Fuels Management—

How to Measure Success: 

Conference Proceedings

28-30 March 2006; 

Portland, OR

United States

Department

of Agriculture

Forest Service

Rocky Mountain

Research Station

Proceedings

RMRS-P-41

September 2006
Andrews, Patricia L.; Butler, Bret W., comps. 2006. Fuels Management—How to 

Measure Success: Conference Proceedings. 28-30 March 2006; Portland, OR.

Proceedings RMRS-P-41. Fort Collins, CO: U.S. Department of Agriculture, Forest 

Service, Rocky Mountain Research Station. 809 p.

Fuels management programs are designed to reduce risks to communities and to 

improve and maintain ecosystem health. The International Association of Wildland 

Fire initiated the 1st Fire Behavior and Fuels Conference to address development, 

implementation, and evaluation of these programs. The focus was on how to measure success. Over 500 participants from several countries convened in Portland, 

Oregon, to discuss approaches to fuels management and to learn from 158 oral and 

poster presentations. 

Sponsors

•	 International Association of •	 National Fire Plan

Wildland Fire •	 Canadian Forest Service

•	 National Interagency Fuels •	 British Columbia Ministry of 

Coordination Group Forests and Range, Protection Program

•	 Joint Fire Science Program • National Center for Landscape

•	 USDA Forest Service Research Fire Analysis

•	 The Nature Conservancy, Global

Fire Initiative

Conference Coordinators

• Dr. Patricia L. Andrews, Fire Behavior Research Scientist, Missoula Fire Sciences 

Laboratory, Rocky Mountain Research Station, USDA Forest Service

• Dr. Elizabeth E. Reinhardt, Fire Ecology and Fuels Research Project Leader, Missoula 

Fire Sciences Laboratory, Rocky Mountain Research Station, USDA Forest Service

Editors’ Note

Peer technical reviews of manuscripts were obtained by the authors before submission. The views expressed are those of the presenters.

Cover Photo by Bill Gabbert, National Park Service, retired.

Rocky Mountain Research Station

Natural Resources Research Center

2150 Centre Avenue, Building A

Fort Collins, CO 80526

You may order additional copies by sending your mailing information 

in label form through one of the following media. Please specify the 

publication title and number.

Telephone (970) 498-1392

FAX (970) 498-1122

E-mail rschneider@fs.fed.us

Web site http://www.fs.fed.us/rm

Mailing Address Publications Distribution

Rocky Mountain Research Station

240 West Prospect Road

Fort Collins, CO 80526
Contents

Foreword—1st Fire Behavior and Fuels Conference:Fuels Management

How to Measure Success . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Patricia L. Andrews, Conference Chair

Fuels Management

Be a Change Agent and Change the Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Jerry Williams

U.S. Federal Fuel Management Programs: Reducing Risk to

Communities and Increasing Ecosystem Resilience and Sustainability . . . . . . . . . . . . . . . . . 9

Tim Sexton

Canadian Wildland Fire Strategy: A Vision for an Innovative and

Integrated Approach to Managing the Risks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

Canadian Wildland Fire Strategy Project Management Team

Fuel Management—An Integral Part of Fire Management:

Trans-Tasman Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

Jim Gould

Forest Fuels Management in Europe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

Gavriil Xanthopoulos, David Caballero, Miguel Galante, Daniel Alexandrian,

Eric Rigolot, and Raffaella Marzano

Wildland Fire Use — Challenges Associated With Program Management

Across Multiple Ownerships and Land Use Situations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

Thomas Zimmerman, Michael Frary, Shelly Crook, Brett Fay,

	 Patricia Koppenol, and Richard Lasko

U.S. Policy Response to the Fuels Management Problem: An Analysis of 

the Public Debate About the Healthy Forests Initiative and the Healthy

Forests Restoration Act . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

Jayne Fingerman Johnson, David N. Bengston, David P. Fan, and Kristen C. Nelson

Influences on USFS District Rangers’ Decision to Authorize Wildland Fire Use . . . . . . . . . . . 67

Martha A. Williamson

Modeling, Risk Assessment and Decision Support

Considerations in the Use of Models Available for Fuel Treatment Analysis . . . . . . . . . . . . . . 81

Charles W. McHugh

Fuels Management—How to Measure Success: 

Conference Proceedings

28-30 March 2006; 

Portland, OR

i
A Computational Method for Optimizing Fuel Treatment Locations . . . . . . . . . . . . . . . . . . 107

Mark A. Finney

Simulation of Long-Term Landscape-Level Fuel Treatment Effects on

Large Wildfires . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125

Mark A. Finney, Rob C. Seli, Charles W. McHugh, Alan A. Ager,

	 Berni Bahro, and James K. Agee

A Wildfire Risk Modeling System for Evaluating Landscape Fuel

Treatment Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149

Alan Ager, Mark Finney, and Andrew McMahan

Automating the Fireshed Assessment Process with ArcGIS . . . . . . . . . . . . . . . . . . . . . . . . . 163

Alan Ager, Bernhard Bahro, and Klaus Barber

An Analytical Framework for Quantifying Wildland Fire Risk and

Fuel Treatment Benefit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169

Joe H. Scott

Strategic Placement of Treatments (SPOTS): Maximizing the Effectiveness

of Fuel and Vegetation Treatments on Problem Fire Behavior and Effects . . . . . . . . . . . . 185

Diane M. Gercke and Susan A. Stewart

Landfire: Landscape Fire and Resource Management Planning Tools Project . . . . . . . . . . . . 193

Kevin C. Ryan, Kristine M. Lee, Matthew G. Rollins, Zhiliang Zhu,

	 James Smith, and Darren Johnson

Applying Fire Spread Simulators inNew Zealand and Australia:

Results from an International Seminar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201

Tonja Opperman, Jim Gould, Mark Finney, and Cordy Tymstra

An Overview of FlamMap Fire Modeling Capabilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213

Mark A. Finney

Using Landscape-Based Decision Rules to Prioritize Locations of

Fuel Treatments in the Boreal Mixedwood of Western Canada . . . . . . . . . . . . . . . . . . . . 221

Marc-André Parisien, Dave R. Junor, and Victor G. Kafka

Fuel Characterization and Mapping

Fuels Products of the LANDFIRE Project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239

Matthew C. Reeves, Jay R. Kost, and Kevin C. Ryan

FUEL3-D: A Spatially Explicit Fractal Fuel Distribution Model . . . . . . . . . . . . . . . . . . . . . . . 253

Russell A. Parsons

FuelCalc: A Method for Estimating Fuel Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273

	 Elizabeth Reinhardt, Duncan Lutes, and Joe Scott

Accuracy and Precision of Two Indirect Methods for Estimating Canopy Fuels . . . . . . . . . . 283

Abran Steele-Feldman, Elizabeth Reinhardt, and Russell A. Parsons

Mapping Fuels on the Okanogan and Wenatchee National Forests . . . . . . . . . . . . . . . . . . . 293

Crystal L. Raymond, Lara-Karena B. Kellogg, and Donald McKenzie

Fuel Type Classification and Fuel Loading in Central Interior,

Korea: Uiseong-Gun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 305

Myoung Soo Won, Kyo Sang Koo, Myung Bo Lee, and Si Young Lee

ii
Understanding Ozark Forest Litter Variability Through a Synthesis of

Accumulation Rates and Fire Events . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 321

Michael C. Stambaugh, Richard P. Guyette, Keith W. Grabner, and

	 Jeremy Kolaks

Estimating Fuel Bed Loadings in Masticated Areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 333

Sharon Hood and Ros Wu

Variability in Loading of Mechanically Masticated Fuel Beds in Northern

California and Southwestern Oregon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 341

Jeffrey M. Kane, Eric E. Knapp, and J. Morgan Varner

Creating a Fuels Baseline and Establishing Fire Frequency Relationships

to Develop a Landscape Management Strategy at the Savannah River Site . . . . . . . . . . . 351

Bernard R. Parresol, Dan Shea, and Roger Ottmar

Variation in Surface and Crown Fire Hazard With Stand Age in

Managed Coastal Western Hemlock Zone Forests in Southwestern

British Columbia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 367

Michael C. Feller and Stefanie L. Pollock

Evaluation of a Dynamic Load Transfer Function Using Grassland Curing Data . . . . . . . . . . 381

Patricia L. Andrews, Stuart A.J. Anderson, Wendy R. Anderson

Foliar Moisture Contents of North American Conifers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 395

Christopher R. Keyes

Mapping the Relationship Between Wildfire and Poverty . . . . . . . . . . . . . . . . . . . . . . . . . . 401

Kathy Lynn and Wendy Gerlitz

Fuel Treatment and Prescribed Fire

Fire Severity and Intensity During Spring Burning in Natural and

Masticated Mixed Shrub Woodlands......................................... 419

Tim Bradley, Jennifer Gibson, and Windy Bunn

Assessing Mitigation of Wildfire Severity by Fuel Treatments —

An Example From the Coastal Plain of Mississippi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 429

Erik J. Martinson and Philip N. Omi

A Fuel Treatment Reduces Potential Fire Severity and Increases

Suppression Efficiency in a Sierran Mixed Conifer Forest.......................... 441

Jason J. Moghaddas

The Use of Silviculture and Prescribed Fire to Manage Stand Structure and

Fuel Profiles in a Multi-aged Lodgepole Pine Forest . . . . . . . . . . . . . . . . . . . . . . . . . . . . 451

Colin C. Hardy, Helen Y. Smith, and Ward McCaughey

Effectiveness of Prescribed Fire as a Fuel Treatment in Californian

Coniferous Forests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 465

Nicole M. Vaillant, JoAnn Fites-Kaufman, Scott L. Stephens

Changes in Downed Wood and Forest Structure After Prescribed Fire in 

Ponderosa Pine Forests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 477

Victoria Saab, Lisa Bate, John Lehmkuhl, Brett Dickson, Scott Story,

	 Stephanie Jentsch, and William Block

Biomass Consumption During Prescribed Fires in Big Sagebrush Ecosystems . . . . . . . . . . . 489

Clinton S. Wright and Susan J. Prichard

iii
The Lick Creek Demonstration — Forest Renewal Through Partial Harvest and Fire . . . . . . 501

Benjamin Zamora and Melinda Martin

Response of Fuelbed Characteristics to Restoration Treatments in

Piñon-Juniper-­Encroached Shrublands on the Shivwits Plateau, Arizona . . . . . . . . . . . . . . 513

Helen Y. Smith, Sharon Hood, Matt Brooks, JR Matchett, and Curt Deuser

Effect of a Spaced Thinning in Mature Lodgepole Pine on Within-Stand

Microclimate and Fine Fuel Moisture Content . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 523

R. J. Whitehead, G. L. Russo, B. C. Hawkes, S. W. Taylor, B. N. Brown,

	 H. J. Barclay, and R. A. Benton

Predicting Ground Fire Ignition Potential in Aspen Communities . . . . . . . . . . . . . . . . . . . . 537

S. G. Otway, E. W. Bork, K. R. Anderson, and M. E. Alexander

Fire Ecology and Fire Effects

Integrating Fuel Treatments into Comprehensive Ecosystem Management . . . . . . . . . . . . . . 549

Kevin Hyde, Greg Jones, Robin Silverstein, Keith Stockmann, and Dan Loeffler

Comparison of the Sensitivity of Landscape-Fire-Succession Models to

Variation in Terrain, Fuel Pattern, Climate and Weather . . . . . . . . . . . . . . . . . . . . . . . . . 563

Geoffrey J. Cary, Robert E. Keane, Robert H. Gardner, Sandra Lavorel,

	 Mike D. Flannigan, Ian D. Davies, Chao Li, James M. Lenihan,

	 T. Scott Rupp, and Florent Mouillot

Assessing Ecological Departure from Reference Conditions with the

Fire Regime Condition Class (FRCC) Mapping Tool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 575

Stephen W. Barrett, Thomas DeMeo, Jeffrey L. Jones, J.D. Zeiler, and Lee C. Hutter

Predicting Post-Fire Severity Effects in Coast Redwood Forests

Using FARSITE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 587

Hugh Scanlon and Yana Valachovic

Measuring Ecological Effects of Prescribed Fire Using Birds as Indicators of

Forest Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 593

Nathaniel E. Seavy, John D. Alexander

Monitoring Changes in Soil Quality from Post-fire Logging in the

Inland Northwest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 605

Deborah Page-Dumroese, Martin Jurgensen, Ann Abbott, Tom Rice,

	 Joanne Tirocke, Sue Farley, and Sharon DeHart

The Relation Between Forest Structure and Soil Burn Severity........................ 615

Theresa B. Jain, Russell T. Graham, and David S. Pilliod

Economics and Biomass Utilization

Design and Objectives of FTM–West Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 635

Peter J. Ince and Henry Spelter

FTM-West Model Results for Selected Fuel Treatment Scenarios . . . . . . . . . . . . . . . . . . . . . 647

Andrew D. Kramp and Peter J. Ince

Estimating Woody Biomass Supply From Thinning Treatments to Reduce

Fire Hazard in the U.S. West . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 657

Kenneth E. Skog and R. James Barbour

Biomass Utilization Modeling on the Bitterroot National Forest . . . . . . . . . . . . . . . . . . . . . . 673

Robin P. Silverstein, Dan Loeffler, J. Greg Jones, Dave E. Calkin,

	 Hans R. Zuuring, and Martin Twer

iv
Communication and Collaboration

A Collaborative Approach to Community Wildfire Hazard Reduction . . . . . . . . . . . . . . . . . 691

Marc Titus and Jennifer Hinderman

Organizational Characteristics that Contribute to Success in Engaging

the Public to Accomplish Fuels Management at the Wilderness

Non-Wilderness Interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 703

Katie Knotek and Alan E. Watson

Communicating the Wildland Fire Message:Influences on Knowledge and

Attitude Change in Two Case Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 715

Eric Toman and Bruce Shindler

Reduction of Potential Fire Behavior in Wildland-urban Interface

Communities in Southern California: A Collaborative Approach . . . . . . . . . . . . . . . . . . . 729

Christopher A. Dicus and Michael E. Scott

Fire Management in the Inter Galatic Interface or 30 Years of Fire

Management at Merritt Island National Wildlife Refuge/­Kennedy

Space Center, Florida . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 739

Frederic W. Adrian

Measuring Success in Your Fuels Program: From the Report Card to

Valuable Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 751

Paula Nasiatka and David Christenson

Fire Weather

Predicting Fire Season Severity in the Pacific Northwest . . . . . . . . . . . . . . . . . . . . . . . . . . . 757

Paul Werth

Employing Numerical Weather Models to Enhance Fire Weather and

Fire Behavior Predictions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 769

Joseph J. Charney and Lesley A. Fusina

WindWizard: A New Tool for Fire Management Decision Support . . . . . . . . . . . . . . . . . . . 787

Bret W. Butler, Mark Finney, Larry Bradshaw, Jason Forthofer, Chuck McHugh,

	 Rick Stratton, and Dan Jimenez

Appendix: Presentations for which papers were not submitted for these proceedings . . . . . 797

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 807

v

Fuels Management

Modeling, Risk Assessment and 

Decision Support

Fuel Characterization and Mapping

Fuel Treatment and Prescribed Fire

Fire Ecology and Fire Effects

Economics and Biomass Utilization

Communication and Collaboration

Fire Weather

USDA Forest Service Proceedings RMRS-P-41. 2006. 1

Foreword

1st Fire Behavior and Fuels Conference:

Fuels Management—How to Measure Success

Patricia L. Andrews, Conference Chair

The 1st Fire Behavior and Fuels Conference: Fuels Management—How 

to Measure Success was held in Portland, Oregon, March 28-30, 2006. 

The International Association of Wildland Fire (IAWF) initiated a conference 

on this timely topic primarily in response to the needs of the U.S. National 

Interagency Fuels Coordinating Group (www.nifc.gov/fuels).

Fuels management programs are designed to reduce risks to communities 

and to improve and maintain ecosystem health. The conference addressed 

development, implementation, and evaluation of these programs, with a focus 

on how to measure success. The scope included not only the how to, but also the 

what and why of fuels management.

The 500 conference participants represented a wide range of organizations, 

disciplines, and countries. The conference program included workshops, invited 

speakers, oral and poster presentations, panels, and vendor displays.

Rather than having a single keynote speaker set the tone for the entire conference, each day began with invited speakers who presented a range of viewpoints. 

Topics included a broad view of fi re as it relates to other “disasters,” fi re as an 

ecological process, and fuels management policy and direction of U.S. federal 

agencies, Canada, Australia and New Zealand, and Europe.

Panels addressed two key topics: “Wildland fi re use: it’s not just for wilderness anymore” and “How do we defi ne success in fuels management?”

About 250 people took advantage of the optional pre-conference workshops. 

They attended several of the 10 workshops that described and demonstrated 

computer systems, models, and methods that can be used in support of fuels 

management. The short workshops showed how to get additional information, 

publications, and computer programs.

In addition to the seven invited speakers, there were 151 presentations (97 

oral and 54 poster). Presenters described their research and experience on 

topics including

• modeling, risk assessment, and decision support systems,

• fuel characterization and mapping,

• fuel treatment and prescribed fi re,

• fi re ecology and fi re effects,

• economics and biomass utilization,

• communication and collaboration, and

• case studies.

Sixty-fi ve of the presenters elected to submit a paper for the published proceedings. Some of the papers will also be in a special issue of the International 

Journal of Wildland Fire. Titles and authors of presentations without papers are 

listed in the appendix to give an indication of the scope of the conference.
2 USDA Forest Service Proceedings RMRS-P-41. 2006.

The published proceedings is a partial record of the conference content. An 

important element was the interactions and sharing of information that occurred 

outside of the formal presentations. Many of those who responded to the afterconference survey listed “networking” as one of the most valuable aspects of 

the conference. They noted the mix of managers, researchers, academia, practitioners, and policy makers. The fi eld of fuels management will undoubtedly 

benefi t from the many personal contacts made at the conference.

Special thanks are owed to the steering committee, who formulated the 

structure of the conference, and to the conference organizing committee, 

who planned and implemented details of the conference. The conference was 

a success due to the contributions of dedicated individuals.

Conference Chair:

• Patricia L. Andrews, USDA Forest Service, Rocky Mountain Research 

Station, Fire Sciences Lab, Fire Behavior Research, Missoula, Montana

Conference Co-chair:

• Elizabeth Reinhardt, USDA Forest Service, Rocky Mountain Research 

Station, Fire Sciences Lab, Fire Ecology and Fuels Research, Missoula, 

Montana

Steering Committee: 

• Dick Bahr, National Interagency Fuels Coordination Group, National 

Park Service

• Jack Cohen, Fire Behavior Research, RMRS Missoula Fire Sciences 

Lab

• Lynn Decker, The Nature Conservancy, Global Fire Initiative

• Nathalie Lavoie, British Columbia Ministry of Forests and Range

• Carol Miller, Aldo Leopold Wilderness Research Institute, RMRS, Missoula

• Steve Taylor, Canadian Forest Service

Conference Organizing Committee:

• Bret Butler, Fire Behavior Research, RMRS Missoula Fire Sciences Lab

• Michelle Ekstrom, State of Montana

• Bill Gabbert, IAWF Executive Director

• Dan Jimenez, Fire Behavior Research, RMRS Missoula Fire Sciences 

Lab

• Matt Jolly, Fire Behavior Research, RMRS Missoula Fire Sciences Lab

• Ed Mathews, Fire Ecology and Fuels Research, RMRS Missoula Fire 

Sciences Lab

• Mikel Robinson, University of Montana, Continuing Education 

• Jen Schimmenti, The Nature Conservancy

• Diane Trethewey, Fire Behavior Research, RMRS Missoula Fire Sciences 

Lab

• Paul Woodard, IAWF offi cer, University of Alberta
USDA Forest Service Proceedings RMRS-P-41. 2006. 3

Fuels Management
4 USDA Forest Service Proceedings RMRS-P-41. 2006.
USDA Forest Service Proceedings RMRS-P-41. 2006. 5

In: Andrews, Patricia L.; Butler, Bret W., 

comps. 2006. Fuels Management—How to 

Measure Success: Conference Proceedings. 

28-30 March 2006; Portland, OR. 

Proceedings RMRS-P-41. Fort Collins, 

CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research 

Station.

1 Retired from the State of Montana and 

is currently owner/manager of Sonoran 

Wings Flight Training Centre, Inc., 

Tucson, AZ info@sonoran-wings.com

I was invited to speak at this conference on the subject of disasters and the 

relative importance of wildfi res because of the breadth of my experience. The 

fact that I currently manage a fl ight school gives me an outside perspective 

of wildland fi re and fuels management.

I have spent a fair amount of time in wildland fi re and disaster management. 

This experience has been in the management of incidents, and in training 

others to manage incidents. My experience includes wildfi res (I quit counting 

at 600), fl oods, blizzards, hurricanes, tornados, volcanoes, earthquakes and 

disease epidemics. I was even on a cruise ship that sank, and my wife and I 

ended up managing the triage and recovery center.

Disasters have been around since man was there for the event. By UN defi -

nition, a disaster is “A natural or human-caused event, which causes negative 

impacts on people, goods, services and/or the environment, exceeding the 

affected community’s capability to respond.”

Over time, events that would not have been disasters, or even emergencies, 

are now major catastrophes. The increase in world population, the movement 

of this population to vulnerable areas, has created a situation where 100’s 

of thousands of people die, and 100’s of billions of dollars are incurred in 

response, relief and reconstruction. This results in an on-going cycle of disasters. Around the world, disasters are a growth industry. At any one time 

there are as many as 40 major relief efforts by US government agencies and 

non-governmental organizations.

Hundreds of thousands of people on the African continent are dying from 

AIDS. Millions are dying from civil wars. Millions more are about to die 

from starvation and disease.

Every year in Bangladesh, 100 thousand children under the age of 5 die 

from diarrhea. Every day 700 die from malnutrition. I spent 6 weeks in 

Bangladesh at a research hospital working on a training program for NGO’s 

on the prevention and treatment of diarrheal disease in disasters.

No one knows for sure how many died from the South Asia Tsunami but 

the number is probably well over 300 thousand.

The death toll from Katrina is still not known and the damages will be in 

the billions of U.S. dollars. An impact of Katrina and the Florida hurricanes is 

that the re-insurers are telling the underwriters to cancel policies on structures 

built on the beaches and outer banks. Allstate just last week announced the 

cancellation of more than 22 thousand policies in Massachusetts alone.

There is also a worldwide attitude that “the government will take care of 

me.” An Arizona Daily Star (March 22, 2006) AP article told of a California 

homeowner who cancelled his earthquake insurance because it was too high, 

saying that he is going to rely on the government to take care of him.

Be a Change Agent and Change the Strategy

Jerry Williams1
6 USDA Forest Service Proceedings RMRS-P-41. 2006.

Williams Be a Change Agent and Change the Strategy

These major events make the news and some stay in the media focus for 

months or years. A large wildland fi re might stay on the radar for a week or 

two and then disappear. Hurricane Andrew is still referred to, and Katrina 

and the Tsunami will be in the news for many years to come.

In the world of disasters, wildfi res are a passing thing. Since 1871, the 

death toll from wildfi res is less than 3400. There is no count on structures 

lost, but then, how many have burned off of the same foundation more than 

once?

Sixty years ago, we suppressed fi res to protect the renewable resources 

that we were managing for the products they produced, and the economies 

they supported. Foresters were the good guys in the white hats. The Forest 

Service was described in management books as a great example of management excellence.

We said fi re in the forest was bad. The most successful ad campaign in 

history put our symbol in everyone’s brain. All hail Smokey.

Then the situation changed. Foresters were not able to continue the cultural 

practices the land needed. We learned a great deal about the effects of fi re 

thanks to some great researchers. So now we said fi re was natural and good. 

The drip torch brigades were on the march.

Then a bunch of folks lost their homes from our “prescribed” fi res. The 

system went awry. But since we thought we were still the good guys in the 

white hats, we said, “Hey, we’re from the government and we know what 

is best.” The public has lost trust in the government to do the right thing 

in a disaster. Another impact of Katrina and the Florida hurricanes is that 

the American public now has no trust in the ability of their government to 

respond in disasters. A recent media survey indicated only 15 percent had 

any confi dence at all (AP, Tucson Citizen, March 3, 2006).

Today houses are wrapped in aluminum foil to protect them from the results of our actions, or non-actions, over the years. In my opinion, if people 

want the experience of living in the woods, they should have an opportunity 

to get all of the experience. Just as the wilderness hiker has the opportunity 

to be eaten by a grizzly, maybe the wildland homeowner should have the 

opportunity to get burned up.

We have fi re managers that are afraid to fi ght fi re aggressively. The courts 

and the agencies have put in “rules of engagement” that make an Incident 

Commander (IC) think long and hard about taking action. In the old days, 

if we had two fi refi ghters and a couple of tools, we set an anchor point and 

started making line. If we were lucky we had some C-rations and maybe a 

ham and cheese sandwich that the ladies in the offi ce made and sent out. 

We didn’t have TV and foosball and movie set catering services in our fi re 

camps. The idea that a fi re boss would wait ten days to establish an anchor 

point and start building line just baffl es me. And you know what, the public 

knows this too.

Those big air tankers full of money sure do make good clips on the evening 

news but somebody has to still build line.

There is a well known axiom of management, “If you do things the way 

you have always done them, you will get the results you have always gotten.” 

If you like sitting in the offi ce doing those Environmental Impact Statements 

(EIS) and all the other stuff you do, don’t change a thing. Otherwise, it’s 

time to fi nd a new approach and a new horse to ride. When the insurance 

companies stopped paying for burned down buildings in Boston and Butte, 

the urban renewal stopped. When the insurance companies stopped paying 

for blown down houses in the Caribbean, the people started following the 
USDA Forest Service Proceedings RMRS-P-41. 2006. 

7

Be a Change Agent and Change the Strategy Williams

building codes to build new ones. The same thing is happening in Florida 

and now the Gulf Coast.

In Latin America, the West Indies and the South Pacifi c, business and 

government are working together to reduce the risk of disaster by eliminating 

hazards where possible and really focusing on reducing vulnerability. The best 

results have been obtained at the individual and community levels.

The Fire Safe Councils and Firewise programs are a good start but they 

need a bigger stick to wield. A recent article in the Arizona Daily Star (March 

5, 2006) about a Firewise effort said “It would be heartbreaking to see one 

homeowner’s effort be overcome by a neighbor who didn’t participate in 

Firewise.”

Instead of asking for money for Public Relations programs, ask for positive 

action. The insurance companies, the banks and lenders, the power companies, all have a fi nancial interest in reducing the losses due to wildfi res. They 

need to support the enforcement of strong codes for location and construction of structures in fi re prone areas. After the fi res on Mt. Lemmon near 

Tucson, Arizona, Pima County wrote new codes and guides for construction 

in wildland areas. The insurance companies are supporting the effort by not 

paying for reconstruction and not reinsuring structures, or their contents, 

that do not meet the codes.

These fi nancial institutions also have an interest in good land management 

using the best cultural practices. You can’t lobby congress, but they can, and 

they do. If every local insurance agent and lender went to company meetings 

and pushed for corporate action, action will happen. These companies have 

tremendous political and economic power. I know this from my work with 

the insurance companies and lenders in the aviation industry.

The world of general aviation, where I am, is changing dramatically. Technology that was only available to the military and airlines is now available in 

virtually every small airplane. I have a new Cessna 172 trainer coming next 

week that has the latest in glass cockpit technology. This is the same technology that’s in the most sophisticated commercial jets. And, soon to be at an 

airport near you are the small personal jets.

The Federal Aviation Administration, that large monolithic agency made 

of stone, has great concern that this technology is overwhelming the average 

pilot and causing accidents. And they are correct. They could not do their 

usual approach of writing regulations to make something happen, but the 

insurance companies could, and have, with minimum qualifi cation training 

and recertifi cation requirements for insurance coverage.

The FAA has proposed a whole “new” approach to reduce the risk of 

general aviation accidents. And it is not regulation. We are going to change 

the way we teach people to fl y. The FAA has asked me to develop a whole 

new course of instruction using a lot of the techniques we learned in the 

wildland fi re training program during the past 30 years. We have already 

started implementing the use of scenario based training and advanced aviation training devices.

The insurance companies are a key player in this effort with the requirement for pilots to be recertifi ed annually to fl y complex aircraft. I have been 

meeting with the major aviation underwriters this past month and we are 

beginning to do insurance company recertifi cations using the same strategies. They reward the pilot and business that have risk reduction programs 

and increase rates on those that do not. I have had an 18% reduction over 

the past two years.
8 USDA Forest Service Proceedings RMRS-P-41. 2006.

Williams Be a Change Agent and Change the Strategy

The following is a review of my thoughts in the form of some brief 

statements:

 1. We created this situation with our fi re suppression success and loss of 

management options.

 2. The traditional PR programs (e.g., Smokey Bear) are not working.

 3. A new approach to risk reduction is needed and the government isn’t 

going to be able to make it happen.

 4. The public understands the economics and options of high insurance 

costs and premium breaks.

 5. The folks at the local level ultimately have the power to make something 

happen. The lenders and the insurers have to take action.

 6. A change is needed and you have to make it happen.

There are a lot of very creative folks in the wildland fi re business. Quite 

frankly, it’s time for you to get off your bureaucratic backsides, become change 

agents, and get on with it. I’m going home and change the way people learn 

to fl y airplanes. What are you going to change?
USDA Forest Service Proceedings RMRS-P-41. 2006. 9

In: Andrews, Patricia L.; Butler, Bret W., 

comps. 2006. Fuels Management—How to 

Measure Success: Conference Proceedings. 

28-30 March 2006; Portland, OR. 

Proceedings RMRS-P-41. Fort Collins, 

CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research 

Station.

1 Program Manager, USDA Forest 

Service. timsexton@fs.fed.us

There is no doubt that wildland fuel conditions on large portions of federal 

wildlands in the United States have changed signifi cantly over the last 100 

years. The changes include:

 • Increased density of woody species

 • Artifi cial fragmentation of fuel mosaics

 • Exotic species invasions

 • Structural changes which reduce ecosystem resilience to fi re

Fire suppression, especially in plant communities which evolved with frequent fi re, has allowed fuel to accumulate to levels far above what would have 

existed without fi re suppression. The fi re suppression era also contributed 

to forest densifi cation. Many more stems of living shrubs and trees occupy 

landscapes today than would have existed without fi re suppression. Forest 

densifi cation tends to predispose areas to insect and disease mortality, further 

loading up the dead fuel mass.

Roads, farms, cities and other human developments have broken up fuel 

mosaics. Fragmented fuels inhibit fi re spread and contribute to fuel accumulation.

Exotic species such as cheat grass, phragmites, salt cedar (tamarisk), and 

others have added to live fuel mosaics or even completely replaced previous 

plant communities. Many exotics (such as those listed above) are much more 

fl ammable than the native species that would otherwise occupy sites. The 

increased fl ammability has resulted in larger and more damaging wildfi res 

in these invaded areas.

Logging, grazing and other human activities have altered plant community 

structure and composition. In many cases the new structure is more susceptible to fi re damage and/or more fl ammable. Small trees are fi re-killed more 

readily than large trees and provide a more effective “ladder” for a surface 

fi re to climb into the crowns.

Last, but not least, social changes in the United States have caused a huge 

change in the potential consequences of wildfi res. Homes, infrastructure, and 

public use have become embedded in these altered, volatile fuel mosaics.

The last twenty years have witnessed a signifi cant increase in large, costly 

wildfi res which have damaged natural resources and improvements on public 

and private lands. A great deal of scientifi c research points to increases in 

wildland urban interface, fuel accumulations, alteration of species composition, and changes in plant community structure as principal reasons for these 

costly, damaging wildfi res.

The National Fire Plan and associated initiatives have provided a framework 

for managing fuels to reduce impacts from wildfi re. The primary fi ve federal 

agencies with wildland fi re management responsibilities (US Forest Service, 

U.S. Federal Fuel Management Programs: 

Reducing Risk to Communities and Increasing 

Ecosystem Resilience and Sustainability

Tim Sexton1
10 USDA Forest Service Proceedings RMRS-P-41. 2006.

Sexton U.S. Federal Fuel Management Programs: Reducing Risk to Communities and Increasing Ecosystem Resilience and Sustainability

Bureau of Land Management, National Park Service, Bureau of Indian Affairs, 

and US Fish and Wildlife Service) are coordinating efforts to manage fuels. 

The National Interagency Fuels Coordination Group (NIFCG) with representatives from these fi ve federal agencies has been tasked to coordinate federal 

strategies for mitigating wildfi re hazards through fuel treatments. NIFCG 

is currently composed of Dennis Dupuis (BIA), Erik Christiansen (BLM), 

Dick Bahr (NPS), John Segar (USF&W), and Tim Sexton (USFS).

I encourage you to engage these agency representatives when you have issues with national policy rather than simply complain to your counterparts. 

We need to know how our efforts are working. You need to tell us. We have a 

website which can be accessed through the National Interagency Fire Center 

home page. This website is intended to be a resource for keeping the fi eld 

informed on our actions and proposed changes to our business.

The primary goal of federal wildland fuel management is to reduce the 

unwanted impacts from wildfi re, including threats to public safety, suppression costs, damage to natural and cultural resources, and damage to public 

and private improvements. It must be clear that we are not trying to reduce 

the number of acres burned by wildfi re. In fact, we will likely facilitate an 

increase in acres burned by unplanned ignitions. Wildland Fire Use and less 

aggressive attack on many suppression-objective wildfi res present opportunities for suppression cost savings, reduced exposure of fi refi ghters to hazards, 

and reductions in hazardous fuel.

The federal wildland fi re agencies have agreed on several key action areas in support of the goal to reduce impacts from wildfi res. These action areas include:

 • aligning federal fuels management policies, practices, and procedures

 • prioritizing fuel treatments which:

 o have been identifi ed as key components of Community Wildfi re Protection Plans,

 o provide by-products for local economies and energy production,

 o reduce hazard on a landscape scale, and

 o are cost-effective

 • expanding wildland fi re use as a means of treating fuels

 • providing support for development and deployment of technologies (such 

as LANDFIRE and associated planning tools) for facilitating planning 

and implementation of fuel reduction projects

 • managing ecosystems so that they are resilient to disturbance and sustainable in the goods and services which they provide to the American 

Public

 • development of a work force which has the capacity and the capabilities 

to strategically manage fuels to obtain the greatest reduction in impacts 

from wildfi re

Successes

In fi scal year 2005 more than 4 million acres of hazardous fuel were 

treated on USDA and USDI lands. We recognize that gross area treated is 

not a particularly good indicator of progress toward the goal of reducing 

unwanted impacts from wildfi re. However, it is a good indicator of our increasing capability to implement treatments. We believe that LANDFIRE will 

enable us to develop metrics which will correlate more closely with progress 

toward our goal.
USDA Forest Service Proceedings RMRS-P-41. 2006. 11

U.S. Federal Fuel Management Programs: Reducing Risk to Communities and Increasing Ecosystem Resilience and Sustainability Sexton

We have many accounts of wildfi res which were contained or where communities were prevented from burning by the fuels treatments accomplished 

since the National Fire Plan was developed.

In July 2004, the Waterfall Fire, near Carson City, Nevada burned over 

8,700 acres. Fifteen homes were destroyed. However, many times that number were saved due to reduced fi re behavior in fuel treatment areas on BLM 

lands adjacent to subdivisions.

Recently, the February Fire on the Tonto National Forest in Arizona was 

contained at about 4,200 acres due, in part, to a recently completed fuel 

treatment area. Post fi re review indicated that the containment opportunity 

afforded by the fuel treatment area contributed to protecting many homes in 

the fi re area including one owned by Mike Johns, US Attorney and frequent 

defender of us in fi re-related litigation.

In October 2004 on the Eldorado National Forest, the Fred and Power 

Fires burned over 20,000 acres near the communities of Kyburz and Silver 

Fork, California. Fuel treatment areas in the wildland urban interface enabled 

fi refi ghters to protect all homes in these communities.

One of the best examples of successful fuel treatment is the Cone Fire 

which burned on the Blacks Mountain Experimental Forest in northern 

California in 2002. This fi re burned through several well documented fuel 

treatment areas, enabling comparisons of burn severity related to treatment 

type and intensity.

While these are impressive accomplishments we need to do more. We 

continue to see many examples of urban interface and intermix in extremely 

vulnerable fuel conditions. Beyond the WUI, we see extensive areas of overly 

dense forests; cheat grass-invaded rangelands, and watersheds which have been 

left to develop multi-story fl ammable conditions. Historically, an average of 

over 25 million acres burned annually from wildfi re on lands that are now 

managed by these agencies in the coterminous United States. Some national 

analyses have suggested that we need to double our efforts in order to make 

signifi cant progress in reducing the impacts of wildfi re. Other analyses indicate 

that strategic placement of treatments might achieve that same signifi cant 

progress with much less area treated.

We have had a few failures along the way. In early 2006 the US Forest 

Service has experienced two large, damaging escaped prescribed fi res. In 

January, on the Cleveland National Forest, the Sierra Prescribed Fire escaped 

eventually burning about 12,000 acres and costing over 7 million dollars to 

suppress. In February on the Shasta-Trinity National Forest the Hot Lum 

Prescribed Fire escaped burning 3,000 acres and a residence.

We are working hard to determine the reasons for the escapes and any 

unit-level or programmatic actions which would prevent additional escapes. 

We are using Learning Organization concepts so that we, as an organization, 

can benefi t from the losses.

Future

What do we need to do to become more effective in managing fuels and 

unwanted impacts from wildfi re? The NIFCG is working to improve our 

organizations and business practices so that we have:

• Increased capacity

 o Utilize our agency and partners workforces
12 USDA Forest Service Proceedings RMRS-P-41. 2006.

Sexton U.S. Federal Fuel Management Programs: Reducing Risk to Communities and Increasing Ecosystem Resilience and Sustainability

 • Increased capabilities

 o Skills in using new technology and recently developed science

 • Internally integrated Fuel and Other Resource management programs

 • Logic-based allocation process for prioritizing funds from National to 

Regional/State and then to local unit levels

 • Improved Collaboration with all stakeholders

 • Interagency Fuels Training Strategy

 • Enhanced planning skills

 o SPOT

 o LANDFIRE has great promise for increasing our abilities to develop 

strategic fuel treatment plans

 o Treatment longevity

 o Treatment effectiveness

 o Treatment cost effi ciency

 o Trade-off analysis

 o Smoke management

 • Focused science needs and delivery

 o Risk quantifi cation

 o Treatment effectiveness longevity

 • Streamlined, “enabling” policies such as might be developed through a 

doctrinal approach

 • Programs at National, Regional, and local levels which are “opportunistic”

What can you do? Keep current on national initiatives such as LANDFIRE, 

FPA, FRCC, the revised ten year implementation plan, and others. Most of 

what is initially put forth has room for improvement and thoughtful critiques 

are welcome. The most effective improvements will come from fi eld-level folks 

who are being asked to implement these initiatives.

In summary, the US federal fuel management policies provide guidance 

and support to manage fuels to reduce the unwanted impacts from wildland 

fi re and to manage plant communities so that they are resilient to disturbance 

and can continue to provide the socially-desired goods and services in the 

long run.
USDA Forest Service Proceedings RMRS-P-41. 2006. 13

In: Andrews, Patricia L.; Butler, Bret W., 

comps. 2006. Fuels Management—How to 

Measure Success: Conference Proceedings. 

28-30 March 2006; Portland, OR. 

Proceedings RMRS-P-41. Fort Collins, 

CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research 

Station.

1 The Canadian Wildland Fire Strategy 

Project Management Team members (in 

alphabetical order) are:

Brian Emmett, Natural Resources Canada, 

Canadian Forest Service, Ottawa, ON, 

Canada.

Peter Fuglem, British Columbia Ministry 

of Forests and Range, Victoria, BC, 

Canada.

Kelvin Hirsch, Natural Resources Canada, 

Canadian Forest Service, Edmonton, AB, 

Canada. khirsch@nrcan.gc.ca

Gordon Miller, Natural Resources Canada, 

Canadian Forest Service, Edmonton, AB, 

Canada.

Tim Sheldan, British Columbia Ministry 

of Forests and Range, Victoria, BC 

Canada.

Abstract—The Canadian Wildland Fire Strategy (CWFS) provides a vision for a new, 

innovative, and integrated approach to wildland fi re management in Canada. It was 

developed under the auspices of the Canadian Council of Forest Ministers and seeks 

to balance the social, ecological, and economic aspects of wildland fi re through a 

risk management framework that emphasizes hazard mitigation, preparedness, and 

recovery as well as effi cient fi re suppression and response. This strategic and holistic 

approach is needed to address both the root causes and symptoms of current and 

future wildland fi re management challenges.

The desired future state advocated in the CWFS consists of communities that are 

empowered to enhance their own safety and resilience, forest ecosystems that are 

healthy and productive, and wildland fi re management agencies that utilize modern 

business practices. To foster change in attitudes, policy, and practices, the provincial, 

territorial, and federal governments are currently working collaboratively to create 

a joint cost-shared program in excess of 1 billion dollars over 10 years to address 4 

strategic objectives: (i) pan-Canadian FireSmart initiative, (ii) wildland fi re preparedness and response capability, (iii) public awareness and risk and policy analysis, and 

(iv) innovation. The underlying tenet is that managing the risks from wildland fi re is 

a shared responsibility of individuals, stakeholder groups, the private sector, and all 

levels of government and therefore requires integrated and cooperative actions.

Introduction

Each summer the news media carry stories of wildfi res raging across the 

Canadian landscape, threatening our communities, causing evacuations, and 

at times burning public and private property. This portrayal of fi re as a menace 

to society is often accurate but it is only part of the story. In Canada, fi re is 

nature’s primary way of keeping the wildlands (including forests, grasslands, 

and parks) healthy and productive. As a result, policy makers and practitioners 

are faced with the complex and diffi cult task of managing wildland fi res so 

that their environmental benefi ts are maximized and simultaneously the risk 

to people and property is minimized.

Recognizing that the challenges of today and the future cannot be solved 

by simply using the thinking and methods of the past, the provincial, territorial and federal governments have worked together under the auspices 

of the Canadian Council of Forest Ministers (CCFM) on a new Canadian 

Wildland Fire Strategy (CWFS). Based on the principles of risk management, the CWFS will address the symptoms and the root causes of wildland 

fi re management by modernizing approaches and capabilities. It provides a 

comprehensive vision of integrated activities that will increase public safety, 

improve the health and productivity of Canadian forests, enhance intergovernmental cooperation, and apply public funds effi ciently.

Canadian Wildland Fire Strategy: A Vision 

for an Innovative and Integrated Approach to 

Managing the Risks

Canadian Wildland Fire Strategy Project Management Team1
14 USDA Forest Service Proceedings RMRS-P-41. 2006.

Canadian Wildland Fire Strategy Project Management Team Canadian Wildland Fire Strategy: A Vision for an Innovative and Integrated Approach to Managing the Risks

Fire and Fire Management in Canada

The Role of Fire in Canada’s Forests

Fire has been a very dominant feature in Canada’s forests since the last Ice 

Age, particularly in the vast boreal region that stretches from the Yukon to 

Newfoundland. Many plant species — such as pine, spruce and birch, to name 

just a few — have not only adapted to fi re but rely on it for their renewal. Fire 

has also created a mosaic of habitat types and ages, which are needed by various 

animal species. Wildfi res burned freely in most of Canada until the late 19th 

century after which European-infl uenced views of fi re and forestry resulted 

in policies that sought to suppress all fi res. In recent decades there has been 

a growing recognition that fi re exclusion is neither ecologically desirable, nor 

economically possible, to eliminate all fi res from our wildlands.

The Risk from Wildfi re

Currently in Canada there is an annual average of 8,600 fi res that burn 

2.5 million hectares, or an area larger than Lake Ontario. Provincial and territorial agencies and Parks Canada are world leaders in forest fi re suppression, 

controlling 97% of all wildfi res when only a few hectares in size. But just as 

with hurricanes, fl oods, and tornados, there are times when Mother Nature 

presents conditions that make wildfi res unstoppable. As more Canadians 

live, work, and recreate in or near fl ammable vegetation, wildfi res are posing an increasing threat to public safety. Over the past 10 years more than 

700,000 people have been threatened by wildfi res in over 200 communities 

– many of which are inhabited by Aboriginal peoples. A recent, vivid example 

was in western Canada in 2003, when hundreds of homes were lost, tens of 

thousands of people were evacuated, and combined damage and fi refi ghting 

costs exceeded $1 billion.

The Looming Crisis

Extensive analysis conducted by federal, provincial, and territorial government offi cials has found that the vulnerability of people, property, and 

natural resources to wildfi re has reached an unprecedented level and is projected to continue to rise rapidly. The main reasons for this include more 

frequent and intense fi res resulting from severe droughts and climate change; 

insect infestations that leave dead and highly fl ammable forests in their wake; 

and the growing number of homes, cottages, businesses and activities located 

in or near fl ammable forests. Meanwhile current wildland fi re suppression 

capacity is eroding as aircraft, facilities, and equipment age and experienced 

fi refi ghting professionals retire. Many believe it is only a matter of time until 

another major fi re season occurs again in Canada and the greatest concern is 

that next time the tragic consequences may include the loss of human lives 

as seen recently in other parts of the world.

Moving Forward

Taking a Strategic Approach

To address current and emerging challenges, the CWFS recommends 

expanding the toolkit available to wildland fi re managers to include hazard 
USDA Forest Service Proceedings RMRS-P-41. 2006. 15

Canadian Wildland Fire Strategy: A Vision for an Innovative and Integrated Approach to Managing the Risks Canadian Wildland Fire Strategy Project Management Team

mitigation, preparedness, and recovery programs that complement an effi cient 

fi re suppression and response system. New ways of sharing and managing the 

risks are also required.

To put this another way, on a personal level all Canadians, in their daily 

lives, face decisions about risks from house fi res and how to deal with them. 

Some people buy insurance, others purchase smoke detectors, and many 

schoolchildren have helped their families plan escape routes from a burning home as part of a homework assignment. At the community level, local 

governments invest in fi refi ghting equipment and the training of fi refi ghters 

to stop fi res, if possible, before they become devastating. However, perhaps 

most important has been the considerable effort that has gone into creating 

building materials that are increasingly fi re-resistant and the rigourous use 

of building codes that demand high standards of fi re protection in the construction of residential homes and offi ce buildings. The principles that have 

worked in our homes and communities for house fi res can also work in the 

Canadian wildlands to reduce the risk from unwanted wildfi res.

Action Plan

In October 2005, the provincial, territorial, and federal forestry ministers 

signed the CWFS Declaration and committed to a shared vision and common set of principles for wildland fi re management in Canada (see www.

ccfm.org). They also agreed to approach their respective governments to 

invest over $1 billion dollars over the next 10 years to implement the CWFS. 

Working with relevant partners and stakeholders, a joint cost-shared program 

would target four main initiatives:

(1) pan-Canadian FireSmart activities that empower individuals and communities to directly reduce the risk from wildfi re;

(2) improved preparedness and response capability through, for example, 

replacement of aging aircraft and equipment, plus a stepped-up recruitment and training program to create the next generation of professional 

fi re management staff (including extensive capacity building in aboriginal 

and rural communities); and

(3) a public awareness campaign about the role of wildland fi re and the associated risks;

(4) innovation that includes the development and application of new science 

and technology in support of early warning systems, better predictive 

models, and the increased use of prescribed fi re.

All of these actions build upon a strong spirit of intergovernmental cooperation that has existed in the wildland fi re community for many years, and 

is evidenced in the thousands of fi re fi ghting resources that are exchanged 

among agencies during times of need.

The CWFS is an ambitious initiative, but one whose time has defi nitely 

come. At fi rst glance it may appear costly; however, in the face of increasing 

threats from wildfi res, it is an investment that will avoid escalating costs 

and losses in the future. When implemented, the CWFS will make Canada’s 

wildland fi re management policies and programs among the most progressive 

in the world – thereby enhancing the safety of Canadians, facilitating forest 

sustainability, and ensuring the effi cient use of public funds.
USDA Forest Service Proceedings RMRS-P-41. 2006. 17

Abstract—Although Australia and New Zealand have quite different fi re climates 

and fuels, the common understanding of fi re behaviour underlies many facets of fi re 

management in both countries. Fire management is the legal responsibility of various 

government land management agencies that manage public lands and individuals, 

local governments or corporations that manage private land. Volunteer bushfi re/rural 

brigades have been formed throughout rural and peri-urban areas and are coordinated 

by rural and metropolitan fi re authorities for specifi c activities such as fi re suppression 

and fuel management. During the last two decades there has been an increasing interaction between Australia and New Zealand rural and land management fi re agencies 

exchanging fi re management practices, lesson’s learnt, common incident command 

systems and more recently, through partnership in their research programs.

Both countries face a similar array of challenges in meeting their fi re management 

objectives and the task is becoming increasingly diffi cult. As overarching services 

provided by governments, fi re management has been subject to fi nancial pressures, 

resulting in staff reductions and erosion of traditional levels of fi re management resources. Resources are declining at a time when demands for protection by the general 

community are increasing. Concurrently, the demands for ecologically appropriate 

fi re management practices and concerns about the long-term impacts of prescribed 

burning have led to the suggestions that, in some areas, fi re is adversely affecting biodiversity and long-term sustainability of natural ecosystems. These issues are overlain 

by debate about how fi re can affect climate change, greenhouse gas balance at the 

landscape and national level, and whether such changes are being exacerbated by 

managed and/or wildland fi res.

Australian Fire Environment

Bushfi res have been part of Australia’s environment for millions of years. 

Australia’s natural ecosystems have evolved with fi re, and the landscapes 

and their biological diversity have been shaped by both historical and recent 

patterns of fi re. Because of the climatic variation across Australia, at any time 

of the year some part of the continent is prone to bushfi res. Thus, bushfi re 

occurs throughout Australia, although they may be very infrequent in some 

climatic zones, such as those dominated by rainforest or wet eucalypt forests. 

In any give year, the greatest extent of bushfi res is in the tropical savannas 

regions of northern Australia; in some seasons these extend into the semi-arid 

and arid interior regions (Luke and McArthur 1978). Table 1 shows area of 

Australian burnt between 1997 and 2003 and percentage of total land area 

fi re affected (Ellis and others 2004).

Fuel Management—An Integral Part of Fire 

Management: Trans-Tasman Perspective

Jim Gould1 

In: Andrews, Patricia L.; Butler, Bret W., 

comps. 2006. Fuels Management—How to 

Measure Success: Conference Proceedings. 

28-30 March 2006; Portland, OR. 

Proceedings RMRS-P-41. Fort Collins, 

CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research 

Station.

1 Research Leader, Ensis- Forest 

Biosecurity and Protection CSIRO; and 

Program Leader for the Bushfi re CRC, 

Australia. Jim.Gould@ensisjv.com
18 USDA Forest Service Proceedings RMRS-P-41. 2006.

Table 1—Approximate fi re-affected areas across Australia, 1997 to 2003a.

 Percentage of fi re-

 Area Percentage of total affected area that is

Calendar year (million hectares) land area fi re affected tropical savannab

 1997 48.3 6.3 86

 1998 26.3 3.4 92

 1999 60.0 7.8 86

 2000 71.5 9.3 65

 2001 80.1 10.4 84

 2002 63.8 8.3 63

 2003 31.6 4.1 85

a Source: Western Australian Department of Land Information in Ellis and others 2004. 

b Defi ned by the Department of Land Information, Western Australia, for the purposes of monitoring 

fi re-affected areas, as being the area north of 21°S and east of 120°E.

Planned fi res to achieve specifi c objectives (ecological, fuel reduction, etc) 

have been and remain a fundamentally important land management tool 

for Australia’s land managers and fi refi ghters. Australians who work with 

bushfi res- indigenous Australians, farmers and pastoralists, fi re fi ghters, public 

land mangers and scientists- recognise that there are good, as well as bad, 

bushfi res. Good bushfi res help to meet land management and fi re mitigation 

objectives without adverse impacts on people, property or the environment; 

bad bushfi res threaten lives, property or environmental assets and do so in 

ways that are diffi cult to control (Ellis and others 2004).

Since European settlement nearly 70 percent of Australia has been occupied 

by agricultural, forestry and livestock grazing enterprises resulting in the 

extensive modifi cation and conversion of forest woodland, open woodland, 

shrubland and grassland systems (Thackway and Lesslie 2005). The native 

forests cover is classifi ed into three classes by the density of their crown cover 

(National Forest Inventory 2001). Thus, there are:

 − 118 million hectares of woodland (tree crowns cover 20 to 50 percent of 

the land area when viewed from above), including just under 10 million 

hectares of woodland mallee; 

 − 43 million hectares of open forest (51 to 80 percent crown cover), made 

up of 38 million hectares of what are commonly called wet and dry 

sclerophyll forests and 5 million hectares of open forest mallee; and

 − 5 million hectares of closed forest (81 to 100 percent crown cover), made 

up of over 4 million hectares of rainforest and almost 1 million hectares 

of mangroves.

Most of the woodland and open forest areas of Australia, composed of 

fi re-dependent and fi re-adapted species and ecosystems, have evolved in the 

presence of a fi re regime driven originally by natural sources of fi re ignition 

(i.e. lightning) and by cultural practices of aboriginal people. The forests are 

a source of raw material for the forest industry, and a source of many tangible 

and intangible products and services including recreational and cultural opportunities for all Australians. In recognition of these values, forest protection 

efforts commenced in the early 1900s, and have steadily developed to the 

point where Australian State public land management agencies are recognized 

among the world’s leaders in fi re management.

Forest fi re management in Australia is the responsibility of the State and 

Territorial governments. Fire management on public lands (e.g. State forests, 

Gould Fuel Management—An Integral Part of Fire Management: Trans-Tasman Perspective
USDA Forest Service Proceedings RMRS-P-41. 2006. 19

National parks, State parks, Crown lands, etc.) is the responsibility of the 

State agency charged with managing those areas. Fire suppression may be 

carried out by individual agencies or placed with one agency, e.g. in Victoria 

suppression on all State lands is carried out by the Fire Management Section 

of the Department of Sustainability and Environment. Fire management on 

private lands is carried out by volunteer bushfi re brigades or industry brigades 

that are co-coordinated and supported by the State rural fi re agencies. In 

recent years there has been an increase in the corporatisation of State-owned 

plantations and the fi re management responsibility for these forests, along 

with new plantation forests established on private land, rests increasingly 

with the State rural fi re authorities. This shift in fi re responsibility has mainly 

occurred in South Australia and Victoria over the last fi ve years.

Most of the States provide fi re management directly as a government service, 

generally by the departments that manage lands, forests and other natural 

resources. Their fi re management programs provide for varying levels of planning, fuel management (i.e. prescribed burning), detection, pre-suppression 

and suppression operations. The level and type of activity in each category varies with each agency’s natural resource polices, protection priorities, fi nancial 

resources and, in particular, the ecological and biogeographical conditions 

of the forest itself. Consistent with the statutory obligations and policies of 

public management agencies, their fi re management objectives include:

 • Protection of people from bushfi re.

 • Protection of buildings and facilities from bushfi re.

 • Prevention of bushfi re burning onto neighbouring property.

 • Conservation of natural and cultural values including:

 - Native plant and animal species, habitats and communities;

 - Soil and water resources;

 - Scenic and landscape values; and

 - Aboriginal and European heritage values.

All agencies deliver an organised detection program. Fire towers are the 

most common detection system offering regular surveillance of high-value 

areas and community assets. The used of fi xed wing aircraft for detection has 

increased in the past 15 years. There are recent attempts to use satellite-based 

remote sensing as a tool for fi re detection.

Suppression strategies use a mix of resources from the land management 

agencies with support from rural bushfi re authorities. Ground crews using fi re appliances (fi re tankers), heavy equipment (dozers) and hand tools are the 

backbone of the suppression system. Aircraft for aerial suppression have been 

used in Victoria for more than thirty years, and over the past decade other 

land management agencies have increasingly used air attack on bushfi res.

Different suppression strategies are used by the agencies, which are based 

on the nature of the forest and fi re regimes that they deal with and, to some 

extent, on the organisational philosophy. Some agencies, such as those in 

Victoria and Western Australia, have relatively large full-time fi re management organisations compared to those in other States.

New Zealand Fire Environment

Although not having one of the most severe fi re climates in the world, 

New Zealand has as a long history of large and damaging wildfi res. Northern and eastern New Zealand are characterized by a mix of fl at and steeply 

Fuel Management—An Integral Part of Fire Management: Trans-Tasman Perspective Gould
20 USDA Forest Service Proceedings RMRS-P-41. 2006.

divided terrain, occasional drought, strong wind conditions and fl ammable 

grass and scrub fuels. New Zealand climate ranges from subtropical in the 

far north to cool temperature in the south, but the steep and divided relief 

causes dramatic variation along the length of the country. As frontal weather 

systems approach New Zealand, the winds preceding it often reach gale force 

and are force to rise over the Southern Alps resulting in hot dry fohn winds 

in the eastern part of the South Island. These regions in the South Island 

Canterbury Plains can experience extreme fi re weather on more than 40 days 

per year (Pearce and Majorhazi 2003).

The approximate cover of different land uses in New Zealand is listed in 

table 2. Natural and plantation forests cover 23 percent (6.2 million hectares) 

and 7 percent (1.8 million hectares) of the New Zealand land area respectively (New Zealand Ministry of Agriculture and Forestry 2005). Areas of 

pastures, arable land and other non-forested land (tussock and scrub vegetation) cover approximately 70 percent (18.9 million hectares). These areas of 

tussock and scrub fuels are very fl ammable, and recent research results show 

that extreme fi re behaviour will often occur under Low to Moderate forest 

fi re danger conditions (Fogarty and others 1998).

New Zealand native vegetation consists of species that are not specifi cally 

adapted to fi re, but there are xeromorphic elements thought to be adapted to 

disturbance from longer term climatic fl uctuations. Margins of beech (Nothofagus spp.) and podocarp forest are sensitive to fi re and after fi re or other 

disturbance (e.g. landslides), fl ammable species (e.g. Leptospermum spp. and 

Dracophyllum spp.) invade the site such that the potential for decline and 

fragmentation by fi re is increased (Fogarty and Pearce 1995).

New Zealand experiences approximately 3,000 vegetation wildfi res each 

year and these fi res are attended by the Department of Conservation, forest 

companies or local government Rural Fire Authorities make up of both permanent (land management) staff and volunteer fi re fi ghters. These fi res are 

primarily human-caused and many continue to occur as a result of escapes 

from (both permitted and unauthorised) prescribed burning activities and 

increasing arson (Pearce and Majorhazi 2003).

The number of hectares that are burnt annually by wildfi res varies considerable being driven predominantly by the weather conditions during the 

summer season. The summer of 1946 represents the most disastrous fi re year 

in New Zealand history when, following periods of drought in the north 

east central regions of the North Island, over 200,000 ha of indigenous forest, exotic plantations, cutover forest, tussock and scrub were burnt. More 

recently, the 1998/99 fi re season resulted in 18,000 ha being burnt. Since 

1988/98 there has been an annual average of 7,000 ha of rural lands (including forestry) have been burnt (Fogarty and Pearce 1995).

Large and devastating bushfi res occur relatively infrequently in New 

Zealand when compared with Australia, Canada and USA. However, the 

Table 2—Different land uses in New Zealand.a

 Hectares (millions) % of total

Pasture & arable land 11.8 44%

Natural forest 6.2 23%

Other non-forested land 7.1 26%

Plantation forest 1.8 7%

a Source: New Zealand Ministry of Agriculture and Forestry, 2005.

Gould Fuel Management—An Integral Part of Fire Management: Trans-Tasman Perspective
USDA Forest Service Proceedings RMRS-P-41. 2006. 21

potential exists in most parts of the country for signifi cant events to occur 

(Pearce and others 2004, Fogarty and others 1998). Like Australia, New 

Zealand will face an increase in the severity and impact of bushfi res in the 

next decade and beyond. The increasing trend in the expansion of the ruralurban interface is one of the major factors contributing to increased future 

risk from wildfi res. Also, changes in forestry and land management practices 

may increase the likelihood of major wildfi re events. This includes potential 

changes in long-term fi re danger such as those associated with projections of 

future global warming and climate change (Pearce and others 2005; Hennessy and others 2006).

Fuel Management Strategy

The damage caused by wildfi res and the ability of suppression forces to 

control them is strongly linked to fi re intensity, which is governed by fuel, 

weather and topography. Of these factors, only the fuel level can be manipulated, and fuel management is the basis of wildfi re prevention throughout 

much of Australia. New Zealand is beginning to consider use of fi re to manage 

fuels (for fuel reduction or ecosystem management) despite a long history of 

using fi re as a land management tool for land clearing and forest establishments. In the natural landscape, this requires the periodic removal of part of 

the surface litter and understorey vegetation. This can be achieved by manual, 

mechanical, or chemical methods or through the use of fi re.

Prescribed burning is defi ned as the burning of vegetation under specifi ed 

environmental conditions and within a predetermined area to achieve some 

predetermined objective. The objective may include habitat management 

for native fauna, species regeneration, maintenance of specifi c eco-types or 

hazard reduction, etc.

Studies conducted by McArthur (1962), Peet (1965), and others since the 

1960s (Cheney and others 1992) have provided the technology for fi re to be 

used effectively to manage fuels. These studies enable the behaviour of fi res 

that are lit under given conditions to be predicted. A range of operational 

procedures provide a high level of security against fi re escape. Due to the 

improvements in techniques and the application of fi re behaviour knowledge, 

prescribed burning has become a reliable fuel management tool. To date the 

only effective way of reducing fuels over large areas is through the use of 

low-intensity prescribed fi res and, in Australia, this is generally synonymous 

with broad-area fuel reduction. In most of the eucalypt forest the aim of fuelreduction programs is to keep the load of fi ne fuel (fuels less than 6 mm in 

diameter) on the forest fl oor to less than 10 tonnes per hectare (t ha-1). This 

will prevent the development of crown fi res in medium to tall forests and 

will limit the rate of spread and damage done by wildfi res. The frequency of 

burning is determined by litter accumulation rates so that burning rotations 

to manage fuel reduced areas are normally between 5 and 10 years.

Prescribed fi re is also used in native forests to remove slash accumulations 

and to prepare a seed bed for the regeneration of native forest species, and 

more recently to regenerate understorey species and manipulate vegetation 

to provide suitable habitat for native fauna. Although these operations also 

remove fuels, they are generally of higher intensity than low-intensity prescribed burning specifi cally for fuel reduction and the intensity prescribed is 

determined by the requirements for good regeneration.

Fuel Management—An Integral Part of Fire Management: Trans-Tasman Perspective Gould
22 USDA Forest Service Proceedings RMRS-P-41. 2006.

Hazard reduction burning—Hazard reduction burning will reduce the 

total load of fi ne fuel and is also effective in reducing the height and fl ammability of elevated fi ne fuels such as shrubs and suspended dead material. 

Burning is the only practical way of reducing the fi brous bark on trees, which 

is the prime source of fi rebrands that cause spotting. Hazard reduction reduces fi re behaviour by:

 • reducing the rate of development of growth of the fi re from its ignition 

point;

 • reducing the height of fl ames and rate of spread;

 • reducing the spotting potential by reducing the number of fi rebrands and 

the distance they are carried downwind; and,

 • reducing the total heat output or intensity of the fi re.

Prescribed burning is not intended to stop forest fi res but it does reduce 

their intensity and this makes fi re suppression safer and more effi cient. Prescribed burning does not provide a panacea, nor does it work in isolation. It 

must be used in conjunction with an effi cient fi re fi ghting force.

Hand crews can suppress a fi re up to a maximum intensity of 1000 kilowatts per metre (kW m-1) (Loane and Gould 1986). If the fuel load is greater 

than 15 t ha-1 (which is typical of dry eucalypt forests between 8 to 15 years 

since the last fi re) this intensity will be exceeded under low to moderate fi re 

danger conditions. If the fuels are reduced to 10 t ha-1, fi res will not develop 

an intensity of 1000 kW m-1 until fi re danger gets into the moderate to high 

range. This means that the range of weather conditions that fi re fi ghting 

with hand tools is effective is increased and more time is available to bring 

the fi re under control. If the fuels are reduced further to less than 7.5 t ha-1

then suppression with hand tools is effective under weather conditions of very 

high fi re danger. Under extreme conditions, provided there is suffi cient fuel 

to carry fi re, fi re suppression by any means is virtually impossible because the 

strong dry winds associated with conditions will cause burning embers to 

breach any fi reline. Nevertheless, the result of the lighter fuel load will reduce 

the rate of spread of the fi re and the area burnt so that the fi re suppression 

task will be easier when the weather conditions ameliorate.

Silvicultural burning—Silvicultural burning is usually a moderate-intensity prescribed burn carried out after a partial-cut logging operation designed 

to remove logging slash, prepare the seed bed and stimulate regeneration 

and/or the growth of rootstock regeneration. Silvicultural burning is conducted in the jarrah forest of Western Australia and the silvertop ash forests 

of New South Wales.

Ecological burning—The main aim of using fi re for ecological management is to provide an appropriate fi re regime (of specifi c fi re frequency, 

intensity, seasonality and patchiness) to meet specifi c goals for the management of a particular species, populations or communities (e.g. as part of a 

recovery plan for a threatened species). Since fi re has a fundamental role in 

the development of forest ecosystems, it follows that fi re has a place in maintaining them. Good (1981) indicated that because fi re is the major and only 

environmental factor over which some control can be exercised, and many 

native species depend on fi re for their continued existence, and the use of fi re 

will always have a place in ecological management. Fire has a place in both fl ora and fauna management but its effective application in Australia has been 

infrequent.

Gould Fuel Management—An Integral Part of Fire Management: Trans-Tasman Perspective
USDA Forest Service Proceedings RMRS-P-41. 2006. 23

Application of prescribed burning—There is a perception among people 

unfamiliar with fi re management that prescribed burning is simply lighting fi res to burn-off the undergrowth and that this can be carried out with 

only a basic understanding of fi re behaviour. Indeed, where burning-off has 

been carried out in this way the results have been less than optimal and have 

resulted in escapes, injury and/or death (e.g. Kur-Ring Gai National Park, 

New South Wales 2000). Like any land management operation, prescribed 

burning requires the setting of clear priorities and objectives, planning and 

the application of technical guidelines to meet those objectives. In general 

terms the process of conducting a prescribed burn is as follows:

 • Set the objectives and desired outcome for the fi re.

 • Determine the fi re intensity and the associated heat pulse that is required 

to meet that objective (in forestry and for fuel management this may be 

determined by an acceptable height of scorch of the overstorey canopy 

or an acceptable level of heat damage to the cambium of regenerating 

trees).

 • Determine the level of fi re behaviour (for example fl ame height, intensity) 

that will produce this heat pulse for the particular fuel type.

 • Determined the weather conditions and the ignition pattern that will 

produce this fi re behaviour.

 • Light the fi re in a planned way when prescription conditions are met and 

confi ne it to a predetermined area.

The key to conducting the operation is a good fi re behaviour guide that 

predicts fi re behaviour in the selected fuel type. In Western Australia, the 

Department of Conservation and Land Management has been conducting 

prescribed burning to meet fi re protection, forestry and ecological objectives 

in a scientifi c way since mid-60s. The planning process starts seven years in 

advance of each prescribed burn. Individual burning guides have been developed through empirical research for all their major fuel types including dry 

jarrah forest, tall wet karri forest, conifer plantations and mallee shrublands 

(for example Sneeuwjagt and Peet 1998).

In the eastern states prescribed burning is largely carried out using rules 

of thumb based on a McArthur's original burning guide for dry eucalypt 

forests produced in the 1960s (McArthur 1962). However, in one case a new 

burning guide has been developed and that was for burning under young 

regeneration of silver top ash in New South Wales State Forests (Cheney and 

others 1992). Clearly, if prescribed burning is to be conducted in a more 

professional way in there is an urgent need for new and better burning guides 

that can be applied to a whole range of different fuel types.

Advances in fuel management—The development of more sophisticated 

burning guides requires a better understanding of fi re behaviour in fuels of 

different structure and composition. Recent work undertaken by CSIRO 

and Department of Conservation and Land Management Western Australia 

as part of Project Vesta (Cheney and others 1998, Gould and others 2001, 

McCaw and others 2003) has identifi ed the importance of fuel structure in 

determining fi re behaviour and has developed a system for quantifying fuel 

structure with a numerical index that can be used as a fuel predictor variable 

to replace fuel load.

Although fuel structure is diffi cult, if not impossible, to measure reliably 

and consistently, all natural fuels can be divided into easily recognisable layers. It is the characteristics of these layers that determine the particular fuel 

type and its characteristic fi re behaviour and the diffi culty of suppression. For 

Fuel Management—An Integral Part of Fire Management: Trans-Tasman Perspective Gould
24 USDA Forest Service Proceedings RMRS-P-41. 2006.

example, the simplest fuel type is annual grassland like wheat. This is a single 

layer of relatively uniform compaction. The main factor that determines rate 

of spread is the continuity of the grass. Although height of the sward the 

affects the fl ame height, and thereby the suppression diffi culty, it has only a 

minor effect on the rate of spread. In contrast dry eucalypt forest with a tall 

shrub understorey has fuels that can be identifi ed into several layers of different compaction. These are in order of decreasing compaction:

 • Compacted surface litter bed of leaves twigs and bark that makes up about 

60 percent of the total fuel load,

 • Near surface layer above it of the low shrubs containing suspended litter 

and bark,

 • Elevated layer of tall shrubs,

 • Intermediate layer of small trees,

 • Fibrous bark of the overstorey trees, and

 • Canopy of the overstorey trees.

All of these layers make an important contribution to the fi re behaviour and 

each layer becomes progressively involved in fi re as the intensity increases. A 

visual hazard rating system is being developed (Gould and others 2001) takes 

into account the height, continuity and fraction of dead fl ammable material 

in each layer. The latter that appears to be most important in determining fi re 

spread is the near surface fuel layer and the best fuel variable for predicting 

the rate of spread is an index based on the hazard score and height of the near 

surface fuel layer (Gould and other 2001, McCaw and other 2003).

Effectiveness of fuel reduction over time—The period of time over which 

fuel reduction remains effective in assisting suppression depends upon the 

number of fuel layers involved, the rate of accumulation of fuels and the time 

that it takes for the key layers to build up to their full potential hazard for 

the site. This may be a relatively short time for fuels with a simple structure 

or take many years in more complex fuel types (table 3).

Table 3—Period that fuel reduction burning will assist suppression activities and the main 

factors that contribute to diffi culty of suppression.

 Persistence of reduced Factors contributing to

 Fuel type fi re behaviour (years) diffi culty of suppression

Annual grass 1 (year of burning) 

Tussock grassland 5 Development of persistent

 tussock fuel

Tall shrubland 10 to15 Height of shrubs accumulation

 of dead material (ROS,

 fl ame height)

Forest, short shrubs, gum bark 10 to 15 Surface fuel, near-surface

 fuels structure (ROS fl ame

 height)

Forest, tall shrubs, stringybark 15 to 25 Near-surface fuel, shrub

 height and senescence,

 bark accumulation (ROS,

 fl ame height, spotting

 potential

Gould Fuel Management—An Integral Part of Fire Management: Trans-Tasman Perspective
USDA Forest Service Proceedings RMRS-P-41. 2006. 25

Although the effect of prescribed burning may persist for a considerable 

time, most fi re management agencies consider that suffi cient fuels have accumulated after 5 to 8 years to warrant re-burning.

Trans-Tasman Partnership

Australia and New Zealand have had a long history of sound fi re management through a number of coordinating organisations. Building on this 

history and accumulated relevant fi re management expertise, fi re managers 

in Australia and New Zealand have been able and will continue to contribute 

the technical capacity of fi re management in Australasia and internationally. 

In addition to the obvious positive economic and environmental outcomes 

from fi re management their contributions have complementary social benefi ts 

to both countries. The major Trans-Tasman co-ordinating bodies include:

Forest Fire Management Group (FFMG)—is a committee of Australian 

and New Zealand land management agencies with responsibility for forest 

fi re management together with representatives from research, education 

and the forest industry. FFMG reports to the federal government Forestry 

and Forest Products Committee (FFPC) which is comprised of the heads of 

federal, state, and territory and New Zealand government forestry agencies. 

The FFPC is a sub-committee of the Primary Industries Ministerial Council. 

FFMG’s aims are to provide a centre of expertise on forest fi re management 

and control, and particularly to:

 • Provide a high level of technical and policy advice on fi re management and 

fi re control matters to the Forestry and Forest Products Committee through 

the Primary Industries Standing Committee;

 • Assist interstate and international liaison and consultation between fi re controllers and managers; and

 • Assist in the development of effective fi re management and control philosophy 

and profi ciency.

Australasian Fire Authorities Council (AFAC)—is the peak representative body for fi re, emergency services and land management agencies in the 

Australasian region. It was established in 1993 and has 26 full members and 

10 affi liate members. AFAC’s mission is to improve collaboration between the 

fi re, emergency services and land management agencies in the Australasian 

region, particularly in the exchange of strategic information and the sharing 

of expertise.

As the national peak body, it is also committed to:

 • Developing national standards for the fi re industry;

 • Advocating to State and Federal government on behalf of its member 

agencies;

 • Creating national policies on a range of issues;

 • Acting as an industry peak body on issues of national importance.

Research partnership—The resources of Australia’s and New Zealand’s 

pre-eminent forest research organisations has come together in a world leading joint forest research venture. Ensis- the joint venture between Australia’s 

CSIRO Forestry and Forests Products and New Zealand’s Scion (formerly 

Forest Research) - combines and enhances the breadth, depth and scale of 

Australasia’s bushfi re research and development capability. This research 

capability is also enhanced by the research partnership with the Bushfi re 

Cooperative Research Centre (Bushfi re CRC). The integrated Ensis bushfi re 

Fuel Management—An Integral Part of Fire Management: Trans-Tasman Perspective Gould
26 USDA Forest Service Proceedings RMRS-P-41. 2006.

research group created a strong Australasian bushfi re science capability with 

signifi cant benefi ts to end users in Australia and New Zealand, including:

• Gaining critical mass, economies of scale, and enhanced overall capability, with immediate benefi ts in the areas of bushfi re science.

 • A signifi cant increase of expertise available to New Zealand in terms of 

fi re behaviour, fuel assessment and suppression research. Integration of 

the bushfi re research groups has increased its research capabilities in the 

Bushfi re CRC.

 • An increased capacity to quickly deal with the various activities generated 

from major wildfi re events which in most cases assume top priority.

Conclusion

Australia and New Zealand have quite different fi re environments and 

diverse land cover but the importance of understanding fi re behaviour is 

recognised in both countries as an aid to fi re management. Fire management 

agencies in both countries face a similar array of challenges in meeting their 

fi re management objectives and the task is becoming increasingly diffi cult. 

As a government service, fi re management has traditionally been combined 

with other forest management skills, notably sustainable timber production. 

Financial pressures and changes in policy relating to timber production from 

native forests are resulting in staff reductions and erosion of traditional levels 

of the fi re management skills base and resources. Resources are declining at a 

time when demands for protection by the general community are increasing. 

Concurrently, the demands for ecologically appropriate forest management 

practices and concerns about the long-term impacts of prescribed burning 

practices have led to the suggestion that, in some areas, fi re is adversely affecting biodiversity and long-term sustainability of forest ecosystems. It is 

also widely recognised that there will be increase in the severity and impact of 

bushfi res in the next decade in the Australasian region. This includes potential 

changes in long-term fi re danger such as those associated with projections 

of future global warming and climate change. These issues are overlain by 

debate about how fi re can affect climate change, greenhouse gas balance at 

the landscape and national level, and to whether these changes are being 

exacerbated by managed and/or wildland fi res.

Accurate interpretation of the effect of fi re management practices on forest 

management requires not only accurate measurement of area burnt but also 

the classifi cation of all fi res by vegetation type and burning conditions, the 

measurement of the fuel dynamics and equilibrium fuel loads for each type 

and the measurement of consumption rates under a wider range of burning 

conditions than is currently available. Also, fuel management using prescribed 

fi re has an important role in protection of forests, community assets, other 

valued resources and biodiversity. Forest and rural landscapes in Australia 

and New Zealand are becoming increasingly more fragmented because of 

human activities, is also having an impact on the fi re management practices 

that could contribute more to the amount of area burnt by wildfi res. The 

critical role of fi re management and using fi re as a management tool for fuel 

management requires a better understanding of fuel characteristics and fi re 

behaviour leading to the development of improved guides for prescribed 

burning in different fuel types.

Gould Fuel Management—An Integral Part of Fire Management: Trans-Tasman Perspective
USDA Forest Service Proceedings RMRS-P-41. 2006. 27

Acknowledgments

I would like to thank British Columbia Ministry of Forests and Range for sponsoring the invited speaker to the First Fire Behaviour and Fuels Conference- “Fuel 

Management- How to Measure Success”. The shared views from my colleagues 

of the Forest Fire Management Group and Phil Cheney have been wonderful 

sounding board and contributor for many years on this subject are greatly 

appreciated. Formal internal review for CSIRO and Ensis- Bushfi re Research 

before submission was carried out by A. Sullivan and G. Pearce.

References

Cheney, N. P.; Gould, J. S.; Knight, I. 1992. A prescribed burning guide for young 

regrowth forest of Silvertop ash. Forestry Commission of New South Wales, 

Research Paper No. 16, pp 92.

Cheney, N. P.; Gould, J. S.; McCaw, L. 1998. Project Vesta: Research initiative into 

the effects of fuel structure and fuel load on behaviour of wildfi res in dry eucalypt 

forest. In: Proceedings: 13th International Fire and Meteorology Conference. 

Lorne Victoria. IAWF. pp 375-378.

Ellis, S.; Kanowski, P.; Whelan, R. 2004. National Inquiry on Bushfi re Mitigation 

and Management. Commonwealth of Australia, Canberra.

Fogarty, L. G.; Pearce, G. H. 1995. Forest and rural fi re research in New Zealand. 

Fire Technology Transfer Note. NZ Forest Research Institute Rotorua. Number6 October 1995. 24 p.

Fogarty, L.G.; Pearce, G. H.; Catchpole, W. R.; Alexander, M. E. 1998. Adoption 

vs. adaptation: Lesson from applying the Canadian Forest Fire Danger Rating 

System in New Zealand. In: Proceedings. III International Conference on Forest 

Fire Research, 14th Conference on Fire and Forest Meteorology. 16-20 November 

1998. Luso, Portugal. Volume 1. pp. 1011 – 1028.

Fogarty, L. G.; Jackson, A. F.; Lindsay, W.T. 1996. Fire behaviour, suppression and 

lesson for the Berwick Forest Fire of 26 February 1995. New Zealand Forest 

Research Institute, Rotorua, in association with the National Rural Fire Authority, 

Wellington. FRI Bulletin No. 197, Forest and Rural Fire Scientifi c and Technical 

series, Report no 3. 38 p + Appendices.

Good, R. 1981. Heathland fi re management. In: Heaths in New South Wales. C. 

Haigh (editor). NSW National Parks and Wildlife Service, Sydney, pp. 57-61.

Gould, J. S.; Cheney, N. P.; McCaw, L. 2001. Project Vesta- Research into the 

effects of fuel structure and fuel load on behaviour of moderate to high-intensity 

fi res in dry eucalypt forest: Progress Report. Australasian Bushfi re Conference 

In: Proceedings. 3–6 July 2001. Christchurch, New Zealand. pp 13-21.

Hennessy, K.; Lucas, C.; Nicholls, N.; Bathols, J.; Suppiah, R. Ricketts, J. 2006. 

Climate change impacts on fi re-weather in south-east Australia. CSIRO Marine 

and Atmospheric Research. Aspendale, Victoria. 88 p.

Loane, I.T.; Gould, J.S. 1986. Aerial suppression of bushfi res - Cost/benefi t study 

for Victoria. National Bushfi re Research Unit, CSIRO Division of Forest Research, 

Canberra, ACT. 213 p + Appendices.

Luke, R. H.; McArthur, A. G. 1978. Bushfi res in Australia. Australian Government 

Publishing Service, Canberra, Australia.

McArthur, A. G. 1962. Control burning in eucalypt forests. Aust. Forestry and 

Timber Bureau, Leafl et No. 80, 31 p.

Fuel Management—An Integral Part of Fire Management: Trans-Tasman Perspective Gould
28 USDA Forest Service Proceedings RMRS-P-41. 2006.

McCaw, L.; Gould, J. S.; Cheney, N. P. 2004. Do eucalypt forest fi res burn faster 

and hotter in older fuels? – New experimental evidence from Project Vesta. In: 

Proceedings. 3rd International Wildland Conference. October, 2003. Sydney, 

NSW.

National Forest Inventory. 2001. Commonwealth of Australia Department of 

Agriculture, Forestry and Fisheries. (Web site: http://www.daff.gov.au/content/ 

output.cfm?ObjectID=D2C48F86-BA1A-11A1-A2200060B0A06330).

New Zealand Ministry of Agriculture and Forestry. 2005. New Zealand Forest 

Industry Facts and Figures 2005/2006. 23 p.

Pearce, H.G.; M. Majorhazi. 2003. Application of fi re behaviour to fi re danger and 

wild fi re treat modelling in New Zealand. In: Proceedings. 3rd International 

Wildland Conference. October, 2003. Sydney, NSW.

Pearce, H.G.; Hamilton, R. W.; Millman, R. I. 2004. Fire behaviour and fi refi ghter 

safety implication associated with the Bucklands Crossing fi re burnover of 24 

March 1998. Forest Research, Rotorua, in association with New Zealand Fire 

Service Commission and the National Rural Fire Authority, Wellington. Forest 

Research Bulletin No. 197, Forest and Rural Fire Scientifi c and Technical Series, 

Report no. 4. 63 p.

Pearce, G. H.; Mullan, A. B.; Salinger, M. J.; Opperman, T. W.; Woods, D.; Moore, 

J.R. 2005. Impact of climate change on long-term fi re danger. NZFSC Research 

Report No 50. 75 p.

Peet, G. B. 1965. A fi re danger rating and controlled burning guide for northern 

jarrah forest of Western Australia. Forest Department. Western Australia. Bulletin. 

No. 74, 37 pp.

Thackway, R.; Lesslie, R. 2005. Vegetation Assets, States and Transitions (VAST): 

accounting for vegetation condition in the Australian landscape. BRS Technical 

Report, Bureau of Rural Sciences, Canberra.

Sneeuwjagt, R. J.; Peet, G. B. 1998. Forest fi re behaviour tables for Western Australia. 

Department of Conservation and Land Management. Western Australia 59 p. 

Gould Fuel Management—An Integral Part of Fire Management: Trans-Tasman Perspective
USDA Forest Service Proceedings RMRS-P-41. 2006. 29

In: Andrews, Patricia L.; Butler, Bret W., 

comps. 2006. Fuels Management—How to 

Measure Success: Conference Proceedings. 

28-30 March 2006; Portland, OR. 

Proceedings RMRS-P-41. Fort Collins, 

CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research 

Station.

1 Fire researcher, National Agricultural 

Research Foundation (NAGREF), 

Institute of Mediterranean Forest 

Ecosystems and Forest Products 

Technology, Terma Alkmanos, Athens, 

Greece. gxnrtc@fria.gr

2 Fire researcher, TECNOMA SA, 

Isla del Hierro 7, S.S. de los Reyes, 

Madrid, Spain.

3 Forest engineer, General Directorate 

of Forest Resources, Forest Fire 

Protection Division, João Crisostomo, 

Lisboa.

4 Fire consultant, Agence MTDA, Aixen Provence, France.

5 Fire researcher, Institut National de 

la Recherche Agronomique (INRA), 

Unité de Recherches Forestières 

Méditerranéennes (UR629), Equipe 

prévention des incendies de forêt, 

Avenue A. Vivaldi, Avignon, France.

6 Assistant professor of Fire Ecology 

and Forest Management, University 

of Torino, Department of Agronomy, 

Silviculture and Land Management 

(Agroselviter), Grugliasco, Torino, 

Italy.

Abstract—Current fuel management practices vary considerably between European 

countries. Topography, forest and forest fuel characteristics, size and compartmentalization of forests, forest management practices, land uses, land ownership, size of 

properties, legislation, and, of course, tradition, are reasons for these differences.

Firebreak construction, although not as clearly favored as in the past, is still a 

prominent fuel management technique. Fuelbreak construction has been adopted 

quite extensively in the last decades. Fuel treatments along the sides of roads are common. Use of prescribed burning is generally very limited. However, in most countries, 

shepherds use fi re quite extensively, but illegally. Furthermore, stubble burning is a very 

common type of fi re use, which often becomes source of wildfi res. Grazing of cattle, 

sheep and goats is a traditional practice in the wildlands of Mediterranean countries. 

In spite of many recent social changes, it is still prevalent. Although its effect is often 

negative, when the carrying capacity of the land is exceeded, it does offer a signifi cant 

contribution toward controlling fuel accumulation. In some cases animal herds are 

actively used as means for controlling vegetation re-growth in areas of fuel treatment. 

This paper is an effort to provide an overview of current fuel management activities 

in the European countries, mainly those with Mediterranean climate.

Introduction

Europe is a diverse continent with a large number of nations and countries 

that differ signifi cantly from each other. Their differences range from the 

characteristics of their people to the prevailing environmental conditions, and 

from their culture and heritage to their social and economic structure. The 

European forest cover is characterized by a great diversity of forest types, extent, ownership structure and socio-economic conditions. However, in regard 

to forest fi res, things are much simpler: the countries in northern Europe are 

not really concerned with fi res. On the other hand, the southern European 

countries (Portugal, Spain, the south departments of France, Italy, Greece and 

Cyprus), most of them lying next to the Mediterranean Sea, face a profound 

forest fi re problem. The Mediterranean countries contribute 94% of the total 

burned area in Europe, according to an analysis of the 1975-2000 statistics 

by the European Forest Institute. Fire is the most important natural threat 

to forests in Southern Europe.

The countries of Southern Europe have seen their fi re problem getting 

worse in the second part of the 20th century. Abandonment of rural areas, 

prolonged protection of forest lands, and expansion of fast growing species 

that are highly fl ammable (mostly pines and eucalypts) have aggravated fi re 

Forest Fuels Management in Europe

Gavriil Xanthopoulos1, David Caballero2, Miguel Galante3, 

Daniel Alexandrian4, Eric Rigolot5, and Raffaella Marzano6
30 USDA Forest Service Proceedings RMRS-P-41. 2006.

Xanthopoulos, Caballero, Galante, Alexandrian, Rigolot, and Marzano Forest Fuels Management in Europe

hazards. Tourism growth and development of extensive wildland-urban interface areas have sharply increased fi re incidence and disaster potential. The 

annually burned area has more than doubled since the 1970s.

Realizing they had a problem but not fully understanding the reasons 

behind it, all south European countries responded by increasing their fi re 

suppression capacity, especially through the 1990s, necessarily increasing 

their fi refi ghting budgets. The outcome of this effort is a reduction in total 

annually burned area in relatively easy fi re seasons. However, the potential for 

major disasters is still there. As more fuels accumulate, in diffi cult fi re seasons, 

the burned area climbs again to high levels. Furthermore, the damages are 

very high as fi res often originate or easily reach the extensive wildland-urban 

interface areas that have emerged in all these countries, mainly close to the 

coastline. This has been demonstrated very clearly in the last three catastrophic 

fi re seasons in Portugal (2003-2005), with the occurrence of extremely large 

(over 10,000 ha) and destructive fi res. Currently, the need for reducing fi re 

hazard through active fuel management is becoming more and more obvious, but, to this day, the funding that is diverted from suppression to fuel 

treatment and general fi re prevention is limited.

Fuel occupies one of the three sides of the fi re triangle. Heat and oxygen 

form the other two sides. In the forest, fuel can be manipulated effectively 

before the start of a fi re, infl uencing the probability of fi re ignition and 

potential fi re behavior. The other two contributing factors to fi re behavior 

(weather and topography) cannot be altered by fi re managers. Thus, forest 

fuel management is one of the cornerstones of successful fi re management.

There are many defi nitions of fuel management in literature, most of them 

quite similar to each other. According to the one adopted by the Food and 

Agriculture Organization of the United Nations, fuel management is the “act 

or practice of controlling fl ammability and reducing resistance to control of 

wildland fuels through mechanical, chemical, biological, or manual means, 

or by fi re, in support of land management objectives.”

Managers can modify the load and the arrangement of both live and dead 

fuels. Available options are quite well known. They include horizontal isolation 

of fuel through fi rebreaks, fuelbreaks and greenbelts, fuel reduction through 

physical removal, prescribed burning and intensive utilization, change of fuel 

bed compactness by methods such as lopping and scattering (manually or by 

tractor crushing) and chipping, breaking vertical continuity through pruning 

and surface fuel reduction, and change of fuel moisture content through dead 

fuel removal and even local irrigation (Chandler and others 1983). Fire-aware 

silviculture is yet another broad option. The choice of which methods are 

used varies depending on factors such as vegetation type and characteristics, 

seriousness of the fi re problem, available funds, available experience and expertise, tradition, social concerns, etc. How these factors weigh in the fi nal 

decision has a direct effect on the selection of fuel management methods and 

the scale of their application. This is where differences exist between Europe 

and the other continents, as well as within Europe.

Most of the above mentioned methods of fuel treatment are used somewhere in Europe. This paper provides an overview of current fuel management 

practices in European countries, mainly those with Mediterranean climate, 

based on literature and on the personal knowledge of the contributing authors. In doing so, we tried to explain the reasons that have led to the current 

practices.
USDA Forest Service Proceedings RMRS-P-41. 2006. 31

Forest Fuels Management in Europe Xanthopoulos, Caballero, Galante, Alexandrian, Rigolot, and Marzano

Fuel Management Practices in European 

Countries

Horizontal Continuity Disruption

Firebreaks—Firebreak construction was the most widely applied fuel 

treatment in the past. It still is to a large extent but it is not as clearly favored anymore. The preference for creating fi rebreaks can be explained by 

the obviousness of their objective (to stop the fi re through fuel continuity 

disruption) that is visible to laymen and politicians alike. However, through 

time, a number of disadvantages became evident: high construction cost, high 

maintenance cost (need for annual clearing), poor aesthetics and signifi cant 

potential for erosion when built on medium to steep slopes. Furthermore, 

their effectiveness proved to be quite limited. They may help to stop small 

fi res with little fi refi ghting support under mild weather conditions, but they 

are easily breached through spotting under strong winds and low relative 

humidity. The relatively small extent of forests in Europe, presence of villages and agricultural properties, and concerns about aesthetics and erosion, 

practically preclude construction of very wide fi rebreaks. The width (30-40 m) is 

often inadequate for averting breaching by direct fl ame contact when crown 

fi res are fanned by strong winds.

Currently fi rebreak construction is a regular practice in Portugal, Spain, 

France, Greece and Cyprus. In Italy it exists as a practice but its use is not as 

regular. It should be pointed out that in some of these countries, especially 

in Spain (where the regions are largely autonomous) and in Italy, there are 

signifi cant differences in the natural environment (colder north vs. warmer 

south, elevation infl uence, maritime infl uence, vegetation composition), in the 

societal structure and in the overall political management practices, including 

budgeting. This is refl ected to a large extent in the decisions made on fuel 

management in general and in fi rebreak construction in particular.

Building fi rebreaks is only a start. Maintaining them is much more diffi cult 

as budget shortages often make it impossible to keep them free of low vegetation (mainly grasses and shrubs) on a short period (usually annual) basis. The 

longer the fi rebreak network, the more the yearly budget required for maintenance. With poor maintenance fi rebreaks cannot serve their purpose.

Fuelbreaks—Fuelbreak construction has been adopted quite extensively 

in the last two decades. Sometimes fuelbreaks are built “by the book” trying 

to permanently convert vegetation to a cover of low fuel volume and/or low 

fl ammability (Chandler and others 1983). In general this is not easy when 

dealing with Mediterranean shrubs, either in an open shrubland or under 

the canopy of trees, because most of these shrubs are vigorous resprouters. 

On the other hand, use of phytocides has been tried experimentally in various situations with interesting results but their costs and the associated risks 

make this practice diffi cult to accept in both ecological and economical terms 

(Rego 1997).

In the European countries road networks are quite dense. Clearing vegetation on the sides of forest and rural roads, either manually or mechanically, 

results in fuelbreak-like belts of reduced fi re hazard from which fi refi ghters 

can try to stop a fi re, for example, by lighting a backfi re. Also, when understory vegetation is removed along the sides of the roads, usually up to a 
32 USDA Forest Service Proceedings RMRS-P-41. 2006.

Xanthopoulos, Caballero, Galante, Alexandrian, Rigolot, and Marzano Forest Fuels Management in Europe

distance of 30 m on each side, the spread of a fi re that starts by the road is 

slowed down. Crown fi re initiation is also delayed increasing the probability 

of successful initial attack.

Regular fuelbreak construction is common in Spain, France and Italy, while 

it is less common in Portugal and quite uncommon in Greece.

Greenbelts—Chandler and others (1983) referred to greenbelts as “the 

next logical progression after the fuelbreak.” They defi ned greenbelt as “a 

strip that has been converted to a nonfl ammable cover type and is maintained 

in that state by irrigation and mechanical treatment.” They suggested a golf 

course as an example of a greenbelt, but admitted that greenbelts are prohibitively expensive for a forestry organization.

In Southern Europe, however, some agricultural cultivations play the role 

of breaking horizontal fuel continuity by providing a strip of nonfl ammable 

cover type. The abundance of such fi elds around villages is one of the reasons 

for reduced fi re damages in the past. Vineyards are one of the commonly 

encountered cultivations that can function as a greenbelt. Orange and lemon 

orchards are another. Even olive groves, when properly cultivated, with grass 

and other surface fuels removed, can stop a fi re effectively. However, as much 

of the rural population abandons agriculture and leaves for the cities, the 

effectiveness of these greenbelts is greatly reduced. Their size decreases and 

without the usual treatment of grasses under the cultivated woody plants the 

fi re can easily breach them. Olive groves are the most pronounced example 

of this change: when left with grasses in the understory they become a major 

problem for fi refi ghting because the olive trees, once ignited, are very hard 

to extinguish completely.

Fuel Reduction

Physical fuel reduction—Fuel reduction by manual or mechanical means 

is the main method used by fi re protection organizations for the creation of 

fi rebreaks and fuelbreaks. However, the cost of such treatments is generally 

very high and the area that can be treated is quite limited.

Prescribed burning—Prescribed burning was introduced in Europe—

Portugal, Spain and France, in the early 1980s (Botelho and Fernandes 

1998). However, after 25 years, its operational use remains very limited. In 

some cases, as in Greece, it is not possible as there is no provision for it in the 

existing laws. There is neither long-term experience in the fi re management 

organizations nor much willingness to assume the risks associated with this 

practice. The existence of towns and villages, agricultural lands and other 

private property imposes signifi cant restrictions in regard to smoke management, liability issues and safety. Furthermore, since any type of fi re in the 

forest has been described in all fi re prevention campaigns as bad in the past, 

there is concern of the public receiving mixed signals if prescribed burning 

is not introduced properly.

Currently, the European Union (EU) is trying to improve its knowledge 

on prescribed fi re as applied in European ecosystems, hoping to expand its 

usage where it could be benefi cial and offer practical solutions. An EU funded 

Integrated Research Project titled “FIRE PARADOX” was started in the 

beginning of 2006 and will continue until 2010. It involves 31 institutions 

from 13 countries, including in addition to the European partners, institutions from Northern Africa (Maroc and Tunis). The aim of the project is 

to study the use of prescribed fi re and the application of backfi re in Europe 
USDA Forest Service Proceedings RMRS-P-41. 2006. 33

Forest Fuels Management in Europe Xanthopoulos, Caballero, Galante, Alexandrian, Rigolot, and Marzano

in four main domains: Prevention, ignitions, spread and suppression (www.

fi reparadox.org).

Shepherds often do what is not done by fi re management organizations in 

regard to fuel reduction by fi re. It is a long-standing tradition for them to set 

small fi res at times of low fi re danger that burn patches of land, stimulating 

new succulent growth of grasses and (mainly resprouting) shrubs for their 

animals. This procedure could be considered as a management scheme under 

certain conditions as it is profi table for the shepherds and also reduces fuel 

hazard. However, in recent years it has become a problem, often leading to 

desertifi cation. The reason for this is an increase of the number of animals to 

levels far beyond the carrying capacity of the available land. EU subsidies to 

shepherds, in the 1985-2000 period, were based on the number of animals 

they had, becoming a motive for increasing the size of their fl ocks. This 

policy has been changed nowadays after its detrimental effects became evident. The large number of animals quickly reduced available forage, making 

shepherds reburn the land every 1-4 years. Such a frequency, combined with 

immediate overgrazing of the young vegetation, quickly denuded many sites 

leaving them covered with non-palatable, mostly thorny, plants, and having 

a signifi cant soil erosion problem.

Although “effective” for fuel reduction, this method also has a side effect. 

As fi refi ghting organizations easily manage to stop shepherd fi res in the low 

fi re danger season, it has been observed that the shepherds turn to new lands 

where they start fi res on high danger days.

Biomass utilization—Fuels accumulate in forests when biomass production through photosynthesis is higher than the rate of decay. This is common 

in most ecosystems but the rate at which such accumulation occurs, varies 

depending on the characteristics of the ecosystem and its environment. When 

fuels accumulate beyond a certain point fi re becomes the alternative that 

Figure 1—Burnt area in the EU Mediterranean region in the 1980-2003 period 

(European Communities 2004).
34 USDA Forest Service Proceedings RMRS-P-41. 2006.

Xanthopoulos, Caballero, Galante, Alexandrian, Rigolot, and Marzano Forest Fuels Management in Europe

breaks down the biomass and initiates a new circle of life. Biomass utilization 

is a third alternative that can maintain balance and reduce the probability 

of fi re.

In the Northern European countries active forest management with good 

timber utilization that leaves relatively little slash behind is key to keeping 

the potential for fi re disasters low. Timber production is one of the main 

products fueling the economy of the Scandinavian countries. With such 

practices, fi res like those in the boreal forests of Alaska, Canada, and Siberia 

are highly unlikely in Sweden or Finland.

On the other hand, in Mediterranean ecosystems fuels accumulate quite fast 

as biomass production is quick and decay is slow. Active forest management 

for timber production with appropriate silvicultural practices is mostly carried 

out where there is fi nancial incentive: the forest products have a higher value 

than the cost for managing the forest. Examples are the eucalypt plantations in 

Portugal, the Pinus nigra forests in Greece and the Pinus pinaster and Pinus 

sylvestris forests in Spain. In many cases, however, as with the forests of Pinus 

halepensis and Pinus brutia in Greece, the active management of forests is not 

economically viable. Without biomass utilization these forests are expected 

to burn with relatively high frequency. Traditionally, much of the biomass 

produced by these forests and the evergreen Mediterranean shrublands was 

harvested and used as an energy source for cooking and heating by the rural 

populations living close to the forests. Also, resin collectors managed these 

pine forests in a traditional way, guarding them, maintaining access trails 

and removing old and non-productive trees to be used as fuel wood, in an 

effort to create open spaces for regeneration of new clubs of trees. In this 

way, a balance was maintained, at least close to the numerous villages, where 

approaching fi res were easy to control. The migration of these populations 

toward the cities and the substitution of other energy sources (electricity, oil, 

gas) for wood upset this “natural” balance and led to the current worsening 

condition and the need for fuel management for fi re hazard reduction.

Currently, grazing in the shrublands is the most common form of biomass 

utilization in the non-timber producing forest lands in the Mediterranean. 

When this practice is planned and controlled at appropriate levels it functions 

as a very effective and productive method of fuel management.

A Short Summary for Each Country

France

France is the most active southern European country in regard to fuel 

management. The French approach is that fi refi ghting implies strategy, and 

good strategy means preparing wildlands for fi refi ghting to achieve effi ciency 

of suppression operations and safety of fi re crews. In this respect, the core of 

French strategy in fi re management is “wildland partitioning” (Figure 2).

The “tools” for achieving wildland partitioning are fuelbreaks and fi re 

fi ghting areas. Furthermore, as part of the overall strategy, protection of 

human assets is a priority. A “let it burn” policy is applied on a very limited 

scale. It is very diffi cult to apply such a policy in France, because human assets are too many and interspersed in most forests.

A “fuelbreak working group” has been established in France. The Group 

works on:

 • Building fuelbreaks
USDA Forest Service Proceedings RMRS-P-41. 2006. 35

Forest Fuels Management in Europe Xanthopoulos, Caballero, Galante, Alexandrian, Rigolot, and Marzano

 • Fuelbreak maintenance

 • Real study cases analysis

 • Economic assessment of fuelbreaks

 • Preparing national standards for the “tools” of fuel management (such 

as fi rebreaks) that are applied in the fi eld

In France, fuelbreaks are categorized in three types according to their 

objective:

 • Type 1: The objective is to limit fi re ignitions: fuel management aims to 

decrease ignition hazard and to increase success of early fi re fi ghting 

operations. It is mostly applied in or around Wildland Urban Interface 

areas.

 • Type 2: The objective is to limit fi re effects on assets: fuel management 

focuses on making the circulation of fi refi ghting crews and the public 

easier and safer (safer escape routes). It is mostly applied in or around 

Wildland Urban Interface areas. Fuel management for forest autoprotection (i.e. to avoid stand replacement fi res) is included in this type of 

fuelbreak.

 • Type 3: The objective is to limit the size of burned areas by breaking 

forest continuity. These are fuelbreaks built at strategic locations to 

help fi refi ghters control the head or the fl anks of probable fi res. They 

are generally built between 2 non-burning (usually agriculture) areas. 

In building type 3 fuelbreaks two objectives are:

 o To provide at least a safety zone for fi re crews.

 o To enable effi cient fi re suppression actions.

Scenarios that must be taken into account include the case of a large fi re 

and fi res under severe fi re weather conditions (Figure 3).

Fuelbreaks are built by forest authorities but in cooperation with the 

fi refi ghters (Civil Protection) in order to take their requirements into 

Figure 2—The concept of “wildland partitioning” in France.
36 USDA Forest Service Proceedings RMRS-P-41. 2006.

Xanthopoulos, Caballero, Galante, Alexandrian, Rigolot, and Marzano Forest Fuels Management in Europe

consideration. Their construction takes into account the fi refi ghting strategy. 

Necessary elements, such as water supply, access (roads), road signs, etc., must 

be available along a fuelbreak.

The standard approach for fuelbreak maintenance is mechanization. When 

possible, grazing and agriculture are also used. Prescribed burning is also used 

to some extent and its application is increasing. Of course, social constraints 

have to be integrated when choosing to use it.

In a 1999 study of nine fuelbreaks in France, which took into consideration the cost of construction (amortizement), maintenance, outcomes from 

grazing (production) and external costs, over a 5 to 15 year period, it was 

found that the annual cost of a 30 to 40 ha fuelbreak is equal to one hour of 

aircraft fl ying time delivering three retardant drops.

Portugal

The Portuguese Forest Services structure is based in a Central Offi ce at 

Lisbon and three regional offi ces, each with 7 sub-regional offi ces. These 

sub-regional offi ces have the responsibility of promoting the Regional Plans 

of Forest Management. Recently, a Sub-Directory for Forest Fires Prevention 

was created under the General-Directorate of Forest Resources, to accomplish 

the execution of the National Plan for Forest Fire Prevention and Management. The Regional Plans of Forest Management also defi ne the primary 

and secondary fuels break network planning, to promote rural landscape 

fragmentation and control the spread of large fi res.

Figure 3—The concept of a fuelbreak network in France.
USDA Forest Service Proceedings RMRS-P-41. 2006. 37

Forest Fuels Management in Europe Xanthopoulos, Caballero, Galante, Alexandrian, Rigolot, and Marzano

There is the assumption that the solution to the forest fi re problem relies 

on the promotion of private forest management, although this is a mid-term 

solution. Several incentives are now being developed, such as reduction of 

the Value Added Tax (VAT) for preventive silviculture services and funding 

support for the installation of “Integrated Forest Zones”, that unite, for 

management purposes, a large number of small size private forest parcels. 

These zones have a size of at least 1,000 ha, a size that is considered as the 

minimum needed for professional management of the forest resources and 

for fi re prevention planning at the landscape level.

At the municipal level, Forest Fire Prevention plans include characterization of hazardous areas and set the fuels treatment strategy. Techniques like 

prescribed fi re, grazing and localized mechanical interventions are defi ned in 

those plans. Fuel treatments in the forest/urban interface are also planned, as 

well as pre-suppression infrastructures (water points, lookout towers, forest 

roads and fuelbreaks).

Firebreaks are the most widely used fuel management technique in Portugal, mostly in the mountainous areas, in the public lands and in the eucalypt 

plantations of the pulp and paper companies. Directed grazing for cattle at 

the landscape level is starting to be promoted. Localized manual fuel treatment by hand crews is another technique used in strategic areas. Recently, 

Portugal puts an emphasis in the reintroduction of prescribed fi re and for 

this purpose has started a broad training program of foresters and support 

crews. A technical exchange program with the U.S. Forest Service (USFS) is 

being prepared to support this initiative (Figure 4).

 Figure 4—One of the fi rst prescribed burns executed in 2006 in Portugal with the cooperation of 

USFS prescribed fi re specialists (Photo: Mike Crook (USFS)). 
38 USDA Forest Service Proceedings RMRS-P-41. 2006.

Xanthopoulos, Caballero, Galante, Alexandrian, Rigolot, and Marzano Forest Fuels Management in Europe

Spain

In Spain the responsibility for forest fi re prevention belongs to the 17 

autonomous regions (Autonomies). Every region has its own regulation and 

rules for forest fuel management, hence the methods, intensity and allocated 

budget varies from region to region.

Out of the 26 million hectares classifi ed as forest land, about 18 million 

are privately owned. Landowners are in charge of the exploitation and 

maintenance of the ecosystems, and also responsible for the forest fuel 

treatments. Although specifi c regulations apply in the regions most affected 

by forest fi res, landowners do not respond in the same way in regards to fuel 

management. Hence the methods, extension and intensity vary within the 

regions as well.

Every region, by law, has to provide a forest fi re defense plan, including a 

chapter for preventive measures, which include operations on the forest fuel. 

However, common objectives are followed, mostly thanks to the yearly CLIF 

meeting which is hosted by the Ministry of Environment and in which main 

target priorities are discussed and set among all autonomous regions.

In the last two decades, an important change in fuel structure and load 

has occurred, mostly caused by the de-population of rural areas. Land use 

change, in many cases followed by the abandonment of activities in the forested lands, has brought about an increase of burnable biomass in grasses and 

shrubs, and the modifi cation of the vegetation structure, favoring horizontal 

and vertical fuel continuity.

Three areas can be considered in terms of fuel structure and load, hence 

giving an idea of the requirements of forest fuel treatments.

In the Atlantic zone, which is humid, there are several vegetation structures. 

The forested areas frequently have an overload of fl ammable fuels creating 

explosive situations. This is caused by the low budget invested in the forest 

stands, and the poor investment of landowners in fuel treatments. The situation is aggravated further by the fact that the shrubs in this zone regenerate 

quickly, leading to heavy accumulations of very fl ammable biomass in a short 

time after fi res or fuel treatments. The agricultural lands have mostly been 

abandoned. Natural vegetation has invaded these lands, mostly in the interior. The situation is made worse by the uncontrolled use of fi re in an effort 

to control the invading vegetation. Removal of forest fuels in the Atlantic 

zone is costly due to the high rate of biomass production, and is traditionally 

limited to the removal of fern and grasses. Their biomass is normally burned 

in piles. Today, a new practice is being explored: it is the mechanical removal 

of the fast growing shrubs and their use in biomass-energy production plans. 

In the Atlantic zone, it is normal to apply systemic herbicides on fi rebreaks 

and cleared zones built along the perimeter of forest lands to reduce future 

regeneration of shrubs and tree sprouts. Although very effi cient as a vegetation control tool, Administrations are generally reluctant to use prescribed 

burning, perhaps due to the many agricultural burnings that end-up as large 

forest fi res. As a result, at least in the vicinity of large and/or dense forest 

stands, prescribed burning is avoided, although is the most effi cient and cheap 

method of forest fuel removal. In the Atlantic zone, grazing is not applied 

systematically for fuel control.

In the Mediterranean zone, Spain has a mosaic of forest land patterns, 

including young forest stands and reforested areas, abandoned agricultural 

lands, and mature forest stands. They are always subject to the pressure of 
USDA Forest Service Proceedings RMRS-P-41. 2006. 39

Forest Fuels Management in Europe Xanthopoulos, Caballero, Galante, Alexandrian, Rigolot, and Marzano

shrub species. Most forests have a more or less dense shrub understory. In 

this zone, silvicultural and other treatments of the forest fuel are generally 

economically unfeasible for widespread application. Hence, hazard reduction 

efforts are localized and more focused. A combination of silvicultural treatments and livestock grazing is the measure of choice.

In calculating risks in the Mediterranean zone, it is very important to 

consider soil erosion and other hydrological phenomena, which could take 

place under sparse vegetation coverage. Large forest stands are infrequent, and 

when they exist, they are protected by a strip of low combustibility around 

their perimeter. Networks of fi rebreaks are combined with other low-load 

vegetation patterns (i.e. agriculture) to avoid horizontal continuity, while 

taking into consideration the protection of settlements and housing areas 

in the increasing wildland-urban interface domain. Planning the extent and 

location of fuel management takes into consideration the quality and extent 

of the various ecosystems, regeneration capacity, vegetation coverage and 

special protection priorities, if any.

In the Southern zone of Spain, the forest structure is very variable and has 

a direct correlation with the ownership regime. The forest stands belonging and managed by the Administration are subject to periodic silvicultural 

treatments, such as thinning, pruning and understory removal. In contrast, 

in privately owned forests the response is quite poor, except for some cases, 

in which several owners associate and cooperate in managing their forest. In 

this Southern zone, the most common fuel management practices applied 

are grass and shrub removal, prescribed burning and grazing. Due to budget 

restrictions mechanization is still not totally achieved.

Firebreak construction is perhaps the most common fuel control measure 

in Spain. All fi re-prone areas in Spain are criss-crossed by a network of linear fi rebreaks. The main objective is to fragment the territory into cells to 

minimize the spread of large fi res. Regardless of whether they serve their 

purpose well, fi rebreaks are unpopular among citizens in Spain, mostly due 

to the visual impact on the landscape, although the rural population has 

accepted them more quickly due to the forest protection benefi ts they offer. 

Maintenance of fi rebreaks, which is required, takes a large part of the fuel 

treatment budget. Often, budget constraints lead to poor maintenance in 

certain regions. However, in some regions, such as Valencia, application of 

intense grazing by goats in fi rebreaks keeps costs low and helps to maintain 

the fi rebreak network.

The standards for building new fi rebreaks are summarized below:

 • Width of fi rebreak has to be two and a half times the dominant canopy 

height, with a minimum of 15 m in the vicinity or forest stands.

 • Width of fi rebreak has to be 10 m in the vicinity or inside of shrublands.

 • Width of fi rebreak has to be 5 m in the vicinity or inside grasslands.

 • In all cases, fi rebreak vegetation has to be totally removed to mineral 

soil.

In areas where lightning is a main cause of forest fi res, fi rebreaks are often 

built along mountain crests where they serve as an effi cient transport corridor 

for ground forces in addition to hindering fi re growth.

Fuelbreaks are becoming more popular in Spain lately. They are favored 

by many because they have a more natural-looking structure. Their width is 

normally about 30% more than that required for the fi rebreaks.

Prescribed burning is not a generalized and accepted practice for forest 
40 USDA Forest Service Proceedings RMRS-P-41. 2006.

Xanthopoulos, Caballero, Galante, Alexandrian, Rigolot, and Marzano Forest Fuels Management in Europe

fuel reduction. It is regulated and applied in some regions (i.e. Andalusia), 

it is slowly being accepted in others, such as Catalonia and Castilla Leon, 

but it is totally banned in several others, such as Madrid. In general, it is a 

rather unpopular practice, perhaps due to the fact that the use of fi re as a 

tool in agricultural activities has frequently been the cause of large and very 

destructive fi res (Vega and Velez 2000).

The frequency of burning for grazing by shepherds varies between regions 

but it is more or less general practice in Spain to obtain pasture by burning 

shrubs. This practice is more prevalent in the Atlantic zone as mentioned 

above. Grazing of cattle, sheep or goats is a common practice for fuel reduction 

in Valencia and other provinces of the Mediterranean zone. In Galicia, Castilla 

Leon and many other regions of the Atlantic zone grazing is used just to 

contain shrub sprouts. Other fuel management practices include mechanical 

and manual clearance around heavily traveled roads, and under high-voltage 

(1,000 to 220,000 V) power lines. Furthermore, silviculture in Spain takes 

into consideration the need to reduce fi re hazard. Treatments include shrub 

removal, tree thinning, and pruning of lower branches and are often applied 

at locations of special interest (Figure 5).

Figure 5—An example of a silvicultural treatment that also aims at crown fi re potential reduction on 

Tenerife Island, Spain. 
USDA Forest Service Proceedings RMRS-P-41. 2006. 41

Forest Fuels Management in Europe Xanthopoulos, Caballero, Galante, Alexandrian, Rigolot, and Marzano

Italy

The 20 Italian Regions have unique administrative competencies concerning wildland and forest management in their territory. They are also in charge 

of forest fi re protection, supported by the State Forestry Corps through special 

agreements at regional level.

Law dispositions on wildland fi res in Italy are mainly established by the 

national law 353/2000. This law states that each Region is in charge of setting 

up a Fire Management Plan for its regional territory. The plan should identify 

priorities and arrange all fi re protection activities, including interventions on 

woodlands. The national law is inspired by the principle that the best approach to protect forests from wildfi res is to promote and provide incentives 

for prevention activities, instead of just focusing on suppression. In spite of 

this declared goal, neither the law nor its specifi c guidelines discuss in detail 

the subject of fuel treatment and management for wildfi re prevention. The 

law simply states that each regional plan must provide for silvicultural activities to clean and manage woodlands. The greatest investments are still made 

in fi re fi ghting, with a varying amount destined to prevention activities from 

Region to Region.

Each Region must plan, realize and maintain fuelbreaks (and other structural and infrastructural interventions), establishing typologies and standards 

according to its environmental characteristics. To reduce the risk of fi res 

spreading from agricultural areas to forests, within some Regions, plowed 

or mowed buffers are realized along cultivated and abandoned fi elds located 

next to forests (Figure 6).

Figure 6—Mowed buffer strip separating a forest from agricultural land 

in Italy.
42 USDA Forest Service Proceedings RMRS-P-41. 2006.

Xanthopoulos, Caballero, Galante, Alexandrian, Rigolot, and Marzano Forest Fuels Management in Europe

Each regional plan also has to design all forest cleaning and management 

interventions for those areas with the greatest wildfi re risk. These interventions must specifi cally aim at:

 • Reducing fuel biomass and removing coarse woody debris.

 • Creating mixed and well structured stands, with a heterogeneous forest 

composition.

 • Favoring, where possible, coppice conversion to high forest.

 • Favoring natural regeneration.

 • Thinning old and too dense coniferous plantations.

 • Slashing, mowing and cleaning in the proximity of railway lines, forest 

and ordinary roads and road banks, especially if they are located next 

to forested areas.

This last treatment is mainly applied in the summer. It is realized by the 

organizations responsible for the road network and railway management. 

Along railroads chemical weeding is a common practice, while around roads 

both manual and mechanical clearance are applied.

In Italy there is clearly lack of experience concerning prescribed burning; 

moreover there is not a clear set of rules that would defi ne for all the country 

the use of fi re for ecological and management purposes. The national law 

does not mention the possibility to use prescribed burning; thus, it is up to 

each Region to adopt the use of prescribed fi re in its fi re management plan. 

Only a few Regions currently have plans that allow and regulate the use of the 

prescribed burning technique. For these reasons and because of a widespread 

mistrust of fi re for ecological and management purposes, in Italy prescribed 

burning is not applied. Recently some experiments were conducted by the 

Agroselviter Department of the University of Torino to investigate the use 

of prescribed burning both for the management of particular biotopes and 

to reduce fuel load (Ascoli and others 2005).

The practice of burning for grazing by shepherds was more widespread in 

the past; currently it is quite limited and is exercised mainly in a few areas of 

the southern regions and in the islands (mostly Sardinia). Stubble and shrub 

burning is instead a traditional practice adopted by farmers; it is one of the 

most frequent sources of wildfi res.

Grazing of cattle was also more common in the past. Recently some attempts are being conducted to use sheep grazing to reduce fuel biomass within 

fuelbreaks, instead of mechanical treatments (Antona and others 2003).

Greece

In Greece, the responsibility of fi refi ghting passed from the Forest Service 

to the Fire Service in 1998 (Xanthopoulos 2004). As a result, prevention and 

suppression are not seamlessly tied anymore. The cost of fi refi ghting tripled 

in the years that followed. Funding for prevention decreased. Subsequently, 

fuel management efforts are relatively limited today.

The General Secretariat for Civil Protection which was established in the 

late 1990s tries to organize cooperation of all organizations involved in fi re 

management. It organizes public education and fi re prevention campaigns 

every summer, co-ordinates general planning and, in regard to fi re hazard 

reduction, it distributes some prevention funds to local authorities for fuel 

management work, mainly in the vicinity of settlements and along roads.

Firebreaks are the most common fuel management measure taken by the 

Forest Service. Forestry offi cers struggle to keep them clear of vegetation 

re-growth before every fi re season with the limited funding they get.
USDA Forest Service Proceedings RMRS-P-41. 2006. 43

Forest Fuels Management in Europe Xanthopoulos, Caballero, Galante, Alexandrian, Rigolot, and Marzano

In Greece, few fuelbreaks are built “by the book” (i.e. trying to permanently 

convert vegetation to a cover of low fuel volume and/or low fl ammability). 

The road network in the forests is quite dense. Clearing vegetation on the 

sides of forest and rural roads, either manually or mechanically, results in 

fuelbreak-like belts of reduced fi re hazard from which fi refi ghters can try to 

stop a fi re (Figure 7). The cost of this work, when performed manually, has 

been studied in Greece by Xanthopoulos (2002).

Grazing of sheep and goats is very common in the wildlands of Greece. 

In all regions of the country, the number of animals exceeds the carrying 

capacity of the available grazing land. This high grazing pressure has obvious 

negative ecological effects but also keeps fuels under control. On the other 

hand, fi res lighted by shepherds to rejuvenate vegetation in the overgrazed 

shrublands are a signifi cant problem as they constitute more than 10%, probably close to 20% if fi res listed as “of unknown cause” are considered, of all 

wildfi res in the country (Figure 8).

Figure 8—Two small burned areas near a sheep and goat fold in western Crete, Greece.

Figure 7—An example from mount Parnis, near Athens, of shrub 

understory removal around heavily used forest roads, chipping the 

resulting biomass. 
44 USDA Forest Service Proceedings RMRS-P-41. 2006.

Xanthopoulos, Caballero, Galante, Alexandrian, Rigolot, and Marzano Forest Fuels Management in Europe

Conclusions

Table 1 summarizes the fuel management methods in the southern European countries. In spite of the differences between them, there are many 

similarities worth noting here:

 • Firebreak construction, although not as clearly favored as in the past, is 

still a prominent fuel management technique.

 • Fuelbreak construction has been adopted quite extensively in the last 

decades. Fuel treatments along the sides of roads are common.

 • Use of prescribed burning is generally very limited. The existence of villages and other infrastructures within and around forests is one of the 

reasons discouraging its adoption. It can be concluded that efforts to 

expand its use are underway.

 • In most countries, fire is used quite extensively, but illegally, by 

shepherds.

 • Stubble burning is a very common type of fi re use, which often becomes 

source of wildfi res.

 • Grazing of cattle, sheep and goats is very common in the wildlands of 

Mediterranean countries. In spite of many recent social changes, it is 

still prevalent. Although its effect is often negative, when the carrying 

capacity of the land is exceeded, it does offer a signifi cant contribution 

toward controlling fuel accumulation. In some cases animal herds are 

actively used as means for controlling vegetation re-growth in areas of 

fuel treatment.

In general, efforts are concentrated mainly close to inhabited areas and 

focus on protecting humans and infrastructures. Firebreaks and fuelbreaks 

mainly aim to aid in limiting the spread of large fi res but their density in 

areas where there is little population and low forest value is generally limited. 

Preventive silviculture, including prompt timber harvesting and development 

of mixed forests rather than monocultures are often solutions in seeking fi re 

resistance in productive forests.

Acknowledgments

This paper was prepared following an invitation from the organizing 

committee of the Conference. It is mainly based on work performed in the 

context of the “EUFIRELAB” project that is funded by the European Commission, Directorate General for Research, 5th Framework Program (Contract 

EVR1-CT-2002-40028). Relevant information can be found on the project 

web-site (www.eufi relab.org). The support of the Protection Branch of the 

Ministry of Forests and Range of the Province of British Columbia, Canada, 

for making the trip and presentation to the Conference possible, is gratefully 

acknowledged.
USDA Forest Service Proceedings RMRS-P-41. 2006. 45

Forest Fuels Management in Europe Xanthopoulos, Caballero, Galante, Alexandrian, Rigolot, and Marzano Table 1—A summary of current fuel management practices in southern Europe. Manual Mechanical Grazing Prescribed clearance clearance of cattle, burning Burning for Clearance Firebreak Fuelbreak around around sheep for fuel grazing by Silvicultural under Country construction construction roads roads or goats reduction shepherds treatments powerlines France R R R R I I I I R Greece R I R I R N R E R Italy I R R R N N – E N R - I R Portugal R I I R R E R I R Spain R R R R R I R I R Where: R : Regular operation I : Applied but not on a regular basis E : Applied in small scale or experimentally N : Not applied
46 USDA Forest Service Proceedings RMRS-P-41. 2006.

Xanthopoulos, Caballero, Galante, Alexandrian, Rigolot, and Marzano Forest Fuels Management in Europe

References

Antona, Madeleine; Casanova, Jean-Baptiste; Franca, Antonello; Seddaiu, Giovanna; 

Argenti, Gianni; Lombardi Piero. 2003. Les interventions pastorales au service de 

la prevention des incendies, en Corse, en Toscane et en Sardaigne. Proceedings of 

the Euro-Mediterranean Conference “The future of the green Mediterranean”; 

2001 June 1-2.; Alghero. (SS).

Ascoli, Davide; Marzano, Raffaella; Bovio Giovanni. 2005. Fuoco prescritto 

sperimentale per la gestione conservativa della brughiera. Linea Ecologica, 37 

(3), 19-26.

Botelho, Herminio S.; Fernandes, Paulo M.. 1998. Controlled burning in the 

Mediterranean countries of Europe. In: Eftichidis, George; Balabanis, Panayotis; 

Ghazi, Anver., ed. Advanced Study Course on Wildfi re Management: proceedings. 

1997 October 6-14; Marathon, Greece, 1997 Algosystems S.A., Athens, Greece 

under the auspices of the European Commission, DG XII: 163-179.

Chandler, Craig C; Cheney, P.; Thomas, P; Trabaud, L.; Williams, D. 1983. Fire in 

Forestry, Vol. II: Forest fi re management and organization. John Wiley & Sons, 

N.Y. 298 p.

European Communities. 2004. Forest Fires in Europe: 2003 Fire Campaign. 

SPI.04.124. 51 p.

Rego, Francisco Castro. 1997. Fuel management and prescribed fi re. In: Balabanis, 

Panayotis; Eftichidis, George; Fantechi R., ed. Forest Fire Risk and Management: 

European School of Climatology and Natural Hazards course: proceedings; 1992 

May 27 – June 4; Porto Carras, Halkidiki, Greece. 133-142.

Vega, José Antonio; Vélez, Richard Muñoz. 2000. Actas de la reunion sobre quemas 

prescritas en Lourizán 1998. Cuadernos de la Sociedad Española de Ciencias 

Forestales N9-2000.

Xanthopoulos, Gavriil. 2002. Shrub removal cost estimation for fi re hazard reduction 

in Mediterranean forest conditions. In: Viegas, Domingos Xavier, ed. 4th Int. Conf. 

on Forest Fire Research: proceedings; 2002 November 18-23; Luso-Coimbra, 

Portugal. Millpress Science Publishers, Rotterdam, The Netherlands: Book of 

Abstracts, p. 33. Full paper on CD accompanying the book of abstracts.

Xanthopoulos, Gavriil. 2004. Who should be responsible for forest fi res? Lessons 

from the Greek experience. p. 128 In book of abstracts of the “II International 

Symposium on Fire Economics, Planning and Policy: A Global View”, April 19-

22, 2004, Cordoba, Spain. University of Cordoba, and USDA Forest Service, 

Pacifi c Southwest Research Station. Full paper on CD accompanying the book 

of abstracts.
USDA Forest Service Proceedings RMRS-P-41. 2006. 47

In: Andrews, Patricia L.; Butler, Bret W., 

comps. 2006. Fuels Management—How to 

Measure Success: Conference Proceedings. 

28-30 March 2006; Portland, OR. 

Proceedings RMRS-P-41. Fort Collins, 

CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research 

Station.

1 Director, Fire and Aviation Management, 

USDA Forest Service, Southwestern 

Region, Albuquerque, NM. 

tomzimmerman@fs.fed.us

2 Fire Ecologist, Bureau of Land 

Management, Colorado State Office, 

Denver, CO.

3 Fire Management Offi cer, USDA Forest 

Service, Gila National Forest, Silver City, 

NM.

4 Fire Use Specialist, USDA Forest Service, 

Intermountain Region, Ogden, UT.

5 Deputy Director, Fire and Avia tion, 

USDA Forest Service, Intermoun tain 

Region, Ogden, UT.

6 Strategic Fuels Planner, USDA 

Forest Service, Headquarters Offi ce, 

Washington, DC.

Abstract—The application and use of wildland fi re for a range of benefi cial ecological 

objectives is rapidly expanding across landscapes supporting diverse vegetative complexes and subject to multiple societal uses. Wildland fi re use originated in wilderness 

and has become a proven practice successful in meeting ecological needs. The use 

of wildland fi re in non-wilderness is emerging as an important practice but its success 

is predicated on the acknowledgment of the fundamental inseparability and equal 

importance of ecological, social, and economic needs and requirements. The 2005 

western fi re season resulted in the single largest scale application of wildland fi re use 

in non-wilderness to date and illustrated that managing wildland fi re use in these areas 

is associated with a higher level of complexity driven by a number of elements including: spatial scale differences; presence of multiple ownerships and increased values 

to be protected; increased needs to plan and implement mitigation actions; temporal 

scale differences for implementing mitigation actions; greater social and economic 

concerns and needs; and increased public information needs. Continuing expansion 

of wildland fi re use implementation across federal, state, and private land ownerships 

and all land use situations will encounter additional infl uences and new challenges, 

situations not previously experienced, and ancillary implementation questions which 

could potentially limit program growth and development.

Introduction

Wildland Fire Use (WFU) is the application of the appropriate management response to naturally ignited wildland fi res to accomplish specifi c 

resource management objectives in predefi ned designated areas outlined in 

Fire Management Plans (USDA/USDI 2005). What is currently wildland 

fi re use has its origins in ground-breaking management decisions and actions 

in wildernesses, national parks, and other areas managed as de facto wildernesses over three and one-half decades ago. As this program expanded and 

evolved, planning processes, assessment procedures, and implementation 

techniques continued to progress. But, to successfully accomplish objectives 

as a land management practice in support of ecosystem maintenance, restoration, and community protection at the necessary scale, both temporal and 

spatial increases must be achieved and sustained. Consequently, wildland fi re 

use applications must expand beyond wilderness into other suitable areas and 

broaden from a wilderness only application to one having potential applications across all land-use situations.

Wildland Fire Use — Challenges Associated 

With Program Management Across Multiple 

Ownerships and Land Use Situations

Thomas Zimmerman1, Michael Frary2, Shelly Crook3, Brett Fay4, 

Patricia Koppenol5

, and Richard Lasko6
48 USDA Forest Service Proceedings RMRS-P-41. 2006.

Zimmerman, Frary, Crook, Fay, Koppenol, and Lasko Wildland Fire Use—Challenges Associated with Program Management Across Multiple Ownerships...

Managing wildland fi re in wilderness has prompted development of specifi c 

procedures and processes in response to risks and challenges and has become 

a proven and widely applied practice to meet ecological needs. Actual accomplishments by all agencies shows the average annual level of achievement from 

2001 – 2005 to be about three times higher than the average annual output 

for the previous fi ve years (fi gure 1). Managing WFU in non-wilderness, 

while having been applied since the late 1990’s, has not achieved widespread 

use. However, the 2005 fi re season exemplifi ed the expanding nature of this 

program; the single largest scale application of WFU in non-wilderness in 

the United States occurred. The advent of WFU expanding into non-wilderness adds a substantial management component and accomplishments can 

be expected to increase over historic levels. Figure 1 illustrates WFU accomplishments since the implementation of the Federal Fire Policy in 1995 and 

the 2005 non-wilderness accomplishment.

Continued programmatic expansion of wildland fi re use is presenting new 

challenges, previously unexplored situations, and additional implementation 

questions which could potentially limit implementation. To support sustained 

program expansion, these questions need addressed, management effi ciency 

must be improved, potential barriers to success should be eliminated, and all 

prerequisites to continued implementation must be defi ned and in place.

Existing Challenges to Wildland Fire Use

Wildland fi re use, regardless of the land use situation it is applied in, is affected by a large number of factors that are supportive or potentially limiting 

to this activity. These factors as experienced from a predominantly wilderness 

land use situation are shown in table 1.

Figure 1—Wildland fi re use accomplishments for all agencies, 1995-2005; comparison 

of annual total and non-wilderness for 2005 (source USFS, NPS data on fi le at National 

Interagency Fire Center, Boise, ID and National Fire Plan Annual Performance Reports, 

2001- 2004. NOTE: NFP data is tabulated by fi scal year, not calendar year.)
USDA Forest Service Proceedings RMRS-P-4. 2006. 49

Wildland Fire Use—Challenges Associated with Program Management Across Multiple Ownerships... Zimmerman, Frary, Crook, Fay, Koppenol, and Lasko

Table 1—Current supportive and potentially limiting factors to wildland fire use (adapted

from Zimmerman, in press).

Supportive Factors Potentially Limiting Factors

 To date, the most supportive federal

fire policy for using wildland fire as part

of the full spectrum of appropriate

management responses,

 To date, the highest level of advocacy

for using wildland fire to accomplish

resource benefits,

 To date, the highest level of scientific

support for and technical capabilities to

use fire,

o Fire behavior prediction

models,

o Long-term risk assessment

techniques,

o Geographic information

system capabilities,

o Satellite imagery useful in

assessing live fuel moisture,

smoke production and

dispersion, and fire locations,

o Improved meteorological

analysis and record keeping,

o Fire effects prediction models,

o Fuel measurements

techniques,

 To date, the highest level of knowledge

of fire effects and the natural role of

fire,

 Higher levels of public awareness and

understanding,

 Better definition and clarification in land

management planning process in

regard to the use of fire.

 More dominant temporal limitations in

response to changing fuel complexes,

 More assertive social demands, needs,

and tolerances which strongly sway

public opinion, affect management

opportunities, and in combination with

continually expanding wildland-urban

interfaces and associated protection

concerns, dramatically affect the ability

to apply fire across a wide spatial

spectrum,

 Significant influence of threatened and

endangered species and sensitive

natural and cultural resource

considerations, protection, and

management in fire use decisionmaking,

 Changing fuel complexes and fire

spread and intensity rates effects on

increasing risk and complexity levels,

 Continuing needs for expanded public

information,

 Smoke management concerns.

Emerging Challenges to Wildland Fire Use

The array of factors exerting infl uence on wildland fi re use in nonwilderness encompasses the full set of factors listed in table 1. However, 

programmatic expansion into non-wilderness has encountered new situational 

elements presenting additional diffi culty and complexity in wildland fi re use 

management. It is apparent that prerequisite to full implementation in nonwilderness is the acknowledgement of the inseparability and equal importance 

of ecologic, social, and economic needs and requirements. During the past 

35 years, wildland fi re use has focused on ecologic needs and requirements 

as the most important objective. This focus is shifting as implementation 

moves out of wilderness and specifi c challenges are emerging during nonwilderness wildland fi re use involving social and economic needs, planning 

considerations, and implementation procedures. Areas where concerns and 

questions associated with managing wildland fi re use in non-wilderness have 

surfaced are shown in table 2.
50 USDA Forest Service Proceedings RMRS-P-41. 2006.

Zimmerman, Frary, Crook, Fay, Koppenol, and Lasko Wildland Fire Use—Challenges Associated with Program Management Across Multiple Ownerships...

Supportive Factors

Communication, Education, and Community Relations—Perhaps one 

of the best opportunities to accomplish local communication and outreach is 

available during implementation of wildland fi re use events in non-wilderness. 

The proximity of these fi res to communities and increased public and media 

awareness due to the fi re visibility, while likely adding diffi culty to management actions, creates a virtual “classroom” where program and fi re benefi ts 

can easily be explained and illustrated to increase public understanding and 

support. Such opportunities should be fully explored and utilized.

Increased Collaboration in the Use of Wildland Fire to Accomplish 

Benefi cial Effects—

 • Private Landowner Support for Using Wildland Fire on Private Lands—

Much of the public and many but not all, private landowners are 

recognizing the value of restoring and maintaining fi re-adapted ecosystems. This year, as wildland fi re use expanded outside wilderness and 

Table 2—Emerging challenges supportive to, adding more management considerations, and potentially

increasing complexity for wildland fire use, based on the 2005 fire season non-wilderness applications.

Supportive Factors

Concerns and Questions -

Planning, Implementation, and

General Understanding

Additional Complexity

Influences

 Communication,

education, and community

relations opportunities

 Private landowner support

for using wildland fire by

on private lands in

conjunction with federal

activities,

 State agency support for

using wildland fire for

resource benefits by in

cooperation with federal

agencies,

 Support for State-led

efforts to improve forest

and watershed health and

reduce potential wildfire

effects

 Expansion of ecosystem

restoration and

maintenance and

hazardous fuel strategy

and accomplishments into

all land use situations.

 Expanded implementation

capability and greater

accessibility.

 Number and kind of mitigation

actions needed for successful

management of the fire

 Size constraints/limitations on

WFU in non-wilderness,

specifically in regard to

minimum size limits or

thresholds (size thresholds) and

a perceived similarity between

non-wilderness wildland fire

use management and

prescribed fire

 Managing fire immediately

adjacent to an MMA

 Equivalency to non-fire

treatments

 Internal support for wildland

fire use

 Communication, education,

and community relations

opportunities

 Cost containment

 Inclusion or exclusion of

private lands within the MMA

and wildland fire use affected

areas

 Economic concerns –

protection of necessary natural

resources or establishment of

alternatives

 Allotment fence protection –

protection of necessary socialeconomic values

 Proximity to values –

additional hazards

 Increased smoke management

needs

 Fuels and fire behavior of

lower elevational zones

 Susceptibility of nonwilderness to post-fire

proliferation of invasive

species
USDA Forest Service Proceedings RMRS-P-4. 2006. 51

Wildland Fire Use—Challenges Associated with Program Management Across Multiple Ownerships... Zimmerman, Frary, Crook, Fay, Koppenol, and Lasko

proximate to private lands, signifi cant interest in support of managing 

fi res and numerous requests to include private lands in management 

areas were received. This unprecedented level of interest and request 

for collaborative involvement and management by private landowners 

illustrates a growing trend toward greater support for the use of wildland 

fi re where feasible. Management agencies are actively responding to this 

interest in all possible ways and future wildland fi re use applications 

in non-wilderness will be collaborative efforts, with federal, state, and 

private partners involved.

 • State Agency Support for Using Wildland Fire for Resource Benefi ts—New 

initiatives aimed at the improvement of ecosystem health are providing 

an impetus to capitalize on all possible fuel treatment activities, biomass utilization opportunities, increased use of wildland fi re, and the 

restoration of fi re-adapted ecosystems throughout western states (State 

of New Mexico 2004, State of Arizona 2005). As implementation plans 

are developed, collaborative activities are receiving increasing attention. 

While some State agencies are limited in their authority to use fi re, 

they recognize the role of fi re in restoration and maintenance of forest 

and watershed health and are providing increasing support to Federal 

agencies in the use of wildland fi re. In situations where authorities permit it, State agencies are becoming actively involved in planning and 

implementing wildland fi re use. Increasing collaborative implementation 

of wildland fi re use is occurring. This type of cooperative involvement 

includes federal agencies, state agencies, private organizations, and private 

landowners to some degree and will lessen barriers to implementation, 

potentially reduce costs, and advance the use of wildland fi re for resource 

benefi ts.

Additional Support for State-Led Efforts to Improve Forest and 

Watershed Health and Reduce Potential Wildfi re Effects—As State agencies seek to implement forest and watershed health initiatives and programs, 

they are incorporating all viable strategies. Since wildland fi re has been such 

an important factor infl uencing the structure and composition of many ecosystems, fi re risk reduction in many areas can be achieved by restoration of 

natural fi re and community protection capability can be enhanced by WFU. 

Wildland fi re use is a viable and increasingly important management option, 

especially as expanding experience demonstrates the mitigating role fi re can 

perform. Expanding application of WFU directly supports state-led efforts 

and compliments new initiatives and programs.

Expanded Implementation Capability and Greater Accessibility—

Managing wildland fi re in non-wilderness presents a different capacity for 

implementation than in wilderness. Specifi cally, most areas have a well- defi ned 

road network and improved access. A wider range of tools and tactics to 

complete mitigation actions is available and improved access increases the 

ability to implement mitigation actions. However, fi res are often closer to 

Wildland-Urban Interface (WUI) areas. This spatial situation can affect timing, duration, and kind of mitigation actions that can or must be applied.

Concerns and Questions – Planning, Implementation, and 

General Understanding

Wildland fi re use implementation in non-wilderness will by necessity, frequently, but not always, be implemented on a smaller scale than in wilderness. 
52 USDA Forest Service Proceedings RMRS-P-41. 2006.

Zimmerman, Frary, Crook, Fay, Koppenol, and Lasko Wildland Fire Use—Challenges Associated with Program Management Across Multiple Ownerships...

This requires closer attention to maximum manageable areas, potentially more 

in-depth operational planning, and a need for greater mitigation actions to 

successfully manage the fi re within the desired area, respond to other societal 

concerns, infl uence fi re behavior, and protect sensitive areas. A primary difference between this application and wilderness implementation is, commonly in 

wilderness, size and time are the primary mitigation measures used to ensure 

the fi re will remain within the desired area and mitigate potential threats.

Number and Kinds of Mitigation Actions—Management of WFU does 

not have a strict requirement of no on-the-ground action; in fact, smaller area 

management actions must be commensurate with values to be protected, desired objectives, and are described in detail in Wildland Fire Implementation 

Plans (WFIP). The number of management actions identifi ed in WFIPs will 

always be in response to the fi re risk (based on values, hazards, and probability) (USDA/USDI 2005). Non-wilderness fi res are proving in general, 

to present a slightly higher risk level. Consequently, more management actions are often necessary in these areas than for comparable size wilderness 

wildland fi re use events.

In addition to the amount of mitigation actions, the kind of actions 

also can vary. While wilderness fi re implementation can have a high focus 

on monitoring, mapping, and closures with some on-the-ground holding 

or checking actions, non-wilderness fi res frequently require more intense 

containment actions including wider use of standardized fi refi ghting operations. The scale of burn out operations can vary dramatically and range from 

small site-specifi c actions that carry fi re along a road, fence line, or property 

boundary to larger applications of burning through sensitive resource areas 

or adjacent to private property with ground or even large-scale aerial ignition. 

These types of focused and more intense management actions, seemingly 

inconsistent with the original philosophy of restoring fi re to wilderness, are 

not inconsistent with objectives of ecosystem restoration and maintenance in 

all land use situations. In fact, they may be a necessity on a specifi c piece of 

ground and are no more than the specifi c situational requirements of using 

wildland fi re to accomplish resource benefi ts.

Size Thresholds and Similarity to Prescribed Fire—Questions have 

arisen regarding size thresholds of non-wilderness WFU applications; specifi -

cally, are more intense efforts to manage long-duration wildland fi res justifi ed 

for smaller areas or would prescribed fi re more effi ciently accomplish this? 

Wildland fi re use is a viable tool for accomplishing landscape scale ecosystem 

restoration and maintenance. Prescribed fi re has high applicability for sitespecifi c applications conducted on small to mid-scale levels. As scale increases, 

prescribed fi re becomes a longer duration proposition with less specifi city in 

objectives. A key difference between prescribed fi re and wildland fi re use is 

the degree of precision necessary to accomplish objectives. For site-specifi c 

actions identifying specifi c measurable objectives, greater precision in application may be required. Small-scale prescribed fi re affords the ability to obtain 

higher precision through more control over area burned, time of burning, 

direction of spread, rates of spread, intensity and severity, duration of burning, and potential fi re effects. But, the larger the scale, the more diffi cult it 

becomes to exercise and maintain this level of specifi city. Wildland fi re use 

affords more infl uence over restoration of fi re as a natural process but less 

infl uence over specifi c effects. When objectives relate to process restoration 

across a landscape with differential fi re behavior, differential fi re effects, 

and alteration of fuel complexes, stand structure, and stand composition as 
USDA Forest Service Proceedings RMRS-P-4. 2006. 53

Wildland Fire Use—Challenges Associated with Program Management Across Multiple Ownerships... Zimmerman, Frary, Crook, Fay, Koppenol, and Lasko

desired attributes; wildland fi re use is an effective tool. In non-wilderness, 

size thresholds for WFU have limited value; there is no clearly defi nable lower 

size limit for WFU application. Wildland fi re use in non-wilderness, while 

at times appearing operationally similar to prescribed fi re, is appropriate to 

restore fi re as a natural process and accomplish ecosystem maintenance and 

restoration objectives across landscapes, and in the majority of situations, will 

be as effective ecologically and economically. It should be considered/applied 

in all cases where it can accomplish landscape level effects (could occur in 

relatively small areas; the majority of all wildland fi re use events are small size, 

short duration, inactive, and ecologically insignifi cant) and total application 

size will be infl uenced primarily by fuel types and continuity, just as wilderness fi res are. But, a key difference will be the effect of land-use activities and 

land ownership patterns on implementation activities.

Managing Fire Adjacent to MMAs—Managing WFU in smaller landscapes creates numerous situations where the fi re is immediately adjacent to 

a MMA. Past experience portrays this scenario as an undesirable situation. 

Textbook examples of MMAs nearly always show a fi re well within an MMA 

in order to provide potential spread area for the fi re and increased opportunities for management action points to mitigate or eliminate threats throughout 

the life of the fi re. The smaller areas encountered in non-wilderness present 

situations where the fi re can be immediately adjacent to the MMA from 

the onset or management actions burn out fuels between the fi re and the 

MMA causing the fi re to be adjacent to the MMA. These situations may be 

encountered during WFU implementation, will be more frequent in non-wilderness applications than in wilderness situations, and are not inappropriate 

or undesirable. Having fi re against the MMA is only inappropriate when it 

taxes control capabilities, results from situations not described in the WFIP 

management actions, and/or is unanticipated. So long as management actions 

facilitate the accomplishment of objectives, having fi re immediately adjacent 

to the MMA is acceptable.

Equivalency to Non-Fire Treatments—Managing WFU in non-wilderness in smaller areas or within the bounds of established road systems where 

additional mitigation actions are needed or where the fi re is adjacent to the 

MMA introduces the question of whether objectives can be accomplished 

easier, quicker, and/or less expensively through the application of non-fi re 

fuel treatments. Again, the precision of the objectives dictates what the most 

appropriate treatment technique should be. It is very diffi cult for non-fi re 

treatments to simulate a natural fi re and its effects. The timing of natural 

fi re, its ability to present differential fi re behavior and its indefi nite duration across a range of weather conditions all contribute to the effects of fi re. 

Non-fi re treatments are more structured, lack the range of effects, and can 

be completed in fi nite timeframes that may be shorter than for a natural fi re. 

In terms of expense, wildland fi re use is proving to be less expensive than 

non-fi re treatments, depending upon the fi nal size. The long-term benefi ts 

of wildland fi re use in terms of hazardous fuel removal, restoration of overall 

ecosystem health as refl ected through changed fi re regime condition class 

levels, restoration of fi re as a natural process, and reduction of the threat of 

future wildfi re spreading across landscapes and land ownerships outweigh 

short-term economic investments.

Internal Support for Wildland Fire Use—Some internal agency and 

interagency groups are resistant to accept wildland fi re use as a legitimate fi re 
54 USDA Forest Service Proceedings RMRS-P-41. 2006.

Zimmerman, Frary, Crook, Fay, Koppenol, and Lasko Wildland Fire Use—Challenges Associated with Program Management Across Multiple Ownerships...

management option. The individuals and groups are either “holding on” to 

old traditions or lack a complete understanding of the Federal Wildland Fire 

Management Policy. While the concentration of such attitudes vary among 

agencies and organizations, this current position must mature before WFU 

can be totally integrated into fi re management strategies.

Communication, Education, and Community Relations Opportunities—Objectives of WFU, associated risks, planning procedures, 

implementation practices, and potential tradeoffs have not always been understood and were sometimes not well accepted. An understanding of the guiding 

principles and objectives of the WFU program by the public and media is 

essential for social and political acceptance and endorsement. Currently, this 

understanding is increasing and may be at an all time high, but there is still 

a continuing need to establish and maintain a proactive communication and 

education effort for both the program and individual fi re level.

While general public awareness of the role of fi re in western ecosystems 

is increasing, smoke on the horizon will remain unsettling to much of the 

public, particularly as more fi res are managed in proximity to and visible from 

urban areas adjacent to wildlands. An understanding of the full range of appropriate management responses to wildland fi re is needed as opposed to an 

oversimplifi ed belief that all fi res can and should be extinguished, preferably 

by fi re retardant dramatically delivered by large air tankers.

Increasing programmatic accomplishments can provide a basis for improving long-term community relations in regard to the wildland fi re use 

program. Fire restoration in highly visible areas can graphically demonstrate 

that wildland fi re use operational actions are safe, well planned, adequately 

funded, and effectively executed. Strengthened awareness of the natural role 

of fi re and fi re effects, the role and value of ecosystem restoration needs in 

all land use situations, and removal or reversal of professional and public 

controversies surrounding fi re management perspectives and philosophy can 

result from successful implementation. Landowners and community leaders 

may be stimulated to complete Community Wildfi re Protection Plans and 

become much more proactive in hazard fuel reduction.

Cost Management—Cost management has become a signifi cant topic 

of concern by agency administrators regarding both suppression fi res and 

WFU events. High scrutiny and review of large fi re suppression costs seem 

to be fostering a general feeling that equates low cost as a principle measure 

of success. Implementing an appropriate management response that is truly 

the best action for a given set of circumstances will have an associated cost. 

This cost should always be monitored and managed at an effi cient level. But, 

it must be accepted as the price of implementing the proper action and not 

be the cause for reactive alteration of strategies and tactics.

Additional Complexity Infl uences

Inclusion of private lands—In many previous applications of the use of 

wildland fi re to accomplish resource benefi ts, it was common to protect private 

lands and, in the process, exclude fi re from burning outside federal lands. 

In 2005, there was considerable interest on the part of private landowners 

to be included in many wildland fi re use applications if possible. Since this 

is converse to past planning and implementation practices, procedures to 

include private lands are not clear.
USDA Forest Service Proceedings RMRS-P-4. 2006. 55

Wildland Fire Use—Challenges Associated with Program Management Across Multiple Ownerships... Zimmerman, Frary, Crook, Fay, Koppenol, and Lasko

Wildland fire use is part of the full range of appropriate management response actions consistent with the Interagency Strategy for the 

Implementation of Federal Wildland Fire Management Policy (WFLC 

2003). Some States support the implementation of WFU and are prepared 

to serve as cooperators in the management of the wildland fi re including the 

development of systems and methods for the use of wildland fi re on private 

lands. In addition, several states have developed statewide plans that address 

forest and watershed health. Other states are currently developing new policy 

to allow for the orderly proposal and designation of areas where alternative 

suppression strategies may be employed consistent with values at risk, fi re 

ecology, and historic fi re return intervals, and potential fi re severity. This 

policy will provide a process to manage wildland fi res under predetermined 

conditions, criteria, and prescriptions on federal, state, county, and private 

lands, as appropriate.

Specifi c authorities allow the Forest Service to enter into agreements with 

willing State governments and landowners for the protection, restoration, 

and enhancement of fi sh and wildlife habitat, and other resources on public 

or private land that benefi t those resources within the watershed. The Wyden 

Amendment provides for benefi ts that include improving, maintaining, or 

protecting ecosystem conditions through collaborative administration and/or 

implementation of projects; improving collaborative efforts across all ownerships, not just limited solely to adjacent Forest Service lands; and increase 

operational effectiveness and effi ciency through coordination of efforts, 

services, and products.

Collaboration to explore and utilize all opportunities to maximize ecological restoration activities and cross-jurisdictional, landscape efforts has yielded 

procedures for wildland fi re use implementation adjacent to or potentially 

impacting private lands. Three scenarios have been developed to date: where 

State agencies can represent private landowners and collaboratively work with 

Federal agencies to implement WFU, where State agencies are limited in their 

capacity to implement WFU and agreements between Federal agencies and 

private landowners must be developed, and where agreements between Federal 

agencies and County governments must be developed. These scenarios are:

 • State representation of private landowners and collaborative implementation—In some states, the state agency will be a cooperator in the 

management of the fi re, including the development of systems and 

methods for the use of wildland fi re on private lands. The State agency 

will provide the Federal agency with a Delegation of Authority to the 

Incident Commander or Fire Use Manager that directs them to manage 

the fi re across private lands under State authority with the appropriate 

management response that could move across/around/remain outside 

of private lands.

 • Individual Landowner Agreements—In some states, the State Forester 

may furnish advice to the people of the state on forestry matters and 

has the authority to prevent and suppress any wildfi res on state and 

private lands located outside incorporated municipalities, and if subject 

to cooperative agreements, on other lands located in this state or in 

other states. The State Forester has the responsibility to prevent and 

suppress wildfi res only on lands covered by cooperative agreements. 

However, no provision exists for the responsibility of wildland fi re on 

private lands to rest with the State Forester. Therefore, he/she cannot 

re-delegate authority to the Forest Service to include private lands as part 

of WFU activities. So, procedures for WFU implementation adjacent to 
56 USDA Forest Service Proceedings RMRS-P-41. 2006.

Zimmerman, Frary, Crook, Fay, Koppenol, and Lasko Wildland Fire Use—Challenges Associated with Program Management Across Multiple Ownerships...

or potentially impacting private lands in these states must either involve 

excluding private lands from the WFU area or developing individual 

landowner agreements between the Federal agencies and landowners.

 • Pre-existing agreements with County Governments—During the period 

between 1999 and 2001, the Bureau of Land Management (BLM), in 

coordination with the USDI Solicitors Offi ce, developed an agreement 

format to utilize when developing pre-existing agreements allowing for 

wildland fi re use (on fi le, BLM Colorado State Offi ce). The National Fire 

Plan emphasized that local and county governments should develop fi re 

management plans for their jurisdictions that may or may not incorporate 

wildland fi re use into their management schemes.

Economic Concerns – Protection of Necessary Natural Resources or 

Establishment of Alternatives—From an economic standpoint, wildland 

fi res in non-wilderness potentially pose increased economic threats. A notable 

example is the impact to livestock operators. In some cases, these impacts 

can be mitigated by movement of livestock to alternative areas, delaying or 

checking the spread of fi re through a specifi c area, or by maintaining a set of 

alternate grazing areas (vacant allotments, seasonal exceptions, etc.) that could 

constitute “grass banks.” Whatever the specifi c action taken is, managers face 

additional concerns that must be planned for and effectively implemented. If 

not fully accounted for and addressed, these situations could severely limit 

wildland fi re use applications.

Allotment Fence Protection – Protection of Necessary Social-Economic 

Values—Using wildland fi re to accomplish resource benefi ts is almost universally accepted as producing only benefi cial effects. But in fact, these are 

wildland fi res, burning with differential fi re behavior from random points of 

ignition and across widely ranging and partially mitigated areas. While fi res 

have defi nite ecological benefi ts, they can also have some social and economic 

impacts. Allotment and pasture fences represent an additional concern, if not 

properly planned for, could limit or restrict wildland fi re use applications. 

Many fences across federal lands are constructed of wood posts and stays. Even 

low intensity surface fi res can remove most or all of these wood materials. 

There are also fences on private lands that can be impacted. If the allotment 

or pasture integrity is lost from fi re damage, economic impacts to livestock 

operators can be incurred from movement of livestock or loss of grazing 

opportunities. Long-term impacts can result from inability to re-construct 

fences on both public and private lands; there is no avenue currently available 

to the federal land management agency to assist landowners in repairing or 

replacing damaged structures on private lands.

Threats to fences must be addressed as a social-economic concern during 

the planning process and mitigation actions must be developed that protect 

the fences or allow for movement of livestock to alternative sites. Such mitigation actions would need to be coupled with a strategy for either protection 

or reconstruction to eliminate longer-term impacts.

Proximity to Values – Additional Hazard—Many wildland fi res in 

non-wilderness will be situated in closer proximity to private lands and even 

to communities and developed areas. Decreased distance from values to be 

protected can result in higher probabilities of rare fi re spread events, greater 

spread potential depending on fuel types, and a likelihood of more area 

covered by fi ner fuel types. Overall, non-wilderness land use situations will 

present a higher hazard and correspondingly, increasing risk.
USDA Forest Service Proceedings RMRS-P-4. 2006. 57

Wildland Fire Use—Challenges Associated with Program Management Across Multiple Ownerships... Zimmerman, Frary, Crook, Fay, Koppenol, and Lasko

Proximity to Values – Increased Need for Communication, Education, 

and Community Relations—While an aggressive and effi cient communication and education effort for wildland fi re use programs and for each wildland 

fi re that is managed is important, it is imperative for this to occur when fi res 

are closer to developed areas or are visible daily. Without this, inaccurate 

perceptions, assumptions, or beliefs could strongly sway public opinion, affect management opportunities, and have fast-acting impacts on our ability 

to use fi re across diverse landscapes.

Increased Smoke Management Need—Having fi res closer to urban areas 

increases concerns over smoke management. Since WFU events may be of 

longer durations, smoke production will ebb and fl ow according to weather 

and fi re behavior and present an increased element of complexity. Some 

weather combinations will result in undesirable smoke conditions. Additional 

planning will be required to ensure fi res can be managed while meeting air 

quality and smoke management needs.

Fuels and Fire Behavior of Lower Elevation Zones—Public lands are 

managed with signifi cant industrial, commercial, agricultural and recreational 

use on-going almost on a year-round basis. Fuel types typically found on 

lower elevation areas tend to support fi re behavior characterized by rapid 

spread rates and high intensity. Using wildland fi res to accomplish resource 

benefi ts in such areas can be diffi cult and require a much more aggressive 

timetable to complete planning requirements as well as constant awareness and 

attentiveness to the escalating fi re situation in order to maintain the ability 

to implement timely mitigation actions. Various levels of pre-planning can 

help but generally, all planning and implementation activities after ignition 

occurs must take place in a more accelerated timeframe than in areas supporting less fl ammable fuel types.

Susceptibility of Non-Wilderness to Post-Fire Proliferation of Invasive 

Species—A concern in much of the arid western United States is the invasion 

of burned areas by non-native and noxious species. Though managed fi re is 

benefi cial in the long term, short-term protection against invasive species 

until native plants are established may be needed. If invasive species invade an 

area, fi re hazard can become considerably more severe. There are no simple 

methods available to mitigate the potential for invasive species entering a 

burned area once the fi re has passed. Current policies do not permit the 

use of emergency stabilization funds on WFU events. This has created the 

need for fi re and land managers to pursue a variety of means to implement 

short-term mitigation actions that reduce or minimize the risk of invasive 

species spread and intensifi cation and soil erosion on burned areas. In some 

instances, a lack of mitigation options has caused agency administrators to 

choose a suppression strategy so that emergency rehabilitation and stabilization funds can be accessed.

Summary

The long history of fi re suppression and protection of natural resources has 

fostered defi nitive and well-established attitudes regarding “good” and “bad” 

aspects of wildland fi re. As wildland fi re became increasingly important to 

accomplish benefi cial effects, general understanding and acceptance did not 
58 USDA Forest Service Proceedings RMRS-P-41. 2006.

Zimmerman, Frary, Crook, Fay, Koppenol, and Lasko Wildland Fire Use—Challenges Associated with Program Management Across Multiple Ownerships...

keep pace. A “let burn” perspective that evolved over the years pervaded the 

general thinking about fi re management. Confusion associated with seemingly confl icting objectives of fi re suppression and fi re management resulted 

and general program endorsement suffered. Appreciation and understanding 

of the natural role of fi re and fi re effects are now reaching an all time high 

and attitudes are changing accordingly, although slowly.

Wildland fi re use has proven to be an effective management practice in 

wilderness and is now expanding into non-wilderness situations with highly 

successful results. The use of wildland fi re in non-wilderness must be applied under certain circumstances and within specifi c bounds. Even though 

success has been achieved, this practice is not suitable in all non-wilderness 

situations, and may not even be feasible in others. As this program expands 

across multiple ownerships and land use situations, new challenges, higher 

complexity, and needs to address additional management concerns, onthe-ground mitigation actions, and public concerns are surfacing. Specifi c 

challenges facing managers in these areas include: private lands, protection 

of economic concerns, values to be protected and their proximity, increased 

smoke management concerns, and numerous planning, implementation, and 

interpretation questions.

Expansion of wildland fi re use outside wilderness has the potential to 

increase vegetation mosaics, decrease long-term wildfi re potential, and increase community protection capability. Expanding wildland fi re use beyond 

wilderness and across all land-use situations will broaden fi re management 

accomplishments, strengthen ecosystem maintenance and restoration and 

community protection strategies, and advance land management practices. 

But, successful management must be predicated upon continued and proactive collaboration among federal and state agencies, private organizations, 

and private landowners.

References

State of Arizona. 2005. The Report of the: Governor of Arizona Forest Health 

Oversight Council. Executive Order 2003-16. AZ Dept. Commerce. Phoenix, 

AZ. 26 p.

State of New Mexico. 2004. The New Mexico Forest and Watershed Health Plan, An 

Integrated Approach to Ecological Restoration. New Mexico Forest and Watershed 

Health Planning Committee. New Mexico Forestry Division, Energy, Minerals, and 

Natural Resources Department. Santa Fe, NM. 33 p.

USDA/USDI. 2005. Wildland fi re use: implementation procedures reference guide. 

USDA Forest Service. 71 p.

Wildland Fire Leadership Council (WFLC). 2003. Interagency strategy for the 

implementation of federal wildland fi re management policy. National Interagency 

Fire Center, Boise, ID.

Zimmerman, G.T. in press. Management implications of fi re use in wildland areas 

subject to mixed severity fi re regimes in the Southwestern United States. Spokane, 

WA, November 2004. Washington State University Pullman.
USDA Forest Service Proceedings RMRS-P-41. 2006. 59

In: Andrews, Patricia L.; Butler, Bret W., 

comps. 2006. Fuels Management—How to 

Measure Success: Conference Proceedings. 

28-30 March 2006; Portland, OR. 

Proceedings RMRS-P-41. Fort Collins, 

CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research 

Station.

1 Ph.D. candidate in the Conservation 

Biology Graduate Program, University of 

Minnesota. fi ng0006@umn.edu

2 Research social scientist with the 

Northern Research Station, USDA Forest 

Service, St. Paul, MN. 

3 Professor at the University of Minnesota 

and President of InfoTrend, Inc., St. 

Paul, MN.

4 Associate professor of Human Dimensions 

of Natural Resources and Environmental 

Management, Department of Forest 

Resources, University of Minnesota, 

St. Paul, MN.

Abstract—The Healthy Forests Initiative (HFI) and Healthy Forests Restoration Act 

(HFRA) represent major policy and legislative responses to the fuels management 

problem in the United States. This study examined the nature and evolution of the 

public discussion and debate about these policy responses. Computer content analysis 

was used to analyze favorable and unfavorable beliefs about HFI / HFRA expressed in 

about 2,800 news stories published from August 1, 2002 through December 31, 2004. 

The most frequently mentioned favorable beliefs that emerged included the view that 

HFI / HFRA will (1) reduce the risk of catastrophic wildfi re, (2) protect people, communities, and property, and (3) cut red tape and speed up decision making processes. 

The most commonly expressed unfavorable beliefs included the view that HFI / HFRA 

(1) is an excuse to increase logging, (2) will weaken environmental protections, and 

(3) will reduce public input. Some evidence was found of a growing consensus on 

the problem of fuel buildup and the need to reduce the risk of wildfi re. But mistrust 

was found to be an ongoing issue as the HFRA is implemented. Building public trust 

will be a key to continuing to gain support.

Introduction

The Healthy Forests Initiative (HFI) and Healthy Forests Restoration Act 

(HFRA) represent major policy and legislative responses to the fuels management problem in the United States. This study examined the nature and 

evolution of the public discussion and debate about these policy responses, 

as expressed in the news media.

Research by communications and public opinion researchers has found that 

the news media both shape and refl ect public attitudes and beliefs about a 

wide range of social issues (Burgess 1990; Fan 1988; McCombs 2004). For 

example, Elliott and others (1995) found a signifi cant impact of changes in 

media coverage on the level of public support for environmental protection. 

The news media also strongly infl uence agenda-setting for public policy issues (Dearing and others 1996; McCombs 2004). In other words, there is a 

relationship between the relative emphasis given by the media to issues and 

the degree of salience these topics have for the general public. Therefore, 

analysis of the public debate about social issues contained in the news media 

is not mere “media analysis,” it is a window into the broader social debate 

and a means to gauge, indirectly, public attitudes.

U.S. Policy Response to the Fuels Management 

Problem: An Analysis of the Public Debate 

About the Healthy Forests Initiative and the 

Healthy Forests Restoration Act

Jayne Fingerman Johnson1, David N. Bengston2, David P. Fan3, and 

Kristen C. Nelson4
60 USDA Forest Service Proceedings RMRS-P-41. 2006.

Johnson, Bengston, Fan, and Nelson U.S. Policy Response to the Fuels Management Problem: An Analysis of the Public Debate...

Part of the explanation for the infl uence of the news media on public 

attitudes is the importance of the media as the primary information source 

for public policy issues, including forestry and other environmental issues. 

For example, a survey in Oregon found that “The most important sources 

of information about forestry issues tend to be newspaper and television, 

followed by radio, other printed materials, friends and relatives, and interest 

groups. Only 16 percent overall considered natural resource agencies to be 

important sources” (Shindler and others, 1996: 7).

The news media have also been found to be important information sources 

with respect to wildfi re. In a study of public support for fuel reduction 

strategies in forest-based communities, Shindler and Toman (2003) asked 

respondents to rate the usefulness of information sources. Newspapers and 

magazines were rated as most useful, and the percent of respondents who 

rated the USDA Forest Service as a useful source dropped from 60 percent 

in 1996 to 48 percent in 2000.

Given the strong infl uence of the news media on public attitudes and the 

importance of the news media as an information source about wildfi re, fi re 

managers and policy makers need a better understanding of the way in which 

fi re and fi re policy is discussed in the media. Lichtman (1998: 4) argued that 

building support for fi re policy will require paying close attention to the ways 

in which fi re is portrayed in the public discourse. This paper contributes to 

this understanding by analyzing the news media discussion of the Healthy 

Forests Initiative (White House 2002) and the Healthy Forests Restoration 

Act of 2003. The following section describes the data and methodology used 

in this study, followed by a discussion of the main fi ndings. A fi nal section 

discusses the conclusions and implications for wildfi re policy in the United 

States.

Methodology and Data

This analysis involved fi ve main steps: (1) identifying news media stories 

dealing with HFI / HFRA and downloading them from an on-line commercial database, (2) “fi ltering” the text to eliminate irrelevant news stories, (3) 

identifying favorable and unfavorable beliefs about HFI / HFRA contained 

in the stories, (4) developing computer instructions to score the paragraphs 

for the identifi ed beliefs, and (5) assessing the accuracy of the analysis. These 

steps are briefl y described in the following paragraphs.

Data for this study consisted of the text of articles from over 200 U.S. 

news media sources downloaded from the LexisNexis® online database. A 

Boolean search term was developed to identify articles about HFI / HFRA. 

The time frame for the analysis covered August 1, 2002 (the month in 

which the Healthy Forests Initiative was fi rst proposed) through December 

31, 2004. The downloaded text was then “fi ltered” using the InfoTrendTM 

method (described briefl y below) to remove news stories that were not about 

the HFI or HFRA.

Favorable and unfavorable beliefs about HFI / HFRA were identifi ed by 

reviewing a random sample of news stories. Eight main favorable beliefs and 

seven unfavorable beliefs were identifi ed. The specifi c favorable and unfavorable beliefs are discussed in the following section.

Scoring the text for expressions of the favorable and unfavorable beliefs was 

done using the InfoTrend computer content analysis method and software. 

An algorithm was developed to score the text, that is, to count the number 
USDA Forest Service Proceedings RMRS-P-41. 2006. 61

U.S. Policy Response to the Fuels Management Problem: An Analysis of the Public Debate... Johnson, Bengston, Fan, and Nelson

of expressions of each of the beliefs. Briefl y, this involves development of a 

dictionary (composed of a list of ideas related to the favorable and unfavorable beliefs, and groups of words and phrases associated with each idea) and 

a series of idea transition rules (computer instructions specifying how pairs 

of ideas in the dictionary are combined to give new meanings).

For example, one favorable belief that was expressed in the news stories 

and scored in this analysis is that HFI and HFRA will reduce the risk of 

wildfi re. For this belief, a set of dictionary terms such as “avert,” “control,” 

“curb,” “eliminate,” “decrease,” “risk of,” etc., was developed and used to 

identify expressions of the concept of reduce risk. Another set of terms such as 

“blaze,” “burn,” “fi re,” etc., was used to identify expressions of the concept 

wildfi re. An idea transition rule was then developed specifying that when 

a “reduce risk” term and a “wildfi re” term are in close proximity of each 

other within a paragraph that mentions HFI or HFRA, then one expression of the belief that HFI / HFRA will reduce wildfi re risk is counted. For 

example, the statement “With 190 million acres at high risk of catastrophic 

fi re across the country, this is the kind of partnership we need if we are going 

to conserve forests...” (Norton 2003: B7) connects the ideas “wildfi re” and 

“reduce risk” in the context of a paragraph discussing HFI / HFRA, and 

was counted as one expression of the belief that HFI / HFRA will reduce 

the risk of wildfi re.

To identify expressions of the belief that HFI or HFRA do not reduce 

the risk of fi re, the same process was used but with the addition of a set of 

negation terms (for example, “not,” “won’t,” “can’t,” “fail”) in close proximity to a statement that HFI or HFRA reduces wildfi re risk via another idea 

transition rule.

Finally, an assessment of the accuracy of the scoring was done by reviewing a random sample of paragraphs to check the accuracy of computer-coded 

results. After fi nal refi nements in the dictionary and idea transition rules, 

accuracy rates for the scoring of beliefs about HFI / HFRA were all in excess of 80 percent, which is used as an acceptable accuracy level in content 

analysis (Krippendorff 1980).

Findings and Discussion

We found approximately 2,800 news stories about HFI / HFRA for the 

analysis time period August 1, 2002 through December 31, 2004. To put the 

number of stories in perspective, for the same time period and for the same news 

sources, there were more than 45,000 stories about wildfi re, so news media discussion of HFI / HFRA was only about 5 percent of the volume of all wildfi re 

discussion. The most commonly expressed favorable beliefs that we found about 

HFI / HFRA, in order of prevalence, included the beliefs that HFI / HFRA: 

(1) will reduce the buildup of fuels in forests and reduce the risk of catastrophic 

wildfi re, (2) will cut red tape, streamline bureaucracy, and speed up decision 

making processes, (3) will protect people, communities and property, (4) will 

restore “forest health,” (5) will help deal with insect infestation and disease, 

(6) will create economic benefi ts, such as job creation and sustaining the local 

economy in forest-based communities, and (7) involves a collaborative approach 

with community involvement and partnerships.

In addition to these seven specifi c favorable beliefs about HFI / HFRA, 

we found many non-specifi c favorable expressions, such as the belief that HFI 

was “a step in the right direction” or HFRA was a “common sense” approach. 
62 USDA Forest Service Proceedings RMRS-P-41. 2006.

Johnson, Bengston, Fan, and Nelson U.S. Policy Response to the Fuels Management Problem: An Analysis of the Public Debate...

A “general favorable” category was created to count all of these non-specifi c 

expressions of support for HFI / HFRA. There were also a number of infrequently expressed favorable beliefs, such as the view that HFI / HFRA will 

help protect wildlife and wildlife habitat, or that it will pay for itself. These 

beliefs were not tracked in this analysis because they were rarely expressed.

Figure 1 shows the share of each favorable belief as a percent of all expressions of favorable beliefs about HFI / HFRA in our database. The most 

frequently expressed favorable belief was “reduces fi re risk,” the view that 

HFI / HFRA will reduce fuel buildup and reduce the risk of catastrophic 

wildfi re. This belief accounted for 38 percent of all expressions of favorable 

beliefs. An example of an expression of this belief scored by our computer 

content analysis instructions is: “If signed, the bill will give foresters the 

funds and tools they need to prevent catastrophic wildfi res from threatening 

homes and watersheds, supporters say,” (deYoanna 2003: B1). This text was 

also scored as an expression of the belief that HFI / HFRA will “protect 

people, communities, and property.”

“General favorable” expressions about HFI / HFRA was the second most 

frequently expressed favorable belief, accounting for 26 percent of all favorable 

beliefs. “Cuts red tape” was the third most frequently expressed, followed 

by “protects people, communities and property,” and “restores health.” The 

other three favorable beliefs were not often expressed and were not a signifi -

cant part of the public discussion.

The most commonly expressed unfavorable beliefs that emerged in the 

news media debate included the beliefs that HFI / HFRA will: (1) be an 

excuse to increase logging and is really a subsidy to the timber industry, often referred to in the news media discussion as “stealth logging,” (2) reduce 

or weaken important, long-standing environmental protections, (3) reduce 

public input and threaten citizens’ rights to be involved in decision-making 

on U.S. National Forests, (4) fail to reduce the risk of catastrophic wildfi re, 

(5) fail to protect people, communities, and property, and (6) fail to restore 

forest health.

Figure 1—Share of favorable beliefs about the Healthy Forests Initiative and Healthy 

Forests Restoration Act, August, 2002 through December, 2004.
USDA Forest Service Proceedings RMRS-P-41. 2006. 63

U.S. Policy Response to the Fuels Management Problem: An Analysis of the Public Debate... Johnson, Bengston, Fan, and Nelson

There were also many general, non-specifi c unfavorable expressions related 

to HFI / HFRA. These included unfavorable characterizations of HFI / 

HFRA such as “deceptive,” “double-speak,” “smoke and mirrors,” and so on. 

In addition, there were also a number of infrequently expressed unfavorable 

beliefs, such as the view that HFI / HFRA will be too costly, will result in 

more roads in National Forests, or will harm wildlife habitat due to increased 

logging. These infrequently expressed unfavorable beliefs were not tracked 

in this analysis.

Figure 2 shows the share of each unfavorable belief as a percent of all expressions of unfavorable beliefs. The most frequently expressed unfavorable 

belief was “stealth logging,” the view that HFI / HFRA is primarily about 

logging and subsidizing the timber industry. This belief accounted for 32 

percent of all expressions of unfavorable beliefs. An example of an expression of this belief is: “The “Healthy Forests Restoration Act” passed by the 

U.S. House this week has nothing to do with healthy forests and everything 

to do with a return to environmentally reckless, taxpayer-subsidized timber 

cutting,” (The Columbian 2003: C8).

“General unfavorable” expressions also accounted for 32 percent of all 

unfavorable beliefs (fi g. 2). “Reduces environmental protection” was the 

third most frequently expressed unfavorable belief, followed by the belief 

that HFI / HFRA “limits input.” The other three unfavorable beliefs were 

not often expressed and were not a signifi cant part of the public discussion 

as refl ected in the news media.

Figure 3 shows an aggregation of all favorable and all unfavorable beliefs 

about HFI / HFRA expressed in the news media over time. Peaks in the 

volume of discussion are associated with major events. The biggest spike in 

discussion occurred in August, 2003 and coincided with President Bush using wildfi res in the western U.S. as a backdrop for promoting the Healthy 

Forests Initiative. Other spikes in coverage are associated with the introduction of HFI by President Bush in August, 2002, the passage of HFRA by 

the U.S. House of Representatives in May, 2003, Senate passage of HFRA 

in October, 2003, and the signing of HFRA by President Bush in December, 

2003. Since that time, there has been a dramatic drop in the volume of news 

media discussion of HFI / HFRA.

Figure 2—Share of unfavorable beliefs about the Healthy Forests Initiative and Healthy 

Forests Restoration Act, August, 2002 through December, 2004.
64 USDA Forest Service Proceedings RMRS-P-41. 2006.

Johnson, Bengston, Fan, and Nelson U.S. Policy Response to the Fuels Management Problem: An Analysis of the Public Debate...

We also found evidence in our database of HFI / HFRA news stories of 

a growing consensus about the fuel buildup problem and the need to deal 

with it. Although we did not develop computer instructions to explicitly 

identify expressions of this idea, this growing consensus was evident in the 

news stories we analyzed. For example:

“There’s strong consensus that the forests, particularly the federal forests, 

are in fuel conditions that are unnatural because of fi re suppression and past 

management choices. There’s probably strong consensus on what can be 

done” (Cruz 2002: B1).

“We have serious reservations about some details of the President’s Healthy 

Forests Plan. But we have no lingering doubts about the need for Congress 

to approve fi re legislation” (Oregonian 2003: B1)

“It doesn’t matter your race, religion or political beliefs—you have to make 

sure you don’t have a forest fi re in your backyard” (Ratt 2004).

Other researchers have argued that there is a growing consensus among 

many stakeholders that fuel buildup and the risk of catastrophic wildfi re is 

of great concern, especially in the wildland urban interface (Vaughn and 

Cortner 2005).

Concluding Comments

This study examined the national debate about the Healthy Forests Initiative and Healthy Forests Restoration Act as refl ected in the news media. 

A primary conclusion is that the Bush administration has been successful in 

connecting the Healthy Forests Initiative and the Healthy Forests Restoration 

Act with the need to reduce the risk of catastrophic wildfi re and excess fuel 

buildup. The most frequently expressed belief in the news media discussion 

and debate, either favorable or unfavorable, was that HFI / HFRA will reduce 

Figure 3—All favorable and all unfavorable beliefs about the Healthy Forests Initiative 

and Healthy Forests Restoration Act, August, 2002 through December, 2004.
USDA Forest Service Proceedings RMRS-P-41. 2006. 65

U.S. Policy Response to the Fuels Management Problem: An Analysis of the Public Debate... Johnson, Bengston, Fan, and Nelson

the risk of wildfi re. Reducing wildfi re risk has been the main selling point of 

HFI / HFRA and it has resonated loudly in the public discourse.

It is notable given the term “healthy forests” in the titles of the HFI and 

the HFRA that there was very little discussion of the favorable belief “restores health” in the news media discussion. Even if the “bugs and disease” 

category were combined with “restores health” in a broader forest health 

category, this would still only rank fourth in frequency of expression among 

the favorable beliefs.

The most frequently expressed unfavorable belief, “stealth logging,” indicates a strong lack of trust in the legislation, the Administration’s motives, 

and in the Forest Service’s implementation of HFRA. In addition, the terms 

used to identify “general unfavorable” expressions about HFI / HFRA also 

conveyed deep distrust. Examples of these terms include “cynically named,” 

“deceptive,” “dishonest,” double-speak,” “duplicitous,” “insidious,” “misleading,” “Orwellian,” “pernicious,” “smoke and mirrors,” “untruthful,” 

and so on. Others have noted the vital role of building and maintaining trust 

in fuels management (Winter and others, 2004). Building trust will be a 

key concern for the Forest Service as it implements HFRA. The public and 

other stakeholders will be watching closely to see how the Healthy Forests 

Restoration Act is implemented.

Acknowledgment

Funding was provided as part of a North Central Research Station National 

Fire Plan research project coordinated by Sarah McCaffrey. The authors thank 

Cindy Chojnacky of the USDA Forest Service Policy Analysis Staff in Washington, D.C. for guidance and input during the early stages of this research, 

and they thank Sarah McCaffrey and Bernie Lewis for helpful comments on 

an earlier version of this paper.

Literature Cited

Burgess, Jacquelin. 1990. The production and consumption of environmental 

meanings in the mass media: A research agenda for the 1990s. Transactions of 

the Institute of British Geographers. 15(2): 139-161.

Columbian, The. 2003. Clearcut at will; fi re-prevention bill targets big trees. The 

Columbian. May 23, 2003: C8.

Cruz, Laurence M. 2002. Fires are not preventable, forester says. The Statesman 

Journal. September 30, 2002: B1.

Dearing, J. W.; Rogers, E. M.; Chaffee, S. H. 1996. Agenda-setting (Communication 

Concepts, Vol. 6). Thousand Oaks, CA: Sage. 149 p.

DeYoanna, Michael. 2003. Forest thinning measure ok’d. Fort Collins Coloradoan. 

November 22, 2003: B1.

Elliott, E.; Regens, J. L.; Seldon, B. J. 1995. Exploring variation in public support 

for environmental protection. Social Science Quarterly. 76(1): 41-52.

Fan, D. P. 1988. Predictions of public opinion from the mass media: computer content 

analysis and mathematical modeling. New York, NY: Greenwood Press. 220 p.

Krippendorff, K. 1980. Content analysis: an introduction to its methodology. 

Newbury Park, CA: Sage Publications. 191 p.
66 USDA Forest Service Proceedings RMRS-P-41. 2006.

Johnson, Bengston, Fan, and Nelson U.S. Policy Response to the Fuels Management Problem: An Analysis of the Public Debate...

Lichtman, Pamela. 1998. The politics of wildfi re: lessons from Yellowstone. Journal 

of Forestry. 96(5): 4-9.

McCombs, Maxwell. 2004. Setting the agenda: the news media and public opinion. 

Cambridge: Polity Press. 184 p.

Norton, Gale. 2003. New law will keep forest healthy. Seattle Post-Intelligencer. 

December 10, 2003: B7.

Oregonian, The Sunday. 2003. Case study for the fi re bill. The Sunday Oregonian. 

September 14, 2003: B1.

Ratt, Lily. 2004. Neighborhood wildfi re plan sparks concerns. The Bend Bulletin. 

May 27, 2004. (online edition).

Shindler, Bruce; Steel, B. and List, P. 1996. Public judgments of adaptive management: 

a response from forest communities. Journal of Forestry. 94(6): 4-12.

Shindler, Bruce; Toman, E. 2003. Fuel reduction strategies in forest communities: 

a longitudinal analysis of public support. Journal of Forestry. 101(6): 8-15.

Vaughn, J.; Cortner, H. 2005. George W. Bush’s healthy forests: reframing the 

environmental debate. Boulder, CO: University Press of Colorado. 231 p.

White House. 2002. Healthy forests: an initiative for wildfi re prevention and stronger 

communities. Washington, DC: The White House. August 22, 2002. (Online: 

http://www.whitehouse.gov/infocus/healthyforests/toc.html).

Winter, Greg; Vogt, Christine A.; McCaffrey, Sarah. 2004. Examining social trust 

in fuels management strategies. Journal of Forestry. 102(6): 8-15.
USDA Forest Service Proceedings RMRS-P-41. 2006. 67

In: Andrews, Patricia L.; Butler, Bret W., 

comps. 2006. Fuels Management—How to 

Measure Success: Conference Proceedings. 

28-30 March 2006; Portland, OR. 

Proceedings RMRS-P-41. Fort Collins, 

CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research 

Station.

1 Fire management specialist on the 

Humboldt-Toiyabe National Forest. 

This project was done while a masters 

student in the College of Forestry and 

Conservation at University of Montana. 

marthaawilliamson@yahoo.com

Abstract—United States wildland fi re policy and program reviews in 1995 and 2000 

required reduction of hazardous fuel and recognition of fi re as a natural process. 

Although an existing policy, Wildland Fire Use (WFU), permitted managing natural 

ignitions to meet resource benefi ts, most fuel reduction is still achieved through mechanical treatments and prescribed burning. However resource constraints suggest that 

successful fuel and ecosystem management hinges on expanding WFU. The decision 

to authorize WFU in the U.S. Forest Service rests with line offi cers, and the ‘go/no go’ 

decision constitutes a time-critical risk assessment. Factors infl uencing this decision 

clearly impact the viability of WFU.

This study examined infl uences on line offi cers’ go/no go decision. A telephone 

survey was conducted of all U.S. Forest Service district rangers with WFU authority in 

the Northern, Intermountain, and Southwestern Regions. The census was completed 

during February 2005 and obtained an 85 percent response rate. Data were analyzed 

using classifi cation and regression tree (CART) analysis.

Personal commitment to WFU provided the primary classifi er for 91 percent of the 

district rangers who authorized WFU. External factors, negative public perception, 

resource availability, and a perceived lack of support from the Agency were the main 

disincentives to authorizing WFU.

Introduction

Fuel buildup resulting from a century of fi re exclusion has left millions 

of acres prone to higher severity wildland fi res than those that historically 

visited the landscape. Active fi re seasons in 1994 and 2000 drew attention to 

this unanticipated consequence of fi re suppression. As a result, national fi re 

policy has shifted towards hazardous fuel reduction and recognition of fi re as 

an essential ecological process. In an attempt to reduce the immediate likelihood of ‘catastrophic’ wildfi re while providing performance measures, agency 

direction has focused on mechanical treatments and prescribed burning.

Despite this effort to address fuel accumulation, fuels still accumulate at 

two to three times the current treatment rate (USDA-FS 2004). The most 

accessible, and therefore least expensive, treatments may already have been 

done (Calkin, personal communication 2005; GAO 2005), and in the current 

climate of budget rescissions, it seems doubtful that all the acres that need 

treatment to remedy 100 years of fuel buildup will receive it. Furthermore, 

treatments focus mostly on the 0-to-35 year return interval fi re regimes, and 

one-time treatments will not resolve the problem of fuel accumulation. These 

areas will need maintenance treatments on regular intervals to truly resolve 

the forest structure problems resulting from fi re exclusion (Black 2004).

Infl uences on USFS District Rangers’ Decision 

to Authorize Wildland Fire Use

Martha A. Williamson1
68 USDA Forest Service Proceedings RMRS-P-41. 2006.

Williamson Infl uences on USFS District Rangers’ Decision to Authorize Wildland Fire Use

While mechanical treatments and piecemeal prescribed-burns do alter the 

forest structure responsible for the higher severity fi re events, they do not 

remedy the underlying problem of almost systematic fi re exclusion. In contrast 

to these two treatments, wildland fi re use (WFU) provides another option 

to the suppression policy.

Wildland fi re use is the fi re management strategy that allows natural 

ignitions to burn in predetermined locations under scripted conditions. This 

strategy allows fi re to assume its role as a vital ecosystem process, as encouraged by changes to national fi re policy since 1995. This new direction, in 

conjunction with the ability of WFU to restore both structure and process, 

suggests that WFU should assume a more prominent role as a fuel management tool. However, in 2004 U.S. land management agencies managed a 

mere 2.7 percent of all lightning ignitions as WFU (NICC 2005).

Policy Framework

The decision to allow WFU (called ‘go/no go’) can only come after 

meeting three planning requirements (NWCG 1995a). The Land/Resource 

Management Plan (L/RMP) provides general direction for the wildland 

fi re management direction. In the USFS, the L/RMP corresponds to the 

Forest Plans that must go through a public comment period (36 CFR 219). 

Fire Management Plans (FMP) tier to this document. These plans identify 

the fi re management strategies available for every burnable acre. For areas 

determined as eligible for wildland fi re use by the FMP, managers must create guidelines that specify the burning conditions acceptable for wildland 

fi re use (NWCG 2003).

Finally, the Wildland Fire Implementation Plan Stage 1 (WFIP1) must be 

done to further scrutinize any ignition that meets the criteria outlined in 

the FMP. This time-critical process, with an 8-hour deadline1, fi rst evaluates 

the candidate fi re’s physical elements against the prescriptions established 

in the FMP and in the WFU guidebook. Criteria considered in this step 

include: threat to life, property, or public and fi refi ghter safety that cannot 

be mitigated; potential effects on cultural and natural resources outside the 

range of desired effects; relative risk indicators and/or risk assessment results 

unacceptable to the appropriate agency administrator; other proximate fi re activity that limits or precludes successful management of the fi re; other agency 

administrator issues that preclude wildland fi re use. Existence of any one 

criterion results in the decision to suppress. Foremost, public and fi refi ghter 

safety take precedence over any other concern (USDA-FS 2000), and only 

trained and qualifi ed personnel may implement a WFU project (USDA-FS 

2000). Beyond this stipulation, only natural ignitions may be managed for 

resource benefi ts (NWCG 2005). In addition, each wildland fi re may have 

only one objective, and suppression overrides resource benefi t in case two 

fi res merge (NWCG 2005).

The decision to authorize WFU ultimately rests with agency administrators (NWCG 2005). The need for managerial accountability has created a 

decision process that places all of the authority (and consequent liability) on 

these administrators. Specifi cally in the U.S. Forest Service (USFS), District 

1 Until January of 2005, including the fi re season preceding this study, agency administrators operated under a 2-hour time constraint.
USDA Forest Service Proceedings RMRS-P-41. 2006. 69

Infl uences on USFS District Rangers’ Decision to Authorize Wildland Fire Use Williamson

Rangers are the administrators, or line offi cers, most frequently presented 

with the ‘go/no go’ decision on whether to allow WFU.

All federal land management agencies must follow national policy direction 

that mandates allowing fi re to function in its natural role (NWCG 1995a). 

Assessing the feasibility of this policy and facilitating WFU implementation 

demands understanding the drivers of the so-called ‘go/no go’ decision.

Drivers of the Go/No Go Decision

Several authors have touched on factors potentially affecting the decision 

to authorize wildland fi re use. The considerations either discourage or bolster 

a ‘go’ decision.

The principal factors acting against authorizing WFU include risk, liability, 

lack of public support, air quality, and inadequate staffi ng. Most frequently, 

authors cited the risk of a WFU event escaping as a barrier to authorizing 

WFU (Arno and Brown 1991; Daniels 1991). This risk assumes greater importance when calculated with potential damage to private property, natural 

resources, and professional consequences (Czech 1996; Miller and Landres 

2004; Arno and Fiedler 2005). Negligence could indicate liability for ensuing 

damages (White 1991), further raising the stakes. In the case of employee 

injury, decision-makers could be held liable without evidence of negligence 

(Stanton 1995).

Lack of public support (Daniels 1991), coupled with the documented need 

for public buy-in for successful fi re and fuels management (Cortner and others 

1990; Shindler and Toman 2003; Weible and others 2005) could also factor 

into the agency administrator’s decision. Further, air quality concerns from 

both regulatory and public opinion perspectives could also (NWCG 1995b; 

Cleaves and others 2000).

Staffi ng concerns affect the decision to authorize WFU in two ways. The 

managerial endurance required to commit to managing a WFU event for 

an extended and indeterminate period enters into the go/no go decision 

(Bonney 1998; Daniels 1991; Tomascak 1991). Suffi cient availability of 

highly qualifi ed personnel also weighs heavily in the decision to use WFU 

(Cortner and others 1990; Daniels 1991; Cleaves and others 2000; Miller 

and Landres 2004).

While these authors predominantly suggest factors that tip the decision 

towards “no go,” others indicate infl uences in favor of authorizing WFU. 

Anecdotal evidence of cost savings through wildland fi re use suggests this as 

a possible motivator (Daniels 1991; Czech 1996; Bonney 1998; Calkin and 

others forthcoming). In addition to reducing costs, the desire to minimize 

fi refi ghter exposure to the dangers of wildland fi res could also infl uence the 

go/no go decision (Bonney 1998). Finally, a dedication to stewardship that 

dictates a commitment to restoring fi re could inspire a ‘go’ decision (Pyne 

1995; Miller and Landres 2004; Arno and Fiedler 2005).

Although the agency administrator ultimately makes the decision to authorize wildland fi re use, no study has sought their input as to the relative 

importance, if any, of the elements found in the literature. Understanding 

the drivers of the ‘go’ decision requires identifying the factors affecting the 

people who must assume authority for the consequences.

This study aims to determine the factors infl uencing the line offi cers’ 

go/no go decision.
70 USDA Forest Service Proceedings RMRS-P-41. 2006.

Williamson Infl uences on USFS District Rangers’ Decision to Authorize Wildland Fire Use

Methods

The question addressed in this study narrowed the potential population to 

those agency administrators able to authorize wildland fi re use in their areas. 

As an agency with a mandate to manage for multiple-use, the USFS presented 

an ideal candidate for examining the complex decision-making behind wildland fi re use. Meteorological and ethical factors indicated that USFS district 

rangers with wildland fi re use authority on their districts in USFS Regions 

1, 3, and 4 provided an appropriate population to investigate. These regions 

represent a swath through the Intermountain west, and include forests with 

WFU authority in Montana, Idaho, Nevada, Wyoming, Utah, Arizona, and 

New Mexico. This study did not include district rangers in USFS Regions 2 

and 6 because too few rangers in these regions have WFU authority on their 

districts to guarantee confi dentiality in their responses.

The USFS employee directory, available on the internet, provided names, 

email addresses, and phone numbers of district rangers. Unpublished data, 

provided by the USFS Rocky Mountain Research Station Aldo Leopold 

Wilderness Research Institute, identifi ed forests with WFU approved in their 

forest plans.

This identifi cation process led to a potential population of 81 district 

rangers with WFU authority both in and out of designated wilderness across 

Regions 1, 3, and 4. Twenty-nine rangers with WFU authority work in 

Region 1, 27 in Region 3, and 25 in Region 4. Given the small population 

size, this study conducted a census rather than a sample of the identifi ed 

district rangers.

This study relied on a telephone questionnaire due to the associated improvements in response rate and effi ciency over a mailed one (Dillman 1978; 

Groves and others 2004). Questionnaire construction followed widely accepted guidelines (Sudman and Bradburn 1982; Groves and others 2004).

Previously-identifi ed, potential drivers of the go/no go decision provided 

guidance in developing appropriate questions to include in the survey instrument. A subset of line offi cers, not included in the population, verifi ed the 

survey instrument’s content, organization, and clarity. Question formulation for followed guidelines outlined by Groves and others (2004). The 

questionnaire included 50 multiple-choice questions, and six open-ended 

ones. Respondents were invited to expand on their answers, although these 

discussions did not contribute to statistical analysis.

The questions included in the fi nal questionnaire covered eight subject 

groups: respondent eligibility, external factors (including resource availability), past experience with fi re, concern for public perception, confi dence 

in staff, perception of internal support, perception of agency protocol, and 

demographics. The data reduction conducted to facilitate analysis refl ected 

these question groups.

I conducted the telephone interviews between February 9, 2005 and 

March 21, 2005.

Classifi cation and regression tree analysis (CART) offered the most appropriate analysis tool for this data set. The go/no go decision amounts to a 

detailed risk assessment that weighs potential costs against potential resource 

benefi ts. The Decision Criteria Checklist in the WFIP Stage 1, described 

previously, specifi es fi ve tiers to this process. If, at any of these levels, cost 

exceeds benefi t then the decision tips to ‘no go’ and the risk assessment 

stops. Other factors entering into the go/no go decision that this study explored could follow a similar tiered pattern. CART provides a ‘road map’ to 
USDA Forest Service Proceedings RMRS-P-41. 2006. 71

Infl uences on USFS District Rangers’ Decision to Authorize Wildland Fire Use Williamson

navigate such a hierarchical decision process. The classifi cation marks each 

intersection and determines whether a case progresses towards ‘go’ or if the 

risk assessment halts.

The model used a binary target variable, WFU. The binary variable resulted from collapsing the number of lightning strikes in the WFU-approved 

area managed as WFU in the last three seasons. A score of 0 was attributed 

to answers of ‘none’ or ‘few.’ ‘About half,’ ‘most’ or ‘all’ were attributed a 

score of 1. Model runs used Salford Systems CART 5.0 software (Steinberg 

and Colla 1997) and kept the default settings of the Gini splitting criterion, 

10-fold cross-validation, minimum parent node N=10, and minimum child 

node N=1. The best tree was selected based on minimum probability of 

misclassifi cation estimated through cross-validation. Cross-validation (test) 

prediction success provides the most accurate estimate of model performance 

(Steinberg and Colla 1997).

The model used a reduced group of factors to classify the district rangers 

as having authorized WFU on their unit. These factors refl ect the question 

groups explored in the questionnaire. These independent variables include 

confi dence in staff, external factors, experience with fi re, agency support, 

protocol, perceived program value, staffi ng level and concern for public perception. For all variables, larger scores indicate higher levels of the variable 

in question.

Results

Contact with 22 district rangers revealed that they did not have WFU 

authority on their districts and reduced the actual population to 59. The 

American Association of Public Opinion Research (AAPOR 2004) defi nes 

six methods of obtaining response rate, ranging from conservative to expansive. Using the most conservative computation yields a response rate of 84.75 

percent. Twenty-one (of 25) district rangers from Region 1, 12 (of 16) from 

Region 3, and 17 (of 18) from Region 4 participated.

As a census with an 84.75 percent response rate, errors of non-observation 

cause minimal concern. Conducting a census eliminates concerns of sampling 

errors. Although not eradicated, errors associated with coverage and nonresponse were minimized.

Of nine non-respondents, four corresponded to either vacant positions or 

positions that had been fi lled since the 2004 fi re season. The remaining fi ve 

non-respondents face contexts (terrain, weather, fuel, and political) similar to 

their neighbors who participated. This similarity in geographical and political 

situations suggests that their responses would resemble their neighbors’ and 

would therefore not alter the study’s results.

A combination of residual instrument errors and respondent errors may 

have contributed the most signifi cant source of error in the data collected. 

Several of the questions either refl ected areas of Agency direction or inquired 

after professional motivations. Despite confi dentiality guarantees, the respondents could have opted to ‘toe the Agency line’ and not provide completely 

candid answers.

Analysis

Model 1 from the CART analysis used eight variables to classify the dependent variable. This classifi cation resulted in a tree with fi ve decision nodes and 

six terminal nodes (Figure 1). Program value, concern for public perception, 
72 USDA Forest Service Proceedings RMRS-P-41. 2006.

Williamson Infl uences on USFS District Rangers’ Decision to Authorize Wildland Fire Use

staff trust, external factors, and agency support successfully identifi ed 63.6% 

of respondents who authorized wildland fi re use. Table 1, below, summarizes 

Model 1 performance.

Figure 1, on the following page, depicts Model 1. Each intersection, or 

node, provides a make-or-break rule for whether or not the respondent will 

continue down the tree. Respondents whose answers meet the splitting rule 

move down the path to the left. The tree shunts respondents who fail the 

splitting rule to the right.

The fi rst intersection, at program value (PROGVAL <= 3.8), diverts 11 

respondents and classifi es them as not authorizing WFU (terminal node 1). 

This indicates that program value is the most important factor, and progression to the next decision rules hinges on the score for this variable.

Respondents who make it through the intersection at program value move 

to the next one, at concern for public perception (PUBPERC <=–0.2). Here, 

though counter-intuitive, respondents who reported less concern for public 

support are classifi ed as not authorizing WFU (terminal node 6). Survey 

participants who reported higher concern for public support (lower negative 

score) continue to the next intersection, which occurs at staff trust.

This more intuitive split (STFTRST<= 2.4) indicates that staff trust plays 

the next most important role in determining whether or not respondents have 

authorized WFU. Respondents who reported a level of confi dence in their 

staff below 2.4 are classifi ed as not authorizing WFU (terminal node 2) and 

do not continue down the tree.

The next criterion involves external factors. Respondents who scored at 

the upper end of external considerations (EXT>6.5) do not authorize WFU 

(terminal node 5). Those who meet the splitting rule of EXT <= 6.5 move 

on to the fi nal intersection, at agency support.

This fi nal tier separates those respondents who perceive that the Agency 

facilitates the decision to use WFU. Again counter-intuitively, respondents 

who scored above the threshold value of 2.5 did not authorize WFU (terminal 

node 4). Conversely, respondents who met the decision rule AGSPRT<=2.5 

did authorize WFU (terminal node 3).

Ninety-one percent (20 of 22) of respondents who authorized WFU follow 

the tree all the way through to the fi nal intersection at agency support.

Table 1—Model 1 test prediction success.

 Predicted Class

 0 1

 Actual Class Total Cases Percent Correct N=19 N=27

Test 0 28 67.9 19 9

data 1 22 63.6 8 14
USDA Forest Service Proceedings RMRS-P-41. 2006. 73

Infl uences on USFS District Rangers’ Decision to Authorize Wildland Fire Use Williamson

Figure 1—CART Model 1.
74 USDA Forest Service Proceedings RMRS-P-41. 2006.

Williamson Infl uences on USFS District Rangers’ Decision to Authorize Wildland Fire Use

Discussion

Interpretation of CART-analysis results indicates that the go/no go decision 

rests on personal commitment to returning fi re to the landscape. This overarching theme helps explain the somewhat counter-intuitive modeling results. 

The decision structure presented by Model 1 highlights potential deterrents to 

WFU, and responses to individual survey questions expand on them.

“You are acting outside the scope of your employment if 

you do not do what is best for the land”

The CART model suggests that the value placed on the WFU program 

provides the most important determinant of whether a respondent authorized 

wildland fi re use.

From Model 1 emerges a group of decision-makers that stands behind 

returning fi re to the landscape, and is strongly motivated by ‘doing the right 

thing’ for the land. Beyond this belief, these district rangers have confi dence 

in their staff, but worry about public perception and do not feel supported 

by their employer. As one respondent said, “the nexus of temporal, spatial, 

and political factors doesn’t always align” and yet individuals driven by their 

desire to do right by the land will proceed with WFU.

The results of Model 1 suggest that “the laudable, noble goal of ecosystem 

restoration” motivates a cohort of district rangers, convinced that WFU will 

accomplish this goal. According to the CART model, this cohort will predictably see potential benefi ts to the resource outweighing potential risks, and 

decide to ‘go.’ The model suggests the idealistic nature of those who reliably 

authorize WFU, but also highlights the obstacles that prevent district rangers 

from authorizing WFU across the board.

“There is more value to the resources at risk than value to 

allowing fi re back on the landscape”

Responses to the open-ended questions in this study fl esh out the backbone suggested by the CART model and draw attention to the risks that 

make implementing a stewardship ethic a costly gamble. External factors, 

public perception, resource availability, and agency support all surfaced as 

top considerations that inhibited the ‘go’ decision.

External Factors: “WFU is Risky Business”

Environmental factors came up as the main consideration infl uencing the 

go/no go decision, and a key to managing non-suppression fi res to meet objectives. Specifi cally, fi re danger indices were mentioned seven times in the 

context of managing a non-suppression fi re and 21 times as the top consideration in the go/no go decision. Location and time of year surfaced 17 and 

16 times, respectively, as the most important factors infl uencing the go/no 

go decision. Beyond these repeated concerns, weather, ignitions, smoke, and 

threatened and endangered species habitat all came up as considerations that 

weighed in the go/no go decision. These factors refl ect concern for “risk of the 

unknown” that 8 respondents mentioned as a disincentive to use WFU.

Deciding to authorize a WFU event can engage a district’s management 

capacity for an extended period. The time commitment involved depends on 

unpredictable events such as weather and lightning ignitions. In the midst of 

this uncertainty, air quality and endangered species regulations, in addition to 
USDA Forest Service Proceedings RMRS-P-41. 2006. 75

Infl uences on USFS District Rangers’ Decision to Authorize Wildland Fire Use Williamson

private property considerations impose defi nite restrictions on management 

activity. Even for those supportive of fi re restoration, the daunting requirements to ensure in this uncertain environment often prove prohibitive.

Public Perception: “Dick Cheney is not too hip on smoke”

Public support and public perception surfaced six times as a requirement 

for managing non-suppression fi res to meet objectives and seven times as a 

disincentive to using WFU. Respondents evoked concerns for the political 

fallout of the external considerations described previously. Smoke, perceived 

or real threats to threatened and endangered species habitat, and resource 

damage perceived as unacceptable by the public or by others within the agency, 

all came up as specifi c areas of public concern. These concerns stem to some 

extent from a partially misinformed public that still views all wildland fi res 

as a threat.

Resource Availability: “We need trained people with the 

right qualifi cations”

Resource availability surfaced 20 times as the top factor entering into the 

go/no go decision, 14 times as what was needed to manage a non-suppression 

fi re to meet objectives, and in 18 of 43 unprompted discussions that arose 

during the interviews. Respondents mentioned that the level of qualifi cations 

required for fi re use managers constrained WFU authorization. In addition, 

several respondents indicated that they lacked skilled personnel in suffi cient 

numbers to manage WFU.

Respondents also indicated that candidate lightning ignitions frequently 

occurred when other fi re activity was high. In these situations, the line offi cers 

did not have the staff on hand to manage the ignitions as WFU. Potential staff 

shortages cause concern given the indeterminate duration of WFU events.

Respondents mentioned the need for aerial resources in addition to 

personnel. Two respondents specifi cally indicated that the availability of helicopters had allowed them to manage WFU events to meet their objectives. 

In both cases, water-bucket drops by the helicopters cooled down fl anks that 

would have otherwise hit management action-points and triggered a shift 

to suppression.

Agency support: “Signing ‘go’ is a lonely feeling”

The need for agency support surfaced as a requirement for managing nonsuppression fi res to meet objectives. Respondents also cited a perceived lack 

of agency support as a disincentive to authorizing WFU. This perceived lack 

of agency support takes two forms. First, respondents expressed a doubt that 

the agency would stand behind their decision if a WFU event went awry. 

Second, respondents indicated that the current focus on meeting hazardous 

fuel reduction targets impeded their use of WFU.

Potential career impacts surfaced seven times as a disincentive, and 14 

times in unprompted discussions. Three respondents mentioned specifi c 

concerns about the potential for criminal charges as a result of recent afteraction reviews of suppression fi res that led to fatalities. Weighing resource 

benefi ts against potential damage to the decision-maker’s family makes ‘no 

go’ more attractive.

Pressure to meet targets and lack of credit for WFU came up as disincentives to using WFU and surfaced in 14 unprompted discussions. These 

respondents indicated that they could not credit acres restored through WFU 
76 USDA Forest Service Proceedings RMRS-P-41. 2006.

Williamson Infl uences on USFS District Rangers’ Decision to Authorize Wildland Fire Use

towards fuels targets. At the same time, they suggested that prescribed burn 

targets confl icted with using WFU. Further, two respondents reported that 

they would suppress lightning fi res within areas prepared for prescribed burns 

because the WFU fi re would not count towards the prescribed fi re targets.

Conclusion

The position of line offi cer in the U.S. Forest Service draws people with a 

strong commitment to working for the good of the land. As with many public 

sector careers, there are few benefi ts other than satisfying a personal land stewardship ethic—a characteristic that holds true in the context of using lightning 

ignitions to restore fi re to the landscape. This study suggests that authorization 

of WFU by district rangers primarily stems from their personal commitment to 

restoring fi re for the good of the land, despite multiple disincentives. If national 

policy mandates restoring fi re as a natural process, then implementation should 

not rely uniquely on those willing to take risks for their personal ethic.

References

36 CFR 219 (2005) National Forest System land management planning. Federal 

Register 70, 1023-1061.

AAPOR [The American Association for Public Opinion Research] (2004) ‘Standard 

defi nitions: fi nal dispositions of case codes and outcome rates for surveys. 3rd 

edition.’ (AAPOR: Lenexa, KS).

Arno S.F. and Fiedler C.E. (2005) ‘Mimicking nature’s fire.’ (Island Press: 

Washington, DC).

Arno S.F. and Brown J.K. (1991) Overcoming the paradox in managing wildland 

fi re. Western Wildlands 17, 40-46.

Black A. (2004) ‘Wildland fi re use: the “other” treatment option.’ USDA Forest 

Service, Rocky Mountain Research Station Research Note RMRS-RN-23-6-WWW. 

(Missoula, MT). 2 pp.

Bonney B.J. (1998) Use of alternative suppression strategies during 1994 on the 

Clearwater National Forest. In ‘Fire in ecosystem management: shifting the paradigm 

from suppression to prescription.’ (Eds. T.L. Pruden and L.A. Brennan) Tall Timbers 

Fire Ecology Conference Proceedings, No. 20. (Tallahassee, FL). pp. 280-283.

Calkin, D. (2005) Personal communication. Research forester, U.S. Department of 

Agriculture Forest Service, Rocky Mountain Research Station. (Missoula, MT).

Calkin D., Gebert K., Jones G., Neilson R. (Forthcoming) Forest Service large fi re area 

burned and suppression expenditure trends, 1970-2002. Journal of Forestry.

Cleaves D.A., Martinez J., Haines T.K. (2000) Infl uences on prescribed burning 

activity and costs in the National Forest System. USDA Forest Service, Southern 

Research Station General Technical Report GTR-SRS-37. (New Orleans, LA).

Cortner H.J., Taylor J.G., Carpenter E.H., Cleaves D.A. (1990) Factors infl uencing 

Forest Service fi re managers’ risk behavior. Forest Science 36, 531-548.

Czech, B. (1996) Challenges to establishing and implementing sound natural fi re 

policy. Renewable Resources Journal 14,14-19.

Daniels O.L. (1991) A Forest Supervisor’s perspective on the prescribed natural fi re. In 

‘High intensity fi re in wildlands: management challenges and options.’ Tall Timbers 

Fire Ecology Conference Proceedings, No. 17 (Ed SM Hermann) (Tallahassee, FL). 

pp. 361-366.
USDA Forest Service Proceedings RMRS-P-41. 2006. 77

Infl uences on USFS District Rangers’ Decision to Authorize Wildland Fire Use Williamson

Dillman D.A. (1978) ‘Mail and telephone surveys: the total design method.’ (John 

Wiley and Sons: New York, NY).

GAO [Government Accountability Offi ce] (2005) ‘Wildland fi re management: 

important progress has been made, but challenges remain to completing a cohesive 

strategy.’ Government Accountability Offi ce Report GAO-05-147 (Washington, 

DC). 37 pp.

Groves R.M., Fowler F.J. Jr, Couper M.P., Lepkowski J.M., Singer E., Tourangeau 

R. (2004) ‘Survey methodology.’ (John Wiley and Sons: Hoboken, NJ).

Miller C., Landres P. (2004) ‘Exploring information needs for wildland fi re and fuels 

management.’ USDA Forest Service, Rocky Mountain Research Station General 

Technical Report RMRS-GTR-127 (Fort Collins, CO). 36 pp.

NICC [National Interagency Coordination Center] (2005) ‘2004 Statistics and 

summary.’ (National Interagency Coordination Center: Boise, ID).

NWCG [National Wildfi re Coordinating Group] (1995a) ‘1995 Federal Wildland 

Fire Management Policy and Program Review.’ (National Interagency Fire Center: 

Boise, ID).

NWCG [National Wildfi re Coordinating Group] (1995b) Prescribed fi re and fi re 

effects working team 1995 user needs survey summary. Available online: http://

fi re.r9.fws.gov/ifcc/fuwt/Customer.htm.

NWCG [National Wildfi re Coordinating Group] (2003) ‘Interagency strategy 

for the implementation of federal wildland fi re management policy.’ (National 

Interagency Fire Center: Boise, ID).

NWCG [National Wildfire Coordinating Group] (2005) ‘Wildland fire use: 

implementation procedures reference guide.’ (National Interagency Fire Center: 

Boise, ID).

Pyne S.J. (1995) Vestal fi res and virgin lands: a reburn. In ‘Proceedings: symposium 

on fi re in wilderness and park management; 1993 March 30-April 1; Missoula, 

MT.’ (Tech coords JK Brown, RW Mutch, CW Spoon, RH Wakimoto). USDA 

Forest Service Intermountain Research Station General Technical Report INTGTR-320. (Ogden, UT). pp. 15-21.

Shindler B. and Toman E. (2003) Fuel reduction strategies in forest communities: 

a longitudinal analysis of public support. Journal of Forestry 101, 8-14.

Stanton R. (1995) Managing liability exposures associated with prescribed fi res. 

Natural Areas Journal 15, 347-352.

Steinberg D., Colla P. (1997) ‘CART – classifi cation and regression trees.’ (Salford 

Systems: San Diego, CA).

Sudman S., Bradburn N.M. (1982) ‘Asking questions.’ (Jossey-Bass, Inc: San 

Francisco, CA).

Tomascak W. (1991) Improving a prescribed natural fi re program: the Northern 

Region’s approach. Fire Management Notes 52, 6-8.

USDA-FS [United States Department of Agriculture Forest Service] (2000) ‘FSM5100 – Fire management.’ Available online: http://www.fs.fed.us/im/directives/

dughtml/fsm5000.html, accessed 4/18/05.

USDA-FS [United States Department of Agriculture Forest Service] (2004) ‘Forest 

Service performance and accountability report – fi scal year 2003.’ (USDA Forest 

Service: Washington, DC).

Weible C., Sabatier P., Nechodom M. (2005) No sparks fl y: policy participants agree 

on thinning trees in the Lake Tahoe Basin. Journal of Forestry 103, 5-9.

White D.H. (1991) Legal implications associated with use and control of fi re as a 

management practice. In ‘High intensity fi re in wildlands: management challenges 

and options.’ Tall Timbers Fire Ecology Conference Proceedings, No. 17. (Ed 

S.M. Hermann) (Tallahassee, FL). pp. 375-384.
USDA Forest Service Proceedings RMRS-P-41. 2006. 79

Modeling, Risk Assessment and 

Decision Support
80 USDA Forest Service Proceedings RMRS-P-41. 2006.
USDA Forest Service Proceedings RMRS-P-41. 2006. 81

In: Andrews, Patricia L.; Butler, Bret W., 

comps. 2006. Fuels Management—How to 

Measure Success: Conference Proceedings. 

28-30 March 2006; Portland, OR. 

Proceedings RMRS-P-41. Fort Collins, 

CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research 

Station.

1 Forester, USDA Forest Service, RMRS, 

Fire Sciences Lab, Missoula, MT. 

cmchugh@fs.fed.us

Abstract—Fire managers are required to evaluate and justify the effectiveness of 

planned fuel treatments in modifying fi re growth, behavior and effects on resources 

and assets. With the number of models currently available, today’s fi re manager can 

become overwhelmed when deciding which model to use. Each model has a required 

level of expertise in order to develop the necessary data, run the model(s), and analyze 

and interpret their associated outputs. In addition, each model has an appropriate 

temporal and spatial scale for its use, e.g., stand level versus landscape level. Traditional 

fuel treatment analyses have focused on stand level changes in fi re behavior and effects. This approach does not to account for the topological effects of treatments in 

modifying fi re growth, fi re behavior and fi re effects. To fully investigate fuel treatment 

effectiveness requires the examination of the spatial interaction of fuel treatments. This 

requires the use of spatial models to analyze and display these effects.

Introduction

The fi re behavior triangle consists of fuels, weather, and topography. The 

only element managers have direct control over is fuels. Management strategies of the 20th century, and the unintended consequences of fi re suppression 

have contributed to the increase in large fi res across the western U.S. (Agee 

and Skinner 2005). During the time period 1970-2002, 97-99% of all fi res 

reported on U.S. Forest Service lands were still less than 121 ha (300 acres) 

(Calkin and others 2005) suggesting that suppression is still effective on 

small fi res. However, 1.1% of all fi res (greater-than 121 ha) during this time 

period accounted for 97.5% of the burned area and current data trends suggest that the number of large fi res and their average size is increasing (Calkin 

and others 2005). It is the 1% of fi res that are of greatest concern because 

of their effect across large portions of forested landscapes. This is the great 

irony of successful fi re suppression; past success has contributed to failure 

today (Brown and Arno 1991; Agee and Skinner 2005).

The success of fi re suppression, especially in dry forest types of the western 

United States has lead to drastic changes in the horizontal and vertical continuity of fuels as well as a buildup of surface fuels (Agee and Skinner 2005). 

This success has changed the fi re regime from frequent low-intensity surface 

fi res to more intense fi res capable of becoming crown fi res in these forest types 

(Arno and Brown 1991; Agee and Skinner 2005). The accumulation of fuels 

contributing to larger fi res in the future has long been identifi ed (Weaver 

1943; Dodge 1972; Brackebusch 1973). Individual large fi res as well as large 

scale fi re events since 1988 have garnered much political attention and resulted 

Considerations in the Use of Models Available 

for Fuel Treatment Analysis

Charles W. McHugh1
82 USDA Forest Service Proceedings RMRS-P-41. 2006.

McHugh Considerations in the Use of Models Available for Fuel Treatment Analysis

in numerous reports by federal land management agencies (for example, 10-

Year Cohesive Strategy; National Fire Plan). The focus of these reports on 

fuels treatments and fuels management aimed at reducing “catastrophic” and 

large wildfi res resulted in increased funding for these activities.

In the last decade the annual area burned by wildfi re has continued to rise 

despite an increase in funding for fi re suppression resources and fuel hazard 

reduction projects (Stephens and Ruth 2005). This should not be a surprise 

for several reasons. There is an inherent delay from the initial identifi cation 

and funding to implementation of projects. It takes time to work through the 

NEPA process and then implement the plans. Additional delay or failure to 

implement a planned project can occur due to funding availability, personnel availability, weather conditions and administrative appeals or litigation 

(GAO 2003a). Also, the amount of area treated nationally has been focused 

around high-risk high priority areas, primarily Wildland Urban Interface 

(WUI) where from 2001-2004 greater than 65% of available fuel treatment monies have been spent and more than 50% of the treated acres have 

occurred (USDI-USDA 2006). As such, the opportunity for wildfi res to test 

fuel reduction treatment areas at a landscape scale and their potential effect 

on reducing fi re size and changing fi re movement has likely been low. Lastly, 

most projects fail to adequately analyze treatments from a spatial perspective. 

While treatments are often recognized as having a positive localized effect 

on fi re behavior and fi re effects (Graham and others 1999; Graham and others 2004) the topological effect of strategically placed treatments designed 

to interact with one another at changing fi re movement and size across a 

landscape are not (Finney 2001, 2003; Agee and Skinner 2005; Finney and 

others 2005). Thus, large fi res still generally have free reign to move across 

the landscape essentially unimpeded.

This has lead to a number of reports and reviews of Federal agencies’ fuels 

management practices, primarily by the General Accounting Offi ce (GAO). 

Most, if not all are critical of federal agencies in this area. Criticisms include 

a need for improved planning to identify and prioritize those areas needing 

treatment (GAO 2003b) and that a systematic and defendable approach was not 

being used to assess risks and environmental consequences of fuels treatments 

as well as taking no-action, e.g., no fuel treatment (GAO 2004). No treatment 

is still an action and not always with the desired outcome (Agee 2002).

The General Accounting Offi ce (GAO 2004) identifi ed that neither the 

Forest Service nor Bureau of Land Management (BLM) have an institutionalized systematic way to analyze fuel treatment effectiveness at the landscape 

level. Furthermore, it concludes that fi re management planning guidance is 

not specifi cally provided by the National Environmental Policy Act (NEPA) 

on how fuel management projects are to be accomplished (GAO 2004). 

Franklin and Agee (2003) also identifi ed a need for a comprehensive national 

fi re policy dealing with all aspects of wildfi re management including, fi re 

suppression, fuels management and issues surrounding when, how, and why 

fuels projects are conducted, as well as salvage and restoration treatments 

following wildfi re events.

However, localized grassroots efforts have been undertaken to look at 

fuels treatments at the landscape level and the topological interaction of fuel 

treatments. The Fireshed Assessment Process as developed in the Pacifi c 

Southwest Region (R-5) of the USDA, Forest Service is one such program. 

Fireshed assessments offer a methodology to assess the strategic placement 

of treatments across large landscapes that are meant to interact with each 

other in reducing adverse fi re behavior and ecological effects (Bahro and 

others, in press).
USDA Forest Service Proceedings RMRS-P-41. 2006. 83

Considerations in the Use of Models Available for Fuel Treatment Analysis McHugh

In the past fi re managers were left to their own devices when it came to 

analyzing fuel treatments and navigating through the often confusing and 

contradictory list of models to choose from. Currently an abundance of tools 

exists with a dearth of data, especially for spatial fi re models, and the local 

manager is often uncertain as to which model to select and how to go about 

the process. Recent publications (Graham and others 1999, 2004; Agee 

and Skinner 2005; Peterson and others 2005) have provided the principles, 

concepts, effects, and scientifi c basis for changing stand structure through 

the use of thinning, prescribed fi re or a combination of these on altering fi re 

behavior and fi re effects. However, rarely is there an in-depth discussion of 

various fi re decision support systems for analyzing pre- and post-treatment 

effects. The goals of this paper are to leave the manager with information on 

models and modeling concepts, a general discussion of fuel treatment analysis, 

and the models currently available for conducting these types of analyses.

Models in General

Models are used for a variety of reasons such as making inferences concerning processes and parameters of interest, determining potential effects of 

management actions or changes in environmental conditions on the subject 

of interest, evaluation of management alternatives by displaying the modeled 

effects, to display or communicate the effects of changes to concerned parties 

or in public meetings, and to test theories and assumptions one may have 

about conditions that can not easily be accomplished with formal experiments, 

for example, crown fi re experiments.

Modeling itself is both an art and a science (Burnham and Anderson 1998). 

Modeling is an art because one must understand the inner workings of the 

model and the data and how adjustments must be made to achieve realistic, 

defendable, and quantifi able results. It is a science, because the user must have 

a fundamental understanding of the biological, mathematical and statistical 

relationships contained and used within the model as well as the assumptions 

and limitations inherent to the model. The user must be cognizant of the 

limitations and assumptions of the respective model, realize that all models 

need to be validated and calibrated to some past or current documented and 

observed condition, and that all results need to be tempered based on the 

user’s experience, knowledge and observations.

It is incumbent on the end user to understand and comprehend these 

issues. After all, good models used poorly are no better than guessing. One 

must remember that all models are simplifi cations of reality (Burnham and 

Anderson 1998). If the model developer adheres to the concept of parsimony 

then the model itself cannot hope to account for all the complexities and 

interactions encountered in the modeling of natural or biological phenomena. A statement that rings very true is “All models are wrong but some are 

useful” (Box 1976; Burnham and Anderson 1998). Therefore the end user 

must accept and realize that some level of error and uncertainty exists, even 

if the “best” available model is used.

The advantage of using models is they have often undergone some type 

of peer review process, either through academia, evaluation through the 

formal publication process or by use and acceptance by experts in the fi eld. 

Models generally incorporate previously published relationships, interactions 

or equations often bundling them as sub-models into a decision support 

system (Finney 1998; Andrews and Queen 2001). By using widely accepted, 
84 USDA Forest Service Proceedings RMRS-P-41. 2006.

McHugh Considerations in the Use of Models Available for Fuel Treatment Analysis

formally published and peer reviewed models one can state they are using the 

best available science for the time in their analysis. Models also provide an 

instrument to quickly evaluate multiple sets of conditions, alternatives, and 

theories or assumptions the end user may have.

However, models have a downside. Models that operate at the landscape 

scale are data intensive and require a certain level of expertise by the end 

user. Because computers are capable of making very fast and very accurate 

mistakes, the modeled solution may become generally accepted as fact. That 

is, it does not matter about the quality of the data, the expertise of the user, 

the applicability of the model to appropriately assess the situation or the 

interactions of all these in contributing to the solution. A common mantra 

is “Garbage In Garbage Out (GIGO)” perhaps a more appropriate revision 

would be the warning “Garbage In Gospel Out.”

Models do not replace your knowledge and experience; beware of falling 

into the “Black Box Syndrome.” The nirvana of modeling expertise and understanding tempered with an adequate knowledge and expertise in fi re behavior 

is diffi cult to achieve and not often found. Harry T. Gisborne (1948), one 

of the founding fathers of fi re research coined the term, experienced judgment, and defi ned it as: Opinion based on knowledge acquired by experience. 

However, Harry also points out the following (Gisborne 1948, page 23):

“If you have fought forest fi res in every different fuel type, under all possible different kinds of weather, and if you have remembered exactly what 

happened in each of these combinations your experienced judgment is probably pretty good. But if you have not fought all sizes of fi res in all kinds of 

fuel types under all kinds of weather then your experience does not include 

knowledge of all the conditions.”

Sage advice and something to consider, especially when model outputs 

are contrary to your experienced judgment. When this occurs it is easy to 

disregard the model output as spurious and thus unworthy of consideration. 

However, are the results fl awed due to the data, the model or the user, or 

are there other plausible explanations? Are the results within your acquired 

knowledge base? Are the model parameters outside those you have experienced 

fi rst-hand? Perhaps the model has shown you some possible outcome that 

you have not considered or previously experienced. While all model output 

should be evaluated with an appropriate level of skepticism, do not disregard 

it off-hand without due consideration.

Error Sources

General sources of error in the modeling process can be attributed to the 

data, the user, the model or the complex intersection and unions of these 

respective areas (Personal Communication, Dave Sapsis) (Figure 1). From 

personal experience, data and associated issues can account for up to 75% 

of encountered modeling diffi culties. For example, Jones and others (2004) 

found that errors in the fuels map resulted in greater changes to model outputs 

than errors in terrain while erroneous weather information resulted in highly 

unstable outputs for a wildfi re threat model for Australia. The remaining 

general sources of modeling error can be attributed to the user, model and 

interactions of error components (data, user, model).

Spatial modeling requires a seamless landscape of data attributes regardless 

of ownership or jurisdictional boundaries. Rarely, do the necessary data exist 

wall-to-wall thus necessitating some larger modeling exercise be undertaken 
USDA Forest Service Proceedings RMRS-P-41. 2006. 85

Considerations in the Use of Models Available for Fuel Treatment Analysis McHugh

to populate the landscape. This is typically accomplished using biophysical 

attributes, remote sensing, and statistical techniques such as Most Similar 

Neighbor (MSN; Crookston and others 2002) or Gradient Nearest Neighbor 

(GNN; Ohmann and Gregory 2002). Issues related to data such as inappropriately collected fi eld data, limitations of the data in modeling natural 

or biological phenomena, inappropriate construction of data sets, assignment 

of the wrong units associated with the data, extrapolation of existing data to 

areas without data based on some other modeling exercise, and inappropriate 

rule sets used to develop data sets given the respective ecology and biological 

processes for the area. Additionally, data are often developed over large areas 

and assumed to represent some homogeneous condition within an artifi cially 

assigned boundary, such as a timber-stand. Because of this assumption critical 

data attributes may often represent the “average condition” for data values. 

This can have adverse consequences for some fi re behaviors such as crown 

fi re. In these instances the “average” crown base height likely does not refl ect the minimum values that permit crown fi re to initiate. Rarely, does the 

average condition account for problem fi re behavior (Fulé and others 2001; 

Scott and Reinhardt 2001).

The end user can introduce error into the process by using an inappropriate model or user-defi ned model settings, simulating scenarios outside the 

limitations and assumptions of the model, from errors introduced during 

input of data, and in output interpretation. All models have assumptions and 

limitations associated with them. Error associated with the limitations of 

the data used to develop the model, using the model for phenomena outside 

that which the model was intended for and the models themselves have an 

associated error factor.

Figure 1—Components of modeling errors and their respective interactions.
86 USDA Forest Service Proceedings RMRS-P-41. 2006.

McHugh Considerations in the Use of Models Available for Fuel Treatment Analysis

Finally, the area where all three components interact through the process 

of compensating errors can introduce uncertainty into the modeling process 

as well. Results may be correct or incorrect and the complexity of these interactions can make it diffi cult to discern which component is contributing 

to the modeled outcome.

Modeling Process

Fire modeling should not be thought of as a linear process, but rather as 

an open system of feedback loops whereby each idea can interact with the 

other causing a completely open system (Figure 2). While the following is 

offered in a linear fashion it is only to facilitate discussion and not intended 

to imply a linear, step-by-step operation.

 • Defi ne the modeling objective or question

 • Model selection based on modeling objective or question

 • Spatial and temporal data development required by selected model

 • Gather supporting spatial and temporal data

 • Data critique and analysis of developed data

 • Calibration of the model to a past event(s)

 • Simulations, evaluation and critique of results, and documentation

 • Gaming-out, and what-if scenarios of fuel treatment location and prescription

 • Evaluation, write-ups, and presentation of results

The framing of any project and defi ning the question or objective in a very 

specifi c nature is critical to selection of an appropriate model. For example, 

fuel treatments should be designed for a specifi c prescription of wind, weather 

and fuel moistures, should identify and target those fuel properties able to 

impact the targeted fi re behaviors, e.g., surface fi re versus crown fi re, and take 

into account the short lived nature of treatments (Finney 2001; Finney and 

Cohen 2003; Agee and Skinner 2005; Finney and others 2005). Objectives 

and treatment prescriptions must be specifi c if the analysis is to adequately 

address the identifi ed problem. Finally, after the questions and objectives 

have been defi ned this will assist in guiding one to a likely list of models to 

select from.

However, choosing the appropriate model is also data dependent. If a model 

relies on spatial data, these data must be available. If not, what data do you 

have and how long will it take to collect and develop the missing data sets? 

Can you develop the required data and still meet project deadlines? Are the 

data you have appropriate for the question you want to answer? For example, 

to examine the occurrence of crown fi re do you have the additional required 

information (crown base height, crown bulk density, and stand height)? Do 

monitoring data exist from past fuel treatment or timber sale projects that 

could provide information on fuel models, fuel loadings and stand structure 

pre- and post-treatment? Additionally, does monitoring data from previous 

prescribed burn projects within treated areas exist that could provide insight 

into fi re behavior and intensity following treatment? Monitoring data from 

previous projects, informal feedback, and an adaptive management approach 

is useful in developing and refi ning data and modeling scenarios as well as in 

the evaluation and validation of simulation results.

Temporal information on weather, wind, and fuel moisture parameters 

for the model will also be needed. This requires the user acquires, critiques, 
USDA Forest Service Proceedings RMRS-P-41. 2006. 87

Considerations in the Use of Models Available for Fuel Treatment Analysis McHugh

Figure 2—Conceptual representation of the modeling process. 

The process should not be considered linear in nature but rather 

as an open series of feedback loops within the circle.

analyzes historical climatological data or base all modeling scenarios on well 

known critical fi re weather and fuel moistures for the respective area. Other 

potential sources of this information include fi re behavior summaries from 

past fi re events or monitoring reports from previous prescribed burn projects. 

Either can be very useful in the calibration of simulations and in developing 

critical fi re weather and fuel moisture information for modeling scenarios.

Critique and analysis of weather data can be performed using FireFamily 

Plus (Bradshaw and Brittain 1999; Bradshaw and McCormick 2000). FireFamily Plus allows the user to interrogate historical climatological information 

for erroneous data as well as summarize fuel moistures and analyze critical 

weather and wind variables. Prior to any analysis the user should evaluate the 

values recorded for windspeed and direction, temperature, relative humidity, 

and precipitation amount and duration for erroneous values.
88 USDA Forest Service Proceedings RMRS-P-41. 2006.

McHugh Considerations in the Use of Models Available for Fuel Treatment Analysis

The modeler should also be cognizant that windspeed values based on the 

reported observation time from the selected Remote Automated Weather 

Station (RAWS) will likely be too low for most extreme fi re weather modeling scenarios. Crosby and Chandler (1966, 2004) provide some guidance on 

interpolating average wind speeds to maximum wind speeds and the probable 

momentary gust speed. Another option is to access the Western Regional 

Climate Center’s RAWS climate archive (http://www.raws.dri.edu/index.

html). From this location one can access the hourly wind information for 

10-minute average as well as the peak gust speed and direction, conduct a 

wind probability analysis and develop a Wind Rose for an individual RAWS 

station.

The gathering of ancillary spatial data can be very useful in providing spatial 

context to simulations and greatly assist in the critique and analysis of data 

and model outputs. At a minimum the following data should be considered; 

fi re history (point, polygon, and progression), roads, hydrology, ownership, 

and vegetation.

Data critique and review should be considered an ongoing and never 

ending practice throughout the modeling exercise. During this process it 

is important to document your modeling assumptions, weather/wind and 

fuel moisture development used in simulations, limitations in the data, and 

data/model interactions. Data critique and review should take place during 

the initial spatial and temporal data development, immediately following 

the completion of data development, during the calibration of datasets to 

past fi re events, during actual simulations, and again while analyzing model 

outputs. Critique of data during simulations and post-simulation is critical. 

Often, erroneous results due to model and data interactions will not manifest 

themselves until model simulations are run. Error examples may include but 

are not limited to the fi re moving in the wrong direction, due to bad wind 

direction information, or too little crown fi re activity due to the interaction 

of crown base height and fi re behavior model.

Calibration can be defi ned as a procedure by which the factors controlling 

fi re growth are verifi ed and adjusted to make the predicted fi re behavior match 

the observed or past event as closely as possible (Personal Communication, 

Robert C. Seli). Calibration of modeling scenarios to past events is critical. 

Calibration provides a mechanism for testing interactions of the data and 

model, allows one to evaluate model and data performance in predicting or 

matching to past documented fi re events, provides insight into the respective 

fi re models and how the interactions of data and user-defi ned model settings 

can affect modeled outputs. Additionally, and most importantly, it provides a 

means to evaluate the relevancy and accuracy of the data and instill confi dence 

in future modeling projections.

Simulations allow the user to test silvicultural and fuel treatment prescriptions, treatment location and size and their effectiveness in altering 

fi re behavior and growth under specifi ed wind/weather and fuel moisture 

conditions for a specifi c project. However, simulations can also be used to 

game-out different scenarios which is critical in evaluating treatment locations, 

treatment prescriptions, and their effectiveness. The modeler can effi ciently 

evaluate changes in treatment location, prescription intensity, treatment effectiveness, and treatment longevity based on temporal changes to fuels, stand 

structure, and user defi ned model settings. With the gaming-out of each new 

scenario an alternative pattern of treatment locations and prescription can be 

evaluated providing insight into fi re spread and behavior across a landscape 

under various ignition locations, model duration, and wind/weather, and 

fuel moisture scenarios (Bahro and others, in press).
USDA Forest Service Proceedings RMRS-P-41. 2006. 89

Considerations in the Use of Models Available for Fuel Treatment Analysis McHugh

Evaluation and critique of results not only occurs during calibration but 

during modeling scenarios. This provides another opportunity to evaluate 

predicted fi re behavior and fi re growth, the review of spatial and temporal 

data quality and model-data interactions. It is important the user consider 

the predicted fi re behavior and growth for the fuels and weather conditions, 

the effects of user-defi ned model settings and whether the selected model is 

appropriate for the simulated fi re type. At this stage, documentation of results, 

user-defi ned model settings, wind/weather and fuel moisture values and assumptions concerning changes to landscape data pre- and post-treatment, fuel 

model assignment logic, treatment time span and time since treatment, and 

an organized fi ling structure is encouraged. Having a succinct and organized 

record of what was done can prove to be very benefi cial in the future.

Evaluation and presentation of results following fuels treatments is the 

last 5% of the process and perhaps the most critical. Analyzing the results 

appropriately and presenting them in a concise and meaningful manner can 

be diffi cult. The modeling results should demonstrate reduced fi re damage from an ecological standpoint, and show improved controllability, e.g., 

increased line production rates, reduced rates of spread, reduced spotting, 

changes in the proportion of crown fi re type, thus allowing for fl exibility in 

employed tactical and strategic options. This section should also address short 

and long-term risks, fi re effects, fi re behavior, and compare and contrast the 

consequences of no fuel treatment to conducting fuel treatments.

Fuel Treatment Analysis

In the dry forest types of the western United States the effect of fuel 

treatments on fi re behavior at the localized or stand level is well documented 

(Graham and others 1999, 2004; Graham 2003; Finney and others 2005; 

Peterson and others 2005; Raymond and Peterson 2005; Cram and others 

2006). Documentation has consisted of anecdotal and observational, literature 

reviews, theoretical analyses, and more recently empirical evidence. Published 

papers on this topic have done an adequate job of identifying and reviewing 

past work in these areas (Graham and others 1999, 2004; Raymond and 

Peterson 2005; Stephens and Moghaddas 2005b; Cram and others 2006). 

Due to the opportunistic nature of wildfi re impact studies, empirical evidence 

of fuel treatment effi cacy has been severely lacking. However, empirical evidence on the effi cacy of fuel treatments is becoming more evident and for a 

larger portion of the western United States (Pollet and Omi 2002; Agee and 

Skinner 2005; Finney and others 2005; Raymond and Peterson 2005; Cram 

and others 2006). In some instances, the effects of fuel treatments have been 

captured by Landsat-7 satellite imagery (Figure 3), the most demonstrative 

to date has been the Rodeo-Chediski fi re in northern Arizona (Finney and 

others 2005).

Fuel Treatment Principles

Fuels treatments can be effective when they are targeted at specifi c components of the fuel complex; specifi cally surface, ladder and canopy fuels (Agee 

1996; Scott and Reinhardt 2001; Finney 2004; Graham and others 2004; 

Agee and Skinner 2005; Peterson and others 2005). Treatment of these 

fuels components should occur in a hierarchal manner from the bottom up 

(Figure 4) with the following order of precedence: 1) Surface fuels, 2) ladder 
90 USDA Forest Service Proceedings RMRS-P-41. 2006.

McHugh Considerations in the Use of Models Available for Fuel Treatment Analysis

Figure 3—Landsat-7 ETM Plus satellite image of the Rodeo-Chediski fi re on June 21, 

2002 (a); Chevron shaped areas within the fi re front (a) correspond to recent fuel 

treatment activities 1999 and 1993 (b); (c) Natural barriers such as lakes can cause 

fi res to exhibit the same behaviors as fuel treatments. Note area on the leeside of the 

lake in the foreground. Example from the Big Fish Fire 2002, arrows show path of fi re 

movement.
USDA Forest Service Proceedings RMRS-P-41. 2006. 91

Considerations in the Use of Models Available for Fuel Treatment Analysis McHugh

fuels, and lastly 3) canopy fuels (Agee 2002; Hessburg and Agee 2003; Finney 

2004; Graham and others 2004; Agee and Skinner 2005). Fuels treatments 

must be of appropriate intensity to signifi cantly change those fuel components 

at the stand or local scale critical to identifi ed fi re behaviors of concern in the 

context of their spatial location and size, anticipated treatment effectiveness, 

temporal changes to fuel and stand structure (treatment longevity) under the 

targeted wind/weather and fuel moisture conditions.

Surface fuel treatments consist mainly of broadcast prescribed burning 

which has proven effective at changing fi re behavior, fi re severity, and even 

in limiting the extent of future fi res (Weaver 1943, 1957; Wagle and Eakle 

1979; van Wagtendonk 1996; Pollet and Omi 2002; Fernandes and Bothello 

2003). Removal of surface fuels limits the spread rate and intensity of surface 

wildfi re, which reduces the ability for fi re to heat and ignite tree canopies 

(Van Wagner 1973, 1977).

Ladder fuels consist of small trees and limbs that facilitate the vertical 

movement of a surface fi re into the tree canopy (Van Wagner 1973; Scott and 

Reinhardt 2001). Mechanical methods can be used to remove small trees or 

even prune lower limbs, which increases the vertical gap between the surface 

and canopy fuel stratum (Graham and others 1999, 2004; Scott and Reinhardt 

2001). When followed by prescribed burning, such low thinning hinders the 

initiation of crown fi re (Graham and others 2004). Cram and others (2006) 

and Raymond and Peterson (2005) reported that a combination of mechanical 

treatments followed by prescribed burning had the greatest effect on reducing 

fi re severity in the Rodeo-Chedeski and Biscuit fi res respectively.

Figure 4—Fuel treatment triangle. Treatment of fuel components 

should be considered a hierarchal process starting with the 

surface fuels moving upward into the canopy fuels.
92 USDA Forest Service Proceedings RMRS-P-41. 2006.

McHugh Considerations in the Use of Models Available for Fuel Treatment Analysis

Finally, canopy fuels might be thinned to limit the potential for active 

crown fi re spread presuming fi re has been initiated into the crowns (Finney 

2004; Graham and others 2004; Agee and Skinner 2005). Large trees with 

foliage held high above the ground surface and their thicker bark are more 

resistant to fi re damage (Peterson and Ryan 1986). Dominant canopy trees 

offer the least available fuel source and are generally more fi re resistant 

and thus should be the last stratum considered purely for fuel treatment 

prescriptions. However, balancing the fi nancial feasibility of fuel treatment 

prescriptions may require removal of some large trees to offset costs of the 

treatments themselves (Larson and Mirth 2001; Lynch 2001; Franklin and 

Agee 2003). Over time, the costs of treatments will be much less-than the 

costs associated with fi re suppression, post-fi re rehabilitation, and long-term 

ecological damage to watersheds (Lynch 2004).

Fuel Management Scale

Fuel treatments affect fi re behavior at two spatial scales: the stand or local 

scale, and at the landscape scale (Finney 2001, 2003). A stand is considered 

a reasonably homogeneous unit that can be clearly differentiated from surrounding stands by its age, composition, structure, site quality, or geography 

(Daniel and others 1979). While there is no precise size attached to a stand, 

their smallest unit of size is defi ned by the land management agency based on 

a minimum mapping unit, typically around 2-4 ha (5-10 acres). Landscapes

represent larger areas and typically are aggregations of individual stands 

that can be burned by a wildland fi re. This is a functional defi nition related 

specifi cally to fi re and fuel treatment analysis. While there is no set number 

of stands or area limitation to defi ne a landscape it should be large enough 

to allow treatments the ability to disrupt large fi re growth that has occurred 

in the past. While watershed boundaries are the most common, ecological 

management units, range allotments, fi reshed assessment area or some other 

arbitrarily defi ned extent may be used to defi ne an individual landscape.

McKenzie and others (1996) found in previous fi re ecology studies, generally the area represented by a stand to vary from 10 to 1,000 ha (24.7 to 

2,417 acres) while landscapes ranged from 1,000 to 1,000,000 ha (2,471 

to 2,471,044 acres). Recently, Finney and others (In Review (a)) analyzed 

landscapes ranging in size 40,500 to 54,600 ha (100,077 to 134,919 acres) 

with stand sizes ranging from 4 to 515 ha (10 to 1,273 acres) to simulate the 

spatial-temporal effects of landscape level fuel treatments and their affect on 

large wildfi res. Both studies report sizes similar to those proposed by Simard 

(1991) for analogous classifi cations.

Fuel Treatments and Topology

Fire whether at the stand or landscape scale is a contagion process, meaning the fi re behavior at one site or point in space is directly infl uenced by the 

characteristics of the adjacent site or point in space (McKenzie 1991). Topology

is the spatial relationship of one feature to another (ESRI 2004). Because of 

fi re’s contagion process the topological relationship of treatments and their 

interaction become important when locating fuel treatments at the landscape 

scale (Finney 2001). However, rarely is placement of treatment units based on 

topological considerations and this effect on altering fi re growth and behavior 

across a landscape (Finney 2001, 2003). Treatment placement is typically 

based on other management considerations and thus unlikely to have little 

impact on changing the overall growth and size of fi res until signifi cant portions of any one landscape are treated (Finney 2001). Topologically placed 
USDA Forest Service Proceedings RMRS-P-41. 2006. 93

Considerations in the Use of Models Available for Fuel Treatment Analysis McHugh

treatments collectively interact at changing fi re growth and fi re behavior across 

the landscape and can require fewer acres treated than when treatments are 

located without this consideration (Finney 2001, 2003; Finney and others, 

in review (a)). It is important to remember the importance of treatment 

topology, treatment intensity, and the proportion of the landscape treated 

when designing landscape level fuel treatments (Figure 5). Based on these 

findings and the impracticality of treating every acre, the strategic placement 

of treatments, treatment intensity, and the proportion of treated landscape 

and their resultant effect in changing fi re behavior and growth should be 

considered when designing and implementing fuel treatments (Finney 2001, 

Finney and others, in review (a)).

Analyses of fuel treatment effectiveness typically display, describe and 

summarize fi re behavior at the stand or local level. While this is important 

in displaying stand level treatment effects in changing fi re behavior, it does 

not model the topological effects of treated stands in altering fi re movement 

between treated stands at the landscape scale. This is the problem of stand 

or local-scale fi re behavior analyses. This methodology can indicate a change 

in fi re behavior at the stand scale as well as an overall change in fi re behavior 

and fi re effects summarized across the defi ned landscape. However, standlevel analyses offer no means to analyze, display, or show the topological 

effect of these treatments in changing fi re growth and movement nor the 

off-site leeward protection (leverage) afforded by fuel treatments (Finney 

2001; Loehle 2004; Agee and Skinner 2005; Finney and others 2005). Thus, 

aggregation of localized effects is not an appropriate method to analyze at 

the landscape scale, the effects of treatments in changing fi re behavior, fi re 

growth and movement.

Figure 5—Theoretical illustration of the relative importance (starting 

from the base up) of treatment topology, fuel treatment prescription 

intensity, and proportion of landscape treated.
94 USDA Forest Service Proceedings RMRS-P-41. 2006.

McHugh Considerations in the Use of Models Available for Fuel Treatment Analysis

Fire Modeling Tools

With increased emphasis on fuel management, fi re managers are continually asked to offer analytical support related to fuel management decisions. 

There are numerous tools available to the fi re manager to model fi re behavior 

which can provide critical insight in assessing current situations, forecasting 

future fi re scenarios, and evaluating fuel treatment alternatives (Andrews and 

Queen 2001). However, the availability of these tools and supporting data 

(or lack of data) from the internet can often lead to confusion on which tool 

is appropriate and at what spatial scale (stand or landscape). Additionally, 

users are commonly guilty of the misapplication of the respective model, the 

data and interpretation of outputs (Andrews and Queen 2001).

Terminology

The terminology proposed by Andrews and Queen (2001) is used when 

referring to the tools discussed here. They defi ne a fi re model as a mathematical relationship that describes an aspect of fi re while a decision support system

incorporates multiple individual fi re models under a unifying architecture 

to provide various outputs related to fi re behavior and fi re growth (Andrews 

and Queen 2001). Fuel models are lists of numbers that describe the fuels 

as required by the respective fi re model (Andrews and Queen 2001). All of 

the decision support systems here use fuel models as described by Anderson 

(1982) and initially formulated by Rothermel (1972) and Albini (1976) and 

those recently developed by Scott and Burgan (2005). For the purposes of 

this discussion I presume all the tools discussed here are decision support 

systems (DSS) (table 1).

Table 1 outlines each of the individual DSS applicable scale (stand, point 

or landscape), their required inputs and associated outputs. Tools designated 

as a point or stand can only be used to analyze or display fi re behavior for an 

individual stand or location. Decision support systems with a landscape designation are also capable of analyzing the topological effects of treatments as 

Table 1—Decision support systems available for fuel treatment analysis.

 Applicable Inputs Outputs

Model Scale Spatial Temporal Spatial Tabular Graphical

Behave/BehavePlus

Andrews (1986) Andrews 

and others (2005) Point/Stand NO NO NO YES YES

FARSITE Vector &

Finney (1998) Stand/Landscape YES YES Raster YES YES

FlamMap Vector &

Finney (this proceedings) Stand/Landscape YES NO Raster NO NO

FMA Plus

Carlton (2005) Point/Stand NO NO NO YES YES

Fire Fuels Extension (FFE)

Reinhardt and Crookston (2003) Point/Stand NO YES NO YES NO

NEXUS Scott (1999) Point/Stand NO NO NO YES YES
USDA Forest Service Proceedings RMRS-P-41. 2006. 95

Considerations in the Use of Models Available for Fuel Treatment Analysis McHugh

fi re moves across a landscape. Those DSS with Spatial inputs require data for 

fuel model, topography, stand structure and canopy fuels generated within a 

GIS and provided to the model in the form of Grid-ASCII raster data. Decision support systems with temporal inputs allow for modeling fi re behavior 

and growth over time thus requiring information on duration and intervals of 

the modeling scenario as well as time dependant wind and weather streams. 

Spatial outputs indicate the DSS is capable of generating simulation results 

as shapefi les or GRID-ASCII raster data that can be directly ingested into a 

GIS system for display and further analysis.

Decision Support Systems

The DSS listed in table 1 comprise the more common tools used to analyze 

fuel treatments from either a stand or landscape level. While some are more 

appropriate for certain scales of analysis than others, all essentially incorporate 

the same mathematical fi re models for surface fi re spread rate, crown fi re initiation and crown fi re spread rate models (Scott 2006). Furthermore, all the 

DSS discussed here use the previously discussed fi re behavior fuel models.

The most common approach to analyzing fuel treatment effects have been 

on a stand or point basis. BEHAVE (Andrews 1986), BehavePlus (Andrews 

and others 2005), Fuels Management Analyst Plus (FMAPlus; Carlton 2005), 

Fire Fuels Extension (FFE; Reinhardt and Crookston 2003), and NEXUS 

(Scott 1991; Scott and Reinhardt 2001) have all been used to conduct these 

types of analysis (table 2). All these systems can generate information on fi re 

behavior, crown fi re, and fi re type. However, they are only valid for the point 

or stand information provided. None of the DSS are capable of simulating 

Table 2—Publications using fi re decision support systems in fuel treatment analysis. NO: 

Study tested treatments based on Non-site specifi c locations.

Author Modeling System(s) Location

Kalabokidas and Omi (1998) BEHAVE Colorado

Brose and Wade (2002) BEHAVE Florida

Keyes and O’Hara (2002) BEHAVE NO

Hummel and Agee (2003) BEHAVE Washington

Van Wagtendonk (1996) FARSITE NO

Rice and Miller (1998) FARSITE California

Stephens (1998) FARSITE California

Finney and others (2002) FARSITE California 

Stratton (2004) FARSITE/FlamMap Utah

Stephens and Moghaddas (2005a,b) FMAPlus California

Scott (1998a) NEXUS Montana

Fulé and others (2001a) NEXUS Arizona

Fulé and others (2001b) NEXUS Arizona

Fulé and others (2002) NEXUS Arizona

Scott (2003) NEXUS NO

Fulé and others (2004) FVS-NEXUS Arizona

Raymond and Peterson (2005) NEXUS Oregon

Scott (1998b) FFE-FVS Montana

Fiedler and Keegan (2003) FFE-FVS New Mexico

Fiedler and others (2004) FFE-FVS Montana
96 USDA Forest Service Proceedings RMRS-P-41. 2006.

McHugh Considerations in the Use of Models Available for Fuel Treatment Analysis fi re spread between points or stands. However, using GIS, simulation outputs 

from these models such as, fl ame length or crown fi re can be linked back to a 

stand boundary data layer and displayed spatially across a landscape. However, 

this still provides no information on the topological effects of the treatments 

in altering fi re behavior and growth at the landscape scale.

Analyzing topological effects of fuels treatment must also be done to examine their effectiveness in altering fi re movement and behavior at the landscape 

scale (Finney 2001, 2003). To accomplish this a DSS capable of modeling 

fi re behavior at the stand level and incorporating the topological effect of 

treatments in altering fi re movement across the landscape is needed. Only 

FARSITE (Finney 1998) and the Minimum Travel Time (MTT; Finney 2002) 

or Treatment Optimization Model (TOM) component within FlamMap 

(Finney, this proceedings) are widely available for this analysis. Simulation 

outputs of fi re growth and behavior from these models are spatial and easily 

brought into a GIS for display and further analysis.

Available Tools

BEHAVE/BehavePlus—BEHAVE (Andrews 1986) and BehavePlus 

(Andrews and others 2005) are decision support systems that are only capable 

of determining fi re behavior at the point or stand level, do not require spatial 

information or model fi re behavior temporally and can only provide limited 

outputs in the form of tables, graphs or diagrams (table 1). Previous efforts 

using this system were completed using BEHAVE (Andrews 1986) and examined the effectiveness of fuel treatments in changing fi re behavior as well as 

changes in fi re behavior associated with changes in stand structure following 

a spruce budworm outbreak (table 2). While BEHAVE and BehavePlus can 

be used for evaluating fuel treatment effectiveness, its primary uses have been 

in projecting the behavior of ongoing fi res and in planning prescribed fi res. 

BehavePlus (Andrews and others 2005) is the successor to BEHAVE and 

should be used for future modeling exercises. BehavePlus can model surface 

fi re spread and intensity, crown fi re spread and intensity, safety zone size, 

size of point source fi re, fi re containment, spotting distance, crown scorch 

height, tree mortality, wind adjustment factors, and probability of ignition 

(Andrews and others 2005).

NEXUS—NEXUS links existing models of surface and crown fi re behavior that can be used to assess crown fi re potential at the point or plot scale 

(Scott and Reinhardt 2001). NEXUS does not require spatial or temporal 

data inputs and the outputs from NEXUS include tables, graphs, and text 

fi les (table 1). While NEXUS can provide an index of crown fi re hazard, fi re 

type, and potential fi re behavior it cannot determine or simulate fi re spread or 

the spread of fi re between stands or points. NEXUS has been used to analyze 

changes in crown fi re hazard following fuel treatments with crowning index 

and torching index used to refl ect these changes (table 2). NEXUS version 

2.0 has the ability to run in batch mode, which can process up to 32,000 

records at one time.

FVS-FFE—The Fire and Fuels Extension (FFE; Reinhardt and Crookston 

2003) contained within the Forest Vegetation Simulator (FVS; Wykoff and 

others 1982) allows one to model stand dynamics and changes to fuels over 

successional time and the associated behavior and impacts of fi re as stands 

grown and change (Reinhardt and Crookston 2003) or associated with specifi c 

treatments (table 2). Because of this ability FFE can add a temporal effect to 

the analysis and display the changes in stand structure and potential fi re hazard 
USDA Forest Service Proceedings RMRS-P-41. 2006. 97

Considerations in the Use of Models Available for Fuel Treatment Analysis McHugh

as a time-series over periods of a year to decades. While FFE can determine 

potential fi re behavior it cannot determine fi re probability or simulate fi re 

spread or the spread of fi re between stands (Reinhardt and Crookston 2003). 

The data used to determine potential fi re behavior and effects is based on 

the stand averages which leads to some homogenization of the data and can 

infl uence the simulated outputs. Results can be displayed visually using the 

Stand Visualization System (SVS) (McGaughey 1997) or in tabular format. 

Exporting the data out in a spreadsheet format allows the user to construct 

graphs of the simulation. Since data sets used in the modeling are typically 

tied to a spatial representation of stand boundaries, simulation results can be 

displayed spatially in GIS. With the spatial relationship of the stand data, and 

the ability of FVS-FFE to model stand dynamics and fuel accumulations over 

time one could use the model to develop spatial-temporal data (fuel models, 

stand structure, canopy fuels) that can be imported into spatial models such 

as FARSITE or FlamMap. A recent study used FVS-FFE in just this way 

(Finney and others, In Review (a)).

FMAPlus—FMAPlus incorporates previously established methodologies 

and fi re models to produce fuel inputs, outputs of fi re behavior, crown fi re 

potential, crown scorch and mortality based on fi eld collected data (Carlton 

2005). It is appropriate for point or stand level analyses. FMAPlus requires 

no temporal or spatial inputs, and only produces tabular or graphical outputs 

(table 1). Because it can incorporate fi eld-collected data and import tree list 

data from FVS (Wykoff and others 1982), temporal analysis over time (year 

or decades) could be accomplished. Within FMAPlus fuel treatment analysis 

is accomplished by altering the tree list data to refl ect post-treatment stand 

conditions. Only recently has the use of this model to display fuel treatment 

effects been published (table 2).

FARSITE—With the development of the Fire Area Simulator FARSITE

(Finney 1998) the ability to analyze fi re growth at the landscape scale in a 

spatially dependent environment became available. FARSITE uses spatial information on elevation, slope, aspect, canopy cover, fi re behavior fuel model, 

stand height, crown base height, and crown bulk density as well as temporal 

information concerning weather, wind, and fuel moisture to run fi re growth 

simulations and generate fi re behavior outputs (table 1). Outputs consist of 

spatial (shapefi les and GRID ASCII) for fi re growth and behavior as well as 

tabular and graphical information on area burned, fi re perimeters, and fi re 

numbers. Examples of the use of FARSITE in evaluating the effi cacy of fuel 

treatments in changing fi re behavior and the effectiveness of fi re suppression 

following fuel treatments in changing fi re size and economic damage have 

been published (table 2). Its reliance on spatial data and the common lack of 

availability of these data have been a major impediment to its use. However, 

the LANDFIRE (http://landfi re.gov/) project is tasked with the development of spatial data for use in FARSITE for the lower 48 States, which should 

alleviate this data barrier over time.

FlamMap—FlamMap (Finney, this proceedings) is a simplifi ed version 

of FARSITE requiring the same data inputs while generating similar fi re 

behavior outputs. One of the benefi ts of FlamMap over FARSITE is many 

scenarios can be run very quickly and many of the assumptions required by 

FARSITE are simplifi ed or eliminated. However, there is no temporal component in FlamMap, as such it uses spatial information on topography and 

fuels to calculate potential fi re behavior characteristics at a specifi c instant 
98 USDA Forest Service Proceedings RMRS-P-41. 2006.

McHugh Considerations in the Use of Models Available for Fuel Treatment Analysis

for the landscape under a specifi ed set of conditions for every raster cell on 

the landscape with without contagion between data cells. FlamMap does not 

simulate temporal variations in fi re behavior caused by weather and diurnal fl uctuations, nor will it display spatial variations caused by backing or fl anking fi re behavior. Because of this, FlamMap is an ideal tool to compare relative fi re 

behavior changes resulting from fuel modifi cations. Using just the fi re behavior 

option, FlamMap is much like a point/stand fi re model, however, the ability to 

analyze many stands in their georeferenced position does offer some degree of 

landscape perspective, albeit without topological infl uences. To get around this 

issue Stratton (2004) incorporated both FlamMap and FARSITE to analyze fuel 

treatment effectiveness in southern Utah. However, Version 3 of FlamMap can 

also evaluate the topological effects of fuel treatments by using the Treatment 

Optimization Model (TOM; Finney, In Review (b)) and evaluate the spatial 

movement of fi re across a landscape based on Minimum Travel Time (MTT; 

Finney 2002; Finney, this proceedings; Finny In Review (b)).

Model Selection

Given this list of potential models, which model is the best or appropriate 

model? As mentioned previously the objective and questions that need to be 

answered are of paramount importance, however, one should select the model 

based on the intricacy of the question(s) or objectives. Associated with this 

decision process one should also consider the following.

First, what are the intended uses of the model? Is the objective to assess changes to crown fi re hazard following fuel treatments? Then perhaps 

NEXUS is the appropriate model. If one wishes to examine the effects of 

fuel treatments on changes to fi re behavior then, BehavePlus or FlamMap 

may be more appropriate.

Secondly, what are the required inputs of the model? If one decides to 

use a spatial model such as FARSITE or FlamMap do you have the required 

spatial data? If not, do you have the time to acquire any needed ancillary 

data, develop the data, critically analyze and assess the accuracy of the data, 

and develop the required data layers needed for the questions of interest?

Thirdly, what are the outputs generated by the model and will they allow 

the user to fully answer and analyze the questions or objectives of the modeling exercise? Do you need only tabular or graphical outputs of fi re behavior 

characteristics or do you need these outputs to be spatially represented across 

the landscape. All of the previously mentioned fi re behavior tools generate 

tabular and graphical outputs of data, but only FARSITE, FlamMap, FVSFFE generates outputs that can easily be applied spatially within GIS.

Lastly, what is the complexity and time frames associated with the current 

project? Complexity is directly related to the type of analysis being performed, 

e.g., Categorical Exclusion (CATEX), Environmental Analysis (EA), or Environmental Impact Statement (EIS). All of these processes require a certain 

level of analysis and time commitments to meet their respective legislative 

intents. As such, it can have a direct impact on the required decision support system. For a CATEX the use of BehavePlus may be the appropriate 

tool to use to assess changes in fi re behavior associated with fuel treatments, 

especially if one needs to develop many of the required spatial layers to use 

FARSITE or FlamMap. The project timelines associated with each of these 

also controls whether the fi re manager has the time to develop the required 

data layers taking into account those items mentioned in previously.
USDA Forest Service Proceedings RMRS-P-41. 2006. 99

Considerations in the Use of Models Available for Fuel Treatment Analysis McHugh

Summary

Large wildfi res have impacts well outside the localized effect of individually treated stands. Treatment placement that does not consider treatment 

topology is unlikely to have any impact on changing the overall growth and 

size of fi res until signifi cant portions of any one landscape is treated (Finney 

2001, 2003). Based on this and the fact it is impractical to treat every acre 

the strategic placement of treatments and their collective effect in changing 

fi re behavior and growth needs to be considered (Finney 2003). An effective 

landscape fuel management scheme should do the following:

 • Treatments burn at a reduced severity

 • Treatments are located to collectively reduce fi re sizes and severity

 • Change probability of fi re movement

 • Facilitate a change in suppression tactics and strategy

 • Reduced fi re sizes and fi re severity under target environmental conditions

 • Reduced large-fi re suppression costs

When using any decision support system to analyze fuel treatment effectiveness or ongoing fi re events, there are three potential sources of error: data, 

user, and model (DUM). The end user needs to be cognizant of these errors 

at all times during data development and the modeling process.

Fuel treatment prescriptions should focus on defi ned elements of wildland 

fuel structure following a hierarchal order of treatment (surface, ladder and 

canopy fuels). Treatment design should also consider treatment topology, 

treatment prescription intensity, treatment size and planned longevity, and 

the amount of landscape treated.

While treatments are focused on altering fi re behavior at the stand level, 

it is the topological placement of treatments that is important in changing 

fi re growth and behavior at a larger landscape scale. Treatment locations that 

are strategically placed and based topologically may actually require fewer 

acres be treated to achieve an equivalent reduction in affected acres when 

this is not considered (Finney 2001, 2003; Finney, in review (a)). Because 

topology of treatments is important we can no longer rely on the number of 

acres treated as a metric for treatment success. Developing a more appropriate 

metric to measure fuel treatment effectiveness and success is an area requiring further investigation and should be included in any future national fi re 

policy (Franklin and Agee 2003; Stephens and Ruth 2005).

Modeling fuel treatment effectiveness is one of the more diffi cult applications of fi re decision support systems. It requires the modeler to make 

assumptions about the future conditions of fuels and vegetation structure, 

which is diffi cult at best. This analysis however is critical to assist in management decisions. When modeling fuel treatment effectiveness, it is best to 

remember the advice of Yogi Berra: “Prediction is very hard, especially when 

it’s about the future.”

Acknowledgments

The author thanks Mark Finney, Dave Sapsis, Bernie Bahro, Rob Seli, 

and Rick Stratton for their helpful comments on an earlier version of the 

manuscript.
100 USDA Forest Service Proceedings RMRS-P-41. 2006.

McHugh Considerations in the Use of Models Available for Fuel Treatment Analysis

References

Agee, James K.1996. The influence of forest structure on fire behavior. In: 

‘Proceedings of the 17th annual forest vegetation management conference, 

Redding, CA, January 16-18, 1996’. pp. 52-68.

Agee, James K. 2002. The fallacy of passive management. Conservation Biology in 

Practice. 3(1): 18-25.

Agee, James K.; Skinner, Carl N. 2005. Basic principles of forest fuel reduction 

treatments. Forest Ecology and Management. 211: 83-96.

Albini, F. A. 1976. Estimating wildfi re behavior and effects. Gen. Tech. Rep. 

INT-GTR-30. Ogden, Utah: U.S. Department of Agriculture, Forest Service, 

Intermountain Forest and Range Experiment Station. 92 p.

Anderson, Hal E.1982. Aids to Determining Fuel Models For Estimating Fire 

Behavior. Gen. Tech. Rep. INT-GTR-122. Ogden, UT: U.S. Department of 

Agriculture, Intermountain Forest and Range Experiment Station. 22 p.

Andrews, Patricia L. 1986. BEHAVE: fi re behavior prediction and fuel modeling 

system - BURN subsystem, part 1. Gen. Tech. Rep. INT-260, Ogden, UT: 

U.S. Department of Agriculture, Intermountain Forest and Range Experiment 

Station. 130 p.

Andrews, Patricia L.; Queen, LLoyd P. 2001. Fire modeling and information system 

technology. International Journal Wildland Fire. 10(3-4): 343-352.

Andrews, P. L.; Bevins, Collin D.; Seli, Robert C. 2005. BehavePlus fi re modeling 

system, version 3.0: User’s Guide. Gen. Tech. Rep. RMRS-GTR-106WWW 

Revised. Ogden, UT: U.S. Department of Agriculture, Forest Service, Rocky 

Mountain Research Station. 142 p.

Bahro, Bernard; Barber, Klaus H.; Sherlock, Joseph W.; Yasuda, Donald A. In Press. 

Stewardship and fi reshed assessment: A process for designing a landscape fuel 

treatment strategy. Paper presented at the National Silviculture Workshop, June 

6-10, 2005, Lake Tahoe, California. Gen. Tech. Rep. PSW-GTR-XXX. Riverside, 

CA: U.S. Department of Agriculture, Forest Service, Pacifi c Southwest Research 

Station.

Box, George E. P. 1976. Science and Statistics. Journal of the American Statistical 

Association. 71(356): 791-799.

Brackebusch, Arthur P. 1973. Fuel management: A prerequisite not an alternative 

to fi re control. Journal of Forestry. 71(10): 637-639.

Bradshaw, Larry S.; Brittain, Stewart. 1999. FireFamily Plus: fi re weather and fi re 

danger climatology at your fi ngertips. In Proceedings of the 3rd symposium on 

fi re and forest meteorology, Long Beach, CA. American Meteorological Society: 

Boston, MA: 112-113.

Bradshaw, Larry S.; McCormick, Erin. 2000. FireFamily Plus user’s guide, version 

2.0. General Technical Report. RMRS-GTR-67-WWW. Ogden, UT: U.S. 

Department of Agriculture, Forest Service, Rocky Mountain Research Station.

Brown, James K; Arno, Stephen F. 1991. Overcoming the paradox in managing 

wildland fi re. Western Wildlands. (Spring): 40-46.

Brose, Patrick; Wade, Dale. 2002. Potential fi re behavior in pine fl atwood forests 

following three different fuel reduction techniques. Forest Ecology and 

Management. 163: 71-84.

Burnham, K.P.; Anderson, D.R.1998. Model Selection and Inference: A Practical 

Information-Theoretic Approach. Springer-Verlag, Inc., New York. 353 pp.

Calkin, David E.; Gebert, Krista M.; Jones, Jeff G.; Neilson, Ronald P. 2005. Forest 

Service large fi re area burned and suppression expenditure trends, 1970-2002. 

Journal of Forestry. 103(4): 179-183.
USDA Forest Service Proceedings RMRS-P-41. 2006. 101

Considerations in the Use of Models Available for Fuel Treatment Analysis McHugh

Carlton, Don W. 2005. User’s guide to using fuels management analyst suite version 

3.0.1. Fire Program Solutions, Estacada, OR. Available at www.fi reps.com.

Cram, D.; Baker, T.; Boren, J. 2006. Wildland fi re effects in silviculturally treated 

vs. untreated stands of New Mexico and Arizona. Research Paper RMRS-RP-55. 

Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain 

Research Station. 28 p.

Crookston, Nick L.; Moeur, Melinda; Renner, David. 2002. Users guide to the 

Most Similar Neighbor Imputation Program Version 2. Gen. Tech. Rep. RMRSGTR-96. Ogden, UT: U.S. Department of Agriculture, Forest Service, Rocky 

Mountain Research Station. 35 p

Crosby, John S.; Chandler, Craig C. 1966. Get the most from your windspeed 

observation. Fire Control Notes. 27(4): 12-13.

Crosby, John S.; Chandler, Craig C. 1966. 2004. Get the most from your windspeed 

observation. Fire Management Today. 64(1): 53-55.

Daniel, Theodore W.; Helms, John A.; Baker, Fredrick S. 1979. Principles of 

Silviculture. Second Edition. McGraw-Hill Inc., New York. 500 pp.

Dodge, Marvin. 1972. Forest fuel accumulation - a growing problem. Science. 177: 

139-142.

ESRI. 2004. ARC/INFO Version 9.0. Environmental Systems Research Institute, 

Inc. Redlands, California.

Fernandes, Paulo M.; Botelho, Herminio S. 2003. A review of prescribed burning 

effectiveness in fi re hazard reduction. International Journal Wildland Fire. 12(2): 

117-128.

Fiedler, Carl E.; Keegan, Charles E., III; Woodall, Christopher W.; Morgan, Todd 

A. 2004. A strategic assessment of crown fi re hazard in Montana: potential 

effectiveness and costs of hazard reduction treatments. Gen. Tech. Rep. PNWGTR-622. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacifi c 

Northwest Research Station. 48 p.

Fiedler, Carl E.; Keegan, Charles E. 2003. Reducing crown fi re hazard in fi re-adapted 

forests of New Mexico. In Fire, fuel treatments, and ecological restoration: Omi, 

Philip N.; Joyce, Linda A., technical editors. Conference proceedings; 2002 16-

18 April; Fort Collins, CO. Proceedings RMRS-P-29. Fort Collins, CO: U.S. 

Department of Agriculture, Forest Service, Rocky Mountain Research Station. 

39-48.

Finney, Mark A. 1998. FARSITE: Fire area simulator – model development and 

evaluation. Res. Pap. RMRS-RP-4, Ogden, Utah: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research Station. 47 p.

Finney, Mark A. 2001. Design of regular landscape fuel treatment patterns for 

modifying fi re growth and behavior. Forest Science. 47(2): 219-228.

Finney, Mark A. 2002. Fire growth using minimum travel time methods. Canadian 

Journal Forest Research. 32(8): 1420-1424.

Finney, M.A. 2003. Calculating fire spread rates across random landscapes. 

International Journal Wildland Fire. 12(2): 167-174.

Finney, Mark A. 2004. Landscape fi re simulation and fuel treatment optimization. 

In: Methods for Integrated Modeling of Landscape Change: Interior Northwest 

Landscape Analysis System. Hayes, Jane L.; Ager, Alan. A.; Barbour, R. James 

(Tech. Editors) Gen. Tech. Rep. PNW-GTR-610. Portland, OR: U.S. Department 

of Agriculture, Forest Service, Pacifi c Northwest Research Station. 117-137.

Finney, Mark A.; Cohen, Jack D. 2003. Expectation and evaluation of fuel 

management objectives. In: Omi, P.N.; Joyce, L.A. technical editors. Fire, fuel 

treatments, and ecological restoration: conference proceedings; 2002 April 16–18; 

Fort Collins, CO. Proc. RMRS-P-29. Fort Collins, CO: U.S. Department of 

Agriculture, Forest Service, Rocky Mountain Research Station: 353-366.
102 USDA Forest Service Proceedings RMRS-P-41. 2006.

McHugh Considerations in the Use of Models Available for Fuel Treatment Analysis

Finney, Mark A.; McHugh, Charles W.; Grenfell, Isaac C. 2005. Stand -and 

landscape- level effects of prescribed burning on two Arizona wildfi res. Canadian 

Journal Forest Research. 35(7): 1714-1722.

Finney, Mark A.; Sapsis, David B.; Bahro, Bernard. 2002. Use of FARSITE 

for simulating fi re suppression and analyzing fuel treatment economics. In: 

Proceedings of Fire in California Ecosystems: Integrating Ecology, Prevention, 

and Management, N.G. Sugihara, M.E. Morales, and T.J. Morales (eds). 17-20 

November 1997, San Diego, CA. Miscellaneous Pubublication No. 1, Association 

for Fire Ecology. 121-136 pp.

Finney, Mark A.; Seli, Robert C.; McHugh, Charles W.; Ager, Alan A.; Bahro, 

Bernard; Agee, James K. In Review (a). Simulation of long-term landscape-level 

fuel treatment effects on large wildfi res. International Journal Wildland Fire.

Finney, Mark A. In Review (b). A computational method for optimizing fuel 

treatment locations. International Journal Wildland Fire.

Franklin, Jerry F.; Agee, James K. 2003. Forging a science-based national forest fi re 

policy. Issues in Science and Technology. 20(1): 59-66.

Fulé, Peter Z.; McHugh, Charles; Heinlein, Thomas A.; Covington, W.W. 2001a. 

Potential fi re behavior is reduced following forest restoration treatments. In: 

Vance, Regina K.; Edminster, Carleton B.; Covington, W. Wallace; Blake, Julie 

A., Compilers. Ponderosa pine ecosystems restoration and conservation: steps 

toward stewardship; 2000 April 25–27; Flagstaff, AZ. Proceedings RMRS-P-22. 

Ogden, UT: U.S. Department of Agriculture, Forest Service, Rocky Mountain 

Research Station. 28–35.

Fulé, Peter Z.; Waltz, Amy E.M.; Covington, W.W.; Heinlein, Thomas A. 2001b. 

Measuring forest restoration effectiveness in hazardous fuels reduction. Journal 

of Forestry. 99(11): 24–29.

Fulé, Peter Z.; Covington, W.W.; Smith, H. B.; Springer, Judith D.; Heinlein, 

Thomas A.; Huisinga, Kristin D.; Moore, Margaret M. 2002. Comparing 

ecological restoration alternatives: Grand Canyon, Arizona. Forest Ecology and 

Management. 170: 19-41.

Fulé, Peter Z.; Crouse, Joseph E.; Cocke, Allison E.; Moore, Margaret M.; 

Covington, W.W. 2004. Changes in canopy fuels and potential fi re behavior 

1880-2040: Grand Canyon, Arizona. Forest Ecology and Management. 175: 

231-248.

Gisborne, Harry T. 1948. Fundamentals of fi re behavior. Fire Control Notes. 9(1): 

13-24.

GAO [U.S. General Accounting Offi ce]. 2003a. Information on appeals and litigation 

involving fuels reduction activities. Report GAO-04-52. U.S. General Accounting 

Offi ce, Washington, D.C., USA. 95 p.

GAO [U.S. General Accounting Offi ce]. 2003b. Additional actions required to better 

identify and prioritize lands needing fuels reduction. Report GAO-03-805. U.S. 

General Accounting Offi ce, Washington, D.C., USA. 60 p.

GAO [U.S. General Accounting Offi ce]. 2004. Forest Service and BLM need better 

information and a systematic approach for assessing the risks of environmental 

effects. Report GAO-04-705. U.S. General Accounting Offi ce, Washington, 

D.C., USA. 88 p.

Graham, Russell T.; Harvey, Alan E.; Jain, Threasa B.; Tonn, Jonalea R. 1999. 

The effects of thinning and similar stand treatments on fi re behavior in Western 

Forests. Gen. Tech. Rep. PNW-GTR-463. Portland, OR: U.S. Department of 

Agriculture, Forest Service, Pacifi c Northwest Research Station. 27 p.

Graham, Russell T., Technical Editor. 2003. Hayman fi re case study. Gen. Tech. 

Rep. RMRS-GTR-114. Ogden, UT: U.S. Department of Agriculture, Forest 

Service, Rocky Mountain Research Station. 396 p.
USDA Forest Service Proceedings RMRS-P-41. 2006. 103

Considerations in the Use of Models Available for Fuel Treatment Analysis McHugh

Graham, Russell T., McCaffrey, Sarah; Jain, Threasa B. Technical Editors. 2004. 

Science basis for changing forest structure to modify wildfi re behavior and 

severity. Gen. Tech. Rep. RMRS-GTR-120. Fort Collins, CO: U.S. Department 

of Agriculture, Forest Service, Rocky Mountain Research Station. 43 p.

Hessburg, Paul F; Agee, James K. 2003. An environmental narrative of Inland 

Northwest United States forests, 1800-2000. Forest Ecology and Management. 

178: 23-59.

Hummel, S.; Agee, James K. 2003. Western spruce budworm defoliation effects on 

forest structure and potential fi re behavior. Northwest Science. 77(2): 159-169.

Jones, S. D.; Garvey, M.F.; Hunter, G.J. 2004. Where’s the fi re? Quantifying 

uncertainty in a wildfi re threat model. International Journal Wildland Fire. 

13(1): 17-25.

Kalabokidis, K. D.; Omi, P. N. 1998. Reduction of fi re hazard through thinning/

residue disposal in the urban interface. International Journal of Wildland Fire. 

8(1): 29–35.

Keyes, Christopher R.; O’Hara, Kevin L. 2002. Quantifying stand targets for 

silvicultural prevention of crown fi res. Western Journal Applied Forestry. 17(2): 

101-109.

Larson, Debra; Mirth, Richard. 2001. Projected economic impacts of a 16-inch tree 

cutting cap for ponderosa pine forests within the greater Flagstaff urban-wildlands. 

In: Vance, Regina K.; Edminster, Carleton B.; Covington, W. Wallace; Blake, 

Julie A. comps. Ponderosa pine ecosystems restoration and conservation: steps 

toward stewardship; 2000 April 25–27; Flagstaff, AZ. Proceedings RMRS-P-22. 

Ogden, UT: U.S. Department of Agriculture, Forest Service, Rocky Mountain 

Research Station: 154-160.

Loehle, Craig. 2004. Applying landscape principles to fi re hazard reduction. Forest 

Ecology and Management. 198: 261-267.

Lynch, Dennis L. 2004. What do forest fi res really cost? Journal of Forestry. 102(6): 

42-49.

Lynch, Dennis L. 2001. Financial results of ponderosa pine forest restoration in 

southwestern Colorado. In: Vance, Regina K.; Edminster, Carleton B.; Covington, 

W. Wallace; Blake, Julie A. comps. Ponderosa pine ecosystems restoration and 

conservation: steps toward stewardship; 2000 April 25–27; Flagstaff, AZ. 

Proceedings RMRS-P-22. Ogden, UT: U.S. Department of Agriculture, Forest 

Service, Rocky Mountain Research Station: 141-148.

McGaughey, Robert J. 1997. Visualizing forest and stand dynamics using the 

stand visualization system. Proc. 1997 ACSM/ASPRS Annual Convention and 

Exposition.

Bethesda, MD: American Society for Photogrammetry and Remote Sensing. 

248-257.

McKenzie, Donald; Peterson, David L.; Alvarado Ernesto. 1996. Extrapolation 

problems in modeling fi re effects at large spatial scales: A review. International 

Journal Wildland Fire. 6(4): 165-176.

Ohmann, Janet L.; Gregory, Matthew J. 2002. Predictive mapping of forest 

composition and structure with direct gradient analysis and nearest neighbor 

imputation in coastal Oregon, U.S.A. Canadian Journal Forest Research. 32(4): 

725-741.

Peterson, David L.; Ryan, Kevin C. 1986. Modeling post-fi re conifer mortality for 

long-range planning. Environmental Management. 10: 797–808.

Peterson, David L.; Johnson, Morris C.; Agee, James K.; Jain, Theresa B.; McKenzie, 

Donald; Reinhardt, Elizabeth D. 2005. Forest structure and fi re hazard in dry 

forests of the Western United States. Gen. Tech. Rep. PNW-GTR-628. Portland, 

OR: U.S. Department of Agriculture, Forest Service, Pacifi c Northwest Research 

Station. 30 p.
104 USDA Forest Service Proceedings RMRS-P-41. 2006.

McHugh Considerations in the Use of Models Available for Fuel Treatment Analysis

Raymond, Crystal L.; Peterson, David L. 2005. Fuel treatments alter the effects 

of wildfi re in a mixed-evergreen forest, Oregon, USA. Canadian Journal Forest 

Research. 35(12): 2981-2995.

Reinhardt, Elizabeth; Crookston, Nicholas L. (Technical Editors). 2003. The Fire 

and Fuels Extension to the Forest Vegetation Simulator. Gen. Tech. Rep. RMRSGTR-116. Ogden, UT: U.S. Department of Agriculture, Forest Service, Rocky 

Mountain Research Station. 209 p.

Rice, Carol L.; Miller, Cheryl. 1998. FARSITE modeling in the urban/wildland 

intermix. In: 13th Fire and Forest Meterology Conference, Lorne, Austrailia. 

561-570.

Rothermel, Richard C. 1972. A mathematical model for predicting fi re spread in 

wildland fuels. Res. Pap. INT-115. Ogden, UT: U.S. Department of Agriculture, 

Forest Service, Intermountain Forest and Range Experiment Station. 40 p.

Scott, Joe H. 1998a. Sensitivity analysis of a method for assessing crown fi re hazard 

in the northern Rocky Mountains, USA. In: Proceedings of the III International 

Conference on Forest Fire Research and 14th Conference on Fire and Forest 

Meteorology, Luso, Portugal. 16/20 November 1998, VOL II: 2517-2532.

Scott, Joe H. 1998b. Using the fi re and fuels extension to the forest vegetation 

simulator to assess long-term changes in crown fi re hazard. In: Proceedings of 

the III International Conference on Forest Fire Research and 14th Conference 

on Fire and Forest Meteorology, Luso, Portugal. 16/20 November 1998, VOL 

II, p. 2621-2629.

Scott, Joe H. 1999. NEXUS: A system for assessing crown fi re hazard. Fire 

Management Notes. 59(2): 20-24.

Scott, Joe H. 2003. Canopy fuel treatment standards for the wildland-urban interface. 

In: Omi, Philip N.; Joyce, Linda A., technical editors. Fire, fuel treatments, and 

ecological restoration: conference proceedings; 2002 April 16-18; Fort Collins, 

CO. Proceedings RMRS-P-29. Fort Collins, CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research Station. 29-37.

Scott, Joe H. 2006. Comparison of crown fi re modeling systems used in three fi re 

management applications. Res. Pap. RMRS-RP-5. 58. Fort Collins, CO: U.S. 

Department of Agriculture, Forest Service, Rocky Mountain Research Station. 

25 p.

Scott, Joe H.; Reinhardt, Elizabeth D. 2001. Assessing crown fi re potential by 

linking models of surface and crown fi re behavior. Res. Pap. RMRS-RP-29. Fort 

Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain 

Research Station. 59 p.

Scott, Joe H.; Burgan, Robert E. 2005. Standard fi re behavior fuel models: A 

comprehensive set for use with Rothermel’s surface fi re spread model. Gen. Tech. 

Rep. RMRS-GTR-153.Fort Collins, CO: U.S. Department of Agriculture, Forest 

Service, Rocky Mountain Research Station. 72 p.

Simard, Albert J. 1991. Fire severity, changing scales, and how things hang together. 

International Journal Wildland Fire. 1(1): 23-34.

Stephens, Scott L. 1998. Evaluation of the effects of silvicultural and fuels treatments 

on potential fi re behavior in Sierra Nevada mixed conifer forests. Forest Ecology 

and Management. 105: 21-35.

Stephens, Scott L.; Moghaddas, Jason J. 2005a. Silvicultural and reserve impacts on 

potential fi re behavior and forest conservation: Twenty-fi ve years of experience from 

Sierra Nevada mixed conifer forests. Biological Conservation. 125: 369-379.

Stephens, Scott L.; Moghaddas, Jason J. 2005b. Experimental fuel treatment impacts 

on forest structure, potential fi re behavior, and predicted tree mortality in a 

California mixed conifer forest. Forest Ecology and Management. 215: 21-36.

Stephens, Scott L.; Ruth, Lawrence W. 2005. Federal forest-fi re policy in the United 

States. Ecological Applications. 15(2): 532-542.
USDA Forest Service Proceedings RMRS-P-41. 2006. 105

Considerations in the Use of Models Available for Fuel Treatment Analysis McHugh

Stratton, Richard D. 2004. Assessing the effectiveness of landscape fuel treatments 

on fi re growth and behavior. Journal of Forestry. 102(7): 32-40.

USDI-USDA. 2006. Protecting people and natural resources: A cohesive fuels 

treatment strategy. Available at: http://www.fi replan.gov/documents/cohesive_

fuels_strategy03-03-06.pdf.

Van Wagner, Charles E. 1973. Height of crown scorch in forest fi res. Canadian 

Journal Forest Research. 3(3): 373-378.

Van Wagner, Charles E. 1977. Conditions for the start and spread of crownfi re. 

Canadian Journal Forest Research. 7(1): 23-24.

van Wagtendonk, Jan W. 1996. Use of a deterministic fi re growth model to test fuel 

treatments. Pages 1155-1165 in Sierra Nevada Ecosystem Project: Final report 

to congress, Vol II. Assessements and Scientifi c Basis for Management Options. 

Centers for water and wildland resources. University of California, Davis, CA.

Wagle, R. F.; Eakle, Thomas W. 1979. A controlled burn reduces the impact of a 

subsequent wildfi re in a ponderosa pine vegetation type. Forest Science. 25(1): 

123-129.

Weaver, Harold. 1943. Fire as an ecological and silvicultural factor in the ponderosa 

pine region of the Pacifi c slope. Journal of Forestry. 41(1): 7-15.

Weaver, Harold. 1957. Effects of prescribed burning in ponderosa pine. Journal of 

Forestry. 55(2): 133-138.

Wykoff, William R.; Crookston, Nicholas L.; Stage, Albert R. 1982. User’s guide 

to the Stand Prognosis Model. Gen. Tech. Rep. INT-GTR-133. Ogden, UT: U.S. 

Department of Agriculture, Forest Service, Intermountain Forest and Range 

Experiment Station. 112 p.
USDA Forest Service Proceedings RMRS-P-41. 2006. 107

In: Andrews, Patricia L.; Butler, Bret W., 

comps. 2006. Fuels Management—How to 

Measure Success: Conference Proceedings. 

28-30 March 2006; Portland, OR. 

Proceedings RMRS-P-41. Fort Collins, 

CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research 

Station.

1 USDA Forest Service, Missoula Fire 

Sciences Laboratory, Missoula MT. 

mfi nney@fs.fed.us

Abstract—Modeling and experiments have suggested that spatial fuel treatment patterns 

can infl uence the movement of large fi res. On simple theoretical landscapes consisting 

of two fuel types (treated and untreated) optimal patterns can be analytically derived 

that disrupt fi re growth effi ciently (i.e. with less area treated than random patterns). 

Although conceptually simple, the application of these theories to actual landscapes 

is made diffi cult by heterogeneity (fuels, weather, and topography) compared to the 

assumptions required for analytical solutions. Here I describe a computational method 

for heterogeneous landscapes that identifi es effi cient fuel treatment units and patterns 

for a selected fi re weather scenario. The method requires input of two sets of spatial 

input data: 1) the current fuel conditions and 2) the potential fuel conditions after a 

treatment (if it were possible). The contrast in fi re spread rate between the two landscapes under the weather scenario conditions indicates where treatments are effective 

at delaying the growth of fi res. Fire growth from the upwind edge of the landscape 

is then computed using a minimum travel time algorithm. This identifi es major fi re 

travel routes (areas needing treatment) and their intersections with the areas where 

treatments occurred and reduced the spread rate (opportunity for treatment). These 

zones of treatment “need and opportunity” are iteratively delineated by contiguous 

patches of raster cells up to a user-supplied constraint on percentage of land area to 

be treated. This algorithm is demonstrated for simple and for complex landscapes.

Introduction

Fuel treatment effects on wildland fi re behavior have long been documented at the stand level (Biswell et al. 1973, Wagle and Eakle 1979, Helms 

1979, Pollet and Omi 2002, Fernandes and Botelho 2003, Graham 2003). 

Prescribed burns and thinning operations change fuel structure and have 

together been successful in modifying fi re behavior and consequent effects in 

the areas treated (Weaver 1943, Kallender et al. 1955, Cooper 1961, Martin 

et al. 1989, Graham et al. 1999, Schoennagle et al. 2004, Graham et al. 2004, 

Agee and Skinner 2005, Cram et al. 2006). The landscape level, however, 

is composed of many stands and mixtures of fuel conditions through which 

large fi res burn, and there has been little work on strategies for treatment at 

this broad scale. Prescribed burning and general fuel management will be 

a necessary part of mitigating and even reversing effects of fi re suppression 

(Arno et al. 1991). Evidence shows that even widespread treatments that 

change fi re behavior at the stand-level can be circumvented by larger fi res 

(Salazar et al. 1987, Dunn 1989, Finney et al. 2005). This paper reports on 

an algorithm that optimizes the placement of treatment units to limit this 

circumvention and thereby interrupt the movement of large fi res.

A Computational Method for Optimizing Fuel 

Treatment Locations

Mark A. Finney1
108 USDA Forest Service Proceedings RMRS-P-41. 2006.

Finney A Computational Method for Optimizing Fuel Treatment Locations

Precedence for landscape-level fuel modifi cations is found in the patchwork or mosaic formed by free burning fi res in large wilderness areas in the 

western United States. Here, patterns of old burns delay and detour later fi res 

(van Wagtendonk 1995, Parsons and van Wagtendonk, Rollins et al. 2001). 

These interactive effects are possible when fi re frequency is high enough to 

maintain some unknown fraction of the landscape in a modifi ed condition. 

By comparison, intensive fuel treatment methods are expensive and wholesale 

treatment of large landscapes is impossible for practical reasons including 

land ownership, confl icting management objectives, and funding. Typically, 

the amount of land area and the locations of treatments are constraining, 

thus, the question of where to place treatments becomes a problem suitable 

for optimization.

Theoretical work for artifi cial landscapes has shown optimum effi ciency 

from a pattern of rectangular treatment units that reduces fi re growth rates 

with a minimum of area treated (Finney 2001a). Rectangular units that 

partially overlap in the predominant fi re spread direction (determined from 

historic climatology) allow the fi re to move through and around them at 

the same rate. Fire growth is slowed by the pattern because fi re progress is 

dominated by lateral movement. When small fractions of land are treated, 

these patterns are effi cient compared to random arrangements (Finney 2003, 

Loehle 2005). Random patterns may require several times as much treatment 

to reduce fi re growth rates to comparable levels (Gill and Bradstock 1998, 

Bevers et al. 2004). Although conceptually simple, the application of these 

theories to actual landscapes is only just beginning (Hirsch et al. 2001) and 

is made diffi cult by the heterogeneity of real landscapes (fuels, weather, and 

topography) compared to the assumptions required for analytical solutions 

(Finney 2001b).

The computational method reported here uses spatial GIS data to represent the heterogeneity of actual landscapes and produces a map of treatment 

areas that collectively disrupt fi re growth at scales coarser than the individual 

treatment units. The algorithm is applied to simple and complex landscape 

conditions showing that the treatment pattern is one of many that achieve 

effective results comparable to those suggested by the analytical theory.

Assumptions And Methods

Fire Sizes and Severity

The objective explicitly assumed by this analysis for landscape fuel management is to delay the growth of large or “problem” fi res. Information on such 

fi res is readily obtained for most wildland areas from local or regional fi re 

history or fi re atlases (Figure 1). The reasoning for this assumption follows 

from the conditions that foster the growth of such fi res in areas dominated 

by suppression-oriented management in western North America. Here, fi res 

become large by escaping initial attack and then spreading far from where 

they start. Large fi res are resistant to suppression efforts because of the dry 

and windy weather that contributes to their rapid growth, the sheer size and 

length of perimeter they present to control, and the fi re behaviors produced 

under the extreme weather conditions originating their escape (crown fi re, 

spotting). Suppression success typically occurs only when durable changes 

in the weather abate rapid fi re growth. During periods of active spread, such 

fi res are responsible for the greatest damages to watersheds, ecosystems, and 
USDA Forest Service Proceedings RMRS-P-41. 2006. 109

A Computational Method for Optimizing Fuel Treatment Locations Finney

Figure 1—Fire history atlas around Flagstaff, Arizona shows large fi res are mostly 

oriented along a southwest-northeast axis. Wind conditions associated with these 

fi res are about 35 mph (56 kph) with fuel moistures from 3 to 5 percent.

present the greatest threats to human developments beyond the borders of 

the wildlands per se. Managing the condition of the landscape and the spatial 

fuel structure, therefore, offers the only possible means to resist the growth 

of fi res under such conditions, reducing the spread rate and ultimate size of 

the fi res (Gill and Bradstock 1998, Brackebusch 1973 ). This contrasts with 

the use of fuel breaks (Green 1977, Weatherspoon and Skinner 1995, Agee 

et al. 2000) which require active fi re suppression for benefi ts to be realized. 

Fuel is the only element of fi re behavior that is manageable, since weather 

and topography are beyond human control.

Weather Conditions

By targeting large fi res for treatment efforts, the analysis of fi re behavior 

can be restricted to a small subset of weather conditions contributing to the 

growth of those kinds of fi res. Large fi res typically occur under the most 
110 USDA Forest Service Proceedings RMRS-P-41. 2006.

Finney A Computational Method for Optimizing Fuel Treatment Locations

extreme weather conditions that originate their escape from initial attack. 

The weather during historic large fi res is well known to local fi re management 

offi cials and can be synthesized from climatological records (Rothermel 1998, 

Mutch 1998). These weather conditions provide critical data on general fi re 

spread directions and spread rates for all fuel types on the landscape and narrows the focus of fuel management efforts to specifi c ranges of humidity, fuel 

moisture, and winds (Figure 1). By assuming a single set of specifi c weather 

conditions for large fi res (fuel moistures across a landscape, wind speed and 

direction) fi re behavior can be calculated for all areas of each landscape.

Sizes of Fires Greater than Fuel Treatment Units

The large size of these fi res relative to the size of treatment units also 

suggests that the starting locations of fi res can be ignored. This assumption 

allows the analysis to focus on the directions of fi re movement. Large fi res 

moving across landscapes encounter smaller treatment units with relatively 

wide fronts that have become largely independent of the exact ignition location. The major direction of fi re movement is, however, critical because the 

rapid spread rates of the heading fi re (moving with wind and slope) burn the 

most acreage with the highest intensities (Catchpole et al. 1982). Heading 

fi re is more important to modify than fl anking and backing portions of the 

fi re which have lower intensities and cause less severe fi re impacts.

Fuel Treatments

The fuel treatment optimization procedure described below depends on 

fi re behavior contrasts between the two fuel profi les burning under the target 

weather conditions: one represents the starting conditions or current state 

of fuels and forest structure, and the second represents the fuel conditions 

following treatment (Figure 2). The assumption here is that desired fuel 

conditions can be identifi ed on a stand-by-stand basis across the landscape for 

all stands where treatment is possible. These fuel conditions are represented 

across a large landscape as a rectangular grid at a fi xed resolution. The cells 

of the grid are assumed uniform at scales fi ner than the resolution in terms 

of fuels, topography, and weather. The treated landscape describes the potential areas for treatment that must total more than the constraint imposed 

on total area treatable within the planning horizon. Treatment prescriptions 

within each stand or cell on a landscape, such as prescribed burning or various 

stand-level thinning guidelines can vary to refl ect local objectives or restrictions on activities. Although any prescription can be applied, fi eld evidence 

consistently suggests that fuel treatment prescriptions achieve reductions in 

wildfi re spread rate and intensity by removing surface fuels through prescribed 

burning and decreasing the continuity between surface and canopy fuel strata 

through “low-thinning” (van Wagtendonk 1996, Pollet and Omi 2002, Agee 

and Skinner 2005). Mechanical treatments that leave slash or don’t remove 

pre-existing surface fuels may not change fi re behavior suffi ciently (Graham 

2003, Raymond and Peterson 2005, Cram et al. 2006) or even exacerbate 

fuel hazards (Alexander and Yancik 1977). Lands excluded from treatment 

consideration retain the identical fuel descriptions in both landscapes or involve prescriptions that increase the fi re spread rate. Thus, the optimization 

will choose from the lands where treatments change fi re behavior to achieve 

the greatest collective reduction in landscape fi re spread rate.
USDA Forest Service Proceedings RMRS-P-41. 2006. 111

A Computational Method for Optimizing Fuel Treatment Locations Finney

Algorithm

The objective of the fuel treatment optimization is to fi nd the specifi c 

treatment areas that reduce fi re growth for the target weather conditions 

by the greatest amount. In other words, it is attempting to maximize the 

minimum travel time for fi re moving across the landscape. With the emphasis on fi re travel time, a critical component of this optimization is a method 

for calculating fi re growth under the target weather conditions. Fire growth 

simulation using a minimum-travel-time algorithm (Finney 2002b) is well 

suited to this task because it rapidly produces a fi re arrival time fi eld for a 

given ignition (which can be contoured to visualize fi re growth at constant 

time intervals) and records the travel routes of fi re movement from one node 

to the next (Figure 3). Both fi re growth contours and travel routes are used 

by the fuel treatment optimization.

The optimization algorithm begins by dividing the landscape into a 

series of parallel strips oriented perpendicular to the main fi re spread direction (Figure 3). The width of these strips is determined as a user input that 

Figure 2—Two landscape fuel conditions area required for the optimization algorithm. 

The fi rst landscape represents the pre-treatment or current fuel conditions whereas the 

second landscape represents the potential treatment conditions (i.e. modifi cations of fuel 

strata) everywhere treatments can potentially be located. Both landscapes are processed 

for fi re behavior under the “target” weather conditions (i.e. those weather conditions 

that the treatments are designed for).
112 USDA Forest Service Proceedings RMRS-P-41. 2006.

Finney A Computational Method for Optimizing Fuel Treatment Locations

regulates the maximum dimension of treatment units allowed. This is similar 

to the method described by Finney 2002b, but this algorithm produces a 

deterministic solution:

 1. Beginning with the upwind strip, fi re growth and minimum travel routes 

are computed (Figure 4a). Concave segments along the fi re arrival time 

contour are identifi ed. These segments are concave in terms of the fi re 

arrival time at a particular row of the landscape, which means that they 

start and end with a local maximum arrival time (Figure 4b).

 2. Within the strip, the minimum travel time path for each segment is 

identifi ed and followed backwards in time and space to record intersections with areas where fi re behavior differences exist between the two 

landscapes (Figure 4c).

 3. A choice is made for the best place to start the fuel treatments for each 

segment. The choice was based on criteria of having the earliest time 

where fuel treatments are possible. Arrival times are reset to infi nity for 

all nodes (on the entire landscape) having an arrival time later than the 

time at the starting location.

Figure 3—The fi re behavior simulation uses a minimum travel-time (MTT) algorithm that 

(a) for a given landscape (b) produces an arrival time map which can be contoured to 

indicate fi re progression and (c) displayed along with fi re travel routes which correspond 

to calculations of “fi re infl uence” (i.e. the area burned as a result of burning through 

that grid cell). All travel paths are shown in (c) as fi ne black lines and the “major” travel 

paths chosen at specifi ed distance intervals and are indicated by bold yellow lines.
USDA Forest Service Proceedings RMRS-P-41. 2006. 113

A Computational Method for Optimizing Fuel Treatment Locations Finney

 4. The minimum travel-time algorithm is re-run for the strip using the 

post-treatment landscape data (Figure 4d). This is done for the entire 

strip separately for each segment identifi ed in #1 above since the time 

contour used as starting point for fuel treatments identifi ed in step 3 is 

typically different for each segment. The new arrival time map is stored 

for each segment and represents the rate of fi re growth assuming all fuel 

treatments have occurred.

 5. An iterative procedure identifi es and delineates treatment units within 

the strip that have sizes and shape for effi ciently retarding fi re growth. 

A treatment unit is identifi ed as a contiguous group of cells marked as 

treated using a contagion algorithm (Figure 4e). For each travel path 

the process marks treatable contiguous cells with arrival times greater 

than the starting point up to a time limit that is iteratively increased 

until the specifi ed fraction of the landscape (strip) is treated. For each 

treatment unit, the contiguous block of marked cells is expanded laterally until the forward time difference is also reached. This creates a 

treatment unit that approximates the balance between time required 

for spreading through the unit and spreading around the unit (Finney 

2001a).

Figure 4—Optimization process begins by dividing the landscape into rows. For each row beginning with the 

row farthest upwind (a) fi re growth for the pre-treatment landscape is calculated using the MTT calculation 

to identify travel routes and produce an arrival time map (b) the concave segments of arrival time are 

identifi ed at the ending row, (c) intersections of the major fi re travel paths and the treatment opportunity are 

identifi ed (areas where treatments reduce the fi res spread rate) and the point with the earliest arrival time 

of this intersection is recorded, (d) fi re growth for the potential landscape is calculated from the starting 

time identifi ed in (c), and (e) iteration of treatment unit size and shape is performed.
114 USDA Forest Service Proceedings RMRS-P-41. 2006.

Finney A Computational Method for Optimizing Fuel Treatment Locations

The algorithm assumes that the fi re front will have a rippled time contour 

or “fi ngers” at the forward edge produced by varying spread rates that result 

from fuels, topography, or wind patterns. The algorithm targets fuel treatments to block these fi ngers since they are local zones of faster spread. For 

relatively uniform conditions, where little or no variation exists, the algorithm 

must be modifi ed to place fuel treatments by some other rule. The rule used 

here within a given strip was a systematic and regular spacing, which produces 

the ripples at later time periods.

In optimal regular patterns (Finney 2001a) the most effi cient treatment 

unit size depends on overlap and separation of neighboring treatment units. 

These dimensions are constant among units, and as such, are diffi cult to 

transfer directly to actual landscapes that contain complex variation in fuels, 

topography, and perhaps weather (wind direction, fuel moisture). The regular 

patterns don’t apply here because the size and orientation of a given treatment unit is only effi cient in the context of other possible units encountered 

immediately before and after by fi re moving across the landscape. Yet, each 

unit modifi es the path of fi re into succeeding units. Thus, a compromise 

was undertaken for the algorithm that assumes that the delay of fi re spread 

through the unit must be twice the delay in circumventing it. This will not 

be strictly valid if the fuel conditions downwind of the treatment unit are 

substantially different from those upwind.

Evaluation of the Algorithm

Two kinds of landscapes were used to evaluate the performance of the 

algorithm. First, an artifi cial simple landscape with several slow-burning fuel 

patches was used to test the ability of the algorithm to produce treatment 

patterns similar to the theoretical patterns described by Finney (2001) and 

illustrate the sensitivity to localized non-uniformities in the landscape. Here 

fuel treatments were implemented to reduce spread rate to 1/20th of the untreated rate. The second landscape was located near Flagstaff Arizona. The 

historic fi res were plotted and the predominant SW to NE orientation of the 

large wildfi res was used to orient the treatment units against this major spread 

direction. Weather for the fi re simulations were chosen at 99th percentile of 

the historic National Fire Danger Rating System (NFDRS) index Energy 

Release Component for fuel model “g” (ERC(g)) which provides the fuel 

moisture content of the fuel components required for fi re behavior modeling. 

Wind speed and wind directions were chosen to refl ect the period of major fi re growth associated with the historic large fi res (Figure 1) that have burned 

in this area. Treatment prescriptions were only applied to ponderosa pine and 

mixed conifer forest areas in public ownership and consisted of changing surface fuels to fuel model 9 (Anderson 1982) increasing the crown base height 

and decreasing crown bulk density (both making crown fi re more diffi cult). 

No treatment was permitted in meadows, on privately owned lands, or in a 

designated USFS Wilderness area in the north part of the area.

The response of the fi re behavior to the various treatment options was 

measured in terms of average spread rate, relative change in wildfi re size, and 

conditional burn probability. The conditional burn probability was determined 

by random fi res simulated under the target weather conditions (Finney 2002a) 

for varying amounts of time (resulting in various fi re sizes after 360, 720, 

and 1080 minutes of spread). This probability is “conditional” because is 

represents the probability of burning once a fi re becomes large (>100 ha) or 
USDA Forest Service Proceedings RMRS-P-41. 2006. 115

A Computational Method for Optimizing Fuel Treatment Locations Finney

escapes initial attack, which typically occurs at a rate of less than 2% per year 

(NIFC 2002, Neuenschwander et al. 2000). Mean spread rate was obtained 

by dividing the average arrival time for the last row in the landscape by the 

linear travel distance.

Results

Treatment optimization for the simple landscape produced partially 

overlapping patterns similar to those of the analytical model (Finney 2001a) 

with the exception of the downwind edge of the few slow-burning patches 

(Figure 5). Patterns were similar when the optimization was directed to 

vary the sizes of treatment units. The average spread rate of the fire across 

the entire landscape showed the same response to increasing amounts of 

treatment as average fire sizes and average burn probability (Figure 6) for 

a given size of simulated fire. Burn probabilities were higher when larger 

fires were simulated but responded the same across the range of treatment 

percentages (Figure 6b,c). In addition, the increased efficiency of the optimal pattern compared to random treatment patterns was similar to the 

same comparisons for theoretical patterns (e.g. Finney 2003).

Figure 5—Treatment optimization runs for a simple fl at landscape that contains eight small 

patches of slow-burning fuel (purple). From left to right the maximum treatment dimension 

is increased from 800 m to 2,500 m. Treatment units are shown independently along with 

the fi re progression which reveals that treatment units cause repeated disruptions of fi re 

movements.
116 USDA Forest Service Proceedings RMRS-P-41. 2006.

Finney A Computational Method for Optimizing Fuel Treatment Locations

The optimal patterns for the Flagstaff landscape were less systematic than 

the patterns on the real landscape (Figure 7) and were strongly infl uenced by 

areas where treatment was precluded by ownership (private and designated 

wilderness) or vegetation type (i.e. meadows represented by grass fuels (Fuel 

Model 1)). The optimal pattern was more effi cient at all levels of treatment 

than the random pattern (Figure 8a). However, the presence of untreatable 

area interspersed among the forests provided conduits for rapid fi re spread 

and decreased the effi ciency of the optimal pattern in retarding overall fi re 

growth compared to random patterns as seen in the simple landscape and 

theoretical comparisons (Figure 8a). As with the simple landscapes, the 

relative fi re sizes and conditional burn probability decreased with amount of 

treatment (Figure 8b,c).

Figure 6—Summary of optimization results for simple landscapes over ranges of 

treatment amount were measured in terms of (a) average spread rate across the 

landscape, (b) average fi re sizes for 1,000 simulated randomly ignited fi res of different 

durations, and (c) average burn probability for the landscape determined from 1,000 

random ignitions.
USDA Forest Service Proceedings RMRS-P-41. 2006. 117

A Computational Method for Optimizing Fuel Treatment Locations Finney

Figure 7—Optimal treatment patterns for the Forest Ecosystem Restoration Analysis project (FERA) for an 

area surrounding Flagstaff, Arizona. Each pattern represents 20% of the analysis area in treatments with 

only the treatment size varying by alternative. 2,500 m (a), 1,200 m (b), 800 m (c), and 600 m (d). The analysis 

area is 2,906 km2 and 168,853 ha within the Kaibab and Coconino National Forests and is a portion of a 

larger landscapes (809,375 ha).

Discussion

This study showed that an optimization algorithm produced treatment patterns on simple landscapes with impacts on spread rate similar to the analytical 

solutions for similar landscapes (Finney 2001a). This is encouraging because 

performance on complex landscapes cannot be directly assessed relative to 

theoretical results. Relative performance of optimal patterns on both simple 

and complex landscapes could be assessed in relation to random patterns. This 

comparison suggested that optimization effi ciently reduced spread in both 

landscapes but that the presence of untreatable areas within the landscape 

compromises the effi ciency of the overall pattern. The poor effi ciency of the 

random patterns is also similar to theoretical results (Finney 2003).
118 USDA Forest Service Proceedings RMRS-P-41. 2006.

Finney A Computational Method for Optimizing Fuel Treatment Locations

The intent of the optimization was to target treatment locations in areas 

where fi re fl ow is greatest, meaning that these areas have greater infl uence 

on the area burned downwind. The position of the treatment units relative 

to the slow-burning patches that existed before treatment illustrated how 

treatment units were positioned to avoid the lee-side wake on the back-side 

of each of these patches. The major fl ow paths are located laterally around 

the left and right fl anks of the slow-patches and directed the location of the 

treatment units.

Maximum treatment unit dimension was varied from 800 to 2,500 m (~0.5 

to 1.5 mile diameter, or up to 160 to 960 acres if the units were square) in the 

optimization but made little difference to the aggregated spread rate, burn 

probabilities, or the average fi re sizes. The fl exibility of treatment size would 

be important to application of treatment units to different landscapes, ecology, topography, and constraints on treatment as illustrated by the Flagstaff 

Figure 8—Optimal and random treatment patterns for 1,500 m units on the Flagstaff 

landscape reduced fi re spread rate (a), mean fi re sizes (b), and conditional burn 

probability (c) effi ciently compared to random treatments. Although fuel treatments 

individually reduced fi re spread rate by about 90%, the collective benefi t of even the 

optimal pattern was compromised by the presence of large grass meadows that could 

not be treated. Grass fuels with full wind exposure had spread rates more than four 

times faster than the forest fuel types and served as conduits for fi re growth which 

reduced effectiveness of treatment pattern in minimizing overall fi re growth.
USDA Forest Service Proceedings RMRS-P-41. 2006. 119

A Computational Method for Optimizing Fuel Treatment Locations Finney

example where meadows could not be treated (Figure 7). Treatment unit 

sizes also affect the optimal spacing between units and appropriateness for 

wildfi res in different fi re regimes. Large fi re patterns may permit large treatment units and wide spacing, but smaller fi res are theoretically little affected 

by widely spaced treatment units. The possible enhancement of treatment 

longevity associated with larger units (Finney et al. 2005) may be an additional 

consideration in selecting treatment sizes for the optimization.

The algorithm developed here was intentionally designed to produce 

“greedy” solutions for individual treatment units by blocking fl ow-paths that 

are identifi ed as “major” only within the current strip. An alternative would 

be to identify and block fi re fl ow-paths that become important farther downwind than the immediate strip. These two approaches will probably diverge 

for more complex landscapes because remote downwind landscape conditions (e.g. fuels and topography) may obviate a local pathway. The emphasis 

on a greedy solution has two advantages. First, it is faster computationally 

because fi re growth does not have to be simulated far downwind from the 

current strip. Second, and perhaps more importantly for fi re management 

applications, the greedy solution situates a treatment unit on a locally major 

pathway which increases the proximity of a well-placed treatment unit to a 

randomly located ignition source.

Amount of treatment tested was limited to 40 percent because theoretical 

differences between the optimal and random treatment patterns diminish with 

treatment cover above some level around this point (Finney 2003, 2004). 

This means that if fi nancial or operational resources permit landscape treatment at a rate suffi cient to maintain a landscape at about 30 or 40% treatment 

annually, then the spatial pattern becomes less important and optimization 

is not as useful. In natural fi re regimes, observed interference by fi re history 

patterns on subsequent wildfi re growth (van Wagetndonk 1995, Parsons and 

van Wagtendonk 1996) is derived from largely random ignition patterns only 

because the frequency of fi re is suffi cient to maintain a large fraction of the 

landscape in a fuel-modifi ed condition.

The spatial optimization assumes that the spatial pattern is extant at a given 

instant in time. In reality, however, treatments are accomplished on an annual 

basis and treatment effects to reduced fi re behavior diminish with time. To 

achieve an effective spatial pattern means that the annual rate of treatment 

or maintenance must be high enough to achieve the cumulative spatial pattern while treatment effectiveness decreases. Little is known about treatment 

longevity but a few studies suggest that benefi ts to fi re effects are limited to 

about 10 to 15 years (Biswell et al. 1973, Fernandes et al. 2004, Finney et al. 

2005). Consequently, this suggests that the minimum annual treatment rate 

can be estimated to be about 1/10th of the total treatment cover desired. 

For example, if treatment of 20% of the landscape is a desired state, then the 

annual rate of treatment must be no lower than approximately 2%.

Spatial constraints are accommodated in the treatment optimization automatically where fi re behavior is identical between pre-treatment and potential 

treatment landscapes. Areas where fuel treatment changes fi re spread rate will 

be considered available for treatment and perhaps selected if intersected by 

major fi re travel paths. Those areas where treatments are not possible contain 

the same fuel conditions in both landscapes, thereby offering no contrast in 

fi re behavior and no reason for selecting them for treatment even if major fl ow paths intersect these areas. Such effects can be seen in the large areas 

with no treatment in the Flagstaff example because of the location of grass 

meadows and designated wilderness areas that are not available for treatment 

even though the fi re may spread very rapidly (Figure 7).
120 USDA Forest Service Proceedings RMRS-P-41. 2006.

Finney A Computational Method for Optimizing Fuel Treatment Locations

The current algorithms neglect effects of spotting on fi re progression and 

fuel treatment locations. Spotting is a fi re behavior that includes the lofting and transport of burning embers downwind which start new fi res and 

permit fi re to breach barriers and discontinuities of fuels. Models for ember 

production and transport (Albini 1979) are included in other fi re behavior 

systems (Finney 1998) and can be included here in future. The exact effect 

of spotting on treatment performance is not clear because fuel treatments 

often limit the source of new embers as well as retard the growth of eventual 

spot fi res. Spotting effects may be minimized by manually increasing the size 

of treatment units to mitigate overfl ight possibilities. But longer separation 

distances between larger treatments permit wider headfi res to develop in between treatment units which may increase spot fi re generation. Even if spot 

fi res breach the treatment units, an extensive landscape pattern of treatments 

would impose repeated interruption of any new fi res.

Conclusions

The optimization procedure was developed with the intent of obstructing 

the movement of large wildfi res rather than containing them. The algorithm 

was capable of reducing the average fi re growth rate effi ciently for complex 

and simple landscapes. This procedure can be useful for inclusion in fi re 

management planning activities because it offers a means of measuring the 

performance of fuel treatments at both a stand- and landscape-level.

Acknowledgments

This study was funded by the U.S. Joint Fire Sciences Program, the Bureau 

of Land Management (BLM), and the USDA Forest Service, Missoula Fire 

Sciences Laboratory. The author is grateful to Charles McHugh, Rob Seli, 

and Rick Stratton for their efforts in testing the optimization model and 

helping prepare data and graphics for this paper. Howard Roose provided 

critical funding from the BLM for software development.

Literature Cited

Agee, J.K., B. Bahro, M.A. Finney, P.N. Omi, D.B. Sapsis, C.P. Weatherspoon. 

2000. The use of fuelbreaks in landscape fi re management. Forest Ecology and 

Management. 127(1-3):55-66.

Agee, J.K. and C.N. Skinner. 2005. Basic principles of forest fuel reduction 

treatments. For. Ecol. Mgt. 211:83-96.

Alexander, M.E., and R.F. Yancik. 1977. The effect of precommercial thinning on 

fi re potential in a lodgepole pine stand. Fire Management Notes. 38(3):7-9.

Anderson, H.E. 1982. Aids to determining fuel models for estimating fi re behaviour. 

USDA For. Serv. Intermountain Forest and Range Experiment Station, General 

Technical Report INT-GTR-122.

Arno, Stephen F.; Brown, James K. 1991. Overcoming the paradox in managing 

wildland fi re. West Wildl. 17(1):40-46.
USDA Forest Service Proceedings RMRS-P-41. 2006. 121

A Computational Method for Optimizing Fuel Treatment Locations Finney

Bevers, M., Omi, P.N., and Hoff, J. 2004. Random location of fuel treatments in 

wildland community interfaces: a percolation approach. Can. J. For. Res. 34: 

163–173.

Biswell, H.H., Kallander, H.R., Komarek, R., Vogl, R.J., and Weaver, H. 1973. 

Ponderosa fi re management: A task force evaluation of controlled burning in 

ponderosa pine forest of central Arizona. Tall Timbers Research Station Misc. 

Publ. No. 2. Tallahassee, Florida.

Brackebusch, A.P. 1973. Fuel management: a prerequisite not an alternative to fi re 

control. Journal of Forestry. 71(10):637-639.

Catchpole, E.A.; de Mestre, N.J.; Gill, A.M. 1982. Intensity of fi re at its perimeter. 

Australian Forest Research. 12:47-54.

Cooper, C.F. 1961. Controlled burning and watershed condition in the White 

Mountains of Arizona. J. For. 59:438-442.

Cram, D.; Baker, T.; Boren, J. 2006. Wildland fi re effects in silviculturally treated 

vs. untreated stands of New Mexico and Arizona. Research Paper RMRS-RP55. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky 

Mountain Research Station. 28 p.

Dunn, A.T. 1989. The effects of prescribed burning on fi re hazard in the chaparral: 

toward a new conceptual synthesis. In: N.H. Berg (Tech. Coord). Proc. of the 

Symp. on Fire and Watershed Management. USDA For. Serv. Gen. Tech. Rep. 

PSW-109. Pp 23-29.

Fernandes, P. and H. Botelho. 2003. A review of prescribed burning effectiveness 

in fi re hazard reduction. Intl. J. Wildland Fire 12(2):117-128.

Fernandes, P.A.M., C.A. Loureiro, and H.S. Botelho. 2004. Ann. For. Sci. 

61:537-544.

Finney, M.A. 2001a. Design of regular landscape fuel treatment patterns for 

modifying fi re growth and behavior. For. Sci. 47(2):219-228.

Finney, M.A. 2001b. Spatial strategies for landscape fuel treatment. In J. Bento and 

H. Botelho, eds. Proc. Workshop on Tools and methodologies for fi re danger 

mapping. March 9-14, Vila Real Portugal: 157-163.

Finney, M.A. 2002a. Fire growth using minimum travel time methods. Can. J. For. 

Res. 32(8):1420-1424.

Finney, M.A. 2002b. Use of graph theory and a genetic algorithm for fi nding 

optimal fuel treatment locations. In: Proc. 4th International Conf. on Forest Fire 

Research, Luso Portugal.

Finney, M.A. 2003. Calculating fi re spread rates across random landscapes. Intl. J. 

Wildl. Fire. 12(2):167-174.

Finney, M.A. 2004. Chapter 9, Landscape fi re simulation and fuel treatment 

optimization. In: J.L. Hayes, A.A. Ager, J.R. Barbour, (tech. eds). Methods for 

integrated modeling of landscape change: Interior Northwest Landscape Analysis 

System. PNW-GTR-610: 117-131.

Finney, M.A., C.W. McHugh, and I.C. Grenfell. 2005. Stand- and landscape-level 

effects of prescribed burning on two Arizona wildfi res. Can. J. For. Res 35: 

1714-1722.

Gill, A.M.; Bradstock, R.A. 1998. Prescribed burning: patterns and strategies. 

In: 13th conference on fi re and forest meteorology. International Association of 

Wildland Fire, USA: 3-6.

Graham, R.T., A.E. Harvey, T.B. Jain, and J.R. Tonn. 1999. The effects of thinning 

and similar stand treatments on fi re behaviour in western forests. USDA For. Serv. 

Gen. Tech. Rep. PNW-GTR-463.

Graham, R.T. 2003. Hayman fi re case study. USDA For. Serv. Gen. Tech. Rep. 

RMRS-GTR-114.
122 USDA Forest Service Proceedings RMRS-P-41. 2006.

Finney A Computational Method for Optimizing Fuel Treatment Locations

Graham, R.T., S. McCaffrey, and T.B. Jain. 2004. Science basis for changing forest 

structure to modify wildfi re behavior and severity. USDA For. Serv. Gen. Tech. 

Rep. RMRS-GTR-120.

Green, L.R. 1977. Fuelbreaks and other fuel modifi cation for wildland fi re control. 

USDA Ag. Handbook 499.

Helms, J.A. 1979. Positive effects of prescribed burning on wildfi re intensities. Fire 

Management Notes. 403:10-13.

Hirsch, K., V. Kafka, C. Tymstra, R. McAlpine, B. Hawkes, H. Stegehuis, S. Quintilio, 

S. Gauthier, and K. Peck. 2001. Fire-smart forest management: a pragmatic 

approach to sustainable forest management in fire-dominated ecosystems. 

77(2):357-363.

Kallander, H., Weaver, H., and Gains, E.M. 1955. Additional information on 

prescribed burning in virgin ponderosa pine in Arizona. J. For. 53:730-731.

Loehle, C. 2004. Applying landscape principles to fi re hazard reduction. For. Ecol. 

Management. 198:261–267.

Martin, R.E., J.B. Kauffman, and J.D. Landsberg. 1989. Use of prescribed fi re 

to reduce wildfi re potential. In: N.H. Berg, tech. coord. Proc. of the Symp. on 

Fire and Watershed Management. USDA For. Serv. Gen. Tech. Rep. PSW-109: 

17-22.

Mutch, R.W. 1998. Long-range fi re behavior assessments: your fi re behavior future. 

In: Close, K.; Bartlette, R., eds. Proceedings of the 1994 interior West fi re 

council meeting and program. International Association of Wildland Fire. Roslyn, 

Washington, USA: 69-74.

National Interagency Fire Center (NIFC). 2002. Wildland fi re season summaries. 

(www.nifc.gov/stats/summaries/summary_2002.html).

Neuenschwander, L.F., Menakis, J.P., Miller, K.M., Sampson, R.N., Hardy, C., 

Averaill, B., Mask, R. 2000. Indexing Colorado watersheds to risk of wildfi re. J. 

Sustain. For. 11 (1/2): 35-55.

Parsons, D.J.; van Wagtendonk, J.W. 1996. Fire research and management in the 

Sierra Nevada. In: Halvorson, W.L. and G.E. Davis, eds. Science and ecosystem 

management in the National Parks. Chap. 3. Tucson, AZ: University of Arizona 

Press.

Pollet, J., and Omi, P.N. 2002. Effect of thinning and prescribed burning on crown 

fi re severity in ponderosa pine forests. Int. J. Wildland Fire. 11:1-10.

Raymond, C.L. and D.L. Peterson. 2005. Fuel treatments alter the effects of wildfi re 

in a mixed evergreen forest, Oregon, USA. Canadian Journal of Forest Research, 

35:2981-2995.

Rollins, M.G., Swetnam, T.W., and Morgan, P. 2001. Evaluating a century of fi re 

patterns in two Rocky Mountain wilderness areas using digital fi re atlases. Can. 

J. For. Res. 31:2107-2123.

Rothermel, R.C. 1998. Long range assessments. In: Close, K.; Bartlette, R., 

eds. Proceedings of the 1994 interior West fi re council meeting and program. 

International Association of Wildland Fire. Roslyn, Washington, USA: 169-180.

Salazar, L.A. and Gonzalez-Caban, A. 1987. Spatial relationship of a wildfi re, 

fuelbreaks, and recently burned areas. West. J. Appl. For. 2(2):55-58.

Schoennagle, T., T.T. Veblen, and W.H. Romme. 2004. The interaction of fi re, fuels, 

and climate across Rocky Mountain forests. BioScience. 54(7):661-676.

van Wagtendonk, J.W. 1996. Use of a deterministic fi re model to test fuel treatments. 

In: Sierra Nevada Ecosystem Project: Final report to Congress Volume II. Centers 

for Water and Wildland Resources, University of California, Davis: 1155-1167.
USDA Forest Service Proceedings RMRS-P-41. 2006. 123

A Computational Method for Optimizing Fuel Treatment Locations Finney

van Wagtendonk, J.W. 1995. Large fi res in wilderness areas. In: Brown, J.K.; Mutch, 

R.W.; Spoon, C.W.; Wakimoto, R.H., tech. coords. Proceedings: symposium on 

fi re in wilderness and park management. Gen. Tech. Rep. INT-GTR-320. Ogden, 

UT: U.S. Department of Agriculture, Forest Service, Intermountain Research 

Station: 113-116.

van Wagtendonk, J.W., and C.A. Sydoriak. 1987. Fuel accumulation rates after 

prescribed fi res in Yosemite National Park. Proc. 9th Conf. Fire For. Meteor. 

9:101-105.

Wagle, R.F., and Eakle, T.W. 1979. A controlled burn reduces the impact of a 

subsequent wildfi re in a ponderosa pine vegetation type. For. Sci. 25: 123-129.

Weaver, H. 1943. Fire as an ecological and silvicultural factor in the ponderosa pine 

region of the Pacifi c slope. J. For. 41:7-15.
USDA Forest Service Proceedings RMRS-P-41. 2006. 125

In: Andrews, Patricia L.; Butler, Bret W., 

comps. 2006. Fuels Management—How to 

Measure Success: Conference Proceedings. 

28-30 March 2006; Portland, OR. 

Proceedings RMRS-P-41. Fort Collins, 

CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research 

Station.

1 USDA Forest Service, Missoula Fire 

Sciences Laboratory, Missoula, MT. 

mfi nney@fs.fed.us,

2 USDA Forest Service, La Grande Forestry 

and Range Sciences Lab, La Grande, OR.

3 USDA Forest Service, Regional Fuels 

Specialist, Region 5, Sacramento, CA.

4 University of Washington, College 

of Forest Resources, Anderson Hall, 

Seattle, WA.

Abstract—A simulation system was developed to explore how fuel treatments placed 

in random and optimal spatial patterns affect the growth and behavior of large fi res 

when implemented at different rates over the course of fi ve decades. The system 

consists of a forest/fuel dynamics simulation module (FVS), logic for deriving fuel 

model dynamics from FVS output, a spatial fuel treatment optimization program, and 

spatial fi re growth and behavior model to evaluate the performance of the treatments 

in modifying large fi res. Simulations were performed for three study areas: Sanders 

County in western Montana, the Stanislaus National Forest in California, and the Blue 

Mountains in eastern Oregon. Response variables reported here include: (1) fi re size 

distributions, (2) large fi re spread rates, and (3) burn probabilities, and all revealed the 

same trends. For different spatial treatment strategies, our results illustrate how the rate 

of fuel treatment (percentage of land area treated per decade) competes against the 

rates of fuel recovery to determine how fuel treatments accrue multi-decade cumulative impacts on the response variables. Using fuel treatment prescriptions that involve 

thinning and prescribed burning, even optimal treatment arrangements (designed to 

disrupt the growth of large fi res) require at least 10% to 20% of the landscape to be 

treated each decade. Randomly arranged units with the same treatment prescriptions 

require about twice that rate to produce the same effectiveness. The results also show 

that the fuel treatment optimization tends to balance maintenance of previous units 

with treatment of new units. For example, with 20% landscape treatment, fewer than 

5% of the units received 3 or more treatments in 5 decades with most being treated 

only once or twice and about 35% remaining untreated the entire planning period.

Introduction

Benefi ts of fuel treatments for mitigating the severity of wildfi res have 

been documented at the stand level for much of the 20th century (Weaver 

1943, Cooper 1961, Biswell et al. 1973), particularly in ponderosa pine and 

dry mixed conifer forests in the western United States (ponderosa pine and 

Douglas-fi r). Recent large wildfi res have stimulated renewed interest in fuel 

treatments and prompted new studies that have confi rmed these fi ndings 

(Pollet and Omi 2002, Graham 2003, Graham et al. 2004, Raymond and 

Peterson 2005, Agee and Skinner 2005, Cram et al. 2006). Beyond the 

immediate stand level (i.e. fuel changes over time and large spatial scales) 

treatment effects are poorly understood. Only a few studies of treatment 

longevity exist (Biswell et al. 1973, van Wagtendonk and Sydoriak 1987, 

Finney et al. 2005) and indicate diminishing benefi ts beyond about a decade. 

Landscape-level effects from various treatment patterns are still largely theoretical (Finney 2001a, 2003, Hirsch et al. 2001) with few observations of 

Simulation of Long-Term Landscape-Level 

Fuel Treatment Effects on Large Wildfi res

Mark A. Finney1, Rob C. Seli1, Charles W. McHugh1, Alan A. Ager2, 

Berni Bahro3, and James K. Agee4
126 USDA Forest Service Proceedings RMRS-P-41. 2006.

Finney, Seli, McHugh, Ager, Bahro, and Agee Simulation of Long-Term Landscape-Level Fuel Treatment Effects on Large Wildfi res

treatment performance in altering fi re movement (Finney 2005). Given the 

diffi culty with implementing large-scale and long-term experiments in fuel 

treatment, this study sought to use computer simulation to explore complex 

interactions of landscape treatment pattern and temporal vegetation/fuel 

changes in addressing the following questions:

 1. What effect does spatial treatment pattern have on fi re growth on complex 

landscapes?

 2. At what rate must fuel treatments be implemented across a landscape to 

produce aggregated or cumulative effects on wildfi re growth?

 3. For purposes of disrupting fi re growth, should existing fuel treatment 

units be maintained or should effort be made to implement new treatment units?

 4. How do restrictions or constraints on fuel treatment location (because 

of confl icting land management objectives) affect treatment benefi ts?

 5. How do landscape-level fuel treatment patterns perform under weather 

scenarios more moderate than the extreme conditions specifi ed in their 

design?

Methods

Our objectives were to produce a simulation system that implements fuel 

treatments over large landscapes in order to evaluate the impact on potential 

fi re behavior over multiple decades. The system (Figure 1) consisted of:

 1. The Forest Vegetation Simulator (FVS) for simulating the changes over time 

in forest vegetation (Crookston and Stage 1991) and fuels (Reinhardt and 

Crookston 2003). The FVS models were used for multiple stands comprising a landscape and for implementing the treatment prescriptions.

Figure 1—The simulation system was run for each decade. This system consisted of the 

Parallel Processing version of the Forest Vegetation Simulator (PPE-FVS) that simulated 

forest development with and without treatment, derivation of surface fuel models from 

the biomass categories and production of spatial landscapes for each scenario, spatial 

optimization of fuel treatment locations for disrupting fi re growth, and implementation 

of treatments as feedback for the next simulation cycle in PPE-FVS.
USDA Forest Service Proceedings RMRS-P-41. 2006. 127

Simulation of Long-Term Landscape-Level Fuel Treatment Effects on Large Wildfi res Finney, Seli, McHugh, Ager, Bahro, and Agee

 2. A spatial model for choosing the location of treatment units using optimal 

or random selection logic (Finney 2002a, 2004, Finney in prep.).

 3. A fi re growth simulation model used to evaluate the impact of treatments 

in terms of fi re growth rate, fi re sizes, and relative burn probability 

(Finney 2002b).

Simulating Forest and Fuel Conditions and Treatment 

Prescriptions using FVS

The Forest Vegetation Simulator (FVS) is widely used in the U.S. for forest growth and yield modeling (Wykoff et al. 1982) and has recently been 

modifi ed to record information on fuels and woody debris (Reinhardt and 

Crookston 2003). FVS has multiple “variants” that correspond to species, 

growth rates, and fuel types of forests in numerous regions throughout the 

U.S. Our system relied on a custom verson of the Parallel Processing Extension (PPE) of FVS (Crookston and Stage 1991) which processes the stand list 

cycle-by-cycle (rather than one at a time for all cycles as in the normal version 

of FVS) and implements specifi c silvicultural and fuel treatment prescriptions 

(i.e. modifi es forest and fuel structures). This custom version of PPE controls 

the simulation loop that calls separate routines outside of PPE that identify 

specifi c stands to treat. The PPE module then implements the prescriptions 

and processes the growth and fuel deposition for the next simulation cycle.

The stand-level prescriptions representing fuel treatments in FVS were 

specifi cally developed for treating fuels rather than to extract forest products 

(e.g. timber volume) or meet long-term ecological objectives. Treatments 

that include removal of surface fuels by prescribed burning have shown the 

greatest effectiveness in reducing fi re intensity and severity (Helms 1979, 

Martin et al. 1989, Fernandes 2003, Raymond and Peterson 2005, Agee and 

Skinner 2005), either alone or in combination with silvicultural activities 

that reduce vertical and horizontal continuity of canopy fuels (Hirsch and 

Pengelly 1999, van Wagtendonk 1996, Stephens 1998, Graham et al. 1999, 

Agee et al. 2000, Cram et. al. 2006). Canopy fuel parameters that infl uence 

crown fi re include crown base height and canopy bulk density (Agee 1996, 

Scott and Reinhardt 2001, Agee and Skinner 2005). Treatments that only 

involve cutting or canopy manipulation without surface fuel mitigation were 

not implemented here because these activities often increase fuel availability 

(Alexander and Yancik 1977, van Wagtendonk 1996, Brown et al. 2004, 

Stephens and Moghaddas 2005, Raymond and Peterson 2005). Based on the 

precedence of modifying surface fuels whenever canopy fuels are manipulated, 

prescriptions were developed for each stand on the entire landscape based on 

the forest species composition, structural stage, and general understory fuel 

type (e.g. shrubs, grass, litter).

 • Prescribed burning only. This prescription was used for maintenance of 

the surface fuels when there was no need to reduce aerial fuels. This 

prescription reduces surface fuels only and may kill small understory trees 

and regeneration using the mortality functions in FVS (Table 1).

 • Prescribed burning after various harvest prescriptions (typically low-thinning). This treatment removes slash from the mechanical activities as 

well as the pre-existing surface fuels (Table 1).

FVS requires a “tree-list” to be supplied for each stand. A tree list contains 

the number of trees by species and stem-diameter class. FVS also requires 

initialization of dead and downed “fuel pools” which represent the current 

loading states of various fuel components and are critical to consequent fuel 
128 USDA Forest Service Proceedings RMRS-P-41. 2006.

Finney, Seli, McHugh, Ager, Bahro, and Agee Simulation of Long-Term Landscape-Level Fuel Treatment Effects on Large Wildfi res

dynamics. Since a landscape is composed of polygons that delineate individual 

stands, all stand polygons must be assigned a tree list. We used a process 

called Most Similar Neighbor (MSN, Crookston et al. 2002) that uses a 

representative sample of tree lists from areas throughout the landscape to 

imput tree lists to polygons with no local measurements. The MSN process 

uses canonical correlation analysis, a multivariate technique, to select the 

tree list that corresponds to the polygon with minimum weighted distance 

of predictor variables. Tree lists for measured stands were obtained from existing data collected by a) local forest stand exams, and b) Forest Inventory 

and Analysis plots (FIA) (Van Deusen et al. 1999, McRoberts et al. 2000, 

Reams et al. 2001). The size of stand polygons was approximately the same 

for each study site, varying from 5 ha to 10 ha.

The output from the PPE version of FVS is contained in a table of stand 

conditions each year in the planning period (we used a period of 10 years). 

This table contains the fuel conditions that would have occurred with no 

treatment along with those that resulted from application of the treatment 

prescription that is critical for assessing the impact of the treatment on potential fi re behavior. The fuel conditions specifi ed are those required of the 

fi re behavior models used to evaluate wildfi re impacts (Finney 1998). The 

FVS polygon fuels data specifi cally includes canopy cover, stand height, crown 

base height, canopy bulk density, as well fuel pools, treatment history, and 

stand species information for assigning a fuel model (Anderson 1982, Scott 

and Burgan 2005). Because FVS currently does not utilize the Scott and 

Burgan surface fuel models, the fuel model assignment for each stand was 

accomplished outside of FVS-PPE. When the stand conditions are mapped 

Table 1—FVS treatment prescriptions were developed to work inside of FVS/PPE 

which provided a variety of general fuel treatments based on stand and 

fuel conditions at the beginning of each decade.

Seedling/Sapling size class

Thin from below to 1580 trees/ha (640 trees/acre)

If 0 to 7.62cm diameter fuel loading (0-3 inch) >= 5.6Mg/ha (2.5 tons/acre)

 Pile and burn fuel treatment

Poletimber size class

For fi re tolerant forest types (PP & DF)

 Thin from below to 30 m2/ha (130 ft2/ac) of basal area

 Prescribe burn

For fi re intolerant forest types (all others)

 Thin from below to 34 m2/ha (150 ft2/ac) of basal area

 Pile and burn fuel treatment

Sawtimber size class

For lodgepole pine forest type

 Clearcut with reserves

 Prescribe burn

For fi re tolerant forest types (PP, DF, WP, & WL)

 Thin from below to 32 m2/ha (140 ft2/ac) of basal area

 Prescribe burn

For fi re intolerant forest types (all others)

 Thin from below to 34 m2/ha (150 ft2/ac). of basal area

 Pile and burn fuel treatment
USDA Forest Service Proceedings RMRS-P-41. 2006. 129

Simulation of Long-Term Landscape-Level Fuel Treatment Effects on Large Wildfi res Finney, Seli, McHugh, Ager, Bahro, and Agee

spatially to the polygon locations, a forest landscape can be constructed 

to contrast the effects of treatment for all stands in terms of fi re behavior 

variables. Non-forested polygon fuel conditions (e.g. grass, rock) were held 

constant through the simulation.

Spatial Locations of Fuel Treatments

Having two sets of landscape fuel conditions each decade (depicting conditions with and without treatment) makes it possible to spatially delineate 

areas where fuel treatments are effective at changing stand-level fi re behavior. 

Treatments were only considered possible for areas where fi re behavior would 

be modifi ed by implementing that prescription (e.g. thinning and prescribed 

burning of a particular stand could not be conducted in sequential decades 

if the second treatment did not reduce fi re spread rate). Thus, the landscape 

confi guration of areas suitable or available for fuel treatment would vary from 

decade to decade.

To move from the stand-level to the landscape-level, the spatial treatment 

optimization attempts to locate a specifi ed percentage of these stands to treat, 

which optimally disrupt the growth or movement of large fi res across that 

landscape (Finney 2002a, 2004, Finney in prep.). This optimization numerically implements the concepts described by Finney (2001a) for an optimal 

spatial arrangement of discrete units on a simple landscape that can be solved 

analytically. For complex real landscapes, a numerical technique is required, 

and makes use of a fi re growth technique (Finney 2002b) to identify major 

travel paths produced by fi res growing under a set of specifi ed weather conditions. These weather conditions are obtained from historic local climatology 

associated with large and extreme fi res.

The algorithm fi nds intersections between the fi re travel paths and stands 

where the treatments slow the fi re under the specifi ed “target” weather 

conditions. Target weather conditions are synthesized for a particular study 

area from weather associated with historic large fi res for which suppression is 

ineffective (Finney 2001a). Weather parameters include fuel moisture, wind 

speed and wind direction for the afternoon burning period (when the majority of fi re area is burned). Typically, most large fi res in a particular region 

have a similar orientation produced by the wind fl ow of a synoptic weather 

system that repeatedly contributes to the escape and rapid growth of fi res. 

Thus, selecting these conditions ensures that treatment prescriptions modify 

fuels to suffi ciently change fi re behavior when fi re suppression is impossible. 

Stands that slow the fi re are identifi ed by the contrast in fi re behavior between 

treated and untreated stands. Fire behavior is calculated for each grid cell of 

each landscape using an implementation of fi re behavior models described 

by (Finney 1998). Thus, a comparison of spread rate between two locations 

indicates where treatments reduce spread and can thereby contribute to retarding fi re movement.

The spatial optimization technique begins by dividing the landscape into 

rectangular strips oriented normal to the predominant wind direction (Finney 

2002a, 2004, Finney in prep.). Beginning with the strip farthest upwind, 

fi re growth is simulated to identify major fi re travel routes and their intersection with potential treatment areas (areas where the fi re is slowed by the 

treatment). The process then iterates to delineate separate treatment units 

(one for each travel route) as constrained by unit size total treatment area. 

The orientation of the treatment units will typically be perpendicular to the 

major fi re spread direction because this intercepts the main direction of fi re 

movement. This procedure is followed for each strip moving successively in 
130 USDA Forest Service Proceedings RMRS-P-41. 2006.

Finney, Seli, McHugh, Ager, Bahro, and Agee Simulation of Long-Term Landscape-Level Fuel Treatment Effects on Large Wildfi res

the direction of the wind because treatments imposed on the landscape affect 

the downwind fi re travel routes and subsequent treatment areas.

For purposes of comparison of the spatial optimization, the spatial fuel 

treatment module linked to PPE was enabled to perform a random selection 

of forest stands.

Modeling Landscape-Effectiveness of Fuel Treatments

The performance of the various fuel treatment patterns at each decade were 

evaluated in terms of the responses of fi re growth (Finney 2002b) under the 

99th percentile “target conditions. Effects of treatment are measured entirely 

assuming an absence of fi re suppression because the weather conditions targeted for fuel treatment performance have historically been associated with 

large fi res for which suppression efforts were ineffective (i.e. 99th percentile). 

However, reductions in overall fi re growth rates, fi re intensity, and fi re sizes 

that would be expected to facilitate suppression action in treated areas and by 

linking or connecting treatment units by fi re control lines (Bunnell 1998).

Wildfi re responses were measured with the following metrics:

 1. Total fi re travel time (and thus, aggregated spread rate across the landscape) under the target weather conditions

 2. The sizes of a randomly ignited fi res on the landscapes, and

 3. The average relative burn probability for all places on the landscape by 

randomly ignited fi res.

The fi re travel time was used to calculate the aggregated average fi re spread 

rate of a fi re from the upwind to the downwind edge of the landscape. This 

was performed by igniting the upwind edge of the landscape and running 

the simulation until it arrived at the downwind edge. The fi re size distributions were obtained from simulations of 3,000 randomly located fi res across 

each landscape. These fi res were simulated for the same weather conditions 

identifi ed as the “target” conditions used for the optimization because the 

fi res targeted for treatment performance are those that escape initial attack 

efforts. This assumes that fi re management policies attempt to suppress all 

fi res, leaving to spread only those that cannot be controlled under extreme 

weather conditions (Table 2). The simulated fi res are used to estimate the 

relative burn probability for the landscape which is derived by tallying the 

total number of fi res that cross each grid cell of the landscape.

Study Areas and Simulation Scenarios

A large number of scenarios were developed for simulating fi ve decades 

of vegetation dynamics and treatment activity. The main variables evaluated 

were:

 1. Treatment amount (e.g. proportion of the landscape, from 0 to 50%),

 2. Maximum treatment unit size (400 to 1600 meters per unit),

 3. Treatment unit pattern (optimal vs. random),

 4. Reserves of randomly selected areas in the proportion of 15% to 65% of 

the landscape,

 5. Fire simulations under weather percentiles of 90th, 95th, to test treatment 

performance designed at the 99th percentile.

The study areas were selected to represent some of the variability in forest 

conditions that exist in the western U.S. The study sites selected for modeling actual landscapes are based on data availability and differences in the fi re 

regime, policy, land ownership, and social context. The variety of conditions 
USDA Forest Service Proceedings RMRS-P-41. 2006. 131

Simulation of Long-Term Landscape-Level Fuel Treatment Effects on Large Wildfi res Finney, Seli, McHugh, Ager, Bahro, and Agee

at these sites is intended for comparison of how fuel management objectives 

(specifi c in both space and time) can be accomplished in the context of realistic variability, constraints on management activities, and understanding of 

fi re weather conditions. Table 2 contains the fi re weather conditions used for 

each study area associated with 99th percentile Energy Release Component 

(ERC) from the U.S. National Fire Danger Rating System (Deeming et al. 

1977).

Sanders County, Montana—Sanders County consists of 680,000 ha in western Montana along the Idaho border from which a study area of 51,700 ha was 

selected (Figure 2, Table 2). Land ownership is about 65% National Forest, 

10% Plum Creek Timberlands, 5% school trust lands administrated by the 

Montana Department of Natural Resources and Conservation, and 20% 

small private landowners. Topography consists of the Bitterroot Mountains 

with the Flathead and Clark Fork Rivers fl owing the length of the county. 

A wide variety of fuel types are present, with sagebrush/grasslands at the 

lower elevations in the eastern half of the county, frequent fi re interval ponderosa pine (Pinus ponderosa) stands throughout, western red cedar (Thuja 

plicata) stands at the west end of the county and lodgepole (Pinus contorta) 

and whitebark pine (Pinus albicaulis) stands perpetuated by stand replacement fi res at higher elevations. Private lands with the associated towns and 

improvements are concentrated in the lower elevations along the rivers and 

consist of the fl ashier fuel types. Barriers to fuel treatment include habitat 

concerns for a variety of endangered species; grizzly bear, wolves, lynx, and 

bull trout. Other issues are water quality limited streams and checkerboard 

ownership.

Table 2—Summary of study area attributes and fire weather conditions simulated for fuel treatment 

optimization.

 Fire Weather

 Study Area, Fire Regimes (general conditions used for

Location and size Land Ownership severity classes) fi re modeling

Blue Mountains, OR • Wallowa-Whitman NF • Low-Mixed Severity • Wind 48kph, West

54,600 ha • Umatilla NF • Fuel Moisture (1hr 3%, 

 • Tribal (Umatilla) 10hr 4%, 100hr 5%, 

 • Private (non-industrial) Live Herb 100%, Live 

 • Private (industrial) Woody Shrubs 100%)

Sanders County, MT • Lolo NF • Low, Mixed, High • Wind 48kph, West

51,700 ha • Kootenai NF • 10hr 4%, 100hr 5%, 

 • Private (non-industrial) Live Herb 100%, Live

 • Private (industrial) Woody Shrubs 100%)

 • Salish and Kootenai Tribes

 • MT Department of Natural 

 Resources & Conservation

 • Sanders County, Montana•

Stanislaus NF, CA • Stanislaus National Forest • Currently Mixed-High, • Wind 48kph, West

40,500 ha • Private (non-industrial) but historically low- • 10hr 4%, 100hr 5%, 

 • Private (industrial) mixed. Live Herb 100%, Live

 Woody Shrubs 100%)
132 USDA Forest Service Proceedings RMRS-P-41. 2006.

Finney, Seli, McHugh, Ager, Bahro, and Agee Simulation of Long-Term Landscape-Level Fuel Treatment Effects on Large Wildfi res

Data for the study area of Sanders County, Montana consisted of continuous 

polygon coverage across all land ownership categories attributed with tree list 

data for the forested polygons. The polygon coverage was derived from that 

used in the Northern Region Vegetation Mapping Project (Brewer 2004). 

Data from USDA Forest Service Forest Inventory and Analysis (FIA) and 

Salish-Kootenai Tribe Continuous Stand Inventory (CSI) plots (commonly 

referred to as stand exam, forest inventory data, or observations) were used 

to create tree lists. Each tree list location or observation was attributed to 

the polygon it was located in and then imputed to other similar polygons, 

using nearest neighbor analysis, resulting in all forested polygons having a 

tree list attributed.

Two sub-areas were chosen from Sanders County (labeled Prospect and 

Baldy) because of the large size of the County and varying forest types and 

treatment options. The Prospect area represents the north Idaho forest types 

such as western hemlock (Tsuga heterophyla) and true fi rs (Abies spp.), limited 

past management activities, continuous dense forest cover, prevalent brush 

fuels beneath the forest canopy, and predominance of National Forest ownership. The Baldy landscape was smaller and more variable than Prospect. 

It contained a large rocky area at high elevation surrounded by drier forest 

Figure 2—The study areas were located in western Montana (Prospect, Sanders County), the Sierra Nevada 

mountains of California (Stanislaus National Forest), and eastern Oregon (Mill Creek).
USDA Forest Service Proceedings RMRS-P-41. 2006. 133

Simulation of Long-Term Landscape-Level Fuel Treatment Effects on Large Wildfi res Finney, Seli, McHugh, Ager, Bahro, and Agee

types including ponderosa pine and Douglas-fi r and was composed of lands 

administered by Indian tribal governments (Salish and Kootenai tribes) and 

U.S. National Forest. Signifi cant past management activities have created a 

variety of age classes, forest structures, surface and aerial fuel conditions.

Stanislaus National Forest, California—The Stanislaus National Forest 

is 363,000 ha and lies in the heart of the central Sierra Nevada from which 

40,500 ha was selected for simulation (Figure 2, Table 2) with 7,754 tree-list 

polygons. The administrative boundary includes industrial private timberlands 

and small private parcels, many of which have been developed for housing. 

Vegetation varies from hard chaparral (manzanita species), oak (Quercus 

species) woodlands and ponderosa-pine (Pinus ponderosa) stands at the lower 

elevations to the west to mixed-conifer and red fi r (Abies mognifi ca) forest at 

middle and upper elevations to the east. The western edges of this area are 

representative of the wildland-urban intermix of the Sierra Nevada foothills. 

The fi re management strategy for the area was outlined recently in the forest 

plan amendment Record of Decision. This directs the forests in the Sierra 

Nevada to reduce threats to urban intermix areas and maintain 30 to 40% 

of the landscape in strategically placed treatments. Treatment effectiveness, 

landscape design, and monitoring effectiveness are key implementation questions. The fi re regime has changed from a predominantly surface fi re regime 

among all forest type prior to settlement to more of a mixed-high severity 

fi re regime since about 100 years of fi re exclusion. Surface and crown fuels 

on all lands now contribute to a relatively continuous fuel complex with the 

potential for broad destruction and loss of life if a fi re should occur under 

extreme conditions. The foothills of the central and northern Sierra Nevada 

have recently been prone to these kinds of fi res and result in losses and costs 

in the hundreds of millions of dollars.

Data for the California study area consisted of continuous polygon coverage across all land ownership categories attributed with tree list data for 

the forested polygons. The Pacifi c Southwest Region Vegetation Inventory 

Strata map was used for the polygon coverage. USDA Forest Service, Forest 

Inventory and Analysis (FIA) data, supplemented with additional plots in rare 

types and plantations, were used for the tree lists. Each tree list location or 

observation was attributed to the polygon it was located in and then imputed 

to other similar polygons, using most-similar-neighbor analysis, resulting in 

all forested polygons having a tree list attributed.

Mill Creek, Oregon—The Mill Creek study area consists of 256,780 ha 

of federal and privately owned lands situated southeast of Walla Walla, WA. 

(Figure 2, Table 2). A subset of this area (54,600 ha) was used for the simulations with a total of 5,732 different stand polygons simulated. The entire area 

is situated on the west slope of the Blue Mountains, bordered by agricultural 

lands on the west and the USFS wilderness on the east. The private lands are 

located on the western edge. About half of the study area is forested with 

the remaining area covered by a mixture of dry grasslands, wet meadows, 

and shrubs. Elevations range between 500 m along the lower western edge 

to over 1,800 m in the east. The forest composition follows elevation, with 

dry forests of ponderosa pine (Pinus ponderosa) intermixed with grasslands 

in the west, cold forests dominated by subalpine-fi r (Abies lasiocarpa) and 

Engelmann spruce (Picea engelmannii) in the east, and a transition zone 

containing grand fi r (Abies grandis), Douglas-fi r (Pseudotsuga menziesii), 

and western larch (Larix occidentalis) in the mid elevations.
134 USDA Forest Service Proceedings RMRS-P-41. 2006.

Finney, Seli, McHugh, Ager, Bahro, and Agee Simulation of Long-Term Landscape-Level Fuel Treatment Effects on Large Wildfi res

Forest stand delineations on the Forest Service portion of the study area 

were obtained from existing vegetation GIS layers on fi le at the Umatilla 

National Forest. Vegetation data and fuel loadings for these stands were 

obtained from the Umatilla National Forest vegetation database. Tree lists 

were a mix of fi eld exams and data obtained from nearest neighbor analysis. 

Stands outside the Forest Service boundary were digitized on orthophotos 

fl own in year 2000, and vegetation and fuels data obtained by fi eld surveys. 

Photo series including Fischer (1981) were used to estimate initial surface 

fuel loadings.

We simulated stand-level treatments that consisted of selective thinning 

from below, mechanical fuels treatment, and underburning. The thinning 

prescription used the stand density index (SDI), and we triggered a thin in 

FVS when a stand’s SDI exceeded 65% of the maximum SDI as specifi ed in 

Cochran and others (1994). The thinning prescriptions targeted removal of 

late-seral, fi re intolerant species like grand fi r in mixed-species stands, favoring early seral species such as ponderosa pine, western larch and Douglas-fi r. 

We simulated site removal of fuels and underburning after thinning.

Results

The simulation system was designed for multi-processor computers because of the intensive nature of the treatment optimization program and fi re 

growth model. The fi re growth algorithms (Finney 2002a) and the treatment 

optimization module were the most intensive and were run on 16-processor 

systems. Run times for fi ve decades of simulation ranged from 6 hours to 

several days depending on the size of the landscape (area and number of 

cells) and the resolution of the treatments. Treatment units were identifi ed 

by the treatment optimization (Figure 3) for each landscape for the target 

weather conditions.

The performance of the treatments was measured in terms of the change 

in landscape-level fi re behavior, including average spread rate, conditional 

burn probabilities, and average fi re sizes. All measures showed identical 

responses to the treatments (Figure 4) because slower moving fi res burning 

for a specifi ed period of time will be smaller and thus contribute to a lower 

overall probability of burning any portion of the landscape. Thus, only the 

relative spread rate is reported for the remaining simulation results. All measures revealed that the landscape fuel conditions, and thus fi re behavior, were 

changing over time even in the absence of treatment (top line in all graphs 

on Figure 4). The treatment effects must be evaluated with respect to the 

untreated condition at each decade.

Optimal patterns of treatment units were found to reduce the average fi re 

spread rate effi ciently for all study areas in comparison to random patterns 

(Figure 5). Treatment unit size varied from 400 m to 1,600 m but unit size 

had little infl uence on the effect of optimal treatment patterns on fi re spread rate 

regardless of the rate of treatment, simulation time, or study area (Figure 5). The 

Baldy study size (Sanders County, Montana) showed the greatest variation 

of relative spread rate (Figure 5f) in relation to treatment sizes from 200 m 

to 1,600 m, especially as the percentage of area treated increased.

For each study area, the average fi re spread rate decreased with percentage of 

treatment but the amount of reduction varied by study area (Figure 5). Treatments were found to be more effi cient for the Prospect study site in Montana 

than for any of the other study areas (Figure 5). With 10% treatment per 
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Simulation of Long-Term Landscape-Level Fuel Treatment Effects on Large Wildfi res Finney, Seli, McHugh, Ager, Bahro, and Agee

Figure 3—Example data and outputs from the Montana, California, and Oregon study 

areas showing surface fuel types and examples of optimized treatment locations along 

with major fi re travel routes prior to placement of treatment locations (treatment 

location are intersected by travel routes).

decade, the fi re spread rate was reducing to about 40% at Prospect, Montana (Figure 5a), 60% at Baldy Montana (Figure 5b), and 80% in California 

(Figure 5c), and 60% in Oregon (Figure 5d). Increasing rate of treatment 

to 30% per decade improved the overall reduction in spread rate to 20% for 

Prospect, Montana (Figure 5e), 40% for Baldy Montana (Figure 5f), 60% 

for California (Figure 5g), and 40% for Oregon (Figure 5h). For all study 

areas and treatment rates the effects of treatment were the greatest the fi rst 

decade and the cumulative effect of additional treatment was negligible after 

the second decade of simulation. These trends occurred irrespective of the 

amount of treatment but were more noticeable with high treatment rates.
136 USDA Forest Service Proceedings RMRS-P-41. 2006.

Finney, Seli, McHugh, Ager, Bahro, and Agee Simulation of Long-Term Landscape-Level Fuel Treatment Effects on Large Wildfi res

Figure 4—Average fire spread rate across the landscape, conditional 

probability of burning produced by simulating 3,000 fi res (conditional upon 

having a large wildfi re), and the mean fi re sizes revealed nearly identical 

trends. Shown here are only the results for the Prospect, Montana study 

area, although all study areas had identical comparisons among the response 

variables.
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Simulation of Long-Term Landscape-Level Fuel Treatment Effects on Large Wildfi res Finney, Seli, McHugh, Ager, Bahro, and Agee

Figure 5—The magnitude of the treatment effect on average fi re spread rate varied by study area although the 

cumulative effects over time of random and optimally placed treatments were similar for all areas. Treatment unit 

size had little effect on the average fi re spread rate.
138 USDA Forest Service Proceedings RMRS-P-41. 2006.

Finney, Seli, McHugh, Ager, Bahro, and Agee Simulation of Long-Term Landscape-Level Fuel Treatment Effects on Large Wildfi res

The rate of treatment in optimal patterns had a large effect on the cumulative treatment effectiveness up to approximately 20% per decade (2% per year) 

for all study areas (Figure 6). Increasing treatment rate beyond this point had 

little effect on the ultimate fi re spread rates. For each rate of treatment (1% 

to 3% per year), the results suggested that cumulative effects of the optimal 

patterns reached a steady state after the second decade (Figure 6) as well as 

for random treatment patterns (Figure 5). Higher rates of treatment (40% to 

50% per decade) produced little cumulative benefi t to landscape fi re spread 

beyond the fi rst decade.

Effectiveness of optimal treatment patterns in reducing fi re spread rate was 

little affected by randomly reserving less than about 20% of the area from 

consideration from treatment (Figure 7). However, reserving 45% to 65% of 

the area from treatment diminished the effectiveness of optimal patterns to 

about the level of random patterns.

Figure 6—Treatments implemented at a rate of about 20% per decade produced overall reductions 

in average fi re spread rate similar to higher treatment rates for all study areas. Treatment rates of up 

to 20% per decade required about two decades to reach the cumulative benefi t reached in the fi rst 

decade for higher rates of treatment. All results are displayed for 800 treatment units, but trends are 

nearly identical for unit sizes of 200 m, 400 m and 1,600 m.
USDA Forest Service Proceedings RMRS-P-41. 2006. 139

Simulation of Long-Term Landscape-Level Fuel Treatment Effects on Large Wildfi res Finney, Seli, McHugh, Ager, Bahro, and Agee

Figure 7—Simulated reserves of land area from fuel treatment reduced the effectiveness of optimal 

treatment patterns to the point that reserving 45% to 65% produced results similar or even less effective 

than random patterns.

The treatment preferences for re-treating or maintaining fuel conditions 

in the optimal patterns was increasingly different from a random pattern as 

the rate of treatment increased beyond 10% (Figure 8). The trends were so 

similar for study areas that only the Prospect, Montana results are shown in 

Figure 8. The random treatments produced the expected Poisson distributions 

of treatment frequency (Figure 8a) which were similar to the treatment frequency produced for optimal patterns at a rate of 10% per decade (Figure 8b). 

However, treatment frequency was not random at higher rates of treatment 

in optimal patterns (Figures 8c-8f). Specifi cally, about 35% of the landscape 

would never be treated in an optimal pattern even with the highest rate of 

treatment (50% per decade). Where treatment rates were the highest (40% 

to 50% per decade), most fuel treatments were not maintained every decade 

(Figure 8e, 6f).

Optimal treatments in all study areas remained more effective than random 

treatments (Figure 9) in reducing fi re growth rate under weather conditions 

more moderate (90th and 95th percentile) than specifi ed in the design (99th

percentile). The relative benefi t of treatment, however, decreased as conditions became more moderate because fi re behavior contrasts decrease between 

treated and untreated areas.
140 USDA Forest Service Proceedings RMRS-P-41. 2006.

Finney, Seli, McHugh, Ager, Bahro, and Agee Simulation of Long-Term Landscape-Level Fuel Treatment Effects on Large Wildfi res

Discussion

The simulations for the three study areas consistently suggested that all 

treatment rates (10% to 50% per decade) accumulated benefi ts to reduced fi re 

spread rate, wildfi re sizes, and burn probability out to about two decades in 

all study areas. This is probably a result of the inherent fuel accumulation and 

decomposition rates which determine longevity of individual treatments. Beyond that point, additional treatments produced little cumulative reduction in 

the landscape fi re metrics. Additionally, treatment rates beyond approximately 

Figure 8—The question of maintaining treatment areas or implementing new treatments was 

summarized by the frequency of treatment over fi ve decades. Random treatment resulted in Poisson 

frequency distributions. At treatment rates of 20% and greater per decade, the optimal treatment 

strategy consistently excluded some areas from treatment more frequently than random selection 

and refused frequent treatment for other areas.
USDA Forest Service Proceedings RMRS-P-41. 2006. 141

Simulation of Long-Term Landscape-Level Fuel Treatment Effects on Large Wildfi res Finney, Seli, McHugh, Ager, Bahro, and Agee

20% per decade in optimal patterns produced little added benefi t for the 

study areas. Few studies have directly measured fuel accumulation, but van 

Wagtendonk and Sydoriak (1987) found that litter and fi ne twigs returned 

to preburn levels in 5-7 years in the Sierra Nevada mountains of California. 

The results of this study are generally similar to the fi ndings of Biswell et al. 

(1973), Fernandes et al. (2004), Finney et al. (2005) who reported fuel treatment mitigation of wildfi re severity out to 15 years, 13 years, and 9 years, 

respectively. These timeframes for treatment longevity imply certain rates of 

treatment by land management planners, namely that a substantial level of 

effort is required over the course of about two decades to realize the cumulative benefi ts to mitigating large fi re behaviors. Such effort has long been 

advocated as a critical part of overall fi re management (Brackebusch 1973, 

Arno and Brown 1991). Evidence for effectiveness of such large scale-efforts 

were documented by Weaver (1957) and showed prescribed burning in eastern 

Washington State over 11 years, which covered about 6% of the landscape, 

reduced fi re occurrence on the treated lands by 97% and area burned by 90% 

compared to the untreated areas. We did not study the trajectories of treatment benefi t related to changing the treatment rate through time, but, since 

higher treatment rates certainly accelerated the production of benefi ts, higher 

rates might be desirable in the fi rst decade followed by later decreases.

Figure 9—Comparison of fuel treatment effects on relative fi re spread rate across a range of fi re weather 

percentiles suggests that optimal treatment effects are robust under weather more moderate than the 

conditions specifi ed for optimization (99th percentile). Spread rates are shown for the 2nd decade of 

simulation (when collective treatment effects are maximal) and normalized for each study area relative 

to the spread rate at the beginning of the simulation (i.e. zero years). Weather percentiles are expressed 

in terms of Energy Release Component (ERC) from the National Fire Danger Rating System and primarily 

refl ect changes in moisture content.
142 USDA Forest Service Proceedings RMRS-P-41. 2006.

Finney, Seli, McHugh, Ager, Bahro, and Agee Simulation of Long-Term Landscape-Level Fuel Treatment Effects on Large Wildfi res

The three response variables of large fi res (growth rate, fi re sizes, burn 

probabilities) all showed identical trends in relation to fuel treatments. Fire 

growth rates (aggregated spread rate across the landscapes), mean fi re size, and 

the burn probability all decreased as fuel treatment amounts increased, both 

for optimal and for random patterns. The explanation is straightforward, given 

that faster fi res will produce larger fi res in an equal amount of time; larger 

fi res burn a larger fraction of the landscape each time and thereby increase 

the burn probability. This is useful information for landscape fuel treatment 

planning in the context of risk assessment (Miller et al. 2000, Priesler et al. 

2004, Finney 2005) because burn probabilities are a main component of risk. 

Fuel treatments can be designed to decrease burn probability by considering 

both the treatment prescription at the stand level and the spatial arrangement 

of the stands at the landscape level.

Differences in the maximum reduction of fi re spread rate were found among 

study areas for random and optimal treatment patterns, probably because of 

different fuel treatment prescriptions and the changes simulated by FVS in 

the forest structures for those geographic locations. Differences could also 

be a function of the particular spatial confi gurations of fuel types for each 

landscape because treatments that dictated the areas suitable for treatment. 

Both of these factors likely affect the outcome of the simulations because the 

differences among study areas were consistent regardless of the use of optimal or random spatial fuel treatment patterns. Thus, either rapid recovery of 

fuels after treatment or limited positions of candidate treatment areas would 

have similar effects on reducing overall effectiveness on the landscape-level 

fi re metrics.

Despite the complexity of the landscapes studied here and the complexity 

of modeling required to characterize fuels, fi res, and treatment units, these 

results of the optimal and random landscapes correspond well with those based 

on the theoretical analysis of simple landscapes (Finney 2001a,b, 2003). For 

spatially optimal patterns, increasing the treatment rate reduces fi re spread 

rate and exhibits a negative-exponential-type shape. This was found for all 

study sites and treatment unit sizes, although the magnitude of the decrease 

depends on the particular landscape. This is interpreted to be the consequence 

of different patterns of fast- and slow-burning fuel types on the real landscapes 

that dictate the opportunities and impacts of the particular treatment units. 

The decrease in spread rate with increasing treatment amount arranged in 

random patterns did not exhibit the sigmoidal trend found from analysis of 

simple spatial landscapes (Finney 2003), however, the random pattern was 

much less effi cient in reducing large fi re spread than the optimal patterns. 

The ineffi ciency of random patterns is also verifed by other theoretical studies (Loehle 2004, Bevers et al. 2004). Together, these results are useful for 

drawing general conclusions about the role of spatial treatment patterns on 

fi re movement. The theoretical and spatially simple results apply quite well 

to the expected trends for treatments on actual landscapes.

The benefi ts of optimal treatment patterns appear to be robust to uncertainties in weather (wind speed and fuel moisture) as revealed for weather 

conditions more moderate than those for which the patterns were designed 

(Figure 9). Under moderate weather conditions, the contrast in fi re behavior between treated and untreated areas is diminished (fi re spread rate and 

intensity tend toward similar values). This means that the treatments will 

result in a smaller proportional reduction in fi re area than under extreme 

conditions. However, the primary reason that treatments are not designed 

for moderate fi re weather is that modern suppression policies do not permit 
USDA Forest Service Proceedings RMRS-P-41. 2006. 143

Simulation of Long-Term Landscape-Level Fuel Treatment Effects on Large Wildfi res Finney, Seli, McHugh, Ager, Bahro, and Agee

large wildland fi res to spread when suppression organizations are generally 

successful in limiting fi re spread. Thus, fi re behavior is generally more benign, 

fi re suppression more effective, and treatments less necessary for changing 

fi re behavior when weather conditions are moderate.

The effects of reserving areas from treatment, irrespective of the location or 

need for treatment, decreased the effectiveness of an optimal treatment pattern 

and compromised the optimal solutions entirely at about 50% reserved. This 

has bearing on the treatment planning process in land management operations 

where restrictions are imposed for a variety of reasons, including concern for 

treatment impacts on wildlife habitat, restrictions on proximity to streams 

or rivers, road access, budget limitations, or ownership. These simulations 

generally suggest that treatment restrictions amounting to more than about 

40% of a landscape would diminish any advantage an optimal solution would 

achieve over purely random treatment placement. The specifi c topology of 

the various fuels and restrictions for a particular landscape, however, would 

likely be different than this generalization. Nevertheless, if land managers 

intend to achieve reductions in large fi res, collaboration with all concerned 

parties would likely be necessary to accommodate treatment locations to 

achieve landscape-level effects.

The fi ve-decade simulations suggest that both maintenance of existing 

units and implementation of new units are important to the optimization 

of spatial treatment patterns. The frequency of re-treatment in the optimal 

landscape was different than produced by chance with the random treatments 

(Poisson distributed) which indicates that the choice of fuel treatment activity 

was driven by functional concerns. Compared to the random patterns, the 

optimization attempted more treatments on new stands than on re-treating 

old stands, probably because the treatment benefi ts endured for more than 

one decade. It is unknown how the pattern would change if the simulation 

were to have continued for 100 years, for example, that would have greatly 

exceeded the time-frame of treatment performance.

Variation in treatment unit sizes had the least impact on modifying large 

fi res compared to treatment pattern and rate of treatment. Large and small 

units typically produced similar reductions in fi re sizes, spread rates, and burn 

probabilities at all levels of treatment. Slightly lower effi ciency (e.g. amount of 

reduced spread rate per unit treated) of the smallest treatment unit sizes for 

all study areas, however, suggests that emphasizing small units may restrict 

opportunities to block fi re movement in some critical locations which require 

large units. That is, small units cannot effectively block the movement through 

large corridors where fi re easily moves. The optimization algorithm used here 

is not fl exible enough to effectively mix both small and large units.

Conclusions

The simulations suggested that long-term treatment effects are primarily 

dependent on the rate of application of treatments and the spatial patterns of 

treatment units. Treatment rates of 10% to 30% per decade reached a cumulative maximum effectiveness in about two decades in all study areas. Higher 

rates of treatment did not improve the cumulative effects beyond the fi rst 

decade. Random treatment patterns also produced cumulative effects on fi re 

behavior but were less effi cient than the optimized patterns, requiring about 

twice the area to be treated compared to optimal patterns.
144 USDA Forest Service Proceedings RMRS-P-41. 2006.

Finney, Seli, McHugh, Ager, Bahro, and Agee Simulation of Long-Term Landscape-Level Fuel Treatment Effects on Large Wildfi res

Acknowledgments

This study was funded by the U.S. Joint Fire Sciences Program and the 

U.S. Forest Service, Missoula Fire Sciences Laboratory. Special thanks to 

Nick Crookston for his willingness to make custom modifi cations to PPE 

for our uses and to Howard Roose with the Bureau of Land Management 

who also provided funding.

Literature Cited

Agee, J.K. 1996. The infl uence of forest structure on fi re behavior. In: Sherlock, J., 

ed. Proceedings of the 17th annual forest and vegetation management conference. 

Jan. 16-18, 1996. Redding, CA: 52-68.

Agee, J.K., B. Bahro, M.A. Finney, P.N. Omi, D.B. Sapsis, C.P. Weatherspoon. 

2000. The use of fuelbreaks in landscape fi re management. Forest Ecology and 

Management. 127(1-3):55-66.

Agee, J.K. and C.N. Skinner. 2005. Basic principles of forest fuel reduction 

treatments. For. Ecol. Mgt. 211:83-96.

Alexander, M.E., and R.F. Yancik. 1977. The effect of precommercial thinning on 

fi re potential in a lodgepole pine stand. Fire Management Notes. 38(3):7-9.

Anderson, H.E. 1982. Aids to determining fuel models for estimating fi re behaviour. 

USDA For. Serv. Intermountain Forest and Range Experiment Station, General 

Technical Report INT-GTR-122.

Arno, Stephen F.; Brown, James K. 1991. Overcoming the paradox in managing 

wildland fi re. West Wildl. 17(1):40-46.

Bevers, M., Omi, P.N., Hof, J., 2004. Random location of fuel treatments in wildland 

community interfaces: a percolation approach. Can. J. Forest Res. 34:164-173.

Biswell, H.H., Kallander, H.R., Komarek, R., Vogl, R.J., and Weaver, H. 1973. 

Ponderosa fi re management: A task force evaluation of controlled burning in 

ponderosa pine forest of central Arizona. Tall Timbers Research Station Misc. 

Publ. No. 2. Tallahassee, Florida.

Brackebusch, A.P. 1973. Fuel management: a prerequisite not an alternative to fi re 

control. Journal of Forestry. 71(10):637-639.

Brown, R.T., J.K. Agee and J.F. Franklin. 2004. Forest restoration and fi re: 

principles in the context of place. Conservation Biology. 18(4):903-912.

Brewer, K.C., D. Berglund, J.A. Barber, and R. Bush. 2004. Northern Region 

Vegetation Mapping Project Summary Report and Spatial Datasets. USDA Forest 

Service, Northern Region, Missoula, MT.

Bunnell, D. 1998. Old growth lodgepole pine and the Little Wolf Fire. In: Close, K.; 

Bartlette, R., eds. Proceedings of the 1994 interior West fi re council meeting 

and program. International Association of Wildland Fire. Roslyn, Washington, 

USA: 155-160.

Cochran, P.H., Geist, J.M., Clemens, D.L., Clausnitzer, R.R., Powell, D.C., 

1994. Suggested stocking levels for Forest Stands of Northeastern Oregon and 

Southeastern Washington. General Technical Report PNW-RN-513. Portland, 

OR: U.S. Department of Agriculture Forest Service, Pacifi c Northwest Research 

Station. 21 p.

Cooper, C.F. 1961. Controlled burning and watershed condition in the White 

Mountains of Arizona. J. For. 59:438-442.
USDA Forest Service Proceedings RMRS-P-41. 2006. 145

Simulation of Long-Term Landscape-Level Fuel Treatment Effects on Large Wildfi res Finney, Seli, McHugh, Ager, Bahro, and Agee

Cram, D.; Baker, T.; Boren, J. 2006. Wildland fi re effects in silviculturally treated 

vs. untreated stands of New Mexico and Arizona. Research Paper RMRS-RP55. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky 

Mountain Research Station. 28 p.

Crookston, Nicholas L. and Stage, Albert R. October 1991. User’s Guide to the 

Parallel Processing Extension of the Prognosis Model. Gen. Tech. Report-INT281. Ogden, UT: U.S. Department of Agriculture, Forest Service, Intermountain 

Research Station. 93 p.

Crookston, Nicholas L.; Moeur, Melinda; Renner, David. 2002. Users guide to the 

Most Similar Neighbor Imputation Program Version 2. Gen. Tech. Rep. RMRSGTR-96. Ogden, UT: U.S. Department of Agriculture, Forest Service, Rocky 

Mountain Research Station. 35 p.

Deeming, J.E., R.E. Burgan, and J.D. Cohen. 1977. The national fi re-danger rating 

system — 1978. USDA For. and Range Exp. Station, Gen. Tech. Rep. INT-39.

Fernandes, P.A.M. and H. Botelho. 2003. A review of prescribed burning effectiveness 

in fi re hazard reduction. Intl. J. Wildland Fire 12(2):117-128.

Fernandes, P.A.M., C. A. Loureiro, and H.S. Botelho. 2004. Fire behaviour 

severity in a maritime pine stand under differing fuel conditions. Ann. For. 

Sci. 61:537-544.

Finney, M.A. 1998. FARSITE: Fire Area Simulator — model development and 

evaluation. USDA For. Serv. Res. Pap. RMRS-RP-4. 47 p.

Finney, M.A. 2001a. Design of regular landscape fuel treatment patterns for 

modifying fi re growth and behavior. For. Sci. 47(2):219-228.

Finney, M.A. 2001b. Spatial strategies for landscape fuel treatment. In J. Bento and 

H Botelho, eds. Proc. Workshop on Tools and methodologies for fi re danger 

mapping. March 9-14, Vila Real Portugal: 157-163.

Finney, M.A. 2002a. Use of graph theory and a genetic algorithm for fi nding 

optimal fuel treatment locations. IN Proc. 4th International Conf. on Forest Fire 

Research, Luso Portugal

Finney, M.A. 2002b. Fire growth using minimum travel time methods. Can. J. For. 

Res. 32(8):1420-1424.

Finney, M.A. 2003. Calculating fi re spread rates across random landscapes. Intl. J. 

Wildl. Fire. 12(2):167-174.

Finney, M.A. 2004. Chapter 9, Landscape fi re simulation and fuel treatment 

optimization. In: J.L. Hayes, A.A. Ager, J.R. Barbour, tech. eds. Methods for 

integrated modeling of landscape change: Interior Northwest Landscape Analysis 

System. PNW-GTR-610: 117-131.

Finney, M.A. (In prep.) A computational method for optimizing fuel treatment 

locations.

Finney, M.A. 2005. The challenge of quantitative risk assessment for wildland fi re. 

For. Ecol. and Mgt. 211:97-108.

Finney, M.A., C.W. McHugh, and I.C. Grenfell. 2005. Stand- and landscape-level 

effects of prescribed burning on two Arizona wildfi res. Can. J. For. Res 35: 

1714-1722.

Fischer, W.C., 1981. Photo guide for appraising downed woody fuels in Montana 

forests: Grand fi r – larch – Douglas-fi r; western hemlock – western redcedar; 

and western redcedar cover types. For. Serv. Gen. Tech. Rep. INT-96. United 

States Department of Agriculture, Intermountain Forest and Range Experiment 

Station, Ogden, UT. 53 p.

Gill, A.M.; Bradstock, R.A. 1998. Prescribed burning: patterns and strategies. 

In: 13th conference on fi re and forest meteorology. International Association of 

Wildland Fire, USA: 3-6.
146 USDA Forest Service Proceedings RMRS-P-41. 2006.

Finney, Seli, McHugh, Ager, Bahro, and Agee Simulation of Long-Term Landscape-Level Fuel Treatment Effects on Large Wildfi res

Graham, R.T., A.E. Harvey, T.B. Jain, and J.R. Tonn. 1999. The effects of thinning 

and similar stand treatments on fi re behaviour in western forests. USDA For. Serv. 

Gen. Tech. Rep. PNW-GTR-463.

Graham, R.T. 2003. Hayman fi re case study. USDA For. Serv. Gen. Tech. Rep. 

RMRS-GTR-114.

Graham, R.T., S. McCaffrey, and T.B. Jain. 2004. Science basis for changing forest 

structure to modify wildfi re behavior and severity. USDA For. Serv. Gen. Tech. 

Rep. RMRS-GTR-120.

Helms, J.A. 1979. Positive effects of prescribed burning on wildfi re intensities. Fire 

Management Notes. 403:10-13.

Hirsch, K. and I. Pengelly. 1999. Fuel reduction in lodgepole pine stands in Banff 

National Park. In: Neuenschwander, L.F., Ryan, K.C., Gollberg, G.E., Greer, J.D., 

eds. Proceedings of the Joint Fire Science Conference and Workshop: ‘‘Crossing 

the Millenium: Integrating Spatial Technologies and Ecological Principles for 

a New Age in Fire Management,’’ Boise, ID, June 15-17, 1999, University of 

Idaho: 251-256.

Hirsch, K., V. Kafka, C. Tymstra, R. McAlpine, B. Hawkes, H. Stegehuis, S. Quintilio, 

S. Gauthier, and K. Peck. 2001. Fire-smart forest management: a pragmatic 

approach to sustainable forest management in fire-dominated ecosystems. 

77(2):357-363.

Kallander, H., Weaver, H., and Gains, E.M. 1955. Additional information on 

prescribed burning in virgin ponderosa pine in Arizona. J. For. 53:730-731.

Loehle, C. 2004. Applying landscape principles to fi re hazard reduction. For. Ecol. 

Mgmt. 198:261-267.

Martin, R.E., J.B. Kauffman, and J.D. Landsberg. 1989. Use of prescribed fi re 

to reduce wildfi re potential. In: N.H. Berg, tech. coord. Proc. of the Symp. on 

Fire and Watershed Management. USDA For. Serv. Gen. Tech. Rep. PSW-109: 

17-22.

McRoberts, Ronald E.; Reams, Gregory A; Van Deusen, Paul C. 2000. Proceedings 

of the First annual forest inventory and analysis symposium. 1999 November 2-3; 

San Antonio, TX. Gen. Tech. Rep. NC-213. St. Paul, MN: USDA Forest Service, 

North Central Research Station. 57 p.

Miller, C., Landres, P.B., Alaback, P.B., 2000. Evaluating risks and benefi ts of 

wildland fi re at landscape scales. In: Neuenschwander, L.F., Ryan, K.C., Gollberg, 

G.E., Greer, J.D., eds. Proceedings of the Joint Fire Science Conference and 

Workshop: ‘‘Crossing the Millenium: Integrating Spatial Technologies and 

Ecological Principles for a New Age in Fire Management,’’ Boise, ID, June 15-

17, 1999, University of Idaho: 78-87.

Pollet, J., and Omi, P.N. 2002. Effect of thinning and prescribed burning on crown 

fi re severity in ponderosa pine forests. Int. J. Wildland Fire. 11:1-10.

Preisler, H.K., D.R. Brillinger, R.E. Burgan, and J.W. Benoit. 2004. Probability 

based models for estimation of wildfi re risk. Intl. J. Wildl. Fire. 13(2):133-142.

Raymond, C.L. and D.L. Peterson. 2005. Fuel treatments alter the effects of wildfi re 

in a mixed evergreen forest, Oregon, USA. Canadian Journal of Forest Research. 

35:2981-2995.

Reams, Gregory A.; McRoberts, Ronald E.; Van Deusen, Paul C., eds. 2001. 

Proceedings of the First annual forest inventory and analysis symposium. Salt Lake 

City, UT. October 17-18, 2000. Gen. Tech. Rep. SRS-47. Asheville, NC: U.S. 

Department of Agriculture, Forest Service, Southern Research Station. 143 p.

Reinhardt, Elizabeth; Crookston, Nicholas L. (tech. eds.). 2003. The Fire and Fuels 

Extension to the Forest Vegetation Simulator. Gen. Tech. Rep. RMRS-GTR-116. 

Ogden, UT: U.S. Department of Agriculture, Forest Service, Rocky Mountain 

Research Station. 209 p.
USDA Forest Service Proceedings RMRS-P-41. 2006. 147

Simulation of Long-Term Landscape-Level Fuel Treatment Effects on Large Wildfi res Finney, Seli, McHugh, Ager, Bahro, and Agee

Scott, J.H.; R.E. Burgan. 2005. Standard fi re behavior fuel models: A comprehensive 

set of fuel models for use with the Rothermel’s surface fi re spread model. USDA 

For. Serv. Rocky Mountain Research Station, General Technical Report RMRSGTR-153.

Scott, J.H. and E.D. Reinhardt. 2001. Assessing crown fi re potential by linking 

models of surface and crown fi re behavior. USDA For. Serv., Rocky Mountain 

Research Station, Research Paper RMRS-RP-29.

Stephens, S.L. 1998. Evaluation of the effects of silvicultural and fuels treatments 

on potential fi re behavior in Sierra Nevada mixed conifer forests. For. Ecol. Mgt. 

105:21-35.

Stephens, S.L. and J.J. Moghaddas. 2005. Silvicultural and reserve impacts on potential 

fi re behavior and forest conservation: Twenty-fi ve years of experience from Sierra 

Nevada mixed conifer forest. Biological Conservation. 125:369-379.

Van Deusen, P.C., S.P. Prisley, and A. A. Lucier. 1999. Adopting an annual inventory 

system: user perspectives. J. For. 97(12): 11-14.

van Wagtendonk, J.W. 1996. Use of a deterministic fi re model to test fuel treatments. 

In: Sierra Nevada Ecosystem Project: Final report to Congress Volume II. Centers 

for Water and Wildland Resources, University of California, Davis: 1155-1167.

van Wagtendonk, J.W., and C.A. Sydoriak. 1987. Fuel accumulation rates after 

prescribed fi res in Yosemite National Park. Proc. 9th Conf. Fire For. Meteor. 

9:101-105.

Weaver, H. 1943. Fire as an ecological and silvicultural factor in the ponderosa pine 

region of the Pacifi c slope. J. For. 41:7-15.

Weaver, H. 1957. Effects of prescribed burning in ponderosa pine. J. For., February: 

133-138.

Wykoff, William R.; Crookston, Nicholas L.; Stage, Albert R. 1982. User’s guide 

to the Stand Prognosis Model. Gen. Tech. Rep. INT-133. Ogden, UT: U.S. 

Department of Agriculture, Forest Service, Intermountain Forest and Range 

Experiment Station. 112 p.
USDA Forest Service Proceedings RMRS-P-41. 2006. 149

In: Andrews, Patricia L.; Butler, Bret W., 

comps. 2006. Fuels Management—How to 

Measure Success: Conference Proceedings. 

28-30 March 2006; Portland, OR. 

Proceedings RMRS-P-41. Fort Collins, 

CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research 

Station.

1 Operations Research Analyst with 

USDA Forest Service, Pacifi c Northwest 

Research Station, La Grande, OR, 

currently stationed at the Western 

Wildlands Environmental Threat Center 

in Prineville, OR. aager@fs.fed.us.

2 Research forester, USDA Forest Service, 

Rocky Mountain Research Station, 

Missoula, MT.

3 Research Ecologist, ITX Inc, Forest 

Health Technology Enterprise Team, 

Fort Collins, CO.

Abstract—Despite a wealth of literature and models concerning wildfi re risk, fi eld units 

in Federal land management agencies lack a clear framework and operational tools to 

measure how risk might change from proposed fuel treatments. In an actuarial context, 

risk is defi ned as the expected value change from a fi re, calculated as the product of 

(1) probability of a fi re at a specifi c intensity and location, and (2) the resulting change 

fi nancial or ecological value. The expected value defi nition accounts for landscapescale wildfi re spread, intensity, and damage in a single measure, providing a relatively 

robust metric for comparing the effects of fuel treatment scenarios. New advances in 

calculating burn probabilities and recent work on resource valuation has set the stage 

for actuarial risk analysis in fuels treatment planning. To demonstrate this approach, we 

estimated expected net value change on 16,000 ha wildland-urban interface using 12 

fuel treatment scenarios and four hypothetical value schemes. Burn probabilities were 

estimated by simulating 200 randomly-ignited wildfi res. The results showed a nonlinear response in expected value with increasing treatment area. Fuels treatments on a 

relatively minor percentage of the landscape (20%) resulted in 20% to 50% increases 

in expected net value for most scenarios. The modeling advances the application of 

actuarial science to wildfi re risk management and fuels treatment planning

Introduction

Despite an overwhelming literature concerning wildfi re risk, fi eld units in 

the federal land management agencies lack a clear framework and the appropriate risk assessment tools for prioritizing and measuring the effectiveness of 

proposed fuel treatment projects. It has been suggested that the lack of risk 

assessment tools has led to short term, risk-averse management that has perhaps 

exacerbated longer term risks from natural disturbances (Irwin and Wigley 

2005). With few exceptions, existing wildfi re risk systems are not well founded 

in the actuarial sciences. None we know of model the interactions among 

landscape-scale wildfi re spread, fi re intensity, and wildfi re effects on the net 

value of resources. The actuarial defi nition of wildfi re risk is the expected net 

value change calculated as the product of (1) probability of a fi re at a specifi c 

intensity and location, and (2) the resulting change in fi nancial or ecological 

value (e.g., Bachmann and Allgöwer 2001; Brillinger 2003; Finney 2005). 

The net value change can include both present and future discounted values. 

Assumptions about the effects of wildfi re suppression on wildfi re probability 

and value change can also be incorporated into the expected net value change 

equation. The process of wildfi re risk assessment is concerned with changes 

in expected loss in response to fuel treatments, structure improvements, and 

assumptions about fi re weather and suppression capabilities.

A Wildfi re Risk Modeling System for Evaluating 

Landscape Fuel Treatment Strategies

Alan Ager1, Mark Finney2, and Andrew McMahan3
150 USDA Forest Service Proceedings RMRS-P-41. 2006.

Ager, Finney, and McMahan A Wildfi re Risk Modeling System for Evaluating Landscape Fuel Treatments Strategies

Major advances in the spatial modeling of burn probabilities (e.g. Finney 

2005; Miller and Parsons 2005; Parisien and others 2005) has set the stage 

for rapid improvements in tools and methods for wildfi re risk assessments. 

Extensive work on resource valuation and ecosystem services has also provided 

many frameworks for valuing resources in the expected loss equation (Calkin 

and Hummel 2005; Calkin and others 2005; Rideout and Ziesler 2005). 

We believe that a risk assessment model for fuels treatment planning is now 

within our grasp, and such a model would signifi cantly improve our ability to 

measure the performance of fuel treatments over existing methods, especially 

those that do not consider fuel contagion (Parisien and others 2005). Even 

simplistic valuation schemes that weight key resources such as homes, wildlife 

habitat, visual areas, combined with coarse estimates of burn probabilities 

would provide far more robust measures for comparing fuel treatment options 

compared to existing methods often used by fi eld units.

In this paper we describe a wildfi re risk model based on established concepts 

and defi nitions of risk from the actuarial sciences. The modeling approach 

was designed to be compatible with project level application on federal lands 

in terms of resolution and data requirements. The system estimates expected 

change in net value considering wildfi re spread, intensity, and the effects of 

fi re on resources of concern. We tested the model on a 16,000 ha wildlandurban interface in eastern Oregon, USA, using several hypothetical valuation 

schemes. We report our initial fi ndings here and discuss further work towards 

an operational risk model for fuels treatment planning.

Materials and Methods

Study Area and Data

The Mt. Emily wildland urban interface extends 30 km along a northsouth ridge immediately north of La Grande, Oregon, where the forested 

slopes of Mt. Emily and adjacent ridges descend to the agricultural lands in 

the Grande Ronde Valley. For analysis purposes, a boundary was established 

around the area using major drainages and natural breaks in vegetation, and 

the area within contained 16,296 ha of federal, state, and privately owned 

lands. About 12,471 ha within the study area are classifi ed as forested lands 

based on inventory data. Approximately 9,432 ha are managed by the U.S. 

Department of Agriculture, Forest Service. The forest composition ranges 

from dry forests of ponderosa pine (Pinus ponderosa), cold forests dominated 

by subalpine-fi r (Abies lasiocarpa) and Engelmann spruce (Picea engelmannii), and a transition zone containing grand fi r (Abies grandis), Douglas-fi r 

(Pseudotsuga menziesii), and western larch (Larix occidentalis). Surface 

fuel loadings exceed 140 metric tons/ha in some areas, with high loading 

of dead ladder fuels in a large number of the stands (Wallace 2003). Fuel 

accumulations accelerated after the 1980-1986 Western spruce budworm 

(Choristoneura occidentalis) epidemic. Vegetation and surface fuels data acquisition was accomplished in concert with a fuels reduction project on the 

La Grande Ranger District and have been described in detail elsewhere (Ager 

and others 2005; Wallace 2003).

Modeling Overview

We used a three step modeling process that involved: (1) Simulating landscape 

fuel treatment scenarios with the Forest Vegetation Simulator linked to the Parallel 
USDA Forest Service Proceedings RMRS-P-41. 2006. 151

A Wildfi re Risk Modeling System for Evaluating Landscape Fuel Treatments Strategies Ager, Finney, and McMahan

Processing Extension (PPE, Crookston and Stage 1991), (2) Calculating fi re spread 

parameters (elliptical dimensions) with FlamMap, and (3) simulating random fi res 

and net value change with the mechanistic fi re spread program RANDIG (developed by M. Finney). We integrated the fi rst two steps into ArcGIS using Visual 

Basic scripts (Pattison 1998) and the ArcObjects library (Chang 2004) to facilitate 

the design and simulation of fuel treatment scenarios. Step three involved batch 

processing landscapes with the RANDIG program.

Simulating Fuel Treatment Scenarios

We simulated fuel treatments using the Blue Mountains variant of the PPE 

and the Fire and Fuels Extension (FFE, Reinhardt and Crookston 2003). 

PPE simulates multiple stands in a parallel fashion, i.e., the simulation is completed for all stands each time period before cycling to the next time period. 

PPE can model multiple, spatially explicit treatment constraints and priorities at the stand scale for a given landscape (Crookston and Stage 1991). We 

simulated 12 treatment scenarios by combining six treatment intensities with 

two treatment priorities. Treatment intensities were created by constraining 

the total treatment area to 0, 10, 20, 30, 40, and 66 percent of the forested 

lands. The 66 percent constraint represents treating every overstocked stand 

in the landscape (Ager and others 2005). The treatment priorities were based 

on stand density index (SDENS) and residential density (RDENS). SDENS 

chose stands for treatment based on their level of overstocking as defi ned 

by the current SDI relative to the site potential (Cochran and others 1994). 

RDENS prioritized stands based on the spatial density of homes in the surrounding area. Residential density (residences/km2) was calculated from a 

point layer of homes obtained from the Oregon Department of Forestry using a kernel density estimator with a 2 km search radius. Each stand polygon 

was assigned the average residential density of the pixels within the polygon. 

In the RDENS scenario, stands were also required to meet the same stand 

density as in the SDENS scenario.

Fuel Treatment Prescriptions

The fuels treatment prescription consisted of selective thinning, site removal of surface fuels, and underburning. We triggered a thin when a stand’s 

SDI exceeded 65 percent of the maximum. Removal of trees was ordered 

from smallest to largest so that the thinning treatments were effective at 

reducing ladder fuels. Stands were thinned to a target SDI of 35 percent of 

the maximum for the stand. The thinning prescriptions targeted removal of 

late-seral, fi re intolerant species like grand fi r in mixed-species stands, favoring early seral species such as ponderosa pine, western larch and Douglas-fi r. 

The species preferences were varied slightly by plant association group as 

described in Ager and others (2005). Although the treatment prescriptions 

were simplistic in relation to the diversity of ecological conditions in the 

project area, they conformed to overall management practices in the area. 

Underburning and mechanical treatment of surface fuels was simulated with 

the FFE (Reinhardt and Crookston 2003). Fuel loadings were initialized in 

FFE for each stand using the surface fuels data in the vegetation database. 

We simulated mechanical treatment of surface fuels to remove 90 percent of 

the 7.6 cm to 14.8 cm and 40 percent of the 2.5 cm to 7.6 cm surface fuels 

(Wallace 2003). Underburning was simulated using weather conditions and 

fuel moisture guidelines provided by fuels specialists on the La Grande Ranger 

District. The treatment simulations were performed on a 1600 mhz single 

processor PC and required about 5 minutes per scenario.
152 USDA Forest Service Proceedings RMRS-P-41. 2006.

Ager, Finney, and McMahan A Wildfi re Risk Modeling System for Evaluating Landscape Fuel Treatments Strategies

FVS database outputs (Crookston and other 2006) for crown bulk density 

(kg/m2), height to live crown (ft), total height (ft), canopy closure (percent), 

fl ame length, and crown fi re activity were examined to quantify the effects 

of the treatments on stand structure. For space considerations those results 

are omitted from the present paper. We converted the databases to FlamMap 

landscape fi les using Visual Basic scripts (Ager 2005). After fi nding problems 

with the post thinning, FFE fuel model selection logic, we overrode the FFE 

fuel model selection on treated stands and, based on expected fi re spread rates 

and behavior for the treated stands, assigned them to fuel model 181 (Scott 

and Burgan 2005). This assignment was based on expected fi re spread rates 

in the post-treated stands.

Wildfi re Simulations

For each management scenario, wildfi re spread parameters (elliptical dimensions, Finney 2002) for each 30 x 30 pixel in the study area were calculated 

with a command line version of FlamMap (M. Finney). A fi xed set of weather 

conditions and fuel moistures were used to represent 97th percentile weather 

conditions generated from local RAWS weather stations (Ager and others 

2005; Bradshaw and McCormick 2000). The spread parameters for each 

30 x 30 m pixel were then used as input for the RANDIG.fi re simulation 

program. RANDIG simulates fi re spread using the minimum travel time 

methods (Finney 2002) and inputs on wind, fuel moisture and topography. 

We used RANDIG to simulate 200 random ignitions for each of the 12 fuel 

treatment scenarios. The number of ignitions was chosen after preliminary 

runs showed that burn probability estimates rapidly stabilized at this value, 

which is similar to the fi ndings of Parisien and others (2005 fi g. 16). The duration of each fi re was determined using a Monte Carlo approach that sampled 

a frequency distribution of spread event days developed from a database of 

recorded fi res on the Umatilla National Forest from 1970 to 2005 (data on 

fi le, Umatilla National Forest). We simulated a range of wildfi re burn periods 

with FlamMap for the study area, and then assigned a burn period value to 

each fi re in the database by matching it to a FlamMap simulated fi re with 

similar size. We recognize a number of assumptions and limitations in this 

approach, although alternative methods are not readily available.

Calculating Risk

Using the defi nition of risk as the expected net value change, we incorporated 

risk calculations into the RANDIG program with the following process. We created 

four pairs of landscape value grids (30 x 30 m), each pair containing data on the 

potential positive and negative impacts from wildfi re (table 1), and representing a 

particular valuation scenario. Loss functions were then created for each valuation 

scenario that defi ned proportional changes in value for different fl ame lengths (table 

1). RANDIG was then modifi ed to tally the value change at each pixel for each 

simulated wildfi re and report the net change at the end of simulation (200 fi res 

per scenario). It is important to note that the loss functions and value layers were 

purely hypothetical and created for the purpose of demonstrating the utility of the 

wildfi re risk approach in the current study area. The fi rst valuation scenario (FX) 

assumed a fi xed value of $500 per ha, and the loss function specifi ed a loss directly 

proportional to fl ame length (table 1). The scenario was included to demonstrate a 

simple method to incorporate both fi re intensity and spread in measuring landscape 

effects of fuel treatment scenarios. The second valuation scenario was developed 

using home locations. We assigned a fi xed value of $200,000 to each of the 176 

homes in the WUI and then smoothed the point data using a kernel density 
USDA Forest Service Proceedings RMRS-P-41. 2006. 153

A Wildfi re Risk Modeling System for Evaluating Landscape Fuel Treatments Strategies Ager, Finney, and McMahan

f unction with a search radius of 200 m to generate a smooth grid of home values. 

The goal was to represent the value of individual homes on a number of pixels to 

refl ect the uncertainty in the modeling about loss from direct ignition, and the fact 

that signifi cant value in the rural residences exists around the main structure. Each 

residence was represented by 125 pixels having a maximum and minimum value 

of about $2000 and $8,000 respectively. A loss function was then created that assumed linear damage with increasing fl ame length (table 1). No benefi ts from fi re 

were assumed in this scenario. Again, the scenario was purely hypothetical and built 

to demonstrate the utility of the risk system. A third valuation scenario assumed 

that fl ame lengths under 1.2 m (4 ft.) constituted a fuels treatment, and generated 

a positive value of $350/ha. Negative values were not included in this scenario. A 

fourth scenario (RES+WB) combined portions of the third and second scenarios 

to consider both the loss of residences at fl ame lengths > 1.2 m, and the benefi ts of 

low intensity fi re when fl ame length was less than 1.2 m.

Within each simulated fi re, the fl ame length at each pixel was used to calculate the net value change using a loss function. The loss function translated 

fl ame length into an expected change in value expressed as a proportion. 

We simulated 200 wildfi res for each combination of management intensity, 

treatment priority, and valuation scheme, for a total of 9,600 fi res. For demonstration purposes we ignored a number of important factors including 

the cost of fi re suppression and fuel treatments, and revenues from harvests. 

These factors will be considered in future work

Results

Burn Probabilities

Burn probability (BP) and average wildfi re size decreased linearly with 

increasing treatment intensity for both the RDEN and SDEN scenarios (Table 2). 

The highest BP’s were observed for the scenarios without treatments, and 

were in the range of 0.049 – 0.060, averaging 0.057. At the maximum treatment rate, where all overstocked stands were treated (8,495 ha, 66 percent), 

Table 1—Loss functions for different valuation schemes used in the study. Data in the table are the proportional change 

in the value of each pixel at different fl ame lengths. Positive values represents benefi ts, negative values represent 

losses. Loss functions are hypothetical and were developed to illustrate the process for modeling expected change 

in net value. 

 Valuation scenario

Flame length Residential values Wildfi re benefi ts Residential and wildfi re 

 (m) Fixed value (FX) (RES) (WB) benefi ts (RES+WB)

 Loss Benefi ts Loss Benefi ts Loss Benefi ts Loss Benefi ts

 0.0 – 0.30 –0.1 0.0 –0.1 0.0 0.0 1.0 0.0 1.0

 0.30 – 0.61 –0.2 0.0 –0.2 0.0 0.0 1.0 0.0 1.0

 0.61 – 0.91 –0.3 0.0 –0.3 0.0 0.0 1.0 0.0 1.0

 0.91 – 1.22 –0.4 0.0 –0.4 0.0 0.0 1.0 0.0 1.0

 1.22 – 1.52 –0.5 0.0 –0.5 0.0 0.0 0.0 –0.5 0.0

 1.52 – 1.83 –0.6 0.0 –0.6 0.0 0.0 0.0 –0.6 0.0

 1.83 – 2.13 –0.7 0.0 –0.7 0.0 0.0 0.0 –0.7 0.0

 2.13 – 2.44 –0.8 0.0 –0.8 0.0 0.0 0.0 –0.8 0.0

 2.44 – 2.74 –0.9 0.0 –0.9 0.0 0.0 0.0 –0.9 0.0

 >2.74 –1.0 0.0 –1.0 0.0 0.0 0.0 –1.0 0.0
154 USDA Forest Service Proceedings RMRS-P-41. 2006.

Ager, Finney, and McMahan A Wildfi re Risk Modeling System for Evaluating Landscape Fuel Treatments Strategies

average BP was reduced to 0.016 for the RDEN and 0.017 for the SDEN 

management scenarios. Thus, treating every overstocked stand reduces the 

average probability of a 30 x 30 m pixel burning by 0.04. The BP estimates 

varied only slightly among the four different simulations for each scenario 

with the range averaging 0.0043. Considerable spatial variation in BP was 

observed, with the largest values (0.22) along the eastern edge in the middle 

of the study area. This same area also showed the largest reduction in BP 

from the thinning treatments. For instance, BP was reduced from 0.22 to 

0.07 between the no treatment and treating 66 percent of the landscape using the SDEN treatment priority.

Average Wildfi re Size

Wildfi re sizes in the 9600 simulations (12 management scenarios by 4 

valuation schemes x 200 fi res) ranged from 1 to 5,600 ha. Average wildfi re 

size decreased linearly with increasing treatment at the rate of about 0.07 ha 

per ha treated for both the RDEN and SDEN (table 2). Thus, for every 100 

ha treated, the average wildfi re on the entire study area was reduced by 6 ha. 

On a proportional basis, treating 20% of the landscape (3,255 ha) reduced the 

average wildfi re size by about 34%. Differences among the spatial treatment 

scenarios (RDEN, SDEN) in terms of wildfi re size were minor.

Expected Loss

The hypothetical valuation schemes (FX, RES, WB, RES+WB, see table 

1) showed large differences among the six management intensities and only 

slight differences for the two different treatment priorities (fi g. 1, table 2). 

Average net change for the FX valuation scheme, where we arbitrarily valued each ha at $500 and assumed a loss function directly proportional to 

fl ame length (table 1), equaled -$46.65 and -$53.35 per ha for the SDEN 

and RDEN treatment priorities at the 0 percent treatment level (table 2). 

Table 2—Results from wildfi re risk simulations on the Mt. Emily study area. Each scenario represents a management 

intensity (0, 10, 20, 30, 40, 66 percent of forested area treated) and spatial treatment priority (SDEN = stand 

density index, RDEN = residential density). Two-hundred random ignitions were simulated for each management 

scenario. Four resource valuations schemes were used to calculate an expected net change in landscape 

value. Valuation schemes are: FX= flamelength, RES = value of residences, WB = wildfire benefits from low 

intensity wildfire, RES+WB = combined RES and WB. The reported average and range is for the 4 simulations 

per management scenario (one for each valuation scheme).

 Average Average Expected net value change 

 burn Range in average wildfi re ($/ha) by valuation scheme

Scenario Ha treated probability burn probability size (ha) FX RES WB RES+WB

SDEN-0 0 0.057 0.049 – 0.060 883 –53.4 –47.7 40.6 10.9

SDEN-10 1690 0.040 0.038 – 0.045 624 –28.4 –34.0 37.1 6.6

SDEN-20 3255 0.038 0.036 – 0.040 624 –30.0 –34.3 32.0 –4.9

SDEN-30 4887 0.029 0.027 – 0.032 499 –25.6 –24.4 24.2 2.0

SDEN-40 6547 0.025 0.024 – 0.026 408 –19.5 –23.5 21.8 –6.9

SDEN-66 8495 0.017 0.017 – 0.018 278 –12.8 –16.9 14.4 –74.6

RDEN-0 0 0.053 0.052 – 0.056 893 –46.6 –38.4 46.9 18.2

RDEN-10 1690 0.040 0.037 – 0.041 683 –40.5 –22.2 31.1 –40.7

SDEN-20 3255 0.033 0.032 – 0.035 552 –32.7 –19.1 28.6 –16.8

RDEN-30 4887 0.026 0.025 – 0.028 435 –21.1 –14.9 23.2 –17.9

RDEN-40 6547 0.021 0.018 – 0.024 344 –15.0 –12.8 18.0 –46.1

RDEN-66 8495 0.016 0.016 – 0.017 273 –12.8 –12.4 13.1 –58.4
USDA Forest Service Proceedings RMRS-P-41. 2006. 155

A Wildfi re Risk Modeling System for Evaluating Landscape Fuel Treatments Strategies Ager, Finney, and McMahan

Figure 1—Expected net value for the SDEN (top) and RDEN (bottom) treatment 

priorities by treatment level ( percent of landscape treated) and resource valuation 

scheme. SDEN and RDEN prioritized stand treatments based on stand density and 

residential density, respectively.

At the maximum level of treatment (66 percent of landscape), the net value 

increased to -$12.8 per ha for both treatment scenarios. On a proportional 

basis, there was a 44% increase in net value when the treatments rate was increased from 0% to 20%. We observed a slightly more rapid increase in value 

with the SDEN versus the RDEN spatial priority between 0 percent and 10 

percent treatment levels, perhaps due to the treatment of stands with higher 

SDI and more extreme fi re behavior.

Maps comparing the expected loss (FX) between two treatment priorities 

(RDEN, SDEN) at 20 percent treatment showed distinct treatment patterns 

(fi g. 2). At higher treatment levels, the difference between spatial priorities 

was diminished (fi g. 1). The SDEN treatment priority resulted in a relatively 
156 USDA Forest Service Proceedings RMRS-P-41. 2006.

Ager, Finney, and McMahan A Wildfi re Risk Modeling System for Evaluating Landscape Fuel Treatments Strategies

large increase in the net value in the central portion of the project area which 

currently supports a higher proportion of overstocked stands. In contrast, 

the RDEN treatment priority, which selected stands for treatment along the 

eastern edge of the study area near residences, showed relatively low expected 

values in the central portion of the project area.

Mapping the difference in expected value (FX) between the untreated and 

treating 20 percent of the forested area for the SDEN treatment priority was 

used to compare the effects of fuel treatments on expected loss inside and 

outside the treatment areas (fi g. 3). Changes in expected loss were apparent 

especially in the treated areas (fi g. 3). However, the effect of the treatments 

outside the treatment units were also apparent (fi g. 3), thus illustrating the 

secondary (landscape) benefi ts of the treatments.

Simulation results for the RES valuation schemes, which valued homes at 

$200,000 with a linear loss function (table 1) appeared very similar to the 

FX valuation scheme, with increasing net value with increasing treatment 

levels. The effect of prioritizing treatment according to residential density 

(RDEN) versus the stand density (SDEN) was evident in the results, with 

the expected value for the former scenario substantially higher for the lower 

treatment levels (10 to 30 percent, table 2). For instance, at the 30 percent 

treatment level the expected value for the RDEN spatial treatment priority 

was about $15 per ha higher as compared to SDEN where stands were treated 

based only on their SDI. The expected value for the RES valuation scheme 

Figure 2—Map of the study area showing expected value loss ($ per ha) from wildfi re 

for the SDEN (left panel) and RDEN (right panel) treatment priorities with 20 percent 

of the landscape treated and fi xed land values ($500 per ha). Values shown in legend 

represent value loss.
USDA Forest Service Proceedings RMRS-P-41. 2006. 157

A Wildfi re Risk Modeling System for Evaluating Landscape Fuel Treatments Strategies Ager, Finney, and McMahan

and RDEN treatment priority increased about 50% when the treatment rate 

was increased from 0% to 20% of the landscape. The increased expected value 

(decreased loss) between the SDEN and RDEN treatment priorities were 

largely due to localized treatments around residences (fi g. 4). Nevertheless, 

treating stands based on density alone, which resulted in the bulk of the 

treatments several kilometers from the residences, reduced the expected loss 

in the RES valuation scheme (fi g. 4, lower right panel)

As expected, the WB scenario, which valued wildfi re fuel treatments at 

$350 per ha when the fl ame length was less than 1.2 m showed decreasing 

expected value with increasing levels of treatments (fi g. 1). The drop in expected value with increasing treatment intensity mirrored the reduction in 

average wildfi re size (table 2). With less area burned by wildfi re under the 

higher treatment levels the expected value would be expected to drop.

The valuation scheme that considered both residential values and wildfi re 

benefi ts (RES+WB) produced some erratic results, although in general the 

decrease in wildfi re benefi ts with increasing treatment levels overshadowed the 

effects on residential values (table 2, fi g. 1). The variable results are diffi cult 

to explain and are perhaps a larger number of simulated fi res are required to 

estimate expected values when there are multiple valuations on a landscape.

Figure 3—Difference in expected loss between no treatment and treating 20 percent 

of the landscape (left panel) for the SDEN treatment priority, and fi xed land valuation 

($500 per ha). Darker areas represent benefi ts (reduced loss) from the fuel treatments. 

Left panel is a zoomed image of the central portion of the study area showing the 

same data and treatment units (horizontal hatching). Darker shading shows the offsite, landscape effect of the fuel treatments in terms of reducing burn probability and 

intensity, and potential loss from fi re.
158 USDA Forest Service Proceedings RMRS-P-41. 2006.

Ager, Finney, and McMahan A Wildfi re Risk Modeling System for Evaluating Landscape Fuel Treatments Strategies

Discussion

The results demonstrate a wildfi re risk assessment process that incorporates 

important interactions among wildfi re spread, intensity, and resource values, 

and illustrates how landscape fuel treatment strategies may affect the expected 

net value change. The simulations assumed that, from a risk standpoint, the 

primary concern is escaped fi res and extreme fi re conditions (Finney 2005), 

since these are the fi res that are responsible for the most damage. Suppression 

Figure 4—Expected loss for the RES valuation scheme at the 20 percent treatment level for the RDEN and 

SDEN treatment priorities. The RES valuation scheme valued each home at $200,000. Left panel shows treated 

stands (horizontal hatching) for the RDEN treatment priority where stands were prioritized for treatment 

based on residential density. Black circles denote residences. Panels on right show the expected value 

change for the stand density (SDEN, upper right) and residential (RDEN, lower left) treatment priorities. 

Values in the legend represent expected loss.
USDA Forest Service Proceedings RMRS-P-41. 2006. 159

A Wildfi re Risk Modeling System for Evaluating Landscape Fuel Treatments Strategies Ager, Finney, and McMahan

activities are generally ineffective in these types of fi res, and thus were excluded from the model. Modifying the expected value equation to account 

for mitigation such as suppression capability is discussed by Smith (2001).

We envision this modeling framework as a useful one for simulating fuel 

treatment scenarios and analyzing their performance with measures like 

burn probability and net value change, especially within the context of collaborative fuels treatment planning (Bahro 2004; Gercke and Stewart 2006). 

For instance, the change in expected value per ha treated could be used as 

a measure of treatment performance. This measure can be partitioned between the treated area and non-treated area to measure the effi ciency of the 

treatment package in terms of local (treated stands) versus landscape-scale 

(non-treated stands) effects.

From a research perspective, this modeling framework could also help 

resolve one of the key risk assessment questions concerning fuels treatment 

on federal lands: the tradeoff between potential short term impacts of fuel 

treatments versus long term benefi ts of wildfi re mitigation (Irwin and Wigley 

2005). In this case the net value formulation will require discounted, future 

losses and benefi ts, and the vegetation simulations will require a temporal 

component. This type of problem is tractable with the FVS-Parallel Processing 

Extension and the RANDIG program.

There are important differences between the methods used here to estimate burn probabilities versus probabilistic models built with historical fi re 

occurrence and size data (Martell and others 1989; Mercer and Prestemon 

2005; Preisler and others 2005). We have estimated a conditional burn probability to compare the effects of management, and set the number of fi res 

to a value that sample the landscape in terms of fi re spread, intensity, and 

value. Until we factor in spatio-temporal probabilities for ignition, escape, 

and burn conditions (Davis and Miller 2004; Miller 2003; Parisien and 

other 2005), there is most likely little relationship to the burn probabilities 

estimated here and the actual probability of a wildfi re on the Mt. Emily area. 

However, precisely what parameters most infl uence burn probabilities and 

whether a more complex model is necessary for modeling the effectiveness 

of fuel treatments remains to be seen.

Although our resource value layers were hypothetical, they were useful for 

demonstrating the application of the system. The modeling framework is also 

well suited for analyzing long-term risk tradeoff between wildland fi re benefi ts 

and the cost of wildfi re suppression and fuels treatments (Calkin and Hyde 

2004). More realistic valuation and loss scenarios have been used in other 

studies to examine treatment costs, potential timber revenues, and wildlife 

habitat impacts over time (Hummel and Calkin 2005).

Resource valuation is a complex problem (Freeman 2003) especially on 

federally-managed lands where planners need to integrate monetary and nonmonetary valuations for analyzing and comparing risk among fuel treatment 

alternatives. Valuation schemes that use a common, relative weighting system 

for multiple resource values have been proposed for federal lands (Rideout 

and Zielser 2005). Many other kinds of valuation data are readily available 

online or in agency GIS systems. The risk framework described here could 

be easily expanded to accommodate multiple loss and benefi t grids and loss 

functions in an integrated measure of risk.

Future work will involve experimenting with a number of factors that 

affect burn probabilities, such as ignition location, weather conditions, and 

the effectiveness of suppression. We also plan to simulate treatment scenarios 

through time to address the temporal aspects of wildfi re risk assessment.
160 USDA Forest Service Proceedings RMRS-P-41. 2006.

Ager, Finney, and McMahan A Wildfi re Risk Modeling System for Evaluating Landscape Fuel Treatments Strategies

Acknowledgments

We are indebted to Carol Miller and Marc Parisien for discussions on 

estimating burn probabilities. We thank Dave Calkin and Carol Miller for 

reviewing an earlier version of the paper. The simulation of fuel treatments 

on Mt. Emily was funded by a Joint Fire Science Program grant (03-4-1-04) 

to the senior author. Additional support was provided by the Forest Service 

Western Wildlands Environmental Threat Center, Prineville, OR.

Literature Cited

Ager, A. A. 2005. ArcFuels: Forest planning tools for managing wildland fuels. 

Proceedings of the 25th ESRI international users conference, July 25-29, San 

Diego, CA. 5 p. http://gis.esri.com/library/userconf/proc05/papers/pap1296.

pdf. Accessed 3/15/06.

Ager, A. A.; Barbour, R. J.; Hayes, J. L. 2005. Examination of long-term fuels 

management scenarios on a wildland-urban interface in northeastern Oregon. 

In: Bevers, Michael; Barrett, Tara M., tech. comps. Systems analysis in forest 

resources: proceedings of the 2003 symposium. Gen. Tech. Rep. PNW-GTR-656. 

Portland OR: U.S. Department of Agriculture, Forest Service, Pacifi c Northwest 

Research Station: 215-228.

Bachmann, A.; Allgöwer, B. 2001. A consistent wildland fi re risk terminology is 

needed! Fire Management Today. 61: 28-33.

Bahro, B. 2004. Fireshed assessment: An integrated approach to landscape planning. 

R5-TP-017. U.S. Department of Agriculture, Forest Service, Pacifi c Southwest 

Region. http://199.128.173.130/psw/centennial/agenda/prefi re_planning/

BerniBahro%20fi reshed.pdf Accessed 3/15/06.

Bradshaw, L.; McCormick, E. 2000. FireFamily Plus user’s guide, version 2.0. Gen. 

Tech. Rep. RMRS-GTR-67WWW. Ogden, UT: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research Station. 124 p.

Brillinger, D. R. 2003. Three environmental probabilistic risk problems. Statistical 

Science. 18: 412-421.

Calkin, D.; Hyde, K. 2004. Break-Even Point. Wildfi re. September 1 issue. http://

wildfi remag.com/ar/breakeven_point/index.htm Accessed 3/15/06.

Calkin, D.; Hyde, K.; Gebert, K.; Jones, G. 2005. Comparing resource values at 

risk from wildfi res with Forest Service fi re suppression expenditures: Examples 

from 2003 western Montana wildfi re season. Res. Note RMRS-RN-24WWW. 

Missoula, MT: U.S. Department of Agriculture, Forest Service, Rocky Mountain 

Research Station. 6 p.

Chang, K. T. 2004. Programming ArcObjects with VBA: a task oriented approach. 

Boca Raton, FL. CRC Press. 352 p.

Cochran, P. H.; Geist, J. M.; Clemens, D. L.; Clausnitzer, R.R.; Powell, D.C. 

1994. Suggested stocking levels for forest stands of northeastern Oregon and 

southeastern Washington. Gen. Tech. Rep. PNW-RN-513. Portland, OR: U.S. 

Department of Agriculture, Forest Service, Pacifi c Northwest Research Station. 

21 p.

Crookston, N. L.; Gammel, D. L.; Rebain, S. 2006. User’s guide to the Database 

Extension of the Forest Vegetation Simulator Version 2.0. Moscow, ID. U.S. 

Department of Agriculture, Forest Service, Rocky Mountain Research Station. 

50 p. http://www.fs.fed.us/fmsc/ftp/fvs/docs/gtr/DB-FVS-UserGuide.pdf. 

Accessed 3/15/06.
USDA Forest Service Proceedings RMRS-P-41. 2006. 161

A Wildfi re Risk Modeling System for Evaluating Landscape Fuel Treatments Strategies Ager, Finney, and McMahan

Crookston, N. L.; Stage, A. R. 1991. User’s guide to the Parallel Processing Extension 

of the Prognosis Model. Gen. Tech. Rep. INT-281. Ogden, UT: U.S. Department 

of Agriculture, Forest Service, Rocky Mountain Research Station. 87 p.

Davis, Brett; Carol Miller. 2004. Modeling wildfi re probability using a GIS. In: 

Proceedings of the ASPRS 2004 annual conference, Denver, USA. May 23-28. 

Bethesda, MD: American Society for Photogrammetry and Remote Sensing, 

2004. Available on CD only.

Finney, M. A. 2002. Fire growth using minimum travel time methods. Canadian 

Journal of Forest Research. 32:1420-1424.

Finney, M. A. 2005. The challenge of quantitative risk analysis for wildland fi re. 

Forest Ecology and Management. 211:97-108.

Freeman, A. M. 2003. The measurement of environmental and resource values. 2nd 

ed. Washington, D.C.: Resources for the Future. 491 p.

Gercke, D. M.; Stewart, S. A. 2006. Strategic placement of treatments (SPOTS): 

maximizing the effectiveness of fuel and vegetation treatments on problem 

fi re behavior and effects. In: 1st Fire behavior and fuels conference on fuels 

management—How to measure success; March 28-30, 2006; Portland, 

Oregon. [Abstract] http://www.iawfonline.org/pdf/Abstracts-1.pdf. Accessed 

3/16/06.

Hummel, S.; Calkin, D. E. 2005. Costs of landscape silviculture for fi re and habitat 

management. Forest Ecology and Management. 207: 385-404.

Irwin, L. L.; Wigley, T. B. 2005. Relative risk assessments for decision-making related 

to uncharacteristic wildfi re. Forest Ecology and Management. 211: 1-2.

Martell, D.L.; Bevilacqua, E.; Stocks, B. J. 1989. Modelling seasonal variation in 

daily people-caused forest fi re occurrence. Canadian Journal of Forest Research. 

19: 1555-1563.

Mercer, D. E.; Prestemon, J. F. 2005. Comparing production function models for 

wildfi re risk analysis in the wildland urban-interface. Forest Policy and Economics. 

7: 782-795.

Miller, C. 2003. The spatial context of fi re: A new approach for predicting fi re 

occurrence. In: Galley, K. E. M.; Klinger, R. C.; Sugihara, N. G., eds. Proceedings 

of fi re conference 2000: The fi rst national congress on fi re ecology, prevention, and 

management. Misc. publication No. 13. Tallahassee, FL: Tall Timbers Research 

Station: 22-29.

Miller, C.; Parsons, D. P. 2005. Can wildland fi re use restore natural fi re regimes 

in wilderness and other unroaded lands? Final report to the Joint Fire Science 

Program, Project #01-1-1-5. Hardcopy on fi le at Joint Fire Science Program offi ce 

and Aldo Leopold Wilderness Research Institute. http://leopold.wilderness.

net/research/fprojects/pdfs/FinalReport_JFSP01_Jan01_05.pdf Accessed 

3/15/06.

Parisien, M. A.; Kafka, V. G.; Hirsch, K. G.; Todd, B. M.; Lavoie, S. G.; Maczek, P. 

D. 2005. Using the BURN-P3 simulation model to map wildfi re susceptibility. 

Canadian Forest Service Report NOR-X-405. 36 p.

Pattison, T. 1998. Programming distributed applications with COM and Microsoft 

Visual Basic 6.0. Redmond, WA: Microsoft Press. 500 p.

Preisler, H. K.; Brillinger, D.R.; Burgan, R. E.; Benoit, J. W. 2004. Probability 

based models for estimating wildfi re risk. International Journal of Wildland Fire. 

13: 133-142.

Reinhardt, E.; Crookston, N. L., tech. eds. 2003. The Fire and Fuels Extension 

to the Forest Vegetation Simulator. Gen. Tech. Rep. RMRS-GTR-116. Ogden, 

UT: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research 

Station. 209 p.
162 USDA Forest Service Proceedings RMRS-P-41. 2006.

Ager, Finney, and McMahan A Wildfi re Risk Modeling System for Evaluating Landscape Fuel Treatments Strategies

Rideout, D. B.; Ziesler, P. S. 2005. Weight System (EOWEP) for FPA-PM1. Working 

Draft Version 2.5 Department of Forest, Rangeland, and Watershed Stewardship. 

Colorado State University, Fort Collins, CO. February 4, 2005. http://www.fpa.

nifc.gov/Library/Papers/Docs/EOWEP_V2_2005_02.pdf

Scott, J. H.; Burgan, R. E. 2005. Standard fi re behavior fuel models: a comprehensive 

set for use with Rothermel’s surface fi re spread model. Gen. Tech. Rep. RMRSGTR-153. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, 

Rocky Mountain Research Station. 72 p.

Smith, Keith. 2001. Environmental Hazards: Assessing Risk and Reducing Disaster. 

London: Routledge 392 p.

Wallace, P.L., 2003. Mt. Emily fuels reduction project. Specialist report on fi le, 

Wallowa Whitman National Forest, La Grande Ranger District, La Grande, 

OR. 22 p.
USDA Forest Service Proceedings RMRS-P-41. 2006. 163

In: Andrews, Patricia L.; Butler, Bret W., 

comps. 2006. Fuels Management—How to 

Measure Success: Conference Proceedings. 

28-30 March 2006; Portland, OR. 

Proceedings RMRS-P-41. Fort Collins, 

CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research 

Station.

1 Operations Research Analyst for the 

USDA Forest Service, Pacifi c Northwest 

Research Station, La Grande, OR, currently assigned to the Western Wildlands 

Environmental Threat Center, Prineville, 

OR aager@fs.fed.us.

2 Regional Fuels Manager for Planning 

for the USDA Forest Service, Pacifi c 

Southwest Region, McClellan, CA.

3 Regional Analyst with the USDA 

Forest Service, Pacific Southwest 

Region, McClellan, CA.

Abstract—A library of macros was developed to automate the Fireshed process within 

ArcGIS. The macros link a number of vegetation simulation and wildfi re behavior 

models (FVS, SVS, FARSITE, and FlamMap) with ESRI geodatabases, desktop software 

(Access, Excel), and ArcGIS. The macros provide for (1) an interactive linkage between 

digital imagery, vegetation data, FVS-FFE, and SVS, creating a map-based interface 

for designing and testing stand fuel treatments; (2) rapid scale-up of stand-specifi c 

treatments to simulate project-wide changes in vegetation and fuels; (3) data linkages 

between FVS outputs and FlamMap/FARSITE to allow for simulation of landscape-scale 

fi re behavior and evaluation of fuel treatment scenarios; and (4) data linkages between 

FVS outputs and ArcMap for rapid mapping of FVS database outputs. The library is 

distributed as an ArcMap project fi le (.mxd) and is implemented on custom toolbars 

on the ArcMap interface. The system was designed to automate geospatial analyses 

performed in the Fireshed process to design and test fuel treatments in a collaborative 

setting. A beta version of ArcFuels is available from the senior author.

Introduction

Planning fuel treatment projects on large forested landscapes requires a 

number of wildfi re and vegetation models to simulate and test the merits of 

proposed management activities (Finney and Cohen 2002; Stratton 2006). 

Treatment scenarios are typically constructed by iteratively selecting stands 

for treatment, and subsequently evaluating the aggregate effects of treatments 

on landscape-scale wildfi re behavior by using wildfi re simulators. Ideally, the 

selection of specifi c stands is based on both the potential fi re behavior within 

the stand, and the stand’s topological relationship to other treated stands 

(Finney 2004). Fuel treatment projects that do not address both stand and 

landscape aspects of the problem may be ineffective in terms of reducing the 

threat from large wildfi res (Finney 2004; Finney and Cohen 2002).

The process for designing fuel treatments is complicated by multiple management goals and constraints on public lands (Hayes and others 2004). A 

further, perhaps more challenging problem for federal land managers is that 

wildfi re does not recognize land ownership boundaries, and thus treatments 

must be designed in collaboration with other landowners. To address these 

problems, a cadre of Forest Service fi re specialists created a collaborative 

process for building multi-ownership fuel treatment plans (Amboy 2006; 

Bahro 2004; Bahro and others 2006). The process integrates multiple land 

and resource management objectives when addressing and evaluating fuel 

treatments (Ewell and others 2006). The “Fireshed” process starts with the 

delineation of geographic units (10,000 to 50,000 ha) with similar fi re regimes, fi re history, and wildland fi re risk issues. In a collaborative setting, fuel 

treatments are designed and tested in near real time with wildfi re simulation 

Automating the Fireshed Assessment Process 

with ArcGIS

Alan Ager1, Bernhard Bahro2, and Klaus Barber3
164 USDA Forest Service Proceedings RMRS-P-41. 2006.

Ager, Bahro, and Barber Automating the Fireshed Assessment Process with ArcGIS

models (FARSITE, FlamMap). The process requires a number of support staff 

including geographic information system (GIS) specialists, database analysts, 

and fi re modelers, as well as a library of GIS macros and other programs. 

The Fireshed process represents a major advance in fuel treatment planning, 

and led to the Stewardship and Fireshed Assessment Pilot Program in the 

Forest Service (Gercke and Stewart 2006). The framework is increasingly 

being used as an organizing and operational framework for landscape fuel 

treatment planning (Gallagher 2005). The concept of a fi reshed also has 

ecological science value and is being used as a research framework (Jordana 

and others 2003).

In 2004, one of the authors (Ager) received funding from the Joint Fire 

Science Program to streamline the process of fuel treatment planning (Ager 

and McGaughey 2003). The project proposal identifi ed a gap in the integration and data linkages among fi re behavior models, vegetation and fuel data, 

GIS, and desktop software. The Fireshed process was adopted as the design 

template for the work. In this paper, we describe our progress to streamline 

and integrate fuel treatment planning and the Fireshed process, and a new 

library of macros (ArcFuels) within ArcGIS.

ArcFuels

Overview

We used the ArcObjects library (Chang 2004) and Visual Basic for Applications (VBA) (Pattison 1998) within ArcMap as the development framework. 

The VBA development interface is integrated within ArcMap and Microsoft 

(MS) Offi ce products (Excel and Access); ArcFuels macros are distributed 

within ArcGIS project fi les (.mxd). The project fi le is loaded into ArcMap, 

and the macros appear as toolbars. Project defaults that specify the paths of 

installed fi re behavior models, vegetation databases, GIS themes, and various 

other parameters are stored in a MS Access database.

The selection of models and linkages within the ArcFuels interface was 

aimed at providing the user with the following functionality for fuel treatment 

planning: (1) an interactive system within ArcMap to develop stand-specifi c 

silvicultural prescriptions and fuel treatments, including thinning, underburning, and mechanical fuel treatment; (2) automated generation of data plots 

showing how stand fuel treatments change wildfi res in terms of fl ame length, fi re 

behavior [surface or crown], and stand mortality over time; (3) rapid scale-up 

of stand-specifi c treatments to simulate project-wide changes in vegetation and 

fuel from proposed management activities; (4) tight data linkages to FlamMap or FARSITE to simulate landscape-scale fi re behavior and measure the 

treatment performance in terms of wildfi re probabilities, spread rates, and 

fi reline intensity (Finney 2004); (5) ability to easily modify and reevaluate 

fuel treatment scenarios; and (6) integration of fi re modeling spatial outputs 

into ArcGIS and other programs to facilitate the evaluation of fuel treatments 

with multi-resource objectives.

Data

Detailed modeling of fuel treatments for project-level planning requires 

tree list data and information on surface fuel loadings. Forest Service tree list 

data are stored within the FSVEG database system. In many projects, data for 

polygons without stand exams are imputed by using a most similar neighbor 
USDA Forest Service Proceedings RMRS-P-41. 2006. 165

Automating the Fireshed Assessment Process with ArcGIS Ager, Bahro, and Barber

approach (Crookston and others 2002). The Forest Service FSVEG system 

can generate spatial vegetation databases that are compatible with the FVS 

database extension (Crookston and others 2006). For the Fireshed process, 

these databases can be augmented with key information about land management strata and other factors important for building management scenarios 

(for example ownership, management emphasis) and prescriptions.

Stand Modeling of Fuel Treatments

Developing and testing treatment prescriptions for specifi c stands is an 

iterative process that seeks to fi nd the best prescription to meet multiple 

objectives. ArcFuels provides interactive linkages to the Forest Vegetation 

Simulator (FVS) (Dixon 2003) and the FVS Fire and Fuels Extension (FFE), 

which are widely used to simulate thinning, prescribed fi re, and mechanical 

treatment of downed fuels, and the post-treatment potential fi re behavior. 

These simulations use a well-defi ned weather scenario, usually generated from 

fi eld weather stations (http://www.fs.fed.us/raws/) by using FireFamily Plus 

(Bradshaw and McCormick 2000). Stand prescriptions are developed with a 

number of FVS keywords (for example THINSDI, SIMFIRE, FUELMOVE, 

see Dixon 2003). FVS and FFE can also be used to examine the longer term 

(for example 50 years) effects of the treatments on forest density and dead 

fuel dynamics provided a forest regeneration model is available.

In the fuels treatment planning process, signifi cant work is required to validate stand data, defi ne values for model parameters, and design stand-specifi c 

treatments. To automate this process, we built a stand query function into 

ArcFuels to allow users to interact with stand data and fi re models within the 

ArcMap interface. Users can also load digital color imagery for their project 

area (http://www.apfo.usda.gov/NAIP.html) and overlay stand polygon 

maps, and then test different management prescriptions by clicking on specifi c stands to execute one or more fi re models. For instance, clicking on the 

stand within ArcMap can be used to: (1) simulate management activities and 

potential wildfi re within FVS; (2) generate Excel graphs of stand metrics, fuel 

loadings, and fi re behavior and; (3) Visualize treatments and wildfi re effects 

in the Stand Visualization System (SVS, McGaughey 2002). A direct link on 

the ArcFuels forms to the FVS prescription keywords allow for rapid changing 

of management prescriptions and testing of different fuel treatment options. 

The system provides a rapid method for browsing a landscape in a spatial 

context, examining and visually validating the data representing the stand, 

and iteratively testing stand-level treatment prescriptions within a GIS.

Landscape Design and Testing of Fuel Treatments

Landscape analysis of fuel treatment scenarios examines the aggregate effect 

of all treatments on potential wildfi re behavior. The effects of fuel treatments 

on other landscape-scale goals are measured at this stage. Goals for wildlife, 

visuals, aquatics, and forest restoration may also be examined (Hayes and 

others 2004). Of key importance is the spatial arrangement and size of the 

fuel treatments relative to the direction of a likely wildfi re event. Testing 

the performance of fuel treatment strategies can be accomplished with the 

FlamMap program in terms of fi re spread, travel time, and burn probabilities. 

The FVS parallel processing extension (Crookston and Stage 1991) is a key 

part of this system. FVS-PPE is a little used extension that recognizes stand 

contagion and can model harvest constraints, treatment goals, fuels, and 

generates many of the specifi c inputs needed by landscape fi re models.
166 USDA Forest Service Proceedings RMRS-P-41. 2006.

Ager, Bahro, and Barber Automating the Fireshed Assessment Process with ArcGIS

ArcFuels automates the process of selecting and/or assigning stand-specifi c prescriptions within a Fireshed and building the input fi les required by 

FlamMap. The assignment of treatments to stands is accomplished in six ways: 

(1) ArcGIS selection; (2) stand query function; (3) database queries that key 

off of data in the stand database; (4) importing a treatment optimization 

grid from FlamMap; (5) dynamic selection by using FVS-PPE variable; and 

(6) external algorithms. FVS-PPE can prioritize and constrain on multiple 

activities and land strata. The external algorithm approach was used by Finney 

(2004) for fuel treatment optimization.

ArcFuels builds scenario fi les for the FVS-PPE from MS Access vegetation databases (Crookston and others 2006). Subsets of a landscape can be 

selected by using the Select command in ArcMap, providing a simple method 

to interactively simulate landscape subunits or specifi c stand types (for example, select all stands within 200 meters of homes). FVS database outputs 

can be automatically joined to stand GIS coverages for rapid mapping of the 

simulation outputs. ArcFuels macros can be used to convert FVS database 

outputs to the binary landscape fi les required by FlamMap and FARSITE. 

This system can be used to generate sets of landscape fi les for multi-period 

and multi-scenario FVS simulations.

ArcFuels uses a database approach to organize management prescriptions 

for stands within a project area, and codes prescriptions within the stand 

database required by FVS (Crookston and others 2006). This simplifi es the 

process of replicating complex constraints and management goals for multiowner Firesheds. Key information about land management strata and other 

factors important for building management scenarios (for example, ownership, 

management emphasis) are stored in the FVS stand database.

Mapping Outputs

With the database extension, FVS outputs can be written to an Access 

database containing tables for stand summary statistics, potential fi re behavior, fuels, and others (See Crookston and others 2006). A VBA script 

on the simulation interface joins these tables to the Arc feature class layer 

representing the stand polygons. Once joined, an array of map queries can 

be performed with ArcMap commands to analyze FVS outputs in a spatial 

context. The joining of other databases can be automated by editing the 

underlying VBA macro.

Summary and Future Work

Our work addresses a major gap in the integration of wildfi re behavior 

models with GIS and desktop software used for the Fireshed process. The 

approach was made possible by the recent development and release of ESRI’s 

ArcObjects, and integration of the Visual Basic development tools within 

ArcMap. The development strategy here permits rapid integration of new models within ArcGIS, and sharing of the VBA macros among other applications 

and projects. We are continuing to test ArcFuels in several Fireshed projects 

in the western United States. Further development is ongoing, including 

a system for modeling and manipulating grid-based fuel data (for example 

Landfi re) for projects where tree-list type data are not available.
USDA Forest Service Proceedings RMRS-P-41. 2006. 167

Automating the Fireshed Assessment Process with ArcGIS Ager, Bahro, and Barber

Acknowledgments

This work was funded by a Joint Fire Science Program grant (03-4-1-04) 

to the senior author. We thank Chuck Tilly for providing ArcObjects code 

early in the project, Linda Dillavou for editorial assistance, Andrew Lacey 

and Andrew McMahan for technical reviews.

Literature Cited

Ager, A.A.; McGaughey, R. 2003. Developing an analysis and planning framework 

for district-level fuels treatment projects. Project Proposal to the Joint Fire Sciences 

Program. 12 p.

Ager, A.A. 2005. ArcFuels: forest planning tools for managing wildland fuels. 

Proceedings of the 25th ESRI International Users Conference, July 25-29, San 

Diego, CA. 5 p. http://gis.esri.com/library/userconf/proc05/papers/pap1296.

pdf. Accessed 3/15/06.

Amboy, N. 2006. Using Fireshed Assessments to Measure Landscape Performance. 

ESRI Federal User Conference, January 31-February 2. Washington D.C. http://

www.esri.com/events/feduc/sessions/descriptions/fi reshed_assessment.html. 

Accessed 3/15/06

Bahro, B. 2004. Fireshed assessment: An integrated approach to landscape planning. R5-

TP-017. USDA Forest Service Pacifi c Southwest Region. http://199.128.173.130/

psw/centennial/agenda/prefi re_planning/BerniBahro%20fi reshed.pdf Accessed 

3/15/06

Bahro, B.; Barber, K.; Perrot, L.; Sherlock, J.; Taylor, A.; Wright, K.; Yasuda, D. 

2006. Using fi reshed assessments to measure landscape performance. In: 1st Fire 

behavior and fuels conference on fuels management – How to measure success; 

March 28-30, 2006; Portland, Oregon. [Abstract] http://www.iawfonline.org/

pdf/Abstracts-1.pdf. Accessed 3/16/06

Bradshaw, L.; McCormick, E. 2000. FireFamily Plus user’s guide, version 2.0. 

General Technical Report RMRS-GTR-67WWW. Ogden, UT: U.S. Department 

of Agriculture Forest Service, Rocky Mountain Research Station.

Chang, K.T. 2004. Programming ArcObjects with VBA: a task oriented approach. 

Boca Raton, FL USA; CRC Press 352 p.

Crookston, N.L.; Stage, A.R. 1991. User’s guide to the parallel processing extension 

of the prognosis model. Gen. Tec. Rep INT-281. Ogden, UT: U.S. Department 

of Agriculture, Rocky Mountain Research Station. 87 p.

Crookston, N.L.; Gammel, D.L.; Rebain, S. 2006. Users Guide to the Database 

Extension of the Forest Vegetation Simulator Version 2.0. Moscow, ID. USDA, 

Forest Service, Rocky Mountain Research Station. 50 p. http://www.fs.fed.us/

fmsc/ftp/fvs/docs/gtr/DB-FVS-UserGuide.pdf. Accessed 3/15/06.

Crookston, N.L.; Moeur, M.; Renner, D. 2002. Users Guide to the Most Similar 

Neighbor Imputation Program Version 2. Gen. Tec. Rep RMRS-GTR-96. 

Moscow, ID: USDA Forest Service, Rocky Mountain Research Station. 35p.

Dixon, G.E., comp. 2003. Essential FVS: A user’s guide to the Forest Vegetation 

Simulator. Internal Rep. Fort Collins, CO: USDA, Forest Service, Forest 

Management Service Center. 193 p.

Ewell, C.; Fites, J.A.; Noonan, E. 2006. Measuring effectiveness of fuel treatments 

across national forests in California: a practical, programmatic approach. In: 1st 

Fire behavior and fuels conference on fuels management – How to measure success; 

March 28-30, 2006; Portland, Oregon. [Abstract] http://www.iawfonline.org/

pdf/Abstracts-1.pdf. Accessed 3/16/06
168 USDA Forest Service Proceedings RMRS-P-41. 2006.

Ager, Bahro, and Barber Automating the Fireshed Assessment Process with ArcGIS

Finney, M.A. 1998. FARSITE: Fire Area Simulator - model development and 

evaluation. Research Paper RMRS-RP-4. Missoula MT: USDA Forest Service, 

Rocky Mountain Research Station. 47 p.

Finney, M.A. 2004. Landscape fi re simulation and fuel treatment optimization. In: 

Hayes, J.; Ager, A.; Barbour, J., tech. eds. Methods for integrated modeling of 

landscape change. Gen. Tec. Rep PNW GTR-610. Portland OR: USDA Forest 

Service, Pacifi c Northwest Forest and Range Experiment Station. P. 117-131.

Finney, M.A.; Cohen, J.D. 2002. Expectation and evaluation of fuel management 

objectives. In: fi re, fuel treatments, and ecological restoration: conference 

proceedings. Gen. Tec. Rep RMRS-P-29. Missoula MT: USDA Forest Service, 

Rocky Mountain Research Station. p 353 – 366.

Gallagher, T. L. 2005. North Fork of the San Jacinto Healthy Forest Project fi re 

and fuels report. Fuel Specialist T.E.A.M.S. Enterprise Unit. http://www.fs.fed.

us/r5/sanbernardino/documents/northfork_031905.pdf. Accessed 3/15/06.

Gercke, D.M.; Stewart, S.A. 2006. Strategic placement of treatments (SPOTS): 

maximizing the effectiveness of fuel and vegetation treatments on problem fi re 

behavior and effects. In: 1st Fire behavior and fuels conference on fuels management 

– How to measure success; March 28-30, 2006; Portland, Oregon. [Abstract] 

http://www.iawfonline.org/pdf/Abstracts-1.pdf. Accessed 3/16/06

Hayes, J.; Ager, A.; Barbour R. (tech. eds.). 2004. A framework for the Interior 

Northwest Landscape Analysis System. In: Methods for integrated modeling of 

landscape change. Gen. Tec. Rep PNW GTR-610. Portland OR: USDA Forest 

Service, Pacifi c Northwest Forest and Range Experiment Station. 210 p.

Jordana, M.J.; Patterson, W.A.; Windisch, A.G. 2003. Conceptual ecological models 

for the Long Island pitch pine barrens: implications for managing rare plant 

communities. Forest Ecology and Management, 185:151–168.

McGaughey, R.J. 2002. Creating visual simulations of fuel conditions predicted by 

the fi re and fuels extension to the Forest Vegetation Simulator. In: Crookston, 

N.L.; Havis, R.N. compilers. Second forest vegetation simulator conference. 

RMRS-P-25. Fort Collins, CO: USDA Forest Service, Rocky Mountain Research 

Station.

Pattison, T. 1998. Programming distributed applications with COM and Microsoft 

Visual Basic 6.0 Redmond, WA: Microsoft Press.

Reinhardt, E.; Crookston, N.L. (technical editors). 2003. The Fire and Fuels 

Extension to the Forest Vegetation Simulator. Gen. Tec. Rep. RMRS-GTR-116. 

Ogden, UT: USDA Forest Service, Rocky Mountain Research Station. 209 p.

Stratton, R.D. 2006. Guidance on Landscape Wildland Fire Analysis: Models, Tools, 

and Techniques. In: 1st Fire behavior and fuels conference on fuels management—

How to measure success; March 28-30, 2006; Portland, Oregon. [Abstract] 

http://www.iawfonline.org/pdf/Abstracts-1.pdf. Accessed 3/16/06
USDA Forest Service Proceedings RMRS-P-41. 2006. 169

In: Andrews, Patricia L.; Butler, Bret W., 

comps. 2006. Fuels Management—How to 

Measure Success: Conference Proceedings. 

28-30 March 2006; Portland, OR. 

Proceedings RMRS-P-41. Fort Collins, 

CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research 

Station.

1 Forester, Systems for Environmental 

Management, Missoula, MT.

joe.scott@montana.com

Abstract—Federal wildland fire management programs have readily embraced the 

practice of fuel treatment. Wildland fire risk is quantified as expected annual loss 

($ yr –1 or $ yr –1 ac–1). Fire risk at a point on the landscape is a function of the probability of burning at that point, the relative frequency of fi re behaviors expected if the 

point does burn, and the response of various resources to those expected fi re behaviors 

(net value change). The probability of fi re burning at any point on the landscape is a 

function of the spatial arrangement of fuel, weather, topography, and ignition locations 

surrounding the point of interest, but not characteristics of the point itself. Relative 

frequency of fi re behavior is a function of the local fi re environment and the likelihood 

of burning at various portions of an assumed elliptical fi re. Fire loss is assumed to be 

a function of fi re behavior characteristics. Fire behavior can be measured by the Fire 

Intensity Index (FII), the common logarithm of fi reline intensity. A risk reduction treatment is an investment of capital today for a benefi t to be reaped in the future. The 

benefi t of a risk reduction treatment is the present value of the difference in risk with 

and without treatment. Cost is the present value of current year and future treatment 

expenditures. Fuel treatment benefi t-cost ratio is a measure of effi ciency; it is one of 

many factors that inform a fi re management decision.

Background

The 1995 Federal Wildland Fire Management Policy and Program Review 

established that (1) life safety as the highest fi re management priority, (2) wildland 

fi re is a natural ecosystem process, and (3) fi re management decisions must be 

consistent with approved land management plans. The 2001 review and update 

of the 1995 Federal Wildland Fire Management Policy included as guiding 

principles that (1) “sound risk management is a foundation for all fi re management activities,” and (2) “fi re management programs and activities are 

economically viable, based upon values to be protected, costs, and land and 

resource management objectives.” The document establishes the objectives 

and priorities of fi re management on federal land in the United States, but 

does not require that the objectives be achieved in any particular way.

In the late 1990s the United States Forest Service refocused its fi re management program and budget toward hazardous fuel reduction. Congress 

established the Joint Fire Sciences Program in 1998 to better assess fuel 

management problems and solutions. In 1999, the General Accounting Offi ce (GAO) noted that signifi cant barriers existed to achieving the agency’s 

stated goal of mitigating wildland fi re threat by 2015, and recommended 

development of a cohesive wildland fi re mitigation strategy (GAO 1999). 

In 2000, the Secretaries of Interior and Agriculture prepared a report to 

An Analytical Framework for Quantifying 

Wildland Fire Risk and Fuel Treatment Benefi t

Joe H. Scott1
170 USDA Forest Service Proceedings RMRS-P-41. 2006.

Scott An Analytical Framework for Quantifying Wildland Fire Risk and Fuel Treatment Benefi t

President Clinton outlining how to (1) respond to the still-burning fi res of 

that year, (2) reduce the impacts of wildland fi res on rural communities, and 

(3) ensure suffi cient fi refi ghting resources in the future. That report recommended a budget increase of $1.2 billion for the next fi scal year, including 

$390 million for fuel treatment and burned area rehabilitation. Under the 

heading of investing in projects to reduce fi re risk, the report recommended 

the “establishment of a collaborative effort to expedite and expand landscapelevel fuel treatments.”

The National Interagency Fuels Coordination Group (NIFCG) was chartered in 2004 with the purpose of developing and implementing “an effective, 

interagency fuels management program to address risks from severe fi res...” 

One of the group’s enumerated objectives is to “[d]evelop strategies that safely 

and effectively mitigate [wildland fi re] threats to communities and resource 

values...” The NIFCG’s 2005 Strategic Action Plan ranks encouragement of 

landscape-level fuel treatments among its highest priorities.

In 2004, the GAO noted that “Without a risk-based approach at the 

project level, the [United States Forest Service and Bureau of Land Management] cannot make fully informed decisions about which effects and projects 

alternatives are more desirable” (GAO 2004). The report recommended the 

agencies develop a better understanding of the negative effects of wildland 

fi re, and create a systematic framework for landscape-level risk assessment in 

order to effi ciently locate risk reduction activities.

Clearly, federal fi re policy as fi rst set in 1995 and updated in 2001 not 

only allows but encourages a holistic, risk-based approach to wildland fi re 

management. Federal fi re policy recognizes that wildland fi re is neither good 

nor bad; it simply exists, and causes both losses and benefi ts at different places 

and times. Federal fi re policy suggests that the cost of our response to the 

existence of wildland fi re (prevention, suppression, fuel treatment, etc.) should 

be in balance with the benefi ts and losses that it confers. Despite the lack 

of a systematic framework for assessing wildland fi re risk, fuel treatment has 

emerged as a signifi cant risk management tool of the new millennium. Even 

so, no scientifi cally defensible metric has yet emerged to guide managers in 

deciding where, when, and how such treatments should be implemented, much 

less to confi rm whether they are even cost-effective to implement.

This paper presents a framework for quantifying wildland fi re risk and 

the benefi t of risk reduction activities, including fuel treatment. The analysis 

framework suggests alternative strategies for mitigating wildland fi re loss, as 

well as a means of comparing the relative effi ciency of the alternatives.

Introduction

A fuel treatment is an intentional modifi cation of fuelbed characteristics 

(load, bulk density, horizontal and vertical continuity, fuel particle size class 

distribution, etc.) for the purpose of mitigating negative fi re effects (fi re loss), 

either directly by making fi re characteristics more benign, or indirectly by 

reducing the probability of fi re burning a particular area. Negative fi re effects 

include (1) socio-cultural losses, and (2) uncharacteristically severe wildfi re. 

Socio-cultural losses—damage to or destruction of buildings, utility lines, 

recreation facilities, watersheds, commercial timber, etc.—can occur wherever 

those values are exposed to wildland fi re. In fact, protection of socio-cultural 

values from fi re is the primary reason for suppressing fi re in the fi rst place. 

Thus, to the extent that fuel treatment is undertaken to reduce the ultimate 
USDA Forest Service Proceedings RMRS-P-41. 2006. 171

An Analytical Framework for Quantifying Wildland Fire Risk and Fuel Treatment Benefi t Scott

size of a future fi re, fi re suppression and fuel treatment are two sides of the 

same coin; the main difference between them is where and when the activities are undertaken.

Modern fi re suppression is highly successful at containing incipient fi res. As 

noted in a 2004 panel report to the Wildland Fire Leadership Council, from 

1980 through 2002, almost 99 percent of wildland fi res were contained to 300 

acres or less; cost to suppress the remaining 1.4 percent of fi res accounted for 

94 percent of the total suppression expenditures. The fi res that escape initial 

attack and burn more than 300 acres are not determined randomly; they are 

“selected” because their behavior (spread rate, intensity, fuel consumption) 

exceeds our ability to contain them. In other words, they burn in extreme fi re 

environments that often result in uncharacteristic severity. By eliminating the 

most benign 99 percent of fi re starts from the landscape, fi re suppression has 

resulted in much longer fi re return intervals—and higher fi re severity when 

a fi re does occur—compared to the historic fi re regime. This unintended 

change in fi re-regime is most pronounced in high-frequency, low-severity 

historic fi re regimes (Heinselman 1981), but is also present in longer-interval 

fi re regimes. To paraphrase Shakespeare, we have suppressed fi re not wisely 

but too well. Thus, fi re suppression was a solution to one problem (sociocultural fi re loss) that created another (too much uncharacteristically severe 

fi re, too little low-severity fi re).

Landscape-scale application of fuel treatments may reduce the incidence of 

uncharacteristic fi res. However, in areas where fi re was frequent but not severe, 

restoring the historic fi re regime will also require dramatically increasing the 

incidence of low-severity fi res. Increasing the prevalence of low-severity fi res—

through fi re use, prescribed fi re and fi re surrogates—over time should result in 

a reduction of uncharacteristic fi res. Treating fuel to reduce uncharacteristically 

severe fi re, however, does nothing to increase the desirable fi res.

The change in value associated with suppression-caused fi re regime change 

is diffi cult to quantify (Finney 2005). Socio-cultural fi re losses, on the other 

hand, are amenable to quantitative analysis. Therefore, this framework is 

focused on socio-cultural resources at risk; a different framework must be 

used to support fuel treatment decisions regarding restoration of historic 

fi re regimes.

Minimizing cost plus net-value-change (C+NVC) is an accepted objective 

for optimizing fi re program level (Althaus and Mills 1982, Mills and Bratten 1982, Mills and Bratten 1988). In this paper, the C+NVC optimization 

concept is adapted to project-level analysis of fuel treatment options. A fuel 

treatment is an investment of capital today for benefi ts—reduction in expected 

annual NVC—to be received in the future. Therefore, investment analysis 

tools such as benefi t-cost (BC) ratio should be useful for comparing fuel 

treatment options.

Quantitative Wildland Fire Risk Assessment

Quantitative wildland fi re risk is defi ned as expected annual NVC (Bachman 

and Algöwer 2000, Finney 2005, Finney and Cohen 2003) for any spatially 

explicit land area (plot, pixel, stand, parcel, watershed, etc.). Expected annual 

NVC is the sum-product of NVCi (cost plus net-value-change should fi re 

occur at the ith fi re behavior) and p(Fi) (the annual probability of observing 

the ith fi re behavior). If NVCi is expressed on a per-acre basis (e.g., $ ac –1), 

then annual risk density ($ ac–1 yr –1) is
172 USDA Forest Service Proceedings RMRS-P-41. 2006.

Scott An Analytical Framework for Quantifying Wildland Fire Risk and Fuel Treatment Benefi t

E NVC p F NVC i i

i

N

( ) () = ∗

=

∑

1

 [1]

The annual probability of observing fi re of any behavior at a particular location is the sum of probabilities over all fi re behaviors. Geographic extent for 

equation [1] is not explicitly specifi ed; it refers to any homogeneous land unit 

(pixel, plot, or stand). Risk density is the appropriate quantitative metric for 

mapping wildland fi re risk, especially where mapping units may be of varying sizes. Risk accumulates to larger geographic or political reporting units 

(e.g., stands, watersheds, or political units like counties or states) composed 

of many land units. Landscape-level wildland fi re risk ($ yr–1) is the sum of 

risks of the M land units that comprise the landscape.

E NVC E NVC A landscape k

k

M

k () () = ∗

=

∑

1

 [2]

where

Ak = the area of land unit k

E(NVC)k = E(NVC) for land unit k (eqn. [1])

Assessing the effects of a spatial fuel treatment array (Finney 2001) requires calculating risk at this larger landscape scale to fully account for their 

potential landscape-level effects.

A wildland fi re risk assessment consists of two separate parts: p(Fi) and 

NVCi. Conceptually, p(Fi) is a function of p(F), the overall probability of 

fi re burning under any behavior, and p(Fi)/p(F), the relative frequency of 

different fi re behaviors given that a fi re does occur

pF pF p F

p F i

i ( ) = ( ) ⎛ ( )

⎝

⎜

⎞

⎠

⎟ *

( ) [3]

Mitigating risk entails modifying p(F) , p(Fi)/p(F) or NVCi.

Probability of Burning — p(F)

The probability of fi re burning any particular point on the landscape is a 

function of ignition locations and fi re travel from the ignitions to the point 

of interest. The factors affecting whether fi re can reach a given point on the 

landscape from a given ignition point include: spatial and temporal arrangement of fuel, weather and topography across the landscape, and the level of 

perimeter containment (suppression) attempted. The probability of burning 

is inversely proportional to the general level of suppression effort. The fi re 

environment at any point of interest has no bearing on whether a fi re might 

reach that point—that is determined by the up-fi re environment—but does 

affect how the fi re would behave if it does reach it.

Two approaches are possible for estimating p(F)—simulation modeling and 

fi re data. Simulation modeling, like that implemented in FlamMap (Finney 

and others 2006) uses a fi re spread model in conjunction with spatial and 

temporal fi re environment information and an assumed or measured pattern 

of ignition locations to estimate the probability of fi re burning each landscape 

element, assuming no suppression action is taken. This approach provides 

spatially resolved estimates of p(F), as a function of spatial arrangement of 

the surrounding fi re environment and distribution of ignition locations. 

However, without some kind of verifi cation, the accuracy of the method is 

unknown.
USDA Forest Service Proceedings RMRS-P-41. 2006. 173

An Analytical Framework for Quantifying Wildland Fire Risk and Fuel Treatment Benefi t Scott

The fi re data approach relies on records of past fi res to indicate the probability of burning of future fi res. The annual probability of burning for a 

landscape is estimated as the average annual landscape fraction burned

p F

B A xt

tx

( )=

∑( ) [4]

where Bt is the area burned in year 

t

A is the analysis area, and x is the length of the time period

There are many limitations when using this method to predict future burn 

probability: fi re climate, suppression effort, ignition density, and fi re regime 

are all assumed to be constant. The fi re-data estimate is not spatially resolved; 

it applies to the whole landscape regardless of spatial pattern of fuel, weather, 

topography and ignitions. Increased precision may be obtained with this 

method by replacing the geography-based landscape with a fi re environment 

classifi cation within which the fi re environment and ignition pattern are more 

homogeneous. For example, applying equation [4] for individual vegetation 

types will produce an estimate of p(F) for that vegetation type, regardless of 

geographic location. Although this method may be more accurate because it 

is based on observation, its poor spatial resolution limits its use for assessing 

the effects of landscape-level fuel treatments.

The advantages and disadvantages of the simulation and fi re data approaches suggest that a hybrid method combining the spatial resolution of 

the simulation method with the accuracy of the fi re data method would be 

worth pursuing. For example, one could apply the simulation method heuristically, adjusting simulation parameters as necessary until the weighted 

average landscape level p(F) from the simulation method equals that of the 

fi re data method. More research and development of methods of estimating 

p(F) is obviously necessary.

Relative Frequency of Fire Behaviors — p(Fi)/p(F)

The relative frequency distribution of fi re behaviors at a particular point, 

given that the point does burn, is the fi nal piece of information needed to 

estimate p(Fi) using equation [3]. Relative frequency distribution of fi re 

behavior at a point is a function of the fuel and topography at the point, the 

weather at the time it burns, and the direction of spread (with respect to the 

heading direction) as fi re passes the point. Fuel and topographic characteristics can be known and mapped without consideration for any particular 

fi re. The weather history for a location can be analyzed to identify live and 

dead fuel moisture contents when burning is most likely (that is, during the 

extreme conditions during which two percent of all fi res escape initial attack 

and go on to burn most acres). Because most acres are burned under very 

dry conditions (98th percentile ERC), it is reasonable to simplify the analysis 

by focusing on very dry conditions.

Head fi re behavior predicted for very dry conditions is then predicted over 

a range of open wind speeds (fi g. 1a). Fireline intensity, the product of fuel 

consumption and fl ame front spread rate (Byram 1959), seems a logical choice 

for measuring fi re behavior as it “... contains about as much information about 

a fi re’s behavior as can be crammed into one number” (Van Wagner 1977). 

Alexander (1982) provides an excellent discussion of the calculation and interpretation of Byram’s fi re intensity. The Fire Intensity Index (FII; Scott in 
174 USDA Forest Service Proceedings RMRS-P-41. 2006.

Scott An Analytical Framework for Quantifying Wildland Fire Risk and Fuel Treatment Benefi t

preparation) is the common logarithm of fi reline intensity (kW/m). Like the 

Richter Scale for earthquakes, a unit change on the FII scale corresponds to 

an order of magnitude change in fi re intensity (table 1). Slow-spreading fi res 

burning in very light fuels may exhibit FII < 1; fast-spreading active crown 

fi res through heavy forest fuels may exhibit FII > 5. The effect of wind speed 

on FII is analyzed separately from fuel moisture because it is not necessarily 

correlated with fuel moisture. For determining how often the FII predicted 

in fi g. 1a would be observed, a distribution of open wind speeds must be 

obtained from the weather record (fi g. 1b).

Figure 1—Two components for estimating p(Fi): (a), Fire Intensity Index (FII) over a range of open 

wind speeds for the very dry moisture conditions during which most acres are burned, and (b) the 

relative frequency of observing those wind speeds. FII is the common logarithm of fi reline intensity 

expressed in kW/m. 

Table 1—The behavior characteristics of a wildland fi re can be measured using the Fire Intensity Index 

(FII; Scott in preparation). FII is the common logarithm of fi reline intensity (FLI; kW/m). Slow-spreading 

surface fi res in very light fuels exhibit FII < 1; fast-spreading crown fi res in heavy forest fuels may exhibit 

FII approaching 5. FII is classifi ed into six classes (I – VI); each class represents a 10-fold increase in 

fi reline intensity. The range of fl ame length (FL) as predicted by Byram’s (1959) and Thomas’ (1963) 

models is shown for each FII class. 

 FL range, m FL range, m

Category FII FLI range, kW/m (Byram’s FL model) (Thomas’ FL model)

 I FII < 1 FLI < 10 < 0.22 FL < 0.12

 II 1 ≤ FII < 2 10 ≤ FLI < 100 0.23 – 0.64 0.13 – 0.58

 III 2 ≤ FII < 3 100 ≤ FLI < 1000 0.65 – 1.86 0.59 – 2.72

 IV 3 ≤ FII < 4 1 000 ≤ FLI < 10 000 1.87 – 5.36 2.73 – 12.7

 V 4 ≤ FII < 5 100 000 ≤ FLI < 100 000 5.37 – 15.46 12.8 – 59.42

 IV FII ≥ 5 FLI ≥ 100 000 ≥ 15.47 ≥ 59.43
USDA Forest Service Proceedings RMRS-P-41. 2006. 175

An Analytical Framework for Quantifying Wildland Fire Risk and Fuel Treatment Benefi t Scott

The fi nal factor affecting the distribution of FII at a point is the effect of 

spread direction (relative to the head fi re) as fi re passes the point. By assuming fi re spreads as a simple ellipse (Van Wagner 1969), we can predict the 

area burned in different FII classes through different areas of a fi re (fi g. 2). 

Probability of burning in each FII class is proportional to the relative area 

burned in those classes.

Using the above factors, a relative frequency distribution of FII for any given 

fi re environment can be constructed (fi g. 3). The product of that frequency 

distribution and probability of burning is p(Fi) (fi g. 4a).

Figure 2—Distribution of FII class 

as a function of location within an 

elliptical fire. At moderate wind 

speed, the head of the fi re falls in FII 

class IV, which extends around to the 

fl ank. Backing and fl anking intensity 

fall in class III. At high wind speeds, 

the head of the fi re falls in FII class 

V, the fl anks are in class IV, and the 

extreme rear of the fi re is in FII class 

III. The probability of burning in an 

FII class is assumed proportional to 

the ratio of area in that class to total 

fi re area.

Figure 3—Relative frequency of Fire 

Intensity Index (the common logarithm 

of fireline intensity) given that a fire 

does occur at a given point. The sum of 

probabilities of observing individual FII 

classes is one. Relative frequency of FII is 

a function of the local fi re environment 

at the time of the fi re and the distribution 

of fire intensity at different parts of 

an assumed elliptical fi re. FII class is 

indicated in Roman numerals.
176 USDA Forest Service Proceedings RMRS-P-41. 2006.

Scott An Analytical Framework for Quantifying Wildland Fire Risk and Fuel Treatment Benefi t

Net Value Change — NVCi

Conceptually, net value change due to fi re is a function of initial value 

and susceptibility. For example, consider two buildings of equal initial value 

surrounded by fl ammable wildland fuel, one with a fl ammable roof covering 

and the other with a non-fl ammable covering. The building with a non-fl ammable roof covering is less susceptible to fi re damage—it is more resistant to 

loss should it experience a fi re—and therefore has a lower NVCi. Conversely, 

for two buildings of equal susceptibility, NVCi is proportional to their total 

value.

Net value change is the post-fi re minus pre-fi re value of a given place on the 

landscape (expressed as present value). Net change in land value due to fi re 

is assumed to be a function of fi re behavior, and includes both positive and 

negative effects of fi re. NVCi is quantifi ed by summing over the many different market and non-market values or resources present at a given place.

Figure 4—A “Risktogram”—a graphical display of the elements of a quantitative fi re risk 

analysis. Chart (a) displays the frequency distribution of the Fire Intensity Index (FII) 

at a point. The sum of probabilities in individual FII classes is the annual probability of 

burning. Chart (b) displays the predicted net change of different values to fi re of the 

various FII classes. Chart (c) displays the resulting wildland fi re risk. The sum of E(NVC)

over all FII classes is wildland fi re risk.
USDA Forest Service Proceedings RMRS-P-41. 2006. 177

An Analytical Framework for Quantifying Wildland Fire Risk and Fuel Treatment Benefi t Scott

NVC NVC i ij

j

n

=

=

∑

1

 [5]

The mix of values and resources that can be affected by fi re at any given 

point depends on ownership and management emphasis. The values can include market values (timber, water, forage, commercial mushroom production, 

etc.), human developments (buildings and infrastructure), and non-market 

values (recreation, fi sheries, clean air, wildlife habitat, ecosystem function, 

etc.). Net value change must include the potential benefi ts of fi re. One often 

overlooked benefi t of an otherwise destructive wildfi re is the reduction of 

future loss it confers [by reducing p(F) or p(Fi)/p(F)].

Positive NVCi indicates that expected benefi ts of fi re exceed losses, such as 

might occur in uninhabited areas; negative NVCi indicates a net loss should 

fi re occur (fi g. 4b). Estimating NVCi across a landscape is a diffi cult yet critical task that would support fi re management decisions regarding both fuel 

treatment and fi re suppression. Detailed spatial information on NVCi could 

prove to be even more useful to managers of wildfi res or fi re-use incidents 

than predictions of fi re growth or potential fi re behavior. Due to the large 

areas to be mapped and the wide array of market and non-market values 

that are affected by fi re, it may be necessary to create a stylized set of “value 

models” for estimating fi re loss as a function of FII. Further research into 

how different values are affected by fi re and development operation tools for 

implementing that research is clearly needed.

Suppression cost is not included in a quantitative risk analysis; it is part of 

a larger analysis of fi re program level. However, suppression efforts infl uence 

the burn probabilities as described above.

Wildland Fire Risk — E(NVC)

Equations [1], [3], and [5] form the foundation of quantitative wildland 

fi re risk analysis. Wildland fi re risk is the product of three elements: fi re probability, fi re behavior, and fi re effect (fi g. 5). Fire probability is the whether

component and is estimated through fi re simulation or using fi re data records. 

Fire behavior is the how component, and is estimated by relative frequency 

distribution of FII. Fire effect is the so what component, and is estimated 

by predicting the positive and negative effects of fi re on various values as a 

function of FII (NVCi).

Figure 5—Quantitative wildland fi re risk is a function 

of fire probability, fire behavior characteristics 

(given that a fi re does occur), and fi re effects (for 

given levels of fi re behavior). Fire probability at 

any discrete point on the landscape is a function 

of the upfi re environment (spatial pattern of fuels, 

weather, topography and ignitions in the area from 

which fi re can be expected to arrive at the point) 

and suppression actions. Fire behavior is a function 

of the local fi re environment at the time of the fi re 

and the distribution of fi re intensity around the 

perimeter of an assumed elliptical fi re. Fire effects 

are the costs and value changes as a function of fi re 

behavior.
178 USDA Forest Service Proceedings RMRS-P-41. 2006.

Scott An Analytical Framework for Quantifying Wildland Fire Risk and Fuel Treatment Benefi t

Approaches to Risk Reduction

The framework suggests several theoretical approaches to risk reduction. 

Wildland fi re risk is a function of three main factors: p(F), p(Fi)/p(F) and 

NVCi. Reducing any of those components reduces risk. Risk reduction activities fall under two broad categories—fuel treatment and value treatment. 

A fuel treatment modifi es fuel characteristics with the intention of affecting 

p(Fi). A value treatment modifi es characteristics of a value or resource with 

the intention of reducing NVCi.

Fuel treatment—Fuel treatments are implemented in discrete geographic 

units that are generally small in comparison to the large fi res whose effects 

they are intended to mitigate. There are two primary fuel treatment effects 

on risk reduction —within-unit, and among-units. Because a treatment unit 

is small, changing its fuel characteristics does not change its probability of 

burning; that is determined by the “upfi re” fi re environment (Finney 2005). 

Within-unit effects are limited to changing the relative frequency of FII. 

Within a unit, surface and canopy fuel characteristics are directly modifi ed by 

a treatment. Dead fuel moisture content and midfl ame wind speed are indirectly affected by many fuel treatments, usually adversely. The topography and 

weather elements of the fi re environment are not affected by treatment.

Because the probability of burning in discrete treatment unit is determined 

by the spatial and temporal arrangement of the up-fi re environment, only 

a coordinated array of fuel treatments can potentially reduce the overall 

probability of burning. The reduction in p(F) is not expected to be constant 

throughout the fuel treatment array. At the extreme up-fi re edge of an array, 

probability of burning is dominated by the unmodifi ed up-fi re environment, 

and p(F) is not reduced. At the down-fi re edge of the array, reduction in 

p(F) reaches a maximum because the greatest disruption of fi re growth can 

occur. The maximum theoretical reduction in p(F) (as indexed by the preand post-treatment fi re growth rates), occurs only if the treatment array is 

as large as the largest fi res expected to occur. Otherwise, a fi re could grow 

unmitigated in the untreated area up-fi re of the array before encountering 

the array; the fi re’s growth could have been further disrupted if treatments 

were located in that area as well.

Not only does a fuel treatment array potentially reduce p(F), both within 

and between treatment units, but it can possibly shift the relative frequency 

of fi re behaviors toward lower classes by increasing the amount of fl anking 

fi re compared to the predominantly heading fi re that would have occurred 

without the treatment array (Finney 2005). The magnitude of this effect 

depends on the size of the treatment units relative to fi re and the relative 

spread rates between the treatment unit and the surrounding untreated area. 

Simulation modeling may confi rm and quantify this effect.

Because spatial fuel treatment arrays create effects that occur both within 

and between treatment units, they must be analyzed at the landscape level 

(eqn. [2]) rather than at the treatment unit level (eqn. [1]) to be sure that 

off-treatment benefi ts are fully accounted for.

Value treatment—A value treatment is a risk reduction treatment that 

modifi es a value to reduce NVCi. Recall that NVCi is a function of initial 

value and susceptibility. A value treatment must therefore reduce either 

initial value or susceptibility. Reducing initial value is not within the scope 

of risk reduction activities, so value treatments are limited to activities that 

reduce susceptibility. (However, NVCi can be mitigated proactively by choosing not to place a susceptible value in a hazardous environment in the fi rst 
USDA Forest Service Proceedings RMRS-P-41. 2006. 179

An Analytical Framework for Quantifying Wildland Fire Risk and Fuel Treatment Benefi t Scott

place.) Modifying the physical characteristics of a building—changing to a 

more fi re-resistant roof covering, adding exterior sprinklers, screening attic 

vents—is one example of a value treatment. Value treatments reduce risk by 

reducing damage (NVC) for any given level of fi re behavior without changing exposure to that fi re behavior. Instead of making modifi cations to a 

building, the owner may instead (or in addition) choose to implement a fuel 

treatment in the immediate vicinity of the building. Such a treatment, often 

referred to as defensible space, affects the relative frequency of fi re behaviors 

at the building, but not NVCi or the overall probability of fi re reaching the 

building in the fi rst place.

Analysis of Risk Reduction Treatment 

Alternatives

Quantitative wildland fi re risk is useful for comparing with other risks faced 

by a land manager. For example, homeowners and natural resource managers may be interested in knowing how wildland fi re risk compares with risk 

associated with other natural hazards like fl ood, earthquake, hail, tornado, 

and hurricane. A homeowner may be interested in comparing his wildland 

fi re risk with technological risks he also faces like structure fi re, automobile 

crashes, and terrorism.

By itself, a quantitative risk analysis is insuffi cient to prioritize areas for 

risk reduction treatment because it does not consider the cost or benefi t of 

the possible risk reduction activities. High-risk areas may not respond well to 

treatment (the relatively high risk may not be easily reduced). Low-risk areas 

may be so inexpensive to treat that they are a cost-effective option (many 

more acres can be treated). To make effi cient fuel treatment decisions, we 

must compare treatment benefi ts with their costs. The benefi t-cost ratio of 

a risk reduction treatment is the present value of its benefi ts divided by the 

present value of its costs.

The nominal benefi t of a risk reduction treatment is a reduction in risk—that 

goes without saying—and is quantifi ed as the difference between risk without

treatment and risk with treatment. For example, if risk without treatment is 

–$50 ac–1 yr–1 and a treatment reduces that risk to –$40 ac–1 yr–1, then the 

benefi t of the treatment in that year is [–$40 – (–$50)], or $10 ac–1 yr–1. 

Unless periodic maintenance is incorporated, the amount of risk reduction 

due to fuel treatment will diminish with time since treatment due to fuel 

accumulation and vegetation growth. Even without treatment, risk is not 

necessarily constant over time. NVCi may change as new values are added to 

the landscape, increase in value, or become more (or less) susceptible to fi re; 

and p(Fi) may change due to fuel accumulation, vegetation growth, human 

activity, climate change, or natural disturbance. The present value of fuel 

treatment benefi ts (PVB) over some period of time is therefore

PVB E NVC E NVC

r

t Treatment t noTreatment = −

( +

() ()

= 1 ) ∑ t t

x

1

 [6]

where

r is the discount rate 

x is the planning horizon (yr), 

E(NVCt)Treatment is the risk in year t if the treatment is implemented, and 

E(NVCt)noTreatment is the risk in year t if no treatment is undertaken.
180 USDA Forest Service Proceedings RMRS-P-41. 2006.

Scott An Analytical Framework for Quantifying Wildland Fire Risk and Fuel Treatment Benefi t

Choice of planning horizon and discount rate can affect present value of 

benefi ts. Different landowners have different planning horizons—a forest 

homeowner might not care about benefi ts further than a decade or two in the 

future, while government-managed land is generally planned up to 100 years 

into the future. Because treatment effectiveness diminishes over time, and 

because of the time value of money, marginal fuel treatment benefi t (present 

value) diminishes to near zero after just a couple of decades, so little is to be 

gained with longer planning horizons. Also, natural and anthropomorphic 

changes in the fi re environment during that time are likely to require reassessment of risk.

Risk reduction expenses are comparatively straight-forward to calculate. 

Expenditures for improving fi re resistance or implementing a fuel treatment 

can occur in any year, especially if the fuel treatment plan calls for a spatial 

array of treatments installed over time. Also, maintenance of the fuel treatment may be prescribed for future years or even annually. Therefore, present 

value of risk reduction treatment cost is

PVC

C

rt

t

t

x

=

= ( ) + ∑1 1

 [7]

Expenditures associated with fuel treatment activities are routinely documented and modeled. For treatments that generate revenue (for example, 

commercial thinning), cost is net cost after accounting for revenue. If a 

treatment generates more revenue than the treatment costs to implement, 

then BC ratio analysis is no longer an appropriate analysis tool—there’s no 

economic downside. Such treatments can be ranked by present net value rather 

than BC ratio. Treatment costs depend on many factors, including the type 

and intensity of treatment, location on the landscape, size and shape of the 

treatment unit, access to treatment area, distance to forest products markets, 

and regulatory analysis requirements.

As an investment of capital today for benefi t tomorrow, potential fuel 

treatments should be analyzed in a manner similar to any other forestry 

investment. When choosing among possible projects for which capital is the 

only limiting resource, the economically optimal solution is to implement 

the projects with the highest BC ratios until the available capital is expended 

(Gilles and BuonGiornio????). In reality, many resources may be limiting, 

and operational or political constraints may not allow the optimal economic 

solution. The BC ratio is just one of many factors that inform a fi re management decision. Investments with BC ratios less than one cannot be justifi ed 

based on quantitative analysis of benefi ts and costs alone; other benefi ts not 

included in the analysis must presumably be present to offset the otherwise 

negative return. BC ratio less than one implies that available capital is better 

invested at the specifi ed discount rate and proceeds used to fund any losses 

when a fi re does occur.

Prioritizing Risk Reduction Treatments

In the absence of an analytical framework for estimating the effi ciency of 

alternative fuel treatments, such as that presented here, fuel treatment planners must resort to experience and instinct in selecting the type and location 

of individual fuel treatments. Their selection criteria include potential fi re 

behavior reduction, the general location and value of resources-at-risk and 

variables related to treatment cost (access, ability to meet NEPA analysis requirements, etc.). Treatment locations selected through such a process have 

been termed “easy acres” because they were often the easiest areas to treat. 
USDA Forest Service Proceedings RMRS-P-41. 2006. 181

An Analytical Framework for Quantifying Wildland Fire Risk and Fuel Treatment Benefi t Scott

Such treatments are placed individually without regard for an overall spatial 

pattern that may reduce p(F). Their locations have been considered random for 

comparison against a theoretically optimal spatial pattern designed to reduce 

large fi re growth (Finney 2001). While their locations on the landscape may 

be random in terms of spatial pattern (and therefore sub-optimal in terms 

of reducing p(F)), they are anything but random in terms of treatment cost. 

In fact, the factors used to select the “easy acres” also result in relatively low 

treatment cost. Treatment units that are truly random would be quite costly 

to implement, because factors that affect treatment cost are not considered; 

randomly located treatments could require costly road construction, fi reline 

building, and NEPA analysis. Fuel treatments located based on a theoretically 

optimal spatial pattern can be considered random with respect to treatment 

cost. The BC analysis framework outlined here can shed light on the relative 

cost effi ciency of each strategy.

This analysis framework suggests a new risk reduction treatment strategy—

optimizing landscape-level risk by selecting a spatial and temporal risk 

reduction treatment regime that maximizes the BC ratio.

Discussion

Following signifi cant wildland-urban interface fi res in 1923 and 1991, it is 

well established that the fuels, fi re weather and fi re-susceptible values in the 

Oakland-Berkeley Hills of Northern California present a signifi cant wildland 

fi re risk to area homeowners. That same area is also exposed to potentially 

devastating earthquakes on several faults in the area. According to a recent 

USGS study, there is a 27% chance of a Richter magnitude 6.7 or larger 

earthquake occurring in the immediate vicinity of the Oakland-Berkeley 

Hills between 2003 and 2032 (Hyndman and Hyndman 2005), an annual 

probability of 0.009 (nearly one in one-hundred). Given the proximity of the 

probable fault rupture and magnitude of the potential earthquake, signifi cant 

damage to or total destruction of homes and utilities is likely. Assuming an 

earthquake loss of just $250,000 per home, the resulting annual earthquake 

risk is $2250 per home.

Clearly, eliminating risk of any natural hazard is well beyond the capability of both individuals and governments. In the face of limited mitigation 

resources, a strategy for optimally managing risk is required. It is tempting 

to simply compare the quantitative levels of risk from all natural and technological hazards and allocate mitigation resources to the hazard posing 

the highest risk, or to each hazard in proportion to its relative contribution 

to total risk over all hazards. Neither strategy is effi cient, however, because 

they do not consider the cost of mitigation efforts in relation to the benefi t. 

The economically optimal solution would be to allocate resources to efforts 

with the highest return on investment (that is, the highest BC ratios) until 

all resources have been used up, regardless of the absolute or relative level of 

risk. The economically optimal solution may not be feasible for technological or political reasons, so calculation of risk reduction treatment BC ratio 

must simply be part of a larger decision support framework that accounts for 

constraints other than available capital.

This analysis framework estimates treatment costs and benefi ts without 

considering to whom those benefi ts and costs accrue. Costs may be borne 

by one party while benefi ts are reaped by another. For example, federal or 
182 USDA Forest Service Proceedings RMRS-P-41. 2006.

Scott An Analytical Framework for Quantifying Wildland Fire Risk and Fuel Treatment Benefi t

state governments may implement a fuel treatment on public land that benefi ts nearby private ownerships, or a government agency may subsidize fuel 

treatment on private land. Potential fi re losses may also be transferred among 

parties. Insurance is a risk management tool used to deal with risks that include 

potential for catastrophic loss. In exchange for a periodic premium, an insurance company agrees to repair or replace insured values if a loss should occur. 

The insurance premium is composed of the insurance rate and the insured 

value. Insurance rate is a function of the hazardousness of the environment 

in which the value resides (the physical situation). Insured value represents 

NVC. In other words, an insurance premium is a function of quantitative risk; 

the higher the hazard or NVC, the higher the premium. This is a potentially 

helpful concept because insurance rates and premiums can be used as surrogates for hazard and risk. The majority of wildland homes are covered by an 

insurance policy that includes coverage for fi re loss (no distinction is made 

between wildland fi re loss and fi re loss due to other causes). In other words, 

some of the fi re risk a homeowner faces has been transferred to an insurance 

company. In such cases, benefi ts of risk reduction treatment are received by 

the insurance company rather than the homeowner. The analysis here makes 

no consideration for disconnected costs and benefi ts.

The exclusion of fi re suppression costs from this analysis of fuel treatment 

may seem unjustifi ed. Fire suppression and fuel treatment are similar endeavors—fi re suppression is just-in-time fuel treatment; fuel treatment is fi re 

suppression without prior knowledge of where or when a fi re will escape initial 

attack. Both activities are intended to mitigate fi re loss. In a risk analysis we 

wish to account for the NVC incurred if an area burns, whereas suppression 

is an attempt to prevent areas from burning. Therefore, suppression costs 

should be assigned to the acres that did not burn rather than to the areas 

that did. In this analysis framework, suppression is assumed constant at some 

level. The effects of fuel treatment are simulated as (1) a shift toward more 

benign fi re behavior (lower FII) within treated areas, and (2) a reduction in fi re size and therefore p(F) in fuel treatment arrays. A holistic fi re management 

approach would seek the optimal mix of fuel treatment and fi re suppression 

that minimizes their combined cost plus NVC.

When a fi re near homes escapes initial attack, it is common to witness lastminute fuel treatments around the homes (defensible space) or preparation 

of the home itself to resist fi re damage (a value treatment). Fire suppression 

organizations discourage homeowners from relying on these just-in-time 

mitigation efforts. Instead, homeowners are urged to create defensible space 

and make their homes resistant to ignition well in advance of a fi re start. 

However, the just-in-time mitigation behavior may actually be quite rational 

from a purely economic standpoint. Without a nearby ignition, p(F) at a home 

is quite small, perhaps as low as 1 in 1000 per year, resulting in relatively low 

risk and corresponding low benefi t of defensible space and value treatments; 

their costs may far exceed potential benefi t. Once a fi re has ignited nearby, 

however, p(F) increases drastically, thereby increasing risk, and therefore 

treatment benefi t, by as much as two orders of magnitude. Suddenly, the 

treatment benefi ts may exceed treatment costs by a wide margin. Of course, 

there may not be time or resources available to treat fuels and homes immediately before a fi re, and homes will be destroyed. Unfortunately, that does 

not make treating the home when no fi re is present a better investment.

Just-in-time fuel treatment behavior is not restricted to private landowners. 

Government property is frequently managed in the same manner. The 
USDA Forest Service Proceedings RMRS-P-41. 2006. 183

An Analytical Framework for Quantifying Wildland Fire Risk and Fuel Treatment Benefi t Scott

headquarters area of Glacier National Park, in West Glacier, Montana, which 

includes offi ce, industrial and residential buildings, is located in a fi re-prone 

landscape with a recent history of large fi res in the region. Despite the obvious 

need, defensible space was not maintained throughout the headquarters area. 

Only when the 2003 Robert fi re threatened to burn through West Glacier 

did activities to create defensible space commence. One reason often cited 

for this type of behavior is the availability of suppression resources assigned 

to the fi re. Because those suppression resources are not funded by the local 

unit. Since treatment cost to the local unit decreases to zero, they have even 

more incentive than a homeowner to engage in just-in-time mitigation. Aggressive suppression actions prevented the Robert fi re from reaching West 

Glacier. Interestingly, after experiencing a rapid rise in p(F) when the Robert 

fi re started nearby, p(F) in future years should be expected to fall below preRobert levels because the fi re acts as a large fuel modifi cation directly upfi re 

from West Glacier. The corresponding reduction of risk is an example of an 

unexpected benefi t of an unplanned, unwanted wildland fi re.

The analytical framework presented here considers only values for which 

benefi ts and losses can be quantifi ed. Non-market values are not easily 

quantifi ed and therefore diffi cult to bring into such an analysis. Two possible solutions to this problem are (1) attempt to quantify non-market values 

through techniques such as contingent valuation, or (2) implement the 

framework with the full understanding that it does not account for all values, 

and should be used as one piece of information among many to support a 

fuel treatment decision.

Conclusion

Managing wildland fi re risk is an important function of any fi re management program. Fire risk exists wherever human values are located in areas 

where wildland fi re can occur. Wildland fi re risk is a function of probability 

burning, potential fi re behavior, and fi re effects on human values. Fire risk 

is mitigated by affecting one or more of those factors. The benefi t of a risk 

reduction treatment is the present value of risk reduction. The cost-effi ciency 

of a risk reduction activity can be measured by its benefi t-cost ratio.

Acknowledgments

This work was supported by the Fire Modeling Institute, Missoula Fire 

Sciences Laboratory, Rocky Mountain Research Station.

References

Alexander, M. E. 1982. Calculation and interpretation of forest fi re intensities. Can. 

J. Bot. 60: 349-357.

Althaus, I. A. and Mills, T. J. 1982. Resource values in analyzing fi re management 

programs for economic effi ciency. Gen. Tech. Rep. PSW-57. Berkeley, CA: Pacifi c 

Southwest Forest and Range Experiment Station, Forest Service, U.S. Dept. of 

Agriculture. 9 p.
184 USDA Forest Service Proceedings RMRS-P-41. 2006.

Scott An Analytical Framework for Quantifying Wildland Fire Risk and Fuel Treatment Benefi t

Bachman, A.; Algower, B. 2000. The need for a consistent wildland fi re risk 

terminology. In: Neuenschwander, L.F., Ryan, K.C., Gollberg, G.E., Greer, J. D. 

(Eds.), Proceedings of the Joint Fire Science Conference and Workshop: Crossing 

the Millennium: Integrating Spatial Technologies and Ecological Principles for 

a New Age in Fire Management, Boise, Idaho, June 15-17, 1999, University of 

Idaho: 67-77.

Buongiorno, J.; Gilles, J. K. 2003. Decision methods for forest resource management. 

London, UK: Academic Press. 439 p.

Byram, G. M. 1959. Combustion of forest fuels. In: Forest fi re: Control and use, 

2nd edition. New York, NY: McGraw-Hill: chapter 1: 61-89.

Finney, M. A. 2001. Design of regular landscape fuel treatment patterns for 

modifying fi re growth and behavior. For. Sci. 47(2): 219-228.

Finney, M. A. 2005. The challenge of quantitative fi re risk analysis for wildland fi re. 

Forest Ecology and Management. 211: 97-108.

Finney, M.A; Britain, S.; and Seli, R. C. 2006. FlamMap Version 3.0 Beta. Computer 

program available at www.fi re.org. Last updated February 16, 2006.

Finney, M. A.; Cohen, J. D. 2003. Expectation and evaluation of fuel management 

objectives. In: Fire, Fuel Treatments, and Ecological Restoration: Conference 

Proceedings, 2002 April 16-18; Fort Collins, CO. Omi, P. N; Joyce, L. A., 

technical eds. RMRS-P-29. Ft. Collins, CO: U.S. Dept. of Agriculture, Forest 

Service, Rocky Mountain Research Station: 353-366.

GAO. 1999. A cohesive strategy is needed to address catastrophic wildfi re threats. 

United States General Accounting Offi ce, RCED-99-65. 60 p.

GAO. 2004. Forest Service and BLM need better information and a systematic 

approach for assessing the risks of environmental effects. United States General 

Accounting Offi ce, GAO-04-705. 88 p.

Heinselman, M. L. 1981. Fire intensity and frequency as factors in the distribution 

and structure of northern ecosystems. In: Mooney, H. A. and others (eds.), 

Fire regimes and ecosystem properties: U. S. Department of Agriculture, Forest 

Service, WO-26: 7-57.

Hyndman, D.; Hyndman, D. 2006. Natural hazards and disasters. Belmont, CA, 

USA: Thompson Brooks/Cole. 490 p.

Mills, T.J.; Bratten, F. W. 1982. FEES: design of a Fire Economics Evaluation System. 

Gen. Tech. Rep. PSW-65. Berkeley, CA: Pacifi c Southwest Forest and Range 

Experiment Station, Forest Service, U.S. Dept. of Agriculture. 26 p.

Mills, T. J.; Bratten, F. W. 1988. Economic effi ciency and risk character of fi re 

management programs, Northern Rocky Mountains. Res. Pap. PSW-192. Berkeley, 

CA: Pacifi c Southwest Forest and Range Experiment Station, Forest Service, U.S. 

Dept. of Agriculture. 52 p.

Van Wagner, C. E. 1969. A simple fi re growth model. Forestry Chron. 45: 104-104

Van Wagner, C. E. 1977. In Readers’ Forum. Fire Technol. 13: 349-350.
USDA Forest Service Proceedings RMRS-P-41. 2006. 185

In: Andrews, Patricia L.; Butler, Bret W., 

comps. 2006. Fuels Management—How to 

Measure Success: Conference Proceedings. 

28-30 March 2006; Portland, OR. 

Proceedings RMRS-P-41. Fort Collins, 

CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research 

Station.

1 Spat ial Fire/ Fuels Technical 

Analyst, Systems for Environmental 

Management, Chapel Hill, NC.

dmgercke@hotmail.com.

2 Applied Fire Ecologist, United States 

Department of Agriculture: Forest 

Service, Washington, DC.

Abstract—In 2005, eight U.S. Forest Service and Bureau of Land Management interdisciplinary teams participated in a test of strategic placement of treatments (SPOTS) 

techniques to maximize the effectiveness of fuel treatments in reducing problem fi re 

behavior, adverse fi re effects, and suppression costs. This interagency approach to 

standardizing the assessment of risks and proposing strategically placed treatments 

to mitigate that risk uses an iterative, collaborative strategic approach to proposing 

landscape scale treatment patterns. The pilot teams used FARSITE and FlamMap, spatially explicit fi re behavior prediction models, to evaluate the effectiveness of proposed 

treatments on fi re behavior and effects at scales appropriate to address the expected 

problem fi re event. A primary objective was to develop a consistent, systematic approach that integrates multiple land and resource management objectives when 

addressing and evaluating fuels risks. This paper discusses the accomplishments and 

challenges the pilot project teams faced as they tested strategic placement of treatments 

methods in different landscapes, vegetation, fi re regimes, and ownerships.

Introduction

In 2005, the USFS in partnership with the Bureau of Land Management (BLM) tested the strategic placement of treatments concept using 

sophisticated spatial analysis tools in eight pilot areas across the country. 

This process is not just designed for fi re and fuels planning, but as a holistic 

land management process. While problem fi re is the fi lter through which 

potential treatment patterns are tested, the objectives for many of the treatments planned are related to timber management, silviculture, forest health, 

wildlife, and watershed issues, as well as protection of assets from unwanted 

wildland fi re. The national objectives for the SPOTS pilots were to develop 

a consistent, interagency, systematic approach to evaluating and mitigating 

risks, test a variety of data sets, models and tools, and to identify barriers or 

restrictions to meaningful progress.

The SPOTS concept contributes to overall understanding of spatial dynamics of fuel and related fi re behavior through use of a collaborative planning 

process and fi re modeling tools that describe fi re potential on a specifi c landscape. The placement of fuels treatments has shown promise in reducing the 

undesired effects of large fi res and acres burned and in the modeling environment. (Finney 2005; Bahro 2006 in press; Stratton 2004). The Fireshed 

team in California has successfully implemented this concept since 2003. The 

California Fireshed team conducts integrated workshops to develop long-term 

Strategic Placement of Treatments (SPOTS): 

Maximizing the Effectiveness of Fuel and 

Vegetation Treatments on Problem Fire 

Behavior and Effects

Diane M. Gercke1 and Susan A. Stewart2
186 USDA Forest Service Proceedings RMRS-P-41. 2006.

Gercke and Stewart Strategic Placement of Treatments (SPOTS): Maximizing the Effectiveness of Fuel and Vegetation...

strategic objectives and proposed treatment patterns as the basis for program 

of work planning required for United States Forest Service (USFS) compliance with the National Environmental Policy Act (NEPA).

The SPOTS process broadens fuels projects planning opportunities. Since 

fi res do not stop at ownership boundaries, neither should our planning process. 

Planning should happen not only at the project level, but also the interagency 

broad scale fi re planning level, where budgets are allocated. Integrating other 

resources upfront with fi reshed-scale planning happening ahead of the NEPA 

process’ “purpose and need” phase would reduce pressures on land managers 

and allow for a balanced, clear process.

These strategically placed fuels treatments are not intended to exclude fi re 

from the landscape, but to change the character and ultimate effects of an 

unplanned fi re. Treatments on a fraction of the landscape may or may not be 

suffi cient to restore ecosystems, but may effectively disrupt or reduce large 

wildfi re growth as well as being a step right direction toward the long-term 

goal of restoration of desired conditions at the large scale. Restoration is 

rarely fully realized in the fi rst entry and may be achieved through multiple 

entries over many years. The initial strategic entry, if successful can reduce 

the probability of a large, uncharacteristically severe fi re, and can serve to 

buy more time for mangers to continue working toward the long-term restoration goal.

Methods

A steering committee with members from USFS management and research 

and the Department of Interior was established to guide the pilot efforts, 

evaluate the proposals and participate in selection, interact with ongoing 

pilots, and ultimately develop a performance measure for 2006/2007. Eight 

pilot teams were selected. The projects represent a range of geographic areas, 

vegetative types, potential fi re problems, data sources, and ownership mixes 

(Figure 1, Table 1).

The SPOTS pilot teams were required to attend training with the California 

Fireshed team and report on their lessons learned and current status. Each 

team was asked to defi ne their specifi c problem fi re scenario in an analysis 

area larger than the expected problem fi re, prepare their data for integrated 

spatial analysis, and hold a workshop in which they designed potential treatment patterns in an iterative manner. The workshop was expected to feature 

testing the treatment scenarios with FARSITE or FlamMap fi re behavior and 

spread models (Finney 1998; Finney et al. 2004) and other spatial analysis 

tools to test effects on other resource objectives. An expected outcome of 

the workshops was a transparent spatial, tabular, or graphic display of the 

trade-offs made in the proposed action.

Results

Pilot project teams reported the results of their efforts in October of 2005 

(Table 2). All eight teams were able to describe the problem fi re scenario, 

including probable weather, fi re behavior, and undesired effects. Seven out of 

eight project teams calibrated the FARSITE landscape by validating outputs 

against known fi re behavior or a recorded fi re event. Seven of eight teams 
USDA Forest Service Proceedings RMRS-P-41. 2006. 187

Strategic Placement of Treatments (SPOTS): Maximizing the Effectiveness of Fuel and Vegetation... Gercke and Stewart

Table 1—Participating pilot projects are identifi ed by project name, management unit, location, and a brief 

study area description.

USFS region Project name Unit State Study area size and vegetation

 1 Butte North Beaverhead- MT 45,000 acres mixed lodgepole pine

 Deerlodge NF forest

 2 Yankee Hill Arapahoe CO 35,600 acres high elevation Rocky

 Roosevelt NF Mountain mixed conifer and lodgepole

 3 La Jara Carson NF NM 6,000 acres within the 42,000 acres

 Taos Canyon, fuels range from low

 pinyon/juniper to ponderosa to high

 elevation conifer

 4 Upper Wasatch- UT 90,000 acres, 7-12,000 ft elevation;

 Provo Cache NF aspen, lodgepole, spruce-fi r, mixed

 conifer, and mountain-shrub/oakbrush

 5 Alder Mendocino NF CA 31,000 acres; Sierra conifer and

 Springs chaparal

 5 Sagehen Tahoe NF CA 8,000 acres; mixed conifer, red fi r,

 eastside pine, and pine plantation

 6 Cascade Deschutes NF OR 150,000 acres. BLM and FS. Five

 Front and Buttes/LaPine interface Pondo/mixed

 Prineville BLM conifer, lodgepole

 8 ION/Wando Francis SC 1,030 acres: Longleaf pine, loblolly

 Marion NF regeneration, pocosin, hurricane

 blowdown

Figure 1—Location of the fi nal 8 pilot projects selected across the country.
188 USDA Forest Service Proceedings RMRS-P-41. 2006.

Gercke and Stewart Strategic Placement of Treatments (SPOTS): Maximizing the Effectiveness of Fuel and Vegetation... Table 2—Summary of results from the 2005 SPOTS pilot projects. Created an Completed Calibrated Completed estimate of how Collected an iterative the model by an iterative proposed historic Identifi ed process to validating process to treatments weather and analysis area Created answer outputs answer changed the wind data appropriate FARSITE strategic against strategic problem fi re Defi ned and to the landscape from placement of known fi re placement of outcome Categories of problem evaluated for problem fi re vegetation and treatments event or treatments (screenshots, Workshop persons Project area fi re PF size other data “So What?” behavior “So What?” graphs or tables) and duration attended North Butte, MT X X X X X Plan to Public, city government, local fi re departments Yankee Hill, CO X X X X X X X Yes: 2 days Public, state FS, environmental groups La Jara, NM X X X X Yes = Informs Yes: 1 day Public, inter- burn severity agency partners maps for before and the Taos and after Pueblo Tribal treatment Council Upper Provo, UT X X X X X X X Yes = FARSITE Yes: 3 days FS ID team, and FlamMap summer screenshots for 2 homeowners treatment options and 1 county commissioner Sagehen, X X Plan to X Plan to Plan to Tahoe, CA Cascade X X X X Plan to X Plan to Plan to TNC, BLM, Front, OR USFS, public ION/Wando, X X X X X Yes = FARSITE Yes: 1 day USFS, public, SC screenshot collaborators
USDA Forest Service Proceedings RMRS-P-41. 2006. 189

Strategic Placement of Treatments (SPOTS): Maximizing the Effectiveness of Fuel and Vegetation... Gercke and Stewart

identifi ed an analysis area of appropriate size, suffi ciently large to contain 

the expected fi re event. Five of the eight pilot projects completed some sort 

of collaborative workshop or public meeting. Of the fi ve teams that had a 

workshop, only two teams used FARSITE in an iterative way at the workshop 

so the participants could test various treatment patterns. Two additional 

teams used FARSITE outside of the workshop environment, modeling and 

displaying results from ideas provided by workshop attendees at a later date. 

One team simply used FARSITE to test their existing program against the 

no action alternative, but chose not to evaluate alternative treatment patterns. 

Five teams created an estimate of how proposed treatments changed the fi re 

size or behavior using screenshots, graphs, or tables. A single team shared 

maps of different ultimate fi re sizes in different treatment scenarios.

Pilot teams used several models, but did not even begin to explore the 

dozens of tools available. FARSITE and FlamMap fi re behavior and spread 

models were the common tools used to evaluate treatment patterns. Though 

FARSITE has been taught for a decade, it has been used primarily for incident 

support and has yet to be embraced by the planning community. The projects 

universally recognized the utility of the FARSITE model for fuels planning 

purposes. These fi re modeling programs should become the centerpiece of 

a suite of interconnecting software programs that are designed to evaluate 

treatment patterns.

Four overall national objectives were identifi ed at the start of the 2005 

Pilot Projects. The following is a summation of results concerning those 

national objectives:

 1. Develop a consistent, systematic approach for evaluating and addressing landscape-level risks in an integrated and collaborative way.

This objective was fully met. The seven-step framework outlined in the discussion section of this paper was developed as a direct result of lessons learned 

from the national SPOTS pilots.

 2. Test a variety of available data sets, models, and tools in partnership 

with researchers to determine applicability of some of the many 

tools available.

A total of eleven tools and data sets were tested by the various pilot projects. 

Two teams tested prototype LANDFIRE data.

 3. Identify barriers or restrictions to implementing the selected pattern, 

intensity or timing of fuel treatments that may be imposed by existing Land and Resource Management Plans.

The most common barriers reported by the pilot project teams were:

 • Analyst skills are universally in short supply.

 • The complexity and effort required to generate data layers was 

extraordinary.

 4. Devise an appropriate measure of success to describe accomplishments 

developed and implemented using the landscape-scale effectiveness

approach.

A performance measure will not be built for SPOTS at this time. The Forest 

Service will collect data in the next two years on the use of the seven step 

framework and begin to document cases where strategic treatment patterns 

are tested by wildland fi re.
190 USDA Forest Service Proceedings RMRS-P-41. 2006.

Gercke and Stewart Strategic Placement of Treatments (SPOTS): Maximizing the Effectiveness of Fuel and Vegetation...

Discussion

At the October 2005 SPOTS pilot project meeting, teams identifi ed barriers and successes concerning their efforts. Key themes in this discussion 

included the time and labor-intensive nature of planning and data preparation and calibration, the lack of skilled personnel to complete the preparation 

and analysis, and the overall success of the process as a communication tool. 

Where pilots failed to move forward in the process, lack of line offi cer support was most often the cause.

The collaborative workshop is perhaps the most critical step in the process, 

because the participants can actively test their ideas about treatments, and see 

the results almost immediately. This is the step that allows for transparency 

regarding the trade-offs for the decision maker. The best possible fi re solution may not be desired because of impacts to wildlife, watersheds, or scenic 

quality objectives. The best solution for timber management may not meet 

the fi re objectives. The workshop displays the outcomes of those choices on 

expected fi re behavior as well as the implications for other resources.

The most successful workshops used fi re behavior models to inform and 

support the process. Models increased understanding of fuels and fi re spread 

on the landscape, helping to defi ne the problem and align participants towards 

a common goal. Fire models run on properly calibrated landscapes were very 

successful in demonstrating how well treatments worked to interrupt theoretical large fi re spread on the landscape. Seven out of eight project teams 

calibrated the FARSITE landscape by validating outputs against known fi re 

behavior or a recorded fi re event. Model calibration gives confi dence in model 

output and contributes to overall participant support. The models were most 

useful where live modeling was available within the workshop and multiple 

treatment scenarios could be compared in an iterative manner.

The pilot teams acknowledged the need to identify problem fi re behavior 

within the context of the workshop. This aim establishes modeling parameters, 

facilitates the discussion of treatment intensities, and helps to create “buy in” 

regarding the fi nal outputs. Many of the pilot areas identifi ed multiple fi res 

of concern. Developing a shared understanding of the problem fi re can be 

challenging. The members must understand that the task is not to describe 

everything that could happen under a variety of different conditions, but to 

discuss the worst case scenario with as defi ned by the known local fi re history 

or recorded weather conditions.

A change in the planning culture emphasizing partnership and shared 

decision-making was recognized as a key success by all of the pilot project 

teams. Communities and collaborators appreciated inclusion in the process, 

increasing perceived “buy-in” to decisions and decreasing the perceived likelihood of litigation. Internal cooperation was also a success in many areas, 

when multiple resource disciplines were able to use the tools and collaborative 

process to understand overall fi re risk and achieve hazardous fuels project 

planning and multiple resource benefi ts.

Multiple barriers to the process were identifi ed including: perceived confl icts 

between fuels treatments and the protection of threatened and endangered 

species habitat, smoke issues, limited budgets for project implementation, and 

the tendency for large chunks of these budgets to be spent in the planning 

process. Traditionally, fuels treatments may be constrained by cost-per-acre, 

with acres accomplished taking precedence over higher dollar wildland urban 

interface or remote area treatments. Litigation or the potential for litigation 

was also perceived as a planning constraint.
USDA Forest Service Proceedings RMRS-P-41. 2006. 191

Strategic Placement of Treatments (SPOTS): Maximizing the Effectiveness of Fuel and Vegetation... Gercke and Stewart

Conclusion

A framework was developed, based on the experiences of the eight SPOTS 

pilot project teams, giving general guidelines to follow when attempting to 

implement fi reshed-level fuels treatment planning on individual landscapes. 

While strategic approaches will vary throughout the country to account for 

different fuels, topography, weather, and social factors, all spatial modeling 

approaches targeting undesired fi re behavior should feature:

 1. Explicitly defi ning an analysis area

 2. Identifying assets and protection targets

 3. Defi ning the “problem fi re”

 4. Designing treatment patterns

 5. Testing multiple treatment patterns with a spatial fi re behavior model

 6. Clearly displaying the trade-offs

 7. Monitoring and adaptive management

This framework is discussed in depth at www.nifc.gov/spots. The framework meets the need, described by the United States General Accounting 

Offi ce (2000, 2003, 2004, 2005), to establish a consistent way to defi ne 

risk and test potential solutions. The framework can be used collaboratively 

across agency boundaries and would be useful even lacking complex modeling 

software or data. Critical innovations provided by this framework are tying 

the size of the analysis area directly to the ‘problem fi re’, the development 

of a treatment pattern specifi cally designed to impede fi re spread and severity, and the iterative testing that allows team members to have immediate 

feedback on their ideas.

Challenges to the wide spread adoption of SPOTS seven step approach 

remain. The lack of analyst skills is a critical need that must be fi lled with 

training and employee development. The Fire Modeling Institute at the Fire 

Sciences Lab in Missoula is beginning to supplying skilled analysts who may 

be available to teams that are trying to develop a skills base locally. A great 

deal of work remains to select and integrate models that would form a unifi ed 

national corporate software package. Teams using a SPOTS approach will be 

the early customers of the national LANDFIRE data set. SPOTS analysis 

approaches should dovetail with Fire Program Analysis (FPA) System, and 

could be critical in supporting land and resource management planning.

The seven-step framework for SPOTS is an excellent way to aid in collaboration with a variety of partners and supports policy directives like the 

National Fire Plan and Healthy Forests Initiative. Fire modeling shows that 

a deliberate pattern of slower burning fuels can lead to fi res that are smaller 

and less intense. Fuel treatments and vegetation management efforts can 

change the outcome of the problem fi re consequently reducing suppression 

costs. SPOTS approaches encourage a landscape-level, cohesive fuels treatment strategy that may provide biomass and encourage the development of 

businesses that can use our hazardous fuels to bring value added products 

to market or increase our capability to generate energy.

SPOTS approaches may not be meaningful on all lands, for all problems. 

In an environment where the land management agencies currently only fund 

treatments on about 1% of their lands per year, planning to treat 20% of the 

entire landscape seems unrealistic. The strategic placement of fuel treatments 

should be used in high profi le, high priority areas to increase the likelihood of 

success and secure future treatment opportunities. SPOTS treatment patterns 

may allow managers time to implement long-term management strategies to 
192 USDA Forest Service Proceedings RMRS-P-41. 2006.

Gercke and Stewart Strategic Placement of Treatments (SPOTS): Maximizing the Effectiveness of Fuel and Vegetation...

restore ecosystems. The enhanced understanding of wildland fi re potential 

gained by participants of the SPOTS approach as well as the distribution of 

treated acres with lower fi re severity potential across the landscape may provide 

some comfort to local decision makers considering the highly effective fuels 

treatment option provided by broad-scale Wildland Fire Use.

Literature Cited

Bahro, B.; Barber, K.; Perrot, L.; Sherlock, J.; Taylor, A.; Wright, K.; Yasuda, D. 2006 

in press. Using Fireshed Assessments to Measure Landscape Performance.

Finney, M.A. 1998. FARSITE: fi re area simulator. Model Development and 

Evaluation. USDA Forest Service General Research Paper, RMRS-RP-4. Fort 

Collins, CO: Rocky Mountain Forest and Range Experiment Station.

Finney M.A.; S. Brittain; R.C. Seli. 2004. FlamMap Spatial Analysis of Fire 

Potential. Joint Fire Sciences Program: Rocky Mountain Research Station: 

Missoula MT).

Finney, M.A.; McHugh, C.W.; Grenfell, I.C. 2005. Stand and landscape effects 

of prescribed burning on two Arizona wildfi res. Canadian Journal of Forest 

Resources 35. pp 1714-1722

Stratton, R.D. 2004. Assessing the effectiveness of landscape fuel treatments on fi re 

growth and behavior. Journal of Forestry, vol. 102, no. 7, pp. 32-40.

United States General Accounting Offi ce. 2000. Reducing wildfi re threats: Funds 

should be targeted to the highest risk areas. GAO/T-RCED-00-296. Washington, 

D.C.: September 13, 2000. 8 pp

United States General Accounting Office. 2003. Wildland fire management: 

Additional actions required to better identify and prioritize lands needing fuels 

reduction. GAO-03-805. Washington, D.C.: August 15, 2003. 60 pp.

United States General Accounting Offi ce. 2004. Wildland fi res: Forest Service and 

BLM need better information and a systematic approach for assessing the risks of 

environmental effects. GAO-04-705. Washington, D.C.: June 24, 2004. 88 pp.

United States General Accounting Offi ce. 2005. Wildland Fire Management: Forest 

Service and Interior need to specify steps and a schedule for identifying longterm options and their costs. GAO-05-353T. Washington, D.C.: February 17, 

2005. 15 pp.

USDA Forest Service and Department of Interior. 2002. “National Environmental 

Policy Act documentation needed for fi re management activities; categorical 

exclusions.” Federal Register 67(241).
USDA Forest Service Proceedings RMRS-P-41. 2006. 193

In: Andrews, Patricia L.; Butler, Bret W., 

comps. 2006. Fuels Management—How to 

Measure Success: Conference Proceedings. 

28-30 March 2006; Portland, OR. 

Proceedings RMRS-P-41. Fort Collins, 

CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research 

Station.

1 LANDFIRE program manager, at the 

USFS Rocky Mountain Research Station, 

Missoula Fire Sciences Laboratory, 

Missoula, MT. kryan@fs.fed.us

2 LANDFIRE program analyst, at the 

USFS Rocky Mountain Research Station, 

Missoula Fire Sciences Laboratory, 

Missoula, MT.

3 LANDFIRE science lead at the USFS 

Rocky Mountain Research Station, 

Missoula Fire Sciences Laboratory, 

Missoula, MT.

4 Senior remote sensing scientist at the 

USGS Center for Earth Resources 

Observation and Science, Sioux Falls, 

SD.

5 LANDFIRE program manager at The 

Nature Conservancy, Jacksonville, FL.

6 Northeastern LANDFIRE modeling 

lead at The Nature Conservancy, Fort 

Andross, Brunswick, ME.

Abstract—Managers are faced with reducing hazardous fuel, restoring fi re regimes, 

and decreasing the threat of catastrophic wildfi re. Often, the comprehensive, scientifi -

cally-credible data and applications needed to test alternative fuel treatments across 

multi-ownership landscapes are lacking. Teams from the USDA Forest Service, Department of the Interior, and The Nature Conservancy are completing the LANDFIRE 

Project, which produces consistent and comprehensive spatial data describing vegetation composition and structure, wildland fuel, historical fi re regimes, and ecosystem 

status across the entire United States. LANDFIRE provides a scientifi c foundation for 

assessments of wildland fuel conditions, fi re hazard, and ecosystem status. While 

LANDFIRE products will fi ll immediate needs for testing alternative fi re management 

scenarios, planning fuel treatments, and allocating resources, the data and models have 

much broader applications in research, biodiversity conservation, and strategic forest 

and resource management planning. This paper provides a synopsis of the background, 

objectives, and deliverables of the LANDFIRE Project and the management challenges 

LANDFIRE products address. Presented are potential applications of LANDFIRE data 

for use in fi re research and vegetation ecology studies and in wildland fuel treatments 

and restoration projects to protect communities at risk.

Introduction

LANDFIRE is a fi ve-year wildland fi re, ecosystem, and wildland fuel mapping project that generates consistent, comprehensive products describing 

vegetation, fi re, and fuel characteristics across the United States. Wildland 

fi re managers faced with requirements for reducing hazardous fuel, restoring 

historical fi re regimes, and decreasing threats of catastrophic wildfi re are often 

without adequate, scientifi cally credible data to support their planning and 

decision-making processes. LANDFIRE was conceived to fi ll this need. The 

main objective of LANDFIRE is to generate relevant, integrated geospatial 

products that provide a scientifi c foundation for landscape fi re management 

planning, prioritization of fuel treatments, interagency collaboration, community and fi refi ghter protection, and effective resource allocation. The 

consistent and comprehensive nature of LANDFIRE methods ensures that 

products are nationally relevant, while the 30-m grid resolution assures that 

data can be locally applicable.

Landfi re: Landscape Fire and Resource 

Management Planning Tools Project

Kevin C. Ryan1, Kristine M. Lee2, Matthew G. Rollins3, 

Zhiliang Zhu4, James Smith5, and Darren Johnson6
194 USDA Forest Service Proceedings RMRS-P-41. 2006.

Ryan, Lee, Rollins, Zhu, Smith, and Johnson Landfi re: Landscape Fire and Resource Management Planning Tools Project

Background

The recent United States laws and policies with respect to health and restoration of wildlands share common themes. These include the recognition that 

1) fi re is a landscape-level biophysical process critical to the maintenance of 

ecosystem function; 2) solutions to fuel and fi re problems require collaboration 

among stakeholders at all levels of government; and 3) effective collaboration 

requires consistent, comprehensive, up-to-date data on vegetation, wildland 

fuel, and fi re conditions across the entire country.

In the aftermath of the 1994 fi re season — in which 34 fi re fi ghters lost their 

lives, dozens of communities were threatened, and hundreds of thousands of 

hectares burned — the Secretaries of Agriculture and Interior adopted a new 

federal wildland fi re management policy directing federal land management 

agencies to manage wildland fuel and fi re on an interagency, landscape-scale 

(USDA and USDI 1995). Through this policy, fi re managers were directed 

to develop long-term fi re management plans that incorporate measures to 

treat fuel and increase the utilization of biomass. Additionally, this policy 

called for the implementation of fi re behavior prediction to support both 

strategic planning and tactical suppression and logistics decisions, with special 

consideration of fi refi ghter safety. Clearly, fi re behavior and effects modeling 

and information system technology play a critical role in all future wildland 

fi re planning and management activities.

In 2000, Congress mandated the implementation of the National Fire Plan 

(USDA and USDI 2000). The National Fire Plan is a long-term commitment 

to address problems associated with unsustainable wildland fuel and ecosystem 

conditions that have evolved over many decades of fi re suppression and land 

use. The plan is based on cooperation and communication among federal 

agencies, states, local governments, tribes, and interested publics. To supplement the National Fire Plan, the Western Governors’ Association, working 

with federal land management agencies, developed the 10-Year Comprehensive 

Strategy that directs state and federal agencies to focus high priority on treatments that protect communities and provide defensible space for fi re fi ghters 

(USDA and USDOI 2001). More recently, the Healthy Forests Restoration 

Act (HFRA) was enacted to facilitate the reduction of wildfi re risk, improve 

biomass utilization, protect resources, promote the systematic gathering of 

information on wildland fi re, promote the early detection of insect and disease 

outbreaks, and to protect, enhance, and restore ecosystems.

Managers need for continuous wildland fuel and vegetation data at suffi cient spatial resolution to run commonly used decision support tools (such 

as BEHAVE-Plus [Andrews and others 2005], FARSITE [Finney 1998], 

FlamMap [Stratton 2004], Nexus [Scott and Reinhardt 2001], and FFOFEM 

[Reinhardt and others 1997]) led the Wildland Fire Leadership Council, a 

group of senior administration executives representing all land management 

agencies in the country, to charter the LANDFIRE Project (see www.landfi re.

gov for additional project details).

Mapping Vegetation and Fuel

The three general production objectives of LANDFIRE are 1) mapping 

existing vegetation, 2) mapping wildland fuel, and 3) mapping the departure 

of current landscape conditions from those that existed historically. Maps 
USDA Forest Service Proceedings RMRS-P-41. 2006. 195

Landfi re: Landscape Fire and Resource Management Planning Tools Project Ryan, Lee, Rollins, Zhu, Smith, and Johnson

describing environmental site potential and existing and historical vegetation are important intermediate LANDFIRE products for assessing wildland 

fuel conditions and evaluating departure from historical conditions. Both of 

these assessments are required by federal wildland fi re management policy 

and the HFRA. LANDFIRE describes current and historical vegetation 

characteristics by mapping existing vegetation (EVT) and modeling two types 

of potential vegetation: environmental site potential (ESP) and biophysical 

settings (BpS).

The LANDFIRE environmental site potential (ESP) product represents 

the vegetation that could be supported at a given site based on the biophysical environment in the absence of disturbance. As used in LANDFIRE, ESP 

map units represent the natural plant communities that would become established at late or climax stages of successional development in the absence 

of disturbance. The ESP map is similar in concept to other approaches to 

mapping potential vegetation in the western United States, including habitat 

types (Daubenmire 1968; Pfi ster and others 1977) and plant associations 

(Henderson and others 1989). It is important to note that ESP is an abstract concept and represents neither current nor historical vegetation. In 

LANDFIRE, ESP map units are used for site stratifi cation in the processes 

of mapping surface fuel models and canopy fuel.

The biophysical settings (BpS) product represents the vegetation that can 

potentially exist at a given site based on both the biophysical environment 

and an approximation of the historical disturbance regimes. It is based on 

the ESP map. Unlike the ESP map, the BpS map represents natural plant 

communities that would become established given uninterrupted natural 

disturbance processes, such as fi re. In LANDFIRE, the BpS map is used 

to link the ecological process of succession to simulation landscapes in the 

LANDSUM landscape fi re succession model, which simulates historical fi re 

regimes and vegetation conditions (Keane and others 2002). Each BpS map 

unit is matched with a model of vegetation succession and disturbance pathways, and both serve as key inputs to the LANDSUM landscape succession 

model. The BpS grid is similar in concept to the potential natural vegetation 

groups used in mapping and modeling efforts related to fi re regime condition 

class (Schmidt and others 2002; www.frcc.gov).

The third vegetation map, existing vegetation type (EVT), represents the 

vegetation currently present at a given site. EVT map units are based on 

NatureServe’s Ecological Systems classifi cation (Comer and others 2003). The 

map of EVT is generated using a predictive modeling approach that relates 

Landsat imagery and spatially explicit biophysical gradients to fi eld-referenced 

data that have been classifi ed to LANDFIRE vegetation map units based on 

the dominant vegetation of the plot. Some fi eld-referenced data are withheld 

from the map creation process and are used to test and validate maps and 

model results. To date, the LANDFIRE reference database contains approximately 146,800 fi eld plots from the fi rst 17 mapping zones compiled from 

existing government and non-government inventory databases, including the 

U.S. Forest Service’s Forest Inventory Analysis Program.

The LANDFIRE existing vegetation maps are integrated with maps of 

vegetation structure to represent succession classes (termed vegetation-fuel 

classes in the Interagency Fire Regime Condition Class Guidebook (Hann 

and others 2004). Succession classes form the foundation of fi re regime 

condition class (FRCC) calculation and represent current vegetation conditions with respect to the vegetation species composition, vegetation cover, 

and vegetation height ranges of successional states that occur within each 

biophysical setting.
196 USDA Forest Service Proceedings RMRS-P-41. 2006.

Ryan, Lee, Rollins, Zhu, Smith, and Johnson Landfi re: Landscape Fire and Resource Management Planning Tools Project

LANDFIRE is mapping both surface fuel and canopy fuel. Surface fuel 

represents biomass that occurs on the ground contributing to the behavior 

of fi res burning on or near the surface. Because mapping wildland fuel over 

large regions is very diffi cult using standard indirect remote sensing techniques 

(Keane and others 2001), the LANDFIRE Project relies on combinations 

of existing vegetation composition and structure and biophysical settings to 

create wildland fuel products. The 13 fi re behavior fuel models described 

by Anderson (1982) and the 40 Scott and Burgan fi re behavior fuel models (Scott and Burgan 2005) are mapped to facilitate the modeling of fi re 

behavior variables such as fi re intensity, spread rate, and size using models 

such as Rothermel’s mathematical model for surface fi re behavior and spread 

(Rothermel 1972), BEHAVE Plus (Andrews and others 2005), FARSITE 

(Finney 1998), NEXUS (Scott and Reinhardt 2001) and FOFEM (Reinhardt 

and others 1997).

Fuel models integrate the fuel characteristics necessary for fi re propagation along the ground; however, additional information on the vegetation 

canopy is required to predict the initiation, spread, and intensity of crown 

fi res (VanWagner 1977, 1993; Rothermel 1991; Scott 2003). Canopy fuel 

represents the amount and arrangement of live and dead biomass in the vegetation canopy. Maps of canopy height, canopy cover, and existing vegetation 

were developed using information from the LANDFIRE reference database, 

remote sensing methods, and statistical modeling.

In addition to canopy height and canopy density, two more canopy characteristics serve as critical components for predicting crown fi re potential: 

canopy bulk density (CBD) and canopy base height (CBH). CBD describes 

the density of foliage and branches for a specifi c vegetated stand and is defi ned as the mass of available canopy fuel per canopy volume unit; canopy 

base height (CBH) describes the average height from the ground to a forest 

stand’s canopy bottom. CBD and CBH were calculated for each plot in the 

LANDFIRE reference database using FUELCALC, a fuel summary application developed by Reinhardt and Crookston (2003). FUELCALC computes 

a number of canopy fuel characteristics for each fi eld reference plot based on 

allometric equations relating individual tree characteristics to crown biomass. 

Geospatial data describing canopy fuel provide information for fi re behavior 

models, such as FARSITE (Finney 1998), to determine areas in which a 

surface fi re is likely to transition to a crown fi re (Van Wagner 1977, 1993).

Fuel models and canopy fuel metrics are used to simulate fi re behavior. 

Simulation of the effects of fi re (such as vegetation mortality, soil heating, 

and smoke production) requires systems that describe and integrate the actual 

measurements of fuel for vegetated stands. There are two examples of fi re 

effects models that may be produced by LANDFIRE. Both are currently 

under scientifi c review and at this time are not fully incorporated into the 

LANDFIRE production. Mapping of these products will be initiated upon 

the recommendation of scientifi c review. The fi rst, fuel loading models (FLMs; 

Lutes and others, in preparation), use fuel information from the LANDFIRE 

reference database to characterize representative loading for each fuel component (for example, woody and non-woody) for typical vegetation classifi cation 

systems such as the Society of American Foresters vegetation classifi cation 

system (Eyre 1980). FLMs characterize fuel loading across all vegetation and 

ecological types. The second fi re effects modeling system, called the Fuel 

Characterization Classifi cation System (FCCS) and developed by Sandberg 

and others (2001), summarizes fuelbeds using canopy, shrub, surface, and 

ground fuel stratifi cations. Several fuelbed categories that describe unique 
USDA Forest Service Proceedings RMRS-P-41. 2006. 197

Landfi re: Landscape Fire and Resource Management Planning Tools Project Ryan, Lee, Rollins, Zhu, Smith, and Johnson

combustion environments form the foundation of FCCS. See www.fs.fed.

us/pnw/fera/research for more information on FCCS. Both sets of fi re effects models are formulated to serve as input to existing fi re effects models 

such as FOFEM (Reinhardt and others 1997) and CONSUME (Ottmar and 

others 1993). When incorporated into LANDFIRE production, these sets of 

fi re effects models will be assigned to unique combinations of the integrated 

vegetation products. Geospatial representation of fi re effects fuel models may 

be used to prioritize fuel treatment areas, evaluate fi re hazard and potential, 

and examine past, present, and future fuel loading characterizations. See 

Reeves and others, this proceedings for a full description of LANDFIRE 

fuel products.

In addition to products that describe wildland fuel characteristics, LANDFIRE produces a suite of products related to fi re regime condition class 

(FRCC). The discrete, three-level FRCC classifi cation, established by Hann 

and Bunnell (2001), is defi ned as a descriptor of the amount of “departure 

from the historical natural regimes, possibly resulting in alterations of key 

ecosystem components such as species composition, structural stage, stand 

age, canopy closure, and fuel loadings.” The three condition classes describe 

low departure (FRCC I), moderate departure (FRCC II), and high departure 

(FRCC III). LANDFIRE produces maps of FRCC using methods derived 

from the Interagency Fire Regime Condition Class Guidebook (Hann and 

others 2004). It is important to note that the LANDFIRE FRCC map 

represents the departure of current vegetation conditions from simulated 

historical reference conditions, which is only one component of the FRCC 

characterization outlined in Hann and others (2004).

The historical reference conditions for vegetation succession classes are simulated using LANDSUM (Keane and others 2002). The existing succession 

classes, mapped according to EVT, can additionally represent uncharacteristic vegetation components, such as exotic species, that are not found within 

the compositional or structural variability of successional states defi ned for 

a biophysical setting. In LANDFIRE, current succession class proportions 

within an analysis area are compared to those of simulated historical reference 

conditions to calculate FRCC.

LANDFIRE also produces maps of fi re regime groups representing an 

integration of the spatial fi re regime characteristics of frequency and severity simulated via the LANDSUM model (Keane and others 2002). These 

groups are intended to characterize the presumed historical fi re regimes based 

on interactions between vegetation dynamics, fi re spread, fi re effects, and spatial 

context (Hann and others 2004). Fire regime groups mapped by LANDFIRE 

include: 1) Fire Regime I (0 to 35 year frequency, low to mixed severity), 2) Fire 

Regime II (0 to 35 year frequency, replacement severity), 3) Fire Regime III 

(35 to 200 year frequency, low to mixed severity), 4) Fire Regime IV (35 to 

200 year frequency, replacement severity), and 5) Fire Regime V (200+ year 

frequency, any severity).

Applications

The consistent and comprehensive fuel and vegetation data produced by 

LANDFIRE provide managers and scientists with the ability to systematically 

compare how vegetation, fuel, and fi re potential vary between landscapes. 

LANDFIRE provides the fuel and terrain data necessary for executing 

the FARSITE (Finney 1998), FlamMap (Stratton 2004), BEHAVE-Plus 
198 USDA Forest Service Proceedings RMRS-P-41. 2006.

Ryan, Lee, Rollins, Zhu, Smith, and Johnson Landfi re: Landscape Fire and Resource Management Planning Tools Project

(Andrews and others 2005), and Nexus (Scott and Reinhardt 2001) models. Surface fuel models include the 13 fi re behavior fuel models (Anderson 

1982) and the 40 fi re behavior fuel models (Scott and Burgan 2005). Fuel 

consumption, smoke production, and soil heating calculated using FOFEM 

(Reinhardt and others 1997) and CONSUME (Ottmar and others 1993). 

LANDFIRE products provide managers with the ability to predict potential 

fi re behavior in tactical and strategic planning of suppression activities. The 

ability to model expected fi re behavior with and without fuel treatments 

provides managers with valuable decision support tools for strategic planning (Finney 2001, 2005). The ability to predict and game fi re behavior and 

effects across landscapes provides managers and scientists with a framework 

to explore biophysical mechanisms that entrain fi re regimes and to forecast 

the implications of climate change (Keane and others 1997), fragmentation 

(Finney 2005), and other disturbances across landscapes and regions. Finally, 

the ability to quantify the locations and magnitude of hazardous fuel is critical for designing defensible space and protecting communities.

Acknowledgments

We acknowledge Robert E. Keane; Mark A. Finney, Charles McHugh, and 

James Menakis with the USDA Forest Service Rocky Mountain Research Station, Missoula, Montana for their thoughtful contributions to LANDFIRE 

methods. We acknowledge Gregory Dillon, Donald Long, Matt C. Reeves, 

and Brendan Ward, with the USDA Forest Service Rocky Mountain Research 

Station, Missoula, Montana and Karen Short with Systems for Environmental Management, Missoula, Montana for their professional support in the 

development and implementation of software and procedures critical to the 

creation of LANDFIRE products. A large national project could not succeed 

without a business management team, and we therefore also acknowledge 

Daniel Crittenden, Bruce Jeske, and Timothy Melchert with the USDA Forest Service Offi ce of Fire and Aviation Management and Henry Bastian with 

the U.S. Department of Interior Offi ce of Wildland Fire Coordination, for 

their professional business support.

References

Anderson, H. E. 1982. Aids to determining fuel models for estimating fi re behavior. 

Gen. Tech. Rep. INT-122. Ogden, UT: U.S. Department of Agriculture, Forest 

Service, Intermountain Forest and Range Experiment Station. 26 p.

Andrews, P. L.; Bevins, C. D.; Seli, R. C. 2005. BehavePlus fi re modeling system, 

version 3.0: User’s Guide. Gen. Tech. Rep. RMRS-GTR-106. Ogden, UT: U.S. 

Department of Agriculture, Forest Service, Rocky Mountain Research Station. 

142 p.

Comer, P.; Faber-Langendoen, D.; Evans, R.; Gawler, S.; Josse, C.; Kittel, G.; 

Menard, S.; Pyne, M.; Reid, M.; Schulz, K.; Snow, K.; Teague J. 2003. Ecological 

Systems of the United States: A working classifi cation of U.S. terrestrial systems. 

NatureServe, Arlington, VA. 75 p.

Daubenmire, R. 1968. Plant communities: A textbook of plant synecology. New 

York: Harper and Row Publishing. 300 p.

Eyre, F. H. E. 1980. Forest cover types of the United States and Canada. Washington 

DC., USA: Society of American Foresters. 147 p.
USDA Forest Service Proceedings RMRS-P-41. 2006. 199

Landfi re: Landscape Fire and Resource Management Planning Tools Project Ryan, Lee, Rollins, Zhu, Smith, and Johnson

Finney, M. A. 1998. FARSITE: Fire Area Simulator-model development and 

evaluation. Res. Pap. RMRS-RP-4. Ogden, UT: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research Station. 47 p.

Finney, M. A. 2001. Design of regular landscape fuel treatment patterns for 

modifying fi re growth and behavior. Forest Science 47(2):219-228.

Finney, M. A. 2005. The challenge of quantitative risk analysis for wildland fi re. 

Forest Ecology and Management 211:97-108.

Hann, W. J.; Bunnell, D. L. 2001. Fire and land management planning and 

implementation across multiple scales. International Journal of Wildland Fire 

10:389-403.

Hann, W.; Shlisky A.; Havlina, D.; Schon, K.; Barrett, S.; DeMeo, T.; Pohl, K.; 

Menakis, J.; Hamilton, D.; Jones, J.; Levesque, M. 2004. Interagency Fire 

Regime Condition Class Guidebook. Interagency and The Nature Conservancy 

fi re regime condition class website .USDA Forest Service, US Department of the 

Interior, The Nature Conservancy, and Systems for Environmental Management. 

[Online] http://www.frcc.gov/ [April 2006].

Henderson, J. A.; Peter, D. H.; Lesher, R. D.; Shaw, D. C. 1989. Forested plant 

associations of the Olympic National Forest. Technical Paper R6-ECOL-TP 

001-88. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacifi c 

Northwest Region. 502 p.

Keane, R., E.; Hardy, C. C.; Ryan, K. C.; Finney, M. A. 1997. Simulating effects of 

fi re on gaseous emissions and atmospheric carbon fl uxes from coniferous forest 

landscapes. World Resource Review. 9(2):177-205.

Keane, R. E.; Burgan, R.; vanWagtendonk J. 2001. Mapping wildland fuels for 

fi re management across multiple scales: Integrating remote sensing, GIS and 

biophysical setting. International Journal of Wildland Fire 10:301-319.

Keane, R. E.; Parsons, R.; Hessburg, P. 2002. Estimating historical range and 

variation of landscape patch dynamics: limitations of the simulation approach. 

Ecological Modeling 151: 29-49.

Lutes, D.; Keane, R. E.; Caratti, J. [in prep]. Fuel loading models: a national 

classifi cation of wildland fuels for fi re effects modeling. Submitted to Canadian 

Journal of Forest Research.

Ottmar, R. D.; Burns, M. F.; Hall, J. N.; Hanson, A. D. 1993. CONSUME user’s 

guide. Gen. Tech. Rep. PNW-GTR-304. U.S. Department of Agriculture, Forest 

Service, Pacifi c Northwest Research Station, Portland, OR. 17 p.

Pfi ster, R. D.; Kovalchik, B. L.; Arno S. F.; Presby, R. C. 1977. Forest habitat-types 

of Montana. Gen. Tech. Rep. GTR-INT-34. Ogden, UT: U.S. Department of 

Agriculture, Forest Service, Intermountain Forest and Range Experiment Station, 

Ogden. 174 p.

Reinhardt, E. D.; Crookston, N. L. (tech. eds.) 2003. The Fire and Fuels Extension 

to the Forest Vegetation Simulator. Rocky Mountain Research Station RMRSGTR-116. 218 p.

Reinhardt, E.; Keane, R. E.; Brown, J. K. 1997. First Order Fire Effects Model: 

FOFEM 4.0 User’s Guide. Gen. Tech. Guide INT-GTR-344. Ogden, UT: U.S. 

Department of Agriculture, Forest Service, Intermountain Research Station. 

74 p.

Rothermel, R. C. 1972. A mathematical model for predicting fi re spread in wildland 

fuel. Res. Pap. INT-115. Ogden, UT: U.S. Department of Agriculture, Forest 

Service, Intermountain Forest and Range Experiment Station. 42 p.

Rothermel, R. C. 1991. Predicting behavior and size of crown fi res in the Northern 

Rocky Mountains. Res. Pap. INT-438. Ogden, UT: U.S. Department of 

Agriculture, Forest Service, Intermountain Forest and Range Experiment Station. 

46 p.

Sandberg, D. V.; Ottmar, R. D.; Cushon G. H. 2001. Characterizing fuel in the 

21st century. International Journal of Wildland Fire 10:381-387.
200 USDA Forest Service Proceedings RMRS-P-41. 2006.

Ryan, Lee, Rollins, Zhu, Smith, and Johnson Landfi re: Landscape Fire and Resource Management Planning Tools Project

Schmidt, K. M.; Menakis, J. P.; Hardy, C. C.; Hann, W. J.; Bunnell, D. L. 2002. 

Development of coarse-scale spatial data for wildland fi re and fuel management. 

Gen. Tech. Rep. RMRS-GTR-87. Fort Collins, CO: U.S. Department of 

Agriculture, Forest Service, Rocky Mountain Research Station. 41 p.

Scott, J. H. 2003. Canopy fuel treatment standards for the wildland-urban interface. 

In: Fire, fuel treatments, and ecological restoration conference: proceedings; 

2002 April 16-18; Fort Collins, CO. Omi, Philip N.; Joyce, Linda A., tech. eds. 

Proceedings RMRS-P-29. Fort Collins, CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research Station: 29-37.

Scott, J. H.; Burgan, R. E. 2005. Standard fi re behavior fuel models: a comprehensive 

set for use with Rothermel’s surface fi re spread model. Gen. Tech. Rep. RMRSGTR-153. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, 

Rocky Mountain Research Station. 72 p.

Scott, J. H.; Reinhardt, E. D. 2001. Assessing crown fi re potential by linking models 

of surface and crown fi re behavior. USDA Forest Service Rocky Mountain Research 

Station Research Paper RMRS-RP-29. Fort Collins, CO.

Stratton, R. D. 2004. Assessing the effectiveness of landscape fuel treatments on 

fi re growth and behavior. Journal of Forestry 102(7): 32-40.

U.S. Department of Agriculture, U.S. Department of the Interior. 1995. Federal 

wildland fi re management, policy and program review-fi nal report. National 

Interagency Fire Center: Boise, ID. 45 p.

U.S. Department of Agriculture; U.S. Department of the Interior. 2000. National 

fi re plan: managing the impact of wildfi re on communities and the environment. 

Washington, DC: U.S. Department of Agriculture; U.S. Department of the 

Interior. 35 p.

U.S. Department of Agriculture; U.S. Department of the Interior. 2001. A 

Collaborative approach for reducing wildland fi re risks to communities and the 

environment 10-Year comprehensive strategy, August 2001.

Van Wagner, C. E. 1977. Conditions for the start and spread of crownfi re. Canadian 

Journal of Forest Research 7:23-24.

Van Wagner, C. E. 1993. Prediction of crown fi re behavior in two stands of jack 

pine. Canadian Journal Forest Research 23:442-449
USDA Forest Service Proceedings RMRS-P-41. 2006. 201

In: Andrews, Patricia L.; Butler, Bret W., 

comps. 2006. Fuels Management—How to 

Measure Success: Conference Proceedings. 

28-30 March 2006; Portland, OR. 

Proceedings RMRS-P-41. Fort Collins, 

CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research 

Station.

1 Fire Ecologist, U.S. Department of 

Agriculture, Forest Service, Bitterroot 

National Forest, Hamilton, MT U.S.A. 

and Wildland Fire Scientist, Ensis 

Bushfi re Research, Christchurch, New 

Zealand. tsopperman@fs.fed.us

2 Research Leader, Ensis Bushfire 

Research, Christchurch, New Zealand 

and Research Leader, Ensis Bushfi re 

Research, and Bushfi re CRC Program 

Leader, Kingston, ACT, Australia 

2004.

3 Research Forester, U.S. Department 

of Agriculture, Forest Service, Fire 

Sciences Laboratory, Missoula, MT 

U.S.A.

4 Fire Science and Research Offi cer, 

Wildfi re Policy and Business Planning 

Forest Protection Division, Alberta 

Sustainable Resource Development, 

Edmonton, Alberta, Canada.

Abstract—There is currently no spatial wildfi re spread and growth simulation model 

used commonly across New Zealand or Australia. Fire management decision-making 

would be enhanced through the use of spatial fi re simulators. Various groups from 

around the world met in January 2006 to evaluate the applicability of different spatial 

fi re spread applications for common use in both New Zealand and Australia. Developers and researchers from Canada, the United States, and Australia were invited to 

apply Prometheus, FARSITE, and other similar models to New Zealand and Australian 

wildfi res in grass, scrub, and forested fuel types. Although the lack of site-specifi c fuel 

models and weather data were a concern, coarse spatial and temporal data inputs 

proved adequate for modeling fi res within a reasonable margin of error. The choice 

of grass models proved less important than expected since spread rates were easily 

manipulated through moisture content values during calibration. The fi nal modeled 

perimeters are affected by several user inputs that are impossible to separate from 

model error. These various inputs exist to allow experienced users to approximate local 

environmental variability as closely as possible to obtain successful outputs. Rather than 

attempt to quantify direct comparisons, local users concluded it was more important 

to choose an application that provides an appropriate level of functionality, that is 

compatible with current data and fi re management systems, and that can be easily 

modifi ed to use unique and varied fi re spread equations. Prometheus and FARSITE 

performed very well and will be further investigated to understand how each might 

be customized for use with local fi re spread models. This paper describes the process 

and results of testing some existing fi re growth simulation models for use on fi res in 

New Zealand and Australia.

Introduction

Australian and New Zealand fi re managers have a need for spatial fi re spread 

simulators for planning and operations. The New Zealand Department of 

Conservation (DOC) and the National Rural Fire Authority are interested in 

adopting a spatial fi re growth simulation model for enhanced decision-making. New Zealand’s native vegetation is not generally fi re-adapted, and DOC 

must measure conservation success by comparing the actual area burned to 

the potential area burned without suppression. Australia, a more fi re-prone 

nation, has experienced some of its most devastating wildfi res in the past 

two decades with signifi cant damage to property, infrastructure, and the 

environment, including loss of civilian lives. In response to these wildfi res, 

the Australian government has recommended continued development and 

Applying Fire Spread Simulators in

New Zealand and Australia: Results

from an International Seminar

Tonja Opperman1, Jim Gould2, Mark Finney3, and Cordy Tymstra4
202 USDA Forest Service Proceedings RMRS-P-41. 2006.

Opperman, Gould, Finney, and Tymstra Applying Fire Spread Simulators in New Zealand and Australia: Results from an International Seminar

coordination of wildfi re simulation models to enhance decision-making. Unlike New Zealand, Australian land management agencies need to conduct 

prescribed burning for hazardous fuel reduction. Using a simulator to assess 

changes in risk over time and space would be helpful. Additionally, private 

plantation companies in both nations are interested in how various wildfi re 

scenarios might affect their investments. Fire researchers are investigating 

whether a current model can be adapted for use in Australasia, or whether a 

new simulator requires development.

In recent years, with advances in computer speed and modeling, storage 

capacity and graphical capabilities, some fi re behavior models have been 

implemented in spatial fi re growth simulation models. These models can aid 

in understanding strategic placement of fuel treatments on the landscape to 

reduce overall fi re spread and potential fi re behavior (Finney 2002; Vojtek 

2006). A spatial fi re simulation tool allows fi re managers to quickly simulate several potential fi re scenarios and helps them evaluate fi re effects at a 

landscape scale.

Wildland fi re simulators combine spatial and temporal representations of 

fuels, weather, and topography to propagate point, line or polygon ignitions. 

Fire simulators are not new fi re behavior models. Calculations depend on 

the underlying mathematical expressions representing what are commonly 

referred to as ‘fi re behavior models’. Familiar surface fi re spread models include empirical models developed by McArthur (1967) and Forestry Canada 

Fire Danger Group (1992), and the semi-empirical model developed by Rothermel (1972). Some simulators incorporate additional models to calculate 

spotting and crown fi re initiation (Pastor and others 2003). Worldwide, over 

twenty spatial wildland fi re simulators have been developed for operations, 

planning, and research (Pastor and others 2003). Most of these simulators 

are designed to handle specifi c areas and requirements; few are suffi ciently 

robust for trans-continental applications (Johnston and others 2005). Ensis 

Bushfi re Research hosted an international workshop to evaluate several spatial 

fi re spread simulators that could be adopted in New Zealand or Australia. This 

paper describes the process and results of testing some existing fi re growth 

simulators for that purpose.

Fire Environments

The fi re environments and fi re histories of New Zealand and Australia are 

markedly different.

New Zealand consists of two main islands of 270,000 square kilometers 

isolated in the southwest Pacifi c Ocean. Indigenous vegetation types are not 

generally considered fi re adapted and New Zealand experiences relatively 

few naturally ignited fi res. Pine plantations, pasture grasslands, and exotic 

shrubs comprise the majority of non-native vegetation types that burn readily from human-caused ignitions. Rapidly changing conditions dominate the 

maritime-infl uenced weather and unrelenting winds exceeding 80 km/h are 

common. New Zealand has approximately 2500 rural vegetation fi res each 

year that, combined, burn approximately 7000 hectares. Fires are considered 

“large” if they are greater than 50 hectares and spread for more than one 

burn period.

Australia is located between the Indian and Pacifi c Oceans and is 7 million square kilometers, thus supporting a continental climate. Bushfi res are 
USDA Forest Service Proceedings RMRS-P-41. 2006. 203

Applying Fire Spread Simulators in New Zealand and Australia: Results from an International Seminar Opperman, Gould, Finney, and Tymstra

an inherent part of the Australian landscape. Few areas of Australia are free 

from fi re, and every decade, intense and widespread fi res burn in southeast 

Australia. As an example, the spring of 1974 witnessed 15% of Australia’s 

land area burned (Luke and McArthur 1978), and from 1960-2001 there 

were 224 fi re-related deaths, over 4500 injuries, and $2475 million dollars 

in damages (McMichael and others 2003). As such, this area has a reputation 

as one of the three most fi re-prone areas in the world along with southern 

California and southern France. Although fi re has proven important to the 

local ecosystems by shaping vegetation mosaics and maintaining biodiversity, 

it is one of the most signifi cant threats to human populations and infrastructure. Throughout the 20th century, many fi res have claimed lives, destroyed 

homes and livelihoods, and burned thousands of hectares. Land managers 

and fi re management agencies reduce this risk through a range of measures 

before and during fi res.

Fire Spread Models and the Need for a 

Common Simulator

New Zealand and Australia have approached fi re spread modeling somewhat differently. New Zealand fi re managers have adapted a limited number 

of empirical fi re spread models, mostly from the Canadian Fire Behavior 

Prediction system (FBP, Forestry Canada Fire Danger Group 1992; Pearce 

and Anderson 2004; Opperman and Pearce 2005). Australian researchers 

have developed empirical models based on experimental burns supplemented 

by reliable wildfi re observations. Both nations use qualifi ed fi re behavior 

analysts to predict fi re spread and behavior using computational spreadsheets 

or calculators and paper maps on fi re incidents. The McArthur Forest Fire 

Danger Meter (McArthur 1967) and Western Australia Forest Fire Behavior 

Tables (Sneeuwjagt and Peet 1985) are commonly used for fi re behavior 

prediction in open eucalypt forests in Australia, while Pearce and Anderson’s 

guide (2004) is used in New Zealand. Although fi re behavior analysts can 

readily provide point-based calculations and a perimeter for a single weather 

scenario, this time-intensive process leaves little time to develop potential 

perimeters for a variety of possible weather scenarios. Often, the Incident 

Commander has no basis for judging the error associated with the supplied 

perimeter. In contrast, fi re behavior analysts in the United States and Canada 

have spatial fi re simulators in their suite of predictive tools to quickly develop 

several potential fi re perimeters based on different weather scenarios.

Australia and New Zealand would benefi t from adopting the same fi re 

spread simulator. Although each nation can see immediate benefi ts by adopting the simulator that most closely refl ects current fi re management systems, 

this may prove diffi cult to manage in the long term. Fire management organizations in both nations are experiencing a shortage of fi refi ghting personnel 

and a loss of the technical skill base. Therefore, operational resources are 

often shared. If one simulator could be used in both countries, the resulting 

common technology transfer would represent a cost savings and allow skilled 

fi re behavior analysts to be shared. Although New Zealand and Australia 

differ in regards to fi re history, fuels, and fi re behavior models, both have a 

private and public need for fi re simulation models.
204 USDA Forest Service Proceedings RMRS-P-41. 2006.

Opperman, Gould, Finney, and Tymstra Applying Fire Spread Simulators in New Zealand and Australia: Results from an International Seminar

Simulators Evaluated at the Workshop

Six fi re simulators were presented at the workshop. Five of these simulators 

are systems that combine different fi re behavior models with multi-dimensional mathematical models to predict rates of spread in complex environmental 

conditions varying spatially and temporally. Time-dependent fi re spread is 

calculated appropriate to local conditions to output tabular or graphical representations of fi re area, fi re perimeter, fi re numbers, and fi re characteristics. 

Of the simulators examined at the seminar, FARSITE (Finney 1998) and 

Prometheus (Tymstra and others 2006) are operational in their respective 

countries; the Portable Fire Growth Model (Shamir, pers. comm.) and the 

Bushfi re CRC computer simulation project are under development (Johnston, 

pers. comm.). Networked Fire Chief (Omodei and others 2004) is not a fi re 

spread simulator, but a research decision tool to generate fi re scenarios. A 

new model based on Minimum Travel Time (MTT, Finney 2002) was also 

demonstrated. This technique solves for fi re arrival time across the landscape 

using Fermat’s principle, which is essentially the inverse of Huygen’s and 

produces nearly identical results given homogeneous temporal data. This 

evaluation focuses on the two mature operational fi re spread systems—FARSITE and Prometheus.

FARSITE (Finney 1998) was developed in the U.S. and has been in use 

since the early 1990s (Finney 1994). It relies on a wave-front expansion 

technique called Huygens’ principle to achieve two-dimensional elliptical 

fi re growth (Anderson 1983; Richards 1990) using existing one-dimensional 

models of fi re behavior. Fire behavior support in FARSITE includes surface 

fi re (Rothermel 1972), crown fi re (Van Wagner 1977, 1993; Rothermel 

1991), dead fuel moisture (Nelson 2000) and spotting from torching trees 

(Albini 1979). FARSITE generates vector and raster maps of fi re growth and 

behavior (time of arrival, fi reline intensity, rate of spread, fl ame length, heat 

per unit area, and fi re type), which can be exported as ASCII grids. FARSITE 

inputs may be used with FlamMap, which computes fi re behavior for every 

landscape cell using a single wind and weather scenario. FlamMap includes 

the recently developed and experimental fi re simulation techniques called the 

Treatment Optimization Model (TOM, Finney 2001) and Minimum Travel 

Time (MTT, Finney 2002).

The Canadian fi re growth simulation model, Prometheus, was also tested. 

The foundations of the Prometheus model are the Fire Weather Index (FWI) 

and the Fire Behavior Prediction (FBP) Sub-Systems of the Canadian Forest Fire Danger Rating System (CFFDRS) (Van Wagner 1987; Forestry 

Canada Fire Danger Group 1992). Prometheus incorporates two sets of elliptical growth equations to mathematically expand the elliptical wave front: 

two-dimensional differential equations defi ned in Richards (1990) and threedimensional equations defi ned in Richards (1999) to simulate fi re growth 

over a three-dimensional surface. A variety of FBP outputs (fi re intensity, 

rate of spread, surface fuel consumption, crown fuel consumption, and total 

fuel consumption) can be exported as ASCII grids. Software engineering of 

Prometheus began in 2000. The Microsoft COM architecture of this model 

provides for the reusability and extension of its components. As examples, 

burn probability mapping applications such as Burn-P3 (Parisien and others 

2005) and batch routine applications such as Pandora re-use Prometheus 

functionality.
USDA Forest Service Proceedings RMRS-P-41. 2006. 205

Applying Fire Spread Simulators in New Zealand and Australia: Results from an International Seminar Opperman, Gould, Finney, and Tymstra

Methods

Acquiring Simulation Data

Inputs for the fi re simulators differed slightly, though each required a digital 

elevation model (DEM), weather, and fuel data. Although the DEMs were 

relatively easy to acquire and import into the simulators, it proved diffi cult 

to identify wildfi res with adequate geospatial records and nearby weather stations or on-site weather observations, fi re narratives, or photographs of fi re 

behavior. In New Zealand, all fi nal perimeters are impacted by suppression 

within the fi rst burning period, which makes it diffi cult to assess free-burning fi re behavior. Conversely, Australia experiences very fast moving, high 

intensity fi res that are diffi cult to quantify during the event. Weather data, 

once acquired, had to be manually transformed into unique input fi les for 

each application. In some cases, the nearest weather station data were recorded 15 kilometers from the fi re and did not refl ect conditions at the fi re 

site. Visiting the site, speaking with the Incident Commander, and making 

insightful adjustments to the wind direction values were necessary to spread 

the simulated fi re in the observed direction.

The required fuel model grids were not readily available. FARSITE requires 

ASCII grids of Rothermel-based fuel models (Rothermel 1972; Anderson 

1982; Scott and Burgan 2005) and canopy cover. Prometheus also requires 

ASCII grids of FBP fuel models. New Zealand had a local fuel model map 

derived from the national vegetation database. Australia had fuel maps 

coded in “grass” and “forest” fuel models. We used a satellite-derived land 

cover database with vegetation descriptions to assign the required fuel types 

judged to be reasonably close in fuel depth and loading to those models 

available for each simulator. Estimates were confi rmed through on-site visits 

and discussions with experienced fi re managers, helping to refi ne fuel maps. 

Several optional layers can be used in FARSITE for modeling crown fi re initiation and spotting fi rebrands from trees, but the vegetation databases did 

not contain attributes other than land cover classes. Tree height and crown 

base height were estimated for each fuel type based on local knowledge; a 

constant value was used for crown bulk density. Prometheus was designed to 

use Canadian-based fuel types, and modifi cations were made to incorporate 

the custom New Zealand fuel types that are based on the Canadian models. 

Empirical fi re behavior data were available to assist fuel model assignments 

in some fuel types.

Simulating the Fires

Two New Zealand fi res and one Australian fi re were modeled during the 

workshop. Before modelers were asked to predict fi re spread, it was necessary 

to discuss the local fi re environments. Invited modelers, Ensis research staff, 

and local DOC fi re managers visited several New Zealand fi re sites to discuss 

local fuels, weather, topography, and burn progression. The Australian fi re 

environment, fi re behavior, and fi re reconstruction were detailed in a slide 

presentation (Jim Gould, pers. comm.).

Data were provided to modelers both before and during the workshop. 

Providing data before the workshop allowed modelers to assess data quality 

and convert fi les to formats unique to their applications. New Zealand input 

data were made available to modelers one month prior to the meeting. These 

data included tabular fi re weather data; shapefi les of fi re ignition points and 
206 USDA Forest Service Proceedings RMRS-P-41. 2006.

Opperman, Gould, Finney, and Tymstra Applying Fire Spread Simulators in New Zealand and Australia: Results from an International Seminar

times, fi nal fi re perimeters and times; ASCII grids of elevation, aspect, slope, 

and local vegetation types; and a crosswalk table for creating new ASCII grids 

of fuel models specifi c to each application. The fi nal data were provided at 

the workshop. Australian data were provided at the start of the workshop to 

test the applications’ ability to quickly import data from a new source.

Fire Simulation Results

Participants assembled in one room to concurrently run the simulators 

on each fi re. Input parameters were fi rst discussed to ensure simulators used 

the most similar and accurate inputs as possible with regard to weather stations, wind speed modifi cations, use of fi re spread barriers, manual fuel type 

changes, and simulation duration. The group examined the results in detail 

after each fi re was modeled. These results serve to compare not only the applications but also the underlying fi re behavior fuel models.

Craigeburn Fire, New Zealand

The Craigeburn Fire was a human-caused point ignition in the Canterbury 

region of the South Island in January 2004. It burned 548 hectares in tussock 

grassland with mixed hardwood and native shrub gullies. Full suppression 

actions with aircraft began within an hour of the ignition. The fi re spread 

for approximately seven hours under strong northerly winds.

Figure 1 illustrates the Craigeburn Fire model results from FARSITE and 

Prometheus. When the fi re was fi rst modeled using the wind stream from 

the distant weather station, the fi res spread east rather than south. Therefore, the teams modifi ed the weather fi le wind directions, but left the wind 

Figure 1—For the New Zealand Craigeburn Fire, FARSITE (left) simulated fi re perimeters (white) 

against the fi nal fi re perimeter (black); Prometheus (right) simulated perimeters (black) against 

the actual fi re (red). Both simulations are reasonable, especially if the effect of suppression is 

considered.
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Applying Fire Spread Simulators in New Zealand and Australia: Results from an International Seminar Opperman, Gould, Finney, and Tymstra

speed untouched. FARSITE over-predicted the right and left fl anks and the 

extent of the backing fi re, while predicting the heading fi re well. Prometheus 

under-predicted the fi re’s right fl ank, slightly over-predicted the heading and 

backing fi res, and predicted the left fl ank well. Considering that suppression 

dramatically reduced the actual fi re extent, both models achieved a reasonable outcome on this relatively simple fi re.

Cora Lynn Fire, New Zealand

In March 2001, the Cora Lynn fi re burned 360 hectares of grass, native 

shrubs and native beech forest in steep, rocky terrain. The fi re burned for 

10 hours with full suppression consisting of several helicopters and ground 

personnel. The native beech forest fuel type was interesting to model because 

there are no straightforward fuel models in the Canadian or U.S.-based 

systems. FARSITE used a moderate load humid timber shrub model (TU2) 

with increased fuel moisture to model the very slow fi re spread appropriately. 

Prometheus used the custom New Zealand indigenous forest model based 

on FBP’s M-2 (mixed hardwoods), but found the fuel model was spreading 

Figure 2—The Minimum Travel Time (MTT) model shows a slightly different shape 

for the New Zealand Craigeburn Fire. Although it uses the same fi re behavior models 

as FARSITE, it propagates fi re through regularly spaced nodes (Fermat’s principle) 

rather than wave fronts (Huygens’ principle) and uses constant rather than varied 

wind and weather inputs.
208 USDA Forest Service Proceedings RMRS-P-41. 2006.

Opperman, Gould, Finney, and Tymstra Applying Fire Spread Simulators in New Zealand and Australia: Results from an International Seminar

fi re too rapidly. With some minor calibration and fuel model adjustments 

from brush to rock, both simulators were able to model the Cora Lynn Fire 

reasonably well.

Wangary Fire, Australia

The Wangary Fire burned on the Lower Eyre Peninsula in South Australia 

in January 2005. The fi re spread rapidly in grass, brush, and forested fuel 

types to a fi nal extent of 77,000 hectares. Suppression efforts were hampered 

by extreme fi re behavior during the second burning period when a wind shift 

pushed the left fl ank east and northeast. This simulation was unique in that 

the previous day’s burned area was provided, and in that multiple ignition 

points needed to be modeled only for the second burn period.

The differences between observed and modeled perimeters were within 

acceptable limits. Prometheus over-predicts the fi re’s southern edge; this may 

be because the FBP grass fi re model, which was set at 95% curing, is known 

to over-predict under these conditions (Figure 3). FARSITE uses the stylized Rothermel-based grass model GR6 (moderate load, humid climate grass, 

dynamic) (Scott and Burgan 2005). The FARSITE simulation more closely 

approximates the fi re’s southern edge; however, the fi re was simulated using 

the same ignition time for the four ignition points. Prometheus used the 

actual, varied ignition times for the four ignition points, and this difference 

will certainly have an impact on the generated perimeters.

FARSITE over-predicts the northwest fi re edge where suppression activities 

were occurring, while Prometheus did so to a lesser degree. This may also 

be accounted for by differences in the fuel models and in ignition times. The 

potential actual fi re growth in this direction is diffi cult to approximate when 

one considers the amount of suppression that took place in that area.

Prometheus over-predicts the fi re at the northeast edge, while the FARSITE simulation is closer in that respect. FARSITE was running at a coarse 

tolerance for vertex separation (400m); Prometheus was running at a fi ner 

tolerance (50m). Interestingly, through our discussions of this simulation 

we determined that the vertex resolution was deemed inconsequential due 

Figure 3—FARSITE (left) and Prometheus (right) modeled Australia’s Wangary Fire. Although 

there are differences in the fi re spread model, how suppression was modeled in each simulator, 

and the starting times of the spot fi res, the resulting perimeters still coincide reasonably well 

with the fi nal fi re edge (black).
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Applying Fire Spread Simulators in New Zealand and Australia: Results from an International Seminar Opperman, Gould, Finney, and Tymstra

to the low variability of the spatial data. Despite these differences, both of 

the modeled perimeters successfully approximated the fi nal fi re perimeter in 

a reasonable length of time.

Discussion: What We Learned

The lack of a site-specifi c fuel model and weather data was a concern, but 

our coarse approach regarding crosswalking land cover classes to fuel models 

proved adequate. Site visits were instrumental in determining the most appropriate interpretation of wind observations several kilometers away from 

the fi re. This was imperative because the simulations were not useful without 

local wind data. The Craigeburn fi re illustrates this well; local winds infl uencing the fi nal fi re shape are impossible to know.

The choice of grass models proved less important than expected since they 

could be easily manipulated to spread faster or slower through moisture content values during the calibration process. There is great latitude in deciding 

what fuel types to use, because a particular fi re can be modeled well using 

a variety of combinations of fuels, winds, and moisture contents that are all 

within the uncertainty of actual data. The coarse vegetation maps and fuel 

model crosswalks proved adequate for representing fuel conditions in fi res 

we modeled, but they will constrain use of simulators in diverse fuel complexes—a known problem for any simulator.

The ability to adapt the simulators to the local fi re environments was mixed. 

It was necessary to create solar radiation effects from the north rather than 

the south and simulating summer day lengths in January. Entering a negative 

latitude in FARSITE changed the sun angle and automatically changed day 

length for the fi re date. Adding six months to the date, and selecting New 

Zealand and Australian time zone settings in Prometheus were necessary to 

simulate appropriate conditions. FARSITE was unable to readily input weather 

streams that crossed into a new calendar year, which was problematic for fi res 

igniting on January 1 and requiring three prior days of fuel conditioning 

weather data. Several of these identifi ed problems have since been fi xed in 

both simulators.

The disadvantage of both simulators was that each is built around one 

set of fi re spread equations. FARSITE currently implements fi re behavior 

models based on Rothermel (1972), and Prometheus implements fi re behavior models based on Canadian fi re spread equations (Forestry Canada 

Fire Danger Group 1992). Although fi re spread equation coeffi cients can be 

user-manipulated to some degree, neither Prometheus nor FARSITE supports 

the entry of fully customized fi re spread equations with varying parameters. 

Though some simulation inputs were easily manipulated, the ability to use 

locally developed equations is an important feature of any Australasian spatial 

fi re simulator because several varied fi re spread equations are in use or under 

development.

Each fi re simulator handles timesteps and vertices differently. FARSITE 

uses an internal dynamic time step that is adjusted to control spatial resolution of the calculations for execution performance. Prometheus employs 

user-defi ned fi xed timesteps for direct control. FARSITE merges fi res and 

eliminates vertices on the fi re perimeters that cross, whereas Prometheus 

retains the separate identity of individual fi res and renders vertices inert. 

Prometheus uses many more vertices than FARSITE to represent the active 

fi re front. Prometheus by default uses a vertex resolution that matches that 
210 USDA Forest Service Proceedings RMRS-P-41. 2006.

Opperman, Gould, Finney, and Tymstra Applying Fire Spread Simulators in New Zealand and Australia: Results from an International Seminar

of the grid data, and FARSITE by default uses a coarser resolution to address performance concerns, and to intentionally ignore minor variations in 

the grid fuel map.

In Prometheus and FARSITE, the fi nal modeled perimeter is strongly 

infl uenced by several user inputs and settings that are impossible to separate 

from model error. These various inputs and settings are necessary to allow 

experienced users to approximate the local environmental variability as closely 

as possible and to control the computational intensity of the simulation to 

match time or computer constraints. Interestingly, the two models do not 

share the same reconfi guration options. This fact complicated direct comparisons of outputs.

Even though both simulators were developed independently, they share 

very similar functionalities and user interface designs. The differences were 

infl uenced in part by their operational roles in their respective countries. 

FARSITE is more adept at handling different weather stream formats and 

has more displays of different data. Prometheus can simultaneously simulate 

and display outputs from differently confi gured scenarios (variations in user 

settings, and in spatial and temporal data are allowed) for direct comparisons 

within the model.

Direct comparison of Prometheus and FARSITE is diffi cult because modeling fi re perimeters is as much art as science. We cannot conclude whether 

one application is better based solely on the ability to predict fi re spread, 

size, and shape due to differences in underlying fuel models and computation implementations, and an inability to separate user error from model 

error. Although both models performed reasonably well, they still required 

minor tuning with respect to the computational implementations of the fuel 

equations. This suggests that these models should be operated by expert users who are aware of their intricacies. Exact agreement between models and 

against the observed fi res is not possible for many reasons, but the degree of 

similarity between these systems suggests that the application of Huygens’ 

principle and assumed independence of segments of the fi re front is justifi ed 

for the grass fi res tested. Thus, we conclude that it was more important to 

choose an application compatible with current data availability, current fi re 

management systems, and that can be modifi ed to use unique and varied fi re 

spread equations.

This seminar was an excellent technology transfer opportunity. Modeling 

fi res together in one room with different models was more advantageous than 

we anticipated; the opportunity to run the applications side-by-side is what 

made this seminar extraordinary. Modelers gained an appreciation for the 

need to accommodate a variety of different fi re spread equations and parameters in one fi re spread simulation system. Application developers, computer 

scientists, fi re managers, fi re behavior scientists, and GIS specialists learned 

from each other, were inspired to try new approaches to problems, considered 

new concepts, and established relationships with international fi re modeling 

colleagues.

Conclusions

Determining how to pursue adoption of a New Zealand or Australian spatial fi re growth simulator requires further consideration and will take place 

over the next several months.
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Applying Fire Spread Simulators in New Zealand and Australia: Results from an International Seminar Opperman, Gould, Finney, and Tymstra

This seminar provided a fi rst step in sharing available information. Although 

the scale of wildland fi re in New Zealand versus Australia differs signifi cantly, 

their fi re management and research institutions are geographically and politically linked. Currently, there is no spatial fi re spread simulator used in either 

country, but interest is growing among Australasian fi re managers to adopt 

a common tool to enhance decision-making for operations and planning, 

especially with regard to reconstructing fi re events to measure the success 

of suppression operations or investigate potential fi re behavior. Among the 

numerous considerations, the fl exibility in incorporating local fi re behavior models into one of these systems will be important. Simulators such as 

FARSITE and Prometheus both appear to be well suited to modeling fi res 

in New Zealand and Australia.

Literature Cited

Albini, F. A. 1979. Spot fi re distance from burning trees—a predictive model. For. 

Serv. Gen. Tech. Rep. INT-56. U.S. Department of Agriculture, Forest Service, 

Intermountain Research Station.

Anderson, H. E. 1983. Predicting wind-driven wildland fi re size and shape. For. 

Serv. Res. Pap. INT-305. U.S. Department of Agriculture, Forest Service, 

Intermountain Research Station.

Finney, M. A. 1994. Modeling the spread and behavior of prescribed natural fi res. 

Proc. 12th Conf. Fire and Forest Meteorology: 138-143.

Finney, M. A. 2001. Design of regular landscape fuel treatment patterns for 

modifying fi re growth and behavior. Forest Science 47:219-228.

Finney, M. A. 2002. Fire growth using minimum travel time methods. Canadian 

Journal of Forest Research 32:1420-1424.

Finney, M. A. 1998. FARSITE: Fire Area Simulator—model development and 

evaluation. Research Paper RMRS-RP-4. U.S. Department of Agriculture, Forest 

Service, Rocky Mountain Research Station.

Forestry Canada Fire Danger Group. 1992. Development and structure of the 

Canadian forest fi re behavior prediction system. Canada Information Report 

ST-X-3. Canadian Department of Forestry. Ottawa, Ontario.

Gould, J. 2006. Pers. comm. 16 January.

Johnston, P.; Milne, G.; Klemitz, D. 2005. Overview of bushfi re spread simulation 

systems. Report prepared for the Bushfi re CRC. School of Computer Science and 

Software Engineering, University of Western Australia. 31 March 2005. 24 p.

Johnston, P. 2006. Pers. comm. 16 January.

Luke, R. H.; McArthur, A. G. 1978. Bushfi res in Australia. Australian Government 

Publishing Service, Canberra, Australia.

McArthur, A. G. 1967. Fire behaviour in eucalypt forests. Technical Report Leafl et 

No.107. Commonwealth Forestry and Timber Bureau, Canberra, Australia.

McMichael, A. J.; Woodruff, R. E.; Whitton, P.; Hennessy, K.; Micholls, N.; Hales, 

S.; Woodward, A.; Kjellstrom, T. 2003. Human health and climate change in 

Oceania: a risk assessment. Commonwealth Department of Health and Ageing. 

116 p.

Nelson, R. M. 2000. Prediction of diurnal change in 10-h fuel stick moisture 

content. Canadian Journal of Forest Research 30:1071-1087.

Omodei, M.; Elliott, G.; Walshe, M. 2004. Development of a computer simulated 

wildfi re scenarios for the experimental investigation of unsafe decision-making. 

Bushfi re CRC Report No. 2:2004.
212 USDA Forest Service Proceedings RMRS-P-41. 2006.

Opperman, Gould, Finney, and Tymstra Applying Fire Spread Simulators in New Zealand and Australia: Results from an International Seminar

Opperman, T. S.; Pearce, H. G. 2005. New Zealand fi re behaviour prediction models 

and assumptions. Report No. 38945. Ensis Forest Biosecurity and Protection, 

Bushfi re Research. Christchurch, New Zealand.

Parisien, M. A.; Kafka, V. G.; Hirsch, K. G.; Todd, J. B.; Lavoie, S. G.; Maczek, P. 

D. 2005. Mapping wildfi re susceptibility with the BURN-P3 simulation model. 

Informational Report NOR-X-405.Natural Resources Canada, Canadian Forest 

Service, Northern Forestry Centre, Edmonton, Alberta. 36 p.

Pastor, E.; Zarate, L.; Plana, E.; Arnaldos, J. 2003. Mathematical models and 

calculation systems for the study of wildland fi re behavior. Progress in Energy 

and Combustion Science 29: 139-153.

Pearce, H. G.; Anderson, S. A. J. 2004. Field guide to fi re behaviour in New Zealand 

fuel types. Unpublished report on fi le, Ensis Forest Biosecurity and Protection, 

Bushfi re Research, Christchurch, New Zealand.

Richards, G. D. 1990. An elliptical growth model of forest fi re fronts and its numerical 

solution. Int. J. Num. Methods Eng. 30:1163-1179.

Richards, G. D. 1995. A general mathematical framework for modeling twodimensional wildland fi re spread. Intl. J. Wildland Fire 5(2):63-72.

Richards, G. D. 1999. The mathematical modeling framework and computer simulation 

of wildland fi re perimeter growth over a 3-Dimensional surface. Int. J. Wildland 

Fire 9(3):213-221.

Rothermel, R. C. 1972. A mathematical model for predicting fi re spread in wildland 

fuels. Research Paper INT-115.U.S. Department of Agriculture, Forest Service, 

Intermountain Research Station.

Rothermel, R. C. 1991. Predicting behavior and size of crown fi res in the northern 

Rocky Mountains. Res. Pap. INT-438. U.S. Department of Agriculture, Forest 

Service, Intermountain Research Station.

Scott, J.; Burgan, R. 2005. Standard fi re behavior fuel models: a comprehensive set 

for use with Rothermel’s surface fi re spread model. Gen. Tech. Rep. RMRS-GTR153. U.S. Department of Agriculture, Forest Service, Rocky Mountain Research 

Station, Fort Collins, CO. 72 p.

Shamir, R. 2006. Pers. comm. 16 January.

Sneeujagt, R.; Peet, G. 1985. Forest fi re behavior tables for Western Australia, 3rd ed. 

Department of Conservation and Land Management, Perth, Western Australia.

Tymstra, C.; Bryce, R. W.; Wotton, M; Armitage, B. 2006. Prometheus — the 

Canadian Wildland Fire Growth Model: Model Development and Validation. Inf. 

Rep. In preparation. Alberta Sustainable Resource Development/Nat. Resour. 

Can., Can. For. Serv., North. For. Cent.

Van Wagner, C. E. 1977. Conditions for the start and spread of crownfi re. Canadian 

Journal of Forest Research 7:23-24.

Van Wagner, C. E. 1987. Development and Structure of the Canadian Forest Fire 

Weather Indexs System. Tech. Rep. 35. Agric. Can. For. Serv., Ottawa.

Van Wagner, C. E. 1993. Prediction of crown fi re behavior in two stands of jack 

pine. Canadian Journal of Forest Research 23:442-449.

Vojtek, S. L. 2006. Decreasing average fi re size through random fuel treatments: A 

boreal forest case study. Master of Environmental Studies, University of Waterloo, 

Waterloo, ON, Unpublished thesis, submitted.
USDA Forest Service Proceedings RMRS-P-41. 2006. 213

In: Andrews, Patricia L.; Butler, Bret W., 

comps. 2006. Fuels Management—How to 

Measure Success: Conference Proceedings. 

28-30 March 2006; Portland, OR. 

Proceedings RMRS-P-41. Fort Collins, 

CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research 

Station.

1 USDA Forest Service, Missoula 

Fire Sciences Laboratory, Missoula, 

Montana, mfi nney@fs.fed.us

Introduction

Computerized and manual systems for modeling wildland fi re behavior 

have long been available (Rothermel 1983, Andrews 1986). These systems 

focus on one-dimensional behaviors and assume the fi re geometry is a spreading line-fi re (in contrast with point or area-source fi res). Models included 

in these systems were developed to calculate fi re spread rate (Rothermel 

1972, Albini 1976), fi re shape (Anderson 1983, Alexander 1985), spot fi re 

distance (Albini 1979, 1983) and crown fi re spread rate (Van Wagner 1977, 

Rothermel 1991). The FlamMap program was developed for extending the 

utility of these models to a landscape-level where the necessary inputs have 

been mapped using geographic information systems (GIS). This paper documents the capabilities in FlamMap 3.0 and discusses some of the uses for 

such capabilities.

Features of FlamMap 3.0

General Features

All fi re behavior calculations assume that fuel moisture, wind speed, 

and wind direction are constant in time. FlamMap is designed, however, 

to examine spatial variability in fi re behavior, so it utilizes the same set of 

spatial inputs as the FARSITE fi re simulation system (Finney 1998). The 

fi re behavior calculations are performed independently for each cell on the 

gridded landscape.

These spatial inputs include eight GIS raster themes that describe fuels and 

topography (Figure 1) combined into a Landscape (LCP) File. Any raster 

resolution (the X- and Y-dimensions of the raster cells) can be used, but all 

layers must be identical in resolution, extent, and co-registered. The user is 

required to input initial fuel moisture conditions for each surface fuel model 

and the fuel model parameters for any custom surface fuel models present. 

There are two options for using fuel moistures in the calculations,

 1. Using a fi xed set of fuel moistures (by surface fuel model) is the default 

and allows direct comparison of fi re behavior across the landscape because fuel moisture can be set identically for all surface fuel models.

 2. Fuel moistures conditioned by a wind and weather stream is used to calculate localized moisture contents of dead surface fuel size-classes (1hr, 

10hr) that are infl uenced by the elevation, slope, aspect, and canopy 

cover (Nelson 2000).

An Overview of FlamMap Fire Modeling 

Capabilities

Mark A. Finney1
214 USDA Forest Service Proceedings RMRS-P-41. 2006.

Finney An Overview of FlamMap Fire Modeling Capabilities

Figure 1—Input data themes required for running FlamMap are the same as those for FARSITE 

and are contained in a “Landscape” fi le constructed from ASCII Grid fi les that are of identical 

resolution, co-registered, and of equal extent. 

Winds are entered as a fi xed speed and direction or as spatial wind fi eld 

grids (separate grids for wind speed and direction) that are generated outside 

of FlamMap but are useful for examining fi re spread in complex terrain where 

winds are modifi ed by topography.

Ancillary grid and vector themes (besides those in the LCP fi le or outputs) 

can also be displayed. All grid and vector themes can be viewed in 2- or 

3-dimensions. Outputs can be saved in ASCII Grid or Shapefi le format for 

import and analysis in a GIS.

There are three calculation modes in FlamMap, basic fi re behavior, minimum travel time fi re growth, and treatment optimization.

Basic Fire Behavior

The simplest use of FlamMap is for use in characterizing fi re behavior under 

a constant set of environmental conditions for an entire landscape. Fire behavior can be generated for all cells on the landscape in a number of ways:

 1. For winds blowing uphill, this generates the fastest spread rate because 

wind will be moving in the same direction as slope.

 2. Using a single wind speed and direction combined with the slope to 

produce the resultant vector for fi re spread.

 3. Relative to the maximum direction of spread is the default that results in 
USDA Forest Service Proceedings RMRS-P-41. 2006. 215

An Overview of FlamMap Fire Modeling Capabilities Finney

the heading fi re characteristics. A value of 90 calculates fi re behavior in 

the fl anking direction and 180 calculates fi re behavior in the backing 

direction.

 4. For a direction relative to north (degrees azimuth) allows characterization of the fi re behavior in a particular direction and may be useful for 

looking at fi re progress when a specifi ed wind direction is concerned 

(e.g. winds from west and specifying fi re spread rate to the east).

Basic fi re behavior outputs are generated in raster format for surface and 

crown fi re calculations (Table 1). These can be displayed and saved to a variety image formats (Figure 2a, b). In addition, a combined output can be 

requested to display spread vectors that show the spread rate and maximum 

spread direction of the fi re.

Minimum Travel Time

The minimum travel time (MTT) algorithm (Finney 2002) is used in 

FlamMap for computing fi re growth between the cell corners at an arbitrary 

resolution. Fire growth is computed under the same assumptions as the basic 

fi re behavior – holding all environmental conditions constant in time. Thus, 

the MTT calculations can generate fi re growth in the absence of time- varying 

winds or moisture content which enables analysis only of the effects of spatial 

patterns of fuels and topography.

To run the MTT algorithm, ignitions (points, lines, polygons), the desired 

resolution of the calculations (distance between nodes of a square lattice), 

and the maximum simulation time are required inputs. Alternatively, ignition 

points can be generated randomly for a specifi c number of fi res. As the name 

implies, MTT calculates fi re growth (Figure 2c) by fi nding the paths with the 

minimum fi re travel time among the nodes of the grid. The resolution can 

be selected independently of the input data resolution. This search produces 

both the arrival time grid which can be contoured at any time-interval to 

depict fi re progression, but also the minimum time paths (Figure 2d). These 

paths can be sorted by their fl ow characteristics or prominence in affecting the 

landscape as measured by the magnitude of the number of nodes that burn 

as a result of burning through that node (i.e. logarithm of the number).

Table 1—Outputs from FlamMap.

Fire Behavior Value Output Type Units

Fireline Intensity Raster kW m–1 or BTU ft–1 sec–1

Flame Length Raster meters or feet

Rate of Spread Raster M min–1 or ft min–1 or ch hr–1

Heat per unit Area Raster kW m–2 or BTU ft–2 sec–1

Horizontal Movement Rate Raster M min–1 or ft min–1 or ch hr–1

Midfl ame Windspeed Raster mph or kph

Spread Vectors Vector m min–1

Crown Fire Activity Raster Index, 0 1 2 or 3

Solar Radiation Raster W m–2

1-hr Dead Fuel Moisture Raster Fraction (0.0-1.0)

10-hr Dead Fuel Moisture Raster Fraction (0.0-1.0)
216 USDA Forest Service Proceedings RMRS-P-41. 2006.

Finney An Overview of FlamMap Fire Modeling Capabilities

A different suite of outputs is generated from the MTT calculations than 

for the basic FlamMap products (Table 2). These outputs are produced only 

for the area within the spreading fi re and are affected by the direction of 

fi re movement, revealing heading, fl anking, and backing spread. They will, 

therefore, be different from the values that are generated for outputs listed 

in Table 1. All fi re growth calculations across the landscape are performed 

assuming independence of fi re behavior among neighboring cells (e.g. the 

travel time across a cell does not depend on the behavior in adjacent cells). If 

random ignitions are selected, then the only output will be a burn probability 

map (0.0-1.0). These probabilities are properly interpreted as conditional 

probabilities, since they are conditional upon large fi res occurring.

Figure 2—Example outputs from FlamMap for (a) fi re spread rate, (b) crown fi re activity (0 =none, 1=surface fi re, 

2=torching trees or passive crown fi re, and 3=active crown fi re), (c) fi re progression (white perimeters) simulated 

using the Minimum Travel Time (MTT) method, and (d) the fi re travel paths produced by MTT (bold yellow lines 

distinguish major paths from all paths in light blue).
USDA Forest Service Proceedings RMRS-P-41. 2006. 217

An Overview of FlamMap Fire Modeling Capabilities Finney

Fuel Treatment Optimization

Fuel treatment optimization is accomplished using an algorithm that 

attempts to block the major MTT pathways with fuel treatments that are 

designed to slow large fi res (Finney 2004). Several major assumptions must 

be met before this process can be attempted:

 1. The specifi c objective of the optimization is to fi nd fuel treatment locations that retard the growth rate of large fi res. There are many objectives 

for fuel treatment, some of which are to provide local benefi ts only to 

the area treated. However, the major assumption here is that reduction 

in large fi re growth is obtainable through the collective effect of many 

units occurring on the landscape (Finney 2001).

 2. Wildfi res are larger than the fuel treatment units – this allows the analysis 

to focus on the directions fi res move rather than their start locations.

 3. Treatments are targeted to perform under a specifi c set of weather conditions – target conditions must be specifi ed to contrast fi re behavior 

between the current landscape and the ideal landscape. These are often 

taken from the extreme weather and fuel moisture conditions associated 

with historic large fi re events for which fi re suppression is ineffective.

The treatment optimization model (TOM) process requires the user to 

provide several sets of input data besides the target weather conditions:

 1. Ignition location – this is generally a line fi re or large ignition source at 

the upwind edge of the landscape. This ignition confi guration allows fi re 

movement to be calculated through the entire landscape for identifying 

major travel routes.

 2. An ideal landscape is required that identifi es the fuel conditions everywhere on the landscape where fuel treatments are possible. The changes 

to the fi ve fuel layers of the LCP fi le (Figure 1) can vary across the landscape depending on the appropriateness of the treatment prescription. 

Areas where treatments are not possible remain the same as the current 

landscape.

 3. The resolution of the calculations has the same effect on treatment optimization as on the execution of the minimum travel time algorithm. 

Finer resolutions require more computations but permit greater detail 

in identifying treatment unit locations.

Table 2—Fire behavior outputs from the Minimum Travel Time feature of FlamMap.

Fire Behavior Value Output Type Units

Rate of Spread Raster m min–1 or ft min–1 or ch hr–1

Infl uence Grid Raster Index (logarithm of nodes burned after this node)

Arrival Time Grid Raster minutes

Fireline Intensity Grid Raster kW m–1 or BTU ft–1 sec–1

Flow Paths Vector 

Major Paths Vector 

Arrival Time Contour Vector Interval 1/10th range

Burn Probabilities Raster 0.0-1.0
218 USDA Forest Service Proceedings RMRS-P-41. 2006.

Finney An Overview of FlamMap Fire Modeling Capabilities

 4. The maximum treatment dimension is the maximum length dimension 

that the treatment can be, although multiple treatments may be located adjacent to one-another and form a combined area with a longer 

dimension than this constraint. Practically, this value should be set no 

fi ner than 5 or 6 times the resolution of the calculations (i.e. #3 above) 

in order to allow the treatment unit to be delineated with several cell 

widths.

 5. The maximum fraction of the landscape that can be treated.

The process begins by dividing the landscape into parallel strips beginning 

with the upwind edge. Fire growth is calculated using MTT to identify the 

major fi re movement routes and then identifi es intersections with areas of 

the landscape where the treatments change fi re behavior favorable to slowing 

the fi re. If such intersections are found, an iterative procedure identifi es the 

collection of grid cells that effi ciently blocks each fi re travel route (Finney 

2004) subject to the constraint on treatment size and total area treated.

The outputs from TOM are similar to those from MTT (Table 2) with the 

addition of the treatment opportunities grid, which shows the areas where 

treatments spread faster, slower, or the same as the untreated landscape (values of -1, 0, or 1, respectively), and the fi nal treatment grid which indicates 

the cells which were selected for treatment (fl agged as 0 for untreated and 

1 for treated).

Discussion

The basic fi re behavior calculations in FlamMap are intended for characterizing fuel hazard in fi re management planning. Data on fi re spread rate, 

crown fi re activity, and fl ame length can be quickly calculated and displayed 

to spatially compare fi re behaviors under given weather conditions. FlamMap 

was used near Flagstaff, Arizona (http://forestera.nau.edu/tools_fi remodeling.htm) and in the Sierra Nevada Mountains of California (http://ssgic.

cr.usgs.gov/Pages/mapping_nj.htm) for this purpose.

Fire behavior calculations are at the heart of risk assessment as well because risk assessment requires an assessment of probability of fi re behavior 

occurring. Approaches to quantitative risk assessment have incorporated fi re 

behavior from FlamMap for ranges of weather conditions. Examples of such 

uses include the Florida Risk Assessment (http://www.fl -dof.com/wildfi re/

wf_fras.html), and the CRAFT risk assessment process (http://www.fs.fed.

us/psw/topics/fi re_science/craft/craft/introduction.htm).

FlamMap is also useful in the verifi cation process of spatial data. The fi re 

behavior calculations can easily be compared with expected behaviors for 

the particular fi re environment at each cell (i.e. fuels, weather, topography). 

Display of the landscape, and wind vectors, and various outputs in two- and 

three-dimensions is often helpful for evaluating reasonableness of the fi re 

behavior calculations.

For fuel treatment analysis the MTT and TOM calculations allow effects 

of treatment on fi re movement to be analyzed. These capabilities are relatively 

new, however, and have only recently been applied beyond the research phase. 

However, the basic calculations in FlamMap for comparing effects of fuel 

treatments on fi re behavior have been used to illustrate the stand-level fi re 

behavior changes resulting from treatment (Stratton 2004).
USDA Forest Service Proceedings RMRS-P-41. 2006. 219

An Overview of FlamMap Fire Modeling Capabilities Finney

Summary

Version 3.0 of FlamMap has capabilities of 1) calculating surface and crown 

fi re behaviors and moisture of fi ne dead fuels over an entire landscape, 2) 

simulating fi re growth for constant conditions using a minimum travel time 

(MTT) algorithm, and 3) fuel treatment optimization modeling (TOM) 

for delaying the growth of large fi res. The basic features are useful for characterizing fuel hazard or potential behavior under specifi ed environmental 

conditions. New features of MTT and TOM have potential for analyzing fi re 

movement and fuel treatment interactions.

Acknowledgments

Development of FlamMap (available at http://fi re.org) has been funded by 

grants from Joint Fire Sciences and from the Bureau of Land Management. 

Programming of the graphic interface of FlamMap was accomplished by Stuart 

Brittain of Systems for Environmental Management. The help system was 

developed by Rob Seli (U.S. Forest Service). Rick Stratton provided Invaluable testing and feedback on FlamMap features during development.

Literature Cited

Albini, F.A. 1976. Estimating wildfi re behavior and effects. USDA For. Serv. Gen. 

Tech. Rep. INT-30.

Albini, F.A. 1979. Spot fi re distance from burning trees- a predictive model. USDA 

For. Serv. Gen. Tech. Rep. INT-56. 

Albini, F.A. 1983. Potential spotting distance from wind-driven surface fi res. USDA 

For. Serv. Res. Pap. INT-309.

Alexander, M.E. 1985. Estimating the length-to-breadth ratio of elliptical forest fi re 

patterns. pp. 287-304 Proc. 8th Conf. Fire and Forest Meteorology.

Anderson, H.E. 1983. Predicting wind-driven wildland fi re size and shape. USDA 

For. Serv. Res. Pap. INT-305.

Andrews, P.L. 1986. BEHAVE: fi re behavior prediction and fuel modeling systemBURN subsystem, Part 1. USDA For. Serv. Gen. Tech. Rep. INT-194.

Finney, M.A. 1998. FARSITE: Fire Area Simulator – model development and 

evaluation. USDA For. Serv. Res. Pap. RMRS-RP-4. 47p.

Finney, M.A. 2001. Design of regular landscape fuel treatment patterns for modifying 

fi re growth and behavior. For. Sci. 47(2):219-228.

Finney, M.A. 2002. Fire growth using minimum travel time methods. Can. J. For. 

Res. 32(8):1420-1424.

Finney, M.A. 2004. Chapter 9, Landscape fi re simulation and fuel treatment 

optimization. In: J.L. Hayes, A.A. Ager, J.R. Barbour, (tech. eds). Methods for 

integrated modeling of landscape change: Interior Northwest Landscape Analysis 

System. PNW-GTR-610. p 117-131.

Nelson, R.M. 2000. Prediction of diurnal change in 10-h fuel stick moisture 

content. Can J. For Res. 30:1071-1087.

Rothermel, R.C. 1972. A mathematical model for predicting fi re spread in wildland 

fuels. USDA For. Serv. Res. Pap. INT-115.
220 USDA Forest Service Proceedings RMRS-P-41. 2006.

Finney An Overview of FlamMap Fire Modeling Capabilities

Rothermel, R.C. 1983. How to predict the spread and intensity of forest and range 

fi res. USDA For. Serv. Gen. Tech. Rep. INT-143.

Rothermel, R.C. 1991. Predicting behavior and size of crown fi res in the northern 

Rocky Mountains. USDA For. Serv. Res. Pap. INT-438.

Stratton, R.D. 2004. Assessing the effectiveness of landscape fuel treatments on fi re 

growth and behavior. J. For. October 2004: 32-40.

Van Wagner, C.E. 1977. Conditions for the start and spread of crownfi re. Can. J. 

For. Res. 7:23-24.
USDA Forest Service Proceedings RMRS-P-41. 2006. 221

In: Andrews, Patricia L.; Butler, Bret W., 

comps. 2006. Fuels Management—How to 

Measure Success: Conference Proceedings. 

28-30 March 2006; Portland, OR. 

Proceedings RMRS-P-41. Fort Collins, 

CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research 

Station.

1 Fire scientist with Natural Resources 

Canada, Canadian Forest Service, 

Northern Forestry Centre, Edmonton, 

AB, Canada. mparisie@nrcan.gc.ca

2 Fire scientist with Natural Resources 

Canada, Canadian Forest Service, 

Northern Forestry Centre, Edmonton, 

AB, Canada.

3 Fire ecologist with Parks Canada 

Agency, Quebec Service Centre, National 

Fire Centre, Québec, QC, Canada.

Abstract—This study used a rule-based approach to prioritize locations of fuel treatments in the boreal mixedwood forest of western Canada. The burn probability (BP) 

in and around Prince Albert National Park in Saskatchewan was mapped using the 

Burn-P3 (Probability, Prediction, and Planning) model. Fuel treatment locations were 

determined according to three scenarios and fi ve fuel treatment intensities. Fuel 

treatments were located according to jurisdictional boundaries and BP; BP only; and 

nonfl ammable landscape features, BP, and fuel treatment orientation. First, a baseline 

BP map was created from the original fuel grid. Fuel treatments were then added to the 

grid and BP maps produced for each combination of scenario and treatment intensity. 

BP values for the treated landscapes were compared with those of the baseline BP 

map. Results varied substantially among scenarios and treatment intensities. Locating 

fuel treatments as a function of the jurisdictional boundaries and BP yielded the lowest 

reduction in BP. Results suggest that clumping fuel treatments within a limited area or 

using landscape features to maximize the large-scale spatial benefi ts of the fuel treatments can signifi cantly reduce landscape-level BP. Although these two strategies may 

produce similar overall reductions in BP, their appropriateness and utility depend on 

management objectives.

Introduction

A fuel treatment consists of a stand-level modifi cation of fl ammable vegetation aimed at reducing specifi c aspects of fi re behavior, such as rate of 

spread, fi re intensity, and fi re severity (for example, crown involvement) (Agee 

and others 2000). This strategy is receiving increasing attention from land 

managers and scientists in North America, especially since the catastrophic 

wildland–urban interface incidents of 2003 in Kelowna, BC, and San Diego, 

CA. Furthermore, the alarming rate of expansion of the wildland–urban 

interface (Radeloff and others 2005) is forcing managers and policy-makers 

to fi nd ways of mitigating the negative impacts of large wildfi res (Stephens 

and Ruth 2005).

There is a growing body of evidence on fi re behavior responses to fuels 

modifi cations from empirical (Pollet and Omi 2002; Finney and others

2005) and simulation (van Wagtendonk 1996; Stephens 1998; Stratton 

2004) studies. Although much remains to be learned about fi re behavior in 

fuel-treated areas, a substantial fuel modifi cation will always translate into 

a change in physical fi re processes (Agee and Skinner 2005). For instance, 

a heavy reduction in crown and ladder fuel load (for example, by reducing 

Using Landscape-Based Decision Rules to 

Prioritize Locations of Fuel Treatments in the 

Boreal Mixedwood of Western Canada

Marc-André Parisien1, Dave R. Junor2, and Victor G. Kafka3
222 USDA Forest Service Proceedings RMRS-P-41. 2006.

Parisien, Junor, and Kafka Using Landscape-Based Decision Rules to Prioritize Locations for Placement of Fuel Treatments...

tree density or pruning trees) will reduce fuel consumption and hence fi re 

intensity. Similarly, conversion of a fl ammable coniferous fuel type to a less 

fl ammable deciduous fuel type can signifi cantly reduce fi re behavior severity 

and fi re size (Hirsch and others 2004). At present, most of the research on 

fuel treatments in North America is based on stand-level information. However, the spread of large fi res is also infl uenced by landscape-level factors that 

promote or interrupt fi re spread (Mermoz and others 2005), and these factors 

should be taken into account for fuel treatment design and evaluation.

To this end, equations were developed to determine the optimal shape and 

size of fuel treatments (Finney 2001). This technique, known as strategically placed areas of treatments (SPLATs), represents the fi rst known spatial 

extension of this concept and is gaining popularity with managers working 

in the fi re-dominated biomes of North America. Although assessments of 

effectiveness remain fragmentary, SPLATs reduced fi re behavior in two large, 

high-intensity Arizona fi res (Finney and others 2005). The SPLAT design 

represents an advancement in the science of fuels management, but it does 

not address such critical landscape-level aspects as the placement of fuel treatments or the most suitable treatment fraction (treatment intensity).

Spatial modeling studies have shown that the connectivity of fl ammable 

fuels affects the size of fi res (Miller and Urban 2000; Duncan and Schmalzer 

2004). From a fuel treatment viewpoint, an increase in the relative proportion, as well as aggregation, of less-fl ammable fuels reduces the spread of fi re 

(Bevers and others 2004; Loehle 2004), but this approach is often unrealistic 

on a real landscape. First, a large fraction of the landscape (usually more than 

50 percent) must be treated to achieve an appreciable reduction of fi re spread; 

second, the random placement of treatments implies ineffi ciency (Finney 

2003); and third, the shape of these aggregates may be suboptimal and they 

may therefore provide only a small reduction in area burned.

Despite some preliminary data, the best placement of fuel treatments on 

the landscape remains a crucial but largely unanswered question. This information is particularly important in the Canadian boreal forest, where fuel 

treatments may be necessary to reduce the spread of large fi res burning at 

intensities that preclude direct fi re suppression. Because fi nancial resources 

to create fuel treatments are limited, land managers need to quantify fi re 

risk and apply fuel treatments where they are most needed and can meet 

management objectives (Sanchez-Guisandez and others 2002). A scarcity of 

spatially explicit tools for long-term strategic planning in fi re management 

has inhibited signifi cant progress, but the recent development of approaches 

for mapping burn probability (BP), such as Burn-P3 (Probability, Prediction, 

and Planning) (Parisien and others 2005), represents an opportunity. Reliable 

estimates of BP are necessary to examine the combined effects of altering the 

type and spatial confi guration of forest fuels.

The goal of this study was to develop and assess a rule-based approach to 

prioritizing the placement and level of fuel treatments in a boreal mixedwood 

forest. Our working hypothesis was that incorporating landscape-level features would enhance the effectiveness of fuel treatments. In this article, we 

explore landscape “recipes” for fuel modifi cations using the Burn-P3 simulation model. Our specifi c objectives were (1) to create a BP map for the study 

area, (2) to identify areas where the effectiveness of fuel treatment could 

be maximized, and (3) to assess the relative benefi t of increasing treatment 

intensity (that is, total area treated). The results are discussed in the context 

of current land management objectives for the study area.
USDA Forest Service Proceedings RMRS-P-41. 2006. 223

Using Landscape-Based Decision Rules to Prioritize Locations for Placement of Fuel Treatments... Parisien, Junor, and Kafka

Study Area

The study area, which encompasses Prince Albert National Park (PANP), 

is located in central Saskatchewan (Fig. 1) and covers 1 653 467 ha. The 

area has long, cold winters and short, warm summers. The average monthly 

temperature of the Prince Albert weather station, located in the southern part 

of the study area, ranges from –19.1°C in January to 17.5°C in July. Mean 

annual precipitation is 424 mm, most of it falling between May and August 

(Environment Canada 2005).

The study area can be described as a fl at to rolling plain, a large proportion 

of which is covered by lakes and wetlands. It is characterized by coniferous, 

deciduous, and mixedwood stands of various sizes. The main conifers of the 

study area are white spruce (Picea glauca (Moench) Voss), black spruce (Picea 

mariana (Mill.) BSP), jack pine (Pinus banksiana Lamb.), and tamarack 

(Larix laricina (Du Roi) K. Koch.). The deciduous component is mainly 

represented by trembling aspen (Populus tremuloides Michx.), balsam poplar 

(Populus balsamifera L.), and white birch (Betula papyrifera Marshall).

The fi re regime of the study area — one of the most active in Canada — is 

dominated by infrequent large and intense fi res, more than 80 percent of 

which occur between May and August (Parisien and others 2004). Although 

lightning-ignited wildfi res are frequent and are responsible for most of the 

Figure 1—The study area in central Saskatchewan covering Prince Albert 

National Park and its surroundings.
224 USDA Forest Service Proceedings RMRS-P-41. 2006.

Parisien, Junor, and Kafka Using Landscape-Based Decision Rules to Prioritize Locations for Placement of Fuel Treatments...

area burned, humans ignite most fi res and have had a marked impact on 

the fi re regime since colonization (Weir and Johnson 1998). The main fi re 

management policy in the area consists of aggressive initial attack of small 

fi res and fi re suppression operations aimed at limiting fi re spread. However, 

in recent years Parks Canada has committed to restoring the fi re regime in 

PANP (Weir and Pidwerbeski 2000) to achieve a level of burning similar to 

that of historical fi re cycles and thereby maintain ecological integrity (Weir 

and others 2000).

Methods

Data Types

Three types of data were required as inputs for the Burn-P3 analysis: records 

of historical large fi res, daily fi re weather conditions, and fuel types.

Historical Large-Fire Database—The Canadian Forest Service Large Fire 

Database (Stocks and others 2003), which consists of points of ignition for 

all reported fi res of 200 ha or more in the period 1959 to 2003, was used to 

determine the historical number of large fi res in the study area. A database 

of daily progression of 130 large fi res that occurred in Saskatchewan between 

1991 and 2000 was used to determine the average number of days of signifi cant fi re spread or the number of spread event days (4 percent or more of 

the fi nal fi re size) per fi re.

Daily Fire Weather—Daily noon observations of temperature, relative 

humidity, wind speed, wind direction, and 24-h precipitation, as well as 

the associated fuel moisture codes and fi re behavior indices (from the Fire 

Weather Index System [Van Wagner 1987]), were obtained for 8 weather 

stations in and around the study area for the period 1990 to 2001. To integrate fi re weather into the Burn-P3 model, only daily records for days with 

fi re weather conditions conducive to signifi cant fi re spread, defi ned here as 

having an Initial Spread Index of 8.6 or more (Parisien and others 2005), 

were extracted from the database.

Fuel Types—The fuels were represented as a grid of fuel types of the 

Canadian Forest Fire Behavior Prediction (FBP) System (Forestry Canada 

Fire Danger Group 1992). The FBP System categorizes vegetation into 16 

fuel types; here, however, fuels were grouped into 5 main types: coniferous, 

deciduous, mixedwood, grasses, and slash. The coniferous fuel type produces 

more severe fi re behavior than the deciduous fuel type, whereas the fl ammability of the Boreal Mixedwood fuel type lies between the two. Slash is also 

highly fl ammable, but it is uncommon in the study area. The deciduous and 

mixedwood fuel types are more fl ammable in the spring, before the deciduous trees leaf out. A map of the fuel groups used in the study is presented in 

Figure 2, and percent cover is presented in table 1.

Fuel Treatment Dimensions

Fuel treatment dimensions were determined according to the SPLAT design (Finney 2001). In brief, this design consists of multiple treatment units 

(blocks) that are less fl ammable than the surrounding forest. The aim of a 

SPLAT is to slow down the fi re front and promote fl anking, thereby reducing 
USDA Forest Service Proceedings RMRS-P-41. 2006. 225

Using Landscape-Based Decision Rules to Prioritize Locations for Placement of Fuel Treatments... Parisien, Junor, and Kafka

Figure 2—Major fuel groups of the study area.

Table 1—Area and percentage of each fuel group 

and water within the study area.

Fuel type Area (ha) Proportion (%)

Coniferous 511 478 30.9

Mixedwood 268 527 16.2

Deciduous 222 400 13.5

Grass 411 424 24.9

Other fuels 39 847 2.4

Water 199 791 12.1
226 USDA Forest Service Proceedings RMRS-P-41. 2006.

Parisien, Junor, and Kafka Using Landscape-Based Decision Rules to Prioritize Locations for Placement of Fuel Treatments...

forward spread of the fi re. The width (W) and length (L) of the treatment 

units are based on estimates of typical forward fi re spread (D) (the distance 

between the point of ignition and the fi re front), the distance between rows 

of treatment units (S), the overlap between units (O), and the angle at which 

the units are slanted (Fig. 3) (see Finney 2001 for details). The dimensions 

of the treatment units are arbitrary as long as their spatial confi gurations are 

adjusted according to the SPLAT design equations; however, the units must 

be large or numerous enough to signifi cantly reduce fi re spread.

The fuel treatments in this study consisted of 3 rows of treatment units 

for deciduous fuels (Fig. 3b). The SPLAT dimensions were calculated for 

high-intensity wind-driven fi res (10 000 kW/m and 90th percentile winds) 

in coniferous forests, which represent the threshold conditions above which 

direct fi re suppression is impossible. We used a consistent SPLAT design so 

that our analysis of treatment location and intensity would not be obscured 

by design factors. We opted for a conversion to the deciduous fuel type as a 

treatment because it provides a realistic yet effective way to reduce fi re spread 

in the boreal mixedwood (Hirsch and others 2004) and its fi re behavior 

characteristics are well known. In addition, we selected treatment unit dimensions similar to those for typical cutblocks in the boreal forest, where W = 

300 m and L = 900 m (total area = 27 ha). The separation (S) between unit 

rows was set at 200 m and the units were angled at 20°. These dimensions 

were consistent throughout the study, but the overall length of the area with 

multiple fuel treatments varied according to location on the landscape, as 

dictated by the scenarios.

Figure 3—(a) A multiple “nonslanted” fuel treatment design, where W (width) and 

L (length) defi ne the dimensions of each unit (i.e., block). The location of units in 

relation to each other is determined by the overlap (O) and separation distance (S) 

between unit rows. (b) Slanted units, as used in this study, are inclined at an angle (θ) 

to block openings through the pattern. Both fuel treatments consist of three rows, or 

superimposed layers of treatment units. This fi gure was modifi ed from Finney 2001.
USDA Forest Service Proceedings RMRS-P-41. 2006. 227

Using Landscape-Based Decision Rules to Prioritize Locations for Placement of Fuel Treatments... Parisien, Junor, and Kafka

Modeling Scenarios

We developed three modeling scenarios from different sets of decision 

rules for SPLAT placement and tested each of them according to 5 treatment 

intensities: 1500, 3000, 4500, 6000, and 7500 ha. A baseline BP map was 

produced from the original fuel grid to guide placement of the fuel treatments. In all scenarios, the deciduous fuel treatments had to be embedded 

in areas dominated by coniferous or mixedwood fuels.

In the fi rst (boundary) scenario, fuel treatments were positioned exclusively 

around the periphery of PANP. The 7500-ha treatment intensity covered the 

entire periphery of PANP where it was dominated by coniferous and mixedwood forest (Fig. 4a). The second rule specifi ed that SPLATs for the other 

treatment intensities would be positioned as a function of the highest value 

of BP in the baseline BP map.

In the second (BP-only) scenario, SPLATS were located solely as a function of BP. To identify the areas of highest BP, the values of the baseline BP 

map were “contoured” by intervals of 0.5 percent. The 1500-ha area class 

was thus associated with the highest BP region, the 3000-ha with the highest 

and second-highest BP regions, and so on. To entirely cover the areas of high 

BP, the 3-row fuel treatments were stacked in a clustered, rather than linear 

layout. As a result, the SPLATs for the 7500-ha area class corresponded to 3 

very large areas of high BP and several smaller localized areas (Fig. 4b).

Unlike the fi rst two scenarios, the third (lake-linking) scenario located 

SPLATs according to a hierarchal set of rules based on the linkage of nonfl ammable landscape features (lakes), the highest BP values, and the most suitable 

orientation of fuel treatments. Fuel treatments were used to connect large 

lakes that were no more than 20 km apart, an arbitrary maximum distance 

that is realistic for landscape-level fuel treatments in the area. The minimum 

lake size to be considered was determined by classifying areas of the BP 

map as having either above-average or below-average BP and calculating the 

Figure 4—The fuel treatments (in black) for the boundary scenario (a), BP-only scenario (b), and lake-linking 

scenario (c) for the 7500-ha treatment intensity. The black outline represents the boundary of Prince Albert 

National Park; lakes are shown in gray. BP = burn probability.
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Parisien, Junor, and Kafka Using Landscape-Based Decision Rules to Prioritize Locations for Placement of Fuel Treatments...

frequency of lakes of different sizes (in 50-ha classes) that were adjacent to 

below-average BP areas. Using this method, we determined that lakes of 350 

ha represented the smallest size class in which all lakes were adjacent to areas 

with below-average BP. Among all possible links between lakes, prioritization occurred as a function of BP, as in the BP-only scenario, except that in 

the lake-linking scenario the fuel treatments were not stacked (Fig. 4c). The 

fi nal rule was that if multiple fuel treatments were identifi ed for a BP contour, 

priority was given to the fuel treatments that had an angle of 45° (that is, 

spanning the northeast–southwest axis), the orientation perpendicular to the 

most likely direction of large fi re spread (Parisien and others 2004).

The SPLATs for each combination of scenario and treatment intensity 

were added to the original fuel grid in a geographic information system to 

create a total of 15 modifi ed fuel grids. When the SPLATs were added, cells 

in the grid that were classifi ed as water, nonfuel, or already deciduous were 

not replaced. Because it was nearly impossible to obtain exactly the targeted 

treatment area (for example, 7500 ha), we allowed a variation of 100 ha for 

each treatment intensity.

The Burn-P3 Simulation Model

The Burn-P3 simulation model evaluates BP of large fi re-prone areas by 

simulating the growth of a very large number of fi res (Parisien and others

2005). Burn-P3 models only large fi res because these fi res are responsible 

for virtually all of the area burned in Canada (Stocks and others 2003). 

Individual fi res are simulated deterministically for one fi re season using the 

Prometheus fi re growth model, and this process is repeated for a large number of iterations (for example, 1000). The Prometheus model calculates the 

elliptical growth of each fi re through complex fuels and terrain according to 

the FBP System (Forestry Canada Fire Danger Group 1992) and fi re spread 

mechanisms (Richards 1995). Fires are recorded in Burn-P3 only if they 

reach 200 ha. All other components in Burn-P3 are stochastic: the number 

of fi res per iteration, the location of fi re starts, the burning conditions, and 

the burning period.

The number of fi res per iteration was input as a frequency distribution 

of the number of fi res of at least 200 ha per year (mean 1.06 fi res/year), 

stratifi ed by two seasons: spring (April 1 to May 31) and summer (June 1 

to August 31). The locations of fi re starts were random, but lighting-caused 

and human-caused fi res were distinguished, to prevent lightning ignitions 

in deciduous fuels. No fi re starts were allowed in the grass fuel type, most 

of which is farmland, where very few large fi res occur (P. Maczek, personal 

communication). The duration of the burning period for each fi re was input 

as an exponential frequency distribution, on the basis of the average number 

of spread event days from the daily fi re progression database (mean 3.8 days). 

Burning conditions were randomly drawn from a database of daily fi re weather 

conditions conducive to fi re growth for each spread event day.

In a Burn-P3 run, fi res are simulated according to a given set of landscape 

(fuels and topography, although the latter was not used in this study because 

of the relatively fl at terrain of the study area), fi re, and weather inputs for 

an iteration and recorded in a grid. This process is repeated for each iteration, and the grids of all iterations are compiled in a cumulative grid of area 

burned. Several internal Burn-P3 settings (for example, daily hours of burning, curing of grass fuels) were heuristically adjusted to produce a fi re size 

distribution similar to the historical distribution (compare with Parisien and 

others 2005).
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Using Landscape-Based Decision Rules to Prioritize Locations for Placement of Fuel Treatments... Parisien, Junor, and Kafka

The BP in a given cell i is calculated as follows:

BP

b

N i

i = ×100 [1]

where bi is the number of iterations that resulted in cell i being burned and 

N is the total number of iterations. BPi, expressed as a percentage, represents 

the likelihood of cell i being burned in a single fi re season.

Analysis

Burn-P3 was used to produce 1000-iteration BP maps for the original grid 

with unmodifi ed fuels (the baseline BP map) and for each combination of 

scenario and treatment intensity. A 10-km buffer was added to the study area 

and subsequently removed from the BP maps to prevent an edge effect.

The results for each combination of scenario and treatment intensity were 

compared with those of the same areas in the baseline BP map. The comparison areas consisted of the SPLATs and 2-km buffers around them. The 

buffer distance was selected through comparison of the BP response at several 

buffer distances. The mathematical comparison of the treatment BP maps 

with the baseline BP map was a step-wise process. In the equations below, 

the scenarios are denoted as j, where scenarios 1, 2, and 3 are expressed as j 

= {1,2,3}, respectively, and the treatment intensities are denoted as k, where 

the k = 0 treatment intensity refers to the baseline BP map, and the baseline 

and 1500, 3000, 4500, 6000, and 7500 ha treatment intensities are expressed 

as k = {0,1,2,3,4,5}, respectively.

First, the mean BP, BP jk , for each buffered area was defi ned as follows:

BP

b

n

jk

ijk

i jk

n

=

=

∑

1

 [2]

where bijk is the value of BP for any given cell i for the scenario j and the 

treatment intensity k, and nj k is the total number of cells in the buffered area 

for each combination of j and k.

The calculated values of BP jk were then standardized as follows:

BP s

BP

BP t jk

jk

jk

( ) ( ) = [3]

where BP s jk ( ) represents the standardized BP jk for each combination of j and k,

and BP t jk ( ) is the mean BP of the total of all cells in the baseline BP grid of j and 

k. The purpose of standardization was to account for background variability in BP jk

among BP grids. Although this variability is usually minimal, it can partially obscure 

the patterns of reduction in BP observed between treatment and baseline BP.

Finally, the relative difference (i.e., reduction) in BP jk, ΔBPjk , expressed as 

a percentage, was calculated using the following equation:

ΔBP

BP s BP s u

BP s u jk

jk jk

jk

= − ×

() (,)

(,)

100 [4]

where BP s u jk (,) is the mean BP calculated for the area corresponding to each 

combination of j and k in the baseline BP map.

In an additional analysis, the values of ΔBPjk were adjusted for area in terms 

of an arbitrary comparison area (50 000 ha) to assess the spatial “coverage” 

of the different fuel treatment layouts. The area Aj k (in ha) was obtained for 

each buffered fuel treatment of scenario j and treatment intensity k. Then, 

an area factor, F A j k ( ) , was calculated as follows:
230 USDA Forest Service Proceedings RMRS-P-41. 2006.

Parisien, Junor, and Kafka Using Landscape-Based Decision Rules to Prioritize Locations for Placement of Fuel Treatments...

F A

A

ha jk

jk ( ) = 5 10 × 4 [5]

The area-adjusted mean change in BP ( ΔBPjk ), ΔBP a jk ( ) was obtained for ,

each j and k:

Δ Δ BP a BP F A jk jk jk () ( ) = × [6]

The resulting ΔBPjk and ΔBP a jk ( ) were plotted as a function of treatment 

intensity for each scenario.

Results and Discussion

The baseline BP map had localized areas of high and low BP (Fig. 5). 

High-BP values were usually found in conifer-dominated areas. The relative 

reduction in BP for treated landscapes varied substantially by scenario and by 

treatment intensity (Fig. 6a). At all treatment intensities, the boundary scenario yielded the lowest reduction in BP. A larger relative reduction in BP was 

Figure 5—Baseline (i.e., untreated) burn probability in the study 

area. The black outline represents the boundary of Prince Albert 

National Park.
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Using Landscape-Based Decision Rules to Prioritize Locations for Placement of Fuel Treatments... Parisien, Junor, and Kafka

Figure 6—The relative reduction in burn probability as a function of decreasing treatment intensity by scenario 

without (a) and with (b) an area adjustment to 50 000 ha.

expected at the lower treatment intensities because the minimum treatments 

were linked to areas where fuel treatments would be most needed (high BP). 

That this was not borne out for the boundary and BP-only scenarios suggests 

that there are optimal treatment intensities for large-scale effectiveness of 

SPLATs. The SPLATs in some locations did not always yield an important 

reduction in BP (not shown), especially for the boundary scenario. Some of 

the treated areas at the PANP boundary were virtually unaffected by the 

fuel treatments, which suggests that locating linear SPLATs as a function of 

jurisdictional boundaries and BP represents a poor option. This outcome may 

be due to a number of factors, notably the ineffectiveness of SPLATs that are 

oriented parallel to an oncoming fi re (Finney 2001) and the critically low 

portion of the landscape treated (Bevers and others 2004; Leohle 2004).

Without adjustment for area, the 6000-ha and 4500-ha treatment intensities for the BP-only scenario performed the best, with a BP reduction of more 

than 40 percent (Fig. 6a). The 7500-ha treatment intensity did not yield as 

high a reduction in BP because the small, isolated clumps of fuel treatments 

(Fig. 4b), which were not retained with the smaller treatment intensities, were 

unsuccessful in reducing BP. Large fi res presumably wrapped around these 

clumps readily. In contrast, bigger clumps were highly effective at reducing 

BP, which suggests that stacking fuel treatments is effective in reducing BP. 

These stacked SPLATs not only represented greater impediments to fi re 

spread, but also were more versatile in terms of reducing the spread of fi res 

burning from a range of directions.

The observed reduction in BP in the lake-linking scenario was fairly constant 

among treatment intensities, but this scenario was more effective at the 3000-ha 

and 1500-ha treatment intensities than the other two scenarios (Fig. 6a), which 

suggests that this set of decision rules would be superior to the others when only 

a small area can be treated. The reduction in BP with the lake-linking scenario 

would also be much higher if the BP reduction around the lakes to which the 

fuel treatments were linked had been considered in the calculation of the buffered 

fuel treatment area. Furthermore, the compound effect of closely spaced fuel 

treatments on BP reduction in this scenario (Fig. 7) suggests a synergistic effect 

of adjacent placement of fuel treatments. However, to maximize the treated area, 
232 USDA Forest Service Proceedings RMRS-P-41. 2006.

Parisien, Junor, and Kafka Using Landscape-Based Decision Rules to Prioritize Locations for Placement of Fuel Treatments...

managers should avoid placing too many treatments in the same locations. There is 

thus a need to evaluate the optimal proximity of neighboring fuel treatments.

The high effectiveness of the lake-linking scenario supports previous suggestions that one of the most effective layouts of fuel treatments is one that 

compartmentalizes areas of high wildfi re susceptibility (Agee and others

2000; Hirsch and others 2004). One way to determine the most suitable 

compartments is to take into account the spatial effects of SPLATs on BP 

reduction (Fig. 7). The 2-km buffer proved appropriate for the analysis and 

was consistent with a fi eld study reporting a marked decrease in fi re frequency 

within a 2-km radius around large lakes in the boreal forest of Quebec (Cyr and 

others 2005). However, the reduction in BP may extend far beyond the 2-km 

buffer, depending on several spatial features of the landscape (Parisien and 

others 2003). Consideration of the scale of BP reduction would certainly 

have favored the lake-linking scenario in this analysis.

The potential benefi ts of fuel treatments are explicitly linked to their area 

of infl uence or spatial coverage, which is far greater for the buffered area 

of the boundary and lake-linking scenarios than for the BP-only scenario 

(85 794, 91 917, and 63 290 ha, respectively), where the fuel treatment 

buffers are “shared” among rows of treatments because they are stacked. 

High spatial coverage thus extends the effects of the treated areas to a larger 

proportion of the landscape. If an area adjustment is taken into account in 

the BP reduction, the lake-linking scenario performed the best in terms of 

BP reduction for all but the 1500-ha treatment intensity (Fig. 6b), where all 

three scenarios performed equally.

Figure 7—Part of the burn probability map of the baseline (i.e., untreated) landscape (a) and the same part 

of the burn probability map of the treated landscape with the lake-linking scenario (b) for the 7500-ha 

treatment intensity. The overlaid fuel treatments are shown in black.
USDA Forest Service Proceedings RMRS-P-41. 2006. 233

Using Landscape-Based Decision Rules to Prioritize Locations for Placement of Fuel Treatments... Parisien, Junor, and Kafka

Implications for Land Management

The classic defi nition of wildfi re risk considers two components, BP and 

potential impacts (Finney and others 2005). At present, the main challenge in 

estimating wildfi re risk is that few approaches provide a quantitative estimate 

of landscape-level BP. However, according to Finney (2005), without this 

measure “it is not possible to estimate the cost-effectiveness of management 

activities that may be proposed for mitigating potential fi re impacts.” In this 

respect, BP modeling represents an important advance in assessing wildfi re 

risk. The strength of this approach is that it allows us to directly measure the 

change in BP that results from landscape modifi cations, such as fuel treatments, prescribed burns or wildfi res, and changes in land use.

Given the costs of implementing SPLATs over a large area, a tool such as 

Burn-P3 can provide valuable new information to land managers. A BP map 

is very useful in itself, but is perhaps most helpful when used in “what if” 

scenarios of landscape change (Miller 2003), as showcased in this study. In 

the PANP area, where fi re and land management policies differ substantially 

within and outside the park, strategic management planning can be challenging, especially given the high numbers of large fi res. Our results strongly 

suggest that simple decision rules based on in-depth knowledge of an area 

and its fi re environment provide a robust framework for SPLAT placement 

and that the straightforward nature of this approach makes it simple to explore and implement. However, we acknowledge that much could be learned 

by combining these methods with more sophisticated ones, such as spatial 

optimization (Zuuring and others 2000) and succession modeling of fuels 

(He and others 2004).

Even at the 7500-ha treatment intensity the overall treated area was small 

relative to the entire study area. Converting 7500 ha of coniferous and 

mixedwood forest to deciduous forest appears to represent a massive effort, 

but in most of the commercial forest of western Canada it could easily be 

achieved, given the extensive harvesting and site preparation associated with 

forestry operations. In fact, Stratton (2004) suggested that fuel treatment 

units could be shaped like forest patches without signifi cantly affecting the 

benefi ts of fuel treatments. Where there are no forestry operations, as in 

PANP, fuel conversion could be an alternative in strategic areas because it 

requires minimal maintenance. However, the use of prescribed burns as a fuel 

treatment, either alone or combined with deciduous conversion, is preferred, 

because it also contributes to restoring the historical fi re regime.

Ideally, the effectiveness of fuel treatments should be measured not only 

by their effect on BP but also by the reduction in fi re behavior potential. 

Although the decision rules of the BP-only and lake-linking scenarios produced appreciable reductions in BP (more than 40 percent in some cases), 

in reality fi res would rarely burn freely: some level of fi re suppression, even 

minimal, would be undertaken. In fact, the purpose of fuel treatments in 

PANP is largely to enhance fi re suppression operations (Weir and Pidwerbeski 

2000). If the rate of spread and fi re intensity can be markedly reduced by 

fuel treatments, fi re suppression is more likely to succeed. Furthermore, the 

treated areas can be used for indirect attack to contain burnout operations, 

a technique that is widely used by boreal fi re management agencies.

Our results emphasize the importance of identifying the appropriate spatial scale for decision-making regarding fuel treatments (Finney and Cohen 

2003). At a local landscape scale, it appears that the clumping of fuel treatments is the most effective way to reduce BP. In fact, at an even smaller scale 
234 USDA Forest Service Proceedings RMRS-P-41. 2006.

Parisien, Junor, and Kafka Using Landscape-Based Decision Rules to Prioritize Locations for Placement of Fuel Treatments...

(for example, in a single community) it is often feasible to concentrate fuel 

management efforts and reduce the BP to almost zero, effectively creating a 

“fuel break.” By contrast, at a larger spatial scale it is not always possible or 

even desirable (for example, ecologically) to treat a sizeable portion of the 

landscape. If resources are fi nite, it is preferable to spread out the potential 

benefi ts of fuel treatments by using strategic decision rules. Moreover, at a 

large spatial scale, a decision scheme like that of the BP-only scenario may 

position fuel treatments where they are not needed, whereas the lake-linking scenario not only extends the spatial coverage of the treatment but also 

is more fl exible in terms of identifying adequate fuel treatment locations. 

Comparison of the observed BP reduction among scenarios also suggests that 

the use of nonfl ammable landscape features in our decision rules is highly 

profi table, which further exemplifi es the importance of using spatial data in 

decision-making for placement of fuel treatments.

Acknowledgments

This study was made possible by the Prince Albert Model Forest Science 

and Technology Fund. We are grateful to Jen Beverly, Paul Maczek, Diana 

Stralberg, Nicole Vaillant, and Jeff Weir for their ideas and comments. Alan 

Frank and Dave Wieder provided geographic information system data, and 

Daniel Poirier and Richard Carr provided weather data. We thank Cordy 

Tymstra for his project-long assistance with Prometheus and Peter Englefi eld 

for helping with the geographic information system analysis.

References

Agee, J. K.; Bahro, B.; Finney, M. A.; Omi, P. N.; Sapsis, D. B.; Skinner. C. N.; van 

Wagtendonk, J. W.; Weatherspoon, C. P. 2000. The use of shaded fuelbreaks in 

landscape fi re management. Forest Ecology and Management. 127: 55–66.

Agee, J. K.; Skinner, C. N. 2005. Basic principles of forest fuel reduction treatments. 

Forest Ecology and Management. 211: 83–96.

Bevers, M.; Omi, P. N.; Hof, J. 2004. Random location of fuel treatments in 

wildland community interfaces: a percolation approach. Canadian Journal of 

Forest Research. 34: 164–173.

Cyr, D.; Bergeron, Y.; Gauthier, S.; Larouche, A. C. 2005. Are the old-growth 

forests of the Clay Belt part of a fi re-regulated mosaic? Canadian Journal of Forest 

Research. 35: 65–73.

Duncan, B. W.; Schmalzer, P. A. 2004. Anthropogenic infl uences on potential 

fi re spread in a pyrogenic ecosystem of Florida, USA. Landscape Ecology. 19: 

153–165.

Environment Canada. 2005. Canadian climate normals 1971–2000. Ottawa, ON. 

<http://www.msc-smc.ec.gc.ca/climate/climate_normals/index_e.cfm>.

Finney, M. A. 2001. Design of regular landscape fuel treatment patterns for 

modifying fi re growth and behavior. Forest Science. 47: 219–228.

Finney, M. A. 2003. Calculation of fi re spread rates across random landscapes. 

International Journal of Wildland Fire. 12: 167–174.

Finney, M. A. 2005. The challenge of quantitative risk analysis for wildland fi re. 

Forest Ecology and Management. 211: 87–108.
USDA Forest Service Proceedings RMRS-P-41. 2006. 235

Using Landscape-Based Decision Rules to Prioritize Locations for Placement of Fuel Treatments... Parisien, Junor, and Kafka

Finney, M. A.; Cohen, J. D. 2003. Expectation and evaluation of fuel management 

objectives. In: Conference on fi re, fuel treatments, and ecological restoration: 

proceedings; 2002 April 16–18; Fort Collins, CO. RMRS-P-29. Fort Collins, 

CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research 

Station: 353–366.

Finney, M. A.; McHugh, C. W.; Grenfell, I. C. 2005. Stand- and landscape-level 

effects of prescribed burning on two Arizona wildfi res. Canadian Journal of 

Forest Research. 35: 1714–1722.

Forestry Canada Fire Danger Group. 1992. Development and structure of the 

Canadian Forest Fire Behavior Prediction System. Information Rep. ST-X-3. 

Ottawa, ON: Forestry Canada. 64 p.

He, H.; Shang, B. Z.; Crow, T. R.; Gustafson, E. J.; Shifl ey, S. R. 2004. Simulating 

forest fuel and fi re risk dynamics across landscapes—LANDIS fuel module design. 

Ecological Modelling. 180: 135–151.

Hirsch, K. G.; Kafka, V.; Todd, J. B. 2004. Using forest management techniques to 

alter forest fuels and reduce wildfi re size: an exploratory analysis. In: Engstrom, 

R. T.; de Groot, W. J., eds. 22nd Tall Timbers fi re ecology conference: fi re in 

temperate, boreal, and montane ecosystems: proceedings; 2001 October 15–18; 

Kananaskis, AB. Tallahassee, FL: Tall Timbers Research Station: 175–184.

Loehle, C. 2004. Applying landscape principles to fi re hazard reduction. Forest 

Ecology and Management. 198: 262–267.

Mermoz, M.; Kitzberger, T.; Veblen, T. T. 2005. Landscape infl uences on occurrence 

and spread of wildfi res in Patagonian forests and shrublands. Ecology. 86: 

2705–2715.

Miller, C. 2003. The spatial context of fi re: a new approach for predicting fi re 

occurrence. In: Galley, K. E. M.; Klinger, R. C.; Sugihara, N. G., eds. First 

national congress on fi re ecology and preventive management: proceedings; 

2000 November 27–December 1; San Diego, CA. Publ. 13. Tallahassee, FL: Tall 

Timbers Research Station: 27–34.

Miller, C.; Urban, D. L. 2000. Connectivity of forest fuels and surface fi re regimes. 

Landscape Ecology. 15: 145–154.

Parisien, M. A.; Hirsch, K. G.; Lavoie, S. G.; Todd, B. M.; Kafka, V. G. 2004. 

Saskatchewan fi re regime analysis. Information Rep. NOR-X-394. Edmonton, 

AB: Natural Resources Canada, Canadian Forest Service, Northern Forestry 

Centre. 49 p.

Parisien, M. A.; Kafka, V. G.; Hirsch, K. G.; Todd, B. M.; Lavoie, S. G.; Maczek, 

P. D. 2005. Using the Burn-P3 simulation model to map wildfi re susceptibility. 

Information Rep. NOR-X-405. Edmonton, AB: Natural Resources Canada, 

Canadian Forest Service, Northern Forestry Centre. 36 p.

Parisien, M. A.; Kafka, V. G.; Todd, B. M.; Hirsch, K. G.; Lavoie, S. G. 2003. The 

peripheral reduction in burn probability around recent burns in the boreal forest. 

In: 2nd international wildland fi re ecology and management congress; 2003 

November 16–20; Orlando, FL. Extended abstract.

Pollet, J.; Omi, P. N. 2002. Effect of thinning and prescribed burning on crown 

fi re severity in ponderosa pine forests. International Journal of Wildland Fire. 

11: 1–10.

Radeloff, V. C.; Hammer, R. B.; Stewart, S. I.; Fried, J. S.; Holcomb, S. S.; McKeefry, 

J. F. 2005. The wildland–urban interface in the United States. Ecology. 15: 

799–805.

Richards, G. D. 1995. A general mathematical framework for modeling twodimensional wildland fi re spread. International Journal of Wildland Fire. 5: 

63–72.

Sanchez-Guisandez, M.; Cui, W.; Martell, D. L. 2002. FireSmart management of 

fl ammable wildland urban interface landscape. In: Viegas, D.X., ed. Forest fi re 

research and wildland fi re safety: proceedings; 2003 November 18–23; Luso, 

Portugal. Rotterdam, Netherlands: Millpress: 121–130.
236 USDA Forest Service Proceedings RMRS-P-41. 2006.

Parisien, Junor, and Kafka Using Landscape-Based Decision Rules to Prioritize Locations for Placement of Fuel Treatments...

Stephens, S. L. 1998. Evaluation of the effects of silvicultural and fuels treatments on 

potential fi re behavior in the Sierra Nevada mixed-conifer forests. Forest Ecology 

and Management. 105: 21–35.

Stephens, S. L.; Ruth, L. W. 2005. Federal forest fi re policy in the United States. 

Ecological Applications. 9: 1253–1265.

Stocks, B. J.; Mason, J. A.; Todd, J. B.; Bosch, E. M.; Wotton, B. M.; Amiro, B. D.; 

Flannigan, M. D.; Hirsch, K. G.; Logan, K. A.; Martell, D. L.; Skinner, W. R. 

2003. Large forest fi res in Canada, 1959–1997. Journal of Geophysical Research. 

108: FFR5-1–FFR5-12.

Stratton, R. D. 2004. Assessing the effectiveness of landscape fuel treatment on fi re 

growth and behavior. Journal of Forestry. 7: 32–40.

Van Wagner, C. E. 1987. Development and structure of the Canadian Forest Fire 

Weather Index System. Tech. Rep. 35. Ottawa, ON: Canadian Forest Service. 

35 p.

Van Wagtendonk, J. W. 1996. Use of a deterministic fi re model to test fuel treatments. 

In: Sierra Nevada ecosystem project: fi nal report to Congress. Vol. 2. Davis, 

CA: University of California, Davis, Center for Water and Wildland Resources: 

1155–1167.

Weir, J. M. H.; Johnson, E. A. 1998. Effects of escaped settlement fi res and logging 

on forest composition in the mixedwood boreal forest. Canadian Journal of Forest 

Research. 28: 459–467.

Weir, J. M. H.; Johnson, E. A.; Miyanishi, K. 2000. Fire frequency and the spatial 

age mosaic of the mixed-wood boreal forest in western Canada. Ecological 

Applications. 10: 1162–1177.

Weir, J. M. H.; Pidwerbeski, A. 2000. Prince Albert National Park fi re management 

plan. Parks Canada internal report. Prince Albert, SK.

Zuuring, H. R.; Chew, J. D.; Jones, J. G. 2000. Sequential use of simulation and 

optimization in analysis and planning. In: Bitterroot ecosystem management 

research project symposium: what we have learned: proceedings; 1999 May 18-20; 

Missoula, MT. RMRS-P-17. Ogden, UT: U.S. Department of Agriculture, Forest 

Service: 97–103.
USDA Forest Service Proceedings RMRS-P-41. 2006. 237

Fuel Characterization and Mapping
238 USDA Forest Service Proceedings RMRS-P-41. 2006.
USDA Forest Service Proceedings RMRS-P-41. 2006. 239

In: Andrews, Patricia L.; Butler, Bret W., 

comps. 2006. Fuels Management—How to 

Measure Success: Conference Proceedings. 

28-30 March 2006; Portland, OR. 

Proceedings RMRS-P-41. Fort Collins, 

CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research 

Station.

1 Fuels team leader and GIS specialist for 

the LANDFIRE program, Fire Sciences 

Lab, Missoula, MT, mreeves@fs.fed.us

2 Senior Scientist at the USGS Center 

for Earth Resource Observation and 

Science (EROS), Science Applications 

International Corporation (SAIC), 

Technical Support Services at the USGS 

National Center for Earth Resources 

Observation & Science Sioux Falls, SD.

3 LANDFIRE Program Manager, Fire 

Sciences Lab, Missoula, MT.

Abstract—The LANDFIRE project is a collaborative interagency effort designed to provide seamless, nationally consistent, locally relevant geographic information systems 

(GIS) data layers depicting wildland fuels, vegetation and fi re regime characteristics. 

The LANDFIRE project is the fi rst of its kind and offers new opportunity for fi re management and research activities. Here we introduce the LANDFIRE wildland fuels data 

layers including fi re behavior fuel models, canopy bulk density, canopy base height, 

canopy cover, canopy height and new Fuel Loading Models. Specifi cally, we focus on 

the methods and data used to create these layers and present preliminary assessments. 

These key fuels layers will support fuels and smoke management and fi re behavior 

modeling in addition to providing essential information for evaluating and managing 

wildland fi res, seamlessly and consistently.

Introduction

Wildland fuels are critical elements in wildland fi re planning and management activities. Wildland fuels are needed to parameterize consumption 

models, for example First Order Fire Effects Model (FOFEM) and fi re behavior models such as NEXUS (Scott 1999), BehavePlus (Andrews 2003) 

and FARSITE (Finney 1998). These models can be used for two basic but 

critically important purposes; prioritizing fuel treatments and assessing fi re 

behavior and effects in wildland fi re suppression activities. Data to drive 

these models are lacking for most federal lands. These issues led the Wildland Fire Leadership Council, a group of senior administration executives 

representing all land management agencies in the country, to charter the 

LANDFIRE Project. The LANDFIRE project is currently mapping or developing geospatial data to meet the need for continuous, consistent, unbiased 

and scientifi cally produced fuels layers. In particular, LANDFIRE produces 

the fuels layers needed to run FARSITE including fi re behavior fuel models, 

both the Anderson (1982) models (13 fi re behavior fuel models) and the 

relatively newer Scott and Burgan (2005) set, canopy cover, canopy height, 

canopy bulk density and canopy base height. For fi re effects analysis, a new 

set of Fuel Loading Models is being developed that focus on providing the 

necessary inputs to run FOFEM spatially. This paper explains methods and 

tools employed by LANDFIRE to map each of these fuel products.

Fuels Products of the LANDFIRE Project

Matthew C. Reeves1, Jay R. Kost2, and Kevin C. Ryan3
240 USDA Forest Service Proceedings RMRS-P-41. 2006.

Reeves, Kost, and Ryan Fuels Products of the LANDFIRE Project

Methods

Upstream Products

The fuels layers rely on previously produced LANDFIRE layers and ancillary data (fi g. 1) including existing vegetation type (EVT), canopy cover 

(CC), canopy height (CH), environmental site potential (ESP), Enhanced 

Thematic Mapper (ETM) imagery, digital elevation model (DEM) and associated derivatives and biophysical gradients. A brief explanation of these 

data is required so that the fuels mapping process can be discussed and understood with clarity.

Reference Database—The LANDFIRE reference database forms the 

foundation for nearly all LANDFIRE deliverables. It is used for developing 

training sites for imagery classifi cation; validating and testing simulation 

models; developing vegetation classifi cations; creating empirical models; 

determining and archiving data layer attributes and; assessing the accuracy 

of maps and models (Caratti 2006). The reference database stores all relevant plot level information and provides the means to generate, test, and 

validate predictive models and LANDFIRE deliverables. Data have been 

received from a variety of sources in various forms, though the United States 

Forest Service has been the largest contributor with approximately 56,000 

plots (~40% of the total). Roughly 140,000 plots have been archived in the 

Figure 1—Flow of data, data processing and fi nal products of the LANDFIRE project. Note the dependency of 

the fuels products on upstream LANDFIRE layers. 
USDA Forest Service Proceedings RMRS-P-41. 2006. 241

Fuels Products of the LANDFIRE Project Reeves, Kost, and Ryan

reference database for the fi rst 16 mapping zones (fi g. 2). Once each plot 

is converted to a common format, it is keyed to an existing vegetation type 

(EVT) and environmental site potential (ESP) using sequence table classifi ers based solely on fl oristic composition. A main feature of the reference 

database for fuels mapping is the inclusion of a suite of predictor variables. 

These predictor variables form the basis for the landscape prediction models 

developed for mapping canopy fuels.

Predictor variables fall into one of four categories including; 1) imagery, 

2) DEM and associated derivatives, 3) biophysical gradients, and 4) other 

LANDFIRE layers.

The LANDFIRE program uses the satellite imagery from the Multi-Resolution Land Characterization (MRLC) 2001 project (Homer and others 2004). 

This system divides the nation into separate mapping zones (fi g. 2). There 

are two key elements resulting from this study that are used by LANDFIRE. 

First, the LANDFIRE project uses the same mapping zones as those created 

in the MRLC 2001 project. Second, LANDFIRE uses the satellite imagery 

that was painstakingly mosaicked for each zone for the conterminous U.S. 

The essential characteristics of this satellite imagery database are; 1) image 

dates (time of acquisition) range from 1999 – 2003; 2) imagery is supplied 

by the ETM sensor, and 3) each mapping zone has three sets of associated 

imagery including leaf-on, spring and leaf-off. A full description of these data 

is available in Zhu and others (2006).

The biophysical gradients are derived from WXBGC (Keane and others 

2002), a modifi ed version of the ecosystem simulation model, BiomeBGC 

(Running and Gower 1991; Thornton and others 2002). The meteorological 

data used to drive WXBGC come from the DAYMET meteorological database, which comprises interpolated surfaces of daily meteorology observations 

(Thornton and others 2002). In addition to these gradients, a suite of terrain 

variables such as DEM, slope and aspect are used.

Figure 2—Multi-Resolution Land Characterization (MRLC) mapping zones used by 

LANDFIRE. Numbers in bold circles represent zones completed as of 5 April, 2006. 
242 USDA Forest Service Proceedings RMRS-P-41. 2006.

Reeves, Kost, and Ryan Fuels Products of the LANDFIRE Project

Other LANDFIRE Layers—The fuels mapping process relies extensively 

upon EVT, existing vegetation cover, height and, to a lesser degree, ESP. The 

EVT and associated structural attributes are produced by Earth Resources 

Observation Systems (EROS), a United States Geological Survey LANDFIRE 

partner, while ESP is created at the Missoula Fire Sciences Laboratory.

The EVT depicts the dominant Ecological System (Comer and others 

2003) currently present at each 30 m pixel. Each fi eld plot is assigned a 

life-form and ecological system class, and this information is then used to 

train decision tree models (Quinlan 1993) using imagery, topographic, and 

biophysical data (Zhu and others 2006).

Existing vegetation canopy cover, as defi ned in the LANDFIRE project, 

represents the average percentage of dominant life-form, non-overlapping 

canopy cover for each 30 m pixel. A life-form stratifi cation is used to develop 

independent canopy cover for tree, shrub, and herbaceous life-forms. Canopy 

cover for the shrub and herbaceous life-forms is developed through use of 

fi eld plot information in the reference database combined with imagery, 

topographic, and biophysical data to train regression tree models (Quinlan 

1993), while tree canopy cover is developed by procedures employed for the 

National Land Cover Dataset (NLCD) effort (Homer and others 2004). 

The fi nal existing vegetation cover dataset is comprised of nine, 10 percent 

incremental classes ranging from 10 to 100 percent.

Existing vegetation height represents the average height of the dominant 

life-form for each 30 m pixel. Field plot height measurements, in addition to 

Landsat imagery, topographic, and biophysical spatial data, are used to train 

decision tree models that predict existing vegetation height. Continuous 

tree, shrub, and herbaceous height fi eld data are grouped into 3 to 5 discrete 

classes, depending on plot height ranges and data availability, prior to being 

modeled. Prior to dissemination on the National Map (http://nationalmap.

gov [last visited 24 March, 2006]) as fuels layers, existing vegetation height 

and cover are converted to the canopy height (CH) and canopy cover (CC) 

products. These differ from the existing vegetation height and cover products 

because the thematic classes are converted to ordinal, biologically meaningful 

values so that they can be used directly in a fi re behavior processor (Finney 

1998; Scott 1999). In addition, the CH and CC products only represent 

cover and height of forested systems, as all herbaceous and shrub areas are 

coded as 0.

The environmental site potential (ESP) represents the vegetation that could 

be supported at a site based on the biophysical environment. Map units are 

named according to NatureServe’s Ecological Systems classifi cation (Comer 

and others 2003). As used in LANDFIRE, map unit names represent the 

natural plant communities that would become established at late or climax 

stages of successional development in the absence of disturbance. The ESP 

is similar in concept to other potential vegetation classifi cations in the western United States, including habitat types (for example, Daubenmire 1968; 

Pfi ster and others 1977).

Fuels Mapping

Fire Behavior Fuel Models—Prior to creating maps of fi re behavior fuel 

models (here referred to as FBFM), LANDFIRE fuelbeds are created using 

the spatial intersection of EVT/CC/CH/ESP. Every unique combination 

identifi ed during this process is assigned a fi re behavior fuel model. Use of 

these four variables for identifying fuelbeds is appropriate because it enables 

maps of fi re behavior fuel models to be inferred from vegetation. Existing 
USDA Forest Service Proceedings RMRS-P-41. 2006. 243

Fuels Products of the LANDFIRE Project Reeves, Kost, and Ryan

vegetation type yields information about the type of litter and ultimately, the 

vegetation that will most likely carry the fi re. Canopy cover permits inference 

of the nature of the understory. For example, in more open canopy situations 

a greater preponderance of understory vegetation, such as shrubs and herbs is 

expected. Canopy height can further help the distinction between FBFM’s. 

For example, a grass existing vegetation type will probably burn more like a 

fi re behavior model 1 (Anderson 1982) if it is short, whereas if the grass is 

tall and dense, for example ≥ 1 m, it will likely be categorized as a FBFM 3 

(Anderson 1982). The environmental site potential is infrequently used to 

distinguish relatively more xeric fuelbeds from those that are relatively more 

mesic.

Using this information, rules can be created that divide these ranges of 

possibilities into several categories for each EVT based on expected fi re behavior. For example, the assumption can be made that there are two general 

kinds of fi re behavior typically observed in a Great Basin pinyon-juniper 

environment. The fi rst is a creeping fi re with low fl ame length and rate of 

spread. This situation often occurs on relatively more dense stands with high 

canopy cover and low fuel moistures. The other type of fi re behavior is more 

active, with higher rates of spread and fl ame lengths. This type of behavior 

is typically observed in relatively more open stands, in high winds, where 

herbaceous species are denser and shrubs such as sagebrush are interspersed 

with the larger pinyon pine and juniper.

With this logic, several rulesets can be derived from our example stand 

of pinyon-juniper (table 1). Each ruleset is subsequently assigned two fi re 

behavior fuel models; one from Anderson (1982) and one from Scott and 

Burgan (2005). After these preliminary assignments are made they are refi ned and reviewed by local fi re and fuel managers during fi re behavior fuel 

model assignment workshops. After fuelbeds are reviewed, they are linked 

to a layer in a GIS and fuel model maps are created. After each fuel model 

map is created it goes through a separate cycle of review by local fi re and 

fuel specialists with revision as appropriate. This second revision process 

differs from the assignment workshops because it focuses on the spatial 

expression of the rulesets created by experts during the assignment process. 

These workshops are a critical part of the LANDFIRE process because they 

permit collaboration between specialists, with knowledge about their area, 

and LANDFIRE scientists.

Canopy Base Height and Bulk Density—Canopy base height (CBH) is 

defi ned as the lowest point in the canopy at which there is suffi cient available fuel for propagating the fi re vertically, while canopy bulk density (CBD) 

Table 1—Example LANDFIRE fuelbed assignments from a Great Basin Pinyon-Juniper 

Existing Vegetation Type. ESP is Environmental Site Potential. 

Fuelbed # Cover (%) Height (m) ESP FBFM131 FBFM40

 1 0 - 50 Any Xeric 6 SH1

 2 0 - 50 Any Mesic 2 GS2

 3 50 - 100 ≥ 3 Any 8 TL1

 4 50 - 100 ≤ 3 Any 6 SH1

1FBFM13 and FBFM40 are fi re behavior fuel models from Anderson (1982) and Scott and Burgan 

(2005) respectively. 
244 USDA Forest Service Proceedings RMRS-P-41. 2006.

Reeves, Kost, and Ryan Fuels Products of the LANDFIRE Project

refers to the mass of available canopy fuel per unit canopy volume (Scott and 

Reinhardt 2001). These canopy characteristics are most often used to determine expected crown fi re activity for a stand or larger landscape.

The canopy fuels mapping process begins by attributing each plot with 

estimates of CBH and CBD. These canopy characteristics are computed using 

FuelCalc (Reinhardt and others 2006, this proceedings). The inputs required 

by FuelCalc include species, diameter at breast height (d.b.h), canopy height, 

height to live crown, crown class and trees per acre. These tree lists used 

as input to FuelCalc are simple attributes to collect but not often recorded 

in the fi eld with the exception of the Forest Inventory and Analysis (FIA) 

program. Indeed, 84% of all plots used thus far in the LANDFIRE fuels 

mapping effort come from FIA data. The FIA data used for this effort range 

in date from 1978 to 2005, and therefore were obtained using different fi eld 

methods and plot designs (Bechtold and Scott 2005).

These tree lists are ingested by FuelCalc and canopy biomass is computed 

by linking d.b.h. with total canopy biomass using species allometric equations. 

Using these equations, total crown biomass is computed and crown fuel is 

estimated to be that portion of the crown biomass that may be consumed by 

the fl aming front of a passing fi re (≤ 0.6 cm. [1⁄4 in.] dia.). This fuel biomass 

is apportioned through the canopy of the stand according to the nature of 

the stand being investigated. From this CBD profi le the maximum value is 

chosen to represent the stand. Likewise, the CBH is defi ned as the lowest 

layer in the canopy at which the CBD is ≥ 0.012 kg m–3 (0.0007 lb ft–3).

The goal of the canopy fuels mapping effort is to predict CBH and CBD 

across each LANDFIRE mapping zone by relating these attributes to the 

plethora of predictor variables available for each zone. These predictions derived in this manner are referred to as the FuelCalc — derived estimates of 

canopy characteristics. This distinction is signifi cant to later discussions.

The statistical models used to spatially predict CBD and CBH are formulated using the commercially available regression tree, machine-learning 

algorithm, Cubist (© Rulequest Research 2004) (Quinlan 1993; Rulequest 

Research 2006). Cubist offers a fast, effi cient and relatively accurate approach 

for building regression tree models that can be applied to large areas (Huang 

and others 2001; Xian and others 2002). Other salient features of Cubist are 

discussed in Zhu and others (2006) and Keane and others (2006).

The CBH and CBD regression tree models are evaluated using a 10-fold 

cross validation procedure (Shao 1993). Different combinations of variables 

are tested until a consistently low cross validation error rate is observed. Once 

a suitable regression tree model has been formulated, it is applied spatially 

using a suite of tools developed in support of the NLCD project (Homer 

and others 2004; Vogelman and others 2001). These tools were specifi cally 

designed to integrate and interpret regression trees formulated using Cubist 

with the ERDAS Imagine image processing system (Erdas Imagine 2006) 

(© ERDAS, Inc. 2001).

The landscape predictions of CBH and CBD are then subsequently 

qualitatively and quantitatively evaluated. Quantitative evaluations include 

comparisons of CBD with the LANDFIRE canopy cover and satellite imagery. Canopy bulk density is strongly related to canopy cover (fi g. 3). Thus, 

logical relationships between canopy bulk density and canopy cover should 

be observed in the LANDFIRE products. To evaluate these relationships, 

zonal statistics are performed such that the mean CBD is computed for each 

canopy cover class. In a similar manner CBH is evaluated against canopy 

height for each mapping zone.
USDA Forest Service Proceedings RMRS-P-41. 2006. 245

Fuels Products of the LANDFIRE Project Reeves, Kost, and Ryan

Other quantitative methods of evaluating the canopy fuel products include 

comparisons between the frequency of CBH and CBD from the plot data 

with that of the predicted values in each layer. One might expect a consistent 

pattern in the numerical distribution between plot and image data, provided 

that the fi eld plots suffi ciently cover the range of variability observed in a 

mapping zone. For example, if 50 percent of the fi eld plots fell below a bulk 

density 0.12 kg m–3, then a similar fi nding in the predicted values for a mapping zone would be expected.

These quantitative methods are combined with extensive visual inspections for obvious errors. While not statistically rigorous, these methods yield 

valuable guidance and insight as to the appropriate predictor variables and 

subsequent regression tree formulations that should be used. As a result of 

these processes, a predictive regression tree model may undergo signifi cant 

revision for a mapping zone prior to completion of the fi nal product.

Identifying and Filling Areas of Snow, Cloud and Shadow—Although 

the MRLC project carefully selected scenes of imagery to eliminate clouds, 

there are still a few small areas where it was not possible to get a totally cloud 

free scene. Areas contaminated by snow, cloud and shadow are identifi ed in 

each mapping zone using maximum likelihood supervised classifi cation techniques implemented in Erdas Imagine. Any pixel in a mapping zone dominated 

by snow, clouds or shadow will be fi lled using one of two values. These “fi ll” 

values are generated using plot data by computing mean CBH and CBD for 

each EVT/ESP (Stage 1) and EVT (Stage 2) combination. The “fi lling” 

process occurs in two stages. Stage 1 fi lling draws from the database of mean 

CBH and CBD for each EVT/ESP combination. Use of Stage 1 fi lling is 

preferable because it maintains more spatial heterogeneity than the stage 2 

Figure 3—Relationship between estimated canopy bulk density (kg m–3) 

and canopy cover (percent) from FuelCalc for Mapping Zone 12. Black dots 

represent relatively short trees (average of 5.5 m with standard error of 

± 0.08 m) (usually Juniperus spp.), while open circles represent relatively 

taller trees (average of 12.8 m with standard error of ± 0.85 m). 
246 USDA Forest Service Proceedings RMRS-P-41. 2006.

Reeves, Kost, and Ryan Fuels Products of the LANDFIRE Project

fi lling. However, it is not always possible to use Stage 1 fi lling because not 

every EVT/ESP combination on the landscape has plot data with which to 

compute a mean CBH or CBD. In these instances, the simpler, mean CBH 

or CBD by EVT is used. Finally, if there is an EVT found in a mapping zone 

for which there are no plot data to compute a mean CBH or CBD, then the 

prediction is not altered from its original state (as computed using regression 

tree formulae) regardless of the error associated with that prediction.

Obtaining Canopy Base Height From an Expert System—Canopy 

base height is used to aid in predicting surface to crown fi re transition. 

Thus, it is a critical parameter for accurate simulation of crown fi re activity. 

For maximum effectiveness, however, canopy fuels should not be developed 

independently of surface fuels or illogical combinations might occur (Keane 

and others 2001). In recognition of the need to convolve CBH estimates 

with each LANDFIRE fuelbed, an expert system was developed to crosswalk 

these entities to permit crown fi re simulation.

To accomplish this task a series of fi re behavior and fi re management experts 

were asked to estimate conditions under which each appropriate LANDFIRE 

fuelbed would transition from a surface to a crown fi re. The expert panel was 

shown a picture and a description of each fuelbed and then asked to identify 

specifi c environmental criteria under which, in their experience, they had 

observed transitions from surface to crown fi re. These fuelbeds combined 

with the environmental criteria obtained from the experts were fed into a 

spreadsheet analysis system with the appropriate functions from FARSITE 

(Finney 1998) programmed into it. The necessary CBH to permit passive 

crown fi re was computed from this analytical spreadsheet. This dataset is 

separate from the FuelCalc — derived estimates of CBH described above. 

Indeed, these expert system canopy base height estimates are specifi cally 

designed to be used with LANDFIRE data in fi re behavior processors and 

should not be construed as biologically relevant predictions of CBH across 

the landscape. Instead, this CBH layer simply represents a model parameter 

that is estimated in the context of each LANDFIRE fuelbed.

Fuel Loading Models—The Fuel Loading Models (FLM) represent a 

unique surface fuels classifi cation that incorporates the variability of fuel loading within and across fuel components. The model classifi cation uses surface 

components including fi ne and coarse woody debris (FWD ≤ 7.62 cm [3 in.] 

and CWD ≥ 7.62 cm respectively), duff and litter. Fuel loading models were 

created using four generalized steps: 1) collection of fuels data, 2) compute 

fi re effects from fuels data, 3) cluster fi re effects predictions into “Effects 

Groups” (EG), and 4) classify effects groups to create FLM’s. Roughly 4,000 

plots were used to create these FLM’s spanning a large geographic range.

Using these plots, fi re effects were estimated using the First Order Fire 

Effects Model (FOFEM) (Keane and others 1994; Reinhardt and others 

1997). Each fuels plot was subsequently clustered into one of ten effects 

groups based on total PM2.5 emissions and maximum surface soil heating 

(fi g. 4). Classifi cation tree analysis was then used to build a rule set to predict 

each of these effects groups based on FWD, CWD and duff and litter. These 

FLM’s will eventually be spatially mapped through vicarious linkages with 

vegetation and fuels attributes from the LANDFIRE project. These mapped 

FLM’s will contain the necessary data to parameterize fi re effects models 

such as FOFEM in a spatial manner. 
USDA Forest Service Proceedings RMRS-P-41. 2006. 247

Fuels Products of the LANDFIRE Project Reeves, Kost, and Ryan

Discussion

Fire Behavior Fuel Models

Approximately 130 fi re behavior and fuels specialists have participated in the 

LANDFIRE fi re behavior fuel model assignment and calibration workshops. 

This has greatly increased the effi cacy of the FBFM layers. For example, a 

common problem identifi ed with the LANDFIRE FBFM layers is the lack 

of grass models resulting from invasion by Bromus spp. (for example, cheatgrass). As a result, we implemented a procedure, which resulted in millions of 

acres being updated to grass models due to the preponderance of Bromus spp. 

These and other changes have updated LANDFIRE layers to represent local 

conditions as near as possible given the constraints of mapping consistency 

and objectivity. It is notable that the LANDFIRE EVT mapping process is 

not refi ned enough to detect stands that have been minimally thinned, which 

result in accumulation of slash. Thus, it is rare to observe any of the slash 

models in LANDFIRE data, with one exception. Slash models have been 

assigned to some LANDFIRE fuelbeds in the southwestern United States. 

Some stands in this region are late successional decedent stands of Abies 

concolor (white fi r) where very high fuel loads (> 60 tons acre–1) of coarse 

woody debris are observed and blowdown can be several meters thick. The 

Figure 4—Ten effects groups ordinated by PM2.5 (Mg km–3) emissions 

and maximum soil surface temperature (C). 
248 USDA Forest Service Proceedings RMRS-P-41. 2006.

Reeves, Kost, and Ryan Fuels Products of the LANDFIRE Project

fi re and fuel specialists in these areas felt that the fi re behavior under these 

conditions could only be described by slash models, but these situations are 

relatively rare.

Canopy Base Height and Bulk Density—Examples of the relationships 

developed during the canopy fuels regression tree analysis are shown in fi gures 5 

and 6. Figures 5 and 6 indicate CBD estimates above 0.4 and CBH estimates 

above approximately 6 meters are probably not reliable. In general there are 

not enough plots with large values of CBD or CBH to make a reliable and 

stable regression tree above these values.

There is an inverse relationship between canopy cover and bulk density 

in some mapping zones but only in areas of extremely high CC. This nonlinear relationship typically only occurs in stands with relatively high CH. 

This follows the pattern observed in the plot level estimates of CBD and CC 

(fi g. 3). Figure 3 clearly shows two distinct relationships between CBD and 

CC; one for tall trees and one for short trees.

In comparison to CBD, CBH is more diffi cult to interpret, map and identify 

using fi eld based reconnaissance. This is because CBH is more abstract and 

is not a defi nitively measurable feature of a stand. Thus, few techniques exist 

that can be used to asses the true accuracy of these estimates in LANDFIRE 

data. This is one primary reason for creating the expert system derived CBH 

estimates. Examples of these expert system estimates are shown in table 2.

Figure 5—Predicted and observed canopy bulk density (kg m–3) resulting from a 

regression tree analysis for Mapping Zone 12. Note the asymptotic feature beginning 

at approximately 0.4 kg m–3. 
USDA Forest Service Proceedings RMRS-P-41. 2006. 249

Fuels Products of the LANDFIRE Project Reeves, Kost, and Ryan

Figure 6—Predicted and observed canopy base height (m) resulting from a regression tree 

analysis for Mapping Zone 23. Predictions above approximately 6.0 meters are unreliable.

Table 2—Canopy base heights computed using an analytical spreadsheet informed through an 

expert system. Note that each fuelbed has both Anderson (1982) (FBFM13) and Scott 

and Burgan (2005) (FBFM40) fuel models. The environmental criteria for this analysis 

are as follows: fi ne dead fuel moistures (1,10 and 100 hr time lag fuels) are 4,5 and 6% 

moisture content respectively; 20 ft. wind speed was estimated as 20 mph.

 EVT Cover Ht ESP1 FBFM13 FBFM40 CBH132 CBH403

 (%) (m) - - - - - - (m)- - - - - -

Northern Rocky 

 Mountain

Ponderosa Pine

 Woodland and

 Savannah

 ≥50 ≥ 5 Any 9 TU5 0.29 .71

 < 50 ≥ 5 Any 2 TU3 0.075 2.33

 Any < 5 Any 6 GS2 N/A N/A

Rocky Mountain

Subalpine Mesic

Spruce-Fir Forest

 and Woodland

 ≥ 50 ≥ 5 Any 10 TU5 0.34 1

 30 - 49 ≥ 5 Any 8 TU1 0.25 0.23

 < 30 < 5 Any 5 SH4 N/A N/A

1 ESP is Environmental Site Potential. 

2 Canopy base heights formulated using the Anderson (1982) fuel model.

3 Canopy base heights formulated using the Scott and Burgan (2005) fuel model. 
250 USDA Forest Service Proceedings RMRS-P-41. 2006.

Reeves, Kost, and Ryan Fuels Products of the LANDFIRE Project

Use and Limitations of LANDFIRE Fuels Data

The LANDFIRE fuels data layers can be used for applications at varying 

scales, including project level planning (for example, < 5000 acres), particularly when higher resolution data are lacking. These data are particularly well 

suited for comparative analyses within and between regions. Thus, it is the 

responsibility of the user to determine the appropriate scale and usefulness 

of LANDFIRE fuels data. These fuels layers span all ownerships, a trait not 

likely to be found in other fuels data sets. These layers are expected to form 

the baseline data for interagency planning, while local datasets, which cost 

more and take longer to produce can be used in place of, or in addition to, 

LANDFIRE data. However, because of their objective and comprehensive 

nature LANDFIRE data can be used effi ciently for such activities as strategic 

fuels reduction plans, tactical fi re behavior assessment and estimating fi re effects. These fuels data are the fi rst of their kind because they will seamlessly 

cover the nation. Any project with this scope will have tradeoffs between 

quantity and quality. As a result, there is a need for further research for 

improving the quality of these layers and for assessing their true effi cacy. To 

meet this need we recommend cohesive, scientifi c, interagency assessments 

of LANDFIRE fuels data.

Summary

This paper provides a general overview of the LANDFIRE fuels mapping 

procedures and highlights their interdependency on multiple data sources 

including other LANDFIRE layers. Fire behavior fuel models are linked 

with vegetation type and structural attributes based on rulesets devised by 

local fi re and fuel experts. In turn, the spatial expression of these rulesets is 

evaluated and critiqued in a series of local calibration efforts. Canopy fuels 

are mapped using predictive landscape modeling by relating a multitude of 

predictor variables to CBH and CBD in regression trees. These regression 

trees are subsequently applied across the landscape. Given the nebulous nature 

of CBH and the dependence on this variable by fi re behavior processors, we 

have devised a strategy to map canopy base height across the landscape using 

an expert system approach. At national and regional scales LANDFIRE will 

provide valuable insight for modelers, fi re scientists and managers. Finally, we 

recognize the need for cohesive efforts to assess the effi cacy of all LANDFIRE 

fuels data and hope to initiate this process in the future.

Acknowledgments

We acknowledge Robert E. Keane, Mark A. Finney, Charles McHugh, and 

Joe Scott for their thoughtful contributions to LANDFIRE methods. A large 

national project could not succeed without a business management team. We 

therefore also acknowledge Henry Bastian, Daniel Crittenden, Bruce Jeske, 

and Timothy Melchert for their professional business support. Finally, we 

wish to thank the participants of the various fuels workshops. Their local 

expertise has dramatically improved the LANDFIRE fuels layers.
USDA Forest Service Proceedings RMRS-P-41. 2006. 251

Fuels Products of the LANDFIRE Project Reeves, Kost, and Ryan

Literature Cited

Anderson, H. 1982. Aids to determining fuel models for estimating fi re behavior. 

General Technical Report GTR INT-122. USDA Forest Service, Intermountain 

Forest and Range Experiment Station, Ogden, Utah.

Andrews, P.L., and C.D., B. 2003. BehavePlus fi re modeling system, version 2.0: 

overview. Proceedings of the Second International Wildland Fire Ecology and Fire 

Management Congress and Fifth Symposium on Fire and Forest Meteorology. 

November 16-20, 2003. Orlando, FL: American Meteorological Society. pp. 

5-11.

Bechtold, W.A., and Scott, C.T. 2005. The forest inventory and analysis plot design. 

In: Bechtold, W.A.; Patterson, P.L., eds. The enhanced forest inventory and 

analysis program — national sampling design and estimation procedures. General 

Technical Report GTR - SRS80. U.S. Department of Agriculture, Forest Service, 

Southern Research Station: Asheville, N.C.

Caratti, J.F. 2006. In press. The LANDFIRE Reference Database. In: The 

LANDFIRE Prototype Project: nationally consistent and locally relevant 

geospatial data and tools for wildland fi re management. General Technical Report 

RMRS-GTR-XXX. USDA Forest Service, Rocky Mountain Research Station, 

Fort Collins, CO.

Comer, P., Faber-Langendoen, D., Evans, R., Gawler, S., Josse, C., Kittel, G., 

Menard, S., Pyne, M., Reid, M., Schulz, K., Snow, K., and Teague, J. 2003. 

Ecological Systems of the United States: A Working Classifi cation of U.S. 

Terrestrial Systems. Arlington, VA.

Daubenmire, R. 1968. A textbook of plant synecology. Harper and Row, New York, 

New York.

Erdas Imagine 2006. http://gis.leica-geosystems.com/default.aspx. Last Visited 

22 March, 2006.

Finney, M.A. 1998. FARSITE: Fire area simulator. — model development and 

evaluation. RMRS-RP-4. United States Department of Agriculture, Forest 

Service, Rocky Mountain Research Station, Fort Collins, CO.

Homer, C., Huang, C., Yang, L., Wylie, B., and Coan, M. 2004. Development of 

a 2001 National Land-Cover Database for the United States. Photogrammetric 

Engineering & Remote Sensing 70:829-840.

Huang, C., Yang, L., and Wylie, B.C. 2001. A Strategy for Estimating Tree Canopy 

Density Using Landsat 7 ETM+ and High Resolution Images Over Large Areas. 

Third International Conference on Geospatial Information in Agriculture and 

Forestry:CD-ROM. Disk 1.

Keane, R.E., Reinhardt, E.D., and Brown, J.K. 1994. FOFEM — A fi rst order 

fi re effects model for predicting the immediate consequences of wildland fi re in 

the United States. In: Proceedings of the 12th Conference of Fire and Forest 

Meteorology: 628-632.

Keane, R.E., Burgan, R., and Wagtendonk, J.V. 2001. Mapping wildland fuels for 

fi re management across multiple scales: Integrating remote sensing, GIS, and 

biophysical modeling. International Journal of Wildland Fire 10:301-319.

Keane, R.E., Parsons, R.A., and Hessburg, P.F. 2002. Estimating historical range 

and variation of landscape patch dynamics: limitation of the simulation approach. 

Ecological Modeling 151:29-49.

Keane, R.E., Reinhardt, E.D., Scott, J., Gray, K., and Reardon, J. 2005. Estimating 

forest canopy bulk density using six indirect methods. Canadian Journal of Forest 

Research 35:724-739.

Pfi ster, R.D., Kovalchik, B.L., Arno, S.F., and Presby, R.C. 1977. Forest Habitattypes of Montana. General Technical Report. GTR-INT-34. USDA Forest Service, 

Intermountain Forest and Range Experiment Station, Ogden, Utah.
252 USDA Forest Service Proceedings RMRS-P-41. 2006.

Reeves, Kost, and Ryan Fuels Products of the LANDFIRE Project

Quinlan, J.R. 1993. Combining instance-based and mode-based learning. 10th 

International conference on machine learning. Amherst MD:236-243.

Reinhardt, E.D., Keane, R.E., and Brown, J.K. 1997. First Order Fire Effects Model: 

FOFEM 4.0 Users’s Guide. General Technical Report. INT-GTR-344. United 

States Department of Agriculture, Forest Service, Rocky Mountain Research 

Station, Fort Collins, CO.

Rulequest Research. 2006. Data mining tools. http://www.rulequest.com/ [Online] 

(verifi ed 22 March, 2006).

Running, S.W., and Gower, S.T. 1991. A general model of forest ecosystem processes 

for regional applications. II. Dynamic carbon allocation and nitrogen budgets. 

Tree Physiology 9:147-160.

Scott, J.H. 1999. NEXUS: A System for Assessing Crown Fire Hazard. Fire 

Management Notes (2) 59:20-24.

Scott, J.H., and Burgan, R.E. 2005. Standard fire behavior fuel models: A 

comprehensive set for use with Rothermel’s surface fi re spread model. General 

Technical Report RMRS-GTR-153. U.S. Department of Agriculture, Forest 

Service, Rocky Mountain Research Station, Fort Collins, CO.

Shao, J. 1993. Linear model selection by cross-validation. Journal of the American 

Statistical Association 88:486-484.

Thornton, P.E., Law, B., Gholz, H.L., Clark, K.L., Falge, E., Ellsworth, D.S., 

Goldstein, A.H., Monson, R.K., Hollinger, D., Falk, M., Chen, J., and Sparks, 

J.P. 2002. Modeling and measuring the effects of disturbance history and climate 

on carbon and water budgets in evergreen needle leaf forests. Agriculture and 

Forest Meteorology 113:185-222.

Vogelman, J.E., Howard, S.M., Yang, L., Larson, C.R., Wylie, B.K., and Van Driel, 

J.N. 2001. Completion of the 1990’s National Land Cover Data Set for the 

conterminous United States. Photogrammetric Engineering & Remote Sensing 

67:650-662.

Xian, G.K., Zhu, Z., Hoppus, M., and Flemming., M. 2002. Application of decision 

tree techniques to forest group and basal area mapping using satellite imagery and 

forest inventory data. I/FIEOS Conference, Pecora 15/Land Satellite information 

IV/ISPRS Commision.

Zhu, Z., Ohlen, D., Kost, J., Chen, X., and Tolk, B. 2006. In press. Mapping existing 

vegetation composition and structure for the LANDFIRE Prototype project. In: 

The LANDFIRE Prototype Project: nationally consistent and locally relevant 

geospatial data and tools for wildland fi re management. General Technical Report 

RMRS-GTR-XXX. USDA Forest Service, Rocky Mountain Research Station, 

Fort Collins, CO.
USDA Forest Service Proceedings RMRS-P-41. 2006. 253

In: Andrews, Patricia L.; Butler, Bret W., 

comps. 2006. Fuels Management—How to 

Measure Success: Conference Proceedings. 

28-30 March 2006; Portland, OR. 

Proceedings RMRS-P-41. Fort Collins, 

CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research 

Station.

1 Fuels researcher and modeler at the 

USFS Fire Sciences Lab in Missoula, MT. 

rparsons@fs.fed.us

Abstract—Efforts to quantitatively evaluate the effectiveness of fuels treatments are 

hampered by inconsistencies between the spatial scale at which fuel treatments are 

implemented and the spatial scale, and detail, with which we model fi re and fuel interactions. Central to this scale inconsistency is the resolution at which variability within 

the fuel bed is considered. Crown fuels are characterized by clumps of fuel separated 

by gaps between needles, between branches, and between trees. A growing body of 

evidence suggests that this variability plays an important role in how fi re spreads. A 

new system currently in development for representing fuels with higher detail, called 

FUEL3-D, is presented. FUEL3-D is designed to both facilitate fundamental fuel and 

fi re science research and to provide detailed guidance to managers in the design and 

evaluation of fuel treatments. Unlike existing fuel models that do not deal with spatial 

structure or variability within the fuelbed, FUEL3-D represents fuels with spatially explicit detail; individual branches on individual trees are resolved and quantifi ed using 

fractal geometry and allometric relationships. Fuels can be summarized to 3-D pixels, 

at any scale, as input to advanced physical numerical fi re behavior models such as 

FIRETEC and WFDS. FUEL3-D can thus be used to represent fuels before and after 

treatment with much greater detail than has been possible before. Model development, preliminary validation against destructively-sampled crown fuels data sets, and 

current research inquiries are discussed.

Background

Current fi re management practices and policy emphasize implementation 

of fuel treatments, such as thinning and prescribed burning, that seek to 

modify future fi re behavior by reducing or altering the fuel bed in some way. 

A common objective of many fuels treatments is to reduce the likelihood of 

a fi re spreading from surface fuels, such as litter and fi ne woody debris, to 

the forest canopy. Fuel treatments must generally be implemented at one 

time, and actually tested (by a wildfi re passing through or near them) at a 

different time. As substantial resources must be committed to carry out fuel 

treatments, and conditions at the time the treated area burns are unknown, 

fuel treatment assessments rely heavily on predictions from computer models. 

The accuracy of predictions from such models is dependent on the detail with 

which they represent the main components of the problem, namely, wildland 

fuels and their interactions with fi re.

Spatially explicit models of trees and shrubs have been developed with 

different levels of detail. The most common applications of such models are 

light dynamics and plant growth models (see Brunner 1998 and Busing and 

Mailly 2004 for reviews of several such models, respectively). A common 

FUEL3-D: A Spatially Explicit Fractal Fuel 

Distribution Model

Russell A. Parsons1
254 USDA Forest Service Proceedings RMRS-P-41. 2006.

Parsons FUEL3-D: A Spatially Explicit Fractal Fuel Distribution Model

approach is to represent trees and shrubs crowns as simple geometric forms, 

such as cylinders, cones or ellipsoids (e.g., Canham et al. 1999, Kuuluvainen 

and Pukkala 1987, Pukkala et al. 1993). Such representations are limited to 

particular scales because detail within the tree crown is not modeled. A much 

more accurate approach represents plants as fractal objects (Mandelbrot 1983, 

Godin 2000) and model plant architecture in detail, sometimes extending as 

far as individual branches, twigs and leaves (Berezovskava et al. 1997, OzierLafontaine et al. 1999, Richardson and Dohna 2003, Godin et al. 2004). Such 

approaches are particularly relevant to representation of canopy fuels because 

they successfully capture the natural pattern of clumps of fuel separated by 

gaps, such as those between needles and between branches.

The clumped nature of wildland fuels is important to fi re behavior because 

propagation of fi re is a fundamentally fi ne scale, spatial process, dependent 

on the size, shape, composition and arrangement of fuel particles (Burrows 

2001) and, particularly, distance between fuel particles (Fons 1946, Vogel and 

Williams 1970, Weber 1990, Bradstock and Gill 1993). Current management 

tools used to predict fi re behavior, such as BehavePlus (Andrews 2003) and 

FARSITE (Finney 1998) do not deal with spatial relationships within the 

fuel bed and cannot be used to reliably assess transitional fi re behaviors, such 

as the change from surface fi re to crown fi re, or fi re-atmosphere interactions 

that strongly infl uence the initiation of rapid and intense “blow-up” behaviors 

which may pose great threats to fi re fi ghter safety (Rothermel 1991, Potter 

2002). Fuel treatments can only be assessed with such models as a comparison of average conditions (e.g., Van Wagtendonk 1996). This is problematic 

because the complex and dynamic nature of fi re-fuel and fi re-atmosphere 

interactions may result in cases in which the average conditions either do not 

actually occur (such as mean crown base height in a two storied tree stand) 

or do not result in average fi re behavior.

In recent years more advanced physics-based, numerical fi re behavior models 

have emerged, such as FIRETEC (Linn et al. 2002, Linn and Cunningham 

2005), and WFDS (Mell et al. 2005) that consider spatial variability within 

the fuel bed, fi re-fuel interactions and fi re-atmosphere interactions. The detail 

with which these models address fundamental drivers of fi re behavior, as well 

as the underlying physics basis of the models, facilitates robust prediction of 

fi re behavior and related analyses of fuel treatments at multiple scales.

One of the key limitations in the application of these models is that they 

require fi ne scale spatially explicit fuels inputs that are diffi cult to directly 

measure in the fi eld, such as 3-D cells describing the distribution of fuel 

density within a tree. While the fi re behavior models are very sophisticated 

in their treatment of the physics of fi re spread and heat transfer, fuels information for wildland fuels of commensurate detail is extremely rare or 

non-existent. At present no procedures exist by which fuels data measured in 

the fi eld can be used to develop these inputs or test the accuracy with which 

fuels are represented. Perhaps even more importantly, no tool exists by which 

the fundamental properties of wildland fuels can be assessed, quantifi ed and 

evaluated as to their importance across a range of spatial scales. Wildland fi re 

science will not be able to take full advantage of the advancements that have 

been made in fi re modeling until these knowledge gaps are addressed.

One component of fuel treatment assessments that has not received much 

attention is the change in microclimate resulting from the treatment. The 

size, density and geometry of plants affects solar radiation at the forest fl oor 

(Reifsnyder and Lull 1965, North 1996, Govaerts and Verstraete 1998) and 

the interception of rain by the canopy (Helvey and Patric 1965), which both 

infl uence fuel moisture (Fosberg and Deeming 1971, Nelson 2002). The 
USDA Forest Service Proceedings RMRS-P-41. 2006. 255

FUEL3-D: A Spatially Explicit Fractal Fuel Distribution Model Parsons

canopy structure also infl uences winds within a stand (Jensen 1983, Oke 

1978, Brandle 1980). Fuel treatments may thus result in signifi cant feedback 

relationships with the microclimate, which may alter the future behavior of 

fi re within a stand in unexpected ways. At present we are greatly limited in 

our ability to assess the nature and magnitude of these effects.

Objectives

In this paper I introduce a spatially explicit fuel model called FUEL3-D, 

which can be used to represent fuels in great detail, both as discrete branches 

and as 3-D cells. This model represents a new concept in fuel modeling, in 

which fuel beds are described as a collection of discrete elements such as individual trees and branches within trees. FUEL3-D can be used to provide 

inputs to detailed numerical fi re behavior models that account for spatial 

relationships within the fuel bed and are thus more sensitive to fuel treatments than current operational fi re models.

I describe preliminary parameterization for ponderosa pine crown fuels 

based on destructively sampled crown fuels data and present results of preliminary validation analyses of biomass quantities against independent validation 

data. I then demonstrate two ways in which fi ne scale representations of fuels 

might provide insights relevant to fuel treatment assessments. First, I demonstrate how spatial relationships within the fuel bed infl uence fi re behavior 

using a three-dimensional physical fi re behavior model, WFDS (Mell et al. 

2005). Second, using ray-tracing procedures I demonstrate how the spatially 

resolved structure of wildland fuels can be used to simulate the infl uence of 

the forest canopy on light dynamics at the forest fl oor, an important component of surface fuel moisture dynamics as well as vegetative response to fuel 

treatments. I conclude with discussion of how modeling fuels at fi ne scales 

fi ts into the larger picture of fi re management.

Methods

Parameterization of the FUEL3-D Model for Ponderosa Pine

As the precise number, size and positions of individual branches composing 

the crown of an individual tree will generally never be known, it is necessary 

to simulate this structure. This is done on the basis of relationships identifi ed from fi eld data describing biomass quantities and geometry within the 

crown.

Field Data and Analysis—Detailed crown fuels data were collected 

through a destructive sampling crown fuels study in fi ve locations in the 

western United States in 2000 and 2002 (Scott and Reinhardt 2002). In 

each study location, fi eld crews systematically measured, removed, dissected 

and weighed individual branches for each tree in fi ve stands destructively 

sampled between 2000 and 2002 (Scott and Reinhardt 2002). Tree level 

measurements included height, height to crown base, health status, canopy 

class (dominant, codominant etc.), coordinates of the tree stem and diameter 

at breast height (1.35 m, DBH). Branch level measurements included branch 

basal diameter, height on bole, angle from vertical, total length, width, and 

weight, separated out by component (woody vs. foliage, live or dead, etc.). 
256 USDA Forest Service Proceedings RMRS-P-41. 2006.

Parsons FUEL3-D: A Spatially Explicit Fractal Fuel Distribution Model

Woody fuels were separated and weighed by fuel moisture lag time size classes, 

i.e. 1 hour, 10 hour (Fosberg and Deeming 1970). I used tree and branch 

data measured for ponderosa pine (Pinus ponderosa) trees in a dense, single 

storied stand at the Flagstaff, Arizona fi eld site in this initial development 

and testing of the FUEL3-D model. Of the original 85 trees, 7 trees with 

no individual branches, such as broken snags, were excluded from analysis, 

resulting in a data set of 78 trees and a total of 2207 individually measured 

branches. The trees were mostly codominant and intermediate trees with 

diameters ranging from 2.6 to 38.4 cm (mean 17.2 cm) (Figure 1). The majority (80%, 62 trees) of this data was randomly selected for model-building 

(to develop empirical relationships used in the model), and the remainder 

(20%, 16 trees) was withheld for validation. An additional 16 ponderosa pine 

trees measured at the Ninemile, Montana fi eld site for the same study were 

used to assess how well relationships identifi ed for the Flagstaff data could 

be applied to ponderosa pine trees sampled at other locations.

Figure 1—Three plots showing properties of data for the 78 ponderosa pine trees used 

in this study. All data used were from the Flagstaff fi eld site: A) diameter distribution: B) 

Crown class distribution: D=Dominant, C=Codominant, I=Intermed, S=Suppressed. 

C) Health Status: H=Healthy,S=Sick,D=Dying
USDA Forest Service Proceedings RMRS-P-41. 2006. 257

FUEL3-D: A Spatially Explicit Fractal Fuel Distribution Model Parsons

I supplemented this main data set with additional data collected in 2004 

and 2006 in Montana. These data sets included measurements of angles between sub-branches, lengths and diameters of sub-branches as proportion of 

parent branches, and weights and dimensions of individual clumps of needles. 

This data in combination with the more extensive crown fuels study data 

described above provided information adequate for modeling sub-branches 

and distribution of biomass within a branch.

Using the model-building data I used non-linear regression procedures 

to predict the total branch biomass, and total foliar biomass for a branch 

as a function of basal branch diameter. I then used maximum likelihood 

estimation procedures to fi t theoretical Weibull probability density functions (Grissino-Mayer 1999) describing the branch size class distribution of 

individual branches as a proportion of tree diameter at breast height (DBH) 

(Figure 2). The Weibull distribution is a fl exible continuous positively skewed 

distribution described by the probability density function

 f(y) = ( cy ( c – 1 ) / bc

) e(–(y/b)c

) [1]

for the range 0 <= y < ∞ , scale parameter, b and shape parameter, c. I assessed 

model fi t for branch size distributions with the Komologorov-Smirnov (K-S) 

test. Additional analyses (not presented here for the sake of brevity) assessed 

relationships between the position and orientation of the base of a branch 

along the tree stem and set upper limits for the total length and width of each 

branch, all on the basis of branch basal diameter. A summary of parameters 

used to describe and model ponderosa pine is presented in Table 1.

Figure 2—Distribution of branch basal diameters, as proportion of tree diameter at 

breast height, for 62 ponderosa pine trees destructively sampled near Flagstaff, Arizona. 

Smooth line shows theoretical distribution fi tted on this data.
258 USDA Forest Service Proceedings RMRS-P-41. 2006.

Parsons FUEL3-D: A Spatially Explicit Fractal Fuel Distribution Model

Simulation of Tree Crowns—Simulation of a tree begins with a measurement of DBH. This is used to predict the size class distribution of branch 

basal diameters on the basis of analysis described above. Individual branch 

basal diameters are then sampled from this distribution until the sum of the 

cross sectional areas of the branches equal the tree cross sectional area. This 

relationship, fi rst observed by Leonardo da Vinci and later applied in the 

pipe model theory (Shinosaki et al. 1964), has been shown to be true for a 

wide range of tree species and is a common basis in fractal models of plant 

structure (Berezovskava et al. 1997, Ozier-Lafontaine et al. 1999, Enquist 

2002). For each branch basal diameter total branch biomass and foliar biomass quantities are then predicted using empirical functions described above. 

At this point each branch is defi ned in general terms but has no structure 

of sub branches.

The structure of sub branches which comprises the total branch is modeled 

as a series of frustums of a right circular cone, described by two vertices defi ning the position of the end points, and the radii at each end perpendicular 

to the line connecting the vertices (Figure 3). The branching structure is 

assembled using a static fractal model approach (e.g., Ozier-Lafontaine et al. 

1999), described only briefl y here. An initial segment is defi ned which represents the fi rst part of a branch up to the point where sub branches form. The 

dimensions of this branch, along with geometric parameters describing the 

number of child branches and angles between them are used as the “seed” in 

a recursive function, common to numerous fractal tree models (Berezovskava 

et al. 1997, Niklas 1986). The effect of the recursive function is to continue 

Table 1—Empirical relationships and parameters used to model ponderosa pine crowns.

 Dep. var. Indep. var. Function 

(abbrev), units (abbrev), units type Equation Fit

Allometries

Branch diameter size class distributiona Weibull pdf. f(y) = (cy(c – 1) / bc) e(–(y/b)c

)

 K-S

 b = 0.128 0.06

 c = 2.285 p-value

 0.0002

Total branch Branch basal Power TB = 27.17 * BD2.77 R2 = 0.96

biomass(TB), g diameter(BD),cm Y = axb

Branch foliar Branch basal Power FB = 11.15 * BD2.36 R2 = 0.92

biomass (FB), g diameter(BD),cm Y = axb

Geometry

Total branch Total branch Linear BW = 0.50 * BL R2 = 0.69

width (BW), m length (BL,m) Y = ax

Total branch Branch basal Power BL = 0.47*BD 0.99 R2 = 0.77

length (BL), m diameter(BD),cm Y = axb

Angle between NA Random, Mean = 77 stdev = 9

branches, normal pdf.

degrees

a Branch diameter distribution modeled as a proportion of tree diameter, so y = Branch basal diameter / tree d.b.h. This 

accounts for the increase in branch diameters as trees get larger. 
USDA Forest Service Proceedings RMRS-P-41. 2006. 259

FUEL3-D: A Spatially Explicit Fractal Fuel Distribution Model Parsons

branching until some predefi ned end condition is met. In this manner each 

branch extends itself, splits into smaller branches, which themselves split into 

smaller branches, and so on (Figure 4). The position of each segment in 3-D 

space, dimensions and orientation and other attributes are written to a list for 

future use. In this initial confi guration of the model branching was stopped 

when the distal radius of the segment was small enough to be considered a 

terminal, which represents a clump of needles. A terminal is defi ned in space 

as a frustum of a cone but also has additional attributes describing the total 

number of needles, surface area, foliar biomass etc. For extremely detailed 

simulations (typically only within a small area) it is possible to replace each 

terminal with a series of smaller objects. In this manner it is possible to represent detail down to the level of individual needles if desired.

Figure 3—Planar view of a frustum of a 

cone, defi ned by length h, large radius 

R, and small radius r. The frustum of 

a cone is the basic building block for 

branches within the FUEL3-D spatial 

fuel model.

Figure 4—A simulated branch with sub-branches generated with FUEL3-D.
260 USDA Forest Service Proceedings RMRS-P-41. 2006.

Parsons FUEL3-D: A Spatially Explicit Fractal Fuel Distribution Model

Summarization to 3-D cells—In order to use the fuels data defi ned as 

discrete objects in the numerical fi re behavior models it is necessary to convert 

the data to values associated with three-dimensional grid cells (Figure 5). This 

is accomplished by slicing each branch segment, perpendicular to its main 

axis into a number of circular cross sections. Each circle is “clipped” along 

the line of intersection between the plane within which it lies and each of the 

applicable planes which constitute the limits of the 3-D cell. The area of the 

resulting, possibly irregular, polygon is stored off in a list. All of these areas 

are then numerically integrated to calculate the volume of that branch that 

lies within the particular cell. This procedure is repeated for each cell and for 

all branch segments. Parts of a branch segment that are cut out of one cell 

will be accounted for in an adjacent cell. In this manner the total quantities 

are preserved across whatever spatial scale is desired.

Comparison With Validation Data—Comprehensive validation of a complex model often requires a large number of tests; as the FUEL3-D model 

is still in active development validation efforts are ongoing. I compared the 

measured total crown biomass, for the two independent validation sets, against 

quantities simulated with FUEL3-D (Figure 6). The modeled relationships 

used in testing were all derived from the Flagstaff model building data set.

Figure 5—3-D cell representation of density within the crown of a small tree, for two 

resolutions (columns, left 10 cm cells, right, 5 cm cells) and two perspectives (rows, 

top, side view of vertical slice through volume, bottom, overhead view of horizontal 

slice through volume. Light colors are low values of density within a cell and dark 

cells are higher values. A) 10 cm cells, side view, vertical slice; B) 5 cm cells, side view, 

vertical slice; C) 10 cm cells, overhead view, horizontal slice; D) 5 cm cells, overhead 

view, horizontal slice.
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FUEL3-D: A Spatially Explicit Fractal Fuel Distribution Model Parsons

Simulating Fire and Fuel Interactions—I demonstrate how detailed 

representations of fuel structure may provide insights to fi re and fuel interactions with two related simulations using the physics-based fi re model 

WFDS (Mell et al. 2005). The data used as inputs were similar to outputs 

from FUEL3-D, with values associated with individual 3-D cells, but were 

somewhat simplifi ed as explicit connections between FUEL3-D and WFDS 

are still in development. The simulations were set up within a very small area 

similar to a wind tunnel in dimensions (8m long x 4 m wide x 4 m wide). 

For fi re computations this area was divided into 64 x 32 x 32 cells, 0.125 m 

on a side. Within this small spatial domain I simulated a surface fuel bed 

0.25 m in depth, 2 m wide and 6 m long, with fuel properties of excelsior 

(shredded aspen) and a constant moisture content of 6.3%. Three simulated 

trees were placed with the center of their stems at 2 m, 4.5 m and 6 m along 

the centerline of this fuel bed (Figure 7). WFDS represents trees and other 

Figure 6—Comparison of measured total crown biomass (X axis) against crown biomass 

simulated with FUEL3-D (Y axis) for 16 trees used as independent “holdout” validation 

data from the Flagstaff site (a), and from the Ninemile site (b). Neither set of trees was 

used to construct modeled relationships. Solid lines in both fi gures represent the 1:1 

line, while thinner lines are fi t to the data. Correlations for fi tted lines were 0.94 (a) 

and 0.98 (b), but slopes less than 1.0 show that modeled relationships underpredict 

biomass for larger trees in both sites.
262 USDA Forest Service Proceedings RMRS-P-41. 2006.

Parsons FUEL3-D: A Spatially Explicit Fractal Fuel Distribution Model

elevated fuels as collections of thermally thin particles. Each tree was defi ned 

individually with a height, height to crown base, crown radius, and available 

fuel moisture content. To represent gaps within the crown, the crown for 

each tree was defi ned as frustum of a right circular cone. Within the volume 

of that cone, each cell was either assigned fuels or was empty depending on 

a random number. The fi rst and third trees were parameterized as with more 

gaps, to represent more gappy, live trees while the middle tree was parameterized as less gappy and dead, with a much lower moisture content. An ignitor 

panel was simulated at the left edge of the fuel bed to start the fi re. Winds 

were initialized at zero but were accelerated to a constant 1.5 m/s (3.4 mph) 

three seconds into the simulations. The fi rst simulation used these fuels with 

Figure 7—Comparison of two simulations with a numerical fi re model, WFDS, and highly 

resolved at t = 0. Top fi gure shows “untreated” simulation with three small trees and a 

surface fuel bed in a wind tunnel. The outer trees are live, with high moistures and the 

middle tree is dead with low moisture, representing a recently bug-killed tree. Bottom 

fi gure shows the “treated” simulation in which the middle dead tree has been removed 

and lower branches have been pruned to 0.75 m. 
USDA Forest Service Proceedings RMRS-P-41. 2006. 263

FUEL3-D: A Spatially Explicit Fractal Fuel Distribution Model Parsons

no modifi cations and represents the “untreated” case. The second simulation 

represents an extremely simple fuel treatment, consisting of thinning (removal 

of the dead, middle tree) and branch pruning (removal of fuels in the two 

remaining trees below 0.75 m). Both simulations were run for a duration of 

120 seconds. Graphical outputs from Smokeview, the companion software 

to WFDS used to visualize WFDS outputs for the two simulations for t = 

0, 48, 60 and 72 seconds are shown in Figures 7-10. In these fi gures, the 

small particles represent the fuels, the lighter cloud-like structures represent 

fl ames (as isosurfaces of heat release rate per unit area, in KJ/m2) and the 

darker cloud like structures represent soot density. These simulations were 

not intended to provide defi nitive scientifi c results, as the spatial domains 

are probably too small to eliminate artifacts arising from the proximity of 

the boundaries, but simply to illustrate potential applications of numerical 

fi re behavior models in fuel treatment assessments.

Figure 8—Demonstration of a numerical fi re simulation with the Wildland Urban 

Interface Fire Dynamics Simulator (WFDS), and highly resolved fuels at t = 48 seconds. 

Surface fuels are burning in both simulations but the middle dead tree in the untreated 

simulation (top) is burning intensely. 
264 USDA Forest Service Proceedings RMRS-P-41. 2006.

Parsons FUEL3-D: A Spatially Explicit Fractal Fuel Distribution Model

Figure 9—Demonstration of a numerical fi re simulation with the Wildland Urban Interface Fire 

Dynamics Simulator (WFDS), and highly resolved fuels at t = 60 seconds. Surface fuels are burning 

in both simulations. Heat from the the middle dead tree in the untreated simulation (top), as well as 

from the surface fuels, has caused the tree at right to ignite. In the “treated” simulation (bottom) the 

tree at right is scorched from below but does not ignite. 
USDA Forest Service Proceedings RMRS-P-41. 2006. 265

FUEL3-D: A Spatially Explicit Fractal Fuel Distribution Model Parsons

Figure 10—Demonstration of a numerical fi re simulation with the Wildland Urban Interface Fire Dynamics 

Simulator (WFDS), and highly resolved fuels at t = 72 seconds. Surface fuels are burning in both simulations. 

Heat from the the middle dead tree in the untreated simulation (top), as well as from the surface fuels, has 

caused the tree at right to ignite, and it continues to burn intensely. In the “treated” simulation (bottom) 

the tree at right is scorched from below but does not ignite.
266 USDA Forest Service Proceedings RMRS-P-41. 2006.

Parsons FUEL3-D: A Spatially Explicit Fractal Fuel Distribution Model

Simulating Canopy Shading—To demonstrate the application of fi ne 

scale spatial representation in assessing impacts to the microclimate I used 

ray tracing procedures (North 1996, Govaerts and Verstraete 1998, Brunner 

1998) to simulate the shadows cast by a single tree modeled with FUEL3-

D. The tree was parameterized with data from the Flagstaff fi eld site but 

arbitrarily located in Missoula, Montana, at a point in space (Latitude 46.5 

North, Longitude 114.0 degrees West, Missoula, Montana) and at two 

points in time 30 minutes apart (June 21, 2005, 14:20 and 14:50 local time) 

(Figures 11 and 12). Ray tracing is a spatially explicit approach for light 

modeling which samples beams of light between the light source (the sun) 

and a given object and thus is capable of representing shadows and other 

behaviors related to light with great detail, both in space and in time.

Figure 11—Visualization of a medium sized ponderosa pine tree modeled with FUEL3-D. 

The shadow of the tree, modeled with ray-tracing procedures, is shown at left. 
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FUEL3-D: A Spatially Explicit Fractal Fuel Distribution Model Parsons

Figure 12—Visualization of the same tree as in Figure 11 but 30 minutes later. The shadow 

of the tree, modeled with ray-tracing procedures, is shown at left, has moved slightly 

as the position of the sun changed. 

Results

Field Data Analysis

Several relationships were identifi ed from analysis of the fi eld data (Table 1). 

Two sets of relationships are described: allometric relationships which relate 

easily measured quantities on a tree, such as DBH, to properties within the 

tree, such as the size class distribution of branches, and geometric relationships which describes properties and proportions. The size class distribution 

of individual branches on a tree, as a function of tree DBH, was positively 

skewed and fi t well with the Weibull distribution as measured with the K-S 

statistic (Figure 2, Table 1). Branch biomass quantities were strongly related 
268 USDA Forest Service Proceedings RMRS-P-41. 2006.

Parsons FUEL3-D: A Spatially Explicit Fractal Fuel Distribution Model

to branch basal diameter with power law relationships. These relationships 

provide the basis for the simulation of canopy structure of ponderosa pine 

trees.

Comparison/Validation

Biomass quantities simulated with FUEL3-D compared reasonably well 

with both validation data sets, with correlation coeffi cients of 0.94 for the 

independent holdout data for Flagstaff site and 0.98 for the Ninemile site 

data (Figure 6). Slopes of linear trend lines fi t to the validation data were 

somewhat less than 1.0 (0.95 for Flagstaff and 0.86 for Ninemile), indicating 

that biomass quantities for larger trees might be underestimated. The Ninemile data consisted of generally larger trees, and a very different biophysical 

setting, so it is diffi cult to determine whether the underestimation observed 

for larger trees is purely a function of tree size or if it has some interaction 

with differences between sites.

Numerical Fire Simulations

The two simulations illustrate how spatial relationships within the fuel bed 

can result in differences in fi re behavior. The two simulations had identical 

environmental conditions (wind speeds and fuel moistures) but removal of 

the center dead tree and elimination of lower branches on the remaining trees 

(Figure 7) resulted in differences in fi re behavior between the two simulations. 

Figures 7-10 show the progression of the two simulations at t = 0, 48, 60 and 

72 seconds, respectively. At t = 48 (Figure 8) the center tree in the untreated 

simulation (top) is engulfed in fl ame while in the treated simulation, the fi re 

is confi ned to the surface fuels. At t = 60 (Figure 9), fl ames are moving into 

the crown of the large tree at right in the untreated simulation (top); at t = 

72 that tree is actively fl aming throughout the crown (Figure 10). At these 

points in time in the treated simulation the fi re is burning underneath the 

crown of the rightmost tree but does not ascend into the crown.

Simulating Crown Shading—Visualizations at two points in time 30 

minutes apart (Figures 11 and 12) show the detail with which individual 

trees and their shadows can be modeled. In full sun conditions, shadows 

from trees signifi cantly reduce the direct solar radiation received at a shaded 

point on the ground. Direct solar radiation is a key driver of dead fi ne fuel 

moisture, raising the fuel temperature, heating the boundary layer and accelerating evaporation (Nelson 2002). Modeling shadows from individual 

trees may thus be applied to assess spatial variability in surface fuel moistures 

and changes in such patterns arising from fuel treatments.

Discussion

The models which form the basis of our current operational capacity to 

assess fuel treatments, namely, the fi re behavior model BEHAVE (Rothermel 

1972) and the stand growth model PROGNOSIS (Stage 1973), were developed at a time when many processes in combustion science and plant growth 

were poorly understood, and when both computational resources, and the data 

which could be used as inputs to predictive models were limited. Advances 

in computing resources, information technology and geospatial applications 

such as GPS, GIS and remote sensing change the nature of what is possible 
USDA Forest Service Proceedings RMRS-P-41. 2006. 269

FUEL3-D: A Spatially Explicit Fractal Fuel Distribution Model Parsons

in assessing fuel treatments. New sensors such as LIDAR make it possible 

to measure individual tree stems and branch heights (Henning and Radtke 

2006), individual crown diameters (Popescu et al. 2003) and estimate other 

stand characteristics (Nelson et al. 1988). The continuing development of 

such technologies suggests that detailed modeling of fi re and fuels will only 

become more accessible to the wildland fi re community as time goes on.

The FUEL3-D model is still in development and should be viewed as a 

work in progress. The same holds true, to a lesser degree, for the numerical 

fi re models themselves which represent a rapidly advancing but still emerging fi eld in fi re science. Continuing development of the FUEL3-D model will 

provide avenues by which important knowledge gaps regarding wildland fuel 

properties, microclimate-fuel dynamics, fi re-fuel interactions and fi re effects 

can be addressed. Although the model is currently more appropriate for 

research use, a management appropriate confi guration will be developed as 

soon as the underlying structure of the model is suffi ciently mature.

The ability to represent the spatial structure of vegetation in detail across a 

range of scales will facilitate improvements in our understanding of fundamental fuels science. Fuel beds can be constructed describing any confi guration 

of trees and shrubs of any size. By building fuel beds from individual trees 

and shrubs (and associated surface fuels), loss of relevant detail and scaledependencies associated with fuel classifi cations is avoided (Sandberg et al. 

2001). At present there is no way that fundamental wildland fuel properties, such as surface area to volume ratio, the size distribution of particles 

or distribution of mass within a tree crown, can be easily calculated. With 

FUEL3-D these quantities can be calculated from the simulated structure, 

tested and calibrated. The fl exibility with which FUEL3-D can represent the 

architecture of trees and shrubs makes it possible to develop species-specifi c 

fuel models. Differences in crown architecture between species likely play key 

roles in how fi re burns through a stand and how that stand responds to fuel 

treatment over time. This provides stronger linkages between silviculture, 

ecosystem function and fuel management such that fuel treatments can be 

considered not only in terms of their potential impacts on fi re behavior but 

also on other ecosystem components.

Detailed modeling of wildland fuels in space improves in our ability to assess changes in microclimate arising from fuel treatments, as well as to better 

understand the complexities of natural stands. A large number of spatially 

explicit light models have been developed (see Brunner 1998) but the majority of these focus on plant growth and thus do not consider fl uctuations in 

solar radiation at temporal scales fi ner than a few weeks, as this tends to be 

the limit at which plant growth can be modeled (Brunner 1998). In fi re and 

fuels applications such time scales are likely too coarse to capture much of 

the important dynamics, particularly with respect to dead fi ne fuel moisture, 

which exhibit signifi cant sensitivity to solar radiation over short time periods 

(Nelson 2002). Current FUEL3-D research inquiries in this arena are directed 

at linking a ray tracing procedure to a dynamic fuel moisture model (Nelson 

2002) in space. This will enable spatially and temporally explicit modeling 

of surface fuel moisture dynamics which can be used to quantitatively compare fuel treatments. Such detailed modeling will also likely also be of use 

in modeling shrub and grass growth response over time, a factor important 

to the effective duration of fuel treatments.

By quantitatively describing fuels at higher detail, FUEL3-D will promote 

an improved understanding of fi re and fuels interactions. In conjunction 

with numerical fi re behavior models such as FIRETEC or WFDS it will be 

possible to more precisely study transitions from surface to crown fi re and 
270 USDA Forest Service Proceedings RMRS-P-41. 2006.

Parsons FUEL3-D: A Spatially Explicit Fractal Fuel Distribution Model

develop species-specifi c thinning spacing guidelines. Analyses across scales 

will help to systematically identify conditions when greater complexity in 

modeling is required, and simpler conditions in which it is not. Correlative relationships observed through more intense numerical studies may be 

used to refi ne existing operational models. One advantage of FUEL3-D is 

its independence from any specifi c fi re behavior model and its assumptions 

and limitations. At present the model is being designed to work with two 

numerical fi re models, FIRETEC (Linn et al. 2002) and WFDS (Mell et al. 

2005). As other models appear or as these models change FUEL3-D will be 

able to provide the needed inputs. The independence of the fuel model from 

particular fi re behavior models provides fl exibility and facilitates comparisons 

between models.

Finally, modeling fuel-fi re interactions at fi ne scales will aid in a tighter 

coupling between fi re behavior and fi re effects. Most fi re effects calculations 

are carried out as point calculations, where fuel consumption at a point or 

mortality of an individual tree are considered (Reinhardt et al. 2001). At 

present it is diffi cult to rectify the homogeneous stand-based fi re behavior 

calculations from operational fi re behavior models with point level fi re effects predictions. Incorporation of fi ner detail in representation of fuels with 

FUEL3-D, and detailed spatially explicit fi re behavior models will provide a 

basis for linkages between fi re behavior, fuels and fi re effects than has been 

possible before. This will improve our ability to defi ne burn window prescriptions and anticipate the consequences of treatments or wildfi re.

Acknowledgments

The author wishes to thank Elizabeth Reinhardt, Joe Scott, Jim Reardon, and Bob Keane for use of the crown fuels study data and their helpful 

insights into that remarkable study. Fire modelers Rod Linn, William Mell 

and Mark Finney provided much helpful discussion. Field crews at the Fire 

Sciences Lab in Missoula, Montana played a key role in gathering the data 

used in this project.

Literature Cited

Andrews, P. L., C. D. Bevins, and R. C. Seli. BehavePlus fi re modeling system–version 

2.0: User’s Guide. USDA Forest Service, Rocky Mountain Research Stations, 

General Technical Report-RMRS-GTR-106WWW.

Berezovskava, F .S., Karev, G. P., Kisliuk, O. S., Khlebopros, R. G., and Y. L. 

Tsel’niker. 1997. A fractal approach to computer-analytical modeling of tree 

crowns. Trees 11:323-327.

Bradstock, R. A., and A. M. Gill. 1993. Fire in semi-arid, Mallee shrublands: size 

of fl ames from discrete fuel arrays and their role in the spread of fi re. Int. J. 

Wildland Fire 3(1):3-12.

Brandle, J. R., B. B. Johnson, and D. D. Dearmont.1984. Windbreak economics: the 

case of winter wheat production in eastern Nebraska. Journal of Soil and Water 

Conservation 39(5):339-343.

Brunner, A. 1998. A light model for spatially explicit forest stand models. Forest 

Ecology and Management 107:19-46.
USDA Forest Service Proceedings RMRS-P-41. 2006. 271

FUEL3-D: A Spatially Explicit Fractal Fuel Distribution Model Parsons

Burrows, N. D. 2001. Flame residence times and rates of weight loss of eucalypt 

forest fuel particles. Int. J. Wildland Fire 10:137-143.

Busing, R. T. and Mailly, D. 2004. Advances in spatial, individual-based modeling 

of forest dynamics. Journal of Vegetation Science 15:831-842.

Canham, C. D., K. D. Coates, P. Bartemucci, and S. Quaglia. 1999. Measurement 

and modeling of spatially explicit variation in light transmission through interior 

cedar-hemlock forests of British Columbia. Can. J. For. Res. 29:1775-1783.

Enquist, B. J. 2002. Universal scaling in tree and vascular plant allometry: toward 

a general quantitative theory linking plant form and function from cells to 

ecosystems. Tree Physiology 22:1045-1064.

Finney, M. A. 1998. FARSITE: Fire Area Simulator — Model development and 

evaluation. USDA Forest Service, Rocky Mountain Research Station, General 

Technical Report RMRS-GTR-4.

Fons, W. L. 1946. Analysis of fi re spread in light forest fuels. Journal of Agricultural 

Research 72(3):93-121.

Fosberg, Michael A.; Deeming, John E. 1971. Derivation of the 1- and 10- hour 

timelag fuel moisture calculations for fi re-danger rating. Research Note RM-207. 

Fort Collins, CO: USDA Forest Service, Rocky Mountain Forest and Range 

Experiment Station. 8 p.

Godin, C. 2000. Representing and encoding plant architecture: a review. Ann. For. 

Sci. 57:413-438.

Godin, C., O. Puech, F. Boudon, and H. Sinoquet. 2004.Space occupation by tree 

crowns obeys fractal laws:evidence from 3D digitized plants. 4th International 

Workshop on Functional-Structural Plant Models, 7-11 June 2004, Montpellier, 

France. Edited by C. Godin et al: 79-83.

Govaerts, Y. M. and Verstaete, M. M. 1998. Raytran: a Monte Carlo ray-tracing 

model to compute light scattering in three dimensional media. IEEE Transactions 

on Geoscience and Remote Sensing, 36: 493-505.

Grissino-Mayer, H. D. 1999. Modeling fi re interval data from the American 

Southwest with the Weibull distribution. International Journal of Wildland Fire. 

Vol. 9, no. 1 Mar. 1999: 37-50.

Helvey, J. D., and J. H. Patric. 1965. Canopy and litter interception by hardwoods 

of eastern United States. Water Resources Research 1:193-206.

Henning, J. G., and P. J. Radtke. 2006. Detailed stem measurements of standing 

trees from ground-based scanning LIDAR. Forest Science 52(1):67-80.

Jensen, A. M. 1983. Shelterbelt Effects in Tropical and Temperate Zones. Report 

IDRC-MR80e. Agriculture, Food and Nutrition Sciences Program, International 

Development Research Centre. Ottowa, Canada. 61 pp.

Kuuluvainen, T. and T. Pukkala. 1987. Effect of crown shape and tree distribution 

of shade. Agric. Forest Meterology 40:215-231.

Linn, R. R., and P. Cunningham. 2005. Numerical simulations of grass fi res using 

a coupled atmosphere-fi re model: basic fi re behavior and dependence on wind 

speed. J. Geophysical Research 110:D18107.

Linn, R. R., Reisner, J., Colman, J. J., and J. Wintercamp. 2002. Studying wildfi re 

behavior using FIRETEC. Int. J. Wildland Fire 11:233-246.

Mandelbrot, B. B. 1983. The fractal geometry of nature. W. H. Freeman and 

Company, New York, New York, USA.

Mell, W. E., J. J. Charney, M. A. Jenkins, P. Cheney and J. Gould. 2005. Numerical 

simulations of grassland fi re behavior from the LANL-FIRETEC and NISTWFDS models. EastFIRE conference, May 11-13, 2005. George Mason 

University, Fairfax, VA.

Nelson, R. R., R. Swift, and W. Krabill. 1988. Using airborne lasers to estimate 

forest canopy and stand characteristics. J. Forestry. 86:31-38.
272 USDA Forest Service Proceedings RMRS-P-41. 2006.

Parsons FUEL3-D: A Spatially Explicit Fractal Fuel Distribution Model

Nelson, Ralph. M., Jr. 2002. Prediction of diurnal change in 10-h fuel stick moisture 

content. Canadian Journal of Forest Research 30:1071-1087.

Niklas, K. J. 1986. Computer simulated plane evolution. Scientific American 

254:78-86.

North, P. R. J. 1996. Three-dimensional forest light interaction model using a 

Monte Carlo method. IEEE Transactions on Geoscience and Remote Sensing, 

34:946-956.

Oke, T. R. 1978. Boundary Layer Climates. Methuen, London. 372 pp.

Ozier-Lafontaine, H. O., F. Lecompte and J. F. Sillon. 1999. Fractal analysis of 

the root architecture of Gliricidia sepium for the spatial prediction of root 

branching, size and mass: model development and evaluation in agroforestry. 

Plant and Soil 209(167-180).

Popescu, S. C., R. H. Wynne, and R. F. nelson. 2003. Measuring individual tree 

crown diameter with lidar and assessing its infl uence on estimating forest volume 

and biomass. Can. J. For. Res. 29:564-577.

Potter, B. E. 2002. Dynamics-based view of atmosphere-fire interactions. 

International Journal of Wildland Fire 11:247-255.

Pukkala, T., Kuuluvainen, T. and P. Stenberg. 1993. Below canopy distribution of 

photosynthetically active radiation and its relation to seeling growth in a boreal Pinus 

sylvestris stand: a simulation approach. Scand. J. Forest Research. 8:313-325.

Reifsnyder, W. E. and H. W. Lull. 1965. Radiant energy in relation to forests. USDA 

Forest Service. Technical Bulletin No 1344. 111 pp.

Reinhardt, E. D., R. E. Keane, et al. 2001. Modeling fi re effects. International 

Journal of Wildland Fire 10: 373-380.

Richarson, A. D., and H. Z. Dohna. 2003. Predicting root biomass from branching 

patterns of Douglas-fi r root systems. OIKOS 100:96-104.

Rothermel, R. C. 1991. Predicting behavior and size of crown fi res in the northern 

Rocky Mountains. USDA For. Serv. Res. Pap. INT-438.

Rothermel, Richard C. 1972. A mathematical model for predicting fi re spread in 

wildland fuels. Res. Pap. INT-115. Ogden, UT: U.S. Department of Agriculture, 

Forest Service, Intermountain Forest and Range Experiment Station. 40 p.

Sandberg, D. V., Ottmar, R. D., and G. H. Cushon. 2001. Characterizing fuels in 

the 21st Century. Int. J. Wildland Fire 10:381-387.

Scott, J. H., and E. D. Reinhardt. 2002. Estimating canopy fuels in conifer forests. 

Fire Manage. Today. 62:45-50.

Shinozaki, K., Yoda, K., Hozumi, K. and T. Kira 1964.A quantitative analysis of 

plant form — the pipe model theory. I. Basic analysis. Jpn. Ecol. 14:97-132.

Stage, A. R. 1973. Prognosis model for stand development. USDA Forest Service, 

Rocky Mountain Research Stations, Research Paper INT-137. 32 pp.

VanWagtendonk, J. W. 1996. Use of a deterministic fi re growth model to test fuel 

treatments. Sierra Nevada Ecosystem Project: Final report to Congress, Vol. II, 

Assessments and scientifi c basis for management options. Davis: University of 

California, Centers for Water and Wildland Resources.

Vogel, M., and F. A. Williams. 1970. Flame progagation along matchstick arrays. 

Combustion Science and Technology. 1:429-436.

Weber, R. O. 1990. A model for fi re propagation in arrays. Math’l Comput. Modelling 

13(12):95-102.
USDA Forest Service Proceedings RMRS-P-41. 2006. 273

In: Andrews, Patricia L.; Butler, Bret W., 

comps. 2006. Fuels Management—How to 

Measure Success: Conference Proceedings. 

28-30 March 2006; Portland, OR. 

Proceedings RMRS-P-41. Fort Collins, 

CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research 

Station.

1 Research Forester, Missoula Fire Sciences 

Lab, Rocky Mountain Research Station, 

Missoula, MT, ereinhardt@fs.fed.us

2 Research Foresters with Systems for 

Environmental Management, Missoula, 

MT.

Abstract—This paper describes the FuelCalc computer program. FuelCalc is a tool to 

compute surface and canopy fuel loads and characteristics from inventory data, to 

support fuel treatment decisions by simulating effects of a wide range of silvicultural 

treatments on surface fuels and canopy fuels, and to provide linkages to stand visualization, fi re behavior and fi re effects programs that rely on estimates of fuel loads 

and qualities.

Canopy fuel characteristics, including available fuel, canopy bulk density, canopy 

base height and canopy cover are estimated from a list of trees.

Key words: canopy bulk density, canopy base height, wildland fuel, crown fi re, fi re 

behavior, biomass, stand table

Introduction

Fuel treatment is mandated by the need to protect communities and municipal watersheds and manage ecosystems. Analysis to support fuel treatment 

decisions is required by the National Environmental Policy Act of 1969. In 

order to use the best available fi re science in comparing fuel treatment alternatives, managers need access to high-quality fuel information, as well as 

the impact of fuel treatment alternatives on wildland fuels, fi re behavior, fi re 

effects, and fuel hazard. The most fundamental fuels information is, however, 

surprisingly hard to come by. We receive frequent requests for help from fuels 

managers who want to know simply: how can inventory data be converted 

to fuel quantities and qualities? Surface fuel loads, fi re behavior fuel models, 

and canopy fuel characteristics are needed to model fi re behavior, fi re effects, 

smoke production, and to analyze fuel treatment alternatives. Managers need 

the ability to determine how these fuel quantities and qualities will change 

when treatments are applied to stands.

Site-specifi c, inventory-based data greatly strengthens the scientifi c foundation of fuel treatment decisions. Currently, although a variety of fuel analysis 

tools exist, it is quite daunting to perform these analyses with raw inventory 

data. There is a need for a simple, user-friendly, nationally applicable fuel 

analysis tool that accepts inventory data, allows users to simulate effects of 

silvicultural treatments on surface and canopy fuels, and provides linkages 

to other software for further analysis of fi re behavior and fi re effects in these 

fuels.

The FuelCalc computer program is a tool to meet these information needs. 

This tool, currently under development with support from the Joint Fire 

FuelCalc: A Method for Estimating Fuel 

Characteristics

Elizabeth Reinhardt1, Duncan Lutes2, and Joe Scott2
274 USDA Forest Service Proceedings RMRS-P-41. 2006.

Reinhardt, Lutes, and Scott FuelCalc: A Method for Estimating Fuel Characteristics

Science Program and the USDA Forest Service Rocky Mountain Research 

Station, will support fuel management decision-making directly and also 

provide input to a number of other analysis tools. This paper describes the 

sampling methods supported by FuelCalc, and the calculation procedures it 

uses to convert inventory data to estimates of fuel characteristics. It describes 

linkages and prescription development support provided by FuelCalc. Parts 

of FuelCalc, for example the canopy fuel calculations, are currently available 

in draft form, others are still in the design phase.

Fuel Strata in FuelCalc

Ground Fuels

Duff load information is critical in smoke management, soil heating, carbon 

balance, and site productivity applications. FuelCalc will include a method 

for estimating duff load based on a measurement of duff depth. Duff depth 

is multiplied by duff bulk density to estimate duff load. Duff bulk density 

can be entered or default values used based on cover type.

Surface Fuels

Surface fuel inventory may take a number of forms. FuelCalc will provide 

estimates from data collected using Brown’s (1974) planar intercept method, 

Burgan and Rothermel’s (1984) fuel sampling procedures, and Hardy’s (1996) 

slash pile inventory method, as well as direct entry of fuel loads as estimated 

from photo-guides or other data sources. Crosswalks will be provided to 

standard fi re behavior fuel models, and a fi rst-cut custom fi re behavior fuel 

model developed.

Planar intercept — Brown (1974) developed procedures for sampling 

down woody fuels by counting intercepts across a sampling plane by particles 

of different size classes. This is a well established method of inventorying 

woody fuels; FuelCalc contains procedures to convert this data to estimates 

of fuel loading.

Burgan and Rothermel — Burgan and Rothermel (1984) published a 

simple, effective method of inventorying surface fuel. The method relies on 

the relationship between fuel depth, load and bulk density. Field inventory 

requires estimates of depth and cover by life form, and the assignment of 

bulk density by comparison with photos. These inventory methods are supported in FuelCalc.

Hardy slash pile inventory — Hardy (1996) published guidelines for 

estimating biomass contained in slash piles. FuelCalc allows entry of pile shape 

and dimension, packing ratio and wood density, and uses these guidelines 

to estimate slash biomass.

Linkages to fi re behavior fuel models — FuelCalc will provide a “best 

guess” standard fi re behavior model (Scott and Burgan 2005) that seems to 

represent the sampled fuels.

Creation of custom fi re behavior fuel models — FuelCalc will also 

provide a fi rst cut custom fi re behavior fuel model suitable for testing with 

BehavePlus (Andrews and Bevins 2003) or Nexus (Scott 1999).
USDA Forest Service Proceedings RMRS-P-41. 2006. 275

FuelCalc: A Method for Estimating Fuel Characteristics Reinhardt, Lutes, and Scott

Canopy Fuels

Van Wagner (1977) proposed a theoretical model suggesting that crown 

fi re initiation is dependent on surface fi re intensity and canopy base height, 

while sustained crown fi re spread is dependent on crown fi re rate of spread 

and canopy bulk density. His work has been further developed by Alexander 

(1988), Agee (1996), Scott and Reinhardt (2001), and Van Wagner (1993) 

and is incorporated in the Canadian Fire Behavior Prediction System (Forestry 

Canada 1992), FARSITE (Finney 1998), and NEXUS (Scott 1999).

Fire managers need estimates of canopy base height and canopy bulk density to use these fi re models. The LANDFIRE program (Rollins, in prep.) 

has committed to mapping these variables at a 30 meter resolution for the 

continental U.S. In addition, land managers have a growing concern that 

crown fi re activity may be increasing in some forest types due, in part, to fi re 

suppression and resultant changes in stand structure. Assessing these changes 

in stand structure requires defi ning and consistently evaluating canopy fuel 

characteristics.

A rich body of literature exists quantifying tree crown and forest canopy 

characteristics for purposes other than fuel characterization. A number of 

studies exist that predict foliar and branch biomass from tree dimensions, 

typically diameter, sometimes in combination with height, crown ratio or 

sapwood thickness. Brown (1978) provides predictive equations for the common conifer tree species of the Inland West; Snell and Brown (1980), provide 

similar methods for Pacifi c Northwest conifers. A large number of allometric 

equations of this type from many research studies are summarized in the 

computer software BIOPAK (Means and others 1994). These equations, together with a list of trees representing a stand, may be used to estimate total 

foliar biomass, as well as biomass of branchwood of various sizes.

Canopy bulk density is the weight of available canopy fuel per unit volume 

of canopy space. It is a bulk property of the stand, not an individual tree. Estimates of total canopy biomass can be divided by canopy volume to estimate 

canopy bulk density. This method carries the implicit assumption that canopy 

biomass is distributed uniformly within the stand canopy. This assumption is 

unlikely to be true even in stands with very simple structures; multi-storied 

stands are likely even more poorly represented by this procedure.

Even canopy base height, a simple characteristic to measure on a single 

tree, is not well defi ned or easy to estimate for a stand. Neither the lowest 

crown base height in a stand nor the average crown base height is likely to be 

representative of the stand as a whole. In terms of its consequences to crown 

fi re initiation, canopy base height can be defi ned as the lowest height above 

the ground at which there is suffi cient canopy fuel to propagate fi re vertically 

through the canopy. Using this defi nition, ladder fuels such as lichen, moss 

and dead branches can be incorporated. Sando and Wick (1972) suggested 

describing the canopy fuels by plotting the vertical distribution of available 

canopy fuel in thin (1-foot) vertical layers (fi gure 1). Canopy base height 

can then be computed as the height above the ground at which some critical 

bulk density is reached. Their method could also be used to defi ne effective 

canopy bulk density. Scott and Reinhardt (2001) used the Sando and Wick 

approach in combination with Brown’s (1978) equations to estimate canopy 

base height and canopy bulk density. Canopy base height was defi ned as the 

lowest height above which at least 100 lbs/acre/vertical foot of available 

canopy fuels was present. Canopy bulk density was defi ned as the maximum 

of a 15-foot deep running mean of canopy bulk density for one-foot deep 

vertical layers. This method has been incorporated into the Fire and Fuels 
276 USDA Forest Service Proceedings RMRS-P-41. 2006.

Reinhardt, Lutes, and Scott FuelCalc: A Method for Estimating Fuel Characteristics

Extension to the Forest Vegetation Simulator (FFE-FVS, Reinhardt and 

Crookston 2003) and was validated by destructive sampling of forest canopies 

in fi ve interior west conifer stands (Reinhardt and others, in prep.).

In FuelCalc we use this approach for computing canopy base height 

and canopy bulk density from a stand table or tree list. These methods 

have several advantages: 1. They do not require visual judgment calls or 

extensive interpretation that might result in inconsistent or subjective estimation, 2. They were developed with the underlying fi re behavior models in 

mind, so the computed values are relevant in the context in which they will 

be used, 3. Because they are computed directly from a stand table or tree list 

they are derived using detailed information on stand structure, unlike methods based on image interpretation, 4. They can be performed quickly, using 

data sources that are widely available, so that values can easily be generated 

for thousands of stands.

Available canopy fuel load — Available canopy fuel load is assumed to be 

all the foliage and one-half of the 0-.25” branch material in the stand. We 

use Brown’s (1978) equations for estimating the weight of foliage and small 

(0-1/4”) branchwood for each tree from species and diameter. For some species no estimates of these components are available. In that case we use other 

published equations for total foliage biomass or crown biomass, if available, 

and crosswalk the proportions to Brown’s equations. If no foliage or crown 

biomass equations of any kind are available, we crosswalk the species to a 

Figure 1—Vertical distribution of available canopy fuel as computed from a tree list using 

FuelCalc methods. Canopy bulk density is the maximum of the running mean. Canopy 

base height is the lowest point at which the running mean exceeds 0.012 kg/m3, while 

stand height is the highest such point.
USDA Forest Service Proceedings RMRS-P-41. 2006. 277

FuelCalc: A Method for Estimating Fuel Characteristics Reinhardt, Lutes, and Scott

similar species that has published biomass relationships. These estimates are 

further adjusted to account for crown class (dominant, co-dominant, intermediate, suppressed) using adjustment factors developed in our canopy fuels 

fi eld study (Gray and Reinhardt 2003). Trees less than 6 feet tall are excluded 

from the analysis, however, trees over 6 feet tall can contribute crown weight 

from branches less than 6 feet off the ground.

Canopy bulk density — Canopy base height is calculated by distri buting 

the available crown fuel from each tree between its crown base and its top. 

The fuel is distributed vertically using regression equations developed from 

our destructively sampled data from 600 trees. These equations vary by species, but more biomass occurs higher in the crown. Fuel is summed in 1 foot 

height increments for all the trees in the stand. We smooth this profi le with a 

15-foot deep running mean, and defi ne canopy bulk density as the maximum 

of this running mean.

Canopy base height — Canopy base height is computed in FuelCalc as the 

lowest point at which the running mean exceeds .012 kg/m3 (33 lbs/acre/foot). 

This value, like Sando and Wick’s 100 lbs/acre/foot, is arbitrary and not 

based on any kind of combustion physics, but it seems to perform well.

Stand height — Stand height is calculated in a way analogous to canopy 

base height, using the maximum height within the canopy at which canopy 

bulk density exceeds 0.012 kg/m3.

Canopy cover — Canopy cover is estimated from the sum of the areas 

of individual tree crowns. Individual crown widths are computed from 

tree diameter (Moeur 1981). Following Crookston and Stage (1999), and 

assuming the crowns are randomly distributed within a stand, percent 

cover = 100(1–e–totalcrownarea/43560).

FuelCalc Linkages

FuelCalc is intended to make data management and analysis easy for managers by automating linkages to other software (fi gure 3).

FIREMON Database

For users who wish to store their data in a database, FuelCalc is linked to 

the FIREMON database (Lutes and others 2006). FIREMON provides a 

whole suite of statistical analysis tools. Similarly, FIREMON users will have 

the entire capability of FuelCalc available to them as an analysis tool, capable 

of reading data directly from the database.

SVS

The Stand Visualization System or SVS (McGaughey 1997) produces 

graphic representation of stands from tree list data (fi gure 2). These graphics are very helpful both for managers and even more importantly, for the 

public in assessing thinning treatments. FuelCalc will format data for use 

with SVS.
278 USDA Forest Service Proceedings RMRS-P-41. 2006.

Reinhardt, Lutes, and Scott FuelCalc: A Method for Estimating Fuel Characteristics

Figure 2—Examples of SVS (McGaughey, 1997) outputs. 

Figure 3—FuelCalc linkages.
USDA Forest Service Proceedings RMRS-P-41. 2006. 279

FuelCalc: A Method for Estimating Fuel Characteristics Reinhardt, Lutes, and Scott

FOFEM

FOFEM: a First Order Fire Effects Model (Reinhardt and others 1997, 

Reinhardt 2003) predicts tree mortality, fuel consumption, soil heating and 

smoke production from prescribed fi re and wildfi re. FOFEM requires as input exactly the kind of data that FuelCalc manages. FOFEM is widely used 

for NEPA documentation as well as smoke regulation. FOFEM will be fully 

integrated with FuelCalc so that as fuel treatment alternatives are developed 

within FuelCalc, FOFEM is invoked to assess impacts of those treatments 

on expected fi re effects.

Nexus

Nexus (Scott 1999) is a fi re behavior prediction system as well as a crown 

fi re hazard assessment tool. It computes torching and crowning indices 

(Scott and Reinhardt 2001), as well as the full suite of fi re behavior outputs 

including rate of spread, fi reline intensity, and reaction intensity. Torching 

and crowning indices are windspeeds at which torching and active crowning 

can be expected to occur in a given fuel complex. Lower values indicate fuels 

that are more prone to crown fi re behavior, i.e., crown fi re can be expected at 

lower windspeeds. Torching and crowning indices vary as canopy and surface 

fuels are altered, thus they are useful indicators of crown fi re hazard and of 

fuel treatment success. Nexus, like FOFEM, will be fully integrated with 

FuelCalc, so that as fuel treatment alternatives are developed in FuelCalc, 

expected changes in fi re behavior and crown fi re hazard can be assessed.

FFE-FVS

 FuelCalc will convert data into fi les suitable for use with the Fire and 

Fuels Extension to the Forest Vegetation Simulator: FFE-FVS (Reinhardt and 

Crookston 2003). FFE-FVS can then be used to simulate treatment effects 

on fuels, potential fi re behavior and stand structure over time.

National Volume Estimator Library

When thinning treatments are simulated, FuelCalc will use the National 

Volume Estimator Library of equations maintained by the USDA Forest 

Service Forest Management Service Center (USDA Forest Service 1993) in 

order to estimate the amount of potentially merchantable material that may 

be generated by thinning treatments.

FuelCalc Features

Prescription Design and Assessment

FuelCalc will provide analytical tools for prescription development. A 

user will be allowed to specify criteria such as: thin from below to a residual 

canopy bulk density of 0.05 kg/m3, or thin from below to a residual basal 

area of 100 sq ft/acre, and FuelCalc will identify the number, volume, and 

characteristics of trees to be removed, as well as compute the activity fuels 

that would be generated by such a thinning. This analysis will combine the 

work of the JFSP-funded Canopy Fuels Study (Reinhardt and others 1999) 

with earlier work by Brown and Johnston (1976), and the National Volume 

Estimator Library (U.S. Forest Service 1993).
280 USDA Forest Service Proceedings RMRS-P-41. 2006.

Reinhardt, Lutes, and Scott FuelCalc: A Method for Estimating Fuel Characteristics

Batch Mode for Linking with GIS

FuelCalc is designed as a stand level tool, however, a batch mode will be 

provided to link with GIS and landscape level applications. We have successfully used this approach in developing FOFEM and Nexus. The LANDFIRE 

program has been using the batch FuelCalc program to process data from 

1000s of plots.

Library of Code for Incorporation in Other Software

FuelCalc code will be provided on request to other software developers, 

hopefully resulting in more consistent use of inventory data across agencies 

and for a variety of applications.

Acknowledgments

FuelCalc is being developed with the support of the Joint Fire Sciences 

Program, the USFS Rocky Mountain Research Station, and Systems for Environmental Management. Thanks to Larry Gangi for computer programming, 

and Russ Parsons and Kathy Gray for manuscript reviews.

References

Agee, J. K. 1996. The infl uence of forest structure on fi re behavior. In: Proceedings 

of the 17th Forest Vegetation Management Conference. January 6-18, 1996. 

Redding, CA: 52-68.

Alexander, M. E. 1988. Help with making crown fi re hazard assessments. In: 

Protecting people and homes from wildfi re in the Interior West: Proceedings of 

the Symposium and Workshop. United States Department of Agriculture, Forest 

Service, General Technical Report INT-251, Intermountain Forest and Range 

Experiment Station, Ogden UT: 147-153.

Andrews, Patricia L.; Bevins, Collin D. 2003. BehavePlus fi re modeling system, 

version 2.0: overview. Proceedings of the Second International Wildland Fire 

Ecology and Fire Management Congress and Fifth Symposium on Fire and Forest 

Meteorology. November 16-20, 2003. Orlando, FL: American Meteorological 

Society. P5.11

Brown, J.K. 1974. Handbook for inventorying downed woody material. USDA 

Forest Service GTR-INT-16.

Brown, J.K. and C.M. Johnston. 1976. Debris Prediction System. USDA Forest 

Service, Intermountain Forest and Range Experiment Station, Missoula, MT. 

Fuel Science RWU 2104. 28 p.

Brown, J. K. 1978. Weight and density of crowns of Rocky Mountain conifers. 

United States Department of Agriculture, Forest Service, Research Paper INT197, Intermountain Forest and Range Experiment Station, Ogden UT.

Burgan, Robert E.; Rothermel, Richard C. 1984. BEHAVE: fi re behavior prediction 

and fuel modeling system—FUEL subsystem. Gen. Tech. Rep. INT-167. Ogden, 

UT: U.S. Department of Agriculture, Forest Service, Intermountain Research 

Station. 126 p.

Crookston, Nicholas L.; Stage, Albert R. 1999. Percent canopy cover and stand 

structure statistics from the Forest Vegetation Simulator. Gen. Tech. Rep. RMRSGTR-24. Ogden, UT: U. S. Department of Agriculture, Forest Service, Rocky 

Mountain Research Station. 11 p.
USDA Forest Service Proceedings RMRS-P-41. 2006. 281

FuelCalc: A Method for Estimating Fuel Characteristics Reinhardt, Lutes, and Scott

Finney, M. A. 1998. FARSITE: Fire Area Simulator — model development and 

evaluation. United States Department of Agriculture, Forest Service, Research 

Paper RMRS-4, Rocky Mountain Research Station, Ft. Collins, CO.

Forestry Canada Fire Danger Group. 1992. Development and structure of the 

Canadian Forest Fire Behavior Prediction System. Inf. Rep. ST-X-3. 63 p.

Gray, K.L., Reinhardt, E.D. 2003. Analysis of Algorithms for predicting canopy 

fuel. In: Proceedings of the Second International Wildland Fire Ecology and Fire 

Management Congress and Fifth Symposium on Fire and Forest Meteorology, 

November 16-20, 2003, Orlando, FL. American Meteorological Society.

Hardy, C.C. 1996. Guidelines for estimating volume, biomass, and smoke production 

from piled slash. USDA Forest Service Gen. Tech. Report PNW-GTR-364. 21 p.

Lutes, Duncan C.; Keane, Robert E.; Caratti, John F.; Key, Carl H.; Benson, 

Nathan C.; Sutherland, Steve; Gangi, Larry J. 2006. FIREMON: The fi re effects 

monitoring and inventory system. Gen. Tech. Rep. RMRS-GTR-164-CD. Fort 

Collins, CO. U.S. Department of Agriculture, Forest Service, Rocky Mountain 

Research Station. 1 CD.

McGaughey, Robert J. 1997. Visualizing forest and stand dynamics using the 

stand visualization system. Proc. 1997 ACSM/ASPRS Annual Convention and 

Exposition. Bethesda, MD: American Society for Photogrammetry and Remote 

Sensing.

Means, J.E., Hansen, H.A., Koerper, G.J., Alaback, P.B., and Klopsch, M.W. 1994. 

Software for computing plant biomass – BIOPAK users guide. USDA For. Serv. 

Gen. Tech. Rep. PNW-340.

Moeur, M. 1981. Crown width and foliage weight of Northern Rocky Mountain 

conifers. Res. Pap. INT-283. Ogden, UT: U.S. Department of Agriculture, 

Forest Service, Intermountain Forest and Range Experiment Station. Res. Pap. 

INT-283. 14 p.

Reinhardt, E.D. 2003. Using FOFEM 5.0 to estimate tree mortality, fuel consumption, 

smoke production and soil heating from wildland fi re. In: Proceedings of the 

Second International Wildland Fire Ecology and Fire Management Congress 

and Fifth Symposium on Fire and Forest Meteorology, November 16-20, 2003, 

Orlando, FL. American Meteorological Society.

Reinhardt, E.D., and Crookston, N.L. (Editors). 2003. The Fire and Fuels Extension 

to the Forest Vegetation Simulator. USDA For. Serv. Gen. Tech. Rep. RMRSGTR-116.

Reinhardt, E.D.; Keane, R.E.; Brown, J.K. 1997. First Order Fire Effects Model: 

FOFEM 4.0, User’s Guide. General Technical Report INT-GTR-344.

Reinhardt, E.D., Scott, J.H, Keane, R.E. and Brown, J.K. 1999. Quantifying canopy 

fuels in conifer forests. A proposal funded by the JFSP.

Reinhardt, E.D., Scott, J.H., Keane, R.E., and Gray, K. in review. Comparison of 

computation approaches for estimating canopy fuel load and bulk density with 

measured values.

Rollins, M.G. (Technical Editor). In preparation. The LANDFIRE prototype 

Project: nationally consistent and locally relevant geospatial data for wildland fi re 

management. USDA Forest Service. Rocky Mountain Research Station, Missoula 

Fire Sciences Laboratory. RMRS-GTR-XXX.

Sando, R.W. and C.H. Wick. 1972. A method of evaluating crown fuels in forest 

stands. United States Department of Agriculture, Forest Service, Research Paper 

NC-84.

Scott, Joe H. 1999. Nexus: a system for assessing crown fi re hazard. Fire Management 

Notes 59(2):20-24.

Scott, Joseph H.; Burgan, Robert E. 2005. Standard fi re behavior fuel models: a 

comprehensive set for use with Rothermel’s surface fi re spread model. Gen. Tech. 

Rep. RMRS-GTR-153. Fort Collins, CO: U.S. Department of Agriculture, Forest 

Service, Rocky Mountain Research Station. 72 p
282 USDA Forest Service Proceedings RMRS-P-41. 2006.

Reinhardt, Lutes, and Scott FuelCalc: A Method for Estimating Fuel Characteristics

Scott, J.H. and Reinhardt, E.D. 2001. Assessing crown fi re potential by linking 

models of surface and crown fi re behavior. Res. Pap. RMRS-RP-29. Fort Collins, 

CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research 

Station. 59 p.

USDA Forest Service. 1993 Timber Volume Estimator Handbook. Forest Service 

Handbook 2409.12a.

Snell, J.A.K., and Brown, J.K. 1980. Handbook for predicting residue weights of 

Pacifi c Northwest conifers. USDA For. Serv. Gen. Tech. Rep. PNW-103.

Van Wagner, C.E. 1977. Conditions for the start and spread of crown fi re. Canadian 

Journal of Forest Research 7:23-34.

Van Wagner, C.E. 1993. Prediction of crown fi re behavior in two stands of jack pine. 

Canadian Journal of Forest Research 23:442-449.
USDA Forest Service Proceedings RMRS-P-41. 2006. 283

In: Andrews, Patricia L.; Butler, Bret W., 

comps. 2006. Fuels Management—How to 

Measure Success: Conference Proceedings. 

28-30 March 2006; Portland, OR. 

Proceedings RMRS-P-41. Fort Collins, 

CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research 

Station.

1 Graduate student in Quantitative 

Ecology and Resource Management, 

University of Washington, Seattle, WA. 

abran@u.washington.edu.

2 Research foresters at the USDA Forest 

Service, Rocky Mountain Research 

Station, Fire Sciences Laboratory, 

Missoula, MT.

Abstract—We compared the accuracy and precision of digital hemispherical photography and the LI-COR LAI-2000 plant canopy analyzer as predictors of canopy fuels. 

We collected data on 12 plots in western Montana under a variety of lighting and sky 

conditions, and used a variety of processing methods to compute estimates. Repeated 

measurements from each method displayed considerable variability, but hemispherical 

photography proved to be the more precise method. To evaluate the accuracy of the 

different methods, we correlated measurements with allometrically derived estimates 

of canopy bulk density and available canopy fuel. Measurements from both methods 

were more highly correlated with available canopy fuel than canopy bulk density. 

Hemispherical photography emerged as the superior methodology, displaying greater 

precision and accuracy, at least when measurements must be collected under sub-par 

lighting conditions.

In order to assess the potential risk of crown fi res, accurate estimates of 

canopy fuel loads are needed. Direct met hods for measuring these loads are 

often diffi cult and time consuming, involving destructive sampling of the 

forest canopy or, alternatively, detailed allometric measurements on individual 

trees. As a result, indirect methods are being used increasingly to exploit 

the relationship between the amount of biomass in the forest canopy and 

the amount of light that gets transmitted to the forest fl oor. By measuring 

the relative amount of light reaching the forest fl oor, canopy fuels can be 

estimated indirectly.

This paper examines two indirect methods for measuring canopy fuels, the 

LI-COR LAI-2000 and hemispherical photography. Both of these methods 

have been used extensively to measure leaf area index (LAI), and are much 

less time consuming than direct methods (see Jonckheere and others 2004, 

or Chen and others 1997, for reviews of different methods for estimating 

LAI). Defi ned as the one sided leaf area per unit ground area, LAI is used 

frequently as a measure of canopy structure, and LAI has also been correlated 

with important metrics of canopy fuels loads, for example canopy bulk density 

(Keane and others, 2005). Thus these indirect methods could potentially 

provide an effi cient method for estimating canopy fuel loads.

However, because these indirect methods rely on light transmittance, the 

resulting estimates can be highly sensitive to the ambient lighting conditions. 

Ideally measurements should be taken only at dawn or dusk with the sun 

below the horizon. Less ideally, data can also be collected under uniformly 

cloudy skies. In the former case, data collection is limited to only a few hours 

each day, while in the latter, data collection hinges on weather conditions. In 

practice these constraints may be too prohibitive, greatly limiting the time 

available for data collection. As a result they are often disregarded, and data 

are collected under a wide variety of lighting and sky conditions.

Accuracy and Precision of Two Indirect 

Methods for Estimating Canopy Fuels

Abran Steele-Feldman1, Elizabeth Reinhardt2, and Russell A. Parsons2
284 USDA Forest Service Proceedings RMRS-P-41. 2006.

Steele-Feldman, Reinhardt, and Parsons Accuracy and Precision of Two Indirect Methods for Estimating Canopy Fuels

In this paper, we evaluate the accuracy and precision of these two indirect 

methods with measurements taken under a variety of less than ideal lighting conditions. Using repeated measurements from 12 sites, we evaluate the 

precision of the estimates obtained using each method, and then compare 

these estimates with two allometrically derived metrics of canopy fuel loads: 

canopy bulk density (CBD) and available canopy fuel (ACF).

Background and Theory

Hemispherical photography and the LAI-2000 present different ways to 

measure the gap fraction in a stand: the proportion of sky visible under the 

canopy. With digital hemispherical photography, a digital camera with a 

fi sh eye lens is used to take a photograph of the canopy from which the gap 

fraction is computed. Usually this is accomplished by converting the color 

photograph to a black and white image: a threshold is chosen and all pixels 

darker than the threshold are declared to be not-sky and painted black, while 

all those brighter than the threshold are declared sky and painted white. 

The gap fraction is then equivalent to the proportion of white pixels in the 

image. Hemispherical photography requires little specialized equipment, 

simply a tripod, a digital camera, a fi sh eye lens, and software for processing 

the images.

The LAI-2000, on the other hand, is a specially produced piece of equipment for measuring LAI (LI-COR 1992). It consists of a light sensor mounted 

on a wand that is attached to an electronic control box. To compute gap 

fractions, the LAI-2000 needs to take two measurements of light intensity 

with the light sensor. The fi rst measurement is taken above the forest canopy 

under open sky (usually in a clearing) while the second is taken below the 

canopy. The gap fraction is then computed by taking the ratio of these two 

measurements. Both measurements must be taken with the light sensor leveled and facing the same compass direction.

There is extensive theory detailing the relationship between gap fractions, 

leaf area index, and other canopy structure statistics (Welles and Norman 

1991). Briefl y, in an idealized homogenous full cover forest stand with small, 

randomly distributed foliage, the Beer Lambert law can be used to compute 

leaf are index, L, from gap fraction measurements as

LG d = − ∫ 2

0

2

ln( ( ))cos sin .

/

θ θ θθ

π

 (1)

Here θ denotes zenith angle and G(θ) is the gap fraction as a function of the 

zenith angle. In practice, this integral is usually approximated by dividing the 

continuous range of zenith angles (, ) 0 2

π into a number of concentric rings 

or sectors. The gap fraction is measured at specifi c zenith angles (or over a 

range of zenith angles) and then L is given by a weighted sum,

L GWi i

i

n

= − =2∑

1

ln( ( )) θ ,

 (2)

Where n is the number of zenith angles (or number of rings) used, and Wi

is the weighting term. The light sensor on the LAI-2000 has 5 rings centered at zenith angles of 7, 23, 38, 53, and 68 degrees. With hemispherical 

photography the number of rings and their locations can be controlled by 

the experimenter.
USDA Forest Service Proceedings RMRS-P-41. 2006. 285

Accuracy and Precision of Two Indirect Methods for Estimating Canopy Fuels Steele-Feldman, Reinhardt, and Parsons

The LAI estimates derived from Hemi-photos and the LAI-2000 are very 

sensitive to lighting conditions. Both methods are best used under certain 

restricted light conditions: before sunrise, after sunset, or, less preferably, 

under uniformly cloudy skies (LI-COR 1992; Pepper 1998; Frazer 2001). 

Direct sunlight in a hemispherical photograph often leads to lens fl are, and 

brightly lit foliage can be mistakenly classifi ed as sky when hemispherical 

photographs are converted to black and white images for analysis. Similarly 

direct sunlight can lower resulting estimates from the LAI-2000 by up to 

40% because of sunfl ects (Welles and Norman 1991). In practice appropriate 

lighting conditions can be diffi cult to obtain, greatly limiting the time available for data collection. As a result, these constraints are often neglected, or 

less data is collected. In this study, we examine how collecting data under 

sub-optimal lighting conditions affects the precision and accuracy of the 

measurements obtained.

Materials and Methods

Study Area and Sampling Methodology

The study area, located in Lolo National Forest in western Montana, 

consisted of 11 sample units, each 13m in radius. Each sample unit was either homogenously Douglas-fi r (Pseudotseuga menziessi) or homogenously 

ponderosa pine (Pinus ponderosa). The tree densities varied substantially 

between plots (table 1). Nine of the plots were on south aspects, and 2 were 

on north aspects (plot codes DF-N and PP-N). Two of the Douglas-fi r plots 

were open grown (DF-O-1 and DF-O-1) and located several miles from the 

others, in an area with thinner soil and higher winds.

Height, diameter and crown ratio measurements were collected on each 

tree in the study units, and then these tree lists were used to compute stand 

level canopy fuel load and bulk density, using methods described in Reinhardt 

and others (this proceedings).

Table 1—Fuel characteristics of the plots used in the study. Plots beginning with DF are 

homogenously Douglas fi r, whereas those beginning with PP are homogenously 

ponderosa pine. All plots are circular with a radius of 13 m.

Plots

 Canopy Available

 Plot Bulk Density Canopy Fuel Canopy Tree

Index Code (Kg/m3) (Tons/Acre) Cover (%) per acre

 1 DF-2 0.0801 5.587 48.68 137

 2 DF-3 0.1290 5.444 46.32 107

 3 DF-4 0.2752 9.567 68.40 244

 4 DF-N 0.0633 3.718 35.13 84

 5 DF-0-1 0.0122 0.891 9.27 8

 6 DF-0-2 0.0703 3.518 33.90 84

 7 PP-1 0.0895 2.239 37.18 274

 8 PP-2 0.0922 2.533 39.21 305

 9 PP-3 0.0244 0.508 9.73 53

 10 PP-4 0.1082 2.750 42.06 290

 11 PP-N 0.0848 4.127 42.87 198
286 USDA Forest Service Proceedings RMRS-P-41. 2006.

Steele-Feldman, Reinhardt, and Parsons Accuracy and Precision of Two Indirect Methods for Estimating Canopy Fuels

Hemispherical photographs and readings with the LI-COR LAI-2000 were 

collected in early September 2004. Data were collected under a variety of 

lighting and sky conditions, and in total measurements were taken 12 times 

with each instrument on each sample area.

A Nikon Coolpix 9000 digital camera with a fi sheye lens was used for taking 

hemispherical photographs. The camera was attached to a leveled tripod and 

aligned so that the camera body pointed north. On each visit to a plot, two 

photographs were taken sequentially: one with proper exposure as determined 

by the camera’s automatic metering and one underexposed by two f-stops. 

All photographs were taken using the highest resolution setting.

Two LICOR LAI-2000 units were used to obtain the above and below 

canopy measurements. The fi rst unit was set up in a centrally located clearing, 

leveled, aligned to the North, and automatically logged above canopy readings every 30 seconds. The other unit was used to record the below canopy 

readings, and on each visit to a plot two below-canopy readings were taken 

immediately after the hemispherical photographs. The wand on the below 

canopy unit was leveled and aligned to the north for each measurement. Each 

LAI-2000 unit used a 90° view cap.

Data Processing

To compute gap fractions for the LAI-2000, we individually matched each 

below canopy reading with the above canopy reading that was closest in time, 

and computed gap fractions at each of the fi ve zenith angles. Computing 

gap fractions for the hemispherical photographs was more complicated, as 

the color photographs fi rst had to be converted to black and white images. 

Usually this is accomplished by choosing a threshold and coloring all pixels 

darker than the threshold black (vegetation) and all others white (sky). However, under uneven lighting conditions this approach can result in substantial 

misclassifi cations because foliage near the sun appears brighter than the sky 

far from the sun.

Instead, we used a two-stage supervised clustering algorithm to convert the 

color photographs to black and white images. The algorithm is an example of a 

commonly used iso-clustering algorithm from the image processing literature 

(Richards 1996), and was implemented in ARC-GIS. Briefl y, the algorithm 

uses an automated procedure to assign each pixel in the image to one of a 

user-specifi ed number of bins, based on the color and brightness attributes of 

the pixels in the image. In the fi rst stage of processing, the photograph was 

divided into ten bins and the user was then prompted to classify each bin as 

not-sky (black), sky (white), or unknown (red). Often a single bin contained 

both vegetation and sky, and these bins were classifi ed as unknown in the 

fi rst stage. Any pixels classifi ed as unknown during the fi rst stage were then 

further subdivided into seven bins for a second stage of classifi cation. The 

result was a black and white image with generally more fi ne detail than was 

obtainable using the traditional single threshold approach.

The resulting black and white images were then input into the commercial 

software HemiView for analysis. HemiView divides each image into a userspecifi ed number of concentric circles (rings) of equal width, corresponding 

to different zenith angles, and then computes the average gap fraction in each 

ring. To facilitate comparison with estimates from the LAI-2000, fi ve rings 

were used, centered at zenith angles of 9, 27, 45, 63, and 81 degrees. Note 

that the zenith angles from the two techniques are different, since the rings 

in the LAI-2000 are of unequal width.
USDA Forest Service Proceedings RMRS-P-41. 2006. 287

Accuracy and Precision of Two Indirect Methods for Estimating Canopy Fuels Steele-Feldman, Reinhardt, and Parsons

There are potentially many ways to combine the individual gap fractions 

at each zenith angle into estimates of the overall LAI or fuel on a plot. The 

standard method is to compute LAI using all fi ve zenith rings by taking a 

weighted sum of the logarithm of the gap fractions, i.e. equation 2. Not all 

rings need to be included in the sum however, and we also computed LAI 

values using different subsets of the zenith rings.

Moreover, it may be that the raw un-weighted gap fractions prove to be 

better indicators of canopy fuel loads. In this case the average gap fraction,

G, will be a useful statistic:

G

n

G i

i

n

= =

∑

1

1

( ) θ . (3)

As with the LAI based statistics, this sum can be computed over different 

subsets of the zenith rings. In the following analysis, we utilized several 

different sets of zenith rings and computed predictions using both the raw 

gap fractions and the log transformed and weighted LAI as the predictive 

statistic (table 2).

Results

Comparing the Different Methods

We computed the mean, variance, and coeffi cient of variation (CV), for 

each method on each plot (table 3). The mean variance and CV per plot are 

both consistently larger for the LAI-2000 estimates than for the hemi-photo 

estimates. There is also a tendency for the CV and variance to increase as 

the number of rings used in the analysis is reduced. Note, however, that the 

estimates derived using only the 3rd ring do not conform to this pattern, 

suggesting that the number of rings is less important than the zenith angles 

of the rings used. Estimates derived using the smaller zenith angles exhibit 

more variation than do estimates derived from the larger angles.

Table 2—Factors in the analysis. Gap fractions were obtained 

with either the Licor unit or hemispherical photographs. 

Either the mean gap fraction or the log transformed and 

weighted leaf area index was used to derive predictions. 

The different analysis schemes used between and fi ve 

zenith rings to derive predictions.

Methods

 Licor LAI-2000 plant canopy analyzer

 Hemi Hemispherical photography

Statistics

 GF Mean gap fraction (unweighted)

 LAI Leaf area index (weighted mean of the

 logarithm of individual gap fractions)

Analysis Scheme

 1 Only third zenith ring

 2 Top two zenith rings

 3 Top three zenith rings

 5 All fi ve zenith rings
288 USDA Forest Service Proceedings RMRS-P-41. 2006.

Steele-Feldman, Reinhardt, and Parsons Accuracy and Precision of Two Indirect Methods for Estimating Canopy Fuels

For the hemi-photos, the variance and CV across plots is substantially larger 

than the average variance and CV per plot, suggesting that the method can 

consistently distinguish between some of the plots. However, the LAI-2000 

readings have roughly similar variances between and across plots, and the 

CV across plots is actually smaller than the average CV per plot. The mean 

estimates of LAI from the LAI-2000 are consistently lower than those from 

the hemi-photos for all of the different ring choices. Also, the mean estimated 

LAI values from the hemi-photos decrease as the rings with larger zenith 

angles are removed from the analysis.

To examine the correlation between the LAI-2000 estimates and the hemiphoto estimates, we computed simple correlation coeffi cients for each pair of 

estimates (table 4). The correlation between the LAI-2000 and hemi-photo 

estimates increases as the rings with the larger zenith angles are excluded 

from the analysis. Measurements were most correlated when only the top 

two zenith rings were used.

Table 3—Summary statistics of the LAI estimates produced using different methods. The 

average variance and CV per plot represent the variance (CV) in measurements 

on each plot averaged across all the plots. Similarly the variance (CV) across 

plots denotes the variance (CV) in the mean value of the measurements for each 

plot. Note that these are the results using the LAI statistic.

 Average Average Variance CV

 Variance CV Across Across

 Method Mean Per Plot Per Plot Plots Plots

LAI-2000 

 LAI-5 1.04 0.18 0.46 0.16 0.38

 LAI-3 1.13 0.32 0.71 0.37 0.54

 LAI-2 0.90 0.33 1.05 0.52 0.80

 LAI-1 1.33 0.56 0.70 0.42 0.49

Hemi 

 LAI-5 1.80 0.04 0.09 0.33 0.32

 LAI-3 1.69 0.09 0.15 0.52 0.43

 LAI-2 1.55 0.13 0.19 0.88 0.61

 LAI-1 1.82 0.08 0.14 0.33 0.32

Table 4—Correlation coeffi cients between the hemi-photo and 

Licor LAI values.

 Licor LAI-2000

Hemi-Photo LAI-5 LAI-3 LAI-2 LAI-1

 LAI-5 0.561 0.618 0.673 0.429

 LAI-3 0.602 0.659 0.667 0.499

 LAI-2 0.594 0.683 0.717 0.496

 LAI-1 0.560 0.570 0.543 0.459
USDA Forest Service Proceedings RMRS-P-41. 2006. 289

Accuracy and Precision of Two Indirect Methods for Estimating Canopy Fuels Steele-Feldman, Reinhardt, and Parsons

Relationship with Allometric Data

For all processing methods, we computed regressions using both available 

canopy fuel (ACF) and canopy bulk density (CBD) as computed from the 

stand data as response variables. We tested three different regression models. The simplest, the reduced model, used only the measured LAI or GF 

statistic as a predictor variable, but the other two regressions incorporated 

additional predictor variables. The second regression model introduced tree 

type (Douglas fi r or Ponderosa pine) into the reduced model as a categorical 

predictor variable, including an interaction term. This approach is justifi ed 

due to the homogenous nature of the stands in the study and the common 

use of species specifi c clumping factors for modifying LAI estimates (White 

and others 1998). Finally the third regression model further added canopy 

base height as an additional predictor variable. Canopy base height is defi ned 

as the average height within a stand from the ground to the canopy bottom. 

While more diffi cult to assess than tree type, canopy base height can be 

measured or estimated relatively easily.

To simply the presentation, we use R2 values to measure goodness of fi t 

(fi gure 1). For each of the two instruments there were two possible statistics 

(GF or LAI), four analysis schemes, two response variables, and three types 

of regression models, for a total of 2x2x4x2x3 = 96 different regression 

models.

Several clear patterns emerge from fi gure 1. The reduced regression model, 

using a single predictor, performs uniformly poorly for both instruments and 

both predictor variables. The third regression model, which includes canopy 

base height, performs substantially better than the other two, especially for 

hemispherical photography with CBD as the response variable. For all of the 

Figure 1—R2 values from the different regressions. The x-axis shows the number of 

zenith rings used to derive predictions. Regression model 1 (solid lines) is the reduced 

model, model 2 (dashed lines) includes tree type as a predictor, and model 3 (dotted 

lines) also includes canopy base height. Results are shown with available canopy fuel 

(ACF) or canopy bulk density (CBD) as the response variable.
290 USDA Forest Service Proceedings RMRS-P-41. 2006.

Steele-Feldman, Reinhardt, and Parsons Accuracy and Precision of Two Indirect Methods for Estimating Canopy Fuels

regression models, the fi t was better using ACF as the response variable than 

it was with CBD as the response variable. The LAI-2000 estimates derived 

using only the third ring (analysis scheme 1), as well as those derived using 

the top 3 rings (analysis scheme 3), produced the best fi ts for both CBD and 

ACF. Conversely, the hemi-photo estimates derived using the top two rings 

consistently had the largest R2 values, although only marginally larger than 

those derived using the top 3 rings. For the hemi-photos, correlations generally increase as the zenith angles increase, but for the LAI-2000 correlations 

appear to peak around the third zenith angle. Overall, there appears to be 

little overall difference in performance between the estimates produced using 

LAI and those produced using average GF.

With the simplest regression model, the hemi-photos and LAI-2000 both 

performed similarly. In the more complex regression models, however, the 

hemi-photo results were clearly dominant, with consistently larger R2 values 

than the corresponding LAI-2000 based estimates. This suggests that hemiphoto based estimates of CBD and ACF are more accurate.

Discussion and Conclusions

As is clear from table 2, the hemi-photo measurements are more precise 

than the LAI-2000 measurements, with substantially smaller variances and 

CVs on each plot. The hemi-photos also provided more accurate measures 

of canopy fuels, as indicated by the R2 values from the regressions against 

CBD and ACF.

The number of rings used in the analysis had a somewhat signifi cant impact 

on the accuracy of the different estimates (table 4). The tendency towards 

increased accuracy with reduced zenith angles may be due to the relatively 

small size (13m radius) of the plots used. In any case, as the zenith angles used 

for analysis decreased, the CV of the measurements on each plot tended to 

increase. Taken together these results suggest that accuracy can be increased, 

at least on smaller plots, by only using the smaller zenith angles, but at the 

cost of decreasing the precision of the measurements.

The lower precision of the LAI-2000 estimates is not surprising: the LAI2000 is not intended to derive estimates from individual measurements. 

Indeed, part of the attraction of using the LAI-2000 is the ease of taking 

repeated measurements on a single plot. Whereas repeated measures using 

hemi-photos require analyzing each photograph individually, the LAI-2000 

can automatically combine repeated measures into a single estimate. Thus the 

lower precision of individual measurements is offset by the ease of repeating 

measurements. The large processing time needed to derive estimates from 

the hemi-photos, and the relative ease of incorporating multiple measurements into a single estimate using the LAI-2000, makes the LAI-2000 more 

competitive than the preceding analysis might suggest. Nonetheless, this 

analysis demonstrates that the hemi-photo method is preferable from the 

standpoint of both accuracy and precision. If the processing of the hemiphotos could be completely automated, the processing time would be more 

comparable for the two methods, and the hemi-photo methodology would 

be more clearly preferable.

Surprisingly the hemi-photos provided decent measures of canopy fuels despite the variety of less than ideal lighting and sky conditions under which the 

photographs were taken. In this study we used a very labor intensive processing methodology that allowed for more detailed black and white photographs 
USDA Forest Service Proceedings RMRS-P-41. 2006. 291

Accuracy and Precision of Two Indirect Methods for Estimating Canopy Fuels Steele-Feldman, Reinhardt, and Parsons

even under poor lighting conditions such as direct sunlight. Apparently more 

labor intensive processing in the lab was able to compensate for less than 

ideal sampling conditions in the fi eld. Hemispherical photography thus has 

the potential to reduce the labor, time, and environmental constraints in the 

fi eld, in exchange for more time and labor spent in the lab.

Acknowledgments

This work was supported by the USDA Forest Service Rocky Mountain 

Research Station and Systems for Environmental Management. We appreciate 

Kathy Gray’s assistance with estimation of canopy fuel characteristics.

References

Chen, J. M.; Rich, P. M.; Gower, S. T.; Norman, J. M.; Plummer, S. 1997. Leaf 

area index of boreal forests: theory, techniques, and measurements. Journal of 

Geophysical Research Atmospheres. 102 (D24): 29429-29443.

Frazer, G. W.; Fournier, R. A.; Trofymow, J. A.; Hall, R. J. 2001. A comparison of 

digital and fi lm fi sheye photography for analysis of forest canopy structure and 

gap light transmission. Agricultural and Forest Meteorology. 109: 249-263.

Jonckheere, I.; Fleck, S.; Nackaerts, K.; Muys, B.; Coppin, P.; Weiss, M.; Baret, F. 

2004. Review of methods for in situ leaf area index determination Part I. Theories, 

sensors and hemispherical photography. Agricultural and forest meteorology. 

121: 19-35.

Keane, R. E.; Reinhardt, E. D.; Scott, J; Gray, K.; Reardon, J. 2005. Estimating 

forest canopy bulk density using six indirect methods. Canadian Journal of forest 

research. 35(3): 724-739.

LI-COR, Inc. 1992. LAI-2000 Plant Canopy Analyzer: operating manual. Lincoln, 

Nebraska: LI-COR Inc.

Peper, P. J.; McPherson, E. G. 1998. Comparison of fi ve methods for estimating 

leaf area index of open-grown deciduous trees. Journal of Arboriculture. 24: 

98-111.

Richards, J. A. 1986. Remote Sensing Digital Image Analysis: An Introduction. 

Springer-Verlag: New York.

Welles, J. M.; Norman, J. M. 1991. Instrument for indirect measurement of canopy 

architecture. Agronomy Journal. 83(5): 818-825.

White, J. D.; Running, S. W.; Nemani, R.; Keane, R. E.; Ryan, K. C. 1998. 

Measurement and mapping of LAI in Rocky Mountain montane ecosystems. 

Canadian Journal of Forest Research. 27: 1714-1727.
USDA Forest Service Proceedings RMRS-P-41. 2006. 293

In: Andrews, Patricia L.; Butler, Bret W., 

comps. 2006. Fuels Management—How to 

Measure Success: Conference Proceedings. 

28-30 March 2006; Portland, OR. 

Proceedings RMRS-P-41. Fort Collins, 

CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research 

Station.

1 College of Forest Resources, 

University of Washington, Seattle, 

WA. clrfi re@u.washington.edu

2 College of Forest Resources, University 

of Washington, Seattle, WA.

3 Pacifi c Wildland Fire Sciences Lab, 

USDA Forest Service, Seattle, WA.

Abstract—Resource managers need spatially explicit fuels data to manage fi re hazard 

and evaluate the ecological effects of wildland fi res and fuel treatments. For this study, 

fuels were mapped on the Okanogan and Wenatchee National Forests (OWNF) using a rule-based method and the Fuels Characteristic Classifi cation System (FCCS). 

The FCCS classifi es fuels based on their combustion properties, producing unique 

“fuel beds,” each of which represents a distinct fi re environment. Managers on the 

OWNF identifi ed 187 fuel beds which were consolidated into 40 general fuel beds 

representing the major vegetation forms (forest vs. non-forest) and species groups. Fuel 

beds were assigned to each 25-m cell in the forest domain (27,353,425 cells) using 

decision rules based on a combination of spatial data layers. General fuel beds can 

then be subdivided into specifi c structural types using spatial data on canopy cover, 

quadratic mean diameter, and past disturbances (fi res, insects, and management). 

This rule-based approach allows for the incorporation of more specifi c data if available or a more general classifi cation if they are unavailable, and for reclassifi cation 

when new data become available. Key uses of the fuels map include spatially explicit 

modeling of fi re effects and assessment of spatial patterns of fi re hazard under different management strategies.

Introduction

Fuel mapping is a complex and often multi-disciplinary process, potentially 

involving remote sensing, ground-based validation, statistical modeling, and 

knowledge-based systems (Huff et al. 1995; Burgan et al. 1998; Keane et al. 

2000, 2001; Rollins et al. 2004). There are strengths and weaknesses of each 

technique, and a combination of methods is often the best strategy (Keane 

et al. 2001). The scale and resolution of fuel mapping efforts depend both 

on objectives and availability of spatial data layers. For example, input layers 

for mechanistic fi re behavior and effects models must have as high resolution 

(≤ 30 m) as possible (Keane and Finney 2003).

Because of the time and effort required for ground-based measurements 

and the intrinsic variability of fuel loads, even at fi ne scales, estimation of fuel 

loadings across broad extents must rely on indirect methods. For example, 

Ohmann and Gregory (2002) built stand-level models of vegetation, including 

fuel loads, from inventory plots, satellite imagery, and biophysical variables, 

and used nearest-neighbor imputation to assign them to unsampled plots 

(cells). Keane et al. (2000) used satellite imagery, terrain modeling, and simulation models to develop predictions of biophysical setting, vegetation cover, 

and structural stage, from which they assigned each cell a fi re behavior fuel 

model (Anderson 1982). Both these efforts are model-based classifi cations.

Mapping Fuels on the Okanogan 

and Wenatchee National Forests

Crystal L. Raymond1, Lara-Karena B. Kellogg2, and 

Donald McKenzie3
294 USDA Forest Service Proceedings RMRS-P-41. 2006.

Raymond, Kellogg, and McKenzie Mapping Fuels on the Okanogan and Wenatchee National Forests

At broader scales, or where no ground data are available, fuel-mapping 

relies mainly on classifi cations of remotely sensed imagery and existing 

spatial data (for example, Burgan et al. 1998). Knowledge-based classifi cations (Schmoldt and Rauscher 1996) are often appropriate when there are 

multiple uncertainties associated with scaling predictive models (Rastetter 

et al. 1992, McKenzie et al. 1996). Rule-based classifi cations are knowledgebased methods that invoke a rule set: a collection of inferences that can be 

qualitative, or numerical, or both (Puccia and Levins 1985, Schmoldt and 

Rauscher 1996).

The choice between rule-based and model-based classifi cations involves 

trade-offs. Model-based methods provide quantitative estimates of variance 

and uncertainty whereas rule-based methods only provide qualitative estimates. A poor quantitative model is generally less useful than a qualitative 

model, (Puccia and Levins 1985, Schmoldt and Rauscher 1996, Schmoldt et 

al. 1999), so mapping efforts for which quantitative models perform poorly 

or cannot be validated are good candidates for rule-based methods.

Ecosystems are dynamic and fuel loadings change with succession, in 

response to climatic variability, or after disturbance. Quantitative fuel maps 

can become obsolete rather quickly. In order to keep fuel maps current so 

that they will retain their value for users, methods are needed to update fuel 

layers effi ciently as landscapes change. An advantage to rule-based mapping 

is that new data layers can be incorporated effi ciently because rules only need 

to be built for new attributes. In contrast, bringing updated data layers into 

model-based mapping requires entirely new models because relationships 

between response and predictor variables will change.

In this paper, we demonstrate the use of FCCS for fuel mapping on the 

Okanogan (ONF) and Wenatchee National Forests (WNF) at 25-m resolution. We focus on the process of assigning a unique fuel bed (Riccardi et al., 

in review) to each mapped cell in a spatial data layer and show how the classifi cation scheme in FCCS, based on dominant vegetation, facilitates the use 

of existing GIS layers in developing classifi cation rules and ongoing updates 

of fuel bed maps as new GIS layers become available. We briefl y discuss how 

assigning actual fuel loads to cells can proceed. Finally, we discuss applications 

of FCCS-based fuel maps for both modeling and management.

Methods

Study Area

The Okanogan (690,400 ha.) and Wenatchee National Forests (890,000 

ha.) are in north central Washington State extending from the crest of the 

Cascade Range eastward to savanna-steppe and agricultural lands. Near the 

crest topography is extremely rugged, with deep and steep-sided valleys. 

Climate is intermediate between the maritime climate west of the Cascade 

Crest and the continental climate east of the Rocky Mountains. The Okanogan highlands portion of the ONF lies further east and topography there 

differs from the western portion by having more moderate slopes and broad 

rounded summits. Conifer species dominate, notably subalpine fi r (Abies 

lasiocarpa) and mountain hemlock (Tsuga mertensiana), at higher elevations 

and ponderosa pine (Pinus ponderosa) and Douglas-fi r (Pseudotsuga menziesii) 

at lower elevations.
USDA Forest Service Proceedings RMRS-P-41. 2006. 295

Mapping Fuels on the Okanogan and Wenatchee National Forests Raymond, Kellogg, and McKenzie

Spatial Data Layers

We used GIS layers developed from a variety of sources and archived by 

the ONF and WNF. We selected the best available (highest level of local 

manager confi dence) spatial data layers for each forest so techniques and 

methods differed based on the layers chosen. ArcGIS 9.0 (ESRI 2005) was 

used for all GIS computations.

For the WNF we used a 25-m raster layer (R6) and a photo-interpreted 

polygon layer (WenVeg) of cover type. The R6 layer comprises 6 cover types 

from a direct classifi cation of LANDSAT TM imagery and 9 forested cover 

types from an interpretation of the cover classes in terms of potential natural 

vegetation (Lillybridge et al. 1995). The WenVeg layer distinguishes 26 forest types, each of which has one or more structural or age classes associated 

with it. WenVeg polygons were classifi ed from aerial photos, and range in 

size from less than 1 ha to 28,000 ha, but with only 18 polygons larger than 

4,000 ha. Many polygons were validated by site visits or expert local knowledge of ecologists on individual forest districts. The R6 layer was converted 

to polygons, then overlain with the WenVeg layer. We created a new coverage 

of the combined polygons whose attribute table retained the attributes of 

the original layers.

For the ONF we used a 30-m resolution raster layer of modeled hierarchical potential vegetation consisting of 10 vegetation zones (VZ) subdivided 

into 42 plant association groups (PAG), and a 25-m resolution raster layer 

of 36 cover types classifi ed from LANDSAT TM imagery (USU 1997). 

Forest managers on the ONF conducted an accuracy assessment of the USU 

LANDSAT TM imagery and reclassifi cations were done when necessary (K. 

Davis, personal communication, 2006). The 30-m resolution PAG layer was 

resampled to 25-m and the resampled PAG layer and the USU layer were 

overlain and combined to create a new raster layer of all possible combinations of PAG and USU cover types.

Fuel Bed Development

Forest managers from the ONF and WNF collaboratively designed 187 

fuel beds with distinct species composition, stand structure, and disturbance 

histories. We aggregated these into 35 general fuel beds based on forest 

composition, within which one or more structural or age classes could be 

distinguished (for example, table 1). Additional spatial data on disturbance 

history, canopy cover, and stand structure can be used to distinguish the 187 

specifi c fuel beds (see Discussion).

Table 1—Sub-categories of a generic fuel bed (Douglas-fi r, moist grand fi r) on the Okanogan and Wenatchee 

National Forests based on structure, age class, and disturbance.

Fuel bed ID Age range (yrs) Structure Change agent

 OW020 0-30 Created opening Wildfi re

 OW021 30-60 Seedlings & saplings Pre-commercial thin

 OW022 30-60 Seedlings & saplings, high density & load. None

 OW023 60-90 Poles Selection cut and burn 

 OW024 60-90 Poles None

 OW025 90-200 Multi-layer Selection cut & burn

 OW026 90-200 Multi-layer, high density & load. None

 OW027 Over 200 Layered mature, medium density & load. None

 OW028 Over 200 Layered mature, high density & load. None

 OW029 Over 200 Open parkland, low density & load. None

 OW030 Over 200 Open parkland, medium density & load. None
296 USDA Forest Service Proceedings RMRS-P-41. 2006.

Raymond, Kellogg, and McKenzie Mapping Fuels on the Okanogan and Wenatchee National Forests

We used 1,490 plots from the USFS Pacifi c Northwest Region Current 

Vegetation Survey (CVS) on ONF and WNF to determine if the designated 

fuel beds adequately represented the likely species combinations. Some species 

and species combinations were poorly represented by the original 35 general 

fuel beds, so we added 5 general fuel beds. A limiting factor of using available 

spatial data is that some species are diffi cult to map due to the resolution of 

the data layers. For example, the initial list included fuel beds dominated by 

both whitebark pine (Pinus albicaulis) and subalpine larch (Larix lyallii), but 

the spatial layers lumped these species into one high-elevation parkland classifi cation, so we added a corresponding high-elevation parkland fuel bed.

Fuel Bed Assignment

We assigned a fuel bed to each 25-m cell in the forest layers using a rulebased approach that incorporated the GIS layers for each national forest. The 

overarching criterion for the WNF was that the fuel bed assignment fi rst had 

to be consistent with the WenVeg layer, because this was the one in whose 

accuracy local managers had the most confi dence. Because WenVeg does not 

distinguish species composition as fi nely as the general fuel beds, however, 

we used the R6 layer to narrow possibilities for dominant species. For each 

R6 cell within each WenVeg polygon, the most likely fuel bed was assigned. 

Figure 1 illustrates the logic for three distinct fuel bed assignments within 

Figure 1—Example of logic for identifying a generic FCCS fuel bed for combinations of satellite-mapped 

vegetation and photo-interpreted vegetation on the Wenatchee National Forest. 
USDA Forest Service Proceedings RMRS-P-41. 2006. 297

Mapping Fuels on the Okanogan and Wenatchee National Forests Raymond, Kellogg, and McKenzie

the cover class “Douglas-fi r” in the R6 layer, depending on the WenVeg 

polygon within which they fall. The LANDSAT-based cover type classifi cation was the primary GIS layer used to assign fuel beds on the ONF because 

it was a measure of current vegetation for which an accuracy assessment was 

completed. If the cover type was not specifi c, it was further refi ned using the 

VZ, and if the cover type classifi cation was common and coincided with many 

PAG, the PAG were also used to assign the most likely fuel bed. Figure 2 

illustrates the logic for assigning fuel beds to the “Douglas-fi r” LANDSATbased cover type in the USU layer.

We used the CVS plots to validate the fuel bed assignments based on the 

remotely sensed data. The objective of this validation was to compare the 

frequency distribution of fuel beds represented in the spatial data layer with 

that of fuel beds represented by the CVS plots, not to match individual cells 

to individual plots. First we assigned a fuel bed to each of the CVS plots 

based on the relative tree species composition by basal area giving weight to 

the most dominant species and the presence of rare species. Each CVS plot 

is a cluster of fi ve subplots in which trees were sampled in a 15.6 m radius 

circular plot (0.076 hectares). To compare fuel beds at a commensurate scale, 

only data from the center plot were used, which corresponded to one 25-m 

grid cell.

Figure 2—Example of logic for identifying a generic FCCS fuel bed for combinations of satellite-mapped 

vegetation and modeled potential vegetation on the Okanogan National Forest. 
298 USDA Forest Service Proceedings RMRS-P-41. 2006.

Raymond, Kellogg, and McKenzie Mapping Fuels on the Okanogan and Wenatchee National Forests

Results

The combination of 9 R6 modeled vegetation types and 6 LANDSAT-based 

cover types with 26 classes from the photo-interpreted WenVeg layer yielded 

34 general fuel beds (fi gure 3) including 6 common (greater than 1,000,000 

cells) and 5 rare (less than 10,000 cells) fuel beds (table 2). “Western hemlock, Pacifi c silver fi r, mountain hemlock” was most prevalent, accounting for 

14 percent of the mapped area (2,233,445 cells). The commonness refl ects 

both the range of vegetation and the range of possible fuel bed choices. For 

example, fuel bed choices for the WNF included only two dominated by 

western hemlock and only one dominated by mountain hemlock, but fi ve 

dominated by Douglas-fi r. Five fuel beds with western larch or western white 

pine as a signifi cant component were not mapped on the WNF due to the 

limited resolution of the original GIS layers. These species are problematic for 

the rule-based logic of assigning fuel beds on the WNF, because even when 

present, they rarely dominate stands or represent the climax species.

As would be expected, the rarest fuel beds refl ect the species with more 

restricted ranges in the study area: Oregon white oak (Quercus garryana) 

and Engelmann spruce (Picea Engelmannii). The WNF map showed areas of 

greater homogeneity in the middle elevations on the west side of the forest 

where “Western hemlock, Pacifi c silver fi r, mountain hemlock” and “Mountain 

hemlock, Pacifi c silver fi r, subalpine fi r” occur in large patches. In contrast, 

patterns in the lower elevations on the east side of the forest were more heterogeneous, a consequence of both more fuel bed options and a more patchy 

disturbance regime creating fi ner-scale spatial variability.

The combination of PAG and LANDSAT-based cover types yielded 36 fuel 

beds on the ONF (fi g. 4)including 4 common (greater than 1,000,000 cells) 

and 6 rare (less than 10,000 cells) fuel beds (table 3). The most frequently 

occurring fuel bed was “Subalpine fi r, Engelmann spruce, Douglas-fi r, lodgepole pine” covering 16 percent of the area (1,776,623 cells). All fuel beds 

were mapped except the two Oregon white oak fuel beds because the area is 

beyond its range. The greater specifi city of the LANDSAT-based cover type 

layer on the ONF better captured rare species such as Engelmann spruce, 

white bark pine, western larch, and western white pine. The greater frequency 

of these fuel beds refl ects both the higher number of categories in the USU 

LANDSAT layer and the greater abundance of these species on the ONF. 

The pattern of fuel beds across the ONF domain distinguishes four general 

areas: (1) the western portion of the forest along the Cascade crest and west 

of the crest is dominated by the Mountain hemlock, silver fi r, subalpine fi r” 

fuel bed, (2) the north east is dominated my lodgepole pine fuel beds, (3) 

the south east is dominated by Douglas-fi r and ponderosa pine fuel beds 

and (4) the Okanogan highlands is highly variable with the greatest fuel bed 

heterogeneity.

Validation

Validation of fuel beds on the WNF indicated a bias towards fuel beds 

composed of late seral species (for example, western hemlock, Pacifi c silver 

fi r, mountain hemlock) and dry forest fuel beds were under-represented (for 

example, Douglas-fi r, ponderosa pine, grand fi r) (fi gure 5). This was not 

entirely unexpected as one of the spatial data layers was partially developed 

from modeled potential vegetation. To adjust for this bias, we revisited each 

classifi cation rule, under the assumption that a systematic shift towards the 

early seral species in the R6 plant associations would correct the bias. However, 
USDA Forest Service Proceedings RMRS-P-41. 2006. 299

Mapping Fuels on the Okanogan and Wenatchee National Forests Raymond, Kellogg, and McKenzie

Figure 3—Fuel bed classifi cation for the Wenatchee National Forest, Washington state, at 25-m resolution.
300 USDA Forest Service Proceedings RMRS-P-41. 2006.

Raymond, Kellogg, and McKenzie Mapping Fuels on the Okanogan and Wenatchee National Forests

Table 2—Percentage area of the common (> 1,000,000 cells) and 

rarest (< 10,000 cells) fuel beds in the Wenatchee National 

Forest map.

Common fuel beds Area (%)

Western hemlock, Pacifi c silver fi r, mountain hemlock 13.84

Mountain hemlock, Pacifi c silver fi r, subalpine fi r 9.66

Douglas-fi r, ponderosa pine 9.07

Moist grand fi r, western hemlock 8.47

Non-vegetated 8.07

Montane herbaceous opening 7.10

Rare fuel beds 

Dry hemlock 0.038

Oregon white oak, Douglas-fi r, ponderosa pine 0.032

Engelmann spruce, lodgepole pine 0.011

Wet avalanche opening 0.002

Oregon white oak < 0.001

Figure 4—Fuel bed classifi cation for the Okanogan National Forest, Washington state, at 25-m resolution.
USDA Forest Service Proceedings RMRS-P-41. 2006. 301

Mapping Fuels on the Okanogan and Wenatchee National Forests Raymond, Kellogg, and McKenzie

Table 3—Percentage area of the most common (> 1,000,000) and rarest (< 

10,000) fuel beds in the Okanogan National Forest map.

Common fuel beds Area %

Subalpine fi r, Engelmann spruce, Douglas-fi r, lodgepole pine 15.84

Douglas-fi r, ponderosa pine 14.29

Lodgepole pine 9.92

Lodgepole pine, subalpine fi r 9.10

Rare fuel beds 

Low elevation grassland 0.079

Dry hemlock 0.054

Western larch 0.031

Western larch, lodgepole pine 0.024

Wet riparian 0.016

Ponderosa pine, western larch 0.006

Figure 5—A comparison of fuel bed distributions from two sources on the Wenatchee 

National Forest, 835 CVS plots and remotely-sensed data.
302 USDA Forest Service Proceedings RMRS-P-41. 2006.

Raymond, Kellogg, and McKenzie Mapping Fuels on the Okanogan and Wenatchee National Forests

the only rules amenable to this adjustment represented a small enough number 

of cells that the distributions changed only slightly toward lesser bias.

Validation with the CVS plots on the ONF indicated that the fuel bed 

classifi cation process better captured the spatial distribution of fuel beds 

on the ONF than on the WNF. The distribution of fuel beds represented 

by the spatial data layers on the ONF was remarkably similar to that of the 

CVS plots with a few exceptions (fi gure 6). Classifi cation of the spatial data 

layers over represented the “lodgepole pine” and “lodgepole pine, subalpine 

fi r” fuel beds. Conversely two Douglas-fi r fuel beds, “pure Douglas-fi r” and 

“Western larch, Douglas-fi r,” occurred with much greater frequency in the 

CVS plots than in the classifi cation of the combined spatial data layers.

Figure 6—A comparison of fuel bed distributions from two sources on the Okanogan National Forest, 655 CVS plots 

and remotely-sensed data.
USDA Forest Service Proceedings RMRS-P-41. 2006. 303

Mapping Fuels on the Okanogan and Wenatchee National Forests Raymond, Kellogg, and McKenzie

Discussion

We completed classifi cation of FCCS fuel beds on two national forests using a rule-based method that takes advantage of spatial data layers of current 

and potential vegetation. In order to be useful for management and modeling 

applications, these fuel beds must be translated into fuel loads by fuel type 

(for example, canopy, live surface fuels, dead surface fuels, litter and duff). 

The FCCS has default values so the implementation of mapping fuel loads can 

proceed by assigning each cell its default value for each fuel category. Fuels are 

highly variable in space and time, however, so although this approach might 

produce unbiased estimates of mean fuel loadings, it clearly underestimates 

the variability of fuels across a region.

We can use high-resolution quantitative GIS layers that cover the WNF and 

ONF to quantify the attributes of each fuel bed. The Interagency Vegetation 

Mapping Project estimated both canopy cover and quadratic mean diameter 

(QMD) at 30-m resolution across the forest from LANDSAT TM imagery. 

The USU LANDSAT TM imagery included layers of canopy cover and stand 

size (d.b.h class) at 30-m resolution across the ONF. These layers provide 

structural information that can be linked to specifi c fuel beds (for example, 

table 1), thereby refi ning estimates of fuel loadings for each cell to the more 

precise default values associated with the specifi c fuel beds. This will be particularly valuable for quantifying fuels below the canopy layer—a problematic 

task in mapping fuels and vegetation in general (Keane et al. 2001).

Fuels are also highly variable over time, because of vegetation succession, 

disturbance, and land use. The FCCS includes a facility for incorporating 

“change agents” (Ottmar et al., in review) to account for modifi cation of fuel 

beds by disturbance and management. This feature, along with the FCCS’ 

basis in vegetation, enables straightforward updates of the mapped layers as 

new vegetation layers become available and disturbances are identifi ed and 

mapped. The base maps we developed can be updated to implement a change 

agent for fuel beds assigned to cells affected by disturbance, or in some cases 

changed to a new general fuel bed, by incorporating spatial data layers on 

fi re and insect disturbances and logging activities

Applications to Modeling and Management

Any attribute associated with a fuel bed can be mapped at the same resolution as the fuel bed. Not only can the default fuel loads for each of 16 

categories of fuels be mapped, but also any output from the FCCS calculator 

can be similarly mapped. Mapped FCCS attributes can provide input layers 

for current and future modeling efforts at multiple scales. Managers can use 

these FCCS-based maps as planning tools for the national forest, because 

their forest-wide coverage with fi ne resolution matches the scale of forest plans 

(R. Harrod, personal communication, 2006). The ability to customize fuel 

beds within FCCS facilitates the quantitative evaluation of fuel-treatment 

scenarios across the landscape.

The hierarchical scheme of FCCS enables a crosswalk to existing and future 

spatial data layers using straightforward decision rules. Fuel bed attributes 

such as vegetation cover and fuel loads can likewise be matched to quantitative spatial data layers. Dynamic fuel mapping is necessary as we move into 

the future with rapid climatic and land-use change, and possibly increasing 

disturbance extent and severity. The rule-based methods we describe here 

are well suited for updating with new spatial data, to keep local and regional 

scale fuel assessments current and inform both research and management.
304 USDA Forest Service Proceedings RMRS-P-41. 2006.

Raymond, Kellogg, and McKenzie Mapping Fuels on the Okanogan and Wenatchee National Forests

References

Anderson, H.A. 1982. Aids to determining fuel models for estimating fi re behavior. 

USDA Forest Service Gen. Tech. Rep. INT-122. Intermountain Research Station, 

Ogden, UT.

Burgan, R.E., Klaver, R.W., and Klaver, J.M. 1998. Fuel models and fi re potential 

from satellite and surface observations. Int. J. Wildland Fire 8: 159-170.

ESRI (Environmental Systems Research Institute). 2005. ArcGIS 9.0. Redlands, CA.

Huff, M.H., Ottmar, R.O., Alvarado, E., Vihnanek, R.E., Lehmkuhl, J.F., Hessburg, 

P.F., and Everett, R.L. 1995. Historical and current forest landscapes in eastern 

Oregon and Washington. Part II. Linking vegetation characteristics to potential 

fi re behavior and related smoke production. USDA Forest Service Gen. Tech. Rep. 

PNW-GTR-355. Pacifi c Northwest Research Station, Portland, OR.

Keane, R.E., Burgan, R., and van Wagtendonk, J. 2001. Mapping wildland fuels 

for fi re management across multiple scales: integrating remote sensing, GIS, and 

biophysical modeling. Int. J. Wildland Fire 10: 301-319.

Keane, R.E., and Finney, M.A., 2003. The simulation of landscape fi re, climate, and 

ecosystem dynamics. Pp. 32-68 in: Veblen, T.T., Baker, W.L., Montenegro, G., 

and Swetnam, T.W. (eds.), Fire and Climatic Change in Temperate Ecosystems 

of the Western Americas. Springer-Verlag, New York.

Keane, R.E., Mincemoyer, S.A., Schmidt, K.M., Long, D.G., and Garner, J.L. 

2000. Mapping vegetation and fuels for fi re management on the Gila National 

Forest Complex, New Mexico [CD-ROM]. USDA Forest Service Gen. Tech. Rep. 

RMRS-GTR-46-CD. Rocky Mountain Research Station, Ogden, UT. 126 p.

Lillybridge, T.R., Kovalchik, B.L., Williams, C.K., and Smith, B.G. 1995. Field 

guide to forested plant associations of the Wenatchee National Forest. USDA 

Forest Service Gen. Tech. Rep. PNW-GTR-359. Pacifi c Northwest Research 

Station, Portland, OR.

McKenzie, D., Peterson, D.L., and Alvarado, E. 1996. Extrapolation problems in modeling 

fi re effects at large spatial scales: a review. Int. J. Wildland Fire 6: 165-176.

Ohmann, J.L., and Gregory, M.J. 2002. Predictive mapping of forest composition 

and structure with direct gradient analysis and nearest-neighbor imputation in 

coastal Oregon, USA. Can. J. For. Res. 32: 725-741.

Ottmar, R.D., Sandberg, D.V., Riccardi, C.L., and Prichard, S.J. The Fuel 

Characteristic Classifi cation System (FCCS)—A system to build, characterize, 

and classify fuels for resource planning. ms. in review

Puccia, C., and Levins, R. 1985. Qualitative Modeling of Complex Systems. Harvard 

University Press, Cambridge, MA.

Rastetter, E.B., King, A.W., Cosby, B.J., Hornberger, G.M., O’Neill, R.V., and 

Hobbie, J.E. 1992. Aggregating fi ne-scale ecological knowledge to model coarserscale attributes of ecosystems. Ecol. Appl. 2: 55-70.

Riccardi, C.L., Andreu, A.G., Elman, E., Kopper, K.E., Long, J., and Ottmar, R.D. 

National system to characterize physical properties of wildland fuels. in review.

Rollins, M.G., Keane, R.E., and Parsons, R.A. 2004. Mapping fuels and fi re regimes 

using remote sensing, ecosystem simulation, and gradient modeling. Ecol. Appl. 

14: 75-95.

Schmoldt, D.L., Peterson, D.L., Keane, R.E., Lenihan, J.M., McKenzie, D., Weise, 

D.R., and Sandberg, D.V. 1999. Assessing the effects of fi re disturbance on 

ecosystems: a scientifi c agenda for research and management. USDA For. Serv. 

Gen. Tech. Rep. PNW-GTR-455. Pacifi c Northwest Research Station, Portland, 

OR.

Schmoldt, D.L., and Rauscher, H.M. 1996. Building Knowledge-based Systems for 

Natural Resource Management. Chapman and Hall, New York, NY. 386 p. 
USDA Forest Service Proceedings RMRS-P-41. 2006. 305

In: Andrews, Patricia L.; Butler, Bret W., 

comps. 2006. Fuels Management—How to 

Measure Success: Conference Proceedings. 

28-30 March 2006; Portland, OR. 

Proceedings RMRS-P-41. Fort Collins, 

CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research 

Station.

1 Korea Forest Research Institute. 

mswon@foa.go.kr

2 Kangwon National University.

Abstract—The objective of this study is classifi cation of fuel type and calculation of fuel 

loading to assess forest fi re hazard by fuel characteristics at Uiseong-gun, Gyeongbuk 

located in the central interior of Korea. A database was constructed of eight factors 

such as forest type and topography using ArcGIS 9.1 GIS programs. An on-site survey was conducted for investigating vegetation and fuel loading. Forest distribution 

of Uiseong-gun is composed of mixed forest, about 43.7%, of coniferous trees such 

as Pinus densifl ora, approximately 43.5%, and of broad-leaved trees like Quercus 

variabilis, 8.7%. In order of age class, trees are III-class (11~20 years) 57.6%, IV-class 

(21~30 years) 25.1% and II-class (1~10 years) 14.4%. By diameter at breast height 

(DBH) 82.5% are small diameter, 6~16 cm, and 14.9% of young trees are under 

6 cm diameter. Most trees are are less than 16 cm DBH. Considering Korean forest 

characteristics this study led to a classifi cation of ten fuel types. With the utilization of 

the data taken into account, this research, based on the existing forest type and forest soil map, categorized the 10 fi re fuel types into three coniferous forests (C), one 

broadleaf forest (D), and one mixed forest (M), fi ve fuel type forests in total. In shrub 

layers and below them, fuel load was found to be 7.64 t/ha in Pinus densifl ora pure 

forest (C-1), 10.99 t/ha in the Pinus densifl ora-middle stratum (C-2), 8.62 t/ha in the 

Pinus densifl ora-substratum (C-3), 9.17 t/ha in the mixed forest (M), and 1.01 t/ha 

in the broadleaf forests (D). To categorize fuel types in drawing a forest fi re fuel map, 

the research analyzed the relationship between the density of coniferous forests (C-1, 

C-2, and C-3), fuel load and forest soil conditions.

Introduction

The USDA Forest Service developed a forest fi re danger rating system 

consisting of two fuel models in 1964. The 1972 National Fire Danger Rating System (NFDRS) used nine fuel models (Deeming and others 1972). 

The 1978 NFDRS uses 20 fuel models (Deeming and others 1977). This 

research enables people to predict fi re behavior in wildlife resources, thereby 

allowing one to evaluate and control potential forest fi re damage. Rothermel’s 

(1972) mathematical fi re spread model enables quantitative prediction of fi re 

behavior and forest fi re danger rating. This mathematical model requires 

a description of fuel characteristics to calculate forest fi re danger indices, 

namely, fi re behavior potential. Data collection for fuel characteristics can be 

categorized as fuel models, which consist of four groups: grass, shrub, timber, 

and slash (from logging or fi re or wind damage). Fire danger rating uses 20 

fuel models. Thirteen fuel models are used in the fi re behavior prediction 

and application (Albini 1976). Anderson (1982) provided photographs and 

descriptions of fuel models in particular areas, allowing users to use them 

Fuel Type Classifi cation and Fuel Loading in 

Central Interior, Korea: Uiseong-Gun

Myoung Soo Won1, Kyo Sang Koo1, Myung Bo Lee1, and Si Young Lee2
306 USDA Forest Service Proceedings RMRS-P-41. 2006.

Won, Koo, Lee, Lee Fuel Type Classifi cation and Fuel Loading in Central Interior, Korea: Uiseong-Gun

with ease. Anderson also linked the fi re behavior fuel models with fuel models 

in the National Fire Danger Rating System.

The Canadian Fire Behavior Prediction (FBP) system categorizes fuel types 

into fi ve groups consisting of 16 types. The FBP system fuel types are quality-focused rather than quantity-focused, and are categorized into overstory 

layers (structure and composition of standing tree areas), shrub layers (surface 

and ladder fuel), and surface vegetation and duff layers.

This research seeks to use fuel management programs fi t for Korean circumstances, taking into account geographical and ecological characteristics, 

and develop fuel models to be used in evaluating forest fi re danger levels.

Methods

Study Area

Uiseong-gun belongs to North Gyeongsang Province, and is located in 

the middle inland area of Korea (Figure 1). The county’s topography, except the area of Sinpyeong-myeon to the northwest, is not so rugged. The 

northwestern area is part of Taebaek Mountain Ridges, featuring overlapped 

mountainsides and forming highlands, but is in its old age stage and is relatively well-developed. The county is long east to west, and narrow north to 

south, forming a narrow rectangle. Major mountains include Mt. Geumseong 

(530 m), Mt. Seonam (879 m), and Mt. Bibong (672 m) to the southwest, 

as well as Mt. Bibong (579 m), Guksabong Peak (521 m) and Mt. Munam 

(460 m) to the Northeast. The county’s forests consist of mixed forests, 

coniferous forests, and broadleaf forests. Pine tree forests represent over 

one-third of the forests. By forest type, mixed forests represent 43.7% of the 

total forests, coniferous forests 43.5%, and broadleaf forests 8.7%, thereby 

forming various forest types.

Figure 1—Site map of Uiseong-gun and on-site study area.
USDA Forest Service Proceedings RMRS-P-41. 2006. 307

Fuel Type Classifi cation and Fuel Loading in Central Interior, Korea: Uiseong-Gun Won, Koo, Lee, Lee

Field Methods

The recent-fi ve-year (2001-2005) forest fi re outbreak statistics by season 

indicates an annual average of 543 cases, and of these, in spring (March-May) 

alone, 364 cases broke out, representing 67% of the total. Thus, to accurately 

survey fuel load by forest fi re type, on-site investigation was conducted in the 

spring, which is the driest season and has the greatest danger of forest fi re.

To survey vegetation with the aim of categorizing forest fi re fuel types, 

quadrates (10 m x 10 m) were installed in each vegetation community type 

classifi ed by physiognomy and location conditions, and dominance and sociability by hierarchical level were measured using Braun-Blanquet (1964)’s 

phytosociological method, Z-M tradition. Regarding timbers and Korean 

dogwood existing in the installed quadrats, their species, tree height, crown 

base height, DBH, and crown diameter were measured. Also surveyed were 

each hierarchical level (timber, shrubs, and grass) and the thickness of fallen 

leaves that may infl uence forest fi re ignition. Since fuel types within forest 

areas, even though the related trees are of the same kind, may have different 

structures according to topographical conditions, elevation, aspect, slopes and 

location coordinates were marked in the survey camp. To categorize Uiseonggun’s forest fi re fuel types, live vegetation and dead fuel were surveyed in 46 

survey zones. To estimate fuel load, fuel load in surface fuels in shrubs and 

litter were surveyed (Figure 2).

To survey fuel load, shrub forests were divided in a size of 2 m x 2 m, while 

grass, fallen leaves, fallen branches, and fruits were divided in a size of 1 m x 1 m. 

Also related fuels were collected on site and live load was measured. Each collected 

sample was dried in a drying oven, and dried load was measured again. On-site 

survey items are as follows (Figure 2).

 • Vegetations survey: 10 m x 10 m quadrates

 • Overstory: Tree height, crown base height, DBH, density

 • Understory: Height of shrubs and grass layer, percent cover

 • Fuel load: shrub, grass, fallen leaves, fallen branches

 • Topographical conditions: elevation, slope, aspect

Figure 2—Field methods for fuel type classifi cation.
308 USDA Forest Service Proceedings RMRS-P-41. 2006.

Won, Koo, Lee, Lee Fuel Type Classifi cation and Fuel Loading in Central Interior, Korea: Uiseong-Gun

Building of Database for Fuel Type Classifi cation

To identify the distribution of Uiseong-gun’s forest types, using a forest 

type map with a scale of 1:25,000 crafted by Korea Forest Research Institute 

and Korea Forest Service, maps by forest type and age class were developed. 

Using these forest type maps, survey points were selected to categorize forest 

fi re fuel types, and taking account of the distribution ratios of forest types, 

the survey plan for the Uiseong-gun area was established. Also, to determine 

topographical features of Uiseong-gun, digital elevation models were crafted 

to manufacture a map featuring altitude, slopes and four directions (Figure 3). 

Also, since forest soil conditions have a great effect on the growth of trees 

and plants, (the map) refl ected soil types to be used as reference data in categorizing forest fi re fuel types. In this research, to distinguish the fuel type 

of pine tree forests, which have the highest danger of forest fi re, soil types 

were extracted from the forest type map, and the relationship between the 

density and fuel load by soil type was analyzed.

Figure 3—Topographical and forest conditions in Uiseong-gun.
USDA Forest Service Proceedings RMRS-P-41. 2006. 309

Fuel Type Classifi cation and Fuel Loading in Central Interior, Korea: Uiseong-Gun Won, Koo, Lee, Lee

Results and Discussion

Forest Type and Topographic Information

To structure databases designed for categorizing fuel types, using a forest 

digital map and a forest topographical map with a scale of 1:25,000, maps were 

crafted to refl ect forest types, age class, forest type information by diameter 

class, DEM, slopes, aspect, and altitude, thereby determining the Uiseonggun area’s topographical information (Figure 4). In Uiseong-gun, mixed 

forests with coniferous and broadleaf forests represent the largest portion of 

the total at 43.7%, with pine tree forests accounting for 37.3%. Regarding 

distribution area by forest type, mixed forests represent 43.7% of the total, 

coniferous forests 43.5%, and broadleaf forests 8.7% (Table 1). By age class, 

the third-age class represents 57.6%, 4th-age class 25.1%, and 2nd-age class 

14.4%, showing most of forests (72%) consist of forests under 30 years old 

(Table 2). Regarding distribution by diameter class, small-diameter trees account for 82.5%, thus making trees with the diameter of less than 16cm at 

the chest’s height form the most of the forests (Table 3). Uiseong-gun’s slopes 

are 20-25 degrees for 22.6% of the total area, 25-30 degrees for 40.3%, and 

over 30 degrees for 36.6%, showing most of the area has steep slopes.

Figure 4—Forest type map in Uiseong-gun. D: Pinus densifl ora Sieb. et Zucc; H: 

Deciduous forest; LP: Grass land; M: Mixed forest; PH: Unnatural deciduous forest; 

PK: Pinus koraiensis Sieb. et Zucc., Korean Pine; PL: Larix leptolepis (Sieb. et Zucc.) 

Gordon, Japanese Larch; PR: Pinus rigida Mill, Pitch Pine; R: Agricultural area within 

forest land.
310 USDA Forest Service Proceedings RMRS-P-41. 2006.

Won, Koo, Lee, Lee Fuel Type Classifi cation and Fuel Loading in Central Interior, Korea: Uiseong-Gun

Table 1—Forest distribution of Uiseong-gun by forest type map(1:25,000 scale).

 Division Code Forest type Area Percentage Total

 (ha) - - - - - (%) - - - - -

Coniferous Forest (C) D,PD Pinus densifl ora 29,487.75 37.35

 PK Pinus Koraiensis 553.44 0.7 43.

 PL Larix leptolepis 1,646.93 2.1

 PR Pinus rigida 2,713.03 3.4

Deciduous Forest (D) Q Quercus sp. forest 108.72 0.1 

 PH Unnatural deciduous forest 213.35 0.3 8.7

 H Deciduous forest 6,543.59 8.3

Mixed Forest (M) M Mixed forest 34,542.22 43.7 43.7

Open Land (O) F Cutover 7.32 0.0 

 O Area of canopy cover 30% below 263.18 0.3 2.4

 E Devastated region 2.95 0.0

 LP Pasture 52.6 0.1

 L Agricultural area 1,599.46 2.0

Others R Agricultural area within forest land 1,372.57 1.7 1.7

 W Stream 1.28 0.0

 Others — 0.62 0.0

Total 79,109.01 100 100

Table 2—Distributed area by age class.

Age class Area Percentage

 (ha) (%)

 2 Class 10,944.95 14.4

 3 Class 43,635.66 57.6

 4 Class 19,007.94 25.1

 5 Class 1,951.14 2.6

 6 Class 243.39 0.4

 Total 75,783.08 100.0

Table 3—Distributed area by diameter class.

Diameter class Code Area Percentage

 (ha) (%)

 Sapling 0 11,314.75 14.9

 Small 1 62,605.08 82.5

 Medium 2 1,922.31 2.5

 Large 3 55.70 0.1

 Total — 75,897.84 100.0
USDA Forest Service Proceedings RMRS-P-41. 2006. 311

Fuel Type Classifi cation and Fuel Loading in Central Interior, Korea: Uiseong-Gun Won, Koo, Lee, Lee

Forest Soil

As surveyed from the forest map, Uiseong-gun’s forest soil area covers 

about 760,000ha, accounting for 65% of its total area. By soil attribute, dry 

brown forest soil accounts for 35.6%, slightly dry brown forest soil 31.2%, 

and moderately moist brown forest soil 15.9%, showing most of the forest 

area is brown forest soil (Table 4). Uiseong-gun's forest soil types are shown 

in Figure 5.

Table 4—Status of forest soil type in Uiseong-gun

Forest soil type Percent of area

Dry brown forest soil (B1) 35.56

Slightly dry brown forest soil (B2) 31.18

Moderately moist brown forest soil (B3) 15.91

Slightly wet brown forest soil (B4) 0.34

Dry dark red brown forest soil (DRb1) 4.36

Slightly dry dark red brown forest soil (DRb2) 5.30

Slightly eroded soil (Er1) 3.22

Hardly eroded soil (Er2) 0.02

Lithosol (Li) 2.27

Red forest soil (R) 1.64

Dry reddish brown forest soil (rB1) 0.02

Slightly dry reddish brown forest soil (rB2) 0.19

Figure 5—Forest soil type of Uiseong-gun.
312 USDA Forest Service Proceedings RMRS-P-41. 2006.

Won, Koo, Lee, Lee Fuel Type Classifi cation and Fuel Loading in Central Interior, Korea: Uiseong-Gun

Fuel Type Classifi cation

Vegetation data gathered from on-site surveys were analyzed, thereby categorizing forest fi re types by 10 items. To categorize forest fi re types, based 

on the density of pine tree forests which are vulnerable to forest fi re and 

have a wide distribution, dense and sparse areas were initially divided, and 

then four types were divided by the hierarchical level of pine trees. Also, one 

coniferous forest, one broadleaf forest and one mixed forest were divided. 

On-site-based forest fi re types are divided as shown in Table 5. On the basis 

of Table 5 the results of vegetation survey classifi ed by ten fuel types are 

shown in Table 6.

The ten forest fi re types from on-site surveys are based on currently existing 

forest types, density of forest areas, and vegetation structures by hierarchical 

level, thus presenting limitations in using these forest fi re types, categorizing 

fuel types in the whole survey areas, and crafting a fuel type map. Thus, to 

craft a fuel type map and put it to practical use, existing available forest type 

maps, forest soil maps, topographical data, satellite image data and others 

should fi rst be used to categorize fuel types. This research fi rst took account 

of cost and time in structuring databases as well as practical usage. A categorization of Uiseong-gun's forest fi re types is based on forest type maps 

and forest soil maps, categorized as ten fuel types and reduced to fi ve fuel 

types: three coniferous forest types (C), one broadleaf forest (D), and one 

mixed forest (M). Of these, pine tree forests which are the most vulnerable 

to forest fi re are segmented into sub groups on the basis of forest types and 

hierarchical structures. The adjusted forest fi re fuel types of Uiseong-gun 

are shown in Table 7.

Fuel Load Each Fuel Types

The type and strength of a forest fi re may vary according to fuel load, size, 

and distribution, as well as depth of the fuel bed, fuel moisture, moisture of 

extinction and other conditions in the forest area. Thus, taking account of 

features of Korea's forests, pine tree forests which are the most vulnerable 

to forest fi re were surveyed by hierarchical structure. As a result, in shrub 

levels, grass levels, fallen leaves, fallen branches, cones and other levels below 

the shrub levels, fuel load (ton/ha) of surface fl ammable materials was found 

to appear the most in broadleaf forests, Pinus densifl ora-middle stratum, 

Table 5—Ten fuel type classifi cations by fi eld survey.

 Forest type Density Fuel type

Coniferous forest -Pinus densifl ora (dense): Pinus densifl ora (dense)

 3,000 trees/ha and above Pinus densifl ora (dense)-shrub-grass

 Pinus densifl ora (dense)-shrub

-Pinus densifl ora (sparse): Pinus densifl ora (dense)-grass

 3,000 trees/ha and below Pinus densifl ora (sparse)

 Pinus densifl ora (sparse)-shrub-grass

 Pinus densifl ora (sparse)-shrub

 Pinus densifl ora (sparse)-grass

Deciduous forest Deciduous forest

Mixed forest Mixed forest
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Fuel Type Classifi cation and Fuel Loading in Central Interior, Korea: Uiseong-Gun Won, Koo, Lee, Lee

Table 6—Survey inventory classifi ed by ten fuel types. Overstory (over 8m) Middle story (2-8m) Shrub Grass Fuel loadingFuel Type TH DBH CBH DEN TH DBH CBH DEN SH %C GH %C shrub grass leaf twig cone Total (m) (cm) (m) (trees/ha) (m) (cm) (m) (trees/ha) (m) (%) (m) (%) - - - - - - - - - - - - - - - (ton/ha) - - - - - - - - - - - - - - -  1 8.9 9.9 4.6 17.0 6.0 5.8 3.6 27.0 1.5 21.3 0.5 21.3 0.4 0.3 5.7 1.5 0.3 8.0  2 — — — — 4.8 6.9 2.2 42.0 1.8 30.0 0.7 65.0 0.7 1.4 6.5 1.8 0.3 10.7  3 9.4 12.4 4.7 15.0 6.1 7.0 3.9 23.0 1.5 70.0 1.0 20.0 1.1 0.1 8.0 2.2 0.3 11.7  4 9.1 13.3 3.7 6.5 5.3 6.4 2.7 41.0 1.8 20.8 0.9 62.9 0.3 0.7 6.1 1.6 0.4 9.0  5 11.3 20.1 6.2 14.0 6.4 10.3 3.8 7.0 1.7 23.3 0.4 20.8 1.3 0.3 5.0 1.2 0.5 8.2  6 9.7 29.7 6.4 9.0 2.8 2.3 — 12.0 1.5 60.0 0.7 50.0 4.2 1.0 4.2 1.2 0.3 10.9  7 9.9 16.8 4.6 12.0 5.5 7.2 2.6 16.8 1.8 50.0 0.6 13.8 0.8 0.4 7.1 1.7 0.2 10.0  8 9.3 14.2 3.7 10.3 5.8 9.4 2.7 13.4 1.6 18.1 0.9 70.0 0.4 1.1 4.5 2.0 0.5 8.3  9 11.3 15.0 3.9 8.4 4.8 8.4 2.2 8.8 1.9 29.0 0.8 13.0 1.4 0.2 5.8 3.7 0.2 11.0  10 10.5 13.5 3.5 8.9 5.1 6.5 2.4 17.0 1.7 22.5 0.4 35.0 0.3 0.2 7.0 1.7 0.1 9.2 TH: tree height, DBH: diameter at breast height, CBH: crown base height,%C: percent cover, SH: shrub height

Table 7—Fuel type classifi cation justifi ed in Uiseong-gun. Description Fuel types Fuel type codeConiferous trees such as Pinus densifl ora Pinus densifl ora pure forest C-1

Pinus densifl ora-middle stratum C-2

Pinus densifl ora-substratum C-3

Deciduous trees such as Quercus variabilis Deciduous forest DConiferous and deciduous trees mixed Mixed forest M
314 USDA Forest Service Proceedings RMRS-P-41. 2006.

Won, Koo, Lee, Lee Fuel Type Classifi cation and Fuel Loading in Central Interior, Korea: Uiseong-Gun

mixed forests, and Pinus densifl ora-substratum in this order, and the least 

fuel load was found in Pinus densifl ora pure forest (Table 8). Fuel load, in 

the case of Pinus densifl ora pure forest (C-1 type), was measured at 0.95 t/ha 

in shrubs, 0.22 t/ha in grass, 4.87 t/ha in fallen leaves, 1.27 t/ha in fallen 

branches, and 0.46 t/ha in cones, totaling 7.64 t/ha. In the case of Pinus 

densifl ora-middle stratum (C-2 type), shrubs were measured at 0.88 t/ha, 

grass at 0.70 t/ha, fallen leaves at 7.34 t/ha, fallen branches at 1.89 t/ha, 

and cones at 0.28 t/ha, showing relatively a greater total amount of fuel load 

at 10.99 t/ha. Fuel load in Pinus densifl ora-substratum (C-3 type) totaled 

8.62 t/ha, with shrubs standing at 0.52 t/ha, grass at 0.89 t/ha, fallen leaves 

at 5.16 t/ha, fallen branches at 1.73 t/ha, and cones at 0.37 t/ha, showing 

a relatively greater grass fuel load, compared with other fuel load types. On 

the other hand, mixed forests (M type) where pine trees and oak trees were 

evenly distributed showed 0.29 t/ha, 0.16 t/ha, 6.98 t/ha, 1.67 t/ha, and 

0.15 t/ha for a total of 9.17 t/ha of fuel load, in shrubs, grass, fallen leaves, 

fallen branches, and cones, respectively. Furthermore, fuel load in broadleaf 

forests (D type) totaled 11.01 t/ha, with fallen leaves and fallen branches 

standing at 5.78 t/ha and 3.68 t/ha, respectively, thus showing the greatest 

fuel load (Table 8).

Tree Density and Fuel Loading

To determine fuel features of C-1, C-2, and C-3, equivalent to 33 

coniferous forests among 46 survey places, the relations between the density, 

fuel load below the shrub hierarchical level, tree height and diameter at the 

chest height were analyzed. The density of individual trees and fuel load 

in pine tree forests are displayed in a scatter plot in fi gure 6, which shows 

a distinctive “U” type on the basis of 3,000-4,000 trees per ha. In zones 

below 3,000 trees per ha, the more the density increased, the more the fuel 

load decreased. This is presumably because, in areas with a low density of 

pine trees, the age of pine trees was advanced at 3rd-4th age class (30-40 

years), and biomass increased in forest areas as tree height was in proportion 

to diameter at the breast height. Also it is deemed that there was relatively 

greater volume and distribution ratio of thick branches in forest areas, thus 

increasing fuel load. And, fuel load decreased as the density of forests increased, presumably because competition between individual trees shortened 

tree height and diameter at the breast height, thus reducing biomass as well. 

In addition, artifi cial density management presumably decreased fuel load 

gradually. However, pine tree forests with 3,000 trees per ha showed trends 

that fuel load increased as density rose. These fuel features are characterized 

by low tree height and diameter at the breast height, and a high distribution 

ratio of small branches with small volume within forest areas. Mainly 2nd3rd age class (20-30 years old) pine trees were packed closely, and the low 

hierarchical area had grass well developed, presumably providing a very high 

fuel load. These areas have not received density management and have been 

left abandoned, thus having great absolute amounts of fuel load. Thus, fuel 

load density management beginning with these areas should be conducted 

to reduce forest fi re damage.

Fuel Types Classifi cation by Forest Soil Types

Forest soil conditions have great impact on growth of trees and plants. According to soil moisture conditions, soil is categorized into dry soil, slightly 

dry soil and moderately moist soil. Dry soil includes B1 and Er1, slightly dry 

soil B2 and DRb2, and moderately moist soil B3. Thus, criteria for categorizing 
USDA Forest Service Proceedings RMRS-P-41. 2006. 315

Fuel Type Classifi cation and Fuel Loading in Central Interior, Korea: Uiseong-Gun Won, Koo, Lee, Lee Table 8—Survey inventory by fuel type in Uiseong-gun. Overstory (8m and over) Middle story (2-8m) Shrub Grass Fuel loading Fuel Type TH DBH CBH DEN %C TH DBH CBH DEN %C SH %C GH %C shrub grass leaf twig cone Total (m) (cm) (m) (trees/ha) (%) (m) (cm) (m) (trees/ha) (%) (m) (%) (m) (%) - - - - - - - - - - - - - (ton/ha) - - - - - - - - - - - - - - C-1 10.1 16.1 5.7 1500 67 6.2 8.1 3.7 1700 37 1.6 18 0.4 18 0.95 0.22 4.87 1.27 0.46 7.65 C-2 9.9 17.4 4.6 1200 66 5.2 6.9 2.8 2000 52 1.7 49 0.7 35 0.88 0.70 7.34 1.89 0.28 10.99 C-3 9.2 13.7 3.8 800 55 5.4 7.6 2.7 2800 51 1.6 22 0.9 64 0.52 0.89 5.16 1.73 0.37 8.62 M 10.5 13.5 3.5 900 54 5.1 6.5 2.4 1700 45 1.7 22 0.4 35 0.29 0.16 6.98 1.67 0.15 9.17 D 11.3 15.0 3.9 800 63 4.8 8.4 2.2 900 27 1.9 29 0.8 13 1.36 0.21 5.78 3.68 0.26 11.01 TH: tree height, DBH: diameter at breast height, CBH: crown base height, %C: percent cover, SH: shrub height
316 USDA Forest Service Proceedings RMRS-P-41. 2006.

Won, Koo, Lee, Lee Fuel Type Classifi cation and Fuel Loading in Central Interior, Korea: Uiseong-Gun

Figure 6—Density and fuel loading of the coniferous forest.

forest fi re fuel types for coniferous forests (C-1, C-2, and C-3 types) can be 

established by utilizing soil types with soil moisture conditions refl ected.

The dry soil B1, which is distributed chiefl y in areas near ridgelines at the 

summit, upper areas of mountain slopes and other dry areas, accounts for 

about 70% of Pinus densifl ora pure forest (C-1). In Figure 7, B1 soil maintains 

an average fuel load of 8.0 ton/ha, and the smaller the density is, the bigger 

the tree height and the diameter at the breast height are, thereby increasing 

the volume of fallen branches and amounts of fallen leaves and consequently 

presumably maintaining certain fuel load. With this type, the low hierarchical 

area usually remains a naked forest area, and thus, when a forest fi re takes 

place, it will highly likely develop into surface fi re.

Slightly dry soil B2, which is distributed chiefl y at gentle-sloping summits 

and mountainsides in wind-hit areas, allows forest trees to have relatively good 

growth. Fuel load tends to decrease as the density of individual trees increases 

(Figure 7-B2). B2-soil areas saw mainly pine tree forests-low hierarchical 

type (C-3) distributed (60%), and the low hierarchical area was dominated 

by grasses, thereby boosting the ratio of grasses of fuel load. If a forest fi re 

takes place in this case, grasses will play a role of ladder fuel, presumably 

creating danger of surface fi re and crown fi re in these areas.

Moderately moist soil B3 sees its fallen leaves decompose fast, mostly 

seeping into topsoil, and boosting the productivity of forest areas. Forest 

trees grow well in this soil. Fuel load of pine tree forests in B3 increases as 

density rises. C-1, C-2, and C-3 fuel types are evenly distributed, and the 

middle hierarchy has many broadleaf trees thus providing high possibility 

of developing into mixed forests. Pine tree forests-middle hierarchical type 

(C-2) are distributed at 50%.
USDA Forest Service Proceedings RMRS-P-41. 2006. 317

Fuel Type Classifi cation and Fuel Loading in Central Interior, Korea: Uiseong-Gun Won, Koo, Lee, Lee

Figure 7—Density and fuel loading by forest soil of the coniferous forest. Dry Soil—B1, Er1; Slightly 

dry soil—B2, DRb2; Moderately moist soil—B3.
318 USDA Forest Service Proceedings RMRS-P-41. 2006.

Won, Koo, Lee, Lee Fuel Type Classifi cation and Fuel Loading in Central Interior, Korea: Uiseong-Gun

Conclusion

Forest fi re occurrence probabilities and risk of fi re spread in Korea are the 

greatest in coniferous forests. To ensure future effi cient fuel management 

and make scientifi c and accurate prediction of forest fi re occurrence and fi re 

spread risk, basic surveys of fuel features of coniferous forests, particularly, 

pine tree forests, should be fi rst conducted. Thus, to evaluate and quantify 

forest fi re risk according to characteristics of forest fi re fuel, fuel types should 

be categorized based on hierarchical structures by forest type, and the fi re risk 

should be quantifi ed on the basis of the survey of fuel load that allows one 

to estimate fl ammable amounts on topsoil. With the utilization of the data 

taken into account, this research streamlined forest fi re fuel types from 10 

to 5 (C-1, C-2, C-3, M, and H). In the future, nationwide-based forest fi re 

fuel models will be developed by determining tree height and diameter at the 

breast height by density, and adding topographical factors such as mountain 

foot, mountainside, and summit in connection with fuel types.
USDA Forest Service Proceedings RMRS-P-41. 2006. 319

Fuel Type Classifi cation and Fuel Loading in Central Interior, Korea: Uiseong-Gun Won, Koo, Lee, Lee

References

Albini, F. A. 1976. Estimating wildfi re behavior and effects. GTR-INT-30, USDA 

Forest Service, Ogden, UT.

Anderson, H. E. 1982. Aids to determining fuel models for estimating fi re behavior. 

GTR-INT-122, USDA Forest Service, Ogden, UT.

Deeming, J. E., R. E. Burgan, and J. D. Cohen. 1977. The National Fire-Danger 

Rating System—1978. GTR-INT-39, USDA Forest Service, Ogden, UT.

Deeming, J. E., J. W. Lancaster, M. A. Fosberg, R. W. Furman, and M. J. Schroeder. 

1972. National Fire-Danger Rating System. RP-RM-84, USDA Forest Service, 

Fort Collins, Colorado.

Rothermel, R. C. 1972. A mathematical model for predicting fi re spread in wildland 

fuels. RP-INT-115, USDA Forest Service, Ogden, UT.
USDA Forest Service Proceedings RMRS-P-41. 2006. 321

In: Andrews, Patricia L.; Butler, Bret W., 

comps. 2006. Fuels Management—How to 

Measure Success: Conference Proceedings. 

28-30 March 2006; Portland, OR. 

Proceedings RMRS-P-41. Fort Collins, 

CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research 

Station.

1 S en ior R e s e a rc h S p ec i a l i s t , 

Department of Forestry, University of 

Missouri-Columbia, Columbia, MO. 

stambaughm@missouri.edu

2 Research A ssociate Professor, 

Department of Forestry, University of 

Missouri-Columbia, Columbia, MO.

3 Ecologist, U.S. Geological Survey, 

Biological Research Division, Columbia 

Environmental Research Center, 

Columbia, MO.

4 Forest Community Ecologist, Missouri 

Department of Conservation, West 

Plains, MO.

Abstract—Measuring success of fuels management is improved by understanding rates 

of litter accumulation and decay in relation to disturbance events. Despite the broad 

ecological importance of litter, little is known about the parameters of accumulation 

and decay rates in Ozark forests. Previously published estimates were used to derive 

accumulation rates and combined litter measurements, model estimates, and fi re scar 

history data were used to derive a decay constant (k = 0.38). We used accumulation 

equations to demonstrate temporal changes in litter loading. For example, after a fi re 

event that consumes nearly 100 percent of the litter, about 50 percent of the litter 

accumulation equilibrium is reached within 2 years, 75 percent within 4 years, and 

the equilibrium (99 percent accumulation) after approximately 12 years. These results 

can be used to determine the appropriate prescribed burning intervals for a desired 

fi re severity. For example, fi re history data show that the percentage of trees scarred, 

a surrogate for fi re severity, is infl uenced by the length of historic fi re intervals (i.e., 

amount of litter accumulated). This information will be incorporated into regional fi re 

risk assessments and can be used as a basic knowledge of litter dynamics for both fi re 

management planning and forest ecosystem understanding.

Introduction

The Ozark Highlands lacks a general synthesis of the rate of litter accumulation and temporal variability of litter following fi re events. Information on 

the temporal variability of fuels is needed by fi re and forest managers in order 

to measure the success of management activities. In addition, information on 

litter accumulation is critical for modeling and monitoring of fuel loading and 

fi re effects. This information is regionally specifi c and depends on the balance between rates of litter accumulation and decomposition (Olson 1963). 

Litter accumulation rates are controlled by vegetation type, decomposition 

rate, ecosystem productivity, and their interrelationships. Litter accumulation rates can be diffi cult to predict because of the high variability imposed 

by changes in species, tissues, vertical structure of vegetation, elevation, site, 

and time of year (Gosz and others 1972). Litter decays by leaching, physical 

weathering, faunal activities, and microbial consumption. Microbial consumption is the primary mode of decay and it is a process controlled by physical 

and chemical litter properties and climatic conditions (Meentemeyer 1978, 

McClaugherty and others 1985). Meentemeyer (1978) presented a general 

equation for predicting average annual decomposition rates (k) from actual 

evapotranspiration (AET) and leaf lignin contents.

Understanding Ozark Forest Litter Variability 

Through a Synthesis of Accumulation Rates 

and Fire Events

Michael C. Stambaugh1, Richard P. Guyette2, Keith W. Grabner3, 

and Jeremy Kolaks4
322 USDA Forest Service Proceedings RMRS-P-41. 2006.

Stambaugh, Guyette, Grabner, and Kolaks Understanding Ozark Forest Litter Variability Through a Synthesis of Accumulation Rates and Fire Events

In order to provide general information for the Ozark region we synthesized 

data from existing studies and produced a model for predicting litter accumulation. In this paper we 1) provide a regionally averaged fuel accumulation 

equation for use in estimating fuel loading and 2) describe the long-term 

variation in Ozark fuel loading with fi re history data. The objectives of the 

paper are to develop a quantitative relationship between litter amounts and 

time, and use this relationship to examine the effects of fi re management on 

the accumulation and decay of litter.

Methods

Ozark Litter Accumulation and Decay Estimates

Estimates of litter accumulation and decay parameters were derived from 

four sources: 1) previous published studies, 2) actual litter loading measurements, 3) empirical litter relationships, and 4) analysis of historic fi re intervals 

and tree scarring.

Previous Studies—In a study in the northern Ozarks, Kucera (1959) 

ranked litter from oaks (Quercus alba, Q. rubra, Q. marilandica) as being 

most resistant to decay, followed by sugar maple (Acer saccharum), shagbark 

hickory (Carya ovata), and American elm (Ulmus americana). At the same 

location, Rochow (1974) estimated a litter decomposition rate (k) of 0.35 

for oak-dominated forest. More recently, Ryu and others (2004) arrived at a 

similar estimate for a larger portion of the Missouri Ozarks using an ecosystem productivity model (PnET-II) (Aber and others 1995).

Litter Loading Measurements—Missouri Ozark region litter loading data 

was gathered for many forested sites and time periods (table 1). Litter was 

collected using clip plot methods, dried to a constant weight, and reported 

on a dry-weight basis. In addition, we gathered associated data, including 

collection date (pre- and post-burn), dates of fi res, number of previous fi res, 

and physical plot attributes (slope, aspect, vegetation type, overstory basal 

area, and stand density). Variability in litter sample weights likely occurred 

due to collection by different investigators, years of collection, and forest 

conditions. When possible, we only used measurements that excluded the 

zone of highly decomposed material commonly called the humus or duff 

layer. We estimated the litter decomposition rate (k) using the equation developed by Olson (1963), where the annual production of litter is divided by 

the standing crop litter. The mass of annual litter production was estimated 

using mean litter loading values collected one year after burning. Estimates 

of the average standing crop (steady-state level) of litter were derived from 

litter masses that had accumulated for >20 years and were based on multiple 

measurements taken from many Ozark sites (table 1).

Empirical Litter Relationships—We also estimated litter decomposition 

rates using Meentemeyer’s (1978) general equation, which incorporates lignin 

contents and actual evapotranspiration (AET). Average litter lignin content 

for the important Ozark tree species was derived from previously published 

studies. Tree species included black oak (Q. velutina), scarlet oak (Q. coccinea), white oak (Q. alba), post oak (Q. stellata), and shortleaf pine (Pinus 

echinata) (table 2). No lignin contents were obtained for hickories (Carya

spp.). Though there is likely high variability in decomposition rates due to 
USDA Forest Service Proceedings RMRS-P-41. 2006. 323

Understanding Ozark Forest Litter Variability Through a Synthesis of Accumulation Rates and Fire Events Stambaugh, Guyette, Grabner, and Kolaks

Table 1—Data on oven dry-weights of litter from 35 Ozark Highlands sites. Forest structure codes and site information are 

given at the bottom of the table.

 Basal

 Forest area Years Litter Litter

 Site structure n (ft2/ac) accumulation (tons/ac) (tonnes/ha) Source

Knob Noster S.P. 1 5 80 2 3.02 1.11 authors

HaHa Tonka S.P. 1 5 58 2 3.12 1.14 authors

Meremac S.P. 1 7 108 3 2.50 0.92 authors

Taum Sauk Mnt S.P. 1 7 52 2 2.90 1.06 authors

Bennett Spring S.P. 1 4 66 1 2.56 0.94 authors

USFS - Mark Twain 1 7 51 1 2.71 0.99 authors

University Forest A1 1 2 55 1 2.18 0.80 authors

Baskett WMA A1 2 9 na 1 1.56 0.57 Rochow 1974

Stegall Mtn. 1 3 38 2 2.76 1.01 authors

Chilton Creek 2003 1 26 na 1 2.00 0.73 Hartman 2004

Chilton Creek 1998 1 26 na >20 3.40 1.25 Hartman 2004

University Forest B1 1 na na 1 1.64 0.60 Scowcroft 1965

University Forest B2 2 na na >20 5.45 2.00 Scowcroft 1965

University Forest C1 2 na na >20 3.88 1.42 Meier 1974

University Forest D1 2 na na >20 6.10 2.23 Paulsell 1957

Jerktail Mtn.1 2 18 96 >20 5.77 2.12 authors

Jerktail Mtn. 2 2 6 67 >20 4.17 1.53 authors

Powder Mill 1 2 10 82 >20 4.97 1.83 authors

Powder Mill 2 2 6 93 >20 4.00 1.47 authors

Akers1 2 14 99 >20 3.49 1.28 authors

Akers2 2 10 86 >20 3.88 1.42 authors

Alley Spring 2 6 93 >20 3.76 1.38 authors

Bay Creek 1 2 6 90 >20 3.84 1.41 authors

Bay Creek 2 2 6 73 >20 4.13 1.52 authors

Black River 1 2 15 na >20 3.02 1.11 Kolaks 2004

Black River 2 2 15 na >20 3.19 1.17 Kolaks 2004

Black River 3 2 15 na >23 2.92 1.07 Kolaks 2004

Coot Mtn. 2 6 103 >20 3.23 1.19 authors

Williams Mtn. 2 6 90 >20 6.53 2.40 authors

Wildcat Mtn. 2 8 93 >20 4.29 1.57 authors

Baskett WMA B1 2 102 129 >20 6.52 2.39 authors

Goose Bay Hollow 2 8 110 >20 5.44 2.00 authors

Dent & Iron Co.’s a 2 na na >20 6.60 2.42 Loomis 1975

Sinkin Exp. Forest 1 a 2 na na >20 6.20 2.28 Loomis 1965

Sinkin Exp. Forest 2 b 2 na 30 >20 5.00 1.84 Crosby and Loomis 1968

Mean maximum accumulation (>20 years accumulation) 4.57 1.68

forest structure: 1 = savanna/woodland, 2 = forest

na = not available

a contains organic matter

b shortleaf pine plantation

Table 2—Lignin contents of important Ozark forest species.

 Lignin

 Species content (%) Source

Quercus velutina 25.70 Martin and Aber 1997, Aber (online data)

Quercus coccinea 18.70 Washburn and Arthur 2003

Pinus echinata a 25.50 Washburn and Arthur 2003

Quercus rubra b 23.43 Martin and Aber 1997

Quercus rubra and Quercus alba 23.48 Martinand Aber 1997

 Mean 23.36

a samples include Pinus rigida litter. b samples include Acer rubrum litter.
324 USDA Forest Service Proceedings RMRS-P-41. 2006.

Stambaugh, Guyette, Grabner, and Kolaks Understanding Ozark Forest Litter Variability Through a Synthesis of Accumulation Rates and Fire Events

variability among sites, climatic conditions (for example AET), and numerous 

vegetation assemblages, we utilized a multi-species average of lignin contents 

for the region since our aim is to develop a better general understanding of 

litter dynamics in the Ozarks. We obtained AET estimates for the Ozark 

Highlands region from the Global Hydrologic Archive and Analysis System 

(GHAAS). Data were 0.5 degree gridded average annual AET estimates given 

in millimeters per year (Vörösmarty and others 1998). We averaged long-term 

grid means for the Ozark region to get a mean regional AET value.

Historic Fire Intervals—Historic fi re intervals were derived from four previously constructed published and unpublished fi re scar history studies in the 

Ozarks. Study sites were located in Shannon County, Missouri and included 

Stegall Mountain (Guyette and Cutter 1997), Mill Hollow, MOFEP Site 3, 

and MOFEP Site 4 (Guyette and Dey 1997). Methods for sample collection, 

tree-ring crossdating, and fi re scar dating can be found in several published 

studies (Guyette and others 2003, Stambaugh and others 2005). Site level 

fi re scar chronologies were input to FHX2 software (Grissino-Mayer 2001) 

where fi re intervals were calculated for each fi re at each site as the number 

of years between fi re events. Fire intervals were paired with the percentage 

of trees scarred in the fi re year that ended each interval. The percentage of 

trees scarred was calculated as the number of sample trees scarred in a given 

year divided by the number of recorder sample trees in the same year. All 

data were pooled into a single dataset with 111 paired observations of fi re 

intervals and percentage of trees scarred. Due to the changing characteristics 

of the anthropogenic fi re regime (Guyette and others 2002), we only used 

data from the period A.D. 1700 to 1850 in the analysis. This period was 

selected because it is well replicated (9-20 recorder trees at any given year) at 

all sites and because there exists high variation in the length of fi re intervals. 

We used non-linear regression (exponential equation) to describe the variability in the percentage of trees scarred from fi re intervals. We assumed that 

the variation in percentage of trees scarred is related to fuel accumulation. 

Based on this assumption, an exponential function should approximate the 

litter accumulation rate and the exponential term of the regression model 

would be an estimate of litter decomposition rate (k).

Temporal Litter Variability Model

The mass loss of litter as a function of time is generally expressed as an 

exponential decay model (Bärlocher 2005, Olson 1963). The temporal litter variability for Ozark forests was described using an exponential decay 

function:

Xt = X0*e –kt

,

where Xt is the amount of litter remaining after time t, X0 is the initial 

quantity of litter, and t is time of accumulation. The estimated rate of litter 

decomposition (k = 0.38) was a mean derived from four different procedures 

(table 3). The mean standing crop of litter (4.57 tons/acre, see results on 

next page) was used to defi ne maximum mass accumulation. We used the 

exponential decay function to describe the rate of accumulation of litter and 

the time required to reach maximum litter accumulation. Additionally, the 

equation was applied to historic fi re event data from four Ozark fi re scar history sites (Stegall Mountain, Mill Hollow, MOFEP Site 3, MOFEP Site 4) in 

order to reconstruct past temporal variability in litter loading. Using fi re scar 

chronologies, the model was initiated at the fi rst year of record. Fire event 
USDA Forest Service Proceedings RMRS-P-41. 2006. 325

Understanding Ozark Forest Litter Variability Through a Synthesis of Accumulation Rates and Fire Events Stambaugh, Guyette, Grabner, and Kolaks

years were used to reset the litter accumulation model to zero. Accumulation 

following fi re events assumed 100 percent fuel consumption and a constant 

weight of annual litterfall.

Results

Ozark Litter Accumulation and Decay Estimates

Litter Loading Measurements—The mean mass of annual litter production was 2.11 tons/acre (n = 6, s.d. = 0.47) or 0.77 tonnes/hectare. The 

mean standing crop of litter was 4.57 tons/acre (n = 24, s.d. = 1.22) or 1.68 

tonnes/hectare. Based on the ratio of mean annual production of litter to the 

mean standing crop, the estimated litter decomposition rate (k) was 0.46.

Empirical Litter Relationships—Average percent lignin contents of litter 

for the important Ozark overstory forest tree species (table 2) was 22.63%. 

AET values ranged from 675 to 760 mm/yr and the mean was 712 mm/yr. 

Based on Meentemeyer’s (1978) equation the estimated litter decomposition 

rate (k) ranged from 0.59 to 0.69.

Historic Fire Intervals—The relationship between the percentage of trees 

scarred in a fi re event and the preceding fi re interval (years since last fi re) was 

established using the non-linear equation:

percent trees scarred = 13.8 + 7.72 (ln[fi re interval]),

where the fi re interval is years since last fi re event (model r2 = 0.21, intercept 

and variables signifi cant p<0.0001, n = 111). Although the fi re-free interval 

model explained only about one-fi fth of the variance, the model and variables 

were highly signifi cant. The form of the equation resulted in an exponential 

term (litter decomposition rate (k)) of 0.34.

Temporal Litter Variability Model

The temporal litter variability for Ozark forests was described using an 

exponential decay equation and is presented in terms of percent accumulation 

(eq. 1) and mass accumulation (eq. 2).

 Percent accumulation = 100 – (100e–0.38t) (eq. 1),

 Mass accumulation = 4.57 – (4.57e–0.38t) (eq. 2),

Table 3—Litter decomposition rates (k) from the Missouri Ozark Highlands.

 Method k Source 

Litter loading measurements 0.46 this paper

Climate/leaf lignin model 0.64* this paper

Historic fi re intervals 0.34 this paper

Litter loading measurements 0.35 Rochow 1974

Climate/leaf lignin model 0.35 Ryu and others 2004

Mean 0.38

*not used to calculate mean
326 USDA Forest Service Proceedings RMRS-P-41. 2006.

Stambaugh, Guyette, Grabner, and Kolaks Understanding Ozark Forest Litter Variability Through a Synthesis of Accumulation Rates and Fire Events

where t is the years of litter accumulation. The equation predicts that litter 

accumulates to 25 percent, 50 percent, and 75 percent of maximum accumulation at approximately 1 year, 2 years, and 4 years, respectively (fi g. 1). 

An equilibrium accumulation (99 percent) is reached at approximately 12 

years. In terms of mass accumulation, roughly one ton of litter per acre is 

accumulated per year up to 3 years post-fi re (fi g. 1).

The litter accumulation function showed important differences in litter 

accumulation with burning frequency (fi g. 2). For example, annual burning 

allows a maximum of 32 percent of the total litter to accumulate. A burning 

frequency of 5 years allows a maximum of 85 percent of the total litter to 

accumulate, while a burning frequency of 10 years allows a maximum of 97 

percent of the total litter to accumulate. In terms of litter loading, the difference between annual and 5-year burning frequency is over two times greater 

than the difference between 5-year and 10-year burning frequencies.

The effects of variable burning frequencies were further exhibited by a 

reconstruction of long-term Ozark litter loading (fi g. 3). The long-term variation in historic fuel loading is striking and a result of frequent anthropogenic 

ignitions. Prior to EuroAmerican settlement (pre-1800), fuel loading was 

both spatially (between sites) and temporally variable. Comparisons between 

sites show that Stegall Mountain has undergone conditions of continuous 

burning and rapid fuel replenishment. Mill Hollow and MOFEP Sites 3 and 

4 underwent prolonged frequent fi res (1-3 years) that lasted most of the 19th 

century and had a long-term effect on minimizing fuel loading. Mean fuel 

loading of the four sites was 2.91 tons/acre prior to 1800 and 1.45 tons/acre 

from 1800-1900. Since about 1930 to 1940, the effects of fi re suppression 

has resulted in maximum litter loading and lowered temporal litter variability. 

An exception is Stegall Mountain, where prescribed burning management 

has been in practice since about 1980.

Figure 1—Plot illustrating a litter accumulation function in terms of percent of maximum 

and mass for forests of the Ozark Highlands, Missouri. The decomposition constant (k) 

was based on the mean from multiple sources and methods (table 3).
USDA Forest Service Proceedings RMRS-P-41. 2006. 327

Understanding Ozark Forest Litter Variability Through a Synthesis of Accumulation Rates and Fire Events Stambaugh, Guyette, Grabner, and Kolaks

Figure 2—Litter accumulation dynamics with litter removed by fi re (or other means) at 

different but regular intervals. Given here are litter accumulation patterns for annual 

fi re intervals (solid fi ne line), 2-year fi re intervals (short dashed line), 5-year fi re intervals 

(dot dashed line), and a single 20-year interval (solid bold line).

Figure 3—Litter loading reconstructions for four forest sites in the Ozark Highlands, 

Missouri. Reconstructions are based on fire scar history data and a litter mass 

accumulation function (fi g. 1). Site reconstructions begin and end at different calendar 

years based on the period of fi re scar chronology records.
328 USDA Forest Service Proceedings RMRS-P-41. 2006.

Stambaugh, Guyette, Grabner, and Kolaks Understanding Ozark Forest Litter Variability Through a Synthesis of Accumulation Rates and Fire Events

Discussion

Fire suppression policies of the past 75+ years have altered Ozark forest 

ecosystems, often in ways that are not fully understood at this point in time. 

From fi re scar studies, we know that much of the Ozarks landscape burned 

relatively frequently (8-15 years) for at least 200 years prior to Euro-American settlement. The natural communities that developed during that time 

are now changing, and restoration efforts often include the reintroduction 

of fi re, despite a lack of quantitative information on how fi re might behave 

under the conditions resulting from years of fi re suppression. One of the 

many ways in which fi re suppression has affected Ozark forests is by altering 

the nature of fuels at the forest fl oor, though there has not previously been 

a way to quantify these changes. In this paper, we present a litter accumulation model specifi c to the Ozark region, which we hope will improve our 

general understanding of the temporal variability in litter accumulation and 

our ability to manage fuels effectively in the Ozarks. The litter accumulation equations provide managers and scientists with a standard of expected 

fuel loading, the potential effects of different burning frequencies on fuel 

accumulation and loading, and estimates of the historic variability in fuel 

loading at four Ozark sites.

Estimates of temporal changes in fuel depend primarily on the litter decomposition rate (k) and level of maximum litter accumulation. The best 

estimates of litter decay and accumulation in the Ozarks were based on litter 

loading measurements and the historic fi re record. We chose not to include 

the value of k derived from mean annual AET and lignin contents as the 

estimate was extremely high (k = 0.64). Though litter decomposition rates 

differ from year to year due to changing conditions (for example climate, 

species, forest density), we felt that the value was a gross overestimate and 

outside of a plausible range of rates (Ryu and others 2004). The increased 

rate of decomposition of mixed-species litter (Gartner and Cardon 2004) 

was unaccounted for, and may be one important reason why Meentemeyer’s 

equation yielded a decay constant much higher than other estimates.

The rapid accumulation of litter following disturbance events likely leads to 

large differences in burn coverage and fi re behavior between fi re frequencies 

of 1, 2, and 3 years. To illustrate this point Behave Plus 3.0.1, fi re behavior 

prediction software, was used to estimate the different fi re rates of spread and 

fl ame lengths between fuel accumulation rates at 1, 2, and 3 years (table 4). All 

else equal, fi res occurring at 10-year intervals versus 20-year or longer intervals may not differ signifi cantly in behavior or severity (percent trees scarred) 

Table 4—Behave Plus prediction of fi re behavior using litter accumulation rates from this study. Behave 

Plus was run using fuel model 9 and 1 hour fuel loading was adjusted according to accumulation 

rates.

 Midfl ame 1hr 10hr Rate Flame

Litter Accumulation Windspeed Slope % Moisture % Moisture of Spread Length

 Rate (mph) (%) Content Content (chains/hr) (ft)

1 yr (25% max) 10 5 5 7 24.8 3.3

2 yr (50% max) 10 5 5 7 29.4 4.5

3 yr (65% max) 10 5 5 7 30.1 4.9

10 yr (97% max) 10 5 5 7 29.5 5.3

20 yr (100% max) 10 5 5 7 29.6 5.3
USDA Forest Service Proceedings RMRS-P-41. 2006. 329

Understanding Ozark Forest Litter Variability Through a Synthesis of Accumulation Rates and Fire Events Stambaugh, Guyette, Grabner, and Kolaks

because the level of litter accumulation is similar (table 4). One important 

factor in surface fi re behavior is litter moisture content which can be highly 

variable by aspect and drought condition (Stambaugh and others, in press). 

Litter profi les can also be highly variable with dry litter on the surface covering 

a relatively moist “mat” of partially decomposed but identifi able leaves of the 

previous few growing seasons (Crosby 1961, Loomis 1975). Furthermore, 

although fuel loading following 10 and 20 years of accumulation may be 

marginal, important differences in the development and conditions of the 

underlying litter profi le likely exist.

In addition to the quantifi cation of accumulation and decay rates, the reconstruction of long-term litter loading under different fi re regimes provides 

a unique perspective for fuels management. Although diffi cult to substantiate, frequent burning during the 19th century may have altered the nature of 

Ozark fuels by increasing herbaceous and grass vegetation, possibly leading 

to even lower fuel loading (for example tons/acre) than reconstructed (fi g. 

3). Frequent and long-term burning likely led to a transition in the dominant 

litter type from forest leaf litter to herbaceous grass and forb litter, which 

possibly resulted in increased decomposition rates and decreased total litter 

loading. In the southeastern Missouri Ozarks, Godsey (1988) found that 

both annual and periodic and annual burning of an oak-hickory forest after 

36 years resulted in an increased abundance of grasses, forbs, and legumes 

that only comprised about 0.02 tons/acre. Additionally, Hector and others 

(2000) discussed the differences in decomposition between plant functional 

groups (legumes, grasses, herbs) and showed increasing decomposition rates 

with decreasing litter carbon to nitrogen ratios. The conditions conducive 

to high litter loading potential are most likely found where forest fl oors are 

dominated by leaf litter and have been subject to fi re suppression for more 

than 12 years. Much of the forested area of Missouri has had no fi re disturbance since the mid-20th century, which has resulted in relatively high litter 

loading and reduced variability in litter loading compared to the previous 

200+ years.

The accumulation of organic litter on forest fl oors has implications for many 

processes which involve soils, litter invertebrates, fl oral diversity, hydrology, 

and carbon cycling. Furthermore, the effects of historically frequent fi re and 

reduced litter, as well as current and future effects, are poorly understood. 

Several studies have commented on the slow recovery of endophage populations and activity following burning (Crossley and others 1998). Auten (1934) 

and Meier (1974) found that burned Ozark sites had signifi cant reduction 

in water infi ltration compared to unburned sites. Studying the same Ozark 

experimental burn plots, Scowcroft (1965) speculated that prolonged, frequent 

burning eventually led to decreased soil productivity. Frequent fi re also results in decreased fuel connectivity, particularly as canopy trees are killed and 

inputs of litter are reduced (Miller and Urban 2000). These represent only a 

few of the myriad of ways that frequent fi re may impact forest processes, and 

highlight the value of continued research into the dynamics of fi re frequency 

and severity and the subsequent impact on organic litter accumulation.

Prescribed burning management is faced with multiple challenges in the 

Ozark region. Few studies have been conducted to investigate the effects of 

fi re on multiple ecosystem components. Meanwhile, previously fi re-maintained communities and species are decreasing in area and abundance, and 

require fi re disturbance to persist. Even with relatively general information 

about litter decay and accumulation, decisions about forest management and 

prescribed burning activities are better informed. For example, successful 

regeneration of shortleaf pine, a species of restoration concern in the Ozarks, 
330 USDA Forest Service Proceedings RMRS-P-41. 2006.

Stambaugh, Guyette, Grabner, and Kolaks Understanding Ozark Forest Litter Variability Through a Synthesis of Accumulation Rates and Fire Events

could be greatly enhanced through better understanding of the rate of litter 

accumulation, which often precludes seedling establishment. Also, burning 

prescriptions for areas being managed for multiple resources can be tailored 

to achieve an optimal level of fuel loading and desired fi re behavior.

Though based on regionally specifi c data from the Ozarks, the litter accumulation and decay estimates presented here are generalized and do not take 

into account interannual variability due to variable fi re effects (for example 

partial litter consumption), climate, litter production, litter chemistry, and 

other infl uencing factors. Despite these limitations, the approach to understanding long-term litter variability is new and applicable to other locations. 

Many improvements to this approach are attainable, including: the incorporation of variability in fuel accumulation and decomposition between leaf fall 

events; taking changing climate into account; addressing differences in species 

and vegetation densities; and, addressing differences in modern and historic 

fi re conditions (for example fuel consumption, fi re severity). The estimates 

and equations provide a context for fuels management under current conditions, facilitate a new understanding of historic fi re regimes, and provide the 

foundation for a more refi ned understanding of the fuel-fi re interaction.

Acknowledgments

This study was supported by a grant from the USDA Forest Service, North 

Central Research Station. We are grateful to the support of previous fuels 

studies whose litter dry-weight data were used in the analysis for this paper. 

We appreciate the efforts by Joe Marschall in fuels data collection and measurement and Erin McMurry for her assistance in data analysis and editorial 

comments. In addition we thank John Kabrick and Zhoufei Fan for their 

helpful comments.

References

Aber, John D. Unpublished data. Available online at http://www.aber.sr.unh.edu/

chronicn/hfglbo.txt

Aber, John D.; Ollinger, Scott V.; Federer, Anthony; Reich, Peter B.; Goulden, 

Michael L.; Kicklighter, David W.; Melillo, Jerry W.; Lathrop, Richard G. Jr. 1995. 

Predicting the effects of climate change on water yield and forest production in 

the northeastern United States. Climate Research. 5: 207-222.

Auten, John T. 1934. The effect of forest burning and pasturing in the Ozarks on the 

water absorption of forest soils. Station Note 16. U.S. Department of Agriculture, 

Forest Service, Central States Experiment Station. 5 p.

Bärlocher, Felix. 2005. Leaf mass loss estimated by litter bag technique. In: Graca, 

Manuel, Bärlocher, Felix, and Gessner, Mark, eds. Methods to study litter 

decomposition. Springer Pub., The Netherlands: 37-42.

Crosby, John S. 1961. Litter-and-duff fuel in shortleaf pine stands in southeast 

Missouri. Technical Paper 178. U.S. Department of Agriculture, Forest Service, 

Central States Experiment Station. 10 p.

Crosby, John S.; Loomis, Robert M. 1968. Fuel evaluation for a Missouri pine 

plantation released by aerial spraying. North Central Forest Experiment Station, 

Research Note NC-64.
USDA Forest Service Proceedings RMRS-P-41. 2006. 331

Understanding Ozark Forest Litter Variability Through a Synthesis of Accumulation Rates and Fire Events Stambaugh, Guyette, Grabner, and Kolaks

Crossley, D.A. Jr.; Hansen, Randi A.; Lamoncha, Karen L. 1998. Response of forest 

fl oor microarthropods to a forest regeneration burn of a southern Appalachian 

watershed. In: Oswald, Brian, ed. Proceedings of the First Biennial North 

American Forest Ecology Workshop, 1997 June 24-26; Raleigh, NC. 15 pp.

Gartner, Tracy B.; Cardon, Zoe G. 2004. Decomposition dynamics in mixed-species 

leaf litter. Oikos 104: 230-246.

Godsey, Kevin W. 1988. The effects of fi re on an oak-hickory forest in the Missouri 

Ozarks. M.S. Thesis, University of Missouri-Columbia. 125 pp.

Gosz, James R.; Likens, Gene E.; Bormann, Herbert F. 1972. Nutrient content and 

litter fall on the Hubbard Brook Experimental Forest, New Hampshire. Ecology 

53(5): 769-784.

Grissino-Mayer, Henri D. 2001. FHX2 - software for analyzing temporal and spatial 

patterns in fi re regimes from tree rings. Tree-Ring Research 57(1): 115-124.

Guyette, Richard P.; Cutter, Bruce E. 1997. Fire history, population, and calcium 

availability in the Current River watershed. In: Pallardy, Stephen, et al. eds. 

Proceedings of the 11th Central Hardwood Forest Conference. 1997 March 23-

26; Columbia, MO. USDA Forest Service, General Technical Report, NC-188. 

pp. 354-372.

Guyette, Richard P.; Dey, Daniel C. 1997. Fire and logging history at Huckleberry 

Hollow, Shannon County, Missouri. Forestry Research Report No. 1. Missouri 

Department of Conservation, Jefferson City MO. 10 pp.

Guyette, Richard P.; Dey, Daniel C.; Muzika, Rose-Marie. 2003. Dynamics of an 

anthropogenic fi re regime. Ecosystems 5: 472-486.

Hartman, George. 2004. Changes in fuel loading as the result of repeated prescribed 

fi re within the Ozark forests of Missouri. USDA Forest Service, General Technical 

Report, GTR-NE-316. pp. 162-167.

Hector, Andy; Beale, A. J.; Minns, Asher; and others. 2000. Consequences of the 

reduction of plant diversity for litter decomposition: effects through litter quality 

and microenvironment. Oikos 90: 357-371.

Kucera, Clair L. 1959. Weathering characteristics of deciduous leaf litter. Ecology 

40(3): 485-487.

Loomis, Robert M. 1965. Seasonal interpretation of certain fi re danger factors in 

Missouri. M.S. Thesis, University of Missouri-Columbia. 108 pp.

Loomis, Robert M. 1975. Annual changes in forest fl oor weights under a southeast 

Missouri oak stand. North Central Forest Experiment Station, Research Note 

NC-184. 3 pp.

Martin, Mary E.; Aber, John D. 1997. High spectral resolution and remote sensing 

of forest canopy lignin, nitrogen, and ecosystem processes. Ecological Applications 

7(2): 431-443.

McClaugherty, Charles A.; Pastor, John; Aber, John D.; Melillo, Jerry M. 1985. 

Forest litter decomposition in relation to soil nitrogen dynamics and litter quality. 

Ecology 66(1): 266-275.

Meentemeyer, Vernon. 1978. Macroclimate and lignin control of litter decomposition 

rates. Ecology 59(3): 465-472.

Meier, Calvin E. 1974. The effect of fi re on hardwood forest soil of the Missouri 

Ozarks. M.S. Thesis, University of Missouri-Columbia. 82 pp.

Miller, Carol; Urban, Dean L. Connectivity of forest fuels and surface fi re regimes. 

Landscape Ecology 15:145-154.

Olson, Jerry S. 1963. Energy storage and the balance of producers and decomposers 

in ecological systems. Ecology 44: 322-331.

Paulsell, Lee K. 1957. Effects of burning on Ozark hardwood timberlands. University 

of Missouri College of Agriculture, Agricultural Experiment Station. Research 

Bulletin 640. 24 pp.
332 USDA Forest Service Proceedings RMRS-P-41. 2006.

Stambaugh, Guyette, Grabner, and Kolaks Understanding Ozark Forest Litter Variability Through a Synthesis of Accumulation Rates and Fire Events

Rochow, John J. 1974. Litter fall relations in a Missouri forest. Oikos 25: 80-85.

Ryu, Soung-Ryoul; Chen, Jiquan; Crow, Thomas R.; Saunders, Sari C. 2004. 

Available fuel dynamics in nine contrasting forest ecosystems in North America. 

Environmental Management 33(1): S87-S107.

Scowcroft, Paul. 1965. The effects of fi re on the hardwood forests of the Missouri 

Ozarks. M.S. Thesis, University of Missouri-Columbia. 126 pp.

Stambaugh, Michael C.; Guyette, Richard P.; Dey, Daniel C. In press. Forest fuels and 

landcape-level fi re risk assessment of the Ozark Highlands, Missouri. Proceedings 

of the 15th Central Hardwoods Forest Conference. General Technical Report, 

U.S. Department of Agriculture.

Stambaugh, Michael C.; Guyette, Richard P.; Putnam, Charles. 2005. Fire in the 

pines: a 341-year history of wildland fi re at Big Spring Pines Natural Area, Ozark 

National Scenic Riverways. Park Science 23(2): 43-47.

Vörösmarty, Charles J.; Federer, C. Anthony; Schloss, Annette L. 1998. Potential 

evaporation functions compared on U.S. watersheds: possible implications 

for global-scale water balance and terrestrial ecosystem modeling. Journal of 

Hydrology 207: 147-169.

Washburn, Carol; Arthur, Mary A. 2003. Spatial variability in soil nutrient availability 

in an oak-pine forest: potential effects of tree species. Canadian Journal of Forest 

Research 33: 2321-2330.
USDA Forest Service Proceedings RMRS-P-41. 2006. 333

In: Andrews, Patricia L.; Butler, Bret W., 

comps. 2006. Fuels Management—How to 

Measure Success: Conference Proceedings. 

28-30 March 2006; Portland, OR. 

Proceedings RMRS-P-41. Fort Collins, 

CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research 

Station.

1 Forester, USDA Forest Service, Rocky 

Mountain Research Station, Fire 

Sciences Laboratory, Missoula, MT. 

shood@fs.fed.us

2 Fire ecologist, San Juan National Forest, 

Durango, CO.

Abstract—Masticated fuel treatments that chop small trees, shrubs, and dead woody 

material into smaller pieces to reduce fuel bed depth are used increasingly as a mechanical means to treat fuels. Fuel loading information is important to monitor changes in 

fuels. The commonly used planar intercept method however, may not correctly estimate 

fuel loadings because masticated fuels violate the assumption that fuel particles are 

round. A sampling method was developed for estimating masticated fuel bed loadings 

using percent cover, average depth, and bulk density in three vegetation types: Jeffrey 

pine-white fi r, ponderosa pine-Gambel oak, and pinyon-juniper. Masticated material, 

duff, and litter samples were collected to determine bulk densities. Loadings were 

calculated as the product of bulk density and depth. Total fuel median bulk densities 

equaled 129 (Jeffrey pine-white fi r), 128 (ponderosa pine-Gambel oak), and 226 

kg/m3 (pinyon-juniper). Correlations between loading and depth were best for the 

Jeffrey pine-white fi r type. Bulk density was most variable in pinyon-juniper. Woody 

material loadings calculated from the cover-depth method were generally lower than 

the loadings calculated from the planar intercept method, while duff and litter loadings from the cover-depth method were higher than the loadings calculated from the 

vertical profi le measurements on the planar-intercept transect.

Introduction

Mechanical methods to treat fuels are used increasingly in the wildland 

urban interface (WUI). The goal of many of these projects is to reduce wildfi re 

or prescribed fi re intensity and spread rate through modifi cation of surface 

fuels and increased canopy base heights. Masticating fuels compacts the surface 

fuel bed by both shredding small trees and shrubs and by chipping dead and 

down fuels into smaller size classes. While the mastication treatment reduces 

fuel bed depth, it can also result in a more continuous horizontal surface fuel 

layer and cause mixing of the woody material into the duff and litter layers. 

Because mastication is a relatively new fuels treatment, it is unclear how these 

treatments will affect surface fi re behavior or the resulting fi re effects.

Gathering fuel loading information is important for predicting fi re behavior 

and explaining post-fi re effects for any fuels treatment. However, Brown’s planar intercept and duff/litter profi le method (Brown 1974; Brown and others 

1982) may not estimate fuel loadings accurately in masticated areas because 

masticated fuels are highly irregular in shape and size and may violate the 

assumption of round fuel pieces. In this paper, we propose the cover-depth 

method as an alternative to the planar intercept method when estimating 

masticated fuel bed loadings. For the cover-depth method, square one meter 

frames are placed along a fuel transect and the percent cover of the fuel bed 

(masticated/woody material, litter, and duff) and masticated/woody only 

is estimated. Depth to mineral soil is then measured and the percent that 

Estimating Fuel Bed Loadings in 

Masticated Areas

Sharon Hood1 and Ros Wu2
334 USDA Forest Service Proceedings RMRS-P-41. 2006.

Hood and Wu Estimating Fuel Bed Loadings in Masticated Areas

is masticated/woody and the percent that is litter of the vertical profi le are 

estimated. Loadings can then be estimated by multiplying the bulk densities 

presented here by the fuel bed depth and cover class.

Specifi cally our objectives were: 1) determine bulk densities of the total fuel 

bed and the individual woody, litter, and duff layers, 2) test a new method 

to estimate fuel loadings using cover and depth (cover-depth method), and 

3) compare loadings estimated from the cover-depth method and the planar 

intercept method in masticated areas.

Methods

Study Sites

Treatment areas were located on the San Juan National Forest in southwestern Colorado (CO) and the Lassen National Forest in northern California 

(CA). We chose sites on the San Juan National Forest that had pre-treatment 

fuels data in two vegetation types: pinyon-juniper (Pinus edulis Engelm. 

and Juniperus osteosperma (Torr.) Little) and ponderosa pine-Gambel oak 

(Pinus ponderosa P. & C. Lawson and Quercus gambelii Nutt.). There were 

three pinyon-juniper sites, IC, MAHN, and KRC, and three ponderosa 

pine-Gambel oak sites, HAYD, MLCK, and NJAK. The California site, 

GRAYS, was dominated by Jeffrey pine (Pinus jeffreyi Grev. & Balf.) and 

white fi r (Abies concolor (Gord. & Glend.) Lindl. ex Hildebr.) and had no 

pre-treatment fuels data. It was part of a separately funded Joint Fire Science 

Program proposal.

Both vertical and horizontal shaft machines were employed for mechanical treatment of fuels. Vertical shaft hydro-mowers or hydro-axes were used 

more commonly because of superior maneuverability on steep slopes and less 

ground disturbance. The size and distribution of fuel pieces after a treatment 

was dependent on the equipment, the operator, and site conditions. No material was removed from the CO sites. The CA site was thinned from below 

and merchantable trees were whole tree yarded before mastication treated 

activity fuels and small trees and shrubs.

Field Measurements

Existing fuel transects were used to compare loadings estimated from the 

planar intercept method and the cover-depth method on all sites. The CA 

site had two transects per plot, with transects radiating from plot center at 

right angles to each other. The CO sites had multiple transects per plot and 

followed FIREMON protocols (Lutes and others 2006). All transects were 

established from random start locations. We placed square frames (1 m2 area) 

at 5, 10, 15, 20, and 25 meters at the CA site and at 15 and 24 meters at the 

CO sites on each transect (fi g. 1). Photographs were taken approximately one 

meter above each frame in order to develop a visual aid for estimating cover. 

Total cover of duff, litter, and woody material and only woody cover (dead 

and down fuels and masticated material) were estimated for each frame using 

FIREMON cover classes (Lutes and others 2006).

If fuels were evenly distributed throughout the frame, depth was recorded 

at each corner and the middle of the plot to the nearest 0.5 cm. Fuel depth 

was measured from the top of the masticated material to the mineral soil. We 
USDA Forest Service Proceedings RMRS-P-41. 2006. 335

Estimating Fuel Bed Loadings in Masticated Areas Hood and Wu

estimated the percent of the vertical profi le consisting of masticated/woody 

material and the percent litter following FIREMON methodology at each 

point where depth was measured. If fuel distribution inside the frame was 

markedly uneven, we assessed fuels by visually dividing the area into homogenous clumps. The proportion of each clump was recorded and fuel bed 

depth measured. We took one depth measurement for every 25 percent area 

the clump covered.

A 30 x 30 cm square sub-frame was placed in the lower left-hand corner of 

each one meter frame for collection of fuels to determine bulk density (fi g. 1). If 

fuel bed total cover inside the sub-frame was 100 percent, depth was recorded 

using the same method as described for the 1 m2 frame. Care was taken to 

minimize disturbance to the fuel bed while measuring depth. We did not 

sample the sub-frame if total cover was less than 100 percent because of the 

diffi culty in calculating volume and bulk density. We collected all fuels inside 

the sub-frames with 100 percent cover to mineral soil separately by three fuel 

types: masticated/dead and down fuels, litter, and duff. Duff and litter were 

combined on the pinyon-juniper subplots because of diffi culty in separating 

the two layers. While the fuels were generally arranged in layers, we found 

more mixing and compression of the woody material into the litter and duff 

layers than is seen on unmasticated sites. Woody material was placed into litter and duff collection bags if the particle’s cross-section was in the litter or 

duff layer, leading to higher weights for these layers. Pieces extending outside 

the sub-frame were cut with clippers or a hand saw.

Dead and down woody fuels were counted along 23 m transects using the 

planar intercept method (Brown 1974). Masticated pieces are often irregularly 

shaped; therefore, diameters of the pieces were averaged for placement into 

a time-lag fuel size class (1, 10, 100, and 1000 hour). Duff and litter depths 

were recorded at 14.5 m and 24 m along each transect.

Data Analysis

Fuel bed samples from the sub-frames were dried at 105°C for 48 hours or 

until sample weight stabilized and then weighed to the nearest gram. Total 

fuel bed volume and individual fuel bed component volume was calculated 

by multiplying dimensions of the sub-frame by the average depth of the 

vertical profi le. Bulk density of each sample was then calculated by dividing 

the oven-dry weight of the sample by the volume. Because of the mixing 

and compression of fuel bed layers and diffi culty in separating the layers 

during collection, we feel it is more accurate to use the total subplot sample 

weight and the individual fuel component depth to calculate loadings and 

bulk densities.

Figure 1—Example transect and frame layout of masticated fuel loading study. Each 

plot contained multiple transects.
336 USDA Forest Service Proceedings RMRS-P-41. 2006.

Hood and Wu Estimating Fuel Bed Loadings in Masticated Areas

Fuel bed loading was determined by multiplying the median bulk density 

of each vegetation type by the average depths of the one meter frames as if 

cover was 100 percent. The loadings were then reduced based on recorded 

cover class and clumping proportions. Loadings were calculated individually 

by fuel bed component and together. The total bulk density and loadings 

were calculated using average total depths and summed masticated, duff, and 

litter weights. All loadings reported here were calculated using the median 

total fuel bed bulk density and individual fuel bed component depth.

We also developed linear regression equations by vegetation type to estimate 

loadings using fuel bed depth as the independent variable (SAS Institute Inc. 

v 9.1). If the intercept was not signifi cant (p-value ≥ 0.05), it was dropped 

from the regression equation.

Five sub-samples of duff and litter from each vegetation type were randomly 

selected to determine mineral ash content because of potentially higher mineral soil contents in the fuel bed from the mixing and compression of layers 

during mastication. Higher mineral soil content increases bulk densities. The 

samples were placed in a muffl e furnace at 450°C for 24 hours to combust all 

organic matter. The mineral ash content (percent) was calculated by dividing 

the weight of the mineral ash by the weight of the oven-dried sample.

Loadings were also calculated from data collected using the planar intercept/duff-litter profi le method. We used the FIREMON v. 2.1.2 software 

to calculate these fuel loadings (Lutes and others 2006). All frame loadings 

and transects loadings were averaged by vegetation type and site to determine 

average site loadings.

Results and Discussion

We collected 17, 41, and 26 sub-frame (30 x 30 cm) samples on 3, 17, and 

13 plots in the Jeffrey pine-white fi r, ponderosa pine-oak, and pinyon-juniper 

vegetation types, respectively. Fuel bed depth was highest on the Jeffrey pinewhite fi r site and lowest on the Pinyon-Juniper sites (fi g. 2). The masticated 

layer averaged approximately 3.0 cm for all vegetation types.

Average litter mineral ash content was 3.9 percent in the Jeffrey pine-white 

fi r, 11.2 percent in the ponderosa pine-oak, and 26.2 percent in the pinyonjuniper (includes duff). Average mineral content of the duff samples were 

high. We found 32.4 and 42.4 percent mineral content for Jeffrey pine-white 

fi r and ponderosa pine-oak, respectively. The high mineral content for the 

pinyon-juniper litter samples was probably a result of combining the duff 

and litter into one sample bag during collection. The pinyon-juniper sites 

also have a much higher percentage of bare soil than the other vegetation 

types which may have resulted in mixing of bare soil into the duff and litter 

material when the mastication treatment was applied.

Median fuel bed bulk density was very similar for Jeffrey pine-white fi r and 

ponderosa pine-oak (129 and 128 kg m–3), but pinyon-juniper bulk density was 

much higher (226 kg m–3) (fi g. 3a). Median bulk density of the masticated/

woody layer only was 155, 136, and 218 kg m–3 for Jeffrey pine-white fi r, 

ponderosa pine-oak, and pinyon-juniper, respectively (fi g. 3b). Variability 

decreased when litter, duff, and woody material samples were combined 

into one forest fl oor sample per plot to calculate bulk densities (fi g.3). This 

was likely due to the diffi culty of accurately separating the individual fuel 

components during collection.
USDA Forest Service Proceedings RMRS-P-41. 2006. 337

Estimating Fuel Bed Loadings in Masticated Areas Hood and Wu

Figure 2—Average depth of surface fuels and forest fl oor by vegetation and fuel type.

Figure 3—Bulk density of (a) total surface and forest fl oor fuel loadings and (b) 

only surface masticated and woody fuel loadings in subplots by vegetation type. 

Solid lines represent median values. Dots are 5th and 95th percentile outliers.
338 USDA Forest Service Proceedings RMRS-P-41. 2006.

Hood and Wu Estimating Fuel Bed Loadings in Masticated Areas

Figure 4—Regression showing relationship of total 

fuel bed depth and loading for samples in (a) Jeffrey 

pine-white fi r, (b) ponderosa pine-Gambel oak, and 

(c) pinyon-juniper vegetation types.

Total fuel bed loadings calculated from the sub-frames where cover was 

100 percent were highest in the Jeffrey pine-white fi r type (9.6 kg m–2 (42.8 

tons/acre)), followed by ponderosa pine-oak (8.2 kg m–2 (36.6 tons/acre)) 

and pinyon-juniper (7.3 kg m–2 (32.6 tons/acre)). Average masticated/woody 

fuel loadings were highest in the pinyon-juniper plots (5.6 kg m–2 (25.0 tons/

acre)). Masticated loadings in the Jeffrey pine-white fi r and ponderosa pineGambel oak plots were similar (4.0 and 3.9 kg m–2 (17.8 and 17.4 tons/acre)). 

Loadings increased generally linearly with depth. Variability was high except 

for the Jeffrey pine-white fi r type (fi g. 4). The intercept was non-signifi cant 

for only the Jeffrey pine-white fi r type. Regressions equations for estimating 

total loadings are given in fi gure 4.
USDA Forest Service Proceedings RMRS-P-41. 2006. 339

Estimating Fuel Bed Loadings in Masticated Areas Hood and Wu

Figure 5—(a) Masticated and down woody and (b) litter and duff fuel loading 

estimations using the cover-depth method and Brown’s planar intercept/duff-litter 

profi le method.

 Woody fuel loadings estimated with the cover-depth method were usually 

lower (fi g. 5a) and duff and litter loadings higher (fi g. 5b) than the loadings 

estimated with the planar intercept method. The difference between the two 

methods can be attributed to both differences in average depths and bulk densities. The cover-depth method requires more depth measurements (5 per 1 m2 

per frame) than the planar intercept method (2 per transect). The duff and 

litter bulk densities calculated from the 30 x 30 cm sub-frames were higher 

than the ones used by FIREMON to calculate loadings (44 kg m–3 for litter 

and 106 kg m–3 for duff), especially for the pinyon-juniper vegetation type.

The Jeffrey pine-white fi r vegetation type had the strongest correlation 

between loading and depth. This could be due to more uniform stand conditions than the ponderosa pine-Gambel oak and pinyon-juniper types, both 

inherently and from treatment application. Also, all data collected in the 

Jeffrey pine-white fi r type came from one site, whereas data for the other 

vegetation types were collected across several sites. The pinyon-juniper type 

was the most variable type and had the highest bulk density.
340 USDA Forest Service Proceedings RMRS-P-41. 2006.

Hood and Wu Estimating Fuel Bed Loadings in Masticated Areas

The cover-depth method estimated higher duff and litter loadings and 

lower woody fuel loadings than the planar intercept method for most sites. 

Our next step is to perform an accuracy assessment based on the data collected in the sub-frames to determine which method is better for estimating 

fuel bed loadings in masticated areas. We also plan to assess if fewer depth 

measurements would produce similar results, thereby speeding the data collection process. If the cover-depth method proves to more accurately estimate 

loadings than the planar intercept method, more sampling in more vegetation 

types will be necessary to completely test this method.

Aknowledgements

We wish to thank Duncan Lutes and Robert Keane for their advice during the project development and data analysis stages and for review of the 

manuscript.

References

Brown, J. K. 1974. Handbook for inventorying downed woody material. Gen. Tech. 

Rep. INT-16, USDA Forest Service, Intermountain Forest & Range Experiment 

Station, Odgen, UT. 24 p.

Brown, J. K., Oberheu, R. D. and Johnston, C. M. 1982. Handbook for inventorying 

surface fuels and biomass in the interior west. Gen. Tech. Rep. INT-129, U.S. 

Department of Agriculture, Forest Service, Intermountain Forest and Range 

Experiment Station, Odgen, UT. 48 p.

Lutes, D. C., Keane, R. E., Caratti, J. F., Key, C. H., Benson, N. C., Sutherland, 

S. and Gangi, L. J. 2006. FIREMON: The fi re effects monitoring and inventory 

system. Gen. Tech. Rep. RMRS-GTR-164-CD, U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research Station, Fort Collins, CO. 1 CD.
USDA Forest Service Proceedings RMRS-P-41. 2006. 341

Abstract—The use of mechanical mastication to treat non-merchantable fuels is becoming increasingly popular, but loadings and other characteristics of masticated fuel 

beds are unknown. Surveys of eight recently masticated sites in northern California 

and southwestern Oregon indicate that signifi cant site level differences were detected 

for 1 hr and 10 hr time-lag classes and total woody fuel loading (P < 0.0001). The 

majority of the total woody fuel loading occurred in the 10 hr time-lag class (76.9 ± 

14.1 percent) at all 10 sites. At one particular site, planar intercept estimates of woody 

fuel loading were 181.7 (± 20.3) % higher than estimates using a plot-based method. 

When the actual average squared quadratic mean diameter values (1 hr = 0.06 cm2, 

10 hr = 1.09 cm2 and 100 hr = 11.8 cm2) were used, woody fuel loading estimates 

between the two methods did not differ statistically. Across sites, fuel depth was not a 

signifi cant predictor of fuel loading (R2 = 0.24, P = 0.22). However, a signifi cant relationship between fuel depth and loading was found at the individual site level, except 

for one site (WFR). Species masticated, mastication machinery used, and operator 

experience are some of the potential reasons why the depth to loading relationship 

differed among sites.

Introduction

In the foothill and montane regions of northern California and southwestern Oregon, the combination of weather and fuel conditions has led to many 

recent catastrophic wildfi res (e.g., Fountain, Jones and Biscuit fi res). These 

events are a deviation from the historical fi re regime of relatively frequent, 

low to moderate intensity fi res of this region (Skinner and Chang 1996, 

Taylor and Skinner 2003). Due to the successful fi re suppression over the 

last century (Agee 1993), wildfi re size and intensity has increased, bringing 

national attention to fi re management and policy. Public awareness is especially 

pronounced in residential communities located within or adjacent to areas of 

elevated fuel accumulation. Solutions to reduce the risk of wildfi re in these 

areas have often resorted to the use of mechanical fuel treatments.

One method of mechanically treating non-merchantable fuels that has 

become increasingly popular in the western United States is mastication. 

Mastication is the process of converting live or dead standing biomass into 

surface fuel by “chewing” or breaking up larger pieces into smaller portions 

by the means of a front-end or boom-mounted rotary blade or head (fi g. 1). 

In northern California and southwestern Oregon, mastication equipment is 

primarily used to treat shrub and small tree fuels, typically along fuel breaks 

and within the wildland-urban interface. Machinery used to masticate woody 

fuels is highly varied but have similar mechanical treatment properties. 

Variability in Loading of Mechanically 

Masticated Fuel Beds in Northern California 

and Southwestern Oregon

Jeffrey M. Kane1, Eric E. Knapp2, and J. Morgan Varner3

In: Andrews, Patricia L.; Butler, Bret W., 

comps. 2006. Fuels Management—How to 

Measure Success: Conference Proceedings. 

28-30 March 2006; Portland, OR. 

Proceedings RMRS-P-41. Fort Collins, 

CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research 

Station.

1 Masters student in the Department of 

Forestry and Watershed Management 

at Humboldt State University, Arcata, 

CA, USA. jk55@humboldt.edu

2 Research ecologist for USDA. Forest 

Service Pacific Southwest Research 

Station, Redding, CA, USA. 

3 Assistant professor in the Department 

of Forestry and Watershed Management 

at Humboldt State University, Arcata, 

CA, USA.
342 USDA Forest Service Proceedings RMRS-P-41. 2006.

Kane, Knapp, and Varner Variability in Loading of Mechanically Masticated Fuel Beds in Northern California and Southwestern Oregon

Mastication results in the translocation of typically living shrub and midstory 

fuel beds, thereby increasing dead surface woody fuel loading (fi g. 2). The 

reduction of potential ladder fuels and compaction of surface fuels as a result 

of mastication are appealing to land managers and have contributed to the 

dramatic increase in its use.

While the popularity of mastication to treat fuels is increasing, little work 

has been conducted to quantify and characterize the variability in masticated 

fuel beds. This lack of information is an important shortcoming to installing 

subsequent fuel treatments and an impediment to modeling potential fi re 

behavior and effects in treated areas. In order to provide land managers with 

appropriate information regarding the use of mastication and subsequent 

fi re behavior and effects, research accurately quantifying and characterizing 

masticated fuel beds is necessary.

Figure 1—General masticator types: front-end mounted, Takeuchi®,TL150 w/ FECON Bull 

hog®shredder head (left) and a boom-mounted FECON Bull hog® shredder head mounted on 

an excavator (right). (left-Photo courtesy of Nancy Curran, USDA Forest Service).
USDA Forest Service Proceedings RMRS-P-41. 2006. 343

Variability in Loading of Mechanically Masticated Fuel Beds in Northern California and Southwestern Oregon Kane, Knapp, and Varner

Figure 2—Mad River (MAD) masticated site contrasting 

untreated shrub fuels in the background with treated 

dead woody fuels in the foreground.

The purpose of this study was to provide preliminary analyses characterizing 

the variability among masticated sites in northern California and southwestern 

Oregon as part of a larger study that aims to create custom fuels models for 

masticated fuel beds. Specifi cally, the objectives of this paper were to:

 1) Quantify site level variability in masticated fuel bed loading

 2) Compare and contrast methods of estimating fuel loading in masticated 

areas

 3) Determine if fuel bed depth is signifi cantly related to total woody fuel 

loading
344 USDA Forest Service Proceedings RMRS-P-41. 2006.

Kane, Knapp, and Varner Variability in Loading of Mechanically Masticated Fuel Beds in Northern California and Southwestern Oregon

Methods

Study Sites

Throughout northern California and southwestern Oregon, eight study 

sites were selected to investigate variability in loading of masticated fuel beds. 

Study sites were located primarily on federal land (USFS, BLM and NPS), 

with one site on a private forest (Whitmore). The vegetation masticated within 

each of the study sites varied but was predominantly shrub (Arctostaphylos spp.,

Ceanothus spp.) and/or small hardwood tree species (Lithocarpus densifl orus, 

Arbutus menziesii). All mastication treatments were completed using either 

a front-end or boom-mounted masticator, and all mastication was conducted 

between November 2002 and May 2005 (table 1).

Field Sampling

Surface fuel loading was calculated for each study site using two methods: 

the planar intercept (Brown’s transect) method (Brown 1974) and a plot-based 

sampling method. At each study site, long baseline transects traversing the 

treated areas were placed at random azimuths. At 25 m increments along these 

baseline transects, a Browns transect was established at a random azimuth. 

Brown’s transect lengths were typically 20 m but occasionally less when the 

transect neared the edge of a treated area. At each Brown’s transect, 1 hr 

Table 1—Site names, locations, date of mastication and masticator type for all masticated study sites in northern 

California and southwestern Oregon, U.S.A. (BM= boom-mounted, FE = front-end mounted).

Site Code Site Name Location Mastication Date Masticator Type

APP Applegate Valley Applegate Valley, Apr./May BM-Slashbuster® 

 Oregon (BLM) 2005 brush cutter

CFR Challenge Fuel Plumas National Forest, Dec. 2002 BM-Slashbuster® 

 Reduction California (USFS) Mar. 2003 mounted on an excavator

IMR Iron Mountain Rd. Redding, California (BLM) Nov. 2004 FE-Masticating head on 

 an ASV PositrackTM

MAD Mad River Six Rivers National Forest, Dec. 2004 FE-Takeuchi®,TL150 w/

 California (USFS) FECON Bull hog®

 shredder head

SFR Sierraville Fuel Tahoe National Forest, May/June FE-Rayco® Forestry

 Reduction California (USFS) 2003 Mower (small) on a

 bulldozer

TAY Taylor Ridge Klamath National Forest, Apr./May BM-“Brontosaurus”

 California (USFS) 2005 head on excavator

WFR Whitmore Fuel Whitmore, California May 2003 FE-Rayco® Forestry 

 Reduction (Private) Mower (small) on a

 bulldozer

WHI Whiskeytown Whiskeytown NRA (NPS) Nov. 2002 FE-Slashbuster® on

 California an ASV PositrackTM
USDA Forest Service Proceedings RMRS-P-41. 2006. 345

Variability in Loading of Mechanically Masticated Fuel Beds in Northern California and Southwestern Oregon Kane, Knapp, and Varner

(0.0-0.6 cm-diameter) and 10 hr (0.6-2.54 cm-diameter) time-lag fuel size 

classes were tallied along the fi rst 2 m, while 100 hr (2.54-7.6 cm) fuel particles 

were tallied along the fi rst 4 m. The entire transect length was surveyed for 

1000 hr (>7.6 cm) fuel particles and their actual diameters were measured, 

species recorded, and decomposition category (sound or rotten) assigned. 

Since masticated fuel particles are often irregularly shaped, determination of 

the size class of each particle was made along the narrowest diameter that 

intersected the planar transect. Fuel bed depth measurements were made at 

three points along the transect (5 m, 10 m, and 15 m).

For the plot-based sampling method, a 50 cm x 50 cm metal frame was 

placed at the 7 m mark along the planar intercept transect. All woody fuels 

inside the frame were collected; in the event that a woody fuel particle crossed 

the frame, the piece was cut along the boundary and the interior portion was 

retained. To characterize fuel bed bulk density, four large pins were placed 10 

cm from each of the frame corners. At each pin, fuel bed depth was measured 

by progressive removal of each fuel layer. All woody fuels were separated in 

the lab by time-lag classes and then oven-dried for at least 72 hrs at 75 °C in 

a mechanical convection oven and then weighed on an analytical balance.

At the Mad River (MAD) mastication site, loading estimates for woody 

fuels were calculated using the composite squared average quadratic mean 

diameter values for each fuel size class (1 hr = 0.08 cm2, 10 hr = 1.3 cm2, 

100 hr = 11.9 cm2) provided by Brown (1974). In addition, woody fuel 

loading was calculated using actual squared average quadratic mean diameter 

values (1 hr = 0.06 cm2, 10 hr = 1.09 cm2 and 100 hr = 11.8 cm2) determined from collected fuels. Fuel quadratic mean diameters were generated 

by measuring the average of the minimum and maximum squared diameters 

for a subsample of fuel particle collected with the plot sampling method (1 hr, 

n = 1187; 10 hr, n = 170; 100 hr, n = 4).

Data Analysis

Means and standard errors were calculated for site-level estimates of total 

fuel loading and loading of different time-lag classes for both the planar intercept and the plot-based sampling methods. A one-way analysis of variance 

(ANOVA) was conducted to detect a site level effect for mean total woody fuel 

loading and mean loading by time-lag classes. If differences were detected, 

a post-hoc Bonferoni means comparison test was used to detect signifi cant 

differences among sites (Sokal and Rohlf 1995). Linear regression analysis 

was used to determine the relationship between total woody fuel loading calculations and fuel bed depth across all sites and at the individual site level. All 

statistical tests were computed using STATA (Statacorp 2005) and statistical 

signifi cance was based on an α = 0.05.

Results

Site Level Variation

For estimates made using the plot-based method, sites differed signifi -

cantly in total woody fuel loading and loading by 1 hr and 10 hr time-lag 

classes (P < 0.001; table 2). The MAD site had the highest total woody 

fuel loading (63.4 Mg ha–1) and contained more 10 hr fuel loading than 

all sites except Applegate Valley (APP) and Taylor Ridge (TAY; fi g. 3). The 
346 USDA Forest Service Proceedings RMRS-P-41. 2006.

Kane, Knapp, and Varner Variability in Loading of Mechanically Masticated Fuel Beds in Northern California and Southwestern Oregon

Whitmore fuel reduction (WFR) site had the lowest total woody fuel loading 

(15.3 Mg ha–1) and contained signifi cantly less in 10-hr fuel loading than 

all other masticated sites (fi g. 3). Post-mastication fuel loading was concentrated in the 10-hr and 100-hr time-lag classes, which made up 76.9 (± 14.1) 

percent and 11.5 (± 5.8) percent of the total woody fuel load, respectively. 

Loading of 10-hr time-lag class was approximately 250-300 percent greater 

in some sites (e.g., MAD, APP) than others (e.g., SFR, WFR).

Figure 3—Ten-hour fuel loading in masticated sites in northern California 

and southwestern Oregon across all sites from the plot-based method 

estimates (letters above error bars denote signifi cant difference between 

sites using Bonferoni means comparison test).* = full site names provided 

in table 1.

Table 2—Plot based sampling method estimates of mean fuel loading (± standard error) of woody fuel classes and fuel 

height for masticated sites in northern California and southwestern Oregon.

Plot-based sampling method

Site n 1 hr 10 hr 100 hr 1000 hr Total Woody Fuel Depth

 - - - - - - - - - - - - - - - - - - (Mg ha–1) - - - - - - - - - - - - - - - - - - (cm)

APP 15 12.3 (2.8) 24.6 (4.3) 8.6 (4.8) 5.3 (5.3) 50.7 (9.9) 6.9 (0.7)

CFR 40 8.1 (0.7) 19.2 (1.6) 7.9 (1.7) 3.5 (2.2) 38.7 (7.2) N/A

IMR 15 6.2 (1.7) 13.8 (2.5) 3.6 (1.7) 0.0 (0.0) 23.6 (6.9) 4.9 (0.8)

MAD 15 23.5 (2.6) 34.8 (2.6) 5.1 (2.5) 0.0 (0.0) 63.4 (7.8) 4.6 (0.8)

SFR 15 5.2 (1.0) 11.1 (1.4) 6.6 (2.9) 0.0 (0.0) 22.9 (5.4) 3.2 (0.5)

TAY 15 13.2 (2.9) 21.7 (2.7) 2.1 (0.8) 0.0 (0.0) 37.0 (6.4) 5.0 (0.5)

WFR 40 4.4 (0.7) 9.4 (1.7) 1.6 (0.6) 0.0 (0.0) 15.3 (2.8) 4.4 (0.6)

WHI 15 11.8 (2.4) 16.4 (1.8) 3.6 (1.5) 0.0 (0.0) 31.8 (5.2) 5.8 (0.3)

 All Sites 10.6 (2.2) 18.9 (2.9) 4.9 (0.9) 1.1 (2.8) 35.4 (2.8) 4.9 (0.6)
USDA Forest Service Proceedings RMRS-P-41. 2006. 347

Variability in Loading of Mechanically Masticated Fuel Beds in Northern California and Southwestern Oregon Kane, Knapp, and Varner

Fuel Load Methods Comparison

At the MAD site, total woody fuel estimates using the Brown’s planar intercept method with the composite squared average quadratic mean diameter 

values given by Brown (1974) were 180.5 (± 55.4) percent higher than the 

estimates made using the plot-based sampling method. Preliminary results 

from the MAD site suggest that the actual average quadratic mean diameters 

of masticated particles are smaller than the composite values given in Brown’s 

formula (1974). When the actual quadratic mean diameter measures at the 

MAD site were used in the fuel loading calculations, the total loading values 

no longer differed from those estimated using the plot-based method (fi g. 4). 

Even though the total fuel loading did not differ, Brown’s transect values were 

substantially greater than the plot-based sample values for 10-hr fuels and 

substantially less than the plot-based sample values for 1-hr fuels (fi g. 4).

Predictors of Total Woody Fuel Loading

Land managers and researchers are often interested in simplifying measures 

of fuel loading to improve cost effectiveness and sampling effi ciency. Fuel 

depth is a measure that is often sought to correlate with total woody fuel 

loading. Average fuel depth values for masticated sites ranged from 3.0 to 

6.9 cm. Based on linear regression analysis, fuel depth and total woody fuel 

loading over all study sites were not related (P = 0.22, R2 = 0.24). However, 

within sites, a signifi cant relationship between depth and woody fuel loading 

Figure 4—Total woody fuel loading comparisons of the planar intercept method 

with standard calculation of quadratic mean diameter, planar intercept method 

with actual quadratic mean diameter and estimates from the plot-based 

sampling method for the MAD mastication site.
348 USDA Forest Service Proceedings RMRS-P-41. 2006.

Kane, Knapp, and Varner Variability in Loading of Mechanically Masticated Fuel Beds in Northern California and Southwestern Oregon

was found at all except the WFR site (R2 = 0.03, P = 0.28). The MAD site 

had the strongest relationship between depth, and woody fuel loading of all 

sites (R2 = 0.84), while the R2 values of other sites ranged from 0.24 to 0.74. 

Equations are still being developed and are not shown here.

Discussion

Variation in woody fuel loading has many implications for both fi re behavior 

and effects. The results of this study suggest that large variations in woody 

fuel loading exist across 1-hr and 10-hr time-lag classes within masticated 

areas of northern California and southwestern Oregon. Site level differences 

in total woody fuel loading found in this study were largely driven by the 

MAD and WFR sites, which had both the highest and lowest fuel loading in 

the 10-hr time-lag class, respectively (fi g. 3). Variation in woody fuel loading 

of masticated sites in our study suggests that different fuel models may be 

necessary to accurately assess fi re behavior and effects in these areas.

Site level variation in total woody fuel loading across all time-lag fuel 

classes for masticated sites was not entirely unexpected. Primary sources of 

variation in masticated fuel beds may be linked to pretreatment biomass and 

time since mastication, although secondary factors such as decomposition 

rate and time since disturbance may be important in determining total woody 

fuel loading. Masticator type, mastication intensity, and the size and/or age 

of treated fuels are likely contributors to variation in the proportion of fuels 

in different time-lag classes.

Independent of the variability found in loading, fi ne fuel particles (particularly 10 hr) were the dominant woody fuel across all sites. These fi ndings 

have broader implications, suggesting that in spite of the many different 

types of masticators used and the level of variability in loading, there are 

consistent trends in the size of the fuel particles produced by mastication. 

The presence and quantity of fi ne fuel particles are well-known to infl uence 

fi re behavior (Rothermel 1983) and may strongly infl uence fi re effects in 

masticated areas.

When actual quadratic mean diameter measurements of masticated particles 

were used in the planar intercept fuel loading calculations, the two methods 

produced similar estimates of fuel loading. However, the planar intercept 

method underestimated 1-hr fuel loading while simultaneously overestimating 

10-hr fuel loading. An explanation for this inconsistency may be due to the 

fact that the Brown’s transect estimates were made in the fi eld after signifi -

cant fall rains, while the material collected with the plot-based method was 

dried in an oven prior to sorting into size categories. Prolonged drying of 

fuels may have caused a reduction in particle diameter, with 10-hr fuels in the 

fi eld becoming 1-hr fuels in the lab. Since fi res occur when the fuels are dry, 

the numbers obtained with the plot-based method have greater applicability 

to fi re behavior and fi re effects modeling. Results to date suggest that either 

method can be used to estimate total woody fuel loading (especially if the 

fuels are dry), but that squared averaged quadratic mean diameters specifi c 

to masticated fuels should be used in calculations with the planar intercept 

method. So far we have only made measurements of fuel particle size at one 

site and additional measurements are being made to determine if average 

particle size differs among sites.
USDA Forest Service Proceedings RMRS-P-41. 2006. 349

Variability in Loading of Mechanically Masticated Fuel Beds in Northern California and Southwestern Oregon Kane, Knapp, and Varner

While the plot-based sampling method appears to be useful for estimating 

loading of masticated fuels, several disadvantages exist. The plot-based method 

is time intensive and therefore, more costly and doesn’t evaluate enough area 

to appropriately account for relatively uncommon 1000 hr fuels, compared 

to the planar intercept method.

Fuel depth was not found to be a signifi cant predictor of total woody fuel 

loading possibly because of differences among sites caused by masticator type, 

operator experience, mastication effort, and vegetation type. It may therefore, not be feasible to create a universal equation relating depth to loading 

for this type of masticated fuel. While a relationship across all sites was not 

observed, all but one site’s total woody fuel loading was signifi cantly related 

to fuel depth. Relationships between fuel depth and woody loading may aid 

in determining simpler and faster means to calculating woody fuel loading 

within masticated sites. Surrogate measures of total woody fuel loading have 

been established for other areas (Fulé and Covington 1994) and deserve 

further investigation in masticated fuel beds.

The quantifi cation and characterization of fuel loading in masticated sites 

have ramifi cations for the prediction of fi re behavior and effects. Managers 

and researchers (Bradley and others 2006; Knapp, personal observation) report a high degree of variability in fi re behavior with prescribed burning in 

masticated fuels, which may partially be related to variations in fuel loading. 

Differences in loading have additionally been shown to infl uence depth and 

duration of lethal soil temperatures during burning (Busse and others 2005). 

In spite of the growing popularity and use of mastication, many unknown 

factors still exist in characterizing this novel fuel type. The level of variation 

encountered within our study suggests that several custom fuel models may 

be necessary to adequately predict fi re behavior and effects. Additional work 

to determine if differences in average particle size exist among sites, how these 

differences relate to site parameters, and the extent to which mastication alters 

the surface area to volume ratio of fuel particles, is in progress.

Acknowledgments

These preliminary results are a part of a larger study investigating the 

development of custom models for masticated fuel beds. Funding for this 

project was provided by the Joint Fire Science Program. Earlier drafts of this 

report were improved with the help of Emily Orling. This project was greatly 

advanced by Elishau Dotson and Emily Orling, who assisted with fi eld data 

collection.

Literature Cited

Agee, J. K. 1993. Fire Ecology of Pacifi c Northwest Forests, Island Press, Washington, 

DC. 493 pp.

Busse, M. D.; K. R. Hubbert; G. O. Fiddler; C. J. Shestak; R. F. Powers. 2005. Lethal 

soil temperatures during burning of masticated forest residues. International 

Journal of Wildland Fire 14: 1-10

Bradley, T.; J. Gibson; W. Bunn. 2006. Fuels management and non-native plant 

species: an evaluation of fi re and fi re surrogate treatments in a chaparral plant 

community. Final Report to the Joint Fire Science Program. 38 pp.
350 USDA Forest Service Proceedings RMRS-P-41. 2006.

Kane, Knapp, and Varner Variability in Loading of Mechanically Masticated Fuel Beds in Northern California and Southwestern Oregon

Brown, J. K. 1974. Handbook for inventorying downed woody material. USDA 

Forest Service, Intermountain Forest and Range Experiment Station. General 

Technical Report INT-16.

Fulé P. Z.; W. W. Covington. 1994. Double sampling increases the effi ciency of 

forest fl oor inventories for Arizona ponderosa pine forest. International Journal 

Wildland Fire 4: 3-10.

Rothermel, R. C. 1983. How to predict the spread and intensity of forest and range 

fi res. USDA Forest Service, Intermountain Forest and Range Experiment Station. 

General Technical Report. INT-143.

Sokal, R. R.; F. J. Rohlf. 1995. Biometry: The Principles and Practice of Statistics 

in Biological Research. 3rd edition. W. H. Freeman and Company: New York. 

887 pp.

Skinner, C. N.; C. Chang. 1996. Fire regimes, past and present. In: Sierra Nevada 

Ecosystem Project: Final report to Congress. Vol. II: Assessments and scientifi c 

basis for management options. Wildland Resourcess Center Publication No. 

37. Centers for Water and Wildland Resources, University of California, Davis: 

1041-1070.

StataCorp. 2005. Stata Statistical Software: Release 8.2. College Station, TX: 

StataCorp LP.

Taylor, A. H.; C. N. Skinner. 2003. Spatial and temporal infl uences and controls on 

fi re regimes in the Klamath Mountains. Ecological Applications 13: 704-719.
USDA Forest Service Proceedings RMRS-P-41. 2006. 351

In: Andrews, Patricia L.; Butler, Bret W., 

comps. 2006. Fuels Management—How to 

Measure Success: Conference Proceedings. 

28-30 March 2006; Portland, OR. 

Proceedings RMRS-P-41. Fort Collins, 

CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research 

Station.

1 Biometrician, USDA Forest Service, 

Southern Research Station, Asheville, 

NC. bparresol@fs.fed.us.

2 Fire Planner, USDA Forest Service, 

Savannah River, New Ellenton, SC.

3 Research Forester, USDA Forest 

Service, Pacifi c Northwest Research 

Station, Seattle, WA.

Abstract—The Savannah River Site is a Department of Energy Nuclear Defense Facility 

and a National Environmental Research Park located in the upper coastal plain of South 

Carolina. Prescribed burning is conducted on 15,000 to 20,000 ac annually. We modifi ed standard forest inventory methods to incorporate a complete assessment of fuel 

components on 622 plots, assessing coarse woody debris, ladder fuels, and the litter 

and duff layers. Because of defi ciencies in south-wide data on litter-duff bulk densities, 

which are the fuels most often consumed in prescribed fi res, we developed new bulk 

density relationships. Total surface fuel loading across the landscape ranged from 0.8 

to 48.7 tons/ac. The variables basal area, stand age, and site index were important in 

accounting for variability in ladder fuel, coarse woody debris, and litter-duff for pine 

types. For a given pine stand condition, litter-duff loading decreased in direct proportion to the number of burns in the preceding thirty years. Ladder fuels for loblolly and 

longleaf increased in direct proportion to the years since the last prescribed burn. The 

pattern of fuel loading on the SRS refl ects stand dynamics, stand management and 

fi re management. It is suggested that the Forest Inventory and Analysis Program can 

easily modify sampling protocols to incorporate collection of fuels data.

Introduction

The Savannah River Site (SRS) is a 198,344 ac land base controlled by 

the Department of Energy. The SRS is a Nuclear Defense Facility and a 

National Environmental Research Park. The SRS is located on the Upper 

Coastal Plain and Sandhills physiographic provinces, south of the city of 

Aiken, South Carolina (fi gure 1). Created in 1951, the SRS today contains 

approximately 182,420 ac of forested landscape divided into 6,009 stands 

across six expansive management areas.

When the SRS was established, approximately 80,000 acres were in oldfi elds and the balance consisted of cut over forest land with low stocking (Kilgo 

and Blake 2005). The planting of the old fi elds and cutover forests with (nonnative) slash pine (Pinus echinata), loblolly pine (P. taeda) and longleaf pine 

(P. palustris) created a large block in a narrow age class and a dynamic fuel 

loading problem. Approximately 14 wildfi res, primarily surface fi res, occur 

each year. An effective prescribed burning program was not initiated until 

the mid 1970’s. Today prescribed burning is conducted on 15,000 to 20,000 

acres annually to reduce fi re hazards and to enhance ecological communities 

associated with longleaf fi re savannas. The SRS has also utilized herbicides 

to reduce mid-story vegetation, primarily for management of the endangered 

Creating a Fuels Baseline and Establishing 

Fire Frequency Relationships to Develop 

a Landscape Management Strategy at the 

Savannah River Site

Bernard R. Parresol1, Dan Shea2, and Roger Ottmar3
352 USDA Forest Service Proceedings RMRS-P-41. 2006.

Parresol, Shea, and Ottmar Creating a Fuels Baseline and Establishing Fire Frequency Relationships to Develop a Landscape Management Strategy at the Savannah River Site

red cockaded woodpecker (Picoides borealis), and mechanical shredding. More 

recently sub-merchantable woody fuels are being considered as a fuel supply 

for a bioenergy fi red power facility on-site. However, prescribed burning is 

the most cost effective technique on per acre basis. Because of smoke management constraints, which limits prescribed burning and the high costs of 

alternative fuel treatments, there was an identifi ed need to optimize fuels 

management, including the types of stands to be treated, their location on 

the landscape, and the frequency of treatment.

The Need for Fuels Inventory

There are currently no periodic regional or national fuels inventories being 

conducted. The lack of periodic fi eld inventories makes it impossible to gauge 

the effectiveness of national, regional or local fuels and fi re management policies and strategies. Remote sensing methods are largely unable to accurately 

estimate surface fuels (Keane and others 2000) that are the main contributors 

to fi re behavior in the South. Because of the identifi ed need to optimize fuels 

management at the Savannah River Site, the periodic inventories conducted 

on-site were modifi ed to include measurement of forest fl oor fuel variables. 

Small mid-story trees that contribute to ladder fuel were being captured by 

the existing design. Our objective was to establish a fuel loading baseline as 

a function of stand variables as a reference for Site management, to allocate 

fuel treatment strategies, and to estimate the prescribed burning frequency 

needed to achieve wildfi re behavior objectives.

Figure 1—Location of the Savannah River Site in Aiken, Barnwell, and Allendale counties, South 

Carolina. The six expansive management areas are shown.
USDA Forest Service Proceedings RMRS-P-41. 2006. 353

Creating a Fuels Baseline and Establishing Fire Frequency Relationships to Develop a Landscape Management Strategy at the Savannah River Site Parresol, Shea, and Ottmar

Inventory Design and Fuels Sampling

A systematic layout of sample points was installed using an approximate 

1000- by 1000-meter grid over the entire SRS land base, except for the narrow 

corridor along the Lower Three Runs Creek that extends from the southeast 

boundary to the Savannah River. This resulted in approximately one sample 

plot per every 250 acres of the SRS, or 773 plot locations. This plot density 

is high from the traditional inventory perspective. Of the 773 plots, only 

657 fell on forested areas. An additional data source of 62 plots that fall on 

the SRS from the Forest Inventory and Analysis (FIA) regional inventory 

(conducted by the USDA Forest Service) are included in the plot database. 

Combining the 62 regional inventory plots that fall on the SRS with the 657 

new SRS plots produces a potential sample of 719 points (fi gure 2).

Figure 2—Systematic layout of inventory plots on the Savannah River Site and spatial 

distribution of the broad forest type groups.
354 USDA Forest Service Proceedings RMRS-P-41. 2006.

Parresol, Shea, and Ottmar Creating a Fuels Baseline and Establishing Fire Frequency Relationships to Develop a Landscape Management Strategy at the Savannah River Site

The plot design used is a standard FIA design commonly used in the southeastern U.S. It consists of a cluster of fi ve subplots, 70 feet between points, 

which are normally laid out in the scheme shown in fi gure 3. Two nested 

plot-types are established at each of these fi ve subplot center points. One of 

these plot-types is a variable-radius plot using a 37.5-factor angle prism for 

sampling trees that are 5-inches or larger in diameter at breast height (dbh). 

Nested at the same point is a circular fi xed-radius 1/300th-acre plot for 

sampling trees from 1- to 5-inches in diameter. All sampled trees from the 

fi ve subplots are combined, meaning that the operative prism factor for the 

sample location (that is, the 5 subplots) is 7.5, and the cumulative area of the 

fi xed-radius plots is 1/60th of an acre. The pattern shown in fi gure 3 is the 

standard subplot layout, but the arrangement was altered when necessary to 

insure that all subplots fall within the same stand or forest condition found 

at subplot 1. It was necessary to alter this arrangement in about a third of 

the plots on the SRS. Subplot 1 is never moved from the initially selected 

point location. Rotation only occurs on subplot 2 to 5, for the purpose of 

matching their forest condition with that of subplot 1.

In-between the fi ve subplots are four planar transects used for measuring 

coarse woody debris (CWD) forest fl oor fuel (fi gure 4). These measurements 

are on dead woody material that has separated from the plant (trees and 

shrubs) that produced it, or from main stems of dead trees that have fallen 

down. The method for measuring CWD uses a vertical-plane-intersect plot 

that either counts by size class for smaller material or measures the individual 

diameters for diameters greater than 3 inches the pieces of CWD material 

that break the plot plane (Brown 1974). As shown in fi gure 4, counts were 

made along a 10-foot section of the transect line of dead downed material 

with diameters of 0-0.25 inches (1-hour fuels). Counts of pieces with diameters in the 0.25-1.0 inch range (10-hour fuels) were made at the same 

time along the same 10-foot section of the transect line. Counts were made 

of pieces with diameters of 1.0-3.0 inches (100-hour fuels) along a 20-foot 

transect. Dead downed material larger than 3 inches diameter encountered 

along the full 70-foot transect had their individual diameters at the point 

of intersection measured, and their condition was classed as either solid or 

Figure 3—Plot design used at the Savannah River Site showing 

standard orientation of the 5 subplots and the 4 planar transects.
USDA Forest Service Proceedings RMRS-P-41. 2006. 355

Creating a Fuels Baseline and Establishing Fire Frequency Relationships to Develop a Landscape Management Strategy at the Savannah River Site Parresol, Shea, and Ottmar

rotten. Seven measurements of litter and duff depth to the nearest 1/10th

inch were taken at ten-foot intervals along each of the 70-foot transect lines. 

An inventory of 622 plots (from the 719 possible) was started in March 1999 

and completed in January 2002.

Bulk Density Study

Because of defi ciencies in south-wide data on litter-duff bulk densities, 

which are the fuels most often consumed in prescribed fi res, a study was 

undertaken to develop new bulk density relationships. There have been several studies in the past to collect bulk density values for forested areas of the 

Figure 4—Design of the Brown’s planar transect for measuring coarse woody debris 

and litter and duff depths.
356 USDA Forest Service Proceedings RMRS-P-41. 2006.

Parresol, Shea, and Ottmar Creating a Fuels Baseline and Establishing Fire Frequency Relationships to Develop a Landscape Management Strategy at the Savannah River Site

south. However, these studies were very limited in scope (Scholl and Waldrop 

1999) or were completed at locations other than at the Savannah River Site 

(Ottmar and Vihnanek 2000; Ottmar and others 2003; McNab and others 

1978). The primary objective for the study was to determine bulk density 

conversion factors to convert litter and duff depth values in inches to forest 

fl oor fuel values in tons per acre. This was done for combinations of four 

common forest types (loblolly/slash pine, longleaf pine, pine and hardwood 

mix, upland hardwood), 3 age classes (5-20, 20-40, 40+ years old) and 3 

categories of burning history (0-3, 3-10, 10+ years since last burn).

Bulk density sampling points were randomly selected from the 622 inventory plots of the 1999-2002 inventory period. Random points were selected 

from groups of plots based on the aforementioned stand type, stand age, 

and rough age. Within each sample site, subplot 1 was designated as the plot 

center. The lower left bulk density sample square point was established 33 

feet from the plot center at each of the four cardinal directions (fi gure 5). A 

12-inch beveled steel square was positioned on top of the forest fl oor. Twelve 

markers (6 inch gutter nails) were then placed in a grid pattern evenly within 

the square (fi gure 5). The nails were tapped downwards until the top of the 

Figure 5—Sample plot layout for the Savannah River bulk density project.
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nail was fl ush with the top of the litter layer. Litter was then carefully removed 

from the square and placed within a labeled bag. The distance between the 

top of each marker and the top of the duff layer was measured and recorded. 

The average of these twelve depth measurements represents the litter depth 

for the sample. After measurements were recorded, the markers (nails) were 

again tapped down so that the tops were all fl ush with the top of the duff 

layer. The duff layer was then carefully removed, placed in a labeled bag, and 

the distance between the top of the marker and the substrate was measured, 

the average of these twelve measurements represents the duff depth for the 

sample. All litter and duff samples were taken to the lab and oven dried for 

48 hours. Litter samples were dried at 70 degrees Celsius and duff samples 

at 100 degrees Celsius. For further details and results see Maier and others 

(2004) and Parresol (2005).

Fuels Computation

Computation of biomass for each fuel component was done in a different fashion. For ladder fuels (i.e., non-merchantable arborescents of Pinus, 

Juniperous, Taxodium < 5" dbh and hardwoods < 6" dbh) biomass equations 

were utilized (Brown and others 1997). The coarse woody debris subcomponents were converted to biomass using formulas from Brown (1974). These 

formulas to compute tons/ac are:

0- to 3-inch material: 

3

= 11 64 × × ×× × 2 . nd sac

L

+-inch material : = 11 64 × ×× × 2 . Σd sac

L

where n is number of particles counted in each size class along a line 

transect, d is average particle diameter for the 0- to 3-inch size classes and 

d is measured diameter for pieces 3"+, s is wood specifi c gravity, a is the 

nonhorizontal angle correction factor (the correction factor adjusts weight 

estimates for the fact that all particles do not lie horizontally as assumed in 

the planar intersect theory), c is the slope correction factor for converting 

weight/ac on a slope basis to a horizontal basis, and L is the transect length 

in ft. The percent slope was measured at each inventory plot and the slope 

correction factor was calculated as c = +1 2 (percent slope/100) . The following 

values for average d 2, s, a, and L were used:

Size class d 2 s a L

0 – 0.25" 0.0151 0.7 1.13 40

0.25" - 1" 0.289 0.7 1.13 40

1" - 3" 2.76 0.58 1 80

3"+ sound — 0.58 1 280

3"+ rotten — 0.3 1 280

For the litter and duff calculations subplots were averaged for a combined 

average litter-duff depth for each inventory plot. Bulk density conversion 

factors determined from the bulk density study were applied to the averaged 

depth value for each plot to compute litter-duff tons/ac. See Parresol (2004) 

for a detailed description of the fuel loading computations.
358 USDA Forest Service Proceedings RMRS-P-41. 2006.

Parresol, Shea, and Ottmar Creating a Fuels Baseline and Establishing Fire Frequency Relationships to Develop a Landscape Management Strategy at the Savannah River Site

Broad Species Groups

The SRS contains 25 naturally occurring mixtures of species or stand types 

(see Hansen and others 1992). For analysis purposes we grouped the 25 stand 

types into seven broad species composition groupings defi ned on the basis 

of the forest types as given in table 1. For each of the 622 inventory plots, a 

forest type was assigned based on each individual plot species make-up, by 

applying the following Forest Service defi nitions:

 1) to be assigned to one of the three yellow pine forest types, 70% or more 

of the total basal area of the stand must be in yellow pine, and then it is 

assigned to a particular yellow pine species based on the species (loblolly, 

longleaf, or slash pine) with the largest basal area component,

 2) to be assigned to the pine-hardwood type the plot must have >50% and 

<70% of the total basal area in yellow pines species,

 3) to be assigned to the hardwood-pine type the plot must have >30% and 

<50% of the total basal area in yellow pines species, and

 4) to be assigned to the hardwood type, < 30% of the total stand basal area 

must be in yellow pine species.

 5) to be assigned to the cypress/tupelo type, ≥50% of the total stand basal area 

must be in baldcypress (Taxodium distichum)and/or tupelo (Nyssa sp.).

The inventory plots were grouped into the broad categories previously 

identifi ed in table 1 based on their observed species make up derived from 

applying the above defi nitions. Examples of forest types are shown in fi gure 

6. This resulted in the distribution of inventory plots into the forest type 

groups as given in table 1. The cypress/tupelo stands are set-aside areas and 

are not considered further.

Analysis

For analysis purposes we combined litter and duff, and added all components for total fuel. For each broad species group we ran a factorial analysis of 

variance (ANOVA) on 5 factors, site index class (SIC) where site index (SI) is 

stand height in ft at 50 years, basal area class (BAC) where basal area (BA) is 

measured in ft2/ac, age class (AC) where age is years, number of burns class 

(NBC) where number of burns (NB) is a count of prescribed burns in a stand, 

Table 1—The forest stands on the Savannah River Site categorized into seven broad species composition 

groups linked with the relevant Forest Service forest types.

Group Group Name Forest Types Included # Stands Acres Percent # Plots

 1 Loblolly pine 25, 31, 32 1897 62,602 34.32 277

 2 Longleaf pine 21, 26, 34 1151 43,294 23.73 129

 3 Slash pine 22 618 17,716 9.71 58

 4 Pine-Hardwood mix 12, 13, 14, 35 272 5,340 2.93 23

 5 Hardwood-Pine mix 44, 46, 47, 48, 49 214 5,355 2.94 27

 6 Hardwoods 51, 52, 53, 54, 56, 57, 58, 61,

 62, 63, 64, 68, 72, 82, 98 1739 41,436 22.71 103

 7 Cypress/Tupelo 67 118 6,677 3.66 5

 6,009 182,420 100.00 622
USDA Forest Service Proceedings RMRS-P-41. 2006. 359

Creating a Fuels Baseline and Establishing Fire Frequency Relationships to Develop a Landscape Management Strategy at the Savannah River Site Parresol, Shea, and Ottmar

Figure 6—Examples of forest types occuring on the Savannah River Site: a) longleaf 

pine plantation, b) natural stand of mixed hardwoods.

a

b
360 USDA Forest Service Proceedings RMRS-P-41. 2006.

Parresol, Shea, and Ottmar Creating a Fuels Baseline and Establishing Fire Frequency Relationships to Develop a Landscape Management Strategy at the Savannah River Site

and number of years since last burn class (YSBC) where years since last burn 

(YSB) is time in years or fraction thereof from the most recent prescribed 

burn. The defi nition of SIC is: if SI < 70 ft then SIC=1, if 70 < SI ≤ 80 then 

SIC=2, if SI > 80 then SIC=3. The defi nition of BAC is: if plot BA ≤ 82.5 

ft2/ac then BAC=1, if 82.5 < BA ≤ 111.5 then BAC=2, if plot BA > 111.5 

then BAC=3. The defi nition of age class (AC) is: if age ≤ 4 then AC='A, if 

5 ≤ age ≤ 17 then AC='B', if 18 ≤ age ≤ 35 then AC='C', if age ≥ 36 then 

AC='D'. Number of burns class is 0, 1, 2, 3+. Years since last burn class is 

defi ned as: if YSB ≤ 3 then YSBC=1, if 4 ≤ YSB ≤ 9 then YSBC=2, if YSB ≥

10 then YSBC=3. We also examined the impact of the 5 analysis variables 

through running a series of stepwise linear least squares regressions by broad 

species group. To examine trends in more detail, that is, to investigate the 

role of stand dynamics and effect of prescribed burning, we present a series 

of regression response surfaces using longleaf pine to illustrate.

Results

Bulk Density Study

Bulk density conversion factors are given in table 2. Average litter bulk 

densities ranged from 1.5 tons/ac/in for mixed pine and hardwood stands 

between 5-20 years old without fi re for over 10 years to 2.4 tons/ac/in for 

loblolly and slash pine sites between 5 and 20 years in age and more than 3 

years since fi re. Average duff bulk densities ranged from 2.6 tons/ac/in on 

mixed upland hardwood stands between 5 and 20 years in age with greater 

than 10 years since fi re to 9.0 tons/ac/in for loblolly and slash pine greater 

than 40 years in age and 3 to 10 years since fi re.

Fuel Loading

Fuel loading weight in tons across the entire SRS are given in table 3 by 

broad forest type. Fuel weights are displayed by the fuel categories conifer 

fuel trees, hardwood fuel trees, CWD, and litter-duff. Table 4 has the same 

structure as table 3 except average fuel weight in tons per acre is given in 

Table 2—Litter and duff bulk densities (tons/acre/inch) for forest types by age class (years) 

and rough age (years).

 Forest Type

 Age Rough Lob/Slash LL PH Mix UH Mix

Class Age Litter Duff Litter Duff Litter Duff Litter Duff

 0-3 — — 1.8 3.8 — — — —

 5-20 3-10 2.0 4.4 1.6 4.5 — — — —

 10+ 1.9 4.8 1.8 4.1 1.5 3.9 1.8 2.6

 0-3 2.4 6.0 2.6 8.2 2.8 6.7 — —

21-40 3-10 2.4 6.4 2.9 6.3 1.6 5.3 1.9 5.1

 10+ 1.9 5.9 2.7 8.6 1.7 4.0 2.1 5.7

 0-3 1.9 6.4 2.2 8.2 2.1 8.8 2.2 6.6

40+ 3-10 2.3 9.0 2.1 7.0 2.2 7.0 1.9 6.2

 10+ 2.3 7.2 2.5 8.2 2.0 5.3 2.0 7.1

Note: Lob is loblolly pine, LL is longleaf pine, PH Mix is mixed species pine-hardwood stand, UH Mix is 

mixed species upland hardwood stand, and rough age is number of years since last burn.
USDA Forest Service Proceedings RMRS-P-41. 2006. 361

Creating a Fuels Baseline and Establishing Fire Frequency Relationships to Develop a Landscape Management Strategy at the Savannah River Site Parresol, Shea, and Ottmar

Table 3—Fuel loadings in tons from the 1999-2002 Savannah River Site inventory of 622 plots.

 Forest Type

 Pine- Hdwd- All

Fuel Type Loblolly Longleaf Slash Hdwd Pine Hdwd Types

 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Tons - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 

Conifer trees 160,949.2 86,854.5 36,242.4 1,821.3 15.5 4,120.8 290,003.7

Hdwd trees 232,256.3 80,439.4 50,093.5 34,619.4 40,828.3 314,243.7 752,480.6

CWD 233,994.5 150,301.2 79,926.2 28,655.6 22,147.1 149,620.5 664,645.1

Litter-duff 93,503.5 58,705.5 31,645.3 6,476.7 5,402.6 36,668.3 232,401.9

 Overall Total: 1,939,531.3

Note: Hdwd is hardwood, CWD is coarse woody debris.

Table 4—Average fuel loadings in tons/ac from the 1999-2002 Savannah River Site inventory of 622 plots.

 Forest Type

 Pine- Hdwd- All

Fuel Type Loblolly Longleaf Slash Hdwd Pine Hdwd Types

 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Tons - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 

Conifer trees 2.571 2.006 2.046 0.357 0.003 0.107 1.684

Hdwd trees 3.710 1.858 2.828 6.784 8.109 8.167 4.369

CWD 3.738 3.472 4.512 5.615 4.399 3.888 3.859

Litter-duff 1.494 1.356 1.786 1.269 1.073 0.953 1.349

 Average: 11.261

Note: Hdwd is hardwood, CWD is coarse woody debris.

the table cells. The overall fuel tonnage for the 172,228 acres covered in the 

fuels inventory is 1,939,531 tons giving an average per acre value of 11.3 

tons. This average breaks down as follows: 1.7 tons/ac in conifer fuel trees, 

4.4 tons/ac in hardwood fuel trees, 3.9 tons/ac in CWD, and 1.3 tons/ac 

in litter/duff.

Analysis of Variance

The results of the ANOVAs are outlined in table 5. All factors shown in 

table 5 were signifi cant at the α = 0.05 level. As can be seen in this table, 

loblolly and longleaf pine had a number of signifi cant factors. Our explanation 

for the nonsignifi cance with slash involves land-use history. Slash is an offsite species, planted primarily in old-fi elds with a small range in age, BA and 

SI, so there is very little variability among the stands. However, using stand 

variables as a continuum in the linear regressions shows signifi cant effects 

despite the small range in values, as seen in the next section. The ANOVAs 

indicate the complex interplay of factors involved in trying to understand 

fuel loadings.
362 USDA Forest Service Proceedings RMRS-P-41. 2006.

Parresol, Shea, and Ottmar Creating a Fuels Baseline and Establishing Fire Frequency Relationships to Develop a Landscape Management Strategy at the Savannah River Site

Table 5—Signifi cant (P<0.05) class variables and interactions by forest type.

Forest Type Ladder Fuel CWD Litter-Duff Total Fuel

Loblolly BAC, AC, AC BAC, AC, None

 SIC×YSBC, SIC×BAC

 NBC×YSBC

Longleaf SIC, BAC, AC BAC, SIC×AC, None SIC, BAC, 

 BAC×AC SIC×NBC AC

 SIC×YSBC BAC×AC

Slash None None None None

Pine-Hdwd BAC, NBC None None None

Note: Hdwd is hardwood, CWD is coarse woody debris, BAC is basal area class, AC is age class, SIC 

is site index class, YSBC is years since last prescribed burn class, and NBC is number of prescribed 

burns class. Please see text for defi nitions of classes.

Stepwise Linear Least Squares Regressions

More informative than the ANOVAs are the inferences from the linear 

regressions. The signifi cant variables from the linear regressions are given in 

the table 6. Basal area and age are important explanatory variables for estimating fuel loading in loblolly pine stands. In terms of prescribed burning, 

loblolly ladder fuel and CWD were affected by years since last burn, while the 

litter-duff layers were affected by number of burns. Site index, basal area and 

stand age were all critical in determining longleaf pine stand fuel loadings. For 

longleaf, ladder fuel was affected by years since last burn, but burning in this 

linear context did not seem to affect the CWD or litter-duff layers. Because 

of the importance of longleaf pine management at the SRS, response was 

examined more closely using nonlinear models and log-transformed models. 

Those results are given in the next section. For slash pine, years since the last 

burn was correlated with CWD and number of burns affected the litter-duff 

layers. Finally for the pine-hardwood mix, the CWD was correlated with 

years since last burn. While stand characteristics play a major role in overall 

fuel loads, the prescribed burning program is having signifi cant impacts on 

reducing fuel components.

Response Surfaces

To more fully understand the effects of stand variables and the impact 

of the prescribed burning program, a series of best-fi t empirical regression 

relationships for longleaf pine were developed to generate response surfaces. 

Equations for ladder fuel (equation 1), litter-duff (equation 2), 1 hour fuel 

Table 6—Signicant variables (P<0.05) from the stepwise linear least squares regressions.

Forest Type Ladder Fuel CWD Litter-Duff Total Fuel

Loblolly BA, A, YSB A, YSB BA, A, NB BA

Longleaf SI, BA, A, YSB SI, BA, A BA SI, BA, A, YSB

Slash SI, BA YSB BA, NB BA

Pine-Hdwd SI, BA SI,YSB None BA

Note: Hdwd is hardwood, BA is basal area in ft2/ac, A is age in yeasrs, SI is site index in ft base age 50, YSB 

is years since last prescribed burn, and NB is number of prescribed burns.
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Creating a Fuels Baseline and Establishing Fire Frequency Relationships to Develop a Landscape Management Strategy at the Savannah River Site Parresol, Shea, and Ottmar

(equation 3), 10 hour fuel (equation 4), and the 100+ hour fuel (equation 

5) are given below.

ladder fuel == 50.217 exp 0.036SI + 0.014BA  0.033Age + 0.00102YSB2 ( )

R2 = 0.51,RMSE = 3.50

(1)

litter -duff = 0.598 + 0.0127BA  0.374 / YSB

R2 = 0.45,RMSE = 0.57 (2)

ln1hour fuel = 4.082  0.206ln Age 1.659ln SI  0.257ln NB

R2 = 0.084,RMSE = 0.966 (3)

ln10 hour fuel = 1.429 + 0.272ln Age + 0.075lnYSB

R2 = 0.11,RMSE = 0.836 (4)

ln100 + hour fuel = 6.071 0.939ln BA + 0.803ln Age + 1.710lnSI

R2 = 0.15,RMSE = 1.373 (5)

Figure 7 shows the response surfaces generated from these equations. 

Figure 7a shows that ladder fuels are generally determined by BA and age, 

decreasing as BA decreases and age increases. Equation 1 shows that YSB has 

a small but statistically signifi cant effect in reducing ladder fuels. Figure 7b 

shows the dramatic effect both YSB and BA has on determining litter and 

duff fuel loading. It is clear that litter-duff loadings recover quickly, in as 

little as two to three years after a burn. Figure 7c shows that the 1 hour fuel 

is reduced through repeated burning and that SI also plays a role. Figure 7d 

indicates that recency of burn has some impact on the 10 hour fuel but that 

age is the main factor determining fuel load. Finally, equation 5 and fi gure 

7e reveal that burning has no detectable effect on the 100+ hour fuel, but 

rather the interplay of age, BA and SI.

Discussion

Field fuel inventories are generally not available at local, regional or national 

scales. At the SRS managers identifi ed the need for such information to help 

guide decision making concerning fuels management. We easily modifi ed 

standard forest inventory methods to incorporate a complete assessment of 

fuel components on the SRS. The FIA program of the USDA Forest Service inventories the entire U.S. forest resources periodically and is moving 

towards an annual multi-resource inventory system. A suite of habitat and 

environmental variables are collected along with the more traditional tree 

measurements. From our experience with this project, we were able to easily 

incorporate fuel variables into our inventory design and we strongly believe 

and recommend that the FIA program nationally can achieve the same objective. The average number of man days per plot was equal to the expected 

productivity without the fuel loading modifi cation.

Due to the paucity of forest fl oor bulk density information for southeastern 

forests, new bulk density conversion factors for the dominant forest types on 

the SRS were developed to compute litter and duff fuel loading in tons/ac/in. 
364 USDA Forest Service Proceedings RMRS-P-41. 2006.

Parresol, Shea, and Ottmar Creating a Fuels Baseline and Establishing Fire Frequency Relationships to Develop a Landscape Management Strategy at the Savannah River Site

Figure 7—Response surfaces illustrating trends in fuel 

levels as a function of stand variables and burning 

history: a) ladder fuel based on equation 1, b) litterduff fuel based on equation 2, c) 1 hour fuel based on 

equation 3, d) 10 hour fuel based on equation 4, and 

e) 100+ hour fuel based on equation 5.
USDA Forest Service Proceedings RMRS-P-41. 2006. 365

Creating a Fuels Baseline and Establishing Fire Frequency Relationships to Develop a Landscape Management Strategy at the Savannah River Site Parresol, Shea, and Ottmar

These conversion factors should prove useful for similar forest types of the 

upper coastal plain and piedmont forests of the Southeastern U.S.

The pattern of fuel loading across the forest types, age, stocking and fi re 

frequency refl ects land use history, stand dynamics, stand management and 

fi re management. For the major forest types (loblolly, longleaf, slash, pinehardwood, hardwood-pine, and hardwood) stand variables generally explained 

the larger fraction of the variability in the fuel components. Age, BA, and SI 

explained a large proportion of the variability in individual components, but 

particularly ladder fuels and 100 hour+ fuels. Natural stand dynamics even in 

these highly disturbed systems dominated the observed relationships. Ladder 

fuels decreased with age probably as a result of two factors. Small trees and 

shrubs are predominant in young stands simply as a result of early succession. As the stands age, the mid-story shrub component is suppressed by the 

overstory. In addition, land use history also plays a role on these sites. The 

older pine types were generally planted on old-fi elds established during the 

1950’s. These stands had most of the hardwood shrub component eliminated 

through farming. Later plantations were established in cut-over lands with 

little effected control of the competition. More recent stands were established 

on an array of sites with a wide range in ladder fuel species development.

In contrast, stand management probably has a major infl uence on the relationship between BA and ladder fuels for the managed pine types. The lower 

BA stands have reduced ladder fuels and mid-story components as a result of 

disturbance from mechanical harvesting through repeated thinning operations, coupled with prescribed fi re. The only fi re variable affecting ladder fuels 

was YSB, but the impact was relatively small. Restriction on environmental 

conductions during prescribed burning, particularly wind, humidity, and 

fuel stick moisture, probably limits the fi re intensity such that only smaller 

diameter woody trees and shrubs are killed or controlled. Most prescribed fi re 

activities have also historically been applied during the dormant season, in 

contrast to the growing season. The latter period is recommended for burning 

when the objective is to control mid-story shrubs and ladder fuels.

The major fuel type controlling surface fi re rate of spread in these stands is the 

litter and duff and the 1 hour fuel components. Numerous studies of prescribed 

burning fuel consumption at the SRS demonstrated that these components are 

the largest fraction contributing to fuel consumption following burning (Kilgo 

and Blake 2005). Using longleaf pine as an example, it is clear that the previous dominant management paradigm that stands should be burned every fi ve 

to seven years may not be an effective frequency to reduce hazard fuels within 

stands. Not withstanding the infl uence of the spatial distribution of fuel treatments on the rate of spread of catastrophic large wildfi re, it appears that a two-to 

three year burning cycle is critical to effectively reduce these fuels (Outcalt and 

Wade 2004). This study has established a baseline for future fuels management 

and policies and provides insight into factors contributing to fuel dynamics for 

upper coastal plain forests.

Acknowledgments

The authors wish to thank Dr. John Blake, Assistant Manager Research, of 

the Savannah River Site for his peer review of this manuscript. Funding was 

provided by the Department of Energy-Savannah River Operations Offi ce 

through the U.S. Forest Service Savannah River under Interagency Agreement 

DE-AI09-00SR22188.
366 USDA Forest Service Proceedings RMRS-P-41. 2006.

Parresol, Shea, and Ottmar Creating a Fuels Baseline and Establishing Fire Frequency Relationships to Develop a Landscape Management Strategy at the Savannah River Site

References

Brown, J. K. 1974. Handbook for inventorying downed woody material. General 

Technical Report INT-16. Ogden, UT: U.S. Department of Agriculture, Forest 

Service, Intermountain Forest and Range Experiment Station. 24 p.

Brown, S.; Schroeder, P.; Birdsey, R. 1997. Aboveground biomass distribution of 

US eastern hardwood forests and the use of large trees as an indicator of forest 

development. Forest Ecology and Management 96: 37-47.

Hansen, M. H.; Frieswyk, T.; Glover, J. F.; Kelly, J. F. 1992. The Eastwide forest 

inventory data base: users manual. Gen Tech. Rep. NC-151. St Paul, MN: U.S. 

Department of Agriculture, Forest Service, North Central Forest Experiment 

Station. 48 p.

Keane, Robert E.; Mincemoyer, Scott A.; Schmidt, Kirsten M.; Long, Donald G.; 

Garner, Janice L. 2000. Mapping vegetation and fuels for fi re management on 

the Gila National Forest Complex, New Mexico, [CD-ROM]. Gen. Tech. Rep. 

RMRS-GTR-46-CD. Ogden, UT: U.S. Department of Agriculture, Forest 

Service, Rocky Mountain Research Station, 126 p.

Kilgo, J. C.; Blake, J. I., editors. 2005. Ecology and management of a forested landscape: 

Fifty years on the Savannah River Site. Covelo, CA: Island Press. 430 p.

Maier, B.; Ottmar, R.; Wright, C. 2004. Forest fl oor bulk density and depth at 

Savannah River-draft fi nal report. USDA Forest Service Savannah River, New 

Ellenton, SC. Available online: www.osti.gov.

McNab, Henry W.; Edwards, Boyd M., Jr.; Hough, Walter A. 1978. Estimating fuel 

weights in slash pine-palmetto stands. Forest Science 24 (3): 345-358.

Ottmar, Roger D.; Vihnanek, Robert E. 2000. Stereo photo series for quantifying 

natural fuels. Volume VI: longleaf pine, pocosin, and marshgrass types in the 

Southeast United States. PMS 831. Boise, ID: National Wildfi re Coordinating 

Group, National Interagency Fire Center. 56 p.

Ottmar, Roger D.; Vihnanek, Robert E.; Mathey, Jared W. 2003. Stereo photo 

series for quantifying natural fuels. Volume VIa: sand hill, sand pine scrub, and 

hardwoods with white pine types in the Southeast United States with supplemental 

sites for volume VI. PMS 838. Boise, ID: National Wildfi re Coordinating Group, 

National Interagency Fire Center. 78 p.

Outcalt, Kenneth W.; Wade, Dale D. 2004. Fuels management reduces tree mortality 

from wildfi res in Southestern United States. Southern Journal of Applied Forestry 

28(1): 28-34.

Parresol, B.R. 2004. Point and fi xed plot sampling inventory estimates at the 

Savannah River Site, South Carolina. USDA Forest Service Savannah River, New 

Ellenton, SC. Available online: www.osti.gov.

Parresol, B. R. 2005. Report on analysis of forest fl oor bulk density and depth at 

the Savannah River Site. USDA Forest Service Savannah River, New Ellenton, 

SC. Available online: www.osti.gov.

Scholl, E. R.; Waldrop, T. A. 1999. Photos for estimating fuel loadings before and 

after prescribed burning in the upper coastal plain of the southeast. Gen. Tech. 

Rep. SRS-26. Asheville, NC: U.S. Department of Agriculture, Forest Service, 

Southern Research Station.
USDA Forest Service Proceedings RMRS-P-41. 2006. 367

Abstract—Surface and crown fuels were measured in 186 stands ranging in age from 

0 years after clearcutting to old-growth forests > 300 years old in Douglas-fi r (Pseudotsuga menziesii) – western hemlock (Tsuga heterophylla) – western redcedar 

(Thuja plicata) – dominated forests in southwestern British Columbia. Indexes of 

surface fi re hazard based on woody debris loads, and of crown fi re hazard based on 

5 factors (canopy foliar bulk density, height to live crown, woody debris loads, ladder 

fuels, and snag quantities), were developed. Using the indexes developed, surface 

fi re hazard followed a U-shaped trend with stand age, being highest for the fi rst few 

years after clearcutting, declining to a minimum 20 to 40 years after harvesting before 

increasing. Crown fi re hazard was lowest for the fi rst few years after clearcutting, rose 

to a maximum 20 to 90 years after harvesting and then declined to low values in 100 

to 150 year old forest, before rising to higher values in old-growth. In the absence of 

fuel reduction treatments, some post-harvesting age classes of forests will have higher 

surface or crown fi re hazards than old-growth forests.

Introduction

Fuel management in forests of southern coastal British Columbia, dominated 

by Douglas-fi r (Pseudotsuga menziesii), western hemlock (Tsuga heterophylla), 

and western redcedar (Thuja plicata), in the recent past has been characterized by a dichotomy. On the one hand, in active forest harvesting areas, 

strips of old-growth forest were left between clearcut blocks partly because 

it was believed that the old-growth strips could serve as fuel breaks as they 

presented a lower fi re hazard than the clearcuts (Grant 1984). On the other 

hand, in the water supply watersheds for the city of Vancouver, management 

involved clearcutting old-growth forests to produce younger plantations with 

a perceived lower fi re hazard state (Economic and Engineering Services 1991). 

This raised the question of how fi re hazard varied with forest age.

Forest fi re hazard (a fuel complex defi ned by volume, type, condition, arrangement, and location, that determines the degree both of ease of ignition 

and of fi re suppression diffi culty (Forest Resources Development Branch 

1986)) can be broken into two components – surface fi re hazard and crown 

fi re hazard – which are not necessarily correlated. Assuming surface fi re hazard 

is directly related to surface fuel quantity, different trends with stand age in 

surface fi re hazard have been reported. Brown and See (1981) described three 

different trends for lodgepole pine (Pinus contorta) as well as for subalpine fi r 

(Abies lasiocarpa) forests in the U.S. Rocky Mountains – i) a general increase 

with age, peaking in old-growth, ii) an inverse U-shaped curve with a peak 

occurring in mature (110 – 160-year old) forests, and iii) a U-shaped curve 

Variation in Surface and Crown Fire 

Hazard With Stand Age in Managed 

Coastal Western Hemlock Zone Forests 

in Southwestern British Columbia

Michael C. Feller1 and Stefanie L. Pollock2

In: Andrews, Patricia L.; Butler, Bret W., 

comps. 2006. Fuels Management—How to 

Measure Success: Conference Proceedings. 

28-30 March 2006; Portland, OR. 

Proceedings RMRS-P-41. Fort Collins, 

CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research 

Station.

1 Associate Professor, Department of 

Forest Sciences, University of B.C., 

Vancouver, BC, Canada.

feller@interchange.ubc.ca

2 Graduate student, Département de 

biologie and Centre d’études nordiques, Université Laval, Québec, QC, 

Canada.
368 USDA Forest Service Proceedings RMRS-P-41. 2006.

with maximum values occurring in the youngest as well as the oldest forests. 

Most studies have found U-shaped curves (Feller 2003), particularly in B.C. 

and the adjacent U.S. Pacifi c Northwest (Agee and Huff 1987; Fahnestock 

1976; Spies and others 1988; Wells and Trofymow 1997).

In areas subjected to forest harvesting, surface fi re hazard for the fi rst 

few years after harvesting can be greater than at any other time in the life 

of a forest due to inputs of logging slash (Feller 2003; Wells and Trofymow 

1997). Feller (2003) considered that a U-shaped curve could be the normal 

trend in surface fi re hazard with forest age after harvesting, with deviations 

from this occurring for different reasons. For example, initial hazard may 

not be particularly high if initial post-disturbance inputs are low as a result 

of a severe fi re, slow collapse of snags, or low pre-disturbance vegetation or 

surface fuel biomass. An inverse U-shaped curve may occur if thinning occurs 

or if tree mortality is particularly high during the mid-life period of a forest 

as a result of high tree densities, insects, disease or blowdown.

Surface fi re hazard is likely to depend not only on the total surface fuel 

load, but also on the distribution of size classes and decay states of surface 

fuels (Baker 2003; Van Wagner 1983). Baker (2003) considered that large 

sound fuels are relatively unimportant to fi re behaviour since they are usually 

not consumed, while large well-decayed fuels and fi ne fuels were considered 

important. Fine fuels may increase slowly after a fi re for 150-200 years, and 

then decline, while large sound fuels, legacies of the pre-disturbance forest, 

generally decrease with time for long periods until they are replenished again 

(Baker 2003; Harmon and others 1986; Romme 1982). Van Wagner (1983) 

proposed that surface fi re hazard in northern coniferous forests peaked before 

canopy closure and again in old-growth forests, primarily due to fl uctuations 

in the quantity of fi ne fuels present.

Crown fi re hazard depends on the ease of initiation and of propagation of 

crown fi res. Van Wagner (1977) developed conceptual models of both initiation and propagation, and most subsequent work on crown fi re hazard has 

used these models (for example, Cruz and others 2003; Scott and Reinhardt 

2001). According to Van Wagner (1977), ease of initiation depends on the 

intensity of the surface fi re, the height above the ground of the base of the 

live canopy, and foliar moisture content. Ladder fuels can be considered to 

either increase the surface fi re intensity or increase fl ame length (Alexander 

1988), or decrease canopy height (Van Wagner 1993), facilitating crown 

fi re initiation. Once in the crowns, ease of propagation depends on the bulk 

density of available fuel in the canopy as well as rate of spread of the fi re 

which in turn, depends on wind speed. Scott and Reinhardt (2001), using 

Van Wagner’s (1977) conceptual models, developed a quantitative Torching 

Index and Crowning Index, but did not sample surface and crown fuels across 

all forest ages. The Canadian Fire Behavior Prediction System indicates that 

crown fi re intensity and spread rate are greater in immature than in mature 

lodgepole pine forests for a given set of fuel moisture conditions (Forestry 

Canada Fire Danger Group 1992). No study, however, appears to have determined an index of crown fi re hazard for an entire range of age classes of a 

forest, although Van Wagner (1983) has proposed that crown fi re hazard was 

greatest in young stands with closed canopies, then decreased before increasing again in old-growth stands. Fahnestock (1976), using fi re hazard keys, 

reported a similar trend in subalpine fi r – false box (Pachistima myrsinities) 

forests in north central Washington, but Hawkes (1979), using Fahnestock’s 

keys, found little difference in crowning potential between young and oldgrowth stands in Canada’s southern Rocky Mountains in Alberta.

Feller and Pollock Variation in Surface and Crown Fire Hazard With Stand Age in Managed Coastal Western Hemlock Zone Forests...
USDA Forest Service Proceedings RMRS-P41. 2006. 369

Due to the contrasting beliefs about the fi re hazard in old-growth versus 

managed forests and the lack of quantitative data on successional changes in 

forest fi re hazard in southwestern British Columbia, this study was begun in 

1994 with the objective of determining the relative surface and crown fi re 

hazards of old-growth forests, and those arising from a forest harvesting 

regime.

Study Area

The study occurred in the Coastal Western Hemlock biogeoclimatic zone 

of southwestern British Columbia, within 50 km from the city of Vancouver, 

specifi cally in the dry maritime (CWHdm) and very wet maritime (CWHvm) 

biogeoclimatic subzones (Meidinger and Pojar 1991).

A total of 186 study plots, each approximately 0.5 to 1 ha in size, were 

located in old-growth forests and adjacent areas that had been clearcut up to 

80 years previously, or burned from 80 to 150 years previously. No stands 

aged 151 to 250 years old were sampled due to their unavailability. All 

stands older than 250 years, regardless of their actual age, were classed as 

old-growth. Clearcuts up to 60 years old had not been subjected to any slash 

disposal treatment and had mostly been planted with Douglas-fi r. All forests 

were dominated by western hemlock, western redcedar, and Douglas-fi r and, 

at higher elevations, Pacifi c silver fi r (Abies amabilis) as well. All study plots 

were located on sites intermediate in moisture and nutrient status to avoid 

the confounding factor of site variability.

The CWHdm and CWHvm subzones have wet mild climates, with mean 

annual precipitation of 1800 to 2800 mm, most of which is rain, and mean 

annual temperatures of 8 to 10° C. All months have mean temperatures > 0° 

C. Due to the high forest productivity resulting from this climate, relatively 

long intervals between fi res, and the presence of slowly decaying western 

redcedar, old-growth CWH forests generally contain the greatest surface 

fuel loads of all B.C. old-growth forests (Feller 2003).

Methods

Field Measurements

Within each study plot, 3 surface fuel plots and 3 crown fuel plots were 

randomly located. Each surface fuel plot consisted of an equilateral triangle 

with 20 m or 30 m sides, depending on fuel load and spatial orientation of 

the study plot. The mass of all surface woody fuels > 1 cm diameter was determined using the line intersect technique (Van Wagner 1968) measuring 

along the sides of the triangles. Each piece measured had its species or decay 

state recorded. Volumes calculated from the line intersects were converted 

to masses using relative densities determined for each size class (1.l-3.0, 3.1-

5.0, 5.1-7.0, 7.1-12.0, and > 12 cm) for each species and decay class present. 

Nine to 32 samples per size class for each species or decay class were cut from 

randomly chosen woody materials and taken to the laboratory for density 

analysis. Fine fuels (≤ 1 cm diameter) were collected from nine 1 m2 plots, 

each located 2 m away from each triangle apex along a line projected outwards 

from the centre of the triangle.

Variation in Surface and Crown Fire Hazard With Stand Age in Managed Coastal Western Hemlock Zone Forests... Feller and Pollock
370 USDA Forest Service Proceedings RMRS-P-41. 2006.

Each crown fuel plot consisted of a 20 x 20 m or 20 x 10 m plot, depending on spatial orientation of the study plot. Within each crown fuel plot, 

the species and d.b.h. of every tree present was measured. The dominance 

class and state of decay of each snag present were also recorded. Canopy volume was estimated by multiplying surface area by crown length, which was 

measured as the difference between the height to the base of the live crown 

and the height to the top of the tree canopy, with 1 to 3 measurements per 

crown fuel plot. Relative ladder fuel amount was estimated visually using a 6 

category system. Ladder fuel was considered to be any dead woody material 

or small conifers occurring between the surface fuel bed (up to 1.5 m above 

the ground) and the live canopy.

Stand age was determined from forest cover maps where known, or from 

counting rings in cores extracted from 2 to 3 of the largest trees in each 

crown fuel plot.

Laboratory Procedures

Surface fuel materials—Relative densities of all woody materials were measured using a water displacement technique and an average value calculated 

per size class and species or decay class. Fine fuel samples were dried at 100 

°C for 24 to 48 hours, then weighed. An average fi ne fuel mass was calculated 

from each of the nine samples collected per study plot.

Crown fuel data—For each study plot, an average canopy foliar bulk density, 

height to the base of the live crown, and relative ladder fuel quantity were 

calculated from the 3 crown fuel plot values. Canopy foliar bulk density was 

calculated by dividing the total foliage mass in a plot by the measured crown 

volume. Foliage mass was estimated by applying foliar biomass equations to the 

d.b.h. values of all trees measured in a plot. These equations had either been 

developed by M. Feller or were obtained from Gholz and others (1979).

Development of a surface fi re hazard index (SFHI)—Surface fi re hazard 

was considered to depend on the quantity of surface fuels present, particularly on fi ne fuels (≤ 1 cm diameter). It was assumed that a surface fi re was 

unlikely to start if no fi ne fuels were present. The surface fi re hazard index 

(SFHI) chosen was

SFHI = FF (1 + CWD)

where FF is the quantity (kg/m2) of fi ne fuels present, and CWD is the 

quantity (kg/m2) of coarse woody debris (materials > 1 cm diameter). The 

study plots were placed into different age classes then the average SFHI was 

calculated for each age class. To test the sensitivity of the changes in SFHI 

with age to different age class groupings and different relative weighting of 

FF and CWD, the average SFHI was calculated for combinations of six different age class groupings (table 1) and nine different FF/CWD weightings. 

Thus, for SFHI = FF [1 + a(CWD)], “a” varied from 10 to 0.01.

Development of a crown fi re hazard index (CFHI)—Crown fi re hazard 

indexes which combined both initiation and propagation were developed. It 

was considered that a crown fi re would not occur if it could not be initiated 

or if it could not propagate. Thus -

Crown Fire Hazard Index (CFHI) = (ease of propagation) x (ease of initiation).

Ease of initiation was considered to depend on surface fi re intensity, ladder 

fuels, and height to the live crown, while ease of propagation was considered 

Feller and Pollock Variation in Surface and Crown Fire Hazard With Stand Age in Managed Coastal Western Hemlock Zone Forests...
USDA Forest Service Proceedings RMRS-P41. 2006. 371

Table 1—Different age class groupings used to calculate the Surface and Crown Fire Hazard 

Indexes and relative weightings of FF and CWD used to calculate the Surface Fire 

Hazard Index.

Groupings A B C D E F

Age class 0-3 0-2 0-3 0-3 0-2 0-4

 (years) 4-9 3-5 4-10 4-9 3-6 5-10

 10-15 6-10 11-18 10-16 7-12 11-20

 16-29 11-20 19-30 17-25 13-20 21-30

 30-40 21-39 31-45 26-35 21-35 31-50

 41-61 40-60 46-65 36-55 36-50 51-70

 62-81 61-80 66-85 56-75 51-70 71-90

 82-105 81-100 86-105 76-100 71-90 91-110

 106-150 101-150 106-150 101-150 91-150 111-150

 >150* >150 >150 >150 >150 >150

* All forests > 150-years-old were actually > 250 years old and could be considered old-growth.

to depend on canopy foliar bulk density. It was assumed that foliar moisture 

content would not vary with stand age and could be ignored. Surface fi re 

intensity would depend on surface fi re rate of spread and fuel consumption. 

It was then assumed that rate of spread would be similar beneath forests of 

different ages and that fuel consumption would depend on surface fuel load. 

The presence of tall snags (codominant to dominant in canopy height status) 

with rough surfaces, implying a high probability of blowing embers, was also 

considered as a factor which might enhance the likelihood of a crown fi re.

Therefore, CFHI α [f(FD)] [f(SFL, LF, HC, SD)]

where FD is the canopy foliar density (kg/m3), SFL is the surface fuel load 

(kg/m2), LF is the relative ladder fuel quantity (dimensionless, with scale = 

0-5), HC is the height to the live canopy (m), and SD is the density of tall, 

rough snags (no. snags/ha).

In its simplest form, this equation is CFHI = (FD) (SFL + LF – HC + SD).

The study plots were placed into different age classes then the average 

CFHI was calculated for each age class. Due to missing tree data, canopy 

foliar bulk densities could not be calculated for seven plots, so the analyses 

were conducted using 179 plots. To test the sensitivity of the changes in CFHI 

with age to different age class groupings and different relative weighting of 

SFL, LF, HC and SD, the average CFHI was calculated for combinations of 

six different age class groupings (the same as those used for SFHI (table 1)) 

and different SFL, LF, HC and SD relative weightings in the CFHI equation. 

The weighting given to each of these factors was increased or decreased by 

up to 6-10 times (table 2).

To determine which weighting factors might be most appropriate to use, 

the outputs from these equations were correlated with the Crowning Index 

(CI) of Scott and Reinhardt (2001), calculated for drought summer conditions 

using their fi gure D-1 for each of the study plots except those aged 0-3 years 

(table 2). This left 154 study plots for which the CI was calculated. The CI 

decreases as the ease of crowning increases, whereas the CFHI of the present 

study increases as the ease of crowning increases. Consequently, equations 

which produced CFHIs which were positively or weakly negatively (r > – 0.1) 

correlated with CI values, were not considered to be appropriate.

Variation in Surface and Crown Fire Hazard With Stand Age in Managed Coastal Western Hemlock Zone Forests... Feller and Pollock
372 USDA Forest Service Proceedings RMRS-P-41. 2006.

Table 2—Pearson correlation coeffi cients (r) between the CFHI 

of the present study and the CI of Scott and Reinhardt 

(2001) for different weightings of SFL, LF, HC, and SD used 

in the equation CFHI = FD (SFL + LF – HC + SD).

 r

Weighting SFL LF HC SD

 10.00 -- –0.55 -- –0.18

 6.00 –0.41 -- 0.57 --

 5.00 -- –0.61 -- --

 4.00 –0.36 -- 0.51 –0.16

 3.00 –0.31 -- -- --

 2.50 -- –0.36 -- --

 2.00 –0.22 -- 0.31 –0.13

 1.67 -- –0.24 -- --

 1.33 -- –0.17 -- --

 1.00 –0.06 –0.06 –0.06 –0.06

 0.67 -- 0.07 -- --

 0.50 0.03 0.14 –0.38 --

 0.40 -- -- -- –0.06

 0.33 0.07 0.22 -- --

 0.25 0.08 -- –0.53 --

 0.20 -- -- -- –0.05

 0.17 0.10 0.30 –0.56 --

 0.13 -- -- –0.58 --

 0.10 -- -- –0.59 –0.05

 0.07 -- 0.33 -- –0.04

-- not calculated

The equation in which each of SFL, LF, HC, and SD has an equal 

weighting (1) is CFHI = FD (SFL + 6LF – HC + SD/20)

SFHIs and CFHIs, determined for the 6 different age class groupings 

were compared using a Kruskal Wallis test to identify signifi cantly different 

(P < 0.05) values. All statistical analyses were conducted using SYSTAT 11 

software (SYSTAT 2004).

Results and Discussion

Surface Fire Hazard

Average fi ne fuel and coarse fuel loads each varied approximately three fold 

from 0.1 to 0.3 and from 4.2 to 15.2 kg/m2, respectively (table 3). The SFHI 

suggested that the surface fi re hazard in old growth forests was less than in 

recently harvested areas, regardless of the relative weighting given to coarse 

fuels, which varied over 3 orders of magnitude (fi gure 1). Since the surface 

fi re hazard in old-growth forests, relative to that in recently harvested areas, 

varied little with the magnitude of the coeffi cient “a” in SFHI = FF[(1 + 

a(CWD)], it was decided to use the simplest form of this equation, with a 

= 1, to express the relative surface fi re hazard. When this equation was applied to different age groupings, the general trend in hazard with age was 

an initial very high hazard (up to fi ve years post-harvest) which declined to 

Feller and Pollock Variation in Surface and Crown Fire Hazard With Stand Age in Managed Coastal Western Hemlock Zone Forests...
USDA Forest Service Proceedings RMRS-P41. 2006. 373

Table 3—Average values, with standard errors in parentheses, of each of the variables 

used in the SFHI and CFHI equations for each of the age classes assessed in age 

class grouping F. 

 Number

Age class of plots FF CWD SFL FD LF HC SD

 (years) - - - - - - (kg/m2)- - - - - - (kg/m3) m (no./ha)

 0 - 4 16 0.29 13.45 13.74 0.00 3.3 0.0 0

 (0.03) (1.56) (1.56) (0.00) (0.6) (0.0) (1)

 5 - 10 17 0.10 15.15 15.25 0.03 3.9 0.2 4

 (0.03) (1.73) (1.74) (0.01) (0.4) (0.1) (3)

11 - 20 14 0.12 9.51 9.63 0.05 3.3 1.4 0

 (0.03) (1.62) (1.64) (0.01) (0.4) (0.7) (0)

21 - 30 24 0.12 4.81 4.93 0.13 3.4 7.4 16

 (0.01) (0.52) (0.52) (0.02) (0.2) (0.8) (14)

31 - 50 17 0.24 5.64 5.88 0.10 3.6 10.3 150

 (0.03) (0.60) (0.62) (0.01) (0.3) (1.0) (44)

51 - 70 19 0.29 6.54 6.83 0.13 2.5 15.9 54

 (0.05) (0.74) (0.77) (0.01) (0.3) (1.1) (22)

71 - 90 15 0.23 7.48 7.71 0.13 1.9 17.7 54

 (0.03) (1.62) (1.62) (0.01) (0.3) (1.5) (19)

91 - 110 7 0.26 6.93 7.19 0.09 1.4 14.9 69

 (0.03) (0.60) (0.60) (0.01) (0.2) (2.0) (12)

111 - 150 18 0.20 4.19 4.39 0.10 1.6 18.8 23

 (0.03) (0.53) (0.53) (0.01) (0.2) (1.1) (8)

> 150 32 0.21 10.00 10.21 0.12 2.4 18.9 23

 (0.02) (0.94) (0.94) (0.01) (0.1) (1.0) (4)

Figure 1—Surface Fire Hazard Index in 

old-growth forests as a percentage of that 

in the youngest post-harvesting forests for 

six different age class groupings (A-F).

Variation in Surface and Crown Fire Hazard With Stand Age in Managed Coastal Western Hemlock Zone Forests... Feller and Pollock
374 USDA Forest Service Proceedings RMRS-P-41. 2006.

a minimum around 20 to 40 years post harvest, followed by an increase to 

around 50 to 70 years post harvest, a decrease to around 100 to 150 years 

post harvest, then an increase again in old-growth (fi gure 2). Old-growth 

forests, however, generally had a lower surface fi re hazard than forests 0 to 5 

and 50 to 70 years old (fi gure 2), although the difference between the oldgrowth SFHI and the greatest SFHI was statistically signifi cant for age class 

groupings A, C, D, and F, but not B and E (fi gure 2). The only age classes 

which had a statistically signifi cantly lower SFHI than that of old-growth 

were those in the range of 16 to 35 years (fi gure 2).

Figure 2—Average Surface Fire Hazard Indexes for different aged forests using six different age class groupings 

(A-F) and the equation SFHI = FF(1 + CWD).* designates a SFHI which is signifi cantly higher (P < 0.05) than 

that of old-growth. x designates a SFHI which is signifi cantly lower (P < 0.05) than that of old-growth.

Feller and Pollock Variation in Surface and Crown Fire Hazard With Stand Age in Managed Coastal Western Hemlock Zone Forests...
USDA Forest Service Proceedings RMRS-P41. 2006. 375

The SFHI used total CWD and not just well decayed CWD, which has 

been considered more important in determining surface fi re hazard (Baker 

2003). Quantitative data to support this do not appear to be available, however. Furthermore, several studies in coastal western hemlock forests have 

found that well decayed materials constitute a greater proportion of total 

CWD mass in younger than in old-growth forests (Spies and others 1988; 

Wells and Trofymow 1997; Feller 2003). Consequently, if the SFHI had 

given greater weight to well decayed CWD than to less well decayed CWD, 

the differences in SFHI between old-growth and the youngest forests would 

likely have been greater. It was also assumed that wind speed was unaffected 

by forest age. This is unlikely to be correct as wind speed near the ground 

surface is usually greater in the open than beneath forests (Spittlehouse and 

others 2004), so fi re forward rates of spread, and hence fi re hazard would 

also be greater in the open. Tanskanen and others (2005) have also found 

that surface fi re likelihood in Finnish conifer forests was greatest in recent 

clearcuts and declined with increasing age up to age 60 years, the oldest forest studied, due to increasing surface fuel moisture content. Consequently, 

microclimate differences even further emphasize the difference in surface 

fi re hazard between old-growth and the youngest forests. Thus, it can be 

concluded that the surface fi re hazard of the old-growth forests in the study 

area was less than that of recent clearcuts and was only greater than that of 

forests around 16 to 35 years old.

Crown Fire Hazard

Average surface fuel loads were greatest in 0 to 10 year old stands and least 

in 111 to 150 year old stands; average canopy foliar bulk densities increased 

with age up to 20 years, then remained relatively constant thereafter; ladder fuels were greatest in 0 to 70 year old stands; canopy heights tended to 

increase with stand age; and the density of dominant rough snags was least 

in the youngest stands and greatest in stands aged 31 to 110 years (table 3). 

Canopy foliar bulk densities may be overestimated in some old-growth stands 

as some of the tallest trees had dead tops and the foliar biomass regression 

equations used, which had been developed for trees up to 1.6 m d.b.h., were 

applied to trees up to twice this size.

Although the infl uence on the CFHI of variations in the weighting given 

to individual factors was assessed, the infl uence of variations in the weighting 

given simultaneously to 2 or more factors was not fully analyzed. Consequently, the appropriate CFHI equations chosen must be considered a fi rst 

approximation. When the different crown fi re initiation variables (whose range 

in values between individual plots were - SFL = 0.4 to 30.2 kg/m2, LF = 0 to 

5, HC = 0 to 32 m, and SD = 0 to 592 stems/ha) were given equal weight, 

the CFHI equation became CFHI = FD (SFL + 6LF – HC + SD/20). The 

weighting given to SFL had a major impact on the relative CFHI of oldgrowth versus younger forests. As the weighting increased, so did the CFHI 

of old-growth compared to that of younger forests (fi gure 3). Correlations 

between the CFHI and the CI of Scott and Reinhardt (2001) were > -0.1 for 

weightings of one or less (table 2). Consequently, an appropriate weighting 

factor would be > 1.

Regardless of the weighting given to LF, HC, or SD, the CFHI always 

remained lower in old-growth than in younger forests (fi gure 3). This applied even for weighting factors substantially greater or less than those given 

in fi gure 3. Based on correlations between the CI and CFHIs, appropriate 

weighting factors would be > 1 for LF and SD, and < 1 for HC (table 2).

Variation in Surface and Crown Fire Hazard With Stand Age in Managed Coastal Western Hemlock Zone Forests... Feller and Pollock
376 USDA Forest Service Proceedings RMRS-P-41. 2006.

Figure 3—Crown Fire Hazard Index in old-growth forests as a percentage of the highest CFHI in all age classes 

of forests, using six different age class groupings (A-F) and different weightings for SFL, LF, HC, and SD.

Many possible equations could be chosen using different appropriate 

weighting factors. All equations with SFL preceded by a coeffi cient of 4 or 

less resulted in CFHIs being lower in old-growth than in some younger forests. For simplicity, several equations were chosen for use, using weighting 

factors that were not too extreme. Due to the lack of data or even theoretical models which link snag abundance to crown fi re hazard, the weighting 

given to snag density was kept relatively low. It is currently unclear which 

equation best predicts crown fi re hazard as none have been tested with real 

fi res. CFHIs calculated from a sequence of equations with increasing weight 

being given to SFL from equation 1 through equation 4 are given in fi gure 4. 

The indexes calculated from the equations CFHI = FD(aSFL + bLF + cHC 

+ dSD), with varying a-d, were multiplied by either 25, 10, or 8 to convert 

the index to a scale of 1 to 100. The CFHIs calculated from all 4 equations 

were signifi cantly negatively correlated with the CI of Scott and Reinhardt 

(2001). These correlations progressively improved from r = –0.30 for equation 1 to r = –0.60 for equation 4, suggesting that as the relative weighting 

of SFL increases, the CFHI becomes a better predictor of crown fi re propagation. This only occurs up to a weighting factor of 8, however, after which 

the closeness of the correlation declines.

Feller and Pollock Variation in Surface and Crown Fire Hazard With Stand Age in Managed Coastal Western Hemlock Zone Forests...
USDA Forest Service Proceedings RMRS-P41. 2006. 377

The CFHIs in fi gure 4 are shown only for one grouping of age classes as 

there were no substantial differences between the six different age groupings 

in the relative rankings of old-growth versus younger forests. The CFHI was 

always lowest for 0 to 5 year old age classes, then increased to peak values 

in 20 to 90 year old age classes, before declining in 100 to 150 year old age 

classes then rising again in old-growth. The CFHI for old-growth was lower 

than that of a younger age class forest for all equations in which SFL had a 

weighting factor < 5. However, it was statistically signifi cantly lower (Kruskal 

Wallis tests, P = 0.05) only when the SFL weighting factor was < 2, as in 

equation 1 (fi gure 4). The CFHI for old-growth was also signifi cantly higher 

than that for 0 to 4 and 111 to 150 year old stands (fi gure 4).

It can be concluded that whether or not younger forests have a higher 

crown fi re hazard than old-growth in the study area depends primarily on 

the weighting given to surface fuel load. As the weighting given to this factor 

increases, the relative crown fi re hazard of old-growth forests increases. However, as no reasonable equation could be found which resulted in old-growth 

forests having a statistically signifi cantly higher crown fi re hazard than all 

younger forests, it can also be concluded that simply clearcutting old-growth 

will not produce younger forests that always have a lower crown fi re hazard 

than old-growth forests. Following clearcutting, fuel abatement treatments, 

such as slash reduction and thinning, would be necessary to signifi cantly 

Figure 4—Average Crown Fire Hazard Indexes for different aged forests, calculated using four different CFHI 

equations and age class grouping F. * designates a CFHI which is signifi cantly higher (P < 0.05) than that of 

old-growth. x designates a CFHI which is signifi cantly lower (P < 0.05) than that of old-growth.

Variation in Surface and Crown Fire Hazard With Stand Age in Managed Coastal Western Hemlock Zone Forests... Feller and Pollock
378 USDA Forest Service Proceedings RMRS-P-41. 2006.

reduce crown fi re hazards. Slash reduction would defi nitely be required to 

reduce post clearcutting surface fi re hazard below that of old-growth forests. 

These conclusions are consistent with those of DellaSala and Frost (2001), 

who reported that old-growth forests in the western U.S. were less likely to 

burn catastrophically than younger forests.

Guidelines for fuel reduction treatments which lower fi re hazards in forests 

are becoming available (for example, Keyes and O’Hara 2002; Peterson and 

others 2005). The present study suggests that both surface and crown fi re 

hazard reduction would benefi t from an emphasis on reducing surface fuels. 

However, the ecological benefi ts of surface fuels (Brown and others 2003; 

Feller 2003) as well as their infl uence on fi re hazard must be considered.

Acknowledgments

B. Ford, D. Leard, R. Lehmann, P. Olanski, E. Pierce, and M. Råberg 

as well as many students have assisted with fi eld work and data analysis. We 

thank the staff of the University of B.C. Malcolm Knapp Research Forest, 

and the Greater Vancouver Watershed District for permission to work within 

their areas and for facilitating this work. Funding for the study has come 

from the University of B.C. Research Forest, the government of B.C., and the 

Canada-B.C. FRDA research program. We thank B. Hawkes and C. White 

for helpful reviews of an earlier version of this paper.

References

Agee, J. K.; Huff, M. H. 1987. Fuel succession in a western hemlock/Douglas-fi r 

forest. Canadian Journal of Forest Research. 17: 697-704.

Alexander, M. E. 1988. Help with making crown fi re hazard assessments. In Fischer, 

W. C.; Arno, S. F. Compilers. Protecting people and homes from wildfi re in the 

Interior West: Proceedings of a Symposium and Workshop. Gen. Tech. Rep. INT251. U.S. Department of Agriculture, Forest Service, Intermountain Research 

Station: 147-156.

Baker, W. L. 2003. Fires and climate in forested landscapes of the U.S. Rocky 

Mountains. In Veblen, T. T.; Baker, W. L.; Montenegro, G.; Swetnam, T. W., 

eds. Fire and climatic change in temperate ecosystems of the western Americas. 

Ecological Studies. 160. Springer: 120-157.

Brown, J. K.; See, T. E. 1981. Downed dead woody fuel and biomass in the northern 

Rocky Mountains. Gen. Tech. Rep. INT-117, U.S. Department of Agriculture, 

Forest Service, Intermountain Research Station.

Brown, J. K.; Reinhardt, E. D.; Kramer, K. A. 2003. Coarse woody debris: managing 

benefi ts and fi re hazard in the recovering forest. Gen. Tech. Rep. RMRS-GTR105, U.S. Department of Agriculture, Forest Service, Rocky Mountain Research 

Station.

Cruz, M. G.; Alexander, M. E.; Wakimoto, R. H. 2003. Assessing Canopy fuel 

stratum characteristics in crown fi re prone fuel types of western North America. 

International Journal of Wildland Fire. 12: 39-50.

DellaSala, D. A.; Frost, E. 2001. An ecologically based strategy for fi re and fuels management 

in National Forest roadless areas. Fire Management Today. 61(2):12-23.

Economic and Engineering Services Inc. 1991. Watershed management evaluation 

and policy review. Final Summary Report. Greater Vancouver Water District. 

Burnaby, British Columbia.

Feller and Pollock Variation in Surface and Crown Fire Hazard With Stand Age in Managed Coastal Western Hemlock Zone Forests...
USDA Forest Service Proceedings RMRS-P41. 2006. 379

Fahnestock, G. R. 1976. Fire, fuels, and fl ora as factors in wilderness management: 

the Pasayten case. In Tall Timbers Fire Ecology Conference Annual Proceedings 

No. 15. Tall Timbers Research Station, Tallahassee, Florida: 33-69.

Feller, M. C. 2003. Coarse woody debris in the old-growth forests of British 

Columbia. Environmental Reviews. 11 (Supplement 1): S135-S157.

Forest Resources Development Branch. 1986. Wildland fi re management terminology. 

Forestry Department, Food and Agriculture Organization of the United Nations, 

Rome.

Forestry Canada Fire Danger Group. 1992. Development and structure of the 

Canadian Forest Fire Behavior Prediction System. Forestry Canada Information 

Report ST-X-3. Science and Sustainable Development Directorate, Ottawa, 

Ontario.

Gholz, H. L.; Grier, C. C.; Campbell, A. G.; Brown, A. T. 1979. Equations for 

estimating biomass and leaf area of plants in the Pacifi c Northwest. Oregon State 

University Forest Research Laboratory, Research Paper 41, Corvallis, Oregon.

Grant, D. T. 1984. Fuel management planning, Vancouver Forest Region. British 

Columbia Ministry of Forests, Vancouver Forest Region, Burnaby, British 

Columbia.

Harmon, M. E.; Franklin, J. F.; Swanson, F.J.; Sollins, P.; Gregory, S.V.; Lattin, J. 

D.; Anderson, N. H.; Cline, S. P.; Aumen, N. G.; Sedell, J. R.; Lienkaemper, G. 

W.; Cromack, K., Jr.; Cummins, K. W. 1986. Ecology of coarse woody debris in 

temperate ecosystems. Advances in Ecological Research. 15: 133-302.

Hawkes, B. C. 1979. Fire history and fuel appraisal study of Kananaskis Provincial 

Park, Alberta. M. Sc. In Forest Science Thesis. University of Alberta, Edmonton, 

Alberta.

Keyes, C. R.; O’Hara, K. L. 2002. Quantifying stand targets for silvicultural 

prevention of crown fi res. Western Journal of Applied Forestry. 17: 101-109.

Meidinger, D.; Pojar, J. 1991. Ecosystems of British Columbia. British Columbia 

Ministry of Forests, Special Report 6, British Columbia Ministry of Forests, 

Victoria, British Columbia.

Peterson, D. L.; Johnson, M. C.; Agee, J. K.; Jain, T.B.; McKenzie, D.; Reinhardt, 

E. D. 2005. Forest structure and fi re hazard in dry forests of the western United 

States. Gen. Tech. Rep. PNW-GTR-628, U.S. Department of Agriculture, Forest 

Service, Pacifi c Northwest Research Station.

Romme, W. H. 1982. Fire and landscape diversity in subalpine forests of Yellowstone 

National Park. Ecological Monographs. 52: 199-221.

Scott, J. H.; Reinhardt, E. D. 2001. Assessing crown fi re potential by linking models 

of surface and crown fi re behavior. Res. Pap. RMRS-RP-29: U.S. Department of 

Agriculture, Forest Service, Rocky Mountain Research Station.

Spies, T. A.; Franklin, J. F.; Thomas, T. B. 1988. Coarse woody debris in Douglas-fi r 

forests of western Oregon and Washington. Ecology. 69: 1689-1702.

Spittlehouse, D. L.; Adams, R. S.; Winkler, R. D. 2004. Forest, edge, and opening 

microclimate at Sicamous Creek. Res. Rep. 24: British Columbia Ministry of 

Forests, Research Branch, Victoria, British Columbia.

SYSTAT Software Inc. 2004. SYSTAT. Richmond, California.

Tanskanen, H.; Venäläinen, A.; Puttonen, P.; Granström, A. 2005. Impact of stand 

structure on surface fi re ignition potential in Picea abies and Pinus sylvestris forests 

in southern Finland. Canadian Journal of Forest Research. 35: 410-420.

Van Wagner, C. E. 1968. The line intersect method in forest fuel sampling. Forest 

Science. 14: 20-26.

Van Wagner, C. E. 1977. Conditions for the start and spread of crown fi re. Canadian 

Journal of Forest Research. 7: 23-24.

Variation in Surface and Crown Fire Hazard With Stand Age in Managed Coastal Western Hemlock Zone Forests... Feller and Pollock
380 USDA Forest Service Proceedings RMRS-P-41. 2006.

Van Wagner, C. E. 1983. Fire behavior in northern conifer forests and shrublands. 

In Wein, R. W.; MacLean, D. A. eds. The role of fi re in northern circumpolar 

ecosystems. SCOPE 18. John Wiley and Sons: 65-80.

Van Wagner, C. E. 1993. Prediction of crown fi re behavior in two stands of jack 

pine. Canadian Journal of Forest Research. 23: 442-449.

Wells, R. W.; Trofymow, J. A. 1997. Coarse woody debris in chronosequences of 

forests on southern Vancouver Island. Info. Rep. BC-X-375: Canadian Forestry 

Service, Pacifi c Forestry Centre, Victoria, British Columbia.

Feller and Pollock Variation in Surface and Crown Fire Hazard With Stand Age in Managed Coastal Western Hemlock Zone Forests...
USDA Forest Service Proceedings RMRS-P-41. 2006. 381

In: Andrews, Patricia L.; Butler, Bret W., 

comps. 2006. Fuels Management—How to 

Measure Success: Conference Proceedings. 

28-30 March 2006; Portland, OR. 

Proceedings RMRS-P-41. Fort Collins, 

CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research 

Station.

1 Research Physical Scientist, USDA 

Forest Service, Rocky Mountain Research 

Station, Fire Sciences Laboratory, 

Missoula, MT. pandrews@fs.fed.us

2 Fire Scientist, Ensis Bushfi re Research, 

Ensis-Forest Biosecurity and Protection, 

Scion, Christchurch, New Zealand; and 

Australian Bushfi re Cooperative Research 

Centre.

3 Senior Lecturer in the School of Physical, 

Environmental and Mathematical Sciences, 

University of New South Wales, Australian 

Defence Force Academy, Canberra, 

Australia; and Australian Bushfire 

Cooperative Research Centre.

Abstract—Understanding and calculating fi re behaviour in various fuel types is essential 

for effective fi re management, including wildfi re suppression and fuels management. 

Fire spread in grassland fuel is affected by the curing level, the amount of dead fuel 

expressed as a percentage of the total (live and dead fuel combined). The infl uence of 

live fuel is included in various fi re models in different ways. U.S. fi re behavior prediction 

systems are based on Rothermel’s fi re spread model, which uses the load of live and 

dead fuel and the moisture content of each. Dynamic fuel models include a transfer 

of fuel load from the live to dead class as a function of live fuel moisture. Australian 

and New Zealand grassland fi re behavior models rely heavily on the curing level as 

a major determinant of the ability for a fi re to develop and spread, and place greater 

direct emphasis on both the proportion and moisture content of the dead fi ne fuels. 

A joint Australian and New Zealand study under the Australian Bushfi re Cooperative 

Research Centre (CRC) is addressing various methods of assessing curing levels in 

grasslands. Data from that study are used to evaluate the dynamic fuel load transfer 

function used in fuel models developed for the Rothermel spread model. Results showed 

that live fuel moisture is not an indicator of level of curing. A signifi cant difference is 

demonstrated in calculated rate of spread using the load transfer model versus direct 

entry of live fuel moisture and level of curing.

Introduction

Fuels management planning often involves modeling potential fi re behavior 

to identify areas of risk, assess hazard, and evaluate the effectiveness of various 

fuel treatment options. Fire behavior for a given fuel type can be modeled 

under a range of weather conditions and seasonal changes. Fire behavior 

modeling supports other aspects of fi re management including suppression, 

prevention, and prescribed fi re.

Fire behavior is infl uenced primarily by the fuel type (grass, shrub, etc.), 

fuel condition (moisture content, percentage of dead fuel), wind speed, and 

slope. The moisture content of fi ne dead fuel varies diurnally in response to 

changes in temperature, humidity, solar radiation, and rainfall. Live fuel moisture changes seasonally due to the physiology of the plant and its response to 

seasonal weather conditions. Seasonal curing of live herbaceous plants leads to 

a change in the ratio of dead to live material in the fuel complex, commonly 

referred to as level of curing. Live fuel plays an important role in determining the behavior of grassland fi re (Cheney and Sullivan 1997, Cheney and 

others 1998). The level of curing has a major effect on grass fi re behavior, in 

particular fi re spread (Alexander 1993, Anderson and Pearce 2003).

Evaluation of a Dynamic Load Transfer 

Function Using Grassland Curing Data

Patricia L. Andrews1, Stuart A.J. Anderson2, Wendy R. Anderson3
382 USDA Forest Service Proceedings RMRS-P-41. 2006.

Andrews, Anderson, and Anderson Evaluation of a Dynamic Load Transfer Function Using Grassland Curing Data

The infl uence of live fuel is incorporated into fi re behavior models in several 

ways. The fi re models used in New Zealand and Australia place the emphasis 

on level of curing, whereas U.S. models utilize live fuel moisture. New Zealand 

uses the Canadian grassland fi re behavior models from the Canadian Fire 

Behavior Prediction System based on data collected in Australia (Forestry 

Canada Fire Danger Group 1992). There are models for cut or matted grass 

and for natural standing grass.

In Australia a fi re model has been developed for grassland (three defi ned 

pasture types) by Cheney and others (1998) to replace existing models based 

on the McArthur Mark 3 and Mark 5 Fire Danger Meters (McArthur 1966, 

McArthur 1977) and provided in equation form by Noble and others (1980). 

Although the fi re models used in New Zealand and Australia are quite different, they both predict fi re spread in grasslands from fi ne dead fuel moisture, 

wind speed, and degree of curing.

Fire behavior prediction systems in the U.S. are based on Rothermel’s 

(1972) surface fi re spread model. Calculations are based on a description of 

the fuel, fuel moisture content of each size class of dead and live fuel, wind 

speed, and slope. The fi re model itself does not include the infl uence of curing. Dynamic fuel models are used as a means of modeling changes in fi re 

behavior that occur as herbaceous fuels cure and die.

In this paper we evaluate the dynamic load transfer function that is part 

of fuel models developed for use with Rothermel’s surface fi re spread model 

(Scott and Burgan 2005). We compare the load transfer model predictions 

with fi eld sampled grassland curing data and we examine the infl uence of the 

load transfer function on rate of spread calculations.

Grassland Curing Study

Grassland curing data are being collected as part of an ongoing study 

under the Australian Bushfi re Cooperative Research Centre (CRC): “Improved Methods for the Assessment and Prediction of Grassland Curing” 

(www.bushfi recrc.com). Grass curing describes the annual or seasonal cycle of 

grasses dying and drying out following fl owering. Degree of curing refers to 

the proportion of cured (dead) material in grasslands, expressed as a percentage of the total grassland fuel complex (live and dead material). It is a critical 

input to grassland fi re behavior and fi re danger models used in Australia and 

New Zealand. Current curing inputs are often inaccurate, leading to incorrect determination of grassland fi re danger levels and potential fi re behavior. 

Many important fi re management decisions and strategies are based upon 

this grassland fi re behavior information, and fi re managers need access to 

accurate and reliable information to protect life and property.

The degree of curing is currently assessed visually or by satellite remote 

sensing using an index based on the refl ective properties of grasses at different wavelengths. Visual assessment is often inaccurate, sometimes differing 

vastly from the actual curing value obtained from destructive sampling. 

Problems include diffi culties obtaining and extrapolating estimates over large 

areas, experience of observers, calibration of visual assessments, and timing 

and frequency of observations (Anderson 2005, Anderson and Pearce 2003, 

Millie and Adams 1999).

Remote sensing is used to assess curing levels in grasslands over parts of 

Australia (Paltridge and Barber 1988, Barber 1990, Allan and others 2003). 

However, the algorithms developed have had little validation outside of 
USDA Forest Service Proceedings RMRS-P-41. 2006. 383

Evaluation of a Dynamic Load Transfer Function Using Grassland Curing Data Andrews, Anderson, and Anderson

southern Australia, and there are issues with the accuracy of the technique 

due to atmospheric conditions and lack of uniformity of grasslands within 

pixels.

The Bushfi re CRC project is examining improved remote sensing approaches, and will also include evaluation and modifi cation of agricultural 

pasture growth models for curing determination. These models account for 

environmental and physiological factors regulating grass growth. An Australasian-wide fi eld sampling program is providing data for development and 

validation of techniques. We used preliminary fi eld data for this analysis.

Destructive sampling of grasses is the most accurate method of collecting 

curing data, but is not practical to implement on a large scale. It is laborintensive to collect and process destructive samples, and there are further 

issues with obtaining spatially-representative samples of curing across the 

landscape.

Curing data in the CRC study were obtained by destructive fi eld sampling. 

Sampling quadrats were located along two transects at right angles to each 

other and a total number of approximately fi ve samples were collected. For 

each sample, all the vegetation from within a 0.25m2 frame was clipped with 

shears to the ground level and removed and placed in a bag. In the laboratory, the samples were then separated into live and dead material, oven dried 

at 100°C for 24 hours and then weighed. The degree of curing was then 

determined by calculating the percentage of dead material expressed as a 

percentage of the total (live and dead) material.

Samples of live, dead, and combined fuel moisture were also collected in 

the fi eld, to investigate the feasibility of using grass fuel moisture data to 

calculate curing percentage, assuming that the moisture status of the grasses 

represents a live or dead state. Live and dead fuel moisture samples were collected randomly from within the curing sampling area, sealed in tins, weighed, 

oven-dried for 24 hours at 100°C and reweighed. The moisture content of 

the combined (live and dead) fuel was calculated using the material collected 

as part of the destructive sample from the 0.25m2 sampling frame. Collecting 

representative fuel moisture data in the fi eld can be diffi cult. Live moisture 

varies by the part of the plant and the stage of growth as well as by grass 

species. For example on the same date and location Australian native grass 

may have a moisture content of 125% while improved pasture has a moisture 

content of 250%.

Table 1 shows the data from the grassland curing study used in this analysis. 

There are ten sites, three in New Zealand and seven in Australia. The type 

of grass and level of grazing is noted for each. The grass is characterized by 

loading and height. The date of each of the fourteen sample data points is 

given with the live fuel moisture and level of curing.

Dynamic Load Transfer Function

The dynamic load transfer function is part the of dynamic fuel models 

developed for use with Rothermel’s (1972) surface fi re spread model. Dynamic fuel models are used as a means of modeling changes in fi re behavior 

that occur as herbaceous fuels cure and die. The fi re model itself does not 

include the infl uence of curing. Calculations in Rothermel’s fi re model are 

based on fuel model, fuel moisture content of each size class of dead and live 

fuel, wind speed, and slope. A fuel model is a set of intrinsic fuel parameters 

that are required by the fi re model.
384 USDA Forest Service Proceedings RMRS-P-41. 2006.

Andrews, Anderson, and Anderson Evaluation of a Dynamic Load Transfer Function Using Grassland Curing Data

Table 1—Data from the Australian Bushfi re Cooperative Research Centre (CRC) study: “Improved Methods for the 

Assessment and Prediction of Grassland Curing.”

 Site description Sample data

 Grass Total fuel Live fuel Level of

Location* type** Grazing*** load Grass height Date moisture content curing

 (ton/acre) (ft) - - - - - - - - -(%) - - - - - - - - - -

Monaro IP UG 3.8 1.1 8/8/2005 211 93

Monaro IP UG 3.6 1.1 9/6/2005 270 91

Fisher IP LG 1.4 1.5 8/18/2005 315 87

Majura IP UG 1.5 2.3 1/16/2006 92 79

Majura IP UG 1.2 2.3 2/22/2006 113 71

Umbigong NG LG 2.2 0.8 8/30/2005 124 92

Tidbinbilla IP HG 0.6 0.7 1/24/2006 152 99

Braidwood IP LG 1.0 0.7 1/5/2006 142 80

Braidwood IP LG 0.7 0.7 2/14/2006 192 84

Milton IP UG 7.0 9/12/2005 331 72

Darfi eld IP LG 1.1 0.5 9/16/2005 292 58

Darfi eld IP LG 0.7 0.5 2/20/2006 320 86

Godley Head IP/NG UG 4.1 1.0 9/17/2005 234 80

Lake Lyndon IP/NG UG 2.3 0.8 2/14/2006 165 70

*Darfi eld, Godley Head, and Lake Lyndon are in New Zealand; the rest are in Australia.

**IP = Improved Pasture; NG = Native Grass.

***UG = Ungrazed; LG = Lightly Grazed; HG = Heavily Grazed.

The standard set of 13 fi re behavior fuel models, which has been widely 

used since 1976, are static; fuel model parameters do not change (Albini 

1976, Anderson 1982). Dynamic fuel models, on the other hand, include 

the dynamic load transfer function which changes the fuel description by 

moving some of the load from the live category to dead. Although rarely 

used, the option of developing dynamic custom fuel models was available in 

the BEHAVE fi re behavior prediction and fuel modeling system (Burgan and 

Rothermel 1984). Seventeen of the recently developed set of 40 standard fuel 

models are dynamic (Scott and Burgan 2005). These 40 fuel models were 

designed to represent a wider range of fuel types than the set of 13, and have 

been implemented in the BehavePlus fi re modeling system (Andrews and 

others 2004), the FARSITE fi re area simulator (Finney 1998), and other 

fi re behavior prediction systems in the U.S.

The dynamic fuel load transfer function is shown in fi gure 1. Load is 

transferred from the live herbaceous class to dead as a function of live fuel 

moisture. The same relationship is used for all dynamic fuel models.

 • For live herbaceous fuel moisture content of 120 percent or higher, most 

of the herbaceous fuels are assumed to be green, and the initial live 

herbaceous load for the fuel model stays in the live category.

 • For live fuel moisture of 30 percent or lower, the herbaceous fuels are 

considered fully cured, and all live herbaceous load is transferred to the 

dead category.

 • For live fuel moisture between 30 and 120 percent, part of the live herbaceous load is transferred to dead. For example, if live fuel moisture 

is 75 percent (halfway between 30 and 120 percent), half of the initial 

live herbaceous load is transferred to dead herbaceous, the remainder 

stays in the live herbaceous class.
USDA Forest Service Proceedings RMRS-P-41. 2006. 385

Evaluation of a Dynamic Load Transfer Function Using Grassland Curing Data Andrews, Anderson, and Anderson

Table 2 gives the parameters for the standard fi re behavior fuel models applicable to grasses. Fuel models 1, 2, and 3 are from the original set of 13 and 

are static. Fuel models GR1 through GR9 are dynamic fuel models from the 

set of 40. The listed fuel loadings for the dynamic fuel models are the values 

before the fuel load transfer function is applied. The percentage of the total 

load that is dead fuel prior to load transfer is also given on the table. Scott 

and Burgan (2005) refer to the percent load transferred as curing percent, and 

say that the parameters on the table are for uncured fuel. There is, however, 

dead fuel in the fuel models even before any load is transferred from live to 

dead. As an illustration of the difference between percent load transferred and 

percent dead, table 2 gives the percent dead at 50 percent load transferred. 

Fuel model GR2 is 63 percent dead (63 percent cured) when 50 percent of 

the load is transferred from live to dead. In Australasian and Canadian fi re 

behaviour models, degree of curing in grasslands is defi ned as percent dead. 

For this paper, we use the terminology “percent load transferred” where Scott 

and Burgan used the term “percent cured”.

As an illustration of the effect of live fuel moisture for static and dynamic 

fuel models, the BehavePlus fi re modeling system was used to compare 

calculated rate of spread using Rothermel’s fi re spread model for seven fuel 

models under the same wind and fuel moisture conditions (5 percent dead 

fuel moisture, 5 mi/h wind, no slope) (fi gure 2). Fuel models 1 and 3 have 

no live fuel, so rate of spread is not affected by a change in live fuel moisture. 

Fuel model 2 is a static model with a live fuel component. The effect of live 

fuel moisture is therefore limited to the relationships in the original formulation of the Rothermel (1972) fi re model. Fuel models GR1, GR2, GR4, and 

GR7 are dynamic. Live fuel moisture is not only used in the rate of spread 

Figure 1—Percent of the live herbaceous fuel load that is transferred to the dead category 

(Burgan 1979). There is dead fuel in the fuel model when the percent is zero, for the 

section of the graph labeled “uncured.”
386 USDA Forest Service Proceedings RMRS-P-41. 2006.

Andrews, Anderson, and Anderson Evaluation of a Dynamic Load Transfer Function Using Grassland Curing Data

Table 2—Load and depth for grass fi re behavior fuel models. Percent dead for no load transfer and for 50% load 

transfer from live herbaceous to dead are given to illustrate the difference between percent load transferred and 

percent dead.

 Fuel model parameters

 Fuel load (ton/ac) Depth Percent dead fuel Percent dead at

 Fuel model 1-h 10-h 100-h live herb (ft) prior to load transfer 50% load transfer

1 Short Grass 0.74 1.0 N/A N/A

2 Grass and timber 2.0 1.0 0.5 0.5 1.0 N/A N/A

 understory

3 Tall Grass 3.0 2.5 N/A N/A

GR1 Short, Sparse, 0.1 0.3 0.4 25.0 63

 Dry climate

GR2 Low Load, 0.1 1.0 1.0 9.1 55

 Dry Climate

GR3 Low Load, 0.1 0.4 1.5 2.0 6.3 63

 Very Coarse, 

 Humid Climate

GR4 Moderate Load, 0.25 1.9 2.0 11.6 56

 Dry Climate

GR5 Low Load, 0.4 2.5 1.5 13.8 57

 Humid Climate

GR6 Moderate Load, 0.1 3.4 1.5 2.9 51

 Humid Climate

GR7 High Load, 1.0 5.4 3.0 15.6 58

 Dry Climate

GR8 High Load, 0.5 1.0 7.3 4.0 6.4 59

 Very Coarse, 

 Humid Climate

GR9 Very High Load, 1.0 1.0 9.0 5.0 10.0 59

 Humid Climate

calculations according to the fi re spread model, but also to change the fuel 

model according to the fuel load transfer function. For fuel model GR7 

under the specifi ed dead moisture and wind conditions, there is a four-fold 

increase in calculated rate of spread as live fuel moisture decreases from 100 

to 75 percent; and rate of spread increases by a factor of 2.4 for the very small 

change in live fuel moisture from 100 to 95 percent. In his paper “Sensitivity 

of a fi re behavior model to changes in live fuel moisture” Jolly (2005) found 

that the grass fuel models within the set of 40 new fuels showed the highest 

sensitivity to live fuel moisture changes. They were most sensitive to changes 

in live fuel moisture from 90 to 100%.

The fuel load transfer function was developed as part of a live fuel moisture 

model developed by Burgan (1979) for use in the U.S. National Fire Danger 

Rating System (Deeming and others 1977). The load transfer function is 

a conceptual model; development was not based on live fuel moisture and 
USDA Forest Service Proceedings RMRS-P-41. 2006. 387

Evaluation of a Dynamic Load Transfer Function Using Grassland Curing Data Andrews, Anderson, and Anderson

curing data. The live fuel moisture range of 30 to 120 percent was defi ned 

as the transition stage because 120 percent “roughly defi nes the moisture 

content at which new growth is complete and the foliage is mature.” Thirty 

percent was defi ned as the minimum moisture for transition because “that 

is the approximate fi ber saturation point, below which plants are assumed to 

be dead.” The fuel load transfer function has not previously been evaluated 

using fi eld data.

Results

Table 3 gives the sample data for live fuel moisture and level of curing, 

and the model values for percent load transferred and percent dead calculated 

from the sampled live fuel moisture. We compare the sampled and modeled 

level of curing (percent dead). A fuel model was assigned to each sample 

site based primarily on the fuel loadings of the sample data in table 1. The 

Milton site in coastal Australia was the only one designated as humid. Only 

three of the fourteen sample points had live fuel moisture content below 120 

percent. According to the dynamic fuel load transfer function, moisture above 

120 percent indicates no load is transferred from live to dead. The modeled 

percent dead for the fuel models is calculated from the percent dead for the 

fuel model before the load transfer (see table 2) and the percent transferred 

from live to dead according to the dynamic load transfer function.

Figure 2—Comparison of calculated rate of spread for seven fuel models to illustrate 

sensitivity to live fuel moisture for static (1, 2, 3) and dynamic (GR1, GR2, GR4, GR7) 

fuel models. Dynamic fuel models include the use of the load transfer function.
388 USDA Forest Service Proceedings RMRS-P-41. 2006.

Andrews, Anderson, and Anderson Evaluation of a Dynamic Load Transfer Function Using Grassland Curing Data

Figure 3 is a plot of the sample data, level of curing (percent dead) vs. live 

fuel moisture, with an indication of the assigned fuel model. The dynamic 

fuel load transfer function is used to plot percent dead for each fuel model. 

Figure 4 is a plot of predicted and observed level of curing (percent dead). 

The lowest observed curing level was 58 percent while the highest predicted 

value was 42 percent. A simple look at the plots precludes the need for a 

statistical analysis.

The load transfer function is based on the assumption that fuel is “uncured” 

when live fuel moisture is over 120 percent. Note that a signifi cant amount 

of the grass fuel load is dead at high live fuel moisture values. For example, 

the live fuel moisture content was 315 percent for the Fisher site, corresponding to a measured 87 percent curing level. The load transfer function gives 

no load transfer and 11.6 percent dead fuel for fuel model GR4 and live fuel 

moisture 315 percent.

It is apparent that there is no useful relationship between live fuel moisture 

and curing level for this data. We conclude that for this data set, live fuel 

moisture is not an indicator of level of curing.

Infl uence on Rate of Spread Calculations

The dynamic load transfer model is an intrinsic part of dynamic fuel models. Given that we have shown that live fuel moisture may not be an indicator 

of curing, we examine the option of independent specifi cation of live fuel 

moisture and curing level.

Table 3—Fuel models were assigned to each site primarily based on fuel loading. Field data includes 

live fuel moisture and level of curing (percent dead). The fuel load transfer function is used to 

calculate percent load transferred for the associated live fuel moisture value. Percent dead is 

affected by the fuel model (see table 2).

 Sample data Model values

 Percent load

 Live fuel Level of transferred

Location Fuel model moisture content curing live to dead Percent dead

 - - - - - - - - - (%) - - - - - - - - -

Monaro GR7 211 93 0 15.6

Monaro GR7 270 91 0 15.6

Fisher GR4 315 87 0 11.6

Majura GR4 92 79 34.5 42.1

Majura GR4 113 71 12.1 22.3

Umbigong GR4 124 92 0.5 12.0

Tidbinbilla GR1 152 99 0 25.0

Braidwood GR1 142 80 0 25.0

Braidwood GR1 192 84 0 25.0

Milton GR8 331 72 0 6.4

Darfi eld GR2 292 58 0 9.1

Darfi eld GR2 320 86 0 9.1

Godley Head GR7 234 80 0 15.6

Lake Lyndon GR4 165 70 0 11.6
USDA Forest Service Proceedings RMRS-P-41. 2006. 389

Evaluation of a Dynamic Load Transfer Function Using Grassland Curing Data Andrews, Anderson, and Anderson

Figure 3—Australian and New Zealand live fuel moisture and curing data compared 

to the dynamic load transfer function. There is a different curve for each fuel model 

because of the dead fuel in the fuel models when there is zero load transferred from 

live to dead. The sample data points are from table 3.

Figure 4—Predicted vs. observed level of curing (percent dead). 

Data are from table 3.
390 USDA Forest Service Proceedings RMRS-P-41. 2006.

Andrews, Anderson, and Anderson Evaluation of a Dynamic Load Transfer Function Using Grassland Curing Data

As a means of dealing with the required relationship between curing and 

live fuel moisture, Scott and Burgan (2005) give the following guidance 

in use of the dynamic fuel models: “It will often be preferable to estimate 

live herbaceous moisture content by working backward from observed or 

estimated degree of herbaceous curing. For example, if the fuelbed is observed to be 50 percent cured, use a value of 75 percent for live herbaceous 

moisture content.” A user who knows both live fuel moisture and curing 

level must choose which to use. It is not possible, for example, under the 

current formulation, to calculate rate of spread for live fuel moisture of 200 

percent and 50 percent cured. (Recall that Scott and Burgan use the term 

percent cured for the percent load transferred from live to dead rather than 

the percent dead.)

Table 4 shows the live fuel moisture values that correspond to 100, 75, 50, 

25, and 0 percent load transferred according to the function. The calculated 

rate of spread for fuel model GR4 (5 percent dead fuel moisture, 5 mi/h 

wind, and no slope) is given for each and indicated on the curve in fi gure 5. 

For example, live fuel moisture of 75 percent and a 50 percent load transfer 

gives rate of spread of 53 ch/h. These calculations are as implemented in the 

BehavePlus fi re modeling system using the dynamic fuel models as described 

by Scott and Burgan.

Consider the effect of not using the dynamic load transfer function, but 

rather directly supplying values for live fuel moisture and load transfer percent. Table 5 shows calculated rate of spread for a range of live fuel moisture 

values and load transfer levels. The highlighted values in table 5 correspond 

to those in table 4 and are indicated on fi gures 6 and 7. Figure 6 is rate of 

spread for fi ve levels of load transfer for a range of live fuel moisture. The 

curves in fi gure 6 correspond to the columns in table 5. For a fi xed load 

transfer of 50 percent, live fuel moisture from 30 to 300 percent results in rate 

of spread from 11 to 90 ch/h. Similarly, fi gure 7 is rate of spread for seven 

levels of live fuel moisture for a range of load transfer values. The curves in 

fi gure 7 correspond to the rows in table 5. For a fi xed live fuel moisture of 

75 percent, load transfer from 0 to 100 percent results in rate of spread from 

2 to 110 ch/h. There is a signifi cant difference between the results using the 

dynamic load transfer function and specifying live fuel moisture and curing 

independently.

Table 4—Live fuel moisture and percent load transferred from live to dead according 

to the dynamic load transfer function. Associated calculated rate of spread for 

fuel model GR4, 5 percent dead fuel moisture, 5 mi/h midfl ame wind, and no 

slope. The fi ve highlighted rate of spread values are shown in fi gure 5.

Live fuel moisture Load transferred live to dead* Rate of spread 

- - - - - - - - - - - - - - - - - - (%) - - - - - - - - - - - - - - - - - - (ch/h)

 30 100 110

 53 75 87

 75 50 53

 98 25 11

 120 0 1

 200 0 1

 300 0 1

*Referred to as percent cured by Scott and Burgan (2005).
USDA Forest Service Proceedings RMRS-P-41. 2006. 391

Evaluation of a Dynamic Load Transfer Function Using Grassland Curing Data Andrews, Anderson, and Anderson

Figure 5—Calculated rate of spread for dynamic fuel model GR4, which 

incorporates the fuel load transfer function. Load is transferred from the live to 

the dead class as a function of live fuel moisture. The data points on the curve 

are given in table 4.

Table 5—Calculated rate of spread for a range of live fuel moisture and load 

transfer values. The highlighted values correspond to those in table 4 

and are plotted on the curves in fi gures 6 and 7. The curves in fi gure 6 

correspond to the columns of this table. The curves in fi gure 7 correspond 

to the rows.

 Rate of spread (ch/h)

Live herbaceous moisture (%) Load transferred from live to dead*, %

 100 75 50 25 0

 30 110 103 90 69 3

 53 110 87 66 49 2

 75 110 76 53 33 2

 98 110 66 44 11 1

 120 110 59 38 10 1

 200 110 43 24 6 1

 300 110 32 11 5 1

*Referred to as percent cured by Scott and Burgan (2005)
392 USDA Forest Service Proceedings RMRS-P-41. 2006.

Andrews, Anderson, and Anderson Evaluation of a Dynamic Load Transfer Function Using Grassland Curing Data

Figure 7—Rate of spread is calculated for several values of live herbaceous moisture for 

a range of load transfer percentage. Use of the load transfer function results in only the 

single indicated point on each curve. The curves correspond to the rows in table 5.

Figure 6—Rate of spread is calculated for several levels of load transfer for a range of 

live fuel moisture. Use of the load transfer function results in only the single indicated 

point on each curve. The curves correspond to the columns in table 5.
USDA Forest Service Proceedings RMRS-P-41. 2006. 393

Evaluation of a Dynamic Load Transfer Function Using Grassland Curing Data Andrews, Anderson, and Anderson

Discussion

It is recognized that curing level is an important factor in determining 

fi re behavior in grass fuel types. Because the Rothermel (1972) fi re spread 

model does not include the infl uence of curing, fuel models that incorporate 

a dynamic load transfer function have been developed by Scott and Burgan 

(2005) to refl ect seasonal curing. Live fuel moisture is used to estimate the 

load that is transferred from the live to dead class in the fuel model. Evaluation of the dynamic load transfer function using fi eld sampled data from 

Australia and New Zealand showed that the assumption that level of curing 

is related to live fuel moisture needs to be questioned.

An examination of the use of the dynamic load transfer function compared 

to the option of independent specifi cation of live fuel moisture and curing 

level showed a signifi cant difference in rate of spread calculations using 

Rothermel’s model. Although both live fuel moisture and degree of curing 

are currently diffi cult to determine, we suggest that the required use of the 

dynamic load transfer function be reconsidered in anticipation of improved 

models and methods of assessment.

There is a need for longer term research on the curing process, a description 

of the seasonal changes in the grasslands for fi re modeling, and on the combustion processes involved in the burning of a mixture of live and dead fuel. 

It is imperative that fi re researchers and fi re managers continue to question, 

validate, and refi ne fi re behavior models and their underlying assumptions 

for effective fi re and fuel management.

Acknowledgments

The authors wish to thank the Australian Bushfi re Cooperative Research 

Centre (CRC) for permission to use the curing data for this paper.

References

Albini, F. A. 1976. Estimating wildfi re behavior and effects. GTR-INT-30, USDA 

Forest Service, Ogden, UT.

Alexander, M. E. 1994. Proposed revision of fi re danger class criteria for forest and 

rural fi re areas in New Zealand. Wellington, New Zealand.

Allan, G., A. Johnson, S. Cridland, and N. Fitzgerald. 2003. Application of NDVI 

for predicting fuel curing at landscape scales in northern Australia: can remotely 

sensed data help schedule fi re management operations? International Journal of 

Wildland Fire 12:299-308.

Anderson, H. E. 1982. Aids to determining fuel models for estimating fi re behavior. 

GTR-INT-122, USDA Forest Service, Ogden, UT.

Anderson, S. 2005. Assessing and predicting the curing of grasslands. in Australasian 

Fire Authorities Council/Bushfi re CRC Conference, Auckland, New Zealand.

Anderson, S., W. Anderson, F. Hines, and A. Fountain. N.D. Project Report: 

Determination of fi eld sampling methods for the assessment of curing levels in 

grasslands. Ensis Forest Biosecurity and Protection.

Anderson, S. A. J., and H. G. Pearce. 2003. Improved methods for the assessment of 

grassland curing. in 3rd International Wildland conference, Sydney, Australia.
394 USDA Forest Service Proceedings RMRS-P-41. 2006.

Andrews, Anderson, and Anderson Evaluation of a Dynamic Load Transfer Function Using Grassland Curing Data

Andrews, P. L., C. D. Bevins, and R. C. Seli. 2004. BehavePlus fi re modeling system, 

version 3.0: User’s Guide. RMRS-GTR-106WWW, USDA Forest Service.

Barber, J. R. 1990. Monitoring the curing of grassland fi re fuels in Victoria, Australia 

with sensors in satellites and aircraft. A Country Fire Authority Study in Remote 

Sensing, Country Fire Authority, Victoria.

Burgan, R. E. 1979. Estimating live fuel moisture for the 1978 national fi re danger 

rating system. GTR-INT-226, USDA Forest Service, Ogden, UT.

Burgan, R. E., and R. C. Rothermel. 1984. BEHAVE: fi re behavior prediction and 

fuel modeling system--FUEL subsystem. GTR-INT-167, USDA Forest Service, 

Ogden, UT.

Cheney, N. P., J. S. Gould, and W. R. Catchpole. 1998. Prediction of fi re spread in 

grasslands. International Journal of Wildland Fire 8:1-13.

Cheney, P., and A. Sullivan. 1997. Grassfi res: fuel, weather, and fi re behaviour. 

CSIRO Publishing, Collingwood, Australia.

Deeming, J. E., R. E. Burgan, and J. D. Cohen. 1977. The National Fire-Danger 

Rating System--1978. GTR-INT-39, USDA Forest Service, Ogden, UT.

Finney, M. A. 1998. FARSITE: fi re area simulator--model development and 

evaluation. RP-RMRS-4, USDA Forest Service.

Forestry Canada Fire Danger Group. 1992. Development and structure of the 

Canadian Forest Fire Behavior Prediction System. ST-X-3, Forestry Canada, 

Ottawa, Canada.

Jolly, W. M. 2005. Sensitivity of a fi re behavior model to changes in live fuel moisture. 

in Sixth Symposium on Fire and Forest Meteorology, Canmore, AB, Canada.

McArthur, A. G. 1966. Weather and grassland fire behaviour. No. 100, 

Commonwealth of Australia, Canberra, Australia.

McArthur, A. G. 1977. Grassland fi re danger meter Mk V. CSIRO Division of Forest 

Research Annual Report 1976-1977. Canberra, Australia.

Millie, S., and R. Adams. 1999. Measures of grassland curing: a comparison of 

destructive sampling with visual and satellite estimates. Pages pp 257-263 in

Australian Bushfi re 99 Conference, Albury, NSW.

Noble, I. R., G. A. V. Bary, and A. M. Gill. 1980. McArthur’s fi re-danger meters 

expressed as equations. Australian Journal of Ecology 5:201-203.

Paltridge, G. W., and J. Barber. 1988. Monitoring Grassland Dryness and Fire 

Potential in Australia with NOAA / AVHRR Data. Remote Sensing of 

Environment 25:381-394.

Rothermel, R. C. 1972. A mathematical model for predicting fi re spread in wildland 

fuels. RP-INT-115, USDA Forest Service, Ogden, UT.

Scott, J. H., and R. E. Burgan. 2005. Standard fi re behavior fuel models: a 

comprehensive set for use with Rothermel’s surface fi re spread model. RMRSGTR-153, USDA Forest Service, Rocky Mountain Research Station.
USDA Forest Service Proceedings RMRS-P-41. 2006. 395

In: Andrews, Patricia L.; Butler, Bret W., 

comps. 2006. Fuels Management—How to 

Measure Success: Conference Proceedings. 

28-30 March 2006; Portland, OR. 

Proceedings RMRS-P-41. Fort Collins, 

CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research 

Station.

1 Assistant Professor of Silviculture & 

Applied Forest Ecology, Department 

of Forestry & Watershed Management, 

Humboldt State University, Arcata, 

CA. christopher.keyes@humboldt.edu

Abstract—Foliar moisture content (FMC) is a primary factor in the canopy ignition 

process as surface fi re transitions to crown fi re. In combination with measured stand 

data and assumed environmental conditions, reasonable estimates of foliar moisture 

content are necessary to determine and justify silvicultural targets for canopy fuels 

management strategies. FMC values reported in research publications are best used 

for this purpose. This paper summarizes the results of 11 studies on the FMC values 

and trends for 16 North American conifers. FMC values ranged from 73 to 480 percent but varied by species, foliage age, and season. FMC values presented here and 

the references associated with them will be helpful to managers engaging in canopy 

fuels planning with the use of popular fi re behavior and fuels management software 

(e.g. NEXUS, Fuels Management Analyst, and the Forest Vegetation Simulator’s Fire 

and Fuels Extension).

Keywords: crown fi re, fi re surrogates, wildfi re hazard, canopy ignition, shaded fuelbreak

Introduction

The relationship of stand structure to fi re behavior, and the basis for silviculturally modifying stands to reduce crown fi re susceptibility, have been 

well established (Graham et al. 2004, Agee and Skinner 2005). In planning 

silvicultural treatments to achieve crown fi re resistance, assumptions must be 

made about uncontrolled parameters that are beyond the scope of manipulation (Keyes and O’Hara 2002). One of these is the percent foliar moisture 

content (FMC) of overstory and midstory trees.

The quantitative basis for prescribing silvicultural treatments (such as 

thinning and pruning) to the aerial fuel complex is Van Wagner’s (1977) 

model of the relationships among crown fi re behavior, surface fi re behavior, 

and canopy fuel structure. Since its inception as a tool to predict the occurrence and behavior of crown fi res, Van Wagner’s model has since been 

refi ned and adapted in formats useful for fuels planning (Alexander 1988, 

Scott and Reinhardt 2001, Keyes and O’Hara 2002). It is currently utilized 

by virtually all decision-support software currently used in fuels planning in 

North America, including FARSITE (Finney 1998), NEXUS (Scott 1999), 

the CrownMass program of the Fuels Management Analyst tool suite (Fire 

Program Solutions 2003), and the Forest Vegetation Simulator’s (FVS) Fire 

and Fuels Extension (Reinhardt and Crookston 2003).

Using one or more of those simulation programs, fuels planners identify 

structural targets that can reduce a stand’s susceptibility to crown fi re initiation, crown fi re spread, or both, and then propose fuels treatments to achieve 

these targets. Ideally, the effects of proposed silvicultural fuels treatments 

on fuel dynamics are also considered (Keyes and Varner 2006). To decrease 

Foliar Moisture Contents of North American 

Conifers

Christopher R. Keyes1
396 USDA Forest Service Proceedings RMRS-P-41. 2006.

Keyes Foliar Moisture Contents of North American Conifers

susceptibility to torching or canopy ignition, a target canopy base height is 

determined on the basis of anticipated surface fi reline intensity and foliar 

moisture content. For the former parameter, measured surface fuelbed properties are utilized in combination with a worst-case fi re weather scenario to 

determine the most intense surface fi re behavior that is likely to occur. But 

fuels planners lack a standard basis for determining appropriate values for 

FMC. This paper reviews relevant literature to address that need.

Variability in Foliar Moisture Content

Fuels treatments are expected to be effective over a range of temporally 

changing conditions, so estimates of FMC are best drawn from published 

studies that document changes in foliar moisture content over seasons or years. 

A list of these is given in table 1 for 16 common North American conifer 

species. The table reveals a wide range of moisture content values based on 

species, period of measurement, and foliage age. These values are drawn from 

the primary literature; in some cases values have been visually approximated 

to the nearest 5 percent from published graphs.

Table 1—Published percent foliar moisture content (FMC) values for North America forest conifers. In some cases values are 

visually approximated to the nearest 5 percent from graphs.

 New Old

 Species foliage1 foliage2 Period3 Reference

Abies balsamea – balsam fi r 180-230 130-150 Jul-Sep Kozlowski and Clausen 1965

 130-220 110-150 Jul-Oc Little 1970t

 143-356 75-140 Jan-Dec Chrosciewicz 1986

Abies grandis – grand fi r 167-313 112-138 Jun-Oct Agee et al. 20024

 140-310 110-150 Jun-Sep Agee et al. 20024

Abies lasiocarpa – subalpine fi r 150-225 110-125 Aug-Sep Agee et al. 20024

 115-312 — Jun-Sep Agee et al. 20024

Abies magnifi ca var. shastensis – Shasta red fi r 170-310 — Jun-Sep Agee et al. 2002

Picea glauca – white spruce 146-480 78-139 Jan-Dec Chrosciewicz 1986

Picea engelmanii – Engelmann spruce (mixed 100-130) Jul-Oct Gary 1971

Picea mariana – black spruce 131-349 73-126 Jan-Dec Chrosciewicz 1986

 — 75-115 Jan-Dec Springer and Van Wagner 1984

Pinus banksiana – jack pine 130-190 105-120 Jul-Oct Johnson 1966

 137-288 79-129 Jan-Dec Chrosciewicz 1986

Pinus clausa – sand pine 195-210 145-150 Jul-Oct Hough 1973

Pinus contorta – lodgepole pine 117-148 96-118 Late Aug Hartford and Rothermel 1991

Pinus edulis – pinyon pine (mixed 95-130) Jul-Oct Jameson 1966

Pinus ponderosa – ponderosa pine 125-210 95-115 Jul-Oct Philpot and Mutch 1971

149-275 85-120 Jun-Oct Agee et al. 20024

115-340 85-135 Jun-Sep Agee et al. 20024

Pinus resinosa – red pine 160-250 120-140 Jul-Sep Kozlowski and Clausen 1965

 135-200 110-130 Jul-Oct Johnson 1966

Pinus strobus – eastern white pine 150-230 130-140 Jul-Sep Kozlowski and Clausen 1965

Pseudotsuga menziesii – Douglas-fi r 120-200 80-120 Jul-Oct Philpot and Mutch 1971

Tsuga canadensis – eastern hemlock 170-280 120-150 Jul-Sep Kozlowski and Clausen 1965

1Range of percent FMC values for fi rst-year leaves. 2Range of percent FMC values for second-year leaves or older.

3Month(s) comprising the study duration.

4Two separate studies for each species in same publication.
USDA Forest Service Proceedings RMRS-P-41. 2006. 397

Foliar Moisture Contents of North American Conifers Keyes

Foliar moisture content varies seasonally. Lowest foliar moisture contents 

typically occurring during late spring (Philpot and Mutch 1971), rapidly 

increase to an annual maximum shortly therafter, and then steadily decline 

through summer to fall (Kozlowski and Clausen 1965). This trend is physiologically based, and is more a function of the leaf’s changing carbohydrate 

content than its water content. For example, an analysis of young red pine (Pinus resinosa) foliage revealed a seasonally declining FMC even as the actual 

water content increased (Kozlowski and Clausen 1965).

Like other fuel properties, the moisture content of foliage also varies on 

a diurnal basis. Philpot’s (1965) study of ponderosa pine (Pinus ponderosa) 

summertime FMC revealed diurnal fl uxes of 26 to 34 percent. FMC roughly 

tracked ambient relative humidity measured over the same period. More 

modest fl uxes of 4 to 12 percent for ponderosa pine, Douglas-fi r (Pseudotsuga 

menziesii), and grand fi r (Abies grandis) were observed during a late August 

day in Washington by Agee et al. (2002).

The occurrence of worst-case fi re weather and lowest foliar moisture content 

are usually asynchronous. For conifers such as ponderosa pine and Douglasfi r, old foliage FMC drops below 100 percent, but generally ranges between 

100 percent and 130 percent during the summer months when ignitions are 

most frequent and fi res most intense. In fuels planning, assumed FMC values 

should be kept seasonally consistent with the fi re weather scenario used to 

predict surface fi reline intensity.

Foliage age is another primary determinant of variation in FMC. Moisture 

content of fi rst-year leaves is typically higher than older leaves by a substantial 

margin. For the species in table 1, the range of FMC values for new foliage 

is 120 to 480 percent, versus a range of 73 to 150 percent for older foliage 

(2nd year or later). In a study of eastern white pine (Pinus strobus), FMC 

values between July and September ranged from 130 to 140 percent for old 

foliage, but ranged from 150 to 230 percent for new foliage on the same 

trees (Kozlowski and Clausen 1965). Although studies have identifi ed FMC 

differences in foliage age, none have demonstrated FMC differences in tree 

age. Until this relationship is further examined, values in Table 1 should be 

applied regardless of stand or cohort age.

No reports have addressed FMC among stands of variable densities or 

other attributes of stand structure. Therefore, fuels planners must assume 

that stand structure or treatment history has no bearing on the FMC assumption. Differences between species and regions are apparent (table 1), 

but not with any obvious relationships to shade tolerance, latitude, or other 

useful ordinal characterizations that might suggest a need for regionally 

explicit assumptions, or that would allow extrapolation to other species not 

represented in table 1.

The case of mixed-species stands introduces additional complexity. In stratifi ed even-aged mixtures or mixed multi-cohort stands, it is most appropriate 

to use the FMC value of the species relegated to the lower-most stratum (the 

stratum that will initiate the crown ignition process). For unstratifi ed evenaged mixtures, it is suggested that the lowest FMC value be adopted among 

those species constituting at least 10 percent the stand’s basal area.

Conclusion

Whenever possible, all assumptions in silvicultural fuels management 

should be supported on the basis of best available scientifi c information. 
398 USDA Forest Service Proceedings RMRS-P-41. 2006.

Keyes Foliar Moisture Contents of North American Conifers

The foliar moisture content values summarized here should be utilized in 

the fuels planning process, and their supporting documentation cited in justifying silvicultural treatments of forest fuels. Alexander (1988) lists several 

additional studies of FMC that are more obscure but that could also prove 

useful. For species lacking published FMC data, a low default value of 90 or 

100 percent is a prudently conservative assumption (e.g. Scott 2003). For this 

review, additional details that are present in the original research (table 1) were 

by necessity omitted in order to present all species together in one common 

tabular format. Additional information beyond the values presented here is 

available from the primary literature, and should be consulted and cited as 

necessary to establish the scientifi c basis for value assumptions used in fuels 

planning.

Literature Cited

Agee, J.K., C.S. Wright, N. Williamson, and M.H. Huff. 2002. Foliar moisture 

content of Pacifi c Northwest vegetation and its relation to wildland fi re behavior. 

Forest Ecology and Management 167:57-66.

Agee, J.K., and C.N. Skinner. 2005. Basic principles of forest fuel reduction 

treatments. Forest Ecology and Management 211:83-96.

Alexander, M.E. 1988. Help with making crown fi re hazard assessments. Pp. 147-156 

in Protecting People and Homes from Wildfi re in the Interior West: Proceedings 

of the Symposium and Workshop, Missoula, Montana. USDA Forest Service 

General Technical Report INT-GTR-251.

Chrosciewicz, Z. 1986. Foliar moisture content variations in four coniferous tree 

species of central Alberta. Canadian Journal of Forest Research 16:157-162.

Finney, M.A. 1998. FARSITE: Fire Area Simulator — Model development and 

evaluation. USDA Forest Service Research Paper RMRS-RP-4. 47 p.

Fire Program Solutions. 2003. Users’ guide to CrownMass. Fire Program Solutions 

LLC, Estacada, Oregon. 77 p.

Gary, H.L. 1971. Seasonal and diurnal changes in moisture contents and water 

defi cits of Engelmann spruce-needles. Botanical Gazette 132(4):327-332.

Graham, R.T., S. McCaffrey, and T.B Jain. 2004. Science basis for changing forest 

structure to modify wildfi re behavior and severity. USDA Forest Service General 

Technical Report RMRS-GTR-120.

Hartford, R.A., and R.C. Rothermel. 1991. Fuel moisture as measured and predicted 

during the 1988 fi res in Yellowstone Park. USDA Forest Service Research Note 

INT-RN-396. 7 p.

Hough, W.A. 1973. Fuel and weather infl uence wildfi res in sand pine forests. USDA 

Forest Service Research Paper SE-RP-106. 9 p.

Jameson, D.A. 1966. Diurnal and seasonal fl uctuations in moisture content of pinyon 

and juniper. USDA Forest Service Research Note RM-RN-67. 7 p.

Johnson, V.J. 1966. Seasonal fl uctuation in moisture content of pine foliage. USDA 

Forest Service Research Note NC-RN-11. 4 p.

Keyes, C.R., and K.L. O’Hara. 2002. Quantifying stand targets for silvicultural 

prevention of crown fi res. Western Journal of Applied Forestry 17(2):101-109.

Keyes, C.R., and J.M. Varner. 2006. Pitfalls in the silvicultural treatment of canopy 

fuels. Fire Management Today 66(3):46-50.

Kozlowski, T.T., and J.J. Clausen. 1965. Changes in moisture contents and dry 

weights of buds and leaves of forest trees. Botanical Gazette 126(1):20-26.
USDA Forest Service Proceedings RMRS-P-41. 2006. 399

Foliar Moisture Contents of North American Conifers Keyes

Little, C.H.A. 1970. Seasonal changes in carbohydrate and moisture content in needles 

of balsam fi r (Abies balsamea). Canadian Journal of Botany 48:2021-2028.

Philpot, C.W. 1965. Diurnal fl uctuation in moisture content of ponderosa pine and 

whiteleaf manzanita leaves. USDA Forest Service Research Note PSW-RN-67. 

7 p.

Philpot, C.W., and R.W. Mutch. 1971. The seasonal trends in moisture content, 

ether extractives, and energy of ponderosa pine and Douglas-fi r needles. USDA 

Forest Service Research Paper INT-RP-102. 21 p.

Reinhardt, E.D., and N.L. Crookston. 2003. The fi re and fuels extension to the 

forest vegetation simulator. USDA Forest Service General Technical Report 

RMRS-GTR-116. 209 p.

Scott, J.H. 1999. NEXUS: A system for assessing crown fi re hazard. Fire Management 

Notes 59(2):20-24.

Scott, J.H. 2003. Canopy fuel treatment standards for the wildland-urban interface. 

Pp. 29-38 in Fire, Fuel Treatments, and Ecological Restoration: Conference 

Proceedings, Fort Collins, Colorado. USDA Forest Service Proceedings RMRSP-29.

Scott, J.H., and E.D. Reinhardt. 2001. Assessing crown fi re potential by linking 

models of surface and crown fi re behavior. USDA Forest Service Research Paper 

RMRS-RP-29.

Springer, E.A., and C.E. Van Wagner. 1984. The seasonal foliar moisture trend of 

black spruce at Kapuskasing, Ontario. Canadian Forest Service Research Note 

4:39-42.

Van Wagner, C.E. 1977. Conditions for the start and spread of crown fi re. Canadian 

Journal of Forest Research 7:23-34.
USDA Forest Service Proceedings RMRS-P-41. 2006. 401

In: Andrews, Patricia L.; Butler, Bret W., 

comps. 2006. Fuels Management—How to 

Measure Success: Conference Proceedings. 

28-30 March 2006; Portland, OR. 

Proceedings RMRS-P-41. Fort Collins, 

CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research 

Station.

1 Resource Innovations, University of 

Oregon, Eugene, OR.

kathy@uoregon.edu

2 National Network of Forest Practitioners, Portland, OR.

Abstract—Wildfi res and related government roles and responsibilities for federal 

wildland management are prominent in our national consciousness because of the 

increased severity in the last decade of fi res on and around public lands. In recent 

years, laws, strategies, and implementation documents have been issued to direct 

federal efforts for wildfi re prevention, fi refi ghting, and recovery. Reliable national-level 

information and monitoring are essential to ensure good decision-making and agency 

accountability. Social and economic information about communities at risk from wildfi re is critical to these decisions. Despite the indispensable nature of this information 

for understanding communities, wildfi re risk, and cooperative efforts, there is a void 

in policy direction within the federal agencies to collect, understand, and use social 

and economic information in wildfi re management programs. This study addresses 

community capacity and examines socioeconomic indicators as elements of wildfi re 

risk. The study investigates whether communities most at risk from wildfi re are able to 

access and benefi t from federal programs established to serve these communities. In 

other words, are the dollars, assistance, and fuels-reduction projects hitting the ground 

in the areas throughout the country that are most at risk to wildfi re? This presentation 

will provide a forum to discuss the needs of rural and underserved communities in 

relationship to fi re and fuels management programs.

Introduction

Wildfi res and the related government roles and responsibilities for federal 

wildland management are prominent in our national consciousness because of 

the increased severity in the last decade of fi res on and around public lands. 

In recent years, numerous laws, strategies, and implementation documents 

have been issued to direct federal efforts for wildfi re prevention, fi refi ghting, 

and recovery. Reliable national-level information and monitoring are essential 

to ensure good decision-making and agency accountability.

Social and economic information about communities at risk from wildfi re is critical to these decisions. Despite the indispensable nature of this 

information for understanding communities, wildfi re risk, and cooperative 

efforts, there is a void in policy direction within the federal agencies to collect, understand, and utilize social and economic information in wildfi re 

management programs.

This research project uses the concept of community capacity – a community’s ability to protect itself, respond to, and recover from wildfi re – and 

examines socioeconomic indicators (one component of community capacity) 

as elements of wildfi re risk. Utilizing socioeconomic information, as well 

as ecological factors, this study set out to investigate, through a geographical-information-systems approach, whether communities most at risk from 

Mapping the Relationship Between Wildfi re 

and Poverty

Kathy Lynn1 and Wendy Gerlitz2
402 USDA Forest Service Proceedings RMRS-P-41. 2006.

Lynn and Gerlitz Mapping the Relationship Between Wildfi re and Poverty...

wildfi re are able to access and benefi t from federal programs established to 

serve these communities. In other words, are the dollars, assistance, and fuelsreduction projects hitting the ground in the areas throughout the country 

that are most at risk?

This research project found that federal agencies do not have the information and data necessary to answer this question. Spatial data to inform every 

aspect of this research – including data regarding the ecological conditions 

of federal lands, wildfi re protection capability in and around communities, 

and the federal expenditures under the national fi re plan – are unavailable 

and/or inadequate.

Using the limited data that are currently available, this research focused 

primarily on the relationship between poverty and populated areas at risk to 

wildfi re. Our research indicates that there is a relationship between poverty 

and federal land ownership, and that more poor households are located in 

close proximity to federal lands. Perhaps more signifi cant, the research shows 

a higher percentage of poor households in inhabited wildland areas that are 

not considered part of the Wildland Urban Interface – the areas that federal 

agencies and Congress have prioritized to receive the majority of funds for 

activities under the national fi re plan. The research also indicates that, in the 

one state analyzed, poor households are more likely in areas with low or no 

fi re response capabilities than are non poor households.

This research should be seen as a fi rst step to document the importance of 

social and economic information and community capacity in wildfi re policy 

and implementation. The lack of information about wildfi re risk, including 

ecological conditions, socioeconomic indicators, and resource allocation 

convinced us to focus our recommendations on improving federal agency 

understanding and use of social and economic factors through national inventory and monitoring efforts. Specifi c recommendations include developing 

a method for measuring community capacity in the context of wildfi re and 

using this methodology to redefi ne the concept of risk for implementation 

priorities at the national level and in state, regional, and local planning and 

risk assessment. Federal land management agencies must also improve systems 

for monitoring national fi re plan expenditures and the datasets that support 

the prioritization of these funds.

Understanding the social and economic dynamics of communities is critical 

for providing federal assistance that will help communities protect themselves 

from wildfi re and respond to and recover from an event. We encourage others to build on this effort to understand the complex social, economic, and 

ecological factors that infl uence wildfi re risk. Specifi cally, we encourage federal 

agencies to take steps to understand the social and economic indicators that 

are necessary to understand and serve our nation’s communities.

Research Methods

This study examines the relationship between wildfi re and community risk 

through the concept of community capacity. The research also attempted to 

analyze federal resource allocation in conjunction with data indicating relative risk. To examine these issues, the project team conducted background 

research to identify indicators and nationally consistent data for each element 

of the project. The team also facilitated internal and external data review, 

mapped indicators once data had been collected, and reexamined and reported 

fi ndings through the mapping process.
USDA Forest Service Proceedings RMRS-P-41. 2006. 403

Mapping the Relationship Between Wildfi re and Poverty... Lynn and Gerlitz

To illustrate the study elements, we sought data to use as indicators of community capacity and wildfi re risk. This process was iterative, investigating potential 

datasets, summarizing the benefi ts and drawbacks of each, and obtaining feedback 

from an advisory committee. We also presented preliminary fi ndings of the study 

at two community meetings in southern Oregon and central Oregon.

This section provides a description of the data we initially sought to examine community capacity, wildfi re risk, and federal resource allocation. It 

includes the limitations of the best available data, and a summary of how we 

use the data in this study.

National-Level Data

This report is a national-level analysis that seeks to provide information on 

a national scale. The spatial information included in this report is provided at 

the county and census-block levels. Therefore, the visual analysis is, in many 

cases, more meaningful on a state level. Consequently, the researchers have 

included more detailed maps and analysis for the states of Washington and 

Oregon, as state-level examples. The maps and analysis shown for these two 

states are also available, upon request, for other states.

Identifying indicators that provide consistent and meaningful information 

for a nationwide study became the fi rst challenge. Although some poverty data 

exist on a national scale (from the Census and Department of Housing and 

Urban Development), it was more diffi cult to fi nd consistent national data on 

community capacity, protection capacity, wildfi re risk, and federal resource 

allocation. The researchers encountered major challenges in fi nding spatial 

data, especially in a format conducive to national-level modeling. Specifi cally, 

there is a lack of suitable data in the areas of: (1) community capacity/protection capability (2) ecological conditions on federal lands/populated areas at 

risk from wildfi re; and, (3) federal resource allocation.

Indicators and Data

The following section provides information about the purpose of each 

indicator, the data initially sought, the limitations encountered, and the data 

ultimately selected.

Community Capacity

Examining community capacity requires understanding a complex set of 

issues and indicators that are not easily summarized by a single set of data. 

Below, we explain the purpose for using the concept of community capacity, existing defi nitions of community capacity found in published research, 

the limitations we encountered in identifying data, and the indicators we 

ultimately chose for this research.

Community capacity can be used to assess the relative risk that a community 

faces from wildfi re. Well defi ned, community capacity will provide the social 

information to tell us which communities are at a greater risk—less ready to 

protect themselves from wildfi re, and less able to recover from the impacts of 

a fi re. Understanding the capacity of a community to address the economic, 

social, and environmental costs of wildfi re will lead to more directed policies 

and programs and a more effi cient use of resources. Following are two defi nitions of capacity that we used to help frame the study and the indicators we 

sought to use for the research.
404 USDA Forest Service Proceedings RMRS-P-41. 2006.

Lynn and Gerlitz Mapping the Relationship Between Wildfi re and Poverty...

 • Kusel (1996) defi nes community capacity as “the collective ability of 

residents to respond...to external and internal stresses; to create and 

take advantage of opportunities; and to meet the needs of residents, 

diversely defi ned.”

 • A response by American Forests to the 2001 Federal Register notice 

Urban Wildland Interface Communities within the Vicinity of Federal 

Lands that are at High Risk from Wildfi re, defi nes community capacity 

as the collective ability of residents in a community to respond to external and internal stresses, to create and take advantage of opportunities, 

and to meet local needs. Community capacity in relation to wildfi re 

addresses a community’s ability to mitigate wildfi re threats, respond to 

active wildfi re, and mitigate post fi re damage. This includes the ability 

to implement risk-reduction strategies, including hazardous fuels reduction, fi refi ghting, and restoration activities (American Forests 2001).

For purposes of this research (and because of limited data), two indicators were used as a fi rst step to measure community capacity as it relates to 

wildfi re: (1) socioeconomic elements that infl uence a community’s ability to 

respond to and recover from wildfi re and (2) protection capability - systems 

that are in place that infl uence a community’s ability to protect itself from 

an actual wildfi re. As previously stated, a true assessment of community 

capacity would include a much broader array of social and cultural information; however, this information was not readily available at the time that this 

research was undertaken.

The study uses 2003 Housing and Urban Development (HUD) Income 

Limits, at a comparable census block group level, as the primary layer for 

poverty. HUD Income Limits refl ect income, earnings and employment, 

and housing affordability. The Median Family Income Limit estimates are 

based on the U.S. Census Bureau median family income estimates with an 

adjustment using a combination of earnings and employment data, median 

family income data, and fair market rents. Data are available nationally. HUD 

Income Limits describe family sizes of one to eight persons, and a formula is 

provided to calculate income limits for larger family sizes. Income limits are 

adjusted for family size and areas with unusually high or low family income 

or housing-cost-to-income relationships (Housing and Urban Development). 

Income limit groups include families whose incomes do not exceed 80 percent of the median family income for the area (low-income), families whose 

incomes do not exceed 50 percent of the median family income for the area 

(very low-income), and families whose incomes do not exceed 30 percent of 

the area median income (very, very low-income).

This report also utilizes fi re hazard ratings, used by both public and private 

sector organizations around the nation, as indicators of the capabilities of fi re 

districts to protect their communities from wildfi re. The Fire Suppression 

Rating Schedule is a common method used by the insurance industry in reviewing the fi refi ghting capabilities of individual communities. The schedule 

measures the major elements of a community’s fi re suppression system and 

develops a numerical grading called a “Public Protection Classifi cation.” Ten 

percent of the overall grading is based on how well the fi re department receives 

and dispatches fi re alarms. Fifty percent of the overall grading is based on the 

number of engine companies and the amount of water a community needs to 

fi ght a fi re. Forty percent of the grading is based on the community’s water 

supply, which focuses on whether the community has suffi cient water supply 

for fi re suppression beyond daily maximum consumption.
USDA Forest Service Proceedings RMRS-P-41. 2006. 405

Mapping the Relationship Between Wildfi re and Poverty... Lynn and Gerlitz

This report uses data from the Washington State Independent Fire Hazard 

Rating Bureau to assess the relationship between fi re hazard ratings, poverty, 

and potential wildfi re risk. The Washington State Rating Bureau provides data 

for all of the fi re protection ratings for fi re districts in Washington State.

Ecological Risk/ Populated Areas at Risk from Wildfi re

The research intended initially to examine ecological wildfi re risk—the 

likelihood of fi re occurring in different areas and the potential damage such 

a fi re would pose—through spatial data that would indicate, on a national 

level, the relative risk status of wildlands across the country. This indicator 

was intended to provide information about the ecological condition of lands. 

When it became apparent that there was insuffi cient consistent and up-todate data on the ecological conditions of lands, we focused the study on the 

potential risk of fi re to populated areas.

This study focuses on two distinct elements of the Forest Service study 

and data on wildland urban interface. The fi rst data set that we examine is 

the Wildland Urban Interface as defi ned above. The second set of data that 

we use is the Wildland Intermix —less densely populated areas in wildlands, 

which enabled the study to include signifi cant portions of inhabited land in 

areas vulnerable to wildfi re.

Federal Resource Allocation

Initially, this study intended to include data detailing all federal expenditures under the National Fire Plan, including grants to communities and 

hazardous fuel reduction projects on private and public lands and spatial 

information that would indicate where the activities took place. These data 

would provide a roadmap to track where federal funding was being spent, 

which would allow researchers to examine these data with the data layers 

indicating capacity and wildfi re risk. The combination of these layers would 

provide information about how well the federal agencies were serving the 

areas most at risk from wildfi re.

National Fire Plan Grants—National Fire Plan data for Region 6 are 

available in a multi-agency database (projects funded by BLM, Bureau of 

Indian Affairs, USDA Forest Service, and Fish and Wildlife Service). They 

include zip code and latitude/longitude information for each grant, based on 

the location of the grant recipient, and a designation for the type of project 

funded (fuels reduction, fi re prevention, planning and education, small-diameter marketing and utilization). Because of the limitations of the grants data, 

the decision was made not to analyze the data numerically. This report does 

include maps that illustrate the allocation of National Fire Plan Community 

Assistance grants in Oregon and Washington in comparison with poverty 

and WUI and Inhabited Wildland areas.

Findings

When we began this study, we anticipated that fi ndings would focus on 

the provision of services (or gaps in services) to at-risk communities. Actual 

fi ndings are considerably different from this original intent, due largely to 

the limited availability of data and lack of monitoring information.
406 USDA Forest Service Proceedings RMRS-P-41. 2006.

Lynn and Gerlitz Mapping the Relationship Between Wildfi re and Poverty...

Overall, the fi ndings indicate that using national datasets to illustrate the 

complex social and ecological factors infl uencing wildfi re risk is limited by 

the very nature of these elements. Datasets available for social, economic and 

ecological factors are more refi ned and meaningful on smaller scales. Locally 

specifi c data and information provide a better indication of the relationship 

between wildfi re and poverty and how well services for fi re protection are 

being provided to at-risk communities. This is apparent in the data we reviewed, as well as from comments from public meetings held in southwest 

and central Oregon and through dialogue with national partners. Despite 

these challenges, specifi c research fi ndings include:

 1) a slightly higher percentage of poor households in inhabited wildland 

areas that are not considered part of the WUI;

 2) poor households in Washington State are more likely to be in areas with 

low or no fi re response capabilities than are non poor households;

 3) federal land management agency information about grants to communities and hazardous fuels reduction projects is insuffi cient to allow an 

analysis of areas served or improved.

The following section describes these fi ndings in more detail.

Poverty and Wildland Urban Interface and Inhabited Wildland Areas

The fi rst set of fi ndings is related to the incidence of poverty in the wildland urban interface and other inhabited forested land areas. Initial analysis 

using the WUI dataset resulted in maps that showed a small portion of the 

total forested land area, particularly in the western United States. Further 

investigation indicated that the federally defi ned “Wildland Urban Interface”

is based on residential density that excludes many inhabited forest areas. Expanding the analysis to include wildland intermix, the less densely populated 

areas that are not included in the WUI, which we refer to from here on as 

“Inhabited Wildlands,” allowed us to include signifi cant portions of rural, 

inhabited land in areas vulnerable to wildfi re.

Table 1 illustrates the percentage of households in Oregon, Washington, 

and nationally in WUI and Inhabited Wildland areas and compares non-poor, 

poor, and very poor households. These percentages illustrate a trend in the 

Northwest and nationally of a greater number of poverty areas in inhabited 

wildland areas than in the states or nation as a whole, or in WUI areas or 

non-forested areas.

Results from this analysis indicate that, in general, there are more households in poverty in inhabited wildland areas than there are in the Wildland 

Urban Interface or in areas outside of the vegetated wildlands in the rest of 

the state. The researchers held regional meetings to share preliminary fi ndings with community organizations, agencies, and citizens in poor areas to 

examine data at a local level. These meetings reinforced the fi nding that the 

inhabited wildland areas that do not fall within the federal WUI defi nition 

are areas with a greater number of households in poverty.

Maps of Oregon, Washington, and the United States on the following pages 

illustrate the data described above and provide a visual representation of the 

relationship between wildfi re and poverty. The maps illustrate HUD units 

where 20% of households or more are low-income households in Wildland 

Urban Interface and Inhabited Wildland areas.

The study maps of Oregon and Washington clearly indicate a tremendous 

amount of inhabited wildland, particularly in the western United States, 
USDA Forest Service Proceedings RMRS-P-41. 2006. 407

Mapping the Relationship Between Wildfi re and Poverty... Lynn and Gerlitz

that is not considered part of the WUI under the Federal Register defi nition 

(fi gures 1, 2, and 3). There is a relatively high level of poverty in the nonWUI rural areas (areas where the housing density is too low to be included 

in the WUI).

The maps of Oregon and Washington illustrate a strong relationship between poor areas and the communities in the Inhabited Wildland areas. The 

national numbers support this relationship as well. However, more detail is 

evident from the national map, which illustrates that, although there may be 

more poverty in the inhabited wildlands in some regions, such as the western 

United States, other regions may have more households in poverty in the 

WUI, as appears to be the case in the Southeast.

If agencies are following the Federal Register defi nition, the strategy to 

prioritize WUI lands for hazardous fuels reduction work and the funding 

reserved for those areas means that fewer resources are being allocated in 

some regions to the poorest citizens in communities that may need the most 

assistance.

Poverty and Protection Capability

This study provides data about the level of fi re district capabilities, which 

is only one indicator of the capacity of a community to reduce wildfi re risk. 

This information is provided for the state of Washington.

Table 2 illustrates the percentage of poor and non-poor households in each 

of four fi re response categories in Washington. A small area in the west-central portion of the state did not fall under a particular response category but 

showed that 33.1% of households are poor. Although there are low-income 

populations with all levels of fi re protection, the map illustrates the visual 

relationship between the Wildland Urban Interface and Inhabited Wildland 

areas, as well as poverty and protection capability. In general, a higher percentage of poor households live in areas with no or low fi re response capability 

than do non-poor households.

Figure 4 illustrates the level of fi re protection capability in relation to the 

Wildland Urban Interface and poverty data in the state of Washington. The 

map shows a relationship between high poverty areas that overlap with areas 

with limited to no protection capability.

Table 1—Household Location by Poverty Level and Wildland Urban Interface Designation.

 Fire hazard Designation Inhabited

Income level Location Overall Not vegetated WUI wildlands

Non Poor National 77% 79% 81% 76%

 Oregon 79% 78% 83% 77%

 Washington 79% 79% 83% 78%

Poor National 23% 21% 19% 24%

 Oregon 21% 21% 17% 23%

 Washington 21% 21% 17% 22%

Very Poor National 12% 10% 9% 12%

 Oregon 10% 10% 8% 11%

 Washington 11% 10% 8% 11%
408 USDA Forest Service Proceedings RMRS-P-41. 2006.

Lynn and Gerlitz Mapping the Relationship Between Wildfi re and Poverty... Figure 1—Oregon: Wildland Urban Interface, Inhabited Wildlands, and Low-Income Areas.
USDA Forest Service Proceedings RMRS-P-41. 2006. 409

Mapping the Relationship Between Wildfi re and Poverty... Lynn and Gerlitz Figure 2—Washington: WUI, Inhabited Wildlands, and Low-Income Areas.
410 USDA Forest Service Proceedings RMRS-P-41. 2006.

Lynn and Gerlitz Mapping the Relationship Between Wildfi re and Poverty...Figure 3—National: WUI, Inhabited Wildlands, and Low-Income Areas.
USDA Forest Service Proceedings RMRS-P-41. 2006. 411

Mapping the Relationship Between Wildfi re and Poverty... Lynn and Gerlitz

Table 2—Washington Households, Poverty Level and Fire Protection Capability.

 High Fire Medium Fire Low Fire No Fire 

Income Level Response Response Response Response

Non-Poor 82% 85% 79% 77%

Poor 18% 16% 21% 23%

Very Poor 8% 7% 10% 12%

Figure 4—Washington: Fire District Rating and Low-Income Areas.
412 USDA Forest Service Proceedings RMRS-P-41. 2006.

Lynn and Gerlitz Mapping the Relationship Between Wildfi re and Poverty...

Federal Resource Allocation and Grants

The original goal of this study was to examine the provision of fi re-related 

services and resources to low-income, low capacity communities in high-risk 

wildfi re areas. Because of limited data about actual grant and resource allocation, it is not possible to draw reliable conclusions about resource allocation 

in and around poor communities. Consequently, our fi ndings are limited to 

the discovery that there is inadequate monitoring of NFP expenditures and 

program implementation at the national level to ensure the accountability 

of federal programs to the goals and priorities set forth in the National Fire 

Plan, Healthy Forest Restoration Act, and related wildfi re programs.

National Fire Plan Grants—Data about fi re and aviation community 

assistance grants obtained through the National Fire Plan offi ce in Region 

6 (Oregon and Washington), produced maps that refl ect areas that have received grants that relate to the poverty data in WUI and Inhabited Wildland 

areas.

The limitations of these data, as described in the research methods section, 

above, restricted our ability to provide percentages of poor communities that 

have received (or benefi ted from) National Fire Plan grants. The points on 

the map illustrate where grant funds have been received, not where grants 

were actually expended. In some cases, grants may have been received by 

agencies and organizations in county seats or municipalities that have higher 

income levels than the more rural areas where the funds were expended. The 

point data also lack information on the type and amount of treatment that 

occurred and the extent to which fi re and fuel conditions, and community 

capacity have changed in low-income areas.

Recommendations

Due to the limited availability of data and the limitations of the existing 

data, we have focused our recommendations on improving federal agency 

understanding and use of social and economic factors through national inventory and monitoring efforts, and on increasing and improving assistance for 

low-income and low capacity communities. A summary of recommendations 

is provided below.

 1. Redefi ne the areas prioritized for federal assistance to include rural areas 

with lower residential density (e.g., inhabited wildlands).

 2. Improve systems for monitoring and evaluating the National Fire Plan and 

other federal fi re-related program implementation by including social 

and economic, as well as ecological, information.

 3. Immediately develop nationally consistent standards for monitoring National Fire Plan expenditures that will enable assessment of outcomes 

over time.

 4. Develop a method for measuring community capacity in the context of 

wildfi re.

 5. Provide clear direction to federal and state land management agencies 

for determining “at risk” communities, giving signifi cant consideration 

to social and economic factors. Target assistance and federal programs 

based on community needs.

 6. Integrate indicators of community capacity into state, regional, and local 

planning and risk assessment.
USDA Forest Service Proceedings RMRS-P-41. 2006. 413

Mapping the Relationship Between Wildfi re and Poverty... Lynn and Gerlitz

 7. Increase federal support and funding to programs that target assistance 

to “at risk” communities.

 8. Conduct case studies in high wildfi re risk areas to gain more in-depth 

knowledge about the relationship between wildfi re, poverty and community capacity.

Acknowledgments

Several individuals were instrumental to this research. Special thanks to 

Dacia Meneguzzo and Ron McRoberts from the Forest Inventory and Analysis, North Central Research Station, USDA Forest Service. Dacia provided 

the mapping expertise and labor, and Ron provided knowledge and advice 

regarding spatial mapping and datasets. Krista Gebert, Rocky Mountain 

Research Station, USDA Forest Service and Susan Odell, National Rural 

Community Assistance Coordinator, USDA Forest Service bridged the gap 

between the authors and the Forest Service, providing support and assistance. 

Bonnie Wood and Lauren Maloney assisted in obtaining data about community assistance grants from the Region 6 National Fire Plan offi ce.

References

American Forests. 2001. Comments on the Federal Register Notice, “Urban 

Wildland Interface Communities within the Vicinity of Federal Lands that are 

of High Risk from Wildfi re.” (February 23rd) http://www.americanforests.org/

downloads/fp/AFpolicyviews/fedregltr.pdf

California Fire Alliance. 2001 Characterizing the fi re threat to wildland-urban 

interface areas in California. Sacramento: California Fire Alliance.

Catalog of Federal Domestic Assistance 83.557: Pre-Disaster Mitigation, http://

aspe.os.dhhs.gov/search/cfda/p83557.htm

Center for Watershed and Community Health. 2001. “Wildfi re and Poverty: An 

Overview of the Interactions Among Wildfi res, Fire-related Programs, and Poverty 

in the Western States.” Prepared for CWCH by ECONorthwest. http://www.

econorthwest.com/pdf/wild_pov.pdf

The CED Centre Forest Communities Project. 1997. Promoting Community 

Economic Development for Forest-based Communities: the Process of 

Community Capacity Assessment. http://www.sfu.ca/cedc/forestcomm/

fcbackfi le/assessment/capasproc.html

Council on Environmental Quality. 1997. “Environmental Justice: Guidance Under 

the National Environmental Policy Act.” Executive Order 12898.

Council on Environmental Quality. 2000. Managing the Impact of Wildfi res on 

Communities and the Environment: A Report to the President in Response to 

the Wildfi res of 2000.

Donoghue, E. 2003. Delimiting Communities in the Pacifi c Northwest. USDA 

Forest Service. Pacifi c Northwest Research Station. General Technical Report 

PNW-GTR-570.

Donoghue, E.; and N.L. Sutton. Forthcoming. “Socioeconomic Conditions and 

Trends for Communities in the Northwest Forest Plan Region, 1990 to 2000,” 

Chapter 2, Volume 3, in Northwest Forest Plan: the fi rst ten years. Rural 

communities and economics, ed. S. Charnley, Gen. Tech. Rep. Portland, OR: U.S. 

Department of Agriculture, Forest Service, Pacifi c Northwest Research Station.
414 USDA Forest Service Proceedings RMRS-P-41. 2006.

Lynn and Gerlitz Mapping the Relationship Between Wildfi re and Poverty...

Federal Register, Vol. 66, No. 160, (August 17, 2001), Notices, http://www.fi replan.

gov/communities_at_risk.cfm

Fire Effects Monitoring and Inventory Protocol (FIREMON). http://fi re.org/

fi remon/default.htm

Healthy Forest Restoration Act of 2003. Public Law 108-148. 108th Congress.

Insurance Services Offi ce. 2003. “Public Protection Classifi cation” http://www.

isomitigation.com/fi re1.html

Jakes, P.; Kruger, L.; Monroe, M.; Nelson, K.; and Sturtevant, V. “A Model For 

Improving Community Preparedness to Wildfi re.” http://www.ncrs.fs.fed.

us/4803/highlights.htm

Johnson, K.M. “Demographic Trends in National Forest, Recreational, Retirement 

and Amenity Areas,” Working Papers on Recreation, Amenities, Forests and 

Demographic Change. No. 2. 2002.

Kusel, J. 1996. Well-being in forest-dependent communities, part I: A new approach. 

In Sierra Nevada Ecosystem Project: Final Report to Congress, vol. II, Assessments 

and Scientifi c Basis for Management Options, S. Doak and J. Kusel (eds.), 361-74. 

Davis: University of California, Centers for Water and Wildland Resources.

Landscape Fire and Resource Management Planning Tools Project, (LANDFIRE). 

http://www.landfi re.gov.

McKnight, John L.; Kretzmann, John P. 1996. Mapping Community Capacity. 

Institute for Policy Research, Northwestern University. Neighborhood Innovations 

Network.

Oregon Economic and Community Development Dept. Economic Data – Distressed 

Areas in Oregon. http://www.econ.state.or.us/distarea.htm

Oregon Progress Board. 2003 Benchmark Performance Report to the Oregon 

Legislative Assembly. Chap. 3 Communities – Social Support. http://www.econ.

state.or.us/opb/2003report/Report/2003BPR.pdf

National Association of State Foresters. 2003. Field Guidance: Identifying and 

Prioritizing Communities at Risk. http://www.stateforesters.org/reports/

COMMUNITIESATRISKFG.pdf

National Fire Plan Operations and Reporting System (NFPORS). http://www.

nfpors.gov

Northwest Area Foundation. NWAF Indicator Website – Indicators for Oregon. 

http://www.indicators.nwaf.org/ShowOneRegion.asp?FIPS=41000

Radeloff, V.C., R.B. Hammer; S.I Stewart; J.S. Fried; S.S. Holcomb; and J.F. 

McKeefry. 2005. The Wildland Urban Interface in the United States. Ecological 

Applications 15:799-805.

Smith, N.; Littlejohns, L.B.; and Roy, D. May 2003. “Measuring Community 

Capacity: State of the Field Review and Recommendations for Future Research.” 

David Thompson Health Region Red Deer, Alberta T4N 6H2

Stauber, K.N. 2001. Why Invest in Rural America—And How? A Critical Public 

Policy Question for the 21st Century, Northwest Area Foundation

Stone, W. 2001. Measuring Social Capital: Towards a theoretically informed 

measurement framework for researching social capital in family and community 

life. Research Paper No. 24, February 2001, Australian Institute of Family 

Studies.

Sturtevant, V.; Moote, M.A; Jakes, P.; Cheng, A.S. 2005. Social Science to improve 

fuels management: a synthesis of research on collaboration. Gen Tech. Rep. 

NC-257. St. Paul, MN: U.S. Department of Agriculture, Forest Service, North 

Central Research Station.

Teie, W.C., B.F. Weatherford. 2000. Fire in the west: The wildland/urban interface 

fi re problem. Report to the Council of Western State Foresters. Rescue, CA: Deer 

Valley Press.
USDA Forest Service Proceedings RMRS-P-41. 2006. 415

Mapping the Relationship Between Wildfi re and Poverty... Lynn and Gerlitz

U.S. Census Bureau. The 2003 HHS Poverty Guidelines. U.S. Dept. of Health and 

Human Services. http://aspe.hhs.gov/poverty/03poverty.htm

U.S. Department of Agriculture Economic Research Service. Rural Indicators Map 

Machine. http://www.ers.usda.gov/data/RuralMapMachine/

U.S. Department of Agriculture Forest Service and US Department of the Interior 

Bureau of Land Management. 2004. The Healthy Forests Initiative and Healthy 

Forests Restoration Act: Interim Field Guide. FS-799. http://www.fs.fed.us/

projects/hfi /fi eld-guide/documents/haz-fuel-cvr.pdf

U.S. Department of Agriculture Forest Service. 2003. The Principal Laws Relating 

to USDA Forest Service State and Private Forestry Programs. FS-758.

U.S. Department of Agriculture Forest Service. 2000a. Interim Strategic Public 

Outreach Plan: Reaching Out to America. FS-665. http://www.fs.fed.us/cr/

national_programs/correspondence/spop/fsspop.pdf

U.S. Department of Agriculture Forest Service. 2000b. Protecting People and 

Sustaining Resources in Fire Adapted Ecosystems: A Cohesive Strategy. The Forest 

Management Team Response to the General Accounting Offi ce Report GAO/

RCED-99-65.

U.S. Department of Agriculture and U.S. Department of the Interior. 2001. Urban 

Wildland Interface Communities Within The Vicinity Of Federal Lands That Are 

At High Risk From Wildfi re. Federal Register 66: 751.

U.S. Department of Housing and Urban Development. 2003. FY 2003 HUD 

Income Limits Briefi ng Material. http://www.huduser.org/datasets/il/fmr03/

BRIEFING-MATERIAL-3-1-03.doc

Vogelmann, J.E.; S.E. Howard; L. Yang; C.R Larson; B.K. Wylie; N. van Driel. 2001. 

Completion of the 1990s National Land Cover Data set for the conterminous 

United States from Landsat Thematic Mapper data and ancillary data sources. 

Photogr. Eng. & Remote Sensing 67, 650-662.
USDA Forest Service Proceedings RMRS-P-41. 2006. 417

Fuel Treatment and Prescribed Fire
418 USDA Forest Service Proceedings RMRS-P-41. 2006.
USDA Forest Service Proceedings RMRS-P-41. 2006. 419

In: Andrews, Patricia L.; Butler, Bret W., 

comps. 2006. Fuels Management—How to 

Measure Success: Conference Proceedings. 

28-30 March 2006; Portland, OR. 

Proceedings RMRS-P-41. Fort Collins, 

CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research 

Station.

1 Fire Ecologist, National Park Service, 

Whiskeytown National Recreation Area, 

Whiskeytown, CA. Tim_Bradley@nps.gov

2 Ecologist, National Park Service, 

Whiskeytown National Recreation Area, 

Whiskeytown, CA.

3 Biological Science Technician, National 

Park Service, Whiskeytown National 

Recreation Area, Whiskeytown, CA.

Abstract—Fire risk is an ever present management concern in many urban interface 

regions. To mitigate this risk, land management agencies have expanded their options 

beyond prescribed fi re to include vegetation mastication and other mechanical fuel 

treatments. This research project examined fi re severity and intensity in masticated 

and unmanipulated units that were burned in spring in a Northern California mixed 

shrub woodland. Mastication treatments signifi cantly altered the fuel profi le, resulting 

in an approximate 200 percent average increase in woody fuel cover for 1-hr and 

1000-hr TLFM size classes, and greater than 300 percent average cover increase in 

10-hr and 100-hr TLFM size classes. The mean fl ame length (29 vs. 10 inches/ 74 

vs. 25 cm) and fl ame zone depth (20 vs. 6 inches/ 51 vs. 15 cm) were signifi cantly 

greater (P<0.001) in masticated units than in unmanipulated units as were the mean 

temperatures at the litter surface (657°F vs. 219°F/ 347°C vs. 104°C) and 1.64 ft (0.5 m) 

above the litter surface (277°F vs. 59°F/ 136°C vs. 15°C) (P<0.001). Greater fl aming and heat release in the masticated units led to increased mortality of overstory 

and pole-sized oaks and conifers posing confl icts with the management objective of 

retaining overstory vegetation.

Introduction

Land managers in the Western United States are increasingly faced with 

the challenge of implementing wildland fuel reduction treatments that are 

both effective and achievable within reasonable time frames. Traditionally, 

managers have relied on prescribed fi re as the primary tool for landscape level 

risk reduction and ecosystem restoration in fi re prone plant communities. 

However, a number of challenges complicate the achievement of fuel reduction 

goals using prescribed fi re alone. These challenges include air quality restrictions, limited burn windows, insuffi cient staffi ng, and the liability associated 

with escaped burns. Due to these limitations, managers are increasingly 

turning to the use of mechanical treatments as a supplement to prescribed 

fi re for the accomplishment of fuels management objectives.

One option that has gained popularity with land managers in Western 

states is vegetation mastication, which can allow managers to quickly and 

safely decrease shrub and other understory vegetation at a fraction of the 

cost of comparable manual thinning treatments. Tens of thousands of acres 

of shrubs and other understory species in fi re-prone plant communities are 

being treated with vegetation mastication to reduce fi re hazard. Most land 

management agencies prefer to leave masticated biomass on the ground to 

cycle nutrients, prevent soil erosion, and to impede the establishment of nonnative and invasive plant species. However, since mastication does not remove 

Fire Severity and Intensity During Spring 

Burning in Natural and Masticated Mixed 

Shrub Woodlands

Tim Bradley1, Jennifer Gibson2, and Windy Bunn3
420 USDA Forest Service Proceedings RMRS-P-41. 2006.

Bradley, Gibson, and Bunn Fire Severity and Intensity During Spring Burning in Natural and Masticated Mixed...

this biomass, but rather converts the standing brush to dead surface fuels, fi re 

risk can still be high. Despite gaining acceptance as a landscape-scale treatment, signifi cant uncertainty exists regarding the effects of these alterations 

on fi re behavior in both prescribed fi re and wildland fi re scenarios.

Like many National Park Service units throughout the country, Whiskeytown National Recreation Area recently revised its Fire Management Plan 

(National Park Service 2003). This plan greatly expands the options available 

to park managers and includes a suite of mechanical treatments, such as manual 

thinning, small-scale logging, and vegetation mastication that have yet to be 

tested in the park or in similar habitat types elsewhere. With support funding 

from the Joint Fire Science Program, a research project was initiated in 2002 

to provide managers with a better understanding of the effects of one of these 

treatments, vegetation mastication, on fi re behavior and intensity.

Since fuel beds resulting from the mastication of shrubs and small trees 

are most similar to those of a logging slash fuel model, it is hypothesized 

that the increase in small-sized surface fuels would increase fi re intensity and 

severity. The overall goal of this project was to evaluate key fi re behavior 

indices and severity effects to vegetation in both masticated and unmanipulated vegetation during a spring prescribed burn. Specifi c objectives for the 

unmanipulated vegetation were consistent with prescribed burn treatments 

applied throughout the park, while separate project-specifi c objectives were 

developed for the application of fi re to masticated vegetation (table 1). These 

objectives targeted the reduction in specifi c fuel classes and the retention of 

overstory trees.

Study Site

Whiskeytown National Recreation Area is located on the southeastern 

edge of the Klamath Mountains in Northern California. The climate is 

characterized as Mediterranean, with cool, wet winters and hot, dry summers. Temperature readings are often over 100 ̊F (38  ̊C) from May through 

October and occasional sub-freezing temperatures occur from November 

through March. The annual precipitation averages 60 inches (152 cm) at 

Table 1—Management objectives for the prescribed fi re treatments in masticated and 

unmanipulated fuelbeds.

 Targeted percent change

 Objective Masticated Unmanipulated 

Reduce surface fuel accumulation (litter, duff, 1, 

 10, 100, 1000 hr TLFM) 15 to 35 25 to 70

Reduce live density of small knobcone pine 

 trees (<8 inch/20 cm d.b.h) 0 to 25 10 to 75

Reduce live density of all other small trees 

 (<8 inch/20 cm d.b.h) 0 to 25 0 to 40

Limit mortality of overstory trees 

 (>8 inch/20 cm d.b.h) 0 to 15 0 to 15

Reduce cover of live shrubs 0 to 25 15 to 75
USDA Forest Service Proceedings RMRS-P-41. 2006. 421

Fire Severity and Intensity During Spring Burning in Natural and Masticated Mixed... Bradley, Gibson, and Bunn

park headquarters, most of which falls between November and April. The 

45 acre study site is located in a low elevation (1,250 to 1,400 ft/380 to 

460 m) area that has slopes less than 30 percent (the upper limit for the 

selected machinery). Overstory vegetation is dominated by black oak ( Quercus 

kelloggii) and knobcone pine (Pinus attenuata), with limited presence of 

other species such as canyon live oak (Quercus chrysolepis), grey pine (Pinus 

sabiniana), and interior live oak (Quercus wislizeni). The understory vegetation is typically dense and dominated by whiteleaf manzanita (Arctostaphylos 

viscida), with toyon (Heteromeles arbutifolia) and poison oak (Toxicodendron 

diversilobum) also common.

Experimental Design and Treatments

The research site was stratifi ed based on vegetation, slope, and aspect, resulting in the selection of ten different 1 to 2 acre (0.4 to 0.8 ha) treatment 

blocks. Each treatment block was divided into fourteen approximately equalsized units, with two units from each block representing masticated (n=20) 

and unmanipulated (n=16) vegetation burned in the spring. The remaining 

experimental units are part of a separate long-term research project focusing 

on vegetation response to mastication and other fuels treatments.

Mastication treatments were completed in November of 2002 using an 

ASV Posi-TrackTM with industrial brush-cutter. At least 90 percent of machine 

operations occurred over surfaces covered with chipped wood to limit soil 

disturbance and compaction (Poff 1996). To further minimize soil impacts, 

the tractor specifi cations required rubber tires or tracks, a vehicle no larger 

than 10,000 gross pounds (4,500 kg), an average of less than 3.5 pounds 

per square inch (0.25 kg/cm2) ground pressure, and operation on dry soil 

(Windell and Bradshaw 2000). The goal of this treatment was to reduce understory bulk density by 60 to 95 percent by thinning shrubs and small trees 

less than four meters in height. In areas where overstory trees were absent, 

a limited cover of shrub species was maintained.

Prescribed burn treatments were designed to be representative of treatments typically applied within the park. All fi res were backing with respect 

to slope and/or wind, utilizing drip torches and applying a combination of 

strip and spot ignition patterns. Ambient weather conditions were recorded 

on-site by fi re effects monitors. During the burning period (April-May 2003), 

temperature extremes ranged from 59°F to 71°F (15°C to 22°C), relative 

humidity ranged from 34 to 73 percent, and wind speeds averaged 2 mph 

(3 km/h) with a maximum wind speed of 6 mph (9.5 km/h). Soil moisture 

readings were very high (0.3 to 0.4 kPa tension) as recorded by a Delmhorst 

KS-D1 soil moisture meter at reference locations 18 inches (45 cm) below 

the surface.

Fire behavior and effects measures were recorded for each burn unit in four 

1 m2 fi re behavior plots (n=140). Within each fi re behavior plot, pre- and 

post-burn measurements were collected for litter, duff, 1-hr (<0.25 inches 

or 0.6 cm), 10-hr (0.25 to 1 inch or 0.6 to 2.5 cm), 100-hr (1 to 3 inches 

or 2.5 to 7.6 cm) and 1000-hr (>3 inches or 7.6 cm) time lag fuel moisture 

(TLFM) cover. In addition, percent cover values for herbaceous vegetation 

and bare ground were recorded. Using a method similar to Hobbs and Atkins 

(1988), a garden stake with pyrometers was located at the center of each fi re 

behavior plot to record maximum temperature. Pyrometers were constructed 

using brass tags painted with heat-sensitive paint (OMEGALAQ®, Omega 
422 USDA Forest Service Proceedings RMRS-P-41. 2006.

Bradley, Gibson, and Bunn Fire Severity and Intensity During Spring Burning in Natural and Masticated Mixed...

Engineering, Inc.), and were positioned in three strata: 1) between the duff 

and soil layers; 2) on top of the litter; and 3) 0.5 m (1.64 ft) above the litter 

surface. During the burn, fi re behavior data were recorded on the maximum 

and average fl ame lengths, fl ame zone depths, rates of spread, and fi re types 

(head, backing, or fl anking). One month after the burn, scorch estimates for 

dominant trees and shrubs were recorded for each burn unit and tree and shrub 

mortality estimates were recorded approximately six months post-burn.

To examine potential patterns in fi re behavior, severity, and surface fuels, 

all fi re behavior plots were characterized through a Principal Components 

Analysis (PCA) (Tabachnick and Fidell 1996). A two-tailed t-test (Zar 1996) 

was used to determine the difference in the mean PCA factor scores for 

masticated and unmanipulated vegetation. Similarly, a two-tailed t-test was 

used to determine the mean difference in fl ame length and fl ame zone depth 

for masticated and unmanipulated vegetation. To ascertain differences in 

pyrometer temperature between masticated and unmanipulated vegetation, 

a two-tailed t-test was used. A multiple regression (Zar 1996) was used to 

model relationships for aerial and litter level pyrometers with surface fuels 

and fi ne dead fuel moisture.

Results and Discussion

The effect of the brush mastication treatment did not result in a reduction 

of fuels, but rather the rearrangement of standing live material into dead and 

small-sized surface fuels. Prior to implementation of the mastication treatment, the fuels at the site were best characterized as a mix of fuel models 4, 8, 

and 9 (Anderson 1982). After mastication, the fuel bed changed drastically, 

with post treatment conditions representative of fuel model 11 (logging slash). 

This conversion of standing vegetation into downed woody debris resulted 

in an approximate 200 percent average cover increase in woody fuel loading for 1-hr and 1000-hr TLFM size classes, and greater than 300 percent 

average cover increase in 10-hr and 100-hr TLFM size classes. In addition 

to a surface fuel quantity increase, average shrub canopy cover was reduced 

from 64% down to 2% by the mastication treatment. This removal of canopy 

cover can contribute to an increase in air circulation, surface temperature, 

and direct solar radiation (Aussenac 2000), which can dry fuels quickly and 

increase fl ammability (Weatherspoon 1996). The results from this research 

strongly suggest that the combination of rearranging the structure of fuels 

while simultaneously altering the site microhabitat characteristics, led to an 

increased potential for high intensity fi re.

To examine potential patterns between surface fuels and indices of fi re 

behavior and severity, a Principal Components Analysis (PCA) was used 

(fi gure 1). Positively skewed data were transformed using the square root 

and the Pearson’s product moment correlation coeffi cient (Zar 1996) was 

used to eliminate variables that were highly correlated (>0.6). The PCA illustrated differences between masticated and unmanipulated plots for Factor 

1 scores. A two-tailed t-test on the PCA scores demonstrated a difference 

in the amount of surface fuels, fi re behavior, and fi re severity variables with 

mean Factor 1 scores for masticated plots (0.480) signifi cantly (P<0.001) 

greater than those for unmanipulated vegetation (–0.583). The high Factor 

1 scores for masticated plots indicate a high amount of surface fuels (litter, 

1-hr, 10-hr, and 100-hr fuels), wide fl ame zone depth, and greater aerial 

temperatures. Plots in unmanipulated vegetation had a high percent cover 
USDA Forest Service Proceedings RMRS-P-41. 2006. 423

Fire Severity and Intensity During Spring Burning in Natural and Masticated Mixed... Bradley, Gibson, and Bunn

Figure 1—PCA scores for masticated (left) and unmanipulated (right) plots during the 

burn treatment.

of herbaceous species, bare ground, and low surface fuels, fi re behavior and 

fi re severity values.

A variety of fi re intensity measures showed striking differences between 

the masticated plots and those in unmanipulated vegetation. A two-tailed 

t-test for both fl ame length and fl ame zone depth indicated greater values 

in masticated plots when compared to plots in unmanipulated vegetation. 

Mean fl ame length (29 inches/ 74 cm) and fl ame zone depth (19 inches/ 

48 cm) were signifi cantly greater (P<0.001) in masticated plots than mean 

fl ame length (10 inches/ 25 cm) and fl ame zone depth (6 inches/ 15 cm) 

in the unmanipulated plots. Two of the three strata tested with pyrometers 

also indicated signifi cant temperature differences between masticated and 

unmanipulated plots (fi gure 2). A two-tailed t-test showed that mean temperatures for litter (657°F/ 347 ̊C) and aerial (277°F/ 136 ̊C) pyrometers in 

the masticated plots were signifi cantly greater (P<0.001) than temperatures 

recorded for litter (219°F/ 104 ̊C) and aerial (59°F/ 15 ̊C) pyrometers in 

unmanipulated vegetation. While above ground temperatures were moderate 

to high, high duff and soil moistures moderated intensity effects to the soil, 

with only limited heating recorded by the lowest pyrometer. As a result of 

these conditions, duff reduction was not complete in either masticated (27 

percent consumption) or unmanipulated (16 percent consumption) fuels.

The data for aerial and litter pyrometers were analyzed by multiple linear 

regression models to investigate the relationship among variables. With aerial 

pyrometer temperature as the dependent variable, the best fi tting model (P = 

0.004) included 100-hr fuels and fi ne dead fuel moisture as independent variables (table 2). With litter pyrometer temperature as the dependent variable, 

the best fi tting model was also highly signifi cant (P = 0.026) and included 

litter depth, 10-hr fuels, and 100-hr fuels as dependent variables (table 3). 

Despite their high signifi cance, each of these models demonstrated relatively 

mediocre fi t with r2 = 0.314 for aerial pyrometers and r2 = 0.478 for litter 
424 USDA Forest Service Proceedings RMRS-P-41. 2006.

Bradley, Gibson, and Bunn Fire Severity and Intensity During Spring Burning in Natural and Masticated Mixed...

Figure 2—Average temperatures recorded by pyrometers during the burn treatment in 

masticated and unmanipulated plots. Aerial pyrometers were located 0.5 m/1.64 ft above the 

ground surface, litter pyrometers were located on the surface of the litter, and soil pyrometers 

were located between the duff and soil layers.

Table 2—Regression statistics for aerial (0.5 m/1.64 ft above ground 

surface) pyrometers

Model Term Parameter estimate SE Pr(>|t|)

Intercept 357.9272 122.6933 0.0041

100 hr. Fuelsa 14.4611 2.5365 0.0000

FDFMb –28.1747 12.2782 0.0233

a 100 hour TLFM size class

b Fine dead fuel moisture

Table 3—Regression statistics for pyrometers placed at the litter 

surface.

Model Term Parameter estimate SE Pr(>|t|)

Intercept 453.5598 201.7489 0.0262

Litter Depth 91.0512 21.3446 0.0000

10 hr Fuelsa 8.6276 1.8347 0.0000

100 hr Fuelsb 13.1951 4.0239 0.0013

FDFMc –46.1797 19.2146 0.0176

a 10 hour TLFM size class

b 100 hour TLFM size class

c Fine dead fuel moisture
USDA Forest Service Proceedings RMRS-P-41. 2006. 425

Fire Severity and Intensity During Spring Burning in Natural and Masticated Mixed... Bradley, Gibson, and Bunn

pyrometers. It is probable that a more accurate quantifi cation of the fuelbeds 

would have improved our results, although at a signifi cant increase in time. 

Regardless, given the high level of variability that existed within individual 

fuelbeds, such fi ndings are not surprising and perhaps highlight the differences frequently found between laboratory and fi eld experiments. Of note 

is the correlation shown by fi ne dead fuel moisture in both models. While a 

coarse value, fi ne dead fuel moisture is sensitive to changes in canopy cover 

and regularly recorded ambient weather conditions.

Based on the multiple regression analyses, surface fuel loading was a primary driver of fi re behavior, with signifi cant fuel consumption differences 

noted between treatments (fi gure 3). With the exception of 1-hr fuels, total 

percent consumption in the masticated fuelbeds was higher for all TLFM 

size classes. It is probable that the apparently low consumption of 1-hr size 

class fuels in the masticated fuels (17 percent) was actually much higher, and 

includes larger 10-hr and 100-hr fuels that were only partially consumed 

during the burns. Interestingly, an increase was noted in 100-hr and 1000-hr 

TLFM size classes following the burn treatment in unmanipulated vegetation. While only a marginal increase, this fi nding is consistent with other 

monitoring completed at the park, refl ecting the addition of recently killed 

vegetation to the surface fuelbed.

Figure 3—Consumption of downed woody fuels during the burn treatment in masticated and 

unmanipulated plots. Fuels are categorized as 1 hour, 10 hour, 100 hour, and 1000 hour time lag fuel 

moisture (TLFM).
426 USDA Forest Service Proceedings RMRS-P-41. 2006.

Bradley, Gibson, and Bunn Fire Severity and Intensity During Spring Burning in Natural and Masticated Mixed...

The total surface fuel reduction objectives (table 1) were achieved in both 

masticated and unmanipulated vegetation, but the fi re effects to live vegetation were more complex. In the unmanipulated units, reduction targets 

were met for pole-sized (<8 inches or 20 cm d.b.h.) trees and shrubs and 

there was no mortality of overstory (>8 inches or 20 cm d.b.h.) trees (table 

4). However, in the masticated units, reduction and mortality targets were 

greatly exceeded for pole-sized trees, shrubs and overstory trees (table 4). 

Despite efforts by ignition crews to mitigate effects to overstory trees, the 

heat effects to these trees and to residual shrubs in the masticated units were 

severe. While applying prescribed fi re during the early growing season was 

likely a contributing factor to this mortality, the increased fi re intensity in 

masticated fuels was the primary cause.

Management Implications

Results from this study showed signifi cant differences in fi re behavior and 

effects during spring prescribed burns in units with masticated vegetation 

versus those with unmanipulated vegetation. These results strongly suggest 

that the differences were driven by the surface fuel conditions created as a 

direct result of the mastication treatment. Through time, decomposition and 

compaction of these materials may promote lowered fi re intensity potential, 

but in the short term mastication appeared to contribute to an increase in 

fi re severity and intensity.

While vegetation mastication followed by prescribed fi re was a success 

from a fuel reduction standpoint, fi re intensity in the masticated units was 

lethal for much of the residual vegetation. Since the mastication treatment 

had already eliminated shrubs and small trees, the effect of the prescribed 

burn on retained vegetation was undesirable. In natural areas the retention of 

overstory trees is a primary resource management concern during prescribed 

burns, and these results highlight the potential confl icts of burning in varied 

fuelbeds when objectives extend beyond surface fuel consumption.

While this study was restricted to one site, the results apply to many land 

management agencies that are interested in applying mastication treatments 

Table 4—Average percent mortality of trees and shrubs during the spring burn treatment in masticated 

and unmanipulated plots.

 Overstory (>8 inch/20 cm d.b.h) Pole (<8 inch/20 cm d.b.h.)

 Unmanipulated Masticated Unmanipulated Masticated

 - - - - - - - - - - - - - - - - - - - percent mortalitya - - - - - - - - - - - - - - - - - - -

Knobcone Pine 

(Pinus attenuata) 0 16 15 66

Black Oak 

(Quercus kelloggii) 0 23 17 47

Canyon Live Oak 

(Quercus chrysolepis) 0 49 21 98

Shrubs Unmanipulated Masticated

 30 96

aMortality fi gures for resprouting oak species refers to top-killed individuals.
USDA Forest Service Proceedings RMRS-P-41. 2006. 427

Fire Severity and Intensity During Spring Burning in Natural and Masticated Mixed... Bradley, Gibson, and Bunn

for reduction of understory vegetation. The following list highlights some 

of the management implications derived from this research:

1) Mastication of vegetation results in a short to medium-term increase in 

fi re intensity and severity potential. Where utilized, mastication prescriptions should consider the need for greater canopy retention to increase 

shading at the soil surface, thus increasing fi ne dead fuel moisture and 

contributing to slower seasonal drying of fuels. In addition, lowering 

intensity of mastication will directly reduce total surface fuel load.

2) Mortality of remaining overstory vegetation may be high in areas where 

masticated treatments are followed by prescribed burning. Managers may 

be able to reduce this secondary mortality by:

 • Decreasing the level of mastication intensity. This will contribute to 

lower fi re behavior indices and severity results by reducing surface 

fuel loading, increasing shading of fuels, decreasing wind circulation 

and thus, drying of surface fuels.

 • Applying fi re during mild conditions. Mastication treatments signifi -

cantly alter the fuelbed and result in signifi cantly different fi re behavior 

than in unmanipulated vegetation. Prescriptions must consider these 

differences in expected behavior and subsequent severity.

 • Avoiding spring or early growing-season burns when desirable species 

are in a susceptible period of development. The post green-up application of fi re in this study coincided with a vulnerable phenologic 

period in plant development, when leaf, bud, and cambium tissues 

were particularly susceptible to thermal effects. Prescription windows 

that are scheduled during the dormant season would likely minimize 

severity effects to retained vegetation.

3) Short-term increases in fi re intensity occur following mastication; however, long-term trends are still unknown. This study was conducted six 

months after mastication when the masticated fuelbed was still loosely 

arranged on the surface. Through time, it is expected that decomposition and compaction of the masticated fuels would occur, lowering the 

potential fi re intensity, but the rate of change is not known. Research 

on assessing changes in masticated vegetation over time would provide 

valuable information for long-term management.

Acknowledgments

The authors would like to thank Whiskeytown NRA Fire and Resource 

Management personnel for assistance in all aspects of this project. In particular, we want to thank Jake Blaufuss for setting up the initial treatment 

polygons, and Justin Cully, Johanna D’Arcy, Ed Waldron, James Savage, 

Chris Sprague, Brian Rasmussen and Joe Svinarich for fi re behavior data collection assistance. We also want to thank North Tree Fire International for 

assistance with the mastication treatment, and the California Conservation 

Corps, California Department of Forestry and Fire Protection, and USFS, 

Redding Hotshots and Redding Smokejumpers for assistance with the burn 

treatments. This project was made possible by funds provided by the Joint 

Fire Science Program.
428 USDA Forest Service Proceedings RMRS-P-41. 2006.

Bradley, Gibson, and Bunn Fire Severity and Intensity During Spring Burning in Natural and Masticated Mixed...

References

Anderson, Hal E. 1982. Aids to determining fuel models for estimating fi re behavior. 

Gen. Tech. Rep. INT-122. Ogden, Utah : U.S. Department of Agriculture, Forest 

Service, Intermountain Forest and Range Experimental Station. 22 p.

Aussenac, G. 2000. Interactions between forest stands and microclimate: 

Ecophysiological aspects and consequences for silviculture. Ann. For. Sci. 57: 

287-301.

Deeming, John E.; Burgan, Robert E.; Cohen, Jack D. 1977. The National FireDanger Rating System—1978. Gen. Tech. Rep. INT-39. Ogden, UT: U.S. 

Department of Agriculture, Forest Service, Intermountain Forest and Range 

Experiment Station. 63 p.

Hobbs, R. J.; Atkins, Lyn. 1988. Spatial variability of experimental fi res in southwest Western Australia. Australian Journal of Ecology. 13(3): 295-299.

National Park Service. 2003. Fire management plan: environmental impact statement. 

Whiskeytown, CA: National Park Service, Whiskeytown National Recreation 

Area. 269 p.

Poff, R.J. 1996. Effects of silvicultural practices and wildfi re on productivity of forest 

soils. In: Sierra Nevada Ecosystem Project: Final report to Congress, vol. II, chap. 

16. Davis: University of California, Centers for Water and Wildland Resources.

Tabachnick, B. G.; Fidell, L. S. 1996. Using Multivariate Statistics, 3rd ed. New 

York, NY: HarperCollins Publishers Inc. 880 p.

Tyler, C. M.; D’Antonio, C. M.. 1995. The effects of neighbors on the growth and 

survival of shrub seedlings following fi re. Oecologia (Berlin) 102(2): 255-264.

Weatherspoon, C. P. 1996. Landscape-level strategies for forest fuel management. 

In Sierra Nevada Ecosystem Project: Final report to Congress, vol. II, chap. 56. 

Davis: University of California, Centers for Water and Wildland Resources.

Windell, Keith; Bradshaw, Sunni. 2000. Understory biomass reduction methods 

and equipment. Tech. Rep. 0051-2828-MTDC. Missoula, MT: U.S. Department 

of Agriculture, Forest Service, Missoula Technology and Development. Center. 

32 p.

Zar, J. H. 1996. Biostatistical Analysis, 3rd ed. Upper Saddle River, NJ: PrenticeHall, Inc. 662 p.
USDA Forest Service Proceedings RMRS-P-41. 2006. 429

In: Andrews, Patricia L.; Butler, Bret W., 

comps. 2006. Fuels Management—How to 

Measure Success: Conference Proceedings. 

28-30 March 2006; Portland, OR. 

Proceedings RMRS-P-41. Fort Collins, 

CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research 

Station.

1 Research Associate, Department 

of Forest Rangeland and Watershed 

Stewardship, Colorado State University, 

Fort Collins, CO.

erikm@warnercnr.colostate.edu

2 Professor Emeritus, Department 

of Forest Rangeland and Watershed 

Stewardship, Colorado State University, 

Fort Collins, CO.

Abstract—Fuel treatments such as prescribed fi re are a controversial tenet of wildfi re 

management. Despite a well-established theoretical basis for their use, scant empirical 

evidence currently exists on fuel treatment effectiveness for mitigating the behavior and 

effects of extreme wildfi re events. We report the results of a fi re severity evaluation of 

an escaped prescribed fi re that burned into an area previously treated with repeated 

prescribed fi res. We observed signifi cantly lower scorch heights, crown damage, and 

ground char in the treated area. We attribute the moderated fi re severity in the treated 

area to a signifi cantly altered fuel profi le created by the repeated prescribed fi res. 

Though our results represent just one treatment area in a single wildfi re, they add to 

a depauperate database and bring us a step closer to defi ning the conditions under 

which fuel treatments are an effective pre-suppression strategy.

Introduction

Fuel treatment effectiveness as a pre-suppression strategy is a controversial 

tenet of wildfi re management with a strong theoretical foundation, but scant 

empirical evaluation. Several recent reviews provide a survey of the extant 

literature on the scientifi c justifi cation for fuel treatment programs (Graham 

and others 2004; Carey and Schumann 2003; Fernandes and Botelho 2003). 

A perusal of the publications cited in these reviews and those published 

subsequently (prior to March 2006) reveals that much of the evidence of 

fuel treatment effectiveness comes from the results of simulations based on 

models of fi re spread (Rothermel 1972) and crown fi re potential (Rothermel, 1991). More than half (26 of 49) of the analytical studies conducted 

in North America rely on simulations and, of these, half (13) employ hypothetical treatments as well as hypothetical wildfi res. Many questions related 

to fuel treatments can only be addressed in a modeling environment, such as 

optimal landscape placement (Finney 2001) or potential effectiveness under 

varying climate regimes. However, the ability of current fi re behavior models 

to refl ect reality has received little validation, particularly under the extreme 

conditions that produce large wildfi re events (Cruz and others 2005). Thus, 

the results of modeling experiments are best viewed as hypotheses awaiting 

an empirical test.

Nonetheless, simulation experiments have been necessary to establish a 

scientifi c basis for the effectiveness of fuel treatments, given the obvious 

limitations on experimentation with actual wildfi res. Just one study exists 

that tested the effectiveness of a fuel treatment under experimental conditions extreme enough to produce crown fi re activity (Alexander and Lanoville 

2004). This study was conducted in the boreal forest of the Canadian Northwest Territories and the authors conclude that thinning without treatment 

Assessing Mitigation of Wildfi re Severity by 

Fuel Treatments — An Example From the 

Coastal Plain of Mississippi

Erik J. Martinson1 and Philip N. Omi2
430 USDA Forest Service Proceedings RMRS-P-41. 2006.

Martinson and Omi Assessing Mitigation of Wildfi re Severity by Fuel Treatments — An Example From the Coastal Plain of Mississippi

of surface fuels is largely ineffective, though the sample size was very limited 

and no statistical analysis has been reported.

The remainder of the evidence of fuel treatment effectiveness in North 

American ecosystems relies on natural experiments in which an actual wildfi re serendipitously encountered one or more fuel treatment areas. Though 

there have been recent efforts to collect fi re behavior data in situ as wildfi res 

encounter fuel treatments (Fites-Kauffman 2001), all 22 of the natural experiments published to date have relied on post facto analysis. However, just 

11 of these studies included a statistical analysis of the treatment effect and 

only seven attempted to control for the infl uences of topography and weather, 

which along with fuels are the determinants of fi re behavior. Rather incredibly, 

only four studies have been published that included both a statistical test and 

adequate control to discern a fuel treatment effect in an actual wildfi re.

Pollet and Omi (2002) evaluated the severity of four wildfi res that burned 

over treated areas in ponderosa pine forests in Oregon, Washington, California, and Arizona. One of the fi res encountered a prescribed burn, while the 

other three encountered thinning treatments where the activity fuels were 

effectively removed, either by burning or whole-tree removal. The treatments 

were completed 1 to 11 years prior to wildfi re and in all cases fi re severity 

was found to be signifi cantly lower in treated stands.

Raymond and Peterson (2005) evaluated the severity of a wildfi re in mixed 

conifer forest of coastal Oregon that burned over four thinning treatments, 

one of which included subsequent underburning. All thinning was completed 

6 years prior to the wildfi re and the underburn was done 5 years later. Fire 

severity was found to be signifi cantly greater in two of the three thinned areas 

that were not underburned, while the third showed no effect. However, the 

wildfi re burned around the underburned treatment without entering.

Cram and others (2006) evaluated the severity of three wildfi res that 

burned over treatments in ponderosa pine forests in Arizona and New Mexico. 

All of the wildfi res included areas that were thinned followed by prescribed 

burns and one of them also included areas where the slash was scattered but 

left on-site. All treatments reduced wildfi re severity, but the treatments that 

were not prescribe-burned were less effective.

Skinner and others (in press) evaluated the severity of a wildfi re in ponderosa 

pine dominated forest in northern California that burned over fi ve thinning 

treatments, all but one of which were subsequently treated with prescribed 

fi re. Fire severity was found to be signifi cantly lower in the thinned units 

where the slash was treated, but no effect was observed in the thin-only 

treatment.

This paper describes how the fuel treatment assessment methods followed 

by Pollet and Omi (2002) were applied again to provide much needed additional empirical information from a wildfi re that burned into an area that 

had been previously treated with repeated prescribed burns in coastal Mississippi. We follow with a discussion of how our methods have since evolved 

to overcome certain limitations presented by this site.

Methods

Study Area

The study site is located on and adjacent to the Fontainebleau Unit of 

the Mississippi Sandhill Crane National Wildlife Refuge. The Refuge is approximately 8 km east of Ocean Springs in Jackson County, Mississippi in 
USDA Forest Service Proceedings RMRS-P-41. 2006. 431

Assessing Mitigation of Wildfi re Severity by Fuel Treatments — An Example From the Coastal Plain of Mississippi Martinson and Omi

the Gulf Coastal Plain physiographic province. Topography is fl at throughout at an elevation of 6m. Slash pine (Pinus elliottii Englem.) is dominant 

in the forest canopy with longleaf pine (Pinus palustris Mill.) also present. 

Sub-canopy species include persimmon (Diospyros virginiana L.) and black 

gum (Nyssa sylvatica Marsh). Vines (e.g., Vitis spp. and Smilax spp.), bays 

(Persea spp.), and gallberry (Ilex coriacea (Pursh) Chapm.) are abundant in 

the understory.

The US Fish and Wildlife Service established the Refuge in 1975 to protect 

the endangered Mississippi Sandhill Crane (Grus canadensis pulla Aldrich) and 

its wet pine savannah habitat. Management of the Refuge includes extensive 

use of prescribed fi re to reduce hazardous fuels and restore the open structure 

of longleaf pine savannahs (Platt and others 1988). One such prescribed fi re 

became the Fontainebleau wildfi re at 1430 hours on April 18, 1999 when 

it spotted across a railroad and onto private property containing untreated 

fuels best characterized by Fuel Model 7 (Anderson 1982). The wildfi re exhibited extreme behavior and at 1600 hours spotted back across the railroad 

and into a stand that Refuge managers had burned in 1988, 1992, and 1998 

with the objective of converting fuels to approximate Model 2 conditions. 

The Fontainebleau fi re grew to a fi nal size of 142 ha including 36.5 ha on 

Refuge lands last treated in 1998. Hourly weather conditions from an on-site 

Remote Automated Weather Station are provided in Table 1.

Data Collection

We collected data in September 1999 to quantify fuels and fi re severity 

differences between treated and untreated stands affected by the Fontainebleau Fire. Data were collected in nine variable radius plots in each of the two 

stand types (treated and untreated). Plot areas were defi ned with a Cruiser’s 

Crutch with a metric basal area factor of 2 (Avery and Burkhart 1994). We 

employed a systematic sampling design in which plot centers were separated 

by 60 m along three transects also separated by 60 m. A 60 m buffer on either 

side of the railroad that separates the treated and untreated areas minimized 

edge effects. Figure 1 depicts a map of the fi re perimeter, treatment area, 

and plot locations.

The trees sampled at each plot were distinguished by species and crown 

position and measured for the following aerial fuel descriptors: stand density, 

tree size and height, and height to the base of the pre-fi re live crown. The 

Table 1—Weather conditions during the Fontainebleau Fire on April 18, 1999 (weather data 

from an onsite Remote Automated Weather Station).

 Wind Wind Relative Dead Fuel Moisture Content

 Time speed Direction Temperature Humidity 1hr a 10hr b 100hr c

 hr km per hr Azimuth °C - - - - - - - - - - - - -percent- - - - - - - - - - - -

1400 10.9 315 21.0 27 5.5 6.7 15

1500 9.0 270 22.3 28 5.5 6.7 15

1600 12.1 315 21.3 28 5.6 6.6 15

1700 9.7 315 21.3 29 5.9 5.8 15

a Dead fuel moisture content is expressed by standard equilibrium time lag classes: 1hr refers to fuels less 

than 0.25 inch diameter

b 10hr fuels are less than 1 inch in diameter

c 100hr fuels are less than 3 inches in diameter
432 USDA Forest Service Proceedings RMRS-P-41. 2006.

Martinson and Omi Assessing Mitigation of Wildfi re Severity by Fuel Treatments — An Example From the Coastal Plain of Mississippi

base of the pre-fi re live crown was judged to be the lowest branch with twigs, 

though this may have been an overestimate in severely burned plots if lower 

live branches or twigs were completely consumed; or an underestimate if lower 

branches with twigs were needleless prior to the fi re. We also attempted to 

quantify the pre-fi re density and height of shrub fuels (an important component of Fuel Model 7) by sampling four 1 m2 circular plots located at 90 

degree angles and 17.85 m from each plot center. No attempt was made to 

quantify pre-fi re conditions of other surface fuel components post hoc, since 

the fi ne fuels that contribute most to surface fi re spread are consumed in most 

fi res (Ottmar and others 1993).

Following the methods used by Pollet and Omi (2002), we evaluated 

wildfi re severity at each plot in terms of stand damage, as well as upward 

and downward heat pulse components. Stand damage ratings were adapted 

from Omi and Kalabokidis (1991) while the downward heat pulse was estimated with ground char ratings adapted from Ryan and Noste (1985). 

Rating criteria are provided in table 2. Stand damage was evaluated for the 

plot as a whole, while the downward heat pulse was estimated with ground 

char ratings in four 30 m x 60 m subplots at the same locations as the shrub 

subplots described above.

Figure 1—Plot locations in relation to fuel treatments involved in the 1999 Fontainebleau 

wildfi re on and adjacent to the Mississippi Sandhill Crane National Wildlife Refuge. The fi re 

started as a prescribed burn on the Refuge (in the area shaded with horizontal lines), but was 

declared a wildfi re when it spotted across the railroad and onto private property. The wildfi re 

later spotted back across the railroad and into an area the Refuge had previously treated.

Table 2—Criteria used to evaluate fi re severity in sampled stands. 

Rating Stand Damage Criteria Ground Char Criteria

 0 All tree crowns unscorched. No evidence of surface fi re.

 1 Partial scorch on at least 1 tree, but some Litter and twigs charred.

 trees unscorched. 

 2 Partial scorch on all tree crowns, but few trees All twigs, leaves, and standing grasses 

 completely scorched. consumed, branches and logs charred.

 3 Nearly all tree crowns completely scorched, Branches and logs mostly consumed.

 but few crowns consumed. 

 4 Nearly all tree crowns consumed.
USDA Forest Service Proceedings RMRS-P-41. 2006. 433

Assessing Mitigation of Wildfi re Severity by Fuel Treatments — An Example From the Coastal Plain of Mississippi Martinson and Omi

The height of needle scorch on the coniferous trees sampled at each 

plot was measured as an indicator of fi reline intensity (Van Wagner 1973). 

Percent canopy scorch was ocularly estimated on all trees, as well. Since 

height of needle scorch underestimates fi reline intensity on trees that are 

either unscorched or completely scorched (the upper bound of scorch height 

is limited by tree height, while the lower bound is limited by crown base 

height), we modifi ed calculations for average scorch height at each plot by 

excluding measurements from trees that were uninformative or misleading. 

Specifi cally, only the following measurements contributed to plot averages 

for scorch height:

 1) Scorch heights of all partially scorched trees.

 2) Tree heights of completely scorched trees added sequentially by decreasing height until average scorch height was maximized.

 3) Bole char heights of unscorched trees added sequentially by decreasing 

height until average scorch height was maximized.

 4) Crown base heights of unscorched trees added sequentially by increasing 

height until average scorch height was minimized.

Data Analysis

Standard statistical software (SAS Institute 2001) was used to conduct 

two-sample one-tail parametric tests for comparisons of continuous variables 

between treated (n = 9) and untreated (n = 9) sample plots. Specifi cally, we 

tested the following null hypotheses:

Ho1: Vertical and horizontal fuel profi les do not differ between the area treated 

with prescribed fi res and the untreated area.

 Ho1a: Trees are not larger (in diameter and height) in the treated area.

 Ho1a: Crown bases are not higher in the treated area.

 Ho1a: Shrubs are not shorter in the treated area.

 Ho1b: Densities of trees and shrubs are not greater in the untreated 

area.

Ho2: Wildfi re severity does not differ between the area treated with prescribed 

fi res and the untreated area.

 Ho2a: Scorch height is not greater in the untreated area.

 Ho2b: Crown volume scorch on overstory trees is not greater in the untreated area.

 Ho2c: Stand damage is not greater in the untreated area.

 Ho2c: Ground char depth is not greater in the untreated area.

Non-parametric Wilcoxon tests were used for ordinal categorical data (that 

is the fi re severity ratings). Signifi cance levels for all tests were adjusted by 

partial Bonferonni correction to account for multiple comparisons (the Bonferonni adjustment was increasingly liberalized as the correlations among the 

set of compared variables increased (see ad hoc adjustments to the Bonferonni 

procedure in Sankoh and others (1997) or Uitenbroek (2001)).

Results

The forest treated with repeated prescribed fi res on the Mississippi Sandhill 

Crane National Wildlife Refuge was found to have signifi cantly different fuel 

profi les than the adjacent unmanaged private forest (table 3). The untreated 

plots had nearly seven times as many trees as the treated plots and these 
434 USDA Forest Service Proceedings RMRS-P-41. 2006.

Martinson and Omi Assessing Mitigation of Wildfi re Severity by Fuel Treatments — An Example From the Coastal Plain of Mississippi

were substantially smaller in diameter and height. Trees in the treated plots 

had twice the girth and were 50 percent taller than those in the untreated 

plots. Live crown bases were nearly twice as high off the ground in treated 

plots compared to untreated plots where shrubs were more than twice as tall. 

However, no signifi cant difference was found in shrub density between the 

two sampled areas.

The two areas with distinctly different fuel profi les were observed to have 

experienced distinctly different wildfi re severity (fi g. 2, table 3). Average 

height of needle scorch was nearly twice as high in the untreated plots. With 

very few exceptions crown volume scorch in the untreated plots was 100 

percent and signifi cantly greater than in the treated plots. Ground char was 

light in all the treated plots, but somewhat deeper in the untreated plots.

Table 3—Comparison of stand conditions and fi re severity 

indicators between treated and untreated stands within 

the Fountainebleau fi re (means with standard deviations 

in parentheses).

 Treated Untreated 

 Variable (n = 9) (n = 9)

Tree diameter (cm) 20.9e 10.7

 (3.4) (4.7)

Tree height (m) 16.5c 10.6

 (2.5) (4.2)

Height to crown (m) 11.1c 7.3

 (2.2) (2.7)

Tree density (# per ha) 373b 2,496

 (224) (2,092)

Tree basal area (m2 per ha) 14.2 19.1

 (7.8) (10.8)

Shrub height (cm) 61.2c 164.3

 17.2 (77.9)

Shrub density (# per m2) 15.9 13.7

 (5.8) (3.7)

Scorch height (m) 10.0b 15.4

 (2.9) (5.0)

Crown volume scorch (percent) 14e 99

 (22) (1)

Stand damage rating 0.8e 3.1

 (0.7) (0.8)

Ground char rating 1.0b 1.2

 (0.0) (0.2)

Treatment means followed by a superscript indicate a signifi cant 

difference from the untreated mean in the hypothesized direction as 

follows: 

a p<0.1.

b p<0.05.

c

 p<0.01.

d p<0.001.

e p<0.0001.
USDA Forest Service Proceedings RMRS-P-41. 2006. 435

Assessing Mitigation of Wildfi re Severity by Fuel Treatments — An Example From the Coastal Plain of Mississippi Martinson and Omi

Figure 2—Adjacent treated (a) and 

untreated (b) stands burned by 

the Fontainebleau fi re.

(a)

(b)
436 USDA Forest Service Proceedings RMRS-P-41. 2006.

Martinson and Omi Assessing Mitigation of Wildfi re Severity by Fuel Treatments — An Example From the Coastal Plain of Mississippi

Discussion

The differences we observed between the treated and untreated areas 

burned over by the Fontainebleau fi re were dramatic. Still, our highly signifi -

cant results may be conservative, since the 1998 prescribed fi re that served as 

our treatment was reportedly more intense than the subsequent wildfi re when 

it burned in the treatment area. Thus, the scorch heights and crown damage 

that we observed in the treated area probably resulted from the treatment 

itself, masking the less severe effects that resulted from the wildfi re.

Vertical and horizontal fuel continuity was clearly greater in the private 

unmanaged forest, as evidenced by taller shrubs, lower tree crowns, and higher 

tree density. We attribute the lower severity observed on the Refuge primarily 

to a less hazardous fuel profi le that resulted from distinct land management 

practices, most notably the repeated application of prescribed fi re.

However, several caveats associated with this study bear mention. Unlike 

completely randomized pre-planned experiments, retrospective studies such 

as this one are inherently prone to selection bias both in the choice of study 

sites and the location of sample plots. Further, the availability of treatment 

replicates that might be considered independent samples is beyond the control 

of the investigators.

When the Fontainebleau fi re was selected for investigation, our approach 

to identifying potential study sites relied on advertising our interest and criteria on relevant electronic list serves and at professional meetings attended 

by land managers. It soon occurred to us, however, that we might only be 

contacted in the case of an obviously effective treatment. We have since taken 

a more rigorous approach to defi ning the universe of possible study sites in 

any given year and now contact land managers directly wherever a wildfi re 

exceeds 4,000 ha (10,000 acres).

Wildfi res smaller than 4,000 ha are unlikely to encounter a single fuel 

treatment area, much less multiple treatments. Prior to the recent expansion 

of fuel treatment initiatives it was rare even for large wildfi res to encounter 

more than one treatment. Unfortunately, any analysis of the effect of a single 

treatment must be based on pseudo-replicated samples (Hurlbert 1984), resulting in underestimated variance and compromised statistical tests. Such was 

the case with Fontainebleau, as well as all of Pollet and Omi’s (2002) study 

sites. Few fuel treatment studies have been published based on samples from 

(approximately) replicated treatments and all but Cram and others (2006) 

relied on an analysis of remote sensing data that failed to control for the effects of weather and topography (for example, Weatherspoon and Skinner 

1995; Martinson and others 2003, Finney and others 2005). However, since 

sampling Fontainebleau we have been able to restrict our investigations to 

wildfi res that burned over at least three spatially dispersed areas that were 

similarly treated. We have now completed data collection from eight such 

study areas.

Once a wildfi re is selected for investigation, we follow the procedures established by Pollet and Omi (2002) to minimize potential bias in locating sample 

plots. Plot locations are selected prior to any fi eld visits and based solely on 

maps of treatment boundaries, roads, streams, vegetation, topography, and 

wildfi re progression. Comparison plots are situated such that they straddle 

a treatment boundary, burned on the same day and under similar weather 

conditions, and have similar slope, aspect, elevation and tree species. We 

further seek to avoid areas that were a focus of fi re control activities, as well 

as treatment boundaries defi ned by a signifi cant fuel break, such as a major 
USDA Forest Service Proceedings RMRS-P-41. 2006. 437

Assessing Mitigation of Wildfi re Severity by Fuel Treatments — An Example From the Coastal Plain of Mississippi Martinson and Omi

road or stream. The straddle point along each treatment boundary is then 

chosen by random number generation, if any choice remains after all criteria 

are satisfi ed. The Fontainebleau site met all these criteria with the notable 

exception of a railroad separating the treated and untreated areas. While this 

was a substantial fuel break, it failed to stop fi re spread; not once but twice. 

Further, the fi re was not even curtailed by a much wider divided highway 

that included a mown median. We therefore concluded that any reduction 

in fi re severity accomplished by the prescribed burns was undiminished by 

the presence of the railroad.

A fi nal caveat for the Fontainebleau site is the unknown management history 

of the privately owned stand that served as our untreated control. While no 

activity has occurred in this stand since establishment of the Wildlife Refuge 

in 1975, its condition differed from the treated stand to such a degree that 

we fi nd it diffi cult to believe three prescribed fi res alone accomplished the 

difference. Rather, the untreated stand had probably been clearcut sometime 

in the past with no subsequent management (personal communication from 

Tony Wilder, Refuge Fire Management Offi cer). Nonetheless, the Fontainebleau site illustrates the differential consequences of fuels management and 

lack thereof when a wildfi re occurs.

Conclusion

Like all studies of fuel treatment effectiveness, the data from the Fontainebleau fi re are limited in many respects. Nonetheless, the results of this study 

provide a rare addition to a depauperate literature. Fuel treatment activities 

are expanding rapidly on public lands despite minimal empirical evidence 

to support their use. At least one benefi cial consequence of this should be 

an increase in the number of wildfi res that burn over multiple treatments, 

providing greater opportunity to achieve a semblance of replication and 

control in future retrospective studies of fuel treatment effectiveness. Thus 

fuel treatment activities are perhaps best viewed as experiments that provide 

potential learning opportunities. Knowledge must be gleaned from both the 

successes and the failures so that we might eventually defi ne the conditions 

under which fuel treatments are an effective pre-suppression strategy for the 

mitigation of extreme wildfi re behavior and effects. Every effort to collect 

empirical information from natural experiments such as that presented by 

the Fontainebleau fi re brings us a step closer to this end.

Acknowledgments

We thank Malia Boyum for fi eld assistance and Tony Wilder for facilitating our fi eld visit and providing fi re weather and behavior information. The 

Joint Fire Science Program funded this research.

Literature Cited

Alexander, M. E.; Lanoville, R. A. 2004. The international crown fi re modeling 

experiment fuel treatment trials. Tall Timbers Fire Ecology Conference 

Proceedings 22: 222.
438 USDA Forest Service Proceedings RMRS-P-41. 2006.

Martinson and Omi Assessing Mitigation of Wildfi re Severity by Fuel Treatments — An Example From the Coastal Plain of Mississippi

Anderson, H. E. 1982. Aids to determining fuel models for estimating fi re behavior. 

Gen. Tech. Rep. INT-GTR-122. Ogden, UT: U.S. Department of Agriculture, 

Forest Service, Intermountain Research Station. 19 p.

Avery, T. E.; Burkhart, H. E. 1994. Forest measurements, 4th edition. New York, 

NY: McGraw-Hill Book Co. 408 p.

Carey, H.; Schumann, M. 2003. Modifying wildfi re behavior – the effectiveness of 

fuel treatments. Santa Fe, NM: National Community Forestry Center Southwest 

Region Working Paper #2. 26 p.

Cram, D. S.; Baker, T. T.; Boren, J. C. 2006. Wildland fi re effects in silviculturally 

treated vs. untreated stands of New Mexico and Arizona. Research Paper RMRSRP-55. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky 

Mountain Research Station. 28 p.

Cruz, M. G.; Alexander, M. E.; Wakimoto, R. H. 2005. Development and testing of 

models for predicting crown fi re rate of spread in conifer forest stands. Canadian 

Journal of Forest Research 35: 1626-1639.

Fernandes, P. M.; Botelho, H. S. 2003. A review of prescribed burning effectiveness 

in fi re hazard reduction. International Journal of Wildland Fire 12: 117-128.

Finney, M. A. 2001. Design of regular landscape fuel treatment patterns for 

modifying fi re growth and behavior. Forest Science 47: 219-228.

Finney, M. A.; McHugh, C. W.; Grenfell, I. C. 2005. Stand- and landscape-level 

effects of prescribed burning on two Arizona wildfi res. Canadian Journal of 

Forest Research 35: 1714-1722.

Fites-Kauffman, J. 2001. Real-time evaluation of effects of fuel-treatments and other 

previous land management activities on fi re behavior during wildfi res. Research 

proposal funded by the United States Departments of Agriculture and Interior 

Joint Fire Science Program.

Graham, R. T.; McCaffrey, S.; Jain, T. B. 2004. Science basis for changing forest 

structure to modify wildfi re behavior and severity. Gen. Tech. Rep. RMRS-GTR120. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky 

Mountain Research Station. 43 p.

Martinson, E. J.; P. N. Omi; W. D. Sheppard. 2003. Fire behavior, fuel treatments, 

and fi re suppression on the Hayman fi re. Part 3. Effects of fuel treatments on fi re 

severity. Gen. Tech. Rep. RMRS-114: 96-122. Fort Collins, CO: U.S. Department 

of Agriculture, Forest Service, Rocky Mountain Research Station.

Omi, P. N.; Kalabokidis, K. D. 1991. Fire damage on extensively versus intensively 

managed forest stands within the North Fork Fire, 1988. Northwest Science 65: 

149-157.

Ottmar, R. D.; Burns, M. F.; Hall, J. N.; Hanson, A. D. 1993. CONSUME user’s 

guide. Gen. Tech. Rep. PNW-GTR-304. Portland, OR: U.S. Department of 

Agriculture, Forest Service, Pacifi c Northwest Research Station. 118 p.

Platt, W. J.; Evans, G. W.; Rathburn, S. L. 1988. The population dynamics of a 

long-lived conifer (Pinus palustris). The American Naturalist 131: 491-525.

Pollet, J.; Omi, P. N. 2002. Effect of thinning and prescribed burning on crown 

fi re severity in ponderosa pine forests. International Journal of Wildland Fire 

11: 1-10.

Raymond, C. L.; Peterson, D. L. 2005. Fuel treatments alter the effects of wildfi re 

in a mixed-evergreen forest, Oregon, USA. Canadian Journal of Forest Research 

35: 2981-2995.

Rothermel, R. C. 1972. A mathematical model for predicting fi re spread in wildland 

fuels. Research Paper INT-115. Ogden, UT: U.S. Department of Agriculture, 

Forest Service, Intermountain Research Station. 40 p.

Rothermel, R. C. 1991. Predicting behavior and size of crown fi res in the northern 

Rocky Mountains. Research Paper INT-RP-438. U.S. Department of Agriculture, 

Forest Service, Intermountain Research Station. 46 p.
USDA Forest Service Proceedings RMRS-P-41. 2006. 439

Assessing Mitigation of Wildfi re Severity by Fuel Treatments — An Example From the Coastal Plain of Mississippi Martinson and Omi

Ryan, K. C.; Noste, N. V. 1985. Evaluating prescribed fi res. Gen. Tech. Rep. 

INT-GTR-182: 230-238. Ogden, UT: U.S. Department of Agriculture, Forest 

Service, Intermountain Research Station.

Sankoh, A. J.; Huque, M. F.; Dubey, S. D. 1997. Some comments on frequently used 

multiple endpoint adjustment methods in clinical trials. Statistics in Medicine 

16: 2529-2542.

SAS Institute, Inc. 2001. The SAS system for windows, release 8.2. Cary, NC: The 

SAS Institute, Inc.

Skinner, C. N.; Ritchie, M. W.; Hamilton, T.; Symons, J. In press. Effects of 

prescribed fi re and thinning on wildfi re severity: the Cone fi re, Blacks Mountain 

Experimental Forest. In: Proceedings of the 25th Vegetation Management 

Conference, Redding, CA.

Uitenbroek, DG. 2001. Simple interactive statistical analysis: Bonferonni correction. 

URL: http://home.clara.net/sisa/bonhlp.htm#Corr.

Van Wagner, C. E. 1973. Height of crown scorch in forest fi res. Canadian Journal 

of Forest Research 3: 373-378.

Weatherspoon, C. P.; Skinner, C. N. 1995. An assessment of factors associated with 

damage to tree crowns from the 1987 wildfi res in northern California. Forest 

Science 41: 430-451.
USDA Forest Service Proceedings RMRS-P-41. 2006. 441

In: Andrews, Patricia L.; Butler, Bret W., 

comps. 2006. Fuels Management—How to 

Measure Success: Conference Proceedings. 

28-30 March 2006; Portland, OR. 

Proceedings RMRS-P-41. Fort Collins, 

CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research 

Station.

1 Fire Ecologist, Plumas National Forest, 

Quincy, CA. jmoghaddas@fs.fed.us

Abstract—Fuel treatments are being widely implemented on public and private lands 

across the western U.S. While scientists and managers have an understanding of how 

fuel treatments can modify potential fi re behavior under modeled conditions, there 

is limited information on how treatments perform under real wildfi re conditions in 

Sierran mixed conifer forests. The Bell Fire started on 9/22/2005 on the Plumas National Forest, CA. This fi re burned upslope into a 1-year old, 390-acre mechanical fuel 

treatment on private land. Prior to impacting the fuel treatment, the main fi re ignited 

spot fi res 400 feet into the treated area. Within the treated area, loadings of 1, 10, and 

100-hour fuels averaged 5.2 tons per acre. Stand density averaged 73 trees per acre, 

with a live crown base of 30 feet, and 36% canopy cover. This fuel treatment resulted 

in: 1) increased penetration of retardant to surface fuels, 2) improved visual contact 

between fi re crews and the IC, 3) safe access to the main fi re, and 4) quick suppression 

of spot fi res. This treatment was relatively small and isolated from other fuel treatments 

but resulted decreased severity, suppression costs, and post fi re rehabilitation needs 

leading to cost savings for local public and private land managers.

Introduction

Fuel treatments are being widely implemented on public and private lands 

across the western United States (Stephens 2005). Over 11 million acres of 

hazardous fuel reduction and landscape restoration activities have been implemented since federal fi scal year 2000 (Healthy Forests Report 2005). The 

stated goals of these treatments are to: “1) Directly reduce wildfi re threats 

to homes and communities that are adjacent to or within the wildland urban interface (WUI), 2) Treat areas outside of the wildland-urban interface 

(non-WUI) that are at greatest risk of catastrophic wildland fi re. These high 

priority non-WUI treatments move towards restoring fi re to its historical 

role and 3) Maintain previous treatments to ensure resiliency to catastrophic 

wildland fi re and implement activities that are in line with other long-term 

management goals” (Healthy Forests Report 2005).

While scientists and managers have an understanding of how fuel treatments 

can modify potential fi re behavior under modeled conditions (Stephens and 

Moghaddas 2005), there is limited information on how treatments perform 

under real wildfi re conditions in Sierran mixed conifer forests (Fites and 

Henson 2004). Public land managers are often tasked with designing projects to meet “desired future conditions” for fuel treatments, though there is 

limited information on what these conditions should be across a broad range 

of site classes and forest types. While several fi res have been documented by 

fi re managers burning or spotting into recently established fuel treatments 

A Fuel Treatment Reduces Potential Fire 

Severity and Increases Suppression Effi ciency 

in a Sierran Mixed Conifer Forest

Jason J. Moghaddas1
442 USDA Forest Service Proceedings RMRS-P-41. 2006.

Moghaddas A Fuel Treatment Reduces Potential Fire Severity and Increases Suppression Effi ciency in a Sierran Mixed Conifer Forest

(Beckman 2001; Hood 1999), relatively few of these events are formally 

studied to determine the effects of the fuel treatment on fi re behavior and 

severity in Sierran mixed conifer forests.

The purpose of this paper is to document one example of how a fuel 

treatment infl uenced fi re behavior and enhanced suppression effi ciency in 

a mixed conifer stand within the wildland urban interface. Secondly, this 

paper quantifi es a stand structure which was functioned as an effective fuel 

treatment under the weather conditions described. 

Methods

Study Site

The study area is on the Beckworth Ranger District of the Plumas National Forest, approximately 1 mile south of Highway 89 at Lee Summit. 

The treatment described was established on private timberlands owned by 

the Soper-Wheeler Company. The treatment unit is located within the 1.5 

mile extended wildland urban interface of Spring Garden, a Community at 

Risk (Callenberger and Lunder 2006; PCFSC 2005). The parcel is bordered 

on two sides by untreated National Forest Land (Figure 1, Figure 2). The 

fuel treatment was established on the north side of a ridge, immediately 

above the Middle Fork of the Feather River Drainage. The dominant aspect 

of the treated area is north facing with an average slope of 11 percent. The 

area within the treatment is classifi ed as a site class II (Dunning 1942). 

Data available from the timber harvest plan and associated inventory plots 

were used to establish pre-treatment stand conditions. Post treatment, three 

1/10th acre fi xed radius plot were established along a transect which ran 

through the area impacted by spot fi res. These plots were measured within 

2 months of the fi re.

Treatment Prescription

The forest type is Sierran Mixed Conifer forest dominated by Douglas-fi r 

(Pseudotsuga menziesii (Mirb.) Franco), incense cedar (Calocedrus decurrens 

[Torr.] Floren.), ponderosa pine (Pinus ponderosa Dough. Ex. Laws), sugar 

pine (Pinus lambertiana Dougl.), white fi r (Abies concolor Gord. & Glend.), 

and California black oak (Quercus kelloggii Newb.) (table 3). Prior to treatment, stand basal area was 258 ft2 per acre and tree density was 478 trees 

per acre. Stands were thinned in the summer of 2005 under a selection harvest (CDF 2003) using a leave tree mark. Biomass and sawlog material was 

removed mechanically using a whole tree harvest system. Sub-merchantable 

material and tops were chipped at the landing and hauled to a local mill. An 

average of 2,460 board feet and 8.6 bone dry tons of biomass per acre were 

removed from the project area (Violett 2005).

General Fire Information

The Bell fi re was reported at 12:13 on September 22nd 2005 (Table 1). The 

fi re was accidentally ignited by railroad activity along the tracks immediately 

downhill and below the project area (Figure 1). Relative humilities and peak 

wind speeds averaged 18 percent and 10 miles per hour, respectively, during the 

burning period between 12:00 to 16:00 (Table 2).
USDA Forest Service Proceedings RMRS-P-41. 2006. 443

A Fuel Treatment Reduces Potential Fire Severity and Increases Suppression Effi ciency in a Sierran Mixed Conifer Forest Moghaddas

Figure 1—Location of treated area and fi re perimeter
444 USDA Forest Service Proceedings RMRS-P-41. 2006.

Moghaddas A Fuel Treatment Reduces Potential Fire Severity and Increases Suppression Effi ciency in a Sierran Mixed Conifer Forest

Figure 2—Treated stands (foreground) and untreated stands on public land (background). 

Property line follows edge of thinned area

Table 1—General fi re information

Fire Name Bell Fire

Location Plumas National Forest, 

 Beckworth Ranger District: T 24N, R 8E, Section 9

Elevation 4,125 ft to 4,605 ft

Burning Index on day of fi re 61

Energy Release Component 57

 on day of fi re

Report Date and Time 09/22/2005 at 12:13

Contain Date and Time 09/22/2005 at 19:00

Control Date 09/24/2005 at 18:00

Cause Ignition from railroad activity

Final Size 35 acres
USDA Forest Service Proceedings RMRS-P-41. 2006. 445

A Fuel Treatment Reduces Potential Fire Severity and Increases Suppression Effi ciency in a Sierran Mixed Conifer Forest Moghaddas

Results

Post Treatment Stand Structure

Mechanical treatments resulted in a relatively open stand with vertical and 

horizontal separation of ladder and crown fuels (Figure 2). Treatments reduced the percent species composition of white fi r (Table 3). Treatments raised 

the average height to crown base and reduced canopy cover, basal area, and 

overall stand density (Table 4). Though surface fuels were not treated, residual 

1, 10 , and 100 hour fuels combined averaged 5.3 tons per acre (Table 5). Fuel 

depth average 1.4 inches (Table 5). There was no evidence of brush on the plots 

at the time of measurement.

Predicted and Actual Fire Behavior

The fi re moved quickly up a steep hill from the point of origin to the 

ridgeline which was also the boundary of the fuel treatment. At the ridgeline, 

fl ame lengths from torching trees were observed as high as 30 feet above 

the tree canopy. Trees on the slope between the ridgeline and the point of 

origin generally had over 75% scorch. This level of scorch was observed on 

trees over 20 inches in diameter. From the point the fi re impacted the fuel 

Table 2—Weather parameters during active burn period on 09/22/2005. Weather taken from Quincy 

remote access weather station (#40910).

 Relative Dry Bulb 10-hour Fuel Fuel Peak Wind

Time Humidity Temperature Moisture Temperature Windspeed Direction

 Percent °F Percent °F mi/hr degrees

12:00 25 74 8.9 74 6 260

13:00 18 85 8.7 103 6 144

14:00 15 86 8.0 101 14 224

15:00 14 85 7.5 98 13 243

16:00 17 82 7.2 93 17 267

17:00 21 79 7.1 81 12 256

18:00 23 75 7.0 78 11 256

19:00 31 67 7.0 63 7 259

Table 3—Percent species composition of conifers and 

hardwoods before and after treatmenta.

 Species Pretreatment Post Treatment

 - - - - - - - - - Percent - - - - - - - - -

Douglas-fi r 21 41

Incense cedar 18 21

Ponderosa pine 19 20

Sugar pine 10 12

White fi r 29 6

Black oak 2 na

aNote: pre and post treatment data collected within the same 

stand but from different plots
446 USDA Forest Service Proceedings RMRS-P-41. 2006.

Moghaddas A Fuel Treatment Reduces Potential Fire Severity and Increases Suppression Effi ciency in a Sierran Mixed Conifer Forest

treatment and approximately 200 feet into the fuel treatment, the level of 

scorch decreased. Similar patterns of scorch were observed in the Cone Fire 

at Blacks Mountain Experimental Forest (Skinner and others in press).

Up to four spot fi res were ignited within the fuel treatment area. These 

fi res ignited directly in activity fuels left after the harvest. Predicted fl ame 

lengths and mortality for these spot fi res are shown in table 6. Observed fl ame 

lengths on these spot fi res was less than 2 feet and there was little evidence 

of scorch on trees larger than 10 inches DBH.

The actions taken for suppression of the fi re are based on discussions with 

on-scene personnel (Craggs 2006) and summarized here. Hand crews hiked 

into the base of the fi re along the railroad tracks, anchored their fi reline and 

continued constructing line up the east and west fi re fl anks. The Incident 

Commander (IC) and two bulldozer transports could access the main fi re 

from Highway 89, along a dirt road, and directly through the treated area. 

From this point, the IC could also easily locate established spot fi res. Due 

to relatively low rates of spread and fl ame lengths, the decision was made to 

line spot fi res using the bulldozer. After lining the spot fi res, the bulldozers then cut a line between the approaching fi re front, the untreated USFS 

land, and the treated private property. The dozer line between the main fi re 

and untreated USFS land was completed prior to the main fi re reaching the 

ridge. When the fi re reached the main ridge and the fuel treatment, torching 

stopped though direct scorch still occurred within the fi rst 200 feet of the 

treatment. Finally a water tender and “pumpkin” were brought forward into 

the treated area and used in conjunction with engines to extinguish and mop 

up the spot fi res. Mop up continued the next day.

Table 4—Post treatment vegetation structure

 Quadratic Stand

 Basal area Height to live Tree Canopy Mean Density

 Live Trees per acre crown base Height Cover Diameter Index

 Trees per acre Ft2/acre - - - - - - -Feet - - - - - - - Percent Inches

 Post 

 Treatment 73.3 103.3 30.1 72.5 36.3 15.6 130.3

 Average

 Post 

 Treatment 40 to 73.2 to 24.9 to 59.0 to 25 to 11.9 to 105.5 to 

 Range 130 154.3 40.2 84.0 48 18.3 171.1

Table 5—Post treatment fuel characteristics

 All 1, 10,

 and 100 1,000 hour 1,000 hour Cover of

 Litter & Duff hour fuels sound rotten Fuel Depth Brush

 - - - - - - - - - - - - - - - Tons per acre - - - - - - - - - - - - - - - Inches Percent

Average 73 5.3 1.9 0.6 1.4 0

 

Range 19.5 to 110.5 1.3 to 8.3 0.9 to 2.8 0.0 to 0.9 0.5 to 2 0
USDA Forest Service Proceedings RMRS-P-41. 2006. 447

A Fuel Treatment Reduces Potential Fire Severity and Increases Suppression Effi ciency in a Sierran Mixed Conifer Forest Moghaddas

During the active suppression period, aerial retardant was being delivered 

to the area between the main fi re and both the private treated area and the 

untreated US Forest Service property. Based on visual observations, substantially more retardant reached surface fuels in the treated area than on 

the untreated USFS lands. Within untreated areas, retardant was evident on 

upper foliage of dominant and co-dominant trees where it would not help 

slow the spread of surface fi re.

Discussion

The treatments utilized principles of fuel reduction including thinning 

from below and use of whole tree harvest (Skinner and Agee 2005). While no 

further treatment of activity fuels generated by the harvest were completed, 

residual, post treatment fuel loads and arrangement resulted in observed fl ame 

lengths in spot fi res was less than 2 feet. These low fl ame lengths in conjunction with relatively high crown base heights resulted in limited observed 

scorch in spot fi re areas at the time of measurement. Spot fi res were easily 

lined and allowed to burn out while suppression resources were concentrated 

on the main fi re fl anks.

In terms of suppression tactics, the treated area established a safe access 

point which could be use to move equipment and other resources towards 

the head of the main fi re. Had this area not been in place, crews would have 

likely had to hike in an additional 1⁄4 to 1⁄2 mile. This would have resulted in 

the use of indirect suppression methods, leading to increased suppression 

intensity than the direct control methods utilized. The relative openness of 

the stand allowed the Incident Commander (IC) to maintain visual contact 

with equipment and personnel. In addition, greater penetration and coverage 

of aerial retardant to surface fuels was observed in the treated areas adjacent 

to un-treated areas. In untreated areas, retardant primarily ended up in the 

upper tree crowns were it was less effective at containing and reducing surface 

fi re spread. The overall results of this treatment were decreased suppression 

intensity and increased suppression effectiveness. This in turn resulted in 

decreased damage to the stand due to suppression activities and direct scorch. 

In turn, these factors decreased the relative total cost of suppression and follow up rehabilitation.

Table 6—Predicted fi re behavior and mortality

 Predicted Predicted Predicted

 Mortality Mortality Mortality

 Flame Torching Crowning Trees 1 to Trees 10 to Trees 20 to

 Length Index Index 10 inches 20 inches 30 inches

 Feet - - - Miles Per Hour- - - - - - - - - - - - - - DBH - - - - - - - - - - - -

Predicted 3.2 >40 >40 60 14 5
448 USDA Forest Service Proceedings RMRS-P-41. 2006.

Moghaddas A Fuel Treatment Reduces Potential Fire Severity and Increases Suppression Effi ciency in a Sierran Mixed Conifer Forest

Conclusion

It is important to emphasize that fuel treatments are not designed to stop 

all fi res the purpose of this work is not to make this assertion. Fuel treatments 

are typically designed decrease fl ame lengths, fi re spread, and ideally, reduce 

landscape level fi re severity (Stratton 2004; Finney 2001). Often, they are to 

be used in conjunction with suppression resources (Agee and others 2000). 

This is an important point to bring out when communicating the potential 

effectiveness of fuel treatments with the public. Not all fuel treatments will 

work all the time in all vegetation types or weather conditions. Breaking up 

vertical and horizontal continuity of live and dead fuels in this particular case 

reduced passive crown fi re within treated areas. Decreased fl ame lengths and 

visual contact in treated areas allowed more direct suppression methods to 

be employed. It is diffi cult to say how big the fi re would have been without 

treatments in place or if in indirect methods were used but based on discussions with personnel on-scene, suppression intensity and cost were decreased 

by these treatments. If the fi re had become established in the un-treated 

areas, suppression intensity, cost, and follow up rehabilitation would have 

likely been higher.

Fire managers should be able to easily document their direct experiences 

with fi re behavior within established fuel breaks. Fire fi ghters are often the 

only ones to witness “real time” fi re behavior within fuel treatments- their 

direct observations and experiences are critical in determining when fuel 

treatments work and don’t work, and how they can be modifi ed to be more 

effective in the future. This is imperative considering the limited funds available for establishing fuel treatments in comparison to the number of acres 

that need treatment. If documented and available for public access, these 

observations may inform the research community of sites for possible future 

studies of fi re behavior as well as inform and refi ne current hypothesis used 

for these studies. This information will help provide the necessary feedback 

for changing and improving practices through adaptive management.

Acknowledgments

Thanks to Paul Violett and the Soper-Wheeler Company for allowing access to their land and records for this study. Special thanks to Larry Craggs, 

Fire Management Offi cer, and Ryan Southwick, Dozer 2 Operator both from 

the Mount Hough Ranger District on the Plumas National Forest. Their 

eyewitness experiences on this fi re were essential in conveying the story of 

what happened as seen by the folks who were there as it burned.

References

Agee, James K., Bahro, Berni, Finney, Mark A., Omi, Phillip N., Sapsis, David B., 

Skinner, Carl N., van Wagendonk, Jan W, and Weatherspoon, Phillip C. 2000. 

The use of shaded fuel breaks in landscape fi re management. Forest Ecology and 

Management 127:55-66.
USDA Forest Service Proceedings RMRS-P-41. 2006. 449

A Fuel Treatment Reduces Potential Fire Severity and Increases Suppression Effi ciency in a Sierran Mixed Conifer Forest Moghaddas

Beckman, Sid. 2001. Assessment of the effects of multiple fuel treatments on fi re 

spread and timber stand damage: Stream Fire, Plumas N.F., July 26th, 2001. Fire 

Behavior Analyst, California Interagency Incident Management Team 5.

Callenberger, Barry and Lunder, Zeke. 2006. Plumas County Hazardous Fuel 

Assessment Strategy. January 20, 2006; 58 p.

CDF (California Department of Forestry and Fire Protection). 2003. The California 

Forest Practice Rules. Sacramento, CA: The California Department of Forestry 

and Fire Protection, Resource Management, Forest Practice Program; 202 p.

Dunning, Duncan. 1942. A Site Classifi cation for the Mixed-Conifer Selection 

Forests of the Sierra Nevada. Forest Research Note 28; USDA Forest Service 

California Forest and Range Experiment Station; 21 p.

Finney, Mark A. 2001. Design of regular landscape fuel treatment patterns for 

modifying fire growth and behavior. Forest Science 47(2): 219–228.

Fites, Jo Ann and Henson, Carol. 2004. Real-time evaluation of effects of fueltreatments and other previous land management activities on fi re behavior 

during wildifi res. Final Report of the Joint Fire Sciences Rapid Response Project. 

September 20, 2004; 13 p.

Healthy Forests Report. 2005. Healthy Forests Report. www.healthyforests.gov/

projects/healthy-forests-report-fi nal.pdf. 5 p.

Hood, Larry D. 1999. A defensible fuel profi le zone gets put to the test. Memo: 

Larry Hood, Team Member, Adaptive Management Services, Rapid Response 

Fire Planning and Analysis Team. 3 p.

PCFSC (Plumas County Fire Safe Council), 2005. Plumas County Communities 

Wildfi re Mitigation Plan. February, 2005; 10 p.

Skinner, Carl N., Ritchie, Martin W., Hamilton, Todd, and Symons, Julie. In Press. 

Effects of prescribed fi re and thinning on wildfi re severity: The Cone Fire, Blacks 

Mountain Experimental Forest. Proceedings 25th Vegetation Management 

Conference, Jan. 2004, Redding, CA; 12 p.

Stephens, Scott L. and Ruth, Larry W. 2005. Federal forest fi re policy in the United 

States. Ecological Applications, 15(2):532-542.

Stephens, Scott L. and Moghaddas, Jason J. 2005(a). Experimental fuel treatment 

impacts on forest structure, potential fi re behavior, and predicted tree mortality 

in a mixed conifer forest. Forest Ecology and Management, 215:21-36.

Stephens, Scott L. and Moghaddas, Jason J. 2005(b). Silvicultural and reserve impacts 

on potential fi re behavior and forest conservation: 25 years of experience from 

Sierra Nevada mixed conifer forests. Biological Conservation, 25:369-379.

Personal Communications

Craggs, Larry. 2005. Fire Management Offi cer, Mount Hough Ranger District, 

Plumas National Forest.

Violett, Paul. 2005. Forester, Soper-Wheeler Company.
USDA Forest Service Proceedings RMRS-P-41. 2006. 451

Abstract—This paper presents several components of a multi-disciplinary project designed to evaluate the ecological and biological effects of two innovative silvicultural 

treatments coupled with prescribed fi re in an attempt to both manage fuel profi les 

and create two-aged stand structures in lodgepole pine. Two shelterwood silvicultural 

treatments were designed to replicate as well as enhance the existing multi-aged stand 

structure on the Tenderfoot Creek Experimental Forest in central Montana: the fi rst, 

with reserve trees evenly distributed; the second, with reserves contained within small 

(1/10-1/4 acre) groups. Retention of reserve trees was targeted at 50%, without regard 

to diameter or species. Eight even distribution and eight group-retention treatments 

were applied on 16 units totaling 649 acres. Half of the units were broadcast burned 

following harvest using a common burn prescription on all units. Allowable overstory 

mortality specifi ed in the prescribed fi re plan was 50%. Plot-based fuel inventories 

and fi re effects observations were performed at permanent plot locations prior to and 

following harvest, and after burning. Fuel moisture samples were acquired immediately 

prior to ignition. Data from four prescribed-burned treatment units were evaluated for 

this paper: two even-retention units and two grouped retention units. Harvest activities 

resulted in signifi cant increases in fi ne-fuel loading (1-, 10-, and 100-hour fuel), which 

was subsequently reduced by prescribed fi re to near pre-harvest levels. Consumption 

of large woody fuel was similar for both treatment types. The fi re-induced mortality 

of overstory trees was greater in the even distribution than in the grouped distribution. Despite careful execution of a relatively conservative burn plan, mortality in the 

even treatments exceeded the prescription threshold of 50% by an additional 28%. 

Additional data collected at the plots include trees per acre, residual tree mortality, 

residual tree growth, regeneration, windthrow, hydrologic responses, soil impacts, and 

beetle activity. A comprehensive summary of the treatments will follow subsequent 

monitoring scheduled to occur fi ve and ten years after burning.

Introduction

The Tenderfoot Research Project is a multi-disciplinary effort designed 

to evaluate and quantify the ecological and biological effects of innovative 

restoration treatments in an attempt to both manage fuelbed profi les and 

create two-aged stand structures in lodgepole pine. The suite of sixteen fi re 

and silvicultural treatments were implemented on the Tenderfoot Creek Experimental Forest (TCEF) in the Little Belt Mountains of central Montana 

(fi g. 1). Although the USDA Forest Service has established seventy-seven 

experimental forests and ranges, the TCEF is the only reserve dominated 

by the lodgepole pine forest type (Adams and others 2004). The research 

presented here was guided by the Tenderfoot Creek Research Project mission 

(USDA Forest Service 1997):

The Use of Silviculture and Prescribed Fire to 

Manage Stand Structure and Fuel Profi les in a 

Multi-aged Lodgepole Pine Forest

Colin C. Hardy1, Helen Y. Smith2, and Ward McCaughey3

In: Andrews, Patricia L.; Butler, Bret W., 

comps. 2006. Fuels Management—How to 

Measure Success: Conference Proceedings. 

28-30 March 2006; Portland, OR. 

Proceedings RMRS-P-41. Fort Collins, 

CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research 

Station.

1 Project Leader, Fire Behavior Research 

Work Unit, USDA Forest Service, Rocky 

Mountain Research Station, Missoula 

Fire Sciences Laboratory, Missoula, MT. 

chardy01@fs.fed.us

2 Ecologist, Fire Ecology and Fuels 

Research Work Unit; Rocky Mountain 

Research Station, Missoula Fire Sciences 

Laboratory, Missoula, MT.

3 Research Forester, Ecology and 

Management of Northern Rocky 

Mountain Forests Research Work Unit, 

Rocky Mountain Research Station, 

Missoula Forestry Sciences Laboratory, 

Missoula, MT.
452 USDA Forest Service Proceedings RMRS-P-41. 2006.

“Test an array of management treatments for regenerating and restoring 

healthy lodgepole pine forests through emulation of natural disturbance 

processes, but avoiding catastrophic-scale disturbances.”

This paper documents a preliminary exploration of selected results following completion of all phases of treatment activities. It is our intent to follow 

this paper with a comprehensive compilation of results that synthesize all 

aspects of the multi-disciplinary efforts.

Background

The subalpine lodgepole pine forest type is estimated to cover about 15 

million acres in the western United States and a much larger area (nearly 50 

million acres) in western Canada (Lotan and Critchfi eld 1990). Its latitudinal range extends from Baja (35° latitude) to the Yukon (65° latitude), and 

longitudinally from the Pacifi c coast to the Black Hills of South Dakota. In 

the Rocky Mountains of the Interior West, lodgepole pine is the third most 

extensive forest type. The adaptations of lodgepole pine to severe, stand 

replacement fi re⎯in particular its serotinous cones ⎯have long been acknowledged (Lotan and Perry 1983). Less well-known is that lodgepole pine forests 

also burn in low- to mixed-severity fi re, often creating two-aged stands and 

variable patterns across the landscape (Agee 1993; Arno 1980; Barrett and 

Hardy, Smith, and McCaughey Manage Stand Structure and Fuel Profi les in a Multi-aged Lodgepole Pine Forest

Figure 1—The Tenderfoot Creek Experimental Forest is a 9,125 acre watershed located in 

Central Montana.
USDA Forest Service Proceedings RMRS-P-41. 2006. 453

Manage Stand Structure and Fuel Profi les in a Multi-aged Lodgepole Pine Forest Hardy, Smith, and McCaughey

others 1991). Numerous studies in the interior Northwest have documented 

the intricate mosaic patterns of historical fi res in lodgepole pine forests (Arno 

and others 1993; Barrett 1993; Barrett and others 1991). Newer studies are 

looking more closely at the details of these patterns and their implications 

for management (Hardy and others 2000; Stewart 1996). These studies are 

being used as a basis for designing and refi ning silvicultural and prescribed 

fi re treatments in National Forests of the Northern Rocky Mountains.

Historically, clearcutting and broadcast burning of lodgepole pine forests 

was considered to be economically effi cient and conducive to regeneration. 

These treatments roughly mimic effects of natural, stand-replacement fi res. 

More recently, foresters have recognized that burning irregularly shaped cutting units containing patches of uncut trees, while also creating snags, would 

far more effectively simulate effects of historical fi res. One negative effect 

from leaving patches or individual uncut trees in lodgepole pine forests is the 

vulnerability of the species to windthrow. However, recognition of the extent 

of the mixed-severity fi re regime in lodgepole pine, and the recent success 

and experience gained from other pilot projects have led to continued efforts 

toward more ecologically-based management of lodgepole pine.

Paired watersheds at TCEF have been monitored for several years and serve 

as a basis for comparison of water quantity and quality under different cutting 

and burning treatments. A detailed fi re history study and map completed by 

Barrett (1993) documents a sequence of stand replacement and mixed-severity fi res extending back to 1580 (fi g. 2A). Stand-replacing burns occurred at 

intervals of 100 to over 300 years, with low- or mixed-severity burns often 

occurring within these intervals. Two-aged stands cover about half the area 

at TCEF, ranging in size from a few acres to about 1,000 acres (fi g. 2B). 

Experimental treatments at TCEF were designed to refl ect these historical 

disturbance patterns. The study design for TCEF integrates observations 

of on-site treatment response with water yield and water quality data from 

paired, experimental sub-watersheds that have monitoring fl umes.

In this paper we present new research and preliminary results specifi cally 

related to fuel management that may lead to more complete knowledge and innovative techniques to manage lodgepole pine forests in the Interior West.

Methods

Timeline for Planning and Execution

The timeline for execution of the study is given in table 1. The Tenderfoot 

Creek Experimental Forest is administered by the Rocky Mountain Research 

Station (RMRS) in collaboration with the Lewis and Clark and National 

Forest. Research is proposed and planned by RMRS and timber sales on the 

EF are conducted and administered by the National Forest. Implementation 

of any research on the Experimental forest requires close and continuous 

cooperation between research and National Forest personnel.

Planning for this extensive study was initiated by Forest Service Research 

in 1995, and an interdisciplinary planning team was assembled by the Lewis 

and Clark National Forest to accomplish the Environmental Assessment (EA) 

process required for the project. The EA was completed in 1998 and a fi nal 

decision notice was issued in early 1999. Construction of approximately 2 1⁄2 

miles of roads was accomplished in 1999, with harvesting completed in 2000. 

Prescribed burning operations were executed in 2002 and 2003.
454 USDA Forest Service Proceedings RMRS-P-41. 2006.

Figure 2—An extensive fi re history study done at TCEF in 1986 documented a complex mosaic of fi res dating 

back to 1580 (top), and determined that about half of TCEF is comprised of two-aged stands resulting from 

low- to mixed-severity fi re(s).

Hardy, Smith, and McCaughey Manage Stand Structure and Fuel Profi les in a Multi-aged Lodgepole Pine Forest
USDA Forest Service Proceedings RMRS-P-41. 2006. 455

Manage Stand Structure and Fuel Profi les in a Multi-aged Lodgepole Pine Forest Hardy, Smith, and McCaughey

Treatment Descriptions and Locations

The large-scale set of treatments were implemented on two sub-watersheds 

within the 9,125-acre Experimental Forest, with two adjacent sub-watersheds 

left as untreated controls. The two treatment sub-watersheds are Spring Park 

Creek (north of Tenderfoot Creek) and Sun Creek (south of Tenderfoot 

Creek) (fi g. 3). The silvicultural system used was a two-aged system termed 

“shelterwood with reserves,” with two forms of leave tree retention: one with 

leave trees evenly distributed, and the other with leave trees retained in unharvested retention groups distributed across the treatment units in a noticeably 

uneven pattern. The harvest system utilized in all units included felling by 

excavator-mounted “hot saws” and whole-tree skidding to centralized processing locations where the trees were de-limbed and decked for transport. 

All unutilized materials were piled and burned on site. About 50 percent of 

the basal area and stems were removed in both treatment types, with low 

intensity underburns in one-half of the treatment units. One objective for 

low intensity underburns was mitigation of surface fi re hazard exacerbated 

by high loadings of harvesting debris (slash). The fuelbed components most 

relevant to a hazard reduction objective are the fi ne fuels: 1-hour, 10-hour, 

and 100-hour timelag fuelbed components. It is these fuel particles that 

contribute most signifi cantly to surface fi re behavior, and a reduction in 

loading of these fuelbed components was a principle objective in the treatment prescription. The sum of these three fuelbed components is hereafter 

referred to as “fi ne-fuel loading.”

The treatment labels and descriptions are summarized in table 2, and a 

satellite (IKONOS®) image of the two Sun Creek treatments is shown in 

fi gure 4.

Table 1—Timeline of activities, from project proposal to post-burn 

assessments.

 Date(s) Activity

1995 – 1997 Draft Research Proposal

 MOU between FS Research and L&C Nat’l Forest

1997 – 1998 Planning with L&C Nat’l Forest

 Scoping/public comment

Spring 1999 Environmental Assessment

 

1999 – 2000 Establish treatment units

 Sale administration

 Road installation

 Pre-harvest sampling

 Harvest activities

 Prepare burn prescriptions

Autumn 2001 Burn all piles and windrows

Summer 2002 Post-harvest sampling

2002 – 2003 Burn treatments

2003 – 2005 Post-burn sampling and assessments
456 USDA Forest Service Proceedings RMRS-P-41. 2006.

Hardy, Smith, and McCaughey Manage Stand Structure and Fuel Profi les in a Multi-aged Lodgepole Pine Forest

Figure 3—Treatment units were located in two sub-watersheds of Tenderfoot Creek: Spring 

Park Creek (south aspect, north of Tenderfoot Creek), and Sun Creek (north aspect, south 

of Tenderfoot Creek).

Table 2—Treatment labels and descriptions.

Treatment label Distribution of retention trees Prescribed fi re

 SE Evenly distributed None

 SEB Evenly distributed Burned (B)

 SG Group-retention None

 SGB Group-retention Burned (B)
USDA Forest Service Proceedings RMRS-P-41. 2006. 457

Manage Stand Structure and Fuel Profi les in a Multi-aged Lodgepole Pine Forest Hardy, Smith, and McCaughey

Field Sampling

The average size per treatment unit was 43 acres. An average of 32 sampling plots per unit (about one plot per 1.3 acres) were permanently located 

to facilitate multiple-year sampling at each plot—pre-harvest, post-harvest, 

and post-burning. In addition to a comprehensive assessment of vegetation 

and stand characteristics, fuelbed data were collected on one-half of the plots, 

where two 75’ line-intercept fuel transects were installed and permanently 

located at each plot. Fuel loadings (mass per unit area) of all fuel components 

along each transect were then estimated per Brown (1974). This allows the 

generation of summary statistics and analyses that can be calculated at multiple levels—plot, unit, and treatment type (pooled-unit).

The consumption by prescribed burning of large woody fuel was determined by measuring the reduction in diameter of sampled logs using wires 

installed prior to burning. Following burning, the wires were tightened, and 

the difference in wire length was used to determine reduction in diameter 

and associated mass.

Figure 4—An IKONOS® satellite image showing the two types of “shelterwood with reserves” 

silvicultural treatments.
458 USDA Forest Service Proceedings RMRS-P-41. 2006.

Hardy, Smith, and McCaughey Manage Stand Structure and Fuel Profi les in a Multi-aged Lodgepole Pine Forest

Following burning, annual assessments will continue for several years to 

document windthrow (a problem common to lodgepole pine) and both fi reand insect-caused tree mortality. The burn prescription for both the Even and 

Grouped treatment type specifi ed a maximum target overstory tree mortality 

of fi fty percent. Data from three years of post-burn mortality sampling are 

available for the present analysis.

Analysis

Although the study included treatment units in both Spring Park Creek 

and Sun Creek sub-watersheds, we did not obtain pre-harvest sample data 

from the Sun Creek Units. Therefore, fi re- and fuels-related data spanning 

all phases of the study (pre-harvest, post-harvest, and post-burn) are only 

available for Spring Park Creek.

The fuels analysis in his paper is focused on the four treatment units within 

Spring Park Creek that included prescribed burning following harvest (SEB 

and SGB). This selection constraint for the current analysis provides two pairs 

of treatment units: one pair of Even distribution with burning (SEB—units 

10 and 13), and one pair of Grouped retention with burning (SGB—units 12 

and 16). The Spring Park Creek units are illustrated in fi gure 5.

Prior to pooling the fi ne-fuel loading data from pairs of units, we evaluated the individual unit statistics to ensure similarity of variances and central 

tendencies between units within a pooled pair. This analysis was done for each 

of pre-harvest, post-harvest, and post-burn fi ne-fuel loading data. The boxand-whisker plots given in fi gure 6 present median values and interquartile 

ranges (expressed as tons per acre), and also illustrate the 0.05 Student’s t

statistic. We can conclude from the plots in fi gure 6 that no signifi cant difference existed in fi ne-fuel loading between pairs of units in either the Even

retention pool (fi g. 6A) or Grouped retention pool (fi g. 6B). Therefore, results 

will be presented with respect to the pooled classes.

Figure 5—Two pairs of units in 

Spring Park were selected for 

analysis: SEB (units 10 & 13), 

and SGB (units 12 & 16).
USDA Forest Service Proceedings RMRS-P-41. 2006. 459

Manage Stand Structure and Fuel Profi les in a Multi-aged Lodgepole Pine Forest Hardy, Smith, and McCaughey

Figure 6—Median values, interquartile ranges, and the 0.05 Student’s t statistic (expressed as tons per acre) 

presented as confi rmation that fi ne-fuel loadings in the pooled units are not signifi cantly different for either 

the Even (6A) or Grouped (6B) treatments.
460 USDA Forest Service Proceedings RMRS-P-41. 2006.

Hardy, Smith, and McCaughey Manage Stand Structure and Fuel Profi les in a Multi-aged Lodgepole Pine Forest

Results

We present results of preliminary analyses by comparing pre-harvest, 

post-harvest and post-burn conditions between the two harvest-and-burn 

treatments on Spring Park Creek. As described above in methods, two treatment units are pooled for each of the two treatment types—SEB and SGB. 

Results presented here are limited to fi ne-fuel loading, large-woody fuel 

loading, and fi re-caused overstory tree mortality.

Fine-Fuel Loading—Harvesting activities contributed approximately 3.5 

tons per acre of fi ne fuels in both the Even and Grouped treatments, as illustrated in fi gure 7 by the mean values of all plots within the pooled units for 

each treatment type—this is roughly a one hundred percent increase from 

pre-harvest conditions (fi g. 7). The prescribed burning treatment following 

harvest reduced the fi ne-fuel loading to near pre-harvest conditions in both 

treatment types; reductions were 2.7 tons per acre and 3.0 tons per acre for 

the Even and Grouped treatments, respectively. While the post-harvest fi nefuel loadings were signifi cantly higher (α=0.05) than either the pre-harvest 

or post-burn loadings for both treatment types, the differences between 

pre-harvest and post-burn fi ne-fuel loadings were not statistically signifi cant 

(α=0.05) for either treatment type. In summary, the harvesting activities resulted in signifi cant increases in fi ne-fuel loadings, and post-harvest prescribed 

burning effectively reduced the fi ne-fuel loadings to pre-harvest levels.

Large Woody Fuel Loading—We compared the consumption (mass 

reduction measured in tons per acre) of large woody fuel due to prescribed 

burning between the Even and Group treatment types. Mean values and 

95% confi dence intervals representing all plots within the pooled units for 

each treatment type are presented in fi gure 8. In both treatment types, less 

than one ton per acre of large woody fuel was consumed, with no signifi cant 

difference between the treatment types (α=0.05) (fi g.8). The percent mass 

reduction in large woody fuels for the Even and Group treatment types was 

Figure 7—Changes in fi ne-fuel loading (tons/acre) between pre-harvest, post-harvest, and post-burning 

for pooled Even (left) and Grouped (right) distribution units.
USDA Forest Service Proceedings RMRS-P-41. 2006. 461

Manage Stand Structure and Fuel Profi les in a Multi-aged Lodgepole Pine Forest Hardy, Smith, and McCaughey

14.5% and 12.9%, respectively. Within the grouped treatment types are two 

distinct distributions of overstory: 1. The un-harvested retention groups; 

and 2. The completely harvested (effectively, “clearcut”) open areas between 

grouped reserves. These two strata are labeled in fi gure 8 as “Group Reserve” 

and “Group Open,” respectively. While the total mass consumption of large 

woody fuel in the Group Open plots was somewhat greater than the average 

for the overall group treatment (labeled “All_Group” in fi gure 8), consumption within the Group Reserves was signifi cantly lower than either the Group 

Open or the Even distribution (α=0.05). In terms of percent mass reduction 

in large woody fuels within the two Group treatment strata, there was a 19.5% 

reduction in mass for the Group Open strata and only a 2.7% reduction for 

the Group Reserves strata.

Fire-induced Overstory Tree Mortality—Although most of the results 

presented here have been confi ned to the Spring Park treatments, data on 

fi re-induced overstory tree mortality were acquired and analyzed for treatments in both Spring Park Creek and Sun Creek. Mortality data from each 

of the fi rst three years following burning are presented in fi gure 9. Within 

a treatment type (Even or Group) the three-year trends are similar for both 

sub-watersheds. However, a general comparison of mortality between the two 

sub-watersheds indicates higher levels of mortality in the Spring Park units, 

regardless of treatment type (fi g. 9). By the third year following burning, 

mortality in the Even treatments was twenty-three percent and thirty-seven 

percent higher than for the Group treatments in Sun Creek and Spring Park, 

respectively. The highest mortality, seventy-eight percent, was observed for 

the Even treatment type in Spring Park— twenty-eight percent higher than 

the maximum prescription target of fi fty percent.

Figure 8—Comparison of means and 95% confi dence intervals for consumption of large 

woody fuel (1000-hour) between Even and Grouped (All_Group) distributions. The data 

labeled “Group Reserve” are from plots within the grouped retention areas, and data labeled 

“Group Open” are from plots located in the open (harvested) areas between groups.
462 USDA Forest Service Proceedings RMRS-P-41. 2006.

Hardy, Smith, and McCaughey Manage Stand Structure and Fuel Profi les in a Multi-aged Lodgepole Pine Forest

Figure 9—Fire-caused mortality was much higher for the Spring Park units than for Sun Creek; mortality 

for the even distribution was much higher (23%-37%) than for the grouped distribution; and in the even 

distribution for Spring Park mortality greatly exceeded the prescribed upper limit of 50%.

Discussion

Specifi c evidence presented here regarding the consequences related to two 

of four treatment alternatives is limited to fi ne-fuel loadings, consumption 

of large woody fuel, overstory tree mortality, and anecdotal observations. 

The signifi cant increase in fi ne-fuel loadings resulting from harvest activities 

was well mitigated by post-harvest prescribed burning. Although fi ne-fuel 

loadings were effectively doubled by harvest activities, the absolute loadings 

were not particularly high (3.5 tons/acre). In lodgepole pine, however, the 

vulnerability of the thin-barked species to bole-related mortality is high, relative to most other coniferous species. This makes management of fi ne-fuel 

loadings—the principle contributor to surface fi re intensity—of paramount 

importance. Despite very careful execution of a conservative prescribed fi re 

plan, increased levels of fi ne-fuel loadings caused by the harvesting activities 

in the Even distribution treatment were high enough to cause unacceptable 

fi re-induced mortality. During a typical wildfi re season, most fuel and weather 

conditions would be signifi cantly warmer, drier, and windier than conditions 

under which the prescribed burning treatments were applied. In such cases, 

the fi ne-fuel loadings present following the harvesting activities would lead 

to dramatic, unacceptable increases in overstory tree mortality. For example, 

the comparison of mortality between the Sun Creek units and the Spring 

Park Creek units shown in fi gure 9 indicates much lower mortality for the 

Sun Creek units. Despite our desire to burn all units within similar weather 

and fuel conditions, the relative humidity was considerably higher during the 

burning operations in Sun Creek, with lower temperatures and wind speeds. 

Although not considered in the statistical analyses, these conditions provide 

substantial anecdotal evidence supporting the sensitivity of the lodgepole 

pine forest type to fi re-weather conditions.
USDA Forest Service Proceedings RMRS-P-41. 2006. 463

Manage Stand Structure and Fuel Profi les in a Multi-aged Lodgepole Pine Forest Hardy, Smith, and McCaughey

In contrast to the prescriptions targeting reductions in fi ne-fuel loading 

through prescribed fi re treatments, there is neither a fi re hazard-related nor 

ecological advantage to burning of large woody fuel components (there are, 

in fact, a number of advantages to retaining large woody biomass). When 

the large woody fuel becomes involved in combustion, there are signifi cant 

increases in heat fl ux to the soil and organic surface components, and also 

production of signifi cantly elevated levels of smoke emissions from combustion of the large woody fuels as well as other biomass associated with the 

large fuel combustion. There was no signifi cant difference in large woody 

fuel consumption between the two treatment types, however, so there are no 

management implications associated with large-woody fuel consumption.

Although there is an on-going field effort to assess and document 

windthrow in all treatment units, quantitative data are not yet available. 

However, anecdotal evidence from observations over the short period of 

time since completion of management activities show signifi cant windthrow 

in several of the Even treatment units. In contrast, windthrow in the Group 

treatment have been observed to be limited to an occasional tree at the perimeter of the retention groups.

These preliminary results provide a fi rst-look at the relative successes of 

innovative silvicultural and prescribed fi re treatments targeting restoration 

and maintenance of lodgepole pine forest systems. They are not, however, 

suffi cient enough to support conclusions from which to formulate management direction. The research mission for TCEF directed us to “test an array 

of management treatments for regenerating and restoring healthy lodgepole 

pine forests through emulation of natural disturbance processes, but avoiding catastrophic-scale disturbances.” Results from further examination of the 

complete data set from this study will be integrated with results from other 

Tenderfoot Creek Research Project studies in a comprehensive assessment 

of the feasibility and consequences of these innovative treatments. More 

management direction may be provided at that time.

References

Adams, M. B.; Loughry, L.; Plaugher, L. comps. 2004. Experimental forests and 

ranges of the USDA Forest Service. Gen. Tech. Rep. NE-321. Newtown Square, 

PA: U.S. Department of Agriculture, Forest Service, Northeastern Research 

Station. 178 p.

Agee, J. K. 1993. Fire ecology of Pacifi c Northwest forests. Washington D.C.: Island 

Press. 493 p.

Arno, S. F. 1980. Forest fire history in the northern Rockies. J. Forestry 

78(8):460-465.

Arno, Stephen F.; Reinhardt, E. D.; Scott, J. H. 1993. Forest structure and landscape 

patterns in the subalpine lodgepole pine type: A procedure for quantifying past 

and present conditions. Gen. Tech. Rep. INT-294. Ogden, UT: U.S. Department 

of Agriculture, Forest Service, Intermountain Forest and Range Experiment 

Station. 17 p.

Barrett. S. W. 1993. Fire history of Tenderfoot Creek Experimental Forest, Lewis and 

Clark National Forest. Contract completion report on fi le at the Rocky Mountain 

Research Station, Forestry Sciences Lab, Bozeman, MT.

Barrett, S. W.; Arno, S. F.; Key, C. H. 1991. Fire regimes of western larch-lodgepole 

pine forests in Glacier National Park, Montana. Can. J. Forestry Res. 21:1711-

1720.
464 USDA Forest Service Proceedings RMRS-P-41. 2006.

Hardy, Smith, and McCaughey Manage Stand Structure and Fuel Profi les in a Multi-aged Lodgepole Pine Forest

Brown, J. K. 1974. Handbook for inventorying downed woody material. Gen. 

Tech. Rep. INT-16. Ogden, UT: U.S. Department of Agriculture, Forest Service, 

Intermountain Forest and Range Experiment Station. 24 p.

Hardy, C.C.; Keane, R.E.; Stewart, C.A. 2000. Ecosystem-based management in 

the lodgepole pine zone. In: Smith, H. Y., ed. 2000. The Bitterroot Ecosystem 

Management Research Project: What we have learned—symposium proceedings; 

1999 May 18-20; Missoula, MT. Proceedings RMRS-P-17. Ogden, UT: U.S. 

Department of Agriculture, Forest Service, Rocky Mountain Research Station. 

31-40.

Lotan, J. E.; Critchfi eld, W. B. 1990. Pinus contorta Dougl. ex. Loud.--lodgepole 

pine. IN: Burns, R.M.; Honkola, B. H., Tech. coordinatores. Silvics of North 

America: Vol. 1, Conifer. Agric. Handb. 654,. Washinton, D.C.: U.S. Dept. of 

Agri. pp. 302-315.

Lotan, J.E.; Perry, D.A. 1983. Ecology and regeneration of lodgepole pine. Agric. 

Handb. 606. Washington, DC: USDA Forest Service. 51 p.

Stewart, C. A. 1996. Restoring historic landscape patterns through management: 

restoring fi re mosaics on the landscape. In: Hardy, C. C.; Arno, S. F., eds. 

1996. The use of fi re in forest restoration. Gen. Tech. rep. INT-GTR-341: U.S. 

Department of Agriculture, Forest Service, Intermountain Mountain Research 

Station. 49-50.

USDA Forest Service. 1997. Tenderfoot research project: a proposal for research and 

demonstration of ecosystem-based treatments in lodgepole pine forests on the 

Tenderfoot Creek Experimental Forest. [Unpublished report on fi le]. Missoula, 

MT: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research 

Station. 22 pages.
USDA Forest Service Proceedings RMRS-P-41. 2006. 465

In: Andrews, Patricia L.; Butler, Bret W., 

comps. 2006. Fuels Management—How to 

Measure Success: Conference Proceedings. 

28-30 March 2006; Portland, OR. 

Proceedings RMRS-P-41. Fort Collins, 

CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research 

Station.

1 Graduate student at UC Berkeley and 

a fi re ecologist for AMSET,Division 

of Ecosystem Science, Department of 

Environmental Science, Policy, and 

Management, University of California, 

Berkeley, Berkeley, CA.

vaillant@nature.berkeley.edu

2 Fire ecologist for Nevada City, CA.

3 Assistant professor at UC Berkeley, 

Berkeley, CA.

Effectiveness of Prescribed Fire as a Fuel 

Treatment in Californian Coniferous Forests

Nicole M. Vaillant1, JoAnn Fites-Kaufman2, Scott L. Stephens3

Abstract—Effective fi re suppression for the past century has altered forest structure and 

increased fuel loads. Prescribed fi re as a fuels treatment can reduce wildfi re size and 

severity. This study investigates how prescribed fi re affects fuel loads, forest structure, 

potential fi re behavior, and modeled tree mortality at 80th, 90th, and 97.5th percentile 

fi re weather conditions on eight National Forests in California. Potential fi re behavior 

and effects were modeled using Fuel Management Analyst. Prescription burning did not 

signifi cantly change forest structure at most sites. Total fuel loads (litter, duff, 1, 10, 100, 

and 1000-hour) were reduced by 23 to 78 percent across the sites. This reduction in 

fuels altered potential fi re behavior by reducing rate of spread, fl ame length, and fi reline 

intensity. Increased torching index values coupled with decreased fuel loads reduced 

crown fi re potential post-treatment in some stands. Predicted tree mortality decreased 

post-treatment as an effect of reduced potential fi re behavior and fuel loads. With the 

vast forested areas classifi ed at high risk for catastrophic wildland fi re in California, it 

is most effi cient to target stands that benefi t the most from treatment.

Introduction

In many coniferous forests, fi re suppression has lead to higher tree densities 

(Biswell 1959), changes in species composition (Weaver 1943), and higher 

fuel loads (Dodge 1972), which have altered fi re regimes (Beaty and Taylor 

2001; Stephens and Collins 2004). A recent analysis of fi re cause and extent 

on U.S. Forest Service (USFS) lands from 1940 to 2000 demonstrated that 

California experienced a signifi cant increase in the total number of fi res 

and had the most area burned relative to other regions in the United States 

(Stephens 2005). Although the area burned has not signifi cantly increased 

from 1940 to 2000 in California (Stephens 2005), the wildland fi re problem 

has only worsened as suppression has become more effective (Brown and 

Arno 1991).

Fuels treatments can be effective at reducing the severity (Pollet and 

Omi 2002; Agee and Skinner 2005; Finney and others 2005) and size of 

wildland fi res (Stephens 1998; Piñol and others 2005). Reduction of surface 

fuels, and in some cases crown fuels, can reduce the likelihood of crown fi res 

(van Wagner, 1977). Typically, mechanical methods are used to alter stand 

structure (i.e., reduce tree density, decrease basal area, increase the height to 

live crown base, and reduce canopy cover) (Keyes and O’Hara 2002; Pollet 

and Omi, 2002; Stephens and Moghaddas, 2005a,b). Prescribed fi re alone 

can decrease surface and ladder fuels which reduce potential fi re behavior 

and thus lower the risk of crown fi re and spot fi re ignition (van Wagtendonk 

1996; Stephens 1998).
466 USDA Forest Service Proceedings RMRS-P-41. 2006.

Vaillant, Fites-Kaufman, Stephens Effectiveness of Prescribed Fire as a Fuel Treatment in Californian Coniferous Forests

The objective of this study is to determine how prescribed fi re effects fuel 

loads, vegetation structure, and potential fi re behavior and effects in eight 

National Forests in California. The null hypothesis investigated is that there 

will be no signifi cant difference in vegetation structure, fuel load, fi re behavior, and predicted tree mortality at each study site when comparing pre- and 

post-treatment characteristics. Information from this study could be used to 

assist in the development of forest management plans that use prescribed fi re 

to reduce fi re hazards.

Methods

Study Location

Nine project sites are located on eight National Forests: the Klamath (one 

on the eastern section, KNF E, and one on the western section, KNF W), 

Lassen (LNF), Los Padres (LPF), Modoc (MDF), Mendocino (MNF), Plumas 

(PNF), Shasta-Trinity (SHF) and Sierra (SNF) (fi g. 1). LPF, MDF, MNF, 

and SNF are dominated by yellow pine [>80% of basal area is composed of 

ponderosa pine (Pinus ponderosa Laws) or Jeffrey pine (Pinus jeffreyi Grev.)] 

and KNF E, KNF W, PNF, and SHF are in mixed-conifer forests.

Figure 1—Location of study sites.
USDA Forest Service Proceedings RMRS-P-41. 2006. 467

Effectiveness of Prescribed Fire as a Fuel Treatment in Californian Coniferous Forests Vaillant, Fites-Kaufman, Stephens

Climate in the study sites is Mediterranean with a summer drought period 

that extends into the fall. The majority of precipitation occurs during winter 

and spring. Tree species present include ponderosa pine, Jeffrey pine, sugar 

pine (Pinus lambertiana Dougl.), white fi r (Abies concolor Gord. and Glend.), 

Douglas-fi r (Pseudotsuga menziesii (Mirb.) Franco), incense-cedar (Calocedrus 

decurrens Torr.), western juniper (Juniperus occidentalis Hook.), California 

black oak (Quercus kelloggii Newb.), canyon live oak (Quercus chrysolepis 

Liebm.), and bigleaf maple (Acer macrophyllum Pursh).

The average elevation of the study sites ranges from approximately 1000 to 

1600 m. Average slopes vary from three to 61 percent. Pre-treatment percentcover of tree canopy, shrubs, and grasses varies between study locations.

Treatments

All of the study sites were treated with prescribed fi re. The primary objectives of the prescribed burns were to reduce the potential for catastrophic 

stand replacing fi re events and to reintroduce fi re into the ecosystem. Each of 

the National Forests implemented their own prescribed fi res. The prescribed 

fi res occurred either in spring or fall depending on weather, available personnel, and funding, with the majority of prescribed fi res taking place in the 

spring (six out of nine).

Vegetation Measurements

In each of the nine project sites, vegetation was measured using 0.2 ha 

randomly-placed, permanently-marked circular plots (26 total plots). Tree 

information was collected in two nested subplots; 0.1-ha for all trees greater 

than 15 cm diameter at breast height (d.b.h.), and 0.025 ha for trees 2.5 to 

15 cm d.b.h. Tree measurements (species, d.b.h., height, height to live crown 

base (HTLCB), and tree crown position (dominant, codominant, intermediate or suppressed)) are recorded for live trees; for snags species, d.b.h., and 

total height was recorded. Canopy cover was measured every meter along 

two perpendicular 50 m transects using a Moosehorn sight tube (Gill and 

others 2000). Shrub measurements were also taken along the same transects 

in each of the plots to estimate percent shrub cover. An ocular estimate of 

percent cover by grasses was made along the shrub transect in a 1 m2 frame 

every 10 m.

Ground and Surface Fuel Characteristics

Surface and ground fuels were measured with four transects in each of 

the plots using the line-intercept method (van Wagner 1968; Brown 1974). 

For each transect, one-hour (0 to 0.64 cm diameter) and 10-hour (0.64 

to 2.54 cm diameter) fuels were sampled from 0 to 1.83 m, 100-hour fuels 

(2.54 to 7.62 cm diameter) from 0 to 3.66 m, and 1000-hour fuels (diameter 

>7.62 cm) from 0 to 15.24 m. Species, diameter, and decay status (rotten or 

sound) were recorded for all 1000-hour fuels. Litter, duff, and fuel bed depth 

(cm) measurements were taken every 1.52 m totaling 10 per transect. Surface 

and ground fuel loads were calculated using arithmetically-weighted coeffi cients specifi c to the California tree species based on the average basal area 

fraction of the individual sites (van Wagtendonk and others 1998; Stephens 

and Moghaddas 2005a).
468 USDA Forest Service Proceedings RMRS-P-41. 2006.

Vaillant, Fites-Kaufman, Stephens Effectiveness of Prescribed Fire as a Fuel Treatment in Californian Coniferous Forests

Fire Modeling

Fire behavior and effects were modeled under upper 80th, 90th, and 97.5th

percentile fi re weather conditions. Eightieth, 90th, and 97.5th percentile fi re 

weather represent moderate, high, and extreme fi re weather, respectively. 

Percentile weather was computed using Fire Family Plus (Main and others 

1990). Forty-three years (1961 to 2004) of weather data from the most 

representative Remote Automated Weather Station (RAWS) for each site 

(NFAM 2004) were analyzed to determine percentile weather conditions.

Fuels Management Analyst (FMA) was used to model fi re behavior and 

effects (rate of spread, fl ame length, fi reline intensity, crowning index, torching index, and tree mortality) (Carlton 2005). Fire behavior predictions were 

made for stand and fuel structures before and after prescribed burning. A 

surface fuel model was assigned to each sampling plot based on stand structure, shrub cover, grass cover, and fuel loads (Scott and Burgan 2005).

Data Analysis

Paired t-tests were used to determine if signifi cant differences (p<0.1) 

existed in vegetation (trees ha–1, basal area ha–1, tree height, HTLCB, canopy 

cover, crown bulk density (CBD)) and fuel loads (litter, duff, 1-hr, 10-hr, 100-hr, 

1000-hr sound, 1000-hr rotten, total fuel load (1 to 1000-hr, litter and duff), 

and fuel depth) for each site pre- and post-prescribed fi re (Zar 1999). The 

choice of p<0.1 was made due to high natural variation found between plots 

in each study site. The number of sample plots varied by site location due to 

the ability of the individual National Forests to burn the proposed units and 

because some prescribed fi res did not burn the entire intended area.

Results

Forest Structure

The inventory plots in the nine study locations included 860 live trees 

greater than 2.5 cm d.b.h. pre-treatment and 801 post-treatment. No signifi cant differences were found for any of the measured variables (basal area, 

trees ha–1, d.b.h., tree height, HTLCB, canopy cover, CBD) at KNF W, MDF, 

SHF or SNF (table 1). At LNF, LPF, MNF and PNF some but not all of the 

variables were signifi cantly different (table 1). All variables were signifi cantly 

different at KNF E except HTLCB.

Fuels Characteristics

A total of 104 fuel transects were analyzed over the nine project sites to 

characterize surface and ground fuels pre- and post-prescribed burning. All 

locations had a signifi cant difference post-treatment in at least one of the fuels 

parameters (table 2). All of the locations except PNF experienced a signifi cant 

reduction in litter loads. Total fuel load was reduced at all sites; however, the 

difference was only signifi cant at MNF and LPF.

Potential Fire Behavior

Rate of spread (ROS) increased for all sites with increasing percentile 

weather (table 3). Post-treatment ROS either decreased or experienced no 

change when compared to pre-treatment. Flame length (FL) increased with 
USDA Forest Service Proceedings RMRS-P-41. 2006. 469

Effectiveness of Prescribed Fire as a Fuel Treatment in Californian Coniferous Forests Vaillant, Fites-Kaufman, Stephens Table 1—Average pre- and post-treatment vegetation structure for all trees greater than 2.5 cm d.b.h. by site location for nine stands in eight Californian National Forests. Basal area Tree height Canopy cover Site (m2 ha–1) Trees (ha–1) DBH (cm) (m) HTLCB (m) (percent) CBD (kg m–3) Pre Post Pre Post Pre Post Pre Post Pre Post Pre Post Pre Post KNF E 37.0a 36.0a 706.7a 533.3a 27.2a 29.6a 14.4a 15.6a 4.9 4.6 43.2a 35.3a 0.094a 0.091a KNF W 48.2 42.6 585.0 420.0 33.7 34.5 15.0 17.1 3.4 8.9 76.3 70.6 0.054 0.050 LNF 51.9 48.8 490.0a 405.0a 34.7 36.2 21.0 21.4 8.9 9.4 96.6 93.1 0.046 0.044 LPF 28.1 27.3 600.0 306.7 27.3 37.1 14.3 13.3 3.6 3.2 24.0a 19.8a 0.049a 0.044a MDF 26.9 24.3 313.3 263.3 33.3 34.1 14.3 15.1 3.8 4.8 29.4 30.2 0.057 0.050 MNF 27.3 27.2 520.0 516.0 27.3 26.6 14.0 14.0 4.2 4.2 69.0a 50.7a 0.090 0.089 PNF 38.1 35.9 423.3 360.0 33.9 35.3 19.3a 21.0a 7.8a 11.3a 64.7 62.1 0.069 0.067 SHF 34.4 33.8 163.3 120.0 52.5 58.6 27.9 31.4 10.4 11.7 30.5 27.6 0.034 0.033 SNF 40.7 40.8 525.0 525.0 36.2 36.2 19.1 19.1 7.1 7.1 51.0 44.2 0.074 0.071 HTLCB= height to live crown base, CBD= crown bulk density, a=signifi cantly different pre- versus post-treatment. Table 2—Average fuel loads (metric t ha–1) pre- and post-treatment by site location. 1000-hr 1000-hr 1-1000-h plus Fuel Site Duff Litter 1-hr 10-hr 100-hr sound rotten litter, duff depth (cm) Pre Post Pre Post Pre Post Pre Post Pre Post Pre Post Pre Post Pre Post Pre Post KNF E 21.4 2.2 11.1a 4.4a 1.5 0.9 2.7 1.3 2.8 2.5 3.5 6.5 39.7 0.0 82.7 17.8 28.2a 7.7a KNF W 33.2 7.4 18.5a 3.0a 1.4 0.3 6.1 1.4 5.9 1.3 9.7 6.9 61.4 33.7 136.0 53.9 25.6 12.3 LNF 17.0 9.2 18.9a 3.5a 2.1 0.6 7.7 3.8 4.5 2.7 7.4 0.8 7.4 29.8 65.1 50.3 14.7 11.5 LPF 22.3a 10.0a 4.4a 1.7a 0.6 0.2 1.0 0.8 2.8 4.1 0.0 4.6 13.2 0.0 44.3a 21.3a 9.7 3.4 MDF 13.6 5.2 5.6a 3.8a 0.5a 0.2a 1.2 0.9 1.7a 2.8a 1.2 0.0 12.0 3.6 35.6 16.5 7.9 3.5 MNF 16.7 14.8 12.9a 3.0a 0.3 0.4 2.5 1.6 3.6 4.8 2.7 7.3 64.7a 12.2a 103.5a 44.1a 67.0a 8.3a PNF 22.5 9.0 4.3 10.0 1.3 0.6 2.0 2.1 5.1 3.9 13.8 0.2 25.2 15.3 74.2a 41.0a 16.2a 10.3a SHF 28.9a 6.9a 5.4a 1.9a 0.9a 0.2a 3.4a 1.0a 7.6 3.1 0.4a 9.5a 17.6 7.6 64.3 30.2 11.1a 7.4a SNF 15.4 8.5 12.1a 2.7a 0.9 0.5 2.3 0.8 5.3 3.7 1.5 2.5 6.0 2.6 43.6 21.3 51.3 8.1 a=signifi cantly different pre- versus post-treatment .
470 USDA Forest Service Proceedings RMRS-P-41. 2006.

Vaillant, Fites-Kaufman, Stephens Effectiveness of Prescribed Fire as a Fuel Treatment in Californian Coniferous Forests Table 3—Average modeled fi re behavior under 80th, 90th, and 97.5th percentile weather by site location. ROS FL FI TI CI Site 80th 90th 97.5th 80th 90th 97.5th 80th 90th 97.5th 80th 90th 97.5th 80th 90th 97.5th Pre - - (m min–1) - - - - - - (m) - - - - - - - - - - (kW m–1) - - - - - - - - - - - - (km h–1) - - - - - - - - - - (km h–1) - - - - KNF E 2.9 7.2 15.7 0.6 0.7 6.6 75.5 153 49286.6 152.8 119.5 109.7 41.5 37.2 33.2 KNF W 2.0 12.6 24.9 0.9 4.3 5.2 213.3 10476.3 16751.5 76.1 26.9 24.0 51.6 47.0 41.3 LNF 1.4 1.8 2.1 0.7 0.8 0.9 160.3 201.8 248.6 595.2 462.7 417.3 60.6 56.0 51.4 LPF 3.6 4.6 12.5 1.4 1.5 2.8 570.8 685.4 2950.5 35.3 26.2 23.2 75.9 67.0 60.6 MDF 1.2 1.6 2.9 0.8 1.0 1.2 185.5 245.9 379.3 135.9 104.5 88.5 73.1 68.4 60.8 MNF 6.4 8.3 16.0 3.2 3.7 7.1 5430.0 8500.2 44942.9 48.3 37.6 34.2 34.1 31.4 28.6 PNF 1.5 1.6 1.8 0.6 0.7 0.7 100.8 111.3 129.9 770.3 612.8 560.1 44.2 38.8 32.5 SHF 0.4 0.5 0.7 0.3 0.4 0.4 25.3 30.2 40.4 1894.2 1491.6 1401.0 80.6 75.1 70.4 SNF 1.8 4.5 5.4 0.9 1.4 1.5 22.9 578.4 685.9 212.2 140.1 117.7 45.6 29.8 24.7 Post KNF E 0.7 0.9 15.4 0.4 0.4 6.3 33.9 42.6 46828.1 434.7 347.0 321.2 42.8 38.4 34.2 KNF W 0.5 0.7 0.9 0.3 0.3 0.3 15.6 19.7 24.2 3023.2 2422.0 2218.5 58.0 53.0 46.8 LNF 0.5 0.7 0.8 0.3 0.3 0.3 15.9 19.9 21.9 2167.4 1766.4 1650.3 61.8 57.2 52.7 LPF 0.7 0.7 4.6 0.4 0.4 0.5 31.2 33.9 53.4 299.6 238.2 217.8 84.6 74.8 67.8 MDF 0.4 0.5 0.7 0.2 0.3 0.3 12.1 15.9 24.2 619.0 521.8 466.9 85.6 85.9 71.2 MNF 2.4 2.6 3.9 1.2 1.2 1.5 408.4 451.3 659.7 104.7 83.2 76.2 34.3 31.6 28.8 PNF 1.5 1.7 2.0 0.6 0.7 0.7 94.4 107.2 125.4 981.9 782.0 715.1 51.2 45.1 38.1 SHF 0.4 0.5 0.6 0.2 0.3 0.3 12.1 14.5 19.7 2677.8 2134.9 2012.2 82.9 77.2 72.4 SNF 0.6 0.7 0.7 0.3 0.3 0.3 17.7 21.9 21.9 1299.2 1052.0 968.1 47.2 40.4 38.1 ROS-rate of spread; FL-fl ame length; FI-fi reline intensity; TI-torching index; CI-crowning index.
USDA Forest Service Proceedings RMRS-P-41. 2006. 471

Effectiveness of Prescribed Fire as a Fuel Treatment in Californian Coniferous Forests Vaillant, Fites-Kaufman, Stephens

respect to higher percentile fi re weather pre-treatment except at PNF and 

SHF where no change occurred between the 90th and 97.5th percentiles 

(table 3). FL was shorter post-treatment as compared to pre-treatment in all 

locations except PNF where it did not change. Modeled fi reline intensity (FI) 

increased as percentile weather increased both pre- and post-treatment for 

all site locations except SNF (table 3). FI decreased post-treatment as compared to pre-treatment for all site locations. Torching index (TI) decreased 

as percentile weather increased pre- and post-treatment (table 3). Crowning 

index (CI) decreased with increasing percentile weather except at MDF where 

it only increased between the 80th and 90th percentile. CI increased slightly 

post-treatment for all locations, following the decreasing trend with respect 

to increasing severity of fi re weather.

Fire type (FT) remained 100 percent surface fi re in the LNF, PNF, SHF, 

and SNF sites pre- and post-treatment for all weather scenarios (table 4). 

Prescribed fi re changed predicted FT in the KNF E, KNF W, LPF, MDF, 

and MNF sites by either decreasing the likelihood of crown fi re or decreasing 

the severity of crown fi re. At 80th and 90th percentile fi re weather conditions, 

all post-treatment sites experienced only surface fi re.

Predicted Tree Mortality

Probability of mortality was modeled for four diameter classes (2.5 to 25, 

25 to 51, 51 to 76, >76 cm d.b.h.) as well as for all trees at each study site 

pre- and post-treatment (table 5). For all sites, a higher percentage of trees was 

predicted to die prior to treatment than after treatment. A higher amount of 

Table 4—Modeled fi re type under 80th, 90th, and 97.5th percentile weather by site location.

Site 80th 90th 97.5th

Pre

KNF E 33%PCF,66%SF 33%PCF,66%SF 33%ACFWD, 66%SF

KNF W 33%PCF,66%SF 33%SF, 66%PCF 33%SF,33%PCF,33%ACFPD

LNF 100%SF 100%SF 100%SF

LPF 33%PCF,66%SF 33%PCF,66%SF 33%SF, 66%PCF

MDF 100%SF 100%SF 33%PCF, 66%SF

MNF 40%PCF, 60%SF 40%PCF, 60%SF 20%PCF, 20%ACFPD, 60%SF

PNF 100%SF 100%SF 100%SF

SHF 100%SF 100%SF 100%SF

SNF 100%SF 100%SF 100%SF

Post

KNF E 100%SF 100%SF 33%ACFWD, 66%SF

KNF W 100%SF 100%SF 100%SF

LNF 100%SF 100%SF 100%SF

LPF 100%SF 100%SF 33%PCF, 66%SF

MDF 100%SF 100%SF 100%SF

MNF 100%SF 100%SF 100%SF

PNF 100%SF 100%SF 100%SF

SHF 100%SF 100%SF 100%SF

SNF 100%SF 100%SF 100%SF

SF=surface fi re; PCF=passive crown fi re; ACFWD=active crown fi re wind driven; ACFPD=active crown fi re plume 

dominated.
472 USDA Forest Service Proceedings RMRS-P-41. 2006.

Vaillant, Fites-Kaufman, Stephens Effectiveness of Prescribed Fire as a Fuel Treatment in Californian Coniferous Forests

Table 5—Average pre- and post-prescribed burn percent predicted mortality by diameter class and 

site location for three percentile weather conditions.

 DBH

 range 

 (cm) KNF E KNF W LNF LPF MDF MNF PNF SHF SNF

Pre

80th 2.5-25 62.7 90.6 56.1 99.4 86.0 95.4 64.3 65.5 58.7 

 25-51 21.9 52.1 24.1 70.5 27.5 79.6 17.5 20.4 17.3 

 51-76 6.9 8.0 7.3 6.0 8.4 • 6.4 5.2 4.9 

 >76 • 4.6 2.0 2.8 • • 2.0 3.6 2.0 

 All 30.5 38.8 22.4 44.7 40.6 87.5 22.5 23.7 20.7 

90th 2.5-25 66.5 98.1 57.8 99.6 92.4 96.9 64.7 65.5 77.5 

 25-51 26.7 84.1 25.0 79.6 32.6 85.7 17.5 20.4 28.8 

 51-76 8.2 50.0 8.0 8.9 12.1 • 6.4 5.2 5.4 

 >76 • 48.3 2.0 3.8 • • 2.0 3.6 2.0 

 All 33.8 70.1 23.2 48.0 45.7 91.3 22.6 23.7 28.4 

97.5th 2.5-25 69.5 99.1 59.2 99.6 97.8 99.2 66.0 65.5 83.8 

 25-51 46.6 87.9 26.6 89.0 44.0 95.5 17.6 20.4 37.2 

 51-76 35.6 64.0 9.3 39.4 20.6 • 6.4 5.2 6.5 

 >76 • 58.6 2.0 5.5 • • 2.0 3.6 2.0 

 All 50.6 77.4 24.3 58.4 54.1 97.3 23.0 23.7 32.4

Post

80th 2.5-25 52.3 58.9 46.0 52.4 53.6 81.7 52.2 40.2 48.0 

 25-51 21.1 23.1 23.2 22.5 17.2 58.8 17.8 18.6 13.9 

 51-76 6.9 5.6 8.6 7.1 5.0 • 6.3 5.2 4.3 

 >76 • 2.9 2.0 2.4 • • 2.0 3.6 2.0 

 All 26.8 22.6 19.9 18.3 25.2 70.2 19.6 16.9 17.1 

90th 2.5-25 52.3 58.9 46.0 52.4 53.6 85.7 52.2 40.2 48.0 

 25-51 21.1 23.1 23.2 22.5 17.2 67.2 17.8 18.6 13.9 

 51-76 6.9 5.6 8.6 7.1 5.0 • 6.3 5.2 4.3 

 >76 • 2.9 2.0 2.4 • • 2.0 3.6 2.0 

 All 26.8 22.6 19.9 18.3 25.2 76.5 19.6 16.9 17.1 

97.5th 2.5-25 65.7 58.9 46.0 56.9 53.9 87.6 52.3 40.2 48.0 

 25-51 46.7 23.1 23.2 31.0 17.9 78.0 17.8 18.6 13.9 

 51-76 35.6 5.6 8.6 16.6 5.2 • 6.3 5.2 4.3 

 >76 • 2.9 2.0 2.4 • • 2.0 3.6 2.0 

 All 49.3 22.6 19.9 18.8 25.7 82.8 19.6 16.9 17.1

• = no trees in this diameter class for this location.

mortality was predicted in smaller diameter classes (2.5 to 25 cm and 25 to 

51 cm d.b.h.) regardless of location, weather condition, or treatment status. 

An increase in mortality with respect to increasing predicted fi re weather 

conditions occurred in most study sites prior to prescribed fi re; the trend was 

not the same post-treatment (table 5).
USDA Forest Service Proceedings RMRS-P-41. 2006. 473

Effectiveness of Prescribed Fire as a Fuel Treatment in Californian Coniferous Forests Vaillant, Fites-Kaufman, Stephens

Discussion

Topography, weather, and fuels all play a role in the hazard and severity of 

wildland fi re. Altering the fuel load is the most feasible and important factor 

to decrease hazard and severity of wildland fi re. The vertical and horizontal 

continuity of surface fuels (litter and downed woody debris), ladder fuels (shrubs 

and small trees), and/or canopy fuels (large trees) must be broken to reduce 

fi re severity. Reduction in surface fuels can reduce FI, increasing HTLCB can 

reduce the risk of torching, and reduction in crown density can limit tree-totree spread of crown fi res (Agee 2002; Hessberg and Agee 2003; Agee and 

Skinner 2005).

Many studies in ponderosa pine and mixed-conifer forests document the 

effectiveness of prescribed fi re in reducing future fi re severity (Weaver 1943; 

Biswell and others 1973; Kauffman and Martin 1989; van Wagtendonk 1996; 

Stephens 1998; Miller and Urban 2000; Pollet and Omi 2002; Finney and 

others 2005; Knapp and others 2005; Stephens and Moghaddas 2005a,b). 

Prescribed fi re effectively reduces surface fuel loads as well as kills shrubs and 

small diameter trees which reduce ladder fuels. Understory burning can also 

raise the height to live crown base through scorching of lower branches. One 

unifying goal of the prescribed burns analyzed in this work was to reduce 

the risk of stand-replacing catastrophic fi re.

Stand characteristics did not signifi cantly change in four of the nine site locations after treatment. This is consistent with many of the studies mentioned 

above. However, KNF E did experience a signifi cant change in basal area, trees 

ha–1, d.b.h., tree height, canopy cover, and CBD post-prescribed fi re. This may 

be partially due to a tree blowdown event between plot readings (Kit Jacoby, 

personal communication). In the rest of the sites there were few differences 

in stand structure pre- and post-treatment. TI and CI moderately increased at 

all sites post-treatment, which indicates the need for an increase in wind speed 

to initiate and maintain crown fi re. Overall, the modeled outputs document a 

reduced percentage of crown fi res post-treatment; fi ve treatments had a component of passive crown fi re pre-treatment and two post-treatment (table 4).

If the primary goal of the prescribed fi re treatment is to reduce the potential 

of stand replacing catastrophic wildfi res, then TI and CI might be of particular 

interest. CI only increased slightly for all sites post-treatment indicating that 

the prescribed fi re treatments did not effect the overstory (CBD or tree canopy 

cover). Under the 80th percentile fi re weather condition, the untreated sites 

are unlikely to initiate crown fi re due to high TI (table 3). For the 90th and 

97.5th percentile fi re weather conditions, pre-treatment values of TI and CI 

make the KNF W, LPF, and MNF sites more vulnerable to active crown fi re 

(table 3). The reduction in likelihood of crown fi re is due to a combination 

of changes in stand structures and surface fuel loads. Crown fi re is not solely 

linked to canopy characteristics; surface fuel loads also play a critical role in 

active crown fi re initiation and spread. If surface fi reline intensity exceeds 

the critical level needed to initiate an active crown fi re, the canopy is likely 

to burn as long as high surface fuel loads are present.

Fuel bed depth was signifi cantly reduced at the KNF E, MNF, PNF and 

SHF sites; however, fuel bed depth was reduced by at least 20 percent at 

the remaining fi ve sites, but was not statistically signifi cant. Total fuel loads 

(surface and ground) were reduced signifi cantly at LPF, MNF and PNF. 

The relatively high consumption of ground and surface fuels is consistent 

with past studies (Kilgore and Sando 1975; Kauffman and Martin 1989; 

Stephens and Finney 2002; Knapp and others 2005). Prescribed fi re without 
474 USDA Forest Service Proceedings RMRS-P-41. 2006.

Vaillant, Fites-Kaufman, Stephens Effectiveness of Prescribed Fire as a Fuel Treatment in Californian Coniferous Forests

crown thinning has been shown to greatly reduce fi reline intensity relative 

to no treatment (van Wagtendonk 1996; Stephens 1998). A reduction in 

surface fuel loads generally results in decreased fi re severity, ROS, FL, and 

FI. Altered stand structures also contributed to the increase of surface fi res 

versus crown fi res post-treatment. Smaller diameter trees killed by prescribed 

fi re are initially standing dead fuel. Eventually these trees will fall and contribute to the surface fuel loads (Stephens 1998; Agee 2003), necessitating 

future prescribed fi res to keep hazards low.

Predicted tree mortality was higher pre-treatment than post-treatment for 

all locations under low, moderate, and extreme fi re weather. Probability of tree 

mortality is primarily based on percent crown scorched which is derived from 

crown ratio, species tree height, and tree diameter (Reinhardt and others 

1997). Predicted tree mortality was greatest in the smallest diameter class 

(2.5 to 25 cm d.b.h.) and decreased with increasing diameter classes (table 5). 

Increases in percentile fi re weather post-treatment did not increase the likelihood of overall tree mortality at fi ve sites (KNF W, LNF, PNF, SHF, SNF), it 

only slightly increased tree mortality in two sites (LPF and MDF), and it greatly 

increased tree mortality in two sites (KNF E and MNF). Predicted mortality almost doubled for all diameters at KNF E between the 90th and 97.5th

percentile conditions post-treatment where fi re type also changed; however, 

mortality was still lower relative to pre-treatment conditions (tables 4 and 5).

If reduction of potential stand replacing fi res is the primary goal of prescribed fi re treatments, selection of treatment locations must consider the 

existing fi re hazards. Four of the nine study sites examined here only experienced modeled surface fi re in pre-treatment conditions, including extreme 

fi re weather conditions (table 4). Post-treatment potential fi re behavior (ROS, 

FL, FI) was reduced, but these stands were not at risk of crown fi re before 

treatment. On the other hand, three of the nine sites were at an elevated 

risk of crown fi re (low TI and CI) pre-treatment at 97.5th percentile weather 

conditions (table 3). For the sites that would experience only surface fi re, 

treatment is not warranted based on the reduction of potential fi re behavior 

and effects. Sites experiencing low TI and CI values may benefi t from a mechanical treatment (such as thinning from below) prior to prescribed fi re to 

further reduce the risk of active crown fi re.

In addition to the reduced potential for stand replacing catastrophic wildland fi res, reintroduction of fi re into the ecosystem was a primary goal of 

these prescribed fi re treatments. Seasonality of prescribed fi re is important 

from an ecological and fuels consumption standpoint. Fire history data from 

the southern Cascades in California document that prehistoric fi res occurred 

mostly during the dormant season (starting as early as August and ending in 

October) in both pine dominated and mixed conifer forests (Taylor 2000; 

Beaty and Taylor 2001). In mixed conifer forests of the north-central, southcentral, and southern Sierra Nevada, fi res occurred most frequently just before 

dormancy in latewood growth (Stephens and Collins 2004). If reintroducing 

ecological processes is an important goal of a prescribed burn, it would be best 

if the burns took place in a time consistent with the fi re history records.

Managers must consider many facets when choosing a location for treatment. With the amount of land rated at high hazard in California it would 

be wise to target stands which would benefi t the most from treatment. If 

reintroduction of fi re into the ecosystem is the primary goal and fuel reduction the secondary goal, then choosing treatment locations could include 

both stands with high and low fi re hazards. Unfortunately, there is no one 

size fi ts all for fuel treatments in California; managers must consider many 

factors when implementing a forest restoration plan.
USDA Forest Service Proceedings RMRS-P-41. 2006. 475

Effectiveness of Prescribed Fire as a Fuel Treatment in Californian Coniferous Forests Vaillant, Fites-Kaufman, Stephens

Refrences

Agee, J. A. 2002. The fallacy of passive management: managing for fi re safe forest 

reserves. Conservation Biology in Practice 3 (1): 18-25.

Agee, J. A. 2003. Monitoring post fi re tree mortality in mixed-conifer forest reserves 

of Crater Lake, OR. Natural Areas Journal 23: 114-120.

Agee, J. K. and C. N. Skinner. 2005. Basic principles of forest fuel reduction 

treatments. Forest Ecology and Management 211: 83-96.

Beaty, R. M. and A. H. Taylor. 2001. Spatial and temporal variation of fi re regimes 

in a mixed conifer forest landscape, Southern Cascades, California, USA. Journal 

of Biogeography 28: 955-966.

Biswell, H. H. 1959. Man and fi re in ponderosa pine in the Sierra Nevada of 

California. Sierra Club Bulletin 44: 44-53.

Biswell, H. H., H. R. Kallander, R., Komarek, R. J. Vogl, and H. Weaver. 1973. 

Ponderosa Fire Management. Tall Timbers Research Station Misc. Publication 

No. 2. Tallahassee, FL: Tall Timbers Research Station. FL. 49pp.

Brown, J. K. 1974. Handbook for inventorying downed woody material. Gen. 

Tech. Rep. INT-16. Ogden, UT: U.S. Department of Agriculture, Forest Service, 

Intermountain Forest and Range Experiment Station. 22 p.

Brown, J. K. and S. F. Arno. 1991. The paradox of wildland fi re. Western Wildlands 

Spring: 40-46.

Carlton, D. 2005. Fuels Management Analyst Plus. In: Fire Program Solutions, 

LLC.

Dodge, M. 1972. Forest fuel accumulation- a growing problem. Science 177: 139-142.

Finney, M. A., C. W. McHugh, and I. C. Genfell. 2005. Stand- and landscape-level 

effects of prescribed burning on two Arizona wildfi res. Canadian Journal of 

Forest Research 35: 1714-1722.

Gill, S. J., G. S. Biging, and E. Murphy. 2000. Modeling tree crown radius and 

estimating canopy cover. Forest Ecology and Management 126: 405-416.

Hessburg, P. F. and J. A. Agee. 2003. An environmental narrative of inland Northwest 

U.S. forests, 1800-2000. Forest Ecology and Management 178: 23-59.

Kauffman, J. B. and R. E. Martin. 1989. Fire behavior, fuel consumption, and 

forest-fl oor changes following prescribed understory fi res in Sierra Nevada mixed 

conifer forests. Canadian Journal of Forest Research 19: 455-462.

Keyes, C. R. and K. L. O’Hara. 2002. Quantifying stand targets for silvicultural 

prevention of crown fi res. Western Journal of Applied Forestry 17 (2): 101-109.

Kilgore, B. M. and R. W. Sando. 1973. The ecological role of fi re in Sierran conifer 

forests: it’s application to national park management. Journal of Quaternary 

Research 3: 496-513.

Knapp, E. E., J. E. Keeley, E. A. Ballenger, and T. J. Brennan. 2005. Fuel reduction 

and coarse woody debris dynamics with early and late season prescribed fi re in 

a Sierra Nevada mixed conifer forest. Forest Ecology and Management 208: 

383-397.

Main, W. A., D. M. Paananen, and R. E. Burgan. 1990. Fire Family Plus. Gen. Tech. 

Rep. NC-138. St. Paul, MN: U.S. Department of Agriculture, Forest Service, 

North Central Forest Experiment Station, St. Paul, MN. 35 p.

Miller, C. and D. L. Urban. 2000. Modeling the effects of fi re management alternatives 

on Sierra Nevada mixed-conifer forests. Ecological Applications 10 (1): 85-94.

NFAM, 2004. National Fire and Aviation Management Web Applications. http://

famweb.nwcg.gov/weatherfi recd/california.htm.
476 USDA Forest Service Proceedings RMRS-P-41. 2006.

Vaillant, Fites-Kaufman, Stephens Effectiveness of Prescribed Fire as a Fuel Treatment in Californian Coniferous Forests

Piñol, J., K. Beven, and D. X. Viegas. 2005. Modeling the effect of fi re-exclusion 

and prescribed fi re on wildfi re size in Mediterranean ecosystems. Ecological 

Modeling 183: 397-409.

Pollet, J. and P. N. Omi. 2002. Effect of thinning and prescribed burning on crown 

fi re severity in ponderosa pine forests. International Journal of Wildland Fire 11: 

1-10.

Reinhardt, E. D., R. E. Keane, and J. K. Brown. 1997. First order Fire Effects 

Model: FOFEM 4.0, User’s Guide. Gen. Tech. Rep. INT-344. Ogden, UT: U.S. 

Department of Agriculture, Forest Service, Intermountain Forest and Range 

Experiment Station. 65 p.

Scott, J. H. and R. E. Burgan. 2005. Standard fi re behavior fuel models: A 

comprehensive set for use with Rothermel’s surface fore spread model. Gen. Tech. 

Rep. RMRS-GTR-153. Fort Collins, CO: U.S. Department of Agriculture, Forest 

Service, Rocky Mountain Research Station. 72 p.

Stephens, S. L. 1998. Evaluation of the effects of silvicultural and fuels treatments on 

potential fi re behavior in the Sierra Nevada mixed-conifer forests. Forest Ecology 

and Management 105: 21-35.

Stephens, S. L. 2005. Forest fi re causes and extent on United States Forest Service 

lands. International Journal of Wildland Fire 14: 213-222.

Stephens, S. L. and M. A. Finney. 2002. Prescribed fi re mortality of Sierra Nevada 

mixed conifer tree species: effect of crown damage and forest fl oor combustion. 

Forest Ecology and Management 162: 261-271.

Stephens, S. L., and B. M. Collins. 2004. Fire regimes of mixed conifer forests in the 

north-central Sierra Nevada at multiple spatial scales. Northwest Science 78 (1): 

12-23.

Stephens, S. L and J. J. Moghaddas. 2005a. Experimental fuel treatment impacts on 

forest structure, potential fi re behavior, and predicted tree mortality in a California 

mixed conifer forest. Forest Ecology and Management 215: 21-26.

Stephens, S. L and J. J. Moghaddas. 2005b. Silvicultural and reserve impacts on 

potential fi re behavior and forest conservation: twenty-fi ve years of experience from 

Sierra Nevada mixed conifer forests. Biological Conservation 125: 369-379.

Taylor, A. H. 2000. Fire regimes and forest change in mid and upper montane forests 

of the southern Cascades, Lassen Volcanic National Park, California, U.S.A. 

Journal of Biogeography 27: 87-104.

van Wagner, C. E. 1968. The line intercept method in forest fuel sampling. Forest 

Science 14: 20-26.

van Wagner, C. E. 1977. Conditions for the start and spread of crown fi re. Canadian 

Journal of Forest Research 7: 23-34.

van Wagtendonk, J. W. 1996. Use of a deterministic fi re growth model to test fuel 

treatments. In: Sierra Nevada Ecosystems Project: Final Report to Congress, 

vol. II. Assessments and Scientifi c Basis for Management Options. University of 

California, Davis. Centers for Water and Wildland Resources: 1155-1165.

van Wagtendonk, J. W. J. W. Benedict, and W. M. Sydoriak. 1998. Fuel bed 

characteristics of Sierra Nevada conifers. Western Journal of Applied Forestry 

13: 1145-1157.

Weaver, H. 1943. Fire as an ecological and silvicultural factor in the ponderosa-pine 

region of the Pacifi c slope. Journal of Forestry 41: 7-15.

Zar, J.H. 1999. Biostatistical Analysis. Upper Saddle River, NJ: Prentice-Hall. 663 p.
USDA Forest Service Proceedings RMRS-P-41. 2006. 477

In: Andrews, Patricia L.; Butler, Bret W., 

comps. 2006. Fuels Management—How to 

Measure Success: Conference Proceedings. 

28-30 March 2006; Portland, OR. 

Proceedings RMRS-P-41. Fort Collins, 

CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research 

Station.

1 Research Biologist with the US Forest 

Service, Rocky Mountain Research 

Station, MSU Campus Bozeman, MT. 

vsaab@fs.fed.us

2 Research Wildlife Biologist contracting with the US Forest Service, Rocky 

Mountain and the Pacifi c Northwest 

Research Stations, Kalispell, MT.

3 Research Wildlife Biologist with the 

US Forest Service, Pacifi c Northwest 

Research Station, Wenatchee Forestry 

Sciences Lab, Wenatchee, WA.

4 PhD Graduate Research Assistant at 

the Noon Lab of Conservation and 

Landscape Ecology, Department of 

Fishery and Wildlife Biology, College 

of Natural Resources, Colorado State 

University, Fort Collins, CO.

5 Masters Graduate Research Assistant 

in the Ecology Department, Montana 

State University, Bozeman, MT.

6 Masters degree in Wildlife and 

Fisheries Science at the University 

of Arizona, Department of Natural 

Resources, University of Arizona, 

Biological Sciences, Tucson, AZ.

7 Project Leader/Supervisory Research 

Wildlife Biologist for the Rocky 

Mountain Station, Southwest Forest 

Science Complex, Flagstaff, AZ. 

Abstract—Most prescribed fi re plans focus on reducing wildfi re hazards with little 

consideration given to effects on wildlife populations and their habitats. To evaluate 

effectiveness of prescribed burning in reducing fuels and to assess effects of fuels 

reduction on wildlife, we began a large-scale study known as the Birds and Burns Network in 2002. In this paper we analyze changes in downed wood and forest structure 

(trees and snags) measured within one year after prescribed fi re treatments that were 

completed in ponderosa pine (Pinus ponderosa) forests in Arizona and New Mexico 

(Southwest region), and Idaho and Washington (Northwest region). Apparent reductions 

in downed wood and trees were observed in both regions. However, statistically signifi -

cant reductions of downed wood were found primarily in the Northwest (p < 0.001), 

whereas signifi cant reductions of trees were reported only for the Southwest (p = 0.03). 

No signifi cant post-treatment changes were detected in snag densities, although we 

observed a pattern of non-signifi cant increases in all size classes. Additional fi re treatments are likely needed to meet fuels reduction goals. Results of this study are intended 

to assist managers with developing scientifi cally sound and legally defensible prescribed 

fi re projects that will reduce fuels and concurrently enhance wildlife habitat.

Introduction

Fire regimes of lower elevation forests, particularly ponderosa pine (Pinus 

ponderosa) of the Interior Western United States, have been altered since EuroAmerican settlement (Agee 1993; Schoennagel and others 2004). Alterations 

in fi re regimes and subsequent changes in forest structure and composition 

stem primarily from fi re suppression, logging, and livestock grazing (Allen 

and others 2002; Schoennagel and others 2004; Veblen 2000). After decades of fi re suppression, elevated fuel loads in many ponderosa pine forests 

have increased the likelihood of unusually large and severe fi res (Arno and 

Brown 1991; Covington and Moore 1994), and the area burned annually has 

increased (Grissino-Mayer and Swetnam 2000; Keane and others 2002).

In an effort to restore ponderosa pine forest ecosystems, land managers 

have increasingly relied on prescribed burning (Horton and Mannan 1998; 

Arno 2000; Machmer 2002; Carey and Schumann 2003). Most prescribed 

fi re plans focus on reducing the intensity of wildfi re, with little consideration 

given to effects on wildlife populations and their habitats. Strategies for fi re 

management should not only reduce fi re risk but also maintain habitat for 

wildlife and other components of biodiversity (Saab and others 2005).

Ponderosa pine trees, snags and downed wood are among the most valuable 

habitat components for wildlife species in western North American forests 

(Balda 1975; Bull and others 1997; Hall and others 1997; Szaro and others 

Changes in Downed Wood and Forest 

Structure After Prescribed Fire in 

Ponderosa Pine Forests

Victoria Saab1, Lisa Bate2, John Lehmkuhl3, Brett Dickson4, 

Scott Story5, Stephanie Jentsch6, and William Block7
478 USDA Forest Service Proceedings RMRS-P-41. 2006.

Saab, Bate, Lehmkuhl, Dickson, Story, Jentsch, and Block Changes in Downed Wood and Forest Structure After Prescribed Fire in Ponderosa Pine Forests

1988). Large-diameter ponderosa pine snags, trees with decay, and downed 

logs are relatively easy to excavate by woodpeckers and provide roosting, 

nesting, and foraging habitat for a variety of wildlife (Bull and others 1997; 

Hall and others 1997; Szaro and others 1988; Scott 1979).

Many cavity-nesting birds depend on fi re-disturbed landscapes for breeding, 

dispersal, and other por tions of their life history (Saab and others 2004). Several cavity nesters are designated by state, federal, and provincial governments 

as species at-risk because they are responsive to fi re and timber management 

activities. Stand-replacement fi res in conifer forests are particularly important 

to breeding and wintering cavity-nesting birds (Blackford 1955; Raphael and 

White 1984; Saab and Dudley 1998; Kriesel and Stein 1999; Hannon and 

Drapeau 2005). Little is known, however, about bird population responses 

to prescribed fi re, particularly in the Intermountain region (Bock and Block 

2005; Saab and others 2005). In 2002, we began a regional study to evaluate effectiveness of prescribed fi re in reducing fuels and to assess the effects 

of fuels reduction on habitats and populations of birds in ponderosa pine 

forests throughout the Interior West. Our study is known as the Birds and 

Burns Network (BBN) (see web page http://www.rmrs.nau.edu/lab/4251/

birdsnburns/), with study areas located in seven states encompassing much of 

the range of ponderosa pine in the United States (Arizona, Colorado, Idaho, 

Montana, New Mexico, Oregon, and Washington). As of 2005, study areas in 

Arizona and New Mexico (Southwest region; SW), and Idaho and Washington 

(Northwest region; NW) have received prescribed fi re treatments.

In this paper, our objective was to evaluate the magnitude of change in 

the quantities of downed wood, dead stems (hereafter termed snags), and live 

stems (hereafter termed trees) measured within one year after prescribed fi re 

treatments. Based on previous studies (Horton and Mannan 1988; Machmer 

2002; McHugh and Kolb 2003; Raymond and Peterson 2005), we hypothesized that downed wood of all sizes, large snags (≥ 9 inch diameter breast 

height [d.b.h.]), and smaller trees (< 9 inch d.b.h.) would be reduced as a 

result of prescribed burning, whereas we expected smaller snags (< 9 inch 

d.b.h.) to increase after fi re treatments. Results of this study are intended to 

help managers develop scientifi cally sound and legally defensible prescribed 

fi re projects that will reduce fuels and concurrently maintain and enhance 

wildlife habitat.

Study Area and Methods

Study areas were located in forests dominated by ponderosa pine, where 

prescribed fi re treatments were implemented by the USDA National Forests. 

On each study area, a single treatment unit ranged in size from 500 to 1000 

acres and was paired with an unburned control unit of equivalent area. As of 

2005, prescribed fi re treatments were completed at seven study units in four 

states and data from these units were used in the analyses presented in this 

paper. General objectives of these “low-intensity” fi re treatments included 

fuels reduction, fi re threat mitigation, and forest restoration.

Pre-treatment data were collected during the summers of 2002 and 2003. 

Four units were treated with fi re in the SW on USDA National Forests (NF); 

two units during fall 2003 in Arizona (Apache-Sitgreaves and Coconino 

NFs), and two units that were initiated in fall 2003 and completed during 

spring 2004 in New Mexico and Arizona (Gila and Kaibab NFs, respectively). 

Three units were treated in the NW during spring 2004, one unit in Idaho 
USDA Forest Service Proceedings RMRS-P-41. 2006. 479

Changes in Downed Wood and Forest Structure After Prescribed Fire in Ponderosa Pine Forests Saab, Bate, Lehmkuhl, Dickson, Story, Jentsch, and Block

(Payette NF) and two units in Washington (Okanogan and Wenatchee NFs). 

Post-treatment data were collected one growing season after fi re treatments 

during the summers of 2004 or 2005.

Overstory vegetation (trees ≥ 9 inches d.b.h.) on all units in both regions was 

dominated by ponderosa pine. For trees ≥ 20 inch d.b.h. or larger, ponderosa 

pine was also the dominant tree species for all locations except for the Gila NF, 

where alligatorbark juniper (Juniperus deppeana) had higher densities.

In Arizona, common understory vegetation included green rabbitbrush 

(Chrysothamnus viscidifl orus) and Fendler rose (Rosa woodsii), whereas gambel oak dominated the understory in New Mexico. Arizona fescue (Festuca 

arizonica) and blue gramma (Bouteloua gracilis) were the most common grass 

species throughout the SW. Elevations in the SW region ranged from 6800 

feet on the Coconino NF to nearly 8200 feet on the Gila NF.

The understory vegetation in the NW was comprised of various species, 

including snowberry (Symphoricarpos albus), spirea (Spirea spp.), serviceberry 

(Amelanchier alnifolia), and chokecherry (Prunus spp.). Bluebunch wheatgrass 

(Pseudoroegenaria spicatus) and Idaho Fescue (Festuca idahoensis) were the 

common grass species. Elevations ranged from 2200 feet in Washington to 

6500 feet in Idaho.

Within each unit we established 20 to 40 permanently marked 1-acre 

random plots to measure fuel and vegetative characteristics. All plots centers 

were at least 820 feet apart (Dudley and Saab 2003). To determine the effects of prescribed fi re on downed wood, snags, and trees, we measured these 

forest components at each plot before (pre) and after (post) prescribed fi re. 

We considered the difference in pre and post values by plot to be a measure 

of the treatment effect size.

Measurements were nested within the 1-acre plot confi gured as two 66 x 326 

feet rectangles that crossed in the center (that is, a rectangular cross plot). 

Tree and snag measurements followed methods outlined by Bate and others 

(1999). Snags ≥ 9 inches d.b.h. were counted within 33 feet of the centerline 

in the rectangular cross plot. Trees ≥ 9 inches d.b.h. were counted within 16.5 feet 

of the centerline in the SW and within 9.8 feet in the NW. Plot widths for 

trees and snags were based on power analyses of pilot data from each location 

to maximize effi ciency of data collection (Bate and others 1999). For trees 

and snags < 9 inches, we counted within 6.6 feet of the centerline in the SW 

and within 3.3 feet in the NW.

In this paper we present preliminary results for both snags and trees in four 

categories: (1) < 3 inch; (2) ≥ 3 to 9 inch; (3) ≥ 9 inch; and (4) total density 

of all stems (snags or trees). Snags and trees in the ≥ 9 inch d.b.h. category 

were of special interest to us because they commonly represent the smallest size 

class that woodpeckers use for nesting (for example, Saab and others 2004) 

and the smallest sized trees harvested for timber values (USDA 1996).

We measured the weight (tons per acre) of downed wood following Brown’s 

(1974) protocol. Downed wood is defi ned as the “... dead twigs, branches, 

stems, and boles of trees and brush that have fallen and lie on or above the 

ground” (Brown 1974, page 1). Downed wood pieces less than 1 inch diameter 

(1- and 10-hour fuels) were sampled along 41 feet of transect in two directions (north and south) from the plot center. Material in the ≥ 1 to 3 inch 

size class (100-hour fuels) was measured in the same two directions but along 

twice the length (82 feet). For coarse wood ≥ 3 inches (≥ 1000-hour fuels), 

we recorded the intersection diameter of each woody piece along 164 feet in 

each of the four cardinal directions originating from the plot center (total 

of 656 feet sampled). Downed wood pieces ≥ 3 inch were classifi ed as either 

sound or rotten and we used the specifi c gravities provided by Brown (1974) 
480 USDA Forest Service Proceedings RMRS-P-41. 2006.

Saab, Bate, Lehmkuhl, Dickson, Story, Jentsch, and Block Changes in Downed Wood and Forest Structure After Prescribed Fire in Ponderosa Pine Forests

to obtain a weight estimate for each condition class. That is, we used 24.96 

lbs/ft3 and 18.72 lbs/ft3 for sound and rotten wood, respectively, relative to 

the density of water (62.4 lbs/ft3) (Brown and See 1981). Here, we present 

results for downed wood in four size categories: (1) < 3 inch; (2) ≥ 3 inch; 

(3) ≥ 9 inch; and (4) total weight of all downed wood. Weights calculated for 

the ≥ 9 inch category were based on the large-end diameter (LED), whereas 

weights of other size classes were based on the intersect diameter.

We calculated a response to the prescribed fi re as an “effect size” on each 

plot, which represented the change in fuels attributable to the prescribed fi re. 

The effect size was measured by subtracting pre-fi re fuel quantities from postfi re fuel quantities. We then computed least-squares means (PROC MIXED 

SAS Institute 2003) to test whether the effect size was signifi cantly different 

from zero for weight of downed wood, snag densities, and tree densities. We 

accepted p ≤ 0.05 as the observed probability level for Type I error in hypothesis tests. We used a nested analysis with plots nested within units, and units 

nested within regions. Results are reported for the mean effect size of stems per 

acre (± 1 standard error [SE]) and tons per acre (± 1 SE) at the regional level. A 

likelihood ratio test was computed to compare a model with a pooled estimate 

of variance across regions to a model with a separate variance estimate for each 

region. Generally, the model with separate variance estimates had signifi cantly 

better goodness-of-fi t and was used for the least-squares means analysis. Pooled 

variance results are reported only for trees ≥ 9 inch d.b.h. and for total trees.

Results

Weight of downed wood in all size classes decreased after prescribed fi re 

treatments (Table 1), however most of the statistically signifi cant differences 

were measured in the NW (Table 2). Downed wood was reduced by 25 to 

43 percent in the SW and by 29 to 58 percent in the NW. Total weight of 

downed wood was reduced by nearly half in the NW region, where most of 

the downed material was comprised of large logs ≥ 9 inches LED (Table 1). 

In contrast to the NW, pre-fi re weight of downed wood in the SW region 

was composed almost exclusively of small diameter material < 9 inches LED 

(Table 1).

Our hypothesis about reductions of small diameter (< 9 inches d.b.h.) trees 

(seedlings, saplings, and poles) was generally supported by the data; however, 

trees of all diameter classes in the SW region also decreased signifi cantly after 

fi re treatments (Table 2). Trees were reduced by 19 to 74 percent in the SW 

and 0 to 39 percent in the NW (Table 1). Stems in the smallest size class (<3 

inches) contributed the most to changes in tree densities, whereas large tree 

(≥ 9 inches) densities changed the least.

We hypothesized that snags of the smaller size classes (< 9 inches d.b.h.) 

would increase and that large snags (≥ 9 inches d.b.h.) would decrease after 

fi re treatments. Our results indicated no signifi cant post-treatment changes 

in snag densities (Table 2), although we observed a pattern of non-signifi cant 

increases in all size classes (Table 1). Increases in snags ranged from 30 to 

72 percent in the SW and 29 to 229 percent in the NW. Large snags (≥ 9 

inches d.b.h.) contributed to the greatest changes in dead stems in the SW, 

whereas smaller diameter stems (≥ 3 – 9 inches d.b.h.) contributed most to 

snag changes in the NW.
USDA Forest Service Proceedings RMRS-P-41. 2006. 481

Changes in Downed Wood and Forest Structure After Prescribed Fire in Ponderosa Pine Forests Saab, Bate, Lehmkuhl, Dickson, Story, Jentsch, and Block

Discussion

Decreases in downed wood and trees supported our hypotheses regarding 

changes in these forest components after prescribed fi re treatments. While 

we expected only the smaller size classes of snags (< 9 inch d.b.h.) to increase 

after prescribed fi re, we observed a pattern of non-signifi cant increases in 

large snag (≥ 9 inch d.b.h.) densities as well. Apparently, prescribed fi re treatments were severe enough to kill trees of all size classes, particularly in the 

SW where this result was statistically signifi cant.

Table 1—Means, standard errors (SE), and percent change for downed wood (DW; mean tons per acre), and 

trees, and snags (mean stems per acre) measured pre- and post-fi re treatment by region (Southwest [SW] 

and Northwest [NW]) in the Birds and Burns Network during 2002-2005. Downed wood was measured 

at large end diameter (LED) and stems were measured at diameter breast height (d.b.h.).

 SW [ n = 134] NW [n = 60]

 Pre-fi re Post-fi re Percent Pre-fi re Post-fi re Percent

 Size class mean mean change mean mean change

 (inches) - - - - - - - - - - - (SE) - - - - - - - - - - - - - - - - - - - - - - (SE) - - - - - - - - - -

DW < 3 2.0 (0.2) 1.5 (0.1) –25 1.8 (0.2) 1.3 (0.1) –27.8

(tons/ac) ≥ 3 2.3 (0.2) 1.3 (0.1) –43.5 7.6 (0.8) 3.8 (0.5) –50

≥ 9 0.7 (0.1) 0.4 (0.1) –42.9 6.3 (0.8) 2.6 (0.4) –58.7

 Total 4.3 (0.3) 2.8 (0.2) –34.9 9.4 (0.9) 5.1 (0.5) –46

Trees < 3 256 (26.5) 66.3 (12.6) –74.1 234 (29.4) 144 (26.6) –38.5

(stems/ac) ≥ 3 to 9 124 (9.5) 72.6 (8.4) –41.5 191 (23.6) 133 (17.1) –30.4

≥ 9 52.2 (2.5) 42.2 (2.9) –19.2 45.5 (2.4) 48.1 (2.9) +.057

 Total 432 (33.2) 181 (18.2) –58.1 470 (46.8) 324 (36.2) –31.1

Snags < 3 28.6 (3.9) 44.6 (5.6) +55.9 62.2 (10.2) 110 (16.7) +76.8

(stems/ac) ≥ 3 to 9 15 (2) 19.5 (2.4) +30 12.7 (2.1) 41.8 (7.9) +229

≥ 9 2.5 (0.3) 4.3 (0.7) +72 2.8 (0.4) 3.6 (0.5) +28.6

 Total 46 (5.4) 68.4(7.5) +48.7 77.6 (11.5) 156 (23.5) +101

Table 2—Results of least–square means analysis to test for statistical differences from zero, or no change in the 

quantity of downed wood (DW; tons per acre), trees (stems per acre), and snags (stems per acre) measured 

pre- and post-prescribed fi re in western ponderosa pine forests. Mean estimate of the effect size, standard 

error of the estimate (SE), t-value, p-value, and sample size [n] are reported for each size class by region 

(Southwest [SW] and Northwest [NW]) in the Birds and Burns Network during 2002-2005.

 SW [n =134] NW [n = 60]

 Estimate Estimate

 Size class (effect size) SE t-value p-value (effect size) SE t-value p-value

 (inches)

DW < 3 –0.49 0.32 –1.53 0.19 –0.46 0.43 –1.06 0.34

(Δ in tons/ac) ≥ 3 –1.15 0.43 –2.68 0.04 –3.9 0.58 –6.71 0.001

≥ 9 –0.4 0.18 –2.11 0.09 –3.7 0.54 –6.83 0.001

 Total –1.66 0.73 –2.28 0.07 –4.3 0.36 –11.97 <0.001

Trees < 3 –212.6 28.6 –2.7 0.04 –107.1 47.5 –2.26 0.07

(Δ in stems/ac) ≥ 3 to 9 –60.9 29.5 –2.07 0.09 –61.1 35.2 –1.74 0.14

≥ 9 –13.3 8.06 –1.65 0.16 2.9 9.38 0.31 0.77

 Total –287.1 99.9 –2.37 0.03 –165.2 117.3 –1.41 0.22

Snags < 3 19.4 13.7 1.42 0.21 47.9 19.5 2.45 0.06

(Δ in stems/ac) ≥ 3 to 9 5.4 4.2 1.29 0.25 29.2 17.6 1.65 0.16

≥ 9 1.86 1.06 1.75 0.14 0.79 0.55 1.43 0.21

 Total 26.6 14.8 1.80 0.13 77.9 37.2 2.09 0.09
482 USDA Forest Service Proceedings RMRS-P-41. 2006.

Saab, Bate, Lehmkuhl, Dickson, Story, Jentsch, and Block Changes in Downed Wood and Forest Structure After Prescribed Fire in Ponderosa Pine Forests

Nearly half of large downed wood (≥ 9 inch LED) was consumed by prescribed 

fi re in both regions. Drought conditions, followed by low wood moistures prior 

to fi re treatments, may have contributed to the large loss of downed wood. When 

moisture contents are less than 15 percent, fi re generally consumes about half 

of large downed woody materials (Brown and others 1985). Efforts to retain 

these large structures may require seasonal adjustments for burning times when 

moisture contents are higher and fi re severity effects are lower (Thies and others 

2005). Maintenance of large, downed wood is important ecologically because 

these structures provide foraging habitat, thermal cover, and concealment for 

many sensitive wildlife taxa (Bull and others 1997; Szaro and others 1988), 

although logs may have been a limited resource in low-severity fi re regimes 

(Agee 2002).

Overall tree densities in the SW were signifi cantly reduced after fi re treatments. Although we observed a pattern of decreased tree densities in the NW, 

no statistical differences were detected in densities measured before and after 

prescribed fi re. We think, however, that all observed changes in tree densities 

were important ecologically. For example, in both regions we observed the 

greatest reduction of tree densities in the smallest size class (< 3 inches d.b.h.), 

followed by reductions in the medium size class (≥ 3 to < 9 inch), with little 

change in large (≥ 9 inches d.b.h) tree densities. Small diameter trees function as 

ladder fuels in dense stands by carrying fl ames into the crowns of mature trees, 

where the potential for larger tree mortality increases (Pollet and Omi 2002). 

Indeed, prescribed fi re programs that remove small diameter trees can reduce the 

likelihood and cost of stand-replacing fi res (Arno 1980; Fernandes and Botelho 

2003; Pollet and Omi 2002).

We observed relatively little change in densities of large trees ≥ 9 inch d.b.h. 

This result was not surprising because the thick bark of ponderosa pine is fi reresistant, improving tree survival during low to moderate severity burns (Agee 

1993). Historically, large-diameter ponderosa pines were harvested because of 

their high timber and fuelwood values (Agee 1993). These same trees are also 

among the most valuable for many wildlife species of conservation concern 

(Bull and others 1997; Lehmkuhl and others 2003; Saab and others 2004). 

Retention of large-diameter snags and decayed trees, particularly ponderosa 

pine, can provide vital nesting and roosting habitat for a variety of wildlife 

species (Bull and others 1997; Martin and Eadie 1999). For example, the 

sapwood of ponderosa pine is relatively thick compared to other conifers and 

exceptionally valuable for the excavation of nesting and roosting tree cavities 

(Bull and others 1997).

We observed apparent increases in snag densities, including the large 

diameter size class in both regions. While this pattern was not statistically 

signifi cant, the result has implications for the creation of wildlife habitat. 

Maintenance and recruitment of larger diameter snags is particularly important because large snags have greater longevity and provide wildlife habitat 

for a longer period of time than smaller snags (Raphael and Morrison 1987; 

Everett and others 1999; Saab and others 2004). Additional tree mortality is 

expected two to three years after fi re, because time allows for crown scorch 

and consumption to cause further tree death (McHugh and Kolb 2003).

In contrast to our results that suggest increased densities of large snags 

after fi re, Horton and Mannan (1988) reported that large ponderosa pine 

snags were reduced by about 50 percent within the fi rst year after a moderately-intense prescribed fi re. Detrimental effects of prescribed fi re on suitable 

nesting snags were also reported in ponderosa pine forests of Canada, where 

burning caused heavy scorching of large snags (Machmer 2002). Differences 
USDA Forest Service Proceedings RMRS-P-41. 2006. 483

Changes in Downed Wood and Forest Structure After Prescribed Fire in Ponderosa Pine Forests Saab, Bate, Lehmkuhl, Dickson, Story, Jentsch, and Block

in fi re severity among studies likely contributed to the opposing results of 

snag changes after prescribed fi re.

Several authors suggest protecting nest trees by removing combustible 

materials around their base prior to burning to reduce losses of suitable 

nest/roost snags (Horton and Mannan 1988; Machmer 2002; Tiedemann 

and others 2000). Specifi cally, Horton and Mannan (1988) recommend protecting large (>50 cm [20 inch] d.b.h.) snags and logs with moderate decay. 

Tiedemann and others (2000) recommended removing combustible material 

around snags > 30 cm (12 inch) d.b.h. These methods are labor intensive and 

cost prohibitive for large-scale prescribed fi re programs, unless snag protection is required for Threatened and Endangered species. While prescribed 

fi re consumed some wildlife snags, burning also recruited snags (Table 1). 

Direct effects of prescribed burning on wildlife should also be considered. 

For example, prescribed fi res conducted during spring or early summer may 

cause direct mortality to nestlings and fl edglings (Lyon and others 2000).

Smaller snag (< 9 inch d.b.h.) densities increased 30 to 60 percent in the 

SW and two to four times that amount in the NW region. While still standing, these dead trees contribute to increased risk of spot fi res (Stephens and 

Moghaddas 2005). With time, these stems create ground fuels and increase 

the likelihood of higher fi re intensities (Reinhardt and Ryan 1998). Such 

fuel accumulations can limit the effectiveness of prescribed fi re programs to a 

relatively short period of time such as two to four years (Fernades and Botelho 

2003). Studies suggest that relatively frequent, natural fi res are necessary to 

maintain ponderosa pine forests in a diverse landscape mosaic more common 

to historical conditions (Brown and Cook 2006; Fry and Stephens 2006) 

that existed just prior to European settlement. Similarly, prescribed fi res also 

have the potential to mitigate the likelihood of severe crown fi res (Fernandes 

and Botelho 2003; Finney and others 2005; Pollet and Omi 2002; Raymond 

and Peterson 2005), which were once rare but regular events in ponderosa 

pine forests (Shinneman and Baker 1998).

Few of our results were statistically signifi cant at p ≤ 0.05. Managers willing 

to take more acceptable risk can interpret our results as being more defi nitive by using a signifi cance level of p ≤ 0.10 (Zar 1999). Inherent differences 

in pre-treatment forest structure existed in our ponderosa pine forests, which 

possibly infl uenced fi re behavior and resulted in high variability in the effectiveness of fuels reduction. The power to detect statistically signifi cant changes is 

low without large numbers of replicates. However, long-term prescribed fi re 

programs can still play an important role in reducing fi re hazard potential 

(Fernandes and Botelho 2003), suggesting that our study areas may require 

multiple fi re treatments to reach fuels reduction and restoration goals. In 

addition, wildland fi re can also be used to effectively reduce fuels and to 

closely mimic past disturbance regimes in ponderosa pine forests (Baker and 

Ehle 2001).

In this paper we did not evaluate the infl uence of fi re severity on changes in 

fuels and other vegetation after fi re. In the future, we plan to incorporate fi re 

severity data to help with understanding the infl uences of severity on vegetation 

mortality, and wildlife populations and their habitat (Saab and Powell 2005). 

Also, we recommend monitoring vegetation and wildlife populations for several 

years after prescribed burning because of changes in vegetation and wildlife 

responses with time since fi re (Hannon and Drapeau 2005; McHugh and Kolb 

2003; Reinhardt and Ryan 1998; Saab and others 2004). Severity information 

and monitoring for multiple years after fi re will help in developing guidelines 

for prescribed fi re projects that will reduce fuels and concurrently create wildlife 

habitat.
484 USDA Forest Service Proceedings RMRS-P-41. 2006.

Saab, Bate, Lehmkuhl, Dickson, Story, Jentsch, and Block Changes in Downed Wood and Forest Structure After Prescribed Fire in Ponderosa Pine Forests

Acknowledgments

This study was funded by the Joint Fire Sciences Program, National Fire 

Plan, USDA Rocky Mountain Research Station, USDA Pacifi c Northwest 

Research Station, Intermountain Region of the USDA Forest Service, and the 

USDA Payette National Forest. We thank Craig Bienz and Robin Russell for 

thoughtful comments on the manuscript. Rudy King provided guidance on 

statistical analyses. We are grateful to the many fi eld assistants who worked 

long, hot days to collect the fi eld data.

References

Agee, J.K. 1993. Fire ecology of Pacifi c Northwest forests. Washington, DC: Island 

Press: 493 p.

Agee, J. K. 2002. Fire as a coarse fi lter for snags and logs. In: Shea, P. J.; Laudenslayer, 

Jr., W. F.; Valentine, B.; Weatherspoon, C. P.; Lisle, T. E., eds. Proceedings of the 

Symposium on The Ecology and Management of Dead Wood in Western Forests, 

Gen. Tech. Rep. PSW-GTR-181. Albany, CA: U.S. Department of Agriculture, 

Forest Service, Pacifi c Southwest Research Station: 359-368.

Allen, C. D.; Savage, M.; Falk, D. A.; Suckling, K. F.; Swetnam, T. W.; Schulke, T.; 

Stacey, P. B.; Morgan, P.; Hoffman, M.; Klingel, J. 2002. Ecological restoration 

of southwestern ponderosa pine ecosystems: A broad perspective. Ecological 

Applications. 12: 1418-1433.

Arno, Stephen F. 2000. Fire in western forest ecosystems. In: Brown, J. K.; Smith, 

J. K., eds. Wildland fi re in ecosystems: effects of fi re on fl ora. Gen. Tech. Rep. 

RMRS-GTR-42-vol. 2. Ogden, UT: U.S. Department of Agriculture, Forest 

Service, Rocky Mountain Research Station: 97-120.

Arno, S. F.; Brown, J. K. 1991. Overcoming the paradox in managing wildland fi re. 

Western Wildlands. 17: 40-46.

Baker, W. L.; Ehle, D. 2001. Uncertainty in surface-fi re history: the case of ponderosa 

pine forests in the western United States. Canadian Journal of Forest Research. 

31: 1205-1226.

Balda, R.P. 1975. The relationship of secondary cavity nesters to snag densities in 

western coniferous forests. Wildlife Habitat Technical Bulletin 1. Albuquerque, 

NM: U.S. Department of Agriculture, Forest Service, Pacifi c Southwest Research 

Station. 37 p.

Bate, L. J.; Garton, E. O.; Wisdom, M. J. 1999. Estimating snag and large tree 

densities and distributions on a landscape for wildlife management. Gen. Tech. 

Rep. PNW-GTR-425. Portland, OR: U.S. Department of Agriculture, Forest 

Service, Pacifi c Northwest Research Station. 76 p.

Blackford, J. L. 1955. Woodpecker concentration in burned forest. Condor. 57: 

28-30.

Bock, C. E.; Block, W. M. 2005. Fire and birds in the southwestern United States. 

Studies in Avian Biology. 30: 14-32.

Brown, J. K. 1974. Handbook for inventorying downed woody material. Gen. Tech. 

Rep. INT-GTR-16. Ogden, UT: U.S. Department of Agriculture, Forest Service, 

Intermountain Forest and Range Experiment Station. 24 p.

Brown, J. K.; See, T. E. 1981. Downed dead wood fuel and biomass in the Northern 

Rocky Mountains. Gen. Tech. Rep. INT-GTR-117. Ogden, UT: U.S. Department 

of Agriculture, Forest Service, Intermountain Forest and Range Experiment 

Station. 48 p.
USDA Forest Service Proceedings RMRS-P-41. 2006. 485

Changes in Downed Wood and Forest Structure After Prescribed Fire in Ponderosa Pine Forests Saab, Bate, Lehmkuhl, Dickson, Story, Jentsch, and Block

Brown, J. K.; Marsden, M. A.; Ryan, K. C.; Reinhardt, E. D. 1985. Predicting duff 

and woody fuel consumed by prescribed fi re in the Northern Rocky Mountains. 

Gen. Tech. Rep. INT-GTR-337. Ogden, UT: U.S. Department of Agriculture, 

Forest Service, Intermountain Forest and Range Experiment Station. 23 p.

Brown, P. M.; Cook, B. 2006. Early settlement forest structure in Black Hills 

ponderosa pine forests. Forest Ecology and Management. 223: 284–290.

Bull, E. L.; Parks, C. G.; Torgersen, T. R. 1997. Trees and logs important to wildlife 

in the Interior Columbia River basin. Gen. Tech. Rep. PNW-GTR-391. Portland, 

OR: U.S. Department of Agriculture, Forest Service, Pacifi c Northwest Research 

Station. 55 p.

Carey, H.; Schuman, M. 2003. Modifying wildfi re behavior – the effectiveness of 

fuel treatments: the status of our knowledge. National Community Forestry 

Center, Southwest Region Working Paper 2. (http://www.theforesttrust.org/

images/swcenter/ pdf/workingpaper2.pdf)

Covington, W. W.; Moore, M. M. 1994. Southwestern ponderosa forest structure: 

changes since Euro-American settlement. Journal of Forestry. 92: 39-47.

Dudley, J.; Saab, V. 2003. A fi eld protocol to monitor cavity-nesting birds. Res. Pap. 

RMRS-44. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, 

Rocky Mountain Research Station.16 p.

Everett, R.; Lehmkuhl, J.; Schellhaas, R.; Ohlson, P.; Keenum, D.; Reisterer, H.; 

Spurbeck, D. 1999. Snag dynamics in a chronosequence of 26 wildfi res on the 

east slope of the Cascade Range in Washington state, USA. International Journal 

of Wildland Fire. 9: 223-234.

Fernandes, P. M.; Botelho, H. S. 2003. A review of prescribed burning effectiveness 

in fi re hazard reduction. International Journal of Wildland Fire. 12: 117-128.

Finney, M. A.; McHugh, C. W.; Grenfell, I. C. 2005. Stand- and landscape-level 

effects of prescribed burning on two Arizona wildfi res. Canadian Journal Forest 

Research. 35: 1714-1722.

Grissino-Mayer, H. D.; Swetnam, T. W. 2000. Century scale changes in fi re regimes 

and climate in the Southwest. The Holocene. 10: 207-214.

Hall, L. S.; Morrison, M. L.; Block, W. M. 1997. Songbird status and roles. In: 

Block, W. M.; Finch, D. M., eds. Songbird ecology in Southwestern ponderosa 

pine forests: a literature review. Gen. Tech. Rep. RM-GTR-292. Fort Collins, 

CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research 

Station: 69-88.

Hannon, S.; Drapeau, P. 2005. Bird responses to burning and logging in the boreal 

forest of Canada. Studies in Avian Biology. 30: 97-115.

Horton, S. P.; Mannan, W. R. 1988. Effects of prescribed fi re on snags and cavitynesting birds in southeastern Arizona pine forests. Wildlife Society Bulletin. 16: 

37-44.

Keane, R. E.; Ryan, K. C.; Veblen, T. T.; Allen, C. D.; Logan, J.; Hawkes, B. 2002. 

Cascading effects of fi re exclusion in Rocky Mountain ecosystems: a literature 

review. Gen. Tech. Rep. RMRS-GTR-91. Fort Collins, CO: U.S. Department of 

Agriculture, Forest Service General Technical Report. Rocky Mountain Research 

Station.

Kreisel, K. J.; Stein, S. J. 1999. Winter and summer bird use of burned and unburned 

coniferous forests. Wilson Bulletin. 111: 243-250.

Lehmkuhl, J. F.; Everett, R. L.; Schellhaas, R.; Ohlson, P.; Keenum, D.; Riesterer, H.; 

Spurbeck, D. 2003. Cavities in snags along a wildfi re chronosequence in eastern 

Washington. Journal of Wildlife Management. 67: 219-228.

Lyon, L. J.; Telfer, E. S.; Schreiner, D. S. 2000. Direct effects of fi re and animal 

responses In: Smith, J. K. ed. Wildland fi re in ecosystems: effects of fi re on 

fauna. Gen. Tech. Rep. RMRS-GTR-42-vol. 1. Ogden, UT: U.S. Department of 

Agriculture, Forest Service, Rocky Mountain Research Station. 17-23.
486 USDA Forest Service Proceedings RMRS-P-41. 2006.

Saab, Bate, Lehmkuhl, Dickson, Story, Jentsch, and Block Changes in Downed Wood and Forest Structure After Prescribed Fire in Ponderosa Pine Forests

Machmer, M. 2002. Effects of ecosystem restoration treatments on cavity-nesting birds, 

their habitat, and their insectivorous prey in fi re-maintained forests of southeastern 

British Columbia. In: Shea, P. J.; Laudenslayer, Jr., W. F.; Valentine, B.; Weatherspoon, 

C. P.; Lisle, T. E., eds. Proceedings of the Symposium on The Ecology and 

Management of Dead Wood in Western Forests, Gen. Tech. Rep. PSW-GTR-181. 

Albany, CA: U.S. Department of Agriculture, Forest Service, Pacifi c Southwest 

Research Station. 121-133.

Martin, K.; Eadie, J. M. 1999. Nest webs: a community-wide approach to the 

management and conservation of cavity-nesting forest birds. Forest Ecology and 

Management. 115: 243-257.

Peterson, D. L.; Johnson, M. C.; Agee, J. K.; Jain, T. B.; McKenzie, D.; Reinhardt, 

E. D. 2003. Fuels planning: Managing forest structure to reduce fi re hazard. 

In: Proceedings of the Second International Wildland Fire Ecology and Fire 

Management Congress, Orlando, FL. 10 p.

Pollet, J.; Omi, P. N. 2002. Effect of thinning and prescribed burning on crown 

fi re severity in ponderosa pine forests. International Journal of Wildland Fire. 

11: 1-10.

Raymond, C. L.; Peterson, D. L. 2005. Fuel treatments alter the effects of wildfi re 

in a mixed-evergreen forest, Oregon, USA. Canadian Journal Forest Research. 

35: 2981-2995.

Raphael, M. G.; White, M. 1984. Use of snags by cavity-nesting birds in the Sierra 

Nevada. Wildlife Monographs. 86: 1-66.

Reinhardt, E. D.; Ryan, K. C. 1998. Analyzing effects of management actions 

including salvage, fuel treatment, and prescribed fi re on fuel dynamics and fi re 

potential. In: Pruden, T. L.; Brennan, L. A. eds. Fire in ecosystem management: 

shifting the paradigm from suppression to prescription. Tall Timbers Fire Ecology 

Conference Proceedings. No. 20. Tall Timbers Research Station, Tallahassee, 

FL. 206-209.

Saab, V.; Dudley, J. 1998. Responses of cavity-nesting birds to stand-replacement 

fi re and salvage logging in ponderosa pine/ Douglas-Fir forests of southwestern 

Idaho. Res. Pap. RMRS-RP-11. Ogden, UT: U.S. Department of Agriculture, 

Forest Service. Rocky Mountain Research Station. 17 p.

Saab, V. A.; Dudley, J.; Thompson, W. L. 2004. Factors infl uencing occupancy of 

nest cavities in recently burned forests. Condor. 106: 20-36.

Saab, V. A.; Powell, H. D. W. 2005. Fire and Avian Ecology in North America: 

process infl uencing pattern. Studies in Avian Biology. 30: 1-13.

Saab, V.A.; Powell, H. D. W.; Kotliar, N. B.; Newlon, K. R. 2005. Variation in fi re 

regimes of the Rocky Mountains: implications for avian communities and fi re 

management. Studies in Avian Biology. 30: 76-96.

SAS Institute. 2003. SAS (9.1) PROC MIXED. SAS Institute Inc., Cary, NC.

Schoennagel, T.; Veblen, T. T.; Romme, W. H. 2004. The interaction of fi re, fuels, 

and climate across Rocky Mountain forests. BioScience. 54: 661-676.

Scott, V. E. 1979. Bird response to snag removal in ponderosa pine. Journal of 

Forestry. 77: 26-28.

Shinneman, D. J.; Baker, W. L. 1997. Nonequilibrium dynamics between catastrophic 

disturbances and old-growth forests in ponderosa pine landscapes of the Black 

Hills. Conservation Biology. 11: 1276-1288.

Stephens, S. L.; Moghaddas, J. J. 2005. Experimental fuel treatment impacts on 

forest structure, potential fi re behavior, and predicted tree mortality in a California 

mixed conifer forest. Forest Ecology and Management. 215: 21-36.

Szaro, R. C.; Severson, K. E.; Patton, D. R. eds. 1988. Management of amphibians, 

reptiles, and small mammals in North America. Gen. Tech. Rep. RMRS-GTR-166. 

Fort Collins, CO: U.S. Forest Service, Forest Service, Rocky Mountain Research 

Station.
USDA Forest Service Proceedings RMRS-P-41. 2006. 487

Changes in Downed Wood and Forest Structure After Prescribed Fire in Ponderosa Pine Forests Saab, Bate, Lehmkuhl, Dickson, Story, Jentsch, and Block

Thies, W. G.; Westlind, D. J.; Loewen, M. 2005. Season of prescribed burn in 

ponderosa pine forests in eastern Oregon: impact on pine mortality. International 

Journal of Wildland Fire. 14: 223-231.

Tiedemann, A.; Klemmedson, J. O.; Bull, E. L. 2000. Solution of forest health 

problems with prescribed fi re: are forest productivity and wildlife at risk. Forest 

Ecology and Management. 127: 1-18.

U.S. Department of Agriculture, Forest Service. 1996. Forest Service Handbook. 

Timber Management Control Handbook. FSH: 2409.21e.

Veblen, T. T. 2000. Disturbance patterns in central Rocky Mountain forests. In: 

Knight, R. L.; Smith, F. W.; Buskirk, S.W.; Romme, W. H.; Baker, W. L. eds. 

Forest fragmentation in the southern Rocky Mountains. University Press of 

Colorado, Niwot, CO. 33-56.

Zar, J. 1999. Biostatistical analysis. Prentice-Hall, Upper Saddle River, NJ. 718 p.
USDA Forest Service Proceedings RMRS-P-41. 2006. 489

In: Andrews, Patricia L.; Butler, Bret W., 

comps. 2006. Fuels Management—How to 

Measure Success: Conference Proceedings. 

28-30 March 2006; Portland, OR. 

Proceedings RMRS-P-41. Fort Collins, 

CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research 

Station.

1 Research Forester, USDA Forest Service, 

Pacific Northwest Research Station, 

Pacifi c Wildland Fire Sciences Laboratory, 

Seattle, WA. cwright@fs.fed.us

2 Research Ecologist, USDA Forest 

Service, Pacifi c Northwest Research 

Station, Pacifi c Wildland Fire Sciences 

Laboratory, Seattle, WA.

Abstract—Big sagebrush (Artemisia tridentata) ecosystems typically experience 

stand replacing fi res during which some or all of the ignited biomass is consumed. 

Biomass consumption is directly related to the energy released during a fi re, and is 

an important factor that determines smoke production and the effects of fi re on other 

resources. Consumption of aboveground biomass (fuel) was evaluated for a series of 

operational prescribed fi res in big sagebrush throughout the interior West. Pre-burn 

fuel characteristics (composition, amount, and structure), fuel conditions (live and 

dead fuel moisture content), and environmental conditions (weather and topography) 

affected fi re behavior and subsequent fuel consumption. Total aboveground biomass 

consumption varied from 1.6 to 22.3 Mg ha–1 (18 to 99 %) among the 17 experimental areas. Multiple linear regression and generalized linear modeling techniques were 

used to develop equations for predicting fuel consumption during these prescribed 

fi res. Pre-burn fuel loading, which is infl uenced by season of burn, site productivity, 

time-since-last-fi re, and grazing is the most important predictor of fuel consumption. 

Use of fi re in big sagebrush is desirable for several reasons, including wildlife habitat 

improvement, livestock range improvement, fi re hazard abatement, and ecosystem 

restoration.

Keywords: Artemisia tridentata, big sagebrush, fi re effects, fuel consumption

Introduction

Research to quantify and model fuel consumption during wildland fi res 

has been conducted in managed and unmanaged forest types throughout the 

United States (e.g., Ottmar 1983; Sandberg and Ottmar 1983; Little and 

others 1986; Brown and others 1991; Hall 1991; Albini and Reinhardt 1997; 

Reinhardt and others 1997; Myanishi and Johnson 2002), but is generally 

lacking or of limited scope in shrub-dominated ecosystems (for example, 

Sapsis and Kauffman 1991). Much of the existing fi re research in shrub 

types has focused on fi re behavior prediction in a limited number of shrub 

types (for example, Lindenmuth and Davis 1973; Green 1981; Brown 1982). 

Shrub-dominated ecosystems occur on hundreds of millions of hectares of 

private, state and federal lands in the United States. Sagebrush (Artemisia

spp.) occurs on at least 38.5 million hectares in the interior West, making it 

one of the largest biomes in North America (Shifl et 1994). Sagebrush and 

other shrub-dominated types may be remotely located or they may occur at 

the wildland-rural/suburban/urban interface throughout their range. Many 

shrub-dominated ecosystems are home to sensitive, rare, threatened and endangered species, including numerous species of birds, mammals, mollusks, insects, 

Biomass Consumption During Prescribed 

Fires in Big Sagebrush Ecosystems

Clinton S. Wright1 and Susan J. Prichard2
490 USDA Forest Service Proceedings RMRS-P-41. 2006.

Wright and Prichard Biomass Consumption During Prescribed Fires in Big Sagebrush Ecosystems

plants, fi sh, reptiles and amphibians. In terms of sheer land area, proximity to 

populated areas, and wildlife habitat, research in shrub-dominated types addresses information needs for a diverse array of natural resource managers.

Increasing public awareness of environmental issues necessitates that resource managers fully evaluate regulatory requirements and potential impacts 

of land management decisions (in other words, no action, prescribed fi re use, 

wildland fi re use, grazing, mechanical treatment, chemical treatment, etc.) 

using the best available information. Where fi re is concerned, quantifi cation of 

fuel consumption is critical for evaluating fi re severity (for example, Keely and 

others 2005), and for effectively modeling fi re effects, including smoke emissions, regional haze, nutrient cycling, plant succession, species composition 

changes, plant/tree mortality, wildlife habitat restoration and maintenance, 

erosion, soil heating, and carbon cycling. Fuel consumption is the most 

critical variable for effectively evaluating and managing the consequences of 

prescribed and wildland fi re as related to land management objectives.

Many sagebrush-dominated ecosystems in the western United States have 

experienced periodic, naturally occurring fi re events (Miller and Rose 1999). 

Resource managers use prescribed fi re as a multi-scale treatment for a number 

of specifi c purposes, including fuel and fi re hazard reduction, wildlife habitat 

improvement, and ecosystem restoration. In contrast to forested systems where 

a large proportion of the fuelbed is composed of dead and down organic 

matter, in sagebrush-dominated ecosystems, the fuelbed is composed almost 

entirely of living (and standing dead) vegetation. Prior to the application of 

fi re in forests and shrublands it is desirable to gauge the likelihood of treatment success (in other words, desired change in vegetation or fuel structure) 

by predicting fuel consumption. Change in the vegetation structure (that 

is, fuel composition, amount and arrangement) is often the most signifi cant 

measure of treatment success. If resource managers in the sagebrush biome 

are to develop effective fi re plans and prescriptions designed to meet desired 

objectives for terrestrial and atmospheric resources, research must quantify 

both fuel characteristics and fuel consumption during wildland fi res.

Objective

The primary objective of our research was to develop models to predict 

biomass consumption in big sagebrush ecosystems using variables that are 

relatively easily measured or readily obtained. These fuel consumption models have been incorporated into the software CONSUME 3.0 (Prichard and 

others, in press). Development of consumption models for sagebrush ecosystems and their application in CONSUME 3.0 promotes more effective and 

informed use of emission production, fi re effects, and wildfi re/prescribed fi re 

tradeoff models allowing for better wildland fi re emissions and fi re effects 

accounting and planning at a variety of scales.

Methods

Data were collected at 17 locations on a series of operational prescribed fi res in 

big sagebrush (A. tridentata) ecosystems in southeastern Oregon, northwestern 

Nevada, northwestern Wyoming, and northern California (table 1). Sampling 

for fuel consumption occurred on gentle slopes (0 to 15 percent slope) of all 
USDA Forest Service Proceedings RMRS-P-41. 2006. 491

Biomass Consumption During Prescribed Fires in Big Sagebrush Ecosystems Wright and Prichard

aspects at elevations ranging from 1,331 to 2,056 m. Sites were selected to 

represent a broad range of coverage and biomass of standing big sagebrush of all 

three recognized subspecies: Wyoming big sagebrush (A. t. ssp. wyomingensis), 

mountain big sagebrush (A. t. ssp. vaseyana), and basin big sagebrush (A. t. ssp. 

tridentata). Big sagebrush subspecies occur on sites that follow a gradient of 

increasing precipitation; Wyoming big sagebrush occupies the driest sites (20 

to 32 cm annual precipitation), mountain big sagebrush occupies the wettest 

sites (31 to 149 cm annual precipitation) and basin big sagebrush is found on 

intermediate sites (Francis 2004). Experimental areas were embedded within 

larger operational units, and were burned under a variety of environmental and 

fuel moisture conditions during the fall of 2001 (September 23 to October 

25) and spring of 2002 (March 21; table 2).

Data Collection

Fuel Characterization and Consumption—A regular grid of 2 × 2 m 

plots (or 1.5 × 1.5 m, if vegetation was particularly large or dense) was used 

to determine fuel loading and composition in a relatively uniform stand or 

patch of big sagebrush. A total of 36 plots were numbered sequentially; nine 

plots each were located every 7.6 m along four 76.2-m long transects that 

Table 1—Site information for experimental sagebrush burns.

Site Name # Sites Latitude Longitude Elevation Slope State Admin. Unita

Flook Lake 3 42° 36’ 119° 32’ 1539-1542 m 0 % OR USFWS1

Stonehouse 1 42° 56’ 118° 26’ 1937 m 15 % OR BLM1

V-Lake 5 42° 28’ 118° 44’ 2018-2056 m 0-15 % OR Private

Gold Digger Pass 2 41° 46’ 121° 34’ 1331-1346 m 0-5 % CA NPS

Escarpment 2 41° 52’ 119° 40’ 1672-1693 m 0-5 % NV USFWS2

Sagehen 1 41° 56’ 119° 15’ 1717 m 0 % NV USFWS2

Heart Mountain 3 44° 42’ 109° 09’ 1764-1823 m 0-15 % WY BLM2

aUSFWS1 = U.S. Fish and Wildlife Service, Hart Mountain National Antelope Refuge; USFWS2 = U.S. Fish and Wildlife 

Service, Sheldon National Wildlife Refuge; BLM1= Bureau of Land Management, Burns, OR; BLM2 = Bureau of Land 

Management, Cody, WY; Private = Roaring Springs Ranch; NPS = Lava Beds National Monument.

Table 2—Weather and fuel moisture information for experimental sagebrush burns.

 Weather Fuel moisture

 Live sage Dead sage

Site name Subspp.a Temp. RH Windspeed Grass foliage 10hrb

 °C percent km hr–1 - - - - - - - - -percent - - - - - - - - -

Flook Lake W 17.2-17.8 17-34 12.1-12.9 9.8-10.2 59.9-61.8 9.2

Stonehouse M 7.2 40 6.4 29.9 78.7 8.4

V-Lake M 21.1-23.9 22-28 3.2-12.1 19.9-38.7 60.6-74.9 2.8-6.2

Gold Digger Pass M 16.7 25-26 7.2 13.7 71.9 7.7

Escarpment W-B 17.8 35 6.4 10.6 68.9 6.8

Sagehen B 17.2 23 16.1 14.5 77.1 10.8

Heart Mountain M-W 16.1-20.6 24-28 4.0-12.1 30.3 73.6 5.7

aW = Wyoming (A. wyomingensis); M = Mountain (A. vaseyana); B = Basin (A. tridentata). b10hr fuel particles are 0.64 – 2.54 cm in diameter.
492 USDA Forest Service Proceedings RMRS-P-41. 2006.

Wright and Prichard Biomass Consumption During Prescribed Fires in Big Sagebrush Ecosystems

were spaced 10 to 20 m apart (no plots were placed at transect endpoints). 

Odd- or even-numbered plots were randomly selected to be destructively 

sampled before the fi re; remaining plots were destructively sampled after 

the fi re. Fuels were characterized by clipping at ground level or collecting, 

drying and weighing all standing biomass or surface fuels rooted or located 

inside the plot frame. Biomass was separated into the following categories in 

the fi eld: grasses, forbs, live sagebrush, dead sagebrush, shrubs other than 

sagebrush (hereafter referred to as ‘other shrubs’), dead and down woody 

fuels by size class (1hr, 10hr, 100hr, and 1000hr1), and litter. Dead branches 

and twigs on living sagebrush plants were removed and included in the dead 

sagebrush category. Grasses, forbs, other shrubs, dead and down woody fuels, 

and litter were collected, returned to the laboratory, dried for a minimum 

of 48 hours at 100 °C, and weighed to determine ovendry fuel loading by 

category on an area basis. Sagebrush was harvested, separated into live and 

dead biomass, and weighed in the fi eld. One or two complete branches from 

each fi eld sample were collected in heavy-gauge plastic bags with airtight 

seals. These subsamples were weighed shortly after collection, returned to 

the laboratory, dried for a minimum of 48 hours at 100 °C, and weighed to 

determine live and dead sagebrush moisture content per plot. The following 

formula was used to adjust sagebrush fi eld weight to ovendry weight:

moisture subsample dry weight

moisture subsample wet weight

× undried field weight = ovendry weight (1)

Pre-fi re coverage by category (grass, forbs, sagebrush, other shrubs, litter) 

was measured using the line intercept method (Canfi eld 1941) along the full 

length of all four 76.2-m long layout transects. Grass, forb, sagebrush, and 

other shrub heights were measured at points every 7.6 m along the full length 

of all four transects. As most fi res were patchy, coverage of the area burned 

during the fi re was measured along parallel transects that were offset 3 m 

from the original layout to avoid sampling in areas that had been destructively 

sampled before the fi re.

Fuel consumption was calculated by subtracting average post-burn biomass 

from average pre-burn biomass for sagebrush, and by multiplying average 

pre-burn biomass by the percentage of the area burned for the other fuel 

categories. Based on post-fi re fi eld observations, we assumed that all nonsagebrush biomass was consumed in areas that were burned.

Day of Burn Fuel Moisture and Weather—Five to 10 grab samples 

of grass, sagebrush foliage, and standing dead sagebrush in 1hr, 10hr, and 

100hr size classes were collected in the interplot area prior to the burning of 

each experimental area. A single set of fuel moisture samples was collected to 

represent multiple sites if they were relatively close to one another, and being 

burned at or around the same time. Samples of approximately 50 to 400 g 

each were collected in heavy gauge, plastic bags with airtight seals, weighed 

immediately after collection, returned to the laboratory, ovendried for a minimum of 48 hours at 100 °C, and weighed to determine fuel moisture content 

on a dry weight basis. Weather conditions during the burning period were 

measured every 15 to 30 minutes using a sling psychrometer (temperature 

and relative humidity) and an electronic pocket weather meter (temperature, 

relative humidity, windspeed 2 m aboveground). Weather conditions were 

1 1hr, 10hr, 100hr, and 1000hr timelag fuels are defi ned as woody material ≤0.64 cm, 0.64-2.54 cm, 

2.55-7.62 cm, and >7.62 cm in diameter, respectively.
USDA Forest Service Proceedings RMRS-P-41. 2006. 493

Biomass Consumption During Prescribed Fires in Big Sagebrush Ecosystems Wright and Prichard

also measured with a portable weather station (temperature, relative humidity, 

windspeed 2 m aboveground) logging 15-minute average values at several of 

the experimental locations. Temperature and relative humidity measurements 

taken using the sling psychrometer and windspeed measurements taken using the pocket weather meter were used preferentially, as these are the tools 

available to practitioners on the fi reline.

Ignition—Sites were ignited during the course of daily prescribed burning 

operations. Most experimental sites were ignited by hand with drip torches, 

although a few areas were aerially ignited using incendiary plastic spheres 

containing chemicals that undergo a rapid exothermic reaction when mixed 

(ethylene glycol and potassium permanganate). Experimental areas typically 

burned in a heading or fl anking fi re.

Data Analysis

Model Development—Pre-burn coverage and height data, and coverage 

and height data from the Natural Fuels Photo Series (Ottmar and others 2000) 

were combined to develop a model to estimate sagebrush loading. Models 

to predict consumption of biomass were constructed from the suite of fuel 

characteristics and environmental variables measured before and during the 

fi res. A simple correlation matrix of all variables measured as part of this study 

identifi ed those that were most promising for constructing the predictive 

models. Forward and backward stepwise multiple linear regression (Neter and 

others 1990) was used to identify preliminary models; expert opinion was used 

to select the fi nal models. Criteria for model selection included parsimony as 

well as the presence of reasonable physical explanations for a given variable’s 

inclusion in the full model. A generalized linear model (GLM; McCullagh 

and Nelder 1989) of the binomial family was also developed for predicting 

the proportion of biomass consumed using the same variables included in the 

multiple linear regression model. The binomial GLM predicts proportional 

shrub consumption between [0,1] and therefore avoids predictions of fuel 

consumption that are either less than zero or greater than the pre-fi re fuel 

amount. The GLM was created in S-plus (Insightful 2002) and programmed 

into the CONSUME 3.0 software (Prichard and others, in press). Both models’ predictive capabilities were compared to independent data sets reported 

by Kauffman and Cummings (1989) and Sapsis and Kauffman (1991).

Results

Tables 3, 4, and 5 summarize pre-fi re fuel loading, pre- and post-fi re coverage, and fuel consumption, respectively. Total aboveground pre-fi re biomass 

ranged from 5.3 to 22.6 Mg ha–1; sites dominated by mountain big sagebrush 

tended to have the most aboveground biomass. Pre-fi re sagebrush loading 

ranged from 4.4 to 20.2 Mg ha–1 with site coverage of 14 to 67 percent. All, 

live and dead sagebrush represented from 46 to 92, 25 to 64, and 20 to 56 

percent of the total site biomass, respectively; total sagebrush biomass was >80 

percent of total biomass for 16 out of 17 sites. Mean sagebrush height ranged 

from 0.3 to 0.9 m, although many plants were taller than the mean height. 

Pre-fi re herbaceous vegetation and other shrub loading (and coverage) ranged 

from 0.1 to 0.6 Mg ha–1 (5 to 38 percent) and zero to 3.7 Mg ha–1 (0 to 19 

percent), respectively. Surface fuel loading ranged from 0.3 to 2.7 Mg ha–1.
494 USDA Forest Service Proceedings RMRS-P-41. 2006.

Wright and Prichard Biomass Consumption During Prescribed Fires in Big Sagebrush Ecosystems

Table 3—Pre-fi re fuel loading for experimental sagebrush burns.

 Loading

 Herbaceous Live Dead Other All Surface All

Site name vegetation sagebrush sagebrush shrubs vegetation fuelsa fuels

 - - - - - - - - - - - - - - - - - - - - - - - - - - - - Megagrams hectare–1- - - - - - - - - - - - - - - - - - - - - - - -

Flook Lake 1 0.290 5.521 5.623 0.002 11.435 0.866 12.300

Flook Lake 2 0.109 7.141 5.763 0.000 13.013 1.523 14.536

Flook Lake 3 0.106 6.087 4.214 0.063 10.471 0.714 11.185

Stonehouse 0.614 4.621 1.995 0.580 7.810 2.211 10.021

V-Lake A 0.156 11.113 5.177 0.440 16.885 1.975 18.860

V-Lake 1 0.273 7.919 3.514 0.236 11.942 1.974 13.916

V-Lake 2 0.206 9.207 3.787 0.229 13.430 1.052 14.481

V-Lake 3 0.158 3.239 1.162 0.043 4.602 0.672 5.274

V-Lake 4 0.224 11.062 3.635 0.312 15.233 1.122 16.356

Gold Digger 1 0.543 4.522 3.796 0.191 9.052 0.339 9.391

Gold Digger 2 0.570 6.348 3.396 0.000 10.314 0.511 10.825

Escarpment 1 0.310 3.094 2.652 3.723 9.780 2.709 12.488

Escarpment 2 0.251 7.619 6.626 0.031 14.527 1.562 16.088

Sagehen 0.078 6.081 10.919 0.035 17.112 2.231 19.343

Heart Mtn HM 0.393 12.709 7.492 0.000 20.594 1.994 22.588

Heart Mtn OT 0.411 4.520 2.937 0.409 8.277 0.992 9.269

Heart Mtn SC 0.361 5.531 3.193 0.003 9.088 0.968 10.056

aIncludes litter and all dead and down woody fuels.

Table 4—Pre- and post-fi re coverage for experimental sagebrush burns.

 Pre-fi re coverage Post-fi re coverage

 Herbaceous Other All Area Unburned

Site name vegetation sagebrush shrubs vegetation burned sagebrush

 - - - - - - - - - - - - - - - - - - - - - - - - percentage - - - - - - - - - - - - - - - - - - - - - - - -

Flook Lake 1 10.8 35.9 0.2 46.8 32.7 21.5

Flook Lake 2 20.1 38.1 0.0 58.1 38.6 22.6

Flook Lake 3 4.6 29.0 0.2 33.8 36.9 24.6

Stonehouse 20.0 35.7 6.9 62.5 39.8 29.0

V-Lake A 20.0 49.8 6.1 75.8 50.6 21.9

V-Lake 1 12.3 43.9 9.3 65.4 74.6 13.8

V-Lake 2 14.8 43.2 3.7 61.7 53.8 21.3

V-Lake 3 15.1 34.5 1.5 51.2 23.9 20.0

V-Lake 4 23.0 59.5 3.1 85.6 96.9 1.6

Gold Digger 1 22.9 24.5 5.6 53.0 36.4 19.5

Gold Digger 2 23.7 30.3 2.6 56.6 60.4 10.7

Escarpment 1 13.7 13.5 19.1 46.3 75.9 4.3

Escarpment 2 22.0 35.1 0.5 57.6 78.2 7.2

Sagehen 5.0 43.3 5.9 54.2 14.5 33.1

Heart Mtn HM 37.6 66.5 0.3 98.3 98.4 0.6

Heart Mtn OT 34.3 29.7 2.7 66.7 94.8 0.5

Heart Mtn SC 31.5 42.0 0.1 73.6 99.8 0.3
USDA Forest Service Proceedings RMRS-P-41. 2006. 495

Biomass Consumption During Prescribed Fires in Big Sagebrush Ecosystems Wright and Prichard

Table 5—Fuel consumed during experimental sagebrush burns.

 Consumption

 Herbaceous Other All Surface All

Site name vegetation Sagebrush shrubs vegetation fuelsa fuels

 - - - - - - - - - - - - - - - - Megagrams hectare–1 (percentage of pre-fi re loading) - - - - - - - - - - - - - -

Flook Lake 1 0.097 (33.6) 3.132 (28.1) 0.001 (33.6) 3.230 (28.2) 0.291 (33.6) 3.521 (28.6)

Flook Lake 2 0.042 (38.6) 4.020 (31.2) — 4.062 (31.2) 0.588 (38.6) 4.650 (32.0)

Flook Lake 3 0.040 (38.0) 4.999 (48.5) 0.024 (38.0) 5.064 (48.4) 0.271 (38.0) 5.335 (47.7)

Stonehouse 0.246 (40.0) 1.992 (30.1) 0.232 (40.0) 2.469 (31.6) 0.885 (40.0) 3.354 (33.5)

V-Lake A 0.082 (53.0) 9.750 (59.9) 0.233 (53.0) 10.065 (59.6) 1.046 (53.0) 11.112 (58.9)

V-Lake 1 0.205 (75.3) 7.571 (66.2) 0.177 (75.3) 7.954 (66.6) 1.486 (75.3) 9.440 (67.8)

V-Lake 2 0.129 (62.4) 9.457 (72.8) 0.143 (62.4) 9.728 (72.4) 0.656 (62.4) 10.384 (71.7)

V-Lake 3 0.050 (31.6) 1.322 (30.0) 0.013 (31.6) 1.385 (30.1) 0.212 (31.6) 1.597 (30.3)

V-Lake 4 0.218 (97.2) 13.648 (92.9) 0.304 (97.2) 14.170 (93.0) 1.091 (97.2) 15.260 (93.3)

Gold Digger 1 0.201 (37.0) 4.660 (56.0) 0.070 (37.0) 4.931 (54.5) 0.125 (37.0) 5.057 (53.8)

Gold Digger 2 0.346 (60.7) 5.655 (58.0) — 6.001 (58.2) 0.310 (60.7) 6.311 (58.3)

Escarpment 1 0.242 (78.1) 3.116 (54.2) 2.906 (78.1) 6.264 (64.1) 2.114 (78.1) 8.379 (67.1)

Escarpment 2 0.197 (78.6) 12.662 (88.9) 0.024 (78.6) 12.884 (88.7) 1.227 (78.6) 14.111 (87.7)

Sagehen 0.016 (20.5) 2.737 (16.1) 0.007 (20.5) 2.761 (16.1) 0.763 (34.2) 3.524 (18.2)

Heart Mtn HM 0.390 (99.2) 19.916 (98.6) 0.000 (99.2) 20.306 (98.6) 1.978 (99.2) 22.284 (98.7)

Heart Mtn OT 0.411 (100.0) 7.341 (98.4) 0.409 (100.0) 8.161 (98.6) 0.992 (100.0) 9.153 (98.8)

Heart Mtn SC 0.361 (100.0) 8.525 (97.7) 0.003 (100.0) 8.889 (97.8) 0.968 (100.0) 9.857 (98.0)

aIncludes litter and all dead and down woody fuels.

Total aboveground biomass consumption varied from 1.6 to 22.3 Mg ha–1

(18 to 99 percent) among the 17 experimental areas, with 15 to 100 percent 

of the experimental area burned. Most fi res were patchy, although in excess 

of 90 percent of the area burned for four of the 17 sites. Post-fi re coverage 

of unburned live sagebrush ranged from <1 to 33 percent. Fire spread was 

most limited in the single spring burn (Sagehen) despite temperature, relative 

humidity, and windspeed conditions similar to the fall burns (all others). Five 

out of seven of the study sites where fi re burned less than 40 percent of the 

experimental area had dead 10hr sagebrush fuel moisture values in excess of 

eight percent. Fuel consumption was highest at sites where dead 10hr fuel 

moisture was 6.1 percent and less.

Multiple linear regression and generalized linear models are reported in 

table 6. Percentage of area burned and pre-burn sagebrush loading were 

strong predictors of sagebrush consumption (fi g. 1a). Similarly, percentage 

of area burned and pre-burn loading of non sagebrush fuels were predictors 

of non sagebrush consumption (fi g. 1b). Pre-burn coverage of herbaceous 

vegetation, slope, windspeed, 10hr fuel moisture were chosen as variables to 

predict percentage of area blackened (fi g. 2).

Because of our relatively small sample size (n=17), we chose to retain all 

data points in the model building data set. However, using the generalized 

linear and multiple linear regression models, predicted total fuel consumption 

averaged within ±3.1 and ±1.9 percent, respectively, of observed values for 

four fall prescribed fi res, and within ±11.9 and ±12.6 percent, respectively, of 

observed values for four spring fi res measured by Kauffman and Cummings 

(1989) and Sapsis and Kauffman (1991).
496 USDA Forest Service Proceedings RMRS-P-41. 2006.

Wright and Prichard Biomass Consumption During Prescribed Fires in Big Sagebrush Ecosystems

Table 6—Regression equations for sagebrush loading, sagebrush and non sagebrush 

consumption, and area burned. The generalized linear model (GLM) gives the proportion 

of the area burned or biomass consumed and follows the form: Y = EXP(y)/(1+EXP(y)); 

multiply YAB by 100 to get AB,YCs

 by Ls to get Cs, and YCn by Ln to get Cn.

 Equations a b1 b2 b3 R2

Multiple Linear Regression

 Ls = a + b1(Ps) + b2(Hs) –1.364 0.292 1.365 0.85

 AB = a + b1(Ph) + b2(FM) + b3(W × S) 30.582 1.951 –4.369 1.737 0.69

 Cs = a + b1(Ls) + b2(AB) –7.171 0.681 0.111 0.87

 Cn = a + b1(Ln) + b2 (AB) –1.056 0.706 0.016 0.96

Generalized Linear Model

 yAB = a + b1(Ph) + b2(FM) + b3(W × S) –1.734 0.114 –0.209 0.110 0.75a

 yCs = a + b1(Ls) + b2(AB) –2.657 0.043 0.047 0.82a

 yCn = a + b1(Ln) + b2(AB) –2.206 –0.050 0.052 0.89a

a(null deviance - residual deviance) ÷ null deviance; (analogous to R2 for GLM)

Symbols:

Ls = pre-burn loading of sagebrush, Mg ha–1;

Ln = pre-burn loading of non sagebrush biomass, Mg ha–1;

Ps = pre-burn coverage of sagebrush;

Hs = pre-burn height of sagebrush, meters;

AB = area burned, percentage of total area;

Ph = pre-burn coverage of herbaceous vegetation, percentage;

FM = day of burn 10hr fuel moisture, percentage by dry weight;

W = day of burn windspeed, km hr–1;

S = slope category, <5%=1, 5-15%=2, 16-25%=3, 26-35%=4, >35%=5;

Cs = consumption of sagebrush, Mg ha–1;

Cn = consumption of non sagebrush, Mg ha–1.

Figure 1—Generalized linear models showing (a) sagebrush and (b) non sagebrush consumption as a function 

of loading at 25, 50, 75, and 100 percent of area burned (lines).
USDA Forest Service Proceedings RMRS-P-41. 2006. 497

Biomass Consumption During Prescribed Fires in Big Sagebrush Ecosystems Wright and Prichard

Discussion

Two conditions contribute to fuel consumption (and post-fi re fuel loading); 

partially consumed fuel particles, and fuel left in unburned patches. Fuel loading and coverage, fuel moisture, weather (windspeed), and site characteristics 

(slope) are incorporated in the predictive equations reported here. These 

equations encapsulate all of the consumption that occurs because of partial 

burning of fuels and patchy burning of an area. Sites where fi re spread was 

patchier and fi re carried through less of the plot area typically experienced 

lower overall fuel consumption, although a high proportion of the fuels in 

the burned areas may have consumed.

The fi nal models are relatively simple and incorporate predictor variables 

for which users are likely to have, or can readily acquire the necessary data. 

Pre-burn biomass is a key variable for predicting fuel consumption. Biomass 

can be estimated from locally available inventory data, from fuels assessments using photo guides (for example, Ottmar and others 1998, 2000) or 

calculated using the equation for estimating sagebrush biomass (Ls) from 

sagebrush coverage (Ps) and height (Hs; table 6). While managers and planners 

typically do not have biomass data at their disposal, they often have coverage 

and height data, or can easily acquire it from a variety of sources. Percentage of area burned is the other key variable for predicting fuel consumption. 

We include an equation to predict this value (AB), again, based on data that 

fi re managers and planners are likely to have at their disposal and routinely 

include in prescribed fi re burn plans and prescriptions, including windspeed 

(W), slope (S), and 10hr fuel moisture (FM; table 6).

Users of CONSUME 3.0 can easily predict how environmental, site, and 

fuel conditions will affect potential percentage of area burned and fuel consumption. This is a tool that can be used for developing burning prescriptions 

that meet specifi c management objectives. For example, if one objective of 

a prescribed fi re project is to create a mosaic of burned and unburned vegFigure 2—Generalized linear model showing area burned as a function of windspeed × slope category at 3, 6, 

9, and 12 percent 10hr fuel moisture content (lines) where herbaceous vegetation coverage is (a) 10 percent, 

(b) 25 percent, and (c) 50 percent.
498 USDA Forest Service Proceedings RMRS-P-41. 2006.

Wright and Prichard Biomass Consumption During Prescribed Fires in Big Sagebrush Ecosystems

etation in a specifi c area for wildlife habitat improvement, users can modify 

windspeed and fuel moisture inputs until the model yields the desired amount 

or range of percentage of area burned, thereby defi ning the prescription parameters. Similarly, a desired percentage of area burned can then be used as 

an input along with information about site biomass, to predict potential fuel 

consumption and smoke emissions or other fi re effects.

Energy (heat) is required to drive off fuel moisture, to heat fuel particles to 

pyrolysis and combustion temperatures, and to sustain fl aming combustion. 

Dead 10hr fuel moisture content is an indicator of how readily combustion occurs, how effectively fi re spreads from particle to particle and from dead to live 

fuels, and subsequently how much fuel consumes. Increasing amounts of fuel 

become available to burn as live and dead fuel moisture decline, however, once 

fuel moisture has fallen below a critical value, weather and fuel loading appear 

to become the elements affecting fuel consumption. Where suffi cient amounts 

of fuel are available to burn, prevailing weather conditions (windspeed in our 

model) appear critical for determining fi re spread and fuel consumption. The 

effects of windspeed can be exacerbated or mitigated to some degree by slope. 

The multiplier for slope incorporated in the windspeed × slope variable in 

the equation for predicting area burned is comparable to values suggested 

by Brown (1982). Poor fuel consumption conditions (elevated fuel moisture, 

elevated relative humidity, low windspeeds, lack of carrier fuels, etc.) may be 

mitigated to some degree by an aggressive burning operation. If enough fi re 

can be introduced to the site at once, fi re spread can be facilitated, and fuel 

consumption increased. Use of heli-torches, terra-torches and large numbers 

of hand igniters can be effective for mass ignition.

Individual plant height, plant to plant spacing, interplant “understory” 

vegetation amount, overall biomass, and live fuel:dead fuel ratios all may have 

an effect on how well fi re spreads, how much heat and energy are generated, 

how long fl aming and smoldering combustion persist, and therefore how 

much fuel consumes. Other weather variables, such as temperature, solar 

insolation (or shading), and relative humidity; and other fuel characteristics, 

such as live fuel moisture, likely are also important, although they were not 

useful as predictors of fi re spread and fuel consumption given their limited 

range in our data set. A larger data set with a greater range of values may help 

identify if or how they are correlated with fuel consumption.

The predictive models reported here are empirical. They represent correlations among variables, and not cause and effect relationships. However, 

variables were included in the various models only if there was a reasonable 

physical explanation. For example, cover of herbaceous vegetation was included 

in the model to predict how much of an area was likely to burn, as the grasses 

and forbs growing between and under individual sage plants provide a vector 

for fi re to spread from plant to plant. Similarly, windspeed was included as if 

infl uences convective heat transfer and fl ame contact among adjacent shrubs 

and other fuel particles.

Fuel characterization, fuel moisture, site characterization and onsite weather 

sampling during the burning experiments allowed us to develop models for 

predicting fuel consumption that will be useful to fi re managers and planners. The ability to predict fuel consumption under varying environmental 

conditions will facilitate prescription development, burn planning and burn 

scheduling. The tools available in CONSUME 3.0 will allow resource managers to better assess landscapes for opportunities and hazards, and to develop 

science-based treatment and mitigation strategies to most effectively manage 

fuel consumption, fi re effects and smoke production.
USDA Forest Service Proceedings RMRS-P-41. 2006. 499

Biomass Consumption During Prescribed Fires in Big Sagebrush Ecosystems Wright and Prichard

Acknowledgments

This research was supported by the Joint Fire Science Program and the 

National Fire Plan. Roger Ottmar, David L. Peterson, and Donald McKenzie 

reviewed the manuscript.

Literature Cited

Albini, F. A.; Reinhardt, E. D. 1997. Improved calibration of a large fuel burnout 

model. International Journal of Wildland Fire. 7: 21-28.

Brown, J. K. 1982. Fuel and fi re behavior prediction in big sagebrush. Res. Pap. INT290. Ogden, UT: U.S. Department of Agriculture, Forest Service, Intermountain 

Forest and Range Experiment Station. 10 p.

Brown, J. K.; Reinhardt, E. D.; Fischer, W. C. 1991. Predicting duff and woody fuel 

consumption in northern Idaho prescribed fi res. Forest Science. 37: 1550-1566.

Canfi eld, R. H. 1941. Application of the line interception method in sampling range 

vegetation. Journal of Forestry. 39: 388-394.

Francis, J. K., ed. 2004. Wildland shrubs of the United States and its Territories: 

thamnic descriptions: volume 1. Gen. Tech. Rep. IITF-26. San Juan, PR and 

Fort Collins, CO: U.S. Department of Agriculture, Forest Service, International 

Institute of Tropical Forestry and Rocky Mountain Research Station. 830 p.

Green, L. R. 1981. Burning by prescription in chaparral. Gen. Tech. Rep. PSW-51. 

Berkeley, CA: U.S. Department of Agriculture, Forest Service, Pacifi c Southwest 

Forest and Range Experiment Station. 36 p.

Hall, J. 1991. Comparison of fuel consumption between high and moderate intensity 

fi res in logging slash. Northwest Science. 64(4): 158-165.

Insightful. 2000. S-PLUS 6.1 for Windows. Seattle, WA: Insightful Corporation.

Kauffman, J. B.; Cummings, D. L. 1989. Fuel loads and biomass consumption 

during spring and fall prescribed fi res in central Oregon rangeland ecosystems. 

Unpublished report on fi le at the U.S. Department of Agriculture, Forest Service, 

Pacifi c Northwest Research Station, Pacifi c Wildland Fire Sciences Laboratory, 

Seattle, WA.

Keely, J. E.; Fotheringham, C. J.; Baer-Keely, M. 2005. Determinants of postfi re 

recovery and succession in Mediterranean-climate shrublands of California. 

Ecological Applications. 15(5): 1515-1534.

Lindenmuth, A. W., Jr.; Davis, J. R. 1973. Predicting fi re spread in Arizona’s oak 

chaparral. Res. Pap. RM-101. Fort Collins, CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Forest and Range Experiment Station. 11 p.

Little, S. N.; Ottmar, R. D.; Ohmann, J. L. 1986. Predicting duff consumption from 

prescribed burns on conifer clearcuts in western Oregon and western Washington. 

Res. Pap. PNW-362. Portland, OR: U.S. Department of Agriculture, Forest 

Service, Pacifi c Northwest Research Station. 29 p.

McCullagh, P.; Nelder, J. A. 1989. Generalized Linear Models. London, UK: 

Chapman and Hall. 511 p.

Miller, R. F.; Rose, J. A. 1999. Fire history and western juniper encroachment in 

sagebrush steppe. Journal of Range Management. 52: 550-559.

Myanishi, K.; Johnson, E. A. 2002. Process and patterns of duff consumption in 

the mixedwood boreal forest. Canadian Journal of Forest Research. 32: 1285-

1295.
500 USDA Forest Service Proceedings RMRS-P-41. 2006.

Wright and Prichard Biomass Consumption During Prescribed Fires in Big Sagebrush Ecosystems

Neter, J.; Wasserman, W.; Kutner, M. H. 1990. Applied linear statistical models: 

regression, analysis of variance, and experimental designs. 3rd edition. Boston, 

MA: Richard D. Irwin, Inc. 1181 p.

Ottmar, R. D. 1983. Predicting fuel consumption by fi re stages to reduce smoke 

from slash fi res. In: Proceedings of the Northwest Forest Fire Council Annual 

Meeting; November 21-22, 1983; Olympia, WA. 20 p.

Ottmar, R. D.; Vihnanek, R. E.; Wright, C. S. 1998. Stereo photo series for 

quantifying natural fuels. Volume I: mixed-conifer with mortality, western juniper, 

sagebrush, and grassland types in the interior Pacifi c Northwest. PMS 830. Boise, 

ID: National Wildfi re Coordinating Group, National Interagency Fire Center. 

73 p.

Ottmar, R. D.; Vihnanek, R. E.; Regelbrugge, J. 2000. Stereo photo series for 

quantifying natural fuels. Volume IV: pinyon-juniper, sagebrush, and chaparral 

types in the Southwestern United States. PMS 833. Boise, ID: National Wildfi re 

Coordinating Group, National Interagency Fire Center. 97 p.

Prichard, S. J.; Ottmar, R. D.; Anderson, G. K. [In press]. Consume 3.0 User’s 

Guide. Gen. Tech. Rep. PNW-GTR-xxx. Portland, OR: U.S. Department of 

Agriculture, Forest Service, Pacifi c Northwest Research Station.

Reinhardt, E. D.; Keane, R. E.; Brown, J. K. 1997. First Order Fire Effects Model: 

FOFEM 4.0, user’s guide. Gen. Tech. Rep. INT-GTR-344. Ogden, UT: U.S. 

Department of Agriculture, Forest Service, Intermountain Forest and Range 

Experiment Station. 65 p.

Sandberg, D. V.; Ottmar, R. D. 1983. Slash burning and fuel consumption in the 

Douglas-fi r subregion. In: Seventh Conference on Fire and Forest Meteorology; 

April 25-28, 1983; Fort Collins, CO. pp. 90-93.

Sapsis, D. B.; Kauffman, J. B. 1991. Fuel consumption and fi re behavior associated with 

prescribed fi res in sagebrush ecosystems. Northwest Science. 65(4): 173-179.

Shifl et, T. N., ed. 1994. Rangeland cover types of the United States. Denver, CO: 

Society for Range Management. 152 p.
USDA Forest Service Proceedings RMRS-P-41. 2006. 501

In: Andrews, Patricia L.; Butler, Bret W., 

comps. 2006. Fuels Management—How to 

Measure Success: Conference Proceedings. 

28-30 March 2006; Portland, OR. 

Proceedings RMRS-P-41. Fort Collins, 

CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research 

Station.

1 Associate Professor and Scientist, 

Natural Resource Sciences, Washington 

State University,Pullman, WA.

bzamora@wsu.edu.

2 Fire Planner, Fire/Fuels Management, Pomeroy Ranger District, USFS 

Umatilla National Forest, Pomeroy, WA.

Abstract—The Lick Creek Demonstration Site on the Pomeroy Ranger District, Umatilla 

National Forest, is a Joint Fire Science Program sponsored project to create a demonstration of the effects of fuels management on forest health. The project was initiated 

in 2001 and involved the integration of two levels of partial harvest with prescribed 

fi re, a burn only treatment and an untreated control treatment. Biomass utilization was 

incorporated into the burn preparation following harvest. Objectives of the treatments 

were to improve stand composition and structure, reduce fuel levels, and enhance 

wildlife habitat. Units were harvested in 2001. Prescribed fi re as applied in 2004. 

Monitoring of fuels and stand attributes was implemented in 2005. Harvest reduced 

overstory canopy coverage as much as 70%. Understory tree layers remained intact 

through the harvest but were signifi cantly affected by the prescribed burn. Herbage 

production increased in areas of moderate fi re intensity but showed little response in 

areas of high fi re intensity. Less than 1% mortality was evident in 2005 among leave 

trees in the treatment units but tree conditions indicate future higher mortality. Fuels 

reduction was the most uniform in the commercial yarding treatments but was highly 

varied in the burn only treatments. Contractor revenue profi ts from the harvest and 

biomass fuel were modest and dependent on the provision of service contracts by 

the USFS Pomeroy Ranger District in addition to the release of the products to the 

contractors for independent sale.

Introduction

In 2000, the Joint Fire Science Program (JFSP) requested grant proposals 

for development of fuels management demonstration sites throughout the 

United States. The sites were to provide the public and research interests 

opportunity to observe the effects of fuels management involving prescribed 

fi re on wildland ecosystems. The Pomeroy Ranger District of the Umatilla 

National Forest in southeastern Washington, in conjunction with Washington 

State University, received a grant from the JSFP to initiate the development 

of the Lick Creek Demonstration Site in the northern Blue Mountains of 

southeastern Washington. The project period was originally set for a three-year 

period from FY 2001 through FY 2003. The last of the project components 

was completed in 2005.

The overall goal of the Lick Creek project was to develop a demonstration 

of the application and effects of selective, partial harvest on mid-succession 

forest stands in combination with prescribed fi re to enhance forest condition, 

amenities, and reduce wildfi re hazard. Frequent and timely monitoring of the 

demonstration site would provide documentation to substantiate, clarify, and 

The Lick Creek Demonstration — Forest 

Renewal Through Partial Harvest and Fire

Benjamin Zamora1 and Melinda Martin2
502 USDA Forest Service Proceedings RMRS-P-41. 2006.

Zamora and Martin The Lick Creek Demonstration — Forest Renewal Through Partial Harvest and Fire

explain anticipated ecological relationships and treatment effects throughout 

the demonstration site. The demonstration was to provide opportunity for 

general public examination and for the Pomeroy Ranger District to begin a 

long-term monitoring study of a fuels management strategy applied throughout the District.

Efforts to achieve public understanding and support of forest practices to 

rectify forest health issues is a priority in the Pomeroy Ranger District because 

of the sensitivity of the forest landscapes and the multiple interests in the 

public lands of the District. Successful implementation of any forest practice 

in the District is predicated on public support. The Lick Creek Demonstration is intended to show how forest practices can enhance forest landscapes 

for tree growth, wildlife habitat, and reduction of wildfi re hazard.

The purpose of this proceedings paper is to provide a synopsis of the character and development of the demonstration site.

Objectives

The specifi c objectives of the project were (1) to implement four levels of 

viewable silvicultural and fuels management stand treatment on the Lick Creek 

site in a replicated manner, (2) prepare documentation of the treatments and 

treatment effects for public review, (3) initiate a long-term monitoring study 

of the site to document treatment effects to include response of leave trees, 

and (4) to assess the economic viability of small diameter timber harvest as a 

means of accomplishing silvicultural and fuels management objectives. The 

treatments would represent prescription strategies currently employed by 

District staff to address management of stand structure, species composition, 

and fuel conditions in mid-successional forest stands.

Site Location and Pretreatment Vegetation 

Character

The Lick Creek Demonstration Site lies within the Blue Mountains Physiographic Province of southeastern Washington (Fig. 1). The site is located in 

the eastern portion of the Pomeroy Ranger District of the Umatilla National 

Forest and centered at longitude 117.4833°, latitude 46.2333°. The general 

terrain of the area is a deeply dissected plateau to the south and east of the 

Snake River Canyon that traverses through southeastern Washington. The 

specifi c site terrain is a steep, dissected canyon slope between 4100-5100 ft 

elevation with aspects spanning northwest to northeast. Slopes average 50 to 

60% across the entire site. The area is within the rain shadow cast by the 

central Blue Mountains ridge, thus is within a dry subhumid climate. Total 

annual precipitation is ±35 inches with effective moisture varying according 

to topographic and soil conditions.

The vegetation of the site is a mosaic of forest stands interspersed with 

grassland sites on side-ridges and shallow soils (Fig. 2). Generally, two distinct zones of vegetation are distinguishable across the site. An upper canyon 

wall zone of the Douglas-fi r/snowberry (Pseudotsuga menziesii/Symphoricarpos albus) and Douglas-fi r/ninebark (Pseudotsuga menziesii/Physocarpus 

malvaceus) plant associations covers from 1⁄4 to 1⁄3 of the site slope surface 

(Johnson and Clausnitzer 1992). The width of this zone is dependent on 
USDA Forest Service Proceedings RMRS-P-41. 2006. 503

The Lick Creek Demonstration — Forest Renewal Through Partial Harvest and Fire Zamora and Martin

Figure 1—Location of the Lick Creek Demonstration Site in the Pomeroy Ranger District, 

Umatilla National Forest, southeastern Washington.

Figure 2—Pre-harvest photograph of the Lick Creek Demonstration Site looking east 

along the face of the site in 2000.
504 USDA Forest Service Proceedings RMRS-P-41. 2006.

Zamora and Martin The Lick Creek Demonstration — Forest Renewal Through Partial Harvest and Fire

slope surface confi guration with the narrowest portions associated with concave surfaces and the broadest portions associated with convex surfaces. The 

lower 2⁄3 to 3⁄4 of the canyon wall is dominated by the grand fi r/twinfl ower 

(Abies grandis/ Linnaea borealis) plant association. Ponderosa pine (Pinus 

ponderosa) and western larch (Larix occidentalis) are the seral conifer tree 

species throughout the site.

The existing vegetation over the site prior to treatment generally refl ected a 

mid-successional state (Fig. 3). Most of the area supported a sparse component 

of 100+ year-old trees of ponderosa pine, western larch, and Douglas-fi r that 

formed an upper overstory layer with canopy cover ranging from 5-15%. Tree 

heights were 90 ft with average d.b.h. ranging from 20 to 32 inches. The 

majority of the leave trees in the harvest treatments were in this particular 

cohort. The bulk of the forest canopy was a deep mid-story tree layer composed predominantly of Douglas-fi r (67%) and grand fi r (24%) with sparse 

ponderosa pine (6%) and western larch (3%). Average height of trees within 

this layer ranged from 45 to 50 ft and averaged 9 to 11 inches d.b.h. Most 

of the merchantable timber came from this mid-story layer. A distinct third 

cohart of smaller trees with 3 to 6 inches d.b.h. occurred in the lower portion 

of the layer between 20 and 40 ft. These smaller trees composed 53% of the 

total tree density of the layer, and in combination with an understory layer 

of junvenile trees less than 3 inches d.b.h. and under 10 ft in height, formed 

the greatest barrier to light penetration to the ground surface.

Figure 3—Example of pre-harvest stand conditions in the Lick Creek Demonstration Site.
USDA Forest Service Proceedings RMRS-P-41. 2006. 505

The Lick Creek Demonstration — Forest Renewal Through Partial Harvest and Fire Zamora and Martin

History of the Site

The Problem

The Lick Creek site was selectively harvested through several entries during 

the 1960’s and early 1970’s for large diameter sawlogs by means of a skyline 

system. No additional harvest or fuels treatment have occurred on the site 

since that time. There is no evidence of wildfi re in the immediate drainage 

area of the site in the past 100+ years so the site represents a fi re exclusion 

location. Stand development since the 1960’s progressed to mid-successional 

stages of overstocked, nearly closed stands of small diameter, shade-tolerant 

Douglas-fi r and grand fi r. Competition from these shade-tolerant species reduced the abundance of shade-intolerant ponderosa pine and western larch. In 

addition, surface and ladder fuels were accumulating to the point of creating 

a severe surface and crown wildfi re hazard (Fujishin 1998).

The drainage lies directly above a major Rocky Mountain elk winter range in the 

Asotin Creek watershed adjacent to the Snake River and is a signifi cant part of 

a spring elk calving area and summer range for elk herds that utilize the Asotin 

Creek winter range. Stand closure of forests within the Lick Creek drainage 

was reducing the diversity and abundance of the understory and detrimentally 

affecting the quality of elk and wildlife habitat (Lorentz 1997).

The Solution

The prescription for stand management on the Lick Creek site revolved 

around the principal objectives of opening stands, shifting the balance of 

species composition to favor ponderosa pine and western larch, and reducing fuel loading (Bott 1998). Selective harvest and thinning from below 

combined with prescribed fi re was prescribed to accomplish the objectives 

The effi cacy of these practices was considered to be well established (Agee 

1996, Applegate and others 1997, Graham and others 1999, Williams and 

others 1993). Multiple entries over time with prescribed fi re after harvest 

was thought necessary in order to ultimately achieve the objectives of the 

prescription (Martin 1998). The treatment effects of the combined practices 

were also expected to enhance wildlife habitat in general, and more specifi cally, 

the elk habitat of the site that is a central concern to several local public interest groups. Untreated wildlife leave units were integrated into the treatment 

design to serve a wildlife cover and travel corridors. Non-hazardous snags 

where left standing and a buffer zone was designated along the bottom of 

the Lick Creek drainage to protect watershed values and provide additional 

undisturbed wildlife cover.

Two primary concerns were identifi ed in the development of the Lick Creek 

Demonstration prescription and are refl ected as inclusions in the project objectives. These were (1) the effects of fi re on leave trees and (2) the economic 

viability of small diameter timber harvest.

The mortality of large leave trees from prescribed fi re across the Lick 

Creek site was a major concern (Martin 1998). Several recent studies have 

confi rmed that mortality of trees from a prescribed fi re increases as the depth 

of the duff layer around the base of the tree and the diameter of tree bole 

increases (Ryan and Frandsen 1995, Hille and Stephens 2005, Stephens 

and Finney 2002, Thies and others 2006). Documentation of the leave tree 

post-burn responses was designated as a priority element in the monitoring 

of the demonstration site.
506 USDA Forest Service Proceedings RMRS-P-41. 2006.

Zamora and Martin The Lick Creek Demonstration — Forest Renewal Through Partial Harvest and Fire

The majority of the merchantable sawlog trees on the Lick Creek site 

grade as small diameter timber (5-9 inches d.b.h.), raising questions about 

the profi tability of such a harvest to logging contractors and their interest in 

undertaking this kind of harvest option. Selective harvest and thinning of 

small diameter stands is being increasingly considered in the Interior Northwest as a means of reducing wildfi re hazard, redistributing tree growth, and 

re-directing stand development (Wagner and others 1997, Baumgartner and 

others 2002). But questions remain about the fi nancial viability of such harvest from the standpoint of product marketability and revenue and harvest 

costs (Johnson 1997, Wagner and others 1997). Harvest costs are affected 

by tree size and utilization with harvest costs inversely proportional to tree 

size—small-diameter trees result in small piece sizes with low volumes and are 

more costly to handle (Stokes and Klepac 1997). Johnson (1997) stated that 

an economical harvest of small trees is diffi cult to attain for two reasons—the 

cost per unit of volume to move the material increases dramatically as diameter of the volume decreases, and the value of the unit volume decreases 

as piece size decreases. Harvest costs increase with reduced road accessibility and conditions, less steeper and more complex terrain, smaller trees and 

higher density stands, limited opportunity to use less expensive mechanical 

yarding, and greater hauling distance. Ultimately, the availability of stable 

markets for multiple wood products from the harvest will dictate net profi t 

from the harvest (Johnson 1997, Stokes and Klepec 1997). Documentation 

and evaluation of harvest and fuels treatment costs and product revenues to 

assess economic viability of the silvicultural and fuels management strategy 

was included as a primary objective of the project.

Methods

Treatment Design and Installation

The site was divided into three treatment units with wildlife habitat units left 

between some of the treatment units within the site boundaries (Fig. 4). A 150 

ft buffer zone was maintained of at the bottom of the slope between the treatment units and Lick Creek. Each unit was divided into four subunits to replicate 

the treatment. The following four treatments were installed in each unit—two 

levels of harvest, a control, and a burn only treatment (Table 1). The replication 

subunits range in size from 6 to 17 acres, the size being dictated by uniformity 

of pre-harvest conditions and the facilitation of harvest and prescribed burning 

operations.

Treatment Schedule

Treatment planning, the timber cruise, and pre-logging stand inventory 

were conducted in 2000 and 2001. Harvest of the site was completed during 

the winter of 2001-2002. Preburn inventory was conducted in 2002-2003. 

Slash piles were removed from the site by means of chipping and selected 

pile burns in the fall of 2003. The prescribed burn of the treatment units 

was conducted in September and October 2004. The fi rst year of post-burn 

monitoring was completed in the summer and fall of 2005.
USDA Forest Service Proceedings RMRS-P-41. 2006. 507

The Lick Creek Demonstration — Forest Renewal Through Partial Harvest and Fire Zamora and Martin

Figure 4—Aerial view of treatments at mid-elevation within the Lick Creek Demonstration 

Site showing fuels yarding (a, c) on a wildlife leave unit (b) above Lick Creek (d). 

Permanent monitoring plots are distributed near the center of each unit from top to 

bottom.

Table 1—Replicate (3) treatments implemented on the Lick Creek Demonstration Site, Blue Mountains, 

Pomeroy Ranger District, Umatilla National Forest.

 Treatment Description 

Control No harvest, no prescribed burn - stand left in original state.

Prescribed burn only No harvest, prescribed burn of stand in its original state.

Commercial yarding - prescribed burn All trees unmarked as leave trees that were 6 inches d.b.h. 

or greater that had a minimum 8 foot long piece to a 3 inch 

top were cut and removed to the landings, whole tree yarding 

was not required but generally done to improve effi ciency of 

operation; post-harvest prescribed burn was applied. Objective 

of treatment was to leave a greater quanity of fuel in place on 

the units.

Fuels yarding - prescribed burn All trees unmarked as leave trees that were 3 inches d.b.h. or 

greater that had a minimum 8 foot long piece to a 3 inch top 

were cut and removed to the landings; whole tree yarding was 

required; down material meeting the cutting specifi cations for 

commercial yarding (6+ inches d.b.h.) were cut and yarded; 

post-harvest prescribed burn was applied. Objective of 

treatment was to minimize fuel accumulations on the units.
508 USDA Forest Service Proceedings RMRS-P-41. 2006.

Zamora and Martin The Lick Creek Demonstration — Forest Renewal Through Partial Harvest and Fire

Harvest

A total of 85 acres of the site were harvested in a 40-day period, commencing in mid December 2001. Winter logging in the Blue Mountains is at risk 

of being stopped at any time because of severe storm conditions and snow 

accumulation. Fortunately, severe weather conditions never developed until 

after the harvest had been completed in late January, 2002, allowing harvest 

to proceed with minimal snow cover.

Yarding was conducted for 74% of the harvest area with gravity feed skyline 

system utilizing a skyline yarder and a motorized support carriage. Groundbased yarding with a tracked skidder was conducted over the remaining 26% 

of the harvest area. Whole tree yarding was required as part of the treatment 

prescription to minimize fuel loading on the site.

A stationary, pull-through, motorized, radio-controlled delimber was used 

to process the whole trees that were yarded to the landing. After delimbing, 

the trees were sorted according to merchantable (sawlog) or unmerchantable (tonwood, fi ber wood), cut to specifi ed lengths, and piled into decks for 

loading. One-hundred loads were hauled from the site—51 sawlog loads, 

33 tonwood loads, and 16 fi ber loads.

The total yield of the harvest averaged 27.14 tons per acre for the 85-acre 

harvest area of the Lick Creek Site. Yield was portioned as follows - sawlog 

(1171 t, ~ 194 gross mbf), tonwood (761 t), fi ber wood (374 t).

Chipping for Biomass Fuel

Whole tree removal from the harvest site meant that large slash piles accumulated at the landings at the top of each unit. These piles were large, 

ranging from 1.41 tons/acre to 4.67 tons/acre and were considered a hazard 

to the conduct of the prescribed burn because of their location and potential 

to initiate escape fi re. Removal of the piles proceeded in the late fall of 2003 

through a service contract to a local contractor to chip the slash for sale as 

biomass fuel. Because of limited road access, the chipper was stationed at a 

site that provided access to haul trucks. Slash from the piles was transported 

by trucks to the chipper for processing. Slippage of some slash piles down the 

steep slopes of the site made some of the slash inaccessible to loaders. This 

material was pile burned after the slash chipping had been completed. A total 

of 33 piles yielded 482.44 dry t of chipped wood for sale as biomass fuel.

Prescribed Burn

The burn was conducted from in a 5-day period from September 30-

October 4, 2004. The burn prescription targeted reduction of fuels and 

understory fi re-intolerant and shade-tolerant tree species on the site as the 

principal objectives. The principal Ignition pattern was strip head-fi re over 

most of the site with backing fi re used through heavy fuel accumulations and 

down very steep slopes. Flame-lengths were to be kept under 4 ft to limit 

fi re intensity. Seven burn units are designated, combining treatment units 

to facilitate control and consistency in the character of the burn. Ignitions 

started at highest points of the site and progressed down-slope and to lower 

elevations within the site over the burn period. Surface fuels were typically 

a mosaic of grass and woody fuel patches, intermixed with live shrub and 

tree materials. The small live tree component was especially signifi cant in 

the higher elevation units. The live shrub component was most signifi cant in 

the lower elevation and environmentally warmer units. Woody fuel loading 

varies across the Lick Creek site according to treatment. The highest woody 
USDA Forest Service Proceedings RMRS-P-41. 2006. 509

The Lick Creek Demonstration — Forest Renewal Through Partial Harvest and Fire Zamora and Martin

fuel loadings were in the commercial yarding units harvest with fuelbeds 

in the Fire Behaviror Fuel Model 10 and 11 categories depending on the 

mix of herbaceous and live fuels and amount of overstory.

At the beginning of the burn period, temperatures (55-62 ° F) and relative 

humidity (38-44%) were near the lower limit of the burn prescription providing an advantage in keeping fi re intensity low while still accomplishing the 

prescription objectives. Backing fi res were ineffective under these conditions, 

so strip head-fi re ignition was the principal means of ignition. Temperatures 

climbed into the low 70’s and humidity dropped into the high 20’s by the 

end of the 5-day burn period and back-fi ring became the principal means of 

fi re spread. Winds occurred in the typical fall convective wind pattern and 

were not a factor at anytime during the burn period.

Initial estimates indicate that an average reduction of 80% was achieved in 

the woody fuel and ground fuel loading over the Lick Creek site.

Monitoring System

Five permanent plots are distributed within each treatment unit near 

the center from the top to the bottom of the unit (Fig. 4). The plots were 

inventoried pre- and post-burn. Pre-harvest plots were sampled in the same 

locality as the permanent plots but do not represent the exact location of the 

permanent plots. The plot is circular with a diameter of 50 m. The center of 

the plot is the photo point from which a radial sequence of photos is taken 

of the entire perimeter of the plot. Two 25-m transects from the plot center 

along the contour of the slope are used to collect point and microplot data 

for the following overstory and understory attributes: fuel loading, species 

composition and canopy coverage, tree density by diameter class and species, stand canopy stratifi cation, height, and composition, and soil surface 

coverage and composition. A series of digital photos are taken of 1-m2 microplots along each transect. The data is being entered and summarized in 

FIREMON (2006).

Summary of Preliminary Findings

Stand structure was signifi cantly altered by harvest with reductions of 

overstory canopy coverage by as much as 70% in some treatments. The 

majority of the dominant mid-story canopy layer was eliminated by the 

harvest. However, a substantial amount of the understory tree layer of short 

and less than 3 inches d.b.h. remained intact after harvest. The prescribed 

burn damaged the majority of the understory layer but the full extent of the 

mortality was not fully expressed in the 2005 inventory. Herbage production 

increased dramatically in areas of moderate fi re intensity but did not show 

a similar response in areas of high fi re intensity. Less than 1% mortality was 

evident in 2005 in the leave tree populations across all of the harvest treatment units. A low degree of mortality is evident in the overstory of the burn 

only treatments but the condition of many of the trees suggest that greater 

levels of mortality are to be expected in coming years. Fuels reduction varied 

greatly among treatment replications with the most uniformity reduction in 

the commercial yarding treatments and the greatest variation in the burn 

only treatments. Contractor revenue profi ts from the harvest and biomass 

fuel were modest and dependent on the provision of service contracts by the 

USFS Pomeroy Ranger District in addition to the release of the products to 

the contractors for independent sale.
510 USDA Forest Service Proceedings RMRS-P-41. 2006.

Zamora and Martin The Lick Creek Demonstration — Forest Renewal Through Partial Harvest and Fire

Project Sponsors

Funding to initiate the development of the Lick Creek Demonstration Site 

was provided by the Joint Fire Science Program, USFS Umatilla National 

Forest, Washington State University, Rocky Mountain Elk Foundation, Blue 

Mountain Elk Initiative, and Guy Bennett Lumber Company.

Literature Cited

Agee, J.K. 1996. Critical stand characters for crown-fi re-safe forests. pp. 52-68. In: 

Proc. 17th Annual Forest Vegetation Management Conference, Redding CA, Jan 

16-18, 1996.

Applegate V., Dupuis V. Ablutz M., Klinkhammer S., Slaughter S., Yelczyn B., 

Becker R. 1997. Use of habitat types and fi re regimes in landscape assessments 

and stand level prescriptions. pp. 36-72. In: Forest Management Into the Next 

Century: What Will Make It Work? Proceedings. Spokane WA: Forest Products 

Society and USDA For Serv.

Baumgartner D., Johnson R.L., DePuit E.J. 2002. Small diameter timber resource 

management, manufacturing, and markets. Proc. of Conf. held Feb. 25-27, 2002, 

Spokane WA. Pullman WA: WSU Extension MISC 0509.

Bott K. 1998. Silvicultural prescriptions—Lick Analysis Area. Staffi ng paper, Lick 

Analysis File, Pomeroy Ranger District, Umatilla National Forest, USDA Forest 

Service, Pomeroy, WA.

FIREMON. 2006. Fire effects monitoring and inventory system. Online: http://

fi remon.org.

Fujishin M. 1998. Environmental assessment for the Lick timber sale and fi re 

reintroduction project. Staffi ng paper, Lick Analysis File, Pomeroy WA: Pomeroy 

District, Umatilla National Forest, USDA For. Serv.

Graham R.T., Harvey A.E., Jain T.B., Tonn J.R. 1999. Effects of thinning and 

similar stand treatments on fi re behavior in western forests. General Technical 

Report PNW-GTR-463. Portland, OR: USDA For. Serv., PNW For and Range 

Exp. Sta.

Hille M.G., Stephens S.L. 2003. Mixed conifer forest duff consumption during 

prescribed fi res: tree crown impacts. For. Sci. 51:417-424.

Johnson L. 1997. Overview of timber harvesting technology and costs. pp. 89-

94. In: Forest Management Into the Next Century: What Will Make It Work? 

Conference proceedings sponsored by the Forest Products Society and USDA 

Forest Service, Spokane, WA, November 19-21, 1997.

Johnson C.G. Jr., Clausnitzer R.R. 1992. Plant associations of the Blue and Ochoco 

Mountains. USDA, For. Serv., PNW Region, Wallow-Whitman National Forest, 

R6-ERW-TP-036-92.

Lorentz S. 1997. Vegetative conditions in the Lick Prescribed Fire Project Area. 

Staffi ng paper, Lick Analysis File, Pomeroy, WA: Pomeroy District, Umatilla 

National Forest, USDA Forest Service.

Martin M. 1998. Report on the fi re and fuels management issues in the Lick Analysis 

Area. Staffi ng paper, Lick Analysis File, Pomeroy, WA: Pomeroy District, Umatilla 

National Forest, USDA Forest Service.

Maruoka K.R. 1993. A fi re history survey in selected Pseudotsuga menziesii and Abies 

grandis stands in the Blue Mountains of Oregon and Washington. Pomeroy, WA: 

Pomeroy District, Umatilla National Forest, USDA Forest Service.

Ryan KC, Frandsen W.H. 1992. Basal Injury From Smoldering Fires in Mature Pinus 

ponderosa Laws. International Journal of Wildland Fire 1(2):107-118.
USDA Forest Service Proceedings RMRS-P-41. 2006. 511

The Lick Creek Demonstration — Forest Renewal Through Partial Harvest and Fire Zamora and Martin

Stephens S.L., Finney M.A. 2001. Prescribed fi re mortality of Sierra Nevada mixed 

conifer tree species: effects of crown damage and forest fl oor combustion. Forest 

Ecology and Management 162: 261-271.

Stokes B.J., Klepac J.F. 1997. Ecological technologies for small-diameter tree 

harvesting. pp. 95-101. In: Forest Management Into the Next Century: What 

Will Make It Work? Conference proceedings sponsored by the Forest Products 

Society and USDA Forest Service, Spokane, WA, November 19-21, 1997.

Thies W.G., Westlind D.J., Loewen M., Brenner G. 2006. Prediction of delayed 

mortality of fi re-damaged ponderosa pine following prescribed fi res in Eastern 

Oregon, USA. International Journal of Wildland Fire 15: 19-29.

Wagner F.G., Keegan III, C.E., Fight R.D., Willits S. 1997. Potential for smalldiameter saw timber utilization by the current industry infrastructure in Western 

North America. pp. 115-120. In: Forest Management Into the Next Century: 

What Will Make It Work? Proceedings. Spokane WA: Forest Products Soc. and 

USDA Forest Service.

Wickman B.E. 1992. Forest health in the Blue Mountains: The infl uence of insect 

and disease. Gen. Tech. Rep. PNW-GTR-295. Portland, OR: USDA Forest 

Service, Pacifi c Northwest Forest and Range Experiment Station.

Williams J.T., Schmidt R.G., Norum R.A., Omi P.N., Lee R.G. 1993. Fire related 

considerations and strategies in support of ecosystem management. Staffi ng paper, 

Fire and Aviation Management, Washington Offi ce, USDA Forest Service.
USDA Forest Service Proceedings RMRS-P-41. 2006. 513

In: Andrews, Patricia L.; Butler, Bret W., 

comps. 2006. Fuels Management—How to 

Measure Success: Conference Proceedings. 

28-30 March 2006; Portland, OR. 

Proceedings RMRS-P-41. Fort Collins, 

CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research 

Station.

1 Ecologist, USFS, Rocky Mountain 

Research Station, Fire Sciences Lab, 

Missoula, MT. hsmith04@fs.fed.us

2 Forester, USFS, Rocky Mountain 

Research Station, Fire Sciences Lab, 

Missoula, MT.

3 Research botanist USGS, Western 

Ecological Research Center, Las Vegas 

Field Station, Henderson, NV.

4 Biologist, USGS, Western Ecological 

Research Center, Las Vegas Field 

Station, Henderson, NV.

5 Supervisory restoration biologist, 

NPS, Lake Mead National Recreation 

Area, Boulder City, NV.

Abstract—The recent encroachment of piñon (Pinus edulis) and juniper trees (Juniperus osteosperma) into historically shrub- and grass-dominated landscapes has caused 

major changes in ecosystem structure and function, including dramatic changes in fuel 

structure and fi re regimes. Such encroachment is currently occurring on thousands of 

acres on the Shivwits Plateau in northwestern Arizona and land managers are seeking 

effective techniques to restore these areas to pre-invasion conditions and reduce the 

threat of high severity crown fi res. A study was established on the Shivwits Plateau to 

test the effectiveness of three thinning techniques for reducing the density of recently 

established piñon and juniper trees and to assess changes to the fuelbed structure. 

The thinning treatments were: (1) cut and leave; (2) cut, buck and scatter; and (3) 

herbicide. The line-point intercept method was used to characterize changes in the 

fuelbed structure. Belt transects were used to quantify tree density. Responses of the 

shrubs and suffrutescent plants (herein collectively referred to as ‘shrubs’) are reported. 

Generally, there was more live shrub cover in the treatment units versus the control 

units. In addition, the mechanical treatments added woody fuels to the initially sparse 

sites. These two structural changes are expected to help to carry surface fi re through 

the treated areas.

Introduction

Tausch and others (1981) found evidence of expansion both in tree densities 

and geographical distribution of piñon-juniper (Pinus spp.- Juniperus spp.) 

woodlands over the last 175 years. The type conversion from shrubland to 

woodland leads to a decrease in understory plants such as shrubs, suffrutescent 

plants, bunchgrasses, and herbaceous species as the overstory canopy closes. 

This woodland expansion is a major concern for land managers due to the 

resulting loss of wildlife habitat associated with sagebrush steppe, decreased 

species diversity, loss of soil seedbanks, decreased aquifer recharge, increased 

soil erosion, and increased intensity of wildfi res (Koniak and Everett 1982, 

Wilcox and Breshears 1994, Davenport and others 1998, West 1999, Miller 

and others 2000). 

The range expansion of piñon and juniper is associated with increased fi re 

return intervals due in large part to fi re suppression and the reduction of surface fuels caused by the introduction of livestock grazing by European settlers 

(Miller and Rose 1999). In an attempt to return stands to pre settlement 

Response of Fuelbed Characteristics to 

Restoration Treatments in Piñon-JuniperEncroached Shrublands on the Shivwits 

Plateau, Arizona

Helen Y. Smith1, Sharon Hood2, Matt Brooks3, J.R. Matchett4, and 

Curt Deuser5
514 USDA Forest Service Proceedings RMRS-P-41. 2006.

Smith, Hood, Brooks, Matchett, and Deuser Response of Fuelbed Characteristics to Restoration Treatments in Piñon-Juniper- Encroached...

conditions dominated by sagebrush steppe and shorter fi re return intervals 

and to improve livestock forage and wildlife habitat, land managers have attempted to reintroduce fi re and manipulate fuel conditions using mechanical, 

chemical, and seeding treatments.

Where woodlands are dense, shading inhibits herbaceous development, 

limiting the surface fuels necessary to support a low intensity surface fi re. In 

this situation, fi re does not propagate easily except under extreme fi re weather 

conditions which typically results in undesirable intense overstory crown 

fi res (Miller and others 2000). Where woodlands are more open and surface 

fuels still exist, managers can create low to moderate intensity surface fi res 

with sporadic torching of larger trees, but even in these conditions fi re can 

be diffi cult to propagate. For these reasons, sites have often been prepared 

before burning, typically by chaining landscapes to uproot trees and provide 

opportunities for early successional forbs, grasses, and shrubs to re-establish. 

However, chaining results in removal of both pre and post settlement trees 

and creates signifi cant soil disturbance, which is not compatible with the 

management goals of many land management agencies, especially the National 

Park Service. As a result, mechanical thinning or chemical treatment of post 

settlement trees is becoming more common since such treatments create 

uneven-aged woodland stands which better represent historic conditions, 

provide better wildlife habitat, and do not create signifi cant soil disturbance. 

Minimizing soil disturbance is especially important in areas where cultural 

resources may otherwise be at risk. By reducing overstory canopy cover, 

understory plants will have a chance to grow, increase cover, and create fi ne 

fuels that will support a lower intensity surface fi re through the area.

To this end, this study on the Shivwits Plateau in northwestern Arizona 

was established to compare the effectiveness of two types of mechanical and 

one type of chemical thinning treatments as well as their costs for:

 1. reducing densities of post-settlement piñon (Pinus edulis) and juniper 

(Juniperus osteosperma) trees;

 2. increasing cover and seedbank density of native annual plants and perennial grasses; 

 3. increasing plant species diversity;

 4. minimizing cover and seedbank density of invasive alien plants; and

 5. creating a fuelbed that promotes the re-establishment of historic low to 

moderate intensity surface fi res.

This report examines the fi rst and fi fth objectives.

Materials and Methods

Study Site

The study site (~405 ha/1,000 ac) is located within a single watershed on 

the northern rim of the Grand Canyon on the western Colorado plateau. The 

administrative boundaries of the project are within the National Park Service 

(NPS) portion of the Grand Canyon–Parashant National Monument, an area 

jointly managed with the Bureau of Land Management (BLM), Arizona Strip 

Field Offi ce. The site is at 1,890 m (6,200 ft), with slopes from two to 15 

percent. Mean annual precipitation is 33 to 43 cm (13 to 17 inches), bimodally 

distributed in summer monsoons from late June to early September, and winter 

frontal systems from November through March. Mean annual soil temperature 

is 9 to 13 °C (49 to 56 °F), and the frost-free period is 135 to 150 days. 
USDA Forest Service Proceedings RMRS-P-41. 2006. 515

Response of Fuelbed Characteristics to Restoration Treatments in Piñon-Juniper- Encroached... Smith, Hood, Brooks, Matchett, and Deuser

European settlement of the area occurred in the mid 1800s and included 

extensive cattle grazing until the late 1980s when grazing was terminated 

on the site. Historic evidence of prolifi c cattle grazing remains in the study 

region including corrals, drift fences and earthen water tanks. Some of this 

region was chained in the late 1950s to early 1960s by a local rancher in an 

effort to improve range forage conditions. The area was “withdrawn land” by 

the Bureau of Reclamation in the 1930s and was transferred to the National 

Park Service in 1964. 

Fire suppression has likely occurred concomitantly with European settlement. Organized fi re fi ghting responsibilities have been shared by the BLM 

and NPS since the 1950s. A Prescribed Natural Fire Plan was implemented 

for the area in 1998 and fi res are currently being managed as “wildland fi re 

use” which is synonymous with allowing lightning-ignited fi res to burn under 

certain management approved conditions.

Lightning storms commonly occur in the area throughout the monsoon 

season. There is evidence that moderately-sized fi res burned historically in 

the area [up to 40 ha (100 ac)], but in the last 25 years smaller fi res less than 

one hectare and single tree fi res were more common. In an attempt to reintroduce low- to moderate-intensity surface fi res, the NPS has implemented 

over 2,400 ha (6,000 ac) of prescribed fi res in the area since the program 

started in 1994. Prescribed burn objectives were only met on approximately 

600 ha (1,500 ac), which included the majority of the formerly chained 

areas. Most of the untreated/unchained areas did not carry fi re with the use 

of a helitorch except under extreme fi re weather conditions. Monitoring has 

shown that plant diversity has generally increased in burned areas; however, 

native grasses have only increased in small isolated areas, possibly due to a 

depleted soil seedbank. In order to help meet resource objectives, assessment 

of alternative treatments besides simply attempting to reintroduce fi re appears 

to be necessary.

Current land management goals at this site are to preserve, restore, and 

maintain naturally functioning ecosystems and cultural resources. Other 

goals are to maximize native plant and animal diversity within the natural 

range of variation. Primary management concerns are related to soil erosion 

potential, and it is believed that current site conditions will not adequately 

sustain soil resources in the event of a high severity crown fi re. The site is 

ideal to conduct restoration activities since cattle grazing has been excluded; 

no elk exist in the area; and deer, small mammals, and insects are the only 

remaining grazers. The lack of excessive grazing pressure should facilitate 

the re-establishment of native grasses, forbs, shrubs, and suffrutescent plants. 

The NPS Lake Mead Exotic Plant Management Team is available to control 

invasive plants in the event that they begin to appear in the study area.

Study Design

Thirty-two, 8.1 ha (20 ac) units were laid out and each unit was randomly 

assigned to be left untreated (control) or to have one of three thinning treatments applied. The treatments consisted of two types of mechanical and one 

chemical thinning treatment. The goal of all thinning treatments was an 80 

percent reduction of post settlement trees. Land managers estimated that 

this level of tree reduction would open the stands enough to provide favorable establishment and growing conditions for perennial grasses and other 

vegetation, provide fuels to support a low to moderate intensity surface fi re, 

and provide enough ground cover to reduce the potential for soil erosion. 

Post settlement trees were defi ned as those ranging in age from 1 to 175 years 
516 USDA Forest Service Proceedings RMRS-P-41. 2006.

Smith, Hood, Brooks, Matchett, and Deuser Response of Fuelbed Characteristics to Restoration Treatments in Piñon-Juniper- Encroached...

old (Class 1 to 3 trees; Bradshaw and Reveal 1943). None of the oldest trees 

(Class 4) were to be cut or sprayed. This classifi cation of piñon and juniper 

trees was based on general guidelines such as diameter at stump height or 

breast height, tree height, and growth form. 

The mechanical thinning options consisted of either a cut-leave or a cutbuck-scatter scenario. Trees were not marked prior to cutting, but rather 

the thinning crews were briefed on what factors constitute a post settlement 

tree and were given the direction to cut four post settlement trees and leave 

the fi fth post settlement tree they encountered uncut. In this manner, an 80 

percent reduction in tree density of each species should occur. In the cutleave treatment, trees were cut with either loping shears or chainsaws and left 

where they fell. The cutting methods were the same in the cut-buck-scatter 

treatment, but the larger trees were then limbed to manageable lengths and 

the material scattered across the site, avoiding placing slash under the driplines of uncut trees. Approximately 20 percent of the mechanical thinning 

was accomplished by a National Park Service fi re crew with the remainder 

completed by contract crews.

The herbicide thinning treatment used 15 percent Tordon 22K (DOW) that 

was batch mixed at 11.4 liters (three gallons) increments directly into SP-3 

backpack sprayers at a rate of 709.8 milliliters (24 fl uid ounces) of chemical 

to 3.78 liters (1 gallon) of water with 29.6 milliliters (one fl uid ounce) of 

Blaze-on blue dye and one milliliter (0.03 fl uid ounce) of kinetic nonionic 

surfactant. Since this method is a spot treatment, the rate applied per unit 

area is dependent upon the target tree density. For this treatment, the average 

application was 1.84 liters per hectare (25.15 ounces per acre) of Tordon 22K. 

The spray mixture was applied as a solid stream to the base of the tree at the 

soil interface (Williamson and Parker 1996). A 4.6 m (15 ft) buffer was left 

around each pre settlement tree encountered due to concerns for chemical 

drift in the soil. Other trees, regardless of their classifi cation that fell in this 

zone, were not treated. It was estimated that these trees would constitute 

the 20 percent residual leave tree target; therefore, every post-settlement tree 

located outside the buffer zones was treated with herbicide. Herbicide application was performed by the Exotic Plant Management Team from Lake 

Mead Recreation Area.

No cutting or herbicide application was implemented in the control units. 

All treatments were completed prior to the start of our sampling. 

Sampling

In each treatment unit, three plots were randomly located. At each plot, we 

laid out a 50 m (164 ft) line transect, which ran down the center of a 6x50 m 

(20x164 ft) belt transect. Vegetation data was collected along the 50 m line 

transect using the line-point intercept method (Lutes and others 2006) and 

tree data was collected within the belt transect. Plots were established in 2004 

after completion of the thinning treatments and measured in late August/early 

September of 2004 and 2005. 

Trees—Since cutting took place before plot establishment, we could not 

note the features such as tree height, growth form, or diameter at breast 

height or stump height of cut trees that Bradshaw and Reveal (1943) used 

for their classifi cation system and that the thinning crew used when making the decision of which trees to cut. We used data from Miller and others 

(1981) to develop relationships between diameter at stump height, diameter 

at breast height, and groundline diameter (g.l.d.) and we assigned each of 
USDA Forest Service Proceedings RMRS-P-41. 2006. 517

Response of Fuelbed Characteristics to Restoration Treatments in Piñon-Juniper- Encroached... Smith, Hood, Brooks, Matchett, and Deuser

the trees/stumps in our data set a Class based solely on g.l.d. (table 1). Trees 

that were treated with herbicide were either labeled as dead or “sick.” If, by 

appearance, they were unhealthy and expected to die in the near future they 

were deemed sick. 

All trees/stumps located within the 6x50 m belt transect that had a g.l.d. 

of 7.6 cm (3 inches) or greater were recorded along with the species. This left 

the smallest Class 1 trees unmeasured, leading to the assumption that the 

Class 1 trees measured and those that were thinned were representative of 

smaller trees as well. Although other tree attributes were measured, density 

and percent reduction will be the only tree data presented in this paper.

Surface fuels—Along the 50 m (164 ft) line transect that bisected the belt 

transect, we sampled fuel groups by category (fi ne slash, coarse slash, fi ne 

woody debris, coarse woody debris, grass, live shrubs, dead shrubs, trees by 

species, forbs, and bare soil) using the line-point intercept sampling methods. 

The height of the tallest interception by fuel group was recorded at 0.5 m 

(1.6 ft) intervals. Since we did not sample prior to treatment establishment, 

the distinction between ‘slash’ and ‘debris’ was made in an attempt to determine woody fuel presence prior to and following treatment application. True 

shrubs such as scrub oak (Quercus turbinella), cliffrose (Purshia mexicana) 

and sagebrush (Artemisia tridentate) as well as suffrutescent plants such as 

broom snakeweed (Gutierrezia sarothrae) are combined in our ‘live shrub’ 

and ‘dead shrub’ categories. The fuel that will contribute to fi re spread in 

this system is made up of plants such as shrubs and grasses as much as it is 

woody fuels; therefore, much of our focus was spent on assessing continuity 

of plant growth. Live shrub cover is the only surface fuel component that 

will be presented in this paper.

Data Analysis

For the line-point intercept method of cover determination, percent cover 

is calculated by summing the number of hits per line and dividing by 100. In 

our situation, we had 100 points per line, so it was a matter of simply summing the number of hits. For example, if forbs were encountered at 13 of the 

100 points along a line, this computes to a 13 percent cover for forbs.

Table 1—Groundline diameter classes used to distinguish tree class 

for juniper and piñon trees. Breakpoint diameters based on 

Bradshaw and Reveal (1943) and Miller and others (1981).

 Diameter at groundline

 Juniper Piñon

 - - cm - - - - in - - - - cm - - - - in - -

Class 1 <10.2 <4 <8.9 <3.5

Class 2 10.2-24.1 4-9.5 8.9-21.8 3.5-8.6

Class 3 24.2-35.8 9.6-14.1 21.9-31.8 8.7-12.5

Class 4 >35.8 >14.1 >31.8 >12.5
518 USDA Forest Service Proceedings RMRS-P-41. 2006.

Smith, Hood, Brooks, Matchett, and Deuser Response of Fuelbed Characteristics to Restoration Treatments in Piñon-Juniper- Encroached...

We used general linear mixed models (GLMM) to examine differences 

in live shrub cover and pre treatment tree density between treatments (SAS 

Institute v.9.1, Littell and others 1996). All mixed models used a completely 

randomized design with subsampling and the Tukey-Kramer method to detect 

treatment differences.

Results

Live Tree Density

Prior to treatment, there were no statistical differences in density of either 

juniper (F3,26 = 0.28; p = 0.84) or piñon (F3,26 = 0.65; p = 0.59) between 

treatment types. Across all treatment units, there was an average of 508 juniper trees per hectare (t.p.h.) [206 trees per acre (t.p.a.)] and an average of 

134 piñon t.p.h. (54 t.p.a.). 

The cut-leave treatment reduced post settlement juniper trees by 83 percent 

and piñon by 77 percent. Of the pre settlement trees identifi ed by our defi nition, 11 percent of the juniper and no piñon trees were cut (table 2). 

Ninety-two percent of the post settlement juniper trees were cut in the 

cut-buck-scatter treatment, with 100 percent Class 1 juniper trees cut and 99 

percent of Class 2 trees cut. Seven percent of the pre settlement juniper trees 

were also cut. Of the post settlement piñon trees identifi ed, 64 percent were 

cut. None of the pre settlement piñon trees were cut (table 2).

Of the herbicide-treated juniper trees, 50 percent of the post settlement 

trees were dead three years after application with another18 percent designated as sick. Providing these trees die as a result of the treatment, the juniper 

trees will be reduced by 68 percent. Thirty-two percent of the pre settlement 

juniper trees were killed and another 11 percent were sick. Seventy percent 

of the post settlement piñon trees were dead in 2005 and seven percent were 

sick. Combined, this will result in a 77 percent reduction in post settlement 

piñon trees. There was only one pre settlement piñon tree identifi ed and it 

was killed (table 2).

Table 2—Percent reductions of trees by treatment, species, and tree class. For the herbicide treatment, percent 

reductions based on dead as well as dead plus sick are included.

 Total Post Class 4

Treatment Species Class 1 Class 2 Class 3 Settlement (Pre Settlement)

 - - - - - - - - - - - - - - - - - - Percent reduction - - - - - - - - - - - - - - - - - -

Cut-Leave juniper 93 86 69 83 11

 piñon 82 84 25 77 0

Cut-Buck-Scatter juniper 100 99 69 92 7

 piñon 89 65 18 64 0

 dead + dead + dead + dead + dead +

 dead sick dead sick dead sick dead sick dead sick

 - - - - - - - - - - - - - - - - - - - - - Percent reduction - - - - - - - - - - - - - - - - - - - - -

Herbicide juniper 53 73 49 64 49 75 50 68 32 43

 piñon 95 95 69 77 27 45 70 77 100 na
USDA Forest Service Proceedings RMRS-P-41. 2006. 519

Response of Fuelbed Characteristics to Restoration Treatments in Piñon-Juniper- Encroached... Smith, Hood, Brooks, Matchett, and Deuser

Live Shrub Cover

There were no signifi cant differences in live shrub cover in 2004 (F3,26 = 

0.19; p > 0.9) (fi g. 1). Cover in the areas treated with herbicide was highest 

with 5.5 percent cover. The cut-leave treatment had the lowest cover with 3.8 

percent. Intermediate between the herbicide and cut-leave treatments were 

the cut-buck-scatter and control treatments, with 4.3 and 5.1 percent cover, 

respectively.

Cover increased in all treatments in 2005. Control units had 15 percent 

cover, cut-buck-scatter units had 27 percent cover, cut-leave units had 38 

percent cover, and herbicide units had 36 percent cover. There were statistical 

differences in live shrub cover between treatments (F3,26 = 12.29; p < 0.0001). 

Live shrub cover in the control units was signifi cantly lower than the thinned 

treatments (p < 0.05); however, there were no differences between the thinned 

treatments (fi g. 1).

Discussion

Live Tree Density

By only providing general growth form guidelines to the cutting crew, it 

most likely cost less per unit area to execute the treatments, but it also left more 

ambiguity and room for failure in meeting the treatment objective of 80 percent 

reduction in post settlement tree density. Depending on land management 

goals, the range of reduction in post settlement tree density that we captured 

Figure 1—Percent live shrub cover for 2004 and 2005 by treatment type as measured 

using the line-point intercept method. There were no signifi cant differences between 

types in 2004 (F3,26 = 0.19; p = 0.9033). Uppercase letters (A, B) represent signifi cant 

differences between types in 2005 (F3,26 = 12.29; p < 0.0001). C = control; CL = cutleave; CBS = cut-buck-scatter; and H = herbicide. Error bars represent one standard 

error about the mean.
520 USDA Forest Service Proceedings RMRS-P-41. 2006.

Smith, Hood, Brooks, Matchett, and Deuser Response of Fuelbed Characteristics to Restoration Treatments in Piñon-Juniper- Encroached...

(64 to 92 percent) may be acceptable. In addition, if pre settlement tree retention is only of low to moderate priority, then this method of determination 

for thinning is probably appropriate. If the goal of an 80 percent reduction in 

post settlement trees is an important target and retaining pre settlement trees 

is a high priority, it may well be worth the cost of better assessing tree ages by 

coring the largest trees and marking trees to be thinned or retained. Another 

option may be to thin trees based on a target tree density, rather than a percent 

reduction of a portion of current density based on tree diameter.

Based on our methods of assessment, it appears that the cut-leave thinning 

treatment produced results closest to the objective of 80 percent tree reduction 

(table 2). This may well be due to the relatively simple nature of this method. 

A cut tree provides an immediate measure by which to assess effi cacy and, by 

not taking time to buck and scatter the larger trees, a more consistent fl ow can 

be kept by the thinning crew. Cutting was heavier in the smaller trees (Classes 

1 and 2) in both of the cutting treatments, which may be an indication of the 

level of uncertainty in using general growth form as a cutting guideline.

The intricacies of the herbicide application, with care taken for soil drift, 

may have led to the low reductions in post settlement trees that we documented. Another consideration may be that crews were constrained by maximum 

allowable herbicide application per unit area. 

Live Shrub Cover

Live shrubs responded favorably to the thinning treatments. In 2004, live 

shrub cover was second highest in the control units at 5.1 percent. In 2005, 

following a strong monsoon season, the cover of live shrubs in the control units 

nearly tripled to 15 percent. This threefold increase, however, was the lowest in 

2005 and was dwarfed by the response seen in the treatment units. The cut-leave 

units underwent the greatest increase in live shrub cover with a tenfold increase, 

but were not distinguishable from the other thinned treatments. Live shrub 

cover in the cut-buck-scatter and herbicide units increased by roughly six times 

(fi g. 1). Observationally, most of the increase in shrub cover came from broom 

snakeweed (Gutierrezia sarothrae); a suffrutescent plant which is an increaser on 

disturbed sites (U.S. Department of Agriculture, Forest Service 1937) and can 

help minimize soil erosion (Campbell and Bomberger 1934).

In summary, regardless of the accuracy of the thinning treatments relative 

to the goal of 80 percent post settlement tree reduction, thinning is apparently 

facilitating the creation of a fuelbed which should help to carry a surface fi re 

through the area. The thinning treatments have opened the sites up, allowing 

an increase in live shrub cover as well as adding woody structure that should 

help to support a desirable surface fi re and provide nurse sites for future plant 

germination and establishment. Dependant on funding, the next phase of 

this study will be to burn half of the units to determine the impacts of the 

thinning treatments on fi re behavior and consequent fi re effects.

Acknowledgments

We would like to thank the Joint Fire Sciences for funding this study 

(#03-3-3-58) and Duncan Lutes (SEM; Missoula, MT) for helping to develop our methods. In addition, Duncan Lutes and Mick Harrington of the 

Missoula Fire Lab greatly improved this paper with their reviews. Logistics 

support from Shirley Kodele, NPS and Tim Duck, BLM in St. George, UT 

has been invaluable.
USDA Forest Service Proceedings RMRS-P-41. 2006. 521

Response of Fuelbed Characteristics to Restoration Treatments in Piñon-Juniper- Encroached... Smith, Hood, Brooks, Matchett, and Deuser

References

Bradshaw, K. E.; Reveal, J. L. 1943. Tree classifi cation of Pinus monophylla and 

Juniperus utahensis. Journal of Forestry:100-104.

Campbell, R. S.; Bomberger, E. H. 1934. The occurrence of Gutierrezia sarothrae on 

Bouteloua eriopoda ranges in southern New Mexico. Ecology. 15(1): 49-61.

Davenport, D. W.; Breshears, D. D.; Wilcox, B. P.; Allen, C. D. 1998. Viewpoint 

– sustainability of piñon-juniper ecosystems – a unifying perspective of soil erosion 

thresholds. Journal of Range Management. 51:229-238.

Koniak, S.; Everett, R. L. 1982. Seed reserves in soils of successional stages of piñon 

woodlands. The American Midland Naturalist. 108:295-303.

Littell, R. C.; Milliken, G. A.; Stroup, W. W.; Wolfi nger, R. D. 1996. SAS system 

for mixed modesl. SAS Institute, Cary, N.C.

Lutes, D. C.; Keane, R.E.; Caratti, J. F.; Key, C. H.; Benson, N. C.; Sutherland, 

S.; Gangi, L. J. 2006. FIREMON: Fire effects monitoring and inventory system. 

General Technical Report RMRS-GTR-164-CD, USDA Forest Service Rocky 

Mountain Research Station, Fort Collins, CO USA.

Miller, E. L.; Meeuwig, R. O.; Budy, J. D. 1981. Biomass of Singleleaf Pinion and 

Utah Juniper. INT-RP-273, U.S. Department of Agriculture, Forest Service, 

Intermountain Forest and Range Experiment Station. 18 p.

Miller, R. F; Rose, J. A. 1999. Fire history and western juniper encroachment in 

sagebrush steppe. Journal of Range Management. 52:550-559.

Miller, R. F.; Svejcar, T. J.; Rose, J. A. 2000. Impacts of western juniper on 

plant community composition and structure. Journal of Range Management. 

53:574-585.

Tausch, R. J.; West, N. E.; Nabi, A. A. 1981. Tree age and dominance patterns in 

Great Basin pinyon-juniper woodlands. Journal of Range Management. 34(4): 

259-264.

U.S. Department of Agriculture, Forest Service. 1937. Range Plant Handbook. 

Washington, D.C. 532 p.

West, N. E. 1999. Juniper-pinyon savannas and woodlands of western North America. 

Pp. 288-308 in Anderson, R. C.; Fralish, J. S.; Baskin, J. M. (eds.) Savannas, 

barrens, and rock outcrop plant communities of North America. Cambridge 

University Press, London UK.

Wilcox, B. P.; Breshears, D. D. 1994. Hydrology and ecology of piñon-juniper 

woodlands: conceptual framework and fi eld studies. Pp. 109-119 in Shaw, D. 

W.; Aldon, E. F.; LoSapiro, C. (eds.) Proceedings: desired future conditions for 

piñon-juniper ecosystems. GTR-RM-258, US Department of Agriculture, Rocky 

Mountain Forest and Range Experiment Station, Fort Collins, CO.

Williamson, M.; Parker, D. April 1996. Low-Impact, Selective Herbicide Application 

For Control of Pinon/Juniper: A Field Guide. USDA, Forest Service, Southwestern 

Region.
USDA Forest Service Proceedings RMRS-P-41. 2006. 523

In: Andrews, Patricia L.; Butler, Bret W., 

comps. 2006. Fuels Management—How to 

Measure Success: Conference Proceedings. 

28-30 March 2006; Portland, OR. 

Proceedings RMRS-P-41. Fort Collins, 

CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research 

Station.

1 Research Silviculturists, Natural 

Resources Canada, Canadian Forest 

Service, Victoria, British Columbia, 

Canada. rwhitehe@nrcan.gc.ca

2 Fire Research Officers, Natural 

Resources Canada, Canadian Forest 

Service, Victoria, British Columbia, 

Canada.

3 Senior Research Technician, Natural 

Resources Canada, Canadian Forest 

Service, Victoria, British Columbia, 

Canada.

4 Research Scientist – Modeling, Natural 

Resources Canada, Canadian Forest 

Service, Victoria, British Columbia, 

Canada.

5 Forest Meteorologist, Natural 

Resources Canada, Canadian Forest 

Service, Victoria, British Columbia, 

Canada.

Abstract—Thinning mature forest stands to wide spacing is prescribed to reduce crown 

bulk density and likelihood of severe crown fi re behaviour. However, it may adversely 

affect surface fuel load, moisture content and within-stand wind, which infl uence surface fi re behaviour and crowning potential. Comparison of a mature lodgepole pine 

(Pinus contorta Dougl. var. latifolia Engelm.) stand in southeastern British Columbia 

to an adjacent stand with half the basal area removed by thinning to 4 m inter-tree 

spacing found a decrease in canopy interception of rainfall and increases in solar 

radiation, windspeed, and near-surface air temperature during peak fi re danger hours 

over 13 fi re seasons. Moisture content of needle litter and fuel moisture sticks was 

measured in both stands in 2005. Between-treatment differences in moisture content 

of sticks and litter were greatest after rain, but decreased quickly as fuels dried, to 

very small at moderate fi re danger. Prediction of moisture content of lodgepole pine 

needle litter using the Canadian Fire Weather Index System also improved as fuels 

dried and worked well for both stands at moderate fi re danger. There was only one 

day at higher fi re danger during the study. Further studies should examine physical 

models of fuel moisture and microclimate under a wider range of stand densities, fuel 

types and climatic conditions.

Introduction

Thinning mature forest stands to a wide inter-tree spacing is sometimes 

prescribed to reduce crown bulk density and lower the likelihood of severe 

crown fi re behaviour (Hirsch and Pengelly 1999). However, thinning may 

also affect surface fuel loading, fi ne fuel moisture content and within-stand 

winds, which in turn affect surface fi re behaviour and crowning potential 

(Rothermel 1983; Scott 1998; Scott and Reinhardt 2001). Rates of wetting or 

drying, and consequently moisture content, of fi ne surface fuels are infl uenced 

by microclimatic factors that are expected to change when a stand is thinned. 

These factors include canopy interception of rainfall and solar radiation, and 

near surface air temperature, relative humidity and within-stand windspeed 

(Rothermel 1983; Forestry Canada 1992).

The purpose of this paper is to compare and contrast a natural mature 

lodgepole pine (Pinus contorta Dougl. var. latifolia Engelm.) stand to an 

adjacent stand which was thinned to uniform 4 m spacing in February 1993, 

with respect to:

 • within-stand microclimate parameters that are likely to affect moisture 

content of fi ne surface fuels;

Effect of a Spaced Thinning in Mature 

Lodgepole Pine on Within-Stand Microclimate 

and Fine Fuel Moisture Content

R. J. Whitehead1, G. L. Russo1, B. C. Hawkes2, S. W. Taylor2, 

B. N. Brown3, H. J. Barclay4, and R. A. Benton5
524 USDA Forest Service Proceedings RMRS-P-41. 2006.

Whitehead, Russo, Hawkes, Taylor, Brown, Barclay, and Benton Effect of a Spaced Thinning in Mature Lodgepole Pine on Within-Stand Microclimate...

 • measured moisture content of fi ne surface fuels; and,

 • difference between actual moisture content of lodgepole pine needle litter 

and values predicted by the Canadian Forest Fire Weather Index (FWI) 

System (Van Wagner 1987).

Study Area

This study was conducted at one of three sites where, since 1992, researchers 

from the Canadian Forest Service and Forest Engineering Research Institute 

of Canada and operations staff from the British Columbia Ministry of Forests 

and Range have studied the effi cacy of commercial thinning to uniform wide 

spacing for reaching several stand-level management objectives in natural 70 

to 100 year old lodgepole pine.

The site is located south of Cranbrook in south-eastern British Columbia 

at 49° 25 ́ N, 115° 36 ́ W, on level terrain in a broad valley at 1350 m elevation. The overstorey consists of a single cohort lodgepole pine stand that 

originated after wildfi re in 1912, with a few scattered western larch (Larix 

occidentalis Nutt.) trees of about the same age.

In 1992, one of three adjacent 15 to 20 ha treatment units (fi g. 1) was 

commercially thinned by Galloway Lumber Co. Ltd. to a uniform inter-tree 

spacing of approximately 4 m, a second was clearcut and the third was left 

untreated (Mitchell 1994). Stand characteristics are shown in table 1 and 

the fuel complexes are described in table 2. Sparse understorey vegetation is 

typical of the lodgepole pine/Oregon grape-pinegrass site series of the dry 

cool Montane Spruce biogeoclimatic subzone (Braumandl and Curran 1992). 

Various studies at this site have examined the harvest operations and effects 

on stand and tree growth, wildlife habitat, and forest health (for example, 

Mitchell 1994; Allen and White 1997; Safranyik and others 1999; Safranyik 

and others 2004; Whitehead and others 2004; Whitehead and Russo 2005). 

This paper examines and discusses selected microclimatic parameters that may 

affect fuel moisture from the project’s 13-year database at the Cranbrook site 

(1993-2005) and fi ne fuel moisture content measured in the thinned and 

unthinned stands during the 2005 fi re season.

Figure 1—Aerial overview of site with 

weather station locations represented by 

letters A (unthinned control), B (thinned 

to 4 m spacing), and C (clearcut).
USDA Forest Service Proceedings RMRS-P-41. 2006. 525

Effect of a Spaced Thinning in Mature Lodgepole Pine on Within-Stand Microclimate... Whitehead, Russo, Hawkes, Taylor, Brown, Barclay, and Benton

Table 1—Distribution of all trees >7.5 cm d.b.h., by 5 cm diameter classes in the 

unthinned control and thinned stands at the Cranbrook study site.

 Midpoint of diameter class (cm)

Stand Species 10 15 20 25 30 35 40

 - - - - - - - - - - - - - - - - trees/ha - - - - - - - - - - - - - - - -

Control W. Larch 8 0 0 0 0 0 0

 L. Pine 208 700 633 125 17 0 0

Thinned W. Larch 11 5 5 5 5 5 11

 L. Pine 0 11 155 192 53 5 0

Source: Whitehead, R.J.; Brown, B.N.; Nemec, A.F.L.; Stearns-Smith, S.C. (Submitted 

2006). Stand and tree-level growth response to spaced commercial thinning and fertilization 

treatments in mature lodgepole pine stands in southeastern British Columbia. Natural 

Resources Canada, Canadian Forest Service, Information Report BC-X---.

Table 2—Description of the fuel complexes (July 2005)a, in the unthinned control 

and thinned stands at the Cranbrook study site.

 Fuel Complex Control Thinned

- - - - - - - - - - - - - - - -Overstory Conifers (7.5+ cm d.b.h.)- - - - - - - - - - - - - - - - -

Basal area (m2/ha) 40.0 21.7

Density (live trees/ha) 1692 464

Mean height (m) 20.6 21.8

Mean diameter at breast height (cm) 19.4 23.9

Mean live crown base height (m) 14.0 12.8

Mean dead crown base height (m) 5.0 2.4

Maximum live crown width (m) 2.6 3.6

Canopy bulk densityb - foliage (kg/m3) 0.204 0.053

- - - - - - - - - - - - - - - - - - - - - - - Understory Fuels- - - - - - - - - - - - - - - - - - - - - -

Understory conifer biomass (kg/m2) 0.00 0.03

Shrub biomass (kg/m2) 0.02 0.33

Herbaceous biomass (kg/m2) 0.03 0.06

Litter biomass (kg/m2) 0.18 0.18

Litter bulk density (kg/m3) 19.0 17.1

Litter depth (cm) 0.94 1.06

Duff bulk density (kg/m3) 113.5 104.9

Duff depth (cm) 2.71 3.22

Dead woody fuels 0.1 to 3 cm in diameter (kg/m2) 0.15 0.15

Dead woody fuels 3.1 to 7 cm in diameter (kg/m2) 0.26 0.15

Dead woody fuels 7.1+ cm in diameter (kg/m2) 0.81 1.95

aSource: Russo, G.L.; Whitehead, R.J. Natural Resources Canada, Canadian Forest Service, 

unpublished data.

bBased on foliar weight calculated using equations from Standish and others (1985).
526 USDA Forest Service Proceedings RMRS-P-41. 2006.

Whitehead, Russo, Hawkes, Taylor, Brown, Barclay, and Benton Effect of a Spaced Thinning in Mature Lodgepole Pine on Within-Stand Microclimate...

Methods

Microclimate

Weather stations with dataloggers (Campbell Scientifi c CR-10) were installed in the centre of each treatment unit (at least 125 m from the outside 

edge) in 1992 (fi g. 1). Air temperature and relative humidity (Campbell 

Scientifi c HMP 45C) and full spectrum solar radiation (LiCor LI200SZ 

pyranometer) sensors were mounted on a tower at 1.3 m height and windspeed 

monitors (RM Young Wind Monitors) at 3 m height. Three air temperature 

sensors (Campbell Scientifi c 107B) were also located nearby at 5 cm above the 

forest fl oor. Solar radiation and air temperature sensors at 5 cm height were 

sampled every fi ve minutes, while air temperature at 1.3 m height, relative 

humidity and windspeed were sampled every minute. Hourly data summaries 

and statistics were recorded from May 1 through September 15 from 1993 to 

2005. Daily precipitation was measured by a Sierra Misco tipping bucket rain 

gauge in the clearcut treatment unit only until 2003, when gauges were also 

added in the thinned and unthinned stands. Each rain gauge was mounted 

at 1.3 m above ground-level and 3 m to 5 m from the sensor tower.

Weather station and sensor maintenance was carried out as per Spittlehouse 

(1989). Initial screening of raw data followed procedures described by Meek 

and Hatfi eld (1994) which included between-sensor comparisons where 

sensors were replicated on site (e.g. temperature) and nearby Environment 

Canada station normals, where sensors were not replicated (relative humidity, 

windspeed, precipitation and solar radiation). Manual fi ltering and graphical 

screening were used to account for sensor drift, and records with missing data 

for any treatment were deleted from the database before analysis.

Moisture Content of Fine Surface Fuels

Fuel Moisture Sticks—Five sets of 10-hour fuel moisture sticks (4-stick 

arrays of ponderosa pine dowels weighing 100 g), mounted on wire brackets 

at 20 cm above the forest fl oor were positioned 2-m apart on an east-west 

transect (fi g. 2) near the weather station in the thinned and control stands. 

Each array was weighed on site at 16:00 Mountain Standard Time (MST) 

on 70 days between June 21 and September 25, 2005, and oven dried at the 

end of the season to determine how much weight was lost due to weathering 

effects over the season. Moisture content was calculated on each sampling 

day using equation 1:

 Moisture Content (%) = 100 × ⎛

⎝

⎜

⎞

⎠

⎟

wet weight - dry weight

dry weight (1)

with dry weights adjusted for weathering, using equation 2:

 Adjusted Dry Weight (g) = 100 100 − ⎛ −

⎝

⎜ ⎞

⎠

⎟ ×

a

b

c (2)

where a is dry weight at end of season (grams), b is total number of days 

exposed, and c is number of days exposed before wet weight was measured.

Needle Litter—Ten 1-m2 quadrats, spaced 1 m apart on a transect perpendicular to the moisture stick transect, were established in each stand 

for collection of lodgepole pine needle litter (fi g. 3). Cured lodgepole pine 

needles were collected from the forest fl oor nearby and distributed in a thin 
USDA Forest Service Proceedings RMRS-P-41. 2006. 527

Effect of a Spaced Thinning in Mature Lodgepole Pine on Within-Stand Microclimate... Whitehead, Russo, Hawkes, Taylor, Brown, Barclay, and Benton

Figure 2—Five sets of fuel moisture sticks set up in the thinned stand 

(left) and in the control stand (right).

Figure 3—Plots for sampling lodgepole pine needle litter in the thinned 

stand before adding needles (left) and in the control stand after adding 

needles (right).

even layer (<1.5 cm thick) amongst the quadrats on May 18, 2005 to ensure 

ample litter for sampling throughout the season. Five samples (approximately 

50 g each) of needle litter were collected at 16:00 MST, from alternating 

odd and even quadrats each sampling day, at 16:00 MST on 70 days between 

June 21 and September 25, 2005. Each sample was placed in a numbered tin 

and weighed on site to the nearest 0.1g, then oven dried for at least 24 hours 

at 100°C and re-weighed. Moisture content of needle litter was calculated 

using equation 1.
528 USDA Forest Service Proceedings RMRS-P-41. 2006.

Whitehead, Russo, Hawkes, Taylor, Brown, Barclay, and Benton Effect of a Spaced Thinning in Mature Lodgepole Pine on Within-Stand Microclimate...

Predicted Moisture Content of Needle Litter—In Canada, the codes 

and indices that make up the Fire Weather Index System are calculated from 

weather station outputs. On June 24, 2005, the B.C. Ministry of Forests 

and Range Southeast Fire Centre installed a standard Fire Weather Station 

(Forest Technology Systems) in the clearcut opening to determine daily fi re 

weather indices, including the Fine Fuel Moisture Code (FFMC) and Fire 

Danger Class (B.C. Ministry of Forests 1983). FFMC is a numerical index of 

the moisture content of litter and other cured fi ne fuels (Van Wagner 1987). 

We used FFMC to predict moisture content of lodgepole pine needle litter 

using equation 3 (Van Wagner 1987):

 Predicted Moisture Content (%) = 147 2 101

59 5

.

.

× − ( )

+

FFMC

FFMC (3)

Data Analyses

Microclimate—Mean hourly within-stand windspeed, air temperature, 

total solar radiation, and relative humidity (RH) during peak fi re danger 

hours (12:00 to 16:00 MST) in the control and thinned stand over thirteen 

fi re seasons (1993 to 2005) were compared graphically. Precipitation data over 

three fi re seasons (2003 to 2005) were consolidated into 54 “rain events” 

(periods of 1 or more days when precipitation was recorded at one or more 

stations and separated from other events by at least one day without rain), For 

each rain event, canopy interception of rainfall in the thinned and unthinned 

stands was calculated using equation 4 and between-treatment differences 

were tested with a Wilcoxon signed-ranks test (α = 0.05).

Interception (%) = 100 × Rainfall in clearcut (mm Rainfall in ) − stand (mm)

Rainfall in Clearcut (mm)

⎛

⎝

⎜

⎞

⎠

⎟ (4)

Fine Fuel Moisture Content—The variance was not constant across the 

range of moisture content data for fuel moisture sticks and lodgepole pine leaf 

litter. We grouped the data by Fire Danger Class for graphical comparisons 

and it was clear that this problem was associated with rainfall, which occurred 

primarily when the Fire Danger Class was Very Low (26 days). During periods 

when Fire Danger Class was Low (31 days) or Moderate (12 days) variance 

in the data was small and consistent. We therefore restricted statistical analyses of between-treatment differences to days with Low or Moderate ratings 

and used two-tailed paired sample t-tests to investigate between treatment 

differences in moisture content of fuel moisture sticks (α =0.05) and differences in moisture content of lodgepole pine leaf litter between treatments, 

and between each treatment and values predicted from FFMC with α =0.02 

to approximate an experiment-wise error of 0.05 (Kirk 1968). All statistical 

analyses were conducted using Analyse-it® for Microsoft Excel.

Results and Discussion

During 54 rain events over three fi re seasons, rainfall in the clearcut opening ranged from 0.1 mm in a single day to 99 mm over an 11 day period, and 

mean canopy interception was signifi cantly lower (p<0.0001) in the thinned 

stand (51.1 percent; SE=±2.9254) than in the unthinned stand (65.3 percent; 

SE=±2.9596 percent).
USDA Forest Service Proceedings RMRS-P-41. 2006. 529

Effect of a Spaced Thinning in Mature Lodgepole Pine on Within-Stand Microclimate... Whitehead, Russo, Hawkes, Taylor, Brown, Barclay, and Benton

Mean hourly within-stand windspeed, air temperature, relative humidity and solar radiation in the thinned stand during peak fi re hours (12:00 

to 16:00 MST) are plotted against the corresponding hourly means in the 

untreated control stand in fi gures 4 to 7, respectively. Windspeed and air 

temperature were consistently higher in the thinned stand. Although total 

solar radiation was most often higher in the thinned stand, between-treatment 

differences were not as consistent as for windspeed and air temperature. This 

may have been due to effects of shading from one or more trees at a particular 

sun angle and location relative to the sensors in different treatments. Most 

between-treatment differences were intuitive, with the exception of relative 

humidity, where no difference was detectable within sensor error (±3 percent) 

when measured at 1.3 m above the forest fl oor. However, there was also no 

between-treatment difference in mean air temperatures measured at 1.3 m 

height, although they were consistently higher in the thinned stand when 

measured much closer to the surface fuels of interest (at 5 cm height). We 

did not measure RH at 5 cm and cannot discount the possibility that it may 

also differ nearer the forest fl oor.

Daily mean moisture contents of fuel moisture sticks in the thinned stand 

are plotted against corresponding daily means in the untreated control stand 

in fi gure 8, and within Fire Danger Classes in fi gure 9. Moisture content 

was generally lower in the thinned stand than in the unthinned stand, but 

the magnitude of that difference decreases when moisture content is below 

Figure 4—Hourly means of windspeed (km/h) at 3 m height in the thinned 

stand, measured between 12:00 MST and16:00 MST, vs. corresponding 

means in the control stand (n=7380 hours).
530 USDA Forest Service Proceedings RMRS-P-41. 2006.

Whitehead, Russo, Hawkes, Taylor, Brown, Barclay, and Benton Effect of a Spaced Thinning in Mature Lodgepole Pine on Within-Stand Microclimate...

Figure 5—Hourly means of air temperature (oC) at 5 cm height above ground in the 

thinned stand, measured between 12:00 MST and16:00 MST, vs. corresponding means 

in the control stand (n=7760 hours).

Figure 6—Hourly means of relative humidity (percent) at 1.3 m height in the thinned 

stand, measured between 12:00 MST and 16:00 MST, vs. corresponding means in the 

control stand (n=5809 hours).
USDA Forest Service Proceedings RMRS-P-41. 2006. 531

Effect of a Spaced Thinning in Mature Lodgepole Pine on Within-Stand Microclimate... Whitehead, Russo, Hawkes, Taylor, Brown, Barclay, and Benton

Figure 8—Daily mean moisture content (percent) of fuel moisture sticks at 16:00 MST in 

thinned and unthinned stands on 70 days between June 21 and September 25, 2005.

Figure 7—Total hourly solar radiation (mJ/m2) at 1.3 m height in the thinned stand, 

measured between 12:00 MST and 16:00 MST, vs. corresponding total hourly solar 

radiation in the control stand (n=8503 hours).
532 USDA Forest Service Proceedings RMRS-P-41. 2006.

Whitehead, Russo, Hawkes, Taylor, Brown, Barclay, and Benton Effect of a Spaced Thinning in Mature Lodgepole Pine on Within-Stand Microclimate...

about 20 percent and as Fire Danger Class increases (fi g. 9; table 3). Variance and between-treatment differences in daily mean moisture contents of 

needle litter samples were larger than for fuel moisture sticks, but tended to 

follow the same general trends (fi g. 10 and fi g. 11; table 3). Pook and Gill 

(1993) compared an untreated radiata pine (Pinus radiata D. Don) stand 

with one that had been thinned and pruned. They also found that, although litter 

moisture content was generally higher in the unthinned stand, between-treatment 

differences decreased with declining moisture content and increasing concern 

for fi re danger.

Figure 9—Mean moisture content (percent) of fuel moisture sticks in the thinned and unthinned 

stands, plotted from wettest to driest control value within different Fire Danger Classes, with 

corresponding 24-hour rainfall (mm) shown as bars.

Table 3—Descriptive statistics (mean, standard deviation and standard error 

of the mean) for needle litter and fuel moisture stick moisture content at 

different fi re danger classes.

Danger Class X SD SE X SD SE

 - - - - - litter- - - - - - - - - - - sticks- - - - -

Low (n=31) Control 17.2 7.23 1.30 11.4 1.99 0.36

 Thinned 13.2 3.19 0.57 10.5 1.61 0.29

 Predicted 15.7 6.91 1.24 — — —

 

Moderate (n=12) Control 10.2 2.91 0.84 8.6 1.40 0.40

 Thinned 8.9 2.19 0.63 8.3 1.18 0.34

 Predicted 9.3 1.96 0.57 — — —
USDA Forest Service Proceedings RMRS-P-41. 2006. 533

Effect of a Spaced Thinning in Mature Lodgepole Pine on Within-Stand Microclimate... Whitehead, Russo, Hawkes, Taylor, Brown, Barclay, and Benton

Figure 10—Daily mean moisture content (percent) of lodgepole pine needle litter at 

16:00 MST in thinned and unthinned stands on 70 days between June 21 and September 

25, 2005.

Figure 11—Predicted and actual mean moisture content (percent) of lodgepole pine needle litter in 

the thinned and unthinned stands, plotted from wettest to driest predicted value within different 

Fire Danger Classes, with corresponding 24-hour rainfall (mm) shown as bars.
534 USDA Forest Service Proceedings RMRS-P-41. 2006.

Whitehead, Russo, Hawkes, Taylor, Brown, Barclay, and Benton Effect of a Spaced Thinning in Mature Lodgepole Pine on Within-Stand Microclimate...

Tanskanen and others (2005) found that canopy characteristics of a mixed 

Norway spruce (Picea abies (L.) Karst) and Scotch pine (Pinus sylvestris L.) 

stand in southern Finland, including canopy depth and leaf area index, correlated strongly with ignition success in surface needle litter. They suggested 

that differences in surface fuel wetting and drying due to canopy infl uence 

on precipitation and wind conditions near the forest fl oor might have been 

responsible. Canopy characteristics were quite different in the two lodgepole 

pine stands we studied (tables 1 and 2), and although we observed a consistent 

difference in canopy interception of precipitation, windspeed and temperature, 

the between-treatment differences in moisture content we observed were very 

small except when fuels were too wet to ignite easily. We found statistically 

signifi cant between-treatment differences in mean moisture content of both 

needle litter and fuel moisture sticks when Fire Danger Class was Low, and 

also for sticks when Danger Class was Moderate (table 4). Although statistically signifi cant, it is unlikely that such small differences in fi ne surface 

fuel moisture (for example, 0.3 percent difference in stick moisture content 

at Moderate fi re Danger Class) would have any practical effect on ignition 

probability or crowning potential. However, there was only one sampling 

day with a higher fi re danger during this study and similar measurements at 

High and Extreme Fire Danger Classes are recommended to confi rm these 

fi ndings during periods of most concern to fi re managers.

Moisture content of fi ne surface fuels is one important factor used in the 

Canadian Forest Fire Behaviour Prediction System to model ignition potential 

(Lawson and others 1994), surface fi re intensity and rate of spread (Taylor 

and others 1997). When combined with stand characteristics, surface fi re 

intensity is used to predict potential for crown fi re. The Fine Fuel Moisture 

Code generated by the Canadian Fire Weather Index System is an index of 

moisture content of litter and other cured fi ne fuels, and is used as an indicator of ignition potential or the potential for fi res to start and spread (B.C. 

Ministry of Forests 1983). Our measurements of moisture content of needle 

litter were not signifi cantly different in either treatment from the values predicted from FFMC using Van Wagner’s equation, when Fire Danger Class 

was Low or Moderate, although the values were consistently slightly higher 

than predicted in the control stand and slightly lower than predicted in the 

thinned stand (fi g. 11; table 3). Predictions of moisture content of needle 

litter from daily FFMC values improved as fi re danger increased (fi g. 11) 

and moisture content was predicted well in both stands when Fire Danger 

Class was Moderate. FFMC refl ects fi ne fuel moisture content across a fairly 

wide range of stand conditions within a given fuel type and it appears to be 

robust enough to predict fi ne fuel moisture content in both stand conditions 

we studied.

Table 4—Two-tailed p values from paired sample t-tests comparing needle litter 

moisture content (α = 0.02) and fuel moisture stick moisture content (α = 0.05).

Danger Control vs. Control vs. Thinned vs. Control vs.

 Class Thinned Predicted Predicted Thinned

 - - - - - - - - - - - - - - - litter - - - - - - - - - - - - - - - - - sticks- -

Low 0.0003 0.0278 0.0224 <0.0001

Moderate 0.0281 0.0391 0.3021 0.0112
USDA Forest Service Proceedings RMRS-P-41. 2006. 535

Effect of a Spaced Thinning in Mature Lodgepole Pine on Within-Stand Microclimate... Whitehead, Russo, Hawkes, Taylor, Brown, Barclay, and Benton

Conclusions

Removing approximately half of the basal area of a mature stand of 

lodgepole pine in southeastern British Columbia, by thinning from below 

to uniform 4 m inter-tree spacing, resulted in decreased canopy interception 

of rainfall and increased within-stand solar radiation, windspeed, and nearsurface air temperature. Moisture content of both needle litter and of fuel 

moisture sticks were most different in thinned and unthinned stands following rainfall, but these differences decreased rapidly as fuels dried. Under 

moderate fi re danger conditions, between-treatment differences were very 

small and not practically signifi cant. Values for moisture content of lodgepole 

pine needle litter in both stands were predicted well by the Canadian Fire 

Weather Index System. Further work is needed to examine physical models 

of fuel moisture and microclimate under a wider range of stand densities, 

fuel types and climatic conditions.

Acknowledgments

We would like to thank the B.C. Ministry of Forests and Range (BCMOFR), Protection Branch, Fuel Management Section and the Government 

of Canada Mountain Pine Beetle Initiative for funding this work; BCMOFR 

Southeast Fire Centre for assistance with measuring Fire Weather in 2005; 

John Vallentgoed of the Canadian Forest Service and Jack Selman and Assoc. 

of Cranbrook B.C. for fi eldwork; and, Dr. Nathalie Lavoie (BCMOFR) and 

Dr. Michael Feller (University of British Columbia) for prompt and helpful 

reviews of this manuscript. We also wish to acknowledge our former colleague, 

Bruce D. Lawson for fi rst recognizing the opportunity to do this study.

References

Allen, E.; White, T. 1997. Decay associated with logging injuries in western larch, 

Larix occidentalis, and in lodgepole pine, Pinus contorta. Natural Resources 

Canada, Canadian Forest Service, Pacific Forestry Centre, Victoria, BC. 

Technology Transfer Note 07.

Braumandl, T. F.; Curran, M. P. 1992. A fi eld guide for site identifi cation and 

interpretation for the Nelson Forest Region. B.C. Ministry of Forests, Land 

Management Handbook No. 20. 311 p.

B.C. Ministry of Forests. 1983. Fire weather indices – decision aids for forest 

operations in British Columbia. Forest Protection Handbook 12. British Columbia 

Ministry of Forests, Protection Branch. Victoria, BC.

Forestry Canada. 1992. Development and structure of the Canadian Forest Fire 

Behavior Prediction System. Canadian Forest Service Information Report ST-X-3. 

63 p.

Hirsch, K. G.; Pengelly, I. 1999. Fuel reduction in lodgepole pine stands in Banff 

National Park. In Neuenschwander, L. F.; Ryan, K. C., editors. Proceedings of 

joint fi re science conference and workshop. June 15-17, 1999; Boise, ID. Boise: 

University of Idaho and International Association of Wildland Fire: 251-256.

Kirk, R. E. 1968. Experimental Design: proceedures for the behavioural sciences. 

Brook/Cole Publishing Company, Belmont, California. 577 p.
536 USDA Forest Service Proceedings RMRS-P-41. 2006.

Whitehead, Russo, Hawkes, Taylor, Brown, Barclay, and Benton Effect of a Spaced Thinning in Mature Lodgepole Pine on Within-Stand Microclimate...

Lawson, B. D.; Armitage, O. B.; Dalrymple, G. N. 1994. Ignition probabilities for 

simulated people-caused fi res in British Columbia’s lodgepole pine and white 

spruce-subalpine fi r forests. Pages 493—505 in Proceedings of the 12th conference 

on fi re and forest meteorology, October 26-28, 1993, Jekyll Island, GA, USA. 

Society of American Foresters, Bethesda, MD.

Meek, D. W.; Hatfield, J. L. 1994. Data quality checking for single station 

meteorological databases. Agricultural and Forest Meteorology 69:85–109.

Mitchell, J. L. 1994. Commercial thinning of mature lodgepole pine to reduce 

susceptibility to mountain pine beetle. Forest Engineering Research Institute of 

Canada Special Report SR-94. 19 p.

Pook, E. W.; Gill, A. M. 1993. Variation in live and dead fi ne fuel moisture in Pinus 

radiata plantations of the Australian Capital Territory. International Journal of 

Wildland fi re 3(3): 155-168.

Rothermel, R. C. 1983. How to predict the spread and intensity of forest and 

range fi res. General Technical Report INT-143. Ogden, UT: U.S. Department 

of Agriculture, Forest Service, Intermountain Forest and Range Experiment 

Station. 161 p.

Safranyik, L.; Shore, T. L.; Linton, D. A. 1999. Attack by bark beetles (Coleoptera: 

Scolytidae) following spacing of mature lodgepole pine (Pinaceae) stands. Canadian 

Entomologist 131: 671-685.

Safranyik, L.; Shore, T. L.; Carroll, A. L.; Linton, D. A. 2004. Bark beetle 

(Coleoptera: scolytidae) diversity in spaced and unmanaged mature lodgepole pine 

(Pinaceae) in southeastern British Columbia. Forest Ecology and Management 

200: 23-38.

Scott, J. H. 1998. Sensitivity analysis of a method for assessing crown fi re hazard in 

the Northern Rocky Mountains, USA. In: Viegas, D. X., ed.; III International 

conference on forest fi re research; 14th conference on fire and forest meteorology; 

1998 November 16–20; Luso, Portugal. Proc. Coimbra, Portugal: ADAI. 

Volume II: 2517–2532.

Scott, J. H.; Reinhardt, E. D. 2001. Assessing crown fi re potential by linking models 

of surface and crown fi re behaviour. USDA Forest Service Rocky Mountain 

Research Station Research Paper RMRS-RP-29. 59 p.

Spittlehouse D.L. 1989. Using dataloggers in the fi eld. Forestry Canada - B.C. 

Ministry of Forests, FRDA Report No. 086. 49 pp.

Tanskanen, H.; Venäläinen, A.; Puttonen, P.; Granstöm. 2005. Impact of stand 

structure on surface fi re ignition potential in Picea abies and Pinus sylvestris forests 

in southern Finland. Canadian Journal of Forest Research 35(2): 410-420.

Taylor, S. W.; Pike, R. G.; Alexander, M. E. 1997. Field guide to the Canadian Forest 

Fire Behavior Prediction (FBP) System. Natural Resources Canada, Canadian 

Forest Service, Northern Forestry Centre, Edmonton, Alberta. Special Report 11. 

60 p.

Van Wagner, C. E. 1987. Development and structure of the Canadian Forest Fire 

Weather Index System. Canadian Forest Service, Ottawa, Ontario. Forestry 

Technical Report 35. 37 p.

Whitehead, R. J.; Russo, G. L. 2005. “Beetle-proofed” lodgepole pine stands in 

interior British Columbia have less damage from mountain pine beetle. Natural 

Resources Canada, Canadian Forest Service, Victoria, B.C. Information Report 

BC-X-402. 17 p.

Whitehead, R. J.; Safranyik, L.; Russo, G. L.; Shore, T. L.; Carroll, A. L. 2004. 

Silviculture to reduce landscape and stand susceptibility to the mountain pine 

beetle. Pages 233-244. In T. L. Shore, J. E. Brooks and J.E. Stone, editors. 

Mountain pine beetle symposium: challenges and solutions. October 30-31, 

2003. Kelowna, British Columbia. Natural Resources Canada, Canadian Forest 

Service, Victoria, B.C. Information Report BC-X-399.
USDA Forest Service Proceedings RMRS-P-41. 2006. 537

In: Andrews, Patricia L.; Butler, Bret W., 

comps. 2006. Fuels Management—How to 

Measure Success: Conference Proceedings. 

28-30 March 2006; Portland, OR. 

Proceedings RMRS-P-41. Fort Collins, 

CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research 

Station.

1 Manager Resource Conservation, Parks 

Canada, Elk Island National Park, Fort 

Saskatchewan, AB, Canada.

steve.otway@pc.gc.ca.

2 Associate Professor, Range Management, 

University of Alberta, Department of 

Agricultural, Food, and Nutritional 

Science, 410E AgFor Centre, Canada.

3 Fire Behaviour Researcher, Canadian 

Forest Service, Northern Forestry Centre, 

Edmonton, AB, Canada. 

4 Senior Fire Behaviour Research Offi cer, 

Forest Engineering Research Institute 

of Canada, Wildland Fire Operations 

Research Group, Hinton, AB, Canada. 

Present address: Canadian Forest Service, 

Northern Forestry Centre, Edmonton, 

AB.

Abstract—Fire is one of the key disturbances affecting aspen (Populus tremuloides 

Michx.) forest ecosystems within western Canadian wildlands, including Elk Island 

National Park. Prescribed fi re use is a tool available to modify aspen forests, yet 

clearly understanding its potential impact is necessary to successfully manage this 

disturbance.

Undesirable social consequences of severe, deep burning ground fi res include smoke 

generation and impaired vegetation re-growth. Data on the soil and duff moisture 

conditions under which ground or subsurface fi res may start in aspen are presented, as 

well as experimental test fi re results. Different topographic positions, plant communities and seasons were factored into the research design. The Duff Moisture Code and 

Drought Code components of the Canadian Forest Fire Weather Index System were 

calculated and factors including duff moisture content, bulk density and inorganic 

content measured at the time of ignition. Probability of sustained smouldering ignition 

models were developed for the aspen forest fuel type, with values of 27 for DMC and 

300 for DC at the 50% probability of ignition level. This information will improve the 

capability to effectively manage aspen using fi re in central Alberta.

Introduction

The Duff Moisture Code (DMC) and Drought Code (DC) within the 

Canadian Forest Fire Weather Index (FWI) System (Canadian Forest Service 1984; Van Wagner 1987) are values of great assistance to fi re managers 

in assessing forest fuel dryness and associated fi re risk. Both DMC and DC 

represent soil duff (i.e. LFH) moisture dryness (Van Wagner 1987), and 

therefore, its potential to infl uence fi re behaviour. Changes in DMC track 

moisture in the shallow duff or fi bric soil horizon (F-layer), while the DC 

tracks the humus (H) or deep duff layers as well as heavy downed woody 

materials. Both indices are determined at noon (standard time) each day 

during April to October from the standardized weather readings of dry-bulb 

temperature, 10 m open wind speed, relative humidity and 24 h accumulated 

precipitation (Turner and Lawson 1978).

Currently there are empirical models correlating the probability of smouldering combustion or ignition and DMC-DC values for select boreal forest 

types using commercial peat moss as a fuel source (Frandsen 1987, 1991, 

1997; Hartford 1989; Lawson and others 1997), but none for trembling 

aspen. EINP is dominated by trembling aspen (Populus tremuloides Michx.) 

forest. Although these communities may not burn as readily as other boreal 

forests in the Boreal region (Peterson and Peterson 1992), ground fi re may 

persist in this vegetation under dry conditions for extended periods (Lawson 

and Dalrymple 1996).

Predicting Ground Fire Ignition Potential in 

Aspen Communities

S. G. Otway1, E. W. Bork2, K. R. Anderson3, and M. E. Alexander4
538 USDA Forest Service Proceedings RMRS-P-41. 2006.

Otway, Bork, Anderson, and Alexander Predicting Ground Fire Ignition Potential in Aspen Communities

In this study, the probability of sustained combustion or ignition was examined for soil duff layers in aspen forests of Elk Island National Park, with 

ignition tests conducted in-situ, as per the Lawson and others (1997) fi eld 

trials. We also determined whether the indices of modeled DMC-DC predict 

ignition in aspen forest equal to that of duff moisture, with or without soil 

bulk density and inorganic content considerations.

Materials and Methods

Study Area

EINP is situated 35 km east of Edmonton in central Alberta (approximate 

Lat. 53° N; Long. 112° E), at the north end of the Beaver Hills,a post-glacial dead-ice moraine elevated 10 to 30 m above the surrounding plains, 

suffi cient to place the area within the Lower Boreal Mixedwood ecoregion 

(Strong and Leggat 1991). The dominant vegetation of uplands in the Park 

is trembling aspen, although open grasslands, shrublands, and white spruce 

[Picea glauca (Moench) Voss] forests are interspersed throughout the area 

(Polster and Watson 1979). Six different aspen plant community types have 

been identifi ed within the Park (Best and Bork 2004).

The climate of the area is cool-continental, with long, cold winters and 

short, warm summers (Bowser and others 1962). Annual precipitation over 

the last 44 yrs at the Edmonton International Airport indicates an average 

yearly rainfall of 460 mm (Parks Canada 2004). Precipitation in the Park 

from April to October, inclusive, accounts for 81% of yearly totals (Parks 

Canada 2004), and has ranged from 220 to 470 mm over the last 10 yrs 

(Parks Canada 2004). Mean growing season temperatures vary between 5°C 

in April to 17°C in summer (Rogeau 2004), while the frost-free period is 

about 100 days (Crown 1977).

Both DMC and DC are re-calibrated annually beginning at ‘start-up’, 

either 3 days after snow loss in spring or 3 days after a recorded noon temperature of 12oC (Alexander 1983; Canadian Forestry Service 1984), and are 

continually updated throughout the fi re season until October 31st (Turner 

and Lawson 1978).

Experimental Approach

The approach used in this study was to develop and test empirical relationships between DMC-DC and ignition trials from various sites throughout 

the Park. A main calibration site was utilized, involving intensive, repeated 

sampling and testing to establish a detailed profi le of burning success under 

various DMC-DC levels. Sampling was performed both within in-situ soils as 

found within each plot, as well as within ‘rainfall exclusion’ treatment areas, 

designed to exclude precipitation and simulate drought (Van Wagner 1970). 

Exclusion areas were 3 x 3 m, and tarped 1 m above ground to eliminate soil 

moisture recharge and to ensure low moisture levels (and high FWI values) 

were represented in at least a portion of the plots where test fi res were conducted. Following initial calibration of codes to the primary ignition plots, 

relationships between ignition and DMC-DC were subsequently tested on 

independent replicated plots within each of three main aspen plant community 

types found throughout EINP (Best and Bork 2004).
USDA Forest Service Proceedings RMRS-P-41. 2006. 539

Predicting Ground Fire Ignition Potential in Aspen Communities Otway, Bork, Anderson, and Alexander

Field Sampling

All plots were 20 x 20 m in size and permanently marked. The calibration 

area was situated within a plant community type encompassing traits similar to 

the two most prevalent types previously documented within EINP, accounting for approximately 70% of all aspen communities previously investigated 

within the Park. On average, there were two ignition tests within each plot 

on each day of sampling. Twelve validation plots were randomly selected from 

a series of 96 vegetation permanent sample plots (PSP) situated on forested 

uplands throughout EINP.

Daily fi re weather observations were obtained from the Environment 

Canada (Campbell Scientifi c) automated weather station, 800 m from the 

calibration site. Precipitation was also measured locally within and adjacent 

to the calibration site and at each validation site using a manual rain gauge. 

Unique fi re weather indices (DMC-DC) were calculated for each site using 

localized precipitation and all other observations were from the weather 

station.

Ignition Testing and Analysis

Most tests took place during the months of May to August 2004, on a 

schedule frequent enough to coincide with small increases in DMC-DC and 

to ensure a series of ignitions ranging from 0 to 100% success at each site. 

Ignition trials were conducted similar to the method used by Lawson and 

others (1997). Core samples were taken in each plot as per Nalder and Wein 

(1998), using a cordless drill and hollow, cylindrical tube auger, 5 cm in 

diameter. Extracted core samples were separated into 2-cm increments and 

later oven-dried to determine the moisture content and bulk density of each 

layer. Core holes from moisture sampling were then fi lled with smouldering 

peat moss, obtained from commercial supplies. Peat was heated until approximately 2⁄

3 black in colour and actively smouldering, producing greyish-black 

smoke. The 5-cm diameter and 12- to 15-cm deep hole generally required 

about 500 ml of peat moss. Heated moss was carefully placed into the hole, 

with slight overfi lling to compensate for the eventual collapse of peat moss 

during combustion. Test holes typically smoked for 2 to 5min until a grey 

ash cover formed.

After 2 h had passed, the peat was carefully removed, making sure not to 

scrape the sides of the drill hole at the combustion interface. Bare fi ngers 

were used to promptly test the perimeter of the hole throughout the 2- to 

4-cm and 4- to 6-cm layers for evidence of persistent ignition. The proportion of the cylindrical core found smouldering corresponded to the reported 

percentage of success or probability of ignition, to the nearest 10%.

All extracted soil core samples were measured for duff moisture and bulk 

density using the procedure of Lawson and Dalrymple (1996). A representative 

number of soil core samples were retained for inorganic content determination, 

following the methods of Kalra and Maynard (1991). A total of 117 trials 

were carried out, with 64 on the calibration site and 53 on the validation 

sites. In most areas the ‘burning window’, ranging from 0% to 100% success, 

was duplicated at least twice.

Data Analysis

The variables utilized in all analyses included DMC-DC, moisture content 

(% oven-dry weight basis), bulk density (kg m ̄3), and soil inorganic content 
540 USDA Forest Service Proceedings RMRS-P-41. 2006.

Otway, Bork, Anderson, and Alexander Predicting Ground Fire Ignition Potential in Aspen Communities

(ash, reported as %). To arrive at one model comparing the probability of 

ignition success versus the corresponding observed DMC-DC, a non- linear 

procedure, PROC NLIN (SAS 2001), was used and fi tted to a logistic 

model.

The fi rst analysis involved comparing the probability of ignition versus the 

DMC or DC only on the calibration plots. Coeffi cients derived from initialization were run on SAS to check for convergence and derive the B0 and B1

values of the estimates. The B0 and B1 parameters from SAS were then inserted 

into a simple non-linear regression model. The standard formula used was:

 P=exp(B0+ B1*Code)/(1+exp(B0+ B1*Code)), (1)

where ‘Code’ represents DMC-DC, B0 the intercept and B1 designates the 

slope of the regression coeffi cients. To confi rm the relative accuracy of the 

calibration equations generated, a linear regression analysis was used to determine the goodness of fi t (R2) and other statistical parameters of the models 

in relation to the actual probabilities observed.

The second analysis included development of a multivariate non-linear 

regression model, which included DMC-DC, bulk density (ρB) and soil inorganic content (Ash), using the following formula, after Lawson and others 

(1997):

 P=exp(B0+ B1*Code+B2*Ash+B3*ρB)/

 (1 +exp(B0+ B1*Code+B2*Ash+B3*ρB)) (2)

where ‘Code’ represents DMC-DC, B0 the intercept and B1, B2 and 

B3 designates the slopes as regression coeffi cients. For the multivariate 

non-linear regression analysis, the simple equation coeffi cients B0 and 

B1 were utilized as a starting point, and when combined with the average inorganic content and actual bulk density measurements, as per 

Lawson and others (1997), used to initialize the approximate B2 and 

B3 coeffi cients. Only the DMC or DC value was changed at any one 

time to form the new multivariate models that were checked against the 

results of the fi eld trial ignition probabilities. Next, these approximate 

coeffi cients were inserted into SAS (SAS 2001) along with the actual 

data set of varying bulk density values and different average inorganic 

values from 2003 and 2004. Finally, the derived coeffi cients were run 

once more with the average bulk density and inorganic values in the 

multivariate non-linear regression model run with SAS. Multivariate 

equations were also assessed for goodness of fi t (R2) and other statistical parameters through linear regression with the actual ignition 

probabilities measured.

The 53 validation site trials were subsequently tested against the 

calibration models by comparing actual validation ignition success rates 

(probability values) against the predicted results expected from the 

simple non-linear calibration models. Testing involved the evaluation of 

goodness-of-fi t (R2) and other statistical parameters obtained through 

the use of linear regression with PROC REG (SAS 2001).

Both the calculated moisture content and corresponding DMC-DC 

values were compared against observed ignition trial results through 

linear regression with PROC REG (SAS 2001) to determine any differences between predictive capabilities. Finally, results were compared 

to modelled ignition probabilities from Lawson and others (1997), 

utilizing the results modelled at the 50% probability level, as suggested 

by Cruz and others (2003).
USDA Forest Service Proceedings RMRS-P-41. 2006. 541

Predicting Ground Fire Ignition Potential in Aspen Communities Otway, Bork, Anderson, and Alexander

Results

Calibration Results

 Results of the ignition analysis generated from the calibration site data 

are provided in table 1, and indicate that both the simple and multivariate 

models for both the DMC and DC layers were highly signifi cant (P<0.0001). 

However, overall R2 values were greater, and root mean square error (RMSE) 

and coeffi cient of variation (CV) values less for models generated using the 

DMC layer compared to results for the DC (table 2). While the simple and 

multivariate models resulted in similar R2, RMSE and CV within the DMC 

data, the simple model resulted in a greater R2 and lower CV than the multivariate model within the DC data (table 1).

Final coeffi cients for both the simple and multivariate models in the DMC 

and DC are shown in table 2. Simple and multivariate non-linear models were 

additionally compared graphically within each of the DMC and DC (fi g. 1). 

Results indicate that the simple model predicted a slightly greater probability 

of ignition than the multivariate model at a given DMC-DC code, although 

this difference was more apparent within the DC data (fi g. 1). This fi nding indicates the addition of soil bulk density and inorganic content to the 

model tended to reduce the anticipated probability of ignition. For example, 

the simple model indicated a 50% probability of ignition at DMC and DC 

values of 27 and 300, respectively (fi g. 1). In contrast, DMC and DC codes 

resulting in the same ignition, but using the multivariate model, were 29 

and 336. Given that the results from either model were similar, and because 

Table 1—Linear analysis of calibration site DMC and DC values, and 

observed probability of ignitions using simple or multiple regression 

modelled equations, showing goodness of fi t (R2), root mean square 

error (RMSE), coeffi cient of variation (CV) and probability (Pr>F).

 Linear Analysis

Code Model Type R2 RMSE CV Pr>F

DMC Simple Equation 0.74 0.14 16.69 <.0001

 Multiple Equation 0.74 0.15 18.72 <.0001

DC Simple Equation 0.54 0.23 50.42 <.0001

 Multiple Equation 0.43 0.24 80.93 <.0001

Table 2—Coeffi cient parameters and standard errors for simple and multiple non-linear models comparing DMC 

and DC values to the probability of ignition in the aspen fuel type at EINP.

Code Model Type B0 SEa B1 SE B2 SE B3 SE F Pr>F

DMC Simpleb –3.11 0.63 0.12 0.02 – – – – 1008.31 <.0001

 Multiplec 2.92 1.38 0.12 0.02 –0.16 0.05 –0.002 0.001 485.68 <.0001

DC Simple –8.96 2.22 0.03 0.01 – – – – 147.14 <.0001

 Multiple 7.98 3.03 0.04 0.01 –0.36 0.08 0.0002 0.001 127.55 <.0001

a Standard error.

b Simple non-linear equation is P=exp(B0+ B1*Code)/(1+exp(B0+ B1*Code)).

c Multivariate equation is P=exp(B0+ B1*Code+B2*Ash+B3*ρB)/(1 +exp(B0+ B1*Code+ B2*Ash+ B3*ρB)).
542 USDA Forest Service Proceedings RMRS-P-41. 2006.

Otway, Bork, Anderson, and Alexander Predicting Ground Fire Ignition Potential in Aspen Communities

inorganic soil data were limited, the simple models were chosen for subsequent 

application to the validation data.

Validation of Ignition Prediction Models

Ignition probability values observed at the validation trials were compared 

directly to the values predicted using the simple model developed from the 

calibration site for both DMC and DC layers. For the DMC, a strong relationship (P≤0.001) was evident between observed and predicted ignition, 

but only at the Beaver and Tawayik sites (table 3), with no relationship (P = 

0.52) at the Goose site. Goodness-of-fi t comparisons for the former two were 

Figure 1—Results of the non-linear analysis fi tted to a logistic model showing 

the probability of sustained ignition against the DMC (A) and DC (B) for simple 

(smpl) and multivariate (mltp) equations.
USDA Forest Service Proceedings RMRS-P-41. 2006. 543

Predicting Ground Fire Ignition Potential in Aspen Communities Otway, Bork, Anderson, and Alexander

relatively strong (R2 = 0.46 to 0.49), with a positive relationship between 

predicted and observed ignitions (table 3). Results of the DC analysis were 

similar to DMC, except that a signifi cant relationship (P≤0.01) was evident 

between actual and observed ignition at all three validation sites (table 3). 

Goodness-of-fi t values for the three sites were similar (R2 = 0.33 to 0.54) to 

those observed previously with the DMC.

Comparison of Moisture Content and FWI System Fuel 

Moisture Codes on Ignition Success

Regressions of ignition success with either moisture content or DMC-DC 

were compared for each soil layer (table 4). Results from the calibration site 

and the total pooled data from all validation sites were analysed for both F 

and H-layers. In all comparisons except the calibration F-layer, FWI values of 

DMC-DC were superior predictors of ignition than soil duff moisture. FWI 

values had a higher goodness-of-fi t (R2 = 0.20 to 0.53) and lower RMSE (23 

to 35) and CV (27 to 89%) than moisture content comparisons. All FWI 

comparisons were signifi cant (P <0.001).

Table 3—Comparison of the validation observed fi eld burning data 

to the calibration site modelled results using simple linear 

regression, showing goodness of fi t (R2), root mean square 

error (RMSE), coeffi cient of variation (CV) and probability 

(Pr>F).

 Linear Analysis

Code Validation Site R2 RMSE CV Pr>F

DMC Beaver 0.49 0.20 31.12 0.0006

 Goose 0.04 0.26 34.67 0.5216

 Tawayik 0.46 0.23 33.85 0.0013

DC Beaver 0.50 0.11 78.05 0.0004

 Goose 0.54 0.22 49.84 0.0029

 Tawayik 0.33 0.23 80.79 0.0102

Table 4—Comparison of observed ignition success versus either moisture 

content (MC) or the FWI codes of DMC/DC, showing goodness-of-fi t 

(R2), root mean square error (RMSE), coeffi cient of variation (CV) and 

probability (Pr>F) for the calibration site (Allcal) and combined validation 

data (Allval). 

 Linear Analysis

Soil Layer Parameter R2 RMSE CV Pr>F

F-layer Allcal MC 0.62 18.94 22.42 <.0001

 Allcal DMC 0.44 23.02 27.26 <.0001

 Allval MC 0.02 33.68 40.00 0.2899

 Allval DMC 0.20 30.37 36.06 0.0007

 

H-layer Allcal MC 0.25 40.32 74.47 <.0001

 Allcal DC 0.53 31.98 59.07 <.0001

 Allval MC 0.07 44.14 110.35 0.0570

 Allval DC 0.40 35.45 88.63 <.0001
544 USDA Forest Service Proceedings RMRS-P-41. 2006.

Otway, Bork, Anderson, and Alexander Predicting Ground Fire Ignition Potential in Aspen Communities

Comparison of Results to Other Models

Comparison of the modeled values derived here to Lawson and others 

(1997) indicate the ignition results from EINP were associated with lower 

DMC-DC values relative to similar ignition probabilities in boreal forest 

duff types elsewhere. At the 50% probability of ignition, Lawson and others 

(1997) calculated DMC values between 39 and 58 in upper feather moss and 

upper sphagnum moss vegetation. Using the lower feather moss fuel type, 

the Lawson and others (1997) DC value at the 50% probability was 482. In 

Anderson (2000), the 50% probability of ignition for the DMC layer in the 

D-1 (leafl ess aspen) fuel type was calculated near 79, although the logistic 

regression model utilized in that study was from Hartford (1990).

Discussion

Using the simple ignition models developed in this study, code values of 27 

and 300 for DMC and DC, respectively, were determined to approximate the 

50% probability of ignition. Incorporating inorganic content and bulk density 

into multivariate predictive models led to minimal changes in threshold code 

values (DMC 29 and DC 336 for 50% probability). The addition of physical 

fuel properties only marginally improved the predictability of ignition models. 

Both Frandsen (1987) and Lawson and others (1997) developed multivariate 

equation models for certain duff types; however, neither defi nitively compared 

the accuracy of simple and multivariate models. Ignition tests in these studies 

were also recorded as binary events (yes or no), whereas in the current study 

a range of probabilities were recorded to a fi ner resolution (0.0 to 1.0).

Model goodness-of-fi t values based on comparison of the validation to 

calibration data indicated ground fi re occurrence could be predicted to some 

degree from calibrated ignition models. Variation in model accuracy may be 

explained by the shallow nature of the surface duff profi le and substantial 

inorganic content and bulk density values found in duff layers of the Park.

Validation ignition models for the DC layers, while signifi cant, were found 

to have a lower R2 and higher CV than those for the DMC. The shallow 

depth of the DC layer, coupled with a high inorganic content may explain 

these observations.

Ignition was under-estimated by calibration models on average at actual 

ignition levels over 60% for DMC and 20% for DC. Ignition success in the 

fi eld often increased from less than 20% to over 50% and above, over a very 

short time interval (days). Ignition also appeared to change rapidly with moisture depletion and changing FWI codes. As a result, effective modelling of 

ignition remains diffi cult under rapidly changing environmental conditions, 

in turn affecting the accuracy of ignition models.

The smouldering threshold (i.e. 50% probability of ignition) for the DMC 

and DC in ignition trials of Lawson and others (1997) were much greater 

than that observed in the current study. Lawson and others (1997) also 

found that a narrow range of moisture separated successful from unsuccessful 

ignitions, particularly in white spruce duff, somewhat similar to observations 

within the current study where ignition increased from 20% to more than 

50% over a few days.
USDA Forest Service Proceedings RMRS-P-41. 2006. 545

Predicting Ground Fire Ignition Potential in Aspen Communities Otway, Bork, Anderson, and Alexander

Conclusions

This research established and tested non-linear models relating DMC and 

DC to the probability of duff ignition, or ground fi re. Overall, simple rather 

than complex multivariate models were more effective in relating DMC and 

DC to ignition. During the validation procedure, models developed for the 

independent calibration site were relatively effective at detecting a change in 

ignition, although the accuracy of those models remained quite low.

Results of this study indicate that the aspen forest and D-1 fuel type of 

EINP is quite unique in its properties. Thus, the results of this study are not 

directly comparable to either that of Frandsen (1987) or Lawson and others 

(1997) in conifer vegetation types.

Acknowledgments

The authors wish to thank the management and staff of Elk Island National 

Park for their patience in allowing this research to be carried out and for their 

assistance in conducting fi eldwork. Duff sampling fi eld assistance was also 

received from Matthew Cacka and the family of the corresponding author. 

Support for this study was provided by EINP, the University of Alberta, the 

Canadian Forest Service and FERIC.

References

Alexander, M. E. 1983. Overwinter adjustment to spring starting values of the 

Drought Code. Forest Report No. 28: 5. Edmonton, AB. Canadian Forestry 

Service, Northern Forest research Centre.

Best, J. N.; Bork, E. W. 2004. Aspen understory community classifi cation in Elk 

Island National Park using environmental & overstory characteristics. Parks 

Canada unpublished manuscript. 59 pp. 

Bowser, W. E.; Kjearsgaard, A. A.; Peters, T. W.; Wells, R. E. 1962. Soil 

survey of Edmonton: sheet 83-H. Soil Survey Report number 21. Canadian 

Department of Agriculture, Research Council of Alberta and University of 

Alberta, Edmonton, Alta.

Canadian Forestry Service. 1984. Tables for the Canadian Forest Fire Weather Index 

System, 4th Edition. Canadian Forest Service, Forest Technical Report 25.

Crown, P. H. 1977. Soil survey of Elk Island National Park. Alberta Institute of 

Pedology, publication number S-77-38. 128 pp.

Cruz, M. G.; Alexander, M. E.; Wakimoto, R. H. 2003. Assessing the probability of crown 

fi re initiation based on fi re danger indices. Forestry Chronicle 79: 976-983.

Frandsen, W. H. 1987. The influence of moisture and mineral soil on the 

combustion limits of smouldering forest duff. Canadian Journal Forest Research, 

17: 1540-1544.

Frandsen, W. H. 1991. Burning rate of smouldering peat. Northwest Science, 65: 

166-172.

Frandsen, W. H. 1997. Ignition probability of organic soils. Canadian Journal of 

Forest Research, 27: 1471-1477.
546 USDA Forest Service Proceedings RMRS-P-41. 2006.

Otway, Bork, Anderson, and Alexander Predicting Ground Fire Ignition Potential in Aspen Communities

Hartford, R. A. 1989. Smouldering combustion limits in peat as infl uenced by 

moisture, mineral content, and organic bulk density. Pp. 282-286. In: D.C. 

MacIver and others (eds.) Proceedings of the 10th Conference on Fire and Forest 

Meteorology, Forestry Canada, Ottawa, Ont.

Kalra, Y. P.; Maynard, D. G. 1991. Methods for forest soil and plant analysis. Forestry 

Canada, Northwest Region, Northwest Forestry Centre, Edmonton, Alberta. 

Information Report NOR-X-319.

Lawson, B. D.; Dalrymple, G. N. 1996. Ground-truthing the Drought Code: fi eld 

verifi cation of overwinter recharge of forest fl oor moisture. Canadian Forest 

Service Pacifi c Forest Centre and B.C. Ministry of Forest Research Branch FRDA 

Report 268.

Lawson, B. D.; Frandsen, W. H.; Hawkes, B. C.; Dalrymple, G. N. 1997. 

Probability of sustained smouldering ignition for some boreal forest duff types. 

Canadian Forest Service, Northern Forestry Centre, Edmonton, Alberta. Forest 

Management Notes, 63.

Nalder, I. A.; Wein, R. W. 1998. A new forest fl oor corer for rapid sampling, minimal 

disturbance and adequate precision. Silva Fennica 32: 373-382.

Parks Canada. 2004. Weather records for Elk Island National Park, 2002 through 

2004. Elk Island National Park internal document.

Peterson, E. B.; Peterson, N. M. 1992. Ecology, management and use of aspen 

and balsam poplar in the prairie provinces. Forestry Canada, Northern Forestry 

Centre, Special Report No. 1.

Polster, D. F.; Watson, L. E. 1979. Vegetation classifi cation and evaluation — Elk 

Island National Park. Parks Canada Report prepared by Techman Ltd. 134 pp.

Rogeau, M.P. 2004. Historical fi re weather review: Edmonton Region. Strathcona 

County, Phase II Firesmart Initiative. 19 pp.

SAS Institute Inc. 2001. The SAS system for windows. Release 8.02. Cary, N.C.

Strong, W. L.; Leggat. K. R. 1991. Ecoregions of Alberta. Alberta Forestry, Lands 

and Wildlife, Land Information Services Division, Resource Information Branch, 

Edmonton, Alta. 110 pp.

Turner, J. A.; Lawson, B. D. 1978. Weather in the Canadian Forest Fire Danger 

Rating System: A user guide to national standards and practices. Canadian Forest 

Service Information Report, BC-X-177.

Van Wagner, C. E. 1970. An index to estimate the current moisture content of the 

forest fl oor. Canadian Forestry Service Publication 1288. 23 pp.

Van Wagner, C. E. 1987. Development and structure of the Canadian Forest Fire 

Weather Index System. Canadian Forestry Service, Forest Technical Report 35. 

37 pp.
USDA Forest Service Proceedings RMRS-P-41. 2006. 547

Fire Ecology and Fire Effects
548 USDA Forest Service Proceedings RMRS-P-41. 2006.
USDA Forest Service Proceedings RMRS-P-41. 2006. 549

In: Andrews, Patricia L.; Butler, Bret W., 

comps. 2006. Fuels Management—How to 

Measure Success: Conference Proceedings. 

28-30 March 2006; Portland, OR. 

Proceedings RMRS-P-41. Fort Collins, 

CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research 

Station.

1 Landscape Modeler Hydrologist, METI 

Corp. for USDA Forest Service, Rocky 

Mountain Research Station, Forestry 

Sciences Laboratory, Economic Aspects 

of Ecosystem Management on Forest 

Lands research unit (RMRS Forest Econ 

Unit). kdhyde@fs.fed.us

2 Project Leader, Rocky Mountain 

Research Station, Forest Econ Unit, 

Missoula, MT.

3 Landscape Modeler, METI Corp. 

for Rocky Mountain Research Station, 

Forest Econ Unit, Missoula, MT.

4 Economist, Ecosystem Assessment 

and Planning, Northern Region, USDA 

Forest Service, Missoula, MT.

5 Economist, College of Forestry and 

Conservation, University of Montana, 

Missoula, MT.

Abstract—To plan fuel treatments in the context of comprehensive ecosystem management, forest managers must meet multiple-use and environmental objectives, address 

administrative and budget constraints, and reconcile performance measures from 

multiple policy directives. We demonstrate a multiple criteria approach to measuring 

success of fuel treatments used in the Butte North Strategic Placement of Treatments 

(SPOT) pilot project. Located in the Beaverhead – Deerlodge National Forests, Montana, the project addresses multiple issues: altered wildlife habitat affecting sensitive 

species, grassland conversion to forest, an insect epidemic, water resource concerns, 

wildland-urban interface development, and wildland fi re management. Managers are 

working with researchers to develop dynamic landscape management strategies. They 

employ multiple modeling approaches to conduct an integrated assessment of ecological and resource issues relative to multiple management scenarios. Besides evaluating 

effects of proposed treatments on changes to fi re behavior, they also evaluate effects 

on wildlife habitat, disturbance processes, water quality and economics of treatment 

alternatives. The intent is to effectively integrate fuel management with Forest Plan 

goals and comprehensive ecosystem management. This approach offers a structure to 

use multiple criteria to evaluate success of fuel management activities in the context 

of other resource objectives.

Introduction

Recent dramatic increases in wildland fi res triggered the commitment of 

substantial resources to reduce hazardous fuels. The Government Accounting 

Offi ce (2002) calls for federal land management agencies to develop “consistent criteria to identify and prioritize” areas requiring treatment and “clearly 

defi ned outcome-oriented goals and objectives.” The urgency to reduce forest 

fuels creates tension with expectations that forest management must address 

competing resource objectives while applying the best available ecosystem 

science. The Healthy Forest Restoration Act of 2003 established a framework 

to conduct hazardous fuels reduction projects on federal forested lands to 

protect key ecosystem components, reduce risk to communities and municipal 

water supplies, improve critical habitat for threatened or endangered species, 

restore vegetation structure to refl ect historic variability, improve commercial 

value of forest biomass, and address insect infestation. How do managers effectively integrate the complexities of ecosystem science and multiple resource 

objectives into practical planning strategies?

The scientifi c basis for comprehensive ecosystem assessment is well established (Grumbine, 1997) and issues of applied ecosystem assessment have 

been thoroughly discussed (Haynes et al. 1996; Holt 2001; Jakeman and 

Integrating Fuel Treatments into 

Comprehensive Ecosystem Management

Kevin Hyde1, Greg Jones2, Robin Silverstein3, Keith Stockmann4, and 

Dan Loeffl er5
550 USDA Forest Service Proceedings RMRS-P-41. 2006.

Hyde, Jones, Silverstein, Stockmann, and Loeffl er Integrating Fuel Treatments into Comprehensive Ecosystem Management

Letcher 2003; van der Sluijs 2002). Provisions for conducting environmental impact analysis and managing resources to meet multiple objectives were 

established in the National Environmental Policy Act of 1969 and National 

Forest Management Act of 1976, respectively.

Computer-based decision support systems evolved concurrently with 

ecosystem sciences. Numerous modeling systems seek to transfer ecosystem 

theory and knowledge into practical management solutions. Many modeling 

tools focus on resource specifi c issues such as water quality, wildlife habitat, 

wildland fi re behavior, vegetation processes, management logistics, and economic resource assessment. Many modeling tools coevolved with geographic 

information systems (GIS) permitting spatially explicit model displays. The 

need to assess integrated ecosystem components drives development of the 

emerging fi eld of Integrated Assessment Modeling (IAM) (Jakeman and 

Letcher 2003; van der Sluijs 2002). In principle, IAM accounts for ecological, social, and economic values where planning environmental and resource 

management activities. The objective of IAM is to integrate multiple, relevant 

modeling components into a unifi ed framework to improve how complex 

environmental problems are analyzed and possible solutions identifi ed.

This paper presents a conceptual framework for a modeling-based assessment and planning procedure that integrates forest fuel treatments with 

multiple resource objectives. The framework is an example of an IAM currently used for the Butte North Project, Beaverhead-Deerlodge National 

Forest, Montana. The project is as a pilot of the USDA Forest Service, Strategic Placement of Fuels (SPOT)program. The SPOT program is intended 

to guide development of a “consistent and systematic interagency approach” 

to identify and plan treatments on forested acres deemed most critically in 

need of fuel reduction (Bosworth 2005). The framework is presented in a 

structured, stepwise format, and provides insight into how integrated assessment modeling is practically implemented. We conclude by describing 

a “performance report card” for evaluating treatment success based upon 

multiple resource objectives.

Study Area

The Butte North Project area, located in Silver Bow County, Montana, 

covers 38,600 ac, 80% of which is managed by the Beaverhead-Deerlodge 

National Forest (BDNF) (fi gure 1). In the lower elevations, shallow, highly 

erodible soils support grass and sagebrush lands. The forested lands above are 

dominated by lodgepole pine (Pinus contorta) with 2,800 ac of Douglas-fi r 

(Pseudotsuga menziesii) in drier sites. The area was heavily impacted by mining throughout the late 19th and early 20th century (Lyden 1948). Most of 

the timber was removed to support mining operations. Commercial logging 

of lodgepole pine occurred most recently during the 1980’s. Many forest 

roads intersect stream channels. Over 80 residential structures occupy the 

wildland-urban interface. Small ranch operations run cattle on private lands 

and federal grazing allotments. The National Forest lands are highly valued 

for hunting and other recreation. A small municipal water supply reservoir is 

also located within the project area.
USDA Forest Service Proceedings RMRS-P-41. 2006. 551

Integrating Fuel Treatments into Comprehensive Ecosystem Management Hyde, Jones, Silverstein, Stockmann, and Loeffl er

Current Conditions and Management Issues

The land use history and current environmental conditions result in multiple management issues. Details follow by seven general resource topics as 

defi ned by the BDNF managers. These topics are repeated in major sections 

of the paper as we describe the integrated modeling process.

A. Vegetation: Dense seedling and sapling cohorts occupy stands commercially harvested 20-30 years ago. Conifers continue to encroach upon 

grass and sagebrush lands. Understory development within Douglas-fi r 

stands increases acres of densely stocked, multi-story vegetation. There are 

few stands of large mature trees, limiting the potential development of more 

complex ‘old-growth’ type vegetation structure. Encroachment and increased 

vegetation density generally reduces landscape complexity.

B. Insects: Infestations of mountain pine beetles are present and threaten 

to spread rapidly throughout the conifer forests causing extensive mortality 

to lodgepole and Douglas-fi r stands.

C. Fire and forest fuels: Continuous stands with heavy fuel loading could 

provide conditions for rapid fi re growth. Vegetation on over half of the managed area is classifi ed as Fire Regime Condition Class 3 (FRCC3), indicating 

that conditions are departed from the historic range of variability and that 

signifi cant management may be needed for restoration (Hann and Strohm 

2003). Fuel loadings in beetle infested areas may increase in the future as 

infested trees senesce.

Figure 1—Location of study area within Silver Bow County, Montana.
552 USDA Forest Service Proceedings RMRS-P-41. 2006.

Hyde, Jones, Silverstein, Stockmann, and Loeffl er Integrating Fuel Treatments into Comprehensive Ecosystem Management

D. Watershed: Stream channels are over-widened and contain uncharacteristic volumes of fi ne sediments, probably from past mining activities and 

the extensive forest road network. Willow is regenerating poorly, in part due 

to conifer encroachment and over-grazing in riparian zones.

E. Wildlife habitat: The trend toward lower vegetation complexity probably limits habitat for species which historically inhabited the area. Plans 

for any proposed management activities must consider habitat for multiple 

aquatic and terrestrial sensitive species including red squirrel, Tamiasciurus hudsonicus (nesting, foraging), lynx, Lynx canadensis (den, foraging), 

black-backed woodpecker, Picoides arcticus (habitat), pileated woodpecker, 

Dryocopus pileatus (nesting, foraging), fl ammulated owl, Otus fl ammeolus

(nesting, foraging), northern goshawk, Accipiter gentilis (nesting, foraging), 

fi sher, Martes pennanti (den, foraging) and West Slope Cutthroat Trout 

(Oncorhynchus clarki lewisi).

F. Social: Dense fuel concentrations proximate to residential structures 

and within the municipal watershed could threaten lives, property, and a 

drinking water source should severe wildland fi re occur.

G. Economics: Funds to conduct any management activities are limited. 

Proposed activities must be logistically and economically feasible.

Developing an Integrated Modeling Framework

The core Butte North assessment team consisted of specialists in silviculture, wildlife, GIS, fi re and fuel management, hydrology, fi sheries, and 

landscape modeling. Following background research, group discussions, and 

fi eld reconnaissance, the team defi ned resource issues and developed a list of 

possible management objectives. The objectives were translated into landscape components and relationships that could be defi ned within a GIS and 

modeling applications. Rules were developed to adapt these components and 

relationships into assessment logic within the modeling framework. Modeling 

tools appropriate to resource issues were implemented addressing vegetation, 

insect spread, fuels and fi re, wildlife habitat, and human uses. Modeling results 

were integrated into a fi nal modeling system which assessed the feasibility 

and trade-offs associated with multiple objective scenarios. In summary, the 

IAM process was accomplished through the following steps:

Step 1: Translate Issues to Objectives

Step 2: Translate Objectives to Modeling Logic

Step 3: Build and Integrate Models

Step 4: Defi ne Basis for Scenario Comparison

Step 5: Frame Alternative Scenarios

The IAM process permits visualization of possible consequences of multiple plausible alternatives which may help estimate and confi rm anticipated 

benefi ts and confl icts. IAM may also reveal unanticipated opportunities and 

pitfalls. The intent is to provide spatially explicit comparison across a range 

of alternative scenarios.

Step 1: Translate Issues to Objectives

The core team developed a series of management objectives defi ned by 

specifi c activities, to address the seven identifi ed landscape issues.
USDA Forest Service Proceedings RMRS-P-41. 2006. 553

Integrating Fuel Treatments into Comprehensive Ecosystem Management Hyde, Jones, Silverstein, Stockmann, and Loeffl er

A. Vegetation: Implement pre-commercial thinning in stands commercially 

harvested over the past 2-3 decades. Restore grass and sagebrush lands using 

slashing and broadcast burning. Reduce Douglas-fi r understory vegetation. 

Protect selected stands with larger stem sizes, passively managing for potential 

‘old growth’ conditions. Monitor spatial arrangement of vegetation activities 

for changes to the mosaic of vegetation structure.

B. Insects: Thin beetle infested stands to reduce competition among the 

remaining trees and salvage value of some trees in infested areas.

C. Fire and forest fuels: Reduce forest fuels within stands with highest 

potential for extreme fi re behavior. Reduce vegetation density in FRCC3 

areas. Reduce vegetation density in beetle infested areas.

D. Watershed: Limit or prohibit management activities near stream 

channels, especially where sensitive species are present. Remove conifers 

encroaching into broadleaf riparian vegetation.

E. Wildlife habitat: Monitor and constrain management activities which 

alter potential habitat for species of concern. Minimize impacts to currently 

suitable habitat and favor change which increases suitable habitat.

F. Social: Reduce loading of forest fuel near structures and within the 

municipal water supply watershed.

G. Economics: Use commercial values from vegetation treatments which 

yield merchantable timber to generate revenues to fund other, non-commercial 

resource improvements.

Many of these objectives could be addressed simultaneously through activities within the same landscape area. For example, revenues from harvesting 

to reduce stand density within insect infested areas could help fund stream 

restoration projects. Conversely, activities to meet one objective could directly 

confl ict with other resource objectives. For example, mechanical activity to 

reduce forest fuels could increase sedimentation to streams and alter sensitive 

wildlife habitat. The challenge of the IAM approach is to defi ne resource 

relationships suffi ciently well to illuminate benefi ts, trade-offs, and confl icts 

within the modeling environment.

Step 2: Translate Objectives to Modeling Logic

With objectives defi ned, the next step was to determine which resource 

components to model and to identify available data. Each objective was reviewed to determine which physical and landscape attributes best describe 

the features affected by the objective and how these features relate to the 

planning landscape. Implicit in these defi nitions is the requirement that spatial data be available. This is an iterative process which requires dealing with 

“chicken or egg” logic; prior knowledge of model input requirements may 

limit data that can be used, while available data may limit which modeling 

tools may be used (Mulligan and Wainwright 2004). Also, available data 

may not be suffi cient; more data may need to be collected, parameters may 

need to be estimated from existing data, or alternative modeling approaches 

may be necessary.

The minimum modeling unit, the smallest land area identifi ed as having 

unique characteristics, was also chosen at this step. The convention defi ning 

vegetation stands (hereafter “stands”) as a minimum mapping unit logically 

translated to the minimum modeling unit. All computations and summaries 

are based upon the attributes of the minimum modeling unit. Attributes were 

assigned to stands as a single assignment assuming homogeneity for the entire 
554 USDA Forest Service Proceedings RMRS-P-41. 2006.

Hyde, Jones, Silverstein, Stockmann, and Loeffl er Integrating Fuel Treatments into Comprehensive Ecosystem Management

unit or as a percentage of land area occupied by a given feature within the unit. 

An example of percentage is the portion of a vegetation stand occupied by a 

stream buffer. The stream buffer is also an example of a management zone. 

Zones may defi ne common jurisdictions, areas with common management 

objectives, or other classifi er useful for planning and analysis.

A. Vegetation: The GIS stands layer which established the minimum 

modeling unit was a composite of legacy Timber Stand Management Record 

System (TSMRS) with vegetation updates from Satellite Imagery Land Classifi cation (SILC) data (Redmond and Ma 1996). Each stand was assigned a 

dominant plant/tree species, vegetation structure class, canopy density class, 

and habitat type.

B. Insects: The 2005 Aerial Detection Survey (ADS) GIS layer was used 

to identify stands and label with current beetle infestation (USDA Forest 

Service 2005).

C. Fire and forest fuels: In addition to assigning FRCC classifi cations 

a fi re and fuels specialist used expert opinion to translate vegetation data 

into defi nitions of fuel characteristics required for fi re behavior modeling. 

Topographic information required for fi re behavior modeling was acquired 

from a digital elevation model and historical weather data was acquired from 

a nearby weather station.

D. Watershed: Stream buffers were delineated around perennial stream 

channels after the Inland Native Fish Strategy (INFISH) (USDA Forest 

Service 2006) guidelines. A riparian recovery zone was established at 50 ft 

and an activities monitoring/exclusion zone was established at 300 ft. The 

coincidence of the 300 ft zone was appended to the stands layer as a binary 

attribute and the portion of a stand occupied by the riparian buffer was assigned to each stand. Areas previously identifi ed as high priority for recovery 

were assigned as a priority zone.

E. Wildlife habitat: Wildlife habitat modeling required vegetation characteristics acquired from the GIS stand layer.

F. Social: The locations of structures were approximated using the Montana parcel GIS layer (available at: http://nris.state.mt.us/nsdi/cadastral/) 

to generate a point layer representing building clusters. Points from the GIS 

were adjusted to match recent aerial photos provided by the BDNF. Stands 

within the municipal supply watershed were attributed based on a GIS layer 

provided by the BDNF.

G. Economics: Activity cost estimates were provided by the BDNF. Revenue 

estimates from potential commercial sales were derived from the transaction 

evidence appraisal (TEA) procedures of USDA Forest Service Region 1 (2005), 

explained further in the next section. Estimates of potential harvest volumes 

were derived from the basic vegetation attributes of the stands layer.

Step 3: Build and Integrate Models

The data describing landscape attributes and management effects were 

loaded into individual resource models, or sub-models. Using independent 

sub-models maintains model integrity, greater process transparency, and 

better description of errors and uncertainties inherent in all environmental 

modeling (Beven 2006; van der Sluijs 2002). Sub-models may be sophisticated 

computer programs or very simple rules developed from research or expert 

opinion. Respective model outputs were organized back into the base GIS 

and fi nally compiled into a fi nal Integrated Assessment Model.
USDA Forest Service Proceedings RMRS-P-41. 2006. 555

Integrating Fuel Treatments into Comprehensive Ecosystem Management Hyde, Jones, Silverstein, Stockmann, and Loeffl er

A. Vegetation—Successional pathways: Logic for successional pathways 

following disturbance and management activities was adopted as previously 

developed from research literature and expert opinion (Chew et al. 2004).

B. Insects—Infestation spread model: Based on current conditions defi ned by the ADS, the projected spread of the infestation was modeled using 

a GIS-based approach (Shore and Safranyik 1992) adapted to fi t available 

data. Results of the insect spread modeling were used to construct a future 

landscape used in the fi re behavior modeling to estimate fi re behavior 20-30 

years in the future assuming increased insect spread and increased fuel loading as dead and dying trees senesce.

C. Fire and forest fuels: Potential fi re behavior was modeled using the 

Treatment Optimization Model (TOM) within the FLAMMAP modeling 

system (Finney 2002). TOM uses GIS data layers to analyze fi re spread behavior assuming fi xed ignition sources, and weather and wind conditions. 

The resulting map suggests the location, orientation, and size of fuel treatment polygons, or TOM polygons, which may most effectively and effi ciently 

change large fi re growth. Separate TOM runs were completed using 97-99th

percentile weather conditions, prevailing winds from two directions, NW and 

SW, and two vegetation conditions, current and future bug-infested conditions created by the insect spread model. The GIS stands were attributed to 

indicate coincidence with TOM polygon.

D. Watershed—Specialist analysis: Watershed analysis was limited to 

specialist fi eld assessments and GIS attribution of stream buffer zones previously described.

E. Wildlife Habitat—Model of wildlife habitat zones: Wildlife zones 

were determined by matching GIS vegetation data with the habitat requirements of the species (Hart et al. 1998; Pilliod 2005; Ruediger et al. 2000; 

Samson 2005). The zones were categorized on a 0-3 scale for habitat quality 

and the GIS stands were attributed with the suitability rank for each wildlife 

zone. The wildlife zones values were summed for an overall wildlife habitat 

quality index.

F. Social model: The wildland urban interface (WUI) was modeled by 

generating a buffer extending 1⁄2 mi from each building cluster point. Stands 

intersected by this buffer were assigned the WUI zone attribute.

G. Economic model: Timber value was estimated by the TEA method 

which predicts stumpage value adjusted for sale characteristics and market 

indicators. Polygons in the GIS vegetation layer were assigned a mechanical 

treatment method based on proximity to an existing road and mean slope 

within the polygon; this attribute adjusts the TEA values on a stand by 

stand basis. Estimates of forest product volumes from mechanical activities 

were derived by using Forest Inventory and Analysis (FIA) data in the Forest Vegetation Simulator model (FVS) (Dixon 2002) and the Fire and Fuels 

Extension of FVS (Reinhardt 2003). The modeling results were compiled 

into a “look-up” table which associates volume estimates from activities with 

the antecedent vegetation.

Model Integration—Results from each sub-model were compiled fi rst in 

GIS then into a master IAM system called Multiple-resource Analysis and 

Geographic Information System (MAGIS). MAGIS is an optimization model 

designed to solve complex spatial and temporal scheduling problems in natural 

resource management (Zuuring et al. 1995). The MAGIS modeling system is 

based on mixed-integer mathematical programming that includes vegetation 
556 USDA Forest Service Proceedings RMRS-P-41. 2006.

Hyde, Jones, Silverstein, Stockmann, and Loeffl er Integrating Fuel Treatments into Comprehensive Ecosystem Management

management and an optional roads component for analyzing access and associated costs and resource impacts (Weintraub et al. 1994). Generally, if 

a resource can be defi ned in a GIS and with rules relating the resource to 

management effects, the resource can be accounted for in MAGIS.

Figure 2 presents a schematic of the model integration structure. The 

MAGIS model was prepared for sub-model data by defi ning the attributes 

to import from the GIS layers. Other defi nitions were entered for management activities, costs, and rules for vegetation succession, activity outputs, 

and management activities. Management regimes were defi ned consisting of 

activities, alone or in series that could be applied to accomplish project objectives. Examples included slashing and broadcast burning to restore grass and 

sagebrush lands and mechanical thinning in the commercial management 

zones. With all defi nitions entered, the attributed GIS vegetation layer was 

imported to MAGIS.

Step 4: Defi ne Basis for Scenario Comparison

The fi nal step for building an integrated model was to defi ne effects functions. These establish resource characteristics to be monitored and compared 

between alternative management scenarios run in MAGIS. These are constructed so that the output of each effects function specifi cally relates to a 

project objective. Effects functions commonly summarize acres affected by 

management actions. They may be viewed as an accomplishment meeting an 

objective (e.g. sum of stream project acres treated), or an indicator to be monitored or perhaps constrained (e.g. change in wildlife habitat index or number 

of acres impacted within the 300 ft stream buffer). Virtually any number of 

effects functions can be defi ned limited by project objectives and common 

sense. Effects functions defi ned for the Butte North Project include:

A. Vegetation

 – Acres of lodgepole plantation thinned (accomplishment)

 – Acres of grass/sagebrush restoration candidates treated (accomplishment)

 – Acres of multi-story Douglas-fi r treated (accomplishment)

 – Acres of potential old growth affected (indicator)

Figure 2—Schematic of model relationships and integration structure.
USDA Forest Service Proceedings RMRS-P-41. 2006. 557

Integrating Fuel Treatments into Comprehensive Ecosystem Management Hyde, Jones, Silverstein, Stockmann, and Loeffl er

B. Insects

 – Acres treated intersected by TOM polygons in areas of projected insect 

spread (accomplishment)

C. Fire and fuels

 – Acres treated intersected by TOM given modeled fi re behavior based on 

current vegetation (accomplishment)

 – Acres treated classifi ed as fi re regime condition class 3 (accomplishment)

D. Watershed

 – Acres of priority riparian project treated (accomplishment)

 – Acres of stands treated containing any 300 ft stream buffer (indicator)

E. Wildlife habitat

 – Acres treated containing habitat of key species (accomplishment or 

indicator depending upon associated affects)

 – Index of wildlife habitat value (indicator)

F. Social

 – Acres treated containing WUI buffer (accomplishment)

 – Acres treated around reservoir (accomplishment)

G. Economics: These effects functions are either accomplishments or 

indicators depending upon other associated resource effects

 – Total costs of activities

 – Total product volume

 – Total present net revenue

Step 5: Frame Alternative Scenarios

The process of using IAM to defi ne alternative scenarios is similar to developing alternative land management proposals. Different combinations of 

desired outcomes are compiled, each emphasizing a particular set of resource 

objectives. A primary scenario goal or objective function is determined. Boolean logic is then applied to effects functions to set specifi c goals and apply 

constraints. For example, an objective function might be to maximize acres 

of WUI treated to reduce fuels. Constraints might be set to simultaneously 

limit impact in the stream protection zone, acres of mechanical treatment in 

the WUI zone, and budget. The mathematical solver in MAGIS fi rst determines the feasibility of meeting the objective function within the constraints 

set and then calculates related impacts and outcomes defi ned by each effects 

function. Defi ning scenarios is an iterative and cumulative process. Results 

from one scenario are analyzed, adjusted, and fed into the next. This process 

continues until the users believe they have reached an optimal spatial and 

temporal schedule of treatments to meet objectives. Work on the Butte North 

modeling continues. Examples of basic scenarios which will be used for the 

Butte North analysis will include a fi re threat reduction option, a wildlife 

option, and an economic option.

Forest Health Restoration Report Card

The IAM outlined for the Butte North Project demonstrates application of multiple modeling tools for multi-objective, multi-resource analysis. 

The single issue of fuel reduction does not drive the analysis. Fuels and fi re 

threats are addressed in the context of the other signifi cant environmental 

and management concerns. The opening assessment question is not, “What 
558 USDA Forest Service Proceedings RMRS-P-41. 2006.

Hyde, Jones, Silverstein, Stockmann, and Loeffl er Integrating Fuel Treatments into Comprehensive Ecosystem Management

is the problem fi re?” Instead this approach asks, “What role does fi re play as 

one component of a complex system?” and “What management actions are 

warranted to address overall forest health?”

Expecting that management accomplishments must be accounted for based 

on standard performance criteria, the systematic assessment of key resources 

through the preceding analysis presents a logical foundation for a multiple 

criteria performance reporting tool. Given that fi re and forest fuel will drive 

budgets for the foreseeable future and that the Healthy Forest Restoration 

Act establishes the management directives, the prospective tool is entitled: 

Forest Health Restoration Report Card. Figure 3 presents a working draft 

concept. The intent is to account for and acknowledge multiple costs and 

benefi ts from management activities, to concisely report expected treatments 

objectives, and to convey this information simultaneously to several audiFigure 3—Working prototype for a Forest Health Restoration Report Card. Some cells are intentionally left 

empty to refl ect how the single card can capture the unique character of each project.
USDA Forest Service Proceedings RMRS-P-41. 2006. 559

Integrating Fuel Treatments into Comprehensive Ecosystem Management Hyde, Jones, Silverstein, Stockmann, and Loeffl er

ences. The report card should directly refl ect the project purpose and need. 

It should document the expected resource effects, both positive and negative, expected duration of treatment effectiveness, the economic benefi ts and 

costs, and any other social effects that have been analyzed. The tool provides 

a valuable qualitative and quantitative summary of project goals, merits, 

impacts, and costs; accounts for annual accomplishments comparing treatment targets to actual acres treated; and provides a basis for future project 

monitoring and outcome-based performance reporting. This tool sets the 

foundation for measuring success beyond simply reporting acres treated and 

more robustly captures the value and intent of undertaking fuel and forest 

restoration treatments.

The report card system may be one tool to help restore public trust, because 

it clearly demonstrates that multiple resource and environmental concerns 

were addressed and acted upon. Furthermore, the report card system may 

provide a basis for more consistent multi-objective planning and monitoring 

of future projects with a forest health emphasis. Modeling results may be 

validated and the degree to which intentions are realized is transparent.

Future of Modeling and Performance 

Measures

Models may help guide decisions, not make them. Models are limited by 

errors and uncertainty and, as such, are never a substitute for professional 

judgment and ground verifi cation of planning data. For all the error and uncertainties within the models and modeling processes themselves, we cannot 

hold off decisions until we have perfect systems. Models provide some measure 

of simplicity with the hope of greater clarity as we wrestle with inherently and 

intractably complex systems. Reasonably enough, management of complex 

systems requires tools that adequately represent this complexity. IAM is one 

such tool. Our current abilities to integrate resource modeling systems are 

coarse but will only improve with practice (Jakeman and Letcher 2003) and 

development of improved IAM tools and logic.

We have outlined a practical procedure for integrating fuel treatments 

into comprehensive ecosystem management through integrated assessment 

modeling. This framework provides a tool for systematic analysis of multiple 

resource objectives within a common planning area. Rather than fi re and 

fuels issues driving the process, this framework provides insight into the 

relationship between fi re, forest fuels, and other resources. The results from 

this integrated assessment modeling approach offer a structure to develop a 

multi-criteria performance report card. The outcome may be planning protocols that make better use of ecosystem science and more defensibly meet 

land management directives.

References

Beven, K.J. 2006. A manifesto for the equifi nality thesis. Journal of Hydrology, 

320: 18-36.

Bosworth, D. 2005. Interior Subcommittee Hearing on FY 2006 Budget for USDA 

Forest Service, March 3, 2005, U.S. Senate, Washington, DC.
560 USDA Forest Service Proceedings RMRS-P-41. 2006.

Hyde, Jones, Silverstein, Stockmann, and Loeffl er Integrating Fuel Treatments into Comprehensive Ecosystem Management

Chew, J.D., Stalling, C. and Moeller, K. 2004. Integrating Knowledge for Simulating 

Vegetation Change at Landscape Scales. Western Journal of Applied Forestry, 

19(2): 102-108.

Dixon, G.E. 2002. A user’s guide to the Forest Vegetation Simulator. USDA Forest 

Service, Forest Manager Service Center, Fort Collins, CO.

Finney, M.A. 2002. Use of graph theory and a genetic algorithm for fi nding optimal 

fuel treatment locations. In: Viegas (Editor), Forest Fire Research & Wildland 

Fire Safety. Millpress, Rotterdam.

GAO. 2002. Severe Wildland Fires: Leadership and Accountability Needed to 

Reduce Risks to Communities and Resources, U.S. General Accounting Offi ce, 

Washington, DC.

Grumbine, R.E. 1997. Reflections on “What is Ecosystem Management?” 

Conservation Biology, 11: 41-47.

Hann, W.J. and Strohm, D.J. 2003. Fire regime condition class and associated data 

for fi re and fuels planning: methods and applications. In: P.N. Omi and L.A. 

Joyce (Editors), Fire, Fuel Treatments, and Ecological Restoration. USDA Forest 

Service, Rocky Mountain Research Station, Fort Collins, CO: 337-443.

Hart, M.M. et al. 1998. Montana atlas of terrestrial vertebrates, Montana 

Cooperative Wildlife Research Unit, University of Montana, Unpublished 

report, Missoula, MT.

Haynes, R.W., Graham, R.T. and Quigley, T.M. 1996. A Framework for Ecosystem 

Management In the Interior Columbia Basin and Portions of the Klamath and 

Great Basins. PNW-GTR-374. USDA Forest Service Pacifi c Northwest Research 

Station, Portland, OR.

Holt, R.F. 2001. An Ecosystem-Based Management Planning Framework for the 

North Coast LRMP. North Coast LRMP, Vancouver.

Jakeman, A.J. and Letcher, R.A. 2003. Integrated assessment and modelling: features, 

principles and examples for catchment management. Environmental Modelling 

& Software, 18(6): 491-501.

Lyden, C.J. 1948. The Gold Placers of Montana, Memoir No. 26, Montana Bureau 

of Mines and Geology, Butte.

Mulligan, M. and Wainwright, J. 2004. Modelling and Model Building. In: J. 

Wainwright and M. Mulligan (Editors), Environmental Modelling: Finding 

Simplicity in Complexity. John Wiley & Sons, Chichester, England: 7-73.

Pilliod, D. 2005. Fuels planning: science synthesis and integration; environmental 

consequences fact sheet 15: the Wildlife Habitat Response Model. U.S. Department 

of Agriculture, Rocky Mountain Research Station, Res. Note RMRS-RN-23-15, 

Fort Collins, CO.

Redmond, R.L. and Ma, Z. 1996. Mapping Existing Vegetation and Landcover 

Across Western Montana and Northern Idaho. Wildlife Spatial Analysis Lab, 

Montana Cooperative Wildlife Research Unit, University of Montana, Missoula. 

971 pp.

Reinhardt, E.D., and Nicholas L. Crookston (Tech. eds.) 2003. The Fire and Fuels 

Extension to the Forest Vegetation Simulator. RMRS-GTR-116, USDA Forest 

Service Rocky Mountain Research Station, Ogden, UT.

Ruediger, B. et al. 2000. Canada lynx conservation assessment and strategy. USDA 

Forest Service Northern Region, USDI Fish and Wildlife Service, Bureau of Land 

Management, and National Park Service, Missoula, MT.

Samson, F.B. 2005. A Conservation assessment of the northern goshawk, blackedbacked woodpecker, fl ammulated owl, and pileated woodpecker in the Northern 

Region. USDA Forest Service, Northern Region, unpublished report on fi le:

Missoula, MT.
USDA Forest Service Proceedings RMRS-P-41. 2006. 561

Integrating Fuel Treatments into Comprehensive Ecosystem Management Hyde, Jones, Silverstein, Stockmann, and Loeffl er

Shore, T.L. and Safranyik, L. 1992. Susceptibility and risk-rating systems for the 

mountain pine beetle in lodgepole pine stands. For. Can. Pac. Yukon Reg. Inf. 

Rep. No BC-X-336.

USDA Forest Service. 2005. Forest Health Protection: Aviation Program. USDA 

Forest Service, Intermountain Region, State and Private Forestry, Odgen, UT. 

Available at: http://www.fs.fed.us/r1-r4/spf/fhp/aerial/.

USDA Forest Service. 2006. 2006 PACFISH/INFISH Implementation Monitoring. 

Rocky Mountain Research Station, Boise, ID.

USFS Region 1. 2005. Equation 5-4, Westside and Eastside Appraisal Zones.USDA 

Forest Service, Region 1, Missoula, MT.

van der Sluijs, J.P. 2002. A way out of the credibility crisis of models used in integrated 

environmental assessment. Futures, 34(2): 133-146.

Weintraub, A., Jones, G., Magendzo, A., Meacham, M. and Kirby, M. 1994. A 

heuristic system to solve mixed integer forest planning models. Operations 

Research, 42(6): 1010-1024.

Zuuring, H.R., Wood, W.L. and Jones, J.G. 1995. Overview of MAGIS: A MultipleResource Analysis and Geographic Information System. Res. Note RN-427. USDA 

Forest Service, Intermountain Research Station, Ogden, UT.
USDA Forest Service Proceedings RMRS-P-41. 2006. 563

In: Andrews, Patricia L.; Butler, Bret W., 

comps. 2006. Fuels Management—How to 

Measure Success: Conference Proceedings. 

28-30 March 2006; Portland, OR. 

Proceedings RMRS-P-41. Fort Collins, 

CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research 

Station.

1 Senior Lecturer, The Australian 

National University, Canberra, ACT, 

0200, Australia and a researcher in the 

Bushfi re Cooperative Research Centre. 

geoffrey.cary@anu.edu.au

2 Research Ecologist, USDA Forest 

Service, Missoula Fire Science Laboratory, 

Missoula MT.

3 Professor and Director, University of 

Maryland Center for Environmental 

Science, Frostburg, MD.

4 Research Scientist, Laboratoire d’Ecologie 

Alpine, CNRS, Grenoble, France.

5 Research Scientist, Canadian Forest 

Service, Sault Ste Marie ON, Canada.

6 Software Developer, Research School of 

Biological Sciences, Australian National 

University, Canberra, ACT, 0200, and 

Researcher in the Cooperative Research 

Centre for Greenhouse Accounting.

7 Research Scientist, Canadian Forest 

Service, Edmonton, AB, Canada

8 Research Ecologist, USDA Forest Service, 

Pacific Northwest Research Station, 

Corvallis, OR.

9 Assistant Professor, University of Alaska 

Fairbanks, Fairbanks, AK.

10 Researcher, Centre d’Ecologie 

Fonctionnelle et Evolutive, Montpellier, 

France.

Abstract—The relative importance of variables in determining area burned is an important management consideration although gaining insights from existing empirical 

data has proven diffi cult. The purpose of this study was to compare the sensitivity of 

modeled area burned to environmental factors across a range of independently-developed landscape-fi re-succession models. The sensitivity of area burned to variation 

in four factors, namely terrain (fl at, undulating and mountainous), fuel pattern (fi nely 

and coarsely clumped), climate (observed, warmer & wetter, and warmer & drier) and 

weather (year-to-year variability) was determined for four existing landscape-fi re-succession models (EMBYR, FIRESCAPE, LANDSUM, and SEM-LAND) and a new model 

implemented in the LAMOS modelling shell (LAMOS(DS)). Sensitivity was measured 

as the variance in area burned explained by each of the four factors, and all of the 

interactions amongst them, in a standard generalised linear modelling analysis. Modeled area burned was most sensitive to climate and variation in weather, with four 

models sensitive to each of these factors and three models sensitive to their interaction. 

Models generally exhibited a trend of increasing area burned from observed, through 

warmer and wetter, to warmer and drier climates. Area burned was sensitive to terrain 

for FIRESCAPE and fuel pattern for EMBYR. These results demonstrate that the models 

are generally more sensitive to variation in climate and weather as compared with 

terrain complexity and fuel pattern, although the sensitivity to these latter factors in a 

small number of models demonstrates the importance of representing key processes. 

Our results have implications for representing fi re in higher-order models like Dynamic 

Global Vegetation Models (DGVMs)

Introduction

Wildland fi re is a major disturbance in most ecosystems worldwide (Crutzen 

and Goldammer 1993). Fire interacts with weather and vegetation such that 

forested landscapes may burn quickly whenever fuels are abundant, dry and 

spatially continuous, especially if there is a strong surface wind (McArthur 

1967; Rothermel 1972). The relative importance of variables in determining 

area burned is an important management consideration although gaining 

insights from existing empirical data has proven diffi cult.

Landscape-fi re-succession models, that simulate the linked processes of 

fi re and vegetation development in a spatial domain, are one of the few tools 

that can be used to explore the interaction of fi re, weather and vegetation 

over long time scales. There is a diverse set of approaches to predicting fi re 

regimes and vegetation dynamics over long time scales, due in large part to 

the variety of landscapes, fuels and climatic patterns that foster frequent forest 

Comparison of the Sensitivity of LandscapeFire-Succession Models to Variation in 

Terrain, Fuel Pattern, Climate and Weather

Geoffrey J. Cary1, Robert E. Keane2, Robert H. Gardner3, Sandra 

Lavorel4, Mike D. Flannigan5, Ian D. Davies6, Chao Li7, James M. 

Lenihan8, T. Scott Rupp9, and Florent Mouillot10
564 USDA Forest Service Proceedings RMRS-P-41. 2006.

Cary, Keane, Gardner, and others Comparison of the Sensitivity of Landscape-fi re-succession Models to Variation ...

fi res (Swanson and others 1997; Lertzman and others 1998), and variation 

in modeler’s approaches to representing them in models.

Systematic comparisons among models, using a standardised experimental 

design, offers insight into our understanding of the key processes and parameters affecting diverse ecosystems (Dale and others 1985; Rose and others 

1991; Gardner and others 1996; VEMAP 1996; Pan and others 1998; Cramer 

et al 1999) as well as our confi dence in the reliability of model predictions 

(Bugmann and others 1996; Turner and others 1989). The objective of this 

research is to compare a range of landscape-fi re succession models to gain 

insight into the relative importance of terrain, fuel pattern, weather and climate in determining modeled area burned, and the extent to which fi ndings 

can be generalized across a range of ecosystem types.

We selected a set of landscape-fi re-succession models and performed a 

comparison on neutral landscapes to identify the relative importance and 

sensitivity of simulated fi re to terrain, fuel pattern, weather and climate. We 

originally planned to compare results of models from the twelve classifi cation 

categories of landscape-fi re-succession models of Keane and others (2004) but 

in reality we limited ourselves to models from three classifi cation categories 

selected from modelers with the time and resources to undertake the complex 

simulation design. We compared fi ve models including EMBYR (Gardner 

and others 1996), FIRESCAPE (Cary & Banks 1999), LANDSUM (Keane 

and others 2002), SEMLAND (Li 2000), and a new application of the LAMOS modelling shell (Lavorel and others 2000). These models may appear 

functionally similar but they are quite different in many aspects, including a 

wide diversity in the simulation of fi re spread and ignition, representation of 

vegetation, and the complexity of climate and fi re linkages (Cary and others 

2006).

This study does not represent an exercise in model validation. Rather, we 

selected models that have previously been verifi ed and validated, and one new 

model, and analysed their behaviour with respect to variation in terrain, fuel 

pattern, weather and climate. A more comprehensive description of the study 

is given by Cary and others (2006).

The Models

EMBYR is an event-driven, grid-based simulation model of fi re ignition and 

spread designed to represent the landscapes and fi re regimes of Yellowstone 

National Park (Hargrove and others 2000). The pattern of forest succession 

of lodgepole pine forests is simulated by a Markov model, with fuels suffi -

cient to sustain crown fi res developing as a function of forest stand age. The 

probability of fi re spreading from a burning pixel to each of its neighbors is 

determined by stand age, fuel moisture, wind speed and direction, and slope. 

An index of fi re severity, based on fuel type, fuel moisture, wind speed and 

the rate that the cell burned, determines whether fi re intensity is suffi ciently 

high to cause a stand-replacing fi re.

FIRESCAPE simulates individual fi re events that are combined into patterns of fi re frequency, fi re intensity and season of occurrence (Cary and Banks 

1999). Daily weather is generated by a modifi ed version of the Richardsontype stochastic climate generator (Richardson 1981) so that serial correlations 

within a particular meteorological variable and cross correlations between 

variables are maintained (Matalas 1967). Ignition locations are generated 

from an empirical model of lightning strike modifi ed from McRae (1992). 
USDA Forest Service Proceedings RMRS-P-41. 2006. 565

Comparison of the Sensitivity of Landscape-fi re-succession Models to Variation ... Cary, Keane, Gardner, and others

The rate of spread of fi re from a burning pixel to its neighbors is assumed 

to be elliptical (Van Wagner 1969) and is determined by Huygens’ Principle, although varying topography, fuel load and wind direction result in 

non-elliptical fi res. Head fi re rate of spread is according to the fi re behavior 

algorithms of McArthur (McArthur 1967; Noble and others 1980) with fuel 

loads modeled using Olson’s (1963) model of biomass accumulation which 

has been parameterized for a range of Australian systems.

LAMOS(DS) is an implementation of LAMOS (Lavorel and others 2000) 

with a contagious spread fi re model working on a daily time step. It is a simple 

model, sensible to daily minimum and maximum temperature, precipitation, 

fuel amount and slope. LAMOS(DS) contains two principle functions; one 

to estimate pan evaporation (Bristow and Campbell 1984; Roderick 1999) 

which, together with precipitation, produces a moisture budget, and a second 

equation to modify spread probabilities as a function of slope (Li 2000) and 

intensity. Fire intensity is the product of three linear functions: fuel load 

(0 – 1 kg m–2), moisture (0-200mm) and temperature (5-25°C). Temperature 

during the course of the fi re is interpolated between the daily minimum and 

maximum by a symmetrical sine function. Fires are assumed to begin when 

temperature is at the daily maximum. Fuel is consumed in proportion to the 

resulting intensity.

The LANDscape SUccession Model (LANDSUM) is a spatially explicit 

vegetation dynamics simulation program wherein succession is treated as a 

deterministic process, and disturbances are treated as stochastic processes 

(Keane and others 2002). Fire spread is a function of fuel-type, wind speed 

and direction, and slope using equations from Rothermel (1972) and Albini (1976). The elements that defi ne the fi re regime (for example average 

fi re size, ignition probabilities) are input parameters, whereas fi re regime is 

an emergent property for the other models. Ordinarily, the area burned in 

LANDSUM would not vary amongst the climate factors, however for this 

comparison, the probability of ignition success was made sensitive to the 

Keetch-Byram Drought Index.

The SEM-LAND model (Spatially Explicit Model for LANDscape Dynamics) simulates fi re regimes and associated forest landscape dynamics resulting 

from long-term interactions among forest fi re events, landscape structures, 

and weather conditions (Li 2000). A fi re process is simulated in two stages: 

initiation and spread. The fi re initiation stage continues from the presence 

of a fi re ignition source in a forest stand until most trees in that stand have 

been burned. Once most trees are burned, the fi re has the potential to spread 

to its surrounding cells. The probability of fi re spread is determined by fuel 

and weather conditions and slope using relationships from the Canadian 

Forest Fire Weather Index system (Van Wagner 1987) and Canadian Forest 

Fire Behavior Prediction system (Forest Canada Fire Danger Group 1992; 

Hirsh 1996).

The Comparison Design

The comparison involved determining the sensitivity of modeled area 

burned to systematic variation in terrain, fuel pattern, climate and weather 

(Cary and others 2006). It incorporated three types of terrain, two types of 

fuel pattern, three different climates, and the full extent of weather variability 

for simulation locations. The simulation landscape was an array of 1000 by 

1000 square pixels measuring 50 by 50 meters.
566 USDA Forest Service Proceedings RMRS-P-41. 2006.

Cary, Keane, Gardner, and others Comparison of the Sensitivity of Landscape-fi re-succession Models to Variation ...

Variation in terrain was introduced by varying the minimum and maximum elevation of the simulation landscape by varying the amplitude of the 

two-dimensional sine function used to represent terrain. The sine functions 

had a periodicity of 16.67 km (333.3 pixels). Three landscapes representing 

fl at, rolling and mountainous terrain, with maximum slope values of 0°, 15° 

and 30° respectively and relief of 0 m, 1250 m and 2500 m respectively were 

generated (fi gure 1). The average elevation of each landscape was 1250 m.

Fuel pattern was varied to represent fi nely clumped and coarsely clumped 

fuel patterns (fi gure 2). The fi nely clumped fuel pattern was comprised of 

ten by ten pixel (25 ha) clumps of varying fuel ages, whereas the coarsely 

clumped fuel pattern was comprised of fi fty by fi fty pixel (625 ha) clumps. 

Maps of fuel ages were generated by randomly allocating values from the 

series 0.1, 0.2, 0.3, ....1.0 to both fi nely and coarsely clumped fuel maps so 

that values were represented evenly across the landscapes. Ten replicate maps 

of each fuel pattern type were randomly generated for the model comparison. 

Fuel maps were transformed differently for each model to produce either fuel 

load or fuel age related maps that were meaningful to individual models (see 

Cary and others 2006). The maps of different fuel types were characterised 

by the same average fuel load or age, however the arrangement of different 

aged fuels varied between map types.

Figure 1—Pattern of elevation in mountainous landscape used in comparison of 

landscape-fi re-succession models.
USDA Forest Service Proceedings RMRS-P-41. 2006. 567

Comparison of the Sensitivity of Landscape-fi re-succession Models to Variation ... Cary, Keane, Gardner, and others

Figure 2—Replicate of each type of fuel pattern map used in comparison of landscape-fi re 

succession models: a) fi nely clumped (25 hectare patches) and b) coarsely clumped (625 

hectare patches) fuel pattern (values range from 0 to 1.0 and are transformed into fuel age 

or fuel load separately for each model.
568 USDA Forest Service Proceedings RMRS-P-41. 2006.

Cary, Keane, Gardner, and others Comparison of the Sensitivity of Landscape-fi re-succession Models to Variation ...

Weather and climate are essentially different phenomena at fi ne temporal 

scales and were treated as orthogonal. Variation in weather was introduced 

for most models by selecting ten representative years of daily weather 

records for the landscape where the model has undergone most rigorous 

validation (table 1 ). For EMBYR, weather data from Glacier National Park, 

MT, was used. The ten weather years were selected so that the distribution 

of annual average daily temperature and annual average daily precipitation 

in the selected set best matched the variation in the weather record available 

(around 40 years for most models) (See Cary and others 2006). Three types 

of climate were included in the design, including observed, warmer/wetter, and warmer/drier climate. Daily values for the warmer/wetter and the 

warmer/drier climate were derived from the 10 weather years of observed 

climate by adding 3.6 °C (mid-range of projected global average temperature 

increase (1.4 to 5.8°C) (IPCC 2001) to daily temperature, and by decreasing 

daily precipitation by 20 percent for the warmer/drier climate and increasing 

daily precipitation by 20 percent for the warmer/wetter climate.

A total of 1,800 year-long simulations were run for each model (except for 

LANDSUM) from the 180 unique combinations of terrain (fl at, mountainous, 

mountainous), fuel pattern (fi nely and coarsely clumped), climate (observed, 

warmer/wetter, warmer/drier), and weather (ten one-year replicates), given 

that there were ten replicate maps of each fuel pattern. Approximately 20 

percent of the LANDSUM simulations did not experience fi re and this resulted in a poor estimate of the probability and size of fi res, because of the 

shortness of the simulation periods. This was rectifi ed by performing ten 

Table 1—Available weather data for study regions and associated models.

 Location Data type Variables Model 

Glacier National 42 years, daily Daily maximum temperature (°C) EMBYR

Park, Montana observations. Daily minimum temperature (°C) LANDSUM

 Daily precipitation (cm)

Edson, Alberta 34 years (1960 – Temperature (°C) SEM-LAND

 1993) of daily Relative Humidity (%)

 observation Windspeed (km.h–1)

 (observations at Rainfall (mm)

 1200 LST) from Daily FFMC*, DMC*, 

 approximately the DC*, ISI*, BUI*

 1st April to 30th Daily Fire Weather Index

 September, Number of days since rain

 inclusive. * variables related to Fire 

 Weather Index 

Ginninderra, 42 years of simulated Daily maximum temperature (°C) FIRESCAPE

Australian weather based on Daily minimum temperature (°C)

Capital Territory Richardson-type weather Daily west-east wind speed (km.h–1)

 simulator (Richardson, Daily south-north wind speed (km.h–1)

 1981) modifi ed for all Daily 9 am atmospheric vapour 

 variables required for fi re pressure (kPa)

 behaviour modelling. Daily precipitation (mm)

Corsica 38 years (1960 – 1997) Daily average temperature (°C) LAMOS

 of daily observations. Daily precipitation (mm)

 Daily PET (mm)
USDA Forest Service Proceedings RMRS-P-41. 2006. 569

Comparison of the Sensitivity of Landscape-fi re-succession Models to Variation ... Cary, Keane, Gardner, and others

simulation replicates for each unique combination of terrain, fuel pattern, 

fuel pattern replicate, climate, and weather replicate, and averaging them to 

produce a better estimate of area burned. Fires affected fuel load/age within 

each simulation but, since simulations were for only a single year, no vegetation succession algorithms were invoked. The total area burned per year (m2) 

was recorded for each one-year simulation.

The sensitivity of simulated area burned to terrain, fuel pattern, climate and 

weather was assessed from the variance explained by each of the variables and 

all possible interactions. Variance explained (r2) was determined from a fully 

factorial ANOVA performed in the SAS statistical package. Variance explained 

is a more meaningful measure than statistical signifi cance when comparing 

the importance of environmental variables, particularly when dealing with 

simulated data. It facilitates the comparison of the importance of a range 

of variables on area burned, across a range of models with different input 

requirements and calibrated for widely separated landscapes characterised by 

quite different climate systems and weather syndromes. Plots of residual values 

against fi tted values were constructed for each analysis. Analyses performed 

on untransformed area-burned data produced residuals which were highly 

skewed and the variance in residuals that was highly variable across fi tted 

values. Transformation of area burned by the natural logarithm produced 

patterns of residuals that we considered acceptable for our analyses.

Results

Simulated area burned was more sensitive to climate and weather than 

to fuel pattern and terrain (table 2). Ln-transformed modeled area burned 

was considered sensitive to variation in climate for FIRESCAPE, LAMOS, 

LANDSUM and SEM-LAND while it was considered sensitive to variation in 

weather for EMBYR, FIRESCAPE, LANDSUM and SEM-LAND. The interaction between these two variables was considered important for EMBYR, 

LANDSUM and SEM-LAND. For models sensitive to climate, there was 

a trend for increasing area burned for warmer climates (warmer/drier and 

warmer/wetter) compared with the observed climate, with the warmer/drier 

climate being characterised by larger area burned than the warmer/wetter 

climate in two of four cases (see Cary and others 2006).

Only FIRESCAPE showed sensitivity to variation in terrain (and the interaction between terrain and weather, and that between terrain, climate and 

weather). Modeled area burned was highest for mountainous terrain and least 

for fl at terrain. Only EMBYR showed sensitivity to variation in fuel pattern 

(and the interaction between fuel pattern and weather factors). Modeled area 

burned was higher for the coarsely clumped fuel pattern than for the fi nely 

clumped pattern (see Cary and others 2006).

Discussion

The variance in modeled area burned was greater for weather than climate 

for EMBYR, LANDSUM and SEM-LAND, compared with FIRESCAPE 

and LAMOS, perhaps because the inter-annual variation between the weather 

years for these locations was lower than for other sites. Nevertheless, sensitivity of modeled area burned to weather was considered important for four 
570 USDA Forest Service Proceedings RMRS-P-41. 2006.

Cary, Keane, Gardner, and others Comparison of the Sensitivity of Landscape-fi re-succession Models to Variation ...

Table 2—Relative Sums of Squares attributed to different sources of variation in the comparison of 

sensitivity of ln-transformed area burnt to terrain (Terrain), fuel pattern (Fuel), climate (Climate) 

and weather factors (Weather), and their interactions. Factors and their interactions are considered 

important if they explain more than 0.05 and 0.025 of total variance respectively. Factors and 

interactions considered unimportant are blank. Signifi cant factors and interactions (P < 0.05) 

are indicated by *. 

 Model

 Source DF EMBYR FIRESCAPE LAMOS LANDSUM SEM-LAND

Terrain 2 0.293*

Fuel 1 0.217* * * *

Terrain x Fuel 2 *

Climate 2 * 0.418* 0.278* 0.178* 0.370*

Terrain x Climate 4 *

Fuel x Climate 2 * *

Terrain x Fuel x Climate 4 *

Weather 9 0.329* 0.087* * 0.333* 0.542*

Terrain x Weather 18 0.025* *

Fuel x Weather 9 0.031* * *

Terrain x Fuel x Weather 18 *

Climate x Weather 18 0.096* * * 0.224* 0.046*

Terrain x Climate x Weath 36 0.025*

Fuel x Climate x Weather 18 *

Terr x Fuel x Clim x Weath 36

Model 179 0.744 0.905 0.401 0.766 0.971

Note that not all signifi cant sources are considered important. 

(Source: Cary and others 2006)

out of fi ve models. The overriding importance of weather for fi re activity has 

been highlighted in numerous studies (see Flannigan and Harrington 1988; 

Swetnam 1993; Bessie and Johnson 1995; Hely and others 2001; Flannigan 

and Wotton 2001). Our fi nding regarding the importance of weather across 

a range of models highlights the importance of adequately incorporating 

variability in weather into landscape-fi re-succession models.

Several authors have provided simulated evidence for increasing area burned 

or frequency of fi re under warmer climates (Clark 1990; Cary and Banks 

1999; Li and others 2000; Cary 2002), possibly due to a longer fi re season 

(Stocks et al 1998; Wotton and Flannigan 1993). This is consistent with our 

general fi ndings. Climate was not considered important for EMBYR although 

earlier studies have indicated that a wetter climate would result in larger fi res 

(Gardner and others 1996). A possible explanation for the discrepancy is 

that, in this study, simulations were only one year in length and vegetation 

succession effects were not incorporated. We are planning new research where 

simulations will be centuries long, allowing for the importance of vegetation 

succession to be explored.

Fuel pattern was relatively unimportant, except in the case of EMBYR. Fire 

spread in EMBYR is partly a function of the nature of fuel in the source and 

target pixels of any fi re spread event. Frequently changing fuel condition in 

the fi nely clumped fuel pattern resulted in a decrease in area burned compared 

with the coarsely clumped pattern. While this is a realistic representation of 

fi re spread, fuel pattern accounts for a comparatively small amount of variance 

in EMBYR compared to climate and weather in the other models.

Terrain was considered important for FIRESCAPE, despite all models 

incorporating a similar positive effect of slope on fi re spread. FIRESCAPE 
USDA Forest Service Proceedings RMRS-P-41. 2006. 571

Comparison of the Sensitivity of Landscape-fi re-succession Models to Variation ... Cary, Keane, Gardner, and others

is the only model that varies weather with terrain. The mountainous terrain 

provides a greater proportion of the landscape which is warmer and drier 

(in the “valleys”), compared to the rolling and fl at landscapes, given that all 

landscapes were characterized by an average elevation of 1250 m. Representing the effect of terrain on weather in landscape fi re models is fundamental 

if this aspect of the terrain factor is to infl uence models results in a realistic 

fashion.

Our results have implications for representing fi re in higher-order models 

like Dynamic Global Vegetation Models (DGVMs). The relative unimportance of fi ne scale fuel pattern indicates that coarse scale DGVMs may not 

need to incorporate pattern of vegetation within simulation cells, although 

this depends on the importance of vegetation succession on area burned, 

which was not tested in this experiment. On the other hand, landscape scale 

pattern in terrain was demonstrated to be fundamentally important using 

the one landscape-fi re-succession model that incorporates the effect of terrain on weather. Also, the general fi nding of the importance of inter-annual 

variability in weather (compared with climate) has important implications 

for the inclusion of fi re into DGVMs because an increase in inter-annual 

weather variability resulted in greater effects on area burned than the climate 

variable in some cases.

The results from this study are concerned with comparing landscapes where 

the mean fuel age/load is constant across simulations but varies in the arrangement of fuel (fuel pattern). We are presently using our approach to compare 

the sensitivity of modeled area burned to variation in approach/extent of fuel 

management and ignition probability. It also has considerable potential for 

conducting comparisons amongst groups of other types of models producing variation in landscape dynamics, and for further comparison amongst 

landscape-fi re succession-models.

Acknowledgments

Thanks to the National Center for Ecological Analysis and Synthesis, a 

Center funded by the National Science Foundation (Grant #DEB-0072909), 

the University of California, and the Santa Barbara campus, who partly 

funded this research. We also thank all participants in NCEAS workshops 

especially Andrew Fall, Carol Miller, Don McKenzie and Mike Wotton, and 

Russ Parsons, and Dan Fagre who organised a workshop in Glacier National 

Park. The IGBP Fire Fast Track Initiative are acknowledged for their support 

of this project.

References

Albini, F. A. 1976. Estimating wildfi re behavior and effects. General Technical 

Report INT-30, USDA Forest Service.

Bristow, K. L. and Campbell, G. S. 1984. On the relationship between incoming 

solar radiation and daily maximum and minimum temperature. Agricultural and 

Forest Meteorology. 31: 159-66.

Bessie, W. C. and Johnson, E. A. 1995. The relative importance of fuels and weather 

on fi re behaviour in subalpine forests. Ecology. 76: 747-62.
572 USDA Forest Service Proceedings RMRS-P-41. 2006.

Cary, Keane, Gardner, and others Comparison of the Sensitivity of Landscape-fi re-succession Models to Variation ...

Bugmann H. K. M., Yan, X. D., Sykes, M. T., Martin, P., Lindner, M., Desanker, P. 

V. and Cumming, S. G. 1996. A comparison of forest gap models: model structure 

and behaviour. Climatic Change. 34: 289-313.

Cary, G. J. 2002. Importance of a changing climate for fi re regimes in Australia. In

Bradstock, R. A, Gill, A.M. and Williams, J.E. (eds.) 2002. Flammable Australia: 

The Fire Regimes and Biodiversity of a Continent, pp. 26-46. Cambridge 

University Press, Cambridge, UK.

Cary, G. J., and Banks, J. C. G. 1999. Fire regime sensitivity to global climate change: 

An Australia perspective. J. L. Innes, M. Beniston, and M. M. Verstraete, editors. 

Advances in Global Change Research: Biomass burning and its inter-relationships 

with the climate system. Kluwer Academic Publishers, London, UK.

Cary, G. J., Keane, R. K., Gardner, R. H., Lavorel, S., Flannigan, M., Davies, I. D., 

Li, C., Lenihan, J. M., Rupp, T. S. and Mouillot, F. 2006. Comparison of the 

sensitivity of landscape-fi re-succession models to variation in terrain, fuel pattern, 

climate and weather. Landscape Ecology. 21: 121-37

Clark, J. D. 1990. Fire and climate change during the last 750 years in northwestern 

Minnesota. Ecological Monographs. 60: 135-59.

Cramer, W., Kicklighter, D.W., Bondeau, A., Moore III, B., Churkina, G., Nemry, 

B., Ruimy, A., Schloss, A. L. and The participants of the Potsdam NPP intermodel 

comparison 1999. Comparin global models of terrestrial net primary productivity 

(NPP): overview and key results. Global Change Biology. 5: 1-15.

Crutzen, P. J., and Goldammer, J. G. 1993. Fire in the Environment: The Ecological, 

Atmospheric and Climatic Importance of Vegetation Fires. John Wiley and Sons, 

New York, New York, USA.

Dale, V. H., Doyle, T. W and Shugart, H. H. 1985. A comparison of tree growth 

models. Ecological Modelling. 29: 145-69.

Flannigan, M. D. and Harrington, J. B. 1988. A study of the relation of meteorological 

variables to monthly provincial area burned by wildfi re in Canada 1953-80. Journal 

of Applied Meteorology. 27: 441-52.

Flannigan, M. D. and Wotton, B. M. 2001. Climate, weather and area burned. In 

Johnson E.A. and Miyanishi K. (eds.) Forest Fires: Behaviour and Ecological 

Effects, pp. 335-57. Kluwer Academic Press.Forestry Canada Fire Danger 

Group. 1992. Development and structure of the Canadian Forest Fire Behaviour 

Prediction Systems. Inf. Rep. ST-X-3. Ottawa: Forestry Canada, Science and 

Sustainable Development Directorate. 63 p.

Forestry Canada Fire Danger Group. 1992. Development and structure of the 

Canadian Forest Fire Behaviour Prediction Systems. Report ST-X-3. Ottawa: 

Forestry Canada, Science and Sustainable Development Directorate. 63 p.

Gardner, R. H., Hargrove, W. W., Turner, M. G. and Romme, W. H. 1996. Climate 

change, disturbances and landscape dynamics. Pages 149-172 in B.H. Walker 

and W. L. Steffen, editors. Global change and terrestrial ecosystems. Cambridge 

University Press, Cambridge, MA., USA.

Hargrove, W. W., Gardner, R. H., Turner, M. G., Romme, W. H. and Despain, 

D. G. 2000. Simulating fi re patterns in heterogeneous landscapes. Ecological 

Modelling. 135: 243-263.

Hely, C., Flannigan, M., Bergeron, Y. and McRae, D. 2001. Role of vegetation 

and weather on fi re behaviour in the Canadian mixedwood boreal forest using 

two fi re behaviour prediction systems. Canadian Journal of Forest Research. 31: 

430-41.

Hirsch, K. G. 1996. Canadian Forest Fire Behavior Prediction (FBP) System: User’s 

Guide. Nat. Resour. Can., Can. For. Serv., Northwest Reg., North. For. Cent., 

Edmonton, Alberta, Spec. Rep. 7.

IPCC. 2001. Climate Change 2001: The Scientifi c Basis. IPCC Third Assessment 

Report: Summaries for Policymakers Working Group I Climate Change 2001.
USDA Forest Service Proceedings RMRS-P-41. 2006. 573

Comparison of the Sensitivity of Landscape-fi re-succession Models to Variation ... Cary, Keane, Gardner, and others

Keane, R. E., Parsons, R. and Hessburg, P. 2002. Estimating historical range and 

variation of landscape patch dynamics: Limitations of the simulation approach. 

Ecological Modelling. 151: 29-49.

Keane, R. E., Cary, G. J., Davies, I. D., Flannigan, M. D., Gardner, R. H., Lavorel, 

S., Lenihan, J. M., Li, C., and Rupp, S. T. 2004. A classifi cation of landscape fi re 

succession models: spatial simulations of fi re and vegetation dynamics. Ecological 

Modelling. 179: 3-27.

Lavorel, S., Davies, I. D and Noble, I. R. 2000. LAMOS: a LAndscape MOdelling 

Shell. Pages 25-28 in B. Hawkes and M. D. Flannigan, editors. Landscape fi re 

modeling-challenges and opportunities. Natural Resources Canada, Canadian 

Forest Service, Vancouver, BC, Canada.

Lertzman, K., Fall, J. and Brigitte, D. 1998. Three kinds of heterogeneity in fi re 

regimes: at the crossroads of fi re history and landscape ecology. Northwest Science 

72: 4-23.

Li, C. 2000. Reconstruction of natural fi re regimes through ecological modelling. 

Ecological Modelling. 134: 129-144.

Li, C., Flannigan M. D. and Corns, I. G. W. 2000. Infl uence of potential climate 

change on forest landscape dynamics of west-central Alberta. Canadian Journal 

of Forest Research. 30: 1905-12.

Matalas, N. C. 1967 Mathematical assessment of synthetic hydrology. Water 

Resources Research. 3: 937-45.

McArthur, A. G. 1967. Fire behavior in eucalypt forests. Leafl et Number 107 

Commonwealth of Australia Forestry and Timber Bureau.

McRae, R. H .D. 1992 Prediction of areas prone to lightning ignition. International 

Journal of Wildland Fire. 2: 123-30.

Noble, I. R., Bary, G. A.V. and Gill, A. M. 1980. McArthur’s fi re-danger meters 

expressed as equations. Australian Journal of Ecology. 5: 201-3.

Olson, J. S. 1963. Energy storage and the balance of producers and decomposers in 

ecological systems. Ecology. 44: 322-32.

Pan, Y. D., Melillo, J. M., McGuire, A. D., Kicklighter, D. W., Pitelka, L. F., Hibbard, 

K., Pierce, L. L., Running, S. W., Ojima, D. S., Parton, W. J. and Schimel, D. 

S. 1998. Modeled responses of terrestrial ecosystems to elevated atmospheric 

CO2: a comparison of simulations by the biogeochemistry models of the 

Vegetation/Ecosystem Modelling and Analysis Project (VEMAP). Oecologia. 

114: 389-404.

Richardson, C. W. 1981. Stochastic simulation of daily precipitation, temperature, 

and solar radiation. Water Resources Research. 17: 182-90.

Roderick, M. L. 1999. Estimating the diffuse component from daily and monthly 

measurements of global radiation. Agric. For. Meteorol. 95: 169-185

Rose, K. A., Brenkert, A. L., Cook, R. B., Gardner, R. H. and Hettelingh, J. P. 1991. 

Systematic comparison of ILWAS, MAGIC, and ETD watershed acidifi cation 

models: 1. Mapping among model inputs and deterministic results. Water 

Resources Research. 27: 2577-98.

Rothermel, R. C. 1972. A mathematical model for predicting fi re spread in wildland 

fuels. Research Paper INT-115, US Department of Agriculture, Forest Service, 

Intermountain Forest and Range Experiment Station, Ogden, UT, USA.

Stocks, B. J., Fosberg, M. A., Lynaham, T. J., Mearns, L., Wotton, B. M., Yang, 

Q., Lin J. Z., Lawrence, K., Hartley, G. R., Mason, J. A. and McKenney, D. W. 

1998. Climate change and forest fi re potential in Russian and Canadian boreal 

forests. Climatic Change. 38: 1-13.

Swanson, F. J., Franklin, J. F. and Sedell, J. R. 1997. Landscape patterns, disturbance, 

and management in the Pacifi c Northwest, USA. In Zonnneveld I.S. and Forman 

R. T. T. (eds.). Changing Landscapes: An Ecological Perspective, pp. 191-213. 

Springer-Verlag, New York, NY, USA.
574 USDA Forest Service Proceedings RMRS-P-41. 2006.

Cary, Keane, Gardner, and others Comparison of the Sensitivity of Landscape-fi re-succession Models to Variation ...

Swetnam, T. W. 1993. Fire history and climate change in giant sequoia groves. 

Science. 262: 885-9.

Turner, M. G., Costanza, R. and Sklar, F. H. 1989. Methods to evaluate the 

performance of spatial simulation models. Ecological Modelling. 47: 1-18.

Van Wagner, C .E. 1969. A simple fi re-growth model. Forestry Chronicle. 45: 3-4.

Van Wagner, C. E. 1987. Development and structure of the Canadian Forest Fire 

Weather Index System. For. Tech. Rep. No. 35. Ottawa: Canadian Forestry 

Service. 36 p.

VEMAP. 1996. The vegetation/ecosystem modelling and analysis project (VEMAP): 

Assessing the potential responses of natural ecosystems to climate change. 

EPRI.

Wotton, B. M. and Flannigan, M. D. 1993. Length of fi re season in a changing 

climate. Forestry Chronicle. 69: 187-92.
USDA Forest Service Proceedings RMRS-P-41. 2006. 575

In: Andrews, Patricia L.; Butler, Bret W., 

comps. 2006. Fuels Management—How to 

Measure Success: Conference Proceedings. 

28-30 March 2006; Portland, OR. 

Proceedings RMRS-P-41. Fort Collins, 

CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research 

Station.

1 Consulting Fire Ecologist, Kalispell 

MT, U.S.A. sbarrett@mtdig.net

2 Regional Ecologist, USDA Forest 

Service, Portland, OR.

3 Landscape Ecologist on the National 

Interagency Fuels Technology Team, 

Kalispell, MT.

4 Computer Scientist with USDA 

Forest Service Northern Region, 

Kalispell, MT.

5 Software Engineer with Systems 

for Environmental Management, 

Stevensville, MT. 

Abstract—Knowledge of ecological departure from a range of reference conditions 

provides a critical context for managing sustainable ecosystems. Fire Regime Condition 

Class (FRCC) is a qualitative measure characterizing possible departure from historical 

fi re regimes. The FRCC Mapping Tool was developed as an ArcMap extension utilizing 

the protocol identifi ed by the Interagency Fire Regime Condition Class Handbook to 

derive spatial depictions of vegetation departure. The FRCC Mapping Tool requires a 

biophysical setting layer identifying potential vegetation distribution, a current succession class layer allowing for comparison with historical vegetation, and a landscape 

layer (assessment area boundaries) as input data. The tool then compares existing 

vegetation composition for each biophysical setting to previously modeled reference 

conditions for those types. As described in this paper, spatial outputs characterizing 

vegetation departure at the succession class, biophysical setting, and landscape levels 

can be used by land managers to identify restoration objectives and priorities.

Introduction

Severe wildfi res in recent years have prompted Federal action to protect 

communities and restore landscapes and associated fi re regimes (USDA Forest 

Service 2000). A standardized, relatively simple method of landscape assessment was needed to measure progress in ecosystem restoration (Schmidt et 

al. 2002). The Fire Regime Condition Class (FRCC) assessment method was 

developed (Hann et al. 2005) to meet this need, and to evaluate departure 

from a range of reference conditions at multiple scales. Reference conditions 

include the median values for abundance of seral stages, as well as an estimate 

of historical fi re frequency and severity on landscapes and are developed for 

each BpS. FRCC is a classifi cation of the amount of departure of conditions 

at a given time period (such as current or future) from historical ecological 

reference conditions (Hann et al. 2005). Current policy direction for federal 

lands management, embodied in the Healthy Forest Restoration Act of 2003 

(P.L. 108-148), requires FRCC assessments as part of pre-restoration planning and post-restoration monitoring.

Because of the prominence of FRCC in legal and administrative direction, a 

number of national and regional trainings in FRCC methods were conducted 

in 2003 and 2004, with the aim of improving understanding and implementation of FRCC assessments. FRCC training continues at the local level, and 

is also available on line at www.frcc.gov. An understanding of these methods 

is a necessary precursor for effective use of the FRCC Mapping Tool.

Assessing Ecological Departure from 

Reference Conditions with the Fire Regime 

Condition Class (FRCC) Mapping Tool

Stephen W. Barrett1, Thomas DeMeo2, Jeffrey L. Jones3, J.D. Zeiler4, 

and Lee C. Hutter5
576 USDA Forest Service Proceedings RMRS-P-41. 2006.

Barrett, DeMeo, Jones, Zeiler, and Hutter Assessing Ecological Departure from Reference Conditions with the Fire Regime Condition Class (FRCC) Mapping Tool

Central to the FRCC concept is a classifi cation of landscape integrity relative 

natural or “reference conditions.” We defi ne natural conditions as the range 

of ecological structure, function, and composition operating on landscapes 

without post-European settlement infl uence. Because of uncertainties and lack 

of information on what this range would be at present, we use the historical 

range of variation (that prior to European settlement) as an approximation of 

what the current natural range would be. Given the constraints of currently 

available data and knowledge, this historical range of variation (HRV) is assumed to represent the best understanding of a properly functioning ecosystem 

(Landres et al. 1999, Hessburg et al. 1999). When actual historical data are 

available (tree ring studies, legacy photographs, etc.), the historical range of 

variation can be described directly, if often incompletely. Usually, however, 

modeling is required. Modeling this range of historic reference conditions, 

and then comparing it to current conditions, allows us to infer a departure 

from conditions presumably infl uenced by a properly functioning disturbance 

regime (Cleland et al. 2004).

Moving landscapes closer to the historic range of variation can be useful 

if the management goal is to restore ecosystems across landscapes. Note, 

however, that the range of variation is not necessarily the same as a desired 

future condition. Maintaining wildlife habitat and protecting communities 

from wildlfi re risk are examples where management goals are not necessarily 

the same as moving landscapes towards HRV.

A simple, intuitive concept in principle, modeling HRV can be fraught 

with complexity and sources of error. One problem with estimating historic 

landscapes is that we are generally working with very little data (Gill and 

McCarthy 1998, Dillon et al. 2005, Marcot 2005). Another problem is that 

climate change may lead to changing reference conditions; i.e., the historical 

range of variation becomes obsolete as an approximation of the natural range 

of variation. Nevertheless, HRV remains our best approximation of a properly 

functioning system, at least until better models are available.

Dillon et al. (2005) cautioned that modeling HRV has four primary requirements: 1) analyses should be conducted at multiple scales so that important 

ecological processes are not missed or misrepresented; 2) assessments should 

consider spatial variation of vegetation patterns across landscapes (see also 

Arno and Petersen 1983, Johnson and Gutsell 1994); 3) variability can be 

calculated in several ways, and this should be considered for a more meaningful result (see also Marcot 2005); and 4) consider the role of climate change 

over time; e.g., climatic conditions during the Little Ice Age (1700-1850), 

a timeframe often used for the historic range, are very different from those 

today (see also Millar and Woolfenden 1999).

The FRCC Mapping Tool is a menu-driven GIS extension automating 

and spatially applying FRCC calculations. As designed and with subsequent 

refi nements, it addresses each of these considerations. The practical outcomes 

of Mapping Tool use, however, are still unfolding as it is implemented and 

results evaluated. The Mapping Tool can be easily run at multiple scales, 

providing that input layers are delineated or can be aggregated at those 

scales, addressing requirement (1) above. FRCC is based largely on variation 

in spatial patterns, addressing requirement (2). Throughout this paper, the 

reader should fully realize departure is calculated using an estimated mean 

or median value of succession stage abundances. Departure from a range of 

values would be more meaningful, and methods to develop this are under 

active consideration (requirement 3). Finally, as for climate change (requirement 4), there is nothing in FRCC that precludes modeling different climate 

scenarios. As climate change effects on vegetation become better understood 
USDA Forest Service Proceedings RMRS-P-41. 2006. 577

Assessing Ecological Departure from Reference Conditions with the Fire Regime Condition Class (FRCC) Mapping Tool Barrett, DeMeo, Jones, Zeiler, and Hutter

and models more widely available, FRCC reference conditions can be adjusted 

accordingly.

During the initial development of the FRCC methodology, and with subsequent research efforts such as the multi-year LANDFIRE project (www.

landfi re.gov), reference conditions were modeled to estimate HRV. Specifi -

cally, HRV was estimated for vegetative structure and composition, and in 

terms of fi re regime characteristics (fi re frequency and severity). Using a 

combination of literature searches, expert opinion, and simulation modeling, 

HRV metrics were developed for all major vegetation types, or “Biophysical 

Settings” (BpS), in the U.S. Biophysical settings are a potential vegetation 

concept defi ned using a disturbance-constrained approach; i.e., succession 

and vegetation development occur within the bounds of historic natural 

disturbances; non-lethal disturbance frequency and severity can infl uence 

successional trajectories (Hann et al. 2005). To date, more than 300 reference condition models provide the basic foundation for diagnosing FRCC 

at multiple spatial scales.

The FRCC system is an index of departure, with three condition classes. 

Properly functioning landscapes, defi ned as exhibiting less than 33 percent 

departure from the median or average HRV conditions, receive a Condition Class 1 rating. Condition Class 2 represents landscapes with moderate 

departure (33 to 66 percent departure), and Condition Class 3 lands show 

high departure (greater than 66 percent). These classes are generally useful 

for planning and prioritizing ecosystem maintenance and restoration. For 

example, FRCC data might provide baseline data for pre- and post-treatment 

planning, monitoring, and accomplishment reporting.

FRCC assessments can be conducted in several ways. Field-based assessments can be made where an evaluator rates the vegetation (succession stage 

abundance) and fi re regime components (current fi re frequency and severity) 

of the landscape using aerial photography, fi eld observation, and fi re atlas 

data. These landscapes are generally in the range of hundreds to thousands 

of acres. This method is useful for fi eld checking of estimates made at broader 

scales and for local monitoring. Another alternative is to use the FRCC Mapping Tool with remotely sensed vegetation data in a geographic information 

system (GIS) to produce maps at various scales. The Mapping Tool evaluates 

remotely sensed vegetation data to produce spatially specifi c FRCC diagnoses. A third option, not discussed in this paper, is to download the remotely 

sensed FRCC map from www.landfi re.gov. That data layer, however, was 

designed for regional and national-scale analyses and may be too coarse for 

many analyses.

The FRCC Mapping Tool provides an objective, consistent, and spatially 

specifi c way to measure post-European settlement changes across multiple 

geographic scales if suitable data are available. Assessments based on the 

FRCC Mapping Tool can help managers prioritize landscapes for possible 

restoration and maintenance activities from fi ne (e.g., hundreds of acres) to 

coarse (e.g., millions of acres) scales. Finally, the Mapping Tool is relatively 

easy to use and understand—not a minor consideration when a standardized 

method for use at multiple organizational levels is needed.

FRCC Mapping Tool Characteristics

The FRCC Mapping Tool was designed in conjunction with the fi eld-based 

Standard Landscape Method described in the FRCC Guidebook (Hann and 
578 USDA Forest Service Proceedings RMRS-P-41. 2006.

Barrett, DeMeo, Jones, Zeiler, and Hutter Assessing Ecological Departure from Reference Conditions with the Fire Regime Condition Class (FRCC) Mapping Tool

others 2005). In contrast with fi eld-based FRCC assessments, the Mapping 

Tool is a GIS application that produces multiple spatial layers to analyze pixelto landscape scale (ranging from hundreds to millions of acres) departure 

and FRCC.

Both FRCC methods use similar principles to evaluate landscape departure and condition class. Field-based assessments evaluate existing vegetation 

and fi re frequency/severity, whereas the FRCC Mapping Tool currently assesses only the departure of existing vegetation from reference vegetation 

conditions. To date, the software team developing the mapping tool has not 

been able to develop a way to effectively evaluate post-European settlement 

fi re frequency and severity for a given landscape. This is primarily because 

these data layers are lacking or inconsistent for most areas of the country, 

not because of software limitations. Nonetheless, for many biophysical settings the existing condition indicates changes in fi re regimes compared to 

the reference range.

Because of the similarity between the two FRCC methods, potential users 

of the Mapping Tool should fi rst seek FRCC certifi cation (see www.frcc.gov). 

In addition, users should have a fi rm understanding of geographic information systems (GIS) and experience using raster data and ArcMap (Version 

9.0 or later) software. The Mapping Tool software, user guide, and systems 

requirements can be downloaded at www.frcc.gov.

The FRCC Mapping Tool uses three input layers to produce six output 

layers. (See Figure 1 for a diagram of the mapping process used in the Tool.) 

The Mapping Tool also produces a summary spreadsheet known as the Management Report. This report shows the current acres in each BpS succession 

class, and the area that would need to be converted to restore a landscape 

with a range of conditions similar to the historical range.

Figure 1—Diagram of the FRCC Mapping Tool process.
USDA Forest Service Proceedings RMRS-P-41. 2006. 579

Assessing Ecological Departure from Reference Conditions with the Fire Regime Condition Class (FRCC) Mapping Tool Barrett, DeMeo, Jones, Zeiler, and Hutter

Input Data Layers

The Mapping Tool derives its suite of FRCC attributes from three user-provided input layers. These data sources can range widely, from coarse fi eld-level 

data, to data derived from satellite imagery, to photo-interpreted vegetation 

mapping with extensive fi eld checking. Because FRCC is a scale-dependent 

variable (Hann and others 2005), users must fi rst provide a map to support 

scale-appropriate succession class analysis. This Landscape Layer should 

identify the appropriate spatial scale and boundaries for assessing FRCC. It 

may vary by BpS or geographic area. The Mapping Tool allows up to three 

landscape levels for consideration. For example, a tri-level nested hierarchy 

of area hydrologic units or similar nested classifi cation can be used. When 

based on hydrologic units, for example, the map units might range from 

subwatersheds, to watersheds, to subbasins (nested watersheds of increasing 

area, Figure 2). These hierarchical maps allow the FRCC Mapping Tool to 

analyze Succession Classes according to ecologically appropriate scales, which 

differ among fi re regimes. For example, a subwatershed scale can be used 

where small or patchy fi res predominated historically (fi re regime groups I 

and II [Hann and others 2005]). Conversely, BpS’s infl uenced primarily by 

large replacement fi res (Regimes IV and V) should be analyzed at the largest 

landscape scale because large fi res can falsely appear to skew the statistical 

distribution of succession classes for small study areas. Hann and others 

(2005) have developed guidelines for analyzing FRCC based on fi re regimetopography combinations (Table 1).

Figure 2—Example of tri-nested landscape hierarchy based on hydrologic units (from 

Hann et al. 2005). Such ecologically based classifi cations are useful for FRCC analysis, 

where potential analysis units range from the subwatershed to the subbasin scales.
580 USDA Forest Service Proceedings RMRS-P-41. 2006.

Barrett, DeMeo, Jones, Zeiler, and Hutter Assessing Ecological Departure from Reference Conditions with the Fire Regime Condition Class (FRCC) Mapping Tool

To summarize input requirements for the landscape layer, the user must: 

1) provide a base map containing up to three nested landscape sizes, such 

as hydrologic units or ecological units (Winthers et al. 2005), and 2) in an 

associated table, specify for the Mapping Tool which landscape levels are appropriate for FRCC analysis based on BpS, dominant fi re regime types and 

associated terrain dissection. The Mapping Tool then concurrently analyzes 

BpS vegetation succession classes according to each user-specifi ed landscape 

level in the area.

The FRCC Mapping Tool also requires a Biophysical Settings input layer, 

which shows BpS distribution within the analysis area. The Mapping Tool 

analyzes this layer in tandem with a user-provided Reference Condition table 

to document the estimated average amount of each succession class historically. For instance, results from a given BpS model might suggest up to 20 

percent of the type occurred in the early seral succession class, 40 percent 

occurred in the mid-seral open class, 10 percent occurred in the mid-seral 

closed class, and so on.

The LANDFIRE reference condition tables for the entire U.S. will load 

automatically after installing the Mapping Tool software, or users can develop 

custom reference condition tables based on local data. These tables must contain three pieces of information for the Mapping Tool: 1) a comprehensive list 

of all BpS within the study area, 2) reference condition amount (in percent) 

for each BpS succession class, and 3) the appropriate landscape reporting scale 

for each BpS type. Determining this scale generally means identifying a scale 

large enough to encompass the normal range of disturbance (fi re) sizes and 

frequency for the question of interest.

Finally, the user must provide a Succession Classes layer showing the current 

distribution of succession classes within the analysis area. This layer can be 

generated from local current vegetation layers crosswalked to the appropriate FRCC succession class. This allows the Mapping Tool to compare the 

current amount of each succession class to the estimated historical amounts, 

thus assessing FRCC departure and condition class diagnoses. The LANDFIRE project represents a good source of data for succession class and other 

information. Upon completion in 2009, comprehensive U.S. map coverage 

will be available for succession classes, BpS, and other layers.

Table 1—Scale guidelines for determining FRCC (Hann and others 2005). Suggested analysis 

area size range is based on dominant fi re regime type and is inversely related to slope 

steepness and land dissection.

 Terrain

 Flat to rolling Steep (moderately to 

Fire regime group1 (lightly to moderately dissected) highly dissected)

 - - - - - - - - - - - - - - - - - - - acres-- - - - - - - - - - - - - - - - - - - -

I, II 50-2000 50-1000 

III 500-2000 250-1000

IV, 5000-1,000,000 2000-250,000

V (replacement 5000-1,000,000 2000-250,000

 severity)

V (mixed severity) 50-10,000 50-10,000

1 I (0-35 yr/low to mixed severity); II (0-35 yr/stand replacement); III (35-200 yr/mixed severity); IV (35-

200 yr/stand replacement); V (200+ yr/stand replacement [but can include any severity type]).
USDA Forest Service Proceedings RMRS-P-41. 2006. 581

Assessing Ecological Departure from Reference Conditions with the Fire Regime Condition Class (FRCC) Mapping Tool Barrett, DeMeo, Jones, Zeiler, and Hutter

Output Data

To date, the FRCC Mapping Tool produces six output raster (pixel-based) 

GIS coverages (map layers) describing various Fire Regime Condition Class 

metrics. The Mapping Tool also generates a report summarizing the raster 

data. Two additional rasters are now in the fi nal stages of development, as 

discussed below. For more detailed information on all layers, see the FRCC 

Guidebook (Chapter 4 in Hann and others 2005).

Output layers generated by the Mapping Tool fall into two groups: those 

at the BpS/landscape scales and those at the succession class/stand scales. 

The fi rst group (BpS/landscape scales) includes three layers. The fi rst of 

these, the Strata Departure layer summarizes Departure for each BpS, (or 

landscape “stratum,” Hann et al. 2005). (Note that the soon-to-be-replaced 

FRCC Guidebook uses the now outdated name “Stratum S-Class Departure” 

for this layer.) The Strata Departure layer integrates the landscape strata 

according to a number of percent Departure classes. The next layer is the 

“Strata FRCC” layer (previously called the “Stratum S-Class FRCC” layer) 

(Figure 3). This data layer classifi es the various BpS departure results according to the three FRCC Condition Classes described above. The fi nal raster 

currently available is the “Landscape Departure” layer. Here, the Mapping 

Tool rates landscape-scale Departure by calculating an area-weighted average 

of the various strata departure percents, then by generating an overall rating for the appropriate landscape scale. When an area is dominated by large 

replacement fi res, for instance, the tool bases the departure rating on the 

largest landscape scale defi ned by the user, such as a watershed occupying 

tens of thousands of acres.

Figure 3—Example of FRCC Mapping Tool output for a hypothetical analysis area. Map shows 

Fire Regime Condition Class for the various landscape Strata, which typically represent an 

area’s biophysical settings (Key: green is Condition Class 1, yellow is Condition Class 2, red 

is Condition Class 3 [white polygons indicate “No Data”]).
582 USDA Forest Service Proceedings RMRS-P-41. 2006.

Barrett, DeMeo, Jones, Zeiler, and Hutter Assessing Ecological Departure from Reference Conditions with the Fire Regime Condition Class (FRCC) Mapping Tool

In the second group (succession class/stand scales), the fi rst data layer generated by the FRCC Mapping Tool is the Succession Class Percent Difference

layer. This output compares the amount of each BpS succession class during the current period to the estimated average amounts for the Reference 

period. In this case the measurement scale ranges from -100% to +100%, 

with zero representing similar amounts, negative values indicating defi cient 

amounts, and positive percents representing excessive amounts. That is, the 

layer shows the most defi cient to the most excessive (relative to the historic 

median) succession classes on today’s landscape.

The next output layer is the Succession Class Relative Amount. (The current 

version of the FRCC Guidebook (Hann et al. 2005) uses the now outdated 

name “Stratum S-Class Relative Amount” for this layer.) This layer simply 

classifi es the percent difference data according to the FRCC Guidebook (Hann 

and others 2005)(Figure 4). For example, pixels with a percent difference 

value of between minus 33 and minus 66 percent are “under-represented,” 

whereas values between plus 33 and plus 66 percent are considered “overrepresented.” Classifying the myriad results from the percent difference 

layer thus helps users more easily identify which succession classes should 

be maintained, versus those that could be reduced or recruited, in order to 

emulate average BpS Reference Conditions.

Finally, the Stand Condition Class (FRCC) layer, previously called “Stand 

Level FRCC” (Hann et al. 2005), further classifi es the above results. Here, 

the Mapping Tool rates the relative amount output according to the three 

Condition Classes mentioned earlier. For example, pixels in the “similar,” 

“under-represented,” and “trace” relative amount classes are rated as Stand 

Condition Class 1. Pixels in the “over-represented” relative amount class 

are considered to be Stand Condition Class 2, and those in the “abundant” 

relative amount class are Stand Condition Class 3. This layer was developed 

primarily to facilitate reporting and accomplishment. We stress this layer 

should not be used as a proxy for the landscape condition class layer, because 

the latter is a more appropriate layer for identifying FRCC, a landscape-scale 

measure. It is better to think of stands as having membership in successional 

stage classes that are either over-abundant, under-abundant, or within the 

historic range. 

Figure 4—The Percent Difference- and Relative Amount scales used for FRCC assessments.
USDA Forest Service Proceedings RMRS-P-41. 2006. 583

Assessing Ecological Departure from Reference Conditions with the Fire Regime Condition Class (FRCC) Mapping Tool Barrett, DeMeo, Jones, Zeiler, and Hutter

Software for two additional rasters currently is being developed, yielding an 

eventual total of eight data layers. Specifi cally, a Stand Departure layer and a 

Landscape Condition Class (FRCC) layer will likely be available by late 2006. 

The Stand Departure layer will base departure at the local (stand) scale on 

each stands membership in an seral stage abundance class compared to the 

historic average. The Landscape Condition Class layer will generate a single 

FRCC call for a landscape (delineated by the user) that is the weighted average of its member Strata Condition Classes.

The FRCC Mapping Tool also generates a Management Report spreadsheet 

to accompany the output rasters. The spreadsheet serves as the primary tool 

for analyzing and interpreting the GIS results, helping to support various 

planning needs. For instance, the data helps identify the ecological condition of an individual BpS or for multiple BpS in a given analysis area. The 

GIS data can also help managers identify ecological conditions and prioritize 

treatments ranging in scale from individual stands to entire landscapes. Such 

FRCC data can also be useful for fulfi lling various reporting requirements, 

for developing budgets, and for supporting public education.

Mapping Tool Limitations

The FRCC Mapping Tool has several limitations. First, unlike fi eld-based 

assessments, the Mapping Tool cannot be used to document post-settlement 

trends in fi re frequency and severity. In many cases, however, the remotely 

sensed vegetation condition serves as an indirect measure of current fi re 

potential, essentially serving as a proxy for those two FRCC metrics. Using remotely sensed data to identify numerous vegetation types and current 

conditions also can be diffi cult. Distinguishing between closely related BpS 

types and among the various succession classes is frequently challenging, 

particularly when types occupy closely similar terrain. In the western U.S., 

for example, the distinction between early successional Class “A” in pinyon 

pine (Pinus edulis)-juniper (Juniperus spp.) woodlands and similarly grassdominated succession classes in adjacent sagebrush (Artemisia spp.) types 

can be diffi cult, especially for broad ecotones. Identifying various types of 

FRCC-defi ned “Uncharacteristic” succession classes also can be diffi cult 

when using remotely sensed data. Examples include areas invaded by varying 

amounts of exotic cheatgrass (Bromus tectorum), and woodland-grassland 

ecotones experiencing tree encroachment as a result of post-1900 fi re exclusion. To help mitigate such interpretation errors, users of the FRCC Mapping 

Tool might need to conduct local fi eld sampling to help improve the digital 

“signatures” for the remotely sensed data.

Management Applications

To date, land managers have used the FRCC Mapping Tool to support various planning activities. Introduced in late 2004 during a number of training 

sessions in the western U.S., the FRCC Mapping Tool is gaining acceptance 

and use. Although the Tool has not yet been fully implemented, enough 

practical experience has emerged that we can highlight several management 

oriented examples and issues here. As of 2006, the mapping tool has been 

used to determine FRCC on National Forests throughout much of the Pacifi c 

Northwest Region. One of the software’s main strengths as reported by users 

is the personnel time saved with its use. The Tool has helped automate a GIS 

process that would otherwise require a number of time-consuming steps. 

The FRCC Mapping Tool has also helped promote a standardized approach 
584 USDA Forest Service Proceedings RMRS-P-41. 2006.

Barrett, DeMeo, Jones, Zeiler, and Hutter Assessing Ecological Departure from Reference Conditions with the Fire Regime Condition Class (FRCC) Mapping Tool

to determining FRCC (Jane Kertis, Siuslaw National Forest, pers. comm.), 

facilitating communication among land managers.

Improper or inconsistent use of the Mapping Tool, rather than software 

design and function, seems to be the main issue to date. The Mapping Tool 

will not run if the input layers do not agree with each other and with the 

reference condition table. For example, if a BpS on the map layer is not included in the reference conditions table, the software will not run. Hence the 

importance of consistent input data without errors. Also, using inappropriate 

landscape input maps can be expected to produce varying degrees of FRCC 

estimation error for similar vegetation types. Experienced users are currently 

helping to educate their peers about the FRCC scale issue and the appropriate 

uses of the Mapping Tool. Instructions on use of the Mapping Tool can be 

found in the FRCC Guidebook (Hann et al. 2005).

The FRCC Mapping Tool will be used to assess subregions, such as northwest Oregon (Jane Kertis, Siuslaw National Forest, pers. comm.). Similarly, 

the USDA Forest Service Pacifi c Northwest Region’s standardized existing 

vegetation mapping effort, known as the Interagency Mapping and Assessment 

Process (IMAP) also will examine the potential utility of the Mapping Tool 

for assessing FRCC and related metrics at more local landscape scales than 

LANDFIRE does. Given the vast amount of area in the U.S. currently in need 

of ecological assessments, newly emerging GIS software such as the FRCC 

Mapping Tool will become increasingly important to land managers.

Acknowledgments

The FRCC Mapping Tool was developed by the National Interagency 

Fuels Technology Team (NIFTT), the technology transfer unit of the 

National Interagency Fuels Coordinating Group. Both are Federal entities 

representing the U.S. Forest Service, Bureau of Land Management, Bureau 

of Indian Affairs, Fish and Wildlife Service, and National Park Service. Additionally, The Nature Conservancy participates as a formal partner in the 

FRCC/LANDFIRE effort.

References

Arno, S.F. and T.D. Petersen. 1983. Variation in estimates of fi re intervals. A closer 

look at fi re history on the Bitterroot National Forest. USDA For. Serv. Res. Pap. 

INT-301.

Cleland, D.T., T.R. Crow, S.C. Saunders, D.I. Dickmann, A.L. Maclean, J.K. Jordan, 

R.L. Watson, A.M. Sloan, and K.D. Brosofske. 2004. Characterizing historical 

and modern fi re regimes in Michigan (USA): A landscape ecosystem approach. 

Landscape Ecology 19:311-325.

Dahlgreen, M. pers. comm. 2006. GIS specialist, USDA For. Serv., OkanoganWenatchee N.F.s, Wenatchee, WA.

Dillon, G.K., D.H. Knight, and C.B. Meyer. 2005. Historic range of variability for 

upland vegetation in the Medicine Bow National Forest, Wyoming. Fort Collins, 

CO: USDA For. Serv. Rocky Mountain Res. Sta. Gen. Tech. Rep. RMRS-GTR139, 85 pp.

Gill, A. M., and M. A. McCarthy. 1998. Intervals between prescribed fi res in Australia: 

what intrinsic variation should apply? Biological Conservation 85:161-169.
USDA Forest Service Proceedings RMRS-P-41. 2006. 585

Assessing Ecological Departure from Reference Conditions with the Fire Regime Condition Class (FRCC) Mapping Tool Barrett, DeMeo, Jones, Zeiler, and Hutter

Hann, Wendel, Havlina, D., Shlisky, A. 2005. Interagency Fire Regime Condition 

Class Guidebook,[on-line]. National Interagency Fuels Technology Team 

(Producer). Available at: http://frcc.gov/docs/1.2.2.2/Complete_Guidebook_

V1.2.pdf

Johnson, E.A. and S.L. Gutsell. 1994. Fire frequency models, methods, and 

interpretations. Advances in Ecological Research 25:239-287.

Hessburg, P. F., B. G. Smith, and R. B. Salter. 1999. Using estimates of natural 

variation to detect ecologically important change in forest spatial patterns: a case 

study, Cascade Range, eastern Washington. USDA Forest Service PNW-RP-514. 

Portland OR. 65 pp.

Kertis, J. pers. comm. 2006. Fire ecologist, USDA For. Serv., Siuslaw N.F., Corvallis, 

OR

Landres, P.B., P. Morgan, and F.J. Swanson. 1999. Overview of the use of natural 

variability concepts in managing ecological systems. Ecological Applications 

9:1179-1188.

Marcot, B. G. 2005. The vocabulary of range of natural variation. Presented 25 

January 2005 at conference on Using Past Ecological Conditions in Resource 

Planning: Status of the Science and Application Experience. Central Cascades 

Adaptive Management Partnership and the Northwest Oregon Ecology Group 

(invited).

Millar, C.I., and W.B. Woolfenden. 1999. The role of climate change in interpreting 

historical variability. Ecological Applications 9:1207-1216.

Schmidt, K.M., J.P Menakis, C.C. Hardy, W.J. Hann, and D.L. Bunnell. 2002. 

Development of coarse-scale spatial data for wildland fi re and fuel management. 

Fort Collins, CO: USDA For. Serv. Rocky Mountain Res. Sta. Gen. Tech. Rep. 

RMRS-GTR-87, 41 pp.

USDA Forest Service. 2000. Protecting people and sustaining resources in fi readapted ecosystems: A cohesive strategy. Washington, DC: USDA For. Serv., 

80 pp.

Winthers, E., D. Fallon, J. Haglund, T. DeMeo, D. Tart, M. Ferwerda, G. Robertson, 

A. Gallegos, A. Rorick, D. Cleland, W. Robbie, and D. Shadis. 2005. Terrestrial 

ecological unit inventory technical guide. USDA For. Serv. Washington Offi ce—

Ecosystem Management Coord. Staff, 125 pp.
USDA Forest Service Proceedings RMRS-P-41. 2006. 587

In: Andrews, Patricia L.; Butler, Bret W., 

comps. 2006. Fuels Management—How to 

Measure Success: Conference Proceedings. 

28-30 March 2006; Portland, OR. 

Proceedings RMRS-P-41. Fort Collins, 

CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research 

Station.

1 California Department of Forestry 

and Fire Protection, Fortuna, CA. 

hugh.scanlon@fi re.ca.gov

2 University of California Cooperative 

Extension, Eureka, CA.

Abstract—Assessing post-fi re impacts in coast redwood (Sequoia sempervirens) forests 

can be diffi cult due to rough terrain, limited roads, and dense canopies. Remote sensing techniques can identify overstory damage, locating high intensity damage areas, 

although this can underestimate the effects on the understory vegetation and soils. To 

accurately assess understory impacts requires fi eld assessment techniques, which can 

be expensive for larger burn areas. Where geospatial data for fuels and topography can 

be combined with weather data using FARSITE, a fi re behavior simulation model, landscape fi re behavior predictions can be made. Fire behavior outputs can be generated 

to produce a post-fi re predicted landscape map of fi re severity. The 2003 Canoe fi re 

burned 4,000 hectares, primarily in old-growth redwood forests in Humboldt County, 

California. Post-fi re sampling of burn impact was assessed using the Composite Burn 

Index methodology and found to be unrelated to FARSITE produced fi re behavior variables using regression analysis. This fi nding is understandable because basic FARSITE 

landscape data available for this fi re lacked fuel load information for post-combustion 

analysis. The Canoe Fire had a slow rate of spread, and with the deep fuel beds present; long duration burning was observed. Fire severity, as described by the Composite 

Burn Index, was greatest in the forest understory. FARSITE was a useful projection tool 

for perimeter advance and fl ame lengths associated with the fi re front.

Introduction

The short-term effects of wildfi re on vegetation, soils, wildlife, and watersheds are poorly understood in the coastal redwood [Sequoia sempervirens 

(D. Don) Endl.] forests of northern California. The September 2003 4,575 

hectare, (11,214 acre) Canoe Fire, ignited by lightning in Humboldt Redwoods State Park, provided a rare opportunity to better understand the mixed 

effects of fi re following logging and over a half century of fi re exclusion in 

old-growth and second-growth forests.

Assessing post-fi re impacts in coast redwood forests can be diffi cult due to 

rough terrain, limited access, and dense canopies. Remote sensing techniques 

can identify overstory damage, locating high intensity damage areas, although 

this can underestimate the effects on the understory vegetation and soils. 

To accurately assess understory impacts requires fi eld assessment techniques, 

which can be expensive for larger burn areas.

Where geospatial data for fuels and topography can be combined with 

weather data using a fi re behavior simulation model, landscape fi re behavior 

predictions can be made. Fire behavior outputs can be generated to produce 

a post-fi re predicted landscape map of fi re severity.

Predicting Post-Fire Severity Effects in Coast 

Redwood Forests Using FARSITE

Hugh Scanlon1 and Yana Valachovic2
588 USDA Forest Service Proceedings RMRS-P-41. 2006.

Scanlon and Valachovic Predicting Post-Fire Severity Effects in Coast Redwood Forests Using FARSITE

Methods

The 2003 Canoe fi re started in Humboldt Redwoods State Park in 

Humboldt County, California, burning primarily old-growth and younggrowth redwood forests. Stand species included coast redwood, Douglas-fi r 

(Psudeotsuga menziesii), and tanoak (Lithocarpus densifl orus) in the overstory. 

Understory species included suppressed redwood, tanoak, huckleberry (Vaccinium sp.) and Oxalis oregana. The burn included unlogged old-growth 

areas, partially logged areas with a residual old-growth component, and 

previously logged areas that have stands of 60 to 100 year young-growth. A 

fi eld based fi re severity assessment was completed 9 months after the burn 

using the Composite Burn Index Methodology (FIREMON 2003) and was 

used to calibrate a map of the fi re effects based on remotely sensed data. We 

tested the prediction ability of the FARSITE (Finney 2004) fi re simulator 

to produce a similar map.

CBI Analysis

An initial fi re severity map was created using a remote sensing approach. 

Pre- and post-fi re IKONOS imagery (2002 pre-burn versus 2004 post-burn) 

was visually compared to delineate fi re severity boundaries. Oblique imagery 

taken after the fi re from a helicopter in December 2003 was used to validate 

three established severity classes. Severity classes were defi ned as: low with 

no visible change to the canopy; medium with <50% canopy loss; and high 

with a >50% loss. The minimum mapping unit was approximately 5 acres 

and boundaries were drawn with heads-up digitizing.

The forests of the burned area were classifi ed into one of three community 

types (alluvial redwood, slope redwood and Douglas-fi r forests), two management histories (old-growth or second growth stands), and two fi re severity 

types based on observations of canopy conditions, with low representing 

green canopy conditions, and high with canopy mortality. This design created a factorial of 12 stand types and fi ve replicate plots that were assigned at 

random using a GIS application. One type, old-growth alluvial high severity 

did not exist and therefore was excluded. As a result, 55 plots were installed 

and utilized for comparisons.

The Composite Burn Index (CBI) is a fi eld technique developed by the 

interagency FIREMON program to identify and quantify fi re effects over 

large areas. FIREMON is designed for repetitive measures. We applied the 

CBImethodology during the summer of 2004, nine months post-burn in 

0.04 hectare (0.1 acre) circular plots. Characteristics were related to individual strata and scores averaged for the whole plot. The strata consisted of a) 

substrates or soils, b) herbs, low shrubs and small trees < 1 meter tall, c) tall 

shrubs and saplings 1< 5 m, d) intermediate and subdominants trees, and e) 

the dominant trees. The color and condition of the soils, the amount and 

quality of the fuels and vegetation consumed, the regeneration post-fi re, the 

establishment of new seral species, and blacking, scorching and torching of 

the trees was evaluated.

We used the fi eld data to calibrate or validate the remote sensing results. 

Our results are presented as an average of the scores for 1) total plot (i.e. all 

strata) 2) overstory (i.e. only the dominant tree stratum) and 3) understory 

(i.e. soil to vegetation <5 m tall). The CBI produces a score on a 0-3 basis 

with 3 as extremely high severity.

NCSS was used to analyze the data using ANOVA and means separation 

was performed with Fisher’s Protected LSD.
USDA Forest Service Proceedings RMRS-P-41. 2006. 589

Predicting Post-Fire Severity Effects in Coast Redwood Forests Using FARSITE Scanlon and Valachovic

FARSITE Analysis

FARSITE is a spatial fi re behavior simulation system. The base landscape 

data was created at the Northern California Geographic Area Command Center, Redding, California in September 2003, and was used during the fi re to 

predict short and medium range fi re growth. Slope, aspect, and elevation data 

are derived from 30-meter resolution USGS Digital Elevation Models. The 

fuel model layer was derived from the California Department of Forestry and 

Fire Protection’s Forest and Rangeland Assessment Program remote sensing 

data. Crown canopy values were estimated by H. Scanlon during the fi re. No 

fuel loading data for post-frontal combustion analysis was available.

Weather data for analysis are derived from the nearby Eel River Remote 

Automated Weather Station (RAWS) and a portable RAWS.

The Eel River RAWS was used hourly for all wind data. The portable RAWS 

was deployed in the fi re area from September 23 to October 1. These stations 

were used to develop the diurnal cycle of maximum temperature—minimum 

relative humidity, minimum temperature—maximum relative humidity for 

the fi re.

In the early stages of the fi re, perimeter data was estimated visually by 

aircraft and are therefore sparse and imprecise,. No CBI data sample plots 

were within these initial fi re areas. As the fi re increased in size, fi re perimeters were determined primarily using helicopter mounted thermal imaging 

technology. Usually only one perimeter was generated at the end of each 

fl ight day. The daily fi re perimeter was used as an ignition starting point for 

FARSITE, and the burn was projected for at least 48 hours. Initially, a 6 

hour daily burning period was used since the fi re advance was initially slow. 

This was extended to a 10 hour active burning period by the second week of 

the fi re. Additional ignition was added where perimeter conrol fi ring operations are known to have been used and actual fi re advance was not reasonably 

predicted by model.

Where the fi re was projected to advance, FARSITE predicted the following 

values for each 30 m x 30 m raster cell: time of fi re arrival from run initiation; rate of spread; fl ame length; fi reline intensity: heat per unit area. Raster 

output from FARSITE was imported to ESRI ArcMap for compilation and 

analysis. For each overlapping CBI sample site and FARSITE raster cell, the 

resulting fi re behavior values were evaluated against the corresponding fi re 

intensity for the understory, overstory, and combined CBI values using linear 

regression (Microsoft Excel 2003).

Results and Discussion

The Canoe fi re produced a complex mosaic of fi re effects, with the majority 

of the burned area classifi ed as low or low-moderate severity, based on remote 

and fi eld calibrated data. Results of the remote evaluation (Ikonos imagery 

and aerial photos) were well correlated with the fi eld established CBI ratings 

for the overstory, but signifi cantly under-estimated the fi re severity observed 

in the understory. The Canoe Fire had a slow rate of spread, and with the 

deep fuel beds present, long duration smoldering burning was observed. 

Some patches of high severity effects were observed along the ridges where 

fi re intensity was the greatest.

Since fi re severity was under-estimated in all but the high severity areas 

using remote sensing, modeling the fi re using FARSITE had some potential 

to provide better prediction for these sites. As applied, FARSITE only mod-
590 USDA Forest Service Proceedings RMRS-P-41. 2006.

Scanlon and Valachovic Predicting Post-Fire Severity Effects in Coast Redwood Forests Using FARSITE

eled the advancing fi re front, not the long duration burning following the 

front’s passage.

The Composite Burn Index (CBI) results were found to be unrelated to 

FARSITE produced fi re behavior variables using linear regression analysis. 

The FARSITE outputs of fi reline intensity, fl ame length, heat per unit area, 

rate of spread, and reaction intensity were poor predictors (r2 < 0.10) and 

not signifi cant for fi eld derived understory, overstory, and combined CBI 

values.

Knowing that the longer these models project into the future, the more 

inaccurate they become, we reassessed our data to use only those CBI plots 

where the fi re arrived within 48 hours, then 24 hours of the run initiation. 

The linear regression fi t did not improve substantially. Review of scatter plot 

diagrams did not suggest improvement by using transformation functions 

(Figure 1).

Finding the fi re behavior outputs as unrelated to the CBI results is understandable because the basic FARSITE landscape data lacked fuel load 

information for post-combustion analysis. The fi re burned for a long time 

after the passing of the fi re front, which we were unable to model. FARSITE 

was a useful projection tool for perimeter advance and fl ame lengths associated with the fi re front.

Several additional factors contributed to the poor correlation of fi re intensity predictions to fi eld observations. Fire perimeters were usually determined 

between 1900 and 2100 hours for any given day, generally near the end of 

the active burning period. The next day’s projected progression did not begin 

until 1100, about 14 hours after the last known fi re location. In this area, two 

Figure 1—Scatter plot diagrams of fi re behavior output versus understory CBI values.
USDA Forest Service Proceedings RMRS-P-41. 2006. 591

Predicting Post-Fire Severity Effects in Coast Redwood Forests Using FARSITE Scanlon and Valachovic

separate burning periods were observed – one during peak fi re conditions, 

and a second beginning at 0100 hours for the upper slopes. The FARSITE 

simulator is not designed to handle a two-burning period situation, since it 

relatively uncommon.

Fire control actions also infl uenced the burn response. In most areas, 

control lines were established, followed by a fi ring operation to blacken in 

the perimeter prior to the arrival of the main fi re. We attempted to include 

these operations in the modeling. However, the records were sparse for when 

and where these actions were taken and may not have been applied at the 

correct time or date. Aerial ignition spheres were also used in the fi re control 

operation to accelerate interior burn out in some areas. Higher severity was 

observed in some of these areas (southeastern portion of the fi re) than were 

predicted by the model.

Differences in winds were not likely a major factor. The dense canopy cover 

tends to reduce the wind effect in most burn areas. Winds only had substantial 

effects on exposed ridgelines. Those areas were not used in the CBI assessment. Other error may have been introduced in determining and mapping 

CBI plot locations (plots landing in the wrong raster cell), and inaccurate 

assessment of fuel models. However, fuels, topography, and weather did not 

vary substantially within the immediate area of a plot in either the fi eld, or 

as modeled. Post-fi re vegetation was assessed in the FIREMON process, 

challenging the accuracy of the remotely sensed fuel model data.

Conclusions

With improved pre-fi re data we believe that FARSITE could assist in predicting the landscape effects of fi re. Additional research and fuel load data 

is needed to produce better modeling. Users are cautioned to have a good 

understanding of model limitations before applying the results. Predicting 

understory impacts of fi re across large areas will remain a challenge without 

improved remote sensing techniques.

Acknowledgments

We are grateful for the funding provided by Save-the-Redwoods League 

and for the project assistance provided by Humboldt Redwoods State Park.

References

California Department of Forestry and Fire Protection. 2003. Fuel model GIS 

layer. Fire and Resource Assessment Program. PO Box 944246, Sacramento, CA 

94244-2460. www.fi re.ca.gov.

Finney, M. A. 2004 FARSITE Version 4.0.4c Fire Area Simulator USDA Forest 

Service, Fire Sciences Laboratory, PO Box 8089, Missoula, MT 59807.

FIREMON 2003 Wildland Fire Effects Monitoring Assessment. Systems for 

Environmental Management, Missoula, MT. www.fi re.org

Microsoft Excel 2003 Statistical functions.
USDA Forest Service Proceedings RMRS-P-41. 2006. 593

In: Andrews, Patricia L.; Butler, Bret W., 

comps. 2006. Fuels Management—How to 

Measure Success: Conference Proceedings. 

28-30 March 2006; Portland, OR. 

Proceedings RMRS-P-41. Fort Collins, 

CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research 

Station.

1 Klamath Bird Observatory, Ashland, 

OR, Department of Zoology, University 

of Florida, Gainesville, FL.

nseavy@zoo.ufl .edu

2 Klamath Bird Observatory, Ashland, 

OR.

Abstract—To evaluate the ecological effects of prescribed fi re, bird and vegetation 

surveys were conducted in four study areas of the Klamath National Forest where 

prescribed fi res are being used for management. Bird and vegetation data were collected at sites treated with prescribed fi re and nearby untreated control sites. Data 

were collected at stations from 2000 (pre-treatment) to 2004 (1-4 years post treatment). The treated sites ranged from 9 to 30 ha, and during the course of the study 

25-73% of each area was treated with prescribed fi re. Over this time period, there 

was no consistent change in the volume of vegetation in either the tree or shrub strata. 

Similarly, there was no measurable effect of prescribed burning on the composition of 

the overall bird community. Spatial variation and annual variation in abundance appear 

to be more important than the change induced by prescribed burning at this scale 

and intensity. The abundance of eight individual species that have been identifi ed as 

conservation focal species for coniferous forests was also investigated. There were no 

consistent changes in the abundance of these species that we could attribute to the 

application of prescribed fi re. These results suggest that the prescribed fi re applied in 

these treatment units had negligible effects on landbird community composition.

Introduction

Biodiversity and ecosystem function may be closely linked to historical 

fi re regimes. These regimes have been altered by fi re suppression policies 

implemented in the 20th century (Agee 1993). In an attempt to restore fuel 

conditions created by historical fi re regimes (i.e. mixed-severity; Huff and others 2005), management agencies are using prescribed burns and mechanical 

fuels treatments that mimic the effects of natural fi re. However, the ability 

of these management activities to mimic the effects of natural fi re on habitat 

structure and animal populations is not well understood (Tiedemann and 

others 2000). For example, prescribed fi re treatments may fail to create the 

range of habitat conditions used by birds after naturally occurring wildfi res 

(Smucker and others 2005).

Like many national forests across the west, the Klamath National Forest in 

northern California is currently using prescribed fi re as a tool to reduce fuels 

and improve forest health (S. Cuenca, personal communication). However, 

the ability of prescribed fi re to achieve the desired ecological effects is largely 

uninvestigated (Tiedeman and others 2000; Huff and others 2005). Monitoring is essential to evaluate the ability of fi re-related management activities to 

achieve desired ecological conditions (Huff and others 2005). One approach 

to designing monitoring projects is to focus on groups of organisms that can 

provide cost-effective information about ecological conditions of interest 

Measuring Ecological Effects of Prescribed 

Fire Using Birds as Indicators of Forest 

Conditions

Nathaniel E. Seavy1, John D. Alexander2
594 USDA Forest Service Proceedings RMRS-P-41. 2006.

Seavy and Alexander Measuring Ecological Effects of Prescribed Fire Using Birds as Indicators of Forest Conditions

(Vos and others 2000; Gram and others 2001). Birds are an effective tool for 

monitoring because: (1) many species are easily and inexpensively detected 

using standardized sampling protocols; (2) species respond to a wide variety 

of habitat conditions; and (3) accounting for and maintaining many species 

with different ecological requirements can be used to implement landscape 

scale conservation strategies (Hutto 1998). Changes in the abundance of bird 

species associated with desired habitat conditions can thus be used to gauge 

the ability of management actions to maintain or improve that habitat condition and provide inferences about which habitat conditions are contributing 

to these changes.

To evaluate the impacts of prescribed burning in the Klamath National 

Forest, we compared vegetation structure and bird abundance over a fi veyear period. The objectives of this project were to (1) describe the effects of 

prescribed burning on vegetation structure and bird community composition 

and (2) evaluate if these effects are consistent with the ecological goals of 

coniferous forest management.

Methods

Study Sites and Sampling Design

Our study site was on the Klamath National Forest in northern California 

(fi g. 1). The forest vegetation in the area of these prescribed fi res is diverse 

(Whittaker 1960) and includes both conifer and hardwood species. Dominant conifers include Douglas-fi r (Pseudotsuga menziesii), ponderosa pine 

(Pinus ponderosa), incense-cedar (Calocedrus decurrens), and white fi r (Abies 

concolor). Dominant hardwoods include tanoak (Lithocarpus densifl orus), 

Pacifi c madrone (Arbutus menziesii), canyon live oak (Quercus chrysolepis), 

California black oak (Q. kellogii), Oregon white oak (Q. garryana), and bigleaf maple (Acer macrophyllum). The relative composition of these species 

varies with elevation, aspect, and soils. Generally, these forests correspond to 

the Douglas-fi r, Mixed Evergreen Hardwood, or White Fir Types described 

by Huff and others (2005). Fire-related studies in these vegetation types 

show a mix of fi re severities, frequencies, and sizes typically characteristic of 

low and moderate-severity fi re regimes (Agee 1991; Wills and Stuart 1994; 

Taylor and Skinner 1998, 2003). Over time, such mixed-severity fi res create 

forests with multiple age classes, often with Douglas-fi r or ponderosa pine 

as an emergent canopy above various hardwoods.

Working with a fi re planner and district biologist form the Klamath National Forest, we identifi ed four study areas where a series of control burns 

were to be implemented (fi g. 1). Using maps of planned prescribed fi re treatments, we established groups of stations (sites) where fi re treatments were 

planned (treated sites), and where they were at least 1000 m from where fi res 

were planned (control sites). Stations were established at least 250 m apart. 

For all analyses we consider sites as independent replicates and generated a 

single measurement for each site by averaging across stations.

The application of prescribed burns within the study areas was patchy. 

Sometimes, burns were applied such that stations were located along their 

edges or just outside the boundaries of burns. As a result, it is diffi cult to use 

a simple dichotomous classifi cation of treated vs. untreated stations. Furthermore, stations were surveyed each year, but between surveys new treatments 

were applied. As a result the proportion of treated area around the points 

increased throughout the course of the study. To quantify the proportion of 
USDA Forest Service Proceedings RMRS-P-41. 2006. 595

Measuring Ecological Effects of Prescribed Fire Using Birds as Indicators of Forest Conditions Seavy and Alexander

each treated site that was burned, we used a geographical information system 

to create a 50 m buffer around all points that fell within 50 m of a polygons 

that had been treated between 1999 and 2004 and then calculated the percent 

of this area that was treated in each year of the study (table 1).

Data Collection

Vegetation sampling—Vegetation structure was measured at all stations 

in all years of the study. We used a relevé method (Ralph and others 1993) to 

collect vegetation data at each station on variable radius plots. Within these 

Figure 1—Map showing the location of four study areas where we studied the effects 

of prescribed fi re on bird communities in the Klamath National Forest in northern 

California. Triangles represent stations at treated sites, and circles represent stations 

at control sites.
596 USDA Forest Service Proceedings RMRS-P-41. 2006.

Seavy and Alexander Measuring Ecological Effects of Prescribed Fire Using Birds as Indicators of Forest Conditions

plots, we recognized two vegetation layers: a tree layer (generally >5 m), shrub 

layer (generally >0.5 m and <5 m). For each layer, we visually estimated height 

of the top of the tree layer (canopy height) and the bottom of the tree layer 

(canopy base height). We also estimated shrub height and shrub base height. 

For each layer, we recorded total cover of all vegetation in each layer as one of 

six cover classes (0, 0 to 5, 5 to 25, 25 to 50, 50 to 75, and 75 to 100 percent) 

and used the center point of each cover class as the measurements.

Breeding season point counts—Bird abundance was evaluated using standardized point count methodologies (Ralph and others 1993). Five-minute 

bird counts were conducted between sunrise and 1000 PDT on each station, 

and all landbird species seen and heard were recorded. The distance to each 

individual was estimated to the nearest meter. Counts were conducted only 

on days when the wind was <20 kph and it was not raining. All observers 

were experienced and had been trained for distance estimation and species 

identifi cation. Only birds detected ≤50 m of each point were used in the 

analysis. This criterion was chosen to reduce the possibility of double counting 

individuals, including detections that were outside of treated or control areas, 

and alleviate biases introduced if detection rates differed between treated and 

control areas (Schieck 1997; Siegel and DeSante, 2003). Flyover detections 

were excluded from the analysis. We restricted our analysis to passerines and 

woodpeckers, and excluded four species (Common Raven, American Dipper, 

Violet-green Swallow, and American Crow) that we expected would be highly 

infl uenced by habitat characteristics unaffected by prescribed fi re.

Data Analyses

Vegetation structure—We used the relevé data to generate indices that 

represented the volume of vegetation of the tree layer and shrub layer. The 

volume of the tree layer was calculated by subtracting the canopy base height 

from the canopy height, and then multiplying this distance by the total cover 

value for the tree layer. The same method was used to calculate an index for 

the volume of the shrub layer. Within each year, we averaged all measurements within each site, and used this single tree and shrub layer value in all 

subsequent analyses.

Table 1—Four study units in then Klamath National Forest, California, where prescribed burning 

was applied between 2000 and 2004. Location of sites are identifi ed in Figure 1.

 Number of Total Percent treated2

 Area Site stations area (ha)1 2000 2001 2002 2003 2004

Blue Jay treated 12 9 0 0 18 25 25

 control 31

Scott Bar treated 6 5 0 0 0 0 71

 control 8

McGuffy treated 69 53 33 33 33 33 59

 control 8

Canyon treated 39 30 53 62 64 66 73

 control 29

1Number of ha encompassed by a 50 m buffer around the points in each unit.

2Cumulative percent of the buffer-defi ned area that was treated for each year.
USDA Forest Service Proceedings RMRS-P-41. 2006. 597

Measuring Ecological Effects of Prescribed Fire Using Birds as Indicators of Forest Conditions Seavy and Alexander

To describe the difference between vegetation volume of treatment and 

control sites, we used:

 d=log(Vtreatment/Vcontrol),

where d describes the difference between the vegetation volume (V) in the 

control sites and treatment site. When there is no difference between control 

and treatment sites d = 0, when treatment sites have greater vegetation volume 

than controls, d is positive, when treatment sites have less vegetation volume 

d is negative. Because prescribed fi re was expected to raise the canopy base 

height and reduce shrub cover, we predicted that d would become increasingly negative over the course of the study.

Bird community composition— For each site and year we calculated 

average abundance (individuals/station) of all bird species and used these 

values in a species x site matrix. We then tracked the movement of each site 

in ordination space to evaluate the degree to which the bird community 

composition changed over the course of the study. Because our four areas 

covered a wide range of elevations and habitats, we expected substantial 

spatial differences in bird community composition. Therefore, we analyzed 

two sets of birds; ‘all birds’ included all the passerines and woodpeckers that 

were detected during the study and ‘core birds,’ which was a subset that was 

restricted to species that were detected at all sites in at least one year of the 

study. We evaluated changes in bird community composition through time 

using detrended correspondence analysis (DCA) conducted in PC-ORD 

(McCune and Mefford 1999).

Abundance of coniferous forest focal species—To investigate species-specifi c responses to fuels treatments we selected ‘core’ birds that were 

identifi ed by either the California or Oregon/Washington Partners in Flight 

coniferous forest conservation plans (Altman 2000; CalPIF 2002). Within 

each year, we averaged the number of individuals detected per station, and 

used this single value for each site in all subsequent analyses. Similar to the 

analysis of vegetation volume, we described the difference in bird abundance 

between treated and untreated sites as:

 d=log(Atreatment+1/Acontrol+1),

where d describes the difference between bird abundance (A) at control sites 

and treated sites. Because some species were not detected at some sites in 

some years, we used Naperian (N + 1) logarithms.

Results

Application of Prescribed Fire

Prescribed fi res were applied at all four sites over the fi ve years of the study 

(table 1). At two sites (Guffy and Canyon) a third to half of the area had 

already been treated before the study began, however, in both these areas 

treatments continued throughout the course of the study (table 1), thus we 

would expect the trajectory of changes at these areas to be similar to the 

other areas. In most of the sites we monitored for several years after the fi rst 

treatments were applied, with the exception of the Scott Bar site, where we 

collected a single year of post-fi re data.
598 USDA Forest Service Proceedings RMRS-P-41. 2006.

Seavy and Alexander Measuring Ecological Effects of Prescribed Fire Using Birds as Indicators of Forest Conditions

Vegetation Structure

We found no evidence that the volume of live vegetation in the tree layer 

was consistently reduced at treated sites; in each year the difference between 

the treated and control areas was roughly symmetrical around 0, and there 

was no suggestion that this measurement had consistently decreased at any 

of the four areas (fi g. 2). Our results for the volume of the shrub layer were 

similar (fi g. 2), in that there were no sites that showed a consistent pattern 

of change between treated and control sites through the course of the study. 

In both the fi rst and last year of the study, the measurements of the difference in total shrub cover of treated and untreated sites was symmetrically 

distributed around 0 (fi g. 2).

Figure 2—Log response ratios comparing vegetation characteristics of treated and control 

sites from the four study areas over the fi ve-year study period.
USDA Forest Service Proceedings RMRS-P-41. 2006. 599

Measuring Ecological Effects of Prescribed Fire Using Birds as Indicators of Forest Conditions Seavy and Alexander

Bird Community Composition

For ordinations of both ‘all birds’ and ‘core birds’ most of the variation 

in the original multidimensional space was captured in the fi rst two axes 

(table 2), therefore, we limited out our interpretation to these axes. Ordination of bird communities for the treated and untreated units demonstrated 

substantial variation in bird communities among sites (fi g. 3). In particular, 

the Canyon control site and Blue Jay treated site were substantially different 

from all the other study sites. Furthermore, it was not uncommon for sites 

from the same area (e.g., compare Guffy treatment to Guffy control) to be 

more different than sites from different areas (e.g., Guffy treatment versus 

Scott Bar control). These spatial patterns remained roughly the same for ordinations of all birds and core birds (fi g. 3). Although there was substantial 

year to year variation in bird communities, both in treated and control units, 

there was no apparent directional movement in ordination space associated 

with treatments. For instance, although treated units Canyon and Blue Jay 

both moved during the study period, they moved toward each other, suggesting that if there was an effect of prescribed fi re, it had the opposite effect 

in these two units.

Abundance of Focal Species

For the eight Partners in Flight coniferous forest focal species that we investigated, we could discern no obvious changes in abundance that occurred 

as a result of treatment (fi g. 4).

Discussion

Our results suggest that the effects of prescribed fi re on vegetation structure 

and bird community composition have been minimal in these areas of the 

Klamath National Forest. We found no evidence that prescribed fi re treatments were associated with a persistent decrease in the volume of vegetation 

in the tree or shrub layer. There was substantial year to year variation, and 

some of these changes may represent short term changes from recent treatments, but these effects did not appear to persist, or accumulate, over the 

course of the study.

Similarly, our ordination results for the bird community show no evidence 

of a directional change in bird community composition that is unique to the 

treated areas (fi g. 3). Even in the absence of overall community effects, we 

Table 2—Coeffi cient of determination for the correlation between bird community detrended 

correspondence analysis (DCA) ordination distances and relative Euclidean distances in 

the original multidimensional space.

 All birds Core birds

DCA Axis Incremental R2 Cumulative R2 Incremental R2 Cumulative R2

Axis 1 0.39 0.39 0.39 0.39

Axis 2 0.35 0.74 0.40 0.79

Axis 3 0.04 0.79 0.04 0.83
600 USDA Forest Service Proceedings RMRS-P-41. 2006.

Seavy and Alexander Measuring Ecological Effects of Prescribed Fire Using Birds as Indicators of Forest Conditions

Figure 3—Ordination plots of DCA scores for bird communities at treated and 

untreated sites in the Klamath National Forest in northern California.
USDA Forest Service Proceedings RMRS-P-41. 2006. 601

Measuring Ecological Effects of Prescribed Fire Using Birds as Indicators of Forest Conditions Seavy and Alexander

Figure 4—Log response ratios comparing bird abundance of treated and untreated sites from the 

four study areas over the fi re-year study period.
602 USDA Forest Service Proceedings RMRS-P-41. 2006.

Seavy and Alexander Measuring Ecological Effects of Prescribed Fire Using Birds as Indicators of Forest Conditions

may still be concerned about the effects of prescribed fi re if they change the 

abundance of individual species that are of particular conservation concern. 

However, our analyses of the Partners in Flight focal species for coniferous 

forests showed no consistent trends for these species to become either more 

or less abundant after treatment.

There is limited evidence that fuels reduction projects in the western United 

States can be implemented in such a way that they are consistent with the 

goals of wildlife conservation and ecosystem health (Tiedemann and others 2000; Huff and others 2005). However, this study, and a similar study 

comparing thinned and unthinned mixed-conifer forests in the Sierra Nevada 

(Siegel and DeSante 2003), suggest that in conditions were prescribed fi re 

has little effect on the volume of live vegetation, such treatments may have 

relatively minor consequences for bird communities. However, if the goal of 

these treatments includes restoring conditions in such a way that it changes 

the quality of wildlife habitat, our results suggest that prescribed fi re in the 

Klamath National Forest would need to be modifi ed to achieve the desired 

conditions.

Acknowledgments

We thank S. Cuenca and T. Grenvic for logistical support and numerous 

fi eld assistants for their help in conducting fi eld work. Comments from C.J. 

Ralph and S. Janes greatly improved the paper. This project was funded by 

the Joint Fire Sciences Program project 01B-3-2-10 and the US Forest Service 

Region 5 Partners in Flight program.

References

Agee J. K. 1991. Fire history along an elevational gradient in the Siskiyou Mountains, 

Oregon. Northwest Science 65:188-199.

Agee, J. K. 1993. Fire ecology of Pacifi c Northwest Forests. Island Press, Washington 

DC.

Altman, B. 2000. Conservation strategy for landbirds in lowlands and valleys of 

western Oregon and Washington. Oregon-Washington Partners in Flight, Boring, 

OR.

CalPIF (California Partners in Flight). 2002. Version 1.0. The draft coniferous 

forest bird conservation plan: a strategy for protecting and managing coniferous 

forest habitats and associated birds in California (J. Robinson and J. Alexander, 

lead authors). Point Reyes Bird Observatory, Stinson Beach, CA.

Gram, W. K., V. L. Sork, R. J. Marquis, R. B. Renken, R. L. Clawson, J. Faaborg, 

D. K. Fantz, J. Le Corff, J. Lill, and P. A. Porneluzi. 2001. Evaluating the effects 

of ecosystem management: a case study in a Missouri Ozark Forest. Ecological 

Applications 11:1667-1679.

Huff, M. H., N. E. Seavy, J. D. Alexander, and C. J. Ralph. 2005. Fire and birds in 

the maritime Pacifi c Northwest. Studies in Avian Biology 30:46-62.

Hutto, R.L. 1998. Using landbirds as an indicator species group. In: Marzluff, J.M., 

Sallabanks, R. (Eds.), Avian Conservation: Research and Management. Island 

Press, Covelo, CA.

McCune, B. and M. J. Mefford. 1999. PC-ORD, Multivariate Analysis of Ecological 

Data, Version 4.25. MjM Software, Gleneden Beach, OR.
USDA Forest Service Proceedings RMRS-P-41. 2006. 603

Measuring Ecological Effects of Prescribed Fire Using Birds as Indicators of Forest Conditions Seavy and Alexander

Ralph, C. J., G. R. Guepel, P. Pyle, T. E. Martin, D. F. DeSante. 1993. Handbook 

of fi eld methods for monitoring landbirds. U.S.D.A. For. Ser. Gen. Tech. Rep. 

PSW-GTR-144.

Schieck, J. 1997. Biased detection of bird vocalizations affects comparisons of bird 

abundance among forested habitats. Condor 99:179-190.

Siegel, R. B., D. F. DeSante. 2003. Bird communities in thinned versus unthinned 

Sierran mixedconifer stands. Wilson Bulletin 115:155-165.

Smucker, K. M., R. L. Hutto, and B. M. Steele. 2005. Changes in bird abundance 

after wildfire: importance of fire severity and time since fire. Ecological 

Applications 15:1535-1549.

Taylor A. H. and C. N. Skinner. 1998. Fire history and landscape dynamics in latesuccessional reserve, Klamath Mountains, California, U.S.A. Forest Ecology and 

Management 111:285–301.

Taylor A. H. and C. N. Skinner. 2003. Spatial patterns and controls on historical fi re 

regimes and forest structure in the Klamath Mountains. Ecological Applications 

13:704-719.

Tiedemann, A. R., J. O. Klemmedson, and E. L. Bull. 2000. Solution of forest 

health problems with prescribed fi re: are forest productivity and wildlife at risk? 

Forest Ecology and Management 127:1-18.

Vos, P., E. Meelis, W. J. A. Ter Keurs. 2000. A framework for the design of ecological 

monitoring programs as a tool for environmental and nature management. 

Environ. Monit. Assess. 61:317-344.

Whittaker, R. H. 1960. Vegetation of the Siskiyou Mountains, Oregon and 

California. Ecological Monographs 30:280-338.

Wills, R. D., and J. D. Stuart. 1994. Fire history and stand development of a Douglasfi r/hardwood forest in northern California. Northwest Science 68:205-212.
USDA Forest Service Proceedings RMRS-P-41. 2006. 605

In: Andrews, Patricia L.; Butler, Bret W., 

comps. 2006. Fuels Management—How to 

Measure Success: Conference Proceedings. 

28-30 March 2006; Portland, OR. 

Proceedings RMRS-P-41. Fort Collins, 

CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research 

Station.

1 Research Soil Scientist/Project Leader, 

Rocky Mountain Research Station, 

Moscow, ID. ddumroese@fs.fed.us

2 Professor Forest Soils, Michigan 

Technological University, School of 

Forestry and Environmental Sciences, 

Houghton, MI.

3 Biometrician, Rocky Mountain Research 

Station, Moscow, ID.

4 Geographer, Rocky Mountain Research 

Station, Moscow, ID.

5 Biological Technician, Rocky Mountain 

Research Station, Moscow, ID. 

6 Soil Scientist, Helena National Forest, 

Helena, MT.

7 Soil Program Leader, Northern Region, 

Missoula, MT.

Abstract—The wildland fi res of 2000, 2002, and 2003 created many opportunities to 

conduct post-fi re logging operations in the Inland Northwest. Relatively little information is available on the impact of post-fi re logging on long-term soil productivity or 

on the best method for monitoring these changes. We present a USDA Forest Service 

Northern Region study of post-fi re logged sites using a variety of methods to assess 

changes in soil productivity and site sustainability after timber harvesting activities. 

The disparate soil and climatic conditions throughout the Northern Region made it 

an ideal area to study post-fi re logging operations. Our results indicate that post-fi re 

logging during the summer creates more detrimental disturbance (50% of the stands) 

than winter harvesting (0% of the stands). In addition, on the sites we sampled, equipment type (tractor > forwarder > rubber-tired skidder) also infl uenced the amount 

of detrimental disturbance. Number of sample points is a critical factor when determining the extent of detrimental disturbance across a burned and harvested unit. We 

recommend between 80 and 200 visual classifi cation sample points, depending on 

confi dence level. We also provide a summary of methods that will lead to a consistent 

approach to provide reliable measures of detrimental soil disturbance.

Introduction

During the last century, wildfi res in the western USA have been viewed 

by many land managers and the public as catastrophic events (Kuuluvainen 

2002). Until recently, fi re suppression has been used to control the extent of 

these fi res, but now stand-replacing fi res are occurring on many Federal lands 

in the western USA. Consequently, the standard policy on many National 

Forests has been to harvest fi re-killed trees for economic value before they 

decay (Lowell and Cahill 1996; McIver and Starr 2001). Proponents and opponents of post-fi re logging are abundant (Beschta and others 2004; Sessions 

and others 2004; Donato and others 2006), but one critical issue of concern 

to each group is the impact of this practice on the soil resource.

Wildland fi res can impact more than 10,000 ha of forest land at one time 

and, combined with post-fi re logging, signifi cant soil impacts can occur. Loss 

of surface organic matter and nutrients from the fi re, increased decomposition from increased insolation, decreased soil porosity, increased erosion, and 

compaction may all combine to alter site productivity after wildfi re and postfi re logging activities (Poff 1996). There are no specifi c methods that directly 

assess the impact of post-fi re logging on soil productivity, but many methods 

for measuring proxies exist (see Burger and Kelting 1999; Schoenholtz and 

other 2000). Measures of wood production, net primary productivity, or 

changes in some specifi c soil properties (e.g. bulk density, forest fl oor depth, 

Monitoring Changes in Soil Quality from 

Post-fi re Logging in the Inland Northwest

Deborah Page-Dumroese1, Martin Jurgensen2, Ann Abbott3, 

Tom Rice4, Joanne Tirocke5, Sue Farley6, and Sharon DeHart7
606 USDA Forest Service Proceedings RMRS-P-41. 2006.

Page-Dumroese, Jurgensen, Abbott, Rice, Tirocke, Farley, and DeHart Monitoring Changes in Soil Quality from Post-fi re Logging in the Inland Northwest

cover type, etc.) can all be readily determined, but the link between forest 

management, soil properties, and site sustainability is not easily obtained.

Historically, maintenance of soil productivity on public lands in the USA 

has been governed by the Multiple Use Sustained Yield Act of 1960, the Forest 

and Rangeland Renewable Resources Planning Act of 1974, and the National 

Forest Management Act of 1976. As an outgrowth of these policies, each 

USDA Forest Service Region developed soil quality standards and guidelines, 

which were designed to act as a fi rst warning of reduced site productivity 

after harvest and site preparation operations. The general concepts and the 

basis for the various guidelines are described in Griffi th and others (1992). 

Lacking better methods, these standards and guidelines have also been used 

to evaluate soil productivity changes after wildfi re and post-fi re logging.

Concern about an accurate assessment of soil properties has expanded 

because of the growing public interest in the consequences of forest management practices on soil quality and its productive capacity (Burger and Kelting 

1999; Schoenholtz and others 2000). Worldwide initiatives including the 

Helsinki Process (1994) and the Montreal Process (1995) have resulted in 

the development of criteria and indicators for monitoring sustainable forestry practices at broad levels (Burger and Kelting 1999). Recently, progress 

has been made on developing a common approach to soil monitoring in 

northwestern North America (Curran and others 2005). The key questions 

are: What do we measure and what does it mean? The literature is rife with 

examples of how a soil chemical, physical or biological property may contribute to changes in biomass production, hydrologic function, or ecosystem 

sustainability (see Schoenholtz and others 2000 for a summary). However, 

as budgets and personnel dwindle, land managers need a visual assessment 

of disturbance that can be completed quickly, effi ciently, and easily by either 

fi eld soil scientists or others trained in the assessment process (Curran and 

others 2005).

Wildfi res and post-fi re logging generate unique soil surface conditions. 

Visual disturbance criteria estimate the amount of detrimental disturbance 

and may need to be specifi cally designed to encompass the impacts of both fi re 

and logging. Therefore, the objectives of our study were to: (1) determine the 

magnitude and areal extent (as defi ned by current soil quality standards) of 

detrimental disturbance from wildfi re and post-fi re logging across the Northern Region of the USDA Forest Service, (2) determine the most appropriate 

spatial sampling design methods for assessing the magnitude of soil impacts, 

and (3) develop visual criteria that can be used following post-fi re salvage 

harvests to assess disturbance across disparate soil and climatic regimes.

Methods and Materials

Site Descriptions

In the summer of 2004 and 2005, post-fi re logging sites were located on 

the Custer, Helena, Bitterroot, Kootenai, Lewis and Clark, Flathead, and 

Lolo National Forests (Table 1). Thirty-six stands were sampled over 2 fi eld 

seasons; 20 had been post-fi re winter logged and 16 were post-fi re summer 

logged. Sites were selected by local soil scientists in areas that had recently 

burned in a wildfi re (2000, 2002, or 2003) and had subsequently been 

logged. If available, we selected three replicate units on each forest, which 

had similar slope, aspect, soil type, and logging practices.
USDA Forest Service Proceedings RMRS-P-41. 2006. 607

Monitoring Changes in Soil Quality from Post-fi re Logging in the Inland Northwest Page-Dumroese, Jurgensen, Abbott, Rice, Tirocke, Farley, and DeHart

Soil Indicator Assessment

In each post-fi re logging unit, a 100 point systematic grid and a 100 point 

random transect were established from a fi xed corner point. At each grid and 

transect point, we described the soil surface cover (e.g. rill erosion, forest fl oor, 

bare mineral soil, rocks, etc.) and the presence or absence of platy structure 

in the underlying mineral soil in 1 m2 plots. Once the soil surface had been 

described, we assigned a soil disturbance category to each plot (Table 2), 

based on the classifi cation systems of Howes (2001) and Heninger and others 

(2002). In addition to a visual classifi cation, soil strength was determined at 

each sampling point using a RIMIK CP40 recording penetrometer (Agridry, 

Toowoomba, Australia).

Statistical Analysis

Chi-square tests for homogeneity were used to evaluate the relationships 

between disturbance class and soil texture, parent material, season of harvest, and harvest method. Chi-square tests for homogeneity were also used 

to evaluate relationships between detrimental soil disturbance, soil texture, 

parent material, season of harvest, and harvest method. Analysis of variance 

was used to examine relationships between soil strength and soil texture, 

parent material, season of harvest, and harvest method. All analyses were 

performed using SAS 9.1.

Results

In this study, there were no signifi cant differences between the grid and 

random transect methods when visually assessing soil disturbance after fi re 

and post-fi re logging (p < 0.001). Therefore, data from both the grid and 

random transect were pooled for subsequent analyses.

Table 1—Post-fi re logging study site characteristics.

 Season Logging National Year Year of Elevation Surface soil

of harvest method Forest burned harvest (m) Parent material texture

Summer Tractor Custer 2002 2003 1200 Sandstone Loamy sand

 Tractor Helena 2000 2003 1700 Metasediments Sandy loam

 Tractor Helena 2000 2002 1700 Metasediments Loamy sand

 Forwarder Lolo 2000 2005 1400 Metasediments Loamy sand

 Forwarder Flathead 2000 2002 1900 Quartzite Sandy loam

Winter Tractor Bitterroot 2000 2002 1750 Granitic Loamy sand

 Tractor/RTS1 Flathead 2000 2002 1150 Limestone Silt loam

 Tractor Helena 2000 2002/03 2500 Metasediments Sandy loam

 Tractor Helena 2000 2002 1700 Metasediments Loamy sand

 Tractor Lewis & Clark 2001 2003 2200 Limestone Silt loam

 Forwarder Kootenai 2000 2003 1600 Glacial till Silt loam

 Forwarder Lolo 2000 2005 1500 Metasediments Loamy sand

1 RTS= Rubber tired skidder.
608 USDA Forest Service Proceedings RMRS-P-41. 2006.

Page-Dumroese, Jurgensen, Abbott, Rice, Tirocke, Farley, and DeHart Monitoring Changes in Soil Quality from Post-fi re Logging in the Inland Northwest

In the USDA Forest Service Northern Region, a stand is considered detrimentally disturbed if greater than 15% of the area is in disturbance class 

3, 4, or 5 (Table 2). Of the stands we sampled, 50% of the summer-logged 

sites and no winter-logged sites had more than 15% of the sampling points in 

the detrimental disturbance categories (Table 3). The relationship of logging 

season and detrimental disturbance is signifi cant (p < 0.0001) and is primarily 

characterized by platy structure on skid trails or cow trails.

Table 2—Description of soil condition classes used.

Condition class Identifying features

 0 Undisturbed forest fl oor

 1 No evidence of past equipment operation, but records of harvesting

 No wheel ruts

 Forest fl oor intact

 No mineral soil displacement

 2 Trail used by harvester (ghost trails)

 Faint wheel tracks and ruts

 Forest fl oor intact

 No mineral soil displacement and minimal mixing with forest fl oor

 3 Trail used by harvester and forwarder

 Two track trails created by one or more passes

 Wheel tracks are >10 cm deep

 Forest fl oor is missing/partially intact

 4 Skid trails existed prior to reentry and reused

 Old skid trails from 20th century selective harvest

 Recent operation had little impact on old skid trail

 Trails have a high level of soil compaction

 Evidence of mineral soil displacement from trails

 5 Old and new skid trails present

 Mineral soil displacement from area between skid trails

 Forest fl oor is missing

Table 3—Average soil disturbance after summer and winter post-fi re logging.

Season of National Number of Amount of Disturbance 

 harvest Forest stands Not detrimental Detrimental

 - - - - - - - - - -percent - - - - - - - - - -

Summer Custer 4 72 28

 Flathead 3 77 23

 Helena 3 96 4

 Lolo 4 91 9

Average 84 16

 

Winter Bitterroot 3 97 3

 Flathead 3 90 10

 Kootenai 3 97 3

 Lewis & Clark 3 92 8

 Lolo 2 99 1

 Helena 1 3 92 8

 Helena 2 3 87 13

Average 93 7
USDA Forest Service Proceedings RMRS-P-41. 2006. 609

Monitoring Changes in Soil Quality from Post-fi re Logging in the Inland Northwest Page-Dumroese, Jurgensen, Abbott, Rice, Tirocke, Farley, and DeHart

There is a signifi cant relationship (p<0.0001) between site parent material 

and the areal extent of detrimental disturbance. Metasediments, limestone, 

and granitic parent materials were the least detrimentally disturbed with 75% 

of the visual classifi cation points being in class 0 or 1.

Surface soil strength was generally not related to disturbance class; however, 

some exceptions occurred at the 2.5 cm depth. The exceptions were two stands 

on the Helena National Forest (p = 0.0312; p = 0.0236) and two stands on 

the Flathead National Forest (p = 0.0235; p = 0.0033). These four stands 

are unique as there was no relationship between surface soil strength, harvest 

season, type of equipment, or total areal extent of disturbance. However, all 

four of these sites were burned in 2000 and post-fi re logged in 2002. The 

time between post-fi re logging and sampling could have been enough for 

some soil recovery before soil monitoring occurred.

For all sites, there is a signifi cant relationship (p < 0.0001) between visual disturbance class, areal extent of detrimental disturbance, and harvest 

method. In 66% of the forwarder harvested units, 85% of the rubber-tired 

skidder units, and 45% of the tractor units, we detected less than 15% areal 

extent of detrimental disturbance. Many of the sampling sites classifi ed as not 

detrimentally disturbed had less exposed bare mineral soil than detrimentally 

disturbed units (p < 0.0001). On sites with a signifi cant portion of soil cover, 

many had live plants, forest fl oor, moss and lichens present, which may likely 

indicate soil surface recovery after post-fi re harvesting.

Discussion

Severe wildfires greatly impact below-ground ecosystems, including 

development of water-repellent soils (DeBano 2000) and decreased evapotranspiration (Walsh and others 1992), which can lead to overland fl ow of 

water and signifi cant soil erosion. Additionally, the loss of forest fl oor material 

reduces water storage in the surface mineral soil (McIver and Starr 2001). The 

subsequent cumulative effects of fi re followed by logging in such a landscape 

have been diffi cult to measure (McIver and Starr 2001). Soil surface conditions 

after post-fi re logging is highly infl uenced by management decisions, which 

determine equipment type and harvest season. Regardless of disturbance 

origin (fi re or logging), soil productivity in a given area may be infl uenced by 

site characteristics (topography, parent material, revegetation, and climate), 

logging method, and construction of additional roads or skid trails. Our visual 

disturbance classes (0-5) along with a quick presence or absence survey of 

key factors (platy or massive structure, forest fl oor displacement, rut, sheet, 

rill, or gully erosion, mass movement, live plant, forest fl oor, wood debris 

<3 ̋ or >3 ̋, or bare soil) can determine if a harvest unit will meet soil quality 

guidelines. However, our disturbance classes need to be modifi ed to include 

soil burn impacts associated with severe wildfi res. Removal of surface organic 

matter may not be detrimental to site productivity unless it is coupled with 

a change in color in the mineral soil (Neary and others 1999).

Detrimental disturbance was least with rubber-tired skidders, greater when 

using forwarders, and the most with tractors. In addition, the number of 

stands with detrimental disturbance was signifi cantly decreased when logging operations occurred during the winter. This is similar to work by Klock 

(1975) in which he found that tractor skidding over exposed mineral soil 

caused the greatest amount of detrimental disturbance (36%), followed by 

cable skidding (32%), and tractor skidding over snow (10%).
610 USDA Forest Service Proceedings RMRS-P-41. 2006.

Page-Dumroese, Jurgensen, Abbott, Rice, Tirocke, Farley, and DeHart Monitoring Changes in Soil Quality from Post-fi re Logging in the Inland Northwest

Eighty-two percent of our stands were categorized as not having a detrimental soil disturbance after post-fi re logging. The remaining stands that 

approached or exceeded the 15% areal extent of detrimental soil disturbance 

may require amelioration before other management activities are considered. 

Detrimental soil disturbance ratings are generally higher after wildfi re and 

post-fi re logging when compared to green timber sales, since both wildfi re 

and post-fi re logging sites generally lack understory vegetation and forest 

fl oor (Klock 1975). Ground-based logging can mitigate some detrimental 

impacts by leaving logging residue on site or by delaying harvesting until 

after killed trees drop their needles after a wildfi re to establish some forest 

fl oor. Both measures provide additional protection from erosion (Megahan 

and Molitor 1975).

Compaction of the surface soil is also a common concern after groundbased logging operations (Froehlich 1978; Adams and Froehlich 1981; 

Clayton and others 1987; Page-Dumroese 1993; Miller and others 1996), 

and surface soil disturbance is more evident immediately post-harvest. Using 

visual classifi cation categories, we were able to distinguish impacts of summer 

and winter logging, the infl uence of parent material, and harvest methods. 

In some cases, our visual assessments were a direct indication of changes in 

soil physical properties (e.g. platy or structure) or in surface properties (e.g. 

displacement of surface organic matter, churned mineral soil, or ruts), and 

could be used as a surrogate for more intensive sampling. However, the time 

elapsed between the wildfi re and logging activities, and the time between 

post-fi re logging and soil monitoring can be important factors in the degree 

of detrimental disturbance measured. For instance, on sites with several years 

between the fi re and logging and then another time period between logging 

and monitoring, some revegetation would likely occur and deposit plant litter on the soil surface. Plant establishment could improve some soil physical 

properties and infl uence whether a sample point is categorized as detrimental 

(class 3) or not detrimental (class 2). The short times between fi re, logging 

and monitoring (1 year between each) may be a reason the Custer National 

Forest had 28% detrimental disturbance, compared to the Helena National 

Forest (3 years between fi re and logging, and 1 year between logging and 

monitoring) with only 4% detrimental soil disturbance.

Soil resistance, as measured using a penetrometer, could be easily evaluated on many sites, but the infl uence of rocks, roots, and low soil moisture, 

later in the growing season limited its usefulness as tool to make compaction 

comparisons among sites. However, the use of the penetrometer within one 

area of similar soil characteristics during a time when soil moisture is fairly 

high (near fi eld capacity) is feasible for monitoring changes in soil penetration 

resistance (Utset and Cid 2001).

Management Implications

For our study, we used 6 visual disturbance categories (classes 0-5) to 

describe areas that had been burned by wildfi re and subsequently logged. 

These visual disturbance classes described combinations of soil disturbance 

that recur across each harvest unit and can be a relatively quick and easy 

method for quantifying soil disturbance (Howes et al. 1983). However, 

season of logging, equipment used, and time between disturbance activities and monitoring were important variables that determine the extent of 
USDA Forest Service Proceedings RMRS-P-41. 2006. 611

Monitoring Changes in Soil Quality from Post-fi re Logging in the Inland Northwest Page-Dumroese, Jurgensen, Abbott, Rice, Tirocke, Farley, and DeHart

detrimental disturbance. The visual classifi cation measurements do seem to 

be an easy, inexpensive method for timely monitoring, and with more data 

collection, can likely be correlated with long-term vegetation growth. Visual 

classifi cations that encompass burn conditions of the soils (charcoal, mineral 

soil discoloration and ash deposition) are also needed to refi ne the disturbance assessments, which would make them more useful to forest managers 

and soil scientists.

Our data indicate that at the 95% confi dence level, a sample size of approximately 200 sample points in a 10 ha unit would detect 15% (±5%) 

detrimental disturbance (Table 4 and unpublished data). A site with 5% detrimental disturbance would only need 75 sample points; whereas a site with 

a high proportion (>30% of the unit) of detrimental disturbance would need 

340 sample points at this confi dence level. A confi dence level of 80% would 

signifi cantly lower the number of samples needed. For instance, a site with 

little disturbance (<5% of the unit) would need only 32 sample points, but 

a site with a large amount (30% of the unit) of disturbance would need 139 

sample points. Using either random transects or grid points are appropriate 

strategies for laying out monitoring points for similar wildfi re burned and 

post-fi re harvested sites when using our visual classifi cation method.

In the USDA Forest Service, soil assessment of management impacts is 

typically linked to site productivity through soil quality standards (PageDumroese and others 2000). However, these standards are not site-specifi c, 

do not specify collection of baseline data, are not always linked to changes 

in biomass production or carbon accumulation, and, in many cases, the 

monitoring techniques are cumbersome, lengthy, costly and require some 

laboratory analysis. Reliable assessment of soil disturbance and the link to site 

productivity is critical. Visual classifi cations have been used throughout the 

Pacifi c Northwest by the B.C. Ministry of Forests (Forest Practices Code Act 

1995) and Weyerhaeuser Company (Scott 2000), but have not been linked 

to tree growth. To date, visual classifi cation systems only describe surface 

soil conditions, and have not been validated to response variables that are 

ecologically important (e.g. tree growth, survival). A necessary step in the 

acceptance of any visual soil disturbance criteria is to develop direct evidence 

that there is a change in site function, productivity, or sustainability (Curran 

and others 2005). Our test of visual criteria for assessing soil disturbance 

after wildfi re and logging operations could be used to determine areal extent 

of detrimental impacts within a harvest unit.

Although visual classifi cations are not directly linked to ecosystem functions at this time, it is generally recognized in the northwestern USA that 

surface organic matter can help maintain site productivity (Page-Dumroese 

and others 2000; Jurgensen and others 1997; Harvey and others 1981). 

Table 4—Sample points needed to detect 15% areal extent 

of detrimental disturbance in a 10 ha unit at different 

confi dence levels (±5%).

Confi dence level Sample points needed

 95% 196

 90% 139

 80% 84
612 USDA Forest Service Proceedings RMRS-P-41. 2006.

Page-Dumroese, Jurgensen, Abbott, Rice, Tirocke, Farley, and DeHart Monitoring Changes in Soil Quality from Post-fi re Logging in the Inland Northwest

Existing studies such as the North American Long-Term Soil Productivity (LTSP) study, established in the USA and Canada, are investigating the 

effects of OM removal and compaction on soil productivity (Powers and 

others 2004), but fi re was not included as a disturbance variable. However, 

the physical removal of surface OM on LTSP study sites generally resulted 

in lower mineral soil C pools and reduced N availability 10 years after treatment, and tree growth was reduced on low productivity sites (Powers and 

others 2005). Additionally, tree growth declined on compacted clay soils and 

increased on sandy soils, but was strongly related to control of the understory 

vegetation. Recently, the Fire and Fire Surrogate study was started by the 

USDA/USDI to evaluate the effects of mechanical fuel reduction treatments 

and prescribed fi re-severity on above- and below-ground productivity in a 

variety of forest ecosystems across the USA (Weatherspoon 2000). Both of 

these sources of information are needed to complement monitoring data to 

help develop post-fi re harvesting methods that maintain adequate amounts 

of OM and limit soil compaction to maintain soil productivity.

References

B.C. Ministry of Forests and B. C. Environment. 1995. Hazard assessment keys for 

evaluating site sensitivity to soil degrading processes guidebook. Forest Practices 

Code of British Columbia, Victoria, B.C. 24 p.

Beschta, R.L., J.J. Rhodes, J.B. Kauffman, R.E. Gresswell, G.W. Minshall, J.R. Karr, 

D.A. Perry, F.R. Hauer, and C.A. Frissell. 2004. Postfi re management on forested 

public lands of the western United States. Cons. Biol. 18: 957-967.

Burger, J.A. and D.L. Kelting. 1999. Using soil quality indicators to assess forest 

stand management. For. Ecol. and Manage. 122: 155-166.

Clayton, J.L., G. Kellogg, and N. Forrester. 1987. Soil disturbance-tree growth 

relations in central Idaho clearcuts. USDA Forest Service, Intermountain Res. 

Stn., Res. Note INT-RN-372. 6 p.

Curran, M.P., R.E. Miller, S.W. Howes, D.G. Maynard, T.A. Terry, R.L. Heninger, 

T. Niemann, K. van Rees, R.F. Powers, and S.H. Schoenholtz. 2005. Progress 

toward more uniform assessment and reporting of soil disturbance for operations, 

research, and sustainability protocols. For. Ecol. Manage. 220: 17-30.

DeBano L.F. 2000. The role of fi re and soil heating on water repellency in wildland 

environments: a review. J. of Hydrol. 231: 195-206.

Donato, D.C., J.B. Fontaine, J.L. Campbell, W.D. Robinson, J.B. Kauffman, and 

B.E. Law. 2006. Post-fi re logging hinders regeneration and increases fi re risk. 

Sci. 311: 352.

Froehlich, H.A. 1978. Soil compaction from low ground-pressure, torsion-suspension 

logging vehicles on three forest soils. Res. Pap. 36. For. Res. Lab. Oregon State 

Univ., Corvallis. 13 p.

Griffi th, R.W., C. Goudy, and R. Poff. 1992. Current application of soil quality 

standards. In: Proceedings of the soil quality standards symposium. SSSA meeting 

San Antonio, TX, 21-27 October 1990. USDA Forest Service, Watershed and Air 

Management Staff. Washington, DC WO-WSA-2, pp 1-5.

Harvey, A.E., Jurgensen, M.F., and Larsen, M.J. 1981. Organic reserves: importance 

of ectomycorrhizae in forest soils of western Montana. For. Sci. 27: 442-445.

Heninger, R.L., W. Scott, A. Dobkowski, R., Miller, H. Anderson, and S. Duke. 

2002. Soil disturbance and 10-year growth response of coast Douglas-fi r on 

nontilled and tilled skid trails in the Oregon Cascades. Can. J. For. Res. 32: 

233-246.
USDA Forest Service Proceedings RMRS-P-41. 2006. 613

Monitoring Changes in Soil Quality from Post-fi re Logging in the Inland Northwest Page-Dumroese, Jurgensen, Abbott, Rice, Tirocke, Farley, and DeHart

Howes, S., J. Hazard, and J. M. Geist. 1983. Guidelines for sampling some physical 

conditions of surface soils. USDA Forest Service, Pacifi c Northwest Region. R6-

RWM-146. 34 p.

Howes, S.W. (2001). Proposed soil resource condition assessment. Wallowa-Whitman 

National Forest. Unpublished methods.

Howes, S., J. Hazard, and M.J. Geist. 1983. Guidelines for sampling some physical 

conditions of surface soils. USDA Forest Service Pacifi c Northwest Region 

Publication R6-PNW-146-1983. 34 p.

Jurgensen, M.F., A.E. Harvey, R.T. Graham, D.S. Page-Dumroese, J.R. Tonn, 

M. J. Larsen, and T.B. Jain. 1997. Impacts of timber harvesting on soil organic 

matter, nitrogen, productivity, and health of Inland Northwest forests. For. Sci. 

43: 234-251.

Klock, G.O. 1975. Impacts of fi ve postfi re salvage logging systems on soils and 

vegetation. J. Soil Water Conserv. 30: 78-81.

Kuuluvainen, T. 2002. Disturbance dynamics in boreal forests: defi ning the ecological 

basis of restoration and management of biodiversity. Silva Fenn. 36: 5-12.

Lowell, E.C. and J.M. Cahill. 1996. Deterioration of fi re-killed timber in southern 

Oregon and northern California. Western J. Applied For. 11: 125-131.

Megahan, W.F. and D.C Molitor. 1975. Erosional effects of wildfi re and logging in 

Idaho. p. 423-444. In: Proc. of the symposium on watershed management. Am. 

Soc. Civil Engineers.

McIver, J.D. and L. Starr. 2001. A literature review on environmental effects of 

postfi re logging. Western J. Applied For. 16: 159-168.

Miller, R.E., W. Scott, and J.W. Hazard. 1996. Soil compaction and conifer growth 

after tractor yarding at three coastal Washington locations. Can. J. For. Res. 26: 

225-236.

Neary, D.G., C.C. Klopateck, L.F. DeBano, and P.F. Ffolliott. 1999. Fire effects 

on belowground sustainability: a review and synthesis. For. Ecol. and Manage. 

122: 51-71.

Page-Dumroese, D.S. 1993. Susceptibility of volcanic ash-infl uenced soil in northern 

Idaho to mechanical compaction. USDA Forest Service, Intermountain Res. Stn. 

INT-RN-409. 5 p.

Page-Dumroese, D., M. Jurgensen, W. Elliot, T. Rice, J. Nesser, T. Collins, and R. Meurisse. 

2000. Soil quality standards and guidelines for forest sustainability in northwestern 

North America. For. Ecol. and Manage. 138: 445-462.

Poff, R.J. 1996. Effects of silvicultural practices and wilfi re on productivity of forest 

soils. In: Sierra Nevada Ecosystem Project: Final report to Congress, vol. II. 

Assessments and scientifi c basis for management options, University of California, 

Davis. Chapter 16. 477-494.

Powers, R.F., F.G. Sanchez, D.A. Scott, and D. Page-Dumroese. 2004. The North 

American long-term soil productivity experiment: coast-to-coast fi ndings from 

the fi rst decade. Proceedings RMRS-P-34. Ft. Collins, CO. USDA For. Serv. 

Rocky Mtn. Res. Stn. p. 191-206.

Powers, R.F., D.A. Scott, F.G. Sanchez, R.A. Voldseth, D. Page-Dumroese, J.D. 

Elioff, and D.M. Stone. 2005. The North American long-term soil productivity 

experiment: Findings from the fi rst decade of research. For. Ecol. Manage. 220: 

31-50.

Schoenholtz, S.H., H. VanMiegroet, and J. A. Burger. 2000. A review of chemical and 

physical properties as indicators of forest soil quality: challenges and opportunities. 

For. Ecol. Manage. 138: 335-356.

Scott, W. 2000. A soil disturbance classifi cation system. Internal Report. Forestry 

Research Technical Note. Paper #00-1. Weyerhauser Co., Federal Way, WA. 12 p.
614 USDA Forest Service Proceedings RMRS-P-41. 2006.

Page-Dumroese, Jurgensen, Abbott, Rice, Tirocke, Farley, and DeHart Monitoring Changes in Soil Quality from Post-fi re Logging in the Inland Northwest

Sessions, J., P. Bettinger, R. Buckman, M. Newton, and J. Hammann. 2004. 

Hastening the return of complex forests following fi re: The consequences of 

delay. J. For. 102: 38-45.

Utset, A. and G. Cid. 2001. Soil penetrometer resistance spatial variability in a 

Ferrosol at several moisture conditions. Soil and Tillage Res. 61: 193-202.

Walsh, R.P.D., C. deO.A. Cohlho, R.A. Shakesby, and J.P. Terry. 1992. Effects of 

land use management practices and fi re on soil erosion and water quality in the 

Agueda River Basin, Portugal. GEOOKOplus. 3: 15-36.

Weatherspoon, C.P. 2000. A long-term national study of the consequences of fi re and 

fi re surrogate treatments. In: Proceedings Joint Fire Science Conference, Boise, 

ID, June 15-17, 1999. University of Idaho Press, Moscow, ID. p. 117-126.
USDA Forest Service Proceedings RMRS-P-41. 2006. 615

In: Andrews, Patricia L.; Butler, Bret W., 

comps. 2006. Fuels Management—How to 

Measure Success: Conference Proceedings. 

28-30 March 2006; Portland, OR. 

Proceedings RMRS-P-41. Fort Collins, 

CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research 

Station.

1 Research Foresters, Rocky Mountain 

Research Station, Forest Service, U.S. 

Department of Agriculture, Moscow, ID. 

tjain@fs.fed.us

2 Assistant Professor, Department of 

Biological Sciences, College of Science 

and Mathematics, California Polytechnic 

State University, San Luis Obispo, CA.

Abstract—A study funded through National Fire Plan evaluates the relation between 

pre-wildfi re forest structure and post-wildfi re soil burn severity across three forest 

types: dry, moist, and cold forests. Over 73 wildfi res were sampled in Idaho, Oregon, 

Montana, Colorado, and Utah, which burned between 2000 and 2003. Because of 

the study’s breadth, the results are applicable for understanding how forest structure 

relates to post-wildfi re soil burn severity within Rocky Mountains forests. This paper 

discusses a burn severity classifi cation that integrates fi re intensity, fi re severity, and 

post wildfi re response; and discusses the relations wildfi re setting (fi re group), tree 

crown ratio, tree canopy cover, surface fuel condition, and tree size have with different soil burn severity outcomes.

Introduction

Although canopy bulk density, fuel models, canopy base height, and 

other forest metrics have been related to fi re behavior using physical laws, 

controlled experiments, and models (Graham and others 2004, Peterson and 

others 2005), there is limited information to indicate how forest structure 

infl uences or is related to burn severity (what is left and its condition) after 

a wildfi re event (Broncano and others 2004, Loehle 2004, Weatherspoon 

and Skinner 1995). Moreover, the uncertainty of these relations is unknown, 

preventing forest managers from communicating their confi dence in fuel 

treatments that may reduce the risk of wildfi res and their effects. Without 

these estimates, managers and forest stakeholders could have a false sense of 

security and a belief that if a wildfi re occurs after a fuel treatment the values 

they cherish (for example, homes, wildlife habitat, community water sources, 

sense of place) will be protected and maintained both in the short- (months) 

and long- (10s of years) term.

In 2001, we began to defi ne and quantify the relation between forest 

structure and soil burn severity and determine the uncertainty of the relations 

(Jain and Graham 2004). Although other studies have quantifi ed this relationship they often were limited in scope and applicability (Cruz and others 

2003, Martinson and Omi 2003). To avoid these shortcomings, we designed 

our study to sample many different wildfi res (73) that burned throughout 

the inland western United States over multiple years. Because of the study’s 

scope, it incorporated a large amount of variation in forest structure as well 

as disparity in burn severity after extreme wildfi res. The data we collected 

came from wildfi res that burned in the moist, cold, and dry forests between 

2000 and 2003. By including wildfi res that burned throughout the inland 

The Relation Between Forest Structure and 

Soil Burn Severity

Theresa B. Jain1, Russell T. Graham1, and David S. Pilliod2
616 USDA Forest Service Proceedings RMRS-P-41. 2006.

Jain, Graham, and Pilliod The Relation Between Forest Structure and Soil Burn Severity

western United States occurring over multiple years, we were able to include 

a variety of weather (that occurred during the fi res) and physical settings in 

our sampling. The relations between forest structure and soil burn severity 

and the uncertainty of these associations after intense and severe wildfi res 

will provide information that can be used for informing fuel management 

decisions throughout the moist, cold, and dry forests of the inland western 

United States.

Methods

We visited 73 areas in Montana, Idaho, Colorado, Oregon, Utah, and 

Arizona burned by wildfi res between 2000 and 2003 (fi g. 1). These wildfi res 

occurred in three forest cover types: dry (ponderosa pine, Pinus ponderosa

and Douglas-fi r, Pseudotsuga menziesii), moist (western hemlock, Tsuga 

heterophylla, western redcedar, Thuja plicata, grand fi r, Abies grandis, white 

fi r, Abies concolor) and cold (lodgepole pine, Pinus contorta and subalpine fi r, 

Abies lasiocarpa) forests throughout the inland western United States. Since 

not all forest burned in a single year, we included multiple years and multiple 

geographic regions in our data collection (fi g. 1). All areas were sampled the 

summer after they burned, except areas in Flathead and Lincoln counties in 

Montana and the Diamond Peak complex of fi res in Idaho, which burned in 

2000. These wildfi res were sampled the second summer after they burned.

Figure 1—Distribution of the seventy-three wildfi res sampled between 2001 and 2004. 
USDA Forest Service Proceedings RMRS-P-41. 2006. 617

The Relation Between Forest Structure and Soil Burn Severity Jain, Graham, and Pilliod

Sampling Designs

We used three sampling designs to capture the variation in burn severity occurring at different spatial scales. Intensive sampling occurred in 28 

wildfi res that burned between 2000 and 2003. Extensive sampling revisited 

previously established Forest Inventory and Analysis (FIA) plots within 61 

wildfi res that burned in Montana and Idaho in 2000 and those burned in 

Montana during 2001 and two wildfi res were visited using focused watershed 

(142 ha to 6,480 ha) sampling.

Intensive Sampling

For each selected wildfi re (28 fi res), we used stratifi ed random sampling 

to ensure the variation in forest structure, physical setting, and weather were 

represented. Our sampling stratifi cation began with forest cover (dry, moist, 

and cold), followed by burning index (two classes), slope angle (two classes), 

canopy height (two classes), and stand density (two classes). In establishing 

the sampling frame, forest cover type described the broad-scale vegetation. 

We used fi re progression maps, local weather data, and the most applicable 

fuel model for each stand within a fi re perimeter to calculate Burning Index 

(Bradshaw and Britton 2000). We split our sampling at the median burning 

index for all stands burned by a particular wildfi re. The physical settings of 

the stands were placed into two strata: those with slope angles less than or 

equal to 35 percent and those with slope angles greater than 35 percent. The 

Hayman fi re in Colorado and Flagtail fi re in Oregon had moderately steep 

topography where we used a 25 percent slope angle to differentiate the two 

classes. Nested within slope class, stands were divided into sapling to medium 

sized trees (<12.5 m) and mature to old trees (>12.5 m). Within height class, 

two density stratum were identifi ed: those with canopy cover <35 percent and 

those with canopy cover >35 percent. All stands within a fi re perimeter had an 

equal probability of being selected. We randomly selected a stand if it 1) met 

the sampling criteria, 2) had an opportunity to burn, 3) did not have any 

confounding factors (evidence of suppression activities), and 4) was at least 

100 m by 100 m in size.

Extensive Sampling

Interior West Forest Inventory and Analysis staff have randomly located 

permanent forest sample plots throughout the forests of the western United 

States. Several of these plots burned in 2000 and 2001 (61 wildfi res). Wildfi res that burned in Idaho and Montana in 2000, all wildfi res that burned 

in Montana in 2001, and the wildfi res that burned in Utah and Arizona in 

2003 were revisited. Because FIA plots were distributed across spatially defi ned grids and the burned areas varied in size and location, the number of 

plots burned by the fi res varied considerably. As a result, some burned areas 

had multiple FIA plots sampled after a wildfi re while other areas only had 

one plot revisited.

Focused Watershed Sampling

The focused watershed sampling occurred within forests burned by the 

Quartz and Diamond Peak fi re complexes in Idaho and Oregon in 2000 and 

2001. Using GIS based maps, we delineated the watersheds burned by these 

two wildfi re events and subsequently defi ned a 60-m riparian zone along 

each side of the stream reaches. Areas outside the riparian zone within each 

watershed were defi ned as the upland zone. A minimum of twenty-fi ve plots 
618 USDA Forest Service Proceedings RMRS-P-41. 2006.

Jain, Graham, and Pilliod The Relation Between Forest Structure and Soil Burn Severity

were randomly located within both the upland and riparian zones using a 

complete spatial randomness (CSR) Poisson process (Diggle 2003). Using 

this approach, spatial autocorrelation was avoided (Cressie 1991).

Data Collection

Our intention was to develop a continuous variable or post classify the 

burn severity of the forest fl oor. To do so, fi ne resolution descriptors of soil 

burn severity were synthesized from past burn severity characterizations to 

develop the burn severity indicators. Our soil burn severity concentrated on 

what was left after the fi re and not what was consumed (DeBano and others 1998, Key and Benson 2001, Ryan and Noste 1985, Wells and others 

1979). For each randomly located plot, physical setting descriptors (aspect, 

slope angle, topographic position, and elevation), a general stand description 

(species composition, number of stories, and horizontal spacing), and stand 

origin (past harvest evidence and regeneration treatment) were recorded. 

Forest fl oor characterization included total cover and the proportion of total 

cover dominated by each char class (unburned, black, grey, or orange colored 

soils) on a fi xed radius plots (1/741 ha). These included new litter (deposition since the fi re), old litter (present previous to the fi re), humus, brown 

cubical rotten wood (rotten wood at or above the soil surface), woody debris 

less than or equal to 7.6 cm in diameter, woody debris greater than 7.6 cm 

in diameter, rock, and bare mineral soil.

Physical Setting, Fire Weather, and Forest Structure—Fire behavior and 

burn severity, for the most part, are determined by physical setting (location, 

topography, juxtaposition, and so forth), fuels (live and dead vegetation), 

and weather (both short- and long-term). We used the individual fi re to 

refl ect the broad scale physical setting. For each burned area we obtained 

hourly weather observations that occurred during the wildfi re. Data from 

remote automatic weather stations (RAWS) located in the county where each 

wildfi re burned were summarized into daily reports using Fire Family Plus 

3.0 (Bradshaw and McCormik 2000). The weather data included relative 

humidity, maximum temperature, wind speed, and fuel moistures of 1-, 10-, 

100-, and 1000-hour fuels. Because the exact day and time a specifi c plot 

burned was undetermined, we summarized the weather data to the specifi c 

fi re. Weather data was unobtainable for some fi res located in remote wilderness areas (4 fi res).

We used the Forest Vegetation Simulator (FVS) and its Fire and Fuels 

Extension (FFE) to characterize pre-wildfi re forest structure (Wykoff and 

others 1982, Reinhardt and Crookston 2003, Dixon 2004). Forest structure 

characteristics included stand density indices, characteristics associated with 

fi re behavior (surface fuels, canopy bulk density, canopy base height), and 

other miscellaneous stand characteristics (Reinhardt and Crookston 2003). 

In addition to these FFE-FVS derived forest characteristics we estimated 

canopy base height directly from our data and described total cover which 

included canopy overlap as suggested by Crookston and Stage (1999). Also, 

rather than using quadratic mean diameter (QMD) to describe stem dimensions, we used stem diameter at breast height (d.b.h.) (1.4 m) weighted by 

basal area1.

1 Basal area weighted diameter breast height (d.b.h.-in) is ∑ ((d.b.h.*individual 

tree basal area (ft2) * number of trees for each d.b.h. class) divided by ∑ (number 

of trees * individual tree basal area (ft2).
USDA Forest Service Proceedings RMRS-P-41. 2006. 619

The Relation Between Forest Structure and Soil Burn Severity Jain, Graham, and Pilliod

There are several ways to characterize overstory density such as basal area 

per unit area, trees per unit area, percent cover, canopy bulk density, relative stand density index, total cubic volume per unit area, and total standing 

biomass. To avoid collinear variables as predictors, we used canonical correlation for data mining and our expertise to determine which variables had 

promise for identifying the relation between forest structure and soil burn 

severity. For density we chose total canopy cover with overlap, for tree size we 

used basal area weighted d.b.h., average height, and species composition was 

broadly defi ned as dry, moist, or cold forest. To describe the forest canopy 

we used canopy base height (total height minus uncompacted crown length 

then averaged for plot), and uncompacted crown ratio (fi g. 2).

Classifying Burn Severity—Figure 3 illustrates a model we used to develop 

our soil burn severity classifi cation. The fi re literature provided knowledge 

on fi re intensity by describing the heat pulse into the soil (for example, Baker 

1929, Debano and others 1998, Hungerford and others 1991, Wells and 

others 1979). However, the amount of fuel consumed by a fi re event also 

refl ects fi re intensity. Therefore, we incorporated fi re severity into our burn 

severity classifi cation (for example, Debano and others 1998, Key and Benson 

2001, Ryan and Noste 1989) and fi nally, we included ecological responses 

Figure 2—Illustration of how we measured uncompacted crown ratio and canopy base 

height (total height minus length of uncompacted crown ratio).

Total height

Uncompacted

crown ratio

Canopy base

height
620 USDA Forest Service Proceedings RMRS-P-41. 2006.

Jain, Graham, and Pilliod The Relation Between Forest Structure and Soil Burn Severity

that likely occur after a wildfi re (for example, changes in wildlife habitat, 

alterations in soil productivity, changes in soil erosion potential) (Debano 

and others 1998, Neary and others 1999). As a result our soil burn severity 

(what is left) classifi cation linked fi re intensity, fi re severity, and the ecological response (fi g. 3).

The classifi cation included six levels of soil burn severity (fi g. 4). The factors 

in the soil burn severity include proportion of litter, mineral soil, and exposed 

rock present after a fi re and their dominant char class, defi ned as unburned, 

black char specifi c to mineral soil, and gray and orange char specifi c to mineral soil (Wells and others 1979, Ryan and Noste 1989, Debano and others 

1998) (fi g. 4). The soil burn severity levels included: 1) sites that contained 

greater than 85 percent litter cover, all char classes, 2) 40 to 85 percent litter 

cover, all char classes, 3) less than 40 percent litter cover and mineral soil is 

dominated by black char, 4) less than 40 percent litter cover and mineral soil 

is dominated by grey or white char, 5) and mineral soil is dominated by black 

char and no litter cover, and 6) no litter cover and mineral soil is dominated 

by grey or white char (fi g. 4). Wildfi res and their “goodness,” or lack there of, 

depends on the values at risk and the biophysical setting and the management 

Figure 3—The fi re disturbance continuum, of which there are four components, describes 

the interpretation of different factors involved in wildfi res (Jain and others 2004). The 

fi rst component, the pre-fi re environment, includes forest vegetation and state of the 

environment (moisture levels, amount of biomass, and species composition). This can also 

be referred to as the current condition just prior to the fi re event. The second component, 

the fi re environment, is the environment during the fi re event, where fi re intensity and 

fi re behavior are characterized in addition to fi re severity. Changes to forest components 

from the fi re are also referred to as fi rst-order fi re effects. The third component is the 

environment after the fi re is out, referred to as the post-fi re environment. This is the 

environment created by the fi re but also is a function of the pre-fi re environment and is 

characterized by what is left after the fi re. We refer to this as burn severity. In some cases 

when fuel treatments are being applied to create a more resilient forest, this could be 

referred to as the desired condition. The last component is the response, often referred 

to as second-order fi re effects.

Environmental 

characteristics

before the fire

Pre-fire

Environment Fire environment

Post-fire

Environment Response

Environmental

characteristics during 

the fire

“Fire Intensity”

(Fire characteristics)

“Fire Severity”

(Direct effects from 

combustion process)

“First-order fire effects”

Environmental 

characteristics

after the fire

“Burn Severity”

What is left

The biological 

and physical 

response to the 

environment

“Second-order 

fire effects”

Current

Condition Disturbance

Response

Desired Future 

Condition
USDA Forest Service Proceedings RMRS-P-41. 2006. 621

The Relation Between Forest Structure and Soil Burn Severity Jain, Graham, and Pilliod

objectives for a given setting. Therefore, our six levels of soil burn severity do 

not depict a value but rather describe a continuum from an unburned forest 

fl oor to one in which fi re has appreciably altered the physical and biological 

conditions of the forest fl oor.

Analysis and Interpreting Results

We combined our six levels of soil burn severity into three levels to ensure 

our observations were relatively evenly distributed among the different severity classes. Level 2 burn severity (combined level 1 and 2, fi g. 4) consisted of 

areas with greater than 40 percent litter cover ,and the forest fl oor could vary 

from unburned to areas exhibiting black char. Level 4 (combined levels 3 and 

4, fi g. 4) soil burn severity described areas where less than 40 percent litter 

cover existed and the exposed mineral soil was either black or grey in color. 

Level 6 soil burn severity (combined levels 5 and 6, fi g. 4) described sites 

where there was minimal litter cover and the exposed mineral soil was black, 

gray and/or orange colored, or there was an abundance of exposed rock.

Figure 4—Within the post-fi re environment, the soil burn severity classifi cation includes 

six levels. Going from left to right, a range of temperatures associated with the fi re event 

correspond to the probable indicator of what is left after a fi re. For example, to maintain 

litter cover, the heat pulse into the ground had to be between 0 and 1000 °C. When surface 

litter is left, often soil fauna are still alive, which often occurs when within a fi re severity 

context, a possible description, is less than 15% of surface litter is consumed. In contrast, 

by level 6 soil burn severity, the heat pulse into the ground had to exceed 3000 °C in order 

to create white ash or a grey charred soil appearance (Hungerford and others 1991). The 

char in each burn severity level refers to the dominant char present after the fi re.

C

O

N

S

U

M

P

T

I

O

N

CHAR

Potential veg.

Soil surface

Fine root 

location

Climate

Productivity

_____________

Erosion

potential

Time of year

Current veg.

Rock presence

Heat Pulse °C

0 - 100

0 - 180

50 - 180

180 - 400

300 - 400

Still Present

Soil fauna

Seed source

Soil Nutrients

Erosion

Nitrogen loss

50 - 400

Level 1

> 85 % litte r cover

Level 2

40-85% Litter Cover 

Level 3

< 40% Litter / Black char

Level 4

< 40% Litter / White or Grey

Level 5

0-5% Litter / Black

Level 6

0-5% Litter 

White or Grey

Not much 

left

Level 1

> 85% Litter Cover 

Pre-fire

Environment Fire environment Post-fire

Environment Response
622 USDA Forest Service Proceedings RMRS-P-41. 2006.

Jain, Graham, and Pilliod The Relation Between Forest Structure and Soil Burn Severity

We identifi ed relations between forest structure and soil burn severity 

using a nonparametric classifi cation and regression tree technique (CART) 

(Breiman and others 1984, Steinberg and Colla 1997). Figure 5 shows a 

thirteen-outcome classifi cation tree predicting soil burn severity as a function of pre-wildfi re forest structure. Outcomes 1 through 13 (shaded) show 

number of observations correctly classifi ed, total number of observations, and 

the conditional probability of certainty. Forest characteristics occurring at 

the top of a classifi cation tree were clearly related to burn severity compared 

to characteristics that appeared later in the tree. For example, wildfi re groups 

(groups of individual fi res) were often the most important in differentiating soil 

burn severity, followed by uncompacted crown ratio, total cover, and weighted 

basal area d.b.h. (fi g. 5). In addition, the classifi cation tree identifi ed thresholds 

Figure 5—Classifi cation tree for predicting soil burn severity resulting from CART analysis. 

Shaded areas refl ect different predicted outcomes. Each outcome contains the soil burn 

severity, the number of correctly classified observations versus the total number of 

observations in the outcome and a conditional probability referred to as “certainty.” The 

internode is where splits occurred based on either fi re group or forest structure threshold. 

Numbers to the left and right of the node indicate the forest structure threshold used in 

predicting a particular outcome.
USDA Forest Service Proceedings RMRS-P-41. 2006. 623

The Relation Between Forest Structure and Soil Burn Severity Jain, Graham, and Pilliod

at which a forest structure characteristic became related to soil burn severity. 

In our classifi cation, trees with uncompacted crown ratios <31.5 percent were 

highly related to low litter soil burn severities (level 6, outcome 1) (fi g.5). In 

contrast, trees with uncompacted crown ratios >31.5 percent, differentiated 

(internode 3) into several outcomes (2 – 8) later in the CART classifi cation. The CART analysis displays conditional probabilities (certainty) of an 

event happening predicated on earlier classifi cations. For example, the 0.70 

probability of soil burn severity level 6 occurring in outcome 1 is dependent 

not only if trees have uncompacted crown ratios <31.5 percent but also the 

condition needs to occur within fi re group 1 (fi g. 5).

Results and Discussion

Our results show that soil burn severity (what is left after a wildfi re) is strongly 

related to general wildfi re conditions. That is, we identifi ed seven groups of fi res 

showing similarities when related to soil burn severity (fi g. 5). The strength of 

these relations is exemplifi ed in that fi re group 7 only (1 outcome) contained 

sites with level two soil burn severity (> 40% litter cover, outcome 13). Similarly, fi re group 6 only contained sites with level 4 soil burn severity (1 to 40% 

litter cover, outcome 12). The 56 wildfi res in these two groups predominantly 

burned in the moist and cold forests (fi gs. 5, 6).

The wildfi res in group 3 (outcomes 4 – 11) by far had the greatest diversity 

in soil burn severity of the wildfi res we visited, and the stand structural characteristics often infl uenced the soil burn severity. Within this fi re group total 

stand cover (internode 5, 31.5%, fi g. 5) was an important soil burn severity 

differentiating characteristic. Stands with the lower canopy covers (≤31.5%) 

differentiated into two additional fi re groups (internode 6, fi re groups 4 and 

5) and resulted in level 4 (1 to 40% litter cover, outcome 4) and level 6 (no 

litter cover, outcome 5) soil burn severities (fi g. 5). Several of the soil burn 

severity outcomes (6 – 8) occurring in fi re group 3 were related to tree size 

(weighted d.b.h.) and surface fuel amounts (fi g. 5). The wildfi res creating 

these burn severities tended to occur in the dry forests (fi g. 6). Also within 

fi re group 3 total cover (internode 11), after uncompacted crown ratio (internode 7), became an important structural element infl uencing soil burn 

severity (fi g. 5). That is, stands burned in the moist and cold forests with 

total cover less than 76.5 percent tended to have level 4 (1 to 40% litter cover) 

soil burn severity and stands having excess of 76.5 percent cover tended to 

have level 2 soil burn severity (>40% litter cover) (fi g. 5). These outcomes 

(10 and 11) most frequently occurred when wildfi res burned the moist and 

cold forests (fi gs. 5, 6).

The differentiation of soil burn severity as a result of fi re group most likely 

refl ects wildfi re characteristics such as fi re duration, surface fuel moistures, 

heat produced, physical setting (for example slope angle, aspect), and geographic location (elevation, landscape position, watershed orientation and 

juxtaposition). In addition, these results emphasize the importance of observing many wildfi res occurring in different years (weather), among many forest 

types (composition, potential vegetation), and across geographical areas (for 

example, northern Rocky Mountains, central Rocky Mountains) in order to 

understand the relation between wildfi res and forest structure and how they 

may determine soil burn severity (Van Mantgem and others 2001).
624 USDA Forest Service Proceedings RMRS-P-41. 2006.

Jain, Graham, and Pilliod The Relation Between Forest Structure and Soil Burn Severity

Canopy base height, uncompacted crown ratio, and surface fuel conditions 

most often determine whether a fi re will transition from the surface to a crown 

fi re and as a result determine tree burn severity (Scott and Reinhardt 2001, 

Graham and others 2004, Peterson and others 2005). In contrast, soil burn 

severity depends on the amount of heat generated on the soil surface, the 

conduction of heat into the soil layers, and the heat’s duration (DeBano and 

others 1998, Neary and others 1999, Wells and others 1979). These processes are strongly related to the amount of surface fuels, their structure and 

composition, their moisture content, the pre-fi re environment, and the fi re 

environment (fi g. 4). Stand characteristics such as tree canopy cover, canopy 

cover distribution, uncompacted tree crown ratio, and forest composition 

interact and infl uence the amount, composition and distribution of live and 

dead ground-level vegetation (Barnes and others 1998, Oliver and Larson 

1990). Therefore, we were not surprised that within a fi re group, the most 

common forest characteristics related to soil burn severity were uncompacted 

crown ratio, (internodes 2, 7), total cover (internodes 5, 11), tree size (internodes 4, 9, 10), and the amount of surface fuels (internode 8) (fi g. 5). Often, 

these forest characteristics worked in concert and hierarchically to produce a 

given soil burn severity. For example, for burned over soils to exhibit a level 

two burn severity (outcome 9) was predicated on sites occurring within fi re 

group 3, trees on the site containing uncompacted crown ratios between 

41.5 and 59.6 percent, total canopy cover on the site was less than 31.5 

percent, and the surface fuel amounts had to exceed 49.6 Mg ha–1 (fi g. 5). 

These results illustrate how overstory characteristics can infl uence soil burn 

severity within a group of wildfi res and most likely these soil burn severities 

were related to the amount and condition of ground-level vegetation present 

when the wildfi res burned.

The length of tree crowns in relation to the height of the trees (crown 

ratio) surprisingly had a strong (differentiated early in the CART analysis) 

association with soil burn severity, especially with wildfi res occurring in 

group 1 (fi g. 5, outcome 1). Fires burning stands with uncompacted crown 

ratios <31.5 percent tended to have no litter cover left after the fi res burned, 

resulting in a level 6 soil burn severity (fi g. 5). Many of the stands having this 

Figure 6—The distribution of forest type within each soil 

burn severity outcome (see fi g. 5). Dry forests are ponderosa 

pine and/or Douglas-fi r cover type. Moist forests are either 

western hemlock, grand fi r, western redcedar, or white fi r 

cover types. Cold forests are subalpine fi r and/or lodgepole 

pine cover types.
USDA Forest Service Proceedings RMRS-P-41. 2006. 625

The Relation Between Forest Structure and Soil Burn Severity Jain, Graham, and Pilliod

soil burn severity were multi-storied (60 of 127 sites had 3 stories or more) 

with Douglas-fi r trees dominating the dry forests and lodgepole pine trees 

dominating the cold forests. The trees burned had high canopy base heights 

(>10 m), the stands averaged 1,900 trees ha–1 (Sx = 196), the mean canopy 

cover was 40 percent (Sx = 3) and tree diameter (weighted basal area d.b.h.) 

was less than 19 cm (Sx = 1). These results suggest that stands containing trees 

with short crowns occurring primarily in the cold and dry forests most likely 

infl uenced the composition, amount, distribution, structure, and moisture 

content of the surface fuels. The relatively high tree density may have suppressed surface wind speeds, favoring slow fi re spread rates that could have 

combined with the ground-level vegetation conditions and forest fl oor surface 

layers (duff) to favor long duration surface fi res. These burning conditions 

are often attributed to leaving no surface organic matter on a site after a fi re 

and creating black or grey colored mineral soil (Debano and others 1998, 

Key and Benson 2001, Ryan and Noste 1989).

Stands within fi re group 1 and containing trees with uncompacted crown 

ratios exceeding 31.5 percent differentiated into a multitude of soil burn 

severities depending on further fi re groups, tree diameter, canopy cover, 

and surface fuel amounts. Within fi re group 1 soil burn severity was related 

to total canopy cover in a subset of wildfi res (internode 5, group 3). When 

burned, the denser stands (cover >76.5%) with crown ratios exceeding 59.5 

percent tended to have greater than 40 percent litter cover or level two soil 

burn severity (outcome 11, fi g. 5). Stands exhibiting this soil burn severity 

usually contained 3 or more canopy layers with mean canopy cover exceeding 

90 percent (Sx = 3) and canopy base heights exceeding 4 m (Sx = 0.6). This 

soil burn severity most often occurred within moist forests which tend to have 

high moisture contents in the surface fuels as a result of the deep and closed 

canopy conditions. In fact the 1000-hour fuel moisture contents occurring 

in stands exhibiting this soil burn severity averaged 15.5 percent and were 

greater than those observed in stands exhibiting the other outcomes (fi g. 

7). These results indicate that apparently because of the high fuel moistures, 

moist forests can be relatively resilient to wildfi re, even if they contain multiple 

canopy layers, dense canopy cover, and low canopy base heights.

Figure 7—Average fuel moisture and standard errors for the 

1000-hour fuels occurring in the stands for each soil burn 

severity outcome (see fi g. 5). 
626 USDA Forest Service Proceedings RMRS-P-41. 2006.

Jain, Graham, and Pilliod The Relation Between Forest Structure and Soil Burn Severity

Tree crown ratio appears to infl uence many stand characteristics that relate to 

soil burn severity and its infl uence varies by fi re group and canopy cover. After 

uncompacted crown ratio and canopy cover, the amount of surface fuel becomes 

infl uential in determining soil burn severity. However the larger amounts of 

surface fuels do not readily translate into greater soil burn severity when the 

forests burned. For example, when wildfi res burned stands with crown ratios 

exceeding 31.5 percent and less than 59.5 percent, canopy cover exceeding 31.5 

percent, and containing surface fuels in excess of 48.6 Mg ha–1, level 2 soil 

burn severity (>40% litter cover) was observed (outcome 9, fi g. 5). The moist 

and cold forests typifi ed this outcome, which historically tend to accumulate 

large amounts of surface woody debris (80 Mg ha–1, Sx = 2.5).

After uncompacted crown ratio, canopy cover and the amount of surface 

fuel, tree size (d.b.h.) becomes a determinant of soil burn severity. The 

dominance of large trees on a site appear to create conditions that moderate 

soil burn severity. Soil burn severity level 2 was observed in stands that were 

dominated by large trees (46 cm, Sx = 1.0 basal area weighted d.b.h.) even 

though they contained an average of 40 Mg ha–1 (Sx= 0.6) of surface fuels 

(outcome 8, fi g. 5). The canopy cover was moderate (60%, Sx = 3), as was 

the canopy base height (7 m, Sx = 0.6) of stands exhibiting this soil burn 

severity. This outcome was distributed across the dry forests in strands containing tree densities ranging from 700 to 2,100 trees ha–1. In contrast, level 

6 (no litter cover) soil burn severity was observed in predominantly dry forest 

stands similar to those occurring in outcome 8, except tree diameters were 

less than or equal to 33 cm. Stands exhibiting this burn severity averaged 

28 cm (weighted by basal area) in diameter and contained 1,000 to 2,200 

trees ha–1. The mean canopy cover of the stands was 61 percent and the tree 

canopy base height averaged 4 m (Sx = 0.5).

These two contrasting soil burn severity outcomes differentiated by tree 

diameter most likely are related to the tree juxtaposition and variation in 

density of trees occurring within the stands, especially in ponderosa pine 

forests, large trees tend to be distributed irregularly often occurring in clumps 

(Graham and Jain 2005). This irregular horizontal structure would tend to 

perpetuate variable surface fuel amounts and create a diverse fuel matrix. As a 

result, surface fi res burning fuels in these conditions would most likely result 

in variable soil burn severities which on the average would be low (level 2). 

However, small diameter (for example 28 cm) and most likely mid-aged stands, 

particularly when excluded from fi re, tend to develop with more horizontally 

uniform distributions. As a result, the surface fuels and burning conditions 

would also be uniform in these stands and may have resulted in surface fi res 

with long residence times.

Small trees (d.b.h.), after uncompacted crown ratio, canopy cover, and 

the amount of surface fuel were related to level 4 soil burn severity (fi g. 5, 

outcome 6). The dry forest stands dominating this outcome (fi g. 5, outcome 

6) had 62 percent canopy cover, which was similar to that of the stands occurring in outcomes 7 and 9, but the stands contained more trees (2,000 to 

2,800 trees ha–1). Canopy base heights were relatively low (2 m) and average 

tree height was 13 m (Sx = 1).

The range of soil burn severities occurring among outcomes 6, 7, and 8 illustrate how stand development within dry forests infl uences soil burn severity. 

The small diameter young forests when burned tended to create level 4 soil 

burn severities (outcome 6), the stands with mid-sized and likely mid-aged 

trees when burned tended to create level 6 soil burn severities (outcome 7, 

fi g. 5), and when stands containing large and old trees burned, level 2 soil 

burn severities were created (outcome 8, fi g. 5).
USDA Forest Service Proceedings RMRS-P-41. 2006. 627

The Relation Between Forest Structure and Soil Burn Severity Jain, Graham, and Pilliod

In fi re group 2, which is a subset of group 1 fi res, tree size was second only 

to uncompacted crown ratio in explaining soil burn severity. Again, diameter 

most likely refl ects a developmental stage of the stands exhibiting the two 

contrasting burn severities. Stands with the smaller and younger trees (<18.8 

cm, weighted basal area d.b.h.) had level 4 burn severity compared to the 

stands containing the mid-aged and larger trees (>18.8 cm weighted basal 

area d.b.h.) which exhibited level 6 burn severity (no litter). These fi ndings 

were similar to those illustrated in outcomes 6 and 7 except these outcomes 

occurred in fi re group 2 and outcomes 6 and 7 occurred in fi re group 3 

(fi g. 5). The moisture content of the 1000-hour fuels in stands occurring in 

outcome 2 was 14 percent (Sx = 1) and 11 percent (Sx =1) for the 1000-hour 

fuels within stands occurring in outcome 3.

Thinned stands, plantations, and others exhibiting management typifi ed 

stands in outcomes 2 and 6. The forest fl oor conditions of stands in these outcomes most likely resembled those associated with stand initiation structural 

stages. These early structural stages frequently contain moist and robust layers 

of ground-level vegetation. Because these stands were managed, the surface 

fuel matrix was modifi ed through slash disposal and site preparation activities resulting in a discontinuous fuel bed. Particularly, in the cold and moist 

forests, crown fi res would burn around these areas and most often there was 

evidence that fi rebrands landed in these stands but the surface fuel conditions 

prevented suffi cient fi re from developing that could create a smoldering fi re. 

Therefore, these results indicate that high stand densities and low canopy 

base heights do not necessarily lead to severely burned soils and other factors 

such as developmental stage may also infl uence soil burn severity.

After uncompacted crown ratio (>31.5%) and total canopy cover (<31.5%) 

the fi re setting (fi re group) became an important predictor of soil burn 

severity (fi g. 5). Two fi re groups differentiated, one expressing level 4 soil 

burn severity (outcome 4, fi re group 4) and one expressing level 6 soil burn 

severity (outcome 5, fi re group 5). Both outcomes had similar representation from cold, moist and dry forests (fi g. 6) and the stand densities of both 

were low (292 trees ha–1 for outcome 4 and 312 trees ha–1 for outcome 5) 

when compared to stand densities occurring in the other outcomes. Also, 

for both outcomes canopy base heights were near 6 m and the uncompacted 

crown ratios for both were above 60 percent. The greatest difference in the 

stands occurring in the two outcomes was the setting (for example topography, geographic location, watershed juxtaposition and so forth) in which 

they occurred. Outcome 5 consisted of observations from the Hayman 

and Missionary Ridge fi res in Colorado and the Ninemile fi re in Missoula 

County, Montana. Outcome 4 included observations from the Alpine, Bear, 

and Blodget fi res in Ravalli County, Montana and the Flagtale fi re in Grant 

County, Oregon. The stands burned by wildfi res in outcome 4 also had higher 

1000-hr fuel moistures (12.5%) than stands burned by the fi res in outcome 

5 (11%) (fi g. 7). In addition, the average wind speeds occurring during the 

fi res in outcome 5 tended to be higher (7 to 8 miles hour–1) when compared 

to the winds blowing during outcome 4 fi res (4 miles hour–1). The different 

burning conditions (for example fuel moisture, wind speed, location, and so 

forth ) exemplifi ed in these two outcomes probably had a greater infl uence 

on soil burn severity than forest structure, given that both outcomes had 

very similar structural characteristics.

There are several factors (for example, weather, type of vegetation, fuel 

moisture, atmospheric stability, physical setting, ladder fuels, surface fuels) 

that infl uence fi re behavior and burn severity, and forest structure is only one 

(Agee 1996, Graham and others 2004). Therefore, we did not expect forest 
628 USDA Forest Service Proceedings RMRS-P-41. 2006.

Jain, Graham, and Pilliod The Relation Between Forest Structure and Soil Burn Severity

structure to fully explain all of the variation present in soil burn severity after 

a wildfi re. However, through our study and the analysis we performed, we 

were able predict soil burn severity as a function of pre-wildfi re forest structure with probabilities far greater than what would have occurred randomly. 

These variables were not only hierarchally related to soil burn severity, but 

together they very readily predicted three levels of soil burn severities. Because we identifi ed three levels of soil burn severity, a random probability of 

a given soil burn severity occurring would be 0.33. Therefore, any probability 

exceeding 0.33 of the complete CART tree correctly classifying a particular 

soil burn severity indicates the addition of forest structural characteristics 

were signifi cantly related to soil burn severity. The variables, in order of importance, fi re group, uncompacted crown ratio, weighted basal area d.b.h., 

total cover, and surface fuel amounts classifi ed level 2 soil burn severity (>40% 

litter cover) with a 0.46 probability, level 4 soil burn severity (1 to 40% litter 

cover) with a 0.40 probability, and level 6 (no litter cover) soil burn severity 

with a 0.57 probability.

Conclusion

Undoubtedly intense fi re behavior is a primary concern for forest management throughout the western United States and fuel treatments to modify 

this fi re behavior are a primary concern (Graham and other 2004). However, 

in most circumstances what a fi re leaves behind in terms of soils, homes, and 

trees is as important, if not more important than fi re behavior. Therefore, fuel 

treatments need to be designed and implemented as to modify burn severity 

and the traditional thinned forest with high canopy base heights may not 

result in the desired burn severity.

One size does not fi t all. Therefore, we would suggest that fuel treatments 

be designed to consider burn severity as well as fi re behavior. In particular, 

biophysical setting (fi re group, forest type, locale, potential vegetation type, 

and so forth) needs to provide context for planned fuel treatments. Secondly, 

tree canopy base height (refl ected in uncompacted crown ratio) needs to be 

considered when designing fuel treatments, although high canopy base heights 

do not always reduce soil burn severity. Similarly, reducing total forest cover 

does not necessarily reduce soil burn severity; rather its interactions with the 

biophysical setting, canopy base height, and surface fuel amounts and conditions most likely determine soil burn severity. The last characteristics that 

we identifi ed as having a relation with soil burn severity, were tree diameter 

and surface fuel amounts.

The robust data we accumulated from wildfi res that burned throughout the western United States in recent years did not greatly simplify our 

understanding of the relations between forest structure and soil burn 

severity. Nevertheless, we did identify several interactions between forest 

characteristics and soil burn severity that have fuel treatment management 

applications. A signifi cant factor of this work is the estimate of the certainty 

a forest structure (fuel treatment) will have in modifying soil burn severity. 

The conditional probabilities (certainty) we identifi ed of forest structure or 

fi re setting (fi re group) infl uencing soil burn severity always exceeded 0.50 

and occasionally exceeded 0.75 (fi g. 5). In addition, the approach we took 

in identifying the relations between forest structure and burn severity, and 

the level of certainty we provided, was conditional on the circumstances in 
USDA Forest Service Proceedings RMRS-P-41. 2006. 629

The Relation Between Forest Structure and Soil Burn Severity Jain, Graham, and Pilliod

which the forest characteristic occurred. This kind of information will be of 

value when communicating the importance forest structure (fuel treatments) 

has on determining the aftermath of wildfi res. This paper and the analysis 

and results we reported are a continuation of our work in understanding 

how forest structure interacts with wildfi res, their biophysical setting, and 

burning conditions to create a particular burn severity.

Literature Cited

Agee, J.K. 1996. The infl uence of forest structure on fi re behavior. In: 17th annual 

forest vegetation management conference: Proceedings; 1996 January 16-18; 

Redding, CA. Seattle, WA: University of Washington: 52-68.

Baker, F.S. 1929. Effect of excessively high temperatures on coniferous reproduction. 

Journal of Forestry. 27: 949-975.

Barnes, Burton V.; Zak, Donald R.; Denton, Shirely R.; Spurr, Stephen H. 1998. 

Forest Ecology 4th edition. New York: John Wilely & Sons, Inc. 774 p.

Bradshaw, Larry S.; Britton, Stuart. 2000. Fire family plus: fi re weather and fi re 

danger climatology at your fi ngertips. In: Third symposium on fi re and forest 

meteorology; 2000 January 9-14; Long Beach, CA. Boston, MA. American 

Meteorology Society: 112-113.

Bradshaw, L.; McCormick, E. 2000. Fire family plus user’s guide, version 2.0. Gen. 

Tech. Rep. RMRS-GTR-67. Ogden, UT: U.S. Department of Agriculture, Forest 

Service, Rocky Mountain Research Station. 124 p.

Breiman, Leo; Friedman, Jerome; Olshen, Richard; Stone, Charles. 1984. Classifi cation and 

regression tress. New York. Chapman & Hall/CR. 358 p.

Broncano, Maria J.; Retana, Javier. 2004. Topography and forest composition 

affecting the variability in fi re severity and post-fi re regeneration occurring after 

a large fi re in the Mediterranean basin. International Journal of Wildland Fire. 

13: 209-216.

Cressie, Noel A. C. 1991. Statistics for spatial data. New York: John Wiley & Sons, 

Inc. 900 p.

Crookston, Nicholas L.; Stage, Albert R. 1999. Percent canopy cover and stand structure 

statistics from the forest vegetation simulator. Gen. Tech. Rept. RMRS-GTR-24. 

Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain 

Research Station. 11 p.

Cruz, Miguel G.; Alexander, Martin E.; Wakimoto, Ronald H. 2003. Defi nition of 

a fi re behavior model evaluation protocol: A case study application of crown fi re 

behavior models. In: Omi, Philip N.; Joyce, Linda, Technical Editors. Fire, fuel 

treatments, and ecological restoration: Conference proceedings; 2002 April 16-18; 

Fort Collins, CO: Proceedings RMRS-P-29. Fort Collins, CO: U.S. Department 

of Agriculture, Forest Service, Rocky Mountain Research Station: 49-68.

DeBano, Leonard F.; Neary, Daniel G.; Folliott, Peter F. 1998. Fire’s effects on 

ecosystems. New York: John Wiley & Sons, Inc. 333 p.

Diggle, Peter J. 2003. Statistical analysis of spatial point patterns. 2nd ed. London: 

Arnold. 159 p.

Dixon, Gary E. 2003. Essential FVS: A user’s guide to the Forest Vegetation 

Simulator. Internal Rep. Fort Collins, CO: U.S. Department of Agriculture, 

Forest Service, Forest Management Service Center. 194 p.

Graham, Russell T.; Harvey, Alan. E.; Jain, Theresa B.; Tonn, Jonalea R. 1999. 

The effects of thinning and similar stand treatments on fi re behavior in western 

forests. Gen. Tech. Rep. PNW-GTR-463. Portland, OR: U.S. Department of 

Agriculture, Forest Service, Pacifi c Northwest Research Station. 27 p.
630 USDA Forest Service Proceedings RMRS-P-41. 2006.

Jain, Graham, and Pilliod The Relation Between Forest Structure and Soil Burn Severity

Graham, Russell T.; Jain, Theresa B. 2005. Application of free selection in mixed 

forests of the inland northwestern United States. Forest Ecology and Managment. 

209: 131-145.

Hungerford, Roger D.; Harrington, Michael G.; Frandsen, William H.; Ryan, 

Kevin C., and Niehoff, Gerald J. 1991. Infl uence of Fire on Factors that affect 

Site Productivity. In: Harvey, Alan E. and Neuenschwander Leon F., Compilers. 

Proceedings: Management and productivity of western montane forest soils. 1990 

April 10-12; Boise, ID. Gen. Tech. Rep. INT-280. Ogden, UT: U.S. Department 

of Agriculture, Forest Service, Intermountain Research Station: 32-49.

Jain, Theresa B.; Graham, Russell T.; Pilliod, David S. 2004. Tongue-tied. Wildfi re. 

July: 2-6.

Key, Carl H. Benson, Nate C. (April 25, 2003-last update). The composite burn 

index (CBI): fi eld rating of burn severity. USGS, NRMSC research, [online] 

Available: http://nrmsc.usgs.gov/research/cbi.htm [3/14/2006].

Loehle, Craig. 2004. Applying landscape principles to fi re hazard reduction. Forest 

Ecology and Management. 198: 261-267.

Martinson, Erik J.; Omi, Philip N. 2003. Performance of fuel treatments subjected 

to wildfi res. In: Omi, Philip N.; Joyce, Linda, Tech. Eds. Fire, fuel treatments, 

and ecological restoration: Conference proceedings; 2002 April 16-18; Fort 

Collins, CO. Proceedings RMRS-P-29, Fort Collins, CO; U.S Department of 

Agriculture, Forest Service, Rocky Mountain Research Station: 7-14.

Neary, Daniel G. Carole C. Klopatek Leonard R. F. DeBano Peter F. Ffolliott. 1999. 

Fire effects on belowground sustainability: a review and synthesis. Forest Ecology 

and Management. 122: 51-71.

Oliver, C. D.; Larson, B. C. 1990. Forest stand dynamics. New York: McGraw-Hill 

Inc. 467 p.

Peterson, David L.; Johnson, Morris C.; Agee, James K.; Jain, Theresa B.; McKenzie, 

Donald and; Reinhardt, Elizabeth D. 2005. Forest Structure and Fire Hazard 

in Dry Forests of the Western United States. Gen. Tech. Rep. PNW-GTR-628. 

Portland, OR: U. S. Department of Agriculture, Forest Service, Pacifi c Northwest 

Research Station. 30 p.

Reinhardt, Elizabeth; Crookston, Nicholas L. tech. eds. 2003. The Fire and Fuels 

Extension to the Forest Vegetation Simulator. Gen. Tech. Rep. RMRS-GTR-116. 

Ogden, UT: U.S. Department of Agriculture, Forest Service, Rocky Mountain 

Research Station. 209 p.

Ryan, Kevin C.; Noste, Nonan V. 1985. Evaluating prescribed fi res. In: Lotan, James 

E.; Kilgore, Bruce M.; Fischer, William C.; Mutch, Robert, Tech. Coordinators. 

Proceedings-Symposium and Workshop on Wilderness Fire; 1983 November 15-

18; Missoula, MT. Gen. Tech. Rep. INT-182, Ogden, UT. U.S. Department of 

Agriculture, Forest Service, Intermountain Research Station: 230-238.

Scott, Joe H.; Reinhardt, Elizabeth D. 2001. Assessing crown fi re potential by 

linking models of surface and crown fi re behavior. Res. Pap. RMRS-RP-29. Fort 

Collins, CO. U.S. Department of Agriculture, Forest Service, Rocky Mountain 

Research Station. 59 p.

Steinberg, Dan; Colla, Phillip. CART: Tree-structured non-Parametric data analysis. 

San Diego, CA: Salford Systems. 336 p.

U.S. Department of Agriculture, Forest Service. 2004. The healthy forests initiative 

and healthy forests restoration act: interim fi eld guide. FS-799. Washington, DC: 

U.S. Department of Agriculture, Forest Service and U. S. Department of Interior, 

Bureau of Land Management. 58 p.

Weatherspoon, C. Phillip; Skinner, Carl N. 1995. An assessment of factors associated 

with damage to tree crowns from the 1987 wildfi res in northern California. Forest 

Science. 41(3): 123-129.
USDA Forest Service Proceedings RMRS-P-41. 2006. 631

The Relation Between Forest Structure and Soil Burn Severity Jain, Graham, and Pilliod

Wells, C. G.; Campbell, R. E. 1979. Effects of fi re on soil: a state of knowledge 

review. Gen. Tech. Rep. WO-7. Washington, DC: U.S. Department of Agriculture, 

Forest Service. 34 p.

Wykoff, William R.; Crookston, Nick L.; Stage, Albert R. 1982. User’s guide to the 

stand prognosis model. Gen. Tech. Rep. INT-133. Ogden, UT: U.S. Department 

of Agriculture, Forest Service, Intermountain Forest and Range Experiment 

Station. 112 p.
USDA Forest Service Proceedings RMRS-P-41. 2006. 633

Economics and Biomass Utilization
634 USDA Forest Service Proceedings RMRS-P-41. 2006.
USDA Forest Service Proceedings RMRS-P-41. 2006. 635

In: Andrews, Patricia L.; Butler, Bret W., 

comps. 2006. Fuels Management—How to 

Measure Success: Conference Proceedings. 

28-30 March 2006; Portland, OR. 

Proceedings RMRS-P-41. Fort Collins, 

CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research 

Station.

1 Research Forester, USDA Forest 

Service, Forest Products Laboratory, 

One Gifford Pinchot Drive, Madison, 

WI. pince@fs.fed.us

2 Economist, USDA Forest Service, 

Forest Products Laboratory, One Gifford 

Pinchot Drive, Madison, WI.

Abstract—The FTM–West (“fuel treatment market” model for U.S. West) is a dynamic 

partial market equilibrium model of regional softwood timber and wood product markets, designed to project future market impacts of expanded fuel treatment programs 

that remove trees to reduce fi re hazard on forestlands in the U.S. West. The model 

solves sequentially the annual equilibria in wood markets from 1997 to 2004 and 

projects annual equilibria from 2005 to 2020 using detailed assumptions about future 

thinning programs and market trends. FTM–West was designed specifi cally to account 

for economic complexities that stem from unconventional size distributions of trees and 

logs removed in thinning operations (compared with conventional timber supply in 

the West). Tree size directly infl uences market value and harvest cost per unit volume 

of wood; log size infl uences product yield, production capacity, and processing costs 

at sawmills and plywood mills. FTM–West provides a tool to evaluate future market 

scenarios for large-scale fuel treatment programs with various thinning regimes that 

may have varying costs and yield wood with divergent size class distributions. The 

model provides a capability to analyze and project how much harvestable wood the 

markets can absorb from thinning programs over time and the regional timber price 

and timber harvest impacts of expanded thinning under various assumptions about 

fuel treatment program subsidy or administrative costs, variations in thinning regime, 

or alternative projections of future product demands across the spectrum of products 

ranging from wood fuel to lumber, plywood, and wood fi ber products.

Introduction

Decades of fi re suppression, reduced timber harvests on public lands since 

the 1980s, and a build-up of standing timber inventories in fi re-prone forested 

regions of the western United States have created conditions susceptible to 

catastrophic wildfi res. Expanded programs of systematic stand density reduction through mechanical thinning on public lands may reduce fuel build-up. 

Timber market consequences of such programs depend on the scale of program and the type of treatment regime. This paper describes the design and 

objectives of an economic model that can project timber market impacts of 

expanded fuel treatment programs in the U.S. West.

The “fuel treatment market” model for the U.S. West (FTM–West) employs the Price Endogenous Linear Programming System (PELPS). PELPS 

is a general economic modeling system developed originally at the University of Wisconsin (Gilless and Buongiorno 1985, Calmels and others 1990, 

Zhang and others 1993) and more recently modifi ed for applications at 

the Forest Products Laboratory (Lebow and others 2003). PELPS-based 

models employ the technique of spatial equilibrium modeling (Samuelson 

1952), with periodic (for example, annual) market equilibrium solutions 

obtained by economic optimization. Solutions are derived by maximization 

of consumer and producer surplus, subject to temporal production capacity 

Design and Objectives of FTM–West Model

Peter J. Ince1 and Henry Spelter2
636 USDA Forest Service Proceedings RMRS-P-41. 2006.

Ince and Spelter Design and Objectives of FTM–West Model

constraints, transportation and production costs, and price-responsive raw 

material supply curves and product demand curves, all of which can be programmed realistically to shift over time and respond to endogenous shifts 

in market conditions. FTM–West employs the FPL version of PELPS (called 

FPL–PELPS), Lebow and others (2003) and earlier PELPS publications 

provide further mathematical details about the modeling system. PELPS has 

been used fairly widely for partial market equilibrium models of timber and 

forest products for many years (for example, Buongiorno and others 2003, 

Zhang and others 1996, ITTO 1993).

Structure of FTM–West

Forest sector market models commonly include structural features of wood 

product markets, such as a regional market structure with regional product 

demand curves, regional timber supply curves, interregional transportation 

costs, and regional production capacities and manufacturing costs. Those 

general structural features were included also in FTM–West. In addition, 

FTM–West was designed with other features to account for economic complexities that can arise with utilization of wood from fuel treatment programs, 

which may have a more divergent distribution of volume by tree size class 

than does conventional timber supply (for example, wood from fuel treatments may have a larger fraction of volume in smaller trees than conventional 

timber supply).

General Design Features

Among general design features, FTM–West included demands for more 

than a dozen forest product commodities encompassing the full spectrum 

of forest products produced from softwood timber in the U.S. West, three 

product demand regions, eight production or supply regions, and estimated 

wood supplies from conventional timber supply sources and from future fuel 

treatment programs (assumed to be primarily softwoods). Table 1 summarizes 

the regional and commodity structure of the model.

The model included demand only for forest products produced from 

softwood timber in the U.S. West, a partial representation of total U.S. and 

global demands for forest products. Fairly simple demand curves were specifi ed 

in the model based on an assumption that demands for all products are inelastic 

(price elasticity of demand ranged from –0.3 to –0.8 among the various products). 

Aggregate demand quantities for each product were equated to product output 

data for the U.S. West in the base year (1997) and proportioned to each 

of the three product demand regions using estimates of regional shipments 

Table 1—Regional and commodity structure of FTM–West model.

Supply/production regions Demand regions Demand commodities

Coast PNW (OR, WA) U.S. West Softwood lumber & boards

Eastern Washington U.S. East Softwood plywood

Eastern Oregon Export market Poles & posts

California Paper (fi ve grades)

Idaho Supply commodities Paperboard (three grades)

Montana “Pines” Market pulp

Wyoming–South Dakota “Non-Pines” Hardboard

Four-Corners (UT, CO, AZ, NM) (trees, logs, chips) Fuelwood
USDA Forest Service Proceedings RMRS-P-41. 2006. 637

Design and Objectives of FTM–West Model Ince and Spelter

from the West. Product output was based on data published by industry associations, such as WWPA (various years) for lumber, AF&PA (2005) for 

pulp and paper, and APA–The Engineered Wood Association (various years) 

for plywood. FTM–West was designed to derive annual market equilibria sequentially over a 24-year period, 1997 to 2020, which permitted testing and 

calibration of model solutions against overlapping historical data (to 2004). 

Demand curves were shifted each year based on historical shifts in production in the U.S. West (1997 to 2004), and the model was programmed with 

a set of assumed future growth rates in regional demand (2005 to 2020) for 

each forest product commodity. Demand growth rate assumptions matched 

recent Forest Service Resources Planning Act (RPA) Assessment projections 

(2005 draft RPA timber assessment report).

Similarly, simple supply curves were used to model conventional softwood 

timber supply in each of the eight supply regions, while exogenous estimates 

of wood supply from treatment programs (upper bounds on harvest quantity 

and harvest costs) were introduced as policy or program variables. Estimates 

of wood supply from fuel treatment programs were obtained from the Fuel 

Treatment Evaluator, FTE v. 3.0 (Skog and others 2005). Most conventional 

timber supply in the U.S. West is currently obtained from timber harvest on 

state and private forestlands, subjected mainly to even-aged timber management. Thus, inelastic supply curves were used for conventional timber supply 

(with an assumed price elasticity of 0.7). Conventional timber supply curves 

were programmed to shift over time in direct proportion (1:1 ratio) to net 

growth in softwood timber inventory volumes on state and private timberland 

within each supply region. Annual net growth in state and private timber 

inventories are computed in the model by deducting from standing timber 

inventories the harvest volumes from the preceding year and adding timber 

volume growth based on recent growth rates in each region (Smith and others 2004). Thus, FTM–West incorporated techniques similar to those used 

in the Forest Service RPA Assessment to model conventional timber supply 

(that is, inelastic supply curves shifted over time in proportion to projected 

net growth in timber inventories).

In addition to supply and demand curves, FTM–West incorporated estimates of manufacturing capacities for the various products in each of eight 

production regions, manufacturing cost data, and transportation cost data 

(for wood raw material and product shipments). A feature of PELPS is that 

production capacities shift over time in response to projected market conditions, and in FTM–West we used a representation of Tobin’s q model to 

project regional capacity change as a function of the ratio of shadow price 

(or value) of production capacity to cost of new capacity (Lebow and others 

2003).

Structural Complexities in Wood Utilization

Beyond general elements of model structure, FTM–West incorporated some 

unique features to account for economic complexities that were known to be 

associated with utilization of wood from fuel treatments. Specifi cally, it was 

known that the size-class distribution of wood harvest (the distribution of 

wood volumes by tree diameter class) may be signifi cantly different for wood 

removed in fuel treatments than for conventional timber supply. Also, it is 

fairly well known that timber market value and harvest costs per unit volume 

are highly dependent on tree size class or diameter, whereas mill production 

capacity, processing costs, and product yields also vary with log diameter, 

particularly at lumber mills and plywood mills.
638 USDA Forest Service Proceedings RMRS-P-41. 2006.

Ince and Spelter Design and Objectives of FTM–West Model

Divergent Sizes of Trees and Logs—In recognition of divergent size 

classes of trees harvested, both the conventional timber harvest and the 

exogenously specifi ed wood harvest from fuel treatments were modeled in 

FTM–West by 2-inch (5-cm) diameter classes, ranging from trees <5 inches 

d.b.h (diameter at breast height) to trees >15 inches d.b.h. Thus, all wood 

supply is disaggregated into seven tree size classes, each of which can assume 

a unique market value in the FTM–West model. Furthermore, each tree size 

class yields different proportions of logs (by 2-inch log size class) along with 

variable quantities of wood chip raw materials. Estimates of actual log and 

chip volume yields were derived for each tree size class and for each of the 

eight supply regions based on recovery data from regional utilization studies 

conducted at the Forest Service Pacifi c Northwest (PNW) Research Station 

(compiled from mill studies by Dennis Dykstra, PNW Station).

Figure 1 illustrates divergent distributions of harvest volume by tree size 

class as estimated for conventional timber harvest in the U.S. West (in 1997) 

and for two fuel treatment thinning program regimes (derived from the FTE 

program; Skog and others 2005). Both the even-aged TFB (thin-from-below) 

treatment regime and the uneven-aged SDI (stand density index) treatment 

regime yielded proportionately more volume in smaller trees (size classes 

less than 9 inches d.b.h.) than did conventional timber harvest, but the SDI 

treatment also yielded more volume in larger trees (>15 inches d.b.h.).

Figure 2 illustrates the West-wide average log and chip recovery potential from each tree size class (averages for all eight regions in FTM–West). 

In general, smaller trees can yield only small logs and a high proportion of 

volume in wood chips, whereas bigger trees can yield more volume in larger 

logs (which have generally higher value) and a smaller proportion of volume 

as chips.

Figure 1—Estimated volume distributions by tree size class for conventional timber harvest 

and for wood from fuel treatment regimes on federal lands in U.S. West.
USDA Forest Service Proceedings RMRS-P-41. 2006. 639

Design and Objectives of FTM–West Model Ince and Spelter

Variable Stumpage Values and Variable Harvesting Costs—Harvesting 

costs per unit of wood volume vary with tree size class due to effi ciencies 

gained in harvesting larger trees with more wood volume per tree or per 

log harvested. Thus, in addition to modeling wood supply in FTM–West by 

size class of trees and logs, we used harvest cost models to estimate harvesting costs for each tree size class. Harvesting costs for wood removed in fuel 

treatments were estimated by the FTE program (Skog and others 2005) using the calculation routine from My Fuel Treatment Planner (Biesecker and 

Fight 2005). Timber harvesting costs for conventional timber supply were 

estimated by tree diameter class using a conventional timber harvest cost 

model by Keegan and others (2002).

For the simulated fuel treatment programs, we adopted a policy assumption that fuel treatment managers on federal lands would require removal of 

all tree size classes marked for thinning, based on an assumption that fuel 

treatment policies would not allow “high-grading” or just the removal of 

bigger and more valuable trees. Under that policy assumption, the harvesting and transportation costs applied to all wood from fuel treatments are the 

volume-weighted average costs across all tree size classes. Note that average 

costs for fuel treatments (across all size classes) were estimated to be higher 

than conventional timber harvesting and transport costs in the West.

Figure 3 shows our West-wide averages of wood harvesting costs, wood 

transport costs to mill, and stumpage costs in dollars per thousand cubic feet 

(MCF) as assumed or as estimated in the FTM–West model. Costs for conventional timber supply are differentiated by tree diameter class, with notably 

higher estimated stumpage values for larger trees (2005 equilibrium values). 

Figure 2—West-wide average log and chip recovery potential (percentages of cubic wood 

volume recoverable as chips and logs of various sizes) for different tree diameter classes.
640 USDA Forest Service Proceedings RMRS-P-41. 2006.

Ince and Spelter Design and Objectives of FTM–West Model

In our fuel treatment program scenarios we assumed a hypothetical harvest 

fee (equivalent to stumpage fee) of $500 per acre, representing a nominal fee 

for administrative costs. That fee translates to $214/MCF harvested for the 

TFB thinning program and $188/MCF for the SDI program.

As illustrated in fi gure 3, the assumed harvest fees (stumpage costs) for the 

hypothetical fuel treatment programs are considerably lower than the estimated stumpage market values for conventional timber supply in the region, 

but the estimated harvest and transportation costs for the fuel treatments are 

considerably higher than those for the conventional timber supply. In essence, 

we assumed that the hypothetical fuel treatment programs would offer wood 

to the market at low stumpage fees that would compensate somewhat for the 

higher harvest and transport costs of fuel treatments. This is purely a hypothetical assumption, and future fuel treatment programs might potentially 

charge higher or lower fees. Note also that harvest and transportation costs 

shown here are averages that include costs for both logs and chips delivered 

to mills.

Variable Product Yields and Variable Sawmilling Capacity—Sawmill 

capacities are generally constrained by primary saw rigs that break down logs 

at the front end of sawmills. Primary breakdown saws (or “head rigs”) are 

typically designed to process logs within certain size ranges, some designed 

to process small logs and some designed to process large logs. Small log mills 

run logs end-to-end at fairly constant speed, and within a feasible range of 

equipment design, a larger log yields more product because each cut generates more volume (Fight 2002). In contrast, large log mills may not process 

logs in one pass but may require multiple passes before logs are suffi ciently 

broken down to permit further processing, which results in unproductive 

Figure 3—West-wide averages of 2005 delivered wood costs ($/thousand cubic feet) by tree 

diameter class for conventional timber harvest and wood from fuel treatments, including 

stumpage cost (2005 equilibrium values computed by FTM–West), harvesting cost, and 

transportation cost.
USDA Forest Service Proceedings RMRS-P-41. 2006. 641

Design and Objectives of FTM–West Model Ince and Spelter

dead time between passes. Furthermore, the larger cross-sectional areas of 

cuts usually require a slower feed rate with large logs. Thus, effective lineal 

throughput of logs at large log mills is less than that of small log mills, but 

the greater volume of wood in each lineal foot more than compensates for 

the slower feed rate.

In general, sawmill output capacity is determined by (1) the lineal feet 

of logs that the sawmill is capable of processing in a given amount of time 

(throughput), (2) the volume of wood contained in each lineal foot of log 

throughput, and (3) the lumber recovery factor (LRF), which measures yield 

of lumber in board feet from each cubic foot of log throughput. However, 

parameters (2) and (3) are strongly infl uenced by log diameter, and thus 

lumber output capacity of sawmills varies with the size of log inputs. Product 

recovery per cubic foot of log input for both lumber and plywood generally 

increases with log size. Figure 4 is a plot of estimated lumber recovery (in 

board feet) and plywood recovery (in square feet) per cubic foot of log input 

by log diameter as estimated for the FTM–West model (Williston 1981).

Sawmill industry mill capacities are conventionally reported in board feet 

of lumber output rather than lineal feet of log throughput (for example, see 

Spelter and Alderman 2005). To estimate equivalent sawmill capacities in 

lineal feet of log throughput, we started by obtaining wood consumption data 

by log size, available for the states of Washington (Larsen and Aust 2000) and 

Oregon (Ward and others 2000). In each state the volumes of logs processed 

by sawmills, expressed in board feet, were provided for four log size classes, 

as shown for the state of Washington in table 2, row 1.

We then estimated a corresponding distribution of tree harvest volume by 

tree diameter class (d.b.h.) that would produce a mix of logs (table 2, row 

2) exactly matching the actual survey data on log size distribution (table 

2, row 1). To do this, we started with data on log recovery volumes from 

Figure 4—Estimated lumber recovery (board feet) and plywood recovery (square feet) per 

cubic foot of wood input by log diameter.
642 USDA Forest Service Proceedings RMRS-P-41. 2006.

Ince and Spelter Design and Objectives of FTM–West Model

fi eld studies conducted over the years at the Pacifi c Northwest Research 

Station, as compiled and analyzed by Dennis Dykstra. By an iterative process, we varied the numbers of trees within each tree diameter class until 

the derived log volumes matched the survey data (table 2, row 2). Then, 

multiplying numbers of trees by lineal feet of logs from each tree gave derived 

estimates of lineal log throughput consistent with reported log volumes (table 2, 

row 3). Regional industry throughput capacity in lineal feet was derived by 

dividing the estimated lineal throughput by the observed regional capacity 

utilization ratio (derived from WWPA lumber output data and capacity data 

from Spelter and Alderman 2005) Thus, we obtained estimates of lineal log 

throughput capacities at sawmills in western states and FTM–West regions 

that were equivalent to lumber output capacity in those states and regions. 

Similarly, multiplying the number of logs by the cubic volume of each log 

produces estimates of the equivalent cubic foot volumes of mill throughput 

(table 2, row 5).

To model sawmill capacity in relation to log size, we had to estimate the 

relationship between lumber output and log size for a given regional log 

throughput capacity. In other words, we assumed that sawmill capacity is 

constrained primarily by the lineal log throughput capacity of mill head rigs, 

but variation in log size can result in marginal shifts in lumber output capacity. 

Again, for each log size, two variables connect log throughput to equivalent 

board feet of lumber output: cubic volume of wood in an average lineal foot 

of log throughput (what we term the V factor) and lumber recovery factor 

(LRF), the board feet of lumber yielded by a cubic foot of log throughput. 

Given industry throughput capacity in lineal feet, along with the V and 

LRF factors, the theoretical board foot capacity for each log size class can 

be determined. However, portraying lineal throughput capacity as invariant 

with respect to log size is unrealistic. As logs get bigger, at some point the 

log breakdown requires multiple passes through the head saw and/or feed 

speeds must be decreased (Williston 1976). Because we do not have mill 

capacities by feed speed limits, we approximated this aspect of sawmilling 

by introducing an arbitrary log speed adjustment factor, effectively speeding processing up for smaller logs and slowing it down for larger logs. This 

adjustment resulted in a realistic representation of how sawmill throughput 

would respond to changing log diameters and produced throughput capacities from which board foot capacities were derived by multiplying by the V 

and LRF factors, as shown in table 3.

Table 2—Log volumes in coastal Washington.

Log diameter class (top end diameter) (inches) <5 5–10 11–20 21+ Totals

Log volumes (log scale), actual survey data 124.4 908.8 812.0 137.4 1982.7

 (million board feet)

Log volumes derived from assumed tree harvest 124.5 908.8 812.0 137.4 1982.7

 (million board feet)

Derived lineal feet of logs (millions) 170.2 541.1 127.2 6.1 844.6

Average cubic feet per lineal foot 0.164 0.457 1.345 3.447 0.553

Derived cubic feet of logs (millions) 27.9 247.4 171.0 21.2 467.4

Average board feet (log scale) per cubic foot 4.46 3.67 4.75 6.49
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Design and Objectives of FTM–West Model Ince and Spelter

It is self-evident that the V factor (cubic volume per lineal foot of log 

throughput) increases with log size because the wood volume in a lineal foot 

increases by the diameter of the log squared. The LRF also increases because 

the share of edgings and slabs becomes a smaller fraction of total volume as 

logs increase in size (fi g. 1).

Variable Manufacturing Costs—In a similar vein, the V and LRF factors affect non-wood manufacturing costs. A mill’s labor costs and capital 

costs, for example, are invariant with respect to the size of a log that is momentarily being processed, and thus they are marginally fi xed costs relative 

to log throughput but variable with respect to product output. Varying log 

size marginally affects lumber output, and thus fi xed costs will be written off 

against varying volumes of product output. Thus, manufacturing costs per 

board foot of lumber output vary in FTM–West by log diameter class.

To estimate how manufacturing costs vary with log size class we fi rst developed estimates for each region of average industry non-wood costs (labor, 

energy, materials, supplies, overhead, and depreciation) per unit of mill output. 

Multiplying the unit cost estimates by the base year output gave the total dollar value of non-wood manufacturing costs for each region. Given estimated 

relationships between output capacity and log size, as derived above for each 

region, we calculated the theoretical manufacturing costs for each log size 

at a constant log throughput volume as our fi rst approximation of unit costs 

by log size, which exhibit a pronounced inverse relationship to log diameter 

(as shown by the “constant throughput” relationship in fi gure 2). However, 

again, it would be unrealistic to assume that lineal log throughput speed 

could remain constant with varying log diameter, so we applied again the 

log speed adjustment (table 2) to refl ect accelerated throughput with smaller 

logs and slower throughput with larger logs. The result is the relationship 

shown as the “variable throughput” cost curve in fi gure 5, which we used to 

model lumber manufacturing costs by log diameter in FTM–West. Despite 

the log speed adjustment, there is a big cost difference between processing 

small logs and large logs.

Plywood manufacturing capacity, manufacturing costs, and product recovery are modeled in an identical manner, using the same V factors and 

replacing LRF by the plywood recovery factor, whose behavior is identical 

to the LRF for the same basic reasons (fi g. 4).

Finally, as noted previously, regional production capacities in the FTM–West 

model will shift over the projection period from 2005 to 2020 in response to 

Table 3—Board foot lumber output capacity as a function of log size for given log throughput 

capacity (lineal feet of log throughput).

Log size Adjustment

 class Capacity for log Adj. cap. Capacity

(inches) (lin. ft) speed (%) (lin. ft) V LRF (board ft)

 <4 844.6 73 1,461 0.15 6.33 1,387

 4–5.9 844.6 52 1,284 0.27 6.44 2,233

 6–7.9 844.6 24 1,047 0.51 6.87 3,668

 8–9.9 844.6 7 904 0.65 7.25 4,260

 10–11.9 844.6 –6 794 0.91 7.54 5,448

 12–13.9 844.6 –15 718 1.30 7.77 7,252

 >14 844.6 –32 574 2.52 8.20 11,861
644 USDA Forest Service Proceedings RMRS-P-41. 2006.

Ince and Spelter Design and Objectives of FTM–West Model

Figure 5—Non-wood lumber manufacturing costs ($/thousand board feet) with constant log 

throughput and variable-speed log throughput assumptions.

projected economic profi tability of investments (Tobin’s q ratio), simulating 

long-run capital investment responses to economic opportunities. In scenarios 

that introduce increased supply of wood from fuel treatment programs, we 

found that the model responds with capacity expansion, increased regional 

wood harvest, and displacement of conventional timber harvest by wood from 

fuel treatments. However, treatment regimes that introduce marginally higher 

proportions of small-diameter wood than conventional timber harvest will 

also marginally offset regional production capacities, reduce average product 

recovery, and increase manufacturing costs for lumber and plywood. Those 

impacts affect the producer surplus and consumer surplus consequences of 

fuel treatment programs. Net market welfare impacts of alternative treatment 

regimes are described in a companion paper in these proceedings (Kramp 

and Ince 2006).

Summary

The development of FTM–West provided a tool to evaluate future market 

scenarios for large-scale fuel treatment programs with various thinning regimes that may have varying costs and may yield wood with divergent size 

class distributions. It also provided a capability to analyze and project how 

much harvestable wood the markets can absorb from thinning programs over 

time and the regional timber price and timber harvest impacts of expanded 

thinning under various assumptions about fuel treatment program subsidy or 

administrative costs, variations in thinning regime, or alternative projections 

of future product demands across the spectrum of products ranging from 

wood fuel to lumber, plywood, and wood fi ber products.
USDA Forest Service Proceedings RMRS-P-41. 2006. 645

Design and Objectives of FTM–West Model Ince and Spelter

Acknowledgments

This study was funded in part by the Joint Fire Science Program (JFSP), 

a partnership of six federal agencies: USDA Forest Service, Bureau of Indian 

Affairs, Bureau of Land Management, National Park Service, U.S. Fish and 

Wildlife Service, and U.S. Geological Survey. Additional in-kind contributions 

were provided by researchers at the USDA Forest Service Forest Products 

Laboratory (FPL) and Pacifi c Northwest Research Station (PNW). Andi 

Kramp of FPL helped enter data input for the FTM–West market model and 

assisted in running the model and interpreting model results. Ken Skog of 

FPL assisted in developing treatment program scenarios for FTM–West using 

the Fuel Treatment Evaluator (FTE, version 3.0). Dennis Dykstra of PNW 

provided estimates of log and chip volume yields for each tree size class in 

each of the eight supply regions of FTM–West, based on data from regional 

wood utilization studies. The study was part of a larger JFSP-funded project, 

identifi ed as JFSP project 01-1-2-09, “A national study of the economic impacts of biomass removals to mitigate wildfi re damages on federal, state, and 

private lands,” coordinated by Jeffrey Prestemon and Karen Lee Abt of Forest 

Service Southern Research Station (SRS). The authors sincerely appreciate the 

consultation provided by the project coordinators, as well as consultation on 

the PELPS modeling system from Patti Lebow of FPL, additional data input 

from Matt Alderman of FPL, and consultation or input from other members 

of the JFSP project, including Roger Fight and Jamie Barbour of the PNW 

and Bob Rummer and Robert Huggett, Jr., of SRS.

References

AF&PA. 2005. Statistics, paper, paperboard & wood pulp (and earlier years). 

American Forest & Paper Association. Washington, DC.

APA–The Engineered Wood Association. Various years. Engineered wood statistics. 

Tacoma, Washington.

Biesecker, R.L.; Fight, R.D. 2006. My fuel treatment planner: a users guide. Gen. 

Tech. Rep. PNW-GTR-663. Portland, OR: U.S. Department of Agriculture, 

Forest Service, Pacifi c Northwest Research Station. 31 p.

Buongiorno, J.; Zhu, S.; Zhang, D.; Turner, J.; Tomberlin. D. 2003. The global 

forest products model: structure, estimation and applications. Academic Press. 

300 p.

Calmels, P.; Buongiorno, J.; Zhang, D. 1990. PELPS II: A microcomputer priceendogenous linear programming system for economic modeling. Research Report 

R3477. College of Agricultural and Life Sciences, Univ. of Wisconsin–Madison. 

40 p.

Fight, R.D. 2002. Financial analysis of thinning small diameter trees on the Colville 

National Forest. In: Proceedings of the small diameter timber symposium; 2002 

February 25-27; Spokane, WA. Pullman, WA: Washington State University.

Gilless, J.K.; Buongiorno, J. 1985. PELPS: Price-endogenous linear programming 

system for economic modeling. Research Report R3329. College of Agricultural 

and Life Sciences, Univ. of Wisconsin–Madison. 34 p.

ITTO. 1993. Analysis of macroeconomic trends in the supply and demand of 

sustainably produced tropical timber from the Asia-Pacifi c region, phase I. 

International Tropical Timber Organization: Yokohama, Japan.
646 USDA Forest Service Proceedings RMRS-P-41. 2006.

Ince and Spelter Design and Objectives of FTM–West Model

Keegan, C.E.; Niccolucci, M.J.; Fiedler, C.E.; Jones, J.G.; Regel, R.W. 2002. Harvest 

cost collection approaches and associated equations for restoration treatments on 

National Forests. Forest Products Journal. 52(7/8).

Kramp, A.D.; Ince, P.J. 2006. FTM–West model results for selected fuel treatment 

scenarios. (companion paper in these proceedings).

Larsen, D.N.; Aust, P.M. 2000. Washington mill survey. Washington State 

Department of Natural Resources, Olympia, WA.

Lebow, P.K.; Spelter; H.; Ince, P.J. 2003. FPL-PELPS, A price endogenous linear 

programming system for economic modeling, supplement to PELPS III, Version 

1.1. Res. Pap. FPL-RP-614. Madison, WI: USDA Forest Service, Forest Products 

Laboratory. 32 p. www.fpl.fs.fed.us/documnts/fplrp/fplrp614.pdf

Samuelson, P.A. 1952. Spatial price-equilibrium and linear programming. American 

Economic Review. 42(3):283-303.

Skog, K.; Barbour, J.; Abt, K.; Bilek, T.; Burch, F.; Fight, R.; Huggett, B.; Miles, 

P.; Reinhardt, E.; Sheppard, W. [In prepartaion]. An evaluation of silvicultural 

treatments and biomass use to reduce fi re hazard in western States. USDA Forest 

Service.

[An Internet link to FTE 3.0 is at the following website: www.ncrs2.fs.fed.

us/4801/fi adb/. FTE 3.0 was accessed in September of 2005 to obtain data for 

this report.]

Smith, W.B.; Miles, P.D.; Vissage, J.S.; Pugh, S.A. 2004. Forest resources of the 

United States, 2002. USDA, Forest Service GTR-NC-241.

Spelter, H.; Alderman, M. 2005. Profi le 2005, softwood sawmills in the United 

States and Canada. Research Paper RP-FPL-630. USDA Forest Service. Forest 

Products Laboratory: Madison, WI. 15 p.

Ward, F.R.; Lettman, G.J.; Hiserote, B.A. 2000. Oregon’s forest products industry. 

Oregon Department of Forestry, Albany, OR.

Williston, E.M. 1976. Lumber manufacturing. Miller-Freeman Publications: San 

Francisco, CA.

Williston, E.M. 1981. Small log sawmills. Miller-Freeman Publications: San 

Francisco, CA.

WWPA. Various years. Statistical yearbook of the western industry. Western Wood 

Products Association, Portland, OR.

Zhang, D.; Buongiorno, J.; Ince, P.J. 1993. PELPS III: A microcomputer price 

endogenous linear programming system for economic modeling, v. 1.0. Res. 

Pap. FPL-RP-526. Madison, WI: USDA Forest Service, U.S. Forest Products 

Laboratory. 43 p.

Zhang, D.; Buongiorno, J.; Ince, P.J. 1996. A recursive linear-programming analysis 

of the future of the pulp and paper industry in the United States: Changes in 

supplies and demands and the effects of recycling. Annals of Operations Research. 

68:109-139.
USDA Forest Service Proceedings RMRS-P-41. 2006. 647

In: Andrews, Patricia L.; Butler, Bret W., 

comps. 2006. Fuels Management—How to 

Measure Success: Conference Proceedings. 

28-30 March 2006; Portland, OR. 

Proceedings RMRS-P-41. Fort Collins, 

CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research 

Station.

1 Economics Research Assistant, USDA/

FS Forest Products Lab, Madison, WI. 

akramp@fs.fed.us

Abstract—This paper evaluated potential forest product market impacts in the U.S. 

West of increases in the supply of wood from thinnings to reduce fi re hazard. Evaluations are done using the Fuel Treatment Market–West model for a set of hypothetical 

fuel treatment scenarios, which include stand-density-index (SDI) and thin-from-below 

(TFB) treatment regimes at alternative levels of harvest administrative fees or subsidies. Results show that even with industry bearing the assumed administrative costs 

of thinning programs, substantial volumes of wood could be thinned, but more so in 

coastal regions than inland regions of the West. Also, replacing administrative fee assumptions with hypothetical removal subsidies increases the proportion of harvestable 

wood removed; a sensitivity observed primarily in the inland regions. Results show 

also that wood removals from fuel treatment programs could displace a large fraction 

of timber supply from conventional sources, reducing regional timber harvest and 

timber revenues that would otherwise be projected to increase for state and private 

timberland managers in the West. The SDI thinning regime can result in potential gains 

in forest product consumer surplus that more than offset losses in timber producer 

surplus, resulting in positive net market welfare, while the TFB regime can produce 

the opposite result (negative net market welfare).

Introduction

The Fuel Treatment Market (FTM) model for the U.S. West, or FTM–West, 

is a dynamic partial equilibrium model of the markets for softwood timber 

and forest products produced in the western United States. The model projects 

the market for wood from fuel thinning treatments along with the market for 

timber from conventional sources in order to project the market impacts of 

fuel treatments (Ince and Spelter, this proceedings; Ince and others 2005). 

At the present time, only a small fraction of the fuel treatment acreage on 

federal lands in the U.S. West involves wood harvest (over 90% of the fuel 

treatment acreage involves prescribed burning or mechanical treatment without wood byproduct removal). This paper illustrates projected market impacts 

of hypothetical expanded fuel treatment programs involving thinning and 

wood removal on federal lands in the West.

Different scenarios can be run in the FTM-Westmodel with different hypothetical forest treatment programs or with no treatment program at all. The 

two hypothetical thinning regimes analyzed in this study were created using 

the Fuel Treatment Evaluator (FTE 3.0) model (Skog and others 2006) and 

the areas considered for treatment were NFS and other federal land (BLM, 

BIA, etc.). The thinning regimes were developed by a team of researchers 

whose objective was to identify places where the use of woody biomass from 

FTM-West Model Results for Selected Fuel 

Treatment Scenarios

Andrew D. Kramp1 and Peter J. Ince
648 USDA Forest Service Proceedings RMRS-P-41. 2006.

Kramp and Ince FTM-West Model Results for Selected Fuel Treatment Scenarios

thinning can best help pay for hazardous fuel reduction treatments. The effort 

identifi ed USDA Forest Service Forest Inventory and Analysis (FIA) plots 

on timberland in 12 western states—127 million acres—that passed screens 

excluding high severity fi re regime forest types (where crown fi res are normal), 

low fi re hazard plots, plots in roadless areas, and plots in selected counties 

on Oregon and Washington where treatments would be done primarily for 

purposes other than fi re hazard reduction. Twenty four million acres were 

identifi ed as eligible for treatment, of which 14 million acres are on federal 

land. Eligible acres received simulated treatment by one of two silviculture 

treatment regimes to meet certain fi re hazard reduction targets if the treatment would provide at least 300 ft3/acre (~ 4 oven-dried tons/acre). The 

SDI treatment removed trees across all age classes to leave an uneven-aged 

stand. The TFB treatment removed trees beginning with the smallest to leave 

an uneven-aged stand. The paper by Skog and Barbour (this proceedings) 

explains the SDI treatment regime (a combination of treatments 2A and 4A) 

and the TFB treatment regime (a combination of treatments 3A and 4A).

Each regime was run with two different cost assumptions (making four 

total scenarios). In one scenario, administrative fees (stumpage fees) were 

levied on the wood available for treatment to pay for the estimated average 

cost per acre to the Forest Service to make the wood available ($500 per acre), 

whereas the other scenario eliminated the fee and instead offered a subsidy 

for the wood ($200 per MCF). The sensitivity of the volume of wood treated 

to the stumpage fee or subsidy was not intensely analyzed in this study, and 

therefore the cost assumptions are not assumed to maximize possible revenue 

to the Forest Service or the volume of wood treated under any constraints.

Scenario Inputs

Two different hypothetical forest treatment regimes were evaluated using 

the FTM-Westmodel, the inputs of which were obtained using the FTE model. 

In this paper they are referred to as SDI and TFB, respectively. The FTMWest required as input three different aspects of the scenarios: the volume 

distribution of available wood by d.b.h. class for each supply region (table 1), 

the volume of wood to be made available for treatment in each year for each 

supply region, and the weighted average cost of the wood from treatments, 

which includes harvest and transport costs and possibly an administrative cost 

or subsidy, also in each supply region. Most of the fi gures in this paper are 

aggregated for the whole U.S. West. As Skog and others (this proceedings) 

mention, the SDI scenarios consist of more (about twice as much) total wood 

Table 1—Volume of wood by diameter at breast height class for two hypothetical thinning 

programs compared with 1997 estimates on conventionally harvested wood (Ward and 

others 2000; Larsen and others 2000). Rows might not add to 100% due to rounding.

 Wood by diameter at breast height class

 <5 5 to 6.9 7 to 8.9 10 to 11.9 12 to 13.9 14 to 15.9 >15

 - - - - - - - - - - - - - - - - - - - - - - - Inches - - - - - - - - - - - - - - - - - - - - - - -

TFB 9% 20% 15% 17% 12% 7% 20%

SDI 8% 10% 8% 10% 9% 6% 48%

Conventional

(1997) 3% 8% 14% 18% 17% 14% 27%
USDA Forest Service Proceedings RMRS-P-41. 2006. 649

FTM-West Model Results for Selected Fuel Treatment Scenarios Kramp and Ince

and acres available than the TFB scenarios (fi gures 1 and 2). Also note that 

the FTE only gives the total amount of wood available for treatments in each 

region, so a logarithmic-growth function was used to smooth this amount 

over a 16-year period, 2005 to 2020. Each scenario was run once with an 

added $500 per acre administrative fee (equivalent to a stumpage fee) for 

wood available from forest treatments, which is estimated to cover the cost 

of making the wood available, and once with no fee and an unconstrained 

$200/MCF subsidy.

In all the effects discussed here (volume harvested, timber prices, producer 

and consumer surplus) except the change in net market welfare, the SDI scenarios had larger impacts compared with the TFB scenarios. Similarly, the 

scenarios where forest treatments were subsidized had larger effects when 

compared with the scenarios that required administrative fees.

Volume Harvested and Timber prices

In all four scenarios, more than half of the wood made available from forest treatments was utilized (table 2). Subsidizing the programs resulted in an 

additional 3.6 and 3 billion cubic feet representing 16% and 30% of the total 

FTE volume for the SDI and TFB programs, respectively. This additional 

wood treated was located exclusively in the interior region of the U.S. West 

because in every scenario 100% or nearly 100% of wood made available in the 

coastal region (Pacifi c Northwest and California coasts) was treated. For the 

Figure 1—Maximum volume of wood made available annually. SDI, Stand Density Index; 

TFB, Thinning From Below.
650 USDA Forest Service Proceedings RMRS-P-41. 2006.

Kramp and Ince FTM-West Model Results for Selected Fuel Treatment Scenarios

interior regions, this amounted to an increase from 5% to 42% of available 

wood treated and an average of 2.6 million acres for the SDI program, and 5% 

to 66% and an average of 2.1 million acres for the TFB program, as a result 

of dropping the administrative fee and adding the subsidy (fi gure 3).

In all four scenarios, the total harvest of wood increased when compared 

to a scenario with no wood available for treatment (fi gure 4). However, the 

additional utilization of wood from forest treatments displaced wood utilized 

from conventional sources (mostly state and private). This crowding out of 

conventional timber ranges from 5 to 12 billion ft3 over the 16-year time 

period, depending on subsidy and thinning regime (fi gure 5). Over the time 

Figure 2—Acres made available annually assuming a constant average volume per acre.

Table 2—Billion cubic feet, million acres, and percentage of total wood available 

projected to be treated over the 16-year period, 2005 to 2020. SDI, Stand 

Density Index; TFB, Thinning From Below

Regime $500/acre admin fee $200/MCF subsidy

 Billion cubic feet 13.9 17.5

 SDI Million acres 4.7 7.3

 FTE volume (%) 60% 76%

 Billion cubic feet 5.3 8.2

 TFB Million acres 2.4 4.5

 FTE volume (%) 54% 84%
USDA Forest Service Proceedings RMRS-P-41. 2006. 651

FTM-West Model Results for Selected Fuel Treatment Scenarios Kramp and Ince

Figure 3—Percentage of available wood utilized. SDI, Stand Density Index; TFB, Thinning 

From Below.

Figure 4—Total volume of wood harvested annually. SDI, Stand Density Index; MCF, per 

thousand cubic feet; ac, acre.
652 USDA Forest Service Proceedings RMRS-P-41. 2006.

Kramp and Ince FTM-West Model Results for Selected Fuel Treatment Scenarios

period, the wood from treatments accounted for an average of 10% to 30% 

of the total volume of wood harvested, also depending on subsidy and thinning regime. Consequently, the boost in timber supply from thinning and 

reduction in harvest from conventional supply sources is projected to result 

in lower timber prices as well (fi gure 6).

Producer Surplus, Consumer Surplus and Net 

Welfare

All four scenarios project a decrease in potential revenue to conventional 

timber suppliers, a loss of producer surplus, which is a direct result of the 

decrease in regional timber prices and the volume of conventional timber 

harvested (as compared to a no-treatment scenario). The cumulative potential 

losses over the 16 year projection period (2005 to 2020) are quite signifi cant, 

ranging from $34 billion to $70 billion (fi gure 7).

On the other hand, all four treatment scenarios projected lower wood 

product prices and increases in wood products consumption resulting in 

increases in forest product consumer surplus. Over the projection period the 

cumulative increases ranged from $26 billion to $74 billion (fi gure 8).

When we observe the changes in cumulative net welfare, defi ned as the 

change in producer surplus plus the change in consumer surplus, we see a 

deviation from the theme of the other results. Both TFB scenarios result in 

decreasing net welfare totaling as low as –$8.3 billion after 16 years with 

Figure 5—Volume of wood harvested from conventional sources. SDI, Stand Density Index; MCF, 

per thousand cubic feet; ac, acre.
USDA Forest Service Proceedings RMRS-P-41. 2006. 653

FTM-West Model Results for Selected Fuel Treatment Scenarios Kramp and Ince

Figure 6—Weighted average softwood timber price in the U.S. West. SDI, Stand Density 

Index; MCF, per thousand cubic feet; ac, acre.

Figure 7—Cumulative change in producer surplus as compared to a no-treatment scenario. 

SDI, Stand Density Index; TFB, Thinning From Below.
654 USDA Forest Service Proceedings RMRS-P-41. 2006.

Kramp and Ince FTM-West Model Results for Selected Fuel Treatment Scenarios

Figure 8—Cumulative change in consumer surplus as compared to a no-treatment 

scenario. SDI, Stand Density Index; MCF, per thousand cubic feet; TFB, Thinning From 

Below; ac, acre.

the subsidy making little difference. Conversely, the SDI scenarios show an 

increasing net welfare and, in fact, the unsubsidized program shows the largest 

increase in net welfare, $5.7 billion after 16 years (fi gure 9). This can be seen 

mainly as a result of the fact that the SDI treatment makes much more high 

value large timber available than the TFB. This large timber has lower harvest 

costs, higher product yields, higher output capacity, and lower manufacturing 

costs (all per volume), and only a model like the FTM-Westthat models these 

economic complexities of tree and log size class can observe such economic 

effects. Note that these fi gures for changes in net welfare do not include a 

quantifi cation of the effects from reduced fi re hazard; they represent only 

market welfare impacts. The social welfare benefi ts from reduction in fi re 

hazard are diffi cult to assess. However, Lippke and others (2006), in their 

analysis, make a conservative estimate from $1,186/acre to $1,982/acre, 

increasing with initial fi re risk.

Conclusions

We can draw several important conclusions from these results. First, markets would use a substantial volume of wood from fuel treatment programs, 

even if administrative fees are levied. Second, subsidies for wood from forest 

treatments seem unnecessary in the coastal region but are crucial to achieve 

forest treatment goals in the interior region. Third, expanded fuel treatments 

can have substantial positive impacts on forest product consumer surplus yet 

negative impacts on revenue to conventional timber sources. Finally, the SDI 
USDA Forest Service Proceedings RMRS-P-41. 2006. 655

FTM-West Model Results for Selected Fuel Treatment Scenarios Kramp and Ince

Figure 9—Cumulative change in net economic welfare as compared to a no-treatment 

scenario. SDI, Stand Density Index; MCF, per thousand cubic feet; TFB, Thinning From 

Below; ac, acre.

thinning regime can result in potential gains in forest product consumer 

surplus that more than offset losses in timber producer surplus, resulting in 

positive net market welfare, while the TFB regime can produce the opposite 

result (negative net market welfare).

In addition, since the SDI scenarios result in more acres treated and more 

wood per acre removed, logically they would also result in greater reductions 

in forest fuels and related fi re hazard, producing consequently unambiguously higher net welfare than the TFB scenarios, taking into account both 

the market welfare and fuel reduction impacts. Other factors should also be 

considered in judging net welfare, including changes in suppression costs, 

environmental impacts, wildfi re damages, and other less tangible costs and 

benefi ts of reduced fi re hazard that are addressed, for example, by Lippke 

and others (2006). All these factors are important when considering policy 

toward use of thinning treatments that include biomass utilization. In this 

study, we have focused primarily on the market welfare and fuel reduction 

impacts.

Acknowledgments

This study was funded in part by the Joint Fire Science Program (JFSP), 

a partnership of six federal agencies: USDA Forest Service, Bureau of Indian 

Affairs, Bureau of Land Management, National Park Service, U.S. Fish and 
656 USDA Forest Service Proceedings RMRS-P-41. 2006.

Kramp and Ince FTM-West Model Results for Selected Fuel Treatment Scenarios

Wildlife Service, and U.S. Geological Survey. Additional in-kind contributions 

were provided by researchers at the USDA Forest Products Laboratory (FPL) 

and Forest Service Pacifi c Northwest Research Station (PNW). Ken Skog of 

FPL assisted in developing treatment program scenarios for the FTM-Westusing the Fuel Treatment Evaluator (FTE, version 3.0). Henry Spelter of FPL 

helped estimate conventional stumpage prices, lumber production coeffi cients, 

and manufacturing costs. Dennis Dykstra of PNW provided estimates of log 

and chip volume yields for each tree size class in each of the eight supply 

regions of FTM–West, based on data from regional wood utilization studies. 

The study was part of a larger JFSP-funded project, identifi ed as JFSP project 

01-1-2-09, “A national study of the economic impacts of biomass removals to 

mitigate wildfi re damages on federal, state, and private lands,” coordinated 

by Jeffrey Prestemon and Karen Lee Abt (Forest Service Southern Research 

Station). The authors sincerely appreciate the consultation provided by the 

project coordinators, as well as consultation on the PELPS modeling system 

from Patti Lebow of the Forest Products Laboratory, additional data input 

from Matt Alderman of the Forest Products Laboratory, and consultation or 

input from other members of the JFSP project, including Roger Fight and 

Jamie Barbour of the Pacifi c Northwest Research Station and Bob Rummer 

and Robert Huggett, Jr., of the Southern Research Station.

Literature Cited

Ince, Peter J.; Kramp, Andrew D.; Spelter, Henry; Skog, Ken; Dykstra, Dennis. 

2005. FTM-West: Fuel Treatment Market model for U.S. West, submitted 

and accepted for publication in the 2005 Southern Forest Economics Workers 

(SOFEW) proceedings.

Larsen, David N.; and others. 2000. Washington Mill Survey—1996. Washington 

State Department of Natural Resources. 39 p. plus appendices.

Lippke, R.; Mason, Larry C.; Zobrist, Kevin W.; Bloxton Jr., Thomas D.; Ceder, 

Kevin R.; Comnick, Jeffrey M.; McCarter, James B.; Rogers, Heather K.; Jan/Feb 

2006. Investments in Fuel Removals to Avoid Forest Fires Result in Substantial 

Benefi ts. Journal of Forestry. 27–31.

Skog, Ken E.; Barbour, J.R.; Abt, K.; Bilek, T.; Burch, F.; Fight, R.D.; Hugget, 

B.; Reinhardt, E.; Sheppard, W. (forthcoming 2006). Evaluation of silvicultural 

treatments and biomass use for reducing fi re hazard in western states. USDA 

Forest Service, Forest Products Laboratory, FPL-RP-XXX. <http://ncrs2.fs.fed.

us/4801/fi adb/fi re_tabler_us/rpa_fuel_reduction_treatment_opp.htm>
USDA Forest Service Proceedings RMRS-P-41. 2006. 657

In: Andrews, Patricia L.; Butler, Bret W., 

comps. 2006. Fuels Management—How to 

Measure Success: Conference Proceedings. 

28-30 March 2006; Portland, OR. 

Proceedings RMRS-P-41. Fort Collins, 

CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research 

Station.

1 Project Leader at the USDA Forest 

Service, Forest Products Laboratory, 

Madison, WI. kskog@fs.fed.us

2 Program Manager, USDA Forest 

Service, Pacifi c Northwest Research 

Station, Portland, OR.

Abstract—This paper identifi es timberland areas in 12 western states where thinning 

treatments (1) are judged to be needed to reduce fi re hazard and (2) may “pay for 

themselves” at a scale to make investment in forest product processing a realistic option. 

A web-based tool—Fuel Treatment Evaluator 3.0—is used to select high-fi re-hazard 

timberland plots from the Forest Service Forest Inventory and Analysis Program (FIA) 

database and provide results of simulated thinning treatments. Areas were identifi ed 

where either torching or crowning is likely during wildfi res when wind speeds are below 

25 mph. After additional screens are applied, 24 million acres are deemed eligible for 

treatment (14 million acres on federal lands). Uneven-aged and even-aged silvicultural 

treatments analyzed would treat 7.2 to 18.0 million of the 24 million acres, including 

0.8 to 1.2 million acres of wildland–urban interface area, and provide 169 to 640 

million ovendry tons of woody biomass. About 55 percent of biomass would be from 

main stem of trees ≥7 inches d.b.h. Sixty to seventy percent of the area to be treated 

is in California, Idaho, and Montana. Volumes and harvest costs from two treatments 

on the 14 million acres of eligible federal lands are used as inputs to the fuel treatment 

market model for U.S. West (FTM–West) discussed in these proceedings.

Introduction

Fire hazard is unacceptably high on many acres of forest land in the U.S. 

West. For some of these acres, mechanical treatments are a way to reduce fi re 

hazard. A cohesive strategy is needed for identifying the long-term options 

and related funding needed to reduce fuels (GAO 2005). Given limited government budgets, one approach is to identify places where the use of woody 

biomass from thinning can best help pay for hazardous fuel reduction treatments and to use this information to aid in allocating funds for all types of 

hazardous fuel reduction treatments.

We do not attempt to identify all acres in the U.S. West where removal of 

woody biomass would improve resilience to undesirable fi re effects nor did 

we set out to demonstrate that if this were done enormous volumes of wood 

materials could be collected. We focus on areas in surface and mixed-severity 

fi re regime forests, where treatments are needed to reduce fi re hazard.

For 12 western states (table 1), we selected timberland acres (land capable 

of producing 20 ft3/acre/year and not withdrawn from timber utilization) 

eligible for treatment (determined in part by fi re hazard level), applied several 

alternative silvicultural treatments to reduce hazard while seeking to maintain 

ecosystem integrity, and evaluated to what extent revenues from the sale of 

biomass may offset harvest costs. Full results of our study were reported by 

Estimating Woody Biomass Supply From 

Thinning Treatments to Reduce Fire Hazard 

in the U.S. West 

Kenneth E. Skog1 and R. James Barbour2
658 USDA Forest Service Proceedings RMRS-P-41. 2006.

Skog and Barbour Estimating Woody Biomass Supply From Thinning Treatments to Reduce Fire Hazard in the U.S. West

Skog and others (2006). Results are compared to those from a previous Forest Service assessment (Forest Service 2003).

This evaluation of potential acres to be treated and biomass to be removed 

is intended to be the fi rst of several evaluation steps:

 1. Identify locations across the West where hazardous fuel reduction treatments are needed and that would generate amounts of woody biomass 

for use that could offset treatment costs.

 2. For selected localities in the West, evaluate both current market potential 

for using wood and prospects for expanding specifi c markets to use additional wood material.

 3. Evaluate the social acceptability of establishing and supporting the infrastructure necessary to use sales of wood as a means for funding fi re 

hazard reduction within the selected areas.

This paper also notes special estimates of biomass supply and treatment 

costs for two treatments on the 14 million acres of federal lands that are used 

as inputs to the fuel treatment market model for U.S. West (FTM–West) 

discussed by Ince and Spelter and by Kramp and Ince in these proceedings. 

The FTM–West model is used to evaluate the potential impact of increased 

biomass supply on projected conventional timber supply quantity and timber 

prices.

The 12 western states have 127 million acres of public and private timberland and 77 million acres of other forest land (Miles 2006a). Although 

other forest lands have hazardous fuels and wood from treatments that can 

provide higher value products, the volume and value per acre is very likely to 

be lower in relation to treatment costs than it is for timberland. Treatments 

of other forest land may provide an average 7 ovendry tons (odt) of woody 

Table 1—Area treated, by state and treatment scenario (million acres).

 Treatments for forest types other than spruce–fi r and lodgepole Treatments for spruce-

 Uneven-aged treatments fi r and lodgepole,

 High structural Limited structural Even-aged even-aged in

 diversity diversity treatments WUI area only

 50% BA No BA 50% BA No BA 50% BA No BA 25% BA 50% BA

 removal removal removal removal removal removal removal removal

 limit limit limit limit limit limit limit limit

State 1A 1B 2A 2B 3A 3B 4A 4B

AZ 0.5 0.5 0.4 0.4 0.1 0.1 0.0 0.0

CA 4.4 4.4 3.8 3.8 1.5 1.5 0.0 0.0

CO 1.2 1.3 1.1 1.1 0.4 0.5 0.1 0.1

ID 2.4 2.5 2.2 2.2 1.1 1.1 0.4 0.4

MT 2.9 3.0 2.5 2.6 1.5 1.6 0.0 0.0

NV 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

NM 0.9 1.0 0.8 0.8 0.3 0.3 0.0 0.0

OR 2.2 2.2 1.8 1.8 0.9 0.9 0.0 0.0

SD 0.1 0.1 0.1 0.1 0.0 0.0 0.0 0.0

UT 0.4 0.4 0.4 0.4 0.2 0.2 0.0 0.0

WA 1.8 1.8 1.5 1.5 0.6 0.6 0.0 0.0

WY 0.3 0.4 0.3 0.3 0.2 0.2 0.0 0.0

Total 17.1 17.5 14.8 15.1 6.7 6.8 0.5 0.5
USDA Forest Service Proceedings RMRS-P-41. 2006. 659

Estimating Woody Biomass Supply From Thinning Treatments to Reduce Fire Hazard in the U.S. West Skog and Barbour

biomass per acre (Perlack and others 2005) in the 12 states considered in our 

study compared with 24 to 34 odt/acre estimated for timberland thinning 

treatments.

The terms “woody biomass” and “biomass” refer to all wood in all trees—in 

the main stem, tops, and branches of all sizes of trees. “Merchantable wood” 

refers to the main stem of all live trees with a diameter at breast height (d.b.h.) 

≥5 in., from 1 ft above ground to a minimum 4-inch top diameter outside 

the bark of the central stem, or to the point where the central stem breaks 

into limbs and does not include rotten, missing, and from cull.

Methods

Data used were plot-level data from the Forest Inventory and Analysis 

Program (FIA) of the USDA Forest Service (Smith and others 2004), with 

additional plot information from the National Forest System (about 37,000 

plots in 12 states). The area to be treated and woody biomass to be removed 

were estimated as if the treatments were to be done within 1 year. In reality, 

the area treated and amounts removed would extend over many years. Methods were used to simulate treatments on all ownerships, and those results 

are explained in detail. Methods were also used to simulate treatments on 

federal land alone, and those results were used to provide biomass amounts 

and harvest costs to be used in the FTM–West market model.

Screens to Identify Area Eligible for Treatment

Of the 126.7 million acres of timberland in the 12 selected western states 

(Miles 2006a), 23.9 million acres passed an initial screen and were considered eligible for treatment. A second screen was applied when considering 

a specifi c silvicultural treatment, and less than 23.9 million acres actually 

receive simulated treatment.

Initial Screen—The initial screen was applied to two different groups of 

forest types: group 1, forest types with surface or mixed-severity fi re regimes; 

and group 2, forest types with high-severity fi re regimes. Group 2 includes 

lodgepole pine and spruce–fi r forest types. Group 1 contains all other forest 

types.

Plots excluded from fi re severity group 1 include (a) inventoried roadless 

areas, (b) counties west of Cascade Mountains in Oregon and Washington, 

where forests have a long fi re return interval, (c) plots with lower fi re hazard 

(both crowning index (CI) and torching index (TI) >25 mph, or CI alone 

>40 mph). For a map of inventoried roadless areas, see www.roadless.fs.fed.

us/maps/usmap2.shtml

Plots excluded from group 2 include (a) all plots outside wildland–urban 

interface (WUI) areas, (b) inventoried roadless areas, (c) counties west of 

Cascade Mountains in Oregon and Washington, where forests have a long 

fi re return interval, and (d) plots with lower fi re hazard (CI and TI both >25 

mph, or CI alone >40 mph).

Selected counties west of the Cascades were excluded because treatments 

in forests there would be designed to meet objectives other than fi re hazard 

reduction.

Oregon counties excluded were Benton, Clackamas, Clatsop, Columbia, 

Coos, Curry, Lane, Lincoln, Linn, Marion, Multnomah, Polk, Tillamook, 

Washington, and Yamhill. Washington counties excluded were Clallam, Clark, 
660 USDA Forest Service Proceedings RMRS-P-41. 2006.

Skog and Barbour Estimating Woody Biomass Supply From Thinning Treatments to Reduce Fire Hazard in the U.S. West

Cowlitz, Gray’s Harbor, Island, Jefferson, King, Kitsap, Lewis, Mason, Pacifi c, 

Peirce, San Juan, Skagit, Snohmish, Thurston, Wahkiakum, and Whatcom.

Of the 126.7 million acres of timberland, 67.5 million acres (53 percent) 

have lower fi re hazard than our criteria. Of the remaining 59.2 million acres, 

21.6 million acres (17 percent of all timberland) are in roadless areas or in 

excluded counties in Oregon and Washington. Of the remaining 37.6 million 

acres, 13.8 million acres (11 percent of all timberland) are in forest types 

with high-severity fi re regimes, which leaves 23.9 million acres eligible for 

treatment. In total, our screens removed 81 percent of all timberland and 60 

percent of acres with higher fi re hazard.

Second Screen—When applying a specifi c silvicultural treatment, a second 

screen determined which eligible plots actually receive simulated treatment. 

Plots were not treated if they would not provide 300 ft3 of merchantable 

wood per acre (about 4 odt/acre). Previous studies found that mechanical 

treatments that produce <300 ft3 of merchantable wood are unlikely to cover 

costs of the treatment (Barbour and others 2004, Fight and others 2004).

Fire Hazard Reduction Objectives and Assumptions

Selection of Plots for Treatment—Each FIA plot was assessed for fi re 

hazard by estimating CI and TI (Scott and Reinhardt 2001). Torching index is the 20-ft aboveground wind speed at which crown fi re can begin in a 

specifi ed fi re environment; CI is the 20-ft wind speed at which active crown 

fi re behavior is possible (can be sustained) in that environment. Plots were 

selected for treatment if CI < 25 mph alone or TI < 25 mph and CI < 40 mph 

(denoted hereafter as CI<25 and TI<25). The focus on crown fi res is useful 

because, although all stands may burn under certain conditions, stands that 

are likely to burn in crown fi res present particular suppression problems, and 

consequences of crown fi res are more severe than those of surface fi res. Plots 

with CI<25 or TI<25 were chosen for treatment because fi res might commonly be expected to occur at wind speeds between 15 and 25 mph.

Assumptions for Calculating Torching and Crowning Indexes—Torching and crowning indexes were calculated for each plot based on (a) canopy 

fuel profi le as computed from plot data, (b) slope steepness, (c) selected set 

of fuel moisture conditions corresponding to “summer drought” conditions 

(Rothermel 1991), and (d) use of fi re behavior fuel model (FM) 9 to represent 

surface fuels (Anderson 1982).

Fuel model 9 is described as hardwood or long-needle pine litter. It was 

chosen not because we assume that all surface fuels are hardwood or longneedle pine litter, but because FM 9 results in surface fi re behavior mid-range 

between FM 8 and 10 (other timber litter models) and FM 2 (timber grass 

model) (personal communication, Paul Langowski, Branch Chief, Fuels and 

Fire Ecology, USDA Forest Service, Rocky Mountain Region, 2004).

No single fuel model can be expected to adequately represent surface fuels 

in all timberlands. However, no plot data exist to characterize surface fuels. 

Assuming more extreme fi re behavior, such as FM 10, might lead to recommending thinning where none is really needed, whereas a FM 8, which results 

in very low-intensity surface fi res, may not identify stands at risk of crowning. 

Fuel model 9 was a compromise.

We also used FM 9 when computing TI and CI after thinning; that is, we 

assumed that the thinning treatment did not change the surface fuels enough 

to bump the fuel model into a higher fuel class.
USDA Forest Service Proceedings RMRS-P-41. 2006. 661

Estimating Woody Biomass Supply From Thinning Treatments to Reduce Fire Hazard in the U.S. West Skog and Barbour

Targets for Crowning and Torching Indexes after Treatment—The fuel 

hazard reduction objective for each plot was to increase TI and CI to >25 

mph or to increase only CI to >40 mph. These objectives are intended either 

to keep a crown fi re from starting or to prevent a crown fi re from spreading 

if crowns are ignited.

Limits on Removal of Basal Area—In some treatment cases, we limited 

total basal area (BA) removal to keep canopy closure as high as practical. 

Opening the canopy, while reducing canopy fuels, can lead to different fuel 

hazard problems: (1) expose surface fuels to solar radiation and wind, which 

can alter surface fi re behavior; (2) increase herbaceous and shrub growth, 

which may also change surface fi re behavior; (3) enhance conifer regeneration, ultimately creating ladder fuels; and (4) increase the risk that remaining 

trees will be blown down by strong winds.

To the extent that additional objectives call for refi nement of our treatments 

and more removals in local areas, we may be underestimating the amount of 

area that may be treated with positive average net revenue.

Long-Term Effect of Treatments on Fire Hazard—Forest stands are 

dynamic, as are forest fuels. The necessary frequency of treatments should be 

analyzed as part of a much more site-specifi c planning process, using tools 

such as FFE–FVS (Reinhardt and Crookston 2003) or fi re history studies.

We acknowledge that the fuel hazard reduction treatments described here 

do not address constraints on land management activities specifi ed in existing 

land and resource management plans and their potential effects on removals. 

Nor do these scenarios address the effect on importance of maintaining forest 

stocking, ground fuels, and other factors that may negatively contribute to 

CI and TI values on the ecologic health and productivity of forests.

Silvicultural Treatment Objectives and Assumptions—The thinning 

treatments to reduce fi re hazard have an objective to move the stand toward 

either (1) an uneven-aged condition or (2) an even-aged condition. In addition, the objective of some treatments is to limit BA removed to limit change 

in stand structure.

Some authors (Graham and others 1999) have suggested that thinning 

uneven-aged stands in some cases does not reduce fi re hazard. We address 

this concern by designing uneven-aged treatments that take enough trees to 

be effective in reducing TI, CI, and the risk of crown fi re.

Timberland area was divided into forest types that tend to have (1) highseverity fi re regimes (where severe fi res are routine under natural conditions) 

and (2) surface or mixed-severity fi re regimes. High-severity forest types are 

excluded from treatments except in WUI areas because severe fi res (crown 

fi res) are routine in these forest types under natural conditions, and thinning 

to avoid severe fi re does not support normal fi re ecology.

Treatments for Forests with Surface and Mixed-Severity Fire Regimes—

Treatments 1A and 1B—uneven-aged, leaving high structural diversity—remove 

trees so the number of trees remaining in each d.b.h. class after treatment contribute equally toward the numerical value of residual stand density index (SDI) 

for the stand (Long and Daniel 1990). The fi nal level of overall SDI is adjusted 

downward by simulated removal of trees across all d.b.h. classes until TI≥25 and 

CI≥25, or CI≥40. In scenario 1A, removals are limited to 50 percent of initial 

BA; in 1B, there is no limitation. This scenario results in forest structures that 

retain high structural diversity with intact understories of small trees.
662 USDA Forest Service Proceedings RMRS-P-41. 2006.

Skog and Barbour Estimating Woody Biomass Supply From Thinning Treatments to Reduce Fire Hazard in the U.S. West

Restricting removals to <50 percent of the original BA ensures that some 

semblance of an uneven-aged forest structure is maintained (Alexander and 

Edminster 1977, Burns 1983).

Treatments 2A and 2B—uneven-aged, limited structural diversity—attempt to achieve TI and CI goals by removing as many small trees as possible 

while still retaining smaller trees to ensure an uneven-aged structure. The 

remaining trees in a large d.b.h. class contribute more to the residual stand 

SDI than do trees in a smaller d.b.h. class.

The level of overall SDI is adjusted downward by simulated removal of 

trees until the target TI and CI values are reached (treatment 2B) or until 

50 percent of the original BA has been removed (treatment 2A).

Treatments 3A and 3B—even-aged, thin from below—emulate intermediate thinning in an even-aged silviculture system where the intent is to 

ultimately harvest and replace the existing forest. Small trees are completely 

removed in successively larger d.b.h. classes until CI and TI goals are met 

(treatment 3B) or until 50 percent of the original BA has been removed 

(treatment 3A). Thinning more than 50-percent BA may fundamentally alter the character of the forest and should not be prescribed without careful 

consideration of all potential ecosystem effects.

Treatments for Forests with High Severity Fire Regimes—Treatments 

4A and 4B—even-aged, thin from below (spruce–fi r and lodgepole pine 

forest types)—are similar to treatments 3A and 3B, except BA removals are 

restricted to 25 percent of existing stocking (treatment 4A) or 50 percent of 

existing stocking (treatment 4B) and are only in WUI areas. The 25-percent 

removal restriction is based on published partial cutting guidelines and is 

necessary to avoid wind throw in shallow-rooted tree species such as spruce, 

fi r, and lodgepole pine (Alexander 1986a,b).

Harvest Costs and Product Revenue Estimation

The cost to provide biomass ready for transport at the roadside was estimated for each plot using the Fuel Reduction Cost Simulator (FRCS) from 

My Fuel Treatment Planner (Biesecker and Fight 2006, Fight and others 

2006). Cost estimates are made for up to eight harvesting systems, based on 

the number and average volume of trees in various size categories and the 

slope of the site. Ground-based harvesting systems include (a) manual-felling 

log-length system, (b) manual-felling whole-tree (WT) system, (c) mechanized-felling WT system, and (d) cut-to-length (CTL) system. Cable-yarding 

systems include (a) manual-felling log-length system, (b) manual-felling WT 

system, (c) manual WT/log-length system, and (d) CTL system.

The cost for the least expensive suitable system was assigned to each plot. 

We assumed that (1) harvest is only a partial cut, (2) tops and branches are 

collected for use when the low-cost system brings whole trees to the landing, 

(3) trees down to 1 inch d.b.h. are removed, (4) average distance that logs 

are moved from stump to landing is 1,000 ft, (5) average area treated is 100 

acres, and (6) distance to move equipment between harvest sites is 30 miles. 

Costs might be reduced if small d.b.h. trees are not removed from the site 

and treated by another method (e.g., pile and burn).

We assume the product values and hauling costs used in the 2003 Assessment. Actual prices will vary by location and over time.
USDA Forest Service Proceedings RMRS-P-41. 2006. 663

Estimating Woody Biomass Supply From Thinning Treatments to Reduce Fire Hazard in the U.S. West Skog and Barbour

Delivered sawlogs (volume from main stem ≥7inches d.b.h.) $290/103 board feet

Delivered chips (volume from wood and bark <7 inches d.b.h., 

 tops and branches of larger trees) $30/odt

Haul distance 100 miles

Haul cost (for both sawlogs and chips) $0.35/odt/mile

The Fuel Treatment Evaluator 3.0 (FTE), a web-based tool available for 

general use, was used to select areas for treatment, apply treatments to FIA 

plot data, and generate removal information and maps (Miles 2006b).

Findings

Area Treated and Biomass Removed

The 2003 Assessment identifi ed 96.9 million acres of timberland for possible thinning in fi re regime condition classes (FRCCs) 1, 2, and 3, with 28.5 

million acres in FRCC 3. The 2003 Assessment selected plots for treatment 

if timber density, as measured by SDI, was greater than 30 percent of the 

maximum SDI for the plot forest type.

FRCC refers to the degree to which the current fi re regime (including fi re 

recurrence, intensity, severity) is different from the historical pattern, with 

FRCC 3 having the most divergence (see defi nitions at http://ncrs2.fs.fed.

us/4801/fi adb/fi re_tabler_us/rpa_fuel_reduction_treatment_opp.htm).

In contrast, our treatments 3A (all group 1 forest types) and 4A (group 2 

forest types in WUI areas) together would treat 7.2 million acres, and treatments 1B and 4B together would treat 18.0 million acres, with 85 percent 

of acres in FRCCs 2 and 3.

Of the 21.2 million WUI acres identifi ed in 12 western states (Stewart and 

others 2003), an estimated 4.1 million acres are in timberland. For the highseverity types, 0.5 million acres of WUI were included in treatments 4A or 

4B (table 1). For all other forest types, 0.3 to 0.7 million acres of WUI were 

included in treatments 1A to 3B. So the total WUI area to be treated could 

be 0.8 to 1.2 million acres, or 20 to 30 percent of the timberland WUI acres. 

We could be underestimating area to the extent that communities decide to 

treat larger WUI areas.

Treatment 1B would thin the largest area—17.5 million acres, or about 

14 percent of all timberland in the 12 western states. The highest percentage 

of timberland to be treated would be in California (33 percent), followed by 

New Mexico (24 percent), Idaho (21 percent), Montana (21 percent), and 

Arizona (16 percent).

The 2003 Assessment identifi ed total possible removal of 2.1 billion (109) 

odt biomass with treatment of all 94.5 million acres of treatable timberland. 

Removal from 66.3 million FRCC 2 and FRCC 3 acres could provide 1.5 

billion odt of biomass. If only 60 percent of FRCC 3 acres are treated (17.1 

million acres), the yield would be 346 million odt of biomass.

In our assessment, we identifi ed 7.2 to 18.0 million acres for treatment 

that would yield 169 million odt (smallest amount) from treatments 3A and 

4A and 640 million odt (largest amount) from treatments 1B and 4B (tables 

1 and 2).

The distribution of biomass removed by tree size differs greatly between 

the uneven-aged and even-aged treatments (table 3). In addition, the distribution for the uneven-aged treatments differs substantially from the results 
664 USDA Forest Service Proceedings RMRS-P-41. 2006.

Skog and Barbour Estimating Woody Biomass Supply From Thinning Treatments to Reduce Fire Hazard in the U.S. West

of the uneven-aged treatment used in the 2003 Assessment. The 2003 Assessment showed the most biomass removed from the 10-inch d.b.h. class. In 

contrast, our uneven-aged treatments provide most biomass in the ≥21-inch 

d.b.h. classes. Our uneven-aged treatments remove more because residual 

SDI for our treated stands is <20 percent of maximum SDI, compared with 

30 percent of maximum in the 2003 Assessment. Thinning to an average 

20 percent of maximum SDI is needed in our assessment to thin to achieve 

CI>40 when we cannot attain TI>25. We could help attain TI>25 rather 

than having to reach CI>40 by pruning branches to raise canopy base height 

and by decreasing surface fuels.

In our assessment, the proportion of all acres treated and biomass removed 

that comes from National Forest or all Federal land is about 55 or 60 percent, 

respectively, for both even-aged and uneven-aged treatments.

Fire Hazard Reduction Outcomes

Four possible fi re hazard reduction outcomes were identifi ed for the 23.9 

million acres eligible for treatment:

 1. Treatment is applied; both CI>25 and TI>25.

 2. Treatment is applied; CI>40.

 3. Treatment is applied; 50-percent BA removal limit is achieved before 

achieving either (1) or (2).

 4. No treatment is applied; <300 ft3 of merchantable wood could be 

removed.

Uneven-aged treatments with the 50-percent BA removal limit (1A and 2A) 

treat 71 and 61 percent of eligible acres, respectively. These treatments reach 

the medium or high hazard reduction goal for 44 and 30 percent of eligible 

Table 2—Initial standing biomass and biomass removals from this assessment (million ovendry tons).

 Treatments for forest types other than spruce–fi r and lodgepole Treatments for spruce-

 Uneven-aged treatments fi r and lodgepole,

 High structural Limited structural Even-aged even-aged in

 diversity diversity treatments WUI area only

 Initial 50% BA No BA 50% BA No BA 50% BA No BA 25% BA 50% BA

 volume on removal removal removal removal removal removal removal removal

 treatable limit limit limit limit limit limit limit limit

State timberland 1A 1B 2A 2B 3A 3B 4A 4B

 million acres

AZ 29.5 11.0 13.1 8.9 9.9 2.3 2.6 0.1 0.1

CA 419.2 219.5 222.4 144.8 145.2 37.4 40.1 0.2 0.3

CO 49.3 20.6 28.4 17.4 21.8 6.0 7.5 0.8 1.4

ID 171.4 68.1 83.1 57.7 63.4 26.6 29.4 6.4 10.5

MT 166.7 66.8 84.4 58.9 69.2 36.5 41.9 0.1 0.2

NV 0.9 0.3 0.3 0.2 0.2 0.1 0.1 0.0 0.0

NM 41.9 18.3 24.1 15.0 18.4 5.5 6.3 0.0 0.0

OR 210.4 76.8 88.7 53.9 56.2 25.5 26.3 0.0 0.0

SD 3.9 1.3 1.4 1.1 1.1 0.3 0.3 0.0 0.0

UT 18.2 7.5 9.8 6.9 8.0 2.9 3.2 0.0 0.1

WA 128.7 50.0 60.9 38.8 42.4 14.9 15.4 0.0 0.0

WY 17.7 7.5 10.3 7.3 8.9 3.6 4.5 0.1 0.2

Total 1,257.7 547.8 626.8 410.8 444.7 161.6 177.5 7.6 12.8
USDA Forest Service Proceedings RMRS-P-41. 2006. 665

Estimating Woody Biomass Supply From Thinning Treatments to Reduce Fire Hazard in the U.S. West Skog and Barbour

acres, respectively (table 4). When the BA limit is removed (1B and 2B), a 

slightly greater percentage of acres is treated (72 and 62 percent, respecively), 

all reach a hazard reduction target, and biomass removal increases 14 percent 

(548 to 627 million odt) and 8 percent, respectively.

The even-aged treatment with the 50-percent BA removal limit (3A) 

treats 28 percent of all eligible acres but reaches the medium or high hazard 

reduction goal for only 7 percent of the eligible acres (table 4). When the 

50-percent limit is removed (3B), 28 percent of acres are treated and all these 

treated acres reach the medium or high hazard reduction goal. Moving from 

treatment 3A to 3B requires a 10-percent increase in biomass removals, which 

includes the biomass from the additional 1 percent of acres treated.

In general terms, for forest area where there is the need to obtain a minimum level of merchantable wood to yield positive average net revenue and a 

restriction on BA removal, our results suggest that the uneven-aged treatment 

would more likely achieve one of the hazard reduction targets than would an 

even-aged treatment—in our example, 44 percent or 30 percent, compared 

with 7 percent.

If raising TI is a priority, then even-aged treatments are more effective 

than uneven-aged treatments. However, even-aged treatments are less likely 

to produce 300 ft3 of merchantable wood and provide positive net revenue 

from sale of products.

Treatment Costs, Product Revenues, Net Revenues

Average treatment costs per acre for even-aged treatments are about the 

same as for uneven-aged treatments for the acres selected for each treatment, 

though fewer acres are selected for even-aged treatments because fewer acres 

are able to provide 300 ft3/acre.

Table 3—Biomass removal by treatment and tree d.b.h. class (tons per acre).

 Treatments for forest types other than spruce–fi r and lodgepole Treatments for spruce-

 Uneven-aged treatments fi r and lodgepole,

 High structural Limited structural Even-aged even-aged in

 diversity diversity treatments WUI area only

 50% BA No BA 50% BA No BA 50% BA No BA 25% BA 50% BA

 removal removal removal removal removal removal removal removal

d.b.h. limit limit limit limit limit limit limit limit

class 1A 1B 2A 2B 3A 3B 4A 4B

 (in.)

2.0 0.4 0.5 0.5 0.6 0.8 0.9 0.4 0.5

4.0 1.2 1.5 1.5 1.7 2.2 2.4 1.5 2.2

6.0 2.1 2.4 2.8 3.0 4.9 5.1 4.9 5.4

8.0 2.9 3.3 3.6 3.8 6.2 6.5 4.8 6.6

10.0 3.1 3.6 3.6 2.5 2.8 0.7 2.1

14.0 2.5 2.8 2.2 2.4 1.2 1.4 0.4 0.9

16.0 1.9 2.2 1.5 1.6 0.6 0.8 0.4 0.8

18.0 1.4 1.7 0.9 1.0 0.4 0.5 0.0 0.2

20.0 1.0 1.2 0.4 0.5 0.3 0.3 0.0 0.0

22+ 12.5 13.2 7.6 7.7 0.7 0.6 0.0 0.0

Total 32.0 35.8 27.7 29.5 24.2 26.0 16.6 24.5
666 USDA Forest Service Proceedings RMRS-P-41. 2006.

Skog and Barbour Estimating Woody Biomass Supply From Thinning Treatments to Reduce Fire Hazard in the U.S. West

Average net revenues per acre are positive without subsidy for all treatments 

on gentle slopes and for uneven-aged treatments 1A, 1B, and 2B on steep 

slopes (table 5). With a $20/green ton subsidy for chips, average net revenues 

per acre are also positive for uneven-aged treatments 2A and for even-aged 

treatment 3B on steep slopes. Even with a subsidy, even-aged treatment 3A 

on steep slopes incurs a net cost per acre. With the subsidy, we could relax the 

300-ft3 merchantable wood requirement for all treatments on gentle slopes 

and still attain positive average net revenue.

Treatment Costs—The estimated cost to harvest and move biomass to the 

roadside is less than $1,000/acre for about 50 percent of acres treated for all 

treatments except treatment 4A, for which estimated costs are lower. Acres 

on gentle slopes (≤40 percent) tend to cost less, and acres on steep slopes 

(>40 percent) cost more.

Even though the even-aged treatments call for more trees to be harvested 

per acre on average, harvesting cost per acre is lower than or about the same 

as for uneven-aged treatments, which harvest fewer trees. This may be explained in part by the fact that we selected the lowest cost harvesting system 

for each plot analyzed. Costs for even-aged treatments would also be kept 

low by the requirement to provide a certain volume in larger trees to provide 

300 ft3/acre.

Biomass Revenues—The estimated delivered value of biomass per acre 

varies from $1,600 to $,2600, excluding treatments 4A and 4B, if the main 

stem volume of trees ≥7 in. d.b.h. goes to higher value products and the remainder is delivered as fuel chips. If all volume goes for chips, the delivered 

value varies from $430 to $640/acre.

For uneven-aged treatments 1A and 1B, about 67 percent of biomass is 

merchantable wood from trees ≥7 in. d.b.h. For even-aged treatments 3A and 

3B, about 50 percent of biomass is merchantable wood from trees ≥7 in. d.b.h. 

Also, biomass removed per acre is greater for treatments 1A and 1B than for 

treatments 3A and 3B. As a result, if merchantable wood goes to higher value 

products, the revenue from the uneven-aged treatments 1A and 1B is $800 to 

$1,200/acre more than for even-aged treatments 3A and 3B. If all wood goes 

for chips, treatments 1A and 1B provide only $50 to $100 more per acre than 

do treatments 3A and 3B.

Table 4—Fire Hazard outcomes (percentage of treatable acres).

 Goal attainment

 Low (50% BA Not treated

 limit is reached) Total Total (provides less

 (treatment is achieving receiving than 300 ft3

 made but BA Medium High a medium or some merchantable

Treatment limit is reached) CI>40 only CCI&TI >25 high target treatment wood/acre) Total

 1A 28 21 22 44 71 29 100

 2A 31 18 12 30 61 39 100

 3A 21 4 3 7 28 72 100

 1B 0 23 49 72 72 28 100

 2B 0 14 48 62 62 38 100

 3B 0 6 22 28 28 72 100
USDA Forest Service Proceedings RMRS-P-41. 2006. 667

Estimating Woody Biomass Supply From Thinning Treatments to Reduce Fire Hazard in the U.S. West Skog and Barbour

Net Revenue (Costs) from Treatments—Average net revenue from 

uneven-aged treatments is positive for gentle slopes ($340 to $690/acre) 

and negative for steep slopes (−$9 to −$450/acre). Average net revenue for 

even-aged treatments is $400 to $700 less than that for uneven-aged treatments in the same slope category (table 5). Net revenues for treatments on 

steep slopes are least negative for uneven-aged treatments 1B and 2B (−$9 

and −$120/acre, respectively).

In comparison to the uneven-aged treatment analyzed in the 2003 Assessment, our uneven-aged treatments (1A, 1B, 2A, 2B) provide about the 

same net revenue per acre for sites with gentle slopes ($350 to $700/acre). 

For steep slopes, however, our net revenue per acre is about $700 less and 

negative, whereas the estimates from the 2003 Assessment are positive. This 

difference could be due to the difference in plots selected.

If a subsidy of $20/green ton is provided for chips delivered to a mill, then 

the net revenue is positive for all treatments on gentle slopes and uneven-aged 

treatments 1A, 1B, and 2B (table 5). For these treatments and revenues, we 

could relax the requirement for 300 ft3/acre and treat more acres.

Biomass Removal Maps—Areas where biomass removal from thinning 

on timberland is most likely to provide net revenues per acre include northern 

California, northern and central Idaho, western Montana, central and northern Oregon, and Washington. Smaller acreages include central to southern 

Colorado, central/east Arizona, and northern New Mexico. The timberland 

in WUI areas receiving simulated treatment is found primarily in northern 

California, northern Idaho, western Montana, western Washington, and 

central Colorado (fi gs. 1 and 2).

Table 5—Estimated treatment costs, and revenuesa minus fuel treatment costs when larger diameter logs are sold for higher 

value products or for chips.

 Net revenue (cost)

 with merchantable 

 Net revenue (cost) wood used for higher

 with merchantable Net revenue (cost) with value products and 

 Average treatment wood used for higher merchantable wood chips given a subsidy

 cost value products used for chips of $20 per green ton

 ($/acre) ($/acre) ($/acre) ($/acre)

 Slope Slope Slope Slope Slope Slope Slope Slope

Treatment ≤40% >40% ≤40% >40% ≤40% >40% ≤40% >40%

 1A 903 1,774 619 (256) (1,064) (1,933) 1,039 163 

 2A 844 1,831 343 (453) (978) (1,867) 757 (32)

 3A 854 1,966 (112) (833) (973) (1,882) 391 (368)

 4A 692 1,811 (144) (726) (766) (1,550) 202 (478)

 1B 986 1,839 686 (9) (1,161) (1,917) 1,159 479 

 2B 882 1864 356 (120) (1,023) (1,909) 798 114 

 3B 902 1975 (86) (762) (1,024) (1,892) 441 (255)

 4B 952 1,822 (18) (266) (1,073) (1,615) 421 36 

a Product value assumptions: delivered sawlog value, $290/mbf; delivered chip value, $30/od ton; transport cost, $0.35/od ton; haul distance, 

100 miles.
668 USDA Forest Service Proceedings RMRS-P-41. 2006.

Skog and Barbour Estimating Woody Biomass Supply From Thinning Treatments to Reduce Fire Hazard in the U.S. West

Figure 1—Total biomass removed per 160,000-acre area for uneven-aged treatment 1A 

(tons).

Estimates of Biomass Removed and Harvest Costs Used in 

the FTM–West Model

Two sets of treatments were applied to the 14 million acres of federal timberland judged eligible for treatment. These are treatments 1A and 4A and 

treatments 3A and 4A. Volumes and harvest costs from these treatments are 

used as inputs to the FTM–West market model described by Ince and Spelter 

and by Kramp and Ince in these proceedings. Unevenaged treatments 1A 

and 4A combined (SDI treatment) treat 10.9 million acres and provide 347 

million tons (23.2 billion ft3) at an average cost of $1,531/acre ($0.719/ft3). 

Even-aged treatments 3A and 4A combined (TFB treatment) treat 5.6 million acres and provide 148 million tons (9.9 billion ft3) at an average cost of 

$1,420/acre ($0.807/ft3).
USDA Forest Service Proceedings RMRS-P-41. 2006. 669

Estimating Woody Biomass Supply From Thinning Treatments to Reduce Fire Hazard in the U.S. West Skog and Barbour

Figure 2—Total biomass removed per 160,000-acre area for even-aged treatment 3A 

(tons).

Summary

The proportion of the 23.9 million eligible acres that can be thinned 

and provide positive net revenue from the sale of biomass products varies 

substantially, depending on whether an even- or uneven-aged silvicultural 

treatment is used and whether removals are limited or not limited to taking 

50 percent of initial BA.

Under our assumptions, uneven-aged treatments will be able to treat a higher 

proportion of acres with resulting positive net revenue than will even-aged treatments. Moreover, for treated acres, if BA removal is limited to 50 percent limit, 

then uneven-aged treatments are more likely to attain one of our hazard reduction 

targets (CI>25 and TI>25, or TI>40) than are the even-aged treatments.
670 USDA Forest Service Proceedings RMRS-P-41. 2006.

Skog and Barbour Estimating Woody Biomass Supply From Thinning Treatments to Reduce Fire Hazard in the U.S. West

Both uneven-aged and even-aged treatments are able to meet hazard 

reduction targets on all acres if we remove the BA removal limits and the 

requirement to provide 300 ft3/acre of merchantable wood. But the hazard 

reduction benefi t of removing the BA limit may be limited or offset by the 

effect of a more open canopy and more greatly altered stand structure. The 

data on costs and revenues suggest that if uneven-aged treatments were used 

everywhere, revenues could cover a notably higher proportion of costs than 

if even-aged treatments were used everywhere.

If we assume a $20/green ton subsidy for chips, average revenue is positive for all treatments on gentle slopes and increases the most for even-aged 

treatments (about $500/acre) because they provide the most chips. Revenue 

for uneven-aged treatments increases about $410/acre.

The eligible acres and treated acres are predominately in California, Idaho, 

and Montana, which include 65 to 70 percent of the treated acres for both 

uneven-aged and even-aged treatments. There are an estimated 21.2 million 

acres of WUI area in the 12 western states studied, of which an estimated 

4.1 million acres is timberland. Treatments would cover 20 to 30 percent of 

this timberland

Given the concern about removing large trees by uneven-aged thinning, it 

may be possible to reduce large tree harvest by pruning or reducing surface 

fuels to increase torching index rather than thinning to reach a high crowning index. Supplementary treatments are likely to increase harvest costs and 

decrease net revenue per acre.

Acknowledgments

This paper is an outcome of the Wood Utilization Opportunity Areas 

(WUOA) Project that was funded in part by the National Fire Plan Research 

Program (Project 02.FPL.C.1). The project is a collaborative effort of USDA 

Forest Service researchers and managers including Ken Skog, Forest Products 

Laboratory, and Jamie Barbour, Pacifi c Northwest Research Station, co-team 

leaders; Roger Fight, research forest economist (retired), Pacifi c Northwest 

Research Station; Karen Abt and Bobby Hugget, research economists, Southern Research Station; Frank Burch, silviculturist, Washington, DC; Pat Miles, 

research forester, North Central Research Station; Elizabeth Reinhardt, project leader, Rocky Mountain Research Station; and Wayne Sheppard, research 

silviculturist, Rocky Mountain Research Station.

References

Alexander, R.R. 1986a. Silvicultural systems and cutting methods for old-growth 

Spruce–fi r forests in the Central Rocky Mountains. Gen. Tech. Rep. RM-126. 

U.S. Department of Agriculture, Forest Service, Rocky Mountain Forest and 

Range Experiment Station, Ft. Collins, CO. 33 p.

Alexander, R.R. 1986b. Silvicultural systems and cutting methods for old-growth 

lodgepole pine forests in the Central Rocky Mountains. Gen.Tech. Rep. RM-127. 

U.S. Department of Agriculture, Forest Service, Rocky Mountain Forest and 

Range Experiment Station, Ft. Collins, CO. 31 p.
USDA Forest Service Proceedings RMRS-P-41. 2006. 671

Estimating Woody Biomass Supply From Thinning Treatments to Reduce Fire Hazard in the U.S. West Skog and Barbour

Alexander, R.R.; Edminster, C.B. 1977. Uneven-aged management of old-growth 

spruce–fi r forests: Cutting methods and stand structure goals for the initial 

entry. Res. Pap. RM-186. U.S. Department of Agriculture, Forest Service, Rocky 

Mountain Forest and Range Experiment Station, Ft. Collins, CO. 12 p.

Anderson, H.E. 1982. Aids to determining fuel models for estimating fi re behavior. 

Gen. Tech. Rep. INT-122. U.S. Department of Agriculture, Forest Service, 

Intermountain Forest and Range Experiment Station, Ogden, UT. www.fs.fed.

us/rm/pubs_int/int_gtr122.pdf

Barbour, R.J.; Fight, R.D.; Christensen, G.A.; Pinjuv, G.L.; Nagubadi, R.V. 2004. 

Thinning and prescribed fi re and projected trends in wood product potential, 

fi nancial return, and fi re hazard in Montana. Gen. Tech. Rep. PNW-GTR-606. 

U.S. Department of Agriculture, Forest Service, Pacifi c Northwest Research 

Station, Portland, OR.

Biesecker, R.L.; Fight, R.D. 2006. My fuel treatment planner: A users guide. Gen. 

Tech. Rep. PNW-GTR-663. U.S. Department of Agriculture, Forest Service, 

Pacifi c Northwest Research Station, Portland, OR. 31 p. www.fs.fed.us/pnw/

data/myftp/myftp_home.htm

Burns, R.M. 1983. Silvicultural systems for the major forest types of the United 

States. Agric. Hdbk. 445. U.S. Department of Agriculture, Forest Service, 

Washington, DC. 191 p.

Fight, R.D.; Barbour, R.J.; Christensen, G.A.; Pinjuv, G.L.; Nagubadi, R.V. 2004 

Thinning and prescribed fi re and projected trends in wood product potential, 

fi nancial return, and fi re hazard in New Mexico. Gen. Tech. Rep. PNW-GTR605. U.S. Department of Agriculture, Forest Service, Pacifi c Northwest Research 

Station, Portland, OR. 48 p.

Fight; R.D.; Hartsough, B.R.; Noordijk, P. 2006. Users guide for FRCS: Fuel 

reduction cost simulator software. Gen. Tech. Rep. PNW-GTR-668. U.S. 

Department of Agriculture, Forest Service, Pacifi c Northwest Research Station, 

Portland, OR. 23 p.

Forest Service. 2003. A strategic assessment of forest biomass and fuel reduction 

treatments in western states. U.S. Department of Agriculture, Forest Service, 

Washington, DC. www.fs.fed.us/research/pdf/Western_fi nal.pdf

GAO. 2005. Wildland fi re management—Important progress has been made, but 

challenges remain to completing a cohesive strategy. GAO-05-147. January. U.S. 

Government Accounting Offi ce, Washington, DC. 32 p.

Graham, R.T.; Harvey, A.E.; Jain, T.B.; Tonn, J.R. 1999. The effects of thinning 

and similar stand treatments on fi re behavior in western forests. Gen. Tech. 

Rep. PNW-GTR-463. U.S. Department of Agriculture, Forest Service, Pacifi c 

Northwest Research Station Portland, OR. 27 p.

Long, J.N.; Daniel, T.W. 1990. Assessment of growing stock in uneven-aged stands. 

West. J. Applied For. 5(3):93–96.

Miles, P.D. 2006a. RPA 2002 Tabler/Mapmaker, Version 1.0. U.S. Department of 

Agriculture, Forest Service, North Central Research Station, St. Paul, MN. Feb. 6, 

2006. http://ncrs2.fs.fed.us/4801/fi adb/rpa_tabler/webclass_rpa_tabler.asp

Miles, P.D. 2006b. Fuel Treatment Evaluator 3.0. U.S. Department of Agriculture, 

Forest Service, North Central Research Station, St. Paul, MN. January 2006. 

http://ncrs2.fs.fed.us/4801/fiadb/fire_tabler_us/rpa_fuel_reduction_

treatment_opp.htm http://www.ncrs2.fs.fed.us/4801/fi adb/FTE_Version3/

WC_FTE_version3.asp

Perlack, R.D.; Wright, L.L.; Turhollow, A.F.; Graham, R.L.; Stokes, B.J.; Erbach, 

D.C. 2005. Biomass as feedstock for a bioenergy and bioproducts industry: The 

technical feasibility of a billion ton supply. Oak Ridge National Laboratory, Oak 

Ridge, TN. 60 p. http://feedstockreview.ornl.gov/pdf/billion_ton_vision.pdf
672 USDA Forest Service Proceedings RMRS-P-41. 2006.

Skog and Barbour Estimating Woody Biomass Supply From Thinning Treatments to Reduce Fire Hazard in the U.S. West

Reinhardt, E.D.; Crookston, N.L. (eds). 2003. The fire and fuels extension 

to the forest vegetation simulator. Gen. Tech. Rep. RMRS-GTR-116. U.S. 

Department of Agriculture, Forest Service, Rocky Mountain Research Station, 

Fort Collins, CO.

Rothermel, R.C. 1991. Predicting behavior and size of crown fi res in the Northern 

Rocky Mountains. Res. Pap. INT-438. U.S. Department of Agriculture, Forest 

Service, Rocky Mountain Research Station, Fort Collins, CO.

Scott, J.H.; Reinhardt, E.D. 2001. Assessing crown fi re potential by linking models 

of surface and crown fi re behavior. Res. Pap. RMRS-RP-29. U.S. Department 

of Agriculture, Forest Service, Rocky Mountain Research Station, Fort Collins, 

CO.

Skog, K.E.; Barbour, R.J.; Abt, K.; Bilek, E.T.; Burch, F.; Fight, R.D.; Hugget, 

B.; Miles, P.; Reinhardt, E.; Shepperd, W. 2006. Evaluation of silvicultural 

treatmentments and biomass use for reducing fi re hazard in western states. 

Res. Pap. FPL-RP-634. U.S. Department of Agriculture, Forest Service, Forest 

Products Laboratory, Madison, WI. 29 p.

Smith, W.B.; Miles, P.D.; Visage, J.S.; Pugh, S.A. 2004. Forest resources of the 

United States. Gen.Tech. Rep. NC-241. U.S. Department of Agriculture, Forest 

Service, North Central Research Station, St. Paul, MN. 137 p.

Stewart, S.I.; Radeloff, V.C.; Hammer, R.B. 2003. Characteristics and location of the 

wildland–urban interface in the United States. In: Proceedings, 2nd International 

Wildland Fire Ecology and Fire Management Congress, Orlando, FL, November 

19, 2003. http://silvis.forest.wisc.edu/Publications/PDFs/Stewart_etal_2003.

pdf http://silvis.forest.wisc.edu/Library/Stats/uswuistats.pdf
USDA Forest Service Proceedings RMRS-P-41. 2006. 673

In: Andrews, Patricia L.; Butler, Bret W., 

comps. 2006. Fuels Management—How to 

Measure Success: Conference Proceedings. 

28-30 March 2006; Portland, OR. 

Proceedings RMRS-P-41. Fort Collins, 

CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research 

Station.

1 Landscape Modeling A nalyst 

with Management & Engineering 

Technologies, Inc., El Paso, TX.

rsilverstein@fs.fed.us

2 Economist at the College of Forestry 

and Conservation, University of 

Montana, Missoula, MT.

3 Research Forester with the Economic 

Aspects of Ecosystem Management 

on Forest Lands research unit of the 

USDA Forest Service, Rocky Mountain 

Research Station, Missoula, MT. 

4 Economist with the Economic 

Aspects of Ecosystem Management 

on Forest Lands research unit of the 

USDA Forest Service, Rocky Mountain 

Research Station, Missoula, MT. 

5 Chair of Forest Management 

Department at the College of Forestry 

and Conservation, University of 

Montana, Missoula, MT.

6 Ph.D. student at the College of 

Forestry and Conservation, University 

of Montana, Missoula, MT.

Abstract—Utilization of small-sized wood (biomass) from forests as a potential source 

of renewable energy is an increasingly important aspect of fuels management on public 

lands as an alternative to traditional disposal methods (open burning). The potential 

for biomass utilization to enhance the economics of treating hazardous forest fuels was 

examined on the Bitterroot National Forest and surrounding areas. Initial forest stand 

conditions were identifi ed from Forest Inventory and Analysis (FIA) data. The Forest 

Vegetation Simulator (FVS) was used to simulate stand growth and development and 

estimate removed volumes. Harvest and haul cost models were used to estimate stump 

to mill costs and these were integrated into MAGIS, a natural resources decision-support system. Temporal and spatial implications of utilization were examined through 

optimization modeling with MAGIS to identify sustainable quantities and associated 

costs based on accessibility, haul distance, fl ow, and quantity of small-diameter material. This study enables land managers, investors, and policy-makers to make informed 

economic and environmental decisions regarding biomass as a renewable energy 

source in the Bitterroot National Forest area and will serve as a model for biomass 

utilization in other areas.

Introduction

In the western U.S. there are approximately 15.8 million acres of accessible forestland that could benefi t from mechanical fuel treatments to reduce 

hazardous fuels and disastrous effects of severe wildfi res (USFS 2003). Mechanical treatments will produce signifi cant quantities of currently sub- and 

non-merchantable biomass material not suitable for lumber or pulp production that must be disposed to avoid leaving hazardous fuels in the forest. 

Traditionally, this biomass has been disposed onsite by burning, which has 

drawbacks such as potential escape, air quality issues and limited burning 

windows.

Research has indicated that fuel treatments on public lands have the 

potential to produce an abundance of biomass (Barbour and others 2004, 

USDOI Unpublished, USFS 2003), but competitive markets for this material 

are generally lacking. However, gaining popularity, momentum, and fi nancial feasibility is utilization of this traditional waste material for renewable 

energy production, specifi cally, thermal energy production at relatively small 

scales in rural areas throughout the Western U.S. With the establishment 

of the Fuels for Schools Program, a collaboration of federal and state agencies providing fi nancial subsidies and incentives, small scale thermal energy 

production facilities are now being constructed (www.schoolsforfuels.org). 

Other potential uses of biomass are also being investigated (LeVan-Green 

Biomass Utilization Modeling on the 

Bitterroot National Forest

Robin P. Silverstein1, Dan Loeffl er2, J. Greg Jones3, Dave E. Calkin4, 

Hans R. Zuuring5, and Martin Twer6
674 USDA Forest Service Proceedings RMRS-P-41. 2006.

Silverstein, Loeffl er, Jones, Calkin, Zuuring, and Twer Biomass Utilization Modeling on the Bitterroot National Forest

and Livingston 2003). Thus, outlets for biomass are forming, providing an 

alternative to onsite burning.

This paper compares the economic tradeoffs between biomass recovery 

from fuel treatment for renewable energy production and biomass disposal by 

open burning in Ravalli County, Montana. We have integrated fuel treatments 

devised with Bitterroot National Forest personnel with several independent 

and exogenous models to develop a set of biomass disposal alternatives. These 

alternatives refl ect realistic choices managers must make when determining if 

biomass utilization for renewable energy production is economically justifi ed 

or if onsite burning may be the best option. From this notion of alternative 

disposal options, we have devised a spatial and temporal model of biomass 

utilization economics based on site distance from a utilization center.

Methods

Study Area

The location specifi ed for this analysis – the Bitterroot National Forest 

in western Montana – was chosen due to a number of economic and environmental factors it has in common with other communities in the inland 

western U.S. The area has an abundance of National Forest land, a growing 

population particularly in the Wildland Urban Interface (WUI), and contains a signifi cant amount of forestland categorized as moderately to highly 

removed from historical wildfi re regimes (USFS 2003b). Furthermore, this 

area is within proximal distance of a modest amount of existing wood products 

infrastructure with biomass utilization capacity. These include two recently 

established, small-scale facilities within the study area capable of utilizing 

biomass for thermal energy, and in adjacent Missoula county to the North, 

a sawmill and a pulpmill that utilizes biomass as hogfuel.

Silvicultural Treatments Selected for the Bitterroot 

National Forest

A wide variety of silvicultural treatments are available to land managers 

to achieve differing fuel treatment objectives. In this analysis we focused on 

mechanical treatments designed to reduce wildfi re effects and restore forests 

to sustainable and historical conditions, where prescribed burning would not 

be feasible under present conditions. Discussions with Bitterroot National 

Forest (BNF) silvicultural and fi re management personnel yielded the following three prescriptions:

 1) Thin from below (TB9) – cut and remove all trees up to a 9 inches 

diameter breast height (d.b.h.); apply this prescription only to stands 

having 1) basal area (BA) greater than 50 ft2/ac for trees greater than 

9 inches d.b.h., or 2) BA greater than 20 ft2/ac for trees 9 inches d.b.h. 

or greater where there are at least 109 trees per acre that are 9 inches 

d.b.h. or less. This prescription may be applied in all stands excluding 

lodgepole, white pine, grand fi r and sub alpine fi r.

 2) Moderate density (Moderate) – cut and remove all trees up to 7 inches 

d.b.h., plus some larger diameter trees with a target residual stand having 

100 ft2/ac BA in the largest and healthiest trees; apply this prescription 
USDA Forest Service Proceedings RMRS-P-41. 2006. 675

Biomass Utilization Modeling on the Bitterroot National Forest Silverstein, Loeffl er, Jones, Calkin, Zuuring, and Twer

only to stands having a BA greater than 100 ft2/ac for trees 7 inches 

d.b.h. or greater. Grand fi r and sub alpine fi r are removed fi rst, and then 

the smallest Douglas fi r, ponderosa pine and western larch are cut equally 

until the desired BA is achieved. This prescription may be applied in all 

stands excluding lodgepole and white pine.

 3) Comprehensive restoration (Comprehensive) – cut and remove all 

trees up to 7 inches d.b.h., plus some larger diameter trees with a target residual stand having 50 ft2/ac in fi re resistant tree species such as 

ponderosa pine, western larch, and large Douglas fi r. Remaining tree 

sizes, numbers, and their locations will restore the stand to a sustainable structure given current conditions. Apply this prescription only to 

stands having a BA greater than 50 ft2/ac for trees 7 inches d.b.h. or 

greater. This prescription was designed for application in ponderosa pine 

habitat types only.

Timber Volume Estimation

Forest Inventory and Analysis (FIA, http://www.fi a.fs.fed.us/) data were 

used to estimate the volume of merchantable logs (7+ inches d.b.h. to a 4.5 

inch top) and sub-merchantable biomass (whole trees less than 7 inches d.b.h. 

and tops and limbs of harvested trees 7+ inches d.b.h.) that would be removed 

by the three mechanical fuel reduction prescriptions. A whole tree harvest 

system was assumed. To obtain an adequate amount of stand data, FIA plots 

were selected from six western Montana counties having forest conditions 

similar to those found in Ravalli County, yielding a total of 912 FIA plots.

These data were imported into the Northern Idaho/Inland Empire variant 

of the Forest Vegetation Simulator (FVS, http://www.fs.fed.us/fmsc/fvs/) 

to predict merchantable timber volumes and biomass harvested from applying 

each of the three fuel treatment prescriptions described earlier. We assumed 

that no cut stems, tops, or branchwood were left in the stand, in other words 

everything cut was removed.

To capture the dynamic aspect of timber stand composition over time, as 

well as to allow stands to move between vegetation states, the FIA plot growth 

was simulated using FVS for up to fi ve decades from 1997, the most recent 

inventory year, to 2007,..., 2047. Each plot was grown from its inventory 

condition to each of these decadal time periods and then the fuel treatment 

prescriptions were applied. Based on the forest conditions for applying each of 

the three treatments, the Comprehensive prescription set consisted of 2,703 

plots, the Moderate prescription set had 1,346 plots and the TB9 prescription 

set had 2,267 plots. Many plots qualifi ed for more than one prescription.

Weights for all merchantable logs that would be removed from the FIA 

plots by the prescriptions were computed through a combination of the FVS 

Database Extension, tree component ratio equations from Jenkins and others 

(2003), and dry cubic foot weights obtained from Reinhardt and Crookston 

(2004). Quadratic mean diameter (QMD) and trees per acre cut were tallied for both the merchantable and non-merchantable categories. The Fire 

and Fuels Extension was utilized to estimate the weight of the total biomass 

removed. Subtracting the removed merchantable log weight from the weight 

of the total biomass removed yielded weight of the sub- or non-merchantable 

biomass. We assumed that all cut stems and branchwood were removed from 

the stand (FVS YARDLOSS keyword). Statistics are displayed in tables 1 

through 3.
676 USDA Forest Service Proceedings RMRS-P-41. 2006.

Silverstein, Loeffl er, Jones, Calkin, Zuuring, and Twer Biomass Utilization Modeling on the Bitterroot National Forest

Table 1—Summary statistics of quadratic mean diameter (QMD), cubic feet, trees per acre cut, biomass, and harvest costs for trees removed 

using the Comprehensive prescription (n=2,703).

Harvest Cost per Acre

 QMD QMD Trees per Trees per Total With Without

 >7” <=7” Cubic Ft Cubic Ft Acre Cut Acre Cut Removed Biomass Biomass Biomass

Statistics DBH DBH >7” DBH <=7” DBH >7” DBH <=7” DBH (dry tons) (dry tons) Chipping Chipping

Mean 11.93 3.53 1,740.77 269.07 97.69 215.31 39.22 13.09 $1,595 $1,458

Std. Error 0.06 0.04 25.06 6.47 1.20 6.59 0.45 0.15 $19 $17

 of Mean

Std. Deviation 3.13 1.98 1,302.63 336.38 62.57 342.62 23.63 7.97 $980 $897

Median 11.27 3.84 1,471.76 148.41 87.64 95.81 36.00 11.81 $1,468 $1,335

Table 2—Summary statistics of quadratic mean diameter (QMD), cubic feet, trees per acre cut, biomass, and harvest costs for trees removed 

using the Moderate prescription (n=1,346).

Harvest Cost per Acre

 QMD QMD Trees per Trees per Total With Without

 >7” <=7” Cubic Ft Cubic Ft Acre Cut Acre Cut Removed Biomass Biomass Biomass

Statistics DBH DBH >7” DBH <=7” DBH >7” DBH <=7” DBH (dry tons) (dry tons) Chipping Chipping

Mean 10.29 3.71 1,126.87 250.82 80.21 201.11 27.09 10.37 $1,223 $1,117

Std. Error 0.07 0.05 28.17 7.91 1.51 8.79 0.51 0.18 $22 $20

 of Mean

Std. Deviation 2.40 1.89 1,033.35 290.18 55.42 322.38 18.68 6.78 $804 $736

Median 9.83 4.01 834.83 155.24 70.38 94.53 23.00 8.95 $1,067 $968

Table 3—Summary statistics of quadratic mean diameter (QMD), cubic feet, trees per acre cut, biomass, and harvest costs for trees removed 

using the TB9 prescription (n=2,267).

Harvest Cost per Acre

 QMD QMD Trees per Trees per Total With Without

 >7” <=7” Cubic Ft Cubic Ft Acre Cut Acre Cut Removed Biomass Biomass Biomass

Statistics DBH DBH >7” DBH <=7” DBH >7” DBH <=7” DBH (dry tons) (dry tons) Chipping Chipping

Mean 6.93 3.93 261.98 304.70 42.21 250.10 12.30 8.26 $763 $693

Std. Error 0.06 0.04 5.38 7.02 0.82 7.55 0.22 0.16 $16 $14

 of Mean

Std. Deviation 2.77 1.74 255.92 334.10 39.15 359.57 10.36 7.61 $738 $673

Median 7.93 4.12 187.99 192.20 32.13 131.55 10.00 6.00 $562 $517

Modeling Treatment Costs

Treatment costs (excluding administrative and planning) were modeled for 

each application of the three treatments applied to the FIA plots summarized 

in tables 1 to 3 using the Fuel Reduction Cost Simulator (FRCS, Fight and 

others 2006). Required FRCS input variables include trees per acre removed, 

QMD, average tree volume, green wood weight, and residue weight to bole 

weight fractions. These were calculated from the cut tree lists (tables 1 to 3), 

regression equations from Jenkins and others (2003) and dry wood weights 

from Reinhardt and Crookston (2004) adjusted to 50 percent wood fi ber 
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Biomass Utilization Modeling on the Bitterroot National Forest Silverstein, Loeffl er, Jones, Calkin, Zuuring, and Twer

moisture content. We used the average slope of 22 percent for lands identifi ed through GIS analysis. We specifi ed a whole tree system with an average 

skidding distance of 800 feet. The model was calibrated to refl ect western 

Montana wage rates – $24.60/hour for fallers and/or buckers and $16.13/

hour for all others (2002 dollars, ACINET 2003). The model’s default labor 

benefi t rate of 35 percent was retained, and move-in costs were not included. 

Tables 1 to 3 display summary statistics from the harvest cost modeling.

Haul Cost Estimation

 Material delivery costs from the logging unit to an end use facility 

can often determine the fi nancial success of mechanical treatment operations. Western Montana is home to several locations that utilize biomass as 

thermal-energy fuel, and therefore, haul distances are not as great as many 

other locations. For our analysis we specifi ed two end use locations as destinations for the biomass and one end use facility for merchantable logs that 

resulted from implementing the three fuel reduction prescriptions. These are 

respectively the towns of Darby in the southern portion of Ravalli County, 

Frenchtown in western Missoula County and Milltown in southern Missoula 

County (fi g. 1).

Figure 1—Location of delivery 

points. Darby and Frenchtown 

for biomass. Milltown for 

t i m b e r p r o d u c t s . G r a y 

shaded area is the study area 

polygons.
678 USDA Forest Service Proceedings RMRS-P-41. 2006.

Silverstein, Loeffl er, Jones, Calkin, Zuuring, and Twer Biomass Utilization Modeling on the Bitterroot National Forest

Haul costs were estimated on a per mile basis for each of two types of roads, 

paved and non-paved, using the Forest Residue Trucking Model (FoRTS; 

http://www.srs.fs.usda.gov/forestops/) and a GIS road coverage for the 

study area (Loeffl er and others 2006). We further verifi ed our results from 

FoRTS with the Log Truck Haul Cost Appraisal model (http://www.fs.fed.

us/r6/nr/fp/FPWebPage/FP70104A/Programs.htm). Chip truck haul 

costs were based upon hourly roll-off container truck operating costs and 

average miles per hour speed, and log truck haul costs were based upon the 

hourly costs of operating a tractor trailer. We calibrated the haul cost model 

to refl ect local wages and conditions using an average driver wage of $16/

hour with 35 percent benefi t rate. We assumed the chip truck would haul 16 

green tons of chips and the log truck 28 tons, diesel fuel costs $2.50/gallon 

and oil costs $9/gallon.

We estimated haul costs for log trucks delivering merchantable logs to 

Milltown (where a mill exists that purchases logs) and roll-off container 

trucks hauling biomass to both Frenchtown and Darby. Distances in both 

paved and non-paved miles (total miles is the sum of paved and non-paved) 

were tallied from the polygons identifi ed in the GIS portion of this analysis to 

Darby, Frenchtown, and Milltown. Average speeds were estimated at 15 miles 

per hour on non-paved roads and 45 miles per hour on paved roads. Using 

these estimates, costs per mile for each road surface type were estimated using the FoRTS model as the quotient of operating costs per driving hour and 

average miles per hour speed (table 4). Differences in the costs per mile are 

attributable to changes in variable truck operating costs when combinations 

of road types change. These average costs per mile were then multiplied by 

the actual paved and unpaved distances for each polygon to compute unique 

haul costs for each polygon.

Selection of Analysis Area

GIS data were used to identify the stands in the frequent fi re regime class 

where mechanical treatment is appropriate and feasible. The current vegetation was represented by the vegetation states assigned to the stand polygons 

by Chew and others (2004). Based on fuel management objectives, only 

those vegetation states having the dominant tree species displayed in table 5 

were considered for treatment. Additional characteristics of vegetation states 

included size class (QMD of SS = <5”, Pole = 5” to 8.9”, Medium = 9” to 

14.9”, Large = 15” to 20.9”, and Very large = 21”+) and density (crown 

canopy cover of 1 = 0 to 15%, 2 = 15 to 39%, 3 = 40 to 69%, 4 = 70 to 

100%). The FIA plots were categorized into these pre-treatment vegetation 

states. Since FIA data did not exist for certain vegetation states (21 percent 

by area), missing data was interpolated through a method of substituting 

Table 4—Round trip distances and haul cost to the three end use locations.

 Average Round Trip Miles Cost per Mile

End Use Locations - Non-paved Non-paved

 Montana towns Paved Roads Roads Paved Roads Roads

Darby (chip truck) 38 13 $1.26 $3.78

Frenchtown (chip truck) 134 16 $1.37 $4.10

Milltown (log truck) 124 16 $1.36 $4.08
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Biomass Utilization Modeling on the Bitterroot National Forest Silverstein, Loeffl er, Jones, Calkin, Zuuring, and Twer

based on proportional data from other vegetation states. From the GIS data 

we restricted analysis to non-wilderness areas, with slopes less than or equal 

to 35 percent (based on the requirements of the whole tree ground-based 

harvest system), only lands categorized as FRCC 2 or 3 (USFS 2003b) and 

polygons that fell within a 1500 foot buffer of existing roads. The resulting 

polygons are included in fi gure 1.

MAGIS Modeling Parameters

MAGIS (Multi-resource Analysis and Geographic Information System) is 

an optimization model designed to solve complex spatial and temporal scheduling problems in natural resource management (Zuuring and others 1995). 

MAGIS is based on a mixed-integer mathematical programming formulation 

that includes vegetation management options for treatment unit polygons 

and an optional network component for analyzing road access and associated 

costs and resource impacts (Weintraub and others 1994). Decision variables 

for each treatment unit polygon include “no action” and treatment options 

comprised of alternative management regimes that vary by the treatment(s) 

they prescribe over time, and the period when the management regime is 

implemented.

The objective of this study was to analyze the quantities of biomass that 

could be made available by treating hazardous fuels accessible from existing 

roads. Haul distances and costs were incorporated into the vegetation management alternatives along with costs of burning biomass on site. Separate 

decision variables were created for each combination of vegetation management treatment option (TB9, Moderate, and Comprehensive) and the three 

options for biomass disposal from the treatments: Burning (pile burning at 

logging site), biomass hauled to Darby, and biomass hauled to Frenchtown. 

This resulted in up to nine possible treatment choices for the optimization 

solver to choose from for each treatment unit polygon.

Vegetation Succession—Successional pathways were used to determine 

changes in vegetation states in 5 decadal time steps (50 year planning horizon) if no hazardous fuel treatment is undertaken. These predicted states 

describe the vegetation that would exist when the future treatment options 

would occur. The most important successional pathways in terms of acres 

are listed in table 6.

Table 5—Tree species combinations selected for analysis.

Dominant species Descriptions

 DF Douglas-fi r (Pseudotsuga menziesii)

 DF-GF Douglas-fi r - Grand fi r (Abies grandis)

 DF-LP Douglas-fi r - Lodgepole pine (Pinus contorta)

 DF-LP-AF Douglas-fi r - Lodgepole pine - Subalpine fi r (Abies lasiocarpa)

 L-DF-LP Western larch (Laryx occidentalis) - Douglas-fi r - Lodgepole pine

 L-DF-PP Western larch - Douglas-fi r - Ponderosa pine (Pinus ponderosa)

 PP Ponderosa pine

 PP-DF Ponderosa pine - Douglas-fi r
680 USDA Forest Service Proceedings RMRS-P-41. 2006.

Silverstein, Loeffl er, Jones, Calkin, Zuuring, and Twer Biomass Utilization Modeling on the Bitterroot National Forest

Effects Functions—Functions that were used as constraints or objectives 

by period within the model consisted of the following:

 1) Total acreage functions: total acres: treated, treated with TB9, treated 

with Comprehensive, treated with Moderate, with biomass removal, with 

pile burning, of FRCC treated (class 2 and 3, tabulated separately), and 

of WUI treated

 2) Cost functions: total costs, cost of biomass removal (stump-to-truck and 

chipping), site costs (merchantable (stump to truck) and any biomass 

removal or preparation for pile burning), haul costs of biomass (to Darby 

or Frenchtown, tabulated separately), haul costs of merchantable (to 

Milltown), and costs of pile burning

 3) Revenue functions: biomass revenue, merchantable revenue, and total 

revenue

 4) Net value functions: total net value (total revenues minus total costs), 

biomass net value (biomass revenue minus biomass removal and haul 

costs)

 5) Volume/weight functions: merchantable volume and biomass weight

These functions incorporate the volume and cost computations described 

earlier. The value of delivered merchantable material was set at $2 per cubic 

foot, and the value of delivered biomass was set at $13 per green ton. Both 

values were based on current local markets. The cost of pile burning was 

estimated at $100 per acre.

Results

MAGIS can be used to develop many types of spatial and temporal analyses. We present fi ve analyses that capture the economic aspects of utilizing 

biomass produced by mechanical hazardous fuel treatments. For each, we 

describe the question, the MAGIS set up and runs made to address the question, then present the results.

Table 6—Pathways for the major vegetation states in the study area.

Habitat Initial dominant species, Acres Successional changes: resulting dominant

groupa size class, density (1000) species, size class, density

 B2 PP, SS, 2 76 4th decade goes to PP, Pole, 2

 5th decade goes to PP-DF, Pole, 2

 A2 PP, SS, 2 16 3rd decade goes to PP, Pole, 2

 5th decade goes to PP, Medium, 2

 B2 L-DF-PP, Large, 3 13 no changes

 B2 L-DF-PP, Medium, 3 12 2nd decade goes to L-DF-PP, Medium, 4

 5th decade goes to L-DF-PP, Large, 4

 B2 DF, Large, 3 7 no changes

 B2 DF, Medium, 3 5 2nd decade goes to DF, Large, 3

a Habitat group descriptions: A2 is warm and dry, and B2 is moderately warm and dry.
USDA Forest Service Proceedings RMRS-P-41. 2006. 681

Biomass Utilization Modeling on the Bitterroot National Forest Silverstein, Loeffl er, Jones, Calkin, Zuuring, and Twer

Maximum Net Value by Treatment Prescription

This section investigates the extent to which each of the three mechanical 

fuel treatment prescriptions result in a positive net return, and the number 

of treatment acres expected to result in a positive net return. Three scenarios 

were run that constrained treatment prescription to biomass utilization fi rst 

to only the Comprehensive prescription, next to only the Moderate prescription, and last to only the TB9 prescription. Each scenario optimized on the 

objective function of maximum net value in period one. The results showed 

that acres that could be treated with a positive return were 20,984, 56,421, 

and 60,689 for TB9, Moderate, and Comprehensive, respectively, from 

160,954 treatable acres in the study area. The costs, revenues, and net values 

per acre for these prescriptions are displayed in fi gure 2. The vast majority of 

the total revenue predicted for these treatments comes from the commercial 

component that would be removed. The Comprehensive prescription had an 

understandably higher net value than the TB9, with the Moderate prescription 

falling in between, as was expected from the level of commercial products 

each prescription produces. The net values per acre treated for positive valued 

units for TB9, Moderate, and Comprehensive were $83, $1,632, and $2,939, 

respectively, which support the basic fi ndings Fiedler and others (1999) with 

regard to the economic value of the Comprehensive prescription.

A Spatial View of Economic Importance of Biomass Mill 

Location

Haul costs are known to be an important economic component in the 

feasibility of off-site biomass utilization. As such, the location of biomass markets affects the economics of biomass utilization. In this section we compare 

the economics of biomass utilization with on-site burning for three biomass 

market scenarios: 1) markets at both Darby and Frenchtown, 2) market only 

at Darby, and 3) market only at Frenchtown. In each scenario we assume the 

markets can utilize all the biomass these scenarios would deliver. All three 

scenarios maximized net value in period one as the objective function and 

Figure 2—Costs, revenues and resulting net value for the three mechanical fuel 

treatment prescriptions applied where they result in positive returns. Site costs 

include merchantable (stump-to-truck) and any biomass removal (stump-totruck and chipping) or preparation for pile burning. Haul costs include hauling 

merchantable material and biomass for biomass scenario. Merch revenue is the 

revenue for merchantable material.
682 USDA Forest Service Proceedings RMRS-P-41. 2006.

Silverstein, Loeffl er, Jones, Calkin, Zuuring, and Twer Biomass Utilization Modeling on the Bitterroot National Forest

constrained acres treated to include all that were treatable. The fi rst scenario 

(markets at both Darby and Frenchtown) had no other constraints. The second scenario constrained biomass delivery to Darby only. The third scenario 

constrained delivery to Frenchtown only.

Results mapped in fi gure 3, panels a to c, show the most economical disposal 

of biomass for each polygon. When delivery was allowed to both Darby and 

Frenchtown, it was most economical to deliver 82 percent (by area treated) 

of the biomass to centrally located Darby, while the northern 16 percent of 

biomass went to Frenchtown, north of the study area, and only 2 percent was 

burned on the peripheral units (fi g. 3, panel a). When Darby was the only 

location, biomass delivery (97 percent) was more economic than burning (3 

percent) (fi g. 3, panel b). Finally, when Frenchtown was the only location, 

biomass delivery fell to 57 percent and burning increased to 43 percent (fi g. 

3, panel c). In this scenario, burning was more cost effective in the southern 

area away from the northern mill site and the paved delivery routes that run 

down the center of the study area. This result clearly shows the importance 

of biomass markets nearer to the forest resources, whereby Darby, with an 

average haul distance of 25 miles one-way, showed biomass utilization to be 

profi table in 97 percent of the area, whereas Frenchtown, with an average haul 

distance of 75 miles one-way, showed biomass utilization to be profi table in 

only 57 percent of the area.

Figure 3—Spatial view of use of small diameter materials to maximize net value for all treatable acres for 

three biomass market scenarios: a) markets at both Darby and Frenchtown; b) market at Darby only; and c) 

market at Frenchtown only. See fi gure 1 for mill locations.
USDA Forest Service Proceedings RMRS-P-41. 2006. 683

Biomass Utilization Modeling on the Bitterroot National Forest Silverstein, Loeffl er, Jones, Calkin, Zuuring, and Twer

Biomass Utilization versus Burning for Selected Zones

We also compared the economics of utilizing biomass created by mechanical fuel treatments with pile burning within specifi c zones, fi rst all acres in 

FRCC class 3, and next in WUI acres. For this comparison, net value was 

maximized for scenarios that treated all 71,984 acres of FRCC class 3 and 

all 119,126 acres of WUI with either solely biomass utilization or solely pile 

burning in period one. Utilizing biomass while treating all FRCC class 3 acres 

resulted in a positive average net value for applying mechanical fuel reduction 

treatments, whereas pile burning resulted in a negative average net value. As 

can be seen in fi gure 4, the additional revenue came primarily from biomass, 

which offset increased haul costs enough to show the positive return. The 

biomass revenue is understandably high in FRCC 3 areas as this indicates a 

fi re regime condition class that has grown with thicker forests which would 

provide more biomass in these mechanical fuel treatments. Treating WUI acres 

showed positive net values for biomass utilization and burning, with modest 

increases from biomass revenue offsetting haul costs (fi g. 5). The WUI zone 

generated higher merchantable revenue than the FRCC 3 zone because of a 

higher percentage of area in size classes over 9” d.b.h. (27 percent for WUI 

versus 11 percent for FRCC 3).

Figure 4—Costs, revenues and resulting net value for treatment of 

all FRCC 3 acres exclusively using biomass utilization or burning. 

Site costs include merchantable (stump to truck) and any biomass 

removal (stump to truck and chipping) or preparation for pile 

burning. Haul costs include hauling merchantable material and 

biomass for biomass scenario. Merch. revenue is the revenue from 

merchantable material.

Figure 5—Costs, revenues and resulting net value for 

treatment of all WUI acres exclusively using biomass 

utilization or burning. Categories as defi ned in fi gure 4.
684 USDA Forest Service Proceedings RMRS-P-41. 2006.

Silverstein, Loeffl er, Jones, Calkin, Zuuring, and Twer Biomass Utilization Modeling on the Bitterroot National Forest

Comparing Biomass Utilization with Pile Burning for TB9 

on Lands Classifi ed as FRCC 3

Brown (2000) cautioned land management agencies regarding public 

perception of the removal of large merchantable trees during fuel treatment 

projects. Some public factions prefer fuel treatments that remove only understory ladder fuels and no larger trees. Results presented earlier show that this 

approach represented by TB9 in this study is more economically challenging 

than the other two prescriptions which do remove some larger trees having 

a commercial value. Here we investigate what effects biomass utilization has 

on the ability to accomplish TB9 treatments for specifi c budget levels. We 

focus attention on the FRCC class 3 acres, those presumably most in need 

of mechanical fuel treatments. Although treating all FRCC class 3 stands 

resulted in a positive net value with biomass utilization when all treatment 

prescriptions were available (fi g. 4), limiting the options to only TB9 yields 

a negative net value, requiring a net cost outlay to perform treatments. This 

analysis was accomplished by running scenarios with fi ve different budget 

levels for treatments in period one. Budget levels were set at $0, 10, 20, 

30, and 35 million dollars, by constraining net value to be greater than the 

negative of these values. One scenario with only burning and one with only 

biomass utilization were run for each budget level. The objective function 

in each scenario was to maximize total acres treated.

The resulting graphs, comparing with and without biomass utilization, 

suggest that biomass utilization can make a large difference in making limited 

budgets go further in treating the landscape (fi g. 6). For example, at the $20 

million level, utilizing biomass increases acres treated from 60 percent with 

only burning to 76 percent. Similarly, treating 60,000 acres would cost approximately $22 million with biomass and $29 million without biomass.

Figure 6—Period one treatment with TB9 in the FRCC3 zone only, constrained 

by different budget levels.
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Biomass Utilization Modeling on the Bitterroot National Forest Silverstein, Loeffl er, Jones, Calkin, Zuuring, and Twer

Figure 7—The volume of biomass per decade obtainable with 

treatments that maximized even-fl ow net value at different 

levels.

Even Flow of Biomass Utilization Across Five Decades

Is biomass produced by mechanical fuel treatments sustainable over time? 

This is an important question for potential investors in new biomass processing facilities. To address this question multiple scenarios were run to identify 

the maximum sustainable biomass quantity per decade from mechanical 

fuel treatments over fi ve decades. This was accomplished by constraining 

the periods 2 through 5 biomass volumes to identical minimum levels and 

then using the biomass volume in period 1 as the objective function in successive solutions until the resulting period one biomass volume equaled the 

constrained level for the other periods. This occurred at 758,800 tons of 

biomass volume per decade.

Next we looked at the amount of biomass that would be produced at different levels of acres treated per decade. These scenarios set constraints at 

intervals of 5,000 acres treated per decade and used an even-fl ow of net value as 

the objective function. The outcome provides economically effi cient biomass 

volumes per decade at different treatment levels (fi g. 7). After 15,000 acres 

per decade, the rate of increase in additional biomass volume with additional 

acres treated drops as a point of maximum effi ciency is reached.

Discussion

Our fi ndings demonstrate that utilizing small diameter wood can enhance 

the economics of performing fuel treatments to reduce the risk of wildfi re 

and restore forests to natural conditions. By applying a common mechanical 

fuel treatment prescription, in many instances it is more effi cient to extract 

and utilize the biomass than it is to pile and burn it on site. The breakeven 
686 USDA Forest Service Proceedings RMRS-P-41. 2006.

Silverstein, Loeffl er, Jones, Calkin, Zuuring, and Twer Biomass Utilization Modeling on the Bitterroot National Forest

point between biomass utilization and pile and burning is dependent on 

haul distances and costs to biomass markets as shown in the maps presented 

in fi gure 3. The advantage of biomass utilization is also present in the thin 

from below prescription, TB9, which removes very little commercial product. 

These analyses show that the acres that can be treated by TB9 within a fi xed 

budget can be increased by utilizing the biomass created by the treatment 

rather than pile and burning it on site.

For this paper we analyzed the economics of biomass utilization when 

conducting fuel treatments focusing on maximizing net value for the majority of the spatial and temporal modeling. However, the principles and the 

modeling techniques developed here could easily be adopted by managers 

and planners with different objectives. For example, considerable effort has 

been invested into determining where best to place fuel treatments to reduce 

the risk of wildfi re (Weise and others 1999, Agee and others 2000, Hof and 

Omi 2003, Jones and others 2003). Treatment locations can be based on 

predictions of fi re behavior models that do not consider economics (Finney 

2001). However, the modeling system presented here is fl exible and indices 

such as crown fi re reduction or fi re spread rates (Finney 2003) could also be 

used as the driver to guide treatment placement. With this approach, analysis can be conducted that considers both fi re behavior (through use of the 

crown fi re reduction or fi re spread indexes) and economics in locating places 

to apply treatments.

For businesses to establish small diameter wood processing facilities, a 

guaranteed, long term supply is necessary (Stewart and others 2004; Keegan 

and others 2005). The analysis presented in this paper indicates that with the 

current fuels conditions and expected growth of forest fuels in the future as 

quantifi ed in the successional pathways, signifi cant sustainable volumes of 

biomass could be made available from applying mechanical fuel treatments 

to acres in need of fuel reduction treatments over the next fi ve decades. The 

aspect of this question we have not been able to analyze is whether these mechanical treatments will actually occur on the ground, which on public land is 

dependent largely on local as well as national political and legal processes.

There are understandable environmental concerns when proposing the 

removal of vast quantities of woody material from a national forest. Our 

analysis found the Comprehensive prescription to be the most economically 

effi cient method of treating the landscape and utilizing biomass in the process. Although this was designed as a prescription for ecological restoration 

(Fiedler and others 1999), the present political climate which infl uences management decisions indicates extraction of this much material would likely be 

controversial, whether or not environmentally sound. The TB9 prescription, 

on the other hand, has the potential to address the fi re danger problem with 

less controversy, though at higher net costs, as shown here, and perhaps less 

effectively (Fiedler and others 2003). Furthermore, establishing markets for 

biomass utilization to face the immediate problem of overstocked forests has 

the potential to create a future demand for forest products that can not be 

met in an ecologically sound way once the ecosystems are truly restored. The 

even-fl ow analysis indicated this is not an immediate concern in the study 

area, but ecological restoration may occur much sooner in other locations. 

Thus, the question of sustainability is important for environmental as well as 

economic reasons, and would be an important direction for further research 

to expand on what we have begun here.
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Biomass Utilization Modeling on the Bitterroot National Forest Silverstein, Loeffl er, Jones, Calkin, Zuuring, and Twer

Literature Cited

ACINET. 2003. America’s Career Infonet. Available at: http://www.acinet.org/

acinet/default.asp

Agee, J. K.; Bahro, B.; Finney, M. A.; Omi, P. N.; Sapsis, D. B.; Skinner, C. N.; van 

Wagtendonk, J. W.; Weatherspoon, C.P. 2000. The use of fuel breaks in landscape 

fi re management. Forest Ecology and Management. 127:55-66.

Barbour, R. J.; Fight, R. D.; Christensen, G. A.; Pinjuv, G. L.; Nagubadi, R. V. 2004. 

Thinning and prescribed fi re and projected trends in wood product potential, 

fi nancial return, and fi re hazard in Montana. PNW-GTR-606. U.S. Department 

of Agriculture, Forest Service, Pacifi c Northwest Research Station.

Brown, R. 2000. Thinning, fi re, and forest restoration: A science-based approach 

for national forests in the interior Northwest. Defenders of Wildlife. Portland, 

OR. 26p.

Chew, J. D.; Stalling, C.; Moeller, K. 2004. Integrating knowledge for simulating 

vegetation change at landscape scales. Western Journal of Applied Forestry. 

19:102-108.

Fiedler, C. E.; Keegan, C. E. III; Wichman, D. P.; Arno, S. F. 1999. Product 

and economic implications of ecological restoration. Forest Products Journal. 

42(2):19-23.

Fiedler, C. E.; Keegan, C. E. III; Morgan, T. A.; Woodall, C. W. 2003. Fire hazard 

and potential treatment effectiveness: a statewide assessment in Montana. Journal 

of Forestry. 7(2):7.

Fight, R. D.; Hartsough, B. R.; Noordijk, P. 2006. Fuel Reduction Cost Simulator. 

Beta version.

Finney, M. A. 2001. Design and regular landscape fuel treatment patterns for 

modifying fi re growth and behavior. Forest Science. 47:219-228.

Finney, M. A. 2003. Calculation of fi re spread rates across random landscapes. 

International Journal of Wildland Fire. 12(2):167-174.

Hof, J.; Omi, P. 2003. Scheduling removals for fuels management. In USDA Forest 

Service Proceedings RMRS-P-29: 367-378.

Jenkins, J. C.; Chojnacky, D. C.; Heath, L. S.; Birdsey, R. A. 2003. National-Scale 

Biomass Estimators for United States Tree Species. Forest Science, 49(1):12-35.

Jones, G.; Chew, J.; Silverstein, R.; Stalling, C.; Sullivan, J.; Troutwine, J.; Weise, 

D.; Garwood, D. 2003. Spatial analysis of fuel treatment options for chaparral 

on the Angeles National Forest. In: USDA Forest Service Gen. Tech. Rep. PSWGTR (in press).

Keegan, C. E. III; Morgan, T. A.; Wagner, F. G.; Cohn, P. J.; Blatner, K. A.; Spoelma, 

T. P.; Shook, S. R. 2005. Capacity fur utilization of USDA Forest Service, Region 

1 small-diameter timber. Forest Products Journal. 55:143-147.

LeVan-Green, S. L.; Livingston, J.M. 2003. Uses of small-diameter and low-value 

forest thinnings. Ecological Restoration. 21:34-38.

Loeffl er, D.; Calkin, D. E.; Silverstein, R. P. 2006. Estimating Volumes and Costs 

of Forest Biomass in Western Montana Using Forest Inventory and Geospatial 

Data. Forest Products Journal [in press].

Reinhardt, E. D.; Crookston, N. L. 2004. The Fire and Fuels Extension to the 

Forest Vegetation Simulator, Addendum. RMRS-GTR-116. USDA Forest Service 

Rocky Mountain Research Station.

Stewart, H.G.; Blatner, K. A.; Keegan, C. III. 2004. Risk and feasibility of processing 

small-diameter material in the U.S. West Part II: market pulp and oriented 

strandboard. Forest Products Journal. 54:104-108.
688 USDA Forest Service Proceedings RMRS-P-41. 2006.

Silverstein, Loeffl er, Jones, Calkin, Zuuring, and Twer Biomass Utilization Modeling on the Bitterroot National Forest

USDOI. Unpublished. Biomass Energy Opportunities on Public Lands.

Available at: http://www.blm.gov/nhp/efoia/wo/fy04/im2004-227attach3.pdf

USFS. 2003. A Strategic Assessment of Forest Biomass and Fuel Reduction 

Treatments in Western States. http://www.fs.fed.us/research/infocenter.html

USFS. 2003b. USDA Forest Service Northern Region National Fire Plan Cohesive Strategy 

Geospatial Database. http://www.fs.fed.us/r1/cohesive_strategy/datafr.html

Weintraub, A.; Jones, G.; Magendzo, A.; Meacham, M.; Kirby, M. 1994. A heuristic 

system to solve mixed integer forest planning models. Operations Research. 

42:1010-1024.

Weise, D. R.; Kimberlin, R.; Arbaugh, M.; Chew, J.; Jones, G.; Merzenich, J.; Van 

Wagendonk, J. W.; Wiitala, M. 1999. A risk-based comparison of potential fuel 

treatment trade-off models. In: Neuenschwander, L. F.; Ryan, K. C.; Gollberg, 

G. E. (Eds.), Proceedings from the Joint Fire Science Conference and Workshop, 

Crossing the Millennium: Integrating Spatial Technologies and Ecological 

Principles for a New Age in Fire Management. Vol. II, Boise, ID. June 15-17, 

1999. The University of Idaho and the International Association of Wildland 

Fire: 96-102.

Zuuring, H. R.; Wood, W. L.; Jones, J. G. 1995. Overview of MAGIS: a multiresource analysis and geographic information system. USDA Forest Service 

Research Note, INT-427.
USDA Forest Service Proceedings RMRS-P-41. 2006. 689

Communication and Collaboration
690 USDA Forest Service Proceedings RMRS-P-41. 2006.
USDA Forest Service Proceedings RMRS-P-41. 2006. 691

In: Andrews, Patricia L.; Butler, Bret W., 

comps. 2006. Fuels Management—How to 

Measure Success: Conference Proceedings. 

28-30 March 2006; Portland, OR. 

Proceedings RMRS-P-41. Fort Collins, 

CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research 

Station.

1 Washington Department of Natural 

Resources, NW Region Wildf ire 

Prevention Coordinator, Sedro Woolley, 

WA. marc.titus@wadnr.gov

2Skagit County Conservation District, 

Firewise Program Coordinator, Mount 

Vernon, WA.

Abstract—This paper highlights the very successful collaborative approach to community wildfi re hazard reduction being used in the 5 county NW Region of the Washington 

State Department of Natural Resources. NW Region cooperators have created a 

successful model to help affected communities reduce their risks to wildland fi re. 

Identifi ed high risk communities have been approached by a multi-agency team with 

Firewise education and hazard assessment methodology. Participating communities 

have received mini-Firewise workshops, community hazard assessments and hazard 

mitigation planning assistance. By working collaboratively with communities, local 

fi re districts, County Conservation Districts, County Fire Marshal’s Offi ces and Departments of Emergency Management, as well as other State and Federal fi re managers, 

dramatic results in the Region have been achieved. The Firewise Communities/USA 

model has been used to guide communities through a nationally recognized process of 

risk assessment, mitigation planning and community specifi c outcome based solutions. 

Community fuels reduction efforts have focused on the creation of defensible space 

and shaded fuel breaks, reducing structural ignitability, as well as implementation of 

forest stewardship and greenbelt plans. Community recognition by the Firewise Communities/USA program is the measure of success.

Introduction

The Washington State Department of Natural Resources (WADNR) is 

responsible for wildfi re protection on 12.7 million acres of private and state 

forest land. While fi re can play a benefi cial role in the forest ecosystem, it can 

also be a destructive force that endangers our natural resources, our property, 

and even our lives.

In today’s fi refi ghting in rural and forested areas of the state, traditional 

boundaries between those fi ghting wildfi res and those battling structural 

fi res overlap giving way to the common need to help one another. The 

Wildland Urban Interface (WUI), where “the trees meet the eaves,” is an 

area of great concern to the wildland fi re fi ghting community. It is in this 

area, the WUI, that fi re prevention and education activities can have a great 

positive impact.

By educating property owners and community groups on loss mitigation 

strategies in the WUI, fi re managers from all agencies can infl uence positive 

changes in a very hazardous element of the fi re ground (the WUI). It is this 

social change, the change from passive to active behaviors, that can create 

home sites and communities that are more resistant to loss or damage caused 

by wildfi res. In addition, as property owners and communities become more 

educated, the dangers associated with fi refi ghting in the WUI can be greatly 

A Collaborative Approach to Community 

Wildfi re Hazard Reduction

Marc Titus1 and Jennifer Hinderman2
692 USDA Forest Service Proceedings RMRS-P-41. 2006.

Titus and Hinderman A Collaborative Approach to Community Wildfi re Hazard Reduction

diminished. Toward these efforts, the NW Region of the Washington Department of Natural Resources has embarked on a WUI wildfi re education 

campaign that has been very successful.

The Northwest region of Washington Department of Natural Resources 

is located in northwest Washington State, west of the Cascade Crest and just 

south of the Canadian Border (Figure 1). It covers a 5 county area north 

of Seattle that includes Whatcom, Skagit, San Juan, Island and Snohomish 

counties. Puget Sound and the San Juan Islands add considerably to this 

region’s diversity.

Risk Assessment

Using the Wildland Urban Interface Fire Hazard Assessment Methodology and risk assessment components from NFPA 299 (now NFPA 1144), 

the WADNR, NW Region conducted a systematic wildfi re risk assessment. 

Recent census data was queried to identify potential WUI areas. These landscape areas were assessed for risk using a representative sample scored against 

NFPA 299 criteria. Hazard levels were identifi ed and subsequently mapped 

using census polygons. The rating scale as defi ned by NFPA 299 was utilized 

resulting in hazard ranking from Low to Extreme (Figure 2).

Figure 1—Washington State Department of Natural Resources 

Regions
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A Collaborative Approach to Community Wildfi re Hazard Reduction Titus and Hinderman

Landscapes of Similar Risk

Under the Healthy Forests Initiative and the Healthy Forests Restoration 

Act (HFRA), the requirement to identify at-risk communities and conduct 

Community Wildfi re Protection Planning (CWPP) was defi ned. Using guidance provided by the National Association of State Foresters, WADNR used its 

most recent Wildfi re Risk Assessment to identify Landscapes of Similar Risk. 

Members of local fi re management agencies assisted with this effort along 

with County Departments of Emergency Management, Fire Marshal’s Offi ces 

and other local state and federal fi re managers in the spring of 2004. They 

took the current regional risk assessment and consolidated risk assessment 

boundaries down to the landscape level. Landscapes risks were not restricted 

by county borders, therefore a true landscape was considered. These landscapes 

were named and digitized to create a GIS map layer (Figure 3).

Figure 2—Risk Assessment. The fi rst phase to identifying Landscapes of Similar Risk
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Titus and Hinderman A Collaborative Approach to Community Wildfi re Hazard Reduction

Prioritize With RAMS (Risk Assessment & Mitigation 

Strategies)

RAMS is a computer software program designed to systematically perform 

landscape level risk assessments (RA), prioritize landscapes and plan mitigation strategies (MS). Federal agencies, as well as WADNR, have adopted 

RAMS to prioritize, plan and track fi re prevention activities. A component 

of RAMS is the communities’ module. This module allowed us to perform 

a systematic assessment of our landscapes of similar risk using the following 

standard criteria:

 • Fuels Hazard

 • Ignition Risk

 • Historical Fire Ignition

 • Fire Return Interval

 • Values, and

 • Protection Capability

Figure 3—Landscapes of Similar Risk. Identifi ed by regional fi re managers through a collaborative process.
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A Collaborative Approach to Community Wildfi re Hazard Reduction Titus and Hinderman

Inter-Agency Collaboration

A critical component of the National Fire Plan, as well as HFRA, is inter-agency cooperation. This component was also critical to the success of 

our WUI prevention & education program. Generally speaking, in Western 

Washington where catastrophic wildland fi re incidents are not an every day 

occurrence, it is diffi cult to convince WUI residents that they have a fi re 

problem. Residents have been more receptive to Firewise education when 

addressed by a multi-agency team of fi re and education professionals.

In the WADNR NW Region, strong inter-agency relationships were created 

to facilitate the WUI Prevention & Education program. Partner agencies were 

identifi ed based on concurrent agency missions. For example, the mission 

of the Skagit Conservation District is to provide voluntary, incentive based 

options that support working landscapes while protecting and enhancing our 

natural resource land base. This mission, along with the Skagit Conservation 

District’s experience in community education and outreach make them an 

ideal collaborator. Funding and support from the local Skagit County government and Title III funds make it possible for the Conservation District 

to play a vital role in WUI prevention and education.

County Fire Marshal’s Offi ces and Departments of Emergency Management are examples of other agencies whose missions align with the DNR in 

Community Wildfi re Prevention efforts. Partnering with other Federal and 

State fi re managers is important as well. The local fi re department is the fi nal 

key to a successful community wildfi re prevention program.

With this multi-agency team, a strong, coordinated message can be delivered to WUI residents. It becomes very apparent to residents, when speaking 

with one voice, that there really is a fi re problem. As understanding comes, 

residents are more receptive to mitigation strategies and an effective education campaign can begin.

Working With At-Risk Communities

Once the team is assembled and roles and responsibilities have been decided, 

steps to initiate contact with targeted at-risk communities can begin. There are 

two ways that contact is initiated between a community at risk and an agency 

representative. The agency can target a community they have determined is 

a priority for outreach efforts. In this situation the fi rst and most important 

step is to get the community to recognize that there is an ignition risk and 

then take ownership for that risk. This is often the most diffi cult part of the 

education process, but is much easier with a multi-agency team. Another way 

is when the community initiates contact with the agency, seeking guidance 

in dealing with their fi re problem. This situation circumvents the hurdle of 

getting the community to recognize and take ownership of their fi re problem 

because at that point they have already done so. In either scenario, developing a relationship with, and an understanding of, the community is crucial 

to determining how to move forward in the process.

Initial stages of developing a relationship with a target community require 

an effort on the agency’s part to understand the demographics of that community. This includes such factors as community size, community governance, 

resident lifestyles and any other characteristics of the community that play into 

its’ abilities to respond to a wildfi re issue. For example, a community that has 
696 USDA Forest Service Proceedings RMRS-P-41. 2006.

Titus and Hinderman A Collaborative Approach to Community Wildfi re Hazard Reduction

well established governance may be able to enforce a covenant that requires 

fi re resistant roofi ng materials on new construction or any other Firewise 

type of practice; whereas a community without well established governance 

may not be able to enforce such a rule, they may only be able to suggest it. 

In cases like this, the agency representative would want to tailor outreach 

approaches in the community to refl ect these concerns. Understanding the 

community and making the approach specifi c to that community will allow 

for a more successful result.

Community Leadership

Another important aspect of developing a relationship with a community 

is to identify a “community spark plug.” This term refers to a member, or 

members, of the community who has taken on a leadership role or has the 

most interest and/or concern for the matter. The role the community spark 

plug fi lls is crucial to the dissemination of information in the community. 

This person is the front line contact for agency representatives to communicate 

with a community. They are an integral component of all WUI prevention 

programs. They could, for example, be the person who gets permission from 

the community board for the wildfi re experts to do a presentation for the 

community. Having a member of the community take personal responsibility 

to bring forward the message and draw in other community members opens 

the door for further outreach opportunities. In a successful model, there will 

always be an individual or group of people who will emerge to fi ll this role.

The Workshop

In order to reach the community as a whole and disseminate information, 

it is best to host some sort of informational meeting or workshop (Figure 4). 

Whether the community solicits an agency for a presentation or vice versa, 

it is most effective to bring the presentation to the audience. Including the 

presentation as part of some other event that’s already scheduled will be more 

effective because the audience is already there. For example, scheduling a 

presentation as part of a regularly attended board meeting won’t require any 

extra time of the community members.

No matter what you call your meeting or workshop, there are some important aspects to consider. First, the community should be approached by 

a team of experts which should include but aren’t limited to the local fi re 

district, any wildfi re and/or forestry experts that have jurisdiction in the 

area, and a county fi re marshal or warden. A team of experts can provide 

informational presentations of all aspects of wildfi re and can deliver a more 

powerful message than just one person representing one agency. This also 

allows for shared responsibility in communicating information to the community and allows for use of a wider range of resources. Even though the 

experts hosting the meeting may be federal or state representatives, the focus 

of the presentation should be local.

Using materials available at the Firewise website, a tailor-made presentation can be easily created. At a minimum, the workshop should address the 

community fi re problem, information on what makes homes burn (structural 

ignitability) and information on mitigation strategies in the Home Ignition 
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A Collaborative Approach to Community Wildfi re Hazard Reduction Titus and Hinderman

Zone (the home and it’s immediate surroundings). With this basic toolbox, 

property owners can, if they choose, begin to make an impact where the impact is needed, at the home. If the workshop can convince property owners 

that they can greatly reduce their homes potential ignitability, then we have 

begun the necessary paradigm shift. If property owners in the community 

begin to manage their home ignition zones and reduce structural ignitability 

then the community is well on its way to a better outcome when a wildfi re 

does occur.

A good way to get the community to respond to a presentation and initiate 

follow-up contact is to offer free technical assistance. One way to do this is 

to offer home assessments where all homeowners that are interested receive 

individual attention and expert advice on their home ignition risk. Making it 

easy for the community to access these resources will result in a more positive 

and successful response. After the workshop, an introduction to the Firewise 

Communities/USA program can provide the process and motivation for a 

community to become fi rewise.

A Collaborative Approach to Community Wildfi re Hazard 

Reduction: Shelter Bay Community Case Study

The community of Shelter Bay is located in western Washington, on Fidalgo 

Island in western Skagit County, just outside the small town of La Conner 

(Figure 5). Fidalgo Island was identifi ed by the Washington State Department 

of Natural Resources as a high-risk area for wildfi re due to various physical 

characteristics of the landscape and the proximity of homes to the wildlands. 

The community consists of just over 900 lots, as well as greenbelt tracts, community beaches, and recreational areas (Figures 6 & 7). Shelter Bay homes 

Figure 4—Mini-Firewise Workshop.
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Titus and Hinderman A Collaborative Approach to Community Wildfi re Hazard Reduction

Figure 5—Shelter Bay is located just outside La Conner, WA.

Figure 6—Shelter Bay Parcels.
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A Collaborative Approach to Community Wildfi re Hazard Reduction Titus and Hinderman

and streets wind through a maze of steep and hilly topography. Interspersed 

throughout the homesites are varying acreages of designated greenbelt. 

These greenbelts make up approximately 1⁄4 of the community’s acreage. The 

greenbelts vary in their fuel models and range from grass and dense brush 

to heavy timber. Enough ladder fuels are present in the greenbelts to cause 

single-tree and group-tree torching that could result in ember showers on 

adjacent homes. Shelter Bay Community has well established governance that 

allows the management of community issues through the use of standing 

committees. For example, the greenbelt committee deals with management 

issues in the greenbelt such as views, pruning, thinning and tree topping. 

There are building and lot committees that handle issues with building and 

construction covenants, rights, and restrictions. When the Firewise committee was approved, it was appropriate that it become an ad hoc committee to 

provide advice to and interface with other committees in the community. 

The Firewise Committee is dedicated to reducing the ignition potential and 

increasing awareness of WUI issues in the community.

They contacted the wildfi re experts in the region, including the Skagit 

Conservation District (SCD), the Washington State Department of Natural 

Resources (WADNR), and the Skagit County Department of Emergency 

Management/Fire Marshal’s Offi ce (DEM, FMO). Together these agencies 

are responsible for promoting the Firewise program throughout the county 

and the region. The stakeholders also included the Shelter Bay Community 

at large, the local fi re chief and a Skagit County Commissioner. Once the 

community made contacts, the multi-agency team was able to guide the 

community in their actions.

Figure 7—Shelter Bay Aerial Photo.
700 USDA Forest Service Proceedings RMRS-P-41. 2006.

Titus and Hinderman A Collaborative Approach to Community Wildfi re Hazard Reduction

It started with a Firewise presentation in conjunction with an already 

scheduled information session to answer questions about the ongoing use 

of the goats for greenbelt cleanup. The purpose of the presentation was 

to educate the community on the wildfi re hazard and emphasize personal 

responsibility and defensible space regarding protection of private property. 

This presentation was developed and lead by SCD and DNR. Also present 

were the Skagit County Fire Marshal, the district fi re chief, and one of the 

Skagit County Commissioners.

Each representative had a specifi c role and perspective to offer the community as well as specifi c resources for wildfi re safety. The SCD was able to 

effectively communicate the idea of personal responsibility and mitigation 

strategies for around the home. The SCD took on the responsibility of being 

the direct line of communication to the community as a whole, as well as 

individual landowners in offering them technical assistance and free home 

assessments. The DNR was able to offer expertise in fi re behavior and communicating the risk situation. The fi re chief provided perspective on local 

fi re fi ghting resources and current fi re fi ghter capabilities. The fi re marshal 

was able to provide a regulatory perspective, building code information and 

discuss outdoor burning regulations. The County Commissioner was there 

to offer support of the program, recognizing the importance of our/their 

efforts and provide encouragement. This approach not only allowed for all 

aspects of fi re safety to be addressed in an initial presentation, but also as 

the community moves forward with their Firewise mitigation measures, 

this multi-agency team can offer a comprehensive set of resources to aid the 

community. Sixty-fi ve community members attended the presentation. This 

collaboration continued and will continue to be an effective way of guiding 

the Shelter Bay Community through the Firewise process.

Once the relationships between agency representatives and the community 

were established, the multi-agency team was able to assist the community 

with moving forward in their pursuit of Firewise actions. This began with a 

Community Hazard Assessment for the Shelter Bay Community. The hazard 

assessment addressed the various aspects of wildfi re hazards throughout the 

community on a community-wide scale. These hazards were analyzed and 

addressed with a fi nal recommendation of creating an action plan to establish 

mitigation measures.

From here, the residents that had become active and interested in the 

Firewise process formed an ad-hoc Firewise Committee of 11 members in 

order to follow through with an action plan and pursue projects, as well as 

national recognition through the Firewise Communities/USA program. As 

the community had already completed a major project in reducing the fuels in 

their greenbelts, they were already well on their way to meeting the requirements of becoming a recognized Firewise Community. Their second project 

(currently under way) is a Firewise demonstration landscape. The community 

picked one highly visible area of greenbelt as their project site. Between the 

Conservation District and the WADNR, the site was evaluated and a planting design was created that met the objectives of the community: Firewise, 

wildlife habitat enhancement. Currently a fi nal plan is being developed that 

addresses these goals and objectives as well as the planting design and plant 

list, and provides resources on such aspects of the project as proper planting 

methods and proper pruning techniques etc. Once this project is established, 

the community hopes to use it as an education tool. They also hope to pursue 

further Firewise planting projects within the other greenbelt areas.
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A Collaborative Approach to Community Wildfi re Hazard Reduction Titus and Hinderman

As these ideas developed, so did the need for additional community organization. With the guidance of the Conservation District and the WADNR, 

the Firewise Committee is currently working on developing a comprehensive 

fi ve-year action plan for their community. This action plan will be included as 

part of the community’s comprehensive emergency management plan. Also, as 

part of the requirements of being a Firewise Community, they are planning 

a Firewise education event at the end of April where they will showcase their 

Firewise demonstration planting area and invite the community to celebrate 

their Firewise Communities/USA recognition status.

As the Shelter Bay community continues to build upon their fi rst years’ 

accomplishments, momentum continues to build as well. Their most recent 

accomplishment was a covenant change to prohibit the use of cedar shake roofs 

on all new construction (& re-roofi ng projects where greater than 50% of the 

roof is replaced), opting to support more fi re resistant roofi ng materials to be 

used. This represents a major accomplishment and a signifi cant understanding 

of the wildland fi re problem in the community. As the committee fi nalizes 

the 5-year action plan, it is assured that their success will continue.

Shelter Bay Community was recognized as a Firewise Community/USA 

for the year 2005. Requirements of 2006 recognition will be met by May 

2006.

Firewise Communities/USA

The Firewise Communities/USA is a recognition program designed and 

maintained to give communities the maximum fl exibility in creating outcome based site specifi c solutions to identifi ed wildfi re hazards. Briefl y the 

program involves:

• Enlist a wildland/urban interface specialist to complete a community assessment and assist with the creation of a plan that identifi es achievable 

solutions to be implemented by the community.

 • Form a Firewise Committee which promotes and maintains the FWC/

USA program and monitors and reports progress.

 • Observe a Firewise Day annually that is dedicated to a community Firewise project or education event.

 • Invest a minimum of $2.00 per capita on community Firewise Projects

 • Submit an application that documents compliance with recognition requirements and renew annually to maintain status.

It provides community members with the knowledge necessary to maintain 

an acceptable level of fi re readiness, while ensuring fi refi ghter safety during 

a wildland fi re emergency. The program draws on a community's spirit, its 

resolve, and its willingness to take responsibility for its ignition potential.

By implementing the FWC/USA as described, it truly becomes a self-perpetuating program. All of the training, education and tools for a community 

to take action are provided. Ongoing support by the multi-agency team is 

needed, but becomes less and less time consuming the more a community 

learns. Support activities will always be necessary, but the community leadership is always at the forefront. The local fi re department needs to stay 

engaged as the resident expert on emergency management, but this is a good 

relationship to foster as it provides a solid link between the community and 

Emergency Management Services.
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Titus and Hinderman A Collaborative Approach to Community Wildfi re Hazard Reduction

Conclusion

Wildfi re incidents do not have to be large, nor span many days to be catastrophic. Losing just one home in the Wildland Urban Interface becomes a 

signifi cant, life changing problem for those involved. It has been shown that 

with proper preparation, a home does not have to become fuel for a wildland 

fi re. Reducing structural ignitability by focusing on the home ignition zone 

is the easiest way for homeowners to mitigate wildfi re hazards in their community. Every home that has been prepared in this way has a much greater 

chance of surviving a wildland fi re incident. After all, a home that doesn’t 

ignite is a home that doesn’t burn.

The NW Region of Washington State Department of Natural Resources, in 

keeping with our agency mission and mandate, embarked on a collaborative 

WUI wildfi re education campaign that has been very successful. After using 

national standards to identify at-risk communities, the FWC/USA program 

was utilized to engage community groups. It is a model that allows agency 

interaction with the greatest number of communities at a time. With proper 

preparation and a collaborative environment, fi re management agencies can 

greatly impact communities in the WUI, thereby creating behavioral changes 

designed to mitigate losses in communites due to a catastrophic wildland 

fi re event. NW Region has been a leader in implementation of FWC/USA 

in Washington state and has contributed to Washington’s 2005 #2 ranking 

in the nation of recognized communities (Figure 8).

Success has been largely due to excellent inter-governmental and inter- agency 

relationships, a shared vision and the desire to succeed. The collaborative 

environment has been achieved through hard work and committment of 

all parties and continues to be a model that other areas of the state and the 

nation are striving to emulate. 

Figure 8—Firewise Communities/USA Sites.
USDA Forest Service Proceedings RMRS-P-41. 2006. 703

In: Andrews, Patricia L.; Butler, Bret W., 

comps. 2006. Fuels Management—How to 

Measure Success: Conference Proceedings. 

28-30 March 2006; Portland, OR. 

Proceedings RMRS-P-41. Fort Collins, 

CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research 

Station.

1 Research Associate in Social Science 

with the Aldo Leopold Wilderness 

Research Institute, Rocky Mountain 

Research Station, USDA Forest Service, 

Missoula, MT. kknotek@fs.fed.us

2 Research Social Scientist with the 

Aldo Leopold Wilderness Research 

Institute, Rocky Mountain Research 

Stat ion, USDA Forest Ser vice, 

Missoula, MT.

Abstract—In the fall of 2003, the Rocky Mountain Ranger District of the Lewis and 

Clark National Forest initiated a multi-year, large-scale prescribed burn in the Scapegoat Wilderness. The objectives of this burn were to make the non-wilderness side of 

the wilderness boundary more defensible from wildfi re and to establish conditions 

that will allow fi re to play a more natural role within the wilderness in the future. Using this prescribed burn as a case study, qualitative research was conducted in 2005 

to understand the local ranger district’s public outreach efforts and its subsequent 

infl uence on public attitudes towards the burn. A series of in-depth interviews with 

agency personnel involved in the burn, and representatives from local communities 

who were aware of and/or participated in public outreach efforts for the burn, were 

the primary sources of data for this research. A framework of mindfulness processes 

exhibited by high reliability organizations was used in analysis for identifi cation and 

understanding of organizational characteristics that contribute to success in engaging the public in Forest Service efforts to treat hazardous fuels and manage risk from 

wildfi re. As a case study, the methods and results provide a means of comparison to 

additional cases on other management units.

Introduction

Fire suppression policy on public lands over the past century has resulted 

in hazardous accumulations of fuel in forest and grass lands. In many places, 

fi re is a naturally occurring process, and fi re exclusion has spurred greater 

incidents of large-scale, uncharacteristic wildfi re impacting both ecological 

and social values across the wilderness/non-wilderness interface. The urgency, 

complexity, and oftentimes contentious nature of fi re and fuels management 

operations have signaled the need for increased public outreach (public information and involvement efforts) by wildland fi re management organizations. 

The public must be informed about and engaged in decisions concerning appropriate fuels management techniques to reduce the risk of catastrophic fi re 

and restore the health of our wild lands (HFI 2002; USDA/USDI 2000).

Along the Rocky Mountain Front in northwestern Montana, public land 

protected under federal designation as the Bob Marshall Wilderness Complex 

(includes the Bob Marshall, Scapegoat, and Great Bear Wilderness areas) 

interfaces with public and private lands comprising roadless areas, ranches, 

outfi tter/guide operations, recreational residences and other human uses. 

There is a rich history of naturally occurring fi res in the Bob Marshall Wilderness Complex, although years of fi re suppression has reduced the number 

of acres burned by these fi res and created conditions for uncharacteristic fi re 

behavior. In an effort to allow fi re to play a more natural role within the 

Organizational Characteristics that 

Contribute to Success in Engaging the 

Public to Accomplish Fuels Management at 

the Wilderness/Non-Wilderness Interface

Katie Knotek1 and Alan E. Watson2
704 USDA Forest Service Proceedings RMRS-P-41. 2006.

Knotek and Watson Organizational Characteristics that Contribute to Success in Engaging the Public to Accomplish Fuels Management ...

wilderness and to make the non-wilderness side of the wilderness boundary 

more defensible from wildfi re, the Rocky Mountain Ranger District of the 

Lewis and Clark National Forest initiated, in the fall of 2003, the fi rst phase 

of a multi-year prescribed burn inside and along the boundary of the Scapegoat Wilderness. The complex ownership and human uses surrounding this 

area exemplifi es the importance and need for mindful management of public 

outreach concerning such a large-scale fuels management project.

In their book, Managing the Unexpected: Assuring High Performance in 

an Age of Complexity, Weick and Sutcliffe (2001) outline a theory of managing high-risk operations with mindfulness. Their research on managing 

mindfully draws upon the concept of high reliability organizations (HROs). 

They suggest businesses or other organizations “benchmark on the experts 

in reliability” in managing for unexpected events, offering “techniques that 

are worth copying because they ensure faster learning, more alert sensing, 

and better relationships with customers” (p. xv).

It may be appropriate to apply concepts of high reliability to an organization’s management of public outreach, because managing the interaction 

between an organization and the public also involves managing the unexpected. Many unexpected events can occur when managing public outreach: 

contentious public meetings; withdrawal of key publics from participation; 

harassment of personnel within the organization; negative editorial or opinion 

pieces in reference to the organization; and litigation. To reduce the likelihood of such events occurring, an organization needs some framework to 

guide their management of public outreach.

Using the South Fork of the Sun River Prescribed Burn as a case study, 

we applied Weick and Sutcliffe’s theory on managing with mindfulness to 

the USDA Forest Service’s (USFS) management of public outreach for the 

prescribed burn. A framework of mindfulness processes was used as a guide 

to document and analyze the organization’s public outreach during the planning and implementation stages of the prescribed burn and how it infl uenced 

local community attitudes. This research can increase understanding of organizational characteristics that contribute to success or failure in engaging 

the public to accomplish fuels management at the wilderness/non-wilderness 

interface.

Research Framework

Research on high reliability indicates there are fi ve central processes that 

produce mindful behavior within high-risk organizations, including: 1) 

recognizing potential barriers to accomplishment of management objectives 

(preoccupation with failure), 2) resisting simplifi cation of information or 

interpretations (reluctance to simplify interpretations), 3) ensuring situational awareness of events as they occur (sensitivity to operations), 4) being 

prepared to respond to and recover from unexpected events (commitment to 

resilience), and 5) calling upon appropriate expertise in decision-making and 

management efforts (deference to expertise) (adapted from Weick and Sutcliffe 

2001). These fi ve attributes are believed to be the hallmarks of HROs and 

managing with mindfulness.

Research on managing with mindfulness has typically focused on interaction within an organization (i.e., wildland fi refi ghting, nuclear aircraft 

carriers, air traffi c control systems, and emergency medical treatment) without 

necessarily considering interaction that occurs external to an organization 

such as public outreach. It is important to understand how mindfulness can 

be applied to the management of external, as well as internal interaction, 
USDA Forest Service Proceedings RMRS-P-41. 2006. 705

Organizational Characteristics that Contribute to Success in Engaging the Public to Accomplish Fuels Management ... Knotek and Watson

because it is often this interaction that people use to evaluate and respond 

to a particular organization and their management capabilities. Using a 

framework of mindfulness processes to analyze the USFS’s management of 

external interaction (public outreach) should provide new insight into the 

value of managing with mindfulness.

Methods

A case study research design and qualitative methods (in-depth interviews) 

were used to facilitate the research and provide a deeper understanding of the 

contribution of the USFS’s mindfulness in managing public outreach for the 

South Fork of the Sun River Prescribed Burn. Interviews were conducted with 

a sample of agency representatives on the Lewis and Clark National Forest 

and non-agency public representatives from local communities surrounding 

the Rocky Mountain Ranger District.

Interviews were guided by a pre-arranged set of themes and suggested leadin questions, using a semi-structured interview guide, but they did not follow 

a fi xed question format (Patterson and Williams 2002). All interviews were 

tape-recorded in their entirety, transcribed verbatim, and kept anonymous. 

Analysis began, following completion of the transcriptions. Each transcript 

was edited by simultaneously listening to the associated tape-recording and 

reading the text. The fi nal edited transcripts were the empirical data that 

were analyzed using a qualitative data analysis software program, QSR Nvivo 

version 2.0.

In a case study research design, a previously developed theory is used as 

a template for analysis of the study fi ndings (Yin 1989). A framework of 

mindfulness processes was used to guide analysis of agency and public representatives’ perceptions of public outreach for the prescribed burn. Analysis 

emphasized objective description and personal interpretation by the researcher 

with a focus on organizing data to best document the phenomenon of interest 

within the specifi c case (Denzin and Lincoln 1998).

Results

A total of 14 agency representatives (both past and present) from the Lewis 

and Clark National Forest were individually interviewed. Interviews were 

conducted with personnel who had, in some way, been involved with the 

planning and implementation (including public outreach) of the South Fork 

of the Sun River Prescribed Burn. In order to get a diversity of perspectives, 

the intent was to conduct interviews with personnel representing different 

functional positions within the agency. Thus, interviews were conducted with 

personnel in the following positions: decision-making (line offi cers); planning; 

public affairs; information; fi re; recreation; and wilderness.

A total of 24 non-agency public representatives from local communities 

surrounding the Rocky Mountain Ranger District were interviewed. Interviews were conducted with people who were aware of and/or participated in 

public outreach activities (i.e., attended public meetings, submitted public 

comment, read newspaper articles, received informational mailings, etc.) for 

the prescribed burn. To obtain a diversity of perspectives, interviews were conducted with people with varied social resources and interests. Thus, interviews 
706 USDA Forest Service Proceedings RMRS-P-41. 2006.

Knotek and Watson Organizational Characteristics that Contribute to Success in Engaging the Public to Accomplish Fuels Management ...

were conducted with private landowners, outfi tter/guides, representatives 

from cooperating city, county and state organizations, representatives from 

non-governmental organizations, media personnel, local recreationists, and 

recreation residence owners.

The database of interview transcripts serves as empirical evidence for claims 

or conclusions drawn in this Results section, which contains excerpts of raw 

text from interviews that correspond to specifi c subject headings. Detailed 

below are a select set of these interview excerpts, which serve as examples of 

public outreach efforts by the agency that seemed to be indicative of the fi ve 

central processes of mindfulness.

Recognizing Barriers to Accomplishment of Management 

Objectives

Being consistently mindful of potential barriers to accomplishing management objectives, although suggestive of a negative mindset, is actually 

a positive behavior that can benefi t an organization. Being mindful of potential operational failures or mistakes makes it possible for an organization 

to identify and mitigate small barriers that, if ignored, could complicate or 

jeopardize their objectives (adapted from Weick and Sutcliffe 2001). Perceptions of both agency and public representatives indicated personnel on the 

Lewis and Clark National Forest demonstrated this mindfulness process in 

managing public outreach during the planning and implementation stages 

of the prescribed burn.

Agency representatives felt that agency personnel made personal one-on-one 

contacts with landowners who had the greatest potential to be impacted by 

the prescribed burn should it escape. These landowners happened to also be 

outfi tters in the local area preparing their camps for the upcoming hunting 

season. The agency’s decision to contact these members of the public was 

symbolic of its ability to manage mindfully, for the agency saw the potential 

for damage to private property and human resources, and the possible barrier it could create to accomplishment of management objectives before an 

escaped burn occurred:

Interviewer: And how come it was those two resorts that you 

went to?

Agency Representative: Because they are the ones in the 

vicinity that would be the ones that would be the most rapidly 

impacted if something went wrong with that fi re ... it was in 

the early fall, and so both of those resorts have backcountry 

camps and they were going in and out of their camps at that 

time, getting them ready for the hunting season. So they had 

even more stake in the whole scenario, because they had people 

actually in the backcountry hauling hay or doing that kind of 

thing, and so we needed to coordinate with them on those types 

of things so that we made sure that if their packers were on their 

way out we weren’t going to have a problem.

There was also evidence from perceptions of public representatives that 

agency personnel demonstrated an awareness of potential barriers to its 

management objectives by engaging the public early on in the planning of 

the prescribed burn. This early outreach, which included contact with the 

local media, allowed the public to be informed about project details from 

the very beginning and reduced the likelihood of them being “blindsided” 

by the agency’s intentions:
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Organizational Characteristics that Contribute to Success in Engaging the Public to Accomplish Fuels Management ... Knotek and Watson

Public Representative: But from my perspective, I thought what 

they did worked well, partly because they did it in advance. A lot 

of times people say, and this was a big criticism during the Canyon Creek Fire, we just didn’t know what was coming. We just 

didn’t really, we underestimated. We didn’t know. You didn’t 

tell us, etc. ... I don’t think anybody could fault them. Like you 

said, this started in ’97. It happened in 2003. That’s a long time 

and a lot of comment before the actual trees started to burn. So 

I think they did a good job ... I don’t know what else they could 

have done to get information out to people. And I think Augusta’s 

a relatively small community, I think they probably had close to 

saturation knowledge of what was going on.

Resisting Simplifi cation of Information or Interpretations

In the modern world, success is often achieved when a person simplifi es 

work by focusing on key issues or problems; in contrast, managing with 

mindfulness means resisting simplifi cation of information or interpretations. 

When practicing this tenet of mindfulness, organizations intentionally simplify less and seek ways to perceive and discern more about their management 

situation, creating a more holistic, detailed understanding of the context they 

are working within (adapted from Weick and Sutcliffe 2001). It was evident 

from perceptions of both agency and public representatives that, during the 

planning and implementation stages of the prescribed burn, personnel on 

the Lewis and Clark National Forest demonstrated this mindfulness process 

in managing public outreach.

There was a perception among agency representatives that agency personnel 

made an effort to talk about the known risks of the project rather than glaze 

over them or hide their signifi cance when interacting with the public. This 

effort to communicate directly with the public about the risks associated with 

the prescribed burn seemed to be an indicator of the agency’s resistance to 

simplify information or interpretations related to public outreach. Here’s what 

one agency representative said he or she would do in the future when dealing 

with similar fuels management projects and outreach to local communities:

Agency Representative: I’d follow the same model, and I 

would also be, and I believe we did this this time, I would also 

be frank about the risks ... and by that I mean we have all these 

checks in process to be as safe as possible. And sometimes things 

are going to go south on us. And that happens. The fi re could get 

out of our control, and we know that. And put that on the table 

early on in the process, not in terms of sugar coating. And (the 

District Ranger) did a good job of that. (The District Ranger) 

was very real. So, actually that’s a good take-home message for 

other people, other units, other agencies. Sometimes we’re not 

very good about talking about the real risks.

Perceptions of public representatives indicated the agency resisted simplifi cation of information or interpretations in managing public outreach, 

also, by addressing public concerns about the Canyon Creek Fire of 1988 (a 

wildland fi re that escaped the Scapegoat Wilderness boundary) and how it 

related to the prescribed burn. As suggested in the excerpt below, it would 

have been easy for agency personnel to avoid this issue in order to simplify 

their communication with the public, but they chose to speak to the issue 

and to communicate their plans to prevent a similar occurrence:
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Knotek and Watson Organizational Characteristics that Contribute to Success in Engaging the Public to Accomplish Fuels Management ...

Public Representative: ... I keep coming back to ’88 ... 

clearly an event happened there that the Augusta community 

got exposed to. And, again, superfi cially that was something 

that it would have been easy to shy away from, and (the Fire 

Management Offi cer) didn’t do that. (The Fire Management 

Offi cer) says we want to avoid that. And that’s to say (the Fire 

Management Offi cer) took that experience, took that event, 

and presented it to the community saying we’re with you, we 

recognize this is something that’s not very fun to go through. 

It can be devastating to go through. And we think we have an 

idea to, if not prevent it, then potentially minimize it at the 

very least. And so with using that circumstance, it would have 

been easy just to stay away from, just to put a big veneer lacquer 

around it and just say, uh, that was a bad deal and just never 

go there again. But they didn’t do that. They said let’s take 

that and run with it or let’s respond to that. And so bringing 

in that history, I think, was a good part of it.

Ensuring Situational Awareness of Events as They Occur

There is a tendency for people to be forward thinking, but mindfulness 

requires personnel within an organization to display intense focus on what 

is happening in the present. Organizations that manage with mindfulness 

focus their attention on the front line of an operation, ensuring situational 

awareness of events (both planned and unexpected) as they occur. By paying 

attention to events as they unfold, these organizations are more able to reduce 

uncertainty and make operational adjustments as needed (adapted from Weick 

and Sutcliffe 2001). Again, there were perceptions of both agency and public 

representatives that indicated personnel on the Lewis and Clark National 

Forest demonstrated this mindfulness process in managing public outreach 

during the planning and implementation stages of the prescribed burn.

Agency representatives perceived that a big part of the USFS’s engagement 

with the public prior to implementation of the burn was through briefi ngs 

with key segments of the public, such as county commissioners, the governor’s 

staff, and the media. Sensitivity to the information needs of these publics 

during the planning process and a willingness to engage in public dialogue 

about the project are an example of organizational efforts to ensure situational 

awareness in managing public outreach:

Agency Representative: The District Ranger was very proactive. I must compliment him on that, because he was very 

proactive in getting community involvement ... he developed a 

PowerPoint and he went around to various organizations. He 

talked to his county commissioners. We set up a series of briefings for him. He briefed the governor’s staff. He talked to the 

county commissioners from Lewis and Clark County, which is 

where Augusta is. He also talked to Teton County commissioners, which is where Choteau is ... He talked to TV stations. He 

did radio call-in interviews with KGPR and the local station 

that’s in Augusta, KMON. That’s the station that most people 

could hear ... We’ve only briefed the governor on two or three 

issues the whole time that I’ve been here, and this is one that we 

thought would be critical in case we did lose it.
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Organizational Characteristics that Contribute to Success in Engaging the Public to Accomplish Fuels Management ... Knotek and Watson

Public representatives perceived several other examples, which suggest 

agency personnel maintained situational awareness in managing public 

outreach. The agency’s use of press releases and newspaper articles, making 

documents available for public review, providing informational handouts, and 

holding public meetings, all seemed to have helped keep the public informed 

and involved in the planning process and the agency aware of public interest 

and concern related to the project:

Public Representative: They were putting out press releases. 

They had obviously done studies, and they had those documents 

out for public review. And they had, I want to say that they 

had information available in the Augusta Information Station if people wanted to come in and get fact sheets on it. They 

had their personnel available at any time for people to call ... 

They weren’t just touching the Choteau Acantha as media, 

they were also, there were stories being published in the Great 

Falls Tribune, and I am almost certain that there were stories 

published in the Helena newspaper, although I didn’t ever read 

any of those. But I think they were trying to reach as many 

people as they could. Particularly with this project, it seemed 

to me that they made a really big effort to do a really good job 

in informing people about what was going on.

Being Prepared to Respond to and Recover from 

Unexpected Events

The fourth mindfulness process can be described as being prepared to 

respond to and recover from unexpected events that occur. Managing with 

mindfulness means moving beyond a simple anticipation of unexpected 

events to a greater focus on how, once an unexpected event occurs, an organization and its employees can respond to and/or recover from the event. 

This resiliency enables organizations to function responsively and facilitate 

management even when faced with operational obstacles (adapted from 

Weick and Sutcliffe 2001). In interviews with agency representatives, several 

examples were identifi ed where it seemed as though personnel on the Lewis 

and Clark National Forest were prepared to respond to and recover from 

unexpected events when managing public outreach during the planning and 

implementation stages of the burn. These examples were easily identifi able in 

the analysis of the data because agency representatives were giving fi rsthand 

accounts of being prepared to respond to and recover from unexpected events 

that occurred.

For example, when the agency decided it was time to implement the prescribed burn, they realized that the Public Affairs Offi cer for the Forest was 

scheduled to be on a business trip to Washington, DC. As perceived by agency 

representatives, knowing that this position was crucial to public outreach 

during the burn, the agency seemed prepared to respond to this unexpected 

event by fi nding a qualifi ed replacement to fi ll this position, an employee 

within the region with experience in both public relations and fi re:

Agency Representative: And then when it came actually time 

to burn it, it was so frustrating because we didn’t think we 

were going to have a window in the fall. And when the burning window opened up it was the same week we had scheduled, 

they were going to burn on whatever day they ignited the burn, 

I don’t remember if it was Monday or Tuesday, but the Forest 
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Knotek and Watson Organizational Characteristics that Contribute to Success in Engaging the Public to Accomplish Fuels Management ...

Supervisor and the Forest Planner and (the Forest Public Affairs Offi cer) were fl ying out to Washington, DC, because we 

had briefi ngs with our senators and congressmen ... so we had 

to call in other people. And (an employee) from the Regional 

Offi ce came over and actually took the media out, because we 

had planned fi eld trips for the media to be on a lookout to see 

the actual ignition of the burn and to watch the progress of it 

the fi rst day.

In the analysis of data from interviews with public representatives, examples in which the agency appeared prepared to respond to and recover 

from unexpected events were not as easily identifi able. Thus, there were no 

obvious interview excerpts from public representatives that can be used to 

demonstrate that the agency was prepared to respond to and recover from 

unexpected events when managing public outreach during the planning and 

implementation of the burn. There are a couple of possible explanations for 

this occurrence.

First, it might be possible that the public didn’t perceive the unexpected 

events the agency was challenged with during planning and implementation 

and their resiliency in responding to them. This may be especially true in this 

case where several unexpected events occurred and were dealt with internally 

rather than publicly (i.e., having to fi ll in for the Public Affairs Offi cer while 

in Washington, DC). Also, the fact that agency personnel were resilient in 

responding to these unexpected events, may itself have made it more diffi cult 

for the public to perceive such behavior.

Calling Upon Expertise in Decision-Making and 

Management Efforts

The fi nal mindfulness process is calling upon appropriate expertise in 

decision-making and management efforts. Unlike a rigid hierarchy where 

decisions are imposed from the top down, when incorporating mindfulness 

into decisions and operations, personnel with the most expertise, regardless 

of their position within the organization, are utilized. This does not preclude 

the fact that certain decisions must be made and operations led by personnel 

in specifi c positions (adapted from Weick and Sutcliffe 2001). As indicated 

from perceptions of both agency and public representatives, it seemed evident 

that personnel on the Lewis and Clark National Forest often called upon 

appropriate expertise in decision-making and management efforts related to 

public outreach for the prescribed burn.

One key indicator that the agency called upon appropriate expertise in 

decision-making and management efforts was the fact that local agency 

personnel were charged with the planning and implementation of the burn, 

including public outreach. Even though an Incident Management Team was 

brought in to assist in burn operations, agency representatives perceived that 

local personnel on the District were largely in charge of leading the multifaceted operation:

Agency Representative: ... we identifi ed that at the beginning 

that we’re going to help reduce risk by having a (Incident Management) team involved. But one of the major points, debates 

about that with the public was that we want you guys involved. 

You’re not going to hand this over to a team, right? Oh, no, no. 

You know, our Burn Boss was still (a District employee), who’s 

right here out of Choteau. Our ignition specialist in the air was 
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Organizational Characteristics that Contribute to Success in Engaging the Public to Accomplish Fuels Management ... Knotek and Watson

(a District employee), our FMO (Fire Management Offi cer). 

Our ground ignition specialist was (a District employee), our 

AFMO (Assistant Fire Management Offi cer). And then (the 

District Ranger would) be there as the line offi cer making the 

calls for the Forest Supervisor in terms of whether we would 

ignite that day or not. And (he’d) be the one dealing with the 

people, heading up public meetings, talking to the media ...

It was also evident from public representatives that the agency called upon 

appropriate expertise (in this case local expertise) in their management efforts, 

including the Fire Management Offi cer, District Ranger, and Burn Boss, who 

are all employees of the Rocky Mountain Ranger District and members of 

the local communities, Augusta and Choteau. Public representatives, similar 

to agency representatives, talked about the importance of the agency utilizing the local expertise of these individuals, people well known in the local 

communities, in planning and implementing this specifi c project:

Public Representative: I think that they demonstrated to 

people that the local Forest Service personnel, like (the Fire 

Management Offi cer), (the Burn Boss), (the District Ranger), 

that they were local faces that were well-known that were going 

to be connected to this burn and that they were very credible 

and responsible and accountable. And I think people sensed 

that, that there was going to be an enormous amount of local 

accountability for this burn. And I think because of that some 

people probably felt that their concerns were expressed or reduced 

because it wasn’t going to be some nameless face for a federal 

project. It was going to be the responsibility of people that you 

could look in the eye and talk with ... You’re my neighbor and 

I know you.

Perceptions of Changes in Community Attitudes Towards 

the Burn

Through analysis, agency and public perceptions of changes in local community attitudes towards the burn were identifi ed, as well as perceptions 

about whether the agency’s management of public outreach had infl uenced 

these attitudes. Public representatives had mixed thoughts on whether or not 

local community attitudes had changed during the project. Some thought 

negative attitudes among local community members hadn’t changed and 

never would change. There was also a perception that, for the most part, 

community members had become ambivalent towards the burn, knowing 

the agency was actively moving forward with the project. There was however, 

some evidence from public representatives that attitudes were infl uenced 

during project planning and implementation, in particular becoming more 

positive or accepting and supportive of the burn.

Agency representatives also had mixed thoughts on whether or not community attitudes had changed. Similar to public representatives, some agency 

representatives thought community attitudes toward the burn had become 

more positive, while others thought there had been no change. For those who 

thought community attitudes had changed, there was some indication that 

the agency’s evident mindfulness in managing public outreach had infl uenced 

these attitudes. For example, there was some belief that the agency’s openness in public meetings and one-on-one contacts, demonstrating situational 

awareness in managing public outreach, had an infl uence on community 
712 USDA Forest Service Proceedings RMRS-P-41. 2006.

Knotek and Watson Organizational Characteristics that Contribute to Success in Engaging the Public to Accomplish Fuels Management ...

attitudes towards the burn. Thus, agency representatives provided additional 

evidence of some attitude change during the project that can be attributed, 

in part, to the agency’s mindful management of public outreach.

Conclusions

This research offers an example of how a framework of mindfulness processes can be appropriately used to describe an organization’s management 

of public outreach. The use of qualitative methods (in-depth interviews) 

allowed both agency and public representatives to openly talk about the 

agency’s public outreach during the planning and implementation stages of 

the prescribed burn. Agency and public representatives discussed at length 

such things as public meetings, newspaper articles, one-on-one contacts with 

private landowners, briefi ng to key publics (county commissioners, governor’s 

staff, media), and other such efforts detailed in the Results section, utilized by 

the agency in public outreach. Through analysis of the interview transcripts 

it was possible to not only identify but to also categorize and describe these 

outreach efforts by the agency as being indicative of the fi ve central mindfulness processes (i.e., recognizing potential barriers to accomplishment of 

management objectives, resisting simplifi cation of information or interpretations, ensuring situational awareness of events as they occur, responding 

to and recovering from unexpected events, and calling upon appropriate 

expertise in decision-making and management efforts). There was only one 

instance (public perceptions of the agency’s ability to respond to and recover 

from unexpected events) where this was not possible.

Because use of the framework made it possible to analyze agency and public 

perceptions concerning the USFS’s management of public outreach for the 

South Fork of the Sun River Prescribed Burn, this application of Weick and 

Sutcliffe’s management theory seems to be effective at least to guide analysis. 

The USFS itself will have to determine the usefulness and effectiveness of 

this theoretical application as a management tool.

It is possible that the USFS and other wildland fi re management organizations could use this framework of mindfulness processes as sort of a “checklist” 

before, during, and following public outreach to evaluate their management 

efforts. They could use the framework as a brainstorming tool when planning 

public outreach efforts. For example, they might individually, or as a group, 

proactively think about how they might be mindful of potential barriers to 

accomplishment of their management objectives, or how they might help to 

ensure situational awareness in managing public outreach. They could use 

the framework while they are actively conducting public outreach activities 

to incrementally evaluate individual and group behavior as it relates to the 

management of public outreach. For example, they might critique their efforts to resist simplifi cation of information or interpretations related to public 

outreach, or their ability to respond to and recover from unexpected events 

that have or might occur. They could also use the framework following public 

outreach efforts to evaluate and learn from their efforts in a fashion similar 

to an After-Action Review. For example, they might discuss examples of 

where it seemed they had been exhibiting mindfulness processes, or examples 

of where it seemed they hadn’t exhibited mindfulness processes and could 

improve upon their efforts in the future.

Finally, in using the framework of mindfulness processes to facilitate group 

discussion about public outreach efforts, it might be possible to identify 
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Organizational Characteristics that Contribute to Success in Engaging the Public to Accomplish Fuels Management ... Knotek and Watson

where contrasting perceptions about individual or group behavior exist 

among personnel within a wildland fi re management organization. Such 

uses of this framework of mindfulness processes would likely help to improve 

understanding and practice of organizational characteristics that contribute 

to success in engaging the public to accomplish fuels management at the 

wilderness/non-wilderness interface.

References

Denzin, N. K.; Lincoln, Y. S. 1998. Strategies of qualitative inquiry. Thousand 

Oaks, CA: Sage Publications, Inc. 346 p.

HFI [Healthy Forests Initiative]. 2002. Healthy forests: an initiative for wildfi re 

prevention and stronger communities. Washington, DC.

Patterson, M. E.; Williams, D. R. 2002. Collecting and analyzing qualitative data: 

hemeneutic principles, methods, and case examples. Champaign, IL: Sagamore 

Publishing. 127 p.

USDA/USDI [U.S. Department of Agriculture and Department of Interior]. 2000. 

Managing the impact of wildfi res on communities and the environment: a report 

to the President in response to the wildfi res of 2000. Washington, DC.

Weick, K. E.; Sutcliffe, K. M. 2001. Managing the unexpected: assuring high 

performance in an age of complexity. San Francisco, CA: John Wiley & Sons, 

Inc. 200 p.

Yin, R. K. 1989. Case study research: design and methods. Newbury Park, CA: 

Sage Publications, Inc. 165 p.
USDA Forest Service Proceedings RMRS-P-41. 2006. 715

In: Andrews, Patricia L.; Butler, Bret W., 

comps. 2006. Fuels Management—How to 

Measure Success: Conference Proceedings. 

28-30 March 2006; Portland, OR. 

Proceedings RMRS-P-41. Fort Collins, 

CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research 

Station.

1 Faculty Research Associate in the 

Department of Forest Resources at 

Oregon State University, Corvallis, OR. 

Eric.Toman@oregonstate.edu

2 Professor in the Department of Forest 

Resources at Oregon State University, 

Corvallis, OR.

Abstract—Current wildland fi re policy calls for citizen involvement in planning and 

management. To be effective in their efforts to engage outside stakeholders, resource 

professionals need to understand citizens’ understanding and attitudes toward current 

practices as well as how to best communicate about proposed actions. A variety of 

outreach methods have been used to communicate the rationale behind fuel reduction techniques. Limited evaluation of these efforts has occurred resulting in a lack of 

information available to guide the outreach decisions of agency personnel. This paper 

evaluates the effects of two basic communication strategies—unidirectional information 

exchange and interactive approaches—on participant understanding and attitudes. 

Data was collected in two phases; fi rst, citizens completed a survey on-site prior to 

outreach participation, then, a follow-up questionnaire was mailed to each participant 

two weeks following initial contact. Resulting data enable assessment of the infl uence 

of outreach activities on participant understanding and attitudes and evaluation of 

factors that contributed to program success. Findings suggest interactive outreach 

methods may be more effective at infl uencing knowledge. However, unidirectional 

and interactive approaches infl uenced participants with low initial understanding of 

fi re management or less supportive attitudes toward fuel practices. Results also showed 

a strong association between knowledge and attitude change suggesting fi re professionals have a real opportunity to help shape public perceptions about appropriate 

management actions.

Introduction

Recent federal initiatives such as the National Fire Plan and Healthy Forests 

Restoration Act require a new approach to fi re management. These policies 

emphasize two primary themes. First, there is an increased focus on using fuel 

reduction activities (such as prescribed fi re or mechanized thinning) prior to 

a fi re event to decrease the vegetation available to burn as fuel if a fi re occurs. 

Second, both initiatives call for, and in some cases require, collaboration 

with stakeholders (including local citizens) in planning and prioritizing fi re 

and fuel management activities. Natural resource communicators, including 

federal and state agency personnel, county extension agents, and interpretive 

staff, play an essential role in accomplishing these objectives.

Substantial research over the last several years has indicated the necessary 

role of social acceptability in resource management activities (see review in 

Shindler and others 2002) and specifi cally in fuel reduction efforts (Shindler 

and Toman 2003, Winter and others 2002). Accordingly, many management 

units are moving towards greater citizen involvement in the development 

and implementation of fi re and fuel management strategies. To be successful, 

Communicating the Wildland Fire Message:

Infl uences on Knowledge and Attitude 

Change in Two Case Studies

Eric Toman1 and Bruce Shindler2
716 USDA Forest Service Proceedings RMRS-P-41. 2006.

Toman and Shindler Communicating the Wildland Fire Message: Infl uences on Knowledge and Attitude Change in Two Case Studies

resource professionals need to understand citizens’ knowledge of and attitudes 

toward current practices as well as how to best communicate with local communities about proposed actions. Outreach activities, as the primary interface 

between resource agencies and citizens, play an essential role in these efforts 

(Bright and Manfredo 1997).

In recent years, resource agencies have used a variety of methods to communicate the rationale behind fuel reduction techniques. Approaches have 

ranged from traditional text and graphic displays, such as brochures and exhibits, to more targeted activities, including demonstration areas and guided 

fi eld tours. To date, limited evaluation of these efforts has occurred resulting 

in a lack of available information to guide the outreach decisions of resource 

professionals. The purpose of this paper is to fi ll this research gap by exploring 

the infl uence of two basic communication strategies— unidirectional information exchange and interactive approaches—on participant understanding 

and attitudes.

Related Research

Research on the social aspects of fi re management has increased steadily in 

recent years. A review of the literature suggests a number of fi ndings relevant 

to this study. First, research over nearly three decades has identifi ed a positive 

association between fi re-related knowledge and treatment support among 

citizens (e.g., Stankey 1976, Carpenter and others 1986, Shindler and Toman 

2003). This fi nding has prompted researchers to call for increasing fi re-related 

outreach activities to raise public awareness and support (e.g., McCool and 

Stankey 1986, Carpenter and others 1986). However, public acceptance is a 

complex issue and is not based solely upon technical understanding. Support 

for fi re management is particularly infl uenced by the interactions between 

citizens and resource managers over time and refl ects citizen confi dence in 

agencies to effectively manage risk as well as provide an adequate planning 

process that includes a role for the public (Winter and others 2002, Shindler 

and Toman 2003). Ultimately, understanding is a strong precursor to support, but not suffi cient on its own.

Second, public understanding and acceptance of fuel treatments have 

increased over time. Early research found that participants generally overestimated the negative impacts of fi re while underestimating fi re’s benefi cial 

effects (Stankey 1976). Not surprisingly, a majority preferred complete fi re 

suppression. Subsequent research has consistently identifi ed an upward trend 

in citizen understanding of key fi re management principles and acceptance of 

manager-ignited prescribed fi re (e.g., Carpenter and others 1986) as well as 

thinning for fuel reduction (Loomis and others 2001, Shindler and Toman 

2003). However, Manfredo and others (1990) cautioned that the identifi ed 

increases may only be occurring in particular geographic regions, specifi cally 

those areas most affected by fi re, and may not be representative of general 

attitudes. Brunson and Shindler (2004) also found variations in understanding 

and support among locations and cautioned against implementing “one-sizefi ts-all” management or communication approaches.

Third, and particularly relevant to our study, fi re-related outreach activities 

can positively infl uence participant knowledge and, in some cases, attitudes. 

Prior studies have evaluated response change following exposure to various 

communication activities (brochures, slide shows, workshops, etc.). Such 

activities can be classifi ed as interactive or unidirectional based on the type 
USDA Forest Service Proceedings RMRS-P-41. 2006. 717

Communicating the Wildland Fire Message: Infl uences on Knowledge and Attitude Change in Two Case Studies Toman and Shindler

of outreach experience they provide. Interactive activities (including guided 

visits to demonstration sites and agency workshops) allow for two-way communication with resource professionals while unidirectional methods (such 

as brochures, public service announcements, and static displays) consist of a 

one-way fl ow of information. Toman and others (2006) suggest that interactive programs may be more consistent with principles of adult learning by 

relating information to the local context, incorporating citizen experiences 

and concerns, and providing greater opportunities to develop personal relationships between citizens and agency personnel. This project provides an 

opportunity to further test these ideas.

A review of prior research suggests that both unidirectional and interactive activities have achieved some success. For example, brochures have been 

effective at increasing knowledge (Taylor and Daniel 1984) and leading to 

more supportive attitudes (Loomis and others 2001). Nielsen and Buchanan 

(1986) evaluated a unidirectional (slide show) and an interactive activity 

(interpreter guided walk); both of which resulted in higher knowledge and 

attitude scores among participants. Marynowski and Jacobson (1999) report 

outcomes for an ecosystem management education program that targeted 

fi re ecology as one of four content areas. The program consisted of various 

unidirectional communication methods including posters, brochures, youth 

activity booklets, and multiple news releases. These educational materials 

signifi cantly increased knowledge of fi re ecology, but did not result in a corresponding increase in support for fi re management activities.

Recent research has increasingly emphasized interactive activities. For 

example, Parkinson and others (2003) evaluated the infl uence of workshops 

on attitudes and knowledge. The workshops consisted of hands-on activities 

adapted from FireWorks, an education program originally developed by the 

USDA Rocky Mountain Research Station to target middle school students. 

Following the workshop, participants experienced an increase in knowledge 

and more supportive attitudes toward fi re management. Another study evaluated the infl uence of visits to sites treated by prescribed fi re (Toman and 

others 2004). In a self-assessment, a majority indicated that prescribed fi re 

was more acceptable to them as a result of having observed treated sites.

Finally, in a recent evaluation of a multi-faceted information program that 

used both unidirectional (brochures, mass media) and interactive methods 

(personal contact, group presentations, neighborhood meetings), McCaffrey 

(2004) found that personal contact contributed substantially to communication success. Indeed, educational materials, including unidirectional items, 

were more effective if delivered via personal contact. Findings suggest workshops, site visits, and other interactive activities may not only offer a means 

for information provision but also provide an opportunity for meaningful 

interaction with citizens.

Methods

Two study sites were selected; Sequoia and King’s Canyon National Parks 

(SEKI) and the World Forestry Center (WFC). SEKI is comprised of adjacent 

parks located in the Sierra Nevada Mountains in central California. The parks 

have an active fuel management program that emphasizes management- ignited 

prescribed fi res and managing naturally ignited fi res to achieve resource 

objectives. Thinning, though less prevalent, is also used near structures to 

reduce fuel levels.
718 USDA Forest Service Proceedings RMRS-P-41. 2006.

Toman and Shindler Communicating the Wildland Fire Message: Infl uences on Knowledge and Attitude Change in Two Case Studies

A broad range of outreach activities are represented at SEKI, including 

both unidirectional and interactive methods. Upon entering the park, all 

visitors receive a multi-page newsletter with details about park resources and 

facilities as well as general interpretive information. Within SEKI there are 

fi ve visitor centers, each containing various interpretive activities including 

brochures, fi lm strips, and static displays. Among these, the Giant Forest 

Museum offers a different, more sophisticated visitor experience than the 

other centers. Following recent renovation the Museum now provides a broad 

range of interactive and unidirectional activities, many of which emphasize 

the role of fi re in the Sequoia lifecycle. National Park Service interpretive 

personnel are highly visible at the Museum and frequently engage visitors. 

Other outreach activities within SEKI include interpreter and self-guided 

walks and evening “naturalist talks” at the primary park campgrounds.

Given that visitors to SEKI were potentially exposed to both unidirectional and interactive communication methods, their responses provide an 

opportunity to assess the infl uence of communication type. In the follow-up 

questionnaire, respondents from SEKI indicated the specifi c programs they 

participated in while at the parks. Each activity was classifi ed as interactive 

or unidirectional. Interactive programs included conversations with agency 

personnel, guided interpretive walks, evening naturalist programs, and visits 

to the Giant Forest Museum; all others were unidirectional.

The WFC is located in Portland, Oregon. From May through December 

2003 the center presented “Fire: Forces of Nature.” Each aspect of the exhibit 

was unidirectional and included photographs and text descriptions, examples 

of fi re suppression equipment, videos on home protection and Smokey Bear, 

as well as an abridged version of the Nova fi lm “Fire Wars” in the center’s 

theater. The displays provided information about the use of prescribed fi re 

and thinning to reduce fi re risk. Overall, the exhibit represented a series 

of traditional formats that resource agency personnel could use to provide 

interpretive information at visitor kiosks, information centers, or state and 

county fairs. Although these formats are still largely unidirectional, recent 

technological advances have substantially increased the ability of outreach 

personnel to create high quality, visually appealing displays.

Data Collection

Data were collected in two phases. In the fi rst phase, visitors were contacted 

and completed a brief questionnaire on-site before exposure to outreach 

activities. The on-site questionnaire included measures of citizen awareness 

and attitudes toward fuel treatments before soliciting respondents’ contact 

information and agreement to participate in the follow-up survey. The followup was mailed to respondents two weeks following their initial contact. The 

delayed test was used to assess the enduring effects of exposure to outreach 

activities and control for experimenter expectancy effects (Leeming and 

others 1993). A primary benefi t of the pre-test/post-test design is the collection of panel data, responses by the same individuals to the same measures 

at different points in time. Responses from individual participants can be 

“paired,” or linked, over the separate data collection points to identify shifts 

in individual attitudes and beliefs.

Questionnaire design was informed by semi-structured interviews with 

agency personnel and project partners. Two questionnaires were developed, 

one for the on-site survey and another for the follow-up phase. The follow-up 

questionnaires replicated on-site questions while soliciting further information 

on awareness, attitudes, and understanding of fuel treatments, evaluations 
USDA Forest Service Proceedings RMRS-P-41. 2006. 719

Communicating the Wildland Fire Message: Infl uences on Knowledge and Attitude Change in Two Case Studies Toman and Shindler

of the outreach activities, and demographic information. Resulting data enable assessment of between and within-site differences as well as contributory 

factors. Follow-up mailings were conducted using a modifi ed version of the 

“total design method” (Dillman 1978); surveys were sent in three waves 

beginning approximately two weeks following on-site contact.

Sample sizes and response rates are displayed in Table 1. As might be 

expected, overall visitation levels differ greatly between SEKI and the WFC. 

These differences are refl ected in substantially different sample sizes between 

the two sites. Where comparisons are made between locations, chi-square 

tests are used. Because the test is based on the proportion rather than the 

number of responses, the chi-square statistic is robust to differences in sample 

size (Cohen and Lea 2004). The remainder of the comparative analysis is 

based on responses from participants within each location. Thus, the differing sample sizes have little infl uence on fi ndings reported here.

Results

Respondents were similar demographically (age, education, gender, urbanrural residence) between locations. Overall, respondents had a mean age of 49 

and were well educated; two-thirds (66%) had a bachelor’s degree or higher. 

Just under half (44%) were women. Two-thirds (66%) lived in an urban area, 

while 34% came from a rural community.

Geographic Variation

Knowledge—To gauge citizen knowledge specifi c to fi re and fuel management, respondents completed a fi ve-item true/false quiz about treatment 

objectives and potential effects. Item development was based upon prior 

studies (Stankey 1976, Cortner and others 1984, Loomis and others 2001, 

Shindler and Toman 2003). Respondents appeared relatively knowledgeable 

with a majority answering each question correctly (Table 2). Indeed, participants’ average initial score was 76% at SEKI and 82% at the WFC.

Chi-square tests indicate a few differences in responses between study locations. Specifi cally, in the on-site surveys, fewer SEKI respondents understood 

the role of fi res in shaping natural forests or the impact of fi res on wildlife. 

Interestingly, in the follow-up survey signifi cantly more SEKI respondents 

correctly indicated that prescribed fi res effectively reduce the amount of fuel 

in forests.

Table 1—Sample sizes and response rate.

 On-site Post-surveys Response

 sample size* received rate

World Forestry Center (WFC) 92 68 74%

Sequoia and King’s Canyon National

Parks (SEKI) 395 269 68%

Total 653 459 70%

* Represents number who completed the on-site questionnaire and provided valid mailing addresses.
720 USDA Forest Service Proceedings RMRS-P-41. 2006.

Toman and Shindler Communicating the Wildland Fire Message: Infl uences on Knowledge and Attitude Change in Two Case Studies

Attitudes—Participants also responded to a series of fi ve statements regarding their attitudes toward fi re management issues (Table 3). The fi rst 

four items were based on prior research (Stankey 1976, Loomis and others 

2001, Shindler and Toman 2003). The fi nal item about thinning was included 

because previous studies suggest citizens may be concerned that thinning is 

simply an attempt to increase timber harvests on public lands (Shindler and 

others 2002, Shindler and Toman 2003). Results here indicate considerable 

uncertainty (don’t know responses) about thinning activities even following 

exposure to outreach activities.

Overall, on-site responses were positive toward fi re management, indicating 

a generally high level of support for treatments initially. There were no differences between SEKI and WFC on-site responses; however, agreement with 

management burning of underbrush differed in follow-up responses. While 

statistically signifi cant, these differences have relatively minor implications for 

fi re managers; in both cases a strong majority support periodic burning.

Changes Within Locations

Knowledge and Attitude Indices—A primary objective of this study was 

to examine the infl uence of participation in outreach activities on knowledge 

and attitudes. To assess change in understanding we created an index based 

on within-site participant performance on knowledge questions (responses 

presented in Table 2). A correct answer was coded as 1 while incorrect and “not 

Table 2—Between-site differences in response to quiz questions measuring knowledge about fi re management 

issues.

 Percent of respondents

 Generally Generally Not

 Location true false sure X2 Signifi cance

Wildfi res have played a signifi cant role in shaping natural forests in the western United States.

 On-site SEKI 87a 3 10 7.9 .019

 WFC 99 2 0

 Follow-up SEKI 93 2 6 2.0 .361

 WFC 97 2 2

Wildfi res usually result in the death of the majority of animals in the area.

 On-site SEKI 12 66 22 6.3 .042

 WFC 3 79 18

 Follow-up SEKI 9 71 20 2.0 .361

 WFC 7 79 13

Prescribed fi re or controlled burns effectively reduce amounts of fuel in most forests.

 On-site SEKI 70 9 21 1.6 .447

 WFC 77 4 19

 Follow-up SEKI 90 2 8 7.8 .019

 WFC 78 6 16

Prescribed fi res or controlled burns reduce the chance of high-intensity wildfi re.

 On-site SEKI 89 3 9 3.6 .162

 WFC 91 6 3

 Follow-up SEKI 91 3 6 .19 .906

 WFC 90 3 7

A history of suppressing wildfi res has increased the risk of a destructive fi re in the western United States.

 On-site SEKI 68 10 23 .54 .762

 WFC 69 12 19

 Follow-up SEKI 75 8 18 2.5 .277

 WFC 84 4 12

a

The most correct responses are indicated by italics.
USDA Forest Service Proceedings RMRS-P-41. 2006. 721

Communicating the Wildland Fire Message: Infl uences on Knowledge and Attitude Change in Two Case Studies Toman and Shindler

sure” responses were coded as 0; scores were then summed. Each participant 

received a score from 0 to 5. Using paired t-tests, on-site and follow-up indices 

were compared (Table 4). Mean knowledge scores signifi cantly increased 

among SEKI participants, while scores at the WFC remained similar.

An index was also created for attitude scores based on responses presented 

in Table 3. Each variable was recoded with a response of 1 indicating a positive 

attitude toward fi re management and 0 indicating either a negative attitude 

Table 3—Between-site differences in responses to belief statements measuring attitudes toward fi re management 

issues.

 Percent of respondents

 Don’t

 Location Agree Disagree know X2 Signifi cance

All fi res, regardless of origin, should be put out as soon as possible.

 On-site SEKI 16 78 6 2.4 .295

 WFC 9 85 6

 Follow-up SEKI 3 93 4 1.3 .511

 WFC 6 90 4

Managers should periodically burn underbrush and forest debris.

 On-site SEKI 84 3 13 1.4 .494

 WFC 82 6 12

 Follow-up SEKI 86 2 13 7.1 .027

 WFC 82 8 10

Prescribed fi res or controlled burns are too dangerous to be used.

 On-site SEKI 5 83 12 1.8 .393

 WFC 6 88 6

 Follow-up SEKI 2 93 5 4.7 .091

 WFC 6 85 9

Prescribed fi re or controlled burns should not be used because of potential health problems from smoke.

 On-site SEKI 6 81 14 .16 .920

 WFC 4 82 13

 Follow-up SEKI 3 86 12 .77 .678

 WFC 5 82 13

Thinning for fuel reduction will lead to unnecessary harvesting.

 On-site SEKI 15 51 34 2.7 .253

 WFC 19 57 24

 Follow-up SEKI 18 55 27 1.2 .538

 WFC 21 59 21

Table 4—Within location changes—Knowledge 

and attitude indices.

 Mean response

 SEKI WFC

Knowledge index

 On-site 3.81 4.10

 Follow-up 4.21 4.26

 t-statistic 5.864 1.120

 Signifi cance <.001 .267

Attitude index

 On-site 3.78 3.98

 Follow-up 4.11 4.04

 t-statistic 4.446 .414

 Signifi cance <.001 .680
722 USDA Forest Service Proceedings RMRS-P-41. 2006.

Toman and Shindler Communicating the Wildland Fire Message: Infl uences on Knowledge and Attitude Change in Two Case Studies

or don’t know response; responses were then summed. Each respondent received an index score from 0 to 5 for the fi ve attitudinal statements. As with 

the knowledge indices, the WFC scores remained similar throughout the 

study period while attitudes toward fi re management improved signifi cantly 

at SEKI.

Trends in individual change—Comparisons of mean index scores indicate 

whether an aggregate change occurred among the sample at each location, 

but do not provide an assessment of changes experienced by individual participants. To explore such changes we created two new variables, knowledge 

and attitude change, by pairing index ratings across the study period and 

subtracting the on-site from the follow-up scores. Thus, if a respondent answered two questions correctly in the pre-test and four on the post-test, their 

knowledge change would be two. These variables provide a measurement of 

change for each study participant.

The knowledge and attitude change variables revealed two important 

points (Table 5). First, preliminary observation suggested that respondents 

who showed the greatest amount of change were those with the lowest initial 

scores. To quantify this apparent difference, we used a t-test to compare the 

mean change between respondents with low (0-3) versus high (4-5) initial 

index scores. Mean change was signifi cantly greater among those with lower 

initial index ratings. Specifi cally, respondents with low initial understanding or support were signifi cantly more likely to experience positive shifts in 

knowledge or attitude following exposure to outreach activities.

Second, a substantial number of respondents in each location experienced 

a positive shift (increase of one or greater in index scores). At SEKI, 39% of 

respondents improved their performance on quiz questions and over one-third 

had more supportive attitudes following participation in outreach activities. 

Although slightly lower at the WFC, still more than 30% of respondents 

demonstrated higher knowledge and attitude scores in the follow-up.

Table 5—Within location changes—Trends in participant change.

 Mean response

 SEKI WFC

Knowledge changea

 Low initial knowledge group mean changeb 1.16 1

 High initial knowledge group mean changec –0.01 –0.1

 t-statistic 8.32 2.70

 Signifi cance <.001 0.01

 Percent of respondents with positive knowledge change 

 following outreach participation 39% 31%

Attitude changea

 Low initial attitudes group mean changeb 1.44 1

 High initial attitudes group mean changec –0.12 –0.31

 t-statistic 9.29 4.78

 Signifi cance <.001 <.001

 Percent of respondents with positive attitude change 

 following outreach participation 34% 32%

a Change was calculated by pairing responses and subtracting pre-test from post-test scores.

b Initial index score was 0-3.

c Initial index score was 4-5.
USDA Forest Service Proceedings RMRS-P-41. 2006. 723

Communicating the Wildland Fire Message: Infl uences on Knowledge and Attitude Change in Two Case Studies Toman and Shindler

Factors Infl uencing Change

The number of outreach activities available at SEKI provides an opportunity for further exploration of the infl uence of program and participant 

characteristics on responses. Of particular interest is the infl uence that type 

of outreach experience (interactive or unidirectional) has on knowledge and 

attitude change. Certain SEKI activities (conversations with agency personnel, guided interpretive tours, evening naturalist programs, and visits to the 

Giant Forest Museum) were coded as interactive; all others (park newsletter, 

brochures, other visitor centers, and self guided trails) were treated as unidirectional. Each respondent then received a score based on their participation 

in interactive activities. Scores ranged from 0 (for no interactive experiences) 

to 4 (for participation in each interactive activity).

We then created two multiple linear regression models to assess the relative infl uence of respondent and program characteristics on knowledge and 

attitude change (see Table 6). Independent variables in both models include 

demographics (gender, age, education, urban-rural residence), individual 

relevance of fi re topic (as measured by amount of prior thought given to wildfi re), and participation in interactive activities. Because our fi ndings suggest 

initial knowledge and attitudes may infl uence participant change, each model 

also includes the appropriate on-site index (e.g., the on-site knowledge index 

is included in the knowledge change model and the on-site attitude index in 

the attitude change model). Lastly, the models also included knowledge or 

attitude variable.

F-test results indicate that both models are statistically signifi cant. Furthermore, each explains at least half of the variance in participant change as 

indicated by the R-squared statistics. Among the four demographic variables, 

gender and age signifi cantly infl uenced knowledge change, while age and 

education had signifi cant impacts on attitude change. Males and younger 

participants were more likely to increase in knowledge; older individuals and 

those with lower education levels were more likely to experience an attitude 

shift. Interestingly, despite prior research that has identifi ed differences between urban and rural residents (Brunson and Steel 1996), residence type 

did not infl uence change in either model. Personal relevance of wildfi res had 

Table 6—Regression models testing infl uence of variables on knowledge change and attitude change at SEKI.

 Knowledge change Attitude change

 Standardized Standardized

 coeffi cient Signifi cance coeffi cient Signifi cance

Gender

(males = 1, females = 2) –.140 .007 –.059 .240

Age –.120 .020 .136 .007

Education .080 .132 –.167 .001

Urban-rural residence –.013 .800 –.041 .412

Relevance of fi re topic .173 .001 .102 .047

Participation in interactive activities .134 .007 .045 .361

On-site knowledge index –.702 <.001 — —

Attitude change .133 .011 — —

On-site attitude index — — –.692 <.001

Knowledge change — — .106 .039

F-statistic 26.844 <.001 28.673 <.001

Adjusted R squared .500 .518
724 USDA Forest Service Proceedings RMRS-P-41. 2006.

Toman and Shindler Communicating the Wildland Fire Message: Infl uences on Knowledge and Attitude Change in Two Case Studies

a signifi cant effect; in both models, those who had previously thought more 

about wildfi re were more likely to experience positive change.

Participation in interactive outreach activities signifi cantly contributed 

to knowledge change; however, a corresponding infl uence on attitudes was 

not recorded. In both models, the variables with the largest infl uence on 

participant change were initial knowledge or attitudes (as measured by the 

on-site indices); standardized coeffi cients were –.702 and –.692 for on-site 

knowledge and attitude indices respectively. The negative coeffi cients refl ect 

that respondents with low initial knowledge or attitudes were signifi cantly 

more likely to experience a positive increase throughout the study period. 

Findings here demonstrate a signifi cant association between knowledge and 

attitudes even when accounting for the infl uence of other variables; participants who experienced an increase in knowledge were also signifi cantly more 

likely to experience a positive change in attitude.

Discussion

Recent policy directives require substantial public participation in developing fi re management strategies. Successful participation depends upon the 

ability of resource professionals to communicate relevant information via 

effective outreach methods. Findings presented here provide information 

about participant understanding of and attitudes toward fi re management, 

track changes following outreach participation, and assess factors that contribute to knowledge and attitude change. Several important points emerge 

from this study.

First, participants had relatively high knowledge and supportive attitudes 

before exposure to outreach activities. In many cases, responses were more 

positive than had been recorded in prior studies (Cortner and others 1984, 

Loomis and others 2001, Shindler and Toman 2001). While the research 

approach targeted individuals who generally may be more experienced with 

natural resource issues than the public at large (e.g., they chose to spend their 

leisure time at a natural resource site), the increase in scores over prior studies 

were substantial, even when compared with research that targeted wilderness 

visitors (Stankey 1976, McCool and Stankey 1986). Overall, responses here 

show a greater appreciation for the role of fi re, as well as an increasing recognition of the consequences of fi re suppression and the benefi cial outcomes 

of the use of prescribed fi re.

Likely contributors include recent agency emphasis on outreach promoting 

fi re and fuel management as well as media coverage that has increased in both 

volume and depth. In particular, while media stories still highlight dramatic 

fi re events, there has been increased attention paid to the factors contributing to fi re activity (e.g., long-term fi re suppression resulting in increased 

fuel loads) as well as potential responses by management agencies. Results 

here suggest this increased exposure has resulted in higher initial awareness 

of fi re and a basic acceptance of some fi re management practices among the 

general public. The management implication is that outreach activities and 

messages will need to become more sophisticated to continue to be relevant 

to an increasingly knowledgeable public.

Second, despite high levels of understanding and support, there appeared to 

be some uncertainty about thinning treatments. While previous research has 

found substantial support for thinning in some forest communities (Shindler 

and Toman 2003, Brunson and Shindler 2004), citizens have also expressed 
USDA Forest Service Proceedings RMRS-P-41. 2006. 725

Communicating the Wildland Fire Message: Infl uences on Knowledge and Attitude Change in Two Case Studies Toman and Shindler

reservations with thinning treatments as a new means to conduct “business 

as usual” and increase timber harvests on public lands (Shindler and others 

2002). Indeed, much of the discussion in the popular press regarding the 

Healthy Forest Restoration Act has focused on whether the legislation would 

facilitate removal of large, mature trees (for example see McCarthy and others 

2003, New York Times Editorial Desk 2003). Findings here suggest greater 

discussion within communities will likely be necessary before proceeding 

with large-scale thinning projects. Outreach activities can play an important role here, particularly interactive programs, as research has shown that 

personal contact can reduce the controversy surrounding thinning decisions 

(McCaffrey 2004).

Third, although prior research has suggested differences in citizen perspectives among locations (Manfredo and others 1990, Brunson and Shindler 

2004), fi ndings here were generally similar across study sites. This may partly 

be an artifact of our research approach. Specifi cally, contacting individuals at 

a recreation site (outside of their community) and not at their residence potentially reduces the infl uence of local contextual factors on citizen responses. 

That is, they may have responded to questions about fuel treatments in general 

rather than thinking about a prescribed burn near their back yard. It is important to note that while there appears to be good understanding and high 

support for the concept of fuel management practices, gaining acceptance 

among local residents for specifi c treatments will require more than general 

interpretive messages. The implementation of specifi c projects will require 

effective communication tailored to ecological and social issues at the local, 

and perhaps the neighborhood, level (Brunson and Shindler 2004).

Lastly, the data presented here demonstrate that outreach activities can 

positively infl uence citizen understanding and support. While only SEKI 

responses demonstrated an aggregate increase, approximately one-third of 

participants at both locations experienced some positive change throughout 

the study period. These program effects are particularly remarkable given 

the high initial scores as participant change is less likely when knowledge or 

attitudes are already well-developed (Dillard and Peck 2000). Results further 

reveal that participants with low initial knowledge or less positive attitudes 

were more likely to experience improvements across the study period. This 

trend was evident in both locations; even though there was not an increase 

in aggregate scores at the WFC, those with low initial scores were positively 

infl uenced. Importantly, populations with low understanding or less supportive attitudes are a key target audience of agency personnel and results here 

suggest they are likely to benefi t the most from outreach activities.

Factors Infl uencing Change

A primary objective of these case studies was to assess factors that contribute 

to knowledge and attitude change. Of particular interest is the infl uence of 

interactive versus unidirectional outreach activities on participant responses. 

Within-location changes show that SEKI participants (exposed to interactive 

formats) experienced a signifi cant improvement in knowledge and attitudes 

while the WFC responses (following a unidirectional experience) remained 

similar throughout the study period. While suggestive, these results may 

be confounded by additional variables. For example, SEKI responses were 

initially lower, albeit slightly, and these individuals may have been more 

susceptible to change. In addition, most SEKI respondents participated in 

multiple outreach activities; thus, knowledge and attitude changes may be 

infl uenced by greater exposure to fi re-related information.
726 USDA Forest Service Proceedings RMRS-P-41. 2006.

Toman and Shindler Communicating the Wildland Fire Message: Infl uences on Knowledge and Attitude Change in Two Case Studies

The regression models also provide mixed evidence. While participation in 

interactive activities positively infl uenced knowledge change, results provide 

no evidence of a corresponding impact on attitudes. Ultimately, fi ndings 

here are suggestive but inconclusive on the infl uence of interactive outreach 

experiences. Our inability to identify potential effects may be a consequence 

of the measures used in this study. We replicated measures used by others, 

but the high initial performance may indicate it is time to increase the level of 

sophistication in our tests. A different set of knowledge and attitude measures 

may be necessary to identify change and assess contributory factors among 

an increasingly informed public.

Regression fi ndings also provide information on additional infl uencing 

factors among SEKI respondents. Demographic variables had mixed effects; 

infl uences were either inconsistent or contradictory between the models. 

Thus, fi ndings do not suggest a particular portion of the population to target 

through communication activities. Personal relevance of fi re management had 

positive effects on knowledge and attitude change. The implication here is 

that residents in the wildland urban interface are prime candidates for outreach programs and messages will likely be more successful when crafted to 

demonstrate their application to local issues of concern.

Also noteworthy is the strong association between knowledge and attitude change. While substantial research has identifi ed a correlation between 

knowledge and support for fi re management activities (e.g., McCool and 

Stankey 1986, Carpenter and others 1986, Shindler and Toman 2003), such 

associations are not evident for all natural resource issues. For example, attitudes toward clearcutting are unlikely to change simply on the basis of new 

information (Bliss 2000). The consistency of these fi ndings over time suggests that outreach activities may have a greater infl uence on support toward 

fi re than other management issues. Accordingly, resource professionals may 

see greater dividends by focusing their outreach efforts to communicate the 

fi re and fuel message.

Conclusion

Effective communication is essential to building the understanding and 

support necessary for sustainable resource management. Findings here suggest two basic levels of communication are useful. One is general information 

dispersal; this usually involves broad messages that can be conveyed by unidirectional, mass communication formats such as newspapers, brochures and 

public service announcements. Messages delivered through this format are 

typically created for general public consumption and, as such, provide few 

opportunities to target specifi c audiences. Because it is diffi cult to ensure that 

information is received and understood, their effectiveness as an educational 

tool is limited. Indeed, as Atkin writes, “campaign messages that have the 

broadest reach can deliver only a superfi cial amount of information and persuasive content that is seldom customized to the individual recipient” (2001, 

p. 56). However, these programs can still be benefi cial; they are typically 

inexpensive and can contribute to building awareness for important issues 

or projects (Atkin 2001, Jacobson 1999). Moreover, unidirectional activities, as demonstrated here, can positively infl uence citizens with low initial 

knowledge and a lack of formal opinions about these programs.

The second level of communication is more focused in scope and usually 

includes opportunities for interaction at the community or individual level. 
USDA Forest Service Proceedings RMRS-P-41. 2006. 727

Communicating the Wildland Fire Message: Infl uences on Knowledge and Attitude Change in Two Case Studies Toman and Shindler

Because such outreach activities target local priorities and specifi c environmental contexts, they will likely be more effective at infl uencing citizen 

understanding and acceptance (Brunson and Shindler 2004; McCaffrey 

2004). Indeed, as citizen understanding of fi re management becomes increasingly sophisticated, the fl exibility of interactive activities to provide 

context-relevant information will become even more important. Of the factors that contributed to knowledge change in this study, the type of outreach 

experience was the only one that managers can directly control.

The take-home message from these case studies is that effective outreach 

goes beyond simply using standardized tools to provide information. As 

demonstrated here, outreach success is not only a result of the information provided but also the method of delivery. Indeed, “the availability of information 

does not necessarily mean that it will reach its audience or be effective once 

it gets there” (McCaffrey 2004, p. 12). Successful communication requires 

effective planning including consideration of the communication objective, 

the nature of the topic, and audience characteristics including prior knowledge 

and attitudes (Jacobson 1999). Fire and fuel management are resource issues 

that offer a real opportunity for achieving success through communication 

and outreach. The public has long looked to management professionals to 

provide sound information and leadership regarding fi re issues (Shelby and 

Speaker 1990). As fi ndings here suggest, managers can use this leadership 

role to infl uence public understanding and generate positive attitudes for 

management activities.

References

Atkin, C.K. 2001. Theory and principles of media health campaigns. Pages 49-68 in

Public Communication Campaigns 3rd Ed., edited by R.E. Rice and C.K. Atkin. 

Thousand Oaks, CA: Sage Publications, Inc.

Bright, A.D., and M.J. Manfredo. 1997. The influence of balanced information 

on attitudes toward natural resource issues. Society and Natural Resources

10:469-483.

Brunson, M. and B. Steel. 1996. Sources of variation in attitudes and beliefs about 

federal rangeland management. Journal of Range Management 49 (1): 69-75.

Brunson, M., and B. Shindler. 2004. Geographic variation in the social acceptability 

of wildland fuels management in the western U.S. Society and Natural Resources

17:661-678.

Carpenter, E.H., J.G. Taylor, H.J. Cortner, P.D. Gardner, M.J. Zwolinski, and 

T.C. Daniel. 1986. Targeting audiences and content for forest fi re information 

programs. Journal of Environmental Education 17 (3): 33-42.

Cortner, H.J., M.J. Zwolinski, E.H. Carpenter, and J.G. Taylor. 1984. Public support 

for fi re-management policies. Journal of Forestry 82(6): 359-365.

Cohen, B.H. and R.B. Lea. 2004. Essentials of statistics for the social and behavioral 

sciences. New Jersey: John Wiley & Sons, Inc. 289 p.

Dillard, J.P. and E. Peck. 2000. Affect and persuasion: Emotional responses to public 

service announcements. Communication Research. 27(4): 461-495.

Dillman, D.A. 1978. Mail and telephone surveys: The total design method. New York: 

John Wiley and Sons.

Jacobson, S.K. 1999. Communication skills for conservation professionals.

Washington, D.C.: Island Press.
728 USDA Forest Service Proceedings RMRS-P-41. 2006.

Toman and Shindler Communicating the Wildland Fire Message: Infl uences on Knowledge and Attitude Change in Two Case Studies

Leeming, F.C., W.O. Dwyer, B.E. Porter, and M.K. Cobern. 1993. Outcome 

research in environmental education: A critical review. Journal of environmental 

education 24 (4):8-21.

Loomis, J.B., L.S. Bair, and A. Gonzalez-Caban. 2001. Prescribed fi re and public 

support: Knowledge gained, attitudes changed in Florida. Journal of Forestry

99 (11):18-22.

Manfredo, M.J., M. Fishbein, G.E. Haas, and A.E. Watson. 1990. Attitudes toward 

prescribed fi re policies: The public is widely divided in its support. Journal of 

Forestry 88 (7):19-23.

Marynowski, S.B., and S.K. Jacobson. 1999. Ecosystem management education for 

public lands. Wildlife Society Bulletin 27:134-145.

McCaffrey, S.M. 2004. Fighting fi re with education: What is the best way to reach 

out to homeowners? Journal of Forestry 102(5):12-19.

McCarthy, T., D. Cray, P. Dawson, E. Roston, and A. Zagorin. 2003. How Bush 

gets his way on the environment. Time 161(4):48-51.

McCool, S.F., and G.H. Stankey. 1986. Visitor attitudes toward wilderness fi re 

management policy—1971-84. Research Paper INT-357 Ogden, UT: USDA 

Forest Service, Intermountain Research Station.

New York Times Editorial Desk. 2003. Empty Promises. New York Times. Late 

Edition, January 23, 2003: A-28.

Nielson, C., and T. Buchanan. 1986. A comparison of the effectiveness of two 

interpretive programs regarding fi re ecology and fi re management. Journal of 

Interpretation II (1):1-10.

Parkinson, T.M., J.E. Force, and J.K. Smith. 2003. Hands-on learning: Its 

effectiveness in teaching the public about wildland fi re. Journal of Forestry 101(7): 

21-26.

Shelby, B., and R.W. Speaker. 1990. Public attitudes and perceptions about prescribed 

burning. In Natural and Prescribed Fire in Pacifi c Northwest Forests, edited by 

J.D. Walstad, S.R. Radosevich and D.V. Sandberg. Corvallis, OR: Oregon State 

University Press.

Shindler, B., M. Brunson, and G.H. Stankey. 2002. Social Acceptability of Forest 

Conditions and Management Practices: A Problem Analysis. PNW-GTR-537, 

U.S. Department of Agriculture, Forest Service, Pacifi c Northwest Research 

Station, Portland, OR.

Shindler, B. and E. Toman. 2003. Fuel reduction strategies in forest communities: 

A longitudinal analysis. Journal of Forestry. 101 (6): 8-15.

Stankey, G.H. 1976. Wilderness fi re policy: An investigation of visitor knowledge 

and beliefs. Research Paper INT-180. Ogden, UT: USDA Forest Service, 

Intermountain Forest and Range Experiment Station.

Taylor, J.G., and T.C. Daniel. 1984. Prescribed fi re: Public education and perception. 

Journal of Forestry 82 (6):361-365.

Toman, E., B. Shindler, and M. Reed. 2004. Prescribed fi re: The infl uence of site 

visits on citizen attitudes. Journal of Environmental Education. 35(3): 13-18.

Toman, E., B. Shindler, and M. Brunson. 2006. Fire and fuel management 

communication strategies: Citizen evaluations of agency outreach programs. 

Society and Natural Resources 19: 321-336.

Winter, G.J., C. Vogt, and J.S. Fried. 2002. Fuel treatments at the wildland-urban 

interface: Common concerns in diverse regions. Journal of Forestry 100 (1):15-21.
USDA Forest Service Proceedings RMRS-P-41. 2006. 729

In: Andrews, Patricia L.; Butler, Bret W., 

comps. 2006. Fuels Management—How to 

Measure Success: Conference Proceedings. 

28-30 March 2006; Portland, OR. 

Proceedings RMRS-P-41. Fort Collins, 

CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research 

Station.

1 Associate Professor, Natural Resources 

Management Dept., Cal Poly State 

University, San Luis Obispo, CA. 

cdicus@calpoly.edu

2 Urban-Fire Forester, Rancho Santa Fe 

Fire District, Rancho Santa Fe, CA.

Reduction of Potential Fire Behavior in 

Wildland-urban Interface Communities 

in Southern California: A Collaborative 

Approach

Christopher A. Dicus1 and Michael E. Scott2

Abstract—This manuscript details a collaborative effort that reduced the risk of wildfi re in an affl uent, wildland-urban interface community in southern California while 

simultaneously minimizing the environmental impact to the site. FARSITE simulations 

illustrated the potential threat to the community of Rancho Santa Fe in San Diego 

County, California, where multimillion-dollar homes were located immediately above 

a designated open space area that consisted primarily of 60-year-old, decadent chaparral. Post-treatment fi re behavior simulations demonstrated the potential ability to 

moderate fi re behavior.

Results of the fi re behavior modeling led to a recognition for the need for fuels 

treatments by both homeowners and regulatory agencies that were originally adverse 

to any type of treatment. Through a collaborative process, these diverse stakeholders 

worked to create and maintain an effective fuel treatment that was cost effective and 

environmentally sound. This shared approach by fi re personnel, homeowners, and 

regulatory agencies in Rancho Santa Fe is a success story that could be a template for 

interface communities throughout southern California.

Introduction

Nowhere in the United States is the increasing trend of destructive fi res 

in the wildland-urban interface (WUI) better exemplifi ed than in southern 

California. Coupled with a burgeoning population that continues to expand 

into explosive chaparral fuels, there is an ever-increasing potential for widespread destruction to human life and property. For example, eight fi res in 

southern California have grown to over 100,000 acres in size, including the 

2003 Cedar Fire in San Diego County, which burned over 273,000 acres 

(California Department of Forestry & Fire Protection 2005a). And in terms 

of structures lost, 14 of the 20 most destructive fi res in California occurred 

there, again led by the Cedar Fire, which consumed 4847 structures (California Department of Forestry & Fire Protection 2005b).

To reduce the costs and losses associated with wildfi res, fi re agencies allocate 

their limited resources to two primary strategies in the WUI. The fi rst strategy is to maximize success of initial attack by funding additional suppression 

equipment and personnel. Alternately, pre-fi re fuels treatments are a second 

strategy meant to reduce fi re behavior, thereby increasing suppression success 

and decreasing number of structures lost. While proven effective in numerous 

fi re events, the second strategy is seemingly more diffi cult to implement due 

largely to sociopolitical factors such as perceived degradation of viewsheds 

and costly and timely navigation through environmental review.
730 USDA Forest Service Proceedings RMRS-P-41. 2006.

Dicus and Scott Reduction of Potential Fire Behavior in Wildland-urban Interface Communities in Southern California: A Collaborative Approach

Pre-fi re fuels management is also more diffi cult to measure success as 

treatments are not necessarily meant to eliminate fi re spread. For example, 

fuel treatments in the 2002 Rodeo-Chediski fi res in Arizona signifi cantly 

reduced fi re intensity and rates of spread within the treatments, yet did little 

to impede spread across the landscape as the fi re’s path simply fl anked the 

treatments and continued unabated (Finney and others 2005). In the WUI, 

success of fuels treatments may be measured by any number of metrics, 

including initial attack success, percentage of homes survival, and others. 

Additionally, other metrics of success could include the degree to which the 

treatments retained the positive benefi ts of vegetation such as scenic beauty, 

carbon sequestration, mitigation of heat island effect, stormwater retention 

capacity, and others (Dicus and Zimmerman in review).

For WUI areas in southern California, we broadly defi ne a successful 

project as one that is

 (1) completed on the ground,

 (2) cost effective,

 (3) environmentally sound, and

 (4) effectively modifi es fi re behavior to an extent that minimizes structures 

consumed.

Based on the preceding metrics, a case study that examines the relative success of a fuel modifi cation project in Rancho Santa Fe, California follows.

Community Overview

Rancho Santa Fe is an unincorporated community of 3,252 people (2000 

U.S. Census) that is located approximately 20 miles north of San Diego, 

California (fi gure 1). The community is a classic example of a wildland-urban intermix, where homes are interspersed between designated open space 

parcels of mostly unmanaged vegetation. It has been designated by the State 

as a Very High Fire Hazard Zone.

The high value of homes in Rancho Santa Fe set it apart from most WUI 

communities. Data from the California Association of Realtors reveal that 

the median home price there exceeded $2.5 million in 2005. Further, as of 

the 2000 census, Rancho Santa Fe had the highest per capita income of any 

community in the United States with over 1000 households.

In the absence of Santa Ana winds, fuels will have the greatest effect on 

fi re behavior and is subsequently the greatest threat to homes. Topography 

consists mostly of gently rolling slopes and drainages. Weather is Mediterranean and is greatly moderated by proximity to the Pacifi c Ocean. Property 

owners, by ordinance, must “maintain an effective fuel modifi cation zone by 

removing, clearing, or thinning away combustible vegetation and other fl ammable materials from areas within 100 feet of any structure” (Rancho Santa 

Fe Fire Protection District Ordinance No. 02-01). It is the responsibility of 

individual property owners to create and maintain this buffer. However, if 

the 100 ft buffer around a structure exceeds the property line of a specifi c 

homeowner, it is the responsibility of the adjacent landowner to manage 

vegetation on his own property so as to maintain the 100 ft buffer for all 

structures. In many instances in Rancho Santa Fe, the 100 ft buffer from 

structures extends into adjacent open space parcels.

Fuels in the interspersed open space parcels consist largely of decadent, 

highly volatile brush that has not burned in over 60 years. Vegetation in the 
USDA Forest Service Proceedings RMRS-P-41. 2006. 731

Reduction of Potential Fire Behavior in Wildland-urban Interface Communities in Southern California: A Collaborative Approach Dicus and Scott

Figure 1—Location of Santa Rancho Fe, San Diego County, California.

open space areas is typical of southern California chaparral, consisting of such 

native species as scrub oak (Quercus berbidifolia) and chamise (Adenostoma 

fasciculatum). Further, exotics such as red gum eucalyptus (Eucalyptus camaldulensis) and pampas grass (Cortaderia jubata) are commonly found there.

The open space areas are the responsibility of the Rancho Santa Fe Association (hereafter, Association), a homeowners association that administers 

a protective covenant of land use rules in the area. All members of the Association are responsible for paying for the maintenance of the open space 

parcels, regardless if individual property owners are directly affected. The 

only vegetation management in these areas had been to periodically cut the 

brush along horse trails that crossed through the middle of the open space 

areas, which would have minimal effect on the spread of wildfi re.

Structural and wildland fi re protection is provided by the Rancho Santa 

Fe Fire Protection District (hereafter District), which serves a 42-square mile 

area surrounding Rancho Santa Fe. The District, however, is in a designated 

State Responsibility Area for wildland fi re protection, and is thus also served 

by the California Department of Forestry and Fire Protection. This designation served to facilitate the fuels treatments that will be discussed later.

Of note, the District has adopted a shelter-in-place approach for residents 

of some newer subdivisions during a wildfi re because homes there have been 

built with extremely fi re-resistant construction materials and have District-approved landscaping. The District contends that sheltering in the fi re-resistant 

structures during a wildfi re would be safer than attempting to evacuate along 

winding roads adjacent to potentially burning vegetation.
732 USDA Forest Service Proceedings RMRS-P-41. 2006.

Dicus and Scott Reduction of Potential Fire Behavior in Wildland-urban Interface Communities in Southern California: A Collaborative Approach

Figure 2—Aerial photograph of the El Secreto fuel modifi cation project in relationship to 

homes in Rancho Santa Fe, California.

The older, previously developed community of Rancho Santa Fe, however, is not as fi re resistant as the newer developments. Commonly, private 

residences sit atop ridges above the aforementioned open spaces and would 

receive immense convective heating from burning of the explosive chaparral 

fuels. Further, several of the residences still have wood shake roofs, which 

have been shown to be especially susceptible to combustion from burning 

embers (Cohen 2000). Thus, even with a 100 ft managed buffer around 

structures, risk to many residences remains high.

Project Implementation

One particular area in Rancho Santa Fe had long been a concern to the 

District. This area was in a chaparral-fi lled canyon with homes regularly 

located at the tops of the ridges in natural chimneys and saddles (fi gure 2). 

A formal risk assessment across the District confi rmed that this area was at 

elevated risk of loss during a fi re event. Given the pre-treatment conditions of 

the open space parcel in question, the District expected to lose a minimum 

of eight homes during a wildfi re event.

Given the value of these homes and the historic behavior of wildfi res in 

the area, members of the insurance industry were also extremely concerned 

with potential losses from wildfi re. Because of their high replacement costs, 
USDA Forest Service Proceedings RMRS-P-41. 2006. 733

Reduction of Potential Fire Behavior in Wildland-urban Interface Communities in Southern California: A Collaborative Approach Dicus and Scott

Figure 3—Pre-treatment FARSITE simulations from a single ignition point (in white) under 

50th percentile weather and wind conditions (August). Flame length (ft), 5-minute time 

steps, and background fuel models are depicted.

destruction of only a few homes in Rancho Santa Fe would cause a tremendous loss to the industry, translating into an increase in rates for not only San 

Diego County, but potentially for homeowners across southern California.

FARSITE simulations from a single, likely ignition point during historic 

50% and 97% weather illustrate the pre-treatment potential fi re behavior in 

the area (fi gures 3 and 4, respectively). Even with a 100 ft buffer around the 

homes, many would likely experience intense convective heating, if not direct 

fl ame impingement. Pertinent weather and fuel values for all simulations are 

provided in table 1 and were determined by FireFamilyPlus analysis of historic 

weather data from the nearby Flores RAWS station. A custom fuel model (fuel 

model 20) was utilized to simulate fi re spread within the 100 ft buffer. Figures 

3 to 5 depict extent of spread and fl ame length (ft) for a 1-hour simulation 

(5-minute visible time steps) where all inputs were held constant.

The District contacted the Association regarding unmanaged vegetation 

on the open space parcels that were within 100 ft of structures and provided 

suggestions for mitigation. The District did not take a heavy-handed approach 

with the Association, but instead sought an open dialogue with the Association so as to make them aware of the hazards and recommend solutions that 

were in the best interest of the community.

Modeling efforts were presented to members of the Association who, while 

not understanding the nuances of wildland fi re behavior modeling, appreciated the potential for a signifi cant fi re event. Subsequent simulations that 
734 USDA Forest Service Proceedings RMRS-P-41. 2006.

Dicus and Scott Reduction of Potential Fire Behavior in Wildland-urban Interface Communities in Southern California: A Collaborative Approach

Figure 4—Pre-treatment FARSITE simulations from a single ignition point under 97th

percentile weather and wind conditions (August).

Table 1—Average and extreme (August) 

weather, wind, and fuel moisture inputs 

used in FARSITE simulations in Rancho 

Santa Fe, California. Values obtained 

from FireFamilyPlus analysis of nearby 

Flores RAWS station.

 Percentile

 Variable 50th 97th

Max Temp1 76 85

Min RH2 22 13

Wind Speed3 10 20

1-hr FM2 6 3

10-hr FM2 8 5

100-hr FM2 10 7

Herbaceous FM2 60 30

Live Woody FM2 80 60

1 °F

2 Percent

3 mph
USDA Forest Service Proceedings RMRS-P-41. 2006. 735

Reduction of Potential Fire Behavior in Wildland-urban Interface Communities in Southern California: A Collaborative Approach Dicus and Scott

Figure 5—Post-treatment FARSITE simulations from a single ignition point under 97th

percentile weather and wind conditions (August).

accounted for a fuels treatment in the area (conversion to fuel model 8) clearly 

illustrated the potential benefi ts of those treatments to adjacent landowners, 

even under 97% weather conditions (fi gure 5). The District explained to the 

Association that any fuels treatment would not stop a wildfi re, but would 

reduce the fi re intensity, thereby reducing the threat to nearby structures 

and increasing chance of initial attack success. The Association Board of 

Directors created and distributed a simple but compelling brochure to their 

members that detailed the need to allocate funds for the project as it would 

benefi t all members of the Association, not only the homeowners adjacent 

to the proposed fuel modifi cation.

The Association was initially somewhat hesitant to initiate fuels modifi cations in these areas based not on perceived degradation of views or 

environmental impacts, but instead on the potential cost of treatments. Indeed, initial estimates from contractors on the 11.26 acre (4.65 ha) El Secreto 

project ranged from $65,000 to over $200,000. District personnel worked 

with the Association to explore other, more economically feasible options.

The District sought assistance from publicly funded crews because the project area was within a designated State Responsibility Area for fi re protection 

and was by law, technically open to the public (even though the Association 

attempts to discourage outside access as much as possible to the open space 

parcels). CDF-administered inmate crews were subsequently contacted. At 

fi rst, the community members were extremely adverse to inmate crews in the 

community due to perceived safety concerns. Association Board Members 

visited the applicable correctional facilities to personally investigate the crews 
736 USDA Forest Service Proceedings RMRS-P-41. 2006.

Dicus and Scott Reduction of Potential Fire Behavior in Wildland-urban Interface Communities in Southern California: A Collaborative Approach

and subsequently provided assurance to their members that the inmate crews 

would pose no threat to the neighborhoods. That assurance, in addition to 

the extremely low estimated cost of the implementing the project ($30,000), 

eventually won the community over.

After CDF contracts were established, the Association notifi ed the California Department of Fish & Game (CFG) of their intent to carry out the 

fuels modifi cation project per guidelines established in a preexisting Memorandum of Understanding between CFG, the U.S. Fish and Wildlife Service 

(USFW), CDF, the San Diego County Fire Chief’s Association, and the 

Fire District’s Association of San Diego County. The MOU states that after 

notice of intent to clear vegetation for fi re protection purposes is given, CFG 

and USFW biologists have the option to review the project for compliance 

with endangered species requirements, and may suggest voluntary, alternative 

measures if deemed feasible and warranted. While the District was responsible 

for establishing the need and proposed mitigation measures in the project, 

they purposely did not write the notice of intent to CFG in an attempt to 

avoid any potential interagency political wrangling.

Because the proposed El Secreto project exceeded the 100 ft buffer established in the MOU, CFG and USFW regulators required additional review. 

Once again, FARSITE simulations were used to justify the extent of the project. After analyzing the simulations, they agreed to an on-site review of the 

project area. The on-site review confi rmed to the regulators that a majority of 

the vegetation in the proposed project area was dead and that removal of these 

fuels would not negatively impact habitat there. The regulators required that 

no more than 50% of the vegetation be removed, which was unreasonable in 

some locations as over 80% of the existing vegetation was dead at that time. 

They further requested that all fl ammable exotic species such as eucalyptus 

and pampas grass be removed, by herbicides if necessary, which was beyond 

the original scope of the District but welcomed.

Upon approval by CFG and USFG regulators, female inmate crews from 

the local Rainbow Camp began the project, demonstrating both outdoor 

savvy and the care needed to properly treat the area. Of interest, while initially adverse to inmate crews, homeowners quickly became enamored by the 

female crews and tried to offer cookies and cakes to them, which was against 

CDF policy of limiting contact between inmates and private citizens. The 

Association, however, was able to regularly provide Subway sandwiches to 

the inmates, which apparently increased both their productivity and care on 

the project. At the completion of the project, CDF invoiced the Association 

for $14,000, well below early estimates that exceeded $200,000 and the 

$30,000 for which the Association had budgeted. These savings will pay for 

future maintenance costs on the project.

The project had minimal negative environmental impacts and served to 

provide many positive benefi ts to the community. Indeed, only dead material 

was harvested during the project, which was subsequently chipped and spread 

on existing horse trails. This simultaneously eliminated green waste from 

entering the landfi ll and also mitigated erosion on the trails. Exotic pampas 

grass was eliminated from the project area with herbicide, but will likely return 

via seeds from ornamental plants on properties above the project. Further, 

anecdotal evidence suggests that there are more wildlife species present on 

the site after the treatment, but this may be a function of increased visibility 

of the area, which was marred by the abundance of dead vegetation. At the 

conclusion of the project, a shaded fuel break resulted that simultaneously 

lowered fi re risk while having minimal impacts to the positive benefi ts that 

vegetation provide such as stormwater retention, improved air quality, and 
USDA Forest Service Proceedings RMRS-P-41. 2006. 737

Reduction of Potential Fire Behavior in Wildland-urban Interface Communities in Southern California: A Collaborative Approach Dicus and Scott

carbon sequestration. Whereas before there was an almost impenetrable mass 

of dead brush, the site is now regularly used by the community as a location 

to recreate.

Lessons Learned

By the metrics set forth at the beginning of this manuscript, the El Secreto 

Project was a success. Owing to a collaborative effort between local and state 

fi re agencies, homeowners, and environmental regulatory agencies, the project 

was implemented on the ground after much planning, was relatively cost effective, and was environmentally sound. The ultimate test of the success of 

the project will come in a future, inevitable wildfi re.

While this project is extremely benefi cial to the properties immediately 

adjacent to the fuels project, it will have minimal impact to the spread of 

fi re across the landscape, especially during a Santa Ana wind event, due to 

its relatively small size. However, the original strategy of the project was to 

maximize initial attack success on a fi re occurring in the open space parcel, 

not stop a major wildland fi re.

District personnel cite that the key to this project was the development of 

partnerships and collaboration with property owners and regulatory agencies. 

The District was instrumental in initiating meaningful dialogue between fi re 

personnel, Association members, and regulatory agencies, which was vital 

to the scope and completion of the project. Collaboration does not imply 

“educating” the homeowners and regulators to the needs and desires of the 

fi re agencies, but rather is meaningful communication where all viewpoints 

are considered to best serve the community. They also conclude that it is 

critical to adequately plan an environmentally sound and justifi able project 

before regulators participate in an on-site review of a project.

While pleased in the success of the El Secreto project, concerns over 

future projects remain. One concern is the regular turnover of CFG and 

USFG regulators in the region. Historically, many regulators seemed adverse 

to any type of vegetation management until a trust relationship had been 

developed with District personnel. With regular turnover, the fostering of 

mutual trust between the agencies will be hindered. There are also concerns 

about any future needed projects that might lie within the jurisdiction of 

the California Coastal Commission as they have historically been adverse to 

most vegetative management projects, regardless of the potential threats or 

species involved. Indeed, they were the only party that refused to sign the 

original MOU discussed earlier.

Because of the success of this program, other local communities now 

regularly seek to contract with the inmate crews, which could potentially 

limit the District’s ability to use them for future projects. It is hoped that the 

strong working relationship forged between CDF and the District as well as 

the relatively central location within the CDF responsibility area will insure 

Rancho Santa Fe has access to crews.

Also, the continued presence of wood roofs in the area is an immediate 

threat to the community, due to their susceptibility of combustion from fi re 

brands. Of interest, a portion of the residents in this affl uent community are 

asset-wealthy, but simply do not have the means to replace their roofs with fi re 

resistant materials. These property owners consist primarily of retirees who 

purchased their home in the 1970s or earlier when home prices were signifi -

cantly less; while their home equity has appreciated exponentially, they live 
738 USDA Forest Service Proceedings RMRS-P-41. 2006.

Dicus and Scott Reduction of Potential Fire Behavior in Wildland-urban Interface Communities in Southern California: A Collaborative Approach

today on fi xed incomes. A recent grant to FEMA for a cost-sharing program 

to replace fi re-prone roofs remains pending. The grant would fund 70% of 

the costs of roof replacement, with a cap of $40,000 per residence.

There are also concerns about undeveloped lots adjacent to parcels with 

structures. As with the Association’s open space parcels, those property owners 

are responsible for modifying vegetation within 100 ft of a structure, regardless if their individual property is developed or not. Property owners of the 

undeveloped lots, many living outside the state, have sometimes resisted the 

District’s attempts to enforce the 100 ft buffer. While preferring a collaborative approach to generate solutions that mitigate the threat, the District 

is sometimes forced to send outside contractors to those sites, subsequently 

billing the noncompliant property owners for work completed there.

Acknowledgments

This project was funded by the California State University Agricultural 

Research Initiative. We are grateful to Mark Carpenter of KTU&A Landscape 

Architecture and Planning, San Diego, California, for providing critical GIS 

and FARSITE data fi les.

Literature Cited

California Department of Forestry & Fire Protection. 2005a. 20 Largest California 

Wildland Fires (By *Acreage Burned). Available at http://www.fire.ca.gov/

FireEmergencyResponse/HistoricalStatistics/PDF/20LACRES05.pdf, <4-1-06>.

California Department of Forestry & Fire Protection. 2005b. 20 Largest California 

Wildland Fires (By Structures Destroyed). Available at http://www.fi re.ca.gov/

FireEmergencyResponse/HistoricalStatistics/PDF/20LSTRUCTURES05.pdf, 

<4-1-06>.

Cohen, Jack D. 2000. Preventing disaster: home ignitability in the wildland-urban 

interface. Journal of Forestry 98(3): 15-21.

Dicus, C.A., and M.P. Zimmerman. In review. Quantifying fi re behavior vs. 

community benefi ts of southern California shrublands and grasslands. Proceedings 

to the 23rd Tall Timbers Fire Ecology Conference: Fire in grassland and 

shrubland ecosystems. Bartlesville, OK, October 17-20, 2005.

Finney, M.A., C.W. McHugh, and I.C. Grenfell. 2005. Stand- and landscape-level 

effects of prescribed burning on two Arizona wildfi res. Canadian Journal of 

Forest Research 35(7):1714-1722.
USDA Forest Service Proceedings RMRS-P-41. 2006. 739 

Abstract—Prescribed burning is essential on Merritt Island National Wildlife Refuge. 

Not only is it needed to manage the volatile fuels, but also to manage the complex 

system of fi re maintained habitats found here. Fire management on the Refuge presents 

unique challenges. In addition to the restraints to prescribed burning that are common 

to many prescribed burning programs, Refuge fi re managers must also consider the 

special needs of an operational space port. By using an active program of education, 

demonstration and negotiation with the Space Center, the Refuge has been able to 

maintain a prescribed burning program that has reduced the detrimental effects of 

unwanted wildland fi res when they occur.

Introduction

Merritt Island National Wildlife Refuge (Refuge) is located on the east 

central coast of Florida in Brevard and Volusia Counties (fi gure 1). The 

majority of the Refuge is an overlay of the National Aeronautics and Space 

Administration’s (NASA) John F. Kennedy Space Center (KSC). The U.S. 

Fish and Wildlife Service (Service) administers these lands and waters under 

an interagency agreement. This agreement gives the responsibility for land 

management activities for KSC’s non-operational lands to the Service. Included in these management responsibilities are wildland fi re suppression and 

prescribed burning. The Refuge also has agreements with Canaveral National 

Seashore (CNS) to assist with both prescribed burning and wildland fi re suppression and with the Cape Canaveral Air Force Station (CCAFS) to assist 

in prescribed burning. Together, these four federal agencies manage over 

180,000 acres of relatively undeveloped coastal barrier islands and lagoons.

This coastal ecosystem is quite diverse. Schmalzer and others (2002) list 

803 native plants on the Refuge and adjoining federal lands, with, 38 taxa 

listed as endangered, threatened or of special concern by the State of Florida. 

This wide array of plant species has been grouped into 20 native wetland and 

upland vegetative communities (U. S. Fish and Wildlife Service 2006). The 

Refuge’s habitats provides protection and management opportunities for 10 

regularly occurring federally listed threatened and endangered wildlife species, as well as for 36 species of federal management concern and 47 wildlife 

and plant species listed by the State of Florida (Epstein and Blihovde 2006). 

In addition, over 300 species of migratory and resident birds, 30 species of 

mammals, and 71 species of reptile and amphibians have been recorded on 

the Refuge (Adrian and others 2006).

Fire Management in the Inter Galatic 

Interface or 30 Years of Fire Management 

at Merritt Island National Wildlife Refuge/ 

Kennedy Space Center, Florida

Frederic W. Adrian1

In: Andrews, Patricia L.; Butler, Bret W., 

comps. 2006. Fuels Management—How to 

Measure Success: Conference Proceedings. 

28-30 March 2006; Portland, OR. 

Proceedings RMRS-P-41. Fort Collins, 

CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research 

Station.

1 Administrative Forester, Merritt 

Island National Wildlife Refuge, 

Titusville, FL. Fred_Adrian@fws.gov 
740 USDA Forest Service Proceedings RMRS-P-41. 2006.

Adrian Fire Management in the Inter Galatic Interface or 30 Years of Fire Management at Merritt Island Florida...

Figure 1—Location of Merritt Island National Wildlife Refuge and other federal agencies.
USDA Forest Service Proceedings RMRS-P-41. 2006. 741

Fire Management in the Inter Galatic Interface or 30 Years of Fire Management at Merritt Island Florida... Adrian

Fire History And Fire Ecology

Fire has been a component of the Florida ecosystem since before humans 

occupied the landscape. The National Weather Service Offi ce in Melbourne, 

Florida states that Florida is the “lightning capital of the United States”, with 

over 22,000 lightning strikes occurring in Brevard County alone each year 

(National Weather Service 2005). In historic times, lightning frequently 

ignited fi res, which spread readily throughout the landscape. Examination 

of charcoal deposits in lake sediments show that fi res have occurred in south 

central Florida for 50,000 years (Watts and Hansen 1988). It is logical to assume that fi re has been instrumental in favoring the selection of fi re-adapted 

traits in the Florida’s vegetation.

Fire Maintained Vegetative Communities

Of the almost 77,000 acres of non-open water habitat on the Refuge, 

approximately 55,000 acres support plant communities that can be considered fi re maintained. Without periodic fi res, the characteristics of the four 

important fi re-maintained vegetative communities on the Refuge described 

below would change drastically.

Oak Scrub: Oak scrub occurs on xeric sites. The shrub layer plants found 

here include sand live oak (Quercus geminata) myrtle oak (Q. myrtifolia) and 

Chapman’s oak (Q. chapmanii) along with some palmetto (Serenoa repens). 

Occasionally, a sand pine (Pinus clausa) overstory is present. Historically oak 

scrub stands were low and open with many sandy patches.

The fi re regime in the oak scrub can be described as intense and stand replacing. Oak scrub is diffi cult to ignite. In many cases, lightning fi res started 

in more fl ammable areas, such as the fl atwoods, and ran into the scrub areas. 

When ignited however, the oak scrub burns vigorously. Rates of spread are 

rapid and fl ame lengths of 40 to 50 feet were not uncommon. The natural 

fi re return interval was between fi ve and seven years. Stands of oak scrub 

regenerated quickly from root sprouting (Schmalzer 2003).

Scrubby Flatwoods: The scrubby fl atwoods community is found on slightly 

wetter sites than the oak scrub. The shrub species found in the oak scrub are 

also found here, but palmetto is much more abundant. More mesic species 

such as gallberry (Ilex glabra) and Lyonia spp. are also present. In historic 

times a scattered overstory of south Florida slash pine (P. elliottii var. densa) 

was present. Both the oak scrub and the scrubby fl atwoods are habitat for 

the federally threatened Florida scrub-jay (Aphelocoma coerulescens) and are 

the focus of a much of the Refuge’s upland management activities.

Under natural fuel loadings, fi res in the scrubby fl atwoods were generally 

confi ned to the shrub layer, with overstory consuming fi res only occurring 

during periods of extreme weather. Rates of spread were normally moderate as 

were the fl ame lengths. The fi re return interval was between three and seven 

years. Most of the shrub layer vegetation regenerates from sprouting.

Pine and Palmetto Flatwoods: The pine and palmetto fl atwoods community is found on the more mesic soils of the Refuge. The shrub layer is 

predominately palmetto with some gallberry, Lyonia spp. and wax myrtle 

(Myrica cerifera). Wire grass (Aristida stricta var. beyrichiana) is common. 

An overstory of south Florida slash pine is common, with some stands of 

pond pine (P. serotina) present in the wetter areas. Historically, fi res kept the 
742 USDA Forest Service Proceedings RMRS-P-41. 2006.

Adrian Fire Management in the Inter Galatic Interface or 30 Years of Fire Management at Merritt Island Florida...

understory low and open, and the overstory scattered to moderately dense. 

The pines in the fl atwoods provide nesting habitat for the bald eagle (Haliaeetus leucocephalus).

The historic fi re regime in the fl atwoods consisted of moderately intense 

fi res that occurred every three to fi ve years. The understory of the fl atwoods 

burns vigorously and completely. Much of the vegetation is highly fl ammable. Species such as palmetto contain resins and oils which ease ignition 

and increase rates of spread. As was the case in the scrubby fl atwoods, fi res 

in the canopy were infrequent and occurred during periods of drought or 

when fuel loads became excessive.

Marshes: Both saltwater and freshwater marshes occur on the Refuge. The 

saltmarshes, the majority of which are now impounded, occur along the 

edges of the lagoon system on the Refuge. The native vegetation is primarily 

sand cordgrass (Spartina bakerii) a tall grass with some short grasses such 

as saltgrass (Distichlis spicata) mixed in. The freshwater marshes, or swales, 

also contain sand cordgrass along with some Andropogon spp. The swales are 

intermingled with the upland vegetation described above and are important 

in the fl ammability of those landscapes.

The historic fi re regime was similar in both types of marshes. It can best 

be characterized as rapidly moving, intense fi res with a fi re return interval 

between two and four years. Fires usually consumed all of the vegetation 

and the stand was regenerated by sprouting (Schmalzer and others 1991). 

These frequent fi res kept the stands of grass in an open condition. They also 

reduced the encroachment of woody species such as wax myrtle and salt bush 

(Baccharius spp.)

Human Fire Use

Evidence exists that Native Americans used fi re extensively prior to the arrival of the fi rst European explorers (Robbins and Myers 1992). The journals 

of many of the early explorers indicate that in the southeast, Native Americans 

used fi re to clear fi elds and drive game as well as for communications and 

warfare. Many of these fi res were set outside of the natural fi re season.

The early European settlers used fi re extensively for many reasons. Turpentine operations burned in winter, cattlemen burned in the spring and hunters 

burned in the fall. These activities, combined with the naturally ignited summer fi res resulted in fi re on the landscape throughout the year.

The past 50 years have seen controversy over the use of fi re. Ranchers, 

timber companies, wildlife managers and others have continued to use fi re, 

much of the time outside of the natural fi re season. During the 1950s and 

1960s there was a concerted effort to stop burning the landscape. In addition, efforts to suppress wildfi res were increased. This was especially true at 

KSC.

Changes in the Ecosystem

The removal of fi re from the ecosystem caused major changes in the 

landscape. Pine stands in the fl atwoods and scrubby fl atwoods communities 

became dense and overgrown. Mesic forests began to invade marshes where 

frequent fi res once kept this encroachment in check (Duncan and others 

1999). The oak scrub increased in height and density becoming diffi cult to 

ignite except under extreme fi re weather conditions (Schmalzer and Adrian 

2001).
USDA Forest Service Proceedings RMRS-P-41. 2006. 743

Fire Management in the Inter Galatic Interface or 30 Years of Fire Management at Merritt Island Florida... Adrian

These changes to the habitats affected the wildlife utilizing them. The 

thickness of the scrub vegetation made the oak scrub and scrubby fl atwoods 

less suitable for the Florida scrub-jay and other scrub fauna. Unburned marsh 

grasses made movement diffi cult for secretive birds such as black rails (Laterallus jamaicensis). In some cases, brush in the marshes was thick enough 

to shade out grasses changing the habitat entirely.

Fuel loads increased in all of these vegetative communities. When fi res did 

start, they burned with greater intensity than in the past. This was especially 

critical in the pine fl atwoods. While historic fi res tended to stay in the shrub 

layer, the increase in pine density resulting from the lack of fi re increased the 

potential for crown fi res. This removed nesting substrate for the bald eagle.

Refuge Fire Management

Early Fire Management

Fire management on the Refuge has changed considerably over the past 

three decades. Between the time the Refuge was created in 1963 and 1981 

little active fi re management was done. A review of the somewhat sketchy 

early Refuge records shows a few small prescribed burns, and occasional 

suppression activities. During this time, the responsibility for suppression 

of wildfi res was confused with the Refuge taking action on some fi res, and 

with KSC Fire (primarily a structural fi re organization) suppressing others. 

Training of Refuge personnel was minimal and equipment was typically 

converted military vehicles and other used equipment.

Fuels Management Prescribed Burning

With little fi re activity in the ecosystem, fuel accumulated to a point where 

it was only a matter of time before severe fi res would occur. This happened 

in the summer of 1981 when 46 wildfi res burned over 17,000 acres and 

two fi refi ghters were killed. This calamity initiated the second phase of fi re 

management on the Refuge. Training of wildland fi refi ghters was increased, 

new equipment was purchased, and a contract helicopter was acquired for 

both fi re suppression and prescribed burning.

An aggressive prescribed fi re program was begun with fuels management 

as the primary objective. During this time period, burn units were large, with 

some up to 4,000 acres. Between 1982 and 1992 the Refuge had 108 prescribed burns totaling 121,743 acres with an average size of 1,127 acres.

Most units were designated using existing natural and man made-barriers. It was normal to fi nd several different vegetation communities within 

a single burn unit. This meant that fi re prescriptions could not be tailored 

to meet specifi c requirements for individual communities. This phase of the 

Refuge’s prescribed burning did meet the overall objective of reducing the fi re 

danger. In 1992, a year with similar weather conditions to 1981, the Refuge 

experienced 45 wildfi res, but only 378 acres were burned and no injuries or 

fatalities were experienced.

Habitat Management Prescribed Burning

In the early 1990s fi re management objectives began changing from 

simply reducing fuel loads to meeting wildlife and habitat management objectives. Beginning in 1993 the Refuge began to subdivide the larger units 
744 USDA Forest Service Proceedings RMRS-P-41. 2006.

Adrian Fire Management in the Inter Galatic Interface or 30 Years of Fire Management at Merritt Island Florida...

in an attempt to focus more on the burning requirements of the individual 

vegetative communities and the wildlife species they supported. Of primary 

importance was the maintenance and restoration of oak scrub habitat for the 

threatened Florida scrub-jay. Also of great interest was maintaining nesting 

substrate for the bald eagle in the fl atwoods and managing habitat for black 

rails and other marsh birds in the grassy wetlands.

The size of the subdivided burn units was greatly reduced. Between 1993 

and 2002 the Refuge had 202 prescribed burns totaling 93,402 acres in fi re 

maintained habitats. The average burn size was 460 acres. Although some 

large burns are still conducted, especially in the marshes, it is expected that 

the trend for more burns covering smaller areas will continue. This is especially true as the Refuge continues to restore scrub habitat.

Space Exploration and Its Effect on Prescribed Burning

Many of the constraints and restrictions on prescribed burning on the 

Refuge are common to other fi re programs. Concerns such as safety of fi re- fi ghters and the public, increasing urbanization, fi ckle weather, staffi ng and 

funding shortages that are encountered on other stations are likewise present 

here. In addition to these considerations, this Refuge must deal with an active 

space port. While the Refuge fi re program was evolving, the mission of the 

KSC was also changing. The Apollo and Saturn V programs were phased out 

in the late 1970s and the new Space Transportation System (STS) or Space 

Shuttle program was beginning.

At fi rst, with limited launches and non-sensitive payloads, Shuttle operations 

had little impact on fi re management operations. Burning was prohibited 

forty-eight hours prior to a scheduled launch and twenty-four hours prior to 

landing. Pre-launch concerns included danger while fueling the spacecraft, 

exposure of the orbiter to the elements and increased ground and air traffi c 

just prior to launch. Pre-landing concerns revolved around smoke causing 

visibility problems in the Orbiter’s glide path and anomalies (mishaps) during the landing itself. This soon changed. When KSC was determined to be 

the primary emergency landing site, rather than Edwards Air Force Base in 

California, burning was severely curtailed the entire time the Shuttle was in 

orbit. Although this was ten to fourteen days per space mission, with only 

two to three launches per year, suffi cient burning could still be accomplished. 

However, as the number of launches increased, lost burning opportunities 

became substantial.

Additional constraints were established as plans progressed for the launch of 

the $2.2 billion Hubble Space Telescope (HST) in 1990. Original prescribed 

burning restrictions for the HST called for no burning within 25 miles of 

clean rooms where components of the telescope were being processed. This 

would shut down burning on the entire Refuge for the six to nine months of 

the Hubble’s residency on the KSC. This situation did not bode well for the 

Refuge’s fi re management program. Especially since the HST was the fi rst 

in a series of space-based observatories and other smoke sensitive spacecraft 

that were expected to be launched over the next fi fteen years.

Along with restrictions on burning from space operations on KSC, the 

Refuge had to deal with CCAFS. At CCAFS, each different type of launch 

vehicle had its own set of managers, payload processors, and bureaucracy. 

Additionally, some of the payloads were military missions and much of the 

information about timing was secret. When it came to getting authorization 

to burn, almost anyone in either the KSC or CCAFS chain of command 
USDA Forest Service Proceedings RMRS-P-41. 2006. 745

Fire Management in the Inter Galatic Interface or 30 Years of Fire Management at Merritt Island Florida... Adrian

could trigger a no-go for the fi re. Refuge fi re managers spent countless hours fi elding phone calls, explaining the reasons for burning and begging to get 

permission to execute a burn.

The situation was quickly becoming untenable. There was a time when it 

appeared that all of the issues in force would reduce burning on the Refuge to 

a point where fi re would no longer be a viable tool. It was obvious to all fi re 

knowledgeable people that not burning would lead to a continued increase 

in the amount of very fl ammable vegetation. This would not only lead to a 

serious public safety problem from possible wildfi res, but would also prevent 

effectively managing habitat for the numerous wildlife species found on the 

Refuge. Some way had to be found to provide for the integrity of both the 

space program’s mission, and the purposes and objectives of the Refuge.

Confl ict Resolution

The fi rst step in the resolution process was to educate all of the concerned 

parties about the reasons for burning. The best selling point was the possible impact of severe wildfi res that would occur if the vegetation on the 

Refuge/KSC was not burned on a regular basis. Here we had some help 

from Mother Nature. While the memory of the fi res of 1981 were still vivid, 

burn approvals were relatively easy to obtain. As institutional memory faded, 

approval became more diffi cult. Florida’s bad fi re season in 1998 refreshed 

NASA’s collective memory when fi res shut down operations for almost a week. 

This situation precipitated much discussion as to how fi nd more windows of 

opportunity for burning.

The second factor that helped sell the importance of burning was the 

Endangered Species Act. The Florida Scrub-jay Recovery Plan identifi es the 

Refuge as having one of the four Primary Core Recovery Units (PCRU) for 

the threatened Florida scrub-jay (U. S. Fish and Wildlife Service, 2003). 

In the early 1990s jays were discovered on the site where the Space Station 

Processing Facility was to be constructed. As part of mitigation for continued 

use of this and other areas in scrub-jay habitat, NASA agreed to assist the 

Refuge in restoring overgrown scrub (Schmalzer and others 1994). Since 

burning is a critical component of scrub restoration, this compelled the KSC 

to work more aggressively to fi nd windows for burning.

Along with establishing the need for burning, it was also necessary to 

demonstrate a level of competence in fi re management activities. Although 

the vast majority of prescribed burns nationwide are executed with minimal 

impact to the surrounding areas, the small percentage of burns that do cause 

problems are well documented by the media. This situation can cause concern 

to neighbors when the Refuge announced that a burn is forthcoming. We 

in the fi re community are well aware of the amount of planning, training 

and skill required to carry out a successful prescribed burn. In many cases 

however, those we deal with outside our community are not. In most situations, knowledge helps combat the fear of the unknown. This proved to be 

the case when dealing with NASA managers.

The importance of good communication in solving the problems between 

space operations and Refuge fi re activities cannot be over emphasized. To 

ensure proper information fl ow, meetings were set up with all interested 

parties. In addition to stressing the needs for an active prescribed burning 

program, a presentation on the behind the scenes work that goes on was given. 

The extensive training given to burn bosses, fi ring specialists, air operations 

staff and other key fi re personnel was detailed. The prescription development 
746 USDA Forest Service Proceedings RMRS-P-41. 2006.

Adrian Fire Management in the Inter Galatic Interface or 30 Years of Fire Management at Merritt Island Florida...

process, including smoke screening, environmental parameters, equipment and 

staffi ng needs were explained. It was also pointed out that the Service requires 

that a qualifi ed burn boss of appropriate skill level from outside the Refuge 

review the prescription. At the same time, NASA managers had a chance to 

express their concerns, ask specifi c questions concerning fi re operations and, 

most importantly, meet Refuge fi re managers face to face.

To further establish our credentials, key NASA managers were invited 

to observe burns. They were given the whole burn day experience, from 

the crew briefi ng to the critique at the end of the day. The overall result of 

these discussions and observations was an improved level of confi dence in 

the Refuge’s ability to conduct a successful burn. It was also important not 

to hide anything. All of us that have done any burns know that things can 

go wrong that are beyond our control. The most notable problem is fi ckle 

weather. NASA recognized the need for them to be able to initiate emergency protection measures for sensitive areas, such as clean rooms, should 

this occur.

Once the importance of burning was established, restrictions negotiated 

down to an agreeable level and comfort levels established, the fi nal piece of 

the puzzle was to formulate a comprehensive burn notifi cation process. The 

Space Center’s dispatching offi ce agreed to be the focal point for this endeavor 

through its Joint Base Operations Support Contract (JBOSC) Duty Offi ce. 

In its early stages the Duty Offi ce received the Refuge’s request to execute 

a burn, and then notifi ed telephonically a long list of interested parties. Not 

only was this time consuming, but there was still the problem of almost anyone being able to trigger a no-go situation. Over the years this system was 

improved. Through negotiations with NASA Test Director (NTD), Payload 

Processing, the Center Director and the Commander of the Air Force Station, this list of people that could actually cancel a burn was reduced to less 

than ten. All others on the notifi cation list were only provided information. 

Any concerns had to be forwarded to one of the decision makers. The Duty 

Offi ce also fi elded most of the questions concerning the burn and only passed 

on to Refuge fi re managers those calls they could not handle. The fi nal step 

was to send all correspondence electronically.

Compromises Achieved

The process of education and confi dence building resulted in a compromise 

that was acceptable to all parties. NASA managers recognized that burning is 

an essential part of managing the vegetation types that exist on the Refuge/

Space Center. They also realized that no burning would eventually result in 

unacceptable impacts on both the space program and the environment. On 

the other side, Refuge fi re managers became more aware of the sensitivity of 

spacecraft to smoke and the possible economic and scientifi c impacts should 

damage occur to these craft. Both parties recognized the need for compromise and communication.

Through negotiation, the original 25 mile radius burn prohibition when 

sensitive payloads were present was reduced to a more manageable six miles. 

Burns were allowed while the Orbiter was in space so long as all its systems 

were “nominal” and Edwards Air Force Base was available for emergency 

landings. Lines of communication helped fi nd times in payload processing 

streams where burning could be done with minimum risk to space craft. 

Refuge and NASA managers meet several times a year to discuss upcoming 

operations on both sides that may come into confl ict.
USDA Forest Service Proceedings RMRS-P-41. 2006. 747

Fire Management in the Inter Galatic Interface or 30 Years of Fire Management at Merritt Island Florida... Adrian

A Measurement of Success

The real question is: Did all this effort to fi nd ways of maintaining a prescribed burning program in the middle of an operational space port have 

any measurable results? One way to quantify the results is to determine if 

the effects of wildfi re events in years similar to 1981 were in any way less 

catastrophic. In 1981, there were 40 wildfi res burned a total of 19,335 acres. 

Four fi res were over 1,000 acres in size, with the largest being 6,300 acres. 

The average fi re size was 483.8 acres. There were also two fatalities. When 

1981 is compared to several subsequent severe wildfi re seasons, one can see 

a considerable difference in acres burned and average fi re size as shown in 

table 1.

The fi rst of these seasons occurred in 1992. Several years of below normal rainfall preceded this fi re season, as was the case in 1981. Forty-eight 

wildfi res were ignited during the spring and summer. However, only 1,404 

acres were burned, most of this was in one 1,200 acre fi re which occurred 

when resources were spread thin on a day when several fi res were started. 

The average fi re size was 29.7 acres. There were no injuries to fi refi ghters or 

other personnel, nor were any structures damaged.

Another bad fi re season occurred six years later in 1998, when 25 wildfi res 

burned 5,555 acres. As in 1992, multiple starts exceeded initial attack capabilities and several fi res burned together to account for 4,090 acres of this 

total. The average fi re size was 222.2 acres. While this is much larger than 

the average size in 1992, it is still less than half of what was experienced in 

1981. Again no injuries occurred and no structures were damaged.

The drought that began in 1998 continued through 1999 and 2000. In 

1999, 16 fi res burned a total of 1,219 acres. Once again, one large fi re that 

burned 1,084 acres. The average fi re size this year was 76.2 acres. No injuries 

resulted and no structure damage occurred. By 2000, the drought had abated 

somewhat. More thunderstorms resulted in 25 starts a third again more than 

the previous year. This year only 319 acres were burned, with the biggest fi re 

only amounting to 150 acres. No injuries or structure damage resulted.

The Refuge burns between 15,000 and 20,000 acres in a normal year. 

Even in these strenuous wildfi re seasons a number of prescribed burns were 

completed. It is diffi cult to determine how much of this reduction in acreage 

burned should be attributed to the fuels reduction resulting from prescribed 

burning. Training of personnel and improved equipment certainly played a 

role. However, without the consistent application of prescribed fi re to the 

Refuge’s landscape, more acreage would have been burned by unwanted 

wildland fi re in 1992 and the years of 1998 through 2000. More importantly, the risk to Refuge fi refi ghters suppressing of these fi res would have 

been greater.

Table 1—Comparison of severe fire years at Merritt Island National Wildlife 

Refuge.

 Number Acres Av. Fire Largest Number Acres

Year WF burned size fi re Rx fi res burned

1981 40 19,335 483.8 6,300a 2 3,690

1992 48 1,404 29.7 1,200 8 7,552

1998 25 5,555 222.2 4,090 20 5,605

1999 16 1,219 76.2 1,084 19 2,380

2000 24 319 13.3 150 25 7,414

a Four fi res were over 1,000 acres.
748 USDA Forest Service Proceedings RMRS-P-41. 2006.

Adrian Fire Management in the Inter Galatic Interface or 30 Years of Fire Management at Merritt Island Florida...

Conclusions

Carrying out an prescribed fi re program on Merritt Island National Wildlife 

Refuge presents some unique challenges. The dialogue between Refuge fi re 

managers and the various components of the Nation’s space program is an 

ongoing process. As the space program changes, new points of confl ict will 

arise and new ways to meet the objectives of all the agencies involved must 

be developed.

Managing fi re at Merritt Island National Wildlife Refuge has many unique 

aspects, but many of the confl ict resolution processes described here are applicable in other places. Certainly talking with neighbors and other concerned 

parties is necessary to sell a burning program. It is likewise important for fi re 

managers to learn the specifi c concerns of those who live and work in the 

vicinity of burns. Establishment of communication channels through homeowner associations, the media and personal contact is essential to obtaining 

the support of the community for a burning program. Allow the public to 

see the degree of professionalism that is a part of the burning activities.

It is also important to be honest. No amount of planning, no amount of 

training nor the best forecast in the world can guarantee that nothing will 

go wrong. However, up front discussions of this possibility and the presence 

of a good contingency plan can go far in mitigating a bad situation should 

it occur. Remember, use discretion and care. History has shown that one 

mishap can undo years of successful confi dence building. In spite of all this, 

the experience of the Refuge’s fi re program shows that, with perseverance, 

and initiative, an effective prescribed burning program can be developed 

under diffi cult circumstances.

Literature Cited

Adrian, F. W.; Epstein, M.; Lloyd, R.; Lyons, J. 2006. Draft habitat management 

plan for Merritt Island National Wildlife Refuge. U. S. Fish and Wildlife Service. 

Titusville, FL. 175 p.

Duncan, B. W.; Boyle, S.; Breininger, D. R.; Schmalzer, P. A. 1999. Coupling past 

management practices and historic landscape change on John F. Kennedy Space 

Center. Landscape Ecology 14: 291-309.

Epstein, M.; Blihovde, B. 2006. Listed species of the Merritt Island National Wildlife 

Refuge. U. S. Fish and Wildlife Service. Titusville, Florida. 13 p.

National Weather Service, Melbourne, Fl. 2005. http://www.srh.noaa.gov/mlb/

ltgcenter/ltgmain.html

Robbins, L. E.; Myers, R. L. 1989. Seasonal effects of prescribed burning in Florida: 

a review. The Nature Conservancy. Tallahassee, FL. 119 p.

Schmalzer, P.A. 2003. Growth and recovery of oak-saw palmetto scrub through ten 

years after fi re. Natural Areas Journal 23: 5-13

Schmalzer, P.A.; Foster, T. E.; Duncan, B. W. 2002. Revised fl ora and list of threatened 

and endangered plants of the John F. Kennedy Space Center area, Florida. NASA/

TM-2002-211175. John F. Kennedy Space Center, FL. 75 p.

Schmalzer, P.A.; Adrian, F. W. 2001. Scrub restoration on Kennedy Space Center/

Merritt Island National Wildlife Refuge 1992-2000. In: Zattau, D. ed. Proceedings 

of the Florida scrub symposium 2001. U. S. Fish and Wildlife Service. Jacksonville, 

FL. 17-20.
USDA Forest Service Proceedings RMRS-P-41. 2006. 749

Fire Management in the Inter Galatic Interface or 30 Years of Fire Management at Merritt Island Florida... Adrian

Schmalzer, P. A.; Breininger, D. R.; Adrian, F. W.; Schaub, R.; Duncan. B. W. 1994. 

Development and implementation of a scrub habitat compensation plan for Kennedy 

Space Center. Technical memorandum 109202, Kennedy Space Center, Florida. 

54 p.

Schmalzer, P.A.; Hinkle, C. R.; Mailander, J. L. 1991. Changes in community 

composition and biomass in Juncus roemerianus Scheele and Spartina bakeri Merr. 

marshes one year after a fi re. Wetlands 11: 67-86.

U. S. Fish and Wildlife Service. 2006. Draft comprehensive conservation plan and 

environmental assessment for Merritt Island National Wildlife Refuge. Merritt Island 

National Wildlife Refuge. Titusville, FL, 297 p.

U. S. Fish and Wildlife Service. 2003. Draft revised recovery plan for the Florida scrubjay. U. S. Fish and Wildlife Service. Atlanta, Georgia. 169 pp.

Watts, W. A. and Campbell, L. F. 1988. Environments in Florida in the late Wisconsin 

and Holocene. In: B. A. Purdy ed. Wet site archaeology. The Telford Press, Cadwell, 

NJ. pp 307-323.
USDA Forest Service Proceedings RMRS-P-41. 2006. 751

In: Andrews, Patricia L.; Butler, Bret W., 

comps. 2006. Fuels Management—How to 

Measure Success: Conference Proceedings. 

28-30 March 2006; Portland, OR. 

Proceedings RMRS-P-41. Fort Collins, 

CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research 

Station.

1 Center Manager at the Wildland Fire 

Lessons Learned Center, Tucson, AZ, 

pnasiatka@fs.fed.us

2 Assistant Center Manager at the 

Wildland Fire Lessons Learned Center, 

Tucson, AZ.

Abstract—How can a unit learn in everyday fuels programs and from program reviews? How can a unit move from living in the “report card” culture to discovering 

more effective ways to improve what it knows and how it learns? Six specifi c tasks are 

critical to organizational learning according to David A. Garvin of Harvard Business 

School. By engaging in these tasks a unit can signifi cantly improve both its programs 

and its learning. To further assist fi eld units, an organizational learning survey has been 

recently developed by the Harvard Business School in cooperation with the Lessons 

Learned Center. This tool is designed to measure how a unit learns. By examining the 

learning environment, learning processes and leadership one can measure a unit’s 

level of learning and its improvements over time.

Introduction

Fuels programs around the country are faced with their programs being 

evaluated in periodic program reviews. These reviews often follow a report 

card format rather than a true learning format. This paper is aimed at two 

audiences: fuels programs at the unit level and those who serve on program 

review teams. Unit level fuels programs who take the time to practice the six 

critical tasks of a learning organization and periodically take the learning 

survey should fi nd they are better prepared for program reviews. Program 

reviewers who incorporate the six critical tasks into their reviews and then 

share the unit lessons and effective practices will improve the wildland fi re 

organizational learning environment.

Critical Tasks in Fuels Programs

According to Garvin, a learning organization tries to accomplish six 

tasks:

 1. Collect intelligence about the environment.

 2. Learn from the best practices of other organizations.

 3. Learn from its own experiences and past history.

 4. Experiment with new approaches.

 5. Encourage systematic problem solving.

 6. Transfer knowledge throughout the organization.

Measuring Success in Your Fuels Program: 

From the Report Card to Valuable Learning

Paula Nasiatka1 and David Christenson2
752 USDA Forest Service Proceedings RMRS-P-41. 2006.

Nasiatka and Christenson Measuring Success in Your Fuels Program: From the Report Card to Valuable Learning

These six critical tasks can be directly applied in wildland fi re fuels 

programs:

1) In continually collecting intelligence about the fuels environment, make 

certain to collect critical information and regularly incorporate it into your 

planning and implementation. Search, inquiry and observation are the three 

methods for collecting intelligence. When searching, use comparisons and 

remember to cross-check to validate the accuracy of the information. When 

using the inquiry method, be exploratory by asking open-ended questions. 

Observation is particularly important when a lot of the tacit knowledge at a 

unit is in individuals’ heads. If “we know more than we can tell” than the 

observation method is particularly effective in program reviews. Although 

program reviews generally take place in the off season, everyone can learn 

more effectively if a review is done during a prescribed fi re or wildland fi re use 

event because lessons and effective practices can be more clearly illustrated.

2) Learn from the best practices of other organizations by looking at successful processes other fuels or fi re management programs are using and see how 

they may be applied in your unit. One way to do this is through the Wildland 

Fire Lessons Learned Center’s myfi recommunity.net Web site which serves 

as an online community center for the interagency wildland fi re community. 

The member directory identifi es current projects on which individuals are 

working, particularly in fuels. The neighborhoods are specifi cally designed 

for communities of practice (networks of people) to share knowledge about 

their fi re management programs.

Lessons Learned Center Information Collection Team reports (ICT) are 

another way to learn about the effective practices of other fuels organizations. 

Two recent ICTs have focused on wildland fi re use (WFU) programs both 

from a unit that had its fi rst WFU to a unit with a 35 year history. Both of 

these reports are at: http://www.wildfi relessons.net/ICT.aspx

3) Learn from your own experiences and past history by continually examining your unit’s past performance. Use the After Action Review (AAR) 

process to learn from each project whether it be a mechanical fuels treatment, 

prescribed burn, or WFU. The four questions in an AAR are: 1) What was 

the plan? 2) What actually happened? 3) Why was there a difference? and 4) 

What are we going to do next time? (sustain/improve) To properly use the 

AAR process, it is imperative to take the answers to the fourth question and 

incorporate what will be sustained and improved into short and long-term 

planning. Units that successfully do this actually assign individuals to be 

responsible for incorporating the recommendations into the fuels program 

planning process.

4) Experiment with new approaches that you learn from other fuels programs 

or come from your unit AAR process. Try a different approach especially if 

what you have been doing has not been working the way you want. It is extremely important to listen to unit members who have a different perspective 

and be open to adopting a new idea.

5) Encourage systematic problem solving among all members of your unit. 

Follow a systematic path while trying to solve a problem by looking at what 

was planned, what happened, and why it happened. It is common to try and 

correct a problem without analyzing what happened and why.

6) Transferring knowledge throughout the organization is the true test of 

being a learning organization. Make sure you set aside time during planning 

and information meetings to share new knowledge with your fuels and fi re 
USDA Forest Service Proceedings RMRS-P-41. 2006. 753

Measuring Success in Your Fuels Program: From the Report Card to Valuable Learning Nasiatka and Christenson

management staff as well as other units. The Lessons Learned Center is your 

resource center for sharing what you have learned beyond the scope of your 

own unit. The AAR Rollup is the format for units to record and share their 

lessons and effective practices. The Rollup captures the successes, challenges, 

training curriculum and unresolved issues recommendations. Individual 

units and program reviewers should submit these to the Lessons Learned 

Center. The AAR Rollup form can be found at: http://www.wildfi relessons.

net/AAR.aspx

Organizational Learning Survey

The Lessons Learned Center has been cooperating with Harvard Business 

School as they developed the fi rst of its kind organizational learning survey 

to help individuals and units measure their strengths and weaknesses in relation to the six critical tasks of organizational learning. During the summer 

of 2005, approximately 200 interagency wildland fi re personnel took the 

draft survey online. Members of the wildland fi re community completed it 

as an individual working unit, a wildland fi refi ghting crew, or as an incident 

management team member. Initial results illustrated that the wildland fi re 

community rated well in the sections compared with three other organizations that completed the survey.

The survey tool has three sections:

 1) Learning culture and environment – this includes the interpersonal climate, how differences are valued and the openness to new ideas.

 2) Learning Processes – six processes assessed are experimentation, information collection, analysis, education, training and information transfer.

 3) Leadership – eight different aspects of how managers communicate and 

relate to employees are evaluated.

The survey tool is in its fi nal completion stages and should be online for 

the wildland fi re community and other organizations to use in May 2006. 

Individuals will be able to take the survey and have their scores measured 

against others in the wildland fi re community. From the survey scores, individuals and units can see what areas they are strong or what areas need 

work. Units can then take the survey periodically to further improve their 

fuels programs.

Conclusion

Units can continually improve the learning environment of their fuels 

program by using the six critical tasks of a learning organization. Program 

reviewers can move away from a report card format by incorporating the 

six critical tasks into their reviews. Fuels programs and program reviewers 

should share the knowledge with the Lessons Learned Center so others in 

the wildland fi re community can also learn from them. The organizational 

learning survey will also assist fuels organizations in measuring their effectiveness as a learning organization in comparison with others in the wildland 

fi re community.
754 USDA Forest Service Proceedings RMRS-P-41. 2006.

Nasiatka and Christenson Measuring Success in Your Fuels Program: From the Report Card to Valuable Learning

Acknowledgment

David A. Garvin, Harvard Business School professor and author of the book

Learning in Action: A Guide to Putting the Learning Organization to Work

is credited for the six critical tasks of a learning organization. Acknowledgments also go to David A. Garvin, Francesca Gino and Amy Edmonson of 

Harvard Business School for developing the fi rst of its kind organizational 

learning survey.

References

Garvin, David A. 2000. Learning in Action – A Guide to Putting the Learning 

Organization to Work Boston, MA: Harvard Business School Press.
USDA Forest Service Proceedings RMRS-P-41. 2006. 755

Fire Weather
756 USDA Forest Service Proceedings RMRS-P-41. 2006.
USDA Forest Service Proceedings RMRS-P-41. 2006. 757

In: Andrews, Patricia L.; Butler, Bret W., 

comps. 2006. Fuels Management—How to 

Measure Success: Conference Proceedings. 

28-30 March 2006; Portland, OR. 

Proceedings RMRS-P-41. Fort Collins, 

CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research 

Station.

1 Weather Research and Consulting 

Ser vices, LLC, Battle Ground, 

Washington. pwerth@prodigy.net

Abstract—Projections of fi re season severity that integrate historical weather and fi re 

information can be used by fi re managers when making decisions about allocating and 

prioritizing fi refi ghting resources. They enable fi re managers to anticipate fi re activity 

and pre-position resources to maximize public and fi refi ghter safety, reduce environmental impacts, and lower fi refi ghting costs. This research determines the potential 

severity of fi re seasons in the Pacifi c Northwest by using statistical techniques that 

correlate weather data and annual-acreage-burned fi gures for fi ve fi re management 

agencies in Washington and Oregon (U.S. Forest Service, Bureau of Land Management, 

Bureau of Indian Affairs, Oregon Department of Forestry, and Washington Department 

of Natural Resources). Weather and fi re trends for the 1970 to 2004 time period were 

calculated, and thresholds for above average, average, or below average fi re seasons 

were determined based upon annual acres burned. Eight weather parameters were 

then correlated using scatter diagrams, contingency tables, and multivariate regression equations to predict above average, average, or below average fi re seasons based 

upon projected acres burned. Results show considerable variance in predictors by fi re 

agency with accuracy rates of 60 to 85% for predictions of above average fi re seasons 

and 85 to 90% for average and below average fi re seasons.

Introduction

Several considerations affect fi re managers’ decisions regarding allocation of fi refi ghting resources including: (1) public and fi refi ghter safety (2) the 

potential effect of fi res on local environments, and (3) the increasing impact of 

fi refi ghting costs on agency budgets. Over the past several years, the Northwest Interagency Coordination Center has demonstrated that pre-positioning 

resources throughout Washington and Oregon in advance of fi re outbreaks, 

improves their effectiveness in achieving all three of the above-listed goals. 

The obvious question arises, “How do fi re managers determine the most 

effective placement of resources prior to the fi re season?” One tool they use 

is a pre-season assessment of historical weather and fi re information that 

produces projections of expected fi re season severity for any given area in 

the Pacifi c Northwest. This research takes that assessment to the next level 

by applying statistical techniques to weather and annual acres-burned data 

for fi ve, fi re management agencies in Oregon and Washington, including the 

U.S. Forest Service, Bureau of Land Management, Bureau of Indian Affairs, 

Oregon Department of Forestry, and Washington Department of Natural 

Resources.

Predicting Fire Season Severity in the 

Pacifi c Northwest

Paul Werth1
758 USDA Forest Service Proceedings RMRS-P-41. 2006.

Werth Predicting Fire Season Severity in the Pacifi c Northwest

Data

The assumption that wildland fi re severity is primarily driven by low fuel 

moisture has historically directed research towards drought (Westerling and 

others 2002; Hall and Brown 2003) as a pre-season, predictor of fi re season 

severity. This research also uses drought, but expands the list of potential 

predictors to include: seasonal precipitation, mountain snowpack, snowmelt 

date, and the sea surface temperature of the Pacifi c Ocean.

Monthly precipitation fi gures for seven weather stations in Washington and 

Oregon were used in this analysis. The seven stations used were Medford, 

Portland, Redmond, Burns and Pendleton in Oregon, and Yakima and 

Spokane in Washington (fi g. 1) They were selected based on their location 

near fi re-prone areas and completeness of record since 1970. Monthly precipitation data was divided into four groups: (1) winter (November-March), (2) 

spring (April-May), (3) June, and (4) summer (July-August). June is a group 

by itself because precipitation during the month of June can signifi cantly 

impact the duration of signifi cant fi re danger.

Snow pack water equivalency (SWE) data for the Columbia River Basin of 

Washington, Oregon, Idaho and portions of British Columbia, Montana, and 

Wyoming was also used in this analysis. The April 1 SWE is of particular importance because the snowpack typically peaks around April 1st. SWE fi gures 

for May 1st were used to determine the rate of spring snowmelt in the mountains. SWE data was used to track the annual snowmelt date at 39 Natural 

Resources Conservation Service (NRCS) SNOTEL sites in Washington and 

Oregon from 1986 to 2005 (fi g. 2). These sites represent every major river 

basin and different elevations within Washington and Oregon.

Historic Palmer Drought Severity Index (PDSI) values for climate zones in 

Washington and Oregon were collected from the National Climatic Data Center 

(NCDC) database. Average March values for each state along with the number 

of climate zones classifi ed in moderate drought were used in this research.

Monitoring sea surface temperature anomalies in the central Pacifi c Ocean 

is essential in determining the phases of the El Niño / Southern Oscillation 

(ENSO). The warm phase, commonly called El Niño, is characterized by 

abnormally warm sea surface temperatures in the central and eastern equatorial Pacifi c Ocean. The cool phase of this natural cycle is called La Niña. 

El Niño often results in warm, dry winters and below normal snow packs in 

the Pacifi c Northwest. La Niña has the opposite effect, producing cool, wet 

winters and above average snow packs. Both phases appear to have minimal 

effect on summer weather in the Pacifi c Northwest. The Multivariate ENSO 

Index (MEI) combines six variables (sea-level pressure, zonal and meridianal 

components of the surface wind, sea surface temperature, surface air temperature, and total cloudiness fraction of the sky) to monitor ENSO. Negative 

values of the MEI represent the La Niña phase while positive values indicate 

El Niño. Bi-monthly values of MEI were retrieved from the NOAA-CIRES 

Climate Diagnostics Center in Boulder, Colorado.

The Eastern North Pacifi c (ENP) (fi g. 3) sea surface temperature index is 

a component of the Pacifi c (P) index (Castro, McKee, and Pielke 2001) that 

combines tropical and North Pacifi c SSTs into one index. The P index has 

been correlated with upper-level atmospheric circulation patterns over the 

North Pacifi c Ocean and the Western and Central United States. It has also 

been correlated to the onset of the Southwest Monsoon and precipitation 

anomalies in the Great Plains states. Data to compute the ENP was downloaded from the Comprehensive Ocean Atmospheric Dataset (COADS).
USDA Forest Service Proceedings RMRS-P-41. 2006. 759

Predicting Fire Season Severity in the Pacifi c Northwest Werth

Figure 1—Seasonal Precipitation Stations. Figure 2—WA and OR SNOTEL Stations.

Figure 3—ENP and NINO 3.4 Pacifi c SST Regions.
760 USDA Forest Service Proceedings RMRS-P-41. 2006.

Werth Predicting Fire Season Severity in the Pacifi c Northwest

Area-burned fi gures (in acres) for federal- and state-protected land in 

Washington and Oregon was obtained from the Oregon Department of 

Forestry (ODF), Washington Department of Natural Resources (DNR), 

and the Northwest Interagency Coordination Center annual summaries 

dating back to 1970. The acres burned statistics include both lightning and 

human-caused fi res.

Weather and Area Burned Trends

The fi rst step in determining the signifi cance of seasonal precipitation on 

fi re season severity in the Pacifi c Northwest is to determine whether there 

are long-term trends in both weather and fi re data. This was accomplished 

by constructing time lines for each dataset and then performing a regression analysis to determine whether there are identifi able trends in the data. 

Linear regression equations were developed for each data set in the form of: 

y = mx + b. The equation algebraically describes a straight line for a set of 

data with (x) the independent variable, (y) the dependent variable, (m) the 

slope of the line, and (b) the y-intercept. The sign (+ or –) and magnitude of 

m signify whether the independent variable is increasing or decreasing and 

at what rate.

Regression analysis indicates decreasing winter rainfall (November-March) 

and Columbia River Basin April 1 SWE since 1970 (fi gs. 4 and 5). The decrease is more apparent in SWE, indicating warmer winter temperatures are 

also a contributor in addition to decreased precipitation. However, the trend 

in spring rainfall (April and May) is for wetter conditions (fi g. 6). Rainfall 

amounts for July and August also show a trend toward drier weather during 

the summer in the Pacifi c Northwest (fi g. 7).

Similar regression techniques were used to establish trends in acres burned 

for federal and state land management agencies in Washington and Oregon. 

All agencies trend toward more acres burned per year, especially since the 

mid-1980s. This is most evident in the U.S. Forest Service data (fi g. 8), which 

shows the largest trend in acres burned of all the agencies.

Defi ning Fire Season Severity

Defi ning fi re season severity is a diffi cult question, one that may have many 

answers. Some base it on the total number of fi res or the number of days 

in high to extreme fi re danger; others use the number of large fi res during 

the year. In order to predict fi re season severity, one must fi rst defi ne it. The 

standard used in this research is the annual acres burned by fi re agency. The 

dataset includes thirty-fi ve years of annual acres burned by agency from 1970 

to 2004. Data was sorted by agency and by year from the highest to the least 

number of acres burned. Data was then divided into thirds, or terciles. Years 

in the top tercile, (i.e., those with the largest number of acres burned,) were 

classifi ed as “Above Average” fi re seasons. Years in the middle tercile were classifi ed as “Average” fi re seasons, and years in the bottom third were classifi ed 

as “Below Average” fi re seasons. This classifi cation was performed for each 

of the fi ve federal and state fi re agencies. Threshold acres were identifi ed for 

each category as displayed in this graph for the Bureau of Land Management 

(BLM) (fi g. 9).
USDA Forest Service Proceedings RMRS-P-41. 2006. 761

Predicting Fire Season Severity in the Pacifi c Northwest Werth

Figure 4—Winter Precipitation Trend.

Figure 5—April 1 Columbia Basin Snowpack Trend.
762 USDA Forest Service Proceedings RMRS-P-41. 2006.

Werth Predicting Fire Season Severity in the Pacifi c Northwest

Figure 7—Summer Rainfall Trend.

Figure 6—Spring Rainfall Trend.
USDA Forest Service Proceedings RMRS-P-41. 2006. 763

Predicting Fire Season Severity in the Pacifi c Northwest Werth

Figure 9—Sorted BLM Acres Burned and Severity Thresholds.

Figure 8—USFS Acres Burned Trend.
764 USDA Forest Service Proceedings RMRS-P-41. 2006.

Werth Predicting Fire Season Severity in the Pacifi c Northwest

Analysis Methods

Various statistical techniques were used to determine which variables would 

be the best predictors of fi re season severity. Polynomial regression analysis 

was used to create multivariate forecast equations. Graphical regression was 

also used in conjunction with contingency tables. All analysis was performed 

using Microsoft Excel.

Multivariate Equations

The fi rst step in this process was to identify which variables (seasonal precipitation, snow pack SWE, spring snowmelt date, March PDSI, and Pacifi c 

SSTs) were the best predictors of acres burned for each agency. Each variable 

was ranked from best to worst based on its correlation (R-squared value) 

with acres burned. Table 1 displays the rankings of each variable by agency. 

Overall, summer rainfall (July/August) was the best predictor, with March 

PDSI, April 1 SWE, and May 1 SWEs a close second. There were considerable differences in the predictor rankings by agency.

However, even the best predictors did not do a good job of forecasting acres 

burned alone. Much better results were achieved when all the variables were 

used. This was accomplished by creating multivariate (multiple regression) 

equations unique to each fi re agency using all the variables. Each variable 

was “weighted” according to its correlation factor. The equation forecasting 

acres burned took the form y=a1(m1x1

2+n1x1)+...+an(mnxn2+nnxn)+b, where 

(y) is the dependent variable (acres burned), (x1) through (xn) the independent 

variables, (a1) through (an) are variable weighting factors, (m1,n1) through 

(mn,nn) are coeffi cients of each independent variable, and (b) a constant.

The resulting equation predicts acres burned by fi re agency using either 

observed or forecasted values as input for each independent variable.

Scatter Diagrams and Contingency Tables

A second method of predicting acres burned is the utilization of scatter 

diagrams and contingency tables. This technique plots one variable against 

the other (i.e., April 1 SWE versus Spring Precipitation) on an x-y scatter 

diagram, and then labels the intersection of those two variables as either 

an “Above Average” fi re season or not. In this manner, threshold values for 

each variable can be constructed, dividing the diagram into “YES - high 

probability” or “NO - low probability” risk areas of fi re season severity (fi g. 

10). The results from multiple scatter diagrams, correlating a selection of 

variables, are then input into a 2-way YES / NO contingency table (fi g. 11) 

that predicts the probability of an “Above Average” fi re season and the range 

of acres burned in similar years dating back to 1970.

Table 1—Correlation Factor Ratings by Agency.

Parameters USFS BLM BIA ODF WDNR Ave Rank

Winter Rain 9 7 8 3 3 6.00

Apri1 1 Snowpack 5 3 5 4 5 4.40

Spring Rain 4 5 2 5 7 4.60

June Rain 3 7 4 8 8 6.00

Summer Rain 1 1 1 5 1 1.80

March PDSI 6 6 6 1 2 4.20

April ENP 2 7 9 7 9 6.80

Snowmelt Date 6 3 2 9 4 4.80

May 1 Snowpack 6 2 6 2 5 4.20
USDA Forest Service Proceedings RMRS-P-41. 2006. 765

Predicting Fire Season Severity in the Pacifi c Northwest Werth

Figure 10—WA DNR May SWE vs June Rain.

Figure 11—USFS YES/NO Contingency Table.
766 USDA Forest Service Proceedings RMRS-P-41. 2006.

Werth Predicting Fire Season Severity in the Pacifi c Northwest

Results

The combination of scatter diagrams, contingency tables, and multivariate 

equations produces the following outputs used to predict the severity of fi re 

seasons in the Pacifi c Northwest:

 • a defi nition of fi re season severity (above average, average, below average) 

based on acres burned by fi re agency,

 • the projected acres burned for the coming fi re season, and

 • the probability of an “Above Average” fi re season.

The program is based on thirty-fi ve years of weather and fi re data (1970 to 

2004). The relatively small number of data points is near the minimum needed 

to draw confi dence in the statistical analysis. However, signifi cant changes 

in fi refi ghting strategy, resource availability, and wildland fuel regimes over 

the years produce additional uncertainty if data from years prior to 1970 is 

included. Thus, the current evaluation of how well the program performs is 

based upon “dependent” rather than ‘independent” data. Statistics in future 

years will be able to provide more relevant verifi cation.

Accuracy rates indicate the program will produce correct forecasts of fi re season 

severity in Washington and Oregon in 70 to 85% of the years on which the data 

was based. A forecast of an “Above Average” fi re season should verify correctly 

60 to 85% of the time, and a forecast of “average” or “below average” 85 to 90% 

of the time. There appear to be better accuracy rates in predicting acres burned 

in timber fuels compared to grass / brush fuels, which isn’t surprising when 

considering the sensitivity of fi re spread rates in grass fuels.

2006 Northwest Fire Season

Early projections of 2006 fi re season severity in Washington and Oregon 

are based on correlations with past fi re seasons and the following factors: weak 

La Niña conditions, a wet winter, lack of drought, an above normal snowpack, 

and projected late spring snowmelt dates. Additional assumptions are that 

spring and summer will experience “near normal” or “typical” precipitation 

patterns (i.e., periodic rains through June, followed by dry weather during 

July and August) and there will be an average amount of lightning.

Considering the above factors, it is highly unlikely that Washington and 

Oregon will experience a severe fi re season in 2006. However, the threat of 

large fi res will vary considerably by fuel type. Forest fuels in the mid and 

higher elevations of the Cascade and Blue Mountains will have the lowest 

probability of sustaining large fi re growth. The threat of large fi res will be 

the highest in grass fuels, primarily in the “High Desert” of central and 

southeastern Oregon. Other locations that may experience a greater chance of 

large fi res are the pine forests along the lower eastern slopes of the Cascades 

and the lower slopes of the Blue Mountains, where grass is the primary carrier of fi re. Table 2 displays the severity forecast for each of the fi ve federal 

and state agencies, as well as the projected acres burned.

In general, western Washington and western Oregon, including the crest of 

the Cascades, will likely see a Below Average fi re season. Eastern Washington 

can expect an Average fi re season. Eastern Oregon may also see a Below 

Average fi re season in the Klamath Basin and most of the Blue Mountains. 

Central and southeastern Oregon are projected to experience an Average to 

Above Average fi re season (fi g. 11).
USDA Forest Service Proceedings RMRS-P-41. 2006. 767

Predicting Fire Season Severity in the Pacifi c Northwest Werth

Figure 12—Northwest Fire Season Severity.

Table 2—Projected 2006 Acres Burned by Agency.

 Threshold acres

 2006 Probability of an Projected burned for an

 Fire above average 2006 acres above average

Agency season fi re season burned fi re season

USFS Average to 10% 25,000 to 120,000 acres

 below average 50,000 acres

BLM Average to 40% 50,000 to 90,000 acres

 above average 90,000 acres

BIA Average to 30% 10,000 to 20,000 acres

 above average 20,000 acres

ODF Average to 10% 5,000 to 14,000 acres

 below average 9,000 acres

WADNR Average to 10% 4,000 to 10,500 acres

 below average 9,000 acres
768 USDA Forest Service Proceedings RMRS-P-41. 2006.

Werth Predicting Fire Season Severity in the Pacifi c Northwest

Summary and Conclusions

Potential fi re season severity in the Pacifi c Northwest is projected using 

statistical techniques correlating weather data and annual-acreage-burned 

fi gures for fi ve fi re management agencies in Washington and Oregon. Weather 

and fi re trends for the period 1970 to 2004 are calculated. Thresholds for 

above average, average, or below average fi re seasons were determined based 

on annual acres burned. Eight weather parameters were correlated using 

scatter diagrams, contingency tables, and multivariate regression equations 

to predict above average, average, or below average fi re seasons based on 

projected acres burned. Future modifi cations to this research may include 

replacing existing variables with new and better variables, and the development 

of equations that predict fi refi ghting costs and resource needs. Although this 

research is specifi c to the Pacifi c Northwest, the concept of using multiple 

predictors to forecast fi re season severity is adaptable to other areas, nationally and internationally.

Acknowledgments

The author thanks Brian Potter, AirFIRE Team Seattle WA, USDA Forest Service; Tony Westerling, University of California, San Diego; and John 

Werth, National Weather Service for their helpful comments and suggestions 

in the review of this paper.

References

Castro, C. L.; McKee, T. B.; Pielke Sr., R. A. 2001. The Relationship of the North 

American Monsoon to Tropical and North Pacifi c Sea Surface Temperatures as 

Revealed by Observational Analysis. J. Climate. 14: 4449-4473.

Hall, B. L.; Brown, T. J. 2003. A Comparison of Precipitation and Drought Indices 

Related to Fire activity and Potential in the U.S. AMS Fifth Symposium on Fire 

and Forest Meteorology. J11.3.

Westerling, A.L.; Gershunov, A.; Cayan, D. R.; Barnett, T. 2002. Long Lead 

Statistical Forecasts of Area Burned in Western U.S. Wildfi res by Ecosystem 

Province. International Journal of Wildland Fire. 11: 257-266.
USDA Forest Service Proceedings RMRS-P-41. 2006. 769

In: Andrews, Patricia L.; Butler, Bret W., 

comps. 2006. Fuels Management—How to 

Measure Success: Conference Proceedings. 

28-30 March 2006; Portland, OR. 

Proceedings RMRS-P-41. Fort Collins, 

CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research 

Station.

1USDA Forest Service, North Central 

Research Station, East Lansing, MI. 

jcharney@fs.fed.us

Abstract—This paper presents an assessment of fi re weather and fi re behavior predictions produced by a numerical weather prediction model similar to those used by 

operational weather forecasters when preparing their forecasts. The PSU/NCAR MM5 

model is used to simulate the weather conditions associated with three fi re episodes in 

June 2005. Extreme fi re behavior was reported across the Southwest, Great Basin, and 

Southern California Incident Areas during this time period. By comparing the simulation 

results against reports of extreme fi re behavior, the ability of the model to differentiate 

between the three episodes is assessed, and relationships between weather conditions 

and extreme fi re behavior are suggested. The results of these comparisons reveal that 

the most extreme fi re behavior occurred in locations where near-ground temperatures 

were the highest. While relative humidity did not vary substantially across the three 

episodes, variations in temperature led to a greater potential for evaporation and fuel 

drying, which could have been a factor in the observed extreme fi re behavior. Additional analyses reveal that the diurnal variations in mixed layer processes also explain 

some of the variability in fi re behavior in the episodes.

This paper represents a step towards realizing the full potential of atmospheric 

physics models for fi re weather and fi re behavior forecasting. As researchers and 

operational personnel come to understand the relationships between fi re behavior 

and atmospheric processes that can be predicted by weather forecast models, these 

concepts can be tested in the broader context of day-to-day fi re weather forecasting. 

Eventually, these techniques could provide additional information for the fi re weather 

forecasters and fi re managers, using tools that are already available and used routinely 

in weather forecast offi ces.

Introduction

The fi re weather tools that are currently employed in National Weather 

Service (NWS) forecast offi ces are typically the product of empirical studies that were designed to establish statistical relationships between certain 

types of fi re danger or fi re behavior and observed weather conditions (see 

e.g. Fosberg 1978, Lavdas 1986, Haines 1988). As these indices were being 

developed by the fi re weather community, the broader atmospheric science 

community was more focused on severe storms and hurricane research, and 

developed tools such as radar and high-resolution numerical weather prediction (NWP) models to aide in those research endeavors. As the research 

evolved, these tools became intrinsic to the operational weather forecasting 

process, and are now used every day throughout the world for forecasting 

extreme weather events.

Until very recently, these same tools were seldom if ever used as part of 

NWS fi re weather forecasting, nor were they applied in research projects 

Employing Numerical Weather Models to 

Enhance Fire Weather and Fire Behavior 

Predictions

Joseph J. Charney1 and Lesley A. Fusina
770 USDA Forest Service Proceedings RMRS-P-41. 2006.

Charney and Fusina Employing Numerical Weather Models to Enhance Fire Weather and Fire Behavior Predictions

trying to improve our understanding of fi re-atmosphere interactions. While 

radar observations have limited application for fi re weather forecasting, 

beyond determining when and where precipitation is and will soon occur, 

NWP models can provide temporally and spatially detailed information about 

numerous aspects of fi re-weather that could directly or indirectly impact fi re 

behavior. Researchers can employ these models to establish physical relationships between weather phenomena and observed fi re behavior, rather than 

relying upon empirical and statistical relationships whose broad applicability 

is questionable (Potter 2002). These physical relationships lead to the development of new fi re weather indices and diagnostic techniques (Charney 

and Keyser, 2003) that can, in turn, be passed on to operational fi re weather 

forecasters for use in day-to-day fi re weather forecasting. Fire weather forecasters can then implement these new tools to analyze output from existing 

NWP models, enabling them to provide guidance to fi re managers making 

decisions that pertain to prescribed burn planning and ignition, as well as 

wildfi re decision support that can help save lives and property.

This paper will examine the performance of an NWP model during three 

periods of June, 2005: June 17-18 (hereafter referred to as Episode 1), June 

23-24 (Episode 2), and June 27-28 (Episode 3), during which very high to 

extreme fi re indices were reported in Arizona, New Mexico, and Nevada. 

Despite the extreme fi re indices, reports of extreme fi re behavior varied 

considerably across the three episodes. We hypothesize that variations in 

weather conditions during these periods can help explain the variability in 

observed fi re behavior. In section 2, we will detail the observed fi re behavior 

reports. Section 3 will discuss the NWP model employed to study the weather 

conditions during the three periods identifi ed above. Section 4 will present 

the fi re-weather predictions from the NWP model, and discuss relationships 

between the simulated weather conditions and the observed fi re behavior. 

Section 5 will include discussion and concluding statements.

Observed Fire Behavior

In May and June 2005, extreme fi re indices were reported in the National 

Interagency Coordination Center (NICC) Incident Management Reports 

across the Southwest, Southern California, and Eastern Great Basin Incident 

Areas (see e.g. http://iys.cidi.org/wildfi re/ for archived NICC Incident 

Management Reports). This extended period of extreme fi re indices was 

associated with numerous fi res during the period. For the purposes of this 

study, we choose to focus our attention on three periods in the last two 

weeks of June, during which particularly extreme fi re behavior was reported, 

including rapid spread rates, crown fi res, spotting and torching, and fl ame 

lengths of 50 to 80 feet.

Episode 1 occurred on June 17-18 (Fig. 1a). In the areas of interest, 

extreme fi re indices were reported. Three large fi res were reported in New 

Mexico and Arizona, two of which were designated as Wildland Fire Use 

(WFU) fi res. The non-WFU fi re in Arizona reported active but not extreme 

fi re behavior.

Episode 2 occurred about a week later, on June 23-24 (Fig. 1b). During 

this time period, eight large fi res were reported across central Arizona and 

more than twelve fi res were active across southern California, southern Nevada, northwestern Arizona, and southwestern Utah. Extreme fi re behavior 

was observed at all of these fi res. The central Arizona fi res were reported to 
USDA Forest Service Proceedings RMRS-P-41. 2006. 771

Employing Numerical Weather Models to Enhance Fire Weather and Fire Behavior Predictions Charney and Fusina

Figure 1—Locations of NICC Incident Management Reports of extreme fi re behavior 

during large fi re incidents on a) 17-18 June, 2005, b) 23-24 June, 2005, and c) 27-28 June, 

2005. Red dots indicate wildfi re incidents and green dots indicate large fi res designated 

as Wildland Fire Use fi res.
772 USDA Forest Service Proceedings RMRS-P-41. 2006.

Charney and Fusina Employing Numerical Weather Models to Enhance Fire Weather and Fire Behavior Predictions

exhibit plume-dominated behavior, with active running and crowning, and 

rapid uphill rates of spread. Additionally, there was one report of thunderstorm activity in the vicinity of the fi re generating downdrafts that impacted 

fi re behavior. The fi res in California, Nevada, and Utah exhibited extreme 

rates of spread and fl ame lengths. Gusty winds, fl ashing fuels, crowning, and 

dry thunderstorm outfl ow boundaries also inhibited fi refi ghting activities in 

the area.

Episode 3 occurred three days later on June 27-28 (Fig. 1c). On these dates, 

the fi res in central Arizona had slowed considerably, such that few reports 

of extreme fi re behavior were submitted. Rapid spread rates, downdrafts 

from dry thunderstorms, and isolated torching were reported in California, 

Nevada, and Utah.

Overall, the reported fi re behavior can be characterized as moderate to high 

in isolated areas during Episode 1, high to extreme across the region with 

very large fl ame lengths and running fi res during Episode 2, and decreasing 

intensity with localized incidents of extreme fi re behavior during Episode 3. 

It should be noted, however, that situation reports fi led during and after large 

fi re incidents do not accurately represent all of the variations in fi re behavior 

across the region. It is quite probable that extreme fi re behavior occurred 

on smaller fi res that either went unobserved or unreported. The purpose of 

this study is to determine if the extreme fi re behavior that was reported can 

be explained by changes in the weather conditions at those locations and 

across the region.

Numerical Weather Predictions

The variability in fi re behavior reported during the three episodes could 

have been caused by a wide variety of mechanisms, including local terrain 

infl uences (e.g. north vs. south facing slopes), fuel moisture and fuel type, 

and varying weather conditions. Given that fi re indices were reported as 

extreme throughout the period, and that fuel conditions are an important 

component of the fi re indices, we assume for the purposes of this study that 

differences in fuel conditions were not the main reasons for the differences 

in observed fi re behavior. Information is not readily available on all of these 

fi res concerning the specifi cs of the local terrain. Thus, we propose to explore 

whether variations in weather conditions both at the ground and aloft can 

help explain the differences in observed fi re behavior. We explore this question 

by using an NWP model. An NWP model is a physical atmospheric model 

that employs equations describing spatial and temporal variations in weather 

conditions at the ground and aloft to predict future weather conditions. An 

NWP model is initiated with observations that characterize the current state 

of the atmosphere, and then predicts the future weather from that observed 

state. NWP models allow weather forecasters to forecast the weather with 

some degree of accuracy multiple days in advance.

An NWP model can also be used to simulate the weather conditions of 

events in the past, using the observations from that time to initiate the model 

and then simulating the evolution of the weather conditions throughout the 

event. The main advantage of this technique is that the NWP models generate 

much more information about the weather conditions at the ground and aloft 

than can readily be observed. In the vicinity of a fi re and across the region, 

this information can be analyzed to try to understand how the atmospheric 

conditions simulated by the NWP model might have impacted the fi res.
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Employing Numerical Weather Models to Enhance Fire Weather and Fire Behavior Predictions Charney and Fusina

The NWP model employed for this study is referred to as the Penn State 

University/National Center for Atmospheric Research Mesoscale Model version 5.3 (MM5) (Grell et al., 1995). This model has been developed over the 

last thirty years by the meteorological research community, and is one of the 

most widely used “mesoscale” models in the world. A mesoscale model is an 

NWP model that is designed to simulate the weather conditions across an 

area roughly 1/2-1/4 the size of the United States and resolve the detailed 

fl ows associated with thunderstorms, fronts, and other local weather phenomena. As indicated in the Introduction, these models are used routinely 

by NWS (and other) forecasters to produce forecasts of severe storms and 

precipitation systems.

We have employed the MM5 as a research tool to simulate the weather 

conditions associated with the three episodes defi ned in the previous section. 

Separate simulations were performed for the three episodes, such that hourly 

weather conditions at the ground and aloft were generated from 0000 UTC 

on the fi rst day of each episode and continuing for 48 hours. Model output is 

generated in the form of a 3-dimensional cube of weather data (temperature, 

winds, humidity, clouds, rain, sunlight, etc) which can then be analyzed in 

detail. This output is then analyzed to produce horizontal maps and time 

series at specifi c locations.

Fire Weather Predictions

The model results for the three episodes indicate similarities that would 

be expected considering the season and the region, while also revealing some 

notable differences between the episodes. The surface weather conditions 

were very hot and dry throughout the three episodes, as one would expect 

climatologically. Figure 2 shows the surface relative humidity (RH) and wind 

speed and direction for episodes 1, 2, and 3. It is noteworthy that while there 

are variations in RH and wind speeds across the three episodes, the variations are not particularly noteworthy. The RH in central Arizona, southern 

California, and southern Nevada vary from between about 10-15%. While 

these are very low RH values, particularly for a model that is known to often 

overestimate RH, differences of this magnitude would not by themselves 

explain the observed differences in fi re behavior. Similarly, the simulated 

wind speeds across the region were moderately high, with speeds of about 

15 mph commonly occurring, but do not indicate pronounced variations 

among the episodes.

One of the huge advantages of working with NWP model output instead 

of observations is that the weather conditions aloft are as straightforward to 

generate as surface weather conditions. Thus, the model includes information 

about the diurnal evolution of the mixed layer for each of the episodes. This 

enables us to analyze the weather conditions in the layers of the atmosphere 

that are most likely to interact with a fi re, rather than focusing almost exclusively on surface weather conditions. Figure 3 shows mixed-layer averaged 

temperatures for the three episodes. Clearly, the mixed-layer air in the areas 

where extreme fi re behavior was reported was considerably warmer in Episode 

2 than in Episode 1. However, this increase in temperature did not manifest 

as a pronounced change in RH. RH is often used by fi re weather forecasters 

and fi re managers to anticipate when the atmosphere will contribute to fuel 

drying and, by association, more extreme fi re behavior. But RH is dependant 

upon temperature, such that a 20% RH at 30°C indicates a different impact 
774 USDA Forest Service Proceedings RMRS-P-41. 2006.

Charney and Fusina Employing Numerical Weather Models to Enhance Fire Weather and Fire Behavior Predictions

Figure 2—Simulated a) surface relative humidity and b) surface wind speed and direction 

for 2100 UTC 17 June, 2005. c) and d) are the same as a) and b) for 2100 UTC 23 June, 

2005. e) and f) are the same as a) and b) for 2100 UTC 27 June, 2005.

a

b
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c

d
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Charney and Fusina Employing Numerical Weather Models to Enhance Fire Weather and Fire Behavior Predictions

e

f
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Employing Numerical Weather Models to Enhance Fire Weather and Fire Behavior Predictions Charney and Fusina

Figure 3—Simulated mixed-layer averaged temperature for: a) 2100 UTC 17 June, 2005, b) 2100 UTC 

23 June, 2005, c) 2100 UTC 27 June, 2005.

a

b
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Charney and Fusina Employing Numerical Weather Models to Enhance Fire Weather and Fire Behavior Predictions

c

on fuels than a 20% RH at 40°C. A more defi nitive quantity for the potential impact of humidity on fuel drying is the vapor pressure defi cit (VPD), 

which indicates how much water vapor can be evaporated into a volume of 

air regardless of the temperature. Figure 4 shows mixed-layer averaged VPD 

for the three episodes. The VPD varies from around 3500 Pa in Episode 1 to 

about 6000 Pa in Episode 2 along the Arizona/California/Nevada border, 

which corresponds to an increase of over 70%. This sort of difference would 

be expected to have a noticeable impact on fuel moistures during a fi re.

An NWP model also enables the analysis of fi re-weather conditions at an 

arbitrary location in a region. When using an NWP model, a fi re weather 

forecaster or fi re manager can obtain weather data that is locally valid even 

when a weather station is not nearby. By combining this aspect of NWP data 

with the availability of weather data aloft at every location within the model 

area, new insights can be obtained into the diurnal evolution of weather 

conditions throughout the day.

The traditional classifi cation of fi re as surface, ground, or crown relates 

the fi re’s characteristics to fuel. Just as fuel in these three layers has different 

characteristics that infl uence the fi re’s behavior, the atmosphere is not the 

same at all heights. As a fi re grows, and its plume deepens, air from higher 

levels descends to interact with the fi re and fuels (Fig. 5). If that air is drier, 

hotter, or windier than air at the ground, it may cause dangerous and unexpected changes in the fi re’s behavior such as torching, runs, or spotting. 

Looking at the air that is infl uencing fi re behavior at a particular time, we 
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Employing Numerical Weather Models to Enhance Fire Weather and Fire Behavior Predictions Charney and Fusina

Figure 4—Simulated mixed-layer averaged vapor pressure defi cit for: a) 2100 UTC 17 June, 2005, 

b) 2100 UTC 23 June, 2005, c) 2100 UTC 27 June, 2005.

a

b
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c

Figure 5—Conceptual diagram of the 3-layer model showing potential interactions between 

a fi re and layers of the atmosphere.
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Employing Numerical Weather Models to Enhance Fire Weather and Fire Behavior Predictions Charney and Fusina

ask three questions: 1) what type of air infl uences a fi re while it is forming a 

plume, 2) what type of air infl uences a fi re right after it ignites, and 3) what 

type of air infl uences a fi re that has established an identifi able plume? These 

questions lead to a conceptual model which we refer to as the three-layer 

model (Potter, 2002; Charney et al., 2005), in which we employ the NWP 

model to calculate weather variables at the ground, averaged throughout the 

mixed layer, and averaged from the ground to a point 500 m above the mixed 

layer. By looking at how these quantities vary at a point through the day, the 

impact of mixed-layer processes on surface conditions can be diagnosed and, 

in some cases, predicted hours or even days in advance.

Figure 6 shows time series of 3-layer model quantities for Episodes 2 and 

3 for a point in extreme southern Nevada. It is noteworthy that when the 

mixed-layer starts to grow during the daytime, the wind speed at the ground 

in both episodes increases and the RH decreases dramatically. This progression indicates the importance of mixed-layer processes in the development 

of dry and windy conditions for both episodes. The time series for Episode 

2 suggests that prior to sunrise on June 23rd, the surface air was drier and 

windier than the air 500m above the ground. As the mixed layer grew after 

sunrise, this signal was eliminated and the usual structure of drier and windier 

air aloft than at the ground transpired. However, the unusual vertical structure prior to sunrise on the 23rd preceded the fi re reports of extremely high 

fl ame lengths (50-80 feet) on the 23rd. Without exploring the details of the 

atmospheric processes that led to the formation of the anomalous structure 

during the night, we cannot state whether the fi re reports and this unusual 

mixed-layer structure is related. But the anomalous the mixed-layer structure 

and anomalous fi re behavior suggest that a possible cause and effect relationship should be explored in future studies.

Figure 7 shows a time series of 3-layer model quantities for Episode 3 for 

a point in central Arizona. The development of the surface and mixed-layer 

averaged winds is notable in this case. At sunrise, the winds were quite light, 

with values on the order of 3 mph. As the mixed layer grew through the day, 

surface wind speeds increased rapidly to about 15 mph. The wind speeds just 

above the mixed layer, however, remained sharply higher than the mixedlayer wind speeds throughout the day. This is noteworthy in that a strong fi re 

circulation in that environment could “tap into” air above the mixed layer 

and transport momentum from outside of the mixed layer to the ground, 

leading to anomalously strong surface winds, possibly with gusts that are even 

higher than indicated by the time series. Furthermore, note that the strong 

winds aloft remained in place even after the mixed layer collapsed (e.g. when 

the green line in the plot disappears) indicating that even at night, this fi re 

might continue to experience stronger winds than expected.

Discussion And Conclusion

The NWP model results presented in the last section indicate that variations in weather conditions associated with three fi re episodes in late June, 

2005 can help explain some of the variations in observed fi re behavior. The 

simulations demonstrate that substantial differences occurred in the fi reatmosphere interactions during the episodes. And while these interactions 

appear to rely upon the presence of dry air, the simulations reveal that RH is 

not the best quantity for assessing the impact of dry air on fuel conditions, 

and by association, fi re behavior. Since the most pronounced difference 
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Charney and Fusina Employing Numerical Weather Models to Enhance Fire Weather and Fire Behavior Predictions

Figure 6—Time series in southern Nevada of a) surface wind speed, mixed-layer average wind speed, and mixedlayer + 500m average wind speed in mph and b) surface relative humidity, mixed-layer average relative humidity, and 

mixed-layer + 500 m average relative humidity from 0000 UTC 23 June through 0000 UTC 25 June 2005. c) same as a) 

from 0000 UTC 27 June through 0000 UTC 29 June 2005. d) same as b from 0000 UTC 27 June through 0000 UTC 29 

June 2005.

a

b
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c

d
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a

b

Figure 7—Time series in central Arizona of a) surface wind speed, mixed-layer average wind speed, and mixed-layer 

+ 500 m average wind speed in mph and b) surface relative humidity, mixed-layer average relative humidity, and 

mixed-layer + 500 m average relative humidity from 0000 UTC 27 June through 0000 UTC 29 June 2005.
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Employing Numerical Weather Models to Enhance Fire Weather and Fire Behavior Predictions Charney and Fusina

between the episodes was found in near-ground temperatures, RH would 

be expected to be ambiguous. However, the vapor pressure defi cit shows a 

more pronounced change in conditions between the episodes, and in these 

situations, represents a more precise means of diagnosing the potential fuel 

drying due to atmospheric processes.

The potential for local conditions at the ground and aloft to affect the 

fi res was addressed using the so-called three-layer conceptual model, which 

employs NWP model output to calculate the surface, mixed-layer, and mixedlayer plus 500 m winds and humidities. These analyses highlighted highly 

anomalous mixed-layer structures coinciding with the most extreme fi re behavior reported during the episodes. In other locations, the analyses indicate 

the potential for a fi re to tap into fast-moving air just above the mixed layer; 

air that could be mixed down to the surface and produce unexpected and 

potentially hazardous changes in fi re behavior. The preliminary analyses of 

these time series indicate that the three-layer model could be used to anticipate 

the potential for anomalous fi re behavior associated with diurnal variations 

in atmospheric mixed layer processes. Additional work is necessary, however, 

before the ultimate usefulness of this diagnostic tool can be determined.

This paper represents a step towards realizing the full potential of atmospheric physics models for fi re weather and fi re behavior forecasting. As 

researchers and operational personnel come to understand the relationships 

between fi re behavior and atmospheric processes that can be predicted by 

weather forecast models, these concepts can be tested in the broader context 

of day-to-day fi re weather forecasting. Eventually, these techniques could 

provide additional information for fi re weather forecasters and fi re managers, 

producing new information from tools that are already available and used 

routinely in weather forecast offi ces.

References

Charney, J. J., and D. Keyser, 2003: Using mesoscale model simulations to 

better understand the role of surface mixed-layer dynamics in fi re-atmosphere 

interactions. 20th Conference on Numerical Weather Prediction, 12-15 January 

2004, Seattle, WA, American Meteorological Society.

Charney, J. J., B. E. Potter, W. E. Heilman, and X. Bian, 2005: Assessing the potential 

for atmospheric conditions aloft to contribute to extreme fi re behavior. EastFIRE 

Proceedings, May 11-13, 2005, George Mason University, Fairfax, VA.

Fosberg, M.A. 1978: Weather in wildland fi re management: the fi re weather index. 

Proceedings of the Conference on Sierra Nevada Meteorology, South Lake Tahoe, 

NV. pp. 1-4.

Grell, G. A., Dudhia, J. and Stauffer, D. R., 1995: A description of the fi fthgeneration Penn State/NCAR mesoscale model (MM5). NCAR Technical Note, 

NCAR/TN-398#STR, 122 pp.

Haines, D.A., 1988: A lower atmosphere severity index for wildland fi res. National 

Weather Digest 13:23-27.

Lavdas, L.G., 1986: An atmospheric dispersion index for prescribed burning. USDA 

Forest Service Research Paper SE-256. Asheville, NC: Southeastern Forest 

Experiment Station.

Potter, B. E., 2002: A dynamics based view of atmosphere-fi re interactions. Int. J. 

Wildland Fire,11, 247-255.
USDA Forest Service Proceedings RMRS-P-41. 2006. 787

In: Andrews, Patricia L.; Butler, Bret W., 

comps. 2006. Fuels Management—How to 

Measure Success: Conference Proceedings. 

28-30 March 2006; Portland, OR. 

Proceedings RMRS-P-41. Fort Collins, 

CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research 

Station.

1USDA Forest Service, Rocky Mountain Research Station, Fire Sciences 

Laboratory, Missoula, MT.

bwbutler@fs.fed.us

2 Systems for Environmental Management, Missoula, MT.

Abstract—A new software tool has been developed to simulate surface wind speed 

and direction at the 100m to 300 m scale. This tool is useful when trying to estimate 

fi re behavior in mountainous terrain. It is based on widely used computational fl uid 

dynamics technology and has been tested against measured wind fl ows. In recent 

years it has been used to support fi re management decisions to improve fi refi ghter and 

public safety, understand the environmental conditions associated with entrapment 

fi res, improve prescribed fi re prescriptions, and estimate fi re potential. Outputs from 

this tool include tiff images, GIS shape fi les, and FARSITE wind input fi les.

Introduction

Wind is one of the primary environmental variables infl uencing wildland 

fi re spread and intensity (Rothermel 1972, Catchpole and others. 1998). Indeed, wind and its spatial variability in mountainous terrain is often a major 

infl uencing factor in the fi re behavior associated with “blowup” fi res (e.g., 

South Canyon Fire 1994, Thirtymile fi re 2000, Price Canyon Fire 2002, and 

Cramer Fire 2003). The extent, elevation and orientation of mountains, valleys, ridges, and the fi re itself, infl uence both the speed and direction of wind 

fl ows (fi gure 1). The lack of detailed wind speed and direction information is 

one major source of uncertainty in fi re management decisions. Methods to 

obtain estimates of local wind speed and direction at the 100 to 300 m (300 

to 900 ft) scale have not been readily available. In most cases, fi re incident 

personnel estimate local winds based on weather forecasts and/or weather 

observations from a few specifi c locations, none of which may be actually 

near the fi re. A computer based tool is described here that provides fi re and 

land managers with the ability to determine local surface wind fl ows at the 

100-300 m (300 to 900 ft) scale for a given synoptic wind condition. A 

brief discussion of how the tool’s accuracy has been evaluated is presented 

followed by some examples of how this tool is being used in wildland fi re 

management decisions.

Background

As computational and mathematical simulation capabilities have increased, 

methods for obtaining detailed wind information to support fi re management 

efforts have been explored. Ferguson (2001) uses atmospheric scale models to 

assess the dispersion of smoke from natural and prescribed fi res. Zeller and 

WindWizard: A New Tool for Fire 

Management Decision Support

Bret W. Butler1, Mark Finney1, Larry Bradshaw1, Jason Forthofer1, 

Chuck McHugh1, Rick Stratton2, and Dan Jimenez1
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Butler, Finney, Bradshaw, Forthofer, McHugh, Stratton, and Jimenez WindWizard: A New Tool for Fire Management Decision Support

others (2003) are exploring the application of meso-scale atmospheric fl ow 

models for the prediction of surface winds. The National Weather Service 

(NWS) has recently provided public access to the National Digital Forecast 

Database (NDFD). Meso-scale forecast data are available for the entire United 

States on a daily basis at scales ranging from 4 km to 36 km resolution. The 

NDFD currently provides 5.0 (soon to be 2.5) km resolution, 8-day digital 

forecasts (and GIS support) for the conterminous U.S. These approaches 

include all the important physical processes but suffer from relatively coarse 

scale surface wind predictions (nominally greater than 2000 m scale) and 

large computational requirements. Meso-scale models and weather service 

forecast models are not easily confi gured for “what if” applications wherein 

a single user using a laptop computer can simulate multiple scenarios ahead 

of time and explore their impact on fi re intensity and growth.

Others have approached the problem from a fl uid dynamics approach, 

for example Lopes and others (2002) and Lopes (2003) describe a software system that calculates a surface wind fi eld and includes topographical 

infl uences. However, their system remains a research tool; they have not 

provided a process through which their system can be used operationally by 

fi re managers.

We have commonly referred to our approach as gridded wind simulations. 

In the gridded wind approach, typically, the area of interest is 30 km by 30 km 

(18.6 miles by 18.6 miles) square with the fi re located approximately at the 

Figure 1—Example of a gridded wind simulation. The white line represents the fi re perimeter. Wind speed 

and direction are indicated by the vectors, with length representative of relative speed and orientation 

representative of local wind direction. Vectors are also colored by wind speed.
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WindWizard: A New Tool for Fire Management Decision Support Butler, Finney, Bradshaw, Forthofer, McHugh, Stratton, and Jimenez

center. The tool is based on the Fluent® and FloWizard® computational fl uid 

dynamics software packages (http://www.fl uent.com). The atmosphere is 

assumed to be neutrally stable. The simulation assumes a constant temperature fl ow and turbulence is modeled using the rng κ-ε approach (Jones and 

Launder 1972; Yakhot and Orszag 1986).

The tool has been termed WindWizard. The simulation process followed 

by the WindWizard tool comprises the following general steps:

 1) Acquire and import into WindWizard an ASCII raster digital elevation 

data fi le (DEM) for the area of interest, generally on the order of 30 

km by 30 km (18.6 miles by 18.6 miles) in size.

 2) Automatically build a computational domain over the area of interest and 

divide it into computational cells with dimensions on the order of 300 

m by 300 m by 100 m (900 ft by 900 ft by 300 ft) at the surface of 

the terrain. The result is 100,000 to 1,500,000 cells within the overall 

computational domain.

 3) Compute a surface roughness parameter based on user input of the dominant plant species (forest, shrub, grass).

 4) Solve the Navier-Stokes equations describing the wind fl ow over the 

earth’s surface for up to 10 different wind scenarios based on user input 

of the ridge top or synoptic wind conditions. The user specifi ed input 

wind is imposed as an inlet to the simulation domain and is uniform 

with height above the terrain surface.

 5) Display and output the wind speed and direction 6m above the terrain 

surface at a resolution specifi ed by the user.

Wind modeling for specifi c fi res consists of simulating multiple combinations of free-air wind speed and direction. The different cases are selected 

to match forecasted scenarios or are based on historical weather patterns. 

The gridded wind simulation accounts for the infl uence of elevation, terrain, 

and vegetation on the general wind fl ow. We emphasize the gridded wind 

simulations are not forecasts but rather a snapshot at one point in time of 

what the local surface wind speed and direction would be for a given ridge 

top or synoptic wind scenario. WindWizard is a technique for determining 

the fi ne scale winds that result from a specifi c broader scale wind scenario. 

WindWizard has been used to predict and reconstruct fi re behavior during 

ongoing fi re incidents and to support fi re investigations [i.e. Price Canyon Fire 

(Utah) -Thomas and Vergari (2002), Thirtymile Fire (Washington) - USDA 

Forest Service (2001), Cramer Fire (Idaho) - USDA Forest Service (2004), 

Storm King Mountain Fire (Colorado) - Butler and others (1998), Cedar Fire 

(California) - California Dept. of Forestry and Fire Protection (2004)].

The bottom line is that in all of the wind simulations completed so far, we 

have not observed any reason to believe that the simulated winds are not physically realistic representations of actual winds for similar free-air wind events. 

At the very least, the gridded wind tool represents a signifi cant improvement 

over the previous method of using a single wind speed and direction obtained 

from a point measurement such as a weather station or observer.

Methods

Two methods have been utilized to quantify the accuracy and effectiveness 

of computational fl uid dynamics (CFD) based wind simulations. The fi rst 

compares simulated wind speed and direction against direct measurements. 
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Butler, Finney, Bradshaw, Forthofer, McHugh, Stratton, and Jimenez WindWizard: A New Tool for Fire Management Decision Support

The second compares fi re growth simulations with and without the high 

resolution wind.

In comparisons against measured wind data (fi g. 2), generally the modeled 

wind speeds were within 9 percent of those measured except for the leeward 

upper slope of the hill where the simulated wind speed was 32 percent greater 

than the measured value and is likely related to differences between the steady 

state calculations produced by the CFD-based model and the transient nature 

of turbulent eddies forming on the leeward side of the hill (Castro and others 2003). This result suggests that the CFD-based methodology may not 

capture the transient nature of the fl ow. Figure 3 indicates that simulated 

wind direction was within 13 degrees of the measured value for all locations 

(Butler and others 2004). The differences between the simulated wind direction and measured values were greatest near the base of the hill for both the 

upwind and leeward sides. These comparisons suggest that the CFD-based 

methodology for simulating surface wind fl ow over mountainous terrain 

can provide relatively accurate and useful information, but a valid evaluation 

requires comparison against additional data sets.

Metrics for quantifying the impact of this technology on wildland fi re 

management decision making can be defi ned through two methods: 1) the 

degree of interest in and use of the tool as the fi re management community 

becomes aware of it and 2) the response from fi re managers as to its utility. 

One major focus of this project has been to take advantage of opportunities 

to assist IMT’s by proactively producing wind simulations for their area of 

interest.

Figure 2—A comparison of measured and predicted wind speeds reported from the 

Askervein hill data set. Positive values represent distances downstream from apex and 

negative values represent upstream from apex.
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Discussion

Transfer of results from the wind simulations to fi re managers and fi eld 

personnel occurs in three forms: 1) Images consisting of wind vectors overlaid 

on a shaded relief surface image; 2) ArcView or ArcMap shape fi les of wind 

vectors and 3) fi les for use by the FlamMap and FARSITE (Finney 1998) 

programs. The images and fi les display the spatial variation of the wind speed 

and direction and can be used to identify high and/or low wind speed areas 

along the fi re perimeter caused by the channeling and sheltering effects of 

the topography.

CFD based wind simulations have been used to provide wind input to a 

number of FARSITE fi re growth simulations of previous fi re events. In all 

of the simulations the accuracy of short term (< one day) fi re spread projections, as compared to actual fi re spread histories, has markedly increased. 

For example, fi gures 4 and 5 present fi re growth simulations of the South 

Canyon Fire (Butler and others, 1998). The fi re growth simulation developed 

from uniform wind direction (fi g. 4) clearly does not match the actual fi re 

perimeter. The fi re growth simulation developed using the gridded wind (fi g. 

5) is a better fi t to the actual perimeter. The South Canyon Fire comparison 

was chosen to point out that while the use of gridded wind increases fi re 

growth simulation accuracy it does not guarantee perfect fi t. The discrepancy between actual and simulated fi re perimeters can be attributed to input 

information used by the fi re growth simulation such as inaccuracies in the 

Figure 3—A comparison of the variation from the overall 210 degree fl ow direction for 

the measured and predicted winds from apex of Askervien hill. Positive values represent 

distances downstream from apex and negative values represent upstream from apex.
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Figure 4—FARSITE simulation of the Storm King Mountain Fire assuming uniform wind speed and 

direction from the left to right (west winds). Black line represents actual fi re perimeter at same point 

in time as last fi re simulation. Fire growth simulations are shown as successive fi re burned areas 

with color varying. Last perimeter is shown in light blue-green.

vegetation map. It could also be attributed to the wind fi eld. It is important 

to emphasize that the gridded wind represents a “snapshot” of the fl ow fi eld 

at one moment it time. In reality the wind fi eld is varying in both time and 

space. The terrain present at the South Canyon Fire site would have induced 

strong turbulence in the surface wind. The eddies and transient fl ow created 

by that turbulence could signifi cantly affect the fi re growth.

Butler and others (2004) make a similar comparison for the Price Canyon 

Fire, the agreement between simulated and actual fi re perimeters is very close 

when the gridded wind is included. The improvement in agreement between 
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WindWizard: A New Tool for Fire Management Decision Support Butler, Finney, Bradshaw, Forthofer, McHugh, Stratton, and Jimenez

the fi re growth simulations with the use of gridded wind indicates that the 

gridded wind is more representative of reality.

The CFD-based WindWizard tool represents a new technology not previously available to wildland fi re teams and specialists. Consequently part 

of the research team’s work during the past three fi re seasons consisted of 

simply contacting the incident management teams to inform them of the 

new technology and supporting their fi re management activities. Fire incident management teams (IMT) working in Montana, Colorado, Wyoming, 

California, Washington, Idaho, Arizona, Nevada and Utah have been supplied 

with custom wind simulations.

Figure 5—FARSITE simulation of the Storm King Mountain Fire using gridded wind data from CFDbased simulation. General wind fl ow input to CFD was aligned with the Colorado River gorge (west 

winds generally fl owing diagonally from upper left to lower right). Black line represents actual fi re 

perimeter at same point in time as last fi re simulation. Fire growth simulations are shown as successive 

fi re burned areas with color varying. Last perimeter is shown in light blue-green. 
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Butler, Finney, Bradshaw, Forthofer, McHugh, Stratton, and Jimenez WindWizard: A New Tool for Fire Management Decision Support

While it is subjective, one metric of the utility of the gridded wind as a fi re 

management decision support tool is indicated by the responses from IMTs 

and fi re specialists that are exposed to the technology. Generally, fi re Behavior 

Analysts (FBANs), long term analysts (LTANs) and local fi re specialists found 

the wind simulations to be highly useful for visualizing the channeling effect 

of terrain on the wind. The outputs from the WindWizard tool are being used 

in multiple ways: 1) to build shaded relief maps over which vectors representing 

wind speed and direction are placed. The maps could include fi re perimeters. 

These maps proved useful in identifying synoptic wind conditions that might 

result in signifi cant changes in fi re intensity and spread. For example, given 

a particular wind scenario the WindWizard based wind simulations can be 

used to identify areas on or near the fi re perimeter that might be exposed 

to high winds and thus potentially higher intensity fi re behavior. 2) Others 

have used the tools to identify areas that are sheltered from synoptic winds 

and therefore may not be at high risk for high intensity fi re. GIS shape fi les 

produced by the WindWizard tool can be easily used as another layer in addition to vegetation, terrain, resources, roads etc. in building images and 

analyzing relative fi re risk on a spatial scale. 3) More recently, the FARSITE 

and FlamMap fi re growth and potential fi re behavior tools can easily ingest 

gridded wind data. In all cases, simulations of fi re growth and potential have 

more closely matched observed and intuitively expected fi re behavior with 

the use of gridded wind simulations. 4) Fire managers who have studied the 

gridded wind vectors displayed on maps have commented that the information 

presented would be useful in the appendices of fi re management plans and 

could be useful for identifying potential fuel treatment areas. As the technology is used further new and innovative applications are found for it.

In all cases where it has been tested the WindWizard tool has provided 

wildland fi re managers with an objective method for estimating local wind 

fl ows and the potential for changes in fi re spread rate and intensity.

Conclusions

The research team has used this technology to support wildland fi re 

management teams by completing more than 500 wind simulations for approximately 200 fi re incidents located across the country. Additional uses 

for this tool are being found as more people become aware of and use the 

technology.

Because this technology is still new, many fi re management teams are not 

aware of it or do not know how to access or use it. As stated previously the 

gridded wind simulations are not weather forecasts. While it is not a forecast, 

one of the real benefi ts of this approach is that it can be used in a “gaming” 

mode to explore the impact that various forecasted wind scenarios might 

have at the local scale on the fi re, something not possible with meso-scale 

weather models.

Acknowledgments

Financial support for this project has been provided by the USDA Forest 

Service, The Joint Fire Science Program, John Szymoniak from the National 

Interagency Fire Center and Mike Hilbruner from the USDA Forest Service 
USDA Forest Service Proceedings RMRS-P-41. 2006. 795

WindWizard: A New Tool for Fire Management Decision Support Butler, Finney, Bradshaw, Forthofer, McHugh, Stratton, and Jimenez

Washington Offi ce. Signifi cant improvements have stemmed from suggestions 

and trials of the technology by many Interagency Fire Management Teams 

who have contributed time and effort as test cases for the gridded wind tool. 

Finally the contributions of individual FBANs and LTANs willing to take the 

time to explore this new technology have been invaluable to the development 

and improvement of WindWizard.

References

Butler, B.W.; Forthofer, J.M.; Finney, M.A.; Bradshaw, L.S.; and Stratton, R. 

2004. High Resolution Wind Direction and Speed Information for Support of 

Fire Operations. In: Aguirre-Bravo, Celedonio, et al. Eds. 2004. Monitoring 

Science and Technology Symposium: Unifying Knowledge for Sustainability 

in the Western Hemisphere; 2004 September 20-24; Denver, CO. Proceedings 

RMRS-P-37CD. Ogden, UT: U.S. Department of Agriculture, Forest Service, 

Rocky Mountain Research Station.

Butler, B. W.; R. A. Bartlette; L. S. Bradshaw; J. D. Cohen; P. L. Andrews; T. Putnam 

and R. J. Mangan. 1998. Fire behavior associated with the 1994 South Canyon 

Fire on Storm King Mountain. USDA Forest Service RMRS Res. Pap. RP-9.

California Dept. of Forestry and Fire Protection (2004). Review report of Serious 

CDF Injuries, Accidents and Near-Miss Incidents. Engine Crew Entrapment, 

Fatality, and Burn Injuries, October 29, 2003 Cedar Fire. California Dept. of 

Forestry and Fire Protection Serious Accident Review Team Report March 10, 

2004.

Castro, F. A.; Palma, J. M. L. M., and Lopes, A. S. 2003. Simulation of the askervein 

fl ow. part 1: Reynolds averaged navier-stokes equations (k-ε turbulence model). 

Boundary-Layer Meteorology 107:501-530.

Catchpole, W. R.; Catchpole, E. A.; Butler, B. W.; Rothermel, R. C.; Morris, G. 

A.; and Latham, D. J. 1998. Rate of spread of free-burning fi res in woody fuels 

in a wind tunnel. Comb. Sci. Tech. 131:1-37.

Ferguson, S. A. 2001. Real-time mesoscale model forecasts for fi re and smoke 

management: 2001. Fourth Symposium on Fire and Forest Meteorology, 13-15 

November 2001, Reno, NV. American Meteorological Society. 162-167.

Finney, M. A. 1998. FARSITE: Fire Area Simulator-Model Development and 

Evaluation. Res. Pap. RMRS-RP-4, Ogden, UT: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research Station.

Jones, W. P. and B. E. Launder. 1972. The Prediction of Laminarisation with a Ywoequation Turbulence Model. Int. J. Heat and Mass Transfer Vol. 15 p. 301

Lopes, A. M. G.; Cruz, M. G.; and Viegas, D. X. 2002. Firestation-an integrated 

software system for the numerical simulation of fi re spread on complex topography. 

Environmental Modelling & Software. 2002(17):269-285.

Lopes, A. M. G. 2003. WindStation—a software for the simulation of atmospheric 

f lows over complex topography. Environmental Modelling & Software. 

2003(18)81-96.

Rothermel, R. C. 1972. A mathematical model for predicting fi re spread in wildland 

fuels. USDA For. Serv. Intermt. For. Range Exp. Stn. Ogden, Utah. Res. Pap. 

INT-115.

Thomas, D. and Vergari, G. 2002. Price Canyon Wildfi re Staff Ride. USDA Forest 

Service, Region 4, Ogden, UT. 55p.

USDA Forest Service. 2004 www.fs.fed.us/r4/fi re/cramer/ cramer_q&a_air_1_

12_04.doc

USDA Forest Service 2001. Thirymile fi re investigation. as amended on October 

16, 2001. USDA Forest Service. Washington DC.
796 USDA Forest Service Proceedings RMRS-P-41. 2006.

Butler, Finney, Bradshaw, Forthofer, McHugh, Stratton, and Jimenez WindWizard: A New Tool for Fire Management Decision Support

Yakhot, V. and Orszag, S.A. 1986. Renormalization group analysis of turbulence. 

I. Basic theory. J. Scientifi c Computations. 1:3-51.

Zeller, K; Nikolov, N; Snook, J; Finney, M; McGinley, J; Forthofer, J. 2003. 

Comparison of 2-D wind fi elds and simulated wildland fi re growth. In proceedings 

of the Fifth Symposium on Fire and Forest Meteorology and Second Wildland 

Fire Ecology and Fire Management Congress, 16-20 November 2003, Orlando, 

FL. American Meteorological Society. Washington, DC.
USDA Forest Service Proceedings RMRS-P-41. 2006. 797

Appendix: Presentations for which papers 

were not submitted for these proceedings

Invited Speakers

Is There a Normal Fire Regime in an Always Changing 

Environment?

Daniel Botkin, Research Professor, University of California Santa Barbara

Restoring Fire as an Ecosystem Process

Greg Aplet, The Wilderness Society

Conference Summary and Closeout 

Jeff Jahnke, Colorado State Forester

Panels

Wildland Fire Use: It’s not just for wilderness anymore. 

Moderator: Carol Miller USDA Forest Service, Aldo Leopold Wilderness 

Research Institute

Panelists: Marcia Andre, Supervisor, Gila National Forest

 Dick Bahr, Fire Use Specialist, National Park Service-NIFC

 Tom Nichols, Deputy Fire Program Planning Leader, National 

Park Service-NIFC

 Mike Rieser, FMO, Craig/Routt Fire Management Unit, Bureau 

of Land Management 

 George Weldon, Deputy Director - Fire, Aviation, and Air, Forest 

Service, Northern Region

How do we defi ne success in fuels management?

Moderator: Jack Cohen, USDA Forest Service, Missoula Fire Laboratory

Panelists: Greg Aplet, The Wilderness Society, Denver, CO

 Steve Arno, USDA Forest Service, Missoula Fire Laboratory, retired

 Howard Roose, Bureau of Land Management, NIFC

 Paul Langowski, USDA Forest Service, Region 2

 Jon Keely, USGS Western Ecology Center, Sacramento, CA

 Rocky Barker, Idaho Statesman, journalist & author, Boise, ID
798 USDA Forest Service Proceedings RMRS-P-41. 2006.

Appendix: Presentations for which papers were not submitted for these proceedings

Workshops

Science Synthesis and Integration: FuelsTools

 Dave Peterson 

Using Fireshed Assessments to Measure Landscape Performance 

 Bernie Bahro, K. Barber, L. Perrot, J. Sherlock, A.

 Taylor, K. Wright, and D. Yasuda

Introduction to state-and-transition modeling of vegetation change using 

the Vegetation Dynamics Development Tool (VDDT) 

 Leonardo Frid

Spatially explicit landscape-level modeling of vegetation change using the 

Tool for Exploratory Landscape Spatial Analysis (TELSA) 

 Leonardo Frid

FIREMON fi re effects monitoring protocol 

 Duncan Lutes 

Help with using the 40 new fi re behavior fuel models 

 Joe H. Scott

A Suite of Fuel Management Tools: Fuel Characteristic Classifi cation System, 

Natural Fuels Photo Series, and Consume 3.0 

 Roger D. Ottmar, Cynthia L. Riccardi, Susan Prichard,

 Robert E. Vihnanek, and Clint S. Wright 

Fire and Fuels Extension to the Forest Vegetation Simulator (FFE-FVS)

 Stephanie Rebain 

Use of FlamMap for Fire and Fuels Planning 

 Mark A. Finney, Rob Seli, and Chuck McHugh 

Fire Regime Condition Class: Concepts, Methods, and Applications 

(FRCC)

 Steve Barrett
USDA Forest Service Proceedings RMRS-P-41. 2006. 799

Appendix: Presentations for which papers were not submitted for these proceedings

Oral and Poster Presentations

Modeling the Effects of Moderate Severity Burns on Fuel Loading in 

Northwest Wyoming Mixed Conifer Forests

 Diane C. Abendroth, Mohammed A. Kalkhan and Karl E. Brown

Utilizing prescribed fi re to restore endangered species habitat while managing 

potential negative effects to non-target endangered species

 Russ Babiak

Stanislaus Stewardship and Fireshed Assessment Case Study: Measuring 

Landscape Performance

 Bernie Bahro, K. Barber, J. Sherlock, A. Taylor, D. Yasuda,

 N. Amboy and T. Kohler

The Effects of Fire Severity on the Regeneration of Douglas-fi r

 Jason Barker

Successful Fuels Management at The Caribbean Islands National Wildlife 

Refuges

 Boyd Blihovde, James Padilla, Josh O’Connor and Jim Durrwachter

Geospatial statistical modeling-mapping of fuel characteristics in Grand Teton 

National Park, Wyoming: Integration of geospatial information and fi re 

behavior prediction

 Cory B. Bolen, Mohammed A. Kalkhan and Karl E. Brown

2003 Fires in Southern California: Impact of Fuel Age on Fire Severity and 

Vegetation Recovery 

 Teresa Brennan and Jon E. Keeley

Creating an Access-Based Database for Communities at Risk

 Paul Briggs, Dana Cohen, Brett Fay, Bruce Fields, Taiga Rohrer,

 John Schmidt, Cyndi Sidles, Scott Tobler, David Eaker and

 Anne Stanworth

Right Place, Right Time—An Interagency Approach to Prioritizing Fuel 

Treatments

 Paul Briggs, Dana Cohen, Brett Fay, Bruce Fields, Taiga Rohrer,

 John Schmidt, Cyndi Sidles, Scott Tobler, David Eaker and

 Anne Stanworth

An Interagency Approach to Prioritizing Fuels Treatments

 Paul Briggs, Dana Cohen, Brett Fay, Bruce Fields, Taiga Rohrer,

 John Schmidt, Cyndi Sidles, Scott Tobler and David Eaker

Social research and mitigation of wildland fire risk: Success is about 

communication and relationship building

 Jeffrey J. Brooks, Hannah Brenkert, Judy E. Serby, Joseph G. Champ,

 Tony Simons and Daniel R. Williams
800 USDA Forest Service Proceedings RMRS-P-41. 2006.

Appendix: Presentations for which papers were not submitted for these proceedings

CEFA Program Products for Fuels Management

 Timothy J. Brown, Beth L. Hall, Crystal A. Kolden and

 Hauss J. Reinbold

Partnering to Increase Success: Getting the Public to Relate to Wildland 

Fire Mitigation

 Joseph G. Champ, Jeffrey J. Brooks and Daniel R. Williams 

Grid-based monitoring and gradient modeling to quantify cumulative effects 

of fuels treatments

 Samuel A. Cushman and Kevin S. McKelvey

Canadian Community Wildfi re Protection Plans focus on Forest Inventory

 John Davies and Clark Woodward

A Case Study: Using Fuel Reduction Techniques to Enhance the Military 

Mission

 Tamala DeFries

Mapping Fire Regime Condition Class Using the FRCC Mapping Tool

 Tom DeMeo, Jeffrey L. Jones, Joseph D. Zeiler and Lee C. Hutter

Cooperative fi re management in the Dandenong Ranges, Victoria, Australia

 Jack Dinkgreve

British Columbia Fuel Management Program

 Chris D. Duffy and Sue Clark

Tree-to-Sawlog Ratios for the FTM-West Model

 Dennis Dykstra

The Wildland/Urban Interface: Cheatgrass and Fuel Breaks

 Heidi Esh

Modeling equations to quantify coniferous forest litter in Californian National 

Forests

 Carol Ewell, John Stuart and Jo Ann Fites

Measuring Effectiveness of Fuel Treatments Across National Forests in California: a Practical, Programmatic Approach

 Jo Ann Fites, Carol Ewell and Erin Noonan

Evaluating Wildland Fire Use Fires: Beyond Ecological Benefi ts, Measuring 

Their Contribution to Fuel Hazard Reduction

 Jo Ann Fites, Erin Noonan and Carol Ewell

A Method for Rapid Assessment of Historic Frequent-Fire Vegetation 

Communities

 Diane M. Gercke, Gary G. Blank, Thomas R. Wentworth and

 Cecil C. Frost
USDA Forest Service Proceedings RMRS-P-41. 2006. 801

Appendix: Presentations for which papers were not submitted for these proceedings

The Use of Fire Behavior Models in Reconstructing Presettlement Vegetation 

on a Frequent-Fire Landscape

 Diane M. Gercke, Gary B. Blank, Thomas R. Wentworth and

 Cecil C. Frost

The Fire Research And Management Exchange System (FRAMES) and the 

USGS National Biological Information Infrastructure (NBII): Developing Information Technology in Support of Wildland Fire Research and 

Management

 Greg E. Gollberg

Project Vesta: fi re behaviour study of different age fuels in dry eucalypt 

forests

 Jim Gould, Lachie McCaw and Phil Cheney

Tapping the forest inventory for spatially continuous estimates of fuels and 

fi re potential: the GNNfi re approach

 Jeremy S. Fried, Janet L. Ohmann, Michael C. Wimberly,

 Kenneth B. Pierce and Matthew J. Gregory

Evaluation of Fuel Moisture Content Sampling Methods and Processes

 Sally M. Haase and Susan M. Zahn

Impacts of thinning and prescribed burning treatments on predicted wildfi re 

behavior and tree health in an old-growth ponderosa pine and western 

larch stand

 Michael G. Harrington, Anna Sala and Carl Fiedler 

LANDFIRE Outreach and Technology Transfer

 Doug Havlina

Integrating fuels mitigation and wildfi re planning in Skamania County, WA

 Ole T. Helgerson, Rob Thysell and Jeremy Boyer

20 Years of Prescribed Burning and Fire Effects Monitoring in the Big Creek 

Unit, Yosemite National Park

 Jennifer S. Hooke and Monica S. Buhler

Real vs. simulated fi re effects at McDonald Ridge

 Susan S. Hummel and Gail Bouchard

LANDFIRE Rapid Assessment: Data, Tools and Applications for Fire Regime 

Restoration and Planning

 Darren Johnson

City of Kamloops Wildland/Urban Interface Forest Fuel Hazard Reduction

 Kelly P. Johnston and Willy Saari

The Fire Behavior Assessment Tool – Integrating Multiple Fire Behavior 

Variables into a Stand-level Metric Characterizing Fire Behavior

 Jeffrey L. Jones and Dale A. Hamilton
802 USDA Forest Service Proceedings RMRS-P-41. 2006.

Appendix: Presentations for which papers were not submitted for these proceedings

Changing Fuels Spatial Data using the Contextual Raster Editor

 Jeffrey L. Jones, Lee C. Hutter and Wendel J. Hann 

Developing Integrated Fuel Treatment Priorities at a Landscape Level Using 

the Multi-scale Resource Integration Tool

 Jeffrey L. Jones, Joseph D. Zeiler and Dale A. Hamilton

Interagency Fire Effects Monitoring Across Diverse Landscapes

 Wendy Joslin, Amy Waltz and Geoff Babb

Analysis of fuel variability within the landscape-scale of Rocky Mountain 

Region: Integration of Field Data, Geospatial Information, and Spatial 

Statistics

 Mohammed A. Kalkhan, Karl E. Brown, Cory B. Bolen and

 Diane C. Abendroth

Costs and benefi ts of chaparral fuel modifi cations in southern California

 Jon E. Keeley and Richard W. Halsey

The National Fire and Fire Surrogate Study - Effects of alternative fuel

 Jon E. Keeley and Dylan Schwilk

Fire behavior and soil heating impacts with prescribed burning in masticated 

fuel beds

 Eric Knapp, Matt Busse and Carol Shestak

The role of climate in successful fuels management

 Crystal A. Kolden and Timothy J. Brown

A weighted, data-driven GIS model for assessing changes in fi re risk associated with fuels treatment

 Crystal A. Kolden and Timothy J. Weigel

Measuring Success — A Historical Overview of Fuels Treatment Projects in 

the U.S. Fish and Wildlife Service, Southeast Region

 Peter W. Kubiak

Fire in Wilderness--Managing for Resource Benefi ts in Alaska

 Mary Kwart and Brian Anderson

Small Villages in Fire Prone Wilderness--Managing the Risk

 Mary Kwart and Brian Anderson

An Integrated Approach to Fuels Treatment in the Southwestern U.S., The 

Harvest-Cost-Revenue Estimator

 Eini C. Lowell and Dennis R. Becker

An Experimental Study on the Ignition of Fuel Beds by Firebrands in Wildland/Urban Interface (WUI) Fires

 Samuel L. Manzello, Thomas G. Cleary, John R. Shields,

 Alexander Maranghides, William Mell and Jiann Yang

Whooping Crane use on Prescribed Fires

 Kristen Maxfi eld and Brent Woffi nden
USDA Forest Service Proceedings RMRS-P-41. 2006. 803

Appendix: Presentations for which papers were not submitted for these proceedings

The Public and fuels management: Science fi ndings on social understanding, 

beliefs, and acceptability.

 Sarah McCaffrey

Experiments and computer modeling of fi re spread in trees

 William Mell, Alex Maranghides, Samuel Manzello and

 Ronald Rehm

A Rapid Assessment of Fire Regime Condition Class for the Conterminous 

United States

 James Menakis, Ayn Shlisky and Kelly Pohl

Simulating fi re risk within a mixed-ownership, fi re-prone landscape of 

northeastern Wisconsin: Interactions between human ignitions and forest 

dynamics.

 Brian R. Miranda, Brian R. Sturtevant, Eric J. Gustafson and

 Hong S. He

Climate drivers of fi re & fuel in the Northern Rocky Mountains: Past, Present 

& Future

 Penny Morgan, Emily K. Heyerdahl, Carol Miller, Lauren B. Shapiro,

 Carly E. Gibson and James P. Riser

The Use of Landscale-Scale Ecological Units to Plan and Prioritize Vegetation 

and Fuel Treatments in the Umpqua Cascades

 Don Morrison

Applying National Burn Severity Mapping methodology to National Wildlife 

Refuge Lands in Alaska: an assessment

 Karen A. Murphy and Joel H. Reynolds

Fuel Variability in Seasonally Dry Evergreen Forests in Eastern Amazon

 Gustavo H. Negreiros, Kathryn Prengaman and Matthew Othmer

Fire Forecasting with the MC1 model: Past and Future Forecasts

 Ronald P. Neilson, James M. Lenihan, Dominique Bachelet and

 Raymond J. Drapek

CRAFT: A framework for predicting effects and measuring success

 Steven P. Norman, Danny C. Lee, Sandra L. Jacobson and

 Jeffrey G. Borchers

The Northwest and Alaska Fire Research Clearinghouse (FIREHouse)

 Diana L. Olson, David L. Peterson, Jennifer Pollock and

 Jennifer L. Allen

Mulching/Small Wood Utilization: Prevention, Supression, Rehab

 John W. Orban

Roadside Thinning at Yosemite National Park: Monitoring effectiveness and 

other resource concerns

 Kara J. Paintner, Monica S. Buhler and Jennifer S. Hooke
804 USDA Forest Service Proceedings RMRS-P-41. 2006.

Appendix: Presentations for which papers were not submitted for these proceedings

Woody Biomass Utilization: One Measure of Success for Sustainable Fuels 

Management

 Marcia Patton-Mallory and Sue Stewart

Main Boulder River Fuels Reduction Project

 Dick Rath, Bill Avey, Paula Rosenthal and Mike Dannenberg

Making fuels data usable with maps

 Jennifer L. Rechel

Fuel Treatment Success: What are the Metrics?

 Elizabeth Reinhardt

Fuel Management Success on Private Land with Firewise

 Richard Reitz and Claudia Standish

Decision support for evaluating wildland fi re danger and prioritizing vegetation and fuels treatments

 Keith M. Reynolds, Paul Hessburg, Robert Keane

Performance Measures in Fuels Management

 Douglas B. Rideout, Andrew G. Kirsch and Stephen J. Botti

Duff Moisture: A Key Factor For Staying Within The Prescription Window

 Peter Robichaud, Louise Ashmun and Lonnie Newton

Australian Forest Fire Management — At the Crossroads

 Tony Scherl

Incidental Hazardous Fuel Reduction Benefi ts from Biomass Removal for 

Endangered Species Management in Central Georgia – A Case Report

 Carl Schmidt

A case study test of fuel management effectiveness against crown fi res

 Dave Schroeder and Stew Walkinshaw

Best predictors for post-fire mortality of ponderosa pine trees in the 

intermountain west

 Carolyn H. Sieg, Joel M. McMillin, James F. Fowler, Kurt K. Allen,

 Jose F. Negron, Linda L. Wadleigh, John A. Anhold and

 Ken E. Gibson

Evaluation of Hazardous Fuel Reduction Treatments Using LIDAR Measurements in the Pine Barrens of New Jersey

 Nick Skowronski , Kenneth Clark , John Hom , Ross Nelson and

 Robert Somes

How many fuels plots are needed to measure success? An in-depth look at 

sampling fuels in lodgepole pine for the Tenderfoot Research Project

 Helen Y. Smith and Colin C. Hardy

The Effects of Fire and Fire Surrogate Fuel Treatments on the Abundance of 

Snags and Coarse Woody Debris in a Sierran Mixed Conifer Forest

 Scott L. Stephens and Jason J. Moghaddas
USDA Forest Service Proceedings RMRS-P-41. 2006. 805

Appendix: Presentations for which papers were not submitted for these proceedings

Comparing the cost effectiveness for three options of improving modeled 

home survival when wildfi re threatens structures in the wildland urban 

interface

 Keith D. Stockmann

Guidance on Landscape Wildland Fire Analysis: Models, Tools, and 

Techniques

 Rick Stratton

Fuel structures created by commercial forestry in Finland

 Heidi Tanskanen

Fuel Management and Ecological Management - A Balance

 John Travers

Assignment of New Fire Behavior Models in New Jersey Pine Barrens

 Steve Van Tuyl, Jason Cole, Kenneth Clark, John Hom, Nick Skowronski

 and Robert Somes

Development of burn prescriptions to balance duff reduction and overstory 

tree survival

 J. M. Varner, J. K. Hiers, Roger Ottmar and James Furman

Modeling of Smoldering Front Propagation With Improved Emissions 

Estimates

 Carlos A. Veras, Ernesto Alvarado, David Sandberg and

 Joao A. Carvalho Jr.

Promotion of Fine Fuel Management – Western Wildfi re Impact Reduction 

Resource Center

 Jennifer Vollmer

Fit for Success (Tailoring your custom Wildfi re Mitigation Program)

 Brad Wagner

Reducing Hazardous Fuels And Restoring Desert Bighorn Sheep Habitat 

With Landscape-scale Prescribed Fire

 Mara Weisenberger, Mark Kaib, Don Kearney and Kevin Cobble

Changes in fuelbed characteristics and resulting fi re potentials after fuel 

reduction and restoration treatments in dry forests of northeastern 

Oregon

 Andrew Youngblood, Roger D. Ottmar, Clint S. Wright and

 James D. McIver

USDA Forest Service Proceedings RMRS-P-41. 2006. 807

 Abbott, Ann 605 Deuser, Curt 513

 Adrian, Frederic W. 739 Dickson, Brett 477

 Agee, James 125 Dicus, Christopher A. 729

 Ager, Alan 125, 149, 163 Emmett, Brian 13

 Alexander, M. E. 537 Fan, David P. 59

 Alexandera, John D. 593 Farley, Sue 605

 Alexandrian, Daniel 29 Fay, Brett 47

 Anderson, K. R. 537 Feller, M.C 367

 Anderson, Stuart A. J. 381 Finney, Mark A. 107, 125, 149, 201, 213, 787

 Anderson, Wendy R. 381 Fites-Kaufman, JoAnn 465

 Andrews, Patricia L. 381 Flannigan, Mike D. 563

 Bahro, Bernhard 125, 163 Forthofer, Jason 787

 Barber, Klaus 163 Frary, Michael 47

 Barbour, R. James 657 Fuglem, Peter 13

 Barclay, H. J. 523 Fusina, Lesley A. 769

 Barrett, Stephen W. 575 Galante, Miguel 29

 Bate, Lisa 477 Gardner, Robert H. 563

 Bengston, David N. 59 Gercke, Diane M. 185

 Benton, R. A. 523 Gerlitz, Wendy 401

 Block, William 477 Gibson, Jennifer 419

 Bork, E. W. 537 Gould, Jim 17, 201

 Bradley, Tim 419 Grabner, Keith W. 321

 Bradshaw, Larry 787 Graham, Russell T. 615

 Brooks, Matt 513 Guyette, Richard P. 321

 Brown, B. N. 523 Hardy, Colin C. 451

 Bunn, Windy 419 Hawkes, B. C. 523

 Butler, Bret W. 787 Hinderman, Jennifer 691

 Caballero, David 29 Hirsch, Kelvin 13

 Calkin, Dave E. 673 Hood, Sharon 333, 513

 Cary, Geoffrey J. 563 Hutter, Lee C. 575

 Charney, Joseph J. 769 Hyde, Kevin 549

 Christenson, David 751 Ince, Peter J. 635, 647

 Crook, Shelly 47 Jain, Theresa B. 615

 Davies, Ian D. 563 Jentsch, Stephanie 477

 DeHart, Sharon 605 Jimenez, Dan 787

 DeMeo, Thomas 575 Johnson, Darren 193

Author Index

 Author Page Author Page
808 USDA Forest Service Proceedings RMRS-P-41. 2006.

Author Index

 Johnson, Jayne Fingerman 59 McMahan, Andrew 149

 Jones, Greg 549, 673 Miller, Gordon 13

 Jones, Jeffrey L. 575 Moghaddas, Jason J. 441

 Junor, Dave R. 221 Mouillot, Florent 563

 Jurgensen, Martin 605 Nasiatka, Paula 751

 Kafka, Victor G. 221 Nelson, Kristen C. 59

 Kane, Jeffrey M. 341 Omi, Philip N. 429

 Keane, Robert E. 563 Opperman, Tonja 201

 Kellogg, Lara-Karena B. 293 Ottmar, Roger 351

 Keyes, Christopher R. 395 Otway, S. G. 537

 Knapp, Eric E. 341 Page-Dumroese, Deborah 605

 Knotek , Katie 703 Parisien, Marc-André 221

 Kolaks, Jeremy 321 Parresol, Bernard R. 351

 Koo, Kyo Sang 305 Parsons, Russell A. 253, 283

 Koppenol, Patricia 47 Pilliod, David S. 615

 Kost, Jay R. 239 Pollock, S.L. 367

 Kramp, Andrew D. 647 Prichard, Susan J. 489

 Lasko, Richard 47 Raymond, Crystal L. 293

 Lavorel, Sandra 563 Reeves, Matthew C. 239

 Lee, Kristine M. 193 Reinhardt, Elizabeth 273, 283

 Lee, Myung Bo 305 Rice, Tom 605

 Lee, Si Young 305 Rigolot, Eric 29

 Lehmkuhl, John 477 Rollins, Matthew G. 193

 Lenihan, James M. 563 Rupp, T. Scott 563

 Li, Chao 563 Russo, G. L. 523

 Loeffl er, Dan 549, 673 Ryan, Kevin C. 193, 239

 Lutes, Duncan 273 Saab, Victoria 477

 Lynn, Kathy 401 Scanlon , Hugh 587

 Martin, Melinda 501 Scott, Joe H. 169, 273

 Martinson , Erik J. 429 Scott, Michael E. 729

 Marzano, Raffaella 29 Seavy, Nathaniel E. 593

 Matchett, J.R. 513 Seli, Rob C. 125

 McCaughey, Ward 451 Sexton, Tim 9

 McHugh, Charles W. 81, 125, 787 Shea, Dan 351

 McKenzie, Donald 293 Sheldan, Tim 13

 Author Page Author Page
USDA Forest Service Proceedings RMRS-P-41. 2006. 809

Author Index

 Shindler, Bruce 715

 Silverstein, Robin P. 549, 673

 Skog , Kenneth E. 657

 Smith, Helen Y. 451, 513

 Smith, James 193

 Spelter, Henry 635

 Stambaugh, Michael C. 321

 Steele-Feldman, Abran 283

 Stephens, Scott L. 465

 Stewart, Susan A. 185

 Stockmann, Keith 549

 Story, Scott 477

 Stratton, Rick 787

 Taylor, S. W. 523

 Tirocke, Joanne 605

 Titus, Marc 691

 Toman , Eric 715

 Twer, Martin 673

 Tymstra, Cordy 201

 Vaillant, Nicole M. 465

 Valachovic , Yana 587

 Varner, J. Morgan 341

 Watson, Alan E. 703

 Werth, Paul 757

 Whitehead, R. J. 523

 Williams, Jerry 5

 Williamson, Martha A. 67

 Won, Myoung Soo 305

 Wright , Clinton S. 489

 Wu, Ros 333

 Xanthopoulos, Gavriil 29

 Zamora, Benjamin 501

 Zeiler, J.D. 575

 Zhu, Zhiliang 193

 Zimmerman, Thomas 47

 Zuuring, Hans R. 673

 Author Page

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use of the forests and rangelands. Research is designed to meet 

the needs of National Forest managers, Federal and State agencies, 

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an equal opportunity provider and employer.

RMRS

ROCKY MOUNTAIN RESEARCH STATION