United States

Department

of Agriculture

Forest Service

Rocky Mountain

Research Station

Proceedings

RMRS-P-46CD

September 2007

The Fire Environment—Innovations, 

Management, and Policy

26-30 March 2007

Destin, FL
Butler, Bret W.; Cook, Wayne, comps. 2007. The fire environment—innovations, management, 

and policy; conference proceedings. 26-30 March 2007; Destin, FL. Proceedings RMRS-P-46CD. 

Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research 

Station. 662 p. CD-ROM.

The International Association of Wildland Fire sponsored the second Fire Behavior and Fuels 

conference in Destin, Florida. The conference theme was “Fire Environment—­Innovations, Management, and Policy.” Over 450 attendees participated in presentations on the latest innovations 

in wildland fire management, examples of successful and maybe not so successful management 

practices, current and potentially future wildland fire policy and recent advances in wildland 

fire science. Special sessions focused on smoke management, wildland urban interface, fire 

induced tree mortality, and live fuels. Integrated into this conference was the Joint Fire Science 

Program Annual Primary Investigator meeting where cutting edge JFSP sponsored research was 

presented.

Sponsors

• International Association of Wildland Fire

• U.S. Department of Agriculture, Forest Service 

Rocky Mountain Research Station

• National Wildfire Coordinating Group – 

Fire Environment Working Team

• National Center for Landscape Fire Analysis

• National Predictive Services Group

• National Interagency Fuels Coordinating Group

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/rmrs

Mailing Address Publications Distribution

 Rocky Mountain Research Station

 240 West Prospect Road

 Fort Collins, CO 80526

Conference Coordinators

• Mr. Wayne Cook, National Technology Transfer Specialist, Missoula Fire Sciences Laboratory, 

Rocky Mountain Research Station, U.S. Department of Agriculture, Forest Service

• Dr. Bret Butler, Fire Behavior Research Engineer, Missoula Fire Sciences Laboratory, Rocky 

Mountain Research Station, U.S. Department of Agriculture, 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 Wayne A. Cook, August 19, 2002. Flat Tops Wilderness Area, White River 

National Forest.

• Fire/Air Issues Coordinating Group

• Joint Fire Science Program

• National Fire Plan

• National Weather Service

• Florida Division of Forestry

• U.S. Air Force, Eglin Air Force Base

• The Nature Conservancy
Contents

Introduction

Introduction—2nd Fire Behavior and Fuels Conference: The Fire Environment—Innovations,

Management, and Policy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

	 	 Wayne Cook and Bret W. Butler, Conference Co-Chairs and Proceedings Compilers

Fire Behavior

Wildfires, Weather, and Productivity............................................. 7

	 	 Michel Louis Bernard and Noureddine Nimour

Technical Background of the FireLine Assessment MEthod (FLAME) . . . . . . . . . . . . . . . . . . . 27

	 	 Jim Bishop

Influence of Slope on Fire Spread Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

	 	 B.W. Butler, W.R. Anderson, and E.A. Catchpole

Modification of VanWagner’s Canopy Fire Propagation Model . . . . . . . . . . . . . . . . . . . . . . . 83

	 	 James Dickinson, Andrew Robinson, Richy Harrod, Paul Gessler, and Alistair Smith

Ignition and Flame Travel on Realistic Building and Landscape Objects in 

Changing Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97

	 	 Mark A. Dietenberger

Uncertainty Quantification in Rothermel’s Model Using an Efficient Sampling Method . . . . . 111

	 	 Edwin Jimenez, M. Yousuff Hussaini, and Scott L. Goodrick

Differences in Simulated Fire Spread Over Askervein Hill Using Two Advanced 

Wind Models and a Traditional Uniform Wind Field . . . . . . . . . . . . . . . . . . . . . . . . . . . 123

	 	 Jason Forthofer and Bret Butler

Influence of Radiation Absorption by Environmental Water Vapor on Radiation 

Transfer in Wildland Fires . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129

	 	 David Frankman, Brent W. Webb, and Bret W. Butler

Fire Behavior Modeling to Assess Net Benefits of Forest Treatments on Fire Hazard 

Mitigation and Bioenergy Production in Northeastern California . . . . . . . . . . . . . . . . . . 143

	 	 David J. Ganz, David S. Saah, Klaus Barber, and Mark Nechodom

Combining Turbulent Kinetic Energy and Haines Index Predictions for Fire-Weather 

Assessments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159

	 	 Warren E. Heilman1 and Xindi Bian

The Fire Environment—Innovations, 

Management, and Policy

26-30 March 2007

Destin, FL

i
Moisture Dynamics in Masticated Fuelbeds: A Preliminary Analysis . . . . . . . . . . . . . . . . . . . 173

	 	 Jesse Kreye and J. Morgan Varner

Relationships Between Prefire Composition, Fire Impact, and Postfire Legacies 

in the Boreal Forest of Eastern Canada . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187

	 	 Alain Leduc, Yves Bergeron, and Sylvie Gauthier

Role of Buoyancy and Heat Release in Fire Modeling, Propagation, and Instability . . . . . . . 195

	 	 Shahid M. Mughal, Yousuff M. Hussaini, Scott L. Goodrick, and Philip Cunningham

Fuel Dynamics and Fire Behaviour in Australian Mallee and Heath Vegetation . . . . . . . . . . 221

	 	 Juanita Myers, Jim Gould, Miguel Cruz, and Meredith Henderson

Bucklands Crossing Firefighter Burnover—A Case Study of Fire Behaviour and 

Firefighter Safety Implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 229

	 	 H. Grant Pearce

Mathematical Modeling of Forest Fire Initiation in Three Dimensional Setting . . . . . . . . . . . . 241

	 	 Valeriy Perminov

Preliminary Results of Fire Behavior in Maquis Fuels Under Varying Weather and 

Slope Conditions in Turkey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 249

	 	 Bülent Saglam, Ertugrul Bilgili, Ömer Kücük, Bahar Dinc Durmaz, and Ismail Baysal

Fuels Management

Fine Scale Vegetation Classification and Fuel Load Mapping for Prescribed Burning . . . . . . . 261

	 	 Andrew D. Bailey and Robert Mickler

Presettlement Fire Regime and Vegetation Mapping in Southeastern Coastal 

Plain Forest Ecosystems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 275

	 	 Andrew D. Bailey, Robert Mickler, and Cecil Frost

Fuels Management on the National Forests in Mississippi after Hurricane Katrina . . . . . . . . 287

	 	 Danny Bryant1 and Jay Boykin

Calculating Accurate Aboveground Dry Weight Biomass of Herbaceous Vegetation 

in the Great Plains: A Comparison of Three Calculations to Determine the Least 

Resource Intensive and Most Accurate Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 293

	 	 Ben Butler

Testing the Modeled Effectiveness of an Operational Fuel Reduction Treatment 

in a Small Western Montana Interface Landscape Using Two Spatial Scales . . . . . . . . . . 301

	 	 Michael G. Harrington, Erin Noonan-Wright, and Mitchell Doherty

Role of Fire in Restoration of a Ponderosa Pine Forest, Washington . . . . . . . . . . . . . . . . . . . . 315

	 	 Richy J. Harrod, Richard W. Fonda, and Mara K. McGrath

Comparing the Effectiveness of Thinning and Prescribed Fire for Modifying Structure 

in Dry Coniferous Forests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 329

	 	 Richy J. Harrod, Nicholas A. Povak, and David W. Peterson

FireSmart®-ForestWise: Managing Wildlife and Wildfire Risk in the Wildland/Urban 

Interface—a Canadian Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 347

	 	 Alan Westhaver, Richard D. Revel, and Brad C. Hawkes

ii
Smoke Management

Use of Historic Images as a Tool for Estimating Haze Levels—Natural Visibility and the 

Role of Fire . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 369

	 	 Gordon Andersson

Smoke Monitoring Network on 2006 Northern California Fires . . . . . . . . . . . . . . . . . . . . . 385

	 	 Brenda Belongie and Suraj Ahuja

Visualization and Modeling of Smoke Transport Over Landscape Scales . . . . . . . . . . . . . . . 391

	 	 Glenn P. Forney and William Mell

Validation of BlueSky Smoke Prediction System Using Surface and Satellite

Observations During Major Wildland Fire Events in Northern California . . . . . . . . . . . . 403

	 	 Lesley Fusina, Sharon Zhong, Julide Koracin, Tim Brown, Annie Esperanza,

	 	 Leland Tarney, and Haiganoush Preisler

Development and Demonstration of Smoke Plume, Fire Emissions, and Preand Postprescribed Fire Fuel Models on North Carolina Coastal Plain 

Forest Ecosystems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 409

	 	 Robert A. Mickler, Miriam Rorig, Christopher D. Geron, Gary L. Achtemier,

	 	 Andrew D. Bailey, Candice Krull, and David Brownlie

Internet VSMOKE: A User-Oriented System for Smoke Management . . . . . . . . . . . . . . . . . . 427

	 	 James T. Paul, Alan Dozier, and Daniel Chan

Erosion Control and Land Treatments

Cascading Disaster Models in Postburn Flash Flood . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 443

	 	 Fred May

Assessing the Effectiveness of Seeding and Fertilization Treatments for Reducing 

Erosion Potential Following Severe Wildfires . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 465

	 	 David W. Peterson, Erich K. Dodson, and Richy J. Harrod

Fire and Fire Surrogate Treatments in Mixed-Oak Forests: Effects on Herbaceous 

Layer Vegetation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 475

	 	 Ross Phillips, Todd Hutchinson, Lucy Brudnak, and Thomas Waldrop

Soil Physical Property Response to Prescribed Fire in Two Young Longleaf Pine 

Stands on the Western Gulf Coastal Plain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 487

	 	 Mary Anne Sword Sayer

Innovations

Fire Behavior Sensor Package Remote Trigger Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 499

	 	 Dan Jimenez, Jason Forthofer, James Reardon, and Bret Butler

Fire History and Age Structure Analysis in the Sherburne National Wildlife 

Refuge: Establishing Reference Conditions in a Remnant Oak Savanna Woodland . . . . . 507

	 	 Kurt F. Kipfmueller and Tim Hepola

Application of Ground-Based LIDAR for Fine-Scale Forest Fuel Modeling . . . . . . . . . . . . . . . 515

	 	 E. Louise Loudermilk, Abhinav Singhania, Juan C. Fernandez, J. Kevin Hiers,

	 	 Joseph J. O’Brien, Wendell P. Cropper Jr., K. Clint Slatton, and Robert J. Mitchell

iii
Innovations in Fuels Management: Demonstrating Success in Treating a Serious 

Threat of Wildfire in Northern Minnesota . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 525

	 	 Dennis Neitzke

New Technology in Postfire Rehab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 535

	 	 Joe Sabel

Verification of the WFAS Lightning Efficiency Map . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 539

	 	 Paul Sopko, Don Latham, and Isaac Grenfell

Communication and Collaboration

Integrating Climatic and Fuels Information into National Fire Risk Decision Support Tools . 555

	 	 W. Cooke, V. Anantharaj, C. Wax, J. Choi, K. Grala, M. Jolly, G.P. Dixon,

	 	 J. Dyer, D.L. Evans, and G.B. Goodrich

Educating and Engaging the Business Sector in Reducing Wildfire Property Losses . . . . . . . 571

	 	 Anne S. Fege and Christopher Blaylock

Efficacy of the California Bureau of Land Management Community Assistance and

Hazardous Fuels Programs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 585

	 	 David J. Ganz, David S. Saah, Matthew A. Wilson, and Austin Troy

Firefighters United for Safety, Ethics, and Ecology (FUSEE): Torchbearers for a 

New Fire Management Paradigm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 607

	 	 Timothy Ingalsbee, Joseph Fox, and Patrick Withen

Critical Elements in the Development and Implementation of Community 

Wildfire Protection Plans (CWPPs) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 613

	 	 Pamela Jakes, Sam Burns, Antony Cheng, Emily Saeli, Kristen Nelson4

	 	 Rachel Brummel, Stephanie Grayzeck, Victoria Sturtevant, and Daniel Williams

San Diego Declaration on Climate Change and Fire Management: Ramifications for

Fuels Management...................................................... 625

	 	 Brian P. Oswald and the Board of Directors, The Association for Fire Ecology

Analysis of the Risk Management Decisionmaking Processes and the Decision 

Support Systems in the Wildland Fire Agencies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 633

	 	 Patrick Withen

Appendix

Presentations for Which Papers Were Not Submitted for These Proceedings . . . . . . . . . . . . 647

iv
v

CAUTION:

PESTICIDES

Pesticide Precautionary Statement

This publication reports research involving pesticides. It does no contain 

recommendations for their use, nor does it imply that the uses discussed here 

have been registered. All uses of pesticides must be registered by appropriate 

State and/or Federal agencies before they can be recommended.

CAUTION: Pesticides can be injurious to humans, domestic animals, 

desirable plants, and fish or other wildlife—if they are not handled or applied 

properly. Use all pesticides selectively and carefully. Follow recommended 

practices for the disposal of surplus pesticides and pesticide containers.

The use of trade or firm names in this publication is for reader information and does not 

imply endorsement by the U.S. Department of Agriculture of any product or service
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Introduction

Fire Behavior

Fuels Management

Smoke Management

Erosion Control and Land Treatments

Innovations

Communication and Collaboration

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Introduction
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USDA Forest Service Proceedings RMRS-P-46CD. 2007. 

Introduction

2nd Fire Behavior and Fuels Conference:

The Fire Environment—Innovations, 

Management, and Policy

Wayne Cook and Bret W. Butler, Conference Co-Chairs and 

Proceedings Compilers

The 2nd Fire Behavior and Fuels Conference: Fire Environment – Innovations, Management and Policy was held in Destin, FL, March 26-30, 2007. 

Following on the success of the 1st Fire Behavior and Fuels Conference, this 

conference was initiated in response to the needs of the National Wildfire 

Coordinating Group – Fire Environment Working Team.

Fire management programs are designed to reduce risks to communities 

and to improve and maintain ecosystem health. The conference addressed 

recent innovations in technology, management and policy. The scope included 

not only the how to, but also the what and why of the fire environment. The 

more than 450 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. 

Each day began with invited speakers who presented a range of viewpoints. 

Topics included the psychology of wildland fire management, smoke and 

public policy, fire as an ecological process in wilderness areas, and fuels management policy and direction of U.S. Federal agencies. Panels addressed two 

key topics: “Extreme Fire Behavior – What is it?” and “The Human Element 

in Forest Fire Operations: Thinking Deciding, Acting, Learning.” About 

100 people took advantage of the optional preconference workshops that 

described and demonstrated computer systems, models, and methods that 

can be used in support of fire and fuels management. In addition to the six 

invited speakers, there were 159 oral and 73 poster presentations. Presenters 

described their experiences, findings, and ideas on topics including:

• modeling, risk assessment, and decision support systems

• fuel characterization and mapping

• fuel treatment and prescribed fire

• fire ecology and fire effects

• economics and biomass utilization

• communication and collaboration

• case studies

Forty-eight of the presenters elected to submit a paper for the published 

proceedings.Titles and authors of presentations without papers are listed in 

the appendix to give an indication of the scope of the conference.

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

An important element was the interaction and sharing of information that 

occurred outside of the formal presentations. Many of those who responded 

to the after-conference survey listed “networking” as one of the most valuable aspects of the conference. They noted the mix of managers, researchers, 

In: Butler, Bret W.; Cook, Wayne, 

comps. 2007. The fire environment—

innovations, management, and policy; 

conference proceedings. 26-30 March 

2007; Destin, FL. Proceedings 

RMRS-P-46CD. Fort Collins, CO: 

U.S. Department of Agriculture, 

Forest Service, Rocky Mountain 

Research Station. 662 p. CD-ROM.
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 

academia, practitioners, and policymakers. The field of fuels management will 

undoubtedly benefit from the many personal contacts made at the conference. Special thanks are owed to the organizing committee, who formulated 

the structure of the conference, and planned and implemented details of the 

conference. The conference was a success due to the contributions of many 

dedicated individuals.

Conference Co-Chairs:

• Wayne Cook, U.S. Department of Agriculture (USDA), Forest Service, 

Rocky Mountain Research Station (RMRS), Fire Sciences Lab, Fire 

Behavior Research, Missoula, MT

• Bret Butler, USDA, Forest Service, RMRS, Fire Sciences Lab, Fire Behavior Research, Missoula, MT

Invited Speakers:

• Susan Conard, National Program Leader for Fire Ecology Research, 

USDA Forest Service

• Joe Ferguson, National Incident Management Organization, Fire and 

Aviation, USDA Forest Service

• Michael Long, State Forester, Florida Division of Forestry

• William Paleck, Superintendent-Retired, Lake Chelan National Recreation 

Area, Ross Lake National Recreation Area, North Cascades National 

Park, U.S. Department of the Interior (USDI) Park Service

• Sally Shaver, Director Air Quality Standards Division, U.S. Environmental 

Protection Agency

• Karl Weick, Professor of Organizational Behavior and Psychology, 

University of Michigan

Conference Organizing Committee:

• Detlef Decker, Washington State University

• Bill Gabbert, International Association of Wildland Fire Executive Director

• Marc Titus Washington Department of Natural Resources

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

• Robert Ziel, Michigan Department of Natural Resources

• Paul Sopko, Fire Behavior Research, RMRS Missoula Fire Sciences Lab

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

• Tim Swedberg, Joint Fire Science Program

• Louise Kingsbury, USDA Forest Service Research (retired)

• Kyle Shannon, Fire Behavior Research, RMRS Missoula Fire Sciences Lab

• Jason Forthofer, Fire Behavior Research, RMRS Missoula Fire Sciences Lab

• Jim Brenner, Florida Division of Forestry

• Colleen Finneman, USDA Forest Service, Region 1 Fire and Aviation 

Management

• James Furman, Eglin Air Force Base

• Dave Thomas, USDA Forest Service (retired), Renoveling consultant

• Mike Spaits, Eglin Air Force Base

• Paul Schlobohm, USDI Bureau of Land Management

• Zachary Prusak, The Nature Conservancy

• Pete Lahm, USDA Forest Service

Introduction 2nd Fire Behavior and Fuels Conference: The Fire Environment—Innovations, Management, and Policy Cook and Butler
Fire Behavior
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USDA Forest Service Proceedings RMRS-P-46CD. 2007. 

Abstract—The object of this paper is to show the intercorrelations existing between 

statistics of wildfires (occurrences: N; areas burned: A), climatic parameters (precipitation: P; temperature: T) and net primary productivity: NPP. To this purpose, statistics 

of wildfires have been studied in several regions of the world, focusing on temperature 

and precipitation. The present analysis has been performed on French Mediterranean 

Departments (Bouches-du-Rhône, Hérault, and Var), Northern Ontario (Canada), 

Alicante Region (Spain), Yellowstone National Park –YNP-(USA), San Diego (California, 

USA). Concerning the temperature, the monthly analysis of fire occurrence shows two 

distinct periods with different fire production laws, both being of the Arrhenius type: 

the first one corresponding to the vegetation during the rest period, the second one 

during the vegetative production period. Concerning the precipitation, the monthly 

analysis of fire occurrence led to two distinct laws depending on the region climatic 

type (or eventually the seasonal characteristics). Thus, the monthly variation of fire 

occurrence (in log scale) is a linear increasing or decreasing function according to 

precipitation. In French Mediterranean Regions these expressions are bound to the 

rate of soil water extraction by the plant roots (a limiting step of plant production?). 

Out of the intrinsic importance of the presented results, in the field of primary productivity (dependence on Kelvin temperature) it brings a confirmation of the reality 

of our hypothesis on the role of the limiting steps of plant production in the rate of 

wildfire production.

Introduction

Wildland fire studies on a statistical point of view are more fructuous in 

information than the individual determinist studies. They are the only ones 

allowing correlating wildland fires with intrinsic factors (nature and composition of vegetation, water content...) or extrinsic factors (precipitation, 

air temperature, wind, lightning...) given an area and period of time. In the 

first part of this document, we will discuss our studies of the dependence 

of fires occurrence and burnt area on meteorological data (precipitation and 

temperature).We will then show how correlations obtained can be representative of climate of some regions. That will finally lead us to show you how 

they are bound to vegetation productivity on an annual scale.

Wildfire and Climatic Parameters

Statistics of wildfires have been studied in several regions of the world, 

focusing on temperature and precipitation. The present analysis has been performed on French Mediterranean Departments (Bouches-du-Rhône, Hérault, 

and Var), Northern Ontario (Canada), Alicante Region (Spain), Yellowstone 

National Park –YNP-(USA), San Diego (California, USA).

Wildfires, Weather, and Productivity

Michel Louis Bernard1 and Noureddine Nimour1

In: Butler, Bret W.; Cook, Wayne, 

comps. 2007. The fire environment—

innovations, management, and policy; 

conference proceedings. 26-30 March 

2007; Destin, FL. Proceedings 

RMRS-P-46CD. Fort Collins, CO: 

U.S. Department of Agriculture, 

Forest Service, Rocky Mountain 

Research Station. 662 p. CD-ROM.

1 Research Scientist and Research Research Scientist and Research

Technician, respectively, Laboratoire de 

Chimie Macromoleculaire, Université 

de Provence. Lead author ml-bernard@

numericable.fr
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 

Wildfires and Precipitation

Past works—Many works have been done on the influence of water content 

of plants on their flammability and the propagation of wildland fires. On 

the contrary, only a few thorough studies have been published on the role of 

precipitation on wildland fires.

Trabaud (1980) has studied occurrence of fires of Brachypodium Ramosum

in the Montpelier garrigue. According to him, rainfall is the more important garrigue. According to him, rainfall is the more important . According to him, rainfall is the more important 

climatic factor that influences occurrence of fire. It is not really the total 

quantity of rainfall that is a stopping event, but rather its distribution mode. 

According to him, the number of fires is an exponentially decreasing function of precipitation. However, the statistical curve he showed expresses a 

1/X function, not an e–x function.

Trabaud has looked for predictions of fire occurrence taking account of 

rainfall regime, and particularly of the number of days without rain that 

increases risk of fires. He proposed a model that has shown that the risks of 

fire increase linearly as dry periods are longer.

Douguedroit (1992) pointed out the close relation between precipitation 

and wildland fires in the Mediterranean region. Precipitation in summer 

controls actual evapo-transpiration (AET) but no correlation analysis between 

these factors is proposed.

Latham and Rothermel (1993) have investigated the possibility that a useful 

probability of fire stopping precipitation could be developed from historical 

weather records. Persons familiar with weather and fire behavior suggested 

a fire stopping precipitation criterion of “at least 0.5 inches of precipitation 

in 5 days or less.” Using data from weather stations in the Northern Rocky 

Mountains, USA (daily precipitation data for the period between 1970 and 

1985) they found that the Weibull probability distribution was an excellent fit 

for the problem. The method can be applied to other “fire stopping criteria” 

using the same techniques.

Viegas and Viegas (1994) have related the total area burned yearly in Portugal 

(from 1975 to 1992) to rainfall during particular periods of the year. The best 

correlation: annual burned area – precipitation, concerns the modified precipitation observed in Coïmbra from June to September of each year, showing 

that it is possible to use data from a single weather station to represent rainfall 

in a wider area.

Forgeard (1994) has related the monthly number of fires in the Lande Bretonne e Bretonne Bretonne

(west of France) to precipitations for the years 1976 to 1993. Their number (Y) 

is correlated to monthly precipitation (X) according to the equation:

Y = 98.968 + 1.7885 X (R2 = 0,781)

Daily correlations: lightning fires in Northwestern Ontario—Wildland fires 

may have different possible causes, most of them being human causes. To 

avoid any influences coming from various causes we have taken account in a 

preliminary study only a natural cause: lightning ignited fires.

A number of Canadian fires are lightning ignited fires: according to Boulard 

(1993) they represent a proportion of 30 to 50 percent of fires.

Flannigan and Wotton (1990) have investigated the relationship between 

lightning activity and the occurrence of lightning-ignited forest fires in the 

Northwestern region of Ontario. They found that Duff Moisture Code and 

the multiplicity of the negative lightning discharges were the most important 

variables for estimating the number of lightning ignited fires. Fires have been 

counted with corresponding values of local precipitation and temperature. From 

Wildfires, Weather, and Productivity Bernard and Nimour
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 

data files provided by the authors and completed by the Ontario Ministry of 

Natural Resources (Ward 1994), we have analyzed the data relating to UTM:15 

zone, to be 1,152 fires accounted in 1988.

In this region, in a log scale (fig. 1), the variation of fire occurrence is 

a linear increasing or decreasing function of precipitation. The distinction 

between the two regimes has been done comparing temperature periods:

Log N = 1.019 + 0.0622P For humid and warm period

‡ N = 10.44 e +0.143P

Log N = 2.739 + 0.2517P For dry and cold period

‡ N = 548 e –0.579P

Figure 1—Log N = g(P). ������ Ontari �� ������� ���� , UTM15, 1988.

Humid period for precipitation must be associated to warm period for 

temperature. So precipitation highly contributes the evapo-transpiration 

during this period (positive exponent). Dry period for precipitation must 

be associated to cold period for temperature. So precipitation contributes to 

evapo-transpiration at a low rate.

The humid and warm periods approximately correspond to the periods of 

vegetation production in this region (sub artic region).

Daily correlations: wildfires in West Var (1973)—Olivier (1975) has studied 

the role of vegetation and ecological factors on the “Basse Provence” region 

wildfires. Individual wildfires were registered from area as low as 0.01 ha 

over the whole year 1973.

Wildfires, Weather, and Productivity Bernard and Nimour
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 10

Occurrence of fires and precipitations were summarized by half a month 

values (fig. 2). Again we observe the same type of behavior:

(1) Log N = 0.6306 + 0.0107P For humid and cold period

(2) Log N = 1.1716 + 0.0125P For dry and warm period

The warm and dry period approximately corresponds to the period of 

vegetation production in this region (Mediterranean region).

Such typical relations have been found in other Mediterranean regions 

such as:

	 •	 Languedoc-Roussillon region (data from Rambal 1984):

(3)	 Log N = 2.8961 – 0.0047P

	 •	 Alicante region (data from Terol 1987):

(4)	 Log N = 1.537 – 0.029P

So the monthly occurrence of fires in Mediterranean regions in the warm 

period is a function of the exponential form:

N = Constant * e–aP

Figure 2—Log N = g(P). West Var, 1973.

Wildfires, Weather, and Productivity Bernard and Nimour
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 11

In order to interpret these equations, it is necessary to examine the dynamics of water in the ecosystem, that is to say, soil water balance and the relative 

importance of its different terms with season (particularly during the summer 

season, in the Mediterranean regions; Humbert and Najjar 1992).

Rambal (1984) has studied soil water balance and water uptake characteristics by Quercus Coccifera L, which cover in the garrigue more than garrigue more than 

100,000 ha in the French Mediterranean region.

Water balance equation: ΔS = P * R * AET * D

Where ΔS is the daily change in stored water, P is the precipitation, R is the 

surface runoff, AET is the actual evapo-transpiration and D is the flow of 

water at the bottom of the root zone measured in mm/day.

The study of water balance during every year shows that when yearly precipitations are less than 578 mm, deep drainage loss is negligible and almost 

all the precipitations infiltrating the soil are lost by evapo-transpiration. Studying the water uptake at different deepness in the summer, Rambal proposed 

a relation between the relative deep uptake and the precipitation. According 

to Rambal, precipitation modifies relative water uptake Y (%) according to 

an exponential law:

Y = 25.7 * e–0.006 * x r2 = 0.963

X being precipitation in June and July.

This relative water uptake is also the expression of relative actual evapotranspiration, which is the equivalent of photosynthetic fixation of carbon 

within the same period (Frontier and Viale 1991; Ramade 1994). Photosynthetic activity on plants leads to formation of new twigs and chemical 

modifications of old ones, then to production of fire sensible biomass.

Interannual occurrence of fires in Var area—Yearly occurrences of fires have 

been correlated to precipitation with the data of Var area, for the period 1974 

to 1986 (Promethee Databank). Several 1-year periods have been tested, and 

the best result was found taking a period starting in July. The correlation 

equation is:

Log N annual = 2.90 – 0.000365P annual with r2 = 0.98

Thus, we can conclude that in Mediterranean regions, during the vegetation 

production period, occurrences of wildfires (monthly or yearly) are regulated 

by the deep water uptake rate of plant roots.

Wildfires and Temperatures

Quantitative effects of temperature on wildfire statistics have not been 

developed as for precipitation. In order to approach the problem we appealed 

to chemical kinetics laws.

Considering the parameters of fire occurrence N or area burnt A, during 

a defined time interval and in a given region as reaction rates in chemical 

kinetics, we studied their possible dependences to Arrhenius factor E/RT, E 

being the activation energy of the reaction, R the gas constant, and T Kelvin 

temperature (compare: lightning fires dependence on precipitation and temperature, Bernard 2004c).

Wildfires, Weather, and Productivity Bernard and Nimour
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 12

Lightning fires in Northwestern Ontario—Coming again to Northwestern 

fire data (fig. 3), we observe two Arrhenius plots, the first being the warmest 

period and the second the coldest one. During the vegetation production 

period (warmest period) the activation energy corresponds to photosynthesis. 

During the vegetation rest period (coldest) the activation energy corresponds 

to U.V.B. energy.

Figure 3—Log N = f(T). Ontario, UMT 15, 1988.

Log N = 49.52 – 14,122 / T ‡	 Warmest period

E act = 271.5 KJ/mol. ‡ 439.3 nm (Chlorophyll A)

Log N = 72.164 – 20421 / T ‡	 Coldest period

E act = 392 KJ/mol. ‡ 303.8 nm (U.V.B)

In analogy with the chemical kinetics laws, to interpret those results, 

we need to use the law of the bottleneck (the rate of the global reaction is 

determined by the slowest rate among the different successive reactions that 

compose it).

Only old top parts of

vegetation at rest

old top parts

Growing period:

New biomass

Photosynthesis production

(PAR)

Lightning fires

Rate of global processes (N) controlled by rate of the biomass production

Warmest period

Wildfires, Weather, and Productivity Bernard and Nimour
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 13

Wildfires in Bouches-du-Rhône area 1986—Two Arrhenius linear plots are 

observed for monthly wildfire in Bouches-du-Rhône area (fig. 4) with a good 

square correlation coefficient (R2 = 0.93), one for the summer period and 

another for the winter period. These results must be interpreted using the 

same argument as for the wildfires in Northwestern Ontario.

Other equations of Arrhenius type have been observed for Mediterranean 

type regions:

	 •	 Languedoc-Roussillon area (temperature in Montpellier)

Log N = –5797 * 1 / T + 22.034 (R2 = 0.461)

	 •	 Southern California, USA, 2005 (temperature in San Diego)

Log N = –28674 * 1 / T + 99.04 (R2 = 0.497)

Figure 4—Log N = f(T). Bouches-du-Rhône, 1986.

Vegetation at rest

(before or after

growing period)

Lightning fires

Rate of global processes (N) controlled by U.V. flux

Coldest period

Wildfires, Weather, and Productivity Bernard and Nimour
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 14

Annual Wildfires in Yellowstone National Park—They have been extensively analysed as much on area burnt as on wildfire occurrences, and as 

much on lightning caused fire as on human ones (Douglas 1975; Romme 

1989; Despain 1998). See Bernard (1998a): interpretation of fire intensity 

in Yellowstone National Park for the 1700-1990 period.

Most of the wildfires in YNP are class 3 fires (fig. 5). We observe the 

same Arrhenius law with the lightning fires (fig. 6) on the annual occurrence criterion.

Figure 6—Yellowstone National Park: logN= f(T) Lightning fires. Years: 1933, 1948, 1949, 1952, 1960,

1961, 1963.

Figure 5—Yellowstone National Park: log A =f(log N). Human caused fires. Years: 1933,

1934, 1940, 1947, 1948, 1949, 1953, 1954, 1955, 1960, 1967, 1968.

Wildfires, Weather, and Productivity Bernard and Nimour
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 15

For the human caused fires, the Arrhenius law is verified for both annual 

occurrences and area burnt criterion (fig. 7).

Thus, we can conclude that the rate of photosynthetic radiations regulates 

the monthly occurrence of fires during the vegetation production period and 

yearly occurrences of fires and area burnt at the interannual scale.

Figure 7—Yellowstone National Park: log A = f(T) and log N = f(T). Human caused fires. 

Years : 1933, 1934, 1940, 1948, 1949, 1953, 1960.

Wildfire and Climate

During the vegetation period we define the occurrence of fires as function 

of variable P and T.

(1) Log N = a + b * P 

(2) Log N = c – d / T

We observe the following conditions on the constants:

ß	c : c > 0,

ß	d : d > 0,

ß	a : a > 0,

ß	b : b > 0 (NW Ontario Climate)

 b < 0 (Mediterranean type Climate)

In the equation 1, N is univocal of P, in equation 2, N is univocal of T. P 

and T are measured data or data taken from meteorological stations and are 

interdependant in the vegetation production period.

Wildfires, Weather, and Productivity Bernard and Nimour
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 16

Identifying the two equations, we get:

(3) P = (c–a) / b + d / (b *T)

We now need to compare this relation (3) to real temperature and precipitation data of different regions.

Wildfires in Northwestern Ontario

Referring to the figure 1 for precipitation and figure 3 for the temperature, here are the equations expressed with the real data from Northwestern 

Ontario:

(1’) Log N = 1.019 + 0.0622P

(2’) Log N = 49.5 – 14,122 / T

By identification, we get:

(3’) P = 780 + 0.227 *106 / T

To compare those data, we choose the 1988 meteorological data from Sioux 

Lookout station in Ontario, and try to demonstrate this relation monthly from 

May to August 1988. Figure 8 shows what we found for each studied month. 

We found an equation that even has a correlation coefficient equal to 1:

P = 3,607 – 1.044* 106 / T (R2 = 1)

We observe:

	 •	 Climate data from Sioux Lookout verify the linear correlation P, 1/T.

	 •	 Precipitation from Sioux Lookout is found 4.6 times larger than from 

the N.W. Ontario region.

Figure 8—Climatologic chart of Ontario, UTM 15, Canada at Sioux Lookout meteorological station. P = f(Tmax). 

May-August 1988.

Wildfires, Weather, and Productivity Bernard and Nimour
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 17

Wildfires in the Languedoc-Roussillon Area

The next equations (1’’) and (2’’) are explained in a deeper way in a former 

publication (compare “Application of the concept of climatologic chart to 

Mediterranean climate variability,” Bernard and Nimour 2005a).

Here are the equations expressed with the real data from Promethee 

database and from Rambal (1984). For Montpellier, we found:

(1’’) Log N = 2.896 – 0.0047 P

(2’’) Log N = 22.034 + 5,797 /T

By identification, we get:

(3’) P = –4,071.91 + 1.234 *106 / T

To compare those data, we choose the average values of meteorological 

data from Montpellier station in Languedoc-Roussillon (the average values 

of each month are calculated over the 1964 to 1982 period for precipitation, and the 1980 to 1982 period for temperatures). We try to demonstrate 

this relation monthly from July to October. Figure 9 shows what we found 

for each studied month. We found an equation that even has a correlation

coefficient equal to 0.9995:

P = –3,932.7 + 1.00* 106 / T (R2 = 0.9995)

The agreement is very satisfactory: Climate data from Montpelier verify 

the linear correlation P, 1/T

Figure 9—Climatologic chart of Montpellier.

Wildfires, Weather, and Productivity Bernard and Nimour
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 18

A Concept of Climatologic Chart

Other reconstructions of climate from wildfire occurrence have been stated. 

That confirms the generality of the method. So we introduced the concept of 

climatologic chart (“A concept of climatologic chart,” Bernard and Nimour 

2004a), which is based on the law of interaction between the vegetation, the 

soil, the water, the atmosphere, and the solar radiations.

Climatologic charts could be more useful than historic representations of 

climate characteristics (climatogram, Emberger or Gaussen ombrothermal 

diagrams) to build a new classification of climate in order to perceive any 

possible climate changes (compare “Application of the concept of climatologic 

chart to Mediterranean climate variability,” Bernard and Nimour 2005a).

To illustrate the prior statement, we can refer to figure 10 as an example 

of semiarid climate.

Figure 10—Climatologic chart of Willistone (North Dakota) January-December 1971 to 2000.

Hyper-Mediterranean Climate in San Diego (CA, USA)

The San Diego climate belongs to west-facing continents climate, a particular case of Mediterranean climate that we’ll called “Hyper-Mediterranean.” 

Among this type of climate we also find Mexico, Chile, South Africa (Le Cap), 

West Australia (Brisbane).

They are characterized by an attenuation of precipitation along the year 

compared to precipitation we observe in Mediterranean climate. The expression of this attenuation is of an exponential form and leads to a new expression 

of the climatologic charts:

(4) Log P = A / T – B

A and B being characteristic constant of the meteorological station.

Wildfires, Weather, and Productivity Bernard and Nimour
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 19

In order to study the interaction between wildfire and climate data of 

San Diego, we chose the year 2005 because its summer was abnormally warm. 

Monthly occurrence of fires in southern California was taken from N.I.C.C. 

2005 data. As shown in figure 11, the equations are:

Log N2005 = –1.4202 Log P + 3.34

 Log N 2005 = –28,674 / T + 99.04

The identification gives us:

(4’) Log P = 20,190 / T – 67.38

Compared to climatologic chart of San Diego 2005, the agreement is satisfactory:

Log P = 16,835 / T – 56.012 (R2 = 0.46)

In order to study a possible climate change in San Diego, we established 

its climatologic chart for two annual periods 1927 to 1932 (a) and 1995 to 

2004 (b):

(a) Log P = 30,823 / T – 104.33 (R2 = 0.87)

(b) Log P = 12,028 / T – 40.23 (R2 = 0.73)

Figure 11—San Diego, 2005. Log N = f(T).

Wildfires, Weather, and Productivity Bernard and Nimour
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 20

In analogy with the equations found for the Mediterranean climate, we will 

use for the Hyper-Mediterranean one, the next method of reconstruction:

(1) Log N = a + b * Log P

(2) Log N = c – d / T

Identifying the two equations, we get:

(3) Log P = (c–a) / b + d / (b *T)

Comparing the coefficient of the 1927 to 1932 period to the 1995 to 2004 

period, we observe:

	 •	 The only variation of the “b” term. A 61 percent reduction of term “b” 

expresses the increasing value of aridity in the San Diego climate.

	 •	 A stability of the “d” term (containing the activation energy).

Wildfires and Global Warming

A particular interest for the effect of temperature on wildfire comes from 

global warming. Recent well documented publications of Westerling (2006) 

and Running (2006) analyze the different causes of increasing wildfire 

activities with global change, particularly in the Western United States of 

America.

In our own analyses of large wildfires in southern California in October 

2003 (Bernard and Nimour 2004b), we pointed out an abnormal temperature rise in the region some days before the start of large landfires (Bernard 

and Nimour 2004d).

Conversely, the statistics of annual fires in Yellowstone National Park 

(1700 to 1990) show no fire during the little ice age (compare “Interpretation of fire intensity in Yellowstone National Park for the 1700-1990 period,” 

Bernard 1998a).

Using the Arrhenius models of occurrence of fire and burnt areas:

 Log N 2 – Log N1 = – En

act / (2.3 R) (1 / T2 –1 / T1)

Log A2 – Log A1 = – En

act / (2.3 R) (1 / T2 –1 / T1)

A “theoretical” evaluation of the effect of global warming on natural forest 

fire was performed in 2004 (table 1). Different cases have been considered 

according to the two different fire classes (class 1 and class 3) and the two 

natures of control (UV lightning and PAR)

Table 1—Implication of global warming on natural forest fires.

Control by UVB

E act = 392 KJ/mole

Control by PAR

E act = 271.5 KJ/mole

Remarks T (°C)

(t0 = 21°C)

Coefficient for

N or A a

Coefficient for

A b

Coefficient for

N or A a

Coefficient for

A b

0 1 1 1 1

+0.5 1.3 2.23 1.20 1.44

Reference : 1900-2000

(Papadopol 2000)

+0.77 1.5 3.50 1.33 2.35

ESTIMATIONS +2 2.9 25.3 2.10 9.26

21st century +5 13.72 6.12

Extreme thermal event +10 192 (Re-evaluation of basic hypothesis)

Little ice age (no sunspot) –2 0.33 0.04 0.48 0.1 a

 Class 1 fires : log A = log N b

 Class 3 fires : log A = 3 log N

Wildfires, Weather, and Productivity Bernard and Nimour
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 21

Productivity and Climate Parameters

Net primary productivity, biomass, and associated environmental variables 

are now publicly available for 53 detailed study sites, of which more than half 

have data for belowground biomass or biomass dynamics. Aboveground NPP 

ranges from 35 to 2320 g.m–2.year–1 (dry matter) and total NPP from 182 

to 3538 g.m–2.year–1. These data are available from the Oak Ridge National 

Laboratory Distributed Active Archive Center (Scurlock and Olson 2002).

According to Lieth and Whittaker (1975), the range of NPP in g.m–2.year–1

is: for tropical rain forest 1000 to 3000, warm temperature mixed forest 600 

to 2500, Tundra 60 to 1300, Mediterranean sclerophyll forest (chaparral) 

250 to 1500.

According to Gray (1982), when he studied productivity in southern California, the annual aboveground primary production in Ceanothus Chaparral

was 1056 g.m–2.year–1 and in Costal Sage scrubs 355 g.m–2.year–1.

We will refer for our studies to the original works made by Lieth and Whittaker (1975) on modeling of the primary productivity of the world.

Lieth and Whittaker Data

Lieth and Whittaker (1975) have summarized the net primary productivity of four large zones in the world, each of them containing from 12 to 

20 ecoregions with their climatic data of precipitation in millimeters and 

temperatures in Celsius degrees.

For each zone, we analyzed the correlation between net primary productivity with precipitation and with Kelvin temperature according to the Arrhenius 

term (table 2). Identifying the two correlations, we obtain an expression of 

precipitation as function of 1 / T (table 3). For the three first regions, the 

agreement is satisfactory in spite of their low square regression coefficients.

We have particularly examined the fourth region results, which has the 

best square regression coefficient. In this zone, the net primary productivity 

is among the highest of the world, up to 4,000 gr per square meters by year 

(Lieth 1962).

Table 2—Net primary productivity (NPP) as functions of precipitation (P) and 

temperature (T).

NPP = f(P) NPP = φ(T)

Zone NPP �n ���2.year ; P in mm T in Kelvin

1 NPP = 0.528 P + 727.3 NPP = –2.106 (1/T) + 7 881

 R2 = 0.24 R2 = 0.23

2 NPP = 0.4651 P + 858.8 NPP = –2.106 (1/T) + 8 403

 R2 = 0.11 R2 = 0.11

3 NPP = 0.849 P + 356.7 NPP = –2.106 (1/T) + 6 603

 R2 = 0.19 R2 = 0.20

4 NPP = 0.710 P + 911 NPP = –6.106 (1/T) + 22 074

 R2 = 0.46 R2 = 0.65

Wildfires, Weather, and Productivity Bernard and Nimour
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So the net primary productivity of region 4 as function of temperature is:

P2 – P1 = 6* 106 (1 / T2 –1 / T1)

P expressed in g/ m2/year and T in Kelvin degrees.

In the same publication, Lieth and Whittaker (1975) proposed an empirical 

equation of productivity Y as a function of Celsius degrees X:

Y = 3,000 / (1 + e1.315 – 0.119X )

Converting temperature data from Celsius to Kelvin degrees, we obtain 

an expression of productivity containing an Arrhenius term:

P2 – P1 = Constant (1 / T2 –1 / T1)

Taking 10 values of temperature between –13 °C and +28 °C, we obtain 

an Arrhenius law of net primary productivity with a square correlation

coefficient very close to 1 (fig. 12).

P = 5*106 /T + 19,320 (R2 = 0.979)

Wildfire Net Primary Productivity and NDVI

The values of the normalized difference vegetation index (NDVI) 

extracted from satellite sensor data acquired by the National Oceanic and 

Atmospheric Administration’s Advance Very High Resolution Radiometer 

(NOAA – AVHRR) have often been used for estimating forest fire risks.

According to Fang and others (2001), to examine the relationship between 

variability of NPP and precipitation at a broad scale, a long-term NDVI data 

set coupled with a historic climate data set constitute a useful and powerful data source, because NDVI data are strongly correlated with terrestrial 

NPP.

The relative greenness (R.G.) was described by Burgan and Hartford 

(1993) as expressing how green each cell is relative to the range of greenness. 

Relative greenness, which has been used by Burgan and others (1998) for 

the evaluation of fire potential index (F.P.I.), indicates that it is a potentially 

valuable fire management tool for land management agencies.

Table 3—Climatologic charts from productivity and from climate data.

Chart from productivity

Zone f(P) = φ( T) Chart from cl��ate data

1a P = –3.79 106 (1/ T) + 13 554 P = -3 106 (1/ T) + 12 832

 R2 = 0.79

2 P = –4.3 106 (1/ T) + 16 229 P = –4 106 (1/T) + 15 098

 R2 = 0.79

3 P = –2.35 106 (1/ T) + 7 348 P = –2.10 8 P = –2.106 (1/T) + 6 603

 R2 = 0.20

4 P = –8.44 106 (1/ T) + 29 782 P = –5.106 (1/T) + 19 454

 R2 = 0.57

aExcepted October data.

Wildfires, Weather, and Productivity Bernard and Nimour
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 23

Gabban and others (2006) proposed a use of a new index referring to the 

dynamic relative greenness index (DRGI), to consider the interannual variability of NDVI at each precise location within the studied region. It was 

computed over the European Union countries and tested in Spain, France, 

and Italy. DRGI values and the number of fires were fitted using an exponential model. The testing of the DRGI over the 13 years showed a high 

correlation (R2 = 0.94) between the number of fire outbreaks and the level 

of risk determined by the index.

So, this statement introduces the number of fires on a logarithmic

form as a linear increasing function of DRGI.

Conclusions

It was established that the fire monthly frequencies are linearly correlated established that the fire monthly frequencies are linearly correlated

with precipitation. With regard to the temperature, the fires frequencies are 

narrowly correlated with the Arrhenius factor (1/T, T in Kelvin degrees) 

translating the existence of a regulating photochemical activation phenomenon (PAR or UVB according to the season). All these correlations can be 

found on a multiannual scale.

The reconstruction of the studied area climate can be carried out by identifying the expressions of the frequency of fires in function of precipitation 

and temperature. We proposed the concept of “climatologic chart” (Bernard 

and Nimour 2004) starting from such reconstruction process. This graphical 

Figure 12—NPP = f(1/T). Data from Lieth and Whitaker (1975).

Wildfires, Weather, and Productivity Bernard and Nimour
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 24

representation method of the “climatologic chart” that we introduced starting from the climate data, P= F (1/T) is fully justified by the highlighted 

vegetation production laws.

Indeed, the net primary productivity is expressed in function of precipitation or temperature, through laws similar to those found on the fires 

frequency in log scale on an interannual scale. That brings an answer to the 

questions that we had on the role of the biomass production in the fires 

frequency laws. Moreover, the NDVI and its interannual variability, which 

are closely linked to the vegetation production and the fires statistics, still 

reinforce this similarity.

The proposed model set will constitute valuable tools for the study of the 

climatic changes and their repercussions on the fires regimes, aridity, and 

stability of vegetation communities and may contribute to developing new fire 

management strategies as expressed in the San Diego declaration on climate 

changes and fire management.

References

Bernard, M.L. 1998a. Interpretation of fire intensity in Yellowstone National Park 

(1700-1990). Lecture at Y.N.P., May 29 1998; USA.

Bernard, M.L. 1998b. Wildland fires, sunspot and solar flux: a tentative of correlation. 

3rd International Conference on Forest Fire Research, 14th Conference of fire and 

Methodology, 16-20 November 1998; Coimbra, Portugal.

Bernard, M.L.; Carbonel, M.; Nimour, N. 2000. Are large wildland fires –as 

anomalous ecological processes- natural hazards. Phys. Chem. Earth (A). 25 

(12): 763-768.

Bernard, M.L.; Lleras, J.; Nimour, N. 1999. Wildland fires, solar flux and total 

ozone content of the atmosphere: mutual correlations. European Geophysical 

Society 24th General Assembly; The Hague, Netherlands.

Bernard, M.L.; Nimour N. 2004a. A concept of climatologic chart. EMS Annual 

Meeting Abstracts. 1 (00286). European Meteorological Society.

Bernard, M.L.; Nimour N. 2004b. Large fires in South California, October 2003: 

climatologic analysis, theory and reality. Geophysical Research Abstracts. 6 

(00339). European Geosciences Union.

Bernard, M.L.; Nimour, N. 2004c. Lightning fires dependence on precipitation and 

temperature. Geophysical Research Abstracts. 6 (00354). European Geosciences 

Union.

 Bernard, M.L.; Nimour, N. 2004d. Short-range forecasting of large wildland fires. 

EMS Annual Meeting Abstracts; 1 (00285). European Meteorological Society.

Bernard, M.L.; Nimour, N. 2005a. Application of the concept of climatologic chart 

to Mediterranean climate variability. Geophysical Research Abstracts. 7 (01839). 

European Geosciences Union.

Bernard, M.L.; Nimour N. 2005b. Reality of the climatologic theory of wildland fires 

based on UV-B efficiency. Geophysical Research Abstract. 7 (2906). European Geophysical Research Abstract. 7 (2906). European

Geosciences Union.

Boullard, B. 1993. De la poudre aux incendies de forêts...une approche canadienne. 

La Forêt Privée. 212: 84-87.

Burgan, Robert E.; Hartford, Roberta. 1993. Monitoring vegetation greenness 

with satellite data. Gen. Tech. Rep. INT-297. Ogden, UT: U.S. Department of 

Agriculture, Forest Service, Intermountain Research Station.

Wildfires, Weather, and Productivity Bernard and Nimour
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 25

Despain, D.G. 1998. Personnal communication.

Douglas, Arthur V.; Stockton, Charles W. 1975. Long-term reconstruction of 

seasonal temperature and precipitation in the Yellowstone National Park region 

using dendroclimatic techniques. The University of Arizona. Prepared for United 

States Department of the Interior, National Park Service, Yellowstone, WY.

Douguedroit, A. 1992. Sécheresse et incendies de forêts en region mediterranéenne. 

Revue d’Analyse Spatiale Quantitative et Appliquée. 22. Colloque International 

«Le feu; avant-après». Nice, France. Nice, France.

Fang, J.; and others. 2001. Interannual variability in net primary production and 

precipitation. Science. 7 September 2001. 293 (5536): 1723.

Flannigan, M.D.; Wotton, B.M. 1991. Lightning ignited forest fires in Northwestern 

Ontario. Canadian Journal of Forestry research. 21: 227-287.

Forgeard, F. 1994. Importance and characteristics of heathland fires in Brittany 

(France). 2nd Int. Conf. Forest Fire Research. 1 (B 28): 447-458.

Frontier, S.; Pichod, Viale. 1991. Ecosystèmes, structure, evolution. Collection 1991. Ecosystèmes, structure, evolution. Collection

d’écologie. Masson.

Gabban, A.; San-Miguel-Ayanz, J.; Barbosa, P.; Liberta, G. 2006. Analysis of NOAAAVHRR interannual variability for forest fire risk estimation. International Journal 

of Remote Sensing. 27 (8), 20 April 2006: 1725-1732.

Gray, J.T. 1982. Community structure and productivity in Ceanotus Chaparral and 

Coast Sage Scrubs of South California. Ecologic Monographs. 52 (4), December Ecologic Monographs. 52 (4), December 

1985: 415-435.

Humbert, J.; Najjar, G. 1992. Influence de la forêt sur le cycle de l’eau en domaine 

tempéré. Une analyse de la littérature francophone. CEREG, Université de 

Strasbourg I, France.

Latham, D.J.; Rothermel, R. 1993. Probability of fire stopping precipitation events. 

Res. Note INT 410. Ogden, UT: United States Department of Agriculture, Forest 

Service, Intermountain Research Station.

Lieth, Helmuth; Whittaker, M., eds. 1975. Productivity of the biosphere. Springer-Verlag.

NICC. 2005. Incident Management Report 2005. South California National 

Interagency Fire Center.

Nimour, N. 1997. Flammability of the Mediterranean vegetation. Thesis. Laboratoire Thesis. Laboratoire 

de Chimie et Environnement, Université de Provence. Marseilles, France.

Olivier, L. 1975. Première contribution à l’étude de l’intervention de la végétation 

et de certains facteurs écologiques dans les incendies de forêts en Basse Provence. 

Doctorat de spécialité. 28 february 1975. Université d’Aix-Marseille III (SaintJérôme). Marseilles, France.

PROMETHEE. 1985-1997. Forest fires database. Conseil General des Bouchesdu-Rhône, France.

Ramade, F. 1994. Eléments d’écologie. Ecologie fondamentale. Ediscience Ediscience

International.

Rambal, S. 1984. Water balance and pattern of root water uptake by Quercus coccifera 

L. evergreen scrub. Oecologia (Berlin). 62: 18-25.

Romme, W.H.; Despain, D.G. 1989. The long history of fires in the Greater 

Yellowstone ecosystem. Western Wildlands. 15: 10-17.

Running, Steven W. 2006. Climate change: is global warming causing more, larger 

wildfires? Science. 313 (5789). 927.

Scurlock, J.M.; Olson, R.J. 2002. Terrestrial net primary productivity—A brief 

history and a new worldwide database. Environ. Rev. 10 (2): 91-109. Environ. Rev. 10 (2): 91-109.

Terol, C.M.M. 1987. Incendios forestales en Alicante; Publicaciones de la Caja de 

Ahorros Provincial. 143. Alicante, Spain.

Wildfires, Weather, and Productivity Bernard and Nimour
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 26

Trabaud, L. 1980. Impact biologique et écologique des feux de la végétation des zones 

de garrigues du bas-Languedoc. Thesis. Université des Sciences et Techniques 

du Languedoc. France.

Viegas, D.X.; Viegas, M.T. 1994. A relationship between rainfall and burned area A relationship between rainfall and burned area

in Portugal. International Journal of Wildland Fires. 4 (1): 11-16.

Ward, P.; Wotton, M. 1994. Personnal communication.

Westerling, A.L.; Hidalgo, H.G.; Cayan, D.R.; Swetnam, T.W. 2006. Warming 

and earlier spring increase Western US forest wildfire activity. Research articles. 

Science. 313 (5789): 940-943.

Wildfires, Weather, and Productivity Bernard and Nimour
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 27

Abstract—The FireLine Assessment MEthod (FLAME) provides a fireline-practical 

tool for predicting significant changes in fire rate-of-spread (ROS). FLAME addresses 

the dominant drivers of large, short-term change: effective windspeed, fuel type, and 

fine-fuel moisture. Primary output is the ROS-ratio, expressing the degree of change in 

ROS. The application process guides and instills a systematic methodology, utilizing a 

simple worksheet. The information developed provides a basis for safety judgments 

and for applying Lookouts, Communications, Escape routes, Safe zones (LCES). The 

ROS-ratio can be applied to observed fire spread to provide a timeline of future fire 

spread. Compared to four BehavePlus examples FLAME is accurate to within an 

average error of 14 percent. In four fireline-fatality cases FLAME predictions match 

reconstructed ROS-ratios with an average error of 9 percent, and in every case could 

have foretold the rapid changes that impacted the crews. Adjustment factors are developed to account for variations of windspeed across terrain, and for flame height and 

sheltering by vegetation. Field application of FLAME is explained and demonstrated 

with examples.

Part 1: The FLAME System

Essentially the FireLine Assessment MEthod (FLAME) applies fire behavior prediction science to the implementation of the fire order ‘Base all actions 

on current and expected fire behavior.’ With fireline-based observations 

firefighters apply it using only simple paper-and-pencil pocket tools. FLAME 

takes account of the ‘current’ fire behavior as a baseline, directs attention to 

the ‘next big change’ in fire behavior, and evaluates the magnitude of that 

change. The magnitude of the anticipated change in fire behavior is based on 

the relationships embodied in the Fire Behavior Prediction System (FBPS) as 

computed by BehavePlus, on grass fire behavior expressed in the Australian 

CSIRO model, and on observed rates of spread in grass, brush, and timber. 

The measure of the ‘change in fire behavior’ is expressed in FLAME as the 

factor by which fire rate-of-spread (ROS) will increase/decrease, the ‘ROSratio’. (For example, an increase in ROS from 4 ch/hr to 24 ch/hr has an 

ROS-ratio = 24/4 = 6X.) Application of that ROS-ratio to the observation 

of current fire behavior provides an extrapolation of the fire spread-time, 

expressed most practically in terms of the fire’s position on the landscape 

through time (using ‘natural yardsticks,’ things you can see on the land) 

rather than as a rate-of-spread in distance/time.

The two basic things that we must do: provide a systematic, practical, and 

effective tool to firefighters for evaluating fire behavior on the fireline, and 

effectively communicate and instill the important points of basic fire behavior 

training (as in S-290 Intermediate Fire Behavior). Application of FLAME in 

training and on the fireline supports both needs.

Technical Background of the FireLine 

Assessment MEthod (FLAME)

Jim Bishop1

In: Butler, Bret W.; Cook, Wayne, 

comps. 2007. The fire environment—

innovations, management, and policy; 

conference proceedings. 26-30 March 

2007; Destin, FL. Proceedings 

RMRS-P-46CD. Fort Collins, CO: 

U.S. Department of Agriculture, 

Forest Service, Rocky Mountain 

Research Station. 662 p. CD-ROM.

1 Physicist, mathematician, geologist, formerly with the California 

Department of Forestry and Fire. 

cjbishop1991@sbcglobal.net
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 28

Technical Background of the FireLine Assessment MEthod (FLAME) Bishop

 In assessing potential fire behavior firefighters need to be proactive, to 

consider all of the key factors, and not to simply be passively waiting to notice, 

or to be made aware of, a significant development in the fire’s behavior. And 

they need to have a realistic sense of the impact such a change could have 

on them, the magnitude of the change. Seat-of-the-pants assessments are 

not adequate. A common tendency is for firefighters to rely too much on 

current fire behavior as a basis for their actions, with the expectation that 

they will simply notice any developing changes in time to react. That failure 

to foresee dangerous (yet predictable) changes is evident in perceptions 

revealed again and again in fireline fatality cases. Without an organized 

approach to that fire behavior assessment, it is too easy to miss something 

while being unaware of what information is missing, and too easy to be 

unaware of the magnitude of an impending change. Separate short papers 

(Bishop 2005, 2006) describe more fully the place for FLAME in training 

and on the fireline.

FLAME offers a systematic methodology that prompts and guides the user 

to explicitly consider the key factors that drive significant, sudden changes 

in fire behavior—based on a minimum number of inputs, usually only two: 

effective windspeed and fuel type. It is important that the process be usable, 

or it won’t get used, so the emphasis in FLAME is on simple tools and the 

information that is available to the firefighter on the fireline, remembering 

that computers don’t make it to the fireline. FLAME fills a need that is not 

addressed by the other applications of the fire model (nomograms, and so 

forth). It is far simpler to use, requires minimal materials, builds on observed 

fire behavior, directly addresses the important question of ‘change’ in fire 

behavior, and expresses outputs in terms that are easily applied (what’s next, 

how much change, when will the fire be here).

FLAME application proceeds from basic to more refined steps and yields 

these three important results:

1. The identity and timing of the ‘next big change’—Knowing the nature 

and timing of the pending change allows firefighters first of all to be 

aware of that potential, and also to better monitor the environment and 

maximize the value of fireline lookouts.

2. The magnitude of that change (expressed as the ‘ROS-ratio’, the degree 

of relative increase or decrease in ROS)—Knowing the magnitude of 

the pending change alerts the firefighter to the level of possible danger. 

In fact, large ROS-ratio might well be viewed as a universal common 

denominator in fireline accidents.

3. An estimate of the timeline of the fire’s advance—Knowing the fire 

spread-time allows rational planning of escape routes and timing, as well 

as informing well-chosen tactics.

All in all, the FLAME information provides a solid basis for the implemen-

tation of LCES (Lookouts, Communications, Escape routes, Safe zones). It 

is, first and foremost, a tool for making sound safety decisions on the fireline, 

but also provides relevant information for tactical decisions.

Figure 1 illustrates the basic idea of combining a change in fuel type with 

a change in effective windspeed (EWS) to produce a measure of the resultant 

change in fire behavior (the ROS-ratio). Fuel type and EWS changes are the 

dominant changemakers. All of the details of how the FLAME ‘standard 

curves’ were derived and are used, and the relative sizes of fire behavior fac-

tors, are contained in sections below. This graphic simply portrays the basic 

FLAME inputs and output so that subsequent discussion makes more sense. 

The arrow depicts an example of change from ‘current’ (low windspeed, fire 
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 29

0

100

200

300

400

500

600

0 5 10 15 20 25 30

EWS mi/hr

ROS ch/hr

Grass

Litter

Crown

Figure 1—EWS vs. ROS for each 

of the major fuel types (litter, 

crown and grass). The arrow 

depicts an example in which 

fire in litter with an EWS of 3 or 

4 mi/hr changes to fire in grass 

with EWS about 12 mi/hr. The 

initial and final ROS would be 

on the vertical axis, and the 

larger divided by the smaller 

would be the ROS-ratio for 

this change in fire behavior 

(approx. 70X).

in litter) to ‘expected’ (grass, moderate windspeed) fire behavior. The ROSratio would be the higher ROS on the vertical axis divided by the lower ROS 

on that axis. In this example the ROS would increase by a factor of about 

70X as the fire moved from litter into grass and the EWS increased by between 3X and 4X. These curves form the basis for the FLAME predictions 

and illustrate the idea, but they are not the user-application tool (which is 

described in part 3).

The FLAME user observes the fuel type and current EWS affecting the 

fire, and looks ahead to the fuel type and EWS that will prevail after the 

anticipated change in conditions. The ratio of the larger EWS to the smaller 

EWS is obtained either by simple division or table look-up. With the fuel-type 

change and the EWS-ratio, use the FLAME table (table 2, later in this paper) 

to look up the ROS-ratio: join the EWS-ratio row with the column describing 

the fuel change, and read the ROS-ratio. For example, a 6X increase in EWS 

together with a transition from fire in the litter to crown fire would result in 

an increase in ROS of about 35X (or in the opposite case, a decrease of 35X). 

Associated diagrams and a field guide and worksheet help the user obtain 

inputs, move through the process, and determine the needed outputs.

The application of FLAME affords a range of information levels (corresponding to the aforementioned three important outputs), each with its own 

inherent degree of completeness and precision, and determined by the user 

and the circumstances on the fireline. In practice a firefighter can proceed 

from one level to the next, making use of the output at each stage, as time 

and information allow.

1. ‘Initial application’ level: user depicts/describes the current fire behavior, 

the expected fire behavior, and identifies the next big change; this stage 

involves only a qualitative assessment.

Technical Background of the FireLine Assessment MEthod (FLAME) Bishop
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 30

2. ‘Standard application’ level: user inputs current and expected conditions 

and predicts the ROS-ratio.

3. ‘Complete application’ level: user combines an observation of the current 

fire-spread timeline with the ROS-ratio to yield a predicted fire-spread 

timeline (or spread time). The spread-time line lays out at what time 

the fire will reach a given point. If the fire moved this far in a certain 

time under current conditions, how long will it take to go that far under 

expected conditions? It is related to ROS but is a projection of the fire’s 

progress in terms of features visible on the landscape (natural yardsticks), 

rather than an explicit rate in units of distance/time.

The following examples illustrate the idea of the FLAME process, the 

application stages, and the kinds of outputs a firefighter can obtain, with 

suggestions on how the information relates to LCES. They are not intended 

to explain how FLAME is applied (inputs are simply given), and they are 

not to be ‘over interpreted’ in terms of tactical/operational considerations. 

Both examples are of dangerous changes in fire behavior, but the FLAME 

idea applies to any change in behavior, dangerous or benign.

Example 1—an upslope run: • Fireline observations: Fire spreads over the course of 4 hours up the 

lower half of a forested 20 percent slope as a litter fire with midflame 

windspeed 2 mi/hr. By late morning, conditions will allow the midslope 

fire to transition to a crown fire and to be exposed to higher windspeeds 

on the upper slope.

• Initial application: The next big change expected is a transition to crown 

fire and the effects of the higher windspeeds on the upper half of the 

slope.

• Standard application: The litter-to-crown change in fuel will combine 

with a total 8X increase in effective windspeed (wind on the upper slope 

being 16 mi/hr at the crown-fire flame level) to produce an increase in 

ROS of 50X, ROS-ratio = 50X.

• Complete application: The fire took 4 hours spread up the lower half of 

the slope. The crown fire could run the upper half of the slope in about 

1/50th of 4 hours, or about 5 minutes.

• LCES: Lookouts—be especially vigilant for signs of the transition to crown 

fire, such as torching or short crown runs, and also to wind direction as 

revealed in the smoke column to anticipate which side of the upslope run 

might be most threatening to crews in the area. Communications—regular 

reporting of crown-fire precursors, RH, and wind direction. Escape—must 

take much less than 5 minutes and be located out of the line of the crown 

fire run.

Example 2—a major wind increase and direction change: • Fireline observations: Fire is burning on a flank in litter under coni

-

fers; the head of the fire made a short crown-fire run earlier. The wind 

is predicted to blow across that flank at 20 mi/hr when a cold front 

arrives in the next 2 to 4 hours. The flanking fire has moved 1⁄4 of the 

way to a road in the last 2 hours (a rate that puts it at the road in 6 more 

hours).

• Initial application: The next big change is a large increase in wind 

pushing the flank outward toward the road, and the fire is expected to 

transition to crown fire with the increase in wind. The change is expected 

in as little as 2 hours.

Technical Background of the FireLine Assessment MEthod (FLAME) Bishop
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 31 • Standard application: The litter-to-crown change in fuel will combine 

with a 20X increase in effective windspeed to produce an increase in 

ROS of 140X. (Effective wind on the flank is taken as 1 mi/hr, as will 

be explained in part 2.)

• Complete application: The fire could be halfway to the road in 2 more 

hours when the wind hits, and the wind-driven crown fire will com-

plete the remaining half of the distance to the road in about 1/140th of 4 hours, or about 2 minutes (less time if the fire is more than halfway 

to the road when the wind comes).

• LCES: Lookouts—besides having local lookouts, a remote lookout should 

be established ‘upstream’ of the fire to provide early warning of the ap-

proach of the cold front. Communications must be arranged with the 

remote lookout. Escape via the road would allow only about 2 minutes 

after the wind hits, so escape should be initiated earlier based on the 

reports of the remote lookout.

Why not stop at the ‘initial application’ stage? That is certainly a good 

start, and in some cases will be all one needs to know...to have identified the 

next big change. But going further, to the standard application, requires the 

firefighter to explicitly look at the big changemakers: effective windspeed 

(EWS), current and expected; fuel type; fine-fuel moisture (FFM) or RH, 

and therefore to either obtain those critical parameters or to become aware 

that important information is missing. Seeking the explicit ROS-ratio predic-

tion directs a method that prompts and leads specific appraisal of the key fire 

behavior factors. The ROS-ratio gauges the magnitude of coming changes. 

The complete application provides a timeline that can provide a realistic sense 

of fire movement and can guide good choices about the timing of safety 

actions and effective control actions.

Focusing on the Dominant Changemakers

 Many factors in the fire environment contribute to fire behavior: fuel 

physical/chemical characteristics, fuel arrangement, fuel moisture, slope, 

and wind. Fire behavior (as measured by ROS in FLAME) is less sensitive 

to some of these factors than to others, and some factors change less rapidly 

than others. In a given situation current fire behavior demonstrates the in-

tegrated effects of the prevailing fire environment factors...firefighters can 

observe that. If nothing much changes in the fire environment, prediction 

requires only extrapolation of observed fire behavior. But things eventually 

do change, often quickly, and the degree of change from that ‘baseline’ fire 

behavior can be predicted.

We focus on the major changemakers that will cause large changes in 

‘current’ fire behavior on short timescales (those factors will be quantitatively 

identified below). In eliminating some detail in fire behavior inputs, a little ac-

curacy is traded for practical applicability, but without compromising the basic 

capacity to predict significant change. Also, in order to focus on potentially 

dangerous fire behavior FLAME emphasizes the range of fuel conditions that 

accompany active fire behavior (meaning the lower FM ranges, typical of a 

late-season afternoon). The relative response of ROS to a change in effective 

wind or fuel type is similar over a range of fuel moistures, and here we use 

model guidance assuming fairly dry fuels (usually 1-hr FM 6 percent and 

live FM 80 percent). In other words, the ROS change due to a doubling of 

windspeed is essentially the same at 4 percent as at 8 percent FM.

We want to anticipate change that can be ‘sudden’, those changes that can 

develop in minutes (‘minutes’ is the timescale of escape) or tens of minutes. 

Technical Background of the FireLine Assessment MEthod (FLAME) Bishop
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 32

Changes that evolve gradually over many hours, or days, present less of an 

imminent threat to fireline personnel, and one can remain current on such 

gradually changing fire behavior. So in seeking the dominant drivers of shortterm change we look at fire behavior factors that can change significantly in 

roughly an hour or less. Whether such changes actually take place over several 

hours, or in just minutes, the FLAME application is still relevant. And while 

change can occur quickly, it may not get under way for hours. To extend a 

FLAME prediction, a firefighter simply updates the observation of ongoing 

fire behavior and looks ahead to further change.

Live, 10-hour, 100-hour, and 1000-hour fuel moisture are not drivers of 

large, short-term changes. Live fuel moisture (LFM) varies over the season. 

When LFM levels are low enough, fire can propagate through the crowns 

of shrubs or trees. The LFM usually continues to decline throughout the 

fire season, but for a given species of live fuel it usually doesn’t drop more 

than another 20 percentage points or so after crowns become flammable. 

In extreme and prolonged drought it may drop by 40 percentage points in 

timber fuels after the time when crowns become flammable, typically less 

than that (about 20 percentage points) in chaparral.

However, the change in LFM in a given plant over an hour is much less 

than the seasonal changes. Consider a change in LFM of 5 percentage points 

(which is greater than would be typical in an hour), from 80 to 75 percent. As 

indicated by BehavePlus for Model 5, such a variation in LFM would result 

in a change in ROS of only about 5 percent.

Changes in 10-hour FM over the course of an hour are not likely to be 

more than about 2 percentage points. Consider the effect of a 2 percentage 

point 10-hr FM change on ROS in grass and in litter. For fuel model 1, and 

for fuel model 9, BehavePlus shows no change in ROS for a 2 percentage point 

variation in 10-hour FM. Hundred-hour and 1000-hour fuels will undergo 

even smaller changes in FM over an hour than do 10-hour fuels. Short-term 

moisture changes in larger-diameter fuels are not a significant moderator of 

large, sudden changes in ROS (though moisture in larger fuels certainly does 

affect the overall fire intensity).

Variations in fuel conditions on the fire can result from the fire’s move-

ment, as well as through overall change with time. For example, a fire can 

move from one slope-aspect to another, and fuels on those different slopes 

can have different fuel moistures. Such differences might be as high as a few 

percentage points of dead FM and 10 or 20 percentage points of live FM (in 

the same species of plant). Those variations certainly contribute to changes in 

ROS, of order approximately 20 percent, sometimes less. However, there are 

almost certainly other significant changes associated with such a slope-aspect 

change—in plant community, fuel architecture, and the wind-slope influence 

driving the fire. In the face of these significant changes in more dominant 

factors, the variations in live and larger-diameter dead fuel moistures on different slope aspects are a relatively minor influence on the changes in ROS.

For purposes of predicting significant changes in ROS that result from 

1-hour-timescale changes in fuel conditions, live FM, 10-hour FM, 100-hour 

FM, and 1000-hour FM are considered essentially constant and a minor 

influence on ROS change. The effects of those factors on fire behavior will 

be manifested in, and observable in, current fire behavior, the baseline fire 

behavior.

1-hour fuel moisture plays a significant role—Changes in 1-hour FM (FFM, 

fine-fuel moisture) can be significant over 1-hour timescales. The combined 

effects of relative humidity, temperature, and time-of-day typical of ‘summer 

Technical Background of the FireLine Assessment MEthod (FLAME) Bishop
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 33

day’ changes, might lead to a change in FFM on the order of 2 percentage 

points in an hour. As indicated by BehavePlus for fuel models 1 and 9 such 

a variation in FFM would lead to ROS changes of about 12 percent in grass 

or 17 percent in needle litter. If fire moves from an open south slope to a 

canopy-shaded north slope (or vice versa) it can experience a change in FFM 

of about 3 percentage points—corresponding to ROS changes of about 21 

percent in grass or 32 percent in needle litter. The FLAME system handles 

changes in FFM with the following guideline (in two versions):

For a change of FFM of ‘n’ percentage points, the ROS (in a given fuel) 

will change by about 1.nX. For example, FFM dropping by 2 percentage 

points would yield roughly a 1.2X increase (a 20 percent increase) in ROS 

(compared to an average of 1.15X, or 15 percent, as per the BehavePlus 

example cited above).

An alternative way of doing essentially the same thing is based on the 

change in relative humidity (RH). Given that a change in RH of 5 percentage 

points leads to a change of about 1 percentage point in FFM, the change in 

ROS in fine dead fuels is about 2X(RH-change). For example, a drop in RH 

from 40 to 25 percent (= 15 percent) would increase ROS in fine dead fuels 

about 2X15 percent = 30 percent, or by a factor of 1.3X. Such adjustments 

can fine-tune the ROS-ratio that is based initially on just changes in fuel type 

and EWS, though such refinements are usually not necessary.

There can be more dramatic changes in FFM. A good example is the onset 

of foehn winds, where in a few 10s of minutes FFM might drop by about 

6 percentage points. The direct effect of that FFM change would suggest 

roughly a 60 percent increase in ROS. But the other changes, likely a transi-

tion from surface fire to crown fire and the onset of high winds, would dwarf 

the direct effect of changes in FFM on ROS.

Probably the most important effect of changes in FFM on changes in fire 

behavior is the influence it has on transition to crown fire (an important 

change in fuel type) and on spotting. In the FLAME system FFM can be 

used to fine-tune predicted changes in ROS (for changes between largely 

fine-dead fuels grass and litter) as noted above, but more importantly FFM 

(and relative humidity) is explicitly considered in FLAME applications as a 

factor in the onset of crown fire (detailed in part 2).

Fuel type is a major changemaker—FLAME classifies fuels as litter, grass, 

or crown foliage (of shrubs and trees), a classification based on the ROS 

characteristics of those fuels, their similarity within a group. The reasons for 

treating fuels in that way are:

1. Those fuel types are quickly and easily recognized by firefighters, without 

the need for extensive training on determining fuel models.

2. ROS within each group of fuels is sufficiently characteristic of that fuel 

and distinct from the others to allow meaningful predictions of ROSchange as a function of fuel type.

3. Changes in fuel type contribute to major changes in ROS.

As a generalization, with other fire behavior factors constant, ROS in 

crown fuels is about 4X faster than in litter, and in grass fuels about 3X to 

4X faster than in crown foliage. The total range in variation in ROS across 

the three fuel-type averages is on the order of 15X (for comparison, recall 

that changes in ROS due to short-term changes in fuel moisture are no more 

than about 1.2X for live FM and the heavier dead fuels, and about 1.6X for 

changes in FFM).

Technical Background of the FireLine Assessment MEthod (FLAME) Bishop
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 34

Effective windspeed is the greatest changemaker— Effective windspeed 

(EWS) is taken to be the midflame windspeed (MFWS) plus a component that 

accounts for the effect of slope on ROS. In these discussions in part 1 EWS is 

considered to be ‘a given’ and an appropriate measure of the combined effects 

of wind and slope on ROS. Guidelines for obtaining wind-adjustment/reduc-

tion factors used to determine EWS, and for obtaining the wind-equivalent 

of slope, are in part 2.

EWS has a great influence on ROS, and varies rapidly in time and place. 

It is by far the most dominant changemaker for short-term changes in fire 

behavior, capable of driving ROS changes of >200X. The influence of EWS 

on ROS is derived from the curves of ROS vs. EWS inherent in FBPS models, 

the CSIRO grass fire model, and observations of ROS in crown fuels.

In summary, the magnitudes of the short-term changes (~1 hour) in ROS 

produced by the various factors are listed below. It is clear that the dominant 

changemakers are EWS and fuel type, with FFM relevant but less influential 

as a direct factor (remaining useful for fine-tuning and for helping to reveal 

potential for crown fire). • Fuel moisture in live and larger dead fuels ~ 1.2X (~20 percent) • Fine-fuel moisture (and therefore RH) ~ 1.6X (~60 percent) • Fuel type (litter, crowns, and grass) ~ 15X (~1400 percent) • Effective windspeed (including slope) ~200X (~20,000 percent)

The Basic Data Used in FLAME

The dependence of ROS on fuel and EWS was derived from a combination 

of model outputs and observations. The model outputs were obtained from 

BehavePlus (1-hr FM 6 percent, 10-hr FM 7 percent, 100-hr FM 8 percent, 

live FM 80 percent) and from the CSIRO grass fire nomograms (Cheney 

1997). The curve of ROS vs. EWS for each fuel type is derived from the 

averages of inputs summarized below.

1. Grass fuels are represented by FBPS fuel models 1 and 3, the CSIRO 

grassfire model, and at high windspeeds by observed spread of Australian 

grass fires (Cheney 1997).

2. Crown fuels are represented by FBPS fuel models 5, 6, and 7, and 

observations of crown fire in brush and in timber (including Range and 

others 1982, Rothermel 1991).

3. Litter fuels are represented by FBPS fuel models 8, 9, and 10.

Slash fuels are not explicitly considered in FLAME, as they are uncom-

mon in wildfire environments, but they can be considered via adjustment of 

standard ROS-ratios. The number of permutations between fuels rises rapidly 

as the number of fuels considered increases, and to keep the system simple 

unnecessary variations in fuel type are not included. However, it is possible 

to address a lot of variation from the standard by making adjustments to 

the standard FLAME outputs. Specific adjustments to FLAME outputs are 

described later in this paper.

Crown fuel ROS observations—Summarized in figures 2 and 3 are observa-

tions of crown fire ROS as a function of 20-ft windspeed (which in FLAME 

is taken to be the midflame windspeed for crown fire). Best-fit 2nd-order 

polynomials are displayed on the graphs, together with the regression co-

efficients (here R represents regression coefficient, but later R represents ROS). 

Observed crown fire data used in the graphs are shown in the accompanying 

table. The data used to represent the crown-fuel group ROS values for the 

FLAME standard curves were derived from the regression plots.

Technical Background of the FireLine Assessment MEthod (FLAME) Bishop
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 35

The data shown encompass considerable variation in species and structure, 

and in fuel moistures. The brush fires were in Great Basin shrubs (such as 

sagebrush and antelope brush), chaparral (both interior and maritime), and 

Gambel oak. The timber fuels (mostly from the Northern Rockies but also 

New Mexico, included pines, firs, and spruce). Each had its own live fuel 

moisture, and dead FMs varied as well. In spite of that variation there is 

strong and consistent correlation of ROS with windspeed in brush crowns 

and in timber crowns. Changes in ROS can be related to changes in EWS 

with good accuracy, over a broad range of crown fuel types. And crown fuels 

can be treated as a group with adequate results.

Overall, the ROS data for timber fuels tend to represent long-term spread, 

including periods of discontinuous crown fire. The brush fire data tend to 

represent shorter, more continuous crown-fire spread. An upward adjustment 

Brush crown fire <ROS>

y = 0.0623x2 + 4.3338x + 10

R2

 = 0.967

0.00

50.00

100.00

150.00

200.00

250.00

300.00

0 5 10 15 20 25 30 35 40

WS (20-ft) mi/hr

<ROS> ch/hr

Brush fire

cases

20-ft WS 

mi/hr

<ROS>

ch/hr

Ely 1 10 70.40

Carson B 14 65.60

Carson C 14 70.40

Carson A 9 64.00

South Canyon 35 240.00

Pilot 8 49.60

Fort Ord 10 73.60

Timber crown fire <ROS>

y = 0.0684x2 + 1.8122x + 10

R2 = 0.9449

0.00

50.00

100.00

150.00

200.00

250.00

300.00

0 10 20 30 40 50

WS (20-ft) mi/hr

<ROS> ch/hr

Timber fire

cases

20-ft WS 

mi/hr

<ROS>

ch/hr

Sundance 45 240.00

Sundance 30 112.00

Red Bench 12 41.60

Lily Lake 25 73.60

Sandpoint 16 83.20

Pattee Canyon 30 124.80

Mink Creek 15 44.00

Black Tiger 11 40.80

Scott Able 40 192.00

Butte Fire 

(Wallace Crk.) 12 65.60

Figure 3—Observed crown fire ROS in timber fuels are plotted against 20-ft windspeed (for cases 

shown). Overall slope was not a major direct influence on ROS. Best-fit 2nd-order polynomial curve 

and regression coefficient are shown. <ROS> denotes the spread rates as averages.

Figure 2—Observed crown fire ROS in brush fuels are plotted against 20-ft windspeed (for cases 

shown). Overall topographic slope in these cases was slight. Best-fit 2nd-order polynomial curve 

and regression coefficient are shown. <ROS> denotes the spread rates as averages.

Technical Background of the FireLine Assessment MEthod (FLAME) Bishop
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 36

of crown ROS by 1.7X (Rothermel 1991) to better reflect the faster continuous portions of the spread shows it to match the brush ROS data almost 

exactly (see fig. 4, later in this paper). The similarity in brush versus timber 

ROS, together with the well grouped FBPS model outputs, allow a treatment 

of crown fuels as a single fuel type with good results, as will be developed 

below.

Crown fuel-model representatives—FBPS fuel models 5 and 6 are good 

representatives for crown-foliage fire spread in shrub/brush fuels. Model 7 

represents fire spread in crown fuels typical of the Southeastern United States. 

FBPS fuel model 4 tends to predict ROS that are too high. Calibrations of 

fuel model 4 suggest that its outputs be reduced by at least half. Given the 

other, more accurate models for fire spread in brush, and the crown fire 

observations, fuel model 4 was not used. Fuel models 5, 6, and 7 have very 

similar ROS outputs and, together with the above crown fire observations, 

are used to represent fire spread in crown foliage in constructing the FLAME 

standard curves.

Litter fuel representatives—FBPS fuel models 8, 9, and 10 are all used. 

ROS outputs for models 9 and 10 are similar. ROS outputs for fuel model 

8 tend to be only about one-third of the litter-fuel average, and when such 

compact short-needle litter fuels dominate fire spread, the FLAME outputs 

can be adjusted (the ROS-ratio is increased) to reflect the slower spread in 

the litter.

Compiling the data—The following data were averaged to produce the 

points that define the FLAME ‘standard curves’ (fig. 4), the curves that 

characterize ROS for each fuel type as a function of EWS.

• Grass: The average of models 1 and 3, CSIRO grassfire model, and 

grass fire ROS observation on the Australian Narraweena Fire of 1983 

(which helps to define the curve at high windspeeds); data points at 

EWS 1 through 10 mi/hr and 30 mi/hr (30 is an observation; model 

1 hits wind limit at < 9 mi/hr)

• Crowns: The average of models 5, 6, & 7, and best-fit curves for observed 

ROS in brush and in timber (which also reflect higher windspeeds); data 

points at EWS (mi/hr) of 3, 6, 9, 12, 15, & 18.

• Litter: The average of models 8, 9, & 10; data points at EWS 1 through 

9 mi/hr.

Deriving the Dependence of ROS on Fuel Type and EWS

The FLAME standard curves (fig. 4)—The data for each fuel type (grass, 

crown, litter) have some scatter within the group, but do not overlap; each 

fuel type is uniquely and separately characterized (especially when the faster 

components of average timber ROS are considered). The resulting degree 

of variation in predictions will be defined in discussions of accuracy below. 

You can visualize the ‘sector’ of fuels represented by each standard curve in 

figure 4:

• The grass group is at about ‘1 o’clock.’

• The crown group is at about ‘2 o’clock’ and extends from the points above 

the curve to the first set below the curve (the brush and ‘continuous 

timber’ points, but not the ‘average timber’ points, as will be explained 

below).

• The litter group is at about ‘3 o’clock.’

Technical Background of the FireLine Assessment MEthod (FLAME) Bishop
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 37

0.0

50.0

100.0

150.0

200.0

250.0

300.0

0 5 10 15 20

model 1

model 3

csiro

grass avg.

model 5

model 6

model 7

brush obs.

avg. timber obs.

crown avg.

model 8

model 9

model 10

litter avg.

grass std curve

litter std curve

crown std curve

Rgrass = 14.4 (W)1.232

Rcrown = 4.87 (W)1.146

Rlitter = 1.03 (W)1.213

R ch/hr

W mi/hr

Figure 4—Standard FLAME curves. ROS (ch/hr) on the vertical axis as a function of EWS (mi/hr), for each 

model or observation set used to generate the standard curves (grass fuels in yellows, crown fuels in 

greens, litter fuels in purple, blue, and brown). Standard curves are the best-fit power curves for each fuel 

type, with the defining equation shown. As will be explained below the curves are effectively truncated 

at EWS = 0.5 mi/hr.

Best-fit FLAME curves could be defined by polynomials and would offer 

good approximation to the data. However, it is necessary to compare values 

(as a ratio) between standard curves, and quotients of polynomials are not 

easily handled analytically. Therefore the standard curves are defined by bestfit power curves, which are amenable to analytic solutions when combined as 

ratios. The fit of the power curves to the average ROS values within each fuel 

types is quite good, with regression coefficients of at least r2 = 0.99. R, the 

ROS (not to be confused with earlier use of R as the regression coefficient), 

is in ch/hr; W, the EWS, in mi/hr.

The equations of the FLAME standard curves for each fuel type are (in 

the form expressed in equation 1 below):

• Grass: Rgrass = 14.4 (W)1.232

• Crown: Rcrown = 4.87 (W)1.146

• Litter: Rlitter = 1.03 (W)1.213

ROS-ratio for cases involving no change in fuel—Each fuel type has a unique 

ROS dependence on EWS, a unique wind response, as reflected in the exponent in the power equation (this paper would be a lot shorter if that were 

not so). To characterize changes in EWS in cases where there is no change in 

fuel the exponents are averaged, to give the ‘no-fuel-change’ dependence of 

Technical Background of the FireLine Assessment MEthod (FLAME) Bishop
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 38

ROS-ratio on EWS-ratio (for all fuels), as derived below. The accuracy consequences of using an average wind-response coefficient are evaluated in treating 

the wind response of all fuels the same in the ‘no-fuel-change’ case.

The general ROS equation (from regression curves) for a given fuel type is: 

R = α (W)b Eq. 1

Where R is ROS; α is the fuel coefficient; W is the EWS; b is the windresponse coefficient.

For change in EWS (in a given fuel), where RR = Rlarger / Rsmaller is the 

ROS-ratio, WR = Wlarger / Wsmaller is the EWS-ratio, the FLAME ROS-ratio 

will be

RR = Rlarger / Rsmaller = [α (Wlarger)b] / [α (Wsmaller)b] = (WR)b Eq. 2

The average of bgrass = 1.232, bcrown = 1.146, and blitter = 1.213 is: 

baverage = 1.20

Therefore for EWS changes only (no fuel change), the ROS-ratio is given 

by 

RR = (WR)1.20 Eq. 2a

ROS-ratio for cases involving both a change in fuel type and in EWS—ROSratios are formed for change between litter and crown fuels, between crown 

and grass fuels, and between litter and grass fuels. Note that the equations 

express an ROS-ratio that can describe a fuel change in either direction (for 

example litter to crown, or crown to litter), and either a reduction or an increase in EWS. The conventions regarding whole-number (versus fractional) 

ROS-ratio are detailed below in the section on The net effect on ROS.

Consider a change from an initial fuel-type and initial EWS to a final fuel 

type and final EWS, as expressed by the ROS-ratio RR. Here are the quantities involved (where RR and WR apply to a more general case than the ratios 

used in equation 2).

RI = initial ROS; RF = final ROS; WI = initial EWS; WF = final EWS; 

WR = WF/WI

Using equation 1, where here parameters αI and bI correspond to the 

curve for the initial fuel, αF and bF to the curve for the final fuel (as shown 

on fig. 4):

Initial ROS RI = αI(WI)bI; and final ROS RF = αF(WF)bF

RR = RF / RI = [αF(WF)bF] / [αI(WI)bI]; and since WF = (WI)(WR), 

then

RR = (αF /αI) (WI )(bF– bI) (WR)bF , the basic ROS-ratio equation Eq. 3

What do the terms in equation 3 mean?—Consider first the case in which 

WR = 1, and the equation represents a case in which there is no change in 

EWS, strictly a change in fuel, then:

RR = (αF /αI) (WI )(bF– bI) (for no change in EWS)

For a given WI, RR above represents the ratio of ROS typical of fuels αF& 

αI at that EWS. The fraction (αF /αI) itself represents the ratio of ROS in 

the different fuels at an EWS of WI = 1 mi/hr.

 The term (WI)(bF– bI) incorporates the fact that as WI varies, the relative 

ROS between different fuel types changes, reflecting the difference in the 

Technical Background of the FireLine Assessment MEthod (FLAME) Bishop
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 39

wind-response coefficients between fuel types. In other words, the relative 

ROS in the different fuels changes slightly as actual EWS changes...the relative ROS is slightly different at EWS of 3 mi/hr versus that at 6 mi/hr. One 

way to visualize it is that the relative spacing of the FLAME standard curves 

in figure 4 changes slightly as EWS varies, because each curve bends upward 

at slightly different rate (due to their different wind-response coefficients). If 

bF and bI were equal, the dependence of relative ROS for different fuels on 

EWS would vanish. As will be shown later in the sections on evaluating the 

accuracy of FLAME, the ROS-ratio is only weakly dependent on the term 

(WI)(bF– bI), and that effect introduces only a small error (usually less than 

10 percent).

We can consider (WI)(bF– bI) to be a constant, C, once we’ve chosen a 

standard WI. For changes from litter to/from either crown or grass fuels 

WI will be set at 2 mi/hr, and for changes from crown to/from grass fuels 

WI will be set at 15 mi/hr (as explained below in the section on The specific 

equations for combining fuel and wind changes). The consequences of those 

values of WI will be evaluated quantitatively in the section on The dependence 

of ROS-ratio on actual EWS (in addition to the EWS-ratio).

The third term in equation 3, (WR)bF, represents the influence on ROSratio of a change in EWS (via the EWS-ratio). The wind-response coefficients, 

b, are a bit greater than 1 (ranging from 1.146 to 1.232), which indicates that 

a given increase in EWS produces a little greater increase in ROS.

Rewriting Equation 3 with the constant C gives

RR = C (αF /αI) (WR)bF Eq. 3a

Equation 3a embodies the essential point of the FLAME process, with 

the fireline input shown in bold. A huge range of significant fire-behavior 

change, expressed by the ROS-ratio, RR, can be described by just two things: 

the knowledge of the change in fuel types (represented by αF /αI), and the 

degree of change in windspeed (via the EWS-ratio, WR). You can visualize 

the standardized fuel coefficients as representing the relative change in ROS 

between two different fuel-type curves at a standard EWS (either 2 mi/hr 

or 15 mi/hr), with the further effect of changes in EWS represented by 

movement along the appropriate standard curve an amount specified by the 

EWS-ratio.

The net change in ROS, slower or faster?—The expression in equation 3 is 

analytically complete and covers all possible cases of speeding up or slowing 

down. Initial fuels can be slower or faster than final fuels; initial EWS can be 

slower or faster than final EWS. An ROS-ratio <1 will indicate that the fire 

is expected to slow down as fuel and EWS change. But applying fractional 

EWS-ratios and interpreting fractional ROS-ratios can be awkward for the 

FLAME user, so equation 3 is used to generate a user-friendly lookup table 

containing only whole-number ROS-ratios and EWS-ratios.

For most real-world cases (especially those changes that threaten firefighter 

safety), the ‘slower’ fuel experiences the lesser wind, and the ‘faster’ fuel 

experiences the greater wind. For example, litter (the slowest fuel) under 

a stand will feel less wind than will overlying crowns (a faster fuel). So in 

practice ROS-ratio = (larger ROS)/(smaller ROS), and EWS-ratio = (larger 

EWS)/(smaller EWS). The user avoids working with fractional ratios, and 

simply keeps track of whether the change will be an increase or a decrease 

in ROS.

Technical Background of the FireLine Assessment MEthod (FLAME) Bishop
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 40

Table 1 shows the four possible combinations of ‘faster’ and ‘slower’ for 

fuels and EWS. The most common ‘big change’ combinations by far are 

those that fall into the upper left or the lower right quadrants of the table. 

Those are combinations where the change in fuel type and the change in 

EWS reinforce each other in increasing or in decreasing the fire ROS—they 

effectively multiply together to produce the final ROS-ratio that is shown in 

the main section of table 2, the basic FLAME table. The lower left and upper 

right quadrants show combinations where the change in fuel type and the 

change in EWS oppose each other, with the dominant change determining 

whether the net effect is to reduce or to increase the ROS. Direct changes 

between grass and crown fuels are the most likely possibility for such cases. For 

example, a fire backing in grass (a fast fuel) could then move up a slope with 

increased wind in crown fuels (a slower fuel). Such situations are covered in 

the rightmost two columns of the FLAME table (table 2), or by a technique 

described below in the section When changes have opposing effects.

Table 1—The several possible combinations of change in fuel type and change in EWS and the net 

change in ROS that could result.

Change in EWS

Faster Slower

ROS always increases increase or decrease

increase or decrease ROS always decreases

Change

in fuel 

type

Faster

Slower

Table 2—Rate-of-spread ratios (RR in the equations) as a function of changes 

in fuel and changes in effective windspeed. The table is generated by 

application of equation 3a. The left-hand column shows the EWS-ratio 

(WR in the equations), the factor by which EWS changes. Each column 

corresponds to a change between particular fuel types (or to no change). 

Table values express the ROS-ratio that results from the combined change 

in EWS and fuel. The left side applies to cases in which fuel and wind 

changes reinforce. Cases in which changes in wind and fuel have opposing 

effects are handled with the rightmost two columns. Highlighted ROS values 

define a range that includes situations associated with fireline fatalities.

FLAME Table (source of ROS-ratios)

EWSratioNo fuel 

change

Litter

to/from

crown

Litter

to/from

grass

Crown

to/from

grass

Crown

to/from

grass

Litter

to/from

grass

No chg 1 1 4 14 4 4 14

2 2 10 30 8 2 5

3 4 15 60 13 1 3

4 5 20 80 20 2 3

5 7 30 100 27 2 2

6 9 35 130 35 3 2

8 12 50 180 4 1

10 16 60 240 5 1

12 20 80 300 6 2

16 30 100 440 8 2

20 40 140 600 10 3

24 50 180 700 13 3

30 60 220 1000 17 4

40 80 300 1300 23 6

50 110 400 1800 30 8

60 140 500 2200 40 10

80 200 700 3100 60 16

EWS biggest in faster fuel EWS less in grass

Technical Background of the FireLine Assessment MEthod (FLAME) Bishop
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 41

The specific equations for combining fuel and wind changes—The specific 

parameters (analogous to α and b in equation 1) for each fuel type are (see 

the FLAME standard curves in fig. 4):

• For grass fuels: αG = 14.4; bG = 1.232

• For crown fuels: αC = 4.87; bC = 1.146

• For litter fuels: αL = 1.03; bL = 1.213

The ‘α’ coefficients measure the effects of fuel type on ROS (for the above 

values, at EWS= 1 mi/hr)—indicating that litter is the slowest fuel, crowns 

faster, and grass the fastest fuel. The ‘b’ coefficients measure the response 

of ROS to changes in EWS. b-coefficient comparisons reflect the fact that 

grass is a little more wind-responsive than litter fuels, and that litter fuels 

are a little more wind-responsive than crown fuels (as characterized by the 

FLAME standard curves).

The final equations 4, 5, and 6 (below) are obtained from equation 3 by 

inputting the above specific values of α and b to represent the initial and 

final fuels. For changes from litter to/from either crown or grass fuels the 

standard initial EWS is set at WI = 2 mi/hr, and for changes between crown 

and grass fuels the standard EWS is set at WI = 15 mi/hr. Those ‘standard 

values’ of EWS are chosen because each falls at the ‘geometric’ midpoint of 

a reasonable range of actual EWS (+/– 4X or +/– 3X). For cases in which 

there is no change in EWS, WR = 1, and the approximate relative change in 

ROS between fuel types can be gauged from the fuel-change coefficients 

(4.51, 3.73, and 14.2)—that relationship varies slightly at W away from the 

standard WI. ROS in crown fuels is roughly 41⁄2 X faster than in litter (less 

difference with increasing EWS), ROS in grass fuels is roughly 31⁄2 X faster 

than in crowns (more difference with increasing EWS). Those figures do not 

multiply to give exactly the relationship between litter and grass fuels because 

they represent different ‘standard EWS’ values (otherwise they would).

Litter to/from crown fuels:

RC / RL = (4.87/1.03) (2)(1.146-1.213) (WR)1.146 = 4.51(WR)1.146 Eq. 4

Crown fuels to/from grass:

RG/ RC = (14.4/4.87) (15)(1.232-1.146) (WR)1.232 = 3.73 (WR)1.232 Eq. 5

Litter to/from grass:

RG/RL = (14.4/1.03) (2)(1.232-1.213) (WR)1.232 = 14.2 (WR)1.232 Eq. 6

The above equations are used to generate the table of ROS-ratios used in 

FLAME (table 2 and appendix B table B2), as a function of the two fuels 

involved and of the EWS-ratio. In the main part of the table (center three 

columns) a change in fuel and the change in EWS are assumed to reinforce 

(which is usually the case) to cause a net decrease or net increase in ROS. 

The less common cases where the change in fuel opposes the change in EWS 

(such as fire backing down a slope in grass, then running up the next slope as 

a wind-driven crown fire) are handled in the rightmost two columns. Those 

exceptional cases can also be handled by a technique described briefly in 

the section below on Adaptation to nonstandard cases. The same ROS-ratio 

can describe either a net increase or a net decrease in ROS (in other words, 

a fire could go 6X faster or 6X slower). Tabled numbers have been rounded 

off (within about 10 percent accuracy) to make for easier application and 

Technical Background of the FireLine Assessment MEthod (FLAME) Bishop
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 42

interpolation, and physically unrealistic combinations are left blank.

To use the table join the EWS-ratio row with the column describing the fuel 

change, and read the ROS-ratio. For example, a 6X increase in EWS together 

with a transition from fire in the litter into the crowns would result in an 

increase in ROS of about 35X (or in the opposite case, a decrease of 35X).

Adaptation to nonstandard fuels—There are cases where fire is burning in 

a mix of fuel types. In those cases FLAME can be applied assuming each of 

the fuel components separately, and then averaging the two predictions. For 

example, a litter fire moves into a fuelbed of grass and shrubs. The change 

can be separately treated as ‘litter to grass’ and as ‘litter to crown’, and the 

two ROS-ratios averaged.

Also, there are real-world fuels that are not a good match for one of the 

standard fuel types, and those can be treated as described above. For example, 

fuel model 2 ‘grass’ is a mixed fuel that has typical ROS values that lie between 

crown and grass values (at a given windspeed), and averaging FLAME predic-

tions for a change-to-crown with a change-to-grass gives workable results. 

For example, with a fuel change from litter (avg of models 8, 9, 10) at EWS = 

4 mi/hr to ‘model 2’ at EWS = 12 mi/hr BehavePlus predicts ROS-ratio = 

51X. The average of the separate FLAME ROS-ratios = 60X.

Slash fuels have average ROS values that are approximately 1.5X the aver-

age litter ROS. ROS-ratios for slash fuels can be estimated by adjustment of 

the ROS-ratio obtained using the litter fuel type. The ROS-ratio for litterto-slash would be 1.5X the no-fuel-change ROS-ratio. The ROS-ratio for 

changes between slash and crowns (or slash and grass) would be two-thirds 

of the litter-to-crown (or litter-to-grass) ROS-ratio.

When changes have opposing effects—Consider the case of a change in fuel 

type and an opposing change in EWS, such as a backing fire in grass becom-

ing a wind-driven upslope fire in crown fuels. There are two ways to do it.

1. Simply do the FLAME prediction in two parts. First, use the ROS-table 

to predict the effects of just a change in fuel type. Second, predict the 

ROS-ratio that would result from just the change in EWS. Then divide 

the bigger ROS-ratio by the smaller ROS-ratio (keeping track of whether 

the speed-up or the slow-down in ROS will dominate). For example, an 

EWS increase of 20X would alone produce an increase in ROS of 40X. A 

change from ‘faster’ grass to ‘slower’ crown fuels would alone produce a 

decrease in ROS of about 4X. So the net change in ROS would be about 

40/4 = 10X. Note: this ‘two-step’ approach is applicable to any FLAME 

application.

2. The appropriate section of the FLAME table can also be used, ‘EWS is 

less in grass’. For the above example an EWS-ratio of 20X combined with 

a change from grass to crown fuels yields an ROS-ratio of 10X. (Minor 

discrepancies between the methods can result from round-off errors, and 

because the wind response of each fuel differs slightly from the average 

used in the ‘no-fuel-change’ case.)

Evaluating the Accuracy Limits

The FLAME-prediction performance standard—FLAME should be simple 

and practical enough to be used, and accurate enough to be useful. Presently 

firefighters have no routine and systematic process for assessing fire behav-

ior on the fireline, and for focusing on changes. The full application of the 

fire model requires more input information and more processing capability 

Technical Background of the FireLine Assessment MEthod (FLAME) Bishop
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 43

than is realistic or available on the fireline, and yields outputs that require a 

map or a way of gauging distance in units such a feet or chains. FLAME is 

designed to fill that gap between no system and the full system. The specific 

goals for FLAME accuracy are:

1. That at least three-fourths of the predictions of ROS-ratio will be accurate within a factor of +/– 2X compared to FBPS predictions or to 

real-world observations. ‘+/– factor of 2X’ means that the real ROS-ratio 

falls between the half of and twice the FLAME-predicted ROS-ratio—for 

example, for a FLAME ROS-ratio of 80X that the actual ROS-ratio 

falls in the range 40X to 160X. ‘Factor of 2X’ is easy to remember and 

apply, and spans a fairly realistic range of variations in many real-world 

processes. (The uncalibrated application of the FBPS is characterized as 

having that same level of accuracy, +/– factor of 2X.)

2. That no FLAME predictions mislead firefighters in their safety judgments. This qualitative accuracy goal, that FLAME predictions inform 

but do not mislead safety judgments, is the most relevant and important. 

This indeed could probably have called the first goal.

How might the accuracy range of ROS-ratio affect safety judgments, at each 

application level?

Initial application (identifying the next big change): Following the 

FLAME process a firefighter will be able to identify the large potential 

changemakers in the situation. The dominant changemakers are well characterized in FLAME (EWS and fuel type). The relative order of fuel types 

by ROS characteristics is correct and nonoverlapping, and the wind-dependence of ROS is well represented. So the initial application can be relied on 

to highlight the dominant changemakers, identify the next big change, and 

give the correct sense of decrease or increase in ROS.

Standard application (using the ROS-ratio as a guide to dangerous 

situations): Given that fireline fatalities correlate strongly with large ROSratios (preliminary data suggest ROS-ratio > approx. 60X is a common 

denominator), it is valuable for the predicted ROS-ratio to alert a firefighter to 

a potentially life-threatening situation. When the firefighter applies FLAME 

and obtains an ROS-ratio, he or she will double that prediction and consider the larger ROS-ratio as a guide to potential danger. And if the larger 

ROS-ratio is getting near the ‘danger zone’ prudence demands that safety 

judgments will be based on that potential. For example, if the predicted ROSratio is, say, 40X then twice that is 80X, and a firefighter should carefully 

evaluate the risks and benefits before committing to an action. Therefore, a 

FLAME ROS-ratio of 30X (while the actual ROS-ratio is 60X) would still 

alert the firefighter that he/she faces a level of change known to be associated with fireline fatalities. Also, the FLAME system tends to overpredict 

ROS-ratios.

Complete application (predictions of the fire-spread timeline): Consider two extreme possibilities: first a case of large ROS-ratio, and second a 

case of small ROS-ratio.

Large ROS-ratio: Suppose the ROS-ratio is 200X, with ROS increasing, 

and that it suggests the fire will reach a given point in about 10 minutes. 

Applying the error range of 2X means the fire’s travel-time should be considered to fall between about 5 and 20 minutes. The difference between the 

predicted and actual travel times is only –5 minutes or +10 minutes, and 

Technical Background of the FireLine Assessment MEthod (FLAME) Bishop
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 44

operationally that is not a huge difference....certainly one should not try to 

cut any safety-essential actions too close to save 10 minutes. Assume the worst 

case and act wisely (how better to spend the ‘extra’ 10 minutes?). The point 

is that the time scale is short in any case, and even the inaccuracies are only 

on the order of minutes. Suppose the ROS-ratio is 200X, but the fire will 

slow down, suggesting the fire might reach a certain point in 6 hours. Ap-

plying the factor of 2X means that fire travel-time should fall between 3 and 

12 hours. The actual variation from predicted spread time, up to 6 hours, is 

a long time. But the important point is that the firefighter will have hours to 

continue to observe the fire and to reevaluate the FLAME prediction.

In a sense, the consequence (on predicted fire-spread times) of errors 

associated with large ROS-ratios is ‘self limiting’ in that for large increases 

in ROS the response time is short in any case, while for large decreases in 

ROS there is ample time to update and adjust the prediction.

Small ROS-ratio: A small ROS-ratio means that the fire behavior is not 

expected to change dramatically. In that case, changes from the current fire 

behavior will be gradual. Variations of 2X from that ROS could be noticed 

before creating the sudden and extreme changes that put lives in danger. 

With the expectation of modest changes in fire behavior the firefighter would 

have the chance to observe and update the FLAME prediction, all the while 

looking ahead to the next big change.

It is important to keep the accuracy of the FLAME system in perspective. 

It is not perfect, but it is much better to have a helpful prediction than to 

have none, to call attention to important factors and the potential for signifi-

cant change than to be unaware. And any system, even the most accurate, is 

fundamentally limited by the accuracy of inputs on fire environment factors 

(an especially challenging one being the actual midflame windspeed). There 

will be cases where FLAME falls short of the ‘+/– factor-of-2X’ standard. 

The same is true for any of the operational prediction systems. (For example, 

model-predicted ROS for a litter fuel that falls between fuel models 8 and 9, 

in a situation where the sheltering is between wind-reduction factors 0.1 and 

0.2, could vary by a factor of 9X depending on the inputs a practitioner might 

reasonably choose.)

We can only provide the best tools we have and make users aware of their 

limits—much better to have a decent, if imperfect, tool than no tool.

The dependence of ROS-ratio on the chosen standard EWS—Even though 

the main cause of change in ROS (for a given fuel) is the change in EWS, 

there is a weak dependence of changes in ROS on the actual EWS involved. 

Recall the discussion above under What do the terms in equation 3 mean?. 

How much accuracy do we lose in using values of the fuel coefficients fixed 

at a ‘standard’ windspeed?

 Consider two cases, otherwise identical, one in which the initial EWS is 

W

1 and the other in which it is W2. Comparing the ROS-ratio in one case 

(RR1) with that in the other case (RR2), with the initial and final fuels being 

the same in both cases: RR2/RR1 = [(aF / aI) (W2

)

(

bF – bI) (WR

)

b

F] / [(

a

F / 

a

I) (W1

)

(

bF – bI)(WR

)

bF 

]

 = (W2 / W

1

)

(

bF – bI) Eq. 7

Note that W2 / W

1 here represents the ratio of two possible initial EWS values, 

not an initial and final EWS. This can be viewed as a case where one of those 

EWS values is the chosen ‘standard’ EWS, and the other is the actual EWS 

for a particular fireline situation. The error in the ROS-ratio that results from 

Technical Background of the FireLine Assessment MEthod (FLAME) Bishop
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 45

an actual EWS that differs from ‘standard’ EWS is a function of the ratio of 

those EWS values, (W2 / W1).

For fires in litter a realistic range in EWS values might be from EWS = 0.5 

mi/hr (a backing fire) to EWS = 8 mi/hr. Compared to the assumed standard 

EWS which was set at EWS = 2 mi/hr, the maximum value of (W2 / W1) in 

equation 7 would be 4. For the case of transition from litter to crown fire, 

the variation introduced into the ROS-ratio by using that standard EWS 

would then be (using equation 7).

RR2/ RR1 = (4)(1.146 – 1.213) = 0.91, implying an error range of about +/– 9 

percent

For the same assumed range in the actual EWS, and a transition from litter 

to grass,

RR2/ RR1 = (4)( 1.232-1.213) = 1.026, which implies an error range of about 

+/– 3 percent

Similarly, for transitions between crown and grass fuels a variation in 

actual EWS from 5 mi/hr to 45 mi/hr, a maximum 3X deviation from the 

‘standard’ EWS of 15 mi/hr, would result in:

RR2/ RR1 = (3)( 1.232-1.146) = 1.1, which implies an error range of about 

+/– 10 percent

We can see from the above examples that the error introduced by assuming a standard EWS, and using the EWS-ratio alone to judge wind-induced 

change, is small, generally less than +/– 10 percent. Furthermore, due to 

its dependence on a small exponent, that error grows slowly with ranges of 

initial EWS much wider than assumed above.

Treating the wind response of all fuels the same in the ‘no-fuel-change’ 

case—For simplicity in application, changes in EWS only (with no fuel change) 

are treated with an equation that averages the wind-response effects (the

β-coefficients) of the different fuels. How large is the error introduced by that 

approximation? We use equation 2 to express the ROS-ratio for an actual β

(βact) and for the average β (βavg), and compare the resulting ROS-ratios. RR 

ACT and RR AVG represent the ROS-ratios using actual β-coefficients versus 

the average β-coefficient (= 1.20) respectively.

(RR ACT)/(RR AVG) = WRβact / WRβavg = (WR)(βact-βavg) Eq. 8

Considering a large EWS-ratio in grass or litter to be on the order of 20X, 

and in crown fuels to be about 4X (the ‘low end’ of EWS pushing crown 

fires is much greater than for litter or grass, and therefore the total range in 

EWS-ratio for crown fires is less), the error introduced by using the average 

wind response in the ‘no fuel change’ case is about:

• for grass: (RR ACT)/(RR AVG) = 1.1, which implies an error of 10 percent 

(with the FLAME prediction too low)

• for crowns: (RR ACT)/(RR AVG) = 0.93, which implies an error of 7 percent 

(with FLAME prediction too high)

• for litter: (RR ACT)/(RR AVG) = 1.04, which implies an error of 4 percent 

(with the FLAME prediction too low)

Such deviations fall well within the goal of factor-of-2X accuracy, and therefore simplifications embodied in the average wind-response factor for the 

‘no-fuel-change’ case are not problematic.

Technical Background of the FireLine Assessment MEthod (FLAME) Bishop
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 46

Summary of accuracies of the above analytical simplifications—The various 

simplifications that are required to deal with the exponential dependence of 

ROS on EWS and with the different b-coefficients for each fuel type introduce errors in ROS-ratio that typically range from a few percent to about 

10 percent. In a further ‘direct test’ comparisons of ROS-ratios computed 

directly using the raw ROS equations (equation 1, with specific coefficients 

for each fuel) with ROS-ratios computed from equations 4, 5, or 6 also show 

variations of less than 10 percent. Those errors are within the accuracy goal, 

and are normally subordinate to the errors introduced by imperfect inputs 

(especially of uncertainties in windspeed).

The variation of ROS within a FLAME fuel type—The largest simplification embodied in FLAME is treating a variety of fuels as a single fuel type, 

with regard to ROS. For example, crown fires in brush and in timber are 

considered all as one type of ‘crown fire’. To assess the deviations introduced 

by the simplification of treating all fuels within a group as one fuel type, the 

following measure is used. For a given EWS, the associated ROS for a specific 

fuel is compared with the ROS associated with the FLAME standard curve 

(fig. 4) for that fuel type. For example, at EWS = 6 mi/hr, the grass-fuel 

curve shows ROS = 134 ch/hr, while fuel model 3 predicts 148 ch/hr. The 

deviation of fuel model 3 from the FLAME curve in that case is +9 percent. 

Similarly derived deviations are shown for a range of EWS values in tables 

3 through 5 using the formula: [(fuel-specific value)/(standard-curve value) 

–1 ] X 100. Positive values of the deviation indicate that the specific fuel type 

is faster than the standard curve.

The standard curve for crown fuels is constructed from FBPS fuel models 5, 

6, and 7, observed ROS in brush fuels, and observed ROS in timber fuels. The 

timber fuels are the slowest of the lot. However, the timber observations are 

dominated by averages over considerable times and distances, encompassing 

sustained crown runs and discontinuous crown fire. The brush observations 

are dominated by data from shorter, continuous crown runs. We can consider 

the shorter, sustained crown spread in timber to be faster than the long-term 

average (Rothermel 1991 estimates that maximum ROS in timber is often 

approximately 1.7X the average ROS), and that corresponds closely to the 

ROS observations for brush. The larger data set of longer-term-­average timber 

ROS observations was used in constructing the standard curves, and overall 

Table 3—Deviations of ROS for individual grass fuel representatives 

from the grass-fuel standard curve, at a range of effective 

windspeeds. Positive deviation values indicate that the actual 

ROS for a representative fuel would be higher than the ROS 

suggested by the FLAME standard curve; negative deviations 

indicate the representative ROS is lower than the standard curve. 

The apparent jump in average deviation at EWS = 9 mi/hr is due in 

part to the loss of fuel model 1 data points above its wind limit.

EWS in mi/hr

 3 6 9 30

Model 1 -33% +1%

Model 3 +19% +9% +20%

CSIRO +15% -10% -20% -6%

Average absolute

deviation from standard 22% 7% 20% 6%

Technical Background of the FireLine Assessment MEthod (FLAME) Bishop
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 47

Table 4—Deviations of ROS for individual crown fuel components from the crown-fuel standard 

curve, at a range of effective windspeeds. Positive deviation values indicate that the 

actual ROS for a representative fuel would be higher than the ROS suggested by the 

standard curve, negative deviations indicate the representative ROS for that fuel is lower 

than the standard curve. The FLAME standard curve for crown fuels is weighted toward 

the relatively higher ROS expressed in fuel models 5, 6, 7, and lies above observed 

crown fire ROS (except in one low-wind case). It is intentional to err on the side of not 

underpredicting the increase in ROS associated with the transition from litter fire to 

crown fire, because that dangerous event has too often killed firefighters. The original 

data for ROS in timber are dominated by observation of long-term averages (which 

include sustained runs and discontinuous crown fire). FLAME is aimed at predicting the 

shorter term, sustained fire behavior, so a comparison is also made here to a timber 

ROS adjusted by 1.7X to more realistically represent that behavior. In that comparison, 

timber ROS closely matches brush ROS, and therefore supports the strategy of treating 

all ‘crown fuels’ the same with regard to ROS.

EWS in mi/hr

 6 9 12 15 18

Models 5, 6, 7 averaged +12% +18% +22% +24% +25%

Brush observervation ROS +6% -7% -14% - 19% -22%

Timber observation ROS -36% -45% -49% - 48% -53%

Timber ROS adjusted by 1.7X

to represent continuous runs +9% -6% -14% -18% -21%

Average absolute

deviation from standard using 

adjusted timber ROS

9% 10% 17% 20% 23%

Average absolute

deviation from standard

using long-term average

timber ROS

16% 20% 26% 30% 30%

Table 5—Deviations of ROS for individual litter fuel representatives from the litter-fuel standard 

curve, at a range of effective windspeeds. Positive deviation values indicate that the 

actual ROS for a representative fuel would be higher than the ROS suggested by the 

standard curve, negative deviations indicate the representative ROS is lower than the 

standard curve. Fuel models 9 and 10 are similar in ROS, but fuel model 8 has much 

lower ROS (a factor of about 3X less than the standard curve). To more realistically 

treat a case involving ‘slow’ model 8 litter fuels, the user can apply a correction factor 

of 3X to the FLAME ROS-ratio. Deviations of ‘model 8’ litter fire ROS from a ‘corrected’ 

ROS are covered in the lower two rows of the table.

EWS in mi/hr

 1 3 6 9 

Models 9 and10 averaged +40% +36% +33% +41%

Model 8 -70% -70% -67% -72%

Average absolute

deviation from standard 50% 47% 44% 51%

Model 8 deviation from

‘adjusted’ FLAME

standard

-9% -9% 0% -15%

Average absolute 

deviation from standard

curve with adjustment

for ‘compact’ litter

30% 27% 22% 32%

Technical Background of the FireLine Assessment MEthod (FLAME) Bishop
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 48

the balance represented by the standard curve seems satisfactory. Even with 

the timber ‘average’ ROS values built into the standard FLAME curves, the 

standard curve is ‘faster’ than the observed-crown-fire representative points. 

Given the FLAME intent of predicting short-term changes, the comparison 

between the standard curve and the faster ROS in timber is a more appropriate 

measure of how a FLAME prediction might compare to actual continuous 

crown-fire runs in timber. Therefore, in evaluating the fit between ‘timber’ 

ROS and the standard crown-fuel curve the adjusted timber ROS is also 

considered.

The standard curve for litter is constructed from FBPS fuel models 8, 9, 

and 10. Fuel models 9 and 10 have similar ROS characteristics. Fuel model 

8, representing compact, short-needle litter displays considerably slower ROS, 

about 3X slower than the litter fuel average. The effect of model 8 is to lower 

the standard curve, and the effect of lowering the standard curve is to over-

state, if anything, the increase in ROS accompanying a transition to crown 

fire (FLAME intentionally leans toward not underpredicting such a dangerous 

change). Most litter fuels tend to be more like fuel model 9 or 10.

However, given that the ROS in litter is often the denominator in gener-

ating an ROS-ratio, a large error could arise in cases where the fire was in 

‘model 8’ litter. Such cases can be readily handled by multiplying the FLAME 

ROS-ratio by about 3X (though for simplicity, and because most litter is not 

as ‘slow’ as model 8, the practical adjustment guideline is to multiply the 

ROS-ratio by 2X when ‘compact litter’ is involved).

Dealing with the imprecision in fuel types—The simplest way to handle the 

variations of ROS within a given fuel type is to accept them—most of them 

fall well within the +/– factor-of-2X (–50 percent or +100 percent) goal. 

The notable exception is the ‘slowness’ of model-8 litter fuels, where actual 

ROS-ratios could exceed by 3X the FLAME-predicted ROS-ratio. The main 

consequence of such underprediction of a large increase in ROS-ratio would 

be on the fire-spread timeline. In cases of large ROS-ratio the operational 

impacts of such inaccuracies are small because they affect timescales that are 

already short enough to suggest the need for timely actions (as noted in a 

previous section, Complete application). As with the FBPS, a practitioner can 

improve its accuracy considerably by observing and calibrating.

 A practical and straightforward way to improve the accuracy of FLAME 

predictions is to make an adjustment to the ROS-ratio, or to average two 

ROS-ratios, based on known characteristics of the given fuel. For example: 

when dealing with compact litter (‘model 8’ fuels), double the predicted ROSratio; for ‘model 2’ fuels average the change-to-crown and change-to-grass 

outputs. See the section on Adaptation to nonstandard fuels.

A more sophisticated FLAME application tool could significantly improve 

the accuracy of predictions involving changes in fuel type. The key improve-

ment in relating fuel types is to use the actual fuel coefficients (

a) for the 

specific fuels involved. Array the fuels on paired logarithmic scales by fuel 

coefficient (like a slide rule), and apply user-friendly descriptions within each 

fuel group, such as ‘sparse grass’ or ‘tall grass,’ ‘fluffy litter’ or ‘compact litter’ 

to the scale. Sliding the scales to align the two fuels in question would then 

produce the ratio of their specific 

a-coefficients (exactly as a slide rule por-

trays a quotient), and the scale index could act as a pointer to the appropriate 

column of ROS-ratios. The whole affair could easily be built in to a compact 

calculator. It could also be an application on a hand-held computer.

Technical Background of the FireLine Assessment MEthod (FLAME) Bishop
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 49

Comparing FLAME outputs to FBPS predictions—Following are several 

examples of fire behavior events in which the ROS-ratio based on BehavePlus 

output is compared to FLAME predictions (table 6). Where round-off errors 

in the FLAME table would complicate the comparisons the FLAME predictions are obtained directly from the FLAME equations (equations 4, 5, 6) 

rather than the tables. This is a clearer test of the basic system itself.

Table 6—Summary of the results of comparing FLAME ROS-ratio to the ROSratio generated by BehavePlus (and in example 4 also from Rothermel’s 

1991 crown fire nomograms). Absolute error averages 15 percent, standard 

deviation of the errors 8 percent, largest error 26 percent.

Summary of 

accuracy tests

BehavePlus

ROS-ratio

FLAME

ROS-ratio

Deviation from

BehavePlus

Example 1 2.6X 2.3X -12%

Example 2 500X 370X -26%

Example 3 162X 174X +7%

Example 4 56X 64X +14%

Example 1: A fire in burns with 3 mi/hr midflame windspeed in litter on 

the lower portion of a 20 percent slope; as it moves onto the upper slope the 

midflame windspeed increases to 6 mi/hr.

BehavePlus (model 9): the initial ROS = 4.7 ch/hr; final ROS = 12.2 ch/hr; 

ROS-ratio = 2.6X

FLAME: ROS-ratio = 2.3X

Deviation of FLAME from BehavePlus = –12 percent

Example 2: A backing litter fire on a 30 percent slope crosses to the opposite 

slope and moves upslope in grass with an 6 mi/hr eye-level wind.

BehavePlus (models 9 and 1): initial ROS = 0.3 ch/hr; final ROS = 151 ch/hr; 

ROS-ratio = 500X

FLAME: ROS-ratio = 370X

Deviation of FLAME from BehavePlus = –26 percent

Example 3: A fire creeping in litter up a 30 percent slope (EWS = 1 mi/hr) transitions to crown fire in brush when the 20-ft wind increases to 24 mi/hr.

BehavePlus (models 8/9 combined, model 5): initial ROS = 1.6 ch/hr; final 

ROS = 263; ROS-ratio = 162X

FLAME: ROS-ratio = 174X

Deviation of FLAME from BehavePlus = +7 percent

Example 4: A fire in litter (EWS = 2 mi/hr) transitions to crown fire in 

timber with 20 mi/hr winds (at 20-ft level).

BehavePlus (model 9) and Rothermel formula for Rocky Mountain timber, 

adjusted by 1.7X to represent faster portions of the overall crown fire: initial 

ROS = 2.5 ch/hr; final ROS = 83 ch/hr X 1.7 = 141 ch/hr; ROS-ratio = 

56X

FLAME: ROS-ratio = 64X

Deviation of FLAME from BehavePlus-Rothermel solution = +14 percent

Technical Background of the FireLine Assessment MEthod (FLAME) Bishop
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 50

All of above FLAME predictions fall within factor-of-2X accuracy 

compared to BehavePlus (which in this test is considered to represent the 

‘true’ value of ROS-ratio). FLAME overpredicts the danger presented by a 

litter-to-crown fire transition (which errs on the side of safety). In the case 

of underpredicting the grass fire case, the high FLAME ROS-ratio would 

still alert the firefighter to the clear danger and would yield estimates of fire 

travel-time that were within minutes of the BehavePlus prediction. In the 

clearly ‘dangerous’ cases represented above (examples 2, 3, 4) the predicted 

FLAME ROS-ratios (370X, 174X and 64X) fall in the range associated with 

fireline accidents.

Comparing FLAME outputs to actual fireline incidents—The most relevant 

test of the accuracy of FLAME predictions is against real-world fireline 

situations. To provide a common basis for comparison of FLAME versus 

real-world observations the ROS-ratio derived from fireline accident investigations is compared to that predicted by FLAME (fig. 5). The details of the 

assumptions used for FLAME application to these cases are in appendix A. 

The FLAME prediction is based on the fireline information as it could have 

been available to a firefighter applying FLAME—the idea is to evaluate the 

FLAME process with good information, and not muddy the comparison 

with inaccurate input. The documentation of the incidents is derived from 

the official reports, also in the cases of the South Canyon and Dude fires 

from on-site examination, and in all four cases from discussion with people 

who were there or who have studied the incidents.

Figure 5—FLAME ROS-ratios versus documented ROS-ratios for fatality cases cited 

above. The diagonal represents perfect correlation between FLAME predictions and 

documented values. Average absolute error is 9 percent, standard deviation of errors 

15 percent, and maximum error 29 percent.

Incident Documented

ROS-ratio

FLAME

ROS-ratio

Deviation

from documentation

South Canyon Fire 500X 500X 0%

Dude Fire 480X 500X +4%

30-mile Fire 96X 100X +4%

Cramer Fire 124X 160X +29%

0

100

200

300

400

500

600

0 100 200 300 400 500 600

Documented ROS-ratio

FLAME ROS-ratio

Technical Background of the FireLine Assessment MEthod (FLAME) Bishop
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 51

In all of those cases, application of FLAME could have revealed the coming 

change, would have indicated clearly that it was of a magnitude that should 

be considered dangerous, and predicted with accuracies of order minutes the 

time it took for the fatal fire run.

Accuracy summary—By the most important accuracy standard—­informing 

firefighter safety decisions and not providing misleading predictions that 

compromise safety—FLAME succeeds against BehavePlus calculations and 

in the four real-world cases above. Considering the three application levels, 

FLAME would be reliable in identifying the next big change, would provide 

good predictions of the magnitude of the ROS-ratio, and could suggest 

realistic timelines of the fire’s advance.

The approximations inherent in the FLAME system—standardizing 

relative ROS between fuel types, using an average wind response for all 

fuels in cases of wind-change only, and treating similar fuels as a single fuel 

group—all fall within the 2X range, usually well within. The target quantitative accuracy standard (ROS-ratio within +/– factor-of-2X in at least 

75 percent of the cases) is difficult to fully evaluate simply due to a lack of 

well-documented cases against which to test it. FLAME is within 2X of the 

four real-world cases (absolute error averaging 9 percent, standard deviation 

of the errors 15 percent, largest error 29 percent). And it is within 2X of 

the four BehavePlus examples (absolute error averaging 15 percent, standard 

deviation of the errors 8 percent, largest error 26 percent). But compared 

to other possible BehavePlus examples it might not meet the ‘factor-of-2X’ 

standard, and how do you choose a representative set of BehavePlus examples 

for which the 75 percent success rate is meaningful? As far as the incomplete 

set of comparisons above allows, FLAME meets the quantitative ‘factor-of2X’ accuracy standard. Fuller evaluation awaits a larger set of real-world fire 

behavior cases.

A note on future improvements—No doubt, improvements in the basic 

fire behavior data, the models, and the application tools can make FLAME 

more accurate and more usable. Such improvements will not be difficult to 

incorporate and to disseminate to firefighters; they won’t have to ‘unlearn’ 

anything. It might be as simple as issuing a new FLAME table with new 

ROS-ratio values, or it might involve an improved worksheet or a better 

‘field calculator’. The important thing is that firefighters will have already 

learned the process, a systematic process, of foreseeing the next big change, 

of evaluating the dominant changemakers, and of incorporating the expected 

fire behavior change into their fireline judgments. Assimilating a revised 

FLAME application tool will not be difficult.

Part 2: Obtaining Inputs

The main inputs to a FLAME prediction of ROS-ratio (table 2) are changes 

in fuel type and changes in EWS (effective windspeed). Changes in EWS are 

expressed as the ratio of the larger to the smaller EWS.

Changes in Fuel Type

New fuels ahead of the fire—The simplest change in fuel type occurs when 

the fire moves into new fuels ahead of the fire. Such changes are commonly 

encountered with changes in slope aspect, as fire crosses drainages or ridges. 

Technical Background of the FireLine Assessment MEthod (FLAME) Bishop
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 52

A firefighter looks at what fuels lie out ahead of the fire, and can estimate the 

time of a potential fuel-type change by projecting the fire’s current spread.

Transitions between surface and crown fire—An important change in fuel 

type, and a more challenging change to anticipate, is the transition of a fire 

in surface fuels into crown fuels. In essence, in a stand of trees or brush there 

are two fuel types juxtaposed, a surface fuel bed and crown-foliage fuel strata 

overlying it. The fire can transition between them rapidly, and undergo large 

changes in ROS and intensity.

The potential for fire to extend from surface fuels (usually a litter fuel 

type) into crown foliage depends on many factors. A major variable is the fuel 

moisture, both the live FM of the foliage and the FFM of the dead fuels.

The live FM ‘sets the stage’ when it drops below a threshold that permits 

sustained fire spread in the crown fuels. There is much variation of the thresh-

old live FMs from one vegetation type to another, but some generalizations 

are possible and provide some guidance. At or below those live FM levels 

crown fuels are susceptible to sustained crown fire. The following represent 

such thresholds. • California chaparral: live FM of 70 to 80 percent • Great Basin shrubs: live FM of about 100 percent • Rocky Mountain timber: live FM of about 120 percent • Ponderosa pine: above 125 percent (maybe no practical upper limit)

The FFM plays a major role in the intensity of the surface fire, and the 

flammability of the dead attached component of the aerial fuels—essentially 

controlling the ‘burner’ under the foliage layer. FFM is strongly controlled 

by relative humidity (RH), and RH serves as a practical guide for firefighters. 

When live FM is in the range for potential crown fire, then RH (and FFM) 

dropping below a certain threshold-range greatly increases the potential for 

transition to crown fire. The threshold values of RH are surprisingly consis-

tent over most vegetation types. • Most fuels of the Western United States: RH below about 20 to 30 percent, 

especially below 20 percent

• Some fuels of the Southeastern United States: RH below about 35 percent

Firefighters can stay abreast of published trends in live FM and trends 

evidenced in the fire behavior, and they can monitor RH. The NFDRS is a 

valuable guide to crown fire potential. On the fireline the following accessible 

observations provide a practical guide/check-off list to suggest that transition 

to crown fire is a good possibility: • Seasonal drought period prevails (meaning live FM is reaching the 

threshold of flammability).

• Overall drought makes matters worse (live FM will become critical sooner 

and go lower than normal).

• Recent crown fire, on other fires or your fire (crown fire possibility is 

demonstrated).

• Relative Humidity 35 to 20 percent, or less, especially RH below about 

20 percent (FFM is low enough to trigger crown fire).

• Backing fire produces torchouts (a dead giveaway that headfire can 

crown).

• Fire moving up ladder fuels (early indicators of the transition to crown 

fire).

• Torchouts and short crown runs (with any worsening of burn conditions 

crown fire is imminent).

Technical Background of the FireLine Assessment MEthod (FLAME) Bishop
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 53

Determining the Effective Windspeed

EWS combines the influences of wind (at midflame level) and slope (as a bit 

of ‘upslope wind’) into one ‘windspeed’ that represents the driving force of 

wind and slope on the fire. Changes in EWS are the largest potential producers 

of changes in ROS. Making the determination of current and expected EWS 

is not easy, but it must be done as well as possible. Even though EWS is highly 

variable and subject to many influences firefighters can obtain worthwhile 

results from a few practical guidelines and observations. To not account for 

the variation in EWS, at least approximately, is to miss the largest potential 

source of change in fire behavior. In fact in some ways the firefighter on the 

fireline, making predictions of short-term significant changes in fire behavior, 

requires more detail about windspeed variation than is required for a typical 

longer term, average-fire-spread projection by an FBAN.

The following sections describe the adjustment methods for determining 

EWS as a function of topographic location, flame height, sheltering by vegetation, and slope. To predict changes in fire behavior the firefighter must 

estimate the windspeed currently affecting the fire and also the windspeed 

that will affect it in the future (to obtain the EWS-ratio).

Variations of EWS over the terrain—If even a steady, ‘uniform’ wind impinges on terrain it will vary greatly in speed and direction from one point to 

another across the landscape. It will be channeled by drainages, and its speed 

will be strongly influenced by the topographic obstacles it encounters. For 

unstable or neutrally stable unstratified airflows, typical of the well-mixed 

conditions of a sunny afternoon, the following patterns occur.

Windspeed will be topographically enhanced on upwind slopes, and diminished on downwind (lee) slopes; it will generally be higher on an upper 

slope than on a lower slope. A suitably accurate assessment of fire behavior 

depends on accounting for the variations in windspeed across the terrain. We 

want to gauge the windspeed on the upper slope (upper third or so) versus 

that on the lower slope (lower third or so), and on the downwind (lee) versus 

the upwind slope, and to relate them to the forecasted general or ridgetop 

windspeed. The guideline (illustrated in fig. 6) for estimating those variations 

is based on the several sources detailed below, all of which are compatible 

with such a guideline.

1x

 Upper slope 

1/2 x (0.5)

3/4 x (0.75)

1/4 x (0.25)

1x

1/2 x (0.5) Upwind

slope

Lee

slope

Upwind

 slope 

Decimal equivalents of the fractions are shown in parentheses

General/ridgetop, 1x

Figure 6—Schematic illustration of the FLAME guideline for estimating variations in windspeed 

over the terrain. Wind blows from left to right, values normalized to unity for general/ridgetop 

windspeeds. Lee-slope adjustments are valid only on slopes <30 percent and without sharp 

ridgetops. Winds described as ‘winds of critical concern’ by fire weather meteorologists and 

downslope winds are not subject to the above adjustments.

Technical Background of the FireLine Assessment MEthod (FLAME) Bishop
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 54

Equation 9 (below) can be used to estimate the ‘speedup’ of wind (∆Smax, 

the excess of ridgetop windspeed above ambient over the plains) at the tops 

of hills or ridges (Barry 1992). The form below averages the coefficients for 

the ‘isolated hill’ case and the ‘uniform ridge’ case. Here ‘h’ is the height of 

the hill or ridge, and L* is the half width of the hill at its midelevation.

∆ Smax = 1.8 h/L* (note: units cancel in the ratio) Eq. 9

For a hill having symmetrically equivalent topographic profiles on its upper 

and lower halves, equation 9 can be expressed in terms of slope (slope being 

approximately [h/L*]/2)

∆ Smax = 3.6X (slope), where slope is expressed as a decimal figure Eq. 10

For a hill of moderate slope, say 30 or 40percent, that means the wind 

over the top will be about 2X the ambient wind that blows against the hill 

(remember that windspeed at the ridge = ambient + ∆ Smax). For steeper slopes 

the ridgetop wind increases would be somewhat higher.

 Detailed studies of windflow over hills and ridges are reported in Mason 

and Sykes (1979) and Taylor and others (1987). The pattern and magnitude 

of variations in windspeed as air flows around hills or over ridges are reasonably similar to each other, and are in accord with the ‘speedup’ described by 

equation 10. Figure 7 shows the results for two separate wind-speed profiles 

over an elongate ridge. The average values of windspeed at slope positions 

corresponding to figure 5, normalized to unity for the ridgetop are: 0.46X, 

0.89X, 0.70X, and 0.26X (compared to 0.5X, 1X, 0.75X, and 0.25X for the 

FLAME guideline).

Figure 7—Profiles of windspeed over an elongate ridge at two locations (Taylor and 

others 1987). Wind blows more or less perpendicular to ridge axis; ridge topographic 

profiles shown in red. Windspeeds (yellow curve) are normalized to an ambient 

windspeed of one.

Technical Background of the FireLine Assessment MEthod (FLAME) Bishop
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 55

I have conducted several wind-speed surveys (often on S-290 field trips) 

over elongate ridges near Oroville and Susanville, California. Windspeeds 

were measured with handheld anemometers at eye-level, for either a 3-minute 

average, or by averaging 10 instantaneous observations taken over a couple of 

minutes. The normalized results are (moving with the wind from upwind to 

lee side): 0.5X (lower upwind slope), 1X (upper upwind slope), 0.8X (upper 

lee slope), and 0.2X (lower lee slope). This is close to figure 7 relative values 

and to the FLAME guidelines.

All of this pertains to neutrally stable or unstable airflows, but not to stable, 

stratified airflows such as sea breezes, outflow winds, night-time downslope 

winds, or foehn winds (‘winds of critical concern’)—these are often fastest on 

the downwind slope. I have not found data for a corresponding guideline in 

stable airflows, and one must simply use observed or forecasted windspeeds 

without extrapolation to other parts of the terrain. The scale of the topographic relief characterizing the observations is on the order of 100s of feet 

of elevation, and the observations were on slopes of 30 percent or less.

Table 7 compares the theoretical and observational data with the FLAME 

guidelines (which are arithmetically simplified to facilitate their application). 

Figure 8 illustrates a comparison between the FLAME guideline and an 

advanced airflow model.

Table 7—Summarizing the theoretical and empirical basis for the wind reduction factors 

(research studies and personal observations), and the FLAME guidelines, for 

approximating the variations in windspeeds as air flows over hills and ridges in neutrally 

stable or unstable conditions. Ridgetop windspeed is taken as the general windspeed, 

and the below values are normalized to ridgetop windspeed (as 1X).

Equation 9 Taylor and others (1987) Personal observation FLAME guideline

Lower upwind slope 0.5X 0.46X 0.52X 0.5X

Upper upwind slope 1X 0.89X 0.88X 1X

Upper lee slope NA 0.70X 0.64X 0.75X

Lower lee slope NA 0.26X 0.24X 0.25X

Figure 8 — C omparison of FL A M E 

guideline for windspeed variations over 

terrain with wind-field map generated 

by an advanced airflow-terrain model. 

Green arrows are model-generated. 

Yellow arrows along a line that crosses 

the ridge at the head of the basin are 

from the FLAME guideline, scaled to be 

(from upper right to lower left) 0.5X, 1X, 

0.75X, and 0.25X. There is good overall 

agreement of the guideline with the 

model in predicting the magnitude of the 

variations in windspeed. 

Technical Background of the FireLine Assessment MEthod (FLAME) Bishop
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 56

Variation of windspeed with flame height and sheltering by vegetation—What 

is needed for predicting fire ROS is the midflame windspeed (MFWS), or 

less formally the ‘flame level’ wind. For a given location on the terrain the 

MFWS depends upon the height of the flames and the wind obstruction 

by vegetation (for example, 1-ft flames under a stand of trees in litter versus 

30-foot flames in an open brushfield).

The guidelines used in FLAME for adjusting wind for flame height and 

sheltering vegetation are equivalent to the ‘wind reduction factors’ of FBPS, 

and closely match that scale in relative terms. Using the adjustment factors 

to ‘reduce’ 20-ft winds conforms with the practice suggested by fire weather 

meteorologists and utilized in application of the FBPS.

The FLAME wind-reduction factors are keyed to each of the major fuel 

types, and are applied to the 20-ft windspeed. These values correspond 

closely to the FBPS reduction factors of 0.6X, 0.4X, and 0.15X (average for 

sheltered fuels), and bear nearly the same relative values. Reduction factors 

applied to 20-ft windspeeds for FLAME application are (expressed here as 

simple fractions for ease of use):

• crown fire MFWS (using the 20-ft wind directly) is 1X(20-ft WS)

• grass fire MFWS (and low/scattered brush) is (3/4)X(20-ft WS)

• litter fire MFWS (reflecting low flames and obstruction by the stand) is 

(1/4)X(20-ft WS)

Eye-level is the most practical wind observation level for people in the field. 

The following adjustments can be made to eye-level windspeeds for flame 

height and sheltering, based on the relative scale described above:

• crown fire MFWS is (1 1/3) X EL WS

• grass/low-shrub fire MFWS is 1X EL WS

• litter fire MFWS under a stand is (1/3) X EL WS

Where do the FLAME wind-reduction factors come from?—The basis for 

using the 20-ft windspeed at crown level, and the reduction factor for eyelevel winds, lies in the following field observations and the logarithmic wind 

profile expressed in equation 11.

 My observations of eye-level wind at two RAWS stations in open, scattered, 

low shrubs consistently yield eye-level values that are 0.8X of the corresponding 20-ft windspeed. The Australian CSIRO system (Cheney and Sullivan 

1997) also applies a 0.8X reduction to obtain eye-level (2-m) windspeeds 

from 10-m windspeeds. In both cases the crown-fire MFWS, being several 

feet or more above eye-level, would be essentially those observed at 20 ft (or 

10 m) or at least 0.9X of 20-ft windspeed.

 The logarithmic expression for the boundary layer wind profile, applied 

with an average roughness coefficient characteristic of a mix of grass and low 

shrubs (0.09 m), is equation 11. Here uZ is the windspeed at height z; uZo is 

the windspeed at the reference height zo. Heights are in meters.

uZ /uZo = [ln (z/0.09 m)] / [ln (zo /0.09 m)] Eq. 11

From equation 11 the reduction factor for eye-level (taken as 1.8 m) compared to 20-ft (6 m) windspeed is 0.71X. Combining the logarithmic data 

with the observations at RAWS stations (and to keep the rule simple) yields 

a (3/4)X reduction factor for eye-level windspeed (applicable to fires in grass 

and low/scattered brush).

Technical Background of the FireLine Assessment MEthod (FLAME) Bishop
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 57

The FLAME wind reduction factor for fires in litter is (1/4)X. That value 

comes mainly from the relative reduction in winds for fully sheltered litter 

fuels compared to ‘open fuels’ (grass fuel models) and to fuel model 4 brush 

as embodied in the FBPS guidelines: 0.15X for the average of sheltered fuels, 

0.4X for open fuels, and 0.6X for crown fire in deep brush fields. 0.15X is 

about one-third of 0.4X and is one-fourth of 0.6X.

The slope contribution to EWS—BehavePlus incorporates the influence of 

wind and slope on ROS by adding a wind coefficient and a slope coefficient 

to the propagating flux ratio (Rothermel 1972). The combined coefficients 

can be thought of as accounting for the ‘effective windspeed’ (EWS). FLAME 

handles slope in the same way, by adding an increment of ‘upslope wind’ that 

has the same effect on the ROS as does the actual slope.

The slope-equivalent wind is similar for all fuel types. It is greatest at low 

MFWS and on steep slopes. And in many realistic situations it tends to be 

a small factor compared to the actual MFWS. The variation of the actual 

windspeed induced by topography is much more important to variations in 

ROS than is the ‘direct’ effect of slope. The following guidelines for handling 

slope (with upslope wind and upslope fire spread, as is also assumed in the 

FBPS nomograms) are utilized to yield the EWS used in FLAME.

• Slope less than 20 percent, no correction to the actual MFWS

• Slope 20 to 40 percent, add 1 mi/hr to the MFWS

• Slope 40 to 60 percent, add 2 mi/hr to the MFWS

• Slope 60 to 80 percent, add 3 mi/hr to the MFWS

• Slope greater than 80 percent, add 5 mi/hr to the MFWS

Defining the EWS for a backing or flanking fire—At low EWS, and for 

backing (negative EWS) or flanking fires the ROS curve flattens and represents a slowly changing ROS. One cannot use a zero or negative EWS in 

determining the EWS-ratio. A ‘backing-fire EWS’ to use in representing 

backing fires on the standard curve must be determined.

Basically, the problem is that ROS does not go to zero when EWS goes to 

zero (even though the standard FLAME curve, a power function, would go 

through the origin). The physical reason is that fire propagates by processes 

other than wind alone, and does not stop spreading in the absence of wind. 

For fires backing into the wind and/or down a slope intra-fuelbed radiation 

and convective dominate heat transfer, and ROS is relatively insensitive to 

variations in windspeed or slope.

In the original work on which BehavePlus is based the ‘backing’ ROS was 

taken as simply the flat-table, zero-wind ROS (Rothermel, personal communication). The Australian work on grass fires defines a similar relationship in 

that backing-fire ROS is found to remain essentially constant up to windspeeds 

of 20 km/hr (Cheney and Sullivan 1997). Other reports show backing ROS 

to decline slowly with increasing slope.

An important fireline-safety situation involving backing fires is fire in litter 

backing down a slope under largely wind-sheltered conditions. Such instances 

are common, and the change between backing litter fire and running crown 

fire is an extremely important FLAME application. So the backing EWS used 

in FLAME is based largely on the litter fuel type. The backing-fire EWS 

(not the backing ROS) for grass is similar to that for litter. Because backing 

fires do not propagate directly through crown foliage, there is no crown-fuel 

backing-fire EWS in FLAME.

Technical Background of the FireLine Assessment MEthod (FLAME) Bishop
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 58

It is assumed that over a reasonable range of slopes and windspeeds that 

the backing ROS falls between the zero-EWS value and half of that value (for 

example between 0.6 and 0.3 ch/hr). So the representative backing ROS is 

taken as the average of those values, or 0.75X(ROS at EWS=0).

The question of what EWS to use to represent backing litter fires is then: 

‘What EWS on the litter standard curve corresponds to the representative 

backing ROS?’ The representative backing ROS (not EWS) is 0.47 ch/hr, 

which is 75 percent of the average zero-EWS spread (which is 0.63 ch/hr) for 

fuel models 8, 9, and 10. The corresponding EWS is 0.5 mi/hr (fig. 9).

Figure 9—Standard cur ve for 

litter fuel type, with characteristic 

backing fire ROS at 0.47 ch/hr 

(which is 75 percent of the zeroEWS ROS of 0.63 ch /hr). The 

curves intersect at EWS = 0.5 mi/hr, 

and that is taken to be the EWS 

characteristic of backing fires. 

An EWS of 0.5 mi/hr is the lowest 

value of EWS used in FL AME. 

Comparisons (ROS-ratio) to other 

EWS and ROS values on the curve 

are mutually consistent. Vertical 

axis is ROS in ch/hr.

Litter Std Curve at low EWS

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

0 0.5 1 1.5

EWS (mi/hr)

ROS = 1.03 (EWS)1.213

ROS (ch/hr)

So, in FLAME the backing-fire EWS = 0.5 mi/hr. For flanking fires the 

EWS is taken to be 1.0 mi/hr (being slightly less retarded by the opposing 

influence of wind and slope, and given that even slight variations in wind on 

flanking flames can momentarily act to advance them).

A similar analysis to that above for grass yields a backing-fire EWS of 

0.4 mi/hr. An adjustment can be made for cases involving grass, but most 

of the time it is not necessary to a useful FLAME result.

Basically, the use of a backing-fire EWS of 0.5 mi/hr states that a backing fire moves as if it were driven by a wind of 0.5 mi/hr in the absence of 

other heat-transfer processes, within the context of the ROS relationships 

expressed in the FLAME standard curves. That use of an ‘equivalent wind’ 

allows the comparison of ROS under backing conditions with ROS where 

effective wind is the dominant fire-spread influence. The standard curves are 

effectively truncated at EWS = 0.5 mi/hr, and that represents the slowest 

possible ROS.

Computing the EWS-ratio—Given the EWS associated with ‘current’ fire 

behavior, and an estimate of the EWS that is expected, the EWS-ratio is computed. Because it is easier to work with whole numbers versus fractions, the 

EWS-ratio is always the larger EWS divided by the smaller EWS. A FLAME 

user would simply keep in mind whether that change in EWS would be associated with an increase or a decrease in ROS (for example, will the fire go 

6X faster, or 6X slower?). If the arithmetic is easy, the user can simply do 

the division. For example, if the larger EWS is 8 mi/hr and the smaller one 

is 2 mi/hr, the EWS-ratio = 8/2 = 4X. For cases where the arithmetic is not 

Technical Background of the FireLine Assessment MEthod (FLAME) Bishop
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 59

so easy, table B1 in appendix B allows a simple look-up of the EWS-ratio; it 

is nothing more than a paper calculator. That EWS ratio is used to enter the 

left column of table 2.

Part 3: Application in the Field

The Field Worksheet, Stage by Stage

The worksheet is organized to follow the three ‘application stages’ described 

in the section Flexibility in application, initial, standard, and complete application. As seen in figure 10, where the sheet is divided left-right, the left side 

records ‘current’ conditions and the right side records ‘expected’ conditions. 

In the following discussion each application phase will be detailed, using the 

appropriate subsection of the worksheet. Notations and examples will explain 

each worksheet entry. Worked examples follow the illustration of the three 

application phases. Although this description is intended to illustrate the 

FLAME application process, it is not a complete training manual.

Obs.

spread =

Predicted

Obs Sprd/RosR = Obs SprdXRosR =

L

C

E

S

Current Expected

Next big

change

Rel. Hum.

Fuel

Litter (sfc)

Crown (aer)

Grass (sfc)

Litter (sfc)

Crown (aer)

Grass (sfc)

Eye-lvl WS obs

Eye-lvl WS fire

Midflm WS fire

Slope cont fire

Curr EWS fire

Effect.

Wind

Speed

on fire

20 ft or Eye-lvl WS fire

Midflm WS fire

Slope cont fire

Expct EWS fire

EWS Ratio = 

ROS Ratio = Faster Slower

20-ft pred or EL obs WS

Standard

Initial

Complete

LCES

Figure 10—FLAME worksheet. Down to the ‘EWS-ratio’ entry the left side is for ‘current’ 

conditions and the right side for ‘expected’ conditions. Fuels are listed in order from slowest 

to fastest; the EWS spaces follow the adjustment process from raw values to EWS on the fire. 

The ‘LCES’ portion allows for notes about how those guidelines will be implemented. 

Technical Background of the FireLine Assessment MEthod (FLAME) Bishop
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 60

Initial FLAME application—Considering the current and expected fire 

behavior, and identifying the next big change, make up the initial FLAME 

application. The following three examples show how the current and expected 

conditions can be depicted in various styles. The style or art work is not important. The critical thing is that the firefighter is prompted to consider those 

conditions; to be able to complete the pictures means having made a complete 

fire behavior assessment. If some box is empty you have not fully assessed the 

current and expected fire behavior and potential changes. No assessment is 

ever perfect or finished, but it represents the best you know with the present 

information and can reveal important gaps in your information.

Depict the current fire behavior and fire environment, and the situation 

you expect after the next big change. You can sketch a profile of the area, such 

as the two slopes, with the fire and fuel-types shown. You can sketch a map 

view, a diagram of the fire area. Show the winds you expect with arrows. Or 

you can simply list the key points of the description of fire and conditions. 

Note the ‘next big change’, and some idea of when it will come, in the space 

under the sketch.

Figures 11 and 12 show the components of the initial-application phase of 

the worksheet, and three examples of how they might be filled out.

Standard FLAME application—The heart of the FLAME process, the 

standard application, requires the specification of the fuel types and effective 

windspeeds, both current and expected. From that the ROS-ratio is determined, indicating the magnitude of the next big change. The magnitude of 

the change in ROS-ratio is suggestive of the potential danger posed by the 

change.

The standard-application phase requires the firefighter to obtain specific 

observations of relative humidity (RH), fuel type carrying the fire, and 

effective wind acting on the fire. It also requires the firefighter to predict 

the expected conditions of RH, fuel type, and effective wind. That in turn 

prompts projection of the fire’s progress and attention to the current weather 

forecast. The most important effect of completing the standard application phase is that it leads the firefighter to obtain specific information 

on the key fire behavior variables, which can make obvious the absence 

of critical information. Determination of the ROS-ratio also provides a 

measure of the magnitude of change pending, which conveys a sense of the 

level of potential danger.

Current Expected

Next big

change

Show the 

current fire 

behavior

situation

Show the 

expected fire 

behavior

situation

Identify the 

next big 

change

Figure 11—‘Initial application’ portion of the FLAME worksheet, annotated to describe 

the appropriate entries.

Technical Background of the FireLine Assessment MEthod (FLAME) Bishop
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 61

Figure 12—Three examples illustrating the current and expected conditions and identifying the next big 

change, using the worksheet format. The style of illustration is entirely up to the user, and three different 

situations are shown here as a terrain profile, a sketch map, and an outline to depict the current and 

expected conditions.

Current Expected

12

litter

backing

crown fuel 50% slope

Next big

change

Slope reversal as fire crosses drainage

20

road

fire

4

road

fire

Next big

change

Onset of NW winds as cold front passes in the evening

Current Expected

Fire backing in needle litter

light upcanyon winds at 

present

T-storms possible in the -

afternoon

Outflow winds could blow

downcanyon at up to 30

Next big

change

T-storm winds this afternoon blowing downcanyon

Current Expected

Technical Background of the FireLine Assessment MEthod (FLAME) Bishop
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 62

Figure 13 illustrates the components of the standard-application section of 

the worksheet and presents examples of how information would be developed 

in that section.

Note change in RH; use it 

to help decide fuel change

Mark the current & 

expected fuels

Observed wind

Adjusted to fit fire location

Adjusted to midflame level

Add any slope contribution

Current EWS on the fire

Forecasted or observed 

 wind

Adjusted to fit fire location

Adjusted to midflame level

Add any slope contribution

EWS expected on the fire

Ratio of big EWS to 

small EWS

Ratio of big ROS to 

small ROS

Rel. Hum.

Fuel

Litter (sfc)

Crown (aer)

Grass (sfc)

Litter (sfc)

Crown (aer)

Grass (sfc)

Eye-lvl WS obs

Eye-lvl WS fire

Midflm WS fire

Slope cont fire

Curr EWS fire

Effect.

Wind

Speed

on fire

20-ft or Eye-lvl WS fire

Midflm WS fire

Slope cont fire

Expct EWS fire

EWS Ratio = 

ROS Ratio = Faster Slower

20-ft pred or EL obs WS

Indicate if ROS will 

increase or decrease

Figure 13—’Standard-application’ portion of the FLAME worksheet, annotated to describe the appropriate entries. 

Entries on the left side represent current conditions, and on the right side the expected conditions. EWS-ratio 

and ROS-ratio express the degree of change between current and expected conditions. 

Following are three different fire behavior examples illustrating different 

styles.

Example 1: A fire burns up a 30 percent grassy slope and will spread into 

forest litter. You have observed the eye-level wind at a point on the same 

slope not too far from the fire. Overall conditions are not expected to change 

significantly, though the RH will drop to about 38 percent. Crown fire potential is minimal. The next big change will be the passage of the fire from 

open grass into sheltered litter.

Current conditions: RH = 42 percent; Fuel is grass; Eye-level wind = 9 mph; 

Slope = 30 percent

Expected conditions: RH = 38 percent; Fuel will be litter; No large wind 

changes expected; Slope = 30 percent

The completed worksheet section would look like this. The small effect of 

the RH drop can be estimated as in the upper right box.

Rel. Hum.

Fuel

Litter (sfc)

Crown (aer)

Grass (sfc)

Litter (sfc)

Crown (aer)

Grass (sfc)

Eye-lvl WS obs

Eye-lvl WS fire

Midflm WS fire

Slope cont fire

Curr EWS fire

20-ft or Eye-lvl WS fire

Midflm WS fire

Slope cont fire

Expct EWS fire

EWS Ratio = 

ROS Ratio = Faster Slower

20-

X

42% 38%

X

6

6

6

+1

7

6

6

2

+1

3

X

2X

30X

Obs. wind represents fire 

loc. so no adjustment is 

needed here Obs. E-Lvl wind 

represents fire loc., no 

adjustment

In real life no need to fill 

in redundant boxes; you

could fill in MFWS and 

leave boxes above blank

MFWS in litter is 1/3 of 

E-lvl. windspeed

Fire will slow down 

in the litter

MFWS for grass is same 

as E-lvl., no adjustment.

42-38 = 4

x2

8% faster ROS

1 mi/hr to account for 

30% slope

1 mi/hr to account for 

30% slope

Effect.

Wind

Speed

on fire

ft pred or EL obs WS

Technical Background of the FireLine Assessment MEthod (FLAME) Bishop
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 63

Example 2: A fire backs down a slope in litter, RH = 30 percent. It is expected 

to cross the drainage and spread up the next slope (an upwind 50 percent 

slope) as a crown fire. The ridgetop windspeeds are forecast to be 20 mph, 

and RH to be 20 percent. The next big change will be the transition from 

backing litter fire to running crown fire, and it will affect the lower slope 

first. RH value helps evaluate the potential for crown fire, but no adjustment 

for RH change is made for crown fuels.

Rel. Hum.

Fuel

Litter (sfc)

Crown (aer)

Grass (sfc)

Litter (sfc)

Crown (aer)

Grass (sfc)

Eye-lvl WS obs

Eye-lvl WS fire

Midflm WS fire

Slope cont fire

Curr EWS fire

Effect.

Wind

Speed

on fire

20-ft or Eye-lvl WS fire

Midflm WS fire

Slope cont fire

Expct EWS fire

EWS Ratio = 

ROS Ratio = Faster Slower

20-

X

30% 20%

X

backing

20

10

10

+2

12

24X

180X

MFWS for crown fire is 

same as 20-ft.

Forecast wind is for 

ridgetop; lower slope 

speed is 1⁄2 of that. The fire is backing, and 

the EWS is taken to be 

‘backing’ (EWS = 1⁄2 ). 

No need to fill in other 

boxes.

X

Fire will move 80X faster 

in crown fuels, upslope 

with the wind

2 mi/hr to account for 

50% slope

12/(0.5) = 24

RH is in a range that 

can support crown fire

ft pred or EL obs WS

Example 3: A fire is burning up a 45 percent lower slope in litter, midmorning, RH = 36 percent. By afternoon it will reach the upper half of the slope 

and is expected to transition to crown fire. You directly observe the wind 

on the litter fire, at litter flame height, to be 3 mph upslope. The afternoon 

wind will be in the same upslope direction, and 20-ft windspeed is forecast 

to be 8 to 12 mph, RH will be 15 to 20 percent. The next big change will 

be the transition to crown fire this afternoon on the upper slope.

Rel. Hum.

Fuel

Litter (sfc)

Crown (aer)

Grass (sfc)

Litter (sfc)

Crown (aer)

Grass (sfc)

Eye-lvl WS obs

Midflm WS fire

Slope cont fire

Curr EWS fire

20-ft or Eye-lvl WS fire

Midflm WS fire

Slope cont fire

Expct EWS fire

ROS Ratio = Faster Slower

20-ft pred or EL obs WS

45% 15%

X

3

+2

5

12

12

12

+2

14

3X

15X

X

X

2 mi/hr to account for 

50% slope

Use worst case RH to 

judge crown fire potential

Upper slope windspeed 

is same as ridgetop. 

MFWS is 20-ft for crown 

fuel type

Direct obs. of MFWS 

at fire requires no 

adjustment.

Fire will be faster in 

crown fuels, & feeling

higher windspeeds

High end of forecasted

windspeeds

2 mi/hr to account for 

50% slope

14/5 = 3

Effect.

Wind

Speed

on fire

EWS Ratio = 

Eye-lvl WS fire

Technical Background of the FireLine Assessment MEthod (FLAME) Bishop
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 64

Complete FLAME application—Develops a fire spread-time by applying the 

expected change in ROS to the observed spread-time under current conditions. The spread is gauged against ‘natural yardsticks’ (such as a slope, an 

open field, utility or fence poles, road intersections, the distance the fire has 

moved). For example, a fire might be observed to move down a slope in 12 

hours, or halfway across a field in 20 minutes. Such observations constitute 

the ‘observed spread’ (left hand space in fig. 13).

If the ROS increases, the fire would be expected to spread a similar distance 

in a fraction of the time. For example, if the ROS-ratio is 24X, then a fire 

backing down a slope in 12 hours would be expected to go back up the same 

slope, or up the opposing slope, a similar distance in 1/24th of 12 hours, or 

one-half hour. In other words, the expected spread time when ROS increases is 

the observed spread time divided by the ROS-ratio (middle box in fig. 14).

Similarly, if the ROS decreases, then the expected spread time will be the 

observed spread time multiplied by the ROS-ratio (right hand box in fig. 14). 

For example, if a fire observed to cross half a field in 20 minutes slows down 

by 3X, then it will cross the other half of the field in about 60 minutes.

Figure 14—‘Complete application’ portion of the worksheet. Observed 

spread of the fire is noted on the left. The ‘faster’ or ‘slower’ expected 

fire ROS directs the user to the appropriate box, in which is the formula 

for calculating the expected spread time over a similar distance. In 

the case of faster expected ROS the predicted spread time will be the 

observed spread time divided by the ROS-ratio; in the case of slower 

ROS the predicted spread time will be the observed spread time 

multiplied by the ROS-ratio. 

Obs.

spread =

Predicted

Obs Sprd/RosR = Obs SprdXRosR =

Faster Slower

Example 1: A fire moves down a slope in 12 hours, and is expected to spread 

up the adjacent slope 24X faster, an ROS-ratio of 24X and an increase in 

ROS.

Obs.

spread =

Predicted

Obs Sprd/RosR = Obs SprdXRosR =

slope in 12 hrs slope in 1⁄2 hr

Faster Slower

Example 2: A fire moves halfway across a field in 20 minutes. The wind is 

expected to decrease, and the fire is expected to move 3X slower after that, 

an ROS-ratio of 3X and a decrease in ROS.

Obs.

spread =

Predicted

Obs Sprd/RosR = Obs SprdXRosR =

1⁄2 field in 20 min 1⁄2 field in 60 min

Faster Slower

Technical Background of the FireLine Assessment MEthod (FLAME) Bishop
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 65

Illustrating the FLAME Application Process in Full

Example 1: A fire burns up a lower SW slope (over 60 percent) in litter under 

a stand of pine trees, where observation shows RH = 24 percent and eye-level 

windspeed 6 mi/hr. The fire advances at a rate that will cover the lower halfslope in about 3 hours. As the fire moves up the slope it will begin to feel 

more wind, RH is declining, and the fire will transition to a crown fire on 

the upper slopes. Forecasted winds are SW 15-20 mi/hr, RH dropping to 15 

to 20 percent range. Consider a crown fire run on the upper slope.

The RH values help determine the potential for crown fire, but are not a 

significant guide to fine-tuning the predicted ROS-ratio.

EL

6

Obs.

spread =

Predicted

Obs Sprd/RosR = =

L

C

E

S

Current Expected

Next big

change

Rel. Hum.

Fuel

Litter (sfc)

Crown (aer)

Grass (sfc)

Litter (sfc)

Crown (aer)

Grass (sfc)

Eye-lvl WS obs

Eye-lvl WS fire

Midflm WS fire

Slope cont fire

Curr EWS fire

Effect.

on fire

20 ft or Eye-lvl WS fire

Midflm WS fire

Slope cont fire

Expct EWS fire

EWS Ratio =

ROS Ratio = Faster Slower

20-ft pred or EL obs WS

litter

15-20

crown

lower 1⁄2 slope in

3 hrs

upper 1⁄2 slope in 6

min

24 15

more wind, transition to crown fire on upper slope

X

6

2

5

X

6

+3

20

20

20

+3

23

5X

30X X

watch for torching and the beginning of sustained crown runs

provide RH, fire progress, & windspeed and direction every 20 min.

Eye-lvl WS observed

at an open site

Observed wind is

appropriate to fire loc

MFWS for litter fire is

1/3 of Eye-lvl WS

Slope contributes 3

mi/hr to upslope wind

Net EWS currently

on the fire

Forecasted 20-ft WS

(upper end)

Forecasted 20-ft WS

applies to fire area

20-ft WS is appropriate

for crown fire

Slope contributes 3

mi/hr to upslope wind

Net EWS expected on

crown fire

RH low enough to

support crown fire

Upper slope is about the

same length as lower

slope; fire will move a

distance equal to the

observed lower slope

spread in about 1/30th as

much time. 1/30th of 3

hrs is about 6 min.

Wind

Speed

Obs SprdXRosR

Technical Background of the FireLine Assessment MEthod (FLAME) Bishop
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 66

Example 2: Fire in litter under conifer and shrub stands is backing downslope 

into a small canyon; RH is 40 percent. It has backed the previous late afternoon and night and is expected to reach the canyon floor by midmorning, a 

total of 18 hours. General winds of 12 mi/hr are blowing above the nocturnal 

inversion in the up-canyon direction. With the development of up-drainage 

winds, combined with the general wind, the upslope wind in the afternoon 

is forecasted to be 12-16 mi/hr. Foliage is dry and will support crown fire; 

afternoon RH is expected to be in the high teens. Canyon slopes are 40 to 

50 percent.

18 hrs

litter crown

Predicted

Obs Sprd/RosR = =

L

C

E

S

Current Expected

Next big

change

Rel. Hum.

Fuel

Litter (sfc)

Crown (aer)

Grass (sfc)

Litter (sfc)

Crown (aer)

Grass (sfc)

Eye-lvl WS obs

Eye-lvl WS fire

Midflm WS fire

Slope cont fire

Curr EWS fire

Effect.

Wind

Speed

on fire

20 ft or Eye-lvl WS fire

Midflm WS fire

Slope cont fire

Expct EWS fire

EWS Ratio =

ROS Ratio = Faster Slower

20-ft pred or EL obs WS

down major

slope in 18 hrs

40 17-19

onset of upcanyon winds and beginning of crown 

fire, by early afternoon

X

back

X

16

12

12

+2

14

28X

200X X

12-16

major slope run in 5

or 6 min possible

watch for mixout of inversion, onset of gusty upslope winds; note any

torching, sustained movement of crown fire on slopes above drainage

report wind, RH, & smoke drift every 30 min.

RH low enough to

support crown fire

14/(0.5) = 28

Forecasted 20-ft WS

(upper end)

Overall WS for entire

slope, avg of upper & lwr

20-ft WS is appropriate

for crown fire

Slope contributes 2

mi/hr to upslope wind

Net EWS expected on

crown fire

Canyon slopes are all

about the same height;

fire will move a distance

equal to the observed

down slope spread in

about 1/200th as much

time. 1/200th of 18 hrs is

about 5 or 6 min.

The fire is backing, and

the EWS is taken to be

‘backing’ (EWS = 1⁄2 ).

No need to fill in other

boxes.

Choose the ROS-ratio

that falls between

EWS=24 & EWS=30

in the FLAME table

Obs SprdXRosR Obs.

spread =

Technical Background of the FireLine Assessment MEthod (FLAME) Bishop
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 67

Example 3: Fire will run to the top of a 60 percent slope as a crown fire in 

timber. Sustained runs have taken about 20 minutes to sweep up the slope in 

late afternoon. RH is 20 percent, but will be increasing to 30 percent shortly 

after sundown. Eye-level winds on the lower part of the timbered slope were 

observed to be 6 mi/hr. Over the ridgetop the lee slope is primarily grass.

EL wind 6

crown grass

Obs.

spread =

Predicted

Obs Sprd/RosR = Obs SprdXRosR =

L

C

E

S

Current Expected

Next big

change

Rel. Hum.

Fuel

Litter (sfc)

Crown (aer)

Grass (sfc)

Litter (sfc)

Crown (aer)

Grass (sfc)

Eye-lvl WS obs

Eye-lvl WS fire

Midflm WS fire

Slope cont fire

Curr EWS fire

20 ft or Eye-lvl WS fire

Midflm WS fire

Slope cont fire

Expct EWS fire

EWS Ratio =

Faster Slower

20-ft pred or EL obs WS

up slope as crown

fire run in 20 min

20 30

slope reversal, change from crown to grass 

fuel type 

X

back

X

6

9

30X

17X

backing

down slope in grass

in about 6 hours

watch for spotting from crown fire down the grassy slope; upslope runs

pushed by wind eddying on the lee slope

relate wind, RH, fire progress, & smoke drift every 30 min.

12

+3

15

X

Obs E-lvl WS

on lwr slope

E-lvl WS on upper slope

would be 2X6 = 12 mi/hr

Average upper & lwr to

represent overall WS on

the whole slope, 9 mi/hr

ROS-ratio reflects 60X

slowdown due to less

EWS, and a 4X

increase due to faster

fuel (grass vs crown)

Crown WS is 1 1/3 of

E-lvl WS; (1 1/3)x9=12

Slope contributes 3

mi/hr to upslope wind

The fire is backing, and

the EWS is taken to be

‘backing’ (EWS = 1⁄2 ).

No need to fill in other

boxes.

15/(0.5) = 30

Assuming the slopes are

about the same size;

fire will move a distance

equal to the observed

upslope spread in about

17X as much time. 17X

20 min is about 6 hrs

Change in RH is not

used to fine tune ROSratio because a crown

fuel is involved

Effect.

Wind

Speed

on fire

ROS Ratio =

Technical Background of the FireLine Assessment MEthod (FLAME) Bishop
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 68

Appendix A: Input Data for FLAME 

Application to Case Studies

FLAME outputs are compared to the reported fire behavior that has been 

reconstructed in the incident investigations. FLAME is applied as it might be 

by a firefighter who sees the ‘current’ fire behavior as the ongoing behavior 

that is characteristic of the early phases of the work shift, and considering 

‘expected’ fire behavior that would arise from conditions that did occur in 

connection with the burnover. The expected conditions may not have been 

clearly seen or considered at the time, but they could have been. FLAME is 

here being tested as a tool applied to what could have been known.

The reported fire behavior is typically a mix of model outputs, informal 

observations, and reconstructions from travel times, videos, and photographs. 

Even though it is here termed ‘actual’ fire behavior it is subject to many assumptions and approximations. Still, the well investigated cases offer the best 

documented examples available of ‘before and after’ fire behavior in realistic 

situations.

The application of FLAME to these fatality cases is based on the fire 

behavior setting and events that took place, a test of FLAME in realistic 

situations. The purpose is to test the applicability and accuracy of FLAME, 

so the best known conditions are utilized, even though conditions may not 

have been that accurately known to fireline personnel at the time. There is 

no intent here to evaluate or to judge operational or safety decisions.

South Canyon Fire (Butler and others 1998)

FLAME is applied as if crews were evaluating the effects of a ‘next big 

change’ that included a crown run back upslope under the influence of the 

forecasted frontal winds.

‘Current’ fire behavior is taken to be the long, downslope spread of a 

backing fire from the ridgetop to into the West Drainage, once that spread 

was well established, on and after 3 July 1994. The average rate displayed by 

the fire over that interval was 32 ft/hr, and at that rate it would take about 

60 hours to back down the slope. While there was some grass in the fuelbed, 

the fuel carrying the fire was predominantly litter.

‘Expected’ fire behavior is taken to be the upslope spread of the fire from 

the West Drainage bottom at a time estimated to be 1605 hours on the reconstructed perimeter map, a point below the crews and near the beginning of 

the upslope run, to where the constructed fireline left the ridgetop. Upslope 

winds are taken as the 30 mi/hr midpoint of the modeled ‘25 to 35 mi/hr’ 

slope winds reported in appendix C of Butler and others (1998). The 10 percent RH is taken as an approximation from the Rifle RAWS record.

Actual ROS-ratio is calculated from the spread rates reconstructed in 

the report, and distances scaled from the map, over the time interval from 

1605 to 1614 hours. FLAME ROS-ratio assumes an EWS increase from 

backing to 30 mi/hr, EWS-ratio = 60X, and a change from litter to crown 

fuels. Applied to the 60-hour downslope transit of the fire, FLAME predicts 

a 7 to 8 minute upslope run.

‘Actual’ ROS-ratio = (267 ft/min X 60 min/hr)/(32 ft/hr) = 500X; 

Reconstructed upslope run is 9 minutes

FLAME ROS-ratio = 500X; Prediction of ‘expected’ upslope run is about 

7 minutes [(60 hr)/500 = 7.2 min)]. (This differs from 

the actual spread time largely due to the downslope and 

upslope spread distances being unequal.) 

Technical Background of the FireLine Assessment MEthod (FLAME) Bishop
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 69

Dude Fire (USDA 1999)

FLAME is applied as if crews were evaluating the effects of a ‘next big 

change’ that included the possibility of a thunderstorm outflow, originating 

over the fire or on the other side of it, on the active fire edge above their 

proposed control line.

 ‘Current’ fire behavior is taken as the backing fire on the slope above the 

crews. The backing fire ROS is calculated with BehavePlus, assuming Fuel 

Model 9, 1- hr FM 3 percent, 10-hr 4 percent, and 100-hr 6 percent (based on 

NFDRS reported values), and slope about 20percent. The predicted ROS for 

a backing fire under those conditions is 0.5 ch/hr. From the estimated position of the fire on the slope above the crews (approximately 200 to 300 yards) 

that backing fire would have reached the bottom in roughly 20 hours.

 ‘Expected’ fire behavior is taken to be the downslope spread of the main 

fire toward the line being constructed, as a crown fire driven by 30 mi/hr 

outflow winds. Even though the windspeeds have been estimated as greater 

than that by firefighters on the incident, my experience is that windspeed 

estimates by most observers are not very accurate, especially when there is 

fire and smoke and danger involved, and tends to emphasize gust speeds 

rather than average speeds. In the Dude Fire Investigation Reports (1999), 

Goens estimates that the first 5 or 10 minutes the wind was at its estimated 

40 to 60 mph maximum, and about half that for the next 30 minutes. The 

comparison here involves an observation of ROS that took place over about 

a half hour, so the relevant average wind is what prevailed during that halfhour run. That windspeed average is taken as about 30 mi/hr (avg. 50 mi/hr 

for 5 to 10 min., then avg. 25 mi/hr for 20 to 25 min.). Several indicators, 

including the torching of trees by the backing fire, show the potential for 

transition to crown fire.

 After the onset of the winds the fire was observed to advance about 

1.5 miles in 30 minutes, for an approximate ROS of 3 mi/hr or 240 ch/

hr. Actual ROS-ratio is calculated from the observed ROS of the crown 

fire (240 ch/hr) and the modeled ROS of the backing fire in needle litter 

(0.5 ch/hr).

‘Actual’ ROS-ratio = (240 ch/hr)/(0.5 ch/hr) = 480X; Comments from 

the investigation refer to the crews having a couple of 

minutes to see the fire as it runs down the slope toward 

them.

FLAME ROS-ratio = 500X; Prediction of downslope run to reach the crews 

is about 2 or 3 minutes [(20 hrs)/500 = 2.4 min], which 

is in line with comments from the crews.

Thirtymile Fire (USDA 2001)

FLAME is applied as if crews were evaluating the effects of a crown fire 

pushed by the afternoon upcanyon winds.

‘Current’ fire behavior is that which prevailed during the hours of the 

early suppression efforts and is termed ‘Initial Phase’ in the report. The fire 

behavior consisted predominantly of surface spread in litter, both flanking and backing, with some light downcanyon wind. The modeled ROS is 

reported as 1.3 ch/hr. FLAME Application assumes backing and flanking 

fire in litter.

‘Expected’ fire behavior is the sustained crown fire that moved up the 

canyon in the late afternoon and is termed ‘Deployment Phase’ in the report. 

Technical Background of the FireLine Assessment MEthod (FLAME) Bishop
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 70

The modeled ROS is reported as 125 ch/hr. The 20-foot windspeed up the 

canyon is reported as being 9 to 11 mi/hr, and in the FLAME application 

is taken to be 10 mi/hr.

‘Actual’ ROS-ratio = (125 ch/hr)/(1.3 ch/hr) = 96X

FLAME ROS-ratio = 100X (based on an average of backing and flanking 

current fire behavior)

Cramer Fire (USDA 2003; Kelley Close, Investigation 

FBAN, personal communication)

FLAME is applied as if crew on the ridgetop were evaluating the possibil-

ity that the fire that had been backing into the drainage could spread up the 

drainage under the influence of westerly winds and become a crown fire.

‘Current’ fire behavior is taken as predominantly in litter on the slope 

above Cache Bar Creek. The fire had originally moved over the ridge and into 

the drainage the previous night and had backed actively all but the several 

hours before dawn. The total fire movement downslope into the drainage 

is estimated as taking about 17 hours and extends into the early afternoon. 

The backing distance, scaled from the map, is about 0.2 miles, or 1060 ft. 

The overall backing ROS is estimated to be 1 ft/min. There is reported to be 

some grass in the surface fuels, but for this application the spread is presumed 

to be predominantly in litter (and may account for the estimated 1 ft/min 

ROS being a little higher than would be predicted for just litter).

‘Expected’ fire behavior is the spread that moved up the canyon in the afternoon, becoming crown fire in low brush and eventually in trees, under the 

influence of increasing westerly winds (which blow up the canyon, gradually 

becoming more NW and impinging strongly on NW slopes at the head of 

the canyon). Windspeeds during that period average about 10 to 12 mi/hr 

at Lodgepole RAWS, and are taken as reasonable for winds in the canyon. 

Highest winds are estimated at over 20 mi/hr on the top of the NW slopes. 

To characterize the overall wind flow throughout the upcanyon spread of the 

fire, the 20-ft general windspeed is taken as the average of 12 and 24 mi/hr, 

or 18 mi/hr. Windspeeds at the bottom of the slopes would be approximately 

half that or 9 mi/hr. So the overall 20-ft windspeed associated with the up-

canyon and upslope spread of the fire, throughout the depth of the canyon 

is roughly 14 mi/hr. Since much of the spread was in low brush, with flame 

heights less than the tree heights, the flame level winds are estimated to 

be approximately eye-level or about 11 mi/hr. Many assumptions are built 

into this windspeed, but it is not unreasonable for the average that prevailed 

throughout the vertical range of the fire as it spread along the drainage and 

up onto the upper wind-exposed slopes. The average overall ROS of the up-

drainage/upslope run is estimated from the reconstructed fire perimeters to 

be 124 ft/min. The reconstructed ROS on the upper slopes was considerably 

greater, but the average ROS is what is used to compare to the overall ROS 

increase estimated from FLAME. The movement of the fire up the drainage 

(as scaled from the map) is about 4X the distance that it backed down into 

the drainage.

‘Actual’ ROS-ratio = (124 ft/min)/(1 ft/min) = 124X; Reconstructed overall 

upcanyon spread took about 34 minutes (1450 hrs to 

1524 hrs).

FLAME ROS-ratio = 160X; Predicted upcanyon spread would be about 

26 minutes [4X(17 hr)/160 = 26 min].

Technical Background of the FireLine Assessment MEthod (FLAME) Bishop
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 71

Tuolumne Fire (CDF Fire and USDA 2004, 2005; Larry 

Hood, Investigation Team FBAN, personal communication)

 FLAME is applied as if crews were evaluating the effects of a wind gust 

on the backing fire edge, as they constructed control line nearby.

‘Current’ fire behavior is described as fire in litter backing slowly into 

a steady upcanyon wind of 3 to 5 mi/hr. Backing ROS is modeled using 

fuel model 9, 1-hr FM 4 to 5 percent, wind 4 mi/hr with no appreciable 

influence of slope on the backing fire spread. Modeled ROS = 0.5 ch/hr, 

or approximately 1⁄2 ft/min. At that rate the travel time to the crew working 

approximately 7 to 30 ft away is roughly 15 to 60 min.

‘Expected’ fire behavior is anticipated to be a mix of surface fire and crown 

fire spread, as a gust of wind blows across the fire edge and pushes the fire toward the crew. There are no actual onsite observations that provide a measure 

of the conditions that prevailed. The wind shift is estimated in the report to 

be between 90°

 and 120°. I have estimated a wind gust of 12 mi/hr, deviating 

by between 90° and 180° from upcanyon and blowing at an angle outward 

across the fire edge toward the crews. Gusts of 12 mi/hr were observed at 

the Buck Meadows RAWS station on the canyon rim above the fire, and 

it is common for gusts to exceed the average windspeed on a well-mixed 

afternoon by somewhat over 2X. Also, I lived, worked, sailed, and operated 

portable weather stations in that unit for many years, and I have observed 

the common occurrence of turbulent gusts in the major canyons deviating 

strongly from the average upcanyon direction, even blowing in the opposite 

direction. The expected conditions assumed here are hypothetical, but are 

plausible and would have been a realistic expectation upon which to base a 

FLAME application for a crew working close to the fire edge and therefore 

‘within reach’ of even a momentary surge of the fire.

Actual ROS ratio is not accurately known. Estimates of the duration of the 

flareup, made by crew members and by Air Tactics 440, 

fall in the range from about 10 to 30 seconds.

FLAME ROS-ratio = 90X (the average of a transition to crown fuels with 

continuing spread in litter). The expected travel time 

of the fire to the crews when the wind gust hits is 

approximately 10 to 40 sec. [(15 to 60 min)/90 = 

(10 to 40 sec)].

Technical Background of the FireLine Assessment MEthod (FLAME) Bishop
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 72

Appendix B: Lookup Tables

EWS-ratio Table

Smal >

 v Lrg Bck Flnk 2 3 4 5 6 7 8 10 12 14 16 20 24 30

40 80 40 20 13 10 8 7 6 5 4 3 3 2.5 2 2 1.5

36 72 36 18 12 9 7 6 5 5 4 3 3 2 2 1.5 1

32 64 32 16 11 8 6 5 5 4 3 3 2 2 1.5 1.5 1

28 56 28 14 9 7 6 5 4 4 3 2 2 2 1.5 1

24 48 24 12 8 6 5 4 4 3 2 2 2 1.5 1 1

20 40 20 10 7 5 4 3 3 3 2 2 1.5 1.5 1

16 32 16 8 5 4 3 3 2 2 1.5 1 1 1

14 28 14 7 5 4 3 2 2 2 1.5 1 1

12 24 12 6 4 3 2.5 2 2 1.5 1 1

10 20 10 5 3 2.5 2 1.5 1.5 1 1

8 16 8 4 3 2 1.5 1 1 1

6 12 6 3 2 1.5 1 1

5 10 5 2.5 2 1 1

4 8 4 2 1 1

3 6 3 1.5 1

2 4 2 1

Flnk 2 1

Table B1—EWS-ratio lookup table. Larger EWS on left, smaller EWS across top. Look up the EWS-ratio (=EWSLARGE/EWSSMALL) 

for corresponding large and small EWS values. For example, the EWS-ratio for a backing fire that will in the future have an 

EWS of 12 mi/hr is 24X. Tabled values are rounded off to avoid unnecessary detail.

Table B2—Rate-of-spread ratios as a function of changes in fuel and changes in effective windspeed. The 

left-hand column shows the EWS-ratio, the factor by which EWS changes. Each column corresponds 

to a change between particular fuel types (or to no change). Table values express the ROS-ratio that 

results from the combined change in EWS and fuel. The left side applies to cases in which fuel and wind 

changes reinforce. Cases in which changes in wind and fuel have opposing effects are handled with 

the rightmost two columns. Highlighted ROS values define a range that includes situations associated 

with fireline fatalities.

FLAME Table

EWSratioNo fuel 

change

Litter

to/from

crown

Litter

to/from

grass

Crown

to/from

grass

Crown

to/from

grass

Litter

to/from

grass

No chg 1 1 4 14 4 4 14

2 2 10 30 8 2 5

3 4 15 60 13 1 3

4 5 20 80 20 2 3

5 7 30 100 27 2 2

6 9 35 130 35 3 2

8 12 50 180 4 1

10 16 60 240 5 1

12 20 80 300 6 2

16 30 100 440 8 2

20 40 140 600 10 3

24 50 180 700 13 3

30 60 220 1000 17 4

40 80 300 1300 23 6

50 110 400 1800 30 8

60 140 500 2200 40 10

80 200 700 3100 60 16

EWS biggest in faster fuel EWS less in grass

Technical Background of the FireLine Assessment MEthod (FLAME) Bishop
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 73

References

Andersen, H.E. 1982. Aids to Determining Fuel Models for Estimating Fire Behavior. 

General Technical Report INT-122. Ogden, UT: U.S. Department of Agriculture, 

Forest Service, Intermountain Forest and Range Experiment Station.

Barry, R.G. 1992. Mountain Weather and Climate 2nd Ed. Routledge.

Bishop, J. 1999. FireLine Assessment MEthod—A practical means for predicting 

wildland fire behavior from fireline observations. National Interagency Fire 

Behavior Workshop Proceedings. Phoenix, AZ. 1-5 March 1999.

Bishop, J. 2005. Better Fireline Safety Decisions Using The FireLine Assessment 

MEthod, FLAME. Unpublished.

Bishop, J. 2006. The FLAME (FireLine Assessment MEthod) Idea in a Nutshell. 

S-290 Course Materials.

Blumen, W., ed. 1990. Atmospheric Processes Over Complex Terrain. American 

Meteorological Society, Vol. 23, Number 45.

Butler, B.W.; and others. 1998. Fire Behavior Associated with the 1994 South 

Canyon Fire on Storm King Mountain, Colorado. Research Paper RMRS-RP-9. 

Ogden, UT: U.S. Department of Agriculture, Forest Service, Rocky Mountain 

Research Station.

Butler, B.W.; and others. 2000. A Method for Evaluating the Effectiveness of 

Firefighter Escape Routes. Proceedings of 2000 International Wildfire Safety 

Summit, Edmonton, AB, Canada. 10-12 Oct. 2000.

CDF Fire and USDA. 2004. CDF Helitack 404 Crew Burnover. CDF Greensheet 

& USFS 72-hour Report.

CDF Fire and USDA. 2005. CDF Helitack 404 Crew Burnover Accident Investigation 

Report. Tuolumne Fire. CA-STF-2191.

Cheney, P.; Sullivan, A. 1997. Grassfires, fuels, weather and fire behavior. Australia: 

CSIRO Publishing.

Close, K. 2005. Cramer Fire Investigation Fire Behavior Analyst. Personal 

communication.

Close, K. 2005. Fire Behavior vs. Human Behavior: Why the Lessons from Cramer 

Matter. Unpublished.

Hartford. R.A.; Rothermel, R.C. 1991. Fuel Moisture as Measured and Predicted 

During the 1988 Fires in Yellowstone Park. Research Note INT-396. Ogden, 

UT: U.S. Department of Agriculture, Forest Service, Intermountain Research 

Station.

Houze, R. 1993. Cloud Dynamics. San Diego, CA: Academic Press.

Mason, P.; Sykes, R. 1979. Flow over an isolated hill of moderate slope. Quarterly 

Journal of the Royal Meteorological Society. 105: 383-395.

Maclean, J. 1999. Fire on the Mountain. New York, NY: William Morrow and 

Co.

Oke, T. 1987. Boundary Layer Climates 2nd Ed. Methuen & Co.

Range, P.; and others. 1982. Great Basin Rate-of-Spread Study, Fire Behavior Fire 

Effects. Reno, NV: U.S. Department of the Interior, Bureau of Land Management, 

Nevada State Office.

Rothermel, R.C. 1972. A Mathematical Model for Predicting Fire Spread in Wildland 

Fuels. Research Paper INT-115. Ogden, UT: U.S. Department of Agriculture, 

Forest Service, Intermountain Forest and Range Experiment Station.

Rothermel, R.C. 1983. How to Predict the Spread and Intensity of Forest and 

Range Fires. General Technical Report INT-143. Ogden, UT: U.S. Department 

of Agriculture, Forest Service, Intermountain Forest and Range Experiment 

Station.

Technical Background of the FireLine Assessment MEthod (FLAME) Bishop
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 74

Rothermel, R.C. 1991. Predicting Behavior and Size of Crown Fires in the Northern 

Rocky Mountains. Research Paper INT-438. Ogden, UT: U.S. Department of 

Agriculture, Forest Service, Intermountain Research Station.

Shaw, R. 1988. Boundary-Layer Meteorology. Davis: University of California.

Taylor, P.; and others. 1987. Boundary-Layer Flow Over Low Hills. Boundary-Layer 

Meteorology. 39: 107-132

U.S. Department of Agriculture. 2003. Accident Investigation Factual Report, 

Cramer Fire Fatalities. 0351-2M48-MTDC. Salmon, ID: U.S. Department of 

Agriculture, Forest Service, Region 4, Salmon Challis National Forest, North 

Fork Ranger District.

U.S. Department of Agriculture. 1999. Dude Fire Staff Ride. Ogden, UT: U.S. 

Department of Agriculture, Forest Service, Intermountain Region.

U.S. Department of Agriculture. 2001. Thirtymile Fire Investigation Report. 

Washington, DC: Forest Service.

Walmsley, J.; and others 1989. Simple Guidelines for Estimating Windspeed 

Variations due to Small-scale Topographic Features—an Update. Climatological 

Bulletin. 23: 3-14

Whiteman, D. 2000. Mountain Meteorology. New York, NY, and Oxford, England: 

Oxford University Press. 

Technical Background of the FireLine Assessment MEthod (FLAME) Bishop
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 75

Abstract—Data demonstrate the effect of slope on heading and backing fires burning through woody fuels. The data indicate that the upper limit of heading fire rate 

of spread is defined by the rate of spread up a vertical fuel array, and the lower limit 

is defined by the rate of spread of a backing fire burning downslope. The minimum 

spread rate is found to occur at nominally –16 degrees slope.

Introduction

Terrain slope can be a primary influence on wildland fire behavior (Weise 

1993; Murphy 1963). This effect can be observed on any fire burning in 

mountainous terrain; however, few data exist that explore this phenomenon 

in a quantitative manner. This study reports the results of a set of experiments that were designed to provide direct measurements of fire spread and 

intensity as a function of terrain slope. The fuel used was shredded aspen 

(Populus tremuloides) heart wood, otherwise known as excelsior.

While some work has explored the relationship between fire spread rate 

and slope, understanding and data are limited. Curry and Fons (1938, 1940) 

posited that slope resulted in increased heat transfer between the flame and 

fuel ahead of it and that the effect of slope is relatively low in the absence 

of wind, but that the combined effect of wind and slope can be dramatic. 

Barrows (1951) indicates that as the slope increases so does the average size 

of the fire. McArthur (1968) suggests that slope can significantly affect fire 

rate of spread, especially immediately following ignition. He suggests that 

when compared to flat terrain, heading fire spread rates will increase by two 

times on 10 degree slopes and four times on 20 degree slopes. Murphy (1963) 

conducted a set of experiments using a paste consisting of wood flour mixed 

with sodium nitrate in a 4:1 ratio as the fuel. This mixture resulted in smoldering combustion. Both heading and backing fires were observed at slopes 

of 0, 14, 27 degrees over a range of wind speeds varying from 0 to 4.4 m/s 

in increments of 0.4 m/s. The data showed that backing fire rate of spread 

exceeded heading fire rate of spread for all slopes and wind speeds less than 

2.7 m/s. For higher wind speeds the heading fire rate of spread significantly 

exceeded the backing fire spread rates. Weise (1993) presents results from 

a set of 65 fire experiments subjected to slope angles between –30 and +30 

percent and wind speeds from –1.1 to 1.1 m/s. He compares the measurements to several published models for the slope and wind influence on fire 

spread rate. His data indicate that wind is the dominant variable affecting 

fire spread. Viegas (2005) discusses the relation between fire spread rate and 

Influence of Slope on Fire Spread Rate

B.W. Butler1, W.R. Anderson2, and E.A. Catchpole2

In: Butler, Bret W.; Cook, Wayne, 

comps. 2007. The fire environment—

innovations, management, and policy; 

conference proceedings. 26-30 March 

2007; Destin, FL. Proceedings 

RMRS-P-46CD. Fort Collins, CO: 

U.S. Department of Agriculture, 

Forest Service, Rocky Mountain 

Research Station. 662 p. CD-ROM.

1 Research Mechanical Engineer, 

U.S. Department of Agriculture, 

Forest Service, Rocky Mountain 

Research Station, Missoula, MT. 

bwbutler@fs.fed.us

2 University of New South Wales, 

Australian Defence Force Academy, 

Canberra ACT 2600, Australia
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 76

terrain slope in the context of a model of fire spread in upward sloping canyons. 

He approximates the effect of terrain slope as an artificial wind parameter, 

recognizing that the theoretical upward flame spread limit for fire burning 

along a semi-infinite solid is approximated as an exponentially increasing 

function, which agrees with other reported studies (Alpert and Ward 1984). 

Other studies have been presented that explore the relation between flame 

spread rate on cloth and solid surfaces for purposes of determining spread 

rates in structural fires (for example, Markstein and DeRis 1973). The studies 

referenced above indicate that slope is a critical component in wildland fire 

spread; however, the data are insufficient to fully understand the pertinent 

physical mechanisms occurring in slope-driven fire growth.

For this study a set of experiments were designed to measure fire spread 

rate for sloped fuel beds ranging from –16 to 31 degrees. Table 1 lists the 

experiments and associated conditions. All tests were conducted at nominally 

6 percent fuel moisture (dry mass basis), 27 °C ambient air temperature and 

20 percent relative humidity.

Table 1—Sloped fire experiments.

 Fuel

 Packing Slope depth

Test Fuel ratio (%) (cm) Objectives

1 EXSC 0.01 –30, 0, 15, 30, 45, 60 2.5 Fire spread on slope

2 EXSC 0.03 –30, 0, 15, 30, 45, 60 2.5 Fire spread on slope

3 EXSC 0.005 –30, 0, 15, 30, 45, 60 2.5 Fire spread on slope

4 EXSC 0.01 –30, 0, 15, 30, 45, 60 7.62 Fire spread on slope

5 EXSC 0.03 –30, 0, 15, 30, 45, 60 7.62 Fire spread on slope

6 EXSC 0.005 –30, 0, 15, 30, 45, 60 7.62 Fire spread on slope

7 EXSC 0.01 –30, 0, 15, 30, 45, 60 15.24 Fire spread on slope

8 EXSC 0.03 –30, 0, 15, 30, 45, 60 15.24 Fire spread on slope

9 EXSC 0.005 –30, 0, 15, 30, 45, 60 15.24 Fire spread on slope

Methods

Figure 1 is a photograph of the experiment apparatus. The fuel consisted 

of shredded aspen (Populus tremuloides) heartwood, selected for nominally 

uniform size shape (approximately 2.5 x 0.8 mm cross-section) and because 

it is readily formed into a randomly oriented fuel array with uniform bulk 

density and controllable bed depth. The depth and bulk density were adjusted 

by varying the mass of fuel per unit volume of the fuel bed. Rather than 

reporting bulk density, we use the term “packing ratio” (volume of fuel per 

unit volume of fuel bed), a term more common to wildland fire. Bulk density 

is the product of packing ratio and fuel density. Efforts were taken to maintain the fuel particle moisture content as uniform as possible between burns 

by conditioning fuel beds prior to the experiment at fixed temperature and 

humidity (nominally 27 °C and 25 percent RH) and burning the experiments at the same conditions. Measured fuel moistures ranged from 5 to 8 

percent on a dry mass basis. The fuel tray was 1 m wide by 4.6 m long; slope 

angle was measured from the horizontal plane. The fires were ignited by 

applying electric current to a coiled nichrome wire placed in a 2 cm deep by 

1 m wide tray of gasoline/diesel mixture at the base of the starting location 

for the fuel bed.

Influence of Slope on Fire Spread Rate Butler, Anderson, and Catchpole
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 77

Fire rate of spread was measured by dividing the length of the fuel bed by 

the time required for the flame front to move from the ignition location to 

the opposite end of the fuel bed. Time for all fuel on the bed to complete 

burning was not measured. In some cases fire rate of spread was also gathered 

from analysis of video footage. Generally these two methods agreed within 

±10 percent.

Results

Figures 2 through 4 present rate of spread data from the slope burns. The 

data have been normalized by dividing by the zero slope rate of spread. 

The horizontal axis is slope measured from the horizontal in degrees.

Figure 2 presents the data from the 2.5 cm deep fuel arrays. For all three 

packing ratios and all but the steepest slopes, these fires burned as individual 

flamelets along each individual fuel particle. In the case of the 0.005 packing 

ratio, the fire did not burn at all except for the steepest (31 degree) slope. The 

data from the fuel beds with packing ratio of 0.01 and 0.03 show a 25 percent 

decrease in spread rate between horizontal conditions and 16 degree downward sloped beds. No increase in spread rate was observed for slopes from 

0 to 10 degrees. For slopes between 10 and 25 degrees and packing ratios 

of 0.01 and 0.03, fire spread rates increased slightly with slope. A dramatic 

acceleration in spread rate was observed from 25 to 31 degrees.

The burn data from the 7.6 cm deep fuel beds are presented in figure 3. 

These data suggest that for slopes less than 25 degrees, fuel bed rate of spread 

increases with increasing packing ratio. But as slope increases to 30 degrees, 

the relation reverses, and the more tightly packed bed burns approximately 

15 percent slower than the bed with the lowest packing ratio. The data also 

indicate that when backing down a 16 degree downward slope, the rate of 

spread is 17 percent less than for flat conditions.

Figure 4 presents the normalized rate of spread data from the 15 cm 

deep fuel beds. Trends similar to those observed in the 7.6 cm deep beds 

are observed over the slope range of –16 to +30 degrees. Additional data 

Figure 1—Photograph of experiment, upward spreading fire, 

excelsior fuel, bed measured 1 m wide by 3.5 m long.

Influence of Slope on Fire Spread Rate Butler, Anderson, and Catchpole
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 78

0

5

10

15

20

25

30

–50 –25 0 25 50

R/S* ( normalized to zero slope R/S) 2.54-0.01

R/S* ( normalized to zero slope R/S) 2.54-0.03

R/S (m/s) 2.54-0.005

Marginal burning

conditions

R/S actual =0.007 m/s

Slope (degrees from horizontal)

R* (measured rate/zero slope rate)

0

5

10

15

20

25

30

–50 –25 0 25 50

R /S* (normalized to zero slope R/S) 7.6-0.01

R /S* (normalized to zero slope R/S) 7.6-0.03

R/S* ( normalized to zero slope R/S) 7.6-0.005

R* (measured rate/zero slope rate)

Slope (degrees from horizontal)

Figure 2—Normalized rate of spread for the 2.54 cm deep fuel beds. 

Bed depth (cm) and packing ratio values are listed at the end of each 

identifier in the legend.

Figure 3—Normalized rate of spread for the 7.62 cm deep fuel beds. 

Bed depth (cm) and packing ratio values are listed at the end of each 

identifier in the legend.

Influence of Slope on Fire Spread Rate Butler, Anderson, and Catchpole
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 79

were collected for steep down slope backing fires to a slope of –47 degrees. 

These data indicate that the minimum rate of spread occurs at approximately 

15 degrees downslope. For steeper slopes, the downhill rate of spread increases. The increase is attributed to burning fuel “falling” down into the 

bed accelerating the overall spread rate. A steeper slope of 43 degrees was 

explored in the lowest and highest packing ratios. These data show for these 

relatively steep slopes that the lower packing ratio bed spreads significantly 

faster. For the steepest slopes, the fires burned quickly up the 4 m long bed 

(approximately 20 seconds), but took significantly longer to burn down 

through the bed, indicating that spread occurred over the topmost layer of 

the fuel array.

Discussion

The data indicate at least three unique burning regimes. The first is indicated by individual flamelets burning separately along each fuel particle. This 

type of burning occurred in the 2.5 cm deep fuel beds for slopes less than 

25 degrees. The fire spread process for the slopes less than 25 degrees seems 

to be dominated by energy transfer along the individual strands, with radiant or convective energy transport insufficient to “bridge” the gap between 

individual particles. As slope increased above 25 degrees, an abrupt increase 

0

5

10

15

20

25

30

–50 –25 0 25 50

R/S* (normalized to zero slope R/S) 15-0.01

R/S* (normalized to zero slope R/S) 15-0.03

R/S* (normalized to zero slope R/S) 15-0.005

Slope (degrees from horizontal)

R/S increased due to movement

(falling) of flaming mater ial

into unburnt fuel

R* (measured rate/rate at zero slope)

Figure 4—Normalized spread rate for 15 cm deep fuel beds. Bed depth 

(cm) and packing ratio values are listed at the end of each identifier in the 

legend.

Influence of Slope on Fire Spread Rate Butler, Anderson, and Catchpole
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 80

in spread rate with slope occurred (see fig. 2) where the fire changed from 

flamelets burning along fuel strands to a more coherent flame front. This 

change suggests a corresponding shift in the fundamental physics behind 

the fire spread process. We posit that radiant and convective energy transfer 

provides sufficient heating of the fuel ahead of the fire front to produce a 

more uniform flame front.

As bed depth is increased from 2.5 to 7.6 cm, a uniform flame front was 

observed in all cases (see fig. 3). Minimum spread rates occurred not at 

flat slopes (0 degrees) but at downward spreading slopes of –16 degrees. As 

downslope (backing fire) slope was increased further, the rate of spread was 

observed to increase (see note on fig. 4). For slopes between –16 and + 10 

degrees the rate of spread increases at a rate roughly linearly proportional 

to slope. Fuel array packing ratio was not observed to be significant in this 

slope range. For upward spreading fires between 10 and 25 degrees, the fire 

rate of spread accelerates and the fuel arrays with the highest packing ratio 

burn fastest. As slope increases further, it appears that the fires with the 

highest packing ratios do not accelerate as fast as the lower packing ratios. 

At slopes greater than 25 degrees, spread rate was observed to increase again 

in a roughly linear but much greater proportion to slope.

Figure 4 presents the data from the deepest fuel bed (15 cm). Fire spread 

rates seemed to respond similarly at this depth to the observations for the 

7.6 cm deep beds. Again the minimum spread rate occurred at roughly –16 

degrees downslope. A roughly linear increase in fire spread rate with slope is 

observed from –16 to +16 degrees. From 16 to 25 degrees the data indicate 

some separation as a function of packing ratio with the more tightly packed 

beds burning faster. As slopes increase above 25 to 45 degrees, the lower beds 

with lower packing displayed much faster increases in spread rates than the 

more tightly packed beds. From these data we identify a third burning regime 

where convective energy transfer dominates the energy transfer process. The 

lower resistance to convective flow that is present in the less tightly packed 

beds results in the fastest fire spread rates. The beds with tightest packing, 

and highest loading, resulted in the tallest flames, but the fact that the lower 

packing ratio fires burned faster indicates that while radiant energy transfer is 

still present and probably necessary, it does not seem to dominate. However, 

the beds with lower packing ratios will also be conducive to the transport 

of radiant energy farther into the bed, which may lead to an increase in preheating. But this mechanism is believed to be secondary as the bulk of the 

flame transport and spread for the steepest slopes seems to occur along the 

top surface of the fuel array.

A transition zone, characterized by an increase in the spread rate of the less 

tightly packed beds that was more rapid than tightly packed beds, seemed to 

occur in all of the fuel beds as slopes exceed 25 degrees (see fig. 5). The result 

is a dramatic shift in the spread rate. This is supported by the observation 

that the fastest spread rates are exhibited by the beds with the lowest packing 

ratio. These lower density fuel arrays would be less restrictive to fluid flow 

and thus could be associated with enhanced convective heating.

Conclusions

The data presented suggest three burning regimes with respect to fires on 

slopes. The first is dominated by fire spread along individual fuel particles and 

can be characterized as primarily a conductive process. However, as either fuel 

Influence of Slope on Fire Spread Rate Butler, Anderson, and Catchpole
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 81

0

5

10

15

20

25

30

–50 0 50

R/S* (normalized to zero slope R/S) 2.54-0.01

R/S* (normalized to zero slope R/S) 2.54-0.03

R/S normalized (m/s) 2.54-0.005

R/S* (normalized to zero slope R/S) 7.6-0.01

R/S* (normalized to zero slope R/S) 7.6-0.03

R/S* (normalized to zero slope R/S) 7.6-0.005

R/S* (normalized to zero slope R/S) 15-0.01

R/S* (normalized to zero slope R/S) 15-0.03

R/S* (normalized to zero slope R/S) 15-0.005

R/S* (normalized to zero slope R/S)

Slope (deg)

Figure 5—Normalized spread rate for all fuel beds. Bed depth (cm) and packing ratio 

values are listed at the end of each identifier in the legend.

particle density or fuel bed depth increases, fires begin to burn as a coherent 

front. The behavior of this front in the mid slope range (that is, slopes less 

than 25 degrees) indicates that fuel bed bulk densities favoring increased fuel 

loading burn slightly faster; we posit that this is indicative of a radiatively 

controlled regime. Greater fuel loading will likely result in larger and taller 

flames, which implies more radiatively efficient emitters of radiant energy. 

As slopes increase further (that is, greater than 25 degrees) beds that are less 

tightly packed begin to burn faster. This implies a convective dominated regime where fluid flow within and over the surface of the fuel array dominates 

the flame spread rate. The data indicate the need for further experiments and 

comparison of the data against existing fire/slope relations.

Acknowledgments

This work has been supported by the Joint Fire Science Program and the 

USDA Forest Service. The assistance of Bob Schuette, Paul Sopko, Kyle 

Shannon, and Josh Jones in setting up and completing the experiments is 

sincerely appreciated.

Influence of Slope on Fire Spread Rate Butler, Anderson, and Catchpole
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 82

References

Alpert, R.L.; Ward, E.J. 1984. Evaluation of Unsprinklered Fire Hazards. Fire 

Safety Journal 7, 127-143

Barrows, J.S. 1951. Fire behavior in northern Rocky Mountain forests. Pub. No. 29. 

Ogden, UT: U.S. Department of Agriculture, Forest Service, Intermountain 

Forest and Range Experiment Station.

Curry, J.R.; Fons, W.L. 1938, Rate of spread of surface fires in the ponderosa pine 

type of California. J. Agr. Res. 57:239-267.

Curry, J.R.; Fons, W.L. 1940. Forest fire behavior studies. Mech. Engng. N.Y. 

219-25.

Markstein, G.H.; DeRis, J.N. 1973. Upward Fire Spread over Textiles. Proc. 

Fourteenth Symposium (Intl) on Combustion. The Combustion Institute: 

1085-1097.

McArthur, A.G. 1968. Fire Behaviour in Eucalypt Forests. Leaflet No. 107. Ninth 

Commonwealth Forestry Conference, India, 1968.

Murphy, P.J. 1963. Rates of fire spread in an artificial fuel, MSc. Thesis. Bozeman, 

MT: Montana State University.

Viegas, D.X. 2005. A mathematical model for forest fires blowup. Combust. Sci. 

and Tech. 177:27-51.

Weise, D.R. 1993. Modelling wind and slope-induced wildland fire behavior. Ph.D. 

Dissertation. Berkeley: University of California at Berkeley. 130 p.

Influence of Slope on Fire Spread Rate Butler, Anderson, and Catchpole
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 83

Abstract—The conditions necessary for the combustion of canopy fuels are not well 

known but are assumed to be highly influenced by the volume through which the 

canopy fuels are dispersed, known as canopy bulk density (CBD). Propagating crown 

fire is defined as a continuous wall of flame from the bottom to the top of the canopy, 

implying crown fire propagation is actually independent of the vertical fuel distribution. 

We hypothesize that all foliar canopy fuel is available for the propagation of canopy 

fire. Therefore, we focus our effort on simplifying Van Wagner’s (1977) canopy fire 

propagation model to accept a canopy fuel metric that uses only foliar biomass per 

unit area (FBA) rather than the more complex and commonly used CBD. The multiplication of leaf area index (LAI) and the specific leaf area (SLA) of a given tree species 

results in FBA, making FBA easily related back to ecologically meaningful terms at a 

range of spatial scales. A variety of instruments can be used to estimate LAI with high 

accuracy, and SLA values have been published for many species found in fire prone 

forests of the West. Alternatively, allometric equations can be used to compute FBA 

using individual tree-level measurements. Using Van Wagner’s (1977) data we modify 

his propagation model and successfully match his published results. In addition, we 

use Forest Inventory and Analysis data from northern Idaho to compare the critical 

rate of spread (cROS) predicted by the modified model (using FBA) with the predictions of the original model (using CBD). We find that the two models are statistically 

equivalent (α = 0.10).

Introduction

Canopy fire challenges accurate modeling of fire spread because quantification of the canopy fuel stratum is difficult (Cruz and others 2003; Keane and 

others 2005). Particular difficulty arises, as canopy fires can exist in either 

‘passive’ or ‘active’ modes. In passive canopy fires, individual trees or groups 

of individuals ignite and burn from the bottom to the top of the crown, 

resulting in mixed impacts on the environment (Ryan and Noste 1983). In 

active canopy fires the combustion propagates as a solid wall of flame through 

a landscape filled with trees in conjunction with a surface fire (Van Wagner 

1977). In addition, this combination of active canopy and surface fire often 

burns at high intensity having significant impact on soils, vegetation, and 

wildlife habitat (Grier 1975; Ryan and Noste 1983; Romme 1995; Haggard 

and Gaines 2001). Such fires may exhibit flame lengths exceeding 30 m with 

rates of spread exceeding 50 m/min (Stocks and others 2004). Active canopy 

fire also poses great risk to fire personnel, the public, and private property 

(Scott 1998; Clark and others 1999; Scott and Reinhardt 2001).

In: Butler, Bret W.; Cook, Wayne, 

comps. 2007. The fire environment—

innovations, management, and policy; 

conference proceedings. 26-30 March 

2007; Destin, FL. Proceedings 

RMRS-P-46CD. Fort Collins, CO: 

U.S. Department of Agriculture, 

Forest Service, Rocky Mountain 

Research Station. 662 p. CD-ROM.

1 Fire Ecologist, U.S. Department of 

Agriculture, Forest Service, OkanoganWenatchee National Forests, Chelan, 

WA. jddickinson@fs.fed.us

2 Senior Lecturer of Statistics, 

University of Melbourne, Melbourne, 

Victoria, Australia. 

3 Deputy Fire Management Officer, 

U.S. Department of Agriculture, 

Forest Service, Okanogan-Wenatchee 

National Forests, Wenatchee, WA.

4 Associate Professor of Remote Sensing 

and Spatial Ecology, University of 

Idaho, Moscow.

5 A ssistant Professor of Forest 

Measurements, University of Idaho, 

Moscow.

Modification of VanWagner’s Canopy 

Fire Propagation Model

James Dickinson1, Andrew Robinson2, Richy Harrod3, 

Paul Gessler4, and Alistair Smith5
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 84

Modification of VanWagner’s Canopy Fire Propagation Model Dickinson, Robinson, Harrod, Gessler, and Smith

For these reasons fire managers have great interest in preventative treatment of forested landscapes capable of sustaining active canopy fires (Hof and 

Omi 2003; Scott 2003; Peterson and others 2005) and also in predicting the 

behavior of these fires when they initiate (Stocks and others 2004). These 

management applications require knowledge of the minimal conditions sustaining canopy fire to properly plan fuels treatments or to evaluate impending 

conditions that threaten crews during suppression actions.

Most fire planning tools and spatially explicit fire models depend upon 

the original Van Wagner’s (1977) model (we refer to this model as VWcbd) 

to characterize the minimum conditions necessary to sustain active canopy 

fire (Keane and others 2000; Scott and Reinhardt 2001; Finney and others 2003; Reinhardt and Crookston 2003; Finney 2004). Other research 

has attempted to quantify the available canopy fuel for combustion, called 

canopy bulk density (CBD) to parameterize VWcbd for application (Keane 

and others 2000; Fule and others 2001; Hummel and Agee 2003; Riano 

and others 2003; Gray and Reinhardt 2003; Perry and others 2004; Riano 

and others 2004; Falkowski and others 2005; Peterson and others 2005; 

Keane and others 2005).

To date, no study has compared the effect that using different methods 

of CBD estimation might have on the output of the VWcbd. Therefore, this 

paper introduces the reader to two common methods of estimating CBD and 

statistically compares the outputs of the VWcbd model using these inputs. 

We also develop and compare a modified version of VWcbd, which we call 

VWfba, that utilizes a simpler metric of foliar biomass per unit area (FBA) 

as the fuel input.

Crown Fire Defined

Van Wagner (1977) described a conceptual canopy fire model using “a 

stationary wall of flame with a conveyor belt carrying fuel into the flame.” 

The rate of that conveyor belt must maintain a minimum critical rate of spread 

(cROS) to deliver a sufficient quantity of combustible fuel per unit time to 

maintain the wall of flame in the canopy space (Van Wagner 1977).

Van Wagner modeled canopy fire interactions for fuel, flame front rate 

of spread, and the minimum fireline intensity necessary to maintain canopy 

fire in a fashion similar to Byram’s (1959) index. This surface fire index relates fireline intensity, the rate of spread of a flame front, and the quantity 

of combusted fuel (Byram 1959; Scott and Reinhardt 2001). By representing 

the flame front as a line moving at some rate across a plane of homogenously 

distributed fuel mass (multiplied by a constant heat yield) the result is a 

fireline intensity that is the product of the rate of flame movement and the 

homogeneous fuel (Byram 1959). Like Byram’s (1959) index, the VWcbd 

assumes a homogeneously distributed fuelbed, albeit through a volume rather 

than across an area (Van Wagner 1977). The assumption of a homogeneous 

fuel bed with a constant heat yield per unit of fuel makes VWcbd identical 

to Byram’s (1959) index in form.

The Van Wagner (1977) model differs only in form by its calibration to the 

minimal conditions necessary for active canopy fire to persist. Van Wagner 

assumed that the fuel present in a stand would have a constant heat of ignition (per unit mass), and thus avoided the necessity of calculating the energy 

in the propagating heat flux. This changed the crucial element to a simple 
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 85

Modification of VanWagner’s Canopy Fire Propagation Model Dickinson, Robinson, Harrod, Gessler, and Smith

argument that relates only the critical quantity of fuel consumed per unit 

time required for flame maintenance, divided by the available fuel quantity 

(equation 1). The result of this equation is the critical rate of spread (cROS, 

represented by ‘Ro’ in equation 1) required for the fire to consume the available fuel (‘d’) such that the critical mass flow rate (‘So’) is satisfied.

Ro = d

So (Van Wagner 1977) (1)

Where: Ro : cROS for active canopy fire (m.s–1)

So : critical mass flow rate for canopy fire, (0.05 kg.m–2.s–1)

d: foliar (canopy) bulk density (kg.m–3)

Van Wagner’s definition of canopy fire as a wall of continuous flame from 

bottom to top of the canopy must be met to satisfy the implicit assumption that canopy fire has initiated (Van Wagner 1977; Scott and Reinhardt 

2001). The use of a single value to represent a distribution of fuel through 

the canopy space removes any effect that a vertical distribution of fuel may 

have upon canopy fire, requiring a second implicit assumption that horizontal 

canopy fire propagation occurs regardless of the vertical distribution of the 

fuel. The quotient resulting from the division of the available fuel by volume 

(the definition of CBD) is simply a fraction of the total fuel load (the sum of 

which is still available for combustion) and is irrelative to the effect that the 

vertical distribution may (or may not) have on canopy fire.

Regardless of how a metric might incorporate the vertical distribution 

component, it must be input to a model calibrated for that fuel metric. Likewise, a metric with no vertical distribution component used in a properly 

calibrated model, would not preclude the use of vertical distribution as a 

parameter in the model. It would simply require the incorporation of the 

vertical distribution to be explicitly included in the model. The fuel input to 

such a model would be useable at a range of scales depending only upon the 

method used to calculate the available canopy fuel.

Our overarching assumption is that the Van Wagner (1977) model appropriately relates the basic properties necessary to describe the lower boundary 

conditions required for active canopy fire combustion. Namely, that an active 

canopy fire spreading between two points on the landscape must consume a 

minimum quantity of fuel per unit time in order to persist as a canopy fire. 

The quantification of fuel is vital to a model of the combustion process but 

need not incorporate the vertical distribution into the fuel metric as traditional canopy fuel methodologies have done.

Van Wagner’s (1977) published data are used here to recalibrate the model 

for use with FBA in place of the standard CBD input. The VWcbd and the 

VWfba models are applied to a regionwide nonspatial Forest Inventory and 

Analysis (FIA) database, and an equivalence test is used to compare the estimates of cROS from the VWcbd and VWfba models.

Methodology

In this section we introduce two methods used to calculate the fuel input 

necessary for VWcbd. We then modify the existing VWcbd for the use of foliar 

biomass to create the VWfba model. Finally, we introduce our data and the 

statistical methods used to test the equivalence of the VWcbd and VWfba.
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 86

Modification of VanWagner’s Canopy Fire Propagation Model Dickinson, Robinson, Harrod, Gessler, and Smith

Summary of CBD Methods

We present FBA as a more simple alternative to the several existing methods for calculating CBD (Sando and Wick 1972; Brown 1978; Cruz and 

others 2003; Riano and others 2003; Reinhardt and Crookston 2003; Perry 

and others 2004) though only the FFE (Reinhardt and Crookston 2003) 

and Cruz and others (2003) methods are used for comparison because they 

represent the two unique approaches to estimating CBD.

The widespread use of the complex FFE method by Federal land managers 

and researchers (Hummel and Agee 2003; Andersen and others 2005; Perry 

and others 2004; Falkowski and others 2005; Peterson and others 2005) 

is facilitated by its implementation in the Fire and Fuels Extension to the 

Forest Vegetation Simulator (FFE-FVS) computer software (Reinhardt and 

Crookston 2003) and the proprietary Fire Management Analyst Plus suite 

of computer software (FPS 2001).

When applied to a stand inventory list, FFE calculates the foliar biomass 

assuming constant density of foliar biomass through the length of the individual tree-crown space. FFE calculates the amount of biomass contained in 

every vertical 0.3048 m (1 ft) of the volume space for each tree (Scott and 

Reinhardt 2001). Finally, the foliar biomass for all individuals in the stand is 

summed within each vertical 0.3048 m above the ground and is plotted (see 

fig. 1). This represents stand biomass (kg/m2) distributed by height above 

ground (m), as a sum of all individual trees (that is, vertical distribution of 

foliage within the canopy space) (Sando and Wick 1972).

Figure 1—FFE methodology of canopy bulk density calculation. The maximum of 

the 13-ft (3.96 m) running mean (marked with a star) read from the x-axis as the 

maximum average foliar biomass per square meter per average vertical meter, or 

“effective CBD” (kg/m3).
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 87

Modification of VanWagner’s Canopy Fire Propagation Model Dickinson, Robinson, Harrod, Gessler, and Smith

FFE calculates the maximum 0.3048 m (1 ft) increment of a 3.96 m (13 ft) 

running mean applied to the vertical profile of foliar biomass (see fig. 1) 

(Reinhardt and Crookston 2003). This produces an effective CBD value 

that is not equivalent to the traditional CBD definition (Scott and Reinhardt 

2001). Rather, effective CBD provides a value that is the maximum of a running average (fig. 1) and represents the greatest average fuel value, presumed 

to be the least resistant stratum for canopy fire propagation through a stand 

(Scott and Reinhardt 2001). Despite the lack of direct correspondence to the 

CBD definition, it is frequently used as input to VWcbd.

Cruz and others (2003) calculate the foliar biomass for all trees in a stand 

and divide this by the product of the average crown length multiplied by the 

area of the stand. Biomass is assumed to have equal distribution through the 

volume of the canopy. This method appears to be similar to the method used 

by Van Wagner (1977) for calculation of experimental fuels data. We refer to 

this method as the “Cruz” method for the remainder of this paper.

Modification of Van Wagner’s Model

Our research modifies VWcbd (equation 2a) (Van Wagner 1977) by altering 

the value that represents the mass flow rate (So, equation 2a), which represents the minimum quantity of fuel required to be combusted per unit time 

to sustain canopy fire propagation. To determine So, Van Wagner identified 

forested stands for canopy fire experimentation and recorded stand information including stems per hectare, basal area, tree height, height to crown 

base, and biomass per unit area (table 1). The stands were then ignited with 

three out of four fires judged to burn as active canopy fires by Van Wagner 

(table 1).

R ms S

d

kgm s

d kgm o

o ( ) . ( )

( )

−

− −

− = = 1

2 1

3

0 05 (Van Wagner 1977) (2a)

oo ⋅= dRS (2b)

Ro is the critical minimum rate of spread for active canopy fire

So is the critical mass flow rate for solid crown flame

d is the canopy bulk density

Van Wagner (1977) used one stand (‘R1’ in table 1), considered ‘an incipient’ active canopy fire, for model calibration. The observed rate of spread 

of fire in stand ‘R1’ was multiplied by CBD to determine the required mass 

flow (So in equation 2b). It was apparent that this resulting value was less 

than necessary for active canopy fire, so Van Wagner set So at a constant value 

Table 1—Summary of data taken from Van Wagner (1977) used for model recalibration.

Test Basal Height Actual

fire area Tree to live Biomass CBD ROS

name Fire type (m2) Trees/ha height (m) per m2 (kg/m3) (m/sec)

C6 active 50 3200 14 7 1.8 0.23 0.46

C4 active 50 3200 14 7 1.8 0.26 0.28

GL-B active 25 1800 18 6 1.22 0.12 0.41

R1 developing 50 3200 14 7 1.8 0.23 0.18
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 88

Modification of VanWagner’s Canopy Fire Propagation Model Dickinson, Robinson, Harrod, Gessler, and Smith

slightly greater (0.05 kgm–2s–1). This established the minimum mass flow 

value necessary because a slower fuel consumption rate would result in fire 

behavior similar to the incipient canopy fire behavior of ‘R1’ (Van Wagner 

1977).

Our alternative VWfba was created by dividing Van Wagner’s value of 

So= 0.05 kgm–2s–1 by the CBD for stand ‘R1’ (0.23 kgm–3) (equation 3a). 

The result is a cROS of 0.217 ms–1 for stand ‘R1’. The cROS is multiplied 

by the FBA (foliar biomass per unit area, table 1) of stand ‘R1’ resulting in 

a product of 0.39 kgm–2s–1 (equation 3b) giving the predictive equivalent to 

Van Wagner’s published value using CBD of So = 0.05 kgm–2s–1 (equation 

3c). The final form of the VWfba model is shown in equation 4.

0 05 0 23 0 217 2 1 3 1 . ( ) . ( ) . ( ) kgm s kgm ms − − − − ÷ = (3a)

0 217 1 80 0 39 1 2 1 1 . ( ) . ( ) . ( ) ms kgm kgm s − − − − ∗ = (3b)

0 05

0 23

0 217 0 39 2 1

3

1 . ( )

. ( ) . ( ) kgm s . (kgm

ms

− −

−

− = = kgm s

kgm

− −

−

1 1

2 1 80

)

. ( ) (3c)

R ms kgm s

d kgm o ( ) . ( )( )

−

− −

− = 1

1 1

2

0 39 (4)

After recalibration of So, the VWfba model was applied to the rest of the 

data provided by Van Wagner (1977). The VWfba and VWcbd models provide comparable predictions of cROS. Additionally, VWfba identifies that 

stand “GLB Active” (fig. 2) exceeds the cROS and is an active canopy fire 

when VWcbd does not.

Figure 2—Summary of Van Wagner (1977) published results and the VWfba 

recalibration applied to original data. Where the ‘Actual ROS’ line exceeds the 

estimated lines a canopy fire should occur. Note that only the VWfba estimate 

is exceeded by the GLB fire.
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 89

Modification of VanWagner’s Canopy Fire Propagation Model Dickinson, Robinson, Harrod, Gessler, and Smith

Sample Data

We use a database of 2,626 FIA plots collected from the Inland Northwest 

region of the United States (UIFBL, 2006). FIA routinely collects data on 

trees, saplings, and seedlings at each plot; however, we selected only trees at 

least 7.56 cm (3 inches) in diameter at breast height (1.37 m) (USDA Forest 

Service 1990). Each of the 40,000 tree records in this database include the 

variables of tree diameter, height, percent live crown ratio, species, and the 

tree expansion factor (number of trees per hectare that record represents) and 

plot level variables (such as elevation, habitat type, aspect, and slope).

Fire and Fuels Extension Emulator

The size and format of our 40,000 record-database is not compatible with 

the import tool of FFE-FVS that requires separation of the tree level data 

from the plot level data. A stand-alone program provided by the FVS support group in Fort Collins, CO (FMFC 2002) was also considered for use, 

but single stand with a maximum 2,000 tree records would not work for 

our database containing 2,626 plots. Therefore, with permission (FMFC, 

personal communication), a standalone program was rewritten within the 

statistical package ‘R’ (R Development Core Team 2005) to directly interface 

with our database.

This code provides identical CBD values in comparison to the standalone 

FFE program. The allometric equations we use are identical to those used 

in the northern Idaho and Inland Empire variants of the FFE -FVS software 

(Brown 1978; Gholz and others 1979). Our revised program provides us with 

several important values that are not readily available from FFE. It provides a 

CBD value identical in method to FFE, an estimate for the method of Cruz 

and others (2003), and an estimate of the proposed FBA alternative.

Statistical Analysis

Model validation is often difficult because traditional statistics require that 

the null hypothesis be stated such that the two models are not significantly 

different (Welleck 2003). The ability to demonstrate similarity rather than difference based on probability arises by changing the null hypothesis from one 

of similarity to one stating that dissimilarity exists, and is called equivalence 

testing (Wellek 2003; Robinson and Froese 2004). Though the biomedical 

industry has long used equivalence tests to show that two samples are statistically similar, equivalence tests have not been widely applied to ecological 

modeling (Wellek 2003; Robinson and Froese 2004). Robinson and Froese 

(2004) use equivalence tests to validate model predictions of the diameter 

growth model contained within the Forest Vegetation Simulator.

This variety of statistical test requires that a region of indifference (ε), or 

a range of difference that is of negligible concern, be established (Robinson 

and Froese 2004). That is, a range of difference between sample metrics that 

is insignificant in practical application (Wellek 2003). After establishing a 

region of indifference, what is analogous to two separate trials of a one sided 

t-test on the difference between samples is carried out, one for differences 

greater than the test statistic, and one trial for differences less than the test 

statistic (Robinson and Froese 2004). This method is called a ‘two-one-sided 

t-test’ (TOST), and like a regular t-test, this test gains power as the sample 

size increases (Robinson and Froese 2004).

The null hypothesis of dissimilarity is rejected if the range of indifference 

overlaps into the rejection region for a one-tailed 95 percent confidence interval of the mean of the differences ( diffba =− xxx ) (Robinson and Froese 
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 90

Modification of VanWagner’s Canopy Fire Propagation Model Dickinson, Robinson, Harrod, Gessler, and Smith

2004). The mathematics behind equivalence testing do not make p-values 

practical, so we use both a strict (small) and liberal (large) range of indifference to bracket our confidence in the hypothesis test (Wellek 2003).

These ranges of indifference bracket our confidence based not on probability, but on a judgment of how large the difference could be for the models 

to still be considered equivalent. In this paper, we choose a strict range of 

indifference such that | diff x ± ε| < 0.138 m.s–1 (± 0.50 kph) for cROS, while 

the liberal range of indifference was established such | diff x ± ε| < 0.278 m.s–1

(± 1.0 kph). The values were chosen as being insignificant ranges of difference 

from both a management and a modeling standpoint. We use a one sided type 

I error rate of 5 percent (a= .05), which translates to two times a one-sided 

a (2 x .05 = .10) for our TOST test of equivalence.

It is unusual for any two models to produce identical outputs so we attempt 

to use the available data to explain differences that may exist between these 

models. To explain these differences we fit a multiple linear regression model 

(MLR) to the output of the VWcbd (using FFE method) and VWfba. The 

VWcbd and VWfba models do not exhibit consistent differences between 

them; for example, VWfba does not always estimate lower than VWcbd. 

Therefore, we evaluate the ratio of the cROS estimates from the VWcbd and 

VWfba models (VWcbd:VWfba) as the response variable.

Within the ‘R’ statistical package (R Development Core Team 2005) a 

saturated MLR model with two-way interactions was fit to the response values 

to find a response transformation that provides the best model fit. Evaluation 

of residual plots, QQ-normal plots, and Boxcox plots indicate that the best 

response transformation is a square root transformation.

Each predictor was then removed one at a time and we use the extra sum 

of squares F-test (Ramsey and Schafer 2002) using the “drop1” function 

(R Development Core Team 2005) to determine which estimators are 

significant based on their residuals.

Results and Discussion

Our data do exhibit some kurtosis; however, our large sample size lends 

resistance to any departures from normality (Ramsey and Schafer 2002). To 

verify that outliers were not affecting the results, we removed obvious outliers 

and reran the TOST. In each comparison of models, removing the outliers 

made the minimum region of indifference smaller. Hence, the null hypothesis 

was more clearly rejected with the outliers removed in each comparison, and 

we present our results with all data represented.

The mean of the differences between cROS for FIA plots using VWcbd-FFE and VWfba is only 0.010 ms–1 (table 2). Despite a comparatively 

large standard deviation the null hypothesis of dissimilarity is rejected. The 

sum of the minimum range of indifference and the mean of the difference 

(0.010 ± 0.027 ms–1) is substantially less than the conservative range of 

indifference lending strong evidence (in lieu of a p-value) that the rejection 

of the null hypothesis is justified (table 2).

These same patterns are repeated for the comparison of VWcbd-Cruz and 

VWcbd-FFE, though the mean of the difference is larger than the previous 

comparison (0.050 ms–1) (table 2). Again, the sum of the difference and the 

minimum range of indifference (0.050 ± 0.066 ms–1) are well within the predefined conservative range of indifference leading to a comfortable rejection 

of the null hypothesis. In the final comparison between VWcbd-Cruz and 
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Modification of VanWagner’s Canopy Fire Propagation Model Dickinson, Robinson, Harrod, Gessler, and Smith

VWfba, the mean of differences is larger than any of the previous comparisons 

(0.060 ms–1) (table 2). The sum of this large bias between the models and 

corresponding large minimum region of indifference (0.060 ± 0.086 ms–1) 

does not allow a rejection of the null hypothesis of dissimilarity under the 

conservative scenario, though it is still rejected at the liberal range. In all but 

one of these comparisons we reject the null hypothesis of dissimilarity.

The VWfba provides the slowest estimate of cROS between these three 

methods. These differences 0.010 ms–1 and 0.060 ms–1 are interpreted to 

mean that on average the VWfba model estimates the cROS necessary to 

sustain canopy fire to be less than the cROS predicted by the VWcbd-FFE 

and VWcbd-Cruz, respectively. This result is likely due the VWfba utilization of all available fuel and not some fraction of the available fuel, which 

means that the cROS does not need to be as high as the case where less fuel 

is available.

The small (0.010 ms–1) difference between the VWcbd-FFE and VWfba 

method is surprising considering the drastically different methods of fuel 

estimation used in these models. Therefore, we attempt to explain only the 

differences between VWcbd-FFE and VWfba models using MLR. The total variance (R2) explained by the saturated MLR is 57.8 percent (294 and 

2331 degrees of freedom). Only the diameter at breast height of the tree of 

average basal area (dbh.bar) is a significant individual predictor. Nine other 

variables, all interactions, are found to be significant using the extra sum of 

squares F-test (table 3).

Colinearity of variables limits our ability to identify the contributed explanatory power of each significant variable. Instead, we fit many regressions 

using each individual variable, or constituent variables (in the case of interactions), to quantify the remaining variance not explained by the predictor(s). 

For each variable, table 3 lists the root mean square error (RMSE) of a model 

that incorporates only that variable (and its constituents). The RMSE of the 

response variable predicted using a mean parameter was fit for comparison. 

An individual variable associated with a low RMSE in table 3 is a stronger 

explanatory variable than a variable with a larger RMSE.

The interaction between total basal area (BA.sum) and total trees per acre 

(TPA.sum) has the lowest marginal RMSE, but this MLR model would have 

had two more parameters. While this interaction can be biologically explained, 

it is not easily interpreted. In contrast, the dbh of a tree with average basal 

area (dbh.bar) is directly related to the total basal area of a stand, which is 

highly influenced by the total number of trees in a stand (tpa.sum). The high 

explanatory power of this single variable is useful to know but provides no 

further insight to the differences between these models.

Table 2—Results of the equivalence test for FIA data.
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Modification of VanWagner’s Canopy Fire Propagation Model Dickinson, Robinson, Harrod, Gessler, and Smith

The VWcbd and VWfba estimates for the regional FIA data (n=2676) 

are equivalent to one another well within acceptable ranges of indifference 

established for these tests. We demonstrate that VWfba is equivalent to VWcbd in the Inland Northwest when the sampling design of FIA is followed 

and the data used to describe a 0.40 ha (1 acre) stand. Rejection of the null 

hypothesis of dissimilarity suggests that either model can provide reasonable 

cROS estimates in this case study. Based on our analysis of comprehensive 

FIA data, we conclude that the use of VWfba is a reasonable alternative to 

VWcbd, in particular when compared to VWcbd-ffe. Unfortunately, MLR 

analysis of our data does little to explain the observed insignificant differences between the VWcbd-ffe and VWfba.

Some of the failure to quantify the accuracy of the VWcbd model may be 

attributed to the difficulty in measuring, evaluating, or estimating the canopy 

fuels (Keane and others 2005). Reinhardt and Scott (2005) report nearly 

1000 person-hours required to physically measure several canopy biomass 

variables (including their vertical distribution) to calculate canopy fuel CBD 

for a single 10 m radius plot. This inability to physically measure the volume 

space used in CBD calculations has made it difficult to assess the utility of 

the model as put forth by Van Wagner (1977) (Keane and others 2005).

Table 3—Saturated multiple linear regression (MLR) model and significant predictors found 

using the extra sum of squares F-test.
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Modification of VanWagner’s Canopy Fire Propagation Model Dickinson, Robinson, Harrod, Gessler, and Smith

In contrast, VWfba is directly correlated to the foliar biomass within a 

stand and does not vary based on estimated canopy volume. The accuracy of 

allometric equations and the increasing use and accuracy of remote sensors 

that can be used to estimate foliar biomass in a stand make FBA more easily 

measured in the field in front of potential canopy fires. This measurability 

will make nonexperimental field evaluation of the VWfba model a more 

practical endeavor.

Like CBD estimates, FBA is limited by the accuracy of the technologies 

used to estimate foliar biomass (total available fuel). However, CBD methods 

have the added difficulty of measuring the necessary volume. The ability to 

field test, calibrate, refine, or even observe the efficacy of the VWcbd estimates 

has been limited mostly because of the complex, more difficult measurement of the CBD input. The principle of parsimony (Ford 2000) might be 

applied in this situation leading to the adoption of VWfba as a canopy fire 

model because it utilizes a simpler canopy fuel input. This makes the VWfba 

model simpler than the existing VWcbd model while providing a statistically 

significant equivalent estimate when compared to the current model.

Conclusions

The adoption of VWfba may allow for consistent fuel quantifications 

that are less influenced than CBD, by the highly varied canopy structure of 

forested stands. This consistency provides a benchmark by which to adjust 

Van Wagner’s (1977) modified model, VWfba, for further improvement and 

refinement. We assert that the VWfba recalibration of Van Wagner’s model 

provides statistically equivalent estimates and is consistent with the original 

field observations of Van Wagner.

The VWfba model proposed here only quantifies the fuel conditions 

necessary to sustain canopy fire between two points on the landscape with 

the intention of matching the performance of the original Van Wagner model. 

This model does not require knowledge about the vertical distribution of 

fuel, only that enough fuel exists between two points to sustain canopy fire. 

Future work with the VWfba model may require a coefficient to describe 

vertical distribution or a number of other parameters, but these coefficients 

will be independent of the combustible biomass estimate. This approach leads 

to a more subtle refinement of the model output independent of the quantity 

of fuel that is truly available to the advancing canopy fire.

The VWfba model should be applied to a variety of case studies where estimates of foliar biomass can be obtained for observed canopy fires. An example 

of this sort of validation could be the acquisition of LANDSAT imagery over 

an area subsequent to a canopy fire. Using estimates of foliar biomass derived 

from leaf area index (LAI) and specific leaf area (SLA), the resulting VWfba 

prediction could be compared to fire behavior observations from that fire. 

This type of field validation removes the necessity of estimating the probable 

fire spread direction and then placing fuel sampling personnel directly in the 

path of impending canopy fires, allowing the required fire observations to be 

conducted from a safe distance. Given the lack of literature suggesting that 

the use of VWcbd adequately provides estimates of the cROS threshold we 

would not necessarily expect the VWfba model to accurately estimate cROS 

in these field trials, but we would expect it to perform as well as the VWcbd 

model, if reliable CBD data could be collected for comparison.
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 94

Modification of VanWagner’s Canopy Fire Propagation Model Dickinson, Robinson, Harrod, Gessler, and Smith

Ultimately, acceptance of the VWfba model may lead to the development 

of a canopy fire behavior model that can be related simply to LAI. This would 

remove the necessity of knowing the tree species necessary to assign a SLA 

value. This refinement would provide a practical method to take remotely 

sensed imagery and convert it directly to a relevant canopy fuel characteristic 

involving one less step than the VWfba model proposed here.

References

Andersen, H.E.; McGaughey, R.J.; Reutebuch, S.E. 2005. Estimating forest 

canopy fuel parameters using LIDAR data. Remote Sensing of Environment 94: 

441–449.

Bachelet, D.; Lenihan, J.M.; Daly, C.; Neilson, R.P. 2000. Interactions between 

fire, grazing and climate change at Wind Cave National Park, SD. Ecological 

Modeling 134(2-3) 229–244.

Brown, J.K. 1978. Weight and density of crowns of Rocky Mountain conifers. 

Research Paper INT – 197. Ogden, UT: U.S. Department of Agriculture, Forest 

Service, Intermountain Research Station. 56 p.

Byram, G.M. 1959. Combustion of Forest Fuels. In: Forest Fire Control and Use, 

1st edition. New York: McGraw-Hill. Chapter 3: 61-89.

Clark, T.L.; Radke, L.; Coen, J.; Middleton, D. 1999. Analysis of small-scale 

convective dynamics in a crown fire using infrared video camera imagery. Journal 

of Applied Meteorology 38: 1401–1420.

Cruz, M.G.; Alexander, M.E.; Wakimoto, R.H. 2003. Assessing canopy fuel 

stratum characteristics in crown fire prone fuel types of western North America. 

International Journal of Wildland Fire 2003(12) 39–50.

Falkowski, M.; Gessler, P.E.; Morgan, P.; Hudak, A.T.; Smith, A.M. 2005. 

Characterizing and mapping forest fire fuels using ASTER imagery and gradient 

modeling. Forest Ecology and Management 217: 129-146.

Finney, M.A. Revised 2004. FARSITE: Fire Area Simulator-model development 

and evaluation. Research Paper RMRS-RP-4. Ogden, UT: U.S. Department of 

Agriculture, Forest Service, Rocky Mountain Research Station. 47 p.

Finney, M.A.; Britten, S.; Seli, R. 2003. FlamMap2 Beta Version 3.0.1. Missoula, 

MT: Fire Sciences Lab and Systems for Environmental Management.

FMFC. 2002. U.S Department of Agriculture, Forest Service, Forest Management 

Service Center. Acquired from: http://www.fs.fed.us/fmsc/index.php Accessed: 

May 08, 2005.

Ford, E.D. 2000. Scientific method for ecological research. New York: Cambridge 

University Press. Chapter 9: 242.

FPS. 2001. Fire Program Solutions, LLC. Acquired from: http://www.fireps.com/

Accessed: April 05, 2005

Froese, R.E. 2003. Re-engineering the Prognosis basal area increment model for 

the Inland Empire. PhD Dissertation. Moscow: University of Idaho, Forestry, 

Wildlife and Range Sciences. Chapter 2: 10–21.

Fule, P.Z.; Waltz, A.E.; Covington, W.W.; Heinlein, T.A. 2001. Measuring 

forest restoration effectiveness in reducing hazardous fuels. Journal of Forestry 

November, 24–29.

Gholz, H.L.; Grier, C.C.; Campbell, A.G.; Brown, A.T. 1979. Equations for 

estimating biomass and leaf area of plants in the Pacific Northwest. Research 

Paper 41. Corvallis: Oregon State University, School of Forestry Forest Research 

Lab. 39 p.
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 95

Modification of VanWagner’s Canopy Fire Propagation Model Dickinson, Robinson, Harrod, Gessler, and Smith

Gray, K.L.; Reinhardt, E.D. 2003. Analysis of algorithms for predicting canopy fuel. 

2nd International Wildland Fire Ecology and Fire Management Congress and 5th

Symposium on Fire and Forest Meteorology. Orlando, FL, Nov. 16-20, 2003.

Grier, C.C. 1975. Wildfire effects on nutrient distribution and leaching in a 

coniferous ecosystem. Canadian Journal of Forest Research 5: 599–607.

Haggard, M.; Gaines, W.L. 2001. Effects of stand-replacement fire and salvage 

logging on a cavity-nesting bird community in eastern Cascades, Washington. 

Northwest Science 75(4) 387–396.

Hof, J.; Omi, P. 2003. Scheduling removals for fuels management. Proceedings 

RMRS-P-29. Ogden, UT: U.S. Department of Agriculture, Forest Service, Rocky 

Mountain Research Station: 367–377.

Hummel, S.; Agee, J.K. 2003. Western spruce budworm defoliation effects on forest 

structure and potential fire behavior. Northwest Science 77: 159–169.

Keane, R.E.; Mincemoyer, S.A.; Schmidt, K.M.; Long, D.G.; Garner, J.L. 2000. 

Mapping vegetation and fuels for fire management on the Gila National Forest 

Complex, New Mexico. Gen. Tech. Rep. RMRS-GTR-46-CD. Ogden, UT: U.S. 

Department of Agriculture, Forest Service, Rocky Mountain Research Station. 

CD-ROM: 131 p.

Keane, R.E.; Reinhardt, D.; Scott, J.; Graky K.; Reardon, J. 2005. Estimating 

forest canopy bulk density using six indirect methods. Canadian Journal of Forest 

Research 35(3): 724-739.

Perry, D.A.; Jing, H.; Youngblood, A.; Oetter, D.R. 2004. Forest structure and fire 

susceptibility in volcanic landscapes of the eastern high Cascades. Conservation 

Biology 18(4) 913–926.

Peterson, D.L.; Johnson, M.C.; Agee, J.K.; Jain, T.B.; McKenzie, D.; Reinhardt, 

E.D. 2005. Fuel planning: Science synthesis and integration – forest structure and 

fire hazard. Gen. Tech. Rep. PNW-GTR-628. Portland, OR: U.S. Department 

of Agriculture, Forest Service, Pacific Northwest Research Station. 30 p.

R Development Core Team. 2005. R: A language and environment for statistical 

computing. Vienna, Austria: R Foundation for Statistical Computing. ISBN 3-

900051-07-0; URL http://www.R-project.org.

Ramsey, F.L.; Schafer, D.W. 2002. The statistical sleuth: a course in methods of data 

analysis, 2nd edition. Pacific Grove, CA: Duxbury. Chapter 10: 280–285.

Reinhardt, E.D.; Crookston, N.L. 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.

Riano, D.; Chuvieco, E.; Condes, S.; Gonzalez-Matesanz, J.; Ustin, S.L. 2004. 

Generation of crown bulk density for pinus sylvestris L. from lidar. Remote 

Sensing of Environment 92: 345–352.

Riano, D.; Meier, E.; Allgower, B.; Chuvieco, E.; Ustin, S.L. 2003. Modeling 

airborne laser scanning data for the spatial generation of critical forest parameters 

in fire behavior modeling. Remote Sensing of Environment 86: 177–186.

Robinson, A.P.; Froese, R.E. 2004. Model validation using equivalence tests. 

Ecological Modelling 176: 349–358.

Romme, W.H.; Turner, M.G.; Wallace, L.L.; Walker, J.S. 1995. Aspen, elk, and fire 

in northern Yellowstone National Park. Ecology 76(7) 2097–2106.

Ryan, N.C.; Noste, N.V. 1983. Evaluating prescribed fires. Wilderness Fire 

Symposium, Missoula, MT, Nov. 15-18, 1983.

Sando, R.W.; Wick, C.H. 1972. A method of evaluating crown fuels in forest stands. 

Research Paper NC-84. Saint Paul, MN: U.S. Department of Agriculture, Forest 

Service, North Central Forest Experiment Station.
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 96

Modification of VanWagner’s Canopy Fire Propagation Model Dickinson, Robinson, Harrod, Gessler, and Smith

Scott, J.H. 1998. Sensitivity analysis of a method for assessing crown fire hazard in 

the northern Rocky Mountains, USA. International Conference on Forest Fire 

Research. 14th Conference on Fire and Forest Meteorology, Vol. 2; 2517– 532. 

Luso, Portugal, November 16-20, 1998.

Scott, J.H. 2003. Canopy fuel treatment standards for the wildland-urban interface. 

Proceedings RMRS-P-4. Ogden, UT: U.S. Department of Agriculture, Forest 

Service, Rocky Mountain Research Station: 29–38.

Scott, J.H.; Reinhardt, E.D. 2001. Assessing crown fire potential by linking models 

of surface and crown fire behavior. Research Paper RMRS-RP-29. Ogden, UT: 

U.S. Department of Agriculture, Forest Service, Rocky Mountain Research 

Station. 59 p.

Scott, J.H.; Reinhardt, E.D. 2005. Stereophoto guide for estimating canopy fuels 

characteristics in conifer stands. Gen. Tech. Rep. RMRS-GTR-145. Fort Collins, 

CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research 

Station. 49 p.

Stocks, B.J.; Alexander, M.E.; Wotton, B.M.; Stefner, C.N.; Flannigan, M.D.; 

Taylor, S.W.; Lavoie, N.; Mason, J.A.; Hartley, G.R.; Maffey, M.E.; Dalrymple, 

G.N.; Blake, T.W.; Cruz, M.G.; Lanoville, R.A. 2004. Crown fire behaviour in 

a northern jack pine-black spruce forest. Canadian Journal of Forest Research 

34: 1548–1560.

USDA Forest Service. 1990. Idaho forest survey field procedures (1990-1991). 

Ogden, UT: U.S. Department of Agriculture, Forest Service, Intermountain 

Research Station. 181 p.

Van Wagner, C.E. 1977. Conditions for the start and spread of crown fire. Canadian 

Journal of Forest Research 7: 23–34.

Wellek, S. 2003. Testing statistical hypotheses of equivalence. New York: Chapman 

and Hall/CRC.
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 97

Abstract—Effective mitigation of external fires on structures can be achieved flexibly, 

economically, and aesthetically by (1) preventing large-area ignition on structures from 

close proximity of burning vegetations and (2) stopping flame travel from firebrands 

landing on combustible building objects. In using bench-scale and mid-scale fire tests 

to obtain fire growth properties on common building construction and landscaping 

plants, a model is being developed to use fast predictive methods suitable for changing 

environments imposed on the parcel lot consisting of structures and ornamental plants. 

When fully implemented and validated, the property owners and associated professionals will be able to view realistically in real-time (or faster) the various fire scenarios 

with the ability to select building materials and shapes as well as select ornamental 

plant species and placement for achieving the desired fire mitigation. Because of the 

analytical model’s ability to respond to the changing “parcel” environments of wind, 

temperature, humidity, moisture, sunshine, and wildfire sources of heat and embers, 

as well as to variations in building construction and ornamental plants, means that 

analysis can be done eventually for various neighborhoods. The mathematical formulation presented at the 2006 BCC Symposium is partially shown here and some results 

are compared with (1) our refurbished and modified Lateral Ignition and Flame Travel 

Test (ASTM E1321 and E1317), (2) specialized testing of Class B burning brand (ASTM 

E108) in the Cone Calorimeter (ASTM E1354), (3) room-corner tests with OSB (ISO 

9705), and (4) Cone Calorimeter tests of fire resistive materials such as FRT plywood 

and single-layer stucco-coated OSB. A preliminary Fortran dll file has been generated 

for use in other models, such as ecoSmart Fire.

Introduction

With the increasing fire hazards from wildfires, particularly in Southwestern 

United States, the homes built in the wilderness/urban interface (WUI) will 

come under increasing regulatory pressures to adopt exterior fire resistive 

structures, in addition to managing landscape vegetation. However, it is not 

always clear as to the effective strategy for wildfire mitigation, even to a fire 

protection expert. Indeed, homeowners and builders could benefit greatly 

from a calculation tool for evaluating the wildfire hazards to their structures. 

Fire threats in the WUI basically come in two forms: (1) the long-duration 

exposure from firebrands spotting and (2) the short-duration exposure from 

heat flux and/or flame impingement of the wildfire nearing the structure.

The fire hazard threat of high heat flux or flame impingement from shortduration wildfire exposure is primarily mitigated with vegetative management 

in the defense zones around the combustible structure. The kind of vegetative management needed to prevent structural ignition will depend on the 

Ignition and Flame Travel on Realistic 

Building and Landscape Objects in 

Changing Environments

Mark A. Dietenberger1

In: Butler, Bret W.; Cook, Wayne, 

comps. 2007. The fire environment—

innovations, management, and policy; 

conference proceedings. 26-30 March 

2007; Destin, FL. Proceedings 

RMRS-P-46CD. Fort Collins, CO: 

U.S. Department of Agriculture, 

Forest Service, Rocky Mountain 

Research Station. 662 p. CD-ROM.

1 Research General Engineer, U.S. 

Department of Agriculture, Forest 

Service, Forest Products Laboratory, 

Madison, WI. mdietenberger@fs.fed.us
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 98

fire resistant construction, moisture condition of landscape vegetation, and 

the positions/types of ornamental vegetations relative to the combustible 

structure. To establish the nonthreatening distances of rapidly burning 

ornamental vegetations away from a given structure, which may or may not 

be fire resistant, one should ideally use a fire hazard calculation tool, such 

as being partially developed in this paper. The insidious threat from longduration firebrands’ exposure, as particularly blown in from a distant huge 

wildfire, is really the main driving force in requiring fire resistant structures 

in the WUI. Obviously, the owner needs to place wire screens over chimneys, 

vents, and around decks and some windows to prevent ember penetration 

into the highly combustible interiors of buildings (Manzello and others 

2006). However, it is not clear as to how much fire resistance is needed for 

the construction exteriors. The homeowner could well decide that the wood 

deck is expendable as long as the fire (possibly originating in the deck crevice 

with firebrands, Manzello and others 2006) does not spread into the fire 

resistant home. The patio door and windows should also be made resistant 

to the worst-case firebrand, which is likely the Class A or B simulated firebrand in the ASTM E108 test. The Class A firebrand can also be thought 

of as multiple firebrands collecting in a corner wall, where the upward flame 

spread on combustible sidings is likely. The use of an exterior FRT wood 

siding or similarly fire resistive material will instead prevent such flame spread, 

thereby limiting the damage/ignition to the region of direct exposure from 

the firebrands. Our main point is that reasonable and economical design of 

an exterior fire resistant material needs to consider the firebrand threats, even 

with effective vegetative management.

We believe the speed of computer computation has reached the point of 

bettering the real time calculation of damage, ignition, and fire growth on 

combustible objects. Since the CFD codes such as the Fire Dynamics Simulator are far from reaching such a point, we present here certain analytical 

solutions of the dynamic processes of surface heating to ignition/flame travel 

that leads to overall fire growth. The key numerical procedure is using stepping 

boundary conditions to discretize the analytical time integration and which 

then becomes a fully recursive computation method as a bonus. The mathematical formulation presented at the 2006 BCC Symposium (Dietenberger 

2006a) is partially shown here and some results are compared with (1) our 

refurbished and modified Lateral Ignition and Flame Travel Test (ASTM 

E1321 and E1317), (2) specialized testing of Class B burning brand (ASTM 

E108) in the Cone Calorimeter (ASTM E1354), (3) room-corner tests with 

OSB (ISO 9705), and (4) Cone Calorimeter tests of fire resistive materials 

such as FRT plywood and single-layer stucco-coated OSB.

Ignition Predictions With Changing Conditions

The prediction of surface temperature for reaching ignition conditions 

that take into account the changing boundary conditions, and yet avoid the 

use of time-consuming finite difference methods, resulted in an innovative 

mathematical formulation of transient heat transfer problem. In an earlier 

paper (Dietenberger 2006b) we published a recursive analytical solution for 

transient heat and moisture transfer in a finitely thick hygroscopic material 

with step changes of certain boundary conditions. For many materials, moisture is not a consideration and we show here just the solution for temperature 

change, T(xˆ,t) , profile due to boundary conditions of stepping changes in 

Ignition and Flame Travel on Realistic Building and Landscape Objects in Changing Environments Dietenberger
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 99

surface heat fluxes, q′′(,t) and back side heat fluxes, q′′(0,t) , here as,

T(xˆ,t) ≅

Δq′′(,t

i

)

K

q,

S(α, xˆ,t −t

i

)− Δq′′(0,t

i

)

K

q,0

S(α, − xˆ,t −t

i

)

⎡

⎣

⎢

⎢

⎢

⎤

⎦

⎥

⎥

i=0 ⎥

n

∑ (1)

where xˆ is dimensional depth, t is current time, Kq is thermal conductivity 

coefficient, Cq is heat capacity, ρ is dry body density, α is thermal diffusivity, 

and S(α, xˆ,t) is the series expansion solution,

S(α, xˆ,t) = αt

 +  3xˆ

2 −2

62 − 2π2

(−1)n

n

2 exp −αt

1

nπ



⎛

⎝

⎜

⎜

⎜⎜

⎞

⎠

⎟

⎟

⎟

⎟

⎡ 2

⎣

⎢

⎢

⎢

⎤

⎦

⎥

⎥

⎥ n=1

∞

∑ cos

nπxˆ



⎛

⎝

⎜

⎜

⎜⎜

⎞

⎠

⎟

⎟

⎟

⎟

⎧

⎨

⎪

⎪⎪

⎩

⎪

⎪

⎪

⎫

⎬

⎪

⎪⎪

⎭

⎪

⎪

⎪

(2)

Rarely do classical heat conduction texts discuss such stepping heat fluxes, 

probably because the summation in equation 1 can be burdensome. However, 

such texts do not offer the possibility of converting equation 1 to a recursive summation, which is simple and efficient to implement as a computer 

routine, which we have done for this work. If irradiance, q′′

r, is applied to 

one surface, the material responds with radiative and convective cooling on 

the exposed side, and conductive cooling on the unexposed side as in the 

boundary conditions,

Δq′′(,t

i

) =

αs,i

q′′

r (ti

)+ εs,iσ[Ta

4 (t

i

)−T 4 (,t

i

)]+ hc,i[Ta (t

i

)−T(,t

i

)]

−H(t

i −t1) αs,i

q′′

r (ti−1)+ εs,i

σ[Ta

4 (t

i−1)−T 4 (,ti−1)]+ hc,i

[Ta (t

i−1)−T(,ti−1 { )]}

⎧

⎨

⎪

⎪

⎪

⎩

⎪

⎪

⎪

⎫

⎬

⎪

⎪

⎪

⎭

⎪

⎪

⎪

Δq′′(0,t

i

) = Cinsulate[T(0,t

i

)−Ta (t

i

)]− H(t

i −t1)Cinsulate[T(0,ti−1)−Ta (ti−1)]

(3),

then eventually the predicted surface temperatures will reach a steady-state 

value in which the convective and radiative heat losses to the air and conductive heat losses to backside insulation is equal to radiant energy absorbed. The 

heaviside function, H(ti – t1), is used to specify that prior to heat exposure 

the sample is at a uniform temperature, and therefore has zero heat fluxes at 

both surfaces. If the irradiance is high enough, then the surface will reach 

ignition temperature, Tig, prior to reaching steady state temperature. To more 

accurately capture the time at ignition, we used time steps of one second or 

less, although a large time step is feasible if the boundary conditions change 

slowly enough as with the diurnal heating cycle.

As can be seen from equation 3 the changes in the boundary conditions 

with time can be used. That is, we can arbitrarily vary irradiances, convective 

flow, atmospheric temperature, and surface conditions with time. The method 

can also be extended to multilayered samples in which interfacial zones can be 

treated as “conductive backside cooling” heat transfers. To consider ignition 

due to flame impingement, we have the imposed heat flux from the 100 kW 

propane ignition burner (or the firebrand flames), q′′w , in our room-corner 

burn tests to use in place of the term, αsq′′r + εs

σ(Ta

4 −T(,t)

4 )+ hci(Ta −T(,t)),

in equation 3, as,

q′′w = σ (αs

ε f Tf 4+ε s(1−ε f )Ta

4−ε s

T( ,t)

4 ) + hcf (Tf −T(,t)) (4)

Ignition and Flame Travel on Realistic Building and Landscape Objects in Changing Environments Dietenberger
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 100

The parameters that are known in the case of fluxmeters in the wall are 

q′′w = 55kW / m2, T(,t) = 298 K and absorptivity and emissivity as αs = εs

= 0.97. Using averaged measured flame temperature, Tf = 173 K, we derived values of flame emissivity and convective coefficient as, εf = 0.391 and 

hcf = 0.0165kW / m2 K to reproduce the fluxmeter heat flux. Our test materials 

typically have lower surface emissivity, εs = 0.88, and using the above values for 

other parameters the imposed heat flux becomes 51 kW / m2 instead. Therefore 

we would expect the time to ignition on the wall to correlate best with the 

cone heater flux of 50 kW / m2, as was found by Karlson (1993). However, he 

used a multiplication factor of 1.7 times the time to ignition from the cone 

calorimeter to obtain the actual time to ignition for the room-corner test, 

which is equivalent to adding about 11 seconds (90 percent level) to ignition 

time due to burner lagging.

Fire Growth Simulation With 

Changing Conditions

In an earlier paper reporting on our ISO9705 tests (Dietenberger and 

Grexa 1996), we described the complex-variable Laplace transform solution 

of the Duhamel integral for flame spread, HRR, and pyrolysis area that 

involved four stages requiring solution restarts: (1) ignited corner area due 

to a sluggish propane burner, (2) upward spread of corner flame to the ceiling, (3) lateral spread of top-wall flame for the unlined ceiling, and (4) the 

preflashover rapid downward spreading of the entire three walls flame. This 

analytical solution was modified for application to the changing conditions 

of the WUI fire scenario, and the formulation reported in the 2006 BCC 

Symposium (Dietenberger 2006a) is briefly repeated here. First step in the 

analysis is the description of the extended flame flux profile as an imposed 

flux applied over surface distance, yc, followed by an exponential decay with 

characteristic length, δ f, as in

q′′wf( y) = q′′w0 H( y)+ exp −( y − yc)

δ f

⎧

⎨

⎪⎪

⎩

⎪

⎪

⎫

⎬

⎪⎪

⎭

⎪

⎪

−1

⎛

⎝

⎜

⎜

⎜

⎜

⎞

⎠

⎟

⎟

⎟

⎟⎟

H( y − yc)

⎡

⎣

⎢

⎢

⎢

⎤

⎦

⎥

⎥

⎥ (5)

where H(y) is the heaviside function. With the length of constant flux, yc, 

identified with the pyrolysis front, yp, the characteristic length was found to 

be proportional to extended flame length and correlated as, δf =(yf – yp) / cf

, 

with value of cf approximately as 1.3 for upward spread. With this spatial 

profile of flame heat flux, we then analyzed for the quasi-steady speed, vp, 

of the pyrolysis front by using the formula, y – yig = vp(tig – t), in equation 5 

to represent the sliding movement of imposed heat flux profile over a given 

spot until ignition temperature is reached. With this substitution, Duhamel’s 

supposition integral is the convolution of material’s thermal response to a 

constant imposed flux with time changing imposed flux as in

Tig −Tm = d(T(,t))

dt ⊗ q′′wf(vp (tig −t)+ yig) = (T(l,t))⊗

dq′′wf( yig −vp(t −tig))

dt (6)

where the integration is taken from zero to the time of ignition, tig, to correspond to ignition temperature, Tig. We note that equation 6 becomes exactly 

equation 1 providing the heat flux profile of equation 5 is approximated by 

incremental flux changes with incremental time steps, which we will show 

later in evaluating the LIFT test data. Because it is possible to have a wide 

Ignition and Flame Travel on Realistic Building and Landscape Objects in Changing Environments Dietenberger
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 101

variation in the characteristic flame length, depending on the direction of 

the flame spread, then the time step sizes will have to be highly adaptable to 

ensure a reasonably accurate and efficient descretization of equation 5 for its 

use in equation 1. If there are multiple flame spread directions on multiple 

combustible items, then it would be impossible to determine the optimum 

time steps. This is the fundamental reason why the CFD codes, such as the 

FDS, will fail to predict some types of flame spreading problems. To avoid 

this problem, the intricate analytical solution to equation 6 (instead of a discretization solution) for both thermally thick and thermally thin materials and 

with interpolation between the regimes is given Dietenberger (1991) as:

δ f = vF τm = vF K

qρCp

Tig −Tm

q′′w0−q′′iq

⎛

⎝

⎜

⎜

⎜⎜

⎞

⎠

⎟

⎟

⎟

⎟

2

1

2

+

1

4

+

K

q (Tig −Tm)

 (q′′w0−q′′ig)

⎛

⎝

⎜

⎜

⎜

⎜⎜

⎞

⎠

⎟

⎟

⎟

⎟

⎟⎟

⎛ 1.3

⎝

⎜

⎜

⎜

⎜

⎜

⎜

⎜

⎜

⎞

⎠

⎟

⎟

⎟

⎟

⎟

⎟

⎟

⎟

⎟⎟

−2

1.3

⎛

⎝

⎜

⎜

⎜

⎜

⎞

⎠

⎟

⎟

⎟

⎟⎟

(7)

where q′′ig = εsσ(Tig

4 −T(,0)4 )+ hc(Tig −T(,0)) (8)

One then realizes that all of the material’s parameters for thermal response 

are contained in the material time constant, τm, during flame spreading. 

Closer examination of equation 7 shows that the flame travel rate, vF, can be 

made quite small with large values for thermal conductivity, material density, 

heat capacity, material ignition temperature, and material thickness, or with 

small values for preheated surface temperature, flame heat flux, and flame 

footprint. Obviously, to completely stop flame spreading for any direction, 

the local flame foot heat flux has been reduced to the critical heat flux needed 

for ignition (via equation 8). The use of fire retardants merely improves upon 

this flame spread halting, even to the point of diminishing upward flame 

spreading under a strong radiant source. We note that supposed “constant” 

fire properties used in equations 7 and 8 are also changing with time, especially the flame foot and ignition fluxes.

As the next step in analytical modeling of fire growth, the flame oversize 

area, Af – Ap, as a nonlinear function of HRR, Qt, and flame width, w, for 

the corner flame (Dietenberger and Grexa 1996) is linearized at each time 

step as,

2w( yf − yp) = 0.0433(2w)

1 3Qt

2 3 ≈ Afm+

∂Af

∂Qt

(Qt −Qm)+

∂Af

∂Ap

(Ap− Apm) = cf(a + bAp + cQt) (9)

The flame area for other geometries, such as the single vertical wall, a tunnel 

ceiling, or a circular pool fire, can be similarly linearized for their respective 

nonlinear functions. The fire growth problem, by rearranging equation 7, 

can now be stated concisely as the Voltera type integral as,

dAp

dt = 2wvF = a + bAp + cQtτ m

+ Aig, iΛ(t −ti) (10)

where the total HRR is given by a sum of ignition-burner and material-flamespreading heat release rates as,

Qt = ΔQb, iH(t −ti)+

i

∑ Ap(ti) Q′′m(t −ti)

i

∑ + Q′′m(t −ti−ξ)A p dξ 0

t−ti

∫ (11)

and Q′′m(t) = Q′′m, ig H(t) exp(−ω m t) (12)

Ignition and Flame Travel on Realistic Building and Landscape Objects in Changing Environments Dietenberger
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 102

whereas an exponentially decaying HRR profile (with decay coefficient, ωm) is 

assumed for a given sample surface, with the peak HRR flux, Q′′m,ig , also changing with time as a result of the changing radiant source. The recursive Laplace 

solution to equation 10 given for each time step is (with t

* = t – ti

 > 0),

Ap(t) = a + c(Qmi− ApiQ′′m, iq + Qbi + ΔQbi)

τ m

+ Apiω m

⎛

⎝

⎜

⎜

⎜⎜

⎞

⎠

⎟

⎟

⎟

⎟

exp(s1t

*

)−exp(s2t

*

)

s1−s2

⎛

⎝

⎜

⎜

⎜

⎜⎜

⎞

⎠

⎟

⎟

⎟

⎟

⎟

+Api s1exp(s1t

*

)−s2 exp(s2t

*

)

s1−s2

⎛

⎝

⎜

⎜

⎜

⎜⎜

⎞

⎠

⎟

⎟

⎟

⎟

⎟

+ a + c(Qbi + ΔQbi)

τ m

⎛

⎝

⎜

⎜

⎜⎜

⎞

⎠

⎟

⎟

⎟

⎟

ω m

s1−s2

⎛

⎝

⎜

⎜

⎜⎜

⎞

⎠

⎟

⎟

⎟

⎟

exp(s1t

*

)−1

s1

− exp(s2t

*

)−1

s2

⎛

⎝

⎜

⎜

⎜

⎜⎜

⎞

⎠

⎟

⎟

⎟

⎟

⎟

(13)

Qt(t) = Qb(t)+ Qm(t) = Qbi + ΔQbi + (a + cQbi)Q′′m, ig −b(Qmi− ApiQ′′m, ig)

τ m

⎛

⎝

⎜

⎜

⎜⎜

⎞

⎠

⎟

⎟

⎟

⎟

exp(s1t

*

)−exp(s2t

* )

s1−s2

⎛

⎝

⎜

⎜

⎜

⎜⎜

⎞

⎠

⎟

⎟

⎟

⎟

⎟

+ cQ′′m, ig

τ m

ΔQbi exp(s1t

* )−exp(s2t*

)

s1−s2

⎛

⎝

⎜

⎜

⎜

⎜⎜

⎞

⎠

⎟

⎟

⎟

⎟

⎟

+ Qmi s1exp(s1t

* )−s2 exp(s2t* )

s1−s2

⎛

⎝

⎜

⎜

⎜

⎜⎜

⎞

⎠

⎟

⎟

⎟

⎟

⎟

(14)

where growth acceleration coefficients (in complex variable form) are,

si = b+ cQ′′m, ig −ω mτ m

2τ m

−(−1)i b+ cQ′′m, ig −ω mτ m

2τ m

⎛

⎝

⎜

⎜

⎜⎜

⎞

⎠

⎟

⎟

⎟

⎟

2

+

bω m

τ m

(15)

For brevity we define the recursive terms, Api = Aig, i + Ap(ti), Qbi = Qb(ti),

and Qmi = Q′′m, igAig, i + Qm(ti). The size of the overflame area as a function of time 

is merely given with equation 9. Since equations 13 and 14 are framed in a 

recursive form, the coefficients and parameters treated as constants during a 

time step can be allowed to vary from time step to time step. Indeed the material time constant, τm, is in actuality a fairly strong function of time via the 

changing preheat temperature, Tm, in equation 7, which in turn is calculated 

with equation 1 using the time-changing external radiant flux boundary conditions. Therefore, one could conceive that the overall fire growth can switch 

from a damped fire spread to an accelerative fire spread, or vice versa, through 

the mere time variation of the material time constant. Because the roots are 

considered complex numbers, the above solutions are considered to be in the 

complex variable domain. Specialized computer algorithms were developed 

for complex evaluations so that the above functions could be programmed 

directly as a Fortran code called by the Excel spreadsheet. Because of the 

recursive nature of fire growth equations, it should be possible to consider 

various changing conditions without recalibrating the coefficients.

Class B Firebrand Tests in the Cone Calorimeter

To understand the challenges presented with a typical fire scenario in the 

WUI we burned the Class B firebrand of ASTM E108 in the Cone Calorimeter (ASTM E1354). A modified sample holder was used that allowed 

air flow into the sample as well as exposed the sample partially into the air. 

This necessitates us to turn the cone heater into the vertical position to keep 

it out of the way, and we opted not to use the cone irradiance, although we 

may do that in the future. Use of a Bunsen burner to ignite the brand would 

Ignition and Flame Travel on Realistic Building and Landscape Objects in Changing Environments Dietenberger
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 103

have been required in the ASTM E108 test to ensure a self-burning brand, 

but instead for our test the brand was partially soaked in methanol bath. 

With ignition started at the corners of the brand, the ensuring flame took 

several minutes to spread around the brand. The HRR history as shown in 

figure 1 somewhat increases linearly to a broad peak value of 10 kW and 

decreases gradually afterwards. Although a simple charring wood surface 

has a strong initial peak HRR and then decays approximately exponentially 

for many seconds, the phenomenon of flame spreading around the specimen 

is rapid enough to result in a net increasing HRR with time. Once flame 

spreading is finished, the HRR should decay somewhat exponentially, but 

the increasing glowing HRR makes the decrease in the overall HRR to be 

not so rapid. The fire growth process and the effect on the HRR profile is 

similarly imagined for flaming vegetations, roof fires, deck fires, and so on. 

The challenge for analytical fire growth modeling is to reproduce the HRR 

profile with the use of several burning regions in the model.

The heat flux from a burning firebrand, however, varies according to size, 

distance, shape, viewfactor, and time-dependent HRR profile. For example, 

in our cone calorimeter test of Class B brand we were able to place one 

fluxmeter directly underneath the 150 X 150 X 60 mm wood crib with a 

5 mm gap and 25 mm inward from the edge and another fluxmeter located 

at 45 mm outward from the wood crib. The heat flux data are shown in 

figure 2, which clearly shows the effects of viewfactors of the developing 

flame on the measured value. That is, the outside-fluxmeter seems to mimic 

the HRR trend and has a peak heat flux of 6.7 kW/m2, which is not enough 

to ignite most materials but can still char some materials. On the other hand, 

the underneath-fluxmeter at first could not view the flame, and when the 

flame came into view, the flux levels eventually reached 50 kW/m2. Then 

after the flame subsided and the wood crib was glowing throughout, the 

flux became as high as 80 kW/m2. This is the high flux that rapidly ignites 

most materials, and also some fire resistant materials, albeit with a little more 

time to ignition.

0

2

4

6

8

10

12

0 200 400 600 800 1000 1200 1400 1600 1800

Time (seconds)

HRR (kW)

HRRconv (kW)

Figure 1—Heat release rate of class A brand.

Ignition and Flame Travel on Realistic Building and Landscape Objects in Changing Environments Dietenberger
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 104

-10

0

10

20

30

40

50

60

70

80

90

0 200 400 600 800 1000 1200 1400 1600 1800 2000

Time (sec)

Total Heat Flux (kW/m^2)

25 mm Inward Flux #87891 (kW/m^2)

45 mm Outward Flux #87892 (kW/m^2)

Figure 2—“A” brand heat fluxes.

Figure 3 shows the surface temperature response of dried redwood decking as calculated with equation 1 using the imposed heat flux profiles from 

figure 2 as the time dependent input data. The high temperatures obtained 

under the 80 kW/m2 flux from the contact with wood crib glowing confirm 

the assertion that most combustible building materials will ignite. Yet, at a short 

distance away, the imposed heat flux exposure drops to the levels such that most 

combustible materials will not ignite.

These facts would place exterior cladding surfaces such as roofs and decks 

and unprotected interior flooring as highly susceptible to ignition by the 

“worse-case” firebrand. Therefore, designing fire resistant claddings to 

prevent flame spreading or avoid fire penetrating through the exposed layer 

after the inevitable ignition would be a desirable trait. Indeed, at least among 

wood materials, one could observe similar ignition behavior among different 

species, but that their flame spreading behavior is remarkably different.

0

200

400

600

800

0 500 1000 1500 2000

Time (sec)

Temperature (Celsius)

Outside - Surface

Outside - Backside

Inside - Surface

Inside - Backside

Figure 3—Analytical prediction of dried redwood response to class B brand 

heat exosure.

Ignition and Flame Travel on Realistic Building and Landscape Objects in Changing Environments Dietenberger
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 105

Refurbished Lift Apparatus and Analysis

Almost a couple decades ago we built the LIFT apparatus to duplicate 

the original at NIST BFRL, which was developed mainly by Margaret 

Harkleroad. The intent was to follow the ASTM E1321 standard to obtain 

ignition and flame spread properties for wood based materials. The standard 

called for the 6 inch by 30 inch vertically mounted specimen to be exposed 

continuously to the burner radiant heat until there was a distribution of 

surface temperature in equilibrium. This distribution of temperature then 

gave rise to a variation of lateral flame travel rate, which was to be measured 

manually. However, at the heat fluxes required, the wood was experiencing 

surface charring, which negated possibility of deriving flame travel property. 

Another factor creating difficulties was the unrealistic high convective flow 

exposures to cause ignition and flame travel, as compared to, for example, 

the low convective flow involving vitiated hot air in the lateral flame travel 

phase in the Room Corner test (ISO9705). Finally, we were dependent on 

the venturi tube to control the burner output with the air flow valve, which 

created a problem for us when the cyclic central air source caused a highly 

wandering burner output.

With the current emphasis on the WUI applications, installation of a mass 

flow controller on the air source, and utilization of faster and more accurate 

data acquisition, we embarked on refurbishing the LIFT apparatus. Our 

modified test protocol involves no surface preheating, numerous tiny surface 

thermocouples, and a crank-operated computer-recorded indicator for tracking flame position as function of time. The first detailed test involved the 

OSB that was set aside for the LIFT tests when the series of room-corner 

tests were done.

0

100

200

300

400

500

600

700

800

0 50 100 150 200 250 300

Test Time (s)

Temperature (Celsius)

250 mm

325 mm

350 mm

400 mm

450 mm

500 mm

550 mm

750 mm

650 mm

Figure 4—Surface temperatures measured on OSB surface in the LIFT flame travel test.

Ignition and Flame Travel on Realistic Building and Landscape Objects in Changing Environments Dietenberger
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 106

For the exposure to the 50 kW/m2 radiant imposed flux at the 50 mm 

position from the specimen end, the data (not shown) shows surface temperatures profiles at various locations up to 200 mm which is consistent with 

the flame spread rapidly proceeding downwards from the pilot ignition. In 

figure 4, the surface temperatures profiles at positions greater than 200 mm 

is shown in which there was a lateral flame spread that decreased in the travel 

rate until the flame stopped spreading at around 550 mm. Although it is apparent when the flame has traveled over a thermocouple, it was not apparent 

as to what the ignition temperature is or just how the temperature rapid rise 

has occurred just prior to the flame front arrival. Indeed, with the rapid rise 

in temperature after a radiant preheat period indicates that a small flame-foot 

heating feature must be captured by a credible model of fire growth.

The typical temperature profiles were easily simulated with the recursive 

formulation of equation 1 using reasonable heat flux profiles shown in figures 5 and 6 with the corresponding temperatures predictions in figures 7 

and 8 in comparison with the data. The thermophysical properties for OSB 

were taken from our previous ignitability results. The imposed heat fluxes 

had three phases to properly predict surface temperatures. The first phase 

is the few seconds increase in heat flux as a result of sliding the specimen 

into place. At the 50 mm location where the radiant flux was set at about 

50 kW m2, the calculated temperature response reached 301 °C at 12 seconds 

in figure 7. The third phase of heat flux is caused by the flame foot modeled 

with time-changing form of equation 5, having the flame foot heat flux of 

60 kW/m2 and a time constant of 0.4 seconds. The rapid exponentially upturn of the temperature was captured using 0.2 seconds time steps so that 

the surface temperature of 408 °C was obtained at 15.4 seconds (flame sheet 

arrival time). Further, but damped, temperature rise was in response to the 

imposed flux set at 110 kW/m2. Similar pattern is noted for figure 8, which 

required a flame foot heat flux of 60 kW/m2, time constant of 4.0 seconds, 

and flame sheet arrival time at 83 seconds. With the relative increase of the 

time constant by 10 times, meant that the local flame travel rate at 50 mm is 

also 10 times of that at 200 mm. Note that the net surface heat flux due to 

surface emitting radiation and reduction in convection heat flux has rapidly 

changing profile adequately captured by the analytical model to predict the 

temperature response.

It is interesting that no charring of the wood surface was needed for making close temperature predictions, allowing us to take the planned steps 

to validate the lateral flame travel rate formula given by equation 7. Since 

we have measurements from the thermopile in the flue gas and from fume 

stack thermocouple (ASTM E1317), we can derive the sensible HRR profile 

(Dietenberger 1994) and compare it with the model estimated HRR profile 

from equation 14. Success with this approach can be applied to other situations involving flame travel opposing the air flow, such as ground flame 

propagation or fire on a deck surface.

Selected Room-Corner Tests

Because the analytical fire growth model for changing conditions differs 

somewhat from the original model, we decided to focus on predicting the 

upward fire growth behavior in corner walls, particularly if no fudging of 

material properties was required and that it provided a good representation 

of the exterior environment (far below the flashover conditions). In the case 

Ignition and Flame Travel on Realistic Building and Landscape Objects in Changing Environments Dietenberger
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 107

0

20

40

60

80

100

120

0 10 20 30 40 50

Test Time (s)

Heat Flux (kW/m^2)

qltnew

qradnew

Constant Flame Heat

Exponentially Rising

Flame Foot Heat at

Time Constant 0.4 sec

Insertion Heat at 50 mm

0

20

40

60

80

100

120

0 50 100 150 200 250 300

Test Time (s)

Heat flux (kW/m^2)

qltnew

qradnew

Constant Flame Sheet Heat

Exponentially Rising

Flame Foot Heat at

Time Constant 4 sec

Insertion Heat at 400 mm Position

0

100

200

300

400

500

600

700

800

0 10 20 30 40 50

Test Time (s)

Temperature (s)

Insulated

Backside

Exposed

Surface

50 mm

<- Flame Sheet Heat

<- Ignition

via Flame Foot Heat

<- Radiant Preheat

0

100

200

300

400

500

600

700

800

0 50 100 150 200 250 300

Test Time (s)

Temperature (Celsius)

Insulated

Backside

Exposed

Surface

400 mm

<- Radiant Preheat

<- Ignition

 via Flame Foot Heat

<- Flame Sheet Heat

Figure 5—Imposed heat fluxes modeled for temperature 

predictions at the 50 mm location.

Figure 6—Imposed heat fluxes modeled for temperature 

predictions at the 400 mm location.

Figure 7—Prediction of measured surface temperature 

using imposed heat fluxes in figure 5.

Figure 8—Prediction of measured surface temperature 

using imposed heat fluxes in figure 6.

of OSB we used the properties published earlier (Dietenberger and Grexa 

1996). Figure 9 shows our Room-Corner flashover test with OSB linings on 

the walls and gypsum board on the ceiling. We also show with the dotted 

smooth curve the ignition burner going to 100 kW as it is observed by the 

gas analyzers, in which we take into account gas mixings in the test room 

and gas sensors and time travel of the sampled gas to the sensors. The OSB 

ignited 25 seconds after exposure to the ignition burner and led to an upward fire growth that is shown as the HRR profile rising above that of the 

ignition burner. The dashed smooth curve is predicted by equation 14 that 

was also numerically filtered with a time constant of 18 sec for gas lag in the 

Ignition and Flame Travel on Realistic Building and Landscape Objects in Changing Environments Dietenberger
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 108

room and a time constant of 10 seconds for the gas lag in the sensors. The 

dot-dashed smooth curve is the result of applying an external radiant flux, 

40 kW/m2, in addition to that from the ignition burner. This ignited the 

targeted region at about the same time (23.8 seconds) as the ignition burner. 

The “instantaneous” rise in the HRR to a higher peak HRR (and which 

has a delayed peak because of the numerical filters required to simulate gas 

mixings) demonstrate the capability of the recursive analytical fire growth 

model to adapt to changing conditions. Several other examples of changing 

conditions have been applied that showed reasonable results (not shown). 

Another type of changing conditions we have recently simulated is the effect 

of fire resistive linings on reducing and even stopping upward fire growth. 

Our examples include FRT polyurethane foam, FRT plywoods and the Type 

X gypsum board (shown at the conference presentation).

Conclusions

The introductory discussions on wildfire threats to construction and their 

mitigation have shown the need to understand damage, ignition, and fire 

growth as exposed to changing conditions on realistic combustible items, 

including those considered to be fire resistive. The calculations should 

ultimately be able to provide (1) the fuel clearance (both vegetation and 

structure) needed for mitigating large fire threats of high radiant flux/flame 

impingements on structures and (2) the mitigation of firebrand threat (from 

both woodland and neighborhood) to an uninvolved structure with several 

different and economical fire resistive claddings. Thus far we show how the 

use of data from the bench scale Cone Calorimeter and of various flame travel 

tests such as LIFT, Room-Corner test, Radiant Panel, and so on can be used 

in analytically based fire growth models adaptable to changing conditions. 

We believe that by providing a fire hazard tool based on fire growth algorithms associated with ornamental vegetations and fire resistive exteriors in 

changing environments as proposed here, that the client will be able to find 

an optimum and economical fire-safe construction and landscaping.

0

100

200

300

400

500

600

0 200 400 600

Test Time (s)

Heat Release Rate (kW)

OSB Walls

Ignition Burner

Upward Spread at

0 kW/m^2

Upward Spread at

40 kW/m^2

Wall Ignition ->

Upward Spread

Lateral Spread

Downward Spread

Flashover

Figure 9—Analytical prediction and Room-Corner test for OSB on the walls that varies 

the external radiant flux from 0 to 40 kW/m^2.

Ignition and Flame Travel on Realistic Building and Landscape Objects in Changing Environments Dietenberger
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 109

References

Dietenberger, Mark A. 1991. Piloted Ignition and Flame Spread on Composite Solid 

Fuels in Extreme Environments. Ph.D. Dissertation. Dayton, OH: University of 

Dayton.

Dietenberger, Mark A. 1994. Protocol for Ignitability, Lateral Flame Spread, and 

Heat Release Rate Using Lift Apparatus. In: Nelson, Gordon L. ed. Fire and 

polymers II. Materials and tests for hazard prevention: Proceedings of 208th 

National meeting of the American Chemical Society. August 1994. http://www.

fpl.fs.fed.us/documnts/pdf1995/diete95c.pdf

Dietenberger, Mark A. 2006a. Analytical Modeling of Fire Growth on FireResistive Wood-based Materials with Changing Conditions. In: 17th Annual 

BCC Conference on Flame Retardancy, May, 2006, Stamford, CT. Norwalk, 

CT: Business Communications Co.

Dietenberger, M.A. 2006b. Using a Quasi-Heat-Pulse Method to Determine Heat 

and Moisture Transfer Properties for Porous Orthotropic Wood Products. Journal 

of Thermal Analysis and Calorimetry Vol. 83, No. 1: 97-106.

Dietenberger, M.A.; Grexa, O. 1996. Analytical Model of Flame Spread in Full-Scale 

Room/Corner Tests (ISO9705), 6th Int. Fire & Materials Conf., San Antonio, 

TX, Feb. 22-23, Interscience Comm. Ltd.: 211-222.

Karlsson, Bjorn. 1993. A Mathematical Model for Calculating Heat Release Rate 

in the Room Corner Test. Fire Safety Journal.Vol. 20: 93-113.

Manzello, S.L.; Cleary, T.G.; Shields, J.R.; Yang, J.C. 2006. On the Ignition of 

Fuel Beds by Firebrands. Fire and Materials Vol. 30: 77-87.

Ignition and Flame Travel on Realistic Building and Landscape Objects in Changing Environments Dietenberger
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 110

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Ignition and Flame Travel on Realistic Building and Landscape Objects in Changing Environments Dietenberger
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 111

Abstract—The purpose of the present work is to quantify parametric uncertainty in 

Rothermel’s wildland fire spread model (implemented in software such as BehavePlus3 and FARSITE), which is undoubtedly among the most widely used fire spread 

models in the United States. This model consists of a nonlinear system of equations 

that relates environmental variables (input parameter groups) such as fuel type, fuel 

moisture, terrain, and wind to describe the fire environment. This model predicts 

important fire quantities (output parameters) such as the head rate of spread, spread 

direction, effective wind speed, and fireline intensity. The proposed method, which 

we call sensitivity derivative enhanced sampling (SDES), exploits sensitivity derivative information to accelerate the convergence of the classical Monte Carlo method. 

Coupled with traditional variance reduction procedures, it offers up to two orders of 

magnitude acceleration in convergence, which implies that two orders of magnitude 

fewer samples are required for a given level of accuracy. Thus, it provides an efficient 

method to quantify the impact of input uncertainties on the output parameters.

Introduction

One of the primary goals of wildland fire management is to minimize 

the negative impact of fire on property and society through prevention and 

research. To meet this goal, fire researchers employ a variety of tools such as 

satellite imagery, experiments, fire danger indices, as well as mathematical 

models. A mathematical model typically consists of a set of nonlinear equations 

that describe the interaction of the various environmental variables. These 

equations can be used to predict valuable fire environment information such 

as the head rate of spread and spread direction.

Fire models typically fall into one or more of the following categories: 

physics-based, derived empirically, or constructed from statistical considerations. A fire model that is physics-based uses physical principles such as 

conservation of mass and energy to derive a formula for the rate of spread and 

other quantities of interest (see Weber 2001 and the references therein for an 

in-depth discussion). It is also possible to use a statistical description of test 

fires to predict fire behavior occurring under similar conditions. The McArthur models (McArthur 1966) used for grassland and forest fires in Australia 

are one such example. Finally, laboratory experiments can be performed to 

empirically determine quantities such as the propagating flux, which can, 

in turn, be used to obtain an expression for the rate of spread. Rothermel’s 

model (Rothermel 1972), a fire spread model that spans the physical and 

empirical classes, is perhaps the best known model in the United States, and 

although more recent models include a wider range of fire phenomena, it is 

still in wide use today.

Uncertainty Quantification in Rothermel’s 

Model Using an Efficient Sampling Method

Edwin Jimenez1, M. Yousuff Hussaini1, and Scott L. Goodrick2

In: Butler, Bret W.; Cook, Wayne, 

comps. 2007. The fire environment—

innovations, management, and policy; 

conference proceedings. 26-30 March 

2007; Destin, FL. Proceedings 

RMRS-P-46CD. Fort Collins, CO: 

U.S. Department of Agriculture, 

Forest Service, Rocky Mountain 

Research Station. 662 p. CD-ROM.

1 School of Computational Science, 

Florida State University, Tallahassee, 

FL. ejimenez@scs.fsu.edu.

2 Research Meteorologist, U.S. 

Department of Agriculture, Forest 

Service, Center for Forest Disturbance 

Science, Athens, GA.
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 112

Uncertainty Quantification in Rothermel’s Model Using an Efficient Sampling Method Jimenez, Hussaini, and Goodrick

Not all fire models, however, can be employed as efficient fire prediction 

tools. Some of the more complex models, which couple atmospheric and fire 

behavior effects, for instance, are currently too computationally expensive 

to serve as viable real-time prediction tools. These complex models, nevertheless, assist researchers in gaining a more profound understanding of fire 

behavior.

Rothermel’s Fire Spread Model

Rothermel’s wildland fire spread model was one of the first models to 

describe the fire environment through equations derived, for the most part, 

from thermodynamic principles. The fire environment describes the complex 

chemical and physical interaction of fuels, terrain, and weather. The term fire 

behavior is used to describe the physical characteristics of a fire such as its rate 

of spread, fireline intensity, flame length, and so forth. In North America, 

where forests and grasslands provide an abundant source of wildland fuel, 

wildland fires are of particular interest. Wildland fuels are fuels that consist 

primarily of vegetation, both live and dead, but may also include organic 

layers within the soil. A surface fire, the type of fire Rothermel’s model was 

developed for, spreads through a layer of contiguous fuel extending from the 

ground up to approximately 2 m.

Rothermel’s model groups input parameters into four main categories: fuel 

type, fuel moisture, topography, and wind. Some simplifying assumptions 

regarding the fuels are that for a small area and short-time periods, the fuel 

is taken to be homogeneous. The output variables we shall consider are the 

rate of spread (ros in m/s), the direction of maximum spread (sdr in °), and 

the effective wind speed (efw in m/s).

Parametric Uncertainty in Fire Spread Models

To properly use a fire model, it is essential to understand its limitations and 

scope of applicability. However, even when fire models are used adequately, 

discrepancies between the observed phenomena and model results are inevitable 

as models are derived under idealized conditions. Model errors can result from 

several factors including inadequate physical description, numerical errors, and 

parametric uncertainty (Walters and Huyse 2002). In this work we concentrate 

our efforts solely on those errors originating from parametric uncertainty. In 

order to quantify the impact of parametric uncertainty, it is important to describe the uncertainty mathematically. Because the value of an input parameter 

is seldom known exactly, a common approach (and the one we will pursue) is 

to assign it a mean value and an associated probability density function. The 

standard deviation can then be taken as a measure of the uncertainty in the 

parameter value. The impact of parametric uncertainty on the results can then 

be estimated using, for instance, a Monte Carlo simulation.

Sometimes the uncertainty associated with an input parameter is not only 

a consequence of the intrinsic complexity of the phenomena being modeled. 

Instead, uncertainties may be the inevitable byproducts of economic and 

efficiency constraints. For example, it may be expensive or intractable to 

measure fuel data directly as in the case of a large area.

On the other hand, it is not always necessary to concern ourselves with 

the uncertainty associated with every single parameter. A sensitivity analysis 

(Saltelli and others 2004) can help us identify the input parameters that 

have the greatest influence on output variables. Those parameters that have 

only a marginal impact on the quantities of interest can be assigned constant 

characteristic values to reduce computational demands.
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 113

Uncertainty Quantification in Rothermel’s Model Using an Efficient Sampling Method Jimenez, Hussaini, and Goodrick

Problem Formulation

We will focus on quantifying the impact and propagation of parametric 

uncertainty only on the following output variables: the rate of spread (ros in 

m/s), the direction of maximum spread (sdr in °), and the effective wind speed

(efw in m/s). Using the same notation as in Bachmann (2001) (the full set 

of equations as well as their derivatives can also be found there), Rothermel’s 

steady-state rate of spread is given by

ros

I

Q

R w s

b ig

 X  

R E

( ), 1 & &

(1)

where IR is the reaction intensity, ξ is the propagating flux ratio, ρb is the 

ovendry bulk density, ε is the effective heating number, Qig is the heat of preignition. Φw and Φs are the wind and slope correction factors, respectively. 

The output variables depend on the following parameters: fuel loading w0d1, 

w0d2,w0d3, w0lh, w0lw, (in kg/m2), surface-area-to-volume ratio svd1, svd2,svd3,

svlh, svlw, (in 1/m), fuel moisture content md1, md2, md3, mlh, mlw, (in %), fuel 

bed depth d (in m), wind speed wsp (in m/s), wind direction θ (in °), aspect 

ratio asp (in °), and slope slp (in rad). The subscripts d1, d2, d3, lh, lw, denote 

the size classes traditionally used to categorize the different fuel moisture 

timelag classes (see Deeming and others 1978): dead fuel 0 – 0.6 cm, dead 

fuel 0.6 – 2.5 cm, dead fuel 2.5 – 7.5 cm, live herbaceous fuel, and live woody 

fuel, respectively. Throughout this paper we assume that those parameters 

that are not held constant follow a normal distribution with a given mean 

and standard deviation.

The primary focus of this work is to quantify the propagation and impact 

of parametric uncertainty via an efficient Monte Carlo method. The Monte 

Carlo convergence rate is accelerated through the use of a sensitivity derivative enhanced sampling method that exploits derivative information of the 

output functions with respect to the input parameters to make more judicious use of the samples generated in a simulation. We estimate the mean and 

standard deviation of the output variables using a traditional Monte Carlo 

method as well as with the sensitivity derivative enhanced sampling method 

(SDES). We compare the advantages of SDES over the Monte Carlo method 

via improvement ratios and timing performance. The distributions of output 

variables will also be generated. It should be noted that the computation of 

the required sensitivity derivatives accounts for only a fraction of the total 

cost of a simulation; these can be easily extracted using, for instance, an 

automatic differentiation package.

Numerical Method

To investigate the propagation and impact of input variable uncertainties, 

Bachmann and Allgower (2002) used a first-order Taylor method in place of 

a full-fledged Monte Carlo simulation to avoid the prohibitive computational 

expense incurred through a direct application of the classical Monte Carlo 

method. Indeed, because of its slow convergence rate and the costly generation 

of correlated input variables in the multivariate case, Monte Carlo methods 

are usually reserved to establish a reference against which other methods are 
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 114

Uncertainty Quantification in Rothermel’s Model Using an Efficient Sampling Method Jimenez, Hussaini, and Goodrick

compared. However, by identifying and generating stochastic versions of 

only those parameters to which the output variables are most sensitive and 

at the same time improving the convergence rate of traditional Monte Carlo 

methods, it is possible to perform simulations utilizing the original model 

to capture the more intricate behavior that a low-order approximation might 

otherwise sacrifice. The modified Monte Carlo method we describe below 

is a step toward this goal.

Although Monte Carlo methods have long been popular because they are 

simple to implement and use the underlying model as a ``black box,’’ their slow 

convergence rate often proves to be too inefficient especially when multiple 

simulations are required. A common approach to improve the convergence rate, 

which is known to be proportional to the variance of the objective function, is 

to reduce the variance via a suitable reformulation of the problem. Variants of 

this approach encompass a large class of methods collectively known as variance-reduction methods. The SDES method, which we describe below, is a 

variance-reduction method that has already been employed with success in fields 

such as optimal control (Cao and others 2003, 2004) and computational fluid 

dynamics (Mathelin and others 2004). In this section we review the theory 

underlying the method; our discussion closely follows Cao and others (2004, 

2006). The methods we shall employ are described in their proper mathematical setting, but for the reader who is unfamiliar with some of the concepts 

discussed below, the textbooks by Ross (1997) and Shiryayev (1984) should 

elucidate some of the mathematical details that are omitted.

Monte Carlo Method

Suppose X is a random variable with finite expectation and let p x x ( )( ) ∈R

be an associated probability density function (pdf). If f : R R → is a smooth 

function of X , we recall that the expectation Ef X( ) of f is defined by

Ef X f x p x dx ( ) := ( ) ( ) , ∫ (2)

where the integration is taken over the support of the pdf. The variance 

Vf X( ) is defined by

2 Vf X E f X Ef X ( ) := ( ( ) ( )) . − (3)

For brevity, we will sometimes write μx and 2 σx for the expectation and 

variance of the random variable X , respectively.

In the classical Monte Carlo method, we estimate Ef X( ) by

=1

1 ( ) ( ).

N

i

i

Ef X f x N ≈ ∑ (4)

The N samples 1, , N x x K are generated according to the probability density 

of X. The convergence of this estimate to Ef X( ) as N → ∞ is guaranteed 

by the large number theorem (Shiryayev 1984; Ross 1997).

It is well-known that the approximation error made using (4) is proportional 

to Vf X( )

N . For computationally intensive problems this slow convergence 

might render the Monte Carlo approximation impractical. In the results 

section, the extent to which the sensitivity derivative Monte Carlo method 

alleviates this slow convergence will be shown.
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Uncertainty Quantification in Rothermel’s Model Using an Efficient Sampling Method Jimenez, Hussaini, and Goodrick

Sensitivity Derivative Enhanced Sampling

Recently, Cao and others (2003, 2004) developed a variance-reduction 

method that exploits information regarding the sensitivity of f with respect 

to the random variable X (measured via derivatives of f with respect to X ) 

to speed up the convergence of the Monte Carlo method. The result of their 

efforts was the sensitivity derivative enhanced sampling Monte Carlo method 

(SDES). The first-order SDES method is described below.

Given the first-order Taylor expansion of f about μx

1( ) := ( ) ( )( ), x x x J x f f x μ μ μ + − ′ (5)

and using

( ) = 1 and ( ) ( ) = 0, x p x dx x p x dx μ− ∫ ∫

it is clear that

1 ( ( ) ( )) ( ) = ( ) ( ) ( ). x f x J x p x dx f x p x dx f − − μ ∫ ∫

Upon rearranging for Ef X( ), this suggests the sensitivity derivative Monte 

Carlo approximation of the expectation of f

1

=1

1 ( ) ( ) ( ( ) ( )).

N

x i i

i

Ef X f f x J x N μ≈ + ∑ − (6)

The N samples are again generated according to the pdf of X.

The following inequalities illustrate the extent to which the variance of 

(6) is reduced. Let

1 = ( ) and = ( ) . max 2 max s s

m f s m f s ∈ ∈

′ ′′ R R

Then

2

1

2

2 2 4

1

( ) 2 ( )

( ) ( ( ) (( ) )). 2 x

Vf X m V X

m V f J V X E X μ

≤

− ≤ + −

 (7) (8)

Where (7) and (8) indicate that the SDES method is most efficient when 

V X( ) is small (V(X) << 1). See Cao and others (2006) for a rigorous proof 

of these results as well as a generalization to the th n -order SDES method.

SDES for Rothermel’s Model

Although in this article we concentrate our efforts on Rothermel’s model, 

we will state the mathematical model as a general nonlinear system of equations. The SDES method is applicable to any fire behavior model satisfying 

the appropriate smoothness assumptions.

Let the vector X X X = ( , , ) 1 K m represent the ensemble of input parameters 

that comprise the local fire environment and suppose y f X = ( ) is a function 

of the random variable vector X . Here y may represent the effective wind 

speed efw, the maximum rate of spread ros, or the spread direction sdr . 

The vector X is composed of the fuel type, fuel moisture, terrain, and wind 

parameters. We shall denote the expectation of the parameter vector X by 

1 = ( , , ) x x xm μ μ μ K and the covariance of X by Σ. In this case the secondorder SDES method is given by

2

2

=1

1 1 ( ) ( ( ) ( )) ( ) trace( ( ) ), 2

N

i i x x

i

Ef X f x J x f f N ≈ ∑ − + + ∇ Σ μ μ (9)
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 116

Uncertainty Quantification in Rothermel’s Model Using an Efficient Sampling Method Jimenez, Hussaini, and Goodrick

where

2

2

1 ( ) = ( ) ( )( ) ( ) ( )( ). 2

T

x x x x x x J x f f x x f x μ μ μ μ μ μ + ∇ − + − ∇ −

Here ∇f and 2 ∇ f denote the gradient and Hessian of f , respectively.

To further improve the efficiency of the present sampling method, we 

couple SDES with stratified sampling. The standard stratified sampling 

technique is discussed in Cao and others (2004), for example.

Results and Discussion

To compare the efficiency of SDES with traditional Monte Carlo methods, 

we compute the rate of spread ros, effective wind speed efw, and spread 

direction sdr using two of the original fuel models found in Rothermel 

(1972): the short grass and chaparral fuel models. The main fuel parameters 

are summarized in tables 1 and 2. The following parameters are held constant 

throughout for both fuel models: the dead fuel moisture at 8 percent, the 

live fuel moisture at 150 percent, and the low heat content at 18622 kJ/kg. 

We shall also examine the additional speed-up obtained by coupling SDES 

with standard stratified sampling.

The uncertainty associated with an input parameter is described by assigning it a normal distribution with a typical mean (taken to be the value given 

in the original model) and a corresponding standard deviation (typically 

between 10 and 50 percent of the mean value). Two types of computations 

will be performed for each fuel model. First, we take the fuel bed depth d

and the 1-h surface area/volume ratio d1 sv to be normally distributed random 

variables; all other parameters are fixed. Then, we include a random wind 

speed wsp and wind direction θ in addition to d and 1. d sv

To measure relative errors and improvement ratios of Monte Carlo approximations versus SDES, we use the L2 -norm (Euclidean norm). Recall 

that the L2 -norm is defined for a vector = ( , , ) 1 n x x x K as 

2 2 1/2

2 = ( ) 1 n x x x + + L . 

Table 1—Chaparral fuel model parameters. 

	 Parameter	 Symbol μ σ Units

1-h fuel load 1 0w d 1.12 -- 2 kg/m

10-h fuel load 2 0w d 0.90 -- 2 kg/m

100-h fuel load 3 0w d 0.45 -- 2 kg/m

Live herbaceous fuel load 0w lh 0.0 -- 2 kg/m

Live woody fuel load 0w lw 1.12 -- 2 kg/m

1-h surface area/vol. ratio d1 sv 6562 740 2 3 m /m

Live herb surface area/vol. ratio lh sv 4921 -- 2 3 m /m

Live woody surface area/vol. ratio lw sv 4921 -- 2 3 m /m

Dead fuel moisture of extinction mx 20 -- %

Fuel bed depth d 1.83 0.3 m

Midflame windspeed wsp 2.3 0.5 m/s

Direction of wind vector (from upslope) θ 45 20 o
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 117

Uncertainty Quantification in Rothermel’s Model Using an Efficient Sampling Method Jimenez, Hussaini, and Goodrick

If = ( , , ) 1

MC MC MC E E E K M is a sequence of relative errors obtained from M

Monte Carlo simulations, then a measure of the average error is given by

1/2

2

2

=1

1 = [ ] .

M

MC MC

i

i

E E

M

      ∑

The L2 improvement ratio is then computed from

2

2

2

= .

MC

SDES

E

I

E

Suppose, for example, that we use a Monte Carlo and a first-order SDES 

simulation to approximate the rate of spread using 5,000 samples. If the improvement ratio is I2 = 10 , then the average relative error obtained from SDES 

is 10 times smaller than that obtained from a Monte Carlo method using the 

same number of samples. Put another way, if we use SDES we need only 500 

samples to achieve the same accuracy as the Monte Carlo method.

Table 3 shows that even with a simple first-order SDES method the convergence rate over a traditional Monte Carlo simulation can be as much as 20 

times faster. It is important to note that SDES might require the computation of several derivatives of the objective function. In our computations, an 

automatic differentiation package (see Stamatiadis and others 2000) was used 

to find the relevant derivatives. Table 4 illustrates that even when we couple 

SDES with stratified sampling (denoted by SSD1, for a first-order SDES with 

stratified sampling), the extra computational expense incurred is marginal.

Table 2—Short grass fuel model parameters.

	 Parameter	 Symbol μ σ Units

1-h fuel load 1 0w d 0.17 -- 2 kg/m

10-h fuel load 2 0w d 0.0 -- 2 kg/m

100-h fuel load 3 0w d 0.0 -- 2 kg/m

Live herbaceous fuel load 0w lh 0.0 -- 2 kg/m

Live woody fuel load 0w lw 0.01 -- 2 kg/m

1-h surface area/vol. ratio d1 sv 11483 1150 2 3 m /m

Live herb surface area/vol. ratio lh sv 4921 -- 2 3 m /m

Live woody surface area/vol. ratio lw sv 4921 -- 2 3 m /m

Dead fuel moisture of extinction mx 12 -- %

Fuel bed depth d 0.30 0.05 m

Midflame windspeed wsp 2.3 0.5 m/s

Direction of wind vector (from upslope) θ 45 20 o

Table 3—SDES error improvement ratios for first-moment estimates of the rate of spread using N = 512

and M = 100 different sets of samples.

Fuel model MC ratio (2vars) MC ratio (4vars) S = 4 ratio (2vars) S = 2 ratio (4vars)

Chaparral 29.8 5.1 11.4 3.4

Short grass 24.2 4.3 10.0 3.1
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 118

Uncertainty Quantification in Rothermel’s Model Using an Efficient Sampling Method Jimenez, Hussaini, and Goodrick

Figure 1 illustrates that although the input parameters follow a normal 

distribution (as described in tables 1 and 2), this is not the case with the 

output functions efw ros , , and sdr. This is to be expected as the output 

functions depend nonlinearly upon the random parameters. Using four 

random variables in the chaparral fuel model, the mean values are given by 

efw = 2.41 μ m/s, ros = 0.353 μ m/s, and sdr = 41.3 μ

o

. The corresponding 

standard deviations are 5 efw = 2.0325 10 σ

− ⋅ m/s, 4

ros = 2.8921 10 σ

− ⋅ m/s, 

and 4 sdr = 5.0674 10 σ

− ⋅ . The standard deviation provides us with a measure 

of the uncertainty in the outputs.

Table 4—Timing results (in seconds). Average computational time comparison 

of Monte Carlo versus first-order SDES coupled with stratified sampling 

(four strata).

N (Chap) MC (Chap) SSD1 (Shtgrs) MC (Shtgrs) SSD1

32 4.297E–02 4.437E–02 4.063E–02 4.344E–02

64 8.188E–02 8.250E–02 7.922E–02 8.250E–02

128 1.617E–01 1.656E–01 1.567E–01 1.566E–01

256 3.231E–01 3.295E–01 3.113E–01 3.158E–01

512 6.423E–01 6.255E–01 6.216E–01 6.262E–01

Figure 1—Output distributions of the effective wind speed, rate of spread, and spread direction 

using four random variables ( 1 , , , d d sv wsp and θ ). The mean values for the Chaparral fuel model 

are given by efwμ = 2.41 m/s, rosμ = 0.35 m/s, and sdrμ = 41.3°.

2.414 2.4141 2.4142

0

500

1000

Chaparral

Effective wind speed

0.352 0.353 0.354 0.355 0.356

0

100

200

300

Rate of spread

0.721 0.7211 0.7211 0.7211 0.7211 0.7211

0

200

400

600

Spread direction

2.3901 2.3901 2.3902 2.3903 2.3903

0

500

1000

1500

2000

Short grass

Effective wind speed

0.548 0.55 0.552

0

100

200

300

Rate of spread

0.7117 0.7118 0.7118 0.7118 0.7118 0.7118

0

200

400

600

800

Spread direction
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 119

Uncertainty Quantification in Rothermel’s Model Using an Efficient Sampling Method Jimenez, Hussaini, and Goodrick

Figures 2 and 3 show that the relative errors decay at the expected rate 

proportional to 1/ N , where N is the number of samples used. Throughout, 

we use M = 100 different sets of samples and then average the relative errors. 

We observe that Monte Carlo coupled with stratified sampling is as much as 

five times faster than the traditional Monte Carlo method. In all cases we 

observe that first-order SDES produces results that are up to two orders of 

magnitude more accurate (when coupled with stratified sampling) than plain 

Monte Carlo. With this convergence rate, it would take a Monte Carlo method 

as many as 100 times more samples to achieve comparable results.

Figure 2—(Chaparral fuel model) 

Average relative errors in first 

moment estimates for the rate of 

spread ros using (a) two random 

variables svd1 ~ N(6562,740) and 

d ~ N(1.83,0.3) and (b) four random 

variables s v d1 ~ N(6562,740),

d ~ N(1.83,0.3), wsp ~ N(2.3,0.5), and 

θ ~ N(45,20).

Figure 3—(Short grass fuel model) 

Average relative errors in first 

moment estimates for the rate of 

spread ros using (a) two random 

variables svd1 ~ N(11483,1150) and 

d ~ N(0.30,0.05) and (b) four random 

variables svd1 ~ N(11483,1150), 

d ~ N(0.30,0.05), wsp ~ N(2.3,0.5), and 

θ ~ N(45,20).

102 10−4

10−3

10−2

10−1

100

(a)

1/sqrt(N)

MC

SS2

SDES

SSD1

102 10−3

10−2

10−1

100

(b)

1/sqrt(N)

MC

SS4

SDES

SSD1

102

10−3

10−2

10−1

100

(a)

1/sqrt(N)

MC

SS2

SDES

SSD1

102

10−2

10−1

100

(b)

1/sqrt(N)

MC

SS4

SDES

SSD1
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 120

Uncertainty Quantification in Rothermel’s Model Using an Efficient Sampling Method Jimenez, Hussaini, and Goodrick

Conclusions

Although the simplicity of the Monte Carlo method makes it an attractive 

method to use in simulations that use fire spread models such as Rothermel’s 

model, its slow convergence can render its application infeasible especially in 

time-sensitive situations such as in the prediction of an ongoing fire. In this 

work we have demonstrated that the sensitivity derivative enhanced sampling 

method and its variants provide fire researchers an economic alternative to 

traditional Monte Carlo methods in quantifying parametric uncertainty. 

Quantifying the impact of parametric uncertainty is of utmost importance 

since key input parameters used by fire spread models are seldom known exactly. The speed-up of up to two orders of magnitude in the SDES gives fire 

managers the ability to effectively and efficiently run simulations in real-time 

using only minimal computational resources.

The results indicate that coupling SDES with stratified sampling can further accelerate the convergence rate. This suggests that coupling SDES with 

more sophisticated sampling techniques such as Latin hypercube sampling or 

orthogonal sampling, while not as easily implemented as stratified sampling, 

might improve the convergence rate even further. We will explore these possible enhancements in future investigations.

References

Bachmann, A. 2001. Collection of Derivatives of the Rothermel Model. 1-25.

Bachmann, A.; Allgower, B. 2002. Uncertainty propagation in wildland fire 

behaviour modeling. Int. J. Geographical Information Science. 16(2): 115-127.

Cao, Y.; Hussaini, M.Y.; and others. 2006. A variance reduction method based on 

sensitivity derivatives. Applied Numerical Mathematics. 56(6): 800-813.

Cao, Y.; Hussaini, M.Y.; and others. 2003. An efficient Monte Carlo method for 

optimal control problems with uncertainty. Computational Optimization and 

Applications. 26: 219-230.

Cao, Y.; Hussaini, M.Y.; and others. 2004. Exploitation of sensitivity derivatives for 

improving sampling methods. AIAA Journal. 42(4): 815-822.

Deeming, J.E.; Burgan, R.E.; and others. 1978. The National Fire Danger Rating 

System. Gen. Tech. Rep. INT-39. Ogden, UT: U.S. Department of Agriculture, 

Forest Service, Intermountain Research Station. 63 p.

Mathelin, L.; Hussaini, M.Y.; and others. 2004. Uncertainty propagation for a 

turbulent, compressible nozzle flow using stochastic methods. AIAA Journal. 

42(8): 1669-1676.

McArthur, A.G. 1966. Weather and grassland fire behaviour. A.C.T. Leaflet 100. 

Canberra: Department of National Development, Forestry and Timber Bureau, 

Forestry Research Institute. 23 p.

Ross, S. 1997. A First Course in Probability. New York: Prentice-Hall.

Rothermel, R.C. 1972. A mathematical model for predicting fire spread in wildland 

fuels. Research Paper INT-115. Ogden, UT: U.S. Department of Agriculture, 

Forest Service, Intermountain Forest and Range Experiment Station.

Saltelli, A.; Tarantola, S; and others. 2004. Sensitivity Analysis in Practice: A Guide 

to Assessing Scientific Models, New York: John Wiley & Sons, Ltd.

Shiryayev, A.N. 1984. Probability. Amsterdam: Springer-Verlag.
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 121

Uncertainty Quantification in Rothermel’s Model Using an Efficient Sampling Method Jimenez, Hussaini, and Goodrick

Stamatiadis, S.; Prosmiti, R.; and others. 2000. Auto_deriv: Tool for automatic 

differentiation of a FORTRAN code. Computer Physics Communications. 127: 

343-355.

Walters, R.W.; Huyse, L. 2002. ICASE Report No. 2002-1. Uncertainty analysis 

for fluid mechanics with applications.

Weber, R.O. 2001. Wildland Fire Spread Models in Forest Fires: Behavior and 

Ecological Effects. New York: Academic Press.
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 122

Uncertainty Quantification in Rothermel’s Model Using an Efficient Sampling Method Jimenez, Hussaini, and Goodrick

This 

page is 

blank 

on purpose
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 123

Abstract—A computational fluid dynamics (CFD) model and a mass-consistent model 

were used to simulate winds on simulated fire spread over a simple, low hill. The results suggest that the CFD wind field could significantly change simulated fire spread 

compared to traditional uniform winds. The CFD fire spread case may match reality 

better because the winds used in the fire simulation were more accurate.

Introduction

The influence of wind simulations from two microscale wind models on 

simulated fire spread over a simple, low hill was investigated. The models 

were a computational fluid dynamics (CFD) model and a mass-consistent 

model (Forthofer in prep.). The hill, called Askervein Hill, had previously 

been the site of a detailed field study of wind flow over isolated hills (Taylor 

and Teunissen 1983, 1985). The simulated winds were compared to these 

data. The mass-consistent model and the CFD model were able to accurately simulate the wind flow on the upwind side and top of the hill. On the 

downwind side of the hill, the CFD model showed lower wind speeds than 

the mass-consistent model. These lower speeds matched the measured data 

better. The simulated winds were then used in FARSITE (Finney 1998) 

simulations to identify how the different wind fields affected fire spread. 

For reference, the traditional method of using a spatially uniform wind field 

was also used in the fire spread simulations. The resulting fire progressions 

showed that the mass-consistent wind field produced fire spread similar to 

the uniform wind field case, but the CFD simulation was noticeably different. The uniform and mass-consistent wind based fire growth simulations 

did not show appreciable effects of reduced wind speed on the lee side of 

the hill. These results suggest that the CFD wind fields could significantly 

change simulated fire spread compared to traditional uniform winds. Also, 

the CFD fire spread case may match reality better because the winds used in 

the fire simulation were more accurate.

Discussion

Askervein Hill was the site of a large wind measurement field campaign 

in 1982 and 1983 (Taylor and Teunissen 1983, 1985). More than 50 wind 

measurement towers were placed in the hill area to characterize the surface 

Differences in Simulated Fire Spread Over 

Askervein Hill Using Two Advanced Wind 

Models and a Traditional Uniform Wind Field

Jason Forthofer1 and Bret Butler1

In: Butler, Bret W.; Cook, Wayne, 

comps. 2007. The fire environment—

innovations, management, and policy; 

conference proceedings. 26-30 March 

2007; Destin, FL. Proceedings 

RMRS-P-46CD. Fort Collins, CO: 

U.S. Department of Agriculture, 

Forest Service, Rocky Mountain 

Research Station. 662 p. CD-ROM.

1 Mechanical Engineer and Research 

Mechanical Engineer, respectively, 

U.S. Department of Agriculture, Forest 

Service, Rocky Mountain Research 

Station, Missoula, MT. Lead author 

at jaforthofer@fs.fed.us
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 124

Differences in Simulated Fire Spread Over Askervein Hill Using Two Advanced Wind Models and a Traditional Uniform Wind Field Forthofer and Butler

flow field. The hill was 116 m tall and surrounded by flat ground. Towers 

were placed along three lines running over the hill, as shown in figure 1. 

We have compared simulations from two types of microscale wind models, a 

CFD model and a mass-consistent model, to the measured winds to evaluate 

their ability to reproduce the flow field.

Comparisons of the simulated and measured winds are shown in figure 2 

for sensors placed along lines A, AA, and B. Both models predicted the flow 

on the upwind side of the hill and at the top of the hill well. The CFD model 

compared better on the lee side of the hill than the mass-consistent model. 

The noticeable overprediction here of the mass-consistent model is probably 

due to the model’s inadequate representation of momentum, which becomes 

important on the lee side of the hill.

With the accuracy of the simulated flow fields assessed, hypothetical fire 

spread over the hill was computed using FARSITE (Finney 1998) with the 

two simulated wind fields and a traditional spatially uniform wind field. 

Table 1 shows the settings used in FARSITE for the spread simulations. 

As seen in figure 3, the CFD fire progression was markedly different than 

the mass-consistent and uniform wind cases. It appears that the low wind 

speeds on the lee side of the hill had a significant impact on the simulated 

fire spread. These low speeds were reproduced by the CFD model, but not 

well by the mass-consistent model and not accounted for by the uniform 

wind field (of course).

Figure 1—Contour map of Askervein Hill showing locations of lines A, AA, 

and B. The wind measuring devices were placed along these lines. The 

contour interval is 5 m.
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 125

Differences in Simulated Fire Spread Over Askervein Hill Using Two Advanced Wind Models and a Traditional Uniform Wind Field Forthofer and Butler

Figure 2—Simulated wind speeds from the mass-consistent and CFD models 

compared with measured wind speeds along lines A, AA, and B over Askervein 

Hill. The reported winds are 10 m above the ground.

Table 1—Inputs for FARSITE fire spread simulations.

 Fuel model 2

 Canopy cover 0 percent

 Temperature 80 degrees F

 Relative humidity 20 percent

 1 hour fuel moisture 5 percent

 10 hour fuel moisture 6 percent

 100 hour fuel moisture 7 percent

 Live herbaceous fuel moisture 100 percent

 Live woody fuel moisture 100 percent

 Fire spread rate adjustments 1

 Time step 10 min

 Perimeter resolution 25 m

 Distance resolution 25 m

 Only surface fire, no spotting
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 126

Differences in Simulated Fire Spread Over Askervein Hill Using Two Advanced Wind Models and a Traditional Uniform Wind Field Forthofer and Butler

Figure 3—Comparison of three fire spread simulations for the Askervein Hill area using 

different wind fields. Dark lines denote the fire progression spaced 10 min. apart, light 

lines are the 5 m elevation contour lines. (Third simulation appears on the next page.)
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 127

Differences in Simulated Fire Spread Over Askervein Hill Using Two Advanced Wind Models and a Traditional Uniform Wind Field Forthofer and Butler

Conclusions

This study indicates that spatially varying wind flow occurring from terrain modification can have a large impact on simulated fire spread. Accurate 

simulations of surface influenced wind flow are improved when the simulation 

model includes both mass and momentum conservation. It appears that this 

may be true even in cases of relatively simple, gently sloping terrains such as 

Askervein Hill. Because wind often has such a large impact on the behavior of 

a spreading wildland fire, a significant increase in the accuracy of fire spread 

predictions might be obtained by incorporating a wind model such as the 

CFD model into fire behavior prediction systems.

References

Finney, M.A. 1998. FARSITE: Fire area simulator-model development and 

evaluation. Research Paper RMRS-RP-4. Ogden, UT: U.S. Department of 

Agriculture, Forest Service, Rocky Mountain Research Station.

Forthofer, J.M. In prep. Modeling wind in complex terrain for use in fire spread 

prediction. Fort Collins, CO: Colorado State University. (Expected completion 

2007)

Taylor, P.A.; Teunissen, H.W. 1983. Askervein ‘82: Report on the September/

October 1982 Experiment to study boundary layer flow over Askervein, South 

Uist. MSRB-83-8. Downsview, Ontario, CAN: Atmospheric Environment 

Service.

Taylor, P.A.; Teunissen, H.W. 1985. The Askervein Hill Project: Report on the 

September/October 1983 Main Field Experiment. MSRB-84-6. Downsview, 

Ontario, CAN: Atmospheric Environment Service.
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 128

Differences in Simulated Fire Spread Over Askervein Hill Using Two Advanced Wind Models and a Traditional Uniform Wind Field Forthofer and Butler

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page is 

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on purpose
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 129

Abstract—Thermal radiation emission from a simulated black flame surface to a fuel 

bed is analyzed by a ray-tracing technique, tracking emission from points along the 

flame to locations along the fuel bed while accounting for absorption by environmental 

water vapor in the intervening medium. The Spectral Line Weighted-sum-of-gray-gases 

approach was adopted for treating the spectral nature of the radiation. The flame and 

fuel bed for the simulations are modeled two-dimensionally with the flame being onetenth as long as the fuel bed. Flame heights of 1 and 10 m were explored, and the 

angle between the flame and the fuel bed was specified to be either 60 or 90 degrees. 

Simulated flame temperatures of 1000 K and 1500 K were investigated. The study 

reveals that water vapor at 100 percent humidity will reduce the incident radiation at 

the base of a 1000 K flame by 9 percent for a 1 m flame and 16 percent for a 10 m 

flame oriented normal to the fuel bed. Radiation from an angled flame (oriented at 

60 degrees from the fuel bed) experiences slightly less attenuation from water vapor 

than the 90-degee flame. Further, local attenuation of the hotter flame (1500 K) from 

environmental water vapor is higher than for the 1000 K flame. The relative effect of 

the water vapor attenuation is increased with distance from the flame base.

Introduction

It is understood that thermal radiation transfer plays a significant role in 

wildland fire spread (De Mestre and others 1989). High temperatures in 

gaseous and particulate products of combustion result in significant radiation transfer to the unburned fuel ahead of advancing flames. Attenuation 

of flame radiation can occur from smoke and combustion products that are 

entrained in the intervening air between flame and fuel. This mechanism 

for attenuation of flame radiation is a complex function of fuel, flame, wind, 

and other environmental conditions. It is also recognized that environmental 

water vapor may be a mechanism for attenuation of flame radiation. However, 

the magnitude of this influence is unknown. This paper explores the attenuation of radiation from flame to fuel bed by environmental water vapor in 

the intervening medium.

Literature Review

Wildland fire spread has been modeled using a variety of approaches. 

Some models attempt to conserve energy generally without separating convection from radiation heat transfer. Still others attempt to model radiation 

Influence of Radiation Absorption by 

Environmental Water Vapor on Radiation 

Transfer in Wildland Fires

David Frankman1, Brent W. Webb1, and Bret W. Butler2

In: Butler, Bret W.; Cook, Wayne, 

comps. 2007. The fire environment—

innovations, management, and policy; 

conference proceedings. 26-30 March 

2007; Destin, FL. Proceedings 

RMRS-P-46CD. Fort Collins, CO: 

U.S. Department of Agriculture, 

Forest Service, Rocky Mountain 

Research Station. 662 p. CD-ROM.

1 Lead author at Department of 

Mechanical Engineering, Brigham 

You ng Un iversit y, Provo, U T. 

dave.frankman@gmail.com

2 U.S. Department of Agriculture, 

Forest Service, Rocky Mountain 

Research Station, Fire Sciences 

Laboratory, Missoula, MT. 
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 130

and convection separately (Weber 1991). Those that do model the radiation 

transfer invoke varied approximations in the radiation submodels. Fons 

(1946) treats the radiation transfer in an approximate fashion by assuming 

that a fixed fraction of combustion energy comes from radiation. Emmons 

(1964) assumes radiation is attenuated with distance from the flame by an 

exponential relationship. Hottel and others (1965) were the first to model 

the flame shape, introducing radiation configuration factor relations that 

quantify the fraction of radiation emitted by one surface that is incident on 

another. One model considered radiation heat transfer from a planar flame to 

a planar fuel bed, and the other used a relation that accounted for radiation 

attenuation through the fuel bed using an exponential factor. Albini (1967) 

formulated an approach that employed the same radiation sub-model as Hottel 

and others, considering radiation as the dominant heat transfer mechanism. 

Thomas (1967) proposed that radiation from the flame was insignificant 

in comparison to radiation through the porous fuel bed. Other models 

were introduced using the same radiation configuration factor relations to 

track radiation transfer from flame to fuel until Cekirge (1978) introduced 

a formulation that could account for a circular flame front. Albini (1985) 

first introduced a solution to the Radiative Transfer Equation (RTE), the 

differential equation governing transport of radiative energy. The RTE was 

solved using the discrete ordinates method, but no volumetric effects in the 

fuel, flame, or the intervening medium were included.

To the authors’ knowledge, no prior study explores the effect of attenuation 

of radiation between flame and fuel bed by environmental water vapor. This 

paper investigates this mechanism for attenuation of flame radiation.

Model

Consider a planar flame of length H and characterized by uniform temperature Tf advancing along a horizontal fuel bed, as shown in figure 1. The 

length of the fuel bed L is specified arbitrarily in this study to be 10 times the 

flame length, L/H = 10. The angle between flame and fuel bed is ø. Simulations were conducted with and without environmental water vapor in the air 

separating the flame from the fuel bed. It is assumed that the intervening 

Figure 1 – Schematic illustration of configuration investigated.

Influence of Radiation Absorption by Environmental Water Vapor on Radiation Transfer in Wildland Fires Frankman, Webb, and Butler
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 131

medium (air/H2O vapor) and the fuel bed are at a uniform temperature of 

300 K. It should be clearly stated that attenuation by entrained combustion 

products (ash, H2O, CO2, soot, and so forth) is not considered here. Attenuation of flame radiation from such will dominate any effects by environmental 

water vapor. Rather, the purpose of this study is to characterize solely the 

effects of water vapor.

Absorption of radiation is governed by the Beer-Lambert Law (Modest 

2003), which states

( ) ( )0ss

oeIsI −− = κηη η (1)

Equation (1) indicates that the spectral radiation intensity of magnitude Iηo 

incident on a radiatively absorbing medium and propagating in a particular direction is attenuated exponentially with distance s from the point of incidence 

so along this direction by the factor e s s 
 
 o KH  	, where κη is the volumetric absorption coefficient (Modest 2003). As indicated by the subscript η, equation 

(1) is a spectral relation; the radiative intensity Iη, incident radiation intensity 

Iηo, and absorption coefficient κη vary with wavenumber (or wavelength). 

While significant absorption may occur in some regions of the spectrum, 

others are transparent to radiative transfer. For gases capable of volumetric 

absorption in the infrared spectrum, the spectral absorption coefficient is 

characterized by vibration-rotation bands. For water vapor, the principal absorption bands are centered at wavenumbers 3760 and 1600 cm–1 (wavelengths 

of 2.7 and 6.3 μm, respectively). Within each absorption band, thousands of 

narrow absorption lines exist, with strong absorption of radiation within each 

narrow line. Several hundred thousand absorption lines are associated with 

the two principal vibration-rotation bands for water vapor. The absorption 

strength of each line is a function of the temperature and partial pressure of 

water vapor. The challenges associated with rigorously modeling mathematically the attenuation of flame radiation by water vapor are significant. The 

spectral emission characteristics of the flame are a strong function of fuel 

type, humidity, water content, wind conditions, and so forth. Further, as 

described herein, absorption of flame radiation by the environmental water 

vapor occurs within the hundreds of thousands of extremely narrow spectral 

lines within both infrared vibration-rotation bands. In this study, the flame 

is characterized as a planar surface whose spectral emission of radiation can 

be taken to be that of a blackbody at the flame temperature Tf.

The radiative flux from black surface i incident on surface j may be expressed 

generally as (Modest 2003)

q e

s

i j dA

A A

s i j

i j

m

d


 

±

3

2

2

±

3

2

2

±

3

2

2

0

2

KH Q Q

P

cos cos

i j bi dA E d,H H (2)

Equation (2) is a triple integration over wavenumber (η), the area of the 

emitting surface (Ai), and the incident surface (Aj). The double integration 

over area is required to track the radiant emission from a location on surface 

i that arrives at all possible locations on surface j. This tracking must be 

done for all points of radiation emission on surface i. The geometric term 

cos cos Q Q P i j s

2

 accounts for the varying field of view for differential elements 

on surface i and surface j, separated by a distance s. Ebi,η is the spectral blackbody emission from surface i, described by the Planck blackbody radiation 

spectral distribution at the temperature of surface i. The exponential term s e κ− η in equation (2) accounts for absorption of radiation by the intervening 

Influence of Radiation Absorption by Environmental Water Vapor on Radiation Transfer in Wildland Fires Frankman, Webb, and Butler
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 132

medium of spectral absorption coefficient κη. Integrating over the spectrum 

as in the development of the classical weighted-sum-of-gray-gases model 

(Modest 2003), it can be shown that the radiative flux emitted by surface i

which is incident on surface j is

q s s E d i j i j bi m

d

  	 ±

3

2

H ,H H

0

(3)

where ( )ji η

ss is the spectral volumetric exchange factor, expressed as

s s e

s

i j dA dA

A A

s i j

i

i j

 	 

±

3

2

2

±

3

2

2




H

KH Q Q

P

cos cos

2 j (4)

The dependence of both the volumetric exchange factor defined in equation (4), and the total radiant flux, equation (3), on wavenumber is evident. 

Because of the complex spectral variation in the absorption coefficient of 

water vapor, the Spectral Line Weighted-sum-of-gray-gases (SLW) model 

(Denison and Webb 1993a,b) was used to account for its spectral absorption 

characteristics. The SLW model has been shown to yield accuracy approaching that of computationally intensive line-by-line integrations for a small 

fraction of the computational cost. Rather than calculate the absorption of 

flame radiation by integrating on a line-by-line basis over the hundreds of 

thousands of lines using the Beer-Lambert Law, the SLW model specifies 

several discrete values of the absorption coefficient κk (called gray gas absorption coefficients), determines the total radiation source spectral content 

corresponding to each discrete value of κk (characterized by gray gas weights 

wk), and sums (or integrates) the total radiation from a blackbody source over 

the total number K of discrete gray gas absorption coefficients specified. 

The integration over a few carefully chosen values of the gray gas absorption 

coefficient thus takes the place of spectral integration over wavelength in the 

traditional line-by-line method.

The integration of equation (4) is used to evaluate the radiant flux from 

one finite area Ai to another Aj. As shown in figure 2, øi and øj are the angles 

between the normal vector to each surface and the line joining the differential elements on surfaces i and j, respectively, and s is the distance between 

the two endpoints of the joining line. In order to determine the variation 

of local incident flux along the fuel bed, the fuel was divided into small but 

finite spatial strips running parallel to the shared flame/fuel edge, as shown 

in figure 2. The incident flux on the fuel was then calculated according to 

equation (4) for each strip.

Invoking the Spectral Line Weighted-sum-of-gray-gases model, the total 

(spectrally integrated) radiative flux leaving the planar flame surface and arriving at an arbitrary spatial strip j along the fuel bed may be shown to be 

the sum of radiative contributions from all gray gases:

q E s s w flame fuel j b flame flame fuel j k k

k

K

m , ,  ¤ 	 , (5)

where qflame→fuel,j is the total (spectrally integrated) incident radiation flux 

emitted by the flame that arrives at (is incident on) strip j along the fuel bed, 

Eb,flame is the total blackbody radiation flux emitted by the flame at temperature Tf, s s flame fuel j k  	 , is the volumetric exchange factor for radiant transfer 

from the flame to spatial strip j corresponding to gray gas coefficient κk, and 

wk is the gray gas weight associated with each gray gas absorption coefficient 

κk. Once the discrete gray gas absorption coefficients κk are specified, the 

corresponding gray gas weights wk are determined by evaluating the area 

Influence of Radiation Absorption by Environmental Water Vapor on Radiation Transfer in Wildland Fires Frankman, Webb, and Butler
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 133

under the Planck spectral blackbody radiation distribution Eb,flame,η where the 

gray gas absorption coefficient prevails for the gas specie in question (water 

vapor in this case) (Denison and Webb 1993a). The gray gas weights sum 

to unity, wk

k

K

¤  1. Increasing the number of gray gases employed improves 

the accuracy of the predictions. In this study, K = 20 gray gases were used 

in equation (5). Further increases in the number of gray gases yielded no 

appreciable change in the solution.

Because the closed-form integration of equation (4) to evaluate the volumetric exchange factor for each gray gas, s s flame fuel j k  	 , , is generally not possible 

for general cases involving radiation attenuation in the intervening medium, 

the expression was evaluated using numerical integration. Referring again to 

figure 2, the approach followed here tracks radiation emitted from specified 

locations along the planar flame to destination strip j along the fuel bed. A 

reduction of two-dimensional flame and fuel surfaces (shown in fig. 2) to 

the one-dimensional variation in incident flux along the fuel suggested in 

figure 1 is not forthcoming in the integration of equation (4). Consequently, 

two-dimensional flame and fuel bed surfaces were used in the numerical integration as shown. The lateral extent of the flame and fuel surfaces as then 

sequentially increased in exploratory simulations to determine the dimension 

large enough to yield the one-dimensional variation in radiative flux along 

the fuel bed illustrated in figure 1. The lateral dimension required to achieve 

predictions independent of end effects was 20 times the flame height for 

all configurations. The surfaces of both flame and fuel were discretized in 

two directions for numerical evaluation of the integrals in equation (4). The 

multiple spatial strips along the fuel bed were clustered near the base of the 

flame in order to accurately resolve the steep gradient of incident radiant flux 

with position along the fuel. Each strip j was subsequently discretized into 

smaller differential elements used to evaluate the integrals in equation (4). The 

numerical integration procedure sweeps through differential area elements 

Figure 2—Detailed illustration of geometry and nomenclature for 

determination of volumetric exchange factor and local incident flux.

Influence of Radiation Absorption by Environmental Water Vapor on Radiation Transfer in Wildland Fires Frankman, Webb, and Butler
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 134

on the fuel bed and on each strip of the flame, evaluating the local angles θi

and θj for each of the elements and the corresponding distance s separating 

the two differential elements in question. The solution thus determines the 

fraction of radiation emitted by a given element on the flame that is incident 

on its destination element along the fuel after attenuation by the water vapor 

in the intervening medium. This accounting must be done for all elements 

on the flame. The double integration must be performed for the volumetric 

exchange factor corresponding to each gray gas. Thereafter, the summation 

over all K gray gases in equation (4) is carried out to determine the total 

radiative flux.

The volumetric exchange factor s s flame fuel j k  	 , presented above is identical to 

the classical configuration factor Fi,j (used for predicting radiative exchange 

between diffuse surfaces in the absence of a radiatively participating intervening medium), with the exception of the exponential absorption term, s k e κ− . 

Analytical expressions for the radiative configuration factor Fi,j for commonly 

encountered surface-to-surface exchange configurations are tabulated in the 

literature (Siegel and Howell 2002). The accuracy of the evaluation of the 

volumetric exchange factor can therefore be verified by comparing the numerical integration of equation (4) for the transparent intervening medium case 

(κk → 0) to published analytical expressions for the radiation configuration 

factor between surfaces with adjoining edges and included angle ø. Figure 3a 

illustrates the error between the numerically evaluated volumetric exchange 

factor s s flame fuel j k  	 , for κk → 0 and the corresponding classical radiation configuration factor Fi,j (Siegel and Howell 2002) for radiative transport from the 

entire flame surface to a 10-cm long element at the base of the fuel bed as a 

function of number of points used in the numerical integration. Both φ = 60 

and 90 degree flame configurations for a flame length H = 1 m are shown. 

Radiative exchange between the flame and the small segment at the base was 

selected for this validation exercise because it is the most rigorous accuracy 

Figure 3—(a) Error between the volumetric exchange factor for κk → 0 

(for K = 1) and the corresponding classical radiation configuration factor, 

and (b) variation in calculated volumetric exchange factor with number of 

integration points.

Influence of Radiation Absorption by Environmental Water Vapor on Radiation Transfer in Wildland Fires Frankman, Webb, and Butler
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 135

test of the numerical integration. It is for this configuration that the numerical evaluation of the exchange and configuration factors incurs the greatest 

error. As expected, figure 3a shows that the error decreases as the number of 

integration points increases. The difference between numerical integration and 

analytical result is less than 1 percent for a surface discretization employing 

more than 1011 integration points for both flame angle configurations. This 

favorable comparison for the limiting case κk → 0 demonstrates the accuracy 

of the numerical integration of equation (4), if only for the transparent intervening medium case.

While the data of figure 3a constitute evidence that the numerical integration is accurate for the transparent-medium scenario, there is still some 

uncertainty that with the inclusion of the transmission term sk e κ− , the solution may not be convergent. Therefore, a study was undertaken to demonstrate 

that a convergent solution, independent of the number of points in the numerical integration, was achieved for the case of finite attenuation (κk ≠ 0). 

Figure 3b illustrates the variation in calculated volumetric exchange factor 

s s flame fuel j k  	 , between the flame and a fuel bed surface strip near the base of 

the flame for a single gray gas (K = 1) of gray gas absorption coefficient κ1 = 

5 m–1, with the number of points in the numerical integration for both the 

normally oriented and angled flame configurations. This value of the gray 

gas absorption coefficient used in the exercise is in the mid-range of the coefficients used in the simulations reported hereafter in the results. The figure 

reveals that the value of s s flame fuel j k  	 , returned by the numerical integration 

is unchanging beyond 1010 integration points for both flame angles studied. 

Further, to ensure that this choice produced a solution free of round-off error 

in addition to a solution independent of the number of integration points, all 

variables associated with the numerical integration were increased in computer 

precision to confirm that the answer remained the same. It was found that 

increasing the computer precision (to the so-called “long double” precision) 

yielded no change in eight significant figures over the double precision results 

used in all predictions shown hereafter. The results suggest a convergent solution, lending confidence in the accuracy of the volumetric exchange factor 

for finite gray gas absorption coefficient.

Based on the results of the validation exercise illustrated in figure 3, 1011

integration points were used in all simulations reported here. The prediction 

of incident flux variation along the fuel bed for a given set of conditions was 

computationally intensive. The calculation of the volumetric exchange factor 

for each gray gas for all strips on the fuel bed required approximately 12 hours 

of computation time. This computation was performed for each of the 20 gray 

gases. In practice, s s flame fuel j k  	 , was evaluated for all gray gases simultaneously 

using parallel computing, after which the summation of equation (5) was 

performed to determine the local radiant flux incident on the fuel.

Results And Discussion

Simulations were conducted for two flame heights, H = 1 and 10 m, with 

corresponding fuel bed lengths of L = 10 and 100 m, respectively. Included 

flame angles φ of 60 and 90 degrees were explored. Two flame temperatures 

were investigated, Tf = 1000 and 1500 K, bounding the reasonable nominal 

range of flame temperatures in wildland fires (Butler and others 2004). All 

simulations were conducted with and without environmental water vapor in 

the intervening medium between flame and fuel bed. For those predictions 

Influence of Radiation Absorption by Environmental Water Vapor on Radiation Transfer in Wildland Fires Frankman, Webb, and Butler
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 136

including water vapor, a uniform mole fraction of 3.5 percent H2O was imposed, corresponding approximately to a relative humidity of 100 percent at 

a temperature of 300 K. This represents the upper bound on the influence 

of environmental water vapor on radiation attenuation in wildland fires.

Figure 4 illustrates the variation in predicted local incident radiant flux 

with position along the fuel bed for a 1 m long flame (H = 1 m) under normal flame conditions (φ = 90 deg) for a flame temperature Tf = 1000 K. Two 

cases are shown, with and without the effect of radiation attenuation by 3.5 

percent environmental water vapor. The trends for both cases are qualitatively 

similar. As expected, the highest flux incident on the fuel bed is at the base 

Figure 4—Predicted local incident radiant flux along the fuel bed 

for the cases with and without water vapor absorption, H = 1 m, L = 

10 m, φ = 90 deg, and Tf = 1000 K.

of the flame, where the flame is viewed most intensely by the fuel bed. The 

local radiant flux drops dramatically with increasing distance along the fuel 

bed. As expected, the influence of water vapor is to attenuate the radiation 

incident on the fuel bed. At the base of the flame where the radiant flux is 

the highest, the environmental water vapor reduces the flux incident on the 

fuel bed by approximately 9 percent. Also plotted in figure 4 is the difference 

in local incident radiant flux due to water vapor, expressed as a local percentage difference relative to the otherwise identical case with no absorption. 

The fractional influence of water vapor is lowest near the base of the flame 

(9 percent), and increases with increasing distance along the fuel bed. This 

may be explained by the increasing absorbing path length through which 

emission from the flame must pass for fuel bed locations farther from the 

flame base. It should be recognized that although the fractional influence 

is higher farther from the flame, the incident flux decreases rapidly in this 

direction. Thus, the incident flux is lowest in regions where the percentage 

influence is highest. The data of figure 4 are used as benchmark against which 

the parametric effect of varying flame temperature, flame height, and flame 

angle will be explored in sections to follow.

The effect of varying flame temperature is illustrated in figure 5 for the 1 m, 

normal flame configuration (φ = 90 deg). Predictions for two flame temperatures are presented, Tf = 1000 and 1500 K. As expected, the magnitude of 

Influence of Radiation Absorption by Environmental Water Vapor on Radiation Transfer in Wildland Fires Frankman, Webb, and Butler
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 137

Figure 5—Effect of flame temperature on local incident radiant flux along the 

fuel bed for H = 1 m, L = 10 m, and φ = 90 deg.

the incident flux is considerably higher for the hotter flame. The predictions 

reveal that the incident radiation at the base of the 1500 K flame is attenuated by water vapor an amount 13 percent relative to the case with no H2O, 

compared to 9 percent for the 1000 K flame. The greater influence of water 

vapor at higher Tf is due to the fact that the spectral emission from the hotter 

flame is concentrated more heavily in the spectral regions corresponding to the 

two primary infrared absorption bands of water vapor. It may be concluded 

that a hotter flame scenario results in greater attenuation by environmental 

water vapor than is experienced by a relatively cooler flame.

The effect of flame length is shown in figure 6, where local incident flux 

predictions for H = 1 and 10 m (with L = 10 and 100 m, respectively) are 

plotted for the normal flame configuration with Tf = 1000 K. The differences 

Figure 6—Effect of flame length on local incident radiant flux along the fuel 

bed for φ = 90 deg and Tf = 1000 K.

Influence of Radiation Absorption by Environmental Water Vapor on Radiation Transfer in Wildland Fires Frankman, Webb, and Butler
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 138

in flux magnitude near the base of the flame are not significant for the two 

flame length simulations. This is not surprising, since the region near the 

flame base “sees” little of the flame beyond the 1 m length, and is therefore 

exposed to nearly the same radiative environment in both cases. Farther 

from the flame base along the fuel bed, however, the longer flame yields 

higher incident flux magnitudes produced by the effect of the additional 

flame length for H = 10 m. It appears that attenuation of radiation by water 

vapor is more significant for the longer flame. This may be explained by the 

fact that radiation from the longer flame must, on average, traverse greater 

distance of absorbing medium before reaching the fuel bed.

Figure 7 illustrates the influence of flame angle on the incident radiant flux 

for the 1 m flame, Tf = 1000 K. Relative to the normal flame configuration 

(φ = 90 deg), the radiative flux is substantially higher for the angled flame. 

As the flame angle decreases from φ = 90 degees, it occupies a greater field of 

view for all locations along the fuel bed. In other words, the fuel bed “sees” 

the flame more prominently for angled flame conditions, resulting in high 

incident radiant flux. While not evident in the figure, the fractional decrease 

in incident flux due to absorption by water vapor is reduced for the angled 

flame. Whereas the reduction in incident flux at the base of the flame for the 

φ = 90 degee case was 9 percent, the flux is reduced there by only 5 percent 

for the φ = 60 degee case. Again, this may be explained by the fact that the 

average path length for radiation between flame and fuel bed is smaller for φ

< 90 degees. Consequently, there is less attenuation by water vapor.

The local influence of flame radiation absorption by water vapor for the 

cases presented in the foregoing sections is summarized in figure 8. As was 

done in figure 4, the effect of absorption by water vapor is expressed in figure 8 

as a local percentage difference relative to the otherwise identical case with 

no absorption. To facilitate the presentation of data for both flame lengths 

studied, the data are plotted as a function of normalized position along the 

fuel bed, x/L, where x is the coordinate along the fuel bed measured from 

the base of the flame. For all cases the fractional influence is lowest near the 

Figure 7—Effect of flame angle on local incident radiant flux along the fuel 

bed for H = 1 m, L = 10 m, and Tf = 1000 K.

Influence of Radiation Absorption by Environmental Water Vapor on Radiation Transfer in Wildland Fires Frankman, Webb, and Butler
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 139

base of the flame, and increases with increasing distance along the fuel bed. 

It should be recognized, however, that although the fractional influence is 

higher farther from the flame, figures 4 through 7 indicate that the local 

incident flux decreases rapidly in this direction. Thus, the flux is lowest in 

regions where the percentage influence is highest. It is again observed that 

the relative effect of water vapor absorption is greater for increasing flame 

temperature, flame length, and inclination angle. As outlined previously, 

this is explained by the greater average path-length through which the flame 

radiation must pass before arriving at the fuel bed for these scenarios.

The foregoing sections have presented the effect of absorption by environmental water vapor on the local radiative flux incident on the fuel bed. Of 

interest is the aggregate effect of radiation absorption by water vapor on the 

overall heat transfer to the fuel, found by summing the local incident flux 

spatially over the entire fuel bed area:

Qflame fuel flame fuel j j q A

j

m ¤ m , (6)

Here, Q → fuelflame is the total radiant heat transfer (as opposed to local heat 

flux) emitted by the flame that is incident on the entire fuel bed. The results of 

the calculation of equation (6) are found in table 1 for all of the cases explored, 

again expressed as a percentage difference relative to an otherwise identical 

case with no H2O absorption. The general trends illustrated by figure 8 are 

confirmed by the tabulated results. For the range of parameters investigated 

here, the attenuation of flame radiation by environmental water vapor affects 

the total heat transfer to the fuel bed by an amount ranging from 11.9 to 

26.7 percent. As observed and explained previously, the overall influence of 

environmental water vapor is less important for lower-temperature flames and 

for angled flames. While its effect is modest (but perhaps nonnegligible) for 

small flames, it can become quite significant for larger flames.

Figure 8—Difference in local incident radiant flux due to water vapor absorption 

as a function of normalized position along the fuel bed for all cases studied.

Influence of Radiation Absorption by Environmental Water Vapor on Radiation Transfer in Wildland Fires Frankman, Webb, and Butler
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 140

Conclusions

Predictions have been made for radiative transfer from black, isothermal, 

planar flame to a black fuel bed maintained at 300 K. The effect of flame 

inclination, flame temperature, and flame length were explored for cases with 

and without absorption by environmental water vapor in the intervening air 

(at 300 K). From these simulations one can see that, depending on the conditions, water vapor has a modest but nonnegligible effect on the radiative 

transfer from flame to fuel. The effect is more pronounced for larger flames 

at higher flame temperatures. The influence of water vapor on attenuation 

of radiation is reduced for angled flames.

References

Albini, F.A. 1967. A physical model for fire spread in brush. Pittsburg, PA: Eleventh 

Symposium (Int.) on Combustion, The Combustion Institute: 533-560.

Albini, F.A. 1985. A model for fire spread in wildland fuels by radiation. Combustion 

Science and Technology. 22:229-258.

Butler, B.W.; Cohen, J.; Lathan, D.J.; Schuette, R.D.; Sopko, P.; Shannon, K.S.; 

Jimenez, D.; Bradshaw, L.S. 2004. Measurements of radiant emissive power and 

temperatures in crown fires. Canadian Journal of Forest Research. 34:1577-

1587.

Catchpole, W.R.; Catchpole, E.A.; Tate, A.G.; Butler, B.W.; Rothermel, R.C. 2002. 

A model for the steady spread of fire through a homogeneous fuel bed. Forest 

Fire Research and Wildland Fire Safety. Rotterdam: Millpress. ISBN 90-77017-

72-0.

Cekirge, H.M. 1978. Propagation of fire fronts in forests. Computers and 

Mathematical Applications. 4:325-332.

De Mestre, N.J.; Catchpole, E.A.; Anderson, D.H.; Rothermel, R.C. 1989. 

Uniform propagation of a planar fire front without wind. Combustion Science 

and Technology. 65: 231-244.

Denison, M.K.; Webb, B.W. 1993a. A spectral line-based weighted-sum-of-gray-gases 

model for arbitrary RTE solvers. ASME Journal of Heat Transfer. 115:1004-

1012.

Denison, M.K.; Webb, B.W. 1993b. An absorption-line blackbody distribution 

function for efficient calculation of gas radiative transfer. Journal of Quantitative 

Spectroscopy and Radiative Transfer. 50:499-510.

Emmons, H. 1964. Fire in the forest. Fire Research Abs. Rev. 5:163-178

Fons, W.L. 1946. Analysis of fire spread in light forest fuels. Journal of Agricultural 

Research. 72:93-121.

Table 1—Overall reduction in total radiative heat transfer incident on the fuel bed due 

to absorption by environmental water vapor.

Flame length φ = 90 deg

Tf = 1000 K

φ = 90 deg

Tf = 1500 K

φ = 60 deg

Tf = 1000 K

H = 1 m (L = 10 m): 13.5 percent 16.7 percent 11.9 percent

H = 10 m (L = 100 m): 26.7 percent 25.1 percent

Influence of Radiation Absorption by Environmental Water Vapor on Radiation Transfer in Wildland Fires Frankman, Webb, and Butler
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 141

Hottel, H.C.; Williams, G.C.; Steward, R.R. 1965. The modeling of firespread 

through a fuel bed. Tenth Symposium (Int.) on Combustion. Pittsburg, PA: The 

Combustion Institute: 997-1007.

Modest, M.F. 2003. Radiative Heat Transfer, 2nd Edition. New York: Academic 

Press.

Siegel, R.; Howell, J.R. 2002. Thermal Radiation Heat Transfer, 4th Edition. New 

York: Taylor and Francis.

Thomas, P. H. 1967. Some aspects of the growth and spread of fire in the open. 

Forestry. 40:139-164

Weber, R.O. 1989. Analytical models for fire spread due to radiation. Combustion 

and Flame. 78:398-408.

Weber, R.O. 1991. Modelling fire spread through fuel beds. Progress in Energy and 

Combustion Science. 17:67-82.

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USDA Forest Service Proceedings RMRS-P-46CD. 2007. 142

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Abstract—The fire behavior modeling described here, conducted as part of the 

Biomass to Energy (B2E) life cycle assessment, is funded by the California Energy 

Commission to evaluate the potential net benefits associated with treating and utilizing forest biomass. The B2E project facilitates economic, environmental, energy, 

and effectiveness assessments of the potential public benefits associated with: (1) 

various options for treatment, disposition, and utilization of forest biomass and (2) 

energy production from biomass produced by forest remediation activities. The 

study models forest conditions, fire behavior and fuel changes over a 40-year period, under three fuel treatment scenarios: no treatment; harvest and thinning on 

industrial private lands; and a range of prescriptions on industrial private and public 

multiple use ownerships. Effects of three fuel treatment scenarios are evaluated on 

fuel treatment effectiveness, economic feasibility, energy production supported, 

ecosystem impacts, and the location and capacity of modeled biomass facilities. The 

B2E project is novel in its scale of analysis, modeling the landscape effects of fire and 

treatments on 2.7 million acres of forest and brushland in the northern Sierra Nevada. This landscape represents high-hazard fuel areas, a broad range of ownerships, 

diverse habitats, complex infrastructure, and other values at risk. With 50 percent 

public multiple use and 17 percent industrial private lands, this landscape provides 

a unique opportunity to evaluate the effectiveness of Strategically Placed Area Treatments (SPLATs) and compare them with industrial private thinning and harvest. With 

average pretreatment biomass levels of 79 bone-dry tons (bdts) per acre, the private 

treatments removed an average of 31 bdts/acre while SPLATs removed an average of 

24 bdts/acre. Wildfire modeling of these treatments showed a 6 percent reduction 

in the number of acres burned from private treatments and a 22 percent reduction 

from both private and SPLATs on public lands. While the ownerships, forest type, 

density, and slope dictated the type of treatment prescriptions, the spatial arrangement of treatments has a greater impact on their ability to change fire intensity and 

extent than the prescription applied.

Introduction

California’s wildlands and forests have accumulated an excess of small diameter woody material, or biomass. Fire suppression over the past century, 

combined with intensive forest management and a generally warmer and 

wetter climate, have led to increasingly dense vegetation. When wildfires 

occur, the heavy accumulation of biomass often makes those fires larger and 

more severe. The increase in forest biomass threatens public health and safety, 

watersheds, and wildlife habitat with unacceptable losses to wildfire. Public 

land management agencies and local landowners are focusing their efforts 

on thinning forests to reduce wildfire risks and to make them more resilient 

Fire Behavior Modeling to Assess Net 

Benefits of Forest Treatments on Fire Hazard 

Mitigation and Bioenergy Production in 

Northeastern California

David J. Ganz1, David S. Saah2, �Klaus Bar� 3, and Mark Nechodom4

In: Butler, Bret W.; Cook, Wayne, 

comps. 2007. The fire environment—

innovations, management, and policy; 

conference proceedings. 26-30 March 

2007; Destin, FL. Proceedings 

RMRS-P-46CD. Fort Collins, CO: 

U.S. Department of Agriculture, 

Forest Service, Rocky Mountain 

Research Station. 662 p. CD-ROM.

1 TSS Consultants, Oakland, CA. 

Dganz@tssconsultants.com

2 Landscape Ecologist, Spatial Informatics 

Group, LLC, San Leandro, CA.

3 Regional Analyst, U.S. Department of 

Agriculture, Forest Service, Region 5, 

Ecosystem Planning, McClellan, CA. 

4 Social Scientist, U.S. Department of 

Agriculture, Forest Service, Pacific 

Southwest Research Station, Davis, CA. 
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 144

to insects and diseases. These forest thinnings produce significant volumes 

of biomass as a waste product. Because this material currently has little commercial value, most agencies and landowners are faced with the expense of 

disposal by burning, chipping, and spreading, or hauling to a remote disposal 

site. Using forest biomass to generate electrical power is another disposal option. However, at this time, the costs of removing forest biomass to generate 

electrical power are generally higher than the costs of generating electricity 

from traditional sources, such as natural gas.

The social and environmental benefits of using forest biomass to generate 

electrical power are potentially substantial. In 1999, a major study conservatively placed the value of environmental benefits associated with biomass 

energy production in the United States at 11.4 cents per kilowatt-hour over 

and above the retail value of the energy generated (Morris 1999). In this 

study, the use of biomass from in-forest treatments is the least developed 

analysis, due in large measure to a lack of data and other analytical studies. 

While many studies have concluded that overall benefits of biomass energy 

production substantially outweigh costs, researchers face considerable challenges in quantifying the relevant economic values, particularly the benefits. 

A more accurate accounting of costs and benefits for forest biomass-to-energy strategies is needed to develop coherent policies that link forest health 

management, fuel loading reduction, and energy production.

Current inventory information indicates that in-forest fuels reduction may 

provide one of the largest sources of biomass fuel for power production in 

California. Removal of excess biomass from California’s wildland areas to 

achieve public safety and environmental benefits could theoretically produce 

more than 30 million bone-dry tons (bdt) of biomass annually, of which approximately 18 million bdt would come from commercial and noncommercial 

forest management (CEC 1992; Shelly and others 1998; Kadam and others 

1999). Assuming that this volume of biomass could be environmentally and 

economically available, it would comprise nearly eight times the biomass 

volume from all sources currently consumed for biomass power production 

in California (Morris 2002). The potential for power production would be 

substantial: 30 million bdt could produce more than 3,000 megawatts of 

power. Current biomass power production in California stands at about 650 

megawatts annually, with a total capacity of approximately 750 megawatts. 

Biomass energy contributes 15 percent of the renewable power currently 

produced in the State, but has the potential to provide many times more 

(Morris 2002).

Life Cycle Assessment Approach

One approach used to identify and quantify the costs and benefits of 

biomass energy production is through a life cycle assessment. A life cycle assessment, or LCA, models the environmental impacts and related economic 

values associated with a product, process, or activity by identifying energy and 

materials used and wastes released to the environment. Decisionmakers can 

use LCA models to evaluate opportunities to reduce negative environmental 

impacts and achieve economic efficiencies. LCA is a systematic analytical 

method used to quantify the benefits and drawbacks associated with the entire life cycle of a product. In LCA, all stages of a product’s life are analyzed, 

from the extraction of raw materials needed to make the product through 

final product distribution. An LCA is ideal for comparing new technologies 

with existing technologies to identify overall costs and benefits in terms of 

economic, environmental, and energy effects.

Fire Behavior Modeling to Assess Net Benefits of Forest Treatments on Fire Hazard Mitigation... Ganz, Saah, �er, and Ne�
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 145

The Pacific Southwest Research Station of the U.S. Department of Agriculture, Forest Service is working with the California Energy Commission’s 

Public Interest Energy Research Program; the University of California at 

Davis; energy, forestry, and environmental consultants; and several State and 

Federal agencies to construct a cradle-to-grave forest biomass LCA model. 

The model, called the Biomass to Energy (B2E) LCA model, will be used to 

identify and analyze social, economic, and environmental costs and benefits 

of using forest biomass to generate electrical power.

Study Objectives

The objective for the Biomass LCA Project is to develop a comprehensive 

economic, environmental, and energy LCA model that can be used to evaluate 

the potential net public benefits associated with treating and utilizing forest 

biomass. This computer-based model will be designed to facilitate economic, 

environmental, energy, and effectiveness assessments for the potential public 

benefits associated with (1) various options for treating, disposing, and utilizing forest biomass, and (2) electricity production from forest remediation 

biomass

The model will require synthesis of existing studies and additional research 

to populate individual modules. A wide range of research and peer-reviewed 

data will be incorporated into the model, such as wildlife habitat impacts; costs 

of vegetation management, collection, processing, and transport of biomass 

materials; air and water quality impacts and benefits; changes in wildland 

fire behavior and impacts; and so forth. Model users will be able to game out 

different options (or scenarios) within the various modules, and to change 

modeling assumptions such as forest remediation prescriptions, transportation 

distances, types of equipment used, biomass generating technologies, and so 

forth. Ultimately, the model will be used to explore opportunities for converting forest biomass to electricity, based on economic viability, environmental 

impacts, and energy efficiency. It will also allow policymakers to evaluate the 

effectiveness of alternative forest biomass management policies in meeting 

public goals, stakeholder needs, and government regulations.

Study Site Selection

One important risk in complex environmental modeling concerns the 

degree of generality one assumes about the impacts of the unit processes 

within the model. To increase the accuracy of the modeling assumptions 

and impacts, the LCA project team will select specific geographic locations 

that correspond to the kinds of forest remediation needs in California. Each 

location will represent a different landscape archetype. The team will draw 

data from these selected areas to resolve fuzziness in the model, test assumptions, and provide opportunities to “ground truth” the model. Selection of 

the number and kinds of landscape archetypes was a key challenge early on in 

the project. Possible criteria for selecting areas include the following: (1) vegetation condition, (2) human population density, (3) sensitive ecological 

systems (habitats), and (4) existing infrastructure-related opportunities (for 

example, roads to provide access to treatment areas and transport materials 

from treatment sites) (table 1).

Fire Behavior Modeling to Assess Net Benefits of Forest Treatments on Fire Hazard Mitigation... Ganz, Saah, �er, and Ne�
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 146

Table 1—B2E landscape archetype selection criteria.

Criterion Specifics

High-hazard Fuel FRCC3 fuel loading over a substantial 

portion of the landscape

Ownership Mix Must have a reasonable mix of Public 

Multiple Use (PMU), Public Conservation 

and Recreation (PCR), Industrial Private 

Forestry (IPF), Non-industrial Private 

Forests (NIPF)

Human Settlement & Assets Must have substantial areas of WUI

Capital Assets A reasonable number of key infrstructure 

assets, such as dams, power line 

corridors, etc.

TES/SSC habitat Habitat at risk of wildfire for several 

species of concern

Data Quality Data available in several categories 

required by the model (e.g., private land 

use, WUI described, habitat (WHR) 

mapped, Fuel loading mapped, etc.)

Landowner/Agency Interest Demonstrated interest in working with the 

B2E Project from public agencies, 

communities, environmental NGOs and 

private sector industries

Current Management Baseline conditions, fire histories and 

current vegetation management 

prescriptions must be described and 

ideally mapped

Geographic Scope Must be of sufficient size to measure 

changes in large-scale impacts, such as 

carbon cycling, habitat change across 

populations of T&E species or cumulative 

watershed effects

Representative Ecoregion Must represent landscape characteristics 

of diverse forest/chaparral dominated 

ecoregions in California

The B2E LCA Beta model, selected as a landscape archetype using 

the criteria described above, is novel in its scale of analysis, modeling the 

landscape effects of fire and treatments on 2.7 million acres of forest and 

brushland in the northern Sierra Nevada (fig. 1). The Beta landscape was 

originally chosen to represent high-hazard fuel areas with a reasonable mix 

of ownerships encompassing a broad range of infrastructure and other values 

at risk. This landscape represents high-hazard fuel areas, a broad range of 

ownerships, diverse habitats, complex infrastructure, and other values at risk. 

With 50 percent public multiple use and 17 percent industrial private lands, 

this landscape provides a unique opportunity to evaluate the effectiveness 

of Strategically Placed Area Treatments (SPLATs) and compare them with 

industrial private thinning and harvest.

The Beta landscape has more than 240 vegetation strata, or types of vegetative assemblages, ranging from lower elevation scrub and manzanita (for 

example, around Oroville Dam on the lower west side), to midelevation mixed 

conifer, to eastside pine and western juniper. Many of these vegetation types 

are in overstocked condition, with a Fire Regime Condition Class (FRCC) 

rating of 2 and 3.

Fire Behavior Modeling to Assess Net Benefits of Forest Treatments on Fire Hazard Mitigation... Ganz, Saah, �er, and Ne�
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 147

Fire Modeling Strategy and Assumptions

The Biomass to Energy (B2E) Team has constructed a comprehensive 

forest biomass-to-electricity model, which has identified and analyzed the 

economic and environmental costs and benefits of using forest biomass to 

generate electrical power while changing fire behavior at the landscape level. 

Recognizing the urgent need for reducing “catastrophic” fires at a landscape 

level, the B2E Team identified a modeling strategy for depicting fire behavior 

changes on the landscape as a result of emerging forest remediation treatment 

opportunities. This modeling strategy depends on a series of assumptions, 

which will be described in the following section.

Fundamental to the assumptions of the B2E treatments is the concept of 

SPLATs as described by the research and fire behavior modeling of Mark 

Finney of the Missoula Fire Lab in the Rocky Mountain Research Station, USDA Forest Service (Finney 2001). Finney’s research on optimized 

treatments reveals that how you spatially arrange fuel treatments across the 

landscape is much more important than how much of the area is treated. 

Using the fire behavior modeling software FARSITE and FlamMap, Finney 

and his colleagues at the Missoula Fire Lab have shown that treatments on 

only 20 to 30 percent of the landscape can be effective in reducing the threat 

of crown fires and other severe fire behavior if the spatial arrangement of the 

treatments interrupts the fire’s rate of spread (fig. 2).

Figure 1—Location and features of B2E Beta landscape.

Fire Behavior Modeling to Assess Net Benefits of Forest Treatments on Fire Hazard Mitigation... Ganz, Saah, �er, and Ne�
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 148

For this study, fire behavior was summarized into three classes of severity 

to distinguish and report changes in wildfire effects across the B2E landscape 

(fig. 3). Burned areas were classified based on spatially explicit FlamMap 

(Finney and others 2006) model results of fireline intensity and the crowning behavior of the fires. The effects of wildland fire behavior on vegetation 

were tracked in the vegetation portion of the larger B2E-LCA project (domain), and overseen by the USDA Forest Service Region 5’s Stewardship 

and Fireshed Assessment (SFA) Team. The severity of wildfire was assigned 

to three classes (nonlethal, mixed lethal, or lethal effects) depending on its 

flame length and fire type (ground fire, passive crowing fire, or active crowing 

fire). Fire severity determines the numbers of trees killed and the quantity 

of vegetation consumed by fire. Simulations were performed on a 10-year 

temporal sequence for 40 years with a series of fires taking place immediately 

at the beginning of each decade in each fireshed.

Figure 2—The effects of various fuel treatment patterns on fire size (adapted from 

Finney 2001). Left to right: homogenous fuel conditions (untreated), random treatments, 

complete overlap of parallel strip treatments, and strategic, slanted overlapped 

treatments.

Figure 3—Classes of fire severity used in B2E fire modeling (Stewardship and Fireshed 

Assessment Team): (N) – Nonlethal, (X) – Mixed-lethal, (L) – Lethal.

Fire Behavior Modeling to Assess Net Benefits of Forest Treatments on Fire Hazard Mitigation... Ganz, Saah, �er, and Ne�
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 149

The modeling strategy was to measure and treat changes in wildfire effects 

as expected value outcomes, reflecting average outcomes over long periods 

of 10 years or more. The team averaged probabilities of wildfire occurrence 

across space and time. The 10-year intervals over a 40-year timeframe for the 

fire modeling effort have been selected because this timeframe fits well with 

the economics that drive timber harvest (single entry at 20-year intervals 

for uneven aged management) as well as the life cycle of the technology being evaluated (biomass conversion plants are likely to become obsolete and 

depreciate after 20 years).

Modifying landscape-scale fire behavior when only a portion of the landscape can be realistically treated requires attention to layout. Mark Finney’s 

research indicates that fire spread rates can be reduced, even outside of 

treated area, if a fire is forced to flank treated areas where fuels have been 

reduced. However, two criteria must be met for the strategy to be effective: 

(1) the pattern of treatments must be laid out in a manner that interrupts 

fire spread, and (2) prescriptions within the treatments must be designed to 

modify fire behavior.

Of course, forest management has to be conducted with multiple objectives in mind. The impact of fuel treatments on wildlife habitat, threatened 

and endangered species, and recreational opportunities are essential considerations. In addition, forest managers often have an opportunity to generate 

revenue through timber sales to cover or offset the costs of management 

activities. This means that optimal pattern for preventing wildfires is not 

a realistic option. The treatments are adjusted to protect sensitive wildlife 

habitat, reduce negative watershed effects, shape recreational opportunities, 

and capture timber volume to help pay for treating more areas.

Methods

This study was able to track the contribution of private and public land 

treatments toward modifying large-scale fire behavior by comparing the difference in fire behavior between three management scenarios:

	 •	 Scenario 1 – No treatment: This scenario assumes no treatments on private 

or public lands, thereby providing a reference for the interaction of the 

environment and fire. Vegetation is grown across the beta landscape 

over the 40-year period, and the resulting fire effects are modeled. The 

scenario assumes no salvage harvest or reforestation after wildfires. 

When compared to scenario 2 (below), the no treatment scenario allows 

the team to track the contribution of private land treatments (including 

salvage) toward modifying large-scale fire behavior.

	 •	 Scenario 2 – Industrial Private Forests (IPF) only: This scenario assumes 

treatments on private lands only; no treatments are assumed for public 

lands. On public lands, vegetation is grown from the current date and 

only fire effects are tracked, much as in scenario 1. It is assumed that 

the mix of IPF ownership managed under even aged and uneven aged 

management is 50-50 percent.

	 •	 Scenario 3 – IPF and Public Multiple Use (PMU) combined: This scenario 

assumes the overriding goal is to achieve fire behavior modification at a 

landscape scale. Private lands are treated as under the same prescriptions 

as scenario 1 (IPF only), and PMU lands are treated using a variety of 

strategic approaches (defensible fuels profile zones and SPLATS).

Fire Behavior Modeling to Assess Net Benefits of Forest Treatments on Fire Hazard Mitigation... Ganz, Saah, �er, and Ne�
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 150

Fire modeling outputs included the number of acres in three classes of fire 

severity, the number of burned acres with crown fire behavior, and flame 

lengths less than and greater than 4 feet. Historical fire occurrence was used 

to locate ignitions. Discrete ignition points at locations across the landscape 

were chosen recognizing that demographics, human activities, and climatic 

conditions will vary with time (fig. 4). This study used predetermined ignitions per decade instead of a random generator. Randomization of ignition 

locations did not yield the “catastrophic” events needed to measure differences between the three main treatment scenarios. While purely a means to 

the end, the rule sets generated for performing these three treatments across 

2.7 million acres (table 2) have received attention for similar modeling ventures in California.

Results

With a no treatment weighted average biomass levels of 79 bone-dry tons 

(bdts) per acre, the private treatments removed an average of 31 bdts/acre 

while SPLATs removed an average of 24 bdts/acre (table 3). Downstream 

models are evaluating the effects of these three fuel treatment scenarios on 

economic feasibility, energy production supported, ecosystem impacts and 

the location and capacity of modeled biomass facilities. For the purposes of 

Figure 4—B2E Beta landscape fire history and ignition placement by decade.

Fire Behavior Modeling to Assess Net Benefits of Forest Treatments on Fire Hazard Mitigation... Ganz, Saah, �er, and Ne�
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 151

this study, by comparing the difference in fire behavior between three management scenarios over the 40-year management trajectory, we can evaluate 

the contribution of private and public land treatments toward modifying 

large-scale fire behavior.

While the ownerships, forest type, density, and slope dictated the type of 

treatment prescriptions, we found that the spatial arrangement of treatments 

has a greater impact on their ability to change fire intensity and extent than 

the prescription applied. While we recognize that the optimal pattern for 

preventing catastrophic wildfires (or reducing their impacts) is not always a 

realistic option, we have modeled scenarios 2 and 3 with the necessary adjustments to protect sensitive wildlife habitat, reduce negative watershed effects, 

shape recreational opportunities, and capture timber volume under industrial 

private forest ownerships (that are both realistic in turns of net revenues and 

Table 2—Treatment allocation rule base and logic.

PMU Public Multiple Use NIFL Non Industrial Forested Lands

PCR Public Conservation-Recreation NON Others, Non Forested Lands, Urban

IFL_U Industrial Forest Lands [1, 2] SMZ Streamside Management Zone.

Mgnt.

Regime

Lands

Applied to:

Treatment

Cycle

Treatment

Unit Size

Regime

Code or ID Per-1 Per-2 Per-3 Per-4 Rx Desc Salvage

Wildland

Fire Use

Public

Conservation n/a n/a n/a n/a n/a n/a n/a no

31 Rx Fire Rx Fire

32 Rx Fire Rx Fire

21

Initial

Integ.Fuel

s Treat

Maint -

Integ.Fuels

Treat

thin for 

fuelsretain 40% 

cc

22

Initial

Integ.Fuels

Treat

Maint -

Integ.Fuels

Treat

DFPZs as 

mapped under 

QLG [1/4mi 

fb]

23

Initial

Integ.Fuel

s Treat

Maint -

Integ.Fuels

Treat

thin for 

fuelsretain 40% 

cc

n/a

Initial

Integ.Fuel

s Treat

Maint -

Integ.Fuels

Treat

thin for 

fuelsretain 40% 

cc

n/a

Initial

Integ.Fuels

Treat

Maint -

Integ.Fuels

Treat

11 Selective

Harvest

Selective

Harvest

Thin for 

product

[~5mbf/ac]

& stand 

vigor

12 Selective

Harvest

Selective

Harvest

11 Selective

Harvest

Selective

Harvest

12 Selective

Harvest

Selective

Harvest

1 Regen

Harvest PCT ComThin

Regn

HarvestClearcut

2,7 Regen

Harvest PCT

3,6 ComThin Regen

Harvest PCT comthin to 

a Ave. BA 

4,5 ComThin Regen

Harvest

IFL[2]

Plantations , 

<15yrs

70-years as mapped 8 PCT ComThin

percom

thin to 150-

160 tr/ac

no

No

Treatment all others n/a n/a 1-4 n/a n/a n/a n/a no

Public-MU

<50% 20-yrs

140-acres no

yes

Rx Fire 

[initial

Public-MU

>50% 20-yrs

yes

Restricted

Thin

150-acre ave. 

SPLAT

[Finney

Herring Bone 

Psattern]

SMZ-IFL[1]

treated

along with 

interesect

unit

300-ft perin. 

Unevn-aged

IFL [1] all 

Slopes and

IFL[2] w/SLp 

>50% only

20-yrs 140-acres

Even-aged

IFL[2] slp 

<50%,

mature

70-years 20-acres yes

yes

All NIFL Slp 

<50% 20-yrs 20-acres no

Fire Behavior Modeling to Assess Net Benefits of Forest Treatments on Fire Hazard Mitigation... Ganz, Saah, �er, and Ne�
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 152

Table 3—B2E pretreatment inventory and amounts of 

biomass removed by scenario and year (in BDTs/

acre)

Scenario Year Inventory Treatment

2006 64

2016 73

2026 80

2036 86

2046 91

Average 79

2006 64 30

2016 69 35

2026 73 28

2036 76 31

2046 79

Average 72 31

2006 64 28

2016 67 34

2026 70 23

2036 73 26

2046 75

Average 70 28

2006 64 26

2016 66 34

2026 67 17

2036 70 20

2046 72

Average 68 24

3: (IPF) & 

Public

Multiple Use 

(PMU)

3b: (PMU) - 

SPLATS

1: No 

treatment

2: Industrial 

Private

Forests (IPF) 

only

yet protects their proprietary information). Many of these assumptions are 

depicted in the treatment allocation rule sets and logic described above.

Quantifying effectiveness of fire mitigation treatments is a challenge as 

there is no accepted system of measurement. Evaluations of fire hazard mitigation programs tend to focus primarily on the number of acres treated and 

treatment costs associated with mitigation without adequately assessing the 

benefits of these treatments. These programs also tend to focus on monitoring the total number of acres burned from 1 year to the next to determine 

efficacy of certain fire mitigation strategies (for instance, comparing DFPZs 

and/or SPLATs with traditional fuel break systems). Wildfire behavior modeling, especially with FlamMap, lends itself well to landscape comparisons (for 

example, pre- and posttreatment effectiveness) and for identifying hazardous 

fuel and topographic combinations, thus aiding in treatment prioritization 

and landscape-level assessments such as the B2E Beta model. The B2E Beta 

wildfire behavior modeling of these three treatment scenarios showed a 

6 percent reduction in the number of acres burned from private treatments 

and a 16 percent reduction from SPLATs on public lands (table 4). Scenario 

3 had the overall greatest effect on the number of acres burned (that is, fire 

perimeters) with a 22 percent reduction from the no treatment scenario. For 

scenario 2, decade 2 had the greatest impact on reducing the fire perimeter 

with a 19 percent reduction in total acres (table 4). We expected to see a 

similar trend for reducing fire perimeters across all four decades but recognize 

Fire Behavior Modeling to Assess Net Benefits of Forest Treatments on Fire Hazard Mitigation... Ganz, Saah, �er, and Ne�
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 153

that differences existed due to modeling assumption and fire placements. 

These heightened effects for decade 2 were attributed to the location of the 

ignitions, higher proportion of private industrial ownership, and the topography within the fire perimeters.

As expected, scenario 1 generated the most acres burned with an average 

of 74,833 acres. While not all fires will achieve the same size, the burned 

acreage per decade averaged 67,802 acres for all three scenarios. Ignoring the 

small fires, the B2E Beta landscape’s fire history on record (past 80 years) 

averaged around 65,000 burned acres per decade. The wildfire behavior 

modeling efforts for this B2E LCA Beta model have tried to mimic the fire 

history on record burning 65,000 acres in a variety of fire size and intensities with acres in each severity class approaching 30 percent based on work 

by Miller and Fittes (2006).

Evaluations of fire hazard mitigation programs tend to focus primarily on 

changes in the number of acres burned (since those are easiest to monitor). 

A modeling venture such as the B2E LCA Beta model allows us to evaluate 

the contribution of private and public land treatments toward modifying 

large-scale fire behavior using intensity as the change metric. Across all 

scenarios, 30.8 percent of the acres burned were characterized as nonlethal; 

that is, surface fires with flame lengths between 1 and 4 feet (table 5). The 

percentages of fire severity classes from the B2E wildfire modeling effort correspond well with Forest Service severity monitoring for the Sierra Nevada 

(Miller and Fites 2006).

These fire severity classes are important to the B2E LCA Beta modeling 

project because many of the downstream models are evaluating the effects 

of these three fuel treatment scenarios based upon these three classes. For 

Table 4—Summary of B2E Beta Model burned acres by scenario and year.

2: Industrial 

1: No Private 3: (IPF) & 

Year treatment Forests (IPF) 3b: (PMU) (PMU)

2006 92,684 92,168 81,004 80,487

2016 60,153 48,616 51,383 39,846

2026 69,953 65,241 49,097 44,385

2036 76,543 75,758 68,582 67,796

Total Acreage 299,334 281,782 250,066 232,514

% Change from 

 No TMT 0% –6% –16% –22%

Table 5—Summary of B2E Beta Model severity class acres by scenario.

Scenario Summary

Fire Severity Class 1 2 3 Total %

N - nonlethal 81,471 82,160 86,586 250,216 31%

X - mixed lethal 136,887 125,156 98,560 360,603 44%

L - lethal 80,976 74,465 47,368 202,809 25%

Grand Total 299,334 281,782 232,514 813,629 100%

Fire Behavior Modeling to Assess Net Benefits of Forest Treatments on Fire Hazard Mitigation... Ganz, Saah, �er, and Ne�
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 154

instance, fire consumption rates for canopy fuels and resultant wildfire 

emissions for green house gases are all modeled and calibrated to these fire 

severity classes.

As expected with the higher total number of acres burned, the percentages 

of acres with lethal and mixed-lethal fire severity classes were also highest in 

decade 2 (table 6). All three fire severity classes were favorably affected by 

applying both the private and public treatments over the four decades. Only 

decade 3 showed a decrease in the number of acres in the nonlethal severity 

class (3,880 acres) but that is due to the dramatic drop in total acres burned 

from implementing both public and private treatments in this particular 

decade with a positive change of 25,568 acres or 36.5 percent from the nontreated scenario 1 (table 4).

Table 6—B2E Beta Model severity class acres by 

scenario by year.

Fire Severity Classes

Scenario Decade N X L

1 1 36,579 33,176 22,929

 2 19,447 20,947 19,759

 3 19,296 31,691 18,965

 4 6,148 51,072 19,324

2 1 37,953 30,592 23,623

 2 21,491 13,208 13,917

 3 14,312 32,791 18,138

 4 8,404 48,566 18,787

3 1 37,889 24,740 17,858

 2 19,914 15,452 4,480

 3 15,417 18,496 10,472

 4 13,366 39,873 14,557

Despite a 6 percent positive effect on the number of acres burned, applying 

private treatments alone does not always result in a favorable effect on changing the fire severity. Decade 1’s lethal severity class increased by 694 acres 

and decade 3’s mixed lethal increased by 1,100 acres (albeit 827 acres of these 

can be attributed to a decrease in severity from lethal to mixed-lethal classes). 

Crown fire behavior in even-aged managed stand, especially during early 

stages of plantation development, can explain for these two increases in fire 

severity classes (out of 12 represented in table 6). Overall, the majority of the 

acres modeled in this effort demonstrated favorable impacts of implementing 

industrial private forest treatments with a decrease of 7 percent in the lethal 

severity class across the entire B2E Beta landscape.

Conclusions

Assuming that collection, processing, and transportation are economically 

viable, the conversion of forest biomass to useful energy becomes a critical 

economic and environmental issue. The B2E LCA Team has constructed 

a comprehensive forest biomass-to-electricity model, which has identified 

Fire Behavior Modeling to Assess Net Benefits of Forest Treatments on Fire Hazard Mitigation... Ganz, Saah, �er, and Ne�
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 155

and analyzed the economic and environmental costs and benefits of using 

forest biomass to generate electrical power while changing fire behavior at 

the landscape level. The B2E wildfire behavior modeling of three treatment 

scenarios showed reductions in the number of acres burned and changes in 

intensity classes. Whether compared for the entire B2E Beta landscape or 

within perimeters of the 67,800 acres burned per decade, treatments applied 

to both public and private lands changed fire behavior and growth. The B2E 

wildfire modeling venture has demonstrated that treating public multiple 

use lands with SPLATs, albeit not nearly as strategically applied as originally 

intended by its designers, contributes more (percentage wise) than private 

sector treatments for modifying landscape-scale fire behavior. The goal of 

such modeling efforts is not to differentiate between public and private sector treatments, but rather, to improve our understanding of implementing 

forest treatments across ownerships to prevent catastrophic wildfires (or at 

least reduce their impacts). The next steps for improving the B2E wildfire 

modeling component of this B2E project will be to move to another landscape 

archetype using the criteria described in this paper and design a growth model 

for brush types that will complement the vegetation growth simulations over 

a 40-year timeframe.

The California Biomass to Energy project and other similar projects will 

help provide information about potential economic, energy, and environmental tradeoffs associated with various options for managing forest biomass 

and using forest biomass material to produce renewable energy. The results, 

findings, and conclusions of these efforts will help government organizations 

establish policies, legislation, incentives, and funding initiatives relative to 

biomass power, as well as assist private, academic, and government organizations in setting priorities and establishing plans for forest research and 

development programs.

Discussion

A primary assumption of the B2E fire behavior modeling approach is that 

SPLATs, as both a theoretical and an applied approach, will indeed fragment 

the fire-prone environment of the Beta landscape for the desired effect of 

reducing fire behavior, growth and/or severity. The modeled outcomes demonstrated in our results show a favorable effect from the spatial arrangement 

of treatments, but it is obvious that policymakers will need more empirical 

data to justify greater application of SPLATs on public lands. Recognizing 

this need, the USDA Forest Service and the Joint Fire Science Program have 

funded several empirical studies that are designed to demonstrate landscapes 

that have been treated for fuels with SPLATs, DFPZs, and other strategic 

approaches that can effectively change the behavior of wildfires. One such 

study currently producing empirical results is being performed at the Sagehen 

Experimental Forest by Dr. Scott Stephens and Dr. John Battles from the 

University of California at Berkeley (Saah and others 2006). Other studies 

have begun to report the efficacy of earlier treatments in reducing the effects 

of wildfires (for example, see Fulé and others 2001a,b; Finney and others 

2005). In 2002, the Cone Fire on the Blacks Mountain Experimental Forest and funding from the Joint Fire Science Program provided Skinner and 

others (2004) with the opportunity to document changes in fire behavior on 

a landscape where fuels treatments had been conducted. Skinner and others 

(2004) stated, “In the case of both treatments the fire dropped quickly out 

Fire Behavior Modeling to Assess Net Benefits of Forest Treatments on Fire Hazard Mitigation... Ganz, Saah, �er, and Ne�
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 156

of the crowns to become either a surface fire or die out upon entering the 

treated areas. The rapidity of apparent change from a high-intensity crown 

fire to a much lower-intensity surface fire may have significant implications 

for management of wildland/urban interface zones as well as wildlands in 

general.”

As Mark Finney’s research indicates, modifying landscape-scale fire behavior when only a portion of the landscape can be realistically treated requires 

attention to layout. We agree that the spatial arrangement of treatments is 

critical. On the B2E Beta landscape we noticed that strategic placement had 

a greater impact on fire intensity and extent compared to treatments themselves (dictated by ownership, forest type, density, and slope). Fire severity, 

as defined by the three classes, decreased by both private and public forest 

remediation treatments with a greater effect on public multiple-use lands. 

We were not surprised to find that the mass SPLAT implementation on 

public lands had a greater effect on fire behavior than the treating of private 

lands for commercial timber values. The downstream models utilizing these 

modeling outputs for further analysis are undoubtedly going to question the 

large-scale implementation and lack of strategic direction when SPLATs were 

applied to the B2E landscape. In our B2E LCA model, all stages of a product’s 

life are analyzed, from the extraction of raw materials needed to make the 

product through final product distribution. In this LCA, biomass is the raw 

material (considered here as a waste product) and energy is the desired final 

product. The mass application of SPLATs on public lands to generate this 

raw material while positively reducing fire behavior, growth and/or severity 

(and subsequently reducing the emissions that would have been emitted by 

these fires), will either tip the balance for generating renewable energy from 

this waste product or drive up the costs of removing forest biomass due to 

the need for strategic planning and treatment implementation.

References

CEC. 1992. [The] 1991 biomass resources assessment report for California - draft 

report. Sacramento, CA: California Energy Commission.

Finney, M.A. 2001. Design of regular landscape fuel treatment patterns for modifying 

fire behavior and growth. Forest Science 47: 219-228.

Finney, M.A.; Britten, S; and others. 2006. Flammap. Missoula, MT: U.S. 

Department of Agriculture, Forest Service, Rocky Mountain Research Station, 

Fire Sciences Lab; and Systems for Environmental Management.

Finney, M.A.; McHugh, C.W.; and others. 2005. Stand- and landscape-level effects 

of prescribed burning on two Arizona wildfires. Canadian Journal of Forest 

Research 35(7): 1714-22.

Fulé, P.Z.; McHugh, C.W.; and others. 2001a. Potential fire behavior is reduced 

following forest restoration treatments. Ponderosa pine ecosystems restoration 

and conservation: Steps toward stewardship. Ogden, UT: U.S. Department of 

Agriculture, Forest Service.

Fulé, P.Z.; Waltz, E.M.; and others. 2001b. Measuring forest restoration effectiveness 

in reducing hazardous fuels. Journal of Forestry 99: 24-29.

Kadam, K.L.; Camobreco, V.J.; and others. 1999. Environmental life cycle 

implications of fuel oxygenate production from California biomass - technical 

report. Golden, CO: U.S. Department of Energy, National Renewable Energy 

Laboratory.

Fire Behavior Modeling to Assess Net Benefits of Forest Treatments on Fire Hazard Mitigation... Ganz, Saah, �er, and Ne�
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 157

Miller, J.D.; Fites, J. 2006. Sierra Nevada fire severity monitoring: Internal report. 

Nevada City, CA: U.S. Department of Agriculture, Forest Service, Pacific 

Southwest Region, Adaptive Management Services Enterprise Team: 62.

Morris, G. 1999. The value of the benefits of U.S. biomass power. Golden, CO: 

U.S. Department of Energy, National Renewable Energy Laboratory.

Morris, G. 2002. Biomass energy production in California 2002: Update of the 

California biomass database. Golden, CO: U.S. Department of Energy, National 

Renewable Energy Laboratories.

Saah, D.S.; Battles, J.J.; and others. 2006. Translating SPLATs from a theoretical 

to a real world landscape. http://sagehen.ucnrs.org/research.htm.

Shelly, J. R.; Weiant, P.; and others. 1998. Assessment of urban/wildland biomass 

utilization and disposal options. Sacramento, CA: California Department of 

Forestry and Fire Protection.

Skinner, C.; Ritchie, M.; and others. 2004. Effects of prescribed fire and thinning 

on wildfire severity: The cone fire, Blacks Mountain Experimental Forest. 

Proceedings 25th Vegetation Management Conference, Redding, CA.

Fire Behavior Modeling to Assess Net Benefits of Forest Treatments on Fire Hazard Mitigation... Ganz, Saah, �er, and Ne�
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 158

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page is 

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on purpose

Fire Behavior Modeling to Assess Net Benefits of Forest Treatments on Fire Hazard Mitigation... Ganz, Saah, �er, and Ne�
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 159

Abstract—The 24- to 72-hour fire-weather predictions for different regions of the 

United States are now readily available from the regional Fire Consortia for Advanced 

Modeling of Meteorology and Smoke (FCAMMS) that were established as part of the 

U.S. National Fire Plan. These predictions are based on daily real-time MM5 model 

simulations of atmospheric conditions and fire-weather indices over specific modeling domains. Included in the suite of fire-weather indices provided by the FCAMMS 

is the well-known Haines Index (HI), an operational “mesoscale-type” index that 

characterizes the atmospheric risk of extreme fire behavior based solely on stability 

and moisture conditions in the lower to middle troposphere. However, there are other 

atmospheric variables that also influence the risk of extreme fire behavior, especially 

those that characterize conditions in the atmospheric boundary layer where small-scale 

fire-atmosphere interactions are so important. One of those variables is atmospheric 

turbulence (that is, wind gustiness), as measured by turbulent kinetic energy (TKE). 

TKE can be classified as a “boundary-layer-type” index, with its generation and dissipation dependent on wind shear and buoyancy conditions near the surface. Like 

the HI, predictions of TKE are available from the daily FCAMMS MM5 model simulations. This study examines the utility of combining the HI with TKE to assess potential 

atmospheric risk of extreme fire behavior. Output from the FCAMMS - Eastern Area 

Modeling Consortium (EAMC) MM5 simulations of fire-weather conditions over the 

western Great Lakes region is used to identify regional patterns of HI and TKE on a 

daily basis. A comparison of the patterns of the two indices allows for an assessment of 

whether large HI values typically occur with large near-surface TKE values, a potentially 

dangerous fire-weather condition.

Introduction

The regional Fire Consortia for Advanced Modeling of Meteorology and 

Smoke (FCAMMS) (http://www.fs.fed.us/fcamms), established by the 

U.S. National Fire Plan (USDA Forest Service 2002), are providing daily 

24- to 72-hour real-time fire-weather predictions for different regions of the 

United States as part of their research programs focused on developing new 

and improved tools for predicting fire-fuel-atmosphere interactions. These 

predictions are based on simulations performed with the Fifth Generation 

Penn State University (PSU)/National Center for Atmospheric Research 

(NCAR) Mesoscale Model (MM5) over specific modeling domains set up to 

cover the conterminous United States. The well-known Haines Index (HI) 

(Haines 1988) is one of many fire-weather indices routinely provided by the 

FCAMMS as part of their fire-weather predictions. As an operational index 

for fire-weather forecasting, the HI can be considered a mesoscale-type index 

that characterizes the stability and moisture conditions in the lower to middle 

troposphere. Its value is meant to provide an indication of the atmospheric 

risk of extreme fire behavior due solely to these atmospheric conditions.

In: Butler, Bret W.; Cook, Wayne, 

comps. 2007. The fire environment—

innovations, management, and policy; 

conference proceedings. 26-30 March 

2007; Destin, FL. Proceedings 

RMRS-P-46CD. Fort Collins, CO: 

U.S. Department of Agriculture, 

Forest Service, Rocky Mountain 

Research Station. 662 p. CD-ROM.

1 R esea rch Meteorolog ist a nd 

Meteorologist, respectively, U.S. 

Department of Agriculture, Forest 

Service, Northern Research Station, 

East Lansing, MI. Lead author wheilman@fs.fed.us

Combining Turbulent Kinetic Energy 

and Haines Index Predictions for 

Fire-Weather Assessments

Warren E. Heilman1 and Xindi Bian1
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 160

While the HI has proven to be a valuable tool for fire-weather forecasters 

in some regions of the United States, there are other atmospheric properties 

and processes that can affect the severity of fires, especially those properties 

and processes that characterize the atmospheric boundary layer where smallscale fire-atmosphere interactions are so important. Atmospheric turbulence, 

or wind gustiness, is one of those properties. Wind gusts are a reflection of 

turbulent eddies imbedded within the general circulation of air flow, with 

the energy associated with these eddies defined as turbulent kinetic energy 

(TKE). The generation and dissipation of turbulent eddies and TKE in the 

atmosphere are dependent on wind shear and buoyancy conditions. Strong 

vertical wind shears and unstable temperature lapse rates tend to increase 

atmospheric turbulence, while stable temperature lapse rates tend to dissipate 

turbulence. Predictions of TKE in the atmospheric boundary layer using the 

higher order level 2.5 closure from the Mellor-Yamada turbulence hierarchy 

(Mellor and Yamada 1974, 1982; Gerrity and others 1994) are available 

from the daily FCAMMS MM5 model simulations. However, unlike the HI 

predictions from the FCAMMS, TKE predictions have not been used in the 

past for characterizing atmospheric risk of extreme fire behavior.

The FCAMMS—Eastern Area Modeling Consortium (EAMC) has been 

investigating the utility of combining the mesoscale-type HI with TKE, a 

boundary-layer type index, for assessing the atmospheric potential for extreme 

fire behavior. For example, Heilman and others (2003) reported some initial 

results from an analysis of regional patterns of HI and TKE over the Northeastern United States based on daily EAMC MM5 fire-weather simulations 

covering the period 1 March 2003 to 18 July 2003. While the analyses were 

limited to the Northeast and covered a short period of time, they provided 

some important insight into where high HI and high TKE values typically 

occur in this region of the nation. The analyses also demonstrated that combining the HI and TKE via a simple product of the two indices may have some 

utility in predicting where both lower to middle tropospheric conditions and 

boundary layer conditions are especially conducive to extreme fire behavior, 

as shown by an application of this “combined” index to the Double Trouble 

State Park wildfire in New Jersey on 2 June 2002.

As a follow-up to the Heilman and others (2003) investigation, this paper 

describes a more comprehensive analysis of seasonal HI and TKE patterns 

over the western Great Lakes region of the United States derived from 

EAMC MM5 daily fire-weather simulations for year 2006. Comparisons of 

seasonal patterns of HI and TKE patterns along with analyses of the relative 

significance of wind shear and buoyancy effects in the atmospheric boundary layer in contributing to high TKE values when high HI values are also 

present provide new insight into the atmospheric dynamics that contribute 

to extreme fire behavior.

Haines Index Description

The well-known HI (Haines 1988) is a simple index that provides a measure of the lower to middle tropospheric instability and dryness. The index 

characterizes the stability and moisture content of specific atmospheric layers, 

depending on the elevation above sea level of the underlying terrain. The 

index is defined as

A B

HI = (Tp1 – Tp2) + (Tp – Tdp) (1)

Combining Turbulent Kinetic Energy and Haines Index Predictions for Fire-Weather Assessments Heilman and Bian
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 161

where Tp1 is the temperature (oC) at pressure level p1, Tp2 is the temperature 

(oC) at pressure level p2, and Tp and Tdp are the temperature (oC) and dewpoint temperature (oC), respectively, at one of the pressure levels. The pressure 

levels, p1 and p2, are set at 950 mb and 850 mb, respectively, for low terrain 

elevations; 850 mb and 700 mb, respectively, for mid terrain elevations; and 

700 mb and 500 mb, respectively, for high terrain elevations. The defined low, 

mid, and high terrain elevation regions for the United States can be found 

in Haines (1988). For the HI calculations performed in this study, both the 

low and mid terrain elevation designations are used.

Haines (1988) defined specific temperature lapse rate and dew-point 

depression thresholds for the low, mid, and high terrain elevation designations. 

Integer values of 1, 2, or 3 are assigned to the lapse rate (A) and dew-point 

depression (B) components of the HI, as shown in equation 1, depending on 

the actual values of the lapse rates and dew-point depressions in comparison 

to the defined thresholds. The two integers are added to create an index 

varying from 2 to 6, with the following adjective definitions for the potential 

for large plume dominated fires:

(A + B) = 2 or 3 [very low]

(A + B) = 4 [low]

(A + B) = 5 [moderate]

(A + B) = 6 [high].

The lower to middle tropospheric temperature and dew-point temperature 

data required for calculating the HI typically come from radiosonde observations at 0000 UTC and 1200 UTC or from numerical weather prediction 

models that can provide output data specific to any time of the day. Haines 

Index observations or predictions are often presented in the form of maps 

that allow for a spatial analysis of regional patterns of the index. The HI can 

be classified as a mesoscale-type index because it attempts to capture the 

stability and moisture conditions of atmospheric layers that extend above the 

atmospheric boundary layer. As a mesoscale-type index, it can be useful for 

describing the atmospheric risk for extreme fire behavior over relatively large 

spatial areas. When fire plumes penetrate atmospheric layers characterized by 

high HI values, the potential exists for increased lofting of the plume and 

the downward transport of high-momentum, dry air from these layers to the 

surface, a potentially dangerous wildfire scenario.

Turbulent Kinetic Energy Description

While stability and moisture conditions in atmospheric layers above the 

boundary layer can influence fire behavior, turbulent atmospheric circulations 

(that is, wind gusts) within the boundary layer can also create an environment conducive to extreme fire behavior. Wind gusts are manifestations of 

turbulent eddies generated by wind shear and buoyancy effects, which can be 

large in the boundary layer. The amount of energy in these turbulent eddies 

is defined as turbulent kinetic energy, and is given by 0.5q2 where

q u v w

2 2 2 2    ' ' ' (2)

and u '

2 , v'

2 , and w'2 are the variances of the departure (turbulent) velocities 

in the horizontal x, horizontal y, and vertical z directions, respectively. Large 

vertical wind shears under thermally unstable (convective) conditions lead to 

Combining Turbulent Kinetic Energy and Haines Index Predictions for Fire-Weather Assessments Heilman and Bian
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 162

a highly energetic turbulence regime (that is, large TKE values), whereas a 

thermally stable environment will tend to suppress any turbulence generated 

through mechanical wind shears and produce more laminar-type flows (low 

TKE values). Irrespective of the enhanced atmospheric turbulence generated by buoyancy and wind shears associated with a fire, an already highly 

turbulent atmospheric boundary layer can contribute to even more erratic 

fire behavior through interactions between the fire-induced and ambient 

boundary-layer turbulence regimes.

Simulations and predictions of TKE are possible in many of the current 

research and operational atmospheric mesoscale and boundary layer numerical models, including MM5. Turbulent kinetic energy can be simulated and 

predicted using the level 2.5 closure scheme from Mellor and Yamada (1974, 

1982) given by

u

u

¥

§

¦  ́

¶

μ  
 u

u

u

u

¥

§

¦  ́

¶

μ

 ̈

a

© ·

1

 ̧

t

q V q

z

K

z

q

q

2 2 2

2 2 2 •   
 P P s b E (3)

where the terms on the left side of the equation represent the local time rate 

of change of TKE, the advection of TKE by the three-dimensional mean 

wind V, and the vertical diffusion of TKE (parameterized in terms of diffusion coefficient Kq). The terms on the right side of the equation represent the 

production of TKE through vertical wind shear effects (Ps), the production 

or dissipation of TKE through buoyancy effects (Pb), and the nonbuoyant 

dissipation of TKE (ε) via the breakdown of turbulent eddies into smaller 

and smaller sizes. The production (Ps) of TKE through vertical wind shear 

effects is given by

P u w u

z

v w

v

z s  
 uu 
 uu ' ' ' ' (4)

and the buoyant production or dissipation (Pb) of TKE is given by

P g

b w

v

 v Q

Q ' ' (5)

where g is the acceleration due to gravity, u and v are the horizontal components of the mean wind, θv is the virtual potential temperature, and u w' ', 

v w' ', and Qv ' ' w are the vertical turbulent fluxes of momentum and heat. The 

MM5 mesoscale model used in this study includes the Mellor and Yamada 

(1974, 1982) TKE formulation and is described in more detail by Gerrity 

and others (1994).

Unlike the HI, which can be easily computed from radiosonde observations or numerical model output, TKE as a potential fire-weather index has 

not been used extensively because it is fairly complex and is rarely, if ever, 

included in the suite of fire-weather variables made available to fire managers. 

However, the increasing availability and delivery of TKE predictions from 

research and development groups such as the FCAMMS have now made it 

possible to assess the feasibility of using TKE in some fashion as a potential 

fire-weather index.

Analyses of HI and TKE Patterns

The analyses presented here are built upon daily 48-hour real-time EAMC 

MM5 simulations (0000 UTC initialization) over a 4-km grid spacing domain 

covering the western Great Lakes region for the period of 1 January 2006 

Combining Turbulent Kinetic Energy and Haines Index Predictions for Fire-Weather Assessments Heilman and Bian
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 163

through 31 December 2006. Simulated patterns of the mid-afternoon frequency 

of occurrence of different HI and near-surface TKE values were analyzed as 

a first step in determining: (1) how frequently high HI values (5 or 6) occur 

concurrently with significant near-surface atmospheric turbulence in this region; 

(2) whether there are preferred locations where high HI and high near-surface 

turbulence tend to occur in this region; and (3) whether there is a seasonal 

dependence on those occurrence patterns.

Figure 1 shows the simulated frequency of occurrence of HI values equal 

to 5 or 6 (moderate or high atmospheric risk of large plume dominated fires) 

at 2000 UTC during the January-February-March (JFM), April-May-June 

(AMJ), July-August-September (JAS), and October-November-December 

(OND) periods in 2006. During the JFM period, mid-afternoon high HI 

values were most common over northern Iowa, southern Minnesota, western and southern Wisconsin, and central Michigan. The highest frequencies 

occurred over northeastern Iowa, with 25 to 30 percent of the days during 

this 3-month period characterized by HI values equal to 5 or 6 at 2000 UTC. 

During the spring and summer periods (AMJ and JAS), high HI values at 

2000 UTC were more frequent and widespread in this region. HI values of 

5 or 6 occurred more than 20 percent of the time at 2000 UTC over most 

of Minnesota, Iowa, southern Wisconsin, northern Illinois, and northern, 

western, and eastern Michigan. The autumn period (OND) was characterized 

Figure 1—Simulated frequency (percent) of HI values equal to 5 or 6 at 2000 UTC for the periods (a) JFM, 

(b) AMJ, (c) JAS, and (d) OND in 2006.

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USDA Forest Service Proceedings RMRS-P-46CD. 2007. 164

by maximum frequencies of high HI occurrence at 2000 UTC exceeding 

30 percent over large parts of Minnesota and Iowa, and minimum frequencies 

over much of northern Michigan. Overall, the simulated patterns of high HI 

occurrence over the western Great Lakes region in 2006 suggest that midafternoon lower to middle tropospheric stability and moisture conditions 

were more frequently conducive to extreme fire behavior over the western 

sections of the region than elsewhere during all seasons.

The simulated frequencies of occurrence of near-surface TKE values exceeding 3 m2s–2 (significant turbulence) at 2000 UTC over the same four 3-month 

periods in 2006 are shown in figure 2. The percentage of days when near-surface 

turbulence was significant during the winter months (JFM) in 2006 was low 

over the western sections of the Great Lakes region. Only the upper peninsula 

and northern part of the lower peninsula of Michigan and along the shores of 

Lake Superior had relatively frequent occurrences of significant near-surface 

turbulence, generated primarily by mechanical wind shear effects. The increased 

stability of the atmospheric boundary layer during the winter months reduces 

the buoyancy contribution to TKE production. The spring months (AMJ) were 

characterized by increases in occurrence of high near-surface turbulence over 

the upper peninsula and northern sections of the lower peninsula of Michigan, 

northern Wisconsin, and large areas of Minnesota and Iowa. More than 30 percent of the days in some of these areas had near-surface TKE values exceeding 

3 m2s–2 at 2000 UTC. Overall frequencies of occurrence of high near-surface 

Figure 2—Same as figure 1 except for simulated frequency (percent) of near-surface TKE values greater than or 

equal to 3 m2 s–2.

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USDA Forest Service Proceedings RMRS-P-46CD. 2007. 165

turbulence were lower during the summer (JAS) and autumn (OND) periods 

over the western Great Lakes region in comparison to the maximum frequencies observed during the spring months.

A comparison of the HI and TKE frequency of occurrence maps in 

figures 1 and 2, respectively, reveals that large HI values in 2006 often did 

not occur in the same areas where large near-surface TKE values occurred. 

In fact, episodes of concurrent high HI and near-surface TKE values were 

infrequent. This suggests that combining the HI and near-surface TKE in 

some fashion could produce a highly discriminatory index that captures those 

relatively rare events when both the atmospheric mesoscale environment, as 

quantified by the HI, and the atmospheric boundary-layer environment, as 

quantified by near-surface TKE, are highly conducive to extreme fire behavior. One possible way of combining the indices is to simply take the product 

of the two indices. Figure 3 shows the simulated frequency of occurrence at 

2000 UTC of episodes where HI x TKE > 15, a threshold meant to roughly 

capture those cases when the HI and TKE values are greater than or equal to 

5 and 3 m2s–2, respectively. Over much of the western Great Lakes region, the 

occurrence of concurrent high values of HI and near-surface TKE in 2006 

was relatively rare. It was only during the spring season (AMJ) that frequencies of occurrence above about 10 percent were common over large sections 

of Minnesota, Wisconsin, and Michigan. Frequencies above 20 percent also 

Figure 3—Same as figure 1 except for simulated frequency (percent) of the product of the HI and near-surface 

TKE exceeding 15 (HI x TKE > 15).

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USDA Forest Service Proceedings RMRS-P-46CD. 2007. 166

characterized the local urban areas of Minneapolis, Chicago, Milwaukee, and 

Detroit during the spring. During the summer months (JAS), the highest 

frequencies (~10 to 15 percent) occurred over northern Minnesota and these 

same urban areas. Frequencies in the fall were generally less than 10 percent 

everywhere except along the southern shore of Lake Superior.

Fire Case Studies

In order to test the feasibility of combining the Haines Index and TKE for 

assessing the atmospheric mesoscale and boundary layer risk of extreme fire 

behavior, preliminary analyses were carried out to determine the behavior of the 

product of HI and near-surface TKE values during actual wildland fire events 

in the western Great Lakes region in 2006. Twenty-one wildland fire cases in 

the western Great Lakes region were identified in 2006, ranging from 100 to 

nearly 32,000 acres in size. For each wildland fire case, the HI, near-surface 

TKE, and the product of the HI and TKE were computed each hour for the 

entire duration of the fire at locations corresponding to each wildland fire event 

(based on archived, real-time hourly output from the EAMC MM5 daily fireweather simulations over the western Great Lakes region). Results from those 

simulations are shown in table 1. The five largest fires all had occurrences of 

maximum HI x TKE values greater than 15, with the largest value (32.196) 

observed during the Cavity Lake Fire in the Boundary Waters Canoe Area of 

northern Minnesota. Significant near-surface turbulence (TKE > 3 m2s–2) was 

simulated during these fires, with HI values ranging from 4 to 6 at the time 

Fire name Start

date

End

date HI TKE Max.HI*TKE

Date:time (UTC)

of max HI x TKE Ri Acresburned

Cavity Lake 7/14 9/1 6 5.366 32.196 07/17: 0200 -0.314 31,830

Peatland 10/6 10/8 4 8.214 32.856 10/08: 1900 -0.018 6,625

East Zone Cmp. 9/8 10/1 5 6.282 31.410 09/22: 2300 -0.005 5,898

Red Lake 16 4/6 4/7 4 5.253 21.012 04/06: 2300 -0.028 3,650

Turtle Lake 7/13 8/3 4 4.651 18.604 07/16: 0300 -0.012 2,085

Grain Bin 4/26 4/27 5 2.519 12.595 04/26: 2000 -0.280 1,496

20 Mile 4/26 4/27 5 2.519 12.595 04/26: 2000 -0.280 1,456

Cederbend 11/21 11/22 5 1.232 6.160 11/23: 0600 0.036 727

Trail 4/10 4/11 6 3.068 18.408 04/11: 2300 -0.012 676

Richardville 4/22 4/23 5 2.214 11.070 04/24: 0000 -0.124 640

Red Lake 197 4/16 4/16 5 1.815 9.075 04/16: 1900 -0.281 550

Black River 4/16 4/17 5 0.620 3.100 04/17: 0300 -0.140 500

Easter Sunday 4/16 4/17 6 1.565 9.390 04/16: 2000 -0.026 348

Parkers Prairie 4/9 4/11 4 1.798 7.192 04/09: 2000 -0.028 326

Sharptail Burn 4/17 4/18 4 3.222 12.888 04/17: 2000 -0.280 317

219 7/19 7/24 5 5.735 28.675 07/19: 1900 -0.078 240

Shack 4/6 4/7 3 4.655 13.965 04/07: 0000 -0.012 200

Clementson 9/4 9/12 5 1.817 9.085 09/05: 1900 -6.807 149

Hammer 11/9 11/10 5 2.131 10.655 11/09: 0700 -0.002 115

Keystone 8/3 8/5 4 0.565 2.260 08/05: 0700 -0.184 106

Wobble Grade 7/12 7/13 6 1.418 8.508 07/12: 2000 -2.992 100

Table 1—Maximum values of the product of the Haines Index (HI) and near surface turbulent 

kinetic energy (TKE), and the dates, times, and Richardson number (Ri) values when the HI 

x TKE maximum values occurred during selected wildland fire episodes in the western Great 

Lakes region in 2006. Values of maximum HI x TKE exceeding the threshold of 15 are shown 

in bold.

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USDA Forest Service Proceedings RMRS-P-46CD. 2007. 167

when maximum HI x TKE values were simulated. Fourteen of the 21 fire cases 

included in this study had simulated maximum HI x TKE values less than 15. 

Most of the maximum HI x TKE values during the analyzed fires occurred 

in the local afternoon or evening hours.

The application of a combined HI and near-surface TKE for spatially 

pinpointing where atmospheric mesoscale and boundary layer conditions 

are both highly conducive to extreme fire behavior was tested for the Cavity 

Lake Fire that burned nearly 32,000 acres from 14 July to 1 September 

2006. Figure 4 shows the simulated patterns of HI, near-surface TKE, and 

HI x TKE values at 0200 UTC on 17 July 2006 (9:00 pm CDT on 16 July 

2006). Most of the western Great Lakes region had HI values of 5 or 6 at 

this time, with large areas of HI = 6 covering parts of Minnesota, Wisconsin, and Michigan (fig. 4a), including the Boundary Waters Canoe Area of 

northern Minnesota. Significant near-surface turbulence (TKE > 3 m2s–2) 

also occurred in the region at this time, but was confined to much smaller 

areas located over northern Minnesota, southwestern Minnesota, northern 

Wisconsin, and parts of northern and western Michigan (fig. 4b). Figure 4c 

shows the spatial pattern of the product of the HI and near-surface TKE 

across the region, and clearly indicates that the mesoscale and boundary-layer conditions were highly conducive to extreme fire behavior in the 

BWCA of Minnesota where the Cavity Lake Fire was spreading rapidly at 

the time (McDaniel 2006). Values of HI x TKE exceeded 20 over much of 

the arrowhead region of northern Minnesota.

The product of the HI and near-surface TKE presented here represents 

a simple means of combining the two indices for capturing the concurrent 

atmospheric mesoscale and boundary layer risk of extreme fire behavior. The 

preliminary analyses of case studies carried out in this study suggest that 

computing the product may provide a useful tool for predicting when and 

where the stability/moisture conditions in the lower and middle troposphere 

and atmospheric boundary-layer turbulence could all contribute to extreme 

fire behavior at the same time, a relatively rare but dangerous situation.

Haines Index and Turbulence Dynamics

Beyond the spatial and temporal variability patterns of the HI and near-surface 

TKE in the western Great Lakes region, the atmospheric dynamics associated 

with concurrent lower tropospheric instability and dryness (as measured by the 

HI) and near-surface turbulence (as measured by TKE) are also of interest. 

As part of our analyses, we examined how the mid-afternoon production of 

near-surface turbulence through wind shear and buoyancy processes (equations 

3 through 6) varied with changing HI values during each season in 2006 across 

the western Great Lakes region. Figure 5 shows the frequency distribution of 

simulated near-surface TKE values at 2000 UTC for all HI classes for the JAM, 

AMJ, JAS, and OND periods in 2006. Considering all HI classes (2 through 

5), mid-afternoon TKE values between 0 and 1 m2s–2 were most common across 

the western Great Lakes region during the winter (2,530,087 occurrences – 53.9 

percent; fig. 5a), summer (2,191,371 occurrences – 39.0 percent; fig. 5e), and 

fall (2,781,916 occurrences – 49.0 percent; fig. 5g) seasons. During the spring 

season, mid-afternoon TKE values between 1 and 2 m2s–2 were most common 

in the region (1,887,489 occurrences – 34.4 percent; fig. 5c). The occurrence 

of mid-afternoon TKE values greater than 3 m2s–2 for all HI classes was a 

relatively rare event. The percentages of all model grid points having TKE 

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USDA Forest Service Proceedings RMRS-P-46CD. 2007. 168

Figure 4—Simulated patterns of (a) HI, (b) near-surface TKE 

(m2s–2), and (c) HI x TKE at 0200 UTC on 17 July 2006. The 

location of the Cavity Lake Fire in northern Minnesota is 

highlighted with an “x” in each figure.

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Figure 5—Frequency of occurrence (percent) of simulated near-surface TKE values in bins 

0-1, 1-2, 2-3, 3-4, 4-5, 5-6, and > 6 m2s–2 for all HI classes (2-6) (a, c, e, g) and for the high HI 

classes (5-6) (b, d, f, h) during the JFM (a and b), AMJ (c and d), JAS (e and f), and OND (g 

and h) periods in 2006 over the western Great Lakes region. The numbers at the top of each 

stacked bar indicate the total number of occurrences of TKE values within each bin, while 

the different colors indicate relative TKE occurrence percentages under different Richardson 

number (Ri) categories.

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USDA Forest Service Proceedings RMRS-P-46CD. 2007. 170

values greater than 3 m2s–2 at 2000 UTC in the winter, spring, summer, and 

fall seasons were 6.3, 16.2, 7.5, and 8.4 percent, respectively.

When only the high HI classes are considered (HI = 5 or 6), mid-afternoon 

TKE values between 0 and 1 m2s–2 were most common during the winter 

(JFM) (359,585 occurrences – 59.1 percent; fig. 5b) and autumn (OND) 

(542,412 occurrences – 54.9 percent; fig. 5h) seasons in 2006. The spring 

(AMJ) (377,843 occurrences – 34.2 percent; fig. 5d) and summer (JAS) 

(413,660 occurrences – 37.4 percent; fig. 5f) seasons had more occurrences 

of mid-afternoon TKE values in the 1-2 m2s–2 range than any other range. 

The occurrence of mid-afternoon TKE values greater than 3 m2s–2 was still a 

relatively rare event even when HI values reached 5 or 6 in 2006. The percentages of all model grid points having TKE values at 2000 UTC greater than 

3 m2s–2 were 5.7, 16.1, 8.6, and 5.4 percent for the winter, spring, summer, 

and fall seasons, respectively.

Figure 5 also provides insight into the relative significance of vertical wind 

shear and buoyancy in the production and/or dissipation of near-surface 

turbulence under low or high HI conditions, as measured by the gradient 

Richardson number (Ri):

Ri g z

U z V z  u u

Q  	 u u  u u  	

Q /

/ / 2 2 (6)

where g is the gravitational constant and θ is the potential temperature. As 

Ri becomes more negative, the production of turbulence through vertical 

wind shears becomes less and less important compared to the production of 

turbulence through buoyancy. When Ri is less than about –0.03, buoyancy 

completely dominates the production of turbulence. For –0.03 < Ri < 0, 

both shear and buoyancy effects play a role in the production of turbulence. 

Positive values of Ri indicate that buoyancy is acting to suppress turbulence 

generated by vertical wind shears, with complete suppression of turbulence 

occurring when Ri ≥ 0.25. As shown in figure 5, buoyancy effects dominated the production of TKE (Ri ≤ –0.03) at 2000 UTC during the spring 

(fig. 5c,d) and summer (fig. 5e,f) periods regardless of the amount of turbulence (that is, TKE) present or the values of the mesoscale HI. However, 

for the winter (fig. 5a,b) and fall (fig. 5g,h) periods, there was a significant 

drop-off (increase) in the frequency of occurrence of buoyancy-dominated 

(shear-dominated) turbulence regimes as TKE increased. Unlike the other 

seasons in 2006, the spring months were characterized by numerous occurrences of Ri ≥ 0.25 and 0 ≤ Ri < 0.25 when near surface turbulence was weak 

(TKE < 2 m2s–2) (fig. 5c,d).

Summary

We have followed up our initial study of HI and TKE behavior in the 

Northeastern United States region (Heilman and others 2003) with a new 

study that is examining the utility of combining the HI, a mesoscale-type 

fire weather index, with near-surface TKE, a boundary-layer-type index, for 

assessing the potential atmospheric risk of extreme fire behavior in the western 

Great Lakes region. Using the daily, MM5-based fire-weather predictions now 

readily available from the EAMC, we identified the 2006 seasonal patterns 

of occurrence of high HI, high near-surface TKE, and concurrently high 

HI and TKE, expressed as the product of the two indices. Broad areas of the 

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USDA Forest Service Proceedings RMRS-P-46CD. 2007. 171

western Great Lakes region experienced high mid-afternoon (2000 UTC) 

HI values (5 or 6) on more than 20 percent of the days during each season, 

with the highest frequencies of occurrence happening in the summer (JAS) 

and fall (OND) seasons over Iowa and Minnesota. The high HI occurrence 

patterns differ significantly from the frequency of occurrence patterns of 

high near-surface TKE (> 3 m2s–2). Episodes of significant, mid-afternoon 

near-surface turbulence were most common over the northern sections of 

Michigan, Wisconsin, and/or Minnesota, with the highest frequencies of occurrence (>30 percent of the days) associated with the spring (AMJ) period 

in these areas. The contrasting patterns of occurrence for these two indices 

during all seasons suggest that episodes of high HI and high near-surface 

turbulence at the same time, a potentially dangerous fire-weather condition, 

are relatively infrequent in this region of the United States. However, this 

infrequency does provide an opportunity for combining the Haines and 

TKE indices in some fashion so that the timing and location of these rare 

but important events can be anticipated.

In this study, the HI and near-surface TKE values were combined via a 

simple product of the two, and seasonal patterns of occurrence of HI x TKE 

exceeding 15 across the western Great Lakes region during 2006 were examined. Like the high near-surface TKE patterns of occurrence, mid-afternoon 

(2000 UTC) occurrences of HI x TKE exceeding 15 were most frequent in 

the spring over the northern sections of Michigan, Wisconsin, and Minnesota. 

The urban areas of Minneapolis, Milwaukee, Chicago, and Detroit also had 

occurrence maxima during the spring. Although the occurrence of high HI 

and high near-surface TKE at the same time may be relatively rare in this 

region, the simulation results from 2006 suggest that when it does happen, 

it tends to occur in those areas of the region that are most prone to wildfires 

(that is, northern Michigan, northern Wisconsin, and northern Minnesota) 

and during the springtime when wildfires in the region are most common.

The application of a new index based on the simple product of predicted 

HI and near-surface TKE values to actual western Great Lakes wildland fire 

episodes in 2006 suggests that this type of index may be a useful tool for 

pinpointing when and where atmospheric stability, moisture, and boundary-layer turbulence may collectively contribute to creating an ambient local 

atmospheric environment highly conducive to extreme fire behavior. The 

five largest wildlfires in the western Great Lakes region in 2006 occurred in 

locations where and during periods when the product of the HI and nearsurface TKE exceeded a threshold (15) indicative of dry, unstable middle 

tropospheric layers above a highly turbulent boundary layer. Further testing 

of this type of combined index is planned for the western Great Lakes region 

and other regions of the United States via the regional modeling activities in 

the EAMC and other modeling consortia in the FCAMMS.

The primary mechanism responsible for mid-afternoon turbulence generation in atmospheric boundary layers over the western Great Lakes region 

during the spring and summer seasons in 2006 was buoyancy, regardless of 

the level of turbulence present or the value of the mesoscale HI. During the 

winter and fall seasons, large TKE values were more frequently associated 

with shear-dominated turbulence regimes than buoyancy-dominated regimes. 

This suggests that during the springtime wildfire season in the western Great 

Lakes region, atmospheric instability within the atmospheric boundary layer 

and above is more often than not the primary factor in generating near-surface turbulence that can interact with wildland fires. However, significant 

near-surface turbulence generated by ambient vertical wind shears can certainly create atmospheric environments conducive to erratic fire behavior, as 

Combining Turbulent Kinetic Energy and Haines Index Predictions for Fire-Weather Assessments Heilman and Bian
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 172

shown by the Ri values (–0.03 < Ri < 0) for four of the five largest analyzed 

fires in this study and for five of the seven analyzed fires that had maximum 

HI x TKE values exceeding the 15 threshold (table 1).

The analyses described here represent the first step in assessing the feasibility of combining the HI with near-surface TKE for fire-weather predictions 

in different regions of the United States. Additional analyses of HI and TKE 

behavior for the Northeast and for years prior to 2006 will be carried out 

using the historical MM5 output data archive developed by the EAMC as 

part of its fire-weather prediction program. With these analyses and our further examinations of the dynamic behavior of the HI and near-surface TKE 

before and during actual wildland fire events, we hope to not only improve 

our understanding of atmospheric mesoscale and boundary-layer interactions 

during fire-weather events, but also to determine the potential for combining 

these indices in some fashion for enhancing operational forecasts of extreme 

fire weather and fire behavior.

References

Gerrity, J.P.; Black, T.L.; Treadon, R.E. 1994. The numerical solution of the MellorYamada level 2.5 turbulent kinetic energy equation in the ETA model. Monthly 

Weather Review. 122:1640-1646.

Haines, D.A. 1988. A lower atmospheric severity index for wildland fires. National 

Weather Digest. 13:23-27.

Heilman, W.E.; Bian, X.; Charney, J.; Potter, B. 2003. Combining the Haines 

Index and turbulent kinetic energy for fire-weather predictions. 5th Symposium 

on Fire and Forest Meteorology, 16-20 November 2003, Orlando, FL. American 

Meteorological Society, Boston, MA. [On CD-ROM]

McDaniel, J. 2006. That thing was rolling: the Cavity Lake Fire in the Boundary 

Waters Canoe Area Wilderness. Wildland Fire Lessons Learned Center, National 

Wildfire Coordinating Group. [On line]. Available: <http://www.wildfirelessons.

net/Additional.aspx?Page=74>

Mellor, G.L.; Yamada, T. 1974. A hierarchy of turbulence closure models for planetary 

boundary layers. Journal of the Atmospheric Sciences. 31:1791-1806.

Mellor, G.L.; Yamada, T. 1982. Development of a turbulence closure model for 

geophysical fluid problems. Reviews of Geophysics and Space Physics. 20:851-

875.

USDA Forest Service. 2002. National Fire Plan research and development – 2001 

business summary. St. Paul, MN: U.S. Department of Agriculture, Forest Service, 

North Central Research Station. 29 p.

Combining Turbulent Kinetic Energy and Haines Index Predictions for Fire-Weather Assessments Heilman and Bian
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 173

Abstract—Mastication has become a popular fuels treatment in the Western United 

States, but predicting subsequent fire behavior and effects has proven difficult. Fire 

behavior and effects in masticated fuelbeds have been more intense and erratic in 

comparison with model predictions. While various particle or fuelbed characteristics 

in these fuels may contribute to the inaccuracy of model predictions, an increase in 

particle surface area to volume ratio by the mastication process may affect moisture 

dynamics. The prediction of fuel moisture is critical to predicting fire behavior and 

effects in prescribed fire or wildfire scenarios. Moisture dynamics in masticated fuels is 

characterized here by analyzing desorption rates in masticated and intact manzanita 

and compared with pine and maple dowels under laboratory experiments. Preliminary 

analysis shows that desorption rates are similar in masticated and intact manzanita as 

well as pine dowels by comparing relative moisture contents throughout desorption 

as well as by calculating response times using the timelag concept. These results held 

true both at the particle and fuelbed level, although masticated manzanita and pine 

dowels were both found to desorb moisture more quickly as individual particles compared to within fuelbeds. Particle density was strongly linked to desorption although 

it is not fully explored in terms of its significance as compared with other physical 

properties. Physical and chemical differences due to particle weathering and species 

differences may play significant roles as well. While this may be some of the first work 

to address the effects of mastication on moisture dynamics in forest fuels, future work 

should focus on other aspects where fuelbed or particle characteristics in masticated 

fuels may influence fire behavior and effects.

Introduction

Mechanical mastication of forest fuels has become a popular method of 

reducing fire risk by disrupting the vertical continuity of shrub and small-tree 

fuels. While mastication projects are being conducted over large areas in the 

Western United States, little is known about the effects of mastication on 

subsequent fire behavior. Prescribed fires have occurred within masticated 

sites resulting in unexpected fire behavior and effects (Knapp and others 

2006). Currently, fire modeling systems are poor at predicting fire behavior 

parameters in these types of treatments. Changes in moisture dynamics due 

to increases in surface area:volume ratios associated with the fractured nature of masticated fuels may be a primary reason for inaccurate fire behavior 

predictions.

Fuel moisture is a primary predictor of fire behavior. Understanding the 

response of fuel moisture to changes in environmental conditions is required 

to predict daily or seasonal fuel moisture. The adsorption and desorption of 

moisture in fuels during a change in environmental conditions occur differently (Blackmarr 1971) and are referred to as sorption hysteresis. The resulting 

In: Butler, Bret W.; Cook, Wayne, 

comps. 2007. The fire environment—

innovations, management, and policy; 

conference proceedings. 26-30 March 

2007; Destin, FL. Proceedings 

RMRS-P-46CD. Fort Collins, CO: 

U.S. Department of Agriculture, 

Forest Service, Rocky Mountain 

Research Station. 662 p. CD-ROM.

1 Graduate Student and Assistant 

Professor, respectively, Department of 

Forestry and Watershed Management, 

Humboldt State University, Arcata, CA. 

Lead author jkreye@suddenlink.net 

Moisture Dynamics in Masticated Fuelbeds: 

A Preliminary Analysis

Jesse Kreye1 and J. Morgan Varner1
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 174

equilibrium moisture content following desorption is higher than the equilibrium moisture content resulting from the adsorption process across various 

environmental conditions (relative humidity and temperature). Desorption 

of moisture is critical because it occurs when fuels are drying in response to 

decreases in relative humidity and/or increases in ambient air temperature. 

The irregular shape of masticated fuel particles, resulting in a higher surface 

area to volume ratio, may affect the way in which fuel moisture responds 

to diurnal or seasonal changes in environmental conditions. Fuel moisture 

values that are input into fire behavior/effects models and fire danger rating 

systems (for example, BehavePlus and NFDRS) are often estimated from the 

weighing of standard 1⁄2 inch ponderosa pine dowels.

To address the deficiencies in prediction of fire behavior in masticated 

fuelbeds we evaluated fuel moisture during desorption in mechanically 

masticated fuels (Arctostaphylos manzanita ssp. wieslanderi) at the particle 

and fuelbed scale and compared them to standard fuels (Pinus ponderosa) 

as well as similar density fuels (Acer sp.). Two experiments were conducted 

addressing: (1) desorption at the individual particle level and (2) desorption 

at the fuelbed level. The results presented here are a preliminary analysis, 

and ongoing research is currently investigating site, species, and time since 

treatment level differences.

Methods

Mechanically masticated fuels were collected from a fuelbreak in the Six 

Rivers National Forest near the community of Mad River, CA, approximately 

50 miles east of the Pacific Ocean. The site was dominated by dense common 

manzanita (Arctostaphylos manzanita ssp. wieslanderi) greater than 6 ft tall 

prior to treatment. The elevation of the study site is 940 ft with a 6 percent 

slope and a NW aspect. Mastication was conducted in December 2004. Within 

the site, all woody fuels were collected from the surface down to mineral 

soil at four 2 x 2 m plots. Collected fuel was transported to the laboratory 

for desorption experiments under controlled conditions. Desorption experiments were conducted on individual fuel particles (experiment 1) as well as 

constructed fuelbeds (experiment 2). Desorption rates were analyzed in two 

ways: (1) by comparing relative moisture contents over time using analysis of 

variance and (2) by estimating response times in terms of the original timelag 

concept as developed by Byram (1963).

Experiment 1

Masticated manzanita particles with an average diameter between 6.35 mm 

(1⁄4 inch) and 25.4 mm (1 inch) were selected (n=19) for analysis of moisture 

desorption along with 1.27 x 12.7 cm (1⁄2 inch x 5 inch) standard ponderosa 

pine dowels (n=10) for comparison. Masticated particles were measured for 

minimum and maximum diameter at two equidistant locations along the 

longitudinal axis of the particle. The arithmetic mean of the four measurements was used as average diameter. Specific gravity of masticated fuels and 

pine dowels was measured by submersion of individual particles in water and 

measuring the resulting buoyant force as recorded on a balance whereby

specific gravity = oven dry weight (g) / buoyant force (g)

Moisture Dynamics in Masticated Fuelbeds: A Preliminary Analysis Kreye and Varner
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 175

Masticated manzanita particles and pine dowels were oven dried at 60 oC 

for 72 hours, weighed, and submerged in a water bath for 7 days. Following the water bath all particles were weighed and subsequently placed in a 

temperature and humidity controlled room (4.5 x 3.2 m). Temperature and 

humidity were controlled at 28 percent relative humidity (± 2.7 percent) and 

23 oC (± 1.6 oC) by sealing off all ventilation and the use of a ComfortAire® BHD-301 electronic dehumidifier. All particles were placed on racks 

to allow desorption of moisture until equilibrium moisture content was 

reached. Fuel particles were weighed periodically for 384 hours during the 

desorption process. Temperature and relative humidity data were recorded 

hourly throughout the experiment.

Moisture content (m) of fuel particles at time t was calculated by

mt = (fuel weight t – oven dry weight) / oven-dry weight

Fuel moisture content was converted to relative moisture content (Fosberg 

1970) for comparing desorption rates and to estimate time lag response 

times by

E = (mt – mf) / (mi – mf)

where:

E = relative moisture content

mt = moisture content at time t

mf = final moisture content

mi = initial moisture content

Relative moisture was compared across time periods (t = 0, 10, 24, 50, 

100, and 288 hours) between masticated manzanita and pine dowels using 

GLM analysis of variance. Relative moisture contents were regressed with 

specific gravity as well as average diameter for both fuel types at time periods 

(t = 10, 24, 50, 100, 288 hours). Specific gravity was regressed with average 

diameter as a predictor for both fuel types as well.

The timelag concept developed by Byram (1963) is a common method 

of describing moisture responses in fuels resulting from changes in environmental conditions. Relative moisture content (E) is the remaining fraction of 

moisture that is evaporable at a specific time during desorption from initial 

moisture content to an equilibrium moisture content following a change in 

temperate and/or relative humidity. Nelson (1969) described the timelag 

parameter as characteristic of physical and chemical processes that follow 

an exponential decay function and that E could be described in terms of 

response time (τ) by

m m

m m

E Ke t f

i f

t 



   
T

where

K = 1 when at t = 0, mt = mi

e = base of natural logarithm

t = time (hours)

τ = response time (hours) for which 1/e (.368) of the change between 

two steps remains

The logarithmic form of this equation (below) can be differentiated to 

calculate the rate of change in relative moisture content and the resulting 

slope defined in terms of τ since the logarithmic form will be linear under 

the theoretical negative exponential function. Response time can then be 

calculated by solving this equation for τ.

Moisture Dynamics in Masticated Fuelbeds: A Preliminary Analysis Kreye and Varner
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 176

d

dt (ln ) E  
 1

T

Empirical studies (Anderson 1990; Mutch and Gastineau 1970; Nelson 

1969) have shown that moisture response in forest fuels does not follow a 

pure negative exponential function. Different techniques have therefore been 

used to describe response times (τ) throughout desorption and adsorption 

processes. While response time may be thought of in terms of the time required for 63.2 percent (1 – 1/e) of the total change to occur as moisture 

is adsorbed or desorbed from an initial stable state to that of equilibrium at 

another stable state, this response time fluctuates throughout the process. 

Since moisture response does not follow a pure exponential decay function, 

the derivative of its true function should result in a nonlinear function where 

its slope will not be constant. Response time (τ) fluctuates throughout the 

process because the instantaneous rate of change along the differentiated 

logarithmic form of E changes across time (t). The instantaneous rate of 

change at a single time (t) can result in the calculation of a constant response 

time (τ), but may only be true at that particular time point.

It is common to plot E as a function of time (t) on a semilogarithmic axis 

and the resulting curves partitioned into separate linear portions. Response 

times are then calculated for the separate linear sections. Nelson (1969) 

described two timelags, or “response times,” τ1 and τ2, which represent the 

initial stage of drying and the final stage of drying, respectively, but these 

were separated by a curvilinear portion in the middle. Mutch and Gastineau 

(1970) found two linear portions occurred in desorption and adsorption 

of reindeer lichen. Anderson (1990) studied more than one timelag period 

whereby E = 0.63, 0.86, and 0.95, respectively, for the first three timelag 

periods. All of these studies have used standard conditions of 80 oF and a 

step change from 90 percent RH to 20 percent RH for desorption and the 

reverse for adsorption. Qualitative analysis can be conducted by plotting E

over time on a semilogarithmic axis and comparing different fuel types in 

terms of response times over various described timelag sections to show the 

variation in moisture response of different fuels as Anderson (1990) did with 

Douglas-fir needles, lodgepole pine needles, 1⁄2 inch square pine sticks, and 

lichen. Linton (1962) and Viney and Hatton (1989) described a different use 

of the term “timelag” in regard to the lag time of fuel moisture behind that 

of a theorized equilibrium moisture content that would occur on a diurnal 

cycle of changing temperature and relative humidity under field conditions. 

Viney and Hatton (1989) and Viney and Catchpole (1991) therefore suggested using the term “response time” as the time with which 63.2 percent 

of evaporable moisture content has been lost between a shift in two stable 

conditions, which usually occurs under laboratory experiments. Nonetheless the timelag concept and the use of “response times” are widely used 

in fire and fuels research and management and can be analyzed through 

empirical methods without having detailed information regarding specific 

fuel characteristics.

To address this nonlinearity in the research presented here, piecewise 

polynomial curve fitting was conducted to separate plots of the natural logarithm of E over desorption time (t) into two linear portions for both fuel 

types over 7 days of desorption. Linear-linear piecewise models were used to 

partition the curves into two (τ1 and τ2) timelag sections. Response times (τ) 

were then calculated for each timelag section. A response time for the entire 

desorption process (T), whereby 63.2 percent of the evaporable moisture had 

in fact been lost, was compared with calculated response times of timelag 

Moisture Dynamics in Masticated Fuelbeds: A Preliminary Analysis Kreye and Varner
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 177

sections τ1 and τ2 for each fuel type. This response time (T) is not defined 

by the differential equation above, but rather by estimating the average time 

that 63 percent of the evaporable moisture had in fact been lost within each 

fuel type during desorption experiments.

Experiment 2

Twelve fuelbeds were created using masticated manzanita particles from the 

Mad River fuel break. Fuels were separated into traditional 1-hour (<6.35 mm 

diameter) and 10-hour (6.36 to 25.4 mm diameter) fuel categories. Fuels 

greater than 25.4 mm in diameter were excluded from experimentation because they compose a minor component of fuel loading in masticated sites in 

the region (Kane and others 2006). Each fuelbed was constructed of 294 g of 

1-hour fuels and 435 g of 10-hour fuels, matching proportions and loading 

of masticated fuels on the site (Kane and others 2006). Fuelbed heights were 

5 to 7 cm and were created in 26 x 38 cm aluminum baking pans.

Five manzanita particles between 6.36 and 25.4 cm average diameter 

(10-hour fuels) were selected within each fuelbed and marked with wire and 

metal tags. Two of these manzanita particles were intact while three were 

fractured from mastication. Intact particles did not appear to be physically 

altered, or fractured, by the mastication process. These five marked particles 

were placed at the upper layer of their respective fuelbeds whereby the upper 

surface of each was exposed to the atmosphere directly above the fuelbed, 

representing the driest portion of the fuelbed.

Pine (Pinus ponderosa) and maple (Acer sp.) dowels were also marked with 

wire and tags and added to all fuelbeds. Two pine dowels (12.5 x 127 mm) and 

two maple dowels (12.8 x 127 mm) were placed at the upper layer of each fuel 

bed to compare moisture dynamics with that of the manzanita particles.

All fuelbeds were submerged in a water bath for 7 days, drained and placed 

in the humidity-controlled environment as in experiment 1. Temperature and 

humidity were controlled at 31 percent relative humidity (± 3.4 percent) and 

24 oC (± 1.0 oC). Holes were placed in the bottoms of each pan and pans 

were elevated on wooden slats to allow excess moisture to drain throughout 

desorption. Fuelbeds were allowed to desorb moisture for 336 hours. Fuelbeds and marked particles were weighed throughout the experiment. Average 

diameters of all particles (manzanita, pine dowels, and maple dowels) were 

then measured in the same manner as experiment 1. Fuelbeds were oven dried 

at 60 oC for 72 hours. Specific gravity for all marked particles was calculated 

using the same methods as experiment 1. Fuel moisture content and relative 

moisture content values were calculated for marked particles and the fuelbeds 

themselves. For fuelbed moisture content, weights of pine and maple dowels 

were subtracted at each time point to obtain fuelbed moisture values of the 

manzanita particles exclusively.

Specific gravity was compared across fuel types using GLM analysis of 

variance (see table 1 in the Results section). Relative moisture content was 

compared between the following fuel types: masticated manzanita, intact 

manzanita, pine dowels, and maple dowels, at time periods 10, 24, 50, 100, 

and 288 hours using GLM analysis of variance. Timelag response times were 

calculated using the same methods as experiment 1 for all four fuel types 

from individual marked particles. Response times were also calculated for the 

12 fuelbeds. Results of timelag response times for both experiments 1 and 2 

were combined and reported under experiment 2 results (see table 2 in the 

Results section).

Moisture Dynamics in Masticated Fuelbeds: A Preliminary Analysis Kreye and Varner
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 178

Results

Desorption Experiment 1

Average diameters of masticated manzanita particles (n=19) ranged from 

7.84 to 20.44 mm, while diameters of pine dowels (n=10) ranged from 

12.34 –12.57 mm. Masticated manzanita particles were significantly higher 

in density with a mean specific gravity of 0.69 (±0.011) as compared with 

pine dowels with a mean specific gravity of 0.48 (±0.025).

Relative moisture content did not differ significantly between masticated 

manzanita fuels and pine dowels (fig. 1) across time periods t = 0, 10, 24, 

50, 100, 288 hours (α = 0.05). Variation in relative moisture content appears to be higher in masticated manzanita than that of pine dowels (fig. 2), 

although the Levene’s test rejected homogenous variance between fuel types 

at 10 hours of desorption only.

Average diameter and specific gravity were related (r2 = 0.427) within masticated manzanita particles, but not within pine dowels (r2 = 0.002; fig. 3). 

In masticated manzanita linear regression resulted in a stronger relationship 

between relative moisture content and diameter in early stages of desorption 

(fig. 4), while relative moisture content had a stronger relationship with specific gravity in later stages. Relationships of relative moisture content with 

both diameter and specific gravity became less strong as particles approached 

equilibrium moisture content. In ponderosa pine dowels relative moisture 

content was strongly related to specific gravity in early stages also (fig. 5), 

but relative moisture content and average diameter was not related at all. It 

of course should be noted that variation in diameters of pine dowels was 

minuscule and these results were expected.

Figure 1—Relative moisture content in standard 10-hour 

pine dowels and masticated manzanita fuel particles at 

throughout desorption.

Moisture Dynamics in Masticated Fuelbeds: A Preliminary Analysis Kreye and Varner
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 179

Figure 3—Relationship of specific gravity and average diameter in masticated manzanita (r2 = 0.472) and 

ponderosa pine dowels (r2 = 0.002).

Figure 2—Variation in relative moisture content at 10, 24, 50, 100, and 288 hours of desorption in masticated 

manzanita fuel particles and standard 10-hour pine dowels.

Moisture Dynamics in Masticated Fuelbeds: A Preliminary Analysis Kreye and Varner
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 180

Figure 4—The relationship of relative moisture content with specific gravity and diameter at 0, 

10, 24, 50, 100, and 288 hours of desorption in masticated manzanita. Results are from linear 

regression.

Moisture Dynamics in Masticated Fuelbeds: A Preliminary Analysis Kreye and Varner
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 181

Figure 5—The relationship of relative moisture content with specific gravity and diameter 

at 0, 10, 24, 50, 100, and 288 hours of desorption in standard pine dowels. Results are from 

linear regression.

Moisture Dynamics in Masticated Fuelbeds: A Preliminary Analysis Kreye and Varner
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 182

Desorption Experiment 2

Specific gravity differed significantly (p<0.001) between pine dowels and 

all other fuel types (intact masticated, masticated, and maple dowels) used in 

the fuelbed experiments. Masticated manzanita was higher in specific gravity 

(0.70) than maple dowels (0.65), although the difference was not substantial. 

Intact manzanita did not differ from either masticated manzanita or maple 

dowels (table 1).

Table 1—Specific gravity of maple dowels, pine dowels, 

intact manzanita, and masticated manzanita used in 

desorption experiment 2.

 Fuel type Specific gravity Std Error

Maple dowela 0.65 0.011

Pine dowelc 0.47 0.011

Intact manzanitaab 0.68 0.012

Masticated manzanitab 0.70 0.0009

abc No significant difference (p<0.001) between fuel types with 

like notation.

Fuelbed relative moisture content differed significantly from all fuel types 

marked individually across time periods 10, 24, 50, 100, and 288 hours using 

the Tukey-Kramer multiple-comparison test ά = 0.05 (fig. 6). Relative moisture content of maple dowels was significantly different from all other fuel 

types. Pine dowels and intact manzanita did not differ in regards to relative 

moisture content. Relative moisture content of intact manzanita did not differ 

from masticated manzanita. It is apparent that desorption in intact manzanita, 

masticated manzanita, and pine dowels are fairly similar in comparison to 

maple dowels, which desorb more slowly at the fuelbed level.

The development of response times (τ) under the timelag concept yielded 

similar results as comparing relative moisture contents by fuel type. Linear 

portions developed for each fuel type are shown in figure 7. Results include 

those from experiments 1 and 2. Response times (τ) calculated from slopes 

(b) of linear sections, or timelag sections, are shown in table 2 by fuel type. 

Intact and masticated manzanita as well as pine and maple dowels all had 

Table 2—Response times (τi) for piecewise linear portions and overall response time 

(T) where ~63 percent of evaporable moisture was actually lost.

Slope Response time

 Fuel type J c b1 b2 r2 τ1 τ2 T

hours	 - - - - x10–2- - - -	 - - - -hours - - - -

Fuelbed 98 –1.41 –2.32 .977 71 43 70

Maple dowela 61 –2.76 –1.36 .966 36 74 40

Pine dowela 36 –5.00 –1.47 .950 20 68 23

Intact manzanitaa 39 –5.38 –1.40 .943 19 71 20

Masticated manzanitaa 36 –5.09 –1.52 .940 20 66 20

Pine dowelb 51 –7.94 –0.949 .976 13 105 10

Masticated manzanitab 51 –8.56 –0.687 .912 12 146 10

a Fuel particles desorbing within fuelbeds (experiment 2).

b Fuel particles desorbing individually (experiment 1).

c J is the upper limit (hours) of the 1st piecewise linear portion of lnE vs. time (hours).

Moisture Dynamics in Masticated Fuelbeds: A Preliminary Analysis Kreye and Varner
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 183

Figure 7—Fraction of evaporable moisture (E) throughout 

desorption developed from linear-linear regression using 

piecewise polynomial curve fitting. Response times (τi) are 

developed from the inverse of the slope of each linear portion.

Figure 6—Relative moisture content in fuelbeds, intact manzanita (“intact 

masticated”), masticated manzanita, maple dowels, and pine dowels 

throughout desorption.

Moisture Dynamics in Masticated Fuelbeds: A Preliminary Analysis Kreye and Varner
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 184

initial timelag sections with shorter response times (τ1) than that of later 

stages (τ2). The transition between τ1 and τ2 occurred late in desorption where 

80 percent or more of the evaporable moisture has been lost. The increase 

in response time in the later stage of desorption indicates a decrease in the 

rate of desorption during later stages of drying. Fuelbeds on the other hand 

desorbed moisture at a higher rate during the later stage of drying as indicated 

by the increase in response time in the second timelag section (τ2).

Pine dowels and masticated manzanita dried faster at the individual particle level (experiment 1) as compared to any of fuels at the fuelbed level 

(experiment 2) during the first timelag section (τ1). Although initial response 

time of masticated manzanita was shorter than pine dowels at the individual 

level, 12 hours versus 13 hours, respectively, they appear to have desorbed 

moisture fairly similarly. The transition from timelag section τ1 to τ2 both 

occur at 51 hours. Response time τ2 of pine dowels was shorter than that of 

masticated manzanita at the individual level.

All fuels initially (τ1) desorbed moisture more quickly than do fuelbeds 

(fig. 7). After 4 days fuelbed desorption appeared to increase, as shown by the 

shift of response time from 71 to 43 hours (table 2), which differs from all 

other fuel types. Desorption rates decreased during later stages (τ2) in all other 

fuel types. Of all fuel types, maple dowels had the longest initial response time 

(τ1) of 36 hours. Pine dowels, intact manzanita, and masticated manzanita 

had similar initial response time of 20, 19, and 20 hours, respectively. Later 

stages of desorption (τ2) are fairly similar between all four fuel types (maple 

dowels, pine dowels, intact manzanita, masticated manzanita) with response 

times of 74, 68, 71, and 66 hours respectively, although the transition (J) 

from timelag section τ1 to τ2 was later in maple dowels (table 2). Transition 

times (J) are similar between pine dowels, intact manzanita, and masticated 

manzanita occurring at 36, 39, and 36 hours, respectively.

The times at which 63 percent of evaporable moisture was actually lost (T) 

during desorption were similar to response times calculated for the initial 

timelag sections τ1 (table 2) for all fuels in both experiments including the 

fuelbed.

Discussion

Analyzing moisture dynamics in masticated fuels is important in attempting to understand observed fire behavior and fire effects within masticated 

fuels treatments. Desorption of moisture within fuels addressed here occurs 

differently at the individual level as compared with those fuels drying at the 

fuelbed level. Also while diameter affects moisture dynamics within forest 

fuels, the effect of particle density is shown to affect moisture dynamics.

Comparing relative moisture content over time as well as calculating 

response times under the timelag concept reveals no substantial differences 

in the way that masticated manzanita desorbs moisture as compared with 

intact manzanita or ponderosa pine dowels. The similarity of desorption rates 

between manzanita and pine dowels and the difference between manzanita 

and maple dowels suggests density and species differences having a role in 

moisture dynamics. Because intact manzanita and masticated manzanita do 

not appear to differ in desorption rates, the similarity of masticated manzanita 

in experiment 1 with that of pine dowels may not necessarily be explained by 

Moisture Dynamics in Masticated Fuelbeds: A Preliminary Analysis Kreye and Varner
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 185

the idea that surface area to volume ratio and density are working against each 

other. Although it may be that physical and chemical properties of manzanita 

versus ponderosa pine at the species level may be significant in moisture dynamics, the fact that the manzanita had weathered for 2 years on site before 

moisture experiments had been conducted may be a factor in these results. 

Discrepancies in modeling fire behavior in masticated fuels do not lie in an 

inability of predicting fuel moisture based on surface area to volume ratios 

being altered by the mastication process. It should be noted though that the 

effect of surface area to volume (SA:V) ratio in fire danger rating systems 

has been in regard to the effect on the heating of fuels ahead of the flaming 

front during combustion as moisture is evaporated and the interior portions 

of fuel particles increase in temperature to the point of combustion. The differences in SA:V ratio between masticated and intact fuels may play a role in 

the combustion process and should not be disregarded altogether in terms of 

modeling discrepancies in fire behavior prediction in masticated fuels.

The role of density, as analyzed through specific gravity here, plays a role 

in desorption rates in these fuels. The role of density in the timelag concept 

was addressed by Byram (1963), but the extent of its role might not be fully 

understood. While timelag categories have been developed based on fuel 

diameter, specific gravity appears to be positively correlated with diameter 

and plays a similar role in moisture dynamics. Both increases in diameter 

and increases in fuel density result in slower desorption rates. The degree of 

effect between diameter and density on moisture dynamics in fuels analyzed 

here appears to change temporally whereby diameter and density shift in 

their dominance in controlling desorption. Diameter appears to have more 

control in early stages of desorption while density becomes more dominant 

during later stages. While the preliminary analysis of these data suggests an 

insignificant role in the mastication of fuels in regards to moisture dynamics, 

these results are from a single site conducted with one species. Future analysis 

of experiments with masticated fuels from other sites, different species, and 

various times since treatment should increase our understanding of the role 

that mastication has on influencing moisture dynamics in these fuels.

The response times and their respective linear portions described here 

are from desorption of fuels from initial fuel moisture contents much higher 

than fiber saturation, through soaking, to that of equilibrium moisture 

contents in an environment of approximately 25 oC and 30 percent RH. 

Previous studies referenced here have conducted moisture dynamics experiments under standard conditions of 26.7 oC while shifting relative humidity 

from 90 to 20 percent and then from 20 to 90 percent for desorption and 

adsorption, respectively, allowing fuels to come to equilibrium at each stage. 

Differences in timelag sections developed here and that of previous studies 

may be a result of the scale at which desorption is being analyzed. The use 

of developing response times for certain portions of desorption curves by 

partitioning may be useful in attempting to describe desorption or adsorption rates in general, but these curves appear to be curvilinear following 

logarithmic transformation and more precise modeling may be useful in 

further understanding how various factors affect moisture dynamics in forest 

fuels. Further research regarding other particle and fuelbed characteristics 

of masticated fuels is needed to explore the questions regarding the inability 

of current fire modeling systems to accurately predict fire behavior and fire 

effects in these types of treatments. 

Moisture Dynamics in Masticated Fuelbeds: A Preliminary Analysis Kreye and Varner
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 186

References

Anderson, H.E. 1990. Moisture diffusivity and response time in fine forest fuels. 

Canadian Journal of Forest Research 20: 315-325.

Blackmarr, W.H. 1971. Equilibrium moisture content of common fine fuels found 

in southeastern forests. Research Paper SE-74. Asheville, NC: U.S. Department 

of Agriculture, Southeastern Forest Experiment Station.

Byram, G.M. 1963. An analysis of the drying process in forest fuel material. 

Unpublished report. Missoula, MT: U.S. Department of Agriculture, Forest 

Service, Rocky Mountain Research Station, Fire Sciences Laboratory.

Fosberg, M.A. 1970. Drying rates of heartwood below fiber saturation. Forest 

Science 16: 57-63.

Kane J.M.; Knapp, E.E.; Varner, J.M. 2006. Variability in loading of mechanically 

masticated fuel beds in northern California and southwestern Oregon. In: 

Andrews, P.L., Butler, B.W. comps. Fuel Management – How to Measure Success: 

Conference Proceedings. Mar. 27-30, 2006. Portland, OR. Proceedings RMRSP-41. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky 

Mountain Research Station: 341-350.

Knapp, E.E.; Busse, M.D.; Varner, J.M.; Skinner, C.N.; Powers, R.F. 2006. Behavior 

and short-term effects of fire in masticated fuel beds. Proceedings of the Third 

International Fire Ecology and Manageme4nt Congress, Nov 13-17, 2006, San 

Diego, CA.

Linton, M. 1962. Report on moisture variation in forest fuels-prediction of moisture 

content. Melbourne, Autralia: Commonwealth Scientific and Industrial Research 

Organization, Division of Physical Chemistry.

Mutch, R.W.; Gastineau, O.W. 1970. Timelag and equilibrium moisture content 

of reindeer lichen. Research Paper INT-76. Ogden, UT: U.S. Department 

of Agriculture, Forest Service, Intermountain Forest and Range Experiment 

Station.

Nelson, R.M., Jr. 1969. Some factors affecting the moisture timelags of woody 

materials. Research Paper SE-44. Asheville, NC: U.S. Department of Agriculture, 

Forest Service, Southeastern Forest Experiment Station.

Viney N.R.; Catchpole, E.A. 1991. Estimating fuel moisture response times from 

field observations. International Journal of Wildland Fire 1: 211-214.

Viney, N.R.; Hatton, T.J. 1989. Assessment of existing fine fuel moisture models 

applied to Eucalyptus litter. Australian Forestry 52: 82-93.

Moisture Dynamics in Masticated Fuelbeds: A Preliminary Analysis Kreye and Varner
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 187

Abstract—Canadian mixedwood forests have a high compositional and structural diversity. It includes both hardwood (aspen, balsam poplar, and white birch) and softwood 

(balsam fir, white spruce, black spruce, larch, and white cedar) species that can form 

pure stands or mixed stands. This heterogeneity results in a variety of vertical structural strata that can potentially interact with fire behaviour. Fourteen fire impact maps 

including information on preburn stand composition and structure were gathered in a 

Geographical Information System. The relative influence of prefire forest composition, 

stand density, and surficial deposits on postfire forest cover attributes (such as variation 

in proportion of green/red/charred trees) was analyzed using contingency tables. Many 

attributes of postfire forests (fire legacy) can be related to preburn forest composition 

and structure. Highest fire impact was observed in coniferous stands. At the other 

end of the spectrum, aspen stands and wetlands contributed to most of the fire skips. 

Within coniferous stands, there was a difference between species with regard to their 

susceptibility to windthrow following fire. Jack pine stands had less severe windthrow 

allowing for an abundance of snags, whereas windthrow is common in balsam fir 

stands. Impacts vary with regard to fire severity, suggesting that observed differences 

between stand types may be less important when fires are very intense. These results 

have consequences on the maintenance of the diversity of the forest mosaics through 

time as well as our capability to predict fire behaviour and impacts.

Introduction

The Canadian boreal forest has often been described as a region where 

large severe crown fires control vegetation dynamics (Johnson and others 

1998). These severe fires are usually recognized as leaving few surviving 

trees in burned areas. Many forest managers used this conventional wisdom 

to justify a relatively low level of retention after harvesting. In fact, there are 

relatively few studies that address the question of how much area is spared 

from fire and on how those unburned islands are spatially distributed after 

severe crown fire events (Schmiegelow and others 2006).

Canadian mixedwood forests have a high compositional and structural 

diversity. It includes both hardwood (aspen, balsam poplar, and white birch) 

and softwood (balsam fir, white spruce, black spruce, larch, and white cedar) 

species that can form pure or mixed stands. This heterogeneity results in a 

variety of vertical structural strata that can affect fire behaviour (Cumming 

2001; van Wagner 1977).

Relationships Between Prefire Composition, 

Fire Impact, and Postfire Legacies in the 

Boreal Forest of Eastern Canada 

Alain Leduc1, Yves Bergeron2, and Sylvie Gauthier3

In: Butler, Bret W.; Cook, Wayne, 

comps. 2007. The fire environment—

innovations, management, and policy; 

conference proceedings. 26-30 March 

2007; Destin, FL. Proceedings 

RMRS-P-46CD. Fort Collins, CO: 

U.S. Department of Agriculture, 

Forest Service, Rocky Mountain 

Research Station. 662 p. CD-ROM.

1 Adjunct Professor, Centre d’étude de 

la forêt and Département des sciences and Département des sciences

biologiques, Université du Québec à 

Montréal, Montréal, Québec, Canada. 

leduc.alain@uqam.ca

2 Director and Professor, NSERC 

UQAT/UQA M Industrial Chair 

in Sustainable Forest Management, 

Université du Québec en AbitibiTémiscamingue, Québec, Canada.

3 Research Scientist (Ecology), Natural 

Resources Canada, Canadian Forest 

Service, Laurentian Forestry Centre, 

Quebec, Quebec, Canada.
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 188

In this study, we used 14 fire impact maps (fig. 1) that were overlaid on 

forest inventory maps to define the original forest cover before fire event. The 

relative influence of prefire forest composition, stand density, and surficial 

deposits on postfire forest cover attributes (such as variation in proportion 

of green/red/charred trees) was analyzed using contingency tables.

Figure 1___Map of Quebec showing the location of 14 fire events.

Methods

Fire impact maps are produced by the Quebec Ministry of Natural Resources after large wildfires in order to plan salvage logging operations. These 

maps describe timber damage immediately after fire in six severity classes 

(table 1). Forest inventory maps are available for the commercial zone as raster 

format at a resolution of 14 ha. These maps (inventory and fire impacts) were 

overlaid in ARC-GIS in order to produce a table of 15,200 records for which 

we obtained the prefire forest cover composition, the percentage of forest 

cover, the site type (a combination of surface deposit and moisture regime) 

and the fire severity class.

In the first step, each fire event was clustered based on the overall impact on 

black spruce stands (the dominant cover in all fires) defining a fire intensity 

index. This index corresponds to a weighted mean of area affected by fire 

severity classes where partially burned area in which green trees dominate was 

Relationships Between Prefire Composition, Fire Impact, and Postfire Legacies in the Boreal Forest of Eastern Canada Leduc, Bergeron, and Gauthier
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 189

weighted by 1, mixed zones dominated by scorched tree was weighted by 2, 

and area dominated by crown scorched or charred trees was weighted by 3 

(fire skips were not counted). In the second step, the relationships between 

six fire severity classes and prefire or site characteristics were illustrated by 

deviance; in other words, the relative difference between observed frequency 

and expected frequency reported on expected frequency, overall or taking 

into account the intensity index.

Results

Fires were clustered in three severity classes corresponding to weak (fire 

481, 775, 826), moderate (fire 392, 523, 560, 651, 697, 749, 1084) and high 

overall impact (fire 864, 974, 995, 996; fig. 2). All fire events that burned 

more than 30,000 ha belong to the high severity class showing a good relationship between the area burned and the severity of damage registered on 

tree cover (fig. 3).

Table 1—Relative importance (area) for fire severity class for the overall 14 fires.

Fire severity class Relative importance (%)

5: Charred trees > 40% blowdown 8.6 

4: Charred trees < 40% blowdown 51.3

3: Crown scorched < 25% blowdown 1.8

2: Mixed zones of scorched trees > green trees 7.7

1: Mixed zones of green trees > scorched trees 26.1

0: Fire skips 4.4

Figure 2—Impact map of fire number 974 that occurred in 1995 near Parent township.

Relationships Between Prefire Composition, Fire Impact, and Postfire Legacies in the Boreal Forest of Eastern Canada Leduc, Bergeron, and Gauthier
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 190

Figure 4 shows how site types can interact with fire severity in black spruce 

forest cover type in all fire events. Deviance (percent deviation from expected 

value) indicates an overabundance of weak severity (classes 1 and 0) on hydric soil types such as organic soil and sub-hydric coarse sand. In contrast, 

rocky outcrops and xeric coarse sand show an overabundance of the highest 

severity (class 5).

Figure 5 shows the influence of black spruce stand density on severity class 

distribution. Fire skips (class 0) are overrepresented in closed black spruce 

stands (> 80 percent closed canopy). Note that this stand density appears 

also slightly overabundant in impact class 4 (charred trees with < 40 percent blowdown). Open canopies (25 to 40 percent closed canopy) generate 

an overabundance of weak severity class 1 with a dominance of green trees 

after fire.

Figure 6 shows the influence of forest cover composition for each fire 

intensity level. Generally, fire skips and green tree dominated zones are 

overrepresented in wetlands and stands with prefire forest cover dominated 

by deciduous cover such as trembling aspen and white birch stands. Balsam 

fir stands can form fire skips when fire severity is low but usually fire impact 

was high in these stands. Open forested lands also usually burned intensively. 

Among high intensity fires, jack pine stands could provide fire skips or green 

tree dominated zones but with less propensity than trembling aspen stands. 

Jack pine stands appear also more wind firm (less than 40 percent blowdown) 

than white birch and mixed white birch stands (fig. 6c).

Figure 3—Relationship between fire size and fire intensity index such as estimated by proportion 

of black spruce stands (area) having undergone heavy impact.

Relationships Between Prefire Composition, Fire Impact, and Postfire Legacies in the Boreal Forest of Eastern Canada Leduc, Bergeron, and Gauthier
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 191

Figure 5—Influence of stand density (for black spruce forest cover only) 

on prevalence of fire severity classes.

Figure 4—Influence of site types on the distribution of fire severity 

classes. A positive deviance indicates an overabundance of a severity 

class for a given site type. Conversely, a negative deviance means a 

under abundance.

Relationships Between Prefire Composition, Fire Impact, and Postfire Legacies in the Boreal Forest of Eastern Canada Leduc, Bergeron, and Gauthier
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 192

Figure 6—Influence of prefire forest cover composition on the 

prevalence of fire severity classes. Fires have been grouped by 

estimated intensity index as shown in figure 3, following they 

are weakly (a), moderately (b) or highly (c) intense. Some fire 

severity classes were absent from weakly (a) or moderately (b) 

intense fire groups. Stand types are coded as follows: Wb: white 

birch; MxWb; mixed white birch; Bs: black spruce; Ta: trembling 

aspen; JP: jack pine; Bf: balsam fir.

Relationships Between Prefire Composition, Fire Impact, and Postfire Legacies in the Boreal Forest of Eastern Canada Leduc, Bergeron, and Gauthier
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 193

Discussion

The Canadian boreal forest is known to be characterized by severe crown 

fires. These fire events usually leave few residual patches (less than 5 percent of burned areas are composed of fire skips). Our results confirm this 

observation but also show that a relatively large portion of a fire may be 

occupied by partially burned zones in which green trees dominate postfire 

forest cover. Our results support the idea that forest cover composition and 

structure have an influence on fire behaviour and thus on the resulting fire 

severity. For instance, deciduous forest cover is more likely to generate more 

residual green trees (Cumming 2001; Kafka and others 2001). Moreover, 

our results highlight that fire behaviour may differ according to the overall 

fire intensity. For example, in fires with a low or moderate intensity index, 

partially burned stands are mainly composed of mixedwood whereas in the 

fires with an overall high intensity only the more fire resistant, trembling 

aspen stands (and to some extent jack pine stands) are partially burnt. This 

interaction between fire intensity and forest cover composition suggests that 

complex postfire outcomes, in which residual trees could survive individually or in small groups, are possible in the eastern part of Canadian boreal. 

This spatial pattern of postfire residual trees has implications for the spatial 

planning of postharvesting tree retention (Schmiegelow and others 2006). 

More analyses are needed, however, to characterize this spatial pattern at a 

finer resolution.

References

Cumming, S.G. 2001. Forest type and wildfire in the Alberta boreal mixedwood: 

what do fires burn? Ecological Applications 11: 97-110.

Johnson, E.A.; Miyanishi, K.; Weir, J.M.H. 1998. Wildfires in the western Canadian 

boreal forest: landscape patterns and ecosystem management. Journal of Vegetation 

Science 9: 603-610.

Kafka, V.; Gauthier, S.; Bergeron, Y. 2001. Fire impacts and crowning in the boreal 

forest: study of a large wildfire in western Quebec. International Journal of 

Wildland Fire 10: 119-127.

Schmiegelow, F.K.A.; Stepnisky, D.P.; Stambaugh, C.A.; Koivula, M. 2006. 

Reconciling salvage logging of boreal forests with a natural-disturbance 

management model. Conservation Biology 20: 971-983.

Van Wagner, C.E. 1977. Conditions for the start and spread of crown fire. Canadian 

Journal of Forest Research 7: 23-34.

Relationships Between Prefire Composition, Fire Impact, and Postfire Legacies in the Boreal Forest of Eastern Canada Leduc, Bergeron, and Gauthier
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 194

This 

page is 

blank 

on purpose

Relationships Between Prefire Composition, Fire Impact, and Postfire Legacies in the Boreal Forest of Eastern Canada Leduc, Bergeron, and Gauthier
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 195

In: Butler, Bret W.; Cook, Wayne, 

comps. 2007. The fire environment—

innovations, management, and policy; 

conference proceedings. 26-30 March 

2007; Destin, FL. Proceedings 

RMRS-P-46CD. Fort Collins, CO: 

U.S. Department of Agriculture, 

Forest Service, Rocky Mountain 

Research Station. 662 p. CD-ROM.

1 Research Associate, Institute for 

Mathematical Sciences, Imperial 

College, London, UK. s.mughal@

imperial.ac.uk

2 Professor of Mathematics, with 

School of Computational Science, and 

Assistant Professor with Department 

of Meteorology, respectively, Florida 

State University, Tallahassee.

3 Research Meteorologist, U.S. 

Department of Agriculture, Forest 

Service, Southern Research Station, 

Athens, GA.

Abstract—In an investigation of the dynamics of coupled fluid-combustion-buoyancy 

driven problems, an idealised model formulation is used to investigate the role of 

buoyancy and heat release in an evolving boundary layer, with particular emphasis on 

examining underlying fluid dynamics to explain observed phenomena arising in forest fire propagation. The role played by the Froude number and background ambient 

wind in affecting various characteristics observed in propagating fires is addressed. A 

simplified flow situation is modeled in which controlled amounts of volumetric heat 

is injected into the system. By varying the strength of the heat source and the ambient 

winds the study examines the impact of these variables on the flow dynamics and the 

consequential development of instability waves that may provide insight into environmental conditions that contribute to erratic fire behaviour. Analysis suggests that 

there are two routes to fire behaviour and destabilisation, with Froude number playing 

a crucial role in the boundary layer development and consequential instability of the 

convecting flow. In low Froude number situations, the mechanism of fire induced local 

winds may arise as a product of heat release and buoyancy, which induces favourable 

pressure gradients accelerating local winds to above the ambient. The analysis shows 

that a massive destabilisation takes place and that stationary, zero streamwise and 

nonzero spanwise wavenumber, viscous disturbances have the highest growth rates. 

These stationary spanwise vortex disturbances, commonly referred to as pure vortex 

longitudinal “roll cell” modes, might well be linked to cross roll features that have been 

identified in some recent fire related numerical simulations. The simple model shows 

that a key requirement in breakup of the fireline, is a low enough ambient wind. In 

high Froude number cases, the most probable factor is the sensitivity of the boundary layer to separate and lift off the surface; this being caused by massive updrafts of 

buoyant air. The relatively weak nature of instabilities in this regime suggests that the 

convecting fireline development would otherwise be well behaved.

Introduction

Our concern is with combustion related problems, in particular forest 

fires, where intense volumetric heat and mass release arise during the burning process. This is a complex process whereby entrainment and mixing lead 

to chemical reaction, release of heat, and mass, which in turn affect the flow 

dynamics and hence flow entrainment, mixing and combustion; that is, the 

effect of one on the other is coupled and may be cyclic. It is now fairly well 

recognised through observations that fire propagation characteristics fall into 

two distinct categories. For weak wind conditions, the fire front is mainly 

Role of Buoyancy and Heat Release in Fire 

Modeling, Propagation, and Instability

Shahid M. Mughal1, Yousuff M. Hussaini2, Scott L. Goodrick3, and 

Philip Cunningham2
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 196

Role of Buoyancy and Heat Release in Fire Modeling, Propagation, and Instability Mughal, Hussaini, Goodrick, and Cunningham

affected by the expansion of the hot gases around the combustion zone, 

and in the literature this wildfire propagation regime is identified as plume 

dominated. For greater wind speeds the flame trajectory is affected more so 

by the lateral wind flow, and hot gasses are pushed toward the unburnt solid 

fuel, inducing additional heat source/release points.

To understand the effect of heat release on this quite complex process, we 

examine a simplified flow situation in which controlled amounts of volumetric 

heat is injected into the system. By varying the strength of our heat source 

and the ambient winds, we seek to examine the impact of these variables on 

the flow dynamics and the consequential development of instability waves 

that may provide insight into environmental conditions that contribute to 

erratic and dangerous fire behaviour.

An evolving fire is of course a complex flow, involving flow unsteadiness, 

turbulence, thermo-dynamics, multiple phases, chemistry, radiative effects 

and fluid dynamic coupling. These physical mechanisms are generally considered too complex, outside of a research environment, to be integrated on 

a large scale to simulate fire spread. Thus, present day fire spread models 

simplify the problem by representing the fire front as a curve separating the 

burnt area and the fresh fuel. Each point of this front is regarded as a possible 

ignition source (provided conditions are right), with normally empirical and 

experimentally derived formulae used to continue the computations to the 

next time and spatial level (Rothermel 1972). At the other level of sophistication, and requiring considerable computational resources, we cite the work 

of Morvan and Dupuy (2004), who model a wildfire propagation process by 

a complete multiphase formulation, though due to computational expense 

this work was limited to two dimensions. Typically such works model the full 

burning process, and thus include thermal degradation, pyrolysis, glowing 

combustion of solid fuel particles, convection, radiative heat transfer, effects 

of external wind and ground topography. Furthermore to be as realistic as 

possible, the solid fuel (that is, the natural vegetation) is further broken down 

into families of solid combustible particles, with each family (pine needles, 

twigs, foliage, excelsior, and so forth) characterised by a set of physical properties (density, flammability, moisture content, and so forth), which are also 

included in their mathematical model. A similar approach that allows for 

fully three-dimensional simulations of coupled atmosphere-fire behaviour, 

albeit employing a simplified treatment of combustion, is found in the work 

of Linn (1997) and Linn and Cunningham (2005).

Real fire observations and model simulations have shown that intense vortices of various strengths and sizes often occur; vortices being created either 

directly by the fire front or by and in conjunction with atmospheric convection. Fire vortices on the scale of metres continually occur at the fire front and 

are an essential component of the convection, and are hypothesised to play 

a fundamental role in the physics of fire spread. Heat and moisture supplied 

through the burning of ground, canopy, and/or crown fuel during a forest 

fire generate extreme levels of buoyancy. The horizontal buoyancy gradients 

have in certain fire conditions produced vortices of tornado strength (Banta 

and others1992). The presence of vertical shear in the ambient atmosphere 

is also an important component of fire behaviour. Vertical shear is associated 

with horizontal vorticity that may be converted into vertical vorticity near 

the ground via tilting by updrafts in the vicinity of the fire, thus leading to 

highly nonuniform winds near the fireline. Cunningham and others (2003) 

further suggest that this vertical vorticity is associated with low level horizontal 

winds on the downstream side of the fire that are oriented in the opposite 

direction to the fire spread. Simulations of Clark and others (1996a) lend 
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 197

Role of Buoyancy and Heat Release in Fire Modeling, Propagation, and Instability Mughal, Hussaini, Goodrick, and Cunningham

support to this observation and confirm that presence of low-level wind shear 

can lead to particularly active fire behaviour. Moreover, the presence of low 

level negative shear (that is, winds decreasing with height near the ground) 

has been shown to be a common feature of so-called blowup fires (Byram 

1954). The other feature of note is that winds at the fire scale can either 

be strongly modified or even solely produced by the fire, depending on the 

atmosphere-fire coupling. This coupling or feedback may occur over spatial 

scales from tens of metres at the flame front to kilometres on the scale of the 

total burn area. The magnitude and direction of the wind near the ground 

has been observed to play a crucial role in wildland fire spread.

The potential for erratic fire behaviour arising from buoyancy associated 

with large wildfires and horizontal flow was first theorised by Byram (1954). 

This theory centred upon the notion that erratic fire behaviour (instability) 

becomes more likely as the conversion rate of buoyant energy surpasses the 

flux of kinetic energy by the horizontal flow; hence, the idea of power of the 

fire versus the power of the wind. Byram’s original theory was limited, however, by its assumptions of a neutral atmosphere and a nonentraining plume. 

Nelson (2003) extended the original work of Byram to remove these assumptions. A limitation of this work is that the problem is not posed as a coupled 

system with heat release influencing the development of the flow. Clark and 

others (1996a,b) make use of a numerical atmospheric model coupled to a 

simple fire spread model to investigate the influence of convective processes 

on fireline dynamics in a fully coupled system. This study provided strong 

evidence for a linkage between the convective Froude number Fo

2

, and erratic 

fire behaviour, such as fingering of the fireline and the development of fire 

whirls of near tornado strength. Experiments were conducted for a range of 

wind speeds (1, 2, 3 and 5 m/s) and two fireline lengths (420 and 1500 m). 

These variations in wind speed accounted for a range of Froude numbers. 

For small values of Fo

2

 they encountered strong coupling between the atmosphere and fire with erratic fireline behaviour. However, they did not delve 

into the nature of the instabilities. Thus, vortex tilting and unpredictability 

of ground winds are not only a hazard in itself to nearby firefighters, who 

have on occasions reported being toppled to the ground unpredictably by 

fire whirls, but are an important fire spread mechanism through both local 

dynamics and the ability to loft flaming objects into areas well removed from 

the fire front. The precise mechanisms of how these effects occur are still 

poorly understood.

This paper represents the starting point in a systematic investigation of the 

dynamics involved in the investigation of coupled fluid-combustion-buoyancy 

driven problems. In the present investigation, the basic objective of our work 

is to isolate the roles of the convective Froude number Fo

2 and volumetric 

heat release on fire dynamics and the nature of two and three dimensional 

unstable disturbances that arise. Most previous work is almost all based on 

the low temperature Boussinesq assumption, while detailed simulations use 

combinations of either fully unsteady compressible DNS and or large-eddy 

simulation (LES) models with combustion treated completely or approximated 

somehow. With such complex simulations, considerable difficulty arises in 

trying to isolate the underlying physics. Among the many reasons are the 

large amount of data that require processing and assimilation, and the computational expense of examining a large enough parameter space to isolate 

the role of key parameters. The model we use to examine the issues raised 

above is by way of examining the mixed forced-free convection boundary layer 

problem, which has been much studied in the past from the viewpoint of the 

heat-transfer problem. We contend and show below that this simple model 
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 198

Role of Buoyancy and Heat Release in Fire Modeling, Propagation, and Instability Mughal, Hussaini, Goodrick, and Cunningham

can be used to elucidate some major aspects of fire propagation phenomenon 

too. In particular, we are interested in situations where sizable changes occur 

in density, temperature, and transport properties due to heating. Thus, the 

commonly used approximation, used in most analytic and related numerical 

computations, of using a Boussinesq approximation is dispensed with, and 

we consider the fully coupled problem where temperature variations of the 

order of 1200K may arise.

In this paper we define the appropriate model and discuss our numerical work for the basic nonsimilar steady field, in which we incorporate the 

effects of a volumetric heat source and coupled buoyancy model. Results are 

discussed in the context of forest fire propagation and in view of existing 

knowledge of fire related phenomena. Flow instability aspects of the work are 

discussed by the use of linear stability theory (LST) briefly as we believe that 

a proper investigation of this aspect requires the effects of nonparallelism to 

be included, and LST does not allow this in a straightforward manner. But 

the preliminary instability analysis reported here does allow and will serve 

as a bedrock for a future and fuller investigation using the more advanced 

linear and nonlinear instability analysis tools. The work we believe is an important step in elucidating the coupling of the external fluid (or wind-driven) 

processes and the burning process and how the two interact. Our simplification of the problem describes the two major mechanisms of importance in 

a coupled fluid-combustion-buoyancy dominated problem, namely the role 

of heat release and Froude number on atmospheric dynamics and instability. 

Features such as fireline length and its effect on fire spread and propagation 

are effectively three-dimensional in nature and left for a later investigation.

The tools currently used operationally in the wildland fire community to 

predict fire spread are based on empirical relationships between ambient wind 

speed, topography, and fuel properties (Nobel and others 1980; Rothermel 

1972). The complexities of the combustion process and the intricacies of 

fire-atmosphere feedback processes are largely neglected. The primary focus 

of such tools is on predicting the spread of wind driven surface fires (Froude 

number >1) under homogenous, steady state conditions. These models are 

not typically applicable to the most intense forest fires (crown fires). The 

model presented in the current work, with its boundary layer approximation, 

is well suited to providing a simplified vision of the problem based on physics 

rather than empirically derived relationships. The importance of the physics 

based relationship is that it allows us to explore the response of the system as 

we move toward more buoyancy dominated conditions. While the boundary 

layer assumption precludes application of the model to buoyantly dominated 

flows, it does allow for an initial, though limited, exploration of the transition 

from wind to buoyancy dominated conditions and the potential for associated 

instabilities. Examination of the initial stages of the transition from wind to 

buoyancy dominated conditions (and associated flow instabilities) provides 

insight into the conditions that will cause current operational tools to fail.

Formulation

Basic Steady Field

We consider the fully compressible two-dimensional flat plate natural convection problem that incorporates the Boussinesq assumption, namely that 

density variations may be ignored in all terms apart from the gravitational 

term, and we use the equation of state p R T * * * * = ρ to determine density. Thus, 
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 199

Role of Buoyancy and Heat Release in Fire Modeling, Propagation, and Instability Mughal, Hussaini, Goodrick, and Cunningham

the relevant equations of mass conservation, momentum, and energy to determine the basic boundary layer state are:

∂

∂

+

∂

∂ =

∂

∂

+

∂

∂ = − ∂∂ −

( ) ( ) ,

( )

( )

ρ ρ

ρ

U

x

V

y

U U

x

V V

y

P

x

e

o

0

∂

∂

+

∂

∂

∂

∂

∂

∂ = −

∂

∂

P

x y

U

y

P

y

g

U T

e

( )

( ) *

( ),

( ) ,

(

1

1

μ

ρ ρ

ρ

x

V T

y

U

C

P

x

P

x y

T

y p

e

o

+

∂

∂ = ∂∂

+

∂

∂

+

∂

∂

∂

∂ ) ( ) (

( ) ( )1 1

σ

μ ) ( ) ( , , ). +

∂

∂

+ μ

C

U

y

Q x y z

p

2

(1.1)

P x e

( ) o ( ) is the flow induced pressure field, and we introduce the function 

Q x y z ( , , )to represent the volumetric heat release that arises from a burning 

fire. In the above x and y are streamwise and wall normal coordinates, U V,

the associated velocities, ρ the density, T the temperature, μ the viscosity, 

Cp the specific heat, σ the Prandtl number, and g* the gravitational term.

To reduce the equations into a more suitable form for numerical work, we 

transform y using a Falkner-Skan type transformation

η ρ

μ

ρ

ρ = ( ) ∫ ( ) , e e /

e e

y U

x

s ds 1 2

0

(1.2)

and ψ ρ μ = ( ) /

e e e U x f 1 2 with ψ a stream function, which satisfies mass 

conservation. On assuming Mach number terms of O(M4)are negligible, the 

equations then reduce to

∂

∂

+ − + − = ( ) ∂ ( ) (

'' ' '' ( ) ' ' χ

η

f m s f cff sx dP

dx x f f

1

1 2

∂ − ∂∂

∂

∂ = −

∂

∂

+

x f f

x

P x G s

T

o

''

( ) /

'

),

( ),

( )

1

1 2 1

η

η

χ

σ

( ) ( ) ˆ( '' ' ' ( )γ χ − + + − 1 γ 1 + 2 2

1 2

M f cfT M sf x dP

dx sxQ x x f Tx T fx , ) ( ), ' ' η = ∂∂ − ∂∂

(1.3)

where

χ μρ

μ ρ μ ρ = = = μ ρ

+

+

e e e

e

e e

m e

x

U

dU

dx

c

m x d

dx ; ; ( 1

1 1

2

1

2 e

e

e

M

U

R T

); . *

2

2

=

γ

Here, f U U T T T e e

'

= = / , / and ρ ρ e / = s and are all assumed to be functions 

of η and x; M is the streamwise edge Mach number and the viscosity μ is 

allowed to behave according to Sutherland’s law. The quantities ρ μ e e e e

, , , U T

are functions of the surface conforming coordinate x; however, in this work 

we only consider the case of uniform flow over a flat surface and as such 

m1 = 0; c = 1/2.

The far-field boundary conditions are the usual ones of

f x T x s x P x ' ( ) ( , ) ( , ) ( , ) ; ( , ), η → ∞ = → ∞ = → ∞ = → ∞ = η η 1 η 1 0
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Role of Buoyancy and Heat Release in Fire Modeling, Propagation, and Instability Mughal, Hussaini, Goodrick, and Cunningham

while at the wall η = 0,

f x f x f x w ( , ) ( ); ( , ) ; ' 0 − 0 0 =

and for temperature, either the wall temperature or adiabatic condition (of 

zero heat-transfer) can be satisfied, namely:

T x T x T x T

T x x

x w e m

b s

T

( , ) ( ) / ( ) ( tanh( )) 0

2 = = − +1 −

or

T x '( , ) ;0 0 =

the subscript w refers to the wall value and fw allows for wall suction or blowing 

(mass efflux) to be incorporated into the analysis.

In the above, the quantities of interest are the inverse of the Froude number 

defined

G g L

U R o

e L

=

*

/2 1 2 (1.4)

With L a length scale and R U L L e e e = ρ μ/ a Reynolds number, while the heat 

source term is dimensionalised as follows:

ˆ( , ( , ) Q x . LQ x

C U T

p e e

η η ) = (1.5)

We note from (1.3) that the key parameter that controls the magnitude 

of the buoyancy effect is Go, and if all other free parameters vary, it is this 

parameter that needs to be fixed to obtain similarity of solutions, when two 

apparently quite different flows are compared. Interpretation of the results 

and their relationship to length scales associated with real fires may be seen 

by associating the Sutherland viscosity model used presently with the subgridscale based eddy viscosity and diffusivity concepts (Deardorff 1973) used in 

LES based simulations. Observe that η in (1.2) involves the viscosity μe

, as do 

the definitions of the inverse of the Froude number Go (through the Reynolds 

number RL). Thus, in principle by a simple association of the subgrid based 

eddy viscosity in these scalings, the results presented herein may be applied 

to the length scales associated with evolving fires, albeit rather crudely.

Source Formulation

Our ultimate objective is to consider quite general situations where the 

source function consists of a steady 3-D-forcing term and an unsteady component, namely

Q x y z t Q x y z q x y t e * i z ( , , , ) ˆ = + ( , , ) ( , , ) δ β

where δ << 1. However, in this work we assume the source forcing to be 

steady only and of infinite extent in z (Cunningham and others 2003), having the form

ˆQ x y z q ( , , ) tanh( )exp( )exp( ( x

x

y y

y

x x

o

o

s

o

s = − − − −

xL

) ) 2 (1.6)

with the parameters q x y y x x o o o s s L , , , , , chosen conveniently. A typical example 

is shown in figure 1a, while figure 1b shows a possible wall temperature 

distribution that may be enforced. This particular wall temperature form is 

used to model the fact that behind the fireline, temperatures will have been 

raised by the evolving fire, while ahead of the fireline surface temperatures 

are still presumed to be at ambient levels.
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Numerical Procedure

The system of equations are parabolic and thus solved by a fully implicit 

second-order accurate three-point backward differencing scheme, with a two 

point second-order accurate discretisation in the wall normal direction. The 

two point scheme arises by converting the second-order momentum and energy equations into a system of first-order equations. The nonlinear terms are 

discretised using standard Newton linearisation; thus, ultimately the inversion 

of a block tridiagonal matrix arises, which can be accomplished efficiently 

using standard methods. To initialise the computations at x = 0, the similarity 

form of (1.3) is used and thereafter a marching procedure employed.

For small magnitudes of Qˆ the forced heat source problem will be amenable 

to a marching boundary layer type solution strategy. While as one should 

expect, and investigated by Higuera (1997), where sizable changes occur 

in the wall temperature, such a simple solution strategy will ultimately fail, 

in which case an interactive thermal layer with the pressure determined by 

the external inviscid adjustment of the boundary layer thickness needs to be 

computed or allowed for in any numerical scheme. Thus, either an interactive 

boundary layer type solution procedure or the fully Navier-Stokes solution 

procedure needs to be invoked. Flow reversal, separation, and reattachment 

regions do occur and are predicted by the asymptotic triple-deck analysis of 

Higuera (1997).

Daniels and Gargaro (1993) Boussinesq based buoyancy driven flow investigations conclude that in cases where the Froude number is too low, the 

boundary layer flow cannot be sustained, and the forward motion succumbs 

to the adverse pressure gradient induced by buoyancy. This either leads to 

the onset of reverse flow and the consequent failure of the numerical scheme 

or more generally to the occurrence of a terminal singularity. In cases where 

reverse flow sets in, their computations failed due to numerical instability 

rather than the existence of any local singular behaviour. The flow becomes 

subject to upstream influence, which then requires a much more sophisticated 

numerical treatment.

We examine the situation where both significantly raised temperatures and 

sizable buoyancy effects arise. Thus, at some stage where the flow parameters 

lead to a strong coupling between the normal pressure gradient and convective terms, either from significantly raised temperatures or buoyancy effects, 

we must anticipate a breakdown in the numerical solutions, since then the 

external inviscid field is strongly modified and straightforward boundary layer 

Figure 1—(a) Source strength distribution along the wall y = 0 for the parameters qo = 20; 

xo = 0.001; yo = 3; xs = 0.15; xL = 0.025. (b) Wall temperature distribution for parameters 

T T x x m b s T = = = = 2 1 0 2 0 025 , , . , . of equation 1.6. 
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Role of Buoyancy and Heat Release in Fire Modeling, Propagation, and Instability Mughal, Hussaini, Goodrick, and Cunningham

theory can then no longer be expected to be valid. We restrict our analysis to the parameter regime, where the problem is amenable to a parabolic 

boundary layer marching type solution procedure; namely where Go ~ O(1). 

Ultimately, however, as the buoyancy effects become sizable, boundary layer 

solution technique used here will fail, and at this point recourse to a fully 

Navier-Stokes solution for computation of the underlying basic state has to 

be made.

Basic Flow Solutions

The basic state model to be investigated is that of the forced basic steady 

flow, with Froude number and volume heat source Qˆ as competing/coupled 

effects. We only consider zero pressure gradient, low Mach number flows, 

with flow parameters T K R L ∞ L = = 0.72, = = 300 10 1 5 ,σ , m. The computational 

domain is of extent 0 ≤ x ≤ 1 , with a step length in x o f ∆ x = 0 . 0 0 5 . 

To obtain variations in Froude number we vary the free stream velocity Ue, 

and generally in fire related problems these flow velocities are in the range 

0.5 ~ 10m/s, though as alluded to earlier the key parameter that controls 

the flow structure is Go as defined by (1.4). Typical temperature variations 

examinable are up to 1200K; this may be varied by changing the source 

strength qo.

Zero Volume Source Strength, Qˆ, and Zero Buoyancy

We begin by presenting some results for the zero source strength, zero 

buoyancy case. Of course this problem is the trivial flat plate incompressible 

Blasius problem; however, this serves as a useful guide and comparator when 

the more interesting parameter regime results are presented. Figure 2a shows 

streamwise velocity U and its derivatives, while figure 2b the streamwise 

normal velocity V V Ue = ' ' / distribution at various x-intervals. Since this has 

a similarity structure, as would be expected, the numerical solution for U at 

all x-positions is identical, while that for V shows a decrease in the transpiration velocity or efflux to the external inviscid stream as the flow progresses 

downstream. We note two things: (1) this is of relative small order (as expected) and (2) it attains uniformly constant value sufficiently far from the 

wall. Furthermore, since the wall temperature is specified to be identical to 

the ambient, temperature variations are negligible and are thus not shown. 

Figure 3 shows the effect of maintaining different, above the ambient, uniform wall temperatures on solutions. The boundary layer thickens, the wall 

shear decreases, and the normal velocity magnitude increases; however, the 

near-similarity structure in the U and T profiles is still found to persist.

Figure 2—Blasius profiles at x = 0.065,(0.08),0.785 intervals. (a) Streamwise U velocity and 

derivatives U’, U’’. (b) Wall normal V velocity; arrow is in direction of increasing x.
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To consider the effect of buoyancy on the solutions, we merely switch on the 

buoyancy terms in our equations and repeat the computations and the effect 

is shown in figure 3 by the dashed curves. We note that the differences are 

quite small, though they begin to increase as the wall temperature increases. 

The external wind speed for this particular run is approximately 3.46 m/s and 

the parameter Go = 1/386; thus, one should not expect significant differences 

as the parameters selected are for a large Froude number.

Figure 3—Compressible zero pressure gradient profiles at x = 0.785 for wall temperatures 

Twall = (300,(150),1200),T∞ = 300K,direction of arrows indicate increasing wall temperature. 

Comparison of solutions with (dashed line) and without (solid) inclusion of buoyancy terms 

in governing equations. (a) Streamwise U velocity; (b) Wall normal V velocity; (c) Temperature 

T-1. 

Effects of Source Strength, Qˆ

We next consider how switching on the heat source affects results. We 

begin by examining the large Froude number case, corresponding to a wind 

speed of 10 m/s. The appropriate Q-source distribution selected is determined 

by trial and error; the particular source term distribution used here is that 

shown in figure 1a. Note from (1.6), that Yo fixes the extent in y of the heat 

release, and choosing yo < 5 essentially assumes that most of the heat release 

occurs within the boundary layer; setting ys > 0 maximises the heat release 

off the wall boundary. The strength of the heat release (which determines 

the maximum temperature) is fixed by qo. Figure 4a shows the temperature 

and figure 4b the wall normal (or updraft) velocity distributions achieved for 

varying qo keeping yo constant at various downstream positions. Apart from 

the obvious, namely increasing qo increases boundary layer temperatures, 

we see that the effect is quite localised to the vicinity of the heat source 

(maximum heat release occurs at x= 0.15). Observe that peak temperatures of 

about four times the ambient (~1200K) are achievable near the source, with 

the choice of parameters used. Downstream the overall temperatures decay 

in magnitude but still remain quite substantial.

We observe that changing the magnitude of the heat source has a major 

impact on affecting the magnitude of the vertical velocity. In the vicinity of 

the heat source maximum, a quite strong updraft of air arises for increasing 

qo (note fig. 4b), but then immediately downstream of it a weaker though still 

relatively substantial downdraft also occurs. We note from figure 4b, for the 

choice of parameters or source forcing, the updraft velocity is almost 40 percent of the streamwise velocity, while the downdraft almost 10 percent. This 

downdraft diminishes, quite rapidly as we move downstream of the source, 

unlike the local temperature and streamwise velocity fields. We do compute 
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Role of Buoyancy and Heat Release in Fire Modeling, Propagation, and Instability Mughal, Hussaini, Goodrick, and Cunningham

Figure 4—Effect of increasing source term magnitude qo on nonbuoyancy driven flow 

at various x-locations. U m s q y y x e o s o s = = 10 1 5 10 20 40 0 4 0 1 / ; ( , , , , ); ; ; . 5 0 025; . ; = = = xL =

Twall = T∞ = 300K (arrows point in direction of increasing qo). (a) Temperature profiles. (b) 

Wall normal velocity (or updraft) distributions. (c) Streamwise velocity distributions.

even greater updraft velocities for greater values of qo which in fact exceed the 

value 1, with apparently no ill-effects as regards code convergence problems 

in this limit. We note that boundary layer theory, and the equations used 

presently are derived on assumption of V ~ O RL ( ) −1 2/ , though the only terms 

omitted are the streamwise diffusion terms to render the equation parabolic 

and the assumption that the vertically convected momentum flux is of an order 

less than that induced by density changes. The mass conservation equation 

is solved exactly, so the only crucial weakness in the present model is that of 

assuming that the system is solvable by a parabolic marching procedure.

The effect of the heat source on the streamwise velocity, as may be deduced 

from figure 4c, is that heating modifies this quite drastically too, causing 

in general a reduction of the wall shear and a doubling of the boundary 

layer thickness. Ultimately if the applied heat release qo is large enough, the 

boundary layer shows a tendency to separate. Our code in this limit then fails 

to converge. The above are computed for a ground temperature set to be the 

same as the ambient of Twall = T∞ = 300K, which is clearly impracticable; one 
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assumes real fires have ground temperatures significantly higher. We next 

set the wall temperature to Twall = 600K and repeat the above runs. This is 

seen more clearly in figure 5, which also shows the downstream development 

of the temperature profiles. Apart from the satisfaction of the higher wall 

temperature condition, generally the results and trends are unchanged from 

those presented in figure 4.

In the context of fire propagation and sustainability we observe that well 

ahead of the heat source (for example, x ~ 0.3 in fig. 4) temperatures of the 

order of 600K persist. It is precisely this feature that acts as a preigniter, 

which readies the source material for combustion ahead of the fire line. The 

above is a useful indicator, since the downwind propagation distance of a 

fire front is usually associated with the farthest downwind location where 

the temperature of the solid fuel is raised to a particular threshold to initiate 

or continue the burning process. This is also a criterion used in determining 

the rate of spread of the fire.

Figure 5—Streamwise velocity distributions at various x-locations. qo = 40; yo =4; ys = 0; 

Twall = 600K; T∞ = 300K. All conditions are identical to figure 4, except the wall temperature 

is held fixed at 600K.

Buoyancy Effects

To examine buoyancy effects, based on evidence from the previous section, 

we choose values of qo = 25, yo = 4, ys = 0 and gradually reduce the freestream 

velocity, thus reducing the effective Froude number Fo

2 . As mentioned above 

it would appear to be more realistic to have sizable wall temperature and 

thus in all results presented we fix this at 600K. Moreover based on previous observations and numerical evidence gained presently, we note that the 

Froude number may be redefined to include temperature dependence, since 

we find numerical convergence of our fully coupled system of boundary layer 

equations is Froude number dependent. Thus, results are presented with the 

definition

ˆ , ,

/

*

max

max

max F

U R

Lg T

T

T

T o

o L

e

2

2 1 2

= = −1

∆

∆ (1.7)

with Tmax the temperature maximum in the boundary layer. This we note 

also follows from (1.3), since the term ∆T F max o / ˆ 2 is the total magnitude of 

the buoyancy induced pressure component at any point in the field, and 

quite naturally the larger this becomes, the coupling between the equations 
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Role of Buoyancy and Heat Release in Fire Modeling, Propagation, and Instability Mughal, Hussaini, Goodrick, and Cunningham

increases, until the system is no longer treatable using a parabolic based approach. Clark and others (1996a) in an attempt to characterise wind-driven 

fires use a similar expression, namely

ˆ ˆ *

max

F

U

W g T rc

o

f

2

2

= ∆

(1.8)

with ˆ max ∆T a mean value of the temperature anomaly over the region of intense 

heating and Wf a fireline width in the mean wind direction. In the present 

case we observe that dynamical similarity between two different flows is 

assured provided the parameter Go is identical. The cases examined and correspondence between Ue and Fˆ

o

2 are given in table 1.

Table 1—Values of local Froude number 

Fˆ

o

2 based on (1.7) for cases 

presented.

Ue (m/s) Fˆ

o

2 1 / Go

10.00 1080 3224

7.50 610.5 1813

5.00 274.3 805.9

2.50 71.91 210.5

1.00 13.41 32.24

0.75 8.101 18.13

0.50 4.044 8.059

0.375 2.490 4.533

0.25 1.267 2.015

0.10 0.270 0.322

0.05 0.078 0.081

Profile distributions at a fixed Froude number (corresponding to 

Ue = 0.05 m/s) and varying the source strength qo are shown in figure 6, 

while that of keeping qo fixed and varying the Froude number are shown in 

figure 7. A number of important features may be deduced. The streamwise 

velocity develops distinct maxima; that is, inflectional profiles that exceed 

the freestream value of unity, before ultimately reducing back to unity for

y → ∞, and wind shear at the wall increases for decreasing Fˆ

o

2 . Normal velocity distributions near the heat source release are now downward directed (of 

order of about 30 percent), unlike the earlier high Froude number situation, 

followed by a much weaker updraft downstream of the heat source. This is 

almost the reverse of the situation depicted in figure 4. Temperature profiles 

show a reduction in magnitude for increasing buoyancy. Note a reduction in 

peak temperature of approximately 600K as the Froude number Fˆ

o

2 reduces 

from 1080 to 0.078.

The generation of inflectional profiles is of course related to density (temperature) gradients and the high value of Go that results in sizable buoyancy 

induced pressure gradients, which feed into accelerating the streamwise 

velocities by way of a strongly induced streamwise pressure gradient. In the 

earlier high Froude number situation this mechanism was suppressed, and 

thus the extra energy supplied in the form of the volumetric heat release could 

only be quickly dissipated by increasing the updraft velocity. Furthermore, 

we observe that inflectional profiles result from the temperature difference 

and that our inclusion of a heat release source term in the equations, simply 
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Figure 7—Profile distributions at x = 0.175 for varying Froude numbers corresponding 

to Ue = (10.0,7.5,5.0,2.5,1.0,0.75,0.5,0.375,0.25,0.1,0.05) (arrows point in direction of increasing 

Ue ). qo = 25, yo = 4, Twall = 600K (a) T temperature distributions. (b) U velocity 

distributions.

Figure 6—Effect of increasing source term magnitude qo on basic buoyant flow at various 

x-loc ations. U m s q y y x e o s o s = 0 05 1 5 10 20 40 0 4 0 . / ; ( , , , , ); ; ; . ; . ; = = = = 15 0 025 x T T K 300 ; L wall = = = ∞

(arrows point in direction of increasing qo). (a) Temperature profiles. (b) Wall normal velocity 

(or updraft) distributions. (c) Streamwise velocity distributions.
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offers a facility to generate thermal variations in the boundary layer. In the 

absence of the heat source, inflectional profiles could have been generated 

by maintaining a different wall temperature from the ambient, and provided 

the Froude number (or 1/Go) is small enough, the same mechanism exists to 

generate inflectional profiles. The fact that inflectional profiles do not arise 

in figure 4 is that Go is arranged to be of an almost negligible value, despite 

the fact that significant temperature differences (up to 900K) between the 

ambient and wall exist. To show that this is the case, figure 8 shows results 

for the case of maintaining uniform wall temperature at 600K, but computed 

for varying Froude numbers at a fixed position, and as expected inflectional 

profiles result for the low Froude number runs, and as indicated, thermal 

energy is used to accelerate the flow, thus resulting in less fuller temperature 

profiles compared to the high Froude number case.

Figure 8—Profile distributions at x = 0.3 for varying Froude numbers corresponding to 

Ue = (10.0,0.5,0.25,0.1,0.05,0.02) (arrows point in direction of increasing Ue). Zero 

volumetric heat release; that is, qo = 0, Twall = 600K buoyancy generated purely 

by maintaining a wall to ambient temperature difference. (a) T temperature 

distributions. (b) U velocity distributions. 

The analogous case where, rather than the heated wall, the volumetric heat 

source produces the temperature difference is shown in figure 7. Observe 

that for low free-stream velocities the parameters Fˆ

o

2

 and Go

−1 in table 1 are 

similarly valued, while at high flow velocities this is not the case. This arises 

entirely from the fact that at low Froude numbers, as indicated in figures 6 

and 7 and in relation to the earlier high Froude number results of figure 4, 

energy supplied in the form of heating is used to accelerate the streamwise 

velocity rather than raising field temperatures. Thus, in low Froude number 

situations the preference is for the thermal energy being utilised to accelerate the local flow, rather than raising temperatures. In the limit of extreme 

buoyancy, one then gets the situation where the flow quite aggressively uses 

as much of the heat energy in accelerating the local flow in the streamwise 

direction, leaving little in the temperature field.

The results of figure 6 are for an extreme value of the Froude number or 

an extremely buoyant flow, whereas in practise, provided the Froude number 

is of O(1), one expects a tradeoff or competition between the propensity for 

the boundary layer to use the extra energy supplied by the heat source to 

either increase local boundary layer temperatures or locally accelerate the 

streamwise flow. This competition between the two is shown, for milder 

buoyancy driven case, in figure 9. In this case, free-stream velocity is about 

0.5 m/s, and the effects of keeping the buoyancy fixed and increasing the 

heating through the source term are shown. We see a more typical situation 
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Role of Buoyancy and Heat Release in Fire Modeling, Propagation, and Instability Mughal, Hussaini, Goodrick, and Cunningham

that probably arises in fire situations, where both overshoots in the streamwise 

velocity and substantially increased temperature profiles arise. Figure 9c shows 

the induced pressure gradient, which is the factor in causing the acceleration 

of the streamwise flow.

The main point to note, however, is that the crucial parameter that governs 

the strength of the buoyancy is Go, so the value of the ambient wind speed 

is immaterial and by appropriate choice of parameters—that is, viscosity or 

density say—a parameter situation can be created where buoyancy is a sizable 

factor, even though wind speeds may appear large.

Finally we present in figures 10 through 12 the main features uncovered in 

the analysis. Figure 10 shows temperature and streamwise velocity for a mildly 

buoyant flow, while figure 11 the case where the buoyancy is considerably 

stronger. In these computations we have also specified a wall temperature 

distribution shown in figure 1b; thus, beyond x ~ 0.2, the wall temperature is 

specified to be 300K, while upstream it is fixed at 600K. We note the extreme 

velocity overshoot arising for the Ue = 0.05 case; these overshoots in velocity 

persist well downstream of the heat source as do the raised temperatures, in 

spite of the fact that the wall temperature is specified to be the ambient. As 

already noted, buoyancy terms effectively force or use the energy supplied by 

the heating to accelerate the streamwise velocity at the expense of increasing 

the temperature field. This additional acceleration of the flow stream (due 

to heating) thus forces the horizontal boundary layer motion to be sustained 

for a larger extent downstream before it ultimately succumbs to the adverse 

pressure gradient generated by the buoyancy. Presumably in situations of 

continuous applied heat source in the downstream direction, this motion 

may well then be self-sustaining.

We further note the quite concentrated and strong downdraft region in 

the vicinity of the heat source in figure 12 and the switch over from a strong 

downdraft to an equally strong updraft in varying Froude number from low 

to high. The occurrence of weak downdraft behind the fireline, followed 

by relatively stronger updraft ahead of the fireline, has also been found in 

LES simulations of Linn and Cunningham (2005). Cunningham and others 

(2003) find that the magnitude of the heat source is a major factor in the 

magnitude of the vertical velocity produced and then go on to suggest that 

this plays a key role in the amount of time it takes for a buoyant parcel to rise 

through the shear layer and that the counter rotating vortex pair observed in 

their simulations has its origin near the ground as a pair of vortices oriented 

Figure 9—Profile distributions at x = 0.175 for fixed Froude number for varying source strength 

magnitude qo = (0,1,5,10,20,40,60,80,100) (arrows point in direction of increasing qo). Ue = 5m/ s, 

yo = 4, Twall = 600K (a) T temperature distributions. (b) U velocity distributions. (c) Streamwise 

pressure gradient distributions.
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Role of Buoyancy and Heat Release in Fire Modeling, Propagation, and Instability Mughal, Hussaini, Goodrick, and Cunningham

Figure 12—Wall normal velocity distribution for varying Froude number, for fixed source strength magnitude. 

Contours denote the superposed temperature field. (a)Ue = 0.05m/s; (b) Ue = 0.25 m/s, (c) Ue = 2.5 m/s. 

qo = 40, yo = 4, Twall = 600K.

Figure 10—Mild buoyancy case corresponding to Ue = 0.25m/s; yo = 4, ys = 2.0, qo = 40, xL = 0.05, 

Twall = 600K (a) Temperature field. (b) Streamwise velocity field.

Figure 11—Strongly buoyant case corresponding to Ue = 0.05m/s; yo = 4, ys = 2.0, qo = 40, xL = 0.05, 

Twall = 600K (a) Temperature field. (b) Streamwise velocity field.
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Role of Buoyancy and Heat Release in Fire Modeling, Propagation, and Instability Mughal, Hussaini, Goodrick, and Cunningham

primarily in the vertical direction. Since buoyancy can only directly generate 

horizontal vorticity, the vertical vorticity can only be generated by tilting of 

the horizontal into vertical direction. In this regard, the strongly buoyant 

updraft plays a key role.

Cases where the streamwise velocity exceeds unity may play a role in the 

propagation of crown fires (fires burning through the tree canopy rather than 

through surface fuels). A recent observational study using infrared imagery to 

examine crown fire dynamics noted horizontal flows far greater than ambient 

winds by an order of magnitude (Coen and others 2004). They hypothesise 

that these bursts, as they refer to them, are the result of nonlinear interactions that transfer vertical momentum generated by buoyancy to horizontal 

momentum before buoyancy takes over and produces a more vertical plume 

rise. This theory would seem to be supported by the present work, in that we 

observe that the buoyancy term effectively forces or uses the energy supplied 

by the heating to accelerate the velocity.

Inflectional profiles have been noted before as being tied to extreme fire 

behaviour (Byram 1954).

However, the focus on the ambient wind profile is inflectional, not the input 

of heat generating an inflectional profile. The reasoning is that hot columns 

of rising air draw in air from the surroundings, thus leading to wind-induced 

acceleration of the local winds in the vicinity of the fire line. Clark and others (1996a) present cases of similar dynamics in a fully 3-D coupled model 

that produces significant fingering of the fireline in the spanwise direction 

at low Froude number values. They suggest that when the fireline’s speed is 

slow enough, fire-generated winds break up the fireline. Though how this 

breakup occurs, from a rigorous physics or mathematical modeling viewpoint, 

is not addressed. They surmise that eventually convection and local dynamics 

become nonlinear, producing erratic shapes to both the fire line and convection. They hypothesise that large horizontal buoyancy gradients at the fire 

front produce strong horizontal vortices, and the vertical motions within the 

fire eventually lead to vortex tilting, producing vertical vorticity, and that 

in some cases these vertical vortices cause the breakup of the fireline. After 

breakup of the convection column, the formation of fingers is identified in 

the vicinity of the fire. They conclude that fire line breakup and formation 

of fingers are the result of light winds and thus a dynamically unstable fire 

line leading to the breakup of the two-dimensional column of hot air into 

multiple columns. Moreover, in addition to Fˆ

o

2 being small, they conjecture 

that another ingredient required for fingering to occur is that of low-level 

negative shear, where the wind blows faster near the surface than just aloft. 

They suggest that this arises either from gust fronts, convective downdrafts 

and mountain valley flows, whereas our analysis shows that the fire itself can 

generate such inflectional flows. They speculate that dynamic fingering is 

caused by vertical tilting regions of intense horizontal negative shear at the 

fire front leading to narrow regions of high speed, hot air shooting out in 

front of the fire. This is a major process in fire spread on the micro-scale, 

which causes fires to jump as they spread.

Summary of Basic Flow Solutions

We have shown that using quite a simple two-dimensional model formulation, a wide variety of forced heat and buoyancy coupled problems 

can be examined. Adding in heat release by way of distributed heat release 

over a finite extent allows many of the basic features arising in fire propagation to be identified or captured. Our intention has been to strip away the 
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Role of Buoyancy and Heat Release in Fire Modeling, Propagation, and Instability Mughal, Hussaini, Goodrick, and Cunningham

complexity of using fully unsteady DNS multiphase based formulations and 

solution techniques in favour of a more rudimentary formulation in order to 

identify the key mechanisms arising and playing a major role in observed fire 

propagation. As such the model does produce features that typically arise in 

fires. Namely velocity overshoots or so-called super velocities caused by the 

coupling of buoyancy and heat release near the ground. The model suggests 

that in the low Froude number regime, heat release is directly responsible for 

causing the local streamwise wind speeds to accelerate drastically. Strongly 

inflectional stream wise velocity profiles are generated; thus, we can expect 

the more dangerous (from a boundary layer instability viewpoint) inviscid 

instabilities to arise. The analysis in this regime also shows that regions of high 

shear arise close to ground and that temperature profiles are generated that 

have been observed in typical forest fires. The model also correctly predicts 

the generation of strong updraft velocity followed by a weaker downdraft, 

though a role reversal is also predicted, whereby in high wind conditions heat 

release causes highly buoyant updrafts of air, while in low Froude number 

regimes the reverse happens and an equally strong downdraft arises. The 

model is easily extended to properly three-dimensional volumetric forcing, 

thus allowing for buoyancy dependence in (x, z)-directions and thus would 

allow the effect of fire-length in the spanwise direction to be simulated at a 

fraction of the computational cost of full DNS investigations.

Our interest in this problem originally arose from the viewpoint of addressing the instability properties of velocity and temperature profiles that 

typically arise in fire problems. There does not appear to have been any 

systematic study of examining the stability properties of boundary layer 

profiles of the type that do occur in fire-related problems. In particular as 

may be noted from figures 4, 5, and 7, typically quite dramatic and sizable 

temperature profiles arise, and the combination with the inflectional velocity 

profiles does not appear to have been examined or quantified from a boundary 

layer instability viewpoint. Of course, inflectional velocity profiles and their 

instability properties are well known, but the issue of sizable temperature 

profiles and their combination with inflectional velocity profiles have to our 

knowledge not been the subject of any systematic investigation. Moreover, 

in the study of the high Froude number case, we have shown temperature 

profiles of the order of 1200K are generated. The stability properties at this 

high temperature, low Mach number regime are of interest from the viewpoint of erratic fire behaviour, propagation, and also from the viewpoint of 

how vertical rotors, rotating rolls, and intense vortices develop. As shown 

in computations in this paper, strong horizontal gradients in buoyancy near 

the surface produce strong horizontal vorticity. As the horizontal vorticity 

is tilted into the vertical by the fire updraft, intense vertical vorticity may 

develop where large buoyancy and horizontal gradients of the vertical wind 

coexist. As is well known, within fires there are small vortical structures 

on the millimetre scale that tightly bend the flame fronts, up to vortex 

structures on length scales of many metres. Simulations of plumes from line 

sources by Cunningham and others (2003) find that as the plume rises, it is 

bent downstream, and a regular array of steady perturbations develop in the 

cross-stream direction along the plume cap. As time progresses, the plume 

then undergoes a transition from its initial two-dimensional structure to an 

eventual three-dimensional one, with this transition being evident in a region 

far from the heat source. The vorticity field shows that initially the dominant 

vortical structure is the spanwise vortex tube associated with the plume cap; 

while at later times streamwise vortex tubes become dominant.
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Role of Buoyancy and Heat Release in Fire Modeling, Propagation, and Instability Mughal, Hussaini, Goodrick, and Cunningham

To initiate an understanding of such complex features, the basis for investigation is the use of linear stability theory to look at trends and identify 

features that do lead to those observed and hypothesised above. Previous 

instability work has been almost wholly based on buoyancy associated with the 

low temperature Boussinesq assumption. The majority of earlier studies have 

been confined to examine stability of vortex structures where the coupling 

between the energy and momentum fields is weak and the fluid density or 

compressibility essentially plays no role apart from introducing the buoyancy 

associated pressure term into the equations. Thus, few works have considered 

fully compressible nonsimilar boundary layers and their consequent instability 

of the boundary layer profiles where the coupling between the energy and 

momentum fields is strong. Our principal interest in the following is how 

the effects of buoyancy influences the stability of the basic flows discussed 

above, in a fully compressible problem.

Linear Instability of Basic Flow

To conduct a linear stability analysis of the flow, first the standard decomposition of the flow variables into a main flow component and an (infinitesimally) 

small disturbance is applied. That is the total flow Z' described by the exact 

unsteady fully compressible Naiver-Stokes equations can be split into a basic 

steady flow and an unsteady component, hence

Z Z '( , , , ) ( ) '( , , , ), x y z t y x y z t = + Π

with Z?∏'. In the present case only a 2-D and steady mean flow is assumed. 

On the other hand, the disturbances are assumed to be 3-D and thus can 

be written as

Π Π ' ( ) ( , , , ) )exp . x y z t y i x z t = ( α β ω + −

An eigenvalue problem arises and thus a dispersion relation of form

F( , , , , ) α β ω δ R Fo

2 = 0

requires to be satisfied. We specify (β,ω) as free parameters, with (Rδ, Fo

2

)

flow dependent and solve for the spatial eigenvalue α. The objective is to map 

out the parameter space in β,ω,Rδ and Fo

2

. We concentrate on two aspects: 

(1) the stability of profiles of typical form given by figures 4 and 5; (2) low 

Froude number coupled with the heat source basic flows of form given by 

figures 6 and 9.

Influence of Source Distribution, Zero Buoyancy

Neutral curves corresponding to figures 4 and 5 (Twall = 300K and 

Twall = 600K respectively) are shown in figure 13 for varying heat source 

magnitudes. We observe two effects: one is that increasing qo increases the 

instability frequency envelope, and second, we see that the neutral curves (for 

a large enough value of qo) are closed. That is, heat release first causes a local 

(or further) destabilisation of the flow, totally rearranging the flow structure, 

but this is beneficial in that downstream of the heat release, the flow is totally 

stabilised. For the Twall = 600K simulation, the flow in the absence of the 
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Role of Buoyancy and Heat Release in Fire Modeling, Propagation, and Instability Mughal, Hussaini, Goodrick, and Cunningham

volumetric heat source, becomes naturally unstable beyond Rδ ~ 66; at about 

Rδ ~ 100, corresponding to x ~ 0.1 (refer to fig. 1a), the instability envelope 

increases having its upper bound at about the point where peak heat is being 

released (x= 0.15) and it then diminishes in size, and given the appropriate 

amount of heat the flow can be completely stabilised. Though not shown, further downstream we do find that as the flow readjusts to normalcy, the region 

of instability reappears, but this point can be shifted farther downstream by 

further increments in qo. Of course qo cannot be increased indefinitely, since 

ultimately the boundary layer shows a tendency to separate. But clearly we 

see that heat release causes a destabilisation of the flow in the vicinity of the 

heat source release region. These points are confirmed and reemphasised in 

the Twall = 300K case. In this case, heat is released well upstream in a region, 

which in normal circumstances exhibits no instability until about Rδ ~ 520

(the critical Reynolds number for incompressible Blasius flow). Thus, heating 

generates the pocket of instability, and we further see that this is localised to 

the vicinity of the heat release region. Though not shown, the eigen-functions 

reveal that the unstable modes are thermally induced, in that temperature 

disturbances are the largest in magnitude. Though overall, the growth rates 

of these thermally induced unstable waves are found to be quite small.

Buoyancy Effects

Impact of decreasing Froude numbers on the neutral curves for 2-D disturbances is shown in figure 14a, while growth rates over the frequency span at 

various x positions for the lowest Froude number neutral curve, corresponding to Ue = 0.5m/s, are shown in figure 14b.We note that the Ue = 10.0m/s 

curve is closed and almost total stabilisation of flow is achieved, whereas for 

the lowest Froude number curve shown, the unstable frequency spectrum 

enlarges considerably in the heat release region while disturbances down to the 

stationary are unstable. In the limit of ω→0 the growth rate and wavenumber 

both approach zero, while as ω→∞ or large steamwise wavenumber limit, the 

disturbances ultimately return to being stable. Note that with increasing Rδ

the growth rates increase substantially; thus, a rapid destabilisation occurs as 

a result of the heat release. Another feature is that as x increases the curves 

coalesce, and this may be due to the instability being essentially inviscid in 

structure, and so the value of Rδ becomes irrelevant.

Figure 15a shows growth rates for 3-D disturbances arising in the mildly 

buoyant flow examined in figure 14b and shows that in a certain frequency 

Figure 13—Zero buoyancy (Ue = 10 m/s) neutral curves for parameters of figures 

5 and 4 for varying heat source magnitudes. F = 103 ω / Rδ m/is the circular 

frequency. (a) qo = (1,5,10,20,40,60); Twall = 600K. (b) qo = (120,40,60,80); Twall = 300K. 

Also superposed is the qo = 40 result from figure (a) (shown as dashed curve). 
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Role of Buoyancy and Heat Release in Fire Modeling, Propagation, and Instability Mughal, Hussaini, Goodrick, and Cunningham

Figure 15—(a) Amplification curves for varying frequency for 3D disturbances at 

x = 0.224; Rδ =150; b = 1000, β/Rδ =for the mildly buoyant flow of Ue = 0.5 m/s. (b) The 

corresponding phase speed c variation with wavenumber αr.

Figure 14—Neutral curves for increasing levels of buoyancy, computed for 

parameters corresponding to figure 7. Neutral curves are shown for Froude numbers 

corresponding to(Ue = 0.5, 0.75,1.0, 2.5,5.0,10.0); Twall = 600K. (b) Amplification 

curves for varying frequency at various x-positions corresponding to the plot shown 

in (a) for the Ue = 0.5m/s case; arrow points in direction of increasing downstream 

distance in x corresponding to Rδ =100, 110,120, 130, 140, 150,158.

regime the 3-D disturbances are more destabilising than 2-D disturbances; 

with the stationary vortex being most unstable, while at high frequencies it 

is the 2-D disturbance that is most unstable. For stationary disturbances, 

we note from figure 15b that the streamwise wavenumbers are zero, and so 

these disturbances (the most dangerous from an instability viewpoint) are 

purely periodic in the spanwise direction only. Instability occurs over a finite 

range of β and at large enough β, disturbances return to being stable, while 

(though not shown) as β→0 the growth rates behave as αt ~ β1/2.

The phase speed c, as ω (and thus αr the wavenumber) tends to large values 

asymptotes to the maximum velocity overshoot value (in case shown this limit 

is ~ 1.05). This result is consistent with the Boussinesq based incompressible 

inviscid works of Mureithi and others (1997) and Denier and others (2001) 

who essentially build upon the more complete earlier, again Boussinesq based, 

asymptotic and numerical analysis of Hall and Morris (1993). The former 

two authors’ findings were deduced primarily by a numerical solution to the 

3-D version of the inviscid Taylor-Goldstein equation while our solutions are 

based at finite Reynolds number and allow for full compressibility, though for 
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Role of Buoyancy and Heat Release in Fire Modeling, Propagation, and Instability Mughal, Hussaini, Goodrick, and Cunningham

the quite low external wind speeds considered the Mach number effects are 

insignificant. Their inviscid solutions suggest that the flow is unstable across 

the whole wavenumber spectrum and that growth rates become progressively 

larger as α increases and then state that viscosity ultimately must return the 

flow to a stable regime as α → ∞. Our viscosity included results confirm this 

result. They also confirm the findings of Hall and Morris (1993) that for 

nonzero β the mode with the largest growth rate occurs for ω = 0. That is, 

the dominant wave mode becomes purely steady and two-dimensional in 

the spanwise direction, and at leading order the waves travel with a phase 

speed equal to the maximum of the streamwise velocity. This situation with 

the phase speed though only really arises in the limit of ω → ∞ in regions 

where our viscous included stability analysis, suggesting that disturbances 

have returned to a stable regime.

We only show neutral curves for values of Froude number down to 

Ue = 0.5m/s; this is merely for brevity, in that for even lower Froude numbers the instability envelope increases considerably, both in terms of the 

frequency space and even larger growth rates. An analogous result to figure 

15 but corresponding to the highly buoyant flow at Ue = 0.05m/s is shown 

in figure 16. This merely confirms the findings deduced from the mildly 

buoyant case, in that decreasing Froude number increases the instability and 

unstable parameter space. We note from the eigenfunctions (fig. 17) the high 

frequency most amplified 2-D disturbance is localised off the wall about the 

position where the streamwise velocity attains its maximum overshoot value, 

while the purely spanwise periodic disturbance (fig. 17a) is situated close to 

the wall. This is clearly viscous in nature, while the former is of the inviscid 

type (fig. 17b). In the above, we only report on the first or most amplified 

mode, though as Reynolds number increases unstable higher modes were 

also identified.

Figure 16—(a) Amplification curves for varying frequency for a number of 3-D 

disturbances (b = 0,(2),12) at x = 0.169, Rδ = 130; b= 1000 β/Rδ for the highly buoyant 

flow of Ue = 0.05 m/s. (b) The corresponding phase speed c variation with wavenumber 

αr. The dashed line corresponds to the maximum velocity overshoot value of ~3.78, as 

αrv → ∞ the curves asymptote to this value.
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Role of Buoyancy and Heat Release in Fire Modeling, Propagation, and Instability Mughal, Hussaini, Goodrick, and Cunningham

Conclusions

We may conjecture that in the two Froude number limits, there are two 

different routes to fire behaviour and destabilisation, with the Froude number 

playing a crucial role in the boundary layer development and consequential 

instability of the convecting flow. The work lends support to some of the 

earliest criteria established (Byram 1954), and recently put forth by Nelson 

(2003), that erratic fire behaviour occurs when the ratio of the power of the 

wind to the power of the fire is ≤ 1 (essentially a Froude number definition). 

In view of the low growth rates in the high Froude number case, perhaps the 

significant factor is the sensitivity of the boundary layer to separate and lift 

off the wall; this being caused by massive updrafts of buoyant air. The relatively weak nature of instabilities in this regime suggests that the convecting 

boundary layer or fireline development would otherwise be well behaved. 

This is consistent with the work of Clark and others (1996b), who find that 

in high ambient windflows, a stable fireline arises forming a continuous 

parabolic shape (their model was fully three-dimensional and unsteady) as 

the fire evolves.

We have shown that the addition of heat goes directly into forming a highly 

accelerated streamwise velocity or jetlike flow in the low Froude number 

limit. In this limit, certainly the inflectional nature of profiles plays a strong 

role and must be a key feature in observed fire features. Local acceleration 

of flows by buoyancy and instability of inflectional profiles has been known 

about for some considerable time; however, its relevance in fire related problems has not been identified until now. The work suggests that if the ambient 

profile is initially inflectional, the transition to the unstable modes should 

be more rapid. The work essentially shows that a massive destabilisation via 

the inflectional route takes place in the low Froude number regime and that 

stationary, zero streamwise and nonzero spanwise wavenumber viscous disturbances have the highest growth rates. The results show that the flow can 

Figure 17—(a) Three-dimensional disturbance eigenfunctions computed for parameters 

of figure 16 at F = 0, b =12, the pure vortex most amplified mode. (b) Two-dimensional 

eigenfunctions at F = 120, b = 0. The dashed curve in the v-plots is the overshot 

inflectional basic velocity profile, while the curve in the T-plot is the basic temperature 

profile. 
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Role of Buoyancy and Heat Release in Fire Modeling, Propagation, and Instability Mughal, Hussaini, Goodrick, and Cunningham

support purely spanwise periodic disturbances (that is, vortices) and that it 

is feasible for these modes to have the largest growth rates. These are commonly referred to as pure vortex longitudinal “roll cell” modes. These might 

well be linked to cross roll features mentioned in the work of Cunningham 

and others (2003). Clark and others (1996b) identify similar features and 

state that a key requirement in breakup of the fireline is a low enough ambient wind (low Froude number) and thus speculate that the breakup is due to 

fire generated winds. Remarkably, in one of their figures, a distinct periodic 

spanwise vorticity field is seen, which they describe as erratic behaviour of 

the fireline.

In particular as regards the fingering phenomenon, Clark and others 

(1996b) hypothesise that once a fire line is long enough it cannot sustain 

a single convective updraft column and develops multiple columns due to 

long-line instabilities within the convection column. The convection attempts to form a long, vertically deep, nearly two-dimensional structure. Such 

two-dimensional structures are typically dynamically unstable due to either 

convective or shear instabilities and result in so-called cross-roll instabilities. 

Certainly fire-length (arising from a 3-D finite heat source) and side winds 

being drawn in may well play a role in the fingering phenomenon, but our 

analysis suggests that a possible mechanism might well be due or linked to 

the periodic spanwise stationary disturbances identified in our work. We find 

that it is the 3-D stationary disturbances that are the most unstable; these 

modes may have some bearing on the above description.

Acknowledgments

Part of this work was carried out while the first author was visiting the 

School of Computational Science at Florida State University. This work was 

supported by U.S. Department of Agriculture, Forest Service, Southern 

Research Station, Athens, GA, under a collaborative research agreement 

contract SRS 03-CA-11330136-056.

References

Banta, R.M.; Oliver, E.T.; Holloway, E.T.; Kropfli, R.A.; Bartram, B.W.; Cupp, 

R.E.; Post, M.J. 1992. Smoke column observations from two forest fires using 

Doppler Lidar and Doppler radar. J. Appl. Meteor. 31: 1328-1349.

Byram, G.M. 1954. Atmospheric conditions leading to blowup fires. Station 

Paper No. 35. Asheville, NC: U.S Department of Agriculture, Forest Service, 

Southeastern Forest Experiment Station.

Clark, T.L.; Jenkins, M.A.; Coen, J.; Packham, D. 1996a. A coupled atmospherefire model: Role of the Convective Froude number and dynamic Fingering at the 

fireline. Int. J. Wildland Fire 6(4): 177-190.

Clark, T.L.; Jenkins, M.A.; Coen, J.; Packham, D. 1996b. A coupled atmosphere-fire 

model: Convective feedback on fireline dynamics. J. Appl. Meteor. 35: 875-901.

Coen, J.; Mahalingham, S.; Daily, J. 2004. Infrared Imagery of Crown-Fire 

Dynamics during FROSTFIRE. J. Appl. Meteor. 43(9): 1241-1259.

Cunningham, P.; Hussaini, M.Y.; Goodrick, S.L.; Linn, R.R. 2003. Numerical 

simulations of buoyant plumes in a vertically sheared crossflow. Proceedings, 

American Meteorological Society Tenth Conference on Mesoscale Processes. 

Portland, OR. 3 p.
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 219

Role of Buoyancy and Heat Release in Fire Modeling, Propagation, and Instability Mughal, Hussaini, Goodrick, and Cunningham

Daniels, P.G.; Gargaro, R.J. 1993. Buoyancy effects in stably stratified boundary 

layer flow. J. Fluid Mech. 250: 233-251.

Deardorff, J.W. 1973. The use of subgrid transport equations in a three-dimensional 

model of atmospheric turbulence. J. Fluids Eng. 95: 429-438.

Denier, J.O.; Stott, J.A.K.; Bassom, A.P. 2001. Three-dimensional inviscid waves in 

buoyant boundary layer flows. Fluid Dynamics Research. 28: 89-109.

Hall, P.; Morris, R. 1993. On the instability of boundary layers on heated flat plates. 

J. Fluid Mech. 245: 367-400.

Higuera, F.J. 1997. Boundary layer separation due to gas thermal expansion. Phys. 

Fluids. 9(10): 2841-2850.

Linn, R.R. 1997. A transport model for prediction of wildfire behaviour. 

Los Alamos National Laboratory Scientific Report. LA-13334-T, 195 pp. 

Los Alamos, NM.

Linn, R.R.; Cunningham, P. 2005. Numerical simulations of grass fires using a 

coupled atmosphere-fire model: Basic fire behaviour and dependence on wind 

speed. J. Geophysical Research. 110: D13107.

Morvan, D.; Dupuy, J.L. 2004. Modelling the propagation of a wildfire through 

a Mediterranean shrub using a multiphase formulation. Combustion and Flame 

138: 199-210.

Mureithi, E.W.; Denier, J.P.; Stott, J.A.K. 1997. The effect of buoyancy on upperbranch Tollmien-Schlichting waves. IMA J. App. Math. 58: 19-50.

Noble, I.R.; Bary, G.A.V.; Gill, A.M. 1980. McArthur’s fire-danger meters expressed 

as equations. Australian J. Ecology. 5: 201-203.

Nelson, R.M. Jr. 2003. Power of the fire—a thermodynamic analysis. Int. J. Wildland 

Fire. 12: 51-65.

Rothermel, R.C. 1972. A mathematical model for predicting fire spread in wild-land 

fuels. Gen. Tech. Rep. INT-115. Ogden, UT: U.S. Department of Agriculture, 

Forest Service, Intermountain Research Station.
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 220

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This 

page is 

blank 

on purpose
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 221

Abstract—In southern Australia, shrubby heath vegetation together with woodlands 

dominated by multistemmed eucalypts (mallee) comprise areas of native vegetation 

with important biodiversity values. These vegetation types occur in semiarid and mediterranean climates and can experience large frequent fires. This study is investigating 

changes in the fuel complex with time, fuel moisture dynamics, vertical wind profile 

characteristics, fire propagation thresholds, rates of spread, and flame characteristics. 

The project is being conducted at Ngarkat Conservation Park in South Australia with 

data coming from experimental and prescribed burns conducted under a range of 

weather conditions. The final output of this project will be a prescribed burning guide 

to assist land management agencies to plan and safely conduct effective hazard reduction and ecological management burns in mallee and heath fuel types.

Introduction

Low woodland and heath vegetation are fire prone environments in 

Australia and around the world. In mediterranean climates, typical shrub 

dominated vegetation such as chaparral (California) (Keeley and others 1999), 

kwongan (Western Australia) (Hassell and Dodson 2003; Keith and others 

2002), fynbos (South Africa) (Schwilk and others 1997), and maquis and 

matorral (Mediterranean Basin) (Bilgili and Saglam 2003) are known for 

their flammability. In southern Australia, shrubby heath vegetation together 

with woodlands dominated by multistemmed eucalypts (mallee) represents 

significant areas of native vegetation that burns frequently (Bradstock and 

Cohn 2002; Keith and others 2002).

In the past, tracts of mallee woodlands covered much of the semiarid parts 

of southern Australia, including southern Western Australia (WA), southern South Australia (SA), northwestern Victoria, and western and central 

New South Wales (NSW) (Noble 1984). However, about 35 percent of this 

vegetation has been cleared (ANVA 2001) and relatively little of it remains 

in conservation reserves. One such reserve is the Ngarkat Conservation 

Park (CP), in eastern South Australia. This reserve comprises a mosaic of 

heath and mallee vegetation and experiences bushfires almost annually. This 

270,000 ha (1,042 miles2) reserve is part of an 800,000 ha (3,088 miles2) 

section of contiguous native mallee and heath vegetation extending into the 

neighbouring state of Victoria.

Ngarkat CP is habitat for a number of threatened flora and fauna, contains 

floral resources for the apiary industry, and is used for recreation. These values 

are threatened by large bushfires. These bushfires usually occur in late spring 

and summer, can be of high intensity, and have on occasion burnt in excess 

of 100,000 ha (386 miles2) in single events (Department for Environment 

and Heritage, South Australia, unpublished fire history database, 2007).

Fuel Dynamics and Fire Behaviour in 

Australian Mallee and Heath Vegetation

Juanita Myers1, Jim Gould1, Miguel Cruz1, and Meredith Henderson2

In: Butler, Bret W.; Cook, Wayne, 

comps. 2007. The fire environment—

innovations, management, and policy; 

conference proceedings. 26-30 March 

2007; Destin, FL. Proceedings 

RMRS-P-46CD. Fort Collins, CO: 

U.S. Department of Agriculture, 

Forest Service, Rocky Mountain 

Research Station. 662 p. CD-ROM.

1 Experimental Scientist, Research 

Leader, and Research Scientist, respectively, Ensis Forest Biosecurity and 

Protection, Commonwealth Scientific 

and Research Organisation (CSIRO), 

Ca nber ra, Aust ra l ia, Bush f i re 

Cooperative Research Centre (CRC) 

East Melbourne, Victoria, Australia. 

Lead author at juanita.myers@ensisjv.

com

2 Senior Fire Research Off icer, 

South Australian Department for 

Environment and Heritage, Adelaide, 

South Australia, Australia, Bushfire 

CRC East Melbourne, Victoria, 

Australia.
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 222

The loss of large tracts of native vegetation in a single fire in this fragmented 

landscape is not a desirable management or conservation outcome. Most of 

the fires in the park result from lightning ignitions and may be exacerbated by 

extended drought periods and suppression difficulty. Therefore, the current 

focus of management is on the prevention of large fires across the reserve, 

managed through prescribed burning. For managers to use prescribed burns 

effectively in this landscape requires more robust information on both fuel 

dynamics and fire behaviour.

Studies of fire behaviour and fuels have been undertaken in the mallee 

heathlands of southwest Western Australia (McCaw 1997). McCaw (1997) 

found existing fire danger rating systems and fire behaviour models are not 

suitable in mallee heath fuel types. The rate of spread in these fuel types is 

consistently faster (up to 200 percent) than those predicted for other scrub 

fuel types. The exception to this is the South African fynbos scrub fuel, which 

tends to exhibit faster rates of spread under similar conditions (Van Wilgen 

and others 1985). A major problem encountered was that the current fire 

danger rating systems and spread models are not able to accurately predict 

conditions of fire spread. Inaccuracies in model output arise from the inability of these models to account for the threshold dead fuel moisture content, 

above which fires will not spread in mallee heath. This critical threshold for 

Western Australian mallee heathlands was found to be a dead fuel moisture 

content in the litter layer of no greater than 8 percent (McCaw and others 

1995, 2003; McCaw 1997).

A number of other studies have been carried out in Australian heathlands, 

investigating fuel moisture relationships and prediction of fuel moisture 

content (Catchpole and others 2001; Pippen 1999), determining ignition 

thresholds related to fuel moisture and fire development (Plucinski and 

Catchpole 2002; Plucinski 2003).

The aim of this project is to develop a prescribed burning guide to assist 

land management agencies to plan and safely conduct prescribed burning for effective hazard reduction and ecological sensitive management in 

South Australian mallee and heath vegetation. The specific objectives are: 

(1) characterise changes in the fuel complex with time; (2) model the seasonal and diurnal fuel moisture dynamics of live and dead fuel components; 

(3) determine the vertical wind profile in these fuel types; (4) model the fire 

environment conditions that will sustain fire spread (propagation thresholds); 

and (5) model rate of fire spread and flame characteristics.

This study will be collecting data from controlled experimental and 

prescribed burns in Ngarkat CP. The models developed from this research 

in South Australia will be tested for their applicability in mallee and heath 

in other states of Australia. In this paper we describe a project in progress 

and highlight the context and experimental and modelling approaches 

proposed.

Outline of Experiments

Quantifying Fuels for Fire Behaviour

Mallee and heath in semiarid and mediterranean Australia are characterised by a highly discontinuous fuel complex (Bradstock and Cohn 

2002). Mallee woodlands are made up of short (2 to 10 m; 7 to 33 ft tall) 

multi­stemmed eucalypts, often (but not always) with a shrubby understory. 

Fuel Dynamics and Fire Behaviour in Australian Mallee and Heath Vegetation Myers, Gould, Cruz, and Henderson
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 223

In the mallee, the surface fuel and suspended bark fuel are the main fuel 

layers that carry the flame front and are concentrated around the base of 

individual multistemmed eucalypt clump. The heath fuel type is made up 

of scattered small-leafed shrubs, or clumps of shrubs (up to 1 m; 3 ft tall), 

the sparse litter of which is usually in a tightly packed litter bed and can be 

partially buried by sand (Bradstock and Gill 1993). In this fuel type, the fire 

spreads in the low shrub canopy. In both cases (mallee and heath), the fuel 

layer that carries the flame front is discontinuous, with little fuel in the gap 

between the clumps of fuel.

The fuels that provide the energy flux that enables a fire to spread have 

generally been assumed to be those that are consumed in the continuous 

flaming zone of a fire front. The load of fine fuels, such as dead leaf, bark 

and twig litter <6 mm (1⁄4 inches) in diameter, has been used as the major 

fuel variable (in many studies the only fuel variable) to predict fire spread 

(McArthur 1967; Peet 1965). Although fine fuel load has been the basis of 

Australian fire spread models in the past, and may be useful in providing 

practical information for burning guides, it has not been a significant variable in a number of recent studies (Buckley 1992; Burrows 1994; Cheney 

and others 1992, 1993). In complex fuels, the simple measure of total fine 

fuel is inadequate to describe the fuels that contribute to the forward rate 

of spread.

It is important to get a good estimate of fuel quantity, structure, composition, continuity, and height in order to quantify the effect of low-intensity 

prescribed burning in modifying the behaviour of wildfires. Detailed fuel 

sampling techniques can quantify the fuel loads of the surface (ground litter 

fuel) and near-surface (suspended live and dead fine fuel above the ground 

surface, but not on it) fuel layers, but these methods are not suited to operational use for assessment of fuels.

The development of more sophisticated burning guidelines requires a 

sound understanding of fire behaviour and suppression difficulty in fuels of 

different structure and composition. Australian studies (Cheney and others 

1992; Gould and others 2001; McCarthy and others 1999; McCaw and others 

2003; Project Vesta unpublished reports) have identified the importance of 

fuel structure in determining fire behaviour and ease of suppression. They 

have also developed a system for quantifying fuel structure with a numerical 

index that can be used as a fuel predictor variable to replace fuel load.

Rating systems that assess the relative hazard of fuel factors that affect fire 

behaviour and suppression difficulty represent a new approach in fuel assessment (McCarthy and others 1999; Gould and others 2001; Project Vesta 

unpublished progress reports). The fuel hazard rating systems developed by 

Wilson (1992, 1993) and McCarthy and others (1998) for eucalypt bark, 

elevated fuel, and surface fuel into a combined overall fuel hazard rating provided a simple, easy-to-use method for operational assessment of the hazard 

presented by fuels. This assessment emphasises the whole fuel complex by 

combining a hazard rating for each of the different fuel layers—bark, elevated, 

and surface fuels—using visual fuel characteristics.

This project will examine what changes need to be made for the system 

to be applicable to mallee and heath vegetation. Fuels load will be quantified through a combination of destructive and nondestructive sampling and 

visual systems of scoring structure and hazard as described above. The fuel 

assessment will characterise the changes in the fuel complex with time and 

compare the mallee and heath fuel types.

Fuel Dynamics and Fire Behaviour in Australian Mallee and Heath Vegetation Myers, Gould, Cruz, and Henderson
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 224

Fire Behaviour

Models to better predict fire behaviour in mallee and heath fuels are an 

important tool for managing prescribed burns. Improved fire spread predictions 

will be invaluable for the management of wildfires, providing more timely 

warnings of threat, and aiding decisionmaking on suppression tactics.

In order to improve fire behaviour models, it is necessary to encompass a 

wide range of fuel moisture and weather conditions in experimental burns. 

To achieve this, experimental burnings are being carried out in three seasons: 

in mid autumn (May 2006), early autumn (April 2007), and late summer 

(Feb/March 2008). To capture the differences in fire behaviour between the 

mallee and heath fuel types, and between different fuel ages under identical 

atmospheric conditions, simultaneous ignitions between different fuel types 

and ages will be conducted wherever possible. The fire behaviour under these 

conditions can then be related to the fuel characteristics.

Fire propagation threshold—Of the range of fire behaviour information 

necessary for the planning and conducting of prescribed burning, the determination of the threshold conditions under which the fire will spread (the 

fire propagation threshold) is critical. This is because prescribed burnings 

are mostly conducted under marginal burning conditions.

Due to the discontinuous nature of mallee and heath fuel complexes 

(Bradstock and Gill 1993; Bruner and Klebenow 1979; McCaw 1997), fire 

spread requires conditions that will allow the development of a flame angle, 

depth, and length that will bridge the gaps between individual shrubs or clumps, 

and/or initiate short range spotting that allows the fire to bypass the small 

scale fuel discontinuities. Under the conditions sought for prescribed burning, 

this creates highly nonlinear fire behaviour, with an abrupt increase in rate of 

spread when the conditions for sustained fire propagation are met.

Modelling of the propagation threshold in these discontinuous fuel complexes can be approached through a mechanistic framework as initiated by 

Bradstock and Gill (1993) or through logistic regression analysis, which 

attempts to describe the likelihood of an event, in this case fire spread, occurring (Fernandes and others 2002; Marsden-Smedley and Catchpole 2001).

We will use the logistic regression approach to model the propagation 

threshold. Propagation will be defined as successful if the fire spreads 100 m 

without self-extinguishing. A series of smaller scale (1 ha; 2.5 acre) fire spread 

experiments will be conducted under a range of conditions in each fuel type 

and age class in order to determine the propagation threshold in each. To 

achieve sufficient replication, these data will be supplemented by data collected 

at operational prescribed burns in these fuel types.

Fire behaviour parameters—During experimental burns, fine fuel moisture 

and weather parameters will be measured, including wind measurements. The 

fire behaviour parameters being measured will include forward and lateral 

rates of spread, flame dimensions, intensity, head fire width, spotting distance, 

fuel consumption, and the fuel layers influencing fire spread.

The limited size of the experimental fire plots (3 ha; 7 acres) will mean 

that we need to test whether the predictions from our models can be confidently extrapolated to fires occurring on a larger scale (Marsden-Smedley 

and Catchpole 1995; McCaw 1997). The experimental design includes model 

validation against larger scale experimental fires and against reliable prescribed 

burn and wildfire data.

Fuel Dynamics and Fire Behaviour in Australian Mallee and Heath Vegetation Myers, Gould, Cruz, and Henderson
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 225

Contribution to Improved Bushfire 

Management

The results of this research will better quantify the effects of fuel structure, 

fuel moisture dynamics, and wind on fire behaviour in mallee and heath 

vegetation. This will provide fire spread models that have an application to a 

wider range of mallee, heath, and other shrubland fuel types for both wildfire 

and prescribed burn situations.

New functions describing the relationship between fire spread and wind 

will be designed so that they can be used to predict the behaviour of highintensity wildfires. This could be useful for analysing zones of potential 

wildfire impact and providing timely public warning. Fire spread predictions 

can be applied to data on suppression effectiveness limits to develop better 

fire management strategies.

Acknowledgments

We thank the South Australian Department for Environment and Heritage 

Murraylands Region staff and Brett Stephens (Regional Fire Management 

Officer) for their support in conducting the experimental burning program. 

Also, the Bushfire Cooperative Research Centre contributed to the research 

funding and provided financial support for attending this conference.

References

ANVA. 2001. Australian native vegetation assessment. National Land and Water 

Resources Audit and Commonwealth of Australia, Canberra.

Bilgili, E.; Saglam, B. 2003. Fire behaviour in maquis fuels in Turkey. Forest Ecology 

and Management. 184: 201-207.

Bradstock, R.A.; Cohn, J.S. 2002. Fire regimes and biodiversity in semi-arid mallee 

ecosystems. In: Flammable Australia: the fire regimes and biodiversity of a 

continent. (eds R.A. Bradstock, J.E. Williams and A.M. Gill) Cambridge, UK: 

Cambridge University Press: 238-258.

Bradstock, R.A.; Gill, A.M. 1993. Fire in semi-arid Mallee shrublands: size of flames 

from discrete fuel arrays and their role in the spread of fire. Int. J. Wildland Fire 

3: 3-12.

Bruner, A.D.; Klebenow, D.A. 1979. Predicting success of prescribed fires in pinyonjuniper woodland in Nevada. Res. Pap. INT-219. Ogden, UT: U.S. Department 

of Agriculture, Forest Service, Intermountain Research Station.

 Buckley, A.J. 1992. Fuel reducing a stand of eucalypt regrowth in east GippslandA case study. Research Report No. 33. Victoria, Australia: Department of 

Conservation and Environment. 29 p.

Burrows, N.D. 1994. Experimental development of a fire management model 

for Jarrah (Eucalyptus marginata Donn ex. Sm) forest. PhD thesis. Canberra, 

Australia: Australian National University, Department of Forestry. 293 p.

Catchpole, E.A.; Catchpole, W.R.; Viney, N.R.; McCaw, W.L.; Marsden-Smedley, 

J.B. 2001. Estimating fuel response time and predicting fuel moisture content 

from field data. International Journal of Wildland Fire 10(2): 215-222.

Fuel Dynamics and Fire Behaviour in Australian Mallee and Heath Vegetation Myers, Gould, Cruz, and Henderson
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 226

Catchpole, W.R.; Bradstock, R.A.; Choate, J.; Fogarty, L.G.; Gellie, N.; McCarthy, 

G.J.; McCaw, W.L.; Marsden-Smedley, J.B.; Pearce, G. 1998. Cooperative 

development of prediction equations for fire behaviour in heathlands and 

shrublands. In: Proceedings, 3rd International Conference on Forest Fire Research 

and 14th Fire and Forest Meteorology Conference. (ed. Viegas, D.X.) 16-20 

November 1998, Luso, Coimbra, Portugal: 631-645.

Cheney, N.P.; Gould, J.S.; Catchpole, W.R. 1993. The influence of fuel, weather 

and fie shape variables on fire-spread in grassland. Int. J. of Wildland Fire 3(1): 

31-44.

Cheney, N.P.; Gould, J.S.; Knight, I. 1992. A prescribed burning guide for young 

regrowth forest of Silvertop ash. Research Paper No. 16. For. Comm. of New 

South Wales. 92 p.

Fernandes, P.M.; Botelho, H.S.; Loureiro, C. 2002. Models for the sustained ignition Models for the sustained ignition

and behaviour of low-to-moderately intense fires in maritime pine stands. In: 

Forest Fire Research & Wildland Fire Safety. Proceedings of the IV International 

Conference on Forest Fire Research/2002 Wildland Fire Safety Summit. (ed. 

D.X. Viegas) Rotterdam, The Netherlands: Millpress Scientific Publications. 

[CD-ROM]. 11 p.

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 

fires in dry eucalypt forest: Progress Report. Australasian Bushfire Conference 

Proceedings. 3–6 July 2001. Christchurch. New Zealand: 13-21.

Hassell, C.W.; Dodson, J.R. 2003. The fire history of south-west Western Australia 

prior to European settlement in 1826-1829. In: Proceedings, Fire in ecosystems 

of south-west Western Australia: Impacts and management. 16-18 April 2002 

Perth, Australia. (eds I. Abbott and N. Burrows) Leiden, The Netherlands: 

Backhuys Publisers: 71-85.

Keeley, J.E.; Fotheringham, C.J.; Morais, M. 1999. Reexamining fire suppression 

impacts on brushland fire regimes. Science 284: 1829-1832.

Keith, D.A.; McCaw, W.L.; Whelan, R.J. 2002. Fire regimes in Australian heathlands 

and their effetcs on plants and animals. In: Flammable Australia: the fire regimes 

and biodiversity of a continent. (eds R.A. Bradstock, J.E. Williams and A.M. Gill) 

Cambridge, UK: Cambridge University Press: 199-237.

Marsden-Smedley, J.B.; Catchpole, W.R. 1995. Fire behaviour modelling in 

Tasmanian buttongrass moorlands. II. Fire behaviour. Int. J. Wildland Fire 5(4): 

215-228.

Marsden-Smedley, J.B.; Catchpole, W.R. 2001. Fire modelling in Tasmanian 

Buttongrass Moorlands. IV. Sustaining versus non-sustaining fires. Int. J. 

Wildland Fire 10: 255-262.

McArthur, A.G. 1967. Fire behaviour in Eucalypt Forests. Commonwealth of 

Australia Forestry and Timber Bureau Leaflet No. 107. Canberra. 36 p.

McCarthy, G.J.; Tolhurst, K.G.; Chatto, K. 1998. Overall fuel hazard guide. Fire 

Management Research Report No. 47. Victoria, Australia: Department of Natural 

Resources and Environment. 33 p.

McCarthy, G.J.; Tolhurst, K.G.; Chatto, K. 1999. Overall fuel hazard guide. 

3rd edition. Fire Management Research Report No. 47. Victoria, Australia: 

Department of Natural Resources and Environment. 28 p.

McCaw, W.L. 1997. Predicting fire spread in Western Australian mallee heath 

shrubland. PhD thesis. Canberra, Australia: University of New South Wales, 

University College, School of Mathematics and Statistics. 235 p.

McCaw, W.L.; Burrows, N.D.; Friend, G.R.; Gill, A.M. 1995. Predicting fire 

spread in Western Australian mallee-heath. In: Proceedings, Landscape Fires ‘93: 

Proceedings of an Australian Bushfire Conference, Perth, Western Australia. 27-

29 September 1993. CALM Science, No. 4 Supplement: 35-42.

Fuel Dynamics and Fire Behaviour in Australian Mallee and Heath Vegetation Myers, Gould, Cruz, and Henderson
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 227

McCaw, W.L.; Cheney, N.P.; Gould, J.S. 2003. Do eucalypt forest fires burn 

faster and hotter in older fuels? Implication for hazard reduction burning. In: 

Proceedings, 3rd International Wildland Fire Conference and Exhibition. 3–6 

October 2003. Sydney, NSW, Australia. [CD-ROM]

 McCaw, L.; Cheney, P.; Sneeuwjagt, R. 2003. Development of a scientific 

understanding of fire behaviour and use in south-west Western Australia. In: 

Proceedings, Fire in ecosystems of south west Western Australia: Impacts and 

management. 16-18 April 2002. Perth, Australia (eds I. Abbott and N. Burrows). 

Leiden, The Netherlands: Backhuys Publishers: 171-188.

Noble, J.C. 1984. Mallee. In: Management of Australia’s Rangelands. (eds G.N. 

Harrington, A.D. Wilson, and M.D. Young) Melbourne, Australia: CSIRO: 

223-240.

Peet, G.B. 1965. A fire danger rating and controlled burning guide for northern 

jarrah forest of Western Australia. Forests Department Western Australia Bulletin 

No. 74.

Pippen, B.G. 1999. Predicting fine fuel moisture in shrubby vegetation. Bachelor 

of Science (Forestry) Honours thesis. Canberra, Australia: Australian National 

University, Department of Forestry. 71 p.

Plucinski, M.P. 2003. The investigation of factors governing ignition and development 

of fires in heathland vegetation. PhD thesis. Canberra, Australia: University of 

New South Wales, Australian Defence Force Academy, School of Mathematics 

and Statistics. 347 p.

Plucinski, M.P.; Catchpole, W.R. 2002. Vertical development of fire in shrubland 

fuels. In: Forest fire research and wildland fire safety: Proceedings of IV 

International Conference on Forest Fire Research 2002 Wildland Fire Safety 

Summit. (ed. D.X. Viegas). Luso, Coimbra, Portugal, 18-23 November 2002. 

Rotterdam, The Netherlands: Millpress Science Publishers. [CD-ROM]

Schwilk, D.W.; Keeley, J.E.; Bond, W.J. 1997. The intermediate disturbance 

hypothesis does not explain fire and diversity pattern in fynbos. Plant Ecology 

132: 77-84.

Van Wilgen, B.W.; Maitre, D.C.; Kruger, F.J. 1985. Fire behaviour in South African 

fynbos (macchia) vegetation and predictions from Rothermel’s fire model. Journal 

of Applied Ecology 22(1): 207-216.

Wilson, A.A.G. 1992. Eucalypt bark hazard guide. Research Report No. 32. Victoria, 

Australia: Department of Conservation and Environment, Fire Management 

Branch. 17 p.

Wilson, A.A.G. 1993. Elevated fuel guide. Research Report No. 35. Victoria, 

Australia: Department of Conservation and Environment, Fire Management 

Branch. 22 p.

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USDA Forest Service Proceedings RMRS-P-46CD. 2007. 228

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Abstract—On March 24, 1998, a crew of eight rural firefighters were burned over 

while attempting to suppress a backburning sector of the Bucklands Crossing Fire in 

North Otago, New Zealand. The fire demonstrates how factors typical of the New 

Zealand fire environment – steep slopes, highly flammable shrub fuels, and a strong 

foehn wind effect – combined to produce extreme fire behaviour. Several firefighters 

sustained varying degrees of injury. They were hit by a blowup that was most likely 

caused by a rapid reburn through previously underburned shrub fuels. The exact trigger for upslope spread and transition to a crown fire was not definitively identified; 

however, it was most likely the result of strong winds and localised turbulence, combining with steep topography and highly flammable, preheated shrub fuels. The case 

study report prepared on this burnover describes the activities of personnel leading 

up to and during the incident in relation to the fire environment and fire behaviour. 

Possible causes of the blowup are reviewed, and observed fire behaviour compared to 

that predicted by available models. Particular emphasis is placed on the contribution 

and alignment of fire environment factors and their role in triggering fire behaviour 

escalation. Aspects of firefighter safety during the incident are also discussed, including 

the performance of protective clothing. The findings highlight the need for increased 

training of firefighters in fire behaviour and, in particular, greater situational awareness 

of the fire environment and indicators of extreme fire behaviour potential. The case 

study provides a number of lessons learned that have relevance worldwide.

Introduction

On March 24, 1998, a crew of eight New Zealand rural firefighters were 

burned over while attempting to suppress a sector of the Bucklands Crossing 

Fire in North Otago, New Zealand, that was burning downhill. Three firefighters sustained burn injuries, one serious, and a fourth crew member was 

hurt escaping into previously burned fuels. The crew were hit by a blowup 

that was most likely caused by a rapid reburn through previously underburned 

shrub fuels, a situation highly reminiscent of the 1994 South Canyon Fire 

in Colorado, USA, where 14 firefighters were killed. The fire demonstrates 

how factors typical of the New Zealand fire environment – steep slopes, 

highly flammable shrub fuels, and a strong foehn wind effect – can combine 

to produce extreme fire behaviour. A case study produced on the incident 

describes the factors leading up to the burnover, and presents a number of 

key lessons learned that have relevance in New Zealand as well as for the 

wider global fire community.

Bucklands Crossing Firefighter Burnover—

A Case Study of Fire Behaviour and 

Firefighter Safety Implications

H. Grant Pearce1

In: Butler, Bret W.; Cook, Wayne, 

comps. 2007. The fire environment—

innovations, management, and policy; 

conference proceedings. 26-30 March 

2007; Destin, FL. Proceedings 

RMRS-P-46CD. Fort Collins, CO: 

U.S. Department of Agriculture, 

Forest Service, Rocky Mountain 

Research Station. 662 p. CD-ROM.

1 Fire Scientist, Ensis - Forest Biosecurity 

and Protection, Scion, Christchurch, New 

Zealand. grant.pearce@ensisjv.com
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 230

Case Study

A detailed case study entitled “Fire behaviour and firefighter safety implications associated with the Bucklands Crossing Fire burnover of 24 March 

1998” by Pearce and others (2004; see References section for ordering information) describes the activities of personnel leading up to and during the 

incident in relation to the fire environment and fire behaviour. Within the 

case study, possible causes of the blowup are reviewed, and observed fire behaviour is compared to that predicted by available models. Particular emphasis 

is placed on the contribution and alignment of fire environment factors and 

their role in triggering the temporary escalation to extreme fire behaviour. 

Aspects of firefighter safety during the incident are also discussed, including 

the performance of protective clothing. The findings highlight the need for 

increased training of firefighters in fire behaviour and, in particular, greater 

situational awareness of the fire environment and indicators of extreme fire 

behaviour potential. The case study provides a number of lessons learned that 

have relevance worldwide.

This paper (and the associated poster) aims to promote wider international 

awareness of the case study’s existence, and represents a further effort at 

information transfer that follows on from the communication of initial findings soon after the incident (Pearce and others 1998), the production of the 

detailed case study (Pearce and others 2004), and a brief review of the case 

study and key lessons learned (Anderson 2004).

Incident Summary

At 07:47 a.m. on Tuesday, 24 March 1998, a fire was reported by a local 

musterer on his way to work near Bucklands Crossing, some 40 km (25 miles) 

north of Dunedin (fig. 1). The fire is believed to have been ignited by sparks 

resulting from powerlines contacting adjacent vegetation in high winds (see 

fig. 2). Initially, NZ Fire Service structural firefighters from Waikouaiti and 

Palmerston responded. They were later supported by rural fire crews from 

Dunedin City Council, local forestry companies and the Department of 

Conservation. Two helicopters were also used, although the strong, gusty 

winds initially prevented effective aerial fire suppression.

At around 11:25 a.m., a crew of eight rural firefighters were burned over 

while attempting to suppress a backing sector of the fire. The crew had parked 

their fire appliance on the crest of a steep ridge in a burnt out sector of the 

fire, and were deploying a hoseline downhill toward a fire edge burning slowly 

downslope (see fig. 3). The fire had already burnt out the catchment on one 

side of this ridge and was backing downhill beneath manuka (Leptospermum 

scoparium) shrub fuels down into the adjacent catchment where the crew were 

deploying the hoseline. Before being able to charge this hoseline, the crew 

were overrun by a “fireball” exploding from the gorse-filled (Ulex europaeus) 

gully beneath them (see fig. 4). Three firefighters sustained burn injuries, one 

serious, while a fourth crew member received minor injuries whilst evacuating. 

The driver and another crew member took shelter behind the appliance and, 

along with the remaining two crew members, were uninjured. (Fire shelters 

are not used in New Zealand, so their use was not an option available to 

firefighters in this situation. However, it is unlikely that crew members would 

have had sufficient time to properly deploy shelters due to the rapid approach 

Bucklands Crossing Firefighter Burnover—A Case Study of Fire Behaviour and Firefighter Safety Implications Pearce
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 231

Figure 1—Location of the Bucklands Crossing Fire in relation 

to local population centres (●) and weather stations (μ).

Figure 2—General view of the Bucklands Crossing 

Fire looking northeast to the fire’s point of ignition 

and burnover location.

of the fire and strong, gusty wind conditions.) Damage to the fire appliance 

as a result of the burnover was also significant (see fig. 5).

The fire continued to burn for several hours after the incident before being contained later in the day. However, mopping up of hot spots continued 

over the next 7 days and the fire was not declared out until April 2. The fire 

burned an area of around 200 ha (495 acres), including two small woodlots 

of Pinus radiata totalling 20 ha (50 acres); the remainder of the area was 

grazed pasture and manuka or gorse-covered slopes. Several kilometres of 

fencing were damaged and some stock were also lost.

Bucklands Crossing Firefighter Burnover—A Case Study of Fire Behaviour and Firefighter Safety Implications Pearce
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 232

Figure 3—Locations and escape paths taken by the fire crew members during the 

burnover (letters denote individual firefighters).

Figure 4—Topography in the area of the ridge where the burnover incident occurred.

Figure 5—Damage to the fire appliance, including tail lights, foam proportioner, 

pump cover and rubber hose fittings.

Bucklands Crossing Firefighter Burnover—A Case Study of Fire Behaviour and Firefighter Safety Implications Pearce
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 233

Fire Environment

The fire occurred in rugged terrain comprising steep slopes that drop 

sharply to the meandering course of the Waikouaiti River below. Slopes of 

30 to 40o (55 to 85 percent) are common, and many rock outcrops occur 

throughout the fire area. Several side gullies drain into the main river course 

with steep, narrow ridges in between. The burnover occurred on one of these 

ridges (fig. 4; also see fig. 6), which consisted of a 30o (58 percent) slope on 

the lee side which had been burned over earlier, a narrow ridge crest some 4 to 

5 m (13 to 16 ft) wide, and a 25o (47 percent) slope on the upwind side leading down to the shrub fuels under which the fire was backing downhill.

Fuels in the broader fire area consisted of radiata pine woodlots, manuka, 

and gorse shrubs, and grazed pasture, some with scattered tussock grasses. 

Dense 2 to 3 m (6 to 10 ft) tall manuka shrubs covered the lee side of the 

ridge on which the incident occurred. This had been burnt out earlier in a 

rapid uphill fire run. Grass fuels with scattered short tussocks covered the 

open ridgetop where the appliance was parked (see fig. 6), and this too had 

been burnt out prior to the crew arriving. These grass fuels extended some 

30 to 40 m (100 to 130 ft) down the upwind slope to 3 to 4 m (10 to 13 

ft) tall manuka shrubs (fig. 7 left). This shrub vegetation was initially only 

underburnt, but burned out completely at a later time. The manuka stand 

extended some 30 to 50 m (100 to 165 ft) to the gorse-filled gully bottom 

below (fig. 7 right).

Figure 7—Fuels in the burnover area, including (left) partially burned manuka shrub 

fuels on the ridge adjacent to where the burnover occurred, and (right) burnt gorse 

fuels below the location of the burnover, in the area where the blowup is believed to 

have originated.

Figure 6—Views of the ridge on which the burnover incident occurred, illustrating the 

steepness of the slope in the direction from which the flame front came, the burnt out 

grass fuels along the ridge crest, and the distance between the vegetation and the site 

where the fire appliance was parked (as indicated by the utility).

Bucklands Crossing Firefighter Burnover—A Case Study of Fire Behaviour and Firefighter Safety Implications Pearce
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 234

The fire occurred during warm, windy conditions in a period of extended 

dryness. Weather and Fire Weather Index (FWI) System (Van Wagner 1987) 

values (see fig. 8a) for midday on March 24 were: temperature 25.5 oC (78 oF), 

relative humidity 35 percent, wind speed 16 km/h (10 mph), 6 days since 

>0.6 mm (0.02 inches) rain, Fine Fuel Moisture Code (FFMC) 92.0, Duff 

Moisture Code (DMC) 41, Drought Code (DC) 569, Initial Spread Index (ISI) 

12.9, Buildup Index (BUI) 70, and Fire Weather Index (FWI) 31. Recorded 

temperatures on the day of the fire reached 27 oC (81 oF) and higher values 

were reported at the fire site (fig. 8b); however, these values of temperature 

and relative humidity were not unusually high or low, respectively, for this 

area. Gale force northwesterly winds that exceeded 80 km/h (50 mph) initially restricted aerial suppression operations and, although they dropped off 

during the day, the erratic strength and direction of the wind contributed to 

unpredictable fire behaviour.

Figure 8—(a) Wetting and drying cycles for the FWI System moisture codes leading up to 

ignition of the Bucklands Crossing Fire on 24 March 1998; and (b) diurnal weather patterns 

recorded before, during, and after the initial run of the Bucklands Crossing Fire.

Fire Behaviour

The firefighters involved in the Bucklands Crossing Fire burnover reported 

being hit by a “fireball” originating from the gully below them. This may 

have been the result of a collapsing fire whirl or convection column, flashover, or even a carbon dust explosion. However, the most likely cause was 

a flame extension or “rollover” associated with an uphill run and reburn 

through the preheated shrub canopy. The collapse of the fire front as such a 

run reached the end of the shrub stand (see fig. 9) would push flames along 

the ground and, together with the pulsing of the flame front as volatile gases 

are burned, would create an effect similar to that of a fireball or flashover 

observed by the firefighters. It would also produce extreme flame lengths 

capable of reaching the 30 to 50 m (100 to 165 ft) to the ridge where the 

fire appliance was parked.

a b

Bucklands Crossing Firefighter Burnover—A Case Study of Fire Behaviour and Firefighter Safety Implications Pearce
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 235

While the situation may seem unusual, similar incidents have happened 

in the past (surface fuels consumed/canopy fuels unburned and preheated, 

steep slope, severe fire weather, including upslope winds or flow)—for example, the 1994 South Canyon Fire in the USA (Butler and others 1998). 

In particular, the potential for extreme or “unusual” fire behaviour existing 

from a preheated canopy has been recognised for many years (Editor 1958; 

NWCG 1993, 2006). Fatalities and “near hits” are often intimately linked to 

temporary escalations in extreme fire behaviour (see for example Alexander 

2004; Wilson 1977). The Bucklands Crossing Fire illustrates that the development of extreme fire behaviour can take many forms. In this case, the 

fire effectively burned as an independent crown fire.

So was the situation predictable? It is unlikely that the momentary escalation in extreme fire behaviour would have been predicted in a quantitative 

sense with currently available models (such as Viegas 2006). The situation 

is not one that would necessarily be modelled in a computer simulation, 

addressed through outdoor experimental burning or studied in a laboratory 

setting (M. Alexander, pers. comm.). However, there have been enough similar 

situations in the past to at least qualitatively be cognizant of the potential for 

extreme fire behaviour. While the exact cause of the blowup could not be 

definitively identified, a number of factors relating to recognition of extreme 

fire behaviour potential and associated implications for firefighter safety were 

highlighted as a result of the incident and are described in detail in the case 

study, including:

	 •	 Common denominators of fatal and “near hit” fires (Millman 1993, 

2000; NWCG 1996, Wilson 1977).

	 •	 Alignment of fire environment factors (Campbell 1991; Cheney and others 2001).

	 •	 Effect of atmospheric stability and upper level winds (Byram 1954).

	 •	 Transition from surface to crown fires (Butler and others 1998).

Firefighter Safety

The Bucklands Crossing Fire is also of interest from the human factors 

standpoint. The firefighters perceived this situation as benign; otherwise, they 

would not have engaged the fire in the manner they did (that is, approaching the fire from an upslope location). However, obviously some degree of 

Figure 9—Example of flame front collapse in New Zealand 

manuka shrub fuels during experimental burning trials 

observed from consecutive photographs.

Bucklands Crossing Firefighter Burnover—A Case Study of Fire Behaviour and Firefighter Safety Implications Pearce
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 236

complacency and/or lack of situational awareness were also involved. Several 

fundamental principles of safe firefighting were breached, and the authors 

of the case study have taken care to point these out without criticising the 

individuals (and with their permission and support) or decisions taken, in 

the hope that these valuable lessons can be learned from.

One of the basic safety rules recognised by firefighters around the world 

is to never place oneself uphill on a slope above unburned fuels with a fire 

burning beneath you (see for example NWCG 2004). In deploying where 

they did in this instance, the crew leader and his crew broke this basic rule 

and immediately placed themselves in a potentially dangerous situation. While 

the crew leader did recognise the potential for the fire to “flare-up” and 

spent a considerable amount of time sizing up the fire prior to choosing to 

locate where they did, he believed that the fire would spread upgully under 

the influence of the local windflow. With the benefit of hindsight, he should 

have recognised the potential that existed for a rapid upslope run toward 

the crew’s location, due to the combined influences of the slope steepness, 

partially burned shrub fuels, and predominant, rather than localised, wind 

direction. Downhill fireline construction from above a fire has been identi-

fied as a particularly dangerous tactic only to be undertaken when there is 

no tactical alternative; so much so, that it has its own checklist of guidelines 

surrounding its use (NWCG 2004). In addition to the potential for a rapid 

uphill run of fire, the inherent dangers in use of this tactic also include the 

inability to establish a safe anchor point and that a safe escape route from a 

fire travelling uphill cannot be assumed (ETC and CIFFC 2000).

The concept of LCES (Lookout(s), Communication(s), Escape routes 

and Safety zone(s); after Gleason 1991) or LACES (where the ‘A’ stands for 

Awareness or Anchor points; after Teie 1994; Thorburn and Alexander 2001) 

is also an internationally recognized set of fireline safety reminders that can 

dramatically reduce the probability of an entrapment or burnover. While it 

was not used in firefighter training in New Zealand at the time (but has sub-

sequently been adopted as a result of this incident), consideration of a number 

of the factors encapsulated within the LACES concept may have resulted in 

a different outcome. The deployment of a lookout with a good overview of 

the fire area may have identified the potential for an escalation in fire activ-

ity that might have altered the crew leader’s deployment tactics or provided 

earlier warning. In conducting his sizeup, it could also be debated that the 

crew leader reacted to observed fire activity as opposed to being aware of the 

fire behaviour potential presented by the immediate fire environment factors, 

both individually and in combination. In addition, the ability to assess the 

broader fire environment to anticipate or recognise factors that may lead to 

dangerous situations is also essential. In this case the crew were unaware of 

the exact location of the fire or associated level of fire activity in the gully 

below them, and therefore of the blowup potential that existed as a result. 

The provision of information from adjacent sectors or use of a lookout to 

observe the wider fire area could therefore have facilitated broader awareness 

and appreciation of both current and potential fire behaviour. Suppression 

tactics and strategies should also consider the values-at-risk against the need 

for suppression. In this instance, the injuries were sustained in attempting to 

suppress an area of vegetation burning within the fire perimeter with little or 

no value. A culture of situational awareness, and continual review of strategies 

and tactics based on the fire environment, therefore needs to be instilled in 

all firefighters and fire management personnel (Anderson 2004).

The firefighters involved in the burnover incident were saved from more 

severe injuries by the short duration of their exposure to heat and flame, the 

Bucklands Crossing Firefighter Burnover—A Case Study of Fire Behaviour and Firefighter Safety Implications Pearce
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 237

fact that they were correctly attired in their protective clothing (fig. 10), and 

that they received immediate attention from onsite medical services. While 

these were positive outcomes, there were also a number of other safety issues that were identified as a result of the incident that are addressed in the 

case study. In addition to the performance of personal protective equipment 

(PPE), these include:

	 •	 firefighter training and competency

	 •	 safety rules and reminders

	 •	 operational procedures

	 •	 alternative escape options (also see Alexander 2006)

Figure 10—Damage to personal protective clothing 

from Firefighter C (who received the most serious burn 

injuries), including Nomex coveralls, cotton T-shirt and 

fibreglass helmet.

Key Lessons Learned

The Bucklands Crossing burnover incident highlighted a number of factors that, had they been considered, might have indicated that the crew were 

undertaking suppression in a potentially dangerous situation.

	 •	 The crew were approaching the fire from above, on a steep slope with 

partially burned fuels between them and the fire in the gully below (that 

is, downhill fireline construction).

	 •	 Firefighters must have an understanding in the subjects of personal safety 

and vegetation fire behaviour, and must apply this knowledge at all times 

during sizeup and ongoing fire suppression.

	 •	 Firefighters must consider both the individual and combined influences of 

the fire environment factors on current and potential fire behaviour.

	 •	 Fire crews must also have an appreciation of fire behaviour potential in 

the broader fire environment, in addition to the immediate area in which 

they are working (that is, situational awareness).

	 •	 The lack of a lookout that could monitor fire activity in the area beneath 

the crew was a serious oversight in this particular case.

	 •	 A culture of situational awareness, and constant review of strategies and 

tactics in light of changes in the fire environment, need to be instilled 

in all fireline and supervisory personnel.

Bucklands Crossing Firefighter Burnover—A Case Study of Fire Behaviour and Firefighter Safety Implications Pearce
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 238

	 •	 Suppression strategies and tactics should also consider the values-at-risk 

against the need for suppression.

	 •	 Firefighter training should use reminders such as the Common Denominators and LACES to reinforce potentially problematic aspects of fire 

behaviour and firefighter safety.

	 •	 All training undertaken must also emphasise the correct use of protective 

equipment.

	 •	 Examples such as this incident can be used to clearly demonstrate the 

benefits of picking up on the lessons learned.

There are a great many fuel/vegetation types and fire environments in the 

world where a similar situation could develop to the Bucklands Crossing Fire 

burnover – hence the importance of disseminating this kind of information 

internationally to the wider global fire community.

Acknowledgments

The advice and feedback received from Dr. Marty Alexander (Canadian 

Forest Service) on this paper and the associated poster are greatly appreciated. 

Comments provided by internal reviewers (Stuart Anderson and Jim Gould, 

Ensis Bushfire Research Group) are also gratefully acknowledged.

References

Alexander, M.E., pers. comm. Senior Fire Behavior Research Officer, Natural 

Resources Canada, Canadian Forest Service, Northern Forestry Centre, 

Edmonton, and Adjunct Professor, Department of Renewable Resources, 

University of Alberta, Edmonton, Alberta, Canada.

Alexander, M.E. 2004. What triggered the Brewer Fire blowup remains the mystery. 

Fire Management Today 64(2): 56-57.

Alexander, M.E. 2006. You are about to be entrapped or burned over by a 

wildfire: what are your survival options? In: D.X. Viegas (ed.). Proceedings of 

5th International Conference on Forest Fire Research, 27-30 November 2006, 

Figueira da Foz, Portugal. [CD-ROM]. Amsterdam, The Netherlands: Elsevier 

B.V. 12 p.

Anderson, S. 2004. A close call in Otago – some lessons to be learned: A review of 

“Fire behaviour and firefighter safety implications associated with the Bucklands 

Crossing Fire burnover of 24 March 1998” by H.G. Pearce, R.W Hamilton and 

R.I. Millman. Fire Technology Transfer Note 29 (May 2004). Christchurch: New 

Zealand Forest Research Institute, Forest and Rural Fire Research Programme. 

2 p.

Butler, B.W.; Bartlette, R.A.; Bradshaw, L.S.; Cohen, J.D.; Andrews, P.L.; Putnam, 

T.; Mangan, R.J. 1998. Fire behaviour associated with the 1994 South Canyon 

Fire on Storm King Mountain, Colorado. Research Paper RMRS-RP-9. Ogden, 

UT: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research 

Station. 82 p.

Byram, G.M. 1954. Atmospheric conditions related to blowup fires. Station 

Paper No. 35. Asheville, NC: U.S. Department of Agriculture, Forest Service, 

Southeastern Forest Experiment Station. 30 p. [reprinted as National Fire 

Equipment System (NFES) 2239. Boise, Idaho: National Wildfire Coordinating 

Group, Boise Interagency Fire Center].

Campbell, D. 1991. The Campbell Prediction System. Ojai Printing, California: 

Wildland Fire Specialists.

Bucklands Crossing Firefighter Burnover—A Case Study of Fire Behaviour and Firefighter Safety Implications Pearce
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 239

Cheney, N.P.; Gould, J.S.; McCaw, L. 2001. The Dead-Man Zone – a neglected 

area of firefighter safety. Australian Forestry 64(1): 45-50.

Editor. 1958. Safe practices under blowup conditions – a training outline for the 

crew boss. Fire Control Notes 19(1): 3-7.

ETC and CIFFC. 2000. Wildland fire – safety on the fireline. [CD-ROM]. Hinton, 

Alberta, Canada: Environmental Training Centre (ETC); and Winnipeg, Manitoba, 

Canada: Canadian Interagency Fire Centre (CIFFC), National Training Working 

Group.

Gleason, P. 1991. LCES – a key to safety in the wildland fire environment. Fire 

Management Notes 52(4): 9. [reprinted as Fire Management Today 64(1): 70-71].

Millman, R.I. 1993. Firefighting tragedies and near misses. In: Proceedings, Forest 

and Rural Fire Association of New Zealand (FRFANZ) 3rd Annual Conference, 

4-6 August 1993, Wellington. Rotorua: Forest and Rural Fire Association of 

New Zealand: 84-101.

Millman, R.I. 2000. Injuries and near hits at vegetation fires. In: Conference 

Proceedings, Forest and Rural Fire Association of New Zealand (FRFANZ) 

Annual Conference, 2-3 August 2000, Napier. Rotorua: Forest and Rural Fire 

Association of New Zealand.

National Wildfire Coordinating Group. 1993. Fireline safety reference. National 

Fire Equipment System Publication NFES 2243. Boise, ID: National Wildfire 

Coordinating Group, Boise Interagency Fire Center.

National Wildfire Coordinating Group. 1996. Common denominators of fire 

behaviour on tragedy and near-miss wildland fires. National Fire Equipment 

System Publication NFES 2225. Boise, ID: National Wildfire Coordinating 

Group, Boise Interagency Fire Center. 25 p.

National Wildfire Coordinating Group. 2006. Incident response pocket guide. 

National Fire Equipment System Publication NFES 1077. Boise, ID: National 

Wildfire Coordinating Group, Boise Interagency Fire Center. 96 p.

Pearce, G.; Hamilton, R.; Millman, I. 1998. The Bucklands Crossing fire burnover. 

Fire Technology Transfer Note 19 (December 1998). Christchurch: New Zealand 

Forest Research Institute, Forest and Rural Fire Research Programme. 4 p.

Pearce, H.G.; Hamilton, R.W.; Millman, R.I. 2004. Fire behaviour and firefighter 

safety implications associated with the Bucklands Crossing Fire burnover of 24 

March 1998. Forest Research, Rotorua, in association with the New Zealand 

Fire Service Commission and National Rural Fire Authority, Wellington. Forest 

Research Bulletin No. 197, Forest and Rural Fire Scientific and Technical Series, 

Report No. 4. 62 p. (Copies of the case study are available for download in PDF 

format from the Ensis Bushfire Research website at ww.ensisjv.com/bushfire, or 

in hard copy for purchase from: Publications, Scion/Ensis, Private Bag 3020, 

Rotorua, New Zealand.)

Teie, W.C. 1994. Firefighter’s Handbook on Wildland Firefighting. Rescue, CA: 

Deer Valley Press.

Thorburn, W.R.; Alexander, M.E. 2001. LACES versus LCES: Adopting an “A” for 

“Anchor Points” to improve wildland firefighter safety. In: Proceedings of the 

2001 International Wildland Fire Safety Summit, November 6-8, 2001, Missoula, 

Montana. Montana City, MT: International Association of Wildland Fire. 10 p.

Van Wagner, C.E. 1987. Development and structure of the Canadian Forest Fire 

Weather Index System. Forestry Technical Report 35. Ottawa, Ontario: Canadian 

Forestry Service. 37 p.

Viegas, D.X. 2006. Parametric study of an eruptive fire behaviour model. 

International Journal of Wildland Fire 15(2): 169-177.

Wilson, C.C. 1977. Fatal and near-fatal forest fires – the common denominators. 

International Fire Chief 43(9): 9-10, 12-15.

Bucklands Crossing Firefighter Burnover—A Case Study of Fire Behaviour and Firefighter Safety Implications Pearce
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 240

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on purpose

Bucklands Crossing Firefighter Burnover—A Case Study of Fire Behaviour and Firefighter Safety Implications Pearce
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Abstract—In this study, the assignment and theoretical investigations of the problems 

of forest fire initiation were carried out, including development of a mathematical 

model for description of heat and mass transfer processes in overterrestrial layer of 

atmosphere at crown forest fire initiation, taking into account their mutual influence. 

Mathematical model of forest fire was based on an analysis of experimental data and 

using concept and methods from reactive media mechanics. In the context of the 

general mathematical model of forest fires, this study gives a new mathematical setting 

and method of numerical solution of a problem of a forest fire modeling. The boundary-value problem is solved numerically using the method of splitting according to 

physical processes. In this paper the assignment and theoretical investigations of the 

problems of forest fire initiation are carried out.

Introduction

One of the objectives of these studies is the improvement of knowledge 

on the fundamental physical mechanisms that control forest fire initiation. 

A great deal of work has been done on the theoretical problem of forest fire 

initiation. Crown fires are initiated by convective and radiative heat transfer 

from surface fires. However, convection is the main heat transfer mechanism. 

The first explanation of this process was given by Van Wagner (1977). The 

theory proposed there depends on three simple crown properties: crown 

base height, bulk density, and moisture content of forest fuel. Also, crown 

fire initiation and hazard have been studied and modeled in detail (see for 

example Alexander 1998; Van Wagner 1999; Xanthopoulos 1990; Rothermel 

1991a,b; Cruz and others 2002; Albini and others 1995; Scott and Reinhardt 

2001).The more complete discussion of the problem of modeling forest fires 

is provided by a cycle of works produced by a group of coworkers at Tomsk 

University (Grishin 1997; Grishin and Perminov 1998; Perminov 1995 and 

1998). In particular, a mathematical model of forest fires was obtained by 

Grishin (1997) based on an analysis of known and original experimental data 

(Grishin 1997; Konev 1977), and using concepts and methods from reactive 

media mechanics. The physical two-phase models used in Morvan and Dupuy 

(2001 and 2004) may be considered as a continuation and extension of the 

formulation proposed by Grishin and Perminov. However, the investigation 

of crown fires has been limited mainly to cases of forest fires initiation without taking into account the mutual interaction of the forest fires and three 

dimensional atmosphere flows.

Mathematical Modeling of Forest Fire 

Initiation in Three Dimensional Setting

Valeriy Perminov1

In: Butler, Bret W.; Cook, Wayne, 

comps. 2007. The fire environment—

innovations, management, and policy; 

conference proceedings. 26-30 March 

2007; Destin, FL. Proceedings 

RMRS-P-46CD. Fort Collins, CO: 

U.S. Department of Agriculture, 

Forest Service, Rocky Mountain 

Research Station. 662 p. CD-ROM.

1 Belovo Branch, Kemerovo State 

University, Belovo, Kemerovo region, 

Russia. p_valer@mail.ru
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 242

Problem Formulation

The basic assumptions adopted during the deduction of equations and 

boundary and initial conditions:

1. The forest represents a multiphase, multistoried, spatially heterogeneous 

medium.

2. In the fire zone the forest is a porous-dispersed, two-temperature, singlevelocity, reactive medium.

3. The forest canopy is supposed to be nondeformed media (trunks, large 

branches, small twigs, and needles) and affects only the magnitude of 

the force of resistance in the equation of conservation of momentum in 

the gas phase—that is, the medium is assumed to be quasisolid (almost 

nondeformable during wind gusts).

4. Let there be a so-called “ventilated” forest massif, in which the volume 

of fractions of condensed forest fuel phases, consisting of dry organic 

matter, water in liquid state, solid pyrolysis products, and ash, can be 

neglected compared to the volume fraction of gas phase (components 

of air and gaseous pyrolysis products).

5. The flow has a developed turbulent nature and molecular transfer is 

neglected.

6. Gaseous phase density doesn’t depend on the pressure because of the low 

velocities of the flow in comparison with the velocity of the sound.

Let the coordinate reference point x1, x2 , x3= 0 be situated at the centre 

of the surface forest fire source at the height of the roughness level, axis 0x1

directed parallel to the Earth’s surface to the right in the direction of the 

unperturbed wind speed, axis 0x2 directed perpendicular to 0x1 and axis 0x3

directed upward (fig. 1).

Figure 1—Coordinate reference point information.

Mathematical Modeling of Forest Fire Initiation in Three Dimensional Setting Perminov
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 243

Using the results of Grishin and Perminov from their studies conducted 

1995 through 1998, and known experimental data (Konev 1977), we have 

the following sufficiently general equations, which define the state of the 

medium in the forest fire zone, written using tensor notation:

∂ ρ

∂

∂

∂ ρ

t x

v m j i

j

+ = = = j ( ) , , , , , , ; 1 2 3 1 2 3 (1)

ρ

∂

∂

∂

∂ ρ ρ ρ

dv

dt

P

x x

v v sc v v g i

i j

= − + − i j d i i ( ) | | ′ ′ − − − mvi ; (2)

ρ

∂

∂

c ρ α

dT

dt x c v v T q R T T k p

j

= − p j i v s g ( ) ( ) ′ ′ ′ + − − + 5 5 ( ); cU T R − 4 4 σ (3)

ρ

∂

∂ ρ α α

α α α

dc

dt x

v c R mc

j

= − j ( ) ′ ′ + − = , , ; 5 1 5 (4)

∂

∂

∂

∂

σ σ

x

c

k

U

x

kcU k T k T

j

R

j

R S S g 3

4 4 0 4 4















− + + = , ; k k k g S = + (5)

ρ φ

∂

∂ i p i i σ α S

i

s R S c

T

t

q R q R k cU T

=

∑ = − + − +

1

4

3 3 2 2

4 4( ) V S ( ); T T− (6)

ρ ∂ φ

∂ ρ ∂ φ∂ ρ ∂ φ∂ 1 α 1

1 2

2

2 3

3

1

1

3

t

R

t

R

t

R M

M C R C = − = − = − , , , ; ρ ∂ φ

∂ 4

4 0

t = (7)

, 

c p RT c

M α e v v v v g

α

α

α α

= = ρ = =

= =

∑ ∑ 1

1

5

1

1

5

2 3 , , ( , , ), ( , , ), ( ) 0 0 1 . 1 2

1

g m R R 3 54 55

M

M c R R R c = − + + + + α

Here and above 

d

dt is the symbol of the total (substantial) derivative; αv is 

the coefficient of phase exchange; ρ - density of gas – dispersed phase, t is 

time; vi - the velocity components; T, TS, - temperatures of gas and solid 

phases, UR – density of radiation energy, k – coefficient of radiation attenuation, P – pressure; cp – constant pressure specific heat of the gas phase, cpi, 

ρi, φi

 – specific heat, density and volume of fraction of condensed phase: 

1 – dry organic substance, 2 – moisture, 3 – condensed pyrolysis products, 

4 – mineral part of forest fuel), Ri – the mass rates of chemical reactions, 

qi – thermal effects of chemical reactions; kg , kS – radiation absorption coefficients for gas and condensed phases; Te – the ambient temperature; cα – mass 

concentrations of α - component of gas – dispersed medium, index α=1,2,...,5, 

where 1 corresponds to the density of oxygen, 2 – to carbon monoxide CO, 

3 – to carbon dioxide and inert components of air, 4 – to particles of black, 

5 – to particles of smoke; R – universal gas constant; Mα , MC, and M molecular mass of α – components of the gas phase, carbon and air mixture; g

is the gravity acceleration; cd is an empirical coefficient of the resistance of 

the vegetation, s is the specific surface of the forest fuel in the given forest 

stratum, νg – mass fraction of gas combustible products of pyrolysis, α4 and

α5 – empirical constants. To define source terms that characterize inflow 

(outflow of mass) in a volume unit of the gas-dispersed phase, the following 

formulae were used for the rate of formulation of the gas-dispersed mixture 

ṁ, outflow of oxygen R51, changing carbon monoxide R52, generation of 

black R54 and smoke particles R55.

Mathematical Modeling of Forest Fire Initiation in Three Dimensional Setting Perminov
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 244

R R

M

M

R R R R R R 51 3 g c

1

2

5 52 1 5 53 54 2 = − − , ( ) , , = − − = ν α 1 0 = = +

α

α

4 1 55

5 3

3 3

3 R R

v

v v , R .

*

Reaction rates of these various contributions (pyrolysis, evaporation, combustion of coke, and volatile combustible products of pyrolysis) are approximated 

by Arrhenius laws whose parameters (preexponential constant ki and activation energy Ei) are evaluated using data for mathematical models (Grishin 

and Perminov 1995-1998):

R k E

RT

R k T

s

1 1 1 1 s

1

2 2 2 2

0 5 = −













 = − ρ φ exp , ρ φ e . xp ,

exp

−

















= −









E

RT

R k s c E

RT

s

s

2

3 3 3 1

3 ρφ σ







 = 













 − , ex

.

. R k M c M

M

c M

M

T 5 5 2

1

1

0 25

2

2

2 25 p . −





 



 E

RT

5

Coefficients of multiphase (gas and solid phase) heat and mass exchange are 

definedα α γ φ V = − = S C m S d P S S , /4 . Here α = Nuλ/dS – coefficient of heat 

exchange for sample of forest combustible material (for example, needle), 

Nu – Nusselt number for cylinder, λ – coefficient of heat conductivity for pine 

needle; γ – parameter, which characterizes the relation between molecular 

masses of ambient and inflow gases.

The system of equations 1 through 7 must be solved taking into account 

the initial and boundary conditions:

t v v v T T c c T T e e s e ie = = = = = = = = 0 0 0 0 1 2 3 1 : , , , , , , α α φ φ ; (8)

x x v V v

v

x

T T c c c 1 1 1 2 e e e e

3

1

0 0

3 = − = = = = = − : , , , , ,

∂

∂ α α k

U

x

R cUR

∂

∂ 1

+ = / ; 2 0 (9)

x x

v

x

v

x

v

x

c

x

1 1e

1

1

2

1

3

1 1

= = = = = : , , , , 0 0 0 0 ∂

∂

∂

∂

∂

∂

∂

∂

α ∂

∂

∂

∂

T

x

c

k

U

x

c R UR

1 1

0

3 2

= + = , 0 ; (10)

x x

v

x

v

x

v

x

c

x

2 2 0

1

2

2

2

3

2 2

= = = = = : , , , , 0 0 0 0 ∂

∂

∂

∂

∂

∂

∂

∂

α ∂

∂

∂

∂

T

x

c

k

U

x

c R UR

2 2

0

3 2

= − + = , 0 ; (11)

x x

v

x

v

x

v

x

c

x

2 2 e

1

2

2

2

3

2 2

= = = = = : , , , , 0 0 0 0 ∂

∂

∂

∂

∂

∂

∂

∂

α ∂

∂

∂

∂

T

x

c

k

U

x

c R UR

2 2

0

3 2

= + = , 0 ; (12)

x v v

c

x

c

k

U

x

c

U v R

3 1 2 R

3 3

0 0 0 0

3 2

= = = = − + = : , , , 0 , ∂

∂

∂

∂

α

3 30 1 2

3 1 2 0

= = ≤ ≤

= = > >

v T T x x

v T T x x

g

e

, , , ,

, , , ;

∆ ∆

∆ ∆

(13)

x x

v

x

v

x

v

x

c

x

3 3e

1

3

2

3

3

3 3

= = = = = : , , , , 0 0 0 0 ∂

∂

∂

∂

∂

∂

∂

∂

α ∂

∂

∂

∂

T

x

c

k

U

x

c R UR

3 3

0

3 2

= + = , 0 . (14)

The conditions of symmetry are used because of the patterns of flow and distributions of all scalar functions are symmetrical relatively to the axes Ox2.

The source of ignition is defined as a function of time at x x 1 x 2 x 1 2

≤ ≤∆ ∆ ,

and turned off after the forest fire initiation. It is supposed that the optical 

properties of a medium are independent of radiation wavelength (the assumption that the medium is “grey”), and the so-called diffusion approximation 

Mathematical Modeling of Forest Fire Initiation in Three Dimensional Setting Perminov
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 245

for radiation flux density was used for a mathematical description of radiation transport during forest fires. The components of the tensor of turbulent 

stresses, as well as the turbulent fluxes of heat and mass, are written in terms 

of the gradients of the average flow properties (Grishin 1997). It should be 

noted that this system of equations describes processes of transfer within 

the entire region of the forest massif, which includes the space between the 

underlying surface and the base of the forest canopy, the forest canopy, and 

the space above it, while the appropriate components of the data base are 

used to calculate the specific properties of the various forest strata and the 

near-ground layer of atmosphere. This approach substantially simplifies the 

technology of solving problems of predicting the state of the medium in the 

fire zone numerically. The thermodynamic, thermophysical and structural 

characteristics correspond to the forest fuels in the canopy of a different type 

of forest; for example, pine forest (Grishin 1997; Perminov 1995).

Calculation Method and Results

The boundary-value problems 1 through 7 we solve numerically using 

the method of splitting according to physical processes (Perminov 1995). In 

the first stage, the hydrodynamic pattern of flow and distribution of scalar 

functions was calculated. The system of ordinary differential equations of 

chemical kinetics obtained as a result of splitting was then integrated. A 

discrete analog was obtained by means of the control volume method using 

the SIMPLE like algorithm (Patankar 1981). The accuracy of the program 

was checked by the method of inserted analytical solutions. The time step 

was selected automatically. Fields of temperature, velocity, component mass 

fractions, and volume fractions of phases were obtained numerically.

Figure 2 illustrates the time dependence of dimensionless temperatures of 

gas and condensed phases (a), concentrations of components (b), and relative 

volume fractions of solid phases (c) at crown base of the forest

a T T T T T T T K

b C C

e s s e e )( / , / , ),

)( /

1 2 300

1 1 1

− = − = =

− = C C C C C

c

e e e

e

1 2 2 1 1

1 1 1

2 0 23

1 2

, / , . ),

)( / ,

− = =

− = φ φ φ − = − = φ φ φ φ φ φ 2 2 2 3 3 3 2/ , / ). e e

Figure 2—Relationships of (a) dimensionless temperatures, (b) concentrations, 

and (c) volume fractions in the lower boundary of the forest canopy.

Mathematical Modeling of Forest Fire Initiation in Three Dimensional Setting Perminov
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 246

At the moment of ignition the gas combustible products of pyrolysis burn 

away, and the concentration of oxygen is rapidly reduced. The temperatures 

of both phases reach a maximum value at the point of ignition. The ignition 

processes are of a gas-phase nature—that is, initial heating of solid and gaseous 

phases occurs and moisture is evaporated. Then the decomposition process 

into condensed and volatile pyrolysis products starts, the latter being ignited 

in the forest canopy. Note also that the transfer of energy from the fire source 

takes place due to radiation; the value of radiation heat flux density is small 

compared to that of the convective heat flux. As a result of heating of forest 

fuel elements, moisture evaporates, and pyrolysis occurs accompanied by the 

release of gaseous products, which then ignite. The effect of the wind on the 

zone of forest fire initiation is shown in figures 3 through 5, which present the 

space distribution of field of temperature for gas phase for different instants 

of time when a wind velocity Ve= 7 m/s. We can note that the isosurfaces 

are deformed by the action of wind. The isosurfaces of the temperature of 

gas phase 1,2,3 и 4 correspond to the temperatures T = 1.2., 2, 3 and 4. 

In the vicinity of the source of heat and mass release, heated air masses and 

products of pyrolysis and combustion float up. The wind field in the forest 

canopy interacts with the gas-jet obstacle that forms from the surface forest 

fire source and from the ignited forest canopy base. Recirculating flow forms 

beyond the zone of heat and mass release, while on the windward side the 

movement of the air flowing past the ignition region accelerates. Under the 

influence of the wind the tilt angle of the flame is increased. As a result this 

part of the forest canopy, which is shifted in the direction of the wind from 

the center of the surface forest fire source, is subjected to a more intensive 

warming up. The isosurfaces of the gas phase are deformed in the direction 

of the wind. Figures 4 and 5 present the distribution of the velocity and 

isosurfaces of the temperature at the different instants of time when a wind 

velocity Ve= 7 m/s.

Figure 3—The vectorial field of velocity and temperature at t=3.3 s.

Mathematical Modeling of Forest Fire Initiation in Three Dimensional Setting Perminov
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 247

Figure 5—The vectorial field of velocity and temperature at t=4.8 s.

Figure 4—The vectorial field of velocity and temperature at t=3.8 s.

Conclusions

Mathematical model and the result of the calculation give an opportunity 

to evaluate critical condition of the forest fire initiation and spread, which 

allows applying the given model for preventing fires. The model overestimates 

the rate of the crown forest fires spread. The results obtained agree with the 

laws of physics and experimental data (Grishin 1997; Konev 1977). This 

work represents the attempt for application of three dimensional models for 

description of crown forest fires initiation and spread.

Mathematical Modeling of Forest Fire Initiation in Three Dimensional Setting Perminov
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 248

Acknowledgment

This paper was supported in part by the RFBR Grant 07-01-96047-p_ural_a.

References

Albini, F.A.; and others. 1995. Modeling ignition and burning rate of large woody 

natural fuels. Int. Journal of Wildland Fire 5(2): 81-91.

Alexander, M.E. 1998. Crown fire thresholds in exotic pine plantations of Australasia. 

PhD thesis. Department of Forestry, Australian National University.

Cruz, M.G.; and others. 2002. Predicting crown fire behavior to support forest fire 

management decision-making. [CD-ROM]. In: IV International conference on 

forest fire research. Luso-Coimbra, Portugal. (Ed. D. X. Viegas). 11. Millpress.

Grishin, A.M. 1997. Mathematical Modeling Forest Fire and New Methods Fighting 

Them. Tomsk: Publishing House of Tomsk University.

Grishin, A.M.; Perminov, V.A. 1998. Mathematical modeling of the ignition of tree 

crowns. Combustion, Explosion, and Shock Waves 34: 378-386.

Konev, E.V. 1977. The physical foundation of vegetative materials combustion. 

Novosibirsk: Nauka, Russia.

Morvan, D.; Dupuy, J.L. 2001. Modeling of fire spread through a forest fuel bed 

using a multiphase formulation. Combustion and Flame 127: 1981-1994.

Morvan, D.; Dupuy, J.L. 2004. Modeling the propagation of wildfire through a 

Mediterranean shrub using a multiphase formulation. Combustion and Flame. 

138: 199-210.

Patankar, S.V. 1981. Numerical Heat Transfer and Fluid Flow. New York: Hemisphere 

Publishing Corporation.

Perminov, V.A. 1995. Mathematical Modeling of Crown and Mass Forest Fires 

Initiation With the Allowance for the Radiative-Convective Heat and Mass Transfer 

and Two Temperatures of Medium. Ph.D thesis. Tomsk State University.

Perminov, V.A. 1998. Mathematical modeling of crown forest fire initiation. In: III 

International conference on forest fire research and 14th conference on fire and 

forest meteorology. Luso, Portugal. (Ed. D.X.Viegas): 419-431.

Rothermel, R.C. 1991a. Crown fire analysis and interpretation. In: 11th International 

conference on fire and forest meteorology. Missoula, Montana, USA.

Rothermel, R.C. 1991b. Predicting behavior of the 1988 Yellowstone Fires: 

projections versus reality. Int. Journal of Wildland Fire. 1: 1-10.

Scott, J.H.; Reinhardt, E.D. 2001. Assessing crown fire potential by linking models 

of surface and crown fire behavior. RMRS-RP-29, Fort Collins, CO, USA: U.S. 

Department of Agriculture, Forest Service, Rocky Mountain Research Station.

Van Wagner, C.E. 1977. Conditions for the start and spread of crown fire. Canadian 

Journal of Forest Research 7: 23-34.

Van Wagner, C.E. 1979. Prediction of crown fire behavior in conifer stands. In: 

10th conference on fire and forest meteorology. Ottawa, Ontario. (Eds D. C. 

MacIver, H. Auld, and R. Whitewood): 207-212.

Van Wagner, C.E. 1999. Prediction of crown fire behavior in two stands of jack 

pine. Canadian Journal of Forest Research 23: 445-449.

Xanthopoulos, G. 1990. Development of a wildland crown fire initiation model. 

PhD thesis. University of Montana.

Mathematical Modeling of Forest Fire Initiation in Three Dimensional Setting Perminov
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 249

Abstract—The prediction of fire behavior is of vital importance to all fire management 

planning and decisionmaking processes including fire prevention, presuppression 

planning, and fire use. The effect of slope on fire behavior is well acknowledged, yet 

its effect on fire behavior is not well accounted for. Determining the effects of slope 

on fire behavior under field conditions can prove invaluable and will allow for the 

testing of earlier studies conducted under laboratory conditions and help increase the 

accuracy of fire behavior prediction models. The present study was carried out in Kesan 

Forest Enterprise in the province of Edirne, Turkey (40°35’ N and 26°31’ E). Although 

the site was selected for its structural homogeneity, there was an apparent variation in 

the fuel loadings in different plots. Surface fine fuel (0 to 0.5 cm) loading ranged from 

1.07 to 2.10 kg m–2, coarse fuel (0.6 to 2.5 cm) loading from 0.97 to 1.75 kg m–2, and 

total fuel loading from 1.52 to 5.67 kg m–2. Within the plots burned in this study slope 

ranged form 1 to 15 percent. Weather conditions during the burns were within the 

narrow range without wind speed. Air temperature ranged from 25.6 to 33.5 °C, relative humidity from 37 to 62 percent, and wind speed from 5.3 to 17 m min–1. Rate of 

spread ranged from 0.58 to 8.43 m min–1, fuel consumption from 1.02 to 2.30 kg m–2, 

and fire intensity from 134 to 2847.6 kW m–1. Of the fire behavior characteristics, 

rate of spread was related to wind speed, slope and moisture contents of live fuels, 

and fuel consumption was related to fuel loading and moisture contents of live fuels, 

whereas fire intensity was related to wind speed, slope and moisture contents of live 

fuels. Results obtained in this study should be invaluable in overall fire management 

practices, especially in the Mediterranean Region.

Introduction

Fire is a common feature of Mediterranean landscapes and has a pervasive 

influence on its forests and their management. An increasingly important 

requirement of forest and land management in fire-prone ecosystems is the 

ability to predict fire behavior. Shrublands are one of the most important 

fire-prone plant communities and have crucial importance for fire management in Turkey. Shrub fuel types are known to be exceptionally flammable 

and capable of sustaining extreme fire intensity even at moderate fire danger 

levels (Wouters 1993; Fogarty 1996), thus posing a threat to human life 

and property (Fernandes 2001). The shrub complexes are known by various 

names such as fynbos (South Africa), mattoral (Chile), garrique (France), 

chaparral (California, USA) (Zhou and others 2007), and maquis in Turkey. 

Maquis grows extensively, neighboring on or adjacent to open forests of oak 

and pine, and as an understory in these forest types. Thus, determining the 

effect of varying weather and slope conditions on fire behavior in maquis 

fuel types has vital importance to all fire management planning and decision 

making processes.

Preliminary Results of Fire Behavior in 

Maquis Fuels Under Varying Weather 

and Slope Conditions in Turkey

Bülent Saglam1, Ertugrul Bilgili2, Ömer Kücük3, 

Bahar Dinc Durmaz2, and Ismail Baysal4

In: Butler, Bret W.; Cook, Wayne, 

comps. 2007. The fire environment—

innovations, management, and policy; 

conference proceedings. 26-30 March 

2007; Destin, FL. Proceedings 

RMRS-P-46CD. Fort Collins, CO: 

U.S. Department of Agriculture, 

Forest Service, Rocky Mountain 

Research Station. 662 p. CD-ROM.

1 Arvin Coruh University, Faculty of 

Forestry, Artvin, Turkey. saglambul@

gmail.com.

2 Karadeniz Technical University, 

Faculty of Forestry, Trabzon, Turkey.

3 Kastamonu University, Faculty of 

Forestry, Kastamonu, Turkey.

4 Düzce University, Faculty of Forestry, 

Konuralp, Düzce, Turkey.
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 250

The literature review reveals that only a limited number of studies have established the relation between slope and fire behavior (Ward 1971; Rothermel 

1972; Hirano and others 1974; Van Wagner 1977; Drysdale and Macmillan 

1992; Weise 1993; Weise and Biging 1994; Viegas and others 1994; Dupuy 

1995; Van Wagner 1968, 1988; Mendes-Lopes and others 2003; Bilgili and 

Saglam 2003; Viegas 2004; Zhou and others 2007). Testing slope in fire 

behavior models is mostly conducted in experimental burns in laboratories, 

and field burns are conducted on level terrain.

However, results obtained under laboratory conditions do not always reflect 

the situation under field conditions. Determining the effects of slope on fire 

behavior under field conditions can prove invaluable and will allow for the 

testing of earlier studies conducted under laboratory conditions and help 

increase the accuracy of fire behavior prediction models. The results generated from this study will constitute the basic and fundamental steps toward 

establishing a fire danger rating system in Turkey and should be invaluable 

in all phases of fire management planning.

Materials and Methods

The Study Area

The study is situated in Kesan Forest Enterprise in the province of Edirne, 

Turkey, at 40°35’N and 26°31’E on various slope conditions, and is 30 m 

above sea level. The community studied includes maquis formations of the 

Quercus-Phillyraea alliance that occupy areas with different degrees of perturbation and usually have Q. coccifera as the dominant species. Soils in the 

area are generally shallow and loam and sandy loam of limestone origin. The 

vegetation was an open shrubland with an average height of 1.30 m.

In July 2005, a series of 17 burning plots were established at the experimental burning site. Each plot was more or less 0.04 ha (15×25 m) and was 

delimited by a 5 m wide firebreak in which vegetation was eliminated through 

bulldozing to mineral soil. A complete fire weather station was established 

on the site 10 days prior to the burnings. Air temperature, relative humidity, 2 m open wind speed and precipitation were recorded at 13:00 local 

standard time.

Preburn Fuel Sampling

Fifteen plots (2×2 m) were randomly selected to determine the fuel loadings. The sampled maquis plots were cleared and categorized by diameter. 

Regression relationships between fuel loadings, vegetation cover, and mean 

height were established using the sampling data. Fuels loading in the burning 

plots were estimated using the relationships generated.

Postburn Fuel Sampling

Postburn fuel loading was estimated after each fire to determine fuel 

consumption. Remaining fuel in each plot was estimated by clipping, oven 

drying, and weighing all material from three randomly selected sample plots 

(2×2 m). Fuel consumption was calculated based on the difference between 

pre- and postburn fuel loadings.

Preliminary Results of Fire Behavior in Maquis Fuels Under Varying Weather and Slope Conditions Saglam, Bilgili, Kücük, Durmaz, and Baysal
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 251

Fuel Moisture Contents

Moisture content of vegetation was obtained from clipped samples immediately before each burning. Samples were oven-dried at 105 °C for 24 hours, 

and the fuel moisture was expressed as a percentage of dried weight.

Environmental Variables and Fire Behavior

Wind speed, relative humidity, and air temperature were recorded at 15 second intervals during each fire. Plots were burned over 7 days under varying 

temperature, relative humidity, moisture, wind speed, and slope conditions. 

Fires were started with a drip torch to rapidly establish a fire line along the 

windward edge of each plot. Fire intensity was calculated using Byram’s 

equation, I=hwr, where, I is the fire line intensity (kW m–1), h is heat yield 

of the fuel (kJ kg–1), w is the dry weight of the fuels consumed by the fire 

(kg m–2), and r is the rate of spread of the flame front (m s–1). In this study, 

energy contents of 19000 kJ kg–1 were used based on the relevant information (Brown and Davis 1973; Alexander 1982).

Results

Preburn fuel characteristics for each plot are presented in table 1. Although 

the site was selected for its structural homogeneity, there was an apparent 

variation in the fuel loadings in different plots. Surface fine fuel (0 to 0.5 cm) 

loading ranged from 1.07 to 2.10 kg m–2, coarse fuel (0.6 to 2.5 cm) loading 

from 0.97 to 1.75 kg m–2, and total fuel loading from 1.52 to 5.67 kg m–2.

Table 2 displays the observed variations in fire behavior values and fire 

weather conditions recorded on site during each experimental fire. Weather 

Table 1—Preburn fuel characteristics associated with the experimental fires.

Fire H Fuel loadings (kg m–2) Moisture contents (%)

no. (m) VC HxC <0,5 cm <1 cm Dead 0,6-2,5 cm Total Live fuels Dead fuels

1 0,63 0,70 0,44 1,11 1,35 0,02 0,98 1,60 77,88 11,07

2 1,00 0,75 0,75 1,35 1,70 0,15 1,19 2,68 102,78 17,99

3 1,50 0,83 1,24 1,86 2,28 0,35 1,51 4,42 102,78 17,99

4 1,30 0,80 1,04 1,64 2,04 0,27 1,38 3,70 109,33 9,40

5 1,15 0,80 0,92 1,55 1,90 0,22 1,30 3,28 100,00 8,26

6 0,60 0,70 0,42 1,10 1,32 0,01 0,97 1,52 100,00 8,26

7 2,00 0,80 1,60 2,05 2,69 0,50 1,75 5,67 98,00 8,26

8 1,05 0,83 0,87 1,56 1,84 0,20 1,26 3,11 100,00 8,26

9 1,50 0,83 1,24 1,86 2,28 0,35 1,51 4,42 89,47 13,86

10 1,70 0,80 1,36 1,87 2,41 0,40 1,59 4,83 69,40 8,73

11 1,50 0,65 0,98 1,26 1,96 0,24 1,34 3,48 69,40 8,73

12 1,75 0,85 1,49 2,10 2,56 0,46 1,68 5,28 89,26 14,34

13 1,35 0,85 1,15 1,82 2,16 0,31 1,45 4,08 89,26 14,34

14 0,75 0,70 0,52 1,15 1,44 0,05 1,03 1,89 79,84 9,76

15 1,15 0,80 0,92 1,55 1,90 0,22 1,30 3,28 81,07 10,49

16 1,55 0,85 1,32 1,96 2,36 0,38 1,56 4,68 75,97 7,58

17 0,75 0,65 0,49 1,07 1,40 0,04 1,01 1,76 75,97 7,58

Mean 1,25 0,78 0,99 1,58 1,98 0,25 1,34 3,51 88,85 10,88

Min. 0,60 0,65 0,42 1,07 1,32 0,01 0,97 1,52 69,40 7,58

Max. 2,00 0,85 1,60 2,10 2,69 0,50 1,75 5,67 109,33 17,99

SD 0,41 0,07 0,37 0,35 0,43 0,15 0,25 1,30 12,79 3,50

SE 0,10 0,02 0,09 0,09 0,10 0,04 0,06 0,32 3,10 0,85

N 17 17 17 17 17 17 17 17 17 17

Preliminary Results of Fire Behavior in Maquis Fuels Under Varying Weather and Slope Conditions Saglam, Bilgili, Kücük, Durmaz, and Baysal
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 252

Table 2—Fire behavior values and fire weather conditions associated with the experimental fires.

Fire T RH W S ROS (m min–1) FC (kg m–2) FI (kW m–1)

 no. (°C) (%) (m min–1) (%) Observed Predicted Observed Predicted Observed Predicted

1 25,6 62 11,2 1 1,94 1,53 1,15 1,28 497,61 657,58

2 32,2 42 7,8 10 2,23 2,36 1,40 1,38 720,69 1022,78

3 33,2 39 8,6 10 2,80 2,60 1,95 1,96 1215,07 1070,61

4 31,8 42 10,9 10 3,30 3,52 1,74 1,64 1279,08 1394,34

5 32,2 41 7,2 8 2,84 1,50 1,62 1,61 1025,24 678,55

6 32,2 41 7,8 8 0,58 2,06 1,02 1,17 134,00 882,02

7 32,2 55 7,7 10 1,56 2,37 2,30 2,12 861,92 978,97

8 33,0 41 7,5 9 3,11 1,86 1,57 1,63 1088,75 823,66

9 33,5 37 5,3 10 1,64 1,30 1,95 1,99 710,45 643,38

10 32,2 42 13,5 3 2,76 3,09 2,18 2,08 1473,53 893,24

11 32,5 46 12,9 4 3,19 2,90 1,52 1,45 1189,58 1062,68

12 32,6 38 10,4 12 2,20 3,90 2,11 2,27 1156,68 1541,47

13 32,6 38 11,6 15 5,95 4,61 2,01 1,94 2847,60 1744,58

14 32,7 41 15,7 13 5,10 6,14 1,44 1,26 1742,65 2399,98

15 31,4 45 16,2 13 5,93 6,21 1,76 1,70 2677,75 2265,03

16 27,7 62 15,2 14 5,10 6,17 2,01 2,21 2288,85 2310,22

17 27,0 60 17,0 14 8,43 6,03 1,19 1,24 2243,23 2647,17

Mean 31,4 45 11,0 10 3,45 3,42 1,70 1,70 1361,92 1353,90

Min. 25,6 37 5,3 1 0,58 1,30 1,02 1,17 134,00 643,38

Max. 33,5 62 17,0 15 8,43 6,21 2,30 2,27 2847,60 2647,17

SD 2,3 9 3,6 4 2,01 1,78 0,38 0,37 764,88 674,45

SE 0,6 2 0,9 1 0,49 0,43 0,09 0,09 185,51 163,58

N	 	 17 17 17 17 17 17 17 17 17 17

conditions during the burns were within the narrow range without wind 

speed. Air temperature ranged from 25.6 to 33.5 °C, relative humidity 

from 37 to 62 percent, and wind speed from 5.3 to 17 m min–1. Rate of 

spread ranged from 0.58 to 8.43 m min–1, fuel consumption from 1.02 to 

2.30 kg m–2, and fire intensity from 134 to 2847.6 kW m–1 (table 2).

Correlation and regression analyses were undertaken to investigate the relationships between fire behavior characteristics and associated fuel properties 

and weather conditions. Table 3 displays the correlation coefficients showing 

trends and relationships among the independent and dependent variables. 

The most fitted relationships are given in table 4. Equations are presented 

with one or two independent variables as the second independent variable 

increased the percent variability explained by the equation.

Rate of spread of fire was highly correlated with wind speed (r= 0.791; 

P=0.001) and slope (r= 0.597; P=0.01). Wind speed alone explained 63 percent of the observed variation (P<0.001) in the rate of fire spread. Slope as the 

second independent variable improved the rate of spread prediction significantly (R2=0.791; P<0.0001). Rate of spread observed by these experimental 

fires was poorly related to the slope alone (R2=0.357; P<0.01). The addition 

of moisture contents of live fuels as the second independent variables significantly improved the percent variability explained (R2=0.657; P<0.001). Wind 

speed and slope had a positive effect on rate of spread fire and fire intensity, 

while live fuel moisture content had a negative effect (table 3). The expectation of critical effect of live fuel moisture content on fire behavior would be 

significant; mainly its influence on ignitability (for example, Wilson 1985) 

and combustion rate (for example, Rothermel 1972; Catchpole and others 

1998) was established in this study, while the effect of dead fuel moisture 

content, because of too few fuels, was not determined. Figure 1a shows the 

predicted versus observed rate of spread values.

Preliminary Results of Fire Behavior in Maquis Fuels Under Varying Weather and Slope Conditions Saglam, Bilgili, Kücük, Durmaz, and Baysal
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 253

Table 3—Correlation matrix between the variables used in the analyses.

T RH ROS FI FC W S MCl MCd H VC H×C FLf FLt

T 1

RH –,897(**) 1

ROS –,367 ,298 1

FI –,163 ,132 ,890(**) 1

FC ,383 –,199 –,068 ,290 1

W –,487(*) ,436 ,791(**) ,736(**) –,093 1

S ,105 –,089 ,597(*) ,634(**) ,221 ,260 1

MCl ,424 –,432 –,412 –,400 –,024 –,715(**) ,208 1

MCd ,299 –,468 –,183 –,073 ,129 –,320 ,152 ,268 1

H ,383 –,137 –,198 ,113 ,933(**) –,165 ,058 –,059 ,071 1

VC ,365 –,350 –,140 ,205 ,778(**) –,331 ,333 ,361 ,286 ,608(**) 1

HxC ,380 –,173 –,194 ,143 ,968(**) –,204 ,140 ,024 ,123 ,982(**) ,741(**) 1

FLf ,356 –,217 –,182 ,170 ,956(**) –,252 ,246 ,144 ,194 ,893(**) ,882(**) ,962(**) 1

FLt ,382 –,175 –,196 ,141 ,968(**) –,206 ,139 ,025 ,125 ,981(**) ,742(**) 1,000(**) ,963(**) 1

** Correlation is significant at the 0.01 level (2-tailed).

* Correlation is significant at the 0.05 level (2-tailed).

JT, air temperature (°C); RH, relative humidity (%); ROS, rate of spread (m min–1); FI, fire intensity (kW m–1); FC, fuel consumption (kg m–2); W, 

wind speed (kph), S, slope (%); MCl, moisture contents of live fuels (%); MCd, moisture contents of dead fuels (%); H, mean vegetation 

height (cm); VC, vegetation cover (%); H×C, (mean vegetation height × vegetation cover)/10000; FLf, fine fuel loading (<0.6cm; kg m–2); 

FLt, total fuel loading (kg m–2).

Table 4—Regression equations for predicting fire spread, fuel consumption, and fire intensity in maquis 

fuels based on the data in this study.

Constant Coefficients R2 SEE

No Model a b c

1a ROS=a+b S 0.534 0.302 0.357 1.664

1b ROS=a+b S+c MCl 14.925 0.361 –0.321 0.657 1.257

1c ROS=a+b W –1.357 0.438 0.626 1.268

1d ROS=a+b W+c S –2.747 0.378 0.213 0.791 0.982

 

2a FC=a+b FLt 0.709 0.283 0.937 0.098

2b FC=a+b FLf 0.720 1.031 0.914 0.115

2c FC=a+b FLf+c MCl 0.865 1.056 –0.018 0.941 0.099

 

3a FI= a+b S 183.264 122.178 0.402 611.044

3b FI = a+b S+c MCl 5623.701 144.498 121.189 0.698 449.628

3c FI =a+b W –341.268 155.326 0.542 534.456

3c FI =a+b W+c S –939.026 129.342 91.497 0.752 406.905

3d FI =a+b W (2 fire excluded) –209.409 139.598 0.689 367.365

3e FI =a+b W+c S (2 fire excluded) –637.126 127.621 65.608 0.846 268.743

0

1

2

3

4

5

6

7

0 2 4 6 8 10

Observed ROS

Predicted ROS

1,0

1,5

2,0

2,5

1,0 1,5 2,0 2,5

Observed FC

Predicted FC

a b

Figure 1—Relationship between predicted and observed rates of fire spread (a), and predicted 

and observed fuel consumption (b).

Preliminary Results of Fire Behavior in Maquis Fuels Under Varying Weather and Slope Conditions Saglam, Bilgili, Kücük, Durmaz, and Baysal
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 254

Fuel consumption generated by these experimental fires was significantly 

related to fine fuel loading (<0.6 cm), total fuel loading, and moisture contents 

of live fuel. Total fuel loading and fine fuel loading alone explained 94 and 

91 percent of the observed variation (P<0.001) in the fuel consumption, 

respectively (table 4). The addition of the moisture contents of live fuels as 

a second independent variable with the fine fuel loading, slightly improved 

the variability explained (94 percent; P<0.001). The relationship between 

predicted and observed fuel consumption is shown in figure 1b.

Fire intensity was closely related to wind speed (r=0.736; P=0.001) and 

slope (r=0.634; P=0.001), and slightly related to moisture contents of live 

fuel (r=-0.400; P=0.001). Slope alone explained 40 percent of the observed 

variation of fire intensity. The addition of live fuel moisture content as a 

second independent variables improved the fire intensity prediction significantly (R2=0.698; P<0.001). By the same token, wind speed alone explained 

54 percent of the observed variation of fire intensity. The addition of slope 

as a second independent variables improved the fire intensity prediction 

(R2=0.752; P<0.0001). When the minimum and maximum observed fire 

intensity data (fig. 2a) are excluded from the analyses, wind speed alone explained 69 percent of the observed variation of fire intensity. The addition of 

slope as a second independent variable improved the fire intensity prediction 

(R2=0.846; P<0.0001; fig. 2b). When this study is completed with a wide 

range of weather and slope conditions for about 45 experimental fires, these 

data could be used in the analyses.

0

500

1000

1500

2000

2500

3000

0 500 1000 1500 2000 2500 3000

Observed FI

Predicted FI

0

500

1000

1500

2000

2500

3000

0 500 1000 1500 2000 2500 3000

Predicted FI

Observed FI

a b

Figure 2—Relationship between predicted and observed fire intensity with whole fire intensity 

data (a), and (b) without the minimum and maximum fire intensity data.

Discussion and Conclusions

The preliminary results presented in this study come from the first efforts 

dealing with the prediction of fire behavior under various weather and slope 

effect in maquis fuels in Turkey. In that respect, the study makes an invaluable 

contribution to fire behavior analyses in maquis fuels in the Mediterranean 

Region. The results were based on 17 experimental fires. Differences in fire 

behavior were clearly shown to be a function of wind speed, slope, moisture 

contents of live fuels and fuel loading.

Preliminary Results of Fire Behavior in Maquis Fuels Under Varying Weather and Slope Conditions Saglam, Bilgili, Kücük, Durmaz, and Baysal
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 255

Analyses indicated that wind speed was the most significant single predictor of rate of spread of fire and fire intensity, explaining 63 and 69 percent of 

the variance observed, respectively. Similarly, rate of spread and fire intensity 

observed by these experimental fires was slightly related to the slope alone 

(R2=0.357; R2=0.402), respectively. It is important to address the effect of 

slope on fire spread as topography has a pronounced effect on fire behavior (see 

for example Noble and others 1980; Forestry Canada 1992; Mendes-Lopes 

and others 2003; Viegas 2004; Zhou and others 2007). Our results on rate 

of spread of fire agree with literature (Cheney and others 1993; Johnson and 

Miyanishi 1995; Burgan and others 1998; Piñol and others 1998; Baeza and 

others 2002), and support a negative relationship with moisture contents of 

fuels. On the contrary, because of the little amount of dead fuels, the effect 

of dead fuel moisture content on fire behavior has not been addressed in this 

study. Fernandes (2001) indicated that when live fuels are an important fraction of the total fuel load, dead fuel moisture content has a limited influence 

on the overall moisture content, and consequently, it is logical to expect also 

a limited effect on fire spread. Fuel consumption generated by these experimental fires was significantly related to fine fuel loading (<0.6 cm), total fuel 

loading, and moisture contents of live fuel. The addition of the moisture contents of live fuels as a second independent variable with the fine fuel loading 

explained 94 percent of the observed variation fuel consumption (P<0.001). 

Fire intensity observed in this study (mean 1361.92 kW m–1) agree with those 

found in Trabaud (1979) in Quercus coccifera shrubland (1880 kW m–1).

Given that the study is based on a relatively small number of fires with 

relatively narrow range of weather, slope, and fuel conditions in open area 

conditions, more extensive experimentation is required for a comprehensive 

explanation of fire behavior and the effect of slope and weather parameter on 

fire behavior for developing fire behavior prediction models in maquis fuels. 

Future effort will attempt to complete a series of about 45 experimental fires 

with a wide range of weather and slope conditions to analyze and understand 

fire behavior in maquis fuels.

Acknowledgments

This study was conducted with the cooperation and efforts of many people. 

The authors greatly acknowledge the assistance of Kesan State Forest District 

staff who contributed to the field work. Special thanks are due to State Forest 

Fire Command Unit staff that cooperated and assisted throughout the study. 

This study supported by The Scientific and Technological Research Council 

of Turkey, Career Development Program (3501), Agriculture, Veterinary and 

Forestry Sciences Division, Project No: 105O523.

References

Alexander, M.E. 1982. Calculating and interpreting forest fire intensities. Can. J. 

Bot. 60, 349-357.

Baeza, M.J.; De Luis, M.; Raventos, J.; Escarre, A. 2002. Factors influencing 

fire behavior in srublands of different stand ages and the implication for using 

prescribed burning to reduce wildfire risk. J. Environ. Mang. 65, 199-208.

Preliminary Results of Fire Behavior in Maquis Fuels Under Varying Weather and Slope Conditions Saglam, Bilgili, Kücük, Durmaz, and Baysal
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 256

Bilgili, E.; Sağlam, B. 2003. Fire Behavior in Maquis in Turkey, Forest Ecology and 

Management 184, 201-207.

Brown, A.A.; Davis, K.P. 1973. Forest fire: Control and use, 2nd Ed. New York: 

McGraw-Hill.

Burgan, R.E.; Klawer, R.W.; Klawer, J.M. 1998. Fuel models and fire potential from 

satellite and surface observations. Int. J. Wildland Fire 8, 159-170.

Byram, G.M. 1959, Combustion of Forest Fuels. In: Forest Fire: Control and Use. 

K. P. Davis (ed). New York: McGraw-Hill: 61-89.

Catchpole, W.; Bradstock, R.; Choate, J., Fogarty, L.; Gellie, N.; McCarty, G.; 

McCaw, L.; Marsden-Smedley, J.; Pearce, G. 1998. Cooperative Development 

of Equations for Heathland Fire Behaviour. In: Viegas, D.X. (ed.). Proc. 3rd Int. 

Conf. On Forest Fire Research/14th Fire and Forest Meteorology Conf., Luso, 

16-20 November 1998. ADAI, University of Coimbra: 631-645.

Cheney, N.P.; Gould, J.S.; Catchpole, W.R. 1993. The influence of fuel, weather 

and fire shape variables on fire-spread in grassland. Int. J. Wildland Fire 8 (1), 

31-44.

Coimbra, H.A. 1968. Control Burning in Eucalypt Forests in Victoria, Australia. 

J. For. 66 (8), 601-605.

Drysdale, D.D.; Macmillian, A.J.R. 1992. Flame spread on inclined surfaces. Fire 

Safety Journal 18:245-254.

Dupuy, J.L. 1995. Slope and fuel load effects on fire behavior: laboratory experiments 

in pine needles fuel beds, Int. J. Wildland Fire 5, 153-164.

Fernandes, P.M. 2001. Fire spread prediction in shrub fuels in Portugal. For. Ecol. 

Manage. 144, 67-74.

Forestry Canada. 1992, Development and Structure of the Canadian Forest Fire 

Behavior Prediction System, Forestry Canada. Fire Danger Group, Inf. Rep., 

St-X-3.

Hirano, T.H.; Noreikis, S.E.; Waterman, T.E. 1974. Postulation of flame spread 

mechanisms. Combustion and Flame 22:353-363.

Johnson, E.A.; Miyanishi, K. 1995. The need for consideration of fire behavior and 

effect in prescribed burning. Rest. Ecol. 3, 271-278.

Mendes-Lopes, J.M.C.; Ventura, J.M.P.; Amaral, J.M.P. 2003. Flame characteristics, 

temperature–time curves, and rate of spread in fires propagating in a bed of Pinus 

pinaster needles. Int. J. Wildland Fire 12, 67-84.

Noble, I.R.; Bary, G.A.V.; Gill, A.M. 1980. McArthur’s Fire Danger Meters 

Expressed as Equations. Aust. J. Ecol. 5,201-203.

Piñol, J.; Filella, I.; Ogaya, R.; Peñuelas J. 1998. Ground-based spectroradiometric 

estimation of live fine fuel moisture of Mediterranean plants. Agr. and For. 

Meteo. 90, 173-186.

Rothermel, R.C. 1972. A Mathematical Model for Predicting Fire Spread in Wildland 

Fuels. Res. Pap. INT-115. Ogden, UT: U.S. Department of Agriculture, Forest 

Service, Intermountain Research Station.

Trabaud, L. 1979. Etude du comportoment du feu dans la garrique de chêne kermes 

à partir des températures et des vitesses de propagation. Ann. Sci. For. 36, 13-

35.

Van Wagner, C.E. 1968. Fire Behavior Mechanisms in a Red Pine Plantation. Publ. 

No. 1229. Ottawa, Ontario, Canada: Canadian Department of Forest and Rural 

Development, Forestry Branch.

Van Wagner, C.E. 1977. Effect of Slope on Fire Spread Rate. Bi-Mon.Res. Notes 

33:7-8. Environ. Can., Canadian Forest Service.

Van Wagner, C.E. 1988. Effect of slope on fire spreading downhill. Can. J. For. 

Res., Notes, 18:818-820.

Preliminary Results of Fire Behavior in Maquis Fuels Under Varying Weather and Slope Conditions Saglam, Bilgili, Kücük, Durmaz, and Baysal
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 257

Viegas, D.X.; Neto, L.P. 1991. Wall shear-stress as a parameter to correlate the rate 

of spread of a wind induced forest fire. Int. J. Wildland Fire 1, 177-188.

Viegas, D.X. 2004. Slope and wind effects on fire propagation. Int. J. Wildland 

Fire 13, 143-156.

Viegas, D.X.; Varela, V.G.M.; Borges, C.P. 1994. On the evolution of a linear fire 

front in a slope. In: Proceedings of the 2nd International Conference on Forest 

Fire Research, Coimbra, Portugal, November 21-24, 1994: 301-318.

Ward, D.E. 1971. A controlled experiment to study factors influencing fire rate of 

spread in Pinus pinaster letter. Forest Notes 9(3):39-47. Perth, Australia: Forest 

Department.

Weise, D.R. 1993. Modeling wind and slope induced wildland fire behavior. Ph.D. 

dissertation, University of California. 130 p.

Weise, D.R.; Biging, G.S. 1994. Effects of wind velocity and slope on fire behavior. 

Fourth International Symposium on Fire Safety Science, Ottawa, Ontario, Canada. 

June 13-17, 1994.

Wilson, R. 1985. Observation of extinction and marginal burning states in free 

burning porous fuel beds. Combust. Sci. Technol. 44,179-173.

Zhou, X.; Mahalingam, S.; Weise, D. 2007. Experimental study and large eddy 

simulation of effects of terrain slope on marginal burning in shrub fuel beds, 

Proc. Combust. Inst. 31, 2547-2555. 

Preliminary Results of Fire Behavior in Maquis Fuels Under Varying Weather and Slope Conditions Saglam, Bilgili, Kücük, Durmaz, and Baysal
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 258

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Abstract—Fire managers in the Coastal Plain of the Southeastern United States use 

prescribed burning as a tool to reduce fuel loads in a variety of vegetation types, 

many of which have elevated fuel loads due to a history of fire suppression. While 

standardized fuel models are useful in prescribed burn planning, those models do not 

quantify site-specific fuel loads that reflect land use change, natural disturbances, and 

previous management. Furthermore, data on the fuel consumed during prescribed 

burning are generally unavailable. In an effort to accurately measure and map fuel 

loading and consumption at a site-specific level, fuels and vegetative communities 

were characterized in five burn compartments at the Air Force Dare County Bombing 

Range and Alligator River National Wildlife Refuge in eastern North Carolina. Aerial 

photography, digital softcopy photogrammetry, and GIS were used to map vegetation 

to the alliance level of the National Vegetation Classification System (NVCS). Within 

each vegetation alliance, fuel loads in the shrub, herbaceous, litter, duff, and 1-, 10-, 

100-, and 1000- hour down woody fuel categories were measured using USDA Forest 

Service Forest Inventory and Analysis (FIA) phase 3 protocols. In addition, FIA phase 

2 protocol plots were used to characterize live and standing dead tree biomass and 

forest canopy. Measured fuel loads were then compared to standardized fuel models 

to describe site-specific deviations. Following prescribed burning, fuel load plots were 

remeasured, and fuel consumption was calculated from pre- and postburn biomass. 

Consumption measurements were used to calculate prescribed fire emission factors, 

assess the achievement of prescribed burn goals, and validate the Blue Sky Smoke 

Modeling Framework in the Southeastern U.S. Coastal Plain.

Introduction

The area burned by uncontrolled wildland fires increased over the latter 

decades of the 20th century (GAO 2005), and into the 21st century. This 

increase can be attributed to effective wildland fire suppression during the 

middle of the 20th century, which has lengthened fire return intervals in many 

parts of the country. Longer fire intervals have led to a buildup of flammable 

dead and live vegetation in unburned areas. Elevated levels of fuel loading, 

combined with the extreme weather conditions under which wildfires typically 

burn, create uncontrollable wildfires that often put human life and private 

property at risk. Fires under these conditions frequently burn with more intensity than areas managed for fuel reduction by mechanical or prescribed fire 

fuel treatments (Graham and others 2004; Carey and Schumann 2003).

In an effort to reduce the risk of wildfire, land managers are using prescribed fire to burn areas under controlled conditions. Using prescribed 

fire, land managers hope to periodically reduce fuel loads and modify forest 

structure to become more resistant to catastrophic wildfire. Managers use 

fire behavior modeling tools such as FARSITE (Finney 1998) and BEHAVE 

Fine Scale Vegetation Classification and Fuel 

Load Mapping for Prescribed Burning

Andrew D. Bailey1 and Robert Mickler1

In: Butler, Bret W.; Cook, Wayne, 

comps. 2007. The fire environment—

innovations, management, and policy; 

conference proceedings. 26-30 March 

2007; Destin, FL. Proceedings 

RMRS-P-46CD. Fort Collins, CO: 

U.S. Department of Agriculture, 

Forest Service, Rocky Mountain 

Research Station. 662 p. CD-ROM.

1 Associate Biologist and Program 

Manager, respectively, Alion Science 

and Technology, Durham, NC. Lead 

author adbailey@AlionScience.com
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 262

(Andrews and others 2005) to plan for prescribed burns and predict fire behavior. The BlueSky RAINS Smoke Modeling Framework (O’Neill and others 

2003; McKenzie and others 2006) and VSmoke (Lavdas 1996) models allow 

managers to determine and mitigate the impacts of smoke from prescribed 

fires. The successful implementation of these tools necessitates the accurate 

quantification of wildland fuel loads.

Fuel loadings are typically reported in tons/acre by component for the standard 1-hour, 10-hour, 100-hour, 1000-hour, and litter fuel load classes. Often, 

fuel reporting also includes fuel bed depth and heights of live and dead herbaceous plants and shrubs. Standardized fuel models developed by Rothermel 

(1972), Albini (1976), Anderson (1982), and Scott and Burgan (2005) use 

a text description or a key so that managers may choose an appropriate set of 

fuel loadings for a specific site. The Natural Fuels Photo Series Publications 

(Ottmar and Vihnanek 2000) use close range stereo photography to depict 

vegetation types and fuel loads. The manager chooses the best representation 

of a site by browsing the photography and then reading the associated fuel 

loadings from a chart. Computer models may also be used to determine fuel 

loads. In the Fuelbed Characterization and Classification System (FCCS) 

(Sandberg 2001) users choose from a series of prototype fuelbeds representing 

vegetation descriptions and are able to modify vegetation composition and 

structure. The model then calculates or infers quantitative fuel characteristics 

and probable fire parameters.

Fuel loads from the approaches described above are based on measurements 

made in the field, but usually have been generalized across the continental 

United States or across a region of the country. The generalizations can lead 

to inaccuracies when applied to a specific site. Site history, including land use 

change, natural disturbances, and previous management actions—including 

previous prescribed burns—can lead to significant deviations from standard 

fuel loading models. Rosenfeld (2003) found that measured fuel loads based 

on ecological associations are more accurate than those provided with standard fuel models.

In situations where a high degree of accuracy is required, plot- or transectbased inventory procedures that directly measure site conditions are more 

appropriate (Otmar and Vihnanek 2000). Georeferenced fuel load measurement plots are a means to describe actual fuel loading characteristics, because 

fine woody fuels, litter, and duff are hidden by the canopy or are too small 

to detect with aerial imagery (Keane and others 2001). The most widely 

used method for the direct measurement of wildland fuels is Brown’s (1974) 

line-intersect method. The U.S. Department of Agriculture, Forest Service 

integrated this method into the Forest Inventory Analysis (FIA) Program plot 

design to determine fuel loads, carbon storage, and wildlife habitat. Coupling 

the FIA plot design with GPS plot locations provides the necessary accuracy 

for fuel load measurement that can be combined with computer mapping 

techniques to make fine scale maps useful for a number of purposes.

In this study, the FIA plot design was applied in tandem with fine-scale 

softcopy aerial photography and digital mapping to quantify and map preand postfire wildland fuel loading for a prescribed burn on the Air Force 

Dare County Bombing Range (DCBR) and Alligator River National Wildlife 

Refuge (ARNWR) in the Coastal Plain of North Carolina. Large-scale maps 

of fuel loading developed during this project were designed to be useful to 

local land managers working on individual burns. These fuel loadings and the 

associated maps were used for prescribed burn planning, assessment of prescribed burn objectives, and to provide data for the validation of the BlueSky 

RAINS Smoke Modeling Framework in the Southeastern United States.

Fine Scale Vegetation Classification and Fuel Load Mapping for Prescribed Burning Bailey and Mickler
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 263

Methods

Site Description

The mainland of Dare County, North Carolina, is made up of numerous 

fire-adapted ecosystems under Federal ownership in proximity to one another. Nearly 200,000 acres in Dare County are managed by the U.S. Fish 

and Wildlife Service and the U.S. Air Force. The Dare County mainland is 

a peninsula 14 miles across, bordered on the north by the Albemarle Sound, 

on the east and south by the brackish Croatan and Pamlico Sounds, respectively, and on the west by the freshwater Alligator River. The long axis of the 

peninsula extends 29 miles from north to south. The Outer Banks barrier 

island chain provides protection from the Atlantic Ocean some 20 miles to 

the east. Though there are two small tidal creeks on the peninsula, there 

is virtually no relief, and elevations range from sea level to 4 ft above sea 

level. Over 90 percent of the peninsula is made up of organic “muck” soils. 

Fire, salinity, and organic soil depth are the main ecological factors affecting vegetation development. Our study site (fig. 1) consisted of two burn 

units totaling 1,525 acres on Ponzer, Belhaven, and Pungo muck soils, with 

peatland pocosin vegetation.

Figure 1—Location of the research site. 

The Alligator River National Wildlife 

Refuge and Dare County Bombing 

Range are located on a long, low 

peninsula in eastern North Carolina 

near the Atlantic Ocean.

Fine Scale Vegetation Classification and Fuel Load Mapping for Prescribed Burning Bailey and Mickler
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 264

Aerial Photography

An aerial photography mission flown in spring 2004 captured 496 colorinfrared photographs with a spatial resolution of 7.5 inches per pixel (Bailey 

and Mickler 2005). Twenty-five of these photographs were used to provide 

100 percent coverage of the research burn units. The digitized photographs 

were orthorectified and used to develop an orthophoto mosaic for use as a 

base layer during fuel load and vegetation community mapping. The digitized photographs were also used to develop a “block file” product, which 

allowed stereo photo pairs to be viewed on a computer in 3-D, as if viewing 

the imagery with a stereoscope. Benefits of this approach include onscreen 

panning and zooming, direct GIS database creation, and image manipulation capabilities. This product allowed the viewer to discriminate between 

objects in the canopy and objects on the ground, providing further analysis 

capabilities for determining canopy and understory vegetation and canopy 

cover estimates.

Mapping

Using the orthophoto mosaic, stereo blockfile, a digital elevation model, 

surface hydrology data, and a digital soil survey, polygons representing distinct 

vegetation communities were delineated to the alliance level of the NVCS 

(http://www.natureserve.org/explorer/servlet/NatureServe?init=Ecol). 

The NVCS is an ecosystem-based classification scheme in which vegetation 

communities are grouped by their characteristic physiognomy and floristic 

composition. To differentiate vegetation types on the orthophoto mosaic and 

stereo blockfile, seven photogrammetric interpretation attributes were used: 

size, shape, shadow, color, texture, pattern, and association with other objects 

(Avery 1992). The heads-up stereo photography allowed easy differentiation 

of vegetation communities with differing dominant tree heights, canopy 

shapes, and canopy closure. These were the critical strata used to discriminate 

between NVC alliances (Grossman 1998). Soil, elevation, and hydrology data 

were used to further inform the vegetation classification. When variation in 

structure within a vegetation alliance appeared great enough to affect fuel 

loading and fire behavior, a modifier was added to indicate this difference. A 

large minimum mapping unit of 2.5 acres was used to ensure that polygons 

captured variation in fuel loading within ecosystems.

Fuel Load Measurement

A permanent plot network was established to directly measure fuel loading 

within the research units. The plot design (fig. 2), based on the USDA Forest 

Service FIA phase 2 and phase 3 plots, was used to characterize live biomass 

and pre- and postburn down woody materials (DWM). A sampling grid was 

laid over the study area, and one plot was placed randomly within each grid 

cell. Grid cells were subsampled to ensure that a minimum of three plots 

were placed within each vegetation alliance. We used field protocols based 

on methods establish by the USDA Forest Service in Field Instructions for 

Southern Forest Inventory (http://fia.fs.fed.us/library/field-guides-methodsproc/). DWM data were collected using a line-intersect method to sample 

down wood along transects (Brown 1974). Down deadwood was characterized 

as coarse woody material (CWM, woody pieces greater than 3.0 inches in 

diameter), or fine woody material (FWM, small = 0.1 to 0.24 inch, medium 

= 0.25 to 0.9 inch, and large = 1 to 2.9 inches in diameter). The extent and 

height of live and dead shrub and herbaceous vegetation were measured on a 

Fine Scale Vegetation Classification and Fuel Load Mapping for Prescribed Burning Bailey and Mickler
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 265

6-ft diameter microplot located within each subplot. FIA methodology was 

augmented with additional data on the vertical distribution of DWM for 

input into the FARSITE fire behavior model. The depths of the litter layer 

and fuelbed were taken at the 24-ft location on each transect. The biomass 

of the duff layer (Oa soil horizon) was estimated from the specific gravity 

of oven dried sampling frame soil samples for each soil series found on the 

field plots.

Biomass Scaling

Plot-level biomass estimates were combined to produce a mean per-unit-area 

biomass value for each vegetation alliance. The measurements were combined 

with material density (specific gravity) values in linear equations to compile 

dry-weight mass (tons/acre) for each DWM component (Mickler and Bailey 

2005). Previous equations have been developed primarily for Western U.S. 

species (Brown 1974), necessitating the development of fuel class biomass 

algorithms for additional forest species and decay classes in the research area. 

Additional microplots were established for destructive sampling of shrub and 

herbaceous vegetation to develop new biomass equations.

Figure 2—The USDA Forest Service Forest Inventory and Analysis plot design, used in this study to 

quantify wildland fuel loading. Each plot contains four circular subplots, each with a radius of 24 ft. 

Fine and coarse woody material is inventoried along three transects on each subplot. Litter layer and 

fuelbed depths are measured at the end of each transect. Live and dead shrub and herbaceous fuels 

are assessed in a 6-ft radius circular microplot within each subplot. (Figure adapted from fig. 14-1 

in the USDA Forest Service Phase 3 Field Guide – Down Woody Materials.)

Fine Scale Vegetation Classification and Fuel Load Mapping for Prescribed Burning Bailey and Mickler
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 266

Prescribed Burn and Post Burn Fuel Load Measurement

Following fuel load measurement, the research units were burned according to the North Navy Shell Compartment Prescribed Fire Plan (Simpson, 

and others 2004) on March 4, 2006. Aerial ignitions were conducted via 

helicopter using a Plastic Sphere Dispenser (PSD) machine to implement a 

grid pattern of ignitions that allowed for a backing fire with short periods of 

downwind fire activity. Fire intensity was generally moderate; most fire activity occurred in the litter and dead shrub strata, with occasional torching of 

the overstory aided by fuel ladders. The burn was substantially extinguished 

by rising humidity overnight with little residual smoldering. Following the 

prescribed burn, all plots were relocated and remeasured following the same 

protocols. The difference between prefire and postfire measurement represents 

the actual amount of fuel consumed during the burn.

Results

The aerial photo mosaic and vegetation map are displayed in figures 3 and 

4, respectively. Seven vegetation alliances were mapped and appeared to occur 

along an increasing moisture gradient from southeast to northwest. The 

Shining Fetterbush – Little Gallberry Saturated Wooded Shrubland (12 acres) 

and the Sweetbay – Swampbay Saturated Forest (3 acres) made up a small 

proportion of the study area and were excluded from fuel loading analysis. 

In order of increasing moisture, the five alliances mapped were:

	 •	 Pond Pine Saturated Woodland (457 acres)

	 •	 Pond Pine Saturated Woodland – Overstocked (1349 acres)

	 •	 Loblolly Pine Saturated Forest (65 acres)

	 •	 Loblolly Pine – Atlantic White Cedar – Red Maple – Swamp Blackgum 

Saturated Forest (mixed pine/hardwood forest, 94 acres)

	 •	 Swamp Blackgum – Red Maple – Tuliptree Saturated Forest (maple forest, 

68 acres)

Within the pond pine woodland alliance, high canopy coverage and low 

canopy coverage variants were observed and mapped separately. Following 

field measurements, the values within these variants were determined to be 

similar and were combined for this analysis into one pond pine woodland 

alliance.

Fuel loadings for each plot before and after the prescribed burn are reported 

in table 1. Fuel loadings by vegetation alliance are reported in figure 5. Prior 

to the prescribed fire, litter and FWM fuel loading were highest in the loblolly 

pine forest (12.09 tons/acre) and pond pine woodland (11.19 tons/acre). 

These two forest alliances each contained more than 7 tons/acre in the litter 

fuel class. The mixed pine/hardwood forest alliance contained 9.64 tons/acre 

of litter and FWM, with 4.94 tons/acre occurring in litter fuel class and 

4.23 tons/acre in the medium (0.25 to 0.9 inch) and large (1.0 to 2.9 inch) 

classes. While duff made up the largest component in the fuel load, the prescribed burn was conducted when the possibility of consuming duff was at 

its lowest. No duff or coarse woody material (3+ inches in diameter, 1000 

hour fuels) were consumed in any vegetation class during the burn.

Consumption is reported in table 2. The fire consumed 4.94 tons/acre in 

the loblolly pine forest alliance, which was 40.8 percent of the preburn litter 

and FWM fuel load. The most complete consumption occurred in the litter 

Fine Scale Vegetation Classification and Fuel Load Mapping for Prescribed Burning Bailey and Mickler
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 267

Figure 3—The finished orthorectified aerial photo mosaic for the study site. Each pixel represents a 7.5 x 7.5 inch 

area on the ground.

Fine Scale Vegetation Classification and Fuel Load Mapping for Prescribed Burning Bailey and Mickler
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 268

Figure 4—The finished vegetation alliance map for the study area. Seven alliances were mapped on 1,525 acres.

Fine Scale Vegetation Classification and Fuel Load Mapping for Prescribed Burning Bailey and Mickler
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 269 7.56 3.93 0.27 0.12 1.95 1.49 2.31 1.62 7.23 1.38 0.34 0.07 1.82 1.17 1.81 1.76 4.94 4.30 0.48 0.48 2.10 2.10 2.13 2.13 1.83 1.83 0.33 0.33 1.14 1.14 1.67 1.67

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

Litter (Pre-) Litter (Post-) FWM small (Pre-) FWM small (Post-) FWM medium (Pre-) FWM medium (Post-) FWM large (Pre-) FWM large (Post-)

Fuel Load in tons/acre

Loblolly Pine Forest Pond Pine Woodland Mixed Pine / Hardwood Forest Maple Forest

Table 1—Pre- and postburn biomass (all measurements in tons/acre).

Preburn biomass Postburn biomass

Plot Vegetation alliance Duff Litter FWM

small

FWM

medium

FWM

large Duff Litter FWMsmall

FWM

medium

FWM

large

2 Loblolly pine forest 299.95 7.23 0.25 1.33 1.94 299.95 7.23 0.15 1.16 1.25

8 Loblolly pine forest 299.95 8.32 0.28 2.52 2.36 299.95 1.80 0.08 1.74 1.67

10 Loblolly pine forest 299.95 7.14 0.29 1.99 2.64 299.95 2.75 0.12 1.58 1.94

Average 299.95 7.56 0.27 1.95 2.31 299.95 3.93 0.12 1.49 1.62

11 Pond pine woodland 513.07 6.75 0.39 1.67 1.11 513.07 2.62 0.15 1.28 0.83

12 Pond pine woodland 513.07 7.37 0.38 2.01 2.22 513.07 0.84 0.03 0.92 2.08

13 Pond pine woodland 189.44 7.57 0.24 1.77 2.08 189.44 0.68 0.04 1.31 2.36

Average 405.20 7.23 0.34 1.82 1.81 405.20 1.38 0.07 1.17 1.76

6

Mixed pine/ hardwood

forest 189.44 3.07 0.42 1.33 1.94 189.44 3.07 0.42 1.33 1.94

14

Mixed pine/ hardwood

forest 299.95 4.52 0.31 1.48 1.39 299.95 4.52 0.31 1.48 1.39

15

Mixed pine/ hardwood

forest 189.44 7.23 0.70 3.49 3.05 189.44 5.30 0.70 3.49 3.05

Average 226.28 4.94 0.48 2.10 2.13 226.28 4.30 0.48 2.10 2.13

5 Maple forest 299.95 1.46 0.40 1.02 1.67 299.95 1.46 0.40 1.02 1.67

7 Maple forest 299.95 1.75 0.30 1.21 0.42 299.95 1.75 0.30 1.21 0.42

9 Maple forest 189.44 2.28 0.29 1.19 2.92 189.44 2.28 0.29 1.19 2.92

Average 263.11 1.83 0.33 1.14 1.67 263.11 1.83 0.33 1.14 1.67

Figure 5—Pre- and postburn fuel loading by component in tons per acre. Colors 

correspond to the colors used on the vegetation map in figure 4.

Fine Scale Vegetation Classification and Fuel Load Mapping for Prescribed Burning Bailey and Mickler
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 270

and small FWM classes. Fire consumed 6.81 tons/acre (60.9 percent) of the 

fuel load in the pond pine woodland alliance, including 80 percent of the 

litter and fine woody material classes. Litter consumption occurred on only 

one of the three plots in the mixed pine/hardwood forest alliance, removing 

0.64 tons/acre. No consumption occurred in the maple forest alliance.

Discussion

The goals of the prescribed fire plan were to reduce accumulations of fine 

fuels, top-kill midstory shrubs encroaching into pine ecosystems, and topkill encroaching hardwoods. Within the loblolly pine forest and pond pine 

woodland alliances, these conditions were met successfully. The litter and 

small fine woody material levels were reduced by 48 percent and 56 percent, 

respectively, in the loblolly pine forest. Litter and small FWM consumption 

was particularly high in the pond pine woodland alliance, where the canopy 

coverage was generally below 80 percent. The open canopy permitted sunlight 

to reach the forest floor, which combined with air circulation to desiccate 

the fine fuel classes and permit more active fire behavior. Within the pond 

pine woodland alliance, the fire consumed 81 percent of the total litter fuel 

load and 80 percent of the small FWM fuel load.

Light fuel loading and a variable fuel bed in the mixed pine/hardwood 

forest alliance limited fire activity. The encroaching hardwood bay (Gordonia 

lasianthus / Persea borbonia) midstory and dense overstory canopy inhibited 

fuel desiccation, which suppressed fire behavior and restricted the spread of 

fire. Future burns in this area may need to be conducted with lower relative 

humidity and 10-hour fuel moistures to successfully reduce fuel loads in this 

vegetation type.

The maple forest alliance burned poorly, as it contained little litter, areas 

of flooded soil, and a discontinuous fuel bed. This area would likely only 

burn under wind-driven wildfire conditions, when medium and large fine 

woody fuels, coarse woody fuels, and live canopy fuels could ignite. Under 

typical prescribed fire conditions, the maple forest serves as an effective fire 

barrier.

The mapping technique distinguished vegetation types that had different fuel loadings. The level of detail in the map was high enough to show 

the distribution of vegetation communities with differing fuel loads in the 

landscape. This information was useful for planning and implementing the 

Table 2—Consumed biomass by vegetation alliance.

Tons/acre consumed

Vegetation alliance Total

consumed Litter FWM small FWM

medium

FWM

large

FWM

total

Loblolly pine forest 4.94

(40.81%)

3.63

(48.07%)

0.15

(56.61%)

0.45

(23.24%)

0.69

(30%)

1.3

(28.69%)

Pond pine woodland 6.81

(60.86%)

5.85

(80.91%)

0.27

(79.53%)

0.65

(35.56%)

0.05

(2.56%)

0.96

(24.27%)

Mixed pine/hardwood forest 0.64

(6.66%)

0.64

(13.01%) 0 0 0 0

Maple forest 0 0 0 0 0 0

Fine Scale Vegetation Classification and Fuel Load Mapping for Prescribed Burning Bailey and Mickler
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 271

prescribed burn. A distinction was drawn between high canopy coverage 

and low canopy coverage areas within the pond pine woodland alliance, due 

to appearances of different fuel loadings and fire behavior potential. This 

distinction did not reveal any actual differences in preburn fuel loading, but 

appeared to be more important for postburn fuel loading. This is likely due 

to increased drying from greater sunlight and wind reaching the fuels in the 

low canopy coverage areas. This illustrates the utility of mapping withinalliance distinctions in order to better anticipate fire behavior.

Comparison of our results to standardized fuel models was problematic 

because the maple forest and mixed/pine hardwood forest alliances could not 

be cross-walked to analogous Coastal Plain fuelbeds from the FCCS system. 

For the pond pine woodland alliance, the FCCS fuel loads underpredicted 

our measured values for total litter by 56 percent (table 3). This is likely due 

to site-specific variation from past fire suppression. Within the loblolly pine 

forest alliance, FCCS underpredicted litter by 27 percent and small FWM 

by 5 percent. Loblolly pine forests on saturated deep organic soils, such as 

those present on this research site, are somewhat atypical throughout much 

of the Southeastern United States and may not be accounted for in the 

FCCS model. Duff measurements were much higher than those reported 

by FCCS for the research unit due to the deep peat soil types typical of the 

Dare County peninsula.

Although detailed accuracy assessment was beyond the scope of this study, 

fuel loads for the pine-dominated ecosystems measured within the research 

unit were similar to those reported by Rosenfeld (2003) and Wendel and 

others (1962). The presence of two vegetation alliances that have no analogue 

in standardized fuel models suggests that detailed site-specific fuel loading 

measurements may be necessary for land managers with nonstandard vegetation types, or standard vegetation types growing on atypical sites.

Table 3—Comparison to fuelbed characterization classification system (FCCS).

Measured biomass FCCS

Vegetation type Duff Litter FWM

small

FWM

medium

FWM

large Duff Litter FWMsmall

FWM

medium

FWM

large

Loblolly pine forest 299.95 7.56 0.27 1.95 2.31 22.10 5.55 0.60 1.70 2.00

Pond pine woodland 405.20 7.23 0.34 1.82 1.81 56.00 3.15 1.00 1.50 1.50

Maple forest 263.11 1.83 0.33 1.14 1.67 N/A N/A N/A N/A N/A

Mixed pine/ hardwood

forest 226.28 4.94 0.48 2.10 2.13 N/A N/A N/A N/A N/A

Conclusions

Fine-scale mapping of vegetation alliances and their associated fuel loads 

is a feasible technique for reducing or eliminating the limitations associated 

with standardized fuel models. Standardized fuel models may provide ballpark 

numbers that are, in many cases, appropriate for prescribed burn planning. 

However, site-specific differences that affect both fuel loading and fuel consumption can become apparent after direct measurements are compared to 

standardized models. These differences may be important to research and 

land management activities where smoke management and fuel reduction 

goals depend on using accurate fuel loadings.

Fine Scale Vegetation Classification and Fuel Load Mapping for Prescribed Burning Bailey and Mickler
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 272

Acknowledgments

Funding for this research project was provided by the Joint Fire Science 

Program (Project 04-2-1-80) and the U.S. Air Force Air Combat Command.

Reviewers Frank Koch (USDA Forest Service Southern Research Station), 

Dwight Otwell (IAVO Research and Scientific), and Candace Krull (USDA 

Forest Service Pacific Northwest Research Station) provided comments that 

substantially improved a draft of this paper.

References

Albini, F.A. 1976. Estimating wildfire behavior and effects. Gen. Tech. Rep. INT-30. 

Ogden, Utah: Department of Agriculture, Forest Service, Intermountain Forest 

and Range Experiment Station. 92 p.

Anderson, H.E. 1982. Aids to determining fuel models for estimating fire behavior. 

Gen. Tech. Rep. INT-122. Ogden, UT: U.S. Department of Agriculture, Forest 

Service, Intermountain Forest and Range Experiment Station. 22 p.

Andrews, P.L.; Bevins, C.D.; Seli, R.C. 2005. BehavePlus fire 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. 

144 p.

Avery T.E.; Berlin, G.L. 1992. Fundamentals of Remote Sensing and Airphoto 

Interpretation, fifth edition. New York: Macmillan Publishing Company.

Bailey, A.D.; Mickler, R.A. 2005. Vegetation Classification and Fuel Load Mapping 

Using Softcopy Photogrammetry. In: Proceedings of the 2005 American Society 

of Photogrammetry and Remote Sensing Annual Conference. Baltimore, MD:

ASPRS.

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.

Carey, H.; Schumann, M. 2003. Modifying Wildfire Behavior - The Effectiveness 

of Fuel Treatments. Santa Fe, NM: National Community Forestry Center.

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.

Government Accountability Office. 2005. Wildland Fire Management: Important 

Progress Has Been Made, but Challenges Remain to Completing a Cohesive 

Strategy. GAO-05-147. Washington, DC: United States Government 

Accountability Office.

Graham, R.T.; McCaffrey, S.; Jain, T.B. (tech. eds.) 2004. Science basis for changing 

forest structure to modify wildfire behavior and severity. Gen. Tech. Rep. RMRSGTR-120. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, 

Rocky Mountain Research Station. 43 p.

Grossman, D.H.; Faber-Langendoen, D.; Weakley, A.S.; Anderson, M.; Bourgeron, P.; 

Crawford, R.; Goodin, K.; Landaal, S.; Metzler, K.; Patterson, K.D.; Pyne, M.; 

Reid, M.; Sneddon, L. 1998. International classification of ecological communities: 

terrestrial vegetation of the United States, Volume I: The National Vegetation 

Classification System: development, status, and applications. Arlington, VA: The 

Nature Conservancy. 139 p.

Keane, R.E.; Burgan, R.; van Wagtendonk, J. 2001. Mapping wildland fuels for 

fire management across multiple scales: Integrating remote sensing, GIS, and 

biophysical modeling. International Journal of Wildland Fire 10:301-319.

Fine Scale Vegetation Classification and Fuel Load Mapping for Prescribed Burning Bailey and Mickler
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 273

Lavdas, L.G. 1996. Program VSMOKE—Users Manual. Gen. Tech. Rep. GTR-SRS006. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern 

Research Station. 156 p.

McKenzie, D.; O’Neill, S.M.; Larkin, N.K.; Norheim, R.A. 2006. Integrating models 

to predict regional haze from wildland fire. Ecological Modelling 199(2006): 

278-288.

Mickler, R.A.; Bailey, A.D. 2005. Quantifying Wildland Fire Fuel Loading and 

Fire Risk in Coastal Plain Forests. In: Proceedings of the 2005 American Society 

of Photogrammetry and Remote Sensing Annual Conference. Baltimore, MD: 

ASPRS.

O’Neill, S.M.; Ferguson, S.A.; Peterson, J.; Wilson, R. 2003. The BlueSky Smoke 

Modeling Framework (www.blueskyrains.org). In: 5th Symposium of Fire and 

Forest Meteorology. Orlando, FL: American Meteorological Society.

Ottmar, R.D.; Vihnanek, R.E. 2000. Stereo photo series for quantifying natural 

fuels, Volume VI: longleaf pine, pocosin, and marshgrass types in the southeast 

United States. PMS 835. Boise, ID: National Wildfire Coordinating Group, 

National Interagency Fire Center. 56 p.

Rosenfeld, B.J. 2003. Developing a New Fuel Load Mapping Strategy Using: Digital 

Photogrammetry; International Classification of Ecological Communities; USDA 

Forest Service, Forest Inventory and Analysis Protocols; and Disturbance History. 

Master’s Thesis. Raleigh, NC: North Carolina State University.

Rothermel, R.C. 1972. A mathematical model for predicting fire 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.; Cushon, G.H. 2001. Characterizing fuels in the 21st 

Century. International Journal of Wildland Fire 10:381-387.

Scott, J.H.; Burgan, R.E. 2005. Standard Fire Behavior Fuel Models: A 

Comprehensive Set for Use with Rothermel’s Surface Fire Spread Model. Gen. 

Tech. Rep. RMRS-GTR-153. Fort Collins, CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research Station.

Simpson, R.B.; Crews, T.G.; Montgomery, R.; Stratton, R.; Smith, S. 2004. 

Prescribed Fire Plan: North Navy Shell Compartment. Manteo, NC: U.S. 

Department of the Interior, Fish and Wildlife Service, Alligator River National 

Wildlife Refuge.

Wendel, G.W.; Storey, T.G.; Byrum, G.M. 1962. Forest fuels on organic and 

associated soils in the coastal plain of North Carolina. SFES Paper 144. Asheville,

NC: U.S. Department of Agriculture, Forest Service, Southeastern Forest 

Experiment Station. 46 p.

Fine Scale Vegetation Classification and Fuel Load Mapping for Prescribed Burning Bailey and Mickler
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 274

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on purpose

Fine Scale Vegetation Classification and Fuel Load Mapping for Prescribed Burning Bailey and Mickler
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 275

Abstract—Fire-adapted forest ecosystems make up 95 percent of the historic Coastal 

Plain vegetation types in the Southeastern United States. Fire suppression over the 

last century has altered the species composition of these ecosystems, increased fuel 

loads, and increased wildfire risk. Prescribed fire is one management tool used to 

reduce fuel loading and restore fire-adapted species, but little information exists on 

the presettlement extent and location of fire-dependent ecosystems at a level of detail 

useful to guide land management decisions at the local spatial scale. In an effort to 

close this knowledge gap, the principles of landscape fire ecology have been applied 

to develop a detailed presettlement fire regime map for ~200,000 acres of Coastal 

Plain ecosystems. Factors evaluated include the effects of fire compartment size in 

the original landscape, fire barriers, fire filters, prevailing wind direction during fire 

season, topographic and soil factors affecting fire intensity, fire frequency, fire spread, 

and fire effects on vegetation. The fire regime map was then combined with remnant 

fire-adapted vegetation surveys, historic aerial photography, digital elevation models, 

and soil survey information to create a map of presettlement vegetation. This map 

is being used to develop prescribed burning plans that restore original fire regimes, 

guide the use of prescribed fire as a management tool, restore fire-adapted vegetation 

structure and understory species diversity for threatened and endangered species, and 

enhance ecosystem sustainability.

Introduction

Ninety-five percent of the forest, shrubland, and grassland ecosystems of 

the Southeastern United States Coastal Plain have been shaped by the occurrence of fire (Frost 1995). Many of the plant species that inhabit these 

ecosystems are adapted to either withstand fire by growing rapidly to take 

advantage of reduced competition following fire events, or regenerate rapidly 

after fire. Longleaf pine (Pinus palustris) / wiregrass (Aristida stricta) is the 

most frequently cited example of a fire-adapted Coastal Plain forest ecosystem. 

Of the estimated 60 to 90 million acres of presettlement forests dominated 

by longleaf pine in the Southeast, less than 4 million acres of poor-quality 

second growth longleaf pine forest remain in the region (Harper and others 

1997). Many of these continue to decline due to lack of prescribed fire, or 

prescribed fire applied at an inappropriate frequency.

In contrast to the mineral soils that support longleaf pine ecosystems, the 

Lower Coastal Plain of the Southeast has extensive areas of deep organic 

soil types that support marshes, canebrakes, pond pine (Pinus serotina) pocosins (evergreen shrub bogs with pond pine), Atlantic white cedar forests 

(Chamaecyparis thyoides), and nonriverine swamps (Fussell and others 1995). 

Fire is important in these ecosystems, and the range of fire intensities and 

In: Butler, Bret W.; Cook, Wayne, 

comps. 2007. The fire environment—

innovations, management, and policy; 

conference proceedings. 26-30 March 

2007; Destin, FL. Proceedings 

RMRS-P-46CD. Fort Collins, CO: 

U.S. Department of Agriculture, 

Forest Service, Rocky Mountain 

Research Station. 662 p. CD-ROM.

1 Associate Biologist and Program 

Manager, respectively, with Alion Science 

and Technology, Durham, NC. Lead 

author adbailey@AlionScience.com

2 Adjunct Faculty, Ecology, University 

of North Carolina – Chapel Hill, 

Chapel Hill, NC. 

Presettlement Fire Regime and Vegetation 

Mapping in Southeastern Coastal Plain Forest 

Ecosystems

Andrew D. Bailey1, Robert Mickler1, and Cecil Frost2
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 276

frequencies is often much wider than those found in upland longleaf pine 

savannas. The effects of fire suppression in these lowland fire-adapted ecosystems are similar to those in other areas of the Southeast: conversion to 

less fire-adapted ecosystems; a loss of threatened, endangered, and other rare 

endemic species; and increased fire risk due to increasing fuel loads (Robertson and others 1998).

Many of these ecosystems occur on Federal lands, where land managers 

are expected to simultaneously provide wildlife habitat preservation and 

enhancement, endangered species protection, and wildland fire protection. 

In addition, Federal lands in the Departments of the Interior (USDI) and 

Agriculture (USDA) are required to manage lands for multiple uses that 

include recreation, grazing, forestry, and historic and cultural values. Department of Defense properties must be managed for the military mission. Forced 

to manage within these constraints, land managers can gain insight for the 

development and implementation of management strategies by examining 

the fire history of their land.

Several strategies have been applied successfully in parts of the United 

States to reconstruct fire history, including fire scar analysis and charcoal 

sediment analysis (NOAA 2007). Fire scar analysis is of limited usefulness 

in Southeastern Coastal Plain ecosystems on deep organic soils, because 

natural fire regimes in these locations began to deviate early after settlement 

(~1600 AD), predating any surviving tree. Typically, these ecosystems have 

experienced either catastrophic stand-replacing fires or clear-cut harvesting, 

destroying the old trees needed for fire history reconstruction. Charcoal sediment analysis is not accurate enough to determine fire regimes in frequent 

fire regions, and fire regimes detected by this method cannot be mapped 

spatially at a scale small enough for local management.

A third technique for determining fire history is to apply the principles of 

landscape fire ecology at a local scale. Using available local data sets, weather 

patterns, and current knowledge of fire behavior, the local landscape may be 

broken up into a series of fire compartments. A fire compartment is a unit 

of the landscape with no natural firebreaks, such that an ignition in one 

part would be likely to burn the whole unit unless there was a change in the 

weather or fuel conditions. Within each fire compartment on the landscape, an 

associated mean fire interval and range of variation may be determined (Frost 

1995). Factors such as fire barriers, prevailing wind direction, topography, 

hydrology, and vegetation determine this interval and range within a specific 

compartment. Using field surveys for remnant vegetation, historical land 

survey documents, and photography, maps can be developed at a fine level of 

detail depicting the most likely distribution of fire regimes and fire-adapted 

vegetation communities in the presettlement landscape (Frost 2000). These 

maps may be used to guide land management decisions, determine areas for 

threatened and endangered species recovery, establish ecological endpoints, 

or guide ecological restoration efforts. Here, we apply the principles of landscape fire ecology to map the presettlement fire regimes and vegetation of 

Dare County, North Carolina.

Methods

Site Description

The mainland of Dare County, North Carolina, provides an outstanding example of several fire-adapted ecosystems in proximity to one another. 

Nearly 200,000 acres in Dare County are managed by the U.S. Fish and 

Presettlement Fire Regime and Vegetation Mapping in Southeastern Coastal Plain Forest Ecosystems Bailey, Mickler, and Frost
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 277

Wildlife Service and the U.S. Air Force. The Dare mainland is a peninsula, 

14 miles across, bordered on the north by the Albemarle Sound, on the east 

and south by the brackish Pamlico and Croatan Sounds, and on the west 

by the freshwater Alligator River. The long axis of the peninsula extends 

29 miles from north to south. The Outer Banks barrier island chain provides 

protection from the Atlantic Ocean some 20 miles to the east. Though there 

are two small tidal creeks on the peninsula, there is virtually no relief, and 

elevations range from slightly below sea level to 4 ft above sea level. Areas 

below sea level are typically located in and around agricultural fields. The 

absence of topography means that the vegetation on the Dare mainland is 

shaped primarily by two major natural factors: fire and salinity.

Soils

A digital soil data layer for Dare County was obtained from the USDA 

Natural Resource Conservation Service (NRCS). These data layers are digital 

representations of USDA county soil surveys that contain a soil classification 

established through field work and aerial photography. Each polygon in the 

data layer is associated with detailed information describing physical characteristics of the soil, the type of terrain it is commonly found in, as well as 

potential vegetation types. The soil layer becomes the base map for classifying 

presettlement vegetation.

Remnant Vegetation

Vegetation survey information for fire adapted plant species and communities were grouped by soil series in the Southeastern United States, including 

Dare County (Frost 2000). This survey information was used for preliminary 

mapping of vegetation. Special attention was afforded to vegetation species that 

are considered to be highly fire adapted, or plant species that are remnants 

of fire-adapted vegetation communities displaced by fire suppression, such as 

pitcher plants (Sarracenia ssp.), wiregrass (Aristida stricta), and canebrake 

(Arundinaria gigantea). In some cases, downed logs and plant remains that 

have been buried in organic soil are preserved and can be identified. These 

provide clues to determine what vegetation may have previously occupied a 

converted site. On rare occasions, old individual trees may have fire scars that 

can be used to age past fires.

A second source used to delineate presettlement vegetation is a series of 

aerial photography taken in 1932 depicting the entire Dare County mainland. 

Because effective fire suppression was not possible in Dare until after the 

establishment of internal roads in the 1960s, these images are representative 

of the fire-shaped landscape. The photo series was digitized using a highquality desktop scanner and orthorectified using a second-degree polynomial 

rectification procedure. Images were then stitched together to form a seamless 

mosaic of the peninsula. From this mosaic, vegetation types and fire patterns 

were identified and used to further refine the base map. Finally, all vegetation data were recorded by soil type and then plotted on a map for a rough 

first draft of presettlement vegetation. Using information from the soil map, 

vegetation plots, and historical photography, a first draft of presettlement 

vegetation was digitized into a GIS database.

Historical Maps

A third source of information was historical timber surveys and older maps 

collected from former forest industry owners and the U.S. National Archives. 

These maps have descriptions of the forest landscape and show the locations of 

remnant timber stands and place names that include natural history clues.

Presettlement Fire Regime and Vegetation Mapping in Southeastern Coastal Plain Forest Ecosystems Bailey, Mickler, and Frost
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 278

Topography

Digital Elevation Models (DEMs) for eastern North Carolina were 

developed beginning in 2000 using Light Detection and Ranging (LiDAR) 

technology, a highly accurate method of measuring elevations on Earth’s 

surface. These models were used to develop a topographic map showing 1 ft 

isopleths of elevation change across the peninsula (fig. 1). Because elevation 

corresponds closely with moisture regime and vegetation type, these data 

were used to further refine the presettlement vegetation map.

264

64

64

Elevation (above msl) Elevation of Dare County, NC

Below Sea Level

0 - 1 feet

1 - 2 feet

2 - 3 feet

3 - 4 feet

Above 4 feet

Water

Contours

1:190,000 0 2 4 6 8 10 1

Miles

Alligator

River

Albemarle

Sound

Croatan

Sound

Pamlico

Sound

Figure 1—Color-coded topographic map for Dare County. Nearly the entire county is below 5 ft 

above sea level. The low pocosin dome (dark green), an area where peat has built up over time to 

rise slightly above the surrounding land, is clearly visible in the southeast.

Presettlement Fire Regime and Vegetation Mapping in Southeastern Coastal Plain Forest Ecosystems Bailey, Mickler, and Frost
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 279

Fire Regime Map Creation

All fire indicator species and communities were plotted over the soil map 

to generate a first draft of presettlement fire regimes. In areas where data 

were scarce, it was useful to construct general fire frequency gradients over 

a larger area that included the study area. Because vegetation and fire interact in a complex mechanism of feedback, frequent fire plant communities 

and infrequent fire plant communities are rarely found to abut one another 

directly; usually a gradient exists between extremely different communities. 

Landscape features that affect the spread of fire were determined and considered with respect to the prevailing winds during fire season. Hydrology 

data were used to identify barriers to fire spread, including nonflammable 

vegetation types and bodies of water. This knowledge is used to delineate 

isopleths between areas of similar fire frequency, where the lines form the 

boundaries between compartments with equal fire frequencies. The effect is 

similar to that of a contour map; the contours represent different fire-return 

intervals rather than elevation.

Feedback into Vegetation Map

The vegetation and fire frequency maps were compared, and areas were 

identified that needed further investigation. These included areas where fireinfrequent species were identified in fire-frequent compartments, or areas 

where gradients between infrequent fire and frequent fire appeared very 

steep. Collaborative discussions between the authors were used to resolve 

discrepancies when they were found, and both maps were adjusted using 

GIS editing tools.

Historical Document Validation

Final examination of the maps was done using historical documents from 

multiple sources. For Dare County, these sources included land grant documents from the 18th century and early survey plats (fig. 2) with witness trees 

and descriptions of vegetation. Personal accounts and newspaper articles were 

collected describing logging work from the late 19th century. In several cases 

these sources were used to adjust vegetation and fire frequency polygons.

Results

Presettlement fire regimes are displayed in figure 3. The largest fire frequency class had a mean fire interval of 4 years, and comprised 63,468 acres 

(32.5 percent) of the Dare County mainland where frequent-fire marsh 

vegetation along the eastern shoreline promoted the spread of fire into the 

peninsula interior under hot, dry conditions. The second-largest fire frequency 

class had a mean fire return interval of 25 years, and covered 35,116 acres 

(18 percent) of deep, wet peat soils toward the western interior portion of 

the peninsula. Transition areas are represented by the 6-year and 9-year fire 

return interval classes. These areas were narrow when wet soils forced a steep 

fire gradient and were wider where broad lenses of mineral soil promoted 

drier conditions. The smallest fire frequency class had a mean fire return 

interval of 2 years, and occurred on 1,192 acres (0.6 percent) of upland in 

the northeast of the county, where drier upland fuels and continuity with 

large areas of frequent-fire marsh, canebrake, and pocosin upwind to the 

south and southwest combined to raise fire frequency. All presettlement fire 

frequencies are listed in table 1.

Presettlement Fire Regime and Vegetation Mapping in Southeastern Coastal Plain Forest Ecosystems Bailey, Mickler, and Frost
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 280

Figure 2—1765 land grant survey for Thomas Pain, the first settler on Pains Bay, southeastern Dare County, just 

north of Long Shoal River. The portion of interest for agriculture was a pine ridge near the center of the tract. 

Beyond the pine ridge to the northeast, the land was described as “the Desart,” referring to treeless canebrake 

and pyrophytic (fire-maintained) low pocosin with sparse pond pine. Sea level rise over the intervening 240 

years have converted the original house site to a brackish marsh today.

Presettlement Fire Regime and Vegetation Mapping in Southeastern Coastal Plain Forest Ecosystems Bailey, Mickler, and Frost
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 281

264

64

64

Presettlement Fire Regimes of Dare County, NC

1:190,000 0 2 4 6 8 10 1

Miles

Alligator

River

Albemarle

Sound

Croatan

Sound

Pamlico

Sound

Fire Frequency (Years)

Mean Range

A 2 1 - 3

B 3 1 - 6

C 4 2 - 10

D 6 3 - 20

E 9 4 - 50

F 25 10 - 100

G 90 35 - 300

H None None

Water

Figure 3—Fire regime maps, using cool colors to represent long fire intervals and warm colors for more frequent 

fire intervals (roads superimposed for reference).

Presettlement Fire Regime and Vegetation Mapping in Southeastern Coastal Plain Forest Ecosystems Bailey, Mickler, and Frost
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 282

Presettlement vegetation is displayed in figure 4. The largest two classes 

were the fire-frequent canebrake (33,823 acres) and pond pine pocosin (38,445 

acres) vegetation types, which occur on the eastern portion of the peninsula. 

These are fire-dependant vegetation communities with similar, sparse pond 

pine overstories. Pocosin vegetation refers to an understory dominated by the 

shrubs little gallberry (Ilex glabra), big gallberry (Ilex coriacea), fetterbush 

(Lyonia lucida), and pepperbush (Clethra alnifolia). Canebrake refers to an 

understory dominated by the herbaceous giant cane (Arundinarea gigantea). 

Typical pond pine/canebrake is found in parts of the peatland landscape 

where fire frequency runs from 2 to 6 years. With fire intervals between 7 

and 12 years, some pocosin shrubs become established, and the community 

consists of alternating canebrake and pocosin, with cane dominant for a few 

years after a fire and pocosin shrubs becoming increasingly prominent toward 

the end of the interval. With longer fire intervals, pond pine/canebrake is replaced by pond pine/pocosin. Fire-infrequent vegetation classes Atlantic white 

cedar (26,681 acres) and peatland long fire interval pyromosaic (31,349 acres) 

dominate on the western side of the peninsula under fire interval classes F 

(10 to 100 years) and G (35 to 300 years). Position in the fire landscape 

along with less flammable litter-layer fuels contribute to a reduction in fire 

frequency in these areas. Fires carried easily by the frequent fire fuel types 

Table 1—Presettlement fire frequencies of Dare County.

Fire

frequency

class

Mean fire

interval

(years)

Estimated historic range of variation

(90% of fires) (in years) Acres Percentof total

A 2 1 - 3 1,192 1

B 3 1 – 6 28,727 15

C 4 2 – 10 63,468 32

D 6 3 – 20 12,484 6

E 9

4 – 50 depending upon

vegetation type and location

in the landscape

21,993 11

F 25

10 – 100 depending upon

vegetation type and location

in the landscape

35,116 18

G 90

35 – 300+ depending on

landscape position along

the fire frequency gradient

26,357 14

H None Nonflammable, tidal

cypress-tupelo swamp 4,712 2

W Water 1,239 1

Presettlement Fire Regime and Vegetation Mapping in Southeastern Coastal Plain Forest Ecosystems Bailey, Mickler, and Frost
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 283

264

64

64

Vegetation Class Presettlement Vegetation of Dare County, NC

1 - Estuarine Fringe

Beaches, Sand Berms 

and Low Dunes,

Sparsely Vegetated

2 - Xeric and Dry-Mesic 

Longleaf Pine/Wiregrass

Savanna

5 - Wet-mesic Longleaf

Pine/Wiregrass Savanna

7 - Mesic Mixed Pine

Savanna and Pyrophytic

Hardwood Woodland

8 - Pine-Gum Flats,

Fire Exposed

9 - Oak-Loblolly Pine

Flats, Fire Sheltered

10 - Maritime Pine-Live 

Oak Forest

12 - Pond Pine Savanna

and Forest

13 - Canebrake

14 - Pond Pine Pocosin

16 - Palustrine Low 

Pocosin

17 - Peatland Long Fire

Interval Pyromosaic

(Multiple Species 

Patch Dominants)

18 - Peatland Very Long

Fire Interval Pyromosaic

(Atlantic White Cedar 

dominant)

20 - Tidal CypressGum Swamp

22 - Pine Marsh and 

Estuarine Scrub 

23 - Oligohaline Marsh

24 - Oligohaline to

Brackish Marsh

26 - Brackish and 

Salt Marsh

Water

1:190,000 0 2 4 6 8 10 1

Miles

Alligator

River

Albemarle

Sound

Croatan

Sound

Pamlico

Sound

Figure 4—Presettlement vegetation map. Frequent fire vegetation types form bands along the eastern side of the 

peninsula, with vegetation becoming less frequently burned from east to west (roads superimposed for reference).

Presettlement Fire Regime and Vegetation Mapping in Southeastern Coastal Plain Forest Ecosystems Bailey, Mickler, and Frost
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 284

on the eastern side of the peninsula have little access to the western forests. 

Fires can approach only from the east and southeast, while any fires carried 

on the prevailing southwest winds are blocked by small lakes, streams, and 

the nonflammable band of cypress-gum along the Alligator River. Moderate 

fire return interval vegetation types such as oak-pine flats (11,977 acres) and 

pine-gum flats (9,756 acres) occur at the transition zones between frequent 

and infrequent fire regimes. These vegetation communities are largely missing 

in the current landscape due to site conversion for plantation forestry and 

agriculture. All presettlement vegetation types are listed in table 2.

Discussion

The pronounced east-west fire frequency gradient on the Dare County 

peninsula is driven by salinity. The brackish water along the eastern margin 

sustains flammable saltmeadow cordgrass (Spartina patens) / saltgrass 

(Distichlis spicata) and black needle rush (Juncus roemarianus) marshes, 

which form a continuous wide shoreline band stretching some 50 miles 

(80 km) from the Albemarle sound, down the length of mainland Dare, to 

the Pamlico sound at the bottom of the peninsula. This fire corridor graded 

into flammable canebrake immediately to the west. The boundary between 

brackish marsh and salt-intolerant canebrake likely occurs at the western limit 

Table 2—Presettlement vegetation of Dare County.

Vegetation type Acres Percent

Estuarine fringe beaches, sand berms and low dunes, sparsely vegetated 130 0.1

Xeric and dry-mesic longleaf pine/wiregrass savanna 197 0.1

Wet-mesic longleaf pine/wiregrass savanna 161 0.1

Mesic mixed pine savanna and pyrophytic hardwood woodland 347 0.2

Pine-gum flats, fire exposed 9,756 5.0

Oak-loblolly pine flats, fire sheltered 11,977 6.1

Maritime pine-live oak forest 223 0.1

Pond pine savanna and forest 160 0.1

Canebrake 33,823 17.3

Pond pine pocosin 38,445 19.7

Palustrine low pocosin 18,560 9.5

Peatland long fire interval pyromosaic 31,349 16.1

Atlantic white cedar 26,681 13.7

Tidal cypress-gum swamp 3,726 1.9

Pine marsh and estuarine scrub 6,812 3.5

Oligohaline marsh 288 0.1

Oligohaline to brackish marsh 5,387 2.8

Brackish and salt marsh 6,023 3.1

Water 1,247 0.6

Total 195,291

Presettlement Fire Regime and Vegetation Mapping in Southeastern Coastal Plain Forest Ecosystems Bailey, Mickler, and Frost
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 285

of storm overwash. In contrast, on the west side, nonpyrophytic cypress-gum 

swamp fringes the fresh waters of the Alligator River in a narrow band. The 

short fire interval marsh and cane communities of the eastern side and the 

nonflamable river swamp on the west comprise the extremes of a cross-peninsula fire frequency gradient. Between these extremes, a kilometer wide 

swath of canebrake graded into pocosin vegetation where fire frequency was 

low enough to allow the development of a dense shrub understory. To the 

west of the pocosin vegetation, a large-scale patch mosaic of wooded wetland 

ecotypes occurred at decreasing fire intervals. This mosaic was made up of 

pocosin, pond pine forest, black gum (Nyssa biflora) forest, pond cypress 

(Taxodium ascendens), loblolly bay (Gordonia lasianthus) forest, and Atlantic 

white cedar. Fire-exposed pine-gum and fire-sheltered oak-pine forests occurred on mineral soil lenses in the peninsula’s interior. The patch mosaic 

was bounded on the west by a relatively narrow band of cypress-gum swamp, 

only 100 to 300 m wide, along the Alligator River.

A certain amount of uncertainty is inherent in the construction of presettlement maps. The polygons delineated represent our best estimate of the 

locations of specific fire-adapted communities and fire regimes. In many 

cases these estimates are confirmed by 1934 aerial photography, land grant 

documents, and historic maps, but it is important to realize these sources 

have limited spatial accuracy. This form of mapping does not lend itself to 

quantitative accuracy assessment, and the resulting maps should not be regarded as truth. Instead, they represent a snapshot of the many likely fire 

and vegetation patterns that have existed since these communities became 

dominant nearly 5,000 years ago.

No ecosystem is static, and many things have changed over the 400 years 

since European settlement, including extensive logging of Atlantic white cedar 

and loblolly pine, conversion of hardwood forests on mineral soils to agricultural fields, widespread alteration of hydrology, changing climate, and rising 

sea level. The changes dictate that these presettlement vegetation and fire 

regime maps should not be used to demonstrate what the current landscape 

should look like, but rather used as background information for managing 

existing vegetation. For example, special attention may be appropriate for the 

oak-pine and pine-gum forest types. These forest types occupied a moderatefrequency fire niche in the landscape that is currently not replicated by fire or 

management activity in Dare County. Frequent-fire vegetation communities 

such as low pocosin and canebrake are much smaller than they would have 

been in the presettlement landscape. At the present, 43 percent of presettlement extent of low pocosin and no pure canebrake communities remain. On 

the western portion of the peninsula, fire-intolerant vegetation types are 

now encroaching into what were once areas of low- and mid-frequency fire. 

This encroachment reduces habitat for threatened and endangered species, 

particularly the red-cockaded woodpecker, which inhabits open, frequently 

burned pond pine woodland ecosystems. Active management to increase the 

acreage of pond pine woodland burned would benefit these species.

These techniques may be useful throughout the Southeastern United 

States, where other techniques for establishing presettlement fire regimes 

and vegetation composition have proven less useful. The GIS datasets used 

in this analysis are freely available through online Federal data distribution 

Web sites, and historic documents may typically be obtained from State and 

Federal archives. Other sources of information, including historic aerial 

photography and maps, may be obtained from local land management offices, 

former landowners, and local history societies.

Presettlement Fire Regime and Vegetation Mapping in Southeastern Coastal Plain Forest Ecosystems Bailey, Mickler, and Frost
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 286

Conclusions

The principles of landscape fire ecology were applied to construct a map 

of presettlement fire regimes and presettlement vegetation in Dare County, 

North Carolina. In an area of the country where traditional fire history 

reconstruction techniques such as fire scar chronologies are not available, these 

techniques provide a method to create a useful product for the land manager. 

These presettlement maps should serve as a useful guide for prescribed fire 

management, restoration ecology, and ecosystem management planning for 

the current and future ecosystems of Dare County.

Acknowledgments

Funding for this research project was provided by the Department of 

Defense Legacy Resource Management Program (Project 05-252).

Reviewers Dwight Otwell (IAVO Research and Scientific), David Bailey 

(NCDOT Project Development and Environmental Analysis Branch), and 

Jennifer Costanza (UNC Landscape Ecology and Biogeography Group) 

provided comments which substantially improved a draft of this paper

References

Climate Assessment for the Southwest. 2007. Fire History Analysis Using Tree Rings. 

[Online] 1p. Available: http://www.ispe.arizona.edu/climas/learn/fire/history.

html [March 1, 2007].

Frost, C.C. 1995. Presettlement Fire Regimes in Southeastern Marshes, Peatlands, and 

Swamps. In: S.I. Cerulean and R.T. Engstrom, ed. Fire in wetlands: a management 

perspective. Proceedings of the Tall Timbers Fire Ecology Conference, No 19. 

Tallahasee, FL: Tall Timbers Research Station; 39-60

Frost, C.C. 2000. Studies in landscape fire ecology and presettlement vegetation of 

the Southeastern United States. Doctural Thesis. Chapel Hill, NC: University 

of North Carolina.

Fussell, J.O.; Webster, W.D.; Hall, S.P.; LeGrand Jr., H.E.; Schafale, M.P. 1995. 

Ecosystem Survey of Dare County Air Force Range, North Carolina. NC Natural 

Heritage Program.

Harper, M.; Trame,A.; Fischer, R.A.; Martin, C.O. 1997. Management of Longleaf 

Pine Woodlands for Threatened and Endangered Species. USACERL Technical 

Report 98/21.

NOAA. 2007. A Brief Introduction to Fire History Reconstruction. [Online]. 

1p. Available: http://www.ncdc.noaa.gov/paleo/impd/impd_data_intro.html 

[March 1, 2007].

Robertson, K.; Harper, M.G.; Woolery, M. 1998. Management of Peatland Shruband Forest-dominated Communities for Threatened and Endangered Species. 

USACERL Technical Report 99/08. Washington, DC: U.S. Army Corps of 

Engineers.

Presettlement Fire Regime and Vegetation Mapping in Southeastern Coastal Plain Forest Ecosystems Bailey, Mickler, and Frost
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 287

Abstract—On August 29, 2005, Hurricane Katrina made landfall in southern Mississippi. As the storm passed through Mississippi, it maintained hurricane force winds 

through the northern part of the State affecting all of the Forests. The eye of the storm 

passed within a few miles of the De Soto Ranger District, the Forest’s southern-most 

district. Much of the District received moderate to heavy damage to timber and facilities. The De Soto Ranger District had historically prescribed burned 60,000 acres per 

year. In the 5 years prior to hurricane Katrina, the District had increased its prescribed 

fire program to an average of 94,000 acres per year. While Hurricane Katrina created a serious large fuel loading, the fact that most of the District has been regularly 

prescribed burned, prevented the problem from being exacerbated by having the 

new fuels being piled atop the new growth of volatile brush, which is common in 

the lower coastal plains. The National Forests in Mississippi developed an aggressive 

three-pronged strategy to deal with the sudden increase in fuel loading and the subsequent potential for catastrophic wildfire: (1) remove the large, downed material as 

quickly as possible, through conventional salvage operations; (2) establish fuel breaks 

in critical Wildland Urban Interface (WUI) areas; and (3) reestablish landscape scale 

prescribed fire treatments immediately. 

Introduction

The National Forests in Mississippi (MNF) consist of 1.2 million acres 

of public lands located in six Forests across Mississippi. These Forests are 

diverse ranging from coastal plain pine to upland hardwood stands. Within 

these lands there are over 2,000 acres of lakes and ponds and 600 miles of 

streams that are open to the public for fishing. 

We have 14 wildlife management areas that are operated by Mississippi 

Department of Wildlife, Fisheries and Parks. In these areas, emphasis is placed 

on intensive management of games species such as white-tailed deer, wild 

turkey, and bobwhite quail. 

On some of the forested lands, as we manage for healthy tree stands, we 

are also able to provide a sustainable harvest to meet the demands of you, 

our constituents, for lumber, paper, and other wood products. Forested areas 

are also managed to provide unique habitats for wildlife and opportunities 

for people to experience primitive natural surroundings. 

The Supervisor’s office, which is located in Jackson, MS, supports the 

activities on all the Districts.

The MNF has a very aggressive prescribed burning program. For the last 

5 years, with the exception of 2006, MNF Districts have accomplished over 

235,000 acres per year. As far back as our burn data goes (1963) Mississippi 

has consistently averaged over 100,000 acres per year. With the approximate number of “burnable” acres around 1,000,000 we are on a consistent 

3 to 5 year rotation. 

Fuels Management on the National Forests in 

Mississippi after Hurricane Katrina

Danny Bryant1 and Jay Boykin2

In: Butler, Bret W.; Cook, Wayne, 

comps. 2007. The fire environment—

innovations, management, and policy; 

conference proceedings. 26-30 March 

2007; Destin, FL. Proceedings 

RMRS-P-46CD. Fort Collins, CO: 

U.S. Department of Agriculture, 

Forest Service, Rocky Mountain 

Research Station. 662 p. CD-ROM.

1 Assistant Fire Management Officer, 

U.S. Department of Agriculture, Forest 

Service, National Forests in Mississippi, 

Jackson, MS. dannybryant@fs.fed.us 

2 District Fire Management Officer, 

U.S. Department of Agriculture, Forest 

Service, National Forests in Mississippi, 

De Soto Ranger District.
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 288

The MNF has a fulltime fire organization in the Supervisor’s office as 

well as on all the Districts including fire managers, air and fire specialists, 

fuel technicians, engine crews, tractor plow crews, and firefighters. During 

the prescribed burning season, generally from January through May, we use 

personnel from other resource areas within the Districts and pull in detailers from all over the country. We usually have two to four helicopters on the 

Forest also.

Leading the way in this accomplishment is the De Soto Ranger District, 

MNF’s southern-most ownership. The De Soto contains 378,538 acres including Camp Shelby military base, several communities and municipalities, 

and many wildland-urban interface issues. In spite of these challenges, the 

De Soto averages 94,000 acres of prescribed burning each year, which is by 

far, the largest program on the MNF. Just to the northeast of the De Soto 

lies the Chickasawhay Ranger District. The “Chick” consists of 122,153 acres 

and burns an average of 35,000 annually. On August 29, 2005, these two 

areas took the brunt of Hurricane Katrina’s force.

Hurricane Katrina Impacts to the De Soto 

Ranger District

On August 29, 2005, the De Soto Ranger District was in the unfortunate position of being in the northeastern quadrant of Hurricane Katrina. 

Although the southern portion of the District lies within 8 miles of the 

Gulf, the western side of the District suffered the most damage. The eye 

of the storm passed within 5 miles of the western boundary of the Forest. 

Winds on the District were estimated between 100 and 140 mph and were 

sustained for several hours. Damage to homes, facilities, and timber ranged 

from moderate to near catastrophic.

Impacts to Employees

The De Soto Ranger District is a large complex District with more than 

55 employees. Most employees rode out the storm at home. Fortunately there 

were no injuries. Those that evacuated had trouble returning due to roads 

being blocked. Every District employee had damage to homes, vehicles, or 

property. Three employees lost their homes. One employee lost every possession. Even 18 months after the storm, employees were still doing repair 

work and dealing with insurance companies.

Impacts to District Facilities

Practically all Forest Service facilities were damaged. The District Office 

sustained major roof damage, causing leaks throughout the building. Temporary offices were utilized during the repair. Work centers had only minor 

damage. Some of the fleet vehicles were not so lucky. All of the District’s 18 

recreation areas were heavily damaged. Over 170 miles of trails were made 

impassable or nearly obliterated. Every recreational or administrative facility 

became unsafe for use due to leaning trees and trees with broken tops and 

limbs. All roads (Forest Service, county, and State) were blocked by fallen 

trees. No form of communication was functional after the storm, including 

telephones, cellular phones, radios, and e-mail.

Fuels Management on the National Forests in Mississippi after Hurricane Katrina Bryant and Boykin
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 289

Impacts to Timber

The De Soto Ranger District’s annual timber target was around 15 million board feet prior to Hurricane Katrina. The storm changed the timber 

program for the foreseeable future. Approximately 30 percent of sawtimber 

sized trees were blown over or broke off. Virtually all the young plantations 

are permanently bent over. The District estimates that 3 to 5 years of regular rotation timber sales will have to be suspended. Through salvage sales, 

hazardous fuel removal was completed on 99,000 acres. Final salvage volume 

(218 MMBF) harvested during 12 months represents 14.5 years of annual 

average De Soto Ranger District timber offered target.

Impacts to Threatened and Endangered Species

There were more than 150 Red-Cockaded Woodpecker (RCW) cavity trees 

on the De Soto. Around half of these cavity trees were blown over or snapped 

in-two by the hurricane. At least two RCWs died in the storm.

The Federally threatened gopher tortoise faired well because they burrow under ground. However, the amount of timber blown down could have 

restricted gopher tortoise movements for foraging and reproduction.

On a positive note, in preparation for salvage sales, approximately 48,200 

acres of Threatened and Endangered Species surveys were completed. Close 

to 4,000 new gopher tortoise burrows were located and flagged to protect.

Impacts to Fuels

Since Hurricane Katrina, dead fuel loadings are obviously a great concern. 

The 1hr, 10hr, 100hr, and 1000hr fuel loadings have all increased dramatically, especially the 1000hr. The forest could be considered “jack-strawed” 

in many areas. Prior to the storm, no fuel models 11, 12, and 13 existed. 

Now these models are present in large areas. The open canopy has drastically 

increased the amount of solar radiation to the fuel bed. This has caused dead 

fuels to dry out more rapidly. This has also caused an increase in the mid 

flame windspeeds. The increase in available fuel has increased the source 

for spotting and therefore increased the spotting potential. In addition, the 

more open canopy will enhance the growth of common volatile understory 

brush species such as ti ti, gallberry, and yaupon. This combination of dense, 

volatile live fuels and the dramatically increased dead fuel loadings will be a 

concern for several years. 

Another critical issue that has impacted mainly the area closest to the 

coast is salt damage to vegetation. Most of the needles have remained on 

the trees, so torching or crowning is an increased possibility. Areas with a 

heavy combination of brush and dead needles will most likely prove to be 

the most dangerous. These fuel types will have a heavy dead component to 

the understory and allow fire to move rapidly into the crowns. 

The rate of fire spread has slightly increased due to more open canopies. 

The added fuel loading has increased the flame length and intensity in all 

areas. Spotting due to accumulations of debris and numerous snags has 

increased.

Prescribe burning has proven to be difficult and hazardous due to the 

volatile nature of the fuels. Prescribe burning has also produced higher concentrations of smoke and large particulate matter due to the above normal 

amount of dead and live fuel.

Fuels Management on the National Forests in Mississippi after Hurricane Katrina Bryant and Boykin
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 290

Fire Occurrence and Severity

The increased fuel loading caused by the hurricane, combined with a 

moderate to severe drought in the months after the hurricane has greatly 

increased southern Mississippi’s fire danger. The combination of drought and 

fuel loading resulted in the following suppression figures for the De Soto 

Ranger District in the 18 months following the hurricane:

	 •	 244 – fires suppressed

	 •	 29 – fires over 100 acres

	 •	 20,800 – acres burned

	 •	 4 – Type 3 fires

	 •	 11 – lightning caused fires

Forest Response

After Katrina made landfall, the first and foremost concern of the Forest 

and District staff was the wellbeing of District employees. Locating our employees and confirming that they (and their families) were safe were difficult 

tasks. Phone lines (including cell phones) were down all across the southern 

end of the State. Some employees had moved inland with family or friends. 

But eventually all employees either were contacted by or made contact with 

their supervisors. 

The next concern of the Forest was to reopen Forest roads for emergency 

and essential travel. Over 1,300 miles of Forest roads were totally or partially 

blocked due to fallen trees and other debris. The Forest brought in two Type 2 

Incident Management Teams to accomplish this task. Over the next 2 weeks, 

using chainsaws and mechanized equipment, personnel opened more than 

1,000 miles of roads. It was only then that we realized the magnitude of 

Katrina’s fury. 

The fuel loading on the two Districts had gone from around 1 ton per acre 

to upwards of 60 tons per acre. We knew this new fuel had to be removed 

quickly due to the rapid rate of decay for wood products in the South. If 

this fuel was not salvaged, it would become available to burn within 1 or 2 

years.

Use of the Healthy Forest Restoration Act (HFRA) authority was critical 

in meeting the time sensitive issue of salvaging these 1000 hr fuels. Proactive 

collaboration played a critical role in expediting the process. Key collaborators 

included U.S. Fish and Wildlife Service, The Nature Conservancy, Mississippi Department of Wildlife Fisheries and Parks, and forest industry. Weight 

scale authority was granted to expedite removal of downed timber. Salvage 

sale preparation was conducted concurrently with the National Environmental Protection Act process in order to expedite offering of merchantable 

timber. 

All sales prepared were sold. Over 1.2 million tons or 276 MBF on 109,828 

acres on the Chickasawhay and De Soto were removed.

Because of all the new fuel on the ground, access through the Forest was 

difficult if not impossible. We knew it was critical to establish fuel breaks 

between the Forest and adjoining private lands, especially those in Wildland 

Urban Interface (WUI) areas. The lines were needed not only to prevent wild 

fires from spreading onto private land from the Forest, but more often to 

prevent the spread into the Forest of fires started by the adjoining landowners 

Fuels Management on the National Forests in Mississippi after Hurricane Katrina Bryant and Boykin
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 291

as they were cleaning up debris. Using existing District resources, detailers, 

and contract resources, the Forest constructed more than 600 miles of WUI 

lines. While we knew that these lines would not stop an approaching wildfire 

on its own, they would serve as fuel breaks, provide access routes into areas 

where we needed to position suppression resources, and serve as escape routes 

if needed. It was not long before we began to see just how valuable the WUI 

lines were. On the Burnt Dog fire, the WUI line put in between the Forest 

and some private houses made it possible for our resources to make a stand 

and save the homes.

With the additional fuel loading, the District was reluctant to try any 

prescribed burning at first. It was actually after a wildfire or two that they 

realized they could conduct prescribed fires without doing resource damage, 

causing excessive smoke issues or creating a situation for extreme fire behavior. 

It turned out that what we had thought to be the case all along was actually 

true. Previous years of aggressively burning the Forest had reduced the fuels 

to such a level that, even with tons of fuel added to the forest floor, it was 

still manageable. The District successfully burned more than 14,000 acres 

but then, the Forest made a difficult decision. To concentrate all efforts and 

resources on assisting with the mechanical removal of fuels that were already 

sold. While the entire Forest burned only 91,000 acres that year, we were able 

to count an additional 112,000 acres as mechanical fuel reduction. This year, 

2007, the De Soto jumped right back into their normal burning routine. At 

the time of this writing, they have successfully burned 74,017 acres with no 

plans to let up. The cost of burning is higher now due to additional preparation and holding resources needed but that will go down as time goes on. 

Lessons Learned, and Our Plans

There are several lessons that we can learn from the events surrounding 

Hurricane Katrina. We know that a quick, organized response was vital to the 

success of our mission. Using HFRA and other agreements that were already 

in place, getting the support of our cooperators and other parties, and the 

aggressive, dedicated attitude of our employees made all of this possible. 

At the time, the National Forests in Mississippi’s Fire Staff Officer was 

a Type 1 Incident Commander. His background and experience played an 

important role in getting much of this started. After his retirement, he came 

back on contract and helped us put together a hurricane response plan that 

we can use for future events like this.

The aggressive fuels program that has been in place on the Forest was essential. Having much of the District in a condition class 1 made it possible 

for our resources to get back on the ground sooner because the new fuel 

was not piled on top of existing fuels and new growth of volatile brush that 

is common in the lower coastal plains. 

Our plans include using supplemental Katrina funds to bring in additional 

resources during the burning season and purchase larger equipment that can 

get through the blow-down. We developed a 3-year plan to use these funds 

on the De Soto and several other Districts.

The first year or two after Katrina, our main goal has been to reduce the 

fuel loading as much as possible, but we plan to return to more of a growing 

season rotation, eventually bringing the De Soto and Chickasawhay back to 

where they were pre-Katrina.

Fuels Management on the National Forests in Mississippi after Hurricane Katrina Bryant and Boykin
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 292

Acknowledgments and References

This presentation was developed by Danny Bryant, Forest Assistant Fire 

Management Officer for the National Forests in Mississippi and Jay Boykin, 

District Fire Management Officer on the De Soto Ranger District. 

We thought it would be beneficial to the whole fuels community to see 

how Mississippi and the entire agency pulled together to address and overcome what could have been a crippling event. Thanks to Katrina Incident 

Commander David Carter and all the Forest Service employees who were 

involved with this crucial event.

Most of the data came from weekly reports and updates submitted by the 

Incident Management Team (IMT) on the incident, David Carter Incident 

Commander. Reports are posted on the Hurricane Katrina Recovery Internet site, which is linked to the Mississippi National Forests home page: 

http://fsweb.ms.r8.ffs.fed.us/. Contact us at National Forests in Mississippi, 

100 West Capitol Street, Suite 1141, Jackson, MS 39269 (601) 965 1600 

References for this paper are:

Boykin, Jay. DFMO De Soto Ranger District

Bryant, Danny. FAFMO MNF

Carter, David. District Ranger, Bienville RD

Smith, Ron. District Ranger, De Soto RD

Fuels Management on the National Forests in Mississippi after Hurricane Katrina Bryant and Boykin
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 293

Abstract—Obtaining accurate biomass measurements is often a resource-intensive 

task. Data collection crews often spend large amounts of time in the field clipping, 

drying, and weighing grasses to calculate the biomass of a given vegetation type. 

Such a problem is currently occurring in the Great Plains region of the Bureau of Indian Affairs. A study looked at six reservations in the Great Plains region to compare 

three methods of calculating aboveground dry weight biomass to determine the least 

resource-intensive method. Data were collected in the six agencies using a modified 

FIREMON plot layout that included plot description (PD), fuel loading (FL), and cover 

frequency (CF). Additionally, grasses were clipped and weighed on ten 20 inch X20 inch 

frames per plot. Analyses were performed on the plot data to calculate the dry weight 

biomass of each plot using three common methods. The first method, considered to 

be the most accurate, calculated biomass using the ECODATA clip-and-weigh (CW) 

protocol where the dry weight of the vegetation is converted to pounds/acre based 

on the frame size of 20 inches X 20 inches. The second method used the FIREMON 

bulk density constant of 0.8 kg/m3, which is multiplied by average height and percent 

cover of the vegetation and was applied to the FL and CF data. Last, a regional bulk 

density constant was established using the CW data. The study then compares the 

accuracy of both the FIREMON bulk density constant and the regional constant to 

the CW method. The results of this study provide a regional bulk density constant that 

can be used to generate accurate biomass calculations, which eliminates the need for 

the resource-intensive CW method.

Introduction

Proper management of grasses through the use of fire and other methods promotes healthy grassland that is free from nonnative, invasive shrub 

and tree species encroachment. Accurate estimations of aboveground dry 

weight biomass are crucial to the management of native grassland and prairie 

ecosystems. Fire, as a management tool, relies on fuel readily available for 

consumption. When the biomass of herbaceous vegetation reaches a desirable 

level, managers are able to effectively use prescribed fire as a tool to promote 

new growth of native grasses and forbs. An increase in biomass often leads 

to increased litter production by herbaceous vegetation which in turn causes 

a decrease in forgeable grass production rates; therefore, timing is critical 

when burning grasslands. 

Gathering biomass data is important for predicting the most effective 

time to use prescribed fire as a tool and to help explain postfire effects for a 

given fuel treatment. However, the gathering of this data is often time- and 

Calculating Accurate Aboveground Dry 

Weight Biomass of Herbaceous Vegetation 

in the Great Plains: A Comparison of Three 

Calculations to Determine the Least Resource 

Intensive and Most Accurate Method

Ben Butler1

In: Butler, Bret W.; Cook, Wayne, 

comps. 2007. The fire environment—

innovations, management, and policy; 

conference proceedings. 26-30 March 

2007; Destin, FL. Proceedings 

RMRS-P-46CD. Fort Collins, CO: 

U.S. Department of Agriculture, 

Forest Service, Rocky Mountain 

Research Station. 662 p. CD-ROM.

1 P rog r a m M a n ager, St udent 

Conservation Association, Boise, ID. 

bbutler@thesca.org
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 294

resource-intensive, leading to a decrease in accurate measurements for a given 

management area. The clip-and-weigh (CW) method is the most time consuming; however, it yields the most accurate biomass value. The FIREMON 

cover frequency (CF) and fuel load (FL) methods allow data collection crews 

to make estimations of cover and height to be recorded for calculations. These 

estimations allow the crew to function at a much faster pace, which in turn 

results in less consumption of time and resources. A field comparison by the 

data collection crew used to gather the data for this study shows that the CW 

method uses 100 percent more time to derive a biomass value when compared 

to the FIREMON CF and FL methods.

The overall goal of this study is to generate a regional bulk density constant that can be used to calculate accurate aboveground dry weight biomass 

values. The regional constant was created in an effort to rely more heavily 

on the CF and FL methods outlined in the FIREMON methods (Lutes and 

others 2006) as the field protocol for generating biomass values within the 

Great Plains region of the Bureau of Indian Affairs (BIA).

Study Sites

Study sites were in the Great Plains region of the BIA in North Dakota, 

South Dakota, and Nebraska (fig. 1). Six reservations were sampled throughout summer 2006 by placing plots within nine preestablished burn units 

(table 1). Plot locations were determined randomly using a standard GIS 

protocol to ensure minimal bias. All plots were placed in burn units that 

consisted of the mixed grass prairie vegetation type.

Calculating Accurate Aboveground Dry Weight Biomass of Herbaceous Vegetation in the Great Plains: A Comparison of Three Calculations... Butler

Figure 1—Map showing the Great Plains Region of the BIA and the reservations sampled.
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 295

Field Measurements

Field sampling was performed by a team of four Student Conservation 

Association (SCA) interns and one SCA field staff member. The team was 

assisted by one or more Tribal staff members who provided local expertise. 

All team members were trained in standard FIREMON and ECODATA 

methods to ensure consistency in data collection techniques.

The plot design employed the FIREMON cover frequency (CF) and fuel 

load (FL) methodologies (Lutes and others 2006) and the ECODATA clipand-weigh (CW) methodologies (Jensen and others 1993). CF and CW data 

were collected using a baseline running in an upslope direction from plot 

center and transects running perpendicular to that baseline across the slope 

(fig. 2). There were five transects running from the baseline upon which 

quadrats were located to sample vegetation data. Each transect contained five 

CF quadrats located on the upslope side of the line. Data were then collected 

in each frame and included cover estimates and the average height of each 

species rooted in the frame.

Calculating Accurate Aboveground Dry Weight Biomass of Herbaceous Vegetation in the Great Plains: A Comparison of Three Calculations... Butler

Table 1—Summary of plots sampled by reservation and corresponding burn unit.

Reservation Burn unit Number

of plots

CF

quadrats

FL veg. 

cylinders

CW

quadrats

Crow Creek Carpenter 8 200 32 80

Fort Berthold North School 8 200 32 80

Wild Boar Ridge 6 150 24 60

Omaha 600/599 8 200 32 80

Altmt. 125 5 125 20 50

S. Harlan 3 75 12 30

Winnebago 148-149 4 100 16 40

Ponca East Pen 13 325 52 130

Santee ST Bar 17 425 68 170

Totals 72 1800 288 720

Figure 2—Plot design showing location and number of clip-and-weight quadrats and 

cover frequency quadrats.
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 296

Five CW quadrats were placed on the down slope side of the first and fifth 

transect. Herbaceous vegetation was clipped in each frame, and the average 

height of the vegetation was recorded. Tree and shrub species were not clipped 

to ensure the sample was comparable to the CF and FL methods.

The FL data were collected along transect lines running from plot center. 

Each transect followed one of the cardinal compass directions and contained 

two sampling cylinders. The sampling cylinders measured 6 inches in diameter 

and height and were located at the 45 foot and 75 foot mark on the transects 

(fig. 3). The average height of dead and live herbaceous vegetation, and the 

total cover were recorded for each cylinder using FIREMON cover classes 

(Lutes and others 2006).

Figure 3—Plot design showing location and number of fuel load vegetation 

cylinders.

Data Analysis and Exploration

Weights were recorded for each CW quadrat sampled throughout the study 

area. Each sample was dried for 1 week or until the sample weight stabilized 

and then weighed to the nearest 0.1 gram. The dry gram weights were converted to pounds by multiplying by 0.0022 resulting in a pounds/20 inch 

x20 inch frame. Next, the values were multiplied by a 15681.6 conversion 

factor to generate a resulting tons/acre value for each quadrat. The individual 

quadrat biomass values were then averaged for each plot to generate a biomass 

value recorded in tons/acre.

FL vegetation cylinders were used to calculate the biomass values of herbaceous vegetation per plot. The FIREMON v. 2.1.2 software was used to 

perform this calculation (Lutes and others 2006). The software generated a 

Calculating Accurate Aboveground Dry Weight Biomass of Herbaceous Vegetation in the Great Plains: A Comparison of Three Calculations... Butler
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biomass value for both the live and dead herbaceous component and these 

values were summed to generate a total biomass value for each plot. Again, 

the individual quadrat biomass values were averaged, resulting in an average 

biomass value for the study area recorded in tons/acre.

For the CF frames a biomass value was calculated for each herbaceous species inventoried by using the FIREMON bulk density equation B=H*C*BD 

(where H = average height, C = average cover, BD = bulk density (.8 kg m3), 

and B = biomass) (Lutes and others 2006). The biomass values of each 

individual species rooted in the quadrat were then summed resulting in 

a kg/m2 biomass value for the plot. The values were then multiplied by a 

4.46 conversion factor to generate a resulting tons/acre biomass value for 

each plot. The plot values were then averaged to generate a biomass value 

for the study area.

The FIREMON biomass equation (B=H*C*BD) was also used to generate 

a regional bulk density constant. The CF data were used to calculate the values for the equation. The cover estimates for each plot were summed and the 

plot totals were averaged resulting in 12 percent as the average cover estimate 

for the study area. The height estimations from the CF data were averaged 

resulting in 0.48m as the average height of vegetation in the study area. Finally 

the average biomass of 0.397 kg/m2 was determined from the CW data. The 

results produced a regional bulk density constant of 6.89 kg/m3.

The regional bulk density constant was then used in the FIREMON biomass equation (B = H*C*BD) and calculations were performed on the CF 

and FL (resulting biomass values noted as CF-R, FL-R). Biomass values for 

the CF plots were calculated for each species rooted in a plot quadrat. The 

individual biomass values were then summed resulting in a biomass value 

for each plot. Calculations were also performed on the FL data set using 

the regional bulk density constant. Two biomass values were calculated for 

each plot (live herbaceous and dead herbaceous) using the FIREMON bulk 

biomass equation. The two biomass values were then summed to calculate 

the total biomass of each plot. 

Results and Discussion

The relationship between CW, CF, and FL biomass values was consistently 

poor for each burn unit in the study area. The CW method produced an average of 1.773 tons/acre for the entire study area with the CF and FL methods 

averaging 0.325 and 0.308 tons/acre respectively (fig. 4). There was noted to 

be an average difference of 1.448 tons/acre difference between the CW and CF 

method and a 1.462 tons/acre difference between the CW and FL methods. 

The FIREMON bulk density constant of 0.8 kg/m3 therefore proved to be 

an inaccurate constant when compared to the CW data set. 

A comparison of the biomass values calculated using the regional bulk density constant was also performed. The average biomass values were calculated 

for the CF-R and FL-R datasets and were noted to be 2.655 tons/acre and 

1.50 tons/acre, respectively (fig. 5). When compared to the biomass value 

of 1.773 recorded from the CW method the relationship proved to be much 

improved.

It was determined that further investigation of the poor performance of the 

FIREMON method’s data set was needed to confirm the CW values. The three 

average biomass values (CF, FL, CW) were compared to the Natural Fuels 

Photo Series (Ottmar and Vihnanek 1999). Volume V was used as a compariCalculating Accurate Aboveground Dry Weight Biomass of Herbaceous Vegetation in the Great Plains: A Comparison of Three Calculations... Butler
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 298

son to validate the CW biomass value. The northern tall grass prairie photo 

series was used and the area was determined to be most closely related to the 

TP 5 photo. The photo series provides a total biomass value of 1.718 tons/acre 

when subtracting the surface fuel and shrub components. This value compared 

well with the average CW biomass of 1.773 for the reservation, however, when 

compared to the CF and FL biomass values of 0.325 and 0.308 respectively, 

the relationship proved once again to be poor.

Cover estimates were examined as a possibility of the seemingly low biomass 

values yielded through the CF and FL methods. An average of 12 percent 

herbaceous vegetation cover was recorded by the field crew for the study 

area. Examination of photo documentation from the field crew solidified 

their cover estimates for each quadrat (fig. 6). Crew precision did not prove 

to be a factor in the FIREMON biomass calculations. 

The bulk density constant used in the FIREMON calculations (0.8 kg/m3) 

was questioned as a potential influence in the low biomass values. This constant 

was compared to Brown’s Bulk Densities of Nonuniform Surface Fuels and 

Their Application to Fire Modeling (Brown 1981), which lists a bulk density 

constant of 0.8 kg/m3 for upright surface strata located in the grass-shrub 

vegetation type. Being that the constant used in the FIREMON calculations 

was the same, the constant was considered to be accurate. Both constants, 

however, rely on forest vegetation types and do not necessarily represent the 

mixed grass prairie ecosystem. 

Figure 5—Comparison of the CW method to the CF-R and FL-R.

Figure 4—Comparison of the CW method to the FL and CF.

Calculating Accurate Aboveground Dry Weight Biomass of Herbaceous Vegetation in the Great Plains: A Comparison of Three Calculations... Butler
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The excellent relationship between the FL-R value and the CW value proves 

to be one of significance and it was therefore determined to be the most 

accurate, least time consuming method of producing biomass values.

Summary and Conclusions

The data clearly show a poor performance of the 0.8kg/m3 bulk density 

constant when applied to the FIREMON CF and FL. Exploration into possible causes of this poor performance leads to the fact that national bulk 

density constants make poor predictors of actual biomass values when reduced 

to a smaller scale. This is noted in the loose relationship between the CW, 

CF, and FL methods when applying the national bulk density constant. The 

creation of local constants is essential to calculating accurate biomass values 

for a given ecosystem.

Accuracy of data collection crews also proves important when determining 

biomass in grasslands. The creation of a regional constant cannot be completed 

without accurate cover and height estimates, and therefore crew calibration 

is necessary in the early stages of sampling design. 

The regional bulk density constant of 6.89 kg/m3 proves to be a sound 

value and displayed a consistent relationship with the CW data when applied 

to the FL data set. Further analysis will be performed on the data set to 

examine the loose relationship between the CF and CW methods when applying the regional bulk density constant. Additional comparisons will also 

be performed in the future with the project set to be reexamined throughout 

summer 2007, with the overall goal still being to provide the most accurate 

bulk density constant possible for use by fire professionals throughout the 

Great Plains region of the BIA.

Acknowledgments

I thank Duncan Lutes and John Caratti for their advice and assistance 

during the project development and data analysis stages. The project was 

greatly enhanced by Morgan Beverage and all of the coordinators located in 

the Great Plains region and I thank them for their continued support. I also 

thank the members of the 2006 SCA FIREMON Great Plains crew, under 

the direction of Thomas Hanson, for their tireless data collection efforts in 

the field. Finally, I thank Jill Kolodzne and Mary Taber for their support and 

funding of this project from its conception to the final results.

Figure 6—Documentation showing quadrat before clipping (left) and after 

clipping (right).

Calculating Accurate Aboveground Dry Weight Biomass of Herbaceous Vegetation in the Great Plains: A Comparison of Three Calculations... Butler
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 300

References

Brown, J.K. 1981. Bulk Densities of Nonuniform Surface Fuels and Their Application 

to Fire Modeling. Forest Science. Vol. 27. No. 4: 667-683. 

Jensen, M.E.; Hann, W.; Keane, R.E.; Caratti, J.; Bourgeron, P.S. 1993. 

ECODATA—A multiresource database and analysis system for ecosystem 

description and evaluation. In: M.E. Jensen and P.S. Bourgeron, ed., Eastside 

Forest Ecosystem Health Assessment, Vol. II, Ecosystem Management: Principles 

and Applications. National Forest System Information Report. Washington, DC: 

U.S. Department of Agriculture, Forest Service: 249-265.

Lutes, D.C.; Keane, R.E.; Caratti, J.F.; Key, C.H.; Benson, N.C.; Sutherland, S.; 

Gangi, L.J. 2006. FIREMON: The fire 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.

Ottmar, R.D.; Vihnanek, R.E. 1999. Stereo photo series for quantifying natural 

fuels, vol. V: red and white pine, northern tall grass prairie, and oak/hickory in 

the Midwest. PMS 834. Boise, ID: National Interagency Fire Center, National 

Wildfire Coordinating Group. 99 p.

Calculating Accurate Aboveground Dry Weight Biomass of Herbaceous Vegetation in the Great Plains: A Comparison of Three Calculations... Butler
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 301

Abstract—Much of the coniferous zones in the Western United States where fires 

were historically frequent have seen large increases in stand densities and associated 

forest fuels due to 20th century anthropogenic influences. This condition is partially 

responsible for contemporary large, uncharacteristically severe wildfires. Therefore, 

considerable effort is under way to reduce the potential for extreme wildfire behavior 

and effects, especially near communities, by manipulating canopy and surface fuels 

using mechanical thinning and/or prescribed burning. Treatment effectiveness, however, 

has been difficult to quantitatively assess, but methods are now available for estimating 

stand-level canopy and surface fuels with which fire behavior can be more accurately 

modeled and compared among different fuel treatments. The Sheafman Fuels Reduction Project was recently conducted by the Bitterroot National Forest incorporating 

various fuel treatments to reduce the probability of high intensity crown fire impacting 

a watershed adjacent to Pinesdale, MT. Detailed sampling of stand conditions was 

conducted before and after two treatments: overstory thinning with understory cutting, and understory cutting only. Both were followed by slash burning. Surface fuel 

loadings were estimated from intercept data, and canopy fuels were estimated from 

tree data using the FuelCalc program. Pre- and posttreatment fuels data were used to 

model surface and crown fire behavior using the NEXUS program. A comparison was 

made between fire behavior computed from treatment mean fuels data and individual 

plot fuels data. Results indicate that because surface fuels and estimated mid-flame 

wind speeds increased modestly with treatments, surface fire behavior was not greatly 

altered until 20-ft wind speeds exceeded 20 mph. Treatment effects on canopy base 

heights and canopy bulk densities within the small landscape determined the level of 

estimated treatment effectiveness. Both treatments reduced the probability of crown 

fire initiation by raising canopy base heights. The likelihood of crown fire spread was 

appreciably reduced only in the overstory thinning treatment. An examination of plot 

scale surface and crown fire behavior gave a more detailed assessment of treatment 

effectiveness than average treatment values.

Introduction

A large proportion of the forested regions in the Western United States, 

which historically were exposed to frequent, low intensity wildfire (Arno 

and others 1997; Everett and others 2000), are currently represented by 

altered stand structure and composition compared to pre-European settlement (Cooper 1960; Parsons and others 1979). An alteration of historical 

disturbance trends, from regular fire occurrence to general fire exclusion 

and tree harvesting, is the supposed cause for these forest changes. These 

changes are generally characterized by large numbers of trees, typically with 

a considerable proportion of shade tolerance (Hartwell and others 2000), 

Testing the Modeled Effectiveness of an 

Operational Fuel Reduction Treatment in a 

Small Western Montana Interface Landscape 

Using Two Spatial Scales

Michael G. Harrington1, Erin Noonan-Wright2, and Mitchell Doherty2

In: Butler, Bret W.; Cook, Wayne, 

comps. 2007. The fire environment—

innovations, management, and policy; 

conference proceedings. 26-30 March 

2007; Destin, FL. Proceedings 

RMRS-P-46CD. Fort Collins, CO: 

U.S. Department of Agriculture, 

Forest Service, Rocky Mountain 

Research Station. 662 p. CD-ROM.

1 Research Forester, U.S. Department 

of Agriculture, Forest Service, Rocky 

Mountain Research Station, Missoula, 

MT. mharrington01@fs.fed.us

2 Fire Ecologist and Forestry Technician, 

respectively, Systems for Environmental 

Management, Missoula, MT.
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 302

and often with significant amounts of dead vegetation litter (Pyne and others 

1993; Arno and others 2000). Therefore, across many low to mid-elevation 

forested landscapes, large amounts of both living and dead biomass have accumulated. Furthermore, wildland fires of uncharacteristically high intensity 

and severity are now common, in part because of this condition, and because 

communities and many recreational activities occur within or close to this 

forest type, the fire risk is high (Skinner and Chang 1996; Hardy 2005). In 

addition, because historically these ecosystems were defined by low to mixed 

severity rather than high severity systems, they have developed little resilience, 

and postfire recovery to some acceptable condition is uncertain (Barrett and 

others 1991; Brown and others 1994).

A large effort is currently under way to lessen this hazard by manipulating canopy and surface fuel characteristics to reduce the probability of severe 

wildfire behavior and impacts (USDA, USDI 2004; http://www.healthyforests.gov/accomplish-report_2004.pdf). Most fuel reduction treatments 

use mechanical thinning, prescribed burning, or a combination to reduce 

horizontal and vertical fuel quantities and homogeneity (Agee and Skinner 

2005). Ideally, areas with high probability of severe fire because of a fuel 

hazard can be delineated for active management consideration (GAO 2003). 

Whereas techniques for assessing surface fuel characteristics and surface fire 

behavior have been available for some time, canopy fuel treatment prescriptions and their assumed effects on crown fire initiation and spread have been, 

until recently, qualitatively assessed, typically by expert opinion. This was 

primarily due to the inability to accurately quantify canopy fuels and crown 

fire spread (Scott and Reinhardt 2001).

The Sheafman Fuels Reduction Project was recently conducted by the 

Bitterroot National Forest incorporating various canopy and surface fuels 

treatments in a dense forest condition to reduce the probability for high intensity crown fire impacting the watershed and community of Pinesdale, MT. 

This study evaluates pre- and posttreatment fuels and models the change in 

anticipated fire behavior following two treatments across this small, interface 

landscape. Predicted fire behavior is compared at two scales: the more traditional average treatment-level scale, and a more detailed plot-level scale.

Methods

Treatment History

In May 2001, The Bitterroot National Forest signed a Decision Notice 

to conduct the Sheafman Fuels Reduction Project within a 475-acre area 

adjacent to and west of private property in the community of Pinesdale, MT, 

with several hundred rural homes. The Purpose and Need statement from 

the document (Bitterroot National Forest 2001) reports:

Breakup the existing fuel continuity on National Forest System lands 

near the town of Pinesdale by reducing the density of trees. This will 

decrease the probability that a future wildland fire would develop into, 

or be sustained as, a stand replacing or crown fire, and increase the 

probability of being able to defend life and property within the wildland/urban interface from wildland fire.

Based on existing fuels and site conditions, a series of units were established 

by the National Forest for either prescribed burning only (not discussed in 

this paper), understory cutting with prescribed fire, or overstory thinning 

Testing the Modeled Effectiveness of an Operational Fuel Reduction Treatment in a Small Western Montana Interface... Harrington, Noonan-Wright, and Doherty
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 303

with understory cutting and prescribed fire. The silvicultural prescription 

for the overstory thinning treatment called for leaving a single or two-story 

semiopen stand with 50 to 80 trees per acre favoring ponderosa pine with 

a heavy reduction of understory trees. In the understory cutting treatment, 

trees less than 7 inches diameter breast height (dbh at 4.5 ft) were cut and 

piled for burning, mostly on slopes steeper than 45 percent, where equipment 

for overstory thinning could not safely operate.

Field Methods

In 2002, sample plots were established in six locations within the Sheafman treatment area. Five plots were established for sampling surface fuels, 

stand structure, and other site attributes in four locations and 10 plots in the 

remaining two locations for a total of 40 plots. Throughout the project area, 

the final decision between overstory thinning (OT) or understory cutting 

(UC) was made by the forest operator with the most liberal treatment, OT, 

preferred. Therefore, 32 plots received the OT treatment and eight received 

the UC treatment (fig. 1).

Figure 1—Examples of Sheafman 

fuel treatments: pre (a) and post 

(b) overstory thinning, and pre (c) 

and post (d) understory cutting. 

a

b

Testing the Modeled Effectiveness of an Operational Fuel Reduction Treatment in a Small Western Montana Interface... Harrington, Noonan-Wright, and Doherty
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 304

c

d

The first plot at each location was randomly located, and subsequent plots 

were systematically located along a previously selected azimuth and distance. 

At each plot, two or three 50-ft transects were laid out at previously chosen 

azimuths to measure surface fuel characteristics according to FIREMON 

protocols, version 2.1.1 (Lutes 2006) in which intercepts of down and dead 

woody fuels were counted by size class; canopy cover and height of shrub and 

herbaceous vegetation were measured; and forest floor depth measurements 

were made separating litter and duff. The intercept, canopy cover/height, 

and litter/duff depth data were used in standard algorithms to calculate loadings by fuel type. Note that posttreatment shrub characteristics could not be 

measured on 30 of 40 plots because of dormancy. Therefore, because shrub 

canopy cover and heights on the 10 measured plots were similar between 

pre- and posttreatment, pretreatment values were used in the posttreatment 

analysis for those 30 plots. Also at each plot, the following measurements 

were made for all trees > 4.5 ft tall within a 1/10th-acre circular subplot: 

species, dbh, height, height to live crown base, and crown class (dominant, 

codominant, intermediate, and suppressed). Few trees <4.5 ft tall occurred 

Testing the Modeled Effectiveness of an Operational Fuel Reduction Treatment in a Small Western Montana Interface... Harrington, Noonan-Wright, and Doherty
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 305

at this site. Tree data were used in the FUELCALC program, Beta version 

(Reinhardt and others 2006) to compute canopy fuel attributes, such as loading by fuel size class, canopy bulk densities, and canopy base heights. Crown 

class adjustment factors that were most appropriate for the Sheafman site were 

used in the calculations. Pretreatment sampling took place in 2002 and 2003, 

overstory thinning, understory cutting and pile burning occurred between 

2002 and 2004, and posttreatment sampling took place in 2004.

Fuel and Fire Behavior Modeling

Modeled fire behavior—namely surface fire spread rate and flame length 

along with the torching and crowning indices—was used to evaluate the 

pre- and posttreatment level of wildfire hazard and assess the effect of the 

fuel treatments. In this analysis, we compared pre- and posttreatment stand 

conditions, surface and canopy fuel characteristics, and the resulting fire behavior in the 32 plots with overstory thinning/understory cutting (OT) and 

the eight plots with understory cutting (UC) only. Fire behavior modeling 

was conducted at two scales for comparison: using average treatment-level 

fuels data and using individual plot-level fuels data. In the first method, the 

surface and canopy fuels data for all plots within a treatment were averaged 

resulting in one custom fuel model per treatment each for pre and post conditions. These single custom fuel models were used to predict single values 

for each surface and crown fire behavior attribute by treatment before and 

after thinning. This method is commonly reported when assessing treatment 

effectiveness (Raymond and Peterson 2005; Stephens and Moghaddas 2005). 

In the second method, custom fuel models were developed for each plot from 

plot-level surface fuel characteristics and canopy fuel attributes. These plotlevel models were used to estimate surface and crown fire behavior for each 

plot to assess a distribution of predicted fire response within a treatment. 

Therefore, this is an assessment of scale, examining surface and crown fire 

behavior at the larger treatment scale and at the smaller plot scale, which may 

be the fuel environment level at which fire actually responds. Fulé and others 

(2002) also reported that average stand conditions fail to represent canopy 

fuel variability, reducing the chance of predicting real crown fire behavior.

The 95th percentile fire weather and computed fuel moistures used in 

the wildfire scenario and representing a very high level of fire danger were 

retrieved using the Fire Family Plus 3.0 program with data from the Smith 

Creek Remote Automatic Weather Station (#242912) 7 miles north and at a 

similar elevation and aspect as the fuel treatment site. Data were analyzed for 

the 7 years the station has been in operation and covered May 1 to Oct. 30, 

2000 to 2006. The fuel moisture scenario used in fire behavior calculations 

for both pre- and posttreatment was 1-hr = 3 percent, 10-hr = 4 percent, 

100-hr = 6 percent, live woody = 70 percent, herbaceous = 30 percent, 

foliar = 100 percent. Because winds are inherently transient, they were modeled at four 20-ft wind speeds (10, 20, 30, and 40 mph) and across slope, 

which is the most likely wind direction in the Sheafman area. Stand height, 

crown ratio, and canopy cover were used to derive the wind adjustment factor 

for calculation of the mid-flame wind speeds (Albini and Baughman 1979). 

Both the treatment-level or plot-level custom surface fuel models, canopy fuel 

attributes, and slopes along with the 95th percentile computed fuel moisture 

values as well as the selected 20-ft wind speeds were used for fire behavior 

modeling at the treatment or plot level using the NEXUS 2.0 program (Scott 

1999). Note that, even though crown fire behavior values are computed by 

NEXUS, only fire behavior from surface fire is reported because the focus 

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of this assessment is whether or not crown fire will initiate and spread, not 

how intense the crown fire will be.

Crown fire terminology used in this paper comes from Scott and Reinhardt (2001):

	 •	 Passive crown fire occurs as individual or small groups of trees torch out 

but lateral crown fire spread is not necessary.

	 •	 Active crown fire occurs as the entire fuel complex from surface to canopy 

is burning and the crown fire is dependant on the surface fire.

	 •	 Conditional surface fire occurs when passive crown fire requirements are 

not met but crown fire spread requirements are.

	 •	 Torching index is the 20-ft wind speed at which crown fire activity can 

initiate.

	 •	 Crowning index is the 20-ft wind speed at which active crown fire is 

possible.

Results

Much of the Sheafman treatment area had been heavily logged, most likely 

a century earlier, leading to dense Douglas-fir stands that occupied the site 

at the beginning of this project. Before treatment, the overstory thinning 

plots (OT) averaged about 580 trees/acre and 140 ft2/acre basal area, with 

individual plots ranging from 230 to 1770 trees/acre (fig. 2). Tree density and 

basal area averaged just under 380 trees/acre and 125 ft2/acre in the understory cutting plots (UC) with a much narrower range than the OT plots.

Thinning reduced the average OT plot tree density by 87 percent (73 trees/

acre) and basal area by 63 percent (53 ft2/acre) compared to 59 percent (154 

trees/acre) and 17 percent (102 ft2/acre) reductions, respectively, for the UC 

plots (fig. 2). The relatively high stand densities resulted in substantial mean 

canopy bulk density (CBD) values, and the range of plot values was large, 

0.06 to 0.54 kg/m3 for the OT plots and 0.06 to 0.27 kg/m3 for the UC 

Figure 2—Box plot of trees per acre (a) and basal area (b) grouped by treatment, overstory thin (n=32) and understory 

cut (n=8) and status, pretreatment or posttreatment. The box portion of the box represents the interquartile range 

(25th to 75th percentile). The red line is the mean; the black line is the median. The lines extending from the bottom 

and top of the box represent the minimum and maximum values, respectively. Outliers are not shown.

a b

Testing the Modeled Effectiveness of an Operational Fuel Reduction Treatment in a Small Western Montana Interface... Harrington, Noonan-Wright, and Doherty
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plots (fig. 3). The canopy base height (CBH) mean was moderately high for 

pretreatment conditions, again with a large range for individual OT plots (4 

to 39 ft). Thinning in the OT plots resulted in a 74 percent average CBD 

reduction (from 0.20 to 0.05 kg/m3) and 88 percent average increase in CBH 

(from 17 to 32 ft). In the UC plots, mean CBD decreased by only 13 percent 

(from 0.14 to 0.12 kg/m3) and CBH increased by 58 percent (from 12 to 19 

ft) (fig. 3). A more detailed examination shows that all plots experienced an 

expected decrease in CBD with the exception of one UC plot which had no 

change. Postthinning CBD values ranged from 0.01 to 0.10 kg/m3 in the OT 

plots and 0.05 to 0.21 kg/m3 in the UC plots. CBH predictably rose in all 

plots, with increases ranging from 3 ft to over 40 ft with the most substantial 

increases occurring in the OT plots.

Surface fuel loadings were similar in the two treatments prior to thinning 

with fine dead fuels (1-, 10-, 100-hr, litter) averaging about 4 tons/acre with 

a three to four fold range in individual plot values (fig. 4). UC plot shrub 

fuel loads were roughly double those in the OT plots, but herbaceous fuels 

were equally low in both treatments. With thinning treatments, which were 

followed by pile burning, average fine fuel loads in the UC plots rose by 

about 30 percent to 5 tons/acre, whereas those in the OT plots increased by 

Figure 3—Box plot of canopy bulk density (a) and canopy base height (b) grouped by treatment, overstory 

thin (n=32) and understory cut (n=8) and status, pretreatment or posttreatment. The box portion of the plot 

represents the interquartile range (25th to 75th percentile). The red line is the mean; the black line is the 

median. The line extension represents the minimum and maximum values. Outliers are not shown.

a b

Figure 4—Box plot of downed, surface fuels including 

litter (needles, vegetative plant parts, and so forth) and 

1-hr, 10-hr, 100-hr woody fuels grouped by treatment, 

overstory thin (n=32) and understory cut (n= 8) and 

status, pretreatment or posttreatment. The box portion 

of the plot represents the interquartile range (25th to 75th 

percentile). The red line is the mean; the black line is the 

median. The line extension represents the minimum and 

maximum values. Outliers are not shown.

Testing the Modeled Effectiveness of an Operational Fuel Reduction Treatment in a Small Western Montana Interface... Harrington, Noonan-Wright, and Doherty
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 308

almost 70 percent to 8 tons/acre. These fuel loading increases were primarily due to added 10-hr and 100-hr woody biomass. Live herbaceous fuels 

changed little, and shrub loads were similar in the analysis, primarily because 

pretreatment shrub biomass was used for posttreatment in 30 of 40 plots as 

described in the methods.

Effective mid-flame wind speeds are also expected to increase with higher 

wind adjustment factors resulting from the more open posttreatment stand 

features. The wind adjustment factor as computed using Albini and Baughman 

(1979) averaged about 0.12 in both treatments before thinning but almost 

doubled (0.22) in the OT plots with thinning compared to only a 25 percent 

increase (0.15) in the UC plots. The range of wind adjustment factors across 

pretreatment OT plots was predicted to reduce a 20 mph, 20-ft wind to 2 to 

3 mph at mid-flame height but would increase to 3 to 6 mph after thinning. 

UC plot mid-flame wind speed range would also be 2 to 3 mph pretreatment 

and would only increase to 2 to 4 mph posttreatment.

To repeat from the Fuels and Fire Behavior Modeling section above, two 

methods were used to compute and summarize the fire behavior outputs. In 

the first method, treatment-level fire behavior values were computed from the 

NEXUS model using a single mean custom fuel model for each treatment, 

for pre and post conditions. The second method used plot-level outputs that 

were the distribution of fire behavior values from NEXUS-produced custom 

fuel models for each plot. Treatment-level predicted surface fire spread rates 

and flame lengths are compared in table 1 for pre- and postthinning for the 

two treatments under four wind speeds. Postthinning treatment-level spread 

rates and flame lengths were similar to prethinning values in the OT plots at 

lower wind speeds but increased more rapidly with increasing wind speeds. 

The ranges of plot-level spread rates and flame lengths broadened to higher 

maximums after treatment and at wind speeds higher than 20 mph.

Table 1—Rates of surface fire spread and flame lengths at four 20-ft wind speeds pre- and posttreatment 

in the Overstory Thinning and Understory Cutting treatments. Treatment-level (trt-level) values are 

computed from mean fuel loadings, slopes, and wind speeds. Plot-level values include the maximum 

and minimum values computed from individual plot fuel loadings, slopes, and wind speeds.

Rate of fire spread (chains/hour)

Overstory thinning Understory cutting

Pretreatment Posttreatment Pretreatment Posttreatment

20-ft

wind

speed

(mph) Trt-level Plot-level Trt-level Plot-level Trt-level Plot-level Trt-level Plot-level

10 6 2-23 5 2-24 15 3-31 11 4-23

20 7 3-39 9 4-53 17 6-34 14 7-30

30 10 4-52 16 8-96 22 9-48 20 11-45

40 14 6-73 25 12-148 30 14-67 29 17-66

Flame length (feet)

Overstory thinning Understory cutting

Pretreatment Posttreatment Pretreatment Posttreatment

20-ft

wind

speed

(mph) Trt-level Plot-level Trt-level Plot-level Trt-level Plot-level Trt-level Plot-level

10 3 2-10 3 2-10 6 2-9 5 2-8

20 4 2-10 4 2-11 6 3-9 6 3-9

30 4 2-11 5 2-14 7 3-10 7 4-11

40 5 2-12 6 3-18 8 4-12 8 5-13

Testing the Modeled Effectiveness of an Operational Fuel Reduction Treatment in a Small Western Montana Interface... Harrington, Noonan-Wright, and Doherty
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 309

The treatment and plot-level rates of fire spread and flame lengths in the 

UC plots were predicted to change little as a result of the thinning (table 1). 

Looking at individual plots, figure 5 shows that a general increase in fire 

behavior after treatment is predicted for a growing number of plots as wind 

speed rises. With winds at or above 20 mph, rates of fire spread and flame 

lengths after the OT treatment are expected to increase in over 60 percent 

of the plots. Increases in fire spread and flame length are predicted to occur 

posttreatment in over 50 and 35 percent of the UC plots, respectively, with 

wind speeds 30 mph and greater.

The treatment-level torching index for plots in the OT treatment was 

quite high for both pre- and posttreatment, exceeding 80 mph (table 2). 

Plot-level indices were expansive because of the broad range of CBH values 

(fig. 3). For the UC treatment, the treatment-level torching index rose from 

27 mph pretreatment to almost 50 mph posttreatment. The treatment-level 

Table 2—Torching and crowning indexes in miles/hour winds speed pre- and posttreatment for the overstory thinning 

and understory cutting treatments. Treatment-level (trt-level) values are computed from mean fire behavior 

values, CBD, and CBH. Plot-level values include the maximum and minimum values computed from individual 

plot fire behavior values, CBD, and CBH.

Index Overstory thinning Understory cutting

(mph) Pretreatment Posttreatment Pretreatment Posttreatment

Trt-level Plot-level Trt-level Plot-level Trt-level Plot-level Trt-level Plot-level

Torch 89 0-469 118 21-392 27 0-95 49 10-99

Crown 12 4-32 34 21-89 17 8-32 19 11-34

Figure 5—Percent of plots with increasing surface rates of spread (ROS) and flame 

lengths (FML) following the overstory thinning and understory cutting treatments.

Testing the Modeled Effectiveness of an Operational Fuel Reduction Treatment in a Small Western Montana Interface... Harrington, Noonan-Wright, and Doherty
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 310

crowning index almost tripled in the OT plots from 12 to 34 mph with thinning as did the range, but changed minimally in the UC treatment. This 

reflects the relative changes in CBD by treatment shown in figure 3. Figure 6 

shows the percent of plots with torching potential under four wind speeds. 

Throughout the tested wind speed range, 6 to 28 percent of the OT plots 

were predicted to torch before treatment, which was reduced to a maximum 

of 13 percent at 40 mph winds after thinning. Between 25 and 75 percent 

of the UC plots were initially predicted to torch with 10 to 40 mph winds. 

These values were reduced approximately in half with treatments, indicating 

that some crown fire initiation was still predicted at all wind speeds in the 

UC plots after thinning.

Combining active and conditional crown fire potential, 31 percent of the 

OT plots were predicted to support crown fire spread at a 20-ft wind speed 

of 10 mph before treatment, up to 100 percent with winds at 40 mph (fig. 7). 

After thinning, no plots were expected to support crown fire at winds below 

20 mph, but with 30 to 40 mph winds, 34 and 63 percent of plots were predicted support crown fire. In the UC plots before treatment, the range of 

plots predicted to sustain crown fire spread ranged from 13 to 100 percent 

within the tested wind speed range. With understory tree cutting, these 

percentages decreased by only 13 percent with winds at or below 30 mph, 

and remained at 100 percent with a 40 mph wind speed.

Figure 6 —Percent of plots in the 

over s tor y t hinning ( n = 3 2 ) a n d 

understory cutting (n=8) treatments in 

which crown fire initiation is predicted 

before and after treatment at four wind 

speeds.

Figure 7—Percent of plots in the 

over s tor y t hinning ( n = 3 2 ) a n d 

understory cutting (n= 8) treatments 

in which crown fire spread is predicted 

before and after treatment at four wind 

speeds.

Testing the Modeled Effectiveness of an Operational Fuel Reduction Treatment in a Small Western Montana Interface... Harrington, Noonan-Wright, and Doherty
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 311

Summary and Discussion

The purpose of the Sheafman Fuels Reduction Project as stated in the 

Decision Notice (see Methods section) was to breakup fuel continuity through 

tree density reduction thereby lessening the probability of stand replacement 

crown fire. The monitoring and analysis reported here quantified the surface 

fuel status and the assumed canopy fuel continuity, determined the effect of 

thinning on fuel structure, and modeled the predicted fire behavior before 

and after fuel reduction treatments. We presented average values for surface 

fuel loadings and canopy fuel structure before and after thinning and also 

treatment-level predicted surface and crown fire behavior based on these 

average fuel characteristics and a specified wildfire weather/fuel moisture 

scenario for the two treatments implemented in the project area. These average values offer an assessment of general fuel conditions within treatments 

as well as broad generalization of predicted fire behavior, which is standard 

reporting procedure.

An additional evaluation features a comparison of pre- and posttreatment 

fire behavior on a plot scale with the assumption that fire response is more 

closely tied to small scale surface and canopy fuel characteristics than to larger 

stand scale average conditions and therefore, perhaps, more informative. 

Because surface fire behavior at any particular site depends on the quality and 

quantity of available fuels, spatial variability in fuels should lead to variable fire 

behavior. Further, because surface fire behavior and canopy base height are 

the primary factors controlling crown fire initiation, quantifying the plot-level 

coincidence of these two factors should provide a more accurate assessment 

of the spatial probability of torching than do treatment averages. Additionally, plot-level canopy bulk density values, which may exceed or fall short of 

wind speed thresholds for crown fire spread, should provide a more detailed 

picture of the potential variability of crown fire behavior across a treatment 

area than does an average CBD value, representing the entire treatment.

The initiation of crown fire is determined by the relationship between 

surface fire intensity and adjoining canopy base height. The fire intensity 

input variables that were affected by the thinning treatments were primarily 

fuel loadings and mid-flame wind speed through the wind adjustment factor. 

Average surface fuel loading (fig. 4) and wind adjustment factor increased by 

about 70 and 100 percent in the OT plots, respectively, and 30 and 25 percent 

and the UC plots, respectively. However, predicted posttreatment spread rates 

and flame lengths, as indicators of fire intensity, remained similar to pretreatment values until 20-ft wind speeds exceeded 20 mph in the OT plots and 

at all wind speeds in the UC plots (table 1). The torching index computed 

from treatment-level fire intensity and CBH was very high for the OT plots 

before (89 mph) and after (118 mph) treatment (table 2), implying that 

crown fire initiation in either case would be highly unlikely. However, with 

a plot-level assessment, there clearly are individual plots predicted to initiate 

crown fire at lower wind speeds than predicted by treatment-level values. 

Before treatment, the percent of plots with torching potential ranged from 

6 percent at 10 mph winds to 28 percent at 40 mph (fig. 6). After treatment, 

torching was not predicted in OT plots with winds below 30 mph but could 

be expected in 13 percent with 40 mph winds. In the UC plots, the torching 

index computed from treatment-level fire intensity and CBH was moderately 

low at 27 mph before treatment, but rose to 49 mph with thinning. At the 

plot scale, torching was predicted in 25 percent of the plots at 10 mph and 

up to 75 percent at 40 mph (fig. 6). With thinning, these percentages were 

Testing the Modeled Effectiveness of an Operational Fuel Reduction Treatment in a Small Western Montana Interface... Harrington, Noonan-Wright, and Doherty
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 312

reduced in half, 13 to 38 percent, with the same wind speeds, indicating that 

the torching potential, even though lessened, is still apparent because CBH 

values were significantly raised in only a portion of the plots relative to variable plot-level changes in surface fire intensity.

Crown fire spread is primarily determined by canopy bulk density and 

wind speed once the transition from surface to crowns has occurred (active 

crown fire) or once the crown fire has moved in from an adjacent stand 

(conditional surface fire) (Scott and Reinhardt 2001). Using average CBD 

values for each treatment, the crowning index for the OT plots is predicted to 

rise from 12 mph to 34 mph (table 2) with thinning which reduced CBD by 

almost 74 percent (fig. 3). In the UC plots, the modest 13 percent decrease in 

average CBD resulted in an inconsequential change in crowning index from 

17 to 19 mph. Figure 7 illustrates percent of plots in which sustained crown 

fire spread is predicted as either active or conditional crown fire under four 

wind speeds. With 10 mph winds, a modest percentage of pretreatment UC 

and OT plots, 10 and 30 percent, respectively, would have sufficient CBD to 

support crown fire spread. With incrementally higher winds, the percentage 

of pretreatment plots with expected crown fire spread increases rapidly, including 90 percent of the plots or greater in both treatments at 30 to 40 mph 

winds. Because the UC plots had significantly less CBD reduction than the 

OT plots, the former continues to support high proportion of crown fire after 

thinning, with 40 to 100 percent of plots still capable of crown fire spread 

at wind speeds between 20 and 40 mph. No crown fire spread is predicted 

in the posttreatment OT plots until 30 mph wind speeds are reached, but 

63 percent still have that potential with 40 mph winds.

It should be pointed out that when a fuel reduction treatment is implemented the expected result is an anticipated reduction in fire behavior. 

Figure 5 shows a predicted increase in surface fire behavior after treatment 

due to increased fuel loads and mid-flame wind speeds. This seems to be a 

reasonable tradeoff for the significant reduction in probability of crown fire 

initiation and spread (fig. 6 and 7), since management options for response 

to crown fire are greatly limited compared to surface fire.

In their concluding remarks, Scott and Reinhardt (2001) state: “Spatial 

and temporal variability in the fire environment leads to higher crown fire 

activity than predicted using average conditions.” The sampling and analysis 

reported here captures the spatial variability of a portion of the fire environment, specifically, surface and canopy fuel structure, slope, and predicted 

mid-flame wind speed. Using this spatially variable data provides more detail 

for predicted fire behavior than that from average values. For example, where 

crown fire initiation was not predicted from treatment-level values, a certain 

percentage of plots were predicted to torch based on that smaller scale assessment. This should give managers of the Sheafman Fuels Reduction Project 

a better appraisal of crown fire probability. It should be noted that the appropriate size of a plot-scale fuels assessment has not been determined, and 

the plot size used in this analysis is, at this point, only for illustration.

References

Agee, James K.; Skinner, Carl N. 2005. Basic principles of forest fuel reduction 

treatments. Forest Ecology and Management 211: 83-96.

Albini, F.A.; Baughman, R.G. 1979. Estimating wind speeds for predicting wildland 

fire behavior. Res. Pap. INT-221. Ogden, UT: U.S. Department of Agriculture, 

Forest Service, Intermountain Forest and Range Experiment Station. 12 p.

Testing the Modeled Effectiveness of an Operational Fuel Reduction Treatment in a Small Western Montana Interface... Harrington, Noonan-Wright, and Doherty
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 313

Arno, S.F.; Smith, H.Y.; Krebs, M.A.1997. Old growth ponderosa pine and western 

larch stand structures: influences of pre-1900 fires and fire exclusion. Res. Pap. 

INT-RP-495, Ogden, UT: U.S. Department of Agriculture, Forest Service, 

Intermountain Research Station. 20 p.

Arno, Stephen F.; Parsons, David J.; Keane, Robert E. 2000. Mixed-severity fire 

regimes in the Northern Rocky Mountains: consequences of fire exclusion and 

options for the future. In: Cole, David N.; McCool, Stephen F.; Borrie, William T.; 

O’Loughlin, Jennifer, comps. Wilderness science in a time of change conference, 

Volume 5: Wilderness ecosystems, threats, and management; May 23-27, 1999. 

Proceedings RMRS-P-15. Ogden, UT: U.S. Department of Agriculture, Forest 

Service, Rocky Mountain Research Station: 225-232.

Barrett, S.W.; Arno, S.F.; Key, C.H. 1991. Fire regimes of western larch-lodgepole 

pine forests in Glacier National Park, Montana. Canadian Journal Forestry 

Research 21:1711-1720.

Bitterroot National Forest. 2001. Sheafman Fuel Reduction Project: Environmental 

Assessment. On file: Stevensville Ranger District, 88 Main, St., Stevensville, 

MT. 130 p.

Brown, J.K.; Arno, S.F.; Barrett, S.W.; Menakis, J.P. 1994. Comparing the prescribed 

natural fire program with presettlement fires in the Selway-Bitterroot Wilderness. 

Int. Journal of Wild-land Fire 4:157-168.

Cooper, C.F. 1960. Changes in vegetation, structure and growth of southwestern 

pine forest since white settlement. Ecological Monographs 30: 129-164.

Everett, R.L.; Schellhaas, R.; Keenum, D.; Spurbeck, D.; Ohlson, P. 2000. Fire 

history in the ponderosa pine/Douglas-fir forests on the east slope of the 

Washington Cascades. Forest Ecology and Management 129: 207-225.

Fulé, Peter Z.; Covington, W.; and others. 2002. Comparing ecological restoration 

alternatives: Grand Canyon, Arizona. Forest Ecology and Management 170: 

19-41.

Hardy, Colin C. 2005. Wildland fire hazard and risk: Problems, definitions, and 

context. Forest Ecology and Management 211: 73-82.

GAO. 2003. Additional actions required to better identify and prioritize lands 

needing fuels reduction. Report to Congressional Requesters GAO-03-805. 

Washington, DC: United States General Accounting Office.

Lutes, D.C. 2006. FIREMON: fire 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.

Parsons, D.J.; DeBenedetti, S. 1979. Impact of fire suppression on a mixed-conifer 

forest. Forest Ecology and Management 2:21-33.

Pyne, S.J.; Andrews, P.L.; Laven, R.D. 1996. Introduction to wildland fire. New 

York: John Wiley and Sons. 769 p.

Raymond, Crystal L.; Peterson, David L. 2005. Fuel treatments alter the effects of 

wildfire in a mixed-evergreen forest, Oregon. Canadian Jour. Forest Research 

35: 2981-2995.

Reinhardt, E.; Lutes, D.; Scott, J. 2006. FuelCalc: A method for estimating fuel 

characteristics. In: Andrews, P.; Butler, B., comps. 2006. Fuels Management—

How to Measure Success: conference proceedings. 28-30 March 2006; Portland, 

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

Service, Rocky Mountain Research Station: 273-282.

Scott, J.H. 1999. NEXUS: a system for assessing crown fire hazard. Fire Management 

Notes 59(2): 20-24.

Scott, J.H.; Reinhardt, E. 2001. Assessing crown fire potential by linking models 

of surface and crown fire behavior. Res. Pap. RMRS-RP-29. Fort Collins, CO: 

U.S. Department of Agriculture, Forest Service, Rocky Mountain Research 

Station. 59 p.

Testing the Modeled Effectiveness of an Operational Fuel Reduction Treatment in a Small Western Montana Interface... Harrington, Noonan-Wright, and Doherty
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 314

Skinner, C.N.; Chang, C. 1996. Fire regimes, past and present. In: Sierra Nevada 

Ecosystem Project: Final report to Congress, vol. II chap. 38. Davis, CA: 

University of California, Centers for Water and Wildland Resources.

Stephens, Scott L.; Moghaddas, Jason J. 2005. Experimental fuel treatment impacts 

on forest structure, potential fire behavior, and predicted tree mortality in a 

California mixed conifer forest. Forest Ecology and Management 215: 21-36.

USDA, USDI. 2004. Achieving Healthy Forests and Rangelands: National Fire Plan 

Performance Update 2004. Washington, DC: U.S. Department of Agriculture 

and U.S. Department of the Interior. http://www.healthyforests.gov/accomplishreport_2004.pdf 

Testing the Modeled Effectiveness of an Operational Fuel Reduction Treatment in a Small Western Montana Interface... Harrington, Noonan-Wright, and Doherty
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 315

Abstract—Ponderosa pine forests in the Eastern Cascades of Washington support 

dense, overstocked stands in which crown fires are probable, owing to postsettlement 

sheep grazing, logging, and fire exclusion. In 1991, the Okanogan-Wenatchee National 

Forests began to apply long-term management techniques to reverse postsettlement 

changes in ponderosa pine forests. For 9 years, the effects of thinning and burning in 

a ponderosa pine/pinegrass forest were evaluated, using prescribed fire in fall 1997 in 

forest stands that had been thinned in 1996. Thinning and burning had little effect on 

the canopy layer of ponderosa pine, western larch, and Douglas-fir, but small grand 

fir trees, and nearly half of the saplings of all species, were killed. Shrubs in the middle 

forest layer were top-killed, but resprouted during the first postfire growing season, and 

increased dramatically after 3 years and 9 years. Frequency and cover maintained or 

increased for species in the lower forest layer in postfire years 1, 3, and 9. The thinning and fire treatments reduced the middle layer of small trees and shrubs in the first 

postfire year, but by the third and ninth postfire years tree seedlings, especially grand 

fir and ponderosa pine, and small shrubs were abundant in the understory. Thinning 

trees and removing excess fuels, coupled with low intensity late season prescribed 

burns, offers a promising management strategy for restoring the presettlement structure 

of the ponderosa pine/pinegrass community in Beehive Forest.

Introduction

Historically, ponderosa pine (Pinus ponderosa) dominated forests depended on periodic fire to maintain or increase the frequency and density of 

low-growing plants (Armour and others 1984; Campbell and others 1977; 

Covington and others 1997; Leege and Hickey 1971; Old 1969; Ruha and 

others 1996; Tveten and Fonda 1999; Wilson and Shay 1990). In pre1900 

fire regimes of the Eastern Cascade Mountains, Washington, ponderosa 

pine forests supported frequent low intensity fires (Agee 1993; Everett and 

others 1996, 2000; Weaver 1943, 1951, 1961, 1967). The periodic surface 

fires in these dry forests maintained an open, parklike structure of expansive, 

grassy understories and uneven-aged overstories formed by even-aged patches 

of trees <0.4 ha in area (fig. 1; Harrod and others 1999; Everett and others 1996, 2000). These disturbances resulted in a mosaic of pine stands on 

the landscape (Everett and others 2000), a pattern that was disrupted only 

when the patches became senescent or when diseased or insect-infested trees 

torched (Agee 1994).

The presettlement fire regime in the Eastern Cascades produced pine 

forests with three layers. Mature pine trees formed the upper layer, the discontinuous canopy of the forest that ranged from 15 to 30 m high (fig. 1). 

In contrast to forests in wetter regions, dry forests in the Eastern Cascades 

Role of Fire in Restoration of a Ponderosa 

Pine Forest, Washington

Richy J. Harrod1, Richard W. Fonda2, and Mara K. McGrath3

In: Butler, Bret W.; Cook, Wayne, 

comps. 2007. The fire environment—

innovations, management, and policy; 

conference proceedings. 26-30 March 

2007; Destin, FL. Proceedings 

RMRS-P-46CD. Fort Collins, CO: 

U.S. Department of Agriculture, 

Forest Service, Rocky Mountain 

Research Station. 662 p. CD-ROM.

1 Deputy Fire Management Officer, 

U.S. Department of Agriculture, 

Forest Service, Okanogan-Wenatchee 

National Forests, Wenatchee, WA. 

rharrod@fs.fed.us

2 Biology Professor Emeritus, Western 

Washington University, Bellingham, WA.

3 Botanist, Ecological Services, Inc., 

Longview, WA.
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 316

had large gaps among the groves of ponderosa pine maintained by periodic 

fire (Harrod and others 1999; Everett and others 1996). Although mature 

pines resisted fire, small pines often were killed by fire. Well-maintained pine 

stands had few saplings (Everett and others 2000). Little information is available on the structure of the middle layer, ~2 to 4 m high. It may have been 

a fragmentary mix of shrubs, most likely bitterbrush (Purshia tridentata), 

spirea (Spiraea spp.), and mountain balm (Ceanothus velutinus) (Franklin 

and Dyrness 1988; Weaver 1951). The mostly continuous lower layer, ~1 to 

2 m high, was formed by grasses, forbs, and low-growing shrubs. Pinegrass 

(Calamagrostis rubescens), elk sedge (Carex geyeri), Idaho fescue (Festuca 

idahoensis), lupines (Lupinus spp.), hawkweeds (Hieracium spp.), and roses 

(Rosa spp.) were common members of the lower layer (Franklin and Dyrness 

1988; Weaver 1951).

Fire suppression in ponderosa pine forests in the Okanogan-Wenatchee National Forests (OWNF) began about 1910, and in the intervening years the fire 

Figure 1—Ponderosa pine stand on the Okanogan-Wenatchee National Forests in the early 1900s 

(Forest Service photo). The parklike structure consists of widely spaced trees in the canopy layer, 

few shrubs in the middle forest layer, and grasses and forbs in the lower forest layer.

Role of Fire in Restoration of a Ponderosa Pine Forest, Washington Harrod, Fonda, and McGrath
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 317

regime changed from frequent, low intensity surface fires to infrequent, high 

intensity crown fires (Everett and others 1996, 2000). For example, two fires 

in OWNF, the Dinkleman fire in 1988 (22,275 ha) and the Tyee fire in 1994 

(56,700 ha), burned with greater intensity and covered more area than they 

would have under presettlement fire regimes (Everett and others 1996, 2000). 

After nearly a century of fire exclusion, the forests now are ~10 to 12 fire-free 

intervals removed from historical conditions (Everett and others 2000), forest 

composition has shifted away from fire resisters and endurers (Agee 1993; Fonda 

and others 1998; Fonda 2001; Rowe 1983), and forest structure has shifted 

toward denser stocking of smaller understory trees in a dense, multicanopy arrangement (Everett and others 1996; Weaver 1961, 1967). Fire exclusion has 

had two consequences: (1) a continuous middle forest layer was created in an 

otherwise open forest by invading, drought sensitive trees (Everett and others 

1996), which (2) died a decade or two after germination, adding to the total 

fuel loadings (Harrod and others 1999).

Weaver (1943, 1951, 1961, 1967) advocated management fires in these 

dry forests to reverse the changes in forest structure and composition. Using 

fire to reduce density of overstory species, however, presents challenges that 

must be managed carefully during the burn (Kilgore and Curtis 1987). Many 

ponderosa pine stands in the Cascades have dense canopies and continuous 

ladder fuels, so that torching of trees, spotting, and crown fires are possible. 

Thus, in today’s forests prescribed surface fires might erupt into devastating 

crown fires (Everett and others 1996), unless the middle layer saplings and 

high density overstories are thinned to lower crown bulk density and remove 

ladder and dead woody fuels. Prefire management tactics that thin small trees 

to remove ladder fuels to the canopy and reduce fire intensity have come to 

be viewed as necessary in burning programs to restore presettlement forest 

structure (Agee 1991, 1996; Covington and others 1997; Scott 1998; Swezy 

and Fiedler 1996).

In 1991, the Leavenworth Ranger District of OWNF formed an interdisciplinary team to apply management techniques to dry forest stand to reverse 

postsettlement changes. The decision to thin and burn 865 ha in the Beehive 

Reservoir area was signed in December 1993. This project had six objectives: 

(1) improve forest health and develop stands of timber tolerant of disease, 

insect attack, drought, and wildfire; (2) develop/maintain park-like stands 

of ponderosa pine on slopes north of Beehive Reservoir; (3) maintain natural 

fire-dependent plant communities; (4) maintain/improve the health of large 

mature trees; (5) promote the development of wildlife habitats that require 

larger tree structure; and (6) use excess trees. The data in this study were gathered in conjunction with this forest management project, specifically objectives 

2 and 3. In sum, the project was designed to rehabilitate and mitigate past 

disturbances that included sheep grazing from the 1880s to 1940, extensive 

logging in the 1920s and 1930s, and fire exclusion that became effective in the 

1930s (Harrod and others 1999). The objectives above were expected to be 

achieved over a long period of forest management to restore forest structure to 

approximate the historical low intensity, low severity fire regimes described in 

many papers (Agee 1993; Everett and others 1996, 2000; Harrod and others 

1999; Weaver 1943, 1951, 1961, 1967).

The first stages in the sequence to reach the desired future condition of the 

forest north of the Beehive Reservoir began with thinning in 1996. Our research began in September 1997 with the objective of assessing the first stages 

of the project. We wanted to know if a combination of thinning and burning 

could shift community structure toward parklike stands while maintaining 

composition of native species.

Role of Fire in Restoration of a Ponderosa Pine Forest, Washington Harrod, Fonda, and McGrath
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 318

Materials and Methods

Study Site

The study site for this research was located in OWNF, 13 km southwest of 

Wenatchee, Washington, near Beehive Reservoir, in a ponderosa pine/pinegrass community that we call Beehive Forest for convenience (fig. 2). The 

overstory was dominated by large diameter ponderosa pine with clumps of 

small diameter Douglas-fir and grand fir. Western larch saplings were scattered throughout. The understory was dominated by grasses and forbs, namely 

pinegrass and broadleaf lupine (Lupinus latifolius). Bitterbrush, Scouler willow 

(Salix scouleriana), bittercherry (Prunus emarginata), and oceanspray (Holodiscus discolor) were important shrubs in the matrix of herbaceous species. The 

study site has a northeast aspect and gentle topography with 7° slopes. The site 

averages 590 mm of precipitation annually, with most falling as winter snow 

(Franklin and Dyrness 1988). Snow cover accumulates up to 3 m. Cumulative summer rain averages 44 mm, mostly from thunderstorms. Diurnal air 

temperatures fluctuate by more than 20 °C in the summer. Beehive Forest 

and the surrounding area (fig. 2) are part of the Wenatchee formation, which 

was created by fluvial and lacustrine processes in the lower Oligocene (Glesens 

and others 1981). Soils are Andisols (Meurisse and others 1991), and are 

derived from shale, siltstone, and sandstone (Glesens and others 1981).

Forest Restoration

This study comprised burned and unburned treatments. Before the fire, all 

stands were treated to approximate historical stand structure and composition 

as described in the management plan. Stands were thinned in a range of size 

classes, but the treatment focused on the understory to reduce density and 

vertical fuel structure. Litter was raked from the bases of large trees, lower 

branches were pruned to remove ladder fuels, and logging slash was scattered. Mechanical harvesting units were used to thin trees more than ~1 m 

of snow, which prevented damage to understory plants and soil. Stands in 

the unburned treatments had been unburned for >20 years. After thinning, 

the stands fit fire behavior fuel model SB1, in which spread rate is moderate 

and flame length low (Scott and Burgan 2005). We selected a 14-ha unit in 

the thinned part of Beehive Forest for the unburned treatment.

The prescribed burn was intended to enhance the growth of large, fireresistant trees, kill ~95 percent of all saplings <10 cm dbh, improve the vigor 

of grasses and forbs, and reduce surface fuel loadings. Fifteen ha of Beehive 

Forest, comprising the burn treatment, were underburned on 22 September 

1997. The prescribed burn was a relatively low intensity surface fire with flame 

lengths generally <1.5 m. Approximately 80 percent of the area burned was 

blackened, and ~25 percent of the area consisted of white ash, suggesting 

higher burn intensity and complete litter consumption.

Vegetation Sampling

Postfire data for trees in the upper and middle forest layers, and taller shrub 

species in the middle forest layer, were gathered on six 20 x 50 m macroplots 

in each treatment. Two lines parallel to the 50-m side of the macroplot were 

located at the 5 m and 15 m marks on the 20-m side. Data on species in the 

lower forest layer were gathered on 20 1 x 1 m microplots placed every 5 m 

along these two 50-m lines. Data were gathered on the unburned plots in 

Role of Fire in Restoration of a Ponderosa Pine Forest, Washington Harrod, Fonda, and McGrath
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 319

Figure 2—Location of the study site, ~13 km southwest of Wenatchee, 

WA. The area north of the Beehive Reservoir was thinned in 1996, and 

the burn treatment was applied in 1997.

summer 1998, and on the burned plots in summer 1997 (preburn), 1998 

(burn +1), 2001 (burn +3), and 2006 (burn +9). The unburned plots were 

burned in October 1998, and thus were unavailable afterwards.

Density, basal area, and fire response (alive or dead) were recorded in 1998, 

2001, and 2006 for all tree species >2 cm dbh on the macroplots. All tree 

seedlings were tallied on the macroplot. Crown scorch volume (CSV), the 

best indicator of postfire survivability/mortality (Peterson 1985, Wyant and 

Role of Fire in Restoration of a Ponderosa Pine Forest, Washington Harrod, Fonda, and McGrath
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 320

others 1986, Ryan and Reinhardt 1988), was scored for each tree in 1998 

using scorch classes: <1 percent, 1-5 percent, 6-25 percent, 26-50 percent, 

51-75 percent, 76-99 percent, 100 percent. Mean CSV was calculated using 

midranges of each scorch class. Scorch evidence was gone by 2001.

Density of taller shrubs in the middle layer was recorded on the microplots. 

Fire response of the taller shrubs (alive/unharmed; top-killed/resprouted; 

or, dead) was tallied in 1998 on the macroplots. Because the trees and taller 

shrubs are woody, prefire density was easily reconstructed. We knew that all 

woody plants were alive before the fire, thus skeletons of dead and top-killed 

plants were created by fire.

Percent frequency and cover of herbaceous species were recorded on 

the microplots. Cover was scored to the nearest 1 percent for each species. 

Postfire data for herbaceous species were compared to the unburned plots 

because no prefire data were collected for herbaceous species on the burned 

plots. We judged fire responses according to the same frequency categories 

used by Tveten and Fonda (1999). Species whose frequency changed by <10 

percentage points, compared to the unburned treatment, were maintainers. 

Those whose frequency changed by >10 percentage points, compared to the 

unburned treatment, were increasers or decreasers.

Four hawkweed species were present in the study site and could not be 

distinguished when young; their data were combined and reported as hawkweed spp. Likewise, the data for taxonomically and ecologically similar bristly 

Nootka rose (Rosa nutkana var. hispida) and baldhip rose (R. gymnocarpa) 

were combined and reported as rose spp. Botanical nomenclature follows 

Hitchcock and Cronquist (1973). Voucher specimens are deposited in the 

Biology Department Herbarium (WWB), Western Washington University.

Data Analysis

This research was designed as a Randomized Complete Block ANOVA, 

with the significance level set at 0.05 before the research began. The years 

relative to fire constituted the treatments in the analysis: 1997 (preburn), 

1998 (burn +1), 2001 (burn +3), and 2006 (burn +9). The six macroplots 

constituted the blocks. Because the postfire treatments were defined by a 

numerical component (+1, +3, +9), the data also could be analyzed by Linear 

Regression to identify possible functional relationships among the data with 

time since fire. We used ANOVA and Linear Regression to analysis relationships among percent cover in the lower layer, shrub and seedling density in 

the middle layer, and tree density and basal area in the tree layer.

Results

Overstory Response

Ponderosa pine was well represented on the burned plots in all prefire and 

postfire diameter classes, with the most young and mature trees of any species (fig. 3). Mean height of the canopy layer was 20 m. Douglas-fir, western 

larch, and grand fir were absent from diameter classes >40 cm. Douglas-fir 

was concentrated in the 11 to 40 cm diameter classes preburn and burn 

+1 year, but more trees were present in the <4-10 cm diameter classes after 

burn +3 and burn +9 years. Western larch was concentrated in the 11 to 40 

cm diameter classes in all years. Grand fir had fewer trees in the smallest 

diameter classes after burn +1, but had a substantial increase in <4 cm trees 

Role of Fire in Restoration of a Ponderosa Pine Forest, Washington Harrod, Fonda, and McGrath
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 321

Figure 3—Prefire and postfire mean diameter distribution (trees/ha) for trees >2 cm dbh.

10

60 60

20

30

40

50

60

ponderosa pine

Douglas-fir

grand fir

western larch

Trees/ha Trees/haTrees/ha Trees/ha

10

20

30

40

50

60

Diameter Class (cm)

<4 4-10 11-20 21-40 >40

0

10

20

30

40

50

Diameter Class (cm)

<4 4-10 11-20 21-40 >40

0

10

20

30

40

50

pre-burn

burn +3 burn +9

burn +1

Table 1—Mean tree density (trees/ha) and mean basal area (m2/ha) at 

Beehive Forest, from 1997 (preburn), 1998 (burn +1), 2001 (burn +3), 

to 2006 (burn +9). No significant differences for a species across 

years for density and basal area and only ponderosa pine significantly 

different in burn +9 (in bold) when comparing species density.

 Preburn Burn +1 Burn +3 Burn +9

Density

Ponderosa pine 153 105 101 114

Grand fir 51 22 48 25

Douglas-fir 44 35 37 42

Western larch 32 25 21 24

Total 280 187 207 205

Basal area

Ponderosa pine 7.84 7.60 8.05 8.73

Grand fir 0.26 0.22 0.06 0.09

Douglas-fir 1.06 1.00 0.64 1.25

Western larch 1.01 0.96 0.69 0.92

Total 10.17 9.78 9.44 10.99

after burn +3. However, these trees were not maintained into burn +9 years. 

Few trees >11 cm of any species were killed by fire (fig. 3), but the smaller 

trees in the middle layer were most affected. Young trees of all species in the 

<4 cm diameter class were reduced by 43 percent by the fire.

Fire had little effect on density and basal area for total trees (table 1). 

The small trees killed by fire resulted in lower density, but these differences 

were not significant for any species across years. In addition, there were no 

significant differences among species for any postfire year, except in burn +9 

year ponderosa pine was significantly greater than any other species (table 1). 

Ponderosa pine clearly dominated the canopy layer with significantly greater 

Role of Fire in Restoration of a Ponderosa Pine Forest, Washington Harrod, Fonda, and McGrath
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 322

basal area in each of the treatment years compared to grand fir, Douglas-fir, 

and western larch (table 1). Grand fir, Doulgas-fir, and western larch were 

not significantly different from each other in the preburn year or any postfire 

year. Basal area for a given species was not significantly different across the 

treatment years.

Trees <4 m had generally greater CSV than trees >4 m (table 2). Grand 

fir had greater CSV than any other tree species, and the differences among 

the larger ponderosa pine, Douglas-fir, and western larch were trivial.

Table 2—Postfire mean crown scorch volume (CSV) 

for all trees >2 cm dbh on the burned plots. 

Species are listed by order of increasing CSV 

in the >4 m tall category.

Species >4 m tall <4 m tall

Ponderosa pine 21 72

Western larch 23 1

Douglas-fir 45 53

Grand fir 80 86

Middle Layer Response

The most dramatic change in community structure was represented by 

new tree seedlings (<4 cm dbh) in burn +3 and burn +9 years (table 3). In 

1998, two ponderosa pine seedlings (= 3/ha) were tallied postfire on the 

six macroplots in the burned treatment. No other species had individuals 

<2 cm dbh. By burn +3 years, seedlings of five species were present on the 

burned plots, with grand fir and ponderosa pine the most abundant, followed 

by Douglas-fir and western larch. After burn +9 years, ponderosa pine and 

Douglas-fir seedling density was not significantly different from burn +3 

years, nor was there a significant change over time (table 3). Grand fir and 

western larch seedlings were not significantly different among sample years, 

but the increase over time was significant (regression coefficient for grand 

fir was b= 7.53 (that is, an increase of 7.53 seedlings per ha per year), and 

for larch b= 1.56).

Table 3—Composition of the middle layer, and changes by tree seedling and shrub density 

(per hectare) after fire. Means with the same superscript are not significantly different, 

by species.

Regression

Postfire year relationship

Species 1 3 9 with time

Tree seedlings

Ponderosa pine 3 410a 372a Not significant

Douglas-fir 0 190a 125a Not significant

Grand fir 0a 538a 713a Significant

Western larch 0a 118a 150a Significant

 Total seedling density 3 1,256 1360

Shrubs

Shiny-leaf spirea 2,530a 870a 9982a Not significant

Moutain balm 0 48a 57a Significant

Oceanspray 15a 337a 238a Not significant

Scouler willow 55a 63a 73a Not significant

Bitterbrush 100a 87a 100a Not significant

Serviceberry 0a 30a 48a Not significant

Squaw currant 0a 7a 3a Not significant

 Total shrub density 615 617 10,501

Role of Fire in Restoration of a Ponderosa Pine Forest, Washington Harrod, Fonda, and McGrath
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 323

Shiny-leaf spiraea was the most abundant shrub species in the middle layer 

in all three postfire years (table 3). However, only moutain balm significantly 

increased density over time; postfire years +3 and +9 were significantly greater 

than the first postfire year. Other abundant postfire shrubs were oceanspray, 

Scouler willow, and bitterbrush, all of which readily resprouted in the first 

year postfire (78, 100, and 64 percent resprouted, respectively). Fewer than 

14 percent of shrub individuals were killed on the burned plots, so shrub 

species were consider important endurers or maintainers.

Lower Layer Response

Although postfire responses of the lower forest layer components were 

pronounced on burned plots, the postfire community structure and composition did not change substantially compared to the unburned plots. Based 

on changes in frequency, most of these species responded as maintainers or 

increasers in an intact lower layer after burn +1, burn +3, and burn +9 years 

(table 4). Mean cover of each species was not significantly different among 

the unburned site and any postfire year. In fact, nearly all grasses, forbs, and 

shrubs were endurers. The understory dominant and fire maintainer, pinegrass, was uniformly distributed and had the highest cover of any understory 

species (table 4). One of the latest flowering understory species, pinegrass 

had abundant, tall flower stalks after burn +1 in August.

Broadleaf lupine was a uniformly distributed, dominant species and fire 

maintainer, but it had about half of the cover of pinegrass (table 4). Lupine 

flowered in early summer and set seed by mid-July. It was completely senesced 

by August when pinegrass became prominent. The remaining maintainers, 

with low frequency and cover, were structurally unimportant (table 4).

Table 4—Unburned and postfire percent frequency and mean percent cover of grasses, forbs, and shrubs in the lower 

forest layer. Species listed have at least 1 percent cover in one treatment. Mean cover for each species did not differ 

significantly among the four treatments.

Frequency Cover

Unburned Burn +1 Burn +3 Burn +9 Unburned Burn +1 Burn +3 Burn +9

Maintainers in all postfire years

Pinegrass 86 81 84 84 23.3 23.3 26.8 20.7

Broadleaf lupine 82 75 75 73 12.1 13.8 14.4 8.4

Hawkweed spp. 43 41 53 38 3.8 1.4 3.0 1.7

Sharptooth angelica 13 16 6 12 0.6 <0.1 0.4 0.4

Yellow penstemon 12 15 10 16 0.9 1.0 0.6 0.8

Increaser by burn +9

Kinnikinnick 26 21 31 37 8.1 3.8 7.0 10.3

Decreaser in burn +1 and burn +3, maintainer by burn +9

Elk sedge 68 45 50 67 8.7 6.2 3.4 8.2

Decreasers by burn +9

American vetch 26 28 20 10 3.0 2.8 1.0 0.1

Northwest sedge 18 28 13 1 2.5 1.5 0.3 0.2

Increaser in burn +1 and burn +9

Heart-leaf arnica 22 37 18 33 2.3 4.1 0.9 1.0

Increaser in burn +1 and burn +3, decreaser by burn +9

Fireweed 12 34 28 0 1.5 2.5 1.7 0

Increasers in burn +3

Yarrow 53 58 68 63 5.2 3.9 3.2 1.9

Idaho fescue 9 14 27 3 0.6 1.5 1.5 0.2

Decreaser in burn +1 and burn +9

Rose spp. 49 38 42 33 5.6 5.1 3.8 1.8

Role of Fire in Restoration of a Ponderosa Pine Forest, Washington Harrod, Fonda, and McGrath
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 324

American vetch (Vicia americana) and northwest sedge (Carex concinnoides) were decreasers by burn +9 and contagiously distributed (Whittaker 

1975) within the pinegrass-lupine matrix (table 4).

There were five increasers (table 4). Kinnikinnick (Arctostaphylos uva-ursi) 

commonly formed extensive mats, which contributed strongly to sustaining 

fire on the burned plots. Heart-leaf arnica (Arnica cordifolia) was an increaser 

after burn +1 and burn +9 years. Fireweed (Epilobium angustifolium) was 

an increaser in the first two postfire year measurements, but disappeared 

by burn +9. Yarrow (Achillea millefolium) and Idaho fescue were increasers 

into the third postfire year. They were prominent in late summer, often in 

flower with pinegrass.

Rose spp. response was variable over time, but were decreasers in burn +1 

and burn +9 years. Elk sedge was a consistent decreaser in burn +1 and burn 

+3 years and cover for this species was less than 50 percent of the unburned 

cover. However, elk sedge was a maintainer by burn +9.

Discussion

The forest structure achieved by thinning and burning suggests that 

Beehive Forest approached the conditions established by the management 

objectives in the first postfire year. The data for postfire shrubs, however, 

indicate that more time and treatments will be needed before the forest 

reaches the structure depicted in figure 1. Few plants in Beehive Forest were 

completely killed by fire, and species composition, comprising maintainers 

and a few increasers, were stable after burning. This is a similar finding to 

thinning and burning treatments in ponderosa pine-Douglas-fir in northeastern Oregon, where understory vegetation was largely unchanged (Metlen 

and others 2004). As in the northeastern Oregon study, individual species 

response at Beehive to thinning and burning treatments was consistent their 

life history characteristics. Most grasses, forbs, and shrubs were endurers 

that maintained the low forest layer through rapid resprouting and postfire 

growth. Resisters and endurers commonly are maintainers, occasionally increasers, whose frequency or density does not change substantially after fire. 

The postfire responses of plant species in Beehive Forest were similar also to 

species on Fort Lewis, WA, where dominant species in fire-maintained Idaho 

fescue prairies and Garry oak (Quercus garryana) woodlands were maintainers 

whose frequency changed by <10 percentage points from prefire conditions 

(Tveten and Fonda 1999). Few native species on Fort Lewis or in Beehive 

Forest were increasers or decreasers.

Prefire thinning and burning initially opened the middle layer in Beehive 

Forest, primarily by top-killing at least 50 percent of taller shrubs and secondarily by killing sapling trees. About 13 percent of individual shrubs were 

killed by fire; all survivors were endurers that resprouted <6 weeks after the 

fire. Densities of smaller ponderosa pine and Douglas-fir, and grand fir of all 

sizes, in the middle layer were reduced by fire in the first postfire growing 

season. The current forest structure now fits fire behavior fuel model 2, in 

which low intensity surface fires are carried through fine herbaceous fuels 

(Anderson 1982). Developing and maintaining parklike stands by changing 

the structure of this middle layer with a combination of thinning and burning was a management objective. Third and ninth year data on tree seedlings 

and shrubs indicate that community structure has shifted only slightly toward 

Role of Fire in Restoration of a Ponderosa Pine Forest, Washington Harrod, Fonda, and McGrath
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 325

parklike stands while maintaining native species on the site. The low percentage of total killed seedlings and shrubs indicates that more thinning and fire 

will be needed before the goal is achieved.

The canopy layer remained intact on the burn site. Mature ponderosa 

pine, western larch, and Douglas-fir were resisters. Mature Douglas-fir in the 

canopy layer resisted the direct effects of fire. If the desired forest structure 

requires lower Douglas-fir density, future management efforts may focus 

on mechanical thinning of Douglas-fir, rather than low intensity fires, to 

achieve that structure.

Prescribed fire objectives usually define acceptable values for overstory 

mortality. Because CSV of >80 percent invariably indicates mortality in 

conifers (Ryan and Reinhardt 1988), many burn programs use low intensity 

surface fires to avoid burning the canopy. For instance, mean flame lengths 

in a thinned stand of ponderosa pine in Arizona were ~15 cm, with occasional flareups to ~64 cm (Covington and others 1997), compared to 1 to 2 

m flame lengths for Beehive Forest. The fires in Beehive Forest did involve 

the canopy, with good results. Mean CSV for grand fir (avoider) exceeded 

80 percent, whereas ponderosa pine (resister) was <80 percent (table 2). Furthermore, successive fires enhance the resister strategy of ponderosa pine by 

raising the height to live crown, thereby reducing potential CSV and postfire 

tree mortality in the future.

Conclusions

The treatments in Beehive Forest have started the process to restore forests 

of widely spaced groves of large ponderosa pine trees with scattered western 

larch and Douglas-fir, a grassy understory with sporadic clumps of young 

trees, and light surface fuel loadings (Agee 1993; Everett and others 1996, 

2000; Harrod and others 1999; Weaver 1951). Ponderosa pine forests in 

the Cascades have missed 10 to 12 fire episodes (Everett and others 1996, 

2000), and one thinning and burning treatment will not restore presettlement conditions. The restoration process has started in Beehive Forests, and 

no counterindications exist that suggest management objectives cannot be 

achieved. Prescribed burning alone fails to restore ponderosa pine forests to 

presettlement conditions, since forest structure has changed so drastically 

with fire exclusion (Agee 1996; Covington and others 1997; Everett and 

others 1996, 2000; Fiedler 1996; Harrod and others 1999; Scott 1998; 

Swezy and Agee 1991). Consequently, thinning trees and removing excess 

fuels, coupled with low intensity late season prescribed burns, offers a promising management strategy for restoring the presettlement structure of the 

ponderosa pine/pinegrass community in Beehive Forest.

Acknowlegments

Josie Melton, James Dickinson, and Pete Ohlson assisted with the field 

work, and Jeannie Gilbert created figure 2. We thank the Wenatchee River 

Ranger District, Okanogan-Wenatchee National Forests, for implementing 

both the prescribed burning and thinning treatments. Funding was provided 

by the USDA Forest Service, Okanogan-Wenatchee National Forests.

Role of Fire in Restoration of a Ponderosa Pine Forest, Washington Harrod, Fonda, and McGrath
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 326

References

Agee, J.K. 1993. Fire ecology of Pacific Northwest Forests. Covelo, CA: Island 

Press. 493 p.

Agee, J.K. 1994. Fire and weather disturbances in terrestrial ecosystems of the Eastern 

Cascades. Gen. Tech. Rep. PNW-GTR-320. Portland, OR: U.S. Department of 

Agriculture, Forest Service, Pacific Northwest Research Station. 52 p.

Agee, J.K. 1996. Achieving conservation biology objectives with fire in the Pacific 

Northwest. Weed Technology 10:417-421.

Anderson, H.E. 1982. Aids to determining fuel models for estimating fire behavior. 

Gen. Tech. Rep. INT-122. Ogden, UT: U.S. Department of Agriculture, Forest 

Service, Intermountain Research Station. 22 p.

Armour, C.D.; Bunting, S.C.; Neuenschwander, L.F. 1984. Fire intensity effects 

on the understory in ponderosa pine forests. Journal of Range Management 

37:44-49.

Campbell, R.E.; Baker, M.B.; Ffolliott, P.F.; Larson, F.R.; Avery, C.C. 1977. Wildfire 

effects on a ponderosa pine ecosystem: an Arizona case study. Res. Pap. RM-191. 

Ogden, UT: U.S. Department of Agriculture, Forest Service, Rocky Mountain 

Research Station. 12 p.

Covington, W.W.; Zule, P.Z.; Moore, M.M.; Hart, S.C.; Kolb, T.E.; Mast, J.N.; 

Sackett, S.S.; Wagner, M.R. 1997. Restoring ecosystem health in ponderosa pine 

forests of the southwest. Journal of Forestry 95:23-29.

Everett, R.L.; Schelhaas, R.; Anderson, T.A.; Lehmkuhl, J.F.; Camp, A.E. 1996. 

Restoration of ecosystem integrity and land use allocation objectives in altered 

watersheds. In: Proceedings on Watershed Restoration Management. Middleburg, 

VA: American Water Resources Association: 271-280. (McDonnel, J.J.; Stribling, 

J.B.; Neville, L.R.; Leopold, D.J., eds).

Everett, R.L.; Schelhaas, R.; Keenum, D.; Spurbeck, D.; Ohlson, P. 2000. Fire 

history in the ponderosa pine/Douglas-fir forests on the east slope of the 

Washington Cascades. Forest Ecology and Management 129:207-225.

Fiedler, C.E. 1996. Silvicultural applications: restoring ecological structure and 

process in ponderosa pine forests. In: The use of fire in forest restoration. Gen. 

Tech. Rep. INT-GTR-341. Ogden, UT: U.S. Department of Agriculture, Forest 

Service, Intermountain Research Station: 34-40. (Hardy, C.C.; Arno, S.F., 

editors).

Fonda, R.W. 2001. Burning characteristics of needles from eight pine species. Forest 

Science 47:390-396.

Fonda, R.W.; Belanger, L.A.; Burley, L.L. 1998. Burning characteristics of western 

conifer needles. Northwest Science 72:1-9.

Franklin, J.F.; Dyrness, C.T. 1988. Natural Vegetation of Oregon and Washington. 

Corvallis, OR: Oregon State University Press. 452 p.

Glesens, R.L.; Naeser, C.W.; Whetten, J.T. 1981. Stratigraphy and age of the 

Chumstick and Wenatchee Formations: tertiary fluvial and lacustrine rocks, 

Chiwakum graben, Washington: summary. Geological Society of America Bulletin 

92:233-236.

Harrod, R.J.; McRae, B.H.; Hartl, W.E. 1999. Historical stand reconstruction in 

ponderosa pine forests to guide silvicultural prescriptions. Forest Ecology and 

Management 114:433-446.

Hitchcock, C.L.; Cronquist, A. 1973. Flora of the Pacific Northwest. Seattle, WA: 

University of Washington Press. 730 p.

Kilgore, B.M.; Curtis, G.A. 1987. Guide to understory burning in ponderosa pinelarch-fir forests in the Intermountain West. Gen. Tech. Rep. INT-233. Ogden, 

UT: U.S. Department of Agriculture, Forest Service, Intermountain Research 

Station. 39 p.

Role of Fire in Restoration of a Ponderosa Pine Forest, Washington Harrod, Fonda, and McGrath
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 327

Leege, T.A.; Hickey, W.O. 1971. Sprouting of northern Idaho shrubs after prescribed 

burning. Journal of Wildlife Management 35:508-515.

Metlen, K.L.; Fiedler, C.E.; Youngblood, A. 2004. Understory response to fuel 

reduction treatments in the Blue Mountains of northeastern Oregon. Northwest 

Science 78: 175-185.

Meurisse, R.T.; Robbie, W.A.; Niehoff, J.; Ford, G. 1991. Dominant processes and 

properties in western-montane forest types and landscapes - some implications for 

productivity and management. In: Proceedings of the Symposium on Management 

and Productivity of western-montane forest soils. Gen. Tech. Rep. INT-280. 

Ogden, UT: U.S. Department of Agriculture, Forest Service, Intermountain 

Research Station: 7-19. (Harvey, A.E.; Neuenschwander, L.F., comps).

Old, S.M. 1969. Microclimate, fire, and plant production in an Illinois prairie. 

Ecological Monographs 39:335-384.

Peterson, D.L. 1985. Crown scorch volume and scorch height: estimates of postfire 

tree condition. Canadian Journal of Forest Research 15:596-598.

Rowe, J.S. 1983. Concepts of fire effects on plant individuals and species. In: The 

Role of Fire in Northern Circumpolar Ecosystems. New York, NY: John Wiley 

& Sons:135-154. (Wein, R.W.; MacLean, D.A., eds).

Ruha, T.L.; Landsberg, J.D.; Martin, R.E. 1996. Influence of fire on understory 

shrub vegetation in ponderosa pine stands. In: Proceedings of the Symposium 

on Shrubland ecosystem dynamics in a changing environment. Gen. Tech. Rep. 

INT-GTR-338. Odgen, UT: U.S. Department of Agriculture, Forest Service, 

Intermountain Research Station: 108-113. (Barrow, J.R.; McArthur, E.D.; 

Sosebee, R. E.; Tausch, R. J., comps).

Ryan, K.C.; Reinhardt, E.D. 1988. Predicting postfire mortality of seven western 

conifers. Canadian Journal of Forest Research 18:1291-1297.

Scott, J.H. 1998. Fuel reduction in residential and scenic forests: a comparison of 

three treatments in a western Montana ponderosa pine stand. Res. Pap.-RMRSRP-5. Ogden, UT: U.S. Department of Agriculture, Forest Service, Rocky 

Mountain Research Station. 19 p.

Scott, J.H.; Burgan, R.E. 2005. Standard fire behavior fuel models: a comprehensive 

set for use with Rothermel’s surface fire spread model. Gen. Tech. Rep. RMRS 

GTR-153. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, 

Rocky Mountain Research Station. 72 p.

Swezy, D.M.; Agee, J.K. 1991. Prescribed fire effects on fine-root and tree mortality 

in old growth ponderosa pine. Canadian Journal of Forest Research 21:626-634.

Tveten, R.K.; Fonda, R.W. 1999. Fire effects on prairies and oak woodlands on Fort 

Lewis, Washington. Northwest Science 73:145-158.

Weaver, H. 1943. Fire as an ecological and silvicultural factor in the ponderosa pine 

region of the Pacific slope. Journal of Forestry 41:7-15.

Weaver, H. 1951. Observed effects of prescribed burning on perennial grasses in 

the ponderosa pine forests. Journal of Forestry 49:267-271.

Weaver, H. 1961. Ecological changes in the ponderosa pine forest of Cedar Valley 

in southern Washington. Ecology 42:416-420.

Weaver, H. 1967. Fire as a continuing ecological factor in perpetuation of ponderosa 

pine forests. Advancing Frontier of Plant Sciences 18:137-153.

Whittaker, R.H. 1975. Communities and Ecosystems. New York, NY: MacMillan 

Publishing Company. 387 p.

Wilson, S.D.; Shay, J.M. 1990. Competition, fire, and nutrients in a mixed-grass 

prairie. Ecology 71:1959-1967.

Wyant, J.G.; Omi, P.N.; Laven, R.D. 1986. Fire induced tree mortality in a Colorado 

ponderosa pine/Douglas-fir stand. Forest Science 32:49-59.

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Role of Fire in Restoration of a Ponderosa Pine Forest, Washington Harrod, Fonda, and McGrath
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Abstract—Forest thinning and prescribed fires are the main practices used by managers to address concerns over ecosystem degradation and severe wildland fire potential 

in dry forests of the Western United States. There is some debate, however, about 

treatment effectiveness in meeting management objectives as well as their ecological 

consequences. This study assesses the effectiveness of thinning and prescribed fire 

treatments, alone and combined, for modifying forest structure and potential fire behavior in the Eastern Cascade Mountains of Washington State. Treatments were applied 

to 12 management units (~10 ha each), with each treatment combination replicated 

three times (including untreated controls). Thinning modified forest structure by reducing overall tree stocking and canopy fuels to ≤50 percent of pretreatment values. 

Furthermore, thinning greatly reduced the modeled probability of severe wildfire and 

reduced stand densities to below critical levels for insect outbreaks. The prescribed 

fire treatment, conversely, did not appreciably reduce stocking levels or canopy fuel 

loadings, but was effective for raising canopy height and increasing the density of 

standing dead trees. Prescribed fire effects were more pronounced when used in 

combination with thinning. While thinning was a more reliable method for altering 

stand structure, the spring burns conducted in the experiment were cooler and spottier than were desired and may have led to results that downplay the efficacy of fire 

to meet forest restoration goals.

Introduction

Structure is an important ecological aspect of dry, coniferous forests. 

Dry forests throughout the Interior West have increasingly become dense, 

closed-canopy stands of mostly small, fire-intolerant trees as compared to 

the more open, fire-tolerant, large-tree dominated stands of the past (Agee 

1994; Covington and Moore 1994; Everett and others 2000; Harrod and 

others 1999). Dense overstories with continuous ladder fuels lead to high 

probabilities of torching and crown fires (Peterson and others 2005; Scott 

1998; Scott and Reinhardt 2001) and increased vulnerability to many insects 

and diseases (Hessburg and others 2005). Understory diversity, composition, 

and abundance have been altered due to these modified structural conditions 

(Covington and others 1997; Hall 1977; Smith and Arno 1999). Wildlife are 

also affected as many dry forest species, such as the white-headed and hairy 

woodpeckers, lack suitable habitat (Gaines and others 2007). In general, 

changes in structure and composition have lead to an overall deterioration in 

forest ecosystem integrity and an increased probability of large, high-severity 

wildfires throughout the West (Dahms and Geils 1997; Patton-Mallory 1997; 

Stephens 1998; Weatherspoon and Skinner 1996).

Comparing the Effectiveness of Thinning and 

Prescribed Fire for Modifying Structure in 

Dry Coniferous Forests

Richy J. Harrod1, Nicholas A. Povak2, and David W. Peterson2

In: Butler, Bret W.; Cook, Wayne, 

comps. 2007. The fire environment—

innovations, management, and policy; 

conference proceedings. 26-30 March 

2007; Destin, FL. Proceedings 

RMRS-P-46CD. Fort Collins, CO: 

U.S. Department of Agriculture, 

Forest Service, Rocky Mountain 

Research Station. 662 p. CD-ROM.

1 Deputy Fire Management Officer, 

U.S. Department of Agriculture, 

Forest Service, Okanogan-Wenatchee 

National Forests, Wenatchee, WA. 

rharrod@fs.fed.us

2 Biological Science Technician and 

Research Forester, respectively, U.S. 

Department of Agriculture, Forest 

Service, Pacific Northwest Research 

Station, Wenatchee Forestry Sciences 

Laboratory, Wenatchee, WA. 
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 330

Comparing the Effectiveness of Thinning and Prescribed Fire for Modifying Structure in Dry Coniferous Forests Harrod, Povak, and Peterson

The structure and composition of today’s forests have been created by 

disturbance regimes that are very different from those of the past. Prior to 

Euro-American settlement, fire regimes characterized by high-frequency, lowseverity fire promoted open structure with large trees, particularly ponderosa 

pine (Pinus ponderosa; Agee 1998; Hessburg and others 2005). Fire burned 

every 6 to 7 years in the Eastern Cascade Mountains (Everett and others 

2000), acting as a natural thinning agent that selectively removed small trees 

while causing minimal overstory mortality (<20 percent of basal area; Agee 

1990, 1993). Trees often grew in clumps, creating numerous canopy gaps. 

Canopy layering was minimal as shade-tolerant but fire intolerant species such 

as grand fir (Abies grandis) were limited by frequent fires (Harrod and others 

1999; Hessburg and others 2005). Fire regimes were altered during the past 

century, however, by active fire suppression, land-use changes, and removal 

of fine fuels through heavy grazing by sheep and cattle (Agee 1994). In the 

absence of fire, dense forests developed, creating intense competition for light 

and water, greater tree mortality and increased hazards of severe wildfires. 

Past logging practices further altered forest structure and fire resistance by 

removing large, fire-resistant trees and promoting prolific regeneration of 

small trees (Habeck 1990). These changes are of great concern to forest 

managers because extensive crown fires have threatened lives and property, 

and fire suppression activities have proven to be costly. This paper focuses on 

forest restoration treatment options that modify forest structure, and their 

effectiveness in reducing crown fire hazard.

Forest restoration opportunities are constrained by available methods. 

Thinning and prescribed fire used alone or in combination are options available for modifying forest structure and reducing potential for severe wildland 

fire. However, there is disagreement about the appropriate balance among 

mechanical treatments and prescribed fire (Stephens 1998; van Wagtendonk 

1996; Weatherspoon 1996) and whether or not the treatments can meet 

restoration objectives (Brown and others 2004). Thinning allows for control over species selection and size classes of material removed or retained. 

However, thinning can be problematic if the value of the wood removed does 

not offset treatment costs; if slash significantly increases surface fuel loads 

(Pollet and Omi 2002; McIver and Ottmar 2007); or if activities are limited 

by road access, slope steepness, or sensitive soils. Prescribed fire can be used 

to reduce surface and some ladder fuels and is less limited by road access, 

but does not allow for much control over species and size class selection and 

may produce secondary fire effects, such as bark beetle mortality (Ganz and 

others 2003; McHugh and others 2003). Prescribed fire may also kill large 

trees that are intended to be retained after treatment (Agee 2003). Thinning 

and prescribed fire may complement each other when used in combination, 

although empirical data are still lacking (Weatherspoon and Skinner 2002). 

Further, the added costs associated with completing both treatments may not 

be warranted if management objectives can be met with a single treatment.

The purpose of our study was to assess the effectiveness of thinning and 

prescribed fire alone and in combination for modifying overstory structure 

in dry forests with the purpose of restoring a low-severity fire regime. Data 

were collected from the Mission Creek Fire and Fire Surrogates site, which is 

part of a network of sites throughout the United States (Weatherspoon 2000), 

in which thinning and prescribed fire treatments were applied in factorial 

combinations. We asked the following research questions:

1. Can prescribed fire or thinning alone produce the structural changes 

necessary to restore a low severity fire regime?
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Comparing the Effectiveness of Thinning and Prescribed Fire for Modifying Structure in Dry Coniferous Forests Harrod, Povak, and Peterson

2. If prescribed fire and thinning are used in combination, are the results 

additive or is there an additional benefit to combining treatments?

3. Which treatments reduce stand density sufficiently to reduce the risk of 

insect outbreaks?

4. How do treatments affect current snag inventories and future snag 

recruitment?

Methods

Study Area

The study was conducted in the Eastern Cascade Mountains of central 

Washington State within the Okanogan-Wenatchee National Forests. Forests 

within the study area are dominated by ponderosa pine (Pinus ponderosa) and 

Douglas-fir (Pseudotsuga menziesii) with grand fir (Abies grandis) and western 

larch (Larix occidentalis) occurring at higher elevations. Plant associations 

include Pseudotsuga menziesii/Calamagrostis rubescens, P. menziesii/Spirea 

betulifolia, P. menziesii/Carex geyeri, P. menziesii/S. betulifolia/C. rubescens, 

P. menziesii/S. albus, P. menziesii/ Symphoricarpos albus/C. rubescens, Pinus 

ponderosa/C. rubescens/Agropyron spicatum, Abies grandis/Mahonia nervosa, 

and A. grandis/S. albus/C. rubescens (Lillybridge and others 1995).

Climate is continental due to the rain shadow produced by the Cascade 

Mountains. Annual precipitation averages 22 cm and occurs mostly as snow 

between November and April; mean January and July temperatures are –3 °C 

and 17 °C, respectively. Soils are stony, sandy loams (USDA 1995) and slopes 

vary from 30 to 40 percent. A more complete description of general site conditions will be found in Harrod and others (in progress).

Treatment Descriptions

Approximately 30 candidate stands were identified in 1999 in the Mission 

Creek watershed and other smaller watersheds immediately adjacent to the 

west. We constrained the search to eliminate units with (1) north aspects, (2) 

average slopes >40 percent (there are locally steeper slopes), (3) >10 percent 

rock or nonforest vegetation cover, and (4) known plant or animal species of 

concern. Twelve treatment units, each approximately 10 ha, were ultimately 

chosen for the study.

The treatments included: (1) thinning from below (thin only), (2) prescribed fire alone (burn only), (3) commercial thinning followed by prescribed 

fire (thin + burn), and (4) untreated control. Treatment combinations were 

assigned to the 12 study units, producing three replicates of each treatment 

combination. Treatment assignment was random except that four units were 

not considered for prescribed fire treatments due to access limitations.

The specific treatment objectives were to restore low-density dry forest 

stand structure, reduce ladder and surface fuels, reduce the risk of extensive 

bark beetle attack, and reduce the risk of high-severity fire. The overall objective of the Fire and Fire Surrogate Study was to achieve an 80 percent survival 

rate in codominant/dominant trees in the posttreatment stand for modeled 

wildfires under 80th percentile weather conditions (Weatherspoon 2000). 

The thinning treatment objectives on thin-only and thin + burn units were 

to reduce stand density and favor drought and fire resistant species (largely 

ponderosa pine or Douglas-fir). Target thin densities were designed to lower 

current stand density to the estimated upper management zone for low bark 
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Comparing the Effectiveness of Thinning and Prescribed Fire for Modifying Structure in Dry Coniferous Forests Harrod, Povak, and Peterson

beetle risk (Cochran and others 1994), which is also the estimated historical 

density for this area (for example, stand density index = 263 for pine sites; 

Harrod and others 1999). Stands were thinned from below (largely understory trees) leaving the largest and most vigorous trees at irregular spacing. 

Treatments were tailored to individual forest stands, and target densities 

were dependent on the predominant plant association and site stockability. 

Thinning was accomplished with contract fallers in the winter of 2002/2003, 

and logs were yarded by helicopter to landings outside the study units. Unmerchantable tree tops and branches were left in the study units.

Prescribed burning was conducted on four of the six experimental units in 

spring 2004 and, because of lack of appropriate burning conditions, on the 

remaining two units in spring 2006. Ignition of units was by hand (drip torch) 

and helicopter with polystyrene spheres filled with potassium permanganate 

crystals, which are injected with ethylene glycol and dropped to the ground. 

Flame lengths ranged from 0.2 to 1.0 m and burns were generally patchy (20 

to 50 percent area blackened) because of spring burning conditions.

Sampling Design

Preliminary reconnaissance of the treatment units was used to identify areas 

of continuous forest vegetation and to avoid sampling in areas dominated by 

nonforest vegetation. A series of six, 20x50m plots were permanently established 

within each unit in 2000 (prior to treatments), and plots were systematically 

stratified within the most abundant plant associations within each unit (Lillybridge and others 1995). Based on a preliminary analysis of adjacent forest 

stands we determined that six plots per treatment unit would adequately capture the within unit variability and provide an accurate estimate of vegetation 

characteristics.

Within each sample plot, all live and standing dead trees >7.62 cm d.b.h. 

were identified by species, permanently numbered and measured for diameter, 

total height, height to crown base (height from tree base to the intersection 

of the lowest live limb at the tree bole), bole scarring, and crown condition 

(Keen 1943; Hawksworth 1977). Saplings (height >1.37 m, diameter <7.62 

cm) were also tallied. Canopy closure was measured in the center of the plot 

using a Lemmon Spherical Densiometer, Model-C. Slope, aspect, and elevation 

were also recorded for each plot.

In general, sampling occurred 1 year before and after the treatments were 

implemented; however, the actual years each unit was measured depended on the 

timing of the treatments (Harrod and others in progress). Upon reassessment 

of the units following treatment we noted that two plots located in burn units 

escaped the fire because they were located on the wrong side of the handline 

and had no possibility of being burned. These two plots were subsequently 

removed from all analyses.

Data Analysis

Data were averaged up from the plot-level to the experimental unit level 

for all analyses (Hurlbert 1984). Live trees and snags >7.62 cm, and saplings 

(>1.37 m tall and <7.62 cm d.b.h.) were analyzed separately. Response variables 

of interest included: density (ha–1) and basal area (m2 ha–1), quadratic mean 

diameter (cm), stand density index, canopy bulk density (kg m–3), canopy 

fuel loading (Mg ha–1), and canopy base height (m).

A two-factorial analysis of variance (ANOVA) was used on the pretreatment 

data to determine if there were significant differences in forest structural 

components between treatments prior to the experiment. Factors were burn 
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Comparing the Effectiveness of Thinning and Prescribed Fire for Modifying Structure in Dry Coniferous Forests Harrod, Povak, and Peterson

(two levels, burn or no burn) and thin (two levels, thin or no thin). Of all 

the response variables tested in the pretreatment analysis, only snag basal area 

(BA) was significantly different among treatments. Even though there were 

largely no significant differences in pretreatment forest structure, there was 

enough variability among treatment units to warrant the use of a pretreatment covariate for the analysis of treatment effects. Therefore, to determine 

the treatment effects on stand structure, a two-factorial analysis of covariance 

(ANCOVA) was used with absolute change as the dependent variable (posttreatment – pretreatment) and the respective pretreatment condition included 

as a covariate in the model. This analysis was chosen because it (1) accounts 

for pretreatment variability in structural components, and (2) accounts for 

the repeated measures aspect of the experiment by analyzing the absolute 

change of the response variables rather than the posttreatment values.

Least square means were used to compare main treatment and interaction 

effects on stand structure, and the Tukey-Kramer adjustment for multiple 

comparisons was used to control the experiment-wise error rate. An error 

rate of 0.1 was chosen over the more traditional 0.05 level to (1) reduce the 

probability of Type II errors and (2) because a 90 percent success rate is favorable for most management decisions.

Because treatments were designed to remove trees based on their size, trees 

were grouped into four diameter classes for some analyses to elaborate upon 

the effects of treatments on different size classes of trees. Diameter classes 

included small (7.62 to 19.9 cm d.b.h.), medium (20.0 to 39.9 cm d.b.h.), 

large (40 to 59.9 cm d.b.h.) and very large (>60.0 cm d.b.h.).

Stand density index is a useful measure of competition in forest stands 

and has been applied to many forest types around the world (Shaw 2006). 

Originally developed for even-aged pure stands, SDI has since been applied 

to uneven-aged and mixed-species forests (Shaw 2000; Woodall and others 

2003, 2005). Stand density index for each sample plot was calculated using 

the summation method following Cochran and others (1994) and Shaw 

(2000):

[1] SDI = ∑(Di/25)a

Where, Di = individual tree diameter (cm), a = 1.77 for ponderosa pine and 

1.51 for Douglas-fir and all others.

A variant of Reineke’s maximum size-density model was used to construct 

Gingrich-type stocking charts, following the methods of Cochran and others 

(1994), for use in forest types similar to those in the current study. Average 

model parameters for Douglas-fir and ponderosa pine were calculated to 

determine tree density and basal area at full stocking over a range of average 

stand diameters:

[2] TPH=exp[a–b(log(Dq)]*(SDIf/SDIn)

Where TPH = trees per hectare, Dq = quadratic mean diameter, a = 9.70 and 

b = 1.64, SDIf = stand density index at full stocking for our stands (777), 

and SDIn = average maximum SDI for self-thinning stands (922). Basal area 

(BA; m2 ha–1) was then calculated for each Dq as:

[3] BA=TPH*0.0000785398*Dq2

While SDI is reportedly robust to changes in site quality and stand age, 

full-stocking values have been shown to vary by region and plant association 

(Woodall and others 2000). The (SDIf/SDIn) in equation 2 is an adjustment 

factor used in the model to tailor maximum size-density models to specific 

forests of interest. An SDIf value of 777 was calculated by averaging the SDI 
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Comparing the Effectiveness of Thinning and Prescribed Fire for Modifying Structure in Dry Coniferous Forests Harrod, Povak, and Peterson

values for the individual plant associations (PA) in our study area (based on 

Lillybridge and others 1995) and weighting them by the total number of 

sample plots on which each PA occurred. An SDIn of 920 was used as an 

average maximum density for ponderosa pine and Douglas-fir over a wide 

range of plant communities as summarized by Cochran and others (1994). 

Upper and lower management zones were calculated as 75 and 50 percent 

of full stocking, respectively.

Canopy fuels were modeled at each time period using the Crown Mass® 

software in the Fuels Management Analyst 3® (FMA) package. Canopy bulk 

density, canopy fuel loading, and canopy base height were calculated using 

pre- and posttreatment, plot-level data for each unit. Canopy bulk density is 

the crown mass per unit volume in a forest stand and is an estimate of available fuel in the canopy. Crown Mass® calculates canopy bulk density as the 

maximum of the running mean estimates for 0.3048 m canopy segments on 

the basis that fires will most likely spread in the denser portions of the canopy. 

Canopy fuel loading is the crown mass per unit area and is calculated as the 

cumulative foliage, 1-hr live and dead timelag canopy fuel mass. Canopy base 

height is defined as the vertical distance from the ground to the lowest point 

in the canopy where bulk density is high enough to propagate fire into the 

canopy (0.011 kg m–3; Scott and Reinhardt 2001).

Results

Treatment Effects on Tree Density and Stocking

The thinning treatment greatly reduced stand density over the course of 

the experiment, particularly in the small and medium size classes. Thinning 

significantly reduced total live tree density by >60 percent (P<0.0001), with 

similar declines found using thin only and thin + burn treatments (fig. 1A). 

Thinning removed over two-thirds of the trees in the small and medium size 

classes, and removed some trees >40cm d.b.h. (fig. 2). Residual trees were 

generally vigorous, with few signs of mistletoe or other insects or diseases. Less 

than 2 percent of the residual trees had signs of mechanical scarring related 

to the thinning treatment. The percentage of Douglas-fir stems increased 

slightly following thinning; however, all changes in species composition were 

<10 percent (by density) and no significant differences were found among 

treatment combinations (P>0.5847).

The burning treatment caused minimal tree mortality, most of which was 

restricted to the smaller diameter classes. Evidence of bole scorching was 

found on over half of the trees on plots that received only a burn; however, 

fires burned into the crowns of only 20 percent of the trees on these plots. 

The burn-only treatment caused only 8 percent mortality for all size classes 

of trees, which was not significantly different than the 1 percent background 

mortality observed on control plots (P=0.5061; fig. 1A). Burning alone removed 20 percent of small diameter trees and its effect on larger size classes 

was negligible, with more than twice as many trees in the 20 to 40cm d.b.h. 

class remaining on burned only units as compared to the thinned areas 

(fig. 2). Under the thin + burn treatment, fire scorched about two-thirds of 

the trees and fire-related mortality was higher under this treatment as compared to burning alone (fig. 2). The burn-only treatment had no effect on 

species composition with burning causing <2 percent increase in Douglas-fir 

on average (P>0.6020).
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Comparing the Effectiveness of Thinning and Prescribed Fire for Modifying Structure in Dry Coniferous Forests Harrod, Povak, and Peterson

Figure 1—Changes in (A) live tree density, (B) live tree basal area, (C) sapling 

density, (D) canopy cover, (E) quadratic mean diameter, and (F) height to live 

crown base following treatments in dry-forests of central Washington. Error 

bars represent standard deviation. Treatments with the same letter indicate no 

significant differences between the absolute change in the attribute from pre- to 

posttreatment (P<0.1).

Figure 2—Causes of tree mortality by diameter class (cm) within each treatment 

combination. The full bars in each treatment are equal to the pretreatment 

diameter distribution for live trees and the bottom “surviving tree” bars 

are approximately equal to the posttreatment diameter distribution (actual 

posttreatment diameter distributions may differ due to tree recruitment 

into other diameter classes over the course of the experiment). The “other” 

mortality category includes trees that died of either unknown or natural 

causes not directly attributable to the experimental treatments.
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Comparing the Effectiveness of Thinning and Prescribed Fire for Modifying Structure in Dry Coniferous Forests Harrod, Povak, and Peterson

All treatment combinations caused a decline in sapling densities over the 

course of the experiment. Thinning caused a ~45 percent decline in sapling 

density when used alone and >80 percent when used in combination with 

burning. The burn-only treatment also reduced sapling densities, but to a 

somewhat lesser extent (fig. 1C). The thin + burn treatment led to a drastic 

and significant decline in future tree recruitment compared to the control 

(P=0.0809), and thinning alone and burning alone had a similar yet lesser 

effect on reducing sapling densities.

Burning alone was not effective at reducing tree stocking, while thinning 

led to a marked reduction in basal area (BA) and an increase in average stand 

diameter. Prior to the experiment, most stands were at or near full stocking 

for their respective plant associations (as defined by Cochran and others 

1994; see fig. 3). Basal area on burn-only units increased slightly over the 

course of the experiment, but was not significantly different than the trends 

observed on control units (P=0.9986; fig. 1B). The thin-only treatment 

reduced BA by almost half following treatment, which was significantly less 

than observed on control units (P<0.0001). The thin + burn treatment had 

a somewhat greater effect on reducing BA compared to the thin-only treatment; however, no significant differences were found between treatments 

(P=0.8754). Thinning, in general, reduced stocking to between 50 and 75 

percent of full stocking, whereas burning alone led to an increase in stocking 

(fig. 3). Ponderosa pine dominated units responded similarly to the thin-only 

and thin + burn treatments and were well below 50 percent stocking following 

treatment. The decline in stocking levels on thin-only and thin + burn units 

led to a significant increase in average live tree diameter (P<0.0001; fig. 1E). 

Average diameter on burn-only units also increased following treatment; 

however, this change was not significant and mimicked control units where 

increases in stand diameter were a function of tree growth and suppressed 

tree mortality (P=1.000).

Figure 3—Pre- and posttreatment 

stocking for the 12 treatment sites 

located in central Washington. The 

dotted lines show the change in 

stocking for individual units following 

treatments. Upper management zone 

(UMZ) and lower management zone 

(LMZ) values are equal to 75 and 50 

percent full stocking, respectively. 

Quadratic mean diameters (cm) 

are presented for each calculated 

stocking level.
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Comparing the Effectiveness of Thinning and Prescribed Fire for Modifying Structure in Dry Coniferous Forests Harrod, Povak, and Peterson

Snag Dynamics Under Thinning and Burning Treatments

Burning and thinning treatments had dissimilar effects on snag density, 

and snag populations did not follow the same trends as live trees. The density 

of snags prior to treatment was highly variable between sites; however, no 

significant differences were found among treatments (P>0.3087). Pretreatment snag densities ranged from 8 to 83 ha–1 and averaged 47 ± 7 ha–1 across 

all units with a higher density of snags on control units and fewer snags on 

thin + burn units (fig. 4). About three-quarters of the snags in all treatments 

were <20cm d.b.h. with densities attenuating in sequentially larger size classes 

in a reverse-J pattern. Burning significantly increased snag densities on both 

burn-only and thin + burn units (P=0.0124). Increases in snag density on 

burn-only units were generally restricted to trees <20 cm d.b.h.; however, 

the thin + burn treatment created snags in all size classes (fig. 4). Thinning 

alone, conversely, led to a ~50 percent reduction in snag density, which was 

significantly different compared to the thin + burn (P=0.0286). Thinning 

alone removed snags in all diameter classes, with the largest reductions occurring in the <20 cm d.b.h. class.

Treatment effects on snag basal area differed somewhat from the changes 

observed in snag density. Pretreatment basal area of dead standing trees 

ranged from 0.2 to 2.9 m2 ha–1 and averaged 1.6 m2 ha–1 across all treatments. 

Thin-only units had the highest snag BA prior to the experiment, and was 

significantly different to the thin + burn treatment with the least amount of 

snag BA (2.4 and 0.8 m2 ha–1, respectively; P=0.0359). Snag BA remained 

relatively constant on both control and burn-only units, whereas the thinonly treatment reduced snag BA by >50 percent. Snag BA more than doubled 

under the thin + burn treatment, and had the most snag BA of all treatments 

by the end of the study. The main effect of burning was a significant increase 

in snag BA (P=0.0178); however, there were no significant differences found 

among treatments (P>0.1020).

Figure 4—Diameter distribution (cm) and species composition of dead standing trees pre- and 

posttreatment in dry forests of central Washington. The “other” species category includes western 

larch and grand fir.
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Comparing the Effectiveness of Thinning and Prescribed Fire for Modifying Structure in Dry Coniferous Forests Harrod, Povak, and Peterson

Changes in Canopy Structure Following Treatments

Canopy base height (CBH) increased over the course of the experiment 

under all treatment combinations; however, this increase was greatest under 

the thin + burn treatment. The burn only treatment raised CBH by ~1 m, 

but this was not significantly different as compared to the control (P=0.9208; 

fig. 1F). Thinning had a much larger effect on CBH, with the thin-only treatment and thin + burn treatment causing a ~3.5 and ~5.5 m increase in CBH, 

respectively. The rise in CBH on both thin-only and thin + burn treatments 

was significantly different compared to the control (P=0.0917, P=0.0101, 

respectively), and the thin + burn was significantly different compared to 

the burn-only (P=0.0232).

Canopy fuel loadings were greatly reduced by the thinning treatments, 

while burning was not effective at altering canopy structure. Canopy bulk 

density and canopy fuel loading remained relatively constant under the control and burn-only treatments, with values somewhat higher on control plots 

(fig. 5). The thin-only and thin + burn treatments both caused a significant 

two-fold reduction in canopy bulk density on average, compared to burnonly and control units (P=0.0006). Canopy bulk densities in both thin-only 

and thin + burn treatments were significantly different from burn-only and 

control treatments (P<0.0294). Similar trends were observed for canopy fuel 

loadings, with significant reductions occurring on thin-only and thin + burn 

units (P<0.0001). Both thin-only and thin + burn treatments led to significant declines in canopy fuel loadings as compared to the control treatment; 

however, the thin + burn treatment reduced canopy fuels by >6 Mg ha–1, 

while thinning alone caused only a 3.5 Mg ha–1 decline. The greater reductions on thin + burn units may be due to greater pretreatment canopy fuel 

loading (fig. 5).

Figure 5—Changes in (A) canopy bulk density and (B) 

canopy fuel loading as calculated by Crown Mass®. Bars 

with differing letters are significantly different (P<0.1).
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Comparing the Effectiveness of Thinning and Prescribed Fire for Modifying Structure in Dry Coniferous Forests Harrod, Povak, and Peterson

Canopy cover increased slightly when burning was used alone; while the 

reduction in canopy fuels on thin-only and thin + burn units led to a more 

open canopy. The minimal effect of burning alone on canopy cover was 

similar to the unmanaged control (P=0.9991, fig. 1D). However, thinning 

accounted for a 10 percent reduction in canopy cover on average over all 

thin-only and thin + burn units. This decline was significant compared to 

control and burn-only units (P=0.0363). No added benefit was found when 

thinning and burning were used in combination as compared to thinning 

alone (P=0.9662, fig. 1D).

Discussion

Tailoring Management Prescriptions Using 

Density Management Guidelines

Density management guidelines can be used to regulate forest stands to 

achieve a broad range of management objectives (Newton 1997). The natural 

range of variability (NRV) in forest density is often used to determine reference points in which to judge the ultimate success of restoration treatments. 

Using NRV to tailor management prescriptions is an attractive way to revert 

forest stands to previous structural conditions that were controlled by disturbance processes in place prior to the influence of Euro-Americans. Previous 

studies have identified a range of historic densities for forest types similar to 

our study (Avery and others 1976; Covington and Moore 1994; Harrod and 

others 1999; Morrow 1985). Harrod and others (1999) reconstructed stand 

structure in forests adjacent to our study site and found presettlement stocking varied between plant association groups (PAG) and ranged from 27 to 

68 trees ha–1 and 10 to 22 m2 ha–1. In our study, thin-only and thin + burn 

units following treatment differed somewhat from historical forests, with 

densities and BA averaging 4.6 and 2.8 times greater than historical values, 

respectively. However, these values may not be directly comparable due to 

the relatively young age and early seral stage of development of our study 

forests. For instance, historical stands investigated by Harrod and others 

(1999) were dominated by trees nearly twice the average diameter of posttreatment stands in our study.

Insect populations are often dependent on host densities, with increasing 

tree densities leading to higher probabilities of insect outbreaks. Sartwell 

and Stevens (1975) studied mountain pine beetle (Dendroctonus ponderosae) 

outbreaks in ponderosa pine forests of the Interior Western United States and 

found beetle outbreaks to be appreciably increased in even-aged ponderosa 

pine forests with >34 m2 ha–1 BA. In our study, none of the thin-only or 

thin + burn units exceeded the BA threshold following treatment; however, seven out of the 16 burn-only plots (44 percent) had BA >34 m2 ha–1. 

Because we were working primarily in mixed-conifer stands, 34 m2 ha–1 is 

likely a conservative threshold for our study sites, and higher stocking values 

may be acceptable. The upper management zone (UMZ) defined by Cochran and others (1994) is the stocking level at which a substratum of small 

diameter trees begins to develop, making forests increasingly susceptible to 

insect-­related mortality. After accounting for the lower maximum-stocking 

associated with the PAs in this study we calculated a UMZ of 583, which 

is similar to other UMZ values for interior ponderosa pine and Douglas-fir 

forest types (Day 2005; Cochran and Barrett 1999; Long and Shaw 2005). 
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Comparing the Effectiveness of Thinning and Prescribed Fire for Modifying Structure in Dry Coniferous Forests Harrod, Povak, and Peterson

However, Harrod and others (1999) reported a lower UMZ for stands adjacent to the Mission Creek watershed (SDI=263). These values differ because 

Harrod’s calculations (1) were based on ponderosa pine dominated stands 

that had considerably lower full-stocking values compared to the Douglas-fir 

PAs used in the current study (Lillybridge and others 1995) and (2) used 

slightly different UMZ calculations that were adjusted for site productivity 

(Cochran and others 1994).

Role of Fire in Altering Forest Structure

Since the use of fire suppression activities 50 to 100 years ago, interior 

ponderosa pine and Douglas-fir forests have experienced a drastic decline in 

fire frequency (Cooper 1960; Covington and Moore 1994; Everett and others 

2000; Fry and Stephens 2006), which has led to the development of a dense 

substratum of small-diameter trees and an accumulation of coarse woody 

debris. Prescribed fire is often reintroduced into these ecosystems as a way to 

restore past forest structure; however, little empirical evidence supports the 

use of prescribed fires alone in forest restoration (Sackett and others 1996; 

Thomas and Agee 1986; Youngblood and others 2006). Possible solutions for 

manipulating overstory structure with fire under these circumstances include 

using a single high-intensity prescribed fire (Fulé and others 2004) or to use 

repeated burnings where treatment effects are amortized over time (Miller 

and Urban 2000). Peterson and others (1994) found that repeated burnings 

at 4- to 6-year intervals reduced surface and ladder fuels and increased growth 

in larger residual ponderosa pine trees.

The prescribed burns used in the current study removed only the smallest 

diameter trees and had an overall negligible effect on forest structural components. Observations made during the burns suggest that the fire intensity was 

variable within and among study sites. For our study area, Agee and Lolley 

(2007) reported on the treatment effects on surface fuels and noted that the 

effect of fire was negligible over 50 to 75 percent of the study area. In one 

burn-only unit in particular, fire scorched <5 percent of the crowns of trees 

and less than a third of all trees had any signs of burning. These results suggest that a single, early season burn is ineffective at reducing tree density, but 

may be effective at reducing some forest floor fuels (Agee and Lolley 2007). 

Spring burns often cause lower overstory mortality as compared to fall burns, 

due to the high moisture content in surface and foliar fuels following greenup. Youngblood and others (2006) studied the effect of autumn prescribed 

fires on overstory structure in ponderosa pine/Douglas-fir forests of eastern 

Oregon; however, they found that the late-season burns had a similar null 

effect on overstory structural components as observed in our study.

Trees killed directly by the burn were often left standing following treatment, thereby adding to snag recruitment. Snags are an important aspect of 

forest structure, particularly for primary and secondary cavity nesters and 

insect populations (Raphael and White 1984; Harmon and others 1986; 

Bull and others 1997). While the prescribed burns did increase snag density in this study, most were <20 cm d.b.h.. Cavity nesting birds select trees 

with larger than average diameters for nesting (Raphael and White 1984) 

and several species require snags >25 cm d.b.h. for suitable nesting habitat 

(Ganey and Vojta 2004; Pilliod and others 2006; Thomas and others 1979). 

Therefore, the general increase in snags should be viewed cautiously in terms 

of wildlife habitat.
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 341

Comparing the Effectiveness of Thinning and Prescribed Fire for Modifying Structure in Dry Coniferous Forests Harrod, Povak, and Peterson

The use of prescribed fire has obvious other advantages as a management 

tool including reducing ground fuels, increasing nutrient availability and 

net primary productivity, and reintroducing a natural process back into a 

historically fire-prone system (Weatherspoon 1996). The spring burns conducted in the experiment were cooler and spottier than were desired and 

may have led to results that downplay the efficacy of fire to meet forest restoration goals. Evaluations of drier spring burns or fall burns are needed to 

fully understand the utility of prescribed burning to meet restoration goals 

(McCandliss 2002).

Reducing Fire Potential with Mechanical Thinning

Thinning alone was capable of reducing canopy fuels, canopy bulk density, and tree stocking by >50 percent. Similar results were found in fire and 

fire surrogate experiments in eastern California and Oregon (Stevens and 

Moghaddas 2005; Youngblood and others 2006). However, these results 

ignore the deposition of additional fine- to medium-sized surface fuels created by the thinning treatments that increase flame lengths and torching 

potential (Agee and Skinner 2005; Peterson and others 2005). Youngblood 

and others (2006) studied treatment effects on forest structure in ponderosa 

pine forests of eastern Oregon and found that coarse woody debris density 

increased when thinning was used alone as compared to when burning was 

used alone and in combination with thinning. These results are typical for 

thinning operations (Cram and others 2006; McIver and Ottmar 2007), 

and the increased fire hazard from the accumulation of these fuels is often a 

deterrent for the use of thinning alone. However, when the torching index 

(a measure of the windspeed required to propagate fires into the canopy) 

was modeled in Crown Mass® using fuel loads comparable to those present following thinning in our study areas indices averaged ~100 km hr–1. 

This suggests that thinning alone was capable of increasing fire resiliency by 

reducing tree stocking and crown fuels enough to counteract the resultant 

accumulation of slash.

Treatment rotations for individual stands often exceed 15 to 20 years; 

therefore, treatments should reduce stocking levels sufficiently to cover the 

lag between subsequent treatments. In this study, the limited effects of burning on controlling tree stocking and canopy fuels may diminish quickly and 

stands in our study will most likely revert to pretreatment stand structures. 

The drastic effect of thinning on stocking suggests that this treatment is 

necessary if only a single-entry treatment is feasible and large time gaps between treatments are expected.

The use of mechanical thinning also leaves open the opportunity to combine fuels reduction treatments with other silvicultural treatments elsewhere 

on the landscape. For instance, group selections and other uneven-aged 

management practices are becoming increasingly popular in ponderosa pine 

forests (Youngblood 2005). Incorporating alternative silvicultural prescriptions with regular fuels reduction operations will increase heterogeneity on 

the landscape and benefit wildlife (Graham and others 2004), aesthetics, 

increase landscape heterogeneity, and also help recoup some of the costs 

associated with fuels reduction treatments. Uneven-aged management has 

proved successful at reducing wildfire severity in similar forest types (Omi 

and Martinson 2002; Pollet and Omi 2002).
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 342

Comparing the Effectiveness of Thinning and Prescribed Fire for Modifying Structure in Dry Coniferous Forests Harrod, Povak, and Peterson

Conclusions

Dry forests of the Western United States have undergone drastic structural 

changes over the past century that have contributed to an apparent threat 

of intense crown fire and insect outbreak potential over much of the region. 

Foresters need to be provided with sound, science-based treatment alternatives to effectively and responsibly manage these public lands. Results from 

our study suggest that thinning alone is capable of enhancing forest resiliency 

to fire and insect mortality by reducing overstory density and canopy fuel 

levels. Burning was not an effective method of controlling overstory fuel 

loads; however, by reducing surface and other ladder fuels (see Agee and 

Lolley 2007), burning may be used in conjunction with thinning to restore 

ecosystem structural and functional components.

Acknowledgments

This is Contribution Number 147 of the National Fire and Fire Surrogate 

Project (FFS), funded by the U.S. Joint Fire Science Program. We appreciate the field assistance of numerous workers: Scott Conlan, Beth Armbrust, 

Sara O’Neal, Mara McGrath, Dottie Knecht, Todd Jensen, Tim Hatch, 

Chad Yenney, Megan Whitmore, Kathleen Moran, Darci Carlson, Alisha 

Toombs, Pete Ohlson, James Dickinson, and many others. Jim Agee, Bob 

Gray, and Andy Youngblood provided helpful reviews which greatly improved 

the manuscript. We thank the Wenatchee River Ranger District, OkanoganWenatchee National Forests, for implementing both the prescribed burning 

and thinning treatments.

References

Agee, J.K. 1990. The historical role of fire in Pacific Northwest forests. In: Natural 

and prescribed fire in Pacific Northwest forests. J.D. Walstad, S.R. Radosevich, 

and D.V. Sandberg, eds. Corvallies, OR: Oregon State University Press: 25.37.

Agee, J.K. 1993. Fire Ecology of Pacific Northwest Forests. Washington, DC: 

Island Press.

Agee, J.K. 1994. Fire and weather disturbances in terrestrial ecosystems of the eastern 

Cascades. Gen. Tech. Rep. PNW-GTR-320. Portland, OR: U.S. Department of 

Agriculture, Forest Service, Pacific Northwest Research Station.

Agee, J.K. 1998. The landscape ecology of western forest fire regimes. Northwest 

Science, 24-34 (special issue).

Agee, J.K. 2003. Historical range of variability in eastern Cascade forests, Washington, 

USA. Landscape Ecology 18: 725-740.

Agee, J.K.; Lolley, M.R. 2007. Thinning and prescribed fire effects on fuels and 

potential fire behavior in an eastern Cascades forest, Washington, USA. Fire 

Ecology 2(2): 142-158.

 Agee, J.K.; Skinner, C.N. 2005. Basic principles of forest fuel reduction treatments. 

Forest Ecology and Management 211: 83-96.

Anderson, H.E. 1982. Aids to determining fuel models for estimating fire behavior. 

Gen. Tech. Rep. INT-122. Ogden, UT: U.S. Department of Agriculture, Forest 

Service, Intermountain Forest and Range Experiment Station. 22 p.
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 343

Comparing the Effectiveness of Thinning and Prescribed Fire for Modifying Structure in Dry Coniferous Forests Harrod, Povak, and Peterson

Avery, C.C.; Larson, F.R.; Schubert, G.H. 1976. Fifty-year records of virgin stand 

development in southwestern ponderosa pine, Gen. Tech. Rep. RM-22. Fort 

Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain 

Forest and Range Experiment Station. 71 p.

Brown, R.T.; Agee, J.K.; Franklin, J.E. 2004. Forest restoration and fire: principles 

in the context of place. Conservation Biology 18: 903-912.

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, Pacific Northwest Research 

Station. 55 p.

Cochran, P.H.; Barrett, J.W. 1999. Growth of ponderosa pine thinned to different 

stocking levels in central Oregon: 30-year results. Res. Pap. PNW-RP-508. 

Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest 

Research Station. 27 p.

Cochran, P.H.; Geist, J.M.; Clemens, D.L.; Clausnitzer, R.R.; Powell, D.C. 1994. 

Suggested stocking levels for forest stands in northeastern Oregon and southeastern 

Washington. Research Note PNW-RN-513. Portland, OR: U.S. Department of 

Agriculture, Forest Service, Pacific Northwest Research Station. 21 p.

Cooper, C.F. 1960. Changes in vegetation, structure, and growth of southwestern 

pine forest since white settlement. Ecological Monographs 30: 129–164.

Cortner, H.; Moote, M.A. 1999. The politics of ecosystem management. Washington, 

DC: Island Press. 179 p.

Covington, W.W.; Fule, P.Z.; Moore, M.M.; Hart, S.C.; Kolb, T.E.; Mast, J.N.; 

Sackett, S.S.; Wagner, M.R. 1997. Restoring ecosystem health in ponderosa pine 

frests. Journal of Sustainable Forestry 2: 153-181.

Covington, W.W.; Moore, M.M. 1994. Southwestern ponderosa forest structure, 

changes since white settlement. Ecological Monographs 30(2): 129-164.

Cram, D.; Baker, T.; Boren, J. 2006. Wildland fire effects in silviculturally treated 

vs. untreated stands of New Mexico and Arizona. Res. Pap. RMRS-RP-55. Fort 

Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain 

Research Station. 28 p.

Dahms, C.W.; Geils, B.W., tech. eds. 1997. An assessment of forest ecosystem 

health in the Southwest. Gen. Tech. Rep. RM-GTR-295. Fort Collins, CO: U.S. 

Department of Agriculture, Forest Service, Rocky Mountain Research Station.

Day, K. 2005. Stocking standards for uneven-aged interior Douglas-fir. University of 

British Columbia Faculty of Forestry Report. [Online] 29 pp. Available: http://

www.forestry.ubc.ca/resfor/afrf/Reports_Index.htm [March 26 2007].

Everett, R.L.; Schellhaas, R.; Keenum, D.; Spurbeck, D.; Ohlson, P. 2000. Fire 

history in the ponderosa pine/Douglas-fir forests on the east slope of the 

Washington Cascades. Forest Ecology and Management 129: 207-225.

Fry, D.L.; Stevens, S.L. 2006. Influence of humans and climate on the fire history 

of a ponderosa pine-mixed conifer forest in the southeastern Klamath Mountains, 

California. Forest Ecology and Management 223: 428-438.

Fulé, P.Z.; Cocke, A.E.; Heinlein, T.A.; Covington, W.W. 2004. Effects of an 

intense prescribed forest fire: Is it ecological restoration? Restoration Ecology 

12: 220–230.

Gaines, W.L.; Haggard, M.; Lehmkuhl, J.F.; Lyons, A.L.; R.J. Harrod. 2007. 

Short-term response of land birds to ponderosa pine restoration. Restoration 

Ecology XX.

Ganey, J.L.; Vojta, S.C. 2004. Characteristics of snags containing excavated cavities 

in northern Arizona mixed-conifer and ponderosa pine forests. Forest Ecology 

and Management 199: 323-332.
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 344

Comparing the Effectiveness of Thinning and Prescribed Fire for Modifying Structure in Dry Coniferous Forests Harrod, Povak, and Peterson

Ganz, D.J.; Dahlsten, D.L.; Shea, P.J. 2003 The post-burning response of bark 

beetles to prescribed burning treatments. Fire, Fuel Treatments, and Ecological 

Restoration: conference proceedings. (P. N. Omi and L. A. Joyce, eds.). Proc. 

RMRS-P-29. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, 

Rocky Mountain Research Station: 143–158.

Graham, R.T.; McCaffrey, S.; Jain, T.B. 2004. Science basis for changing forest 

structure to modify wildfire behavior and severity. Gen. Tech. Rep. RMRS-GTR120. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky 

Mountain Research Station. 43 p.

Habeck, J.R. 1990. Old-growth ponderosa pine-western larch forest in western 

Montana: ecology and management. The Northwest Environmental Journal 

6: 271–292.

Hall, F.C. 1977. Ecology of Natural Underburning in the Blue-Mountains of Oregon. 

PNW R6-ECOL-79-001. Portland, OR: U.S. Department of Agriculture, Forest 

Service, Pacific Northwest Research Station.

Harmon, M.A.; 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.; Lienkamper, 

G.W.; Cromack Jr., K.; Cummins, K.W. 1986. Ecology of coarse woody debris 

in temperate ecosystems. Adv. Ecol. Res. 15: 133–302.

Harrod, R.J.; McRae, B.H.; Hartl, W.E. 1999. Historical stand reconstruction in 

ponderosa pine forests to guide silvicultural prescriptions. Forest Ecology and 

Management 114: 433-446.

Hawksworth, F.G. 1977. The 6-class dwarf mistletoe rating system. Gen. Tech. 

Rep. RM-48. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, 

Rocky Mountain Research Station.

Hessburg, P.F.; Agee, J.K.; Franklin, J.F. 2005. Dry forests and wildland fires of the 

inland Northwest USA: contrasting the landscape ecology of the pre-settlement 

and modern eras. Forest Ecology and Management 211(1-2): 117-139.

Hurlbert, S.H. 1984. Pseudoreplication and the design of ecological field experiments. 

Ecological Monographs 54: 187-211.

Keen, F.P. 1943. Ponderosa pine tree classes redefined. Journal of Forestry 41: 

249–253.

Lillybridge, T.R.; Kovalchik, B.L.; Williams, C.K.; Smith, B.G. 1995. Field guide 

for forested plant associations of the Wenatchee National Forest. Gen. Tech. Rep. 

PNW-GTR-359. Portland, OR: U.S. Department of Agriculture, Forest Service, 

Pacific Northwest Research Station. 335 p. In cooperation with: Pacific Northwest 

Region, Wenatchee National Forest.

Long J.N.; Shaw, J.D. 2005. A density management diagram for even-aged ponderosa 

pine stands. Western Journal of Applied Forestry 20: 205-215.

McCandliss, D.S. 2002. Prescribed burning in the Kings River Ecosystems Project 

Area: lessons learned. In: Proceedings of a symposium on the Kings River 

Sustainable Forest Ecosystems Project: progress and current status. Gen. Tech. 

Rep. PSW-GTR-183. Albany, CA: U.S. Department of Agriculture, Forest Service, 

Pacific Southwest Research Station: 37-46.

McHugh, C.W.; Kolb, T.E.; Wilson, J.L. 2003. Bark beetle attacks on ponderosa 

pine following fire in northern Arizona. Environmental Entomology 32(3): 

510-522.

McIver, J.D.; Ottmar, R. 2007. Fuel mass and stand structure after post-fire logging 

of a severely burned ponderosa pine forest in northeastern Oregon. Forest Ecology 

and Management 238(1-3): 268-279.

Miller, C.; Urban, D.L. 2000. Connectivity of forest fuels and surface fire regimes. 

Landscape Ecology 15(2): 145-154.

Morrow, R.J. 1985. Age structure and spatial pattern of old-growth ponderosa pine 

in Pringle Falls Experimental Forest, central Oregon, M.S. thesis. Corvallis, OR: 

Oregon State University. 80 p.
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 345

Comparing the Effectiveness of Thinning and Prescribed Fire for Modifying Structure in Dry Coniferous Forests Harrod, Povak, and Peterson

Newton, P.F. 1997. Stand density management diagrams: Review of their development 

and utility in stand-level management planning. Forest Ecology and Management 

98: 251-265.

Omi, P.N.; Martinson, E.J. 2002. Effect of fuels treatment on wildfire severity. Final 2002. Effect of fuels treatment on wildfire severity. Final 

Report. Submitted to the Joint Fire Science Program Governing Board. Available 

online: http://www.cnr.colostate.edu/frws/research/westfire/FinalReport.pdf 

[March 7, 2007].

Patton-Mallory, M. 1997. Southwest wildland/urban interface fire risk reduction 

workshop; August 4-5, 1997, Flagstaff, AZ. Unpublished summary report on 

file. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky 

Mountain Research Station. 15 p.

Peterson, D.L.; Johnson, M. C.; Agee, J. K.; Jain, T.B.; McKenzie, D.; Reinhardt, 

E. 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, Pacific Northwest Research Station.

Peterson, D.L.; Sackett, S.S.; Robinson, L.J.; Haase, S.M. 1994. The effects of 

repeated prescribed burning on Pinus ponderosa growth. International Journal 

of Wildland Fire 4(4): 239-247.

Pilliod, David S.; Bull, Evelyn L.; Hayes, Jane L.; Wales, Barbara C. 2006. Wildlife 

and invertebrate response to fuel reduction treatments in dry coniferous forests of 

the Western United States: a synthesis. Gen. Tech. Rep. RMRS-GTR-173. Fort 

Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain 

Research Station. 34 p.

Pollet, J.; Omi, P.N. 2002. Effect of thinning and prescribed burning on crown fire Effect of thinning and prescribed burning on crown fire 

severity in ponderosa pine forests. International Journal Wildland fire 11: 1-20.

Raphael, M.G.; White, M. 1984. Use of snags by cavity-nesting birds in the Sierra 

Nevada. Wildlife Monographs 86: 1–66.

Sackett, S.S.; Haase, S.M.; Harrington, M.G. 1996. Lessons learned from fire use 

for restoring southwestern ponderosa pine ecosystems. Gen. Tech. Rep. RMGTR-278. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, 

Rocky Mountain Research Station: 53-60.

Sartwell, C.; Stevens, R.E. 1975. Mountain pine beetle in ponderosa pine: prospects 

for silvicultural control in second-growth stands. Journal of Forestry 73: 136-

140.

Scott, J.H. 1998. Fuel reduction in residential and scenic forests: a comparison of 

three treatments in a western Montana ponderosa pine stand. Res. Pap. RMRSRP-5. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky 

Mountain Research Station. 19 p.

Scott, J.H.; Reinhardt, E.D. 2001. Assessing crown fire potential by linking models 

of surface and crown fire behavior. Res. Pap., RMRS-29. Fort Collins, CO: U.S. 

Department of Agriculture, Forest Service, Rocky Mountain Research Station. 

59 p.

Shaw, J.D. 2000. Application of stand density index to irregularly structured stands. 

Western Journal of Applied Forestry 15: 40–42.

Shaw, J.D. 2006. Reineke’s stand density index: where are we and where do we 

go from here? In: Proceedings of the Society of American Foresters National 

Convention Proceedings, October 19-23, 2005. Ft. Worth, TX.

Smith, H. Y.; Anro, S.F., eds. 1999. Eighty-eight years of change in a managed 

ponderosa pine forest. Gen. Tech. Rep. RMRS-GTR-23. Ogden, UT: U.S. 

Department of Agriculture, Forest Service, Rocky Mountain Research Station. 

55 p.

Stephens, S.L. 1998. Evaluation of the effects of silvicultural and fuels treatments 

on potential fire behaviour in Sierra Nevada mixed-conifer forests. Forest Ecology 

and Management 105: 21-35.
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 346

Comparing the Effectiveness of Thinning and Prescribed Fire for Modifying Structure in Dry Coniferous Forests Harrod, Povak, and Peterson

Stevens, S.L.; Moghaddas, J.J. 2005. Experimental fuel treatment impacts on forest 

structure, potential fire, and predicted tree mortality in a California mixed-conifer 

forest. Forest Ecology and Management 215: 21-36.

Thomas, J.W.; Anderson, R.G.; Maser, C.; Bull, E.L. 1979. Snags. In: Thomas, 

J.W. (tech. ed.), Wildlife Habitats in Managed Forests: The Blue Mountains of 

Oregon and Washington. USDA Agricultural Handbook 553. Washington, DC: 

U.S. Department of Agriculture: 60–77.

Thomas, T.L.; Agee, J.K. 1986. Prescribed fire effects on mixed-conifer forest 

structure at Crater Lake, OR. Canadian Journal of Forest Research 16: 1082-

1087.

van Wagtendonk, J.W. 1996. Use of a deterministic fire model to test fuel treatments

In: Sierra forests in Sierra Nevada ecosystem project, final report to Congress. Vol. 

II Assessments and scientific basis for management options. Davis, CA: University 

of California, Centers For Water And Wildland Resources.

Weatherspoon, C.P. 1996. Fire-silviculture relationships. In: Sierra forests in Sierra 

Nevada ecosystem project, final report to Congress. Vol. II Assessments and 

scientific basis for management options. Davis, CA: University of California, 

Centers For Water And Wildland Resources: 1167-1176.

Weatherspoon, C.P. 2000. A proposed long-term national study of the consequences 

of fire and fire surrogate treatments. In: Neuenshwander, L.F., Ryan, K.C., 

Goldberg, G.E. (eds.), Proceedings of the Joint Fire Science Conference and 

Workshop, Boise, ID June 15-17. Moscow, ID: University of Idaho:. 117-126.

Weatherspoon, C.P.; Skinner, C.N. 1996. Landscape-level strategies for forest fuel 

management. In: Sierra Nevada Ecosystems Project: Final report to Congress, Vol. 

II: Assessments, scientific basis for management options. Davis, CA: University 

of California, Centers For Water And Wildland Resources.

Woodall, C.W.; Fiedler, C.E.; Milner, K.S. 2003. Stand density index in uneven-aged 

ponderosa pine stands. Canadian Journal of Forest Management 33: 96-100.

Woodall, C.W.; Miles, P.D.; Vissage, J.S. 2005. Determining maximum stand 

density index in mixed species stands for strategic-scale stocking assessments. 

Forest Ecology and Management 216: 367-377.

Youngblood, A. 2005. Silvicultural Systems for Managing Ponderosa Pine. In: 

Ritchie, Martin W.; Maguire, Douglas A.; Youngblood, Andrew, tech. coord. 

2005. Proceedings of the Symposium on Ponderosa Pine: Issues, Trends, and 

Management. 2004 October 18-21; Klamath Falls, OR. Gen. Tech. Rep. PSWGTR-198. Albany CA: U.S. Department of Agriculture, Forest Service, Pacific 

Southwest Research Station. 281 p.

Youngblood, A.; Metlen, K.L.; Coe, K. 2006. Changes in stand structure and 

composition after restoration treatments in low elevation dry forests of northeastern 

Oregon. Forest Ecology and Management 234: 143-163.
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 347

Abstract—Reducing the risk of losses from wildfires that threaten homes and communities is a growing priority in Canada. To reduce risk, “FireSmart®” standards have 

been adopted nationwide for managing forest fuel. However, these standards largely 

disregard interests of wildlife and conservation of wildlife habitat – thus raising concerns among residents and other stakeholders. To be acceptable, fuel treatments in 

wildland/urban interface areas of Jasper National Park, Alberta, required that potential 

environmental impacts and the requirements of wildlife also be carefully considered. 

A research project conducted in conjunction with the Foothills Model Forest used 

literature and experimental manipulations to develop ecologically based approaches 

for treating fuel in ways that optimize conditions for wildlife, within constraints of current standards. The research was conducted during a 30-month prototype project on 

more than 250 ha of forest surrounding the community of Jasper, Alberta. The study 

concluded fuel treatments for the purpose of reducing wildfire risk can be compatible 

with wildlife habitat conservation and ecosystem restoration goals. This paper describes 

the interface challenges faced by park managers, explains the adaptive management 

approach used to develop practicable solutions, and describes resulting species-specific 

mitigations, guidelines, and best practices that satisfy community wildfire protection 

standards and ecosystem management objectives, concurrently.

Introduction

Canada is experiencing an increase in interface fires. The vulnerability of 

people, property, and forests has reached alarming levels during recent fire 

seasons and helped trigger the Canadian Wildland Fire Strategy (Canadian 

Intergovernmental Secretariat 2005). Bothwell (2006) documented more 

than 900 cases of structure loss to interface fire since 1980, and in the past 

10 years more than 250 communities and 700,000 Canadians have been 

threatened directly by wildfires (Natural Resources Canada 2005). In British 

Columbia, recent fire seasons showed a large upward trend related to the 

interface when compared to the 10-year average (Fuglem 2004). In 2003, 

more than 100 of 2,517 wildfires in British Columbia struck interface areas, 

and 15 were major incidents that caused evacuation of 50,000 people and 

destroyed 334 homes or businesses.

Several factors combine to underpin the need for more effective community 

wildfire protection, particularly in Western Canada: (1) increasingly dense 

country residential development (Duke and others 2003), (2) growing risk of 

human-caused ignitions, (3) warmer climate resulting in increased frequency, 

size, and severity of wildfires (Flannigan and others 1998; Flannigan and others 

2003), and (4) rising socio-economic costs of fire control (Filmon 2004).

FireSmart®-ForestWise: Managing Wildlife 

and Wildfire Risk in the Wildland/Urban 

Interface—a Canadian Case Study

Alan Westhaver1, Richard D. Revel2, and Brad C. Hawkes3

In: Butler, Bret W.; Cook, Wayne, 

comps. 2007. The fire environment—

innovations, management, and policy; 

conference proceedings. 26-30 March 

2007; Destin, FL. Proceedings 

RMRS-P-46CD. Fort Collins, CO: 

U.S. Department of Agriculture, 

Forest Service, Rocky Mountain 

Research Station. 662 p. CD-ROM.

1 Vegetation/Fire Specialist, Parks 

Canada, Jasper National Park, Alberta, 

Canada. alan.westhaver@pc.gc.ca

2 Faculty of Environmental Design, 

University of Calgar y, A lberta, 

Canada.

3 Natural Resources Canada, Canadian 

Forest Service, Victoria, British 

Columbia, Canada.
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 348

Western Canadian wildland fire managers and researchers have observed 

disturbing changes in the structure and density of forests formerly subject to 

frequent disturbance by fire, an upsurge in wildfire intensity at these locations, 

and a corresponding increase in the difficulty of wildfire control (Quintilio 

2005). For example, dense forest annually encroaches on ~30 km2 of open 

forest and grassland in the Rocky Mountain Trench of British Columbia 

(Kootenay National Park 2002). Remote sensing and ground plots revealed 

significant stand and vegetation changes in Jasper National Park (Rhemtulla 

1999; Mitchell 2005). Following the catastrophic 2003 fire season in British 

Columbia, a Provincial Review (Filmon 2004) concluded past fire suppression has contributed directly to fuel buildup in forests and that this buildup 

will result in more significant and severe wildfires, including more interface 

fires, unless action is taken. In Canada, many of the fire-adapted forests 

most severely impacted are at drier, low elevation areas most attractive to 

recreational and residential development (Duke 2001). Fuel buildup may be 

a lesser problem in the boreal forest where stand-replacing fires prevail.

These trends mirror the experience in the United States, where decades 

of fire exclusion policies have resulted in high accumulations of combustible 

fuels relative to conditions prior to 1900 (Mutch 1994; Graham and others 

2004; United States Department of Agriculture 2005). Covington and Moore 

(1994) described how once frequent low-intensity surface fires served to clean 

the forest floor of fine fuels and remove regenerating conifers. From a fire 

perspective, reduced fire activity results in increased fuel loads, increased fuel 

continuity, and enhanced probability of crown fire (Daigle 1996; Scott and 

Reinhardt 2001; Graham and others 2004).

Improvements to fire management such as training, response, and enhancing structural resistance to fire are important, but fuel reduction remains 

as a leading method to decrease the incidence and severity of interface fires 

(Partners in Protection 2003). The principles of wildfire behaviour form the 

basis for fuel modification standards. Byram’s equation for fireline intensity 

(Byram 1959) and more recent analysis by Countryman (1974) and Graham 

and others (2004) reveal that fuel is the only variable humans can manipulate to reduce the energy released by fire, whereas there is no control over 

weather or topography. Studies of wildfire behavior and severity in treated and 

untreated fuels by Martinson and Omi (2003) lend further support for fuel 

reduction approaches. Specifically, the ignition, development, and spread of 

wildfire are affected by characteristics of the fuel complex (Canadian Forest 

Service 1968). These include the total fuel load, fuel size and arrangement, 

the moisture and chemical content of fuels, and the “canopy base height” 

and bulk density of fuels (Scott and Reinhardt 2001; Cruz and others 2002). 

Interface residents and fire prevention agencies manipulate these fuel properties to reduce risk. When they do so, they are also affecting key aspects of 

wildlife habitat.

Fuel Management Standards in Canada

Current Canadian standards for interface fuel management were developed 

by the nonprofit organization “Partners in ProtectionTM” and first published 

in the manual FireSmart®: Protecting Your Community from Wildfire, in 

1999.

The FireSmart manual sets out preventive standards for management of 

forest fuels by individual homeowners, or agencies working at larger scales 

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100m 30m 10m

10m 20m 70m

Priority Zone 1

Priority Zone 3

Priority Zone 2

Figure 1—FireSmart fuel management Priority Zones within the wildland/urban 

interface (Partners in Protection 2003). 

to protect communities. The purpose of these standards is to limit wildfire intensity, ease fire suppression efforts, and prevent structural ignitions 

(Partners in Protection 2003). Canadian FireSmart standards are based on 

the National Fire Protection Association code, NFPA 1144 Standard for 

Protection of Life and Property from Wildfire (NFPA 2002). The standards 

employ the four basic strategies of fuel removal, reduction, isolation, or conversion to alter fuel bed properties to reduce the potential for fire initiation 

and propagation of crown fire.

These standards require that more fuel be removed as the distance to a 

home or structure decreases. Based on this, the concept of three concentric 

“interface Priority Zones” was adopted by Partners in Protection (2003). 

These zones and the treatments that are found in them served as one basis 

for this study (fig. 1).

Overall, Canadian fire protection agencies are meeting with limited success in convincing individuals or communities at the interface to voluntarily 

modify or manipulate forest structure on and around private property. The 

Auditor General for British Columbia (British Columbia 2001) noted that 

only 3 percent of communities in the province had undertaken significant 

levels of risk mitigation, and that little was being done in 55 percent of 

communities where wildfire risk levels were rated moderate to high. Only 

11 percent of communities had undertaken fuel reduction programs of any 

significance. Independent reviews following large interface fires in Alberta 

(DeSorcy 2001) and British Columbia (Filmon 2004) both concluded the 

lack of prefire preparation is a factor, and there is a great need to accelerate 

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fuel management programs, which can ameliorate this danger. Since these 

surveys, both provinces have instituted improved hazard assessment and fuel 

management programs; however, the reluctance of residents and communities to treat fuel more aggressively frustrates many wildland and municipal 

fire managers.

Several reasons for this lack of action have been documented (Boura 1996; 

Winter and others 2004; McCaffrey 2004b; Mc Gee and others 2005; Brenkert and others 2005): resident perceptions that wildfire risk is lower than 

it actually is; lack of knowledge about risk reduction measures; willingness 

by residents to accept the risk of wildfire losses; constraints on the ability, 

funds, or time to implement solutions; skepticism about the effectiveness of 

risk reduction measures; lack of trust in public agencies responsible for fuel 

management; and conflicts between risk reduction measures and other resource values held by residents such as wildlife, conservation, or aesthetics.

Evidence is building that recurrent conflicts between existing standards for 

risk/fuel reduction and other resource values such as wildlife conservation 

may deter interface residents or communities from taking preventive actions. 

In Australia, fire hazard reduction practices reduced environmental qualities 

and caused bitter social divisions within local communities (Boura 1996). 

In Arizona local citizens opposed forest thinning “because they moved here 

for the forest,” making it clear that not everyone was comfortable with the 

concept of cutting trees or openly protested such actions as being destructive 

(Winter and others 2004). Graham (2003) listed privacy, wildlife viewing, 

recreation, aesthetics, and ideas of naturalness as key landscape values that 

influenced the acceptability of fuel management activities. For many Jasper 

residents and property owners, it seemed difficult to reconcile FireSmart fuel 

treatments with the personal importance they placed on aesthetic values, 

wildlife viewing opportunities, and the “natural” environment they live in. 

Citizens were also concerned about secondary environmental impacts, such 

as soil erosion or stream pollution, which may result from mechanized fuel 

treatment (Westhaver 2003). Consequently, it is easy for conflict to arise 

between fire managers who advocate manipulation of vegetation, and conservationists who view these actions as destructive (Brown 2002).

Such controversies suggest deficiencies in current approaches to community and residential wildfire protection. Further evidence for deficiencies is 

found in careful review of current FireSmart standards, which reveals a preoccupation with physical characteristics of the fuel complex and disregard for 

other resource values such as wildlife, wildlife habitat, biodiversity, cultural, 

and aesthetic qualities. Likewise, few procedures for limiting the secondary 

environmental impacts associated with major fuel manipulation projects are 

included. Interestingly, FireSmart authors recognized these shortcomings 

but could find little information to address these issues (Partners in Protection 2003).

Conversely, fuel initiatives have the best chance of being implemented 

if managers provide effective responses to the questions, objections, and 

concerns of residents (Winter and others 2002; McCaffrey 2004a). A better understanding of how fuel reduction treatments affect forest resources is 

required to minimize and resolve conflicts and to more effectively manage 

wildlife habitat (Kotliar and others 2002). The hesitancy of interface residents to implement FireSmart measures, due to conflicts with other resource 

values or needs such as wildlife, conservation, and aesthetics, was a primary 

motivation for this research.

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Study Problem and Purpose

Based on the foregoing description, it is clear that conflicts between fuel 

modification to reduce wildfire risk and conservation of other resource values must be reconciled if objectives for protecting wildland/urban interface 

communities from wildfire are to be achieved. An urgent need for increased 

community wildfire protection, deep concerns of local citizens for wildlife 

and environmental protection, and Parks Canada’s mandate for ecological 

integrity provided an opportunity to integrate innovation and research into 

a prototype program of fuel treatment in the wildland/urban interface at 

Jasper, Alberta. This situation also makes the town of Jasper an ideal site for 

gauging public support for modified fuel treatments.

The primary purpose of this research was to develop, implement, and 

recommend practicable, ecologically based approaches for managing vegetation at the wildland/urban interface in ways that optimize conditions for 

wildlife, within constraints of current fuel treatment standards. A secondary 

purpose, not discussed in this paper, was to establish a methodology for 

monitoring the long-term effects of fuel management on wildlife habitat and 

use (Westhaver 2006).

Study Area

The 250+ ha study area is within a heavily forested area of Jasper National 

Park, Alberta, at the confluence of three large glacial valleys. These fuel 

treatment areas are arrayed around the town of Jasper (fig. 2) and the recreation cottage subdivision at Lake Edith. Research was conducted under the 

joint auspices of Parks Canada, the Foothills Model Forest, and University 

of Calgary.

The area lies in the valley-bottom “Montane” ecoregion (Holland and Coen 

1982) at an elevation of about 1100 m. This area is among the most productive and biologically diverse ecosystems in Jasper National Park (Holroyd and 

Van Tighem 1983). A wide range of forest types comprise much of the park’s 

critical ungulate winter range, and other specialized wildlife habitats.

Coniferous forests of the study area are mostly composed of mature 

lodgepole pine (Pinus contorta Dougl. ex Loud.), with less area of Douglasfir (Pseudotsuga menziessi (Mirb) Franco) forest and mixed conifer forest. 

White spruce (Picea glauca (Moench) Voss) and Douglas-fir are typically 

late-succession species with white spruce dominating conifer regeneration 

in mesic and hygric sites, and lodgepole pine and Douglas-fir regeneration 

in xeric sites. Plant species and communities in this ecoregion are adapted 

to frequent, low-intensity surface fire or mixed-intensity fire (Tande 1979; 

Achuff 1996; Andison 2000). Low intensity (stand maintaining) surface 

fires predominated in grasslands and open canopy Douglas-fir and lodgepole pine stands. High intensity (stand replacing) crown fires prevailed in 

moister, continuous pine stands on the valley sides and some valley bottom 

areas (Tande 1979). Due to the absence of fire for much of the past century, 

formerly open, savannah-like Douglas-fir forest and lodgepole pine forest in 

the study area have changed significantly. They are now characterized by a 

scattering of dominant widely spaced, large diameter Douglas-fir veterans 

(200 to 300 years old) that are ingrown with a dense multilayered canopy of 

shorter, smaller-diameter lodgepole pine and Douglas-fir.

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Figure 2—Wildland/urban interface operating areas surrounding the town of Jasper. 

FireSmart®- ForestWise: Managing Wildlife and Wildfire Risk in the Wildland/Urban Interface—a Canadian Case Study Westhaver, Revel, and Hawkes
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 353

Prior to Euro-American arrival, aboriginal peoples influenced these Montane ecosystems and the vegetation found here for more than 10,000 years 

(Heitzmann 2001; White 2001). Although aboriginal peoples appear to 

have ignited the majority of Montane fires, lightning ignitions also played 

a role in establishing ecosystem patterns, processes, and the plant composition now being managed (Wierzchowski and others 2002). Since settlement 

by nonnatives, anthropological use of fire has dramatically declined in this 

area (White 2001).

Impacts of the recent “fire-free” period on vegetation are significant. Over 

the past 65 years the amount of Montane forest older than 100 years has 

nearly quadrupled from 21 to 78 percent (Andison 2000). Between 1915 

and 1995, the proportion of the Montane occupied by grasslands and open 

forest habitats decreased by more than 50 percent, with similar decreases 

in the number of these patches/100 ha (Rhemtulla 1999; Mitchell 2005). 

Hammond (2003) stated that much of the current structure of Montane 

forest is an artifact of recent fire management practices, rather than reflective of natural processes and the historical range of variation. Subsequently, 

Parks Canada concluded that, due to fire control, the current fire regime and 

forest conditions are outside the historical ranges of variation, that fire must 

be actively restored, and that risks to developed areas must be ameliorated 

(Parks Canada 2000).

For Parks Canada, it is also important to avoid wildlife/human conflicts 

(Ralf and Bradford 1998); maintain grizzly bear habitat effectiveness (Parks 

Canada 2000); provide connectivity and corridors so that wildlife, particularly carnivores, are able to move freely through the landscape (Mercer and 

Purves 2000); and provide adequate protection for species listed under the 

Canadian Species at Risk Act.

The interface is also the portion of Jasper National Park that receives 

the majority of human development and use. Jasper, a community of about 

5,000 residents and 500 businesses, is the service centre for park visitors 

and administration, and a division point for Canadian National Rail. Up to 

20,000 visitors can be accommodated nightly and more than 2 million visitors use the park each year (Parks Canada 2000). Wildfire risk (La Morte 

and Associates 1996) in the town area is substantial and has been recognized 

for many years (Carnell and Anderson 1974; Haney and Anderson 1978; 

Fenton 1986; FireLine Consulting 1997; Mortimer 1998, 1999; Blackwell 

and Mortimer 2004). Risk arises from a combination of high probability of 

fire ignition from frequent lightning, industrial, transportation, and other 

human-caused sources and extreme consequences that would result, given 

the expectation of a fast-moving high intensity wildfire.

Starting in 1999, solutions to reduce wildfire risk were methodically implemented. Initial efforts focused on infrastructure improvements, structural 

modifications, and better emergency preparedness (Jasper Interface Steering Team 2002). By 2001, efforts shifted to fuel reduction, by landowners 

and Parks Canada. At the same time, residents and park managers became 

increasingly aware that fuel manipulations could adversely affect wildlife and 

wildlife habitat, and deficiencies in FireSmart standards grew more apparent. Without more appropriate fuel measures to address resource and social 

concerns, it seemed unlikely that sufficient public or management support 

for fuel reduction programs would be obtained. It also seemed obvious that 

improvements to standard fuel treatments could, and should, be made. This 

study addresses these deficiencies.

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USDA Forest Service Proceedings RMRS-P-46CD. 2007. 354

Methods

This study employed a combination of literature review, experimentation 

learning through adaptive management (Walters and Holling 1990), and 

deductive analysis to develop innovative fuel treatments that better accommodate wildlife while managing interface vegetation to reduce wildfire risk. 

Improved fuel treatments were applied by labor crews and specialized industry 

contractors, then evaluated and refined by Parks Canada in a 250-ha prototype 

fuel management project at Jasper, Alberta. The work took place over three 

winters between 2003 and 2005. This approach was adopted as the best means 

of achieving the goals of this study and overall risk reduction objectives at 

Jasper, given the constraints of time, and the near absence of reproducible 

scientific studies specific to fuel treatments in the wildland/urban interface. 

Figure 3 summarizes the overall sequence of analytical steps employed to 

achieve the study purpose.

The key principles of wildland fire behavior (Van Wagner 1977; Forestry 

Canada Fire Danger Group 1992; Scott and Reinhardt 2001; Cruz and 

others 2002; Graham and others 2004) and home ignition (Cohen 2000a,b; 

1

IDENTIFY IMPACTS OF

STANDARD FUEL

TREATMENTS

2

DEVELOP FUEL

TREATMENTS TO

ACCOMMODATE WILDLIFE

3

DESCRIBE MEANS OF

IMPLEMENTING IMPROVED

TREATMENTS

4

DEVISE METHODOLOGY

FOR MONITORING

EFFECTIVENES OF 2.2–2.5

5

CONCLUSIONS,

RECOMMENDATIONS, AND

KNOWLEDGE GAPS

F

E

E

D

B

A

C

K

L

O

O

P

Analyse Current

Fuel Standards

Determine Effects

on Forest + Habitat

Identify Wildlife

Requirements

Synthesize Findings, Determine Impacts of Standard Fuel Treatments on Wildlife

Identify

Opportunities

for Wildlife in

Current

Standards

Develop Species-specific

Mitigations, Eco-based

Guidelines, and Modified Fuel

Management Practices

Select Preferred Techniques +

Equipment to Implement

Improved Treatments

Apply Modified Treatments to

Prototype Project at Jasper, AB.

1.1 1.2 – 1.4 1.5

1.5

2.1 2.3 – 2.5

Reconcile Ecological

Restoration and Fuel

Reduction Needs

2.2

3.1 3.2

Figure 3—Schematic of research methods.

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USDA Forest Service Proceedings RMRS-P-46CD. 2007. 355

Cohen and Stratton 2003) were reviewed at the onset of the study. This was 

done to avoid violating the intent or efficacy of existing fuel treatment standards when proposing more environmentally sensitive methods. FireSmart 

standards were dissected and reorganized in relation to six horizontal fuel 

bed layers as described by Sandberg and others (2001) to facilitate a logical 

review of the ecological effects of fuel treatments and practical presentation 

of improved methods (table 1). For biological reasons, Sandberg’s ground 

fuel stratum (2001) was sub-divided into fine and coarse woody fuels, and 

the tree canopy sub-divided into overstory and understory, thus creating 

eight layers in this study.

Extensive literature reviews were conducted to identify potential impacts of 

fuel treatments on abiotic forest components such as insolation, temperature, 

wind flow, effective precipitation, relative humidity, soil moisture, and soil 

nutrient status. Literature was also surveyed about the habitat roles of each 

fuel bed stratum to help predict the direct and/or indirect effects of fuel treatments on each of these. Existing literature was reviewed to determine how 

fuel management treatments alter important habitat features. We chose habitat 

trees, forest edge, coarse woody debris, and wildlife corridors as important 

habitat attributes, and grasslands, aspen forest, and wetland/riparian areas as 

being significant habitat types. Next, we identified the potential impacts of 

fuel treatments on 41 species of wildlife common to the interface. Selected 

species included four cavity excavators, eight songbirds, six raptors, 12 small 

mammals, one bat, six carnivores, three ungulates, and one amphibian.

Table 1—Fuel bed layers (from Sandberg and others 2001) used to assess wildlife habitat 

impacts and develop mitigation techniques.

Fuel bed strata Characteristics

1. Ground fuel stratum Duff or organic soil horizons, roots, and buried wood.

 Generally, ground fuels are consumed by long

 duration smoldering fire, after passage of the flame

 front characteristic of surface and crown fires.

2. Moss-lichen-litter stratum “Fine” fuels consisting of bryoids and loose undecayed

 needles and leaves. These fuels burn during flaming

 and glowing combustion phases of surface fires.

3. Woody fuel stratum Fine (<0.76 cm), medium (0.76 to 7.5 cm), and

 coarse (>7.5 cm) woody material on, or in contact

 with, the ground; may be sound or rotting, and

 arranged individually (stumps), loosely, or in piles.

4. Low vegetation stratum Grasses and herbs. Cured fine fuels burn quickly

 contributing to surface fire intensity, but not to fire

 residence time.

5. Shrub stratum Dwarf shrubs, tall shrubs, and coniferous regeneration.

 Burn vigorously in surface fires to increase fire intensity

 depending on fuel moisture content. These fuels serve

 as connecting “fuel ladders” and transmit surface fire

 into the canopy.

6. Tree canopy stratum Crown or “aerial” fuels consisting of live and dead trees

 (needles and branches <0.76 cm) and arboreal lichens.

 Continuous, high density, canopies lead to crown fire

 and ember spotting.

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Current fuel reduction standards were examined, by fuel bed layer and 

interface Priority Zone, to discern prospects for incorporating measures that 

could improve wildlife and habitat quality, or at least reduce adverse impacts, 

without reducing effectiveness of fuel treatments. Literature pertaining to 

forest health, natural disturbance, and ecological restoration was then surveyed to establish possible linkages with vegetation manipulation for fire 

protection purposes.

Once potential mitigations for protection of wildlife and habitat were 

identified, these were incorporated into prototype fuel treatments and presented in the form of “prescriptions.” Unique prescriptions were developed 

for each forest type in the study area. To aid in assessing the practicability 

of the prototype fuel treatments they were first applied at several 0.5 to 

1.5 ha “demonstration sites.” Initially, Parks personnel worked with resident 

volunteers during neighborhood “work-bees” to implement fuel treatments. 

Later, between April 2003 and October 2005 these treatments were refined 

and applied on much larger areas around the town of Jasper, by specialized 

timber firms under contract to Parks Canada.

Results

Detailed information about FireSmart fuel standards, literature reviews, and 

step-wise analyses of impacts of fuel treatments on abiotic forest components 

and biological attributes of fuel beds, and the impacts of fuel treatments on 

41 species of birds, mammals, and amphibians common to wildland/urban 

interface areas are found in Westhaver (2006), which also provides the results 

of an analysis to identify opportunities for accommodating wildlife and habitat 

attributes in each forest/fuel bed stratum (within the constraints of current 

fuel treatment standards). See Westhaver also for details of other results of 

the prototype project not discussed in this paper, including a methodology 

for long-term monitoring of wildlife and habitat responses to fuel treatments, 

and assessment of mechanized techniques for implementing large-scale fuel 

treatments.

We investigated the potential for achieving ecological restoration concurrent 

with measures for community wildfire protection. That approach is advocated 

by several authors (Arno and Wakimoto 1987; Covington and Moore 1994; 

Fule and others 2001; Brown 2002; Omi and Joyce 2003) but was, to our 

knowledge, untested in Canada. We found strong similarities between the 

solutions required to resolve ecological problems in fire-dependent forests 

such as forest in-growth, forest encroachment, and replacement of deciduous species by conifers, and fire protection issues caused by hazardous fuel 

accumulations. We judged that, by selectively thinning the forest canopy to 

restore stand structure and composition to within their historic ranges of 

variability, the net effect is also to reduce wildfire risk. We also concluded 

that by using information about historical forest density and structure as a 

guide to thinning intensity, wildfire risk can be reduced (in some areas) to 

levels below what could be expected by applying FireSmart standards alone. 

These overlapping objectives did not extend to even-aged lodgepole pine 

forests initiated by stand-replacing fires. In these stands, prescribed thinning 

standards result in habitat conditions that depart from historic norms, but 

may still benefit wildlife. For example, marten may find more meadow vole 

prey within thinned stands, but the density of red squirrels in the interface 

zone is likely to be less than found during pretreatment levels.

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Numerous opportunities for maintaining or enhancing wildlife conditions 

within constraints of current fuel treatments were identified after examining 

FireSmart standards. Due to stricter needs for fuel removal close to structures, opportunities increased with increasing distance from structures. Five 

strategies were identified for managing the forest canopy to benefit wildlife: 

(1) variations of single-tree thinning, (2) cluster thinning, (3) selective 

preservation of habitat trees, (4) stand type conversion, and (5) selection for 

prevention of posttreatment windthrow. The opportunity analysis was carried 

out through other fuel bed/forest layers and yielded many more wildlife 

prospects in treated fuels (Westhaver 2003).

Species-Specific Mitigations for Wildlife and Habitat 

Conservation

A key result of this study was to synthesize information about the life/

habitat needs of wildlife common to the interface and identify mitigations 

to minimize the impact or obtain wildlife benefits within the context of 

current fuel treatments. A sampling of that content from Westhaver (2006) 

is provided in table 2. This synthesis resulted in a variety of species-specific 

conservation measures to minimize adverse impacts to wildlife or, alternatively, 

optimize wildlife benefits from fuel management treatments. Overall, we 

suggest that protection of habitat trees, coarse woody debris, and structural 

diversity within stands are the most significant mitigation factors. Speciesspecific mitigations were refined during three operating seasons of the Jasper 

prototype fuel management project to ensure their practicability. The full 

set of wildlife habitat requirements and mitigations are summarized in seven 

tables, grouped by species with similar habits and life requirements, and are 

presented in Westhaver (2006).

Ecosystem Based Fuel Management Guidelines – 

by Priority Zone and Fuel Bed Strata

For each interface Priority Zone and within each of the eight fuel bed strata, 

we developed and field-tested ecosystem based fuel treatment guidelines for 

benefits to wildlife or reducing potential adverse impacts of fuel management 

activities. Once again, these guidelines respect the overriding principle that 

FireSmart standards for reducing fire intensity be followed.

In Priority Zone 1, these guidelines provided for preserving or planting 

deciduous trees to provide important seasonal habitat, measures to allow 

selective retention of existing snags and creating additional snags by topping 

mature conifers, suggestions for preserving “feature” trees while reducing 

ignition potential, managing native shrubs to optimize forage, shade, and 

cover for wildlife, cultivating fire-resistant ground covers, and preserving 

isolated logs.

Even more extensive opportunities for accommodating wildlife are possible in Priority Zones 2 and 3. Guidelines were provided that recommend 

retention of all deciduous trees and offer a series of principles to preserve 

long-term wildlife benefits when deciding which conifers to retain or remove 

from mixedwood or pure conifer stands. Guidelines to identify and preferentially retain long-lived tree species and individuals with windfirm traits are 

presented. Trees with twin, multiple, and broken tops or fire scars should 

be retained since these deformities, and associated decay, make these trees 

highly suitable for wildlife nesting, roosting, and feeding sites. Exceeding the 

single-tree spacing standards is recommended to create forest gaps or open 

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forest habitats that provide habitat diversity while further decreasing fuel 

continuity, which significantly reduces fire spread rates while increasing ease 

of fire control. All habitat trees with nests and cavities should be preserved. 

Wildlife use can be increased by pairing habitat trees with living trees, or by 

preserving clusters of habitat trees. A minimum of 12 to 15 snags per hectare 

should be retained for optimal wildlife conditions. In the forest understory, 

rather than removing all coniferous regeneration, overtopping mature trees 

can be removed in some cases to provide increased wildlife cover and security while also allowing for long-term tree replacement and seral succession. 

At least 25 to 350 linear metres of logs should be left on the ground, with 

preferential protection for older, larger, and more decayed individuals.

S

Pileated

Woodpecker

Widespread but relatively uncommon year

round resident of most Canadian forests; has

a large territory; needs minimum 33 cm

Diameter at Breast Height (DBH) snags or

live trees with decay for excavating nests and

roosts; ants and insects in trees and logs are

main year round food; uses live hollow or

decaying trees for drumming; attracted to

sheltered clumps of dead trees and downed

logs.

Retain a mix of forest ages and types in the region;

retain 12-15 snags and 12-15 living trees with decay

(legacy trees) per ha of all diameters, species and

sizes with bias towards large diameter (>33 cm

DBH) trees; broken-top trees most important; use

cluster thinning technique to retain cover adjacent

habitat trees; retain trees infested with ants/insects;

retain up to 50 logs/ha on ground (long and large is

best) and extra snags for forage and future downed

logs; keep tall stumps of all sizes; survey areas for

active use by woodpeckers first.

Blackcapped

Chickadee

Common year-round resident Canada-wide;

feed by gleaning insects and insect eggs from

bark, twigs, boles, and foliage of trees and

shrubs from ground to crowns; seeds, berries

augment diet; can excavate nests in rotted

wood; use existing cavities/hollow trees;

stubs are important nest sites; select nest

trees <or= 10 cm. DBH, often in open areas;

roost in cavities/dense conifers out of wind.

Retain or create a variety of dead or dying trees of

different diameters and species for nesting and

foraging; preserve broken-topped trees, even short

stubs; thinning will encourage seed sources from

native flowering plants and berry production;

augment these with planted landscapes around

home and/or bird feeders; preserve shelter around

habitat trees and small clusters of conifers for

roosting out of the wind and rain.

Red-backed

Vole

Common in boreal and mountain forest

across Canada; closely linked with moist,

mossy, mature conifer forest; downed woody

material very important for cover; feeds

heavily on fungi (mushrooms) associated

with decaying wood; also eats seeds, insects,

berries; use squirrel middens; key prey items.

Leave abundant coarse woody debris, large logs,

small brush piles, and decaying matter to foster

fungus foods and provide shelter and moisture; use

cluster thinning and protect shrubby understory to

preserve pockets of dense forest and shaded sites;

limit thinning in moist forest areas where possible;

protect squirrel middens.

Weasel Coarse woody debris provides access to

under-snow environments and cover for

potential prey species; most common in

regenerating forest and grassy areas suited

to prey species, residual trees.

Leave abundant coarse woody debris, large logs,

and small brush piles where possible to foster

abundant prey and provide cover. Leave protruding

debris to provide access routes and under snow

travel routes; use cluster thinning and protect

shrubby understory to preserve pockets of dense

forest and shaded sites; protect squirrel middens.

White-tailed

Deer

Found across Canada in grassland, parkland

and boreal mixedwood; spring/summer diet

mostly flowering plants, grasses; browse on

deciduous trees and shrubs in winter; mostly

inhabit forest edges to feed in open and seek

cover in forest/shrubs; small conifer thickets

are winter refuge.

Interface areas can provide forest edge favorable to

white-tailed deer; encourage and preserve

deciduous shrubs and aspen during thinning; open

canopy will increase summer forage availability;

preserve thickets of coniferous saplings in

deciduous/mixedwood forest for cover (remove

mature conifers that overtop regeneration to

reduce fire spread).

Species Habitat Requirements

Mitigations to Minimize Impact or

Obtain Benefits

Table 2—Example habitat requirements and mitigations to minimize impacts of fuel management or obtain benefits 

for interface wildlife.

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“Best Practices”: Guidelines for Modified 

Fuel Management

This study also developed a series of general guidelines for modified fuel 

practices more sensitive to ecological considerations in the wildland/urban 

interface. These are not specific to wildlife species or interface priority zone.

Burning of woody debris in many smaller piles is recommended in preference to disposal methods such as chipping and spreading. This encourages 

nutrient cycling and hastens establishment of native plant cover. Restricting 

mechanized fuel operations to winter when organic layers are frozen and 

covered with insulation snow will minimize soil disturbance and disruption 

of low habitat structure. Curtailing the season and hours of mechanical 

operations allows wildlife migrations and diurnal movements. We found that 

key habitats such as forest edge could be enhanced by enlarging and making 

existing forest openings more irregular in shape, and that knowledge of 

wildlife habits and local animal movements can be used to preserve existing 

wildlife corridors or ensure adequate hiding/security cover for animals as 

they move between foraging sites. We recommend minimal vegetation disturbance around groundwater discharge areas, temporary pools, and moist 

depressions but compensating for this with adjacent areas of more stringent 

fuel reduction.

We also produced guidelines for adapting thinning from below methods 

to Douglas-fire and open lodgepole pine forest types and regimes of frequent 

fire. These guide tree retention decisions in terms of age, diameter, species, 

and spacing and utilize large veteran trees as “anchor” points to start the 

process. Conversely, decision rules were developed to adapt thinning from 

above methods for use in denser, even-aged lodgepole pine stands to encourage 

stand/habitat diversity and succession of shade tolerant species. Guidelines 

for minimizing posttreatment windthrow include selective retention of trees 

with both lateral and tap root systems, greater height and width of live crown, 

and low slenderness index along with liberal use of cluster thinning. Detailed 

guidelines for all types of wildlife trees are provided (Westhaver 2006).

Altogether, the foregoing species-specific mitigations, Priority Zone and 

fuel bed layer guidelines, and general best practices form a set of ecologically based criteria for modifying current fuel management practices in ways 

that benefit wildlife, but do not compromise risk-reduction objectives of 

FireSmart standards.

Discussion

Developing the above guidelines and mitigations by adaptive management approaches, during an operational fuel management project, proved to 

be an effective method for testing and refining practicable solutions. This 

approach allowed for continuous exchange between researchers and the 

manual crews, specialized forestry harvesting contactors, and equipment 

operators responsible for implementing them. That feedback resulted in many 

practical improvements. Also, adopting a collaborative approach involving 

operators and crews improved communications regarding wildlife conservation objectives, and led to a shared interest in achieving positive results. As 

an example, enthusiastic equipment operators offered up many abilities and 

mitigative actions that heavy equipment is capable of performing, but fuel 

managers were unaware.

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USDA Forest Service Proceedings RMRS-P-46CD. 2007. 360

Our assessment of potential ecological effects of standard fuel treatments 

revealed that manipulating fuel load, arrangement, and size distribution also 

resulted in substantial alterations to important wildlife habitat qualities. Specifically, we noted that fuel modification directly or indirectly affected most 

aspects of forest structure, forest composition, and forest function, and that 

these effects can lead to a wide range of adverse impacts upon wildlife and 

wildlife habitat. As a corollary, we also concluded that knowledge of these 

effects was useful to guide fuel manipulation programs in a more informed 

way, thus allowing adverse impacts to be avoided and potential wildlife benefits to be realized. We judged that the correlation between fuel bed strata 

and ecological or habitat layers was strong, and this analogy was useful for 

organizing, understanding, and presenting the effects of fuel manipulation, 

as well as developing more holistic fuel treatments.

Existing literature provided sound information about life requirements for 

wildlife. However, we found that most literature concerning wildlife response 

to forest disturbance was related to major events such as clearcut logging or 

wildfire, but there are few studies, and few experimental data, to verify the 

response of wildlife to fuel treatments that leave significant canopy cover, 

a less severe form of disturbance. Our forecasts of potential impacts of fuel 

treatments were hampered by this knowledge gap. Future wildlife studies in 

fuel treatments would be beneficial; in aid of this, pretreatment sampling of 

small mammal and ungulate activity was conducted in permanent 90 x 90 

m grids (Westhaver 2006).

The ability to provide benefits for habitat and wildlife varies between 

vegetation and fuel types. For example, it is desirable to preserve an ongoing 

supply of snags of all structural forms (Bull and others 1997), in a variety of 

topographic positions, and with a range of adjacent cover (Brittingham and 

De Long 1998). However, we found that the ability to do so was significantly limited in even-aged lodgepole pine stands, whereas opportunities to 

meet these objectives were plentiful in mixed conifer and Douglas-fir forest 

types. Forest edge can be increased or decreased during fuel treatments by 

varying the pattern and degree of canopy thinning, creating or augmenting forest openings, and aligning treatments with topographic or soil type 

boundaries. However, the utility of artificially created openings and edges 

is poorly known.

Study Limitations and Knowledge Gaps

This and other attempts to develop ecologically based fuel treatments 

will continue to be hampered until more field studies to scientifically assess 

the effects of fuel treatments and the responses of habitat and wildlife are 

conducted. In aid of this, a fixed plot sampling methodology for monitoring 

long-term changes in vegetation conditions and wildlife use was developed 

and put into place prior to fuel manipulations at Jasper (Westhaver 2006). 

Followup studies are strongly recommended in this prototype area, and other 

locations, to help fill current knowledge gaps. Aside from limitations that 

result in applying literature from other forest disturbances that are similar, but 

not identical to fuel treatments, are limitations that may result from conclusions drawn from studies conducted in similar but different ecosystems.

Given that this study was partly motivated by perceived conflicts between 

fuel treatments and other values held by interface residents, it is advisable 

that sociological research be initiated to determine if the improved treatments described by this study are, in fact, more acceptable to these people. 

We also suggest that, due to the great importance of coarse woody debris 

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USDA Forest Service Proceedings RMRS-P-46CD. 2007. 361

for wildlife, studies be directed at better determining threshold levels of 

debris that can be retained for wildlife purposes without compromising fire 

protection objectives.

Through this research, we are hopeful that wildland and municipal fire 

managers will augment the dominant viewpoint of “vegetation as fuel” with 

a more holistic perspective of vegetation as the basis for wildlife populations 

and other social or ecological values held by interface residents. In this way 

concerns of interface residents can be addressed, and a significant barrier to 

fire prevention actions removed.

Acknowledgments

The authors thank Parks Canada and the Foothills Model Forest for 

encouraging this research, a component of a large-scale operational undertaking called the FireSmart – ForestWise Communities Project in Jasper, 

Alberta. Our sincere gratitude goes to many local stakeholders who provided 

encouragement, provoked ideas, and shared in the passion of this project. 

We are indebted to Brian Wallace, Kevin Van Tighem, and Ron Hooper of 

Jasper National Park, and to Don Podlubny of the Foothills Model Forest. 

Our particular thanks go to the gifted field supervisors and members of field 

crews from the Metis Nation of Alberta, to the exceptional people from the 

forest industry, and especially to FireSmart Technicians Clayton Syfchuck, 

Mike Suitor, and Lorraine Wilkinson. Drs. Cormack Gates, Darren Bender, 

Bill Ross, Michael Mc Mordie, and Pauline Fisk of the University of Calgary 

provided invaluable support and feedback during this research. Above all, the 

lead author wishes to express his great appreciation to Lisa, Heather, Robin, 

and Theresa Westhaver for their loving support and patience throughout 

this adventure.

References

Achuff, P.L. 1996. Fire in the Rocky Mountain National Parks. Unpublished Parks 

Canada Paper presented at the National Prescribed Fire Workshop, La Maurice 

National Park. 19 p.

Andison, D.W. 2000. Landscape level fire activity on foothills and mountain 

landscapes of Alberta. Bandaloop Landscape-Ecosystem Services. Foothills Model 

Forest. Ecology Research Series, Report No. 2.

Arno, S.F.; Wakimoto, R.H. 1987. Fire ecology of vegetation common to wildland 

homesites. In: Fischer, W.C. and S.F. Arno, compilers. 1987. Protecting People 

and Homes From Wildfire in the Interior West. Proceedings. Gen. Tech. Rep. 

251. Ogden, UT: U.S. Department of Agriculture, Forest Service. Intermountain 

Research Station. 213 p.

Blackwell, B.A.; Mortimer, D. 2004. Jasper National Park municipal wildfire 

protection strategy. Final Report. Prepared by B.A. Blackwell and Associates 

and FireLine Consulting and Instruction Ltd. for Jasper National Park. Internal 

report.

Bothwell, P. 2006. Unpublished data. Fire Research Officer, Canadian Forest Service, 

Northern Forestry Centre, Edmonton, Alberta.

Boura, J. 1996. Fire and biodiversity: the effects and effectiveness of fire management. 

Biodiversity Series, Paper No. 8. 9p. http://www.environment.gov. au/life/

general_info/biodiver_8/paper22.html, accessed May, 2005.

FireSmart®- ForestWise: Managing Wildlife and Wildfire Risk in the Wildland/Urban Interface—a Canadian Case Study Westhaver, Revel, and Hawkes
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 362

Brenkert, H.; Champ, P.; Flores, N. 2005. Mitigation of wildfire risk by homeowners. 

Res. Note RMRS-RN-25. Fort Collins, CO. U.S. Department of Agriculture, 

Forest Service. Rocky Mountain Research Station. 9 p.

British Columbia. Office of the Auditor General. 2001. Managing interface fire 

risks. Report; 2001/2002:1. 111 p.

Brittingham, M.C.; De Long, C.A. 1998. Management practices for enhancing 

wildlife habitat. Pennsylvania State University. College of Agricultural Sciences, 

Cooperative Extension. 10 p.

Brown, R. 2002. Thinning, fire, and forest restoration: a science-based approach 

for National Forests in the Interior Northwest. Washington, DC: Defenders of 

Wildlife. 40 p.

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, Pacific Northwest Research 

Station. 55 p.

Byram, G.M. 1959. Combustion of forest fuels. Chapter 1: 61 – 89. In: Davis, 

K.P. (ed.). Forest fire control and use, 2nd edition. New York: McGraw-Hill.

Canadian Intergovernmental Conference Secretariat. 2005. Backgrounder: Canadian 

Wildland Fire Strategy Declaration, Canadian Council of Forest Ministers. 

3 p. Ref: 830-858/005. http://www.scics.gc.ca/cinfo05/830858005_e.html 

Accessed January, 2006.

Canadian Forest Service. 1968. Canadian forest fire danger rating system, fire 

behavior prediction system (FBP) component.

Carnell, D.; Anderson, G. 1974. Proposal for the development of a fire protection 

zone around Jasper town site. Jasper Warden Service. Unpublished.

Cohen, J.D. 2000a. Examination of the home destruction in Los Alamos associated 

with the Cerro Grande fire. Ogden, UT: U.S. Department of Agriculture, Forest 

Service, Rocky Mountain Research Station.

Cohen, J.D. 2000b. Preventing disaster: home ignitability in the wildland/urban 

interface. Journal of Forestry 98(3): 15-21.

Cohen, J.D.; Stratton, R. 2003. Home destruction within the Hayman fire perimeter. 

In: Graham, R.T. (tech ed). 2003. Hayman Fire case study. Gen. Tech. Rep. 

RMRS-GTR-114. Ogden, UT:. U.S. Department of Agriculture, Forest Service, 

Rocky Mountain Research Station. 396 p.

Countryman, C.M. 1974. Can Southern California conflagrations be stopped? Gen. 

Tech. Report. Albany, CA: U.S. Department of Agriculture, Forest Service, Pacific 

Southwest Research Station.

Covington, W.W.; Moore, M.M. 1994. Post-settlement changes in natural fire 

regimes and forest structure: ecological restoration of old-growth ponderosa pine 

forest. In: Sampson, R. Neil: Adams, D.L., (eds.) Assessing forest ecosystem health 

in the inland West. New York: The Haworth Press Inc.: 153–181.

Cruz, M.G.; Alexander, M.E.; Wakimoto, R.H. 2002. Predicting crown fire behavior 

to support forest fire management decision-making. In: Forest fire research and 

wildland fire safety. Rotterdam, Netherlands: Millpress.

Daigle, P. 1996. Fire in the dry interior forests of British Columbia. Ministry of 

Forests Research Program. Extension Note #8. Victoria, BC, Canada: British 

Columbia Ministry of Forests. 6 p.

DeSorcy, G.J. 2001. Chisholm fire review committee: Final report. Commissioned 

by Alberta Sustainable Resource Development. 50 p.

Duke, D.L. 2001. Wildlife use corridors in the Central Canadian Rockies: multivariate 

use of habitat characteristics and trends in corridor use. M.Sc. Thesis. Edmonton, 

Alberta, Canada: University of Alberta. Department of Biological Sciences.

FireSmart®- ForestWise: Managing Wildlife and Wildfire Risk in the Wildland/Urban Interface—a Canadian Case Study Westhaver, Revel, and Hawkes
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 363

Duke, D.L.; Quinn, M.; Butts, B.; Lee-Ndugga, T.; Wilkie, K. 2003. Spatial analysis 

of rural residential expansion in Southwestern Alberta. Calgary, Alberta, Canada: 

University of Calgary, Miistakis Institute for the Rockies. 49 p.

Fenton, G. 1986. Pyramid bench fire protection plan. Jasper Warden Service. 

Unpublished. Park. Internal Report. 300 p.

Filmon, G. 2004. Firestorm 2003, provincial review. Report to the Provincial 

Government of British Columbia. Gary Filmon, Chair. 100 p.

FireLine Consulting and Instruction. 1997. Inter-park hazard evaluation project: 

Prairie and Mountain Parks. Report to Parks Canada – Western Fire Management 

Centre.

Flannigan, M.; Stocks, B.; Wotton, M. 2003. Climate change and boreal fire activity: 

fact sheet 9. Sault Ste. Marie, Ontario, Canada: Natural Resources Canada, 

Canadian Forest Service, Great Lakes Forestry Centre. http://www.nofc.forestry.

ca/climate/en/factsheets/factsheet9_e.html

Flannigan, M.; Wotton, M.; Carcaillet, C.; Richard, P.; Campbell, I.; Bergeron, 

Y. 1998. Fire weather: past, present and future. In: Proceedings of the 3rd

International Conference of Forest Fire Research and the 14th Conference Fire 

and Forest Meteorology. Vol. 1: 113-128.

Forestry Canada Fire Danger Group. 1992. Development and structure of the 

Canadian forest fire behavior prediction system. Information Report ST-X-3. 

63 p.

Fuglem, P. 2004. Personal communication. Victoria, British Columbia, Canada: 

Director, British Columbia Ministry of Forests, Protection Program.

Fule, P.Z.; McHugh, C.; Heinlein, T.A.; Covington, W.W. 2001. Potential fire 

behavior is reduced following forest restoration treatments. In: Ponderosa pine 

ecosystems restoration and conservation: steps toward stewardship. Compiled 

by R.K. Vance, C.B. Edminster, W.W. Covington and J.A. Blake. Proceedings 

RMRS-P-22. Fort Collins, CO: U.S. Department of Agriculture, Forest Service. 

Rocky Mountain Research Station.

Graham. R.T. (tech ed). 2003. Hayman Fire case study. Gen. Tech. Rep.. RMRSGTR-114. Ogden, UT: U.S. Department of Agriculture, Forest Service, Rocky 

Mountain Research Stations. 396 p.

Graham, R.T.; McCaffrey, S.; Jain, T.B. (tech ed). 2004. Science basis for changing 

forest structure to modify wildfire behavior and severity. Gen. Tech. Rep. RMRSGTR-120. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, 

Rocky Mountain Research Station. 43 p.

Hammond, H. 2003. Technical review of FireSmart – ForestWise Methods Jasper 

National Park. Unpublished Parks Canada report by Silva Ecosystem Consultants 

Ltd. Slocan Park, British Columbia. 33 p. and follow-up letter report to Parks 

Canada, Jasper. January, 2006.

Haney, B.; Anderson, G. 1978. Proposal for reduction of forest fire fuel hazards 

bordering Jasper townsite. Jasper Warden Service. Unpublished report.

Heitzmann, R.J. 2001. In: Collection of Papers from the 33rd Meeting of the 

Canadian Archeological Assoc. Edited by Jean-Luc Pilon. Ontario Archeological 

Society.

Holland, W.D.; Coen, G.M. (gen ed). 1982. Ecological (Biophysical) Land 

Classification of Banff and Jasper National Parks. Volume II: Soil and Vegetation 

Resources. Publication No. SS-82-44. Alberta Institute of Pedology.

Holroyd, G.L.; Van Tighem, K.J. 1983. Ecological (Biophysical) Land Classification 

of Banff and Jasper National Parks. Volume III, Parts A and B: The wildlife 

inventory. Edmonton, Alberta, Canada: Canadian Wildlife Service. 667 p.

Jasper Interface Steering Team. 2002. Unpublished meeting minutes of the resident’s 

wildland/urban interface advisory group. Monthly meetings. Jasper, Alberta.

FireSmart®- ForestWise: Managing Wildlife and Wildfire Risk in the Wildland/Urban Interface—a Canadian Case Study Westhaver, Revel, and Hawkes
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 364

Kootenay National Park. 2002. Environmental assessment for the Redstreak 

ecological restoration in Kootenay National Park. Resource Conservation file 

report. 36 p.

Kotliar, N.B.; Hejl, S.J; Hutto, R.L.; Saab, V.A.; Melcher, C.P.; McFadzen, M.E. 

2002. Effects of fire and post-fire salvage logging on avian communities in 

conifer-dominated forests of the Western United States. Studies in Avian Biology 

25:49 – 64.

La Morte and Associates. 1996. Risk assessment of fir management alternatives: 

Mountain Parks, Parks Canada. Final report. Victoria, British Columbia. 65 p.

Martinson, E.J.; Omi, P.N. 2003. Performance of fuel treatments subjected to 

wildfires. In: Omi, P. N. and L.A. Joyce, tech ed. 2003. Fire, fuel treatments, and 

ecological restoration: conference proceedings. Proceedings RMRS-P-29. Fort 

Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain 

Research Station. 475 p.

McCaffrey, S. 2004a. Fighting fire with education: What is the best way to reach 

out to homeowners? Journal of Forestry. July/August 2004: 12-19.

McCaffrey, S. 2004b. What factors are most important in shaping the views 

of residential-wildland intermix homeowners about wild hazard and fuels 

management? In: 2nd symposium on fire economics, planning and policy: a global 

view. Cordoba, Spain, April 19 – 22, 2004.

McGee, T.; McFarlane, B.; Varghese, J. 2005. An exploration of wildfire risk 

reduction within communities directly affected by the Lost Creek fire in 2003. 

Foothills Model Forest Final Report. 19 p.

Mercer, G.; Purves, H. 2000. An initial assessment of wildlife movement corridors 

in the Three Valley Confluence of Jasper National Park. Warden Service Report. 

Unpublished. November, 2000. 32 p.

Mitchell, M.P. 2005. Montane landscape heterogeneity and vegetation change in 

Jasper National Park (1949-1997). M.Sc. thesis. Prince George, British Columbia, 

Canada: University of Northern British Columbia. 132 p.

Mortimer, D. 1998. Town of Jasper interface protection project, Jasper National Park. 

Fireline Consulting and Instruction. Report prepared for Parks Canada. 65 p.

Mortimer, D. 1999. Lake Edith cottage development facility protection project, 

Jasper National Park. FireLine Consulting and Instruction. Report prepared for 

Parks Canada. 65 p.

Mutch, R.W. 1994. Fighting fire with prescribed fire: a return to ecosystem health. 

Journal of Forestry. 92(11): 31-33.

National Fire Protection Association. 2002. NFPA 1144 – Standard for protection 

of life and property from wildfire, 2002 edition. NFPA. Batterymarch Park, 

MA. 19 p.

Natural Resources Canada. 2005. Canadian Forest Service – Viewpoint: a new 

wildland fire strategy for Canada. 3 p. http://www.nrcan.gc.ca/cfs-scf/national/ 3 p. http://www.nrcan.gc.ca/cfs-scf/national/

what-quoi/viewpoint/index_e.php?ArticleId=221. Accessed January, 2005. Accessed January, 2005.

Parks Canada. 2000. Jasper National Park of Canada management plan. Catalogue 

Number R64-105/28-2000E. Ottawa, Canada: Minister of Public Works and 

Public Services Canada. Department of Canadian Heritage. 78 p.

Partners in Protection. 2003. FireSmart: Protecting your community from wildfire. 

Second Edition. Edmonton, Alberta, Canada: Capital Color Press Ltd.

Quintilio, D. 2005. Presentation to the Jasper Interface Steering Team, March 14, 

2005, Jasper, Alberta.

Ralf, R.; Bradford, W. 1998. Bear/human conflict management plan, Jasper National 

Park. Internal Report, Warden Service. 65 p.

Rhemtulla, J.M. 1999 Eighty years of change: the Montane vegetation of Jasper 

National Park. M.Sc. Thesis. Alberta, Canada: University of Alberta.

FireSmart®- ForestWise: Managing Wildlife and Wildfire Risk in the Wildland/Urban Interface—a Canadian Case Study Westhaver, Revel, and Hawkes
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 365

Sandberg, D.V.; Ottmar, R.D.; Cushon, G.H. 2001. Characterizing fuels in the 21st

century. International Journal of Wildland Fire 10: 381-387.

Scott, J.H.; Reinhardt, E.D. 2001. Assessing crown fire potential by linking models 

of surface and crown fire behavior. Res. Paper RMRS-RP-29. Fort Collins, CO. 

U.S. Department of Agriculture, Forest Service, Rocky Mountain Research 

Station. 59 p.

Tande, G.F. 1979. Forest fire history around Jasper townsite, Jasper National Park, 

Alberta. M.Sc. Thesis. Alberta, Canada: University of Alberta. 169 p.

U.S. Department of Agriculture, Forest Service. 2005. A strategic assessment of 

forest biomass and fuel reduction treatments in Western States. Gen. Tech. Rep. 

RMRS-GTR-149. Fort Collins, CO: U.S. Department of Agriculture, Forest 

Service, Rocky Mountain Research Station. 17 p.

Van Wagner, C.E. 1977. Conditions for the start and spread of crown fire. Canadian 

Journal of Forest Research. 7: 23-24.

Walters, C.; Holling, C.S. 1990. Large-scale management experiments and learning 

by doing. Ecology 71: 2060-2068.

Westhaver, A.L. 2003. Environmental Screening Report. FireSmart – ForestWise

community protection and forest restoration project; Foothills Model Forest, 

Jasper National Park. Park Registry File J03–004. Jasper National Park. April, 

2003. 81 p.

Westhaver, A.L. 2006. FireSmart – ForestWise: Managing wildlife and wildfire 

risk in the wildland/urban interface. M.Sc. thesis. Alberta, Canada: University 

of Calgary, Faculty of Graduate Studies, Resources and Environment Program. 

224 p.

White, C.A. 2001. Aspen, elk, and fire in the Canadian Rocky Mountains. Ph. D Thesis. 

Faculty of Graduate Studies. Department of Forestry. University of British Columbia.

Wierzchowski, J.; Heathcott, M.; Flannigan, M.D. 2002. Lightning and lightning 

fire, Central Cordillera, Canada. International Journal of Wildland Fire. 11, 

41-51.

Winter, G.; Vogt, C.; Friedy, J.S. 2002. Fuel treatments at the wildland/urban 

interface: common concerns in diverse regions. Journal of Forestry. January/

February 2002: 15-21.

Winter, G.; Vogt, C.A.; McCaffrey, S. 2004. Examining social trust in fuels 

management strategies. Journal of Forestry. September 2004: 8-15.

FireSmart®- ForestWise: Managing Wildlife and Wildfire Risk in the Wildland/Urban Interface—a Canadian Case Study Westhaver, Revel, and Hawkes
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 366

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Abstract—The Regional Haze rule addresses visibility impairment in 156 Federal 

Class I areas. The goal of the rule is to remove all anthropogenic air pollution from 

the National Parks and Wilderness areas. Determining natural visibility conditions is 

an interesting and complicated problem. There is a large archive of pre- and earlysettlement narratives, landscape paintings, and photographs that could be used as part 

of a weight-of-evidence demonstration in regional haze State implementation plans 

(SIPs), especially for Western States. With an understanding of inherent limits, these 

materials could be used for qualitative evaluation of the early American atmosphere. 

Despite large uncertainties and with knowledge of film properties, application of quantitative analysis of contrast in photographs is possible and could provide estimates of 

visual range. These issues are introduced here as a theoretical treatment with some 

reference material for further investigation and practical application.

Background

The Regional Haze Rule (RHR), published July 01, 1999 (64 FR 35714), 

addresses prescribed fire as one sector of visibility impairment (with stationary, 

mobile, and area sources). The national goal expressed in the Clean Air Act 

is “the prevention of any future, and the remedying of any existing, impairment of visibility in mandatory Class I Federal areas which impairment results 

from manmade air pollution.” States need to establish “reasonable progress 

goals” for each Class I area with the objective of achieving natural visibility 

conditions by 2064. The 20 percent worst visibility days must be continuously 

improved, and the 20 percent best days must not deteriorate. The VIEWS 

Web site (Visibility Information Exchange Web System at http://vista.cira.

colostate.edu/views/) serves as the national repository of visibility data and 

analysis, and incorporates the IMPROVE database (Interagency Monitoring 

of Protected Visual Environments, the national monitoring network of air 

samplers and database to implement the regional haze program: http://vista.

cira.colostate.edu/improve/). VIEWS provides estimates of natural component concentrations at the Class I areas based on measured values at the 

IMPROVE monitors. Natural visibility is also sorted into 20 percent best 

and worst days.

In the preamble to the RHR, the Environmental Protection Agency (EPA) 

writes that States should not attempt to recreate historical (pre-Euro-American 

settlement) conditions, but rather determine current natural conditions. In 

related guidance documents, EPA allows considerable flexibility in the use of 

the IMPROVE and other air monitoring data as well as weight-of-evidence 

arguments addressing aspects of modeling and source attribution.

Use of Historic Images as a Tool for 

Estimating Haze Levels—Natural Visibility 

and the Role of Fire

Gordon Andersson1

In: Butler, Bret W.; Cook, Wayne, 

comps. 2007. The fire environment—

innovations, management, and policy; 

conference proceedings. 26-30 March 

2007; Destin, FL. Proceedings 

RMRS-P-46CD. Fort Collins, CO: 

U.S. Department of Agriculture, 

Forest Service, Rocky Mountain 

Research Station. 662 p. CD-ROM.

1 Planner A ir Policy, Minnesota 

Pollution Control Agency, St Paul, 

MN. gordon.andersson@state.mn.us
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 370

Although it addresses concentrations of fine particle (PM2.5) in the air, 

the RHR is not a NAAQS (National Ambient Air Quality Standards, which 

are established by EPA for six “criteria” pollutants). NAAQS primary standards are human health-based, and secondary standards are welfare-based. 

Welfare includes ecological effects and visibility, among other effects, and so 

the RHR is similar to a secondary standard. The RHR is basically an esthetic 

rule about what you see. It is appropriate to include photographs and art 

work as records and representations in reviews of visibility. Photographs are 

used in signage about diminished views at many National Parks, and they are 

part of the documented daily record at a number of Class I areas. This paper 

proposes that some of these esthetic media can also be used for analysis of 

historic visibility conditions.

The Historical Record

A large body of 19th century historical evidence bears on this issue—written 

narratives, landscape paintings, and landscape photographs—that, especially 

in the West, records conditions before the widespread use of internal combustion engines, large industries, and other human-made pollution sources (with 

the exception of fire). Despite the position expressed by EPA, such images 

would include depictions of natural visibility at that time, which would resemble in most part the contribution of natural sources today. It is somewhat 

unexpected that so many of these records describe or portray some amount 

of haze on the landscape.

The Western Class I areas would benefit most from a review of historic 

images (fig. 1). In general, they include more extreme topography and vertical 

surfaces with longer views and measurable “targets” than Eastern areas. (It is 

these same features that resulted in their designation as Parks or Wilderness 

areas.) Also, natural contributions were then (and are now) a larger component 

of haze in the West than in the East and, therefore, more likely to appear in 

the records than haze due to human-made sources.

Both Western landscape photographers and painters were instrumental in 

establishing National Parks in the West based on these same productions. 

William H Jackson’s photographs and Thomas Moran’s paintings convinced 

Congress to protect Yellowstone as a National Park in 1872 and Jackson’s 

and Albert Bierstadt’s pictures of Yosemite played a large part in its designation in 1890. This presentation will mostly address the use of depictions in 

landscape painting and photographs with a few examples.

Written Records

Written narratives can provide descriptions of relative haziness or clarity, 

but they would best be used as accompaniment to photographs, paintings, 

or sketches made of that same scene on the same day, or, with less specific 

application, the same season. Notebooks by the painter or photographer that 

describe the view would either support or qualify the conditions presented 

in the rendered image. Albert Bierstadt, for example, on each of his trips 

to the West made numerous notes that described the scenes painted in his 

oil sketches. In these he recorded from direct observation the colors, light, 

clouds, and atmospheric conditions.

Travelers’ descriptions of landmarks in diaries and letters could provide 

independent dated information or might supplement recorded images. The 

overland migrants to the West Coast traveled chiefly through summer and 

Use of Historic Images as a Tool for Estimating Haze Levels—Natural Visibility and the Role of Fire Andersson
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 371

used the same routes. Such limits of time and place provide for comparisons 

that include similar season, feature orientation, and position of sun. In addition to such records, compilations of regional histories based on primary 

materials concentrate descriptions to a place. Stegner (1982), for example, 

includes extensive quotations of Clarence Dutton, a geologist and companion in John Wesley Powell’s exploration, and a description from 1882 of the 

haze in the Grand Canyon, a present day Class I area. The review of written 

records would be fairly labor-intensive requiring extensive filtering of texts 

to find pertinent descriptions.

Landscape Paintings

More than narrative descriptions, the use of pictorial media have limitations 

that must be understood and applied to estimating haze levels. This is more 

so because they provide immediate appearances that seem to be real.

The landscape painters of this period—Frederick Church, Jasper Cropsey, Asher Durand, George Inness, Thomas Moran, Sanford Gifford, and 

others—painted from nature, often creating oil sketches out of doors and 

completing canvases in the studio (fig. 2). The painters of the old West (such 

as Bierstadt and Moran) were from the Mid-Atlantic States. Their paintings 

were a medium of communicating the grandeur and vistas of the Western 

Use of Historic Images as a Tool for Estimating Haze Levels—Natural Visibility and the Role of Fire Andersson

Figure 1—“Picture Rock at Crooked Lake” by Francis Lee Jaques, 1947 (courtesy of Minnesota Historical 

Society © 2003). A location in the Boundary Waters Canoe Area Wilderness, Superior National Forest, 

Minnesota, a Class I area. The vistas in the Class I areas in the Great Lakes region are seen across large 

lakes or the length of long lakes. These cliffs in the BWCAW provide vertical surface—a rare topography. 

Note the obscured view to the left of the cliffs.
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 372

topography. For example, Bierstadt’s large-scale paintings were exhibited for 

paid admission on tours of Eastern cities. Moran’s huge paintings “Grand 

Canyon of the Colorado” (1872) and “The Chasm of the Colorado” (1874) 

were both purchased by Congress and hung in the Capitol in Washington, 

DC. There would be a natural tendency for these depictions to portray distant 

peaks and open spaces through clear air, whether the air was clear or not. In 

terms of the RHR, these paintings would more likely depict a day from among 

the “20 percent best days” to “20 percent middle days” than the most hazy 

days with greatly diminished views. Whether Eastern or Western scenics, some 

pieces portray crystalline air and many miles of view; some, dramatic storms 

and clouds over distant peaks; and many, a golden light of late afternoon 

with a lot of light scattering from fine aerosols. Many of these last still have 

a long visual range to show distant formations through the haze.

Problem of Sampling Error

The use of paintings to look at visibility must be done with a knowledge 

of the artist and his body of work. This may include styles and phases and 

the placement of a particular painting in the artist’s development. Of course, 

to draw conclusions from a single painting (or photo) of a single feature is 

Use of Historic Images as a Tool for Estimating Haze Levels—Natural Visibility and the Role of Fire Andersson

Figure 2—“A Scene on Tohickon Creek: Autumn” by Thomas Moran, 1868 (courtesy of Minneapolis Institute of Art 

© 2003). An early image from the East (Pennsylvania) by an artist most famous for later panoramas of the West. Due 

to the season, the background haze may represent smoke from vegetative burning with little biogenic contribution. 

(Note for this and some other figures: Images of paintings are made from scans of 35 mm slides obtained from 

museums. In general they are significantly degraded in sharpness and color and are for illustration only. Museum 

Web sites, Web searches for specific paintings, exhibit catalogues and books, available in museums and libraries, 

and the original paintings and photos in museum collections provide the best reference material.)
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 373

not reasonable. Any small number of records presents a large problem of 

“sampling error.” For comparison, the IMPROVE air monitors sample the 

air every third day. The RHR apportions the air data into quintiles with each 

20 percent representing at maximum 24 days a year.

There are a few ways that sample size might be increased if one were to use 

the historic record. A series of paintings of one landscape feature by one artist 

over time would provide one preferred “database.” It would show the range 

of the artist’s representation and scene changes through the year at different 

seasons and times of day in different light with different sun angles.

Researchers at the University of Birmingham, England, propose this very 

process to analyze air pollution conditions in London from evidence in a 

series of paintings by Claude Monet of the Houses of Parliament in 1899 

to 1901 (Baker and Thornes 2006). Monet was a master of Impressionism 

whose canvases include many series of the same subject in different light 

conditions. In all he completed 19 paintings of the Parliament buildings, 

and nine of them show the sun. These London paintings were made in the 

period of his mature style. Despite this, the authors determined that he cor-

rectly rendered the effects of sunlight on the building and they matched the 

solar inclination to the dates of his paintings. Having determined “elements 

of accurate observation” and “real quantitative information” in this series, 

they believe that historical conditions of air quality can be derived from the 

same paintings. Monet chose to paint the sun effects on the façade as they 

were presented to him. Every painter from nature chooses which physical 

elements of the subject to include and with what specificity, and which ele-

ments to omit, alter, or add.

Late Impressionist paintings are less representational than the landscape 

paintings of both the American East and the West. The 19th century land

-

scapers practiced realism or naturalism in large part. On the American scene, 

Albert Bierstadt’s series of paintings of the Yosemite Valley (1865, 1868, 1872) 

might be compared to a Monet series (fig. 3). However, both Bierstadt’s largescale paintings of the Yosemite Valley and Thomas Moran’s painting of the 

canyon of the Yellowstone River are composites, created from oil sketches, 

that include landscape features within the same view that are not present in 

the three dimensional world (Wilton and Barringer 2002). Right away, this 

should diminish the credibility of either of these paintings as physical records, 

including the relative clarity of the atmosphere. If one can move a mountain, 

one can even more easily clarify or obscure the view at a given time with less 

interpretation. The mountain subjects that Bierstadt painted in large format, 

after joining the Frederick Lander railway survey expedition to present-day 

Colorado in 1858, were similarly largely invented. Moran became more stylized 

in later years adding many atmospheric effects to his landscapes (fig. 4). To use 

fanciful (less realistic) representations of landscape at face value is problematic, 

as they depict momentary events in the artist’s imagination, whether or not 

they happened, or might have happened, in nature

A second approach would be to compare different artists’ renderings of 

the same scene, such as Sanford Gifford’s painting (1860) of Mt Katahdin 

in Maine (fig. 5 ) and Frederic Church’s (1853) picture of the same feature. 

If different artists portrayed the same atmosphere conditions affecting the 

view of a landmark, at the same season and time of day, one could argue that 

the similarity may be due to those actual conditions and not to the artist’s 

preferences. To be fair in using such a method, where different depictions 

show different levels of haze, one must acknowledge that the samples are too 

disparate to make estimates of average conditions and, at most, conclude that 

the differences lie somewhere within a range of actual conditions.

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USDA Forest Service Proceedings RMRS-P-46CD. 2007. 374

Figure 3— “Looking Down Yosemite Valley, California” by Albert Bierstadt, 1865 (courtesy of Birmingham Museum 

of Art © 2003).

Figure 4—“Grand Canyon of the Colorado” by Thomas Moran, 1892 (courtesy of Philadelphia Museum of Art © 2003).

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USDA Forest Service Proceedings RMRS-P-46CD. 2007. 375

Yet a third approach to address the issue of “sampling error” is one that adds 

corroborative information to photographs and paintings. The photographer 

William Henry Jackson and the painter Thomas Moran both accompanied 

the Ferdinand Hayden geological survey expedition to Yellowstone in 1871. 

There, Moran made numerous watercolor and gouache drawings of landscape 

features with an “intense concern for accuracy” (Wilton and Barringer 2002). 

As with many artists, a separate “reality” was later created on the final oil canvases completed in the studio, which may be quite independent of the studies, 

sketches, and notes that were made in the field. Jackson made numerous silver 

albumen photographic plates in Yellowstone (fig. 6). In 1873, Moran also 

traveled with the Powell expedition to the Grand Canyon of the Colorado 

River. And in 1892, he again visited the Grand Canyon with Jackson. In 

the case of these two Western Class I areas, there exist contemporary written descriptions, photographic prints, and pencil and paint sketches as well 

as completed oil paintings that hang in several museums. This information 

recording the same landscape and features could be compared for agreement, 

consistency, and anomaly in visibility.

Many collections and “coffee table” books include landscape reproductions 

with descriptive text, biographical information, and the name of the museum 

that owns the original artwork. Wilton and Barringer (2002) provide an 

extensive treatment of representations of this period in American painting.

Figure 5—“The Wilderness” by Sanford Gifford, 1860 (courtesy of Toledo Museum of Art © 2003). The view is of 

Mt Katahdin in Maine, now in Baxter State Park, and climbed by Henry David Thoreau.

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USDA Forest Service Proceedings RMRS-P-46CD. 2007. 376

Figure 6—“The Three Tetons” by William Henry Jackson, 1873 (courtesy of Minneapolis Institute of Art © 2003). 

Taken from Yellowstone National Park; one of 11 Jackson photos in the MIA collection and one of the less hazy 

in appearance.

Landscape Photographs

Photographs, either inside or outside Class I areas, are much less subject 

to “interpretation” by the artist. Photography and painting are differentiated by manipulation of subject matter and by the time to create the image. 

Though each present a moment in time, the one is made in an instant and the 

other may be completed after weeks or months of work. Landscape paintings 

of the 1800s too lack the intrinsic credibility of photographs and, without 

supplemental information, one can neither conclude that a scene is average 

or typical, nor that it never happened.

The preponderance of the 156 Federal “mandatory” Class I areas addressed 

by the RHR are in the West (112, outside of Alaska and Hawaii), and this is 

where, at the time, there was less anthropogenic influence on the atmosphere. 

William Henry Jackson and others (Carleton Watkins, Timothy O’Sullivan, 

Samuel Bourne, Alfred Hart) made series of photos inside present-day Yellowstone and Yosemite National Parks and other Western regions (fig. 7).

Qualitative and quantitative assessment—The paintings and photos of 19th

century artists have intrinsic historic interest, but with qualitative assessment 

they can be useful in helping to define natural visibility conditions. Using 

Use of Historic Images as a Tool for Estimating Haze Levels—Natural Visibility and the Role of Fire Andersson
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 377

such historical records is a different method of reconstructing “natural” 

conditions and it might be used to supplement or compare the quantitative 

methods of natural source concentrations derived from measured filter data. 

If measurement is possible, the visual historical record becomes more applicable to haze research and estimation of natural conditions.

The simplest measure that might be done from images is visual range. 

This does require that distances from the point of view in the photograph 

(or painting) to features in the landscape be known to the individual who 

examines the image. Such distances can be approximated in the real world 

by a visit to the featured site or with the use of topographical maps. Visual 

range is demonstrated with light extinction (bext) and deciviews on the 

IMPROVE homepage, and it is also the visibility metric used by the “haze 

cams” on State and RPO Web sites across the country (fig. 8, 9). With an 

estimated visual range, one can calculate an estimated general light extinction and estimated deciviews.

To correctly measure visual range, one observes a large black object against 

a light background or the bright horizon. Simply stated, the observer visual 

range is the distance at which the object disappears. A painting (or a photograph) can indicate a visual range if features in the background become 

invisible, but this can only be an approximation of actual visual range, and it 

would probably be less than that measured with a black object. Actual visual 

Figure 7—American Indians on horseback at Glacier National Park; photographer unknown, 1912 (courtesy of Minnesota 

Historical Society © 2003).

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USDA Forest Service Proceedings RMRS-P-46CD. 2007. 378

Figure 8—St Paul, MN, “haze cam” 

image showing “current conditions. 

The live image is updated every 15 

minutes. Visual range, measured by 

nephelometer, has been reported 

with the met data beneath the 

picture on many haze cam sites.

Figure 9—“St Paul from Dayton’s Bluff, July 6, 1888” by Alexis Fournier (courtesy of Minnesota Historical Society © 2003). 

This watercolor shows a view of early St Paul, MN, from a location and orientation similar to the haze camera (fig. 8).

Use of Historic Images as a Tool for Estimating Haze Levels—Natural Visibility and the Role of Fire Andersson
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 379

range will usually be greater than the distance to the target object. With 

any painting, one must bear in mind that the apparent visual range is in the 

painting and not in nature, although it might have been accurately recorded 

from the landscape at a point in time.

Large uncertainties exist in contrast measurements from transparencies 

(Molenar undated). Even “live” visual range is affected by the sun angle, the 

target object characteristics, and sky conditions. Measuring contrast from 

photographs depends on these factors as well as the qualities of the film. Lack 

of supplementary information of archival photographic prints that includes 

the properties of the film, its density and response to light, and the deterioration of the prints make calculated measurements of extinction more difficult 

(Molenar, pers. comm.). Quantification of visual range in photographic prints 

by experienced technicians with contrast instruments (densitometer that 

measures reflectance) can derive estimates but not fixed values.

The IMPROVE photographic record—35 mm cameras have been collocated 

with the aerosol monitors at IMPROVE sites in at least 46 National Parks, 

Refuges, and Wilderness areas. These daily images (three slides per day) 

have been archived as a record of visibility conditions. The VIEWS Web site 

includes a large number of images from these Class I areas in “spectrum” 

series with graduated differences in ug/m3 of aerosols, light extinction 

(bext), and deciview (dv) values for each image: (http://vista.cira.colostate.

edu/views/Web/IMPROVE/Data_IMPRPhot.htm)

Light extinction coefficients were estimated for each of these photographs with contrast measurements made with a transmittance densitometer. 

Copeland (undated) found large uncertainties using a 68 percent confidence 

interval when comparing measured visual range to actual visual range even 

where a dark target was in the frame.

William Henry Jackson—Although a number of photographers made images 

of the old West, Jackson was unusually prolific, including in a 1875 catalogue 

more than 2,000 albumen silver prints taken between 1869 and 1875 as part 

of the Hayden U.S. Geological Survey (Sandweiss 2002).

In addition to the catalogs published by Jackson, more recent works 

provide side-by-side comparisons of photographs by Jackson with contemporary photographs taken of the same scene from about the same point 

(Fiedler and Jackson 1999, 2005). This allows for the direct comparison of 

the air quality on those 2 days separated by more than 100 years. By using 

the same latitude/longitude and orientation, these photos provide equal 

distances to vertical features in the distance. It would be most useful if these 

images were taken on the same dates to account for seasonal differences 

and it does not reduce the sampling error issue discussed above. As they are 

largely made in summer, the chances would be about one in 100 that the 

images were made on the same date. However, because daily variation is 

often greater than seasonal difference, valid comparisons of visibility do not 

depend on same-day images. One could examine a large number of paired 

images and estimate the range and the mean of clarity/haziness for the 

“before” and “after” pictures. These references and others (such as Hales 

1988) would seem to be useful to Colorado, with its 12 Class I areas, to 

supplement its air monitoring data.

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The Role of Fire

Haze is apparent in many paintings and photographs from the 19th century. 

A good argument can be made that much of this is due to fire on the landscape. Many of the Eastern paintings (by Church, Cropsey, Durand, Moran, 

Gifford) might represent smoke from the felling and burning of the Eastern 

hardwood forest as a component of visible air pollution. Large portions of 

the land area were clearcut and burned for cropland by the Euro-American 

settlers. Primary forest was converted to log piles that burned for days, and 

the wood was readjusted for further combustion. Due to land-use practices, 

the annual net flux of carbon (C) to the atmosphere was positive from before 

1850, peaked at 328.74 Tg/yr in 1881, and did not go negative until 1923 

(Houghton and Hackler 2002) (fig. 10).

In the East, emissions from coal-burning factories and some coal-burning 

trains, and residential cooking and heating would also be a component of visible air pollution especially after 1850. In 1850, some 5,402 1000 metric tons 

(mt) of carbon were produced by coal burning in the United States; 15,775 

1000 mt were generated in 1865; and 101,603 1000 mt in 1890 (Marland 

and others 2006). The amount of carbon added to the atmosphere by landuse changes in 1881, however, was six times that added by coal burning in 

that year.

Figure 10—Annual net flux of carbon to the atmosphere from land-use changes worldwide, 1850 through 

2000 (Houghton and Hackler 2002). Note that carbon release peaked in the United States in 1881 from 

clearing and burning the Eastern forest.

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With little industry or transportation sources in the West, much of the 

visibility impairment would be due to biogenic sources, including wildfire, 

and intentional burning. American Indians used fire extensively for multiple 

purposes (see Pyne and others 2000).

According to Leenhouts (1998), wildland fires burned 34 to 86 x 10

6 ha 

(megahectares) annually in the preindustrial era, consuming 530 to 1228 

teragram (Tg) of biomass. By comparison, he calculates that contemporary 

wildland, prescribed, and agricultural fires combined burn 4.5 to 7.4 Mha 

per year and consume 24 to 136 Tg of biomass per year (data are from his 

“Results” section). He added that about 10 times more landscape was burned, 

eight times more biomass was consumed, and seven times more emissions 

were produced in the preindustrial conterminous United States than at pres-

ent due to biomass burning.

The pollutants that affect visibility and that are derived from vegetative 

burning are PM10, PM2.5, nitrogen oxides (NOx), ozone, organic carbon, 

and elemental carbon. Ozone, which can form “smog” or haze, is not directly 

produced by fires but from other combustion products (NOx and volatile or-

ganic compounds or VOCs). Nitrates too are not primary products of burning 

but are formed by the chemical transformation of NOx. About 90 percent of 

smoke particles are PM10 and 70 percent are PM2.5 (EPA 1998).

EPA Treatment of Fire in Rules

The Clean Air Act Amendments (1977 and 1990 parts 169A & B) and the 

Regional Haze Rule (1999) require that human-made pollution that affects 

visibility be prevented and reduced. In a regulatory sense, whether the smoke 

component of haze is natural or anthropogenic is a fundamental issue. The 

convention among fire agencies is that lightning-ignited fires are natural. 

This is a source-based definition of emissions determined by the mode of the 

fire ignition. Reasonably, arson fires are clearly anthropogenic. Accidental 

fires (such as cigarette, railroad, and escaped camp fires) too are considered 

human-caused. Extending this logic to prescribed burns classifies them too 

as anthropogenic. Fire emissions are usually treated as area sources in air 

quality emission inventories, and the emission components from vegetative 

burning are similar, regardless of the mode of ignition (fig. 11).

Despite this, sec. 1.15 of EPA “Guidance for Estimating Natural Visibility 

Conditions Under the Regional Haze Rule” (Sept 2003) states, “In some 

cases, regional organizations [RPOs] have found it useful to classify fire 

emissions into two categories, natural and man-made, for the purposes of es-

timating natural visibility conditions. While EPA is not expressing an opinion 

on the importance of classifying fires, it supports those organizations who 

wish to do so for the purposes of estimating visibility conditions. However, 

the EPA does not require the distinction between natural and man-made 

fires.” Apparently, this means that EPA could consider all fires as natural (and 

part of natural haze) or all fires as man-made (and subject to the RHR). This 

lack of clarity is perpetuated by the final Exceptional Events (EE) Rule (72 

FR 13559) published March 22, 2007. Congress passed the SAFE-TEA-LU 

bill (2005) that revised sec. 319 of the CAA to address exceptional events 

and defined them in part as “not reasonably controllable or preventable.” In 

the EE rule, EPA says that prescribed fires may qualify as exceptional events 

if they meet the statutory definition and a State smoke management program 

(SMP) is in place. This confusion might be addressed in part when EPA re-

leases new guidance to replace the “Interim Air Quality Policy on Wildland 

and Prescribed Fires” (April 23, 1998), due in July 2008.

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USDA Forest Service Proceedings RMRS-P-46CD. 2007. 382

Summary

A number of artistic, technical, and regulatory issues attend an effort to 

use the large archive of descriptions and representations of the early American 

landscape as a tool to estimate natural visibility conditions and regional haze 

in early settlement (East) and presettlement (West) America. The haze seen 

in many paintings and photographs could contain a large component of fire 

emissions that would combine both natural and human-ignited sources. Estimating the proportions of each by region presents a problem. Further, EPA 

has not formally separated fire emissions by source in RHR implementation, 

in apparent conflict with the Clean Air Act Amendments.

Despite these problems, work could be done on each of these topics by individual States to derive or to support the values that it includes in the regional 

haze SIP. In particular, a number of the Class I areas in the West are supported 

by a large history of narratives, drawings, paintings, and photographs that describe or portray the atmosphere at different times in those areas or regions. 

Each of these media has limitations that must be considered. The fire science 

literature on natural and Native American fire could be correlated to the historical writings and images. Despite large uncertainties, application of quantitative 

analysis of contrast in photographs can provide estimates of haze concentrations and light extinction. In many locations, qualitative analysis of the image 

archive and additional research could be used to supplement the calculations 

of natural visibility based on current concentrations of air pollution.

Figure 11—Burning the piled brush and debris after lumbering has been completed, Minnesota National 

Forest Reserve; photo by W.E. LaFontain, November 18, 1904 (courtesy of Minnesota Historical Society 

© 2003). Slash burning after clearcutting the red and white pine forests of Minnesota for construction of 

Midwestern cities. Lumbering began in the 1830s and peaked at more than 1 billion board-feet in 1900. 

The last sawmill closed in 1930.

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USDA Forest Service Proceedings RMRS-P-46CD. 2007. 383

References

Baker, Jacob; Thornes, John E. 2006. Solar position within Monet’s Houses of 

Parliament. Proc. R. Soc. A. 462(2076): 3775-3788.

Copeland, Scott. [undated] Summary of limitations and value of slide-based visual 

range measurements. [Online] Available: http://vista.cira.colostate.edu/improve/

Publications/GrayLit/gray_literature.htm

Environmental Protection Agency. 1998. Interim Air Quality Policy on Wildland 

and Prescribed Fires.

Fiedler, John; Jackson, W.H. 1999. Colorado 1870 - 2000. Englewood, CO: 

Westcliffe Publishers. 224 p.

Fiedler, John; Jackson, W.H. 2005. Colorado 1870 - 2000 II. Englewood, CO: 

Westcliffe Publishers. 108 photos; 156 p.

Hales, Peter B. 1988. William Henry Jackson and the Transformation of the American 

Landscape. Philadelphia, PA: Temple University Press. 198 photos; 368 p.

Houghton, R.A.; Hackler, J.L. 2002. Carbon Flux to the Atmosphere from Land-Use 

Changes. In: Trends: A Compendium of Data on Global Change. Oak Ridge, TN: 

U.S. Department of Energy, Oak Ridge National Laboratory, Carbon Dioxide 

Information Analysis Center.

Leenhouts, Bill. 1998. Assessment of biomass burning in the conterminous United 

States. Conservation Ecology [Online]. 2(1): 1. URL:http://www.consecol.

org/vol2/iss1/art1.

Marland, G.; Boden, T.A.; Andres, R.J. 2006. Global, Regional, and National CO2

Emissions. In: Trends: A Compendium of Data on Global Change. Oak Ridge, 

TN: U.S. Department of Energy, Oak Ridge National Laboratory, Carbon Dioxide 

Information Analysis Center.

Molenar, John V. [undated] Uncertainty analysis of calculated extinction from 

apparent contrast measurements from images of natural targets. [Online] Available: 

http://vista.cira.colostate.edu/improve/Publications/GrayLit/gray_literature.

htm

Pyne, Stephen J.; Williams, Gerald W.; Barrett, Stephen W.; Brown, Hutch. 2000. 

Fire Management Today. 60(3):4-48.

Sandweiss, Martha A. 2002. Print the Legend: Photography and the American West. 

New Haven, CT: Yale University Press. 402 p.

Stegner, Wallace. 1982. Beyond the Hundredth Meridian: John Wesley Powell and 

the Second Opening of the West. Lincoln, NB: University of Nebraska Press. 

438 p.

Wilton, Andrew; Barringer, Tim. 2002. American Sublime: Landscape Painting in the 

United States 1820-1880. Princeton, NJ: Princeton University Press. 282 p.

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USDA Forest Service Proceedings RMRS-P-46CD. 2007. 384

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page is 

blank 

on purpose

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USDA Forest Service Proceedings RMRS-P-46CD. 2007. 385

Abstract—Long-duration fire activity during the 2006 northern California fire season 

presented an excellent opportunity to create a temporary air-quality/smoke-monitoring network in the complex terrain across northwestern California. The network 

was established through cooperative interagency coordination of Federal officials, 

the California Air Resources Board (CARB), and local Air Pollution Control Districts 

(APCDs). This network addressed concerns over air quality and potential firefighterand citizen-health issues. Equipment deployed included real time PM2.5 monitors 

(EBAMs and DataRAM’s) at seven stations. The reported network data were posted 

daily on the Internet (in the public domain). The archived field-gathered data set 

will also provide a database for future research and verification of modeled Blue Sky 

output for particulate concentrations (run by the University of Nevada, Reno). Lessons learned from establishing and supporting such a temporary network will help 

make future projects more successful. Overall, the network data were well received, 

especially by local residents.

Introduction

In July and early August 2006, a long-term, multifire episode developed 

in northwestern California over adjacent areas of the Shasta-Trinity, Six Rivers, and Klamath National Forests. Based on previous experience with dense 

smoke events that occurred in 1977, 1987, 1999, and 2002 under similar fire 

regimes, fire and air quality officials soon concluded that significant amounts 

of smoke were likely to blanket the area for a minimum of several weeks.

A closer look at the physical shape of the terrain in the area (fig. 1) shows 

that deep river valleys with steep hillsides are typical in this region. This 

creates efficient “smoke traps,” especially overnight when inversions become 

established, and local downslope, downdrainage winds carry smoke through 

the terrain and valleys. Dense smoke can limit the amount of daytime sunshine 

that reaches the ground, which in turn can inhibit the atmospheric mixing 

possible due to surface heating by the sun. Without much daytime mixing, 

smoke can linger in these canyons and valleys, creating long periods of poor 

air quality conditions. Lower sun angles and longer days in late summer also 

exacerbate these conditions. 

Smoke Monitoring Network

A quantitative sampling network had never been used to monitor heavy 

smoke events during previous long-term fire episodes in the area (Fontana 

2007). In 2006, however, a temporary smoke monitoring network was 

Smoke Monitoring Network on 2006 

Northern California Fires

Brenda Belongie1 and Suraj Ahuja2

In: Butler, Bret W.; Cook, Wayne, 

comps. 2007. The fire environment—

innovations, management, and policy; 

conference proceedings. 26-30 March 

2007; Destin, FL. Proceedings 

RMRS-P-46CD. Fort Collins, CO: 

U.S. Department of Agriculture, 

Forest Service, Rocky Mountain 

Research Station. 662 p. CD-ROM.

1 Predictive Services Meteorologist, 

U.S. Department of Agriculture, 

Fore st S er v ice, R edd i ng Fi re 

Weather Center, Northern California 

Ser v ice Center, R edd i ng, C A . 

bbelongie@fs.fed.us

2 North Zone Air Quality Specialist, 

U.S. Department of Agriculture, 

Forest Service, Region 5, Mendocino 

National Forest, CA.
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 386

deployed for the first time through cooperative interagency actions between 

local air quality district, Federal, and State officials. Initial discussions and 

development of an action plan were done via conference calls. The principal 

cooperating agencies included the North Coast Unified and Shasta County 

Air Quality Management Districts, Siskiyou County Air Pollution Control 

District, USDA Forest Service, USDI Bureau of Land Management, California 

Air Resources Board (CARB), and the California Department of Forestry 

and Fire Protection (CDF).

The network monitored PM2.5 values from August through October using 

both EBAMs and DataRAMs provided by several agencies (USDA Forest 

Service, BLM, CARB). Most of the monitors were borrowed from operations outside the local area. The seven sampling sites (fig. 1) were chosen 

after considering population center locations, likely areas of the largest or 

longest lasting fires, and local meteorology. None of the sampling locations 

were existing full-time air quality monitoring sites. A contract agreement was 

set up with Air Resource Specialists, Inc., of Boulder, CO, to have them do 

onsite calibrations of the sampling units to ensure as accurate and comparatively equal data as possible.

Local Air Quality Management District (AQMD), Air Pollution Control 

District (APCD), and fire officials were concerned about both the exposure 

duration and PM2.5 concentrations experienced by local residents and firefighters. Although there are published Environmental Protection Agency 

Figure 1—This map of the project area in northwestern California shows the sampling sites: (1) Somes 

Bar, (2) Callahan, (3) Orleans, (4) Petersburg, (5) Burnt Ranch, (6) Willow Creek, (7) Junction City.

Smoke Monitoring Network on 2006 Northern California Fires Belongie and Ahuja
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 387

(EPA) standards relating PM2.5 concentrations to human health (reflected 

in table 1), there are no established Federal or documented CDF standards 

or policies uniformly applying those values to firefighter activities. 

A daily summary text product with expected weather affecting dispersion 

and the previous 3 days’ peak PM2.5 concentrations at each site was produced 

collaboratively by Redding Fire Weather Center and CARB meteorologists 

(tables 1 and 2). It was posted on the Redding Fire Weather Center Web 

site by 1400 hours (2 p.m.) daily. 

The 2 months of detailed information the network yielded has been archived and will be used for future research and verification of modeled Blue 

Sky output for particulate concentrations (run by the University of Nevada, 

Reno, for the northern California area).

Challenges Encountered

Network maintenance and logistical support were somewhat problematic 

at times during this project, in part because there were no other examples of 

such networks used on large fires that we could emulate. Some of the instruments required a person to report the observed values, and it was difficult 

at times to find someone who could reliably perform the necessary duties 

daily. Unfortunately there were occasional data gaps when some paper sensor 

tapes ran out or sampling was rendered invalid because of holes in the tape. 

Overall, however, most of these issues were resolved fairly soon, and the data 

input became quite consistent. 

Table 1—Air Quality Guide for Fine Particulate Matter (PM2.5) Pollution. This table was included at the end of the daily 

summary product (source: CARB). The assumption was that supplying local citizens with air quality information 

would help them decide which actions to take to address their individual health situation.

24-hr average

concentration Air quality Air quality

(ug/m3) index category Cautionary statement

0 - 15 Up to 50 Good None

>15 - 40 51-100 Moderate Unusually sensitive people should consider reducing 

prolonged or heavy exertion.

>40 - 65 101-150 Unhealthy for People with heart or lung disease, older adults,

sensitive groups and children should reduce prolonged or heavy exertion.

>65 - 150 151-200 Unhealthy People with heart or lung disease, older adults, and children 

should avoid prolonged or heavy exertion. Everyone else 

should reduce prolonged or heavy exertion.

>150 - 250 201-300 Very unhealthy People with heart or lung disease, older adults, and children 

should avoid all physical activity outdoors. Everyone else 

should avoid prolonged or heavy exertion.

>250 - 500 301-500 Hazardous Everyone should avoid all physical activity outdoors. People 

with heart or lung disease, older adults, and children should 

remain indoors and keep activity levels low.

Smoke Monitoring Network on 2006 Northern California Fires Belongie and Ahuja
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 388

Table 2—Sample content of the daily product posted on the Web site.

SMOKE SUMMARY FOR NORTHERN CALIFORNIA WILDFIRE AREAS

Tuesday October 31, 2006

1400 PST 

Issued by the Redding Predictive Service Unit in cooperation with the California Air

Resources Board

For Air Quality Advisories in your area, please call your local air district at the

following numbers...

DISCUSSION: A weak high pressure ridge that has been over northern CA for the past

several days will drift east on Wednesday as a Pacific storm approaches the north coast.

There should be little change in smoke concentrations today from Monday’s readings.

On Wednesday stability will decrease as southwesterly winds increase, leading to better

mixing and lower smoke concentrations. Higher humidity and occasional rain from

Wednesday night through Saturday should reduce smoke production and smoke

concentrations.

LATEST PM 2.5 MEASUREMENTS

(micrograms per cubic meter)

All data are preliminary

SOMES BAR

Date 1-hr Peak/Time 24hr Average Air Quality

10/30/06 61/0900 7 Good

10/29/06 76/0900 13 Good

10/28/06 55/0900 17 Moderate

ORLEANS

Date 1-hr Peak/Time 24hr Average Air Quality

10/30/06 37/2200 13 Good

10/29/06 29/0300 15 Moderate

10/28/06 33/0700 16 Moderate

************************************************************************

Overall air quality numbers are mainly in the Good category. A weak disturbance making

its way into the north state will keep air quality numbers in the same range tomorrow

through the end of the week.

Depending on where you are, you may or may not be impacted, and the situation could

change very quickly. If you see or smell smoke, consider restricting your outside

activities. Until the weather conditions change and the smoke conditions improve,

individuals should consider taking the following actions:

Smoke Monitoring Network on 2006 Northern California Fires Belongie and Ahuja
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 389

• Healthy people should delay strenuous exercise, particularly when they can see or

smell smoke

• Children and the elderly should consider avoiding outdoor activities, particularly

prolonged outdoor exertion

• People with heart or upper respiratory illnesses should remain indoors

• Asthmatics should follow their asthma management plan

• Contact your doctor if you have symptoms such as chest pain, chest tightness,

shortness of breath, or severe fatigue

• Keep airways moist by drinking lots of water

See the table below for advice that is specific to each air quality category....[see table 1]

Conclusions

The time, effort, and costs involved with creating and maintaining a 

temporary smoke monitoring network make such projects most feasible only 

during long-term large fire incidents. Providing summarized information via 

the Internet is efficient and, in this case at least, was reportedly well received 

by the public. Establishing some monitoring network parameters and plans 

ahead of time would help define its operation. For instance, outlining the 

environmental conditions that warrant when/where to establish a network, 

and prearranging equipment loans and cooperators to help with network 

maintenance, could all be preestablished protocols. These actions would help 

expedite network deployment and minimize data gaps during its operation. 

References

Fontana, Chris. 2007. Personal conversation.

Missoula Technology and Development Center. Spring 2001. Health Hazards of 

Smoke. Missoula, MT: U.S. Department of Agriculture, Forest Service, Missoula 

Technology and Development Center. 10 p.

Reinhardt, Timothy E.; Ottmar, Roger D.; Hanneman, Andrew J.S. 2000. Smoke 

Exposure Among Firefighters at Prescribed Burns in the Pacific Northwest. 

PNW-RP-526. Portland, OR: U.S. Department of Agriculture, Forest Service, 

Pacific Northwest Research Station. 45 p.

Sharkey, Brian. 1997. Health Hazards of Smoke, Recommendations of the Consensus 

Conference, April 1997, Part 1 of 6. Missoula, MT: U.S. Department of 

Agriculture, Forest Service, Technology and Development Program. 10 p.

Smoke Monitoring Network on 2006 Northern California Fires Belongie and Ahuja
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 390

This 

page is 

blank 

on purpose

Smoke Monitoring Network on 2006 Northern California Fires Belongie and Ahuja
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 391

Abstract—Computational tools have been developed at the National Institute of Standards and Technology (NIST) for modeling fire spread and smoke transport. These tools 

have been adapted to address fire scenarios that occur in the wildland urban interface 

(WUI) over kilometer-scale distances. These models include the smoke plume transport 

model ALOFT (A Large Open Fire plume Trajectory model) and WFDS (Wildlandurban interface Fire Dynamics Simulator) for fire spread and smoke transport in the 

wildland-urban interface. The visualization tool is called Smokeview. In this paper, an 

overview of the physical basis of the fire spread and smoke transport models will be 

discussed briefly along with the visualization of characteristic results using Smokeview. 

A technique will be described for visualizing smoke realistically, and indications will 

be given how Smokeview can be applied to other fire models.

Introduction

The National Institute of Standards and Technology (NIST) has developed 

a suite of validated computational tools for the simulation and visualization 

of fire spread and smoke transport. These tools have been adapted for addressing fire scenarios that occur over distances of the order of kilometers, 

in particular, fire scenarios that address the wildland urban interface (WUI) 

problem. These models include the smoke plume transport model ALOFT 

(A Large Open Fire plume Trajectory model; Walton and others 2003) and 

the model WFDS (Wildland-urban interface Fire Dynamics Simulator) for fire 

spread and smoke transport in the wildland-urban interface (Evans and others 

2004; Mell and others 2007). The visualization tool is called Smokeview 

(Forney and others 2003; Forney and McGrattan 2004). These tools were 

developed with an emphasis on ease of use on affordable computer platforms. 

In this paper, the physical basis of the fire spread and smoke transport models will be discussed briefly along with presentation of characteristic results 

using Smokeview. Smokeview visualizes data in several ways: by animating 

two-dimensional slices of gas phase quantities such as temperature or smoke 

concentration, by animating flow vectors and animating surface conditions 

such as incident heat flux or burning rates at the forest floor and also by 

animating iso-surfaces showing all places in the simulation scenario where a 

gas phase quantity takes on a specified value.

Smokeview is capable of rendering smoke and fire realistically using smoke 

concentrations computed by WFDS. By taking account of smoke properties, 

visibility can be assessed. We will discuss the potential use of Smokeview for 

the visualization of predictions from other smoke transport models.

Visualization and Modeling of Smoke 

Transport Over Landscape Scales

Glenn P. Forney1 and William Mell1

In: Butler, Bret W.; Cook, Wayne, 

comps. 2007. The fire environment—

innovations, management, and policy; 

conference proceedings. 26-30 March 

2007; Destin, FL. Proceedings 

RMRS-P-46CD. Fort Collins, CO: 

U.S. Department of Agriculture, 

Forest Service, Rocky Mountain 

Research Station. 662 p. CD-ROM.

1 Research Scientists, Building and 

Fire Research Laboratory, National 

Institute of Standards and Technology, 

Gaithersburg, MD. Lead author at 

glenn.forney@nist.gov
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 392

Modeling Fire Spread in the Outdoors

Computer modeling and visualization are important tools for understanding many complex processes. Fire behavior is no exception. Fire models range 

in complexity from simple algebraic correlations for predicting quantities such 

as flame heights or flow velocities to moderately complex differential equation 

based zone fire models for predicting quantities spatially averaged over large 

regions. Both classes of models work well when used appropriately but break 

down for complicated flows or geometries. For such cases, computational 

fluid dynamics (CFD) techniques are required.

Computing Fire and Smoke Spread with CFD

The Fire Dynamics Simulator (FDS) has been developed at NIST to simulate the effects of fire and smoke spread (McGrattan 2004; McGrattan and 

Forney 2004). FDS predicts smoke and/or hot air flow movement caused by 

fire, wind, and other factors by solving numerically the fundamental equations governing fluid flow, commonly known as the Navier-Stokes equations. 

The fire model WFDS builds on FDS by adding algorithms needed for solving the wildland interface problem. In particular, algorithms for modeling 

flame spread on or among the types of materials encountered in the WUI 

such as grassland, trees, shrubs, and so on. An experimental program is also 

proceeding to determine important material properties required by the WUI 

models (Manzello and others 2006).

WFDS is a physics-based fire-atmosphere coupled model that uses a form 

of CFD known as large eddy simulation (LES) to predict the thermal conditions resulting from a fire. These types of models require significantly more 

computational resources than the most commonly used fire spread models 

such as BEHAVE (Andrews and Bevins 1999) and FARSITE (Finney and 

Andrews 1999), which are semiempirical or empirical. However, there are a 

number of fire behavior problems, of increasing relevance, that are outside the 

scope of empirical and semiempirical models. Examples are wildland-urban 

interface fires, assessing how well fuel treatments work to reduce the intensity 

of wildland fires, and investigating the mechanisms and conditions underlying blowup fires and fire spread through heterogeneous fuels. WFDS uses 

approximations to the governing equations of fluid dynamics, combustion, 

and the thermal degradation of solid fuel. The LES approximation for solving 

the governing equations is a way of describing the effect of turbulence on 

the flow field. Turbulence is a phenomenon that causes gases to mix over a 

wide range of length scales making it hard to replicate with even the fastest 

computers. The combustion model assumes that fuel and oxygen burn readily 

when mixed. The fire itself is a heat source term in the governing equations, 

creating buoyant motion that drives the smoke and hot gases throughout 

the domain of the simulation. The smoke yield is specified for a given fuel 

type based on measurements.

The downside of a CFD calculation such as WFDS is that depending on 

the computational resources, it can easily take days to run. As a result, parallelization techniques become important for splitting the work load among 

multiple computers, thereby speeding up the calculation and allowing results 

to be generated in a reasonable time.

Visualization and Modeling of Smoke Transport Over Landscape Scales Forney and Mell
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 393

Simulation Overview

WFDS, like any CFD model, requires that the scenario of interest be 

divided into small control volumes called computational cells. The model 

then computes the density, velocity, temperature, pressure, and species concentration of the mass distribution (gas + particulates) in each cell based on 

the conservation laws of mass, momentum, and energy. WFDS simulates the 

spread of the fire by calculating the thermal degradation of the vegetative 

fuels. This is driven by the radiative and convective heat fluxes from the fire 

and depends on material properties of the vegetation. The spatial resolution of 

the simulation depends on the number of cells used to discretize the volume 

of interest, much like the quality of a digital photograph depends mainly on 

the number of pixels. The number of cells is ultimately limited by the computing power available and the time available for the calculation. Current 

PCs limit the number of cells to a few million. Many more cells may be used 

if calculations are run in parallel by splitting up the problem into parts and 

solving each part on a different PC. Model users must balance how much 

detail they want to incorporate with run times required to perform the computation. In general, finer grids result in longer calculations, which produce 

better results. Ultimately, one reaches a point of diminishing returns where 

the answer becomes insensitive to the increasing resolution of the grid.

The temporal resolution or time step size is determined from the grid 

resolution and the flow speed. The time step is chosen by WFDS, so that 

flow does not cross more than one grid cell during a single time step.

Both FDS and Smokeview would not have been possible without the recent advent of high-speed computers for performing computations, fast video 

cards for visualizing results and the Internet for exchanging information and 

ideas. These programs also would not have been possible without the research 

needed to develop the underlying fire models and the techniques needed to 

implement these models accurately and efficiently.

While simple line plots are adequate for visualizing the results of simple fire 

models, more sophisticated techniques are needed for interpreting the massive 

amounts of data generated by CFD models. This is where visualization tools 

such as Smokeview, the companion to WFDS, become essential.

Visualizing Smoke

Smokeview displays smoke allowing quantitative assessment using standard 

visualization techniques such as animated tracer particles that follow the 

flow, animated shaded 2D and 3D contours that display flow quantities, and 

animated flow vectors that display flow quantities and direction. Smokeview 

also visualizes smoke realistically by converting soot density to smoke opacity, 

displaying smoke as it would actually appear. These visualization techniques 

highlight different aspects of the underlying flow phenomena.

Visualization is essential at all stages of the process. It is used before a run 

to verify the correctness of scenario geometry (locations and size of simulation features, for example), during a run to monitor the simulation (ensure 

boundary flows are behaving as intended), and after the run has been completed to analyze the results.

Smokeview consists of about 70,000 lines of code. Most of it is written in C 

using standard libraries such as OpenGL (Shreiner and others 2005) and GLUT 

(Kilgard 1996) for graphics; GD (Web site: http://www.boutell.com/gd/), 

libpng (http://www.libpng.org/pub/png/), and libjpeg (http://www.ijg.org) 

Visualization and Modeling of Smoke Transport Over Landscape Scales Forney and Mell
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 394

for generating image files; and libzip (http://www.gzip.org/zlib/) for compression. A portion of Smokeview is written in Fortran 90 to input data generated by 

WFDS. The use of portable libraries allows Smokeview to run on many platforms 

including Windows, and various versions of Unix such as IRIX (for the SGI), 

Linux, and OSX (for the Macintosh). (Any mention of commercial products is for 

information only; it does not imply recommendation or endorsement by NIST.)

Though Smokeview is usually used to visualize the results of WFDS or 

FDS simulations it is also used by other models. For example, Smokeview is 

used to visualize simulation results of the zone fire model CFAST (Jones and 

others 2004). An earlier version of Smokeview was adapted for visualization of 

the constituents of concrete during its formation in Concreteview (Bentz and 

Forney 2000) and molecular dynamics simulations in Molecview (Stoliarov 

and others 2003). The file formats that Smokeview uses for visualizing data 

are documented in both the Smokeview (Forney and McGrattan 2004) and 

FDS (McGratan and Forney 2004) users guide, enabling other fire models 

to make use of Smokeview as a visualization postprocessor.

Scientific Visualizations

Figures 1, 2, and 3 show simulations of a crown fire spreading left to right 

from a virgin, untreated, forest stand into a treated stand. The fuel properties in the untreated stand are based on those measured in the black spruce 

and Jack pine overstory stands in the Northwest Territory of Canada during 

the International Crown Fire Modeling Experiments (Alexander and others 

2004).

Particles—WFDS uses particles as modeling elements to account for heat 

transfer and momentum drag that occurs during tree burning. This is illustrated in figure 1a. Particles may also be used to visualize the fire and smoke 

flow. Figure 1b shows a realistic view of the trees.

Slice planes—Smokeview allows animated color shaded contours of calculated gas quantities to be drawn at any horizontal or vertical plane in the 

simulation. To minimize file output, the user specifies the particular slice 

planes to be visualized. If disk space is not an issue, then the user may specify 

the entire 3D volume. Smokeview then allows the user to scroll through the 

3D volume of data one slice at a time displaying any horizontal or vertical 

plane. Figure 2 illustrates temperature contours in a vertical plane through 

the center of a grassland fire. Temperatures below 100 oC are truncated. 

Figure 2a shows solid shaded contours while figure 2b shows a vector plot. The 

data are colored the same way in both cases. Vector animations as illustrated 

in figure 2b are better than regular slice animations at highlighting flow 

changes, especially in regions where temperatures are uniform.

Isosurfaces—Smokeview uses isosurfaces to identify where a specified level 

of a gas phase quantity occurs rather than how much. For example, WFDS uses 

a mixture fraction model to simulate combustion. In this model, there is a 

critical or stoichimetric mixture fraction value, such that regions greater than 

the critical value are fuel rich and regions less than the critical value are fuel 

lean. Burning then occurs, according to the model, on the level surface where 

the mixture fraction equals this stoichimetric value. Therefore, it is of interest 

to visualize these locations. This is done using animated isosurfaces.

Visualization and Modeling of Smoke Transport Over Landscape Scales Forney and Mell
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 395

Figure 1—Particle and realistic view of a crown fire simulation burning with wind from the left at 4.0 m/s 

(8.9 mph). Colors in the particle view represent temperature. Regions colored red are 200 oC (410 oF) or 

warmer, regions colored blue are about 25 oC (77 oF). Tree section is 50 m on a side. (1a) Trees drawn and 

modeled using particles. Particles release heat. Particles also drag or slow down air flowing past. (1b) Trees 

drawn realistically.

a

b

Visualization and Modeling of Smoke Transport Over Landscape Scales Forney and Mell
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 396

Figure 2—Snapshot of shaded temperature contours and flow vectors through the center of a crown 

fire simulation with wind velocity boundary condition (from the left) of 4.0 m/s (8.9 mph). Colors 

represent temperature. Regions (or vectors) colored red are 200 °C (410 °F) or warmer, regions colored 

blue are about 25 oC (77 oF) . The region in the plot with temperature below 50 oC (122 oF) is hidden. 

Tree section is 50 m on a side. (2a) Shaded temperature contours. (2b) Colored flow vectors.

a

b

Visualization and Modeling of Smoke Transport Over Landscape Scales Forney and Mell
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 397

Figure 3—Snapshot of an iso-surface for of stoichimetric mixture fraction, a reasonably accurate surrogate 

for visualizing flames.

The isosurfaces are generated at each desired time step using a marching 

cube algorithm (Lorensen and Cline 1987) modified to remove ambiguities. 

A decimation procedure is used to reduce the number of resulting triangles 

by collapsing nodes of triangles with large aspect ratios and retriangulating. 

This makes the isosurface look better and also reduces storage requirements. 

Figure 3 illustrates the use of iso-surfaces for visualizing the stoichimetric 

mixture fraction at the same time and view point as seen in figure 2.

Realistic Visualization

Visualizing smoke realistically is challenging for three reasons. The storage 

requirements for describing smoke throughout the simulation scene at every 

time step can easily exceed the disk size capacities of present 32 bit operating 

systems, which would typically be 2 GB. The computation required both by 

the CPU and the video card to display each frame can easily exceed 0.1 s, 

the time corresponding to a 10 frame/s display rate. The physics required to 

describe smoke and its interaction with itself and surrounding light sources 

is complex and computationally intensive. Approximations and simplifications are required.

Smoke visualization techniques described previously, such as the use of 

tracer particles or shaded 2D contours are useful for analyzing data quantitatively, but are not suitable for applications where realism is required. Some 

examples of such applications are using Smokeview as a virtual fire fighter 

trainer or using Smokeview to examine the obscuration effects of smoke on 

an outdoor environment.

The approach used by Smokeview for visualizing smoke realistically is similar to that taken in Fedkiw and others (2001) except that interactions with 

smoke and light are not considered (only the effects of smoke obscuration 

are visualized). The video hardware is exploited to perform an obscuration 

calculation by using OpenGL to display a series of partially transparent parallel 

Visualization and Modeling of Smoke Transport Over Landscape Scales Forney and Mell
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 398

planes. The planes are chosen from the 3D obscuration data set computed 

by WFDS to be the ones most perpendicular to the viewer’s line of sight. 

Different plane orientations are chosen in real time as the viewer rotates the 

scene. The transparencies are computed based on physics using data derived 

from a WFDS calculation. Vertices in each plane are colored black. The vertices are also assigned an OpenGL a opaqueness parameter. The assigned 

value depends on the optical smoke thickness, with 0.0 used for completely 

transparent smoke and 1.0 for completely opaque.

Computing opaqueness—The a values are precomputed by WFDS using 

Beer’s law (Siegel and Howell 2001):

a = − − 1 exp( ) ks x ∆ (1)

for a particular view direction (down the x axis) where ∆x is the distance 

between two nodes, k is the soot mass extinction coefficient and s is the soot 

density. Beer’s law is an empirical relationship relating light absorption to 

the material properties of the media the light is traveling through, in this 

case soot or smoke. Smokeview currently does not consider light scattering 

effects with smoke.

Adjusting opaqueness—The absorption parameter, a needs to be adjusted 

when the view direction is not aligned along the axis orthogonal to the viewing planes (as in fig. 4), the distance between adjacent smoke planes changes, 

or viewing planes are skipped.

Ten million exponential operations per second are required to display 

smoke with corrected a’s at 10 frames per second if the simulation has grid 

dimensions of 100*100*100. Recent advances in CPU and video hardware 

makes these types of visualizations possible. These corrections may also be 

performed in the video card (GPU), resulting in increased display rates since 

the GPU performs the corrections simultaneously at all or many of the grid 

nodes rather than one at a time as the CPU would.

Figure 4—Illustration of the adjustment needed to the opaqueness parameter, α, for 

non axis aligned views. The α value along the ray containing the xˆ segment needs to 

be larger to account for the longer path length.

Visualization and Modeling of Smoke Transport Over Landscape Scales Forney and Mell
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 399

The a obscurations are precomputed using the distance ∆x between adjacent planes along the x-axis. The adjusted aˆ expressed in terms of ∆xˆ is 

given by

aˆ = − − 1 exp( ks x ∆ˆ) (2)

Equations 1 and 2 may be used to solve for aˆ in terms of a to obtain

ˆ ( ) ˆ/ a a = − − 1 1 ∆ ∆ x x

(3)

after noting that

1− = − = − = − ˆ exp( ˆ) exp( ) ( ) 1 ˆ/ ˆ/ a ks x ks x a x x x ∆ ∆ ∆ ∆ ∆ ∆x

The computation of equation 3 is expensive because the exponential is 

computed at each grid node for every time step. In addition, numerical cancellation may occur for a close to zero leading to loss of significant digits. 

Both problems may be solved by expanding equation 3 in a Taylor series and 

keeping only the first few terms:

a a ˆ ≈ − − + − − r ( ) ( )( ) ar r

a

r r r

2 3

2

1

2

1 2

where r x x x x n x x = = = − ⋅ − p e p e sec( ) θ ∆ ∆ ˆ / / ( ), n is the unit vector normal 

to the current plane being drawn, a is the angle between the view direction 

and the normal vector n, xe is the observers position, and xp is the vertex being 

drawn (along the view direction). These terms are illustrated in figure 4.

When planes are skipped equation 3 may be simplified. In particular when 

every second plane is skipped, ∆ ∆ x x ˆ / = 2 , so that equation 3 simplifies to

a a a a ˆ = − − = − 1 1 2 ( )2 2

The video hardware uses a values contained in the smoke planes to obscure 

the background much like a camera uses a neutral density filter to darken 

a scene. Extending the analogy, Smokeview uses one neutral density filter

(numerically) for each plane of smoke data. On a node by node basis then, 

each smoke plane obscures the current image stored in the OpenGL back 

buffer by the amount (1-a) to form a new back buffer image. A simplistic 

description of one step of this process is given by

new buffer image = (1-a) * old buffer image

This process is repeated for each smoke plane. Figure 5 illustrates this 

process for smoke and fire spread over a large grassland fire simulated by 

WFDS. The fuel properties are based on those measured in grassland plots 

during experiments conducted in Australia. A further description of the fuels 

and simulations is in Mell and others (2007).

The visualization is performed by displaying a series of partially transparent 

planes. For illustration, these planes are made more conspicuous (in fig. 5a) 

by skipping smoke planes (displaying every third plane) and orienting them 

along the x axis. Figure 5b shows the visualization as it would normally appear 

with all slice planes shown and oriented toward the viewer.

Visualization and Modeling of Smoke Transport Over Landscape Scales Forney and Mell
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 400

Figure 5—Realistic visualization of a large grass fire simulated using WFDS. Planes in 

the top image are drawn to be conspicuous by skipping two out of every three planes 

and by aligning planes along the x axis. All planes in the bottom image are displayed 

(none are skipped) and they are aligned to be closest to perpendicular of all possible 

plane orientations. (5a) Slices skipped and oriented along x directions. (5b) All slices 

shown and oriented toward viewer.

a

b

Visualization and Modeling of Smoke Transport Over Landscape Scales Forney and Mell
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 401

Summary

Smokeview is used to visualize data simulated by WFDS, the NIST wildland fire dynamics simulator. Smokeview uses several techniques to visualize 

data, some scientific and some realistic. The realistic technique uses the video 

hardware found on modern computers to convert soot densities computed 

by WFDS to opacities displayed on the computer screen. The local values of 

obscuration are computed by WFDS for all grid nodes using soot density, 

grid spacing, and the soot mass extinction coefficient appropriate for visible 

light and the fuel being burned. This is a one time computation. Smokeview, 

on the other hand, integrates these local values along the line of sight each 

time the view position or direction changes and for each frame of data.

Smokeview is a scientific visualization tool that has been adapted to display 

data for several types of applications and it may be adapted to display data 

simulated by fire models used to solve other aspects of the WUI problem.

Additional Information

“Fire on the Web” (http://fire.nist.gov) contains resources related to fires 

including images and movies of real fires along with measured heat release 

data, software for modeling fire flow, and links to NIST publications related 

to fire. Information on the fire modeling software FDS and Smokeview may 

be found at http://fire.nist.gov/fds. Further information about the wildland 

urban interface adaptation of FDS (WFDS) may be found at http://www2.

bfrl.nist.gov/userpages/wmell/public.html.

Acknowledgment

The authors are grateful to acknowledge Kevin McGrattan for developing 

FDS, the foundation of WFDS, and making it available for use in this WUI 

modeling work.

References

Alexander, M.; Stefner, C.N.; Mason, J.A.; Stocks, B.J.; Hartely, J.R.; Maffey, M.A.; 

Wotton, B.M.; Taylor, S.W.; Lavoie, N.; Dalrympte, G.N. 2004. Characterizing 

the Jack pine - black spruce fuel complex of the international crown fire modeling 

experiments (ICFME). NOR-X 393. Canadian Forest Service, Northern Forestry 

Centre.

Andrews, P.L.; Bevins, P.L. 1999. BEHAVE Fire Modeling System: Redesign and 

Expansion. Fire Management Notes 59:16-19.

Bentz, D.P.; Forney, G.P. 2000. User’s Guide to the NIST Virtual Cement and 

Concrete Testing Laboratory, Version 1.0. Technical Report NISTIR 6583. 

Gaithersburg, MD: National Institute of Standards and Technology.

Evans, David D.; Rehm, Ronald G.; Baker, Elisa S. 2004. Physics-Based Modeling 

for WUI Fire Spread - Simplified Model Algorithm for Ignition Structures by 

Burning Vegetation. NISTIR 7179, 2004.

Visualization and Modeling of Smoke Transport Over Landscape Scales Forney and Mell
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 402

Fedkiw, Ronald; Stam, Jos; Jensen, Henrik Wann. 2001. Visual simulation of smoke. 

In: Eugene Fiume, editor, SIGGRAPH 2001, Computer Graphics Proceedings. 

ACM Press/ACM SIGGRAPH: 15-22.

Finney, M.A.; Andrews, P.L. 1999. A Program for Fire Growth Simuluation. Fire 

Management Notes 59:13-15.

Forney, G.P.; Madrzykowski, D.; McGrattan, K B.; Sheppard, L. 2003. Understanding 

fire and smoke flow through modeling and visualization. Computer Graphics and 

Applications 23(4):6-13.

Forney, G.P.; McGrattan, K.B. 2004. User’s Guide for Smokeview Version 4 - A 

Tool for Visualizing Fire Dynamics Simulation Data. NIST Special Publication 

1017. Gaithersburg, MD: National Institute of Standards and Technology.

Jones, W.W.; Peacock, R.D.; Forney, G.P.; Reneke, P.A. 2004. CFAST, Consolidated 

Model of Fire Growth and Smoke Transport (Version 5). Technical Reference 

Guide. NIST Special Publication 1030. Gaithersburg, MD: National Institute 

of Standards and Technology.

Kilgard, Mark J. 1996. OpenGL Programming for the X Window System. Reading, 

MS: Addison-Wesley Developers Press.

Lorensen, William E.; Cline, Harvey E. 1987. Marching cubes: A high resolution 

3d surface construction algorithm. Proceedings of the 14th annual conference 

on Computer graphics and interactive techniques. ACM Press: 163-169.

Manzello, Samuel L.; Cleary, Thomas G.; Shields, John R.; Yang, Jian. 2006. On 

the ignition of fuel beds by firebrands. Fire and Materials 30:77-87.

McGrattan, K.B. 2004. Fire Dynamics Simulator (Version 4), Technical Reference 

Guide. NIST Special Publication 1018. Gaithersburg, MD: National Institute 

of Standards and Technology.

McGrattan, K.B.; Forney, G.P. 2004. Fire Dynamics Simulator (Version 4), User’s 

Guide. NIST Special Publication 1019. Gaitherburg, MD: National Institute of 

Standards and Technology.

Mell, William; Jenkins, Mary Ann; Gould, Jim; Cheney, Phil. 2007. A physicsbased approach to modeling grassland fires. International Journal of Wildland 

Fire 16(1):1-22.

Shreiner, Dave; Woo, Mason; Neider, Jackie; Davis, Tom. 2005. OpenGL Programming 

Guide - The Official Guide to Learning OpenGL, Version 2. 5th edition. OpenGL 

Architecture Review Board. Stoughton, MS: Addison-Wesley.

Siegel, Robert; Howell, John R. 2001. Thermal Radiation Heat Transfer. 4th edition. 

New York: Taylor & Francis, Inc.

Stoliarov, S.I.; Westmoreland, P.R.; Nydeb, M.R.; Forney, G.P. 2003. A reactive 

dynamics model of thermal decomposition in polymers: I. Poly (methyl 

methacrylate). Polymer 44(3):883-894.

Walton, W.D.; McGrattan, K.B.; Mullin, J.V. 2003. A Smoke Plume Trajectory 

Model for Personal Computers. Technical Report NIST SP 995. Gaithersburg, 

MD: National Institute of Standards and Technology.

Visualization and Modeling of Smoke Transport Over Landscape Scales Forney and Mell
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 403

Abstract—The BlueSky Smoke Prediction System developed by the U.S. Department 

of Agriculture, Forest Service, AirFire Team under the National Fire Plan is a modeling 

framework that integrates tools, knowledge of fuels, moisture, combustion, emissions, 

plume dynamics, and weather to produce real-time predictions of the cumulative impacts of smoke from wildfires, prescribed fires, and agricultural burn activities. Currently, 

BlueSky smoke predictions are available daily across the contiguous United States. The 

output has been used by air regulators, burn bosses, and smoke managers as a guide to 

help make ‘go’ and ‘no-go’ decisions about prescribed fires and plan burn operations. 

It also helps track day-to-day emissions from wildland and prescribed fires. BlueSky 

is establishing its reputation as a one-stop shopping for regional smoke concentration 

and emissions tracking across all land ownership, and is being used by more and more 

users especially in the West. On the other hand, little is known about the accuracy 

of its predictions of smoke transport and dispersion under different meteorological 

conditions. This ongoing study aims at validating BlueSky predictions using in-situ and 

satellite observations. The study domain is northern California and southern Oregon 

during the last 2 weeks of August 2006 when several major wildland fires broke out 

in the region. The predicted smoke concentrations are evaluated by the PM2.5 data 

at several stations, and the plume trajectories are compared with satellite images. 

Sensitivity tests are performed to identify potential sources in the smoke predictions 

so that improvements can be made to the BlueSky prediction system. 

Introduction

BlueSky, developed by the U.S. Department of Agriculture, Forest Service, 

AirFire Team, is a smoke modeling framework that integrates consumption, 

emissions, meteorology, and dispersion models to predict smoke trajectories 

and concentrations of particulate matter. BlueSky smoke predictions are available daily across the contiguous United States and the output has been used 

by air regulators, burn bosses, and smoke managers as a guide to help make 

‘go’ and ‘no-go’ decisions about prescribed fires, plan burn operations, and 

help track day-to-day emissions from wildland and prescribed fires. BlueSky 

is establishing its reputation as a one-stop shopping for regional smoke concentration and emissions tracking across all land ownership, and is being 

used by more and more users especially in the West, as this area is subject to 

higher risk of wildland fires than other parts of the country. Few studies have 

compared BlueSky predictions with actual observation. Validation efforts to 

date have been limited to isolated fire cases in the Northwest (Adkins and 

others 2003; Berg and others 2003; Larkin and others 2006). 

Validation of BlueSky Smoke Prediction 

System Using Surface and Satellite 

Observations During Major Wildland Fire 

Events in Northern California

Lesley Fusina1, Sharon Zhong1, Julide Koracin2, Tim Brown2, 

Annie Esperanza3, Leland Tarney4, and Haiganoush Preisler5

In: Butler, Bret W.; Cook, Wayne, 

comps. 2007. The fire environment—

innovations, management, and policy; 

conference proceedings. 26-30 March 

2007; Destin, FL. Proceedings 

RMRS-P-46CD. Fort Collins, CO: 

U.S. Department of Agriculture, 

Forest Service, Rocky Mountain 

Research Station. 662 p. CD-ROM.

1 Department of Geography Michigan 

State University, East Langing, MI. 

Lead author at fusinale@msu.edu.

2 Desert Research Institute, Nevada 

System of Higher Education, Reno, 

NV.

3 U.S. Department of the Interior, 

National Park Ser vice, Sequoia 

and Kings Canyon National Parks, 

California.

4 U.S. Department of the Interior, 

National Park Service, Yosemite 

National Park, California.

5 U.S. Department of Agriculture, 

Forest Service, Pacific Southwest 

Research Station, Albany, CA.
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 404

As part of an effort to provide better information on fire weather and smoke 

dispersion/transport, the California and Nevada Smoke and Air Committee 

(CANSAC) has been running the coupled MM5 model and BlueSky smoke 

prediction system in real-time for California and Nevada. In this paper, we 

describe some preliminary results from an effort to validate the CANSAC 

BlueSky smoke predictions as part of a project funded by the Joint Fire Science 

Plan to develop tools for estimating contributions of wildland and prescribed 

fires to air quality in the Sierra Nevada.

Study Domain, Cases, Data

The study focuses on the area of northern California and southern Oregon 

during the last 2 weeks of August 2006 when a stream of major wildfires 

broke out in this area. These large fires provide a clear smoke signal on satellite imagery and surface PM monitors, creating an ideal environment for 

comparison efforts. Figure 1 shows the station locations where in-situ particulate matter and meteorological variables were collected. The predictions 

of BlueSky PM concentrations associated with these fires were produced by 

CANSAC at the Desert Research Institute. Four nested domains were used 

with the innermost domain of 4-km grid spacing covering California and 

Nevada. The meteorological input was provided by the MM5 forecast. Initial 

and boundary conditions for the MM5 forecasting were obtained from the 

Figure 1–Locations of PM2.5

monitoring sites and surface 

meteorological observation 

sites in the study domain. 

Validation of BlueSky Smoke Prediction System Using Surface and Satellite Observations... Fusina, Zhong, Koracin, Brown, Esperanza, Tarney, and Preisler
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 405

6 hourly 40-km Eta forecasts from the National Center for Environmental 

Predictions. The details of BlueSky and MM5 configuration can be found 

at the CANSAC Web site at: http://www.cefa.dri.edu/COFF/. 

PM2.5 in-situ hourly measurements for approximately a dozen stations 

were obtained from both the Environmental Protection Agency (EPA) and 

Oregon’s Department of Environmental Quality. Daily satellite images were 

downloaded from the National Geophysical Data Center’s Satellite Fire 

Detections Web site (map.ngdc.noaa.gov/website/firedetects/viewer.htm). 

Hourly extracted PM2.5 concentrations by station were compared with time 

series of observed measurements. An average of PM2.5 concentrations taken 

from a fire free period during (July 16-24, 2006) was subtracted from the 

observed PM2.5 value in an effort to view the fire induced effect on PM2.5. 

Qualitative comparisons of smoke trajectories were done between the daily 

satellite images and the BlueSky output. Time series analysis of the MM5 

model was necessary to be sure the meteorological variables being supplied to 

BlueSky were not causing inaccuracies in predicted smoke plume trajectories. 

In-situ hourly weather data were collected for 15 stations from the National 

Climatic Data Center Web site (www.ncdc.noaa.gov/oa/ncdc.html). 

Results

Past studies concerning the accuracy of BlueSky have found that plume 

trajectories seem to agree well with satellite observations, but tend to 

underpredict PM2.5 concentrations (Larkin and others 2006). Our investigation into BlueSky’s accuracy concerning fires in northern California has 

produced similar results. As figure 2 shows, BlueSky has captured very well 

Figure 2– 4km model domain 

used. The left panel shows the 

National Geophysical Data 

Center satellite images of smoke 

plume. The right panel shows 

the corresponding images from 

BlueSky output.

Validation of BlueSky Smoke Prediction System Using Surface and Satellite Observations... Fusina, Zhong, Koracin, Brown, Esperanza, Tarney, and Preisler
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 406

the trajectory and shape of the observed smoke plumes from the fires near 

the California and Oregon border. There are smoke plumes in Nevada in the 

BlueSky predictions that are absent from satellite images. The cause for this 

discrepancy is being investigated, and is likely due to inaccurate information 

on fire emissions. 

Time series analysis of PM2.5 concentrations at selected stations (fig. 3) 

shows increases of modeled values when observed values increase. The observed concentrations of PM2.5 are a combination of emissions from fires and 

other emissions sources including primary and secondary sources. The only 

emission sources in BlueSky, however, are wildland and prescribed fires. To 

isolate the contributions to PM concentrations from fires, the hourly observed 

values during this period were subtracted from the mean hourly values from 

a fire-free period prior to the beginning of these fires. The negative values 

at a particular hour, therefore, imply that the observed value is less than the 

‘fire-free’ mean value for that hour. Although these time series show relatively 

good agreement between BlueSky PM2.5 concentrations and the observed 

measurements, BlueSky does not do well in capturing all of the smaller 

PM2.5 fluctuations. Klamath Falls in Oregon is a good example. The model 

shows two large increases in PM concentration but fails to pick up the many 

smaller fluctuations. That said, the model in all cases has good quantitative 

agreement of PM2.5 concentrations, which is different from previous studies 

where BlueSky was found to underpredict PM2.5 concentrations (Larkin and 

others 2006). 

The time series in figure 3 also shows small time lags between the model 

and observed concentrations. Figure 4 shows a sequence of meteorological 

time series at Klamath Falls. There tends to be a good agreement between the 

modeled and observed variables, but the model is underpredicting windspeed, 

which is probably the cause for the delay in the BlueSky predicted peak PM2.5

value compared to the observed onset time of the peak. The time series of 

Figure 3–Time series of observed and modeled PM2.5 concentrations at 

study sites. To isolate the contributions to PM from the fires, the hourly 

observations of PM2.5 were subtracted by the hourly mean obtained using 

data from a fire-free period just before the study period.

Validation of BlueSky Smoke Prediction System Using Surface and Satellite Observations... Fusina, Zhong, Koracin, Brown, Esperanza, Tarney, and Preisler
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 407

the three remaining stations in figure 3: Portola, Illinois Valley, and Provolt, 

demonstrates the opposite situation, where the impact of PM2.5 is sooner than 

observations show. Most likely, one could follow the same logic as above and 

assume the reason for this is due to overprediction of windspeeds, as figure 5 

shows. However, in this study, the overprediction of MM5 windspeeds was 

not common in respect to how many times PM2.5 impacts were predicted 

sooner than observed. One possibility is that MM5 overpredicts upper level 

winds. At the time of this writing, the predicted upper level meteorological 

conditions including windspeed, wind direction, stability, and boundary layer 

heights, are being compared with the twice daily rawinsonde soundings at 

Medford, OR. Other factors could also contribute to the disagreement between the modeled and predicted concentrations and sensitivity simulations 

will be performed to isolate these factors. 

Figure 4–Time series of meteorological 

variables of Klamath Falls, OR. MM5 

shows relatively good agreement 

with observations but slightly slower 

windspeeds, which was found to be 

normal in this study.

Figure 5–Same as figure 4, but for Arcata, CA. 

Validation of BlueSky Smoke Prediction System Using Surface and Satellite Observations... Fusina, Zhong, Koracin, Brown, Esperanza, Tarney, and Preisler
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 408

Summary

In attempts to validate the accuracy of the BlueSky smoke model used 

by air regulators, burn bosses, and fire managers across the United States, 

a study over northern California during the last 2 weeks in August is being 

investigated. Preliminary results show good agreement between BlueSky 

smoke plume trajectories and satellite images. Although plume shape does not 

seem to be overly consistent between the two, BlueSky does do a reasonably 

good job in capturing accurate concentrations for large increases of PM2.5 

at surface stations. Meteorological analysis has also shown good agreement 

between observed and predicted values. Errors in MM5 windspeed predictions were evident, but alone those were not consistent enough to account 

for all inaccuracies within BlueSky. 

Ongoing work includes more analysis of the meteorology supplied to 

BlueSky by the MM5 model. Specifically, upper air data will be reviewed 

to see if the plume trajectory behavior exhibited by BlueSky is in agreement 

with the MM5 model. Upper air data can also help to clarify discrepancies 

at the surface when predicted surface concentrations of PM2.5 do not agree 

with the observed concentrations, but smoke plume trajectories do. More 

study into the actual smoke plumes predicted by BlueSky and the PM2.5 concentrations within the plumes will be done by comparing them with aerosol 

optical depth data. Sensitivity studies will be done, forcing components of 

BlueSky to behave a certain way, in hopes to isolate which of those components of BlueSky—meteorological variables, emissions, plume dispersion, and 

so forth—are causing the major inconsistencies between BlueSky predictions 

and observations. 

Acknowledgments

This study is made possible with the support and funding by the interagency 

Joint Fire Science Plan. Thanks to Anthony Barnack at the Oregon Department of Environmenal Quality for his help in obtaining PM2.5 data. 

References

Adkins, J.W.; O’Neill, S.M.; Rorig, M.; Ferguson, S.A.; Berg, C.M.; Hoadley, J.L. 

2003. Assessing the Accuracy of the BlueSky Smoke Modeling Framework During 

Wildfire Events. Paper read at 5th Symposium on Fire and Forest Meteorology 

and the 2nd International Wildland Fire Ecology and Fire Management Congress, 

at Orlando, FL.

Berg, C.M.; O’Neill, S.M.; Ferguson, S.A.; Adkins, J.W. 2003. Application of the 

BlueSky Smoke Modeling Framework to the Rex Creek Wildfire. Paper read at 5th 

Symposium on Fire and Forest Meteorology and the 2nd International Wildland 

Fire Ecology and Fire Management Congress, at Orlando, FL.

Larkin, N. K.; O’Neill, S.M.; Soloman, R; Krull, C.; Raffuse, S.; Rorig, M.; Peterson, 

J; Ferguson, S.A. 2006. The BlueSky Smoke Modeling Framework. Atmospheric 

Environment, in review.

Validation of BlueSky Smoke Prediction System Using Surface and Satellite Observations... Fusina, Zhong, Koracin, Brown, Esperanza, Tarney, and Preisler
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 409

Abstract—Wildland fuels have been accumulating in the United States during at 

least the past half-century due to wildland fire management practices and policies. 

The additional fuels contribute to intense fire behavior, increase the costs of wildland 

fire control, and contribute to the degradation of local and regional air quality. The 

management of prescribed and wildland fire on Federal, State, and private lands 

pose critical challenges for the characterization of preburn fire fuels and postburn 

carbon consumption assessments, predicting smoke trajectories and concentrations, 

and modeling air quality emissions. Prescribed and wildland fires are both important 

sources of airborne fine particulate matter (PM2.5) and ozone (O3) precursors such as 

nonmethane volatile (VOCs) and semivolatile organic compounds (SVOCs), nitrogen 

oxides (NOx), carbon monoxide (CO), and methane (CH4). We quantified pre- and 

postburn belowground and aboveground biomass to determine fuel consumption for 

fine and coarse woody material, shrub, herbs, litter, and duff, and assessed fire effects 

on plant communities. The BlueSky smoke prediction modeling framework, and the 

BlueSky Rapid Access Information System (BlueSkyRAINS) were implemented to 

model smoke trajectory and PM2.5 concentrations at ground level in the downwind 

smoke plume. PM10 and 2.5 and photochemically and radiatively important trace gases 

during the flaming and smoldering stages of prescribed burns were characterized and 

fire emission modeled to determine emission factors for chemical species.

Introduction

Fire has played a major role in determining the distribution of plants 

across the Coastal Plain of the Southeastern United States. The extent of 

fire dependent ecosystems has been reduced as a result of fire exclusion and 

landuse conversion. Wildland fire fuel loading throughout the United States 

has become a hazard to life, property, ecosystem health, and the habitat of 

threatened and endangered species as a result of past fire exclusion policies and 

practices. Land managers are concerned that fuel loads are reaching hazardous 

levels that can lead to widespread catastrophic wildfires in forest ecosystems 

and the wildland/urban interface. This wildland fire risk is currently impacting ecosystem management planning throughout the Southeast.

Development and Demonstration of 

Smoke Plume, Fire Emissions, and Pre- and 

Postprescribed Fire Fuel Models on North 

Carolina Coastal Plain Forest Ecosystems

Robert A. Mickler1, Miriam Rorig2, Christopher D. Geron3, 

Gary L. Achtemier4, Andrew D. Bailey1, Candice Krull2, and 

David Brownlie5

In: Butler, Bret W.; Cook, Wayne, 

comps. 2007. The fire environment—

innovations, management, and policy; 

conference proceedings. 26-30 March 

2007; Destin, FL. Proceedings 

RMRS-P-46CD. Fort Collins, CO: 

U.S. Department of Agriculture, 

Forest Service, Rocky Mountain 

Research Station. 662 p. CD-ROM.

1 Program Manager and Forest 

Scientist, respectively, Alion Science and 

Technology, Inc., Durham, NC. Lead 

author rmickler@alionscience.com

2 Meteorologists, U.S. Department of 

Agriculture, Forest Service, Seattle, WA.

3 A t mospher ic S cient i st , U. S. 

Environmental Protection Agency, 

Research Triangle Park, NC.

4 Meteorologist, U.S. Department of 

Agriculture, Forest Service, Athens, GA.

5 Regional Ecologist, U.S. Fish and 

Wildlife Service, Tallahassee, FL.
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 410

Measurement and Modeling of Down Woody Debris and 

Fuels

Fuel classification during the past 75 years has evolved from a fire control planning focus to the beginning of predictive fire behavior modeling 

in the 1970s. Current fuel classification models have focused on the rate of 

spread, resistance to control, and the flame length of fires in surface fuels. 

Fire behavior is predicted by using 13 stylized fuel models (Rothermel 1972; 

Albini 1976). Decision support systems such as FARSITE and the National 

Fire Danger Rating system are based on the Rothermel fire spread model 

and are the basis of predicting fire behavior today. Land managers recognize 

that these models are limited in their ability to predict extreme fire behavior, 

persistent fires, and fuel consumption. Some of these limitations are currently 

being addressed by a fuel characteristic classification (FCC) (Sandberg and 

others 2001).

The availability of fire-spread models has increased the need for quantitative fuel field data. A line-intersect method developed by Brown and others 

(1982) has been widely adopted to quantify fuel-loading inputs. The USDA 

Forest Service Forest Inventory and Analysis (FIA) program recognized the 

need for extensive information on fuels across the landscape. Fuel field protocols (http://fia.fs.fed.us/library/field-guides-methods-proc/) were piloted 

by the former Forest Health Monitoring Program between 1998 and 2000, 

and implemented in 2001 on a 1/16th subset of the standard base FIA grid 

plots. FIA methods generally partition the forest ecosystem into pools for 

live trees, down deadwood, standing dead trees, understory vegetation, forest 

floor materials, and soil. Estimating site-specific fuels from this database has 

been particularly problematic. The data are not consistently available from 

the FIA inventory data source and there are little data on fuel loading in the 

scientific literature. Additionally the biomass algorithms are based nationally 

on available data collected regionally for tree, shrub, and herbaceous species 

and associated wood density for decay classes.

Past fuel and fire behavior research has resulted in only qualitative measures of fuel loads and rates of spread. A more detailed fuel classification, 

based on species composition, standing dead and down deadwood, fuel size 

classification, understory vegetation, and vertical distribution of fuels, would 

have much more utility than the broad fuel model classification system now 

in use. Fire in the deep organic soil areas of the Coastal Plain is frequently 

associated with costly blowup wildfires and soil fires started from prescribed 

fire fuel reduction and habitat management burns. Wildfires in this area can, 

under certain combinations of fuel and weather, grow from a low intensity 

burn to a virtually uncontrollable burn until weather conditions change or 

the fire has run out of fuel. Control efforts are often hampered by inaccessibility, poor soil trafficability on wet organic soils in the area, and fires that 

tend to burn deeply into the organic soils. A better understanding of the 

behavior of fires and the role of fuel loading in fire behavior will contribute 

to the control of wildfires and the use of prescribed fire as a management 

tool in the region.

Fire Emissions Monitoring and Emission Factor Modeling

Landscape scale emissions of trace gases and PM are typically determined 

using the approach of Taylor and Zimmerman (1991) and Hao and Liu 

(1994):

M=A•B•α •β

Development and Demonstration of Smoke Plume, Fire Emissions, and Pre- and Postprescribed Fire... Mickler, Rorig, Geron, Achtemier, Bailey, Krull, and Brownlie
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 411

where M is the amount of biomass consumed annually, A is the total land 

area burned annually (ha yr–1), B is the average organic matter (fuel load) per 

unit area in individual biomes (metric tons or MT ha–1), a is fraction of above 

ground biomass to total, and β is the burning efficiency (fraction consumed) 

of the above ground biomass. Total emission of a given compound is given 

by multiplying M by an emission factor, which is typically expressed in units 

of g-C/kg-C fuel consumed.

These emission factors and total emissions of trace gases and PM from individual fires are typically determined using a carbon mass balance approach 

as described in Ward and Hardy (1988) as:

F C

C + C + C + C C t

t

CO2 CO CH4 TPM VOC

   	

 ̈

a

©

©

·

1

 ̧

 ̧

CSF

where Ft is the flux of the target compound(s), Ct is the concentration of 

the target compound(s), CCO2,CO,CH4,TPM,VOC are sample concentrations of 

CO2, CO, CH4, total PM, and total VOC, respectively, and CSF is the fuel 

consumption (carbon mass) per unit area. Nitrogenous emissions are calculated similarly. These approaches have been used to calculate emission factors 

(EFs) and total emission fluxes from the Coastal Plain prescribed fires in 

North Carolina.

The mass balance approach takes advantage of the turbulent state of mixing in fire plumes, which means that particles and gases will be transported 

in similar proportions as they move from the source. This allows for the 

calculation of valid mass balance estimates of PM and gases in plumes that 

cool to ambient conditions, appropriate for assessing impacts on air quality 

and atmospheric chemistry.

For wildfire sampling, remote platforms are needed to access smoke plumes. 

This is necessary to maintain safe working conditions and ensure that samples 

are collected in air parcels dominated by the fires. Ground based sampling 

is not practical for wildfire studies because adequate planning horizons or 

sampling sites are not typically available. Even when sites are located, shifting 

winds often move smoke away from sampling systems. Manned aircraft has 

been used to collect smoke samples in the past, but this is dangerous and can 

be prohibitively expensive.

Biomass burning in the Southeast can be a potentially significant source 

of photochemically active and radiatively important trace gases as well as PM 

(Barnard and Sabo 2003). Areas burned in the region vary annually but are 

typically several million acres per year, resulting in trace gas and PM emissions that range from 2 to 15 percent of total emissions from other sources. 

Little data on emissions from prescribed burning are currently available, and 

this fire type in particular is projected to increase in the Southeastern United 

States. Emissions of reduced compounds, many of which are air toxins, are 

thought to be lower during prescribed fires compared to wildfires covering 

the same area. This is suspected largely because it is known that wildfires 

occur typically during excessively dry periods when much of the forest floor 

is dry and susceptible to smoldering incomplete combustion, the source of 

many toxic compounds.

Continuous monitoring of O3, PM, and NOx have shown that air pollutant concentrations are enhanced by forest fire emissions. In the rural 

environment, the influence of the forest fire on air quality can be detected, 

and significantly higher (50 to 150 percent) pollution levels than seasonal 

median values have been documented (Cheng and others 1998). While fire 

Development and Demonstration of Smoke Plume, Fire Emissions, and Pre- and Postprescribed Fire... Mickler, Rorig, Geron, Achtemier, Bailey, Krull, and Brownlie
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 412

events can cause high transient air pollutant concentrations, for most criteria pollutants, the fire emissions are a relatively small fraction of the annual 

emission inventory. For fine PM, however, the annual emission estimates 

from biomass burning represent a significant fraction of many Southern 

States’ emission inventories, especially in the counties where the emissions are 

concentrated (Dennis and others 2002). Given the current emphasis by the 

EPA on particles, it is imperative that real-world emission data be developed 

from open burning sources.

It is generally thought that emission factors or pollutants are among the 

more consistent and reliable components of biomass burning emission models. 

However, comparisons of recent studies suggest that under some conditions, 

especially where smoldering combustion is important, EFs are still quite 

uncertain (Andreae and Merlet 2001; Hays and others 2002). Residual smoldering combustion (RSC) emissions from forest floor burns can be produced 

for up to several weeks after the passage of a flame front and they are mostly 

unaffected by flames. Fuels prone to RSC include downed logs, litter, and 

organic soils. These fuels are important in our proposed study area. Limited 

observations suggest that RSC is a globally significant source of emissions to 

the troposphere (Bertschi and others 2003). These authors used a model that 

predicts trace gas EF for fires in a wide variety of aboveground fine fuels. It 

failed to predict emission factors for RSC. For many compounds, the EF for 

RSC-prone fuels is different from the EF for the same compounds measured 

in fire convection columns above forest ecosystems. Some large changes resulted in estimates of biomass fire emissions with the inclusion of RSC. For 

instance, EF increases by a factor of 2.5 even when RSC accounts for only 

10 percent of fuel consumption. This shows that many more measurements 

of fuel consumption and emission factors for RSC are needed to improve 

estimates of biomass burning emissions.

Smoke Trajectory and Concentration Modeling

Smoke emissions from wildland fires are one the most important constraints 

on land mangers conducting prescribed burns. The quantity, duration, time 

of day, and spatial dispersion of smoke must all be considered when assessing the impacts on human health and safety. Existing smoke models do a 

poor job of estimating smoke production and duration. This is especially 

true on the deep organic soils found in the Coastal Plain of the Southeast. 

Many of the dispersion models in use by wildland mangers today—SASEM 

(Sestak and Riebau 1988), VALBOX (Sestak and others 1989), VSMOKE 

(Lavdas 1996), NFSpuff (Harison 1995), TSARS (Hummel and Rafsnider 

1995), and CALPUFF (Scire and others 1995)—have been adapted from 

industrial stack models for use in wildland fires. Smoke models for prescribed 

burning differ from point-source industrial models due to additional data 

requirements for pattern of ignition, fuel moisture by size, fuel loading by 

size, fuel distribution, and local weather that influences burn rates and dispersion. The FARSITE (Finney 1998) model was developed to address these 

data requirements and is used to model forest fire behavior in variable fuels, 

terrain, and changing local weather conditions. FARSITE does not model 

smoke dispersion, but output from the combined models can now be used 

in smoke dispersion models.

The BlueSky smoke modeling framework (http://blueskyrains.org) is a 

smoke prediction tool used by land managers to facilitate wildfire containment 

and prescribed burning programs, which are necessary for ecosystem health, 

while minimizing impacts to human health and scenic vistas. The BlueSky 

Development and Demonstration of Smoke Plume, Fire Emissions, and Pre- and Postprescribed Fire... Mickler, Rorig, Geron, Achtemier, Bailey, Krull, and Brownlie
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 413

smoke modeling framework links computer models of weather prediction, fuel 

consumption and emissions by fire, and smoke dispersion into a system for 

predicting the cumulative impacts of smoke from prescribed fires, wildfires, 

and agricultural fires (O’Neill and others 2003, 2005). BlueSky is currently 

functional over the conterminous United States. While differences exist regionally, each night BlueSky obtains regional meteorological predictions and 

reported burn information from available private, State, and Federal agencies, merges these data with models of fuel consumption and emissions, and 

processes dispersion and trajectory models to produce regional estimates of 

smoke concentrations for the next 1 to 3 days (Ferguson and others 2001). 

Smoke and fire managers access these predictions via the internet as a tool 

to aid their “go/no-go” decisions for burning operations and other real-time 

decision support (Ferguson and others 2003).

Methods

Vegetation Classification and Pre- and Postburn Biomass

We acquired 2004 leaf-off color infrared (CIR) negatives of stereo aerial 

photography for study areas and collected field data on fuel loads and/or 

fuel accumulation. Aerial photos were scanned to generate digital data layers and stereo models for interpretation as well as orthorectified mosaics of 

the study areas. We incorporated existing GIS vegetation data from the U.S. 

Fish and Wildlife Service and the U.S. Air Force using onscreen stereoscopic 

techniques to create a digital vegetation database. Vegetation was classified 

to the Alliance level using the National Vegetation Classification System 

(http://biology.usgs.gov/npsveg/nvcs.html). Field plots were randomly assigned within each vegetation Alliance. Fire fuel data from field based sample 

plots, digital photos, and vegetation data were used to develop fire fuel polygons. Additional field data were used to assess the thematic accuracy of the 

vegetation classification, the positional accuracy of the digital orthophoto 

mosaic, and the fuel load polygons. Metadata were created for the digital 

orthophoto mosaics and vegetation and fuel load databases.

We established a permanent plot network on the Alligator River National 

Wildlife Refuge and the Air Force Dare County Bombing Range modeled 

on USDA Forest Service FIA P2 and P3 plots to measure and characterize 

live biomass and pre- and postburn down deadwood (DWD). We used field 

protocols based in methods establish by the USDA Forest Service in “Field 

Instructions for Southern Forest Inventory.” The collection of DWD data 

was collected using a line-intersect method to sample down wood along 

linear transects based on Brown’s transects (Brown and others 1982). Plotlevel data on the amount, distribution, and characterization of DWD were 

related to the detailed attribute data for other ecosystem components on the 

same plot—that is, shrub and herbaceous understory, live and dead herbs 

(including grasses), and litter. FIA methodology was augmented with additional data on the vertical distribution of DWD for input into the FARSITE 

fire behavior model. Down deadwood was characterized as coarse woody 

debris (woody pieces greater than 3.0 inches in diameter), or fine woody 

debris (small = 0 to 0.24 inch, medium = 0.25 to 0.9 inch, and large = 1 to 

2.9 inches, which correspond to 1-hour, 10-hour, and 100-hour fuels, respectively). The depth of the duff layer, litter layer, and overall fuelbed was 

taken at the 24-ft location on each transect. FIA cluster subplot design with 

three 24-ft. transects (slope corrected, horizontal distance) were established 

Development and Demonstration of Smoke Plume, Fire Emissions, and Pre- and Postprescribed Fire... Mickler, Rorig, Geron, Achtemier, Bailey, Krull, and Brownlie
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 414

at each subplot location. All subplot clusters were laid out in a fixed pattern 

regardless of different condition classes and only the transect segments that 

fell in the forest condition being sampled. The 6.8-ft radius micro plots were 

used to estimate the percent cover and height of live and dead shrubs, live 

and dead herbs (includes grasses) and litter.

These components were used to estimate fire behavior, fire spread, fire 

effects, and smoke production. Plot-level per-unit-area sums were expanded 

by the area associated with the inventory plot or averaged across the plots 

to produce a mean per-unit-area biomass value. Biomass computations for 

each fuel class have been previously developed in a pond pine/high pocosin 

forest type. Fuel class biomass algorithms were developed for additional forest species and decay classes in the forest type. Additional micro plots were 

established for destructive sampling of shrub and herbaceous vegetation to 

develop biomass equations. Previous equations were developed primarily for 

Western U.S. species (Brown and others 1982).

Prescribed Burning Emissions Monitoring and Modeling

In order to assess emissions of fine PM from prescribed fire, we investigated 

emissions from this burning practice during the course of prescribed burns 

for the fall and spring of 2005 and 2006. Mass balance techniques were used 

to support flux measurements of dioxin, methyl chloride, methyl bromide, 

and other compounds. Grab samples—stainless steel canisters for trace gases, 

filter packs for PM, and polyurethane foam traps for semivolatiles (Hays and 

others 2002)—were collected in the plumes of the fire during both flaming 

and smoldering conditions. These samples were then subjected to particle 

and total gaseous carbon analysis using a thermo-gravimetric analysis and 

gas chromatography/mass spectroscopy (GC/MS). Total emissions were 

determined by multiplying emission ratio by the estimate of total fuel carbon 

consumption. Total fuel carbon by mass is approximately half of the fuel dry 

weight following the general cellulosic molecular formula of C6H9O4.

We sampled air that had cooled to approximately ambient temperature 

(within 100 m of the fire) to allow partitioning of semivolatiles between 

the gas and aerosol phase. We sampled directly through PM2.5 cyclones using high volume pumps, onto 47 mm quartz (for organic PM components) 

and Teflon filters (for inorganic ions and elements). These were backed by 

polyurethane foam plugs for quantitative analysis of semivolatile organic 

compounds that pass through or are volatilized from the filters. Trace gases 

(methane, C2-C12 VOC) were characterized separately using Summa stainless steel canisters and GC/MS. Target compounds in the gas and PM phase 

included saturated (alkane) and unsaturated hydrocarbons, aldehydes, ketones, 

organic acids, and polycyclic organic hydrocarbons (PAH). We also measured 

CO via gas filter correlation (Teco 46C) and CO2 via infrared gas techniques 

(Licor 7000) to characterize carbon fluxes for the mass balance flux techniques and to characterize the nature of plume dispersion and proximity to 

the combustion. We used the CO/CO2/VOC measurements to help us in 

chemically identifying the flaming and smoldering stages of the fires.

We quantitatively analyzed these samples to determine emission factors 

for individual trace gases. These were compared with factors from other fuel 

types. We also compared current source apportionment chemical fingerprints 

from these fires with those from our laboratory and “burnhut” studies (Hays 

and others 2002). This allowed us to assess these signatures with in situ

data. We compared emission factors and fluxes with any available data from 

wildfire emissions from corresponding forest types.

Development and Demonstration of Smoke Plume, Fire Emissions, and Pre- and Postprescribed Fire... Mickler, Rorig, Geron, Achtemier, Bailey, Krull, and Brownlie
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 415

Smoke Modeling

A forecast of expected weather and smoke behavior was developed before 

each experimental prescribed burn. On-site meteorological information was 

gathered to enable a run and test the BlueSky smoke prediction system. The 

forecast was necessary to help anticipate fire and smoke behavior and to 

determine the most effective observational configuration. Onsite meteorological information was used to validate and improve the weather components of 

BlueSky and PB-Coastal Plain. A standard configuration of BlueSky was run 

to help with preburn forecasting and used to demonstrate the uncertainty 

in predicting smoke in the region. An enhanced configuration of BlueSky 

was run with measured information from each experiment to help quantify 

areas of needed improvement. The meteorological observations included 

four surface weather stations at strategic points around the fire perimeter to 

capture surface drift smoke and influencing weather. Each station measured 

wind, temperature, humidity, and carbon monoxide every 5 minutes. Additional stations—Data-logging Real-time Aerosol Monitors (DataRAM) and 

Environmental Beta-Attenuation Mass Monitor (E-BAM)—outfitted with a 

PM2.5 sampler were placed in expected downwind locations away from the fire 

perimeter to capture outflow rates. The location of each sampling station was 

based on the topographic configuration of the area, expected weather, and 

anticipated fire and smoke behavior. The meteorological data were monitored 

during each experiment to ensure quality control. This sampling methodology has been successfully deployed at the FROSTFIRE experimental burn in 

Alaska (Ferguson and others 2003) and the surface measurements have been 

tested during experimental burns in Washington, Oregon, and the Piedmont 

region of northern Georgia with success. Before each experiment a standard 

configuration of the BlueSky smoke modeling system was run that used the 

CALPUFF dispersion model. The system was rerun following each experiment in an enhanced mode that adjusts available preburn information with 

information that was measured during the experiment. The two runs were 

compared to quantify uncertainty in the modeling system and determine 

areas of needed improvement.

Results and Discussion

Vegetation Classification and Current Forest Biomass

Six research prescribed burns were monitored during the first 2 years of 

the study. The following preliminary information characterizes two research 

burns conducted on March 3 (day 1) and 4 (day 2), 2006 at the Alligator River 

National Wildlife Refuge and the Air Force Dare County Bombing Range 

in North Carolina (fig. 1 and 2). Seven vegetation alliances were identified 

on the research site (table 1). The Pond Pine Saturated Woodland Alliance 

was delineated as woodland and overstocked woodland for establishment of 

field plots but were combined for the fuel loading analysis. The Shining Fetterbush-Little Gallberry Shrubland and the Sweetbay-Swampbay Saturated 

Forest Alliances did not meet the minimum mapping unit size and were not 

included in the fuel loading analysis.

Preliminary trends in pre- and postburn biomass for North Navy Shell 

prescribed burns day 1 and day 2 are shown in tables 2 and 3. Fuels were 

characterized in 12 ft diameter microplots into live and dead herbaceous 

Development and Demonstration of Smoke Plume, Fire Emissions, and Pre- and Postprescribed Fire... Mickler, Rorig, Geron, Achtemier, Bailey, Krull, and Brownlie
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 416

Figure 1—Delineation of vegetation alliances and acres for prescribed burn day 1.

Development and Demonstration of Smoke Plume, Fire Emissions, and Pre- and Postprescribed Fire... Mickler, Rorig, Geron, Achtemier, Bailey, Krull, and Brownlie
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 417

Figure 2—Delineation of vegetation alliances and acres for prescribed burn day 2.

Development and Demonstration of Smoke Plume, Fire Emissions, and Pre- and Postprescribed Fire... Mickler, Rorig, Geron, Achtemier, Bailey, Krull, and Brownlie
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 418

Table 1—Summary of North Navy Shell prescribed burns day 1 and day 2 forest type acres.

 Rx burn Forest type Acres Pct.

 North Navy Shell day 1 Pond pine saturated woodland 325.83 28.96

 Pond pine saturated woodland, overstocked 799.30 71.04

 Total 1,125.13 100

 North Navy Shell day 1 Loblolly pine-AWC-maple-blackgum forest 93.96 6.19

 Loblolly pine saturated forest 64.91 4.28

 Pond pine saturated woodland 352.08 23.21

 Pond pine saturated woodland, overstocked 922.84 60.84

 Swamp blackgum-swampbay saturated forest 68.32 4.50

 Shining fetterbush-little gallberry shrubland 11.86 0.08

 Sweetbay-swampbay saturated forest 2.83 0.02

 Total 1516.80 100

Table 2—Summary of biomass for 1,125.33 acres day 1 prescribed burn (March 3, 2006) on the Alligator River National Wildlife Refuge and 

the Air Force Dare County Bombing Range, NC.

Vegetation alliance Duff

(Oa)

Litter

1 hr fuels

Fine

woody

1 hr fuels

Fine

woody

10 hr fuels

Fine

woody

100 hr fuels

Coarse

woody

1000 hr fuel

Total litter

and woody

fuels

Dead

shrub

Live

shrub

Dead

herb

Live

herb Litter (0-0.25 in) (0.25-1.0 in) (1-3 in) (>3 in)

Preburn

Pond pine woodland tons/acre 334.1 0.36 1.66 0.00 0.03 9.97 0.41 1.41 1.81 1.19 14.79

total tons 375,972.75 405.12 1,868.05 0.00 33.76 11,219.54 461.39 1,586.72 2,036.85 1,339.14 16,643.63

Postburn

Pond pine woodland tons /acre 334.1 0.36 1.66 0.00 0.03 6.30 0.11 1.22 1.81 1.19 10.63

total tons 375,972.75 405.121,868.05 0.00 33.76 7,089.58 123.79 1,372.90 2,036.85 1,339.14 11,962.26

Consumed

Pond pine woodland tons/acre 0.00 0.00 0.00 0.00 0.00 3.67 0.39 0.30 0.00 0.00 4.36

total tons 0.00 0.00 0.00 0.00 0.00 4,129.96 337.60 213.81 0.00 0.00 4,681.37

Development and Demonstration of Smoke Plume, Fire Emissions, and Pre- and Postprescribed Fire... Mickler, Rorig, Geron, Achtemier, Bailey, Krull, and Brownlie
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 419 Table 3—Summary of biomass for 1,516.8 acres day 2 prescribed burn on the Alligator River National Wildlife Refuge and Air Force Dare County Bombing Range, NC. Vegetation alliance Plot Duff (Oa) Litter 1 hr fuels Fine woody 1 hr fuels Fine woody 10 hr fuels Fine woody 100 hr fuels Coarse woody 1000 hr fuels Total litter and woody fuels Dead shrub Live shrub Dead herb Live herb Litter (0-0.25 in) (0.25-1.0 in) (1-3 in) (>3 in) Loblolly pine forest Preburn 64.91 acres tons/acre 299.95 0.53 2.22 0.00 0.00 7.70 0.27 1.95 2.20 2.59 14.71 total tons 454,918.66 803.90 3,367.30 0.00 0.00 11,679.36 409.54 2,957.76 3,336.96 3,928.51 22,312.13 Postburn tons/acre 299.95 0.50 2.14 0.00 0.00 3.93 0.12 1.49 1.62 2.59 25.12 total tons 454,918.66 758.40 3,245.95 0.00 0.00 5,961.02 182.02 2,260.03 2,457.22 3,928.51 14,813.92 Consumed tons/acre 0.00 0.03 0.08 0.00 0.00 3.77 0.15 0.46 0.69 0.00 5.07 total tons 0.00 45.50 121.35 0.00 0.00 5,718.34 227.52 697.73 879.74 0.00 7,523.33 Pond pine woodland Preburn 1,274.92 acres tons/acre 405.19 0.81 1.02 0.00 0.00 7.23 0.34 1.82 1.80 1.10 12.29 total tons 614,592.19 1,228.61 1,547.14 0.00 0.00 10,966.46 515.71 2,760.58 2,730.24 1,668.48 18,641.47 Postburn tons/acre 405.19 0.64 0.99 0.00 0.00 1.38 0.07 1.17 1.66 1.10 5.38 total tons 614,592.19 970.75 1,501.63 0.00 0.00 2,093.18 106.18 1,774.66 2,517.89 1,668.48 8,160.39 Consumed tons/acre 0.00 0.17 0.03 0.00 0.00 5.85 0.27 0.65 0.14 0.00 6.91 total tons 0.00 257.86 45.51 0.00 0.00 8,873.28 409.54 985.92 212.35 0.0010,481.09 Loblolly pine-AWC-maple- blackgum forest Preburn 93.96 acres tons/acre 226.28 0.53 1.98 0.00 0.00 4.94 0.48 2.10 2.13 17.66 27.31 total tons 343,221.50 803.90 3,003.26 0.00 0.00 7,492.99 728.06 3,185.28 3,230.78 26,786.68 41,423.79 Postburn tons/acre 226.28 0.53 1.98 0.00 0.00 4.30 0.48 2.10 2.13 17.66 26.67 total tons 343,221.50 803.90 3,003.26 0.00 0.00 6,522.24 728.06 3,185.28 3,230.78 26,786.6840,453.04 Consumed tons/acre 0.00 0.00 0.00 0.00 0.00 0.641 0.00 0.00 0.00 0.00 0.64 total tons 0.00 0.00 0.00 0.00 0.00 970.75 0.00 0.00 0.00 0.00 970.75 Blackgum-maple forest Preburn 68.32 acres tons/acre 226.28 0.36 1.05 0.00 0.01 1.83 0.33 1.14 1.67 6.21 11.18 total tons 343,221.5 546.05 1,592.64 0.00 15.17 2,775.74 500.54 1,729.15 2,533.06 9,419.33 16,957.82 Postburn tons/acre 226.28 0.32 1.00 0.00 0.01 1.83 0.33 1.14 1.67 6.21 11.18 total tons 343,221.5 485.38 1516.80 0.00 15.17 2,775.74 500.54 1,729.15 2,533.06 9,419.33 16,957.82 Consumed tons/acre 0.00 0.04 0.05 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 total tons 0.00 60.67 75.84 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Development and Demonstration of Smoke Plume, Fire Emissions, and Pre- and Postprescribed Fire... Mickler, Rorig, Geron, Achtemier, Bailey, Krull, and Brownlie
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 420

plant biomass, and live and dead shrub biomass. Litter, 1-, 10-, 100-, and 

1,000-hour fuels were estimated from line intersects. The large source of 

carbon emissions was found in the litter layer, followed by 1-hour DWD, 

10-hour DWD, and 100-hour fuels.

Emission Factors

The emission factors (EFs) resulting from the samples collected during 

each phase of the prescribed burn are presented in table 4. PM2.5 typically 

composes at least two-thirds of total PM, and roughly 80 percent of PM10

from biomass combustion sources (Andreae and Merlet 2002). Our data 

are consistent with these relative proportions. The PM2.5 and PM10 EFs 

determined using the Met One sensor are lower than the filter based methods, 

likely because this instrument has a 0.5 μm lower diameter detection limit. A 

large fraction of biomass burning particles falls below this size cut (Hays and 

others 2002). CO2 emission factors are important because they represent the 

largest carbon component of emissions. The integrated concentration values 

from the continuous CO2 emission monitor exceeded or were nearly equal to 

the concentration determined from the Summa canister during the flaming 

stages of the fires. It is not known if possible reactions within the canister 

might affect this comparison. In any case, this method-induced variability in 

CO2 concentrations induced an uncertainty in emission factors of all gasses 

and particles of 0 to 10 percent.

In general, the PM2.5 and PM10 EFs are lower those published in other 

open biomass burning studies. Observed NOx EFs are also lower, while 

those for SO2 and total NMHC fall within the ranges of previously reported 

biomass burning studies.

For all of the prescribed fires, emission factors for CO2 are higher than 

reported ranges (Andreae and Merlet 2002). The combination of lower PM 

EFs and higher CO2 EFs indicates that the prescribed fires exhibited more 

efficient combustion than wildfires or slash reduction burns. This is likely 

due to effectively burning primarily fine fuels under prescription conditions, 

Table 4—Emission factors derived from smoke samples of the North Navy Shell prescribed burn day 1 (emission 

factors in g kg (fuel dry weight)–1). 

Stage* Time PM10 PM10M PM2.5 PM2.5Q PM2.5M CO CO2 CO2c NOX THC SO2

Fl 11:41-13:02 6.49 2.92 6.49 4.87 2.22 44.18 1730

950

1700 2.07 10.1 0.55

Sm 13:47-16:30 3.38 1.67 2.07 2.41 0.84 34.09 920 0.81 303 0.43

Stage* Fl=Flaming stage, Sm=smoldering stage

PM10 determined gravimetrically from Teflon filter in single stage impactor.

PM10M determined from Met One detector, measures PM 0.5 to 10 m.

PM2.5 determined gravimetrically from Teflon filter in single stage impactor.

PM2.5Q determined gravimetrically from Quartz filter in single stage impactor.

PM2.5M determined from Met One detector, measures PM 0.5 to 2.5 m.

CO determined using TECO 46C continuous emission monitor.

CO2 determined using GC/TCD analysis on Summa can samples.

CO2c determined using TECO continuous emission monitor.

NOX determined using TECO 42S continuous emission monitor.

THC determined using TECO continuous emission monitor.

SO2 determined using TECO continuous emission monitor.

Development and Demonstration of Smoke Plume, Fire Emissions, and Pre- and Postprescribed Fire... Mickler, Rorig, Geron, Achtemier, Bailey, Krull, and Brownlie
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 421

which minimizes consumption of soil organic layers and smoldering of heavy 

fuels. Combustion of these fuel components often results in higher emission 

of products of incomplete combustion.

Smoke Trajectory and Concentrations

The BlueSky modeling framework output products include animations 

of PM2.5 concentrations and wind flow patterns at the surface. An example 

of an hourly image from the animation is shown in figure 3. Fire characteristics are processed through the Emissions Production Model (EPM) to 

give emission estimates of particulates (PM2.5, PM10, and total PM), carbon 

compounds (CO, CO2, CH4, nonmethane hydrocarbons), and heat. The 

emission estimates from EPM along with meteorology from the fifth generation Pennsylvania State University-National Center for Atmospheric Research 

Mesoscale Model (MM5) are processed for the CALPUFF dispersion model 

and the HYSPLIT trajectory model. The BlueSky framework merges meteorology with emission estimates to yield an integrated regional-scale analysis 

of smoke and aerosol concentrations.

Primary inputs to BlueSky include weather, fire characteristics, and fuels. 

Predictions of wind speed and direction as well as mixing height are required 

to determine smoke trajectories and PM2.5 concentrations. Weather inputs 

come from the MM5 mesoscale meteorological model. In order to arrive at 

an accurate prediction of smoke emissions it is necessary to get detailed information about size, location, and timing of the prescribed burn. Fuel model 

and fuel loading information is essential to emissions modeling. BlueSky uses 

general fuel characteristics derived from one of several fuel model systems, 

including the Fuel Characteristic Classification System (FCCS), but for this 

Figure 3—Prescribed fire simulation from BlueSky for North Navy Shell prescribed fire 

research burn. PM2.5 ground concentrations (μg/m3) and wind flow patterns are shown 

as houly image. MM5 meterology simulation was done on a 1.33 km resolution.

Development and Demonstration of Smoke Plume, Fire Emissions, and Pre- and Postprescribed Fire... Mickler, Rorig, Geron, Achtemier, Bailey, Krull, and Brownlie
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 422

project detailed pre- and postburn fuel characteristics were collected from 

permanent field plots. Emissions are computed using Consume/EPM v1.03, 

which calculates the heat release rate and emissions for particulate matter and 

carbon compounds as a function of time since fire ignition. These emission 

values are input data for CALFUFF v5.711 that calculates the dispersion and 

plume rise. Trajectories are computed using the HYSPLIT model. HYSPLIT 

uses a full three-dimensional wind field for computational purposes but does 

not include any heat or buoyancy effects from fire.

The trajectory of the BlueSky smoke plume correlates well with a NOAA 

GOES satellite image of the smoke plume (fig. 4). The Hazard Mapping System Fire and Smoke Product image is derived from an interactive processing 

system that allows the trained satellite analysts in the Satellite Analysis Branch 

(SAB), within the Satellite Services Division (SSD), to manually integrate 

data from various automated fire detection algorithms with GOES and polar 

(Advanced Very High Resolution Radiometer; AVHRR), Moderate Resolution Imaging Spectroradiometer Fire Algorithm (MODIS), and Defense 

Meteorological Satellite Program/Operational Linescan System (DMSP/

OLS) images. The result is a quality controlled display of the locations of 

fires and significant smoke plumes detected by meteorological satellites. 

Prior to ignition of the prescribed burn, VSMOKE, a computer program for 

predicting the smoke and dry weather visibility impact of a single prescribed 

Figure 4—Hazard Mapping System Fire and Smoke Product image derived from various automated fire detection 

algorithms with GOES and polar Advanced Very High Resolution Radiometer (AVHRR), Moderate Resolution 

Imaging Spectroradiometer Fire Algorithm (MODIS) and Defense Meteorological Satellite Program/Operational 

Linescan System (DMSP/OLS) images.

Development and Demonstration of Smoke Plume, Fire Emissions, and Pre- and Postprescribed Fire... Mickler, Rorig, Geron, Achtemier, Bailey, Krull, and Brownlie
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 423

fire at several downwind locations, was run for current weather conditions. 

Figure 5 illustrates the smoke plume trajectory and visibility impacts. The 

BlueSky modeled plume trajectory and PM2.5 concentrations may result in an 

accuracy improvement when compared to V-SMOKE predictions. Accurate 

representation of the wind field and other meteorology parameters within the 

BlueSky modeling framework may further our understanding of fire behavior 

and its relationship to smoke management on deep organic soils.

Summary and Conclusions

Land managers in the Coastal Plain of the Eastern United States recognize general fuel types on organic soils and mineral soils. Past fuel and fire 

behavior research has resulted in only qualitative measures of fuel loads and 

rates of spread. A more detailed fuel classification based on species composition, standing dead and down deadwood, fuel size classification, understory 

vegetation, and vertical distribution of fuels would have much more utility 

than the broad fuel model classification system now in use. Fire in the organic 

and mineral soil areas of the Coastal Plain centers around the frequent and 

costly blowup wildfires occurring there and the use of fire as a fuel reduction and habitat management tool. Wildfires in this area can, under certain 

combinations of fuel and weather, grow from a low intensity burn to a virtually uncontrollable burn until weather conditions change or the fire has 

run out of fuel. Control efforts are often hampered by inaccessibility, poor 

soil trafficability on wet organic soils in the area, and fires that tend to burn 

deeply into the organic soils. A better understanding of the behavior of fires 

and the role of fuel loading in fire behavior in the pocosins, especially the 

factors that contribute to the occurrence of major fires, will contribute to 

the control of wildfires and the use of prescribed fire as a management tool 

in the region.

Figure 5—VSMOKE prediction of smoke and dry weather visibility impacts.

Development and Demonstration of Smoke Plume, Fire Emissions, and Pre- and Postprescribed Fire... Mickler, Rorig, Geron, Achtemier, Bailey, Krull, and Brownlie
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 424

The methodology of using modified National Vegetation Classification 

Alliance level vegetation maps, created from digital photogrammetry and FIA 

P3 data, shows promise as an approach to fuel mapping. Softcopy photogrammetry, coupled with ground truthing, provides a high level of accuracy for 

mapping to the association level of the ICEC system. Fuel loads generated 

from the FIA P3 plots differ from fuel loads estimated using the standard 

fuel models. These differences could have an impact on the prediction of 

fire spread and behavior. Fuel loads within fuel size classes did vary between 

the modified association level classifications. Disturbance history appears to 

play a significant role in explaining why fuel loads differ and could help in 

creating more accurate fuel maps. Research of this nature may lead to use of 

FIA P3 plot data to generate an index of fuel load by ICEC association level 

vegetation classification. This could lead to a valuable multipurpose tool for 

land managers and researchers for use in predicting, preventing, and managing forest biomass for wildfire.

The U.S. Environmental Protection Agency (EPA) has implemented new 

regulations for the management of PM2.5, tropospheric ozone, and regional 

haze. In accordance with sections 108 and 109 of the Clean Air Act, EPA 

has reviewed the air quality criteria and national ambient air quality standards (NAAQS) for PM2.5. Based on these reviews, EPA revised the current 

primary PM10 standards by adding two new primary PM2.5 standards set at 

15 g m–3, annual mean, and 50 g m–3, 24-hour average, to provide increased 

protection against a wide range of PM-related health effects. These include 

premature mortality and increased hospital admissions and emergency room 

visits; increased respiratory symptoms and disease; decreased lung function; 

and alterations in lung tissue and structure and in respiratory tract defense 

mechanisms. Recent proposals would reduce the 24-hour average PM2.5

standard to 35 g m–3. Fire generates PM2.5 and other ozone precursor gases 

that reduce visibility. Hence, natural area and agricultural land management, 

nationwide, may come under increased scrutiny as regulators seek reductions 

in pollutant emissions that contribute to NAAQS violations.

Little data on emissions from prescribed burning are currently available, 

and this fire type in particular is projected to increase in the Southeastern 

United States. We hypothesize that emissions of PM2.5 and PM10 and gas 

phase reduced compounds, many of which are air toxics, will be lower during 

prescribed fires compared to wildfires covering the same area. This is suspected 

largely because it is known that wildfires occur typically during excessively dry 

periods, when much of the forest floor is dry and susceptible to smoldering 

and incomplete combustion, the source of many toxic compounds. It is likely 

that burning under prescriptions will reduce human exposure and emissions 

of hazardous air pollutants, and net risk to property and human welfare.

References

Albini, F.A. 1976. Estimating wildfire behavior and effects. Gen. Tech. Rep.INT30. Ogden, UT: U.S. Department of Agriculture, Forest Service, Intermountain 

Forest Range Experiment Station.

Anderson, J.R.; Hardy, E.E.; Roach, J.T.; Witmer, R.E. 1976. A land use and land 

cover classification system for use with remote sensor data: U.S. Geological Survey 

Professional Paper 964. 28 p.

Andreae, M.O.; Merlet, P. 2001. Emission of trace gases and aerosols from biomass 

burning. Global Biogeochemical Cycles 15(4): 955-966.

Development and Demonstration of Smoke Plume, Fire Emissions, and Pre- and Postprescribed Fire... Mickler, Rorig, Geron, Achtemier, Bailey, Krull, and Brownlie
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 425

Barnard, W.; Sabo, E. 2003. Review of 1999 NEI and recommendations for 

developing 2002 VISTA inventory for regional haze modeling. Asheville, NC: 

North Carolina DENR.

Bertschi, I.; Yokelson, R.J.; Ward, D.; Babbitt, R.; Susott, R.; Goode, J.; Hao, 

W.M. 2003. Trace gas and particle emissions from fires in large diameter and 

belowground biomass fuels. Journal of Geophysical Research 108(D13): 1-12.

Brown, J.K.; Oberheu, R.D.; Johnston, C.M. 1982. Handbook for inventorying 

surface fuels and biomass in the interior west. Gen. Tech. Rep. INT-129. Ogden, 

UT: U.S. Department of Agriculture, Forest Service, Intermountain Forest Range 

Experiment Station.

Cheng, L.; McDonald, K.M.; Angle, R.P.; Sandhu, H.S. 1998. Forest fire enhanced 

photochemical air pollution. A case study. Atmospheric Environment 32(4): 

673-681.

Dennis, A.; Fraser, M.; Anderson, S.; Allen, D. 2002. Air pollutant emissions 

associated with forest, grassland, and agricultural burning in Texas. Atmospheric 

Environment 36(23):3779-3792.

Ferguson, S.A.; Collins, R.L.; Ruthford, J.; Fukuda, M. 2003. The vertical 

distribution of nighttime smoke following a wildland biomass fire in boreal 

Alaska. Journal of Geophysical Research 108 No. D23, 4743.

Ferguson, S.A.; McKay, S.J.; Nagel, D.E.; Piepho, T.; Rorig, M.L.; Anderson, C.; 

Kellogg, L. 2003. Assessing Values of Air Quality and Visibility at Risk From 

Wildland Fires. Res. Pap. PNW-RP-550. Portland, OR: U.S. Department of 

Agriculture, Forest Service, Pacific Northwest Research Station. 59 p.

Ferguson, S.A.; Peterson, J.; Acheson, A. 2001. Automated, real-time predictions 

of cumulative smoke impacts from prescribed fire and agricultural fires. In: 

Proceedings of the Fourth Symposium on Fire and Forest Meteorology, 13-

15 November 2001, Long Beach, CA. Boston, MA: American Meteorological 

Society: 168-175.

Finney, M.A. 1998. FARSITE: Fire Area Simulator-model development and 

evaluation. Res. Pap. RMRS-RP-4. Fort Collins, CO: U.S. Department of 

Agriculture, Forest Service, Rocky Mountain Research Station.

Friedli, H.R.; Radke, L.F.; Lu, J.Y.; Banic, C.M.; Leaitch, W.R.; MacPherson J.I. 

2003. Mercury emissions from burning of biomass from temperate North American 

forests: laboratory and airborne measurements. Atmospheric Environment 37(2): 

253-257.

Hao, W.M.; Liu, M. 1994. Spatial and temporal distribution of tropical biomass 

burning. Global Biogeochemical Cycles 8(4): 495-503.

Harrison, H. 1995. A user’s guide to PUFFx: a dispersion model for smoke 

management in complex terrain. WYNDSoft Inc. 42 p.

Hays, M.D.; Geron, C.D.; and others. 2002. Speciation of Gas-Phase and Fine 

Particle Emissions from Burning of Foliar Fuels. Environmental Science and 

Technology 36(11): 2281-2295.

Hummel, J.; Rafsnider, J. 1995. User’s guide: TSARS plus smoke production and 

dispersion model, version 1.1. National Biological Service and the Interior Fire 

Coordination Committee. 107 p.

Lavdas, L.G. 1996. VSMOKE user’s manual. U.S. Department of Agriculture, Forest 

Service, Southern Research Station. GTR-SRS-6. 147 p.

Lavdas, L.; Achtemeier, G.L. 1995. A fog and smoke risk index for estimating 

roadway visibility hazard. National Weather Digest 20, 26-33.

O’Neill, S.M.; Ferguson, S.A.; Peterson, J.; Wilson, R. 2003. The BlueSky smoke 

modeling framework. 5th Symposium on Fire and Forest Meteorology. 16-20 

November 2003. Orlando, FL. American Meteorological Society: 161-162.

Development and Demonstration of Smoke Plume, Fire Emissions, and Pre- and Postprescribed Fire... Mickler, Rorig, Geron, Achtemier, Bailey, Krull, and Brownlie
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 426

O’Neill, S.M.; Hoadley, J.L.; Ferguson, S.A.; Solomon, R.; Peterson, J.; Larkin, 

N.K.; Peterson, R.; Wilson, R.; Matheny, D. 2005. Applications of the 

BlueSkyRAINS smoke prediction system. EM: The Magazine for Environmental 

Managers. September 2005. Air and Waste Management Assoc.: 42-45.

Rothermel, R.D. 1972. A mathematical model for predicting fire spread in wildland 

fuels. Gen. Tech. Rep. INT-115. Ogden, UT: U.S. Department of Agriculture, 

Forest Service, Intermountain Forest Range Experiment Station.

Ryan, K.C.; Noste, N.V. 1985. Evaluating prescribed fires. In: J.E. Lotan, B.M. Evaluating prescribed fires. In: J.E. Lotan, B.M. 

Kilgore, W.C. Fischer, and R.W. Mutch, Tec. Cord. Proc. Symposium and 

Workshop on Wilderness Fire. Gen. Tech. Rep. INT-182. Ogden, UT: U.S. 

Department of Agriculture, Forest Service, Intermountain Forest and Range 

Experiment Station: 230-238.

Sandberg, D.V.; Ottman, R.D.; Cushon, G.H. 2001. Characterizing fuels in the 1st

Century. International Journal of Wildland Fire 10:381-387.

Scire, J.; Strimaitis, D.G.; Yamartino, R.J.; Xiaomong, Zhang. 1995. A user’s guide 

for CALPUFF dispersion model. Doc. 1321-2. Concord, MA: Sigma Research/

Earth Tech. 315 p.

Sestak, M.L.; Riebau, A. R. 1988. SASEM, simple approach smoke estimation model. 

U.S. Bureau of Land Management, Technical Note 382. 31 p.

Sestak, M.L.; Marlatt, W.E.; Riebau, A.R. 1989. VALBOX: ventilated valley box 

model. Unpublished report on file with: Michael Sestak, Fort Collins, CO: U.S. 

Department of the Interior, Bureau of Land Management, and Colorado State 

University, Environmental Science and Technology Center. 32 p.

Taylor, J.A.; Zimmerman, P.R. 1991. Modeling trace gas emissions from biomass 

burning. MIT Press (Chapter 41): 345-350.

Ward, D.E.; Hardy, C.C. 1988. Organic and elemental profiles for smoke from 

prescribed fires. APCA/EPA conference.

Acknowledgments

Funding for this study was provided by the Joint Fire Science Program 

(Project 04-2-1-80). Additional support was provided by the U.S. Air 

Force Air Combat Command, Richard Lemaire, Dr. Terry Bashore, Bryan 

Henderson, Joe Lafferty, Scott Smith, and Harry Mann, U.S. Navy. The 

authors wish to acknowledge the U.S. Fish and Wildlife Service (USFWS) 

support of Mike Bryant, Kathy Whaley, Tom Crews, Vince Carver, Donnie 

Harris, Kelley Van Drueten, Greg Suszek, Pete Kubiac, Glen Stratton, the 

prescribed fire staffs on USFWS Fire District 1, and Gary Curcio and the 

North Carolina Division of Forest Resources.

Development and Demonstration of Smoke Plume, Fire Emissions, and Pre- and Postprescribed Fire... Mickler, Rorig, Geron, Achtemier, Bailey, Krull, and Brownlie
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 427

Abstract—The Georgia Forestry Commission has developed an Internet-based, user 

friendly version of a USDA Forest Service smoke dispersion model called “VSMOKE.” 

The program provides an easy to use method to quickly evaluate what areas will likely 

be impacted by smoke from a wild or prescribed fire. This is particularly important in 

assessing air quality, public safety and health, and visibility along highways. This paper 

explains the program and use and interpretations, and looks in detail at the example 

of highway safety.

Introduction

Smoke from wild and prescribed fire has been an increasing concern in 

public health and safety over the last few decades. The Georgia Forestry 

Commission (GFC) encourages safe use of fire on forest lands in Georgia, 

and provides a number of smoke management tools that may be viewed at: 

http://weather.gfc.state.ga.us/

A smoke dispersion model was developed at the Southern Forest Fire 

Laboratory (U.S. Department of Agriculture, Forest Service) and published 

by Lavdas (1996). It was developed before the widespread use of the Internet 

and was for the “...smoke management professional” (Lavdas 1996).

In order to encourage the use of prescribed fire with due regard to air 

quality, the GFC has developed an Internet-based, user friendly version of 

VSMOKE.

To use the original VSMOKE model one must:

1. Set certain dispersion model parameter

2. Provide fuel loading data

3. Provide emissions data

4. Provide weather data

A number of additions and modifications were required to extend the use 

of VSMOKE to the general landowner in Georgia. The guiding philosophy 

in developing the system was ease of use for the general land owner, flexible 

for use in planning burns, and with sufficient sophistication to appeal to 

professionals in forestry and smoke management.

Internet VSMOKE: A User-Oriented System 

for Smoke Management

James T. Paul1, Alan Dozier2, and Daniel Chan2

In: Butler, Bret W.; Cook, Wayne, 

comps. 2007. The fire environment—

innovations, management, and policy; 

conference proceedings. 26-30 March 

2007; Destin, FL. Proceedings 

RMRS-P-46CD. Fort Collins, CO: 

U.S. Department of Agriculture, 

Forest Service, Rocky Mountain 

Research Station. 662 p. CD-ROM.

1 President of SciTrans Inc.

2 Chief of Forest Protection and 

Meteorologist, respectively, Georgia 

Forestry Commission. Corresponding 

author dchan@gfc.state.ga.us
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 428

System Components

There are four main components in the system:

1. The INPUT PROCESSOR – Interactive user input.

FALCON VIEW (http:/falconview.org) extracts the latitude and 

longitude of the burn from a map mouse click.

Date and time of burn.

Acres burned.

Type of burn – headfire, backfire, flankfire.

User input options:

Fuels

Select fuel and emission information from the best match 

fuel photograph, or

Enter basal area, age of rough, height of the understory 

vegetation, and Society of American Foresters (SAF) 

cover type, or

Enter tons of fuel per acre and emission factor.

Weather

The program is preset to use weather from the GFC weather 

database. However, one can elect to manually enter the 

needed weather data.

2. VSMOKE – Hour by hour dispersion.

3. VSMOKE-GIS (Harmes and Lavdas 1997) – Hourly digital isolines 

output of concentrations (called each hour by VSMOKE).

4. FALCON VIEW (http:/falconview.org) – Map output of concentrations 

from VSMOKE-GIS.

The Input Processor

When the GFC Smoke Management System is accessed from the GFC 

Web site, some choices are required. First, the date and time of the burn, the 

location of the burn, and firing technique are always required.

Location—A Georgia Tech Research Institute adaptation of FALCON 

VIEW (http:/falconview.org) is used to aid the user in map location of the 

burn in order for the system to extract latitude and longitude. The user is 

presented with a map of Georgia with county outlines and names (fig. 1). 

The user clicks on the county where the burn will occur, then chooses 

successively more detailed maps to locate the fire (fig. 2). When properly 

located, a mouse click on the location will send a latitude and longitude to 

the system. This is used for generating weather for the location, and for the 

output plume graphics.

Date and time and other fixed data input—After selecting a map location 

the user clicks the “SUBMIT BURN LOCATION” bar at the bottom of the 

page. This will generate the screen to input date, time burn start, time burn 

end, acres burned, firing method, and fuel photo display option (fig. 3). Date 

and times are selected from drop down boxes. Acres burned are entered in 

the labeled box. Firing method and photo display option are selected from 

radio buttons.

Internet VSMOKE: A User-Oriented System for Smoke Management Paul, Dozier, and Chan
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 429

Figure 1—Map with county names for burn site selection in the Georgia Forestry Commission’s Webbased VSMOKE.

Figure 2—The most detailed site map selection in GFC Web-based VSMOKE. It is an aerial photo of 

1 m resolution.

Internet VSMOKE: A User-Oriented System for Smoke Management Paul, Dozier, and Chan
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 430

Weather—The user selects either manual weather input, or elects to automatically use weather from the GFC weather system localized to the burn 

latitude and longitude. The option to manually input weather data (fig. 4) 

can be used for preburn “what if” planning, or for those cases when the user 

has a high quality weather data set. The option to have weather automatically 

supplied by the system is recommended for the general user.

Fuel—There are three options to choose from in the fuels and emissions 

section. First, the user may choose to match his burn site with a forest scene 

photo stored on the system (fig. 5). When the best photo match is chosen, 

the model returns available fuel and emission factor. The second choice is 

for the user to input SAF cover type, basal area, age of rough, and height 

of the understory vegetation (fig. 6). When these variables are entered, the 

system will return available fuel and emission factor. The third option is for 

the user to directly input emission factor and available fuel (fig. 7). The first 

option is recommended for the general user. Its advantage is ease of use, and 

no specialized knowledge of forestry, meteorology, or smoke management is 

required. The disadvantage is that available fuel computations are not as site 

specific as other fuel options.

Figure 3—Date of burn, time of burn, acreage burned, firing technique, and fuel data selection 

screen.

Internet VSMOKE: A User-Oriented System for Smoke Management Paul, Dozier, and Chan
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 431

Figure 4—Weather data selection screen.

Figure 5—Fuel photo screen for fuel data input.

Internet VSMOKE: A User-Oriented System for Smoke Management Paul, Dozier, and Chan
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 432

Figure 6—Stand information input.

Figure 7—Manual fuel data input.

Internet VSMOKE: A User-Oriented System for Smoke Management Paul, Dozier, and Chan
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 433

Output Processor

Running VSMOKE—When all inputs are complete, the user clicks on the 

“SUBMIT VSMOKE DATA” bar. The input processor then collects all user 

inputs, and computations based on these inputs, and runs the models.

Hour by hour outputs include:

1. Smoke plume concentrations displayed on a map background. The 

user may elect to view the output plotted on maps of different scale; 

figure 8. A plume that tends to be circular indicates most smoke is 

being lofted upward with associated good dispersion. A long pencil 

shaped plume indicates poor dispersion with most smoke remaining 

near the ground.

2. List of most smoke sensitive areas near the burn; figure 9.

Optional output selected from the screen following the plume output 

display; figure 10.

1. Inputs used by the model; figure 11.

2. Crosswind visibility downwind to 100KM (62.1 miles); figure 12.

3. Centerline smoke concentrations downwind from the fire to 100 km 

(62.1 miles); figure 13.

4. Low Visibility Risk Occurrence Index (LVORI) from time of fire until 

1200 the next day; figure 14. The hours when the LVORI is greater 

than 7 indicate the atmosphere is conducive to low visibility, and the 

risk becomes greater as LVORI approaches its maximum value of 10. 

Values of LVORI less than 7 indicate smoke is unlikely to contribute to 

low visibility, with the likelihood becoming very small when LVORI 

reaches its minimum value of 1.

Cautions

Some cautions are in order. First, smoke dispersion models are an attempt 

to represent concentrations as sets of mathematical equations. Most models 

in use by the forestry community seem to do an acceptable job of representing this rather complex process. Two sources of error may make a large 

impact on the results. First, fuel data including emission factor, emission 

rate, and available fuel are potential sources of error. The authors regard 

the fuels inputs that were largely gleaned from the literature as being “Best 

operational available,” but recognize this as being an area in need of major 

improvement. Second, the model is obviously sensitive to errors in forecast 

wind direction. By inspection of the output plume, one can easily imagine a 

small change in wind direction might place the plume in an undesired location. Lavdas (1997) in an analysis of National Weather Service forecasts at 

the Macon, GA, airport, observed wind direction was within plus or minus 

22.5 degrees of forecast wind about 38 percent of the time. When the difference was extended to plus or minus 67.5 degrees, the accuracy increased 

to about 79 percent. He also found that at higher wind speeds (15 mph and 

greater) accuracy increased by about 15 percent.

In a recent inhouse study, the GFC staff found similar differences in observed versus daily district forecast wind at Dawsonville, GA (table 1). In this 

case the wind was accurate to within plus or minus 45 degrees 79 percent of 

the time. These studies draw attention to the need for caution in interpreting 

the plume impact, especially at low wind speeds.

Internet VSMOKE: A User-Oriented System for Smoke Management Paul, Dozier, and Chan
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 434

Figure 8—Plume output screen.

Figure 9—Listing of smoke sensitive areas.

Internet VSMOKE: A User-Oriented System for Smoke Management Paul, Dozier, and Chan
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 435

Figure 10—Optional outputs.

Figure 11—Weather data input used for the application.

Internet VSMOKE: A User-Oriented System for Smoke Management Paul, Dozier, and Chan
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 436

Figure 12—Centerline concentration for each hour in the burn period.

Figure 13—Centerline concentration graph for each hour in the burn period.

Internet VSMOKE: A User-Oriented System for Smoke Management Paul, Dozier, and Chan
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 437

Wind

direction

(obs–fcst)

Months

Degrees JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC TOT

-91 < 1 0 2 1 3 2 1 1 1 2 1 1 16

-46 to -90 2 3 5 4 2 0 2 1 1 1 3 1 25

-1 to -45 4 7 9 8 6 2 3 3 3 9 3 6 63

HIT 11 11 6 14 9 14 5 3 7 5 13 14 112

1 to 45 4 7 8 1 2 3 5 5 2 5 3 6 51

46 to 90 0 0 0 1 2 0 2 0 0 2 0 1 8

> 91 4 0 1 0 1 0 0 0 0 3 1 2 12

Total 26 28 31 29 25 21 18 13 14 27 24 31 287

Table 1—Wind direction (observed - forecast) frequencies for the GFC weather station at 

Dawsonville, GA, from January 2002 to December 2002.

Figure 14—Low Visibility Occurrence Risk Index from the end of the burn to the following 

morning.

Internet VSMOKE: A User-Oriented System for Smoke Management Paul, Dozier, and Chan
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 438

Use and Interpretation

The plume depiction provides a method to quickly evaluate what areas will 

likely be impacted by smoke from a prescribed fire. Unsafe highway visibility 

resulting from smoke typically extends outward to about 0.25 to 0.50 mile, but 

under poor dispersion can be much farther. The light to dark gray sections of 

the plume are the areas of most concern. If this section of the plume impacts 

a roadway, the burner should defer burning until more favorable conditions 

occur, or take special precautions such as placing warning signs along the 

highway, or perhaps paying an off-duty law enforcement officer to monitor 

the fire on site and reroute traffic or close the road as needed. Additional 

information concerning concentrations and visibility can be found in the 

screens following the plume screen. For example, the “Centerline Concentration Table” and “Centerline Concentration Graph” screens provide addition 

information on concentrations. “The Centerline Concentration Table” also 

provides estimates of cross plume visibility. These visibility values refer to 

daytime conditions. Additional information such as the LVORI should be 

evaluated for nighttime conditions.

The influence of smoke on highway is intuitive because smoke will obviously reduce roadway visibility if present. The question is what constitutes safe 

visibility? One way to answer this question is to consider automobile stopping 

distance as a function of speed. In the “Georgia Drivers Manual, 2005,” which 

can be viewed on line at http://www.dds.ga.gov/docs/forms/FullDriversManual.pdf, data are presented that relate stopping distance to speed; these 

data were used to construct table 2. These numbers are not absolute, and the 

manual also lists six factors that influence stopping distance. These are:

1. Mental and physical reaction of the driver.

2. Type and condition of the pavement.

3. Kind of tires and tread composition.

4. Chassis (frame) design.

5. Type of brakes, condition, and balance of brakes.

6. Wind direction and velocity.

Suppose one is driving on a State road at the posted speed limit of 55 mph. 

Rounding this to 60 mph, and using values from table 2, the stopping distance would be between 252 and 372 feet. Next, examine the data from the 

“Centerline Concentration Table.” The first value is at 0.062 mile from the 

fire with an associated cross plume visibility of 8.39 miles. In this (as in most 

cases) roadway visibility is not a problem since the estimated visibility is much 

greater than the stopping distance. However, problems do occur, and using 

VSMOKE is a good way to evaluate the existence of a problem.

Table 2—Estimated emergency stopping distance by speed. Source: 

Georgia Drivers Manual, 2005

Driving

speed (mph)

Driver reaction

distance (ft)

Breaking

distance (ft)

Total stopping

distance (ft)

20 44 15-22 59-66

30 66 33-50 99-116

40 88 53-107 141-195

50 110 83-167 193-277

60 132 120-249 252-372

70 154 163-327 317-481

Internet VSMOKE: A User-Oriented System for Smoke Management Paul, Dozier, and Chan
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 439

The boxed numbers on the map refer to smoke sensitive areas such as 

nursing homes. It is assumed the burner will make appropriate arrangements 

when nursing homes and hospitals are within the plumes area.

LVORI is not a perfect indicator of smoke induced low nighttime visibility 

problems on roadways. LVORI is essentially an estimate of the atmosphere 

potential to contribute to low visibility. Other factors that should also be 

considered include:

1. The fire is more than 3 miles from a road. Most nighttime visibility 

problems occur within 3 miles, but in exceptional cases may extend out 

to 30 miles from the fire.

2. The vegetation is continuous and heavy between the burn and a road. 

Heavy vegetation acts as a filter and slows the movement of smoke.

3. Logging roads, power lines, streams, or similar features can provide an 

unobstructed pathway between the burn and a road.

4. The road is at a higher elevation than the burn.

Future Development

The system is structured as a series of stand alone modules with a master 

calling routine integrated into the system. Consequently, the fuels, weather, 

emissions, or dispersion modules can be replaced as desired with a minimum 

of effort. Possible improvements include output from weather models such 

as MM5, better ways to estimate fuel loading, available fuel, and improved 

emission factors.

References

Harms, Mary F.; Lavdas, Leonidas G. 1997. Users guide to VSMOKE-GIS for 

workstations. Research Paper SRS-6. Asheville, NC: U.S. Department of 

Agriculture, Forest Service, Southern Research Station. 41 p.

Lavdas, Leonidas G. 1996. Program VSMOKE - Users Manual. Gen. Tech. Rep. 

SRS-6 Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern 

Research Station. 147 p.

Lavdas, Leonidas G. 1997. Accuracy of National Weather Service wind direction 

forecasts at Macon and Augusta, Georgia. National Weather Digest Vol. 22 No. 

1: 22-26.

Acknowledgments

The authors are indebted to Lee Lavdas for his helpful suggestions during 

the development of Internet VSMOKE, and for his perceptive and helpful 

review of this paper.

Internet VSMOKE: A User-Oriented System for Smoke Management Paul, Dozier, and Chan
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 440

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USDA Forest Service Proceedings RMRS-P-46CD. 2007. 443

Abstract—A useful method of modeling threats from hazards and documenting their 

disaster causation sequences is called “cascading threat modeling.” This type of modeling enables emergency planners to address hazard and risk assessments systematically. 

This paper describes a cascading threat modeling and analysis process. Wildfire and 

an associated postburn flash flood disaster are modeled to serve as examples of the 

modeling and analysis process. Models are developed for both wildfire and flash flood, 

and the two models are then linked at a particular threat interface. Additionally, the 

use of a Federal and State Interagency Technical Team (IAT) for onsite wildfire and 

postburn flash flood assessment is described. The integration of expert field knowledge held by IAT specialists and agency staff is an essential component in developing 

credible cascading disaster models. When applied to local hazard mitigation planning, 

a detailed and systematic picture of local threat, risk, vulnerability, and consequence 

arises. An example wildfire burn and postburn flash flood is provided as a reference. 

Additionally, the use of an IAT for onsite wildfire and postburn flash flood assessment 

is described because this kind of field knowledge is essential in developing credible 

cascading disaster models.

Introduction

Scientists commonly think of nature in terms of systems and disaster events 

occur within natural systems. For some inexplicable reason, disaster practitioners have not typically adopted systems thinking. In current emergency 

management analysis, disasters are usually studied fragmentally (fragmented), 

considering one aspect of a disaster at a time. What are considered in disaster 

assessments are often items arranged in lists or in tables, rather than as component within systems frameworks. Fragmental approaches have public safety 

shortcomings where extremely dangerous threats hidden within a disaster 

system may go unnoticed. It is proposed that cascading disaster models be 

used by emergency practitioners as the basis for conducting hazard and risk 

analyses and developing those analyses further into plans.

It is proposed herein that methods associated with “systems thinking” 

about disasters be called disaster systematics. Two kinds of associated information are presented. The first is an explanation of a disaster modeling technique 

called “cascading disaster modeling” and the second is an explanation of an 

application of this technique to wildfire and postburn flash flood. Although 

the concept of cascading disasters is mentioned often in hazard and disaster 

literature, little has been done in the development of methods for the application of the concept. This author has developed and used a technique for 

many years both for application in the university classroom (disaster studies) 

and for application within governmental contexts, including conducting detailed and systematic hazard and risk analyses at the local government level 

and with State parks.

In: Butler, Bret W.; Cook, Wayne, 

comps. 2007. The fire environment—

innovations, management, and policy; 

conference proceedings. 26-30 March 

2007; Destin, FL. Proceedings 

RMRS-P-46CD. Fort Collins, CO: 

U.S. Department of Agriculture, 

Forest Service, Rocky Mountain 

Research Station. 662 p. CD-ROM.

1 Associate Professor of Emergency 

Management, Jacksonville State 

University Institute for Emergency 

Preparedness, Jacksonville, AL. fmay@

jsu.edu 

Cascading Disaster Models in Postburn 

Flash Flood

Fred May1
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 444

Defining “Analysis” in Hazard Studies

The definition of the word “analysis” requires that the information being 

studied represent an intellectual or material whole, such that the constituent 

parts of the intellectual whole can be studied (see American Heritage Dictionary, Fourth Edition, 2000). Therefore, analyzing a hazard or its associated 

disaster requires understanding disasters in their entirety so that constituent 

parts can then be studied

The nature of the “whole” is explained in hazard and disaster management literature in terms of cascading disasters where one event in a disaster is 

connected through a causal sequence to the next event. Hence, we discover 

that a disaster consists of interconnected cascading causation sequences, from 

the initiation of the disaster to its culmination. This satisfies the definition 

of the whole.

Although a disaster exists as an intellectual or material whole, disasters are 

rarely studied within the framework of a whole. Hazard and disaster analysts 

most often study disasters through the use of selected isolated point threats, 

not related to their constituent cascading threat sequences. As such, according 

to the definition above, it would be impossible to analyze a disaster (break it 

into its component parts) through that process. Thus, there is some contradiction among emergency planners when the term hazard or disaster analysis 

is stated when a fragmental process is actually being used.

Cascading Threat Models

Cascading threat models (also known as cascading emergencies or disasters) have long been mentioned by some authors in the hazard and disaster 

management literature, but generally as only a vague concept. The concept is 

that disasters begin with a single primary threat and then occur as sequences 

of events. These sequences of events are most often referred to collectively 

as “secondary hazards” without the provision of additional definition or 

development. In this present paper, cascading is referred to in the context 

of dynamic disaster systems that consist of branching tree structures from a 

primary threat or event. To make the point that the concept of disaster systems 

is actively presented in disaster management literature, some quotes are provided in table 1. However, while such references exist, they lack clarification 

beyond that provided in the quoted text. Also note that an Internet search 

for the terms “cascading emergencies or disasters” produces something on 

the order of 1 million “hits.”

Cascading and Toppling Dominoes Analogy

The concept of cascading disaster events can be illustrated via an analogy 

involving toppling dominoes. A massive array of dominoes is arranged such 

that once the initial domino is toppled, striking the next, and so forth, the 

dominoes then topple in intricate cascading sequences, from beginning to 

end, often consisting of branching networks. Disasters, as natural systems, 

operate in much the same fashion. The following examples demonstrate this 

analogy.

In a disaster, it is not enough to say that there is a primary threat and then 

all other threats in a sequence are secondary threats. Each threat in a cascading sequence may have its own integral importance, its own degree of damage 

and degree of consequence. Cascading threat sequences can be identified in 

Latin as primary, secondary, tertiary, quaternary, quinary, sextenary (or senary), 

heptenary (or septenary), octenary, nonary, denary, undenary, duodenary, and 

Cascading Disaster Models in Postburn Flash Flood May
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 445

Table 1—Examples from disaster management literature.

Reference Quote

FEMA Independent Study Course, IS 230, Principles of

Emergency Management

p. 3.17. Cascading events are events that occur as a direct or

indirect result of an initial event. For example, if a flash flood

disrupts electricity to an area and, as a result of the electrical

failure, a serious traffic accident involving a hazardous materials

spill occurs, the traffic accident is a cascading event. If, as a

result of the hazardous materials spill, a neighborhood must be

evacuated and a local stream is contaminated, these are also

cascading events. Taken together, the effect of cascading events

can be crippling to a community.

FEMA Independent Study Course, IS 393, Introduction

to Mitigation

p. 1-6. Cascading emergencies—situations when one hazard

triggers others in a cascading fashion—should be considered. For

example, an earthquake that ruptured natural gas pipelines could

result in fires and explosions that dramatically escalate the type

and magnitude of events.

U.S. Department of Homeland Security National

Response Plan, December 2004

p. 4 Additionally, since Incidents of National Significance

typically result in impacts far beyond the immediate or initial

incident area, the NRP [National Response Plan] provides a

framework to enable the management of cascading impacts and

multiple incidents as well as the prevention of and preparation for

subsequent events.

FEMA for Kids Website, Resources for Parents and

Teachers, How Schools Can Become More Disaster

Resistant. http://www.fema.gov/kids/schdizr.htm

. . . disasters can have a cascading effect—forest fires can bring

mudslides; earthquakes cause fires; tornadoes cause downed

power lines.

Resource Materials: Integrating Manmade Hazards into

Mitigation Planning

Risk Management in a Multi-Hazard World

2003 All-Hazards Mitigation Workshop

June 12, 2003

Emergency Management Institute

http://www.fema.gov/txt/fima/antiterrorism/resourcemat

erials.txt

Indirect attacks: infrastructures are really interconnected systems

of systems; an attack on one can lead to cascading losses of

service (ranging from inconvenient to deadly) and financial

consequences for government, society, and economy through

public- and private-sector reactions to an attack.

FEMA 428, Asset Value, Threat/Hazard, Vulnerability,

And Risk

p. 1-17. Extent of damage is determined by type and quantity of

explosive. Effects generally static other than cascading

consequences, incremental structural failure, etc.

FEMA 386-7, FEMA State and Local Mitigation

Planning How-To Guide, Integrating Man-Made

Hazards Into Mitigation Planning. Step. 2, Assessing

Risks.

p. 2-11. What is the likelihood of cascading or subsequent

consequences should the asset be destroyed or its function lost?

Hazard Analysis and Risk Assessment, 2003 Local

Guide, Iowa Homeland Security and Emergency

Management Division,

Hazards create direct damages, indirect effects, and secondary

hazards to the community. Direct damages are caused

immediately by the event itself, such as a bridge washing out

during a flood. Indirect effects usually involve interruptions in

asset operations and community functions, also called functional

use. For example, when a bridge is washed out due to a flood,

traffic is delayed or rerouted, which then impacts individuals,

businesses, and public services such as fire and police

departments that depend on the bridge for transportation.

Secondary hazards are caused by the initial hazard event, such as

when an earthquake causes a tsunami, landslide, or dam break.

While these are disasters in their own right, their consequent

damages should be included in the damage calculations of the

initial hazard event. Loss estimations will include a

determination of the extent of direct damages to property and

indirect effects on functional use.

Regional All-Hazards Mitigation Plan, City of St. Louis

and counties of St. Louis, Jefferson, Franklin and St.

Charles, Missouri, November 2004.

Cascading hazards could include interruption of power supply,

water supply, business and transportation.

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so forth. The author proposes using shortened terms to simplify communication: primary, secondary, tertiary, quartic, quintic, sexic, heptic, octic, nonic, 

decic, undenic, duodenic, and so forth (written communication, Dr. David 

Larmour, Texas Tech University, 2006). Such nomenclature facilitates communicating about threat sequences. In the illustration above, which shows a 

branching domino sequence, a disaster analyst could consider how a wildfire 

would generate a branching set of threat sequences because, for example, the 

fire spreads because fire support is too far away to provide immediate response. 

That sequence of events can be analyzed (separated into its constituent parts to 

be studied). The threat sequence could represent any causation sequence. For 

example: (1) the delay in response would allow the fire to grow, and (2) the 

movement of fire support personnel from distant areas could result in traffic 

accidents, and so forth. Both pathways need to be analyzed.

A single domino, as shown at the right, represents

a point threat, as one might expect were a disaster

to be a static event. This standing domino could

represent a type of electrical transmission line

constructed in an urban wildland interface. It is

simply a power line at risk from wildfire, although

a wildfire has not happened (domino is not yet

toppled).

Here, at the right, we see the same domino but we

consider what might happen if the electrical

transmission line were to be damaged by a

wildfire. Thus, we may consider a degree of

damage and an associated degree of consequence.

In this case the degree of damage would be 100

percent, or complete, and the degree of

consequence is somewhat more than moderate.

This view, at the right, is of an anticipated

dynamic disaster sequence, should the sequence

happen. Should the wildfire provide a low level of

damage, consequence of even a low level of

damage results in a completely negative

consequence (see the top number, one dot, on the

first domino, which represents the primary threat;

see the bottom number on the domino, six dots,

which represents the level of consequence). If the

primary threat (first domino) happens, then the rest

of the cascading sequence may take place, where

one threat causes another, and so forth, each with

its own relationship between degree of damage

and degree of consequence. Numbering is

explained in the text.

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Multihazard Concept

Cascading threat modeling has its origins in the concept of multihazard 

events. The term multihazard was introduced by the Federal Emergency Management Agency (FEMA) in 1982-1983 as part of the Utah Multi-Hazard 

Project. This new concept related multiple hazards to each other through 

causation sequences. The Utah Multi-Hazard Project included the cities of 

Provo and Ogden, and Utah and Weber Counties, showing the relationship 

between earthquake, dam failures, and floods, where a simulated design 

earthquake would logically cause a dam failure, which would then cause a 

flood (personal communication, Wes Dewsnup, Utah Multi-Hazard Project, former Program Manager; Utah Division of Comprehensive Emergency 

Management, July 15, 2006). This concept, though simple, was revolutionary 

because multihazards lead to multidisciplinary considerations, interactions, 

and solutions. The concept required diverse groups of specialists from various agencies to work together to solve multihazard problems. Earthquake 

specialists, dam safety specialists, and flood specialists then needed to work 

together to solve planning problems that emerged from multiple hazards.

Shortly after the awareness of the multihazard concept, various FEMA 

publications began to include the term cascading hazards and cascading disasters (see table of references above). It was from this concept of cascading 

hazards and disasters that this present method of disaster systematics arose 

in the late 1980s, as both a teaching and planning tool. The initial tool was 

called a “hazard tree,” and authoritative hazard trees were created by members 

of the Utah Interagency Technical Team (representatives from several agencies working together) for use in conducting local hazard and risk analyses. 

This tool was used successfully Statewide for such analyses. Hazard trees and 

the larger concept of disaster systematics was used as early as 1988 in the 

classroom at the University of Utah, Center for Natural and Technological 

Hazards, where students were required to develop hazard trees to understand 

cascading disaster processes for analysis purposes. The author first published 

a cascading disaster model in 1991 through the National Research Council’s 

publication “A Safer Future, Reducing the Impacts of Natural Disasters,” 

1991, Chapter 2, Hazard and Risk Assessment.

Modeling Versus Model Analysis

The purpose of creating cascading disaster models is to have credible models 

available for use in analyses. The models, which often are not complicated, 

capture the cascading events in disasters. Disasters often consist of many 

cascading sequences. Computer software can be used to keep track of these 

sequences, facilitating analysis and even supporting standardized analyses.

Model Development

The human mind cannot recall the amount of sequential information contained within a complex disaster system, but computers can store and display, 

both in outline and diagram formats, entire disaster systems. Both outline 

and diagram formats are essential in the development and application of the 

models. Computers can also store additional text and visual information as 

background documents within the models through notes and hyperlinked 

documents of many useful software types (word processing, spread sheets, presentations, Web pages, and so forth). Computers also allow for the creation of 

aesthetically attractive models suitable for public display and presentation.

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Several types of computer software programs are capable of meeting the 

needs of “cascade modelers,” including Inspiration and MindManager. Both of 

these software programs allow the modeler(s) to work within an outline and or 

diagram format, and both are powerful and versatile, allowing the modeler(s) 

to escape the limitations of fragmented hazard and risk thinking–to become 

disaster systems analysts.

Although completed disaster systems models appear complex and intricate, 

few of the threat sequences are particularly complex and are often sequences 

we are familiar with. For example, one sequence from an earthquake model 

might state that a threat from ground motion causes threat from the shak-

ing of buildings that, in turn, causes threat from falling bricks that, in turn, 

causes threat from bricks blocking streets that, in turn, causes delaying of 

emergency vehicles that, in turn, causes threat from the delay of emergency 

services to people in need of emergency services. But then we notice that 

falling bricks can cause more than one threat sequence, and that blocking 

of streets can cause more than one threat sequence. Thus, cascading models 

branch within themselves, but even the branching sequences are often easy 

to follow. The team performing an analysis on any of these sequences can 

perform analyses that range from straightforward to exceptionally complex, 

and can be multi- and interdisciplinary.

The development of a cascading disaster model must follow a process that 

provides credibility to the model. Usually, the modeler (generalist), as men-

tioned, would be an emergency manager or disaster analyst who coordinates a 

technical team of engineers, geologists, natural resource specialists, fire manage-

ment specialists, environmental scientists, planners, and others (all specialists) 

to create a model. Cascading disaster models should be constructed by a team 

of technical experts who collectively provide best available knowledge into the 

model. Computer software is used that enables the modeler (generalist) to enter 

threat sequences directly into the computer during an interview process involv-

ing the technical team (specialists). Team members determine model inputs 

while the modeler enters the information. The software allows the modeler to 

enter the information (causal sequences) into an outline format/view and display 

the model as a cascading tree structure in a diagram format/view. The team of 

specialists collectively determines the threat sequences and the branchings.

Generic and historic models—There are two basic types of cascading disaster 

models: generic and historic. Generic models are those designed by a model-

ing team based on their collective experience to document what can happen 

in a disaster of that type. There is no reference to other disasters in that the 

model is not intended to reflect any particular disaster event. Generic models 

have their own threat nomenclature that is written in the present tense. This 

nomenclature makes the statement throughout the model, from threat to threat, 

stating that “threat from this causes threat from this that caused threat from 

this, and so forth.” Threat nomenclature is mainly found in the way the verbs 

are written within the threat symbols (present tense versus past tense). Most 

example models provided in this paper are representative of generic models. 

This type of threat nomenclature is either written as “ing” endings or as present 

tense verbs. See model examples below for these types of endings.

The other basic type of model is the historic model. Historic models are 

based on research of a particular disaster, and the identified disaster processes 

(sequences) are captured in that model. The intent is to document what 

processes happened in that disaster. Because this information documents an 

event that happened in the past, the threat nomenclature is written in the 

past tense (“ed” endings or past tense verbs) (fig. 1).

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Threat sequence logic transitions one threat logically to another, depicting the causation sequence. Models must be internally coherent to make 

sense in their entirety. Thus, one threat must transition logically (causation 

sequence) and properly (nomenclature) to the next and to the next. This is 

true if a modeler is developing a generic model (present tense) or a historic 

model (past tense).

Model geography—Cascading disaster models have shape, dimension, 

location, and design.

The shape is that of a branching tree structure, called an index tree. Several 

graphical examples of a model are provided. Figure 2 is the model for a wildfire. A cascading model for wildfire would begin with a first level index tree, 

which simply shows our initial sense of what can happen. The fire can then 

spread uphill, laterally, or downhill, threatening an urban wildland interface 

community. It can also burn into the community’s watershed, introducing 

the potential for postburn flash flood. The index tree identified the primary 

and secondary threats that precede the main branches of the model.

The tree can be created and displayed as a top-down tree, a bottom-up 

tree, a right tree, or a left tree. Experience suggests that either a top-down 

tree or a right tree functions best. The shape of a tree structure begins with 

the primary threat, or initial threat that precipitates the cascading sequences 

of the disaster. Branching sequences follow causing the model to expand. The 

portion of the tree structure more near the primary threat is termed “proximal,” and the portion of the tree structure more distant from the primary 

threat is termed “distal.” At the ultimate proximal end is the primary threat, 

and at the ultimate distal end are the terminal threats (where the individual 

model pathways terminate) (fig. 3).

Figure 1—Example of a historic branch of a cascading disaster model of part of the 

Oakland, CA, wildfire (1991) based on studies of that event.

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Figure 3—Model branch showing proximal and distal areas and terminal threats.

Figure 2—A wildfire begins with the primary threat of a small 

localized fire. The display of the first few threat layers constitutes 

an Index Tree, which leads to the rest of an expanded model.

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For dimension, the tree has both height and depth determined by the 

number of branching threat pathways (primary threat to terminal threats). 

There are as many pathways across the model as there are terminal threats. 

If the model has 10 terminal threats then the model has a dimension, top to 

bottom for right trees, of 10 pathways each of which can be analyzed. If the 

model has 10 pathways, from primary threat to terminal threat, that have, for 

example, five threats per pathway, then the model is five threats deep, from 

left to right for a right tree. In this hypothetical example of five threats per 

pathway, the model would consists of 50 threats, all organized within the 10 

pathways. Thus, the model has height (number of pathways - 10) and width 

(numbers of threats per sequence—a hypothetical number of 10).

There are many threat locations within the model. The example in fig-

ure 3 depicts 25 threats, each with a unique location within the model that 

can be identified according to a pathway code. Pathway codes are unique 

to a particular model, its associated team, and its associated draft date and 

time. Through the use of pathway codes, team members, or others using 

the particular model, can communicate about particular threat pathways 

and understand which pathways they are discussing. Each pathway becomes 

of interest in a variety of ways: preparedness, response recovery, recovery, 

training, exercise design, hazard behavior, disaster behavior, and so forth. 

Given that a pathway consists of a unique threat sequence, then the process 

of analysis involves studying each threat in its geographic location within the 

model. It is then important to be able to discuss each threat location with 

others who may be interested in it. Also note that each individual threat has 

its own location code.

Figure 4 shows pathway codes for 11 pathways (each terminal threat denotes 

the end of a pathway) and 24 threats (each box denotes a threat). As shown 

each threat has a position code number. A threat with two numbers in its code 

is a secondary threat, and a threat with three numbers is a tertiary threat, and 

a threat with four numbers is a quaternary threat, and so forth. Numbers are 

counted from each node between threats and from the top down. The threat 

position code for a terminal threat is also the pathway code for that pathway 

(from primary threat at the proximal end of the pathway to the terminal 

threat at the distal end of the pathway). Thus, in figure 4, one can observe 

11 unique pathway codes. As an example of identifying a specific threat, the 

secondary threat of “inadequate or improper water supply” is located within 

the box with pathway code “11.”

The design of the models consists of branches and pathways. Branches are 

units of a cascading disaster model where two or more pathways originate 

from a node (that is, the connecting point between two threats). Pathways 

originating from such a node share design similarities based on the com-

mon threat at the proximal end of the branch. Branches that originate from 

a secondary threat are secondary branches and those that originate from the 

tertiary threat are tertiary branches, and so forth. Thus, a branch can be 

discussed by team members as the associated pathways sharing that common 

characteristic.

Several branches are shown in figure 4. The four pathways of the tertiary 

branch share the common threat characteristic of “Not Fighting All Vegeta-

tion Fires.” The two pathways of the quaternary branch share the common 

threat characteristic of “Fire spreading,” which was caused by “Not using 

all pumping equipment.” The two pathways of the sextec branch share the 

common threat of “Fire spreading.”

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Figure 4—Model branch showing a single pathway highlighted and also showing 

the unique threat and pathway codes.

Branches can be studied by disaster analysis, but pathways (also referred 

to as single-file pathways) form the main study unit of a cascading disaster 

model. Pathways are causation sequences where one threat causes another 

threat causes another threat causes another threat, and so forth. An example 

pathway is shown above in figure 4 in the discussion on pathway codes and 

is separated and shown in figure 5, as well.

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Single-file causation sequences (pathways) can be studied by a team of 

hazard and disaster analysts from a variety of perspectives: disaster preparedness, disaster response, disaster recovery, and hazard mitigation. Causation 

sequences can also be studied from the perspectives of training, exercise 

design, and so forth. For example, a team of disaster analysts could examine 

the pathway shown in figure 5 and ask the questions: how might we prepare 

for, respond to, recover from, or mitigate, such a sequence of events; or how 

might we train for such a sequence or devise a disaster exercise for such a 

sequence? In conducting such an analysis, the team might also choose to provide more detail within the model pathway by inserting additional threats.

Antecedent conditions—Cascading disaster models portray disasters and 

disaster behavior graphically. The same models can also be used to portray 

hazard behavior by including antecedent conditions—the hazard behaviors 

that lead to the activation of the primary threat. For example, the presence 

of propane tanks would also be called a hazard by some disaster analysts. 

Hazards themselves in these models are simply nouns, or the names of the 

threats the mind recognizes as hazards to the human built environment and 

with people themselves.

Figure 6 displays the antecedent conditions that would link a wildfire model 

to a postburn flash flood model. The antecedent conditions begin with a 

Damaged Watershed and conclude 11 threats later with a hyperconcentrated 

flow emerging onto an alluvial fan apex. Antecedent conditions precede the 

associated disaster, which in this case would be a debris flow disaster associated with the damaged watershed and a significant thunderstorm.

Figure 5—Separated single pathway from branch model shown in figure 4.

Figure 6—Antecedent conditions that cause the primary threat in disaster genesis.

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Model Applications

The value of cascading models has become apparent through several years 

of application and in a variety of situations.

Cascading models have been used to:

	 •	 Educate community and governmental leaders: The model as a teach-

ing tool demonstrates to leaders how a particular type of disaster might 

affect their community, showing generic disaster behaviors as causa-

tion sequences, where one threat causes another, causes another, and 

so forth, or how, through the diagram format, a disaster can “unfold” 

in their community. The education is meaningful because disasters are 

commonly thought to happen in random, unpredictable fashion, but 

in reality leaders learn that disasters happen as systematic processes of 

causal sequences.

	 •	 Educate IAT members: Because the human brain is not capable of 

remembering the details of numerous and complex disaster sequences, 

the computer-printouts on paper aid team members when they consider 

what threat pathways are the most dangerous and of highest priority and 

what strategies might be applied to these pathways to prepare, respond, 

recover, or to mitigate potential hazard and disaster events.

	 •	 Educate stakeholders and the public: The process of building commu-

nity capacity to reduce vulnerability requires public support. The public 

most often views disaster processes as being too complex to understand 

and, therefore, tends to avoid gaining an understanding of them. The 

lay people not versed in hazard and disaster processes and knowledge 

can understand the logic of cascading disaster model diagrams and, also, 

the logical threat sequences.

	 •	 Evaluate training: Models can also be used to identify training needs of 

the technical team of hazard and disaster managers from the State and 

Federal agencies, including how to conduct hazard and risk analyses in 

communities. Team members themselves, in studying a model they cre-

ated, could identify what knowledge they need in order to understand 

the sequences in the model.

	 •	 Conduct hazard and risk analyses: State and local governments are 

required by Federal law to prepare and submit to the Federal Emergency 

Management Agency (FEMA, Disaster Mitigation Act of 2000) for 

approval hazard mitigation plans and emergency operations plans. Such 

plans are based on hazard and risk analyses. Many agencies of government 

also conduct these analyses for a variety of needs, or assist local govern-

ments in conducting the analyses. Cascading disaster models provide 

an excellent basis for conducting such analyses that are detailed and 

systematic. A team of experts can use these models to conduct analyses 

in a brief period, perhaps 2 hours, considering most any kind of threat 

that could face a community. Updates to the analyses usually require 

much less time.

	 •	 Design disaster exercises: A visual image of disaster as cascading 

sequences of events provides an excellent basis for designing a disaster 

exercise. The entire model can be used to develop a comprehensive 

disaster exercise or it can be used to develop an exercise for selected 

sequences in a disaster where an exercise would provide the needed 

training for some aspect of response. The simulated messages (injects) 

for the exercise could be designed around the threat sequences and 

relating to the players in the exercise.

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	 •	 Conduct planning: Cascading disaster models can be used for conduct-

ing hazard and risk analyses and support the development of response 

strategies and recommendations. Once a planner comes to the point of 

adding recommendations, then the hazard and risk analysis becomes 

more than just an analysis. It begins to look more like a plan. It could 

become a plan if implementation strategies were also added to the 

model. Plans, however, are best laid out as text within an outline (table 

of contents), but the information from the analyses conducted with the 

model can be transposed into the plan.

Wildfire and Postburn Flash Flood

To relate wildfire to postburn flash flood, refer back to figure 2, which 

shows a simple view of an index tree including wildfire damaging the wa-

tershed. That threat provides the linkage to the cascading threat model for 

postburn flash flood. City officials too often believe that once a threatening 

wildfire is contained, the community’s vulnerability to disaster is largely over. 

However, the community then finds itself facing flash flood threat should a 

significant thunderstorm occur over the damaged watershed (Cornell 1992). 

In this case, there are now two related cascading disaster models needed, the 

first for wildfire and the second for postburn flash flood. The two related 

models would be joined at a terminal threat titled “damaged watershed.”

For example, figure 7 displays a wildfire cascading threat model that 

includes the beginnings of effects of rainfall onto the damaged watershed. 

Remember that such a model can be as complex as a modeling team wishes 

to make it. Such threat models, if developed with the assistance of special-

ized planning teams, gain considerable credibility. This particular model was 

developed by a combination of wildfire hazard and flood hazard specialists 

of the Utah Interagency Technical Team in the early 1990s. The process of 

making a transition from one model (for example, wildfire) to another model 

(postburn flash flood) introduces the concept of a compound model. (Com-

pound models are caused by a preceding “simple model” and superimpose 

their own consequences onto the already-existing consequences of the simple, 

preceding, model. This is a concept too complex to explain in this present 

paper.) The branching in such a model provides an interesting juncture in 

the disaster model because it is at this point that the model would actually 

link to a separate model, representing what happens when the thunderstorm 

does happen. We then must connect to a debris flow tree modified for the 

enhanced sediment load. This is a separate model.

Figure 8 presents an index tree for a postburn flash flood cascading threat 

model. An index tree can be constructed that allows the modeler the option of 

working with any of the shown pathways, beginning with any of the terminal 

threats shown. In fact, the entire comprehensive model could be collapsed 

to the level of the index tree for purposes of illustration.

The Index Tree can be expanded to view a selected branch that would be-

gin with any one of the terminal threats shown in figure 8. This begins the 

process of analysis. The selected branch provided in figure 9 is an extension 

from the index tree, diagramming threat sequences arising from the threat 

of a hyperconcentrated flow impinging onto residential structures. This array 

of threat sequences shows several aspects of the event that relate to the flow 

impinging onto residential structures. They all have this one thing in common, 

which is a characteristic of branches within a cascading disaster model.

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Figure 7—The postburn flash flood (or debris flow/hyperconcentrated flow) model 

begins at a node within the wildfire model.

Figure 8—Index tree for postburn flash flood (or debris flow/hyperconcentrated flow).

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Branch analysis is useful in determining the variety of sequences that can 

arise from a single threat node. This could serve as a justification tool for 

mitigation, which would be an expected objective: to mitigate as many threats 

as possible with one strategy. Still, the main analytical tools of cascading threat 

sequences are the single pathways, as the analyst can focus on one particularly 

interesting or dangerous threat sequence. The single pathway can be displayed, 

according to the domino explanation provided earlier in this paper, where 

each threat in a causation sequence has its own degree of damage and degree 

of consequence. For example, complete damage to a movie theater caused 

by the flow impinging on the movie theater may have little real consequence 

for a community, whereas, a small amount of damage to the fire station may 

have much more consequence. Numerous scenarios of this type (single file 

pathways) can be identified within the cascading model.

Figure 9—Cascading threat model branch of postburn flash flood stemming from the 

index tree shown in figure 8.

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This single file cascading sequence, shown in figure 10, is a simple threestep pathway regarding water flowing into a basement. As the analyst examines 

it, however, it becomes apparent that much more study is warranted. Although 

it is serious enough that adults might be trapped in the basement, one may 

recognize that children often occupy basement bedrooms and may become 

victims of the flooding during a night-time postburn flash flood. If the description, in the next section, of the Santaquin, UT, debris flow is correct, 

that the flow approached silently, then basement rooms would begin filling 

suddenly with liquefied sediment and other debris without warning. Among 

debris flow specialists, it is said generally that 6 inches of water flowing over 

a window well can fill a 10 foot by 10 foot basement room in about 45 seconds, forcing the door closed and making it impossible to open. That being 

the case, then this single file pathway is worthy of considerable study.

A simple example of an analysis approach for single file pathways is provided 

in table 2. Notice how the analyst can arrive at a set of mitigation options, 

addressing each threat in a sequence, and develop an overall mitigation 

strategy.

Figure 10—Debris/hyperconcentrated flow causation sequence trapping victims in a basement bedroom.

Table 2—Example of an analysis approach regarding victims in a basement bedroom.

v Maintain materials for emergency

diversion: Prepositioned jersey

barriers, straw bales.

v Move children to upstairs

bedrooms.

v Use sliding doors for basement

rooms.

v Implement home mitigation

strategies: deploy sand bags to

protect entry points or to divert

flows.

v Elevate window wells.

v Sandbag window wells.

v Board-over window wells.

v Fill window wells with bags of

gravel to displace water.

v Use delicate door materials that

can be easily broken from inside.

v Attach basement doors that open in

outward direction.

v Build homes at 45 degree angle to

uphill slope.

v Construct house without basement

windows on uphill side of house.

v Place basement alarms in

bedrooms that can be activated by

occupants.

v Construct streets in residential area

with inverted crowns angled to

catch and route flows.

v Use double-pane laminated glass

for basement windows.

v Place tools near door so that they

are readily available to break door.

v Construct permanent debris basins

at apex of flow path.

v Cover window wells with

impenetrable plastic shield.

v Remove basement doors until

watershed is reestablished.

v Monitor weather, satellite, and

Doppler images.

v Tape windows so glass is not easily

broken.

v Install plastic film to strengthen

window panes.

v Place a ladder in occupied rooms.

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To conduct such an analysis as shown in table 2, the modeling team of 

Federal and State hazard and disaster experts meets with a local planning 

team. The local team being interviewed can view the overall threat model 

in diagram format (view the tree structure). The analyst leads the local 

team through the process from branch to branch, even pathway to pathway, 

determining which branches and pathways have presented problems to the 

community in the past, or which might present problems in the future. As 

the local team answers, the analyst types the information into the outline 

format of the software under each individual threat. The complete interview 

process, working through an entire model, can take 2 hours, considering that 

not each threat sequence will apply to the community. Once the analysis is 

completed with the interview/analysis, then updating the analysis annually 

will take far less time, based on the local government’s interim experience.

To yet advance the analysis into an initial hazard mitigation plan, as the 

analysis is being conducted, the local team can provide mitigation recommen-

dations as each sequence is discussed. If this is done, then at the end of the 

analysis, the outline format contains all information provided based on experi-

ences and expectations, and also recommendations for mitigation built within 

the systematic framework. The overall information can then be transferred to 

a text document to formulate a hazard mitigation planning document.

Example Scenario: Santaquin, Utah, Postburn 

Flash Flood

Cascading disaster models are developed based on the real-world experience 

of interagency technical teams such as the Utah Interagency Technical Team. 

Since the 1980s, the Utah IAT gained experience working with postburn 

flash floods of the following locations in Utah: Affleck Park, Emigration 

Canyon, 1988; Wasatch Mountain State Park, 1991; North Ogden, 1991; 

Holden, 2000; Orem, 2000; and Farmington, 2003 (State of Utah, Hazard 

Mitigation Plans). The Mollie Wildfire and postburn flash flood of Santa-

quin, UT, was an event that was studied in much detail and resulted in much 

community interaction (fig. 11).

The Santaquin (Utah County) wildfire, named the Mollie Fire, began 

on August 18, 2001, and produced an 8,000 acre burn directly above the 

east bench of Santaquin (pop. 4,834). A new development of homes was in 

proximity to the burn and the wildfire threatened to cause a disaster. Due 

to the potential for disaster, the fire qualified for a Federal Wildfire Sup-

pression Declaration through the Fire Management Assistance Program of 

the Federal Emergency Management Agency. By the time the wildfire was 

contained, it had not burned any homes, due to an excellent fire suppression 

effort by the State and local fire departments. Some homes had minor dam-

age. At the time of the declaration request, the fire was described as: (1) out 

of control with conifer vegetation at higher elevations; (2) oak and sage at 

lower elevations, mixed-in around homes at risk; (3) steep mountain slopes 

with about 3,000 feet of topographic relief; (4) interspersed rugged canyons; 

(5) developed areas lie in fire’s path within 1 to 3 miles; and (5) threat is 

to about 900 people and 250 primary residences. In a sense, the entire 

population on the east side of Interstate 15 was at risk, including 1,315 

housing units. These were also at risk from the potential of postburn flash 

flood. (Mollie Wildfire, BAER Team Report, 2001).

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Figure 11—An eastward view across a Santaquin residential area located 

below a watershed burned by the Mollie Wildfire. The wildfire burned 

to the ridgeline and the width of the watershed shown here. These 

residents lived with the daily reminder of a burned watershed and the 

potential flash flood that could happen with the next thunderstorm or 

unusual snowmelt.

The composition of an interagency technical team for postburn evaluations for flash flood potential represented several agencies of State and 

Federal government and enabled the impacted local government to answer 

most any question and address most any issue. This particular team consisted of the following (Utah Interagency Technical Team ONSITE Report, 

September, 2001):

	 •	 IAT Coordinator, Utah Division of Emergency Services

	 •	 Watershed Geologist, USDA. Natural Resources Conservation Service

	 •	 Hazards Geologist, Utah Geological Survey

	 •	 Engineer/Hydrologist, Division of Water Quality

	 •	 Engineer/Hydrologist, Division of Drinking Water

	 •	 Engineer/Hydrologist, U.S. Army Corps of Engineers

	 •	 Meteorologist, National Weather Service

	 •	 Engineer/Hydrologist, USDA Natural Resources Conservation Service, 

Snow Survey Office

Based on team field assessments, more than 30 field observations were 

made and those observations resulted in 27 hazard mitigation recommendations relative to wildfire and potential postburn flash flood. This body of 

knowledge was incorporated into the cascading disaster model for use in 

future hazard and risk analyses and planning efforts.

In the years following the wildfire, the city of Santaquin experienced two 

postburn flashfloods. These did not happen immediately after the wildfire, 

which highlights one of the major challenges of postburn flash flood. Once 

a watershed is damaged, the threat might remain in effect for up to 6 years 

while the watershed reestablishes itself. In the meantime, local officials and 

residents wait for only the possibility of the event.

Cascading Disaster Models in Postburn Flash Flood May
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 461

On the evening of September 12, 2002, just 1 year after the wildfire 

(August 18, 2001) that damaged 8,000 acres of the watershed above 

Santaquin, an intense thunderstorm settled onto the watershed. This storm 

triggered a wildfire related debris flow that damaged houses in the adjacent 

communities of Spring Lake and Santquin. This debris flow moved and par-

tially buried several automobiles and broke through a wall into a house. It 

entered other homes through doors of basement windows. Gas meters were 

torn from their connections, causing leaks and a small fire. Landscaping and 

property outside of homes were also damaged. The flow also blocked the 

highline irrigation canal causing additional flooding.

Mayor LaDue Scoville (pers. comm. June 29, 2005) reported that the 

debris flow that entered the east side of Santaquin entered the neighborhood 

silently at about dinner time (approximately 6 p.m.). This was unusual in that 

debris flows have been otherwise described as being noisy, much like the sound 

of an approaching locomotive. People eating dinner were not aware of the 

problem until they heard noises of breaking garage doors and the destruction 

of other doors to their homes. On investigating the sounds, people discovered 

that mud was flowing in their streets and around their homes. In one case, 

the mud entered through a main door to the home, breaking it in, and then 

flowing into the main level. The weight of the mud collapsed the main floor 

onto the basement, sending a piano through the broken floor. One lady, be-

ing forced from her home carrying a baby, was “chased” down the street by 

the mud flow, but she was able to keep in front of it, escaping. The flowing 

mix of water and sediment broke through several basement windows filling 

the basements with the mud. The mayor described the mix as crusty on its 

surface, but that pressure placed onto it, as with a foot, caused it to turn into 

a liquid mix and set it to flowing again. The mayor reported that the silent 

approach of the debris flow made it potentially lethal. Fortunately the debris 

flow happened early in the evening before people were in bed. The mix filled 

basement rooms quickly. Had children, or adults, been in basements they likely 

would have been trapped and killed. This has been a message of emergency 

managers for at least a decade, that debris flows can fill a basement bedroom 

in less than a minute, pushing the bedroom door closed and applying such 

weight as to make it nearly impossible to escape.

No fatalities or injuries occurred, which Mayor Scoville explained as a 

miracle. The flow surrounded houses and blocked streets, even partially 

burying automobiles, making it difficult to escape. He explained further 

that the “liquid mix” remained liquid for days, sealing itself and preventing 

evaporation of the water. When the city attempted to haul the mix away it 

liquefied and ran out of the scoops of front end loaders and oozed out the 

rear gates of dump trucks. This was such a problem that the Utah Department 

of Transportation would not allow the city to haul the mix on State roads, as 

it would flow out of the trucks and onto the highway. For several days after 

the city disposed of the mix in a field, it maintained this fluid consistency.

The city Public Works Director, David Banks, indicated that the debris 

flow pushed one house off its foundation and separated the natural gas line 

from the house, causing a leak. The gas company shut off the gas to that 

neighborhood, but gas remained in the lines. As city workers were digging 

in the area, underground power lines still had electricity running through 

them. Excavation by city workers could have severed gas or electrical lines 

and resulted in electrocution, fire, or explosion; fortunately, such an event 

did not happen.

Cascading Disaster Models in Postburn Flash Flood May
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 462

Conclusions

Natural disasters are natural systems and happen as cascading causation 

sequences, and these sequences can be documented using computer software 

as cascading disaster models. These models can be analyzed according to 

their branches and single-file pathways. This is a systems approach to analyzing disasters. The opposing method is called the fragmented approach 

where hazard or disaster concerns to be addressed in assessments are studied 

from lists and tables. Any technique involving a systems approach should be 

included in an aspect of disaster science called disaster systematics. It is also 

proposed that disaster analysts develop and use cascading disaster models 

when conducting hazard and risk analyses. It is also proposed that fragmented 

approaches to studying disasters be largely abandoned as they can lead to 

neglecting dangerous threats that may lie hidden within a disaster system 

(cascading models).

Although, in this paper, wildfire and postburn flash floods are provided as 

simplified cascading threat models, the reader’s imagination would certainly 

suggest that complete models can be highly sophisticated/complex based 

on technical team inputs and model design. The associated analyses would 

likewise be rather sophisticated if performed by technical teams. Detailed 

and systematic hazard and risk analyses can be developed with a local government in about 2 hours per hazard (for example: local wildfire hazard and 

risk assessment), and a mitigation plan with recommendations can also be 

developed simultaneously.

The example herein makes a strong case for the elimination or greater 

protection of basement occupancy on alluvial fans below wildfire burn areas, 

or potential burn areas. The Santaquin, UT, postburn flash flood happened 

much like other such postburn flash flood events that threaten mountainfront communities. Wildfires are on the increase as development increases in 

urban wildland interface areas, and postburn flash floods will likely increase 

as well, similar to the disaster event sequences presented in the Santaquin 

case. Given the increasing level of vulnerability it would seem necessary from 

a public safety perspective to examine complete disaster systems rather than 

fragments of systems (fragmented systems).

References

Ashcroft, Gaylen; Jensen, Donald; Brown, Jeffrey. 1992. Utah Climate. Logan, UT: 

Utah State University, Utah Climate Center.

Cannon, Sylvester Q. 1930. Torrential Floods in Northern Utah, 1930. Circular 

92. Logan, UT: Utah Agricultural Experiment Station, Special Flood 

Commission.

Case, William. Debris Flow Hazards. Public Information Series 70. Salt Lake City, 

UT: Utah Geological Survey.

City of Centerville, Geographic Information System, Alluvial Fan Map.

Cornell, Gary. 1992. Urban/Wildland Interface Fires. In: Utah Natural Hazards 

Handbook. Salt Lake City, UT: Utah Division of Comprehensive Emergency 

Management, Utah Interagency Technical Team: 23-28.

Dewsnup, Wes. 2006. Pers. comm. concerning origin of multihazard concept through 

the Utah Multi-Hazard Project, 1982.

Cascading Disaster Models in Postburn Flash Flood May
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 463

Federal Emergency Management Agency. 1982. National Flood Insurance Program, 

Floodway, City of Layton, Utah (Davis County); Flood Boundary and Floodway 

Map.

Federal Emergency Management Agency. 1991. National Flood Insurance Program, 

Flood Insurance Rate Map, City of Bountiful, Utah (Davis County).

Federal Emergency Management Agency. 1991. National Flood Insurance Program, 

Flood Insurance Study, City of Bountiful, Utah (Davis County). Revised 

September 27.

Federal Emergency Management Agency. 1992. National Flood Insurance Program, 

Flood Insurance Study, City of Centerville, Utah (Davis County). Revised, 

February 19.

Federal Emergency Management Agency. 1992. National Flood Insurance Program, 

Flood Insurance Study, City of Farmington, Utah (Davis County). Revised: 

December.

Federal Emergency Management Agency. 1981. National Flood Insurance Program, 

Flood Insurance Study, City of Kaysville, Utah (Davis County).September.

Federal Emergency Management Agency. 1982. National Flood Insurance Program, 

Flood Insurance Study, City of Layton, Utah (Davis County). December.

Federal Emergency Management Agency. 1982. National Flood Insurance Program, 

Flood Insurance Study, City of North Ogden, Utah (Weber County). July 19.

Hatch, Orrin G. 2001. Hatch Announces $1,250,000 in Federal Relief Grants to 

Help Citizens of Santaquin, Utah Against a Major Flood Threat. United States 

Senator, News Room, News Items from the Senator. September 27, 2001.

May, Fred. 2005. Cascading Threat/Impact Model Applications Wildfire/PostBurn Mudflows, Wasatch Front, Utah. Abstract and Conference Presentation. 

July 18-20, 2006. Boulder, CO.

May, Fred. 2005. Cascading Disaster Model – Post-Burn Mudflows, Wasatch 

Front, Utah, International Conference on Energy, Environment, and Disaster 

(INCEED), Charlotte, SC, Conference Presentation, Abstract and Paper on 

Conference Program CD, July 24-30, 2005. 44 p.

May, Fred. 1992. Introduction, in Utah Natural Hazards Handbook. Salt Lake 

City, UT: Utah Division of Comprehensive Emergency Management, Interagency 

Technical Team: 1.

May, Fred. 2001. Interagency Technical Team, ONSITE Report, Santaquin, Utah 

County, Postburn Flash Flood Evaluation No. 1. September 2.

McDonald, Greg; Giraud, Richard. 2002. September 12, 2002, fire-related debris 

flows east of Santaquin and Spring Lake, Utah County, Utah. Technical Report 

02-09. Salt Lake City, UT: Utah Geological Survey.

National Research Council. 1991. A Safer Future, Reducing the Impacts of 

Natural Disasters. U.S. National Committee for the Decade for Natural Disaster 

Reduction, National Academy Press.

O’Brien, Jimmy. 1990. Flood Hazard Delineation on Alluvial Fans and Urban 

Floodplains. Breckenridge, CO: Lenzotti & Fullerton Consulting Engineers, Inc.

Pickler, Nedra. 2006. Disaster Costs to Swell U.S. Budget. Associated Press. 

September 16.

U.S. Census. 2000. City of Layton, Utah. Fact Sheet. http://factfinder.

census.gov/servlet/SAFFFacts?_event=&geo_id=16000US4943660&_ge

oContext=01000US%7C04000US49%7C16000US4943660&_street=&_

county=Layton&_cityTown=Layton&_state=04000US49&_zip=&_lang=en&_

sse=on&ActiveGeoDiv=&_useEV=&pctxt=fph&pgsl=160

Rasely, R.C. 2001. Emergency watershed protection – Mollie fire hazards impacting 

Santaquin, Utah County, Utah. Unpublished report. Salt Lake City, UT: Natural 

Resources Conservation Service report to Neil Pellman, State Conservation 

Engineer. 4 p., 5 attachments.

Cascading Disaster Models in Postburn Flash Flood May
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 464

Richardson, Arlo E. 1971. Estimated Return Periods for Short-Duration Precipitation 

in Utah. Bulletin No. 1. Logan, UT: Utah State University, Department of Soils 

and Biometeorology.

Whitworth, Paul; May, Fred. 2006. Disaster Planning For Recreation Areas Via 

Cascading Models. Special Issue Introduction: The Role of Public Parks and 

Recreation in Urban Area Homeland Security. Journal of Parks and Recreation. 

Winter, Vol. 24, no. 4: 1-21.

Cascading Disaster Models in Postburn Flash Flood May
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 465

Abstract—Postfire slope stabilization treatments are often prescribed following 

high‐severity wildfires on public lands to reduce erosion, maintain soil productivity, 

protect water quality, and reduce risks to human life and property. However, the ef‐

fectiveness of slope stabilization treatments remains in question. For this study, tests 

were on effectiveness of seeding and fertilization treatments for increasing total live 

plant cover and reducing percent bare soil during the first 2 years following wildfire in 

dry mixed-conifer forests of north-central Washington State. Assessments were made 

on the effects of four seeding treatments (none, two perennial species mixes, and a 

winter wheat monoculture) and three fertilization levels (0, 50, and 100 lb N/acre) 

in factorial combination on percent bare ground, and plant cover using a generalized 

randomized complete block design at eight sites. Half of the sites also received aerially 

applied straw mulch. Surveys of vegetation responses during the first two summers 

following fire showed that seeding, fertilizing, and straw mulching all significantly 

influenced bare ground and/or live plant cover. Fertilizing alone increased mean live 

plant cover by 4 to 9 percent in 2005, and by 8 to 12 percent in 2006. Seeding alone 

increased mean live plant cover by only 1 to 2 percent in 2005 and 0 to 3 percent in 

2006. Fertilizing and seeding together increased plant cover by up to 11 percent in 

2005 and 20 percent in 2006 and reduced bare ground by up to 13 percent in 2005 

and by 21 percent in 2006. Of the seeded species, two native perennial forbs, yar‐

row (Achillea millifolium) and fireweed (Chamerion angustifolium), contributed the 

most to total plant cover. Straw mulching increased litter cover by 10 to15 percent, 

but had little effect on live plant cover. Our results suggest that fertilization treatments 

can increase the effectiveness of seeding treatments and stimulate regrowth of surviv‐

ing native vegetation following wildfires, particularly in forest types with understory 

vegetation dominated by species that resprout following fire. More work is needed to 

determine appropriate levels of fertilization and to better identify species and envi‐

ronmental factors that produce better results for seeding treatments.

Introduction

Controlling erosion and water runoff are important objectives for land 

managers following severe wildfires. High severity wildfires kill vegetation and 

consume surface organic matter, exposing mineral soils to increased erosion, 

particularly during intense rainfall events (DeBano and others 1998; Neary 

and others 2005; Wondzell and King 2003). Fires can also increase hydrophobicity in soils, thereby reducing water infiltration rates and increasing surface 

runoff, soil erosion, and sediment delivery to streams (Benavides-Solorio and 

MacDonald 2001; DeBano 2000). These fire-induced effects on soil erosion 

and water runoff typically diminish as vegetation recovers and replaces lost 

plant and litter cover (Benavides-Solorio and MacDonald 2001).

Assessing the Effectiveness of Seeding and 

Fertilization Treatments for Reducing Erosion 

Potential Following Severe Wildfires

David W. Peterson1, Erich K. Dodson1, and Richy J. Harrod2

In: Butler, Bret W.; Cook, Wayne, 

comps. 2007. The fire environment—

innovations, management, and policy; 

conference proceedings. 26-30 March 

2007; Destin, FL. Proceedings 

RMRS-P-46CD. Fort Collins, CO: 

U.S. Department of Agriculture, 

Forest Service, Rocky Mountain 

Research Station. 662 p. CD-ROM.

1 Research Forester and Ecologist, 

respectively, U.S. Department of 

Agriculture, Forest Service, Pacific 

Nor t hwe s t R e s e a rc h S t at ion , 

Wenatchee, WA. Lead author at 

davepeterson@fs.fed.us

2 Deputy Fire Management Officer, 

U.S. Department of Agriculture, 

Forest Service, Okanogan-Wenatchee 

National Forests, Wenatchee, WA.
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 466

Although elevated levels of soil erosion and water runoff are natural ecosystem responses to severe wildfire, they can present unacceptable hazards to 

human health and property in lower parts of the affected watersheds. Increased 

sediment delivery to streams and/or loss of forest productivity may also be 

undesirable, even if human interests are not threatened, if fires are deemed 

uncharacteristically severe due to previous management activities (such as 

logging or fire suppression). To reduce erosion and flooding hazards and 

protect natural resources, land surface treatments are often applied on Federal 

forest and range lands following wildfires as part of burned area emergency 

response (BAER) or emergency stabilization and rehabilitation (ESR) efforts 

(Robichaud and others 2000).

Land surface treatments to reduce erosion and runoff can include seeding, 

fertilizing, and mulching. Seeding treatments seek to increase plant cover by 

promoting establishment of new plants, typically from fast-growing species 

and readily available seed stocks. Fertilization treatments seek to enhance 

growth and litter production of surviving and newly established plants by 

enhancing soil nutrient availability. Mulching seeks to directly replace surface 

organic matter while having little or no effect on vegetation recovery.

Annual costs for BAER/ESR land treatments have increased in recent years 

due to increased areas burned by high severity wildfires, increased postfire 

threats to human health and property due to expansion of the wildland-urban 

interface, and increasing use of costly mulching treatments. Despite escalating 

expenditures and widespread use, rigorous testing and monitoring of BAER 

rehabilitation treatments have seldom occurred (Robichaud and others 2000; 

Government Accountability Office 2003), making it more difficult for agencies to justify continued expenditures.

In this study, we used an experimental approach to examine the effects of 

seeding and fertilization treatments on plant cover and bare soil at eight sites 

within the Pot Peak Fire (2004) in the eastern Cascade Range of Washington 

State. The effects of mulching are also documented as mulch was operationally 

applied at half of the sites. Study objectives related to this report included:

1. Quantify the effects of seeding, fertilizing, and mulching on vascular 

plant cover and bare soil cover for 2 years following wildfire.

2. Assess differences among sites in rates of native vegetation recovery and 

treatment effectiveness after wildfire.

Methods

Study Site

The Pot Peak-Sisi Ridge wildfire complex burned 47,000 acres of coniferous forest along the southwestern shore of Lake Chelan in north-central 

Washington State during summer 2004. About 45 percent of the area burned 

was classified as moderate to high severity fire with respect to soil effects, 

and erosion hazards were considered high for about 90 percent of the total 

area burned due to combined effects of fire, topography, and soils. Much of 

the area burned by the Pot Peak Fire, which was the subject of this study, 

had previously burned in a large wildfire in 1970.

Forests within the Pot Peak Fire area are dominated by ponderosa pine 

(Pinus ponderosa) and Douglas-fir (Pseudotsuga menziesii) at lower elevations 

and by lodgepole pine (Pinus contorta), Douglas-fir, and subalpine fir (Abies 

lasiocarpa) at higher elevations. Soils generally consist of volcanic ash and 

pumice deposited over colluvium or glacial till. The climate features warm, 

Assessing the Effectiveness of Seeding and Fertilization Treatments for Reducing Erosion Potential Following Severe Wildfires Peterson, Dodson, and Harrod
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 467

dry summers and cold, relatively wet winters. The majority of precipitation 

falls from October to March (much of it as snow), with occasional intense 

summer thunderstorms.

We selected eight study sites within areas of moderate and high fire severity, 

as identified by fire severity maps. The sites were well dispersed and represented a broad range of environmental settings (table 1). At each site, we 

identified a relatively uniform area of about 1 ha on which we established a 

grid of 96 study plots. Each plot was 4 m wide and 10 m long, with the long 

side oriented downslope. We left a 2-m wide untreated buffer between plots 

to reduce risks of across-plot contamination.

Treatments

We tested the effects of four seeding treatments and three levels of fertilization in factorial combination on plant cover and bare soil at each site. 

Seeding treatments included a “warm” seed mix with three grasses and one 

forb species expected to do well in warmer and drier sites; a “cool” seed 

mix with two grasses and one forb species expected to do better on cooler 

and more moist sites; a monoculture of soft white winter wheat (Eltan) that 

is often prescribed as an operational treatment in this area; and a control 

treatment with no seeding (table 2). Most of the seeded species were natives 

(table 2), but local seed sources were not required. We designed the seeding treatments to provide an average of 60 seeds/ft2 for the cool seed mix 

(20 seeds/ft2/species) and the warm seed mix (15 seeds/ft2/species), and 

an average of 15 seeds/ft2 for the winter wheat treatment. By comparison, 

the local operational seeding treatment with winter wheat called for only six 

live seeds/ft2. For the fertilization treatments, we applied an ammonium 

nitrate-ammonium sulfate (30-0-0-6) fertilizer mix at quantities calculated 

to provide 0, 50, or 100 lb of nitrogen per acre. The local operational treatment called for fertilizing at a rate of 50 lb N/ft2. We applied both seed 

and fertilizer with a hand-held Whirlybird spreader, attempting to produce 

a relatively uniform application rate. Treatments were applied shortly after 

snowmelt in the spring following the wildfire.

Assessing the Effectiveness of Seeding and Fertilization Treatments for Reducing Erosion Potential Following Severe Wildfires Peterson, Dodson, and Harrod

Table 1—Site characteristics for Pot Peak Study, including topographic setting 

(slope, aspect, and elevation), fire severity (soil effects), operational mulching 

treatment, mean total plant cover (percent) and shrub cover (percent) on 

untreated control plots.

Site

Slope

(%)

Aspect

(°)

Elevation

(m)

Fire

severity Mulch†

Plant

cover in

2005

(%)

Shrub

cover in

2005

(%)

Hug me 35 280 1196 Moderate No 40 27

Mouse 47 305 1221 High Yes 9 9

Rainbow 57 360 1297 High Yes 21 10

Stairway 45 325 1313 Moderate No 14 13

Beast 68 90 1321 High Yes 27 25

Big Tree 45 320 1380 High Yes 3 2

Nice View 12 20 1393 Moderate No 19 14

Squirrel 43 345 1507 High No 7 5

† Denotes if mulch was operationally applied to the site.
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 468

In addition to the experimental treatments, four of the sites received aerial 

application of wheat straw mulch as part of a larger BAER treatment. Contractors applied the mulch by dropping loose bales of straw from helicopters 

in the fall (shortly after the wildfire) and spring. Although mulching was not 

an experimentally applied treatment, we made use of spatial heterogeneity in 

straw cover and depth to evaluate its effectiveness for reducing bare soil cover 

and its potential effects on vegetation recovery and vegetation responses to 

seeding and fertilization treatments.

Experimental Design

We used a generalized randomized block design for the study. We randomly 

assigned treatments in factorial combination so that each possible combination of fertilization and seeding treatments would be replicated eight times 

per site. Implementation errors on three sites led to as many as 10 replicates 

or as few as seven replicates for some treatment combinations; however, we 

still replicated each fertilization treatment 32 times and each seeding treatment 24 times on each site.

Data Collection and Summary

We collected plot data during midsummer (July to August) in 2005 and 

2006, when total live plant cover was near its annual maximum. At each plot, 

we estimated relative cover for each plant species present, as well as cover of 

bare ground (soil), litter, mulch (straw), woody debris (10 hour fuels and 

larger), cryptogams, and rock for portions of the plot not covered by live 

vascular plants so that total cover summed to 100 percent. All cover estimates were based on visual assessment of what a raindrop would hit if falling 

straight down. In the case of overlapping vegetation, cover was attributed 

to the taller species. Similarly, plant cover took precedence over litter and so 

on. We estimated cover values to the nearest whole percent for values over 

0.5 percent, and recorded values less than 0.5 percent as “trace” amounts 

(using a constant value of 0.2 percent cover for subsequent analyses). We 

summed species cover values to obtain total plant cover for each plot.

Statistical Analyses

Prior to analysis, we chose a Type I error rate of 10 percent (P < 0.10) as an 

acceptable threshold for statistical significance. We accepted a higher error rate 

than is traditional because we believe that managers are primarily interested 

Table 2—Seeded species information for the Pot Peak Study.

Scientific name Seed mix Common name Lifeform Origin

Achillea millefolium Warm common yarrow Forb Native

Chamerion angustifolium Cool fireweed Forb Native

Elymus lanceolatus Cool thick-spike wheatgrass Grass Native

Elymus wawawaiensis Warm Snake River Wheatgrass Grass Native

Festuca idahoensis* Cool Idaho fescue Grass Native

Festuca ovina* Warm sheep fescue Grass Exotic

Poa secunda* Warm Sandberg bluegrass Grass Native

Triticum aestivum Wheat common wheat Grass Exotic

*Due to difficulties in identification of young plants, Festuca and Poa species were

identified to genera only

Assessing the Effectiveness of Seeding and Fertilization Treatments for Reducing Erosion Potential Following Severe Wildfires Peterson, Dodson, and Harrod
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 469

in estimated effect sizes and would likely accept a higher false-positive error 

rate if the potential treatment benefit is high.

We analyzed the effects of seeding, fertilizing, and mulching on bare soil 

and total plant cover in 2005 and 2006 using mixed statistical models (SAS 

PROC MIXED, Littell and others 1996). We included the seeding treatment, 

fertilization treatment, and their interactions as categorical fixed factors in 

the statistical model, and mulch cover (as measured in 2005) as a continuous 

covariate. Because plots were measured in two consecutive years, we included 

“year” as a binary time variable, coding 2005 observations as zero and 2006 

observations as one. We also included year-by-treatment interaction terms in 

the model to test for differences in seeding and fertilization effects between 

2005 and 2006. We included “site” and site-by-treatment interactions as random effects at the site level, where at least marginally significant (P < 0.10), 

to test for site effects on mean plant cover (or bare soil) and/or treatment 

effects. We also included the plot (within site) as a random effect to account 

for correlations among the repeated measurements. Plant cover data required 

the use of a square-root transformation to normalize model residuals.

Results

Fertilization significantly increased mean live vascular plant cover during 

the first two growing seasons following the 2004 Pot Peak wildfire. On 

untreated control plots, plant cover averaged 15 percent in the first year and 

27 percent in the second year. Fertilization alone increased mean plant cover 

in the first year from 15 percent on controls to 19 percent at 50 lb N/acre 

and to 24 percent at 100 lb N/acre (fig. 1). During the second year following 

fire, fertilization increased mean plant cover from 27 percent on untreated 

control plots to 34 percent on plots receiving 50 lb N/acre and 39 percent 

on plots receiving 100 lb N/acre (fig. 1). First year fertilization effects were 

maintained into the second year, but there was no consistent evidence for 

additional fertilization effects in the second year; there was, however, considerable variability among sites in this regard (see below).

Figure 1—Effects of seeding and fertilization, and their interaction on live plant 

cover during the first two growing seasons following wildfire (2005 and 2006). 

Error bars indicate standard errors for the estimated treatment means.

Assessing the Effectiveness of Seeding and Fertilization Treatments for Reducing Erosion Potential Following Severe Wildfires Peterson, Dodson, and Harrod
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 470

Seeding treatments also increased live vascular plant cover, but the effects 

were relatively small. Without fertilization, mean plant cover in the first year 

increased from 15 percent on unseeded controls to 16 percent on plots receiving the winter wheat or cool seed mix and to 17 percent on plots receiving the 

warm seed mix (fig. 1). In the second year, plant cover averaged 27 percent 

on untreated controls and plots receiving the cool species mix, 29 percent on 

plots receiving the wheat seed, and 30 percent on plots receiving the warm 

species mix (fig. 1).

The most effective treatment combination included seeding with the warm 

species mix and fertilizing with 100 lb N/acre; it produced a net increase 

in mean plant cover of 11 percent in 2005 and 21 percent in 2006 (fig. 1). 

Interactions between the seeding and fertilization treatments were statistically insignificant, indicating that treatment effects on live plant cover could 

be treated as being additive.

By increasing live plant cover, fertilization and seeding reduced percent 

cover of bare soil during the first 2 years following fire (2005 and 2006). For 

plots without active seeding, fertilization reduced average percent bare soil 

in the first year from 76 percent on unfertilized plots to 71 percent on plots 

receiving 50 lb N/acre and to 66 percent on plots receiving 100 lb N/acre 

(fig. 2). In the second year, fertilization reduced bare soil from 56 percent on 

unfertilized plots to 44 and 39 percent at 50 and 100 lb N/acre, respectively. 

Unlike plant cover, fertilization produced an additional effect on bare soil 

in the second year after fire, as indicated by a significant year-fertilization 

interaction (table 3). Both active fertilization treatments produced an additional net reduction of about 7 percent bare soil in the second year, relative 

to controls.

As with live plant cover, seeding treatments produced only a small reduction 

in bare soil in the first year after fire, and no significant additional effect in 

the second year after fire. Individually, seeded species varied considerably in 

their effectiveness, although most added little cover. Of all the seeded species, 

yarrow (Achillea millifolium) appeared to be the most effective for increasing 

plant cover and reducing bare soil. Statistical tests found no evidence for any 

significant interactions between seeding and fertilization treatments.

Figure 2—Effect of seeding and fertilization, and their interaction on bare 

soil cover during the first two growing seasons following wildfire. Error bars 

indicate standard error for the estimated treatment means.

Assessing the Effectiveness of Seeding and Fertilization Treatments for Reducing Erosion Potential Following Severe Wildfires Peterson, Dodson, and Harrod
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 471

Mulching reduced bare soil cover by 10 to 15 percent in the first year after 

fire on the four sites receiving the operational mulching treatment. Vegetation 

cover increased slightly with increasing mulch cover in the first year after fire 

(P = 0.083) but was not affected by mulching the following year.

Site factors significantly influenced first year live plant cover and second 

year fertilization effects. First year live plant cover varied from 4 to 37 percent 

and mean bare soil cover varied from 57 to 91 percent (table 1). Fertilization treatment effects on bare soil did not vary significantly among sites in 

the first year (P > 0.10) but did vary significantly in the second year (P = 

0.02). From 2005 to 2006, reductions in bare soil on unseeded plots ranged 

12 to 26 percent at 0 lb N/acre, 21 to 33 percent at 50 lb N/acre and 16 to 

31 percent at 100 lb N/acre. Seeding treatment effectiveness did not vary 

significantly among sites.

Discussion

Fertilization Effects

We found that fertilization accelerated the development of live plant cover 

following severe wildfire, thereby reducing bare soil cover and, presumably, 

water runoff and soil erosion. Fertilization effects varied with treatment 

intensity (0 to 100 lb N/acre), with the 100 lb N/acre treatment increasing 

plant cover more than the 50 lb N/acre treatment. This indicates that soil N 

availability was at least partially limiting native vegetation recovery after the 

Pot Peak wildfire and suggests that higher levels of fertilization may have 

produced even larger increases in plant cover.

Table 3—Type 3 tests of significance tests for fixed effects in mixed model 

analysis of treatment effects on live vascular plant cover.

Fixed effects

Num

DF

Den.

DF F-value Pr. > F

Live vascular plant cover:

Seeding 3 1168 6.57 0.000

Fertilizing 2 15 29.98 0.000

Seeding x Fertilizing 6 1168 1.59 0.147

Year 1 7 99.09 0.000

Year x Seeding 3 736 1.37 0.251

Year x Fertilizing 2 15 0.67 0.526

Year x Seeding x Fertilizing 6 736 1.62 0.140

Mulching 1 1177 3.01 0.083

Year x Mulching 1 743 2.22 0.137

Bare soil cover:

Seeding 3 1257 4.28 0.005

Fertilizing 2 1257 30.42 0.000

Seeding x Fertilizing 6 1257 0.75 0.609

Year 1 7 192.81 0.000

Year x Seeding 3 33 1.16 0.339

Year x Fertilizing 2 17 5.80 0.012

Year x Seeding x Fertilizing 6 717 1.03 0.403

Mulching 1 1246 616.10 0.000

Year x Mulching 1 703 117.32 0.000

Assessing the Effectiveness of Seeding and Fertilization Treatments for Reducing Erosion Potential Following Severe Wildfires Peterson, Dodson, and Harrod
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 472

We expected fertilization effects to be greatest on sites with high densities 

(or cover) of sprouting shrubs and other fire-adapted species that could rapidly take up and use the additional nutrients. However, we found no evidence 

to support this notion. Fertilization effects were not significantly correlated 

with first-year plant cover or bare soil, suggesting that plant uptake capacity 

did not limit the effectiveness of the fertilization treatments, at least on these 

sites and at these fertilization levels. With only eight sites, however, our power 

to detect site differences in treatment effects was limited, and there was some 

evidence to suggest that fertilization effects declined somewhat at higher levels 

of plant cover.

Fertilization as an erosion control treatment has received little study, and, 

where it has been studied, results have been mixed (Robichaud and others 

2000). In the Interior Pacific Northwest, Klock and others (1975) reported 

that fertilization increased establishment and cover of species seeded for 

erosion control. More recently, Robichaud and others (2006) found that 

fertilization increased total plant cover after the North 25 Fire (close to our 

study site), with plant cover on fertilized plots being 8 percent higher during 

the first year after fire and 13 percent higher during the second year, though 

these effects were not judged to be statistically significant. In Colorado, 

nutrients applied after wildfire in the form of biosolids also increased plant 

cover and biomass (Meyer and others 2004).

Results from this study suggest that fertilization could be an effective method 

for accelerating development of live plant and litter cover and reducing erosion hazards following wildfires. Further study is needed to test the possible 

additional benefits of higher levels of nitrogen addition, and to test the potential benefits of adding other nutrients, such as phosphorous and potassium. 

Implementation on sites with diverse soil, climate, and vegetation characteristics 

would also be helpful to assess the consistency of fertilization effects.

Seeding Effects

Compared to natural vegetation recovery and fertilizer effects, seeding 

treatment effects on live plant cover and bare soil were small. Some other studies have found seeding to be ineffective at increasing ground cover or reducing 

erosion (Wagenbrenner and others 2006; Robichaud and others 2006). Yet, 

there are also examples where seeding has produced larger amounts of cover 

after wildfire, even to the point of negatively affecting recovery of native 

vegetation (Beyers 2004; Keeley 2004; Schoennagel and Waller 1999).

Developing a better understanding of the causes of this variability in seeding success is important if seeding is to continue as a land surface treatment. 

Some variability may be due to uncontrollable variables such as year-to-year 

variability in precipitation and soil moisture. However, more consistent results 

may also be attained if seeded species were better matched with biophysical 

environments or, perhaps, if seeding were restricted to environments where 

it is generally successful. In this study, yarrow provided greater benefits than 

winter wheat, the normally prescribed seeding treatment. Is this because yarrow is better adapted to the biophysical settings, established better on burned 

soils, produced faster initial growth, was less susceptible to seed predation, or 

all of the above? The answer is unclear. However, further study appears warranted to identify species for seeding that perform consistently well, or to better 

match seeded species to the environments in which they can be expected to 

perform well. It will also be important to address questions about tradeoffs 

between the practical advantages of seeding highly available nonnative species 

and possible biodiversity benefits of seeding native species (from either local 

or distant seed sources).

Assessing the Effectiveness of Seeding and Fertilization Treatments for Reducing Erosion Potential Following Severe Wildfires Peterson, Dodson, and Harrod
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 473

Mulching Effects

Mulching was effective at reducing bare soil cover during the first year 

after fire, as has been documented previously (Robichaud and others 2000). 

Our analysis indicated that mulching reduced bare soil cover at a rate that 

slightly exceeded the rate of mulch application, perhaps due to germination 

and growth of residual wheat seed in the straw. A potential problem with 

mulching is that it may affect long-term vegetation recovery by introducing 

exotic species (Kruse and others 2004) or interfering with plant establishment (Robichaud and others 2000). We found no evidence that mulching 

reduced live plant cover in either year. However, mulch cover was low overall 

in this study (less than 15 percent average cover on mulched sites) and may 

have been below the threshold needed to influence plant cover. We have not 

yet examined possible mulching effects on plant community composition or 

exotic species.

Site Variability

In the absence of treatments, first year plant cover is determined by prefire plant cover and fire severity (in terms of mortality). In this study, first 

year plant cover was highly dependent on sprouting shrubs and, on one site, 

grasses. Understory plant cover generally declined with increasing elevation, 

probably because lower elevation sites had lower overstory canopy densities 

that supported higher understory plant cover and biomass before the fire. 

Low elevation plant communities may also be better able to survive wildfires. 

Causes for variability in site responses to fertilization are not yet clear but 

are certainly of interest. We are hopeful that clearer patterns of variation in 

treatment effectiveness will emerge in the future as results from similar studies on other wildfire areas become available.

Conclusions

Fertilization is a potentially effective treatment for increasing plant cover 

and reducing bare soil during the first 2 years following wildfire, but more 

work is needed to determine optimal application rates, formulations, and 

variability in effectiveness across a range of climates, fire severities, and soil 

and vegetation types. Seeding was not very effective in this study, suggesting that it may not be the best choice for erosion control in this area. The 

performance of yarrow, however, suggests that seeding effectiveness may 

be improved by choosing different species or species mixtures for seeding. 

Mulching significantly reduced bare soil cover, but it is not clear whether the 

reductions in erosion risk were large enough to justify the high application 

costs and elevated risks of exotic species introduction.

References

Benavides-Solorio, Juan; MacDonald, Lee H. 2001. Post-fire runoff and erosion 

from simulated rainfall on small plots, Colorado Front Range. Hydrological 

Processes 15:2931-2952.

Beyers, Jan L. 2004. Postfire seeding for erosion control: effectiveness and impacts 

on native plant communities. Conservation Biology 18(4):947-956.

Assessing the Effectiveness of Seeding and Fertilization Treatments for Reducing Erosion Potential Following Severe Wildfires Peterson, Dodson, and Harrod
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 474

DeBano, Leonard F. 2000. The role of fire and soil heating on water repellency in 

wildland environments: a review. Journal of Hydrology 231-232:195-206.

DeBano, Leonard F.; Neary, Daniel G.; Ffolliott, Peter F. 1998. Fire’s effects on 

ecosystems. New York: John Wiley and Sons.

Government Accountability Office. 2003. Wildland fires: better information needed 

on effectiveness of emergency stabilization and rehabilitation treatments. Report 

GAO-03-430.

Keeley, Jon E. 2004. Ecological impacts of wheat seeding after a Sierra Nevada 

wildfire. International Journal of Wildland Fire 13:73-78.

Klock, Glenn O.; Tiedemann, Arthur R.; Lopushinsky, William. 1975. Seeding 

recommendations for disturbed mountain slopes in north central Washington. 

Res. Note PNW-RN-244. Portland, OR: U.S. Department of Agriculture, Forest 

Service, Pacific Northwest Research Station. 8 p.

Kruse, Renee; Bend, Eric; Bierzychudek, Paulette. 2004. Native plant regeneration 

and introduction of non-natives following post-fire rehabilitation with straw mulch 

and barley seeding. Forest Ecology and Management 196:299-310.

Littell, Ramon C.; Milliken, George A.; Stroup, Walter W.; Wolfinger, Russell D. 

1996. SAS® system for mixed models. Cary, NC: SAS Institute, Inc.

Neary, Daniel G.; Ryan, Kevin C.; DeBano, Leonard F., eds. 2005. Wildland fire 

in ecosystems: effects of fire on soil and water. Gen. Tech. Rep. RMRS-GTR42-vol. 4. Ogden, UT: U.S. Department of Agriculture, Forest Service, Rocky 

Mountain Research Station. 250 p.

Robichaud, Peter R.; Beyers, Jan L.; Neary, Daniel G. 2000. Evaluating the 

effectiveness of post-fire rehabilitation treatments. Gen. Tech. Rep. RMRS-GTR63. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky 

Mountain Research Station. 85 p.

Robichaud, Peter R.; Lillybridge, Terry R.; Wagenbrenner, Joseph W. 2006. Effects of 

postfire seeding and fertilizing on hillslope erosion in north-central Washington, 

USA. Catena 67:56-67.

Schoennagel, Tania L.; Waller, Donald M. 1999. Understory responses to fire and 

artificial seeding in an eastern Cascades Abies grandis forest, U.S.A. Canadian 

Journal of Forest Research 29:1393-1401.

Wagenbrenner, J.W.; MacDonald, L.H.; Rough, D. 2006. Effectiveness of three 

post-fire rehabilitation treatments in the Colorado Front Range. Hydrological 

Processes 20:2989-3006.

Wondzell, Steven M.; King, John G. 2003. Postfire erosional processes in the Pacific 

Northwest and Rocky Mountain regions. Forest Ecology and Management 

178:75-87.

Assessing the Effectiveness of Seeding and Fertilization Treatments for Reducing Erosion Potential Following Severe Wildfires Peterson, Dodson, and Harrod
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 475

Abstract—Herbaceous layer vegetation responses to prescribed fire and fire surrogate 

treatments (thinning and understory removal) were examined. Results from 3 to 4 

years following treatment are presented for the Ohio Hills Country and the Southern 

Appalachian Mountain sites of the National Fire and Fire Surrogate Study. At the Ohio 

Hills site, changes in forest structure were observed for all treatments, but areas treated 

with fire showed the greatest increase in herbaceous cover and species richness. These 

results indicate that fire effects are unique disturbances that are not mimicked by alterations of the forest structure alone. However, at the Southern Appalachian site, fire 

alone did not produce a response in the herbaceous layer. The combination of fire plus 

mechanical treatment was necessary to increase cover and species richness.

Introduction

The herbaceous layer in oak forests typically contains the greatest number 

of species with perennial forbs and grasses composing the majority of diversity. 

Many of these species respond positively to disturbance through increased 

growth, sexual reproduction, and asexual propagation (Whigham 2004). 

With the exclusion of fire and consequently increased stand density, suitable 

seedbed habitat and available light become limiting factors for herbaceous 

species propagation and survival. Eliminating fire disturbance has allowed 

fire-sensitive, shade-tolerant species to become established leading to changes 

in forest composition and structure (Abrams 1992; Abrams and Nowacki 

1992; Crow 1988; Lorimer 1984; Nowacki and Abrams 1991; Schuler and 

Gillespie 2000).

The use of prescribed fire in these forests has increased in recent years 

in efforts to reduce fuel loadings, encourage oak regeneration, and halt the 

conversion of these stands to forests dominated by mesophytic species. In 

Ohio, acreage treated with prescribed fire grew from less than 2,000 to almost 

20,000 acres from 2001 to 2004 (Mike Bowden, personal communication). 

Thinning has also been suggested as an alternative method for creating stand 

structure to promote and sustain oak dominance. While oak regeneration and 

woody species composition have received considerable attention (for example, 

Abrams 1992; Albrecht and McCarthy 2006; Barnes and Van Lear 1998; 

Brose and others 1999, 2006; Elliott and others 2004; Lorimer 1985; Reich 

and others 1990), only recently has research focused on the herbaceous layer 

in mixed-oak forest types.

Fire and Fire Surrogate Treatments in 

Mixed-Oak Forests: Effects on Herbaceous 

Layer Vegetation

Ross Phillips1, Todd Hutchinson2, Lucy Brudnak1, and Thomas Waldrop1

In: Butler, Bret W.; Cook, Wayne, 

comps. 2007. The fire environment—

innovations, management, and policy; 

conference proceedings. 26-30 March 

2007; Destin, FL. Proceedings 

RMRS-P-46CD. Fort Collins, CO: 

U.S. Department of Agriculture, 

Forest Service, Rocky Mountain 

Research Station. 662 p. CD-ROM.

1 Ecologist, Biological Sciences 

Technician, and Research Forester, 

respectively, U.S. Department of 

Agriculture, Forest Service, Southern 

Research Station, Clemson, SC. Lead 

author at rjphillips@fs.fed.us

2 Research Ecologist, U.S. Department 

of Agriculture, Forest Ser vice, 

Northeastern Research Station, 

Delaware, OH.
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 476

Previous studies in oak dominated forests indicate herbaceous cover 

and abundance typically increase following fire (Elliott and others 1999; 

Hutchinson 2006; Hutchinson and others 2005), whereas fire effects 

on diversity vary. Burning may have little effect on diversity (Dolan and 

Parker 2004; Franklin and others 2003; Hutchinson and others 2005; 

Kuddes-Fischer and Arthur 2002), or it can significantly increase herbaceous 

layer diversity (Ducey and others 1996; Elliott and others 1999; Taft 2003). 

It should be noted the latter studies were conducted in stands that had lower 

basal area and/or experienced higher intensity fires, suggesting that more 

intense management than low-intensity burning is required (Abrams 2005; 

Franklin and others 2003; Ruffner and Groninger 2004).

Many herbaceous species are light limited. Therefore, changes in light 

availability can cause large responses in plant growth and/or reproduction 

(Whigham 2004). Altering the structure of oak stands through mechanical thinning increases resource availability and typically results in greater 

abundance and diversity of herbaceous plants (Bormann and Likens 1979; 

Elliott and others 1997; Gilliam and others 1995). It has also been shown 

that different levels of thinning affect the herbaceous layer in significantly 

different ways (Elliott and Knoepp 2005; Zenner and others 2006). Thus, 

reducing overstory and midstory stem density may provide suitable conditions 

for developing and maintaining diversity within mixed-oak forests.

The objective in this paper is to discuss trends resulting from fire and fire 

surrogate treatments in mixed-oak stands located within the Central and 

Southern Appalachian regions. Similarities and differences between these 

sites will be discussed as they relate to land management objectives.

Materials and Methods

Study Sites

Study locations are within the Central Appalachian Plateau and Southern 

Appalachian Mountains in Ohio and North Carolina, respectively (fig. 1). 

Both sites are part of the National Fire and Fire Surrogate (FFS) Study and 

were selected to represent Eastern forest communities that historically sustained frequent, low-intensity fire disturbances (Weatherspoon 2000).

Figure 1—Site locations for the Ohio 

Hills Country and Southern Appalachian 

Mountain study sites.

Fire and Fire Surrogate Treatments in Mixed-Oak Forests: Effects on Herbaceous Layer Vegetation Phillips, Hutchinson, Brudnak, and Waldrop
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 477

The Ohio Hills Country site is on the Vinton Furnace Experimental 

Forest, Tar Hallow State Forest, and Zaleski State Forest in the Allegheny 

Plateau Province of southeastern Ohio. The overstory is dominated by oak: 

chestnut oak (Quercus prinus L.), white oak (Q. alba L.), northern red oak 

(Q. rubra L.), scarlet oak (Q. coccinea Muenchh.), and black oak (Q. velutina

Lam.). Other common overstory species include pignut hickory (Carya glabra (Miller) Sweet), mockernut hickory (C. alba (L.) Nutt ex Ell.), bitternut 

hickory (C. cordiformis (Wangenh.) K. Koch), red maple (Acer rubrum L.), 

tulip poplar (Liriodendron tulipifera L.), and American beech (Fagus grandifolia Ehrh.). Soils are primarily of the Gilpin series (fine-loamy, mixed, 

mesic Typic Hapludults) with mixtures of Steinsburg (coarse-loamy, mixed, 

mesic Typic Dystrudepts) and Germano (coarse-loamy, mixed, mesic Typic 

Hapludults) soils at two replications and Latham (fine, mixed, mesic Aquic 

Hapludults) and Wellston (fine-silty, mixed, mesic Ultic Hapludalfs) soils at 

the third replication.

The Southern Appalachian site is in the Blue Ridge Province of southwestern North Carolina on the Green River Game Lands. Forest composition is 

mixed-oak with yellow pine on xeric ridges and white pine (Pinus strobus L.) 

in moist coves. Oaks (chestnut oak, scarlet oak, white oak, northern red 

oak, and black oak) dominated all sites, with other common species including: sourwood (Oxydendrum arboreum (L) DC.), red maple, tulip poplar, 

mockernut hickory, blackgum (Nyssa sylvatica Marsh.), and pitch pine (Pinus 

rigida P. Mill.). A dense layer of ericaceous shrubs—mountain laurel (Kalmia 

latifolia L.), rhododendron (Rhododendron maximum L. and R. minus

Michx.), flame azalea (R. calendulaceum (Michx) Torr.), and blueberry 

(Vaccinium species L.)—is found throughout. Soils are primarily Evard series 

(file-loamy, oxidic, mesic Typic Hapludults) with portions of two replications (blocks 1 and 2) of the Cliffield series (loamy-skeletal, mixed, mesic 

Typic Hapludults). These soils are moderately deep, well drained, mountain 

uplands (USDA Natural Resources Conservation Service 1998). Elevations 

range from 366 to 793 m.

Experimental Design and Treatments

Treatments at each study site followed national protocols (Weatherspoon 

2000) with accommodations for regional differences. The four treatments 

included: untreated control (C); a mechanical treatment (M); prescribed 

burning (B); and a combination of the mechanical treatment plus burning 

(MB). Treatments were randomly assigned and replicated three times. Herbaceous layer vegetation (herbaceous and woody vegetation less than 1.4 m 

in height) was measured on 1 m2 quadrats within larger 0.1-ha rectangular 

plots randomly placed throughout the treatment areas (and stratified by 

moisture class at the Ohio site). Within quadrats species composition and 

abundance were recorded using cover classes: <1, 1-10, 11-25, 26-50, 51-75, 

>75 percent. Plots were originally sampled in 2000 (Ohio Hills) and 2001 

(Southern Appalachian). The first posttreatment sampling occurred immediately following treatment. The second posttreatment occurred 3 (Southern 

Appalachian) or 4 years after treatment (Ohio Hills).

The mechanical treatment at the Ohio Hills site was a commercial thinning from below whereas the Southern Appalachian site used chainsaw 

felling of small, suppressed trees (dbh < 10 cm) and all shrubs. Slash created 

from these treatments was left onsite. Mechanical treatment was completed 

in the winter of 2000 to 2001 in Ohio and the winter of 2001 to 2002 in 

North Carolina.

Fire and Fire Surrogate Treatments in Mixed-Oak Forests: Effects on Herbaceous Layer Vegetation Phillips, Hutchinson, Brudnak, and Waldrop
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 478

Prescribed burning immediately followed the mechanical treatment at the 

Ohio Hills site in spring 2001. Temperature and humidity (RH) during the 

burns ranged from 11 to 23 °C and 23 to 44 percent, respectively. Winds 

were generally light at 5 to 8 km/hr. Fires were patchy, with the burn-only 

treatment having greater coverage (acreage burned) and greater intensity than 

those in mechanical plus burn. Mean maximum temperatures measured by 

thermocouples were 152 °C in B and 133 °C in MB with flame lengths =1 

m. At the Southern Appalachian site, burning was conducted in March 2003, 

1 year after mechanical treatment, allowing slash to cure. Ambient temperatures during burns were 17 to 27 °C, the minimum RH was 30 percent, 

and southwest winds were light (3 to 5 km/hr). Fire intensity in MB was 

consistently greater than B, as mean maximum temperatures exceeded 370 °C 

as opposed to 180 °C for B.

Results

Fuel reduction treatments changed species abundance and richness within 

the herbaceous layer of the mixed-oak forests at both sites. Woody cover 

increased across all treated areas at the Ohio Hills site but changed only in 

MB areas at the Southern Appalachian site (fig. 2). Treatments caused a reduction in overstory basal areas for all treated areas at Ohio Hills: M decreased 

from 29 to 20 m2 ha–1; B declined from 27 to 24 m2 ha–1; and MB dropped 

from 28 to 20 m2 ha–1. Pretreatment woody cover in the herbaceous layer 

increased from 17 to 20 percent to greater than 53 percent by the second 

posttreatment measurement for M, B, and MB in Ohio. Woody cover relative to total herbaceous layer cover increased slightly for C (67 to 70 percent) 

and M (82 to 84 percent), whereas B and MB showed slight decreases, 77 to 

71 percent and 78 to 74 percent, respectively.

Figure 2—Woody abundance in the herbaceous layer at the Ohio Hills (a) and Southern Appalachian (b) 

study sites. Vegetation sampling occurred prior to treatment (“Pre”), immediately following treatment 

(“1st Post”), and 3 (Southern Appalachian) or 4 years (Ohio Hills) following treatment (“2nd Post”).

Fire and Fire Surrogate Treatments in Mixed-Oak Forests: Effects on Herbaceous Layer Vegetation Phillips, Hutchinson, Brudnak, and Waldrop
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 479

At the Southern Appalachian site, basal area was reduced from 23 to 18 m2

ha–1 in MB, whereas little effects were observed on overstory basal area for the 

other treatments. Pretreatment herbaceous layer woody abundance averaged 

26 percent for MB, but increased to 40 percent after 3 years. Mechanicalonly and burn-only were relatively unchanged from pretreatment levels after 

3 years, 22 and 20 percent, respectively, while C gradually declined. Relative to total herbaceous layer cover, woody abundance decreased for all areas 

(C: 83 to 77 percent; M: 92 to 91 percent; B: 82 to 80 percent; and MB: 

88 to 76 percent).

Forb abundance showed a positive response to burning at the Ohio Hills site 

with increases in B and MB from 5 percent to more than 22 and 18 percent, 

respectively; M differed little from the control after 4 years (fig. 3a). Graminoids followed similar trends with large increases in B and MB and twice as 

much graminoid cover after 4 years as compared to C and M (fig. 3c). Forbs 

and graminoids composed almost 30 percent of total herbaceous layer cover 

in C prior to treatment but cover had dropped to 24 percent after 4 years. The 

mechanical treatment showed little change in relative cover during the same 

period. The burn-only treatment increased from 20 to 25 percent in relative 

cover whereas herbaceous species had a relative cover of 21 percent in MB.

Figure 3—Cover of forbs (a and b) and graminoids (c and d) at Ohio Hills (a and c) and Southern Appalachian 

(b and d) sites.

Fire and Fire Surrogate Treatments in Mixed-Oak Forests: Effects on Herbaceous Layer Vegetation Phillips, Hutchinson, Brudnak, and Waldrop
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 480

At the Southern Appalachian site, forb and graminoid cover were higher 

in MB as compared to other treatments after 3 years (fig. 3b,d). Forbs recovered to almost twice the pretreatment levels in MB after declining briefly 

immediately after burns. Graminoids in MB increased immediately, a trend 

that continued into the third year. In C, M, and B, no differences from 

pretreatment values were observed after 3 years. Relative cover of forbs and 

graminoids, combined, increased from 19 to 25 percent in C, in contrast 

to expected results. The mechanical-only and burn-only treatments had no 

effect on relative cover for forbs and graminoids after 3 years (9 percent M 

and 18 percent B). However, relative cover of forbs and graminoids in MB 

increased from 13 to 20 percent.

Trends for total species richness were similar for both sites as large increases 

were observed in burned areas (B and MB) immediately after treatment, followed by gradual increases from the first to the second posttreatment sampling 

(fig. 4a,b). The mechanical-only treatment also increased total species richness at both sites, although these areas contained considerably fewer species 

than B and MB areas. At the Ohio Hills site, M resulted in more species m–2

than the controls, but at the Southern Appalachian site, M still had lower 

species richness m–2 than C after 3 years. However, the differences between 

C and M lessened over time.

Figure 4—Total richness (a and b) and herbaceous species richness (c and d) for the Ohio Hills (a and c) 

and Southern Appalachian (b and d) sites.

Fire and Fire Surrogate Treatments in Mixed-Oak Forests: Effects on Herbaceous Layer Vegetation Phillips, Hutchinson, Brudnak, and Waldrop
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 481

Burning increased herbaceous species richness at Ohio Hills with almost 

two more species m–2 for burned versus unburned areas (fig. 4c). The 

mechanical-only treatment also resulted in greater species richness, with little 

differences between C and M by the second posttreatment measurement. 

At the Southern Appalachian site, burned areas (B and MB) showed similar 

trends as immediate increases were observed following burning, whereas 

unburned areas (C and M) gradually increased over time (fig. 4d). After 

3 years MB contained the greatest number of herbaceous species m–2; B and 

C were similar; and M had the least.

Discussion

Mechanical treatment alone had little effect on herbaceous species at 

the Ohio Hills site, but it increased woody cover and total richness. At the 

Southern Appalachian site, there were no significant effects for woody or 

herbaceous species as a result of mechanical treatment. Structural differences between the mechanical treatments for each location were distinct as 

basal area and density were significantly reduced at Ohio Hills (Albrecht and 

McCarthy 2006), whereas few changes were observed for those attributes 

at the Southern Appalachian site (Phillips and others, in press). Dolan and 

Parker (2004) reported no changes in herbaceous vegetation in an Indiana 

oak forest after a mechanical understory reduction treatment similar to that 

used in the Southern Appalachians, although they observed slight increases 

in shade-intolerant tree species. Other studies using more intense harvesting 

practices have shown increasing herbaceous layer abundance and diversity 

associated with greater intensity harvests (Elliott and Knoepp 2005; Zenner 

and others 2006). Zenner and others (2006) suggest that until certain harvest intensity thresholds are reached, few effects will be observed. The lack 

of response for both sites may be partially attributed to the manner in which 

these treatments were conducted. As specified by treatment prescriptions, 

all slash was scattered and left onsite. This material may have served as a 

physical barrier to seed germination and prevented plant growth by limiting 

light at the forest floor. The changes in forest structure from the mechanical 

treatments used at the Ohio Hills and Southern Appalachian sites did not 

promote herbaceous layer richness.

Prescribed fire had positive effects on herbaceous layer abundance and richness at the Ohio Hills site. Abundance and species richness were considerably 

higher in burned areas than in unburned areas. The prescribed fires in B at 

Ohio Hills were of sufficient intensity to create canopy gaps, allowing enough 

light to elicit a response in the herbaceous layer. Changes in the understory 

environment (for example, consumption of the litter layer, increased available 

light) provided suitable conditions for seed germination and plant growth. 

Initial changes in composition richness are typically dominated by establishment of seed-banking, shade-intolerant species; while long-term changes 

result as perennial forbs and graminoids become established (Hutchinson 

2005). Long-term application of fire is necessary to maintain this increased 

diversity (Hartman and Heumann 2003).

At the Southern Appalachian site, prescribed fire alone had little effect 

on herbaceous layer vegetation. No differences were observed between B 

and C after 3 years for herbaceous species abundance or richness, although 

B had higher total species richness. These results are similar to those of 

Kuddes-Fischer and Arthur (2002) where a single low-intensity fire produced 

Fire and Fire Surrogate Treatments in Mixed-Oak Forests: Effects on Herbaceous Layer Vegetation Phillips, Hutchinson, Brudnak, and Waldrop
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 482

little differences in herbaceous and shrub cover and slight, nonsignificant 

increases in richness after 4 years. While mean maximum fire temperatures 

were greater in the Southern Appalachians than in the Ohio Hills, small 

declines in overstory basal area and density at the Southern Appalachian 

site may have been offset by sprouting from ericaceous shrubs. Only minor 

reductions in the shrub component resulted from the burn-only treatment 

(Phillips and others, in press); therefore, shading may have limited the light 

needed for seed germination. Ducey and others (1996) and Elliott and others (1999) indicate that more intense fires are required to reduce mountain 

laurel abundance (K. latifolia) and thus increase herbaceous layer richness 

and diversity in the Southern Appalachians.

The combination of mechanical treatment plus burning showed similar effects as burning alone at the Ohio Hills site. Greater herbaceous layer cover in 

addition to increased species richness was observed with lower-­intensity fires. 

Decreased basal area was achieved by mechanical felling of the overstory, while 

midstory cover and leaf litter were reduced by burning. The combination of 

mechanical treatment and burning may be less cost-effective than burning 

alone, but it provides greater control of shade-tolerant species by reducing 

sprouting vigor through application of multiple disturbances (Albrecht and 

McCarthy 2006).

In the Southern Appalachians, only a combination of mechanical treatment 

and burning resulted in increased herbaceous layer cover and richness. Fires 

within these treatment areas were considerably more intense than the burnonly treatment, resulting in greater mortality of large diameter trees (Brudnak 

and others, in press) increasing insolation. By using fire after mechanical 

treatment, sprouts from shade-tolerant species and ericaceous shrubs were 

further reduced (Phillips and others, in press), allowing herbaceous vegetation to become established.

The lack of response of herbaceous species to mechanical treatment compared to burning indicates that fire may promote processes (that is, nutrient 

cycling, seed germination, and so forth) that cannot be mimicked by structural 

alteration alone. Changes to forest structure in some stands, as a result of 

fire exclusion, may be too extreme to overcome by fire alone (Franklin and 

others 2003). Reducing stem densities and allowing more light to reach the 

forest floor, in conjunction with fire, may be necessary to restore diversity to 

mixed-oak forests, as evidenced at the Southern Appalachian site.

Conclusions

As oak forests decline across the Eastern landscape, prescribed fire and 

other treatments are being applied to encourage oak recruitment and sustain 

this forest type. But the intensities of fire and thinning required to restore 

these communities are not well understood.

These results indicate responses of herbaceous layer vegetation to fire and 

fire surrogate treatments vary by region and are dependant on pretreatment 

species composition and structure. Prescribed fire resulted in greater cover 

and species richness for the herbaceous layer in the Central Appalachian 

region in Ohio. However, in the Southern Appalachian Mountains in North 

Carolina, ericaceous shrubs and lack of sufficient light reaching the forest 

floor required both mechanical treatment and prescribed fire to increase 

abundance and richness in the herbaceous layer.

Fire and Fire Surrogate Treatments in Mixed-Oak Forests: Effects on Herbaceous Layer Vegetation Phillips, Hutchinson, Brudnak, and Waldrop
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 483

References

Abrams, M.D. 1992. Fire and the development of oak forests. BioScience 42(5): 

346-353.

Abrams, M.D. 2005. Prescribed fire in eastern oak forests: Is time running out? 

Northern Journal of Applied Forestry 22(3): 190-196.

Abrams, M.D.; Nowacki, G.J. 1992. Historical variation in fire, oak recruitment, 

and post-logging accelerated succession in central Pennsylvania. Bulletin of the 

Torrey Botanical Club 119(1): 19-28.

Albrecht, M.A.; McCarthy, B.C. 2006. Effects of prescribed fire and thinning 

on tree recruitment patterns in central hardwood forests. Forest Ecology and 

Management 226: 88-103.

Barnes, T.A; Van Lear, D.H. 1998. Prescribed fire effects on advanced regeneration in 

mixed hardwood stands. Southern Journal of Applied Forestry 22(3): 138-142.

Bormann, F.H.; Likens, G.E. 1979. Pattern and Process in a Forested Ecosystem. 

Springer-Verlag, New York, NY.

Brose, P.; Van Lear, D.; Cooper, R. 1999. Using shelterwood harvests and prescribed 

fire to regenerate oak stands on productive upland sites. Forest Ecology and 

Management 113(2): 125-141.

Brose, P.H.; Schuler, T.M.; Ward, J.S. 2006. Responses of oak and other hardwood 

regeneration to prescribed fire: what we know as of 2005. In: Dickinson, M.B., 

ed. Fire in eastern oak forests: delivering science to land managers, proceedings of 

a conference. Gen. Tech. Rep. NRS-P-1. Newton Square, PA: U.S. Department 

of Agriculture, Forest Service, Northern Research Station: 123-135.

Brudnak, L.A.; Waldrop, T.A.; Phillips, R.J. [In press]. HOBO thermocouple 

dataloggers: useful applications for prescribed fire research. In: Stanturf, J., 

ed. Proceedings of the 14th biennial southern silviculture research conference. 

Gen. Tech. Rep. Asheville, NC: U.S. Department of Agriculture, Forest Service, 

Southern Research Station.

Crow, T.R. 1988. Reproductive mode and mechanisms for self-replacement of north 

red oak (Quercus rubra) – a review. Forest Science 34(1): 19-40.

Dolan, B.J.; Parker, G.R. 2004. Understory response to disturbance: an investigation 

of prescribed burning and understory removal treatments. In: Spetich, Martin A., 

ed. Upland oak ecology symposium: history, current conditions, and sustainability. 

Gen. Tech. Rep. SRS–73. Asheville, NC: U.S. Department of Agriculture, Forest 

Service, Southern Research Station: 285-291.

Ducey, M.J.; Moser, W.K.; Ashton, P.M.S. 1996. Effect of fire intensity on understory 

composition and diversity in a Kalmia-dominated oak forest, New England, USA. 

Vegetatio 123: 81-90.

Elliott, K.J.; Boring, L.R.; Swank, W.T.; Haines, B.R. 1997. Successional changes 

in plant species diversity and composition following clearcutting a southern 

Appalachian watershed. Forest Ecology and Management 92: 67-85.

Elliott, K.J.; Hendrick, R.L.; Major, A.E. [and others]. 1999. Vegetation dynamics 

after a prescribed fire in the southern Appalachians. Forest Ecology and 

Management 114(2): 199-213.

Elliott, K.J.; Knoepp, J.D. 2005. The effects of three regeneration harvest methods 

on plant diversity and soil characteristics in the southern Appalachians. Forest 

Ecology and Management 211(3): 296-317.

Elliott, K.J.; Vose, J.M; Clinton, B.D; Knoepp, J.D. 2004 Effects of understory 

burning in a mesic mixed-oak forest of the southern Appalachians. In: Engstrom, 

R.T; Galley, K.E.M; de Groots, W.J., eds. Proceedings of the 22nd Tall Timber 

Fire Ecology Conference: Fire in Temperate, Boreal, and Montane Ecosystems. 

Tallahassee, FL: Tall Timber Research Station: 272-283.

Fire and Fire Surrogate Treatments in Mixed-Oak Forests: Effects on Herbaceous Layer Vegetation Phillips, Hutchinson, Brudnak, and Waldrop
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 484

Franklin, S.B.; Robertson, P.A.; Fralish, J.S. 2003. Prescribed burning effects on 

upland Quercus forest structure and function. Forest Ecology and Management 

184: 315-335.

Gilliam, F.S.; Turrill, N.L.; Adams, M.B. 1995. Herbaceous layer and overstay 

species in clearcut and mature central Appalachian hardwood forest. Ecological 

Applications 5(4): 947-955.

Hartman, G.W.; Heumann, B. 2003. Prescribed fire effects in the Ozarks of Missouri: 

the Chilton Creek Project 1996-2001. [Online]. In: Proceedings of the 2nd

International Wildland Fire Ecology and Fire Management Congress, Orlando, 

FL. Available from: http://ams.confex.com/ams/FIRE2003/techprogram/

paper_65977.htm [Data accessed: March 15, 2007].

Hutchinson, T. 2006. Fire and the herbaceous layer of eastern oak forests. In: 

Dickinson, M.B., ed. Fire in eastern oak forests: delivering science to land 

managers, proceedings of a conference. Gen. Tech. Rep. NRS-P-1. Newton 

Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research 

Station: 136-149.

Hutchinson, T.F.; Boerner, R.E.J.; Sutherland, S. [and others]. 2005. Prescribed 

fire effects on the herbaceous layer of mixed-oak forests. Canadian Journal of 

Forest Research 35: 877-890.

Kuddes-Fischer, L.M.; Arthur, M.A. 2002. Response of understory vegetation and 

tree regeneration to a single prescribed fire in oak-pine forests. Natural Areas 

Journal 22(1): 43-52.

Lorimer, C.G. 1984. Development of the red maple understory in northeastern oak 

forests. Forest Science 30(1): 3-22.

Lorimer, C.G. 1985. The role of fire in the perpetuation of oak forests. In: Johnson, 

J.E., ed. Proceedings Challenges in Oak Management and Utilization. Madison: 

University of Wisconsin, Cooperative Extension Service: 8-25.

Nowacki, G.J.; Abrams, M.D. 1991. Community, edaphic, and historical analysis 

of mixed oak forests of the Ridge and Valley Province in central Pennsylvania. 

Canadian Journal of Forest Research 22: 790-800.

Phillips, R.J.; Waldrop, T.A.; Simon, D.M. [In press]. Third-year responses of 

understory woody species regeneration to fuel reduction treatments in the southern 

Appalachian Mountains. In: Stanturf, J., ed. Proceedings of the 14th biennial 

southern silviculture research conference. Gen. Tech. Rep. Asheville, NC: U.S. 

Department of Agriculture, Forest Service, Southern Research Station.

Reich, P.B.; Abrams, M.D.; Ellsworth, D.S. [and others]. 1990. Fire affects 

ecophysiology and community dynamics of central Wisconsin oak forest 

regeneration. Ecology 71(6): 2179-2190.

Ruffner, C.M.; Groninger, J.W. 2004. Oak Ecosystem Restoration and Maintenance 

in Southern Illinois. In: Spetich, M.A., ed. Upland oak ecology symposium: 

history, current conditions, and sustainability. Gen. Tech. Rep. SRS-73. Asheville, 

NC: U.S. Department of Agriculture, Forest Service, Southern Research Station: 

177-181.

Schuler, T.M.; Gillespie, A.R. 2000. Temporal patterns of woody species diversity in 

a central Appalachian forest from 1856 to 1997. Journal of the Torrey Botanical 

Society 127(2): 149-161.

Taft, J.B. 2003. Fire effects on community structure, composition, and diversity in 

a dry sandstone barrens. Journal of the Torrey Botanical Society 130: 170-192.

USDA Natural Resources Conservation Service. 1998. Soil Survey of Polk County, 

North Carolina. U.S. Government Printing Office: 1998-432-697/60527/

NCRS.

Weatherspoon, C.P. 2000. A proposed long-term national study of the consequences 

of fire and fire surrogate treatments. In: Neuenschwander, L.F., Ryan, K.C., 

Goldberg, G.E., eds. Proceedings Joint Fire Science Conference. Moscow: 

University of Idaho Press: 117-126.

Fire and Fire Surrogate Treatments in Mixed-Oak Forests: Effects on Herbaceous Layer Vegetation Phillips, Hutchinson, Brudnak, and Waldrop
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 485

Whigham, D.F. 2004. Ecology of woodland herbs in temperate deciduous forests. 

Annu. Rev. Ecol. Evol. Syst. 35: 583-621.

Zenner, E.K; Kabrick, J.M.; Jensen, R.G. [and others]. 2006 Responses of ground 

flora to a gradient of harvest intensity in the Missouri Ozarks. Forest Ecology 

and Management 222: 326-334.

Fire and Fire Surrogate Treatments in Mixed-Oak Forests: Effects on Herbaceous Layer Vegetation Phillips, Hutchinson, Brudnak, and Waldrop
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 486

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Fire and Fire Surrogate Treatments in Mixed-Oak Forests: Effects on Herbaceous Layer Vegetation Phillips, Hutchinson, Brudnak, and Waldrop
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 487

Abstract—Prescribed fire every 2 to 4 years is an important component of longleaf 

pine ecosystem restoration. Under some circumstances, repeated fire could change soil 

physical properties on the Western Gulf Coastal Plain. The objective of this study was 

to evaluate the soil bulk density, porosity fractions, and plant-available water holding 

capacity of restored longleaf pine on the Western Gulf Coastal Plain in response to 

two vegetation management alternatives that included the application of three prescribed fires over a 6-year period. Soil microporosity and plant-available water holding 

capacity were influenced by both vegetation management alternatives indicating that 

a reduction in the perturbation of soil by roots may be a mechanism of soil physical 

property change. Apparent soil texture effects on plant-available water holding capacity suggest that further research is needed to determine if repeated prescribed fire 

exacerbates the naturally low plant-available water holding capacity of some soils on 

the Western Gulf Coastal Plain.

Introduction

In the late 1800s, longleaf pine (Pinus palustris P. Mill.) ecosystems occurred across 37 million ha of the Southeastern United States; today, less than 

2 million ha of these forests remain (Landers and others 1995; Outcalt 2000; 

Outcalt and Sheffield 1996). This decrease in range is attributed to extensive 

logging followed by unsuccessful reestablishment due to regeneration problems and exclusion of fire as a management tool (Barnett and Dennington 

1992; Boyer 1989; Outcalt 2000). Because the native plants and animals of 

longleaf pine ecosystems are adapted to, and often depend on, the occurrence 

of fire every 2 to 4 years (Brockway and Lewis 1997; Haywood and others 

2001; Landers and others 1995; Outcalt 2000), successful longleaf pine 

ecosystem restoration is dependent on prescribed fire. Now that fire has been 

reconsidered as a forest management tool in the South (Brockway and Lewis 

1997; Brockway and Outcalt 2000; Gilliam and Platt 1999; Haywood and 

others 2001), and research has identified successful approaches to regenerate longleaf pine (Barnett and McGilvray 1997; Boyer 1989; McGuire and 

others 2001; Ramsey and others 2003; Rodríguez-Trejo and others 2003), 

restoration of this species to portions of its natural range is being realized.

Soil quality will be sustained over a long-term period of repeated prescribed 

fire if the role of vegetation in controlling soil chemical and physical properties 

continues. Under some circumstances, however, repeated crown scorch and 

eradication of understory vegetation may negatively affect various soil physical properties that impact the bulk density and plant-available water holding 

capacity of Western Gulf Coastal Plain soils. For example, if postfire plant 

physiological processes are unable to supply the energy needed to maintain 

In: Butler, Bret W.; Cook, Wayne, 

comps. 2007. The fire environment—

innovations, management, and policy; 

conference proceedings. 26-30 March 

2007; Destin, FL. Proceedings 

RMRS-P-46CD. Fort Collins, CO: 

U.S. Department of Agriculture, 

Forest Service, Rocky Mountain 

Research Station. 662 p. CD-ROM.

1 Research Plant Physiologist, U.S. 

Department of Agriculture, Forest 

Service, Southern Research Station, 

Pineville, LA. msword@fs.fed.us

Soil Physical Property Response to Prescribed 

Fire in Two Young Longleaf Pine Stands on 

the Western Gulf Coastal Plain

Mary Anne Sword Sayer1
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 488

active root growth, both the perturbation of soil by roots and the deposition 

of organic matter in the soil will suffer. Without adequate perturbation and 

organic matter, soil bulk density and strength increase (Fisher and Binkley 

2000). Where inherent soil physical properties approach root-growth limiting 

conditions, root system expansion may be dependent on conduits provided 

by old root channels (van Lear and others 2000). If carbon allocation to 

root growth is inhibited, the network of old root channels and the potential 

for soil resource acquisition will be restricted. The soils of longleaf pine forests on the Western Gulf Coastal Plain are characterized as silty and clayey 

(Peet 2006), with the potential for naturally low subsoil porosity and high 

bulk density (Patterson and others 2004; Pritchett 1979). Where inherent 

soil physical property values approach root growth limitations, fire-induced 

changes could lead to an unnecessary loss of soil quality.

The objective of this study was to evaluate the soil physical properties of 

restored longleaf pine on the Western Gulf Coastal Plain in response to two 

vegetation management alternatives that included the application of prescribed 

fire in spring of 2000, 2003, and 2005. It is hypothesized that repeated fire 

reduced soil quality by decreasing porosity and plant-available water holding 

capacity, and by increasing bulk density.

Materials and Methods

Study Location

The study, on the Kisatchie National Forest in central Louisiana, included 

two replications at latitude 31o 6’N, longitude 92o 36’W on a Ruston fine 

sandy loam (fine-loamy, siliceous, semiactive, thermic Thermic Paleudults) 

with some Malbis fine sandy loam (fine-loamy, siliceous, subactive, thermic 

Plinthic Paleudults) and Gore very fine sandy loam (fine, mixed, active, 

thermic Vertic Paleudualfs) with a slope of 1 to 5 percent (site 1). Three 

replications were at latitude 31° 1’N, longitude 92o 37’W on a gently sloping (1 to 3 percent) Beauregard silt loam (fine-silty, siliceous, superactive, 

thermic Plinthaquic Plaeudults) and Malbis fine sandy loam complex (site 

2). A mixed pine-hardwood forest originally occupied both sites. Site 1 was 

clearcut harvested in 1996 and roller-drum chopped and burned in August 1997. Site 2 was clearcut harvested, sheared, and windrowed in 1991 

and prescribe burned in 1993 and 1996. Vegetation at both sites included 

Schizachyrium, Panicum, and Dichanthelium grass species that are native to 

western longleaf pine ecosystems (Peet 2006). Grass cover was less at site 2 

compared to site 1 due to the prevalence of herbaceous plants such as swamp 

sunflower (Helianthus angustifolius L.), and woody shrubs such as wax myrtle 

(Morella cerifera (L.) Small.

At each location, treatment plots (22 x 22 m; 0.048 ha) were established 

and blocks were delineated by soil drainage and topography. Three vegetation 

management treatments were established:

1. Control (C)—no management activities after planting.

2. Prescribed burning (B)—plots were burned using the strip headfire 

method in spring.

3. Herbicides (H)—herbicides were applied after planting and before age 

3 years for herbaceous and arborescent plant control, and recovering 

brush was cut by hand in 2001 (Sword Sayer and others 2006).

Soil Physical Property Response to Prescribed Fire in Two Young Longleaf Pine Stands on the Western Gulf Coastal Plain Sword Sayer
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 489

Site 2 was burned in May 1998 and both sites were burned in June 2000, 

and May 2003 and 2005. Container-grown longleaf pine seedlings from 

a genetically improved Louisiana seed source (site 1) and Mississippi seed 

source (site 2) were planted at a spacing of 1.8 x 1.8 m in November 1997 

and March 1997, respectively. Treatment plots contained 12 rows of 12 

seedlings. Measurement plots contained the internal eight rows of seedlings 

in each treatment plot.

Measurements

In fall 2002, three saplings of average height per measurement plot were 

randomly identified. In fall 2004 and spring 2006, a tractor-mounted 

hydraulic probe was used to extract one long (5.1 cm diameter x 61 cm 

long) and one short (5.1 cm diameter x 30.5 cm long) soil core 1 m from the 

base of each selected sapling. Cores were placed in capped plastic liners and 

refrigerated until processing. Intact soil core increments were excised from 

the A, Bt1, and Bt2 horizons of long soil cores, and the A horizon of short 

soil cores. During long soil core processing, the depth to the argillic horizon 

(Bt1) was visually estimated by color and texture. Specifically, immediately 

below the darkly colored A horizon, a metal blade was pulled along the core 

length. The upper Bt horizon was identified as the depth where there were 

distinct changes in resistance to the blade and the appearance of the blade 

tracing. The Bt1 core increment was defined as 2 to 12 cm below this depth. 

Two 1 cm deep core sections each from the A horizon core increment (2 to 

12 cm), the Bt1 horizon core increment, and the Bt2 horizon core increment (50 to 60 cm) were excised using a band-saw. Similarly, short soil cores 

were processed so that two intact 1 cm core sections were excised from the 

A horizon core increment. In each year, 90 soil cores were processed for 

A horizon information, and 45 soil cores were processed for Bt1 and Bt2 

horizon information.

From the long and short soil cores, two core sections from each soil horizon were placed in plastic rings. One set of rings was positioned on either 

an equilibrated -0.1 MPa or –1.5 MPa ceramic pressure plate. Bulk density, 

total porosity fraction, microporosity fraction, macroporosity fraction, and 

plant-available soil water holding capacity were determined with data generated by the water retention method (Klute 1986) and the core bulk density 

method (Blake and Hartge 1986). Bulk density (BD) was expressed as core 

section dry weight (g) divided by core section volume (cm-3). Total porosity 

(TOP) was calculated by equation 1.

TOP = 1–[BD (g cm–3)/particle density (2.65 g cm–3)] (1)

Microporosity (MIP) was calculated by equation 2 where WATFC is the 

soil water content of the core section at –0.03 MPa, CSV is the core section 

volume and SGW is the specific gravity of water.

MIP = [WATFC (g)/CSV (cm3)]/SGW (1 g cm–3)] (2)

Macroporosity (MAP) was determined by subtracting MIP from TOP. 

Percent plant-available soil water holding capacity (PAWHC) was calculated 

by equation 3 where WATFC and WATWP are the soil water content of core 

sections at –0.03 MPa and –1.5 MPa, respectively.

PAWHC = (WATFC (g)/CSV (cm ))—(WATWP (g)/CS 3 V(cm

SGW (1 g cm

x 100

3

–3

))

)

 ̈

a ·1 (3)

Soil Physical Property Response to Prescribed Fire in Two Young Longleaf Pine Stands on the Western Gulf Coastal Plain Sword Sayer
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 490

Statistical Analysis

Values of BD, TOP, MIP, MAP, and PAWHC for each horizon were transformed, as needed, to natural logarithm or square root values to establish 

normality, and evaluated by analysis of variance using a split plot in time, 

randomized complete block design with five blocks. Year was the whole plot 

effect and vegetation management treatment was the subplot effect. Effects 

were considered significant at P ≤ 0.05. Means were compared by the Tukey 

test and considered significantly different at P ≤ 0.05.

Results and Discussion

All soil physical properties except PAWHC were significantly affected by 

year in at least two soil horizons (table 1). Values of bulk density in the A, 

Bt1, and Bt2 horizons were, respectively, 5, 8, and 8 percent lower in 2006 

compared to 2004 (table 2). Value of TOP and MAP were greater and values 

a degrees of freedom

b A horizon MIP, and A and Bt1 horizon PAWHC were transformed

 to square root values; BD was transformed to its natural logarithm. 

Effect dfa A Horizon Bt1

Horizon

Bt2

Horizon

Bulk density(BD)b

Block (B) 4 0.0351 0.2296 0.0690

Year (Y) 1 0.0250 0.0172 0.0056

B x Y 4 0.3364 0.1514 0.5146

Treatment (T) 2 0.2789 0.4019 0.7547

T x Y 2 0.5698 0.3928 0.7447

Total porosity (TOP)

B 4 0.0351 0.2296 0.0801

Y 1 0.0250 0.0172 0.0066

B x Y 4 0.3364 0.1514 0.4829

T 2 0.2789 0.4019 0.7419

T x Y 2 0.5698 0.3928 0.7619

Microporosity (MIP)

B 4 0.0025 0.0613 0.5435

Y 1 0.1688 0.0455 0.0187

B x Y 4 0.8664 0.1618 0.1960

T 2 0.0047 0.0741 0.0184

T x Y 2 0.7568 0.2509 0.7785

Macroporosity (MAP)

B 4 0.0167 0.1120 0.1374

Y 1 0.0298 0.0163 0.0039

B x Y 4 0.5552 0.1048 0.1086

T 2 0.0309 0.8419 0.2650

T x Y 2 0.8553 0.4261 0.5020

Plant-available water holding capacity (PAWHC)

B 4 0.0057 0.0209 0.1167

Y 1 0.5735 0.9603 0.8585

B x Y 4 0.7954 0.0667 0.4637

T 2 0.0063 0.2323 0.5001

T x Y 2 0.6581 0.4449 0.5909

Table 1—Probabilities of a greater F-value for soil physical properties 

of restored longleaf pine in central Louisiana in response to three 

vegetation management treatments.

Soil Physical Property Response to Prescribed Fire in Two Young Longleaf Pine Stands on the Western Gulf Coastal Plain Sword Sayer
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 491

of MIP were lower in 2006 compared to 2004. The magnitude of these differences increased with soil horizon depth. It is speculated that these effects 

were caused by soil water content at the time of soil core collection. In 2004, 

soil cores were collected in fall when the soil was dry; in 2006, soil cores were 

collected in spring when the soil was wet. The Ultisol soils in this study are 

characterized by a suite of clay minerals dominated by kaolinite, which exhibits no shrink-swell potential (Buol and others 1980; Kerr and others 1980). 

However, some soil core expansion was possible after removal from the soil 

profile due to the influence of organic matter and minor clay minerals such 

as vermiculite on expansion (Buol and others 1980; Foth 1978). The clay 

fraction of the soil series in this study increases with soil horizon depth (Kerr 

and others 1980; Soil Survey Staff 2007). The effect of this clay fraction on 

soil core expansion may have increased with depth causing annual differences 

in bulk density, TOP, MAP, and MIP to also increase with depth.

With one exception, all significant effects of vegetation management 

treatment on soil physical properties were found in the A horizon (table 1). 

Specifically, MAP, MIP, and PAWHC in the A horizon, and MIP in the 

Bt2 horizon were influenced by vegetation management treatment. Values 

of MAP were 25 percent greater on the B plots, and values of MIP were 

15 and 18 percent lower, respectively, on the B and H plots compared to the 

C plots (fig. 1). Values of PAWHC were 18 percent lower on the B and H 

plots compared to the C plots. In the Bt2 horizon, MIP was 7 percent lower 

on the B plots compared to the C plots. Bulk density was not significantly 

affected by either repeated prescribed fire or chemical vegetation control.

Unwanted vegetation was successfully controlled by repeated prescribed fire 

and chemical vegetation control during seedling establishment. It is likely that 

the production and activity of roots associated with this unwanted vegetation 

were also reduced by B and H treatments. These observations suggest that 

a reduction in the perturbation of soil by roots is a potential mechanism of 

soil porosity change in response to B and H.

Noncapillary pore space or MAP is the fraction of soil porosity containing 

both gravitational water and air space depending on the degree of saturation (Kramer 1983). As the soil drains, MAP provides conduits for aeration. 

The mortality of woody roots as repeated fire weakens or kills unwanted 

shrubs and small trees may be linked to greater MAP in the A horizon on 

Variable

Year BDa TOP MIP MAP

A horizon

2004 1.44 ± 0.03 a 0.46 ± 0.01 b 0.26 ± 0.01 0.19 ± 0.02 b

2006 1.37 ± 0.01 b 0.48 ± 0.01 a 0.25 ± 0.01 0.23 ± 0.01 a

Bt1 horizon

2004 1.61 ± 0.03 a 0.39 ± 0.01 b 0.33 ± 0.01 a 0.07 ± 0.01 b

2006 1.48 ± 0.01 b 0.44 ± 0.01 a 0.30 ± 0.01 b 0 14 ± 0.01 a

Bt2 horizon

2004 1.67 ± 0.03 a 0.37 ± 0.01 b 0.33 ± 0.01 a 0.05 ± 0.01 b

2006 1.54 ± 0.01 b 0.42 ± 0.01 a 0.30 ± 0.01 b 0.12 ± 0.01 a a BD: Bulk density (g/cm3

); TOP: total porosity fraction; MIP: microporosity fraction;

MAP: macroporosity fraction.

Table 2—Means and standard errors of soil physical property values of restored longleaf pine over 

a 3-year period in central Louisiana averaged across three vegetation management treatments 

and two locations. Within a variable and soil horizon, means associated with different letters 

are significantly different at P ≤ 0.05 by the Tukey test. 

Soil Physical Property Response to Prescribed Fire in Two Young Longleaf Pine Stands on the Western Gulf Coastal Plain Sword Sayer
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 492

0.0 0.1 0.2 0.3 0.4

B

C

H

B

C

H

B

C

H

Vegetation managment treatment

MAP porosity fraction

A horizon

Bt1 horizon

Bt2 horizon

a

ab

b

A

0.0 0.1 0.2 0.3 0.4

B

C

H

B

C

H

B

C

H

MIP porosity fraction

a

ab

b

a

b

b

B

0 10 20 30

B

C

H

B

C

H

B

C

H

PAWHC (% volume)

a

b

b

C

Figure 1—Effects of vegetation management treatment on soil physical properties.

the B plots compared to the C plots. Silty and clayey soils on the Western 

Gulf Coastal Plain are often poorly drained and remain saturated in winter 

(Kerr and others 1980). As evapotranspiration increases and these soils dry 

in spring, perhaps dead root cavities from unwanted shrubs and small trees 

improve soil aeration on burned sites.

Capillary pore space is the source of water held against gravity at a tension 

greater than 0.03 MPa. From this, water held at a tension above 1.5 MPa 

is available for plant uptake (in other words, PAWHC) (Kramer 1983). It is 

true that unwanted vegetation was successfully controlled by repeated prescribed fire and chemical vegetation control during seedling establishment. 

However, this was accompanied by 15 to 18 percent reductions in A horizon 

MIP and PAWHC. Obviously, adequate water is needed to maintain longleaf pine physiology and growth on the Western Gulf Coastal Plain where 

seasonal drought is common and prolonged drought is possible (Allen and 

others 1990; Sword Sayer and Haywood 2006). During drought, management-induced reductions in PAWHC have the potential to affect the supply 

of water to young trees and, therefore, their production. Further research is 

needed to determine the significance of reduced A horizon PAWHC on the 

Western Gulf Coastal Plain possibly caused by vegetation control.

Significant block effects were observed for all soil physical property variables in the A horizon, and for PAWHC in the Bt1 horizon (table 1). The 

separation of some block means suggests that the physical properties at site 

1 were distinctly different from those at site 2. The bulk density of the A 

horizon was variable with a higher value on block 3 compared to most other 

blocks (fig. 2A). Similarly, MAP was higher on block 4 compared to the 

other four blocks. It is possible that past management activities such as logging and site preparation introduced variation to the bulk density and MAP 

of the A horizon.

Soil Physical Property Response to Prescribed Fire in Two Young Longleaf Pine Stands on the Western Gulf Coastal Plain Sword Sayer
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 493

For MIP and PAWHC, block means exhibited trends that distinguished site 

1 from site 2 (fig. 2B,C). Specifically, A horizon MIP and PAWHC at site 1 

were 24 and 26 percent lower, respectively, compared to A horizon MIP and 

PAWHC at site 2. It is likely that soil texture controlled soil physical property 

differences at the two sites. Total sand fractions of the A horizon of soils at 

site 1 and site 2 were similar (Soil Survey Staff 2007). However, the sand 

fraction of the A horizon of the Ruston soil at site 1 was dominated by fine 

sand; whereas, that of the A horizon of the Beauregard/Malbis soil at site 

2 was dominated by very fine sand (Soil Survey Staff 2007). It is proposed 

that as sand particle size increased, less MIP contributed to TOP because 

the larger particle size predisposed porosity to MAP.

Soil texture may have also led to site differences in the Bt1 horizon. The clay 

fraction of the Bt1 horizon of the Ruston soil at site 1 was greater than that 

of the Beauregard/Malbis soil at site 2 (Soil Survey Staff 2007). The small 

particle size of clay enables it to hold more water than sand or silt (Kramer 

1983). Small pore sizes associated with clay, however, also result in a large 

fraction of soil water that is plant-unavailable because it is held at a tension 

greater than 1.5 MPa. Perhaps greater clay content in the Bt1 horizon resulted 

in 55 percent less PAWHC at site 1 compared to site 2 (fig. 2C), because the 

clay fraction of the Ruston soil at site 1 retained more plant-unavailable soil 

water than the Beauregard/Malbis soil at site 2.

The results of this study suggest that frequent prescribed fire has the potential to affect the soil porosity and plant-available water holding capacity of 

restored longleaf pine forests on the Western Gulf Coastal Plain. However, 

soil physical property responses to the application of three prescribed fires 

over a 6-year period were not severe enough to affect bulk density. Similar 

1.0 1.2 1.4 1.6 1.8 2.0

Site 1 B3

Site 1 B4

Site 2 B1

Site 2 B2

Site 2 B5

Site 1 B3

Site 1 B4

Site 2 B1

Site 2 B2

Site 2 B5

Site 1 B3

Site 1 B4

Site 2 B1

Site 2 B2

Site 2 B5

Bulk density (g/cm3)

A horizon

Bt1 horizon

Bt2 horizon

bc

ab

a

c

bc

A

0.00 0.20 0.40 0.60 0.80

Porosity fraction

MAP

MIP

b

b

b

b

a

a

ab

ab

bc

c

B

0 10 20 30

PAWHC (% volume)

a

ab

abc

bc

c

a

a

a

b

b

C

Figure 2—Block effects on the study sites.

Soil Physical Property Response to Prescribed Fire in Two Young Longleaf Pine Stands on the Western Gulf Coastal Plain Sword Sayer
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 494

trends in MAP, MIP, and PAWHC between the repeated prescribed fire and 

chemical vegetation control treatments suggest that changes in soil physical 

properties were associated with altered understory vegetation dynamics. It is 

hypothesized that vegetation control by prescribed fire or herbicide application 

reduced soil perturbation by roots, subsequently reducing MIP and PAWHC. 

Apparent relationships between clay content and soil expansion, and between 

sand and clay content and PAWHC were noted. Further research is needed to 

determine if repeated prescribed fire exacerbates the naturally low PAWHC of 

soils with a higher sand fraction in the A horizon and a higher clay fraction in 

the Bt1 horizon. This information would help ascertain whether prescribed 

fire on some Western Gulf Coastal Plain soils influences the supply of water 

necessary to maintain tree physiological processes.

References

Allen, H.L.; Dougherty, P.M.; Campbell, R.G. 1990. Manipulation of water and 

nutrients- practice and opportunity in southern U.S. pine forests. Forest Ecology 

and Management 30: 437-453.

Barnett , J.P.; Dennington, R.W. 1992. Return to longleaf. Forest Farmer 52:11-12.

Barnett, J.P.; McGilvray, J.M. 1997. Practical guidelines for producing longleaf pine 

seedlings in containers. General Technical Report SRS-14. Asheville, NC: U.S. 

Department of Agriculture, Southern Research Station. 28 p.

Blake, G.R.; Hartge, K.H. 1986. Bulk density. In: Klute, A., ed. Methods of Soil 

Analysis Part 1 physical and mineralogical methods. 2nd ed. Madison, WI: Soil 

Science Society of America, Inc.: 363-375.

Boyer, W.D. 1989. Response of planted longleaf pine bare-root and container stock to 

site preparation and release: fifth-year results. In: Miller, J.H. comp. Proceedings of 

the Fifth Biennial Southern Silvicultural Research Conference. General Technical 

Report SO-74. New Orleans, LA: U.S. Department of Agriculture, Southern 

Forest Experiment Station: 165-168.

Brockway, D.G.; Lewis, C.E. 1997. Long-term effects of dormant-season prescribed 

fire on plant community diversity, structure and productivity in a longleaf pine 

wiregrass ecosystem. Forest Ecology and Management 96: 167-183.

Brockway, D.G.; Outcalt, K.W. 2000. Restoring longleaf pine wiregrass ecosystems: 

hexazinone application enhances effects of prescribed fire. Forest Ecology and 

Management 137: 121-138.

Buol, S.W.; Hole, F.D.; McCracken, R.J. 1980. Soil Genesis and Classification. 2nd

ed. Ames, IA: The Iowa State University Press. 406 p.

Fisher, R.F.; Binkley, D. 2000. Ecology and Management of Forest Soils. 3rd ed. 

New York: John Wiley and Sons, Inc. 489 p.

Foth, H.D. 1978. Fundamentals of Soil Science. 6th ed. New York: John Wiley & 

Sons. 436 p.

Gilliam, F.S.; Platt, W.J. 1999. Effects of long-term fire exclusion on tree species 

composition and stand structure in an old-growth Pinus palustris (Longleaf pine) 

forest. Plant Ecology 140: 15-26.

Haywood, J.D.; Harris, F.L.; Grelen, H.E.; Pearson, H.A. 2001. Vegetative response 

to 37 years of seasonal burning on a Louisiana longleaf pine site. Southern Journal 

of Applied Forestry 25: 122-130.

Kerr, A. Jr.; Griffis, B.J.; Powell, J.W.; Edwards, J.P.; Venson, R.L.; Long, J.K.; Kilpatrick, 

W.W. 1980. Soil survey of Rapides Parish Louisiana. Baton Rouge, LA: U.S. Department 

of Agriculture, Soil Conservation Service and Forest Service in cooperation with 

Louisiana State University, Louisiana Agricultural Experiment Station. 86 p.

Soil Physical Property Response to Prescribed Fire in Two Young Longleaf Pine Stands on the Western Gulf Coastal Plain Sword Sayer
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 495

Klute, A. 1986. Water retention: laboratory methods. In: Klute, A., ed. Methods of 

Soil Analysis Part 1 physical and mineralogical methods. 2nd ed. Madison, WI: 

Soil Science Society of America, Inc.: 635-660.

Kramer, P.J. 1983. Water Relations of Plants. New York: Academic Press. 489 p.

Landers, J.L.; Van Lear, D.H.; Boyer, W.D. 1995. The longleaf pine forests of the 

southeast: Requiem or renaissance? Journal of Forestry 93: 39-44.

McGuire, J.P.; Mitchell, R.J.; Moser, E.B.; Pecot, S.D.; Gjerstad, D.H.; Hedman, 

C.W. 2001. Gaps in a gappy forest: plant resources, longleaf pine regeneration, 

and understory response to tree removal in longleaf pine savannas. Canadian 

Journal of Forest Research 31: 765-778.

Outcalt, K.W. 2000. The longleaf pine ecosystem of the south. Native Plants Journal 

1: 42-53.

Outcalt, K.W.; Sheffield, R.M. 1996. The longleaf pine forest: Trends and current 

conditions. Resource Bulletin SRS-9. Asheville, NC: U.S. Department of 

Agriculture Forest Service, Southern Research Station. 23 p.

Patterson, W.B.; Sword, M.A.; Brooker, S. 2004. Response of soil physical properties 

to the application and intensity of prescribed fire in a young longleaf pine (Pinus 

palustris Mill.) stand. In: Conner K, ed. Proceedings of the Twelfth Biennial 

Southern Silvicultural Research Conference. General Technical Report SRS-71. 

Asheville, NC: U.S. Department of Agriculture Forest Service, Southern Research 

Station: 74-78.

Peet, R.K. 2006. Ecological classification of longleaf pine woodlands. In: Jose, S, 

Jokela, E.J.; Miller, D.L. eds. The Longleaf Pine Ecosystem, ecology, silviculture, 

and restoration. New York: Springer Verlag, Inc.: 51-93.

Pritchett, W.L. 1979. Properties and Management of Forest Soils. New York: John 

Wiley & Sons. 500 p.

Ramsey, C.L.; Jose, S.; Brecke, B.J.; Merritt, S. 2003. Growth response of longleaf 

pine (Pinus palustris Mill.) seedlings to fertilization and herbaceous weed control 

in an old field in southern USA. Forest Ecology and Management 172: 281-

289.

Rodríguez-Trejo, D.A.; Duryea, M.L.; White, T.L.; English, J.R.; McGuire, J. 2003. 

Artificially regenerating longleaf pine during a year of drought. Forest Ecology 

and Management 180: 25-36.

Soil Survey Staff 2007. National Soil Survey Characterization Data [Online]. Lincoln, 

NE: Soil Survey Laboratory, National Soil Survey Center, U.S. Department of 

Agriculture, Natural Resources Conservation Service. Available: www.ssldata.

nrcs.usda.gov [April 17, 2007].

Sword Sayer, M.A.; Goelz, J.C.G.; Haywood, J.D. 2006. Effects of prescribed fire 

on production of foliage by sapling longleaf pine. In: Conner K, ed. Proceedings 

of the Thirteenth Biennial Southern Silvicultural Research Conference. General 

Technical Report SRS-92. Asheville, NC: U.S. Department of Agriculture Forest 

Service, Southern Research Station: 478-485.

Sword Sayer, M.A.; Haywood, J.D. 2006. Fine root production and carbohydrate 

concentrations of mature longleaf pine (Pinus palustris P. Mill.) as affected by 

season of prescribed fire and drought. Trees 20: 165-175.

Van Lear, D.H.; Kapeluck, P.R.; Carroll, W.D. 2000. Productivity of loblolly pine 

as affected by decomposing root systems. Forest Ecology and Management 138: 

435-443.

Soil Physical Property Response to Prescribed Fire in Two Young Longleaf Pine Stands on the Western Gulf Coastal Plain Sword Sayer
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 496

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USDA Forest Service Proceedings RMRS-P-46CD. 2007. 499

Abstract—Fire behavior characteristics (such as temperature, radiant and total heat 

flux, 2- and 3-dimensional velocities, and air flow) are extremely difficult to measure 

insitu. Although insitu sensor packages are capable of such measurements in realtime, 

it is also essential to acquire video documentation as a means of better understanding 

the fire behavior data recorded by individual fire behavior sensor packages. Therefore, 

coupling each sensor pack with a digital video recorder for simultaneous recording of 

video and insitu measurements allows viewing of the fire behavior as flames approach, 

envelope, and disperse past individual sensor packs. The limiting factor in this process is 

the amount of recordable digital video tape, typically 60 to 90 minutes of record time. 

This raises both tactical and safety-related concerns, requiring researchers to remain 

in proximity to the advancing fire in order to activate the fire behavior sensor and 

video packages. A new remote trigger that eliminates the need for human interaction 

in order to activate data collection hardware and video equipment has been designed 

and tested. This trigger system allows the fire behavior and video packages to stay in 

“sleep” mode until a measurable rise in heat flux is detected. The detection activates 

the fire behavior sensor package to begin logging data and sends a wireless signal to 

activate the video package. The setup has been tested for range and interference in 

open and densely treed plots as well as in fire and nonfire settings, effectively and 

consistently activating the equipment at distances up to 100 yards.

Background

Insitu fire behavior measurements are an integral part of fire behavior 

research (Butler and others 2004). Fire behavior research often starts as an 

idea that evolves to practical, reproducible experiments in the laboratory 

setting. Many of the models currently being used by fire managers are tools 

that were developed in this way.

Models developed in this manner are often calibrated and validated in 

a controlled environment by changing the key factors that influence fire 

behavior. Under experimental conditions, air temperature, relative humidity, wind speed, fuel loading, and fuel moisture are systematically changed 

in order to define and parameterize fire behavior models under various fire 

conditions. Examples of fire behavior models developed in this manner include Behave (Andrews 1986), Farsite (Finney 1998), and FireStem (Jones 

and others 2004).

Even though it is possible to manipulate the internal environment within 

the burning chamber and wind tunnels at the Fire Sciences Lab (Forest 

Service, Rocky Mountain Research Station), it is often quite different from 

the outside world. To further calibrate and validate fire behavior models 

Fire Behavior Sensor Package 

Remote Trigger Design

Dan Jimenez1, Jason Forthofer1, James Reardon1, and Bret Butler1

In: Butler, Bret W.; Cook, Wayne, 

comps. 2007. The fire environment—

innovations, management, and policy; 

conference proceedings. 26-30 March 

2007; Destin, FL. Proceedings 

RMRS-P-46CD. Fort Collins, CO: 

U.S. Department of Agriculture, 

Forest Service, Rocky Mountain 

Research Station. 662 p. CD-ROM.

1 Chemical Engineer, Mechanical 

Engineer, Physical Science Technician, 

and Research Engineer, respectively. 

U.S. Department of Agriculture, Forest 

Service, Rocky Mountain Research 

Station, Missoula, MT. Lead author 

at djimenez@fs.fed.us
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 500

Fire Behavior Sensor Package Remote Trigger Design Jimenez, Forthofer, Reardon, and Butler

developed in the lab, insitu measurements are often gathered on prescribed 

and wildland fires using ruggedized fire behavior sensor packages (FBP). 

These packages are equipped with heat flux, air temperature, and air flow 

sensors contained in a compact field setup able to withstand the extreme fire 

environment (see fig. 1 for FBP details).

Figure 1—View of fire behavior package with sensors labeled.

Although the insitu sensor packages are capable of detailed measurements 

in real time, the researcher’s ability to understand and interpret the data is 

greatly enhanced when digital video footage of the specific fire behavior at 

the sensor location is provided. Therefore, each FBP sensor pack is typically 

coupled with a digital video recorder for simultaneous recording of video and 

insitu measurements. This coupling allows researchers to see video footage of 

the fire behavior as flames approach, envelope, and continue past individual 

sensor packs (see fig. 2 for video box and camera details). Collection of this 

field data is vital to the model calibration and validation process.

Unfortunately, the success of field campaigns is often marginal due to the 

inherent logistical and safety issues and difficulty associated with anticipating the spread of the fire. The limiting factor in most of these efforts is the 

short record time associated with the digital video tape, which is typically on 

the order of 60 to 90 minutes. These short record times often make it too 

risky for researchers to manually turn on the video cameras when the fire is 

within an hour or less from burning over the sensor locations. This marginal 

success rate and increased risk to field personnel lead to the design, testing, 

and development of a wireless triggering system that eliminates the need for 

human involvement once the equipment is placed.
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 501

Fire Behavior Sensor Package Remote Trigger Design Jimenez, Forthofer, Reardon, and Butler

Materials and Methods

A field deployable ground based sensor package and digital video camera 

provide insitu time resolved measurements of radiant and convective energy 

transfer from the fire, horizontal, and vertical air flow, air temperature, and 

digital video footage. The data and video are critical to the success of the 

field experiments. Research efforts have provided the opportunity to improve 

these sensors through a series of field trials. The result is a system that is not 

only robust, but also easy to operate, simple to deploy, fire proof, and light 

weight. Two packages have been developed:

	 •	 Fire Behavior Flux Package (FBP)—These packages measure 27 cm by 

15 cm by 18 cm and provide an insulated protective enclosure for the 

data logger, sensors, and other electronics. The standard instruments 

consist of radiometers that measure total and radiant energy fluxes, 

small-gauge thermocouples (nominally 0.13 mm diameter wire) to 

sense flame and air temperature, and pitot-static type velocity probes 

that detect the magnitude and direction of airflow before, during, and 

after the fire passes. The packages are typically deployed so that the 

sensors are directed toward the oncoming fire front. The data loggers 

are capable of logging more than 1 million samples, which translates to 

a maximum logging duration of 30 hours at a 1 second sampling rate 

The most recent version of the packages includes a wireless transmitter 

that allows the data logger to send a signal to turn on a nearby video 

camera and begin recording.

Figure 2—View of digital camera enclosure and camera mounting plate.
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 502

Fire Behavior Sensor Package Remote Trigger Design Jimenez, Forthofer, Reardon, and Butler

• Video Acquisition Box (VID)—Digital video imagery is an integral 

component of our field campaigns. Digital video is acquired by camera(s) 

housed within 10 cm by 18 cm by 19 cm fireproof enclosures. The 

camera boxes are designed to be lightweight and compact in order to 

minimize bulk; they are constructed of 1.6 mm aluminum, weigh approximately 1.0 kg. The boxes have a double lens configuration of high 

temperature Pyrex glass and a second lens of hot mirror coated glass 

(Edmund Optics). This multilayer dielectric coating reflects harmful 

infrared radiation (heat), while allowing visible light to pass through. 

The cameras can either be turned on manually or can be set to trigger 

and record through a wireless link to the FBP data loggers. The system 

has been used throughout a full range of fire size and intensity, from 

slow moving surface fires to full scale crown fires. Analysis of the visual 

video images provides an objective method for measuring flame height, 

flame length, flame depth, flame angle, and fire rate of spread.

Currently four of the latest generation fire behavior/digital camera packages have been manufactured to collect insitu measurements in wildland fire 

environments. Table 1 provides details about individual sensors and their 

engineering specifications.

Recognizing the need to reduce risk to research team members and improve use and reliability of the instrument system, efforts were directed at 

developing a safe, ruggedized low cost data logger/camera triggering system. 

The development of this technology was not trivial and required a constant 

level of effort over a significant amount of time to develop, construct, and 

test various trigger methods and designs. Some of the options considered 

included radio frequency, infrared technology, handheld remote, and automatic systems.

The end result was a wireless trigger design based on the SONY proprietary LANC camera control technology (thus, the system is only compatible 

with SONY cameras). While any digital video camera that meets the size 

requirements can be used in the VID boxes, presently only SONY cameras 

are compatible with the automatic trigger system. The preferred model is the 

SONY PC-1000 HandyCam digital video camera. These cameras were chosen 

for their relatively high quality construction, image capability, availability, 

and associated hardware (such as batteries, cables). The system allows users 

to trigger the recording mechanism of the camcorder remotely by using its 

own unique internal computer source code. Although the LANC connector is 

wired directly to the camcorder, by reverse engineering the signals within the 

LANC system we were able to determine how to remotely trigger the on/off 

switch using Radio Frequency (RF), much like a remote garage door opener. 

Radio frequency was chosen over Infra Red (IR) technology due primarily 

to line-of-sight and interfering reflectance issues. In order to incorporate the 

wireless technology into our FBP design each package needed to be fitted 

with a RF transmitter and likewise the video boxes needed to be fitted with 

a RF receiver. This equipment was designed and assembled in-house using off 

the shelf supplies (see fig. 3 and 4). The transmitter and receiver operate off 

the internal 12V battery power sources available in the FBP and VID cases. 

Once the FBP and VID boxes are deployed the trigger system is armed from 

readily accessible switches in the respective enclosures.
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 503

Fire Behavior Sensor Package Remote Trigger Design Jimenez, Forthofer, Reardon, and Butler

Table 1—Insitu fire behavior package (FBP) specifications.

Narrow angle radiometer

Sensor Thermopile (Meggett Avionics) 

Spectral band of sensor 0.15 – 7.0 μm with sapphire window

Field of view ~4.5o controlled by aperature in sensor housing

Transient response Time constant of sensor nominally 30 m sec

Units of measurement Calibrated to provide emissive power of volume in FOV in kW-m-2

Total energy sensor

Sensor Schmidt-Boelter thermopile

Spectral band of sensor All incident thermal energy

Field of view ~130o controlled by aperature in sensor housing

Transient response < 290 m sec

Units of measurement Total heat flux incident on sensor face in kW-m-2

Hemispherical radiometer

Sensor Schmidt-Boelter thermopile (Medtherm Inc)

Spectral band of sensor 0.15 – 7.0 μm with sapphire window

Field of view ~130o controlled by window aperature

Transient response < 290 m sec

Units of measurement Radiant energy incident on sensor face in kW-m-2

Air temperature

Sensor Type K bare wire thermocouple, new, shiny, connected to 27ga lead wire

Wire diameter 0.13 mm

Bead diameter ~0.16-0.20 mm

Units of measurement Degrees celsius

Air mass flow

Sensor SDXL005D4 temperature compensated differential pressure sensor

Pressure range 0-5 in H2O

Sensor design Pressure sensor is coupled to custom designed bidirectional probe with 

±60o directional sensitivity

Units of measurement Calibrated to convert dynamic pressure to velocity in m-s-1 assuming 

incompressible flow

Sensor housing dimensions 150× 180 × 270 (mm)

Housing weight 7.7 kg

Power requirements 12V DC

Power supply Rechargeable internal battery

Data logging Campbell Scientific Model CR10X

Sampling frequency Variable but generally set at 1 Hz

File format ASCII
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 504

Fire Behavior Sensor Package Remote Trigger Design Jimenez, Forthofer, Reardon, and Butler

Figure 3—View of RF electronics for automatic camera trigger function.

Figure 4—View of trigger electronics mounted in fire behavior package.
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 505

Fire Behavior Sensor Package Remote Trigger Design Jimenez, Forthofer, Reardon, and Butler

Results

This new trigger system allows the fire behavior and video packages to 

stay in “sleep” mode until a measurable rise in heat flux or air temperature 

is detected. The detection activates the fire behavior sensor package to begin 

logging data and sends a wireless signal to activate the video camera package 

into record mode. The unique capability and hardware have been tested for 

range and interference over wide range of fire intensities and fuel types in open 

and densely treed plots as well as in fire and nonfire settings. In all the cases 

the system effectively and consistently activated the equipment at distances 

up to 100 yards. The result is a system that is reliable, able to withstand the 

high temperatures of fires and provides researchers and managers with the 

capability to quantify fire intensity and behavior safely and effectively. The 

components required for this conversion cost approximately US$100.

These systems have been used to collect quantitative fire information for 

support of fire spread models, fire-induced plant and tree mortality studies, 

firefighter safety zone studies, crown fire transition studies, and for comparing ecosystem management methods and techniques. The systems have 

been deployed on prescribed and natural fires from Alaska to Florida in the 

United States, and in Europe and Australia. The designs can be adapted to 

fit other sensors and data loggers. The FBP enclosures can be constructed for 

approximately US$300 per box plus cost of data loggers, and sensors. The 

VID enclosures can be constructed for US$450 per box plus cost of cameras. 

Users of the hardware and designs include Dr. Bret Butler (Manager of the 

USDA Forest Service Fire Behavior Team), JoAnn Fites-Kaufman (Manager 

of the USDA Forest Service Adaptive Management Services Enterprise Team), 

Dr. Matthew Dickinson (Research Ecologist with the USDA Forest Service 

Northeastern Research Station), Dr. Miguel Cruz (Research Ecologist with 

Australian CSIRO Forestry Research Group in Canberra, Australia), Jason 

Simmons (USDI Bureau of Land Management Fire Ecologist for experiments 

at Knife-River Village in North Dakota).

Acknowledgments

Funding for this project was provided by the U.S. Department of Agriculture, Forest Service and the Joint Fire Science Program.

References

Andrews, P. 1986. BEHAVE: fire behavior prediction system and fuel modeling 

system - burn subsystem, part 1. Gen. Tech. Rep. INT-194. Ogden, UT: U.S. 

Department of Agriculture, Forest Service, Intermountain Research Station.

Butler, B.W.; Cohen, J.; Latham, D.J.; Schuette, R.D.; Sopko, P.; Shannon, K.S.; 

Jimenez, D.; Bradshaw, L.S. 2004. Measurements of radiant emissive power and 

temperatures in crown fires. Canadian Journal of Forest Research 34:1577- 1587.

Finney, M. 1998. FARSITE: fire area simulator - model development and validation. 

In: Res. Pap. RMRS-RP-4. Ogden, UT: U.S. Department of Agriculture, Forest 

Service, Rocky Mountain Research Station.

Jones, J.L.; Webb, B.W.; Jimenez, D.; Reardon, J.; Butler, B.W. 2004. Development 

of an advanced one-dimensional stem heating model for application in surface 

fires. Canadian Journal of Forest Research: 20-30.
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 506

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Abstract—Oak savanna woodlands were once a dominant ecotone throughout the 

upper Midwest. These ecosystems represented a transitional zone between prairie 

communities to the west that eventually graded into Big Woods forest. Most of the 

oak savanna landscapes of most of the Midwest were extensively homesteaded and 

farmed during the middle 1800s and few intact savanna landscapes remain today. 

Given the current interest in preserving, maintaining, and restoring these systems, it is 

imperative that the natural factors that have shaped these areas are investigated. This 

research investigates the potential of developing reference conditions in a relatively 

intact oak savanna in the Sherburne National Wildlife Refuge, Minnesota. This research 

provides a context for current management activities centered on maintaining and 

restoring oak savanna ecosystems.

Introduction

Oak savanna woodlands were once a dominant ecotone in southwestern 

Minnesota and throughout the upper Midwest. These ecosystems represented 

a transitional zone between prairie communities to the west that eventually 

graded into Big Woods forest. Most of the oak savanna landscape of southern 

Minnesota (and indeed most of the Midwest) were extensively homesteaded 

and farmed during the middle 1800s and few intact savanna landscapes remain today.

The structure, origin, and factors that have maintained presettlement oak 

savanna have not been well documented or established. Fires are thought 

to have maintained these landscapes, perhaps ignited by Native Americans, 

but the disturbance regime of these landscapes prior to widespread EuroAmerican impact remains elusive. Given the current interest in preserving, 

maintaining, and restoring these systems, it is imperative that the natural 

factors that have shaped these areas are investigated. Prescribed fires have 

become an important tool in this regard, but the fire management activities 

can be better implemented if the natural role of fire is known.

Reconstructing the historic fire regime in oak savanna woodlands is 

challenging because few are of sufficient age to extend to periods before appreciable impacts by human activities. However, this information is critical 

for establishing reference conditions that can be utilized to guide restoration 

activities and initiate management activities.

Fire History and Age Structure Analysis 

in the Sherburne National Wildlife Refuge:

Establishing Reference Conditions in a 

Remnant Oak Savanna Woodland

Kurt F. Kipfmueller1 and Tim Hepola2

In: Butler, Bret W.; Cook, Wayne, 

comps. 2007. The fire environment—

innovations, management, and policy; 

conference proceedings. 26-30 March 

2007; Destin, FL. Proceedings 

RMRS-P-46CD. Fort Collins, CO: 

U.S. Department of Agriculture, 

Forest Service, Rocky Mountain 

Research Station. 662 p. CD-ROM.

1 Assistant Professor, Department of 

Geography, University of Minnesota, 

Minneapolis, MN. kurt@umn.edu

2 Federal Cooperator and Regional 

Fire Ecologist, Region 3, U.S. Fish and 

Wildlife Service, Fort Snelling, MN.
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 508

This research will investigate the potential of developing reference conditions in a relatively intact oak savanna in the Sherburne National Wildlife 

Refuge, Minnesota. Vegetation plots will be established throughout the 

remnant to determine the variability in savanna structure and composition. 

Fire scars will be collected throughout the remnant to determine the variability in fire frequency and associated changes in savanna age-structure and 

composition related to fire activity.

This research provides a context for current management activities centered 

on maintaining and restoring oak savanna ecosystems. The principal questions to be answered include:

	 •	 What is the fire frequency of the oak savanna remnant and how much 

variation exists over space?

	 •	 How has fire frequency changed over time?

	 •	 Are there particular time periods where fire is more/less prominent?

	 •	 Are forest compositional and structural characteristics related to fire 

frequency?

	 •	 Has the seasonality of fire changed over time?

Identifying the historic fire regime of oak savanna landscapes, the associated forest structures, and spatial variations is critical to developing sound 

management guidelines for the restoration of these landscapes. Since this study 

area is one of the few old-growth remnants of this ecosystem in the refuge, 

information developed in this study will be useful to management of other 

parts of the refuge. Few detailed investigations of oak savanna fire history 

and age structure have been completed. This research will add substantially 

to our understanding of the vegetation dynamics and disturbance regime 

that characterize this landscape.

The information in this paper is taken from the study’s “Progress Report, 

Spring 2007.”

Field Sampling

Field sampling for age structure and compositional patterns was completed 

during summer 2006 at 55 points within the study area. Our sampling used 

an alternating pattern of grid points. At every other grid point we collected 

increment cores for age determination. Each grid point was inventoried for 

compositional attributes. Age structure data were collected from 28 points.

Our original design included sampling of 65 grid points. However, some 

points fell in wetland areas with little or no tree cover and some points were 

omitted due to their proximity to a cemetery, road, or other impediment to 

sampling.

Age and composition data collection is complete for the study area; however, 

we have not collected fire-scar samples as of March 2007. Fire scar samples 

will be collected during spring and summer 2007 outside the time period 

where oaks are susceptible to oak wilt.

Fire History and Age Structure Analysis in the Sherburne National Wildlife Refuge:... Kipfmueller and Hepola
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 509

Stand Composition

A great deal of variation in stand composition exists across the study area 

(table 1). This is mostly manifested as differences in relative amounts of pin 

and bur oak as stem density and basal area are quite variable. The study area 

is largely composed of northern pin oak that dominates both basal area and 

stem density (and therefore species importance) with lesser amounts of bur 

oak. In fact, only a limited number of sites are dominated by bur oak. Of 

the grid points sampled, 92.7 percent contained pin oak and/or bur oak. 

Aspen and sugar maple were also present at a few sites, sometimes in large 

numbers.

Four-letter

code

Scientific name Common name Number inventoried 

QUEL Quercus ellipsoidalis Northern pin oak 1,386

QUMA Quercus macrocarpa Bur oak 515

POTR Populus tremuloides Quaking aspen 241

POGR Populus grandidentata Big-tooth aspen 15

ACSA Acer saccharum Sugar maple 75

ACRU Acer rubrum Red maple 18

FRPE Fraxinus pennsylvanica Green ash 31

FRNI Fraxinus nigra Black ash 43

ULAM Ulmus Americana American elm 9

OSVI Ostrya virginiana Eastern hophornbeam 5

PRPE Prunus pensylvanica Pin cherry 7

PRVI Prunus virginiana Choke cherry 7

PRSE Prunus serotina Black cherry 14

SASP Salix sp. wolliW 1

Table 1—Species identification code for tree species located in plots. Number inventoried indicates the 

number of each species present in all plots combined. Codes are used in figures in this paper.

Stem density (fig. 1, 2) and basal area (fig. 3, 4) appear to have no spatial 

pattern at a landscape level. That is, there does not initially appear to be 

any clustering of large/small values (fig. 5). However, basal area and stem 

density appear greatest south of the road running east-west through the 

study area. This has not been investigated using a spatial autocorrelation or 

variogram approach as of yet to determine if there are spatial patterns that 

can be quantified.

Tree diversity is generally higher south of the road and includes sugar maple, 

ash, and cherry in greater abundance than are found elsewhere within the 

study area (fig. 6). Corylus sp. is present in high abundance at nearly every 

sampled grid point (note we have not yet summarized sapling/shrub data). 

There are places that have less Corylus than others. This may be related to 

some aspect of the environmental conditions within a stand but more likely 

reflects fire history to some degree. We suspect that those areas that are 

burned most frequently might be enabling hazel to germinate readily from 

root stock. This has led to enormously high stem densities in some areas of 

the savanna at the sapling/shrub level.

Fire History and Age Structure Analysis in the Sherburne National Wildlife Refuge:... Kipfmueller and Hepola
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 510

Stem Density (stems/ha)

Species

QUEL

QUMA

POTR

POGR

ACSA

ACRU

FRPE

FRNI

ULAM

OSVI

PRPE

PRVI

PRSE

Stem Density (stems/ha)

0

200

400

600

800

1000

n=55

Total Stem Density

Stem Density Class(stems/ha)

0-100

101-200201-300301-400401-500501-600601-700

701-800

800+

Number of Plots

0

2

4

6

8

10

12

(A)

(B)

Figure 1—Stem density of sampled forest structure plots in the Sherburne National 

Wildlife Refuge. (A) Box and whisker plots of stem density of individual species where 

present in a plot. Horizontal line in each box represents the median value. Each outliner 

is depicted as an open circle. (B) Total stem density determined in individual plots.

Figure 2—Stem density by species across the landscape. The relative size of each circle 

reflects the total stem density of each plot.

Stem Density Information

Fire History and Age Structure Analysis in the Sherburne National Wildlife Refuge:... Kipfmueller and Hepola
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 511

Basal Area (m2/ha)

Species

QUEL

QUMA

POTR

ACSA

ACRU

FRPE

FRNI

ULAM

OSVI

PRPE

PRVI

PRSE

SASP

Basal Area (m2/ha)

0

5

10

15

20

25

30

n=55

Basal Area (m2/ha)

Basal Area Class (m2/ha)

0-6

6-10

10-1515-2020-2525-30

30-35

35-4040-45

Number of Plots

0

2

4

6

8

10

12

14

16

18

(A)

(B)

Figure 4—Basal area by species across the landscape. The relative size of each circle 

reflects the total stem density of each plot.

Figure 3—Basal area of sampled forest structure plots in the Sherburne National Wildlife 

Refuge. (A) Box and whisker plots of basal for individual species where present in a 

plot. Horizontal line in each box represents the median value. Each outlier is depicted 

as an open circle. (B) Total basal area determined in individual plots.

Basal Area Information

Fire History and Age Structure Analysis in the Sherburne National Wildlife Refuge:... Kipfmueller and Hepola
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 512

Species Importance Values

Importance Values

Species

QUEL

QUMA

POTR

POGR

ACSA

ACRU

FRPE

FRNI

ULAM

OSVI

PRPE

PRVI

PRSE

Importance Value

0

25

50

75

100

125

150

175

200

Percentage of Stands Containing Each Species

Species (four letter code)

QUEL

QUMA

POTR

POGR

ACSA

ACRU

FRPE

FRNI

ULAM

OSVI

PRPE

PRVI

PRSE

Percent Occurence

0

10

20

30

40

50

60

70

80

90

100

n=55

(A)

(B)

Figure 6—Basal area by species across the landscape.

Figure 5—(A) Box and whisker plots of importance values for individual species where 

present in a plot. Importance value is calculated by adding together the relative density 

and basal area of a given species (B) after first multiplying each by 100. Importance 

values range from 0 to 200, with 200 indicative of a stand that is composed of only one 

tree species. Horizontal line in each box represents the median value.

Fire History and Age Structure Analysis in the Sherburne National Wildlife Refuge:... Kipfmueller and Hepola
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 513

Age Structure Data

We are processing increment cores for determination of age-class patterns. 

We hope to use the age-structure information to refine our search for firescarred trees. We will concentrate our search effort for fire scars near areas that 

contain older trees to help extend the fire history further back in time.

Preliminary analyses indicate that most of the sampled trees germinated in 

the mid 1900s. There appears to be a dramatic pulse in regeneration during 

the early 1950s. The oldest trees dated so far have inner-ring dates in the 

middle 1800s with a few that extend further into the past.

The low density area near the center of figure 7 contains trees dating to 

the early 1800s (1801 is the oldest so far). Increment cores from these trees 

were collected separately from the age-structure grid points for a dendrochronology class during fall 2006. Older trees also appear to be near the 

outer edges of the study area.

We have not assessed the relative ages of bur oak versus pin oak in a formal 

sense, but it appears the oldest trees are mostly bur oak.

Figure 7—Oldest sampled trees by species.

Fire History and Age Structure Analysis in the Sherburne National Wildlife Refuge:... Kipfmueller and Hepola
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 514

Tree Size Observations

In nearly every plot where both Quercus ellipsoidalis and Q. macrocarpa 

were present, ellipsoidalis was larger in diameter on average (fig. 8). However, 

initial examination of the age data suggests that Q. macrocarpa were nearly 

always older.

DBH Distribution: QUEL

DBH Class (5 cm increments)

0-5

5-10

10-1515-2020-25

25-30

30-35 35-4040-45

45-5050-55 55-60 60-65

65-7070-1575-80

80-85

85-90 90-95

95-100 100+

Frequency

0

50

100

150

200

250

300 (A)

DBH Distribution: QUMA

DBH Class (5 cm increments)

0-5

5-10

10-1515-2020-25

25-3030-35 35-4040-45

45-5050-55 55-6060-65

65-70

70-1575-80

80-8585-90 90-95

95-100 100+

Frequency

0

50

100

150

200

250

300 (B)

Figure 8—Diameter distribution of Quercus ellipsoidalis (A) and Q. macrocarpa (B) within the study area. Diameter 

at breast height is reported in 5-cm classes. Trees less than 5 cm in diameter were considered saplings and were 

recorded elsewhere.

Fire History and Age Structure Analysis in the Sherburne National Wildlife Refuge:... Kipfmueller and Hepola
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 515

Abstract—Frequent (1 to 5 year) low intensity fire regimes of longleaf pine (Pinus 

palustris) savannas of the Southeastern United States create a continuous fuelbed 

of understory grasses, forbs, flammable pine needle litter, with interstitial hardwood 

shrubs. Measuring the spatial heterogeneity of these fine-fuels can be difficult, requiring intensive field sampling. Ground-based LIDAR (LIght Detection and Ranging) 

may prove useful in this aspect, collecting accurate three-dimensional point-clouds 

of objects at the sub-centimeter level. Here we present the methods and discuss the 

applicability of using a ground-based LIDAR system, the Mobile Terrestrial Laser Scanner (MTLS), to measure variation in fuelbed structure. The MTLS consists of Optech’s 

ILRIS 36D ground based laser scanner mounted on a lift atop a mobile platform, which 

increases its versatility in capturing details about the terrain at multiple angles, vertically 

and horizontally. We recorded sub-meter, spatially explicit fuel characteristics using 

the MTLS and manual point-intercept sampling techniques in multiple plots within a 

longleaf pine woodland. The LIDAR data required additional processing to make it 

comparable to the field data. This process involved merging individual scans of a plot 

taken at different angles and cropping out the areas of interest from the point clouds. 

Preliminary results illustrate that the fuelbeds can be classified into distinct categories 

with distinct characteristics, such as bulk density. The MTLS may be applicable for 

measuring spatially explicit plot-to-stand level fuel structure, continuity, and volume. 

Coupling MTLS derived fuel maps with fire behavior, through thermal imagery and 

modeling, combine to produce a promising strategy to connect fuels and fire at much 

finer scales than attempted before. This approach is particularly well suited to pine 

savannas with high frequency, low intensity fire regimes and other fuel types with fuel 

heights <10 m.

Objectives

Our purpose was to introduce a new approach to measuring fine-scale 

fuelbed structure in forested ecosystems. Our first objective was to provide 

a detailed background of the technology used; the Mobile Terrestrial Laser 

Scanner (MTLS), a ground-based LIght Detection and Ranging (LIDAR) 

system. Our second objective was to present the field data collection procedures and beginning stages of the data processing methodology. Our third 

objective was to provide a brief discussion of the possible applicability of the 

MTLS in forest fuel, fire behavior, and fire effects modeling.

Application of Ground-Based LIDAR for 

Fine-Scale Forest Fuel Modeling

E. Louise Loudermilk1, Abhinav Singhania2, Juan C. Fernandez2,

J. Kevin Hiers3, Joseph J. O’Brien4, Wendell P. Cropper Jr.5,

K. Clint Slatton6, and Robert J. Mitchell7

In: Butler, Bret W.; Cook, Wayne, 

comps. 2007. The fire environment—

innovations, management, and policy; 

conference proceedings. 26-30 March 

2007; Destin, FL. Proceedings 

RMRS-P-46CD. Fort Collins, CO: 

U.S. Department of Agriculture, 

Forest Service, Rocky Mountain 

Research Station. 662 p. CD-ROM.

1 Graduate Research Assistant, School 

of Natural Resources and Environment, 

University of Florida, Gainesville, FL. 

louisel@ufl.edu

2 Graduate Research Assistants, 

Geosensing Engineering and Mapping 

Center, Un iversit y of Flor ida, 

Gainesville, FL. 

3 Research Fellow in Fire Ecology, 

Joseph W. Jones Ecological Research 

Center, Ichauway, Newton, GA. 

4 Research Ecologist, U.S. Department 

of Agriculture, Forest Service, Athens, 

GA. 

5 Assistant Professor, School of Forest 

Resources and Conservation, University 

of Florida, Gainesville, FL. 

6 Assistant Professor, Department 

of Civil and Coastal Engineering 

and Department of Electrical and 

Computer Engineering, University of 

Florida, Gainesville, FL. 

7 Forest Ecology Scientist, Joseph W. 

Jones Ecological Research Center at 

Ichauway, Newton, GA. 
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 516

Application of Ground-Based LIDAR for Fine-Scale Forest Fuel Modeling Loudermilk, Singhania, Fernandez, Hiers, O’Brien, Cropper, Slatton, and Mitchell

Introduction

The frequent (1 to 5 year) low intensity fire regimes of longleaf pine (Pinus 

palustris) savannas of the Southeastern United States create a relatively continuous fuelbed of understory grasses, forbs, flammable pine needle litter, 

with scattered hardwood shrubs. Although this understory fuelbed is often 

considered homogenous within a stand, considerable heterogeneity exists at 

fine spatial scales (sub-meter; see table 1). Understanding the variation in these 

fine fuels is important for linking fuel, fire behavior, and fire effects modeling. 

Capturing the heterogeneity in fine-fuels can be difficult, requiring intensive 

field sampling. A LIDAR (LIght Detection and Ranging) system with threedimensional (3D) capabilities and high accuracy information may provide 

critical spatial information needed to assess these fuel characteristics.

Table 1—Results from cluster analysis identifying discrete categories of ‘fuel cells’ at sub-meter scales. Heights 

are in centimeters.

Cluster % of Mean fuelbed Mean litter

no. fuelbed height height Description of fuel cells

2 40 20 11 Wiregrass with perched pine litter

4 25 52 10 Shrub, wiregrass, and perched litter

3 13 11 6 Pine litter in interstitial space between wiregrass

5 10 29 5 Shrubs with oak litter 

8 6 53 25 Other graminoids with perched pine

1 5 4 3 Oak and pine litter on mineral soil

Airborne LIDAR has been used extensively for measuring multidimensional 

forest metrics, namely tree canopy height, canopy density, tree volume/biomass (Hall and others 2005; Lefsky and others 1999; Nelson and others 

1988) as well as understanding the spatial structure of a forest (Drake and 

Weishampel 2000) over large landscapes. Airborne LIDAR has not, however, 

been able to measure forest metrics related to understory vegetation at the 

sub-meter scale. Ground-based LIDAR could be used as a potential tool to 

address this problem.

Ground-based LIDAR is a new tool that brings the elements of airborne 

LIDAR to a finer-scale, collecting 3D point-clouds of objects at the subcentimeter level. A complete description on the technology can be found in 

Lichti and others (2002) and Fröhlich and Mettenleiter (2004). In this study, 

we present the methods and discuss the applicability of using a ground-based 

LIDAR system, the Mobile Terrestrial Laser Scanner (MTLS), for measuring small-scale spatial variation in fuelbed structure. The MTLS consists of 

Optech’s ILRIS 36D (Intelligent Laser Ranging and Imaging System) ground 

based laser scanner that is mounted on a lift atop a mobile platform (fig. 1), 

increasing its versatility in capturing details about the terrain at multiple 

angles. The ILRIS uses a 1,500 nm wavelength laser with a pulse frequency 

of 2,500 points per second, recording first or last returns of each laser pulse 

(user defined). The field of view is 40 ̊ in both horizontal as well as vertical 

plane. It has a range of 5 m to 1,500 m (at 80 percent reflectivity). The ILRIS has a pan-tilt base providing it a 360 ̊ rotation in the horizontal plane 

and about ± 40 ̊ in the vertical plane. The lift makes possible the vertical 
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 517

Application of Ground-Based LIDAR for Fine-Scale Forest Fuel Modeling Loudermilk, Singhania, Fernandez, Hiers, O’Brien, Cropper, Slatton, and Mitchell

movement of the scanner up to a height of about 9 m. Mean point spacing 

of the laser data is user defined, typically ranging from 1 mm to a few cm. 

The ILRIS collects: (1) x, y, z values with respect to the position of the laser 

sensor; (2) intensity values of the return; (3) true color RGB values for each 

point obtained from an integrated and calibrated digital camera within the 

instrument.

The strength of the ground-based unit lies in its ability to produce accurate 

high-resolution data. This is not possible using airborne LIDAR, which covers large areas, while producing data at an average resolution of about 0.5 m 

horizontally. The MTLS, on the other hand, is a static, stop and scan laser 

scanner that covers limited area, but captures data at an average spot spacing 

at a sub-cm level. The MTLS, with its high-density point data, provides a 

useful tool for characterizing the fuelbed, particularly highly accurate fuel 

volumes.

As a newly developed instrument, there are few published examples of the 

application of ground-based LIDAR in forestry (but see Hopkinson and others 2004; Watt and Donoghue 2005). We know of no published work on the 

use of ground-based LIDAR to measure understory vegetation traits. This 

research provides a unique opportunity to develop an approach to accurately 

measure spatially explicit sub-meter vegetation characteristics.

Figure 1—A ground-based LIDAR system: The MTLS 

(Mobile Terrestrial Laser Scanner) is used to mobilize 

Optech’s ILRIS 36D (yellow instrument) for collecting 

high-resolution 3D point-cloud laser data.
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 518

Application of Ground-Based LIDAR for Fine-Scale Forest Fuel Modeling Loudermilk, Singhania, Fernandez, Hiers, O’Brien, Cropper, Slatton, and Mitchell

Study Areas

Ichauway Preserve

A portion of the research was performed at Ichauway, an 11,000 ha reserve 

of the Jones Ecological Research Center in southwestern Georgia, USA. 

Ichauway is located within the Plains and Wiregrass Plains subsections of 

the Lower Coastal Plain and Flatwoods section (McNab and Avers 1994). 

Ichauway has an extensive tract of second-growth longleaf pine and has been 

managed with low intensity, dormant-season prescribed fires for at least 70 

years, at a frequency of 1 to 3 years. The specific study site used had a 1 year 

rough.

Ordway-Swisher Biological Station

A portion of the research was also performed at the Ordway-Swisher 

Biological Station, a 3,800 ha reserve in north-central Florida, USA. It is 

managed by the University of Florida’s Department of Wildlife Ecology and 

Conservation. Ordway has a large amount of second-growth longleaf pine as 

well and has been intensively managed for the past two decades with prescribed 

fire. The current fire frequency is 2 to 5 years, with some areas reaching more 

than 10 years. The specific study site used had a 2 year rough.

Field and Ground-LIDAR Data Collection

In early spring 2007, a total of 30 georeferenced 4 m x 4 m plots were 

set up throughout the forest matrix with a goal to capture fine-scale (< m) 

variation in fuelbed characteristics in relation to overstory structure (that is, 

within forest gaps, along gap edges, and so forth). The 4 m x 4 m area was 

chosen because it was a large enough area to capture heterogeneity at submeter scales, but small enough to support intensive sampling with minimal 

impact to the vegetation. Spatially explicit point-intercept sampling data were 

recorded for each plot using a graduated dowel rod. We used 0.33 m spacing 

between traditional point-intercept samples, including sampling along the 

edge of the plot, totaling 169 sample points within each plot (fig. 2). A ladder was suspended horizontally across each plot using make-shift saw-horses 

at each end to sample the interior of the plot, taking care not to disturb the 

vegetation. The spatially explicit arrangement and sampling intensity were 

performed to capture the spatial variation of the fuelbed found within this 

small (16 m2) area and to relate to the cm-level 3D laser data collected from 

the ground-based LIDAR. At each sample point, fuelbed and litter depth 

(or height), as well as presence/absence of fuel and vegetation types, were 

recorded.

Within 2 weeks of field data collection, the MTLS collected ground-LIDAR 

data on all 30 plots. Prior to data collection, reference targets (consisting of 

a Styrofoam ball on top of a metal rod, fig. 3) were placed at all four corners 

of the plot. A double reference target (two Styrofoam balls on one metal 

rod) was used at the northwest corner of each plot to orient the plot for data 

processing. An additional one to four reference targets were placed just outside the 4 m plots to align lidar volume estimations with biomass clip plots. 

Biomass reference targets were placed in relatively homogeneous fuel types, 

with a circular area of 0.3 m2. The MTLS was restricted to mapped roads 
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 519

Application of Ground-Based LIDAR for Fine-Scale Forest Fuel Modeling Loudermilk, Singhania, Fernandez, Hiers, O’Brien, Cropper, Slatton, and Mitchell

Figure 2—Example of the spatial grid pattern of field data points and 

corresponding LIDAR heights (color gradients) within the 4 m x 4 m plot 

(2D aerial view). Point-intercept data on fuel traits were collected every 

0.33 m (cross sections of the grid) throughout the plot.

Figure 3—Example of a 4 m x 4 m plot, with reference targets set up for 

ground-LIDAR acquisition and subsequent data processing.
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 520

Application of Ground-Based LIDAR for Fine-Scale Forest Fuel Modeling Loudermilk, Singhania, Fernandez, Hiers, O’Brien, Cropper, Slatton, and Mitchell

and trails, as well as a buffer of 6 to 10 m around each plot, to reduce site 

degradation and vegetation disturbance. The ILRIS was lifted to an appropriate height at each plot (6 to 7 m), with a goal to capture as much of an aerial 

view as possible, without bole or canopy obstruction. The ability to vary the 

height and angle of the ILRIS (hence, using the MTLS) allows significant 

reduction in shadowing effects within the fuelbed that may be found when 

using the ILRIS on a tripod. The ILRIS was set to a downward angle tilt 

of 25° (from horizontal). A true color digital photograph was taken by the 

ILRIS for each plot, and used in the field to delineate the focus area. This 

eliminates any unnecessary data collection, enhancing efficiency in the field 

and reducing file storage size. First-return laser pulses were recorded with 

5 mm mean point spacing. One scan was taken on opposite sides of the plot 

to further reduce shadowing effects and ensure more accurate and complete 

sub-cm-scale data for both the 4 m and biomass plots. These two scans were 

merged in the processing stage to a single spatial coherent data set. Data collection with the MTLS took approximately 20 minutes per plot.

After laser data collection, the biomass plots were analyzed with traditional 

field methods. The biomass reference targets were used as the center of a 

40 cm diameter circular area (area of 0.3 m2). First, 20 random point-intercept 

samples were taken throughout the plot. The same fuelbed traits were measured as described above. All vegetation was removed from the plot, separated 

into perched litter, fine fuels (grasses, forbs, ground litter), woody vegetation, 

pine cones, and other woody debris, and subsequently dry weighed.

Data Processing

Initially, data processing involved converting the collected laser data from 

binary to ASCII format (that is, parsing). This raw data includes a four column text file containing x, y, z coordinates and laser return intensity values 

for each of the sampled laser points. Of the two scans taken per plot, the 

first was horizontally rectified by compensating for the original scanning 

geometry, where the instrument had a downward tilt of 25° with respect to 

horizontal. Then common points (reference targets) between the two scans 

were identified and their coordinates were used to compute the parameters 

of a 3D conformal transformation with a unit scale factor (Wolf and Ghilani 

1997). The conformal transformation was then applied to the second scan to 

bring it to the same coordinate frame of the first scan, combining them into a 

single spatially coherent data set. The merged data set was then rotated about 

the z axis to orient the point cloud in cardinal space. A constant value offset 

was added to the height values in order to set the ground points to a value of 

zero. The digital image and the double reference target for the NW corner 

of the plot were especially helpful in this merging process and in orienting 

the plot in cardinal space. The 4 m x 4 m plot area (fig. 4) and biomass plot 

areas were clipped from the resulting merged scans using the reference targets. 

Roughly 600,000 to 700,000 sample laser points were found within each 

4 m x 4 m plot. Point-densities, volume estimates, and height distributions 

were calculated for each biomass plot and each 4 m x 4 m plot.

Total volume (cm3) was calculated in each plot by determining the presence/absence of laser points within each cm3 space. The process involved using 

a 1 cm3 3D window to move through each plot’s point cloud in the x, y, and 

z direction, respectively. Every time a point (or points) was found in the 3D 

window, 1cm3 of volume was added to the volume counter for that plot.
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 521

Application of Ground-Based LIDAR for Fine-Scale Forest Fuel Modeling Loudermilk, Singhania, Fernandez, Hiers, O’Brien, Cropper, Slatton, and Mitchell

Applicability: Fuel Analysis and Modeling

We used cluster analysis to categorize variation across the fuelbed (table 1). 

These distinct clusters of fuels at these fine scales are termed ‘fuel cells.’ The 

distribution and arrangement of these fuel cells represent within-fuelbed 

variation, each with distinct bulk density, height, and composition. These 

categorized fuel cells may be used to classify the laser data into fuel types and 

then used to project fuel types across a similar dataset. Ultimately, these fuel 

cells may produce fine-scale burn heterogeneity, which may be important to 

managing prescribed fire effects.

With this new approach to measuring fine-scale fuels, future work will 

explore several aspects of fuelbed assessment that have been difficult to measure and understand thus far. We plan to use geostatistical techniques (for 

example, variogram analysis, model fitting, and so forth) to assess fuelbed 

variability across spatial scales, utilizing both the laser and field data. For 

instance, we can gain insight into how fuel depth varies with fuel type across 

the fuel bed. As fuel structure is apparent within the laser data outputs 

(fig. 4), it may be possible to run a pattern recognition analysis (with both 

field and laser data) to ultimately train the model to recognize certain fuel 

types across the fuelbed. With respect to fine-fuels, we are currently using 

ground-based LIDAR to accurately estimate fuel volumes, then relate this 

to field biomass, for more precise fuel volume measurements in bulk density 

calculations. We also hope to better understand bulk density estimates of 

particular fuels, such as common shrubs and grasses, utilizing the volumetric 

measurements (cm3) extracted from the laser data. For instance, the volume 

estimates can be segregated in particular horizontal plains of data (such as 

every 10 cm) within the understory or for just one plant and related to leaf 

Figure 4—Example output 3D ground-LIDAR point-cloud of a 4 m x 4 m plot. (This is not the same 

plot as fig. 3.) Color gradients represent fuelbed height variation.
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 522

Application of Ground-Based LIDAR for Fine-Scale Forest Fuel Modeling Loudermilk, Singhania, Fernandez, Hiers, O’Brien, Cropper, Slatton, and Mitchell

area. Such detailed information is critical for improving our understanding of 

the physical structure of fuelbeds that influence fire behavior, such as surface 

area to volume ratios, packing ratio, and fuel arrangement (such as horizontal 

continuity, patchiness). Furthermore, fine-scale variation in fuels, as measured 

by ground-based LIDAR, may provide insight to variation found within the 

plots that may be attributed to the forest structure (for example, forest gaps, 

edges), or spatial arrangement of the adult pines near the plot.

In a linked study, we are investigating how fine-scale variation in fuels 

may drive fire behavior and second order fire effects. We are using timeelapsed thermal imagery within these plots (as they burn) to assess fine-scale 

fire intensity, with a goal to relate the output thermal signatures to the fuel 

structure captured with both field and laser data. This imagery was recorded 

atop a custom telescoping lift above each plot. The results showed that fuel 

and fire behavior heterogeneity at this fine-scale varied together (spatial autocorrelation). Understanding fine-scale fuel heterogeneity and fire behavior 

will aid in better understanding the varying fire intensity effects on pine 

seedling and young oak demographics, which have been complex and difficult to study (Rebertus and others 1989).

Conclusions

Here, we introduce a novel approach to assessing fine-scale fuels and a 

preliminary description and assessment of some of the data collected and 

possible research applications. As this research progresses, we hope to learn 

more about the complexities of the fuelbed and its influence on fire behavior 

and secondary fire effects.

The variation in longleaf pine fuelbeds captured using this new technique is 

complex in physical structure. These structures (within fuelbed heterogeneity) 

may affect fine-scale fire behavior and subsequent plant response, especially 

related to pine seedling and young oak interactions. Ground-based LIDAR is 

a new tool that may be used to measure and analyze these vegetative features 

at multiple spatial scales and more accurately quantify important fuelbed 

properties, such as bulk density, packing ratios, and continuity.

References

Drake, J.B.; Weishampel, J.F. 2000. Multifractal analysis of canopy height measures 

in a longleaf pine savanna. Forest Ecology and Management 128: 121-127.

Fröhlich, C.; Mettenleiter, M. 2004. Terrestrial laser scanning – new perspectives in 

3D surveying: conference proceedings. 3-6 October 2004; Freiburg, Germany. 

Proceedings ISSN 1682-1750: International Archives of Photogrammetry, Remote 

Sensing and Spatial Sensing: 7-13.

Hall, S.A.; Burke, I.C.; Box, D.O.; Kaufmann, M.R.; Stoker, J.M. 2005. Estimating 

stand structure using discrete-return lidar: an example from low density, fire prone 

ponderosa pine forests. Forest Ecology and Management 208: 189-209.

Hopkinson, C.; Chasmer, L.; Colin, Y.-P.; Treitz, P. 2004. Assessing forest metrics 

with a ground-based scanning lidar. Canadian Journal of Forest Research 34: 

573-583.

Lefsky, M.A.; Harding, D.; Cohen, W.B.; Parker, G.; Shugart, H.H. 1999. Surface 

Lidar Remote Sensing of Basal Area and Biomass in Deciduous Forests of Eastern 

Maryland, USA. Remote Sensing of Environment 67: 83-98.
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 523

Application of Ground-Based LIDAR for Fine-Scale Forest Fuel Modeling Loudermilk, Singhania, Fernandez, Hiers, O’Brien, Cropper, Slatton, and Mitchell

Lichti, D.D.; Gordon, S.J.; Stewart, M.P. 2002. Ground-based laser scanners: 

operation, systems and applications. Geomatica 56: 21–33.

McNab, H.; Avers, P.A. 1994. Ecological subregions of the United States. Section 

descriptions. Washington, DC: U.S. Department of Agriculture, Forest 

Service.

Nelson, R.; Krabill, W.; Tonelli, J. 1988. Estimating forest biomass and volume 

using airborne laser data. Remote Sensing of Environment 24: 247-267.

Rebertus, A.J.; Williamson, G.B.; Moser, E.B. 1989. Longleaf pine pyrogenicity and 

turkey oak mortality in Florida xeric sandhills. Ecology 70: 60-70.

Watt, P.J.; Donoghue, D.N.M. 2005. Measuring forest structure with terrestrial 

laser scanning. International Journal of Remote Sensing 26: 1427-1446.

Wolf, P.R.; Ghilani, C.D. 1997. Adjustment Computations: Statistics and Least 

Squares in Surveying GIS. New York, NY: John Wiley & Sons, Inc.584 p.
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 524

Application of Ground-Based LIDAR for Fine-Scale Forest Fuel Modeling Loudermilk, Singhania, Fernandez, Hiers, O’Brien, Cropper, Slatton, and Mitchell

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Abstract—This case study illustrates the positive effects of strategic fuels treatments in 

continuous heavy fuels. In 1999, a severe windstorm blew down close to 1,000 square 

miles of forest land in northern Minnesota and Canada. As much as 400,000 acres of 

the blowdown occurred in the Boundary Waters Canoe Area Wilderness. Fire experts 

were invited to assess the hazardous fuels problem and to design and implement a 

treatment strategy that would effectively slow the spread of wildland fires and reduce 

the threat of a wildfire moving out of the Boundary Waters Canoe Area Wilderness 

(BWCAW) and into adjacent homes and businesses along a highly used area of the 

Superior National Forest. Treatment blocks were strategically placed in a brick/grid 

pattern across the blowdown landscape in order to slow a wildfire’s progress while 

only treating 15 to 20 percent of the total area. Success of those treatments was demonstrated when a large fire threatened the area of businesses and homes along the 

Gunflint Trail in July 2006. While the brick/grid pattern treatments were not completely 

in place, the fuel treatments were effective in containing the 32,000 acre Cavity Lake 

Fire. The fire behavior dramatically dropped within the big treatment units, allowing 

firefighters to successfully implement control tactics and protect $31 million worth of 

structures in the direct path of the fire.

Introduction

The setting for this case study is northeastern Minnesota, the home of the 

Superior National Forest (fig. 1). This part of the State is dominated by boreal 

forest, combining aspen, paper birch, balsam fir, white and black spruce, jack 

pine, red pine, and white pine. All of these species with the exception of red 

and white pine are short lived trees (60 to 100 years) and are adapted to largescale disturbance, including wild fire. After heavy logging at the turn of the 

19th century, the area was designated as a National Forest where reforestation 

Innovations in Fuels Management: 

Demonstrating Success in Treating a Serious 

Threat of Wildfire in Northern Minnesota

Dennis Neitzke1

Figure 1—The Superior National 

Forest, in the northeastern part of 

Minnesota, borders Canada and 

shares a vast Wilderness resource 

with Canada, known as the Boundary Waters Canoe Area Wilderness.

In: Butler, Bret W.; Cook, Wayne, 

comps. 2007. The fire environment—

innovations, management, and policy; 

conference proceedings. 26-30 March 

2007; Destin, FL. Proceedings 

RMRS-P-46CD. Fort Collins, CO: 

U.S. Department of Agriculture, 

Forest Service, Rocky Mountain 

Research Station. 662 p. CD-ROM.

1 District Ranger, U.S. Department 

of Agriculture, Forest Ser vice, 

Superior National Forest, Minnesota. 

ddneitzke@fs.fed.us
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 526

and protection became the primary objectives for decades. By the 1990s the 

Superior National Forest had abundant second growth forest. Although by this 

time forest management was common, a large part of the Forest had become 

mature to overmature and more than 1 million acres had been protected as 

the Boundary Waters Canoe Area Wilderness (BWCAW).

On July 4, 1999, a tremendous wind and rain storm caused substantial 

damage to the Superior and Chippewa National Forests as well as other public 

and private lands throughout northeastern Minnesota and Ontario, Canada. 

The storm, described as a Derecho, brought the jet stream to the surface of 

the forest, and winds of nearly 100 miles per hour flattened roughly 1,000 

square miles of forest land. Approximately 477,000 acres of the Superior National Forest were damaged by the blowdown, concentrated in the BWCAW 

and in small, scattered areas on the remainder of the Forest. Fuel loadings 

in the damaged areas increased from an average of 10 tons per acre to more 

than110 tons per acre. A five county area in northeastern Minnesota was declared a Federal disaster by the President on July 28. The path of blowdown 

stretched over 125 miles and at times was 20 miles wide and as is typical for 

the Lake States lies in a direction from southwest to the northeast. Dozens 

of homes and businesses were damaged, hundreds of miles of roads were 

closed by tree fall, and more than 60 people camping in the BWCAW received 

injuries. The Superior National Forest recognized the immediate threats to 

life and property and initiated search and rescue efforts for those injured. 

Our second priority was to open roads and clean up property and businesses 

outside of the BWCAW.

Once the immediate health and safety of residents and visitors had been 

taken care of, the Forest set about to understand the long-term changes created by this new massive fuel bed. Obvious, at least to the Forest Service, was 

the increased risk of a major wildfire threatening everything in the path of 

the blowdown. Of particular concern were three narrow corridors of heavy 

private ownership that bisected the Wilderness; those corridors lie in the direct 

path of the blowdown. This case study involves one of those corridors, called 

the Gunflint Trail, basically a County Highway that bisected the BWCAW 

and dead ended near Canada. Directly in a downwind path was the urban 

interface of the upper Gunflint Trail where more than $31 million of homes 

and business exist. This area is also one of the most popular entry points for 

the Boundary Waters Canoe Area Wilderness (fig. 2).

Assessment

To fully understand the magnitude of the threat and to help develop alternative strategies, the Forest enlisted the help of a team of experts in fire 

behavior and fuels management analysis to provide an assessment of the 

Forest’s situation. The team was lead by Dr. Mark Finney of the Rocky Mountain Research Station of the USDA Forest Service. Analysis was completed 

on several aspects of fire behavior to assist in planning necessary recovery 

measures. Modeling was done on fuel to estimate the risk of a fire spreading 

into the areas of homes and businesses within the Wilderness (fig. 3). This 

information was used to determine the breadth of fuels treatments necessary 

within the Wilderness in order to reduce the probability of a fire exiting the 

Wilderness. Other modeling was done to determine the intensity of behavior 

expected from a blowdown fire to allow us to determine appropriate suppression strategies and tools.

Innovations in Fuels Management: Demonstrating Success in Treating a Serious Threat of Wildfire in Northern Minnesota Neitzke
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 527

Figure 3—Comparison of probabilities that a wildfire might exit 

the BWCAW boundary and enter 

the urban interface. Top chart 

demonstrates that farther than 

3 miles from the boundary, the 

probability drops well below 20 

percent. Bottom chart demonstrates that even at 11 miles from 

the boundary, the probability is 

still greater than 20 percent.

Figure 2—The nearly complete blowdown near the 

Seagull Lake area at the end 

of Gunflint Trail. Seagull 

Lake is a highly popular 

recreation site.

Preblowdown, standing forest

Blowdown fuels

Innovations in Fuels Management: Demonstrating Success in Treating a Serious Threat of Wildfire in Northern Minnesota Neitzke
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 528

Figure 4 demonstrates the dramatic increase in fire intensity in the low 

to moderate fire danger conditions. The yellow arrows indicate when fires 

under each set of conditions will exhibit such intensity that direct suppression 

tactics will have little impact. The two blue lines display the narrow window 

estimated to be available to implement prescribed fires in blowdown fuels.

Conclusions regarding fire behavior were presented to the Forest upon 

completion of the Fuels Risk Assessment that lead to a new strategy for 

managing fuels on the Forest. Those conclusions were:

• Fire will impact the BWCAW; it is not “if” a fire will happen, but 

“when.”

• Blowdown will burn more intensely under a wider range of burning 

conditions.

• Blowdown will result in more consistent fire growth (less sensitive to 

weather variations).

• Blowdown fires will demonstrate faster growth, but not faster than crown 

fire.

• Blowdown fires will resist control, particularly using standard tactics.

• Treatments will slow, but not stop, fire growth.

Fuels from similar blowdown events in Canada demonstrated the ability 

to contribute to extreme fire behavior to beyond 30 years after the event. 

Further, the Superior National Forest has a history of at least 1 year each 

decade where weather conditions contribute to large fire growth. Adding 

those two factors together, we understood that there will be a significant 

wildfire in the blowdown if not treated. Therefore, a strategy for addressing 

blowdown fuels was necessary even though much of the blowdown occurred 

within the Wilderness.

Figure 4—Hauling chart showing comparison of fuels related fire behavior.

L M H E

Intensity Level Blowdown Fuels FM 13

Standing Forest FM 10

Fire Danger Class

Innovations in Fuels Management: Demonstrating Success in Treating a Serious Threat of Wildfire in Northern Minnesota Neitzke
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 529

Developing a Plan

Four critical focal points were emphasized in the Forest’s response to the 

increased threat of a wildfire in the blowdown:

1. Intensify fire prevention.

2. Change fire suppression tactics and increase suppression capabilities.

3. Intensify emergency action planning, including evacuation planning.

4. Initiate fuel treatment techniques

Prevention

History tells us that prior to the blowdown, nearly 90 percent of the fires 

outside the Wilderness and 50 percent of the fires inside the Wilderness 

were human caused. Because of the volatility of the fuel bed left from the 

storm, time was necessary in order to implement all four prongs of the response strategy. It was critical to delay the impending wildfire long enough 

to complete the planning phases for our suppression tactics and evacuation 

planning, as well as time to employ the appropriate level of fuels treatment. 

Using the information gained from the Fuels Risk Assessment allowed all the 

levels of government to develop key messages and strategies to implement a 

prevention program. In 2006, the Superior National Forest experienced 128 

fire starts of which only 10 were human caused, and none developed into a 

significant fire.

Emergency Action and Suppression Capability

Within the first year, county, State, and Federal governments had developed evacuation plans for each of the areas of urban interface. Each level of 

government brought a new set of fire resources to northeastern Minnesota 

including hand and engine crews, helicopters, and air tankers. Adding to 

our complement of available tools were the air tankers made available from 

Canada through border mutual aid agreements.

Fuel Treatment Techniques

With the information at hand, an overall strategy for treating the blowdown 

fuels was crafted: “start in close and work our way out.” Fuels in and around 

homes, businesses, and areas where people congregate would be treated first. 

A two-pronged approach was used. First, fuels were completely removed from 

homeowner sites using either machinery or hand tools and transported to 

disposal sites. Second, homeowners took it upon themselves to install sprinkler systems on and around their buildings. With the abundant water in the 

vicinity of the Gunflint Trail, and given enough time, homeowners could 

create a blanket of wet fuels surrounding their homes.

Next in our strategy, we would treat areas adjacent to the previous points using both machinery and prescribed fire, creating a fuels treatment buffer. The 

final step would be to use prescribed fire to create spatially located treatments 

within the BWCAW to slow the spread of a wildfire (see fig. 5). The formal 

decision to carry out that strategy was done through three Environmental 

Impact Statements, two Environmental Assessments, Alternative Arrangements coordinated through CEQ, and some small Categorical Exclusions.

Innovations in Fuels Management: Demonstrating Success in Treating a Serious Threat of Wildfire in Northern Minnesota Neitzke
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 530

18, 000 acres treated (FM-8) 18, 000 acres treated (FM-8)

13.5% of the landscape 13.5% of the landscape

Figure 5—Strategic placement of 

treatments (SPOTS). Dr. Mark Finney 

(USDA Forest Service) developed 

a theory for treatment within the 

BWCAW where patches of prescribed 

fire land would connect to bodies of 

water. The concept is to interrupt the 

fuel bed to slow a fire and allow an 

opportunity for control measure to 

be used.

Implementing the Plan

Expediency was critical and the first two phases of the plan were completed 

by the end of 2001. To the greatest extent reasonable fuels treatments outside 

the BWCAW were completed using logging and other mechanical methods. 

However, Forest Service policy combined with limited, if any, access and steep, 

rugged terrain, precluded us from using mechanical tools to treat fuels inside 

the Wilderness. Prescribed fire with a heavy component of aerial resources 

was necessary to complete the fuels reduction within the BWCAW.

Inside the BWCAW, roughly 32,000 acres of complex blowdown burning 

to create the spatially located treatment units had been completed by the end 

of 2005. This included treating a band of the heaviest blowdown surrounding 

the upper end of the Gunflint Trail. We started slowly with smaller units that 

included better opportunities for control. We chose conditions where fuel 

and soil moistures were high enough so that the fire only consumed fuels 

generally 3 inches in diameter and smaller. The concept was to treat only 

those fuels that would contribute to high fire intensity and spread rates.

Following each year of burning we would conduct a formal review of 

operations, organization, cooperation, planning, fire behavior, and fuels 

consumption. Each year the lessons learned were rolled into the next year’s 

burn plans to improve our knowledge, skills, abilities, and success. From the 

first fires, it was evident that the fuels Risk Assessment contained credible 

information and projections. Our ignitions efforts demonstrated that on 

the same day, same location, and same conditions, fire in standing timber 

would barely creep while fire in immediately adjacent blowdown displayed 

50 to 60 ft flame lengths. As our experience grew, so did our confidence in 

burning larger units at one time, and eventually we were burning 3,000 to 

4,000 acres in one unit.

Monitor, Learn, Adapt, and Tell the Story

Conducting complex prescribed fire, using a myriad of aircraft, experiencing 

fairly intense fire behavior, and generating copious amounts of smoke in the 

backyards of homeowners does not necessarily lead to great public relations. 

Innovations in Fuels Management: Demonstrating Success in Treating a Serious Threat of Wildfire in Northern Minnesota Neitzke
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 531

One point we implemented from the beginning was to bring information 

back to the public in an open forum where questions could be asked and our 

progress could be discussed. We took the lessons from our after action review 

and presented those each year, along with a discussion on the changes we 

made as a result of lessons learned. Five years of success with fire, talking to 

people regularly, providing information, and inviting the public to observe 

our actions brought the comfort of most of our public to an acceptable level. 

However, those at that comfort level were not necessarily the vocal ones. It 

seemed that we could not convince everyone of the benefits of our actions. 

However, the positive relations we held with the majority proved to be a great 

asset in our interactions with critics.

Testing the Plan with Our First Significant Wildfire

The year 2005 brought dry conditions and in August a lightning strike 

pushed by strong winds brought about our first project size fire. Although 

the Alpine Lake fire was in the general blowdown area, the fire spread for 

the first day was in standing conifer. An early break in the weather allowed 

us to control the fire spread right at the trigger point we had outlined for 

evacuation. We took the time to compare fire behavior and fire effects be-

tween this fire and the blowdown burns we have implemented. One notable 

point was that a crown fire stops its major movement when the wind stops 

blowing, whereas a blowdown fire continues to spread until it runs out of 

blowdown fuels.

We conducted a BARC (burn area reflectance correlation) analysis to look 

at the effects between the fires. Because we implemented our prescribed fires 

with soil moistures at higher levels, we noticed the consumption of organic 

soil layers was much less with prescribed fire than with wildfire.

However, the real unanswered question was: Would our prescribed fire 

treatments withstand the test of a major wildfire?

With 6 years of preparation under our belts, 2006 once again proved to be 

dryer than normal. On July 14, the lightning storm moved across northeast-

ern Minnesota and Ontario, Canada, igniting about 40 fires across northern 

Minnesota and several hundred in Ontario. The Cavity Lake Fire was reported 

by a patrol aircraft at 1533 and air tankers were ordered at 1537. The fire was 

6 miles inside the BWCAW but was also in some of the heaviest blowdown 

fuels and upwind of several homes, businesses, a heavily used campground, 

and a youth camp located within the Gunflint Trail corridor. Although we 

had not fully implemented our fuels reduction plan, we did have a buffer of 

prescribed fire between Cavity Lake Fire and the values at risk.

Heavy action by water scooping air tankers had little effect on the blow-

down fire and it grew to over 100 acres within 4 hours. Flame lengths were 

estimated to be 100 to 200 ft in blowdown causing spotting of up to 1,000 ft. 

The fire grew steadily through the night and each of the next 2 days so that 

by late afternoon of July 16 it covered about 3,000 acres.

We had a fire in the same general area 2 weeks earlier called the Rog Lake 

Fire. Our airtankers proved successful at halting the forward spread of that fire 

and provided enough control to allow firefighters to cut a path through the 

blowdown to actively suppress the fire. This particular fire was only 150 yards 

from the shore of Seagull Lake, but it took a crew of six firefighters, using 

chainsaws, 9 hours to cut a path to get to the fire. Cavity Lake Fire’s origin 

was nearly 1.5 miles from the closest point of entry for firefighters. There 

was no safe method to approach the fire from the ground because the entire 

distance to the fire was through some of our heaviest blowdown.

Innovations in Fuels Management: Demonstrating Success in Treating a Serious Threat of Wildfire in Northern Minnesota Neitzke
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 532

The BWCAW is the heaviest used Wilderness in the National Forest System 

with nearly 12,000 persons per night camping somewhere in the Wilderness 

during the summer, the heaviest use time of year. The primary concern of 

our ground forces was to interact with the Wilderness visitors to ensure they 

were safely moving out of harm’s way.

As the end of the third day approached, Cavity Lake Fire had not reached the 

prescribed fires treatments; however, the evening of July 16 brought a different 

story. A thunderstorm with winds from 40 to 50 miles per hour created a fire 

storm of significant size. Our meteorologists gave enough advanced warning 

to ensure all aircraft and firefighters were out of harm’s way. However, the 

storm began late in the evening and continued past dusk and provided us no 

opportunity to assess the extent of fire growth or location. Reports persisted 

through the night that fire had spread into the forest surrounding the homes 

and youth camp that our prescribed fires were supposed to protect.

Years earlier the public had been vocal about our prescribed fire treatments 

in and around Seagull Lake, a major attraction for the end of the Gunflint 

Trail. The view and water quality were vital to the success of several businesses 

and were important to residents of the lake. We did treat the one major island 

that was about 3 miles long (oddly called 3-Mile Island), but we did consent 

to leave some smaller islands untreated by our prescribed burns. The public 

insisted these islands would not be affected in a fire advance, but we had some 

doubt; nevertheless, the islands remained with untreated blowdown.

The thunderstorm of the evening of July 16 lasted 2 to 3 hours, but it 

brought no rain. Our patrols through the night indicated no structures lost, 

but it was not known exactly how close the fire had advanced. As daylight 

came, we were able to put aircraft up and assess the situation and were stunned 

at what we saw. During the hours of the thunderstorm, the fire tripled in 

size to roughly 12,000 acres. Those small islands that we had agreed to leave 

untreated had in fact become involved in the advance of the fire (fig. 6) as 

it apparently island-hopped across nearly 4 miles of water and ignited the 

mainland in the vicinity of the homes. What we also saw was that the fire had 

Figure 6— One of the small islands in Seagull Lake that was not supposed to carry fire. 

Several smaller islands were not treated with prescribed fire, and looking back, “No 

Treatment” proved to be a valuable teachable moment. (Photo by Carol DeSain)

Innovations in Fuels Management: Demonstrating Success in Treating a Serious Threat of Wildfire in Northern Minnesota Neitzke
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 533

Figure 7—Final map of the Cavity Lake 

Fire. Origin of the fire is shown with the 

X while the blue shows the final extent 

of the fire. Prescribed fire treatments are 

shown in green on the eastern edge of the 

fire; further blowdown is shown in red to 

the west. Gunflint Trail corridor is shown 

within lines east of the fire. The area with 

many homes is near the symbol for the 

campground. The oval shows the islands 

that were not treated with prescribed fire 

but contributed to its spread. 

spotted into the prescribed fires and was smoking at up to a half mile into 

the burns. However, the treatments had taken enough energy out of the fire 

to halt the spread toward the homes. A striking contrast existed between the 

black of the wildfire and the remaining green within the treated areas.

Over the next 2 weeks the fire continued to grow until it reached its final 

size of 32,400 acres (fig. 7). It challenged our suppression crews in other 

areas, but our fire specialists were able to use a combination of lakes and our 

prescribed fires to anchor control tactics that included burnout operations. 

Finally, 1.5 inches of rain stopped the spread and allowed more direct control 

measures, and though it smoldered into September, the fire made no further 

progress.

Results

During the course of the fire, we again brought fire behavior experts 

back to provide both a long-term assessment of fire growth potential and 

also to evaluate the Fuels Risk Assessment assumptions and our treatments 

as they related to Cavity Lake Fire (fig. 8). Our overhead team relied on the 

assessment to make decisions on control tactics including implementing the 

concepts of cost containment that are important to the Forest Service. Some 

of the major points from the evaluation:

	 •	 Fire in blown-down fuels behaved as predicted.

	 •	 The fuel treatment areas have functioned as designed.

	 •	 Prior analyses were based on “average” fire season climatology data.

	 •	 Probabilities for fires to exit the Wilderness as well as the projected fire 

sizes are greater than previously thought in a higher than normal fire 

danger year.

	 •	 It was a “Good Plan.”

Looking back at the untreated islands that contributed to the fire spread 

across Seagull Lake, I conclude that it was not necessarily a bad decision. 

Although the fire was carried across the lake, enough energy was removed 

from the fire that movement on the mainland was minimal, and we easily 

controlled it there. The event fully demonstrated to the public that the conclusions from the Fuels Risk Assessment were not overstated.

Innovations in Fuels Management: Demonstrating Success in Treating a Serious Threat of Wildfire in Northern Minnesota Neitzke
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 534

Lessons Learned

As we do each year, we gathered in the fall and conducted a full after action review and looked for lessons learned. While there is always something 

to improve on, the positive factors and lessons learned from our review are 

as follows:

	 •	 Enlist outside expert analysis to aid your planning.

	 •	 Assess your fuels situation and develop strategies and tools to manage 

them.

	 •	 Fuels treatments are essential to reducing the threat of wildfire in an 

urban interface setting.

	 •	 Prescribed fire is effective as a method to take the energy out of a blowdown fire.

	 •	 You can never do too much public education or provide too much information on fires in the interface, and proper planning with practice pays 

off.

	 •	 Evacuation planning and practice (whether you use it or not) pays high 

dividends in terms of calming public response.

	 •	 The right decision is not always the popular decision.

	 •	 Using national resources to assist with long-term assessments provides 

critical information for decisionmaking on large events.

	 •	 Incident business advisors and buying teams help keep a District Ranger 

out of hot water during transition fires.

Figure 8—Fire behavior experts returned to help assess long-term fire behavior 

period, and to assess what the fire would have looked like without prior treatments. The darker the color equals higher probability of fire spread. The upper 

Gunflint Trail would have been inundated with fire.

Innovations in Fuels Management: Demonstrating Success in Treating a Serious Threat of Wildfire in Northern Minnesota Neitzke
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 535

Abstract—PAM-12TM is a recycled office paper byproduct made into a spreadable 

mulch with added Water Soluble Polyacrylamide (WSPAM), a previously difficult 

polymer to apply. PAM-12 is extremely versatile and can be applied through several 

methods. In a field test, PAM-12 outperformed straw in every targeted performance 

area: erosion control, improving soil hydrophobicity, and plant establishment. ENCAP, 

LLC in Green Bay, WI, holds the patent to PAM-12. 

Introduction

A new technology, available for postfire rehabilitation and restoration 

work, stabilizes soil, improves native seed establishment, and reduces hydrophobicity in the soil. Government entities that need to rehab fire-decimated 

forests are effectively demonstrating it, testing it, using it more and more, 

and discovering the same thing: this innovation not only does a better job 

than traditional means, but it is more cost effective, allowing double the acreage on average to be restored for the same amount of money. The product 

revolves around the use of a highly researched Water Soluble Polyacrylamide 

(WSPAM). WSPAMs have been used in agriculture for decades; however, 

it has always been a difficult polymer to apply. ENCAP, LLC in Green Bay, 

WI, has patented an innovative carrier technology to make application of 

WSPAMs easy and cost effective.

The specific product is called PAM-12TM and with the help of Dr. Aicardo 

Roa, from the University of Wisconsin-Madison, it has been perfected as 

an extremely powerful yet cost effective tool that can be added to any erosion control program, especially postfire rehab. Specifically, PAM-12 is a 

recycled office paper byproduct made into a spreadable mulch impregnated 

with WSPAMs patented as Advanced Soil TechnologyTM (ASTTM). PAM-12 

is extremely versatile in its application methods as it can be applied through 

traditional spreaders, drill-seeded, or hydro-seeded. Or uniquely, PAM-12 

can be combined with any seed variety need and can be applied in one step 

saving even more time and money.

TRI, a reputable, independent testing agency, put PAM-12 to the test (as 

requested by Wisconsin Department of Transportation) by requiring it to 

pass the test that is required for erosion control blankets to pass. The passing 

grade is a C factor of 0.2 or below. The 0.2 is the amount of soil loss that 

2,000 lb/acre of straw produces. At only 600 lb/acre, PAM-12 produced a 

C factor of 0.12. This paper reports the field trial results that show at only 

600 lb/acre, PAM-12 outperformed straw (at 3,000 lb/acre) in every targeted 

performance area: erosion control, improving soil hydrophobicity, and plant 

establishment.

New Technology in Postfire Rehab

Joe Sabel1

In: Butler, Bret W.; Cook, Wayne, 

comps. 2007. The fire environment—

innovations, management, and policy; 

conference proceedings. 26-30 March 

2007; Destin, FL. Proceedings 

RMRS-P-46CD. Fort Collins, CO: 

U.S. Department of Agriculture, 

Forest Service, Rocky Mountain 

Research Station. 662 p. CD-ROM.

1 Market Manager, ENCAP, LLC, 

Green Bay, WI. jsabel@encap.net
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 536

Methods

Uinta National Forest Red Bull Fire

The Red Bull Fire burned through the Uinta National Forest in the 

Spanish Fork Canyon near Provo, UT, in July/August 2004. The 1,836 acre 

fire burned for 6 days before being contained on August 3, 2004. Specialists with backgrounds in soil science, hydrology, ecology, archaeology, range 

management, and erosion control analyzed the burned area and proposed 

several treatments as part of the Burned Area Emergency Response (BAER) 

report/plan.

The rehabilitation treatments revegetated burned slopes through use of 

combining native seed species with use of straw mulch and PAM-12 to aid 

vegetative recover, reduce erosion, and prevent degradation of soil and water 

resources.

High severity burn areas experience higher rates of soil/vegetation loss 

from erosion due to high overland flow, decrease in infiltration (water repellency), and loss of vegetation coverage.

Objectives—Compare performance capabilities of PAM-12 to straw in the 

following targeted performance areas relating to revegetation:

• Soil hydrophobicity—scarred soil’s tendency to resist water intake

• Erosion control

• Seed establishment and revegetation

Demonstration/evaluation method—In total, four demonstration sites were 

created (with seed): PAM-12, straw, Pam-12 plus straw, and a control

Participation/evaluation leadership team—Team members were:

• USDA Forest Service Soil Scientist Bob Davidson

• USDA Forest Service and BAER Leader: Ken Luckow

• USDA Forest Service Hydrologist Jeremy Jamecke

• Manufacturer: ENCAP, LLC, Mike Krysiak, President

• Distributor: UAP Timberland, Pat Thomas

• Pilot: Stephen West from Reeder Flying Service

Application—A helicopter aircraft was used for all aerial applications. 

A seeding bucket was used by the aircraft for application of the seed and 

PAM-12. Traditional aerial application methods were used for straw mulching and hay bale bombing. Application rates of products were as follows: 

seed = 46 lb/acre, PAM-12 = 600 lb/acre, straw =3,000 lb/acre.

Test protocol—Objective test protocols were established by the USDA Forest 

Service for each aforementioned Targeted Performance Area. Data were collected in triplicate per demonstration site for accurate representation.

Results and Discussion

After more than 9 months of field observation relating to revegetation, 

data conclusively demonstrate that PAM-12 outperformed straw in all three 

targeted performance areas: seed establishment, erosion control, and soil 

hydrophobicity.

New Technology in Postfire Rehab Sabel
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 537

Hydrophobic Soil Test Results

Both demonstration sites that utilized PAM-12 saw significant improvements in water infiltration of the treated soil. When directly comparing 

PAM-12 to straw, PAM-12 showed a 38 percent improvement in the hydrophobicity of soil, versus a 0 percent improvement shown in the straw plot 

(fig. 1).

0% 0%

29% 38%

0%

20%

40%

Control Straw Straw &

PAM-12

PAM-12

Hydrophobic Soil Improvement (%)

Figure 1—Comparison of four treatments and the hydrophobic 

soil improvement.

Figure 2—Erosion control test results on four treatments.

Erosion Control Test Results

PAM-12 outperformed straw in its ability to control erosion and reduce 

the total soil movement by over 60 percent. In addition, both demonstration 

plots treated with PAM-12 (strictly PAM-12 and PAM-12 with straw) did 

significantly better than plots not treated with PAM-12 (fig. 2).

Plant Establishment Test Results

Use of PAM-12 resulted in more plant life than straw by a ratio of more 

than 2:1. On the plot where PAM-12 was added with straw, the plot generated 78 percent more plant life than straw alone.

1.51

0.90

0.60

0.75

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

Straw PAM-12 Straw/Pam-12 Control

Avg. Soil Movement (Abs Value in Inches)

New Technology in Postfire Rehab Sabel
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 538

Figure 3—Plant establishment on four treatments.

Observations

Successful germination and establishment of plant life is impacted by 

variables such as erosion, soil’s hydrophobic nature, and soil type. PAM-12’s 

performance in the other two targeted performance areas was certain to aid 

in helping to achieve better results in this third and final targeted performance area (fig. 3).

PAM-12 Straw With Pam-12

Control Straw

Rock

0%

Plant Life

26%

Dead Plant

5%

Soil

69%

Rock

7%

Plant Life

59%

Soil

31%

Dead Plant

3%

Rock

3%

Plant Life

50%

Dead Plant

4%

Soil

43%

Rock

2%

Plant Life

28%

Dead Plant

5%

Soil

65%

Summary and Conclusions

In conclusion, PAM-12 is a proven product for forest fire rehabilitation 

and restoration. More and more government entities are adding PAM-12 to 

the list of tools in their toolbox. PAM-12 has the ability to not only control 

erosion, but to improve plant establishment and improve the hydrophobic 

layer formed in the soil after a burn. In addition, PAM-12 is effective in conjunction with other tools as the data above suggest. This gives one a “belts 

and suspenders” approach for those extreme cases where multiple means are 

needed to restore a mountainside.

In addition to all the aforementioned benefits PAM-12 brings, it is extremely cost effective as well. This is important as year after year, budgets 

get tighter and tighter. Utilizing PAM-12 in the rehab program allows an 

end user to rehab on average twice the amount of acreage than traditional 

methods (600 lb of PAM-12/acre versus 3,000 lb of straw per acre). Or instead of applying it by itself, PAM-12 has the unique ability to be combined 

with seed to do application of mulch and seed both at the same time saving 

time and money.

New Technology in Postfire Rehab Sabel
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 539

Abstract—A Lightning Ignition Efficiency map was added to the suite of daily maps 

offered by the Wildland Fire Assessment System (WFAS) in 1999. This map computes 

a lightning probability of ignition (POI) based on the estimated fuel type, fuel depth, 

and 100-hour fuel moisture interpolated from the Remote Automated Weather Station 

(RAWS) network. An attempt to verify the efficiency map was made using cloud-toground (CG) lightning discharge data acquired at the Missoula Fire Lab (through the 

Vaisala Corporation network), lightning fire location data from within USDA Forest 

Service (FS) Region 1 boundaries, and daily 100-hour fuel moisture values retrieved 

from historical National Fire Danger Rating System (NFDRS) archives for the 2003 fire 

season. Daily POI maps were recomputed for 153 days (May through September). 

Daily CG lightning density grids for the same timeframe were computed from lightning 

discharge data and were multiplied by the POI grid to yield a daily 1-km predicted 

number-of-fires or fire “possibility” grid. A daily lightning-caused fire density grid was 

produced using various agency fire occurrence databases from May through September. 

While preliminary spatial neighborhood analysis showed some predictive capability, 

the predicted number of lightning caused fires exceeded the actual reported fires. This 

overestimation could be due to the lack of differentiation between an ignition and 

reported fire and errors in fire occurrence databases. Being able to predict whether 

lightning resulted from a “wet” or “dry” storm could improve predictive ability. More 

refined and spatially accurate fuel maps have become available and are planned to 

be incorporated into future models.

Introduction

Lightning is the primary cause of wildland fire ignitions in the Western 

United States. Networks to detect cloud-to-ground (CG) lightning strikes 

have covered this region in various forms since the 1970s. Presently, the 

network covering the continental United States, operated by Vaisala Corporation, advertises an 80 to 90 percent detection efficiency and a 500 m spatial 

accuracy. To date, we are not aware of any attempts to correlate individual 

CG discharges to actual lightning fires. This is due to a variety of reasons 

including the questionable quality of fire start data and the tendency of 

lightning ignitions to become “holdover” fires.

In 1999, a Lightning Ignition Efficiency map (USDA Forest Service 2002) 

was added to the Wildland Fire Assessment System (WFAS). This map computes a lightning probability of ignition (POI) based on the estimated fuel 

type, fuel depth, and 100-hour fuel moisture interpolated from the Remote 

Automated Weather Station (RAWS) network. The efficiency values are on 

a per-CG discharge basis, and the map is based on a 1-km2 pixel. The values 

on the map are generated by an algorithm based on a laboratory research 

In: Butler, Bret W.; Cook, Wayne, 

comps. 2007. The fire environment—

innovations, management, and policy; 

conference proceedings. 26-30 March 

2007; Destin, FL. Proceedings 

RMRS-P-46CD. Fort Collins, CO: 

U.S. Department of Agriculture, 

Forest Service, Rocky Mountain 

Research Station. 662 p. CD-ROM.

1 Physical Scientist, U.S. Department 

of Agriculture, Forest Service, Rocky 

Mountain Research Station, Missoula, 

MT. psopko@fs.fed.us

2 Scientist, Emeritus, U.S. Department 

of Agriculture, Forest Service, Rocky 

Mountain Research Station, Missoula, 

MT.

3 Statistician, Systems for Environmental 

Management, Missoula, MT.

Verification of the WFAS Lightning 

Efficiency Map

Paul Sopko1, Don Latham2, and Isaac Grenfell3
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 540

study (Latham and Schlieter 1989). Verification of the algorithm was delayed 

by lack of a suitable data set covering a range of input variables and a large 

number of fires. We generated such a data set for the year 2003 spanning 

the months of May through September for an area covering approximately 

Region 1 of the USDA Forest Service.

This study is a “first cut” at analyzing the data. We wanted to become 

familiar with use of GIS-based data storage, presentation, and analysis techniques. In particular, we wanted to explore presentation of point data, such 

as lightning and fires, as gridded density data on the landscape, and examine 

the results both visually and statistically.

Data

Lightning flash and stroke data came from Vaisala Corporation (Cummings 

and others 1998). Each flash in the data set has a date, time, latitude, and 

longitude. Values needed for calculating environmental variables came from 

the National Fire Danger Rating System (NFDRS) through operational 

RAWS stations. The base fuel map used to decipher fuel type and depth for 

the POI calculation came from Schmidt and others (2002). During May 

through September 2003, a total of 709,879 flashes were recorded and used 

in this study. One day’s data are shown in figure 1, comprising 21,192 flashes. 

Flashes are shown here regardless of the polarity of the flash, because the 

WFAS algorithm was generated to be polarity-free.

Fire locations were obtained from the USDA Forest Service, Montana 

Department of Natural Resources and Conservation (DNRC), Idaho Department of Lands (IDL), Bureau of Indian Affairs (BIA), Bureau of Land 

Management (BLM), National Park Service (NPS), and Fish and Wildlife 

Service (FWS). In all, the recorded latitude and longitude of 1,948 fires attributed to lightning along with the discovery dates were entered into the 

set. The results for 1 day’s data are shown in figure 2.

Figure 1—Cloud-to-ground lightning strikes (positive and negative), 

Northern Rockies, August 5, 2003.

Verification of the WFAS Lightning Efficiency Map Sopko, Latham, and Grenfell
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 541

For our analysis, we needed to calculate the WFAS daily lightning probability (efficiency). The algorithm requires a fuel type, depth, and the 100-hour 

NFDRS fuel moisture. Ignition probability in most fuels depends on fuel 

moisture. Short needled fuels depend more on duff depth (USDA Forest 

Service 2002). The ignition probabilities that depend on fuel moisture are 

not really in the 1-hour fuel moisture class but are closer to 25 to 50 hour. 

Because we are estimating the probability in the morning for the day, we 

chose the 100-hour class as the best representation. The 100-hour moistures 

were obtained from RAWS stations through the NFDRS data warehouse using FireFamily Plus (Bradshaw and McCormick 2000). The 100-hour fuel 

moisture values were converted to 1-km2 grids using the weighted inverse 

distance-squared interpolation method (Burgan and others 1997). The 

moisture field for a sample day is shown in figure 3.

Figure 2—Lightning-caused fires, August 5, 2003.

Figure 3—Interpolated 100-hour fuel moisture map, Northern Rockies, August 5, 2003.

Verification of the WFAS Lightning Efficiency Map Sopko, Latham, and Grenfell
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 542

The lightning probability algorithm has six fuel types: long-needled species such as ponderosa pine (Pinus ponderosa), short-needled species such as 

Douglas-Fir (Pseudtsuga menziesii), intermediate needle such as lodgepole 

pine (Pinus contorta), mixed high-altitude such as Englemann spruce (Picea 

engelmannii), grasses and hardwoods, and other nonconsidered such as water, 

farmland, cities, and barren land. The map of lightning fuel types, derived 

from Schmidt and others (2002), is static and applied here on a 1-km2 pixel 

basis (fig. 4).

A typical WFAS ignition probability product is shown as figure 5. The 

probability of ignition for the Northern Rockies on August 5, 2003, is shown 

in figure 6.

Figure 4—Lightning fuel types.

Figure 5—WFAS lightning probability of ignition, March 21, 2007.

Verification of the WFAS Lightning Efficiency Map Sopko, Latham, and Grenfell
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 543

Figure 6—Probability of ignition, Northern Rockies, August 5, 2003.

Analysis

Because we are exploring GIS techniques for the data analysis, we need 

to convert the point values for the lightning flashes and fire locations into 

densities; that is, the lightning flash and fire locations need to be converted 

into flashes or fires per square kilometer per day. In this way, we can apply 

the ignition probability on a daily basis to the daily flash density and compare 

to the daily fire density. In order to account for spatial error and to smooth 

the coarse edges, the density grids were refined using the ArcInfo spatial low 

pass filter “focalmedian” function. The focalmedian is a local neighborhood 

function and, in this instance, returned the median of the nine pixel values in 

the surrounding 3 X 3 matrix. Flash density for August 5 is shown in figure 

7 and fire density in figure 8.

The lightning flash density can be directly combined with the probability 

of ignition data by a simple pixel-by-pixel multiplication. The results are 

presented here as an ignition “possibility” map (an invented name for this 

paper only) (fig. 9). For example, if a given pixel has a 20 percent probability 

of ignition, five flashes would be needed within that pixel to generate a fire 

possibility of one. Note that at this stage the fires are treated as a continuous variable. To improve interpretation the continuous data are grouped into 

eight classes in figure 9. Obviously there cannot be 0.06 fires, but a possible 

ignition/fire density map does convey information of possible use to wildland 

fire organizations.

Figures 10 and 11 show actual fires superimposed on the fire possibility 

map for August 5, 2003. The fire locations are shown as point data. We 

formulated the fires as a density, or fires per square kilometer per day, for 

the preliminary statistical examination described later. For now though, we 

wanted to have a visual representation of how accurate the prediction could 

be. Figure 11, a “blowup” of the outlined section of figure 10, shows an 

example of how well we succeeded.

Verification of the WFAS Lightning Efficiency Map Sopko, Latham, and Grenfell
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 544

Figure 8—Lightning fire density, Northern Rockies, August 5, 2003.

Figure 7—Lightning flash density, Northern Rockies, August 5, 2003.

Verification of the WFAS Lightning Efficiency Map Sopko, Latham, and Grenfell
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 545

Figure 9—Predicted ignition possibility, Northern Rockies, August 5, 2003.

Figure 10—Predicted ignition possibility with lightning-caused fires, Northern 

Rockies August 5, 2003.

Verification of the WFAS Lightning Efficiency Map Sopko, Latham, and Grenfell
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 546

Figure 11—Closeup of outlined portion of figure 10.

On the whole, the visual picture is adequate. There are, of course, some 

fires ostensibly produced by lightning that are in places where there should 

not be any fires (fire possibility of zero). In general, however, the fires do 

seem to occur more often in the pale green patches rather than in the dark 

green ones; that is, fires are appearing in places where the expected density 

is higher as the legend in figure 8 shows. For comparison, figure 12 shows 

actual fires superimposed on the POI map for August 5, 2003.

For this preliminary examination we looked at the total data set in a more 

conventional manner. For the whole study period, the fires were “binned” 

by the probability of ignition categories they fell in for that day. Figure 13 

presents a breakdown of the number of pixels of lightning ignition probability 

having fires in them. There are 168 fires in locations with a zero probability 

that they could be in unclassified places such as farmland or the like, or the 

fire locations are off slightly. Most fires fall in ignition probability classes up 

to about 0.25 and the remainder in classes greater than that.

Next, for each day in the study period, we multiplied the lightning strike 

density grid by the probability of ignition grid yielding a fire possibility grid. 

In other words, we only included pixels that had one or more lightning strikes 

in them. This analysis was done on a per day basis; that is, if a fire occurred, 

we looked to see what the corresponding fire possibility value was for the 

corresponding pixel on the same day. The results are shown in figures 14 and 

15. Looking at the number of fires in the fire possibility classes in this more 

restricted way presents a different picture. According to figure 14, almost all 

of the fires occurred in 1 km2 pixels having no flashes. Figure 15 shows that 

the remainders are distributed as one might expect because the flash density 

seldom exceeds more than one flash per pixel.

Verification of the WFAS Lightning Efficiency Map Sopko, Latham, and Grenfell
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 547

Figure 12—Lightning caused fires and probability of ignition, Northern Rockies, 

August 5, 2003.

Figure 13—Number of fires in ignition probability categories.

Verification of the WFAS Lightning Efficiency Map Sopko, Latham, and Grenfell
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 548

Figure 15—Total fires in fire possibility (Fp) categories—zero Fp excluded.

Figure 14—Total fires in fire possibility (Fp) categories.

Discussion

We need to further consider the results from the data set. Presently, the POI 

map is calculated using a 1-km2 grid size. Perhaps this grid size is too small 

given the errors inherent in the lightning and fire point data. In particular, 

the error in the lightning flash data is known to be on the order of 500 m in 

radius around the flash location and can be larger. Because the fire location 

data came from multiple agencies using different fire reporting protocol, the 

spatial and temporal error of the combined fire dataset is unknown. Across 

the study area, we assumed locations were determined with various techniques 

including Global Positioning System (GPS) receivers, maps, memory, best 

Verification of the WFAS Lightning Efficiency Map Sopko, Latham, and Grenfell
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 549

guess, and so forth. We need to get an idea of the size of the potential fire 

location error by asking some questions of the agencies supplying the data. 

For example, DNRC warned that the fire location data from the multiagency 

jurisdictional area of eastern Montana was of poor quality. There appears to 

be a need for a multiagency standard protocol for determining fire ignition 

locations. Given the low cost and ready availability of GPS devices, we think 

they should be a standard tool provided to all initial attack crews.

The number of reporting RAWS stations affects the accuracy of the POI 

calculation. Figures 3 and 6 show the relationship between the 100-hour fuel 

moisture value and the probability of ignition. The “bull’s eye” pattern observed for some areas in figure 3 is an artifact of the interpolation algorithm 

caused by low RAWS station densities. Figure 6 shows how the bull’s eye effect 

caused by the 100-hour moisture calculation manifests itself in the POI map. 

The number of operational RAWS stations typically waxes and wanes through 

the fire season. For example, we had 64 operational RAWS stations on May 

1, the first day of the study period. On August 5, our example day, there were 

81 operational RAWS stations and 78 on September 30. The station density 

is highest in the western part of Region 1 and lowest in the east.

The 1-day example shown in figures 10 and 11 highlights the model’s 

tendency to overpredict the potential number of ignitions/fires. Each colored pixel (brown represents zero probability) represents a probability of a 

predicted ignition or fire “possibility” greater than zero. The values of these 

pixels are derived from a simple multiplication of the probability of ignition 

and number of lightning strikes falling within that pixel.

There appears to be a need to distinguish between a potential ignition and 

an actual reportable fire. A certain percentage of lightning ignitions never 

become reportable fires. Ignitions can decay, grow and decay before being 

detected, or grow and become detected as fires (Latham 1979). “Holdover” 

fires are another subset that needs further study. The current POI map predicts potential ignitions. We attempted to verify the map using reported fires. 

Because no dataset of all lightning ignitions, including those that decay, exists, 

we were forced to use the reported fire database for verification.

We need to identify all of the potential factors that influence lightning 

ignition/fire growth. Variables accounted for in the POI/fire possibility calculation include fuel moisture (before the storm), fuel type, duff depth, and 

number of lightning strikes. Other factors that determine whether an ignition 

grows and becomes a reportable fire include fuel and duff moisture after the 

storm (“wet” versus “dry” lightning storm), available fuel for fire spread and 

for holdover prediction, temperature, relative humidity, and wind speed in 

the days following a lightning strike. The National Weather Service provides 

a Doppler radar estimate of accumulated rainfall (Storm Total Precipitation). 

This product can be used to estimate daily accumulated precipitation. Rorig 

and Ferguson (1999) developed a discriminant rule using the dewpoint 

depression at 85kPa and temperature difference between 85 and 50 kPa to 

classify convective days as either “wet” or “dry”. We plan to retrieve the appropriate upper air and daily estimated rainfall data for our study period and 

see if it proves to be a significant factor in predicting fires.

The NFDRS index Energy Release Component (ERC) is an indicator of 

potential heat release in the flaming zone at the head of a fire. The ERC can 

be considered an indicator of available fuel since it represents the contribution 

of all live and dead fuel to fire spread. It is also considered one of the best fire 

danger components for indicating the effects of intermediate to long-term 

drying (Hall and others 2003). Using the same techniques we employed to 

recreate the daily 100-hour fuel moisture values and POI index, we plan to 

Verification of the WFAS Lightning Efficiency Map Sopko, Latham, and Grenfell
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 550

calculate the daily ERC values, combine with the POI model, and see if any 

correlation with fire prediction exists.

LANDFIRE (Rollins and Frame 2006) will soon have a more refined 

and spatially accurate fuel map available for the Western United States. We 

would like to use these data to update our lightning fuel type map used for 

fuel type and depth inputs in the POI calculation.

Our goal is to produce a fire probability model that provides managers with 

immediate information on where lightning caused fires are likely to occur. 

What has the best potential for success is a daily grid or contour map that 

incorporates lightning strike data, probability of ignition with other variables 

that prove significant such as the NFDRS index Energy Release Component 

(ERC), Doppler radar estimated precipitation, and upper air soundings.

What other questions can we attempt to answer with the 2003 data set?

• How are fires distributed by lightning probability fuel type? This can be 

easily accomplished by statistically comparing the fire database with the 

lightning fuel type map.

• How do we account for holdover fires? This will be tricky. There are large 

variances and random factors that drive some of the hidden processes 

that influence holdover fires. In the present verification effort, we only 

consider the lightning and probability of ignition values for the fire’s 

reported ignition date. We could, for example, expand the scope of 

analysis of fire possibility out 7 days after the fire ignition date. Initial 

attempts to do this have proven problematic. By increasing the number 

of days in the analysis searching for a predicted fire, large numbers of 

predicted fire pixels with values of zero increase the statistical noise and 

resultant binary correlation values.

• What is the relative error between the lightning location and a possible 

resulting fire? Each lightning strike has an associated error ellipse. The 

fire locations do not. The relative error of each strike can be quantified 

using the error ellipse data. The lack of a cross-agency location reporting protocol along with other variables makes the errors in fire location 

point data hard to measure. We hope to use the error ellipse data to 

measure the lightning location error.

• Does the ignition probability actually predict fire probability? Given 

our preliminary verification analysis, the answer to this question is 

no. Increasing the pixel size is an obvious first step. We think that the 

incorporation of other predictors such as ERC (fire season drought or 

available fuel), radar rainfall estimates (Storm Total Precipitation), upper 

air sounding grids, or a spread variable into the fire probability model 

will significantly improve its predictive ability.

• Will stroke data rather than flash data improve the analysis? Each lightning 

strike is made up of multiple “strokes.” This is referred to as the strikes’ 

multiplicity. We have the stroke data for the study period and plan to 

substitute them for the strike data to see if they improve the analysis.

Acknowledgments

Dr. Latham thanks the Fire Behavior Project at the Missoula Fire Lab for 

their support. We thank Matt Jolly and Charles McHugh for their assistance 

with GIS analysis and Larry Bradshaw for his extensive knowledge and assistance with FireFamily Plus and WFAS algorithms. The detailed comments 

of Miriam Rorig and Jim Reardon are greatly appreciated.

Verification of the WFAS Lightning Efficiency Map Sopko, Latham, and Grenfell
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 551

References

Bradshaw, L.S.; McCormick, E. 2000. FireFamily Plus User’s Guide, Version 2.0., 

General Tech. Rep. RMRS-GTR-67. Fort Collins, CO. U.S. Department of 

Agriculture, Forest Service, Rocky Mountain Research Station. 124 p.

Burgan, R.E.; Andrews, P.L.; Bradshaw, L.S.; Chase, C.H.; Hartford, R.A.; 

Latham, D.J. 1997. WFAS: wildland fire assessment system. Fire Management 

Notes 57:14-17.

Cummins, K.L.; Murphy, M.J.; Bardo, E.A.; Hiscox, W.L.; Pyle, R.B.; Pifer, A.E. 1998. 

A combined TOA/MDF technology upgrade of the U.S. National Lightning Detection 

Network. J. Geophys. Res. 103: 9035-9044.

Hall, B.L.; Brown, T.J.; Bradshaw, L.S.; Jolly, W.M.; Nemani, R. 2003. National 

standardized energy release component (ERC) forecasts. Proc. 5th Symposium on 

Fire and Forest Meteorology and the 2nd International Wildland Fire Ecology and 

Fire Management Congress, Orlando, FL. J11.10

Latham, D.J. 1979. Progress Toward Locating Lightning Fires. Research Note INT-269. 

Ogden, UT: U.S. Department of Agriculture Forest Service, Intermountain Forest and 

Range Experiment Station. 6 p.

Latham, D.J.; Schlieter, J.A. 1989. Ignition probabilities of wildland fuels based on 

simulated lightning discharges. Research Paper INT-411. Ogden, UT: U.S. Department 

of Agriculture Forest Service, Intermountain Research Station. 16 p.

Rollins, M.G.; Frame, C.K. tech. eds. 2006. The Landfire Prototype Project: nationally 

consistent and locally relevant geospatial data for wildland fire management. General 

Tech. Rep. RMRS-GTR-175. Fort Collins, CO: U.S. Department of Agriculture, Forest 

Service, Rocky Mountain Research Station. 416 p.

Rorig, M.L.; Ferguson, S.E. 1999. Characteristics of Lightning and Wildland Fire Ignition 

in the Pacific Northwest. Journal of Applied Meteorology 38: 1565-1575

Schmidt, K.M.; Menakis, J.P.; Hardy, C.C.; Hann, W.J.; Bunnell, D.L. 2002. Development 

of coarse-scale spatial data for wildland fire and fuel management. General Tech. Rep. 

RMRS-GTR-87. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, 

Rocky Mountain Research Station. 41 p +CD.

U.S. Department of Agriculture, Forest Service. 2002. WFAS Lightning Ignition Efficiency 

webpage. 30 April 2007. < http://www.wfas.net/content/view/18/33/>

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USDA Forest Service Proceedings RMRS-P-46CD. 2007. 552

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Abstract—The Wildland Fire Assessment System (WFAS) is a component of the U.S. 

Department of Agriculture, Forest Service Decision Support Systems (DSS) that support fire potential modeling. Fire potential models for Mississippi and for Eastern 

fire environments have been developed as part of a National Aeronautic and Space 

Agency-funded study aimed at demonstrating the utility of NASA assets in fire potential 

decision support systems. Climate, fuels, topography and ignition are recognized as 

important components for modeling fire potential in Eastern forests and grasslands. 

We produced temporal and spatial water budget estimates using daily assessments of 

precipitation and evaporation (P-E) in a Geographic Information System. Precipitation 

values are derived from Doppler radar-based estimates of hourly rainfall accumulation, 

published on the Hydrologic Rainfall Analysis Project (HRAP) grid. Precipitation data 

are routinely available, but evaporation data are not. Regional estimates of evaporation have been produced to fill this void. Regression models that estimate daily 

evaporation in the Southern region of the United States were developed from readily 

available weather station observations. Evaporation estimates were combined with 

precipitation to compute the cumulative water budget. Improvement of these estimates 

when compared to Keetch-Byrum Drought Index (KBDI) was demonstrated using fire 

location data in Mississippi. Evapotranspiration (ET) from the NASA Land Information 

System (LIS), is currently being evaluated as a landscape moisture variable. We have 

implemented a hierarchical modeling methodology that combines information derived 

from ICESat (GLAS) data and MODIS Enhanced Vegetation Indices (EVI) to describe 

fuels structure. A graphical user interface (GUI) has been developed using Visual Basic 

(VB) that accesses an ESRI geospatial database that integrates water budget and fuels. 

The ignition component is derived from gravity models that assess the interaction of 

population density and forest areal size.

Introduction

The Mississippi State University Departments of Forestry, Geosciences, and 

the GeoResources Institute have received National Aeronautical and Space 

Agency (NASA) funding to develop linear additive Geographic Information 

System (GIS) models designed to determine fire potential in Eastern forest 

regimes. Modeling concepts have been developed in cooperation with the 

USDA Forest Service Fire Sciences Laboratory (Missoula, MT), the Forest 

Inventory and Analysis (FIA) unit (Knoxville, TN), and the Mississippi 

Forestry Commission (MFC).

Much of the existing literature on fire potential modeling in the United 

States is oriented toward Western U.S. fire regimes. Fire is also of ecological 

and economic importance in the Southeastern United States. In Mississippi, 

fire frequency data obtained from the MFC indicates that on average, for a 

Integrating Climatic and Fuels Information 

into National Fire Risk Decision Support Tools

W. Cooke1, V. Anantharaj2, C. Wax1, J. Choi1, K. Grala1, M. Jolly3, 

G.P. Dixon1, J. Dyer1, D.L. Evans4, and G.B. Goodrich5

In: Butler, Bret W.; Cook, Wayne, 

comps. 2007. The fire environment—

innovations, management, and policy; 

conference proceedings. 26-30 March 

2007; Destin, FL. Proceedings 

RMRS-P-46CD. Fort Collins, CO: 

U.S. Department of Agriculture, 

Forest Service, Rocky Mountain 

Research Station. 662 p. CD-ROM.

1 Depa r t ment of G eosciences, 

Mississippi State University, Mississippi 

State, MS. Lead author whc5@geosci.

msstate.edu

2 GeoResources Institute, Mississippi 

State University, Mississippi State, MS.

3 U.S. Department of Agriculture, 

Forest Service, Rocky Mountain 

Research Station, Missoula, MT.

4 Department of Forestry, Mississippi 

State University, Mississippi State, MS.

5 Department of Geography, Western 

Kentucky University, Bowling Green, 

KY.
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 556

15-year period beginning in 1990, fire personnel were dispatched to more 

4,000 fires per year. Decisions regarding fire response and suppression, 

personnel and equipment staging, prescribe burning for fuel reduction, and 

implementation of burn bans could benefit from spatial and temporal depictions of fire potential. GIS-based fire potential models can be a valuable 

aid for describing the temporal and spatial conditions that are favorable (or 

unfavorable) for fire ignition and spread.

Morgan and others (2001) compared several approaches for mapping fire 

regimes including two rule-based approaches, a vegetation succession modeling approach, and a statistical modeling approach for the Interior Columbia 

River Basin. Results of these comparisons show that fire frequency is related to 

four factors: climate, vegetation (fuels), anthropogenic influences (ignition), 

and topography (Morgan and others 2001). Topography is an important fire 

variable in the Western United States where changes in elevation and aspect 

are determinants of vegetation and climate. However, topography has less 

influence on fire potential in the Eastern United States where topography is 

characterized by much gentler slopes (Zhai and others 2003).

Results of modeling efforts designed to assess the importance of climate, 

fuels, and ignition are presented. The climate component is discussed in terms 

of the usefulness of water-budget indices and global climate processes in predicting fire potential. Topography is treated as a modifier of total precipitation 

in our models. The fuels variable is examined in more detail, particularly in 

light of damage to forests due to hurricane Katrina. The ignition component 

is presented as a comparison between road density and gravity models and 

their application in predicting fire potential. Finally, development of the data 

acquisition interface for fire potential modeling is described.

Discussion

The modeling flowchart (fig. 1) illustrates the conceptual approach used 

for modeling fire potential. Models are being developed and validated for 

Mississippi with the ultimate goal of expanding the modeling concepts regionally. GIS-based linear algebra approaches using raster (cell) data were 

developed to characterize fire potential in Mississippi. Climate, fuels, and 

ignition are the primary (weighted) variables, and topography is implemented 

as a modifier of precipitation. Different types of base data have been tested 

for usefulness for each variable. Initially, each variable’s correlation to historic 

fire occurrence in Mississippi was tested. The results of these tests ultimately 

guide decisions about the importance of each variable and variable weights 

for the full model.

Climate

KBDI evaluation—The importance of climate for fire potential models in 

Mississippi is not clear. Figure 2 illustrates a 14-year history of the relationship between climate and fire frequency in Mississippi. Drought conditions 

generally occur in the late summer and fall in Mississippi and are associated 

with increased fire frequency, but the temporal period with highest fire frequency is associated with periods of luxury rainfall (spring). Indices like the 

Keetch-Byram Drought Index (KBDI) are often used (Texas Forest Service, 

WFAS, and others) to estimate fire potential. The usefulness of KBDI alone 

for determining fire potential in Mississippi has not been documented in 

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Figure 1—Modeling flowchart.

Figure 2—Relationship between climate and fire. 

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Table 1— KBDI as a measure of fire potential analysis results.

January – April 1989-2003

X Y Pearson’s r R2 Adjusted R2 p-value for r

KBDI Fire total .035 .001 .001 .166

May – August 1989-2003

X Y Pearson’s r R2 Adjusted R2 p-value for r

KBDI Fire total .220 .048 .048 .000

September – December 1989-2003

X Y Pearson’s r R2 Adjusted R2 p-value for r

KBDI Fire total .257 .066 .065 .000

Monthly KDBI average for 1989-2003

0

100

200

300

400

500

600

700

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Average KDBI value

Figure 3—Monthly KBDI averages for 1989 through 2003. 

the literature. We tested the usefulness of KBDI alone as a measure of fire 

potential. KBDI was a poor predictor of forest fire potential in southern 

Mississippi. Results are presented in table 1 and figure 3. Pearson’s r = 0.220 

(May through August) and r = 0.257 (September through December) are 

significant, but regression results indicate poor model fit and little of the 

variance in fire frequency or fire size is explained by KBDI (r-square = 0.048 

and r-square = 0.066 respectively). Based on these results, other measures of 

climate were considered.

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Comparison of multisensor precipitation estimate (MPE) and weather station 

estimates—KBDI is calculated from data obtained from weather stations’ data. 

We tested whether increased sample density of MPE is superior to weather 

station-based precipitation. Interpolated Doppler (NEXRAD) radar estimates 

of daily precipitation recorded at 4 km intervals on the Hydrologic Rainfall 

Analysis Project (HRAP) point grid were tested against precipitation estimates 

interpolated at more widely distributed weather stations. Estimated precipitation at high spatial densities (HRAP grid) offers increased spatial precision 

necessary for recording the relatively small convective weather events that are 

characteristic of summer weather conditions.

Continuous surface raster grids of precipitation estimates derived from 

weather station data were compared with precipitation estimates from MPE 

data (Gilreath 2006). For each day, MPE and weather station data were 

interpolated to a continuous raster surface grid. All days in a month were 

added for a summary of the district’s cumulative monthly precipitation. To 

compare monthly cumulative precipitation between different months the 

MPE and weather station data value ranges were normalized using a z-score 

transformation. The normalized MPE and weather station grids were then 

standardized to five fire potential classes via histogram slicing using Jenk’s 

Natural Breaks. A value of 5 represented highest fire potential and 1 represented lowest fire potential. The standardized fire potential values for both 

the MPE and weather station surface grids were extracted at each known 

fire location using a ‘zonal’ function in a GIS. The extracted fire potential 

estimates for summer 2003 and summer 2004 are compared in bar graphs 

in figures 4 and 5. In general, monthly mean fire potential at known fire 

locations is higher when precipitation is derived from MPE although exceptions are noted for September and October 2003.

Figure 4—Comparison of the 2004 monthly fire 

potential estimates of the MPE and weather station 

layers

Figure 5—Comparison of the 2003 monthly mean fire 

potential estimates of the MPE and weather station 

layers. 

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P-E estimate—Precipitation (P) is a critical fuel moisture component, but 

high temperatures in Mississippi are associated with high rates of evaporation 

(E). Including evaporation in water budget calculations is important when 

determining available moisture in the environment. Pan evaporation was 

compared to regression-based predictions of evaporation developed in association with the Mississippi State Climatologist (Charles Wax). Pan evaporation 

stations that provide evaporation data exist but are sparsely distributed across 

the Southeastern United States. Therefore, regression models for inland and 

coastal locations have been developed that characterize the lower and higher 

humidity environments of the landscape respectively. Regression models were 

constructed using minimum daily relative humidity, maximum daily temperature, and total daily solar radiation data acquired at several pan evaporation 

locations. Evaporation predictions can now be made at any weather station 

that records daily minimum relative humidity and maximum temperature. 

These regression models enable interpolations of evaporation at a much denser 

point pattern than previously available at pan evaporation locations.

Daily water budget estimates were calculated by accumulating (summing) 

each day’s precipitation minus evaporation (P-E) estimates and comparing these 

to long-term daily averages. Therefore, the water budget variable is an index 

that is calculated by measuring the daily departure of P-E from the historic 

P-E averages. While not a direct measure of fuel moisture, this index is representative of landscape moisture conditions critical for assessing fuel moisture 

in the various ‘hour’ fuels that exist in Mississippi and the Southeastern United 

States. The spatial depiction of cumulative wet or dry landscape conditions, 

used in conjunction with vegetation, ignition, soil, and topography, provide 

both a spatial and temporal view of patterns of fire potential.

Climate (P-E) modifier—A constructed variable similar to the soil textural pyramid is being developed by combining slope, vegetation, and soil 

texture. Slope was derived from the USGS National Elevation Data (NED) 

30m Digital Elevation Model (DEM) data. Vegetation is characterized using summer 16-day MOderate Resolution Imaging Spectrometer (MODIS) 

Normalized Difference Vegetation Index (NDVI) data. Soil permeability 

from the State Soil Geographic (STATSGO) database was used as a surrogate 

for soil texture. This variable was designed to estimate the amount of runoff 

expected under various states of vegetation, slope, and soil texture and will 

act as a modifier of daily precipitation estimates.

Teleconnections—potential for fire potential predictions—In addition to traditional climate variables we examined the effects of global climate processes 

on regional weather of the Southeastern United States to better understand 

the impacts of climate variability on forest fires in Mississippi. Results of these 

analyses have the potential to support prediction of fire size and frequency 

in the region. Teleconection indices that were analyzed included: El Niño - 

Southern Oscillation (ENSO), Pacific-Decadal Oscillation (PDO), Atlantic 

Multidecadal Oscillation (AMO), North Atlantic Oscillation (NAO), and 

Pacific-North America Oscillation (PNA).

The literature documents that the El Niño – La Niña climate cycle has an 

effect on fuel load buildup and fire potential in the Western United States. 

During wet El Niño years, vegetation tend to thrive and expand. Big fire years 

in the West tend to occur when wet El Niño years are followed by dry La 

Niña conditions (Kitzberger and others 2001; Simard and others 1985).

Unfortunately, assessing fire potential is not as simple as identifying the 

phase of ENSO. Norman and Taylor (2003) also found that fires are more 

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widespread during warm, dry years that followed cool, wet years. However, 

the effect of ENSO appears to be mediated by the PDO, such that fires are 

more widespread when the PDO is in a warm or normal phase (Norman and 

Taylor 2003). Further, it seems that the AMO may be the most dominant 

teleconnection index with respect to forest fire probability in the Western 

United States (Sibold and Veblen 2006). Nevertheless, no single climate 

variable appears sufficient for assessing and/or predicting fire risk. Rather, a 

combination of negative ENSO (La Niña), negative PDO, and positive AMO 

seems to be the most reliable method for predicting wildfires in the Western 

United States (Sibold and Veblen 2006).

Monthly fire data for each month during the study period (July 1990 to 

June 2006; 192 months) were aggregated into three categories: total fire 

events, average acres per fire event, and total acres burned. These categories 

are treated as dependent variables for this study. Squared bivariate correlation coefficients (r2) were then calculated for the relationships between each 

teleconnection index and each fire variable for each month of the year. It 

is possible that variations in the teleconnection indices may not affect Mississippi fires until a few months later, and/or the effects may last several 

months. Therefore, fire variables were also correlated with teleconnection 

index values from each of the 6 previous months in order to identify any 

lagging relationships.

Best results for analyzed lagging relationships between teleconnection indices and fire events organized monthly are shown in figure 6. More specific 

correlations are outlined below:

	 •	 Niño 3.4: Late summer (August, September) is the only time of year that 

consistently displays strong statistical relationships (α = 0.05) between 

fire variables and Niño 3.4. August fire variables display the strongest 

relationships with Niño 3.4 values.

	 •	 NAO: The relationships between fire variables and the NAO appear to 

be driven by NAO values in early spring and fall. NAO values during 

February and March display notable correlations with the average fire 

size during the months of March, June, and July. In addition, April NAO 

values appear to affect the number of fires in September and October. 

Finally, NAO values during September and October display consistent 

correlations with fire variables in October, November, and December.

	 •	 PDO: The number of fire events in February show consistent correlations with PDO values during the months of December, January, and 

February, but the average fire size shows essentially no relationship to 

antecedent PDO values. All three fire variables for the month of August 

display impressive correlations with August PDO values. Of course, such 

a relationship provides little in the way of predictive ability. Nevertheless, 

despite no statistically significant relationships (α = 0.05), PDO values 

during the previous 5 months appear to have some effects on fires in 

August, and PDO values in the previous February (6-month lag) have 

strong relationships with fire variables in August. Similarly, March PDO 

values display strong correlations with fire events in July.

	 •	 PNA: PNA values during the month of July illustrate the most impressive 

relationships with fire variables, as at least two of the three fire variables 

in each of the months July through October display statistically significant correlations with July PNA values. In addition, fire totals in January 

show some correlation with PNA values in the previous September, 

February fires are related to PNA values in January and February, and 

March fires appear to be affected by previous November PNA values.

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Figure 6—Best results for analyzed lagging relationships between teleconnection indices and fire 

events organized monthly.

0.00

0.10

0.20

0.30

0.40

0.50

0.60

1 2 3 4 5 6 7 8 9 10 11 12

Month

R-squared

Nino3.4

NAO

PDO

PNA

Our analyses point out some correlations between indices and soil moisture. For example, all of the ENSO-fire relationships are negative (that is, 

positive Niño 3.4 anomalies lead to decreased fire potential). This is most 

likely due to the annual minimum in precipitation that typically occurs in late 

summer. During these dry conditions, antecedent soil moisture becomes a 

primary factor controlling the fire occurrence and extent. Since positive Niño 

3.4 anomalies (El Niño) usually result in increased precipitation across the 

Southeast, soil moisture remains high. There is also a negative relationship 

between PDO and fire events in Mississippi. Again, positive PDO periods 

are accompanied by increased precipitation across the Southeastern United 

States., so soil moisture is likely to remain above normal. Finally, most months 

display a negative PNA-fire relationship, but positive PNA values during and 

around the month of September seem to result in increased fires throughout 

the fall and winter. Usually, positive PNA leads to lower temperatures across 

the Southeast, which should yield fewer fires. However, fewer fires in August 

may mean more residual fuels available for fires in fall and winter, which could 

explain the reversal in sign of the correlation.

Fuels

Typically, fuel load is a relatively static or slowly changing variable; however, 

sudden changes in moisture conditions and substantial vegetation damage can 

contribute to rapid changes in fire potential. Development of GIS layers that 

enable rapid characterization of changes in forest fuel conditions is important 

for determining how fire potential can change due to natural disasters such 

as hurricanes. For that reason, in response to damage caused by Katrina, 

we developed GIS fuel-based models that assessed fire potential in areas of 

Mississippi that were most severely impacted by the hurricane.

Long time-series of Landsat imagery, pre- and post-Katrina satellite 

imagery, and aerial imagery were integrated into the fuel-based model. Preand post-Katrina fire potential was compared for six counties in southern 

Mississippi using information on forest age classes, forest type and damage 

categories. Information on forest age classes and types were derived from 

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Landsat satellite data (Collins and others 2005). Hurricane damage information was derived from AWIFS imagery in form of binary damage mask, while 

aerial imagery interpretation of forest stand conditions was used to classify 

the damage categories and assess the accuracy of the satellite damage mask.

Pre-Katrina fire potential was derived a-priori from fire occurrence data. 

Forest type/age groups of similar fire frequency characteristics were determined by summarizing 20 months of pre-Katrina fire occurrence data. 

Number of fires, average fire size, and percentage of area burned in each class 

were evaluated and used as criteria for the assignment of fire potential for 

each group. This resulted in the several unique age/type group combinations 

that were assigned fire potential ranks ranging from 0 (no fire potential) to 

5 (very high fire potential). Post-Katrina fire potential was determined by 

assigning the same ranks as pre-Katrina fire potential for undamaged areas 

and assigning an increased fire potential rank to areas classified as damaged by the satellite damage mask. A unique number was assigned to each 

unique combination of forest type, age, and damage layers to ensure that all 

combinations were assigned with appropriate fire potential rank. This is an 

important consideration in GIS modeling that enables the analyst to assess 

the exact conditions at a given cell location that give rise to a fire potential 

rank value.

The analysis resulted in spatial depictions (fig. 7) and statistical summaries 

of pre- and post-Katrina fire potential. Overall accuracy of the remotely sensed 

damage assessment was 72 percent. Based on the modeling results, validated 

with actual 2006 fire data, the fire potential in the region increased after the 

hurricane. The post-Katrina landscape is characterized by reduction in the 

contiguity of areas classified as very low fire potential, and increases in the 

amount and contiguity of areas classified as very high fire potential (fig. 7). 

Due to the hurricane, areas of very low potential decreased from 19 to 3 percent, while areas of very high potential increased from 3 to 13 percent.

Overall, the proportion of the landscape that was classified as very high 

fire potential after Katrina is greatest in the western counties (Hancock and 

Pearl River), somewhat lower in the central counties (Harrison and Stone), 

and lowest in the eastern counties (Jackson and George). This west-to-east 

fire potential gradient corresponds closely with the strongest wind speeds 

and highest amount of rainfall that occurred in Hancock, Harrison, Stone, 

and Pearl River counties.

The modeling results were validated with actual fire occurrence data. 

The greatest increase in post-Katrina fire potential was observed in the very 

low potential class (mixed and broadleaf stands), which may indicate that 

areas traditionally considered fire resistant change dramatically in terms of 

fire potential following hurricanes. Overall modeling results point out that 

increased numbers of fire suppression personnel may be needed for coming 

fire seasons in the impacted region.

Ideally, the fuels variable should characterize the amount of 1-hour, 

10-hour, and other fuels (Anderson 1982). We are currently testing the usefulness of space-borne waveform Light Detection and Ranging (LiDAR) data 

for measuring vertical structure and fuel loads. Similar to the Fire Potential 

Index (FPI) described by (Burgan and others 1998), we use NDVI derived 

from Advanced Very High Resolution Radiometer (AVHRR) and MODIS 

data for regional fuels analyses. We have tested regression approaches for 

describing the sub-pixel components relevant to fuel loads using Landsat 

Thematic Mapper (TM) data (Berryman 2004), ‘QuickBird’ high resolution 

multispectral imagery, and airborne pulse LiDAR data.

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Figure 7—Pre- and post-Katrina fire potential comparison for six southern 

Mississippi counties.

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Field work was recently begun designed to calibrate ICESAT GLAS data 

waveform LiDAR with fuel characteristics in a study area on the MSU ‘Starr’ 

Forest in Mississippi. Data were acquired over a variety of forest and some 

nonforest conditions. The ground data collection includes understory and 

overstory measurements for 60 field plots that are coincident with the elliptical 

footprint of the GLAS data. Figure 8 contrasts waveform differences between 

Anderson’s fuel model 9 (pine plantation with little understory and downed 

woody debris) and fuel model 10 (mature pine plantation with hardwood 

understory and downed woody debris). Note that the earliest waveform returns are from the canopy, the next waveform returns are from the midstory 

and understory, and the last (and highest intensity) returns are from the 

ground. Our goal is to characterize fuel models from LiDAR samples for a 

variety of conditions, then extrapolate those conditions to larger (regional) 

landscapes via coarse resolution MODIS data using a subpixel prediction 

algorithm under development.

Ignition

Anthropogenic factors play a major role in fire ignition in Mississippi. 

Humans affect wildfire ignition by altering the vegetative fuel load characteristics and by providing an ignition source (Petrakis and others 2005; 

Pye and others 2003). Pye and others (2003) demonstrated that proximity 

to roads and certain levels of road density are significantly correlated with 

increased fire risk. Gilreath (2006) showed that in Mississippi, road density 

calculated using the gaussian kernel for all primary, secondary and county 

roads is a good predictor of fire frequency. Findings indicate that areas of 

very high and very low road density are at very low fire potential. Conversely, 

areas of moderate road density are at significantly higher risk. Sadasivuni 

(2007) tested the application of gravity models that measure the interaction 

among cities and medium-age large contiguous pine forests for fire frequency 

Figure 8—C ontra sting 

waveforms for Anderson’s 

fuel model 9 and 10. 

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relationships. Independent t-tests were used to test for significant differences 

among levels (ranks) of gravity models and road density models for all fires, 

winter fires and summer fires. Finally, t-tests for significant differences between levels (ranks) of gravity models versus road density models were made 

to test the hypothesis that no differences exist between the two methods for 

predicting fire frequency. Results of the tests for no differences between the 

two methods are shown in table 2. The gravity model proved to be better 

overall for estimating fire potential at the very low fire potential level for all 

seasons. It was also better at estimating fire potential in the medium level 

during the winter and for low and medium levels in the summer. For all other 

comparisons, there was no significant difference between road density and 

gravity models as predictors of fire potential.

Fire Potential Modeling Tool (FTMT)

Our geospatial database integrates water budget and fuels for wild fire 

potential modeling for the Eastern United States. A fire potential modeling 

tool (FPMT) has been developed to manage the geospatial database and to 

extract water budget (P-E) for the fire potential model. Development of an 

extension is underway for tools that will incorporate data from the Noah 

land model.

The database adopts the ESRI Geodatabase structure (fig. 9). Feature 

classes include radar sample points and weather station location. Two object 

classes store radar data and weather station data that are connected to the corresponding feature table based on measurement time in the time table. Time 

table is the third object class. Precipitation and evaporation are accumulated 

based on the period chosen as user’s input. Individual accumulation data are 

stored as object tables, which are connected to the corresponding feature 

tables. To populate climatic data in the database, the automated procedures 

were developed in FPMT.

Table 2—Results of t-tests for gravity models and road density models (*indicates statistically 

significant results).

Gravity and road

density

Annual

critical

Annual

p-value

Winter

critical

Winter

p-value

Summer

critical

Summer

p-value

Very low gravity

and very low road

density

3.51* 0.0085 3.64* 0.0058 3.58* 0.007

Low gravity and 

low road density

1.6 0.11 1.56 0.13 2.0* 0.05

Medium gravity and 

medium road

density

3.09* 0.003 2.82* 0.0064 2.78* 0.007

High gravity and 

high road density

0.62 0.534 0.67 0.5 0.29 0.77

Very high gravity

and very high road

density

0.44 0.664 0.08 0.58 0.42 0.68

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FPMT is an application designed to facilitate data management, spatial modeling, and map generation. Therefore, FPMT consists of three sub modules: 

data management, fire fuel modeling, and mapping modules. Current FPMT 

includes the data management module that includes data input, formatting, 

coordinate transformation, and accumulation procedures. Fire fuel modeling 

and mapping modules are under way, which include interpolation, fire fuel 

model, and creating map documents.

Conclusions and Plans

Each variable that has the potential of being included in the Mississippi fire 

potential models has been tested with historic fire occurrence data. Once the 

model variables and weights have been calibrated for Mississippi landscapes, 

we will extend the models to the southern region to test the usefulness of 

these modeling concepts over a larger landscape area.

We believe that measures of climate should include the effects of precipitation and evaporation (P-E) and that calculation of environmental moisture is 

cumulative and compared through time against the long-term P-E average. 

Although KBDI is a cumulative (soil) moisture index, our studies show that 

KBDI is a poor predictor of fire frequency and fire size for southern Mississippi. Precipitation and evaporation are important components for water 

budget calculations. Our goal was to interpolate precipitation and evaporation 

from the densest available source. Local measurements of precipitation and 

evaporation for our models result in descriptive products that are designed 

Figure 9—Geodatabase structure.

+Shape : esriFieldTypeGeometry

ESRI Classes:: Feature

+OBJECTID : esriFieldTypeOID

ESRI Classes:: Object

-sampleID : esriFieldTypeInteger

-observationID : esriFieldTypeInteger

radarLocation

-observationID : esriFieldTypeInteger

-obsDate : esriFieldTypeDate

-precip : esriFieldTypeDouble

-sampleID : esriFieldTypeInteger

radarObservations

observed

-stationID : esriFieldTypeInteger

-stationName : esriFieldTypeString

-formulaCode : esriFieldTypeInteger

-observationID : esriFieldTypeInteger

StationLocations

-obasevationID : esriFieldTypeInteger

-obsDate : esriFieldTypeDate

-temp : esriFieldTypeDouble

-RH : esriFieldTypeDouble

-solarRad : esriFieldTypeDouble

-evap : esriFieldTypeDouble

-stationID : esriFieldTypeInteger

StationObservations

observed

-featureID : esriFieldTypeInteger

-featureType : FeatureType

-dateOfUpdate : esriFieldTypeDate

-dateOfStart : esriFieldTypeDate

-dateOfCurrent : esriFieldTypeDate

updateHistory -FormulaCode : esriFieldTypeInteger

-tempCoeff : esriFieldTypeDouble

-rhCoeff : esriFieldTypeDouble

-srCoeff : esriFieldTypeDouble

-yIntercept : esriFieldTypeDouble

-Description : esriFieldTypeString

Domain::evapCoeff

-featureCode : esriFieldTypeInteger

-typeName : esriFieldTypeString

Domain:: FeatureType

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USDA Forest Service Proceedings RMRS-P-46CD. 2007. 568

to estimate relative fire potential on the basis of comparisons of current climatic conditions with long-term averages. The ability to forecast future fire 

potential depends on understanding how global climatic processes influence 

fire occurrence (teleconnections). We are testing historic information derived 

from Pacific and Atlantic oscillation indices against historic fire occurrence 

in Mississippi, particularly concentrating on the temporal lag between index 

values and fire frequency and size.

This project demonstrated the utility of combing GIS raster modeling 

and remote sensing change analysis in assessing fire potential following hurricane Katrina. In this study actual change in fire threat was demonstrated 

empirically by validating fire potential predictions with actual fire occurrence 

data. This study demonstrates the capability of GIS-based analysis to provide 

rapid assessment of landscape conditions that favor fire ignition in coastal 

regions following destructive hurricane events. Such information is essential 

for emergency and wood recovery personnel to allocate their resources within 

areas of elevated fire hazard.

Understanding the anthropogenic factors that affect fire ignition is important in the Southeastern United States. We examined two methods of 

determining ignition from a spatial perspective. We found that city/fuels 

interaction (gravity) models are better than road density for characterizing 

fire potential in very low and low fire potential strata.

We have recently acquired 18 years of AVHRR NDVI bi-monthly composites for the United States. We calculated the 18-year average greenness for 

physiographic regions in Mississippi. We plan to test correlations between fire 

variables and NDVI (greenness) as a departure from the 18-year average.

We are designing a Web-based graphical interface for our fire potential 

modeling tools (FPMT) to display results. Initially, fire potential estimates 

will be summarized on a bi-monthly basis. The data acquisition interface is 

user-friendly and can adapt to a variety of precipitation input data. We hope 

to have the fire models calibrated, functional and available on the Web in 

the near future.

References

Anderson, H.E. 1982. Aids to determining fuel models for estimating fire behavior.

INT-GTR-122. Ogden, UT: U.S. Department of Agriculture, Forest Service, 

Intermountain Research Station.

Berryman, B.N. 2004. Investigating of relationships between Landsat ETM+ data 

and ground-adjusted LIDAR measurements in southern pine stands. Thesis. 

Mississippi State, MS: Mississippi State University.

Burgan, R.E.; Klaver, R.W.; Klaver, J.M. 1998. Fuel Models and Fire Potential from 

Satellite and Surface Observations. Int J Wildland Fire 8:159-170.

Collins, C.A.; Wilkinson, D.W.; Evans, D.L. 2005. Multi-temporal Analysis of 

Landsat Data to Determine Forest Age Classes for the Mississippi Statewide 

Forest Inventory –Preliminary Results. In: Proceedings of the Third International 

Workshop on the Analysis of Multi-Temporal Remote Sensing Images. Biloxi, 

MS. CD-ROM.

Gilreath, J.M. 2006. Validation of variables for the creation of a descriptive fire 

potential model for the Southeastern Fire District of Mississippi. Thesis. Mississippi 

State, MS: Mississippi State University.

Integrating Climatic and Fuels Information into National Fire Risk Decision Support Tools Cooke, Anantharaj, Wax, Choi, Grala, Jolly, Dixon, Dyer, Evans, and Goodrich
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 569

Kitzberger, T.; Swetnam, T.W.; Veblen, T.T. 2001. Inter-hemispheric synchrony 

of forest fires and the El Nino-Southern Oscillation. Global Ecol Biogeogr 10: 

315-326.

Morgan, P.; Hardy, C.C.; Swetnam, T.W.; Rollins, M.G.; Long, D.G. 2001. Mapping 

fire regimes across time and space: Understanding coarse and fine-scale fire 

patterns. Int J Wildland Fire 10: 329-342.

Morris J.A. 2007. An Analysis of the Keetch-Byram drought index as a predictor 

of forest fire potential. Thesis in preparation. Mississippi State, MS: Mississippi 

State University.

Norman, S.P.; Taylor, A.H. 2003. Tropical and north Pacific teleconnections 

influence fire regimes in pine-dominated forests of north-eastern California, 

USA. J Biogeogr 30: 1081-1092.

Petrakis, M.; Psiloglou, B.; Lianou, M.; Keramitsoglou, I.; Cartalis, C. 2005. 

Evaluation of forest fire risk and fire extinction difficulty at the mountainous 

park of Vikos-Aoos, Northern Greece: use of remote sensing and GIS techniques. 

International Journal of Risk Assessment and Management 5: 50-65.

Pye, J.M.; Prestemon, J.P.; Butry, D.T.; Abt, K.L. 2003. Prescribed Burning and 

Wildfire Risk in the 1998 Fire Season in Florida. In: Fire, fuel treatments, and 

ecological restoration. 2002 April 16-18; Fort Collins, CO. Proceedings RMRSP-29. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky 

Mountain Research Station.

Sadasivuni R. 2007. Gravity model to detect forest fire prone areas in the southeast 

fire district of Mississippi. Thesis. Mississippi State, MS: Mississippi State 

University.

Sibold, J.S.; Veblen, T.T. 2006. Relationships of subalpine forest fires in the Colorado 

Front Range with interannual and multidecadal-scale climatic variation. J Biogeogr 

33: 833-842.

Simard, A.J.; Haines, D.A.; Main, W.A. 1985. Relations between El Niño/Southern 

oscillation Anomalies and wildland fire activity in the United States. Agric. Forest 

Meteorology 36; 93-104.

Zhai, Y.S.; Munn, I.A.; Evans, D.L. 2003. Modeling forest fire probabilities in the 

South Central United States using FIA data. South J Appl For 27; 11-17.

Integrating Climatic and Fuels Information into National Fire Risk Decision Support Tools Cooke, Anantharaj, Wax, Choi, Grala, Jolly, Dixon, Dyer, Evans, and Goodrich
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Integrating Climatic and Fuels Information into National Fire Risk Decision Support Tools Cooke, Anantharaj, Wax, Choi, Grala, Jolly, Dixon, Dyer, Evans, and Goodrich
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Abstract—Most wildfire education programs have relied overwhelmingly on homeowner information and education, limiting the role of such business professionals as 

architects, builders, insurance agents, landscape architects and contractors, planners, 

media, and realtors. This project was undertaken to engage and educate professionals who design landscapes and homes, work for home and property owners, and 

make decisions that affect homeowners and communities—and in the long run, to 

change best management practices in these industries. Market surveys for the target 

professions were conducted at the outset, reaching 58 individuals and organizations 

and gathering information on continuing education requirements, subject relevance, 

training format, pricing, and marketing. About 30 business professionals were involved 

in the selection of existing materials and development of curriculum for four modules: 

industry issues, fire environment, building materials and design, and site planning and 

maintenance. Four seminars were held in March 2007 at sites that provided geographic 

and time-of-day distribution, and 100 business professionals attended from the target 

audiences. Ongoing project evaluation incorporated the market scoping, interaction 

with business groups, and classes. The project team identified new business practices 

and cooperative efforts that contribute to property-loss prevention, are economically 

viable, are likely to be adopted, and extend the reach of the traditional fire and land 

management professionals.

Introduction

The challenges of reducing wildfire property losses, while sustaining healthy 

natural environments, grow with the increase in homes built at the wildlandurban interface and intermix. Substantial gains have been made in the past 

half-century on the technology of building ignition-resistant homes; selecting 

and maintaining landscapes that minimizes radiation exposure of the house; 

and siting structures and developments. Yet many individuals, businesses, 

and agencies are still unaware of, or are unwilling to apply, these measures. 

Furthermore, most wildfire education programs have relied overwhelmingly 

on homeowner information and education, limiting the contributions of such 

business professionals as architects, builders, insurance agents, landscape 

architects and contractors, planners, media, and realtors.

The planning profession produced and published “Planning for Wildfires” 

in their professional series (Schwab and Meck 2005). Schwab and Meck 

emphasize wildfire planning in a comprehensive context, regulation and 

enforcement that stresses property owner responsibility, and education and 

outreach to affected residents and property owners. Whereas the “Planning 

for Wildfires” manual exemplifies the application of wildfire risk reduction 

Educating and Engaging the Business Sector 

in Reducing Wildfire Property Losses

Anne S. Fege1 and Christopher Blaylock1

In: Butler, Bret W.; Cook, Wayne, 

comps. 2007. The fire environment—

innovations, management, and policy; 

conference proceedings. 26-30 March 

2007; Destin, FL. Proceedings 

RMRS-P-46CD. Fort Collins, CO: 

U.S. Department of Agriculture, 

Forest Service, Rocky Mountain 

Research Station. 662 p. CD-ROM.

1 Botany Research Associate and 

Wildfire Education Project Manager, 

respectively, San Diego Natural History 

Museum, San Diego, CA. Lead author 

afege@sdnhm.org
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 572

principles and practices by planning professionals, the authors do not address the importance of accurate and effective advice to homeowners from 

architects, landscape designers, realtors, and other business professionals. The 

extensive information published by the National Fire Protection Association 

(2002) on planning, construction, maintenance, education, and management 

elements for reducing wildfire losses is written for fire officials and is likely 

referenced by few business professionals.

Various studies underscore the value of specific information applied to 

local community settings. Although these studies do not mention business 

professionals, who advise and do work for homeowners, it is their expertise 

and individual advice that could contribute to decisions by homeowners to 

reduce their wildfire risks. Kumagai and others (2004) reported that people 

are more likely to believe wildfire hazard risk information if it is specific, 

consistent, certain, and disseminated repeatedly. Brenkert and others (2005) 

conducted interviews with residents in Larimer County, Colorado, regarding 

wildfire mitigation actions, and found that the most common motivating 

factors were the informal social processes by which homeowners learn about 

and form opinions about wildfire risk; their perceptions about the biophysical setting of their property, community, and nearby public lands; and their 

knowledge about the effectiveness of various mitigation measures. The authors 

noted that one-on-one information tailored to a particular property, from a 

credible source, was associated with homeowners taking mitigation action.

The sessions at a national wildfire education conference (FireWise Communities 2006) highlighted public education, community involvement, and 

government partnerships. Only three of the 75 presentations focused on the 

role of business professionals: how forestry and arborists can communicate 

with potential homeowner clients and provide quality services; how landscape 

architects can balance safety, aesthetics, and ecology in their designs; and how 

insurance agents work cooperatively with homeowners and fire agencies.

Social science and marketing research are now being applied to better 

understand the attitudes and behaviors of homeowners about fire prevention investments in the wildland-urban interface. Social marketing builds on 

social exchange theory wherein people adopt behaviors that they believe have 

a positive benefit for them or their community, and that results in both individual and societal gains. Social marketing principles suggest that information 

should be tailored to the cultural, political, and economic conditions of the 

community that is expected to invest in survivable space (Absher and others 

2006; Andreasen 1995; Kotler and others 2002; Hoban and others 2003). 

Similar gains could be made with the business sector, as objectives can be 

adapted to fit their physical, social, economic, and political environment.

With the support of the Joint Fire Sciences program and the U.S. Fish 

and Wildlife Service, San Diego Wildlife Refuge Complex, this project was 

undertaken to engage and educate business professionals who design landscapes and homes, work for home and property owners, and make decisions 

that affect homeowners and communities. The San Diego Natural History 

Museum developed, marketed, and conducted seminars entitled, “Wildfire 

Education for Business Professionals,” cooperating with local professional 

associations to promote and provide certification or sponsorship. Curriculum 

for these interdisciplinary seminars was developed with local professionals, 

emphasizing property risk reduction while minimizing the impact to the 

native environment.

Educating and Engaging the Business Sector in Reducing Wildfire Property Losses Fege and Blaylock
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 573

San Diego Setting

San Diego is a large urban area with a rapid rate of development; the 

scientific, business, educational, and professional resources are extensive; 

the October 2003 wildfires affected virtually every community, business, 

and individual, at least indirectly. The biodiversity, fire-adapted landscapes, 

low rainfall, and rapid rate of development in San Diego are unique and 

unequaled—and San Diego is always “between wildfires.” In late October 

2003, three wildfires in San Diego County burned 152,000 ha, almost 

15 percent of the land area of the county—including the Cedar Fire, the 

largest in California since historical records were first kept in 1910. Sixteen 

lives and more than 3,200 structures were lost.

A Damage Assessment Team was assigned to document damaged structures 

and property after the Cedar Fire, and the findings were included in a report 

about wildfire losses in California (Fire Cause Analysis 2004). Destruction 

and damage were attributed to poor accessibility, inadequate clearance of 

vegetation, wooden landscape improvements, wood or composition shingles, 

flammable exterior siding, and uncovered vents to attics and crawlspaces.

Absher and Kyle (2006) surveyed homes in the wildland-urban interface 

zone in San Diego County, near the Cleveland National Forest. Of the 770 

returning surveys (35 percent), 98 percent of the households saw smoke in 

2003; 97 percent saw flames; 40 percent were evacuated; 42 percent had 

their work or livelihood disrupted; and 30 percent had property damage. 

They found high compliance for some behaviors (such as cleaning roof and 

gutters, stacking firewood away from the house); moderate compliance for 

others (such as reducing vegetation density); and low to moderate levels of 

community participation (49 percent received information, 22 percent attended meetings, 12 percent volunteered time, 26 percent willing to get 

involved with a FireSafe Council).

In the county of San Diego, local building and brush reduction codes have 

been passed in virtually all jurisdictions and are fairly similar though not 

identical. There is abundant, if somewhat scattered, technical information 

for homeowners and business professionals on how to reduce wildfire risks. 

Numerous forums and workshops have been held by groups such as the San 

Diego Fire Recovery Network in cooperation with the Museum, the San Diego 

Horticultural Society, the Fire and Rescue and the Water Departments in the 

city of San Diego, National Fire Protection Association, local chapters of the 

American Society of Landscape Architects and the California Native Plant 

Society, and the University of California Cooperative Extension program.

Methods

Market Surveys

Because business professionals have generally not been targeted in wildfire 

education and prevention activities, market research was needed to understand 

their vested interests in wildfire risk reduction and continuing education. 

The audience was defined as businesses and professionals who work in and 

around home sites and community developments, and included architects, 

builders, insurance agents, landscape architects and contractors, planners, 

media, and real estate professionals. Market surveys for the target professions 

were conducted at the outset, reaching 58 individuals and organizations 

and gathering information on continuing education requirements, subject 

relevance, training format, pricing, and marketing.

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USDA Forest Service Proceedings RMRS-P-46CD. 2007. 574

Market research into these business sectors was focused initially on national 

professional organizations that are membership-based, provide services that 

range from continuing education to advocacy and lobbying, and have unique 

insights into their industry trends and needs. Information was gathered in 

phone calls to officers and staff within each organization, asking questions 

regarding how to best accommodate the learning style of professionals in that 

industry; how to provide value to the professionals to motivate them to attend 

and utilize the concepts once taught; and how to address budget and time 

constraints to accommodate the greatest number of potential students.

A second phase of market research focused on the local chapters of those 

organizations and/or companies unique to southern California. This outreach 

gathered information on the curriculum and training products that address 

local business practices in wildfire loss prevention, market forces behind 

changes in business practices, ways to market seminars to local professionals 

and companies, and how to develop partnerships and involvement with local 

organizations and businesses.

Curriculum Development

The project “Wildfire Education for the Business Sector” assumed that 

existing wildfire education materials were adequate and could be adapted 

by local professionals and experienced national and regional experts for the 

seminars. Wildfire education materials applicable to the southern California 

setting had already been gathered and used in local wildfire education grants 

after the 2003 wildfires (McElhinney and Younkman 2005), and more 

recently gathered by the University of California Extension Service (Janis 

Gonzales, personal communication, March 2007).

Resource professionals have for decades known most of the principles 

and practices for reducing wildfire-related property and resources losses in 

southern California. A homeowners’ guide—Radtke (1982)—was produced 

from earlier research, and this guide was more recently slightly modified and 

reprinted (Radtke 2004). These publications describe chaparral ecology and 

fire regimes, the effect of topography on fire behavior, risk reduction through 

planning and site design, modification of existing structures to reduce risks, 

and more. Compared with contemporary wildfire education publications, 

Radtke (1982) placed greater emphasis on watershed values, erosion reduction, 

rooting depth and drought tolerance, slope engineering, hillside landscaping, 

and postfire rehabilitation.

An advisory committee provided guidance throughout the development 

of the project, helped with curriculum development, and helped train and 

evaluate the instructors in the material to be presented. Originally a group of 

about 10, it was informally expanded to include 29 local professionals involved 

in curriculum development from various fields (table 1). Whereas involvement 

by national experts was originally planned, it was discovered that several local 

experts were on the “leading edge” of various subject areas, and consultation 

with national experts was done occasionally and informally.

The local experts assisted with the selection of existing materials, development of curriculum objectives, and PowerPoint presentations for four 

modules:

1. Industry issues. Representatives from the various target industries were 

asked to prepare a 3 to 5 minute presentation about the issues they face 

in the field with regard to wildfire. A conference call was held with all 

participants to provide an opportunity for practice and feedback.

Educating and Engaging the Business Sector in Reducing Wildfire Property Losses Fege and Blaylock
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 575

2. Fire environment (including fire basics, local habitats, and local fire ecology). The fire basics objectives and illustrations were drawn from basic 

wildland firefighting training materials. The local habitats section was 

developed by RECON Environmental, Inc., a consulting firm based in 

San Diego, CA, as a partnership and donation to the project. The fire 

ecology section was developed by California Chaparral Institute, based 

in Escondido, CA.

3. Building materials and design. Information from damage assessments after 

the 2003 Cedar and Paradise Fires, as well as Statewide deliberations 

about local building codes, had been incorporated into a PowerPoint 

presentation by Rancho Santa Fe Fire Protection District (2006). This 

was shortened and otherwise unaltered for this module.

4. Site planning and maintenance. A task force of about a dozen landscape 

architects, arborists, and planners met four times to outline, discuss 

extensively, and develop the objectives and the presentation for site design, defensible space, and fuels management. Two landscape architects 

provided most of the photos and review, one in private practice and one 

working for a local agency planning review department.

Drafts of the written outlines and corresponding PowerPoint presentations 

were distributed electronically for review by all of those who contributed to 

the curriculum development, as well as local fire officials and professionals 

willing to support the review process. Finally, two dry-run presentations were 

held with an open invitation to attend. This provided instructors with the opportunity to practice, the team with an opportunity to refine the curriculum, 

and local stakeholders an opportunity to review the materials.

Seminar Marketing Efforts

The Museum’s marketing department assisted with media outreach, sending three press releases in the 6 months prior to the seminars. Contacts were 

made with local professional organizations and associations, to request that 

they include the wildfire seminar information in scheduled e-mail messages 

and newsletters. Announcements were sent directly to about 500 real estate 

professionals and were distributed as “e-mail blasts” to such other organizations as local chambers of commerce, Rotary Club and other service clubs, 

Sierra Club, and California Native Plant Society.

Table 1—Professional affiliations of participants in wildfire education project.

Participants

Curriculum

development

Seminar

attendance

Architects 1 5

Consultants (environmental, wildfire prevention planning) 10 14

FireSafe council and homeowners’ association members 1 9

Insurance agents, brokers and underwriters 1 8

Landscape architects, landscape designers, and contractors 6 17

Property managers, real estate agents, and appraisers 1 16

Public employees (planning, wildfire, land management) 18 26

Total 38 98

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Results

Market Surveys

Respondents generally recommended a training format of about 4 to 6 

hours, and indicated that a cross-training (interdisciplinary) format would be 

beneficial to most industries. Most organizations indicated that there are few 

wildfire education opportunities targeted toward their respective industry, 

either currently or in the past. Education programs commonly are in the 

form of offsite seminars with presentations and handouts.

Education is generally pursued by individual professionals, with companies reimbursing them when it is applicable to their jobs and benefits their 

performance. When continuing education credits are required to maintain 

licensure, the course provided must be approved by the regulatory agency or 

organization, and professionals expect to pay for the courses that they attend. 

Courses can be comarketed with professional associations, and information 

provided in e-mail announcements and newsletters.

There are clear differences between the five business sectors that were the 

focus of this project (table 2). These differences underscore the importance 

of adapting seminar offerings and marketing to various business sectors, to 

enhance participation and educational value.

Table 2—Summary of market survey of business sectors.

Architecture, building,

and design industries

Formal wildfire information is limited and generally is obtained

inefficiently as professionals deal with codes and enforcement in each

jurisdiction. Architects in California have annual CE requirements for

maintaining their licenses, and few courses are offered to meet the

hours required in health, safety, and welfare.

Insurance agents,

brokers, and

underwriters

These professionals are required to complete some CE-certified

training annually and are most interested in how to gain local sitespecific information and make onsite assessments. Most contacts

indicated that existing programs charged a nominal fee, and one contact

from the Insurance Education Association recommended a $199 fee for

a half-day class.

Landscape architects,

landscape designers,

and contractors

Landscape professionals’ interests include the effectiveness of various

mitigation techniques to meet local codes, and how to plan for clients in

the back country. Most have no CE requirements and are accustomed

to attending offsite programs, workshops, and seminars that are free or

have only a nominal charge.

City planners,

environmental

consultants

Planning professionals attend both luncheon meetings and workshops

and are particularly interested in knowing county fire protection plan

requirements, different jurisdictional standards, and ways to address

community fire planning early in the development decision process.

Real estate agents,

brokers, and appraisers

Real estate professionals are looking for courses on either “how to

make money or how to stay out of trouble (risk management).”

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Curriculum

Protecting property from wildfire requires a systems approach. That is, 

no one aspect of mitigation protects the structure entirely but instead works 

within a larger system of mitigations and preparation to save the home. It is 

important then for landscapers not only to have a deeper understanding of 

how their work contributes to a fire-safe landscape, but that they also have 

an understanding of how it fits into the larger system that includes structural 

design and fire physics. It is therefore useful for them to learn about “Building materials and design” and other subject areas in order to understand the 

larger system. It is also likely that larger system information has not been 

presented to landscape architects in their traditional education, whereas they 

may already be familiar with fire-safe landscaping principles.

Therefore, it was decided that all sections would be presented to all professionals, reinforcing the comprehensive systems approach that underlies the 

concepts of wildfire risk reduction and protection of natural environments. 

By giving different industries the full curriculum, better interaction and 

partnership were facilitated among professionals and industries. Landscapers 

can theoretically work better with building contractors or insurance professionals, for example, now that they both have a better understanding of the 

full scope of what is involved, not just the aspects that relate to their narrow 

scope of work.

Concerns about wildfire risk mitigation practices, when accomplished 

initially by excessive fuel reduction, have been expressed repeatedly by 

biologists and other environmental professionals in southern California 

(Halsey 2004). This curriculum was developed to teach the principles of 

wildfire mitigation while minimizing the impact to the native environment. 

The wildfire education seminars focus on appropriate vegetation reduction 

practices that reduce water use, plant maintenance, erosion, slope instability, 

invasion by highly flammable nonnative plants, and therefore also costs to 

the homeowner. When ignition-resistant structures are considered the first 

“line of defense,” and when the landscape practices minimize the width of 

defensible space necessary to protect structures, the acreage of natural habitat 

loss will be minimized.

Reports, brochures, State codes, county and city of San Diego codes, contacts for smaller jurisdictions, sample fire management plans, and Web site 

links were provided on a CD to seminar participants. For example, copies 

of materials produced by the Center for Fire Research and Outreach at the 

University of California in Berkeley (2006) were included, from their Webbased “Fire Information Engine Toolkit” that has extensive information 

on mitigating home-related fire hazards based on observations of wildfire 

damage, data from fire tests, and input from firefighters. The “Homeowner’s 

Wildfire Mitigation Guide” has descriptions and illustrations of problems and 

solutions for roofs and gutters, vents, siding, windows, garages, decks, fences, 

plants, and trees. The CD also included copies of the fire performance tests 

of roofs, decks, walls, and windows that provide detail suitable for architects 

and construction professionals (Quarles 2006).

The core curriculum consisted of PowerPoint presentations with written 

outlines and slide printouts distributed to each participant, for the four modules: industry issues, the fire environment, building materials and design, 

and site planning and maintenance (San Diego Natural History Museum 

2007). PowerPoint presentations were included, and attendees were encouraged to use and adapt them in their work with clients, employees, and other 

contacts. The Museum synchronized audio from the live presentations with 

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USDA Forest Service Proceedings RMRS-P-46CD. 2007. 578

their corresponding PowerPoint slides, suitable for Web-casts or for training 

new instructors. Attention was given to the exact wording of the outlines, 

as they are the principles that seminar participants will apply and share with 

their clients. An excerpt from the “fire environment” module is provided in 

table 3.

Following the development of curriculum objectives and outlines, professional associations were approached for their willingness to assign continuing 

education units (CEUs) for these classes, ostensibly as an attractive motive 

for professionals to attend. By partnering with local organizations, CEUs 

were secured for the following:

1. Certified arborists, through the Western Chapter International Society 

of Arboriculture

2. Certified urban foresters, through the California Urban Forests Council

Table 3—Excerpts from wildfire education curriculum.

a. Local fire ecology – Why Sunshine, Shrubs and Wildfire?

i. Survival in a Mediterranean climate

1. Mediterranean climate exists in only five places in the world, representing

only 2% of the world’s land area. This unique climate helps makes Southern

California’s flora and fauna some of the most unique and diverse in the world.

2. Seasons dictated by rainfall that is distributed differently than other climates.

a. Spring: After first rains, usually in November/December

b. Fall: Very brief, usually in June (or August at higher elevations)

c. Summer drought

ii. Adaptation: An adaptation is a pre-existing behavioral or physical trait of a group of

organisms that allows it to survive an environmental condition.

1. Drought adaptations

a. Leaf adaptations

b. Avoiders: Usually have deep tap roots that find water

c. Persisters: Shallow roots but hang on through conservation

d. Retreaters: Annuals

e. Chameleons: Hang out but leaves change; semi-deciduous

2. Fire adaptations

a. Obligate resprouters: Don’t completely die, resprout after fire

b. Obligate seeders: Adults die, seeds can’t germinate unless scarified by

fire in some way

c. Facultative seeders: respond both by resprouting and germination

d. Annuals and short-live perennial “fire followers”

e. Geophytes: bulbs that bloom after until fire removes the canopy and

allows sunlight on these sites

iii. Fire regimes

1. Difference between “fire” and fire regimes with distinct frequency, intensity

and seasonality of fire

2. Chaparral not dependent on fire for regeneration, but has survived certain fire

regimes in Mediterranean climate and human occupation

3. Fire ignitions have increased linearly with population increases

4. Fire suppression has not led to bigger fires in Southern California

iv. So what do we do about it?

1. Create sustainable, fire-safe environments for our homes by starting from the

house out, rather than from the wildland in.

2. Systems approaches

a. Community design

b. Building design

c. Landscape design

d. Personal responsibility

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USDA Forest Service Proceedings RMRS-P-46CD. 2007. 579

3. Community managers, through the California Association of Community 

Managers

4. Architects, through the American Institute of Architects (self-reporting)

5. Planners, through the American Planning Association (self-reporting)

Seminar Marketing Efforts

Through periodic press releases and contacts with local media, the Museum 

was able to secure media coverage in four publications. Two local newspaper 

articles were written from the press release information, and two articles 

in national insurance and real estate trade publications were written from 

interviews with the project manager and local professionals referred by the 

project manager.

Professional associations provided a major outlet for distribution of seminar 

information. Different organizations took different levels of interest in the 

project and promoted them accordingly. Some simply forwarded the seminar 

invitations in their e-mail blasts. Others, such as the local chapter American 

Society of Landscape Architects (ASLA) and California Association of Community Managers (CACM), were far more aggressive with their promotion 

and, as a result, landscape and property professionals constituted 13 and 

17 percent of the participants, respectively.

Class registration fees played a role in marketing. Market research indicated 

that some industries were used to paying over $100 for similar classes, and 

other industries commonly pay no registration fees or only nominal fees for 

similar offerings. When the seminars were initially announced with $100 

tuition, some industry representatives indicated they would register multiple 

employees if fees were lower, and some in the government and nonprofit 

sector advised us that the fee was too high. A “scholarship” was originally 

envisioned, but that become awkward to administer. In order to promote 

the classes for this initial offering, the seminar fee was reduced to $25, and 

no one commented further about the fee.

Seminar Offerings

Four seminars (classes) were held in March 2007 at sites that provided 

geographic and time-of-day distribution in order to attract the widest possible 

audience. They were held on Friday morning, Thursday afternoon, Friday 

afternoon, and Saturday morning, each for five hours (including 10 minute 

breaks after every 50 minutes of instruction). Professional affiliations of 

attendees are listed in table 2.

One class was cohosted by the San Miguel Fire Protection District, at a 

location convenient for professionals in eastern San Diego County, where 

there is extensive wildland-urban interface. Another class was hosted by 

the San Diego chapter of the American Society of Landscape Architects. 

Although it was expected that the class would be heavily slanted toward 

landscape professionals, a diverse range of professionals attended and it was 

one of the more dynamic classes. The Rancho Santa Fe Fire Protection District cosponsored a class at their facilities, and this drew professionals from 

northern San Diego County.

The insurance industry was an active, enthusiastic participant in the curriculum development, marketing, and attendance at the seminars. Chubb 

Group of Insurance provided extensive perspectives and suggestions, and 

an industry panelist for three seminars. Chubb Group of Insurance and the 

Museum cohosted an introductory 1.5-hour wildfire course in February 2007 

with an out-of-town instructor and CEUs through the California Department 

Educating and Engaging the Business Sector in Reducing Wildfire Property Losses Fege and Blaylock
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 580

of Insurance. The class reached 22 insurance professionals and likely contributed to insurance sector participation in the wildfire education seminars 

in March 2007. It is expected that this course will continue to be available 

to insurance professionals in southern California, though it is cost-effective 

for insurance offices to attend the local Wildfire Education for the Business 

Sector seminars, rather than bringing out-of-town instructors.

A fire prevention course was initiated about 3 years ago by the FireSafe Council of San Diego County and was accredited by the California 

Department of Real Estate in March 2007, “Rural Home Fire Safety for 

Real Estate Professionals Course.” This 6-hour course includes an overview 

of the wildfire problem in California, factors that increase the fire danger to 

a home, features that make a structure more fire-resistant, and how to assist 

their clients in selling and selecting homes located and built with fire safety 

in mind. Course instructors will be qualified professionals approved by the 

FireSafe Council of San Diego County, and six Consumer Service CEUs will 

be given to real estate professionals who complete the course. Because of the 

many common elements, it is expected that it will be comarketed with the 

Wildfire Education Seminars in the future.

Evaluation

Feedback forms were provided to each participant in order to gather 

information about the effectiveness of the training; 39 feedback forms were 

returned. The feedback indicated that participants understood the fundamental points. Many professionals indicated a new understanding of the need 

to look at fire-wise building and landscape design within the context of the 

natural environment. Professionals who are in a position to advise homeowners 

indicated they would use this information to better educate their clients. 

Design professionals indicated that they received ideas that would help them 

early in the planning process. Some design professionals indicated a desire 

for greater detail and potentially a more advanced course. Selected comments 

are provided in table 4.

Project evaluation elements also included the market scoping, interaction 

with business groups, and feedback from professionals developing the curriculum. From these insights, new business practices and cooperative efforts 

are already being identified that will contribute to property-loss prevention, 

are economically viable, are likely to be adopted, and extend the reach of the 

traditional fire and land management professionals.

Future Seminars

Businesses and professionals gained greater understanding of fire-safe 

principles and codes, an understanding that will benefit their clientele in the 

form of knowledgeable homeowners and fire-resistant homes or communities. 

As the wildfire knowledge base in southern California expands through such 

wildfire education efforts, property protection will likely increase and losses 

in the next wildfire will likely decline.

Discussions are ongoing, about whether and how to shorten the seminars 

from 5 to 3 hours. Some possible scenarios for continuing the wildfire education seminars have been explored and include:

1. Offer seminars regularly by local organizations with ongoing educational 

programs on related topics, including the Museum, the Water Conservation Garden (which is sponsored by water companies), Quail Botanical 

Gardens, and the Burn Institute.

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2. Package the seminars and arrange for local organizations and companies 

to undertake marketing, registration, and logistics. To retain the valuable cross-training aspects of the seminars, it would be advisable for two 

or more professional organizations to cohost a seminar and market to 

their professional base. Examples include American Society of Landscape 

Architects and American Institute of Architects, or a real estate office 

and a nearby homeowner’s association, or a fire marshal and local FireSafe council.

3. Package the seminars and market them to other regions of southern 

California. If this approach is taken, it would be advisable for the hosts 

to arrange a short review process involving local regulators and professionals and to arrange for a local industry panel.

4. Incorporate any related courses, targeted to the insurance and real estate 

industries, into the marketing efforts.

Conclusions and Recommendations

The businesses and individuals who advise and interact with homeowners 

are in a unique position to affect the physical properties of homes, communities, and landscapes. This project successfully developed and held seminars for 

business professionals to impart the fundamentals of mitigating for wildfire 

with minimal impact to the surrounding environment.

Table 4—Comments from participant feedback forms.

“How very naïve we are, regarding our native environment” *

“Homeowners must understand where they live and accept the responsibility through a fire-safe

lifestyle that includes house, landscape, and evacuation (or staying to defend the house).”

“Industry panel was very insightful; include city code enforcement and planning department as

panelists.” “Include local land use regulation perspective on panel—I volunteer!”

“Suggest alternatives for building materials during architectural review.” Property manager.

“Great to have real examples of building ignitions, alternative materials, landscaping techniques,

and local ecology.”

“Printing brush management codes on landscape architects’ plans (City of San Diego) provide

ongoing reminders.”

“Establish demonstration gardens around a typical home to show ‘fire-safer’ gardens, perhaps a

garden at the local fire station or a Sunset ‘idea home’ that goes on tour periodically to

demonstrate the latest designs and products.”

“Suggest more information on how to combine defensible space with aesthetic appeal, annual

calendar for maintenance, considerations for wildlife plantings, and more time for questions.”

Property manager.

“As a land conservancy, we’ve had illegal brush management clearing adjacent to our preserves;

we need to work with insurance agents, developers and homeowners to address this problem.”

Biologist.

“Talk about how local communities and organized neighbors could organize to review their

needs and take corrective actions to help the fire department prevent disasters.” Real estate

professional.

*Professional identity listed, if provided on feedback form.

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The wildfire knowledge base in southern California has been enhanced by 

engaging professionals in wildfire education efforts. If these efforts continue, 

property protection will likely increase and, potentially, losses from the next 

fire would decrease. Businesses and professionals gained greater understanding of fire-safe principles and codes, an understanding that will benefit their 

clientele (homeowners, property owners, and communities). By participating 

in this project, businesses and professionals gained information that can be 

applied as greater understanding of local codes, better business practices with 

respect to mitigation investments and maintenance, and perhaps greater accountability for wildfire risk reduction without constant compliance checks 

by fire marshals and other officials.

The development of this project revealed that engaging professionals in 

the development of seminars or similar programs may be just as valuable 

as getting students into the wildfire education seminars. Education in this 

instance is not a one-way street with information flowing from the fire 

knowledge base to professionals, but instead a two-way street of information 

to address complex wildfire issues. Application of this information can shift 

“best management practices” and reduce wildfire property losses through 

better design, construction, maintenance, and advice. As professionals are 

engaged, they add their own field expertise to the larger fire knowledge base, 

and develop new business practices and cooperative efforts that are economically viable and will increase investments in property-loss prevention. Such 

discussions also revealed the need for further adaptation of some practices, 

notably the use of targeted goat grazing, width of defensible space approved 

in new developments, irrigation demands in light of future water shortages, 

and investments in existing, nonconforming structures.

The collaboration that developed around this project is a priceless product of the wildfire education efforts. The development of the project both 

required and provided the ideal conditions for stakeholders of every bias and 

background to come together and bring their respective specialized knowledge 

to the table. In that respect many individuals and organizations played several 

roles throughout the project: providing written materials, developing the 

outlines and PowerPoint presentations, instructing class sessions, marketing 

the seminars through their business and professional contacts, and providing 

feedback and other valuable assistance. This project succeeded only with these 

invaluable investments from business and government partners.

By engaging business professionals, the fire agencies gain a larger “militia” 

in the effort to reach homeowners and help protect homes and communities 

in wildfire prone areas. When they understand the potential of improving 

their services to homeowners and the principles of wildfire-resilient homes in 

harmony with natural lands, business professionals can be a large, effective 

asset that complements the traditional fire prevention programs of fire and 

land management agencies.

References

Absher, J.D.; Kyle, G.T. 2006. Perceptions of wildfire management issues by 

San Diego County wildland-urban interface residents. Presentation to Third 

International Fire Ecology and Management Congress; San Diego, CA; 

November 13-17. [available from first author]

Absher, J.D.; Vaske, J.J.; Bright, A.D.; Don Carlos, A. 2006. Effective communication 

about wildfire management along the Colorado Front Range. Proceedings of the 

Third International Fire Ecology and Management Congress; 2006 November 13-

17; San Diego, CA. [on DVD, track 2, community wildfire management] 6 p.

Educating and Engaging the Business Sector in Reducing Wildfire Property Losses Fege and Blaylock
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 583

Andreasen, A.R. 1995. Marketing social change: Changing behavior to promote 

health, social development, and the environment. San Francisco, CA: 

Jossey-Bass.

Brenkert, H.; Champ, P.; Flores, N. 2005. Mitigation of wildfire risk by homeowners. 

Gen. Tech. Rep. RMRS-RN-25WWW. Fort Collins, CO: U.S. Department 

of Agriculture, Forest Service, Rocky Mountain Research Station. Available at 

http://www.fs.fed.us/rm/pubs/rmrs_rn025.pdf. Accessed on 4/10/07.

Fire Cause Analysis. 2004. Fire at the urban wildland interface: Performance of 

California homes and buildings. Report prepared for California Department of 

Forestry and Fire Protection. 103 p. Available at http://osfm.fire.ca.gov/pdf/

regulations/UWIRpt1-ALL091004.pdf. Accessed 4/15/07.

FireWise Communities. 2006. National Wildland/Urban Interface Fire Education 

Conference, “Backyards and Beyond.” November 2-4, 2006, Denver, CO. 

Available at http://www.firewise.org/fw_youcanuse/conference/. Accessed on 

4/10/07.

Halsey, R. 2004. Fire, chaparral, and survival in southern California. El Cajon, CA: 

Sunbelt Publications. 224 p.

Hoban, V.; Leary, P.; Rivaldi, M.; Weis, J. 2004. Give yourself a fighting chance: 

How to protect homes from destructive wildfires through education and action. 

San Diego, CA: San Diego State University, Graduate School of Business 

Administration (unpublished)

Kotler, P.; Roberto, N.; Lee, N. 2002. Social marketing: Improving the quality of 

life (2 ed.) Sage Publications.

Kumagai, Y.; Carroll, M.S.; Cohn, P. 2004. Coping with interface wildfire as a human 

event: Lessons from the disaster/hazards literature. J. For. 102(6):38-32.

McElhinney, M.; Younkman, D. 2005. Final report on wildfire education programs. 

Report provided to San Diego Women’s Foundation and San Diego Foundation 

“After the Fires” Fund.

National Fire Protection Association. 2002. NFPA 1144: Standard for Protection 

of Life and Property from Wildfire, 2002 edition. Quincy, MA.

Quarles, S. 2006. Fire performance testing: Roofs. [Also for decks, walls, and 

windows.] Available at http://firecenter.berkeley.edu/quarles/roof_kit-SQ.pdf. 

Accessed on 4/16/07.

Radtke, K.W.H. 2004. A homeowners’ guide to fire and watershed management at 

the chaparral/urban interface. City of San Diego Water Department. 47 p. Also 

available at http://www.sandiego.gov/water/pdf/firewatershed.pdf

Radtke, K.W.H. 1982. A homeowners’ guide to fire and watershed management at 

the chaparral/urban interface. Gen. Tech. Rep. 67. Berkeley, CA: U.S. Department 

of Agriculture, Forest Service, Pacific Southwest Research Station.

Rancho Santa Fe Fire Protection District. 2006. Rancho Santa Fe shelter-in-place 

resource guide. Elements available at http://www.rsf-fire.org/. Accessed on 

4/15/07.

San Diego Natural History Museum. 2007. Wildfire education for the business 

sector: Course materials. Unpublished documents on CD, available from afege@

sdnhm.org.

Schwab, J. and S. Meck. 2005. Planning for wildfires. Chicago, IL: American 

Planning Association. 124 p.

University of California, Center for Fire Research and Outreach. 2006. Homeowners’ 

wildfire mitigation guide. Available at http://groups.ucanr.org/HWMG/. 

Accessed on 4/16/07.

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Educating and Engaging the Business Sector in Reducing Wildfire Property Losses Fege and Blaylock
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 585

Abstract—This study provides a framework for assessing the social and environmental benefits and public education outcomes associated with the U.S. Department of 

the Interior, Bureau of Land Management’s Community Assistance and Hazardous 

Fuel Programs in California. Evaluations of fire hazard mitigation programs tend to 

focus primarily on the number of acres treated and treatment costs associated with 

mitigation without adequately assessing the benefits of these treatments. While some 

evaluations account for the value of protected structures or the avoided costs of suppression, few account for the ecosystem service value of protected natural capital. 

Examples include the water purification and flood abatement functions of wetlands, 

the hydrologic regulation functions of forests, and the recreational value of various 

natural landscapes. The total economic value approach to environmental assessment 

used in this study includes both the market-based and nonmarket values that are at 

risk from wildfire, particularly ecosystem goods and services. Using a decision support 

methodology, the data allows the BLM to more effectively quantify and account for 

the social and environmental benefits derived from fire mitigation treatments. Suggestions are provided for how this approach could effectively be scaled up and used 

at a national, regional, or Statewide level to analyze the efficacy of all BLM programs. 

Although this approach is currently compatible with BLM current reporting system, the 

assessment provides recommendations on how to augment the evaluation system so 

that future program elements or “system” elements that enable (or prevent) communities to take part in raising awareness and taking action for themselves are evaluated 

at the broader BLM program level for the Community Assistance and Hazardous Fuel 

Programs in California.

Introduction

Not all fire is harmful, and it is important to differentiate between harmful and beneficial fires (Ganz and Moore 2002). Federal fire policy has been 

significantly modified since 1995 to recognize and embrace the role of fire 

as an essential ecological process (USDA 1995; USDI–USDA 1995; NWCG 

2001). The value of ecosystem goods and services should be recognized when 

considering the positive and negative effects of fire on a landscape. Traditionally, economic assessment methodologies such as Cost-Benefit Analysis have 

not accounted for the value of many ecosystem services because the tools and 

techniques to evaluate ecological goods and services in a cost-effective manner were not widely available (EPA 2000; National Research Council 2004). 

When tradeoffs are made between alternative land use and fire management 

decisions, the best available information is needed to avoid systematic biases 

in the resulting decision.

In: Butler, Bret W.; Cook, Wayne, 

comps. 2007. The fire environment—

innovations, management, and policy; 

conference proceedings. 26-30 March 

2007; Destin, FL. Proceedings 

RMRS-P-46CD. Fort Collins, CO: 

U.S. Department of Agriculture, 

Forest Service, Rocky Mountain 

Research Station. 662 p. CD-ROM.

1 Fire Scientist, TSS Consultants, 

Oakland, CA. Dganz@tssconsultants.

com.

2 Landscape Ecologist, Economist, and GIS 

Analyst, respectively, Spatial Informatics 

Group, LLC, San Leandro, CA. 

Efficacy of the California Bureau of Land 

Management Community Assistance and 

Hazardous Fuels Programs

David J. Ganz1, David S. Saah2, Matthew A. Wilson2, and Austin Troy2
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 586

If hidden costs or benefits are not fully accounted for, people will tend to 

make uninformed choices leading to inefficient outcomes. For example, as 

witnessed in the Katrina hurricane, the valuation literature has long shown 

that offshore barrier islands and near shore saltwater wetlands in the Gulf 

Coast region do tend to provide significant benefits to coastal communities 

in the form of alleviating flooding and hurricane storm surge (Farber 1987). 

Yet, when these ecological benefits are not adequately quantified and incorporated into short-term land use development decisions, critical information 

is left outside of the market calculus and inefficiencies arise with sometimes 

disastrous results. The same quantification needs to be applied when evaluating the significant benefits to communities living in a fire adapted ecosystem. 

While this assessment focuses on the avoided costs of losing these benefits to 

an unwanted fire, we recognize that some ecological goods and services may 

benefit from periodic low intensity fires. To assist the U.S. Department of 

the Interior, Bureau of Land Management (BLM) in California gather such 

knowledge, we have developed a conservative, baseline ecological-economic 

assessment of the ecosystem goods and services for three selected counties 

in that State. Counties were selected based upon the frequency of BLM 

projects, the availability of land cover data, and the landscape heterogeneity 

and transferability. Our goal has been to use the best available methods, data 

sources, and spatial analysis techniques to generate defensible value estimates 

that can then be integrated into better land use planning and environmental 

decisionmaking throughout the region.

Study Objectives

This study provides the basis for a quantified assessment of the benefits, 

cost effectiveness as related to National Fire Plan (NFP) funded projects 

administered by the California BLM. The projects evaluated cover those 

implemented during the 2002 through 2004 study period. Primary objectives of the study are:

• Quantification of the economic values associated with the Community 

Assistance and Hazardous Fuels Programs (HFP) in three counties that 

are representative of California’s heterogeneous landscapes.

• Providing a framework and analysis of which BLM fuel reduction projects offer the highest return on the investment when considering the 

ecosystem goods and services included as part of the HFP.

The assessment differs from previous ones in that it takes into consideration both the market-based and nonmarket values likely to be impacted by 

a catastrophic fire. Specifically, it provides a first-order baseline estimate of 

the ecosystem goods and services provide by California’s natural landscapes 

that might be threatened by catastrophic fire. Using a decision support methodology developed by Spatial Informatics Group LLC, the NaturalAssetsTM 

Information System, the study presents data that will allow the BLM to more 

effectively quantify and account for the social and environmental benefits 

derived from fire mitigation treatments.

Study Site Selection

Study sites were chosen by first generating a query map of the concentrations of community assistance grants, and fuels projects funded by the BLM 

and the Rural Fire Assistance Program (RFA) from 2002 through 2004. 

Efficacy of the California Bureau of Land Management Community Assistance and Hazardous Fuels Programs Ganz, Saah, Wilson, and Troy
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 587

A density analysis map (fig. 1) was created by the BLM with data generated 

from the National Fire Plan Operations and Reporting System (NFPORS). 

The purpose of this first level of analysis was to determine those locations in 

California that have been targeted for receiving the most funding through 

these Federal programs; three counties were chosen that had high concentrations of grant recipients. The three counties selected for performing the 

NaturalAssetsTM Information System evaluation were Napa, Humboldt, and 

San Bernardino. They were selected because:

• These counties represent a good cross section of vegetative communities, 

latitude, and development patterns.

• All three have significant BLM lands within their boundaries.

• All three counties have a diverse number of land cover types and hazardous fuel treatments.

• All three counties are covered by the 1997 to 2001 California Land Cover 

Mapping and Monitoring Program.

Figure 1—Density analysis of BLM funded projects in California (adapted from BLM 2004). 

The density analysis was conducted by BLM using the following data (in a point format): 

2002 to 2004 Fuels Treatments (NRPORS), 2002 to 2004 Rural Fire Assistance Grants, 2002 to 

2004 Community Assistance Activities only from the following counties: Humboldt, Butte, 

Nevada, Napa, Madera, Kern, Los Angeles, San Bernardino, and Orange.

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We also provided the BLM with two case studies: the town of Petrolia in 

Humboldt County and Morongo Valley in San Bernardino County (fig. 2). 

These two case studies are used to compare the costs of treatment to the 

estimated benefits from those treatments, including both protected structures and Ecosystem Service Valuations (ESVs). Morongo Valley is a highly 

developed part of San Bernardino County while Petrolia is in a rural part of 

Humboldt County. Both of these communities are in the wildland urban 

interface with Petrolia listed as a Community at Risk and Morongo Valley 

listed as a Community of Interest.

Figure 2—Case study locations include Napa, 

Humboldt, and San Bernardino Counties.

Methods

Economic valuation can help to ensure that ecosystem services that are not 

traded in markets and do not have market prices receive explicit treatment 

in economic assessments. Our goal is not to “create” values for ecosystems. 

Rather, our purpose is to generate a conservative baseline estimate of the 

values that people already hold with respect to these ecosystems through 

an assessment of the best available literature. Such information will in turn 

assist in our assessments of the benefits provided by community assistance 

and hazardous fuels programs in California. This approach is consistent with 

that being taken in the international Millennium Ecosystem Assessment,

which focuses international policymakers’ attention on the contributions of 

ecosystems to human wellbeing (Millennium Ecosystem Assessment 2003; 

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Argady and others 2005). Due to the lack of credible metrics for evaluating 

the effectiveness of the BLM projects, we have made a series of assumptions 

for this study including:

• Hazardous fuel treatments, irrespective of size, will have an impact on 

reducing the fire hazard on a landscape scale. Although the focus of 

this assessment is not to model (spatially or temporally) how much of 

an impact these treatments might have on fire behavior, there is credible research to indicate that strategically placed treatments can change 

fire behavior (Finney 2001; Knapp and others 2004). We assume that 

all BLM funded hazardous fuels projects have a beneficial impact on 

protecting market and nonmarket assets regardless of the probability of 

burning or the level of changes to fire behavior.

• Communities value their structures more than any other market asset. 

The research to date on community perceptions of fire support this 

assumption (Hodgson 1994; Winter and Fried 2000; Everett 2002). 

As far as generating monetary values for marketable assets, this assessment focuses on parcels and the improvement values (generated from 

the Grand list) and not other market assets. While the market value of 

standing timber is clearly high in Humboldt County, we chose not to 

attempt to quantify it due to the lack of data on forest accessibility, size, 

and age structure.

• Environmental aesthetics and recreational opportunities are important 

services provided by forests in the urban/wildland interface. The natural landscape in and around communities has amenity and recreational 

values that tend to be quite high in California. In California’s Sierran 

foothills, for example, Hodgson (1994) did a survey of residences and 

found that one in five respondents considered protection of the landscape 

more important than the protection of structures. Californians value 

their natural landscape and are willing to pay for the costs associated 

with living in a fire-prone area for the other natural amenities that these 

surroundings provide.

• There are additional ecosystem goods and services from which local 

California residents benefit even though they may not be as aware of 

them. These include flood avoidance, wildlife habitat refugia, and clean 

water provision, all of which provide real benefits to society. This assessment includes value estimates for those ecosystem goods and services 

that have been quantified in the peer-reviewed literature.

• If hazardous fuel treatments are going to be effective in California, they 

need to be coordinated with an outreach effort to raise awareness of 

why landscape scale treatments are needed. This is especially important 

in California where the environmental assessments (performed under 

the National Environmental Protection Act and/or the California Environmental Quality Act) require public input and often turn into legal 

battles over whether fuel treatments are appropriate in certain ecological 

and socio-political systems. Although it is not within this assessment’s 

scope to address or resolve these conflicts, we should be aware that they 

exist in the State and contribute to the costs of implementing a hazardous fuel treatment program.

Building on these assumptions, we recognize that the protection of forests from fire damage can generate real benefits to society—benefits that go 

beyond the protection of market goods and structural assets. Scenic views, 

recreational opportunities, flood control, wildlife habitat protection, sediment retention, and water supply all contribute to the wellbeing of people 

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and the communities they live in. The challenge is that currently many of the 

economic values associated with fire mitigation efforts remain unaccounted 

for because they are not easily quantified in conventional policy assessments 

or cost-benefit analyses.

Value Transfer of Nonmarket, Nonuse Estimates

One of the primary goals in this the study is to shed light on the nonmarket 

economic benefits of ecosystem goods and services associated with the landscapes that are affected by fire hazard mitigation efforts. While a fair amount 

of research has been done on the economic value of ecosystem services globally (Costanza and others 1997; Millennium Ecosystem Assessment 2003), 

relatively limited peer-reviewed work has been done to estimate the specific 

economic values of ecosystem services located in San Bernardino, Napa, and 

Humboldt Counties. Because limited empirical ecosystem service valuation 

research has been done at the study sites, we were required to “transfer” 

values from other sites.

Measuring the use values associated with marketed goods and services simply requires monitoring market data for observable trades; but the nonmarket 

values of goods and services are much more difficult to measure (Bingham 

and others 1995). When there are no explicit markets for ecosystem goods and 

services, more indirect means of assessing economic values must therefore be 

used. A subset of economic valuation techniques commonly used to establish 

values when market values do not exist are identified in table 1. (This list of 

nonmarket valuation techniques is not intended to be all-inclusive. Rather, 

it is intended to reveal the breadth of available empirical techniques that 

have been and are currently being explored in the field of ecosystem service 

valuation.)

Avoided Cost (AC): services allow society to avoid costs that would have been incurred in the absence of

those services; flood control (barrier islands) avoids property damages, and waste treatment by wetlands

avoids incurred health costs.

Marginal Product Estimation (MP): Service demand is generated in a dynamic modeling environment

using production function (that is, Cobb-Douglas) to estimate value of output in response to

corresponding material input.

Factor Income (FI): services provide for the enhancement of incomes; water quality improvements

increase commercial fisheries harvest and, thus, incomes of fishermen.

Travel Cost (TC): service demand may require travel, whose costs can reflect the implied value of the

service; recreation areas attract distant visitors whose value placed on that area must be at least what they

were willing to pay to travel to it.

Hedonic Pricing (HP): service demand may be reflected in the prices people will pay for associated

goods: For example, housing prices along the shore of pristine freshwater lakes tend to exceed the prices

of inland homes.

Contingent Valuation (CV): service demand may be elicited by posing hypothetical scenarios that

involve some valuation of alternatives; people would be willing to pay for increased water quality in

freshwater lakes and streams.

Table 1—Conventional nonmarket valuation techniques.

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As the descriptions in table 1 suggest, each nonmarket valuation methodology represented in the NaturalAssetsTM information system (NAIS) has 

its own strengths and limitations, often limiting its use to a select range of 

ecosystem goods and services within a given landscape. For example, the 

economic value generated by a naturally functioning ecological system can be 

estimated using avoided cost (AC), based on the estimated cost of damages 

due to lost services. However, because these estimates are highly sensitive 

to market conditions used to estimate costs, they must be used with great 

caution. While rigorous and well established in the field, travel cost (TC) is 

primarily limited to estimating recreation values, while hedonic pricing (HP) 

is used for estimating property values associated with aesthetic qualities of 

natural ecosystems. On the other hand, contingent valuation (CV) surveys 

are often widely used to estimate the economic value of less tangible services 

such as critical wildlife habitat or biodiversity. The challenge with CV and 

related methods such as choice modeling is that estimated values are highly 

sensitive to the survey format and context of valuation (Heberlein and others 2005).

In this study, the full suite of ecosystem valuation techniques is used to 

account for the economic value of goods and services provided by natural 

landscapes in San Bernardino, Napa, and Humboldt Counties.

Value transfer by definition involves the adaptation of existing valuation 

information or data to new policy contexts with little or no data. (Following 

Desvouges and others [1998], the term “value transfer” is used instead of 

the more commonly used term “benefit transfer” to reflect the fact that the 

transfer method is not restricted to economic benefits, but can also be extended to include the analysis of potential economic costs, as well as welfare 

functions more generally.) The transfer involves obtaining an estimate for 

the economic value of nonmarket goods or services through the analysis of 

a single study, or group of studies, that have been previously carried out to 

value similar goods or services. The transfer itself refers to the application 

of estimated point values, derived utility functions, and other information 

from the original “study site” to a “policy site” (Loomis 1992; Desvousges 

and others 1998).

While we accept the fundamental premise that primary valuation research 

will always be a “first-best” strategy for gathering information about the 

value of ecosystem goods and services (Smith 1992; Downing and Ozuna 

1996; Kirchhoff and others 1997), we also recognize that value transfer has 

become an increasingly practical way to inform policy decisions when primary 

data collection is not feasible due to budget and time constraints, or when 

expected payoffs are small (EPA 2000; National Research Council 2004).

In other words, value transfers will always represent a policy-relevant compromise solution. When primary valuation research is not possible or plausible, 

then value transfer, as a “second-best” strategy, is important to consider as a 

source of meaningful baselines for the evaluation of management and policy 

impacts on ecosystem goods and services. However, the real-world alternative is to treat the economic values of ecosystem services as zero; a status quo 

solution that, based on the weight of the empirical evidence, will often be 

more error prone than value transfer itself.

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USDA Forest Service Proceedings RMRS-P-46CD. 2007. 592

Ecosystem Service Valuation (ESV) Data

The raw material for the value transfer exercise comes from previously 

published studies that empirically measured the economic value of environmental goods and services. Three types of valuation research exist in the 

literature today:

• Peer-reviewed journal articles, books and book chapters, proceedings, 

and technical reports that use conventional environmental economic 

valuation techniques and that are restricted to an analysis of social and 

economic values.

• Non peer-reviewed publications that include PhD dissertations, non peerreviewed technical reports and proceedings, as well as raw data available 

on the Internet.

• Secondary analysis (for example, meta analysis) of peer-reviewed and/or 

non peer-reviewed studies that use both conventional and nonconventional valuation methods.

The critical underlying assumption of NAIS is that the ESVs for ecosystem 

goods or services can be inferred with sufficient accuracy from the analysis 

of existing nonmarket valuation studies. Clearly, as the level of information 

increases within the source literature (in other words, more studies are done), 

the accuracy of the value transfer likewise improves. The research team developed a set of explicit decision rules for querying economic results from the 

raw data contained in NAIS that would allow us to estimate with sufficient 

accuracy the economic value of ecosystem services in San Bernardino, Napa, 

and Humboldt Counties. The research team selected valuation studies that 

were:

• Peer reviewed and published in recognized journals

• Focused on temperate regions in either North America, Canada, or Europe

• Focused primarily on nonconsumptive use

Using these search criteria, we were able to obtain data from a set of viable 

studies (n=84) whose results were then standardized to 2004 U.S. dollar 

equivalents per acre to provide a consistent basis for comparison. (All dollar 

values are standardized to 2004 using Consumer Price Index tables published 

by the U.S. Department of Labor; http://www.bls.gov/cpi/home.htm.) 

Because each study may contain more than one estimate of value, the end 

result is a collection of valuation data points that are coded by temporal (that 

is, time of study), spatial (place where study was done), and methodological 

(method used) criteria, thereby allowing the research team to derive a lower 

bound and upper bound estimate of dollar values for the study site. For this 

study, we were able to generate a total of (n=205) individual point estimates 

for reviewed land cover types. Given the aforementioned restrictions and gaps 

in the available literature, this approach yields conservative, baseline economic 

values for San Bernardino, Napa, and Humboldt Counties.

In sum, the transfer method adopted in this report involves obtaining an 

estimate for the value of ecosystem goods or services through the analysis of 

peer-reviewed research that has been previously collected and stored in NAIS 

in a standardized format so that it can further be augmented with site-specific 

GIS data (that is, land cover, socioeconomic characteristics) to ensure reliable 

valuation estimates at the study site.

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USDA Forest Service Proceedings RMRS-P-46CD. 2007. 593

Spatial Analysis Methods

Another principal goal in this study is to link the ESV estimates for ecosystem goods and service to available land cover/land use data in San Bernardino, 

Napa, and Humboldt Counties. Thanks to the increased ease of using GIS 

and the availability of land cover data sets derived from satellite images, ecological and geographic entities can more easily be attributed with ecosystem 

services and the values they provide to people (Wilson and others 2004; 

Wilson and Troy 2005). In simplified terms, the technique discussed here 

involves combining one land cover layer with another layer representing the 

geography to which ecosystem services are aggregated—that is, a watershed. 

While the aggregation units themselves are likely to be in vector format, because vector boundaries are most precise, the land cover layer may be either 

raster or vector. (The vector data model represents spatial entities with points, 

lines and polygons. The raster model uses grid cells to represent quantities or 

qualities across space.) Spatial disaggregation increases the contextual specificity of ecosystem value transfer by allowing us to visualize the exact location 

of ecologically important landscape elements and overlay them with other 

relevant themes for analysis—biogeophysical or socioeconomic. A common 

principle in geography is that spatially aggregated measures of geographic 

phenomena tend to obscure local patterns of heterogeneity (Openshaw and 

others 1987; Fotheringham and others 2000).

Development of Land Cover Typology

Two types of values were spatially mapped for this project: ecosystem 

service values and structural improvement values. These require accurate, 

high resolution, and categorically meaningful depictions of land cover. Before developing these maps, a land cover typology was created. To do this, 

we assessed available data coverages to determine which land cover classes 

at what level of categorical precision could be mapped at a usable scale and 

with acceptable levels of accuracy. Table 2 shows the resulting typology with 

the code name for each cover class, its description and the counties in which 

it was present.

Table 2—Land cover typology with applicable counties.

Code Description Counties

AGR Agriculture All

CON Conifer All

DSHB Desert scrubland San Bernardino

DWLD Desert woodland San Bernardino

EST Estuary and tidal bay Napa, Humboldt

FWET Fresh wetland All

HDW Hardwood oak woodland All

HEB Herbaceous All

MIX Mixed hardwood, conifer All

OWLF Forested areas suitable for spotted owl habitat Humboldt

RIPF Riparian forests (50 m buffer) All

RW2 Redwood-second growth Napa, Humboldt

RWOG Redwood-old growth Humboldt

SHB Shrubs All

SWET Salt wetland Napa, Humboldt

URB Urban and barren All

URBG Urban green (forest and grass) All

VIN Vineyard Napa

WAT Open water All

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Spatially Explicit ESV Calculation Methods

Ecosystem service values were then determined by multiplying areas of 

each cover type, in acres, by the dollar per acre ecosystem service value for 

that cover type. The economic values used to estimate the values associated 

with each ecosystem good or service are drawn from the NAIS ESV data. 

The total ESV of a given cover type for a given watershed can thus be determined by adding up the individual, nonsubstitutable ecosystem service values 

associated with that cover type. The following formula is used:

V(ESk) = A LU V ES i ki

i

n

( ) ( ) s



¤

1

Where A(LUi) = area of land use (i)

and V(ESki) = annual value of ecosystem services (k) for each 

land use (i).

Resulting values were estimated for the entire study area using value transfer 

methods. Following that, the ESVs were aggregated by county study area, 

broken down for each county by land cover, and cross-tabulated for each 

study site by (1) land cover and watershed and (2) land cover and zip code. 

Assessed structural improvements were also summarized to generate a total 

economic value estimate for critical human-modified land uses.

Results

Using the value-transfer search criteria, the research team obtained data 

from a set of 84 viable empirical studies, whose results were then standardized 

to 2004 U.S. dollar equivalents per acre/per year to provide a consistent basis 

for comparison in the tables in this section. (All economic valuation data in 

this report are have been standardized to represent total net present values, 

not discounted. This allows for the results to be incorporated into forwardlooking scenarios that might weight future costs and benefits differently than 

current costs and benefits when summing over time using specific discount 

rates; Heal 2004.) The aggregated baseline ESV results for all land cover 

types represented within the study area are presented in table 3.

The ESV data in table 3 show the minimum, the maximum, and the average 

nonmarket ecosystem service valuation estimates aggregated across all land 

cover types contained in the study. (Not all land cover types generated for the 

spatial analysis of San Bernardino, Napa, and Humbolt Counties by the Spatial 

Informatics Group team could effectively be matched with equivalent ESV 

estimates as denoted in table 4.) Clearly, not all land cover types represented 

in this report provide benefits to society equally. Rather, consistent with previously published literature (Daily 1997; Wilson and Carpenter 1999), the data 

reveal how land cover types in the study area that are associated with water 

(wetlands, estuaries, and riparian forest) tend to yield the largest ecosystem 

service values per area unit. Also consistent with previous findings, it also 

appears that both agricultural systems (in this report, the same ESVs were 

assigned to agricultural and vineyard land cover types) and urban greenspace 

tend to yield fairly large values per unit of measurement (Pretty and others 

2000; Ricketts and others 2004). While nonriparian forest systems tend to be 

less valuable per acre unit, there is still a range of variability evidenced among 

different forest types, with old growth and spotted owl habitat yielding the 

highest values per unit and oak woodland yielding the least.

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Table 3—Aggregate ecosystem services for all available land cover types.

Code Description Min $ acre/yr Max $ acre/yr Avg $ acre/yr

AGR Agriculture $83.47 $1,689.04 $887.06

CON Forest-conifer $32.48 $999.79 $332.35

DSHB Desert shrub NA NA NA

DWLD Desert woodland NA NA NA

EST Estuary $1,483.90 $5,239.01 $2,386.75

FWET Fresh wetland $1,761.07 $9,180.73 $4,440.73

HDW Hardwood oak woodland $61.68 $486.84 $177.82

HEB Herbaceous NA NA NA

MIX Mixed hardwood, conifer $34.32 $1,001.63 $334.19

OWLF Spotted owl habitat $100.53 $1,113.86 $403.86

RIPF Riparian forest $122.17 $15,126.99 $3,558.03

RW2 Redwood second growth $29.89 $997.20 $329.76

RWOG Redwood old growth $84.63 $1,051.94 $384.50

SHB Shrubs NA NA NA

SWET Saltwater wetland $229.18 $8,845.04 $2,446.06

URB Urban and barren NA NA NA

URBG Urban green $602.29 $4,289.91 $2,268.21

VIN* Vineyards $83.47 $1,689.04 $887.06

WAT Open fresh water $227.79 $13,073.87 $2,928.72

*Note: Assumption that AGR and VIN ESVs are equivalent as both are intensively managed and 

represent human dominated systems.

Spatially Explicit Ecosystem Service Valuation Results

Building on the ESV data generated with NAIS, the research team was 

able to use the spatially explicit ESV calculation methods, to generate ESV 

results. Tables 4 and 5 provide summaries of total ESVs by land cover class 

and reveal that significant differences exist between the three counties in 

the study.

Significant economic benefits clearly accrue to society from forests in 

Humboldt County. As the data in table 4 show, forest-related land cover 

types account for an overwhelming proportion (almost 80 percent) of total 

ESV delivered by naturally functioning ecological systems in the study area. 

Thus, while on a per-unit basis, forest land types may tend to provide less 

economic value than nonforested systems, the large study area currently under forested cover brings the total economic value associated with forests to 

the foreground. After forests, it appears that freshwater wetlands (FWET) 

and open water (WAT) provide the next most significant ESVs in the study 

area.

In contrast to Humboldt County, forested systems appear to account for 

only approximately 30 percent of the total ESV delivered by functioning ecological systems in Napa County. Napa’s open freshwater (WAT) alone in the 

form of streams, lakes, and rivers appears to provide a significant economic 

benefit to society (31 percent). And as might be expected, both agricultural 

land (AGR) and vineyards (VIN) also provide a substantial positive impact 

on the economic value associated with ecosystem services in the region 

(approximately 20 percent). For Napa County, the zip codes of high value 

(both structural and ESV) are within Napa and St. Helena.

The data in table 6 reveal that similar to Napa County, forested systems 

deliver approximately 31 percent of the total ESV delivered by ecological 

systems in San Bernardino County. Freshwater wetlands (FWET) account 

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Table 4—Humboldt County study areas land cover and ESV estimates.

Class Acres ESV/acre Total ESV Acres ESV/acre Total ESV

AGR 38,973 887 34,571,513 39,380 887 34,932,508

CON 271,121 332 90,107,174 282,303 332 93,823,306

EST 4 2,387 10,085 4 2,387 10,085

FWET 23,676 4,441 105,140,399 23,704 4,441 105,261,803

HDW 272,587 178 48,471,365 277,209 178 49,293,301

HEB 201,869 NA - 205,292 NA -

MIX 628,282 334 209,965,524 647,218 334 216,293,687

OWLF 221,523 404 89,464,211 221,580 404 89,487,414

RIPF 117,270 3,558 417,250,658 122,248 3,558 434,960,966

RW2 230,466 330 75,998,467 246,197 330 81,185,900

RWOG 90,604 385 34,837,363 98,005 385 37,682,967

SHB 53,085 NA - 55,556 NA -

SWET 1,344 2,446 3,287,882 1,356 2,446 3,317,256

URB 41,821 NA - 42,944 NA -

URBG 8,042 2,268 18,239,981 8,043 2,268 18,242,491

WAT 17,266 2,929 50,566,177 17,655 2,929 51,707,928

TOTAL ESV 1,177,910,801 TOTAL ESV 1,216,199,612

Known market values Known market values

 improvement 

value of 

structures $4,376,522,485

 improvement 

value of 

structures $4,499,321,899

TOTAL $5,554,433,286 TOTAL $5,715,521,511

Study area zip codes Entire county

Table 5—Napa County study areas land cover and ESV estimates.

Class Acres ESV/acre Total ESV Acres ESV/acre Total ESV

 AGR 26,265 887 23,298,875 27,700 887 24,571,316

 CON 16,891 332 5,613,779 17,327 332 5,758,593

 EST 1,110 2,387 2,648,298 1,115 2,387 2,661,834

 FWET 4,409 4,441 19,577,352 4,412 4,441 19,592,412

 HDW 141,771 178 25,209,687 145,867 178 25,938,010

 HEB 64,207 - - 66,148 - -

 MIX 11,704 334 3,911,425 13,619 334 4,551,190

 RIPF 16,880 3,558 60,060,503 17,479 3,558 62,189,858

 RW2 1,257 330 414,390 1,262 330 416,315

 SHB 113,065 - - 119,967 - -

 SWET 3,438 2,446 8,409,695 3,450 2,446 8,438,390

 URB 18,408 - - 18,462 - -

 URBG 1,808 2,268 4,099,948 1,808 2,268 4,099,948

 VIN* 35,032 887 31,073,702 35,034 887 31,075,280

 WAT 29,688 2,929 86,947,804 29,918 2,929 87,621,444

TOTAL ESV 271,265,459 TOTAL ESV 276,914,591

Known market values Known market values

improvement

value of 

structures $10,957,341,955 

improvement

value of 

structures $11,256,915,849

TOTAL $11,228,607,414 TOTAL $11,533,830,440

Study area zip codes Entire county

Efficacy of the California Bureau of Land Management Community Assistance and Hazardous Fuels Programs Ganz, Saah, Wilson, and Troy
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for the majority of ecosystem service benefits delivered to society (55 percent)—by far the single most important ecosystem type in the study area 

from an ecosystem services perspective. Given that desert shrub is the most 

predominant land cover type in the county and that no ESVs were estimated 

for desert land cover types in this study, we anticipate that fire-related ESVs 

would be forthcoming for these critical ecosystem types as this information 

is gathered and included in this type of analysis.

An overwhelming proportion of ecosystem service values in Humboldt 

County comes from its forests. Humboldt’s relatively large area of forested 

cover accounted for nearly 80 percent of total ESV delivery by naturally 

functioning ecological systems in the study area. On a per-unit basis, some 

forest types provide a lower stream of benefits than many non-forested types, 

but the size of forested area in Humboldt County means that ESV benefits 

from forests dominate. For instance, the Six Rivers National Forest contributes $293 million in ESV to Humboldt County with an additional $19 

million in market values (such as structures). This contribution is primarily 

due to its size, and to the dominance of redwood old growth and spotted 

owl habitat.

In Napa County, forested systems only accounted for 30 percent of ESVs 

delivered by functioning ecological systems. Napa’s open freshwater, in the 

form of streams, lakes and rivers, provided 31 percent of measured economic 

benefits to society. Both agricultural land and vineyards also provide a substantial positive impact on the economic value associated with ecosystem services 

in the region (approximately 20 percent). The communities of Napa (zip codes 

94558 and 94559) and Saint Helena (94574) have the highest estimated 

quantities of ESVs and structural values within Napa County (fig. 3).

Table 6—San Bernardino study areas land cover and ESV estimates.

Class Acres ESV/acre Total ESV Acres ESV/acre Total ESV

AGR 39596.26 $887 $35,124,255 71,762 $35,124,255 $63,657,272

CON 282191.6 $332 $93,786,377 333,674 $93,786,377 $110,896,564

DSHB 5260821 10,189,383

DWLD 596696.3 606,121

FWET 113840.5 $4,441 $505,534,805 185,251 $505,534,805 $822,650,494

HDW 37049.87 $178 $6,588,207 47,948 $6,588,207 $8,526,125

HEB 34444.69 55,833

MIX 69668.12 $334 $23,282,391 85,968 $23,282,391 $28,729,641

RIPF 84104.76 $3,558 $299,247,244 93,540 $299,247,244 $332,816,821

SHB 384160.9 482,529

URB 441379.6 660,011

URBG 121.74 $2,268 $276,129 152 $276,129 $344,531

WAT 27602.46 $2,929 $80,839,872 42,117 $80,839,872 $123,347,887

TOTAL ESV $1,044,679,280 TOTAL ESV $1,490,969,334

 Known market values Known market values 

improvement

value of 

structures $35,770,650,855

improvement

value of 

structures $68,941,985,365

TOTAL $36,815,330,135 TOTAL $70,432,954,699

Study area zip codes Entire county

Efficacy of the California Bureau of Land Management Community Assistance and Hazardous Fuels Programs Ganz, Saah, Wilson, and Troy
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Figure 3—Total estimated ecosystem service valuation by zip code for Napa County study area.

Efficacy of the California Bureau of Land Management Community Assistance and Hazardous Fuels Programs Ganz, Saah, Wilson, and Troy
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Similar to Napa County, forested systems delivered approximately 31 

percent of the total ESVs delivered by ecological systems in San Bernardino 

County. From an ecosystem services perspective, freshwater wetlands accounted for the majority (55 percent) of ecosystem service benefits delivered to 

society. For instance, the community of Twenty Nine Palms (zip codes 92277 

and 92278) has low assessed structural values relative to other communities 

in San Bernardino County, but the freshwater resources of this community 

yield considerable ESVs compared with the rest of the communities within 

this county (fig. 4). Desert shrub is the most predominant land cover type in 

San Bernardino County. However, there are two reasons why this land cover 

shows few societal benefits in this study. First, this desert-related land cover 

type tends not to burn, and second, the value transfer analysis did not yield 

any ESV studies that estimated economic values for desert cover types.

Figure 4—Total estimated ecosystem service valuation by zip code for San Bernardino County study area.

Cost Effectiveness of Fire Hazard Mitigation Efforts

In this section, we provide a cost effectiveness framework by which the BLM 

fuel hazard mitigation programs can be evaluated relative to their return on 

investment and agency management goals. This framework takes into consideration both the capital costs and the avoided losses to ecosystem services 

associated with fire mitigation. This will allow the BLM and other agencies 

to consider the real losses to ecosystem goods and services that might occur 

in the event that such fuel treatments were not implemented. For example, in 

Humboldt or Napa Counties, the treatment costs per acre range from $306 

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to $600/acre but the estimated benefits of fire mitigation are conservatively 

between $117 to $4,440/acre (using the lower range average ESV for hardwood oak woodland and higher range average ESV for fresh water wetland). 

Regardless of whether the proposed fuel treatment uses prescribed burning 

or mechanical treatment (in either of these two counties), the costs per acre 

to benefit ratio will be higher when we include the accounting of avoided 

losses of ESVs. This application is further explained in two case studies of 

Morongo Valley and Petrolia. Using these case studies, we compared the costs 

of treatment to the estimated benefits from those treatments, including both 

protected structures and ESVs.

An additional analysis was used to evaluate the cost effectiveness of all 

projects within these three counties by agency and by treatment. NFPORS 

is an interagency system designed to assist field personnel in managing and 

reporting accomplishments for work conducted under the National Fire Plan. 

As it is spatially explicit, NFPORS allows for the accounting of natural assets 

surrounding BLM projects using both artificial boundaries (like zip codes 

or parcels) and natural boundaries (watershed boundaries and tributaries). 

The NFPORS system also allows us to evaluate the contribution of the BLM 

projects to the overall fire mitigation framework within these three counties 

and compare their efficiencies with metrics such as per acre treatment costs 

and their ESV avoided costs. For instance, in Humboldt County, of all the 

money spent by Federal agencies on fuel hazard reduction treatments, BLM 

spent 5.6 percent of the total on fire treatments and 29 percent on mechanical treatments. For Humboldt County, the BLM spent $306/acre on fire 

treatments and $377/acre on mechanical treatments. In Napa County where 

all of the treatments were performed by the BLM, 63 percent of the treatment costs were mechanical (at $600/acre), nearly 4 percent went for fire 

treatments, and the remaining 33.5 percent went toward other treatments 

(biological and chemical). While these may seem high compared with the 

national averages for fire mitigation treatment, they are comparable with other 

parts of California. (The Congressional Research Service Report for Congress 

on Forest Fires and Forest Health reported a national average of treatment 

costs at $250/acre. On the Shasta Trinity National Forest, treatment costs 

for slopes <30 percent ranged from $250 to $600/acre and average $400/ 

acre.) This would indicate that statements made about the transferability of 

these three counties generally apply to these treatment costs.

In an area like Humboldt County, where 6,043 acres were treated in a 

variety of ways by the four Federal agencies and their local partners, ESVs 

are estimated at $1,177,910,801 while structural values are assessed at 

$4,376,522,485, for a total accounting within the Humboldt study area zip 

codes of $5,554,433,286. Given the modeling assumptions, the net benefit of 

performing these treatments and protecting market and nonmarket assets on 

the landscape level from wildfire is $2,504/acre in Humboldt County. Using 

the same cost effectiveness approach, we can state that the net benefit from 

treatments that protect market and nonmarket assets in San Bernardino and 

Napa Counties are $4,994/acre and $22,904/acre, respectively. Compared 

with the $2,504/acre “avoided costs” of protecting market and nonmarket 

assets in Humboldt, we can easily see that there would be a greater net loss to 

society resulting from a major wildfire in Napa County. Yet by reviewing the 

overall treatment acreages for Napa across all Federal agencies, the numbers 

of acres treated are substantially less than Humboldt and San Bernardino. 

This is probably due to the lack of Federal agency land, the overall sociopolitical climate for accepting fuel reduction treatments, and the costs of 

doing business in Napa County.

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There is a national tendency to focus on treatment costs and the total number of acres treated. These metrics tend to favor fire mitigation programs in 

other Western States where the costs of labor and materials are lower. Over 

the past 36 years, the average annual price increase in California has been 

8.9 percent. In 2004, the median home price was $523,150 compared with 

the national average of $219,000. These housing trends are undoubtedly 

contributing to the differences in costs of treatment between the three counties (table 7). Humboldt County, with its dynamically lower housing costs 

and the presence of the timber industry, benefits in the presence of a cost 

competitive labor force for implementing fuel treatment projects.

Table 7—BLM planned acres, treatment costs, and costs/acre.

County Treatment Planned acres Cost $/acres

Humboldt Fire 331 101,246 306

Mechanical 1369 515,946 377

Napa Fire 50 5,625 113

Mechanical 158 94,730 600

Other 255 50,500 198

San Bernardino Mechanical 576 243,122 422

Other 20 10,000 500

Source: Table generated from the NFPORS database. Data from 2004-2005

Our geographic disaggregation of ESVs by watershed (fig. 5) and zip 

code (fig. 3 and 4) allows us to depict ESV hotspots and assist the BLM in 

prioritizing funding to protect those areas with the highest values. From this 

analysis, we can compare the overall funds expended within the three counties 

and a per acre “avoided costs” using the total market and nonmarket values 

and the acres in the zip code study areas. This then allows for the computation of “net benefits per acre” for each study area acre. Although these costs 

and benefits are averaged over an entire zip code or watershed unit, in the 

case studies presented below, we have a spatial treatment footprint and will 

demonstrate the application at the finest scale possible—that is, of evaluating individual projects. From both of these examples, we can see that as the 

BLM partners become familiar with GIS, or as the BLM adjusts its grant 

tracking system to include spatial footprints, it should be possible to track 

costs and benefits of fuel treatments on different slopes and across different 

land cover types.

Case Studies

The two specific case studies, Petrolia in Humboldt County and Morongo 

Valley in San Bernardino County, are both within the wildland/urban interface 

(WUI). The Healthy Forest Restoration Act (HFRA; HR 1904) requires that 

50 percent of the funds expended upon HFRA projects are within the WUI 

and municipal watersheds surrounding private homes and communities. In this 

Act, the WUI is defined as a 1.5 mile radius around communities; however, 

communities can define their own WUI by completing a “community fire 

Efficacy of the California Bureau of Land Management Community Assistance and Hazardous Fuels Programs Ganz, Saah, Wilson, and Troy
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Figure 5—Total estimated ecosystem service value by watershed for Humboldt County study area.

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plan.” These case study sites were selected because the BLM has created a spatial 

foot print for the actual treatment and sphere of influence. An additional 1.5 

mile “buffer” was added to each treatment site because of the Healthy Forest 

Restoration Act’s call for vegetative buffers to protect communities. (The 1.5 

mile buffer is relevant because when National Environmental Protection Act 

analysis is required, the study needs to include only the proposed action and 

no action alternative for projects within 1.5 miles of an at-risk community or 

in the WUI as defined in the Community Wildfire Protection Plan [CWWP].) 

The Total Economic Value (TEV) framework discussed in this study is used 

to identify and measure both the nonmarket, ecosystem service values and

the market-based value of protected structures (such as homes) associated with 

hazardous fuels treatment. When compared with the actual costs of treatment 

for mechanical thinning and chipped/biomass utilization in Petrolia and 

Morongo Valley, respectively, these data can be used to evaluate the net social 

economic benefit associated with treatments on the ground (see table 8).

The Petrolia mechanical thinning project, implemented by the Mattole 

Restoration Council was selected as a case study for Humboldt County. The 

project is designed to protect Petrolia, a Community at Risk. It covered 85 

acres, and as table 8 shows, the direct cost (excluding administrative) was 

$332 per acre for a total one-time cost of $28,188.

The Morongo Valley chipping and biomass removal project, implemented 

by the Morongo Valley Fire Safe Council, was selected as a case study for 

San Bernardino County. The Morongo Valley chipping and biomass removal 

portions of the project covered 40 acres and 35 acres, respectively, and were 

designed to protect Morongo Valley, a Community of Interest that is spread 

over a larger geographic area. The direct cost (excluding administrative) of 

this project was $914 per acre for a total of $69,432.

Table 8 data demonstrate, purely from the total economic value perspective, that both fire treatments considered in this case study appear to be cost 

effective. When both the nonmarket and market-based values of protected 

structures, goods, and services within the 1.5 mile buffer zone are taken into 

consideration, there appears to be a net economic benefit for each community. 

For instance, in the case of Petrolia, the data show that treatment project costs 

Table 8—Cost effectiveness of treatment in two communities.*

Project community Petrolia Morongo Valley

Project type Mechanical thinning Chipped/biomass utilization

Acres treated 85 76

Total acres within buffer 10,479 17,993

Project cost $28,188 $69,432

Project costs per acre $332 $937

Market value of protected structures $2,073,213 $107,494,431

Nonmarket ecosystem service values $4,570,692 $379,680

Total economic value $6,643,905 $107,874,111

Total economic value per acre $634 $5,995

Net benefit per acre $302 $5,058

* All dollar values are standardized to 2004 equivalents

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were $332 per acre, yet the total economic value of market and nonmarket 

goods and services within the protected buffer zone yields approximately $634 

per acre, resulting in a net benefit of $302 per acre. In the case of Morongo 

Valley, while the costs of treatment were somewhat higher at $937 per acre, 

the total economic value of the protected area is also considerably higher 

resulting in a net benefit of $5,058 per acre (fig. 6).

What the case study data also show is that the source of economic value 

differs considerably for each community. In the case of Petrolia, it appears 

that nonmarket ecosystem service values contribute approximately twice as 

much to the total economic value of the protected buffer as market-based 

values. As a result, if one were to leave out the nonmarket component of 

total value in the cost-effectiveness estimate, the end result would have been 

quite different: the total economic value would have been only $197 per acre, 

resulting in a net cost of $135 per acre for treatment. On the other hand, in 

Morongo Valley, the market-based value of homes and structures appears to 

far outweigh the nonmarket goods and services associated with the protected 

buffer zone, so that the net cost effectiveness of treatment would remain the 

same regardless of the nonmarket benefits.

With available time and resources, the approach used in this case study 

comparison could effectively be expanded to include all communities in 

the Hazardous Fuels Program throughout California. Given the nature of 

value transferability, the baseline nonmarket valuation information provided 

by NAIS could be linked to other land cover types affected by treatment 

Figure 6—Morongo Valley case study area in San Bernardino County; assessed value of structures 

by parcel versus total ecosystem service values.

Efficacy of the California Bureau of Land Management Community Assistance and Hazardous Fuels Programs Ganz, Saah, Wilson, and Troy
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programs throughout California, and once this information is coupled with 

market-based value estimates, the total economic value can be estimated and 

compared to treatment costs. The end result would provide the possibility 

for a rigorous assessment of total social benefits associated with every BLM 

fire treatment project implemented in California. In sum, the information in 

this study effectively answers recent calls by policymakers to better account 

for the full social costs and benefits associated with environmental programs 

(National Research Council 2004). Armed with such information, it appears 

that more informed decisions can be made in the future about protecting the 

natural and built assets that matter most to the people in the wildland/urban 

interface.

References

Argady, T.; Alder, J.; and others. 2005. Coastal systems and coastal communities. 

Millennium ecosystem assessment: Conditions and trends. Washington, DC: 

Island Press.

Bingham, G.; Bishop, R.C.; and others. 1995. Issues in ecosystem valuation: 

Improving information for decision making. Ecological Economics 14: 73-90.

Costanza, R.; d’Arge, R.; and others. 1997. The value of the world’s ecosystem 

services and natural capital. Nature 387: 253-260.

Daily, G.C. 1997. Nature’s services: Societal dependence on natural ecosystems. 

Washington DC: Island Press.

Desvousges, W.H.; Johnson, F.R.; and others. 1998. Environmental policy analysis 

with limited information: Principles and application of the transfer method. 

Northampton, MA: Edward Elgar Publishing.

Downing, M.; Ozuna, T. 1996. Testing the reliability of the benefit function transfer 

approach. Journal of Environmental Economics and Management 30: 316-322.

Environmental Protection Agency, U.S. (EPA). 2000. Guidelines for preparing 

economic analyses. Washington, DC.

Everett, Y. 2002. Community participation in fire management planning: A case 

from California, USA. Communities in Flames Proceedings of 1st International 

Conference on Community Involvement in Fire Management. RAP Publication 

2002/25. Bangkok, Thailand: United Nations, Food and Agricultural 

Organization, Regional Office for Asia and the Pacific.

Farber, S. 1987. The value of coastal wetlands for protection of property against 

hurricane wind damage. Journal of Environmental Economics and Management 

14(2): 143-151.

Finney, M.A. 2001. Design of regular landscape fuel treatment patterns for modifying 

fire behavior and growth. Forest Science 47: 219-228.

Fotheringham, A.S.; Brunsdon, C.; and others. 2000. Quantitative geography: 

Perspectives on spatial data analysis publication. London, England: Sage.

Ganz, D.J.; Moore, P.F. 2002. Living with fire. Communities in Flames Proceedings 

of 1st International Conference on Community Involvement in Fire Management. 

RAP Publication 2002/25. Bangkok, Thailand: United Nations, Food and 

Agricultural Organization, Regional Office for Asia and the Pacific.

Heal, G.M. 2004. Intertemporal welfare economics and the environment. Handbook 

of environmental economics. K.G. Maler and J.Vincent. Amsterdam, North 

Holland:Elsevier Publishers.

Heberlein, T.A.; Wilson, M.A.; and others. 2005. Rethinking the scope test as a 

criterion for validity in contingent valuation. Journal of Environmental Economics 

50(1): 1-22.

Efficacy of the California Bureau of Land Management Community Assistance and Hazardous Fuels Programs Ganz, Saah, Wilson, and Troy
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 606

Hodgson, R. 1994. Strategies for and barriers to public adoption of fire safe 

behavior. The Biswell symposium: Fire issues and solutions in urban interface and 

wildland ecosystems. General Technical Report GTR-PSW-158. Albany, CA: U.S. 

Department of Agriculture, Forest Service, Pacific Southwest Research Station.

Kirchhoff, S.; Colby, B.G.; and others. 1997. Evaluating the performance of 

benefit transfer: An empirical inquiry. Journal of Environmental Economics and 

Management 33(1): 75-93.

Knapp, E.E.; Stephens, S.L.; and others. 2004. Fire and fire surrogate study in 

the Sierra Nevada: Evaluating restoration treatments at Blodgett Forest and 

Sequoia National Park Proceedings of the Sierra Nevada Science Symposium; 

2002 October 7-10, Kings Beach, CA, Gen. Tech. Rep. PSW-GTR-193. Albany, 

CA: U.S. Department of Agriculture, Forest Service, Pacific Southwest Research 

Station. 287 p.

Loomis, J.B. 1992. The evolution of a more rigorous approach to benefit 

transfer - benefit function transfer. Water Resources Research 28(3): 701-705.

Millennium Ecosystem Assessment. 2003. Ecosystems and human well-being: A 

framework for assessment. Washington DC: Island Press.

National Research Council. 2004. Valuing ecosystem services: Toward better 

environmental decision making. Washington, DC: National Academies Press.

National Wildfire Coordinating Group (NWCG). 2001. Review and update of the 

1995 Federal wildland fire management policy. . Boise, ID: National Interagency 

Fire Center. .

Openshaw, S.; Charlton, M.E.; and others. 1987. A mark i geographical analysis 

machine for the automated analysis of point data sets. International Journal of 

Geographical Information Systems 1: 359-377.

Pretty, J.N.; Brett, C.; and others. 2000. An assessment of the total external costs 

of U.K. Agriculture. Agricultural Systems 65: 113-136.

Ricketts, T.H.; Daily, G.C.; and others. 2004. Economic value of tropical forest to 

coffee production. Ecology 101(34).

Smith, V.K. 1992. On separating defensible benefit transfers from smoke and mirrors. 

Water Resources Research 28(3): 685-694.

U.S. Department of Agriculture (USDA). 1995. Course to the future: Positioning fire 

and aviation management. Washington, DC: USDA, Forest Service, Department 

of Fire and Aviation Management.

U.S. Department of the Interior-U.S. Department of Agriculture (USDI–USDA). 

1995. Federal wildland fire management and policy and program review. Boise, 

ID: USDI, Bureau of Land Management.

Wilson, M.A.; Carpenter, S.R. 1999. Economic valuation of freshwater ecosystem 

services in the United States 1971-1997. Ecological Applications 9(3): 772-783.

Wilson, M.A.; Troy, A. 2005. Accounting for ecosystem services in a spatially 

explicit format: Value transfer and geographic information systems. International 

Workshop on Benefits Transfer and Valuation Databases, Washington DC.

Wilson, M.A.; Troy, A.; and others. 2004. The economic geography of ecosystem 

goods and services: Revealing the monetary value of landscapes through transfer 

methods and geographic information systems. Cultural landscapes and land use. 

M. Dietrich and V. D. Straaten. The Netherlands: Kluwer Academic Publishers.

Winter, G.; Fried, J.S. 2000. Homeowner perspectives on fire hazard, responsibility, 

and management strategies at the wildland-urban interface. Society and Natural 

Resources 13: 33-49.

Efficacy of the California Bureau of Land Management Community Assistance and Hazardous Fuels Programs Ganz, Saah, Wilson, and Troy
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 607

Abstract—Firefighters United for Safety, Ethics, and Ecology (FUSEE) is a nonprofit 

organization promoting safe, ethical, ecological wildland fire management. FUSEE 

believes firefighter and community safety are ultimately interdependent with ethical 

public service, wildlands protection, and ecological restoration of fire-adapted ecosystems. Our members include current, former, and retired wildland firefighters, other 

fire management specialists, fire scientists and educators, forest conservationists, and 

other citizens who support FUSEE’s holistic fire management vision. FUSEE’s primary 

function is to provide public education and policy advocacy in support of a new, 

emerging paradigm that seeks to holistically manage wildland fire for social and ecological benefits instead of simply “fighting” it across the landscape. We seek to protect 

fire-affected wildlands, restore fire-adapted ecosystems, and enable fire management 

workers to perform their duties with the highest professional, ethical, and environmental 

standards. Our long-term goal is the creation of fire-compatible communities able to 

live safely and sustainably within fire-permeable landscapes.

Introduction

Firefighters United for Safety, Ethics, and Ecology (FUSEE) is a nonprofit 

organization promoting safe, ethical, and ecological wildland fire management. FUSEE believes firefighter and community safety are ultimately 

interdependent with ethical service by public agencies and private companies, 

and environmental protection and restoration of fire-adapted ecosystems. 

Our members include current and former firefighters, other fire managers 

and workers, fire scientists and educators, forest conservationists, and other 

citizens who support FUSEE’s holistic fire management vision.

Triad of Safety, Ethics, and Ecology

FUSEE’s alternative fire management mission is based on an interdependent triad of safety, ethics, and ecology (fig. 1 is our organization logo). 

Firefighters United for Safety, Ethics, and 

Ecology (FUSEE): Torchbearers for a New Fire 

Management Paradigm

Timothy Ingalsbee1, Joseph Fox2, and Patrick Withen3

Figure 1—The FUSEE logo 

exhibits the triad of Safety, 

Ethics, and Ecology.

In: Butler, Bret W.; Cook, Wayne, 

comps. 2007. The fire environment—

innovations, management, and policy; 

conference proceedings. 26-30 March 

2007; Destin, FL. Proceedings 

RMRS-P-46CD. Fort Collins, CO: 

U.S. Department of Agriculture, 

Forest Service, Rocky Mountain 

Research Station. 662 p. CD-ROM.

1 Executive Director of Firefighters 

United for Safety, Ethics, and Ecology, 

Eugene, OR. fire@efn.org.

2 Board President of Firef ighters 

United for Safety, Ethics, and Ecology, 

McCall, ID.

3 Associate Professor of Sociology, 

University of Virginia at Wise, Wise, 

VA.
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 608

Firefighters United for Safety, Ethics, and Ecology (FUSEE): Torchbearers for a New Fire Management Paradigm Ingalsbee, Fox, and Withen

Management actions that degrade ecosystems or waste taxpayer resources 

adversely affect firefighter and community safety. For example, use of taxpayer dollars and resources on deficit timber sales that remove fire-resilient 

old-growth trees and leave behind untreated logging slash, violate Federal 

environmental laws in planning or implementation, or are labeled as “fuels 

reduction” or “forest restoration” projects when they actually increase fuel 

hazards or degrade ecological integrity—we believe this is an ethical as well as 

an ecological issue. These kind of antiecological, unethical forest management 

projects also adversely affect firefighter and community safety by diverting 

limited Federal dollars away from genuine hazardous fuels reduction activities, 

and by degrading ecological conditions in ways that increase wildfire rate of 

spread, intensity, or severity. Ethical and efficient use of taxpayer resources 

coupled with environmental protection and ecological restoration are the 

best strategies for improving the safety and working conditions of wildland 

firefighters. Thus, safety, ethics, and ecology are interconnected values in 

FUSEE’s holistic vision of forest and fire management.

FUSEE Mission: A New Fire 

Management Paradigm

FUSEE’s mission is to promote safe, ethical, and ecological wildland fire 

management, and we seek to enable fire management workers to perform their 

duties with the highest professional, ethical, and environmental standards. 

Our primary function is to provide policy analysis and public education in support of a new, emerging paradigm that seeks to holistically manage wildland 

fire for social and ecological benefits instead of simply “fighting” it across the 

landscape. We want to end modern industrial society’s socially conditioned 

fear of forest fires, and instead, wisely use fire on the landscape to benefit 

a multitude of ecosystem functions and processes, vegetative communities 

and wildlife habitats, as well as our own human communities and economies. 

Our long-term goal is the creation of fire-compatible communities able to 

live safely and sustainably within fire-permeable ecosystems.

Efforts to fundamentally shift the policy debates and change the paradigm 

of fire management will not work if efforts are focused solely on “top-down” 

strategies that appeal only to policymakers in Congress or the Administration. Unless and until public attitudes and opinions about wildland fire are 

changed from the “bottom up,” progressive fire policy reforms will continue 

to be blocked or reversed—as evidenced by the use of wildfire to justify the 

roll back of environmental protection laws and regulations. Consequently, 

FUSEE believes in the strategic, long-term importance of educating the public, policymakers, and the press about fire ecology, and empowering people 

to become actively involved in fire management policies and practices.

Educating and empowering people to become informed stakeholders 

actively involved in fire management programs would have a number of practical benefits for land and fire managers, particularly given shrinking levels 

of staffing and funding within Federal agencies. For example, fire managers would benefit from greater stakeholder involvement in fire management 

planning when information from scientists provides the most current references from the literature, when indigenous communities offer place-based 

traditional ecological knowledge, and when residents give local knowledge 

of valued natural assets and human developments. This input would help 

managers better understand the local values-at-risk needed to prioritize fuels 
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 609

Firefighters United for Safety, Ethics, and Ecology (FUSEE): Torchbearers for a New Fire Management Paradigm Ingalsbee, Fox, and Withen

reduction and forest restoration projects, and devise suppression strategies 

and tactics. Greater stakeholder involvement in fire policy formation at regional and national levels would also nurture more public commitment to 

fire management programs—for example, taxpayer funding streams needed 

for long-term monitoring, research, and restoration projects. Expanding 

the number and kind of stakeholders actively involved in fire planning and 

policy development should above all include ground-level wildland firefighters—arguably the stakeholders with the most at stake in sound policies and 

practices. Fundamentally, the expansion of informed public involvement in 

all aspects of wildland fire management is an ethical calling for an expansion 

of democratic principles and processes.

FUSEE has a four-pronged approach to our education and advocacy 

work:

	 •	 First, FUSEE proactively engages journalists and public opinionmakers

with information they can use in reporting on a broader range wildland 

fire issues. We emphasize working with the news media because the 

media are often the source of inaccurate, one-sided information and 

sensationalist stories that intensify the public’s socially conditioned 

fear of wildfire. We promote alternative and investigative journalism 

that explores the vast breadth of fire management issues that are being 

neglected by the media’s near-exclusive focus on emergency wildfire 

suppression. Our Web site (www.fusee.org) features a handbook, “A 

Reporter’s Guide to Wildland Fire” (Ingalsbee 2005) that provides 

journalists with resources such as alternative story angles and new language in order to wean them away from their hackneyed use of the “war 

metaphor” and “catastrophe mentality” in reporting on wildfire events. 

We work to get reporters to expand their usual sources—official press 

spokespersons in fire camp—and get journalists out on the frontlines 

talking to ground-level firefighters, prescribed fire crews, fire effect 

monitors, fire planners, and other fire management workers who can 

articulate the diversity of fire management issues.

	 •	 Second, FUSEE actively informs and empowers fire management 

workers, especially ground-level wildland firefighters, to stand up and 

speak out on behalf of wildlands protection and ecosystem restoration 

not only in the public arena but also out on the fireline. Even minor 

changes in the location or intensity of fireline construction, for example, 

can have major effects on the impacts to soils, streams, and vegetation. 

We seek to change both the methods and the culture of fire management from the bottom up and inside out by instilling a renewed sense 

of civic duty and a new identity among firefighters as forest protectors 

and restoration workers. From their position on the frontlines of fire 

management, ground-level firefighters have the potential to fundamentally change their mission from the ground up and perform their duties 

with the highest professional, ethical, and environmental standards. 

The FUSEE Web site (www.fusee.org) hosts critical fire science papers, 

fire policy documents, fire education materials, personal essays, and an 

interactive “blogger” site that helps raise the ethical and environmental 

awareness of firefighters and citizens, and discusses such things as the 

interrelationship between firefighter safety and forest ecosystem health. 

This investment in firefighter education helps mitigate the firefighting damage done to forests in the present, and nurtures the kind of 

educated, empowered forest/fire restoration workforce needed for the 

future. Our hope is that one day fire-fighters will become something 
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 610

Firefighters United for Safety, Ethics, and Ecology (FUSEE): Torchbearers for a New Fire Management Paradigm Ingalsbee, Fox, and Withen

more like “fire-guiders,” adept at starting prescribed fires and steering 

wildland fires rather than just suppressing wildfires, and envision that 

the label “firefighter” will become as anachronistic in the future as the 

label “smokechaser” is today.

	 •	 Third, FUSEE informs policymakers and decisionmakers about fire 

ecology and management issues, and advocates for alternative ecological restoration policies that improve the health, safety, and working 

conditions of wildland firefighters. In general, policymakers propose 

fire-related legislation and regulations without ever consulting with 

wildland firefighters—arguably the “stakeholders” with the most at 

stake in having sound fire management policies. FUSEE approaches 

policymakers from the perspective of ground-level firefighters and 

veteran fire management experts, most of whom work for low pay, few 

job benefits, and have little job security. Fire management workers facing hazardous and unhealthy working conditions are often forced to 

do environmentally destructive activities that do not serve the public 

interest, yet they often feel disenfranchised, voiceless, and powerless to 

improve their working conditions or alter their work tasks. FUSEE offers 

a safe vehicle for firefighters in public agencies and private companies 

to speak out on fire-related policies and legislation without fearing job 

reprisals.

	 •	 Finally, FUSEE educates homeowners and rural residents living in fireprone areas about their rights and responsibilities to manage defensible 

space and use fire-resistant building materials. Our Web site features “A 

Homeowner’s Guide to Fire-Resistant Home Construction” (Fairbanks 

and Ingalsbee 2005) that provides tips on vegetation management and 

home construction to reduce the risks of wildland fire damage. We work 

to transcend the current defensive, narrow focus on “community wildfire 

protection,” and instead, promote proactive community fire preparation for the whole array of fire management including prescribed fire, 

wildland fire use, as well as wildfire suppression. The sooner homes and 

communities are prepared for fire, the sooner ecosystems can be restored 

with fire. Our long-term vision is the creation of “fire-­compatible communities” dwelling within ecologically restored “fire permeable landscapes” 

where wildland fire can safely move through fire-dependent ecosystems 

without causing property damage.

Conclusions

FUSEE informs, inspires, and empowers wildland firefighters, other fire 

management workers, and their citizen supporters to become torchbearers for 

a new paradigm in fire management. We seek a paradigm shift not only in 

management policies and practices, but in society’s relationship with wildland 

fire. We work to end the public’s fear of wildfire by providing information on 

how to protect homes and communities from wildfire damage, how native 

ecosystems depend upon and benefit from fire, and how society benefits from 

genuinely managing fire instead of endlessly “fighting” it. We also educate 

the press, policymakers, and the public about the safety risks, economic costs, 

and environmental impacts of reactive wildfire suppression, making the case 

for proactive ecological fire restoration to reduce the risks, costs, and impacts 

of firefighting. Most important, we educate firefighters and the public they 

serve about the many benefits from practicing safe, ethical, ecological fire 
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 611

management, with the goal of crafting a new identity and mission for firefighters as forest protectors and restoration workers. We invite members of 

the wildland fire management and forest conservation communities to visit 

our web site and become members and contributors of Firefighters United 

for Safety, Ethics, and Ecology.

References

Fairbanks, Rich; Ingalsbee, Timothy. 2006. A homeowner’s guide to fire-resistant 

home construction. Firefighters United for Safety, Ethics, and Ecology. Online: 

http://www.fusee.org/content_pages/docs/homeowners_guide.pdf

Ingalsbee, Timothy. 2005. A reporter’s guide to wildland fire. Firefighters United 

for Safety, Ethics, and Ecology. Online: http://www.fusee.org/content_pages/

docs/Reporters_guide.pdf

Firefighters United for Safety, Ethics, and Ecology (FUSEE): Torchbearers for a New Fire Management Paradigm Ingalsbee, Fox, and Withen
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 612

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page is 

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on purpose

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USDA Forest Service Proceedings RMRS-P-46CD. 2007. 613

Abstract—Community wildfire protection plans (CWPPs) are being developed and 

implemented in communities across the United States. In a series of case studies, researchers found that the process of developing a CWPP can lead to benefits beyond 

those associated with fuels reduction, including enhancing social networks, developing 

learning communities, and building community capacity.

Introduction

The [Healthy Forest Restoration Act] provides communities 

with a tremendous opportunity to influence where and how 

federal agencies implement fuel reduction projects on federal 

lands. A Community Wildfire Protection Plan (CWPP) is 

the most effective way to take advantage of this opportunity. 

(Healthy Forest Initiative 2007)

Communities across the Untied States have engaged in the development 

and implementation of community wildfire protection plans (CWPPs) in an 

effort to “clarify and refine [their] priorities for the protection of life, property, and critical infrastructure in the wildland-urban interface” (Society of 

American Foresters 2004, p. 2). As might be expected, Western communities 

have embraced the idea of CWPPs; for example at least 44 counties in Idaho 

have developed CWPPs, 33 communities in Washington, and 28 communities 

in New Mexico. However, communities in Eastern States such as Arkansas, 

Florida, Minnesota, Virginia, and Wisconsin are also developing CWPPs. 

CWPPs are developed collaboratively and include (1) a prioritized list of 

areas requiring hazardous fuels treatments and the type of treatment to be 

used, and (2) recommended measures to reduce structural ignitability. In a 

project funded by the Joint Fire Science Program, we conducted a series of 

case studies to identify how CWPPs enhance collaboration between communities and fire management agencies, and how the development of CWPPs 

builds community capacity. We are interested in the contexts in which CWPPs 

are developed, processes used to develop CWPPs, and outcomes from the 

development of CWPPs (fig. 1).

In: Butler, Bret W.; Cook, Wayne, 

comps. 2007. The fire environment—

innovations, management, and policy; 

conference proceedings. 26-30 March 

2007; Destin, FL. Proceedings 

RMRS-P-46CD. Fort Collins, CO: 

U.S. Department of Agriculture, 

Forest Service, Rocky Mountain 

Research Station. 662 p. CD-ROM.

1 Project Leader with the U.S. 

Department of Agriculture, Forest 

Service, Northern Research Station, St. 

Paul, MN. pjakes@fs.fed.us

2 Research Director, Of f ice of 

Community Services, Fort Lewis 

College, Durango, CO. 

3 Professor of Forestry and Natural 

Resource Policy, and Graduate Research 

Assistant, respectively, at Colorado 

State University, Fort Collins.. 

4 Professor of Human Dimensions, 

and Graduate Research Assistants, respectively, at University of Minnesota, 

St. Paul.

5 Professor of Sociology at Southern 

Oregon University, Ashland.

6 Research Social Scientist with the 

U.S. Department of Agriculture, Forest 

Service, Rocky Mountain Research 

Station, Fort Collins, CO.

Critical Elements in the Development and 

Implementation of Community Wildfire 

Protection Plans (CWPPs)

Pamela Jakes1, Sam Burns2, Antony Cheng3, Emily Saeli3, 

Kristen Nelson4, Rachel Brummel4, Stephanie Grayzeck4, 

Victoria Sturtevant5, and Daniel Williams6
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 614

• Physical

• Biological

• Social

• Initiation

• Participants

• Issue framing

• Decision-making

process

• Networking

• Information sharing

• Physical

• Biological

• Social

Context Process Outcomes

Figure 1—A model of community wildfire protection planning focusing on the context in 

which the CWPP is developed, the CWPP process, and the outcomes of the process.

Although data collection and analysis are ongoing, in this paper we discuss three themes that have emerged across all the communities studied to 

date:

• social networks

• learning communities

• community capacity

These themes emerged as critical to the CWPP process and as important 

outcomes resulting from the development of CWPPs.

Methods

We are conducting case studies in eight States: California, Colorado, 

Florida, Minnesota, Montana, Oregon, Virginia, and Wisconsin. A case, or 

unit of analysis, was defined as a CWPP. The case study CWPPs have been 

developed at several scales. The smallest geographic scale for CWPP development in our study was the neighborhood (High Knob near Front Royal, 

VA). We also had a small-scale CWPP made up of different holdings and a 

neighborhood (East Portal, CO). At a larger scale are villages and towns, 

unincorporated (Auburn Lake Trails, CA; Grizzly Flats, CA; Post Mountain, 

CA; and Taylor, FL) and incorporated (Ashland, OR). We have two cases that 

are multicommunity regions (the Barnes-Drummond area of northwestern 

Wisconsin and Harris Park, CO, southwest of Denver in Jefferson and Park 

Counties). The largest scale for CWPP development was at the county level 

(Josephine County, Oregon; Lake County, Minnesota; and Lincoln County, 

Montana).

Critical Elements in the Development and Implementation of Community Wildfire ... Jakes, Burns, Cheng, Saeli, Nelson , Brummel, Grayzeck, Sturtevant, and Williams
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 615

Case study CWPPs can be nested in other plans. For example, Em Kayan 

in Lincoln County, Montana, has a Firewise Communities/USA plan that 

identifies projects that feed into the Lincoln County CWPP, which serves as 

a chapter in the county’s pre-disaster mitigation plan (fig. 2). The Lincoln 

County Pre-Disaster Mitigation Plan is tied to the Montana Multi-Hazard 

Mitigation Plan. The Post Mountain CWPP is an appendix to the Trinity 

County Fire Safe Council Fire Plan. In turn, some case study CWPPs have 

other plans nested in them, such as the Josephine County CWPP, which has 

the Illinois Valley CWPP within it. This nestedness adds to the complexity 

of the context in which CWPPs are developed and makes it difficult to study 

one CWPP without considering linkages to other CWPPs.

Key informant interviews were conducted in each CWPP community. 

Interviews followed an interview guide covering the topics of inquiry identified in figure 1. Interviews were conducted with members of the CWPP team, 

adjacent land management agencies and local governments, and interested 

local residents. Each interview was audio-taped, with a transcript created 

of each tape. Analysis is being conducted around themes identified by the 

research team.

Figure 2—Community wildfire protection plans can be nested in other plans and other 

plans can be nested in the CWPP, as in this example from Montana.

State of Montana multi-hazard

mitigation plan

Lincoln County 

CWPP

Em Kayan

Firewise Communities/

USA Plan

Lincoln County pre-disaster

mitigation plan

State of Montana multi-hazard

mitigation plan

Lincoln County 

CWPP

Em Kayan

Firewise Communities/

USA Plan

Lincoln County pre-disaster

mitigation plan

Critical Elements in the Development and Implementation of Community Wildfire ... Jakes, Burns, Cheng, Saeli, Nelson , Brummel, Grayzeck, Sturtevant, and Williams
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 616

Findings

Social Networks

A social network is “a set of individuals or groups and the ties representing 

some interrelationship between them” (Brass 1992, p. 300). Social networks 

create bridges between groups that facilitate interaction and collaboration 

and/or bonds that build group identity. Social networks played a critical role 

in the CWPP process, and the development of social networks was a positive 

outcome from the creation of CWPPs.

During the CWPP process, a variety of networks were used to obtain 

the information, skills, and resources to complete the plan. Members of the 

CWPP teams served as nodes, connecting the CWPP team to other networks 

in which members participated. For example, local fire departments often 

have strong networks with other fire and emergency management agencies, 

and in locations such as Harris Park and Josephine County, having fire department representatives at the table gives access to these networks during 

CWPP development. Environmental groups are linked to other environmental 

groups, and in Post Mountain and Ashland, representatives of these groups 

tap into their environmental networks to achieve CWPP objectives. Having 

local units of government at the table is especially important for community 

support and buyin. In Ashland, the city forester is connected with private 

landowners, and this relationship was critical for reducing fuels around homes 

and reducing structural ignitibility.

Representatives from non- or quasigovernmental organizations are important nodes on many networks. In our California cases, the networks developed 

by the local Fire Safe Councils were critical to identifying where necessary 

information could be obtained and where funds might be tapped to support 

planning activities. In Auburn Lake Trails and Em Kayan, designation as a 

Firewise Community/USA means that they are tied to other Firewise communities across the country, and residents have access to a vast array of resources 

to guide them in eliminating hazardous fuels around their homes and reducing structural ignitability. In East Portal the YMCA has strong relationships 

with the county mitigation specialist and Colorado State Forest Service due 

to previous Firewise activities, which were valuable in CWPP development. In 

the isolated community of Post Mountain, the strong relationships built by 

the Watershed Training and Research Center brought resources and expertise 

to the CWPP process. In communities such as Auburn Lake Trails and High 

Knob the local homeowners associations provide the links to local residents 

and other organizations that can aid in the development of CWPPs.

The CWPP processes we studied improved relationships within the community, strengthened existing networks, and developed new networks. In 

northwestern Wisconsin, tension between the Drummond town board and 

Chequamegon-Nicolet National Forest over Federal land use decisions eased 

as representatives of the two organizations worked on the CWPP. In Lake 

County, Colorado, networks that linked the fire department and community 

members were strengthened through the CWPP process, as were the relationships between the fire and county emergency agencies in Josephine County, 

Oregon. New networks among community members facilitated bonding in 

Grizzly Flats and High Knob.

The importance of social networks to the CWPP process and as an outcome of the CWPP process was enhanced by several process characteristics. 

First, it is important to have the right people at the table—meaning, people 

who are well connected in the community. Second, the CWPP goals and 

Critical Elements in the Development and Implementation of Community Wildfire ... Jakes, Burns, Cheng, Saeli, Nelson , Brummel, Grayzeck, Sturtevant, and Williams
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 617

objectives must be relevant to team members so they are willing to draw 

on their networks to move the CWPP process forward. Third, members of 

the CWPP team need to have opportunities to get to know one another as 

representatives of their agencies or organizations and as individuals. Finally, 

the probability of having these process characteristics is enhanced by having 

a facilitator who keeps team members involved, informed, and organized.

Learning Communities

Another outcome of the CWPP process relative to social networks is the 

creation of learning communities. Learning communities develop in environments that encourage information sharing and are places where people 

come together to share knowledge that affects performance. One benefit of 

a learning community is that members find common ground in their areas 

of interest (Finneran 2007). In our cases, common ground was found in 

the way in which the wildland fire issue is framed, in the identification of 

high priority fuels reduction projects, and in approaches to reduce structural 

ignitibility.

Different types of knowledge are shared during the CWPP process. This 

knowledge varies in its complexity from relatively simple information of low 

complexity to multidimensional information of high complexity, and the 

knowledge also varies in its scale, from information that was applicable to 

an individual home or forest stand to information applicable to a region or 

landscape. Figure 3 illustrates some of the information shared in the development of our case study CWPPs. Although the placement of any one piece of 

information on the graph may be debated, the range of information is clearly 

depicted. Different members of the CWPP teams brought different information to the processes. Using East Portal as an example, we find that the USDA 

Forest Service provided access to GIS layers on fuel levels and topography 

included in a pre-existing regional risk assessment; the National Park Service 

provided information on forest ecology; the Colorado State Forest Service 

Figure 3—Knowledge shared during the community wildfire protection planning process by 

complexity and scale.

Critical Elements in the Development and Implementation of Community Wildfire ... Jakes, Burns, Cheng, Saeli, Nelson , Brummel, Grayzeck, Sturtevant, and Williams
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 618

provided information on forest ecology, fire behavior, and forest management; 

the county hazard mitigation specialist provided information on fire defense 

and response; the fire department provided information on fire response; and 

community members identified and mapped community values.

An array of methods is used to share information with team members and 

the public. Continuing with East Portal as an example, we find that they use 

many different outlets to share information including team meetings, team 

field visits, homeowner association meetings, demonstration projects, and 

community events such as cleanup days. In addition, community members 

who heard the information at one these outlets carried that information to 

their groups and associations, and were effective in face-to-face interactions 

with neighbors and friends. Interagency funding supports a community 

educator who has developed and staffed booths at public events in the Estes 

Valley.

The development of learning communities as part of the CWPP process 

lead to several outcomes in regards to the CWPP document itself and community perceptions of wildland fire management and the agencies involved. 

We observed that strong, active learning communities tended to produce 

CWPPs that provide:

• strategic direction for wildland fire mitigation

• clarification and coordination of responsibilities

• information-sharing approaches that can be used in future CWPP efforts 

such as implementation

Learning communities resulted in (1) homeowners who are better informed 

about fire dependent ecosystems and wildfire risk, (2) better understanding 

between agencies and a community and its homeowners, (3) improved trust 

in agencies and among community members, and (4) increased community 

and homeowner support for and participation in wildland fire mitigation 

actions.

Community Capacity

Community capacity is the ability of a community to meet the day-to-day 

needs of its residents (Forest Ecosystem Management Assessment Team 1993). 

Community capacity is often monitored or evaluated using indicators, with 

one common set of community capacity indicators being the capitals. Capital 

is generally described as resources that are invested to create new resources 

(Rule and others 2000). Communities transform the capitals through policies, 

laws, social relations, incentives, and institutions to achieve desired outcomes 

such as community wildfire protection. Sustainable communities accumulate 

capital over time, while unsustainable communities deplete capital. In addition, Flora (2000, p. 85) argues that favoring one form of capital over other 

forms “can destroy the ecosystem, create a dependent, fragile economy, and 

increase social inequity.”

Flora and others (2004) have identified seven capitals:

• financial capital

• physical capital

• natural capital

• human capital

• cultural capital

• social capital

• political capital

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Financial capital is the monetary resources available for local use (Kusel 

2002). Financial capital includes debt capital, investment capital, tax revenue, 

savings, tax abatements, and grants (Flora 2000). Rule and others (2000, 

p. 378) suggest that this type of capital is the “easiest to identify and the 

most commonly accounted for in many cost-benefit analyses.” In Auburn 

Lake Trails, the Auburn Lake Trails Property Owners Association Board of 

Directors was able to create financial capital by increasing the membership 

fees paid by property owners. These new funds are being used to carry out 

fuels reduction projects on common property and to meet requirements for 

matching funds for some grants.

Physical capital is the local infrastructure that facilitates community 

activities (Pretty 2000). Physical capital includes housing and other buildings, 

roads and other transportation systems, communication systems, and markets. 

Building infrastructure was a high priority in the Grizzly Flats CWPP, as 

residents focused on the need for an alternative evacuation route and worked 

with National Forest staff to develop an egress through Federal land.

It is becoming increasingly common for communities to view ecological 

systems as capital assets (Wills and Gray 2001). Pretty (2000, p. 77) defines 

natural capital as “nature’s economic and cultural goods and services.” People 

are drawn to places like our case study communities by their natural beauty. 

In Lake County, Minnesota, the strong attachment to place and strong sense 

of responsibility that characterizes residents are due, in part, to northeastern 

Minnesota’s natural capital.

Pretty (2000) sees human capital as the knowledge, skills, and abilities 

individuals develop and accumulate over time. Human capital was critical to 

the success of our case study communities in developing CWPPs. In Ashland, 

highly trained professionals, many educated in the ecological sciences, are a 

major influence on National Forest land management planning and CWPP 

development. In Auburn Lake Trails, the knowledge, skills, and abilities of 

retired professionals are facilitating CWPP implementation.

Some social scientists distinguish between human capital and cultural 

capital—with human capital acquired through education or other formal 

training, and cultural capital through informal learning, as a result of a 

person’s culture or environment (Kusel 2002). We see evidence of the importance of cultural capital in Post Mountain where the experience and technical 

knowledge of third generation loggers is being melded with the traditional 

scientific knowledge of Forest Service professionals to develop and implement 

CWPP projects that fit the local biological, physical, and social contexts.

Social capital can be defined as “the ‘glue’ that binds a community together 

and enables collective action for the benefit of the community. It has also 

been referred to as the ‘grease’ that enables things to happen smoothly” 

(Kay 2005, p. 166). Local leadership is one indicator of social capital, and 

in East Portal there was an abundance of local leadership built through the 

community’s Firewise activities. In the area around Libby (Lincoln County, 

Montana), social capital developed as community residents came together to 

address a number of issues, including the closing of the local mill and cleanup 

of an asbestos mine. Experience handling these challenges has facilitated 

the community coming together to address wildland fire issues. The strong 

neighborhood networks in Harris Park are examples of networks that build 

trust and facilitate collaboration, a critical component of social capital.

Some researchers have identified the networks and relations that facilitate 

the use of political systems to accomplish goals as a separate capital—political 

capital (Flora 2003). In Grizzly Flats, experience and relationships developed 

during the CWPP process have empowered the community to work with its 

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local U.S. Representative to obtain funding for a new community center and 

fire hall. Other communities have been able to take advantage of Federal 

programs not directly related to wildland fire to support planning efforts. 

The Secure Rural Schools and Community Self-Determination Act (SRS) 

provides transitional assistance to counties affected by the decline in revenue 

from timber harvests on Federal land (FirstGov 2007). SRS authorized the 

establishment of 55 Resource Advisory Committees (RACs) in 13 States. The 

RACs have implemented more than 4,500 projects on National Forests and 

National Grasslands and adjacent non-Federal lands. In Josephine County 

and Post Mountain, county governments were willing to work with their 

local RACs to invest SRS funding in the CWPP process.

We discovered several truths about our communities in regards to 

community capacity. First, our communities had a varying constellation of 

capitals, and high levels of stocks were not sufficient to produce and implement 

a CWPP. It was critical for a community to have mobilizers (flow capitals), 

such as local leadership and networks, to produce a CWPP.

In addition, it was important to have participation of agencies or intermediary organizations that link the CWPP team to the resources necessary to 

complete the plan. An intermediary organization is typically a nongovernmental or quasigovernmental organization that serves as a bridge between 

private individuals and government institutions, or between neighborhoods 

and communities and public organizations (Berger and Neuhaus 1996). 

More formally, intermediary organizations help communities mobilize their 

own resources and gain access to outside inputs (information, technology, 

finances) that enhance their capacities (Lee 2006). In several communities 

we observed consultants playing the role of intermediary.

Community organizations provided collaborative “spaces” for the development of CWPPs. Examples of community organizations active in our 

case study communities include Fire Safe Councils, Firewise committees, 

collaborative stewardship groups, homeowners associations, public utility 

districts (PUDs) and water boards, regional planning commissions, and 

various local social groups.

In addition to providing links to outside resources, we observed community organizations and intermediaries filling a number of roles in CWPP 

development, including:

• generate interest in natural resource issues

• gather together residents and important players

• facilitate meetings (CWPP and community)

• provide administrative assistance with communication, organizational 

structure, collaboration, and monitoring

• lend technical skills (for example with GIS)

• assist in grant writing

• help implement fuels reduction in neighborhoods

As mentioned above, contractors play an important role in the development 

of CWPPs. When we initiated this research, we heard stories about contractors 

who were irresponsible in dealing with communities in the development of 

CWPPs, but we also saw examples of contractors who played important roles in 

developing networks, connecting communities to other similar communities 

for advice and counsel, and facilitating collaboration. In several cases these 

contractors were retired Forest Service employees who had valuable contacts 

in the local fire management community.

Finally, agencies at the county, State, and Federal level provide funding, 

data, and key leadership in the development of CWPPs. In Lake County, 

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Minnesota, the Superior National Forest hired a partnership coordinator 

who has initiated the CWPP process in counties with large Federal holdings. 

She provides key leadership, is a node to a variety of networks, and facilitates 

access to the resources (data, maps, and staff) of the National Forest.

Case study communities that could be classified as high capacity communities based on the amount of capital at their disposal were able to engage 

in activities not possible for lower capacity communities. We found that 

several of these high capacity communities increased property owner fees to 

generate funds to hire contractors or to implement projects identified in the 

CWPP. The retired professionals found in these communities bring a high 

degree of human capital to any local endeavor—especially valuable in the 

development of CWPPs are grant writing and planning skills. Residents in 

our high capacity communities had experience in other programs, such as 

stewardship or fire programs or fire cooperatives, which could be directly 

applied to the development of CWPPs. Finally, agencies and programs will 

often target high capacity communities for collaborative projects because 

they believe that their potential for success is greater than in lower capacity 

communities. This practice can benefit the broader community or region by 

providing models for action, but eventually lower capacity communities will 

need to be brought into the process.

Although communities described as low capacity may be seen as having 

fewer resources to bring to the CWPP process, we found many characteristics 

of these communities that facilitated CWPP development. Lower capacity 

communities often exhibited a stewardship ethic that encourages involvement in fuels mitigation projects that would result in more healthy forests. 

The social cohesion and sense of mutual obligation and responsibility found 

in several of these communities provides a foundation for working together 

collaboratively on the CWPP. A history of self-reliance means that several 

of our low capacity communities believed that they could accomplish an 

objective like fuels mitigation. Communities that lack many of the characteristics that enhance community capacity can be more responsive to goals 

defined by agencies or organizations (such as developing a CWPP), and to 

offers for outside assistance. Networks in lower capacity communities tend to 

be informal, but they do exist, and can serve community wildfire protection 

planning. Leadership in lower capacity communities can be more diffuse, but 

the community leaders that are found tend to be more multifaceted because 

they need to be. Finally, community members exhibit a high degree of trust 

in their volunteer fire departments. The knowledge, skills, and abilities found 

in volunteer fire departments in lower capacity communities can provide the 

nucleus for broader community projects.

At the beginning of our discussion of community capacity, we cited 

examples of how the CWPP process contributed to each of the capitals. 

In summary, we observed many examples of the CWPP process increasing 

community capacity by:

• building leadership in communities and organizations

• strengthening relationships among agencies

• providing visibility for players

• gaining access to networks and participating in coordinated efforts

• enhancing stewardship and community buyin for projects

• facilitating social learning

• producing successful projects that spawn other projects

• creating a sense of hope and trust

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The impacts of the CWPP process on community capacity were not 

all positive. If merely imported from another community and treated as a 

fill-in-the-blank exercise, the CWPP process may not realize many of the 

benefits listed above. If agency staff direct the process with little community 

involvement or collaboration, the CWPP process will build little community 

capacity. We also saw examples of the CWPP process creating or enhancing 

conflict among agencies, across programs, or among interest groups.

Several findings from our case studies related to community capacity are 

surprising. We have often heard that communities that are bedroom communities—with part time and/or commuting residents—will not be successful 

in collaborative activities requiring community involvement. This was not 

the case in our communities if the communities had high natural capital, 

human capital, and social capital (including a stewardship ethic). We found 

that economic advantage and political support are not enough to produce a 

CWPP; rather social and human capitals are most significant. Finally, early in 

our interviews we would hear complaints about the fact that the Healthy Forest Restoration Act did not designate a leadership agency for the development 

and implementation of CWPPs. However, we found that by not designating 

a leadership agency, there was flexibility for leadership to emerge at the scale 

where the capacity was the greatest.

Discussion

Agencies and groups interested in monitoring and evaluating the success 

of CWPPs often focus on number of plans signed or acres treated. In our 

preliminary analysis of findings from case studies in eight States, we find that 

benefits from the development and implementation of CWPPs in regards to 

building networks, learning communities, and community capacity may be as 

significant and enduring as fuels reductions. By building community capacity, CWPPs will help ensure that the initial fuels work done in communities 

and on public land adjacent to communities will be maintained as well as the 

reduction in risk from wildland fire.

There remain a number of questions yet to be answered by this and future 

research projects. Regarding information sharing we ask:

• What information is lacking or needed?

• What are the most effective information sharing mechanisms?

• What information sharing method would community members like to 

learn more about?

• How has new information improved work in CWPPs?

Regarding embedding CWPPs in other plans:

• What priorities exist for embedding CWPPs in existing plans?

• How important is this for success?

• What does it mean for CWPP implementation?

Regarding networks:

• What network lessons from CWPP planning transfer to CWPP 

implementation?

• How do we track resource flows along bridging networks?

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Finally, regarding community capacity:

• Do CWPPs provide new opportunities for community-based organizations and for building community capacity?

• Are more low-capacity communities being reached through the CWPP 

process than through other wildland fire management planning efforts?

• What are the needed capacities and essential components for moving from 

strategic plans to sustainable implementation?

• Because intermediaries and contractors play such an important role in 

CWPP development, how can they receive sustained support in order 

to assist in implementation?

References

Berger, P.L.; Neuhaus, R.J. 1996. To empower people: from state to civil society. 

Washington, DC: American Enterprise Institute. 223 p.

Brass, D.J. 1992. Power in organizations: a social network perspective. In: Moore, 

B; Whitt, J.A. (eds), Research in politics and society. Greenwhich, CT: JAI Press: 

295-323.

Finneran, T. 2007. A component-based knowledge management system. The Data 

Administration Newsletter. Available: www.tdan.com/i009hy04.htm [April 27, 

2007].

FirstGov. 2007. Secure Rural School and Community Self-Determination Act of 

2000. Available: wwwnotes.fs.fed.us:81/r4/payments_to_states.nsf [April 27, 

2007].

Flora, C.B. 2000. Measuring the social dimensions of managing natural resources. 

In: Fulton, D.C.; Nelson, K.C.; Anderson, D.H.; Lime, D.W. (eds), Human 

dimensions of natural resource management: emerging issues and practical 

applications, workshop proceedings. 1-3 February 2000; St. Paul, MN. BRD2000-1. St. Paul, MN: University of Minnesota and USGS-Biological Resources 

Division, Minnesota Cooperative Fish and Wildlife Research Unit and Cooperative 

Park Studies Program: 81-99.

Flora, C.B. 2003. Outcomes and measures. North Central Regional Center 

for Rural Development. Available: http://www.nrfc.org/ln/documents/

Monitoring%20outcomes.pdf [April 27, 2007].

Flora, C.B.; Flora, J.L.; Fey, S. 2004. Rural communities: legacy and change. 2nd 

ed. Boulder, CO: Westview Press. 372 p.

Forest Ecosystem Management Assessment Team. 1993. Forest ecosystem 

management: an ecological, economic, and social assessment; Report of the Forest 

Ecosystem Management Assessment Team. Washington, DC: U.S. Department 

of Agriculture. Forest Service.

Healthy Forest Initiative. 2007. A community wildfire protection plan: how-to-guide. 

Available: www.healthyforests.gov/community/cwpp.html [April 4, 2007].

Kusel, J. 2002. Assessment of the Northwest Economic Adjustment Initiative. 

Taylorsville: Forest Community Research.

Lee, L. 2006. Social capital, the social economy and community development. 

Community Development Journal. 41(2):160-173.

Pretty, J. 2000. Towards sustainable food and farming systems in industrialized 

countries. International Journal of Agricultural Resources, Governance and 

Ecology. 1:77-94.

Critical Elements in the Development and Implementation of Community Wildfire ... Jakes, Burns, Cheng, Saeli, Nelson , Brummel, Grayzeck, Sturtevant, and Williams
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 624

Rule, L.C.; Flora, C.B.; Hodge, S.S. 2000. Social dimensions of agroforestry. 

Garrett, H.E.; Rietveld, W.J.; Fisher, R.F. (eds). North American agroforestry: 

an integrated science and practice. Madison, WI: American Society of Agronomy: 

361-386

Society of American Foresters. 2004. Preparing a community wildfire protection 

plan: a handbook for wildland-urban interface communities. Washington, DC: 

Society of American Foresters. 11 p.

Wills, L.; Gray, G.J. 2001. Exploring reinvestment from a community-based 

watershed perspective. Journal of Sustainable Forestry. 19(1/2): 385-399.

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USDA Forest Service Proceedings RMRS-P-46CD. 2007. 625

Abstract—Climate plays a central role in shaping fire regimes over long time scales 

and in generating short-term weather that drives fire events. Recent research suggests 

that the increasing numbers of large and severe wildfires, lengthened wildfire seasons, 

and increased area burned are, in part, related to shifts in climate. The historical fire 

regimes in many ecosystems have been disrupted. Efforts to restore ecosystem structure 

and integrity are facing both a changed fuel load condition and a changing climate. 

The San Diego Declaration on Climate Change and Fire Management attempted to 

consolidate a variety of perspectives on climate change and wildland fire management 

into a common statement on the overriding need to prepare and plan for wildland fire 

regimes of the future. These changes will not only directly affect wildland fire events, 

but also require a more proactive management of fuels to reduce potential disruptions 

to plant communities, fire regimes, and ultimately, ecosystem processes and services. 

The effective management of landscapes may require novel targets for community 

structure and compositions, even if the specific objectives for fuels treatments may 

focus on measure of resilience then the restoration of presettlement conditions.

Introduction

At the Third International Fire Ecology and Management Congress held 

November 13-17, 2006, in San Diego, CA, USA, the Association for Fire 

Ecology (AFE) presented a declaration on climate change and fire management for comment and approval. The draft presented at the Congress was 

the cumulative effort of many scientists within AFE as well as non-AFE 

members. About 200 signed the declaration at that time, and at the General 

AFE meeting held during the Congress, the membership of AFE also approved the declaration. The declaration attempted to consolidate a variety 

of perspectives on climate change and wildland fire management into a 

common statement on the overriding need to prepare and plan for wildland 

fire regimes of the future. While other forums have addressed the issue of 

global climate change (former Vice President Gore’s movie “An Inconvenient 

Truth” and the more recent Intergovernmental Panel on Climate Change),

this declaration was the first to approach the issue from the wildland fire 

perspective. Wildland fire regimes are likely to be significantly affected by 

global climate changes (Swetnam 1993), especially when changes in precipitation patterns (Grissino-Mayer and Swetnam 2000) and/or warmer climatic 

conditions (Williams and others 2001) occur. The result could be increases 

in the number of large fires and increases in the total area burned (Pinol and 

others 1998; Lefort and others 2003).

San Diego Declaration on Climate Change 

and Fire Management: Ramifications for 

Fuels Management

Brian P. Oswald1 and the Board of Directors, The Association for 

Fire Ecology2

In: Butler, Bret W.; Cook, Wayne, 

comps. 2007. The fire environment—

innovations, management, and policy; 

conference proceedings. 26-30 March 

2007; Destin, FL. Proceedings 

RMRS-P-46CD. Fort Collins, CO: 

U.S. Department of Agriculture, 

Forest Service, Rocky Mountain 

Research Station. 662 p. CD-ROM.

1 Professor of Forestry, Arthur Temple 

College of Forestry and Agriculture, 

Stephen F. Austin State University, 

and President, The Association for Fire 

Ecology. boswald@sfasu.edu

2 Davis, CA. cashaw@fireecology.net
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 626

It should be noted that the declaration represents the position of AFE and 

non-AFE member signatories and may not have represented the positions of 

organizations or agencies that sponsored the Congress. Since the Congress, 

the National Park Service (NPS) has launched a program to identify the 

most threaten resources and develop possible mitigations for future climate 

scenarios. In this context the AFE fire and climate change declaration has 

been embraced as an effective starting point to adaptive fuels management 

treatments designed for specific Park landscapes. The declaration has been 

widely distributed within the agency and is a point of specific discussion 

at multiple workshops addressing this issue. The declaration has helped to 

facilitate decisionmaking, up to the directorate level, regarding this complex 

and difficult issue. More recently Conservation Northwest has also endorsed 

the declaration.

The declaration begins with a preamble that addresses the overall issue of 

climate change, acknowledges the inability to identify the specific impact of 

climate change on specific ecosystems, and links potential changes to other 

important issues such as the wildland-urban interface. Some basic premises 

and then a short list of recommendations for research, education, and management follow the preamble.

Summary of Contents of the Declaration

Part One: Preamble

As scientists and land managers who focus on fire and its effects on natural ecosystems, we recognize that climate plays a central role in shaping fire 

regimes over long time scales and in generating short-term weather that 

drives fire events. The science surrounding human-caused climate change 

continues to strengthen, and the weather patterns that shape the ecosystems 

where we live and work may be altered dramatically over the coming decades. 

In anticipation of such changes it is important to consider how fire management strategies may enable us to respond to a changing global climate and 

thereby reduce potential disruptions to plant communities, fire regimes, and 

ultimately, ecosystem processes and services.

Currently, we are observing serious wildland fire conditions, such as increasing numbers of large and severe wildfires, lengthened wildfire seasons, 

increased area burned, and increasing numbers of large wildfires in fire-sensitive ecosystems (such as tropical rain forests and arid deserts). Recent research 

suggests that these trends are, in part, related to shifts in climate.

As temperatures increase, fire will become the primary agent of vegetation 

change and habitat conversion in many natural ecosystems. For example, 

temperate dry forests could be converted to grasslands or moist tropical 

forests could be converted to dry woodlands. Following uncharacteristic 

high-severity fires, seedling reestablishment could be hindered by new and 

unsuitable climates. Plant and animal species already vulnerable due to human activities may be put at greater risk of extinction as their traditional 

habitats become irreversibly modified by severe fire. Streams and fisheries 

could be impacted by changing climates and fire regimes with earlier peak 

flows, lower summer flows, and warmer water even if ecosystems don’t burn. 

Finally, extreme wildfire events and a lengthened fire season may greatly 

increase the risk to human lives and infrastructures, particularly within the 

wildland urban interface.

San Diego Declaration on Climate Change and Fire Management: Ramifications for Fuels Management Oswald and the Board of Directors, The Association for Fire Ecology
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We acknowledge that there are uncertainties in projecting local impacts of 

climate change. However, without taking action to manage fire-dependent 

ecosystems today and in the absence of thoughtful preparation and planning 

for the future, wildland fires are likely to become increasingly difficult to 

manage.

We, the members of the Association for Fire Ecology that endorsed this 

document at the Third International Fire Ecology and Management Congress, support the considerations outlined in this paper for planning and 

management to enhance ecosystem resiliency to wildland fire in a changing 

global climate.

Part Two: Background Premises

The second part of the declaration presents a number of premises that were 

the basis for the declaration. These premises begin with the acknowledgment 

of the role of fire as part of ecosystems as well as the historical disruptions 

of natural fire regimes. Additional premises address the potential impact of 

climate change on vegetation and the need to have proactive management 

programs to address climate change on ecosystems.

Both fire and climate regimes interact with other natural processes to direct 

the formation of vegetation in ecosystems. Given that climate and fire regimes 

are linked through vegetation, changes in climate can lead to large or small 

changes in fire regimes. Climate and fire regimes are also directly connected 

through the climate drivers of ignitions and fire weather. Climate influences 

both where and how vegetation grows and thereby creates the fuel conditions 

that drive fire frequency, intensity, severity, and seasonality. Precipitation 

and temperature patterns regulate the accumulation of fuels. In some ecosystems, wet years may promote “boom” vegetative (fuels) conditions, while 

drought years promote “bust” and the burning of the “boom” vegetation. 

Further, we know that the inevitable dry years, particularly when warm, are 

associated with larger fires, both in size and number, especially where fuel is 

abundant. Fire can also contribute to the problem of increasing green house 

gas emissions because it is a source of CO2 and particulate emissions, which 

may affect local and regional air quality and worldwide climate.

Historical fire regimes have been disrupted in many ecosystems. Factors 

such as human activities and land development, loss of indigenous burning 

practices, and fire suppression have all led to changes in some plant communities historically shaped by particular fire regimes. Human activities have 

significantly increased the number of ignitions in many temperate, boreal, 

and tropical regions. Fuel loads have increased in some temperate forests 

where low intensity fires were historically the norm. In some rangelands, 

shrubs have been replaced by annual grasses or colonizing trees. Humancaused burning has increased fire frequency in some tropical regions where 

fire-sensitive ecosystems dominate.

Not all vegetation types have been significantly altered by fire suppression. Many shrubland ecosystems, such as California chaparral, burn with 

high severity under extreme weather conditions, and fire management in the 

20th and 21st centuries has not appreciably changed their burning patterns. 

Coastal, mesic coniferous forests in the Northwestern United States have 

not been modified to a great extent by fire suppression policies because fire 

rotations in this area are much longer than the period of fire suppression. 

In other forests such as Rocky Mountain lodgepole pine, high severity fires 

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every 100 to 300 years are ecologically appropriate, and fire suppression has 

probably not affected these ecosystems to a great extent. The ecosystems 

most impacted by fire suppression are forests that once experienced regimes 

of frequent, low-moderate intensity fires; these ecosystems are probably the 

most vulnerable to altered fire regimes from changing climates.

Approaches to restore fire-adapted ecosystems often require treatment 

or removal of excess fuels (for example, through mechanical thinning, prescribed fire, or mechanical-fire combinations), reducing tree densities in 

uncharacteristically crowded forests, and application of fire to promote the 

growth of native plants and reestablish desired vegetation and fuel conditions. Excess fuels are those that support higher intensity and severity fires 

than those under which the particular ecosystem evolved or are desired to 

meet management objectives. For example, in dry Western U.S. forests that 

once burned frequently, a high density of trees and a large surface fuel load 

often promotes crown fires that burn over large areas. Some of these same 

forests once flourished under a fire regime where frequent, nonlethal lowintensity surface fires were the norm, and large-scale crown fires were rare. 

Managers should determine if forests can be restored to what they once were 

or if another desired condition is more appropriate. If it is not appropriate to 

restore ecosystems to a previous condition because of expected novel climate 

conditions, then managers should develop new conservation and management 

strategies and tactics aimed at mitigating and minimizing uncharacteristic 

fire behavior and effects.

Climate change may interact with other human activities to further change 

fire regimes. For example, in much of the Western United States, since the 

1980s, large fires have become more common than they were earlier in the 

century. This has often been attributed to increased fuel loads as a result of 

fire exclusion. However, a number of research studies suggest that climate 

change is also playing a significant role in some regions, elevations, and ecosystem types. In the Western United States, researchers recently identified 

an increase in fire season duration in mid-elevation forests. These changes 

were correlated with earlier spring snowmelt dates. With global temperatures 

projected to rise throughout this century, increases in fire season length and 

fire size can be expected to continue.

Climate change can lead to rapid and continuous changes that disrupt 

natural processes and plant communities. Are managers safe in assuming that 

tomorrow’s climate will mimic that of the last several decades? Increased 

temperatures are projected to lead to broad-scale alteration of storm tracks, 

thereby changing precipitation patterns. Historical data show that such 

changes in past millennia were often accompanied by disruption of fire 

regimes with major migration and reorganization of vegetation at regional 

and continental scales. Exercises in modeling of possible ecological responses 

have illustrated the potential complex responses of fire regimes and vegetative 

communities. These exercises indicate that dramatic changes in fire regimes 

and other natural disturbance processes are likely. Indeed, some believe that 

the impacts of climate change may already be emerging as documented in 

widespread insect infestations and tree dieoffs across some areas in the Western 

United States and British Columbia, and more rapid and earlier melting of 

snow packs. Developing both short- and long-term fire and fuels management responses that improve the resilience of appropriate ecosystems while 

reducing undesired impacts to society will be critical.

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Changes in climate may limit the ability to manage wildland fire and apply 

prescribed fire across the landscape. Under future drought and high temperature 

scenarios, fires may become larger more quickly and could be more difficult 

to manage. Fire suppression costs may continue to increase, with decreasing 

effectiveness under extreme fire weather and fuel conditions. In some temperate and boreal regions, it is expected that more acres will burn and at higher 

severities than historically observed. In humid tropical regions exposed to 

severe droughts, vast forests could burn making it difficult for forest managers to prevent farmers from entering destroyed forests and establishing new 

farms. Globally, new fire regimes would be associated with shifts in ecosystem 

structure and function and, likely, changes in biodiversity.

Approaches to fire management that recognize the potential for greater 

variability and directional change in future climates may help to reduce 

ecological and societal vulnerability to changing fire regimes. Such approaches 

are likely to improve fire management and ecosystem health. A goal could be 

to reduce the vulnerability, both ecologically and socially, to the uncertainties 

that accompany a changing climate. For example, if managers restore some 

forests as a means to increase ecosystem resiliency to climate change, they will 

also be improving biodiversity and protecting important forest resources. In 

the humid tropics, if managers make a concerted effort to prevent fire from 

entering rain forests during drought years, then they would be reducing the 

risk of future fires and illegal logging, even if droughts did not become more 

frequent and severe with a changing climate.

Part Three: Considerations for Management, 

Research, and Education

The last section of the declaration presents some specific recommendations or considerations for management, research, and education. Fuels 

management is an important part of the suggested activities, since it is with 

fuels management that we can remediate ecosystems from past fire regime 

disruptions while also preparing for changes in those same ecosystems as a 

result from climate change.

Recent changes in climate and fire patterns have been observed in many 

areas of the world, and current projections are that ongoing and long-term 

changes are likely. We believe that the actions outlined below could help 

managers to be better prepared to anticipate and mitigate potential negative 

effects of variable and changing future environments.

Fire and ecosystem management

	 •	 Incorporate the likelihood of more severe fire weather, lengthened wildfire seasons, and larger sized fires in some ecosystems when planning 

and allocating budgets, which traditionally are based on historical fire 

occurrence.

	 •	 Make use of both short-term fire weather products AND season-to-season 

and year-to-year climate and fire outlooks that are increasingly available 

from “predictive services” groups in Federal agencies, and particularly the 

sub-regional variations in anticipated fire hazards that enable strategic 

allocation of fire fighting and fire use resources nationally.

	 •	 Continually assess current land management assumptions against the 

changing reality of future climates and local weather events.

	 •	 Develop site-specific scenarios for potential weather events linked to 

climate change, and redesign fire management strategies to make room 

for rapid response to these events.

San Diego Declaration on Climate Change and Fire Management: Ramifications for Fuels Management Oswald and the Board of Directors, The Association for Fire Ecology
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 630

	 •	 Consider climate change and variability when developing long-range 

wildland fire and land management plans and strategies across all ownerships.

	 •	 Consider probable alternate climate scenarios when planning postfire 

vegetation management, particularly when reseeding and planting.

Fuels management

	 •	 Prepare for extreme fire events by restoring some ecosystems and reducing 

uncharacteristic fuel levels through expanded programs of prescribed 

burning, mechanical treatments, and wildland fire use to meet resource 

objectives. Burning under the relatively mild weather conditions of a 

prescribed fire produces lower intensity burns and, generally, less carbon 

emissions than would a fire burning under wildfire conditions. Burning 

and thinning treatments should be strategically placed on the landscape 

in locations where they are more likely to influence fire spread. Some 

ecosystems will continue to burn in high severity stand replacement 

fires, and this is appropriate for their sustainability.

	 •	 Incorporate emerging scientific information on the impact of changing 

temperature and precipitation on plant communities into fuels management project design and implementation at the local level.

	 •	 Expand wildland fire use at the landscape scale in fire-adapted ecosystems to restore fire regimes and reduce fuel loads. Be more aggressive 

in promoting fire use during lower hazard fire seasons, and fire use in 

landscapes that offer particular opportunities for relatively low-risk, 

large-scale burning. This will allow more acres to be burned under less 

extreme fire weather conditions than fires that might occur in the future 

under extreme heat or drought conditions.

	 •	 Control highly flammable nonnative plant species and develop management options to address their increased spread and persistence. In some 

ecosystems appropriately timed prescribed fires can be used to reduce 

nonnative species, while in others, continued fire exclusion may be the 

best management strategy. In some areas, reseeding and active restoration may be the best option.

	 •	 In some cases the removal and use of small diameter forest products 

(engineered lumber, pulp and paper, biofuels) and chipped fuels (for 

electrical energy generation) could be used to reduce fire hazards in 

appropriate vegetation types. Burning excess fuels in a cogeneration 

plant has the additional advantage of producing lower emissions when 

compared to prescribed fires.

Research, education, and outreach

	 •	 Implement long-term biodiversity and fuels monitoring programs in the 

fire-adapted ecosystems that are expected to undergo the widest range 

of change and variability linked to climate change, such as those that 

once experienced frequent, low-moderate intensity fire regimes.

	 •	 Expand interdisciplinary research to forecast potential fire season severity and improve seasonal weather forecasts under future climate change 

scenarios.

	 •	 Integrate the subject of climate change and its influence on ecosystem 

disturbance into curricula within natural resource management programs at the university and continuing education levels, and in science 

programs within primary schools.

	 •	 Disseminate information to the general public and government agencies 

regarding the potential impacts of changing climate on local natural resources 

and disturbance regimes, particularly those that interact with fire.

San Diego Declaration on Climate Change and Fire Management: Ramifications for Fuels Management Oswald and the Board of Directors, The Association for Fire Ecology
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 631

	 •	 Hold conferences or symposia to enhance communication among researchers and managers and to engage the general public in discussion on how 

best to adapt public land management to cope with fire in a changing 

environment.

	 •	 Form interdisciplinary teams of researchers that include fire ecologists 

and climate scientists to identify and pursue emerging areas of climate 

and fire research.

Conclusions

The San Diego climate change declaration was an attempt by AFE to enunciate how this issue may impact fire regimes across the country. The issue of 

global climate change will be in the forefront of worldwide discussions on 

land management for decades to come. For those of us involved in natural 

resource management, proactive management will be required to minimize 

the potential negative impacts of climate change. Fuels management is, and 

will be, one of the most important tools we have available. It is with fuels 

management that we can rectify the effects of past actions that resulted in 

modified fire regimes and restore both ecosystem sustainability and landscape-level processes. Fuels management will also give us the opportunity to 

mitigate potential negative effects of changing future environments.

References

Grissino-Mayer, H.D.; Swetnam, T.W. 2000. Century-level climate forcing of fire 

ergimes in the American Southwest. The Holocene 10:213-220.

Lefort, P.; Gauthier, S; Bergeron, Y. 2003. The influence of fire weather and land 

use on the fire activity of the Lake Abitibi area, eastern Canada. Forest Science 

49:509-521.

Pinol, J.; Terradas, J.; Lloret, F. 1998. Climate warming, wildfire hazard and wildlfire 

occurrence in coastal eastern Spain. Climate Change 38:345-357.

Swetnam, T.W. 1993. Fire history and clamate change in giant sequoia groves. 

Science 262:885-889.

Williams, A.A.; Karoly, D.J.; Tapper, N. 2001. The sensitivity of Australian fire 

danger to climate change. Climate Change 49:171-191.

San Diego Declaration on Climate Change and Fire Management: Ramifications for Fuels Management Oswald and the Board of Directors, The Association for Fire Ecology
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 632

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Abstract—This paper offers an analysis of the strengths, weaknesses, opportunities, 

and threats in the risk management process, decision support systems (DSSs), and 

other types of decisionmaking, including recognition primed decisionmaking, bricolage 

with the goal of improving DSSs and decisionmaking. DSSs may be thought of as any 

technology or knowledge that is used as an aid in decisionmaking. Many types of risk 

management processes and DSSs exist in wildland fire, wildland fire, and prescribed 

fire at the tactical, operational, and strategic levels. In the wildland fire community, 

DSSs exist as check-lists, handbooks, implementation guides, computer programs, and 

more. Many wildland fire suppression agencies and other high reliability organizations 

have embraced what may be called a rationalistic based decisionmaking process in the 

form of risk management, programmed decisions, and more. Critics charge that while 

an attempt is made to rationalize decisions, many “judgments” within the rationalistic 

systems reduce their logic, making their rationality questionable. While rationalistic 

based decisionmaking processes exist at all levels of the fire suppression agencies, 

naturalistic decisionmaking is found primarily at the tactical level in the form of recognition primed decisionmaking, or bricolage. Many argue that the risk management 

decisionmaking school of thought is contraindicated by the naturalistic decisionmaking 

school of thought. Finally the role of DSSs in the naturalistic and rationalistic based 

decisionmaking is explored.

Introduction

This paper is based on the premise that the decision support systems (DSSs) 

used in wildland fire suppression should be integrated so that they function 

optimally together in a seamless manner. Ultimately this type of integration 

would begin to integrate the DSSs and their uses at the tactical, middle, 

and strategic levels of action. DSSs may be thought of as any technology or 

knowledge, whether in a material or immaterial form, that is used as an aid 

in decisionmaking. Many types of DSSs exist in wildland fire, wildland fire 

use, and prescribed fire at the tactical, middle, and strategic levels. In the 

wildland fire community, DSSs exist as check-lists, handbooks, implementation guides, computer programs, and more.

The decision support systems examined here include the 59 fire guidelines (which include the 10 Standard Firefighting Orders, the 18 Watch 

Out Situations, the seven Downhill Line Construction Checklist, the four 

Common Denominators of Fire Behavior on Tragedy Fires, the seven Look 

Up, Down & Around Factors, the four LCES Components, and the nine 

Wildland Urban Watch Outs) and the Region 4 Incident Organizer (Version 

2004). It is believed that these DSSs (the 59 fire guidelines and the incident 

organizer) should be integrated with the basic components of an incident 

Analysis of the Risk Management 

Decisionmaking Processes and the 

Decision Support Systems in the 

Wildland Fire Agencies

Patrick Withen1

In: Butler, Bret W.; Cook, Wayne, 

comps. 2007. The fire environment—

innovations, management, and policy; 

conference proceedings. 26-30 March 

2007; Destin, FL. Proceedings 

RMRS-P-46CD. Fort Collins, CO: 

U.S. Department of Agriculture, 

Forest Service, Rocky Mountain 

Research Station. 662 p. CD-ROM.

1 McCa l l Smokeju mpers, U. S. 

Department of Agriculture, Forest 

Service, McCall, ID, and University 

of Virginia at Wise, Department of 

Social Sciences, Wise, VA. pwithen@

virginia.edu
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 634

action plan (IAP), the Wildland Fire Use Implementation Procedures Refer-

ence Guide, and the proposed new fire doctrine. These documents provide 

the entire spectrum of rules and strategies that guide the fire and fire use 

communities. Despite the fact that such a large-scale project is beyond the 

scope of this paper, it is nevertheless important to address the issue of a “full 

spectrum” of integration of DSSs because they operate at all levels of action 

from the tactical level, to middle level, to the strategic level. They operate on 

all sizes of fire and fire use. The levels operate independently of each other, 

in other ways they are closely tied together, and an action or aim at one 

level may impact one or more of the other levels. So while this project is just 

beginning the discussion of the integration of rules, actions, and aims, the 

integration proposed here is only part of the picture, and it must be taken in 

a context of all levels of action and all levels of DSSs.

The need for the integration of the DSSs and planning and action at the 

tactical, middle, and strategic levels can be seen in two manners. First, the 

examination below will demonstrate that the 59 fire guidelines and the typical 

incident organizer (IO) (as well as the typical IAP, WFU Guide, and other 

DSSs not covered here) do not have any overlap written into them, and yet 

conceptually they have much in common. Second, Vergari demonstrated 

in “Back to Basics for Fire Program Managers” (2005) that there is little 

consistency in the manner in which firefighters plan and act, and also that 

there is a wide variance of understanding and usage of tactics, objectives, 

and strategies. The present study attempts to integrate two DSSs, the 59 fire 

guidelines, and the typical IO, in order to gain efficiency and effectiveness 

by allowing the overlap between the DSSs to create a more seamless operat-

ing tempo, by gaining consistency in operation, and by integrating tactical, 

middle, and strategic level planning through a common body of DSSs.

Because the DSSs have never been integrated, this analysis will address only 

some of the possibilities in integrating the documents. First the integration 

of the 59 fire guidelines and the incident organizer will be presented.

Information Overload

The very necessity of using checklists, shortcuts, handbooks, DSSs, and so 

forth demonstrates that there are innumerable factors impacting any given 

situation on the fireline. And because the environment is turbulent—that is, 

changing rapidly in real time—the factors impacting fireline situations also 

change rapidly. Furthermore, while there are innumerable factors impacting a 

typical situation on a fireline, in a crisis situation, with the additional factors that 

arise, the number of factors interacting becomes infinite in a practical sense.

In other words, the fireground and the firefighter training—factors 

creating an extremely large body of situational knowledge and background 

knowledge—are both capable of creating information overload on the fire-

line. Some people use the existence of information overload to argue that 

a development of integrated, comprehensive DSSs or standard operating 

guidelines (SOGs) is impossible. Their solutions lie in either eschewing 

rational decisionmaking (for recognition primed decisionmaking, RPDM, 

or bricolage) or in standing by the status quo. I contend that it is quite pos-

sible to overcome information overload, and that integrated and systematized 

DSSs can operate well using the decisionmaking styles of risk management, 

RPDM, or bricolage.

However, there are principles of information organization that need to 

be looked at.

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Standard Operating Guidelines

Recently, I presented work consolidating the 59 fire guidelines into the 10 

essential factors in (wildland) firefighting (TEFF). The TEFF consolidated 

59 firefighting guidelines, including the 10 Standard Firefighting Orders, 

the 18 Watch Out Situations, the seven Downhill Line Construction Checklist, the four Common Denominators of Fire Behavior on Tragedy Fires, the 

seven Look Up, Down & Around Factors, the four LCES Components, and 

the nine Wildland Urban Watch Outs, into 10 essential factors in wildland 

firefighting. (See appendix A for more on the TEFF.) The principles behind 

the development of the TEFF are important in understanding both the purpose and the functioning of an integrated set of DSSs. As was reported in the 

TriData studies (1998) after the South Canyon Fire of 1994, firefighters and 

fire managers do not believe any more checklists or other DSSs are needed. 

Despite this, rules and guidelines have continued to grow. As a result of the 

30 Mile Fire, Incident Organizers and other documents and procedures 

have been changed and expanded. It is not clear what changes will occur as 

a result of the Cramer Fire, and all the implications of the 30 Mile Fire have 

not yet played out. And it is likely that tactical level rules and guidelines will 

continue to growth despite the pleas to the contrary. The analysis presented 

here holds that the solution to this problem is not to force a moratorium on 

guidelines, but rather it is to systematize and organize them in order to eliminate fluff, redundancy, and confusion and replace them with a tighter, more 

efficient system. Ultimately that system would develop from what it is now, 

a series of “orders,” “watch outs,” “checklists,” “common denominators,” 

and so on, and replace them with standard operating guidelines (SOGs) that 

direct flexible action with greater clarity than currently exists. An example 

of the weakness of the current system can be seen in the debate within the 

wildland fire community that occurred in the years following South Canyon 

over whether the fire orders were in fact invioble orders or whether they were 

simply guidelines. (No clear policy directive on this debate was ever reached 

even though consideration of it occurred in the upper echelons of the firefighting community.) A clear set of SOGs would end such a debate because 

they would clearly state where a standard operating procedure functioned 

and where it did not. And as discussed at the end of this document, there are 

methods for addressing situations where SOGs usefulness ends.

While the principle of establishing SOGs is not the focus of this paper, 

the integration of the 59 fire guidelines, the IO, and other DSSs is a step in 

that direction. And both SOGs and the integrated DSSs aim to systematize 

and standardize planning and action, as well as offer an aid to dealing with 

information overload.

Nesting

The TEFF consolidated the 59 fire guidelines into just 10 factors or 

guidelines that the firefighter on the ground needs to follow. The TEFF was 

designed so that if firefighters notice or sense that there is a problem meeting the requirements of an individual TEFF, then they need to refer to the 

numerous rules that resulted in the formulation of that individual TEFF. For 

example, if a firefighter notices that communications are problematic, under 

a fully developed TEFF system, the firefighter might refer to any or all of the 

appropriate former guidelines such as Fire Order 7: Remain in communication 

w/ crew members, your supervisor, & adjoining forces; or Fire Order 8: Ensure 

instructions are given & understood; or Watch Out 5: Uninformed on strategy, tactics, & hazards; or Watch Out 6: Instruction & assignments not clear; 

Analysis of the Risk Management Decisionmaking Processes and the Decision Support Systems in the Wildland Fire Agencies Withen
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 636

or Watch Out 7: No communication link w/ crew members or supervisor; or 

Downhill Line Construction Rule 3a: Crew supervisor is in direct contact w/ 

lookout who can see the fire; or any of a total of nine fire guidelines dealing 

with communications that are scattered throughout the 59 fire guidelines. In 

addition, as my analysis of the 59 fire guidelines demonstrated, there are also 

gaps and omissions of common situations, indicating that there may need to 

be new tactical guidelines added to any comprehensive curriculum on tactics 

and tactical DSSs.

The principle demonstrated here is that, like the TEFF, the DSSs are 

always shortcuts with a large body of knowledge, or rules, behind them. 

Large amounts of knowledge are nested within shortcut guidelines such as 

the TEFF, the Fire Orders, and many other fireline guidelines. Thus, under 

the proposed new system of integrated DSSs and SOGs, no information is 

lost, it is only organized or nested for easy access.

Hierarchicization

The existence of information overload and the necessity of nesting that 

information implies that information must be arranged in a hierarchy—in 

other words, that some information is more important than other information. The principle of hierarchy implies that the firefighting community must 

decide the best short-hand methodology and terminology for its DSSs so that 

general categories are examined first, and then as they either become problems 

or enter into the risk management decisionmaking process, the information 

that backs them up would become apparent.

Status of Information Use

If these principles of standardization, nesting, and hierarchicization were 

firmly established in wildland fire, then both the curriculum and the DSSs 

that attempt to summarize the curriculum would be arranged so that firefighters could address problems with the most general rules, and then address 

finer points by moving into the more detailed levels of the nested hierarchy 

of information.

Returning to the example of the firefighter who notices that communications are problematic: if that firefighter had a DSS, backed by a parallel 

training curriculum, she or he would examine that problem in a similar 

manner to any other firefighter and presumably in a more efficient manner 

than a firefighter would today. My research confirms Vergari’s (2004) contention that there are only minimal standard operating procedures in that 

most firefighters report that they rarely refer to any of the Fire Orders, Watch 

Outs, or any of the other fire guidelines or DSSs, and that they interpret and 

utilize them in different manners. Most firefighters have their own personal 

“core” of the 59 fire guidelines that they use regularly. Any of the DSSs or 

anything that looks like a SOG is only referred to when there is some minor 

or major crisis that makes a decision difficult.

Today none of the DSSs are good examples of dealing with information 

overload, standardization, nesting, or the hierarchicization of knowledge. The 

existence of 59 fire guidelines that have been compiled over the entire history 

of modern wildland fire suppression, the fact that many locales have their 

own model of an incident organizer, their own model of an IAP, or their own 

model of a Fire Plan, and the fact that locales are left to interpret strategic 

direction in such a way that results in different policies all demonstrate that 

there is little coordination in the wildfire suppression community.

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Integrating the Fire Guidelines and the 

Incident Organizer

How might the 59 fire guidelines and the incident organizer be integrated? 

First, many IOs today are designed to meet 30 Mile Accident Prevention 

requirements. This is fine, but in the interest of integration, the 30 Mile Ac-

cident Prevention requirements should not be the center around which an 

IO is formulated. Below are some suggestions as to how IOs and the 59 fire 

guidelines might be improved through integration, standardization, recogniz-

ing information overload, nesting, or the hierarchicization of knowledge.

Because the curriculum of fire training is not nested or hierarchicalized, the 

fact that the 59 fire guidelines and the IOs are not nested or hierarchicalized 

is not readily apparent. Firefighters have not been trained to think or oper-

ate based on any hierarchy of knowledge or SOGs. This point is important 

for the following analysis because the integrations proposed here would be 

much more valuable if firefighters were trained in a manner that encour-

aged the use of SOGs, nested knowledge, and hierarchicalized knowledge. 

This analysis’ proposal for the integration of all DSSs, for the integration 

of tactical, middle, and strategic level planning and action implies that such 

an integration of all firefighter training would also prove valuable. In fact, 

the wildland fire agency’s development of a new foundational doctrine is a 

good beginning.

Currently the organization of the IO is accomplished in 14 sections:

1. size up

2. resource summary

3. objectives

4. organization

5. map sketch

6. radio frequencies

7. risk management

8. decision points

9. risk analysis

10. incident complexity analysis

11. summary of actions

12. spot weather forecast

13. work rest ratio documentation

14. the after action review

Most IOs begin appropriately with a fire size up section. The size up 

should clearly be divided into two segments: the physical fire size up, and 

the resource size up. Currently this is done in section 1: the size up; and 

section 2: the resource summary. Some of the information about resources 

is done in its own section and is not seen as part of the size up. Some of the 

fire size up is done in section 10: the incident complexity analysis; and sec-

tion 12: the spot weather forecast. It is important to integrate the fire and 

resources size up so that the incident commander (IC) and firefighters start 

their planning based on what they can do, rather than simply on what needs 

to be done. This ties both the IO and the incident’s plan to the strategic 

plans of the administrative unit under which this fire suppression effort is 

operating in that the IC may request more resources, but the fire program 

manager (FPM) may want to allocate those resources to another incident. 

The pairing of the fire situation with the available resources establishes the 

idea that a safe incident is one that accomplishes what it can with the available 

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resources. It is felt that this would minimize any chance of “over-zealousness” 

as spurious as that argument was.

The fire size up itself should be divided into clear segments that address 

the TEFF factors of fire behavior, fire status, fuel type, weather, and terrain. 

In combination, these five factors represent more than 40 fire guidelines in-

cluding some of the Fire Orders, Watch Outs, Urban Watch Outs, and others. 

This focuses the firefighters’ thinking in terms of these five critical factors 

related to the physical fire, and should be accompanied by a similar focus on 

the fire guidelines that apply to these critical physical fire factors.

A radical change from current practice would have the IC, or the person 

filling out the size up, indicate both current conditions, as well as expected 

conditions. While admittedly this is not standard practice today, training 

firefighters to track specific conditions and to tie these conditions to the 59 

fire guidelines and the IO would integrate the practice of tracking trends, 

tying trends to specific actions on the fireline, and to link both of these to the 

incident’s plan in the form of the newly integrated IO. Tracking conditions 

is inherently part of the risk management process in that one is to engage 

another iteration of risk management any time conditions change, whether 

these be physical or firefighting resource conditions on the fireground. The 59 

firefighting guidelines also inherently require trend tracking in, for example 

FO 3: Base all action on current and expected fire behavior; Downhill Line 

Construction Guideline 7: Bottom of the fire will be monitored; if the poten-

tial exists for the fire to spread, action will be taken to secure the fire edge; 

WO 11: Unburned fuel between you & fire; and more. Tracking conditions 

on the fireline is another DSS schema to encourage firefighters to link SOGs, 

conditions, and tactics (or actions). An example of one possible method to 

track trends can be found in appendix A: “Side 1 of the TEFF card.”

As discussed above, section 2: the Resource Summary should be part of the 

initial size up in order to facilitate the standard procedure of establishing a plan 

that fits the resources. This should emphasize the principle that the IC and the 

firefighters need to be aware of the situation and the resources that they have to 

deal with that situation. The Resource Summary should divide resources into 

the categories of those actually on scene, those assigned to the incident but not 

yet on scene, and resources who are unfilled requests. Again the purpose is to 

emphasize the importance of ingraining into the thinking of firefighters the 

necessity of matching plans and tactics to resources. My analysis of the 59 fire 

guidelines, and the development of the TEFF indicate that the fire guidelines, 

as currently constructed, do not emphasize the necessity of matching tactics 

to available resources unless one counts “Fight fire aggressively, but provide 

for safety first.” There are six other fire guidelines that refer to firefighting 

resources (see appendix A), such as FO 9: Retain control at all times. And all 

of the other resource oriented fire guidelines, as currently constituted, refer 

to controlling people, and not to matching available resources to tactics. This 

is one of the critical lapses in our fire fighting guidelines that have resulted 

from the haphazard development of these guidelines, SOGs, over the decades 

of modern fire suppression.

Next in the IO is the tactical planning section. The tactical planning sec-

tion might include the already existing section of Incident Objectives, the 

Incident Organization, the Map, and Radio Frequencies. Following that, in 

the existing IO are the sections on Risk Management & Decision Points, 

Incident Risk Analysis, Incident Complexity Analysis. These sections should 

be integrated in a different manner in order to solidify the linkages between 

the fire guidelines, which are the best axioms that we have for SOGs, tactics, 

and decisionmaking.

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USDA Forest Service Proceedings RMRS-P-46CD. 2007. 639

Note that while IOs could be reformulated in such a manner as to integrate 

in SOGs, this analysis focuses only on integrating in the DSSs that we have: 

the 59 firefighting guidelines and the IO.

In the present IO, the first Incident Objective is written in: SAFETY of 

firefighters and public. After that, the IC is free to enter in one or more ob-

jective of her or his own. Because it was not their original intended purpose, 

there are no specific objectives written into the current firefighting guidelines; 

however, some objectives are implied in WO 8: Constructing line without 

safe anchor point; WO 9: Building fireline downhill with fire below; and 

several implied objectives in the Downhill Line Checklist. These firefighting 

guidelines imply a direct or indirect line placement objective. Thus, the current 

firefighting guidelines are noticeably lacking in supporting the development 

of objectives. The generalist nature of all the current firefighting guidelines 

fails to provide clear direction for the setting of objectives that make it clear 

that, while these guidelines do present the firefighter with the possibility of 

information overload, they are nonetheless incomplete. While the current 59 

firefighting guidelines do not address incident objectives, incident objectives 

are perhaps the most important single factor impacting firefighter safety. Safe 

objectives make for safe operations.

Next in the current IO is the Risk Management & Decision Points, Inci-

dent Risk Analysis, Incident Complexity Analysis. These sections have many 

facets that strongly imply, but do not make, connections to the 59 firefighting 

guidelines and other DSSs, especially the Incident Response Pocket Guide 

(IRPG 2006). For example, in the Decision Points Section, firefighters are 

asked to confirm that “Controls in place for identified hazards?” Here is the 

connection to the 59 firefighting guidelines that could be made more apparent, 

nested, and hierarchicalized. The Incident Risk Analysis (215a) has a similar 

connection that needs to be integrated with the 59 firefighting guidelines in 

that certainly some of the Fire Orders, nearly all of the Watchouts, as well as 

many others may be applied here under the rubric in the Incident Risk Analysis 

of “Hazardous Actions or Conditions.”

In the Incident Complexity Analysis (Type, 3, 4, 5) many of the 59 fire-

fighting guidelines can be implied, but none are directly referenced. For 

example, under the sub-section Fire Behavior, one of the components is 

“Weather forecast indicating no significant relief or worsening conditions.” 

This component of the IO is represented in several of the weather related 59 

firefighting guidelines such as FO 1: Recognize current weather conditions & 

obtain forecasts; WO 14: Weather becoming hotter & drier. WO 15: Wind 

increases and/or changes direction; CD 3: When there is an unexpected 

shift in wind direction or in wind speed; UW 8: Strong winds. Certainly the 

references to weather could be improved in both the IO and the 59 firefight-

ing guidelines. Many other components in the IO Fire Behavior section and 

sub-sections could be more effectively integrated with the 59 firefighting 

guidelines creating a seamless set of DSSs.

While many of the components of the IO Incident Complexity Analysis are 

closely related to the DSS of the 59 firefighting guidelines, there are also com-

ponents of that are not found in the those guidelines, and probably should be. 

For example, the sub-section Firefighter Safety has three components: (a) Per-

formance of firefighting resources affected by cumulative fatigue; (b) Overhead 

overextended mentally and/or physically; (c) Communication ineffective with 

tactical resources or dispatch. The only firefighting guideline referred to here 

is, possibly, WO 18: Taking a nap near the fireline. This demonstrates again 

that the current 59 firefighting guidelines are incomplete. And perhaps in an 

integrated set of DSSs, these factors would be addressed more effectively.

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The Decisionmaking Process

Inherent in the argument for a seamless integrated set of DSSs that 

function to integrate tactical, middle, and strategic planning and action is 

an argument for a particular type of decisionmaking. Recent discussions 

within the wildland firefighting community have often implied that rational 

decisionmaking, such as that found in the risk management decisionmaking 

process, is inadequate. Many wildland fire suppression agencies and other 

high reliability organizations have embraced naturalistic decisionmaking, 

often specifically in the form of recognition primed decisionmaking (RPDM) 

(Klein 1993) and bricolage (Weick 1993, 2001) wherein the decisionmaker’s 

expertise allows him or her to make the correct decision. RPDM is a method 

of decisionmaking wherein the decisionmaker utilizes a first impression, 

through intuitive or blink decisionmaking, to develop an alternative that is 

then analyzed to develop the final decision that is implemented. Weick holds 

that the bricoleur demonstrates intimate knowledge of the situation, makes 

careful observations, listens, trusts her or his ideas, and proceeds while being 

open to feedback.

On the other hand, one of the primary models of decisionmaking used 

by wildland fire suppression agencies is the risk management. The risk man-

agement process is listed on page 1 of the Incident Response Pocket Guide 

(USDA 2006). Many argue that the risk management decisionmaking school 

of thought is contraindicated by the naturalistic decisionmaking school of 

thought. However, this is not the case. I contend that all of these widely var-

ied decisionmaking systems can, and indeed do, work together in a manner 

that is beneficial to all. The system proposed here is one that would employ 

a seamless set of DSSs, SOGs and the risk management process of decision-

making to plan and act on fires at the tactical, middle, and strategic levels. 

RPDM and bricolage are in many ways based on this system in that, however 

different, they take as their starting point the examination of excellent deci-

sions made by accomplished actors. While standard operating procedures as 

embodied in DSSs and SOGs are designed to address all contingencies, in 

practice this is impossible. When standard models reach their limits, when 

as Weick (1993) calls it, there is a “collapse of decisionmaking,” RPDM and 

bricolage are useful decisionmaking strategies.

As discussed above, the line between the use of the risk management 

process and RPDM and bricolage appears clear. However, the distinction is 

seldom clear in practice. Utilizing Weber’s analysis of the types of rational-

ity (1948), RPDM and bricolage operate via substantive rationality, which is 

the dominance of norms and values in the rational choice of means to ends. 

Substantive rationality creates the ability to draw on norms and values to 

make decisions and to motivate people to behave in a rational manner. The 

values and norms (social rules) used by RPDM or the bricoleur are effec-

tiveness and success. The risk management decisionmaking process operates 

via intellectual rationality, also known as instrumental rationality or the 

rational cognitive process, which is the ability to utilize people’s rational 

problem solving capacities. In practice most people rely on several decision-

making processes at once. For example, in the risk management process in 

the situation awareness sub-process, one is to judge fire behavior. Because 

of the state of fire behavior analysis on the fireline today, most firefighters 

make their best estimate of that fire behavior, thus employing substantive 

rationality within the process of instrumental rationality. The people who 

successfully use RPDM or bricolage successfully are usually accomplished in 

Analysis of the Risk Management Decisionmaking Processes and the Decision Support Systems in the Wildland Fire Agencies Withen
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 641

their field. Thus, they have employed instrumental rationality many times, 

and it is based on this unquantifiable body of expertise that they make their 

substantive decisions.

In figure 1, Legarza (2006) exemplifies the situations firefighters find 

themselves in. The DSSs, SOGs, and instrumental rationality in the form of 

the risk management process, operate best in the zone of optimal performance, 

that is, between the dashed red lines. The ultimate goal of the DSSs, SOGs, 

and instrumental rationality must be to expand the area between the dashed 

red lines. But as Legarza recognizes, because firefighters are operating in a 

turbulent, high tempo environment, even if firefighters do everything right, 

they may find themselves outside of the zone of optimal performance. Then 

one has the option: (1) use one’s training and education to gain the awareness or choices one needs to return to normal operations, or (2) operate on 

instinct, using one’s best estimate, possibly using RPDM or bricolage, to 

choose a path of operation. Killion (2000) notes that RPDM can also be 

used by those with a large reservoir of experience to make decisions more 

quickly than would be possible using what he calls multiattribute decisionmaking, which is a form of instrumental decisionmaking similar to the risk 

management process.

Decision

Making

Time/Experience

 Unexpected elements

Unexpected elements

Analytical

Intuitive

ICT5 ICT4 ICT3 ICT2 ICT1

The Zone of Optimal Performance

SA

 SA

Figure 1—The Zone of Optimal Performance occurs in the middle of each circle, respectively, 

inside of the dashed red lines. @Legarza

Analysis of the Risk Management Decisionmaking Processes and the Decision Support Systems in the Wildland Fire Agencies Withen
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 642

The importance of understanding these varied decisionmaking processes 

is that in the development of DSSs, decisionmaking skills, plans of action, 

and so forth, it is useful for actors to understand how and why they are making particular decisions based on particular decisionmaking schemas. As we 

train and educate ourselves and others in fireline decisionmaking, we must 

realize that we can move ahead on several fronts. We can move ahead in the 

integration of the DSSs, and we can move ahead in advancing the skills of 

RPDM and bricolage.

References

Gerth, H.H.; Mills, C. Wright. 1948. From Max Weber: Essays in Sociology. 

London: Routledge.

Killion, Thomas H. 2000. Military Review. November-December: 66-70.

Klein, G.A. 1993. A Recognition-Primed Decision (RPD) Model of Rapid Decision 

making. In: Decision Making in Action: Models and Methods. Norwood, NJ: 

Ablex Publishing.

Legarza, Shawna. 2006. The Zone of Optimal Performance. Unpublished 

manuscript.

TriData Corporation. 1998. Wildland Firefighter Safety Awareness Study: Phase 

III—Implementing Cultural Changes for Safety. Arlington, VA: TriData.

U.S. Department of Agriculture. 2006. Incident Response Pocket Guide. PMS #461, 

NFES #1077. Boise, ID: National Interagency Fire Center, National Wildfire 

Coordinating Group.

Vergari, Greg. 2005. Back to Basics for Fire Program Managers. Eighth International 

Wildland Fire Safety Summit, April 26-28, 2005, Missoula, MT.

Weick, K. 1993. The Collapse of Sensemaking in Organizations: The Mann Gulch 

Disaster. Ithaca, NY: Administrative Science Quarterly. 38: 628-652.

Weick, K.; Sutcliffe, K. 2001. Managing the Unexpected. San Francisco, CA: JosseyBass, Inc.

Withen, Patrick. 2005. TEFF: The Ten Essential Factors In Firefighting: Lookouts, 

Communications, Escape Routes, Safety Zones, Firefighting Resources, Fire 

Behavior, Fire Status, Fuel Type, Weather, Terrain (LCES FFFF WT). Eighth 

International Wildland Fire Safety Summit, April 26-28, 2005, Missoula, MT. 

http://www.myfirecommunity.net/documents/Withen.pdf

Analysis of the Risk Management Decisionmaking Processes and the Decision Support Systems in the Wildland Fire Agencies Withen
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 643

Appendix A: TEFF—The Ten Essential Factors 

in Firefighting

This table shows the breakout of the 59 firefighting guidelines within 

TEFF.

TEFF Number of

firefighting guidelines

included

1. Lookouts 5

2. Communications 9

3, Escape Routes 4

4. Safety Zones 4

5. Fire Resources 7

6. Fire Behavior 6

7. Fire Status 10

8. Fuel Type 7

9. Weather 8

10. Terrain 10

Total 70 (some guidelines

appear in more than

one TEFF)

Average 7 per TEFF

Definitions of the factors listed above:

TEFF 1: Sufficient Lookouts are in place given the hazard assessment.

TEFF 2: Sufficient Communications are in place: generally communications 

are needed w/ lookout(s), crews, supervisors, & adjoining forces, 

but there may be other critical links.

TEFF 3: A suitable Escape Route(s) is known to all.

TEFF 4: A suitable Safety Zone(s) is known to all. The Safety Zone may 

be to exit the fire area.

TEFF 5: While more Firefighting Resources may be on order, Firefighting 

Resources are sufficient for firefighters to remain safe & to successfully implement current tactics.

 Factors over which you have total or limited control.

 Factors over which you have no control, but must monitor.

TEFF 6: Fire Behavior is understood in light of Weather, Terrain, & Fuel 

Type. Fire behavior is not doing anything unexpected, thus Firefighting Resources’ tactics are succeeding as expected.

Analysis of the Risk Management Decisionmaking Processes and the Decision Support Systems in the Wildland Fire Agencies Withen
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 644

TEFF 7: The Status or Scope of the Fire is known to Firefighters, & current 

tactics are successful in light of amount of Firefighting Resources 

& to keep current Firefighters safe.

TEFF 8: Fuel Type is understood, and is exhibiting expected Fire Behavior

TEFF 9: The Weather is doing what is expected; no RH or wind trigger 

points have been crossed.

TEFF 10: The Terrain is not causing unexpected fire behavior, creating a 

hazard for Firefighting Resources, or compromising the Escape 

Route.

Side 1 of the TEFF card, which provides a matrix:

Side 2 of the TEFF card, which provides a brief overview of Fire Suppression Tactics 

trisecting them into Engagement, Modification, and Disengagement:

Analysis of the Risk Management Decisionmaking Processes and the Decision Support Systems in the Wildland Fire Agencies Withen
Appendix
This 

page is 

blank 

on purpose
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 647

Appendix: Presentations for Which Papers 

Were Not Submitted for These Proceedings

Invited Speakers

Susan G. Conard, USDA Forest Service, Washington Office

Joe Ferguson, USDA Forest Service, Washington Office

Michael C. Long, State Forester, Florida

William F. Paleck, USDI National Park Service, retired

Sally Shaver, U.S. Environmental Protection Agency, North Carolina

Karl E. Weick, Professor of Organizational Behavior and Psychology, University 

of Michigan, Ann Arbor

Panels

Extreme Fire Behavior—What is It?

Moderator: Robert Ziel, Michigan Department of Natural Resources

Panelists: Jack Cohen, Research Forester, USDA Forest Service Research

Mark Finney, Research Forester, USDA Forest Service Research

Brian Potter, Research Meteorologist, USDA Forest Service

John Saltenberger, Fire Weather Program Manager, Northwest 

Interagency Coordination Center

Tim Sexton, Wildland Fire Use Program Manager, USDA Forest 

Service

The Human Element in Forest Fire Operations: Thinking, Deciding, Acting, 

Learning

Moderator: Dave Thomas, Consultant, Renoveling; USDA Forest Service (retired)

Panelists: Riva Duncan, Deputy Forest Fire Management Officer, USDA 

Forest Service

Paula Nasiaka, Center Manager, Wildland Fire Lessons Learned 

Center

Larry Sutton, Fire Operations Risk Management Officer, USDA 

Forest Service

Jennifer Thackaberry Ziegler, Assistant Professor, Purdue University

Karol E. Weick, Professor, Organizational Behavior and Psychology, 

University of Michigan

Workshops

Fire Regime Condition Class: Concepts, Methods, and Applications

Stephen Barrett

WindWizard: A New Tool for Fire Management Decision Support

Rick Stratton, Jason Forthofer, Kyle Shannon

BehavePlus Fire Modeling System

Patricia L. Andrews, Tobin Kelley
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 648

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

Mapping Fire Regime Condition Class: Using LANDFIRE Data and the 

FRCC Map Tool

Wendel Hann, Morgan Pence

Use of FlamMap for Fire and Fuels Planning

Mark A. Finney, Rob Seli, Rick Stratton

FLAME, Fire Science for the Fireline

Jim Bishop

From Start to Finish: Creating an Effective Outreach Program for Educators

Christine Denny

FireFamily Plus

Larry Bradshaw, Stuart Brittain, Luck Shelvan

Advanced Meteorology for Fire Behavior Analysis

John Saltenberger

Smoke Monitoring Techniques

Andy Trent

Measuring Canopy Fuels and Assessing Crown Fire Potential

Joe Scott, Elizabeth Reinhardt

The Canadian Forest Fire Danger Rating System: Interpretation and 

Implementation Outside of Canada

Mike Wotton, Nathalie Lavole, Grant Pearce

A Suite of Fuel of Fuel Management Tools: Fuel Characteristic Classification 

System, Natural Fuels Photo Series, and Consume 3.0

Roger D. Ottmar, Susan J. Prichard, Clint S. Wright, Bob Vihnanek

BlueSkyRains

Miriam Rorig, Sim Larkin, Robert Solomon

Oral Presentations

Evaluating the Benefits and Costs of Project Level Fuel Treatments

Karen Abt, Alan A. Ager, Robert J. Haggett Jr., Eric D. Twonbly, 

Jeffrey P. Prestemon, Mark Finney, Aaron M. Ortego

A Fire Spread Model for Calculating Emissions Productions from Prescribed 

Burns

Gary Achtemeier

Evaluation of Methods to Represent Local Winds in Mountainous Areas 

of Eastern United States in Support of Prescribed Burning and Wildfire 

Suppression

Gary Achtemeier, Warren E. Hellman, Scott Goodrick, 

Joseph J. Charney, Bret Butler, Xinndi Bian

Field Validation of a Model to Predict the Movement of Smoke Along the 

Ground at Night

Gary Achtemeier
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 649

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

On the Roles of Prescribed Burn “Engineering” on Degraded Air Quality Over 

an Urban Area of 18 March 2006—Experimental Results with Daysmoke

Gary Achhtemeier, Scott A. Goodrick, Yongqiang Liu, William Jackson

Fire Emissions, Regional Haze, and Air Quality in the Upper Midwest

Gordon Andersson

Examination of Fire Behavior and Fire Danger Rating Systems in the Context 

of a Temporal-Spatial Framework

Patricia L. Andrews

National Wildland Fire Weather Needs Assessment

David Andrus, Samuel P. Williamson, Robert J. Dumont

Smoke on the Horizon—The Future of the Partnership

Chadwick Avery

Getting Fire on the Ground

J.J. Bachant-Brown

Factors Influencing Ponderosa Pine Seedling and Sapling Mortality after 

Prescribed Burns in the Black Hills, South Dakota

Mick Battaglia, Frederick W. Smith, Wayne D. Shepperd

Prescribed Burning Impacts on Urban Air Quality

Karsten Baumann, Eric S. Edgerton, Sangill Lee, James J. Schauer, 

Donald R. Blake, Luke P. Naeher, John J. Brent

Assessing High Reliability Practices in the Wildland Fire Community

Anne Black, Michhelle Barton, Deirdre Dether

Prescribed Fire Lessons Learned Center

Anne Black, Paula Nasiatka, Deirdre Dether

The Nez Perce Tribe Go/No-Go Decisionmaking Process

Andrea Boyer

FireFamily Plus, Version 4

Larry Bradshaw

What is a Fond? And Why Does it Burn GREEN!

James Brenner, Tim Elder, Deborah Hanley

A Climatology of North American Fire Danger from the North American 

Regional Reanalysis Dataset

Timothy Brown, Matt Jolly, Beth L. Hall

Verification of North Carolina Mixing Height Forecasts for Smoke Management

Timothy Brown, Gary Curcio, Beth L. Hall, Doug Pibal

What is the Value of Climate Information for Smoke Management?

Timothy Brown, Pete Lahm

Future Range of Variability: Landscapes as Probabilities

Teresa Burcsu, Miles A. Hemstrom, James Merzenich, Allison Reger

External Human Factors in Incident Management Team Decisionmaking

David Calkin, Janie C. Canton-Thompson, Krista M. Gebert,

Brooke Y. Thompson, Geoffrey H. Donovan, J. Greg Jones
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 650

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

Improving Calculation of Values at Risk for Burned Area Emergency Response

David Calkin, Kevin Hyde, Peter Robichaud

Wildland Fire Decision Support System and Rapid Assessment of Values at Risk

David Calkin, John Szymoniak, Mark Finney, Kevin Hyde

Field Evaluation of a Next-Generation Dead Fuel Moisture Model versus 

Currently Used Dead Fuel Moisture Algorithms in the National Fire 

Danger Rating System

J.D. Carlson, Larry S. Bradshaw, Ralph N. Nelson Jr., Randall R. Bensch,

Rafal Jabrzemski

OK-FIRE: A Weather-Based Decision Support system for Wildland Fire 

Managers in Oklahoma

J.D. Carlson, Terry G. Bidwell

The Potential Impact of Smoke from Prescribed Burns in Southern Missouri

Joseph Charney, Xindi Bian, Warren E. Heilman, Lesley A. Fusina

A Sub-Grid Mixture-Fraction-Based Thermodynamic Equilibrium Model for 

Gas Phase Combustion in FIRETEC: Development and Results

Michael M. Clark, Rodman R. Linn

Fire Incident Mapping Tools

Tim Clark

Plume Energetics and Dynamics of Grass Fires

Craig Clements, Zhong Shiyuan

Examining Crown Fire Behavior with Implication for Modeling

Jack D. Cohen, Mark A. Finney, Kara M. Yedinak

The Home Ignition Zone Principally Determines the Potential for WildlandUrban Interface Fire Disasters

Jack Cohen

Laying the Foundation for Success

Vernon Compton

Does Fire Behavior Change with Stand Age in Black Spruce Forest of Interior 

Alaska?

James Cronan, Ann. E. Camp

Investigating the Dynamics of Fire Behavior in Grasslands Using a PhysicsBased Fire Model

Phillip Cunningham, Rodman R. Linn

Modeling Buoyant Plumes from Wildland Fires: Fundamentals and Applications

Phillip Cunningham, Scott L. Goodrick, Michael J. Reeder

Using a PC Based Smoke Plume Dispersion Model (VSMOKE) and the 

National Fire Danger Rating System in Support of Go-No GO Decisions 

for Prescribed Fire

Gary Curcio

Development and Implementation of a National Fuel Moisture Database

Edward Delgado, Shelby I. Sharples, Annie Brown, Chuck Buescher
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 651

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

Facts About Fire-Increasing Public Awareness and Support of Prescribed 

Burning through the Fire in Florida’s Ecosystems Program

Christine Denny, James Brenner

Process Modeling of First Order Fire Effects: Science and Application

Matthew Dickinson, Bret Butler, William Massman, Elizabeth Reinhardt,

Anthony Bova, Todd Engstrom, Kathleen Kavanagh, Kirsten Stephen,

Alistair Smith, Robert Kremmens

Tree Heating in Surface Fires and the Injury That Results

Matthew B. Dickinson, Bret W. Butler, Anthony S. Bova

Ignition and Flame Travel on Realistic Building and Landscape Objects in 

Changing Environments

Mark A. Dietenberger

Fire History of Red Pine-Dominated Forests of Seney Wildlife Refuge, Upper 

Michigan

Igor Drobyshev, Charles P. Goebel, David Hix, Marie Semko-Duncan,

Greg Corace III

Experiments on Fire Spread in Discontinuous Fuelbeds

M.A. Finney, J.D. Cohen, I.C. Grenfell, K.M. Yedinak

Approving Prescribed Burns in Arizona

Mark Fitch

Applications of a Fire Acceleration Model for Firefighters Safety

Jo Ann Fites-Kaufman

In Situ Fire Wildland Fire Behavior, Fuels and Immediate Postfire Effects

Jo Ann Fites-Kaufman, Alicia Reiner

Collaborative Process for Priorization of Hazard Fuel Reduction Needs

Eric Geisler, James Twoteeth, Frank Roberts

Effects of Single Season Mechanical and Prescribed Fire Treatments in 

Restoring Inland Pitch Pine Scrub Oak Barrens

Neil Gifford, G. Thoman Dooley, Jason Bried, Craig Kostrzeski

A Prototype Fire Activity Database for Tracking Prescribed Fire Emissions

Scott Goodrick

Smoke Screening with Google Earth

Scott Goodrick

Silvicultural Smoke Management and the “Go/No Go” Decision Process 

in Washington

David Grant

Cleared for Take-Off: Smoke Management and the Military Mission

Sonny Green, Marty Martin

The History and Role of Prescribed Fire Councils: a Success Story

Lane Greene

Smoldering Combustion of Biomass Fires—Modeling and Experimental Results

Carlos Gurgel Veras
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 652

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

Institutional Factors Affecting the Use of Decision Support Tools and Ecological Data in Fuels Management a the Project Planning Level in the 

U.S. Forest Service

Erick Hakanson

Fire Situation Reporting

Deb Hamill, Marco Perea, Charlie Leonard

Florida Fire Season 2006

Deborah Hanley

Comparison of Fire Regime Condition Class Results

Wendel Hann, James Smith, Ted Tower, Douglas W. Havlina,

James Smith

A Suite of Multiscale Remote Sensing Approaches for Measuring Thermal 

Characteristics and Their Relationships to Associated First-Order Fire 

Effects

Colin Hardy

National Seasonal Assessment Workshops

Robyn Heffernan

Evaluating Wildland Fire Hazard and Prioritizing Vegetation and Fuels 

Treatments—A Multiscale Decision Support System

Paul Hessburg, Keith M. Reynolds, Robert E. Keane

Assessing the Role of the Fine Scale Heterogeneity in Fuels and Fire Behavior 

in a Low-Intensity Surface Fire Regime

John Hiers, E. Louise Loudermilk, Wendell P. Cropper Jr.,

K. Clint Slatton, Joseph J. O’Brienn, Robert J. Mitchell

Where to Burn? A GIS-Based Prioritization Tool

Kevin Hiers

Application of the Ventilation Index in Smoke Management

Jeanne L. Hoadley

Application of Online Fire Knowledge Management Tools in the United States

William Hubbard, Greg Gollberg, Ken Outcalt, Jennifer Pollock

The Future of Fire Regime Condition Class—Where We Have Been, Where 

We Need to Go

Menakis James, Wendal Hann

The Influence of Live Fuels on the Rothermel Surface Fire Spread Model

William Jolly

Regional Changes in Effective Growing Season Lengths Under Future 

Climate Scenarios

William Jolly

A Cost Effectiveness Analysis of Preventative Mitigation Options for Wildland 

Urban Interface Homes Threatened by Wildfire

J. Greg Jones, Keith D. Stockman, David E. Calkin
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 653

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

Analysis of Fuel Loading-Variability and Burn Severity within the Landscape-Scale of Grand Teton National Park-Bridger Teton National Forest, 

Wyoming: Integration of Field Data, Geospatial Information, and Geospatial Statistics

Mohammed Kalkhan, Karl E. Brown

How to Keep Plantations from Burning: the Efficacy of Fuels Reduction 

Treatments in a Sierra Nevada Pine Plantation

Leda Kobziar, Scott L. Stephens, Joe R. McBride

Different Species, Different Patterns: the Predictors of First-Order Fire 

Mortality in Seven Trees of the Sierra Nevada Mixed-Conifer Forest

Leda N. Kobziar, Jason Moghaddas, Scott L. Stephens

Fire Spread Model Applied to Prescribed Burns in Chaparral

Eummo Koo, Patrick J. Pagni, Scott L. Stephens, David R. Weise

The Studies to Support a Fire Danger Rating System in Turkey

Omer Kucuk, Bulent Saglam, Bahar Dinc Durmaz, Ismail Baysal

Fire History and Vegetation Change in a Network of Pine-Oak Stands in the 

Central Appalacian Mountains

Charles Lafon

The BlueSkyRAINS West Project: Lessons Learned and Plans for BlueSky

Narasimhan Larkin, Susan O’Neill, Robert Solomonn, Sean Raffuse,

Dana Sullivan

Verification of the Lightning Ignition Efficiency Map Using Lightning Discharge and Fire Occurrence Data from the Northern Rockies in 2003

Don Latham, Paul Sopko

Simulated Fire Behavior in Shrubs and Canopies Using HIGR AD/

FIRETEC

Rodman R. Linn, J. Winterkamp, F. Pimont, J. Dupuy, D. Weise,

J. Sauer, J. Canfield

Simulation and Real-Time Prediction of Air Quality Effects of Prescribed 

Burns in Florida with SHRMC-45

Yongqiang Liu, Gary Achtemeier, Scott Goodrick

Developing a New Monitoring Tool: the Integration of FEAT and FIREMON

Duncan Lutes, Nate Benson, Martha Isbister, Kim Johnson,

Austin Streetman, Carter Barnes, John Caratti

The Montana/Idaho Prescribed Burn Reporting System

Lee Macholz, Carol A. Seielstad, Eric Tangedahl, Haiiying Liang,

Limel Piao, Lloyd Queen, Don Helmbrechht, Dave Grace

Marginal Burning in Chaparral—Experiments and Models

Shandar Mahalingam, David R. Weise

On the Size and Mass Distribution of Firebrands Generated from Burning 

Korean Pine Trees

Samuel Manzello, Aleander Maranghides, William Mell,

Yoshihiko Hayyashi, Daisaku Nii
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 654

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

Firebrand Generation from Vegetation and the Ignitability of Fuel Beds of 

Firebrands

Samuel L. Manzello

Heat Release Rate and Mass Loss Experiments of Douglas Firs

Alexander Maranghides, William Mell, Samuel Manzello

Assessing the Impact of Fuel, Weather, and Fire Suppression Effort on the 

Area Burning by Lightning-Caused Forest Fires in Ontario

David Martell, Hua Sun

Daily Regional Fire Weather and Fire Behavior Graphical Products

Chuck Maxwell, Timothy C. Stubbs

Agricultural Burning in the Southeastern United States Detected by MODIS

Jessica McCarty, Chris O. Justice, Stefania Korontzl

Using Spatial Technologies to Assess Historical Fire Data Trends in Florida’s 

Wetland Ecosystems for Improved Fire Planning and Response

Susan McLellan, James Brenner, Jeff Vowell, Karen L. Cummins

Numerical Simulations of Burning Vegetation: Laboratory Experiments and 

Crown Fires

William Mell, Bret Butler, Alexander Maranghides, Samuel Manzello,

Shankar Mahalingham

Firespread in the Wildland Urban Interface

William Mell, Ron Rehm, Alexander Maranghides, Samuel Manzello

Creating a Coalition of Prescribed Fire Councils: Taking It Up a Level

Mark Melvin, Pete Kubiak

Building a National Coalition of Prescribed Fire Councils

Mark Melvin, Pete Kubiak, Lane Green

WUI Microenvironment

Robert Meroney

Communicating the Wildland Fire Use Message

Punky Moore

Spatial Modeling of Fire in Shrublands with HFire

Max Moritz, P.E. Dennison

Interagency Prescribed Fire Planning and Implementation Reference Guide

David Mueller, Kim Van Hemelryck, Dan Buckley, Ed Bratcher, 

Gene Lonning, Dan Severson

Fire Regimes and Management Strategies for Restoring Cascadian Whitebark 

Pine Ecosystems

Michael Murray

Organizational Tips and Tactics for Moving Forward in Wildland Fire Use

Paula Nasiatka, Jonetta Holt

Innovations in Fuels Management, Demonstrating Success in Treating a 

Serious Threat of Wildfire in Northern Minnesota

Dennis Neitzke, Ellen Bogardus-Szymaniak, Patricia J. Johnson
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 655

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

US FS RMC: Real-Time Fire-Weather Intelligence and Smoke Forecast for 

the West

Ned Nikolov, Karl Zeller

Bark, Duff, and Soil Temperatures in Smoldering Duff Fires

Joseph J. O’Brien, J. Moran Varner, J. Kevin Hiers, F.E. Putz, 

Doria Gordon

Physiological Effects of Duff Fires on Longleaf Pines

Joseph J. O’Brien, J. Kevin Hiers, Kathryn Mordecai

Predictive Services Round Table Discussion

Rick Ochoa, Brenda L. Belongie, Deb Hamill, Tom A. Wordell

Litter and Duff Depths and Bulk Densities in the Southern United States

Roger Ottmar, Anne G. Andreu, Robert E. Vihhnanek

Fuels Management Options for the Longleaf Pine Ecosystem of the Gulf 

Coastal Plain

Kenneth Outcalt, Dale G. Brockway

Wildfire Mapping in the 21st Century

Tom Patterson

Fuel Assessment Techniques: Technology Transfer by the National Interagency 

Fuels Technology Team

Morgan Pence, Kathy H. Schon

Measurements of Mass and Temperature During Ignition of Fresh Foliage

Brent M. Pickett, Thomas H. Fletcher, David R. Weise

The Fire Strike Team Concept—Getting Fire on the Ground

Zachary Prusak

Lessons Learned Information Sharing

John Rabin, Jennifer Smither

Integration and Reconciliation of Satellite Detected and Incident Command 

Reported Wildfire Information in the BlueSky Smoke Modeling 

Framework

Sean Raffuse, Narasimhan K. Larken, Robert Solomon,

Dana C. Sullivan, Lyle R. Chinkin, Lawrence Friedl, Amber Soja

Updating Wildland Fire Susceptibility Index Inputs I: Change Detection and 

Classification and Regression Tree Analysis to Develop Surface Fire Fuel 

Models and Canopy Cover

Andrew Ralowicz, Jim Brenner, Sue McLellan, Don Charlton

Fire Emissions and Regional Modeling—Specific Case Studies from the 

WRAPs Regional Haze Modeling. How Small Fires and Plume Rise 

Techniques Influence Near- and Far-Field Model Results

David Randall, Mark J. Fitch

WRAP Fire Emission Tracking System

David Randall, Mark J. Fitch
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 656

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

WRAP Seasonal Suites of Emission Reduction Techniques Developed for 

Implementation of Smoke Management Programs and Regional Modeling Applications (Projections)

David Randall, Mark J. Fitch

Salvage Logging and Fuel Treatment

Elizabeth Reinhardt

Expediting the Community Wildfire Protection Plan

Cheryl Renner, Terry K. Haines, Margaret A. Reams

Forecasting Significant Fire Potential

Tom Rolinski

The Hazard Mapping System—Fire and Smoke Detection Utilizing 

Environmental Satellites

Mark Ruminski

LANDFIRE: Providing Geospatial Products in Support of Fire and Fuel 

Management

Kevin Ryan, Matthew G. Rollins, Matthew C. Reeves

Effects of Fire on Conifer Survival: Practical Considerations for Fuels 

Treatments Projects

Kevin Ryan

Statewide Mapping of Fire Hazard Zones in California

David Sapsis, Mark Rosenberg, James G. Spero, Elsa K. Hucks

Regional Monitoring of Key Fire Weather Parameters

Kevin Scasny

Nomographs for Predicting Surface Fire Behavior without a Computer

Joe Scott

Developing Multitiered Portable Wireless Weather Monitoring Networks for 

Wildland Fire

Carl Seielstad, Richard Han, Regan Zane, Carl Hartung,

Saxon Holbrook, Lloyd P. Queenn, Jim Riddering

Using Laser Altimetry-Derived Surface Roughness to Predict Coarse Woody 

Debris Volume in Mixed Conifer Forests

Carl Seielstad, Eric Rowell

Communication Strategies for Fire Management: Effective Citizen-Agency 

Partnerships

Bruce Shindler, Ryan Gordon

LANDFIRE East of the Rockies: Challenges and Responses

James Smith, Zhi-Liang Zhu, Kevin Ryan, Wendel Hann

How Well Does Satellite Data Quantify Fire in the United States?

Amber Soja, Jassim A. Al-Saadl, Louis Gigliio, R. Brad Pierce,

Dave Randall, Sean Raffuse, James Szykman

Evaluation of the BlueSky smoke Modeling Framework

Robert Solomon, Susan M. O’Neill, Sim Larkin
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 657

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

A Fuel Hazard Model for Forests of the Missouri Ozark and Illinois and 

Indiana Interior Lowland Plateau Ecoregions

Michael Stambaugh, Richard P. Guyette, Daniel C. Dey

Planning for Success: Leveled Supply of Woody Biomass with Coordinated 

Resource Offering Protocol and Strategic Placement of Treatments

Sue Stewart, Edmund A. Gee

Applying Remote Mobile Weather Observations to Tactical Operations in 

the Wildland Fire Urban Interface

Carter Stone, Deborah E. Hanley

Current Status of the Ozone National Ambient Air Quality Standard Review and Potential Revision to the Air Quality Index for the New PM2.5 

Standard

Susan Stone

Burning and Mulching to Reduce High Fuel Loading in Beetle-Killed Stands: 

Effects of Fuel Reduction Treatments on Ectomycorrhizal Inoculum 

Potential

Aaron Stottlemyer, G. Geoff Wang, Christina E. Wells,

Thomas A. Waldrop, Mac A. Callaham

Current Operations and Ongoing Development of the BlueSky System of 

Smoke Impact Predictions

Dana Sullivan, Narasimhan K. Larkin, Robert Solomon, Sean M. Raffuse,

Lyle R. Chinkin, Christian D. Ovard, Lawrence Friedl

An Investigation of Unsteady Effects on the Dynamics of Crown Fire Initiation

Watcharapong Tachajapong, David R. Weise

Sparks That Started the Fire

Gary Taylor

Capturing the Deep Smarts of Fire Behavior and Fuels Experts

Dave Thomas, Dorothy A. Leonard, Carol Miller, Paula Nasiatka

Effect of Fire on Ant Assemblages in Central Semi-Arid Argentina

F. Rodrigo Tizon, Daniel V. Pelaez, Roberto M. Boo, Omar R. Elia,

Mirta D. Mayor, Dolores Rodriguez-Rey

Comparison of Real-Time Smoke Particulate Monitors

Andy Trent

Advancing the Understanding of Postfire Tree Death

J. Morgan Varner

Identification of User Errors in Modeling Fire Behavior and Effects

J. Morgan Varner, Christopher R. Keyes

The ClearSky Field-Burning Decision Support System

Joseph Vaugh, Kyle Heitkamp, Rahul Jain Rahhul, Charleston Ramos,

Brian K. Lamb

The National Fire and Fire Surrogate Study in Eastern Hardwoods—Early 

Results and Future Challenges

Tom Waldrop, Daniel A. Yaussy
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 658

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

Firebrand Review

David Weise, Eunmo Koo, Patrick J. Pagnil, David R. Weise

“Ecosystem Support Team”—Kindling the Flame

Brett Williams

Forecasting Fire Danger Indices

Thomas Wordell, Timothy J. Brown

I Need It and I Need It Now: Improving Predictive Services Outreach to 

Fire Managers

Thomas Wordell, Heidi Bigler-Cole, Patricia L. Winter

Meeting Predictive Services User Needs: An Assessment Survey

Thomas Wordell, Patricia L. Winter, Heidi Bigler-Cole

Relationships Influencing Forest Fire Occurrence in Canada

Mike Wotton, David L. Martell

Investigation of the Laminar Flame Assumption

Kara Yekinak, Jason M. Forthofer, Jack D. Cohen, Mark A. Finney

Testing and Classification of Individual Plants for Fire Behavior

Wayne C. Zipperer, Robert H. White

Poster Presentations

Landscape-Level Assessment of Prescribed Fire Effects on Oak Regeneration

Heather Alexander, Mary A. Arthur, David Loftis, Stephanie R. Green

Use of the BehavePlus Fire Modeling System for Prescribed Fire Planning

Patricia Andrews, Tobin M. Kelley

BehavePlus Fire Modeling System, Version 4.0 Overview

Patricia Andrews, Collin D. Bevins

Modeling Temporal Dynamics to Fuels Management Treatments

John Bailey, Masashi Konoshima

The Development of a Grassland Fire Breaching Model

Gregory Baxter, Martin E. Alexander, Rex Hsieh

Fire Effects on Chaparral-Associated Mammals: Responses to Fire Severity 

and Distance from Unburned Edge

Jan L. Beyers, Jay E. Diffendorfer, Wayne D. Spenser, Paul Schuette,

Scott B. Tremor, Genie M. Fleming, Paul Schuette, Julio Angel Sato-Centeno

Quantifying Burn Severity in Alaska’s Boreal Black Spruce Forest: Comparing 

Composite Burn Index and a Destructive Assessment Method

Leslie Boby

Comparison of Live Fuel Moisture Sampling Methods for Big Sagebrush in 

Utah

Annie Brown, Phillip N. Omi, Jolie Pollet
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 659

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

Southeast Fire Ecology Partnership

David Brownlie, Caroline L. Noble, Bruce Davenport, Paula Seamon,

Kevin Robertson, Ronald E. Masters

Determining Alaska Values for Denali Wildland Fire Use Reference Guide

Brad Cella, Dan Warthen, Larry L. Weddle, Brian Sorbel

Historical Fire Regimes and Fire Climatology in Utah Forests

Linda Chappell, Peter M. Brown, Emily K. Heyyerdahl, Stanley Kitchen,

Marc H. Weber

Tools for Estimating Contributions of Wildland and Prescribed Fires to Air 

Quality in the Southern Sierra Nevada

Ricardo Cisneros, Haiganouch Preisler

An Index of Crown Fire Potential Based on LIDAR Measurements

Kenneth Clark, John Hom, Nicholas Skowronski, Steve Van Tuyl, 

Jason Cole, Matt Patterson, Matthew Duveneck

Characterizing Moisture Dynamics for Assessing Fuel Availability in North 

Carolina Vegetation Communities

Gary M. Curcio, Roberta A. Bartlette, James J. Reardon

Retrospective Fire Modeling to Quantify the Cumulative Effects of Suppression

Brett Davis, Carol Miller

Using Fire to Restore Pine/Hardwood Ecosystems Severely Impacted by 

Southern Pine Beetle in the Southern Appalachians

Katherine Elliott, James M. Vose, Jennifer D. Knoepp, Barton D. Clinton

Wildland/Urban Interface of the Ancients—A Strategy for Reducing Fire 

Risk in the Canyons of the Ancients National Monument

Allen Farnsworth, Linda C. Farnsworth

The Fire Research and Management Exchange System (FLAMES): Technology 

in Support of Wildland Fire Research and Management

Greg Gollberg, Jean Freeney

Reducing Wildfire Risk in the Wildland-Urban Interface: State, County, 

and Municipal Governments’ Initiatives to Protect Human Assets and 

Natural Resource Values

Terry Haines, Cheryl R. Renner, Margaret Reams

Fire Behavior Analysis to Evaluate the Clearance Requirements Around 

Structures

Carol Henson, David D. Kerr

The Effects of Architecture and Loading on Timelags in Longleaf Pine 

Needles

Kevin Hiers, Ralph M. Nelson

Integrating Fire Weather, Fuel Mapping and Atmospheric Science Research 

in New Jersey: National Fire Plan and Eastern LANDFIRE Prototype

John Hom, Kenneth Clark, Steve Van Tuyl, Jason Cole, Yude Pan
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 660

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

Parcel-Based Fire Hazard Rating with Hyperspectral Imagery

Tonya L. Hoover, Brian Collins, Richard Burch, Carol Rick,

Chris Hallford

Relationship of Prescribed Burning and Wildfire Occurrence at Fort Benning 

1985-2006

Thomas F. Hutcherson, Stephen J. Hudson, Jack M. Greenlee

Fire Fighter Math

Daniel M. Jimenez, Brad J. Monnahan

Optimizing Spatial and Temporal Treatments to Maintain Effective Fire and 

Nonfire Fuels Treatments at Landscape Scales

Greg Jones, Woodam Chung, Janet R Sullivan, Kurt J. Krueger

Fuels Management and Structural Complexity: Silvicultural Alternatives for 

Discordant Forest Objectives

Christoper Keyes, J. Morgan Varner

Efficient Landscape Fire Risk Management: A Spatial Endogenous Risk 

Framework

Masashi Konoshima, Heidi J. Albers, John D. Bailey

Validation of the BlueSky Smoke Modeling Framework: Field Campaigns

Candace Krull, Miriam Rorig, Robert Solomon, Narasimhan K. Larkin

Prescribed Fire Notification in Urban Palm Beach County, Florida

Kraig Krum

The Fire Modeling Institute—Connecting Management and Research

Laurie Kurthh, Duncan Lutes, Jim Menakis, Jane Kapler Smith, 

Don Helmbrecht

Sensitivity of CMAQ-Daysmoke Simulations to Smoke Properties and 

Ambient Conditions

Yongqiang Liu, Gary L. Achtemeier, Scott Goodrick, William A. Jackson

Seasonal Response of Elk to Forest Fuels Reduction

Ryan Long, Janet L. Rachlow, John G. Kie

Predictive Services Weather Support to Prescribed Fire and Wildland Fire 

Use Fire Operations

Stephen Marien, Brenda Belongie

Linking State Prescribed Fire Councils as a Coalition: An Initiative to 

Promote Public Understanding of Rx Fire, and to Nationally Address 

Key Management, Policy, and Regulatory Issues

Mark A. Melvin, Johnny P. Stowe, Frank T. Cole, Lane Green,

Peter Kubiak

Climate Drivers of Regional Fire Years in the U.S. Northern Rockies: Past, 

Present, and Future

Carol Miller, Emily Heyerdahl

Evaluating Approaches to Mapping Burn Probability: Phase I

Carol Miller, Marc Parisien, Mark Finney, Alan Ager
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 661

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

Communicating the Wildland Fire Use Message

Punky Moore

The Savannah River Smoke Project—Measurements of Ground-Level PM2.5 

smoke Concentrations Downwind from Prescribed Burns

Luke P. Naeher, Gary L. Achtemeier, John Blake, John Pierce,

David MacIntosh

Light Versus Litter: What Limits Understory Diversity in Unburned Xeric 

Longleaf Stands

Joseph O’Brien, J. Kevin Hiers, Robert Mitchell, Rodney Will

Compiling, Synthesizing, and Analyzing Existing Boreal Forest Fire History 

Data in Alaska

Diana Olson, James B. Cronan, Ann E. Camp, Jennifer L. Allen, 

Donald McKenzie, Jennifer Hroback, Mike Tjoelker

The Agricultural Burn Outlook—Using Forecast Mesoscale Meteorological 

Parameters to Predict Windows of Opportunity for Agricultural Burn 

Operations

Susan O’Neill, Rob Wilson, Sue McCarthy, Ray Peterson

Fire Reintroduction in Fire Dependent Conifer Ecosystems—Unintended 

Pathological Consequences

William Otrosina, Pauline C. Spaine

Predicting and Mapping Canopy Parameters to Aid in Fire Management at 

the Savannah River Site

Bernard Parresol, Dan Shea, Roger Ottmar

Understory Vegetation Responses to Thinning and Prescribed Fire in Dry 

Coniferous Forests of Washington State

David W. Peterson, Erich K. Dodson, Richy J. Harrod

Fire Councils in Florida: A Success Story

Zachary Prusak

Wildland Fire Decision Support System in Aggtelek National Park, Hungary

Agoston Restas, Daniel Nagy, Sandor Rozsa

The Effects of Prescribed Fire on Amphibian and Reptile Biodiversity Patterns 

in Northern Longleaf Ecosystem Restoration

Leslie Rissler, Jimmy Stiles, Sierra Stiles

Prescribed Fires Affect Seedling Establishment and Survival in a Central 

Appalachian Forest

Jacob Royse, Mary A. Arthur, David Loftis

The National Wildfire Coordinating Group’s Fire Environment Working 

Team

Paul Schlobohm, Larry Bradshaw

Tribal Smoke Management in the Southwest

Ronald Sherron

LANDFIRE Vegetation Models: Description, Effects, and Applications

Jim Smith, Elena Contreras, Kori Blankenship, Darren Johnson,

Ayn Shlisky, Randy Swaty
USDA Forest Service Proceedings RMRS-P-46CD. 2007. 662

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

Continental and Landscape-Scale Approaches to Modeling Historic SpatioTemoral Variability in Fire Frequency from Fire Scar Data

Michael Stambaugh, Richard P. Guyette, Daniel C. Dey

The Southern Fire Portal: Science and Technology for the Fire Community

Christopher Szell, David Brownlie, Shelaine Curd-Hetrick, 

Greg Gollberg, Kevin Hiers, Bill Hubbard, Kevin Robertson

Comparison of Browns Planar Intercepts and Photo Series for Fuel Loading 

at Sagehen Creek Basin, Truckee, CA

Nicole Vaillant, Scott I. Stephens, John J. Battles

BlueSky/AIRPACT-3 Predictions of Wildfire Air Quality Impacts

Joseph Vaughan, Jack C. Chen, Brian K. Lamb, Jeremy C. Avise, 

Susann M. O’Neill, Sim Larkin, Robert Solomonn

Fuel Loading Across the Southern Appalachian Landscape

Tom Waldrop, Lucy Brudnak, Sandra Rideout-Hanzak

Modeling Fire-Induced Mortality of Longleaf Pine

Geoff Wang, Steve R. Wangen, William D. Boyer, E.D. Reinhardt, 

K.W. Outcalt, J.L. Walker, T.A. Waldrop

Delayed Mortality of Eastern Hardwoods—A Function of Fire Behavior, 

Site, or Pathology?

Dan Yaussy, Tom Waldrop

Plant Flammability Key for Creating a Firewise Plant List

Wayne C. Zipperer, L. Annie Hermansen-Baez, Alan J. Long, 

Marthha C. Monroe, Cotton K. Randall, Anna L. Behm
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