United States

Department of

Agriculture

Forest

Service

Northern

Research Station

General Technical

Report NRS-P-105

Moving from Status to Trends: 

Forest Inventory and Analysis 

(FIA) Symposium 2012
Abstract

These proceedings report invited presentations and contributions to the 2012 biennial 

Forest Inventory and Analysis (FIA) Symposium, which was hosted by the Research 

and Development branch of the U.S. Forest Service. As the only comprehensive and 

continuous census of the forests in the United States, FIA provides strategic information 

needed to evaluate sustainability of current forest management practices across all 

ownerships. Papers and abstracts included in the publication have been sorted into topic 

areas that match the sessions presented during the meeting. Symposium papers cover 

high priority and timely issue-based topics including climate change, wildlife, fire, 

bioenergy, geo-spatial extensions, monitoring over time, integrating remote sensing 

and GIS applications, statistical and related quantitative solutions to emerging needs, 

and many others.

Papers published in these proceedings were submitted by authors in electronic media. Some 

editing was done to ensure a consistent format. Authors are responsible for content and 

accuracy of their individual papers and the quality of illustrative materials and equations. The 

views and opinions expressed in this report are those of the presenters and authors and do not 

necessarily reflect the policies and opinions of the U.S. Department of Agriculture. 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.

Visit our homepage at: http://www.nrs.fs.fed.us/

Published by: For additional copies, contact:

USDA FOREST SERVICE USDA Forest Service

11 CAMPUS BLVD., SUITE 200 Publications Distribution

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November 2012 Fax: 740-368-0152

Manuscript received for publication August 2012

Cover design by Suzy Stephens, U.S. Forest Service
Moving from Status to Trends: 

Forest Inventory and Analysis 

(FIA) Symposium 2012

December 4-6, 2012

Baltimore, MD

United States Department of Agriculture

Northern Research Station

General Technical Report NRS-P-105

November 2012

Compiled by

Randall S. Morin

Greg C. Liknes


Preface

The eleventh biennial Forest Inventory and Analysis Symposium was held 

December 4-6, 2012, in Baltimore, MD. The symposium featured 128 presentations by 

scientists from across the United States and 10 foreign countries. In addition, the trend for 

participation by scientists from outside the formal Forest Inventory and Analysis Program 

continues to increase. The symposium organizers thank all participants and presenters and 

convey special thanks to those who submitted their papers for these proceedings.

Randall S. Morin

Greg C. Liknes

Steering Committee

Vicki Berrett

Renate Bush

Glenn Christensen

Sean Healey

Greg Liknes

Randall Morin

Chris Oswalt

Greg Reams

Paul Van Deusen
ii

Forest Products Industry Status and Trends: 

National and Regional Perspectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Effects of the “Great Recession” on the Forest Products Sector 

 in the Northern Region of the United States . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Christopher W. Woodall, William G. Luppold, Peter J. Ince, Ronald J. Piva, and Kenneth E. Skog

Impact of the Great Recession on the Forest Products Industry 

 in the Western United States . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Charles E. Keegan, Colin B. Sorenson, Todd A. Morgan, Jean M. Daniels, and Steven W. Hayes 

Results and Issues Encountered while Investigating Southern Forest Industry 

 and Market Responses to Changing Economic Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

Andrew J. Hartsell, Thomas J. Brandeis, James W. Bentley, Consuelo Brandeis, and Donald Hodges

Social Dimensions of Forest Inventory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Assessing Forest Ownership Dynamics in the United States: Methods and Challenges . . . . . 12

Brett J. Butler, Brenton J. Dickinson, and Jaketon H. Hewes

Using Multiple Research Methods to Understand Family Forest Owners . . . . . . . . . . . . . . . . . . 16

John Schelhas

Forest Values and the Impact of the Federal Estate Tax on Family Forests . . . . . . . . . . . . . . . . 20

Brenton J. Dickinson, Brett J. Butler, Michael A. Kilgore, Paul Catanzaro, John Greene, 

 Jaketon H. Hewes, David Kittredge, and Mary Tyrrell

Nontimber Forest Product Harvesting on Family Forests: 

 Results from the National Woodland Owner Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

Marla R. Emery, Zhao Ma, Stephanie Snyder, and Brett J. Butler

Assessing and Mitigating Denied Access on FIA Plots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

Brett J. Butler and Andrew Hill

Methods for Measuring and Assessing Landscape Change . . . . . . . . . . . 28

Comparison of Forest Area Data in the Chesapeake Bay Watershed . . . . . . . . . . . . . . . . . . . . . 29

Tonya W. Lister and Andrew J. Lister

Assessment of Land Use Change in the Coterminous United States and Alaska 

 for Global Assessment of Forest Loss Conducted by the Food and Agricultural 

 Organization of the United Nations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

Tanushree Biswas, Mike Walterman, Paul Maus, Kevin A. Megown, 

 Sean P. Healey, and C. Kenneth Brewer

Image-based Change Estimation for Land Cover and Land Use Monitoring . . . . . . . . . . . . . . . 46

Jeremy Webb, C. Kenneth Brewer, Nicholas Daniels, Chris Maderia, Randy Hamilton, 

 Mark Finco, Kevin A. Megown, and Andrew J. Lister

Forecasting Sustainability: Growth to Removals Ratio Dynamics . . . . . . . . . . . . . . . . . . . . . . . 54

Natasha A. James, Robert C. Abt, Karen L. Abt, Raymond M. Sheffield, and Fredrick W. Cubbage

Development of a Remote Sensing-based System for Monitoring Landscape Change . . . . . . . 59

C. Kenneth Brewer, Sean P. Healey, Brian Schwind, and Kevin A. Megown

Contents
iii

The North American Forest Dynamics Project: 

Moving from Status to Trends through Landsat Time Series . . . . . . . . . . 60

Attributing Causal Agents to Nationwide Maps of Forest Disturbance . . . . . . . . . . . . . . . . . . . . 61

Gretchen G. Moisen, Todd A. Schroeder, Karen Schleeweis, Chris Toney, 

 Warren B. Cohen, and Samuel N. Goward

The North American Forest Dynamics Study: 

 A Decade of Learning about U.S. Forest Disturbance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

Samuel N. Goward, Warren B. Cohen, Chengquan Huang, Jeffery Masek, 

 Gretchen G. Moisen, Rama Nemani, Karen Schleeweis, and Nancy Thomas

Validation Strategy for NAFD Landsat Time Series Forest Change Maps . . . . . . . . . . . . . . . . . 66

Warren B. Cohen, Steve Stehman, Susmita Sen, Peder Nelson, Chengquan Huang, 

 Karen Schleeweis, and Gretchen G. Moisen

Post-disturbance Recovery Analyzed from Remote Sensing Time Series and FIA Data . . . . . . 67

Jeffrey Masek, Khaldoun Rishmawi, and Samuel N. Goward

Cool Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

New Features Added to EVALIDator: Radio Estimation and County Choropleth Maps . . . . . . . 69

Patrick D. Miles and Mark H. Hansen

Using FIESTA, an R-based Tool for Analysts, 

 to Look at Temporal Trends in Forest Estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

Tracey S. Frescino, Paul L. Patterson, Elizabeth A. Freeman, and Gretchen G. Moisen

Area Change Reporting Using the Desktop FIADB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79

Patrick D. Miles and Mark H. Hansen

The Development of a Legacy FIA Report Simulator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83

Stephen P. Prisley, W. Brad Smith, and John W. Coulston

Nontimber Forest Products Output Information System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84

James Chamberlain, John Munsell, Stephen P. Prisley, and Tom Hammett

Connecting Forest Inventory with Climate Data . . . . . . . . . . . . . . . . . . . . . . 85

New Approaches to FIA Data for Understanding Distribution, Abundance, 

 and Response to Climate Change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86

Kai Zhu, Souparno Ghosh, Alan E. Gelfand, and James S. Clark

Investigating Forest Inventory and Analysis-collected Tree-ring Data from Utah 

 as a Proxy for Historical Climate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92

R. Justin DeRose, W. Shih-Yu (Simon) Wang, and John D. Shaw

Unlocking the Climate Riddle in Forested Ecosystems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99

Greg C. Liknes, Christopher W. Woodall, Brian F. Walters, and Sara A. Goeking

Adapt, Move, or Die: FIA Data in Assessments of Forest Tree Genetic Degradation Risk 

 from Climate Change and Other Threats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104

Kevin M. Potter, Barbara S. Crane, and William W. Hargrove

Tree Species Migration in the Pacific Coastal United States . . . . . . . . . . . . . . . . . . . . . . . . . . . 105

Heather Lintz, Andrew N. Gray, Andrew Yost, and Vincente Monleon
iv

Establishing Baselines and Projecting Trends . . . . . . . . . . . . . . . . . . . . . . 106

Emerald Ash Borer Modeling Methods for Future Forest Projections . . . . . . . . . . . . . . . . . . . 107

Ryan D. DeSantis, W. Keith Moser, Robert J. Huggett, Jr., 

 Ruhong Li, David N. Wear, and Patrick D. Miles

Expansion and Contraction Tension Zones in Western Pinon-Juniper Woodlands 

 under Projected Climate Change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115

Jacob Gibson, Gretchen G. Moisen, Tracey S. Frescino, and Thomas C. Edwards, Jr.

Projected Trends in Forest Habitat Classes 

 under Climate and Land-Use Change Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119

Brian G. Tavernia, Mark D. Nelson, Brian F. Walters, and Chris Toney

Site Productivity - Current Estimates, Change, and Possible Enhancements 

 for the Northern Research Station . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123

Scott A. Pugh

The Myth of a Business-As-Usual Baseline: A Review of Forest Inventory Projections . . . . . 129

Stephen P. Prisley, W. Brad Smith, and John W. Coulston

Mapping Forest Disturbance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130

Understanding Trends in Observations of Forest Disturbance 

 and Their Underlying Causal Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131

Karen Schleeweis, Samuel N. Goward, Chengquan Huang, Jeffrey Masek, and Gretchen G. Moisen

Improving Automated Disturbance Maps 

 Using Snow-covered Landsat Time Series Stacks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137

Kirk M. Stueve, Ian W. Housman, Patrick L. Zimmerman, Mark D. Nelson, Jeremy Webb, 

 Charles H. Perry, Robert A. Chastain, Dale D. Gormanson, Chengquan Huang, 

 Sean P. Healey, and Warren B. Cohen

Adding Value to the FIA Inventory: Combining FIA Data and Satellite Observations 

 to Estimate Forest Disturbance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143

Todd A. Schroeder, Gretchen G. Moisen, Sean P. Healey, and Warren B. Cohen

Species Diversity, Species Distributions, 

and Vegetation Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149

Using Forest Inventory Data Along with Spatial Lag and Spatial Error Regression 

 to Determine the Impact of Southern Pine Plantations on Species Diversity 

 and Richness in the Central Gulf Coastal Plain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150

Andrew J. Hartsell

Incorporating Evolutionary Relationships into Regional Assessments 

 of Forest Biodiversity across Forest Inventory and Analysis Plots . . . . . . . . . . . . . . . . . . . . 157

Kevin M. Potter and Christopher W. Woodall

Recent Trends in Maple and Oak-Hickory Distribution for the United States . . . . . . . . . . . . . 158

Michael Farrell

Analysis of Urban and Natural Forest Composition Across the United States . . . . . . . . . . . . 159

Mark J. Ambrose
v

Forest Carbon: Accounting and Science . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160

Technical Aspects of the Forest Carbon Inventory of the United States: 

 Recent Past and Near Future . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161

Christopher W. Woodall, James E. Smith, Grant M. Domke, Sean P. Healey, 

 John W. Coulston, and Andrew N. Gray

Recent Changes in the Estimation of Standing Dead Tree Biomass 

 and Carbon Stocks in the U.S. Forest Inventory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164

Grant M. Domke, Christopher W. Woodall, and James E. Smith

The Climate Change Performance Scorecard 

 and Carbon Estimates for National Forests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170

John W. Coulston, Kellen Nelson, Christopher W. Woodall, David Meriwether, and Gregory A. Reams

Assessing Estimation Techniques for Missing Plot Observations 

 in the U.S. Forest Inventory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177

Grant M. Domke, Christopher W. Woodall, Ronald E. McRoberts, James E. Smith, and Mark A. Hatfield

Gain-loss Estimation of Components of Change in Forest Carbon: 

 An Example from Oregon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182

Andrew N. Gray, Thomas R. Whittier, and David L. Azuma

Managing for Climate Benefits in Dry Mixed-conifer Forests: 

 Tracking the Carbon Implications of Fuel Treatments at Landscape Scale . . . . . . . . . . . . . 183

Jeremy S. Fried, Theresa Jain, Jonathan Sandquist, and Larry D. Potts

Issue Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184

Mapping Forest Soil Organic Matter on New Jersey’s Coastal Plain . . . . . . . . . . . . . . . . . . . . 185

Brian J. Clough, Edwin J. Green, and Richard G. Lathrop

Beech Status in New England’s Aftermath Forests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192

George L. McCaskill and Randall S. Morin199

Estimating Tree Cavity Distributions from Historical FIA Data . . . . . . . . . . . . . . . . . . . . . . . . . 199

Mark D. Nelson and Charlotte Roy

Building Improved Models of Sugar Maple Mortality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204

Charles H. Perry and Patrick L. Zimmerman

Utility of Tree Crown Condition Indicators to Predict Tree Survival 

 Using Remeasured Forest Inventory and Analysis Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 210

Randall S. Morin, Jim Steinman, and KaDonna C. Randolph

Large Landscape Assessments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 216

Temporal Trends of Forest Interior Conditions in the United States . . . . . . . . . . . . . . . . . . . . . 217

Kurt Riitters and James Wickham

Monitoring Trends and Burn Severity (MTBS): 

 Monitoring Wildfire Activity for the Past Quarter Century Using Landsat Data . . . . . . . . . . 222

Mark Finco, Brad Quayle, Yuan Zhang, Jennifer Lecker, Kevin A. Megown, and C. Kenneth Brewer

Approaches for Landscape-scale Forest Carbon Assessment . . . . . . . . . . . . . . . . . . . . . . . . . 229

Richard Birdsey, Yude Pan, Kris Johnson, Fangmin Zhang, and Jing Chen

U.S. Forest Service International Program Activities 

Related to Forest Monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 230

Analysis of the Deforestation Problem in Tropical Latin America . . . . . . . . . . . . . . . . . . . . . . . 231

Jorge Malleux
vi

Forest Inventory and Analysis canopy cover activities . . . . . . . . . . . . . 236

A GIS-Based Tool for Estimating Tree Canopy Cover 

 on Fixed-radius Plots Using High-resolution Aerial Imagery . . . . . . . . . . . . . . . . . . . . . . . . . 237

Sara A. Goeking, Greg C. Liknes, Erik Lindblom, John Chase, Dennis M. Jacobs, and Robert Benton

Building Capacity for Providing Canopy Cover and Canopy Height 

 at FIA Plot Locations Using High-resolution Imagery and Leaf-off LiDAR . . . . . . . . . . . . . . 242

Rachel Riemann, Jarlath O’Neil-Dunne, and Greg C. Liknes

Canopy Cover Estimates for Individual Tree Attributes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 248

James A. Westfall and Randall S. Morin

Relating FIA Data to Habitat Classifications Via Tree-Based Models of Canopy Cover . . . . . 254

Mark D. Nelson, Brian G. Tavernia, Chris Toney, and Brian F. Walters

Densiometers and Canopy Density Measurements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 260

Kenneth W. Stolte, Stanley J. Zarnoch, and Robert L. Eaton

The Past, Present, and Future of Nonnative Plant Monitoring 

by the Forest Inventory and Analysis Program . . . . . . . . . . . . . . . . . . . . . 261

The National Picture of Nonnative Plants in the United States According to FIA Data . . . . . . 262

Sonja N. Oswalt and Christopher M. Oswalt

The Distribution and Occupancy of Introduced Species: 

 A Baseline Inventory from Phase 3 Plots Across the Country . . . . . . . . . . . . . . . . . . . . . . . . 268

Bethany K. Schulz and W. Keith Moser

Updating the Southern Nonnative Plant Watch List: 

 The Future of NNIP Monitoring in the South . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 274

Christopher M. Oswalt, Sonja N. Oswalt, and Lewis Zimmerman

Invasive Potential of Invasive Plants 

 in the Forest of the Southern Region, United States . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 278

Dawn Lemke, John W. Coulston, Philip Hulme, Clint Paterson, and Jennifer A. Brown

Invasive Plant Monitoring for Northern U.S. Forests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283

William H. McWilliams, Randall S. Morin, Katherine Johnson, W. Keith Moser, and James A. Westfall

Nonnative Invasive Plant Inventory in the Northern Research Station: 

 Patterns and Trends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 288

W. Keith Moser and Cassie M. Kurtz

Modeling Regenerative Capacity across the Eastern United States: 

Can FIA Inform Contemporary Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 289

Sampling Forest Regeneration Across Northern U.S. Forests: 

 Filling a Void in Regeneration Model Input . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 290

William H. McWilliams, Charles D. Canham, Randall S. Morin, Katherine Johnson, 

 Paul Roth, and James A. Westfall

Information for Forest Process Models: A Review of NRS-FIA Vegetation Measurements . . 295

Charles D. Canham and William H. McWilliams

Transitioning from Phase 3 Vegetation Data to Phase 2+ Vegetation Data 

 in the Northern Research Station . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 303

W. Keith Moser, Katherine Johnson, Cassie M. Kurtz, Cassandra Olson, and William H. McWilliams
vii

Integrating NFI data with remote sensing and GIS applications . . . . . . 304

Development and Applications of the LANDFIRE Forest Structure Layers . . . . . . . . . . . . . . . 305

Chris Toney, Birgit Peterson, Don Long, Russ Parsons, and Greg Cohn

Characterizing Environmental Change in Interior Alaska (1982-2012) 

 Using Multi-temporal, Multi-scale Remote Sensing Data and Field Measurements . . . . . . . 310

Hans-Erik Andersen and Robert Pattison

A Model for Quantifying Forest Variables Over Landscapes with Uncertain Forest Areas . . 314

Andrew O. Finley, Sudipto Banerjee, and David W. MacFarlane

Climate Change and Forest Health: integrating FIA, climate data, 

and spatial technology for long-term monitoring . . . . . . . . . . . . . . . . . 315

Relationship Between Crown Dieback and Drought in the Southeastern United States . . . . 316

Michael K. Crosby, Zhaofei Fan, Martin A. Spetich, Theodor D. Leininger, and Xingang Fan

Assessing Forest Mortality Patterns Using Climate and FIA Data at Multiple Scales . . . . . . . 319

Michael K. Crosby, Zhaofei Fan, Xingang Fan, Theodor D. Leininger, and Martin A. Spetich

Incorporating Local Statistics Based Spatial Weight Matrix 

 into Simultaneous Autoregressive Model to Predict the Distribution 

 of Nonnative Invasive Shrubs in the Upper Midwest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 325

Weiming Yu, Zhaofei Fan, and W. Keith Moser

Projecting Climate Change Impacts on Down Woody Materials 

 in the Eastern U.S. Forests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 326

Zhaofei Fan, Christopher W. Woodall, and Stephen R. Shifley

Spread of Chinese Tallow in East Texas Using FIA Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 327

Zhaofei Fan

Tempo-spatial Trends of Oak Decline and Mortality 

 Under Periodic Regional Droughts in the Ozark Highlands of Arkansas and Missouri . . . . 328

Shirley X. Fan, Zhaofei Fan, W. Keith Moser, Martin A. Spetich, and Stephen R. Shifley

Methods for Improving Forest Inventory . . . . . . . . . . . . . . . . . . . . . . . . . . . 329

Estimators Used in the New Mexico Inventory: 

 Practical Implications of “Truly” Random Nonresponse within Each Stratum . . . . . . . . . . . 330

Paul L. Patterson and Sara A. Goeking

Estimating Root Collar Diameter Growth for Multi-stem Western Woodland Tree Species 

 on Remeasured Forest Inventory and Analysis Plots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 334

Michael T. Thompson and Maggie Toone

The Improvement of Precision for Estimating the Abundance of Standing Dead Trees 

 Using Auxiliary Information Under the FIA Plot Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 338

Hong Su An, David W. MacFarlane, and Christopher W. Woodall

Optimized Endogenous Post-stratification in Forest Inventories . . . . . . . . . . . . . . . . . . . . . . . 342

Paul L. Patterson

Properties of the Endogenous Post-stratified Estimator 

 Using a Random Forests Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 348

John Tipton, Jean Opsomer, and Gretchen G. Moisen

Improved Prediction of Hardwood Tree Biomass Derived from 

 Wood Density Estimates and Form Factors for Whole Trees . . . . . . . . . . . . . . . . . . . . . . . . . 352

David W. MacFarlane and Neil R. Ver Planck
viii

Challenges and Opportunities for Improved Biomass 

and Carbon Monitoring of US Tree Species . . . . . . . . . . . . . . . . . . . . . . . . . . 356

Biomass Measurement and Modeling Challenges 

 for Hardwood Species in the Northern Region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 357

James A. Westfall, David W. MacFarlane, and Aaron R. Weiskittel

Strategies for Assessing Inter- and Intra-specific Variation 

 in Tree Biomass in the Interior West . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 361

David L.R. Affleck, John M. Goodburn, and John D. Shaw

Estimating Biomass and Carbon in Forests of the Pacific States: Statistical Issues . . . . . . . 365

Andrew N. Gray and Hailemariam Temesgen

Role of FIA data in evaluating forest ecosystem responses 

to air quality stressors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 366

Impacts of Nitrogen and Sulfur Deposition on the Growth of Red Spruce 

 and Sugar Maple in the United States . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 367

Jennifer N. Phelan, Paramita Sinha, George Van Houtven, Marion Deerhake, Randall G. Waite, 

 Anne W. Rea, and Ginger M. Tennant

Yellow Birch and Acid Deposition in the Southern Appalachians . . . . . . . . . . . . . . . . . . . . . . . 371

Kenneth W. Stolte, Walter C. Shortle, Kevin T. Smith, Michael Amacher, Charles H. Perry, 

 Lindsay Majer, and Owen Carson

Models for Estimating Carbon and Biomass . . . . . . . . . . . . . . . . . . . . . . . . . 372

Verification of the Jenkins and FIA Sapling Biomass Equations 

 for Hardwood Species in Maine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 373

Andrew S. Nelson, Aaron R. Weiskittel, Robert G. Wagner, and Michael R. Saunders

Assessing the Uncertainty of Forest Carbon Estimates 

 Using the FVS Family of Diameter Increment Equations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 378

Matthew B. Russell, Aaron R. Weiskittel, and Anthony W. D’Amato

Analysis of Tracheid Development in Suppressed-growth Ponderosa Pine 

 Using the FPL Ring Profiler . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 383

C. Tim Scott and David W. Vahey

Ring Profiler: A New Method for Estimating Tree-ring Density 

 for Improved Estimates of Carbon Storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 386

David W. Vahey, C. Tim Scott, J.Y. Zhu, and Kenneth E. Skog

Cumulative Volume and Mass Profiles for Dominant Stems 

 and Whole Trees Tested for Northern Hardwoods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 391

Neil R. Ver Planck and David W. MacFarlane

FIA’S Volume-to-Biomass Conversion Method (CRM) 

 Generally Underestimates Biomass in Comparison to Published Equations . . . . . . . . . . . . 396

David C. Chojnacky
ix

LIDAR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 403

Applying Inventory Methods to Estimate Aboveground Biomass 

 from Satellite Light Detection and Ranging (LiDAR) Forest Height Data . . . . . . . . . . . . . . . 404

Sean P. Healey, Paul L. Patterson, Sassan Saatchi, Michael A. Lefsky, Andrew J. Lister, 

Elizabeth A. Freeman, and Gretchen G. Moisen

Utility of LiDAR for Large Area Forest Inventory Applications . . . . . . . . . . . . . . . . . . . . . . . . . 410

Nicholas S. Skowronski and Andrew J. Lister

Improving LiDAR Based Prediction of Forest Biomass Using Models 

 with Spatially Varying Coefficients . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 414

Chad Babcock, Andrew O. Finley, and John B. Bradford

Alternative Estimation Techniques for FIA Data . . . . . . . . . . . . . . . . . . . . . . 415

An Efficient Estimator to Monitor Rapidly Changing Forest Conditions . . . . . . . . . . . . . . . . . 416

Raymond L. Czaplewski, Michael T. Thompson, and Gretchen G. Moisen

The Fourth Dimension in FIA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 421

Francis A. Roesch

Improving FIA Trend Analysis Through Model-based Estimation 

 Using Landsat Disturbance Maps and the Forest Vegetation Simulator . . . . . . . . . . . . . . . . 427

Sean P. Healey, Gretchen G. Moisen, and Paul L. Patterson

Advancements in LiDAR-based Registration of FIA Field Plots . . . . . . . . . . . . . . . . . . . . . . . . 432

Demetrios Gatziolis

Hot-deck Matching of Annual Inventory Plots to Enhance Trends and Projections . . . . . . . . 438

Paul C. Van Deusen

Posters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 439

A Comparison of FIA Plot Data Derived from Image Pixels and Image Objects . . . . . . . . . . . 440

Charles E. Werstak, Jr.

Is Lodgepole Pine Mortality Due to Mountain Pine Beetle 

 Linked to the North American Monsoon? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 448

Sara A. Goeking and Greg C. Liknes

Trends in Standing Biomass in Interior West Forests: 

 Reassessing Baseline Data from Periodic Inventories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 453

Sara A. Goeking
x

Poster Abstracts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 461

Tamarack in Minnesota: 

 Investigating Mortality from Eastern Larch Beetle Using FIA Data . . . . . . . . . . . . . . . . . . . . 462

Susan J. Crocker, Jana Albers, Fraser R. McKee, Brian Aukema, and Greg C. Liknes

Agent-specific Tree Mortality Rates in the Eastern United States from FIA Data . . . . . . . . . . 463

Alan V. Di Vittorio and Jeffrey Q. Chambers

Remote Sensing Data as a Monitoring Tool: 

 Trends in Wildfire Activity for the Past Quarter Century 

 and the Relationship to Other Biophysical and Environmental Variables . . . . . . . . . . . . . . . 464

Mark Finco, Brad Quayle, Kevin A. Megown, C. Kenneth Brewer, and Jennifer Lecker

Detailed Maps of Tropical Forest Types are Within Reach: Forest Tree Communities 

 for Trinidad and Tobago Mapped with Multiseason Landsat and Google Earth . . . . . . . . . . 465

Eileen H. Helmer, Thomas S. Ruzycki, Jay Benner, Shannon M. Voggesser, Barbara P. Scobie, 

 Courtenay Park, David W. Fanning, and Seepersad Ramnarine

Dots and Plots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 466

Dennis M. Jacobs and Joseph M. McCollum

Progression of the Inventory and Monitoring of Nonforest Lands with Trees . . . . . . . . . . . . . 467

Dacia M. Meneguzzo, Greg C. Liknes, and Charles H. Perry

Forest Atlas of the United States . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 468

Charles H. Perry, Linda R. Smith, Mary A. Carr, Randy Vreeke, and others

Assessing Changes in Vegetation Composition and Structure: 

 What Can We Learn from 500 Plots? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 469

Bethany K. Schulz and W. Keith Moser

Logging Residue Utilization in the State of Idaho 2008 and 2011 . . . . . . . . . . . . . . . . . . . . . . . 470

Eric A. Simmons, Erik C. Berg, Todd A. Morgan, Charles B. Gale, 

 Stanley J. Zarnoch, and Steven W. Hayes

Mining Historical FIA Reports to Develop Estimates of Forest Land Through Time 

 in the North Central Region of the United States . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 471

Paul A. Sowers

Mapping Aspen in the Interior West . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 472

Charles E. Werstak, Jr.

Sustainability of Oaks in West Virginia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 474

Richard H. Widmann

Does Standing Water or Snow Pack Bias Assessments of Tree Regeneration 

 Within Large-scale Forest Inventories? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 475

Christopher W. Woodall, James A. Westfall, Brian F. Walters, Daniel J. Johnson, and Kai Zhu

Rates of Coarse Woody Debris Biomass Loss and Carbon Debt Implications 

 in Eastern U.S. Forests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 476

Christopher W. Woodall, Matthew B. Russell, Anthony W. D’Amato, 

 Shawn Fraver, and Brian F. Walters

Framework for Assessing Climate Change Risks to Forest Carbon Stocks . . . . . . . . . . . . . . 477

Christopher W. Woodall, Grant M. Domke, Karin L. Riley, Christopher M. Oswalt, 

 Susan J. Crocker, and Gary W. Yohe

Biomass and Carbon Attributes of Down Woody Materials 

 Across Forests of the United States . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 478

Christopher W. Woodall, Brian F. Walters, Grant M. Domke, Chris Toney, Andrew N. Gray, 

 Sonja N. Oswalt, and James E. Smith
Forest Products Industry 

Status and Trends: 

National and Regional 

Perspectives
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105

EFFECTS OF THE “GREAT RECESSION” ON THE FOREST PRODUCTS 

SECTOR IN THE NORTHERN REGION OF THE UNITED STATES

Christopher W. Woodall, William G. Luppold, Peter J. Ince, Ronald J. Piva, 

and Kenneth E. Skog1

Abstract.—The forest industry within the northern region of the United States has 

demonstrated a notable decline in terms of employment, number of mills, wood 

consumption, and forest harvests since 2000—a downturn exacerbated by the “Great 

Recession” of 2007-2009. Longer term industrial decline (since 2000) has been evidenced 

by reductions in secondary product (e.g., furniture) and print paper manufacturing, 

which can be attributed to the lack of global competitiveness of U.S. wages and ascent 

of electronic media. In contrast, more recent (since 2008), yet sharper declines can be 

found in industries such as composite panel production that service the housing industry. 

Despite a decade of decline, future prospects for this region’s forest industry may be 

viewed as positive. The region’s forests are predominantly within private ownership 

and represent some of the world’s most valuable sawtimber. Coupled with the natural 

resource is a present but underutilized industry with spare capacity and a highly skilled 

work force. 

INTRODUCTION

The northern region, which for the purposes of this 

paper broadly includes New England, upper midAtlantic, Great Lakes, central hardwood, and eastern 

Plains states, supports 27 percent of the Nation’s 

timber volume and 46 percent of its hardwood resource 

(Smith et al. 2009). These hardwood-dominated forests 

contain over two-thirds (FIA 2012) of the Nation’s 

high-valued select hardwoods (e.g., select white oaks, 

select red oaks, hard maple, ash, and black walnut) 

and accounted for approximately 18 percent of the 

Nation’s hardwood removals in 2006 (Smith et al. 

2009). These hardwood removals supply a large share 

(48 percent; Smith et al. 2009) of U.S. hardwood 

lumber production and this region is the principle 

source of high grade hardwood lumber, plywood, and 

veneer production. These products, in turn, are used 

by secondary manufacturers (e.g., kitchen cabinet, 

millwork, and furniture manufacturers) domestically 

and internationally. 

Following the same trends as found across the United 

States (Woodall et al. 2012a), the northern region 

had declines in numerous forest products industries 

beginning in 2000 followed by a precipitous drop 

in the wake of the housing construction downturn 

(~2006) and recession of 2007 to 2009 (Woodall et al. 

2012b). 

Declines in production have been accompanied by 

declines in the number of mills and wood consumption 

for saw, pulp, and composite panel mills within the 

north central subregion (IL, IN, IA, KS, MI, MN, 

MO, NE, ND, SD, WI) of the region and reflect 

recent trends in these industries both nationally and 

regionally (Figs. 1 and 2). Mill numbers and wood 

consumption have generally fallen since 2000 for 

both pulp and composite panel sectors, with steep 

declines starting in 2005 (Fig. 1). By 2009 there were 

25 percent fewer pulp mills processing 13 percent less 

1

 Research Forester (CWW), U.S. Forest Service, Northern 

Research Station, 1992 Folwell Ave., St. Paul, MN 55108; 

Economist (WGL) and Forester (RJP), U.S. Forest Service, 

Northern Research Station; Research Forester (PJI) and 

Supervisory Research Forester (KES), U.S. Forest Service, 

Forest Products Lab. CWW is corresponding author: to 

contact, call 651-649-5141 or email at cwoodall@fs.fed.us.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105

Figure 1.—Change (percent) in number of pulp and composite wood panel mills since 2000 and wood consumed by pulp and 

composite panel mills, 2000-2009, north-central states (IL, IN, IA, KS, MI, MN, MO, NE, ND, SD, WI).

Figure 2.—Percentage change in number of sawmills and quantity of processed wood since 2000, 2000-2009, north-central 

states (IL, IN, IA, KS, MI, MN, MO, NE, ND, SD, WI).
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105

pulpwood. These disproportional declines (percentage 

decline in mill number exceeds percentage decline in 

feedstock use) suggest a shift in productive capacity 

to larger and potentially more efficient mills despite 

the economic downturn. In contrast, 36 percent fewer 

composite panel mills in 2009 processed 56 percent 

less wood than peak production in 2005, a direct 

consequence of the fall of the housing market. Trends 

in the sawmill sector mirror those of the composite 

panel sector and are linked to the plight of the 

housing market (Fig. 2). From 2000 to 2006, sawmill 

numbers and wood consumption demonstrated similar 

percentages of decline. After 2006, the amount of 

wood consumed decreased faster than the decline in 

mill counts. This trend resulted in 17 percent fewer 

sawmills processing 26 percent less wood by 2009—

an indicator that the remaining sawmills are running at 

less than full capacity.

Despite a decade of decline, future prospects for this 

region’s forest industry may be viewed as positive. 

The region’s forests are predominantly within private 

ownership and represent tremendous volumes of 

some of the world’s most valuable sawtimber. The 

forest landowners of the northern region have been 

diligently managing their forests for generations as 

evidenced by the ever-increasing volumes for decades 

(USDA 2011). Coupled with this natural resource is a 

present but underutilized industry with spare capacity 

and a highly skilled work force. The continued 

development of competitive secondary industries in 

the United States to receive this region’s high quality 

select hardwoods (e.g., walnut and select oaks) might 

increase regional employment while maintaining 

critical positive international trade balances. The 

export of hardwood saw logs may provide a positive 

trade balance today but also represents the offshoring 

and loss of skilled U.S. labor into the future. In total, 

the highly abundant forest resource, mill capacity, and 

secondary product workforce skills in the northern 

region provide an economic opportunity to foster 

increased competitiveness and evolve beyond the 

downturn precipitated by the housing crash and years 

of offshoring the Nation’s forest products industry.

LITERATURE CITED

Smith, W.B.; Miles, P.D.; Perry, C.H.; Pugh, S.A. 

2009. Forest resources of the United States, 2007.

Gen. Tech. Rep. WO-78. Washington DC: U.S. 

Department of Agriculture, Forest Service. 336 p.

U.S. Department of Agriculture. 2011. National 

report on sustainable forests, 2010. WO-FS-979. 

Washington, DC: U.S. Department of Agriculture, 

Forest Service. 200 p.

Woodall, C.W.; Ince, P.J.; Skog, K.E.; Aguilar, F.X.; 

Keegan, C.E.; Sorenson, C.B.; Hodges, D.G.; 

Smith, W.B. 2012a. An overview of the forest 

products sector downturn in the US. Forest 

Products Journal. 61: 595-603.

Woodall, C.W.; Luppold, W.G.; Ince, P.J.; Piva, 

R.J.; Skog, K.E. 2012b. An assessment of the 

downturn in the forest products sector in the 

northern region of the United States. Forest 

Products Journal. 61: 604-613.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105

IMPACT OF THE GREAT RECESSION ON THE

FOREST PRODUCTS INDUSTRY IN THE WESTERN UNITED STATES

Charles E. Keegan, Colin B. Sorenson, Todd A. Morgan, Jean M. Daniels, and Steven W. Hayes1

1

 Emeritus Director of Forest Industry Research (CEK), 

Research Economist (CBS), Director of Forest Industry 

Research (TAM), and Research Forester (SWH), The 

University of Montana, Bureau of Business and Economic 

Research, Missoula, MT 59812; Research Forester (JMD), 

U.S. Forest Service, Pacific Northwest Research Station. 

CBS is corresponding author: to contact, call 406-243-5113 

or email at colin.sorenson@business.umt.edu.

Abstract.—Wood product prices and production fell dramatically in 2009 as a severe 

recession and massive decline in U.S. housing led to a global financial crisis. In 2009 

and 2010, virtually every major western mill suffered curtailments and 30 large mills 

closed permanently. Sales value of wood and paper products in the West dropped from 

$49 billion in 2005 to $34 billion in 2009. Employment declined by 71,000 workers and 

lumber production fell by almost 50 percent from 2005 to 2009. Capacity utilization at 

sawmills and other timber-using facilities in the West fell from more than 80 percent in 

2005 to just over 50 percent in 2009 and 2010. With the exception of exports and some 

paper markets, U.S. wood products markets have improved little since the recession 

officially ended in 2009. Modest improvements are expected in domestic markets in the 

short term, but substantial improvements are unlikely until 2014 or later, as U.S. home 

building recovers and global demand increases. Much of the West retains the bulk of 

its pre-recession (2006) capacity and mills could respond quickly to increased demand 

spurred by economic recovery.

NOTE: A longer version of this paper with additional tables, figures, and a full Literature Cited 

section was published in the Forest Products Journal, Vol. 61, No. 8, in July 2012. 

INTRODUCTION

The first decade of the 21st century proved tumultuous 

for the global and U.S. economies and the western 

forest products industry. Following a relatively mild 

recession in 2001, a booming economy, low interest 

rates, easy access to credit, and real estate speculation 

fostered more than 2 million U.S. housing starts in 

2005 and record lumber consumption from 2003 to 

2005 (Western Wood Products Association 2010). 

The falloff in U.S. housing markets beginning in 

2006 abruptly ended the credit-fueled growth that had 

been based on the rapid increase in home prices up 

to that point. This period was followed by an official 

recession from December 2007 through June 2009 

(National Bureau of Economic Research 2010), a 

massive decline in home values and housing-related 

financial instruments, and ultimately a global financial 

crisis in the last quarter of 2008. Tightening credit, 

an oversupply of foreclosed homes, and a collapse in 

speculative housing investment made 2009 through 

2011 the worst years for home building and wood 

products markets since the Great Depression. Housing 

starts hit a 50-year record low of 554,000 in 2009, 

with only slight increases during 2010 and 2011. Wood 

product prices and production fell dramatically during 

the recession and have remained quite low throughout 

the post-recession “recovery.” 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105

IMPACTS ON THE

FOREST PRODUCTS INDUSTRY

IN THE WEST

The Great Recession and housing collapse had largescale impacts on the western forest products industry. 

Value of industry outputs fell 31 percent from $49 

billion in 2006 to $34 billion (fob the producing mill) 

in 2009 (U.S. Census Bureau 2010). Employment 

dropped 29 percent from 249,000 workers in 2005 to 

177,000 in 2009 (Table 1) and further to 170,000 in 

2010, while earnings of workers fell almost 22 percent 

from $12.9 billion in 2005 to $10.1 in 2009 and 2010 

(Fig. 1). 

Every sector of the western forest products industry 

was negatively impacted by the economic downturn. 

The wood products sectors, which are more heavily 

dependent on housing and construction than paper 

products, experienced the largest impacts. Sales value 

of wood products fell from $28 billion in 2005 to $14 

billion in 2009, while the primary and secondary pulp 

and paper industry was virtually unchanged at $21 

billion (in current year dollars) over the same period. 

Table 1.—Western states forest products industry employment, 2005 vs. 2009

2005 2009 Net change % Change

Pacific Coast states

Alaska 1,805 1,465 (340) -19

California 79,513 56,630 (22,883) -29

Hawaii 1,091 984 (107) -10

Oregon 58,858 42,578 (16,280) -28

Washington 44,416 32,612 (11,803) -27

Pacific Coast subtotal 185,683 134,269 (51,414) -28

Interior West states

Arizona 14,222 7,160 (7,062) -50

Colorado 9,521 7,293 (2,228) -23

Idaho 14,116 10,028 (4,088) -29

Montana 9,783 7,464 (2,319) -24

Nevada 3,358 2,003 (1,355) -40

New Mexico 3,715 2,623 (1,092) -29

Utah 7,282 5,941 (1,341) -18

Wyoming 1,148 629 (519) -45

Interior West subtotal 63,145 43,141 (20,004) -32

Grand Total 248,827 177,410 (71,418) -29

Sources: U.S. Department of Commerce 2012; U.S. Department of Labor 2012; Forest Industries Data Collection System 2012.

LUMBER PRODUCTION 

AND TIMBER HARVEST

For at least 50 years before the recent housing collapse, 

the West was the major softwood lumber producing 

region in the country. Softwood lumber was the largest 

single component of the industry in the West, accounting 

for approximately $8 billion per year or almost 40 percent 

of total value from the primary industry in 2004 and 

2005. As U.S. housing starts fell by 75 percent and the 

U.S. consumption of softwood lumber dropped by half, 

lumber production in the West fell from more than 19 

billion board feet (BBF) lumber tally (48 percent of U.S. 

production) in 2005 to an estimated 10.4 and 11.1 BBF 

in 2009 and 2010, respectively (less than 45 percent of 

U.S. production). In 2007, output of softwood lumber 

from the South exceeded output from the West for the 

first time since at least 1955. In 2009, lumber production 

in the West was at its lowest level in more than 50 years 

(Fig. 2). Because of the substantial decline in both prices 

and production, the value of lumber produced by western 

sawmills fell by more than 60 percent (in current year 

dollars) from nearly $8 billion in 2004 and 2005 to less 

than $3 billion in 2009 and 2010 (U.S. Census Bureau 

2010).
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105

Figure 1.—Forest products industry worker earnings and employment in the West, 2001-2010.

Figure 2.—New U.S. home construction and western states timber harvest and lumber production, 1959-2010.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105

Timber harvest volume in the West also fell 

dramatically during the downturn, from more than 13 

BBF Scribner in 2004 and 2005, of which more than 

70 percent was used to produce lumber, to 7.9 BBF 

in 2009 (Fig. 2). During 2009 and 2010, West-wide 

harvest and lumber output were at the lowest levels 

since the late 1940s. These extremely low timber 

harvest and lumber production figures are particularly 

notable when considering U.S. population in the late 

1940s was less than half of the 2011 population of 

311 million people. Timber harvest fell by just over 

40 percent in both the Pacific Coast and Interior West 

regions from 2005 to 2009, while lumber production 

fell more than 45 percent.

LOSSES IN INDUSTRY

INFRASTRUCTURE: TIMBER 

PROCESSING CAPACITY

At the start of the Great Recession in 2007, annual 

capacity to process timber in the West was an 

estimated 14.4 BBF, and by 2010, capacity had 

declined by 10 percent to an estimated 13 BBF. 

Capacity utilization, however, declined much more 

precipitously from around 80 percent during the 2003 

to 2006 period to 64 percent in 2008 and 56 percent in 

2010 (Forest Industries Data Collection System 2012, 

Smith and Hiserote 2010). 

EXPORTS

From 2000 to 2009, log exports from the West 

remained relatively consistent, and Japan was the 

dominant destination (Fig. 3). This trend began to 

change in 2009 when the proportion of log exports 

going to China began to rise. By 2011, the volume 

of logs shipped to China had jumped by almost 

600 percent to reach 1.2 BBF Scribner, equivalent to 

14 percent of the West-wide harvest in 2010. This log 

export trend is likely to persist until domestic demand 

for wood products returns, Chinese demand decreases, 

or policy changes impact log exports. 

Figure 3.—Volume of softwood logs exported from western customs districts, 2000 to 2011. 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105

FUTURE OUTLOOK

In 2012, U.S. lumber and wood products markets have 

shown little improvement. Modest improvements are 

expected in domestic wood products markets in 2013, 

with substantial improvements not predicted until 2014 

or beyond, contingent upon recovery in U.S. home 

building and continued increases in global demand. 

Low capacity utilization rates in the West suggest the 

possibility of additional mill closures, but because 

much of the reduced output is due to curtailments 

rather than permanent closures, the industry retains the 

ability to quickly increase production at existing mills 

when market conditions improve.

ACKNOWLEDGMENTS

The authors would like to acknowledge support from 

the U.S. Forest Service Pacific Northwest and Rocky 

Mountain Research Stations’ FIA units and the InlandNorthwest Forest Products Research Consortium 

funded through the U.S. Department of Agriculture 

NIFA Special Grant program for Wood Utilization 

Research.

LITERATURE CITED

Forest Industries Data Collection System. 2012. 

Information system maintained by The University 

of Montana’s Bureau of Business and Economic 

Research in cooperation with the U.S. Forest 

Service’s Rocky Mountain Research Station Forest 

Inventory and Analysis and Pacific Northwest 

Research Station Forest Inventory and Analysis.

National Bureau of Economic Research (NBER). 

2010. US business cycle expansions and 

contractions. Available at www.nber.org/cycles.

html. (Accessed February 21, 2012).

Smith, D.; Hiserote, B. 2010. Washington mill 

survey 2008. Series Report #20. Olympia, 

WA: Washington State Department of Natural 

Resources. 98 p.

U.S. Census Bureau. 2010. Annual survey of 

manufactures 2010. Available at http://factfinder.

census.gov/servlet/DatasetMainPageServlet?_

program=EAS. (Accessed Jan 2012).

U.S. Department of Commerce, Bureau of Economic 

Analysis. 2012. Regional accounts data. Available 

at www.bea.gov/regional/spi/default.cfm?selTable

=SA25N&selSeries=NAICS. (Accessed February 

2012).

U.S. Department of Labor. 2012. Bureau of labor 

statistics. Available at www.bls.gov/. (Accessed 

June 2012). 

Western Wood Products Association. 2010. Statistical 

yearbook of the Western lumber industry.

Portland, OR.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 10

Results and Issues Encountered

while Investigating Southern Forest Industry and

Market Responses to Changing Economic Conditions

Andrew J. Hartsell, Thomas J. Brandeis, James W. Bentley, Consuelo Brandeis, and Donald Hodges1

Abstract.—Recent Forest Inventory and Analysis (FIA) studies revealed the impact 

that the recent economic downturn had on southern forest industries, markets, and 

forests. We used FIA data, timber product output (TPO) surveys, and IMpact analysis for 

PLANing (IMPLAN) output to quantify these impacts. This analysis involves comparing 

recent changes to the pre-downturn trend. IMPLAN analysis suggests that the total jobs 

associated with the wood products industry (direct, indirect, and induced employment) 

fell from 1.2 million jobs to 0.97 million jobs between 2004 and 2009. TPO results 

indicate that total softwood output declined 22 percent between 2005 and 2009, while 

hardwood total product output fell 30 percent over the same time period. Data collected 

from FIA Phase 2 plots suggests that landowners are performing less final harvests and 

more thinnings and other silvicultural treatments. Issues pertaining to correlating annual 

TPO data with moving average FIA inventory data will be discussed.

1

 Research Forester (AJH), U.S. Forest Service, Southern Research Station, 4700 Old Kingston 

Pike, Knoxville, TN 37919; Supervisory Research Forester (TJB) and Foresters (JWB and CB), 

U.S. Forest Service, Southern Research Station; Professor and Director Natural Resource Policy 

Center (DH), University of Tennessee, Knoxville, TN. AJH is corresponding author; to contact, 

call 865-862-2000 or email at ahartsell@fs.fed.us.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Social Dimensions of

Forest Inventory
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 12

ASSESSING FOREST OWNERSHIP DYNAMICS 

IN THE UNITED STATES: METHODS AND CHALLENGES

Brett J. Butler, Brenton J. Dickinson, and Jaketon H. Hewes1

Abstract.—The National Woodland Owner Survey (NWOS) is conducted by the U.S. 

Forest Service, Forest Inventory & Analysis (FIA) Program as the social complement to 

its biophysical inventory. The NWOS is aimed at understanding who owns the forests 

of the United States, why they own it, what they have done with it in the past, and what 

they plan to do with it in the future. On a recurring basis, self-administered surveys are 

sent to randomly selected private forest owners from across the U.S. The sample points 

correspond with plot center of the FIA Phase 2 field plots. For the first time, in 2011, 

the NWOS began to resample points that were sampled between 2002 and 2006. If the 

same owner still owned the sample point, they were resurveyed and if there was a new 

ownership, they were surveyed for the first time. These results will provide the most 

comprehensive examination of forest ownership dynamics in the U.S. to date. Topics that 

will be explored include parcellation and changes in forest owners’ attitudes, behaviors, 

and demographics. This information should prove useful to state forestry agencies, policy 

makers, nongovernmental organizations, forest industry, educators, researchers, forest 

landowner organizations, and anyone who is interested in understanding forest owners 

and/or interacting with them. 

Introduction

There are 751 million acres of forest land in the United 

States and 56 percent of this land is owned by 11 

million families, individuals, corporations, and other 

private groups (Butler 2008). The collective decisions 

of these owners will have profound effects on the 

future forest resources. If the forestry community 

is interested in understanding the factors affecting 

the sustainability of forests and designing effective 

policies, programs, and services that foster this 

sustainability, then it is imperative to understand forest 

ownerships and forest ownership dynamics. 

1

 Research Forester (BJB), U.S. Forest Service, Northern 

Research Station, 160 Holdsworth Way, University of 

Massachusetts, Amherst, MA 01003; Research Fellow 

(BJD) and Research Coordinator (JHH), U.S. Forest 

Service, University of Massachusetts Amherst Family Forest 

Research Center. BJB is corresponding author: to contact, 

call 413-545-1387 or email bbutler01@fs.fed.us.

The U.S. Forest Service, Forest Inventory and Analysis 

(FIA) Program conducts inventories of the biophysical 

forest resource in order to answer questions related 

to the composition, extent, health, and trends in 

this resource. As a complement to this biophysical 

inventory, FIA conducts the National Woodland Owner 

Survey to answer the following questions: 

• Who owns the forests of the United States? 

• Why do they own forests? 

• How have they used their forests in the past? 

• How do they plan to use their forests in the 

future?

This information is used by policy analysts, 

researchers, nongovernmental organizations, forest 

industry, and others in order to create policies, 

programs, and services that more effectively and more 

efficiently aid private forest owners.

The objectives of this paper are to provide some 

background on the National Woodland Owner Survey, 

present some of the current challenges, and discuss 

some things being done to overcome these challenges.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 13

BACKGROUND

The National Woodland Owner Survey has been 

conducted on a periodic basis for many decades. The 

first national data on private forest ownerships comes 

from Josephson and McGuire (1958). This simply 

provided numbers of private forest ownerships and 

area of private forest ownership by size of forest 

holdings by region. The next iteration of a national 

survey was conducted by Birch, Lewis, and Kaiser 

(1982). Here more detailed information was collected 

including demographics and methods of land 

acquisition along with size of forest holdings. The 

finest resolution was again regions. 

Birch (1996c) greatly expanded on the previous 

efforts by providing state-level summaries (Birch 

1996a, 1996b, 1996d) and information on ownership 

objectives, management practices, size of forest 

holdings, and other topics. The National Woodland 

Owner Survey switched from a periodic to an annual 

basis in 2002 (Butler 2008). The most recently 

completed cycle, completed between 2002 and 2006, 

contained many of the same elements as Birch (1996c) 

and added some additional elements. Like Birch 

(1996a, 1996b, 1996d), Butler (2008) also provided 

state-level summaries. 

The most current iteration of the National Woodland 

Owner Survey was initiated in 2011. One major 

advance is that for the first time, the same points on 

the ground are being included, i.e., point-to-point 

remeasurement will be feasible. If the same ownership 

still owns the ground on which the point is located, 

then that ownership is resurveyed, otherwise the new 

ownership is surveyed.

METHODS

The National Woodland Owner Survey, beginning 

with Birch (1996c), is coupled with the national FIA 

sample design. The FIA sample design (Bechtold and 

Patterson 2005) consists of dividing the United States 

into hexagons (approximately 6,000 ac/hexagon) 

and randomly locating a sample point within each 

hexagon. These sample points correspond to plot 

center of the FIA Phase 2 field plots. Twenty to 

fourteen percent of the sample points in the East and 

10 percent of the sample points in the West are visited 

each year resulting in 5 to 7- and 10-year inventory 

cycles, respectively. For those sample points that are 

determined to be forested and privately owned, the 

landowner is invited to participate in the National 

Woodland Owner Survey. The ownerships of record 

are determined through county and municipal property 

tax records. 

The National Woodland Owner Survey is a mail-based 

survey that follows the procedures outlined by Dillman 

(2009). Following an introductory postcard, each 

potential respondent receives a questionnaire, cover 

letter, and business reply envelope. The next mailing is 

a reminder/thank you postcard followed by, for those 

who have not responded, another questionnaire, cover 

letter, and business reply envelope. For those who 

still have not responded, a random subset is contacted 

via telephone in order to facilitate a nonresponse 

bias assessment. The cooperation rate to the National 

Woodland Owner Survey between 2002 and 2006 was 

51 percent. 

CURRENT CHALLENGES

A primary objective of the National Woodland Owner 

Survey is to monitor trends in forest ownership over 

time. Therefore, consistency over time is paramount. 

Changes are periodically made to the survey and 

estimation procedures, but only when deemed 

necessary and when the benefits sufficiently outweigh 

the costs. 

Recent work has shown that there was an error in 

the underlying estimation algorithm for calculating 

numbers of owners (Metcalf 2010). While the 

impact of this error on estimates is minimal, the 

estimation algorithms are being reworked. Once this 

is completed, data from the 2002-2006 and the newer, 

2011 and future, surveys will be (re)calculated using 

the adjusted procedures.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 14

A challenge with presenting results from the National 

Woodland Owner Survey is how to clearly and 

precisely report different units, i.e., ownerships versus 

acres. The units can have profound impacts on the 

interpretation of the results (e.g., Fig. 1). In addition to 

reporting statistics in terms of ownerships and acres, 

the latest iteration will, for the first time, allow for 

estimates of the number of owners—an ownership is 

a legal entity and can, and often does, consist of more 

than one owner.

The ability to analyze remeasured data is a large 

opportunity and a great challenge. How does one know 

when an ownership has changed? A seemingly simple 

question that quickly becomes complicated. When a 

sample point is remeasured, the new and old owners 

are recorded in the database. If the names and mailing 

addresses are the same, it is the same owner. If the 

names and mailing addresses are completely different, 

they are different owners. But often, the differences 

are more subtle (e.g., Bob Smith to Bob and Sue 

Smith). Therefore, it will be necessary to quantify 

change on a sliding scale from definitely the same 

owner to definitely different owners, and thus be able 

to quantify and analyze the subtle differences.

NEXT STEPS

Next steps will include continued honing of the 

National Woodland Owner Survey estimation 

procedures with special emphasis on quantifying 

change over time. This will allow unprecedented 

examination of forest ownership dynamics.

0%

10%

20%

30%

40%

50%

60%

70%

1-9 10-19 20-49 50-99 100-199 200-499 500-999 1000+

Size of Family Forest Holdings (Acres)

Area

Owners

Figure 1.—Percentage of family forest acres and family forest owners by size of forest holdings (Butler 2008).
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 15

LITERATURE CITED

Bechtold, W.A.; Patterson, P.L., eds. 2005. The 

enhanced Forest Inventory and Analysis 

program - national sampling design and 

estimation procedures. Gen Tech Rep. SRS-80. 

Asheville, NC: U.S. Department of Agriculture, 

Forest Service, Southern Research Station. 85 p. 

Birch, T.W. 1996a. Private forest-land owners of the 

Northern United States, 1994. Resour. Bull. 

NE-136. Radnor, PA: U.S. Department of 

Agriculture, Forest Service, Northeastern Forest 

Experiment Station. 293 p. 

Birch, T.W. 1996b. Private forest-land owners of the 

Southern United States, 1994. Resour. Bull. 

NE-138. Radnor, PA: U.S. Department of 

Agriculture, Forest Service, Northeastern Forest 

Experiment Station. 195 p. 

Birch, T.W. 1996c. Private forest-land owners of the 

United States, 1994. Resour. Bull. NE-34. Radnor, 

PA: U.S. Department of Agriculture, Forest Service, 

Northeastern Forest Experiment Station. 183 p. 

Birch, T.W. 1996d. Private forest-land owners of 

the Western United States, 1994. Resour. 

Bull. NE-137. Radnor, PA: U.S. Department of 

Agriculture, Forest Service, Northeastern Forest 

Experiment Station. 249 p.

Birch, T.W.; Lewis, D.G.; Kaiser, H.F. 1982. The 

private forest-land owners of the United States.

Resour. Bull. WO-1. Washington, DC: U.S. 

Department of Agriculture, Forest Service. 64 p.

Butler, B.J. 2008. Family forest owners of the United 

States, 2006. Resour. Bull. NRS-27. Newtown 

Square, PA: U.S. Department of Agriculture, Forest 

Service, Northern Research Station. 72 p. 

Dillman, D.A.; Smyth, J.D.; Christian, L.M. 2009. 

Internet, mail, and mixed-mode surveys: the 

tailored design method. Hoboken, NJ: Wiley & 

Sons. 499 p.

Josephson, H.R.; McGuire, J.R. 1958. Ownership of 

forest land and timber. In: USDA Forest Service, 

ed. Timber Resources for America’s Future. 

For. Res. Rep. No. 14. Washington, DC: U.S. 

Department of Agriculture, Forest Service: 

289-321.

Metcalf, A.L. 2010. Human dimensions of private 

forestland ownership: Sampling, estimation, 

decision making processes, and implications.

State College, PA: The Pennsylvania State 

University. 279 p.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 16

USING MULTIPLE RESEARCH METHODS 

TO UNDERSTAND FAMILY FOREST OWNERS

John Schelhas1

Abstract.—Applied research on family forest owners ensures that we understand who 

they are, what they do, and why they do it. This information enables us to develop 

policy, management, and outreach approaches that can optimize the social, economic, 

cultural, and environmental benefits of private forests at the landowner, community, 

and national levels. The three principal scientific sources of information are National 

Woodland Owner Survey (NWOS) data, Forest Inventory and Analysis (FIA) data, and 

individual research efforts focused on specific geographical areas. NWOS and FIA data 

aim to be geographically comprehensive and are collected in intervals to provide time 

series data but are also less responsive to change in order to provide time series data. 

Individual research studies are tailored to specific questions, but their place specificity 

makes generalization difficult. We need to identify key management questions, use 

multiple research methods and data sources, and work collaboratively to maximize the 

effectiveness of our research.

1

 Research Forester, U.S. Forest Service, Southern Research 

Station, 320 Green St., Athens, GA 30602. To contact, call 

706-559-4260 or email at jschelhas@fs.fed.us.

INTRODUCTION

Family forests (sometimes called Nonindustrial 

Private Forest, or NIPF, owners) represent 40 

percent of the forested acres in the United States 

and are particularly prevalent in the Eastern United 

States, where 83 percent of the forest is in private 

ownership (Butler and Leatherberry 2004). In the 

South, family forests represent about 95 percent of 

the private forest owners and 63 percent of the private 

forest land (Birch 1996). Family forests provide 

many important private and public benefits. Family 

forest owners benefit from their forest lands through 

economic returns from sales of timber and other 

forest products, returns on investments, harvesting 

of products for home or farm use, enjoyment of 

recreation opportunities, appreciation of aesthetics and 

wildlife, and perpetuation of family legacy. Family 

forest lands also provide public goods related to land 

ownership, timber and pulpwood supplies, economic 

development, biomass for energy, forest health, 

watersheds, biodiversity, and global climate change. 

Research on and outreach to family forest owners can 

enhance these benefits, and private forests have long 

been a key target of public policy and outreach (Best 

and Wayburn 2001).

The study of relationships between people and forests 

is complex. First, family forests are subject to myriad 

social, economic, cultural, political, and biophysical 

factors and changes. These influences include: (a) the 

ways that people value forests, (b) markets for forest 

products, (c) government policies and programs that 

provide assistance to forest owners and restrictions 

on their actions, (d) market-based governance 

mechanisms such as certification programs, (e) 

long- and short-term environmental changes, and (f) 

changing population and residence patterns. Second, 

private forests can be studied at various scales, 

including the individual family forest holding; smaller 

social or biophysical areas such as watersheds or 

communities; and larger political units such as states, 

regions, and the nation. Finally, people and forests 

are studied by many academic disciplines, including 

human dimensions of natural resources, anthropology, 

geography, rural sociology, economics, and political 

science.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 17

FAMILY FOREST RESEARCH

Studies of family forest owners have described their 

social and economic characteristics (Birch 1996, 

Hartsell and Brown 2002), attitudes and values (Bliss 

and Martin 1989, Schelhas and Pfeffer 2009), forest 

management practices in response to policies (Brockett 

and Gebhard 1999, Zobrist and Lippke 2003), and 

use of government and professional assistance (Zhang 

et al. 1998). Other research has examined social 

relationships among forest owners, the dynamics 

between communities and forests, and institutions and 

governance affecting people’s interactions with forests 

(Gibson et al. 2000, Lee and Field 2005).

As a result of these studies, we know that family 

forest owners are diverse in terms of demographics, 

ownership objectives, and amount and type of 

forest owned (Best and Wayburn 2001). Families 

own forests for many reasons, such as aesthetics, 

wildlife, recreation, income, and investment, but 

non-commercial reasons tend to predominate (Koontz 

2001). Timber harvest is rarely the main reason for 

owning forests, although most owners do harvest and 

sell timber (Koontz 2001). Only a small percentage 

of forest owners make use of professional forestry 

assistance and/or have written management plans 

(Butler and Leatherberry 2004). Most have only 

a limited knowledge of professional forestry, and 

many forest landholdings receive little systematic 

management attention (Birch 1996). Recent research 

has examined differences by owner type and parcel 

characteristics (Koontz 2001) and used cluster analysis 

to group forest owners by similarities (Majumdar 

et al. 2008). Research in the United States on social 

relationships, communities, and governance has lagged 

behind international research in these areas (Schelhas 

et al. 2003).

The body of research on family forests helps us 

to develop conceptual models that link people to 

forests, with a critical nexus being human behavior 

and forest conditions. We know that human forestrelated behavior is determined by complex mixes 

of economics, values, social characteristics and 

relationships, institutions, and policies across scales. 

Environmental conditions that limit or enable 

various types of forest management and uses at 

particular places are also important in determining 

what people do. Complex people-environment 

interactions over time—and forests have a long-term 

temporal dimension—ultimately produce specific 

environmental conditions (habitat and biodiversity, 

biomass and carbon sequestration, watersheds) and 

human conditions (well-being of individuals and 

communities, economic and rural development). The 

practical value of research is learning about these 

broad relationships in a way that helps us to make 

educational, management, and policy interventions 

that steer human-forest relationships toward more 

environmentally and socially beneficial outcomes or 

identify tradeoffs.

HOW DO WE LEARN AND KNOW?

Ultimately, the science of people-forest relationships 

is cumulative—we are able to develop the knowledge 

we need only through multiple studies of different 

natures that draw on the full range of data types. It 

is important for us to step back occasionally and 

reflect on the goals of our research, the data we are 

collecting, and how we can most effectively move our 

knowledge forward. For family forest owners, we have 

three major data sources, each of which has different 

strengths and weaknesses.

National Woodland Owner Survey 

(NWOS) Data

Today’s NWOS builds on earlier periodic surveys 

of forest owners (e.g., Birch 1996) and is now an 

annual survey of forest owners in 5- and 10-year 

cycles (Butler and Leatherberry 2004). The NWOS 

represents our only comprehensive effort to understand 

family forest owners over time. Its value to forestry is 

similar to the value of the U.S. Census of Agriculture 

in understanding farmers (Schelhas et al. 2003). The 

NWOS is the fundamental source of information 

about the characteristics and practices of family 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 18

forest owners, and how these change over time. It has 

limitations that include the following: (a) questions 

change slowly to enable longitudinal studies, but limit 

tailoring to new issues, (b) it has historically focused 

primarily on owner characteristics and practices, and 

not social relationships and governance, (c) both the 

general and forest owner population are suffering from 

survey fatigue, reducing response rates and forcing 

researchers to limit questionnaire length. 

Forest Inventory and Analysis (FIA) 

Data

FIA data provide a systematic sampling of a fixed set 

of plots, measuring topography, site conditions, and 

forest characteristics (Majumdar et al. 2009). When 

linked to NWOS or other survey data, FIA data add 

the important dimension of environmental data and 

forest characteristics. These data are limited to the 

standard FIA measurements, and linking to other data 

is hindered by non-response rates in surveys and the 

NWOS and by the low density of FIA plots in the case 

of more geographically focused surveys.

Individual Research Projects

Researchers at universities and Federal agencies 

conduct question-driven research projects. 

These projects at times use data from systematic 

monitoring efforts like NWOS and FIA to answer 

specific questions, but often collect original data. 

The many forest owner surveys with econometric 

analysis (Beach et al. 2005) show relationships 

among different social and economic variables and 

are important in understanding forest and people 

relationships. Qualitative research provides more 

nuanced understanding of values and behaviors (Bliss 

and Martin 1989). Projects focusing on policies and 

institutions may use combinations of ethnographies, 

interviews, and text analysis (Gibson et al. 2000). 

What these individually focused projects gain in depth 

and focus, they lose in breadth; they generally cover 

single sites or a few selected comparison sites, thereby 

hindering generalizability.

FORMULATING POLICY- AND MANAGEMENT-RELEVANT QUESTIONS

The goal of applied research on family forest owners 

is to formulate questions relevant to policy and 

management and to draw on the full range of research 

methods and data types to answer them. Two examples 

highlight research projects that drew on diverse 

data sets to answer complex and important policy 

questions.

Kaetzel (2011) used both NWOS and FIA data from 

Alabama, Georgia, and South Carolina to conduct 

an analysis of timber availability that adjusted 

the standing timber according to forest owners’ 

willingness to harvest timber. By taking into account 

the diverse values, objectives, and practices of forest 

owners along with their timber inventory, Kaetzel 

obtained a more refined and useful measurement of 

timber supply.

Schelhas and Pfeffer (2009) combined qualitative 

interviewing and a survey to develop a conceptual 

model of the formation of forest owner values from 

both global environmental messages and local 

conditions, and how these values interact with 

livelihood needs to determine behavioral outcomes. 

The results provide guidance to programs that 

encourage family forest owners to manage forests for 

environmental benefits in buffer zones and corridors.

CONCLUSION

Forestry and social science researchers are asking 

critical questions and collecting a great deal of relevant 

data. There are many advantages to using multiple 

methods. Combining both survey and qualitative 

research can lead to nuanced understanding that can 

be used to develop better questionnaires; statistical 

analysis of quantitative data obtained from these 

questionnaires can then be used to support qualitative 

findings (Schelhas and Pfeffer 2009). Adding forest 

and site data adds an important environmental 

component. Yet many research efforts are 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 19

compartmentalized by institutional, disciplinary, and 

data boundaries. We can learn more if we endeavor to 

identify key management and policy questions, draw 

on multiple sources of data, and work collaboratively 

in our research.

LITERATURE CITED

Beach, R.H.; Pattanayak, S.K.; Yang, J.; Murray, B.C.; 

Abt, R.C. 2005. Econometric studies of nonindustrial private forest management a review 

and synthesis. Forest Policy and Economics. 

7: 261-281.

Best, C.; Wayburn, L.A. 2001. America’s private 

forests: status and stewardship. Washington, DC: 

Island Press. 224 p.

Birch, T.W. 1996. Private forest-land owners of 

the United States, 1994. Resour. Bull. NE-134. 

Radnor, PA: U.S. Department of Agriculture, Forest 

Service. 183 p.

Bliss, J.C.; Martin, A.J. 1989. Identifying NIPF 

management motivations with qualitative 

methods. Forest Science. 35(2): 601-622.

Brockett, C.D.; Gebhard, L. 1999. NIPF tax 

incentives: Do they make a difference? Journal 

of Forestry. 97(4): 16-21.

Butler, B.J.; Leatherberry, E.C. 2004. America’s 

family forest owners. Journal of Forestry. 

102(7): 4-9.

Gibson, C.C.; McKean, M.A.; Ostrom, E., eds. 2000. 

People and forests: communities, institutions, 

and governance. Cambridge, MA: MIT Press. 

274 p. 

Hartsell, A.J.; Brown, M.J. 2002. Forest statistics for 

Alabama, 2000. Resour. Bull. SRS-67. Asheville, 

NC: U.S. Department of Agriculture, Forest 

Service, Southern Research Station. 76 p.

Kaetzel, B.R. 2011. Family forest landowner 

research: attitudes about forest management 

and information needs. Auburn, AL: Auburn 

University. Ph.D. dissertation. 81 p.

Koontz, T.M. 2001. Money talks—but to whom? 

Financial versus nonmonetary motivations in 

land use decisions. Society and Natural Resources. 

14: 51-65.

Lee, R.G.; Field, D.R., eds. 2005. Communities and 

forests: where people meet the land. Corvallis, 

OR: Oregon State University Press. 300 p.

Majumdar, I.; Teeter, L.; Butler, B. 2008. 

Characterizing family forest owners: a cluster 

analysis approach. Forest Science. 54(2): 176-184.

Majumdar, I.; Teeter, L.D.; Butler, B.J. 2009. Using 

extant data to determine management direction 

in family forests. Society and Natural Resources. 

22: 867-883.

Schelhas, J.; Pfeffer, M.J. 2009. When global 

environmentalism meets local livelihoods: policy 

and management lessons. Conservation Letters. 

2: 278-85. 

Schelhas, J.; Zabawa, R.; Molnar, J.J. 2003. New 

opportunities for social research on forest 

landowners in the South. Southern Rural 

Sociology. 19(2): 60-69.

Zhang, D.; Warren, S.; Bailey, C. 1998. The role of 

assistance foresters in nonindustrial private 

forest management: Alabama landowners’ 

perspectives. Southern Journal of Applied Forestry. 

22(2): 101-105.

Zobrist, K.; Lippke, B.R. 2003. Case studies 

examining the economic impacts of new forest 

practices regulations on NIPF landowners. In: 

Teeter, L.; Cashore, B.; Zhang, D., eds. Forest 

policy for private forestry. Oxon: CABI Publishing: 

203-210.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 20

FOREST VALUES AND THE IMPACT OF THE FEDERAL ESTATE TAX 

ON FAMILY FORESTS

Brenton J. Dickinson, Brett J. Butler, Michael A. Kilgore, Paul Catanzaro, John Greene, 

Jaketon H. Hewes, David Kittredge, and Mary Tyrrell1

Abstract.—Previous research has suggested that heirs to family forest land may sell 

timber and/or land in order to pay state and/or federal estate taxes, which could result 

in land use conversion or other adverse ecological impacts. We estimated the number 

of Minnesota family forest landowners and the associated acreage that could be subject 

to estate taxes at various exemption levels. Using 2011 Minnesota forest land sale 

transactional data we calculated the minimum acreage that would trigger the federal 

estate tax at different hypothetical tax exemption levels and estate compositions (percent 

of gross estate whose value is comprised of forest land). Using the U.S. Forest Service’s 

National Woodland Owner Survey data for Minnesota and population mortality rates, we 

estimated the number of family forest ownerships and acres potentially affected under 

various scenarios. 

1

 Research Fellow (BJD), U.S. Forest Service, University 

of Massachusetts Amherst-Family Forest Research Center, 

Holdsworth Hall, 160 Holdsworth Way, Amherst, MA 

01003; Research Forester (BJB), U.S. Forest Service, 

Northern Research Station; Professor (MAK), University 

of Minnesota, Dept. of Forest Resources; Extension Asst. 

Professor (PC), University of Massachusetts Amherst, Dept. 

of Environmental Conservation; Research Forester (JG), 

U.S. Forest Service, Southern Research Station; Professor 

(DK), University of Massachusetts, Amherst, Dept. of 

Environmental Conservation; and Executive Director (MT), 

Global Institute of Sustainable Forestry, Yale University. 

BJD is corresponding author: to contact, call 413-545-6641 

or email at brenton.dickinson@gmail.com.

INTRODUCTION

Of the United States’ estimated 751 million acres of 

forested land, 35 percent is owned by families and 

individuals (Butler 2008). Family forests, sometimes 

referred to as non-industrial private forests, are owned 

by individuals, married couples, trusts, and other 

unincorporated entities. 

The federal government has taxed the intergenerational 

transference of estates through various laws since 

1916 (Jacobson et al. 2007). Some 20 states and the 

District of Columbia also currently impose estate and/

or inheritance taxes; 12 impose estate taxes and six 

impose inheritance taxes. Two impose both (Michael 

2011). The legal environment surrounding the estate 

tax has been particularly dynamic and uncertain over 

the past decade (Greene et al. in press). 

From the perspective of large landowners in particular, 

the worst case scenario for estate tax law is that 

Congress allows the 2010 Tax Relief Act to sunset, 

causing the estate tax exemption and rate to revert to 

2001 levels (Public Law 111-312, 124 Stat. 3296). 

That would mean that owners of estates worth more 

than $1 million ($2 million for couples) would have 

to pay a rate of 55 percent on estate value over that 

threshold. If Congress extends the current exemption 

and rate, owners of estates worth more than $5 million 

for individuals and $10 million for couples (plus 

inflation since 2001) will pay a rate of 35 percent. 

The U.S. Forest Service expects that fully one-sixth 

of family forest land will be transferred over the next 

5 years (USDA Forest Service 2012). This conclusion 

is derived in part by the fact that 19 percent of family 

forest owners fall within the ages 65-74 and an 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 21

additional 15 percent are 75 or older. Collectively, this 

group of retirement-aged individuals owns 44 percent 

of family forests (Butler 2008). Some estates have had 

to sell parcels of land or timber to pay the estate tax 

(Greene et al. 2006). Given the area of land expected 

to be transferred in the near future, this raises two 

questions: How many owners and acres of forest land 

will be affected?; and Will affected landowners sell 

timber and/or land as a result?

The objective of this study was a) to estimate the 

number of owners and associated acres that could 

be affected by the federal estate tax; and b) estimate 

the degree to which timber and land might be sold as 

a result under various tax law scenarios within five 

states. 

BACKGROUND

Greene et al. (2006) attempted to quantify the effects 

of the federal estate tax on land ownership patterns 

among family forest owners. The authors surveyed 

about 1,400 members of two forest owner groups: 

the National Woodland Owner Association and the 

American Tree Farm System. With a 33 percent 

response rate, they found that 9 percent of family 

forest owners were involved in an estate transfer 

between 1987 and 1997, during which there was a 

constant $600,000 exemption. Extrapolating, they 

estimated that 77,200 such transfers take place each 

year in the United States. Using average acreage 

owned by family forest owners, Greene et al. estimated 

the corresponding acreage to be 79.1 million per year. 

Extrapolating from average acreage of timber in their 

sample that was sold to pay the estate tax, Greene et 

al. estimated that each year 4,900 estates sell timber to 

pay the estate tax, amounting to 2.4 million acres. The 

authors similarly estimated that 3,300 family forest 

owners sell on the order of 1.3 million acres annually 

to meet their tax liability. Since 29 percent of those 

sold parcels were developed, the authors estimated 

that about 400,000 acres of family forest are lost to 

development each year in such a fashion.

While the estimates made by Greene et al. (2006) 

need to be viewed with suspicion due to likely sample 

selection bias and other issues, they suggest that the 

estate tax has significant implications for the fate 

of family forests. No subsequent study has tried to 

reproduce or broaden their results. In this uncertain 

legal environment, there is a need for thorough 

understanding of how various permutations of estate 

tax law affect family forests. 

METHODS

The degree to which forest land is affected by the 

estate tax depends in part on the forest land owners’ 

estate compositions. Only forest estates worth more 

than $1 and $5 million would be affected by “worst 

case” and “status quo” tax scenarios, respectively. 

Assuming a worst-case tax scenario, an extreme case 

of “all-forest” estate composition, and using our 

estimated median per-acre price for Minnesota parcels 

greater than 100 acres ($975), only plots in excess 

of 730 acres would be affected by the estate tax. 

National Woodland Owner Survey (NWOS) data from 

2006 suggest that an estimated 1,000 landowners in 

Minnesota own such parcels.

The extreme case of “all-forest” estate composition 

seems highly unlikely. We must take into account 

nonforest components of the estate: housing, 

investments, savings, and other assets. But we are 

faced with a near absence of data. We can only 

examine the affected owners and acres under differing 

assumptions of estate composition.

We began with a pilot study estimating forest land 

values in Minnesota. We obtained a sample of 489 

forest land sales transactions in Minnesota that include 

the town, county, size of plot, and sales price.2 These 

sales transactions occurred in 2009 and 2010 and 

represent only unimproved (no buildings) forested 

tracts, 20 acres or greater in size. We excluded tracts 

with improvements because we did not want to 

2

 Source: Michael Kilgore, University of Minnesota
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 22

conflate forest land value with the value of buildings. 

We limited our sample to parcels of 20 acres or more.

We calculated hypothetical minimum acreage 

thresholds, assuming a variety of per-acre forest land 

values, estate compositions (percent of total estate that 

is forest by value), and estate tax exemption levels. 

Put another way, we constructed a set of simplifying 

assumptions about the population of forest land 

owners and then calculated the minimum number of 

acres a landowner would have to own to be affected 

by the estate tax given those assumptions. The 489 

forest land sales transactions informed the range of 

per-acre price assumptions and the possible tax law 

scenarios informed the range of tax exemption level 

assumptions. We then used 2006 NWOS data from 

Minnesota landowners to estimate the number of 

landowners and associated forested acreage meeting 

specified acreage thresholds. 

An estimated 1,700 family forest owners could be 

affected by the estate tax at a $1 million dollar tax 

exemption level, assuming estates are 50 percent 

forest and given a $975 per-acre price (Fig. 1). At a 

$5 million exemption level, the number of potentially 

affected ownerships drops to less than 50. The number 

of affected acres increases similarly for higher per-acre 

prices, estates with lower percent forest (by value), 

and lower tax exemption levels (Fig. 2). It is important 

to realize that these graphs indicate estimated owners 

and acres who might be affected by the estate tax, 

but not all of these landowners are expected to die 

(and therefore face the tax) every year. Nor does this 

information suggest how the families will react to the 

tax (e.g., sell land, sell timber, or some other action). 

We can cautiously draw several conclusions. Under 

current estate tax law, it is mainly landowners who are 

relatively cash-rich and land-poor who will face the 

estate tax. That is because there are very few (if any) 

privately owned tracts of forest land valued near $5 

million. If, on the other hand, the estate tax law is not 

continued at the status quo and instead reverts back 

to 2001 levels, many more landowners are expected 

to face the tax, including the relatively land-rich and 

cash-poor. 

NEXT STEPS

To obtain reasonable estimates of the number of 

forest land owners who might face the estate tax, we 

need more details about forest land owners’ estate 

compositions. Specifically, we need to know what 

percentage of their total estates are composed of 

forest land. We are currently surveying Minnesota 

landowners to request information on, among 

other things, the value of their total estate and what 

percentage of that estate value is forest land. With this 

information, we can assign a more realistic range of 

estate compositions to the analysis behind Figures 1 

and 2. 

We also will expand the analysis to four other 

states—California, Michigan, North Carolina, and 

Washington—following similar procedures. There 

appears to be at least some high quality forest land 

sales transaction data from Michigan, but the types of 

data we seek may not be available in all states. In that 

case we will alter our analytical approach and/or select 

different states. 

Indeed, a preliminary search for forest land sales 

transaction data suggests a near total lack of 

availability. This highlights the need to develop a 

nationally consistent periodic assessment of forest land 

values across the United States, something we will 

explore further upon completion of this analysis.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 23

Tax Exemption Level (millions)

Total Potentially Affected Ownerships

$0 $2 $4 $6 $8 $10

0 500 1,000 1,500 2,000

Potentially affected ownerships

'' at $1m tax exemption

'' at $5m tax exemption

Figure 1.—Numbers of forest land owners potentially affected by the estate tax at varying tax exemption levels with estate = 

50 percent forest and per-acre price = $975.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 24

Tax Exemption Level (millions)

Total Potentially Affected Acres (millions)

$0 $2 $4 $6 $8 $10

0 1 2

3

4

5

Price = sample median; estate = 50% forest

Price = sample 3rd quartile; estate=25% forest

Figure 2.—Acres of forest potentially affected by the estate tax at varying estate values with varying estate composition and 

per-acre price.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 25

LITERATURE CITED

Butler, B.J. 2008. Family forest owners of the United 

States, 2006. Gen. Tech. Rep. NRS-27. Newtown 

Square, PA: U.S. Department of Agriculture, Forest 

Service, Northern Research Station. 72 p.

Cushing, T.; Bullard, S.; Greene, J.; Beauvias, T. 

1998. The effects of the federal estate tax 

on nonindustrial private landowners. In: 

Proceedings of the Society of American Foresters 

1998 National Convention; Traverse City, MI; 1998 

Sept. 19-23; Bethesda, MD: Society of American 

Foresters: 270-274. 

Greene, J.L.; Bullard, S.H.; Cushing, T.L.; Beauvais, 

T. 2006. Effect of the federal estate tax on 

nonindustrial private forest holdings. Journal of 

Forestry. 104(1): 15-20.

Greene, J.L.; Straka, T.J.; Cushing, T.L. In press. 

Chapter 11: effect of taxes and financial 

incentives on family-owned forest land. In: Wear, 

D.N.; Greis, J.G., eds. Southern forest futures. 

Gen. Tech. Rep. SRS-115. Asheville, NC: U.S. 

Department of Agriculture Forest Service, Southern 

Research Station.

Jacobson, D.B.; Raub, B.G.; Johnson, B.W. 2007. 

The estate tax: ninety years and counting.

Washington, DC: U.S. Department of Treasury, 

Internal Revenue Service, SOI Bulletin, Statistics 

of Income 2007. 27(1): 118-128.

Michael, J. 2011. Survey of state estate, inheritance, 

and gift taxes. Information Brief. St. Paul, MN: 

Minnesota House of Representatives, Research 

Department. 

USDA National Agricultural Statistics Service, 

2011. Land values 2011 summary August 2011.

Washington, DC: U.S. Department of Agriculture, 

National Agricultural Statistics Service. Available 

at http://usda01.library.cornell.edu/usda/current/

AgriLandVa/AgriLandVa-08-04-2011.pdf. 

(Accessed Jan. 11, 2012).

USDA Forest Service. 2012. Forest stewardship: 

estate planning options for family forests.

Newtown Square, PA: U.S. Department of 

Agriculture, Forest Service, Northeastern Area 

State and Private Forestry. Available at www.na.fs.

fed.us/stewardship/estate/estate.shtml. (Accessed 

Jan. 4, 2012).

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 26

Nontimber forest product harvesting on family forests: 

Results from the National Woodland Owner Survey

Marla R. Emery, Zhao Ma, Stephanie Snyder, and Brett J. Butler1

Abstract.—The 2002-2006 National Woodland Owner Survey asked a series of three 

questions about nontimber forest products (NTFPs) to assess the current status of 

their use on family forests. We report on responses to those questions. Logit models 

showed positive relationships between NTFP use and other forms of active engagement 

with family forests. Higher age and lower levels of educational attainment correlated 

negatively with likelihood of gathering, although not strongly. A history of gathering was 

the best predictor of future plans to do so.

1

 Research Geographer (MRE), U.S. Forest Service, Northern Research Station, 705 Spear St., 

Burlington, VT 05403; Assistant Professor (ZM), Department of Environment and Society, Utah 

State University; Operations Research Analyst (SS) and Research Forester (BJB), U.S. Forest 

Service, Northern Research Station. MRE is corresponding author: to contact, call 802-951-6771 

or email at memery@fs.fed.us. 

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 27

Assessing and Mitigating Denied Access on FIA Plots

Brett J. Butler and Andrew Hill1

Abstract.—Nationally, field crews are denied access to approximately 11 percent of 

Forest Inventory and Analysis (FIA) forested plots that are privately owned. The denied 

access rate varies from less than 1 percent in Alabama, Georgia, Louisiana, Maine, 

South Carolina, and Virginia to more than 30 percent in Arizona, California, Colorado, 

Montana, New Jersey, Rhode Island, and Texas. The large disparity is due, in part, to 

state laws that govern access. We hypothesize that there are also significant differences in 

the socio-demographics of the private owners that are influencing access rates. The first 

part of this presentation will focus on the current patterns of denied access rates across 

the United States and present a model to help explain some of its causes. There is a rich 

body of literature, primarily related to surveys, that has theoretically and empirically 

examined ways for maximizing response rates. This literature deals with topics such 

as personalization of communications, financial and nonfinancial incentives, modes of 

contacts, and timing of contacts to name but a few. We believe these findings may be 

very useful for minimizing FIA denied access rates. The second part of our presentation 

will focus on a literature synthesis and experiment that we are conducting to identify best 

practices for minimizing denied access rates.

1

 Research Forester (BJB), U.S. Forest Service, Northern Research Station, 160 Holdsworth Way, 

University of Massachusetts, Amherst, MA 01003; Research Forester (AH), U.S. Forest Service, 

Northern Research Station. BJB is corresponding author; to contact, call 413-545-1387 or email at 

bbutler01@fs.fed.us.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Methods for Measuring 

and Assessing 

Landscape Change
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 29

COMPARISON OF FOREST AREA DATA 

IN THE CHESAPEAKE BAY WATERSHED

Tonya W. Lister and Andrew J. Lister1

Abstract.—The Chesapeake Bay, the largest estuary in the United States, has been 

designated by executive order as a national treasure. There is much interest in monitoring 

the status and trends in forest area within the bay, especially since maintaining forest 

cover is key to bay restoration efforts. The Chesapeake Bay Land Cover Data Series 

(CBLCD), a Landsat-based, multi-temporal change detection raster geographic 

information system (GIS) product was developed by the U.S. Geological Service 

(USGS) to monitor land cover change in the bay. The objective of this study was to 

assess relationships between the CBLCD dataset and Forest Inventory and Analysis 

(FIA) estimates of land use in order to provide a better understanding of the nature of 

the CBLCD and its potential for use in assessing forest cover dynamics. Data were 

summarized at different geographic scales, and differences between datasets were 

highlighted with the goal of providing information that will help users of the CBLCD 

interpret findings. Our analyses suggest there is a strong, positive relationship between 

the CBLCD forest information and that from the FIA data. Misclassifications can be 

explained by analyses created by integrating the CBLCD data with the FIA data and 

standard FIA reporting tools. 

1

 Research Foresters (TWL and AJL), U.S. Forest Service, 

Northern Research Station, 11 Campus Blvd, Newtown 

Square, PA 19073. TWL is corresponding author: to contact, 

call 610-557-4033 or email at tlister01@fs.fed.us.

INTRODUCTION

The Chesapeake Bay Watershed, which includes 

parts of Maryland, Delaware, Pennsylvania, New 

York, Virginia and West Virginia, contains the largest 

estuary in the United States and provides habitat for 

thousands of species of plants and animals. The forests 

in this area provide many ecological services including 

protecting drinking water, serving as buffers against 

sedimentation and nutrient enrichment for estuarine 

species, and providing economic and other benefits for 

humans. However, the forests in the Chesapeake Bay 

Watershed have experienced a 2 percent net loss of 

forest land since the 1980s (Lister and Perdue 2011). 

Claggett et al. (2004) predict significant perturbations 

of the bay region’s forests in the coming decades, due 

largely to development pressures. 

In recognition of the importance of and current and 

future threats to the bay’s natural resources, Congress 

and several of the states containing parts of the 

watershed established the Chesapeake Bay Program 

partnership in the early 1980s. This partnership was 

based in part on an agreement to protect and restore 

the Bay’s ecosystems and has since been reaffirmed 

and updated. One component of the agreement is 

that the signatories will work to establish a system to 

monitor the status of and trends in forest cover. 

The Chesapeake Bay Land Cover Data Series 

(CBLCD) was developed to help with these efforts 

to track and monitor forest land in the bay (Irani and 

Claggett 2010). The CBLCD is a 30-m Landsatbased, multi-temporal geographic information system 

(GIS) change detection dataset produced by the U.S. 

Geological Survey (USGS 2006). The data series 

was created by merging the National Land Cover 

Dataset (NLCD) from the USGS (Homer et al. 2007) 

with land cover data from the National Oceanic and 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 30

Atmospheric Administration’s (NOAA) Coastal 

Change Analysis Program (C-CAP) (NOAA 2007) and 

improving the classification of urban and agricultural 

land. The CBLCD can be used to characterize land 

cover conversions for several dates between 1984 

and 2006. It is not clear, however, how the land cover 

classification system used by the CBLCD, which is 

based on that of Anderson et al. 1976, agrees with 

that used by other agencies, such as the U.S. Forest 

Service’s Forest Inventory and Analysis (FIA) 

Program. The FIA Program uses a network of ground 

plots to characterize land use on all lands along with 

tree and site information associated with forest land. 

The goal of the current study was to assess 

relationships between the CBLCD dataset and FIA 

estimates of land use in order to provide a better 

understanding of the nature of the CBLCD and its 

potential for use in assessing forest cover dynamics.

MATERIALS AND METHODS

The original classification system used for the 2006 

CBLCD image (USGS 2006) was reclassified into 

eight categories: open water/perennial ice and snow, 

developed open space, developed low-high intensity, 

barren land, forest including woody wetlands, shrub/

scrub, grassland/herbaceous, pasture/hay/cultivated 

crops, and emergent herbaceous wetlands.

A GIS was used to intersect the U.S. Census Bureau’s 

Tiger Line 2010 GIS county boundary files with a GIS 

file of the outline of the Chesapeake Bay Watershed. 

Within each of the counties that were completely 

within the watershed, areas of each of the eight 

reclassified CBLCD land cover classes were tabulated. 

Within these same counties, the area of forest land 

use was calculated from the FIA data using the PC 

EVALIDator tool, and a linear regression analysis 

with accompanying R2 values was calculated to assess 

relationships. Next, the individual FIA plots were used 

to characterize the CBLCD. Locations of the 4,784 

FIA plots within the watershed (Fig. 1) were used to 

generate 43-m radius circular buffers which were then 

intersected with the CBLCD. The reclassified CBLCD 

class with maximum area within the buffer was 

assigned to each FIA plot location. This new attribute 

was then incorporated into the PC EVALIDator 

reporting tool and was used as a categorical variable 

for conducting cross tabulations of FIA data (Miles 

2009). Cross tabulations included estimates of forest 

area by combinations of CBLCD class and FIA major 

land use, stocking, stand size, and physiographic 

classes (Woudenberg et al. 2010). In addition, a 

land use matrix from the PC EVALIDator tool that 

compares FIA major land use (forest, nonforest, and 

water) with the equivalent classes from the CBLCD 

was generated.

RESULTS AND DISCUSSION

An assessment of the relationship between the county 

FIA forest use and the CBLCD forest cover area 

estimates is shown in Figure 2. Summary statistics 

that describe the fit of the linear relationship indicate 

a strong, positive, nearly 1:1 linear relationship 

between the two attributes for all counties in each 

of the states, with R2 values greater than 0.80 in all 

cases. This suggests that there is no spatial trend in 

the strength of the relationship, or spatial variation in 

the CBLCD product. The land class matrix that was 

created to compare forest, nonforest, and water classes 

on FIA-sampled land with the equivalent classes 

from the CBLCD (Table 1) also indicates strong 

agreement, with 87 percent agreement between the 

FIA and CBLCD estimates. This is further evidence 

that there is a strong correspondence between forest 

information from the CBLCD and the land use 

information associated with the FIA data. The strength 

of the relationship is somewhat surprising given the 

differences in the definitions of the attributes. For 

example, FIA defines forest land using a combination 

of stocking, aerial extent and shape of the land cover 

patch, and an assessment of the ability of the land to 

provide forest regeneration. The CBLCD definition 

of forest land is based on that of the USGS National 

Land Cover Dataset (NLCD) (Homer et al. 2007) 

and includes tree canopy cover thresholds instead of 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 31

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State boundaries

Chesapeake Bay watershed

Forested FIA plot

Nonforest FIA plot

Figure 1.—Distribution of FIA plots within the Chesapeake Bay. Plot locations are approximate.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 32

Figure 2.—Comparison of county-level FIA forest land estimates with county-level CBLCD within the Chesapeake Bay 

Watershed. Dashed line represents a 1:1 linear relationship.

Table 1.—Land use matrix comparing FIA to collapsed Chesapeake Bay Land Cover Data series (CBLCD) 

cover class data in the Chesapeake Bay Watershed

 CBLCD (acres)

FIA (acres) Forest Nonforest Water Total

Forest 21,636,051 1,948,338 21,198 23,605,587

Nonforest 2,981,308 13,679,780 60,660 16,721,748

Water 202,872 97,682 457,811 758,365

Total 24,820,231 15,725,800 539,669 41,085,700
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 33

stocking and different aerial extent requirements for 

inclusion in the forest class. Although the distinction 

between land cover and land use can sometimes 

be stark (e.g., a clearcut with no trees can still be 

a forest land use), it is clear that in the case of the 

CBLCD and the FIA data, the majority of the land 

cover, as detected by the satellite, is also forest land 

use as detected by a ground observer on an FIA plot. 

Generally, it must be assumed that forest cover maps 

like the CBLCD are surrogates for forest land use 

maps, since forest land use maps are challenging to 

produce and are not widely available.

The area of forest land from the CBLCD classes 

assigned to the FIA plots was 5 percent higher than 

that derived from the FIA forest classes from the same 

plots (Table 1). This is an expected result given that 

the CBLCD estimates forest cover and FIA estimates 

are land use based. For example, the CBLCD may 

assign a forest cover class to treed areas where the 

underlying use is not forest, but rather a treed, low 

density residential development. Accuracy statistics 

from the NLCD 2001 dataset in Massachusetts and 

Rhode Island show a similar (almost 8 percent) 

overestimate of forest, with most misclassifications 

occurring where the NLCD forest class was actually 

developed for agricultural land use (Hollister et al. 

2004).

Ninety-two percent of the land FIA plots classified 

as forest is also classified as forest by the CBLCD 

(Table 1). The majority of the remaining 8 percent 

is classified as agricultural and shrubland (Fig. 3). 

These types of differences may be due to the presence 

of marginal agricultural lands at various stages of 

succession in the area. It is likely that the CBLCD 

classification procedures had difficulty distinguishing 

old or fallow fields from forest. We had expected to 

see this hypothesis reinforced by an analysis of the 

areas of CBLCD classes within areas classified as 

FIA forest, partitioned by stocking class and stand 

diameter class (Fig. 4) since one might expect areas 

0

5,000

10,000

15,000

20,000

25,000

Forest

FIA Forest Land (thousand acres)

0

200

400

600

800

1,000

1,200

1,400

Ag Shrub Urban Grass Wetland Barren Water

CBLC Data Land Use Class

Figure 3.—Composition of the FIA forest land estimate by corresponding CBLCD land use class. Error bars represent 

68 percent confidence intervals.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 34

with a higher stocking class or larger trees to generate 

more of a “forest signal” in the CBLCD classification 

procedure. Although the data do not suggest that 

forested plots with low stocking classes are more 

likely to be misclassified, the data do suggest that 

there is generally a large proportion of medium and 

higher stocked plots in the forest class. Two notable 

exceptions to this pattern, however, are the barren 

and the grassland CBLCD classes, which also show 

relatively high areas of medium and higher stocked 

Figure 4.—Forest land area of select FIA forest class variables including stocking class (a), stand size class (b), and 

physiographic class (c), within each CBLCD land use class. Error bars represent 68 percent confidence intervals.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 35

stands. This could be explained by the relatively large 

areas of small diameter stands within these classes 

(Fig. 4b), suggesting that the high stocking values are 

due to large numbers of small trees or seedlings. These 

areas may confuse the classification algorithm as they 

may resemble barren sites or grasslands. 

Another interesting finding from our comparisons 

is the amount of FIA forest land area misclassified 

as water by the CBLCD (Fig. 3). While this is a 

fairly serious misclassification, Figure 4c suggests 

that the physiographic classes associated with 

these misclassified areas tend to be wetter (coves, 

floodplains, bottomlands, swamps, ponds) than areas 

associated with other types of misclassifications. 

In these areas, the signal imparted by these wet 

sites likely led to the confusion of the classification 

algorithm.

Our main conclusion from this study is that, assuming 

FIA data are representative of the forest attributes 

that Chesapeake Bay land managers want to monitor, 

the strong relationship between CBLCD and FIA 

data indicates that the CBLCD can be a valuable tool 

for characterizing land cover in the Chesapeake Bay 

Watershed. Our analyses suggest that in aggregate 

(at the county scale) there is a strong, positive 

relationship between the CBLCD forest information 

and that from the FIA data. Misclassifications that 

occurred can be explained by analyses created by 

integrating the CBLCD data with the FIA data and 

standard FIA reporting tools. Results of this study 

shed light on the relationship between the two land 

classification systems and will provide managers with 

information that can be used to not only interpret land 

class changes in the Chesapeake Bay Watershed, but 

also to help improve future versions of the CBLCD 

product. Future work will involve looking at species 

assemblage data and their effects on relationships 

between CBLCD and FIA data, as well as the 

relationship between other land cover products and 

FIA data in this important region.

LITERATURE CITED

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.

Geological Survey Professional Paper 964. A 

revision of the land use classification system as 

presented in U.S. Geological Survey Circular 671. 

Washington, DC: U.S. Government Printing Office. 

41 p.

Claggett, P.; Jantz, C.; Goetz, S.; Bisland, C. 

2004. Assessing development pressure in the 

Chesapeake Bay Watershed: an evaluation of 

two land-use change models. Environmental 

Monitoring and Assessment. 94(1): 129-146.

Hollister, J.W.; Gonzalez, L.M.; Paul, J.F.; August, 

P.V.; Copeland, J.L. 2004. Assessing the accuracy 

of national land cover dataset area estimates 

at multiple spatial extents. Photogrammetric 

Engineering and Remote Sensing. 70(4): 405-414.

Homer, C.; Dewitz, J.; Fry, J.; Coan, M.; Hossain, N.; 

Larson, C.; Herold, N.; McKerrow, A.; VanDriel, 

J.N.; Wickham, J. 2007. Completion of the 

2001 National Land Cover Database for the 

conterminous United States. Photogrammetric 

Engineering and Remote Sensing. 73(4): 337-341.

Irani, F.M.; Claggett, P.R. 2010. Chesapeake 

Bay Watershed land cover data series. U.S. 

Geological Survey Data Series 2010-505. 

Lister, T.W.; Perdue, J. 2011. Maryland’s forest 

resources, 2010. Res. Note. NRS-124. Newtown 

Square, PA: U.S. Department of Agriculture, Forest 

Service, Northern Research Station. 4 p.

Miles, P. 2009. EVALIDatorReports: reporting 

beyond the FIADB. In: McWilliams, W.; Moisen, 

G.; Czaplewski, R., comps. 2009. 2008 Forest 

Inventory and Analysis (FIA) Symposium; 2008 

October 21-23; Park City, UT. RMRS-P-56CD. 

Fort Collins, CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research Station. 

1 CD.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 36

National Oceanic and Atmospheric Administration 

(NOAA). 2007. C-CAP zone 65: 2001-2006-era 

land cover metadata. Charleston, SC: National 

Oceanic and Atmospheric Administration, Coastal 

Services Center. Available at http://www.csc.noaa.

gov/digitalcoast/data/ccapregional/. (Accessed July 

15, 2012).

U.S. Geological Survey (USGS). 2006. Chesapeake 

Bay Watershed land cover data series 2006 

metadata document. Reston, VA: U.S. Geological 

Survey. Available at http://www.pasda.psu.edu/uci/

FullMetadataDisplay.aspx?file=CBWLCD_2006.

xml#Metadata_Reference_Information. (Accessed 

July 15, 2012).

Woudenberg, S.W.; Conkling, B.L.; O’Connell, B.M.; 

LaPoint, E.B.; Turner, J.A.; Waddell, K.L. 2010. 

The Forest Inventory and Analysis database: 

database description and users manual. Version 

4.0 for phase 2. Gen. Tech. Rep. RMRS-245. 

Fort Collins, CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research Station. 

336 p.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 37

Assessment of Land Use Change in the Coterminous

United States and Alaska for Global Assessment

of Forest Loss Conducted by the Food and Agricultural 

Organization of the United Nations

Tanushree Biswas, Mike Walterman, Paul Maus, Kevin A. Megown, Sean P. Healey, and C. Kenneth Brewer1

Abstract.—The Food and Agricultural Organization (FAO) of the United Nations 

conducted a global assessment for forest change in 2010 using satellite imagery from 

1990, 2000, and 2005. The U.S. Forest Service was responsible for assessing forest 

change in the United States. A polygon-based, stratified sampling design developed by 

FAO was used to assess change in forest area within 10 km by 10 km tiles at every 1° 

from 1990, 2000, and 2006 using Landsat TM and ETM+ data. The assessment included: 

1) mapping land cover (tree and non-tree) and land use (forest and nonforest) within these 

tiles for each time period; 2) a segment-based analysis of land use transition between 

1990 and 2000, and 2000 and 2005; 3) reporting forest change (area) by FAO ecoregions; 

and 4) comparing the estimates from segment-based analysis of land cover and land use 

change in the coterminous United States between the study periods. The current paper 

summarizes the estimates of land use change by FAO ecoregions in the United States 

between 1990 and 2000, and 2000 and 2006 based on the survey and compares land 

cover and land use change estimates for the coterminous United States. Our analysis 

shows that most forested and nonforested areas remained unchanged during each time 

period. Overall rate of forest loss was higher between 1990 and 2000 than between 

2000 and 2006. Net forest loss in the United States for the entire study period was 0.79 

percent. The ecoregion stratum subtropical humid forest showed the highest net forest 

loss, followed by temperate continental forest and temperate mountain system. Net forest 

and tree cover change was higher in 1990-2000 than 2000-2006 in the coterminous United 

States and confirmed that land cover change does not necessarily indicate land use change. 

1

 Remote Sensing Specialist (TB), Geospatial Analyst 

(MW), Remote Sensing Specialist (PM), RMIM Program 

Manager (KAM), U.S. Forest Service, Remote Sensing 

Application Center, 2222 West 2300 South, Salt Lake City, 

UT 84119; Ecologist (SPH), U.S. Forest Service, Rocky 

Mountain Research Station, Ogden, UT; National Remote 

Sensing Research Program Leader (CKB), U.S. Forest 

Service, Research and Development, Washington, DC. TB 

is corresponding author: to contact, call 801-975-3754 or 

email at tbiswas@fs.fed.us.

INTRODUCTION

The Remote Sensing Application Center conducted a 

survey of land cover and land use change in the United 

States (including Alaska) using a forest sampling 

design developed by the Food and Agricultural 

Organization (FAO) of the United Nations (Ridder 

2007) for global assessment of forest loss. The survey 

included a segment-based assessment of land use and 

land cover types within 10 km by 10 km grids at every 

1° interval for 1990, 2000, and 2006 (Fig.1). Land 

cover (tree, woodland, other, and water) and land use 

(agriculture, bare, built-up, forest, natural herb, other 

wooded land, wetland, and water) were mapped within 

these grids (Fig.1). For simplicity of analysis the land 

cover types were merged into two broad classes “Tree” 

and “NonTree” while the land use types were merged 

into “Forest” and “NonForest”’ classes to assess 

change and meet FAO’s goal of global assessment 

of forest loss (Table 1). Woodland and other wooded 

land were included in “NonTree” and “NonForest,” 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 38 Figure 1.—Distribution of the 10 km x km study grids and the rule set used to classify the segments within the grids into land cover and land use types based on the corresponding NLCD data for assessing land use and land cover changes within the United States between 1990, 2000, and 2006. These rule sets were only used to classify the segments within the survey grids within CONUS based on pixel (30 m resolution) information available from NLCD data cross walked to corresponding land use and land cover product for each time period. If equal percent area in two classes, then segment call → not calculated
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 39

respectively (Table 1). Changes in land use and land 

cover within these grids were summarized by different 

ecoregions (Commission for Environment Cooperation 

2006) in the United States between 1990 and 2000, 

and 2000 and 2006. The current paper provides an 

overview of the methods and assessment of land use 

change conducted in the United States. 

METHODS

We assessed forest loss in the entire United States 

based on 938 grids provided by FAO (Fig.1). The 

following section discusses the methods used for 

mapping, change detection, and validation of change 

in the coterminous United States (CONUS) (812 grids) 

and Alaska (126 grids). 

Mapping 

(i) Coterminous United States. The National Land 

Cover Database (NLCD) product (30-m resolution 

raster, available from the corresponding time period 

Table 1. Cross walk between NLCD land cover types and FAO land use and land cover types

FAO Classes-Level I FAO Classes-Level II

NLCD Class (1990, 2000, 2006) Land Use Land Cover Land Use Land Cover 

Deciduous Forest Forest Tree Forest Tree

Evergreen Forest Forest Tree Forest Tree

Mixed Forest Forest Tree Forest Tree

High Intensity Residential Built-Up Other NonForest NonTree

Low Intensity Residential Built-Up Other NonForest NonTree

Commercial Industrial Built-Up Other NonForest NonTree

Open Water Water Water NonForest NonTree

Orchard/Vineyards Agriculture Other NonForest NonTree

BareRock/Sand/Fallow/Barren Bare Other NonForest NonTree

Pasture/Hay Natural Herb Other NonForest NonTree

Grassland/Herbaceous Natural Herb Other NonForest NonTree

Emergent Herbaceous Wetlands Wetlands Other NonForest NonTree

Row Crop/Small Grain Agriculture Other NonForest NonTree

Shrubland Woodland Shrub NonForest NonTree

Transitional Woodland Shrub NonForest NonTree

Urban Built-Up Other NonForest NonTree

Woody Wetland Other Wooded Land Other NonForest NonTree

Quarry, Mine Bare Other NonForest NonTree

Perennial Ice/Snow Bare Other NonForest NonTree

Dwarf Scrub Other Wooded Land Shrub NonForest NonTree

[1990, 2001, and 2006]) was used for the assessment 

in the coterminous United States (Fry et al. 2011, 

Homer et al. 2007, Vogelmann et al. 2001). The NLCD 

land cover types corresponded closely to FAO’s land 

cover and land use classes of interest by FAO (Table 

1). The segments within the grids (n=812) were 

classified based on pixel (30-m resolution) using 

information available from NLCD-derived land use 

and land cover product for each time period (Fig. 1). 

(ii) Alaska. Absence of NLCD product for Alaska 

from all study periods led to independent unsupervised 

classification of Landsat TM imagery of 126 grids 

into forest and nonforest classes for each time period. 

Zonal majority of area under forest and nonforest 

within each segment was eventually used to classify 

the segments to the dominant land use type. 

Change Detection 

(i) Land Use. The land use types from each time 

period from CONUS were merged to forest and 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 40

nonforest classes (Table 1) to assess change in the 

United States across four transition types: forest to 

nonforest (forest loss), nonforest to forest (forest 

gain), forest to forest (no change forest) and nonforest 

to nonforest (no change nonforest). Change in area 

(ha) under each transition type was summarized by 

each ecoregion for 1990 to 2000, and 2000 to 2006. 

A latitude correction factor was used to account for 

the variation in area of each segment in the study area 

based on location relative to the equator (Equation 1). 

The correction factor was applied to all the segments 

prior to assessing area under different land use 

transitions. 

Latitude Correction Factor 

= 2*pi*R*cos(lat) / (2*pi*R*cos(lat0)) 

= cos(lat)/cos(lat0) Eq (1)

(ii) Land Cover. Land cover change was conducted 

only for the coterminous United States across four 

transition types: tree to non-tree, non-tree to tree, no 

change tree, and no change non-tree. The latitude 

correction factor was used to summarize the area 

estimates of land cover change within the four 

transition types. 

Validation 

The validation exercise was conducted only for land 

use change to meet FAO’s goal of estimate of forest 

loss. A stratified random sampling approach was 

used to photo interpret (PI) a minimum of 20 random 

points within each land use transition in each FAO 

region for each time period. Google EarthTM, National 

Agricultural Imagery Program imagery from 2000 and 

2006, and ground knowledge of the PI personnel were 

used to validate the land use transition of the segment 

that intersected with the random points. The agreement 

matrix for validation was enumerated from the weights 

of sampled points instead of actual count of points 

within each transition. The weight of each sampled 

point within each stratum was developed as a function 

of the area of the land use change stratum relative to 

the total area and total number of points within the 

stratum (Fig. 2). The proportion of agreement and 

disagreement in the land use transitions observed 

between the PI land use change call and NLCD results 

was used to develop a correction factor for the final 

estimate of area under each transition type in each 

FAO region. 

A total of 2,536 points were validated for observed 

land use transitions between 1990 and 2000 with 

120 points in Alaska and the rest in CONUS. For 

the second time period 1,280 points were validated 

for all the land use transition types observed in each 

FAO region between 2000 and 2006. For the first 

time period sampling for validation was done within 

64 land use transitions before merging the land 

use classes to forest and nonforest and without the 

knowledge of the FAO ecoregions. Using the weights 

of the sampled points instead of actual counts also 

accounted for the difference in sampled size under 

each land use transition between the study periods. 

RESULTS

Land Use Change

Land use change (overall forest loss and forest gain) 

was assessed based on the original area of segment, 

area corrected by latitude, and latitude-corrected 

area post-validation and by different ecoregion (Fig. 

3). These results suggest the importance of latitude 

correction and an accuracy assessment of change, and 

effect of location and relative size of the segments, 

on the overall estimate of forest loss. Segments from 

Alaska (Ecoregion - Polar and Boreal) after the 

latitude correction had considerable influence on the 

overall estimate and percent forest loss and forest gain. 

The final estimates used to report and summarize land 

use change during this study were derived from the 

latitude correction function and validation.

The results of the survey indicate more nonforested 

area than forested area, with most of the forest (28.8% 

in 1990-2000, 30.8% in 2000-2006) and nonforest 

(68.6% in 1990-2000, 68.2% in 2000-2006) area 

remaining stable (Table 2). Overall rate of loss of 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 41

Figure 2.—Calculating the weight of each sample point by land use change strata. 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 42

forest was higher between 1990 and 2000 (1.8%) 

than between 2000 and 2006 (0.63%). Rate of overall 

forest loss was higher than forest recovery during both 

the study periods. Overall rate of both forest loss and 

forest gain declined by 1.17 percent and 0.44 percent, 

respectively (Fig. 3, AreaWTbyLatitude_validated), 

reducing the overall net forest loss in the United 

States. Net loss of forest was less than 1 percent 

between 2000 and 2006. Given the large proportion of 

area under stable forest, the observed change in forest 

area was minimal.

Subtropical humid forest region underwent the 

highest net forest loss, followed by the Polar region, 

Subtropical steppe, and temperate mountain system. 

Figure 3.—Comparing differences in assessment of forest loss and forest gain, based on the original area of the segments, 

after the latitude correction function on the original segments (AreaWeightedby Latitude) and validation of the latitude 

corrected segments (AreaWTbyLatitude_Validated) within the coterminous United States. 

Table 2.—Comparison of overall estimates of area (ha) (validated and weighted by latitude) under different 

land use transitions in the United States between 1990 and 2000, and 2000 and 2006, based on the survey 

grids

Land Use Change 1990-2000 2000-2006

Forest to nonforest 123,927.05 (1.8%) 43,133.15 (0.63%)

No change forest 1,968,040.55 (28.8%) 2,106,957.36 (30.86%)

No change nonforest 4,683,794.57 (68.6%) 4,653,012.52 (68.2%)

Nonforest to forest 51,730.53 (0.8%) 24,389.67 (0.36%)

Total Area 6,827,492.71 6,827,492.71

The pattern was consistent for both study periods 

(Fig. 4a). Net percent forest loss was higher between 

2000 and 2006 than between 1990 and 2000 in most 

ecoregions except Boreal and Temperate steppe. 

Boreal tundra woodland and Temperate steppe showed 

net forest gain between 1990 and 2000 and net forest 

loss between 2000 and 2006, although the percent 

change was small. Percent forest gains in these 

ecoregions were higher than percent forest loss in 

1990-2000. Percent forest gain in Polar, Subtropical 

humid forest, Temperate continental forest, and 

Temperate mountain system was lower than percent 

forest loss for each time period, resulting in net forest 

loss (Fig. 4a).
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 43

a.

b.

Figure 4.—(a) Comparing net percentage change in forest (Forest Gain – Forest Loss) between 1990-2000 and 2000-2006 by 

ecoregion; (b) Overall net change in forest area within the United states by FAO ecoregion for the entire study period. 

Overall net forest loss in the United States was less 

than 2 percent (Fig. 4b). Net forest area increased 

the most in Boreal Tundra (4,965.4 ha, Table 3) and 

Boreal mountain system (495 ha, Table 3) for 1990-

2000 and 2000-2006. Net forest loss was highest in 

Subtropical Humid Forest (36,255.6 ha, Table 3) and 

Temperate Continental Forest (32,782.9 ha, Table 3) 

followed by Temperate Mountain system (14,638.7 ha) 

and Subtropical Steppe (11,546.5 ha, Table 3) with net 

percent loss being less than 1 percent. Increased forest 

loss and reduced forest gain led to increased forest 

loss in these regions. A similar pattern was observed 

in other regions with net loss being less than 9,000 ha 

(1%, Fig. 4b). According to the recent Forest Inventory 

and Analysis report (Smith et al. 2009), forest land 

across the Nation has increased by 4 percent since 

1987; it decreased 3 percent between 1953 and 1987. 

In the North region, forest area has increased by nearly 

7 percent while it declined by 5 percent in the South 

region since 1953. In the Rocky Mountain region 

forest acreage rose by 6 percent whereas in the Pacific 

Coast region (including Alaska) forested acreage 

declined by 4 percent between 1953 and 2007. These 

trends seem to be consistent with the patterns observed 

during this survey (Fig. 4b). 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 44

Table 3.—Change in area (ha) under forest gain and forest loss between 1990 and 2000, and 2000 and 

2006 in the United States by FAO region (estimates are based on the segments from the survey grids that 

were corrected for latitude and validated)

FAO Ecoregions Forest to Nonforest Nonforest to Forest Net Change 

Boreal Mountain System -2078.4 2,527.8 449.4

Boreal Tundra Woodland 2,994.1 1,971.3 4,965.4

Polar -10,908.2 3,316.2 -7,592.0

Subtropical Desert 0.0 8.9 8.9

Subtropical Dry Forest -188.9 -0.6 -189.5

Subtropical Humid Forest -11,229.2 -9,391.4 -36,255.6

Subtropical Mountain System -17,845.7 67.0 248.2

Subtropical Steppe -11,229.2 -317.3 -11,546.5

Temperate Continental Forest -17,845.7 -14,937.2 -32,782.9

Temperate Desert -935.0 8.4 -926.6

Temperate Mountain System -11,199.7 -3,438.9 -14,638.7

Temperate Oceanic Forest -1,193.0 -92.8 -1,285.8

Temperate Steppe -1,242.6 -7,212.8 -8,455.4

Tropical Moist Forest -368.3 0.0 -368.3

Segments in Unclassified FAO Region 84.2 150.6 234.8

Overall net change in forest area -79,761.4 -31,387.0 -10,8134.8

Land Cover Change

Land cover change estimates from the coterminous 

United States showed higher estimates of net change 

in tree cover (3.2%, 201,761.4 ha in 1990-2000 and 

0.6%, 40,126.4 ha in 2000-2006) than net forest 

change (2.2%, 139,067.3 in 1990-2000 and 0.8%, 

53,267.8 in 2000-2006) (Fig.5). Both net forest and 

tree cover change was higher in 1990-2000 than 2000-

2006. These estimates for the coterminous United 

States were calculated prior to the validation and 

hence cannot be used to report actual change. These 

patterns suggest, however, that estimates calculated 

from land cover change, as opposed to land use 

change, overestimate change. Area under forest loss 

included areas with gain in tree cover. Likewise area 

under forest gain included area with conversion of tree 

to non-tree (loss of tree cover). Stable non-forest area 

included 1.4% (58850.7 ha) gain in tree cover. Forest 

loss observed between 1990 and 2000, and 2000 and 

2006, showed 82,203 ha and 29,736 ha of persistent 

tree cover, respectively, and included gain in tree cover 

as well (19.7 ha and 58.2 ha, respectively). These 

patterns primarily confirm that land cover change 

does not necessarily indicate land use change. Unless 

a forest segment has undergone a change in its use to 

a non-forest class, observed change in the land cover 

type is only a transitional state to the next successional 

or vegetation type. 

CONCLUSION

Based on the survey conducted within the FAO grids, 

most of the forested area remained stable between 

1990, 2000, and 2006. Overall forest gain and loss 

ranged from less than 1 percent to 2 percent during 

the study periods. Subtropical humid forest showed 

the highest net forest loss, followed by temperate 

oceanic forest. Overall rate of loss of forest was 

higher between 1990 and 2000 than between 2000 

and 2006. Accounting for the latitude of the segments 

significantly altered the estimate of area under 

different land use change. Accuracy assessment of the 

land use change further reduced the estimated area 

under change. This study demonstrated the importance 

of a latitude correction and validation on actual 

estimates and indicated that most of the forest area 

within the surveyed grid in the United States remained 

stable after 2000. 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 45

Figure 5.—Comparing net percent change in forest (NonForest to Foreste – Forest to NonForest) and tree (NonTree to Tree 

– Tree to NonTree) between 1999-2000 and 2000-2006 within the coterminous United States. 

LITERATURE CITED

Commission for Environment Cooperation. 2006. 

North American environmental atlas terrestrial 

eco-region Level II. Available at www.cec.org/

Page.asp?PageID=924&ContentID=2336. [Date 

accessed unknown].

Fry, J.; Xian, G.; Jin, S.; Dewitz, J.; Homer, C.; Yang, 

L.; Barnes, C.; Herold, N.; Wickham, J. 2011. 

Completion of the 2006 National Land Cover 

Database for the conterminous United States.

Photogrammetric Engineering & Remote Sensing. 

77(9): 858-864.

Homer, C.; Dewitz, J.; Fry, J.; Coan, M.; Hossain, N.; 

Larson, C.; Herold, N.; McKerrow, A.; VanDriel, 

J.N.; Wickham, J. 2007. Completion of the 

2001 National Land Cover Database for the 

Conterminous United States. Photogrammetric 

Engineering & Remote Sensing. 73(4): 337-341. 

Ridder, R.M. 2007. Options and recommendations 

for a global remote sensing survey of forests.

FAO Forest Resource Assessment Programme 

Working Paper 141. Rome, Italy: Food and 

Agriculture Organization of the United Nations. 

68 p.

Smith, W.B., tech. coord.; Miles, P.D., data coord.; 

Perry, C.H. map coord.; Pugh, S.A., Data CD 

coord. 2009. Forest resources of the United 

States, 2007. Gen. Tech. Rep. WO-78. Washington, 

DC: U.S. Department of Agriculture, Forest 

Service, Washington Office. 336 p.

Vogelmann, J.E.; Howard, S.M.; Yang, L.; Larson, 

C.R.; Wylie, B.K.; Van Driel, J.N. 2001. 

Completion of the 1990’s National Land Cover 

Data Set for the conterminous United States.

Photogrammetric Engineering & Remote Sensing. 

67: 650-662.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 46

IMAGE-BASED CHANGE ESTIMATION FOR LAND COVER 

AND LAND USE MONITORING 

Jeremy Webb, C. Kenneth Brewer, Nicholas Daniels, Chris Maderia, Randy Hamilton, 

Mark Finco, Kevin A. Megown, and Andrew J. Lister1

Abstract.—The Image-based Change Estimation (ICE) project resulted from the need to 

provide estimates and information for land cover and land use change over large areas. 

The procedure uses Forest Inventory and Analysis (FIA) plot locations interpreted using 

two different dates of imagery from the National Agriculture Imagery Program (NAIP). 

In order to determine a suitable project workflow, interpretation methods and database 

options were explored. The results provide useful information for the change occurring 

between land cover and land use types across two prototype landscapes.

1

 Remote Sensing Specialist (JW), U.S. Forest Service, 

Remote Sensing Applications Center, 2222 West 2300 

South, Salt Lake City, UT 84119; Program Manager 

(CKB), U.S. Forest Service, Research and Development–

Quantitative Sciences Staff; Interns (ND and CM), Group 

Leader (RH), Contract Leader (MF), Program Leader 

(KAM), U.S. Forest Service, Remote Sensing Applications 

Center; Research Forester (AJL), U.S. Forest Service, 

Northern Research Station. JW is corresponding author: to 

contact, call 801-975-3462 or email at jwebb02@fs.fed.us.

INTRODUCTION

Understanding changes in land use and land cover 

(LULC) over space and time provides a means to 

evaluate the complex interactions between human 

and biophysical systems, to project future conditions 

(absent policy changes), and to design mitigation 

and adaptive management strategies. Understanding 

and modeling LULC change is evolving into a 

foundational element of climate, environmental, 

and sustainability science. Land use and land cover 

data are core to applications including: carbon 

accounting, greenhouse gas emission reporting, 

biomass and bioenergy assessments, hydrologic 

function assessments, fire and fuels management, and 

forest and rangeland health assessments. The lack 

of comprehensive and spatially-explicit historical 

vegetation data for the entire United States challenges 

the ability of scientists and land managers to 

understand cumulative effects of natural disturbances 

and human activities. By extension, our ability to 

understand and model future scenarios is limited by 

a lack of information about the disturbance processes 

that shape land cover changes and land use decisions 

by private landowners and public land managers.

A recent resolution from the National Association of 

State Foresters (NASF) reflects both the importance of 

these data and the need to generate reliable estimates 

of land cover and land use change. The NASF 

resolution suggests an enhanced Forest Inventory and 

Analysis (FIA) program that prioritizes the “Use of 

remote imagery to track harvest intensity, land-use 

change, and land cover change” (NASF State Forester 

Resolution 2009-6). The FIA program and the Remote 

Sensing Applications Center (RSAC) were specifically 

identified as partners for accomplishing this. In 2011, 

RSAC initiated this Image-based Change Estimation 

(ICE) project in response to the NASF resolution. 

The specific objectives of the project were to: (1) 

develop an efficient, repeatable workflow that could be 

implemented nationwide in conjunction with the FIA 

program for assessing LULC change from imagery, 

and (2) test and demonstrate the workflow in two 

diverse pilot study areas. 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 47

METHODS 

The study areas for the ICE project were the states 

of Colorado and Georgia. They were selected to 

provide diverse and dissimilar landscapes where 

the effectiveness of photo-based data collection for 

identifying and classifying LULC change could be 

evaluated. LULC change was assessed in the two 

states by interpreting two dates of National Agriculture 

Imagery Program (NAIP) imagery within plots located 

on each state’s base FIA grid (Bechtold 2005). The 

circular plots were 1.44 acres (144-foot radius) in size, 

which ensured full coverage of the FIA subplots. The 

total number of plots interpreted for the study was 

17,222 with 10,815 in Colorado and 6,407 in Georgia. 

Both Colorado and Georgia had full, state-wide 

coverage of NAIP imagery for 2005 that was used 

for the Time 1 (T1) dataset. The Time 2 (T2) dataset 

consisted of four band (red, green, blue, near-infrared) 

NAIP imagery acquired in 2009 for Colorado and 

2010 for Georgia. The spatial resolution of the imagery 

was 1 meter for all datasets except the 2005 Georgia 

imagery, which was 2 meters. The temporal scale of 

approximately 5 years was chosen to align with the 

FIA panel system and the plot revisit times by FIA 

field personnel.

Classification Schemes

The LULC classes for this project were based on the 

FIA land cover and land use classes (USDA FS 2011) 

for defining condition class as well as those described 

by Anderson et al. (1976). The classification schemes, 

summarized in Table 1, allowed the newly photo-

interpreted data to align with previously collected 

FIA plot data. Along with the LULC classes, an 

agent of change was recorded for any plots that had a 

change in LULC between T1 and T2. This provided 

additional information about the type of change that 

may not have been inferred from the LULC attributes. 

The agent of change classes included: reforestation/

afforestation, partial tree harvest, clear cut harvest, 

fire, development, and other change. 

Photo Interpretation and Data Entry 

Interface

Due to the large number of plots to be interpreted, it 

was important to use a data collection method that 

allowed for rapid image viewing, interpretation, and 

data entry. Two software applications were evaluated 

and compared for this purpose: ESRI ArcMapTM and 

Microsoft Access®. The ESRI ArcMap interface was 

set up using a feature dataset within a file geodatabase. 

Land Cover

 Tree: Deciduous Tree, Evergreen Tree, Uninterpretable Tree

 Other Vegetation: Shrub, Herbaceous, Nonvascular Vegetation, Uninterpretable Vegetation

 Water and Ice: Water, Ice and Snow

 Barren: Soil/Sand, Rock, Paved Surface, Building Rooftop, Other Barren Land

 Uninterpretable 

Land Use

 Forest: Forest, Tree Farm

 Rangeland: Rangeland

 Natural/Semi-natural: Wetland/Riparian, River, Lake, Other Natural/Seminatural

 Agriculture: Cropland/Pasture, Orchards/Groves/Vineyards/Nurseries, Confined Feeding Operations, Other 

Agriculture

 Developed: Residential, Recreational, Commercial/Services, Industrial, Transportation/Communications/

Utilities, Strip Mines/Quarries/Gravel Pits, Mixed Urban/Built-up Land, Canals/Reservoirs, Other 

Developed

 Uninterpretable

Table 1.—Primary and secondary classes for land cover and land use used in ICE project.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 48

The feature dataset contained the FIA plots and the 

fields to be attributed. The reference imagery used was 

provided by image services from the Forest Service’s 

enterprise instance of ArcGIS® Image Server. Since the 

FIA plot data was already in an ESRI file format, setup 

was relatively quick. The main limitation of using this 

method was the inability to customize the interface 

without a programming skillset.

The Microsoft Access interface was a modification 

of prior work conducted by Andrew Lister of the 

Northern Research Station FIA program. The database 

consisted of a data entry form attached to a main table 

which stored the data. One of the greatest advantages 

of using an Access database was the ability to easily 

customize the form to meet the project needs. The 

layout of the ICE form allowed the user to view two 

timeframes of aerial imagery side-by-side and enter all 

pertinent information for an individual plot within a 

single form. The form layout consisted of the imagery 

and data entry fields for T1 on the left and the imagery 

and data entry fields for T2 on the right (Fig. 1). 

Additionally, by using buttons placed at the top of the 

images, the interpreters were able to toggle between 

varying image scales and band combinations. Buttons 

on the form also gave the interpreters quick access 

to supplementary imagery available on BingTM and 

Google MapsTM.

Another design objective for the Access form was to 

maintain data integrity throughout the course of the 

project. To support this, features such as predefined 

dropdown lists for land cover, land use, and agents of 

change were implemented. Other quality control (QC) 

features included a “no change” button to minimize 

data entry errors as well as checks for missing data 

that prevented the user from proceeding until the 

fields were attributed. Along with keeping data entry 

consistent, these built-in QC functionalities saved a 

considerable amount of time. 

To test the efficiency of the two applications, a total 

of 1,000 non-FIA plots were randomly selected from 

the two states. Two interpreters used both methods to 

view and attribute the plots. The results of the study 

showed that the Access approach was 28 percent more 

time efficient than the ESRI ArcMap approach. This 

improved efficiency was attributed to faster image load 

time and the ability to customize tools to maximize 

the ease of data entry. As a result of its increased 

efficiency and availability, the Microsoft Access 

approach was selected for use in the project.

Interpretation Methodology

During the early development phase of this project, 

a study was conducted comparing the interpretation 

of land cover within polygons versus at a point (i.e., 

the plot center). The polygon method required the 

interpreter to evaluate the entire plot and mentally 

draw boundaries around the land cover types. Using 

FIA guidelines for minimum mapping units (MMU), 

polygons representing land cover types were identified 

(USDA FS 2011). The class corresponding to the land 

cover polygon located at the plot center was recorded 

by the interpreter. The point method had no associated 

MMU for specifying a land cover type. The plot 

center was visually assessed by the interpreter and 

the land cover class it intersected was recorded for 

the plot. The two methods yielded different results in 

some cases. For example, if the plot center intersected 

an opening in a coniferous forest, the land cover 

call was evergreen tree for the polygon method and 

herbaceous for the point method. In evaluating the 

two methods, results showed a 31 percent increase in 

time efficiency and a 4 percent increase in agreement 

among interpreters when comparing the point to 

the polygon method. One disadvantage of using the 

point method is the lack of contextual information at 

the plot level. However, by also recording the land 

use class, which was necessarily polygon-based, the 

contextual information was made available. Based 

upon the results of the comparison, the point method 

was selected for assessing the land cover type and 

the polygon method was used to evaluate land use. 

The only attribute that used the plot boundary and 

not the center point was the agent of change, which 

was assigned only when there was an LULC change 

anywhere within the 1.44-acre plot area.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 49 Figure 1.—Microsoft Access form customized for ICE project to view NAIP imagery and record plot data. 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 50

After completing the studies, the interpreters began 

interpreting the actual FIA plots. In order to measure 

consistencies among interpretations, 5 percent of 

the FIA plot locations were randomly selected as 

crosschecks. Half of these plots were repeated by the 

initial interpreter and the other half were assessed 

by both interpreters. The FIA panel system, which 

consists of five panels per state, was used to divide 

the plots between the two interpreters, with one 

interpreting two panels and the other interpreting 

three panels for each state. Photo interpretation was 

first completed for the state of Colorado followed by 

Georgia. The two interpreters worked independently 

of one another throughout the majority of the photo 

interpretation process. Plots that were difficult to 

assess were marked for review and later discussed 

between interpreters to refine class calls and to 

improve consistencies between them.

RESULTS AND DISCUSSION

The photo interpretation process for the ICE project 

took an average of 1 minute per plot and eight-person 

work weeks to complete all plots for Colorado and 

Georgia. The dominant land cover and land use types 

for the state of Colorado were Other Vegetation 

(74 percent of initial total) and Rangeland (32 percent 

of total), respectively. For Georgia, Tree (62 percent 

of initial total) was the dominant land cover class 

with Forest (67 percent of total) being the dominant 

land use class. Please see Figure 2 and 3 for more 

information on the land cover and land use results.

Changes occurring within the land use of forests were 

of particular interest for the ICE project (Fig. 4). 

Results show that 0.45 percent of the Colorado forest 

plots changed to a different class in T2. Conversion to 

rangeland and development had the greatest role in the 

forest change by making up 47 percent and 40 percent 

of the forest change, respectively. For Georgia, 1.14 

percent of the forest plots changed land uses in T2, 

with the majority of these changes (73 percent) being 

a result of development. Additional work, including an 

intensified sampling of the changed plots, is underway 

to better quantify the changes. This information, 

along with other plot specific information (i.e., land 

cover type and agent of change) will be available in 

forthcoming reports.

The methodology developed for this project provides 

a rapid and cost-effective way to assess and estimate 

LULC changes across large areas and shows 

potential for nationwide implementation. Through 

the interpretation of FIA plot locations from high 

resolution aerial imagery, information regarding land 

cover and land use changes that are occurring across 

the United States can be made accessible to scientists 

and forest managers to assist in the creation of 

strategies and the making of informed decisions. 

ACKNOWLEDGMENTS

The authors thank the FIA Techniques Research Band 

for providing support from the 2010 and 2011 Work 

Plans.

LITERATURE CITED

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. 

Washington, DC: U.S. Government Printing Office. 

41 p.

Bechtold, W.A.; Patterson, P.L., eds. 2005. The 

enhanced forest inventory and analysis 

program—national sampling design and 

estimation procedures. Gen. Tech. Rep. SRS-80. 

Asheville, NC: U.S. Department of Agriculture, 

Forest Service, Southern Research Station. 85 p.

U.S. Department of Agriculture, Forest Service. 2011. 

Forest Inventory and Analysis national core field 

guide. Volume 1: field data collection procedures 

for phase 2 plots. Version 5.1. Washington, DC: 

U.S. Department of Agriculture, Forest Service. 

310 p. Available at http://fia.fs.fed.us/library/fieldguides-methods-proc/. (Accessed March 1, 2012). 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 51

Figure 2.—Land cover data summaries for Colorado and Georgia. (A) Comparison of land cover values for T1 and T2 

datasets. (B) Land cover change as a percentage of total plots. (C) Distribution of land cover change by type based on initial 

plot condition.

A

B

C
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 52

Figure 3.—Land use data summaries for Colorado and Georgia. (A) Comparison of land use values for T1 and T2 datasets. 

(B) Land use change as a percentage of all plots. (C) Distribution of land use change by type based on initial plot condition.

A C

B
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 53

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.

Figure 4.—Summary of changes within forested plots categorized by state. (A) Percent of forested plots that changed to other 

land use classes. (B) Distribution of land use types to which the changed forest plots transitioned.

A B
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 54

FORECASTING SUSTAINABILITY: 

GROWTH TO REMOVALS RATIO DYNAMICS

Natasha A. James, Robert C. Abt, Karen L. Abt, Raymond M. Sheffield, and Fredrick W. Cubbage1

Abstract.—The growth to removals ratio (G/R) is often used as a measure of forest 

resource sustainability and as a reference point to forecast future resource sustainability. 

However, little work has been done to determine if any relationship exists between 

G/R over time. Forest Inventory and Analysis data for 12 southern states were used to 

determine if any relationship exists between G/R at a given point in time and G/R in the 

future. Ordinary least squares results indicated a positive relationship over time, meaning 

a high G/R ratio in the past is associated with a high G/R ratio in the future. However, 

after removing the effects of differences across space through the use of fixed effects 

analysis, the results indicated G/R has a negative relationship with itself over time.

1

 Research Economist (NAJ), U.S. Forest Service, Southern 

Research Station, 3041 Cornwallis Road, Research 

Triangle Park, NC 27709; Professor of Forestry (RCA) and 

Professor of Forest Economics and Policy (FWC), North 

Carolina State University, Department of Forestry; Research 

Economist (KLA) and Supervisory Research Forester 

(Retired) (RMS), U.S. Forest Service, Southern Research 

Station. NAJ is corresponding author: to contact, call 919-

549-4029 or email at najames@fs.fed.us.

INTRODUCTION

The concern of negative anthropogenic impacts on the 

natural environment and the concerns of insufficient 

natural resource availability in the future have led to 

an international agenda to engage in practices leading 

to resource sustainability and sustainable development. 

The Montreal Process Criteria and Indicators (MPCI) 

were developed to address sustainability concerns 

of the world’s forests. Adopted by 12 countries 

that represent more than 90 percent of the world’s 

temperate and boreal forest, these criteria and 

indicators define seven broad value criteria which are 

measured by various indicators.

Growth to Removals Ratio

Criterion 2 focuses on maintaining the productive 

capacity of forest ecosystems. Criterion and Indicator 

2.13 focuses specifically on the growth to removals 

ratio (G/R) (Montreal Process Working Group 2009). 

Within the field of forest management, resource 

sustainability is often measured by G/R which is 

calculated by dividing the net growth in inventory 

volume during a certain time period by the removals 

(harvest) within the same time period. A G/R ratio 

greater than 1 indicates growth in inventory outpaces 

removals and the resource management within that 

period could be continued without depleting inventory, 

and thus is considered sustainable. Although G/R 

presents a useful snapshot of the relationship of forest 

growth and removals, it is often used as a means 

of determining forest sustainability. For example, a 

high G/R ratio today often is seen as an indicator of 

a high G/R ratio in the future, thus an assurance of 

sustainability.

Objective

This study examines G/R for growing stock timberland 

of both hardwood and softwood species in the selected 

areas. G/R is calculated as the ratio between net annual 

growth (the difference between gross growth and 

mortality) and annual removals for timberland. Figures 

1-3 depict G/R over time for each state used in this 

analysis. Although Kentucky is part of the southern 

FIA, it was not included due to insufficient data.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 55

Even though it is common practice to use a G/R 

ratio greater than 1 as an indicator of continued 

sustainability in the future, Figures 1-3 show a 

relationship over time where periods of low G/R are 

followed by periods of high G/R, suggesting a cyclical, 

negative relationship (Sheffield 2012). The objective 

of this study was to investigate how well G/R in the 

past explains G/R today.

0

0.5

1

1.5

2

2.5

0 1 2 3 4 5 6

G/R ratio

Period

Gulf States

Alabama Louisiana Mississippi Texas

Figure 1.—G/R ratio over time for southern states bordering the Gulf of Mexico.

0

0.5

1

1.5

2

2.5

0 1 2 3 4 5 6

G/R ratio

Coastal States

Florida Georgia

North Carolina South Carolina

Virginia

Period

Figure 2.—G/R ratio over time for southern states bordering the Atlantic coast.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 56

0

0.5

1

1.5

2

2.5

3

0 1 2 3 4 5 6

G/R ratio

Period

Interior States

Arkansas Oklahoma Tennessee

Figure 3.—G/R ratio over time for interior southern states.

DATA

The data used in this analysis were extracted from 

the Forest Inventory and Analysis (FIA) database. 

Data relating to annual removals and net growth were 

collected at the survey unit level for the 12 southern 

states. Although net growth and annual removals are 

estimates containing their own standard errors, for the 

purposes of this paper they are both being treated as 

population values. 

Historically, the FIA Program collected data using 

a 10-year periodic survey. In 1998, the Agricultural 

Research, Extension, and Education Reform Act (PL 

105-185) required an annual inventory. However, the 

panel design described by Bechtold and Patterson 

(2005) was implemented. This panel design allows 

federal funding for a 10 panel design in the East. 

Many southern states use a 5 panel design in which 20 

percent of plots within a state are sampled each year, 

creating full inventory reports in 5-year cycles (Smith 

and Oswalt 2010).

Due to the inconsistency in survey dates, the years 

in which surveys took place were divided into the 

following periods:

Period 1: 1970-1978 Period 4: 1994-2003

Period 2: 1979-19872 Period 5: 2004-2008

Period 3: 1988-1993 Period 6: 2009-2010

METHODS

Ordinary least squares (OLS) analysis was used to 

determine if any relationship exists between G/R today 

and in the past. In this analysis, each period for each 

state is a single observation. In addition, fixed effects 

analysis was applied which controls for the average 

differences across space (states) in order to observe 

only the effects of time on G/R. In this analysis, the 

cross section was defined as the state and the time 

2

 Florida had 2 surveys during Period 2 (1980 and 1987). 

The 1987 survey data are included in Period 3. 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 57

series was defined in periods, as shown above. The 

models analyzed for both procedures were: 

Model 1: (G/R)T = f ( (G/R)T-1 )

Model 2: (G/R)T = f ( (G/R)T-2 )

Model 3: (G/R)T = f ( (G/R)T-1, (G/R)T-2 ),

where (G/R)T is G/R of the current period; (G/R)T-1

is G/R lagged one period; and (G/R)T-2 is G/R lagged 

two periods.

RESULTS AND DISCUSSION

Three OLS models and three fixed effects models were 

used to examine the relationship G/R has with itself 

over time. The first model focuses on the relationship 

between G/R of the current period and the G/R 

lagged one period. The second model focuses on the 

relationship between G/R of the current period and 

the G/R lagged two periods. The final model focuses 

on the relationship between G/R of the current period 

and the G/R lagged one and two periods. Results from 

OLS and fixed effects analysis are listed in Table 1 and 

Table 2, respectively.

In each model the parameter coefficients are positive 

and significant at the 1 percent level. This result 

suggests G/R has a positive relationship with itself 

over time. However, the R2 values are relatively low, 

ranging from 0.14-0.24. 

Table 1.—Results from Ordinary Least Squares 

(OLS) analysis of G/R on lagged values of 

G/R for 12 southern states (standard errors in 

parentheses) 

Model 1 Model 2 Model 3

Constant 0.83 0.96 0.74

(G/R)T-1 0.45*** 0.30***

(0.08) (0.10)

(G/R)T-2 0.32*** 0.16***

(0.09) (0.10)

Number of observations 102 78 78

Adjusted R2 0.24 0.14 0.23

Note: ***indicates significance at 1%.

Table 2.—Results from fixed effects analysis G/R 

on lagged values of G/R for 12 southern states 

(standard errors in parentheses)

Model 1 Model 2 Model 3

Constant 1.91 2.02 2.63

(G/R)T-1 -0.10a -0.33***

(0.14) (0.14)

(G/R)T-2 -0.18 0.21*

(0.15) (0.14)

Number of cross sections 12 12 12

Time series length 5 4 4

Adjusted R2 0.55 0.66 0.72

a ***indicates significance at 1%, *indicates significance at 20%.

Through the use of fixed effects analysis, the average 

differences across states are isolated to determine 

whether G/R at a point in time has implications for 

determining G/R in the future. In each model, the 

F statistic (Fstat) is significant at the 5 percent level, 

indicating there are group effects across space and that 

OLS would not be expected to produce reasonable 

results. Although the R2

 values are higher (0.55-

0.72), the relationship between G/R today and its 

values lagged one period and two periods (Model 1 

and 2) lack significance even at the 20 percent level. 

However, when analyzed simultaneously (Model 3), 

(G/R)T-1 and (G/R)T-2 show a significant, negative 

relationship with G/R of the current period. This result 

implies an inverse relationship where the higher G/R is 

in the past, the lower it will be in the future.

CONCLUSION AND FUTURE WORK

The results from this study show that when controlling 

for the average differences across space, empirical 

evidence suggests that G/R has a negative relationship 

with itself over time. This analysis is just one of many 

steps in understanding G/R. In the South, the G/R is 

rarely below 1. Future work should include regions 

where G/R is not as stable. Future analysis should 

include examination of hardwoods and softwoods 

separately, as these ratios can be different over time. 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 58

In addition, stronger statistical analyses such as 

spectral analysis or Fourier regression could be applied 

to determine cyclical relationships.

ACKNOWLEGDMENTS

The authors would like to thank Evan Mercer and John 

Coulston for their review of this paper. Their feedback 

contributed greatly to the content.

LITERATURE CITED

Bechtold, W.; Patterson, P., eds. 2005. The enhanced 

forest inventory and analysis program—

National sampling design and estimation 

procedures. Gen. Tech. Rep. GTR-SRS-80. 

Asheville, NC: U.S. Department of Agriculture, 

Forest Service, Southern Research Station. 85 p.

Montreal Process Working Group. 2009. Criteria and 

indicators for the conservation and sustainable 

management of temperate and boreal forests.

Available at www.mpci.org. (Accessed December 

2011).

Sheffield, R. 2012. Growth-Removal Ratio-An 

indicator of stability? In: Proceedings, 2012 

Southern Forest Resource Assessment Consortium 

annual conference; 2012 August 8-9; Durham, NC.

Smith, B.; Oswalt, S.N. 2010. Status and progress 

in large scale assessments of the productive 

capacity of forest ecosystems in the United 

States. Journal of Forestry. 109(4): 226-232.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 59

Development of a Remote Sensing-based System

for Monitoring Landscape Change

C. Kenneth Brewer, Sean P. Healey, Brian Schwind, and Kevin A. Megown1

Abstract.—The Landscape Change Monitoring System (LCMS) is an interagency 

remote sensing-based system under development for mapping and monitoring land 

cover and land use change in the United States. There is a growing need for landscape 

change information that is coherent across time, space, and different cover types. While 

a number of relevant datasets exist, work is needed to promote coordination and fill in 

critical gaps. Assessment of existing agency information requirements, data availability, 

and institutional activity suggests the greatest return on efforts to establish a national 

landscape change monitoring system will be in development of a Landsat-based 

information system. Characteristics that make Landsat data particularly well-suited 

to comprehensive change monitoring include the longest data record of any synoptic 

satellite sensor (1972 to present), relatively fine spatial resolution (30 m), spectral and 

radiometric properties that enable vegetation change detection, no-cost availability and 

accessibility, future data continuity, and a rich history of scientific investigation. Current 

activities in the development of LCMS include an independent needs assessment and 

the formation of a science team, which will evaluate and recommend design criteria and 

available Landsat-based change detection methodologies. These developments will be 

described in the presentation.

1

 Program Manager (CKB), U.S. Forest Service, 1601 N. Kent St., Arlington, VA 22209; Research 

Ecologist (SPH), U.S. Forest Service, Rocky Mountain Research Station; (BS and KAM), U.S. 

Forest Service, Remote Sensing Applications Center.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
The North American 

Forest Dynamics Project: 

Moving from Status to Trends 

through Landsat Time Series
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 61

ATTRIBUTING CAUSAL AGENTS TO NATIONWIDE MAPS 

OF FOREST DISTURBANCE

Gretchen G. Moisen, Todd A. Schroeder, Karen Schleeweis, Chris Toney, 

Warren B. Cohen, and Samuel N. Goward1

Abstract.—Currently in its third phase, the North American Forest Dynamics (NAFD) 

project has launched nationwide processing of historic Landsat data to provide a 

comprehensive annual, wall-to-wall analysis of U.S. disturbance history over the last 30+ 

years. Because understanding the cause of disturbance is important to quantifying carbon 

dynamics, work is underway to attribute causal agents to these nationwide change maps. 

Developing empirical models of the diverse causal agents in this country involves many 

decisions. Alternative response designs (such as varying size, shape, quantity, and level of 

detail in training data) are being evaluated in terms of their costs and benefits for national 

mapping applications. Many classes of predictor variables such as spectral signatures, 

textural metrics, extant geospatial disturbance libraries, and bioclimatic information, are 

being tested for their contribution to classification models. Flexible modeling techniques, 

such as the Random Forests models used here, are powerful predictive tools but must 

be coupled with simple rule-based models reflecting expert knowledge. And decisions 

about appropriate modeling subpopulations are being made in light of available training 

data, diversity of ecological zones, and computational efficiency. We will be synthesizing 

results from our initial exploratory work as well as from pilot analyses conducted over 10 

Landsat TM scenes representing diverse causal agents, forest types, and forest prevalence 

levels. We also discuss how these causal disturbance models will enable extensive 

analyses of temporal and spatial patterns in causal agents across the United States. 

1

 Research Forester (GGM), U.S. Forest Service, Rocky 

Mountain Research Station, 507 25th Street, Ogden, UT 

84401; Research Ecologist (TAS), Research Geographer 

(KS), and Ecologist (CT), U.S. Forest Service, Rocky 

Mountain Research Station; Research Forester (WBC), 

U.S. Forest Service, Pacific Northwest Research Station; 

Professor (SNG), University of Maryland, Department of 

Geography. GGM is corresponding author: to contact, call 

801-625-5384 or email at gmoisen@fs.fed.us.

INTRODUCTION

The job of inventorying and monitoring the nation’s 

forests requires the U.S. Forest Service Forest 

Inventory and Monitoring Program (FIA) to do three 

things. First, we have to provide information about 

the status of our forests, quantifying how much is out 

there now and where is it. Second, we have to provide 

information about how our forests are changing. And 

finally, we have to try to decipher trends to describe 

what is happening over the long term. While strongly 

interrelated, the three questions require different 

techniques and data. Addressing the status issue has 

been FIA’s primary focus for many years. We regularly 

produce useful estimates of forest attributes as well as 

accompanying maps. While there are opportunities to 

improve precision in estimates, make the maps better, 

ensure compatibility between maps and estimates, and 

implement better procedures for small area estimation, 

the topic of forest status is already receiving 

considerable attention. However, assessing change 

and trend both pose larger challenges we are only just 

beginning to tackle.

The overarching objective of the work we are 

conducting is to improve FIA’s approach to assessing 

status, change, and trend through Landsat time series. 

Here we briefly discuss forest disturbance and the 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 62

challenges it poses for monitoring. We also provide 

an overview of the most recent phase of the North 

American Forest Dynamics (NAFD) project with 

emphasis on attribution of causal agents. 

FOREST DISTURBANCE

AND MONITORING

Forest disturbance is becoming increasingly important 

to the inventory and monitoring audience. Although it 

affects only a small percentage of a given forest each 

year, there are cumulative effects through time. It is a 

complicated phenomenon in that it can impact canopy, 

understory, as well as forest floor. The definition of 

disturbance changes dramatically depending on an 

individual’s perspective and area of expertise. People 

are having an increasing influence on the frequency 

and severity of disturbances across the landscape. 

Consequently, monitoring in ways that improve our 

understanding of the role of disturbance in our forests 

is increasingly important.

Disturbances brought on by different causal agents 

vary in terms of the speed with which they affect a 

forest and the length of time that the effects persist. 

For example, urbanization doesn’t happen overnight, 

but once it occurs, it is permanent. Conversely, floods 

happen quickly, but they can recede equally as fast. 

Disturbances caused by a variety of agents are also 

dramatically different in terms of the size of area 

affected and the severity or magnitude of change 

they inflict on the forest in that area. For example, 

the death of one tree is small relative to the area in 

a 30 m pixel, but it is very severe for that individual 

tree. Urbanization is also a severe change but occurs 

over much larger areas. Thinking about disturbance in 

this way brings out three key points. First, the timing 

of observation can be critical to detection. Second, 

the spatial scale of observation on the ground can 

influence our ability to detect disturbance. And finally, 

the techniques we use to observe or measure will also 

factor into the ability to discern different types of 

disturbance. Can we really see it with our eyes? Can 

we “see it” with the spectral bands that our remote 

sensing instrument is equipped with?

Understanding the diversity of disturbance raises 

issues about how forests are monitored. The 

advantages of field data collection are well understood 

and appreciated. FIA produces unbiased estimates at 

broad scales, the sampling error is well understood, 

and the measurement error can be assumed to be 

negligible for many variables that we can physically 

measure (wrapping a tape, observing a species). 

However, these estimates are not spatially explicit 

except at very coarse spatial grains such as counties. 

In addition, our inventory frequency might cause 

us to miss some disturbance events, identifying 

year of disturbance can be problematic, and when 

observing from the ground, it can be difficult to detect 

disturbances that affect only crowns. 

Conversely, using remotely-sensed information such 

as dense time series of Landsat imagery offers some 

advantages for monitoring disturbance, such as a 16-

day repeat cycle and 40-year historical archive. The 

30 m grain size and spectral bands are adequate for 

capturing many changes in the landscape, and there is 

no sampling error because of wall-to-wall coverage. 

In addition, historic Landsat data can be interpreted 

visually with the aid of periodic photos to give very 

detailed information about forest history to augment 

information collected on the plots themselves. 

However, measurement error in the observations 

collected by Landsat can be highly variable and is 

often poorly understood.

DISTURBANCE ATTRIBUTION 

IN NAFD PHASE 3

Funded under the North American Carbon Program 

(NACP), the NAFD is a collaborative project that 

began nearly a decade ago and in its first two phases 

grew to include many partners including University 

of Maryland, National Aeronautics and Space 

Administration (NASA), FIA, the U.S. Forest Service 

Pacific Northwest and Northern Research Stations, 

Canadian and Mexican partners, and others. Because 

of the NACPs interest in determining the role of forest 

dynamics in the North American carbon balance 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 63

(USCCSP 2007), the NAFD group was funded to 

study disturbance patterns and recovery rates of forests 

across the continent. The approach taken in NAFD 

phase 1 and 2 was to process a complete time series 

(1985-2008) on a sample of Landsat scenes, resulting 

in biennial (in phase 1) and annual (in phase 2) maps 

of forest disturbance (Goward et al. 2008). FIA data 

were used for calibration and validation.

A third phase of NAFD was launched in the summer 

of 2011 and consists of three major components. 

The first component of Phase 3 involves conducting 

an annual, wall-to-wall analysis of U.S. disturbance 

history between1985-2010 (Huang et al. 2010). The 

second component includes conducting a detailed 

validation of the national disturbance map using 

TimeSync (Cohen et al. 2010). The third component 

consists of exploring variations in post-disturbance 

forest recovery trajectories using repeat measurements 

from FIA plot data following Masek et al. 2008. 

Causal agent groups are also being modeled in the 

final annual disturbance product (with beginning work 

from Schroeder et al. 2011 and Schleeweis et al., in 

press). Our pilot phase, which began in January 2012, 

is designed to address a number of issues through 

analyses conducted over 10 pilot Landsat scenes which 

were chosen to represent a diverse set of disturbance 

regimes and forest types across the country (Fig. 1). 

Figure 1.— Ten sample scenes selected for pilot testing causal attribution methods for North American Forest Dynamics 

(NAFD) Project Phase 3. Scenes were selected to capture a variety of causal agents in diverse forest types.

NAFD Phase 3 Attribution Modeling Pilot Sites

FIA Forest Group Type map from Ruefenacht et al. 2008
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 64

Issues being addressed in this pilot phase include: 

choosing the target population, identifying realistic 

classes to be modeled, testing which predictor 

variables contribute to our empirical models, choosing 

between pixel or polygon modeling objects, and 

combining empirical and rule-based models for the 

improved predictions. Results from these pilot tests 

will be presented in December 2012.

CONCLUSION

One take-home message from this overview relates to 

FIA and collaboration. Increasingly, various groups are 

approaching FIA requesting access to plot coordinates 

to conduct a wide variety of research. What is different 

about the NAFD project is that it involved FIA as full 

partners in research and helped to secure funding to 

support our involvement. This has resulted in more 

than the FIA logo on maps or names of FIA scientists 

on numerous publications. It has resulted in FIA 

expanding its capacity through acquisition of new 

tools, and more importantly, new people and talent 

within the organization.

ACKNOWLEDGMENTS

This research was supported in part by the 

North American Carbon Program, NASA grant 

NNH11AR18I. 

LITERATURE CITED

Cohen, W.B.; Zhiqiang, Y.; Kennedy, R.E. 2010. 

Detecting trends in forest disturbance and 

recovery using yearly landsat time series: 2. 

TimeSync-tools for calibration and validation.

Remote Sensing of Environment. 114: 2911-2924.

Goward, S.N.; Masek, J.G.; Cohen, W.; Moisen, G.G.; 

Collatz, G.J., Healey, S.; Houghton, R.; Huang, 

C.; Kennedy, R.; Law, B.; Powell, S.; Turner, D.; 

Wulder, M.A. 2008. Forest disturbance and 

North American carbon flux. Eos Transactions. 

89: 105-116. 

Huang, C.; Goward, S.N.; Masek, J.G.; Thomas, N.; 

Zhu, Z.; Vogelmann, J.E. 2010. An automated 

approach for reconstructing recent forest 

disturbance history using dense Landsat time 

series stacks. Remote Sensing of Environment. 

114: 183-198.

Masek, J.G.; Huang, C.Q.; Wolfe, R.; Cohen, W.; Hall, 

F.; Kutler, J.; Nelson, P. 2008. North American 

forest disturbance mapped from a decadal 

Landsat record. Remote Sensing of Environment. 

112: 2914-2926.

Ruefenacht, B.; Finco, M.V.; Nelson, M.D.; 

Czaplewski, R.; Helmer, E.H.; Blackard, 

J.A.; Holden, G.R.; Lister, A.J.; Salajanu, 

D.; Weyermann, D.; Winterberger, K. 2008. 

Conterminous U.S. and Alaska forest type 

mapping using forest inventory and analysis 

data. Photogrammetric Engineering and Remote 

Sensing. 74(11): 1379-1388.

Schleeweis, K.; Goward, S. N.; Huang, C.; Kennedy, 

R. E.; Masek, J. G.; Moisen, G.; Thomas, N. [In 

press]. Regional dynamics of forest canopy 

change and underlying causal processes in 

the conterminous US. Journal of Geophysical 

Research–Biogeosciences.

Schroeder, T.A.; Wulder, M.A.; Healey, S.P.; Moisen, 

G.G. 2011. Mapping wildfire and clearcut 

harvest disturbances in boreal forests with 

Landsat time series data. Remote Sensing of 

Environment. 115: 1421-1433.

USCCSP. 2007. The first state of the carbon cycle 

report (SOCCR): the North American carbon 

budget and implications for the global carbon 

cycle. A report of the U.S. Climate Change Science 

Program and the Subcommittee on Globl Change 

Research. Ashville, NC: National Oceanic and 

Atmospheric Administration, National Climatic 

Data Center. 242 p.

The content of this paper reflects the views of the 

author(s), who are responsible for the facts and accuracy 

of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 65

The North American Forest Dynamics Study: A Decade 

of Learning about U.S. Forest Disturbance

Samuel N. Goward, Warren B. Cohen, Chengquan Huang, Jeffery Masek, Gretchen G. Moisen, 

Rama Nemani, Karen Schleeweis, and Nancy Thomas1

Abstract.—The North American Forest Dynamics (NAFD) study is a core project of 

the interagency North American Carbon Program. The first step in the study, a prototype 

analysis carried out in the mid-Atlantic region, showed the potential of combining time 

series Landsat observations with the U.S. Forest Service Forest Inventory and Analysis 

(FIA) field measurements. With the assistance of FIA staff, the investigators began to 

explore merging the NASA/USGS Landsat observations with the FIA measurements in 

order to evaluate forest disturbance dynamics in the United States in the next 6 years 

the study demonstrated the difficulties encountered in sampling Landsat locations as a 

means to estimate U.S. national rates in disturbance. What was found is that disturbances 

occurring in specific Landsat locations dominated estimated national rates in given years. 

Further by examining Landsat observations every other year, significant low intensity 

disturbance events were missed. These “lessons learned” have led to NAFD phase 

III, in which we will conduct an annual, wall-to-wall analysis of the coterminous U.S. 

disturbance rates and underlying processes and will explore the potential for estimating 

regrowth rates. Further, we are now developing validation methodology which should 

permit estimates of the accuracy and precision of these national forest dynamics.

Throughout this decade of research, the NAFD team has maintained a strong partnership 

with the FIA and related U.S. Forest Service activities. What began as solely North 

American Carbon Program science goal has now developed a strong relation between 

NASA research activities and U.S. Forest Service operational responsibilities.

1

 Professor (SNG), University of Maryland, Department of Geographical Sciences, College Park, 

MD 20742; Research Forester (WBC), U.S. Forest Service, Pacific Northwest Research Station; 

(CH, KS and NT), University of Maryland, Department of Geographical Sciences; Biospheric 

Sciences Lab (JM), NASA Goddard Space Flight Center; Research Forester (GGM), U.S. Forest 

Service, Pacific Northwest Research Station; (RN), NASA Ames Research Center, Moffett Field, 

CA. SNG is corresponding author; to contact, call 301-405-2770 or email at sgoward@umd.edu. 

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 66

Validation Strategy for

NAFD Landsat Time Series Forest Change Maps

Warren B. Cohen, Steve Stehman, Susmita Sen, Peder Nelson, Chengquan Huang, 

Karen Schleeweis, and Gretchen G. Moisen1

Abstract.—Landsat time series (LTS) contain detailed information about land use 

and cover change. To characterize forest disturbance, automated LTS algorithms are 

now being developed, tested, and applied over large areas. Because these algorithms 

are used over broad regions and periods up to 40 years, obtaining a statistically valid 

sample of independent reference data for map validation is both costly and prohibitively 

challenging. We present an alternative strategy based on human interpretation of the 

LTS for a sample of plots, both as image chip series and as spectral plots over time. In 

addition, we use Google EarthTM historical temporal snapshots of high resolution images 

to provide critical LTS interpretation support. 

Our interpretation system, TimeSync, is being used for validation of Vegetation Change 

Tracker (VCT) maps for the conterminous United States as part of the North American 

Forest Dynamics (NAFD) project. Because the collection of validation data must precede 

the completion of map development, we designed a sequential two-step stratified-random 

approach with nonforest, undisturbed forest, and disturbed forest map strata. The first 

step of the sample selection is based on a preliminary VCT map, with approximately 

one half of the intended number of plots interpreted. When the VCT map stabilizes, after 

several iterations of the algorithm’s implementation, the second step in the selection 

process will round out the sample to obtain the desired distribution of plots per stratum. 

Our validation strategy includes interpretation of each plot by two separate interpreters. A 

third interpreter arbitrates disagreements to provide a final high quality reference dataset, 

which has built in confidence scoring, based on the integration over all interpreters. Once 

the plots are interpreted, an agreement matrix is constructed to assess VCT map accuracy. 

In our presentation, we present the validation strategy, as described above, and present 

preliminary results from a set of 10 LTS scenes distributed across the United States.

1

 Research Forester (WBC), U.S. Forest Service, Pacific Northwest Research Station, Corvallis, 

OR 97331; State University of New York (SStehman), Department of Forest and Natural 

Resources Management; Oregon State University (SSen, PN), Department of Forest Ecosystems 

and Society; University of Maryland (CH, KS), College Park, MD; U.S. Forest Service (GGM), 

Rocky Mountain Research Station. WBC is corresponding author: to contact, call 541-750-7322 

or email at wcohen@fs.fed.us.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 67

Post-disturbance Recovery Analyzed from

Remote Sensing Time Series and FIA Data

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.

Jeffrey Masek, Khaldoun Rishmawi, and Samuel N. Goward1

Abstract.—As part of the third phase of the North American Forest Dynamics project 

(NAFD), the fate of all patches disturbed between 1972 and 2010 in the conterminous 

United States is being investigated. While forest succession encompasses a wide range 

of ecological attributes, we are particularly interested in the accumulation rate of 

aboveground biomass during recovery from disturbance. Previous studies have noted the 

relative insensitivity of optical remote sensing for measuring biomass in mature stands. 

However, it has also been demonstrated that early recovery (e.g., the first 10-20 years 

following disturbance) can be tracked successfully via multispectral metrics. Our goal 

is to provide as much detail as possible on the early biomass recovery rate on a perpatch basis, while also providing spatially aggregated mean and variance of recovery 

rate across U.S. ecoregions. Of particular interest is whether recovery rates are changing 

through time (e.g., comparing the 1980s with the 2000s). Changes in management 

and growth enhancement in the eastern United States, as well as increased multiple 

interacting disturbances in the western United States, suggest the hypothesis that recovery 

rate distributions may not be stationary. 

The project involves three approaches for assessing recovery rate: 1) empirical modeling 

using remeasured FIA plots and Landast spectral trajectories; 2) physical modeling of 

observed reflectance using canopy radiative transfer models parameterized with FIA 

attributes; and 3) correlation of time-since-disturbance with contemporary structural 

information from Lidar and stereo imaging. One innovation of the work is that temporal 

trends in the Landsat timeseries (rather than single-date reflectances) will be used to 

model contemporary biomass and biomass change rate. This presentation will review the 

science context for characterizing post-disturbance recovery, and provide initial results 

for a set of pilot frames across the United States.

1

 Landsat Project Scientist (JM), NASA, Biospheric Sciences Lab, Goddard Space Flight 

Center, Greenbelt, MD 20771; University of Maryland (KR and SNG), College Park, MD. JM is 

corresponding author: to contact, call 301-614-6629 or email at jeffrey.g.masek@nasa.gov.
Cool Tools
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 69

New Features Added to EVALIDator: Ratio Estimation

and County Choropleth Maps

Patrick D. Miles and Mark H. Hansen1

Abstract.—The EVALIDator Web application, developed in 2007, provides estimates 

and sampling errors for many user selected forest statistics from the Forest Inventory and 

Analysis Database (FIADB). Among the statistics estimated are forest area, number of 

trees, biomass, volume, growth, removals, and mortality. A new release of EVALIDator, 

developed in 2012, has an option to select two statistics and generate a ratio estimate of 

the pair. The new feature can estimate statistics such as volume or growth per acre or 

the growth to removals ratio. Also, the program now makes county choropleth maps of 

all estimates. We provide information on the data and methods used along with sample 

output from a simple query that demonstrates these new features.

1

 Research Forester (PDM), U.S. Forest Service, Northern 

Research Station,1992 Folwell Ave., St. Paul, MN 55108; 

Research Associate (MHH), University of Minnesota. PDM 

is corresponding author: to contact, call 651-649-5146 or 

email at pmiles@fs.fed.us.

Introduction

The Forest Inventory and Analysis (FIA) Program 

of the U.S. Forest Service monitors the status and 

trends of the Nation’s forest resources. FIA delivers 

data, information, and knowledge to the public via 

a multitude of products, including online estimation 

tools. FIA’s EVALIDator Web application (http://apps.

fs.fed.us/Evalidator/tmattribute.jsp) can produce 

estimates for 125 attributes falling into the 12 groups 

depicted in Table 1. Groups are based on subsets of 

inventory plots, where each subset is a valid sample 

and the attributes in the group are observed on all of 

the plots in the subset. FIA uses the term “evaluation” 

for these subsets of plots and the associated Phase 

1 stratification that provides the sample base for 

the estimation of specific attributes. Area and 

volume estimates, for example, are based on the 

plots measured in the current inventory; removals 

estimates are based on a smaller set of plots measured 

in the previous inventory and then measured again 

in the current inventory. The EVAL_TYP column in 

Table 1 identifies the evaluation used to generate the 

estimates. Down woody material estimates are based 

on a small subset of plots on which forest health 

monitoring (Phase 3) measurements were taken. The 

right column in this table shows the number of plots in 

each evaluation (for Minnesota, as an example) where 

a non-zero value for the first attribute in the group 

was observed in the specified inventory and provides 

information on the intensity of the inventory with 

respect to various attributes.

In addition to the ability to produce population 

estimates with sampling errors for user specified 

inventories, the EVALIDator program has the 

flexibility to place restrictions on the estimates and 

breakdowns of the estimates into user defined tables. 

Users can also obtain the Oracle SQL code that 

produces the estimates for further modification. 

METHODS

EVALIDator is flexible with many options selected 

via radio buttons and dropdown lists. In this new 

version, once the user selects the attribute to be 

estimated, there is now a radio button to select 

between population estimate (default) and ratio 

estimate. When ratio estimate is selected, the attribute 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 70

Table 1.—The EVALIDator program can produce reports for these types of estimates

Non-zero plots

EVAL_TYP ESTIMATE GROUP Minnesota 2011

EXPALL Area of sampled land 17,711

EXPCURR Area of forest land or timberland 6,208

EXPVOL # trees, # seedlings, volume, biomass or carbon on forest land 6,089

EXPVOL # trees, # seedlings, volume, biomass or carbon on timberland 5,751

EXPGROW Average annual net growth on forest land 5,705

EXPGROW Average annual net growth on timberland 5,459

EXPMORT Average annual mortality on forest land 3,296

EXPMORT Average annual mortality on timberland 3,140

EXPREMV Average annual removals on forest land 544

EXPREMV Average annual removals on timberland 605

EXPDWM Down woody material (#, vol., biomass or carbon) on forest land xxx

EXPDWM Down woody material (#, vol., biomass or carbon) on timberland xxx

initially selected will be the numerator, and a second 

dropdown list of attributes appears for the user to 

select the denominator. The user then selects the 

evaluation(s) (identified by state and inventory years) 

to be included in the estimate and a classification 

variable used to break down the estimate in the final 

table. For ratio estimation, an additional radio button 

determines how to apply the classification variable 

to the table of estimates, either to both numerator 

and denominator (default) or to the numerator but 

the total of all classes is used for the denominator in 

every class. For ratio estimates, there are two options 

(population estimates have just one) for the user to put 

more restrictions on the estimates through additions to 

the “where” clause. One option is applied to both the 

numerator and denominator and the other is applied 

just to the numerator. Once all of these selections have 

been made, the program will generate the estimates 

requested in a table and a county choropleth map will 

be generated if county was selected as a classification 

variable. There is an option to generate a second 

table that contains estimates and their associated 

sampling errors. All of the estimates of ratios and their 

associated sampling errors are generated following 

the stratified ratio-of-means estimators (Bechtold and 

Patterson 2005). 

Users of the ratio estimates produced by EVALIDator 

need to be aware of three issues. First, ratio estimation 

requires that the same evaluation be used to estimate 

both the numerator and denominator. This reduces 

the evaluation to the smaller of the two. For example, 

when estimating net growth per acre of forest land, 

the area of forest land estimate is based on a smaller 

sample of plots than is normally used to estimate 

forest area. For this ratio, only those plots measured 

at two points in time (the EXPGROW evaluation) are 

used to estimate both the numerator and denominator. 

Because this estimate of forest land area is based on 

a somewhat different sample, it probably will not 

match other estimates of forest land area produced by 

EVALIDator or those published by FIA.

The second issue is that ratio estimates of all growth, 

removals, or mortality (GRM) attributes are based 

only on the components associated with no change 

in condition classification of the land. These GRM 

estimates exclude diversions and reversions and the 

area associated with the change in classification. 

Thus, the area estimate in the denominator of the net 

growth per acre of forest land estimate is an estimate 

of forest land area that was forest at the time of the 

first measurement and that remained forest at the 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 71

time of the second measurement. The numerator 

and denominator estimates in the ratios usually 

will not match population estimates obtained from 

EVALIDator or those published in most FIA reports 

because those include the diversion and reversion 

components and associated areas. 

The third issue relates to how restrictions on the 

numerator and denominator are applied and how 

breakdowns in the table of ratio estimates are made 

when ratio estimates are computed. Table 2 identifies 

the type of ratio estimates and classification variables 

that can be derived using EVALIDator and provides 

information as to whether the classification variable 

is derived from data collected during the current 

inventory or, in the case of GRM, from information 

collected during the previous inventory (Note: using 

the previous or current classification variable for 

GRM ratio estimates may not be appropriate for all 

analyses). In some cases ratio estimates are not logical 

and the program will not allow the user to produce 

an estimate. For example, although it is possible 

to compute acres of timberland per cubic foot of 

volume, it is not logical to report these estimates by 

a classification such as species because area cannot 

be classified by species. The EVALIDator program 

will not allow the user to select a tree classification 

variable when an area estimate has been selected for 

the numerator. These situations are labeled as “not 

possible” in Table 2.

RESULTS

Partial output from a simple volume per acre retrieval 

is presented in Table 3. In this example the user 

selected “Net volume of live trees in cubic feet on 

forest land” as the numerator, “Area of forest land, 

in acres” as the denominator, the 2011 inventories of 

Minnesota and Wisconsin as the geographic area, and 

“County code and name” as the classification variable. 

The EVALIDator first generates the estimates in 

Table 3 and then, if desired, may also generate more 

Table 2.—Limitations on stand and tree level ratio estimates in EVALIDator

Stand or Tree Classification variable is Denominator can be

 Classification Numerator Denominator derived from current or based on class total

 Variable Estimate Estimate previous inventory rather than grand total

Stand Area Area current YES

Stand Area Volume current YES

Stand Area GRM previous YES

Stand Volume Area current YES

Stand Volume Volume current YES

Stand Volume GRM previous YES

Stand GRM Area previous YES

Stand GRM Volume previous YES

Stand GRM GRM previous YES

Tree Area Area not possiblea not possiblea

Tree Area Volume not possiblea not possiblea

Tree Area GRM not possiblea not possiblea

Tree Volume Area current NO

Tree Volume Volume current YES

Tree Volume GRM current YES

Tree GRM Area previous NO

Tree GRM Volume previous YES

Tree GRM GRM previous YES

a

Not possible - Tree classification variable cannot be applied to area estimate.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 72

statistics that include sampling errors, variances, and 

number of plots used in the estimate. “County code 

and name” was selected as the classification variable 

so the user also has the option of producing a county 

choropleth map as depicted in Figure 1.

DISCUSSION

The EVALIDator program continues to evolve to 

meet user needs. This latest version introduces two 

new features, ratio estimates and county choropleth 

maps. Recent changes to the FIADB have also made 

it possible to generate population estimates for 

down woody material. The ability to produce ratio 

Table 3.—All live volume per acre of forest land by county, Minnesota and Wisconsin 2011

County Code Ratio Estimate Estimate of Numerator Estimate of Denominator

and Name (cf/acre) (cubic feet) (acres)

Total 1,244.22 42,739,454,212 34,350,476

27001 Aitkin 1,114.52 922,606,356 827,809

27003 Anoka 1,402.15 91,676,643 65,383

27005 Becker 1,289.74 430,711,212 333,953

... ... ... ...

estimates for down woody material was not included 

in the EVALIDator program due to a paucity of data 

for down woody estimates. As the FIADB evolves, 

additional reporting of forest health monitoring 

estimates will be added to the EVALIDator program.

LITERATURE CITED

Bechtold, W.A.; Patterson, P.L., eds. 2005. The 

enhanced Forest Inventory and Analysis 

program – national sampling design and 

estimation procedures. Gen. Tech. Rep. SRS-80. 

Asheville, NC: U.S. Department of Agriculture, 

Forest Service, Southern Research Station. 85 p.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 73

Figure 1.—All live volume per acre of forest land by county, Minnesota and Wisconsin 2011.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 74

USING FIESTA, AN R-BASED TOOL FOR ANALYSTS, 

TO LOOK AT TEMPORAL TRENDS IN FOREST ESTIMATES

Tracey S. Frescino, Paul L. Patterson, Elizabeth A. Freeman, and Gretchen G. Moisen1

Abstract.—FIESTA (Forest Inventory Estimation for Analysis) is a user-friendly 

R package that supports the production of estimates for forest resources based on 

procedures from Bechtold and Patterson (2005). The package produces output consistent 

with current tools available for the Forest Inventory and Analysis National Program, such 

as FIDO (Forest Inventory Data Online) and EVALIDator. FIESTA was developed as an 

alternative data retrieval and reporting tool that is functional within the R environment, 

allowing customized applications and compatibility with other R-based analyses. FIESTA 

generates estimates and percent sample errors of the estimates for area, population totals, 

and ratios, while allowing user-defined boundaries, stratification schemes, and data 

filters. The features of the tool are demonstrated using temporally sensitive data over 

diverse areas.

1

 Forester (TSF), U.S. Forest Service, Rocky Mountain 

Research Station, Forest Inventory and Analysis Program, 

507 25th St., Ogden, UT 84401; Statistician (PLP), 

Ecologist (EAF), and Research Forester (GGM), U.S. 

Forest Service, Rocky Mountain Research Station. TSF 

is corresponding author: to contact, call 801-625-5402 or 

email at tfrescino@fs.fed.us.

INTRODUCTION

Traditional analytical tools of the Forest Inventory 

and Analysis (FIA) Program have been developed 

for reporting state-level estimates of forest resources. 

The growing popularity of the FIA Program, the 

diverse range of questions and data requests from our 

customers, and the ongoing advancements of remote 

sensing technology have expanded our analytical 

needs. We now require capabilities to monitor annual 

data trends, report on different areas of interest, and 

incorporate diverse ancillary data layers for reducing 

estimation variances. In essence, we need a flexible 

estimation engine that we can use to answer many 

forest estimation questions using many different 

sources of ancillary data.

FIESTA

FIESTA (Forest Inventory Estimation for Analysis) 

is a package developed in the R environment (R 

Development Core Team 2011). R is a powerful 

statistical computing and graphical program language 

that is becoming more popular within the natural 

resource analysis community. It is free software that 

provides a flexible, cross-platform environment for 

statistical tool development and application through 

R package-building. The comprehensive library of 

contributed package applications along with R’s base 

functions and extensive user-community provides 

powerful resources for analysts including statistical, 

spatial, and graphical tools. The main objective of 

FIESTA is to provide a data-retrieval and reporting 

tool for FIA analysts and scientists that is more 

flexible than existing FIA tools and is adaptable to 

analysts’ changing needs. 

The major components of the FIESTA package are:

1. Extracting data from the FIA database for a userdefined area of interest.

2. Acquiring user-defined stratification layers.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 75

3. Generating area estimates and standard errors 

within user-defined populations.

4. Generating tree attribute and ratio estimates: for 

example, the number of aspen trees per acre in 

the Lodgepole Pine forest type. 

Specific features of FIESTA include: extracting data 

from the national or regional FIA National Information 

Management System database (NIMS) (Woudenberg 

et al. 2011); allowing for user-defined populations, 

strata, and data filters; ability to generate tabled 

estimates with sample error of area, population totals, 

and estimation of ratios; and providing additional tools 

for data exploration and spatial data manipulation, 

such as frequency tables and bar plots as well as 

raster clipping and pixel or polygon data extraction. 

FIESTA can be run through a graphical user interface 

environment or by supplying specific input parameters 

defined by the user. Because it is developed in R 

programming language, it also can easily interact 

with other R functions and packages, opening up 

many opportunities for further customization and 

development of tools within FIESTA. 

EVALIDator AND FIESTA 

COMPARISON

EVALIDator is FIA’s Web-based or personal computer 

tool for producing tables of population estimates 

including sampling errors (this tool is available 

at www.fia.fs.fed.us/tools-data/other/default.asp). 

EVALIDator was used to validate the accuracy of 

output from FIESTA. For example, Table 1 shows a 

comparison of the output from EVALIDator and the 

output from FIESTA for estimating cubic feet per acre 

by forest type in Montana. The percent difference is 

less than 1 percent for the estimates, most likely from 

rounding errors. The percent difference in the sampling 

error is also less than 1 percent in most cases. The 

larger differences are in types where the sampling 

error is already high.

FIESTA EXAMPLES

FIESTA originated from a request from the San Carlos 

Apache Indian Reservation in southeastern Arizona 

to support a cost-effective woodland inventory to 

Table 1.—Comparison of EVALIDator and FIESTA. The difference in per-acre estimates and sampling error 

of net volume of live trees (at least 5 inches diameter) by forest type on forest land, Montana, 2003-2009. 

Subset of actual table.

 EVALIDator FIESTA

 Estimates Estimates Difference EVALIDator FIESTA Difference

Forest type (ft3/acre) (ft3/acre) Estimates (%) Error (%) Error (%) Error (%)

Rocky Mtn. juniper 328.797 327.603 0.363 8.425 8.427 -0.0237

Juniper woodland 358.052 357.705 0.097 23.955 23.281 2.8136

Douglas-fir 1835.605 1820.685 0.813 2.694 2.687 0.2598

Ponderosa pine 963.91 957.834 0.630 5.27 5.264 0.1139

Engelmann spruce 3038.053 3011.842 0.863 6.32 6.301 0.3006

Grand fir 2486.062 2464.538 0.866 18.416 18.325 0.4941

Subalpine fir 1669.218 1654.858 0.860 4.949 4.922 0.5456

Mountain hemlock 3428.542 3402.067 0.772 21.038 21.074 -0.1711

Lodgepole pine 2210.947 2192.483 0.835 3.312 3.304 0.2415

Western hemlock 8401.806 8323.888 0.927 15.173 15.101 0.4745

Western redcedar 4225.783 4196.477 0.694 9.299 9.251 0.5162

Western larch 2722.67 2698.901 0.873 7.252 7.198 0.7446

Limber pine 517.243 513.428 0.738 23.943 24.184 -1.0066

Whitebark pine 1906.364 1888.815 0.921 7.466 7.477 -0.1473
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 76

quantify woodland resources on its 1.8 million acres. 

It is currently difficult to generate estimates using 

FIA data for populations other than a state using FIA’s 

estimation tools. With FIESTA, it is possible to extract 

FIA plots within a specific area, such as the San Carlos 

Reservation boundary, and produce estimates based 

on a given stratification layer and the total area of the 

population. 

We generated estimates of area by forest type 

groupings (Table 2) and area, basal area, and trees 

per acre of woodland types using FIA plot data and a 

spatial layer of biological plant communities that was 

provided for stratification. The resulting information 

Table 2.—Area estimates for San Carlos Apache 

Indian Reservation forest type groups

Number Sampling

Class of Plots Area (acres) error (%)

Nonforest 173 1,073,858 4

Ponderosa 14 78,786 24

PJandJ 61 378,460 10

Oak 30 161,079 16

Non-stocked 21 123,989 21

Mesquite 5 30,870 45

Cotton/Misc. 2 12,445 71

was helpful for an initial look at quantifying the forest 

resources within the boundary and identifying areas 

or forest types having high sampling errors that would 

need further sampling to minimize variance. 

Another topic of interest in the Western region is 

looking at temporal changes of species distributions. 

This information is useful for monitoring trends and 

potential changes in individual species distributions 

through time. It is currently difficult to generate 

estimates by year from FIA’s estimation tools, but 

with the programming and customization features of 

FIESTA, it is a simple routine that can be applied to 

any species. 

We used FIESTA to look at estimates of live net 

cubic-foot volume annually for lodgepole pine (Pinus 

contorta) and aspen (Populus tremuloides) species 

in Colorado, Montana, and Utah (Figs. 1 and 2). 

The scale of the figures reflects the amount of each 

species in the respective state. A simple function 

was developed to generate bar plots of estimates by 

inventory year using the total plots sampled each year 

and a simple statewide stratification scheme. The 

figures are useful as initial data exploratory tools but 

are not conclusive evidence for real changes on the 

ground.

Figure 1.—Net cubic-foot live volume for lodgepole pine by inventory year for three states in the Interior West: a. Colorado; 

b. Montana; c. Utah. Live Net Cubic-foot Volume (millions)

Live Net Cubic-foot Volume (millions)Live Net Cubic-foot Volume (millions)
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 77

STATUS AND FUTURE OF FIESTA

The FIESTA package was designed as a base program 

for accessing the FIA NIMS database and generating 

estimates of forest attributes using user-defined 

boundaries, strata, and filters.

The functionality and efficiency of the base program 

will be periodically analyzed and updated for speed 

and consistency. Updates will also occur to reflect 

changes in the NIMS database schema, including the 

addition of new reference tables or regional tables, 

changes in variable names, or general structural 

changes. The base program will be continuously 

augmented with additional data exploration tools and 

spatial tools to assist with estimation. 

Plans for current and future work with FIESTA, 

incorporating additional customized capabilities and 

tools for different needs, are as follows:

• Adding small area estimation capabilities to 

generate estimates for areas within user-defined 

boundaries that contain only a few FIA fieldsampled plots. 

• Adding compatibility with R packages, 

such as ModelMap (Freeman 2009) and 

PresenceAbsence (Freeman and Moisen 2008), 

to coordinate estimates of FIA attributes with 

pixel-based mapping of the same attributes.

• Adding a mechanism to compensate for 

nonresponse within the FIA sample.

• Adding functionality to integrate remotely 

sensed observations, such as photo interpretation 

from large-scale aerial photographs or 

measurements from Landsat or Light Detection 

and Ranging.

• Exploring alternatives to the moving average.

• Communication with Forest Vegetation 

Simulator to generate estimates of future growth 

simulations.

ACKNOWLEDGMENTS

The authors would like to acknowledge Mark Rubey 

for his assistance with understanding the structure of 

the FIA NIMS database, Jim Menlove for his extensive 

knowledge of EVALIDator, and the reviewers of the 

paper.

Figure 2.—Net cubic-foot live volume for aspen by inventory year for three states in the Interior West: a. Colorado; 

b. Montana; c. Utah. Live Net Cubic-foot Volume (millions)

Live Net Cubic-foot Volume (millions)Live Net Cubic-foot Volume (millions)
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 78

LITERATURE CITED

Bechtold, W.A.; Patterson, P.L., eds. 2005. The 

enhanced Forest Inventory and Analysis 

program - national sampling design and 

estimation procedures. Gen. Tech. Rep. SRS-80. 

Asheville, NC: U.S. Department of Agriculture, 

Forest Service, Southern Research Station. 85 p.

Freeman, E.A. 2009. ModelMap: an R package for 

modeling and map production using Random 

Forest and Stochastic Gradient Boosting.

Ogden, UT: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research Station. 

Available at http://CRAN.R-project.org/. [Date 

accessed unknown].

Freeman, E.A.; Moisen, G.G. 2008. 

PresenceAbsence: an R package for presenceabsence analysis. Journal of Statistical Software. 

23(11): 1-31. 

R Development Core Team. 2011. R: a language and 

environment for statistical computing. Vienna, 

Austria: R Foundation for Statistical Computing. 

Available at www.R-project.org. [Date accessed 

unknown].

Woudenberg, S.W.; Conkling, B.L.; O’Connell, 

B.M.; LaPoint, E.B.; Turner, J.A.; Waddell, K.L.; 

Boyer, D.; Christensen, G.; Ridley, T. 2011. 

The Forest Inventory and Analysis database: 

database description and users manual 

version 5.1 for Phase 2. U.S. Department of 

Agriculture. Available at http://fia.fs.fed.us/library/

database-documentation/current/ver5/FIADB_

user%20manual_5-1_p2_10_2011.pdf. [Date 

accessed unknown].

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 79

AREA CHANGE REPORTING USING THE DESKTOP FIADB

Patrick D. Miles and Mark H. Hansen1

Abstract.—The estimation of area change between two FIA inventories is complicated 

by the “mapping” of subplots. Subplots can be subdivided or mapped into forest and 

nonforest conditions, and forest conditions can be further mapped based on distinct 

changes in reserved status, owner group, forest type, stand-size class, regeneration status, 

and stand density. The boundaries of these mapped conditions may change from one 

inventory to the next, resulting in complex geometries when the two sets of boundaries 

are combined. The SUBP_COND_CHNG_MTRX (CMX) table was added in version 

4.0 of the FIADB to “facilitate the tracking of area change” between annual inventories. 

The AreaChangeReports form (located within downloadable FIADB Microsoft Access 

databases) uses the CMX table to link remeasurement plots from two successive annual 

inventories to produce estimates of area change. An example is provided to illustrate 

shifts in land use over a 5-year remeasurement period. FIADB databases for each of the 

48 contiguous states and southeast Alaska, along with built-in reporting tools including 

the AreaChangeReports form, are available for downloading from the FIADB DataMart 

(http://apps.fs.fed.us/fiadb-downloads/datamart.html) as Microsoft Access 2007TM 

databases.

1

 Research Forester (PDM), U.S. Forest Service, Northern 

Research Station, 1992 Folwell Ave., St. Paul, MN 55108; 

Research Associate (MHH), University of Minnesota. PDM 

is corresponding author: to contact, call 651-649-5146 or 

email at pmiles@fs.fed.us.

INTRODUCTION

FIADB data have been available on the Web since 

FIADB version 1.0 was developed in 2001. Initially 

FIADB data were available for downloading as 

comma-delimited files. In 2007 a small subset of 

FIADB tables, and a few SQL scripts for generating 

population estimates, were available in Microsoft 

Access database format for downloading from the Web 

(Miles 2008). By 2009, however, all of the FIADB 

tables, along with a fully developed reporting tool for 

generating population estimates and their associated 

sampling errors, were available for downloading as 

MS-Access databases (Miles 2009).

Under the annual inventory system, subplots can be 

subdivided or “mapped” into forest and nonforest 

conditions, and forest conditions can be further 

mapped based on distinct changes in reserved status, 

owner group, forest type, stand-size class, regeneration 

status, and stand density. These subplots, when 

remeasured several years later, are mapped again 

based on the same six criteria if there is an obvious 

change. The mapping information from these two 

inventories is combined in the CMX table to form a 

subplot condition change matrix. The SUBP_COND_

CHNG_MTRX (CMX) table was added to the FIADB 

(version 4.0) in 2010. The CMX table facilitates 

the generation of area change reports. A detailed 

explanation of this process is provided in The Forest 

Inventory and Analysis Database: database description 

and users manual version 4.0 for Phase 2 (pp. 230-240 

in Woudenberg et al. 2010).

METHODS

Area change estimates can be computed only for those 

states with remeasured data collected under the annual 

inventory design. Therefore, area change estimates are 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 80

currently not available for Alaska, Arizona, California, 

Colorado, Hawaii, Idaho, Montana, New Mexico, 

Nevada, Oregon, Utah, Washington, and Wyoming. 

Microsoft Access databases containing FIADB data 

and reporting tools can be downloaded from the FIA 

DataMart (http://apps.fs.fed.us/fiadb-downloads/

datamart.html).

The AreaChangeBetweenInventories reporting tool 

is an MS-Access form. Double-clicking on the form 

name will initiate the program and the form in Figure 

1 will appear. The user is required to (1) pick an 

evaluation or reporting year, (2) pick a classification 

variable, (3) pick a set of plots to be used in generating 

the estimate, and (4) specify an additional filter 

if desired. In the following example, a database 

containing FIADB data for Minnesota is used.

Pick an Evaluation

In Minnesota, annual inventory plots were first 

remeasured in 2004 and every year thereafter through 

2011 so there are eight different “evaluations” or 

reporting years to choose from. For the evaluation 

“Minnesota 2011: 2002-2006 to 2007-2011” 

Figure 1.—AreaChangeBetweenInventories reporting form.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 81

(highlighted in Figure 1), plots measured in 2002-2006 

were remeasured in 2007-2011.

Pick a Classification Variable

Classification variable names are stored in the MSAccess table REF_PRC. Only classification variables 

based on a condition characteristic can be used in the 

AreaChangeBetweenInventories reporting tool. A list 

of these classification variable names is provided in 

Table 1. In Figure 1 the classification variable “Land 

use-major” is highlighted.

The FIA inventory is designed to track changes in 

forest land. Most of these classification variables 

above are not observed on conditions that are 

nonforest at both measurements. It is not possible to 

use the FIA data to estimate things such as ownership 

class changes on nonforest lands.

Pick the Area of Interest

The set of plots used in generating the estimate is 

either the entire set of remeasured plots or a subset 

of those plots (Table 2). “All remeasured plots” is 

highlighted in Figure 1 because, in this example, we 

are interested in how land use changed on the entire 

land area of Minnesota. If we were interested in how 

forest land made the transition between forest-type 

All live stocking

Artificial regen species

Basal area live tree

Disturbance code 1

Disturbance code 2

Disturbance code 3

Forest Service Region

Table 1.–Classification variables available in AreaChangeBetweenInventories reporting tool

Forest type

Forest-type group

Growing-stock stocking

Land use-forest

Land use-major

National forest

Ownership

Physiographic class

Previous-stand-size

Site index

Site productivity

Slope

Stand origin

Stand treatment code 1

Stand treatment code 2

Stand treatment code 3

Stand-age

Stand-size

groups over the remeasurement period, we would have 

selected “Only remeasurement plots that were forest 

land at time 1 or time 2.”

Specify an Additional Filter

More specific estimates may be required in some 

instances. For example, if a user wanted to determine 

what happened on timberland that was in the jack 

pine forest type at the time of the previous inventory, 

the user would enter “AND C_PAST.FORTYPCD 

IN (101)” in the filter textbox. In this example no 

additional filter was specified.

RESULTS

A simple retrieval was run (Fig. 1) using the 

AreaChangeReports form. The evaluation chosen 

used plots measured in 2002-2006 and remeasured in 

2007-2011. The classification variable selected was 

“Land use–major” and the report was based on all land 

and water. The output from this retrieval is presented 

in Tables 3 and 4. Table 3 contains a cross-tabulation 

of the number of acres by past and present major land 

use. Table 4 provides information on the number of 

plots used in providing this estimate. The retrieval also 

outputs the SQL script used by the AreaChangeReports 

form to generate the report.

Table 2.–Area of interest options in the AreaChangeBetweenInventories reporting tool

1. All land and water

2. Only land that was forest land at time 1 or time 2

3. Only land that was timberland at time 1 or time 2

4. Only land that was forest land at time 1 and nonforest at time 2

5. Only land that was timberland at time 1 and nonforest at time 2

6. Only land that was nonforest at time 1 and forest land at time 2

7. Only land that was nonforest at time 1 and timberland at time 2
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 82

Table 3.—Acres by past and present major land use, MN, 2002-2006 to 2007-2011

Present Land Use - Major

Past Land Use - Major Total Forest Nonforest Noncensus Water Census Water

Total 54,008,529 17,292,981 33,415,843 214,121 3,085,584

Forest 16,335,759 16,096,331 224,383 9,685 5,360

Nonforest 34,289,917 1,125,577 32,945,644 86,916 131,780

Noncensus water 373,750 40,478 135,668 86,484 111,119

Census water 3,009,103 30,595 110,148 31,035 2,837,325

Table 4.—Number of plots by past and present major land use, MN, 2002-2006 to 2007-2011

Present Land Use - Major

Past Land Use - Major Total Forest Nonforest Noncensus Water Census Water

Total 17,454 5,274 11,106 64 1,010

Forest 4,976 4,902 69 3 2

Nonforest 11,380 353 10,959 26 42

Noncensus water 113 10 43 26 35

Census water 984 9 35 9 931

The estimated area of forest land in 2011, based solely 

on remeasurement plots, is 17,292,981 acres, which 

is close to the estimate using all plots (17,370,394 

acres) and within the 0.54 percent sampling error for 

this estimate. Of this total, 16.1 million acres, or 93.1 

percent, was also forest land in 2006. Approximately 

1.1 million acres, or 6.5 percent, was nonforest 5 years 

earlier, and 0.4 percent was in census or noncensus 

water. Only 0.2 million acres went from being 

classified as forest land in 2006 to being classified as 

nonforest or water in 2011. More than 98 percent of 

the land that was forested in 2006 remained forested in 

2011. The result is a net increase in forest land over the 

5-year period, from 16.3 million acres in 2006 to 17.3 

million acres in 2011. 

LITERATURE CITED

Miles, P.D. 2008. A simplified Forest Inventory and 

Analysis database: FIADB-Lite. Gen. Tech. Rep. 

NRS-30. Newtown Square, PA: U.S. Department 

of Agriculture, Forest Service, Northern Research 

Station. 42 p.

Miles, P.D. 2009. EVALIDatorReports: reporting 

beyond the FIADB. In: McWilliams, W.; Moisen, 

G.; Czaplewski, R., comps. Forest Inventory and 

Analysis (FIA) Symposium 2008; 2008 October 

21-23; Park City, UT. Proc. RMRS-P-56CD. Fort 

Collins, CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research Station. 

24 p.

Woudenberg, S.W.; Conkling, B.L.; O’Connell, B.M.; 

LaPoint, E.B.; Turner, J.A.; Waddell, K.L. 2010. 

The Forest Inventory and Analysis Database: 

database description and users manual version 

4.0 for Phase 2. Gen. Tech. Rep. RMRS-GTR-245. 

Fort Collins, CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research Station. 

336 p.

The content of this paper reflects the views of the 

author(s), who are responsible for the facts and accuracy 

of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 83

The Development of a Legacy FIA Report Simulator

Stephen P. Prisley, W. Brad Smith, and John W. Coulston1

Abstract.—The U.S. Forest Service Forest Inventory and Analysis Program (FIA) has 

a long history of providing crucial data on the nation’s forest resources. Since 1928, 

the Forest Service has periodically conducted forest inventories and compiled and 

published data on the status and trends of the nation’s forests. While more recent data is 

available digitally to the public, much of the historic data are available only in hardcopy 

publications, limiting its utility for computerized access and analysis. This presentation 

will describe a project begun in 2010 to compile information extracted from hundreds 

of published reports into a digital database we refer to as the FIA Legacy DB. This 

database contains tables of published estimates for common inventory parameters, such 

as timberland area by state and ownership, growing stock inventory, growth, removals, 

mortality, and species distributions. In addition to data tables containing estimates from 

published reports, interpolation routines were developed to provide estimates at common 

years by interpolation between report years. A set of reporting tools allows users to 

specify states or combinations of states, a time period, and then select from a variety of 

common reports. Example queries and reports will demonstrate the utility of this tool for 

quickly and easily delivering data previously available only in increasingly rare hardcopy 

reports.

1

 Associate Professor (SPP), Virginia Tech, Blacksburg, VA 24060; Research Forester (BS), 

U.S. Forest Service, Washington, DC; Supervisory Research Forester (JWC), U.S. Forest Service, 

Southern Research Station, Knoxville, TN. SPP is corresponding author; to contact, call 

540-231-7674 or email at prisley@vt.edu.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 84

Nontimber Forest Products Output Information System

James Chamberlain, John Munsell, Stephen P. Prisley, and Tom Hammett1

Abstract.—Nontimber forest products (NTFPs) are important commodities and critical 

components of healthy forests. They have not been sufficiently monitored to assess 

population status or trends in the dynamics of supply and demand. Over the last decade, 

U.S. Forest Service Forest Inventory and Analysis (FIA) of the Southern Research Station 

has reported on the status of NTFPs in particular states through state reports and at the 

national level through RPA assessment and the Sustainable Forests reports. In 2011, 

FIA and Virginia Tech initiated an effort to develop a nontimber product output (NTPO) 

information system similar to the timber product output (TPO) system. The protocol will 

systematically monitor harvested NTFPs. The initial focus of this project is on medicinal 

NTFPs in central Appalachia. This work provides a starting point for developing a 

replicable output system that can periodically report on medicinal NTFP production 

for the entire Appalachian forest region. Findings from the analysis of medicinal forest 

products will be integrated into a geographic information system that will spatially depict 

biological, socioeconomic, infrastructure and political aspects of the medicinal forest 

products segment. The long-term goal of this effort is to create a mechanism whereby 

nontimber forest products outputs across the nation can be tracked regularly and more 

completely valued. Authors will present the status of NTFPs in the south and report 

progress on development of the NTPO system. We also will encourage dialogue and 

suggestions with the audience regarding future efforts.

1

 Research Scientist (JC), U.S. Forest Service, Southern Research Station, Blacksburg VA; 

Professors (JM and TH) and Associate Professor (SPP), Virginia Tech, Blacksburg, VA. JC is 

corresponding author; to contact, call 540-231-3611 or email at jchamberlain@fs.fed.us.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Connecting 

Forest Inventory 

with climate Data
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 86

NEW APPROACHES TO FIA DATA FOR UNDERSTANDING 

DISTRIBUTION, ABUNDANCE, AND RESPONSE TO CLIMATE CHANGE

Kai Zhu, Souparno Ghosh, Alan E. Gelfand, and James S. Clark

Abstract.—We are using Forest Inventory and Analysis data to examine evidence for 

tree responses to climate change. By comparing seedling and tree occurrence data, 

we found that there is not yet evidence that tree populations in the eastern half of the 

United States are shifting geographic ranges to higher latitude in response to warming 

temperature. We are developing novel statistical methods to quantify seedling abundance 

in relation to climate and biotic variables, and to compare seedling and tree responses. 

We summarize how combining abundance data for seedlings and trees is allowing us 

to identify differences in how trees respond to climate change in the eastern half of the 

United States.

1

 Ph.D. Candidate (KZ), Nicholas School of the 

Environment, Duke University, Box 90328, Durham, 

NC 27708; Postdoctoral Associate (SG), Department of 

Statistical Science, Duke University; Professor (AEG), 

Department of Statistical Science and Nicholas School 

of the Environment, Duke University; Professor (JSC), 

Nicholas School of the Environment, Department of 

Biology, and Department of Statistical Science, Duke 

University. KZ is corresponding author: to contact, call 

919-613-8037 or email at kai.zhu@duke.edu.

INTRODUCTION

Forest Inventory and Analysis (FIA) data provide 

evidence for the effects of climate variation at 

broad geographic scales. We are using FIA data to 

quantify relationships between tree distributions and 

environmental variation as part of a larger study on the 

consequences of climate change. 

First, to determine whether trees shift ranges to 

track warming climates, we compared geographic 

distributions of juvenile and adult trees based 

on the assumption that a migrating population is 

characterized by offspring extending beyond adults 

at leading edges and the opposite at trailing edges. 

Through comparisons of approximately 100 species 

having range limits within the eastern United States 

(all states from Minnesota south to Louisiana and 

eastward), we found that there is not yet evidence for 

climate-mediated migration.

Second, to better interpret climate variables important 

for recruitment, we developed a novel zero-inflated 

statistical model for seedling counts with extreme 

incidence of zero observations. FIA seedling counts 

at plot level contain too many zeros to allow accurate 

inference using current models. Our zero-inflated 

general model, with k layers of zero-inflation (k-ZIG 

model) allows more flexible handling of both the 

zero-inflation and the nonzero counts. Whereas current 

zero-inflated models perform poorly, the k-ZIG model 

shows that seedling abundances can be explained by 

climate despite high proportions of zeros.

The third area we summarize here concerns the 

possibility that trees undergo ontogenetic niche 

shifts. We quantified the differences between juvenile 

and adult trees’ climate relationships based on the 

FIA seedling and tree data. We are in the process of 

determining whether there are different patterns of 

ontogenetic niche expansion and contraction from 

juveniles to adults.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 87

RANGE SHIFTS AND 

GEOGRAPHIC DISTRIBUTIONS

Anticipating whether species’ geographic ranges 

can track climate change is a goal of global change 

research. Mean annual temperatures increased 

substantially during the 20th century in the Midwest 

and Northeast, but have only recently begun to show 

substantial change in the Southeast, where warming 

summers were balanced by cooling winters in the past 

(Fig.1a). When viewed in terms of a climate change 

velocity, i.e., the ratio of temporal change over spatial 

gradient, regions in the Northeast and Upper Midwest 

have undergone climate shifts of more than 100 km 

during the 20th century (Fig. 1b).

Can tree distributions keep pace with the rapid climate 

warming? We used FIA data to directly compare 

seedling and tree 5th- and 95th-percentile latitudes for 

approximately 100 species for more than 40,000 plots 

across the eastern United States, and their relationship 

with climate change during the 20th century (Zhu 

et al. 2012). Our analysis is based on the rationale 

−95 −90 −85 −80 −75 −70

25 30 35 40 45

Longitude (°)

Latitude (°)

(a)

−95 −90 −85 −80 −75 −70

25 30 35 40 45 50

0.1

1

10

100

1000

3000

km/100 yr

Longitude (°)

Latitude (°)

(b)

Figure 1.—Temperature change in time and space during the 20th century in the eastern United States. Data are extracted 

from 10 decadal mean annual temperatures from the Climate Research Unit dataset, 1901-2000 (Mitchell and Jones 2005). 

Squares in (a) denote the slope of the linear regression of decadal data (red – increasing trend, blue – decreasing trend); 

square size is proportional to the absolute value of the slope. Spatial velocity of temperature change (b) is defined as the 

quotient of the temporal gradient (a) and the north-south directional spatial gradient of temperature distribution (Loarie et al. 

2009). Adapted from Zhu et al. (2012).

that a population that is migrating north in response 

to warming is expected to have offspring extending 

to higher latitudes than adults in regions that have 

warmed over the last century, but not in regions where 

climate has remained essentially constant. Likewise, 

a population retreating from a warming southern 

boundary is expected to have adults south of the 

southern extent of new recruitment by offspring.

We found that there is essentially no evidence that 

latitudinal migration has yet occurred for more than 

half of the approximately 100 tree species in eastern 

U.S. forests and there is a substantial migration lag 

behind climate change velocity (Fig. 2). In fact, 

results suggest that approximately 60 percent of the 

tree species examined show the pattern expected for a 

population undergoing range contraction, rather than 

expansion, at both northern and southern boundaries. 

Fewer species show a pattern consistent with a 

northward shift and fewer still with a southward shift. 

Only about 4 percent are consistent with expansion at 

both range limits. Comparing results with the 20thcentury climate changes that have occurred at the 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 88

0 200 400 600 800

−1000 −500

0

Temperature Change (km/100 yr)

Northern Range Boundary Change (km)

Figure 2.—Temperature change during the 20th century in comparison with latitudinal difference between seedlings and trees 

at northern range boundary. Each crosshair is a species with mean ± standard error. Positive temperature change means 

temperature distribution is moving to the north; negative, moving to the south. Positive northern range boundary change 

means species is consistent with expansion at the northern range limit; negative means contraction. Species above the 

1:1 line (gray dashed) may be tracking temperature change. Adapted from Zhu et al. (2012).
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 89

range boundaries themselves, we find no consistent 

evidence that population spread is greatest in areas 

where climate has changed most, nor are patterns 

related to seed size or dispersal characteristics. The 

fact that the majority of seedling extreme latitudes are 

south of those for adult trees should increase concerns 

for the risks posed by climate change.

SPECIES ABUNDANCE

AND CLIMATE RELATIONS

To explore the relationship between seedling 

abundance and variables that might be especially 

important for recruitment, we developed a novel 

zero-inflated statistical model for seedling counts with 

extreme incidence of zero observations. All species 

in the FIA data are absent in at least 60 percent of 

seedling plots, and some are absent from more than 90 

percent of seedling plots.

In the statistical literature, zero-inflated count models, 

in particular, the zero-inflated Poisson (ZIP) and 

zero-inflated negative binomial (ZINB) models, along 

with Poisson and negative binomial hurdle models, 

are commonly used to address this problem. However, 

these models struggle to explain extreme incidence of 

zeros (e.g., > 80 percent), especially to find important 

covariates. In fact, the ZIP may struggle even when 

the proportion is not extreme. To redress this problem 

we developed the class of k-ZIG models (Ghosh et al. 

2012). These models allow more flexible modeling of 

both the zero-inflation and the nonzero counts. The 

models are fitted within a Bayesian framework. 

For the FIA data, we fit the model for seedling 

abundance of two representative species: red maple 

(Acer rubrum) and yellow-poplar (Liriodendron 

tulipifera), with 62.7 and 93.8 percent of zeros, 

respectively. We used climate (winter temperature and 

annual precipitation) and biotic variables (basal areas 

of conspecific and all species) as covariates. Whereas 

current ZIP and ZINB models perform poorly, the 

k-ZIG model shows that, despite high proportions of 

zeros, the seedling counts could be explained by both 

types of variables.

SPECIES’ RESPONSES 

TO CLIMATE CHANGE

Biogeographic responses of plant species to climate 

change are determined by the requirements of 

juveniles, which can limit spread to new environments. 

However, most models of climate response are 

calibrated to adults, which may not reflect the climate 

that determined their original establishment. Despite 

a large literature on ontogenetic niche shifts in 

animals, there is little direct evidence for plants that 

could provide insight on the consequences of climate 

change for migration potential. Using the FIA data, we 

built a species distribution model to jointly quantify 

juvenile and adult trees’ abundance distributions 

in relationship with the two most commonly used 

climatological variables, temperature and precipitation. 

To accommodate the mismatch between fine-scale 

biological processes and coarse-scale climate variables 

we introduced an aggregation approach to climate 

calibration. The fitted model allowed us to compare 

differences between juveniles and adults in climate 

relationships.

Adults and juveniles of a given species respond 

differently: some species showed broader climate 

calibrations for adults, whereas others showed broader 

climate calibration for juveniles. The differences 

could be partly because of ontogenetic niche shifts 

and partly because of other factors that impact climate 

response. For example, American hornbeam (Carpinus 

caroliniana) juveniles tend to be abundant in cold and 

dry climates relative to adults (Figs. 3a,b). The relative 

recruitment intensity, i.e., the ratio of seedling counts 

over tree basal areas, also shows cold and dry climate 

benefits juveniles while warm and wet climate benefits 

adults (Fig. 3c). Finally, the 90-percent volume 

under the fitted surface within the geographic range 

suggests a broader climatic niche for juveniles than 

adults (Fig. 3d). This pattern could be consistent with 

ontogenetic niche contraction, but there are alternative 

explanations as well.

Both types of ontogenetic niche differences could have 

important ecological implications. Niche expansion 

would be suggested when adult niches are broader 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 90

Figure 3.—Comparison of juvenile vs. adult of American hornbeam (Carpinus caroliniana) abundance surfaces as an example 

of ontogenetic niche shift. (a) is the observed (red bubble) and fitted (gray surface) seedling counts on log scale, with all 

available climate (yellow box) in the eastern United States. (b) is the observed (red bubble) and fitted (gray surface) tree 

basal areas on log scale, with the same climate boundary (yellow box). (c) is the relative recruitment intensity, defined as the 

ratio of seedling counts over tree basal areas on log scale. Warm colors show high values, cold colors show low values, and 

black dashed line shows zero references. It was calculated only within the available climate, superimposed with the actual 

geographic range mapped into the climatic space (green box). (d) is the 90 percent volume under the fitted surface within the 

geographic range (green box), where a large contour suggests a flat surface, and a small contour suggests a peaked surface. 

It approximates different climatic niche requirements for juveniles (red) and adults (blue).

−10 0 10 20

600 800 1000 1200 1400 1600

(a)

−16

−14

−12

−10

−8

−6

Climate

Winter Temperature (°C)

Annual Precipitation (mm)

−10 0 10 20

600 800 1000 1200 1400 1600

(b)

−13

−12

−11

−10

−9

−8

−7

−6

Climate

Winter Temperature (°C)

Annual Precipitation (mm)

−10 0 10 20

600 800 1000 1200 1400 1600

(c)

Range

(d)

−10 0 10 20

600 800 1000 1200 1400 1600 1800

Juveniles

Adults

Range

Winter Temperature (°C)

Annual precipitation (mm)

Annual precipitation (mm)

Winter Temperature (°C)
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 91

than juvenile niches. This expansion could be expected 

if adults integrated over fluctuating windows for 

juvenile establishment in the past, while juveniles 

more narrowly reflect the current climate conditions. 

Seedlings can have narrower niche requirements 

owing to limited root systems, low carbon reserves, 

and reduced photosynthetic capacity. In contrast, niche 

contraction could be suggested by adult niches that 

appear narrower than those of juveniles. Contraction 

could occur if seeds can establish in sink populations, 

where they ultimately do not replace themselves due 

to competition or herbivory. We are examining a large 

number of species across the eastern United States 

to determine how climate calibrations of both adults 

and juveniles are impacted by climate change and the 

emerging role of new competitive environments and 

natural enemies.

ACKNOWLEDGMENTS

The authors thank Chris Woodall for database 

assistance. The study was supported by National 

Science Foundation grants CDI 0940671 and Coweeta 

Long Term Ecological Research.

LITERATURE CITED

Ghosh, S.; Gelfand, A.E.; Zhu, K.; Clark, J.S. 2012. 

The k-ZIG: flexible modeling for zero-inflated 

counts. Biometrics. 68: 878-885.

Loarie, S.R.; Duffy, P.B.; Hamilton, H.; Asner, G.P.; 

Field, C.B.; Ackerly, D.D. 2009. The velocity of 

climate change. Nature. 462: 1052-1055.

Mitchell, T.D.; Jones, P.D. 2005. An improved 

method of constructing a database of monthly 

climate observations and associated highresolution grids. International Journal of 

Climatology. 25: 693-712.

Zhu, K.; Woodall, C.W.; Clark, J.S. 2012. Failure 

to migrate: lack of tree range expansion in 

response to climate change. Global Change 

Biology. 18: 1042-1052.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 92

INVESTIGATING FOREST INVENTORY

AND ANALYSIS-COLLECTED TREE-RING DATA FROM UTAH 

AS A PROXY FOR HISTORICAL CLIMATE

R. Justin DeRose, W. Shih-Yu (Simon) Wang, and John D. Shaw1

Abstract.—Increment cores collected as part of the periodic inventory in the 

Intermountain West were examined for their potential to represent growth and be a 

proxy for climate (precipitation) over a large region (Utah). Standardized and crossdated 

time-series created from pinyon pine (n=249) and Douglas-fir (n=274) increment cores 

displayed spatiotemporal patterns in growth differences both between species and by 

region within Utah. However, the between-species interrelationship of growth was 

strong over much of the state and indicated both species respond similarly to climate 

variations. Indeed, pinyon pine and Douglas-fir exhibited a significant and spatially 

coherent response to instrumental precipitation data. Previous water year (5-month 

lag) exhibited the strongest relationship to tree-ring increment for both species. Results 

suggest increment cores collected by Forest Inventory and Analysis are excellent proxies 

for historical precipitation. 

1

 Research Ecologist (RJD), U.S. Forest Service, Rocky 

Mountain Research Station, 507 25th St., Ogden UT 84401; 

Assistant Professor (SW), Department of Plants, Soil and 

Climate, Utah State University; Analysis Team Leader 

(JDS), U.S. Forest Service, Rocky Mountain Research 

Station. RJD is corresponding author: to contact, call 

801-625-5795 or email at rjderose@fs.fed.us. 

INTRODUCTION

Tree-ring data have long been recognized as 

trustworthy proxies for historical climate conditions 

because of their potential sensitivity to particular 

climate factors and their precise annual resolution 

(Fritts 1976). The spatial representation of treering chronologies for the western United States, 

however, is sparse and can be exceedingly limited for 

particular areas such as the Great Basin. Due to the 

gridded nature of the Forest Inventory and Analysis 

(FIA) sampling design, forest data are collected at 

a spatial density unparalleled by any station data or 

dendrochronological data. As a result, increment cores 

collected by the Intermountain West FIA during the 

Periodic Inventory (~1990s) represent a potentially 

valuable source of dendro-climatic data. 

The conventional preparation of tree-ring chronologies 

sensitive to climate requires careful selection of 

species and sites (e.g., elevation), sample replication 

of individual trees within sites, crossdating, and finally 

chronology development (Fritts 1976). As a result, 

creating an extensive spatial coverage of tree-ring data 

for paleoclimate reconstruction or growth analysis 

can be expensive and time-consuming, justifying 

an examination of possible alternatives. Previous 

research has indicated that pinyon pine (Pinus edulis

Engelm.) and Douglas-fir (Pseudotsuga menziessii

var. glauca) exhibit substantial tree-ring sensitivity, 

making these species candidates for proxy climate 

data. There are, however, two important differences 

between conventionally collected chronologies and the 

FIA-collected increment cores. These differences can 

be thought of as trade-offs in the quality or quantity 

of proxy climate information. First, a conventional 

tree-ring site is subjectively sampled to maximize 

both the climate signal and chronology length, 

and has associated statistical representation issues 

(Carrer 2011). In contrast, the FIA grid represents a 

systematic random sample for making populationlevel inferences. The trade-offs are (1) the differences 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 93

in temporal length of the resultant proxy time-series 

(conventional are longer, FIA shorter) and (2) the 

degree to which a climate signal is expressed at a 

particular point (conventional should be very sensitive; 

FIA data should reflect normal growing conditions 

within the specifications of the sampling). Second, 

conventional chronologies consist of multiple, 

averaged series, which require intense investment 

in time and resources for each sampling point. 

In contrast, increment cores from FIA plots were 

collected as part of a standardized protocol on plots 

~5 km apart. Given the relatively high spatial density 

of FIA data in a state or region, tree-ring series 

combined within a certain distance might reasonably 

correspond to the variability of tree-ring increment 

recorded in conventional chronologies.

In this study we explore the potential of using FIAcollected increment cores for studies of spatiotemporal 

patterns of growth and as possible proxies for 

historical climate. Specifically, we asked whether 

pinyon pine and Douglas-fir tree-ring series from Utah 

(1) display individual variation in ring width, both 

within and between species and (2) have a predictable 

relationship with instrumental precipitation data. 

METHODS

To address the study questions two data sets were 

constructed from the available increment cores in Utah 

(Fig. 1), one for two-needle pinyon pine (n=249) and 

one for Douglas-fir (n=274). Increment cores were 

mounted, sanded, and digitized following conventional 

Figure 1.—Representation (%) of species in the Utah FIA tree-ring data set and all plot locations (blue triangles). Red asterisk 

indicates pinyon pine and Douglas-fir used in this study. 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 94

dendrochronological methods before crossdating 

with the nearest available chronologies. Dated series 

were then detrended using the “super smoother” 

(Friedman 1984) in program ARSTAN (Cook et 

al. 2007) and exported as individual standardized 

series. The series were used in combination with their 

jittered coordinates (Note: the term “fuzzswapped” is 

used within FIA to denote the process that decouples 

actual spatial locations from landowner information) 

to create gridded maps, after which we applied the 

Cressman (1959) objective interpolation scheme at 

1/8° (longitude by latitude) grid spacing over the 

Utah FIA network. The Cressman scheme does not 

include a first-guess field, which has the disadvantage 

of potentially creating spurious values, as a result 

of interpolation. The advantage, however, is that the 

gridded result completely reflects the time-series and 

is not biased by the first guess, which is impossible to 

construct for tree-ring data. We gridded the data with 

a 1/8° setting (Fig. 2a) using four radii of influence: 

1/8°, 1/4°, 1/2°, and 1° from inner radius to outer 

radius. An inverse-square distance (1/r2) weighting 

centered at the site was then applied to the data. 

To examine the spatiotemporal relationship between 

the FIA data and gridded instrumental precipitation 

data, a point-by-point correlation map was computed 

between each grid point of the FIA data and the 

gridded instrumental precipitation data (1950-

1995). Contours were smoothed by averaging over 

the nearest neighbors. To examine the coherence 

between the FIA tree-ring data, which reflect the 

growing season, and the seasonal climate cycle, we 

calculated the cross-correlation between the FIA data 

and precipitation, both averaged over Utah. A 12-

month precipitation average with a 1-month time-lag, 

from year-12 (previous calendar year) to year+12 

(next calendar year) was calculated to examine lags 

in the relationship between precipitation and the FIA 

data (Fig. 3c). Observed precipitation was obtained 

from the station-based, 1/8°-resolution gridded data 

constructed by Maurer et al. (2002) beginning in 1949.

Figure 2.—Contour plots of root mean square (RMS)*10 (to visually accentuate variability) between all pinyon pine (n=249) 

and Douglas-fir (n=274) tree-ring data. Blue dots indicate FIA plot locations. Lower panel depicts the Utah domain average 

(37°-42°N latitude and 108°-114°W longitude) tree-ring index (mm), 1850-1995. 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 95

Figure 3.—Contour plots of point-to-point correlations between pinyon pine (PP) and Douglas-fir (DF) gridded tree-ring data 

for (a) 1700-1799, (b) 1800-1899, (c) 1900-1995, and (d) the1700-1995. Bold contours indicate significance at the 99-percent 

level. 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 96

RESULTS AND DISCUSSION

Pinyon pine and Douglas-fir were two of the most 

represented species in the Utah tree-ring data set (Fig. 

1), and grow virtually throughout the state (Fig. 2; 

note the evident pinyon pine northern range limit). 

Although the totality of tree-ring data could not be 

crossdated, the preliminary data shown herein included 

30 percent of the pinyon pine and 83 percent of the 

Douglas-fir samples available for Utah. Ongoing work 

seeks to increase the representation of both species and 

other species in Utah and the other Intermountain West 

states (Arizona, New Mexico, Colorado, Wyoming, 

Idaho, Montana, and Nevada). 

Spatiotemporal Patterns of Growth

Contours of the root mean square (RMS) of treering width indicated substantial variability in growth 

for both species (Fig. 2). Pinyon pine had a larger 

range of variability in growth than Douglas-fir, 

which was expected, considering that pinyon pine 

inhabit low-elevation, dry sites. However, there was 

notable correspondence between increasing RMS and 

increasing elevation for both species (Fig. 2). This 

pattern was especially prevalent for both species in 

the central and east-central part of the state (i.e., Book 

Cliffs) and the Wasatch Mountains, which run roughly 

north-south through the state (Fig. 2). Domain average 

ring width exhibited striking similarity between 

species; the pluvials of the late1860s and early1980s 

were evident, as were the droughts of the 1880s, late 

1890s, late1930s, and late1970s. These drought-pluvial 

cycles are consistent with those that have prevailed 

in Utah (Wang et al. 2012). Although occupying 

somewhat different ecological niches, pinyon pine 

and Douglas-fir exhibited similarities in interannual 

growth that suggested both species were suitable for 

spatiotemporal analyses of growth. However, in the 

northern tier of Utah (greater than 41°N latitude), 

where only Douglas-fir were analyzed, the RMS was 

reduced substantially. This result hinted at potential 

larger regional patterns of growth that could be driven 

only by climate. 

Inter-species Correspondence

Although both pinyon pine and Douglas-fir 

chronologies have been previously recognized as 

having tree-ring growth sensitivity to precipitation 

(Watson et al. 2009, Woodhouse et al. 2006), further 

testing was necessary to determine whether the 

FIA data were also sensitive. Indeed, point-bypoint correlation between the two gridded datasets 

revealed strong spatiotemporal relationships 

between the two species. There was an increase in 

significant correlations (bold lines) from 1700-1799 

(Fig. 3a), where strongest coherence was in the 

southwest, to 1800-1899 (Fig. 3b) where the Uinta 

Basin and the southeast mountains become strongly 

correlated. During the period 1900-1995 (Fig. 3c) 

the relationships become significantly correlated for 

nearly all the mountainous portion of the state. By 

looking at the entire tree-ring record (1700-1995, Fig. 

3d) the southwest, Grand Staircase, southeast, and 

western Uinta Basin areas were highly significant. 

It is noteworthy that there were no areas of negative 

correlation between these two species (Fig. 3). Finally, 

although pinyon pine and Douglas-fir occupy different 

ecological niches, their strong interrelationships, 

particularly for certain regions, suggested tree-ring 

increment responded similarly to climatic drivers. 

FIA Tree-rings as 

Precipitation Proxy

If the strong growth interrelationship between pinyon 

pine and Douglas-fir was caused by climate, one 

would expect a similarity between the species in 

the response to that driver. Therefore, we examined 

the point-to-point relationship between gridded 

instrumental precipitation and tree-ring width from 

1949 to 1995 for both species. Results showed 

highly significant relationships for both species and 

coherent spatial relationships between species (Fig. 

4). For example, both species exhibited their strongest 

relationships to precipitation in the southwest and 

western Uinta basin areas of Utah. Additionally, both 

species showed lower correspondence to precipitation 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 97

in the basins and western desert, where data were 

limiting. The peak correlation coefficients between 

ring width and precipitation for both species occurred 

around lag-5 month, with a broad peak in significance 

centered over the water year (previous August through 

current July), which strongly indicated that water year 

precipitation drives tree-ring increment in this region. 

This finding strongly supports using the gridded treering data sets as a precipitation proxy, augmenting 

the currently limited station data-based gridded 

precipitation data sets. 

Figure 4.—Contour plots of point-to-point correlations of the gridded pinyon pine and Douglas-fir data sets to precipitation 

reanalysis data (left panels) and the correlation between Utah domain (37°-42°N latitude and 108°-114°W longitude) average 

tree-ring index and monthly precipitation data from 1949-1995, lagged from 12 months previous (-12) to 12 months following 

(12). Bold contours indicate significance at the 99-percent level. 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 98

ACKNOWLEDGMENTS

Matt Bekker, Eric Allen, Connie Woodhouse, and Jeff 

Lukas provided important unpublished chronologies 

necessary for crossdating the FIA tree-ring series 

in this study. This manuscript was improved by the 

comments of two anonymous reviewers. 

LITERATURE CITED

Carrer, M. 2011. Individualistic and time-varying 

tree-ring growth to climate sensitivity. PLoS 

ONE 7(1): e30002. 

Cook, E.R.; Krusic, P.J.; Holmes, R.H.; Peters, K. 

2007. Program ARSTAN Version 41d. Available 

at www.ldeo.columbia.edu/tree-ring-laboratory. 

[Date accessed unknown].

Cressman, G.P. 1959. An operational objective 

analysis system. Monthly Weather Review. 

87: 367-374. 

Friedman, J.H. 1984. A variable span smoother.

Technical Report 5. Stanford, CA: Stanford 

University, Laboratory for Computational Statistics, 

Department of Statistics. 

Fritts, H.C. 1976. Tree rings and climate. New York, 

NY: Academic Press. 567 p. 

Maurer, E.P.; Wood, A.W.; Adam, J.C.; Lettenmaier, 

D.P.; Nijssen, B. 2002. A long-termhydrologically 

based data set of land surface fluxes and states 

for the conterminous United States. Journal of 

Climate. 15: 3237-3251.

Wang, S.-Y.; Gillies, R.R.; Reichler, T. 2012. Multidecadal drought cycles in the Great Basin 

recorded by the Great Salt Lake: modulation 

from a transition-phase teleconnection. Journal 

of Climate. 25: 1711-1721.

Watson, T.A.; Barnett, F.A.; Gray, S.T.; Tootle, 

G.A. 2009. Reconstructed streamflows for the 

headwaters of the Wind River, Wyoming, United 

States. Journal of the American Water Resources 

Association. 45(1): 1-13. 

Woodhouse, C.A.; Gray, S.T.; Meko, D.M. 2006. 

Updated streamflow reconstructions for the 

Upper Colorado River Basin. Water Resources 

Research. 42: W05415. 

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 99

UNLOCKING THE CLIMATE RIDDLE IN FORESTED ECOSYSTEMS

Greg C. Liknes, Christopher W. Woodall, Brian F. Walters, and Sara A. Goeking1

Abstract.—Climate information is often used as a predictor in ecological studies, where 

temporal averages are typically based on climate normals (30-year means) or seasonal 

averages. While ensemble projections of future climate forecast a higher global average 

annual temperature, they also predict increased climate variability. It remains to be seen 

whether forest ecosystems will respond more to changes in mean climate conditions or 

changes in climate variability. Our objective was to compare the relative importance of 

climate mean versus variability metrics as predictors of tree mortality and regeneration. 

Using the 4-km PRISM and 32-km NARR climate datasets, both mean and variability 

metrics were derived for Forest Inventory and Analysis (FIA) plot locations across the 

eastern United States. Tree mortality and seedling abundance data were obtained from 

FIA plots that were visited twice in the years from 2000 to 2010. A number of statistical 

approaches (including correlation analysis, and an algorithmic method, Random Forests) 

were used to examine the relative importance of mean versus variability of climate data 

in the context of evaluating changes in tree and seedling attributes. 

1

 Research Physical Scientist (GCL), Research Forester 

(CWW) and Forester (BFW), U.S. Forest Service, Northern 

Research Station, 1992 Folwell Ave, St. Paul, MN 55108; 

Biological Scientist (SAG), U.S. Forest Service, Rocky 

Mountain Research Station. GCL is corresponding author: 

to contact, call 651-649-5192 or email at gliknes@fs.fed.us.

INTRODUCTION

The climate system plays an essential role in 

forested ecosystems, controlling inputs of energy 

and moisture and setting constraints that intersect 

with species’ ecological niches to determine forest 

community composition and structure. As such, 

climate information is often used in models of 

species’ ecological niches or climate envelopes. In 

addition, spatial and temporal variability of climate 

can inform investigations of tree mortality and 

regeneration dynamics in forested ecosystems. Such 

studies typically use temporal averages representing 

climate normals (30-year averages) or seasonal means. 

Climate change projections predict not only a shift 

in mean climate regimes in many locations, but also 

increased variability. If trees are locally adapted to a 

particular climate, increased variability could lead to 

increased mortality or decreased regeneration. Using 

forest inventory data from the eastern United States, 

we investigated whether climate variability provides 

more explanatory power of tree mortality and seedling 

abundance than climate means by applying classical 

correlation analysis and the Random Forests algorithm 

(Breiman 2001a). 

METHODS

Forest data from the U.S. Forest Service’s Forest 

Inventory and Analysis (FIA) database (Woudenberg 

et al. 2010) were analyzed. The analysis was restricted 

to plots in the eastern United States that had been 

visited twice during annual inventories between 2000 

and 2010, and more than 23,000 plots were available 

for the analysis. The two continuous response 

variables, volume mortality (m3 ha-1) and change in 

seedling abundance (stems ha-1), were calculated 

from differences in adjacent time periods. Other FIA 

data were used as additional predictor variables, such 

as stand age, relative density, live/dead biomass, 

Lorey’s height, and disturbance history (Table 1). It 

should be noted that plots were revisited several years 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 100

apart (mode = 5 years, range = 1 to 10 years), and 

it is therefore not possible to know when mortality 

occurred (akin to censoring in survival analysis) or 

how changes to seedlings abundance were distributed 

across the time interval.

Monthly climate data were used from the PRISM 

dataset (Daly 2002), including minimum and 

maximum daily temperature (Tmin and Tmax, 

respectively) and total precipitation. Tmin and 

Tmax were averaged to obtain a monthly average 

temperature. In addition, temperature and precipitation 

data from the North American Regional Reanalysis 

Project (NARR) (Mesinger et al. 2006) were 

assembled in order to create a second, comparable 

monthly climate dataset.

To investigate the relative explanatory power of 

mean climate and climate variability, mean and 

interannual standard deviation were calculated from 

the monthly data for both total precipitation and 

Table 1.—Summary of analysis methods, response variables, climate predictor variables, and forest stand 

metrics used to assess the relationships between climate means, climate variability, and tree mortality 

and regeneration

Analysis Method

Source Variable Random Forests Correlation

Response variables FIADB volume mortality x x

FIADB seedling abundance change x x

Climate predictor variables NARR mean annual temperature x x

interannual temperature standard deviation x x

mean annual precipitation x x

interannual precipitation standard deviation x x

PRISM mean annual temperature x x

interannual temperature standard deviation x x

mean annual precipitation x x

interannual precipitation standard deviation x x

Stand predictor variables FIADB stand age x

relative density x

live biomass x

dead biomass x

Lorey’s height x

disturbance code 1 x

disturbance code 2 x

disturbance code 3 x

average temperature. As previously stated, the exact 

timing of tree mortality and changes in seedling 

density is unknown relative to plot observations, 

and furthermore, forest responses are known to 

lag climatic conditions, sometimes by a number 

of years. As a result, six different lag periods were 

investigated, ranging from 1 to 6 years preceding the 

initial plot observation (Fig. 1). The endpoint of each 

climate averaging interval was the midpoint of the 

FIA observation interval. Given the unknown timing 

of mortality or seedling change, this endpoint is a 

compromise and can result in a climate time interval 

that is either offset (precedes) or overlaps with the 

forest response. Climate means and standard deviation 

were calculated for each of the six averaging periods 

for both temperature and precipitation, resulting 

in 24 climate variables from both the PRISM and 

NARR datasets. In turn, correlation coefficients were 

calculated for each climate variable in relation to 

volume mortality and seedling abundance change 

(for 96 variable pairs).
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 101

Figure 1.—Depiction of time periods used to average climate variables relative to repeated forest inventory observations. Six 

different lag periods ranging from 1 to 6 years preceding the first inventory observation were used to calculate annual means 

and interannual standard deviation.

All variables, except seedling abundance change, 

were found to be non-normal and were transformed 

using the Box-Cox method (R v. 2.13, “car” package, 

bcPower function; Weisberg 2005). Pearson product 

correlations were calculated for the transformed 

data. Plots with non-zero mortality were included 

in the mortality correlation analysis (59 percent 

of all available plots). Plots with less than 10 cm 

of floodwater or snow cover were included in the 

seedling analysis in order to avoid observation bias; 

outliers and plots with zero change were removed, 

and 61 percent of all available plots remained for the 

seedling analysis.

While considerable debate exists between proponents 

of classical statistical methods and newer algorithmic 

methods (e.g., Breiman 2001b), we considered such 

methods to be a useful exploratory tool to investigate 

the relative importance of climate predictor variables. 

The Random Forests algorithm has appealing features, 

such as automatic consideration of interactions 

between variables, accommodation of nonlinear 

relationships, and built-in analysis of variable 

importance. We built predictive models of mortality 

and seedling density change using Random Forests (R 

v. 2.13, “randomForest” package) (Liaw and Wiener 

2002). A total of eight models were constructed 

(all possible combinations of mortality vs. seedling 

change, NARR vs. PRISM, and temperature vs. 

precipitation, Table 1).

RESULTS

Correlation analysis resulted in very few statistically 

significant coefficients relating climate variables to 

tree mortality, with interannual standard deviation of 

precipitation the only exception (PRISM dataset; 3, 

4, 5, and 6-year lag periods; coefficients range from 

0.017 to 0.018). While nearly all seedling/climate 

correlation coefficients were significant (exceptions 

were interannual standard deviation of temperature 

for both PRISM and NARR; 1, 2, 3, and 4-year lag 

periods), none exceeded 0.11. A few other patterns are 

apparent, such as a general insensitivity of correlation 

coefficients to the lag period, a higher correlation of 

seedling change to precipitation than to temperature, 

and inconsistency in the relative magnitude of 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 102

correlations for mean and variability climate metrics. It 

should be noted, the climate mean variables followed a 

bi-modal distribution and a more robust transformation 

may be required for an adequate comparison.

Importance measures from the Random Forests 

analysis indicate standard deviation of both 

temperature and precipitation are frequently better 

predictors than the corresponding mean (Table 2). 

Additionally, the top climate predictors often provide 

better or at least comparable information to the three 

highest ranked FIA stand metrics. The most important 

stand metrics for the four mortality models and the 

two temperature/seedling models were consistently 

Lorey’s height, stand age, and live biomass (in various 

orders), while the most important stand metrics for 

the seedling change models involving precipitation 

included disturbance history rather than stand age. The 

difference in importance between mean and standard 

deviation of climate variables derived from PRISM 

was consistently larger than the difference derived 

using NARR data.

DISCUSSION

Our intent was to investigate whether climate 

variability data provides more information about tree 

mortality and regeneration in forested ecosystems 

when compared climate mean data. We present 

initial results toward that aim, using more than 

23,000 remeasured inventory plots spanning a 

variety of climatic regimes in the eastern United 

States. While correlation analysis did not indicate 

that variability provides more information than mean 

values, the Random Forests algorithm does indicate 

interannual standard deviation of both temperature and 

precipitation may be better predictors of mortality and 

seedling regeneration than annual mean values. 

Future work will include a classic regression approach 

to modeling tree mortality and seedling regeneration 

in an attempt to reconcile the seemingly contradicting 

results from the correlation and Random Forests 

analyses. In addition, more robust time series methods 

will be applied in order to quantify variability at 

numerous time scales. For example, interannual 

Table 2.—Variable importance measures for eight Random Forests models of tree mortality and seedling 

abundance change using climate mean, climate standard deviation (SD), and forest inventory data (stand 

metrics) as predictors. Temperature (Temp) and total precipitation (PPT) data were derived from two 

different datasets (PRISM and NARR). Importance is measured by the increase in mean square error 

when a variable is removed from the model. Values are based on the climate variables with the highest 

importance score and the three most important stand metrics.

% Increase in MSE

Tree Mortality

Climate Variable Mean SD Lorey’s Height Stand Age Live Biomass

PRISM Temp 15.17 24.58 24.35 19.03 18.66

NARR Temp 16.16 17.25 26.77 18.39 18.66

PRISM PPT 17.21 22.10 20.59 24.74 16.05

NARR PPT 18.91 22.18 20.85 26.87 17.04

Seedling Change

Climate Variable Mean SD Lorey’s Height Stand Age Live Biomass Disturbance Code 1

PRISM Temp 7.88 17.03 14.24 10.64 11.19 -----

NARR Temp 12.37 13.74 22.86 12.75 10.78 -----

PRISM PPT 9.26 23.60 9.88 ----- 9.48 10.59

NARR PPT 18.83 16.34 10.09 ----- 9.27 7.95
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 103

seasonal variability could be an informative predictor, 

as well as decadal or even intra-annual variability. 

A nationwide, consistent protocol for gathering forest 

inventory information across a large climate gradient 

in the United States presents great opportunity 

for unlocking the complexity of climate/forest 

interactions. The FIA program is beginning to 

assemble remeasurement data on a regular return 

interval (e.g., 5-10 years). This work represents an 

early attempt to connect temporally rich climate data 

with observations of change in the Nation’s forested 

ecosystems.

ACKNOWLEDGMENTS

The authors thank Oregon State University’s PRISM 

Climate Group and the National Climatic Data 

Center of the National Oceanic and Atmospheric 

Administration for making PRISM and NARR data 

available.

LITERATURE CITED

Breiman, L. 2001a. Random Forests. Machine 

Learning. 45(1): 5-32.

Breiman, L. 2001b. Statistical modeling: the two 

cultures. Statistical Science. 16(3): 199-231.

Daly, C.; Gibson, W.P.; Taylor, G.H.; Johnson, G.L.; 

Pasteris, P. 2002. A knowledge-based approach 

to the statistical mapping of climate. Climate 

Research. 22: 99-113.

Liaw, A.; Wiener, M. 2002. Classification and 

regression by randomForest. R News. 2(3): 

18-22.

Mesinger, F.; DiMego, G.; Kalnay, E.; Mitchell, 

K.; Shafran, P.R.; et al. 2006. North American 

regional reanalysis. Bulletin of the American 

Meteorological Society. 87: 343-360.

Weisberg, S. 2005. Transformations. Chapter 7. In: 

Applied linear regression. 3rd ed. Hoboken, NJ: 

John Wiley and Sons: 147-166.

Woudenberg, S.W.; Conkling, B.L.; O’Connell, B.M. 

LaPoint, E.B.; Turner, J.A.; Waddell, K.L. 2010. 

The Forest Inventory and Analysis database: 

database description and users’ manual version 

4.0 for Phase 2. Gen. Tech. Rep. RMRS-GTR-245. 

Fort Collins, CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research Station. 

336 p.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 104

Adapt, Move, or Die: FIA Data in Assessments

of Forest Tree Genetic Degradation Risk

from Climate Change and Other Threats

Kevin M. Potter, Barbara S. Crane, and William W. Hargrove1

Abstract.—Changing climatic conditions may pose a severe threat to forest tree species, 

forcing three potential population-level responses: 1) toleration/adaptation, 2) movement 

to suitable environmental conditions, or 3) extirpation. All could have negative genetic 

consequences. It will be important, therefore, to safeguard existing adaptedness and to 

create conditions conducive for future productivity and evolution. To efficiently conserve 

the genetic variation of species, it is necessary to understand where climate change 

pressure will be greatest, and what species and populations are more highly predisposed 

to genetic degradation from climate change and other threats. Forest Inventory and 

Analysis (FIA) data represent an unmatched resource for conducting broad-scale, 

spatially explicit assessments of the risk posed by climate change and other threats 

to the genetic integrity of forest tree populations and species. We used FIA data to 1) 

generate 4 km2 resolution maps predicting the genetic pressure that could be imposed by 

climate change on forest tree species; and 2) compile information about the biological 

attributes and genetic diversity of individual species. The first assessment tool, Forecasts 

of Climate-Associated Shifts in Tree Species (ForeCASTS), has generated climate 

change pressure maps for more than 300 North American tree species and quantifies 

potential climate change genetic pressure, as defined by the straight-line Minimum 

Required Movement (MRM) distance from the existing locations of each species to the 

nearest favorable future habitat. The second assessment tool, the Forest Tree Genetic 

Risk Assessment System (FORGRAS) framework, ranks the predisposition of forest 

tree species to genetic degradation, based on demographic and occurrence information, 

ecological and life-history traits, species-specific projections of climate change pressure, 

and predictions of pest and pathogen susceptibility. Both assessment tools should 

be valuable for scientists and managers attempting to determine which species and 

populations to target for monitoring efforts and for proactive gene conservation and 

management activities.

1

 Research Assistant Professor (KMP), North Carolina State University, Department of Forestry 

and Environmental Resources, Research Triangle Park, NC 27709; Geneticist (BSC), U.S. Forest 

Service, Southern Region; Research Ecologist (WWH), U.S. Forest Service, Southern Research 

Station. KMP is corresponding author: to contact, email at kevinpotter@fs.fed.us. 

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 105

Tree Species Migration

in the Pacific Coastal United States

Heather Lintz, Andrew N. Gray, Andrew Yost, and Vincente Monleon1

Abstract.—We analyzed mean changes in longitude, latitude, and elevation for tree 

species in the Pacific coastal United States (California, Oregon, and Washington). Our 

analyses show that species migration distance and direction is highly variable, and 

Pinus monticola and Cornus nuttallii are leading the pack in polar opposite directions. 

All drought tolerant species are moving northward. Tree species producing the greatest 

seed abundance are moving the farthest. The distance and direction of migratory 

gradients correspond to changes in both means and anomalies of summer relative 

humidity, summer vapor pressure deficit, summer growing season length, and summer 

precipitation. 

1

 Assistant Professor Senior Research (HL), Oregon Climate Change Research Institute, 

326 Strand Hall, Corvallis, OR 97330; Research Ecologist (ANG), U.S. Forest Service, Pacific 

Northwest Research Station; Forest Ecologist (AY), Oregon Department of Forestry, Salem, OR; 

Resource Monitoring and Assessment Program (VM), U.S. Forest Service, Pacific Northwest 

Research Station. HL is corresponding author: to contact, email at hlintz@coas.oregonstate.edu. 

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Establishing Baselines 

and Projecting Trends
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 107

EMERALD ASH BORER MODELING METHODS 

FOR FUTURE FOREST PROJECTIONS

Ryan D. DeSantis, W. Keith Moser, Robert J. Huggett, Jr., Ruhong Li, David N. Wear, and Patrick D. Miles1

Abstract.—The emerald ash borer (Agrilus planipennis Fairmaire; EAB) is a nonnative 

invasive insect that has caused considerable damage to ash (Fraxinus spp.) in North 

America. Unlike invasive organisms that can be mitigated, contained, controlled, or 

even eradicated, EAB continues to spread across North America. The loss of the North 

American ash resource is possible considering literature suggests close to 100 percent 

probability of host tree mortality. We modeled future spatial and temporal changes in 

forest composition from 2010 to 2060 with and without ash mortality anticipated from 

EAB spread for the purpose of examining anticipated effects of EAB on tree species 

composition. To forecast midwest and northeast United States future forest conditions, 

we utilized Forest Inventory and Analysis (FIA) data, the extent of EAB in the United 

States and Canada, estimated EAB spread rate, estimated EAB host mortality probability, 

and models of human population, energy, consumption, land use, and economics. We 

found that in most cases, EAB will not substantially affect the ecosystem function of 

future forests measured by FIA because ash comprises a small proportion of midwest 

and northeast U.S. forests, and it will be replaced by associated species. Although the 

transition from ash to other species could take decades, forests may eventually recover 

when associated species replace ash.

1

 Postdoctoral Research Associate (RDD), University of 

Missouri Department of Forestry and U.S. Forest Service, 

Northern Research Station, 1992 Folwell Avenue, St. 

Paul, MN 55108; Research Foresters (WKM and PDM), 

U.S. Forest Service, Northern Research Station; Research 

Assistant Professor (RJH) and Research Associate (RL), 

North Carolina State University Department of Forestry 

and Environmental Resources; Research Forester (DNW), 

U.S. Forest Service, Southern Research Station. RDD is 

corresponding author: to contact, call 651-649-5130 or 

email at desantisr@missouri.edu.

Introduction

Knowledge of host tree susceptibility risk and 

temporal and spatial knowledge of insect spread rates 

are important for efforts to help mitigate adverse 

economic and ecological effects of invasive insects 

on forests (e.g., Tobin et al. 2004). Consequently, 

modeling EAB spread can drive the decisionmaking 

process and could help with detecting, monitoring, 

and slowing EAB spread (Prasad et al. 2010). EAB 

ash infestation has negatively affected the economic, 

ecological, aesthetic, and cultural interests of a 

variety of stakeholders including forest land owners, 

landowners in urban areas, tree nurseries, and Native 

American tribes (e.g., using black ash (Fraxinus nigra

Marsh.) as a cultural resource for basket weaving) 

(Poland and McCullough 2006). Private landowners 

can protect individual ash trees with various chemical 

treatments but there does not appear to be any effective 

broad-scale treatment to mitigate the effects of EAB. 

Projections of future forest composition would be 

beneficial for forest resource management in North 

American forests with ash, especially considering 

the economic and ecological consequences. Research 

indicates the effects of EAB on North American 

forests with ash may already be visible in FIA data 

(Pugh et al. 2011). We used FIA data and EAB current 

range, estimated spread rate, and host mortality 

data to project future EAB spread and subsequent 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 108

ash mortality. In this paper, we briefly describe our 

modeling structure, provide some insight into the 

intensity and trajectory of the impact of EAB, and 

discuss consequences for future stand development.

MODELING METHODS

This document is part of an effort to forecast the effect 

of current and future societal and natural resource 

trends on the structure and composition of future 

forests and to project how those effects alter forest 

ecosystem services (Shifley et al. 2012). These efforts 

forecasted future forest conditions for 20 states in 

the Northern Research Station region by assessing 

current forest conditions and recent forest changes 

(USDA Forest Service 2012). Forecasts were created 

in 5-year increments for the period 2010-2060 and 

projections of future forests were based on FIA foresttype groups (Wear et al. in press) which were used to 

group forest types developed from multiple sources 

including lists from FIA and the Society of American 

Foresters. Forecasting used a scenario approach with 

a range of plausible futures responsive to human 

population distributions, global economic conditions, 

energy and technology use, climate (combinations 

of three Intergovernmental Panel on Climate Change 

[IPCC] scenarios and four General Circulation Models 

[GCMs]; hereafter referred to as “storylines”), timber 

harvesting, land use change, other disturbance factors, 

and natural succession.

By incorporating EAB effects into one of the 

storylines (the A2 CGCM 3.1 storyline as described 

in USDA Forest Service 2012), we projected forest 

changes from 2010 to 2060 with and without the 

anticipated effects of EAB. We assigned an EAB 

spread rate of 20 km/year (Prasad et al. 2010) and 

a host mortality probability estimate of 100 percent 

(Herms et al. 2010) without any ash regeneration, in 

order to model the future effects of EAB infestation 

on black, green (Fraxinus pennsylvanica Marsh.), and 

white ash (Fraxinus americana L.) in midwest and 

northeast U.S. forests. Data from the U.S. Department 

of Agriculture, Animal and Plant Health Inspection 

Service (APHIS), Plant Protection and Quarantine 

program and the Canadian Food Inspection Agency 

were used to identify the core infested area of U.S. 

counties and Canadian regional municipalities 

where EAB was detected by 31 December 2010. We 

forecasted a 20 km/year spread rate from this core 

infested area from 2010 to 2060 (Fig. 1). Ash is found 

throughout the midwest and northeast United States 

but the highest concentrations are located in Maine, 

Michigan, Minnesota, New York, Pennsylvania, and 

Wisconsin, so this is where we focused our analyses. 

Ash mortality was applied for each inventory unit 

when it was subsumed by EAB spread, and projections 

commenced in 2015 because the range of EAB 

as of 31 December 2010 was applied to the 2015 

projections. Our projections of future forests were 

carried out through 2060, but the anticipated EAB 

spread encompassed the entire midwest and northeast 

United States by 2050 (Fig. 2). Here, we compare 

results of a non-EAB (“standard”) scenario to results 

of the same scenario with the projected EAB effects 

included (“EAB”).

RESULTS

Future forest projections suggested a decrease in 

the number of all trees ≥2.54 cm diameter at breast 

height (d.b.h.) by 2060 with the standard model. The 

EAB model projected a larger decrease over the same 

period, and it projected the loss of ash in all 20 states 

to occur by 2050 (Table 1, Fig. 2). Volume projections 

suggested substantial variation across states (Table 

2). The standard model projected smaller volume 

decreases in most forest-type groups than did the EAB 

model (Table 3). This trend was apparent with foresttype groups where ash was a major component such 

as elm-ash-cottonwood (E-A-C), but this was not the 

case with forest-type groups where ash was a minor 

component, such as spruce-fir. However, in Maine 

the standard model projected larger volume decreases 

in E-A-C than did the EAB model, in Minnesota 

the standard model projected larger decreases in the 

oak-hickory forest-type group (O-H) than did the 

EAB model, and in Pennsylvania the standard model 

projected larger decreases in O-H than did the EAB 

model.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 109

Figure 1.—United States counties and Canadian regional municipalities where EAB was detected by 31 December 2010 

are shown in purple and were based on data from USDA Animal and Plant Health Inspection Service, Plant Protection and 

Quarantine program and Canadian Food Inspection Agency. The projected EAB spread rate of 20 km/year is shown in 5-year 

intervals in dark red lines, whereby the innermost (from center) dark red spread line corresponds with 2020 and outermost 

2050.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 110

Figure 2.—Using dates when EAB spread subsumes each inventory unit (black lines within each state), the projected 

mortality of ash due to EAB is shown for each midwest and northeast FIA inventory unit. EAB spread in New York, Vermont, 

New Hampshire, and Maine was influenced by EAB infestations in Ontario and Québec, Canada regional municipalities. 

EAB spread was not influenced by EAB infestations in Tennessee, Kentucky, or Virginia or in other Canadian locations. 

This projection assumed EAB spread caused ash mortality once the spread subsumed each inventory unit.

Table 1.—Total number of all trees ≥2.54 cm d.b.h. on forest land in billions by state, year, and model. See 

text for model descriptions.

Standard model EAB model

2010 number of all trees 2060 number of all trees Percent change 2060 number of all trees Percent change

State ≥2.54 cm d.b.h. (billions) ≥2.54 cm d.b.h. (billions) (2010 to 2060) ≥2.54 cm d.b.h. (billions) (2010 to 2060)

Maine 23.32 20.87 -11 20.54 -12

Michigan 14.03 12.25 -13 11.25 -20

Minnesota 13.06 11.37 -13 9.97 -24

New York 12.19 11.54 -5 10.93 -10

Pennsylvania 8.35 7.27 -13 6.94 -17

Wisconsin 10.92 9.56 -12 8.67 -21

Total 81.87 72.86 -11 68.30 -17
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 111

Table 2.—Total volume of all trees ≥12.70 cm d.b.h. on forest land in million cubic meters, by state, year, 

and model. See text for model descriptions.

Standard model EAB model

2010 percent of total Percent Percent

State land area in forest land 2010 volume 2060 volume volume change 2060 volume volume change

Maine 89 721.53 765.77 6 735.67 2

Michigan 55 893.47 970.97 9 927.14 4

Minnesota 33 512.51 634.39 24 591.84 15

New York 63 1,121.59 1,197.25 7 1,152.14 3

Pennsylvania 58 1,002.02 986.75 -2 984.24 -2

Wisconsin 48 658.13 817.09 24 766.66 16

Total 53 4909.25 5372.22 9 5157.69 5

Table 3.—Total volume of trees ≥12.70 cm d.b.h. on forest land in million cubic meters, and percent 

change in total volume of trees on forest land, by state, forest-type group, and model. See text for model 

descriptions.

Maine

Percent change

2010-2060

2010 total Standard EAB

Forest-type group volume model model

White-red-jack-pine 77.38 0 1

Spruce-fir 212.64 11 4

Oak-hickory 18.30 13 5

Elm-ash-cottonwood 11.54 -45 -23

Maple-beech-birch 308.64 10 5

Aspen-birch 67.19 -13 -9

Michigan

Percent change

2010-2060

2010 total Standard EAB

Forest-type group volume model model

White-red-jack-pine 100.58 52 49

Spruce-fir 109.21 -6 -9

Oak-hickory 150.92 1 0

Elm-ash-cottonwood 84.98 -7 -28

Maple-beech-birch 313.00 8 6

Aspen-birch 105.35 2 -9

Minnesota

Percent change

2010-2060

2010 total Standard EAB

Forest-type group volume model model

White-red-jack-pine 49.70 21 23

Spruce-fir 79.76 5 -3

Oak-hickory 91.12 -23 -8

Elm-ash-cottonwood 52.00 115 39

Maple-beech-birch 53.77 -18 -26

Aspen-birch 170.84 48 44

New York

Percent change

2010-2060

2010 total Standard EAB

Forest-type group volume model model

White-red-jack-pine 90.04 22 3

Spruce-fir 35.84 17 33

Oak-hickory 167.49 71 46

Elm-ash-cottonwood 57.72 -79 -83

Maple-beech-birch 663.28 1 3

Aspen-birch 29.89 -57 -61

Pennsylvania

Percent change

2010-2060

2010 total Standard EAB

Forest-type group volume model model

White-red-jack-pine 26.46 16 15

Spruce-fir 1.32 -25 -25

Oak-hickory 538.25 1 -3

Elm-ash-cottonwood 14.62 55 -15

Maple-beech-birch 364.00 -8 -1

Aspen-birch 10.37 61 53

Wisconsin

Percent change

2010-2060

2010 total Standard EAB

Forest-type group volume model model

White-red-jack-pine 80.51 126 114

Spruce-fir 40.27 10 4

Oak-hickory 182.85 4 7

Elm-ash-cottonwood 58.27 23 2

Maple-beech-birch 180.86 -4 -7

Aspen-birch 88.20 11 -1
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 112

DISCUSSION

There was little or no difference in the number of 

all trees ≥2.54 cm d.b.h. and volume between the 

standard and EAB models in states where ash was 

not an important genus (e.g., Maine, 2 percent of 

total growing stock volume) (Table 4). Differences 

between the standard and EAB models were greater 

in states where ash was a more prominent genus (e.g., 

Minnesota, 8 percent of total growing stock volume). 

For example, the effect of ash mortality on differences 

between the EAB and standard models was most 

apparent in Minnesota and least apparent in Maine 

because ash contributed a much larger portion of the 

total growing-stock volume in Minnesota than it did 

in Maine. This led to a greater difference between 

the standard and EAB models in Minnesota than in 

Maine. Ash represented a larger component of forest 

and was predominately found in E-A-C forests in 

Minnesota, so the majority of changes involved forest 

types in the E-A-C forest-type group. Similarly, 

Minnesota volume differed between the standard and 

EAB models (Tables 2 and 3). Ash represented a very 

small component of forest and was more prevalent in 

O-H than E-A-C forests in Maine, so most changes 

in forest types did not involve forest types in E-A-C 

forests. Therefore, in Maine the projected number of 

all trees ≥2.54 cm d.b.h. and volume were similar for 

both the standard and EAB models, indicating that 

the EAB model did not appear to substantially alter 

Maine’s E-A-C projections trajectory. Maine O-H 

volume trends between the standard and EAB model 

results were similar as well. Maine standard and 

EAB model results for E-A-C and O-H forests were 

probably similar because ash represented a very small 

proportion of total growing stock. The forest changes 

predicted by the standard model were based on trends 

observed between FIA inventories prior to 2010. For 

the standard model, those trends greatly influenced the 

probability of forest compositional changes, including 

transitioning into or out of forest type-groups with ash 

such as E-A-C or O-H. For example, the Minnesota 

standard model projected an increase in E-A-C volume 

because E-A-C volume increased between recent 

prior inventories. Likewise, the Maine standard model 

projected a decrease in E-A-C volume because E-A-C 

volume decreased between recent prior inventories. 

For the same reason, the Michigan standard model 

projected a decrease in E-A-C forests. Considering 

EAB has been established in Michigan since the early 

1990s (Siegert et al. 2007), the decrease between 

recent inventories may have been partly due to EAB 

effects on ash.

We summarized the effects of EAB on the number and 

volume of ash trees for the broad category of foresttype groups and the large scale of states. It is important 

to consider geographic differences in the composition 

of each forest-type group since we analyzed changes 

by forest-type group. For example, ash does not 

comprise a substantial amount of any forest-type group 

Table 4.—Total volume of growing-stock trees and total volume of ash growing stock ≥12.70 cm d.b.h. on 

forest land, in million cubic meters, by state.

2010 total trees 2010 ash trees 2010 ash percentage

State growing-stock volume growing-stock volume of growing-stock volume

Maine 673.66 14.87 2

Michigan 827.05 40.08 5

Minnesota 443.82 35.11 8

New York 1032.29 76.11 7

Pennsylvania 939.29 46.54 5

Wisconsin 601.61 37.15 6

Total 4517.71 249.86 6
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 113

in Maine, Minnesota E-A-C is comprised mainly 

of green and black ash, and ash might comprise a 

larger portion of O-H than E-A-C in other states. Our 

modeling suggested the transition from ash to other 

species could progress slowly as ash is replaced by a 

variety of associated species, despite our assumption 

that EAB will cause 100 percent ash mortality. Our 

modeling suggested that EAB effects in non-urban 

forests measured by FIA may not cause forest-type 

group changes because associated species not prone 

to EAB infestation have the potential to offset the loss 

of ash trees and their associated volume. On the other 

hand, EAB infestation could lead to canopy gaps and 

facilitate an increase in native and nonnative invasive 

plant species (Gandhi and Herms 2010). Ultimately, 

our modeling may not apply to urban areas not 

measured by FIA, where there could be a larger 

impact due to the extensive distribution of urban ash.

ACKNOWLEDGMENTS

The authors thank Daniel Herms, Therese Poland, and 

Stephen Shifley for their advice and comments that 

greatly improved the manuscript.

LITERATURE CITED

Gandhi, K.J.K.; Herms, D.A. 2010. Direct and 

indirect effects of alien insect herbivores on 

ecological processes and interactions in forests of 

eastern North America. Biological Invasions. 

12: 389-405.

Herms, D.A.; Klooster, W.; Knight, K.S.; Gandhi, 

K.J.K.; Herms, C.P.; Smith, A.; McCullough, D.; 

Cardina, J. 2010. Ash regeneration in the wake 

of emerald ash borer: Will it restore ash or 

sustain the outbreak? In: Lance, D.; Buck, J.; 

Binion, D.; Reardon, R.; Mastro, V., eds. Emerald 

ash borer research and technology development 

meeting. FHTET-2010-01. Morgantown, WV: U.S. 

Department of Agriculture, Forest Service, and 

Animal and Plant Health Inspection Service: 17-18.

Poland, T.M.; McCullough, D.G. 2006. Emerald 

ash borer: invasion of the urban forest and the 

threat to North America’s ash resource. Journal 

of Forestry. 104: 118-124.

Prasad, A.M.; Iverson, L.R.; Peters, M.P.; 

Bossenbroek, J.M.; Matthews, S.N.; Sydnor, T.D.; 

Schwartz, M.W. 2010. Modeling the invasive 

emerald ash borer risk of spread using a 

spatially explicit cellular model. Landscape 

Ecology. 25: 353-369.

Pugh, S.A.; Liebhold, A.M.; Morin, R.S. 2011. 

Changes in ash tree demography associated 

with emerald ash borer invasion, indicated by 

regional forest inventory data from the Great 

Lakes States. Canadian Journal of Forest Research. 

41: 2165-2175.

Shifley, S.R.; Aguilar, F.X.; Song, N.; Stewart, S.I.; 

Nowak, D.J.; Gormanson, D.D.; Moser, W.K.; 

Wormstead, S.; Greenfield, E.J. 2012. Forests 

of the Northern United States. Gen. Tech. Rep. 

NRS-90. Newtown Square, PA: U.S. Department 

of Agriculture, Forest Service, Northern Research 

Station. 202 p.

Siegert, N.W.; McCullough, D.G.; Liebhold, A.M.; 

Telewski, F.W. 2007. Resurrected from the ashes: 

a historical reconstruction of emerald ash borer 

dynamics through dendrochronological analysis.

In: Mastro, V.; Lance, D.; Reardon, R.; Parra, G., 

eds. Emerald ash borer and Asian longhorned 

beetle research and technology development 

meeting. FHTET-2007-04. Morgantown, WV: U.S. 

Department of Agriculture, Forest Service, and 

Animal and Plant Health Inspection Service: 18-19.

Tobin, P.C.; Sharov, A.A.; Liebhold, A.M.; 

Leonard, D.S.; Roberts, E.A.; Learn, M.R. 2004. 

Management of the gypsy moth through a 

decision algorithm under the STS project.

American Entomologist. 50: 200-209.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 114

USDA Forest Service. 2012. Future scenarios: 

a technical document supporting the Forest 

Service 2010 RPA Assessment. Gen. Tech. 

Rep. RMRS-GTR-272. Fort Collins, CO: U.S. 

Department of Agriculture, Forest Service, Rocky 

Mountain Research Station. 34 p.

Wear, D.N.; Huggett, R.; Li, R.; Perryman, B.; 

Liu, S. In press. Forecasts of forest conditions 

in regions of the United States under future 

scenarios: a technical document supporting 

the Forest Service 2010 RPA Assessment. Gen. 

Tech. Rep. SRS. Asheville, NC: U.S. Department 

of Agriculture, Forest Service, Southern Research 

Station.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 115

EXPANSION AND CONTRACTION TENSION ZONES 

IN WESTERN U.S. PINON-JUNIPER WOODLANDS 

UNDER PROJECTED CLIMATE CHANGE

Jacob Gibson, Gretchen G. Moisen, Tracey S. Frescino, and Thomas C. Edwards, Jr.1

Extended Abstract

Populations of piñons and junipers across the interior west have been highly dynamic 

over the last two centuries, undergoing an overall expansion but punctuated with regional 

mortality. Accumulating demographic studies across the interior west indicate the 

drivers of expansion and contraction of populations are compounded by regional land 

use legacies, but have an underlying climatic component. The key implication is that 

piñon and juniper distributions respond to climatic variation across spatial and temporal 

scales by expansion from, and contraction to, areas of long-term persistence relative 

to their centurial (piñons) and millennial (junipers) life spans. Individualistic climatic 

tolerances among piñon and juniper species lead to temporally dynamic assemblages 

in the landscape that vary regionally. Failure to recognize species-specific zones of 

contraction, expansion, and persistence across the landscape and across regions has 

led to controversial management practices developed for certain species under certain 

conditions but extrapolated to different species under disparate conditions (Romme et 

al. 2009). Contrasting demographics between expanding, contracting, and persisting 

populations of piñons and junipers enables localized field classifications of these speciesspecific zones and their emergent woodland types. However, differential mortality rates 

among piñons and junipers during the last decade indicate distribution-wide shifts in cooccurrence have already begun in response to contemporary climate change (Breshears 

et al. 2009). Needed, therefore, is a similar classification of woodlands at a scale 

relevant to global climate change (e.g., Wiens et al. 2009) to aid in identifying regional 

conservation priorities and provide context for extrapolating management practices, such 

as regeneration prescriptions. 

The response of woody plant distributions to climate can be elucidated in a variety of 

ways, and both piñons and junipers have provided model systems for empirical and 

experimental investigations. Yet the physiological mechanisms underlying mortality 

are far from integrated at the whole-tree level (e.g., Plaut et al. 2012). Although 

mechanistically linking individual trees to global climate change forecasts is infeasible in 

this regard (Sala et al.), strong empirical relationships between growth rates and climate 

over the last century have been found across the greater geographic breadth of several 

western conifer species, including P. edulis, and have been extrapolated to forecast 

climates (Williams et al. 2010). A similar technique is to empirically relate the probability 

of the current presence/absence of a species to current climatic conditions often referred 

to as species distribution models (SDMs, Guisan and Zimmermann 2000). Such models 

may then be extrapolated to forecast climate change scenarios (Iverson et al. 2008, 

Refheldt et al. 2008). 

1

 Research Assistant (JG), Utah State University, College of Natural Resources, Logan, UT; 

Research Forester (GGM) and Forester (TSF), U.S. Forest Service, Rocky Mountain Research 

Station; Professor/Research Ecologist (TCE), Utah State University, USGS Biological 

Resources Division. JG is corresponding author: to contact, call 435-797-3892 or email at 

j.gibson@aggiemail.usu.edu.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 116

There are many interwoven technical and conceptual challenges to the process of 

creating SDMs that have received legitimate criticism. Technically, the development of 

SDMs relies on statistical techniques challenged by the availability of presence/absence 

observations and by the availability of relevant predictor variables at an appropriate scale 

(e.g., Weins et al. 2009). This situation is complicated further by the degree to which 

the potential distribution is realized. Although a species current distribution may be 

modeled with prefect statistical accuracies, such a static SDM may not be amenable to the 

application of climate change scenarios that may introduce no-analog climate conditions 

for which no empirical relationships currently exist for modeling purposes. A statistically 

accurate SDM that is built with appropriate variables will be indicative of geographic 

shifts in the climatic conditions in which a species is currently most competitive, but 

introduces the added complexity of the processes by which species track this shift with 

respect to biotic, topographic, and edaphic interactions. Because sessile species must 

track climate through completion of the life cycle, it is necessary to consider the nested 

nature of life stage-specific niche dimensions (i.e., the regeneration niche, Jackson et al. 

2009). To address these interwoven challenges, we develop a nested scale, life historybased framework for organizing the vast and varied research addressing the life history of 

piñons and junipers to guide an empirical approach to build, parameterize, and interpret 

species-specific distribution models. Biotic interactions modify the overarching influence 

of climate on piñon and juniper distributions, leading to lagged or accelerated responses 

of individuals, landscape populations, and continental distributions. Discordance between 

climatic suitability and spatial distribution generates zones under pressure of expansion or 

contraction but held in tension by spatial constraints and biotic interactions. We delineate 

these tension zones under forecast climate change scenarios of the 21st century.

Technical challenges surrounding this process are minimized by using presence/absence 

observations from the Forest Inventory and Analysis Program (FIA) (McRoberts et al. 

2005) that maintains a systematic inventory of forests across the U.S with an underlying 

probabilistic design. We investigated numerous environmental predictor variables 

hypothesized as related to the species distributions and chose the simplest expressions 

of seasonal temperature and moisture to avoid problems associated with forecast 

climatic conditions having no contemporary analog. We used Random Forests (RF) 

(Brieman 2001; Cutler et al. 2007), a commonly employed statistical tool for distribution 

forecasting. 

Conceptual challenges are minimized by working with presence observations from across 

the entirety of each species distribution, thereby sampling the breadth of the realized 

distribution (Pearman et al. 2008). Absences are drawn from a biologically meaningful, 

spatially defined sample frame (e.g., Zarnetske et al. 2007). This sample frame addresses 

the implication of geographic disequilibrium between species distributions and the 

current climate by limiting model development to areas where the species has actually 

had a likely opportunity to grow and excludes areas where the species has likely never 

had an opportunity to grow. This constraint in absence selection avoids overfitting 

models, which typically leads to higher decay rates in forecast probabilities. Initial 

modeled distributions are limited to this model-building domain and are evaluated in 

their ability to describe the current, observed distribution. The initial distribution models 

are applied to forecast climate change scenarios through life stage-specific thresholds 

within spatial constraints. The modeling domain and spatial constraint are based on a 

review of quaternary distribution dynamics in conjunction with current demographic and 

dispersal studies. The life stage thresholds are empirically estimated by relating FIA plot 

measures of seedlings and mortality with modeled climatic suitability within the context 

of reviewed life history studies. 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 117

These species-specific models were applied to two climate change scenarios, following 

the Intergovernmental Panel on Climate Change (IPCC) storylines A2a and B2a, 

representing extreme and moderate global change at the time of publication (Hijmans 

et al. 2005). We use the Hadley Center Coupled Atmosphere-Ocean Global Circulation 

Model (HadCM3 GCM) as the basis of the climate projections. Climate change scenarios 

were applied at three 30-year time steps over the 21st century. After the initial distribution 

is set for each species, the life stage thresholds and spatial constraints determine the 

ability for distributions to track forecasted climate change. The emergent distribution 

is composed of a sequence of possible local extinction, recruitment, and sustained 

populations expected from a nested-niche model (Jackson et al. 2009). The variety of 

possible states across the forecast climate probabilities of each species are collapsed 

into classes of contraction, persistence, and expansion. With a spatial resolution of 1 

km, these classes designate spatial zones reflective of distribution behavior at both a 

landscape/ multi-decadal scale and at a continent/ centurial scale. Shifts in the landscape 

are measured as changes in modeled elevation bands and correspond to shifts in the 

altitudinal zonation of life zones. Continental shifts in distribution are measured as 

changes in modeled latitudinal and longitudinal distribution and correspond to regional 

zones of expansion, contraction, or persistence. We present the modeled expansion and 

contraction as indicative of pressures corresponding to the leading and trailing edge 

dynamics of these distributions. The assemblage of these species distribution models, 

in turn, is classified into zones of species increase, decrease, or turnover.

LITERATURE CITED

Breiman, L. 2001. Random forests. Machine Learning. 45: 5-32.

Breshears, D.D.; Myers, O.B.; Meyer, C.W.; Barnes, F.J.; Zou, C.B.; Allen, C.D.; 

McDowell, N.G.; Pockman, W.T. 2009. Tree die-off in response to global changetype drought: mortality insights from a decade of plant water potential 

measurements. Frontiers in Ecology and the Environment. 7: 185-189.

Cutler, R.C.; Edwards, T.C., Jr.; Beard, K.H.; Cutler, A.; Hess, K.T.; Gibson, J.; Lawler, 

J.J. 2007. Random forests for classification in ecology. Ecology. 88: 2783-2792.

Guisan, A.; Zimmermann, N.E. 2000. Predictive habitat distribution models in 

ecology. Ecological Modelling. 135: 147-186.

Hijmans, R.J.; Cameron, S.E.; Parra, J.L.; Jones, P.G.; Jarvis, A. 2005. Very high 

resolution interpolated climate surfaces for global land areas. International Journal 

of Climatology. 25: 1965-1978.

Iverson, L.R.; Prasad, A.M.; Matthews, S.N.; Peters, M. 2008. Estimating potential 

habitat for 134 eastern US tree species under six climate change scenarios.

Forest Ecology and Management. 254: 390-406.

Jackson, S.T.; Betancourt, J.L.; Booth, R.K.; Gray, S.T. 2009. Ecology and the ratchet 

of events: climate variability, niche dimensions, and species distributions.

Proceedings of the National Academy of Sciences USA. 106: 19685-19692. 

McRoberts, R.E.; Bechtold, W.A.; Patterson, P.L.; Scott, C.T.; Reams, G.A. 2005. The 

enhanced Forest Inventory and Analysis program of the USDA Forest Service: 

historical perspective and announcement of statistical documentation. Journal of 

Forestry. 103: 304-308.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 118

Pearman, P.B.; Guisan, A.; Broennimann, O.; Randin, C. 2008. Niche dynamics in space 

and time. Trends in Ecology and Evolution. 23: 149-158.

Plaut, J.A.; Yepez, E.A.; Hill, J.; Pangle, R.; Sperry, J.S.; Pockman, W.T.; McDowell, 

N.G. 2012. Hydraulic limits preceding mortality in a piñon-juniper woodland 

under experimental drought. Plant, Cell and Environment. 35: 1601-1617.

Rehfeldt, G.E.; Ferguson, D.E.; Crookston, N.L. 2009. Aspen, climate, and sudden 

decline in western USA. Forest Ecology and Management. 258: 2353-2364.

Romme, W.H.; Allen, C.D.; Bailey, J.D.; Baker, W.L.; Bestelmeyer, B.T.; Brown, P.M.; 

Eisenhart, K.S.; Floyd, M.L.; Huffmann, D.W.; Jacobs, B.F.; Miller, R.F.; Muldavin, 

E.H.; Swetnam, T.W.; Tausch, R.J.; Weisberg, P.J. 2009. Historical and modern 

disturbance regimes, stand structures, and landscape dynamics in piñon-juniper 

vegetation of the western United States. Rangeland Ecology and Management. 

62: 203-222.

Sala, A.; Piper, F.; Hoch, G. 2010. Physiological mechanisms of drought-induced 

mortality are far from being resolved. New Phytologist. 186: 274-281.

Wiens, J.A.; Stralberg, D.; Jongsomjit, D.; Howell, C.A.; Snyder, M.A. 2009. Niches, 

models, and climate change: assessing the assumptions and uncertainties.

Proceedings of the National Academy of Sciences USA. 106: 19729-19736.

Williams, A.P.; Park, J.; Michaelsen, J.; Leavitt, S.W.; Still, C.J. 2010. Using tree rings 

to predict the response of tree growth to climate change in the continental United 

States during the twenty-first century. Earth Interactions. 14: 1-20.

Zarnetske, P.L.; Edwards T.C., Jr.; Moisen, G.G. 2007. Habitat classification modeling 

with incomplete data: pushing the habitat envelope. Ecological Applications. 

17: 1714-1729.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 119

PROJECTED TRENDS IN FOREST HABITAT CLASSES 

UNDER CLIMATE AND LAND USE CHANGE SCENARIOS

Brian G. Tavernia, Mark D. Nelson, Brian F. Walters, and Chris Toney1

Abstract.—Wildlife species have diverse and sometimes conflicting habitat 

requirements. To support diverse wildlife communities, natural resource managers 

need to manage for a variety of habitats across a large area and to create long-term 

management plans to ensure this variety is maintained. In these efforts, managers would 

benefit from assessments of potential climate and land use change effects on habitats. As 

part of the U.S. Forest Service’s Northern Forest Futures Project (NFFP), we assessed 

climate and land use driven changes in the areas of forest (≥66% canopy cover) and 

woodland (66% > canopy cover ≥ 10%) habitat across the Northeast and Midwest 

by 2060. Our assessments were made using NFFP projections based on three future 

storylines developed by the Intergovernmental Panel on Climate Change (IPCC). The 

total area of forest and woodland habitat is currently 173.4 million acres and is evenly 

split between forest and woodland (49% and 51%, respectively). Our assessments suggest 

that total forest and woodland habitat area will decrease in the future, but the magnitude 

of habitat loss differed among IPCC storylines, ranging from 5.9 to 11 million acres. 

Regardless of storyline, forest habitat was projected to gain area and woodland habitat 

was projected to lose area. As a result, forest was projected to represent a slight majority 

of the total habitat area (55% vs. 45% for woodland). Projected declines in woodland 

habitat represent a continuation of historical trends and have the potential to negatively 

affect woodland-dependent wildlife via reduced patch sizes, patch isolation, and edge 

effects. 

1

 Science Coordinator (BGT), Integrated Waterbird 

Management and Monitoring Program, USGS Patuxent 

Wildlife Research Center, Laurel, MD 20708; Research 

Forester (MDN) and Forester (BFW), U.S. Forest Service, 

Northern Research Station; and Biological Scientist (CT), 

U.S. Forest Service, Rocky Mountain Research Station. 

BGT is corresponding author: to contact, call 301-497-5654 

or email at btavernia@gmail.com. 

INTRODUCTION

 A significant challenge in natural resources 

management is providing sufficient habitat for wildlife 

species that have diverse and sometimes conflicting 

habitat needs (Noon et al. 2009). Suites of species 

are associated with particular forest habitat classes 

characterized by different compositions, ages, and 

structures (Patton 2011). For example, some species 

(e.g., Cerulean warbler, Setophaga cerulea) are 

associated with mature, deciduous forests while others 

(e.g., Kirtland’s warbler, Setophaga kirtlandii) are 

found in disturbance-dependent, early successional, 

coniferous habitat. Successful conservation and 

management of species with different habitat 

associations requires management plans that are large 

scale and long term in scope; such plans are necessary 

to ensure that diverse habitat needs are simultaneously 

met and maintained through time (Hamel et al. 2005). 

Efforts to conserve diverse groups of wildlife would 

benefit from assessments of projected climate and 

land use change effects on a suite of forest habitat 

classes. The Northern Forests Futures Project (NFFP), 

a joint effort by the U.S. Forest Service and several 

partners, is projecting and assessing the potential 

impacts of climate and land use changes on forest 

extent, composition, and structure across 20 states in 

the Northeast and Midwest. As part of the NFFP effort, 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 120

we used projections of 2060 forest conditions and 

ancillary datasets to assess potential changes in areas 

of forest and woodland habitat classes. 

DATA AND METHODS

Using a wildlife-habitat matrix developed by 

NatureServe (2011), we identified six current forest 

and woodland habitat classes: forest-hardwood, forestconifer, forest-mixed, woodland-hardwood, woodlandconifer, and woodland-mixed. NatureServe defines 

forest habitats as having ≥66 percent total canopy 

cover and woodland habitats as having 40 to 66 

percent canopy cover. Savanna, another NatureServe 

habitat associated with tree cover, is defined as having 

between 10 and 40 percent cover by trees and shrubs. 

Savanna is a rare ecosystem in northern forests and 

should not be confused with early successional stages 

of woodland or forest habitats, which also exhibit 

canopy cover of 10 to 40 percent. Therefore, we coded 

canopy of 10 to 40 percent as woodland. Canopy 

cover thresholds also are used to separate habitat 

composition within both forest and woodland classes. 

Areas are labeled as hardwood or conifer when >66 

percent of the forest or woodland canopy consists 

of hardwood or conifer tree species, respectively. 

Habitats are labeled as mixed when neither hardwood 

nor conifer tree cover exceeds 66 percent of the total 

canopy cover. 

We used NFFP projections to assess potential climate 

and land use driven changes in the areas of the six 

habitat classes from 2010 to 2060. NFFP’s projections 

of future forest conditions were based on current data 

and historical trends from the U.S. Forest Service’s 

Forest Inventory and Analysis (FIA) Program. FIA 

does not provide estimates of canopy cover, so we 

used a computer algorithm to derive estimates of 

canopy cover from FIA data, enabling us to crosswalk 

NFFP area projections to forest and woodland habitat 

classes. Details of the methods used to derive canopy 

cover estimates are provided by Toney et al. 2009 and 

Nelson et al. (in this proceedings). 

NFFP projected future forest conditions under climate 

and land use change scenarios consistent with the 

Intergovernmental Panel on Climate Change’s (IPCC) 

A1B, A2, and B2 storylines. A1B assumes rapid 

economic growth, a global population that peaks 

in the middle of the 21st century and then declines, 

and mixed energy use from fossil and non-fossil fuel 

resources; A2 assumes that the global population 

continues to grow throughout the century and that 

economic development will be regionally oriented; 

and B2 assumes regional and local economic growth 

with per capita income similar to A2 but assumes 

projected population growth that is lower than the 

other scenarios (USDA Forest Service 2012). For 

each storyline, climate conditions were projected 

using multiple General Circulation Models (GCMs) to 

examine model-based uncertainty. 

RESULTS

Across the Northeastern and Midwestern U.S., the 

current total area of all forest and woodland habitat 

classes (as defined above) is 173.4 million acres (Table 

1). The region is dominated by the forest-hardwood 

(41% of forest habitat) and woodland-hardwood 

(34%) habitat classes with no other class exceeding 10 

percent of forest habitat. Forest land is about evenly 

split between the groups of forest and woodland 

habitat classes (49% and 51%, respectively). 

Projected changes in habitat for IPCC storylines 

did not differ across GCMs, possibly because NFFP 

projected habitat conditions over a relatively short 50-

year time period. For this reason, we did not stratify 

assessments results by GCMs. 

Loss of total forest and woodland habitat area was 

projected under all three IPCC storylines although the 

magnitude ranged from 5.9 million acres under B2 to a 

loss of 11 million acres under A1B (Table 1). Patterns 

of change for habitat classes were consistent across 

all three IPCC storylines (Table 1). All three forest 

habitat classes gained area; percent gains were greatest 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 121

Table 1.—Area (millions of acres) and percent change of six forest and woodland habitat classes across 

the Northeast and Midwest.a

Total Forest- Forest- Forest- Woodland- Woodland- WoodlandIPCC Storyline Habitat Hardwood Conifer Mixed Hardwood Conifer Mixed

Baseline 173.4 70.4 4.3 10.8 59.5 16.7 11.7

A1B 162.4 73.1 5.2 11.4 48.5 14.3 9.9

(-6.4%) (3.8%) (20.9%) (5.6%) (-18.5%) (-14.4%) (-15.4%)

A2 164.1 74.3 5.0 11.6 48.7 14.5 10.1

(-5.4%) (5.5%) (16.3%) (7.4%) (-18.2%) (-13.2%) (-13.7%)

B2 167.5 74.9 5.0 12.1 50.6 14.6 10.2

(-3.4%) (6.4%) (16.3%) (12.0%) (-15.0%) (-12.6%) (-12.8%)

a Estimates are provided for 2010 baseline conditions and for 2060 based on the A1B, A2, and B2 storylines from the Intergovernmental 

Panel on Climate Change (IPCC). Changes in habitat classes between 2010 and 2060 were driven by projected land use changes, forest 

succession, and forest harvest. See text for explicit definitions of forest habitat classes.

for forest-conifer and least for forest-hardwood. 

Conversely, all three woodland habitat classes lost 

area; percent losses were greatest for woodlandhardwood and least for woodland-conifer. Forest 

habitat classes were projected to increase relative to 

woodland habitat classes as a percent of total habitat 

(55% versus 45%, respectively).

DISCUSSION AND CONCLUSION

Our assessments suggest that the total area of forest 

and woodland habitat classes will decrease across the 

Northeast and Midwest by 2060. This loss in total 

forest and woodland habitat acreage has the potential 

to negatively affect wildlife populations. Although 

we did not directly assess spatial patterns of habitat 

loss, reduced habitat area can lead to smaller and 

more isolated forest patches. These patches support 

fewer individuals and are less likely to receive 

immigrants from other areas, increasing the likelihood 

of local extinctions and decreasing the likelihood of 

recolonization or population rescue. Smaller forest 

patches in this region of North America are also more 

exposed to negative ecological influences (e.g., nest 

predators) from surrounding nonforest land uses, 

contributing to local population declines. If habitat 

loss is widespread, regional declines and extinctions 

may result. These effects may be of more immediate 

concern for woodland habitat classes than forest 

habitat classes, which are projected to increase in 

area. Nevertheless, land conversion to nonforest land 

use types ultimately constrains the area and spatial 

distribution of all forest and woodland habitat classes. 

Our assessments suggest that uncertainty about future 

demographic, economic, and technological conditions 

(as represented by different IPCC storylines) 

contributes to uncertainty about the extent of habitat 

loss. Policy (e.g., promoting growth near existing 

urban centers) and financial mechanisms (e.g., tax 

deductions resulting from conservation easements) 

might be used to limit the negative effects of land use 

change on forest wildlife. 

Researchers have reported decades-long declines in 

the area of early successional forest habitat across 

the Northeast and Midwest (Trani et al. 2001). These 

declines have been attributed to a number of different 

causes including forest maturation of abandoned 

farmland, altered forest management practices, forest 

ownership patterns that discourage harvest, disrupted 

natural disturbance regimes (e.g., fire suppression), 

and land use conversion (Trani et al. 2001). Assuming 

that early successional forests can be characterized as 

having more open canopies, projections of woodland 

habitat classes in our assessment suggested that 

declines of this habitat type may continue into the near 

future. We found that all woodland habitat classes 

declined and that regional habitat became dominated 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 122

by forest habitat classes. These projected declines 

may negatively affect not only woodland-associated 

species but also species typically associated with forest 

habitats that are dependent on woodland areas during 

certain times of the year (e.g., Streby et al. 2011, Vitz 

and Rodewald 2006). Ultimately, the future status 

of wildlife species dependent on young forests or 

woodland habitat will depend on the scale, type, and 

frequency of anthropogenic and natural disturbances 

occurring in landscapes across the Northeast and 

Midwest. These disturbance patterns will be affected 

by future management decisions (e.g., type of 

forest harvest) as well as changing socioeconomic 

(e.g., changing ownership patterns) and ecological 

conditions (e.g., climate change). 

ACKNOWLEDGMENTS

The authors thank NatureServe for producing a 

terrestrial vertebrate species-habitat matrix for forestassociated species. The authors also acknowledge the 

assistance of J.M. Reed, T. Will, and S. Oswalt, whose 

suggestions improved the manuscript.

LITERATURE CITED

Hamel, P.B.; Rosenberg, K.V.; Buehler, D.A. 2005. Is 

management for golden-winged warblers and 

Cerulean warblers compatible? In: Ralph, C.J.; 

Rich, T.D., eds. Bird conservation implementation 

and integration in the Americas: proceedings of the 

third international Partners in Flight conference; 

2002 March 20-24; Asilomar, CA, Volume 1. Gen. 

Tech. Rep. PSW-GTR-191. Albany, CA: U.S. 

Department of Agriculture, Forest Service, Pacific 

Southwest Research Station: 322-331.

NatureServe. 2011. Lists of vertebrate species in 

the contiguous U.S. L48_vert_hab_data_022011.

accdb – a relational Access database. http://www.

natureserve.org. (Accessed February 17, 2011).

Noon, B.R.; McKelvey, K.S.; Dickson, B.G. 2009. 

Multispecies conservation planning on U.S. 

federal lands. In: Millspaugh, J.J.; Thompson, F.R. 

III, eds. Models for planning wildlife conservation 

in large landscapes. Burlington, MA: Academic 

Press: 51-83. 

Patton, D.R. 2011. Forest wildlife ecology and 

habitat management. Boca Raton, FL: CRC Press. 

272 p.

Streby, H.M.; Peterson, S.M.; McAllister, T.L.; 

Andersen, D.E. 2011. Use of early-successional 

managed northern forest by mature-forest 

species during the post-fledging period. Condor. 

113: 817-824.

Toney, C.; Shaw, J.D.; Nelson, M.D. 2009. A stemmap model for predicting tree canopy cover 

of Forest Inventory and Analysis FIA plots.

In: McWilliams, W.; Moisen, G.; Czaplewski, 

R., comps. Forest Inventory and Analysis (FIA) 

Symposium 2008; 2008 October 21-23; Park City, 

UT. Proc. RMRS-P-56CD. Fort Collins, CO: U.S. 

Department of Agriculture, Forest Service, Rocky 

Mountain Research Station. 19 p.

Trani, M.K.; Brooks, R.T.; Schmidt, T.L.; Rudis, V.A.; 

Gabbard, C.M. 2001. Patterns and trends of early 

successional forests in the eastern United States. 

Wildlife Society Bulletin. 29: 413-424. 

USDA Forest Service. 2012. Future scenarios: 

a technical document supporting the Forest 

Service 2010 RPA Assessment. Gen. Tech. 

Rep. RMRS-GTR-272. Fort Collins, CO: U.S. 

Department of Agriculture, Forest Service, Rocky 

Mountain Research Station. 34 p. 

Vitz, A.C.; Rodewald, A.D. 2006. Can regenerating 

clearcuts benefit mature-forest songbirds? An 

examination of post-breeding ecology. Biological 

Conservation. 127: 477-486. 

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 123

SITE PRODUCTIVITY – 

CURRENT ESTIMATES, CHANGE, AND POSSIBLE ENHANCEMENTS 

FOR THE NORTHERN RESEARCH STATION

Scott A. Pugh1

Abstract.—Site productivity (SP) is the inherent capacity to grow crops of industrial 

wood. SP identifies the potential growth in cubic feet/acre/year and is based on the 

culmination of mean annual increment of fully stocked natural stands. Changes in SP 

were summarized for timberland and the associated effects on net growth and removal 

estimates were investigated using data from the Forest Inventory and Analysis program 

from the early to late 2000s. Change in area by SP class ranged from 4.8 to 19.6 percent, 

depending on the state. Actual changes on the ground are not this common. Net growth 

credited to unproductive-to-productive change varied from 0.0 to 11.2 percent of total 

net growth depending on the state; removals due to productive-to-unproductive change 

varied from 0.0 to 38.7 percent of total removals, depending on the state. A comparison 

of SP derived from current methods versus complementing with net growth information 

shows 12.5 percent of currently classified unproductive area could be classified 

productive and 10.3 percent of forest land area could receive a more productive SP class. 

Artificial change in SP class should be minimized, especially for sites that are marginally 

productive or unproductive. Restricting new calculations of SP when valid measurements 

already exist will lessen erroneous change.

1

 Forester (SAP), U.S. Forest Service, Northern Research 

Station, 410 MacInnes Drive, Houghton, MI 49931. 

To contact, call 906-482-6303 ext. 17 or email at 

spugh@fs.fed.us.

INTRODUCTION

Forest site productivity (SP) is the inherent capacity 

to grow crops of industrial wood. SP identifies the 

potential growth in cubic feet (ft3)/acre/year and is 

based on the culmination of mean annual increment 

of fully stocked natural stands (U.S. Forest Service 

2010). By definition, forest land assigned a SP less 

than 20 ft3/acre/year is unproductive. Timberland is 

productive, i.e., capable of producing at least 

20 ft3

/acre/year, so changes in SP status affect net 

growth and removal estimates on timberland. This 

study’s objectives include summarizing changes in SP 

and the associated effects on annual net growth and 

removals. Following these results, the study focuses 

on ways to improve SP assignments using observed 

net growth estimates and limit artificial change in SP 

assignments. 

Data from the Forest Inventory and Analysis program 

of the Northern Research Station (NRS-FIA), U.S. 

Department of Agriculture, Forest Service, were 

compared from the early to late 2000s (Table 1). NRSFIA uses site index (SI), the most commonly accepted 

measurement, to estimate SP. SI represents the average 

total height in feet that dominant and codominant trees 

are expected to attain in fully stocked, even-age stands 

at a specific base age (usually 50 years for NRS-FIA 

states). Changes in SI can represent magnitudes of 

change in SP. For eastern white pine (Pinus strobus

L.), an SI of 48-59 feet represents an SP of 50-84 ft3

/

acre/year, and an SI of 60-74 feet represents an SP of 

85-119 ft3

/acre/year. NRS-FIA uses seven SP classes 

expressed in ft3/acre/year as follows: class 1 at 225+, 

class 2 at 165-224, class 3 at 120-164, class 4 at 85-

119, class 5 at 50-84, class 6 at 20-49, class 7 at 0-19.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 124

Region State

Total 

forestto-forest 

area

Forest-toforest area 

changed 

SP class

Total net 

growth

Net growth on 

unproductiveto-productive 

area

Total 

removals

Removals on 

productive-tounproductive 

area

million 

acres

percent million 

ft3

/year

percent million 

ft3/year

percent

Western NRS-FIA Illinois 4.4 19.6 214.9 0.0 66.1 13.2

Indiana 4.5 10.7 274.0 0.0 89.0 0.0

Iowa 2.7 10.3 79.1 0.0 40.6 0.0

Kansas 1.9 9.5 36.6 0.0 12.9 28.6

Michigan 19.1 10.6 695.1 0.6 337.5 2.2

Minnesota 15.9 17.1 384.9 3.4 255.7 3.1

Missouri 14.4 12.3 464.2 2.3 174.4 2.8

Nebraska 1.1 9.5 21.7 2.5 14.2 1.9

North Dakota 0.7 8.1 6.8 11.2 2.6 38.7

South Dakota 1.7 9.5 38.2 6.1 25.5 0.1

Wisconsin 16.1 11.8 563.8 0.5 308.9 1.6

Western NRS-FIA 82.6 12.8 2,779.3 1.2 1,327.4 3.4

Eastern NRS-FIA Connecticut 1.7 7.4 93.4 4.6 17.2 0.0

Delaware 0.3 13.9 15.2 0.0 13.8 7.4

Maine 17.5 11.4 663.2 0.6 587.7 0.0

Maryland 2.4 15.7 145.5 0.0 56.8 0.0

Massachusetts 2.9 10.2 176.0 0.9 40.2 2.0

New Hampshire 4.7 7.4 185.9 0.1 100.0 0.2

New Jersey 1.9 9.0 81.9 4.0 24.2 14.8

New York 18.3 9.5 617.8 1.0 288.9 0.0

Ohio 7.7 11.0 393.6 0.0 182.7 0.0

Pennsylvania 16.1 4.8 762.9 0.1 374.7 1.3

Rhode Island 0.3 7.1 18.0 9.7 2.7 0.0

Vermont 4.5 8.1 195.0 0.0 102.1 0.0

West Virginia 11.7 8.9 608.5 0.9 248.1 3.4

Eastern NRS-FIA 90.3 9.1 3,956.8 0.7 2,039.0 0.9

NRS-FIA NRS-FIA 172.9 10.8 6,736.2 0.9 3,366.4 1.9

Table 1.—Change in SP class and effects on net growth and removal estimates for growing stock on 

timberland, by state, NRS-FIA 2005-2010
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 125

METHODS

Site Productivity via NRS-FIA

NRS-FIA crews measure diameter at breast height 

(d.b.h.) and height of a suitable SI tree to represent 

each forested condition on a plot. This information 

is used to estimate SI and subsequently SP; data is 

maintained in the National Information Management 

System (NIMS). An acceptable SI tree should meet 

minimum requirements of size (≥5 inches d.b.h.), age 

(15 to 120 years), quality, and species. Crews may 

reuse a previous SI tree. Crews have the option to 

reject previous trees if they do not meet the minimum 

requirements. This option is usually employed 

when there is an assessment difference between the 

previous and current crew. If no previous valid tree 

exists, the crew should collect data from a new tree 

pending availability. With no suitable trees, a default 

or estimated SP class is assigned later in NIMS. 

For the western NRS-FIA states2, an average SI 

and associated SP are assigned for the species most 

frequently observed for the field forest type and 

physiographic class of the condition in question. For 

the eastern NRS-FIA states2, a default SP class of 6 is 

assigned and the default class is also assigned when 

the calculation of SI and SP indicate an unproductive 

status (class 7) but the crew in the field classified the 

condition as productive. Unproductive assignments 

by crews for eastern NRS-FIA states also supersede 

productive assignments indicated by SI and SP 

calculations.

Sometimes more than one valid SI tree is available 

from previous inventories and this circumstance 

often results in different SPs. The suitable tree with 

the highest SP should be prioritized (the rule used in 

NIMS) but the crews do not have the SP information. 

At times, crews have chosen the tree associated with 

the relatively lower SP and deleted the other formerly 

valid ones.

2

 Western NRS-FIA states include IL, IN, IA, KS, MI, MN, 

MO, NE, ND, SD, and WI. Eastern NRS-FIA states include 

CT, DE, ME, MD, MA, NH, NJ, NY, OH, PA, RI, VT, and 

WV.

Analysis

This study examined changes in SP and effects of this 

change from the 2005 (2001-2005) to 2010 inventory 

(2006-2010). Structured Query Language (SQL) 

determined estimates. Area by SP for each state was 

compared. Average annual net growth of growing 

stock on timberland was estimated for unproductiveto-productive change. With a change to productive 

status, the volumes of associated trees are credited, 

not only the average annual net growth between 

inventories. Average annual removals of growing 

stock on timberland were estimated for productive-tounproductive change. Volumes of trees cut since the 

previous inventory and volumes of trees still living 

in the recent inventory are credited with changes to 

unproductive status. Only living trees are included 

in the effects on removals presented in this study. To 

potentially improve SP class assignments, a new class 

was assigned using the net growth of growing stock 

for each condition of the 2010 inventory when the 

net growth indicated a higher SP than the normally 

employed methods. To minimize artificial change in 

SP class, the study determined where measurements 

of SI could be prohibited when valid measurements 

were previously acquired. This is currently feasible 

when a one-forest-condition plot occurred in a 

previous inventory (time 1) and remains a one-forestcondition plot in the next inventory (time 2). Lack of 

acceptable SI trees can contribute to changes in SP so 

the study identified how often SP was estimated versus 

calculated from SI.

RESULTS

Change

Working with the forest-to-forest areas (forest at time 

1 to forest at time 2) from the 2005 to 2010 inventory, 

change in area by SP class and state ranged from 4.8 

to 19.6 percent (Table 1). Across NRS-FIA states, 

10.8 percent of area changed SP class with 2.3 percent 

changing by two or more SP classes. Thirty-four 

percent of unproductive area changed to productive 

(460,000 acres) and less than 0.5 percent of productive 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 126

area changed to unproductive (393,000 acres). Net 

growth credited to unproductive-to-productive change 

varied from 0.0 to 11.2 percent of average annual 

total net growth, and removals due to productive-tounproductive change varied from 0.0 to 38.7 percent 

of average annual total removals, depending upon the 

state.

Complementing with Net Growth

Working with the forest-to-forest areas from the 

2005 to 2010 inventory, a comparison of SP derived 

from current methods versus complementing current 

methods with net growth information shows that 12.5 

percent (260,000 acres) classified as unproductive 

could be classified productive and 10.3 percent of 

forest land area (18.4 million acres) could receive a 

more productive SP class assignment across NRS-FIA 

states (Table 2). Results varied by region; 14.3 and 

6.2 percent of currently classed unproductive area 

could be classed productive in the western and eastern 

NRS-FIA states, respectively. A more productive class 

could be assigned to 5.6 and 14.7 percent of forest 

land area in the western and eastern NRS-FIA states, 

respectively.

Consistency

Across the NRS-FIA states for the 2010 inventory, 

76 percent of forest-to-forest plots were one-forestcondition plots at time 1 and time 2 and had previously 

valid SI trees. In the 2010 inventory, about 95 and 

79 percent of SP assignments were derived from SI 

trees for the western and eastern NRS-FIA states, 

respectively. Remaining SPs were estimated or 

assigned a default SP class value.

DISCUSSION

Change

Estimates of change in acreage by SP are substantial 

for some states. Actual changes in acreage by SP 

are not as common on the land as predicted in the 

estimates, but estimates change, usually from using 

different SI trees or obtaining an acceptable SI tree for 

the first time. In many cases there are only marginally 

acceptable trees or no acceptable tree (McRoberts 

1996). Subsequently, this increases the chance of 

choosing different SI trees for a given plot and 

condition over time. In some states, artificial change in 

SP status accounts for substantial portions of average 

annual net growth and/or removals on timberland. For 

net growth, the volume per tree credited with a change 

to productive status is usually many times more than 

the average annual change in volume credited with no 

change. Removal estimates are impacted by counting 

the volumes of living trees associated with a change to 

unproductive status. The most noticeable impacts are 

usually associated with states that have relatively low 

total removals.

Complementing with Net Growth

Complementing current methods with net growth 

estimates can identify productive areas that are 

currently classified unproductive. Barring no major 

site quality changes, holding SP class as productive 

on these sites would decrease effects on net growth 

and removal estimates for timberland. Net growth 

estimates also identify areas that should probably be 

designated at higher SP classes. This is most evident in 

the eastern NRS-FIA states where more investigation 

is warranted. More study is also required for some 

species in the western NRS-FIA states where SP is 

used when calculating volume, which subsequently 

influences net growth. Inaccurate values of SP can 

confound net growth estimates. Benefits of these 

possible enhancements should be weighed against the 

cost of implementation. Maintaining a steady SP class 

over time on marginally productive or unproductive 

areas provides the greatest benefit.

Consistency

SI tree collection on one-forest-condition plots at time 

1 and time 2 with previously valid SI trees should be 

prohibited to minimize erroneous changes in SP. Based 

on observations between the 2005 and 2010 inventory, 

consistency could be ensured on about 76 percent of 

forest-to-forest plots. 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 127

Table 2.—Change from unproductive to productive and increase in productivity using average annual net 

growth of growing stock versus SI, by state, NRS-FIA 2005-2010

Region State

Unproductive 

forest-to-forest area 

using SI

Unproductive area 

using SI but productive 

using net growth

Forest-to-forest area 

more productive using 

net growth vs. using SI

thousand acres percent percent

Western NRS-FIA Illinois 14.9 0.0 5.4

Indiana 0.0 0.0 3.7

Iowa 40.8 46.2 4.4

Kansas 102.9 3.8 2.0

Michigan 247.5 29.4 7.9

Minnesota 488.5 11.9 5.3

Missouri 175.9 16.2 3.7

Nebraska 94.3 0.0 6.4

North Dakota 214.7 1.0 2.3

South Dakota 87.3 13.5 14.6

Wisconsin 155.3 23.1 5.2

Western NRS-FIA 1,622.3 14.3 5.6

Eastern NRS-FIA Connecticut 8.5 0.0 22.4

Delaware 3.6 0.0 17.8

Maine 178.8 3.1 11.7

Maryland 5.3 0.0 11.3

Massachusetts 41.2 0.0 19.7

New Hampshire 25.5 12.7 11.6

New Jersey 13.6 0.0 14.4

New York 41.4 0.0 13.8

Ohio 5.3 0.0 15.0

Pennsylvania 57.7 18.3 18.9

Rhode Island 0.0 0.0 16.8

Vermont 20.2 0.0 14.0

West Virginia 55.2 16.0 14.4

Eastern NRS-FIA 456.4 6.2 14.7

NRS-FIA NRS-FIA 2,078.6 12.5 10.3
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 128

When multiple SI trees are available for a condition, 

NIMS and subsequent public reports use the tree with 

the highest associated SP. Following this precedent, 

only trees with the highest SP should be sent to the 

field. Otherwise, field crews can unknowingly chose 

a different tree which can change the assigned SP 

class. Some SP values are estimated or assigned a 

default value when there are no suitable SI trees. The 

number of sites without suitable trees should decrease 

by measuring the same plots over time. After suitable 

trees are acquired, new SP values and associated 

classes can be used in most subsequent inventories.

LITERATURE CITED

McRoberts, R.E. 1996. Estimating variation in field 

crew estimates of site index. Canadian Journal of 

Forest Research. 26: 560-565.

Woudenberg, S.W.; Conkling, B.L.; O’Connell, B.M.; 

LaPoint, E.B.; Turner, J.A.; Waddell, K.L. 2010. 

The Forest Inventory and Analysis database: 

Database description and users manual version 

4.0 for Phase 2. Gen. Tech. Rep. RMRS GTR-245. 

Fort Collins, CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research Station. 

336 p.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 129

The Myth of a Business-As-Usual Baseline: 

A Review of Forest Inventory Projections

Stephen P. Prisley, W. Brad Smith, and John W. Coulston1

Abstract.—With the advent of forest carbon accounting schemes that compare projected 

forest carbon sequestration against a baseline, there is an increasing demand to project 

forest inventories into the future. Often, the desired comparison baseline is termed 

“business as usual”, implying there is a known or anticipated trajectory of forest growth 

and harvests that will occur and against which we can compare alternate management 

scenarios. This raises the question “how well can we project forest inventories into the 

future?” The U.S. Forest Service has decades of experience with developing national 

projections of forest inventories for the Resources Planning Act (RPA) periodic 

assessments.

We have compiled some of the projections made in the 1965, 1974, 1982, and 1993 

RPA assessments, including timberland area by region and ownership, and timberland 

growing stock, net growth, removals, and mortality by region and softwood/hardwood. 

These projections are compared with interpolated Forest Inventory and Analysis data 

for the same regions and years. Differences between projected and measured values are 

expressed as RMSEs to quantify the performance of projections by length of projection, 

spatial resolution (national versus regional), and quantity being projected (area, growth, 

removals, mortality, inventory). 

Results demonstrate the challenge of making projections based on extrapolation of 

recent trends. We also discuss some of the primary reasons for discrepancies between 

projections and reality.

1

 Associate Professor (SPP), Virginia Tech, Blacksburg, VA 24060; Research Forester (BS), 

U.S. Forest Service, Washington, DC; Supervisory Research Forester (JWC), U.S. Forest Service, 

Southern Research Station. SPP is corresponding author; to contact, call 540-231-7674 or email at 

prisley@vt.edu. 

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Mapping Forest 

Disturbance
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 131

UNDERSTANDING TRENDS IN OBSERVATIONS OF FOREST 

DISTURBANCE AND THEIR UNDERLYING CAUSAL PROCESSES

Karen Schleeweis, Samuel N. Goward, Chengquan Huang, Jeffrey Masek, and Gretchen G. Moisen1

Abstract.—Estimates of forest canopy areal extent, configuration, and change have 

been developed from satellite-based imagery and ground-based inventories to improve 

understanding of forest dynamics and how they interact with other Earth systems 

across many scales. The number of these types of studies has grown in recent years, yet 

few have assessed the multiple change processes underlying observed forest canopy 

dynamics across large spatio-temporal extents. To support these types of assessments, a 

more detailed and integrated understanding of the geographic patterns of forest change 

processes across the contiguous United States (CONUS) is needed.

This work uses forest age estimates from U.S. Forest Service ground inventory data and a 

novel data set from the North American Forest Dynamics project, which provides a dense 

temporal record (1984-2005) of forest canopy history across the United States, as well as 

ancillary geospatial data sets on forest change processes (wind, insect, fire, harvest, and 

conversion to suburban/urban land uses) across the CONUS. Forest area is estimated and 

causal processes of forest change are shown through time across multiple scales. 

1

 Research Geographer (KS), U.S. Forest Service, Rocky 

Mountain Research Station, 324 25th St., Ogden, UT 

84401; Professor (SNG, CH), Department of Geographical 

Sciences, University of Maryland; Research Scientist (JM), 

NASA Goddard Space Flight Center; Research Forester 

(GGM), U.S. Forest Service, Rocky Mountain Research 

Station. KS is corresponding author: to contact, call 

301-405-7910 or email at ska1@umd.edu.

INTRODUCTION

For more than half a century, decadal snapshots of 

forest canopy characteristics have been available from 

the Forest Inventory and Analysis (FIA) Program. 

Annual FIA inventories and remote-sensing satellite 

imagery have recently begun to meet the need for data 

that are consistent across large spatial and temporal 

extents, at finer spatial and temporal grains (Cohen 

and Goward 2004, Gillespie 1999). However, we 

still lack data and understanding regarding the causal 

processes underlying observed forest canopy changes. 

It is important to know not just where and when forest 

canopy losses occur, but also the underlying process 

to help determine whether the losses are temporary or 

permanent and how the process influences other Earth 

systems across many scales (Reams et al. 2010).

Natural (fire, wind, insect) and human-managed 

(harvest) forest disturbances and forest land 

conversion affect millions of hectares of forest land, 

but the spatial and temporal trends of these phenomena 

are not well documented (Smith and Darr 2004). 

Although data are available on a single process for 

static points in time, there are few that focus on 

multiple processes through time (Birdsey and Lewis 

2003). A current synthesis on the trends of specific 

forest canopy change processes including fire, insects, 

wind storms, harvests, and forest land conversion is 

lacking at fine spatial and temporal resolutions. This 

work demonstrates the need for this information below 

a coarse regional-decadal resolution and attempts to 

create an integrated geographic model of all these 

phenomena.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 132

METHODS

To synthesize data on forest canopy change and 

causal processes across different studies, common 

spatial boundaries must be used. We used the six 

historical FIA regions of the Northeast (NE), North 

Central (NC), Southeast (SE), South Central (SC), 

Intermountain West (IW), and Pacific (PAC) (see map 

insert, Fig. 2). These boundaries allow for comparison 

of newer data sets with historical data available only at 

coarse regional scales. 

More than 20 years of forest age history data from FIA 

inventories and the North American Forest Dynamics 

(NAFD) project are discussed in the context of the 

underlying forest causal processes that they might 

have captured. NAFD provides a comprehensive 

look at forest disturbance rates for areas sampled 

biennially across the conterminous United States 

at a 30-m resolution from 1985 to 2005 (Goward 

et al. 2008). Forest age, often used as a proxy for 

disturbance history, has been collected by FIA on more 

than 125,000 ground plots (0.7 ha) laid out on a 5-km 

sampling grid across the conterminous United States. 

One-tenth of these plots are remeasured each year in 

the western United States. One-seventh to one-tenth 

of them are revisited each year in the eastern United 

States. However, measurement frequency and spatial 

sampling schemes varied through time and across 

regions prior to 1999 (Gillespie 1999). 

To interpret the regional forest change estimates in the 

context of possible underlying causal processes, we 

compiled tabular data of forest area affected by each 

process to illustrate their trends through time. To judge 

the need for finer spatial and temporal resolution data 

we utilize NAFD forest history maps and geospatial 

data on recent insect infestations, harvests, forest fires, 

wind storms, and suburban/urban conversion of forest 

land (Table 1). These data are assembled into a single 

geographic information system geodatabase to enable 

multi-scale analysis on the patterns of forest change 

processes and forest canopy changes, and their overlap 

through time and space. 

RESULTS 

The more than 20 years of forest history captured 

in both FIA and NAFD data show some similarities 

across regions (Fig. 1). In general, NAFD disturbance 

rates are higher than FIA estimates within regions, 

with the exception of the SE region. 

Regional trends for forest fire, insect infestations, 

suburbanization, harvest, and canopy cover changes 

vary by decade and region (Fig. 2). Regional-decadal 

scale statistics on change processes were found 

to have limited utility in explaining similar-scale 

canopy change trends. Coarse-scale observations 

and reporting confounded the signatures of localized 

canopy change events that overlap in space and time. 

Data inconsistencies through time and space raised 

questions on the reliability of the data. Comparing 

different data sources for both forest fires and 

suburbanization revealed large differences in area 

estimates that may have implications for end users. 

Assembling available geospatial data into a single 

geodatabase produced a better integrated geographic 

model providing insights into the frequency and 

overlap of multiple forest change processes across 

the CONUS (Fig. 3). Mapping each change process 

individually revealed a unique signature of local 

spatio-temporal variability, suggesting that no one 

sampling scheme will adequately capture the canopy 

change resulting from all of the processes. Overlay 

analysis suggests that because of the limited number 

and location of NAFD samples, the benefit of the 

NAFD data occurs not in aggregate calculations but in 

the new perspective they offer on forest history events 

at the scale of landscapes and individual patches. 

Data gaps and inconsistencies through space and time 

in the various data on forest change processes make 

quantitative linkages with NAFD maps and wall-towall analysis difficult. For example, data on harvest 

area suggest harvesting affects more forest area in the 

CONUS than any other process; however, its spatial 

and temporal characteristics were found to be the most 

poorly characterized.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 133

Change process

Measurement 

method Data source

 Spatial

 Grain Extent

 Temporal

 Grain Extent

Fires Landsat, NDVI 

change

MTBS 

http://MTBS.gov

30-m grid national annual 1984-2007

Hurricanes and 

tornadoes

Ground 

measurementswind speed

U.S. National 

Hurricane Center 

http://www.nhc.noaa.

gov/pastall.html

lines national annual 1851-2008

Suburbanization/

urbanization

Decadal census 

– number of 

new housing 

units

Theobald 2005 100-m grid national decadal 1940-2030

Suburbanization/

urbanization

Landsat, Land 

use change

NLCD Retrofit 

Change Data 

http://www.mrlc.gov/

changeproduct.php

30-m grid national decadal 1992-2001

Suburbanization/

urbanization

Landsat, Land 

use change

NLCD 2001/2006 

Land cover change 

http://www.mrlc.gov/

nlcd2006_downloads.

php

30-m grid national 5 years 2001-2006

Harvests Timber Product 

Output Surveys

USFS FIA http://

srsfia2.fs.fed.us/php/

tpo_2009/tpo_rpa_

int2.php

county 

polygons 

or >

sampled 

-national

5- to 

10- year 

cycles

1997-2007

Pests and 

pathogens

Digitized aerial 

sketches of 

insect damage

U.S. Forest Health 

Program 

http://www.fs.fed.us/

r3/resources/health/

fid_surveys.shtml

polygon 

<1 ha

sampled 

-national

annual WA and OR: 1984-

2005; CA: 1994-2008; 

AZ: 2000-2009; NM: 

1998-2009; states in 

the NC and NE regions: 

1997-2009; NV, UT, and 

southern ID: 1991-2008; 

Northern ID, MT, ND: 

2000-2008; most of 

Wyoming, CO, SD, NE, 

KS: 1994-2009

Southern Pine 

Beetle

Aerial Spot 

Detection 

Surveys

Williams and Birdsey 

2003

county 

polygons

SE and 

SC 

states 

(except 

OK)

annual 1987-2004

Table 1.—Data used to build an integrated geodatabase of forest change processes
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 134

Figure 1.—Averaged annual forest change rates from FIA stand age, reported in the most recent inventory data and NAFD 

forest history maps over two decades for six regions of CONUS.

Figure 2.—Averaged annual rates calculated as a percentage of total forest area in the region using total forest area per 

region from Smith et al. (2009). Reported areas for individual disturbance processes are not mutually exclusive and can lead 

to double counting.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 135

DISCUSSION

Rates of forest canopy disturbance vary with 

differing underlying causal processes. This work 

found estimated disturbance rates from FIA and 

NAFD observations of forest stand age differ. The 

differing estimates may be related to the different 

methodologies used by FIA and NAFD and their 

capability to capture different underlying causal 

processes. The spatial and temporal patterns of the 

underlying causal processes are necessary to interpret 

rates of forest canopy from different remote sensing 

and ground inventory data products.

Estimates of canopy change and change processes 

have traditionally been available at decadal-regional 

scales, which hinder analyses of smaller-scale 

ecological processes and canopy observations within 

a landscape matrix. Estimates of forest area affected 

by individual change process, assembled from tabular 

data, revealed trends that vary both within and across 

regions through time. However, the coarse resolution 

of these data made linkages with regional-scale canopy 

change observations from FIA and NAFD problematic. 

Consolidating the many data on forest change 

processes into one geospatial database allowed for 

both aggregate calculations and preservation of 

information on local patterns. Finer-scale observations 

of canopy change and causal processes revealed new 

insights into these dynamic processes. However, data 

gaps and inconsistencies preclude robust quantitative 

analysis. This work represents a first step towards a 

more integrated geographic model of forest change 

processes and canopy change observations. 

Figure 3.—Overlap of five forest change processes (fire, harvest, insects, windstorms, and suburbanization) over roughly two 

decades. Specifics on temporal and spatial characteristics of data vary with each change process and are available in Table 1.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 136

ACKNOWLEDGMENTS

This research was supported in part by the North 

American Carbon Project, NASA grant numbers 

NNG05GE55G and NNX08AI26G. 

LITERATURE CITED

Birdsey, R.A.; Lewis, G.M. 2003. Current and 

historical trends in use, management, and 

disturbance of U.S. forestlands. In: Kimble, 

J.M.; Heath, L.S.; Birdsey, R.A.; Lal, R., eds. The 

potential of U.S. forest soils to sequester carbon 

and mitigate the greenhouse effect. Boca Raton, 

FL: CRC Press: 15-34.

Cohen, W.B.; Goward, S.N. 2004. Landsat’s role 

in ecological applications of remote sensing.

Bioscience. 54(6): 535-545.

Gillespie, A.J.R. 1999. Rationale for a national 

annual forest inventory program. Journal of 

Forestry. 97(12): 16-20.

Goward, S.N.; Masek, J.G.; Cohen, W.; Moisen, G.; 

Collatz, G.J.; Healey, S.; Houghton, R.; Huang, 

C.; Kennedy, R.; Law, B.; Powell, S.; Turner, D.; 

Wulder, M.A. 2008. Forest disturbance and 

North American carbon flux. Eos Transactions. 

89: 105-116.

Reams, G.A.; Brewer, C.K.; Guldin, R.W. 2010. 

Remote sensing alone is insufficient for 

quantifying changes in forest cover. Proceedings 

of the National Academy of Sciences. 

107(38): E145.

Smith, W.B.; Darr, D. 2004. U.S. forest resource 

facts and historical trends. Publ. FS-801. U.S. 

Department of Agriculture, Forest Service. 40 p. 

Theobald, D.M. 2005. Landscape patterns of 

exurban growth in the USA from 1980 to 2020.

Ecology and Society. 10(1): 32. 

Williams, D.W.; Birdsey, R.A. 2003. Historical 

patterns of spruce budworm defoliation and 

bark beetle outbreaks in North American conifer 

forests: an atlas and description of digital maps.

Gen. Tech. Rep. NE-308. Newtown Square, PA: 

U.S. Department of Agriculture, Forest Service, 

Northern Research Station. 37 p.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 137

IMPROVING AUTOMATED DISTURBANCE MAPS 

USING SNOW-COVERED LANDSAT TIME SERIES STACKS

Kirk M. Stueve, Ian W. Housman, Patrick L. Zimmerman, Mark D. Nelson, Jeremy Webb, 

Charles H. Perry, Robert A. Chastain, Dale D. Gormanson, Chengquan Huang, 

Sean P. Healey, and Warren B. Cohen1

Abstract.—Snow-covered winter Landsat time series stacks are used to develop a 

nonforest mask to enhance automated disturbance maps produced by the Vegetation 

Change Tracker (VCT). This method exploits the enhanced spectral separability between 

forested and nonforested areas that occurs with sufficient snow cover. This method 

resulted in significant improvements in Vegetation Change Tracker outputs at the 95 

percent confidence interval. An estimated 34 percent of the world’s forests receive 

sufficient snowfall to use this method.

NOTE: A longer version of this paper with additional tables, figures, and a full literature 

cited section was published in Remote Sensing of Environment, Vol. 115, Issue 12, in 

December 2011.

1

 Research Scientist (KMS), U.S. Forest Service, Northern 

Research Station; Remote Sensing Specialist (IWH), U.S. 

Forest Service, Remote Sensing Applications Center, 2222 

West 2300 South, Salt Lake City, UT 84119; Research 

Statistician (PLZ), U.S. Forest Service, Northern Research 

Station; Research Forester (MDN), U.S. Forest Service, 

Northern Research Station; Remote Sensing Specialist 

(JW), U.S. Forest Service, Remote Sensing Applications 

Center; Research Soil Scientist (CHP), U.S. Forest Service, 

Northern Research Station; Change Detection Specialist 

(RAC), U.S. Forest Service, Remote Sensing Applications 

Center; Supervisory Forester (DDG), U.S. Forest Service, 

Northern Research Station; Associate Research Professor 

(CH), Department of Geographical Sciences, University of 

Maryland; Research Forester (SPH), U.S. Forest Service, 

Rocky Mountain Research Station; Research Forester 

(WBC), U.S. Forest Service, Pacific Northwest Research 

Station. IWH is the corresponding author: to contact, call 

801-975-3366 or email at ihousman@fs.fed.us.

INTRODUCTION

The extensive Landsat archive is increasingly being 

leveraged to gain an understanding of land cover/land 

use change dynamics by offering insight into the 

spatial and temporal aspects of forest disturbances. 

The Vegetation Change Tracker (VCT) is an automated 

change detection method that uses growing season 

Landsat time series stacks (LTSSs) to detect forest 

disturbances (Huang et al. 2010a). Its efficacy is 

highly dependent on forest density, abruptness of the 

onset of disturbance, and presence of agricultural and 

wetland areas (Thomas et al. 2011). 

STUDY AREA

VCT was applied to quantify landscape-level patterns 

of forest disturbances from 1985 to 2008 in the Lake 

Michigan and Lake Superior basins. This study area 

was chosen to gain an understanding of the impacts of 

forest disturbances on the water quality of the Great 

Lakes. These basins are primarily composed of forests 

(intact and disturbed), cropland, pastureland, wetlands, 

and urban areas. The level of forest fragmentation 

generally decreases along a south-to-north gradient. 

A variety of anthropogenic as well as natural 

disturbances occur throughout the study area. These 

disturbances include harvest, urban development, 

insect mortality, storm damage, and fire (Schulte et al. 

2007, Stueve et al. 2011). 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 138

METHODS

In the course of this study, it was observed that VCT 

often incorrectly classified nonforested wetlands and 

agricultural areas as disturbances due to their dense 

vegetation cover and high inter-annual variability. To 

mitigate this error, a modified VCT workflow was 

developed and employed that incorporates winter 

Landsat imagery. This version of VCT is referred 

to as VCTw due to its utilization of snow-covered 

winter Landsat time series stacks (LTSSw) for the 

creation of a nonforest mask. The VCTw approach 

uses the VCT cloud masking method (Huang et al. 

2010b). Due to similarities of clouds and snow cover 

in the spectral/temperature data space, the VCT cloud 

masking method consistently identifies nonforested 

snow-covered areas as clouds (Fig. 1). Areas that are 

consistently identified as clouds and that do not show 

a long-term recovery trend throughout an LTSSw are 

included in a nonforest mask. Forested and disturbed 

pixels from the standard VCT product that fall within 

the nonforest mask are recoded to nonforest (Fig. 2). 

The full VCTw flowchart can be found in Figure 3.

We produced standard VCT and VCTw change 

products for nearly 25 million hectares within the 

Lake Superior and Lake Michigan drainage basins. 

An accuracy assessment of the VCT and VCTw 

change products was conducted through a two-stage 

sampling design across three geographic regions using 

techniques discussed by Nusser and Klass (2003), as 

well as Stehman et al. (2003). Due to the variability 

in typical disturbance type and size throughout the 

basins, the study area was divided into three regions. 

The three accuracy assessment regions were the lower 

Lake Michigan basin (LLMB), upper Lake Michigan 

basin (ULMB), and the lower Lake Superior basin 

(LLSB). (The upper Lake Superior basin (ULSB) 

made up the Canadian portion of the Lake Superior 

basin. The ULSB could not be included in this 

validation due to a lack of consistent aerial reference 

imagery.) Aerial photography spanning the analysis 

period served as the reference imagery. The standard 

VCT year of disturbance classes were binned into two 

disturbance classes to match the temporal resolution 

of the reference imagery. These classes were disturbed 

early (1985-1999) (D1) and disturbed late (2000-2008) 

(D2). The remaining classes were persisting forest 

(PF), nonforest (PNF), and water (PW).

Figure 1.—Spectral-temperature space for a summer Landsat TM scene with clouds (left plot), and a cloud-free snow-covered 

winter scene (right plot). The cloud masking model misclassifies snow as clouds due to their similar bright/cold spectral/

thermal signature. 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 139

Figure 2.—Example of commission of wetland (right center of panels) and agricultural areas (lower left portion of panels) by 

VCT. VCT initially classified these areas as persisting forest/forest disturbance. They were then recoded to nonforest.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 140

Figure 3.—Flowchart of all major steps of the VCTw workflow. Larger components depicted as different colors are running 

VCT using the standard protocol (green), creating a nonforest mask using VCT’s cloud masking algorithm (blue), and applying 

nonforest mask and minimum mapping unit (red) to create the final VCTw product.

Because the aerial imagery had to be manually georeferenced, the first stage of the sample design was 

intended to optimize our reference image preparation 

time. Therefore, the primary sampling unit (PSU) 

corresponded with the footprint of a 7.5-arc minute 

quadrangle. A simple random sample of 7.5-minute 

quadrangles that fell entirely within the basin was 

taken based on approximate region area. Thirty-five 

PSUs were selected in the LLMB and ULMB, and 17 

were selected in the LLSB. The secondary sampling 

unit (SSU) corresponded to individual pixels. A 

stratified random sample was taken using the five 

VCTw classes as strata. The number of SSUs varied 

between strata and region (Table 1). Each SSU was 

photo-interpreted to determine the proper disturbance 

or persisting land cover class. 

RESULTS

The binned VCT disturbance year product obtained 

an overall accuracy of 86.3 percent, while the binned 

VCTw disturbance year product yielded a statistically 

significant improvement at the 95 percent confidence 

interval with an overall accuracy of 91.2 percent. The 

difference between VCT and VCTw varied across 

the study regions (Fig. 4). The most pronounced 

improvement occurred in the LLMB, likely because 

the LLMB contains the largest proportion of the 

agricultural nonforested areas in which standard VCT 

erred while VCTw was able to discern. The proportion 

of agricultural areas in the ULMB and LLSB is lower 

than in the LLMB. 

Table 1.—Number of SSUs from each stratum 

within each region

PNF PF PW D1 D2

LLSB 9 9 2 10 10

ULMB 5 5 2 6 6

LLMB 7 7 2 8 8
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 141

Figure 4.—Overall accuracy of VCT (diamonds) and VCTw 

(squares) with 95 percent confidence interval bars. 

DISCUSSION

In this study, VCTw was used to create a significantly 

improved landscape-level forest disturbance map 

using LTSSw. VCTw is a robust, practical approach 

for mitigating misclassification of wetlands and 

agricultural areas as forest/forest disturbance in any 

area with sufficient snowfall. Because this method 

uses the VCT cloud masking method, it easily fits into 

the standard VCT workflow. Alternate approaches 

have proved effective, but generally require substantial 

model calibration data (Walterman et al. 2008). VCTw 

requires no calibration data and minimal additional 

time to implement. An estimated 34 percent of the 

world’s forests receive sufficient snow to implement 

VCTw. Forested regions that do not receive reliable 

yearly snowfall would require an alternate approach. 

ACKNOWLEDGMENTS

This project is the result of a cooperative effort 

between the FIA unit of the U.S. Forest Service’s 

Northern Research Station, the Remote Sensing 

Applications Center, EPA’s Great Lakes Restoration 

Initiative, and NASA Applied Science Program’s 

North American Forest Dynamics project.

LITERATURE CITED

Huang, C.; Goward, S.N.; Masek, J.G.; Thomas, N.; 

Zhu, Z.; Vogelmann, J.E. 2010a. An automated 

approach for reconstructing recent forest 

disturbance history using dense Landsat time 

series stacks. Remote Sensing of Environment. 

114: 183-198.

Huang, C.; Thomas, N.; Goward, S.N.; Masek, J.G.; 

Zhu, Z.; Townshend, J.R.G.; Vogelmann, J.E. 

2010b. Automated masking of cloud and cloud 

shadow for forest change analysis using Landsat 

images. International Journal of Remote Sensing. 

31(20): 5449-5464.

Nusser, S.M.; Klaas, E.E. 2003. Survey methods 

for assessing land cover map accuracy.

Environmental and Ecological Statistics. 

10(3): 309-331.

Schulte, L.A.; Mladenoff, D.J.; Crow, T.R.; Merrick, 

L.C.; Cleland, D.T. 2007. Homogenization of 

northern U.S. Great Lakes forests due to land 

use. Landscape Ecology. 22: 1089-1103.

Stehman, S.V.; Wickham, J.D.; Smith, J.H.; Yang, L. 

2003. Thematic accuracy of the 1992 National 

Land-Cover Data for the eastern United States: 

statistical methodology and regional results.

Remote Sensing of Environment. 86(4): 500-516.

Stueve, K.M.; Perry, C.H.; Nelson, M.D.; Healey, 

S.P.; Hill, A.D.; Moisen, G.G.; Cohen, W.B.; 

Gormanson, D.D.; Huang, C. 2011. Ecological 

importance of intermediate windstorms rivals 

large, infrequent disturbances in the northern 

Great Lakes. Ecosphere. 2(1): Article 2.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 142

Thomas, N.E.; Huang, C.; Goward, S.N.; Powell, S.; 

Rishmawi, K.; Schleeweis, K.; Hinds, A. 2011. 

Validation of North American forest disturbance 

dynamics derived from Landsat time series 

stacks. Remote Sensing of Environment. 

115: 19-32.

Walterman, M.; Finco, M.; Healey, S.P. 2008. 

Improving local area accuracy innational level 

disturbance mapping. RSAC-4027-RPT1. Salt 

Lake City, UT: U.S. Department of Agriculture, 

Forest Service, Remote Sensing Applications 

Center. 9 p. 

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 143

ADDING VALUE TO THE FIA INVENTORY: COMBINING FIA DATA AND 

SATELLITE OBSERVATIONS TO ESTIMATE FOREST DISTURBANCE

Todd A. Schroeder, Gretchen G. Moisen, Sean P. Healey, and Warren B. Cohen1

Abstract.—In addition to being one of the primary drivers of the net terrestrial carbon 

budget, forest disturbance also plays a critical role in regulating the surface energy 

balance, promoting biodiversity, and creating wildlife habitat. With climate change and an 

ever growing human population poised to alter the frequency and severity of disturbance 

regimes across the globe, improved monitoring of forest disturbance, especially at the 

landscape scale has taken on renewed importance. Because forest disturbance manifests 

at a variety of spatial and temporal scales and has varying impacts which affect the 

canopy, understory, and forest floor, effective monitoring will likely require a composite 

approach where localized field data collected by the Forest Inventory and Analysis (FIA) 

Program are combined with repeat observations from remote sensing satellites such as 

Landsat. As Landsat offers nearly 40 years of well calibrated and systematically collected 

imagery at no cost, it is now economically feasible to monitor year to year trends in forest 

disturbance over large areas. In addition to its use in mapping forest change, Landsat 

data can also serve as a valuable backdrop for collecting detailed human interpretations 

of disturbance. When collected over a design-based sample such as FIA plots, these 

manually derived interpretations offer a wealth of potential uses ranging from map 

validation to estimation of new disturbance-related attributes. Here satellite observations 

and FIA data are used to estimate the area impacted by several types of forest disturbance 

occurring in the Uinta Mountains of northern Utah. This study aims to evaluate two types 

of satellite observations in the context of FIA’s estimation procedure, including the use of 

human interpretations as an augmented response variable and the use of disturbance maps 

for stratified variance reduction. 

1

 Research Ecologist (TAS) and Research Foresters (GGM 

and SPH), U.S. Forest Service, Rocky Mountain Research 

Station, 507 25th Street, Ogden, UT 84401; Research 

Forester (WBC), U.S. Forest Service, Pacific Northwest 

Research Station. TAS is corresponding author: to contact, 

call 801-625-5690 or email at tschroeder@fs.fed.us.

INTRODUCTION

The U.S. Forest Service Forest Inventory and Analysis 

(FIA) Program is required to produce timely and 

accurate estimates of a wide range of forest attributes. 

These attributes, which often include forest area 

and volume, are used to provide information on the 

current status and health of forests over varying 

geographical extents. Typically, areal estimates 

derived from inventory plots are used to quantify 

how much forest there is while maps derived from 

remote sensing imagery are used to delineate forest 

location and extent. Reporting population estimates 

which describe a forests current condition has long 

been a primary function of FIA. With climate change 

and population growth poised to alter the rate and 

severity with which forests are disturbed, a renewed 

emphasis has been placed on determining how much 

forest is changing, where it’s changing, and what’s 

changing it. Monitoring the status and trends of 

forest disturbance can be challenging for inventory 

programs as they typically have long temporal gaps 

between plot remeasurements (e.g., FIA remeasures 

plots every 5 years in the east and every 10 years in 

the west) likely resulting in missed disturbance events. 

But because small amounts of disturbance occur each 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 144

year it is also important that measurements be made 

frequently and over large areas. Thus there is potential 

for national forest inventories to improve their changemonitoring capabilities by incorporating more frequent 

measurements of disturbance from remote sensing 

satellites such as Landsat.

MONITORING 

FOREST DISTURBANCE

WITH LANDSAT IMAGERY

With a 40+ year historical archive and a 16-day 

repeat cycle, Landsat imagery offers an excellent 

platform for monitoring forest disturbance over 

large areas. The 30 m spatial grain and 7 spectral 

bands are capable of capturing many types of forest 

disturbance, especially those that impact the upper 

canopy. With the entire Landsat archive now freely 

available, it is now economically feasible to monitor 

disturbance through time using dense stacks of images. 

The free availability of images has also resulted in 

the development of several new algorithms which 

are capable of producing maps of disturbance in 

an automated manner. Two of these algorithms, the 

Vegetation Change Tracker or VCT (Huang et al. 

2010) and the Landsat-based detections of trends in 

disturbance and recovery, or LandTrendr (Kennedy 

et al. 2010), are being tested and applied over large 

regions. For example, the North American Forest 

Dynamics (NAFD) project is currently using VCT to 

produce a wall-to-wall, seamless forest disturbance 

map for the conterminous United States. Given the 

increased availability and seamless coverage of these 

maps, it is important to determine if these national 

products can help FIA improve the timeliness and 

accuracy with which they monitor and estimate forest 

disturbance. Early attempts at using disturbance maps 

to stratify FIA variables have been met with mixed 

results, although it is possible this has more to do with 

variable definition and lack of remeasured data than 

poor map quality. Thus, a more comprehensivelydefined disturbance response variable (e.g., one that is 

measured annually and includes all disturbance types) 

which is more in tune with the satellite perspective 

might yield improved stratification results.

One new strategy for collecting measurements of 

disturbance comes through the use of a human 

interpreter to analyze yearly time series of Landsat 

images (Cohen et al. 2010). When analyzed with 

periodic, high resolution imagery from Google Earth, 

these human interpretations yield highly accurate 

records of timing and cause of most natural and 

anthropogenic disturbance events. If these human-

interpreted measurements of disturbance are made 

over a statistically based set of sample locations, such 

as FIA plots, they can be used for estimation purposes 

just like any other FIA variable. The objective of this 

study is to explore the utility of augmenting FIA plots 

with these human-interpreted measurements for the 

purpose of estimating the area impacted by several 

different types of disturbance. In testing potential 

improvements to the disturbance response variable, we 

will also test whether maps from VCT and LandTrendr 

can be used to improve the precision of the derived 

disturbance estimates. 

STUDY AREA

The study area consists of the non-overlapping portion 

(or Thiessen scene area, TSA) of Landsat scene path 

37 row 32, which covers the Uinta Mountains of 

northern Utah (Fig. 1). Approximately 56 percent of 

the study area is forested and includes broad expanses 

of conifer, mixed deciduous and pure aspen stands at 

high elevations, and sparse pinyon-juniper woodlands 

on drier, lower elevation slopes. According to the U.S. 

Forest Service forest type map (Ruefenacht et al. 2008) 

the majority of forest within the study area falls in 

five major group types. These include pinyon-juniper 

(18 percent), aspen/birch (12 percent), fir/spruce/

hemlock (12 percent), lodgepole pine (10 percent) and 

Douglas-fir (3 percent). The diversity of forest types 

and land management objectives in this region have 

led to a number of known disturbance events including 

harvests, fires, insects, and mechanical treatments 

such as chaining. The broad array of disturbances, 

along with the presence of both dense and sparse 

forest types, makes this area an excellent location for 

evaluating FIA’s potential use of satellite observations 

of disturbance.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 145

Figure 1.—Uinta Mountains study area with Vegetation Change Tracker (VCT) and LandTrendr forest disturbance.

METHODS

Satellite Disturbance Observations

The annual FIA plots (n=802) falling within the 

boundary of the study area were queried and loaded 

into a GIS for analysis. The plots containing at least 

one forest condition class (n=478) were identified so 

that a human interpreter could analyze and record the 

disturbance history of each plot. The interpretation 

process, similar to the one outlined in Cohen et al. 

(2010), entailed simultaneously viewing the full time 

series of Landsat images (acquired annually between 

1987 and 2010) so that both the immediate plot area 

and its surrounding neighborhood could be easily 

surveyed for changes and supporting context. In a 

separate window, spectral trajectories for the plot 

were displayed using a number of different bands and 

indices. To aid the interpretation of the Landsat data, 

the interpreter used the high resolution, multi-date 

images found in Google Earth. These high resolution 

images covered several dates and were instrumental 

in detecting the first signs of stress-related crown 

mortality, such as from disease, insects, and drought. 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 146

Taken together, the multi-scale information from 

Landsat and Google Earth allowed the interpreter to 

accurately record the year and type of disturbances 

found on the FIA plots. 

Satellite Disturbance Maps

Both VCT and LandTrendr have been successfully 

used to map disturbances across a broad range of 

forested ecosystems. Although the goal of both 

algorithms is to infer change on the ground from 

changes in the spectral signal captured by multiple 

Landsat images, both do so in different ways. For 

example, VCT is designed to locate abrupt forest 

disturbances such as fire and harvesting, whereas 

LandTrendr is designed to detect both abrupt events as 

well as slower, longer-term changes across all lands. 

Here we used each algorithm to generate maps of 

disturbance for the study area using Landsat images 

acquired annually between 1987 and 2009. Because 

LandTrendr maps disturbance across all lands, a forest 

cover map (Ruefenacht et al. 2008; see inset map Fig. 

1) was used to remove disturbance from nonforested 

areas. For consistency, the VCT map was also merged 

with this forest cover map. Additionally, the time 

period of mapped disturbance was restricted to 1995-

2009 to better coincide with the measurement window 

of the annual FIA plots. 

RESULTS

Satellite and FIA 

Disturbance Observations

The image interpretation process identified a total 

of 129 disturbance events between 1995 and 2009. 

These 129 satellite disturbances were grouped into 

10 categories as shown by the blue bars in Figure 2. 

The most prevalent disturbance types were insect and 

insect/disease which together accounted for more than 

75 percent of the total observations. From the satellite 

perspective, these two classes represent distinct 

patterns of crown mortality which were distinguishable 

in the air photos. The insect label indicates the 

visible presence of red attack damage likely caused 

by mountain pine beetle, whereas the insect/disease 

label reflects conditions where dead gray crowns 

were observed, but the exact cause of death could not 

be inferred. The importance of the high resolution 

air photos was apparent, as more than half of these 

chronic stress related disturbances were only detectible 

in Google Earth. Fires were the next most abundant 

(11 percent) disturbance type, followed by harvesting 

(7 percent) and mechanical treatments such as chaining 

and brush saw (2 percent). Not surprisingly, the types 

and frequency of disturbances detected by FIA were 

similar to those observed by satellite interpretation 

(see red bars in Fig. 2). In total, FIA detected 86 

disturbance events, of which the majority (72 percent) 

belonged to the insect and disease categories. Since 

FIA plots are field measured, we expect their insect 

and disease classes to be more accurate than those 

derived by satellite interpretation. Additionally, FIA 

was also able to detect smaller-scale impacts such as 

from animal grazing and weather, which could not 

be picked up from the satellite perspective. Over the 

14-year period of observation (1995-2009), both the 

satellite and FIA measurements showed a noticeable 

trend of increasing disturbance with time as seen in 

Figure 3a and 3b (note different y axes). Despite the 

temporal similarities, it is important to note that in 

most cases FIA and the satellite captured different 

disturbance events. This results in considerably more 

disturbance when the data sets are combined than was 

found only by FIA (Fig. 3c). 

Figure 2.—Frequency of disturbance types observed by FIA 

and satellite interpretation.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 147

Figure 3.—Frequency of disturbance observed by (a) FIA 

interpretation, (b) satellite interpretation, and (c) FIA and 

satellite combined. 

a

b

c

Satellite Disturbance Maps

Both the VCT and LandTrendr algorithms were 

used to derive forest disturbance maps for the study 

area. The algorithms produced noticeably different 

spatial (Fig. 1) and temporal (Fig. 4) patterns, with 

LandTrendr finding nearly twice as much disturbance 

as VCT. Despite these differences, both maps resulted 

in similar accuracy metrics when compared with the 

satellite interpretations of disturbance (VCT overall 

accuracy 87 percent, kappa = 0.42; LandTrendr overall 

accuracy 86 percent, kappa = 0.38). Unfortunately, 

combining the two disturbance maps did little to 

improve accuracy (combined overall accuracy 86 

percent, kappa = 0.43). 

Figure 4.—Area of disturbance (in hectares) mapped by 

Vegetation Change Tracker (VCT) and LandTrendr.

CONCLUSION

The estimation and stratification results are currently 

being finalized and will be presented at the FIA 

Science Symposium in December 2012. Nonetheless, 

we believe the data gathered so far highlights the 

potential importance of augmenting FIA measurements 

with satellite observations of disturbance. 

Undoubtedly, the combination of these data sets will 

lead to larger estimates of disturbance than would 

otherwise be obtained from FIA data alone. In addition 

to improving estimates of disturbance, we also believe 

better disturbance histories will help enhance the 

overall analysis capability of the FIA plot data. 

ACKNOWLEDGMENTS

This study was funded by NASA and the North 

American Forest Dynamics (NAFD) project with 

additional support provided by FIA’s techniques 

research band.

LITERATURE CITED

Cohen, W.B.; Yang, Z.; Kennedy, R. 2010. Detecting 

trends in forest disturbance and recovery using 

yearly Landsat time series: 2. TimeSnyc–Tools 

for calibration and validation. Remote Sensing of 

Environment. 114: 2911-2924.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 148

Huang, C.; Goward, S.N.; Masek, J.G.; Thomas, N.; 

Zhu, Z.; Vogelmann, J.E. 2010. An automated 

approach to reconstructing recent forest 

disturbance history using dense Landsat time 

series stacks. Remote Sensing of Environment. 

114: 183-198.

Kennedy, R.E.; Yang, Z.; Cohen, W.B. 2010. 

Detecting trends in forest disturbance and 

recovery using yearly Landsat time series: 

1. LandTrendr–Temporal segmentation 

algorithms. Remote Sensing of Environment. 114: 

2897-2910.

Ruefenacht, B.; Finco, M.V.; Nelson, M.D.; 

Czaplewski, R.; Helmer, E.H.; Blackard, 

J.A.; Holden, G.R.; Lister, A.J.; Salajanu, 

D.; Weyermann, D.; Winterberger, K. 2008. 

Conterminous U.S. and Alaska forest type 

mapping using forest inventory and analysis 

data. Photogrammetric Engineering and Remote 

Sensing. 74(11): 1379-1388.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Species Diversity, 

Species Distributions, and 

Vegetation Classification
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 150

USING FOREST INVENTORY DATA ALONG WITH SPATIAL LAG 

AND SPATIAL ERROR REGRESSION TO DETERMINE THE IMPACT 

OF SOUTHERN PINE PLANTATIONS ON SPECIES DIVERSITY AND 

RICHNESS IN THE CENTRAL GULF COASTAL PLAIN

Andrew J. Hartsell1

Abstract.—This study investigates the impacts of southern yellow pine plantations 

on species evenness and richness in the gulf coastal plain. This process involves using 

spatial lag and spatial error regression techniques using GeoDa software and U.S. 

Forest Service’s Forest Inventory and Analysis data. The results indicate that increasing 

plantation area is negatively correlated to species evenness and richness. Preliminary 

results indicate that for every 10 percent increment increase in southern yellow pine 

plantation area, Shannon’s E decreases by 0.02 and species richness declines by 1.6 

species. However, these models account for less than 50 percent of the data’s variance, an 

indication that the models are incomplete and more research is needed.

1

 Research Forester, U.S. Forest Service, Southern Research 

Station, 4700 Old Kingston Pike, Knoxville TN 37919. To 

contact, call 865-862-2032 or email at ahartsell@fs.fed.us.

INTRODUCTION

Biodiversity, synonymous with biological diversity, 

can be defined as “the variety and variability among 

living organisms and the ecological complexes in 

which they occur” (OTA 1987). Humans perceive 

regions with a multitude of diverse species to have 

more value than those that don’t (Ehrlich 1991, Wilson 

1993). Possible reasons that species diversity is valued 

by humans are: larger number of plant species means 

a greater variety of crops and life; greater species 

diversity helps assure natural sustainability for all life 

forms; diverse ecosystems can better withstand and 

recover from a variety of disasters; and finally the 

planets complex systems, ecological networks, and 

energy flows are dependent upon numerous organisms 

and interactions (Gaston 1996, SCBD 2006, Wilson 

1993). 

However, global biodiversity may be threatened 

by anthropogenic sources. The main factors 

responsible for potential biodiversity loss include: 

land use change; habitat change such as forest 

fragmentation and conversion; invasive alien species; 

overexploitation; and pollution. Plantations, which are 

artificially regenerated forests that are often composed 

of genetically modified or alien species, satisfy two 

of these factors. It is important that science ascertains 

the positive and negative impacts of this management 

regime to facilitate public discourse and planning.

STUDY AREA

The initial study area was limited to the states of 

Texas, Louisiana, Mississippi, and Alabama. Only 

counties in those states having the majority of their 

area in the gulf coastal plain, as defined by Bailey 

(1998), were considered. This population was thinned 

further by two more factors. First, the Mississippi

River and its associated alluvial basin bisect the study 

area. Counties in this region were removed. Second, 

any county with less than 200,000 acres of forest land 

was removed from the dataset due to FIA’s sampling 

intensity. This assures that at least 30 forested plots 

are in each county, providing a reasonable estimate 

of species diversity and richness at the county level. 

Additionally, any “island” counties that were isolated 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 151

Figure 1.—Study area.

Species richness (R) is the number of different species 

found in a region or study area. For this study, species 

richness is a count of all tree species found in each 

county. Species richness does not take into account 

the relative abundance distributions of species.

Spatial Statistics

Detecting Spatial Autocorrelation

One of the most common ways of detecting spatial 

autocorrelation in group-level data is the Moran’s I 

statistic. Moran’s I is a weighted correlation coefficient 

used to detect departures from randomness such as 

clusters. The formula for Moran’s I is:

I = ∑i∑jwij (xi − μ)(xj − μ)/ ∑i (xi − μ)

2

where: μ is the mean of the x variable

wij are the elements of the spatial weights matrix.

and not attached to the larger study area were also 

removed. The final dataset was composed of data from 

158 counties (Fig. 1).

DEFINITIONS and CONCEPTS

Measuring Biodiversity

Shannon-Wiener (Shannon’s) evenness index (E) and 

diversity index (H) come from information theory and 

measure the order and disorder within a population 

(Shannon and Weaver 1971). Shannon’s diversity 

index is derived by calculating the proportion of 

species i relative to the total number of species (pi ),

 and then multiplying by the natural logarithm of 

this proportion (ln pi ). The result is summed across 

species, and multiplied by −1:

H = −∑ pi ln pi R

i = 1
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 152

Geographically Weighted Regression: 

Spatial Lag and Spatial Error Models

Geographically weighted regression (GWR) can be 

performed in the presence of spatial autocorrelation. 

GWR accounts for distinctions between spatial 

similarity between the dependent and independent 

variables. Ordinary least squares (OLS) and other 

simple statistics do not do this. The basic formula 

for GWR is:

y = χβ + ei

where: χ is an n×p matrix of regressors

β is a p×1 vector of unknown parameters

e is unobserved scalar random variables (errors).

Spatial lag models (SLM) and spatial error models 

(SEM) are two types of GWR. Spatial lag models 

produce a spatially lagged variable on the right 

hand side of a regression equation. A spatial error 

model (SEM) considers the estimation of maximum 

likelihood of a spatial regression model that includes a 

spatial autoregressive error term on the right hand side 

of the regression equation.

METHODS

Species richness, Shannon’s E, total forest area, 

and percent of forest area in southern yellow pine 

plantations (SYP) were computed for each county 

in the study area. Ordinary least squares (OLS) 

analysis was performed on the both Shannon’s E and 

species richness using GeoDa version 0.95 software 

(OpenGeoDa version 1.2 is now available from the 

GeoDa Center at Arizona State University). Moran’s 

I was calculated to determine if spatial dependence 

was an issue. If the data was determined to be spatially 

autocorrelated, then a series of LaGrange multiplier 

(LM) test statistics were computed. The results of the 

LM would then indicate which GWR model, spatial 

lag model or spatial error model, would be used in the 

final analysis.

RESULTS

Shannon’s evenness index (E) was the first dependent 

variable investigated. The OLS regression of 

Shannon’s E was performed using percent of forest 

land per county in southern yellow pine plantations 

(PCT_SYP_PL) as one independent variable, and 

a dummy variable that indicated if a county was on 

the east side of the Mississippi River. The average 

Shannon’s evenness index was 0.695 (Table 1). The 

R2 and adjusted R2 were 0.368 and 0.360, respectively. 

The F-statistic and associated p-value indicated that 

the model was statistically significant. All three 

predictor variables, the intercept and two independent 

variables, were significant as well. 

Table 1.—Results of ordinary least squares analysis on species evenness index using percent southern 

yellow pine plantations per county and location flag 

Dependent Variable: SHANNONS_E Number of Observations: 158

Mean dependent var: 0.694942 Number of Variables: 3

S.D. dependent var: 0.0565841 Degrees of Freedom: 155

R-squared: 0.368228 F-statistic: 45.1709

Adjusted R-squared: 0.360076 Prob(F-statistic): 3.49482e-016 

Sum squared residual: 0.319599 Log likelihood: 265.867

Sigma-square: 0.00206193 Akaike info criterion: -525.734

S.E. of regression: 0.0454085 Schwarz criterion: -516.546

Sigma-square ML: 0.00202278

S.E. of regression ML: 0.0449753

Variable Coefficient Std. Error t-Statistic Probability

CONSTANT 0.7422254 0.01048335 70.80043 0.0000000

PCT_SYP_PL -0.002851605 0.0003366784 -8.469818 0.0000000

EAST_FLAG 0.03198344 0.007769342 4.116622 0.0000623
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 153

Tests for multicollinearity, normality, and 

heteroskedasticity proved to be insignificant 

(Table 2). However, Moran’s I proved to be highly 

significant (p value =0.000000) indicating that 

spatial autocorrelation was an issue with the data. 

The first two tests (LM-error and LM-lag) were both 

significant, indicating that the robust models are more 

appropriate. The robust versions were to be considered 

only if the standard versions were significant. In 

this instance, both LM-lag and LM-error were 

significant, so the robust versions were then used. 

The Robust LM-error statistic was not significant (p 

value=0.8675), but the Robust LM-lag statistic was 

(p value=0.0087). Therefore, a spatial lag model is 

needed to remove any spatial autocorrelation. Table 3 

shows the results of the spatial lag regression model on 

Shannon’s evenness.

Table 2.—Regression diagnostics on ordinary least squares analysis of Shannon’s species evenness index

REGRESSION DIAGNOSTICS

MULTICOLLINEARITY CONDITION NUMBER 5.998419

TEST ON NORMALITY OF ERRORS

TEST DF VALUE PROB

Jarque-Bera 2 0.2463868 0.8840927

DIAGNOSTICS FOR HETEROSKEDASTICITY

RANDOM COEFFICIENTS

TEST DF VALUE PROB

Breusch-Pagan test 2 1.014434 0.6021692

Koenker-Bassett test 2 1.02169 0.5999884

SPECIFICATION ROBUST TEST

TEST DF VALUE PROB

White 5 N/A N/A

DIAGNOSTICS FOR SPATIAL DEPENDENCE

FOR WEIGHT MATRIX: Queen (row-standardized weights)

TEST MI/DF VALUE PROB

Moran’s I (error) 0.277114 5.5791937 0.0000000

Lagrange Multiplier (lag) 1 33.2970224 0.0000000

Robust LM (lag) 1 6.8814084 0.0087097

Lagrange Multiplier (error) 1 26.4434443 0.0000003

Robust LM (error) 1 0.0278303 0.8675084

Lagrange Multiplier (SARMA) 2 33.3248527 0.0000001

Table 3.—Spatial lag regression model on Shannon’s species evenness index

Spatial Weight: Queen

Dependent Variable: SHANNONS_E Number of Observations: 158

Mean dependent var: 0.694942 Number of Variables: 4

S.D. dependent var: 0.0565841 Degrees of Freedom: 154

Lag coeff. (Rho): 0.510154

R-squared: 0.518880 Log likelihood: 282.131 

Sq. Correlation: - Akaike info criterion: -556.262 

Sigma-square: 0.00154043 Schwarz criterion: -544.012 

S.E. of regression: 0.0392483

Variable Coefficient Std. Error z-value Probability

W_SHANNONS_E 0.5101542 0.07463184 6.83561 0.0000000

CONSTANT 0.3820561 0.05341811 7.152183 0.0000000

PCT_SYP_PL -0.002135705 0.0003113954 -6.8585 0.0000000

EAST_FLAG 0.01523912 0.007230402 2.107645 0.0350616
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 154

The same process was repeated for species richness 

(R). The OLS regression on species richness included 

another independent variable, the amount of forest 

land in a county. This variable was labeled Forest_K, 

as each whole unit represents 1,000 acres of forest 

land. The average species richness for the study area 

was 50.5 (Table 4), indicating that each county in the 

study area has an average of 50 distinct tree species 

greater than 1.0 inch diameter at breast height (d.b.h.). 

The R2 was 0.298 and the adjusted R2 was 0.284, 

indicating that less than 30 percent of the dataset’s 

variation was captured in the model. However, the 

model and all variables were statistically significant. 

Tests for multicollinearity and normality indicated 

that neither was a problem. However, both tests for 

heteroskedasticity revealed that variances may not 

be equal. Furthermore, Moran’s I shows that the data 

are spatially dependent (Table 5). The LM statistics 

indicated that the Robust LM-lag was insignificant. 

Table 4.—Results of ordinary least squares analysis on Shannon’s species richness using percent 

southern yellow pine plantations per county, amount of forested acres per county, and location flag

Dependent Variable: RICHNESS Number of Observations: 158

Mean dependent var: 50.5506 Number of Variables: 4

S.D. dependent var: 8.09715 Degrees of Freedom: 154

R-squared: 0.297692 F-statistic: 21.759

Adjusted R-squared: 0.284011 Prob(F-statistic): 8.38957e-012

Sum squared residual: 7275.27 Log likelihood: -526.734

Sigma-square: 47.242 Akaike info criterion: 1061.47

S.E. of regression: 6.87328 Schwarz criterion: 1073.72

Sigma-square ML: 46.046

S.E. of regression ML: 6.78572 

Variable Coefficient Std. Error t-Statistic Probability 

CONSTANT 36.267 2.222544 16.31779 0.0000000

PCT_SYP_PL -0.1072605 0.05378244 -1.994341 0.0478798

EAST_FLAG 3.60517 1.195587 3.015397 0.0030027

FOREST_K 0.04105437 0.005197799 7.898414 0.0000000

Table 5.—Regression diagnostics on ordinary least squares analysis of Shannon’s species richness index

REGRESSION DIAGNOSTICS 

MULTICOLLINEARITY CONDITION NUMBER 9.475663

TEST ON NORMALITY OF ERRORS

TEST DF VALUE PROB

Jarque-Bera 2 3.64542 0.1615873

DIAGNOSTICS FOR HETEROSKEDASTICITY

RANDOM COEFFICIENTS

TEST DF VALUE PROB

Breusch-Pagan test 3 11.23643 0.0105138

Koenker-Bassett test 3 13.29254 0.0040448

SPECIFICATION ROBUST TEST

TEST DF VALUE PROB

White 9 N/A N/A

DIAGNOSTICS FOR SPATIAL DEPENDENCE

FOR WEIGHT MATRIX: Queen (row-standardized weights)

TEST MI/DF VALUE PROB

Moran’s I (error) 0.432723 8.6153092 0.0000000

Lagrange Multiplier (lag) 1 51.3552467 0.0000000

Robust LM (lag) 1 0.0432789 0.8352011

Lagrange Multiplier (error) 1 64.4791145 0.0000000

Robust LM (error) 1 13.1671467 0.0002849

Lagrange Multiplier (SARMA) 2 64.5223934 0.0000000
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 155

Therefore, a spatial error model must be created to 

counter these issues. Anselin notes that the spatial error 

model is also useful for reducing heteroskedasticity as 

well (Anselin 1992, 2005). A spatial error regression 

was performed to correct for these issues (Table 6).

The R2 improved to 0.56, but as with the SLM model, 

this is a pseudo statistic and probably not directly 

comparable to OLS R2. The best way to determine an 

improvement of goodness of fit over the OLS model 

is to compare LL, AIC, and SC. For the SLE model on 

species richness, all three improved. 

DISCUSSION

The results of this study indicate that the area of 

southern yellow pine plantations in a county has a 

negative impact on species evenness and richness. 

Based on the spatially lagged regressions, Shannon’s 

evenness (E) will decrease by 0.02 for every 10 

percent increment increase in SYP plantation area. 

Likewise, species richness will drop by 1.6 species for 

the same change in plantation area.

However, while both models are statistically 

significant, they fail to account for over half of 

the variation in the dataset. This indicates that 

there are explanatory variables not accounted 

for. Further research needs to be performed to 

Table 6.—Spatial error regression model on Shannon’s species evenness index

Spatial Weight: Queen

Dependent Variable: RICHNESS Number of Observations: 158

Mean dependent var: 50.550633 Number of Variables: 3

S.D. dependent var: 8.097153 Degree of Freedom: 155

Lag coeff. (Lambda): 0.675756

R-squared: 0.559861 R-squared (BUSE): - 

Sq. Correlation: - Log likelihood: -500.089426 

Sigma-square: 28.857203 Akaike info criterion: 1006.18 

S.E. of regression: 5.37189 Schwarz criterion: 1015.366637 

Variable Coefficient Std. Error z-value Probability

CONSTANT 39.73594 2.177353 18.24966 0.0000000

PCT_SYP_PL -0.1636192 0.04842778 -3.378622 0.0007286

FOREST_K 0.04129515 0.004798334 8.606143 0.0000000

LAMBDA 0.6757558 0.06527036 10.35318 0.0000000

determine what these variables may be. Possible 

sources are: population estimates, road densities, 

land fragmentation patterns, or other socioeconomic 

factors.

LITERATURE CITED

Anselin, L. 1992. Spatial data analysis with GIS: 

an introduction to application in the social 

sciences. Tech. Rep. 92-10. Santa Barbara, CA: 

University of California-Santa Barbara, National 

Center for Geographic Information and Analysis. 

53 p. Available at http://www.ncgia.ucsb.edu/

Publications/Tech_Reports/92/92-10.PDF.

Anselin, L. 2005. Exploring spatial data with 

GeoDatm: a workbook. Urbana-Champaign, IL: 

University of Illinois, Urbana-Champaign, Center 

for Spatially Integrated Social Science, Spatial 

Analysis Laboratory. 244 p. Available at http://

geodacenter.asu.edu/system/files/geodaworkbook.

pdf. 

Bailey, R.G. 1998. Ecoregions: The ecosystem 

geography of the oceans and continents.

New York, NY: Springer-Verlag. 176 p.

Ehrlich, P.R.; Wilson, E.O. 1991. Biodiversity 

studies: science and policy. Science. 253(5021): 

758-762.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 156

Gaston, K.J. 1996. Biodiversity: a biology of 

numbers and difference. Oxford, UK: Blackwell. 

396 p.

Secretariat of the Convention on Biological Diversity 

(SCBD). 2006. Global biodiversity outlook 2.

[Summary of the second Global Biodiversity 

conference]. Montreal, Canada: Secretariat of the 

Convention on Biological Diversity. 81 p. Available 

at www.biodiv.org/GBO2.

Shannon, C.E.; Weaver, W. 1971. A mathematical 

theory of communication. Champaign, IL: 

University of Illinois Press. 144 p.

U.S. Congress Office of Technology Assessment 

(OTA). 1987. Technologies to maintain biological 

diversity. Washington, DC: U.S. Congress, Office 

of Technology Assessment.

Wilson, E.O. 1993. The diversity of life. New York, 

NY: W.W. Norton & Co. 448 p.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 157

INCORPORATING EVOLUTIONARY RELATIONSHIPS 

INTO REGIONAL ASSESSMENTS OF FOREST BIODIVERSITY

ACROSS FOREST INVENTORY AND ANALYSIS PLOTS

Kevin M. Potter and Christopher W. Woodall1

Abstract.—Evolutionary diversity metrics may be more biologically meaningful 

indicators of forest biodiversity than traditional measures such as species richness, 

which treat all species as equally important. This is because measures that account 

for evolutionary relationships among species should be better surrogates of functional 

diversity within forest communities, given that taxonomically distinct species should 

contribute more to the diversity present within a community. One measure, phylogenetic 

diversity, has been linked to a variety of plant ecosystem processes, goods and services, 

supporting the argument that it is a more useful conservation criterion for management 

decisions. To investigate patterns of forest functional biodiversity across the United 

States, we calculated plot-level evolutionary diversity measures on approximately 

125,000 Forest Inventory and Analysis plots. Most measures were not well-correlated 

with species richness, while others were decoupled with it at small and medium scales. 

Phylogenetic diversity was consistently better correlated than species richness with 

most plot-level measures of forest productivity, including trees per acre and relative 

density, although the results varied by region. Using data remeasured over time on a 

subset of the forest inventory plots, we detected broad-scale patterns of phylogenetic 

diversity change that were consistent with the expected early effects of climate change. 

Specifically, phylogenetic diversity change was greater among seedlings than trees, was 

associated in some locations with latitude and elevation, and was greater among species 

with high dispersal capacity. These findings demonstrate that demographic indicators of 

evolutionary diversity can refine our understanding of climate change impacts on forest 

community biodiversity and function across broad regions. The importance, statistical 

power, and geographic extent of such indicators will increase as repeated measurements 

occur on all 125,000 inventory plots across the United States.

1

 Research Assistant Professor (KMP), North Carolina State University, Research Triangle 

Park, NC 27709; Research Forester (CWW), U.S. Forest Service, Northern Research Station. 

KMP is corresponding author: to contact, call 919-549-4071 or email at kpotter@ncsu.edu. 

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 158

RECENT TRENDS IN MAPLE AND OAK-HICKORY DISTRIBUTION 

FOR THE UNITED STATES

Michael Farrell1

Abstract.—Many researchers predict that climate change will cause maple-dominated 

forests to move northward and be replaced with oaks and hickories throughout much of 

their current range. Whereas there is strong evidence that the climate has been changing 

and will continue to do so, the current trends in these species abundance and distribution 

over the past 30 years are in direct contrast with many stated predictions. To date, human 

management has played a much larger role than climate in shaping the distribution of 

these tree species throughout the northeast. This presentation utilizes Forest Inventory 

and Analysis (FIA) data for 25 states in the eastern United States to examine the diameter 

distributions for sugar and red maples, oaks, and hickories since the 1980s. Oaks and 

hickories have had trouble regenerating throughout their established range whereas 

shade-tolerant sugar and red maples have been rapidly invading oak-hickory forest types. 

In fact, both sugar and red maples are becoming established much more rapidly along 

the southern and central states than they are in the northeast. Red maple is becoming 

even more dominant than sugar maple in almost every state, especially those along 

the southern and western ranges of sugar maple. This presentation explores the FIA 

data for the past 30 years on a state-level to determine what the future composition of 

maples, oaks, and hickories could be in the eastern United States if these trends continue. 

Implications for the maple syrup industry are explored, including where and what kind of 

trees will be tapped in the future. 

1

 Extension Associate, Cornell University, 157 Bear Cub Lane, Lake Placid, NY 12946. 

To contact, call 518-523-9337 or email at mlf36@cornell.edu. 

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 159

ANALYSIS OF URBAN AND NATURAL FOREST COMPOSITION 

ACROSS THE UNITED STATES

Mark J. Ambrose1

Abstract.—It has been suggested that urban forestry tree planting practices are creating 

a homogenous urban forest, with the same limited number of species dominating 

urban landscapes across North America. There is also concern about urban forests as 

pathways for invasive pests and pathogens to adjacent natural forests. However, to date 

urban forest inventory data have been limited and data collected from different cities 

were often incompatible. To begin to address the issue of urban forest composition, 

tree species data were obtained 150 U.S. and Canadian cities which had conducted 

inventories for use with the i-Tree Eco (UFORE) or i-Tree Streets (STRATUM) models 

or which maintained comprehensive street tree databases. Relative basal area of each 

tree species was calculated by city and land use class and (where possible) for each city 

as a whole. Those data were combined with data from nearby natural forests from the 

U.S. Forest Service Forest Inventory and Analysis (FIA) Program. The data were then 

analyzed using PC-ORD to determine which cities’ urban forests were most similar to 

one another (using cluster analysis) and how species composition related to large-scale 

environmental variables (using non-metric multi-dimensional scaling). Environmental 

and geographic explanatory variables used included latitude, longitude, elevation, total 

annual precipitation, and plant hardiness zone.

Preliminary analysis of urban forest data alone showed that urban forests as a whole 

clustered by species composition data along rough geographic and climatic lines. More 

intensively managed portions of the urban forest (e.g., street trees) tended to cluster in 

ways less closely related to geography and climate. Analyses of the urban data together 

with natural forest data indicate that urban forests are generally more similar to one 

another than they are to nearby natural forests. Street tree populations were most similar 

in their species compositions while other components of the urban forest showed greater 

variation. The more intensively managed segments of the urban forest were also less 

similar to adjacent natural forests. Urban forests also tend to resemble the natural forests 

of the eastern United States more than they resemble western forests.

1

 Research Assistant, North Carolina State University, Research Triangle Park, NC. To contact, 

call 919-549-4078 or email mambrose@fs.fed.us. 

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Forest Carbon: 

Accounting 

and Science
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 161

TECHNICAL ASPECTS OF THE FOREST CARBON INVENTORY

OF THE UNITED STATES: RECENT PAST AND NEAR FUTURE

Christopher W. Woodall, James E. Smith, Grant M. Domke, Sean P. Healey, 

John W. Coulston, and Andrew N. Gray1

Abstract.—The Forest Inventory and Analysis program of the U.S. Forest Service has 

explicitly assumed responsibility for providing an inventory of the U.S. forests’ carbon 

stocks and stock change to the U.S. Environmental Protection Agency for numerous years 

to meet obligations to the United Nations Framework Convention on Climate Change. 

Recent improvements, plans for the future, and implications regarding use of the U.S. 

inventory both nationally and at the project scale are discussed.

1

 Research Forester (CWW), U.S. Forest Service, Northern 

Research Station, 1992 Folwell Ave., St. Paul, MN; 

Research Plant Physiologist (JES) and Research Forester 

(GMD), U.S. Forest Service, Northern Research Station; 

Research Forester (SPH), U.S. Forest Service, Rocky 

Mountain Research Station; Supervisory Research Forester 

(JWC), U.S. Forest Service, Southern Research Station; 

Research Ecologist (ANG), U.S. Forest Service, Pacific 

Northwest Research Station. CWW is corresponding author: 

to contact, call 651-649-5141 or email at cwoodall@fs.fed.us.

It appears unlikely that the 112th Congress of 

the United States will approve incentives for the 

commoditization of biogenic carbon (C) (i.e., “cap 

and trade”), so where should the science of forest C 

accounting head in the near future? Initial versions of 

the 2012 Farm Bill coupled with the National Forest 

System Climate Change Performance Scorecard 

highlight areas of current and future research in C 

accounting. The Forest Inventory and Analysis (FIA) 

Program of the U.S. Forest Service has adopted 

several areas of priority research in the area of carbon: 

reduction of the uncertainty associated with FIA’s 

C estimates; downscaling of national C estimates 

to the national forest or mid-level; and refinement 

of biomass estimation procedures. This represents a 

substantive course of action to improve the science of 

C accounting and to meet user demands. 

Over the past year (2011-2012), the major changes 

to the accounting of U.S. forest C involved adoption 

of the Component Ratio Method (CRM) approach 

(Domke et al. 2012) to biomass/C estimation and 

incorporating standing dead tree measurements 

in the national inventory (i.e., annual Phase 2) 

(Woodall 2012). Incorporating standing dead tree 

measurements greatly improved the sensitivity of the 

national inventory to stand-level dynamics (Woodall 

et al. 2012a). FIA is encouraged that incorporating a 

national downed dead wood inventory (Woodall et al. 

2008) in the 2012 National Greenhouse Gas Inventory 

(NGHGI) may impart the same positive effect. In 2013 

and beyond, it is hoped that many aspects of the FIA 

national inventory of forest health indicators (Woodall 

et al. 2011) may refine C estimation. Sometimes the 

casting of a forest health indicator in a different light 

can greatly inform the estimation of a C stock such as 

found with components of the forest floor (Woodall 

et al. 2012c). This process of “stepping through” each 

forest C pool with the objective to increase estimate 

precision and linking the estimates to recently derived 

empirical information (i.e., Phase 3 inventory) is a 

tremendous benefit to C and biomass accounting.

Refined C stock estimates at the national scale may 

benefit the NGHGI, but can this be downscaled 

to our national forests and other comparable 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 162

scales? Estimates of National Forest System C 

stocks (Heath et al. 2011) are now a requirement 

of climate change scorecard reporting for national 

forests in the United States. Because an intention 

of the scorecard is to understand and consider the 

effects of management activities on carbon budgets 

at the scale of a national forest, the uncertainty 

(sometimes > 5 percent sampling error) associated 

with a “downscaled” NGHGI may not appropriately 

inform such management activities. Ongoing research 

into “downscaling” NGHGI to inform mid-level 

activities (Wilson et al. in review, Coulston et al. 

this proceedings) may improve the statistical power 

(Westfall et al., in review) to detect management/

disturbance effects on C budgets. Whereas the 

techniques used to develop the NGHGI satisfy 

requirements under United Nations Framework 

Convention on Climate Change (U.S. EPA 2012), 

new areas of research are needed to downscale 

NGHGI to empower land managers interested in 

mitigating climate change. 

As renewable biomass is an emerging topic in regards 

to energy and economic development (Woodall 

et al. 2012b), the draft 2012 Farm Bill language 

requests more information on the supply of renewable 

forest biomass in the United States. In addition to 

C accounting, it should be recognized that forest 

C accountants are also biomass experts. The FIA 

program has already initiated a national effort to 

improve the modeling of individual tree biomass/C 

attributes (Coulston et al. this proceedings). This 

same drive should be reflected in non-live tree pools. 

Refining the estimations of standing dead tree, downed 

dead, and understory components should benefit 

more comprehensive biomass assessments. Domke 

et al. (2011) found that biomass estimates could 

be improved by refining the decay reductions and 

structural deductions for standing dead trees. Such 

a research approach across the many diverse pools 

associated with C accounting will benefit renewable 

biomass estimation.

Finally, all of these technical improvements do not 

occur in isolation within the FIA program. State and 

university partners in concert with an array of federal 

partners (e.g., USGS and NASA) are critical to the 

development and application of C/biomass accounting. 

Only through partnership can FIA’s refined approaches 

to C/biomass monitoring be leveraged to meet the 

expectations of our diverse user community.

LITERATURE CITED

Domke, G.M.; Woodall, C.W.; Smith, J.E. 2011. 

Accounting for density reduction and structural 

loss in standing dead trees: implications for 

forest biomass and carbon stock estimates in the 

United States. Carbon Balance and Management. 

6: 14. doi:10.1186/1750-0680-6-14

Domke, G.M.; Woodall, C.W.; Smith, J.E.; Westfall, 

J.A.; McRoberts, R.E. 2012. Consequences 

of alternative tree-level biomass estimation 

procedures on U.S. forest carbon stock 

estimates. Forest Ecology and Management. 

270: 108-116.

Heath, L.S.; Smith, J.E.; Woodall, C.W.; Azuma, D.L.; 

Waddell, K.L. 2011. Carbon stocks on forestland 

of the United States, with emphasis on USDA 

Forest Service ownership. Ecosphere. 2(1): 

article 6. 21 p. 

U.S. Environmental Protection Agency [U.S. EPA]. 

2012a. Land use, land-use change, and forestry.

Chapter 7. In: Inventory of U.S. greenhouse gas 

emissions and sinks: 1990-2010. #430-R-12-001. 

Washington, DC: U.S. Environmental Protection 

Agency. 

U.S. Environmental Protection Agency [U.S. 

EPA]. 2012b. Methodology for estimating net 

carbon stock changes in forest land remaining 

forest lands. Annex 3.12. In: Inventory of U.S. 

greenhouse gas emissions and sinks: 1990-2010.

 #430-R-12-001. Washington, DC: U.S. 

Environmental Protection Agency. 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 163

Westfall, J.A.; Woodall, C.W.; Hatfield, M.A. In 

review. If forest carbon flux made a sound would 

we be able to hear it? A statistical power analysis 

of woody carbon stocks in forests of the Great 

Lakes, U.S.A.

Wilson, B.T.; Woodall, C.W.; Griffith, D. In review. 

From plots to the Nation: imputing forest 

carbon stock estimates from inventory plots to a 

nationally continuous coverage.

Woodall, C.W. 2012. Where did the U.S. forest 

biomass/carbon go? Journal of Forestry. 

110: 113-114.

Woodall, C.W.; Amacher, M.C.; Bechtold, W.A.; 

Coulston, J.W.; Jovan, S.; Perry, C.H.; Randolph, 

K.C.; Schulz, B.K.; Smith, G.C.; Tkacz, B.; WillWolf, S. 2011. Status and future of the forest 

health indicators program of the United States. 

Environmental Monitoring and Assessment. 177: 

419-436.

Woodall, C.W.; Domke, G.M.; MacFarlane, D.W.; 

Oswalt, C.M. 2012a. Comparing field- and 

model-based standing dead tree carbon stock 

estimates across forests of the United States.

Forestry. 85: 125-133. 

Woodall, C.W.; Heath, L.S.; Smith, J.E. 2008. 

National inventories of dead and downed forest 

carbon stocks in the United States: opportunities 

and challenges. Forest Ecology 

and Management. 256: 221-228.

Woodall, C.W.; Ince, P.J.; Skog, K.E.; Aguilar, F.X.; 

Keegan, C.E.; Sorenson, C.B.; Hodges, D.G.; 

Smith, W.B. 2012b. An overview of the forest 

products sector downturn in the US. Forest 

Products Journal. 61: 595-603.

Woodall, C.W.; Perry, C.H.; Westfall, J.A. 2012c. An 

empirical assessment of forest floor carbon stock 

components across the United States. Forest 

Ecology and Management. 269: 1-9.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 164

RECENT CHANGES IN THE ESTIMATION OF STANDING DEAD TREE

BIOMASS AND CARBON STOCKS IN THE U.S. FOREST INVENTORY

Grant M. Domke, Christopher W. Woodall, and James E. Smith1

Abstract.—Until recently, standing dead tree biomass and carbon (C) has been estimated 

as a function of live tree growing stock volume in the U.S. Forest Service, Forest 

Inventory and Analysis (FIA) Program. Traditional estimates of standing dead tree 

biomass/C attributes were based on merchantability standards that did not reflect density 

reductions or structural loss due to decomposition common in standing dead trees. In 

1999, the FIA program began consistent nationwide sampling of standing dead trees. 

That data may now be used to supplant previous approaches to standing dead biomass 

and C stock estimation. The objective of this study was to incorporate density reductions 

and structural loss adjustments into standing dead tree biomass/C estimation procedures 

and assess differences in estimates at multiple spatial scales. The results suggest that 

accounting for density reductions and structural loss in standing dead trees substantially 

decreases estimates of standing dead tree biomass and C at tree, plot, and regional 

scales. Incorporating density reductions and structural loss adjustments may improve the 

accuracy of standing dead tree biomass and C estimates in the U.S. forest inventory as 

well as the consistency with FIA field methods and documentation. 

1

 Research Foresters (GMD and CWW), U.S. Forest 

Service, Northern Research Station, 1992 Folwell Ave. 

St. Paul, MN 55108; Plant Physiology/Modeling (JES), 

U.S. Forest Service, Northern Research Station. GMD is 

corresponding author: to contact, call 651-649-5138 or 

email at gmdomke@fs.fed.us.

Introduction

Standing dead tree carbon (C) is part of the dead 

wood C pool recognized in the Land Use, Land Use 

Change, and Forestry (LULUCF) section of the U.S. 

National Greenhouse Gas Inventory produced annually 

by the U.S. Environmental Protection Agency. Forest 

ecosystem C estimates in the LULUCF are based on 

the national forest inventory conducted by the U.S. 

Forest Service, Forest Inventory and Analysis (FIA) 

Program. Until recently, standing live and dead tree 

(SDT) biomass and C estimates were calculated by 

FIA using the same procedures. It has been recognized 

that the density of dead wood generally decreases with 

each stage of biomass decay (Krankina and Harmon 

1995, Sandstrom et al. 2005) and that structural losses 

(e.g., sloughing and breakage) occur throughout the 

decomposition process (Aakala et al. 2008, Cline 

et al. 1980, Raphael and Morrison 1987). This 

paper briefly describes the process of incorporating 

density reduction factors (DRFs) and structural loss 

adjustments (SLAs) into standing dead tree biomass 

and C estimates (for a complete description, see 

Domke et al. 2011). The study objectives were: 1) 

examine the distribution of SDTs across decay classes 

in the FIA database; 2) test the differences between 

unadjusted and adjusted standing dead tree biomass 

and C estimates (i.e., incorporation of DRFs and 

SLAs); and 3) suggest refinements of proposed SDT 

biomass and C estimation procedures and future 

research directions.

METHODS

DRFs for SDTs were developed using standing and 

down dead wood samples categorized by decay 

class and divided into subsections where wood disks 

were cut from each end and volume and weight 

measurements (wet and dry) were taken to determine 

the density of wood and bark (Harmon et al. 2011). 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 165

DRFs were calculated as the ratio of the average 

current decayed density (current mass/volume) of the 

piece of dead wood to average undecayed (live tree 

mass/volume) density for each species and decay class 

(Table 1). 

Preliminary SLAs for tops and branches and 

belowground biomass were estimated using qualitative 

descriptions from the FIA field guide (USDA Forest 

Service 2010) and other studies documenting structural 

loss by decay class and tree component (Cline et 

al. 1980, Krankina and Harmon 1995, Vanderwel et 

al. 2006). Preliminary SLAs for bark biomass were 

estimated from data collected as part of Harmon et 

al.’s (2011) study. Merchantable stem deductions 

due to rough, rotten, or missing cull were accounted 

for in the conversion from gross to sound volume 

(Woudenberg et al. 2010) so no additional SLAs—

with the exception of bark biomass—were estimated 

for bole or stump components (Table 1). 

Regional Case Study

The most abundant SDT species in the Lake States 

(Michigan, Minnesota, and Wisconsin) and Pacific 

Northwest (Oregon and Washington) were selected 

to compare differences in unadjusted and adjusted 

biomass and C stock estimates. While the two species 

selected (quaking aspen [Populus tremuloides Michx.] 

and Douglas-fir [Psuedotsuga menziesii (Mirb.) 

Franco], respectively) may not be representative of 

all species in their respective regions, they are both 

extremely common in the FIA database and provide a 

sound starting point for consideration. 

Field data for each region and species were taken 

from the FIA database. All standing dead (SD) aspen 

and Douglas-fir trees with a diameter at breast height 

≥ 12.7 cm were included in the analysis. A total of 

9,369 SD aspen trees were sampled on 3,975 plots 

in the Lake States from 2005-2009, and 10,144 SD 

Douglas-fir trees were sampled on 2,825 plots in the 

Pacific Northwest from 2001-2009. 

Mean differences in tree-level biomass estimates for 

the component ratio method (CRM), CRM+DRFs, 

and CRM+DRFs+SLAs were compared by diameter 

class for each tree species. Mean differences of tree 

level biomass estimates for each approach were not 

tested for statistical significance for two reasons. 

First, information was not available to estimate the 

uncertainty of the tree level biomass predictions. 

Second, differences between estimates for individual 

trees on the same plot were expected to be highly 

correlated. Techniques for accommodating these 

correlations, particularly with different numbers of 

trees per plot, are beyond the scope of this study.

Estimates of plot-level SDT biomass were calculated 

using the three approaches and compared for the two 

species and regions. The uncertainty of mean plotlevel estimates can be attributed to two sources, the 

uncertainty of individual tree-level estimates and 

plot-to-plot sampling variability. The uncertainty 

of the tree-level estimates is generally regarded by 

national forest inventory programs as negligible 

relative to the sampling variability and, therefore, 

was ignored for these analyses. Mean differences in 

Density Reduction Factors Structural Loss Adjustment Factors

Decay Class Quaking aspen Douglas-fir Top Bark Bole Stump Roots

1 0.970 0.892 1.00 0.92 1.00 1.00 1.00

2 0.750 0.831 0.50 0.66 1.00 1.00 0.95

3 0.540 0.591 0.20 0.39 1.00 1.00 0.80

4 0.613 0.433 0.10 0.21 1.00 1.00 0.65

5 0.613 0.433 0.00 0.00 1.00 1.00 0.50

Table 1.—Density reduction factors by species and preliminary SLA for each decay class by tree 

component for all tree species in the FIA database; table adapted from Domke et al. (2011)
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 166

plot-level estimates were calculated in three steps. 

First, the estimate for each tree was calculated 

using each approach. Second, plot level differences 

were calculated as the difference between the CRM 

estimate of the plot total and the CRM+DRFs and 

CRM+DRFs+SLAs estimates of the plot total. Mean 

plot-level differences were calculated as the mean 

over all plots of the previously calculated plotlevel differences. A t-test was used to determine the 

statistical significance in the mean differences; in 

effect, the test was a paired t-test because it focused 

on mean differences rather than differences of means. 

The null hypotheses were that the mean differences 

between estimates of C stocks were not significantly 

different from zero (α = 0.01). 

RESULTS

The distributions of SD aspen and Douglas-fir trees 

tended toward a normal distribution centered around 

the third decay class (see Domke et al. 2011). Nearly 

29 percent of SD aspen were missing branches and an 

additional 16 percent lacked top and branch biomass. 

For Douglas-fir stems in the Pacific Northwest, only 5 

percent of sample trees had missing tops and branches 

and more than 73 percent of stems had at least some 

top, branch, and bark biomass present.

The difference in individual tree biomass estimates 

was compared by diameter class in Figure 1. Bole and 

stump biomass estimates were quite similar for the 

CRM+DRFs and CRM+DRFs+SLAs for both study 

species across diameter classes (differences were 

due to SLAs for bark biomass in each component), 

but substantially less than the CRM estimates. The 

CRM+DRFs+SLAs produced an almost uniform trend 

for top and branch biomass across diameter classes, 

while belowground biomass trends increased more or 

less consistently with the other two methods. 

Mean plot-level differences in tree component biomass 

for the three estimation procedures were statistically 

significantly different for both species across the two 

regions. The CRM+DRFs and CRM+DRFs+SLAs 

decreased plot-level SD bole biomass estimates for 

aspen by 35 percent (65.8 kg) and 42 percent (78.1 

kg), respectively across the Lake States (Table 2). 

In the Pacific Northwest, the CRM+DRFs reduced 

plot-level SD Douglas-fir bole biomass by 32 percent 

(595.0 kg) and the CRM+DRFs+SLAs reduced bole 

biomass by 36 percent (672.7 kg) (Table 2). For results 

on individual trees and regional population estimates, 

see Domke et al. (2011).

DISCUSSION

Forest inventories in the United States have 

transitioned from a timber focus toward a more 

holistic sampling of forest ecosystem attributes such 

as C stocks of standing dead trees. Likewise, the 

estimation procedures associated with the changing 

inventory need to be inclusive of tree attributes 

beyond those required by the forest products industry. 

Developing SDT biomass and C stock estimates within 

the construct of an inventory system traditionally 

designed to estimate growing stock volume requires: 

1) the development of a SDT decay class system 

which is both qualitative for ease of use in the field 

and quantitative to account for structural loss by tree 

component and species; 2) the development of DRFs 

for SDT species in each decay class, with specific 

emphasis on advanced decay classes; and 3) the 

development of a flexible SDT estimation procedure 

which incorporates initial SLAs and DRFs and allows 

for continual refinement.

SDTs are an important part of the dead wood forest 

ecosystem C pool recognized by the international 

community. In an effort to improve the accuracy of 

biomass and C stock estimates that are used in various 

facets of the national forest inventory, preliminary 

DRFs and SLAs have been developed for SDTs. 

These adjustments reflect the current state of the 

science on SDT biomass/C estimation and result in 

significantly lower plot-level biomass estimates, and 

thus, substantial differences in regional SDT biomass 

and C stock estimates. The results from this study 

suggest that incorporation of the SDT adjustments will 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 167

Diameter c CRM CRM + DRF CRM +DRF +SLA Diameter c CRM CRM + DRF CRM +DRF +SLA Diameter c CRM CRM + DRF CRM +DRF +SLA

13 9.600610784 7.568823409 4.78878 13 39.29831 37.67888 13 3.343056 2.633066 2.524537

38 38.15783559 29.33056066 17.90667 38 174.0484 166.3818 38 10.98881 8.444377 8.073066

64 116.557756 88.60733742 52.28909 64 557.2621 531.6483 64 26.95705 20.47612 19.53648

0

20

40

60

80

100

120

15 20 25 30 36 41 46 50.8+

Biomass (kg) 

Dbh class (cm)

CRM

CRM + DRF

CRM +DRF +SLA

0

100

200

300

400

500

600

15 20 25 30 36 41 46 50.8+

Biomass (kg) 

Dbh class (cm)

CRM

CRM + DRF

CRM +DRF +SLA

0

4

8

12

16

20

15 20 25 30 36 41 46 50.8+

Biomass (kg) 

Dbh class (cm)

CRM

CRM + DRF

CRM +DRF +SLA

0

20

40

60

80

100

120

140

15 20 25 30 36 41 46 50.8+

Biomass (kg) 

Dbh class (cm)

CRM

CRM + DRF

CRM +DRF +SLA

0

100

200

300

400

500

600

13 38 64 89 114 140 165 190.5+

Biomass (kg) 

Dbh class (cm)

CRM

CRM + DRF

CRM +DRF +SLA

0

500

1000

1500

2000

2500

3000

3500

13 38 64 89 114 140 165 190.5+

Biomass (kg) 

Dbh class (cm)

CRM

CRM + DRF

CRM +DRF +SLA

0

20

40

60

80

100

13 38 64 89 114 140 165 190.5+

Biomass (kg) 

Dbh class (cm)

CRM

CRM + DRF

CRM +DRF +SLA

0

150

300

450

600

750

900

13 38 64 89 114 140 165 190.5+

Biomass (kg) 

Dbh class (cm)

CRM

CRM + DRF

CRM +DRF +SLA

(a)

(b)

(c)

(d)

Quaking aspen Douglas-fir

Figure 1.—Mean (with standard errors) SD biomass (oven-dry kg) by tree species (quaking aspen on left, Douglas-fir on right; 

note the y-axis scale differs by species), estimation method, and d.b.h. class for: a) bole, b) top and branches, c) stump, and d) 

belowground components; adapted from Domke et al. (2011).
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 168

substantially reduce estimates of dead wood biomass 

and C stocks across spatial-scales and tree species of 

the United States. While the preliminary estimates 

offer a sound starting point for SDT biomass and C 

estimation, more work is necessary to refine SLAs 

(perhaps by species and region) for each decay class 

used in national inventory field sampling.

ACKNOWLEDGMENTS

The authors would like to thank Greg Liknes and 

Charles Nock for reviewing the condensed version 

of this manuscript and Paul Van Deusen, Mark 

Vanderwel, and Ronald McRoberts for comments and 

suggestions on the original manuscript.

LITERATURE CITED

Aakala, T.; Kuuluvainen, T.; Gauthier, S.; De 

Grandpre, L. 2008. Standing dead trees and their 

decay-class dynamics in the northeastern boreal 

old-growth forest of Quebec. Forest Ecology and 

Management. 255: 410-420.

Cline, S.P.; Berg, A.B.; Wright, H.M. 1980. Snag 

characteristics and dynamics in Douglas-fir 

forests, western Oregon. Journal of Wildlife 

Management. 44(4): 773-786.

Quaking aspen Douglas-fir

Component Comparison	 t	 df	 p	 d	 t	 df	 p	 d

Top and branches 1 56.4 3966 <0.001 19.6 21.4 2823 <0.001 94.1

2 56.8 3966 <0.001 45.1 21.2 2823 <0.001 203.1

Bole 1 48.1 3966 <0.001 65.8 21.2 2823 <0.001 595.0

2 48.2 3966 <0.001 78.1 21.2 2823 <0.001 672.7

Stump 1 55.0 3966 <0.001 3.4 23.4 2823 <0.001 19.0

2 55.2 3966 <0.001 4.0 23.5 2823 <0.001 21.5

Belowground 1 50.6 3966 <0.001 17.2 21.3 2823 <0.001 158.4

2 49.9 3966 <0.001 26.6 21.2 2823 <0.001 216.5

Table 2.—Mean plot-level difference (d) in standing dead biomass (oven-dry kg) by tree component and 

estimation method (1 = CRM vs. CRM+DRF and 2 = CRM vs. CRM+DRF+SLA) for quaking aspen in the 

Lake States (2005-2009) and Douglas-fir in the Pacific Northwest (2001-2009); table adapted from Domke 

et al. (2011)

Domke, G.M.; Woodall, C.W.; Smith, J.E. 2011. 

Accounting for density reduction and structural 

loss in standing dead trees: Implications for 

forest biomass and carbon stock estimates in the 

United States. Carbon Balance and Management. 

6: 14. doi:10.1186/1750-0680-6-14

Harmon, M.E.; Woodall, C.W.; Fasth, B.; Sexton, J.; 

Yatkov, M. 2011. Difference between standing 

and downed dead tree wood density reduction 

factors: a comparison across decay classes and 

tree species. Res. Pap. NRS-15. Newtown Square, 

PA: U.S. Department of Agriculture, Forest Service, 

Northern Research Station. 40 p.

Krankina, O.N.; Harmon, M.E. 1995. Dynamics of the 

dead wood carbon pool in Northwestern Russian 

boreal forests. Water, Air, and Soil Pollution. 82: 

227-238.

Raphael, M.G.; Morrison, M.L. 1987. Decay and 

dynamics of snags in the Sierra Nevada, 

California. Forest Science. 33(3): 774-783.

Sandstrom, F; Peterson, H.; Kruys, N.; Stahl, G. 2005. 

Biomass conversion factors (density and carbon 

concentration) by decay classes for dead wood 

of Pinus sylvestris, Picea abies and Betula spp. 

in boreal forest of Sweden. Forest Ecology and 

Management. 243: 19-27.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 169

USDA Forest Service 2010. Forest Inventory 

and Analysis national core field guide 

Volume 1: field data collection procedures 

for phase 2 plots, version 5.1. http://fia.fs.fed.

us/library/field-guides-methods-proc/docs/

Complete%20FG%20Document/core_ver_5-1_10_

2011.pdf. (Accessed July 10, 2012).

Vanderwel, M.C.; Malcolm, J.R.; Smith S.M. 2006. 

An integrated model for snag and downed 

woody debris decay class transitions. Forest 

Ecology and Management. 234: 48-59.

Woudenberg, S.W.; Conkling, B.L. ; O’Connell, B.M.; 

LaPoint, E.B.; Turner, J.A.; Waddell, K.L. 2010. 

The Forest Inventory and Analysis database: 

Database description and users manual version 

4.0 for Phase 2. Gen. Tech. Rep. RMRS-GTR-245. 

Fort Collins, CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research Station. 

336 p.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 170

THE CLIMATE CHANGE PERFORMANCE SCORECARD 

AND CARBON ESTIMATES FOR NATIONAL FORESTS

John W. Coulston, Kellen Nelson, Christopher W. Woodall, David Meriwether, and Gregory A. Reams1

Abstract.—The U.S. Forest Service manages 20 percent of the forest land in the United 

States. Both the Climate Change Performance Scorecard and the revised National Forest 

Management Act require the assessment of carbon stocks on these lands. We present circa 

2010 estimates of carbon stocks for each national forest and recommendations to improve 

these estimates. 

1

 Supervisory Research Forester (JWC), U.S. Forest 

Service, Southern Research Station, 4700 Old Kingston 

Pike, Knoxville, TN 37919; Research Assistant (KN), 

University of Wyoming; Research Forester (CWW), U.S. 

Forest Service, Northern Research Station; Ecosystems 

Management Coordinator (DM), U.S. Forest Service, 

Southern Region; and National Program Leader (GAR), 

U.S. Forest Service, Washington Office. JWC is 

corresponding author: to contact, call 865-862-2008 or 

email at jcoulston@fs.fed.us. 

Introduction

The climate change performance scorecard is the 

mechanism through which the U.S. Forest Service 

seeks to increase its capacity to respond to climate 

change (U.S. Forest Service 2011). Under this 

paradigm, there are four dimensions (organizational 

capacity, engagement, adaptation, and mitigation) 

that span a range of important components relevant 

to climate change (Fig. 1). Here we focus on the 

mitigation and sustainable consumption dimension 

with particular focus on carbon (C) assessment 

and stewardship in support of the climate change 

performance scorecard.

The revised regulations (Code of Federal Regulations: 

36 CFR 219, 2012) for implementing the National 

Forest Management Act also include requirements 

for assessments of C stocks for each new national 

forest plan or plan revision. Long-term C storage 

is described as a “regulating” ecosystem service of 

forests, rangelands, and grasslands. Plans must include 

components that provide for social and economic 

sustainability, taking into account the ecosystem 

services and reasonably foreseeable risks. Responsible 

officials should use the assessment of C stocks to 

understand (1) how the plan area plays a role in 

sequestering and storing C and (2) how disturbances, 

management, and resource uses influenced C stocks in 

the past and may affect them in the future.

Quantifying and tracking changes in C stocks are key 

objectives for the U.S. Forest Service on National 

Forest System (NFS) lands, which account for 20 

percent of U.S. forest land. The Forest Inventory 

and Analysis (FIA) Program provides a consistent 

monitoring and assessment framework across all lands 

and ownerships, which can be used to consistently 

estimate C stocks across landscapes. Through regional 

or local partnerships between FIA and NFS, the base 

FIA sample can be intensified to increase the precision 

of estimates at the NFS administrative forest level. 

Several studies have provided C estimates for national 

forests at the region, state, and individual forest spatial 

scales (e.g., Heath et al. 2011). In some cases, these 

approaches have relied on estimating the areal extent 

of each national forest (the population of interest) and 

therefore can be improved by (1) explicitly defining 

the areal extent of the populations of interest, and (2) 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 171

Figure 1.—The four dimensions and ten elements of the U.S. Forest Service climate change performance scorecard (source 

U.S. Forest Service 2011).

including inventory plot intensifications on NFS lands. 

Our goal here is to incorporate these improvements 

and identify additional areas for improvement 

in support of C estimates for the climate change 

performance scorecard, for forest planning, and for 

monitoring

METHODS

To construct C estimates for each national forest, we 

used field-based observations from FIA and intensified 

national forest data collected using FIA protocols 

(Southern and Eastern NFS regions). Most national 

forest intensifications were either 2x or 3x the intensity 

of the standard FIA sample. The area of each national 

forest was obtained from the Automated Lands Project 

(ALP) geospatial database available through the 

corporate U.S. Forest Service Citrix environment. The 

2001 NLCD percent tree canopy cover data (Homer 

et al. 2007) were obtained from the Multi-Resolution 

Land Characteristics consortium.

The standard FIA post-stratified estimator was used 

to estimate population totals and the standard error 

of the estimates (Bechtold and Patterson 2005). The 

population total was where At was 

the population area as defined by the ALP database, 

Wh was the weight of each h stratum, and Ӯh was the 

within strata average from the plot level observations. 

The strata weights were developed from the NLCD 

percent tree canopy cover data using an automated 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 172

stratification routine. Wh = Ah/At where Ah is the area of 

the population in stratum h. The standard error of the 

estimate was then S.E(Ŷ) =

where n was the total number of plots in the 

population, nh was the number of plots in each stratum, 

and v(Ӯh) was the variance of the plot level values 

within each h stratum. Estimates of the following 

C pools were made for each administrative forest: 

down dead wood, litter, overstory trees above ground, 

overstory trees below ground, standing dead wood, 

soil organic carbon, understory story trees above 

ground, and understory trees below ground. Plot level 

estimates of forest C stocks were a combination of 

empirically measured tree/site attributes combined 

with a series of individual tree/site models (see USEPA 

2012 for methods). To estimate stocks at aggregate 

levels (NFS administrative region and for all NFS 

lands), basic error propagation techniques were used.

RESULTS

Across all pools there are approximately 10.88 billion 

tons (0.4 percent sampling error) of C stored on NFS 

lands. The majority of the C is stored in national 

forests in the western United States (Fig. 2). This 

finding is primarily due to the larger areal extents 

of these forests. For example, the Tongass National 

Forest in southern Alaska stores 1.17 billion tons 

Figure 2.—Estimates of total C stocks (i.e., all pools) in thousands of tons for each administrative forest.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 173

(1.9 percent sampling error) of C and occupies about 

16.8 million acres. On a per acre basis, NFS lands 

on average have a C density of 57 tons per acre; the 

lowest density occurs in southern California and the 

highest density occurs in the Pacific Northwest region 

(Fig 3). 

The Pacific Northwest region has an average total 

C density of approximately 103 tons per acre (1.06 

percent sampling error) (Table 1). These substantial 

stocks are primarily driven by highly productive 

forests (e.g., large overstory trees) combined with 

considerable input to detrital forest components with 

concomitantly slow decay rates (e.g., C in soil organic 

carbon and dead wood). The Eastern region has the 

Figure 3.—Estimates of total C density (i.e., all pools) in tons C per acre for each administrative forest.

second highest C densities (87 tons per acre, 0.56 

percent sampling error) primary driven by storage in 

organic soils (50 tons per acre, 0.68 percent sampling 

error).

DISCUSSION

The intent of the climate change performance 

scorecard is to increase the capacity of the U.S. Forest 

Service to respond and adapt to climate change (U.S. 

Forest Service 2011). The revised planning regulations 

include requirements to assess and sustain C in forest, 

rangeland, and grassland management. Monitoring 

C stocks and fluxes is a fundamental step toward 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 174

Northern 2.51 12.25 17.91 4.1 2.97 15.09 0.8 0.09 55.71

(1.22%) (0.69%) (1.47%) (1.46%) (3.08%) (3.08%) (1.01%) (1.01%) (0.78%)

Rocky Mountain 1.88 9.38 12.35 2.77 1.87 10.65 0.61 0.07 39.59

(1.17%) (0.87%) (1.52%) (1.51%) (3.06%) (0.98%) (1.6%) (1.6%) (0.91%)

Southwestern 1.02 8.33 7.71 1.74 1.04 8.02 0.79 0.09 28.73

(1.87%) (0.89%) (1.76%) (1.74%) (3.98%) (0.89%) (1.24%) (1.24%) (1.02%)

Intermountain 1.52 9.29 8.94 2.02 1.8 10.84 0.79 0.09 35.3

(1.85%) (1.01%) (1.88%) (1.87%) (3.34%) (1.07%) (1.39%) (1.39%) (1.08%)

Pacific Southwest 4.03 10.99 23.57 5.19 1.77 13.94 0.92 0.1 60.5

(1.61%) (0.87%) (1.79%) (1.79%) (4.72%) (0.84%) (1.53%) (1.53%) (1.14%)

Pacific Northwest 6.26 14.02 37.56 8.45 3.28 31.76 1.12 0.12 102.58

(1.09%) (0.76%) (1.8%) (1.79%) (3.23%) (0.69%) (0.71%) (0.71%) (1.06%)

Southern 2.71 4.33 22.81 4.69 0.53 22.68 1.13 0.13 59.01

(0.76%) (0.57%) (1.28%) (1.27%) (5.28%) (0.64%) (0.4%) (0.4%) (0.7%)

Eastern 2.81 7.91 21 4.27 0.66 49.7 0.67 0.07 87.11

(0.72%) (0.97%) (1.01%) (0.97%) (3.05%) (0.68%) (0.43%) (0.43%) (0.56%)

Alaska 3.02 10.58 17.49 3.96 1.9 20.09 0.59 0.07 57.69

(2.43%) (1.67%) (2.94%) (2.91%) (5.26%) (1.8%) (2.06%) (2.06%) (1.89%)

Table 1.—Carbon density estimates (tons C/acre) and sampling errors (percent, in parentheses) for each 

carbon pool and administrative region

Carbon Pool

National Forest 

System Region

Down 

dead 

wood Litter

Overstory 

trees above 

ground

Overstory 

trees below 

ground

Standing 

dead 

wood

Soil 

organic 

Carbon

Understory 

trees above 

ground

Understory 

trees below 

ground

Total 

Carbon

these goals. Here we provide baseline estimates of C 

stocks by national forest. However, for brevity, we 

have presented summarized results by NFS region. 

Regardless, all pool estimates (circa 2010) are 

available by national forest. Continued monitoring of 

C stocks will allow the U.S. Forest Service to track the 

changes in individual pools in response to disturbance 

and management. FIA provides a consistent framework 

for providing these estimates in support of the climate 

change performance scorecard and forest planning. 

In this paper we presented solutions to two 

shortcomings in FIA-based carbon estimates for 

national forests. These solutions included explicitly 

defining the areal extent of the populations of interest 

and including NFS intensified data in the eastern 

United States. We also incorporated improvements 

in estimating C in the standing dead pool. The 

standing dead C estimates available through FIA’s 

database were model based and developed by Smith 

et al. (2003) and are not based on direct summaries 

of observed standing dead tree measurements. The 

alternative used here was to estimate standing dead 

C from observed data on standing dead trees and 

incorporating appropriate decay reduction factors 

(Domke et al. 2011); however, this approach, although 

now a part of the national greenhouse gas inventory 

(USEPA 2012), is not slated to be fully incorporated 

in the FIA compilation procedures until early in 2013. 

We incorporated the Domke et al. (2011) methods 

for this analysis for each plot where standing dead 

tree data were available. An effort is underway to 

revisit each C pool delineated within FIA’s inventory 

system to improve the accuracy and precision of 

estimates (Woodall 2012). This investment should 

yield reductions in total uncertainty when coupled with 

other efforts described subsequently.

The second area of improvement is in the ALP 

geospatial database, which was used to define the 

populations and their areal extents. As described in 

the methods section, population totals and sampling 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 175

errors are developed by multiplying per acre estimates 

of each pool by the total area and the total area 

squared, respectively. However, reported acreages in 

the ALP database often differ from reported acreages 

in the Land Area Report of areal extent (U.S. Forest 

Service 2012). The total calculated area from the ALP 

data is approximately 880,000 acres lower than the 

acreages reported in the Land Area Report. Given 

the magnitude of this discrepancy, the total C stored 

on national forests may be 50 million tons larger 

than reported here just because of the accuracy of 

the boundary layer. Also note that the ALP boundary 

layer is used in part to determine strata weights. 

Clearly, if the boundary is not correctly reflected, 

the strata weights may be incorrect because they are 

determined by spatial overlay of the ALP layer and the 

NLCD percent tree canopy cover layer. Errors from 

incorrectly specifying the population are not reflected 

in the reported sampling errors. Typically one assumes 

that the population is defined without error. Increased 

precision of national forest boundaries and geographic 

extents will make this assumption more tenable. 

The third area of improvement is to ensure that the 

NFS funded intensified data are incorporated into the 

public FIA database and state-level estimates. Several 

NFS regions have invested in these intensifications 

so that the precision of estimates will meet their 

individual requirements. Typically, intensifications 

have been either 2x or 3x the base FIA sample. 

The basic rule of thumb is that a 4x intensification 

will reduce the width of the 95 percent confidence 

interval of an estimate by half. Clearly, these 

intensifications will have a substantial influence on 

the sampling errors. However, to date the Northern 

Research Station FIA unit is the only program that 

incorporates intensified national forest data into statelevel estimates and treats each national forest as an 

individual population. Other FIA units should take 

the opportunity to incorporate these intensified data 

into state-level estimates and treat national forests 

as individual populations, which will increase the 

precision of both FIA’s traditional state-level estimates 

and estimates for national forests.

In conclusion, the FIA program is a foundational 

component of Forest Service research, and data 

collected as part of the FIA program can be used to 

estimate C storage on NFS lands. Providing these 

estimates in a consistent way to our NFS partners 

will allow for timely reporting of C storage in 

support of the climate change performance scorecard 

and forest planning. We have identified three areas 

of improvement in support of this effort. These 

improvements require efforts from both FIA and the 

NFS regions, but will foster more accurate, more 

comprehensive, and timelier estimates of C.

LITERATURE CITED

36 CFR 219. 2012. Planning, National Forest 

System Land Management Planning. Code 

of Federal Regulations Title 36, Part 219, 

Subpart A 2012. 

Bechtold, W.A.; Patterson, P.L., eds. 2005. The 

enhanced Forest Inventory and Analysis 

program–national sampling design and 

estimation procedures. Gen. Tech. Rep. SRS-80. 

Asheville, NC: U.S. Department of Agriculture, 

Forest Service, Southern Research Station. 85 p.

Domke, G.M.; Woodall, C.W.; Smith, J.E. 2011. 

Accounting for density reduction and structural 

loss in standing dead trees: implications for 

forest biomass and carbon stock estimates in the 

United States. Carbon Balance and Management. 

6: 14.

Heath, L.S.; Smith, J.E.; Woodall, C.W.; Azuma, D.L.; 

Waddell, K.L. 2011. Carbon stocks on forestland 

of the United States with emphasis on USDA 

Forest Service Ownership. Ecosphere. 2(1): 1-21.

Homer, C.; Dewitz, J.; Fry, J.; Coan, M.; Hossain, N.; 

Larson, C.; Herold, N.; McKerrow, A.; VanDriel, 

J.N.; Wickham, J. 2007. Completion of the 

2001 National Land Cover Database for the 

conterminous United States. Photogrammetric 

Engineering and Remote Sensing. 73(4): 337-341.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 176

Smith, J.E.; Heath, L.S.; Jenkins, J.C. 2003. Forest 

volume-to-biomass models and estimates of mass 

for live and standing dead trees of U.S. forests.

Gen. Tech. Rep. NE-298. Newtown Square, PA: 

U.S. Department of Agriculture, Forest Service, 

Northeastern Research Station. 57 p.

USEPA. 2012. Inventory of U.S. greenhouse gas 

emissions and sinks: 1990-2010. Chapter 7. Land 

use, land-use change, and forestry. Annex 3.12. 

Methodology for estimating net carbon stock 

changes in forest land remaining forest lands. 

Washington, DC: U.S. Environmental Protection 

Agency. #430-R-12-001.2011.pdf. (Accessed June 

27, 2012). 

U.S. Forest Service. 2011. Navigating the climate 

change performance scorecard. Available at 

http://www.fs.fed.us/climatechange/advisor/

scorecard/scorecard-guidance-08-2011.pdf. 

(Accessed June 27, 2012). 

 U.S. Forest Service. 2012. Land areas of the 

National Forest System. WO, Lands FS-383. 

Washington DC: U.S. Department of Agriculture, 

Forest Service, Washington Office. 256 p.

Woodall, C.W. 2012. Where did the U.S. forest 

biomass/carbon go? Journal of Forestry. 

110: 113-114.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 177

ASSESSING ESTIMATION TECHNIQUES FOR 

MISSING PLOT OBSERVATIONS IN THE U.S. FOREST INVENTORY

Grant M. Domke, Christopher W. Woodall, Ronald E. McRoberts, James E. Smith, and Mark A. Hatfield1

Abstract.—The U.S. Forest Service, Forest Inventory and Analysis Program made a 

transition from state-by-state periodic forest inventories—with reporting standards largely 

tailored to regional requirements—to a nationally consistent, annual inventory tailored 

to large-scale strategic requirements. Lack of measurements on all forest land during 

the periodic inventory, along with access issues and misidentification of forest plots as 

nonforest, have resulted in plot-level data gaps spread in the FIA database. In this study, 

we examined several approaches that compensate for missing observations with respect 

to the deviation and precision of stratified estimates of carbon stocks per unit area using 

data from the FIA database. Preliminary estimates of live tree carbon stocks per unit area 

calculated using all missing data approaches were well within one standard error of the 

baseline estimates for the Lake States study region. 

Introduction

Forest ecosystem carbon (C) stocks and stock change 

have been documented by the Intergovernmental 

Panel on Climate Change (IPCC) using 1990 as 

a baseline reference for all IPCC reports. In the 

United States, estimates of forest C stocks and 

stock change are obtained from data collected 

and maintained by the U.S. Forest Service, Forest 

Inventory and Analysis (FIA) program. Over the 

course of the IPCC monitoring period, the FIA 

program made a transition from state-by-state periodic 

inventories—with reporting standards largely tailored 

to regional requirements—to nationally consistent, 

annual inventories tailored to large-scale strategic 

requirements (Bechtold and Patterson 2005). Lack 

of measurements on all forest land during the 

periodic inventory, along with access issues and 

misidentification of forest plots as nonforest due to 

1

 Research Forester (GMD and CWW), Mathematical 

Statistician (REM), and Forester (MAH), U.S. Forest 

Service, Northern Research Station, 1992 Folwell Ave., 

St. Paul, MN 55108; Plant Physiology/Modeling (JES), 

Northern Research Station, Durham, NH. GMD is 

corresponding author: to contact, call 651-649-5138 or 

email at gmdomke@fs.fed.us.

poor aerial imagery, have resulted in plot-level data 

gaps throughout the FIA database. These data gaps 

contribute to large differences in estimates of carbon 

stock change between periodic and annual inventories. 

In this study, we examined several approaches that 

compensate for missing observations with respect 

to the accuracy and precision of stratified estimates 

of carbon stocks per unit area using data from the 

FIA database. The objectives of the study were to: 

1) identify patterns of missingness in the FIA data; 

2) examine approaches for replacement; 3) assess 

approaches under increasing levels of missingness; 

and 4) document strategies for replacement in periodic 

and annual forest inventory data within the context of 

National Greenhouse Gas Inventory.

METHODS

Data

Data came from base intensity FIA plots measured in 

each of the two most recent annual inventory cycles 

(2002-2006 and 2007-2011) in the Lake States region 

(Michigan, Minnesota, and Wisconsin). These plots are 

quasi-systematically distributed approximately every 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 178

2,428 hectares across the 48 conterminous states of the 

U.S. Each plot comprises a series of smaller plots (i.e., 

subplots) where tree- and site-level attributes—such as 

diameter at breast height (d.b.h.) and tree height—are 

measured at regular temporal intervals (Bechtold and 

Patterson 2005).

Because the precision standards established by 

the FIA program are rarely satisfied with the base 

intensity plot sample size, the estimation process is 

enhanced through stratification. Stratification is used 

to reduce the variance of attributes, such as C stocks, 

by portioning the population into strata (Bechtold and 

Patterson 2005). Each FIA plot is assigned to a stratum 

using the National Land Cover Database (Homer et al. 

2004) or other Forest Service databases (Ruefenacht et 

al. 2008). In the Lake States region, strata are assigned 

based on percent canopy cover (i.e., 0-5, 6-50, 

51-65, 66-80, and 81-100 percent). Strata are typically 

grouped into estimation units which are determined by 

a combination of sampling intensity (i.e., number of 

plots) and geographical boundaries (Woudenberg et al. 

2010). 

Stratified estimates of aboveground live tree ( ≥12.7 

cm d.b.h.) C per unit area, , and variance, Var( ), 

were calculated following Cochran (1977):

and

where j = 1,..., J denoted stratum, was the weight 

for the jth stratum, calculated as the proportion of 

pixels assigned to the stratum, was the mean carbon 

per unit area for plots assigned to the jth stratum, and 

 was the within-stratum variance for the jth stratum.

Missing Data Strategies

Strategies used to compensate for missing plot 

observations in forest inventory estimation generally 

fell into two categories, ignoring plots with missing 

observations or replacing missing observations. In 

this study, we examined five approaches: 1) treat 

plots with missing observations as if they had not 

been selected for the sample (IGNORE); 2) replace 

missing plot observations with the observation for the 

same plot from the previous inventory ( PREVIOUS); 

3) replace missing observations with the stratum 

mean (STRATUM); 4) randomly draw from a pool 

(nearest neighbors) of observed plots most similar to 

the plot with the missing observation (NEAREST); 

and 5) compute the expected values for missing plot 

observations by repeatedly updating maximumlikelihood parameter estimates and imputing expected 

values until convergence is achieved (EM). Each 

approach was further divided (beyond strata) by 

ownership domain to account for differences in forest 

land management which may result in different C 

estimates. This subdivision also accounts for bias in 

instances when all missing plot observations fall on 

a particular ownership (e.g., denied access on private 

forest land). 

Analysis and Comparisons

The C estimates generated by each missing data 

approach were compared to the base estimates 

(BASE) by stratum and stratum+ownership (i.e., 

public and private). Stratified base estimates of and 

Var( ) were calculated using observations for all base 

intensity plots across the five canopy-cover strata. This 

BASE estimate served as the standard for comparison 

for estimates obtained with the techniques that 

compensate for missing plot observations. Estimates 

were first compared visually by generating a graph 

of the distribution for the BASE estimates and the 

distributions of the different missing data approaches. 

Estimates for the missing data approaches were then 

compared with the BASE estimates and each other 

for proportions of missing plot observations ranging 

from 0 to 25 percent, which encompassed the range 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 179

RESULTS

Preliminary analyses were restricted to 12,323 base 

intensity plots where at least one accessible forest land 

condition (i.e., area classification on each plot such 

as forest type or ownership group used for analytical 

purposes) was present during the annual inventory 

period. The proportion of missing base intensity 

plots for the most recent inventory was 5 percent in 

Minnesota, 6 percent in Wisconsin, and more than 11 

percent in Michigan. Nearly all missing observations 

(94 percent) were due to private landowners denying 

field crews access to lands with the remaining plots 

deemed hazardous by field crews (3 percent) or 

skipped due to seasonal access (3 percent). The 

distribution of missing plot observations by county 

suggests that denied access areas are not uniformly 

distributed throughout the study region (Fig. 1). 

Projection: Albers Equal Area Conic

Source: USDA Forest Service

Geographic base data are provided by ESRI.

By: G.M.Domke, Printed July 2012

Disclaimer: Information displayed on this map

was derived from multiple sources.

Percent missing

< 4.5

4.5 - 13

13 - 23

23 - 46

> 46

0 100 200

Kilometers

Figure 1.—Proportion of missing plot observations due to denied access by county in the Lake States region of the U.S. for the 

most recent FIA inventory period, 2007-2011. 

of nonresponse in the FIA program reported by 

Patterson et al. (2012). Specifically, the proportion was 

calculated as the ratio of the number of plots classified 

as missing (i.e., hazardous, denied access, skipped) to 

the total number of plots selected for measurement. A 

Monte Carlo procedure (McRoberts 2003) was used 

to simulate random denied access on private forest 

lands to compare the deviation and precision of the 

mean values produced by each missing data approach 

at each level of missingness (0-25 percent) against the 

BASE estimates of . In the initial analysis, standard 

errors of the mean estimates produced by the missing 

data approaches at each missingness level were used 

to compare with the BASE estimates. Mean estimates 

greater than one standard error from the BASE 

mean were consider significantly different from the 

BASE estimate. All analyses were conducted using R 

statistical software (R Development Core Team 2012) 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 180

Table 1.—Stratum statistics for the most recent FIA inventory (2007-2011) in the Lake States

 Live tree C per unit area (Mg ha-1)

2006 2011

State and Stratum Weight Number of plots Mean Mean Standard Error

Michigan 1.00 4,454 28.36 28.80 0.42

 Canopy cover 0 - 5 0.64 391 9.77 12.42 0.90

 Canopy cover 6 - 50 0.07 600 11.66 12.24 0.65

 Canopy cover 51 - 65 0.04 415 20.03 20.50 1.02

 Canopy cover 66 - 80 0.10 1,264 26.63 27.46 0.66

 Canopy cover 81 - 100 0.15 1,784 41.22 40.85 0.74

Minnesota 1.00 3,966 17.24 18.04 0.32

 Canopy cover 0 - 5 0.63 370 8.85 10.59 0.82

 Canopy cover 6 - 50 0.03 97 9.45 11.05 1.36

 Canopy cover 51 - 65 0.03 202 11.42 13.42 1.01

 Canopy cover 66 - 80 0.12 1,231 16.29 17.15 0.56

 Canopy cover 81 - 100 0.19 2,082 20.21 20.53 0.47

Wisconsin 1.00 3,903 23.51 25.23 0.39

 Canopy cover 0 - 5 0.60 686 10.25 11.86 0.56

 Canopy cover 6 - 50 0.02 135 11.53 12.44 1.32

 Canopy cover 51 - 65 0.03 182 10.98 15.31 1.22

 Canopy cover 66 - 80 0.08 619 19.88 21.31 0.82

 Canopy cover 81 - 100 0.27 2,281 30.19 31.86 0.55

Lake States 1.00 12,323 23.24 24.21 0.22

more efficient than the other two approaches initially 

evaluated. Assuming the PREVIOUS and EM 

approaches perform comparably to the STRATUM 

and NEAREST techniques, the likely outcome of the 

initial phase of this study will be that the IGNORE 

approach is optimal for dealing with missing plot 

observations (at current nonresponse levels in the 

study area) due to denied access in annual forest 

inventory data. This approach has merit assuming the 

distribution of missing plot observations is random. 

If not, an alternative approach and/or subdivision of 

strata or domain may be necessary to account for bias 

from missing plot observations due to denied access. 

Furthermore, all missing data approaches must be 

examined across the range of potential nonresponse 

in order to evaluate which approach or approaches 

may be useful at the national level. Assessing the 

distribution of missing plot observations and the range 

of nonresponse is important since it is likely there 

are patterns of missingness in the periodic inventory, 

albeit for a variety of different reasons, which may 

Stratified base estimates of increased with 

increasing canopy cover for the most recent inventory 

period in each of the three Lake States (Table 1). 

Preliminary estimates calculated using the IGNORE, 

STRATUM, NEAREST approaches, at current 

missingness levels, were within one standard error 

of the BASE estimate of using observations for 

all plots. This suggests there were no statistically 

significant differences among estimates obtained using 

the missing data approaches initially investigated 

in the study. That said, the IGNORE approach was 

computationally more efficient than the STRATUM 

approach, which was more efficient than the 

NEAREST approach. 

DISCUSSION

Early results suggest there are a number of strategies 

for dealing with missing plot observations in annual 

forest inventory data. The IGNORE approach at 

current nonresponse levels was computationally 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 181

require similar subdivisions to account for bias. While 

the initial analyses focused on a few approaches 

using annual inventory data, the full suite of missing 

data approaches will be evaluated using the annual 

inventory and then applied to the periodic inventory 

to assess whether any of the missing data approaches 

better align estimates for forest C stocks and stock 

change between periodic and annual inventories in the 

United States. 

ACKNOWLEDGMENTS

The authors would like to thank Dale Gormanson and 

Brian Walters for assistance with map and dataset 

development. We also thank Rob Slesak and David 

Wilson for reviewing the draft manuscript.

LITERATURE CITED

Bechtold, W.A.; Patterson, P.J., eds. 2005. The 

enhanced Forest Inventory and Analysis 

program – national sampling design and 

estimation procedures. Gen. Tech. Rep. SRS-80. 

Asheville, NC: U.S. Department of Agriculture, 

Forest Service, Southern Research Station. 85 p.

Cochran, W.G. 1977. Sampling techniques. 3rd. ed. 

New York, NY: Wiley. 428 p.

Homer, C.; Huang, C.; Yang, L.; Wylie, B.; Coan, M. 

2004. Development of a 2001 national landcover 

database for the United States. Photogrammetric 

Engineering and Remote Sensing. 44: 3999-4005.

McRoberts, R.E. 2003. Compensating for missing 

plot observations in forest inventory estimation.

Canadian Journal of Forest Research. 33: 

1990-1997.

R Development Core Team. 2012. R: a language and 

environment for statistical computing. Vienna, 

Austria: R Foundation for Statistical Computing. 

Ruefenacht, B.; Finco, M.V.; Nelson, M.D.; 

Czaplewski, R.; Helmer, E.H.; Blackard, 

J.A.; Holden, G.R.; Lister, A.J.; Salajanu, 

D.; Weyermann, D.; Winterberger, K. 2008. 

Conterminous US and Alaska forest type 

mapping using forest inventory and analysis 

data. Photogrammetric Engineering and Remote 

Sensing. 74: 1379-1388.

Woudenberg, S.W.; Conkling, B.L.; O’Connell, B.M.; 

LaPoint, E.B.; Turner, J.A.; Waddell, K.L. 2010. 

The Forest Inventory and Analysis database: 

database description and users manual version 

4.0 for Phase 2. Gen. Tech. Rep. RMRS-GTR-245. 

Fort Collins, CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research Station. 

336 p.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 182

Gain-loss Estimation of Components of Change 

in Forest Carbon: An Example from Oregon

Andrew N. Gray, Thomas R. Whittier, and David L. Azuma1

Abstract.—Increases in carbon dioxide in the atmosphere are thought to be a main cause 

of changes in global temperature and sea level. There is thought to be a large carbon 

(C) sink on lands in the Northern Hemisphere, but the amounts and causes are not well 

known. Forests are a focus of efforts to understand changes in carbon storage over time 

because they accumulate larger amounts of carbon than other terrestrial ecosystems. 

Current “stock-change” estimates of forest carbon flux are based on a combination of 

field measurements, surveys, remote sensing classifications, and models. The goal of 

this study was to attempt to improve on existing carbon accounting methods by using 

the “gain-loss” method and directly tracking components of change in tree carbon 

across a large region with strong environmental gradients using field measurements. We 

used repeated Forest Inventory and Analysis (FIA) measurements on permanent plots 

to quantify tree carbon flux due to land use change, disturbance, and harvest on 1,067 

plots across Oregon. Land use change resulted in a net increase of 1.1 x 105 ha of forest 

land in Oregon between 1986 and 1997. However, there was a net loss of 3.4 Tg of live 

tree C because most of the losses were on productive west-side forests and most of the 

gains were on east-side juniper woodlands. Live woody C decreased significantly in 

eastern Oregon (-14.4 Tg), with mortality and harvest exceeding growth. Much of the 

mortality and subsequent harvest was associated with severe defoliation by western 

spruce budworm. However, C stores increased significantly in western Oregon (19.2 Tg) 

due to large accumulations from growth on public lands, and harvest being balanced by 

growth on private lands. Patterns of C density associated with stand age differed by site 

productivity class and forest type. The increase in C density with stand age was greater 

on more productive sites due to higher growth rates in young stands, but contribution of 

those sites to growth decreased as harvest rates increased with stand age. We demonstrate 

that gain-loss accounting from a probabilistic field sample can produce detailed estimates 

of carbon flux that identify causes and components of change, and have the potential to 

produce more consistent estimates than combining alternative approaches.

1

 Research Ecologist (ANG), U.S. Forest Service, Pacific Northwest Research Station, 

620 SW Main, Suite 400, Portland, OR 97205; Research Assistant (TRW), Oregon State 

University; Research Forester (DLA), U.S. Forest Service, Pacific Northwest Research Station. 

ANG is corresponding author; to contact, call 541-750-7252 or email at agray01@fs.fed.us.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 183

Managing for Climate Benefits in Dry Mixed-conifer

Forests: Tracking the Carbon Implications

of Fuel Treatments at Landscape Scale

Jeremy S. Fried, Theresa Jain, Jonathan Sandquist, and Larry D. Potts1

Abstract.—Building on the statistically representative sample of a complex of forest 

types, often described as dry mixed conifer, provided by over 5000 annual Forest 

Inventory and Analysis (FIA) plots in seven western states, we modeled multiple indices 

of existing fire hazard and the extent to which implementation of mechanically focused 

fuels management programs could substantially improve these indices with respect to 

a range of objectives. Design of the generic/stylized fuel treatments tested in this study 

was guided my multiple, sometimes conflicting objectives: 1) leave behind a healthy, 

resilient stand; 2) reduce surface fire intensity; and/or 3) reduce crown fire potential, 

though results varied by pretreatment stand structure and forest type. Fuel treatment 

costs, including costs of on-site activities and transportation of harvested materials, and 

yields and values of merchantable and energy wood resulting from treatments under 

alternative policy scenarios were modeled or calculated, allowing estimation, by forest 

type, ecoregion, landowner class, stand density and size class of: 1) the area over which 

fuel treatment is capable of achieving one or more objectives and at what cost [or net 

revenue]; 2) the amount of carbon that fuel treatment can move into long-term storage 

as harvested wood products or into utilization as fossil-carbon-emission offsetting 

bioenergy; 3) the amount of live tree carbon remaining at risk in treated stands and 

the likelihood of that carbon leaving the live-tree pool in the event of a fire; and 4) the 

potential carbon dynamics of post-fire recovery that includes salvage harvest in treated or 

untreated stands. Results reveal that less than half of the dry mixed-conifer forest would 

benefit from the kinds of fuel treatments simulated in this study; however, nearly all the 

acres with potential benefit are on federal lands, and most can be treated so as to generate 

net revenue, useful products and carbon benefits. Although markets for energy wood 

are essentially absent in some regions where these forest types occur, most of the value 

derived from effective treatments flows from the sale of merchantable wood products, 

so markets for energy wood are rarely the determining factor as to fuel treatment 

feasibility. While clearly linked to fire incidence and return interval, the extent to which 

fuel treatments produce net carbon benefits also depends on how post-treatment carbon 

accumulation capacity is impacted by stocking reduction, the reduction in mortality 

achieved in the event of fire, and assumptions about the feasibility of post-fire salvage 

harvest.

1

 Research Forester (JSF), U.S. Forest Service, Pacific Research Station; Research Forester (TJ) 

and Forestry Technician (JS), U.S. Forest Service, Rocky Mountain Research Station; Information 

Technology Specialist (LDP), U.S. Forest Service, Pacific Research Station. JSF is corresponding 

author: to contact, call 503-808-2058 or email at jsfried@fs.fed.us. 

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Issue Analysis
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 185

MAPPING FOREST SOIL ORGANIC MATTER 

ON NEW JERSEY’S COASTAL PLAIN

Brian J. Clough, Edwin J. Green, and Richard G. Lathrop1

Abstract.—Managing forest soil organic matter (SOM) stocks is a vital strategy for 

reducing the impact of anthropogenic carbon dioxide emissions. However, the SOM pool 

is highly variable, and developing accurate estimates to guide management decisions 

has remained a difficult task. We present the results of a spatial model designed to map 

soil organic matter for all forested land in the Coastal Plain physiographic province of 

New Jersey. SOM stocks from 60 sampling locations, distributed across the region in 

a stratified random design based on vegetation type and drainage class, were used in a 

kriging model that incorporated several indices derived from Landsat Thematic Mapper 

data as predictor variables. This model reduced mean squared error at validation plots 

(n=26) by 10 to 23 percent when compared to kriging models that did not use a predictor 

variable. Our results suggest that this approach, combining SOM inventory and remote 

sensing data in a geostatistical framework, is a useful method for reducing uncertainty in 

forest SOM estimates.

1

 Graduate Assistant (BJC) and Professors (EG and RGL), 

Department of Ecology, Evolution and Natural Resources, 

School of Environmental and Biological Sciences, Rutgers 

University, 14 College Farm Rd., New Brunswick, NJ 

08901. BJC is corresponding author: to contact, email at 

bclough84@gmail.com.

INTRODUCTION

Forest inventory data have been an important tool in 

estimating forest carbon (C) stocks (Birdsey 1992). 

The widespread availability of spatially explicit, 

plot-level forest inventory data has allowed landscape 

ecologists to employ a variety of spatial modeling 

techniques to calculate forest C stocks at the landscape 

scale. Much progress has been made in estimating 

aboveground carbon, but belowground carbon storage 

is still poorly documented. 

Combining geostatistical analysis with plot inventory 

data and remotely sensed covariates may represent 

an effective method for mapping soil organic matter 

distribution. In geostatistics, the spatial covariance 

among estimates at sampling locations is quantified, 

and statistical approaches such as variogram analysis 

are used to model the spatial pattern of the variable 

of interest (Isaaks and Srivastava 1989). The creation 

of a spatially explicit model of covariance provides 

a quantitatively rigorous framework for interpolating 

values across response surfaces. The results of such 

interpolation models are “rasterized,” making them 

ideal for up-scaling plot-level data to broad scales, and 

they avoid the issues introduced by spatial aggregation 

and a priori assumptions in other approaches to 

scaling SOM. 

The development of metrics for inferring forest soil 

organic matter stocks from Landsat imagery presents 

a fruitful line of research, and a reliable methodology 

would be of considerable interest to biogeochemists 

and climate change policy-makers. Formation of forest 

soil organic matter (SOM) is controlled by a variety 

of factors, including forest cover type, topography, 

and disturbance (Chapin et al. 2002). The widespread 

availability of landscape-scale remote sensing data, 

largely funded by governmental agencies such as the 

U.S. Geological Survey (USGS), provides researchers 

with tools to collect data on such factors at broad 

scales. 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 186

In this study, we report on a model to interpolate SOM 

across forests of the Coastal Plain region of New 

Jersey. The model incorporates soil inventory and 

normalized difference vegetation index (NDVI) data 

to map SOM across a grid covering the entire study 

region. Kriging with external drift (KED), also referred 

to as “kriging with a trend model” in the literature 

(Goovaerts 1999), was used as the framework for 

incorporating secondary information into the SOM 

interpolation. Our study has three objectives: (1) 

Establish a relationship between SOM and NDVI to 

validate the latter as a predictor variable for mapping 

SOM distribution, (2) Demonstrate that incorporating 

NDVI in an interpolation model reduces uncertainty of 

prediction estimates relative to a model that does not 

incorporate any secondary information, and (3) Apply 

our model to generate a map of SOM distribution for 

New Jersey’s Coastal Plain physiographic province. 

STUDY AREA

This study was conducted on the Coastal Plain 

physiographic province of New Jersey. Three major 

upland forest communities dominate the region: (1) 

Pinus rigida Mill. forest, (2) Quercus L. spp. forest, 

and (3) mixed communities that span a gradient 

between the two pure types. On the inner coastal 

plain, these communities mix with other hardwood 

species such as Fagus grandifolia Ehrh. and Carya

Nutt. species. Forested wetlands are common along 

river courses or in low areas. Most of these wetlands 

are hardwood swamps dominated by Acer rubrum 

L., Liquidambar styraciflua L., and Nyssa sylvatica 

Marsh. However, forested Sphagnum bogs with pure 

stands of Chamaecyparis thyoides (L.) B.S.P. are also 

present across the landscape. 

Soils in the region are largely typic Hapludults and 

Quartzisappamments of marine or alluvial origin 

(Tedrow 1986). Podzolization occurs in some soils, 

owing to litter inputs rich in tannins and other 

recalcitrant organic compounds (Tedrow 1998). Soils 

range from very poorly to excessively drained and 

are primarily sandy in texture. However, clayey and 

mucky soils are frequent in wet areas. Mean annual 

temperature is 54 °C and average annual rainfall is 

1055 mm. 

METHODS

Eighty-six plots were established throughout all forests 

on New Jersey’s Coastal Plain, using a stratified 

random sampling design based on dominant forest 

type and drainage class. Soil was collected within each 

plot at three depths: 0-10 cm, 10-20 cm, and 20-30 

cm. At each depth interval bulk density was sampled 

using the core method (Blake and Hartge 1986), and a 

second sample was taken for laboratory analysis. 

The analytical samples were air dried for at least 48 

hours, while the bulk density samples were dried 

at 105 °C. Both were sieved to 2 mm, and the fine 

fraction material was ground into powder with a 

mortar and pestle and homogenized. Soil organic 

matter content was estimated using loss-on-ignition at 

400 °C for 24 hours. Soil organic matter stock (t/ha) 

was then calculated for each plot using the calculated 

bulk density and gravimetric soil organic matter 

content data. 

Cloud-free Landsat TM scenes (courtesy of USGS) 

were extracted for a single date during the study, 

July 14, 2011, and tiled into a mosaic of the study 

region. We generated raster files for two indices 

calculated from the Landsat data: NDVI and tasseled 

cap band 2 (TC2). Both are related to photosynthetic 

activity, and have been shown to correlate with net 

primary productivity (Asrar et al. 1984), so it was 

reasonable to expect a relationship with SOM. Values 

of NDVI were extracted for all 86 sampling plots, and 

the complete raster files were collated and retained to 

interpolate SOM across the study region. 

The 86 plots were randomly divided into a prediction 

set (60 plots) and a validation set (26 plots). To 

test if incorporating remote sensing covariates as 

predictor variables reduces uncertainty, we compared 

two univariate models, universal kriging (UK) and 

ordinary kriging (OK) to three multivariate models: 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 187

NDVI only (NDVI), tasseled cap only (TC), and both 

NDVI and tasseled cap data (NDVI+TC). Multivariate 

interpolation of SOM was accomplished using a 

universal kriging procedure with a trend model that 

allows for dependency between the response variable 

and a chosen set of predictors. This approach, also 

referred to as ”kriging with external drift” (KED), 

derives predictions of the response variable by 

extending the covariance matrix of the kriging system 

to incorporate the predictor variables (Goovaerts 

1999). Each model was used to interpolate SOM on 26 

validation plots, and the sum of squared error (SSE) 

between predicted and observed values of SOM was 

computed for each model from 50 simulations. 

To ensure that the composition of the modeling data 

set did not overly influence model results, a new 

set of validation plots was randomly selected at the 

beginning of each trial. Model performance was 

assessed by comparing the mean SSE of each model 

for all 50 trials. The results of the best kriging model 

were used to interpolate values of SOM density for all 

cells in a 90 m2 grid covering the full extent of forested 

land on New Jersey’s Coastal Plain. All geostatistical 

analysis was accomplished in the R statistical 

computing environment (R Development Core Team 

2008), using the gstat package (Pebesma 2004). 

RESULTS

All three multivariate models (NDVI, TC, NDVI+TC) 

reduced mean sum of squared error by at least 5 

percent when compared to the two univariate models. 

The model which used NDVI as the only predictor 

had the lowest mean SSE while the ordinary kriging 

model had the highest SSE (Table 1). NDVI reduced 

mean SSE by 17 and 22 percent, respectively, when 

compared to UK and OK. NDVI also outperformed 

TC, reducing mean SSE by 12 percent, and provided 

slight improvement when compared to the full model 

(NDVI+TC). These results are probably explained 

by the higher correlation of SOM stock with the 

normalized difference vegetation index data than 

with the tasseled cap index (Fig. 1). In general, our 

results demonstrate that incorporating remote sensing 

covariates as predictor variables reduces uncertainty in 

regional SOM estimates by at least 17 percent. 

The modeling data set that produced the lowest 

squared error was used for generating a map of SOM 

distribution for the entire study region. Our fitted 

variogram suggests a range of spatial covariance 

among data points of approximately 12,500 m (Fig. 

2). However, note that considerable variation is 

unaccounted for by this model. The spatial pattern, 

owing to the fairly small sample size in the modeling 

data set (n=60), is not well defined. Additionally, 

the large nugget effect of the variogram indicates 

significant microscale spatial variability below the 

resolution of our sampling regime. Due to the weakly 

fitting variogram model, we observed only weak 

correlation between observed and predicted results at 

the 26 validation plots (Fig. 3). Although this model 

performs better than either univariate model (r = 0.15 

for UK and r = 0.05 for OK using the same data set), 

there is still significant uncertainty owing to the small 

sample size in the modeling data set. 

The model tends to predict higher SOM density 

on the interior portions of the Coastal Plain and 

declining SOM stocks to the southeast (Fig. 4). These 

predictions are consistent with our knowledge of the 

region, where the inland soils tend to be more nutrient 

rich and less excessively drained. 

Table 1.—Mean sum of squared error (SSE) of 

each model, and percent reduction by the best 

performing model (NDVI).

Reduction in

mean SSE when

compared to “best”

Mean SSE model (percent)

Univariate models

 Universal kriging (UK) 1984734 0.17

 Ordinary kriging (OK) 2118974 0.22

Multivariate (KED) models

 NDVI only (NDVI) 1657018 0

 TC only (TC) 1883040 0.12

 Both predictors (NDVI+TC) 1686912 0.02
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 188

Figure 2.—Semivariogram for the 60 modeling plots. The 

open circles represent the binned empirical values of 

semivariance. The solid line is a spherical variogram model 

fitted with a restricted maximum likelihood optimization 

routine (partial sill = 0.09, range=12,500, nugget = 0.31). 

Figure 3.—Observed vs. predicted soil organic matter 

content at 26 independent validation plots. The dotted line 

represents a 1:1 relationship between the two variables. 

Figure 1.—Relationship between (a) soil organic matter stock (SOM) and normalized difference vegetation index (NDVI), and 

(b) SOM and tasseled cap band 2 (TC) at all 86 sampling locations. 

NDVI Tasseled Cap Transform Band 2

SOM Stock (t/ha)

SOM Stock (t/ha)

a. b.

Semivariance

Distance (m)

Predicted SOM Stock (t/ha)

Observed SOM Stock (t/ha)
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 189

Figure 4.—Result of a KED interpolation for a 90-m2 grid covering all forests in New Jersey’s Coastal Plain. Our study region 

is defined by the outline. Note that the model predicts higher SOM stocks on the inner portion of the Coastal Plain, where soils 

tend to be less nutrient- poor. 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 190

DISCUSSION

Including soil sampling in large-scale forest 

inventories, such as the Forest Inventory and Analysis 

Program, will greatly increase the spatial coverage 

and availability of data on belowground carbon 

stocks. Incorporating NDVI reduced uncertainty in 

forest SOM estimates by 17 to 23 percent with a 

relatively small set of modeling plots (n=60) despite 

considerable error between measured and predicted 

values at the validation plots. Large coordinated efforts 

to sample forest SOM, where sampling density is 

increased and the range of variation in SOM can be 

better characterized by the data set, would provide 

improved performance. Our model framework will 

likely be an effective means for modeling SOM on 

a variety of forested landscapes, though appropriate 

predictor variables will have to be selected on a 

case-by-case basis. In general, predictor variables 

should be selected based on local knowledge of the 

environmental factors that are most likely to drive 

variation in SOM stocks.

Combining soil inventories with remotely sensed 

data and geostatistical analysis represents an effective 

framework for quantifying the SOM pool at broad 

spatial scales. Interpolation methods such as kriging, 

which provide a statistically rigorous framework 

for defining spatial patterns, provide an appealing 

alternative to traditional approaches for incorporating 

belowground C into landscape-scale forest carbon 

budgets. These methods are especially useful when 

predictor variables derived from rasterized remote 

sensing data sets are incorporated into the models. 

Our results confirm that such an approach constitutes 

an effective method for reducing uncertainty of soil 

organic matter stock estimates and would be a useful 

addition to forest inventory projects. 

ACKNOWLEDGMENTS

We would like to thank Dr. Charles Perry for his 

advice and guidance while developing this project. We 

would also like to acknowledge the U.S. Department 

of Agriculture and the U.S. Forest Service for their 

support. 

LITERATURE CITED

Asrar, G.; Fuchs, M.; Kanemasu, E.T.; Hatfield, 

J.L. 1984. Estimating absorbed photosynthetic 

radiation and leaf area index from spectral 

reflectance in wheat. Agronomy Journal. 

76: 300-306.

Birdsey, R.A. 1992. Carbon storage and 

accumulation in United States forest ecosystems.

Gen. Tech. Rep. WO-59. Washington, DC: U.S. 

Department of Agriculture, Forest Service, 

Washington Office. 51 p.

Blake, G.R.; Hartge, K.H.; 1986. Bulk density. In: 

Klute, A., ed. Methods of soil analysis Part 1. 

Physical and mineralogical methods. Madison, WI: 

American Society of Agronomy, Inc.; Soil Science 

Society of America: 377-382.

Chapin, F.S.; Matson, P.A.; Mooney, H.A. 2002. 

Principles of terrestrial ecosystem ecology. New 

York, NY: Springer Science & Business Media Inc. 

436 p.

Goovaerts, P. 1997. Geostatistics for natural 

resources evaluation. New York, NY: Oxford 

University Press. 496 p.

Isaaks, E.H.; Srivastava, M. 1989. An introduction 

to applied geostatistics. Oxford, UK: Oxford 

University Press. 561 p.

Pebesma, E.J. 2004. Multivariable geostatistics in S: 

the gstat package. Computers & Geosciences. 30: 

683-691.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 191

R Development Core Team. 2008. R: a language and 

environment for statistical computing. Vienna, 

Austria: R Foundation for Statistical Computing. 

Available at www. R-project.org. [Date accessed 

unknown].

 Tedrow, J.C.F. 1986. The soils of New Jersey. New 

Jersey Agricultural Experiment Station publication 

no. A-15134-1-82.

Tedrow, J.C.F. 1998. Development of the Pine 

Barrens soils. In: Forman, R., ed. Pine Barrens: 

ecosystem and landscape. New Brunswick, NJ: 

Rutgers University Press: 61-78.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 192

BEECH STATUS IN NEW ENGLAND’S AFTERMATH FORESTS

George L. McCaskill and Randall S. Morin1

Abstract.—American beech (Fagus grandifolia) is one of the three most dominant 

tree species occupying the northern hardwoods forest of New England. We studied 

Maine, New Hampshire, Vermont, and New York to capture those areas with higher 

concentrations of beech. The status of beech in the northern hardwood forests is 

important because of the long-term impacts of beech bark disease (BBD) (Neonectria

ssp.) on the composition and regeneration of aftermath forests within the region. We 

assessed the current conditions of beech trees at the stand level by comparing 2011 

Forest Inventory and Analysis survey data with the previous survey conducted on the 

same set of plots (2006). To understand the current impacts of BBD on forests, we 

compared the number of growing-stock trees, number of rough cull trees, number of 

rotten cull trees, number of standing dead trees, and number of mortality trees with an 

important associative species, sugar maple (Acer saccharum). To evaluate the impacts 

of BBD on stand regeneration, we also assessed the number of sapling-size trees of 

American beech and three of its close associates, sugar maple, yellow birch (Betula 

alleghaniensis), and eastern hemlock (Tsuga canadensis). Beech trees had 40 percent 

of their stocking classified as defective or dead; sugar maple stocking had less than 15 

percent. Mortality tree numbers for American beech were highest in the larger diameter 

trees (11.0 inches diameter at breast height and larger) where BBD had been detected for 

less than 37 years, but were more equally distributed in older aftermath forests where 

BBD had been present for more than 60 years. The number of beech saplings increased 

while the numbers of its three associate tree species did not change significantly. Net 

growth of beech was increasing as a proportion of net growth for sugar maple. 

1

 Research Foresters (GLM, RSM), U.S. Forest Service, 

Northern Research Station, 11 Campus Blvd., Suite 200, 

Newtown Square, PA 19073; GLM is corresponding 

author: to contact, call 610-557-4045 or email at 

georgemccaskill@fs.fed.us.

INTRODUCTION

American beech (Fagus grandifolia Ehrh.) is an 

important component of the northern hardwood forests 

of the Northeast. It is more shade tolerant than most 

of its associates and can quickly regenerate in the 

form of sprouts (Jones and Raynal 1987). Unlike its 

common associates, beech has been impacted by the 

beech bark disease (BBD). BBD is an insect-fungus 

complex involving the scale insect (Cryptococcus 

fagisuga Lindinger) and the exotic canker fungus 

Neonectria faginata (M.L. Lohman and A.M.J. 

Watson; Castlebury, Rossman and Hyten) or the native 

Neonectria ditissima (Tul. & C. Tul.). BBD has been 

spread by the scale insect throughout New England 

since the early 1930s (Ehrlich 1934). Three stages of 

BBD development have been identified (Shigo 1972). 

The stages are the advancing front where forests with 

concentrations of beech trees are degraded by the scale 

insect during bark feeding and subsequent infection 

of Neonectria spp., the killing front where forests 

have widespread beech mortality as a result of the 

effects of BBD accompanied by high populations of 

the scale insect, and finally the aftermath zone where 

BBD-related mortality and scale insect numbers have 

declined and greater numbers of smaller beech trees 

occupy the stand (Cale et al. 2012, Munck and Manlon 

2006). This analysis has focused on forest conditions 

within the aftermath zone. As the fungal-insect 

complex progresses toward its southern and western 

limits, how can we analyze Forest Inventory and 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 193

Analysis (FIA) data to identify the current conditions 

of maple-beech-birch (northern hardwood) aftermath 

forests for any given locale? Can these same data be 

used to project temporal changes to BBD aftermath 

forests and their important associate tree species?

DATA AND METHODS

This assessment focused on identifying FIA inventory 

attributes from the 2006 and 2011 surveys that can be 

used to determine the present stand-level conditions 

of BBD-infected beech forests within the northern 

hardwood region (Bechtold and Patterson 2005). 

BBD-infected stands were stratified by the number of 

years since first detection. County-level records of the 

year of initial scale insect establishment were provided 

by the U.S. Forest Service, Northeastern Area State 

and Private Forestry, and are available online (Jones 

et al. 2011). These county-level data were not based 

upon the FIA systematic survey; therefore, slight 

inconsistencies may exist between years and regions 

concerning scale insect detections. Two prior studies 

have used these data to examine the relationship 

between the spread of the scale insect and beech 

density (Morin et al. 2005, 2007). The northern New 

England region was studied because the BBD complex 

has been active there longer than anywhere else in the 

U.S. (Fig. 1). Areas in Maine, New Hampshire, New 

York, and Vermont were broken down into areas where 

scale had been detected for less than 37 years, areas 

where scale had been detected from 37 to 60 years, 

and areas where scale had been detected for longer 

than 60 years (Morin et al. 2007). We compared the 

number of growing-stock trees, number of rough (form 

defect) cull trees, number of rotten (decay defect) cull 

trees, number of standing dead trees, and mortality 

expressed as tree numbers to make comparisons 

between American beech and an important associative 

species, sugar maple (Acer saccharum Marsh.). We 

also compared net growth between these two species. 

On the landscape level, we compared these attributes 

as a whole within the northern New England region 

and by state (Maine, New Hampshire, New York, and 

Vermont). These data were stratified by two broad 

diameter classes: all trees greater than or equal to 

7.0 inches diameter at breast height (d.b.h.) versus 

all trees greater than or equal to 11.0 inches d.b.h. 

Acreage was stratified by year of scale detection 

within a given county. All trees greater than 7.0 inches 

d.b.h. were chosen instead of 5.0 inches to reduce the 

influence of sprout stimulation caused by BBD on 

overall beech tree numbers. The number of saplings 

(all trees between 1.0 and 5.0 inches d.b.h.) was 

used to evaluate BBD impacts on regeneration while 

examining possible interactions between beech and 

three of its close associates, sugar maple, yellow birch 

(Betula alleghaniensis Britt.), and eastern hemlock 

(Tsuga canadensis Carr.).

RESULTS

Aftermath Forests

New England region: Beech inventory showed that 

40 percent of all beech trees (5.0 inches d.b.h. and 

greater) are classified as rough and rotten cull trees or 

standing dead. By comparison, less than 15 percent of 

sugar maple trees are cull or dead (Fig. 2).

Years of Beech

Scale Infestation

< 37

37 - 60

> 60

Figure 1.—Years of scale insect infestation by county in New 

England (ME, NH, NY, VT), 2011.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 194

Figure 2.—Number of sugar maple versus beech trees 11.0 

inches d.b.h. and greater in percent on forest land in New 

England (ME, NH, NY, VT), 2006 versus 2011.

Infection Classes 

New York, and to a lesser degree, Vermont made up 

most of the areas infected for less than 37 years. New 

Hampshire, Vermont, and to a lesser degree, Maine 

contain acreages infected from 37 to 60 years. Finally, 

Maine, and to a lesser degree, New Hampshire make 

up most of the acreages infected for longer than 60 

years. Below are the numbers of defected or dead 

trees as a percentage of total tree numbers, including 

growing-stock trees (Table 1).

Mortality and Standing Dead

Mortality trees differ from all standing dead trees 

because they represent only those trees that died since 

the last measurement (5 years). The mortality trees 

also include fallen trees or recently burned material. 

The greatest proportion of mortality beech trees were 

found in areas infected for less than 37 years when 

counting trees of 11.0 inches d.b.h. or greater. In 

those same areas, when the count included trees with 

diameters of 7.0 inches or greater, the mortality levels 

were lower, which was comparable to northern New 

England beech numbers as a whole (Fig. 3).

Table 1.—Numbers of rough, rotten, mortality, and 

standing dead beech trees as percentage of total 

trees; classified by years since first scale insect 

detection.

Tree

Infection Class Attribute Count (%)

Infection <37 Years Standing dead trees 25

Mortality trees 6

Rough cull trees 8

Rotton cull trees 6

Infection ≥37 and ≤60 Years Standing dead trees 21

Mortality trees 1

Rough cull trees 7

Rotton cull trees 12

Infection >60 Years Standing dead trees 28

Mortality trees 3

Rough cull trees 8

Rotton cull trees 7

Number of Trees Greater 

than 11.0 inches d.b.h. (percent)

Number of Trees Greater 

than 11.0 inches d.b.h. (percent)

Number of Trees Greater 

than 11.0 inches d.b.h. (percent)
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 195

Figure 3.—Number of beech trees 7.0 and 11.0 inches d.b.h. 

and greater in percent on forest land in New England (ME, 

NH, NY, VT), 2006 versus 2011.

Regeneration

The number of saplings increased in BBD-infested 

acreage because beech sprouting was stimulated by 

the BBD complex. Sugar maple and hemlock numbers 

did not statistically change (Fig. 4). In Maine, beech 

regeneration had a lower proportion of the total sapling 

numbers where higher yellow birch and hemlock stock 

were growing.

Net Growth

The net growth of beech trees as a proportion of sugar 

maple growth is increasing. The largest proportions 

were found in the younger aftermath forests (Fig. 5).

DISCUSSION

With the killing front comes the beginning of the 

aftermath forests. The effects are first seen on the 

largest beech trees (d.b.h. >16 inches). Gaps are 

created when these larger beech trees fall (snap) as 

a result of wood defects created by BBD infection. 

The new openings are filled by beech saplings as 

a result of BBD-stimulated sprouting and by some 

released yellow birch and hemlock trees. The beech 

sprouts form “thickets” creating understory shading 

while crowding out sugar maple (Gravel et al. 2011). 

Some researchers believe sugar maple saplings are 

suppressed by possible toxic effects of higher levels 

of beech leaf leachate excreted from these dense 

thickets, coupled with focused deer browsing (Hane et 

al. 2003, Runkle 2007). Scale populations eventually 

decline as aftermath conditions become prevalent 

(Cale et al. 2012). The beech thickets grow and cause 

increased shading, creating a nursery environment 

for young hemlock trees. The aftermath stand will 

contain greater numbers of smaller, defective beech 

trees. These smaller diameter beech trees will produce 

fewer sprouts than the larger diameter beech trees 

exposed to the killing front (Jones and Raynal 1987). 

The impact of greater numbers of beech saplings on 

the regeneration of sugar maple, yellow birch, and 

hemlock in upper New England is not yet clear based 

upon current FIA inventory data. But in areas where 

Number of Trees Greater than 7.0 

and 11 inches d.b.h. (percent)

Number of Trees Greater than 7.0 

and 11 inches d.b.h. (percent)

Number of Trees Greater than 7.0 

and 11 inches d.b.h. (percent)
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 196

Figure 4.—Number of saplings (million trees) between 

1.0 and 5.0 inches d.b.h. in percent on forest land in New 

England (ME, NH, NY, and VT), 2006 versus 2011.

Figure 5.—Annual net growth of growing-stock trees 7.0 

inches d.b.h. and greater (cubic feet) in percent by species 

(beech, sugar maple) on forest land in New England (ME, 

NH,NY,VT), 2006 versus 2011. Annual Net Growth in ft3 of All Trees 7.0 Inches d.b.h. and Greater (percent) Annual Net Growth in ft3 of All Trees 7.0 Inches d.b.h. and Greater (percent) Annual Net Growth in ft3 of All Trees 7.0 Inches d.b.h. and Greater (percent)

Number of Saplings

(million trees) (percent)

Number of Saplings

(million trees) (percent)

Number of Saplings

(million trees) (percent)
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 197

larger numbers of yellow birch and eastern hemlock 

saplings are growing, there may be a defense against 

stimulated beech sprouting through stiff competition 

for light and moisture.

CONCLUSIONS

The condition of the northern hardwood forest varies 

based upon the number of years BBD has been 

infecting an area. Forests that have been infested for 

less than 37 years tend to have higher numbers of 

standing dead trees within the larger diameter classes 

as a result of high tree mortality during the killing 

front phase. They also tend to have an equal number 

of rough and rotten cull trees. Sprout numbers are 

high as BBD-induced breakage prevails in the larger 

beech trees. Beech forests infected from 37 to 60 

years had fewer standing dead, fewer rough cull trees, 

and greater numbers of rotten cull trees, resulting in 

down woody debris accumulation. Beech mortality 

is no longer concentrated in the larger diameter trees. 

Where BBD has been present for longer than 60 years, 

beech forests tend to have larger numbers of standing 

dead trees in the smaller diameter classes and fewer 

rotten cull trees as breakage occurs earlier. The BBDgenerated sprouts initiated during the killing front are 

now infected pole-size trees, facing death at a much 

earlier age than their parents. Tree mortality occurs 

across the diameter classes, and the number of beech 

sprouts tends to be lower in these smaller diameter 

beech trees.

LITERATURE CITED

Bechtold, W.A.; Patterson, P.L., eds. 2005. The 

enhanced forest inventory and analysis 

program-national sampling design and 

estimation procedures. Gen. Tech. Rep. SRS-80. 

Asheville, NC: U.S. Department of Agriculture, 

Forest Service, Southern Research Station. 85 p.

Cale, J.A.; Letkowski, S.K.; Teale, S.A.; Castello, J.D. 

2012. Beech bark disease: an evaluation of the 

predisposition hypothesis in an aftermath forest.

Forest Pathology. 42: 52-56.

Castlebury, L.A.; Rossman, A.Y.; Hyten, A.S. 2006. 

Phylogenetic relationships of Neonectria/

Cylindrocarpon on Fagus in North America.

Canadian Journal of Botany. 84: 1417-1433.

Ehrlich, J. 1934. The beech bark disease: a Nectria

disease of Fagus, following Cryptococcus Fagi.

Canadian Journal of Research. 10: 593-692.

Gravel, D.; Beaudet, M.; Messier, C. 2011. Sapling 

age structure and growth series reveal a shift 

in recruitment dynamics of sugar maple and 

American beech over the last 40 years. Canadian 

Journal of Forest Research. 41: 873-880.

Hane, E.N.; Hamburg, S.P.; Barber, A.L.; Plaut, 

J.A. 2003. Phytotoxicity of American beech 

leaf leachate to sugar maple seedlings in a 

greenhouse experiment. Canadian Journal of 

Forest Research. 33: 814-821.

Jones, R.H.; Raynal, D.J. 1987. Root sprouting in 

American beech: production, survival, and the 

effect of parent tree vigor. Canadian Journal of 

Forest Research. 17: 539-544.

Jones, W.; Twery, M.; Liehold, A. 2011. Beech bark 

disease. Forest Health Protection Web site, U.S. 

Forest Service. http://na.fs.fed.us/fhp/bbd.

Morin, R.S.; Liebhold, A.M.; Gottschalk, K.W.; 

Luzader, E. 2005. Mapping host-species 

abundance of three major exotic forest pests.

Res. Pap. NE-726. Newtown Square, PA: U.S. 

Department of Agriculture, Forest Service, 

Northeastern Research Station.

Morin, R.S.; Liebhold, A.M.; Tobin, P.C.; Gottschalk, 

K.W.; Luzader, E. 2007. Spread of beech bark 

disease in the eastern United States and its 

relationship to regional forest composition.

Canadian Journal of Forest Research. 37: 726-736.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 198

Munck, I.A.; Manlon, P.D. 2006. Landscape-level 

impact of beech bark disease in relation to slope 

and aspect in New York State. Forest Science. 

52: 503-510. 

Runkle, J.R. 2007. Impacts of beech bark disease 

and deer browsing on the old-growth forest.

American Midland Naturalist. 157: 241-249.

Shigo, A.L. 1972. The beech bark disease today 

in the northeastern U.S. Journal of Forestry. 

70: 286-289.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 199

ESTIMATING TREE CAVITY DISTRIBUTIONS 

FROM HISTORICAL FIA DATA

Mark D. Nelson and Charlotte Roy1

Abstract.—Tree cavities provide important habitat features for a variety of wildlife 

species. We describe an approach for using historical FIA data to estimate the number 

of trees containing cavities during the 1990s in seven states of the Upper Midwest. We 

estimated a total of 280 million cavity-containing trees. Iowa and Missouri had the 

highest percentages of cavity-containing trees; Michigan and Minnesota had the lowest. 

The percentage of trees containing cavities was higher for the hard hardwood species 

group and dead trees, and it generally increased with increasing diameter at breast height. 

Abundance of cavities decreased with increasing cavity entrance diameter and increasing 

aboveground cavity height.

1

 Research Forester (MDN), U.S. Forest Service, Northern 

Research Station, 1992 Folwell Ave., St. Paul, MN 55108; 

Research Scientist (CR), Minnesota Department of Natural 

Resources. MDN is corresponding author: to contact, call 

651-649-5104 or email at mdnelson@fs.fed.us. 

INTRODUCTION

Forest Inventory and Analysis (FIA) data (Woudenberg 

et al. 2010) span several decades and are easily queried 

to estimate status and trends of coarse-scale habitat 

characteristics, such as area of young hardwood 

forest or old softwood forest. These data also include 

attributes of tree species and tree size (and, for some 

state inventories, cavity entrance diameters, and 

cavity heights above ground), which can provide finer 

scale habitat information for many forest-associated 

vertebrate species.

Cavity availability is thought to limit populations 

of many secondary cavity nesters. Although 

primary cavity nesters excavate their own cavities 

(e.g., woodpeckers, nuthatches, flickers, and 

chickadees), secondary cavity users depend upon 

existing cavities formed from tree injury or through 

excavation by primary cavity nesters. Secondary 

cavity users include wood duck (Aix sponsa), 

hooded merganser (Lophodytes cucullatus), common 

goldeneye (Bucephala clangula), American kestrel 

(Falco sparverius), eastern bluebird (Sialia sialis), 

prothonotary warbler (Protonotaria citrea), tree 

swallow (Tachycineta bicolor), great crested flycatcher 

(Myiarchus crinitus), boreal owl (Aegolius funereus), 

barred owl (Strix varia), house wren (Troglodytes 

aedon), flying squirrels (Glaucomys spp.), American 

deer mouse (Peromyscus maniculatus), weasels 

(Mustela spp.), fisher (Martes pennant), American 

marten (Martes Americana), and raccoon (Procyon 

lotor). The size and location of tree cavities determine 

their suitability for each wildlife species. 

Cavity formation and presence is related to tree 

species, size (diameter at breast height-d.b.h., height), 

and status (live, dead). Such relationships have been 

used with FIA data to estimate tree cavity abundance 

for mature second-growth timberland in Missouri (Fan 

et al. 2003) and to develop models for cavity-nesting 

waterfowl in hardwood forests of the north central 

United States (Denton et al. 2012). However, strategic 

estimates of tree cavities are lacking for the majority 

of cavity-dependent wildlife species, across most 

forests in the Midwest.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 200

Attributes of tree cavity diameter and cavity height 

above ground historically were recorded in some states 

in the northern FIA region. We analyzed cavity data 

from seven contiguous Midwestern States to produce 

estimates of historical numbers of cavity trees, by tree 

species group, tree diameter, cavity size, and cavity 

position. 

DATA AND METHODS

Tree cavity data were queried from historical North 

Central FIA periodic field inventory plots from seven 

Midwestern States during 1989-1998. The inventory 

years were 1998 for Illinois and Indiana, 1990 for 

Iowa and Minnesota, 1993 for Michigan, 1989 for 

Missouri, and 1996 for Wisconsin. Tree cavity data no 

longer are collected in this FIA region.

The following text from the 1998 FIA field manual 

describes data collection protocols:

At each sample point, examine all live and 

standing-dead trees, > 5.0” D.B.H., for cavities 

that could be used for nesting, resting or 

storage by birds or mammals. To qualify as a 

cavity, an entrance hole must be 1.0” or larger 

in the main stem, fork, or large limb. (A limb 

must be greater than 8.0” DOB.)

For the largest cavity record a two-digit code. 

[Only one cavity–the largest-was recorded for 

each cavity tree, regardless of the number of 

cavities present.] The first digit indicates the 

size of the cavity. Cavity size is the diameter 

of the largest ball that could fit through the 

entrance hole [by 1-inch categories, through 

9+ inches (Table 1)]. The second digit 

indicates the location of the cavity on the tree 

[above-ground, in feet, aggregated into nine 

height categories (Table 1)].

We tabulated the total number of trees sampled and the 

number of those trees containing one or more cavities, 

by tree status (live, dead), tree d.b.h., and major 

species group: (1) pines; (2) other softwoods–spruce, 

fir, hemlock, etc.; (3) soft hardwoods-cottonwood, 

aspen, elm, basswood, soft maple, etc.; and (4) hard 

hardwoods–oak, hickory, beech, walnut, hard maple, 

etc. (Woudenberg et al. 2010: see Appendix F for 

complete list of species.). Tree records were omitted 

when status was absent or populated with unknown 

codes (n = 8,427; 1.1 percent), and when trees were 

down-dead (n = 10,915; 1.4 percent) or stumps (n = 

39,772; 5.2 percent). For counts of trees with cavities, 

tree records were excluded when cavity codes did not 

reveal both cavity entrance diameter and cavity height 

above ground (n = 91; 0.01 percent).

Table 1.—Percentage of cavity-containing trees, by cavity entrance diameter and aboveground cavity 

height during the 1990s, Upper Midwest

Cavity

Entrance Aboveground cavity height (feet)

Dia. (in.) 0-1 2-5 6-9 10-19 20-29 30-39 40-49 50-59 60+ Total

1 5.5 6.6 4.6 3.9 1.2 0.4 0.1 0.0 0.2 22.5

2 5.5 5.1 4.4 5.0 2.2 0.7 0.3 0.1 0.1 23.4

3 4.7 3.0 3.1 4.2 2.2 0.8 0.2 0.1 0.1 18.5

4 3.6 1.7 1.4 2.1 1.3 0.7 0.5 0.0 0.0 11.4

5 2.0 0.8 0.7 1.1 0.6 0.2 0.1 0.0 0.0 5.6

6 2.0 0.7 0.6 0.7 0.5 0.3 0.1 0.0 0.0 4.8

7 0.9 0.3 0.2 0.3 0.2 0.1 0.0 0.0 0.0 2.0

8 0.6 0.3 0.1 0.2 0.1 0.1 0.0 0.0 0.0 1.6

9+ 4.5 1.6 1.2 1.5 0.9 0.3 0.1 0.0 0.1 10.2

Total 29.3 20.0 16.4 19.1 9.4 3.6 1.4 0.3 0.5 100.0
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 201

Numbers of all live and standing dead trees ≥ 5 inches 

d.b.h. on timberland were estimated using standard 

FIA estimators via FIA’s online EVALIDator tool 

(http://apps.fs.fed.us/Evalidator/tmattribute.jsp). In 

brief, counts of trees on each sample plot, proportion 

of each plot in forest, and number of plots in each 

stratum were used in a stratified estimation procedure 

to produce estimates of total number of trees (Scott 

et al. 2005). Numbers of cavity trees were estimated 

by multiplying the estimates of numbers of live and 

standing dead trees times the proportion of sampled 

trees containing cavities, by combinations of tree 

diameter, major species group, and live/dead status. 

No estimates of uncertainty (e.g., standard errors) 

were computed for this preliminary study because 

population estimates of numbers of trees were 

weighted by the proportion of cavity trees in another 

database, with some mismatch in tree records.

RESULTS

A total of 709,638 trees were sampled in this study; 

one or more cavities were observed in 25,424 trees. 

Nearly half of all cavities were observed 0 to 5 feet 

above ground, one-third were 6 to 19 feet above 

ground, and the remainder were more than 20 feet 

above ground. Nearly two-thirds of cavities were 1 to 

3 inches in diameter. Cavity abundance decreased with 

increasing cavity diameter (Table 1). 

Percentage of cavity-containing trees varied by state, 

ranging from 1.7 percent and 2.8 percent in Michigan 

and Minnesota, respectively, to 6.5 percent and 7.8 

percent in Missouri and Iowa, respectively. Standing 

dead trees made up 11 percent of all trees and 15 

percent of cavity trees. The proportion of total trees 

containing cavities was 3.4 percent for live, 5.0 percent 

for dead, and 3.6 percent for live and dead trees 

combined. The largest absolute numbers of cavities in 

live trees were recorded in soft and hard hardwood tree 

species and in trees of 9.0 to 16.9 inches d.b.h. The 

hard hardwood major species group also contained the 

largest percentages of cavities relative to total number 

of live trees recorded for that group (4.9 percent). One 

or more tree cavities were recorded in more than 29 

percent of the largest diameter live trees (41.0+ inches 

d.b.h.) (Fig. 1). Relative to the total number of trees 

by diameter class, cavity-containing trees were less 

abundant in small-diameter trees (<11 inches d.b.h.), 

similar in abundance for trees of 11 to 12.9 inches 

d.b.h., and more abundant for larger trees (13.0+ 

inches d.b.h.) (Fig. 1). 

0

10

20

30

40

50

60

70

80

90

100

68

10

12

1416

1820

22

24

26

283032

34

36

38

40

42+

Percentage of d.b.h. class

Diameter at breast height (in.)

Cavity trees

Non-cavity trees

0

5

10

15

20

6 12 18 24 30 36 42+

Percentage of all trees

D.b.h. (in.)

Figure 1.—Distribution of non-cavity and cavity-containing trees within each tree d.b.h. class during the 1990s, Upper Midwest.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 202

Based on the sample, we estimated a total of 11.3 

billion live trees and 1.6 billion dead trees ≥ 5 inches 

d.b.h. for the seven states during the 1990s, of which 

225 million live trees (2.0 percent) and 55 million 

dead trees (3.4 percent) were estimated to contain one 

or more cavities.

DISCUSSION AND CONCLUSIONS

Using historical FIA data, we estimated a total of 

280 million cavity-containing trees in the sevenstate region during the 1990s. The number of cavity 

trees as a percentage of all trees generally increased 

with increasing d.b.h. Dead trees and live trees in 

the hard hardwood species group both contained 

higher percentages of cavities relative to total tree 

abundance of hard hardwoods, which may explain 

variability among states. The two states with the 

highest percentages of cavity-containing trees, Iowa 

and Missouri, both have <1 percent of all trees in 

softwoods; the two states with the lowest percentages 

of cavity-containing trees, Michigan and Minnesota, 

both have >20 percent of all trees in softwoods.

Almost half of all cavities had entrance diameters 

smaller than 3 inches; about half of all cavities 

were located less than 6 feet above ground. These 

characteristics are unsuitable for many wildlife 

species. We acknowledge that some entrances deemed 

cavities from the ground may not have been actual 

cavities (just knots or scars), and some cavities may 

not have been observed from the ground, despite their 

presence, especially at greater heights above ground: 

tree cavities on FIA plots in the Pacific Northwest 

are frequently missed by field crews (Tara Barrett, 

personal communication). 

This study expands the geographic extent addressed 

in Fan et al. (2003) and introduces potential 

enhancements to the approach described in Denton 

et al. (2012): cavity probabilities were expanded to 

include both softwood and hardwood trees, both live 

and dead, for a wider range of tree diameters, and from 

a larger sample of trees. 

Work is underway to estimate standard errors for 

historical tree cavity data and to refine models of tree 

cavity probabilities that can be applied to current and 

future FIA data for which cavity observations are no 

longer recorded. Ongoing collection of FIA tree cavity 

data could detect changes in cavity probabilities. 

Additional studies are being conducted to estimate 

abundance of cavities meeting species-specific wildlife 

habitat requirements.

ACKNOWLEDGMENTS

The authors thank James Blehm, Mark Hatfield, 

and Pat Miles for their assistance in accessing and 

understanding historical FIA data; and Tara Barrett, 

Pat Miles, and Steve Shifley for reviewing and 

improving this manuscript.

LITERATURE CITED

Denton, J.C.; Roy, C.L.; Soulliere, G.J.; Potter, B.A. 

2012. Current and projected abundance of 

potential nest sites for cavity-nesting ducks in 

hardwoods of the North Central United States.

Journal of Wildlife Management. 76(2): 422-432.

Fan, Z.; Shifley, S.R.; Spetich, M.A.; Thompson, F.R. 

III.; Larsen, D.R. 2003. Distribution of cavity 

trees in midwestern old-growth and secondgrowth forests. Canadian Journal of Forest 

Research. 33: 1481-1494.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 203

Scott, C.T.; Bechtold, W.A.; Reams, G.A.; Smith, 

W.D.; Westfall, J.A.; Hansen, M.H.; Moisen, 

G.G. 2005. Sample-based estimators used by 

the Forest Inventory and Analysis National 

Information Management System. In: Bechtold, 

W.A.; Patterson, P.L., eds. The enhanced Forest 

Inventory and Analysis Program-national sampling 

design and estimation procedures. Gen. Tech. 

Rep. SRS-80. Asheville, NC: U.S. Department of 

Agriculture, Forest Service, Southern Research 

Station: 43-67.

Woudenberg, S.W.; Conkling, B.L.; O’Connell, B.M.; 

LaPoint, E.B.; Turner, J.A.; Waddell, K.L. 2010. 

The Forest Inventory and Analysis Database: 

database description and users manual version 

4.0 for Phase 2. Gen. Tech. Rep. RMRS-GTR-245. 

Fort Collins, CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research Station. 

336 p. Available at http://treesearch.fs.fed.us/

pubs/37446. 

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 204

BUILDING IMPROVED MODELS OF SUGAR MAPLE MORTALITY

Charles H. Perry and Patrick L. Zimmerman1

Abstract.—The decline of sugar maple (Acer saccharum Marsh.) in the northern 

United States is causing concern, and several studies have identified soil properties that 

are linked to the observation of dead/dying trees. Unfortunately, the sample of trees 

supporting these studies is purposive in nature; soil properties are assessed only on those 

plots where dead trees are observed. In this study, we used the U.S. Forest Service’s 

Forest Inventory and Analysis database (FIADB) to conduct an exploratory analysis of a 

broader population of sugar maple (live and dead) across a wide range of soil types. This 

population of plots has a highly skewed, zero-inflated distribution: the number of plots in 

the sample without dead trees is an order of magnitude greater than the number of plots 

with dead trees. One effective method of analysis is a hurdle—or conditional—model 

approach. In the first phase, the response variable is the presence or absence of dead 

sugar maple and the inferential space is the entire population of plots with sugar maple 

trees. The second phase uses the relative abundance of dead sugar maple as the response 

variable; in this case, inference is restricted to those plots where dead sugar maple trees 

are observed. In both sets of models, basal area and geology are significant predictors 

of dead sugar maple, but the most significant soil variables vary between these two 

inferential spaces. Our study highlights important analytical considerations when using 

FIADB for analysis of forest health conditions and presents simple methods to create a 

more comprehensive space for statistical inference.

1

 Research Soil Scientist (CHP), U.S. Forest Service, 

Northern Research Station, 1992 Folwell Avenue, 

St. Paul, MN 55108; Ph.D. Candidate (PLZ), University 

of Minnesota, Minneapolis, MN. CHP is corresponding 

author: to contact, call 651-649-5191 or email at 

charleshperry@fs.fed.us.

INTRODUCTION

Several studies of sugar maple (Acer saccharum

Marsh.) mortality exist (e.g., Horsley et al. 2000, Long 

et al. 2009), but most evaluations focus on an area of 

known decline from Pennsylvania to New Hampshire. 

Sampling of sugar maple decline in these and related 

studies tends to be purposive in nature and evaluates 

only those plots with dead sugar maple.

The U.S. Forest Service Forest Inventory and Analysis 

Program (FIA) collects field data to describe the 

status and trends of forests across the United States. It 

focuses on live trees and live-tree observations vastly 

outnumber those of dead trees in the FIA database 

(FIADB). To wit, the ratio of live-tree to dead-tree 

observations for the complete 2011 5-year inventory of 

the Great Lakes states of Minnesota, Wisconsin, and 

Michigan was 5.8:1.2 However, the inventory is not 

biased systematically against dead trees. Dead trees 

are recognized as particularly important ecologically 

(Woodall et al. 2009), and standing dead trees are the 

subject of specific reporting since Field Guide 2.0 was 

published in 2004 (USDA Forest Service 2004). 

Joint observations of live and dead trees contain 

important ecological information and increasing 

the size of the sample population also increases the 

resulting inferential space. However, a joint analysis 

of live and dead trees in FIADB yields a zero-inflated 

population, and statistical inference which does not 

2

 Calculations may be made using FIA’s online tools 

available at http://fiatools.fs.fed.us. 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 205

account for zero-inflation is likely to be erroneous 

(Martin et al. 2005). Zero-inflation often can be 

accommodated by hurdle and mixture models when 

the additional zeros are “true zeros” (Martin et al. 

2005). Hurdle models (also known as conditional 

models) treat the problem in two stages: first, the 

analyst determines the probability of a species or 

property being present or absent in a binary outcome; 

second, and conditional on its presence, the relative 

abundance of said species/property is found (Cameron 

and Trivedi 1998). Mixture models attempt to answer 

the same two questions in one model, but the resulting 

parameters are more challenging to interpret (Martin et 

al. 2005).

In this paper, we outline the application of a hurdle 

model approach to sugar maple mortality in the 

northern United States. Twenty states were included 

in the analysis: Connecticut, Delaware, Illinois, 

Indiana, Iowa, Maine, Maryland, Massachusetts, 

Michigan, Minnesota, Missouri, New Hampshire, New 

Jersey, New York, Ohio, Pennsylvania, Rhode Island, 

Vermont, West Virginia, and Wisconsin. Our study 

highlights important analytical considerations when 

using FIADB for analysis of forest health conditions 

and presents simple methods to create a more 

comprehensive space for statistical inference.

METHODS

Forest and soil inventory plots in the Northern United 

States were joined and extracted from the FIADB 

(Woudenberg et al. 2010). These data were collected 

between 2000 and 2006. Plots were included in the 

analysis if at least three sugar maple trees with d.b.h. 

greater than 5 inches were measured on the plot.

Plot information included state and county, and 

latitude, and longitude. Plot latitude and longitude 

were used to link plots to spatially explicit geologic 

databases describing the origin of surface material 

(Fullerton et al. 2003). Forest-level attributes included 

the basal area of live and dead sugar maple, ecological 

subsection, forest-type group, stand age, stand-size 

code (a classification of the predominant diameter 

class of live trees), slope, aspect, physiographic 

class (e.g., xeric, mesic, or hydric), and the presence/

absence of disturbance on the plot. Soil plot 

information focused on the suite of soil chemistry 

variables extracted from mineral soil samples (O’Neill 

et al. 2005, Woodall et al. 2010) and their derivatives. 

Statistical analyses were conducted in three stages: (0) 

ordinary linear regression on all plots to demonstrate 

the impact zero-inflation; 1) logistic regression on 

the presence and absence of dead sugar maple; and 

2) ordinary linear regression of those plots with 

dead sugar maple. Given the exploratory nature 

of our investigation using the suite of variables 

available in FIADB, analyses were completed using 

stepwise techniques in R (R Development Core Team 

2011). Appropriate variable transformations were 

suggested by Box-Cox analyses. Zeroes cannot be 

log transformed, so a very small number (0.001) was 

added to variables as required. 

RESULTS

Stage 0

A total of 219 plots were selected that met the defined 

criteria of at least three sugar maple trees and the 

collection of soil chemistry data. A number of terms 

were available as predictors (Table 1). Our first 

effort was directed at modeling the fraction of dead 

sugar maple basal area as the response in a multiple 

regression model. If successful, this would be a simple 

and complete model of sugar maple mortality. This 

investigation collapsed because of the zero-inflated 

distribution; too many plots had zero dead sugar maple 

(Fig. 1).

Stage 1

Given our trouble with the zero-inflated fraction of 

dead sugar maple basal area in stage 0, we adopted 

hurdle modeling. Using the hurdle model, we modeled 

the data in two stages. In stage 1, we modeled the 

presence or absence of dead sugar maple using logistic 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 206

Table 1.—Variables available to predict sugar maple death across the northern United States

Site characteristics Soil characteristics

Latitude, Longitude {lat, lon} pH

Drought index {di} sqrt(ECEC) {secec}

Ecoprovince {eco} log(Ca:Al ratio) {lca.al}

Forest-type group {forest} log(Mg:Al ratio) {lmg.al}

Basal area {ba} log(Mg:Mn ratio) {lmg.mn}

Stand age {age} log(Exchg. K percentage) {lekp}

Stand-size class {size} log(Exchg. Na percentage) {lesp}

Site class {site.class} log(Exchg. Ca percentage) {lecp}

Slope {slope} log(Exchg. Mg percentage) {lemp}

Aspect {aspect} log(Exchg. Al percentage) {leap}

Disturbance {dist}

Geology {geo}

Dead Fraction

Frequency

0.0 0.1 0.2 0.3 0.4 0.5

0 50 100 150 200

(A)

Log(Adjusted Dead Fraction)

Frequency

−3.0 −2.0 −1.0 0.0

0 50 100 150 200

(B)

Figure 1.—Histograms of the observed dead fraction of sugar maple basal area on all plots (A) before and (B) after logarithmic 

transformations.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 207

regression. In stage 2 (below), we modeled abundance 

given dead trees were present. Modeling presence or 

absence was accomplished using the binomial family 

of glm(). As before, 219 plots were available to 

parameterize the model, and the same terms were used 

as predictors (Table 1).

Exploratory binomial models were built using two 

starting points: 1) intercept only; and 2) a full model. 

Stepwise regression found the best model using AIC 

as the selection criteria. Similar models were selected 

from different starting points. The coefficients of the 

most plausible stage 1 model are included in Table 2.

The interpretation of the intercepts in logistic 

regression is done using log-odds. Each unit increase 

in basal area increases the odds of dead basal area by 

a factor of 1.02. Each 10x increase in Mg:Mn reduces 

the odds of dead basal area to 55 percent of that for the 

original landscape. A till landscape reduces the odds of 

dead basal area to 50 percent of that for other glacial 

landscapes. A nonglacial landscape reduces the odds of 

dead basal area to 10 percent of that for a glacial (nontill) landscape.

Stage 2

In the second stage of the hurdle model, we modeled 

the amount of dead sugar maple basal area found on 

those plots that have dead sugar maple. We focused on 

the 58 points where dead sugar maple was observed 

(Fig. 2), representing 26 percent of the population of 

plots with sugar maple. The parameters from the most 

plausible model (below) are included in Table 3.

Table 2.—Parameters for the most plausible model 

of sugar maple death using logistic regression 

with all plots

Variable Coefficient Estimate Std. Error Z Pr(>|z|)

ba 0.0197 0.0057 3.47 0.0005

lmg.mn -0.5959 0.2198 -2.71 0.0067 

geo:glacial -0.3715 0.5001 -0.74 0.4576

geo:till -1.0628 0.3698 -2.87 0.0041 

geo:non-glacial -2.5894 0.5593 -4.63 3.67e-06

Log(Adjusted Dead Fraction)

Frequency

−3.0 −2.0 −1.0 0.0

0

5 10 15 20

Figure 2.—Histogram of the observed dead fraction of sugar 

maple basal area for those plots with dead trees.

Table 3.—Parameters for the most plausible model 

of sugar maple death using linear regression with 

only those plots including dead trees

Variable

Coefficient Estimate Std. Error t value Pr(>|t|)

Intercepta -0.675 0.587 -1.150 0.257

lat 0.018 0.014 1.334 0.190

secec 0.077 0.031 2.473 0.018

lca.al 0.027 0.013 2.066 0.046

lmg.mn -0.018 0.011 -1.636 0.110

lesp 0.080 0.022 3.674 0.001

lemp -0.078 0.033 -2.362 0.023

forest (MBB) -0.110 0.077 -1.428 0.162

forest (OH) 0.213 0.085 2.501 0.017

forest (Other) -0.140 0.104 -1.340 0.188

age 0.002 0.001 1.660 0.105

size (Medium) 0.055 0.042 1.318 0.196

size (Small) 0.157 0.115 1.366 0.180

site.class (4) 0.025 0.103 0.244 0.808

site.class (5) 0.187 0.107 1.743 0.089

site.class (6) 0.111 0.111 1.004 0.322

dist 0.065 0.056 1.164 0.252

geo:nonglacial 0.045 0.098 0.455 0.652

geo:till 0.089 0.039 2.245 0.031

ba -0.001 0.001 -2.543 0.016

a

The model intercept includes forest (AB), size (Large), site (3), 

and geo (glacial, not till). Multiple R-squared: 0.6118, Adjusted 

R-squared: 0.4177, F-statistic: 3.152 on 19 and 38 DF, 

p-value: 0.001269. 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 208

The exploratory model developed in Stage 2 presents 

results affirming and challenging previous evaluations 

of sugar maple decline. Rising Mg levels (lmg.mn 

and lemp) are associated with declines in death, but 

contrary to expectations, increases in other forms of 

mineral soil nutrition available to trees (lecec, lca.al, 

and lesp) are associated with increasing death of sugar 

maple (Horsley et al. 2000, Long et al. 2009). 

DISCUSSION AND CONCLUSIONS

Statistical inference in a hurdle model approach 

is complicated by the use of two stages of model 

building. In stage 1, we analyzed the full dataset using 

logistic regression, and log (odds) can be difficult to 

interpret. The model developed in the second stage is 

constructed more traditionally―by linear regression―

so interpretation of the resulting coefficients is 

relatively straightforward. Additionally, while these 

two sets of models are similar, they are not identical.

Our emphasis here is to outline a process whereby 

more comprehensive datasets (namely those 

including both live and dead trees) can be used to 

evaluate the likelihood of sugar maple death across 

the species’ range, so additional interpretations are 

being set aside for more thorough consideration in a 

subsequent manuscript. Given our use of AIC, multimodel inference will be a useful tool for assessing 

predictors within and potentially between the two 

stages (Burnham and Anderson 2002). Our key point 

is that hurdle models offer an opportunity to model 

comprehensive, zero-inflated datasets, like those 

collected by FIA, where the zero-inflation results from 

the presence of true zeros in the dataset (Martin et al. 

2005).

LITERATURE CITED

Burnham, K.P.; Anderson, D.R. 2002. Model 

selection and multimodel inference: a practical 

information-theoretic approach. 2nd ed. New 

York, NY: Springer. 488 p.

Cameron, A.C.; Trivedi, P.K. 1998. Regression 

analysis of count data. Cambridge, UK: 

Cambridge University Press. 411 p.

Fullerton, D.S.; Bush, C.A.; Pennell, J.N. 2003. 

Map of surficial deposits and materials in the 

eastern and central United States (east of 102 

west longitude). U.S. Geological Survey Geologic 

Investigations Series I-2789. Denver, CO: U.S. 

Geological Survey.

Horsley, S.B.; Long, R.P.; Bailey, S.W.; Hallett, R.A.; 

Hall, T.J. 2000. Factors associated with the 

decline disease of sugar maple on the Allegheny 

Plateau. Canadian Journal of Forest Research. 30: 

1365-1378. 

Long, R.P.; Horsley, S.B.; Hallett, R.A.; Bailey, 

S.W. 2009. Sugar maple growth in relation to 

nutrition and stress in the northeastern United 

States. Ecological Applications. 19(6): 1454-1466. 

Martin, T.G.; Wintle, B.A.; Rhodes, J.R.; Kuhnert, 

P.M.; Field, S.A. [et al.]. 2005. Zero tolerance 

ecology: improving ecological inference by 

modelling the source of zero observations.

Ecology Letters. 8(11): 1235-1246. 

O’Neill, K.P.; Amacher, M.C.; Perry, C.H. 2005. 

Soils as an indicator of forest health: a guide to 

the collection, analysis, and interpretation of 

soil indicator data in the Forest Inventory and 

Analysis program. Gen. Tech. Rep. NC-258. 

St. Paul, MN: U.S. Department of Agriculture, 

Forest Service, North Central Research Station. 

53 p.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 209

R Development Core Team. 2011. R: a language and 

environment for statistical computing. Vienna, 

Austria: R Foundation for Statistical Computing.

USDA Forest Service. 2004. Forest Inventory and 

Analysis national core field guide. Volume 

1: Field data collection procedures for phase 

2 plots, version 2.0 [online]. St. Paul, MN. 

Available at http://fia.fs.fed.us/library/databasedocumentation/. (Accessed August 15, 2012).

Woodall, C.W.; Conkling, B.L.; Amacher, M.C.; 

Coulston, J.W.; Jovan, S. [et al.]. 2010. The Forest 

Inventory and Analysis database version 4.0: 

Database description and users manual for 

phase 3. Newtown Square, PA, U.S. Department 

of Agriculture, Forest Service, Northern Research 

Station. 180 p.

Woodall, C.W.; Smith, J.E.; Miles, P.D. 2009. 

Attributes of standing dead trees in forests of 

the United States. In: McRoberts, R.E.; Reams, 

G.A.; Van Deusen, P.C.; McWilliams, W.H., 

eds. Proceedings of the eighth annual Forest 

Inventory and Analysis symposium; 2006 October 

16-19; Monterey, CA. Gen. Tech. Rep. WO-79. 

Washington, DC: U.S. Department of Agriculture, 

Forest Service: 191-196.

Woudenberg, S.W.; Conkling, B.L.; O’Connell, 

B.M.; LaPoint, E.B.; Turner, J.A. [et al.]. 2010. 

The Forest Inventory and Analysis database: 

database description and users manual for 

version 4.0 for phase 2. Gen. Tech. Rep. RMRS245. Fort Collins, CO: U.S. Department of 

Agriculture, Forest Service, Rocky Mountain 

Research Station. 336 p.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 210

UTILITY OF TREE CROWN CONDITION INDICATORS 

TO PREDICT TREE SURVIVAL

USING REMEASURED FOREST INVENTORY AND ANALYSIS DATA

Randall S. Morin, Jim Steinman, and KaDonna C. Randolph1

Abstract.—The condition of tree crowns is an important indicator of tree and forest 

health. Crown conditions have been evaluated during surveys of Forest Inventory and 

Analysis (FIA) Phase 3 (P3) plots since 1999. In this study, remeasured data from 

39,357 trees in the northern United States were used to assess the probability of survival 

among various tree species using the suite of crown condition variables. Logistic 

regression procedures were employed to assess the importance of individual crown 

condition variables alone and in combination for predicting tree survival. Results of the 

regression analyses indicated that crown dieback was the most important crown condition 

variable for predicting tree survival for all species combined and for the 10 individual 

species in the study. Additionally, one-way analysis of variance (ANOVA) results 

identified differences among the ability of different tree species to survive varying levels 

of crown dieback. The results provide statistical evidence for selecting crown dieback as 

one of the crown condition variables to be collected on a subset of Phase 2 plots (P2+) 

starting in 2012. 

1

 Research Forester (RSM), Northern Research Station, 

11 Campus Blvd., Suite 200, Newtown Square, PA 19073; 

Forest Health Coordinator (JS), U.S. Forest Service, 

Northeastern Area State & Private Forestry; Mathematical 

Statistician (KCR), U.S. Forest Service, Southern Research 

Station. RSM is corresponding author: to contact, call 

610-557-4054 or email at rsmorin@fs.fed.us.

INTRODUCTION

An important indicator of the health of a tree is the 

condition of its crown. The U.S. Forest Service Forest 

Inventory and Analysis (FIA) Program uses visual 

assessments of tree crown condition to monitor trends 

in forest health. Trees with vigorous, healthy crowns 

tend to have higher growth rates. By contrast, trees 

with damaged or degraded crowns have a reduced 

capacity for photosynthesis and slower growth rates. 

Many stressors can cause crown degradation including 

insects, disease, weather events, senescence, and 

competition or other stand conditions (Kenk 1993). 

Additionally, trees with unhealthy crowns are more 

susceptible to mortality (Kulman 1971, Lawrence et al. 

2002).

Assessments of tree crown conditions have been 

conducted as part of the U.S. Forest Service Forest 

Health Monitoring (FHM) Program since 1990 

and as a part of FIA since 1999 (Riiters and Tkacz 

2004). Preliminary analyses of crown condition data 

through the FHM program demonstrated the data’s 

utility in classifying tree health and likelihood of 

survivorship, with crown dieback as the best indicator 

of crown condition (Steinman 2000). The crown 

health indicators for live overstory trees (d.b.h. ≥ 5.0 

in) that have been consistently collected since 2001 

are uncompacted live crown ratio (UNCR), crown 

light exposure (CL), crown density (CDEN), crown 

dieback (CDBK), and foliage transparency (TRANS) 

(Schomaker et al. 2007). Results from crown condition 

data have been presented as frequency statistics for 

individual crown indicators (e.g., Randolph et al. 

2010), summaries of tree health by species in FIA 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 211

5-year reports (e.g., Widmann et al. 2012), and more 

specific analyses investigating changes in forest health 

(e.g., Morin et al. 2004, Will-Wolf and Jovan 2009).

To increase the efficiency of field data collection, 

FIA is evaluating the analytical utility of numerous 

variables such as collected as indicators of forest 

health. Here, we assess the importance of crown 

indicators individually and in combination for 

predicting tree survival; the study objective is to 

provide statistical evidence for choosing crown 

condition indicators for continued inclusion in FIA 

data collection. This study focuses only on the utility 

of the crown condition variables for predicting tree 

survival and does not explore other applications.

METHODS

A three-phase forest inventory and monitoring effort 

is implemented by the FIA program within the U.S. 

Forest Service (Bechtold and Patterson 2005). Phase 

1 (P1) is the development of a post-stratification 

scheme using remotely-sensed data. The second phase 

(P2) entails measuring sample plots on the ground for 

the usual suite of forest mensuration variables such 

as tree species, d.b.h., height, forest type, stand age, 

etc. Overstory trees (d.b.h. ≥ 5.0 in.) are measured on 

four 24-ft radius subplots; saplings (1.0 ≤ d.b.h. < 5.0 

in.) are recorded on four microplots of 6.8-ft radius 

each. Phase three (P3) occurs on a 1/16th subset of 

the P2 plots, where additional data are collected on 

forest health indicators, including the crown condition 

variables.

In this study, remeasured data from 39,357 trees in 

the northern United States (2001-2005 to 2006-2010) 

were used to assess the probability of survival among 

various tree species using the suite of crown condition 

variables. Tree species with at least 1,000 remeasured 

trees were included in the individual species models. 

Survival of an individual tree is a discrete event where 

each remeasured tree can only have the value of 1 

(live) or 2 (dead); removed trees were not included. 

The probability of survival was modeled using the 

logistic equation:

P(1) = 1 / (1 + e(b’x) )

where b’x is a linear combination of parameters b and 

independent variables x, and e is the base of the natural 

logarithm. The PROC LOGISTIC procedure (SAS 

Institute 2009) was used to estimate the parameters 

of the logistic regression using maximum likelihood 

methods. Additionally, one-way analysis of variance 

(ANOVA) tests were used to test the effect of CDBK 

on tree survival. Tests were conducted for all trees 

combined and for five the most abundant species 

individually: red maple, sugar maple, northern whitecedar, balsam fir, and quaking aspen. The StudentNewman-Kuels test was employed to determine 

significant differences in survivorship among CDBK 

classes.

RESULTS

All parameters listed in Table 1 are significant 

(α=0.05) and many are highly significant (p-value 

<0.0001 indicated by an *). For all species combined, 

we found all crown variables to be highly significant 

in predicting survival; for each species individually, 

we found CDBK to be highly significant. The area 

under the receiver operating characteristic (ROC) 

curve is provided for the classification models as an 

indicator of classification accuracy (Table 2). To judge 

the relative importance of the variables, standard errors 

are listed in Table 1 and Chi-square values are given in 

Table 2.

Parameter estimates conform to expectations in nearly 

all cases. The coefficient of CDBK is negative in all 

cases (Table 1), indicating decreasing survival with 

increasing CDBK. Similarly, the coefficient of TRANS 

is negative in the most significant cases, indicating 

decreasing survival with increasing TRANS. The 

coefficients of the other variables are all positive, 

except for CL in the models for balsam fir and eastern 

hemlock (Table 1), which indicates increasing survival 

with increases in those variables.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 212

Table 1.—Estimated parameters for survival models. All parameters are significant at the 95 percent level 

of confidence (α = 0.05)

Sample Variable

Species Size (n) CDBK UNCR CDEN CL TRANS

All species 39,357 -0.0371 (0.00185)* 0.0165 (0.00113)* 0.0290 (0.00168)* 0.0821 (0.0180)* -0.0118 (0.00183)*

Red maple 4,459 -0.0464 (0.00672)* 0.0340 (0.00486)* 0.0356 (0.00582)* 0.5933 (0.0978)* 0.0221 (0.00985)

Sugar maple 3,003 -0.0423 (0.00945)* 0.0219 (0.00651) 0.0525 (0.00900)* 0.4488 (0.1291) 0.0308 (0.0153)

Northern white-cedar 1,962 -0.0436 (0.00594)* 0.3484 (0.1054)

Balsam fir 1,603 -0.1127 (0.0166)* 0.0260 (0.00361)* 0.0264 (0.00560)* -0.1965(0.0483)*

Quaking aspen 1,536 -0.0622 (0.0109)* 0.0233 (0.00585)* 0.0320 (0.00556)*

White oak 1,336 -0.0775 (0.0124)* 0.4609 (0.1686)

Paper birch 1,290 -0.0265 (0.00754) 0.0268 (0.00613)* 0.2979 (0.0725)*

Northern red oak 1,092 -0.0655 (0.0150)* 0.5081 (0.1727) -0.0474 (0.00779)*

Eastern white pine 1,027 0.0661 (0.0149)* 0.0275 (0.00748) 0.4159 (0.1391)

Eastern hemlock 1,009 -0.0908 (0.0178)*

CDBK is crown dieback, UNCR is uncompacted live crown ratio, CDEN is crown density, CL is crown light exposure, and TRANS is foliage 

transparency.

Standard errors are given in parentheses.

* indicates significance (p <0.0001).

Table 2.—Receiver operating characteristic (ROC) curve area and Chi-square values for the parameter 

estimates in Table 1

 Chi-Square Statistic

 Variable

Species ROC Curve Area CDBK UNCR CDEN CL TRANS

All species 0.7176 403 215 298 21 42

Red maple 0.8059 48 49 37 37 5

Sugar maple 0.8015 20 11 34 12 4

Northern white-cedar 0.7200 54 11

Balsam fir 0.7557 46 52 22 17

Quaking aspen 0.7075 32 16 33

White oak 0.7532 39 7

Paper birch 0.7015 12 19 17

Northern red oak 0.7805 19 9 37

Eastern white pine 0.7699 20 14 9

Eastern hemlock 0.7340 26

CDBK is crown dieback, UNCR is uncompacted live crown ratio, CDEN is crown density, CL is crown light exposure, and TRANS is foliage 

transparency.

The Chi-square statistics in Table 2 reveal that the 

most important variable for all species combined and 

most species individually is CDBK. The second most 

important variable for all species combined is CDEN, 

and it is also the most important for sugar maple, 

quaking aspen, and paper birch. Among individual 

species the rankings of importance of the variables is 

inconsistent except that CDBK is the most important 

in most models.

One-way ANOVA analyses indicate that the proportion 

of trees that survived until remeasurement decreased 

as CDBK increased (Figs. 1 and 2). For all species 

combined, the proportion of surviving trees for 

each CDBK class is significantly different from all 

others. For most individual species, the proportion 

of survivors in the greater than 50 percent CDBK 

class is significantly different from all other classes, 

but for balsam fir and quaking aspen, proportion of 

surviving trees in the 26 to 50 CDBK class is similar 

to the greater than 50 percent CDBK class. Based 

on the proportion of surviving trees in the 26 to 50 

and greater than 50 CDBK classes, sugar maple and 

northern white-cedar appear to be able to tolerate high 

levels of CDK better than red maple, balsam fir, and 

quaking aspen (Fig. 2).
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 213

Figure 1.—Proportion of trees that were alive at remeasurement in crown dieback classes from time 1 measurement. Bars 

labeled with the same letter (A-E) are not significantly different (ANOVA, Student-Newman-Keuls test, a = 0.05).

Figure 2.—Proportion of trees that were alive at remeasurement in crown dieback classes from time 1 measurement, by 

species. Bars labeled with the same letter (A-E) are not significantly different (ANOVA, Student-Newman-Keuls test, a = 0.05).
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 214

DISCUSSION

Results of the logistic regression analysis for all 

species combined indicate that the crown condition 

variables are all significant predictors of survival, but 

CDBK was the most important variable. It should be 

noted, however, that even though UNCR was not a 

significant predictor of survivability for all species, by 

way of establishing the base of the live crown UNCR 

forms the basis for assessing all of the other crown 

condition variables and therefore must always be 

assessed. Additionally, results of the logistic regression 

analyses for individual species indicate that CDBK is 

the only crown condition variable that is a significant 

predictor of survival for all 10 species in this study. 

Based on differences in the proportion of survivors 

among individual species, CDBK is also a useful 

metric for assessing the ability of different species to 

tolerate and survive varying levels of crown health. 

The results of this study provide statistical evidence 

for selecting CDBK as one of the crown condition 

variables to be collected on a subset of Phase 2 plots 

(termed P2+) beginning in 2012. The P2+ sample 

will be larger than the P3 sample. Therefore, moving 

the CDBK indicator from P3 to P2+ will increase 

statistical power for this line of research. Additional 

research that is suggested by the results of this study 

include looking at successive measures of CDBK on 

surviving trees to determine whether crown health 

recovery has occurred and predicting future mortality 

based on CDBK values. There is also great potential in 

using crown dieback as a means to parse FIA reporting 

attributes (e.g., volume) of live trees into healthy and 

unhealthy categories.

LITERATURE CITED

Bechtold, W.A.; Patterson, P.L., eds. 2005. The 

enhanced Forest Inventory and Analysis 

program - national sampling design and 

estimation procedures. Gen. Tech. Rep. SRS-80. 

Asheville, NC: U.S. Department of Agriculture, 

Forest Service, Southeastern Research Station. 

85 p.

Kenk, G. 1993. Growth in “declining” forests of 

Baden-Wurttemberg (southwestern Germany).

In: Huettl, R.F.; Mueller-Dombois, D., eds. Forest 

decline in the Atlantic and Pacific region. New 

York, NY: Springer-Verlag: 202-215.

Kulman, H.M. 1971. Effects of insect defoliation on 

growth and mortality of trees. Annual Review of 

Entomology. 16: 289-324.

Lawrence, R.; Moltzan, B.; Moser, W.K. 2002. Oak 

decline and the future of Missouri’s forests.

Missouri Conservationist. 63(7): 11-18.

Morin, R.S., Jr.; Liebhold, A.M.; Gottschalk, K.W. 

2004. Area-wide analysis of hardwood defoliator 

effects on tree conditions in the Allegheny 

Plateau. Northern Journal of Applied Forestry. 

21(1): 31-39.

Randolph, K.C.; Morin, R.S.; Steinman, J. 2010. 

Descriptive statistics of tree crown condition 

in the Northeastern United States. Gen. Tech. 

Rep. SRS-124. Asheville, NC: U.S. Department 

of Agriculture, Forest Service, Southern Research 

Station. 21 p.

Riitters, K.; Tkacz, B. 2004. The U.S. forest health 

monitoring program. In: Wiersma, B., ed. 

Environmental monitoring. Boca Raton, FL: CRC 

Press: 669-683.

SAS Institute Inc. 2009. SAS OnlineDoc® 9.2. Cary, 

NC: SAS Institute Inc. 

Schomaker, M.E.; Zarnock, S.J.; Bechtold, W.A.; 

Latelle, D.J.; Burkman, W.G.; Cox, S.M. 2007. 

Crown-condition classification: a guide to data 

collection and analysis. Gen. Tech. Rep. SRS-102. 

Asheville, NC: U.S. Department of Agriculture, 

Forest Service, Southern Research Station. 78 p.

Steinman, J.R. 2000. Tracking the health of trees 

over time on Forest Health Monitoring plots.

In: Hansen, M.; Burk, T., eds. Integrated tools for 

natural resources inventories in the 21st century: 

an international conference on the inventory and 

monitoring of forested ecosystems; 1998 August 

16-19; Boise, ID. Gen. Tech. Rep. NCRS-212. St. 

Paul, MN: U.S. Department of Agriculture, Forest 

Service, North Central Research Station: 334-339.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 215

Widmann, R.H.; Cook, G.W.; Barnett, C.J.; Butler, 

B.J.; Griffith, D.M.; Hatfield, M.A.; Kurtz, C.M.; 

Morin, R.S.; Moser, W.K.; Perry, C.H.; Piva, R.J.; 

Riemann, R.; Woodall, C.W. 2012. West Virginia’s 

Forests 2008. Resour. Bull. NRS-61. Newtown 

Square, PA: U.S. Department of Agriculture, Forest 

Service, Northern Research Station. 64 p. [DVD 

included].

Will-Wolf, S.; Jovan, S. 2009. Lichens, ozone, and 

forest health - exploring cross-indicator analyses 

with FIA data. In: McWilliams, W.; Moisen, 

G.; Czaplewski, R., comps. Forest Inventory and 

Analysis (FIA) symposium 2008; 2008 October 

21-23; Park City, UT. Proc. RMRS-P-56CD. Fort 

Collins, CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research Station. 

18 p.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Large Landscape 

Assessments
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 217

TEMPORAL TRENDS OF FOREST INTERIOR CONDITIONS 

IN THE UNITED STATES

Kurt Riitters and James Wickham1

Abstract.—Nature’s benefits derived from forest interior environments cannot be 

sustained if the natural capital of forest interior area is not sustained. We analyzed the 

spatial patterns of forest loss and gain for the conterminous United States from 2001 to 

2006 to determine whether forest interior environments were maintained at five spatial 

scales. A 1.1 percent net loss of total forest area translated to net losses of 3.2 percent 

to 10.5 percent of forest interior area over spatial scales of 4.41 ha to 5,310 ha. At the 

65.6-ha scale, the reduction of forest interior area was 50,000 km2—almost double the 

net loss of total forest area. The geographically pervasive discrepancy between total 

forest loss and forest interior loss indicates a widespread shift of the extant forest to more 

fragmented conditions, even in regions exhibiting small net changes in extant forest area. 

Forest dynamics could be monitored spatially to better understand the potential impacts 

of fragmentation on the sustainability of forest interior.

1

 Research Ecologist (KR), U.S. Forest Service, Southern 

Research Station, 3041 Cornwallis Road, Research Triangle 

Park, NC 27709; and Research Biologist (JW), National 

Exposure Research Laboratory, U.S. Environmental 

Protection Agency. KR is corresponding author: to contact, 

call 919-549-4015 or email kriitters@fs.fed.us.

INTRODUCTION

Most forests are naturally extensive, and as they 

become fragmented a variety of physical and 

biological mechanisms begins to limit their capability 

to support the ecological attributes and functions 

that depend on interior environments (Laurance 

2008, Murcia 1995, Ries et al. 2004). Continental 

to global forest monitoring tends to focus on trends 

in the absolute area of forest, but forest interior is 

a contextual attribute that depends on the spatial 

arrangement of forest area at multiple spatial scales 

(Riitters et al. 1997). Trend assessments should 

account for the initial spatial patterns and the patterns 

of forest loss and gain to more accurately reflect 

trends in forest interior area (Kurz 2010, Wickham 

et al. 2008). Riitters and Wickham (2012) analyzed 

the spatial patterns of forest loss and gain for the 

conterminous United States from 2001 to 2006 to 

determine whether forest interior environments were 

maintained at five spatial scales. This paper highlights 

the results and calls for spatial monitoring of forest 

dynamics using land cover maps to better understand 

the potential impacts of fragmentation on forest 

conditions. 

METHODS

Forest interior was measured on the 2001 and 2006 

National Land Cover Database (NLCD) land cover 

maps (Fry et al. 2011), which identify 16 land cover 

classes at a spatial resolution of 0.09 ha/pixel. The 

16 NLCD land cover classes were combined into two 

generalized classes called forest (the NLCD deciduous, 

evergreen, mixed forest, and woody wetlands classes), 

and nonforest (all other NLCD classes). At each date, 

the spatial context of each forest pixel was measured 

by its forest area density (FAD), defined as the 

proportion of all pixels in a surrounding fixed-area 

neighborhood that were forest. A given forest pixel 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 218

was called forest interior if FAD ≥ 0.9. The analysis 

was repeated at five spatial scales with neighborhood 

sizes† of 4.41, 15.2, 65.6, 590, and 5,310 ha (Riitters 

et al. 2002). Thus, maps of FAD at a spatial resolution 

of 0.09 ha/pixel were produced for each date and 

neighborhood size. The corresponding maps of forest 

interior comprised the subset of all extant forest pixels 

which met the criterion defining forest interior.

To relate forest area gains and losses to the dynamics 

of forest interior area from 2001 to 2006, the NLCD 

forest maps from 2001 and 2006 were overlaid, on a 

pixel-by-pixel basis, upon the maps of FAD. Pixels 

that were forest in 2001 but not in 2006 represented 

forest area loss, and pixels that were forest in 2006 

but not in 2001 represented forest gain. Pixels of 

forest loss were evaluated in relation to FAD in 2001 

to determine whether forest area losses were also 

removing forest interior. Pixels of forest gain were 

evaluated in relation to FAD in 2006 to evaluate 

whether forest area gains were adding forest interior. 

The differences between gross gains and gross losses 

for FAD ≥ 0.9 represent the net changes of forest 

interior area.

RESULTS

The total forest area†† in 2001 was 2,352,000 km2. 

Forest area losses and gains were 54,000 km2 and 

†

 Rounded to three significant digits; exact sizes were 4.41, 

15.21, 65.61, 590.49, and 5,314.41 ha. 

†† Area estimates differ from official statistics because of 

differences in the definitions of forest. 

Table 1.—Change in forest interior area in the conterminous United States from 2001 to 2006 for five 

neighborhood sizes

Neighborhood Forest Interior Area

Sizea (ha) 2001 (1,000 km2) 2006 (1,000 km2) Change (1,000 km2) Change (%)

4.41 1,419 1,374 -45 -3.2

15.2 1,151 1,102 -49 -4.3

65.6 867 817 -50 -5.8

590 523 482 -41 -7.8

5,310 277 248 -29 -10.5

 a Rounded to three significant digits.

27,000 km2, respectively, resulting in a net loss 

of 27,000 km2 (1.1 percent of total forest area). In 

comparison, the net loss of forest interior area was 

at least 29,000 km2 with a maximum loss of 50,000 

km2 for the 65.6-ha neighborhood size (Table 1). 

The rate of loss of forest interior area increased with 

neighborhood size and was approximately 3 to 9 times 

larger than the rate of loss of total forest area.

The disproportionate loss rates are explained by the 

patterns of original forest area, forest loss area, and 

forest gain area in relation to FAD in 2001 and 2006 

(Fig. 1). Overall forest losses tended to follow the 

distribution of all forest area in relation to FAD in 

2001, but the area lost at high FAD values exceeded 

the area gained by 2006 at high FAD values. As a 

result, a smaller percentage of the extant forest area 

qualified as forest interior in 2006. Regional analyses 

of 36 ecological provinces (Bailey 1995) showed that 

these observations were typical of a wide range of 

initial forest conditions (Riitters and Wickham 2012). 

In terms of total forest area, most of the naturally 

forested ecological sections (Cleland et al. 2007) 

exhibited a net loss while net gains were concentrated 

in sections where forest is not the dominant land cover 

(Fig. 2a). In comparison, for the 65.6-ha neighborhood 

size there was a net loss of forest interior area in 175 

of 190 ecological sections, and 74 sections exhibited 

losses greater than 5 percent (Fig. 2b). In naturally 

forest-dominated regions, forest interior area losses 

greater than 5 percent were typical in the Pacific 

Northwest and Southeast but were less common 

elsewhere. The Intermountain and Great Plains 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 219

regions had relatively low total forest area and the 

forest interior area changes there had relatively little 

influence on national statistics. The nearly national 

extent of differences between total forest loss and 

forest interior loss (Fig. 2) suggests a widespread shift 

in the spatial pattern of the extant forest to a more 

fragmented condition, including regions exhibiting 

relatively small net changes in extant forest area.

Figure 1.—The area distributions of initial forest, forest gains, and forest losses in relation to forest area density in 2001 or 

2006 for three representative neighborhood sizes. Top row: initial forest area in relation to initial forest area density in 2001 

(triangles) for neighborhood sizes of (a) 4.41 ha, (b) 65.6 ha, and (c) 5,310 ha. Bottom row: gross forest area lost in relation 

to initial forest area density in 2001 (open circles) and gross forest area gained in relation to final forest area density in 2006 

(closed circles), for neighborhood sizes of (d) 4.41 ha, (e) 65.6 ha, and (f) 5,310 ha. The net change for each value of forest 

area density is the difference between gross loss and gross gain. Forest interior area for each data series includes the three 

symbols to the right of the dotted vertical reference lines.

Figure 2.—Net change in forest area from 2001 to 2006. (a) All forest. (b) Forest interior in a 65.6-ha neighborhood. Ecological 

sections are shaded and State boundaries are shown for comparison. In the inset map, forest-dominated ecological sections 

are those that contained more than 50 percent forest in 2001.

Forest Area Density (65.6 ha)

0.0 0.2 0.4 0.6 0.8 1.0

0

5

10

25

30

Forest Area Density (5,310 ha)

0.0 0.2 0.4 0.6 0.8 1.0

0

5

10

25

30

0.0 0.2 0.4 0.6 0.8 1.0

Forest Area (1000 km2)

0

100

200

300

400

1000

1100

0.0 0.2 0.4 0.6 0.8 1.0

0

100

200

300

400

1000

1100

0.0 0.2 0.4 0.6 0.8 1.0

0

100

200

300

400

1000

1100

Forest Area Density (4.41 ha)

0.0 0.2 0.4 0.6 0.8 1.0

Forest Area (1000 km2)

0

5

10

25

30

.a .b c.

d. .e f.

Net change

>1% gain

<1% change

1% to 4% loss

5% to 8% loss

9% to 12% loss

>12% loss

a.

b.

> 50% forest in 2001
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 220

DISCUSSION

The unavoidable dependence of perceived pattern 

on measurement scale requires analysis of forest 

interior at multiple spatial scales. Knowledge of forest 

interior at a single scale is required to understand the 

ecological attributes and functions which interact 

with the forest environment at that scale. A multiplescale analysis can inform a wider range of ecological 

questions and identifies the range of spatial scales over 

which forest interior can be said to exist. Furthermore, 

from an inventory perspective forest interior may 

exhibit net gains, net losses, or equilibrium depending 

on the scale at which it is measured. Thus, a multiplescale analysis is more useful than a single-scale 

analysis when the goal is to assess forest interior as a 

generic constraint affecting many ecological attributes 

and functions.

The recent spatial patterns of forest gains and losses 

have not maintained forest interior area in the 

conterminous United States. Forest losses tended to 

follow the distribution of all forest area in relation to 

FAD in 2001, indicating that preservation of forest 

interior was not usually an important consideration 

when forest was removed. Conversely, forest gains 

tended to occur where the gains did not create new 

forest interior, indicating that creation of forest interior 

was not usually an important consideration when 

forest was added. The dispersed and non-compensating 

patterns of forest losses and gains resulted in rates of 

net change of forest interior area that were at least 3 

times larger than the rate of net change of total forest 

area. While the identity of forest interior is naturally 

scale-dependent, the multi-scale analysis showed that 

the non-compensating pattern of forest loss and gain 

was exhibited over a wide range of spatial scales from 

4.41 ha to 5,310 ha. If the recent patterns of change 

continue, the extant forest interior area will become 

smaller in the future. As a result, maintaining the 

benefits derived from forest interior environments 

will become more difficult and fewer options will be 

available to natural resource managers.

Some degree of forest fragmentation is a natural 

condition, and the loss of interior forest per se does 

not imply an anthropogenic cause. Our analysis did 

not distinguish between natural and anthropogenic 

loss and gain, nor did it compare conditions in 2001 

with the patterns of potential natural vegetation absent 

human influences. Knowledge of potential natural 

vegetation is helpful for understanding specific 

impacts of fragmentation, but it is not essential when 

evaluating trends of forest interior area within the 

human dominated era. More information is needed to 

evaluate quantitatively the relative importance of the 

causes of fragmentation in different parts of the United 

States. As a first approximation, the principal drivers 

of forest area change appear to be human activities in 

the East and intense, yet relatively local (relative to the 

scale of the study area), biotic and abiotic disturbances 

in the West (Riitters and Wickham 2012).

National land cover maps provide the synoptic 

perspective needed to identify indicators of forest 

interior consistently over large regions through 

time. These are coarse-scale indicators of dependent 

ecological changes, yet the specific impacts of 

forest interior loss will naturally depend upon 

local circumstances such as the vegetation type 

experiencing the forest loss, the proximate causes 

of loss, and anthropogenic land uses in the vicinity. 

Some of those details can be incorporated by spatially 

linking the synoptic maps of forest interior and other 

contextual pattern information (e.g., land cover 

adjacency metrics) to in situ inventory systems such 

as Forest Inventory and Analysis that provide better 

thematic resolution of forests and land uses (Riitters 

et al. 2011). Sustainable natural resource stewardship 

must account for fluxes in the natural capital that 

provides the desired benefits, and this research has 

demonstrated how forest patterns could be monitored 

to better understand the impact of human activities on 

the sustainability of forest interior.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 221

LITERATURE CITED

Bailey, R.G. 1995. Description of the ecoregions 

of the United States. Misc. Publ. No. 1391 rev. 

Washington, DC: U.S. Department of Agriculture, 

Forest Service.

Cleland, D.T.; Freeouf, J.A.; Keys, Jr., J.E.; Nowacki, 

G.J.; Carpenter, C.A.; McNab, W.H. 2007. 

Ecological subregions: sections and subsections 

of the conterminous United States [1:3,500,000] 

[CD-ROM]. Gen. Tech. Rep. WO-76. Washington, 

DC: U.S. Department of Agriculture, Forest 

Service.

Fry, J.A.; Xian, G.; Jin, S.; Dewitz, J.A.; Homer, C.G.; 

Yang, L.; Barnes, C.A.; Herold, N.D.; Wickham, 

J.D. 2011. Completion of the 2006 National Land 

Cover Database for the conterminous United 

States. Photogrammetric Engineering & Remote 

Sensing. 108: 858-859.

Kurz, W.A. 2010. An ecosystem context for global 

gross forest cover loss estimates. Proceedings of 

the National Academy of Sciences. 107: 9025-9026.

Laurance, W.F. 2008. Theory meets reality: how 

habitat fragmentation research has transcended 

island biogeography theory. Biological 

Conservation. 141: 1731-1744.

Murcia, C. 1995. Edge effects in fragmented forests: 

implications for conservation. Trends in Ecology 

and Evolution. 10: 58-62.

Ries, L.; Fletcher, R.J.; Battin, J.; Sisk, T.D. 

2004. Ecological responses to habitat edges: 

mechanisms, models, and variability explained.

Annual Review of Ecology, Evolution, and 

Systematics. 35: 491-522.

Riitters, K.H.; Coulston, J.W.; Wickham, J.D. 2011. 

Fragmentation of forest communities in the 

eastern United States. Forest Ecology and 

Management. 263: 85-93.

Riitters, K.H.; O’Neill, R.V.; Jones, K.B. 1997. 

Assessing habitat suitability at multiple 

scales: a landscape-level approach. Biological 

Conservation. 81: 191-202.

Riitters, K.H.; Wickham, J.D.; O’Neill, R.V.; Jones, 

K.B.; Smith, E.R.; Coulston, J.W.; Wade, T.G.; 

Smith, J.H. 2002. Fragmentation of continental 

United States forests. Ecosystems. 5: 815-822.

Riitters, K.H.; Wickham, J.D. 2012. Decline of forest 

interior conditions in the conterminous United 

States. Scientific Reports. 2: 653.

Wickham, J.D.; Riitters, K.H.; Wade, T.G.; Homer, 

C. 2008. Temporal change in fragmentation of 

continental US forests. Landscape Ecology. 23: 

891-898.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 222

MONITORING TRENDS AND BURN SEVERITY (MTBS): MONITORING 

WILDFIRE ACTIVITY FOR THE PAST QUARTER CENTURY

USING LANDSAT DATA

Mark Finco, Brad Quayle, Yuan Zhang, Jennifer Lecker, Kevin A. Megown, and C. Kenneth Brewer1

Abstract.—The Monitoring Trends in Burn Severity (MTBS) project is mapping the 

extent, size, and severity of all large fires greater than 1,000 acres in the west and 500 

acres in the east over the conterminous United States (CONUS), Alaska, and Hawaii. In 

2012 the project reached a milestone, completing the mapping for all fires between 1984 

and 2010. The MTBS project produces geospatial and tabular data using a consistent 

protocol for fire trend analysis at a range of spatial, temporal, and thematic scales. This 

paper reviews the objectives of the MTBS project, describes the data sets and information 

provided, and presents results of the analysis of the 1984-2010 MTBS data set for the 

United States. 

1

 Contract Leader (MF), Program Leader (BQ), Graduate 

Intern (YZ), Group Leader (JL), Program Leader (KAM), 

U.S. Forest Service, Remote Sensing Applications Center, 

2222 West 2300 South, Salt Lake City, UT 84119; Remote 

Sensing Program Manager (CKB), U.S. Forest Service, 

Research and Development - Quantitative Sciences Staff, 

Washington, DC. MF is corresponding author: to contact, 

call 801-975-3767 or email at mfinco@fs.fed.us.

INTRODUCTION

The Monitoring Trends in Burn Severity (MTBS) 

project has mapped all large wildland fires in the 

conterminous United States, Alaska, and Hawaii

from 1984 through 2010 using Landsat imagery. 

This 5-year project was completed in April 2012 by 

analysts at the U.S. Forest Service, Remote Sensing 

Applications Center and the U.S. Geological Survey, 

Earth Resources Observation and Science Center. 

This paper presents the first analysis of the complete 

1984-2010 data set and presents broad-scale trends 

observed in the MTBS data record. Over this period, 

14,945 fires were mapped in the conterminous United 

States (CONUS), Alaska, and Hawaii. In addition, this 

paper demonstrates how MTBS data can be used to 

compare different regions of the country in terms of 

fire frequency, burned area, and burn severity. 

METHODS

For the purposes of this short paper, Geographic 

Area Coordination Center (GACC) boundaries are 

used to define the geographic regions. These GACC 

regions are defined by an interagency fire management 

organization made up of Federal and state wildland 

fire directors and have been chosen because the 

authorizing body for the MTBS project was the 

executive-level Wildland Fire Leadership Council. 

Undoubtedly there are more ecologically relevant 

alternatives to this tessellation of the United States. 

Burn Severity Mapping

Burn severity in the MTBS project refers to “degree 

to which a site has been altered or disrupted by fire; 

loosely, a product of fire intensity and residence time” 

(National Wildfire Coordinating Group 2005). Burn 

severity is mapped by the MTBS project using Landsat 

Thematic Mapper and Enhanced Thematic Mapper 

Plus data and the differenced Normalized Burn Ratio 

(dNBR) (Eidenshink et al. 2007). Analysts use dNBR 

images to delineate fire perimeters and to determine 

the dNBR thresholds for distinguishing between 

severity classes based on both scientific protocol and 

experience (Schwind 2008). For each fire, burned area 

is classified into one of four burn severity classes: 

unburned to low, low, moderate, or high (Fig. 1).
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 223

Figure 1.—Example of a burn severity map developed by the MTBS project.

Data and Analysis Methods

MTBS Fires Analyzed

The objective of the MTBS project is to provide a 

consistent and continuous source of 30-m resolution 

burn severity data for all fires greater than 1,000 acres 

in the western CONUS and 500 acres in the eastern 

CONUS, but many fires smaller than these size limits 

were mapped (MTBS 2012). For consistency in our 

analysis, this paper adheres to the original size limits. 

The number of fires was thus reduced to 13,400 and 

burned area to about 110 million acres over the 

27-year data record. 

The MTBS data record also contains both documented 

wildland fires and prescribed fires, as noted in the 

MTBS fire occurrence database. Because this paper’s 

focus is on the potential influences of biophysical, 

geographic, and climatic factors on natural fire 

behavior, only wildland fires were analyzed. Excluding 

prescribed fires further reduced the number of fires to 

10,874 and burned area to about 104.6 million acres. 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 224

Analysis Methods

The burned area and burn severity information for all 

large wildland fires was compiled in a database along 

with other attributes, such as fire name, type, and 

ignition date. Tabular and geographical summaries 

were generated from this database. This database is 

publicly accessible through a Web portal at www.mtbs.

gov/data/search.html. Statistical summaries available 

through the Web portal include burn severity by state, 

GACC, vegetation cover type, and administrative 

ownership. 

The MTBS database was used to aggregate large 

wildland fires on a yearly basis to derive trends of 

fire frequency, size, and burn severity for each of 

the 11 regions (R Core Team 2012). In this paper 

we compare trends in large fire frequency and size 

for the Eastern Great Basin GACC and the Southern 

California GACC. We selected the Northwest GACC 

and Southwest GACC for comparison of trends in 

burn severity. 

NATIONAL AND REGIONAL

WILDFIRE TRENDS 

Between 1984 and 2010 approximately 22 percent of 

the area burned in CONUS was in the unburned to 

low burn severity class, 42 percent in the low class, 

23 percent in the moderate class, and 12 percent in the 

high class. The following four figures show national 

and regional wildland fire trends in fire frequency, 

size, and burn severity.

Fire Frequency and Size

The MTBS project mapped 10,137 large wildland 

fires with a total burned area of about 79.5 million 

acres in CONUS between 1984 and 2010. Generally, 

both fire frequency and size exhibited trends between 

1984 and 2010 towards a larger number of fires and 

greater burned area despite large annual fluctuations 

(Fig. 2). The Nation experienced the largest fire year 

in 2006 in terms of both frequency and burned area, 

reaching a frequency of 843 fires and burned area of 

Figure 2.—Large fire frequency and size for CONUS, 1984-2010. 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 225

8.8 million acres. Outside of 2006, the years with the 

largest number of fires were, in descending order, 2000 

(701), 2007 (562), and 1999 (555). The years with 

greatest burned area in descending order were 2007 (8 

million acres), 2000 (5.6 million acres), and 1996 (4.8 

million acres). The trend of burned area has a positive 

correlation (R2 = 0.835) with fire frequency. The 

largest mean fire size, 14,000 acres, occurred in 2007. 

The smallest, 3,600 acres, was in 1997. The 27-year 

mean fire size was about 2,500 acres and frequency 

was approximately 375 fires per year. 

We compared trends in large fire frequency and size 

between the Eastern Great Basin GACC (EGB) and 

the Southern California GACC (SC) (Fig. 3). Although 

these regions have few biophysical similarities, using 

these two GACCs demonstrates how MTBS data 

can capture the variability in fire activity and acres 

burned between regions. While beyond the scope of 

this paper, comparison and evaluation of results can be 

made in the context of land cover and administrative 

ownership; immediate and long-term effects of 

weather, climate, and ecological conditions; and land 

management strategies. 

Figure 3.—Large fire frequency and size in the EGB and SC, 1984-2010. 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 226

The EGB showed a small increasing trend in fire 

frequency in the EGB, while the SC did not show an 

obvious trend towards greater number of fires. The 

burned area in both GACCs slightly increased from 

1984 to 2010. The EGB had a larger burned area and 

greater number of fires in most of the years than SC. 

The EGB also had a wider range in terms of both 

burned area and frequency, which parallels the cyclical 

nature of the fire seasons in that part of the country. 

In the EGB, the largest burned area was about 2.56 

million acres in 2007 and the smallest burned area 

was about 0.027 million acres in 1993. The highest 

fire frequency, 121 fires, occurred in 2006. The lowest 

number of fires was nine in 1993. In the SC, the largest 

burned area was in 2003 (0.8 million acres), and the 

smallest was about 0.014 million acres in 1991. The 

year 1996 had the largest fire frequency with 61 fires 

and 1991 had the smallest with 5. The mean fire size 

increased in recent years and reached the highest in 

2007 in both GACCs (the same year of the largest 

mean fire size in CONUS): about 25,000 acres in the 

EGB, and 24,000 acres in the SC. The overall mean 

fire size over 27 years is slightly higher in the EGB 

Figure 4.—Percentage of severely burned area (area with moderate or high burn severity) (scale on left axis) and total burned 

area (scale on right axis) in the Northwest GACC, 1984-2010. 

(about 10,000 acres) than in the SC (about 8,500 

acres), both being much higher than that of CONUS 

(2,500 acres). Approximately 53 fires occur annually 

in the EGB and about 31 in the SC. The fire frequency 

is more correlated with burned area in the EGB (R2 = 

0.696) than in the SC (R2 = 0.325), indicating a more 

stable yearly mean fire size in the EGB and episodic 

occurrences of megafire activity in SC. The trends of 

burned area and frequency in both GACCs do not fully 

correspond (R2 = 0.183 for burned area; R2 = 0.095 for 

frequency). Notable outliers were found in 1991 and 

2000 for frequency, and in 2003 for burned area. 

Regional Differences 

in Burn Severity

Burn severity trends were analyzed and compared for 

the Northwest GACC (NW) (Fig. 4) and the Southwest 

(SW) GACC (Fig. 5). The mean percentage of 

severely burned area (moderate or high burn severity) 

in the NW was 32 percent, slightly above the CONUS 

average (28 percent) and significantly higher than that 

of the SW, which was about 24 percent. The NW also 

showed a wider range and fluctuation in the percentage 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 227

Figure 5.—Percentage of severely burned area (area with moderate or high burn severity) (scale on left axis) and total burned 

area (scale on right axis) in the Southwest GACC, 1984-2010. 

of severely burned area. In the SW, the total burned 

area showed an increasing trend throughout the 

data record while in the NW, the total burned area 

decreased in the 1980s and early 1990s, and then 

greatly increased thereafter. In both GACCs, total 

burned area fluctuated more sharply from year to year 

in more recent years.

Because of the higher proportion of forest and biomass 

in the NW, that region burned more severely than 

the SW. The SW showed a trend toward lower burn 

severity; 2002 was an anomaly. Trends in burn severity 

and burned area did not correspond between the NW 

and SW, which is likely due to different weather and 

climate patterns, vegetation composition, and fire 

management. The R2 between the burned area and the 

sum of the percentage of area in the moderate and high 

burn severity classes was 0.202 for the NW and 0.085 

for the SW. In both GACCs, the years with higher 

burn severity were not necessarily the years with 

larger burned areas. For example, in the NW, the most 

severely burned year was 1995, when the percentage 

of severely burned area was 54 percent, but the area 

burned in that year was among the smallest, only about 

0.052 million acres. 

DISCUSSION

For scientists interested in understanding regional and 

national trends in wildland fire, the MTBS dataset 

has no peer. As with any data set, however, a clear 

understanding of how the MTBS data were created and 

what fires are included is important before analyzing 

trends. Without this knowledge, the trends observed 

could be an artifact of the data selected for analysis or 

the data generation process.

When appropriately filtered, the data show clear 

fluctuations and trends in fire frequency, size, and 

burn severity, both nationwide and between regions. 

Our analysis showed a trend toward increasing fire 

size (though very cyclical), but not such a clear trend 

toward increasing fire severity. Comparison of fire 

frequency, size, and severity for different geographic 

areas highlights what we hypothesize to be biophysical 

and climatic differences between the regions in this 

time period. Further analysis is required to substantiate 

these causal relationships.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 228

ACKNOWLEDGMENTS

The authors would like to acknowledge the crossDeputy funding that comes to the MTBS project from 

the Forest Service’s Information Resources Decision 

Board. 

LITERATURE CITED

National Wildfire Coordinating Group. 2005. 

Glossary of wildland fire terminology. Available 

at www.nwcg.gov/. (Accessed October 22, 2005). 

Eidenshink, J.; Schwind, B.; Brewer, K.; Zhu, Z.; 

Quayle, B.; Howard, S. 2007. A project for 

monitoring trends in burn severity. Fire Ecology. 

3(1): 3-21.

Monitoring Trends in Burn Severity. 2012. MTBS 

frequently asked questions, fire mapping (Why 

are some small fires found in the database?).

Available at www.mtbs.gov/faqs.html. (Accessed 

July 27, 2012).

R Core Team. 2012. R: a language and environment 

for statistical computing. Vienna, Austria: R 

Foundation for Statistical Computing. Available at 

www.R-project.org. (Accessed June 20, 2012).

Schwind, B., comp. 2008. Monitoring trends in 

burn severity: report on the PNW and PSW 

fires—1984 to 2005. Available at http://mtbs.gov/. 

(Accessed July 20, 2012).

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 229

Approaches for Landscape-scale 

Forest Carbon Assessment

Richard Birdsey, Yude Pan, Kris Johnson, Fangmin Zhang, and Jing Chen1

Abstract.—Increasingly, public and private forest landowners need to estimate their 

carbon stocks and analyze the impacts of alternate management plans. Here we describe 

approaches designed to work at landscape scales: one involves estimating carbon stocks 

from existing Forest Inventory and Analysis (FIA) data; another involves downscaling 

results from a continental-scale biogeochemistry model known as InTEC; and the third 

uses Lidar remote sensing to provide high resolution biomass maps. Combining FIA 

data with a biogeochemistry model gives the most useful information for analyzing 

causes of historical trends, while the biomass maps support implementation of 

management decisions. Application of these approaches is illustrated by pilot studies 

in the eastern United States. Analysis of FIA data for northern Wisconsin revealed that 

private landowners held more than half of the forest carbon but that the rate of carbon 

sequestration had slowed dramatically over two decades. Causes of the decline were 

hypothesized to include increased harvesting, aging forests, and increasing disturbances. 

The InTEC model for the same region revealed trends over a much longer historical 

period as well as providing information about changes in soil C that are lacking in the 

FIA data analysis. The effects of long-term forest age dynamics and higher inter-annual 

climate variability became evident, and the model results suggest a significant increase 

of soil C stocks. Using this information base, we identified several ways to increase 

landscape-scale average forest carbon stocks: allow some forests to reach full maturity 

and highest carbon stocks; manage other forests to maximize carbon uptake and transfer 

of harvested carbon into wood products; and avoid conversion of existing forests to 

nonforest land uses. Strategic implementation of these kinds of management decisions 

can be facilitated with high resolution biomass maps. 

1

 Program Manager (RB) and Research Scientist (YP), U.S. Forest Service, Northern Research 

Station, 11 Campus Blvd., Suite 200, Newtown Square, PA 19073; University of Toronto 

(FZ and JC), Ontario, Canada. RB is corresponding author: to contact, call 610-557-4092 or 

email at rbirdsey@fs.fed.us.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
U.S. Forest Service 

International Program

Activities Related to 

Forest Monitoring
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 231

ANALYSIS OF THE DEFORESTATION PROBLEM 

IN TROPICAL LATIN AMERICA

Jorge Malleux1

Abstract.—The driving forces of land use changes have been analyzed and discussed 

for a long time with different solutions proposed and implemented. Unfortunately the 

reduction of natural forest cover continues in the same direction, generating an increasing 

alarm all around the world among scientist and politicians, related to the climate change 

awareness and strategies for its reduction and mitigation. This paper discusses the causes 

of deforestation, causes and effects in tropical Latin America. 

1

 International Consultant, Peru; to contact, email at 

jmalleux@gmail.com.

INTRODUCTION

This paper discusses the causes of deforestation in 

tropical Latin America. 

Deforestation is an issue that has long been a concern 

of the international community, especially in the 

tropics. It is increasingly important to provide updated 

and reliable information based on accountable and 

transparent procedures. There is still much work to be 

done to develop standardization of terms, definitions, 

and procedures for the evaluation of changes in 

forested areas at the national and local levels.

The forest cover of any area is referred to as the 

physical presence at a particular time of continuous 

or fragmented vegetation, defined as forest, which is 

a type of woody vegetation with a minimum height 

of 5 m at maturity, with a minimum coverage of 10 

percent of the canopy on the soil surface. Within this 

classification are dense forest (>40 percent coverage), 

open (between 10 and 40 percent coverage), or 

fragmented forest in isolated stands but which 

collectively cover extensions reaching a minimum of 

0.5 ha.

In ecological terms, the most important concept is the 

forest’s ability to meet minimum production of goods 

and services, i.e., the protection of soil, conservation 

of water sources, regulation of the water system, 

wildlife habitat, recreation, conservation of biological 

biodiversity (understood as the total variety of genetic 

strains), species and ecosystems that exist in the nature 

and are able to survive in a sustained way.

This document is only a part of this broad and complex 

problem of the relationship of deforestation to specific 

socioeconomic parameters. Once both aspects of 

definition and concepts of classification of forests are 

standardized or formalized for a particular purpose, the 

immediate problem is how to measure or evaluate the 

parameters that serve to integrate a reliable database 

that is structured for a particular purpose or agenda, 

such as the FRA, Kyoto, Convention of Biodiversity, 

Reducing Emissions from Deforestation and forest 

Degradation (REDD), and the sustainable forest 

management procedures, generating information 

of the first order, so the accuracy and reliability 

of the information may have fairly broad range of 

consistency and reliability.

METHODS

Based on information provided by 14 reporting 

countries for the FAO-FRA program (between 1980 

and 2010), 71 references were pre-selected. Parameters 

for evaluating the information consistency (IC) were 

given a value from 0 to 5 and a specific weight or 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 232

importance. Based on the calculation of coverage 

and/or changes of the forest cover, the following 

parameters were considered:

a: Quality of the source of information: P1 = 0.15

b. Detail information: P2.20 

Consistency and back: P3, 40

d. Age: P4 0.25

IC. Weighted reliability index = 

(a * b * p1 + p2 + + c * d * p3 + p4)

The sum of multiplying the score of each parameter 

by its specific weight gives a final score which can 

be up to 5. These final scores are grouped into three 

categories:

A: 0 to 3.0: low, removed or held in reserve in case 

there is no other better reference 

B: 3.1 to 4.0: means, we choose the most recent or 

consistent

C: > 4: high is used directly 

To do a quick analysis on the relationship of these 

parameters to annual deforestation rates, some major 

macroeconomic parameters at the country level 

for 14 countries of tropical Latin America were 

identified. However, to bring the analysis to smaller 

or manageable contexts, seven Brazilian states from 

the Amazon region were selected, with a total of 129 

districts and about 60 socioeconomic parameters, 

out of which only 26 were considered significant. 

These 26 parameters were analyzed using linear and 

logarithmic correlation with the annual deforestation 

rates (2000-2005), yielding R2 values, indicating the 

degree of correlation between deforestation rates 

recorded for each country and the seven Amazonian 

Brazilian states.

RESULTS

Of the 14 countries evaluated in terms of the reliability 

of the information provided ( IC), 10 values were 

above 3.1. and 4 countries had rates above 4. This 

is an indication that there is still a lot of work to do 

to collect more reliable and consistent information 

regarding forest deforestation. The results of this 

analysis are expressed in terms of R2 values. 

As shown in Table 1, the factor that is the most 

influential in deforestation is the population density, 

particularly the rural population, which should be 

understood as the necessity of living space and food, 

which is stronger than the need to conserve the 

environment, especially in societies where there are no 

valid alternatives through job opportunities, income to 

fund the family basket, and so on. A second influential 

factor, but in a favorable manner to the preservation 

of forests, is the annual growth of gross domestic 

product, which means improved job opportunities 

for people, better prices for agricultural products, 

and increased consumption, particularly for rural 

populations. 

At the Subnational Level

From the seven states of the Brazilian Amazon, 

numerous socioeconomic parameters were 

considered, including: total population from urban, 

rural, agricultural land, pastures, forests, secondary 

forests, production and productivity agricultural state 

investment in development projects, agricultural 

machinery, etc. These were analyzed for their 

relationship or correlation with the average rate of 

deforestation for the period 2000-2005; results are 

shown in Tables 2 and 3.

Table 1.—Factors related to forest conservation

R2 for Linear Influence Over

Parameters Correlation Forest Conservation

Rural Population 

 Density 0.626 Negative

Gross Domestic 

 Product (GDP) 0.2486 Positive

Percent of 

 Rural Population 0.2103 Negative

Annual Growing 

 Population Rate 0.1514 Negative
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 233

ACRE AMAZONAS M.GROSSO PARA RONDONIA TOCANTINS TOTAL

HA/FARMER 

(AGR & GL) V -0.322 -0.374 -0.297 -0.125 -0.583 -0.281 -0.142

HA/FARMER 

(AGR) U 0.271 -0.383 0.029 -0.390 -0.506 -0.388 -0.040

PRODUCTION 

$/HA (AGR & GL) BJ 0.163 -0.304 0.187 -0.436 0.271 -0.181 0.218

PRODUCTION 

$/HA (AGR) BK -0.333 0.238 0.032 0.059 -0.294 -0.640 0.110

PRODUCTION 

$/FARMER BL -0.237 0.108 0.164 -0.482 -0.204 -0.500 0.065

PRODUCTION 

AGR+GL 

$/FARMER

BY -0.257 -0.074 -0.038 -0.273 -0.543 -0.231 -0.092

PRODUCTION 

AGR. $/FARMER BZ 0.241 -0.024 -0.004 -0.273 -0.538 -0.243 0.062

PRODUCTION 

$/HA GL CA -0.250 -0.245 -0.179 -0.274 -0.542 -0.218 -0.137

ANNUAL

INCOME

$/FARMER

BX -0.194 -0.447 -0.008 -0.261 -0.531 -0.237 0.060

MUNICIPAL 

INVESTMENT 

$/FARMER

CB -0.110 0 0.023 0 0 -0.367 0

MUNICIPAL 

INVESTMENT 

$/RURAL 

HABITANT

CC 0.040 0 0.135 0 0 -0.268 0

POPULATION 

DENSITY 

HAB./KM2

F 0.375 -0.370 0.231 -0.192 -0.249 -0.551 -0.130

ANNUAL YIELD 

$/AGR BR 0.229 0.019 -0.113 0.125 -0.472 0.165 -0.027

ANNUAL YIELD 

$/AGRL BS 0.220 0.061 0.181 -0.491 0.276 -0.175 0.138

Table 2.—R2 Values, parameters are positive for the conservation of the forest
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 234

ACRE AMAZONAS M.GROSSO PARA RONDONIA TOCANTINS TOTAL

AGR&G 

LINVESTMENT $/HA BN 0.475 0.080 0.131 0.058 -0.012 0.040 -0.010

AGR INVESTMENT 

$/HA BO -0.143 0.057 -0.112 0.500 -0.390 0.391 -0.027

GL INVESTMENT 

$/HA BP 0.526 0.593 0.128 -0.086 0.096 0.043 0.266

AGRI & GL ANNUAL 

YELD $/HA BQ 0.623 -0.103 0.202 -0.256 0.114 0.100 0.060

NUMBER OF 

TRACTORS/HA 

AGRI & GL

BT 0.043 0.187 0.322 -0.070 -0.199 0.108 0.086

NUMBER OF 

TRACTORS/HA 

AGR

BU -0.259 0.184 -0.104 0.358 -0.408 0.170 -0.039

NUMBER OF 

TRACTORS/GL BV -0.115 -0.055 0.267 -0.158 -0.156 0.085 0.062

% OF FALLOW 

FOREST/ AGR W -0.397 -0.091 0.096 0.609 -0.247 0.618 0.086

% OF FALLOW 

FOREST/AGRIC 

& GL

X -0.135 -0.022 0.149 -0.255 -0.140 0.264 0.068

POPULATION 

ANNUAL GROWTH G -0.067 0.251 0.336 0.155 0.295 -0.131 0.157

% OF RURAL

POPULATION J -0.322 0.117 0.183 0.370 0.564 0.274 0.163

LIFE STOCK 

INVESTMENT $/HA BM 0.226 0.423 0.106 -0.344 0.153 -0.070 0.018

Table 3.—R2 values, parameters turn out to be negative for the conservation of the forest

Figures 1 and 2 show that some socioeconomic 

indexes are highly significant for forest conservation, 

particularly those related to the per capita National 

Gross Product (NGP) and the productivity of incomegenerating activities, to benefit the local population.

On the other hand, population density, type of 

investments, and population growth are working 

against forest conservation and actually stimulating 

deforestation.

DISCUSSION

There is no single cause for deforestation. In any case 

this does not occur in isolation but there are several 

factors that together establish a context quite complex, 

including:

• Extreme poverty and lack of job opportunities 

and family income sufficient for economically 

disadvantaged people

• Lack of policies and legislation and strategies or 

policy mistakes of the occupation of forest areas 

in the humid tropics

• Insufficient capacity in decisionmaking of the 

forest authority in the occupation and use of the 

forest land

• Lack of planning on land use, use of 

inappropriate technologies, and deficiencies in 

the use of land and forest.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 235

Figure 1.—National gross product per inhabitant by annual 

deforestation rate.

Figure 2.—Population density by annual deforestation rate.

References

BRASIL- Instituto Brasileiro do Meio Ambiente 

e dos Recursos Naturais Renováveis (Diretoria 

de Incentivo à Pesquisa e Divulgação/Centro 

de Sensoriamento Remoto). 1992. Cobertura 

Vegetal Natural e Remanescente do Brasil - Versão 

Preliminar. Brasília – Distrito Federal, Brasil.

INPE, Fundacão S.O.S. Mata Atlántica, São Paulo, 

1998.

Brasil, Compilacão de Informacões para FRA2000, 

IBAMA, Brasilia/DF-Curitiba/PR 2000 Brasil. 

BRASIL-INPE – Instituto Brasileiro de Pesquisas 

Espaciais. 2000. Monitoramento da Floresta 

Amazônica Brasileira por Satélite. Manaus - 

Amazonas, Brasil. 22 p.

COLOMBIA, Análisis Introductivo Gemneral sobre el 

desarrollo de la Colonización en Colombia, IGACMINDEFENSA-CIAF, Bogota Colombia, 1978.

FAO- Informes nacionales de los países participanmtes 

en la 21a reunión de la COFLAC, BogotáColombia, Setiembre 2000.

FAO, Monitoreo de Procesos de Deforestación/

Degradación en bosques Húmedos Tropicales, 

Proyecto GCP/RLA/081/JPN, Documento de 

campo No. 15. Lima, Perú, 1989.

FAO-FRA2000, Directrices para la Evaluación en los 

Países Tropicales y Sub-tropicales, Roma, 1998.

HONDURAS, Inventarios Forestales de Honduras 

(información Básica). 

COHDEFOR, Tegucigalpa, 1979.

López, Soria J. Recursos Forestales de Bolivia y su 

Aprovechamiento, La Paz-Bolivia, 1993.

Malleux, J. La Ecología y los recursos Naturales en la 

Integración y Desarrollo de la Amazonía peruana, 

Universidad nacional Agraria La Molina, LimaPerú, 1979.

Malleux, J. Mapa forestal del perú, Memoria 

Explicativa, Universidad nacional Agraria, La 

Molina, Lima-Perú, 1975.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Forest Inventory and Analysis 

Canopy Cover Activities
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 237

A GIS-BASED TOOL FOR ESTIMATING TREE CANOPY COVER ON 

FIXED-RADIUS PLOTS USING HIGH-RESOLUTION AERIAL IMAGERY

Sara A. Goeking, Greg C. Liknes, Erik Lindblom, John Chase, Dennis M. Jacobs, and Robert Benton1

Abstract.—Recent changes to the Forest Inventory and Analysis (FIA) Program’s 

definition of forest land precipitated the development of a geographic information 

system (GIS)-based tool for efficiently estimating tree canopy cover for all FIA plots. 

The FIA definition of forest land has shifted from a density-related criterion based on 

stocking to a 10 percent tree canopy cover threshold. This definitional change required 

a consistent method of estimating tree canopy cover, which is now a core FIA variable, 

using a combination of field-based and image-based assessments. In order to accomplish 

the image-interpretation task for thousands of plots annually, the FIA program, working 

in collaboration with the Remote Sensing Applications Center, developed a GIS-based 

canopy cover tool. Design considerations for the tool include the following: desired 

precision of the tree canopy cover estimate, seamless use across regions, compatibility 

with the existing Forest Service streaming image technology, and ease-of-use for image 

interpreters. Data from this image interpretation effort will not only serve the needs of 

FIA, but will also be used to update the National Land Cover Dataset tree canopy cover 

data layer. 

1

 Biological Scientist (SAG), U.S. Forest Service, Rocky 

Mountain Research Station, 507 25th Street, Ogden, UT 

84401; Research Physical Scientist (GCL), U.S. Forest 

Service, Northern Research Station; Programmer (EL) and 

Project Manager (RB), U.S. Forest Service, Remote Sensing 

Applications Center; GIS/Remote Sensing Specialist (JC), 

U.S. Forest Service, Pacific Northwest Research Station; 

Research Forester (DMJ), U.S. Forest Service, Southern 

Research Station. SAG is corresponding author: to contact, 

call 801-625-5193 or email at sgoeking@fs.fed.us.

INTRODUCTION

With the release of Forest Inventory and Analysis 

(FIA) Field Guide 5.0, the FIA program began 

collecting canopy cover information (specifically, 

percent canopy cover) on all plots. To complement 

the new field-based canopy cover protocols, the 

national prefield task team designed an image-based 

canopy cover protocol for plots that are withheld from 

field visits via the prefield process (i.e., plots that 

are obviously nonforest). The prefield canopy cover 

assessment protocol is applied at tens of thousands 

of plots annually and involves dozens of image 

interpreters. Therefore, it was necessary to develop a 

user-friendly, geographic information system (GIS)-

based tool that facilitates efficient image-based 

interpretation and documentation of percent canopy 

cover. During inventory year 2012, the canopy cover 

tool was used to assign a percent canopy cover at 

all FIA plots in support of an update of the National 

Land Cover Dataset (NLCD) canopy cover data. 

This paper presents the development history, design 

considerations, and practical advantages of the canopy 

cover tool. 

METHODS

Collaboration

FIA’s national prefield task team, which includes 

members from the four FIA regions, identified 

the need for a GIS-based canopy cover tool and 

prepared a functional needs assessment. Subsequent 

tool development was supported by the Techniques 

Research Band in collaboration with the Forest 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 238

Service’s Remote Sensing Applications Center 

(RSAC). The RSAC and the FIA program have had 

a longstanding partnership applying remote sensing 

technology to the national forest inventory of the 

United States. In addition, RSAC has expertise and 

experience developing software tools to assist with 

image interpretation, such as Digital Mylar (Clark 

et al. 2004). The project team, which consisted of a 

programmer and a project manager from RSAC and 

the national prefield team from FIA, collaboratively 

developed a specification document based on FIA’s 

needs assessment. A number of additional FIA 

personnel served as software testers. The project 

followed a rapid application development paradigm 

with iterative versions of the software prepared by 

RSAC and promptly tested by FIA staff. One of the 

agreements in this partnership was the delivery of the 

tool’s source code to the FIA program, which will 

provide flexibility to adapt the tool to other resource 

settings or dot grid designs.

Design Considerations

Multiple image-based methodologies were considered 

and tested (see Goeking and Liknes 2009), and a 

dot grid approach was selected due to favorable 

repeatability performance and minimal training 

requirements. The question of dot grid sample size 

was investigated using statistical methods, and it was 

determined that 100 dots would provide a suitable 

compromise between precision and interpretation time. 

Dots were to be fit within a circle of 43.9-m radius, 

which contains all four subplots in the FIA plot design. 

Additionally, the dot grid was to be rotated clockwise 

15 degrees in order to reduce alignment with cultural 

features. The final dot arrangement uses an integer 

spacing (8 m) between dots, resulting in 109 dots in 

the 43.9-m circle (Fig. 1).

Software Development

Because all FIA image interpreter personnel have 

experience using ArcGIS® products (e.g., ArcMapTM) 

Figure 1.— Screen capture of dot grid overlain on NAIP imagery using the FIA canopy cover tool. Red crosshairs indicate 

non-tree dots, green indicate tree dots, and yellow indicate dots that have not yet been classified. This example illustrates a 

situation where the plot does not meet the FIA definition of forest land yet includes a substantial amount of tree canopy cover.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 239

and the software can seamlessly utilize imagery from 

RSAC’s ImageServer (Vines 2007), the canopy tool 

was developed as an ArcMapTM add-in using C# 

and ArcObjects® in ArcGIS® 10. The add-in can 

be hosted at a network location, and users therefore 

automatically use the latest version of the software 

each time they restart the ArcMapTM application. 

This reduces the technical support effort required to 

ensure all users have the same version of software 

and enables the use of the software tool at the Forest 

Service’s Enterprise Data Center.

Working from the basic functionality outlined in a 

specification document, a prototype of the tool was 

developed. Image interpreters from all four FIA 

regions tested several iterations of the tool, working 

toward consensus while still meeting the workflow 

requirements in each region. Particular attention was 

paid to optimizing efficiency of the interpretation 

process. A summary of the tool’s components appears 

in Table 1.

Icon Utility Functionality

Select layer/ 

Select plot

Dropdown menus that specify the plot layer and individual plot to which other operations will be 

applied.

Generate dots Creates the 109-dot grid for the currently selected plot.

All no tree Single click operation that assigns all no tree attributes to the current plot’s dot grid.

Select based on 

location

Out-of-the-box ArcMap tools used to select dots; default tools included are select by polygon 

(left) and select by lasso/freehand (right).

Attribute dots Assign either tree (left) or not tree (middle) to currently selected dots. Another option allows 

users to distinguish between tally trees (left) and non-tally trees (right).

Select based on 

attributes: Invert 

select/ select null

Custom selection tools; either selects all dots not currently selected and makes all currently 

selected dots not selected (left) or selects all dots that have not yet been assigned a tree/not 

tree value (right).

Undo Removes the last assigned tree/not tree attribute. Successive clicks of this button step through 

older attribute assignments and remove them.

Confidence Dropdown box that allows user to indicate how confident they are in the percent canopy for the 

current plot. Options are high (default) and low; a low confidence value prompts the user for a 

low confidence reason (e.g., clouds).

Done User can indicate they have completed work on a plot. Saves attribute information for the 

currently selected plot and zooms to the next plot in the plot dropdown list.

Percent canopy Calculates and reports the percent canopy cover based on dots that have been attributed. Also 

reports the number of dots that have not yet been attributed.

Options Launches a dialog that allows the user to specify the following parameters: 

Plot key: Specifies an attribute field in the plot layer used to uniquely identify plots (e.g., CN, 

or a concatenation of state, county, plot). The values of this key populate the “Select plot” 

dropdown list. 

Data frame reprojection: Specifies behavior of data frame in the event imagery or plots are in 

different projections. 

Dot layer projection: Specifies the output projection of dot grids. 

Zoom level: Defines the display scale when application moves to a new plot; default is 1:1000. 

Attribute levels: Option to turn off distinction between tally and non-tally trees. 

Symbology: Option to load custom symbology. 

Canopy cover button: Option to disable the informational Percent Canopy Cover button. 

Big undo: Option to delete all dot files in the directory of the currently active layer and to reset 

the associated plot file to its original state by deleting the canopy cover attributes. 

Dot output folder: User-specified location for storage of dot layers. 

Current data frame: Specifies to which data frame tool operations apply.

Table 1.—Summary of functions within the canopy cover tool. Note that the options function permits 

flexibility in defining parameters that may vary across FIA regions or resource applications.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 240

Practical Advantages 

and Applicability

There are several practical advantages of both the 

tool’s user-friendly design and the simplicity of the 

dot grid protocol. First, the ability to host the tool at 

a network location is critical for minimizing software 

support and troubleshooting. Second, the tool requires 

only minimal GIS experience and technical training, 

which is a significant advantage over object-oriented 

image segmentation methods. Third, no field data 

are required. While some assessments of tree canopy 

cover rely on mapped-plot data including tree size and 

location (see Toney et al. 2009), this approach requires 

only high-resolution imagery along with either a 

point shapefile or comma-separated value file of plot 

locations. As with any photo-interpreted evaluation 

of tree cover, however, photo interpretation skills and 

familiarity with regional vegetation are required for 

accurate and repeatable percent canopy cover values.

A fourth advantage is the ability to assess repeatability 

and sampling error. The tool’s visible output includes 

a new shapefile, which contains each of the 109 dots 

in each plot’s dot grid, as well as the tree/no tree 

attribute for each dot. Therefore, multiple interpreters 

can evaluate quality control plots using consistent dot 

locations and imagery. Further, the existence of dot 

grids allows straightforward calculation of sampling 

error. In contrast, while census-based approaches (e.g., 

image segmentation, which produces polygons) have 

no sampling error, they do have misclassification and 

boundary errors that are difficult to quantify. Finally, 

datasets representing dot grids are relatively small in 

size, parallel in structure given that each plot always 

contains the same number of dots and attributes, and 

generally easier to manage than polygon outputs 

produced by image segmentation.

The straightforward design of the canopy cover 

tool lends itself to applications in other projects and 

resource settings. The tool’s principal requirement is 

a point shapefile or comma-separated values (CSV) 

file, wherein each point or record represents one plot 

and contains a unique identifier in the attribute table. 

Based on user-assigned dot grid values, canopy cover 

is calculated for each sample plot and stored in this 

attribute table. RSAC has made available an adaptable 

version of the tool called Image Sampler, which is 

part of the Digital Mylar toolkit (USDA FS 2012). 

This version of the tool allows the user to specify 

whether the dots should be randomly or systematically 

arranged within an area of interest, and also allows for 

a user-defined classification scheme.

DISCUSSION

A new GIS-based tool facilitates the determination of 

percent canopy cover, which is a critical component 

in FIA’s new definition of forest land, using a dot grid 

and high-resolution imagery. A partnership between 

RSAC and FIA successfully led to delivery of a 

functional tool within a few months’ time. 

The canopy cover tool was used to assign percent 

canopy cover to tens of thousands of FIA plots for 

the 2012 inventory year. The resulting data will not 

only populate the canopy cover variable for nonvisit plots in the national FIA database, but will also 

be used in the update of the NLCD percent canopy 

cover layer, which is currently under development 

as a collaboration among several national agencies. 

In future inventory years, percent canopy cover will 

continue to be assigned at all non-visited FIA plots 

using this method. The determination of percent 

canopy cover at all FIA plots will allow FIA to align 

with international cover-based definitions of forest 

land and also monitor trends in tree cover on nonforest 

lands.

ACKNOWLEDGMENTS

Along with the previous and current members of 

the national prefield team and Remote Sensing 

Application Center collaborators, the authors thank 

the FIA Techniques Research Band for supporting 

development of the canopy cover tool and all of the 

FIA staff involved in testing the tool during the rapid 

development process.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 241

LITERATURE CITED

Clark, J.T.; Finco, M.V.; Warbington, R.; Schwind, B. 

2004. Digital mylar: a tool to attribute vegetation 

polygon features over high-resolution imagery.

Available at www.fs.fed.us/r5/rsl/publications/. 

(Accessed April 5, 2012).

Goeking, S.; Liknes, G.C. 2009. The role of pre-field 

operations at four forest inventory units: we can 

see the trees, not just the forest. In: McWilliams, 

W.; Moisen, G.; Czaplewski, R., comps. Forest 

Inventory and Analysis (FIA) symposium 2008; 

2008 October 21-23; Park City, UT. Proc. RMRSP-56CD. Fort Collins, CO: U.S. Department of 

Agriculture, Forest Service, Rocky Mountain 

Research Station. 12 p.

Toney, C.; Shaw, J.D.; Nelson, M.D. 2009. A stemmap model for predicting tree canopy cover 

of Forest Inventory and Analysis (FIA) plots.

In: McWilliams, W.; Moisen, G.; Czaplewski, 

R., comps. Forest Inventory and Analysis (FIA) 

symposium 2008; 2008 October 21-23; Park City, 

UT. Proc. RMRS-P-56CD. Fort Collins, CO: U.S. 

Department of Agriculture, Forest Service, Rocky 

Mountain Research Station. 19 p.

U.S. Department of Agriculture, Forest Service (USDA 

FS). 2012. Digital mylar. Available at http://www.

fs.fed.us/eng/rsac/digitalmylar. (Accessed May 9, 

2012).

Vines, E. 2007. USDA Forest Service delivers 

high-quality imagery in seconds. ArcUser 

Online. Available at http://www.esri.com/news/

arcuser/0807/forest_image.html. (Accessed May 9, 

2012).

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 242

BUILDING CAPACITY FOR PROVIDING CANOPY COVER 

AND CANOPY HEIGHT AT FIA PLOT LOCATIONS 

USING HIGH RESOLUTION IMAGERY AND LEAF-OFF LIDAR

Rachel Riemann, Jarlath O’Neil-Dunne, and Greg C. Liknes1

Abstract.—Tree canopy cover and canopy height information are essential for estimating 

volume, biomass, and carbon; defining forest cover; and characterizing wildlife habitat. 

The amount of tree canopy cover also influences water quality and quantity in both 

rural and urban settings. Tree canopy cover and canopy height are currently collected 

at FIA plots either in the field or by dot-grid interpretation of digital aerial imagery. 

These techniques can be time consuming and costly. The University of Vermont’s Spatial 

Analysis Laboratory has developed an automated approach using Object-Based Image 

Analysis (OBIA) techniques for extracting canopy cover, canopy height, and land 

cover from readily available high resolution aerial imagery and leaf-off LiDAR. We 

used datasets generated by the OBIA approach for 10 different counties spread across 4 

states, representing a range of conditions. Canopy cover, canopy height, and land cover 

information were computed for each FIA plot, at scales of 144-foot-radius (plot circle) 

and 3,280-foot-(1-km)-radius, and compared to FIA estimates at the plot level. Results 

are discussed in terms of the comparative assessment of the three canopy cover data 

sources (including what is missing when nonforest plot data are not available), and the 

prognosis for using the OBIA techniques to extract this type of information at the county 

and state levels. Acquiring tree canopy cover data using the OBIA approach would allow 

FIA to apply a consistent method for acquiring canopy cover to both visit and non-visit 

plots, and even potentially increase the reliability of the canopy cover data available. This 

approach also provided valuable data on canopy height for FIA plots not visited in the 

field and additional data on landscape context for all FIA plots, improving capacity to 

characterize and analyze forest characteristics with respect to local levels of urbanization.

1

 Research Forester/Geographer (RR), U.S. Forest Service, 

Northern Research Station, c/o USGS, 425 Jordan Rd., 

Troy, NY 12180; Director (JOD), Spatial Analysis Lab, 

University of Vermont; and Research Physical Scientist 

(GCL), U.S. Forest Service, Northern Research Station. 

RR is corresponding author: to contact, call 518-285-5607 

or email at rriemann@fs.fed.us. 

INTRODUCTION

The importance of information on tree cover, 

irrespective of land use, has been recognized by the 

U.S. Forest Service’s Forest Inventory and Analysis 

(FIA) Program for some time. In 2010 FIA decided to 

add it as a standard variable, to be collected in the field 

for all visited plots, and via photointerpretation at the 

prefield stage for all non-visit plots, with some overlap 

for quality assurance (QA) to assess the relationship 

between the two. Once the protocol is fully 

implemented, these data will provide FIA with tree 

canopy cover data on all plots, providing a much more 

complete and consistent estimate of tree canopy cover 

information than is currently available. Data collected 

via these two methods are directly relevant to the 1/6-

acre plot cluster and thus the other inventory variables 

collected on the plot. There are some limitations with 

this data, however. Data from visited (mostly forest) 

and non-visited (mostly nonforest) plots are collected 

by different methods and at different scales, potentially 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 243

resulting in systematic differences in measurement 

quality between the two populations. These effects 

will be unknown until enough QA overlap data are 

available for analysis. Furthermore, collecting tree 

canopy cover data in this manner is labor intensive. 

If semi-automated or fully automated approaches to 

determining canopy cover are available that provide 

data at similar scales and with similar estimates and 

standard errors, it could make the protocol more 

efficient in the long term. 

Another source of tree canopy cover information 

has been developed by O’Neil-Dunne et al. (2009) 

using a combination of high resolution aerial imagery 

and LiDAR (Light Detection And Ranging) data. It 

has been used to generate citywide and countywide 

estimates of existing tree canopy cover for resource 

managers (e.g., O’Neil-Dunne and Pelletier 2011). 

This approach leverages the vast amounts of high 

resolution remotely sensed data available through the 

National Agricultural Imagery Program (NAIP) along 

with LiDAR data acquired by federal, state, and local 

governmental organizations. Object-Based Image 

Analysis (OBIA) techniques are used to extract sevenclass land-cover datasets with an overall accuracy 

exceeding 90 percent. The combination of spectral 

(imagery) and height (LiDAR) data in conjunction 

with OBIA techniques enables features to be extracted 

using the same elements of image interpretation used 

by photointerpreters (see Olson 1960).

Our four primary goals in this study were to (1) 

compare plot-level estimates of tree canopy cover 

obtained from the OBIA technique with those obtained 

by FIA via ground inventory in the field and via 

photointerpretation in prefield procedures, (2) compare 

plot-level estimates of stand height with those obtained 

by FIA in the field, (3) examine the canopy cover and 

canopy height characteristics of forest vs. nonforest 

plots, and (4) illustrate the type of landscape context 

information that is available for each plot from the 

OBIA approach. 

METHODS

Of the 10 counties for which OBIA data are already 

available, only 6 were included in this paper: 

Allegheny and Lancaster Counties in Pennsylvania, 

and Anne Arundel, Montgomery, Prince Georges, 

and Howard Counties in Maryland. The six counties 

range from 16 percent forested in Lancaster, PA, to 

40 percent forested in Prince Georges, MD. Species 

composition is primarily hardwoods. 

Our study was based on FIA field plots visited from 

2006 to 2011. Tree height was available on all forested 

plots. Because canopy cover variables were just 

introduced last year, only a subset of these plots had 

canopy cover data. At the time of this study, about 

20 percent of forested plots had field canopy data, 

collected in 2011. A different subset (~20 percent of all 

plots) had photointerpreted (PI) canopy cover collected 

using imagery from 2007 to 2010. Of the 369 plots in 

the 6 counties for which data were available at the time 

of this paper, we had PI percent canopy cover data for 

75 plots (all counties) and field-collected canopy cover 

data for 19 plots (MD counties only). FIA canopy 

height and field canopy cover were calculated as the 

mean of all live trees on the forested conditions on the 

1/6-acre plot. FIA photointerpreted canopy cover was 

estimated using a 100-point dot grid over a 1.5-acre 

(144-foot-radius) area surrounding each plot. 

The imagery used for all the counties in the study 

consisted of NAIP 4-band 1-m data from 2009 to 

2011. The LiDAR was sourced from a broad range of 

federal, state, and local agencies. The various LiDAR 

datasets were similar in that they had a nominal post 

spacing of 0.6 m to 1.4 m, had ground points classified 

(LAS class = 2), and were acquired during leaf-off 

conditions. Digital Elevation Models (DEM) and 

Normalized Digital Elevation Models (nDSM) were 

generated from the LiDAR data. The LiDAR surface 

models, imagery, and ancillary GIS datasets (e.g., road 

centerlines) were integrated into the OBIA system, 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 244

which was built using the eCognition® software 

platform. Within eCognition®, a rule-based expert 

system was used to extract seven classes of land cover 

at a nominal resolution of 1 m: (1) tree canopy, (2) 

grass/shrubs, (3) bare soil, (4) water, (5) buildings, 

(6) roads/railroads, and (7) other paved surfaces. 

The rule-based expert system was built using the 

Cognition Network Language (CNL), which is the 

underlying programming language for eCognition®. 

In this approach, image processing, segmentation, 

classification, and morphology algorithms are 

iteratively applied, thereby successively building 

contextual information, which can then be used to 

improve the classification. To effectively process the 

billions of data points making up the imagery and 

LiDAR and support iterative processing approach, 

the OBIA system was built on 64-bit computing 

architecture and the processing load was distributed to 

multiple cores.

Following the development of the land cover dataset, 

the FIA plot data were integrated into the OBIA 

system. A separate rule-based expert system iteratively 

processed each FIA plot, extracting canopy coverage, 

topographic, and land cover information.

OBIA estimates of canopy height and canopy cover 

were calculated at two different neighborhood sizes 

for each FIA plot: a 144-foot-radius area around 

plot center (1.5 acres), representing the plot circle 

encompassing all four subplots, and a 3,280-footradius area around plot center (776 acres). OBIA 

estimates were calculated as the mean canopy height 

and canopy cover of all tree canopies greater than 8 

feet in height. FIA and OBIA estimates were compared 

using linear regression (r-squared). 

RESULTS

OBIA canopy cover estimates are strongly correlated 

to the FIA photointerpreted tree canopy estimates 

(r2 = 0.91), but relative to the FIA data they tend to 

overestimate canopy values. OBIA and field-collected 

tree canopy estimates exhibited a poor correlation 

(r2

 = 0.07) (Fig 1). 

FIA % Canopy Cover

OBIA % Canopy Cover

PI % canopy

Field % canopy

Linear (1:1)

Linear (field % canopy)

Linear (PI % canopy)

Figure 1.—Comparison of OBIA-based canopy cover information with FIA canopy cover information. OBIA information is 

derived from an automated classification procedure using NAIP high resolution aerial imagery and LiDAR data. FIA canopy 

cover information is either observed in situ or estimated from a NAIP image using a dot grid.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 245

Agreement between FIA canopy height and LiDARderived OBIA height for forested plots is poor, with 

an r-squared of 0.23 (Fig. 2). Additional investigation 

needs to be done to determine if this is due to a 

locational mismatch between FIA plot locations and 

the OBIA data or if the difference in collection dates 

between LiDAR and FIA field visits accounts for 

the differences (or some combination of both). It is 

likely that the leaf-off nature of the LiDAR data is 

at least partly to blame, particularly in deciduous 

forests where the morphological profile of the trees 

(tall, thin, with few branches) results in relatively few 

LiDAR returns with the 0.6- to 1.4-m post spacing. 

FIA currently has no information on tree heights on 

nonforest plots. Based on the OBIA data, nonforest 

plots have trees ranging from 10 to 60 feet in height 

(Fig. 2), information not available from the FIA 

photointerpreted data for nonforest plots. 

The OBIA data provide information on tree canopy 

cover and stand (mean tree) heights for all plots. From 

this information we can summarize characteristics for 

both forest and nonforest plots for these six counties. 

For example, in Lancaster County, the average canopy 

cover for the entire county is 23 percent, with an 

average height of 17 feet. Breaking this down, we 

find that the averages are 68 percent canopy cover 

and 37 feet high for forested plots, and 12 percent 

canopy cover and 12 feet high for nonforest plots. For 

a county that is 85 percent nonforest, this represents 

considerable tree canopy cover for which very little 

FIA data exist. Table 1 presents this information for six 

counties. 

Figure 2.—Comparison of OBIA-based canopy height information with FIA canopy height information. OBIA-based heights are 

derived from a LiDAR point cloud while FIA height information is obtained using field methodology. FIA Field Canopy Height (feet)

OBIA Canopy Height

Forested plots

Nonforest plots

Linear (1:1)

Linear (forested plots)

Percent Canopy Cover Canopy Height

All Plots Forested Plots Nonforest Plots All Plots Forested Plots Nonforest Plots

County Mean SE (n) Mean SE (n) Mean SE (n) Mean SE (n) Mean SE (n) Mean SE (n)

Anne Arundel 49 5.3 (41) 76 7.1 (13) 36 5.5 (28) 33 2.6 (41) 45 3.1 (13) 28 2.9 (28)

Montgomery 46 4.8 (53) 75 4.7 (22) 26 4.9 (31) 25 3.3 31 5.5 20 1.4

Prince Georges 51 5.3 (53) 83 19.0 (19) 33 5.6 (34) 33 2.6 46 10.6 25 4.3

Allegheny 49 3.5 (84) 68 5.0 (37) 34 3.7 (47) 28 1.2 33 1.8 24 1.4

Lancaster 23 3.1 (107) 68 6.7 (20) 12 2.3 (85) 17 1.6 37 2.8 12 1.5

Howard 54 6.6 (28) 79 8.0 (11) 38 7.2 (17) 33 2.1 40 1.9 28 2.7

Table 1.—Summary of tree canopy cover and canopy height statistics for forest vs. nonforest plots from 

the OBIA data 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 246

The OBIA datasets also provide information on the 

land cover context or neighborhood in which the plot 

occurs. This context can be important to understanding 

the status of an individual plot. For example, plots in 

the study area with relatively low canopy cover within 

the 1.5-acre circle were found to have substantially 

more canopy cover within the larger 776-acre circle 

(Fig. 3). And plots with 100 percent canopy cover 

within the 1.5-acre circle were found to contain other 

land uses within the larger 776-acre context area. 

DISCUSSION AND CONCLUSIONS

The OBIA datasets offer several opportunities. First, 

the OBIA datasets provide an alternative assessment 

of the photointerpreted and field-collected tree 

canopy cover values. Second, it is an opportunity 

to provide consistent tree canopy cover information 

Figure 3.—Summary of the land cover context around individual plots in Anne Arundel County at two scales. Plots are sorted 

by the proportion of canopy cover present within the 144-foot- radius area. 

Individual Plots

for both visited and non-visited plots at a scale 

relevant to the FIA plot data without additionally 

impacting the FIA prefield process. As can be seen 

in Table 1, the magnitude of tree canopy cover in 

nonforest areas supports the need to gather this 

information, whether via prefield interpretation or 

the OBIA approach. Third, the OBIA datasets offer 

an opportunity to gather canopy height information 

on all plots, not just those visited in the field. 

Finally, and equally importantly, these datasets can 

provide landscape context information important 

for understanding local urbanization pressures for 

each FIA plot at a scale and accuracy not possible 

from NLCD data sources and with an efficiency not 

possible from photointerpretation. The OBIA data 

provide information about the larger neighborhood 

and therefore can show us how well the FIA plot is 

representative of its surrounding area. 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 247

OBIA approaches to land cover and FIA metric 

extraction have been shown to be efficient when 

applied to large datasets, providing those datasets 

have somewhat consistent properties. Although OBIA 

systems require a great deal of expertise to design and 

deploy, they are cost effective over large areas due 

to economies of scale. Furthermore, in addition to 

computing plot metrics, the OBIA approach provides 

a means by which to conduct a complete census, 

which is increasingly important as development 

pressures stemming from urbanization and natural 

resource exploitation fragment the forested landscape. 

The greatest barrier to the OBIA approach is the 

availability of data. Although NAIP data are acquired 

for each state aside from Alaska at least every 3 years, 

there is no nationally coordinated LiDAR program. 

For states that do have comprehensive coverage, 

the LiDAR data are typically available for only a 

single point in time and are frequently leaf-off to 

create terrain DEMs. The approach used in this study 

allowed us to take advantage of leaf-off LiDAR for 

estimating tree canopy cover and height to generate 

data comparable to current FIA estimates. 

LITERATURE CITED

Olson, C.E. 1960. Elements of photographic 

interpretation common to several sensors.

Photogrammetric Engineering. 26 (4): 651-656.

O’Neil-Dunne, J.P.M.; Pelletier, K.C. 2011. A 

report on Lackawanna County’s existing and 

possible tree canopy. South Burlington, VT: 

U.S. Department of Agriculture, Forest Service, 

Northern Research Station.

O’Neil-Dunne, J.P.M.; Pelletier, K.; MacFaden, S.; 

Troy, A.R.; Grove, J.M. 2009. Object-based highresolution land-cover mapping: operational 

considerations. Proceedings of the 17th 

International Conference on Geoinformatics; 

2009 August 12-14; Fairfax, VA. 

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 248

CANOPY COVER ESTIMATES FOR INDIVIDUAL TREE ATTRIBUTES

James A. Westfall and Randall S. Morin1

Abstract.—In most forest inventory data, it is not feasible to estimate the canopy 

coverage of trees having certain characteristics due to the lack of information on crown 

size. In this study, data from the Forest Inventory and Analysis (FIA) program was used 

to assign crown sizes to individual trees using published crown width models. This 

process effectively links trees to area such that canopy cover area estimates can be made 

using domains that include tree-level attributes (e.g., canopy cover of red maple having 

total height ≥80 feet). Advantages of implementing this approach are 1) estimation can 

proceed as with any other estimate of area derived from FIA data, and 2) canopy cover 

estimates provide different information than classical indicators such as number of trees. 

A disadvantage is the need to dissolve overlapping crowns after the tree-level domain is 

selected. Two examples are provided to illustrate applications of the method. 

1

 Research Foresters (JAW, RSM), Northern Research 

Station, 11 Campus Blvd., Suite 200, Newtown Square, 

PA 19073. JAW is corresponding author: to contact, call 

610-557-4043 or email at jameswestfall@fs.fed.us.

INTRODUCTION

Tree canopy cover is the proportion of an area covered 

by the vertical projection of tree crowns (Jennings et 

al. 1999). Canopy cover plays a critical role in defining 

current and future forest characteristics via impacts 

on understory species composition and structure 

(Canham et al. 1990). Canopy cover is often a primary 

indicator of wildfire risk and behavior of both modeled 

and actual wildfires (Agee and Skinner 2005). 

Additionally, amounts and types of canopy cover 

and structure influence habitat suitability for many 

forest-dwelling vertebrate species (Massé and Côté 

2009). A key factor in studies of the aforementioned 

relationships between canopy cover and forest-related 

phenomena is that relevant analyses often focus on 

specific aspects of canopy cover, e.g., cover of trees 

having certain characteristics such as species, diameter 

at breast-height (d.b.h.), and total height (Abrahamson 

and Gohn 2004). Thus, expansion of research results to 

landscape-scale assessments can be difficult.

Area coverage estimates for tree-level attributes 

are usually not feasible from forest inventory data 

due to the lack of information on how much area 

is represented by an individual tree. Alternatively, 

the area occupied by each inventory tree can be 

approximated from the plot data using techniques 

such as Thiessen polygons or crown width models 

(Bechtold 2003). Applying crown width models to 

forest inventory data with mapped tree locations 

allows for spatial representation of canopy cover while 

also allowing for gaps between trees. Because the 

crown width approach is specific to individual trees, 

there is an explicit relationship that links area coverage 

to tree-level attributes. In this paper, we present 

methodology to estimate the area coverage of virtually 

any tree-level attribute(s) available in forest inventory 

data.

METHODS

A three-phase forest inventory and monitoring effort 

is implemented by the Forest Inventory and Analysis 

(FIA) program within the U.S. Forest Service 

(Bechtold and Patterson 2005). Phase 1 (P1) is the 

development of a post-stratification scheme using 

remotely-sensed data. The second phase (P2) entails 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 249

measuring sample plots on the ground for the usual 

suite of forest mensuration variables such as tree 

species, d.b.h., height, forest type, stand age, etc. 

Overstory trees (d.b.h. ≥5.0 in.) are measured on four 

24-ft radius subplots; saplings (1.0 ≤ d.b.h. ≤ 4.9 in.) 

are recorded on four microplots of 6.8-ft radius each. 

Phase 3 (P3) occurs on a subset of the P2 plots, where 

additional data on forest health indicators are collected 

(e.g., down woody material). To evaluate the efficacy 

of the methods presented below, one full cycle of data 

from Pennsylvania was used (data collected between 

2006 and 2010). 

To estimate the amount of canopy cover for each 

tree measured in the inventory, the crown width 

models described by Bechtold (2003) were used. 

When species were encountered that were not listed 

in Bechtold (2003), coefficients for species of similar 

form were used. For this analysis, the model using 

d.b.h. and crown ratio as input variables was used.

[1] CW β

ˆ βˆ D βˆ D βˆ

3CR 2210 ^ +++=

where: = estimated crown width (feet)

D = d.b.h. (inches)

CR = crown ratio

 = species-specific coefficients

Spatial representation of plot canopy cover was 

accomplished by centering circles of estimated crown 

width at the tree location recorded during the plot 

visit. To assess cover of overstory trees (d.b.h. 5.0+ 

inches), overlapping crowns from neighboring trees 

were combined such that crown overlap was accounted 

for, i.e., the cover as it would appear as viewed 

from above. A geographic information system (GIS) 

was employed to dissolve the boundaries between 

individual tree crowns in order to create polygons 

of non-overlapping crown cover for each FIA plot 

condition (Fig. 1). A key point in the analytical method 

is that crowns extending beyond the sample plot 

boundary are included in the total canopy cover for the 

plot. It is surmised that, on average, this will account 

for crown areas of nonsampled trees extending into the 

plot that are not explicitly accounted for.

^ CW

β 3-0ˆ

Figure 1.—Maps of (A) individual tree crowns on an FIA plot, and (B) dissolved polygons of crown cover on an FIA plot.

Subplot boundaries

Tree crowns

25 0 25 12.5 Feet

A

Subplot boundaries

Dissolved tree crowns

25 0 25 12.5 Feet

B
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 250

Figure 2.—Maps of (A) dissolved polygons of sapling crown 

cover on an FIA plot, (B) microplot tree cover above sapling 

cover, and (C) sapling cover remaining after tree cover is 

accounted for.

While the models presented by Bechtold (2003) 

were fitted to subplot trees (d.b.h. ≥5.0 in.), these 

same models were used to quantify sapling (d.b.h. 

1.0 to 4.9 inches) crown cover on the microplot area. 

At this point, there are estimates of overstory and 

sapling tree cover on forested portions of subplots 

and microplots, respectively. These cannot simply 

be summed to obtain the total cover as 1) overstory 

trees may partially/wholly obscure sapling cover, 

and 2) the overstory and sapling cover estimates are 

on a different area basis. To reconcile total cover 

at the subplot level, the amount of overstory cover 

for trees whose stems occur within the microplot 

was calculated. Also, the amount of sapling cover 

remaining visible once overstory trees were accounted 

for was quantified. This was accomplished by using 

GIS to remove the sapling cover that had microplot 

tree crown cover growing above (Fig. 2). The ratio of 

remaining sapling cover to overstory microplot cover 

was used to estimate the amount of sapling cover that 

would be visible if saplings were measured over the 

entire subplot area—producing an estimate of cover 

for all trees and saplings combined. Due to the use 

of crown area occurring outside plot boundaries and 

the method of estimating the contribution of saplings, 

the area of crown cover exceeded the forested area 

of the subplot in some cases. When this phenomenon 

occurred, the cover estimate was constrained to the 

forested area. As the plot is the primary sampling unit, 

plot-level cover estimates were obtained by summing 

over subplots.

Under standard FIA protocols, the total height of 

saplings is measured only on P3 plots. To facilitate 

analyses related to vertical structure, tree heights for 

saplings on non-P3 plots were predicted from linear 

models developed using observed sapling height 

data obtained on P3 plots. Due to the relatively small 

amount of model fitting data available, coefficients 

were estimated for hardwood and softwood species 

categories (Table 1).

Microplot boundaries

Dissolved sapling crowns

25 0 25 12.5 Feet

A

Microplot boundaries

Dissolved tree crowns

Dissolved sapling crowns

25 0 25 12.5 Feet

B

Microplot boundaries

Remaining sapling crowns

25 0 25 12.5 Feet

C
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 251

Table 1.—Linear regression analysis results for equation [2] by major species group. RMSE is the 

square-root of mean squared error.

Species Group n (Std. Error) (Std. Error) R2 RMSE

Hardwood 1481 9.3057 (0.3424) 5.4529 (0.1332) 0.53 5.7

Softwood 33 2.0837 (1.5163) 5.3902 (0.5501) 0.76 3.4

θ

ˆ

0 θ

ˆ

1

[2]

where: = estimated sapling height (feet)

D = d.b.h. (inches)

 = coefficients estimated from the data

Hˆ θ

ˆ += θˆ

10S D

HS ˆ

10- θ

ˆ

Computation of area of forest land, area of crown 

cover within forest land, and the proportion of forest 

land area having canopy cover was accomplished 

using the standard FIA methods documented in Scott 

et al. (2005). 

Two example analyses are presented to illustrate the 

utility of the method for assessing contemporary 

forestry issues like forest health and wildlife habitat. 

First, beech bark disease kills or injures American 

beech (Fagus grandifolia Ehrh.). The estimated tree 

cover susceptible to the disease was estimated for trees 

of 8.0 inches d.b.h. and larger, as these are the most 

susceptible to mortality (Houston and O’Brien 1983). 

The second example assesses habitat availability for 

the cerulean warbler (Dendroica cerulean). Jones and 

Robertson (2001) found that successful nesting of 

Attribute Area (SE) (SE)

Total canopy cover 14,821,653.7 (150,823.1) 0.899 (0.007)

Beech bark disease 422,893.6 (25,637.3) 0.029 (0.002)

Cerulean warbler habitat 4,149,383.5 (91,977.7) 0.280 (0.005)

Table 2.—Estimates with standard errors (SE) for area (acres) and proportion of all forested 

canopy cover )ˆ (R

Rˆ

cerulean warblers most often occurred where crown 

cover occurred at 19.7 to 39.4 feet with additional high 

cover above 59.1 feet. In the absence of crown shape 

models, estimates of canopy area were calculated for 

trees having height of 19.7 to 39.4 feet when there also 

existed at least one tree having height of 59.1 feet or 

greater on the same subplot.

RESULTS

A preliminary analysis was conducted to estimate 

the proportion of forest area in Pennsylvania having 

tree cover. The area of forest land was estimated to 

be 16,482,968.6 acres with a standard error (SE) of 

156,471.9 acres. The estimated area of tree cover 

was 14,821,653.7 acres (SE = 150,823.1) (Table 2). 

The resultant proportion was 0.899 (SE = 0.007). 

The roughly 10 percent of forest land area without 

tree cover is due to gaps between tree canopies (9.0 

percent), with a modest contribution (1.0 percent) from 

forested areas having no tree or sapling cover (e.g., 

areas only having seedlings).

Rˆ
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 252

The canopy cover of American beech having d.b.h. 

8.0 inches and larger was estimated to be 422,893.6 

acres (SE = 25,637.3). While this area estimate is 

informative, it is also useful to assess this area in 

context of total canopy cover. The corresponding 

proportion of existing canopy cover this area 

represents is 422,893.6/14,821,653.7 = 0.0285 (SE = 

0.0017). Thus, approximately 3 percent of the canopy 

cover in Pennsylvania is potentially at risk due to 

beech bark disease.

Using the criteria outlined above for cerulean 

warbler habitat, the estimated area of canopy cover 

was 4,149,383.5 acres (SE = 91,977.7). Put in the 

context of total canopy cover, the proportion of area 

containing desirable conditions for cerulean warbler 

is 4,149,383.5 /14,821,653.7 = 0.2800 (SE = 0.0054). 

This outcome suggests that more than one-fourth of 

forest land area having canopy cover in Pennsylvania 

may be suitable for cerulean warbler.

DISCUSSION

Estimates of canopy cover provide a different 

viewpoint than classical indicators such as forest 

land area or numbers of trees. An example is the 

difference between area of forest land and area of 

forest land with canopy cover. At first glance, one may 

assume that all forest has canopy cover. However, 

this study has shown there to be an approximate 10 

percent difference in these two characteristics. Starker 

contrasts are seen when comparing different metrics 

for identical domains of tree variables. FIA estimates 

for number of American beech trees having d.b.h. 

≥8.0 inches as a proportion of all trees is 0.0056. The 

proportion of canopy cover is roughly five times larger 

than the proportion of trees (0.0285).

Forest managers should consider adding canopy 

cover area to their portfolio of analytical metrics. 

Canopy estimates provide new information that has 

largely been unavailable in the past and may suggest 

alternative management strategies. The individual 

tree crown size predictions can be added to most 

existing forest inventory data and estimation can 

proceed in a manner similar to other estimates such 

as area by forest type, stand size, etc. A disadvantage 

is this method is not easily implementable in standard 

analytical tools such as FIA’s FIDO or EVALIDator 

software. This is due to the need to perform the 

dissolution of overlapping crowns, which must be 

accomplished after specification of the domain of 

interest. This exercise takes approximately 5 to 

15 minutes, depending on the number of trees in 

the domain. However, the additional effort may 

be worthwhile in cases where canopy cover is of 

particular importance.

LITERATURE CITED

Abrahamson, W.G.; Gohn, A.C. 2004. Classification 

and successional changes of mixed-oak forests 

at the Mohn Mill Area, Pennsylvania. Castanea. 

69(3): 194-206.

Agee, J.K.; Skinner, C.N. 2005. Basic principles of 

forest fuel reduction treatments. Forest Ecology 

and Management. 211: 83-96.

Bechtold, W.A. 2003. Crown-diameter prediction 

models for 87 species of stand-grown trees in the 

eastern United States. Southern Journal of Applied 

Forestry. 27: 269-278.

Bechtold, W.A.; Patterson, P.L., eds. 2005. The 

enhanced Forest Inventory and Analysis 

program - national sampling design and 

estimation procedures. Gen. Tech. Rep. SRS-80. 

Asheville, NC: U.S. Department of Agriculture, 

Forest Service, Southeastern Research Station. 

85 p.

Canham, C.D.; Denslow, J.S.; Platt, W.J.; Runkle, 

J.R.; Spies, T.A.; White, P.S. 1990. Light regimes 

beneath closed canopies and tree-fall gaps in 

temperate and tropical forests. Canadian Journal 

of Forest Research. 20: 620-631.

Houston, D.R.; O’Brien, J.T. 1983. Beech bark 

disease. Forest Insect & Disease Leaflet 75. 

Broomall, PA: U.S. Department of Agriculture, 

Forest Service. 8 p.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 253

Jennings, S.B.; Brown, N.D.; Sheil, D. 1999. 

Assessing forest canopies and understory 

illumination: canopy closure, canopy cover and 

other measures. Forestry. 72: 59-73.

Jones, J.; Robertson, R.J. 2001. Territory and nestsite selection of cerulean warblers in eastern 

Ontario. The Auk. 118: 727-735.

Massé, A.; Côté, S.D. 2009. Habitat selection of a 

large herbivore at high density and without 

predation: Trade-off between forage and cover?

Journal of Mammalogy. 90: 961-970.

Scott, C.T.; Bechtold, W.A.; Reams, G.A.; Smith, 

W.D.; Westfall, J.A.; Hansen, M.H.; Moisen, 

G.G. 2005. Sample-based estimators used by 

the Forest Inventory and Analysis national 

information management system. In: Bechtold, 

W.A.; Patterson, P.L., eds. The enhanced Forest 

Inventory and Analysis program - national sampling 

design and estimation procedures. Gen. Tech. 

Rep. SRS-80. Asheville, NC: U.S. Department of 

Agriculture, Forest Service, Southeastern Research 

Station: 43-67.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 254

RELATING FIA DATA TO HABITAT CLASSIFICATIONS 

VIA TREE-BASED MODELS OF CANOPY COVER

Mark D. Nelson, Brian G. Tavernia, Chris Toney, and Brian F. Walters1

Abstract.—Wildlife species-habitat matrices are used to relate lists of species with 

abundance of their habitats. The Forest Inventory and Analysis Program provides data on 

forest composition and structure, but these attributes may not correspond directly with 

definitions of wildlife habitats. We used FIA tree data and tree crown diameter models 

to estimate canopy cover, from which we assigned FIA conditions to NatureServe forest 

and woodland habitat domains and National Land Cover Dataset (NLCD) forest classes 

within the 20-state northern FIA region. Hardwood and softwood types were most 

abundant for FIA, least abundant for NLCD, and intermediate for NatureServe classes. 

NatureServe hardwood types were evenly distributed between forest and woodland, but 

softwood types were more abundant in NatureServe woodland than forest. Mixed types 

were substantially more abundant for NLCD, intermediate for NatureServe (equally 

distributed between forest and woodland), and least abundant for FIA. Area of woody 

wetlands, which were defined only for NLCD, exceeded area of NLCD softwoods. These 

habitat assignments are useful for estimating current and potential future abundance of 

habitats for forest-associated terrestrial vertebrate wildlife species in the region. 

1

 Research Forester (MDN) and Forester (BFW), U.S. Forest 

Service, Northern Research Station, 1992 Folwell Ave., 

St. Paul, MN 55108; Science Coordinator (BGT), Integrated 

Waterbird Management and Monitoring Program, USGS 

Patuxent Wildlife Research Center; Biologist (CT), U.S. 

Forest Service, Rocky Mountain Research Station. MDN 

is corresponding author: to contact, call 651-649-5104 or 

email at mdnelson@fs.fed.us.

INTRODUCTION

Strategic estimates of wildlife habitat abundance can 

be obtained from forest composition and structure 

data provided by the Forest Inventory and Analysis 

(FIA) Program (Woudenberg et al. 2010). FIA 

data spanning several decades are easily queried to 

estimate status and trends of coarse-scale habitat 

characteristics, like area of young hardwood forest or 

old softwood forest. However, FIA attributes are not 

directly relatable to wildlife species-habitat matrices, 

such as those developed by NatureServe (2011). 

Furthermore, spatially explicit landscape metrics are 

required for assessing habitat quality for many wildlife 

species but are not available directly from FIA plot 

data. Landscape metrics typically are obtained from 

ancillary geospatial datasets such as the National Land 

Cover Database of 2006 (NLCD) (Fry et al. 2011), but 

NLCD forest classes are defined differently from FIA 

forest types. There is an opportunity for increasing 

the utility of FIA data by relating it to species-habitat 

relationships and to ancillary datasets used for 

assessing habitats.

Tree canopy cover thresholds are used to characterize 

NatureServe forest and woodland habitat domains 

and NLCD forest land cover classes, but historical 

and current FIA data do not include estimates of tree 

canopy cover. Therefore, we adapted a procedure 

described by Toney et al. (2009) to estimate tree 

canopy cover from FIA tree data, which we used to 

assign NatureServe and NLCD classes to conditions 

in the FIA Database (FIADB; Woudenberg et al. 

2010). The NatureServe system defines canopy 

cover thresholds that separate forest from woodland, 

with subcategories of hardwood, conifer, and mixed 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 255

classes, resulting in six habitat types (Table 1). 

Similarly, NLCD forest land cover is separated into 

deciduous, evergreen, and mixed forest classes based 

on canopy cover thresholds. Canopy cover thresholds 

for NatureServe and NLCD differ from one another. 

NLCD’s woody wetland class was included in this 

study as an additional forest class, but is not based 

upon a canopy cover threshold (Table 2).

Table 1.—NatureServe habitat domains (adapted from NatureServe 2011) 

Code Habitat Domain Description

0 No data

Forest Woody vegetation at least 6 m tall (usually much taller) with a fairly continuous and 

complete (two-thirds or greater) canopy closure.

1 Forest-Hardwood Angiosperms comprise over two-thirds of the canopy.

2 Forest-Conifer Gymnosperms comprise over two-thirds of the canopy.

3 Forest-Mixed Composed of both hardwood and conifer trees, neither dominating as much as two-thirds 

of the canopy.

Woodland Crowns often not interlocking; tree canopy discontinuous (often clumped), averaging 

between 40 and 66 percent overall cover [Modified to include tree canopy between 10 

and 40 percent (Savanna), per recommendations from NatureServe.]

4 Woodland-Hardwood Angiosperms comprise over two-thirds of the canopy.

5 Woodland-Conifer Gymnosperms comprise over two-thirds of the canopy.

6 Woodland-Mixed Stand composed of both hardwood and conifer trees, neither dominating as much as twothirds of the canopy.

-- Savanna Mosaic of trees or shrubs and grassland; between 10 and 40 percent cover by trees and 

shrubs.

Table 2.—National Land Cover Dataset (2006) forest cover classes (adapted from NatureServe 2011) 

Code Land Cover Class Description

0 No data

41 Deciduous Forest Areas dominated by trees generally greater than 5 m tall, and greater than 20 percent 

of total vegetation cover. More than 75 percent of the tree species shed foliage 

simultaneously in response to seasonal change.

42 Evergreen Forest Areas dominated by trees generally greater than 5 m tall, and greater than 20 percent of 

total vegetation cover. More than 75 percent of the tree species maintain their leaves all 

year. Canopy is never without green foliage.

43 Mixed Forest Areas dominated by trees generally greater than 5 m tall, and greater than 20 percent 

of total vegetation cover. Neither deciduous nor evergreen species are greater than 75 

percent of total tree cover.

90 Woody Wetlands Areas where forest or shrub land vegetation accounts for greater than 20 percent of 

vegetative cover and the soil or substrate is periodically saturated with or covered with 

water.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 256

DATA AND METHODS

A canopy cover modeling approach (Toney et al. 

2009) was used to estimate canopy cover for trees 

(≥ 5 inches d.b.h., on subplots), if present, or saplings 

(1 to 4.9 inches d.b.h., on microplots) on forested FIA 

conditions within 20 states of the Upper Midwest and 

Northeast, during the inventory period 2004-2008. 

These states include Connecticut, Delaware, Illinois, 

Indiana, Iowa, Maine, Maryland, Massachusetts, 

Michigan, Minnesota, Missouri, New Hampshire, 

New Jersey, New York, Ohio, Pennsylvania, Rhode 

Island, Vermont, Wisconsin, and West Virginia. FIA 

forest land is defined as having “...at least 10 percent 

cover (or equivalent stocking) by live trees of any 

size, including land that formerly had such tree cover 

and that will be naturally or artificially regenerated” 

(Woudenberg et al. 2010). 

Canopy cover estimation was based on tree speciesspecific predicted crown dimensions, and tree stem 

location coordinates recorded by field crews within 

FIA subplots and microplots. Tree and sapling crown 

width predictions are based on Bechtold (2003) and 

Bragg (2001). An optional spatial statistic (Ripley’s K) 

included as a predictor in Toney et al. (2009) was not 

used for canopy cover modeling in the present study. 

Because FIA plots may contain multiple conditions, 

tree and sapling canopy cover estimates were weighted 

based on condition proportion and appended to the 

CONDITION table in a Microsoft Access database.

All forested conditions with more than 0 percent 

estimated canopy cover were assigned a NatureServe 

forest or woodland habitat type and an NLCD forest 

land cover class using tree or sapling canopy cover 

thresholds defined in Tables 1 and 2, respectively, 

with minor modifications. NatureServe wildlife 

habitat categories are distinct from those developed 

by NatureServe and others for the U.S. National 

Vegetation Classification (FGDC 2008). That 

classification defines a “Forest to Open Woodland” 

class that includes all forest stands with tree canopy 

cover >10 percent, including stands where the sapling 

layer is the dominant layer (e.g., regenerating stands) 

(Faber-Langendoen et al. 2012). Conditions with 

canopy cover below the minimum thresholds in Tables 

1 and 2 were assigned to NatureServe woodland 

or NLCD forest class, respectively. Canopy cover 

between 10 and 40 percent is defined as “Savanna” 

in NatureServe’s habitat domains (Table 1), but 

was grouped with woodland in this study because 

sparse canopy cover in this region is predominately 

associated with young/regenerating woodland or forest 

(NatureServe, personal communication).

A small number of forested FIA conditions contained 

no trees or saplings. Thus, no canopy cover estimates 

were available for these conditions, and canopy 

cover could not be used to assign habitat or land 

cover classes to those conditions. During a plot visit, 

a field crew can look beyond subplot boundaries 

to determine some condition attributes via visual 

interpretation, including those conditions containing 

no trees at the time of field data collection. For 

conditions with no trees or saplings (i.e., estimated 

canopy cover = 0), habitat and land cover classes 

were recoded to valid classes using other FIA 

condition attributes, including ALSTKCD–“a code 

indicating the stocking of the condition by live trees, 

including seedlings”; FORTYPCD–“the forest type 

used for reported purposes, primarily derived using a 

computer algorithm, except when less than 25 percent 

of the plot samples a particular forest condition”; 

and PHYSCLCD–“the general effect of landform, 

topographical position, and soil on moisture available 

to trees” (Woudenberg et al. 2010).

NatureServe:

If ALSTKCD = 5 (nonstocked), then assign condition 

to “0”.

Else, if ALSTKCD = 4 (poorly stocked, 10-34 

percent), assign to “Woodland”.

if FORTYPCD is between 500 and 998, assign to 

code “Woodland Hardwood”. (4)

if FORTYPCD <400, assign to code “Woodland 

Conifer”. (5)

if FORTYPCD is between 400 and 499, assign to 

code “Woodland Mixed”. (6)
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 257

Else, if ALSTKCD is between 1 and 3 (overstocked, 

fully stocked, medium stocked; 35-100+ 

percent), assign to “Forest”.

if FORTYPCD is between 500 and 998, assign to 

code “Forest Hardwood”. (1)

if FORTYPCD <400, assign to code “Forest 

Conifer”. (2)

if FORTYPCD is between 400 and 499, assign to 

code “Forest Mixed”. (3)

NLCD2006:

If PHYSCLCD is between 30 and 39 (hydric site), 

assign to code “90” (Woody Wetlands).

Else, if ALSTKCD = 5 (nonstocked), then assign 

condition to “0”.

Else, if FORTYPCD is between 500 and 998, assign to 

code “41” (Deciduous Forest).

if FORTYPCD <400, assign to code “42” 

(Evergreen Forest).

if FORTYPCD is between 400 and 499, assign to 

code “43” (Mixed Forest).

An attribute was added to the Access database 

CONDITION table to record which method was used 

to determine the NatureServe and NLCD condition 

assignment: 1 = tree canopy cover, 2 = sapling canopy 

cover, 0 = other FIA condition attributes. FIA’s PCEVALIDator tool was revised to include NatureServe 

and NLCD categories as row and column variables. 

PC-EVALIDator was used to estimate forest land area 

for NatureServe forest and woodland classes, NLCD 

forest and woody wetland land cover classes, and 

FIA forest type group aggregations-softwoods (100-

390), mixed (400), and hardwoods (500-990) (Fig. 1). 

Different class names have similar, but not identical, 

meaning among FIA, NatureServe, and NLCD (i.e., 

softwood/conifer/evergreen, hardwood/hardwood/

deciduous, respectively); we used FIA’s terminology 

for labeling comparisons.

0

2

4

6

8

10

12

14

16

0-5

6-10

11-1516-20

21-25

26-3031-35

36-40

41-45

46-50

51-55

56-60

61-65

66-70

71-75

76-80

81-85

86-9091-95

96-100

Forest Land Area (million acres)

Canopy Cover (percent)

Figure 1.—Canopy cover distribution on FIA forested conditions, 2004-2008, Midwest and Northeast.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 258

RESULTS

Figure 1 portrays the distribution of predicted canopy 

cover across the region. Because all FIA forested 

conditions were assigned labels, total area was 

essentially equivalent across all three classification 

systems (Fig. 2). Canopy cover of 0 to 10 percent 

was estimated for 4.7 percent of all forest land, most 

of which was defined as nonstocked. Hardwood 

and softwood types were most abundant for FIA, 

least abundant for NLCD, and intermediate for 

NatureServe. NatureServe hardwood types were 

evenly distributed between forest and woodland, but 

softwood types were more abundant in NatureServe 

woodland than forest. Mixed types were substantially 

more abundant for NLCD, intermediate for 

NatureServe (equally distributed between forest 

and woodland), and least abundant for FIA. Woody 

wetlands were defined only for NLCD; total area of 

NLCD woody wetlands exceeded NLCD softwoods 

(Fig. 2). No statistical validations were performed for 

this study.

DISCUSSION AND CONCLUSION

The method presented here provides an operational 

approach to predicting per condition tree canopy cover 

from FIA tree data. The resulting classifications were 

used to assign FIA conditions to NatureServe forest 

and woodland habitat domains and NLCD forest 

land cover classes, for which population estimates 

were produced. Although FIA’s forest land definition 

requires a minimum of 10 percent canopy cover, a 

small area of FIA forest land was characterized by 

canopy cover below this threshold. Such conditions 

likely occur shortly after full canopy removal (e.g., 

harvest, wildfire), but before regenerating seedlings 

0

20

40

60

80

100

120

140

160

180

200

NatureServe NLCD FIA

Forest Land Area (million acres)

Nonstocked

Woody Wetlands

Woodland - Mixed

Forest - Mixed

Mixed

Woodland - Softwoods

Forest - Softwoods

Softwoods

Woodland - Hardwoods

Forest - Hardwoods

Hardwoods

Figure 2.—Forest land area estimates for NatureServe forest and woodland habitat types, NLCD forest and woody wetland 

land cover types, and FIA aggregations of forest-type groups, 2004-2008, Midwest and Northeast.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 259

have established significant canopy. Tree canopy cover 

predictions allowed FIA data to be used with wildlife 

species-habitat matrices and ancillary habitat datasets 

that are based on canopy cover thresholds. Choice 

of habitat classification systems can affect resulting 

estimates of habitat abundance. Resulting assignments 

of FIA data to NatureServe habitats were used to 

estimate habitat abundance. 

ACKNOWLEDGMENTS

The authors thank NatureServe for producing a 

terrestrial vertebrate species-habitat matrix for forestassociated species. The authors also thank Don FaberLangendoen, Randall Morin, and James Westfall for 

reviewing this manuscript.

LITERATURE CITED

Bechtold, W.A. 2003. Crown-diameter prediction 

models for 87 species of stand-grown trees in 

the Eastern United States. Southern Journal of 

Applied Forestry. 27(4): 269-278.

Bragg, D.C. 2001. A local basal area adjustment 

for crown width prediction. Northern Journal of 

Applied Forestry. 18(1): 22-28.

Faber-Langendoen et al. 2012. Classification and 

description of world formation types. Part 

I (Introduction) and Part II (Description of 

formation types). Hierarchy Revisions Working 

Group, Federal Geographic Data Committee, 

FGDC Secretariat. U.S. Geological Survey, Reston, 

VA; and NatureServe, Arlington, VA. Available at 

http://www.natureserve.org/explorer/classeco.htm.

FGDC. 2008. National Vegetation Classification 

Standard. Vegetation Subcommittee, Federal 

Geographic Data Committee, FGDC Secretariat. 

Reston, VA: U.S. Geological Survey. 55 p.

Fry, J.A.; Xian, G.; Jin, S.; Dewitz, J.A.; Homer, C.G.; 

Yang, L.; Barnes, C.A.; Herold, N.D.; Wickham, 

J.D. 2011. Completion of the 2006 National Land 

Cover Database for the conterminous United 

States. Photogrammetric Engineering & Remote 

Sensing. 77(9): 858-864.

NatureServe. 2011. Lists of vertebrate species in 

the contiguous U.S. Microsoft Access database. 

Available at http://www.natureserve.org.

Toney, C.; Shaw, J.D.; Nelson, M.D. 2009. A stemmap model for predicting tree canopy cover of 

Forest Inventory and Analysis plots. 2008 Forest 

Inventory and Analysis Symposium, 2008 October 

21-23; Park City, UT. Fort Collins, CO: U.S. 

Department of Agriculture, Forest Service, Rocky 

Mountain Research Station: CD-ROM. 1-19.

Woudenberg, S.W.; Conkling, B.L.; O’Connell, B.M.; 

LaPoint, E.B.; Turner, J.A.; Waddell, K.L. 2010. 

The Forest Inventory and Analysis Database: 

database description and users manual version 

4.0 for Phase 2. Gen. Tech. Rep. RMRS-GTR-245. 

Fort Collins, CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research Station. 

336 p.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 260

Densiometers and Canopy Density Measurements

Kenneth W. Stolte, Stanley J. Zarnoch, and Robert L. Eaton1

Abstract.—The productivity and vitality of forest ecosystems are dependent on the 

condition of tree canopies. Canopy cover and density are components in estimations 

of tree growth efficiency, fish and wildlife habitat, urban tree cover, composition 

of understory vegetation, soil erosion susceptibility, pollutant deposition, and other 

ecological factors. We found that a simple modification of a convex densiometer with 

standardized protocols eliminated two of three multiplicative measurement error factors 

and improved repeatability among observers. We tested this modification using Forest 

Inventory and Analysis (FIA) plot protocols under three scenarios in three geographical 

areas of North Carolina. One scenario consisted of 23 North Carolina State University 

undergraduate forestry students’ measurements on three subplots in a pine seed orchard 

(piedmont geographic area). Another was the evaluation of pre- and post-harvest of 60 

plots of loblolly pine in Croatan National Forest (coastal plain geographic area). The 

third was the adequate number of points on an FIA subplot to measure canopy density 

on two FIA subplots in each of eight long-term vegetation monitoring plots in pine and 

mixed-hardwood forests at Coweeta Experimental Forest (mountainous geographic area). 

We found that the variance components due to persons, points on subplots, and azimuth 

position of points was 5.3, 34.3, and 60.3 percent, respectively, indicating observer 

differences were a very small percentage of the overall measurement variance. We 

observed decrease in average canopy density from 90 percent to 64 percent in 60 plots 

at Croatan NF following an average thinning intensity of 42 percent of the number of 

trees in all plots. This shows the modified densiometer reflects known changes in canopy 

density. Four of seven sample points tested on FIA subplots were found to be adequate 

for measurement of canopy density at Coweeta Experimental Forest in western North 

Carolina. Measurements on only four of the seven points reduces the amount of time 

needed for data collection. These modifications of a convex densiometer meet criteria 

for a good indicator: high signal/noise ratio; applicable to large areas of different forest 

types; directly related to crown condition; and logistically feasible (e.g., weight, cost, 

time on plot).

1

 Research Ecologist (KWS), U.S. Forest Service, Southern Research Station, 3041 Cornwallis 

Road, Research Triangle Park, NC 27709; Mathematical Statistician (SJZ) and Biological Scientist 

(RLE), U.S. Forest Service, Southern Research Station. KWS is corresponding author; to contact 

call 919-549-4022, or email at kstolte@fs.fed.us.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
The Past, Present, and Future 

of nonnative Plant monitoring 

by the forest inventory 

and analysis program
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 262

THE NATIONAL PICTURE OF NONNATIVE PLANTS 

IN THE UNITED STATES ACCORDING TO FIA DATA

Sonja N. Oswalt and Christopher M. Oswalt1

Abstract.—Data collected by the U.S. Forest Service Forest Inventory and Analysis 

Program was assembled from each region of the United States. Occurrence, measured 

as the percentage of forested subplots within a county with observed nonnative invasive 

plant (NNIP) species, was calculated across the continental United States and Hawaii. 

Each region, and in some cases each state, maintains a specific watch list to constrain 

monitoring to only the most important NNIP species within a given area. Therefore, 

occurrence is based on regionally important species and is inconsistent across the United 

States. NNIP can be found invading forests across all of the United States. Eastern U.S. 

forests, however, currently exhibit high levels of NNIP occurrence. Major U.S. travel 

corridors and areas of considerable forest fragmentation that are often coupled with the 

large human population in the eastern United States can be important drivers of NNIP 

distributions. Travel corridors are known to play a profound role in the spread and growth 

of invasive plants. That fact is evident in maps of NNIP species where many major U.S. 

interstates are apparent. For example, the I-85 corridor from Virginia to Alabama is an 

area of intense invasive plant abundance. When forests are divided into smaller and 

smaller parcels (fragmented), the biological diversity of native animals and plants is 

diminished, water cycles are altered, and often nonnative invasive plants are introduced. 

This could help explain the high degree of plant invasions in the heavily agriculture 

dominated landscapes of the middle southern and middle western United States. 

1

 Forester (SNO) and Research Forester (CMO), U.S. Forest 

Service, Southern Research Station, 4700 Old Kingston 

Pike, Knoxville, TN 37919. SNO is corresponding author: 

to contact, call 865-862-2000 or email at soswalt@fs.fed.us.

INTRODUCTION

Invasive plants in the United States are an expensive 

problem both economically and environmentally. In 

their last update on the economic impact of invasive 

species (plants and other organisms) in the United 

States, Pimentel et al. (2005) suggest that the cost of 

prevention and eradication of nonnative invasive plant 

(NNIP) species in crop, pasture, and forest settings 

is approximately $27 billion every year. In fact, the 

cost of combating just the invasive tree Melaleuca 

quinquefolia in the state of Florida was estimated at 

between $3 and $6 billion dollars in 2005 (Pimentel et 

al. 2005). 

The environmental impacts of nonnative invasive 

plants are hard to quantify. Many impacts are 

difficult or impossible to measure in the field or to 

directly attribute to NNIP because of the relative 

lack of controlled experiments related to NNIP and 

the complexity of co-occurring NNIP. For example, 

most invasion biologists accept that NNIP cause 

disruptions in the ecological systems they inhabit, 

whether by directly altering soil chemistry (e.g., 

Chinese tallowtree [Triadica sebifera]) (Bruce et 

al. 1995, Cameron and Spencer 1989), through 

competition with native species (e.g., Japanese 

honeysuckle [Lonicera japonica]), or by hindering 

regeneration in forested settings (e.g., Nepalese 

browntop [Microstegium vimineum]) (Oswalt et al. 

2007). However, while widely acknowledged, the 

environmental costs of those impacts have not been 

quantified in a consistently applied manner that can 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 263

be scaled up for the nation. Additionally, we are just 

now beginning to recognize the need for valuation of 

ecosystem services, biodiversity, and aesthetics, and 

therefore would be hard-pressed to place a dollar value 

on the environmental impacts even if we were able to 

directly measure and sum them nationally. Many of 

the environmental impacts of NNIP may be secondary 

and virtually impossible to quantify. For example, 

the secondary impacts of herbicides used to control 

NNIP or the secondary impacts of altered regeneration 

pathways on wildlife species that use affected forests 

cannot easily be measured.

One step towards a better understanding of the impacts 

of NNIP at the national level is the task of identifying 

where invasive species occur on forest land. 

Monitoring plants known to be potentially invasive 

in forested environments enables land managers and 

policymakers to identify “hotspots” where efforts 

for eradication and control might be concentrated. 

Monitoring the same species over time can help to 

identify species that are expanding in extent versus 

species that have reached a stasis. Monitoring may 

also help to identify some species that have spread to 

the point that extirpation could be considered a futile 

effort, and thus resources can be directed at efforts that 

might lead to more success. Finally, collecting NNIP 

data in conjunction with forest inventory data can 

shed light on the environmental factors contributing to 

the invasibility of particular sites, as well as allowing 

for some understanding of the potential relationships 

between the presence or absence of NNIP and the 

biodiversity of the site in question.

Given the importance of monitoring NNIP on U.S. 

forest land, regions in the Forest Inventory and 

Analysis Program of the U.S. Forest Service have 

implemented efforts to track NNIP. Previous efforts 

by individual regions have been unique and specific to 

those units, thus no consistent method for identifying 

and tracking NNIP has been applied nationwide (Rudis 

et al. 2004). Efforts are underway to establish some 

modicum of consistency in measurement; however, 

for this paper we use data collected and compiled 

by each regional office. Our objectives were to map 

NNIP nationwide and report spatial patterns observed 

as a result of the compilation and mapping effort. 

Additionally, we discuss difficulties in evaluating 

invasive species at a national scale.

METHODS

Data collected by the U.S. Forest Service Forest 

Inventory and Analysis (FIA) Program were assembled 

from each region of the United States. Occurrence, 

measured as the percent of forested subplots within a 

county with observed NNIP species, was calculated 

across the continental United States and Hawaii. 

Each region and, in some cases, each state maintains 

a specific watch list to constrain monitoring to only 

the most important NNIP species within a given area. 

Therefore, occurrence is based on regionally important 

species and is inconsistently measured across the 

United States.

The data used in the analysis spans 1999 to 2010, 

depending on the state and region. Data from the 

Pacific Northwest were collected from 1999 to 2009, 

though data from Alaska spans 2004 to 2009 and data 

from Hawaii were collected in 2010. Data from the 

Intermountain West spans 1999 to 2009 while northern 

data were collected from 2007 to 2010 and southern 

data were collected from 2001 to 2010.

Each region uses a distinct program for collecting 

invasive species data, though plans are underway 

to provide a nationally consistent method for future 

surveys. For this paper, data collection methods 

differed by region and, in some cases, state. Data were 

normalized to minimize differences between regions 

by calculating the number of forested subplots present 

in a county, the number of forested subplots with at 

least one invasive species present, and by generating 

a “percent invaded” statistic so that counties across 

the country could be compared in a consistent 

manner. County and regional comparisons are based 

on visual observations of mapped data. Rudis et 

al. (2004) describe data collection methods for the 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 264

various regions, and specific data collection details are 

available through the FIA website at http://www.fia.

fs.fed.us/library/field-guides-methods-proc/.

RESULTS AND DISCUSSION

The percentage of forested subplots containing one 

or more NNIP was highest in the North Central, 

Northeast, and South Central subregions of the United 

States with invasives affecting 56, 48, and 45 percent 

of subplots, respectively (Fig. 1). The Intermountain 

region had the smallest proportion of subplots with 

NNIP at 5 percent. In general, forested subplots east 

of the Mississippi River had higher incidences of 

invasive plant occurrences than those in the western 

half of the country (Fig. 2).

Concentrations of invasive plants appear in areas 

dominated by agriculture including the delta region of 

the Lower Mississippi Valley in Arkansas, Mississippi, 

and Louisiana, as well as along the I-85 travel corridor 

from Virginia to Alabama, the bluegrass region of 

Kentucky, heavily populated areas in the North and 

Northeast, and the area around Spokane, Washington 

(Fig. 2). A small concentration occurs along the gulf 

coast in Texas and Louisiana, which reflects large 

populations of Chinese tallowtree. 

Major U.S. travel corridors and areas of considerable 

forest fragmentation that are often coupled with the 

large human population in the eastern United States 

can be important drivers of NNIP distributions. Travel 

corridors are known to play a profound role in the 

Figure 1.—Percentage of forested subplots containing at least one nonnative invasive plant, by subregion.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 265

Figure 2.—National map showing percentage of forested subplots with a nonnative invasive plant (NNIP), calculated at the 

county level. Forest/nonforest mask applied to the contiguous 48 states.

spread and growth of invasive plants. When forests are 

divided into smaller and smaller parcels (fragmented), 

the biological diversity of native animals and plants 

is diminished, water cycles are altered, and often 

nonnative invasive plants are introduced. This could 

help explain the high degree of plant invasions in the 

landscapes of the midsouth and midwest United States 

that are heavily dominated by agriculture. 

In the southern United States, much of the noted 

occurrence of NNIP on forested land is due to the 

ubiquitous presence of Japanese honeysuckle and 

nonnative privets (Ligustrum L.), particularly at the 

forest edge. When Japanese honeysuckle is removed 

from analysis, the NNIP situation in the south 

looks far less dire, and hotspots of other species are 

No Data or Nonforest

Percent Forested Subplots

County-Level

0-8.7

8.8-24.5

24.6-42.6

42.7-62.9

63.0-85.8

85.9-100.0

detectable (Fig 3). In the northern region, the presence 

of nonnative roses (Rosa spp.) accounts for much of 

the widespread invasion. In the Pacific Northwest, 

cheatgrass (Bromus tectorum) is fairly ubiquitous. 

Thus, one question this map and brief analysis raises 

is, should we continue to monitor NNIP that are so 

ubiquitous as to be considered naturalized and that 

we have no realistic expectation of exterminating 

from the system, and does the overwhelming presence 

of those species mask evidence of other invasions? 

From a budgetary standpoint, is it cost effective to 

continue to monitor such ubiquitous species, or should 

monitoring dollars be focused on species that are 

less common now, but are considered very likely to 

become problematic in the future? At the very least, it 

is worth considering removing ubiquitous species from 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 266

analyses in order to uncover potentially more “telling” 

patterns. 

Differing methods between regions limit the 

conclusions that may be drawn from a national map, 

and given differences in physiography and climate, 

perhaps the national scale isn’t the best scale for 

evaluating nonnative invasive plants. Establishing 

consistency in methodology should help. However, 

an overview like this does give an indication of where 

particularly problematic areas exist and gives national 

leaders the opportunity to review where prevention 

education and eradication dollars might be best spent. 

Thus, as we move forward with the FIA invasive 

species program, it will be continually important to 

discuss whether the species we are monitoring are the 

No Data or Nonforest

Percent Forested Subplots

County-Level

0-8.7

8.8-24.5

24.6-42.6

42.7-62.9

63.0-85.8

85.9-100.0

Figure 3.—Map of the southern United States showing the percentage of forested subplots with a nonnative invasive plant 

(NNIP), excluding Japanese honeysuckle, calculated at the county level. Forest/nonforest mask applied to the contiguous 48 

states. Data from Louisiana was unavailable at the time of analysis.

plants most worthy of our time and money, if we are 

answering the questions we set out to answer with the 

program, and finally, how we can use this information 

to make forward progress in combatting the negative 

impacts of invasive plants in our forests.

ACKNOWLEDGMENTS

Thanks are due to the many dedicated field employees 

who collect FIA data in each region throughout the 

year. Additionally, the authors are grateful to Beth 

Schulz, Andrew Gray, Christopher Witt, and Keith 

Moser for providing data specific to their regions 

and for conversations and reviews during the writing 

process.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 267

LITERATURE CITED

Bruce, K.A.; Cameron, G.N.; Harcombe, P.A. 1995. 

Initiation of a new woodland type on the 

Texas coastal prairie by the Chinese tallowtree 

(Sapium sebiferum [L.] Roxb.). Bulletin of the 

Torrey Botanical Club. 122(3): 215-225.

Cameron, G.N.; Spencer, S.R. 1989. Rapid leaf decay 

and nutrient release in a Chinese tallow forest.

Oecologia. 80(2): 222-228.

Oswalt, C.M.; Oswalt, S.N.; Clatterbuck, W.K. 2007. 

Effects of Microstegium vimineum (Trin.) A. 

Camus on native woody species density and 

diversity in a productive mixed-hardwood forest 

in Tennessee. Forest Ecology and Management. 

242: 727-732.

Pimental, D.; Rodolfo, Z.; Morrison, D. 2005. Update 

on the environmental and economic costs 

associated with alien-invasive species in the 

United States. Ecological Economics. 52: 273-288.

Rudis, V.A.; Gray, A.N.; McWilliams, W.; O’Brien, R.; 

Olson, C.; Oswalt, S.; Schulz, B.K. 2006. Regional 

monitoring of nonnative plant invasions with 

the Forest Inventory and Analysis Program. In: 

McRoberts, R.E.; Reams, G.A.; Van Duesen, P.C.; 

McWilliams, W.H., eds. Proceedings of the 6th 

annual Forest Inventory and Analysis symposium. 

Gen. Tech. Rep. WO-70. Washington, DC: U.S. 

Department of Agriculture, Forest Service: 49-64.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 268

DISTRIBUTION AND OCCUPANCY OF INTRODUCED SPECIES: 

A BASELINE INVENTORY FROM PHASE 3 PLOTS 

ACROSS THE COUNTRY

Bethany K. Schulz and W. Keith Moser1

Abstract.—Invasive plant species have significant negative impacts in many ecosystems 

and are found in many forests around the world. Although not all introduced species 

become invasive, there are numerous examples of species escaping cultivation and 

invading natural ecosystems years or even decades after their initial introduction. 

Regional distributions of invasive species are influenced by climatic and physical 

conditions; within the landscape, fragmentation, disturbance, and surrounding land 

use are important factors. Inventory data can help describe the distribution (reported 

as constancy) and occupancy, (reported as relative richness and cover of introduced 

species) in forested ecosystems. Vegetation data from 1690 Phase 3 forest inventory plots 

collected by the U.S. Forest Service’s Northern and Pacific Northwest Research Stations’ 

FIA units are used to examine and compare the distribution and occupancy of introduced 

species. Introduced species were common in forests; 388 species were recorded and 

61 percent of all plots had at least one introduced species. Where introduced species 

occurred, their mean relative richness was 10.7 percent and mean relative cover was 8 

percent. However, this varied across regions, level of fragmentation, and distance from 

roads. Regions with high proportions of forest edge plots had higher overall constancy 

and occupancy of introduced species. The most commonly recorded introduced species in 

our analysis was multiflora rose, however, common species varied by ecological region. 

The most commonly recorded species are highlighted by region, with examples of how 

these results can inform managers who have limited budgets for invasive plant control.

1

 Research Ecologist/Vegetation Indicator Advisor (BKS), 

U.S. Forest Service, Pacific Northwest Research Station, 

161 East 1st Ave., Anchorage, AK 99501; Research Forester 

(WKM), U.S. Forest Service, Northern Research Station. 

BKS is corresponding author: to contact, call 907-743-9424 

or email at bschulz@fs.fed.us.

INTRODUCTION

Tens of thousands of plant species have been 

introduced to the United States, often intentionally. 

Although the majority of introduced species are 

not problematic, many have escaped cultivation to 

become invasive, producing reproductive offspring 

at a distance from their original introduction (e.g., 

>328 ft. in less than 50 years for taxa spreading 

by seed) (Mack 2003, Richardson et al. 2000). A 

subset of invasive plant species can have significant 

negative impacts on ecosystems and are found in many 

forests around the world. These species are capable 

of transforming the ecosystems by excluding native 

species via competition or by exuding substances toxic 

to other plants, and altering water, nutrient, and fire 

regimes, and are hence referred to as “transformers” 

(Richardson et al. 2000). Direct effects on native plant 

populations have cascading indirect effects on other 

biota in the invaded communities. 

Although not all invasive species become ecosystem 

transformers, many naturalized introduced species 

(capable of reproduction without further human 

cultivation, but not spreading beyond original site) 

can become invasive years or even decades after 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 269

their initial introduction. (Mack 2003). Initially 

benign species can slowly expand their presence 

without notice or recognizable impacts. Once they are 

widespread, control of these species becomes difficult 

and expensive. 

Many surveys are conducted to monitor for known 

transformer species. These surveys are often 

implemented along roadsides, trails, waterways, 

and areas subjected to disturbance—both human-

induced and natural. Focusing on species that are 

new to the area and have been evaluated for invasive/

transforming properties ensures that managers make 

the most efficient use of limited resources available 

for invasive species control. From year to year, the list 

of species may change as more is learned about the 

distribution and invasiveness of individual species. 

These surveys are invaluable, but cannot provide a 

systematic measure of the distribution throughout the 

range of forested ecosystems of the United States. A 

regional perspective can help land managers evaluate 

their local conditions and prioritize projects for 

effectiveness. 

The distribution of invasive species in forests is 

influenced by the proximal ecosystem and land use 

patterns, disturbance intensity and distribution, and 

climate. Forest inventory data can help define the 

distribution and occupancy of such species in forested 

ecosystems. Introduced and invasive species are 

common in the forests of the northeastern United 

States. Schulz and Gray (in press) found two-thirds 

of Phase 3 plots in the U.S. Forest Service’s Forest 

Inventory and Analysis (FIA) inventory had at least 

one introduced species present. Fragmentation and 

ecological province were important predictors of the 

occupancy (percent richness and cover) of introduced 

species. We expand upon those analyses to include 

data from the Pacific Northwest to further examine 

and compare the distribution of introduced species 

in relation to forest fragmentation, distance from 

improved roads, and ecological divisions. The most 

abundant species are highlighted by region.

STUDY AREA

Twenty-four states in the northeastern and midwestern 

regions of the United States and three states in the 

Pacific Northwest are included in the study area. Data 

collected on 1,690 Phase 3 plots were used in this 

analysis. Approximate plot locations in the contiguous 

United States are shown in Figure 1. (Alaska plots are 

not shown.)

METHODS

Data were collected by the Northern (NRS) and Pacific 

Northwest (PNW) Research Stations’ FIA Programs 

between 2001 and 2008 as part of the Phase 3 (P3) 

Vegetation Diversity and Structure Indicator (VEG). 

VEG includes a complete census of vascular plants 

on the forested portion of each subplot (Schulz et al. 

2009). Field crews certified for VEG data collection 

record each plant species and estimate percent cover 

over the entirety of each of four 24-ft radius subplots. 

Cover of each unknown species is also estimated and 

samples are collected for later identification. Some 

plants remain unidentified if they were present in 

very early or late stages of development or only a few 

individual plants were on site. Plants not ultimately 

identified to the species level were not included in this 

analysis; we assume that the proportions of native to 

introduced species among unknown plants are similar 

to that for known species.

Species origins were assigned according to the 

Natural Resource Conservation Service’s PLANTS 

database (USDA NRCS 2010). Ecological divisions, 

as described by Cleland et al. (1997) were used 

to distinguish regions with broad climatic and 

physiographic zones. The systematic random sample 

design of the FIA inventory results in some plots 

straddling multiple vegetation conditions defined by 

significant differences in land use, forest type, stand 

size class, and forest/nonforest condition (Bechtold 

and Patterson 2005). Each plot was assigned one of 

three levels of fragmentation based on the number 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 270

Prairie

Temperate Steppe Marine

Warm Continental

Hot Continental

Subtropical

Figure 1.— Approximate location of FIA Phase 3 plots where VEG has been collected in the contiguous United States with an 

initial visit from 2001 through 2008. Shaded areas of map represent labeled ecological divisions.

and types of condition classes assigned. If the plot 

was 100 percent forest and was determined to be a 

single condition, it was designated as an “intact” stand. 

Plots that were 100 percent forest, but had more than 

one condition assigned, were designated as “multiple 

condition.” Plots that were less than 100 percent 

forest were designated as “forest edge”, although only 

forested portions of these plots were sampled, their 

proximity to nonforest land increases their potential 

exposure to introduced plant species flourishing on 

nonforested lands. This assignment creates a coarse 

filter for fragmentation in that some “intact” forest 

may, in fact, be adjacent to forest edge or be contained 

within small parcels of remnant forest and the forest in 

some plots labeled “edge” could be adjacent nonforest 

plant communities unaffected by invasive species. 

Other variables considered included distance from 

improved roads, latitude, and longitude.

The numbers of native and introduced species per fully 

forested subplot were compiled for all plots regardless 

of level of fragmentation. Constancy (the presence 

of at least one introduced species) and measures of 

occupancy (relative richness and relative cover of 

introduced species) were computed at the plot level 

at the site. The relative richness is simply the number 

of introduced species divided by the total number of 

all species identified to species per plot. The relative 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 271

cover of introduced species is the sum of subplot cover 

of all introduced species divided by the sum of subplot 

cover of all species. Estimates and variances for each 

category were computed using the ratio of means 

methods described in Schulz et al. (2009).

RESULTS

Sixty-one percent of the 1690 plots had at least 

one introduced species. Where introduced species 

occurred, their mean relative richness was 10.7 

percent, and their mean relative cover was 8 percent. 

We found 386 species of nonnative plants. We 

included two native grasses—reed canarygrass 

(Phalaris arundinacea L.) and common reed 

(Phragmites australis (Cav.) Trin. ex Steud.)—because 

invasive populations are genetically distinct from 

relatively rare native populations (Olson and Cholewa 

2009). 

The distribution of introduced species on Phase 3 

inventory plots varied by ecological division (Table 

1). The Warm Continental Division had the lowest 

constancy and occupancy of introduced species and the 

highest proportion of intact plots. The Prairie Division, 

with the highest proportion of edge plots, had the 

highest constancy and relatively high occupancy. 

Constancy and occupancy varied predictably with 

distance from improved roads (Fig. 2). Results from 

Alaska differed substantially from other regions: 

constancy of introduced species was only 4.4 percent, 

and when they occurred, their mean relative richness 

average was 2.2 percent, and mean relative cover of 

less than 1 percent; 74 percent of all plots were greater 

than 1 mile from an improved road. Comparisons 

between the Pacific Northwest and the northeastern 

United States were limited to the lower 48 States. 

Table 1.—Ecological divisions represented in the sample, with total number of plots, number of plots 

by level of fragmentation, overall constancy of introduced plant species, and mean occupancy when 

introduced species are present. 

 Level of Fragmentation Occupancy

Total Intact Multi Edge Constancy Mean relative richness (SE) Mean relative cover (SE)

Division number of plots percent percent percent

Warm Continental 538 352 64 122 48.7 7.7 (0.4) 4.0 (0.6)

Hot Continental 571 296 46 229 77.8 12.0 (0.4) 10.9 (0.7)

Subtropical 70 33 4 33 64.3 8.6 (0.9) 6.5 (1.6)

Marinea 173 96 21 56 60.7 11.7 (1.0) 6.3 (1.3)

Prairie 87 27 5 55 86.2 10.9 (0.9) 10.0 (1.6)

Temperate Steppe 93 55 10 28 82.8 13.8 (1.0) 9.2 (1.7)

a Washington and Oregon only

0

20

40

60

80

100

< 500 ft 500 ft - 1 mile > 1mile

Percentage of Plots

a

0

5

10

15

20

25

< 500 ft 500 ft - 1 mile > 1mile

Percentage

Distance from Improved Road

b

Relative richness

Relative cover

Figure 2.— Constancy (a) and occupancy (b) as a function of the distance of plots from improved roads; error bars represent 

plus and minus one standard error.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 272

Despite not being found west of the Rocky Mountains, 

multiflora rose (see Table 2 for scientific and common 

names) remained the most commonly recorded species 

in this expanded analysis, with a constancy of over 20 

percent of all 1690 plots. When the data were compiled 

regionally at the ecological-division level, there were 

some surprises among the most commonly recorded 

species (Table 2). Although many of the listed species 

are known to be problematic transformers, several, 

including broadleaf helleborine and wall-lettuce, are 

naturalized species considered benign in the past.

DISCUSSION

Full species census provides opportunities to monitor 

any species present on FIA plots. Our results find 

introduced species to be quite common in the forests 

of the lower 48 United States. Although some species 

were widely planted (e.g., multiflora rose) most of 

these species can be considered invasive, in that it is 

unlikely that they were planted in most of the stands 

sampled by FIA. Although many known transformer 

Table 2.—The three most commonly recorded introduced species by ecological division with constancies 

(percentage of plots in each division where the species was recorded)

 Division Constancy

Ecological Division N plots Scientific Name Common Name (percent)

Warm Continental 538 Hieracium aurantiacum L. orange hawkweed 8.0

Phleum pretense L. timothy 4.8

Epipactis helleborine (L.) Crantz broadleaf helleborine 4.6

Hot Continental 571 Rosa multiflora Thunb. multiflora rose 49.4

Lonicera japonica Thunb. Japanese honeysuckle 16.8

Alliaria petiolata (M. Bieb.) Cavara & Grande garlic mustard 13.7

Subtropical 70 Lonicera japonica Thunb. Japanese honeysuckle 48.6

Rosa multiflora Thunb. multiflora rose 17.1

Microstegium vimineum (Trin.) A. Camus Japanese stiltgrass 8.6

Marinea 173 Mycelis muralis (L.) Dumort. wall-lettuce 28.9

Rubus laciniatus Willd. cutleaf blackberry 16.2

Digitalis purpurea L. purple foxglove 15.6

Prairie 87 Rosa multiflora Thunb. multiflora rose 47.1

Morus alba Michx. white mulberry 17.2

Torilis arvensis (Huds.) Link spreading hedgeparsley 16.1

Temperate Steppe 93 Tragopogon dubius Scop. yellow salsify 39.8

Bromus tectorum L. cheatgrass 23.6

Hypericum perforatum L. common St. Johnswort 19.3

a Washington and Oregon only

species were captured in the Phase 3 VEG sample, 

there were some surprises concerning the most 

common species by ecological region compiled. 

For example, orange hawkweed is found on forest 

edge plots of many forest types, but in intact forests, 

it is found most often in aspen stands; broadleaf 

helleborine was found most often in intact sugar 

maple stands in the Adirondack Mountain province. 

These results could inform the development of lists of 

species to target in local and other list-based invasive 

species efforts, such as the national FIA inventory, and 

focus research attention to invasive species considered 

to be benign but are more widespread than previously 

thought. 

ACKNOWLEDGMENTS

The authors thank all who made P3 VEG data 

collection and stewardship happen: from program 

manager for their commitment, and especially to VEG 

field crews and information managers.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 273

LITERATURE CITED

Bechtold, W.A.; Patterson, P.L., eds. 2005. The 

enhanced Forest Inventory and Analysis 

Program–national sampling design and 

estimation procedures. Gen. Tech. Rep. SRS-80. 

Asheville, NC: U.S. Department of Agriculture, 

Forest Service, Southern Research Station. 85 p.

Cleland, D.T.; Avers, P.E.; McNab, W.H.; Jensen, 

M.E.; Bailey, R.G.; King, T.; Russell, W.E. 1997. 

National hierarchical framework of ecological 

units. In: Boyce, M.S.; Haney, A., eds. Ecosystem 

management: applications for sustainable forest and 

wildlife resources. New Haven, CT: Yale University 

Press: 181-200. 

Mack, R.N. 2003. Plant naturalizations and 

invasions in the eastern United States: 

1634-1860. Annals of the Missouri Botanical 

Garden. 90: 77-90.

Olson, C.; Cholewa, A.F. 2009. A guide to nonnative 

invasive plants inventoried in the north by 

Forest Inventory and Analysis. Gen. Tech. Rep. 

NRS-52. Newtown Square, PA: U.S. Department 

of Agriculture, Forest Service, Northern Research 

Station. 194 p.

Richardson, D.M.; Pyšek, P.; Rejmánek, M.; 

Barbour, M.G.; Panetta, F.D.; West, C.J. 2000. 

Naturalization and invasion of alien plants: 

concepts and definitions. Diversity and 

Distributions. 6: 93-107. 

Schulz, B.K.; Bechtold, W.A.; Zarnoch, S.J. 2009. 

Sampling and estimation procedures for the 

vegetation diversity and structure indicator.

Gen.Tech. Rep. PNW-781. Portland, OR: U.S. 

Department of Agriculture, Forest Service, Pacific 

Northwest Research Station. 53 p.

Schulz, B.K.; Gray, A.N. In press. The new flora 

of the northeastern United States: quantifying 

introduced plant species occupancy in forest 

ecosystems. Environmental Monitoring and 

Assessment. 

USDA Natural Resource Conservation Service. 2010. 

The PLANTS database. Baton Rouge, LA: U.S. 

Department of Agriculture, Natural Resource 

Conservation Service, National Plant Data Center. 

Avalable at http://plants.usda.gov/java/. [Date 

accessed unknown].

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 274

UPDATING THE SOUTHERN NONNATIVE PLANT WATCH LIST: 

THE FUTURE OF NNIP MONITORING IN THE SOUTH

Christopher M. Oswalt, Sonja N. Oswalt, and Lewis Zimmerman1

Abstract.—The Southern Research Station (SRS) Forest Inventory and Analysis (FIA) 

Program began monitoring nonnative invasive plant (NNIP) species in 2001 in response 

to a growing desire to track potential forest health threats on United States forest 

land. The SRS-FIA NNIP program has produced significant results and contributed 

considerably to the understanding of the distribution and spread of NNIP in the southern 

United States. However, opportunities to improve NNIP monitoring in the South do 

exist. Specifically, the SRS-FIA program monitors only a select number of NNIP 

species. Given the importance of monitoring nonnative invasive plants in southern 

forests coupled with the emergence of newly detected plant invaders, the emergence of 

previously known invasive plants as problematic species, and incomplete knowledge 

of accurately predicting exotic invasives, the select list of NNIP required updating. The 

SRS-FIA watch list was thoroughly reviewed with respect to potential removal of some 

species from the list of monitored plants. For example, a recent analysis found that out 

of over 33,000 subplots, some plant species were detected on 3 or fewer subplots. While 

such small detection rates do not indicate a lack of needed monitoring, with limited 

resources, the SRS-FIA program must review the importance of monitoring such species 

in the future. The watch list must also reflect current knowledge and account for newly 

discovered important southern forest invaders. Both scientists and land managers have 

identified numerous regionally and nationally important nonnative invasive plant species 

not currently on the SRS-FIA watch list. A group of regional and national NNIP experts 

(internal and external to FIA) were assembled with the task of evaluating and updating 

the SRS-FIA watch list. The proposed new watch list for SRS-FIA is presented.

1

 Research Forester (CMO), Forester (SNO), and Biological 

Scientist (LZ), U.S. Forest Service, Southern Research 

Station, 4700 Old Kingston Pike, Knoxville, TN 37919. 

CMO is corresponding author: to contact, call 865-862-2000 

or email at coswalt@fs.fed.us.

INTRODUCTION

Given the importance of monitoring nonnative 

invasive plants (NNIP) in southern forests coupled 

with the emergence of newly detected plant invaders, 

the emergence of previously known invasive plants 

as problematic species, and incomplete knowledge of 

accurately predicting exotic invasives, the Southern 

Research Station (SRS) Forest Inventory and Analysis 

(FIA) Program must periodically evaluate the select 

list of nonnative invasive plants that are monitored 

by the program. The SRS-FIA watch list should be 

thoroughly reviewed with respect to potential removal 

of some species from the list of monitored plants. For 

example, Oswalt and Oswalt (2011) found that out of 

over 33,000 subplots, some plant species were detected 

on 3 or fewer subplots. While such small detection 

rates do not indicate a lack of needed monitoring, 

with limited funding and limited time for adequately 

training personnel on the identification of species, 

the SRS-FIA program must review the importance of 

monitoring such species in the future. The watch list 

must also reflect current knowledge and account for 

newly discovered important southern forest invaders. 

Miller et al. (2010) identified numerous regionally and 

nationally important nonnative invasive plant species 

not currently on the SRS-FIA watch list. The SRS-FIA 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 275

watch list has been stable for approximately 10 years 

(2001-2011). A review of the watch list at this time 

was considered necessary.

METHODS

A three-step approach was defined prior to the formal 

evaluation by a team of national and regional experts 

including Christopher M. Oswalt (Lead, SRS-FIA), 

Sonja N. Oswalt (SRS-FIA), Zimmerman (SRS-FIA), 

Jim Miller (SRS, Auburn, AL), Nancy Fraley (National 

Park Service, Ashville, NC), Chris Brown (Texas 

Forest Service, College Station, TX), David Dickinson 

(Georgia Forestry Commission, Carrolton, GA), Jay 

Frost (SRS-FIA), and Rebekah Wallace (University 

of Georgia, Tifton, GA). First, a list of species for 

potential removal from the watch list was developed 

through a quantitative assessment of occurrence. Next, 

through a comprehensive assessment of potential 

species, the team developed a list of candidate species 

for addition to the watch list. A final proposed watch 

list was then developed by the team for submission to 

the U.S. Forest Service Regional Management Team 

with SRS-FIA programmatic oversight. 

In an attempt to address the potential removal of 

plant species from the watch list, all available data 

(2001 through 2010 for most southern states) were 

assembled from the Southern Nonnative Invasive 

Plant data Extraction Tool (SNIPET) available at 

http://srsfia2.fs.fed.us/SNIPET/. Any species with 

less than 50 total observations across all data were 

considered candidates for removal from the watch list. 

Each candidate species being considered for removal 

or addition was discussed in detail by the team. 

Removals and additions to the list were evaluated 

using the following general criteria: (1) the plant 

species is considered a known invader of forested 

systems; (2) the plant species is considered to be a 

regionally significant invader (or highly likely to 

become regionally significant) of forests; and (3) the 

plant species can be invasive to any stage of forest 

development. 

RESULTS AND DISCUSSION

Candidate species for removal included Russian olive 

(Elaeagnus angustifolia), giant reed (Arundo donax), 

English ivy (Hedera helix), Chinese silvergrass 

(Miscanthus sinensis), and tropical soda apple 

(Solanum viarum). Candidate species for addition to 

the watch list were primarily from the species listed in 

Miller et al. (2010). 

The following decisions were made with respect to 

each candidate species for removal:

(1) Russian olive—primarily due to difficulty 

in field identification, Russian olive will be 

combined with autumn olive (E. umbellata) to 

form the Elaeagnus group. 

(2) Giant reed—due to low occurrence (3 subplots 

out of over 33,000) coupled with being found 

primarily on nonforest conditions, giant reed 

will be removed from the watch list. 

(3) English ivy—although observed infrequently, 

an increasing number of observations over time 

suggests the need for continued monitoring. 

English ivy remains on the watch list. 

(4) Chinese silvergrass—although observed 

infrequently, an increasing number of 

observations over time along with potential uses 

of Miscanthus spp. as a bioenergy crop suggests 

the need for continued monitoring. Chinese 

silvergrass remains on the watch list.

(5) Tropical soda apple—due to low occurrence 

coupled with being found primarily on nonforest 

conditions, tropical soda apple will be removed 

from the watch list.

Two plant species, Brazilian peppertree (Schinus 

terebinthifolius) and camphortree (Cinnamomum 

camphora), were elevated from the Florida-only 

watch list to the regional watch list. A total of 15 

species were added to the watch list from Miller et 

al. (2010). A new species group, Tamarix spp., was 

added due to increasing evidence of saltcedar (Tamarix 

ramosissima) invasions in the western areas of the 

southern region. 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 276

The new SRS-FIA watch list (Table 1) contains a 

total of 49 species (or species groups) that will be 

monitored on all southern FIA plots beginning in 

October 2012 with the implementation of the version 

6.0 field guide. 

ACKNOWLEDGMENTS

Thanks are due the many dedicated field employees 

who collect FIA data in each region throughout the 

year. Additionally, the authors are extremely grateful 

for all participants of the Watch List Update Team. 

Table 1.—Southern Research Station Forest Inventory and Analysis 6.0 nonnative invasive plant watch list

Scientific name Common name

Ailanthus altissima Tree of heaven

Akebia quinata Five-leaf akebia, chocolate vine

Albizia julibrissin Mimosa

Alliaria petiolata Garlic mustard

Ampelopsis brevipedunculata Amur peppervine

Ardisia crenata Coral ardisia, Hen’s eyes

Bambusa Group

 Phyllostachys aurea Golden bamboo

 Phyllostachys spp. Other invasive bamboos

 Bambusa spp. Other invasive bamboos

Berberis thunbergii Japanese barberry

Broussonetia papyrifera Paper mulberry

Celastrus orbiculatus Oriental bittersweet

Cinnamomum camphora Camphortree

Dioscorea Group

 D. bulbifera Air yam

 D. oppositifolia Chinese yam

 D. alata Water yam

Elaeagnus Group: 

 E. umbellata Autumn olive 

 E. angustifolia Russian olive

Elaeagnus pungens Thorny elaeagnus

Eragrostis curvula Weeping lovegrass

Euonymus alatus Winged burning bush

Euonymus fortunei Winter creeper

Firmiana simplex Chinese parasoltree

Frangula alnus Glossy buckthorn

Hedera Group

 H. helix English ivy

 H. hibernica Atlantic ivy

 H. colchica Colchis ivy

Imperata cylindrica Cogongrass

Lespedeza Group

 L. bicolor Shrubby lespedeza

 L. thunbergii Thunberg’s lespedeza

Lespedeza cuneata Chinese lespedeza

Ligustrum Group 1

 L. sinense Chinese privet

 L. vulgare European privet

 L. obtusifolium Border privet

 L. ovalifolium California privet

Ligustrum Group 2

 L. japonicum Japanese privet

 L. lucidum Glossy privet

Scientific name Common name

Liriope Group

 L. muscari Big blue lilyturf

 L. spicata Creeping liriope, Monkey grass

 Ophiopogon muscari

Lonicera Group

 L. tatarica Tatarian honeysuckle

 L. maackii Amur honeysuckle

 L. morrowii Morrow’s honeysuckle

 L. fragrantissima Sweet-breath-of-spring, 

 L. x bella Bell’s honeysuckle

Lonicera japonica Japanese honeysuckle

Lygodium japonicum Japanese climbing fern

Mahonia bealei Leatherleaf mahonia

Melia azedarach Chinaberry

Microstegium vimineum Nepalese browntop

Miscanthus sinensis Chinese silvergrass

Nandina domestica Sacred bamboo

Paulownia tomentosa Princesstree

Polygonum cuspidatum Japanese knotweed

Poncirus trifoliata Trifoliate orange

Pueraria montana Kudzu

Pyrus calleryana Bradford pear

Rosa Group

 Rosa multiflora Multiflora rose

 R. bracteata Macartney rose

 R. laevigata Cherokee rose

 Rosa spp. Other nonnative roses

Schedonorus phoenix Tall fecsue

Schinus terebinthifolius Brazilian pepper

Securigera varia Crownvetch

Spiraea japonica Japanese meadowsweet

Tamarix spp. Saltcedar

Triadica sebifera Tallowtree, Popcorntree

Vernicia fordii Tungoil tree

Vinca Group

 V. minor Common periwinkle

 V. major Bigleaf periwinkle

Wisteria Group

 W. sinensis Chinese wisteria

 W. floribunda Japanese wisteria

Indented species are identified within a group.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 277

LITERATURE CITED

Miller, J.H.; Chambliss, E.B.; Loewenstein, N.J. 

2010. A field guide for the identification of 

invasive plants in southern forests. Gen. Tech. 

Rep. SRS-119. Asheville, NC: U.S. Department 

of Agriculture, Forest Service, Southern Research 

Station. 126 p.

Oswalt, S.N.; Oswalt, C.M. 2011. The extent 

of selected nonnative invasive plants on 

southern forest lands. In: Fei, S.; Lhotka, J.M.; 

Stringer, J.W.; Gottschalk, K.W.; Miller, G.W., 

eds. Proceedings, 17th central hardwood forest 

conference; 2010 April 5-7; Lexington, KY. Gen. 

Tech. Rep. NRS-P-78. Newtown Square, PA: 

U.S. Department of Agriculture, Forest Service, 

Northern Research Station: 447-459.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 278

INVASIVE POTENTIAL OF INVASIVE PLANTS IN THE FOREST 

OF THE SOUTHERN REGION, UNITED STATES

Dawn Lemke, John W. Coulston, Philip Hulme, Clint Paterson, and Jennifer A. Brown1

Abstract.—Alien plants introduced for commercial or landscaping use have caused 

substantial problems as invaders of natural and managed ecosystems. The magnitude of 

the problem has dramatically increased over the past few decades with accelerated land 

disturbance, land use changes, and global and internal transportation. In the southern 

region of the United States, invasive plants are one of the threats to the long-term 

sustainability of our forest ecosystems along with climate change and land use change. 

We assessed the potential distribution of invasive plants in forests of the southern 

region using data from the invasive species component of the U.S.Forest Service Forest 

Inventory and Analysis (FIA) Program and freely available digital data including 

elevation, climate, and land use. Using an ensemble modeling approach, we integrated 

maximum entropy algorithms, logistic regression, random forest, boosted regression 

trees, and support vector machine. Areas of agreement between models were considered 

areas of high probability. This suggests the importance of adaptive management and 

long-term monitoring programs and the need for further development of methods for 

assessing probable future climate conditions. We have used this approach to evaluate the 

relative importance of dependent variables and the application and selection of modeling 

techniques.

1

 Ph.D. Candidate and Research Associate (DL), Canterbury 

University, New Zealand and Alabama A&M University, 

Normal, AL; Supervisory Research Forester (JWC), U.S. 

Forest Service, Southern Research Station; Professor of 

Plant Biosecurity (PH), Lincoln University, New Zealand; 

Research Associate (CP), Alabama A&M University; 

Professor of Statistics (JAB), Canterbury University, 

New Zealand. DL is corresponding author: to contact, 

call 256-372-4562, or email at dawn.lemke@aamu.edu.

INTRODUCTION

Invasive species pose a major threat to the 

sustainability of natural ecosystems through biotic 

homogenization and loss of biodiversity, with negative 

consequences for both social and economic systems 

(Miller et al. 2012). Invasive species are considered 

a major component of global environmental 

change (Vitousek et al. 1997). Identifying areas of 

potential invasion is an important part of ecosystem 

management, and one tool that can be applied to this 

is species distribution models (SDMs) (Gallien et al. 

2010). SDMs can be used to predict spatial patterns of 

potential biological invasions and prioritize locations 

for early detection and control of invasion outbreaks. 

SDMs combine concepts from ecology and natural 

history with more recent developments in statistics and 

geospatial information systems (Franklin 2009). In 

this paper we focused on two questions specific to the 

application of SDMs: 1) Which modeling technique(s) 

is most appropriate for this study?; and 2) Do 

environmental determinates remain consistent among 

models? To address these questions we developed 

SDMs for 22 plants invasive to the forests of the 

southern region of the United States using five SDM 

methods.

METHODS

Invasive plants considered for this study included all 

species with more than 100 plot occurrences in the 

invasive plant component of the FIA database (USDA 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 279

FS 2007) (Table 1). Twenty-two environmental 

variables derived from the national land cover 

database, digital elevation models, and Bioclim data 

were used (full details given in Lemke et al. 2011) 

(Table 2). Environmental variables were checked 

for intercorrelation and the statistical package R 

was used to develop the following five models for 

each species: maximum entropy algorithms, logistic 

regression, random forest, boosted regression trees, 

and support vector machine (R Core Team 2012). 

Data were down sampled to give a 1:4 ratio (for every 

occurrence location, four random absence records 

were selected) for logistic regression, random forest, 

boosted regression trees, and support vector machine 

models to balance the data. Models were derived 

using a manual backward selection method where 

variables that had little or no impact on the model 

were removed based on the results of 10 model runs 

(Lemke et al. 2011). The key variables in determining 

the occurrence of each species were identified by their 

percent contribution to the final model and with a 

jack-knife test on gain and influence on the area under 

the curve (AUC). This approach assisted in reducing 

a model that over fits. Three techniques were used to 

assess model reliability: the performance of test and 

training data, the omission rate, and AUC. Data were 

randomly split with 30 percent in test and 70 percent 

in training datasets for the regional models and were 

run 10 times with random selections. The omission 

rate was calculated using a threshold value defined 

by the maximized sum of sensitivity and specificity. 

Models with an omission rate less than 0.25 and an 

AUC of greater than 0.75 were considered acceptable. 

Ensemble models were built for each species that 

had more than one acceptable model using only the 

acceptable models. When more than 75 percent of 

the models agreed in occurrence, these areas were 

considered highly likely to be invaded, when less than 

25 percent of the models agreed in occurrence, these 

areas were considered highly unlikely to be invaded, 

Table 1.—Comparison of five species distribution modeling techniques (boosted regression trees [BRT], 

logistic regression [LR], maximum entropy algorithms [ME], random forest [RF], and support vector 

machine [SVM]) for 22 species invasive to the forest of the Southern region. An omission rate (OR) less 

than 0.25 and an area under the curve (AUC) of greater than 0.75 were considered acceptable models, 

shown in bold.

BRT LR ME RF SVM

Species n AUC OR AUC OR AUC OR AUC OR AUC OR

Tree of heaven (Ailanthus altissima) 854 0.88 0.14 0.81 0.18 0.88 0.13 0.89 0.43 0.80 0.17

Silktree (Albizia julibrissin) 677 0.76 0.32 0.69 0.42 0.76 0.24 0.72 0.80 0.59 0.49

Princesstree (Paulownia tomentosa) 231 0.81 0.22 0.73 0.23 0.80 0.26 0.79 0.67 0.64 0.33

Chinaberry (Melia azedarach) 468 0.87 0.20 0.79 0.16 0.86 0.22 0.86 0.52 0.82 0.29

Tallowtree (Triadica sebifera) 930 0.93 0.14 0.88 0.22 0.93 0.13 0.94 0.30 0.89 0.22

Autumn olive (Elaeagnus umbellate) 327 0.88 0.16 0.75 0.20 0.98 0.19 0.89 0.44 0.82 0.17

Privets (Ligustrum L.) 7580 0.81 0.23 0.72 0.31 0.77 0.21 N/A 0.62 0.29

Bush honeysuckles (Diervilla spp.) 499 0.89 0.22 0.74 0.38 0.89 0.18 0.90 0.40 0.84 0.18

Nandina (Nandina Thumb.) 143 0.81 0.21 0.77 0.36 0.80 0.38 0.81 0.67 0.73 0.35

Nonnative roses (Rosa spp.) 3031 0.87 0.18 0.76 0.21 0.85 0.16 0.88 0.46 0.74 0.35

Climbing yams (Dioscorea L.) 120 0.78 0.35 0.62 0.31 0.83 0.22 0.79 0.52 0.61 0.31

English ivy (Hedera helix) 104 0.85 0.29 0.79 0.33 0.84 0.21 0.88 0.58 0.81 0.36

Japanese honeysuckle (Lonicera japonica) 15931 0.82 0.21 0.69 0.23 0.72 0.20 N/A 0.71 0.30

Kudzu (Pueraria spp.) 280 0.79 0.33 0.71 0.24 0.82 0.30 0.82 0.64 0.62 0.28

Periwinkles (Vinca spp.) 115 0.74 0.42 0.67 0.47 0.74 0.42 0.77 0.77 0.61 0.20

Nonnative wisterias (Wisteria spp.) 113 0.78 0.35 0.75 0.25 0.80 0.30 0.74 0.80 0.65 0.23

Tall fescue (Schedonorus phoenix (Scop.) Holub) 810 0.85 0.22 0.72 0.28 0.82 0.25 0.85 0.52 0.62 0.32

Nepalese browntop (Microstegium vimineum) 1740 0.86 0.15 0.73 0.22 0.83 0.12 0.86 0.51 0.56 0.21

Japanese climbing fern (Lygodium japonicum) 1299 0.93 0.11 0.89 0.15 0.92 0.08 0.97 0.27 0.90 0.08

Garlic mustard (Alliaria petiolata) 105 0.95 0.17 0.82 0.11 0.97 0.19 0.97 0.19 0.94 0.13

Shrubby lespedeza (Lespedeza frutescens) 964 0.82 0.35 0.67 0.39 0.79 0.30 0.79 0.68 0.54 0.19

Chinese lespedeza (Lespedeza cuneata) 1909 0.77 0.26 0.62 0.39 0.76 0.27 0.78 0.66 0.53 0.10

Percent of acceptable models 86% 59% 45% 55% 91% 68% 90% 5% 36% 50%
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 280

and when the model agreement was between 25 and 75 

percent the area was considered moderately likely to 

be invaded.

RESULTS

The results are reported in two components: model 

comparisons, and the influence and relevance of the 

dependent variables (environmental). Most species had 

at least one acceptable model as assessed by both test 

Tree of heaven 3 1 0 4 2 4 2 2 0 2 3 2 1 0 0 1 1 0 1 0 1 1

Silktree 4 0 0 4 2 3 2 2 1 2 4 3 1 0 2 3 1 0 0 0 0 5

Princesstree 3 4 1 4 2 3 2 3 0 1 2 2 2 0 0 1 1 0 0 1 0 1

Chinaberry 3 0 0 5 2 3 3 3 2 3 2 0 0 0 0 5 0 1 0 0 0 0

Tallowtree 1 0 0 5 2 2 5 3 1 1 3 2 0 0 0 0 3 0 0 0 0 0

Autumn olive 4 0 0 3 3 4 1 1 0 4 2 4 0 0 0 1 2 0 1 0 0 1

Privets 3 0 1 3 1 1 1 2 1 2 1 3 0 0 0 4 0 0 0 0 0 1

Bush honeysuckles 4 0 0 4 1 4 2 2 1 2 2 3 0 0 0 3 2 0 0 0 0 1

Nandina 2 0 0 5 3 3 1 2 2 3 1 5 2 0 1 2 1 0 0 0 0 4

Nonnative roses 4 0 0 4 1 1 2 3 0 1 2 0 0 0 0 4 5 0 1 0 0 1

Climbing yams 4 1 0 4 1 1 0 3 1 3 4 4 1 1 0 0 0 1 0 2 0 0

English ivy 1 0 0 4 1 1 1 0 2 0 1 5 2 0 2 1 0 0 0 1 0 5

Japanese honeysuckle 3 0 0 3 0 0 3 1 2 2 4 1 1 0 0 3 1 0 0 0 0 1

Kudzu 3 0 1 3 2 1 2 3 1 4 4 3 1 1 2 3 1 0 0 1 0 5

Periwinkles 1 4 0 3 2 2 3 1 0 4 3 4 0 1 0 3 2 2 0 1 0 2

Nonnative wisterias 2 0 0 5 0 0 0 0 4 1 0 4 2 2 2 3 3 0 0 1 0 5

Tall fescue 4 0 0 4 2 4 2 4 0 3 2 0 0 0 0 3 2 0 1 0 0 1

Nepalese browntop 3 0 0 4 2 3 4 4 0 2 4 2 0 0 0 1 1 0 1 0 0 1

Japanese climbing fern 1 0 0 5 5 2 2 3 1 1 1 0 0 0 0 0 0 0 0 0 0 0

Garlic mustard 3 0 0 3 1 2 0 2 2 1 2 2 0 0 0 1 1 0 0 0 0 1

Shrubby lespedeza 4 1 0 4 3 4 3 4 2 2 3 0 0 1 0 2 4 0 1 2 0 0

Chinese lespedeza 3 0 0 4 3 4 1 3 1 1 3 0 0 0 0 1 2 0 4 1 0 1

BRT (%) 73 9 0 95 36 45 23 41 14 45 59 64 27 18 18 50 18 9 5 5 0 18

LR (%) 14 14 0 23 5 0 50 0 45 9 32 23 0 5 0 86 59 5 32 0 0 77

ME (%) 59 5 0 95 14 41 32 59 14 41 41 64 14 5 23 45 32 0 5 27 0 27

RF (%) 100 15 0 100 95 95 65 90 20 90 90 20 5 0 0 10 30 0 0 5 0 25

SVM (%) 50 9 14 95 45 68 27 55 18 36 23 55 14 0 0 14 14 5 5 9 5 23

Overall (%) 58 10 3 81 38 49 39 48 22 44 48 45 12 6 8 42 31 4 9 9 1 34 Elevation Slope Dist River Min Temp Rainfall Rain in the Wettest Month Rain in the Driest Month Rain in the Warmest Quarter Temp Range Temp in the Wettest Quarter Temp in the Driest Quarter Pop Density Dist City Dist Main Road Dist Road % Farming % Forest Change in Forest % Grass % Pine Change in Pine Residential

Table 2.—Number of models using each of 22 environmental variables across five species distribution 

modeling techniques (boosted regression trees [BRT], logistic regression [LR], maximum entropy 

algorithms [ME], random forest [RF], and support vector machine [SVM]) for 22 species invasive to the 

forests of the Southern region

AUC and omission rates with the exception of kudzu, 

periwinkles, shrubby lespedeza and Chinese lespedeza 

(Table 1). Only one species, garlic mustard, had five 

accepted models, and four species (tree of heaven, 

tallowtree, autumn olive, and Japanese climbing 

fern) had four acceptable models (logistic regression, 

maximum entropy algorithms, boosted regression 

trees, and support vector machine). Overall, boosted 

regression tree and maximum entropy algorithms 

produced the strongest models with 59 percent of 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 281

models considered acceptable (Table 1). Minimum 

temperature was the most useful of the dependent 

variables, occurring in 81 percent of the models, 

followed by elevation (58 percent) and rainfall in the 

wettest month (49 percent) ( Table 2). Every species 

used minimum temperature in at least three of the 

models and elevation in at least one of the models 

(Table 2). Seven variables (distance to roads and 

rivers, proportion of grass and pine, and change in 

forest and pine) contributed information to less than 

10 percent of the models (Table 2). Tree of heaven was 

the only species to use the dependent variable change 

in pine, and distance to river was only used in the 

support vector machine models for three species. On 

average, the logistic regression models use the fewest 

number of variables (five) and random forest models 

use the highest number (nine). Logistic regression 

differed from the other methods in the selection of 

variables, with few logistic models using minimum 

temperature (23 percent) and elevation (14 percent), 

but instead being dominated by land use (Table 2). 

Eleven ensemble models (combining 2 or more 

models) were developed. 

DISCUSSION

The goal of this study was to assess the impact of 

variable and model selection in SDMs, by comparing 

the consistency of the independent environmental 

variables across models, and the consistency of models 

across species. These issues are fundamental to all 

SDMs but of particular interest to invasive species. 

Invasive species often have expanding distributions, 

and limited information is available on this species, 

resulting in less defined models. Through identifying 

agreement between modeling techniques and variables 

selection, we can have greater confidence in models. 

The area of distribution of a species is determined by 

its ecological and evolutionary history. Many factors 

affect species distribution, but the most important 

are the limits of the species’ tolerances and needs for 

certain abiotic conditions, the suite of other species 

with which it interacts, and the potential for dispersal 

and colonization within a given time period (Soberón 

and Peterson 2005). Aboitic conditions can be used 

to define the potential distribution (the focus of 

our work), with species interactions and dispersal 

constraints defining the realized distribution. Many 

studies have found large-scale environmental factors 

can produce strong SDMs (Franklin 2009). Overall 

we found similar results, with the environmental 

variables used in this study useful in predicting the 

species potential distribution with 40 percent of 

the models considered good (test AUC > 0.75 and 

test omission rate < 0.25). Physiographic variables 

dominated the model over land use variables, 

suggesting these distributions are driven by species 

tolerances. Minimum temperature was the dominate 

variable suggesting many of these species are limited 

by the extreme temperatures of winter or length of 

growing season and competition with other species 

in that niche. Elevation, the second most dominate 

variable, has some correlation with temperature and 

was selected over temperature for inclusion in some 

models, while in others it was selected in conjunction 

with temperature. By selecting in conjunction with 

temperature, it may assist in more narrowly defining 

climatic conditions associated with the species or 

forest communities that occupy the area. When 

used without temperature, elevation is likely a 

representation of climatic conditions, with elevation 

integrating both aspects of temperature (high elevation, 

cooler temperature) and rainfall. Some of the finerscale characteristics such as slope and distance to 

rivers were not widely used in models, suggesting 

the models may apply across a regional scale but not 

necessary at a local scale. Many of these relationships 

are nonlinear, with species having preferences for 

the intermediate temperatures and elevations. The 

two models that gave the strongest results (boosted 

regression and maximum entropy algorithms) capture 

nonlinear relationships well. Logistic regression is 

not designed to assess bimodal relationships, and as 

such, many of the models do not integrate temperature, 

rainfall and elevation-based variables, instead focusing 

on land cover characteristics. Our results were similar 

to other studies (Elith et al. 2006), with maximum 

entropy algorithms coming out as one of the strongest 

modeling techniques. 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 282

ACKNOWLEDGMENTS

We wish to thank the U.S. Department of Agriculture, 

Forest Service for supporting this work (cooperative 

agreement 10-DG-11330101-107). Also, we thank 

Kathy Roberts for her review and suggestions on the 

manuscript.

LITERATURE CITED

Elith, J.; Graham, C.H.; Anderson, R.P.; Dudik, M.; 

Ferrier, S.; Guisan, A.; Hijmans, R.J.; Huettmann, 

F.; Leathwick, J.R.; Lehmann, A.; Li, J.; Lohmann, 

L.G.; Loiselle, B.A.; Manion, G.; Moritz, C.; 

Nakamura, M.; Nakazawa, Y.; Overton, J.M.; 

Townsend Peterson, A.; Phillips, S.J.; Richardson, 

K.; Scachetti-pereira, R.; Schapire, R.E.; Soberon, 

J.; Williams, S.; Wisz, M.S.; Zimmermann, N.E. 

2006. Novel methods improve prediction of 

species’ distributions from occurrence data.

Ecography. 29: 129-151.

Franklin, J. 2009. Mapping species distributions: 

spatial inference and prediction. 3rd ed. New 

York, NY: Cambridge University Press.

Gallien, L.; Munkemuller, T.; Albert, C.H.; 

Boulangeat, I.; Thuiller, W. 2010. Predicting 

potential distributions of invasive species: 

Where to go from here? Diversity and 

Distributions. 16: 331-342. 

Lemke, D.; Hulme, P.E.; Brown, J.A.; Tadesse, 

W. 2011. Distribution modelling of Japanese 

honeysuckle (Lonicera japonica) invasion in the 

Cumberland Plateau and Mountain Region, 

USA. Forest Ecology and Management. 262: 

139-149.

Miller, J.H.; Lemke, D.; Coulston, J. In press. The 

invasion of southern forests by nonnative 

plants: current and future occupation with 

impacts, management strategies, and mitigation 

approaches. In: The southern forest futures project. 

Technical Report. Chapter 15. 

R Core Team. 2012. R: a language and environment 

for statistical computing. Vienna, Austria: R 

Foundation for Statistical Computing. Available at 

www.R-project.org. (Accessed June 1, 2012).

Soberon, J.M.; Peterson, A.T. 2005. Interpretation 

of models of fundamental ecological niches 

and species’ distributional areas. Biodiversity 

Informatics. 2: 1-10. 

U.S. Department of Agriculture, Forest Service 

(USDA FS). 2007. The forest inventory and 

analysis database: database description and 

users guide version 3.0. Washington, DC: U.S. 

Department of Agriculture, Forest Service.

Vitousek, P.M.; D’Antonio, C.M.; Loope, L.L.; 

Rejmanek, M.; Westbrooks, R. 1997. Introduced 

species: aignificant component of human-caused 

global change. New Zealand Journal of Ecology. 

21: 1-16.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 283

INVASIVE PLANT MONITORING FOR NORTHERN U.S. FORESTS

William H. McWilliams, Randall S. Morin, Katherine Johnson, W. Keith Moser, and James A. Westfall1

Abstract.—Invasive plants are monitored through canopy cover estimates for a list of 

species developed by FIA for the northern region of the U.S. that is integrated with a 

national list. Nearly all of the invasive plants on the NRS-FIA list are exotic species, but 

a few native species are listed. Highly invasive native species such as rhizomatous fern 

are absent, making the list limited in scope. Some useful applications include describing 

all vegetation other than trees, competing vegetation, as well as indices for “invasiveness,” “native-ness,” and others. The major advantage of the invasive survey is a fourfold 

increase in sample size. The primary disadvantage is that the list of invasive species does 

not include several important native species at the regional level. The invasive protocols 

offer a flexible system for estimating occurrence and abundance that can be segmented to 

address a wide array of questions.

1

 Research Forester (WHM), U.S. Forest Service, Northern 

Research Station, 11 Campus Blvd., Suite 200, Newtown 

Square, PA 19073; Research Foresters (RSM, WKM, and 

JAW) and Forester (KJ), U.S. Forest Service, Northern 

Research Station. WHM is corresponding author: to contact, 

call 610-557-4050 or email at wmcwilliams@fs.fed.us.

INTRODUCTION

Understanding the composition and cover of the 

forest understory helps explain future forest canopy 

composition and structure, particularly the number 

of established seedlings and the character of other 

vegetation. An underlying objective is to describe 

where the more invasive species are and their impact 

on native forest flora. This information is important for 

managers and policy makers evaluating broad-scale 

forest health, as well as scientists working on forest 

dynamics models that require information on forest 

vegetation. 

The history of invasive plant survey for Northern 

Research Station Forest Inventory and Analysis (NRSFIA) includes methods that have changed over time 

and space, but this has been minimized through careful 

field manual preparation, training, and attention to 

the need for “trend.” Invasive trees have been part of 

the standard FIA tree list for many decades, including 

important species like tree-of-heaven (Ailanthus 

altissimma), Norway maple (Acer plataniodes), and 

paulownia (Paulownia tomentosa). Invasive plants 

survey efforts began in earnest during the 1990s and 

have continued to develop regionally and nationally 

since then. For NRS-FIA, the methodology and list of 

invasive plants has been relatively consistent over time 

and space. Exotic and native species are included in 

the invasive plant survey.

METHODS

NRS-FIA ceased collection of Phase 3 (P3) data in 

2011 for budgetary reasons. To maintain some of 

the P3 indicators into the future, a modified suite 

of indicators, referred to as Phase 2+ (P2+), was 

developed for the 2012 field season. The sampling 

intensity for P2+ is one plot per 24,000 acres, a level 

that falls between the 1/96,000 acres for P3 samples 

and 1/6,000 for standard Phase 2 (P2) samples. 

This sampling intensity is based on the maximum 

number of plots that can be measured in a 3-month 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 284

leaf-on summer window. The P2+ design includes 

measurement of a vegetation profile and adult trees 

on the 24-ft radius subplots, as well as seedlings 

and saplings on 6.8-foot radius micro-plots (USDA 

Forest Service 2012). This paper focuses the invasive 

survey in the context of other measures of vegetation 

character.

The P2+ design includes the following components for 

vegetation other than adult trees: 

1. Invasive plant areal canopy cover

2. Percentage canopy cover for vegetation growth 

habits (trees, shrubs, woody vines, forbs, fern, 

and graminoids) on a 24-ft radius plot in four 

height classes

3. Advance tree seedling regeneration (ATSR) 

on 6.8-ft radius plot in six height classes with 

a browse impact code (USDA Forest Service 

2012) 

Invasive plants are monitored by collecting canopy 

cover for a list that includes exotic and native 

species. NRS-FIA has adopted a list of invasive 

plants developed by screening variables according 

to methodology of Morse et al. (2004). The list was 

developed for the northern United States region and is 

integrated with a national list (Table 1). The definitions 

and conventions used in naming species are adapted 

from the USDA Natural Resources Conservation 

Service, Plants Database (NRCS 2012). 

In 2012, one-eighth of the forested sample conditions 

are being visited as P2+ samples and beginning in 

2013, the percentage will be expanded to include up to 

one-fourth of the conditions.

RESULTS AND DISCUSSION

Perhaps the most important indicators of forest 

understory health are species composition and 

abundance. Vegetation other than trees is vital for 

wildlife food and cover, tree seedling development, 

and native plant diversity. Competing vegetation 

often inhibits native tree regeneration (Jackson and 

Species Common name

Microstegium vimineum Japanese stiltgrass,

Nepalese browntop

Phalaris arundinacea reed canarygrass

Phragmites australis common reed, phragmites

Alliaria petiolata garlic mustard

Centaurea biebersteinii spotted knapweed

Cirsium arvense Canada thistle

Cirsium vulgare bull thistle

Cynanchum louiseae Louise’s or black swallow-wort

Cynanchum rossicum European swallow-wort

Euphorbia esula leafy spurge

Hesperis matronalis Dame’s rocket

Lysimachia nummularia creeping jenny

Lythrum salicaria purple loosestrife

Polygonum cuspidatum Japanese knotweed

Polygonum x bohemicum (P. cuspidatum x 

P. sachalinense hybrid)

Polygonum sachalinense giant knotweed

Berberis thunbergii Japanese barberry

Berberis vulgaris common barberry

Elaeagnus umbellata autumn olive

Frangula alnus glossy buckthorn

Ligustrum vulgare European privet

Lonicera maackii Amur honeysuckle

Lonicera tatarica Tatarian bush honeysuckle

Lonicera x bella Showy fly honeysuckle

Lonicera morrowii Morrow’s honeysuckle

Rhamnus cathartica common buckthorn

Rosa multiflora multiflora rose

Spiraea japonica Japanese meadowsweet

Viburnum opulus European cranberrybush

Acer platanoides Norway maple

Ailanthus altissima tree-of-heaven

Albizia julibrissin silktree

Elaeagnus angustifolia Russian olive

Melaleuca quinquenervia punktree

Melia azedarach Chinaberry

Paulownia tomentosa princesstree

Robinia pseudoacacia black locust

Tamarix ramosissima saltcedar

Triadica sebifera tallow tree

Ulmus pumila Siberian elm

Celastrus orbiculatus oriental bittersweet

Hedera helix English ivy

Lonicera japonica Japanese honeysuckle

Table 1.―List of NRS-FIA invasive plants
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 285

Finley 2005). Understory flora can include both 

invasive and non-invasive plants, as well as exotic 

and native species, thus complicating the analysis 

of impacts. Combining invasive survey data with 

vegetation profile estimates of cover and advance tree 

seedling regeneration (ATSR) data provides a useful 

characterization of forest understory vegetation. To 

illustrate, the following list shows a simple, but useful, 

breakdown of commonly found species: 

• Native/non-invasive: witch-hazel (Hamamelis 

virginiana), lowbush blueberry (Vaccinium 

angustifolium)

• Native/invasive: hay-scented fern (Dennstaedtia 

punctilobula), grape vine, (Vitis vinifera)

• Exotic/non-invasive: dandelion (Taraxacum 

officinale)

• Exotic/invasive: Garlic mustard (Alliaria 

officialis), Japanese stiltgrass (Micriostegium 

vimineum), oriental bittersweet (Celastrus 

orbiculatus), and tree-of-heaven

The NRS-FIA invasive species and vegetation profile 

tallies address a broad range of needs. However, some 

filtering is suggested for concise analyses, e.g., screen 

out the native species from the list of invasive plants. 

The vegetation indicator and its use are well described 

by Schulz and others (2009).

Nearly all of the invasive plants on the NRS-FIA 

list are exotic species (USDA Forest Service 2012), 

but some native species are included, such as black 

locust (Robinia pseudoacacia). Some highly invasive 

native species, such as rhizomatous fern in the MidAtlantic region, are absent thus limiting scope. This 

complicates the analysis, but does not preclude 

development of indices of “invasive-ness,” and 

“native-ness” of understory vegetation. To illustrate, 

Figure 1 depicts the invasive survey nested within the 

vegetation profile and includes most of the growth 

habits encountered. The computations are relatively 

simple, but cover estimates must be adjusted to 

account for overlap since total cover can be greater 

than 100 percent:

• Vegetation other than trees = total cover minus

cover of tally trees

• Invasive-ness = invasive plant cover/vegetation 

other than trees

• Native-ness = native plant cover/vegetation 

other than trees.

Other potential analyses include indices for special 

studies of ecoregions, major forest types or species, or 

areas of forest health concern. Assessments of wildlife 

habitat, biomass, forage availability, grazing potential, 

fuel loadings, and potential site productivity are other 

possible uses.

The advantages of the invasive survey include:

1. The methodology and list of invasive plants is 

consistent over time and space

2. The results can be used with results from other 

FIA regions to fill national needs; regional lists 

will differ

3. Estimates of invasiveness, native-ness, and other 

indices of interest can be derived

Since the invasive plant protocols are nested within 

the 24-foot radius subplot used for vegetation and tree 

monitoring, there are further advantages associated 

with the P2+ framework:

4. Fourfold increase in sample size, allowing finerscale estimates and geo-spatial products

5. Cost reduction by limiting the time for 

measuring vegetation and invasive plants to 1 

hour

6. Integration of all measurements of vegetation, 

including trees 

7. Simplification of training and species 

identification

8. Allows development of new improved methods 

of estimating the competing vegetation layer, 

e.g., assigning more weight to woody vines that 

are found above 16 feet
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 286

Tally Tree (> 5.0 inches DBH)

Non-Tally

Tree (> 5.0 inches DBH)

Tally Tree (>= 1.0 inch length

and < 5.0 inches d.b.h.

Tally Tree (<1.0 inch HT)

Vine (Woody)

Vine (Non-woody)

Shrub (> 0.5 meters)

Shrub (< 0.5 meters)

Exotic

Graminoid

Forb

Fern

Legend

Sample footprint

24.0 feet

16.0 feet

Figure 1.―Example of NRS-FIA nested vegetation samples, including the vegetation profile and exotic/invasive survey for a 

single FIA subplot.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 287

Disadvantages of the invasive plant survey are 

generally limited to the technique that was used to 

define “invasive,” a common issue in plant surveys:

1. The list is not complete and ignores several 

important regional species which limits full 

accounting of the impact on native flora 

2. The list includes only three native species

3. The list includes species that are rare on forest 

land, e.g., purple loosestrife (Lythrum salicaria) 

that is a wetland obligate, and bull thistle 

(Cirsium vulgare) that occurs primarily on land 

other than forest

The invasive protocols offer a flexible system for 

estimating occurrence and abundance that can be 

utilized to address a wide array of questions. The 

system will provide for core national products and 

novel regional analyses.

LITERATURE CITED

Jackson, D.R.; Finley, J.C. 2005. Herbicides and 

forest management, controlling unwanted trees, 

brush, and other competing vegetation. CAT 

UH174. University Park, PA: The Pennsylvania 

State University. 31 p.

Natural Resources Conservation Service (NRCS). 

2010. The PLANTS database. Baton Rouge, LA: 

U.S. Department of Agriculture, Natural Resources 

Conservation Service, National Plant Data Center. 

Available at http://plants.usda.gov. 

McWilliams, W.H.; Alerich, C.L.; Butler, B.J.; 

Crocker, S.J.; Kurtz, C.M.; Lister, T.W.; 

Morin, R.S.; Roth, P.; Westfall, J.A. In press. 

Pennsylvania’s Forests 2009. Resour. Bull. 

Newtown Square, PA: U.S. Department of 

Agriculture, Forest Service, Northern Research 

Station.

Morse, L.E.; Randall, J.M.; Benton, N.; Hiebert, R.; 

Lu, S. 2004. An invasive species assessment 

protocol: evaluating non-native plants for their 

impact on biodiversity. Version 1. Arlington, VA: 

NatureServe. 40 p.

Schulz, B.K.; Bechtold, W.A.; Zarnoch, S.J. 2009. 

Sampling and estimation procedures for the 

vegetation diversity and structure indicator.

Gen. Tech. Rep. GTR-781. Portland, OR: U.S. 

Department of Agriculture, Forest Service, Pacific 

Northwest Station. 53 p.

USDA Forest Service. 2012. Forest inventory and 

analysis national core field guide, volume 1 

supplement: field data collection procedures for 

Phase 2+ plots, version 5.0. Newtown Square, PA: 

U.S. Department of Agriculture, Forest Service, 

Northern Research Station. Available at http://www.

nrs.fs.fed.us/fia/data-collection/field-guides/p2/

NRS%20FG%205%200-Oct%202010. (Accessed 

June 7, 2012).

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 288

Nonnative Invasive Plant Inventory 

in the Northern Research Station: Patterns and Trends

Abstract.—Nonnative invasive plants (NNIPs) are an important component of suite 

of biological disturbances that are influencing forests in the 24 state region of the 

Northern Research Station (NRS) of the U.S. Forest Service. Along with locally high 

deer populations and nonnative earthworms, NNIPs have the potential to impact the 

biodiversity of the ground flora, site productivity, wildlife habitat, and the composition 

and structure of forests, present and future. Northern Research Station Forest Inventory 

and Analysis Program (FIA) has several sets of data that can be used to evaluate NNIP. A 

select list of 25 species were measured on 100 percent of the panels in the 11 midwestern 

states from 2005 through 2006. About 20 percent of all plots were inventoried for the 

presence of 43 species from 2007 through 2010 for all 24 states of NRS-FIA. Our 

presentation discusses the results and the slightly different methodologies between 

the 2005-6 and 2007+ inventories, and examines the implications specifically for tree 

regeneration and future stand development.

W. Keith Moser and Cassie M. Kurtz1

1

 Research Forester (WKM), U.S. Forest Service, Northern Research Station, 1992 Folwell Ave., 

St. Paul, MN 55108; Natural Resource Specialist (CMK), U.S. Forest Service, Northern Research 

Station. WKM is corresponding author; to contact, call 651-649-5155 or email at wkmoser@

fs.fed.us. 

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Modeling Regenerative 

Capacity across the 

Eastern United StateS: 

Can FIA Inform

Contemporary Models?
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 290

SAMPLING FOREST REGENERATION ACROSS NORTHERN U.S. 

FORESTS: FILLING A VOID IN REGENERATION MODEL INPUT

William H. McWilliams, Charles D. Canham, Randall S. Morin, Katherine Johnson, 

Paul Roth, and James A. Westfall1

Abstract.—The Forest Inventory and Analysis Program of the Northern Research 

Station (NRS-FIA) has implemented new Advance Tree Seedling Regeneration (ATSR) 

protocols that include measurements of seedlings down to 2 inches in height. The 

addition of ATSR protocols is part of an evaluation of NRS-FIA Phase 3 indicator 

variables to increase sampling intensity from 1/96,000 acres to 1/24,000 acres per sample 

location. The sampling scheme for understory vegetation consists of (1) estimating 

canopy-cover percent for six vegetation growth habits on 24-foot-radius subplots in 

four height classes and as an aerial view and (2) measuring tree saplings and seedlings 

on 6.8-foot-radius microplots. The Pennsylvania Regeneration Study has used ATSR 

and competing vegetation measurements to evaluate regeneration for 16.7 million acres 

of northern hardwood and mixed-oak forest. The findings revealed that only about half 

the State’s forest had adequate advance regeneration for replacement of canopy species 

if the overstory were removed. The ATSR protocols provide estimates of the number 

of seedlings by species and height class, as well as a flexible method for evaluating 

regeneration adequacy. The level of detail of the measurements is expected to provide 

input that has not been available in the past for models that require tree regeneration 

information. 

1

 Research Forester (WHM), U.S. Forest Service, Northern 

Research Station, 11 Campus Blvd., Suite 200, Newtown 

Square, PA, 19073; Senior Scientist (CDC), Institute of 

Ecosystem Studies; NRS-FIA Research Foresters (RSM 

and JAW) and Forester (KJ); Forest Program Manager 

(PR), Inventory and Monitoring Program, Pennsylvania 

Department of Conservation, Bureau of Forestry. WHM 

is corresponding author: to contact, call 610-557-4050 or 

email at wmcwilliams@fs.fed.us.

INTRODUCTION

The Forest Inventory and Analysis Program of 

the Northern Research Station (NRS-FIA) ceased 

collection of Phase 3 (P3) data in 2011 for budgetary 

reasons. Based on an evaluation of costs, time, and 

efficiencies, a modified suite of existing P3 variables 

and new Advance Tree Seedling Regeneration (ATSR) 

protocols were implemented in 2012. The sampling 

intensity for the modified suite will be as high as 

1/24,000 acres, which is between 1/96,000 acres for 

P3 samples and 1/6,000 for standard Phase 2 (P2) 

samples, referred to as Phase 2+ (P2+). This intensity 

is based on the maximum number of plots that can be 

measured in a 3-month leaf-on summer window. As of 

this writing, the 2012 field season is just beginning. In 

2012, one-eighth of the forested sample conditions are 

being visited as P2+ samples; in 2013 and beyond, the 

sample will be expanded to include up to one-fourth of 

the conditions.

This paper focuses on the ATSR sample methodology. 

More specifically, ATSR sampling protocols, 

preliminary results, and applications for assessing 

forest regenerative capacity are discussed along with 

the utility for use in eco-process and other models that 

require regeneration information.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 291

METHODS

The sampling scheme includes (1) estimating 

canopy-cover percent for six vegetation growth 

habits on 24-foot-radius subplots in four height 

classes (Vegetation Profile) and as an aerial view, 

and (2) measuring tree saplings and tree seedlings 

on 6.8-foot-radius microplots. The study design for 

the P2+ Vegetation Profile was adapted from and 

integrated with the national P3 Vegetation Diversity 

and Structure protocols (USDA Forest Service 

2012). The seedling protocols were adapted from the 

Pennsylvania Regeneration Study (PRS)—a 12-year 

study of regenerative capacity for forests under high 

deer browse conditions (McWilliams et al., in press; 

Westfall and McWilliams 2011). The underlying 

ecological and silvicultural relationships used for 

evaluating regeneration are described by Brose et al. 

(2008) and Marquis (1994). The approach for ATSR 

evaluation was developed during debate within the 

environmental community over the impact of deer on 

forest regeneration (Frye 2006).

Tree seedlings are defined as all stems at least 2 

inches tall and up to 1 inch in diameter at breast height 

(d.b.h.) that have survived for at least a year. The 

ATSR sample provides the number of established and 

competitive seedlings by length class (2 to 6 inches, 

6 inches to 1 foot, 1 to 3 feet, 3 to 5 feet, 5 to 10 

feet, and greater than 10 feet). In addition, a browse 

impact code (five levels from very low to very high) 

is recorded for accessible forest land for all four 

subplots. The code was developed for white-tailed 

deer (Odecoleus virginianus Zimmerman) and may be 

adaptable for other ungulates. Other herbivores (such 

as hare and rodents) impact tree seedlings, but are 

not specifically addressed in the estimate of browse 

impact.

The ATSR protocols recognize that all seedlings are 

not the same. The study uses an “established” and 

“competitive” class for heavy seeded species, such as 

oak, walnut, and hickory (Fig. 1). The determination 

of seedling establishment and competitive status for 

Figure 1.—Advance tree seedling regeneration seedling count guide (USDA Forest Service, Northern Research Station 2012).
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 292

large seeded species depends on the diameter at root 

collar, a better predictor of the future status than height 

for these species (Brose et al. 2008). Only a single 

dominant stem is counted when multiple sprouts are 

encountered. Undetached branches covered with soil, 

or layered stems, are not counted.

When combined with an estimate of “competing 

vegetation” from the Vegetation Profile and the browse 

impact code, the microplots can be evaluated for 

regeneration adequacy using thresholds for the number 

of seedlings by length class. The thresholds are 

determined by the level of browse, i.e., more seedlings 

are needed when browse impacts are higher. Samples 

are further screened for light levels conducive to 

seedling development (from 40 to 75 percent stocked 

with adult trees).

RESULTS

The underlying assumption for analysis of advance 

regeneration is that forest stands encountered will 

eventually have overstory removal or mortality and 

need advance regeneration to replace adult trees. Past 

results of the PRS have provided vital information for 

planners and inter-agency decisions on ameliorating 

the impacts of deer on forest regeneration. ATSR is 

used along with the tally of saplings for full evaluation 

of advance regeneration. Saplings are defined as 

trees larger than 1 inch d.b.h. up to 4.9 inches. In 

Pennsylvania, it was found that only about half of 

the forest land that is receiving adequate light for 

establishment and development of regeneration had 

adequate regeneration (Fig. 2) ( McWilliams et al., in 

press). These results are subject to confidence intervals 

of +/- 4 percentage points at the 95-percent confidence 

level or +/- 2 percentage points at the 67 percent level.

The results of the 2012 measurements will be the first 

NRS-FIA-wide dataset containing these new protocols. 

The full baseline measurements should be complete 

following the 2017 field season. At that point, full 

characterization of regeneration adequacy across the 

NRS-FIA region will be possible.

Figure 2.—Percent and number (in parentheses) of samples adequately stocked with advance tree seedling and sapling 

regeneration for canopy replacement species and samples 40 to 75 percent stocked with trees, Pennsylvania, 2006 to 2010.

44 (36) 47 (154)

48 (102)

51 (119)

51 (86) 38 (26)
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 293

DISCUSSION AND CONCLUSIONS

Seedlings less than 1 foot tall have not previously been 

included as a core variable in NRS-FIA inventories 

and seedling height has not been recorded. The new 

sample provides a flexible method for evaluating 

regeneration adequacy for dominant species subject 

to study assumptions, e.g., that overstory removal will 

occur. An advantage of the new P2+ samples is that all 

vegetation measurements are taken from the 24-foot or 

6.8-foot-radius plots. This approach integrates P2 and 

P2+ measurements together for improved analyses of 

overstory and understory conditions.

The implementation of P2+ samples across the NRSFIA region will cover 175.8 million acres of forest 

compared to 16.7 million acres for Pennsylvania. The 

expanded study area includes the Central Hardwood, 

Lake State, Mid-Atlantic, and New England areas; and 

four Plains States. Needed for regions and forest types 

outside the Mid-Atlantic are protocols and algorithms 

for analyzing and applying ecological thresholds 

for the number of seedlings by height class required 

for adequate regeneration to replace adult trees. 

The complexity and diversity of the Pennsylvania 

experience should provide a useful template for other 

NRS-FIA areas. For example, spruce-fir forests of 

Maine will need to use guidelines for size and density 

of regeneration of various species before overstory 

removal or destruction, such as those suggested by 

Seymour (1992, p. 236). The approach should also be 

adaptable to conditions in the Southern Appalachian 

Mountains, Piedmont, and Coastal Plain because 

advance regeneration is important for predicting future 

stand composition there. Additional research can be 

incorporated into the analyses to better match forest 

composition and silvicultural systems of interest.

The level of detail of the measurements is expected 

to provide input for eco-process and other models 

that has not been available in the past (Canham et 

al. 2006). This includes any model that attempts 

to characterize or project future conditions based 

on an inventory of vegetation and trees, including 

regeneration. Seedling height and competitive status 

are critical for addressing impacts of herbivory and 

the status of advance regeneration. The study will also 

provide input for developing regeneration modules 

for individual tree projection models, such as the 

Forest Vegetation Stimulator (Crookston and Dixon 

2005). Further study by McDill (this proceedings) 

is addressing the ability to discern reliable trend 

information from the results.

LITERATURE CITED

Brose, P.H.; Gottschalk, K.W.; Horsley, S.P.; Knopp, 

P.D.; Kochendorfer, J.N.; McGuiness, B.J.; Miller, 

G.W.; Ristau, T.E.; Stoleson, S.H.; Stout, S.L. 

2008. Prescribing regeneration treatments for 

mixed-oak forests of the Mid-Atlantic region.

Gen. Tech. Rep. NRS-33. Newtown Square, PA: 

U.S. Department of Agriculture, Forest Service, 

Northern Research Station. 100 p.

Canham, C.D.; Papaik, M.J.; Uriarte, M.; 

McWilliams, W.H.; Jenkins, J.C.; Twery, M.J. 

2006. Neighborhood analyses of canopy tree 

competition along environmental gradients in 

New England forests. Ecological Applications. 

16(2): 540-554.

Crookston, N.L.; Dixon, G.E. 2005. The Forest 

Vegetation Simulator: a review of its structure, 

content, and applications. Computers and 

Electronics in Agriculture. 49: 60-80.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 294

Frye, B. 2006. Deer wars: science, tradition, and the 

battle over managing whitetails in Pennsylvania.

University Park, PA: The Pennsylvania State 

University Press. 310 p.

Marquis, D.A., ed. 1994. Quantitative silviculture 

for hardwood forests of the Alleghenies. Gen. 

Tech. Rep. NE-183. Radnor, PA: U.S. Department 

of Agriculture, Forest Service, Northeastern Forest 

Experiment Station. 143 p.

McWilliams, W.H.; Alerich, C.L.; Butler, B.J.; 

Crocker, S.J.; Kurtz, C.M.; Lister, T.W.; 

Morin, R.S.; Roth, P.; Westfall, J.A. [In press]. 

Pennsylvania’s forests 2009. Resour. Bull. 

Newtown Square, PA: U.S. Department of 

Agriculture, Forest Service, Northern Research 

Station.

Seymour, R.S. 1992. The red spruce-balsam fir 

forest of Maine: evolution of silvicultural 

practice in response to stand development 

patterns and disturbances. In: Kelty, M.J.; 

Larson, B.C.; Oliver, C.D., eds. The ecology and 

silviculture of mixed-species forests. Norwell, MA: 

Kluwer Publishers. 217-244.

USDA, Forest Service, Northern Research Station. 

2012. Forest inventory and analysis national core 

field guide, Volume 1 Supplement: field data 

collection procedures for Phase 2+ plots, version 

5.0. Available at http://www.nrs.fs.fed.us/fia/datacollection/. (Accessed June 7, 2012).

Westfall, J.A.; McWilliams, W.H. 2011. Detecting 

change in advance tree regeneration using 

forest inventory data: the implications of 

type II error. Environmental Monitoring 

Assessment. DOI 10.1007/s10661-011-2365-3. 

Available at http://www.springerlink.com/content/

650047517180t108/. (Accessed May 10, 2012).

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 295

INFORMATION FOR FOREST PROCESS MODELS: 

A REVIEW OF NRS-FIA VEGETATION MEASUREMENTS

Charles D. Canham and William H. McWilliams1

Abstract.—The Forest and Analysis Program of the Northern Research Station (NRSFIA) has re-designed Phase 3 measurements and intensified the sample intensity 

following a study to balance costs, utility, and sample size. The sampling scheme consists 

of estimating canopy-cover percent for six vegetation growth habits on 24-foot-radius 

subplots in four height classes and as an aerial view, and measuring tree saplings and 

seedlings on 6.8-foot-radius microplots. In the past two years, all of the basic submodels 

in SORTIE, an individual-tree distance-dependent model, were parameterized for the 50 

most common tree species in the eastern U.S. by using FIA data. A significant challenge 

for modelers is that goodness of fit measures, such as the coefficient of determination, 

for the resulting growth models are often relatively low. Opportunities for using NRSFIA data for broad-scale models include use of a non-spatial model of seedlings as a 

function of basal area of the entire subplot, ability to use a mortality model for seedlings, 

and improvement in knowledge of the impact of the species of neighboring trees. The 

major benefit of the new NRS-FIA sample design is a fourfold increase in sample size 

and integration of all vegetation measurements on the subplot (i.e., adult trees, competing 

vegetation, and seedlings). The primary limitation of using FIA samples is that the 

subplots are too small for full analysis of distance-dependent seedling dispersion around 

parent trees.

1

 Senior Scientist (CDC), Institute of Ecosystem Studies, 

Millbrook, NY 12545; Research Forester (WHM), U.S. 

Forest Service, Northern Research Station. CDC is 

corresponding author: to contact, call 845-677-7600 or 

email at ccanham@ecostudies.org. 

Introduction

Broad-scale models of forest dynamics provide 

projections of future conditions that are used by 

policymakers, managers, and scientists to understand 

and implement major decisions. It is challenging to 

provide forest modelers with information from forest 

inventories because model structure requirements vary 

widely. Prominent approaches include individual-tree 

distance-independent tree, growth/yield (Crookston 

and Dixon 2005), individual-tree distance-dependent 

tree (Pacala et al.1996) and imputation (Wear 2002) 

models. The Forest Inventory and Analysis Program of 

the Northern Research Station (NRS-FIA) vegetation 

measurements are reviewed using SORTIE (Pacala et 

al.1996) to exemplify complex model needs. SORTIE 

stands for a model of trees as they “take over” a site 

(not an acronym). SORTIE is an ecological process 

model that has been parameterized using NRS-FIA 

measurements (Canham et al. 2006). Broad-scale 

models that incorporate or “scale up” finer models of 

ecosystem processes to the scale of FIA are a critical 

need expressed by clients, particularly for projecting 

future conditions. The conclusions should be useful 

to others working at large scales, such as the Forest 

Vegetation Simulator (Dixon 2002). 

In this paper, we focus on models that “mimic” growth 

and composition change for forest trees, or “process” 

models. The goal is to emphasize needs for models of 

tree recruitment, or regeneration models. The SORTIE 

model is used here to illustrate requirements for 

models that operate at finer scale than FIA, because 

it includes basic components needed for most process 

models, including a complex set of submodels.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 296

METHODS

NRS-FIA Vegetation Sample

NRS-FIA ceased collection of Phase 3 (P3) data in 

2011 for budgetary reasons. Based on an evaluation 

of costs, time, and efficiencies, a modified suite 

of P3 variables and new Advance Tree Seedling 

Regeneration (ATSR) protocols were implemented 

in 2012. The sampling intensity for the modified 

suite will be up to 1/24,000 acres, which is between 

1/96,000 acres for P3 samples and 1/6,000 for standard 

P2 samples, referred to as Phase 2+ (P2+). This 

intensity is based on the maximum number of plots 

that can be measured in a 3-month leaf-on summer 

window. The new P2+ design includes measurement 

of a Vegetation Profile and adult trees on the 24-footradius subplots and seedlings and saplings on a 6.8-

foot-radius microplot (USDA Forest Service 2012).

All P2 and P2+ measurements are now integrated 

in a nested sample for all vegetation, including 

merchantable trees (greater than 5 inches in diameter 

at breast height [d.b.h.]) (Fig. 1). The sampling 

scheme includes estimating canopy-cover percent 

for six vegetation growth habits on four 24-footradius subplots in four height classes and an aerial 

view canopy cover estimate; measuring tree saplings 

including total height on 6.8-foot-radius microplots; 

and measuring tree seedlings on 6.8-foot-radius 

microplots in six height classes (USDA Forest Service 

2012). The P2+ Vegetation Profile measurements 

are used to characterize vegetation composition and 

structure, and to develop an estimate of percent cover 

for competing vegetation. The competing vegetation 

information is used along with the seedling and sapling 

measurements to evaluate regenerative capacity. 

SORTIE Primer

SORTIE is a spatially explicit, individual-based 

process model of forest dynamics that emphasizes 

neighborhood tree dynamics (Pacala et al.1996). 

Over the past decade, SORTIE has been improved to 

operate as an object-oriented, open-source platform for 

simulating forest dynamics. The model can easily be 

modified, and the source code, executable programs 

for both Windows and Linux platforms, manuals 

for programmers and users, sample parameter files, 

and sample output are all available on the SORTIE 

Web site (www.sortie-nd.org). Development of the 

model has been tightly linked to statistical methods to 

parameterize the model from field data, particularly 

the landscape-level data available from the FIA 

sample-plot network (i.e., Canham et al. 2006). 

In the past two years, all of the basic submodels in 

SORTIE have been parameterized for the 50 most 

common tree species in the eastern U.S. by using FIA 

data from the entire eastern U.S. (Louisiana north to 

Minnesota and eastward). This includes functions 

for seedling recruitment, and growth and survival of 

seedlings, saplings, and adult trees. The recruitment 

functions include effects of climate, and indirectly 

incorporate the effects of shading (using basal area 

of adult trees within the immediate neighborhood 

of the seedling microplots within each FIA subplot 

as a surrogate). Seed production and dispersal are 

not modeled as separate steps, but as in the original 

SORTIE model are subsumed within the seedling 

recruitment functions. The growth and survival 

functions for saplings and adult trees incorporate 

effects of competition, climate, air pollution (N 

deposition) (i.e., Canham et al. 2006).

Regional-scale results are generated for current 

forest structure, composition, and environmental 

characteristics of randomly selected FIA plots, using 

current climate (from bilinear interpolation of PRISM 

climate data) and N deposition, based on a regional, 

spatially-explicit model of total wet and dry N 

deposition (Weathers et al. 2006). Since the FIA plots 

used to initialize the individual runs represent specific 

locations and known total areas of forestland, variation 

in the regional-scale results can be mapped, and can 

be broken out by sub-region (i.e., states, counties), 

forest type, or mapped as a function of any of the axes 

of environmental variation (climate, N deposition, 

soils) linked to the FIA plots. The model architecture is 

currently being extended to allow interactions among 

FIA plots embedded within a region (to allow, for 

instance, modeling seed dispersal based on regional 

abundance of tree species).
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 297

Figure 1.—Example of NRS-FIA forested subplot depicting nested vegetation samples, 2012 (continued on next page).
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 298

Figure 1 (continued).—Example of NRS-FIA forested subplot depicting nested vegetation samples, 2012.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 299

SORTIE is an individual-tree distance-dependent 

model that simulates light, tree birth, growth, and 

mortality for seedlings, saplings, and merchantable 

trees (Canham et al. 1994). SORTIE consists of four 

submodels: seedling recruitment (reproduction) 

function, resource availability (light) (Beaudet 

et al. 2020), tree growth (Pacala et al. 1994), and 

tree mortality (Kobe et al. 1996). SORTIE allows 

development of “behaviors” to fit the user’s forest 

population or eco-type of interest. Canham et al. 

(2006) parameterized SORTIE for use in New 

Hampshire and Vermont using NRS-FIA data. 

Actual tree growth is a function of potential diameter 

growth using scalars that quantify growth based on 

initial diameter, local environmental conditions, and 

crowding by neighbor trees.

The focus for this discussion is the seedling 

recruitment submodel and associated needs from 

FIA. Seedling recruitment models can be complicated 

considering that they track seed production, dispersal, 

predation, germination, and mortality. To simplify 

model needs, using seedlings above a minimum 

threshold allows a focus on seedlings that have 

germinated in the current year (Ribbens et al. 1994). 

The FIA minimum size for counting seedlings is 6 

inches long for conifers and 1 foot for deciduous 

species. Canham et al. (2006) modeled seedling 

dispersion as a function of the proximity of parent 

trees. Specifically, NRS-FIA seedling counts and adult 

tree basal area were merged with climate variables 

with NRS-FIA sample locations. 

DISCUSSION AND CONCLUSIONS

Opportunities for using NRS-FIA data for broadscale models include use of a non-spatial model of 

seedlings as a function of basal area of the entire 

subplot, ability to use a mortality model for seedlings, 

and improvement in knowledge of the impact of the 

species of neighboring trees. The NRS-FIA sample 

design includes several variables associated with 

SORTIE needs (Figs. 1 and 2).

In the past, the NRS-FIA minimum seedling size 

threshold included cohorts from multiple years and 

excluded mortality of seedlings before achieving the 

minimum size. This threshold is no longer a limitation 

because the Advance Tree Seedling Regeneration 

(ATSR) measurement protocols include all seedlings 

that germinated and survived in the past year. Another 

challenge for modelers is that the coefficient of 

determination of the growth models is relatively low. 

The primary limitation of using FIA samples is that 

the subplots are too small for full analysis of distancedependent seedling dispersion around parent trees.

Other benefits of the new NRS-FIA sample design 

are a fourfold increase in sample size and that all 

vegetation measurements are integrated on the subplot 

(i.e., adult trees, competing vegetation and seedlings). 

Although not utilized yet, the information on seedling 

height and browse pressure should allow for finer 

model refinement. The height class breakdown has 

particular value for refining seedling recruitment 

models. The information could also be used to 

improve the seedling component of FIA’s stocking 

and forest type algorithm that uses seedlings to predict 

future stocking. The basic challenge for modelers is 

to continue to seek methods for finer scale models to 

integrate with FIA variables.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 300

Figure 2.—SORTIE submodels and associated FIA vegetation measurements, 2012 (continued on next page). 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 301

Figure 2 (continued).—SORTIE submodels and associated FIA vegetation measurements, 2012. 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 302

LITERATURE CITED

Beaudet, M.; Messier, C.; Canham, C.D. 2002. 

Predictions of understory light conditions 

in northern hardwood forests following 

parameterization, sensitivity analysis, and tests 

of the SORTIE light model. Forest Ecology and 

Management. 165(1-3): 231-244.

Canham, C.D.; Finzi, A.C.; Pacala, S.W.; Burbank, 

D.H. 1994. Causes and consequences of resource 

heterogeneity in forests: interspecific variation 

in light transmission by canopy trees. Canadian 

Journal of Forest Research. 24: 337-349.

Canham, C.D; Papaik, M.J.; Uriarte, M.; 

McWilliams, W.H.; Jenkins, J.C.; Twery, M.J. 

2006. Neighborhood analysis of canopy tree 

competition along environmental gradients in 

New England forests. Ecological Applications. 

16(2): 540-554.

Crookston, N.L.; Dixon, G.E. 2005. The Forest 

Vegetation Simulator: a review of its structure, 

content, and applications. Computers and 

Electronics in Agriculture. 49(1): 60-80.

Dixon, G.E., comp. 2002. Essential FVS: a user’s 

guide to the forest vegetation simulator. Internal 

Rep. Fort Collins, CO: U.S. Department of 

Agriculture, Forest Service, Forest Management 

Service Center. 248 p. (Revised: June 27, 2012).

Kobe, R.K. 1996. Intraspecific variation in sapling 

mortality and growth predicts geographic 

variation in forest composition. Ecological 

Monographs. 66: 181-201.

Pacala, S.W.; Canham, C.D.; Silander, J.A., Jr.; 

Kobe, R.K. 1994. Sapling growth as a function of 

resources in a north temperate forest. Canadian 

Journal of Forest Research. 24: 2172-2183.

Pacala, S.W.; Canham, C.D.; Saponara, J.; Silander, 

J.A., Jr.; Kobe, R.K.; Ribbens, E. 1996. Forest 

models defined by field measurements: 

estimation, error analysis and dynamics.

Ecological Monographs. 66: 1-43.

Ribbens, E.; Silander, J.A.; Pacala, S.W. 1994. 

Seedling recruitment in forests: calibrating 

models to predict patterns of tree seedling 

dispersion. Ecology. 75: 1794-1806.

USDA Forest Service. 2012. Forest inventory and 

analysis national core field guide, Volume 1 

Supplement: field data collection procedures for 

Phase 2+ plots, version 5.0. Available at http://

www.nrs.fs.fed.us/fia/data-collection/. (Accessed 

June 7, 2012).

Wear, D.N.; Griesbusby, J.D. 2002. Southern forest 

resources assessment. Gen. Tech. Rep. SRS-53. 

Asheville, NC: U.S. Department of Agriculture, 

Forest Service, Southern Research Station. 635 p.

Weathers, K.C.; Simkin, S.M.; Lovett, G.M.; Lindberg, 

S.E. 2006. Empirical modeling of atmospheric 

deposition in mountainous landscapes.

Ecological Applications. 16: 1590-1607.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 303

Transitioning from Phase 3 Vegetation Data to Phase 2+ 

Vegetation Data in the Northern Research Station

W. Keith Moser, Katherine Johnson, Cassie M. Kurtz, Cassandra Olson, and William H. McWilliams1

Abstract.—Phase 3 (P3) vegetation diversity and structure was measured in the Northern 

Research Station (NRS), U.S. Forest Service, intermittently between 2000 and 2010. 

Varying in intensity and location, these data provide a glimpse of native and invasive 

plants across portions of 24 states. Between 2007 and 2010, all NRS states were sampled 

largely on the same schedule and intensity. In 2011, an NRS-Forest Inventory and 

Analysis team evaluated a new sampling protocol (NRS Phase 2+ [P2+]), which includes 

vegetation composition and structure variables from the Phase 2 vegetation profile 

and the invasive plants protocols, combined with a regeneration sampling scheme first 

developed for the Pennsylvania “Regeneration Study.” To compare the P3 and P2+ sets 

of variables, our presentation will apply NRS P2+ vegetation profile, invasive plants, and 

advance tree seedling regeneration variables to the previously collected P3 vegetation 

diversity and structure data and we will discuss the implications for customers of 

these data.

1

 Research Forester (WKM), U.S. Forest Service, Northern Research Station, 1992 Folwell 

Avenue, St. Paul, Minnesota 55108; Biological Scientist (KJ), Natural Resource Specialist (CMK), 

Ecologist (CO), and Research Forester (WHM), U.S. Forest Service, Northern Research Station. 

WKM is corresponding author; to contact, call 651-649-5155 or email at wkmoser@fs.fed.us.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Integrating NFI data 

with remote sensing 

and GIS applications
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 305

DEVELOPMENT AND APPLICATIONS 

OF THE LANDFIRE FOREST STRUCTURE LAYERS

Chris Toney, Birgit Peterson, Don Long, Russ Parsons, and Greg Cohn1

Abstract.—The LANDFIRE program is developing 2010 maps of vegetation and 

wildland fuel attributes for the United States at 30-meter resolution. Currently available 

vegetation layers include ca. 2001 and 2008 forest canopy cover and canopy height 

derived from Landsat and Forest Inventory and Analysis (FIA) plot measurements. 

The LANDFIRE canopy cover layer for the conterminous United States is the first of 

its kind developed with FIA tree measurements as the source of ground reference data. 

The mapping process is based on regression tree models with FIA plot values as the 

dependent variables (canopy cover, canopy height), using predictor variables derived 

from reflectance, terrain, and a Shuttle Radar Topography Mission-based height metric. 

Base vegetation maps are updated beginning with a set of annual disturbance maps that 

combine extant fire severity mapping with Landsat time-series disturbance and polygon 

data describing management activities on Federal and state lands. FIA plot data are used 

in the Forest Vegetation Simulator to model disturbance effects on forest vegetation 

for each disturbance type and severity class. Updated 2010 products are scheduled for 

delivery during early 2013. The 2010 products include a new tree list layer in which stand 

structure is depicted explicitly by approximating a representative list of trees occurring at 

each 30-m pixel. The tree list layer provides a more detailed characterization of canopy 

fuel structure than is available with standard LANDFIRE products, facilitating new 

applications and research in fire behavior and fire effects simulation.

1

 Biologist (CT), U.S. Forest Service, Rocky Mountain 

Research Station, 5775 U.S. West Highway 10, Missoula, 

MT 59808; Scientist (BP), ASRC Research and Technology 

Solutions; Ecologist (DL), Research Ecologist (RP), and 

Forestry Technician (GC), U.S. Forest Service, Rocky 

Mountain Research Station. CT is corresponding author: to 

contact, call 406-829-6980 or email at christoney@fs.fed.us.

INTRODUCTION

The LANDFIRE program, now in its second update 

phase, produces more than 20 different 30-meter 

raster layers of vegetation structure and composition, 

fire behavior, and fire regimes for all of the major 

ecosystems in the United States (more information is 

available at www.landfire.gov). The layers describe 

vegetation and wildland fuel across all lands, and are 

designed to facilitate national and regional planning 

and reporting of fire management activities. This 

program has collaborated closely with the Forest 

Inventory and Analysis (FIA) Program since 2004 and 

FIA data are a critical component of several vegetation 

and fuel layers developed by LANDFIRE.

This paper focuses on a subset of LANDFIRE layers 

depicting forest structure that were based heavily on 

FIA field measurements. Forest canopy cover is the 

proportion of the forest floor covered by a vertical 

projection of the tree crowns. Canopy height is the 

representative height above ground level of the taller 

trees composing the upper canopy layer. Canopy cover 

and height are key variables required for modeling fire 

behavior spatially (Finney 2004).

The LANDFIRE canopy cover layer for the 

conterminous United States is the first of its kind 

developed with FIA tree measurements as the 

source of ground reference data, and is paired with 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 306

an integrated canopy height layer. Here we give an 

overview of the mapping process along with current 

applications in wildland fire management and 

research. We also describe a new LANDFIRE layer 

under development in which stand structure is depicted 

explicitly by approximating a representative list of 

trees occurring at each 30-m pixel.

FIELD REFERENCE DATA

Tree canopy cover estimates for FIA plots were 

based on predicted crown dimensions, using stem 

coordinates recorded by field crews to map the 

locations of trees within the subplots and microplots 

(Fig. 1) (Toney et al. 2009). Canopy height was the 

basal area-weighted average height of the dominant, 

codominant, and open-grown trees in each plot. 

Plot records were filtered based on FIA disturbance 

attributes and location-specific Landsat image dates 

to obtain canopy cover/canopy height training sites. 

Some plots were omitted from the training set if they 

had significant disturbances (such as cutting, fire, or 

wind) recorded after the most recent location-specific 

image date in the multi-temporal Landsat mosaics 

described below.

Figure 1.—Example of mapping the modeled crowns of trees in an FIA plot using stem coordinates recorded by field crews.

PRODUCTION MAPPING PROCESS

Canopy Cover and Canopy Height

Forest canopy cover and height were produced using 

several dates of Landsat imagery and derived layers as 

predictor variables. Landsat images from leaf-off, leafon, and spring (U.S. Geological Survey 2001) were 

used, along with texture images derived from tasseledcap images of the three dates. Elevation, slope, and 

aspect were also used (U.S. Geological Survey 2005). 

A Shuttle Radar Topography Mission-derived metric 

(Kellndorfer et al. 2004) was included in the canopy 

height modeling.

Regression tree models were fit using Cubist software 

(RuleQuest [n.d.]). Model predictions were applied to 

all 30-m pixels within the LANDFIRE forest mask. 

The output layers were visually checked for seam 

lines and presence of clouds and other artifacts from 

the imagery. Problem source data were eliminated or 

localized revisions were made.

A design requirement for the canopy cover layer 

was to map a statistical distribution of cover values 

consistent with the distribution expected for spatial fire 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 307

modeling. Canopy cover rarely exceeds 70 percent in 

western U.S. forest types (Scott and Reinhardt 2005, 

Stratton 2006) but is higher in multi-storied forests of 

the eastern United States. The distribution of stemmapped FIA canopy cover was consistent with the 

distribution as evaluated in fire behavior models.

Updating from the 2001 base vegetation maps 

(Vogelmann et al. 2011) begins with a set of 

annual disturbance maps that combine fire severity 

mapping with Landsat time-series disturbance 

(Huang et al. 2010) and polygon data describing 

management activities on Federal and state lands. 

Products available for fire severity mapping include 

Monitoring Trends in Burn Severity (MTBS) (U.S. 

Forest Service and U.S. Geological Survey 2012), 

Rapid Assessment of Vegetation Condition after 

Wildfire (U.S. Forest Service 2011), and Burned Area 

Reflectance Classification (U.S. Forest Service and 

U.S. Geological Survey [n.d.]). Disturbance effects 

on forest vegetation are modeled for each disturbance 

type and severity class using FIA plot data in the 

Forest Vegetation Simulator. 

Tree List Layer

The tree list layer is composed of a thematic raster 

map in which each 30-meter pixel is assigned a tree 

list identifier, along with a lookup table of tree data 

for each identifier. The tree list information includes 

species, size, status, and density from the FIA plot 

records. Production of the tree list layer involves 

post-processing the existing classified Landsat 

(LANDFIRE vegetation type, canopy cover, canopy 

height) by nearest neighbor imputation within specific 

landscape strata. The vegetation-based stratification 

defines subsets of plots eligible for imputation to 

pixels within each stratum on the map. At each pixel, 

a nearest neighbor search of the eligible plots is 

done in a normalized Euclidean space of variables 

describing geography (x, y coordinates), terrain 

(elevation, slope, transformed aspect), and vegetation 

(continuous canopy cover, canopy height). The tree list 

approach provides much greater detail characterizing 

forest structure attributes (cf. Wilson et al. 2012). For 

example, rather than a single canopy height value, a 

distribution of tree heights is presented, facilitating use 

of these layers as inputs to specialized models or more 

in-depth analysis. 

PRODUCT AVAILABILITY

Canopy cover (Fig. 2) and canopy height products 

from LANDFIRE for 2001 and 2008 are currently 

available for download at www.landfire.gov. Canopy 

cover and height for 2010 are scheduled for delivery 

during early 2013. The tree list product for 2010 is also 

scheduled for early 2013, but availability is by request 

with an FIA confidentiality agreement.

APPLICATIONS

The LANDFIRE vegetation structure layers have been 

widely used since 2005 in fire modeling for incident 

support as well as planning. The layers are used for 

simulations in the Wildland Fire Decision Support 

System (http://wfdss.usgs.gov) and the Fire Program 

Analysis System (http://www.fpa.nifc.gov). Nonfire 

land management applications are also a major use 

(e.g., http://www.conservationgateway.org/topic/

applications).

The LANDFIRE tree list layer represents a new 

approach for fuel mapping, providing detail similar 

to what might be achieved with Light Detection and 

Ranging mapping, but with the benefit of wall-towall coverage consistent with the other LANDFIRE 

layers. This approach has significant potential for 

new applications and analysis. For example, the tree 

list layer can be used to provide realistic inputs to 

new, physics-based 3D fire behavior models (e.g., 

FIRETEC), in which canopy fuels can be represented 

as discrete entities such as individual trees (Linn et al. 

2005) (Fig. 3).

ACKNOWLEDGMENTS

Gretchen Moisen and Mark Nelson provided 

comments that improved the manuscript.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 308

Figure 2.—LANDFIRE 2008 tree canopy cover mapped from FIA plot data and Landsat.

Figure 3.—Application of the LANDFIRE tree list layer as fuels input to the dynamic 3D fire model, FIRETEC. At each forested 

pixel, tree data from the tree list layer look-up table were expanded to the pixel scale and draped over the LANDFIRE elevation 

layer. The simulation area is 1 km2

, centered on Mann Gulch in the Helena National Forest, about 25 miles north-northeast of 

Helena, MT.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 309

LITERATURE CITED

Finney, M.A. 2004. FARSITE: Fire Area Simulator 

- model development and evaluation. Res. 

Pap. RMRS-RP-4. Ogden, UT: U.S. Dept. of 

Agriculture, Forest Service, Rocky Mountain 

Research Station. 47 p.

Huang, C.; Goward, S.N.; Masek, J.G.; Thomas, N.; 

Zhu, Z.; Vogelmann, J.E. 2010. An automated 

approach for reconstructing recent forest 

disturbance history using dense Landsat time 

series stacks. Remote Sensing of Environment. 

114: 183-198.

Kellndorfer, J.; Walker, W.; Pierce, L.; Dobson, C.; 

Fites, J.A.; Hunsaker, C.; Vona, J.; Clutter, M. 2004. 

Vegetation height estimation from Shuttle Radar 

Topography Mission and National Elevation 

datasets. Remote Sensing of Environment. 93: 

339-358.

Linn, R.; Winterkamp, J.; Colman, J.J.; Edminster, C.; 

Bailey, J.D. 2005. Modeling interactions between 

fire and atmosphere in discrete element fuel 

beds. International Journal of Wildland Fire. 14: 

37-48.

RuleQuest Research. 2012. Data mining tools.

Available at http://rulequest.com. [Date accessed 

unknown].

Scott, J.H.; Reinhardt, E.D. 2005. Stereo photo guide 

for estimating canopy fuel 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.

Stratton, R.D. 2006. Guidance on spatial wildland 

fire analysis: models, tools, and techniques. Gen. 

Tech. Rep. RMRS-GTR-183. Fort Collins, CO: 

U.S. Department of Agriculture, Forest Service, 

Rocky Mountain Research Station. 15 p.

Toney, C.; Shaw, J.D.; Nelson, M.D. 2009. A stemmap model for predicting tree canopy cover 

of Forest Inventory and Analysis (FIA) plots.

In: McWilliams, W.; Moisen, G.; Czaplewski, R., 

comps. Proceedings, 2008 Forest Inventory and 

Analysis (FIA) symposium; 2008 October 21-23; 

Park City, UT. Conf. Proc. RMRS-P-56CD. Fort 

Collins, CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research Station. 

19 p.

U.S. Forest Service. 2011. Post-fire vegetation 

conditions: What is RAVG? Available at http://

www.fs.fed.us/postfirevegcondition/whatis.shtml. 

[Date accessed unknown].

U.S. Forest Service and U.S. Geological Survey. 

[N.d.]. Remote sensing application center: What 

is BARC? Available at http://www.fs.fed.us/eng/

rsac/baer/barc.html. [Date accessed unknown].

U.S. Forest Service and U.S. Geological Survey. 2012. 

Monitoring trends in burn severity. Available at 

www.mtbs.gov. [Date accessed unknown].

U.S. Geological Survey. 2001. Multi-Resolution 

Land Characteristics Consortium. Available at 

www.mrlc.gov. [Date accessed unknown].

U.S. Geological Survey. 2005. Elevation Derivatives 

for National Application. Available at http://edna.

usgs.gov. [Date accessed unknown].

Vogelmann, J.E.; Kost, J.R.; Tolk, B.; Howard, S.; 

Short, K.; Chen, X.; Huang, C.; Pabst, K.; Rollins, 

M.G. 2011. Monitoring landscape change for 

LANDFIRE using multi-temporal satellite 

imagery and ancillary data. IEEE Journal of 

Selected Topics in Applied Earth Observations and 

Remote Sensing. 4: 252-264.

Wilson, B.T.; Lister, A.J.; Riemann, R.I. 2012. A 

nearest-neighbor imputation approach to 

mapping tree species over large areas using 

forest inventory plots and moderate resolution 

raster data. Forest Ecology and Management. 

271: 182-198.

The content of this paper reflects the views of the 

author(s), who are responsible for the facts and accuracy 

of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 310

CHARACTERIZING ENVIRONMENTAL CHANGE IN INTERIOR ALASKA 

(1982-2012) USING MULTI-TEMPORAL, MULTI-SCALE

REMOTE SENSING DATA AND FIELD MEASUREMENTS 

Hans-Erik Andersen and Robert Pattison1

Abstract.—We investigate how vegetation in the Tanana Valley of interior Alaska 

(120,000 km2) has responded to a changing climate over the preceding three decades 

(1982-2012). Expected impacts include: 1) drying of wetlands and subsequent 

encroachment of woody vegetation into areas previously dominated by herbaceous and 

bryoid vegetation types, 2) changes in forest composition resulting from succession 

processes within burned areas, 3) mortality and defoliation from increased insect activity 

attributable to a warming climate, 4) effects on tree growth attributable to drought stress 

and/or reduced photosynthetic capacity, and 5) expansion of woody vegetation at the 

tree line. To characterize and quantify these changes occurring over the full range of 

environmental conditions present in this vast region, we plan to use a unique resource of 

multi-temporal and multi-scale remote sensing data to analyze changes observed between 

1) field data and large-scale photographs collected in 1982 over a selection of inventory 

plots within the western Tanana Valley region, and 2) low-altitude airborne digital 

imagery collected over these same inventory plots in 2012. Detailed stand- and plantlevel changes observed over 40-ha remeasured photo plots will be scaled up and used to 

inform an analysis of changes in vegetation condition observed in spectral trajectories 

obtained from a time series of Landsat Thematic Mapper/Enhanced Thematic Mapper 

Plus imagery over this 30-year period. 

1

 Research Forester (HEA), U.S. Forest Service, Pacific 

Northwest Research Station, University of Washington, 

Seattle, WA 98117; Research Ecologist (RP), U.S. Forest 

Service, Pacific Northwest Research Station. HEA is 

corresponding author: to contact, call 206-221-9034 or 

email at handersen@fs.fed.us. 

INTRODUCTION

The impacts of climate change on the condition and 

productivity of vegetation are likely to be particularly 

significant in the boreal forest regions of the world, 

such as interior Alaska, where observed rates of 

temperature change over the last three decades are 

significantly higher than in other terrestrial biomes. 

High northern latitudes have experienced the strongest 

warming trend during the era of satellite observations 

(1972-present), leading to a variety of climate-driven 

changes in vegetation growth and mortality (e.g., 

Wolken et al. 2011). These impacts are highly varied 

across the region’s wide range of environmental 

(e.g., hydrological, elevation) gradients. The 

changes occurring in interior Alaska have important 

implications for local community subsistence through 

impacts on wildlife (Kofinas et al. 2010) and biomass 

for energy (Fresco and Chapin 2009). Due to the vast 

spatial extent and lack of infrastructure in the region, 

there has been very little systematic repeated sampling 

as typified by the permanent plots, such as those of 

the Forest Inventory and Analysis Program, that can 

be found in other regions. As a result, insights about 

changes in interior Alaska have come from localized 

field studies and broad-scale remote sensing efforts, 

often carried out independently (Hollingsworth et al. 

2010, Parent and Verbyla 2010).
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 311

OBJECTIVES

In this study, we are investigating how vegetation 

in interior Alaska has changed over three decades 

(1982-2012) and link those changes to shifts in 

climate regime during that time. We will develop a 

hierarchical approach to evaluating vegetation change 

using a unique resource of multi-temporal and multiscale remote sensing data along with remeasured 

field plots over a 17,000-km2 area in the vicinity of 

Fairbanks, AK (Fig. 1). We expect changes in climate 

to be manifested in a variety of ways that will be 

visible across multiple scales of observation. Expected 

ecological impacts include: 1) drying of wetlands and 

subsequent encroachment of woody vegetation into 

areas previously dominated by herbaceous and bryoid 

vegetation types, 2) increase in deciduous vegetation 

postfire due to increased fire severity, 3) tree mortality 

and defoliation due to increased insect activity 

attributable to a warming climate, 4) impacts on tree 

growth attributable to drought stress and/or reduced 

photosynthetic capacity, and 5) expansion of woody 

vegetation at tree line. A unique feature of this study 

will be that we will capitalize on the permanent plot 

remeasurements to provide more robust insights into 

changes. 

APPROACH

Field data and large-scale photographs collected for 

a previous inventory of the Tanana Valley in interior 

Alaska carried out in the early 1980s will be compared 

with field data and low-altitude airborne digital 

imagery collected over plots in 2010 in a previous 

study and in 2012 (Fig. 1). Detailed stand- and 

plant-level changes observed at field plots and 40-ha 

remeasured photo plots will be scaled up and used to 

inform an analysis of changes in vegetation condition 

observed in spectral trajectories obtained from a 

time series of Landsat Thematic Mapper/Enhanced 

Thematic Mapper Plus (TM/ETM+) imagery over this 

30-year period. 

Remeasurement of 

Adaptive Infrared Imaging 

Spectroradiometer Field Data

We plan to revisit a subset of seven Adaptive Infrared 

Imaging Spectroradiometer (AIRIS) field plots in the 

vicinity of Fairbanks to confirm and clarify patterns 

seen at broader scales (repeat air photography and 

satellite) and to assess feasibility of remeasuring field 

plots over a broader geographic range in the future 

(Fig. 1). Revisited plots will be prioritized by those 

that: 1) are in areas that are near roads (to reduce 

sampling costs), 2) have undergone the greatest change 

(increase or decrease) in Normalized Difference 

Vegetation Index (NDVI) across the study area, and 

3) have not been burned (to simplify analyses for this 

study). Each AIRIS field plot consisted of 19 sample 

points distributed uniformly throughout the 8-ha photo 

plot area. Field data from the 1982-1983 inventory are 

readily available (MS Access database), as are scanned 

copies of the field manual with detailed sampling 

notes, and photos taken on the plot. Monuments were 

placed at all plots to make them easier to find. 

Remeasurement of 

AIRIS Photo Plots

We will refly 30 AIRIS photo plots in the vicinity 

of Fairbanks with low-altitude (large-scale) digital 

stereo imagery, to characterize stand- and plantlevel processes of change, including shrub and tree 

establishment, mortality, species replacement or 

succession, and changes in surface hydrology, for 

assessment of impacts on carbon flux and habitat. 

Forest-type polygons interpreted in the 1982 and 

2012 photos will provide a large footprint (40 ha) to 

compare with changes observed in a time series of 

satellite imagery. Several of the AIRIS photo plots 

within the proposed study area have already been 

reflown for a previous project. 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 312

Figure 1.—Location of AIRIS photo plots (black dots) and AIRIS field plots (green) within Tanana Valley, interior Alaska. 

Black rectangle in upper left area of main map shows proposed 17,000-km2 study area for this project; red dots indicate the 

accessible AIRIS field plots to be visited. Inset graphics show Landsat TM imagery and low-altitude aerial photos from the 

mid-1980s and late-2000s. Photo-interpreted forest type polygons within the 1982 8-ha photo plot are also shown. Note 

terrestrialization and shrinkage of pond area, as well as burned areas evident in Landsat TM imagery.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 313

Time-Series Analysis of 

Changes in Satellite Imagery

All field-visited (~7) and reflown photo plots (~30) 

will be also be examined with a 30-year time- series 

analysis of changes in key spectral indices (e.g., 

NDVI) from Landsat TM/ETM+ using TimeSync 

(Cohen et al. 2010). This analysis will allow us to 

compare how these field and photo plots fit into other 

regionwide satellite-based studies of changes in key 

indices (Parent and Verbyla 2010) and will provide 

a foundation for regionwide (i.e., “wall-to-wall”) 

assessment of changes to the entire Tanana Valley 

by using the 30-year Landsat TM/ETM+ record with 

other approaches such as Landsat-based Detection of 

Trends in Disturbance and Recovery (LandTrendr) 

(Kennedy et al. 2010) in the future. 

LITERATURE CITED

Cohen, W.B.; Yang, Z.; Kennedy, R.E. 2010. 

Detecting trends in forest disturbance and 

recovery using yearly Landsat time series: 2. 

TimeSync - Tools for calibration and validation.

Remote Sensing of Environment. 114: 2911-2924.

Fresco, N.; Chapin, F., III. 2009. Assessing the 

potential for conversion to biomass fuels in 

interior Alaska. Res. Pap. PNW-RP-579. Portland, 

OR: U.S. Department of Agriculture, Forest 

Service, Pacific Northwest Research Station. 56 p. 

Hollingsworth, T.; Lloyd, A.; Nossov, D.; Ruess, 

R.; Charlton, B.; Kielland, K. 2010. Twenty-five 

years of vegetation change along a putative 

successional chronosequence on the Tanana 

River, Alaska. Canadian Journal of Forest 

Research. 40(7): 1302-1312. 

Kennedy, R.E.; Yang, Z.; Cohen, W.B. 2010. 

Detecting trends in forest disturbance and 

recovery using yearly Landsat time series: 

1. LandTrendr - Temporal segmentation 

algorithms. Remote Sensing of Environment. 

114: 2897-2910. 

Kofinas, G.; Chapin, F., III; BurnSilver, S.; Schmidt, 

J.; Fresco, N.; Kielland, K.; Martin, S.; Springsteen, 

S.; Rupp, T.S. 2010. Resilience of Athabascan 

subsistence systems to interior Alaska’s changing 

climate. Canadian Journal of Forest Research. 

40: 1347-1359.

Parent, M.; Verbyla, D. 2010. The browning of 

Alaska’s boreal forest. Remote Sensing. 

2: 2729-2747. 

Wolken, J.M.; Hollingsworth, T.N.; Rupp, T.S.; 

Chapin, F.S., III; Trainor, S.F.; Barrett, T.M.; 

Sullivan, P.F.; McGuire, A.D.; Euskirchen, E.S.; 

Hennon, P.E.; Beever, E.A.; Conn, J.S.; Crone, 

L.K.; D’Amore, D.V.; Fresco, N.; Hanley, T.A.; 

Kielland, K.; Kruse, J.J.; Patterson, T.; Schuur, 

E.A.; Verbyla, D.; Yarie, J. 2011. Evidence and 

implications of recent and projected climate change 

in Alaska’s forest ecosystems. Ecosphere. 2(11): 

1-35.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 314

A MODEL FOR QUANTIFYING FOREST VARIABLES 

OVER LANDSCAPES WITH UNCERTAIN FOREST AREAS

Andrew O. Finley, Sudipto Banerjee, and David W. MacFarlane1

Abstract.—We are interested in predicting one or more continuous forest variables 

(e.g., biomass, volume, age) at a fine resolution (e.g., pixel level) across a specified 

domain. Given a definition of forest/nonforest, this prediction is typically a two-step 

process. The first step predicts which locations are forested. The second step predicts 

the value of the variable for only those forested locations. Rarely is the forest/nonforest 

status predicted without error. However, the uncertainty in this prediction is typically not 

propagated through to the subsequent prediction of the forest variable of interest. Failure 

to acknowledge this error can result in biased estimates of forest variable totals within 

a domain. In response to this problem, we offer a modeling framework that will allow 

propagation of this uncertainty. Here we envision two latent processes generating the 

data. The first is a continuous spatial process while the second is a binary spatial process. 

The continuous spatial process controls the spatial association structure of the forest 

variable of interest, while the binary process indicates presence of a possible nonzero 

value for the forest variable at a given location. The proposed models are applied to 

geo-referenced National Forest Inventory (NFI) data and spatially coinciding remotely 

sensed predictor variables. Due to the large number of observed locations in this dataset, 

we seek dimension reduction not just in the likelihood, but also for unobserved stochastic 

processes. We demonstrate how a low-rank predictive process can be adapted to our 

setting and reduce the dimensionality of the data and ease the computational burden.

1

 Assistant Professor (AOF), Michigan State University, Departments of Forestry and Geography, 

East Lansing, MI 48824; Professor (SB), University of Minnesota; Associate Professor (DWM), 

Michigan State University. AOF is corresponding author: to contact, call 517-432-7219 or email 

at finleya@msu.edu.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Climate Change 

and Forest Health: 

integrating FIA, climate data, 

and spatial technology 

for long-term monitoring
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 316

RELATIONSHIP BETWEEN CROWN DIEBACK AND DROUGHT 

IN THE SOUTHEASTERN UNITED STATES

Michael K. Crosby, Zhaofei Fan, Martin A. Spetich, Theodor D. Leininger, and Xingang Fan1

Abstract.—Forest Health and Monitoring (FHM) and Palmer’s Drought Severity Index 

(PDSI) data were obtained for 11 states in the southeastern United States to assess the 

relationship between drought and crown dieback. Correlation analyses were performed 

at the species group and ecoregion levels within the study area. The results indicate a 

negative correlation between drought and crown dieback within 3 to 5 years of the onset 

of drought conditions.

1

 Post-doctoral Research Associate (MKC) and Assistant 

Professor (ZF), Forest and Wildlife Research Center, 

Mississippi State University, Box 9681 Mississippi State, 

MS 39762-9681; Research Forest Ecologist (MAS) and 

Project Leader (TDL), U.S. Forest Service, Southern 

Research Station; Assistant Professor (XF), Department 

of Geography and Geology, Western Kentucky University. 

MKC is corresponding author: to contact, call 662-325-1527 

or email at mkc34@msstate.edu.

INTRODUCTION

Crown dieback, a crown health indicator collected 

during Phase 3 sampling by the Forest Health and 

Monitoring (FHM) Program of the U.S. Forest 

Service, provides an early indication of tree stress 

by estimating the amount of death that has occurred 

in a tree crown (Schomaker et al. 2007). We seek to 

use crown dieback as a first indication of the impacts 

of drought on forests in the southeastern United 

States. Regional differences in crown dieback such as 

elevated crown dieback in maple species (Acer spp.) 

throughout the northeastern United States and red 

oak species (Quercus, section Lobatae) in the Ozark 

Highlands (Fan et al. 2008, Randolph 2009) indicate 

that trees may react differently to sources of stress. 

Typically, hardwood species will shed leaves to avoid 

drought stress while pine species typically have less 

leaf area from the outset (Barnes et al. 1998). In the 

southeast, Randolph (2009) found that oak species had 

higher levels of crown dieback than pines. Further, 

denser oak stands exhibit a higher susceptibility to 

drought in some areas of Alabama, Georgia, and 

Virginia that could lead to an increase in mortality 

(Klos et al. 2009) or increased susceptibility to damage 

from fires or insects.

Monitoring the susceptibility to damage over a large 

scale is an important first step in understanding the 

interaction between climate and forests. To this end, 

we wanted to determine the relationship between 

crown dieback and drought within various species 

groups by forest type and ecoregion. Our hypothesis 

was that relationships will vary among the selected 

groups and give an overall impression of the impact of 

drought on southeastern U.S. forests.

METHODS

Crown dieback, plot location (i.e., latitude and 

longitude), and inventory year were extracted from 

FHM inventory data obtained from the U.S. Forest 

Service for all trees with a diameter at breast height 

(d.b.h.) greater than 5 inches for available states in 

the southeastern United States (Alabama, Arkansas, 

Florida, Georgia, Kentucky, Louisiana, North Carolina, 

South Carolina, Texas, Tennessee and Virginia). 

PDSI data (Palmer 1965), a commonly used index for 

assessing drought, were obtained from the National 

Climatic Data Center of the National Oceanic and 

Atmospheric Administration to determine the severity 

and duration of drought across the southeastern United 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 317

States. FHM and PDSI data were then displayed in a 

geographic information system (GIS) and joined so 

that PDSI values were extracted to the FHM plot. This 

allowed for a completed dataset of crown dieback and 

PDSI for each tree record in the dataset. The data were 

then extracted by species group within each forest type 

and then by ecoregion. It was then possible to assess 

the relationship between crown dieback and PDSI 

across forest types and ecoregions for each species 

group. 

RESULTS

The pine species group did not yield any significant 

relationships with PDSI, forest type, or ecoregion. 

The red oak, white oak (Quercus section Quercus), 

and other hardwood species groups all exhibited 

significant relationships with forest type, with pine, 

oak/pine, and oak/hickory having elevated levels 

of crown dieback. PDSI and ecoregion were also 

significant explanatory variables for red oak and white 

oak species groups with the highest levels of crown 

dieback occurring in areas impacted by drought (Table 

1). These preliminary findings can be used to further 

refine models to monitor crown health with indicators 

of drought.

ACKNOWLEDGMENTS

This research was supported by the U.S. Forest 

Service Forest Health Monitoring Program (# 

SO-EM-F-10-01) and NASA via subcontract 

(WKU5162201000110070670) from Western 

Kentucky University. The authors also wish to thank 

Mr. Paul Jeffreys and Miss Zhen (Carol) Sui for their 

constructive reviews of this manuscript.

LITERATURE CITED

Barnes, B.V.; Zak, D.R.; Denton, S.R.; Spurr, S.H. 

1998. Forest Ecology. New York, NY: John Wiley 

& Sons. 774 p.

Fan, Z.; Kabrick, J.M.; Spetich, M.A.; Shifley, S.R.; 

Jensen, R.G. 2008. Oak mortality associated 

with crown dieback and oak borer attack 

in the Ozark Highlands. Forest Ecology and 

Management. 255(7): 2297-2305.

Klos, R.J.; Wang, G.G.; Bauerle, W.L.; Rieck, J.R. 

2009. Drought impact on forest growth and 

mortality in the southeast USA: an analysis 

using Forest Health and Monitoring data.

Ecological Applications. 19(3): 699-708.

Table 1.—The most significant variables for assessing crown dieback in the southeastern United States 

by species group

Species Group Significant Variables Crown Dieback (%)

Red Oaks Pine, Oak/Pine, and Oak/Hickory forest types 19.7

Eastern Broadleaf Forest (Oceanic), Outer Coastal Plain Mixed Forest, 

Lower MS Riverine Forest, Ouachita Mixed Forest/Meadow Ecoregions

26.7

3-Year Cumulative PDSI ≥1.28 36.8

White Oaks Prairie Parkland Ecoregion 33.2

Pine, Oak/Hickory, and Oak/Gum/Cypress forest types 39.6

7-Year Cumulative PDSI < -1.802 64.8

Other Hardwoods Pine, Oak/Pine forest types 42.6
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 318

Palmer, W.C. 1965. Meteorological drought.

Research Paper No. 45. Washington, DC: U.S. 

Department of Commerce, Weather Bureau. 58 p.

Randolph, K.D. 2009. Crown condition. In: 

Ambrose, M.J.; Conkling, B.L., eds. 2009. 

Forest health monitoring: 2006 national technical 

report. Gen. Tech. Rep. SRS-117. Asheville, NC: 

U.S. Department of Agriculture, Forest Service, 

Southern Research Station. 118 p.

Schomaker, M.E.; Zarnoch, S.J.; Bechtold, W.A.; 

Latelle, D.J.; Burkman, W.G.; Cox, S.M. 2007. 

Crown-condition classification: a guide to data 

collection and analysis. Gen. Tech. Rep. SRS-102. 

Asheville, NC: U.S. Department of Agriculture, 

Forest Service, Southern Research Station. 78 p.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 319

ASSESSING FOREST MORTALITY PATTERNS USING CLIMATE

AND FIA DATA AT MULTIPLE SCALES

Michael K. Crosby, Zhaofei Fan, Xingang Fan, Theodor D. Leininger, and Martin A. Spetich1

Abstract.—Forest Inventory and Analysis (FIA) and PRISM climate data from 1991-

2000 were obtained for 10 states in the southeastern United States. Mortality was 

calculated for each plot, and annual values for precipitation and maximum and minimum 

temperature were extracted from the PRISM data. Data were then stratified by upland/

bottomland for red oak species, and classification and regression tree (CART) analysis 

was used to determine the influence of climate variables on mortality at ecoregion 

province and section levels. The results presented here will provide a basis for future 

research on the causal factors related to red oak mortality.

1

 Post-doctoral Research Associate (MKC) and Assistant 

Professor (ZF), Mississippi State University, Forest and 

Wildlife Research Center, Box 9681, Mississippi State, 

MS 39762-9681; Assistant Professor (XF), Department of 

Geography and Geology, Western Kentucky University; 

Project Leader (TDL) and Research Forest Ecologist 

(MAS), U.S. Forest Service, Southern Research Station. 

MKC is corresponding author: to contact, call 662-325-1527 

or email at mkc34@msstate.edu.

INTRODUCTION

Increased mortality in red oak species (Quercus, 

section Lobatae) has been attributed to a variety of 

factors ranging from biotic (e.g., oak borers [Fan et 

al. 2008]) to abiotic (e.g., drought [Law and Gott 

1987]). Such widespread loss of these species could 

lead to compositional changes in forests across the 

southeastern United States. While previous studies 

have sought to relate climatic factors to increased 

levels of mortality, few have considered the changes 

in mortality patterns at finer scales of analysis such 

as the ecoregion province level and section level. 

Analyzing the relationship between climatic factors 

and mortality at increasingly finer scales could allow 

for new insights into changing mortality trends for 

red oak species across the region. Thus, we assessed 

changes in mortality for upland and bottomland red 

oak species by: 1) determining the trends in mortality; 

and 2) using classification and regression tree (CART) 

to determine the relationships between mortality and 

climate variables at ecoregion provinces and sections 

for both upland and bottomland red oak species. The 

results of these analyses will help guide future research 

endeavors across the region as we seek to understand 

causal factors related to mortality for all species in the 

southeastern United States.

METHODS

Data including latitude, longitude, species codes, and 

basal areas of live and dead trees (red oaks) were 

extracted from the Forest Inventory and Analysis 

(FIA) database for 10 states in the southeastern 

United States for which inventory data were available 

between 1991-2000 (Oklahoma, Arkansas, Kentucky, 

Tennessee, Mississippi, Alabama, Georgia, Florida, 

South Carolina, and Virginia). Geographic information 

system (GIS) software was then used to extract total 

annual precipitation, maximum temperature, minimum 

temperature, and temperature range from PRISM 

(Oregon State University 2012) for 2 years preceding 

each inventory year as well as ecoregion province and 

section codes. Mortality was calculated as a percentage 

of dead basal area for each species type that occurred 

within each plot by dividing dead basal area by total 

basal area of the plot. The data were then divided 

based on sites upon which each species typically 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 320

occurs (e.g., upland or bottomland [Table 1]) for red 

oak species. This was done in an effort to determine 

both the scale and degree of difference between the 

two groups. R statistical software was utilized to 

perform kernel smoothing using the ”stats” package (R 

Core Team 2012) to allow for the determination of a 

spatial trend in mortality, and CART analysis using the 

“rpart” package (Therneau et al. 2012) to determine 

the relationship between mortality and climate 

variables at the ecoregion province and section levels 

(Table 2).

Table 1.—List of upland and bottomland red oak 

species extracted from 1991-2000 Forest Inventory 

and Analysis (FIA) data for 10 southeastern states

 Common Name Scientific Name

Upland Scarlet oak Quercus coccinea

Southern red oak Quercus falcata

Blackjack oak Quercus marilandica

Northern red oak Quercus rubra

Black oak Quercus velutina

Bluejack oak Quercus incana

Bottomland Cherrybark oak Quercus pagoda

Water oak Quercus nigra

Nuttall oak Quercus texana

Willow oak Quercus phellos

Shumard oak Quercus shumardii

 Code Section Province

A Arkansas Valley Southeastern Mixed Forest

B Atlantic Coastal Flatlands Outer Coastal Plain Mixed Forest

C Blue Ridge Mountains Central Appalachian Broadleaf Forest-Coniferous Forest-Meadow

D Boston Mountains Ozark Broadleaf Forest-Meadow

E Central Ridge/Valley Eastern Broadleaf Forest (Oceanic)

F Coastal Plain/Flatwood, Lower Outer Coastal Plain Mixed Forest

G Coastal Plain, Middle Southeastern Mixed Forest

H Florida Coastal Lowlands, Eastern Outer Coastal Plain Mixed Forest

I Florida Coastal Lowlands, Western Outer Coastal Plain Mixed Forest

J Interior Low Plateau, Highland Rim Eastern Broadleaf Forest (Continental)

K Interior Low Plateau, Shawnee Hills Eastern Broadleaf Forest (Continental)

L Mid Coastal Plain, Western Southeastern Mixed Forest

M Mississippi Alluvial Basin Lower Mississippi Riverine Forest

N North Cumberland Mountains Central Appalachian Broadleaf Forest-Coniferous Forest-Meadow

O North Cumberland Plateau Eastern Broadleaf Forest (Oceanic)

P Northern Ridge/Valley Central Appalachian Broadleaf Forest-Coniferous Forest-Meadow

Q Ouachita Mountains Ouachita Mixed Forest-Meadow

R Ozark Highlands Eastern Broadleaf Forest (Continental)

S Southern Appalachian Piedmont Southeastern Mixed Forest

T Southern Cumberland Mountains Eastern Broadleaf Forest (Oceanic)

U Southern Cumberland Plateau Southeastern Mixed Forest

V Southern Ridge/Valley Southeastern Mixed Forest

W Southern Unglaciated Allegheny Plateau Eastern Broadleaf Forest (Oceanic)

X Upper Gulf Coastal Plain Eastern Broadleaf Forest (Continental)

Table 2.—Ecoregion sections used in analysis of mortality at the ecoregion province and section levels
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 321

RESULTS

The highest level of mortality in upland red oak 

species occurred from coastal areas of southern 

Alabama northeastward through portions of the 

Appalachian Mountains. Portions of the Ozark 

Highlands showed 10-15 percent mortality while 

coastal areas of South Carolina ranged from 25-30 

percent. At the ecoregion province level, CART results 

showed that the first split occurred at provinces, with 

the Eastern Broadleaf (Oceanic) province having the 

highest level of mortality (~25 percent). The second 

split was for current (inventory year) precipitation 

where 54 percent mortality levels were associated with 

precipitation less than 1053 mm, below the minimum 

value for average annual precipitation within the 

province. Analysis at the ecoregion section level was 

similar, with sections located in the eastern portion of 

the study area being associated with higher levels of 

mortality. A second split occurred with precipitation 

values less than 1051 mm (below minimum average 

values), with a third split indicating a few sections 

with mortality values approaching 60 percent (Fig. 1).

Figure 1.—Upland red oak species (a) spatial trend for mortality, (b) CART model at the ecoregion province level , 

and (c) CART model at the ecoregion section level.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 322

The north to south spatial trend for bottomland red 

oak species was similar to the upland species, with 

the highest mortality values (ranging from 25 to 

35 percent) occurring across northern portions of 

Alabama, across Tennessee and Kentucky, and in 

south-central portions of Virginia. The first split in 

the CART analysis at the province level occurred for 

the previous year’s average annual temperature range 

with higher mortality associated with ranges exceeding 

13.16 °C. A second split was associated with minimum 

average temperatures greater than 12.36 °C, although 

only 11 samples met this criterion. The CART model 

for the section level matches the province level but 

has a split for previous year’s temperature range of 

less than 13.16 °C that shows mortality values near 

19 percent for sections in the eastern part of the study 

area (Fig. 2)

Figure 2.—Bottomland red oak species (a) spatial trend for mortality, (b) CART model at the ecoregion province level, 

and (c) CART model at the ecoregion section level.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 323

DISCUSSION

The most notable findings are the changes in mortality 

between the two scales of analysis. The province level 

analysis for upland red oak species shows a split for 

which the highest mortality levels are associated with 

one province; however, when analysis occurs at the 

section level, many sections across the eastern portion 

of the study area show higher levels of mortality than 

those for sections in the west (e.g., Ozark Highlands). 

The number of sections that appear from within 

provinces not associated with increased levels of 

mortality in the province level analysis illustrates 

the importance of the increased level of detail that 

occurs at the section level. The sections are associated 

with detailed environmental and biological features 

(e.g., dominant forest cover) that provide a means of 

assessing differences across the region (McNab and 

Avers 1994). At finer scales, it is likely that microclimatic influences are having a greater impact on 

mortality than at larger scales. Kabrick et al. (2007) 

found that ecological land types, determined at the 

stand scale, can be useful for determining areas where 

mortality is likely to be greatest by identifying areas 

that are less suitable for the support of healthy forests.

Also notable are the differences in mortality between 

upland and bottomland red oak species. The only 

climate variable of importance for upland species was 

average annual precipitation in the same year in which 

the inventory occurred while for bottomland species, 

the most influential variable was average annual 

temperature range. These findings point to the possible 

importance of extreme temperatures during some 

months of the year, which could act to stress trees in 

the impacted areas. The bottomland red oak ecoregion 

sections with increased mortality indicate some local 

or possibly regional influences such as associated 

forest or soil types, elevation, and/or aspect. Oak 

mortality events related to stand and site factors such 

as these can also vary across physiographic regions 

(Oak et al. 1996).

The different variables that influence the level of 

mortality for upland and bottomland red oaks vary at 

the scale of analysis but could also vary temporally. It 

has been established that drought can greatly influence 

red oak species mortality (Fan et al. 2012). Additional 

research could also assess variables such as tree 

age and growing season versus nongrowing season 

precipitation. The analysis performed here shows that 

there exists at least some fine scale interaction between 

mortality and climate variables. Future research 

will analyze the aforementioned variables as well as 

compare them to mortality trends in additional time 

periods. 

ACKNOWLEDGMENTS

This research was supported by the U.S. Forest 

Service Forest Health Monitoring Program 

(#SO-EM-F-10-01) and NASA via subcontract 

(WKU5162201000110070670) from Western 

Kentucky University. The authors also wish to thank 

Dr. Emily Schultz and Mr. Paul Jeffreys for providing 

constructive reviews of this manuscript.

LITERATURE CITED

Fan, Z.; Kabrick, J.M.; Spetich, M.A.; Shifley, S.R.; 

Jensen, R.G. 2008. Oak mortality associated 

with crown dieback and oak borer attack 

in the Ozark Highlands. Forest Ecology and 

Management. 255(7): 2297-2305.

Fan, Z.; Fan, X.; Crosby, M.K.; Moser, W.K.; 

He, H.; Spetich, M.A.; Shifley, S.R. 2012. 

Spatio-temporal trends of oak decline and 

mortality under periodic regional drought in 

the Ozark Highlands of Arkansas and Missouri.

Forests. 3(3): 614-631. DOI: 10.3390/f3030614.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 324

Kabrick, J.M.; Fan, Z.; Shifley, S.R. 2007. Red oak 

decline and mortality by ecological land type in 

the Missouri Ozarks. [CD-ROM]. In: Buckley, 

D.S.; Clatterbuck, W.K., eds. Proceedings of 

the 15th Central Hardwood Forest Conference. 

e-Gen. Tech. Rep. SRS-101. U.S. Department of 

Agriculture, Forest Service, Southern Research 

Station: 181-186.

Law, J.R.; Gott, J.D. 1987. Oak mortality in the 

Missouri Ozarks. In: Hay, R.L.; Woods, F.W.; 

DeSelm, H., eds. Proceedings of the Sixth Central 

Hardwood Forest Conference: 1987 February 

24-26; Knoxville, TN: The University of Tennessee: 

427-436.

McNab, W.H.; Avers, P.E., comps. 1994. Ecological 

subregions of the United States: section 

descriptions. Administrative Publication 

WO-WSA-5. Washington, DC: U.S. Department 

of Agriculture, Forest Service. 267 p.

Oak, S.; Tainter, F.; Williams, J.; Starkey, D. 1996. 

Oak decline risk rating for the southeastern 

United States. Annals of Forest Science. 53: 

721-730.

Oregon State University. 2012. PRISM Climate 

Group. Available at http://prism.oregonstate.edu. 

[Date accessed unknown].

R Core Team. 2012. R: A language and environment 

for statistical computing. Vienna, Austria: R 

Foundation for statistical computing. Available 

at http://www.R-project.org/. [Date accessed 

unknown].

Therneau, T.M.; Atkinson, B.; Ripley, B. 2012. 

Package ‘rpart’. The Comprehensive R Archive 

Network. Available at: http://cran.r-project.org/web/

packages/rpart/rpart.pdf. [Date accessed unknown].

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 325

Incorporating Local Statistics Based Spatial 

Weight Matrix into Simultaneous Autoregressive Model 

to Predict the Distribution of Nonnative Invasive Shrubs

in the Upper Midwest

Weiming Yu, Zhaofei Fan, and W. Keith Moser1

Abstract.—In this study, we extended the spatial weight matrix defined by Getis and 

Aldstadt to a more general case to predict the distribution of invasive shrubs among the 

Upper Midwest counties in a simultaneous autoregressive (SAR) model context. The 

modified spatial weight matrix performs better than the original spatial weight matrix 

since it adjusts weights of observations based on the distance from other observations 

but still maintains the locally adaptive nature of the latter. Both the simulation study 

and the application to the Forest Inventory and Analysis (FIA) plot data for invasive 

shrubs collected in 2005-2006 proved that the modified spatial weight matrix outperform 

its original case in diagnostic statistics (e.g., AIC) and resultant invasion maps. Our 

results provided further evidence that misspecification of spatial autocorrelation 

structure in spatial data will result in misinterpretation of real ecological processes and 

bias estimation of coefficients for important covariates involved. The geographical 

distribution of invasive shrubs in the Upper Midwest was significantly associated with 

latitude; local clusters (a group of counties) of high abundance/presence of invasive 

shrubs was significantly determined by TRPF (a ratio of road density to percent of forest 

cover at the county level), a variable reflecting the intensity of human disturbance. 

Both the multiple linear regression model and the SAR model with the original spatial 

weight matrix incorporated tended to overestimate the effect of forest type (community) 

on invasive shrubs. However, the SAR model with the modified spatial weight matrix 

showed that distribution of invasive shrubs among forest types was only marginally 

different at the significance level of p = 0.05. This result conforms to the plot level study 

and is favorably supported by the data. As a conclusion, the SAR model incorporating the 

modified spatial weight matrix provides an applicable solution to map spatial data and 

estimate spatial autocorrelation structure and covariate effect in ecological studies.

1

 Associate (WY) and Assistant Professor (ZF), Mississippi State University, Department of 

Forestry, Thompson Hall, P.O. Box 9681, Mississippi State University, Mississippi State, MS 

39762-9681; Research Forester (WKM), U.S. Forest Service, Northern Research Station. 

ZF is corresponding author: to contact, call 662-325-5809 or email at zfan@cfr.msstate.edu. 

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 326

Projecting Climate Change Impacts

on Down Woody Materials in the Eastern U.S. Forests

Zhaofei Fan, Christopher W. Woodall, and Stephen R. Shifley1

Abstract.—Down woody material (DWM) is dead biomass that originates from tree/

plant mortality in forest ecosystems. DWM is an indicator of forest ecosystem health 

and can be used to evaluate fire risk, wildlife habitat, and forest carbon stocks. Total 

DWM and components (coarse woody debris, fine woody debris, litter, and duff) are 

measured on the Forest Inventory and Analysis (FIA) Phase 3 plots. In this study we 

employed a two-stage modeling strategy to combine the Phase 2 and Phase 3 plots to 

map the distribution of major DWM components across the eastern United States. We 

first used data from the Phase 3 plots to develop a set of semiparametric penalized spline 

functions to estimate DWM components based on a set of covariates that were measured 

for both the Phase 3 and the Phase 2 plots. Then we used the derived spline models to 

estimate DWM components for all Phase 2 plots and map the distribution of DWM 

components at selected resolutions that are pertinent to management needs. We used 

multivariate classification and regression tree to evaluate the impact of climatic variables 

on distributional patterns of DWM components. By integrating design-based information 

and model-assisted inference this approach improves the efficiency of the estimators of 

DWM components compared to the design-based estimators. Finally, we explored the 

ecological implications of current patterns of DWM (by components) for major forest 

cover types and ecoregions in the eastern United States and discussed the potential 

impacts of management practices on hardwood forest ecosystems.

1

 Assistant Professor (ZF), Mississippi State University, Department of Forestry, Thompson Hall, 

P.O. Box 9681, Mississippi State University, Mississippi State, MS 39762-9681; Research Forester 

(CWW and SRS), U.S. Forest Service, Northern Research Station. ZF is corresponding author: 

to contact, call 662-325-5809 or email at zfan@cfr.msstate.edu. 

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 327

Spread of Chinese Tallow in East Texas using FIA Data

Zhaofei Fan1

Abstract.—Chinese tallow tree (Triadica sebifera [L.] Small) is an invasive species that 

is replacing native ecosystems in areas of eastern Texas. It is imperative that the spatial 

pattern of the spread of this species be identified, as well as causal mechanisms. To that 

end, we seek to determine factors that contribute to the spread of Chinese tallow using 

autologistic regression. We also attempt to identify current locations and spread rates 

across eastern Texas using Forest Inventory Analysis (FIA) data within major forest 

types. Distance to roads, disturbances (natural and anthropogenic), and low slope were 

identified as major factors for the occurrence of Chinese tallow. The highest probability 

of occurrence and spread rate of Chinese tallow were found in the oak/gum/cypress 

forest type. Continued disturbance, from harvest events or natural disasters will allow 

the species to continue to spread throughout the region and could threaten overall forest 

productivity. We also discuss some implications of the continued spread of Chinese 

tallow on forest management. Forest managers could benefit from this analysis and use it 

as a guide for monitoring forest types with the highest risk of invasion.

1

 Assistant Professor, Mississippi State University, Department of Forestry, Thompson Hall, 

P.O. Box 9681, Mississippi State, MS 39762-9681. To contact, call 662-325-5809 or email at 

zfan@cfr.msstate.edu. 

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 328

Tempo-Spatial trends of Oak Decline and Mortality 

under Periodic Regional Droughts

in the Ozark Highlands of Arkansas and Missouri

Shirley X. Fan, Zhaofei Fan, W. Keith Moser, Matin A. Spetich, and Stephen R. Shifley1

Abstract.—Oak decline and mortality trends for major oak species (groups) was 

explored in the Ozark highlands of Arkansas and Missouri at the forest landscape/region 

level, based on annual FIA plot data from 1999 to 2010. Oak decline has elevated 

cumulative mortality of red oak species to 11 and 15 percent in terms of relative density 

and basal area of standing dead oak trees, respectively; this rate is two to three times 

higher than for white oak group and non-oak species. 

Oak decline and associated escalating mortality have occurred primarily within red oak 

species while white oak group maintain a relatively stable mortality rate comparable 

to non-oak species. Cross-correlation analyses indicate that mortality was significantly 

correlated with the growing season Palmer Drought Severity Index (PDSI) and usually 

lagged 2 to 3 years following single drought events. Moreover, based on the past 17 

year’s PDSI data, it appears that the cumulative impacts of drought may last up to 10 

years. The Ozark highlands experienced a severe drought extending from 1998 to 2000 

and another milder drought from 2005 to 2006. These drought events triggered the 

escalation of mortality starting around year 2000. Spatially, high mortality of red oak 

(hot spots proportional basal area mortality > 0.15) occurred mainly in the central area 

of the Ozarks. Moderate mortality (proportional basal area mortality of 0.10 to 0.15) was 

widely dispersed over most of the Ozark highlands, while low mortality was distributed 

around the outer perimeter of the Ozarks. In contrast, in the white oak group and nonoak species, high mortality was rare and moderate mortality was sporadic. Cumulative 

mortality of these species was predominant throughout the Ozarks.

1

 Associate (SXF) and Assistant Professor (ZF), Mississippi State University, Department of 

Forestry, Mississippi State, MS 39762; Research Forester (WKM), U.S. Forest Service, Northern 

Research Station; Research Forest Ecologist (MAS), U.S. Forest Service, Southern Research 

Station; Research Forester (SRS), U.S. Forest Service, Northern Research Station. ZF is 

corresponding author: to contact, call 662-325-5809 or email at zfan@cfr.msstate.edu. 

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Methods for improving

Forest Inventory
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 330

Estimators used in the New Mexico Inventory: 

Practical Implications of “Truly” Random Nonresponse 

within each Stratum

Paul L. Patterson and Sara A. Goeking1

Abstract.—The annual forest inventory of New Mexico began as an accelerated 

inventory, and 8 of the 10 Phase 2 panels were sampled between 2008 and 2011. The 

inventory includes a large proportion of nonresponse. FIA’s estimation process uses poststratification and assumes that nonresponse occurs at random within each stratum. We 

construct an estimator for the New Mexico inventory and derive an estimated variance 

based on the missing-at-random assumption. 

1

 Statistician (PLP) and Biological Scientist (SAG), 

U.S. Forest Service, Rocky Mountain Research Station, 

507 25th St., Ogden, UT 84401. PLP is corresponding 

author: to contact, call 907-295-5966 or email at 

plpatterson@fs.fed.us. 

INTRODUCTION

The national Forest Inventory and Analysis (FIA) 

Phase 2 grid forms the basis for FIA sampling, yet 

not all Phase 2 plots can be sampled. When plots 

are not sampled due to denial of access, logistical 

constraints, or hazardous conditions, we refer to them 

as nonresponse following the convention of Patterson 

et al. (2012). From 2008 to 2011 in New Mexico 8 of 

10 panels (more than 5,000 plots) were sampled under 

an accelerated inventory. For many reasons, there 

is a large amount of nonresponse in New Mexico, 

which warrants an examination of how FIA handles 

nonresponse.

Standard FIA assumptions are that we have a simple 

random sample of a region, R, which can be poststratified and whose total area, AT, is known. The 

number of acres, Ad, in domain d is of interest, and 

is equal to AT Pd, where Pd is the proportion of R that 

is classified in domain d. The FIA post-stratified 

estimator for Pd is 

PP� [1] dd = Σ WWhPP�

ddh

HH

h=1

where H is the number of strata, Wh is the weight of 

the strata, and dh is an estimate of the proportion of 

stratum h that is in domain d. The definition of dh in 

chapter 4 of Bechtold and Patterson (2005) contains 

adjustments for nonresponse plots. The two types of 

nonresponse are entire plots and partial plots. The 

adjustment for plots that are entirely nonresponse 

is to reduce the sample size, while an adjustment 

factor is used to compensate for plots with partial 

nonresponse. Both adjustments are based on the 

assumption that nonresponse is random within each 

stratum. Patterson et al. (2012) showed by example 

that substantial bias can occur if the missing-atrandom assumption is violated in the case where the 

nonresponse is restricted to entire plots. Roesch et al. 

(2012) showed a) why bias occurs when there are plots 

with partial nonresponse, and b) that if the missingat-random assumption is correct, then the adjustment 

factor for the partial nonresponse portion of the 

estimator is unbiased, given the assumption the sample 

size is reduced to adjust for plots that are entirely 

nonresponse. The issue of whether the reduction in 

sample size, through a non-random process, induces 

a bias in the estimator is not addressed. Neither is 

the effect of the reduction in sample size, through a 

non-random process, on the variance and estimated 

variance addressed in Bechtold and Patterson (2005) 

or Roesch et al. (2012).

PP�

PP�
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 331

Roesch et al. (2012) proposed using partitions of the 

strata, for which the assumption that the nonresponse 

is random is tenable for each of the partitions, and 

then for each partition use ( d* ) to estimate the portion 

of the partition that is in the domain d. In Särndal et 

al. (1992) the division of the population into groups 

where the assumption of random nonresponse is valid 

is referred to as the response homogeneity group 

(RHG) model.

The purpose of this manuscript is to: 1) construct the 

estimator used to estimate Pd for the estimation units 

in the New Mexico inventory; 2) use the statistical 

framework in Särndal et al. (1992) to construct an 

estimator equal to the estimator constructed in item 

1 and use this equality to investigate the statistical 

properties of the FIA estimator, namely bias, variance 

and properties of an estimated variance; and finally 3) 

discuss the implications for the current FIA estimated 

variance. 

NEW MEXICO ESTIMATOR

Two potential partitioning criteria arise in many FIA 

estimation units (Patterson et al. 2012). The first is 

ownership class; denied access is higher among private 

owners. The second is an aspect of the FIA pre-field 

procedure, in which high-resolution photos are used in 

conjunction with old field notes to classify each plot 

as either “office plot” or “field plot”. The office plots 

are designated as nonforested. The salient point here is 

that for the office plots the probability of nonresponse 

is zero, while in most field plots the probability of 

nonresponse is greater than zero. Whether a stratum 

needs to be partitioned into office and field domains 

depends on two factors: first, the agreement between 

the stratification scheme and pre-field classification 

and second, the amount of nonresponse in the stratum. 

If a stratum needs to be partitioned into office and field 

substrata, the weights for these two partitions must be 

estimated.

To facilitate the comparison with Särndal et al. 

(1992) we will develop some notation. It is useful 

to differentiate between the strata-partitions where 

PP�

the weight is known and partitions where the weight 

must be estimated. We assume there are H strata with 

known weights. Of the H strata, H1 do not need to 

be partitioned further and H2 of the strata need to be 

partitioned further with the weights for the partitions 

being estimated. Note that H1 + H2= H. Let Jh denote 

the number of partitions in the hth stratum (note: for 

the first H1 strata Jh = 1). In RHG model terminology 

(Särndal 1992) there are response groups 

and each response group is a subset of one of the H

strata.

In FIA’s Interior West region there are typically 

two strata, Green (G) and Brown (B), representing 

forest and nonforest, with weights WG and WB, 

respectively. Because the nonresponse rate for the 

New Mexico annual inventory is unusually high on 

private lands, further stratification into partitions of 

Private owners (P) and Non-Private owners (NP) 

must be considered. The four strata weights are WGP, 

WGNP , WBP , and WBNP , with WGP + WGNP + WBP + WBNP

= 1, where, for example, WGP is the weight for the 

stratum which is green and private ownership (see 

Table 1). Nonresponse rates have been calculated for 

each stratum based on preliminary data from 2008 to 

2011. Nonresponse rates for Private owners within 

the G and B strata are 47 percent and 19 percent, 

respectively, while rates for the Non-Private owners 

are 8 percent and 3 percent. The nonresponse rate 

for the NP partition is similar among all ownership 

subclasses (e.g., National Forest system, Bureau of 

Land Management). In addition, the nonresponse rates 

for both GNP and BNP are small enough that we can 

ignore the slight bias caused by blending the field plots 

and the office plots.

JJ = Σ JJh

HH

h=1

Table 1.—Calculated values of stratum weights 

WGP , WGNP , WBP , and WBNP for New Mexico, 

based on spatial intersection of the green/brown 

stratification and a statewide ownership layer

Partition

Stratum Private (P) Non-Private (NP)

Green (G) 0.057 0.155

Brown (B) 0.382 0.406
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 332

In contrast, the high nonresponse rates for both GP 

and BP warrant investigation of further partitioning 

into “field” (F) and “office” (O), based on pre-field 

determinations. Preliminary data from New Mexico 

indicate that nearly all plots in GP stratum were field 

visits, so there is no further partition of the GP stratum 

while approximately 40 percent and 60 percent of the 

BP stratum was field visit and office respectively. Let 

wF(BP) indicate the estimated weight (or proportion) 

of F partition within the BP stratum with similar 

definitions for weights of the O partition of the BP 

stratum. In terms of the H notation, H = 4 , H1 = 3 and 

H2

 = 1. The estimate is

where is the proportion of the * stratum or 

partition that is in domain d and the superscript o 

indicates the proportion is based on the observed 

values and adjusted for the nonresponse in the stratum 

or partition. The explicit formula for is given next. 

We will now return to constructing the estimator, using 

notation consistent with that in Särndal et al. (1992). 

For the hth stratum denote the total number of plots 

by nh

 and the number of partly or fully observed plots 

by mh

, so the number of entirely nonresponse plots 

is nh – mh. In FIA mh is denoted by nh (Bechtold and 

Patterson 2005) and there is no notation for the total 

number of plots. For the jth partition of the hth stratum 

let nhj denote the total number of plots and mhj denote 

the number of partly or fully observed plots. So 

 and . The estimated weight 

of jth partition of the hth stratum is , where 

if h = 1, ... , H1, then Jh = 1 and wh1 = 1.

The estimate of the proportion of the partition hj that is 

in domain d is

[2]

where ahji is the area of observable land in domain d on 

the ith plot of the jth partition of the hth stratum, and 

ahji is the amount of observable land on the ith plot of 

the jth partition of the hth stratum. For the H1 strata 

PP�

dd|∗

oo

Σ nnhjj

JJh

jj = nnh Σ mmhjj

JJh

jj = mmh

wwhjj = nnhjj

nnh

d

o

that are not further partitioned, this equation reduces to

[3]

Two points are noteworthy. First, we are using the 

superscript notation used in Roesch et al. (2012) 

instead of the notation used in Bechtold and Patterson 

(2005). Second, equations [2] and [3] are the same 

as equation [4.1] in Bechtold and Patterson (2005) 

(with mh substituted for nh), where we have simplified 

equation [4.1] by cancelling terms. Combining all 

these parts, we have

[4]

The denominator in equations [2] and [3] is the 

adjustment for the partial nonresponse plots.

STATISTICAL PROPERTIES 

OF ESTIMATOR

We will be using the statistical model presented in 

section 15.6 of Särndal et al. (1992), which assumes 

each sample is decomposed into response groups and 

probability that an element in the kth group responds is 

a constant. Other technical assumptions are not stated 

here. The estimator of interest is constructed using the 

response groups and auxiliary variables (Särndal et al. 

1992: section 15.6.4); the auxiliary variable in our case 

is strata membership. Two assumptions related to FIA 

merit further discussion.

First, if the adjustment factor is treated as a random 

quantity, then we have the combination of a ratio 

estimator and a response model, and the derivation of 

the statistical properties of the estimator becomes a 

much more onerous task. However, preliminary data 

for New Mexico indicate the number of plots with 

partial nonresponse is less than 1 percent, so treating 

the adjustment factor as fixed (a constant) rather than 

random has little effect on the variance. 

Second, in section 15.6 of Särndal et al. (1992) the 

goal is to estimate the population total. Estimates of 

PP�

dd|∗

oo
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 333

the total can be adjusted to proportions by dividing 

by N, the number of population elements. In using 

Särndal et al.’s results, we are approximating an 

infinite population with a finite population (Roesch et 

al. 2012).

Using these two assumptions, the estimator given 

by Equation 15.6.16 in Särndal et al. (1992) is equal 

to d in Equation [4]. From Result 15.6.2 of Särndal 

et al. (1992) d is approximately unbiased and an 

approximate variance is given. Of interest to us is the 

formula for a variance estimator. For a plot let the 

observed sample value be 

where * indicates the index of the plot and # indicates 

the stratum/partition the plot is in, so the denominator 

is the adjustment factor for partial nonresponse plots. 

Let r# denote the sample plots in stratum/partition # 

with either partial or total response. Then the estimated 

variance is given below. For clarity we have split the 

equation into three parts; the first part is for strata 

with Jh = 1 and the following two parts for strata with 

Jh > 1.

[5]

[6]

[7]

where , with # indicating 

the stratum/partition, is an adjustment factor for 

nonresponse; is the variance of the yk for the 

response portion of the sample contained in stratum/

partition #; and erh*is the average of the residuals, 

 , over rh*.

DISCUSSION

If H2 = 0, then equation [4] is the current FIA 

estimator. The estimated variance we are proposing 

would involve only equation [5], which differs 

PP�

PP�

–

from the estimated variance FIA currently uses. The 

difference between our estimated variance and the 

one used by FIA is that the latter is derived based on 

the assumption that a simple random sample of size 

m = ∑h

mh is drawn, while our estimated variance 

is based on two-phase Bernoulli sampling for 

stratification, that is, a simple random sample sa is 

drawn at the first phase and sa is stratified into the 

response groups. Then a subsample sah is drawn from 

each sa using Bernoulli sampling, with probability of 

selection equal to the response probability. This new 

approach may improve FIA’s estimates in situations 

with high nonresponse where the assumption of 

missing at random within strata is untenable.

LITERATURE CITED

Bechtold, W.A.; Patterson P.L., eds. 2005. The 

enhanced Forest Inventory and Analysis 

program-national sampling design and 

estimation procedures. Gen. Tech. Rep. SRS-80. 

Asheville, NC: U.S. Department of Agriculture, 

Forest Service, Southern Research Station. 85 p.

Patterson, P.L.; Coulston, J.W.; Roesch, F.A.; Westfall, 

J.A.; Hill, A.D. 2012. A primer of nonresponse in 

the U.S. Forest Inventory and Analysis program.

Environmental Monitoring and Assessment 184(3): 

1423-1433.

Roesch, F.A.; Coulston, J.W.; Hill, A.D. 2012. 

Statistical properties of alternative national 

forest inventory area estimators. Forest Science. 

doi.org/10.5849/forsci11-008. Available at http://

masetto.ingentaselect.co.uk/fstemp/b12e1416

7fb0e933f0f6fefca6a3f3c1.pdf. [Date accessed 

unknown].

Särndal, C.; Swensson, B.; Wretman, J. 1992. Model 

assisted survey sampling. New York: SpringerVerlag. 694 p.

The content of this paper reflects the views of the 

author(s), who are responsible for the facts and accuracy 

of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 334

Estimating Root Collar Diameter Growth

for Multi-stem Western Woodland Tree Species

On Remeasured Forest Inventory and Analysis Plots

Michael T. Thompson and Maggie Toone1

Abstract.—Tree diameter growth models are widely used in many forestry applications, 

often to predict tree size at a future point in time. Also, there are instances where 

projections of past diameters are needed. An individual tree model has been developed 

to estimate diameter growth of multi-stem woodland tree species where the diameter is 

measured at root collar. The model was built from radial growth data on trees sampled 

from plots measured in Utah, Arizona, and Nevada. Individual tree growth can be 

predicted from the ratio of live woodland stems to all stems and from mean past 

10-year radial growth of trees by ecological subsection, section, or province. Coefficients 

were estimated for four woodland tree species groups that cover most tree species in the 

Southern Interior West region. 

1

 Forester (MTT) and Ecologist (MT), U.S. Forest Service, 

Rocky Mountain Research Station, 507 25th Street, Ogden, 

UT 84404. MTT is corresponding author: to contact, call 

801-625-5374 or email at mtthompson@fs.fed.us.

INTRODUCTION

Estimating diameter growth is an important aspect 

of forest management and inventory. Determining 

diameter growth is problematic for many western 

woodland tree species because they are measured at 

the root collar, are very slow growing, often contain 

multiple stems, and are poorly suited to measurement 

methods used for other temperate tree species. Site 

quality and stand competition in dry arid regions 

comprised of woodland trees species are not well 

understood. 

Forest Inventory and Analysis (FIA) data on diameter 

growth are often obtained from repeatedly measuring 

the same trees over time or by measuring increment 

cores. Historically, the Interior West FIA (IWFIA) 

region has had to rely on increment core measurements 

for estimates of diameter growth because of 

inconsistent inventories over time or gaps in previous 

inventories where there were insufficient numbers 

of previously established permanent plots. When the 

annual inventory system was implemented in the 

IWFIA region in 2000, FIA plots were established 

on a systematic grid (Reams et al. 2005) across the 

landscape regardless of land use and ownership. Now, 

those initially established plots are beginning to be 

remeasured and there will be a need for procedures 

that reliably and efficiently estimate tree growth from 

paired plots measured at two points in time.

FIA requires measurements or models of tree diameter 

growth to estimate individual tree growth. Diameter 

growth, along with other volume attributes such as 

total height, is used to calculate growth for every 

qualifying tree measured on a plot. Individual tree 

growth is used for compiling inventories made up of 

tree data because growth can be calculated for each 

tree and then summarized in many different ways. 

Therefore, an individual tree modeling approach is 

used in this analysis.

Multi-stem woodland trees are the most problematic 

when comparing measurements between two points in 

time, referred to as time 1 (previous measurement) and 

time 2 (current measurement). Measurement error can 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 335

be very significant because of extremely high numbers 

of stems, presence of obstacles that hinder accurate 

measurements such as thorns and rock outcrops, 

swelling and irregular form, or extremely degraded 

stems. The measurement point can change because 

of a field crew at time 2 disagreeing with where the 

field crew at time 1 took diameter at root collar (d.r.c.) 

measurements. Diameters can also change between 

time 1 and 2 because of stems breaking off due to 

cutting or mortality resulting in a d.r.c. reduction 

between time 1 and time 2. 

It is because of these inherent difficulties in measuring 

d.r.c. at time 1 and time 2 that using a change in d.r.c. 

on multi-stem woodland trees will often yield artificial 

estimates of diameter growth. Chojnacky (1997) 

used radial growth data to estimate diameter growth 

of pinyon and juniper (Juniperus spp.) trees in New 

Mexico. Therefore, for multi-stem woodland trees, 

a measure of radial growth may be a superior metric 

for estimating diameter growth than a change in d.r.c. 

between time 1 and time 2.

STUDY AREAS

The data for this study came from inventory plot data 

collected by the IWFIA crew in Arizona, Nevada, and 

Utah. These three states were selected because of the 

preponderance of multi-stem woodland species and 

because they are located in the largely arid southern 

portion of the Interior West region. All data were 

collected under the annual inventory system that began 

in 2000 in Utah and covered the following years: 

Arizona (2001-2009); Nevada (2004-2005); Utah 

(2000-2009).

DATA

Tree data came from a total of 4,376 inventory plots 

measured across the three states. The plot design used 

during the annual inventory is described in Bechtold 

and Scott 2005. Only multi-stem woodland trees were 

used in the study because they are measured differently 

than single-stem woodland trees and are considered to 

be the most problematic growth form for determining 

growth. All multi-stem woodland trees used to develop 

the models had increment cores extracted and 10 years 

of radial growth measured. The minimum d.r.c. of the 

study trees was 5.0 inches. There were a total of 7,996 

trees used in the analysis.

The woodland trees were grouped into four species 

groups: (1) common pinyon (Pinus edulis Engelm.); 

(2) Utah juniper (Juniperus osteosperma (Torr.) 

Little); (3) other junipers including redberry 

juniper (Juniperus coahuilensis), California juniper 

(Juniperus californica Carr.), alligator juniper 

(Juniperus deppeana Steud.), Rocky Mountain juniper 

(Juniperus scopulorum Sarg.), and oneseed juniper 

(Juniperus monosperma (Engelm.) Sarg.); and (4) 

other woodland trees including singleleaf pinyon 

(Pinus monophylla Torr. & Frem.), Mexican pinyon 

pine (Pinus cembroides Zucc.), Arizona pinyon pine 

(Pinus arizonica Engelm), bigtooth maple (Acer 

grandidentatum Nutt.), curlleaf mountain-mahogany 

(Cercocarpus ledifolius Nutt.), western honey 

mesquite (Prosopis glandulosa Torr.), velvet mesquite 

(Prosopis velutina Woot.), gray oak (Quercus grisea

Liebm.), Emory oak (Quercus emoryi Torr.), Gambel 

oak (Quercus gambelii Nutt.), Mexican blue oak 

(Quercus oblongifolia Torr.), silverleaf oak (Quercus 

hypoleucoides A. Camus), and evergreen oaks.

METHODS

Radial growth measurements were converted into an 

annual change in d.r.c. using the following formula:

madc = ,

where madc is mean annual change in d.r.c. in inches 

of a live stem, and r is the length in inches of a radialgrowth core representing 10 years of growth. Dividing 

by 10 converts the periodic estimate of 10 years of 

diameter growth into an annual estimate, standardizing 

the time frame so the modeled estimate can be applied 

to any number of years. This is common practice 

2r

10

—
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 336

in many FIA diameter growth models (Westfall 

2006). Differing remeasurement intervals, estimating 

diameter growth on trees that either died or were cut at 

various points in time during a measurement interval, 

and proper assignment of trees to the appropriate 

growth component are common applications of an 

annualized change in diameter.

Numerous attributes have been correlated with 

diameter growth rates. Often, stand and tree-level 

measurements that are observed or computed are used 

as predictor variables in diameter growth models. 

Examples of stand-level predictors often include age, 

site productivity, and stand density. Several predictor 

variables were identified for possible inclusion. Least 

angle regression was used to identify variables thought 

to be significant for model development and to create 

model parameters. 

Two variables were deemed significant for predicting 

madc: (1) the number of live woodland tree stems 

divided by the number of live and dead woodland tree 

stems for an individual tree (lpct), and (2) the mean 

madc for a species group for an ecological subsection 

assigned to the plot the tree is sampled on (ecogindex). 

Ecological subsection refers to areas of unique 

geomorphology with distinct boundaries (Cleland et 

al. 2007). All FIA plots nationally are assigned a code 

that identifies the ecological subsection (Woudenberg 

et al. 2010). These two predictor variables yielded the 

following diameter growth equation:

madc = β0 + β1lpct + β2ln(ecogindex)

RESULTS AND DISCUSSION

Parameter estimates for average annual change in d.r.c. 

are given in Table 1. Resulting parameters should be 

sufficient for most broad-scale inventory needs and 

can be used to estimate diameter growth on trees that 

have been cut or have died during the remeasurement 

interval or trees that need a previous or past diameter 

predicted because of field crew measurement error. 

It may even be desirable to use a model developed 

from increment core measurements to predict diameter 

growth for all multiple-stem woodland trees because 

of the high level of d.r.c. measurement error associated 

with these growth forms. 

A more powerful explanatory growth model was 

not developed because of poor correlation between 

diameter growth and available FIA site description 

variables. There are several reasons why the 

correlation is poor. First, the FIA site description 

variables are not refined enough for many western 

forest conditions. For example, physiographic 

variables for arid woodland tree conditions are very 

Table 1.—Parameters for estimating mean annual change in diameter at root collar (d.r.c.) of multi-stem 

woodland trees in Utah, Arizona, and Nevada

Parameter Estimates Number of Trees Regression Statistics

Species Group β0 β1 β2 R2 C.V.

Common pinyon .071 .021 .053 826 .17 48.0

Utah juniper .065 .018 .043 3,934 .12 52.2

Other juniper .067 .027 .060 1,787 .11 54.8

Other woodland species .079 .00 .050 1,449 .15 57.0

Diameter growth equation (Eq. 2): madc = β0 + β1lpct + β2ln(ecogindex) where:

madc = mean annual change in d.r.c (inches) based upon 10 years of past radial growth;

lpct = number of live stems divided the total number of live and dead stems for an individual woodland tree;

ecogindex = mean madc for a species group for an ecological subsection assigned to the plot the tree is sampled on;

R2 = coefficient of determination;

C.V. = coefficient of variation.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 337

limited. Second, measures to estimate site quality 

such as site index and site class are not collected on 

sites dominated by woodland tree species. Site quality 

studies are limited for many woodland tree species. 

Third, uncertainty exists for equating growth rates with 

ring counts for certain species. False and missing rings 

are known to occur for juniper (Despain 1989). Certain 

species, such as curlleaf mountain-mahogany, are 

never cored because of very hard wood characteristics. 

The inherent slow growing nature of these growth 

forms can make ring counting difficult.

CONCLUSIONS

The model described is easily applied and uses a 

minimum amount of field data. What is needed is 

the proportion of live woodland tree stems for an 

individual tree (lpct) and the mean 10-year diameter 

growth by species group for the ecological subsection 

assigned to the plot the tree is sampled on (ecogindex). 

The model could be further refined by improving 

methods to identify growth ring identification such 

as sanding and magnification and conducting further 

analysis to determine optimum number of tree samples 

per species groups for ecological subsection.

LITERATURE CITED

Bechtold, W.A.; Scott, C.T. 2005. The Forest 

Inventory and Analysis plot design. In: Bechtold, 

W.A.; Patterson, P.L., eds. The enhanced Forest 

Inventory and Analysis program-national sampling 

design and estimation procedures. Gen. Tech. 

Rep. SRS-80. Asheville, NC: U.S. Department of 

Agriculture, Forest Service, Southern Research 

Station: 27-42.

Chojnacky, D.C. 1997. Modeling diameter growth 

for pinyon and juniper trees in dryland forests.

Forest Ecology and Management. 93: 21-31.

Cleland, D.T.; Freeouf, J.A.; Keys, J.E.; [et al.]. 2007. 

Ecological subregions: sections and subsections 

for the conterminous United States.

GTR-WO-76. Washington, DC: U.S. Department 

of Agriculture, Forest Service.

Reams, G.A.; Smith, W.D.; Hansen, M.A.; Bechtold, 

W.A.; Roesch, F.A.; Moisen, G.G. 2005. The forest 

inventory and analysis sampling frame. In: 

Bechtold, W.A.; Patterson, P.L., eds. The enhanced 

forest inventory and analysis program – national 

sampling design and estimation procedures. 

Gen. Tech. Rep. SRS-80. Asheville, NC: U.S. 

Department of Agriculture, Forest Service, 

Southern Research Station: 11-26.

Westfall, J.A. 2006. Predicting past and future 

diameter growth for trees in the northeastern 

United States. Canadian Journal of Forest 

Research. 36: 1551-1562.

Woudenberg, S.W.; Conkling, B.L.; O’Connell, B.M.; 

LaPoint, E.B.; Turner, J.A.; Waddell, K.L. 2010. 

The forest inventory and analysis database: 

database description and user’s manual version 

4.0 for phase 2. Gen. Tech. Rep. RMRS-GTR-245. 

Fort Collins, CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research Station. 

336 p.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 338

THE IMPROVEMENT OF PRECISION FOR ESTIMATING 

THE ABUNDANCE OF STANDING DEAD TREES 

USING AUXILIARY INFORMATION UNDER THE FIA PLOT DESIGN

Hong Su An, David W. MacFarlane, and Christopher W. Woodall1

Abstract.—Standing dead trees are an important component of forest ecosystems. 

However, reliable estimates of standing dead tree population parameters can be difficult 

to obtain due to their low abundance and spatial and temporal variation. After 1999, the 

Forest Inventory and Analysis (FIA) Program began collecting data for standing dead 

trees at the Phase 2 stage of sampling. However, the estimates have relatively large 

variations compared to the estimates of live trees. The results represent that EZ-Hurdle 

method shows better precision than the FIA method for both spatial patterns and densities 

of standing dead trees.

1

 Postdoctoral Scientist (HSA) and Associate Professor 

(DWM), Department of Forestry, Michigan State University, 

East Lansing, MI 48824-1222; Research Forester (CWW), 

U.S. Forest Service, Northern Research Station. HSA is 

corresponding author: to contact, call 651-234-5678 or 

email at anhong@msu.edu.

INTRODUCTION

Standing dead trees play an important role in forest 

ecosystems such as maintaining biodiversity and 

nutrients, and carbon sequestration. Therefore, reliable 

estimates of standing dead trees are important to 

assess and monitor healthy forest ecosystems (Gray 

2003). Due to high variation in the abundance of 

standing dead trees and their spatial pattern, designing 

sampling schemes for estimating standing dead tree 

abundance can be challenging. According to previous 

studies, relatively larger plot sizes or higher sampling 

intensities than those used for live trees have been 

applied to estimate the abundance of standing dead 

trees (e.g., Ganey 1999, Stephens 2004). While 

intensification of sampling efforts or changing plot 

designs for estimating standing dead tree attributes 

is a straightforward solution, ultimately the cost of 

intensification of sampling must be considered and 

weighed against the value of increased accuracy 

(Curtis and Marshall 2005, Gregoire and Valentine 

2008). 

One major consequence of surveying too-small an 

area (e.g., using plot sizes that are too small) is that 

there may be a large number of zero observations of 

standing dead trees. Excess zero observations, a.k.a. 

zero-inflated data, will increase variation in estimates 

of standing dead tree parameters (Eskelson et al. 

2009, Potts and Elith 2006). Because of the tendency 

for standing dead trees to be aggregated in space and 

a generally lower abundance of standing dead trees 

relative to live ones, the problem of zero-inflated 

data is likely large. The National Forest Inventory 

and Analysis (FIA) Program collects the attributes 

of standing dead trees such as species, diameter, and 

total height, from Phase 2 plots that consist of four 

7.32 m fixed-radius subplots (Bechtold and Patterson 

2005). About 44 percent of FIA plots observed had no 

standing dead trees on them (Woodall et al., in press). 

In a study by An and MacFarlane (N.d.), the ExpectedZero (EZ)-Hurdle method showed better precision 

than the fixed-area sampling method for estimating the 

abundance of standing dead trees when there are large 

proportions of zero observations in fixed-area plot 

data. 

The main objective of this study was to apply the 

EZ-Hurdle method to the FIA plot design to estimate 

the abundance of standing dead trees using auxiliary 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 339

information describing the distance from each subplot 

center to the nearest standing dead tree. 

METHODS

Data

A simulation study was performed to generate 

distributions of standing dead trees within a simulated 

area of 900 ha. In order to collect data for standing 

dead trees, the FIA plot design was applied, and the 

number of standing dead trees within 7.32 m (24 ft) 

radius subplots was recorded. For the EZ-Hurdle 

method, the distance from each subplot center to 

the nearest standing dead tree was measured. The 

maximum search radius for the nearest standing 

dead tree was 17.95 m (120 ft). Two spatial patterns, 

random and clustered, and two densities, 30 and 

45 standing dead trees per ha, were applied. The 

number of sample plots in each iteration ranged 

from 30 to 60 plots in 10-plot increments. For each 

sampling scenario (spatial pattern, density of standing 

dead trees, and sampling intensity combination), 

approximately 3,000 iterations were applied. For each 

iteration, the number of standing dead trees per ha was 

calculated. The coefficient variation for the estimated 

density of standing dead trees was compared. 

Estimating Density 

of Standing Dead Trees

First, the density of standing dead trees per ha was 

estimated by the published method of the FIA program 

(see Bechtold and Patterson 2005). Second, the EZHurdle method was applied to estimate the density 

of standing dead trees. The EZ-Hurdle method (An 

and MacFarlane, N.d.) is a model-based estimation 

method which employs a modified version of the 

Hurdle model (Mullahy 1986) called the EZ-Hurdle 

model. The main difference between the Hurdle and 

EZ-Hurdle models is how the zero-proportion (�) 

is specified in the model. In the case of the Hurdle 

model, � is estimated directly from the count data it is 

being fitted to. The zero-proportion in the EZ-Hurdle 

method is estimated from the detection probability of 

a standing dead tree, which is modeled from auxiliary 

data describing the distance from a plot center to the 

nearest standing dead tree (An and MacFarlane, N.d.). 

With counts of standing dead trees from FIA subplots, 

the EZ-Hurdle method estimates the density of 

standing dead trees in two steps. The first step is 

estimating the expected-zero proportion for the given 

subplot size (radius = r) from the detection probability 

of a standing dead tree given that plot size. The second 

step is estimating the contribution of nonzero counts 

(plots with at least one standing dead tree) to the 

estimate using zero-truncated count distributions such 

as the Poisson distribution. The procedure to model 

the nonzero counts is the same as that in the original 

Hurdle model (Cameron and Trivedi 1998, Mullahy 

1986), and the estimated expected-zero proportion and 

the contribution of nonzero counts are combined. 

When applied to fixed-radius plot sampling yi , 

Y = {yi

 , ... , yn}, is the number of standing dead 

trees in sample plot i and di , D = {di , ... , dn}, is 

the distance from the plot center of i to the nearest 

standing dead tree; n is the number of samples. In 

order to estimate the expected-zero proportion, the 

detection probability of a standing dead tree for the 

given search radius r is modeled from the auxiliary 

data D because the expected-zero proportion for the 

given (fixed) search radius r is: 

P_ez(r) = 1 – P_dt(r)

where P_ez is the expected-zero proportion, P_dt is 

the detection probability of a standing dead tree, and 

r is a fixed search radius to find the nearest standing 

dead trees. A modified Gompertz function was used to 

estimate the detection probability of a standing dead 

tree from the distance data (An and MacFarlane, N.d.).
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 340

The EZ-Hurdle model is specified as follows:

where y is the number of trees, ƒ(Y = y ǀ μ(r)) /

(1 – (ƒ(Y = 0 ǀ μ(r))) is the zero-truncated count 

distribution with search radius (plot radius) r, 

ƒ(Y = 0 ǀ μ(r)) is the proportion of zero estimated from 

a model, and μ(r) is the estimated expected value from 

zero-truncated count distributions.

For the EZ-Hurdle with a Poisson distribution (EZP) 

model, the expected value and variance for the count 

distribution are:

where P_ez(c,r) is the expected-zero proportion 

estimated by the model for the given search radius r

and forest condition c, μ(c,r) is the expected value of 

the zero-truncated Poisson distribution for the given 

plot size (radius = r) and forest condition c.

RESULTS

The detection probability increased with increasing 

density of standing dead trees and was greater with 

the random pattern than clustered pattern at the 

same density of standing dead trees. Table 1 shows 

the estimated density of standing dead trees from 

3,000 iterations. The EZ-Hurdle method had smaller 

standard deviations than the FIA method for all 

scenarios indicating that the EZ-Hurdle method had 

better precision than the FIA method. However, the 

EZ-Hurdle method showed a small bias when standing 

dead trees were clustered. 

ACKNOWLEDGMENTS

This study was funded by a joint venture agreement 

between Michigan State University and the U.S 

Forest Service, Forest Inventory and Analysis 

Program, Northern Research Station. Some of D.W. 

MacFarlane’s time was paid for with funds from 

Michigan AgBioResearch.

LITERATURE CITED

An, H.S.; MacFarlane, D.W. [N.d.]. Using auxiliary 

data and a model to reduce error in estimating 

standing dead tree abundance where zero 

inflated data are expected under the sample 

design. Manuscript in preparation. On file with 

corresponding author.

Bechtold, W.A., Patterson, P.L., eds. 2005. The 

enhanced Forest Inventory and Analysis 

Program-national sampling design and 

estimation procedures. Gen. Tech. Rep. SRS-80. 

Asheville, NC: U.S. Department of Agriculture, 

Forest Service, Southern Research Station. 85 p.

Table 1.—The estimated density of standing dead trees per ha (± standard deviation) by method, spatial 

pattern, density, and the number of plots. FIA = estimated method by FIA program and EZP = EZ-Hurdle 

method.

Number of Plots

Spatial True 30 40 50 60

Pattern Density FIA EZP FIA EZP FIA EZP FIA EZP

Random 30 29.91 (3.96) 30.60 (3.71) 29.94 (3.39) 30.51 (3.17) 30.02 (3.01) 30.53 (2.77) 30.01 (2.72) 30.51 (2.54)

45 45.12 (4.81) 46.31 (4.49) 44.85 (4.06) 45.98 (3.81) 45.01 (3.64) 46.08 (3.43) 45.03 (3.36) 46.04 (3.17)

Cluster 30 30.56 (5.67) 27.81 (4.90) 29.97 (4.79) 27.57 (4.14) 30.02 (4.48) 27.56 (3.83) 29.97 (4.03) 27.45 (3.42)

45 45.01 (7.74) 40.83 (6.84) 48.18 (6.68) 40.10 (5.86) 45.07 (6.63) 40.48 (4.89) 45.08 (5.64) 40.48 (4.89)
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 341

Curtis, R.O.; Marshall, D.D. 2005. Permanent-plot 

procedures for silvicultural and yield research.

Gen. Tech. Rep. PNW-GTR-634. Portland, OR: 

U.S. Department of Agriculture, Forest Service, 

Pacific Northwest Research Station. 86 p.

Cameron, A.C.; Trivedi, P.K. 1998 regression analysis 

of count data. Cambridge, UK: Cambridge 

University Press. 411 p.

Eskelson, B.N.I.; Temesgen, H.; Barrett, T.M. 2009. 

Estimating cavity tree and snag abundance using 

negative binomial regression models and nearest 

neighbor imputation methods. Canadian Journal 

of Forest Reseach. 39: 1749-1765.

Ganey, J.L. 1999. Snag density and composition 

of snag populations on two national forests 

in northern Arizona. Forest Ecology and 

Management. 117(1-3): 169-178.

Gray, A. 2003. Monitoring stand structure in 

mature coastal Douglas-fir forests: effect of plot 

size. Forest Ecology and Management. 175: 1-16.

Gregoire, T.G.; Valentine, H.T. 2008. Sampling 

techniques for natural and environmental 

resources. New York, NY: Chapman & Hall/CRC 

Press. 474 p.

Mullahy, J. 1986. Specification and testing of 

some modified count data models. Journal of 

Econometrics. 33: 341-365.

Potts, J.M.; Elith, J. 2006. Comparing species 

abundance models. Ecological Modelling. 

199(2): 153-163.

Stephens, S.L. 2004. Fuel loads, snag abundance, 

and snag recruitment in an unmanaged Jeffrey 

pine-mixed conifer forest in Northwestern 

Mexico. Forest Ecology Management. 199(1): 

103-113.

Woodall, C.W.; Domke, G.M.; MacFarlane, D.W.; 

Oswalt, C.M. [In press]. Comparing field- and 

model-based standing dead tree carbon stock 

estimates across forests of the United States.

Forestry.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 342

Optimized Endogenous Post-stratification

in Forest Inventories

Paul L. Patterson1

Abstract.—An example of endogenous post-stratification is the use of remote sensing 

data with a sample of ground data to build a logistic regression model to predict the 

probability that a plot is forested and using the predicted probabilities to form categories 

for post-stratification. An optimized endogenous post-stratified estimator of the 

proportion of forest has been recently proposed in the literature, but there are no known 

literature results describing the operating characteristics of this estimator. This study 

reports the results of a detailed Monte Carlo investigation of the performance of the 

optimized and another endogenous post-stratified estimator under a variety of realistic 

scenarios and compares their performance with earlier approaches.

1

 Statistician, U.S. Forest Service, Rocky Mountain 

Research Station, 507 25th St., Ogden, UT 84401. 

To contact, call 907-295-5966 or email at 

plpatterson@fs.fed.us.

INTRODUCTION

In recent years, estimators have been proposed that 

use remotely sensed data in conjunction with natural 

resource inventory sample data to construct land cover 

or use classifications and then use the classification to 

post-stratify the sample; Breidt and Opsomer (2008) 

proposed the term endogenous post-stratification 

(EPS) to describe the “post-stratification of the 

sample based on categories derived from the sample 

data.” One example is using forest inventory sample 

data in conjunction with Landsat Thematic Mapper 

(TM) imagery to construct a logistic regression 

model that predicts for each pixel the probability the 

land associated with the pixel is forested, and then 

uses a set of strata boundaries so the land associated 

with the TM image can be stratified. An example 

of two strata would be as follows: any pixel with 

probability less than or equal to 0.5 is placed in one 

stratum, and any pixel with probability greater than 

0.5 is in the other stratum. The stratification index 

(the predicted value from the logistic regression 

model) was derived using the sample, so the poststratification is endogenous. Since endogenous poststratification “violates the standard post-stratification 

assumptions that observations are classified without 

error into post-strata and the post-stratum population 

counts are known” (Breidt and Opsomer 2008), the 

statistical properties may differ. Breidt and Opsomer 

(2008) derived statistical properties of endogenous 

post-stratified estimators (EPSEs) in the case that 

the stratification index is the logistic regression 

model, and the stratum boundaries are determined 

independent of the sample data. McRoberts (2010) 

proposed an EPSE where the stratification index is 

a logistic regression model constructed from forest 

inventory sample data and TM imagery, and the strata 

boundaries are constructed based on an optimization 

procedure. To justify certain statistical properties of 

the optimized EPSE (OEPSE) McRoberts (2010) cites 

Breidt and Opsomer (2008), which is incorrect because 

the strata boundaries are derived from the sample data.

The objectives of this study are to (1) construct three 

EPSEs of the proportion of forest; (2) give a process 

for constructing populations that can be used in 

simulation studies to deduce the statistical properties 

of the three EPSEs; and (3) deduce the statistical 

properties of the three estimators based on simulation 

studies for three populations that represent a range of 

geographical regions and vegetative classes.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 343

DATA AND ESTIMATORS

Three study areas were used to construct three 

simulation populations. The three study areas were: 

(1) the portion of path 27, row 27, Landsat scene 

in northern Minnesota, which is the study area in 

McRoberts (2010); (2) the portion of path 37, row 

32, Landsat scene in northern Utah; and (3) path 37, 

row 33, Landsat scene totally within Utah (Fig. 1). 

These scenes, chosen because they represent differing 

geographical regions and vegetative classes, have an 

estimated forest cover of approximately 70, 50, and 

30 percent respectively. TM imagery was acquired 

for three dates corresponding to early, peak, and 

late seasonal vegetative stages (Table 1). For each 

date, the normalized difference vegetative index and 

the tasseled cap transforms (brightness, greenness, 

and wetness) were used. Forest inventory data for 

permanent field plots established by the Forest 

Inventory and Analysis (FIA) Program were obtained 

for each of the scenes. This study used only the central 

Figure 1.—Study areas: A is path 27, row 27; B is path 37, row 32; and C is path 37, row 33.

subplot, which was associated with the image pixel 

that contains the center of the subplot. The numbers 

of totally forested, totally nonforested, and partially 

forested central subplots contained within each scene 

are in Table 1. 

Following McRoberts (2010), the assumptions are (1) 

there is a finite population consisting of N elements 

which are 30 m × 30 m Landsat pixels; (2) there is 

an equal probability sample of n population units 

(3) with observed characteristic yi , the proportion of 

forest for the land associated with each pixel; (4) the 

ancillary information for each population element, xi , 

is 12 Landsat-based spectral transforms; and (5) since 

the sample consists of FIA central subplots, then the 

central subplot is assumed to characterize the entire 

pixel that contains the center of the central subplot. 

The population parameter of interest is the mean 

proportion of forest, 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 344

Table 1.—Dates for the three Landsat scenes, and number and type of FIA plots, used to construct the 

simulation populations. Dates represent early, peak, and late seasonal vegetative stages. Plots are those 

whose center subplot is located in the Landsat scene. 

Forest Inventory and Analysis Field Plots

Remotely Sensed Data Number of Plots

Scene Three Dates Field Seasons Forested Nonforested Partially Forested

Path 27, Row 27 

within Minnesota

April 29, 2000 

July 5, 2001 

November 5, 1999

1999 to 2003 1,677 607 34

Path 37, Row 32 

within Utah

June 14, 2000 

July 30, 2002 October 

10, 1999

2000 to 2009 659 620 15

Path 37, Row 33 

in Utah

July 4, 2007 

August 21, 2007 

October 18, 2004

2000 to 2009 447 909 12

Breidt and Opsomer (2008) present post-stratification 

as a stratification index variable zi , which along 

with a set of strata boundaries is used to partition 

the population into H strata; in endogenous poststratification (EPS) the stratification index, strata 

boundaries, or both, are derived from the sample 

data. For example, the variable zi is a forest/nonforest 

indicator, and is observed on a sample but is unknown 

for the rest of the population. The zi are assumed to 

follow a logistic model, that is

[1]

where the expectation is with respect to the model. 

The model parameters β are estimated by maximizing 

the likelihood using the subset of the sample for 

which the pixel is either completely forested (yi = 1) or 

completely nonforested (yi

 = 0). The estimate β is used 

to define the endogenous stratification index 

 for all population 

elements. To complete an EPS we need to specify 

the strata boundaries, which will be discussed in the 

following paragraph. Once the boundaries have been 

specified, the standard post-stratified estimator (PSE) 

 can be used, that is,

̂

and [2]

where

Wh is the weight of stratum h, yhi is the proportion of 

forest for the ith sample element of stratum h, and 

 is an approximate estimated variance for the 

PSE.

Three methods for specifying strata boundaries will be 

presented and used along with μstr from Equation [2] to 

define an EPSE of the mean proportion of forest. All 

three schemes use the endogenous stratification index, 

zi , defined above. The first EPS scheme uses fixed 

predetermined strata boundaries, 0 = τ0 < τ1 < ... < τH-1

< τH = 1; strata assignment given by zi

 will be denoted 

as the fixed boundary endogenous post-stratified 

estimator (FEPSE). The second EPS scheme specifies 

the strata boundaries using an optimization process. 

For strata boundaries with values rounded 

to the nearest hundredth and for which each stratum 

̂

̂

̂
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 345

contains at least four sample elements, calculate

 (Equation [2]). Determine the set of 

boundaries, over all possible numbers of strata, which 

minimizes ; the OEPSE is calculated using 

this set of strata boundaries. McRoberts (2010) found 

little reduction in when optimizing over two or 

three strata compared to only two strata. Two strata are 

defined by a single stratum boundary; a natural choice 

for that boundary would be the mean proportion of 

forest, which is estimated by the sample mean, that is,

 The third EPSE, which is denoted 

the estimated endogenous post-stratified estimator 

(EEPSE), is calculated using the strata boundaries 

{0, τ̂, 1}.

In EPS the classification of observations and the 

post-stratum population counts depend on the 

sample and these two aspects add additional sources 

of variability. Breidt and Opsomer (2008) showed 

for a class of EPSEs that converges to the 

asymptotic variance of the EPSE, as n,N → ∞ . Breidt 

and Opsomer’s assumptions that are germane to our 

study are: first, the set of stratification indices they 

consider contains zi defined above; and second, the 

strata boundaries are fixed. Hence their result on the 

asymptotic variance applies to the FEPSE, but not to 

the OEPSE or the EEPSE.

̂

SIMULATION POPULATION 

AND SIMULATION STUDY

For each of the three Landsat scenes, the population 

is the landmass covered by the scene; population 

elements are the land delineated by the image pixels. 

The FIA reduced sample refers to the set of FIA 

central subplots which are contained in the scene and 

are completely forested or completely nonforested. 

The goal is assign a value of forest or nonforest to 

each pixel so that the logistic relationship between the 

values of the 12 Landsat-based spectral transforms 

and the forest/nonforest values for the entire scene 

is similar to the logistic relationship between the 

observed forest/nonforest values for the FIA reduced 

sample and the value of 12 Landsat-based spectral 

transforms for pixels associated with the FIA reduced 

sample.

First, the FIA reduced sample and the associated 12 

Landsat-based spectral transforms, xi , were used to 

estimate the parameters β of the logistic regression 

model, Equation [1]. Denote the fitted model by 

m(β'xi) and for 0 < τ < 1 define the variable

 which is a forest/nonforest 

indicator based on the predicted probability of forest 

from the logistic model and the cutoff τ. For the FIA 

reduced sample a measure of misfit between viτ and the 

proportion of forest, yi , was defined as

 where n is the size of the 

FIA reduced sample. The measure of misfit dτ was 

minimized over the grid τ ∈ {0.05, 0.10, ... , 0.95}; 

denote where the minimum occurred by τ0 . Then a 

forest/nonforest indicator variable, zi , was defined 

for every pixel in the scene so the measure of misfit 

between zi and viτ0 over the population was the same 

as the measure of misfit between yi and viτ0 over the 

FIA reduced sample. For the simulation population the 

proportion of forest is 

̂

From the simulation population 4,000 simple random 

samples of size n were drawn. For each sample, j, four 

estimates of the proportion of forest were calculated: 

the simple random sample estimate 

the FEPSE with boundaries {0, 0.5, 1}, the OEPSE, 

and the EEPSE. The estimates for each sample 

are denoted by μ* j , and the value of the proposed 

estimated variance is denoted by V (μ* j ), where the 

* is the acronym for the estimator. To determine both 

the small sample size properties and the asymptotic 

behavior, sample sizes of 100, 200, ... , 700 were used 

(fitting the logistic model was problematic at smaller 

sample sizes). For each sample size the statistical 

properties were evaluated using (1) the empirical 

bias, EBias(*); (2) the empirical mean squared error, 

EMSE(*); and (3) the empirical variance, EV(*), 

where * indicates the estimator.

̂

̂ ̂
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 346

To measure whether the proposed estimated variances 

of the three EPSE converge to the variance of 

the estimator, the actual coverage for 95-percent 

confidence intervals was calculated, that is, the 

percentage of the 4,000 replicates where the interval

 contains the proportion of forest 

Z. A standard measure used to compare two unbiased 

estimators is the relative efficiency between the two 

estimators. For example, the relative efficiency (RE) 

between EEPSE and OEPSE is: RE(EEPSE, OEPSE) 

= (EMSE(EEPSE)) / (EMSE(OEPSE)). 

_

RESULTS AND DISCUSSION

All the EPSEs were empirically unbiased at all sample 

levels. Hence the variance can be used in lieu of the 

Figure 3.—For the simulation population path 37, row 33 

the empirical coverage percentages of the nominal 95-

percent confidence intervals for the optimized endogenous 

post-stratified estimator (OEPSE), the fixed boundary 

endogenous post-stratified estimator (FEPSE), and the 

estimated endogenous post-stratified estimator (EEPSE). 

The empirical coverage percentage is based on 4,000 

realizations. The horizontal line y=94.3 is the lower bound for 

the acceptance region at confidence level of 95 percent.

Figure 2.—For the simulation population path 27, row 27 

the empirical coverage percentages of the nominal 95-

percent confidence intervals for the optimized endogenous 

post-stratified estimator (OEPSE), the fixed boundary 

endogenous post-stratified estimator (FEPSE), and the 

estimated endogenous post-stratified estimator (EEPSE). 

The empirical coverage percentage is based on 4,000 

realizations. The horizontal line y=94.3 is the lower bound for 

the acceptance region at confidence level of 95 percent.

(%)

(%)

mean squared error. The acceptance region for n trials 

of the empirical coverage percentages is 

 where p is the stated confidence 

level, n is the number of simulations, and α is the 

confidence level for the acceptance region. Figures 2 

and 3 show the empirical coverage percentages for two 

simulation populations; the third was similar. For all 

three scenes the pattern of convergence is the same: 

the EEPSE converges at the fastest rate, followed 

by the FEPSE and then the OEPSE. For sample size 

100, the OEPSE empirical coverage percentage is 

well below the empirical coverage percentages of the 

other two EPSEs, indicating V (μOEPSEj ) significantly 

underestimates the true variance. All the empirical 

coverage percentages were in the acceptance region 

when the sample sizes were approximately equal to the 

number of FIA plots in the scene.

̂ ̂
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 347

Table 2.—The empirical relative efficiencies (RE) between the estimated endogenous post-stratified 

estimator (EEPSE), the optimized endogenous post-stratified estimator (OEPSE), and the simple random 

sample estimator (SRS), for three sample sizes for each of the three simulation populations. The empirical 

relative efficiencies are based on 4,000 realizations.

Simulation Population

Path 27 Row 27 Path 37 Row 32 Path 37 Row 33

Sample Size 100 700 2,280 100 500 1,270 100 700 1,350

RE(EEPSE,OEPSE) 1.01 1.15 1.19 0.90 0.97 1.00 0.92 1.03 1.06

RE(SRS,OEPSE) 1.11 1.53 1.59 0.98 1.26 1.35 0.99 1.22 1.28

RE(SRS,EEPSE) 1.10 1.33 1.34 1.09 1.30 1.35 1.08 1.18 1.21

The pattern for relative efficiencies 

RE(EEPSE,OEPSE) and RE(SRS,OEPSE) is the same 

for all three simulation populations: the empirical 

relative efficiency increases as the sample size 

increases. Table 2 contains the relative efficiency 

for sample sizes of 100, either 500 or 700, and the 

sample size approximately equal to the number of 

FIA plots. The results indicate the EEPSE appears to 

be uniformly more efficient than the simple random 

sample estimator (SRS). The rest of the results paint 

a mixed picture. In the arid Interior West (Path 37, 

Rows 32 and 33) the EEPSE is more efficient than the 

OEPSE at small sizes and slightly less efficient for the 

larger sample sizes; the EPSEs appear to have around 

30-percent improvement in efficiency over the SRS 

for large sample sizes and no improvement for small 

sample sizes. In the heavily forested Upper Midwest 

(Path 27, Row 27) the OEPSE appears to have a gain 

in efficiency over the EEPSE for large sample sizes 

and a 50- to 60-percent improvement over SRS. 

This study’s results are applicable to situations where 

most if not all of the sample values for the proportion 

of forest are either 0 or 1. Four conclusions can be 

drawn. First, the three endogenous post-stratified 

estimators appear to be unbiased for sample sizes 100 

and greater. Second, the standard estimated variance 

for post-stratified estimators appears to asymptotically 

converge to an estimate of the true variance for the 

estimated and optimized endogenous post-stratified 

estimators. Third, the estimated endogenous poststratified estimator’s asymptotic variance appears 

to have the faster rate of convergence, followed by 

the fixed boundary estimator and then the optimized 

estimator. Fourth, the estimated and optimized 

endogenous post-stratified estimators appear to have 

a higher efficiency compared to the simple random 

sample estimator. Additionally, it appears that for 

some land cover and large sample sizes the optimized 

endogenous post-stratified estimator has greater 

efficiency than the estimated endogenous poststratified estimator.

LITERATURE CITED

Breidt, F.J.; Opsomer, J.G. 2008. Endogenous poststratification in surveys: classifying with a 

sample-fitted model. The Annals of Statistics. 

36(1): 403-427.

McRoberts, R.E. 2010. Probability- and model-based 

approaches to inference for proportion forest 

using satellite imagery as ancillary data. Remote 

Sensing of Environment. 114: 1017-1025.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 348

PROPERTIES OF THE ENDOGENOUS POST-STRATIFIED ESTIMATOR 

USING A RANDOM FORESTS MODEL

John Tipton, Jean Opsomer, and Gretchen G. Moisen1

Abstract.—Post-stratification is used in survey statistics as a method to improve variance 

estimates. In traditional post-stratification methods, the variable on which the data is 

being stratified must be known at the population level. In many cases this is not possible, 

but it is possible to use a model to predict values using covariates, and then stratify on 

these predicted values. This method is called endogenous post-stratification estimation 

(EPSE). In this paper, we investigate methods to automatically select the number of poststrata for EPSE. We do this in the context of models fitted by Random Forests with the 

stratum boundaries set at quantiles of the predicted distribution. 

1

 Graduate Student (JT), 102 Statistics Building, 

Colorado State University, Fort Collins, CO 80523; 

Statistics Department Chair (JO), Colorado State 

University; Research Forester (GGM), U.S. Forest Service, 

Rocky Mountain Research Station. JT is corresponding 

author: to contact, call 970-231-9494 or email at 

Jtipton25@gmail.com.

INTRODUCTION

Post-stratification is used in survey sampling designs 

as a method to improve variance estimates by 

calibrating to known population quantities (Särndal et 

al. 2003). In the U.S. Forest Service Forest Inventory 

Analysis Program (FIA), the stratum categories are 

often land cover classifications based on remote 

sensing data. In many cases it is desirable to use the 

FIA data itself to develop the very maps used for 

stratification. This method is called endogenous poststratification estimation (EPSE) and the theoretical 

properties of this method were first introduced by 

Breidt and Opsomer 2008, and then extended to a 

broader class of nonparametric models by Dahlke 

et al. 2012. The use of FIA data to construct maps 

and then the subsequent use of these maps as poststratum to construct estimates has the potential for 

substantially reducing variance in these estimates. 

Given the increased use of more complex predictive 

models in developing forest attribute maps in survey 

applications, there is an urgent need for simulation 

studies to investigate the properties of the EPSE 

method and determine under which conditions the 

EPSE estimator works and under which it fails. 

This paper has three main goals. First is to compare 

the EPSE estimator properties using a linear model, a 

spline model, and a Random Forests model (Breimam 

2001) to develop post-stratum maps. The second goal 

is to investigate the effects of using estimated stratum 

boundaries instead of fixed stratum boundaries. The 

third aim of the simulation study is to investigate the 

effects of a minimization of the variance estimate on 

the EPSE estimator. 

THE ENDOGENOUS 

POST-STRATIFIED ESTIMATOR

Following the EPSE framework described by Briedt 

and Opsomer (2008), a sample s of size n is taken 

from a population U = {1, ... , i, ... , N} of size N

according to a probability design p(.) where p(s) is 

the probability of drawing the sample s. For each 

i ∈ U a vector of covariates xi

 and a response yi is 

observed. There is assumed to be a true relationship 

between xi and yi , denoted m(.), where E [yi | xi] = 

m(xi

) which is estimated by m(xi ̂ ).
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 349

The models used for this simulation study are a linear 

model, a spline model, and a Random Forests model. 

Details on the models and dataset are omitted from 

this paper. In EPSE, the model predictions m(xi), 

i = 1, ... , N are sorted into H fixed stratum based on 

the stratum boundaries τ1, τ2, ... , τH–1 where m(xi) is in 

the hth stratum if I(τh-1 < m(xi) ≤ τh) = 1. The estimated 

sample counts in stratum h are given by nh and the 

estimated population counts in stratum h are given by 

Nh . The ratio is the estimated stratum weight for 

stratum h. Using the estimated stratum weights, the 

stratum mean μh is calculated for each stratum h by

̂

̂

̂

̂

Nh

̂

nh

̂ ̂

̂

[1]

The EPSE estimator μy for the population mean is 

calculated by

[2]

̂

The estimates V(μy) for Var(μy) are calculated using the 

post-stratified formulas in Särndal et al. 2003 by

[3]

where 

is the sample variance for stratum h. For the simulation 

study, fixed stratum values for τh are considered 

following Breidt and Opsomer (2008). Also, estimated 

stratum values τh based on quantiles of the model 

predictions for the set of population covariates xi , 

i ∈ U are considered.

̂

̂ ̂ ̂

EMPIRICAL PROPERTIES 

OF THE ESTIMATOR

The data used for this study are from the pilot study of 

Utah for the 2011 National Land Cover Data (NLCD) 

canopy cover map. The study region consisted of 

4151 observations in Utah. At each location, aerial 

photography was interpreted to determine the 

percentage canopy cover. This is the forest response 

variable of interest in these simulations. Empirical 

models of tree canopy cover were then derived by 

modeling this tree canopy cover as functions of 

Landsat TM reflectance values and topographic values 

described in detail in Coulston et al. (2012). These 

empirical models were then used to predict tree canopy 

cover and develop the post-strata through a variety of 

binning rules applied to predicted values. 

Comparison of Fixed vs. Estimated 

Stratum Boundaries

The first simulation study is designed to address two 

questions. First, how does the EPSE performance 

compare between the linear model, spline model, and 

Random Forests model when the stratum boundaries 

are fixed. This is the case where the theory is well 

known. And second, how is the EPSE performance 

affected when the stratum boundaries are estimated by 

sample quantiles as compared to using predetermined 

fixed stratum boundaries. To get the fixed stratum 

boundaries, each empirical model was fit using the 

full dataset. Assuming the empirical model is correct, 

the population quantiles of percentage canopy cover 

are determined. This stratification scheme is fixed and 

not dependent on the sample (i.e. it is the same value 

for all samples, or a priori). The strata for the fixed 

stratum boundaries simulation are (–∞,Q1(y)], 

(Q1

(y), Q2(y)], (Q2(y), Q3(y)], (Q3(y), ∞) where Q1(y) 

represents the 25th percentile of the predicted values 

of percentage tree cover based on the full population. 

The second stratification uses estimated quantiles from 

the empirical model fit using the sample and then the 

estimated quantiles are used as stratum boundaries. 

Note that in this second scheme, the quantiles are 

dependent on the sample s, or a posteriori. The strata 

for the estimated stratum boundaries simulation are 

(–∞,Q1

(y)], (Q1(y), Q2(y)], (Q2(y), Q3(y)], (Q3(y), ∞)

where Q1

(y) represents the 25th percentile of the 

predicted values of percentage tree cover based on 

the full population. For both of these strata definitions 

the behaviour of the EPSE estimator μy and its 

variance estimator V(μy) at different sample sizes was 

investigated for the three different models. All models 

used the full set of covariates as predictors and no 

model selection was performed. For each iteration of 

̂ ̂ ̂ ̂ ̂ ̂

̂

̂

̂ ̂
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 350

the simulation, a sample of size n was taken from the 

4151 observations. The different models were fit using 

the covariates in the sample and the percentage tree 

cover values were estimated as the response.

For each iteration of the simulation, μy and V(μy) 

were calculated using equations (1), (2), and (3). This 

process was repeated for 1000 iterations and the mean 

of the variance estimate

was compared to the variance of the post-stratified 

estimator of the mean Var(μy).

̂ ̂ ̂

̂

equal size by the quantiles of the model predictions 

based on the covariates for the population.

We start this simulation as before by taking a random 

sample of size n from the population of 4151 sites 

in the Utah dataset. For each sample we fit a linear 

regression model, spline regression model, and 

Random Forests model using covariates to model 

percentage tree cover. Predictions were made for the 

sample values based on the model fits.

After computing the model predictions for the 

population, the strata over which optimization will be 

performed can be created. For k = 1, one stratum is 

used and is equivalent to simple random sampling. For 

arbitrary k, the predictions are placed into k equally 

spaced quantiles.

For a fixed n, the optimized EPSE estimator using 

Random Forests has the smallest variances and 

variance estimates of the three models, the spline 

model has the next smallest variances and variance 

estimates, and the linear model has the largest 

variances and variance estimates. The results also 

suggest that both the linear model and the spline 

model are overfitting the data resulting in the variance 

estimates being too small. This simulation study 

supports the use of Random Forests in EPSE, but 

suggests that an EPSE estimator based on linear 

model or spline model can underestimate the variance 

when an additional optimization is performed. This 

statement appears to hold for both the optimized and 

non-optimized EPSE estimates. This is a reasonable 

result since no model selection was performed for 

any of the models and therefore there are too many 

covariates for the spline model and the linear model.

CONCLUSION

This study has shown that use of the EPSE estimator 

should not be applied without a simulation study to 

determine if variance estimates for the EPSE estimator 

are over- or under-estimating the true variance. 

Furthermore, care must be taken in implementing 

Results indicate that the EPSE estimator can be 

extended to include the Random Forests model. 

This simulation also supports the use of the EPSE 

estimator when the stratum boundaries are estimated 

quantiles from the model fits instead of fixed stratum 

boundaries. The justification for using the quantiles 

of the model predictions for the set of population 

covariates is to avoid having to deal with unequal 

sampling weights if present in the sampling design. 

The EPSE estimator appears to be robust for different 

models and under estimated stratum boundaries as 

long as care is taken to correctly specify the model 

and no optimization step is performed. There were 

also some technical issues involving numerical 

integration methods used in the statistical software 

not discussed in this paper. For this study, all elements 

in the population have equal probabilities of being 

sampled, but this method of using the predictions at 

the population level should allow for the use of an 

unequally weighted sampling design. This is an area 

for further research. 

Properties of Minimization 

of EPSE Variance Estimates

For the second simulation the goal is to construct the 

smallest variance estimate V(μy) and to determine if 

this algorithm is performing well as an estimator for 

the true variance Var(μy). The optimization will be over 

the number of strata to be used in the EPSE estimator. 

The model predictions will be split into stratum of 

̂ ̂

̂
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 351

the EPSE method with software that uses numerical 

integration methods to prevent rounding errors from 

influencing the results. Caution is needed when 

attempting to optimize the variance estimates as severe 

under-estimation of the variance of the EPSE estimator 

occurred in this study. The use of a model selection 

step in building the model has been shown to reduce 

this problem.

This study lends strength to the idea that EPSE can 

be applied to stratum boundaries that are estimated 

quanitles of the data rather than fixed stratum 

boundaries. This is an area for further research as in 

practice it is easier to implement the EPSE estimator 

using estimated quantiles and thereby eliminating the 

possibility of empty stratum. The Random Forests 

model performed well in each simulation and across 

all sample sizes considered. This is an exciting result 

in that there is almost no tuning needed by the user 

to fit the Random Forests model. This supports 

FIA’s use of maps of land cover and percentage tree 

cover created by Random Forests as a basis for using 

endogenous post-stratification as a way to increase 

precision of FIA estimates. 

ACKNOWLEDGMENTS

The authors would like to thank the FIA for support 

of this research. In addition, thanks go out to the FIA 

and the Remote Sensing Applications Center for the 

dataset used in this study. 

LITERATURE CITED

Breidt, F.J.; Opsomer, J.D. 2008. Endogenous poststratification in surveys: classifying with a 

sample-fitted model. The Annals of Statistics. 

36(1): 403-427.

Breiman, L. 2001. Random forests. Machine 

Learning. 45: 5-32.

Coulston, J.W.; Moisen, G.G.; Wilson, B.T.; Finco, 

M.V.; Cohen, W.B.; Brewer, C.K. 2012. Modeling 

percent tree canopy cover: a pilot study.

Photogrammetric Engineering and Remote Sensing. 

78(7): 715-727.

Dahlke, M.; Breidt, F.J.; Opsomer, J.D.; Van 

Keilegom, I. [In press]. Nonparametric 

endogenous post-stratification estimation.

Statistica Sinic. 

Särndal, C.E.; Swensson, B.; Wretman, J. 2003. Model 

assisted survey sampling. New York, NY: Springer 

Verlag. 694 p.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 352

Improved Prediction Of Hardwood Tree Biomass

Derived From Wood Density Estimates

And Form Factors For Whole Trees

David W. MacFarlane and Neil R. Ver Planck1

Abstract.—Data from hardwood trees in Michigan were analyzed to investigate how 

differences in whole-tree form and wood density between trees of different stem diameter 

relate to residual error in standard-type biomass equations. The results suggested that 

whole-tree wood density, measured at breast height, explained a significant proportion 

of residual error in standard-type allometric equations, but whole-tree form factors 

explained more. However, such form factors are highly variable from tree to tree and 

may be difficult to predict with any precision from simple tree measurements. Whole-tree 

form factors were found to be highly correlated with the percentage of total aboveground 

mass in tree branches, which likely relates to the allometric scaling of the deliquescent 

hardwood growth form. These results suggest that further studies are needed to 

understand whole-tree form factors and incorporate them into tree biomass equations.

1

 Associate Professor (DWM) and Graduate Research 

Assistant (NRV), Michigan State University, Department 

of Forestry, East Lansing, MI 48824-1222. DWM is 

corresponding author: to contact, call 517-355-2399 or 

email at macfar24@msu.edu. 

INTRODUCTION

The basic problem for accurate forest mass inventory 

is that standing trees cannot be weighed, so biomass 

estimates must be derived from allometric scaling 

principles and dimensional measurements of trees, 

principally tree stem diameter at breast height (d.b.h.). 

Since trees with the same basic measurements can 

have very different form and growth rates, allometricscaling relationships should vary widely over space 

and time due to differences in tree form and wood 

density. In principle, if one could measure tree form 

and wood density on every standing tree, generalized 

allometric scaling equations could be made very 

accurate across diverse ecosystems. Wood density 

can be determined from tree cores (Williamson and 

Wiemann 2010) or can be derived from published 

values (Chave et al. 2005). However, wood density 

estimates are generally taken at breast height and 

may not represent the whole tree. While methods for 

estimating the form of the main stem (a.k.a. the bole) 

of a tree are available (e.g., Flewelling et al. 1998), 

methods for estimating the form of a whole tree are 

lacking (but see Cannell 1984). 

Here, data from hardwood trees in Michigan were 

analyzed to investigate how differences in whole-tree 

form and wood density between trees of different 

stem diameters relate to residual error in standard-type 

biomass equations. Methods of predicting whole-tree 

form from other tree attributes were also examined. 

Data

The study location was Fred Russ Experimental 

Forest, which is owned by Michigan State University 

(MSU) and is located in Decatur, MI. Following a 

major storm event, 32 hardwood trees ranging from 

15 to 91 cm in size were selected from a larger group 

of wind-felled trees for whole-tree measurements 

and destructive sampling. Tree species included 

American basswood (Tilia Americana L.), American 

beech (Fagus grandiolia Ehrh.), black cherry (Prunus 

serotina Ehrh.), slippery elm (Ulmus rubra Muhl.), 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 353

and sugar maple (Acer saccharum Marsh.). Beginning 

at the point where the trunk flares, stem diameter 

outside bark was measured at 2-meter intervals until 

the first forking point (defined as the crown base) 

occurred. From that point, three paths were followed 

from the first fork to a terminal twig including the 

dominant path, which was the largest branch leading 

to an apical control point (i.e., the top of the tree), 

and two random paths selected using random branch 

sampling protocols with probability proportional to 

branch cross-sectional area (Gregoire et al. 1995). 

In addition to measurements along the path at 2-m 

intervals, the stem diameter before each forking point 

and all stem diameters after each forking point were 

also measured, which allowed for variable probability 

sampling. 

After all measurements were taken, tree discs were 

removed from all measurement locations starting at 

d.b.h. The discs were measured and weighed fresh 

after transport to the laboratory and then dried to a 

constant mass at 105 °C. Basic wood density was 

estimated following procedures outlined in Williamson 

and Wiemann (2010). 

Analysis

Whole-tree wood volume (VW) and mass (MW) 

estimates were generated using the sample data 

and expansion factors derived from random branch 

sampling (Gregoire et al. 1995), and component 

volume and mass values were also estimated for 

branches and the dominant stem. Breast height wood 

density (ρ) was calculated from the disk removed 

at breast height, and wood density of the dominant 

stem, branches, and the whole tree were estimated by 

dividing the mass by the volume of those components. 

Diameter of the stem was measured at breast height 

(D) and stem height (H) was measured. Whole-tree 

form factor (F) was computed as the ratio of wholetree volume to a proxy tree volume: F = VW / VP , 

where VP

 = 1⁄4πD2H. 

A standard d.b.h.-based allometric equation was fit to 

the tree data: 

MW = αDβ (1)

where MW is the dry mass of a whole tree (without 

leaves), and α and β are the scaling and power 

coefficients of a power function. The coefficients 

were estimated with least squares regression. Relative 

error (RE) in biomass estimation (predicted-observed/

observed) from equation 1 was related to ρ and F to 

determine their contribution to RE.

RESULTS AND DISCUSSION

When the RE of biomass prediction from the equation 

1 was regressed against breast-height ρ and whole-tree 

F, it was found that both explained significant fractions 

of the relative error in biomass estimation from d.b.h., 

but F explained a greater proportion of the RE than ρ. 

While several studies have shown that adding ρ can 

improve allometric equations (e.g., Chave et al. 2005), 

there has been little attention to F. However, Canell 

(1984) showed that the allometric scaling coefficients 

of aboveground biomass equations derived from stand 

basal area and average tree height were positively 

correlated with the percentage of total aboveground 

biomass comprised of branches, and that the 

percentage of branches was correlated with average 

stand F. The results presented here suggest that wholetree F correlates directly with error in individual 

tree mass estimation and should be a useful addition 

to allometric equations, where it can be estimated. 

However, estimation of F requires that whole-tree 

volume be estimable on standing trees. 

Since F was shown to be related to the percentage 

of whole-tree mass found in the branch component 

(Cannell 1984), F was plotted as a function of the 

percentage of mass in branches (Fig. 1a), and they 

were found to be well correlated (70 percent). 

MacFarlane (2011) suggested several predictors of 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 354

percent branch volume, and these were examined 

as possible predictors of F. Unfortunately, the best 

predictor of F, the product of the diameter of the 

largest branch (d.l.b.) in the tree and d.b.h., explained 

only about 42 percent of the variation in F (Fig. 1b), 

which may not be precise enough to improve accuracy 

of the biomass equation. However, it may be possible 

to directly estimate whole-tree volume on standing 

trees (Van Duesen and Roesch 2011) along with 

standard measurements of d.b.h. and tree height, in 

which case F could be directly estimated rather than 

predicted.

ACKNOWLEDGMENTS

This study was funded through a Sustainability Seed 

Grant from Michigan State University (MSU) and 

with funds from a joint venture agreement between 

MSU and the U.S. Forest Service Forest Inventory and 

Analysis Program, Northern Research Station. Part of 

D.W. MacFarlane’s time was paid for with funds from 

Michigan AgBioResearch and a Fulbright Scholarship 

he received. D.W. MacFarlane would also like to thank 

the Kenya Forest Research Institute who provided 

logistical support to him while he conducted part of 

the research while on sabbatical.

LITERATURE CITED

Cannell, M.G.R. 1984. Woody biomass of forest 

stands. Forest Ecology and Management. 8: 

299-312.

Chave, J.; Andalo, C.; Brown, S.; Cairns, M.A.; 

Chambers, J.Q.; Eamus, D.; Fölster, H. [et al.]. 

2005. Tree allometry and improved estimation 

of carbon stocks and balance in tropical forests.

Oecologia: 145: 87-99.

Flewelling, J.W.; Ernst, R.L.; Raynes, L.M. 2000. Use 

of three-point taper systems in timber cruising.

In: Hansen, M.; Burk, T., eds. Integrated tools for 

natural resources inventories in the 21st century: 

proceedings of the IUFRO conference. Gen. Tech. 

Rep. NC-212. St. Paul, MN: U.S. Department of 

Agriculture, Forest Service, North Central Research 

Station: 364-371.

Gregoire, T.G.; Valentine, H.T.; Furnival, G.M. 1995. 

Sampling methods to estimate foliage and other 

characteristics of individual trees. Ecology. 76(4): 

1181-1194.

MacFarlane, D.W. 2011. Allometric scaling of 

branch volume in hardwood trees in Michigan, 

USA: implications for improvements in aboveground forest carbon biomass inventories. Forest 

Science. 57(6): 451-459.

R2 = 0.7011

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

0 20 40 60 80 100

F - whole tree form factor

% mass in branches

a

R2 = 0.4188

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

0 1000 2000 3000 4000 5000

F - whole tree form factor

dlb*dbh (cm2)

b

Figure 1.—Whole-tree form factor (F) as a function of: 

(a) percent mass in branches; and (b) the product of the 

diameter of the largest branch (d.l.b.) in a tree and the stem 

diameter at breast height (d.b.h.).
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 355

Van Deusen, P.; Roesch, F.A. 2011. Sampling a tree 

for total volume, biomass, and carbon. Journal of 

Forestry. 109(3): 131-135.

Williamson, G.B.; Wiemann, M.C. 2010. Measuring 

wood specific gravity...correctly. American 

Journal of Botany. 97: 448 -524.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Challenges and Opportunities

 for Improved Biomass

 and Carbon Monitoring

 of U.S. Tree Species
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 357

BIOMASS MEASUREMENT AND MODELING CHALLENGES 

FOR HARDWOOD SPECIES IN THE NORTHERN REGION

James A. Westfall, David W. MacFarlane, and Aaron R. Weiskittel1

Abstract.—Biomass models for most commercially important hardwood species in the 

northern region of the U.S. are often based on data of very limited spatial extent and 

range of tree characteristics, suggesting uncertain accuracy when applied at regional 

scales. Also, the current models can have poor predictive ability for the proportions 

of biomass found in major tree components considered for utilization, namely the 

merchantable bole, bole tops, tree branches, and foliage. The Forest Inventory and 

Analysis Program of the U.S. Forest Service is currently undertaking a project to obtain 

regionally representative data to develop new volume/biomass models. This paper 

outlines issues related to challenges in data collection and subsequent modeling of 

biomass components for hardwood species. 

1

 Research Forester (JAW), U.S. Forest Service, Northern 

Research Station, 11 Campus Blvd., Suite 200, Newtown 

Square, PA 19073; Associate Professor (DWM), Michigan 

State University; Assistant Professor (ARW), University of 

Maine. JAW is corresponding author: to contact, call 

610-557-4043 or email at jameswestfall@fs.fed.us.

INTRODUCTION

A number of tree biomass studies were conducted 

in the northern region from the late 1970s into the 

early 1980s (Smith and Brand 1983, Wiant et al. 

1977). These studies provided the initial biomass 

estimation procedures for many hardwood species 

in the region. However, data collection was often 

limited to relatively small areas and ranges of tree 

characteristics. Nonetheless, equations from various 

sources were adopted by the Forest Inventory and 

Analysis (FIA) Program (an entity of the U.S. Forest 

Service) because these equations provided the only 

opportunity to include biomass estimates in forest 

resource reports. Due to differences among studies 

in definitions, data collection protocols, and model 

forms, predicted values of biomass for trees of a given 

size could vary considerably. As biomass estimates 

became increasingly important, FIA implemented a 

nationally consistent method to estimate individualtree biomass components (Heath et al. 2009). This 

change in methodology produced estimates that were 

sometimes incongruous with those of the earlier 

models, raising concerns about which method(s) 

provided the most accurate results (Domke et al. 2012, 

Westfall 2011). In the absence of data required to make 

such assessments, FIA has committed to collecting 

regionally representative biomass data to provide a 

common basis for biomass estimator development. 

Although there are several important conifer 

species in parts of the northern region, the forests 

are largely dominated by hardwood species. Due to 

their decurrent form, hardwoods require innovative 

techniques to appropriately measure and model 

biomass components.

MEASUREMENT CHALLENGES

Several challenges must be faced when measuring 

hardwood species for biomass. Foremost, tree 

form has an unknown effect on allometric scaling 

relationships and is critical to estimating biomass 

components. There is an inherent difficulty in 

recording numerous hierarchical orders of branches 

and foliage, so a complex measurement and recording 

system is needed to permit reconstruction of the 

tree. Measurement protocols should be sufficiently 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 358

prescribed such that no critical information is 

overlooked or measured incorrectly. Conversely, the 

detail and cost of measurements should not exceed 

that needed to meet accuracy objectives in predicting 

biomass. Also, the protocols must be understandable 

and intuitive. Theoretically and technically sound 

protocols are of little use if they cannot be consistently 

and accurately applied in the field. A related issue is 

the need to consistently assign tree parts to component 

categories (e.g., branch vs. top wood), especially as 

merchantability and utilization standards change over 

time. Cost (1978) provides examples of field protocols 

designed for measuring volumes in traditional product 

categories including sawtimber and pulpwood, but 

these protocols are not entirely suitable for biomass 

determination. 

To increase efficiency, sampling may be used for 

branch and foliage components. However, the design 

of an unbiased, high-precision sampling design is 

required before widespread implementation. For this 

study, the first year of data collection will entail a 

high level of detail for individual-tree measurements, 

which will allow assessment of various sampling 

protocols that may be implemented in subsequent 

years. Obviously, this exercise hinges on successful 

implementation of the detailed measurement protocols 

described in the above paragraph. Also, selection 

of trees that well represent the range of branching 

and foliage patterns found in the population will be 

essential.

Unlike many volume attributes, which are based on 

trees of merchantable size, biomass predictions are 

needed for smaller trees (e.g., d.b.h. 1.0 inch and 

greater). Furthermore, current biomass estimates for 

small trees can be suspect because many existing 

biomass models are based primarily on trees of 

merchantable size. Thus, ensuring that saplingsize trees are included in the sample is imperative. 

However, inclusion of small trees can create 

measurement challenges because data collection 

protocols are often specified assuming application to 

trees of merchantable size (e.g., biomass of the main 

stem may be defined as that occurring below the 4-

inch top diameter). Thus, measurement protocols must 

be specified such that there is applicability across all 

tree sizes.

Because prediction models will ultimately be applied 

to standing trees, a key component in the measurement 

phase is understanding how measurements of standing 

trees correlate with more accurate data taken on felled 

trees. An example would be branch length, which may 

or may not be needed in addition to branch diameter 

to accurately predict branch biomass. Perhaps branch 

allometry suggests that knowledge of branch diameter 

alone is sufficient to obtain acceptable biomass 

predictions. Understanding the information needs in 

this context is important, because branch lengths on 

standing trees are likely only accurately measured as 

the linear distance from branch base to tip. However, 

most branches have some degree of curvature that 

would not be accounted for in the linear distance 

measure, and thus recorded branch length would 

not reflect the actual length. If branch length is vital 

to branch biomass prediction, differences between 

linear branch lengths taken on standing trees and the 

actual length would need to be empirically assessed 

on trees that are felled. Identifying and accounting for 

such issues in the data collection process is critical 

if unbiased estimates of biomass are to be produced 

based on data elements collected on standing trees 

(e.g., FIA plots).

MODELING CHALLENGES

Several challenges are also faced when considering 

biomass model development for hardwood species. 

First, one must consider the sample size needed 

to establish statistically significant relationships 

between predictor variables and biomass values. Due 

to the wide variation in tree characteristics, more 

samples are likely needed for hardwoods than for 

softwoods. Unfortunately, the number of sample trees 

is often limited due to financial constraints. In these 

situations, it is advisable to estimate the minimum 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 359

sample size deemed necessary and ensure resources 

exist to attain that base level of sampling. Given that 

an appropriate number of sample trees have been 

determined, additional work is needed to specify tree 

selection processes. For example, it may be desirable 

to specify numbers of trees to be sampled by size 

class, form or crown architectural characteristics, 

and/or spatial distribution. These specifications should 

be thoroughly evaluated, because substantial effort 

may be incurred to find and measure certain relatively 

rare occurrences, e.g., very large d.b.h. trees. On the 

other hand, one must also consider that sampling trees 

based on frequency alone would be sub-optimal in 

that most sample trees would be near the middle of 

the distribution(s) of selection criteria and relatively 

little information would be obtained at the lower and 

upper ranges. Such an approach could lead to poor 

predictions for trees that do not exhibit characteristics 

represented in the modeling data. The goal is for the 

sample to cover the widest range of variation in tree 

size and form attributes across the geographic range 

of occurrence; however, this must be balanced within 

practical cost and time constraints.

Another major issue is that the spatial variation in 

species-specific allometric scaling coefficients of 

hardwoods is largely unknown. Due to the high 

cost of felled-tree data, characterizing changes in 

biomass parameters across various gradients (e.g., 

latitude, longitude, and elevation) will require 

ancillary standing-tree information. The types and 

amounts of ancillary data needed to capture these 

shifts in hardwood tree allometry must be determined. 

Due to the limitations of measuring standing trees 

from ground-position, the ability to obtain accurate 

measurements of specified data elements also must 

be assessed, which includes both unobstructed linesof-sight and types of equipment required. Finally, a 

statistically defensible method to incorporate these 

data into the analysis must be developed. 

Potential grouping of species having similar 

characteristics should also be evaluated. For hardwood 

species, this may be particularly difficult due to 

the excessive geographic and phenotypic variation. 

Typically, such groupings have been accomplished 

using genera or categorical classification, such as 

oak/hickory, although different growth habits among 

species within such groups may make them unsuitable 

for aggregation when modeling biomass. The best-case 

scenario is to have pre-determined groupings such that 

the sampling effort can be lessened by having grouplevel (vs. species-level) sample size requirements. 

However, one then assumes the risk of having species 

within groups that actually are not as similar as 

expected. In the absence of reliable information on 

aggregation criteria, caution should be exercised in 

grouping species before data collection.

A final consideration is the duration of the data 

collection effort. One option is to set the sample 

size requirements for model development and cease 

fieldwork once those requirements are met. An 

alternative scenario would be to not only develop 

biomass models from the initial effort, but also 

to continue to collect data as well. One potential 

option would be to incorporate needed biomass 

measurements into existing Timber Products Output 

(TPO) studies conducted by FIA (Wharton and 

Birch 1999). This would provide a continual stream 

of additional information that could be used to 

periodically update existing models. While the latter 

approach seems more desirable from a scientific 

viewpoint, there are programmatic issues for FIA 

because biomass predictions for trees on inventory 

plots change over time, which can be problematic 

for some clients, e.g., reprocessing of older data with 

newer biomass values will produce different analytical 

results. Implementation of a continual data collection 

paradigm would need to be carefully considered before 

implementation to provide balance between the best 

scientific practices and program credibility.

CONCLUSION

Measuring and modeling biomass for hardwood 

species is not a trivial exercise. Due to the decurrent 

form, substantial effort may be needed to collect multilevel branch and foliage data. The data also need to 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 360

be collected consistently across various species, tree 

sizes/forms, and field personnel. Thus, field protocols 

must be explicitly defined and consistently interpreted. 

Obtaining reliable data for hardwoods also requires an 

understandable and intuitive protocol for recording the 

extensive measurements, such that the tree can later be 

accurately reconstructed or re-analyzed as utilization 

standards change.

A primary difficulty faced in modeling biomass 

of hardwood species is establishing relationships 

between biomass and usual mensurational variables 

such as d.b.h and total height. The wide range of 

tree forms assumed by hardwood species creates 

substantial variability, which may require large sample 

sizes to develop models having acceptable degrees 

of predictive accuracy. Particularly, it is necessary 

to empirically describe how relationships between 

biomass components change in relation to various 

factors (e.g., tree size and form). Practical constraints 

on felled-tree data collection suggest that ancillary 

information from standing trees will be necessary to 

further refine model predictions for local accuracy and 

that grouping of species may be helpful in attaining 

sufficient sample sizes. However, the techniques by 

which these issues are best addressed must still be 

developed and evaluated.

LITERATURE CITED

Cost, N.D. 1978. Multiresource inventories—a 

technique for measuring volumes in standing 

trees. Res. Pap. SE-196. Asheville, NC: U.S. 

Department of Agriculture, Forest Service, 

Southeastern Forest Experiment Station. 18 p.

Domke, G.M.; Woodall, C.W.; Smith, J.E.; Westfall, 

J.A.; McRoberts, R.E. 2012. Consequences 

of alternative tree-level biomass estimation 

procedures on U.S. forest carbon stock 

estimates. Forest Ecology and Management. 

270: 108-116.

Heath, L.S.; Hansen, M.H.; Smith, J.E.; Miles, P.D.; 

Smith, B.W. 2009. Investigation into calculating 

tree biomass and carbon in the FIADB using 

a biomass expansion factor approach. In: 

McWilliams, W.; Moisen, G.; Czaplewski, R., 

comps. Forest Inventory and Analysis (FIA) 

Symposium 2008. Proc. RMRS-P-56CD. Fort 

Collins, CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research Station. 

26 p.

Smith, W.B.; Brand, G.J. 1983. Allometric biomass 

equations for 98 species of herbs, shrubs, and 

small trees. Res. Note NC-299. St. Paul, MN: 

U.S. Department of Agriculture, Forest Service, 

North Central Forest Experiment Station. 8 p.

Westfall, J.A. 2011. A comparison of aboveground dry-biomass estimators for trees in the 

northeastern United States. Northern Journal of 

Applied Forestry. 29: 26-34.

Wharton, E.H.; Birch, T.W. 1999. Trends in timber 

use and product recovery in New York. Res. 

Note NE-367. Radnor, PA: U.S. Department of 

Agriculture, Forest Service, Northeastern Research 

Station. 7 p.

Wiant, H.V., Jr.; Sheetz, G.E.; Golaninno, A.; DeMoss, 

J.C.; Castaneda, F. 1977. Tables and procedures 

for estimating weights of some Appalachian 

hardwoods. West Virginia University Agricultural 

and Forestry Experiment Station Bull. 659T. 36 p. 

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 361

STRATEGIES FOR ASSESSING INTER- AND INTRA-SPECIFIC

VARIATION IN TREE BIOMASS IN THE INTERIOR WEST

David L.R. Affleck, John M. Goodburn, and John D. Shaw1

Abstract.—Wildfire hazard mitigation and bioenergy harvesting have emerged as 

forest management priorities throughout the Interior West (IW) of the USA. Regional 

forest inventory and forecasting applications are therefore increasingly focused on 

tree biomass, including biomass in traditionally non-merchantable components. Yet 

accurate biomass equations for the latter components are typically lacking, even for 

major commercial species. This is because previous assessment efforts have focused on 

distinct components, have used inconsistent methodologies, or have relied on data with 

limited spatial or biophysical extents. Here we review and contrast the current state of 

knowledge of a widely distributed and commercially important species, ponderosa pine 

(Pinus ponderosa), and a locally important species of the southwest, two-needle pinyon 

(Pinus edulis). Owing to high levels of inter- and intra-specific variation in biomass 

allometry and to the high costs of collecting biomass data, an important element of new 

data collection efforts will be the development of standardized and accurate subsampling 

procedures. We describe these subsampling strategies and discuss potential adaptations 

for two-needle pinyon in the southwest.

1

 Associate Professor of Biometrics (DLRA) and Associate 

Professor of Silviculture (JMG), University of Montana, 

32 Campus Drive, Missoula, MT 59810; Analysis Team 

Leader (JDS), U.S. Forest Service, Rocky Mountain 

Research Station. DLRA is corresponding author: to contact 

call 406-243-4186 or email at david.affleck@umontana.edu. 

INTRODUCTION

Across the Interior West (IW) of the United States, 

the management of federally administered forest lands 

is increasingly being shaped by policies related to 

wildfire hazard mitigation, bioenergy development, 

and ecological restoration. At the same time, on private 

lands there is heightened interest in regeneration and 

the growth dynamics of young stands. As such, there 

are growing needs for reliable inventory information 

on submerchantable trees and on forest biomass pools 

such as foliage, branchwood, and tops. These emerging 

needs stand in contrast to the considerable biometric 

knowledge concerning mature tree bole volume and 

taper that has been developed. Even for commercial 

species, few biomass models suitable for regional or 

subregional analyses are available, and many of these 

are incomplete or inconsistent with one another.

In this paper we review the current state of knowledge 

concerning tree biomass allometries, focusing on 

two IW species: the widely distributed ponderosa 

pine (Pinus ponderosa) and the locally important 

southwestern two-needle pinyon (Pinus edulis). We 

also consider the advantages and limitations of modelassisted subsampling strategies that can be used to 

augment tree biomass datasets and refine regional 

allometric models.

CURRENT STATE OF 

KNOWLEDGE CONCERNING 

BIOMASS ALLOMETRIES

Ponderosa Pine

Ponderosa pine is found from northern Mexico to 

southern British Columbia, and from California to 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 362

Nebraska. It is a commercially important species over 

much of the northern reaches of its range. It generally 

occupies relatively dry sites but is found over a broad 

climatic envelope: from the northern Sierras to the 

adjacent Great Basin it grows on sites with annual 

precipitation ranging from approximately 1800 mm/yr 

to 200 mm/yr (Callaway et al. 1994).

Most of our understanding of the biomass allometries 

of ponderosa pine comes from studies conducted in 

the northern portion of the species’ range. Yet even 

the relatively intensive field work by, for example, 

Brown (1978) and Standish et al. (1985), had limited 

geographic support relative to the species’ overall 

distribution. Moreover, past studies often employed 

different (and varying) tree biomass measurement 

protocols and partitioned totals into distinct biomass 

components. Though not irresolvable, the latter issues 

do make it challenging to use previously collected data 

or previously developed models to inform regional or 

subregional analyses. From smaller-scale studies such 

as Callaway et al. (1994) and Bouffier et al. (2003), it 

is evident that this species’ biomass allocation patterns 

and wood density vary within the region owing to 

environmental and genetic differences; differences in 

data collection protocols complicate the detection and 

description of such variations.

Two-Needle Pinyon

In the southwestern United States, two-needle pinyon 

is a locally important species in terms of forest cover 

and secondary forest products. It is a woodland species 

ranging mostly over the states of Utah, Colorado, New 

Mexico, and Arizona. Two-needle pinyon is found on 

warmer and drier sites than ponderosa pine. It grows 

in both single-stem, excurrent forms (on better sites) 

and with spreading crowns and multiple stems (typical 

form at lower elevations). 

Compared to ponderosa pine, little has been published 

on the biomass allometries of two-needle pinyon or on 

its genetic variation or wood density characteristics. 

Few biomass studies have been conducted and these 

have been highly localized in terms of geographic 

extent and have been based on small sample sizes (i.e., 

10 to 15 trees) (Darling 1967, Grier et al. 1992). 

BIOMASS SUBSAMPLING

Motivations and Challenges

Field assessment of tree biomass is an expensive 

undertaking in the mountainous and remote regions 

of the western United States. Besides access costs 

there are often difficulties associated with the 

destructive nature of the sampling (e.g., if large 

d.b.h. trees must be felled or if standard log lengths 

cannot be maintained). Materials processing costs are 

also significant with defoliation of branches being 

particularly time consuming (e.g., defoliation of firstorder branches can take more than one person-day for 

some coniferous species). At the same time, there is a 

multitude of site and stand factors that can influence 

the accumulation and apportionment of biomass, 

investigation of which necessitates the accumulation of 

relatively large samples of trees for any given species. 

To collect a large sample in the face of high per-tree 

costs requires consideration of subsampling strategies 

and potential sources of auxiliary (non-biomass) 

information.

The disadvantage of subsampling for tree biomass is 

that within-tree sampling errors become confounded 

with among-tree variation. Put differently, residual 

among-tree variation is inflated by subsampling 

error, making it more difficult to identify and detect 

systematic variation in biomass attributable to tree, 

stand, or site factors. Thus, effective subsampling 

strategies must allow the size of the sample (i.e., the 

number of independently selected trees) to increase 

faster than tree-level uncertainty adds to the overall 

variance. Specifically, if the subsampling strategy 

is unbiased then the sample size under subsampling 

should be greater than the sample size under full 

measurement by a factor of at least 1 + (σe/σt)2, 

where σe is the standard deviation associated with 

the subsampling procedure and σt is the conditional 

standard deviation in biomass across trees with similar 

characteristics. 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 363

Development and Application

Fundamental principles of tree allometry and past 

empirical work can be brought to bear to devise 

efficient and practical tree biomass subsampling 

strategies. As one example, for a given d.b.h. and 

height much of the tree-to-tree variation in bole 

biomass can be attributed to differences in bole taper. 

This suggests using previous taper modeling efforts to 

minimize costs associated with biomass measurements 

specific to the bole. Multiple taper studies have 

been conducted for ponderosa pine in the northwest; 

particularly noteworthy are multi-point equations 

such as those developed by Flewelling and Ernst 

(1996). Though parameterized for specific subregions 

of the northwest, the latter equations allow for fine 

calibration of predicted profiles to actual tree taper via 

the input of multiple upper stem height-diameter pairs 

(that are easily measured on the downed tree). Pointlevel predictions (inside- and outside-bark) from the 

taper profile, together with integrated volumes, can 

then be used as control variates, allowing for accurate 

estimation of bole biomass components from a random 

or systematic sample of discs.

As a second example, numerous field studies (e.g., 

Monserud and Marshall1999) have shown strong 

relationships between branch basal area and branch 

mass for many coniferous species. Less strong are 

relationships between, on the one hand, branch basal 

area and, on the other, branch foliar mass or branch 

woody biomass. Nonetheless, such relationships 

form an important basis for biomass subsampling 

strategies because branch basal areas are more easily 

measured than branch masses (green or dry; whole 

or by component). Subsampling procedures can draw 

on these relationships via unequal probability designs 

(e.g., list sampling or randomized branch sampling, 

RBS) and at the estimation stage through the use 

of generalized ratio or regression estimators. Such 

strategies obviate the need to measure full crown 

weights in the field, a task that is both time-consuming 

and fraught with difficulties due to the prevalence of 

broken and buried branches. As part of an ongoing 

ponderosa pine biomass study in the northwest 

(Affleck and Turnquist 2012), RBS of first-order 

branches has been used to capitalize on the strong 

quadratic relationship between branch diameter and 

branch mass while carrying out the crown sampling 

in a single pass up the stem. The use of generalized 

regression estimators with RBS is also being studied as 

initial results have indicated that a number of conifer 

species do not exhibit area-preserving branching at the 

first-order level, a characteristic implicitly assumed in 

RBS. 

In developing biomass sampling strategies for twoneedle pinyon, there is considerably less mensurational 

knowledge to draw on. While volume equations have 

been developed for this species, we are aware of 

no work on taper. Flexible taper equations for other 

species might be substituted and used for control 

variate sampling, though the procedure would need 

to be modified for multi-stemmed growth forms. 

Inasmuch as the multi-stemmed growth form is 

common, it is anticipated that crown subsampling 

strategies based on branch scaling relationships 

will be more useful for this species. In particular, 

RBS strategies could be applied in different ways 

to estimate crown characteristics on single stems or 

across multiple stems.

ACKNOWLEDGMENTS

Funding for this research was provided by a joint 

venture agreement between the U.S. Forest Service 

and the University of Montana (11-JV-11242305-120), 

by the Joint Fire Science Program through a grant 

to DLRA and JMG (10-1-02-13), and by the Inland 

Northwest Growth and Yield Cooperative. 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 364

LITERATURE CITED

Affleck, D.L.R.; Turnquist, B.R. 2012. Assessing 

the accuracy of crown biomass equations for 

the major commercial species of the interior 

northwest: study plan and preliminary results.

In: McWilliams, W.; Roesch, F.A., eds. Monitoring 

Across Borders: 2010 Joint Meeting of the Forest 

Inventory and Analysis (FIA) Symposium and 

the Southern Mensurationists. Gen. Tech. Rep. 

SRS-157. Asheville, NC: U.S. Department of 

Agriculture, Forest Service, Southern Research 

Station: 247-254.

Bouffier, L.A.; Gartner, B.L.; Domec, J.-C. 2003. 

Wood density and hydraulic properties of 

ponderosa pine from the Willamette Valley vs. 

the Cascade Mountains. Wood and Fiber Science. 

35: 217-233.

Brown, J.K. 1978. Weight and density of crowns 

of Rocky Mountain conifers. Res. Pap. INT197. Ogden, UT: U.S. Department of Agriculture, 

Forest Service, Intermountain Forest and Range 

Experiment Station. 56 p.

Callaway, R.M.; DeLucia, E.H.; Schlesinger, W.H. 

1994. Biomass allocation of montane and desert 

ponderosa pine: an analogy for response to 

climate change. Ecology. 75: 1474-1481.

Darling, M.L.S. 1967. Structure and productivity of 

a pinyon-juniper woodland in Northern Arizona. 

Durham, NC: Duke University. Ph.D. dissertation.

Flewelling, J.W.; Ernst, R.L. 1996. Stem profile 

estimation - east-side species. Unpublished 

report to the Inland Northwest Growth and Yield 

Cooperative. On file at the U.S. Department of 

Agriculture, Forest Service, Forest Management 

Service Center, Fort Collins, CO.

Grier, H.L.; Elliott, K.C.; McCullough, D.G. 1992. 

Biomass distribution and productivity of Pinus 

edulis-Juniperus monosperma woodlands of 

north-central Arizona. Forest Ecology and 

Management. 50: 331-350.

Monserud, R.A.; Marshall, J.D. 1999. Allometric 

crown relations in three northern Idaho conifer 

species. Canadian Journal of Forest Research. 29: 

521-535.

Standish, J.T.; Manning, G.H.; Demaerschalk, J.P. 

1985. Development of biomass equations for 

British Columbia tree species. Information 

Report BC-X-264. Victoria, BC: Canadian Forestry 

Service, Pacific Forest Research Centre.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 365

Estimating Biomass and Carbon in Forests

of the Pacific States: Statistical Issues

Andrew N. Gray and Hailemariam Temesgen1

Abstract.—Increasingly, large-scale estimates of biomass and carbon play significant 

roles in providing critical information to forest resource management and policy 

decisionmaking. Biomass and carbon estimates are required to monitor CO2 mitigation 

projects, characterize forest productivity, and estimate carbon flux. Yet, estimating 

biomass and carbon for large-scale inventory and monitoring is a difficult task. The 

amount of bias introduced by using locally developed equations to estimate biomass 

and carbon across large regions is not known. Equations are either not available or 

poorly developed for important species and important components of carbon storage 

like standing dead trees. In addition, the Pacific states are characterized by some of the 

largest trees and most remote landscapes in the country, which complicates sampling and 

measurement. 

Using ground data collected from across a range of major forest types in Pacific 

Northwest, we demonstrate selected approaches that might improve regional biomass 

and carbon estimates. In this presentation, we discuss 1) the suitability and predictive 

abilities of selected methods to quantify biomass and carbon in different forest types of 

the Pacific Northwest; 2) the use of selected methods to quantify types and frequency of 

dead trees/snags and their contributions to biomass/carbon pools; and 3) the challenges 

and opportunities in estimating biomass and carbon. In addition, we will discuss existing 

gaps and emerging technologies that might offer opportunities to quantify biomass and 

carbon inventories. 

1

 Research Ecologist (ANG), U.S. Forest Service, Pacific Northwest Research Station, 3200 SW 

Jefferson Way, Corvallis, OR; Associate Professor (HT), Oregon State University, Corvallis, OR. 

AG is corresponding author: to contact, call 541-750-7252 or email at agray01@fs.fed.us. 

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Role of FIA data in evaluating 

forest ecosystem responses

 to air quality stressors
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 367

IMPACTS OF NITROGEN AND SULFUR DEPOSITION 

ON THE GROWTH OF RED SPRUCE AND SUGAR MAPLE

IN THE UNITED STATES

Jennifer N. Phelan, Paramita Sinha, George Van Houtven, Marion Deerhake, Randall G. Waite, 

Anne W. Rea, and Ginger M. Tennant1

Extended Abstract

Total nitrogen (N) and sulfur (S) deposition in forest systems can have either positive 

or negative impacts on tree growth. The growth of many forests in North America is 

limited by N availability (Chapin et al. 1993, Killam 1994). Therefore, N fertilization is 

often a key component of forest management (Allen 2001), and in areas of N deposition, 

tree growth may be stimulated. However, N additions can sometimes be greater than 

what trees require and can negatively impact tree health and growth (Aber et al. 1995, 

Driscoll et al. 2001, McNulty et al. 2005). Systems where atmospheric deposition of 

N and S is greater than the critical load may be examples of such forest conditions. 

When critical loads are exceeded, tree health and growth may be compromised both 

directly and indirectly due to soil nutrient imbalances caused by leaching of base cations 

from the soil. Trees may have an increased susceptibility to drought and pest damage, 

aluminum (Al) toxicity in roots, reduced tolerance to cold, and a greater susceptibility 

to frost injury (Cronan and Grigal 1995, Driscoll et al. 2001, Fenn et al. 2006, McNulty 

et al. 2005, Ouimet et al. 2008). In the context of acidifying deposition of N and S, 

whether deposition has a positive or negative impact on tree growth may depend largely 

upon whether the critical load is exceeded by the deposition level, and it may follow an 

inverted U-shaped relationship similar to that which was hypothesized by Aber et al. 

(1995) for temperate forest systems that receive chronic, long-term N additions (Fig. 1).

To examine the relationships between N and S deposition and tree growth, preliminary 

analyses comparing the growth of sugar maple (Acer saccharum var. saccharum) and red 

spruce (Picea rubens) and critical acid load exceedances (positive and negative) were 

conducted for the full geographical ranges of both species in the United States. Sugar 

maple and red spruce were selected as the test species because both have experienced 

decline in areas of high N and S deposition (Ouimet et al. 2008, Shortle et al. 1997, 

Watmough 2002). Annual tree growth estimates for sugar maple and red spruce were 

obtained from the Forest Inventory and Analysis (FIA) database, and critical acid 

loads were calculated using the simple mass balance model (United Nations Economic 

Commission for Europe 2004) and three different levels of protection to tree health 

(base cation/Al soil solution ratio = 0.6, 1.2, and 10.0). The critical loads were compared 

against 2002 N and S deposition to estimate critical load exceedances. A series of 

multivariate ordinary least squares regressions were then conducted to examine the 

1

 Research Ecologist (JNP), RTI International, 3040 Cornwallis Rd., Research Triangle Park, 

NC 27709; Research Economist (PS), Senior Research Economist (GVH), and Senior Research 

Environmental Scientist (MD), RTI International; Air-Water Program Manager (RGW), Assistant 

Laboratory Director (AWR), and Ecologist (GMT), U.S. Environmental Protection Agency, Office 

of Research and Development. JNP is corresponding author: to contact, call 919-541-7079 or 

email at jenphelan@rti.org.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 368

Tree Growth

not exceeded exceeded Critical Load

Figure 1.—Hypothetical relationship between tree growth and critical load exceedance (based on 

curve describing forest productivity as a function of long-term chronic N additions outlined in Aber 

et al. [1995]).

relationship between tree growth and N and S deposition. A quadratic functional form 

was used to test for evidence of the inverted U-shaped relationship between critical load 

exceedance and tree growth represented in Figure 1. 

In addition, we conducted separate linear regression analyses relating N deposition and 

tree response in plots where critical load exceedance values were negative (i.e., plots 

where deposition levels did not exceed the critical loads), and analyses evaluating critical 

load exceedance and volume growth on plots where N and S deposition levels exceeded 

the critical loads. These separate analyses were conducted to test the hypotheses that 

tree growth is stimulated or “fertilized” by N when critical loads are not exceeded, and 

tree health and growth are impaired when the benefits of N deposition are replaced by 

the negative acidifying impacts of N and S deposition. The statistical results suggested 

that both species exhibited a quadratic functional form relationship between critical load 

exceedance and growth, although not significant at the p = 0.05 level. Based on critical 

loads determined using the base cations/Al ratio of 10.0, the linear regressions showed 

that growth of sugar maple was stimulated by N deposition on plots where critical loads 

were not exceeded (p = 0.0013), and red spruce growth was reduced on plots where 2002 

N and S deposition levels exceeded the critical loads (p = 0.0223).

The objectives of this study will be to expand and improve on the preliminary analyses 

and further our understanding of the relationships between N and S deposition and red 

spruce and sugar maple growth. Parameters including initial tree volumes, plot elevation, 

stand basal area, and plot latitudes and longitudes will be included in the analyses to 

account for other sources of variation in the growth response. The ability to indirectly 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 369

determine species-specific soil solution base cation/Al ratios will also be explored. It 

may be possible to calculate the base cation/Al ratio critical limit for red spruce and sugar 

maple based on the point of inflection in the quadratic relationship determined for each 

species. Previous comprehensive synthesis efforts have reported that the growth of red 

spruce and sugar maple seedlings was reduced by 20 percent, relative to controls at base 

cation/Al ratios of 1.2 and 0.6, respectively (Sverdrup and Warfvinge 1993). To date, 

however, the ability to test or confirm these species-specific critical limits for trees in 

the field has been limited. Furthermore, these exposure-response models of red spruce 

and sugar maple growth to N and S deposition may potentially be applied to evaluate the 

impacts of altered deposition levels on tree growth, and consequently, on provisioning 

ecosystem services through the use of the Forest and Agricultural Sector Optimization 

Model—Green House Gas version. If successful, these analyses and novel approach 

may prove to be a very effective way to evaluate the current condition of U.S. forests in 

response to N and S deposition, and how forest health could be improved with decreased 

deposition levels.

ACKNOWLEDGMENTS

The authors would like to acknowledge Elizabeth LaPoint for all of her assistance with 

acquiring data from the FIA database. 

LITERATURE CITED

Aber, J.D.; Magill, A.; McNulty, S.G.; Boone, R.D.; Nadelhoffer, K.J.; Downs, M.; 

Hallett, R. 1995. Forest biogeochemistry and primary production altered by 

nitrogen saturation. Water, Air and Soil Pollution. 85: 1665-1670. 

Allen, L.H. 2001. Silvicultural treatments to enhance productivity. In: Evans, J., ed. 

The forests handbook. Vol. 2. Oxford, UK: Blackwell Science Ltd.: 129-139.

Chapin, F.S., III; Moilanen, L.; Kielland, K. 1993. Preferential use of organic nitrogen 

for growth by a non-mycorrhizal arctic sedge. Nature (London). 361: 150-153.

Cronan, C.S.; Grigal, D.F. 1995. Use of calcium/aluminum ratios as indicators of 

stress in forest ecosystems. Journal of Environmental Quality. 24: 209-226.

Driscoll, C.T.; Lawrence, G.B.; Bulger, A.J.; Butler, T.J.; Cronan, C.S.; Eager, C.; 

Lambert, K.F.; Likens, G.E.; Stoddard, J.L.; Weathers, K.C. 2001. Acidic deposition 

in the northeastern United States: sources and inputs, ecosystem effects, and 

management strategies. Bioscience. 51(3): 180-198.

Fenn, M.E.; Huntington, T.G.; McLaughlin, S.B.; Eagar, C.; Gomez, A.; Cook, R.B. 

2006. Status of soil acidification in North America. Journal of Forest Science. 

52(Special Issue): 3-13.

Killam, K. 1994. Soil ecology. Cambridge, UK: Cambridge University Press. 211 p.

McNulty, S.G.; Boggs, J.; Aber, J.D.; Rustad, L.; Magill, A. 2005. Red spruce ecosystem 

level changes following 14 years of chronic nitrogen fertilization. Forest Ecology 

and Management. 219: 279-291.

Ouimet, R.; Moore, J.-D.; Duchesne, L. 2008. Effects of experimental acidification and 

alkalinization on soil and growth and health of Acer saccharum Marsh. Journal of 

Plant Nutrition and Soil Science. 171: 858-871.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 370

Shortle, W.C.; Smith, K.T.; Minocha, R.; Lawrence, G.B.; David, M.B. 1997. Acidic 

deposition, cation mobilization, and biochemical indicators of stress in healthy 

red spruce. Journal of Environmental Quality. 26: 871-876.

Sverdrup, H.; Warfvinge, P. 1993. The effect of soil acidification on the growth 

of trees, grass and herbs as expressed by the (Ca+ Mg+ K)/Al ratio. Reports 

in Ecology and Environmental Engineering 2. Lund, Sweden: Lund University, 

Department of Chemical Engineering. 108 p.

United Nations Economic Commission for Europe. 2004. Manual on methodologies 

and criteria for modeling and mapping critical loads and levels and air pollution 

effects, risks, and trends. Convention on long-range transboundary air pollution, 

Geneva, Switzerland. Available at http://www.icpmapping.org. (Accessed August 16, 

2006).

Watmough, S.A. 2002. A dendrochemical survey of sugar maple (Acer saccharum

Marsh.) in south-central Ontario, Canada. Water, Air and Soil Pollution. 136: 

165-187.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 371

Yellow Birch and Acid Deposition

in the Southern Appalachians

Kenneth W. Stolte, Walter C. Shortle, Kevin T. Smith, Michael Amacher, Charles H. Perry, 

Lindsay Majer, and Owen Carson1

Abstract.—Aerial observations of small clusters of large, standing dead trees in higher 

elevations of the southern Appalachians (SoApps) in 2005 were later identified as yellow 

birch (YB) growing in areas of low soil pH and calcium. Yellow birch is a common tree 

in the maple-beech-birch forests of the northeast United States and higher elevations 

of the SoApps. It cannot move calcium from older to newer growing tissues and low 

soil-calcium conditions cause reduced growth and increased vulnerability to damage 

and mortality from drought, insects, fungi, storms, and other stressors. Black cherry is a 

common associate with yellow birch but can translocate Ca within tissues and therefore 

is relatively tolerant of low soil calcium. We used FIA Phase 2 data to locate 48 survey 

sites in parts of ecoregion subsections M221Dc and M221Dd where larger diamater (>15 

inches at breast height) YB were found. At each site we established polygons based on 

randomly located 5+ YB in each of three target size classes (5-12, 12-15, and >15 inches 

d.b.h.) in 2010. Yellow birch was evaluated for crown condition (dieback, transparency, 

ratio, position); damage (type, location, and severity); d.b.h and height; and seedling and 

sapling regeneration. We also recorded sites that had associated black cherry with d.b.h. 

>8 inches. Mineral soil samples from 2 depths (0-4 and 4-8 inches) were collected under 

three randomly selected largest diameter class YB trees and analyzed by the Northern 

Research Station Forest Inventory and Analysis soils laboratory. We found low soil pH 

and calcium, and high aluminum, Fe, S, and other toxins correlated with higher elevation 

sites. The largest diameter yellow birch trees had the highest damage index scores. 

Seedling and sapling regeneration was highly variable with little or no regeneration at 

many sites. In late summer of 2011, we obtained tree ring cores from a subset of 26 sites 

with large diameter yellow birch and black cherry trees for growth of the outer 1 inch of 

the largest diameter trees of both species, and created shallow wound holes in the same 

trees to evaluate would response over time. On average, 33 years were required to add 

1 inch diameter of growth to yellow birch trees (n=126) and 14 years for black cherry 

trees (n=62). Shallow wounds on most black cherry had filled with sap, while most of the 

yellow birch wound holes remained dry 3 months later. A second evaluation of wound 

response and collection of soil humus for chemical analysis in autumn 2012 is scheduled. 

1

 Research Ecologist (KWS), U.S. Forest Service, Southern Research Station, 3041 Cornwallis 

Road, Research Triangle Park, NC 27709; Plant Pathologists (WCS and KTS), U.S. Forest 

Service, Northern Research Station; Research Soil Scientist (MA), U.S. Forest Service, Rocky 

Mountain Research Station; Research Soil Scientist (CHP), U.S. Forest Service, Northern 

Research Station; Associates (LM and OC), Equinox Environmental, Asheville, NC. KS is 

corresponding author: to contact, call 919-549-4022 or email at kstolte@fs.fed.us. 

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Models for Estimating 

Carbon and Biomass
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 373

VERIFICATION OF THE JENKINS AND FIA SAPLING BIOMASS 

EQUATIONS FOR HARDWOOD SPECIES IN MAINE

Andrew S. Nelson, Aaron R. Weiskittel, Robert G. Wagner, and Michael R. Saunders1

Abstract.—In 2009, the Forest Inventory and Analysis Program (FIA) updated its 

biomass estimation protocols by switching to the component ratio method to estimate 

biomass of medium and large trees. Additionally, FIA switched from using regional 

equations to the current FIA aboveground sapling biomass equations that predict woody 

sapling (2.5 to 12.4 cm d.b.h.) biomass using the Jenkins et al. (2003) equations (Forest 

Science 49 (1): 12-35) and then multiplying predictions by species-specific adjustment 

factors. The new equations have not been verified for saplings in Maine where saplingdominated stands make up nearly 24 percent of the forest land. We verified the FIA 

sapling equations and Jenkins et al. (2003) equations for naturally regenerated hardwood 

species from an experiment in eastern Maine. Results demonstrate the FIA sapling 

equations underestimated observed aboveground woody biomass by between 15 and 37 

percent. Our results suggest that the current species-specific sapling adjustment factors 

were inadequate for the trees in this study, and we propose a new set of adjustment 

factors based on the observed data. 

1

 Graduate Research Assistant (ASN) and Assistant 

Professor of Forest Biometrics and Modeling (ARW), 

University of Maine, School of Forest Resources, 

5755 Nutting Hall, Orono, ME 04469; Director (RGW), 

University of Maine; Assistant Professor of Silviculture 

(MRS), Purdue University. ASN is corresponding author: 

to contact, call 207-581-2763 or email at 

andrew.s.nelson@maine.edu.

INTRODUCTION

In 2009, the Forest Inventory and Analysis (FIA) 

Program updated its protocols for estimating 

biomass across the United States by switching to 

the component ratio method (CRM). The CRM 

was designed to provide consistent national-level 

biomass estimates similar to the current FIA volume 

estimates. In particular, the CRM uses specific gravity 

conversions to estimate bole wood and bark biomass 

and component proportions from Jenkins et al. (2003) 

equations to estimate biomass of tops and roots 

(Heath et al. 2009, Woudenberg et al. 2011). FIA also 

switched from using regional equations to predict 

sapling (i.e., trees between 2.5 and 12.4 cm diameter 

at breast height [d.b.h]) biomass to the current FIA 

aboveground sapling biomass equations (Heath et al. 

2009). The new sapling equations predict oven-dry 

woody biomass (stem, stump, and woody crown) of 

saplings using the estimates from Jenkins et al. (2003), 

and then multiplying the estimate by a species-specific 

adjustment factor to ensure a smooth transition into 

estimates obtained for larger size classes of trees. The 

species-specific adjustment factors are the ratio of 

estimated biomass by the CRM and predictions by the 

Jenkins et al. (2003) equations for all 12.5 cm d.b.h. 

trees of a particular species.

Although the FIA sapling equations conform to 

biomass estimation techniques of larger trees, the 

switch to the new equations resulted in a decrease of 

sapling biomass in Maine by 34 percent between 2003 

and 2010, even while corresponding stem densities 

increased by 11 percent during that time period 

(McWilliams et al. 2005, USDA FS 2012). The likely 

reason for the drastic reduction in sapling biomass in 

the region was the shift to the FIA sapling equations 

because the species-specific adjustment factors range 

between 0.7 and 0.8 for the common species in 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 374

Maine. Unfortunately, biomass predictions using the 

FIA sapling equations have not been well verified in 

Maine where nearly 24 percent of the 7 million hectare 

forested area is dominated by sapling-size stands. 

In this investigation, we verified the Jenkins et 

al. (2003) and FIA sapling equations for biomass 

estimation of hardwood saplings common in Maine, 

including red maple (Acer rubrum L.), paper birch 

(Betula papyrifera Marsh.), gray birch (Betula 

populifolia Marsh.), bigtooth aspen (Populus 

grandidentata Michx.), and trembling aspen (Populus 

tremuloides Michx.) from an experiment in eastern 

Maine.

METHODS

Experimental Design and Sampling

As part of a larger study, saplings of red maple, paper 

birch, gray birch, bigtooth aspen, and trembling aspen 

were destructively sampled in summer 2011 from 

a controlled experiment installed in a post-clearcut 

stand dominated by early successional hardwood 

species on the Penobscot Experimental Forest in 

eastern Maine. The experimental design is a 3 x 3 +1 

factorial of silvicultural intensity (thinning, thinning 

plus enrichment planting, and intensive plantations) 

and species compositional objectives (hardwood, 

mixedwood, and conifer), plus an untreated control. 

A full description of the experiment can be found 

in Nelson et al. (2012, In press). Sample tree d.b.h. 

ranged from 2.7 to 12.0 cm and oven-dry woody 

biomass ranged from 0.88 to 48.25 kg (Table 1).

Trees were cut at the root collar and dried at 65 °C 

for a minimum of 2 weeks (foliage and branches) or 

6 weeks (bole) to constant mass. Foliage and branches 

were weighed separately to the nearest 10 mg, while 

boles were weighed to the nearest 10 g.

Analytical Approach

Oven-dry woody (branch, bole, and stump) biomass 

estimates of the Jenkins et al. (2003) and FIA sapling 

equations were compared to the observed data for 

the sample trees. Paper birch and gray birch were 

combined due to low sample sizes and because 

both the Jenkins et al. (2003) equation and sapling 

adjustment factor were the same for both species. Root 

mean square error (RMSE), bias (observed–predicted), 

and the minimum negligible difference (MDND) 

equivalence test (Radtke and Robinson 2006), where 

the null hypothesis is that the observed and predicted 

values are not equal (Robinson and Froese 2004), were 

used to assess model accuracy and precision.

RESULTS

The FIA sapling equations substantially 

underestimated aboveground woody biomass for all 

of the hardwood species in the investigation (Fig. 1). 

The predicted means were between 15.0 percent for 

paper birch and gray birch combined to 36.6 percent 

for trembling aspen lower than the observed means. 

RMSE of the FIA sapling predictions ranged from 

1.0 kg for red maple to 6.7 kg for trembling aspen, 

and bias ranged from 0.8 kg for red maple to 3.7 kg 

for trembling aspen (Table 2). Because the predicted 

Table 1.—Descriptive statistics of destructively sampled trees used to verify the Jenkins et al. (2003) and 

FIA sapling equations. The number of individuals (n), and median values and ranges of d.b.h. (cm) and 

woody biomass (branches and bole) in kg are shown.

D.b.h. D.b.h. Woody Biomass Woody Biomass

Species n Median (cm) Range (cm) Median (kg) Range (kg)

Red maple 6 3.4 2.7 - 6.0 2.75 1.19 - 8.10

Birch species 5 5.2 3.2 - 8.4 7.03 2.20 - 23.40

Bigtooth aspen 13 6.5 2.7 - 9.5 7.85 0.97 - 19.10

Trembling aspen 15 5.2 2.6 - 12.0 4.78 0.88 - 48.25
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 375

Figure 1.—Oven-dry woody biomass (kg) versus d.b.h. (cm) for the five naturally regenerated hardwood species (paper birch 

and gray birch combined). The solid circles represent the observed data, the dotted line represents estimates by the Jenkins et 

al. (2003) equations, and the solid line represents estimates by the FIA sapling equations.

 Jenkins et al. (2003)

Table 2.—Root mean square error (RMSE), bias (observed-predicted), prediction relative to observed 

(PRO), minimum detectable negligible difference (MDND), and equivalence test results for the Jenkins et 

al. (2003) equations (Jenkins) and FIA sapling equations (FIA Sapling)

 Observed Predicted

RMSE (kg) Bias (kg) Mean (kg) Mean (kg) PRO (%) MDND (%) Null Hypothesis

Red maple

Jenkins 0.88 0.57 3.28 3.65 11.31 31.89 reject

FIA Sapling 0.99 0.82 3.28 2.46 -25.13 14.00 not reject

Birch species

Jenkins 1.33 0.86 5.66 6.52 15.16 38.16 reject

FIA Sapling 1.11 0.85 5.66 4.81 -14.98 1.19 not reject

Bigtooth aspen

Jenkins 2.15 1.51 8.12 9.51 17.02 26.83 reject

FIA Sapling 2.42 1.89 8.12 6.23 -23.25 14.54 not reject

Trembling aspen

Jenkins 2.41 1.21 10.18 9.83 -3.41 7.31 reject

FIA Sapling 6.71 3.72 10.18 6.45 -36.57 11.88 not reject
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 376

relative to observed values were all larger than 

the MDND for the FIA sapling equations, the null 

hypothesis of the equivalence test was not rejected. 

This suggested that the predicted mean was outside the 

range of the observed mean ± MDND and there was 

not enough evidence to reject that the two means were 

different. 

Comparatively, the predicted means of the red maple, 

birch species, and bigtooth aspen Jenkins et al. (2003) 

equations were 11.3 percent, 15.2 percent, and 17.0 

percent greater, respectively, than the observed mean, 

while the predicted mean was 3.4 percent lower than 

the observed for trembling aspen. RMSE error was 

12.1 percent, 11.3 percent, and 64.0 percent lower for 

the red maple, bigtooth aspen, and trembling aspen 

Jenkins et al. (2003) equations, respectively, than the 

FIA sapling equation, but was 16.3 percent greater for 

the birch species.

DISCUSSION

The FIA sapling equations substantially 

underestimated woody biomass of the hardwood 

species in this investigation. Although the equations 

are used to facilitate a smooth transition of biomass 

estimates to larger trees estimated with the CRM, 

the substantial underestimation relative to observed 

values of sapling biomass is the likely cause of a 34 

percent decrease in sapling woody biomass estimates 

for Maine, where nearly 24 percent of the forest land 

is dominated by sapling-size stands. Our investigation 

had small sample sizes, but the 15 percent and 37 

percent underestimation corroborates the reported 

decreases in sapling biomass in Maine.

Comparatively, the Jenkins et al. (2003) equations 

overestimated sapling woody biomass for three of the 

species. Because the observed data typically occurred 

between the estimates of the Jenkins et al. (2003) 

equations and the FIA sapling equations, it may be 

useful in the future to modify the species-specific 

sapling adjustment factors to conform to observed 

field data. For instance, the current adjustment factor 

for red maple is 0.74, but using the data from this 

investigation, we find an adjustment factor of 0.90 

multiplied by the Jenkins et al. (2003) estimates 

would provide estimates identical to the mean 

observed woody biomass. Other potential speciesspecific sapling adjustment factors calculated using 

the observed data in this investigation are shown in 

Table 3. Of course, more field data will be necessary 

to calibrate the sapling adjustment factors across sites 

and regions and include more species, but this may 

be a worthwhile venture given the poor estimates of 

sapling woody biomass found in this investigation and 

the drastic reductions in sapling woody biomass in 

Maine. 

ACKNOWLEDGMENTS

The authors thank the University of Maine 

Cooperative Forestry Research Unit (CFRU), 

Northeastern States Research Cooperative–Theme 

3, and the Henry W. Saunders Chair, School of 

Forest Resources, University of Maine, for funding 

this research. We also thank Derek Brockmann for 

assistance in collecting the biomass data.

Table 3.—Species-specific sapling adjustment 

factors calculated as the ratio of the observed 

mean woody biomass and predicted mean from 

the Jenkins et al. (2003) equations

Species Sapling Adjustment Factor

Red maple 0.90

Birch species 0.87

Bigtooth aspen 0.85

Trembling aspen 1.03
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 377

LITERATURE CITED

Heath, L.S.; Hansen, M.H.; Smith, J.E.; Miles, P.D. 

2009. Investigation into calculating tree biomass 

and carbon in the FIADB using a biomass 

expansion factor approach. In: McWilliams, 

W.H.; Moisen, G.; Czaplewski, R., comps. Forest 

Inventory and Analysis Symposium 2008; 2008 

October 21-13; Park City, UT. RMRS-P-56CD. 

Fort Collins, CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Station. 26 p.

Jenkins, J.C.; Chojnacky, D.C.; Heath, L.S.; Birdsey, 

R.A. 2003. National scale biomass estimators for 

United States tree species. Forest Science. 49(1): 

12-35.

McWilliams, W.H.; Butler, B.J.; Caldwell, L.E.; 

Griffith, D.M.; Hoppus, M.L.; Laustsen, K.M.; 

Lister, A.J.; Lister, T.W.; Metzler, J.W.; Morin, R.S.; 

Sader, S.A.; Stewart, L.B.; Steinman, J.R.; Westfall, 

J.A.; Williams, D.A.; Whitman, A.; Woodall, C.W. 

2005. The forests of Maine: 2003. Resour. Bull. 

NE-164. Newtown Square, PA: U.S. Department of 

Agriculture, Forest Service, Northeastern Research 

Station.

Nelson, A.S.; Saunders, M.R.; Wagner, R.G.; 

Weiskittel, A.R. 2012. Early stand production of 

hybrid poplar and white spruce in mixed and 

monospecific plantations in eastern Maine. New 

Forests. 43(4): 519-534.

Nelson, A.S.; Wagner, R.G.; Saunders, M.R.; 

Weiskittel, A.R. [In press]. Influence of 

management intensity on the productivity of 

early successional Acadian stands in eastern 

Maine. Forestry.

Radtke, P.J.; Robinson, A.P. 2006. A Bayesian 

strategy for combining predictions from 

empirical and process-based models. Ecological 

Modelling. 190: 287-298.

Robinson, A.P.; Froese, R.E. 2004. Model validation 

using equivalence tests. Ecological Modelling. 

176: 349-358.

USDA, Forest Service. 2012. EVALIDator version 

1.5.1.2a. Forest Inventory and Analysis Program. 

Available at http://apps.fs.fed.us/Evalidator/

tmattribute.jsp. (Accessed August 3, 2012).

Woudenberg, S.W.; Conkling, B.L.; O’Connell, B.M.; 

LaPoint, E.B.; Turner, J.A.; Waddell, K.L.; Boyer, 

D.; Christensen, G.; Ridley, T. 2011. The Forest 

Inventory and Analysis Database: description 

and users manual version 5.1 for Phase 2. U.S. 

Department of Agriculture, Forest Service.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 378

ASSESSING THE UNCERTAINTY OF FOREST CARBON ESTIMATES 

USING THE FVS FAMILY OF DIAMETER INCREMENT EQUATIONS

Matthew B. Russell, Aaron R. Weiskittel, and Anthony W. D’Amato1

Abstract.—Serving as a carbon (C) accounting tool, the Forest Vegetation Simulator 

(FVS) is widely used by forest managers and researchers to forecast future forest C 

stocks. Assessments of the uncertainty that FVS equations provide in terms of their 

ability to accurately project forest biomass and C would seemingly differ, depending 

on the region and scale of interest to the user. This analysis used permanent sample plot 

data obtained from the annual Forest Inventory and Analysis (FIA) Program database 

to assess the performance of the diameter at breast height (d.b.h.) increment function in 

the Northeast and Lake States variants of FVS. Up to three measurements of FIA plots 

were recorded, representing more than 10 years of observed growth. Total aboveground 

biomass and C were estimated using the FIA’s component ratio method, which served 

as a field-based measure of forest biomass/C. After initial FIA measurements were 

forecasted with the species-specific d.b.h. increment equation from the appropriate 

FVS variant, biomass/C was calculated and compared with the field-based measure. 

Results found that d.b.h. increment was generally underpredicted across both regions, 

which resulted in deviations when comparing model- and field-based predictions of 

biomass. Generally, a 10 percent error in predicting d.b.h resulted in a 25 percent error 

in predicting total aboveground biomass and C. Assessing the amount of uncertainty as 

predictions from FVS are used by managers and researchers will continue to provide 

information for those attempting to quantify the intricate processes of forest C dynamics.

1

 Postdoctoral Research Associate (MBR) and Associate 

Professor (AWD), University of Minnesota, Department of 

Forest Resources, St. Paul, MN 55108; Assistant Professor 

of Forest Biometrics and Modeling (ARW), University of 

Maine. MBR is corresponding author: to contact, call 

612-626-4280 or email at russellm@umn.edu.

INTRODUCTION

Growth models like the Forest Vegetation Simulator 

(FVS) provide predictions for individual trees, but 

forest managers typically make decisions at the 

stand level. Because error begins to compound as 

one scales from the individual tree to the plot and 

stand, quantifying the uncertainty associated with 

this scaling would have a direct impact on stand-level 

estimates. For example, some argue that a 10 percent 

bias in measuring diameter at breast height (d.b.h.) 

can result in a 25 percent error in predicted basal area 

(BA) (Gertner and Dzialowy 1984). BA is a relatively 

straightforward calculation, but for measures such as 

aboveground biomass and C, computations are much 

more complex. This complexity can be seen in the 

component ratio method (CRM) administered by FIA, 

which estimates total aboveground C for individual 

trees (Woodall et al. 2011).

FVS is a distance-independent growth model that 

projects future forest conditions, composition, and 

stand structure. A key determinant of future forest 

stocks in FVS lies in its diameter increment (Δdbh) 

function. Similarly, national biomass equations 

(Jenkins et al. 2003, 2004) rely heavily on d.b.h., 

and d.b.h. is widely used in the CRM to calculate 

aboveground C (Woodall et al. 2011). Increasingly, 

FVS is being used to estimate forest C into the 

future, yet little is known about the uncertainty of 

forest C estimates that might arise from a potential 

bias inherent in the Δdbh equation used. The goal 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 379

here was to use Forest Inventory and Analysis (FIA) 

remeasurement data from the Northeast and Lake 

States to quantify the uncertainty of plot-level C stocks 

using different implementations of d.b.h. increment 

equations in FVS. 

METHODS

FIA Data

Tree and plot records were obtained from the U.S. 

Forest Service’s FIA Program. Many of these 

inventory plots were remeasured but some were not. 

Data were obtained from the online FIA database 

at http://apps.fs.fed.us/fiadb-downloads/datamart.

html (accessed November 11, 2011). Compiled data 

spanned eight ecoregions. Plots began measurement in 

1998.

Diameter Increment in FVS

Diameter increment is estimated differently in the 

Lake States (FVS-LS) and Northeastern (FVS-NE) 

variants of FVS. Key differences in the Δdbh equations 

used in the two regions are (1) diameter increment 

is predicted in FVS-LS, whereas BA increment is 

predicted in FVS-NE and then converted to diameter, 

(2) in the competition modifier, tree crown ratio, 

species maximum and plot BA, and quadratic mean 

d.b.h. are used in FVS-LS, whereas BA is used in 

larger trees in FVS-NE, and (3) an adjustment factor is 

added to ∆dbh predictions in FVS-LS. 

FVS-LS

The ∆dbh for trees ≥5.0 inches in FVS-LS is predicted 

using a potential-modifier approach and adjustment 

factor. First, potential diameter growth is estimated 

using tree d.b.h., crown ratio, and species site index 

(Hahn and Leary 1979). Second, a competition 

modifier is estimated using tree d.b.h., maximum 

species BA, plot BA, and quadratic mean d.b.h. 

(Holdaway 1984). Predicted annual ∆dbh is assumed 

to be the product of the potential and modifier 

components and is then corrected to the cycle length. 

Lastly, an adjustment factor is predicted based on 

tree d.b.h. and d.b.h. squared, and is added to ∆dbh 

Equation coefficients for the three components of 

∆dbh are provided for 28 species groups (Dixon and 

Keyser 2008a).

FVS-NE

The ∆dbh for trees ≥5.0 inches in FVS-NE is also 

estimated using the potential-modifier approach (Teck 

and Hilt 1991). First, potential BA growth is estimated 

using tree d.b.h. and species site index. Second, a 

growth modifier is estimated using the BA in larger 

trees. Lastly, predicted annual BA growth is estimated 

by multiplying the potential and modifier components. 

Basal area growth is added to current tree BA and 

converted to a tree diameter. Equation coefficients are 

provided for 28 species groups (Dixon and Keyser 

2008b).

Analyses

This analysis was limited to all trees with d.b.h. ≥5.0 

inches because (1) it is the threshold for measurement 

trees on FIA Phase 2 plots, and (2) it is the threshold 

for the large-tree ∆dbh equations within FVS. 

Increments for d.b.h. were standardized to a 5-year 

interval for each tree that survived a remeasurement 

period, given that most FIA plots were remeasured on 

a 5-year time step. So, ∆dbh5 represents 5-year d.b.h. 

increment.

Volume, biomass, and C were estimated for each 

initial measurement on each individual tree using the 

CRM (Woodall et al. 2011). Predictions were made 

separately for growing stock and cull trees. Summaries 

of these variables were made for each FIA plot. Plotlevel summaries were calculated using the predicted 

Δdbh5 from FVS, representing a “predicted” plot 

condition. Plot-level summaries were then calculated 

using actual FIA measured d.b.h., representing the 

“observed” value. Only surviving trees in Y2 measured 

in Y1 were used, and FIA plots with no observed 

treatment (e.g., silviculture or harvesting) since the last 

measurement were used. Percent accuracy within 15 

percent and bias were computed for each FIA plot and 

summarized by ecoregion. 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 380

RESULTS AND DISCUSSION

Lake States

Mean Δdbh5 bias (observed-predicted) provided by 

FVS-LS was as low as 0.01 inch/5 years for the trees 

in the Laurentian mixed forest ecoregion and as high 

as 0.25 inch/5 years for trees in the prairie parkland 

ecoregion. This slight underprediction of Δdbh5 differs 

somewhat from Pokharel and Froese’s finding (2008) 

of a general overprediction of the FVS-LS model for 

trees in Michigan. The differences could arise because 

Pokharel and Froese (2008) employed data from 

FIA inventory cycles in the 1980s and early 1990s, 

whereas this analysis employed data from the annual 

inventory design beginning around 2000. Similarly, 

5-year increments were used here, whereas 10-year 

increments were used by Pokharel and Froese (2008). 

The degree that biomass/C predictions are influenced 

by a Δdbh equation for a specific species of interest is 

a subject for further investigation. 

Northeast

Underprediction of 5-year diameter increment was 

similarly observed using FVS-NE. Mean Δdbh5 bias 

provided by FVS-NE was as low as 0.05 inch/5 years 

for the trees in the outer coastal plain mixed forest 

ecoregion and as high as 0.40 inch/5 years for trees in 

the eastern broadleaf (continental) ecoregion. A more 

substantial underprediction was generally observed 

for those ecoregions with fewer FIA plots located on 

the fringes of the northeastern geographic range (e.g., 

Western Allegheny plateau).

Uncertainty in Plot-level Carbon 

Adding the predicted Δdbh5 to initial d.b.h. and 

scaling to the plot level, FVS predicted basal area, 

volume, and biomass/C well for some ecoregions 

in the Northeast (e.g., Laurentian mixed forest 

and eastern broadleaf [oceanic]) and Lake States 

(Laurentian mixed forest) (Table 1). For Northeast 

plots, percent accuracies were generally similar for 

the three variables. For the Lake States, however, 

percent accuracies decreased as one scaled from basal 

area to volume and biomass. This result for FVS-LS 

as opposed to FVS-NE likely arises because of the 

adjustment factors used and the differing volume 

equations employed in the two regions.

Generally, a 10 percent error in predicting d.b.h. 

resulted in a 25 percent error in predicting total 

aboveground biomass and C (Fig. 1). Although 

the CRM uses a myriad of calculations to arrive at 

aboveground biomass and C, initial results do not 

seem to show that errors in individual tree predictions 

lead to larger uncertainties of forest C when compared 

to plot-level basal area predictions. 

Table 1.—Ecoregions examined, number of FIA plots, and evaluation statistics for basal area, volume, and 

biomass/carbon

Mean Δdbh5 Percent Accuracy (±15%)

Ecoregion Code Plots (n) Bias (inches) Basal Area Volume Biomass/C

Northeast

Laurentian mixed forest 212 3,212 0.09 91 90 91

Eastern broadleaf (oceanic) 221 2,262 0.14 64 64 64

Eastern broadleaf (continental) 222 262 0.40 38 38 39

Western Allegheny plateau 223 12 0.19 17 25 25

Adirondack-New England mixed forest 232 115 0.19 12 14 14

Central Appalachian broadleaf forest M211 2,329 0.09 96 96 96

Outer coastal plain mixed forest M221 1,062 0.05 61 61 61

Lake States

Laurentian mixed forest 212 2,376 0.01 97 64 61

Eastern broadleaf (continental) 222 581 0.20 92 43 41

Prairie parkland 251 10 0.25 90 50 50
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 381

Error in tree biomass (%)

Error in d.b.h. (%)

Figure 1.— Percent error in predicting 5-year diameter 

increment with associated uncertainty in predicting tree 

biomass using the FIA component ratio method.

CONCLUSIONS

Using FIA data from the northeastern U.S. and 

Lake States, this analysis found that the current 

implementation of FVS underpredicted tree diameter 

increment throughout the two regions. Previous 

work in the Lake States (Pokharel and Froese 2008) 

and ongoing work in the Northeast suggest that 

recalibrating the diameter increment functions in 

FVS may not prove effective, suggesting new Δd.b.h. 

models be engineered. As managers will continue to 

rely on C accounting tools like FVS to project future 

forest C stocks, assessing the level of uncertainty as 

these models scale output to upper level hierarchies 

will help provide more information for those seeking 

improved methodologies for quantifying forest C 

dynamics.

LITERATURE CITED

Dixon, G.E.; Keyser, C.E., comps. 2008a (revised 

March 16, 2012). Lake States (LS) variant 

overview-Forest Vegetation Simulator. Internal 

Rep. Fort Collins, CO: U.S. Department of 

Agriculture, Forest Management Service Center. 

37 p.

Dixon, G.E.; Keyser, C.E., comps. 2008b (revised 

March 16, 2012). Northeast (NE) variant 

overview-Forest Vegetation Simulator. Internal 

Rep. Fort Collins, CO: U.S. Department of 

Agriculture, Forest Service, Forest Management 

Service Center. 40 p.

Gertner, G.Z.; Dzialowy, P.J. 1984. Effects of 

measurement errors on an individual tree-based 

growth projection system. Canadian Journal of 

Forest Research. 14: 311-316.

Hahn, J.T.; Leary, R.A. 1979. Potential diameter 

growth functions. In: A generalized forest growth 

projection system applied to the Lake States 

region. Gen. Tech. Rep. NC-49. St. Paul, MN: U.S. 

Department of Agriculture, Forest Service, North 

Central Forest Experiment Station: 22-26.

Holdaway, M.R. 1984. Modeling the effect of 

competition on tree diameter growth as applied 

in STEMS. Gen. Tech. Rep. NC-94. St. Paul, MN: 

U.S. Department of Agriculture, Forest Service, 

North Central Forest Experiment Station. 9 p.

Jenkins, J.C.; Chojnacky, D.C.; Heath, L.S.; Birdsey, 

R.A. 2003. National-scale biomass estimators for 

United States tree species. Forest Science. 49(1): 

12-35.

Jenkins, J.C.; Chojnacky, D.C.; Heath, L.S.; Birdsey, 

R.A. 2004. A comprehensive database of 

diameter-based regressions for North American 

tree species. Gen. Tech. Rep. NE-319. Newtown 

Square, PA: U.S. Department of Agriculture, 

Forest Service, Northeastern Research Station. 

45 p. [1 CD-ROM]. 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 382

Pokharel, B.; Froese, R.E. 2008. Evaluating 

alternative implementations of the Lake States 

FVS diameter increment model. Forest Ecology 

and Management. 255: 1759-1771.

Teck, R.M.; Hilt, D.E. 1991. Individual-tree 

diameter growth model for the northeastern 

United States. Res. Pap. NE-649. Radnor, PA: 

U.S. Department of Agriculture, Forest Service, 

Northeastern Research Station. 11 p.

Woodall, C.W.; Heath, L.S.; Domke, G.M.; Nichols, 

M.C. 2011. Methods and equations for 

estimating aboveground volume, biomass, and 

carbon for trees in the U.S. forest inventory, 

2010. Gen. Tech. Rep. NRS-88. Newtown Square, 

PA: U.S. Department of Agriculture, Forest Service, 

Northern Research Station. 30 p. [1 CD-ROM]. 

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 383

ANALYSIS OF TRACHEID DEVELOPMENT IN SUPPRESSED-GROWTH 

PONDEROSA PINE USING THE FPL RING PROFILER 

C. Tim Scott and David W. Vahey1

Extended Abstract

The Ring Profiler was developed to examine the cross-sectional morphology of wood 

tracheids in a 12.5-mm core sample. The instrument integrates a specially designed 

staging apparatus with an optical imaging system to obtain high-contrast, high-resolution 

images containing about 200-500 tracheids. These images are further enhanced and 

analyzed to extract tracheid cross-sectional properties such as shape, double-wall 

thickness, and lumen area. Subsequently, localized density can be calculated for specific 

regions of interest (e.g., earlywood and latewood), as it varies throughout the tree.

We showed that tracheid development in trees can be closely examined with the Ring 

Profiler. In particular, we showed that for the same core sample, recent periods of 

“suppressed” growth result in very narrow growth rings containing as few as 2-10 radial 

fibers/year (Fig. 1). In contrast, normal periods of growth may contain 20 or more 

radial fibers/year, resulting in dramatically different tracheid development (Fig. 2). 

This was observed in ponderosa pine (Pinus ponderosa Dougl. Ex Laws.) trees of 

various diameters harvested from an even-aged, high density stand in the Pringle Falls 

Experimental Forest near Bend, OR. For this study, sample disks were extracted from 

each tree at diameter at breast height (d.b.h.) and every 6 m up the tree. Radial strips 

(Fig. 3) were then prepared for examination by the Ring Profiler. The entire strip was 

scanned and image algorithms were applied to calculate tracheid cell wall area, relative 

proportion of earlywood and latewood, and local density in select growth rings. A 

calculation of yearly mass accumulation was made by assuming that the tree volumetric 

growth can be approximated by a sequence of concentric cones. Measures of stored 

carbon were then estimated based on the chemical composition of the wood.

1

 General Engineer (CTS) and Materials Research Engineer (DWV), U.S. Forest Service, Forest 

Products Laboratory, 1 Gifford Pinchot Dr., Madison, WI 53276. CTS is corresponding author: to 

contact, call 608-231-9435 or email at tscott@fs.fed.us. 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 384

Figure 1.—Narrow growth rings circa 1992.

Figure 2.—Wide growth ring circa 1957.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 385

Figure 3.—Five radial samples from a century-old, 33-m ponderosa pine. The bottom strip was 

prepared from the diameter at breast height (d.b.h.) disk with the rest taken at 6-m intervals up the 

tree.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 386

RING PROFILER: A NEW METHOD FOR ESTIMATING TREE-RING 

DENSITY FOR IMPROVED ESTIMATES OF CARBON STORAGE

David W. Vahey, C. Tim Scott, J.Y. Zhu, and Kenneth E. Skog1

Abstract.—Methods for estimating present and future carbon storage in trees and 

forests rely on measurements or estimates of tree volume or volume growth multiplied 

by specific gravity. Wood density can vary by tree ring and height in a tree. If data on 

density by tree ring could be obtained and linked to tree size and stand characteristics, 

it would be possible to more accurately predict changes in density and weight of tree 

biomass with projected changes in tree size and stand characteristics. Ring Profiler is a 

patented method for characterizing the structure of softwood tracheids and their changes 

from one tree ring to another over time. Measurements can be converted to density and 

can be multiplied by volume to estimate total weight and weight of carbon by ring. A 

sample displaying pith-to-bark ring structure is prepared from a radial core and scanned 

beneath a microscope as images are taken. Despite the sample’s thickness (up to 6mm) 

it is possible to image it with light transmitted from below using a single light-emitting 

diode (LED) for illumination. Near-infrared radiation (NIR) from the LED is captured 

by tracheid walls and travels efficiently to the viewing surface, much as light travels 

through an optical fiber. NIR captured by lumens tends to be absorbed by particulate 

matter introduced during sample preparation. The result is a high-contrast image in which 

tracheid walls are bright, lumens are dark and the interface is sharp. Images containing 

approximately 400 tracheids each are processed by ImageJ, public software available 

from the National Institutes of Health (NIH). Measurements include the distribution of 

radial and tangential diameters and wall thicknesses, from which local density can be 

estimated and applied to improved estimates of carbon storage. Over time, measurements 

from Ring Profiler can increase our understanding of tree response to stress. 

1

 Materials Research Engineer (DWV), General Engineer 

(CTS), Research General Engineer (JYZ), and Supervisory 

Research Forester (KES), U.S. Forest Service, Forest 

Products Laboratory, 1 Gifford Pinchot Dr., Madison, WI 

53276. DWV is corresponding author: to contact, call 

608-231-9304 or email at dvahey@fs.fed.us.

INTRODUCTION

Forests have been a strong carbon sink in the United 

States in recent decades. Forest policy makers and 

forest managers have a keen interest in monitoring 

and projecting how forest carbon stocks will change 

and learning how alternate management practices can 

influence carbon accumulation. 

Current methods to estimate weight of total biomass 

or weight of growth in biomass rely in large part on 

estimates of wood volume in trees and estimates of 

average density of trees by species. Wood density 

can vary by tree ring (age) and height in a tree. The 

density associated with volume growth can change 

as the tree ages and can be influenced by stand and 

site conditions. The weight increase associated with a 

volume increase will depend on the changing value of 

density. 

To what degree is wood density in a tree ring affected 

by tree characteristics, stand characteristics, and 

site characteristics? If data on density by tree ring 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 387

could be obtained and linked to tree size and stand 

characteristics, it would be possible to more accurately 

predict changes in density and weight of tree 

biomass with projected change in tree size and stand 

characteristics. 

With ongoing joint Forest Service and university 

efforts to revise tree biomass equations, information 

will be collected on tree volume and wood weight 

by tree part. With the collection of cross section 

samples for trees of varying sizes, species, and stand 

conditions there is an opportunity to collect density 

for some subsamples by tree ring (age or radius from 

the center). Modeling this link could allow for more 

accurate estimates of tree density associated with a 

given volume or with given volume changes under 

certain species, stand, and site conditions. 

More detailed models could make it possible to answer 

additional questions about managing for carbon 

accumulation. Is it possible that forest treatment 

recommendations to increase carbon weight may 

differ from treatment recommendations to enhance 

tree volume? To what degree is slower volume growth 

in a denser stand offset by higher density in slower 

growing rings? How does this tradeoff differ by forest 

type, stand conditions, or site conditions?

The gold standard for measurement of tree-ring density 

from cores is the SilviScanTM instrument developed by 

the Commonwealth Scientific and Industrial Research 

Organization (CSIRO) in Melbourne, Australia (Evans 

1994). Using robotic control of optical microscopy, 

X-ray densitometry, and X-ray diffraction, a complete 

picture is presented of density, tracheid structure, and 

microfibril angle over the life of the tree. At present, 

SilviScanTM is not an instrument for purchase but 

rather a service. The response time for results may be 

long for FIA requirements.

At the other extreme is the Haglof Tree Core Reader 

(Haglof Sweden AB, Langsele, Sweden). This fieldworthy device accepts a standard core and provides 

a 6x magnifier to measure the narrowest growth 

rings. Though providing much less information than 

SilviScanTM, and no information archiving, it has 

advantages of lower cost, no core preparation, and 

rapid field response.

Ring Profiler is intended to fill a niche midway 

between that of SilviScanTM and the Haglof device 

(Vahey et al. 2007). Like SilviScanTM, it requires 

a processed core for measurement and produces 

archived results in a computer-based instrument. Like 

SilviScanTM, it has an optical microscopic component 

for measuring tracheid structure. Unlike SilviScanTM, 

it avoids the need for an X-ray component by 

producing a density measurement based on a near-

infrared image of tracheid cross-sections. It lacks 

SilviScanTM’s measurement of microfibril angle, but 

this information is not needed for FIA applications. 

In final implementation, it would be an instrument 

for sale rather than a service, allowing the owner-user 

control of turn-around time.

RING PROFILER CONCEPT

The Ring-Profiler concept is illustrated by Figure 1, 

showing an LED illuminating a portion of a radially 

cut sample prepared with the growth direction parallel 

to the light beam. Both exit and entry surfaces are 

sanded to a 1200 grit finish and polished with paper. 

A microscope objective collects transmitted light 

from the sample and images it on a charge-coupled 

device (CCD) camera array. Figure 2 shows a more 

rugged implementation of the optics, along with a 

representative camera image. Light travels through 

and along tracheid walls which show up as bright, 

while the lumens filled with debris from the sanding 

operation show up as dark. The field-of-view in 

the radial direction is 0.7 mm. After the image is 

generated, a scanning mechanism moves the sample 

0.7 mm so that the next image aligns with the first. 

The process continues until the entire radial length is 

examined, or at least a portion of interest nearest the 

bark. This may take a matter of minutes.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 388

Figure 1.—The Ring Profiler concept. Figure 2.—A current version of Ring Profiler with 

example image.

Figure 3.—(a) Latewood (LW)- centered image showing early earlywood growth of the next year (EEW, left) and last earlywood 

growth of the current year (LEW, right); (b) Binary images of the three sections, showing calculated densities.

?

?

?

?

?

?

?

?

?

?

?

?

?

EEW=523 kg/m3 LW=843 kg/m3 LEW=605 kg/m3 700 μm

(a) (b)

The 0.7 mm field of view (FOV) was chosen with 

suppressed growth in mind. There might be three 

complete suppressed growth rings captured within this 

FOV, while for normal growth only a partial growth 

ring is captured. Image processing must be flexible 

enough to deal appropriately with each situation. Our 

focus to date has been on suppressed and intermediate 

growth, such as shown in Figure 3. A single latewood 

(LW) band near the center of the image is flanked on 

the right by the last earlywood (LEW) from the same 

growth year, and on the left by the earliest earlywood 

(EEW) from the next growth year.

Image Processing

By blurring the image and thresholding, we created 

masks for the three distinct bands of the image. The 

masks allowed us to optimally digitize portions of 

the original image. For example, Figure 3b shows 

processed LW, LEW and EEW bands containing 

lumens and rays as all black, and other material, 

mostly cell walls, as all white. By taking the dark 

area density to be 0 kg/m3 and the white area density 

to be 1500 kg/m3, the commonly accepted density of 

cellulose, we estimated the overall densities of the LW, 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 389

LEW and EEW bands of the original image. An added 

benefit is that we can piece together the annual growth 

of the tree from the radial dimensions of the bands. 

This allows the calculation of accumulated mass.

Comparison with SilviScanTM

This information may be sufficient for FIA 

applications. However, further processing of the lumen 

images of Figure 3b leads to measurements of the 

radial and tangential diameters and cell-wall thickness, 

such as are available from SilviScanTM. The main 

point of differentiation from SilviScanTM, with regard 

to measurement capability, is that the SilviScanTM 

measures density using X-rays and calculates cell 

wall thickness from density and tracheid diameters. 

Ring Profiler measures wall thicknesses and tracheid 

diameters and calculates density. The SilviScanTM 

approach allows for measurements of more tracheids, 

producing greater statistical accuracy of results. 

Finally, SilviScanTM X-ray technology is applied to the 

measurement of microfibril angle as well as density.

Both SilviScanTM and Ring Profiler require sample 

preparation from wood cores. This results in the 

greatest time delay between gathering the core and 

learning results. There are three SilviScanTM units to 

serve the world’s needs, so there is an additional delay 

in shipping samples and waiting for prior customers 

to be serviced. At a targeted cost of ~$20,000, Ring 

Profiler will be within purchasing range of most 

testing laboratories, and should become much more 

widely distributed than SilviScanTM. Future marketing 

directions for SilviScanTM are unknown; however, 

Ring Profiler has obtained patent protection in the 

United States (Vahey et al. 2011).

RING PROFILER AND FIA

In the near term, Ring Profiler could be a stationary 

lab instrument to process and measure cores provided 

by FIA. Information would be delivered in rough 

proximity to other FIA information for the same lands 

to inform management decisions. The information 

appears comparable to that which would nominally 

be obtained when the lands are revisited in later 

years. Suppose a course of remediation prompted by 

revisiting a forest in year 10 could be prompted by use 

of Ring Profiler in year 0. This would result in double 

the expanded growth in biomass in year 20. A quick 

calculation suggests that if the unmanaged forest grew 

25 percent in 20 years, the forest remediated in year 

10 would grow 27 percent in 20 years, and the forest 

remediated in year 0, based in part on Ring Profiler, 

would grow 29 percent in 20 years. This calculation 

assumes that remediation improves the content of 

normal-growth trees from 70 to 90 percent. More 

careful determination of these “back-of-envelope” 

numbers provides a starting point for discussion about 

the relevance of Ring Profiler to FIA.

 RING PROFILER IN THE WOODS

In the longer term, Ring Profiler could be qualified 

for use in the woods. The essential imaging hardware 

weighs in the vicinity of 2kg, and much has already 

been accomplished with respect to merging the light 

source, camera, and sample holder into a stable, 

precision mechanical unit. Required computing power 

is commensurate with modern laptops, which adds 

additional weight and battery power requirements. 

However, the downloading of images to remote 

computers by cell or satellite phone is an active area of 

interest in many fields.

The greatest challenge appears to be producing an 

acceptable sample from the green core. This is known 

to be an area of active interest to law enforcement 

charged with detection of contraband trees, so work by 

others toward this end may be to the benefit of Ring 

Profiler.

ACKNOWLEDGMENTS

The authors thank Forest Service patent advisor 

Janet Stockhausen for her support of this technology, 

including financial support from the Forest Service 

Patent Program, along with Linda Schramer, Forest 

Service patent technician.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 390

LITERATURE CITED

Evans, R. 1994. Rapid measurement of the 

transverse dimensions of tracheids in radial 

wood sections from Pinus radiata. Holzforschung. 

48: 168-172.

Vahey, D.W.; Zhu, J.Y.; Scott, C.T. 2007. Wood 

density and anatomical properties in suppressedgrowth trees: comparison of two methods. Wood 

and Fiber Science. 39(3): 462-471.

Vahey, D.; Zhu, J.Y.; Scott, C.T. 2011 (May 17). 

Method for determining the density and 

cross-section morphology of trees. U.S. Patent 

7,945,098.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 391

CUMULATIVE VOLUME AND MASS PROFILES FOR DOMINANT 

STEMS AND WHOLE TREES TESTED FOR NORTHERN HARDWOODS

Neil R. Ver Planck and David W. MacFarlane1

Abstract.—New models were presented to understand the relationship between the 

dominant stem and a whole tree using cumulative, whole-tree mass/volume profiles 

which are compatible with the current bole taper modeling paradigm. New models 

were developed from intensive, destructive sampling of 32 trees from a temperate 

hardwood forest in Michigan. The species in the sample were primarily American beech 

(Fagus grandifolia Ehrh.) and sugar maple (Acer saccharum Marsh.). The new profile 

models allowed for both mass and volume of both the dominant stem and branches to 

be estimated from ground level to the top of the tree. Nonlinear mixed effects models 

were used in the model development to account for the correlations among multiple 

measurements of an individual tree. Allometric scaling relationships between the 

dominant stem and branches can be directly derived from the new models and can be 

used to define sampling approaches to localize predictions of generalized whole-tree 

models via measurements of simple branch parameters.

1

 Research Assistant (NRV) and Associate Professor 

(DWM), Department of Forestry, Michigan State University, 

East Lansing, MI 48824-1222. NRV is corresponding 

author: to contact, call 616-745-6464 or email at 

verplan6@msu.edu.

INTRODUCTION

Integrated whole-tree biomass and volume equations 

are in great demand due to the simultaneous need to 

improve estimation of forest carbon stocks and to 

quantify the distribution of wood volume within trees 

for estimating whole-tree utilization potential. While 

the volume of the dominant stem in a tree, generally 

referred to as the bole, has been extensively studied, 

the relative mass and volume of branches has received 

much less attention. It is particularly challenging 

to quantify the branch volume and branch mass in 

trees with a deliquescent branching architecture (i.e., 

hardwoods), and it is even more difficult to model 

the bole for such trees because the lack of apical 

dominance makes definition of the bole more obscure. 

Here, a dominant path through a tree’s branch network 

is defined as the cumulative bole profile and other 

nondominant branches contribute to cumulative 

volume and mass along the dominant path. 

METHODS

Study Site and Tree Selection

The study site included 20.8 ha of a 36-ha secondgrowth maple-beech stand at the Fred Russ 

Experimental Forest in southwestern Michigan 

owned by Michigan State University. The Fred Russ 

Experimental Forest is located in Cass County, in 

Decatur, MI, with a total area of 381 ha that supports 

a diverse range of species and stand conditions. 

Kalamazoo, Ormas, and Oshtemo are the three 

primary soil series. The Kalamazoo and Oshtemo soil 

series are fine-loamy and coarse-loamy, mixed, mesic 

typic hapludalfs, respectively. The Ormas soil series 

is a coarse-loamy, mixed, mesic arenic hapludalfs on 

an outwash plain landform with a level topography 

(USDA NRCS 2011). The 30-year average annual 

precipitation is 1035 mm (NOAA 2011). The residual 

basal area of the stand is 22.1 m2/ha.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 392

In the spring of 2010, wind-thrown trees with intact 

crowns were sampled following a windstorm that 

uprooted more than 200 trees. Sampling began in 

late spring of 2010 and included as wide a range 

of diameter at breast height (d.b.h.) as possible, 

irrespective of species, with at least one individual in 

each 10 cm size class ranging from 10-95 cm. Selected 

trees were relatively isolated from other fallen trees 

so that the branches were easily measurable. The 32 

sample trees had a mean d.b.h. of 50.9 cm and mean 

height of 29.4 m with 87.5 percent of the trees being 

American beech (Fagus grandifolia Ehrh.) or sugar 

maple (Acer saccharum Marsh.).

Volume Profiles

The dominant stem in this study was defined by 

following the largest and most vertical branch at 

each fork to an apical control point. For each tree, 

circumference was measured at stump height, 37 cm 

above ground level, followed by d.b.h. Additional 

circumference measurements were taken at 2-m 

length intervals up to the first branch junction. 

Upon encountering the first branch, circumference 

measurements were taken at the before fork (BF) 

location of the stem and then at the after fork (AF) 

location of each branch based upon functional branch 

analysis protocols (van Noordwiijk and Mulia 2002). 

The BF was defined as the location where bark due to 

branching was no longer visible, as this is most likely 

the origin of the branches off the dominant stem. The 

AF was the location immediately after the point at 

which the fork occurred. The length from the BF to 

the AF was also recorded. The process of length and 

circumference measurements continued by following 

the dominant stem until the terminal bud was reached. 

The volume of each section was determined by 

Smalian’s formula for volume (Avery and Burkhart 

2002). The cumulative dominant stem volume profile 

is the accumulation of consecutive sections as a 

function of height.

An estimate of the total aboveground volume outside 

bark of an entire tree was obtained by random 

branch sampling (Gregoire and Valentine 2008). 

The same measurement methods as for the dominant 

stem were used, but a segment was selected based 

on probability proportional to size at each branch 

junction. The sectional volume for the random 

path was used to compute the volume of the whole 

tree using the inverse of the cumulative selection 

probability as expansion factors for a section in the 

random path (Gregoire and Valentine 2008). The 

volume of branches was found by subtracting the total 

dominant stem volume from the whole tree volume. 

A second random branch path was sampled on each 

tree to determine mean whole tree volume and the 

variation of whole tree volume estimates between the 

two different selected paths. The mean total branch 

volume determined from the two random paths 

was then distributed back to each first order branch 

immediately off the dominant stem. The volume for 

each first order branch and all higher order branches 

were incorporated into the profile at the height at 

which the first order branch connected to the dominant 

stem. The redistribution of the branch volume ensured 

that the final observation of the cumulative whole tree 

volume profile was the same as the mean total whole 

tree volume outside bark from the two random branch 

paths.

Density Profiles

To obtain a dominant stem mass profile, a profile of 

density for each tree was developed by harvesting tree 

discs. Discs were harvested at every circumference 

measurement location below crown height. Above 

crown height, discs were collected at every AF along 

the dominant stem path. Approximately 5- and 10-

cm-thick discs were harvested for smaller and larger 

circumferences, respectively. Each disc was measured 

for green mass and green volume immediately after 

transportation to the laboratory. Mean disc thickness 

was calculated by averaging the thickness of eight 

locations at 45° angles. Green volume was calculated 

as the product of mean disc thickness and crosssectional area of the disc. After being oven-dried at 

105 °C until constant mass was reached, each disc was 

reweighed to calculate moisture content, dry mass, 

and basic specific gravity (Williamson and Wiemann 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 393

2010). The basic specific gravity of each disc was 

assumed to be constant in the radial direction of the 

disc. The basic density, in units of kg/m3, of each disc 

is the basic specific gravity of the disc multiplied by 

103.

Whole tree cumulative mass profiles were developed 

by harvesting tree discs. To reduce costs, discs were 

collected at every AF for only the second random 

branch path, and the density along the path length was 

assumed to vary equally along the first random branch 

path and the second random branch path.

The statistical analysis was performed using the R 

statistical environment (R Development Core Team 

2010). The nlme package was used to fit the linear 

and nonlinear mixed effects models of the volume and 

density profiles (Pinheiro et al. 2011).

RESULTS

Figure 1a shows an example of a cumulative volume 

profile for a sugar maple using nonlinear mixed effects 

modeling for the dominant stem and whole tree. For 

this particular tree, most of the whole-tree volume 

is in the dominant stem, but a significant amount of 

additional volume accumulates in branches above 

relative crown height (RCH), although slowly at first, 

as the first branch encountered is not of large size 

compared to branches encountered later in the vertical 

profile. The difference between the dominant stem and 

whole-tree profiles gives the volume of branches in the 

tree.

Figure 1b shows the basic density profile for the same 

sugar maple which oscillates around the average 

wood density of sugar maple as determined by the 

Forest Products Laboratory (USDA FS 2010). Stem 

wood is dense at the base of the tree then density 

declines to a point about half-way to the base of the 

crown, increases into the middle of the crown and 

then decreases again toward the tip. The model for the 

basic density shows that after the relative path length 

at which the random path diverges from the dominant 

stem (RHD), the branches are denser at higher 

relative heights of the dominant stem. By combining 

the cumulative volume and basic density profiles, 

a cumulative mass profile can be attained. For this 

example tree (Fig. 1a and b), the branch mass fraction 

of whole-tree mass would be somewhat higher than the 

branch volume fraction of whole-tree volume.

ACKNOWLEDGMENTS

This study was funded through a Sustainability Seed 

Grant from Michigan State University (MSU) and 

with funds from a joint venture agreement between 

MSU and the U.S. Forest Service Forest Inventory and 

Analysis Program, Northern Research Station. Part of 

D.W. MacFarlane’s time was paid for with funds from 

Michigan AgBioResearch and a Fulbright Scholarship 

he received. D.W. MacFarlane would also like to thank 

the Kenya Forest Research Institute who provided 

logistical support to him while he conducted part of 

the research while on sabbatical.

LITERATURE CITED

Avery, T.E.; Burkhart, H.E. 2002. Forest 

measurements. New York, NY: McGraw-Hill. 

456 p.

Gregoire, T.G.; Valentine, H.T. 2008. Sampling 

techniques for natural and environmental 

resources. New York, NY: Chapman & Hall/CRC 

Press. 474 p.

National Oceanic and Atmospheric Administration 

(NOAA). 2011. National climatic data center.

Available at http://ncdc.noaa.gov/. (Accessed 

June 5, 2012).

Pinheiro, J.; Bates, D.; DebRoy, S.; Sarkar, D.; R. 

Development Core Team. 2011. nlme: linear 

and nonlinear mixed effects models. R package 

version 3.1-98. Vienna, Austria: R Foundation for 

Statistical Computing.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 394

0.0 0.2 0.4 0.6 0.8 1.0

0.0 1.0 2.0 3.0

Relative Height

Cumulative Volume (m3

)

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

DOM

WHT

RCH

A

Relative path length

Stem density (kg

m3

)

300 400 500 600 700

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

DOM

RBP

FPL

RHD

B

Figure 1.—(A) The cumulative volume profile for the dominant (DOM) stem and whole-tree (WHT) of a sugar maple with d.b.h. 

= 42.8 cm, total height = 34.0 m, and relative crown height (RCH, vertical line) of 0.500. The measured DOM locations are 

represented by filled circles, and WHT locations are represented by open circles. (B) The mixed-effects vertical stem density 

(wood plus bark) profile for the DOM stem and the random branch path (RBP) of the same sugar maple tree with relative path 

length at which the random path diverged from the dominant stem (RHD, vertical line) of 0.714. The average wood density of 

560 kg/m3

 for sugar maple trees (USDA FS 2010) is shown as a horizontal dashed line. 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 395

R Core Team. 2012. R: a language and environment 

for statistical computing. Vienna, Austria: R 

Foundation for Statistical Computing. Available at 

www.R-project.org. [Date accessed unknown].

U.S.Department of Agriculture, Forest Service 

(USDA FS). 2010. Wood handbook: wood as an 

engineering material. Gen. Tech. Rep. FPL-190. 

Madison WI: U.S. Department of Agriculture, 

Forest Service, Forest Products Laboratory. 509 p.

U.S. Department of Agriculture, Natural Resources 

Conservation Service (USDA NRCS). 2011. Web 

soil survey. Available at http://websoilsurvey.nrcs.

usda.gov/. (Accessed June 4, 2012).

van Noordwijk, M.; Mulia, R. 2002. Functional 

branch analysis as a tool for fractal scaling 

above- and belowground trees for their additive 

and non-additive properties. Ecological 

Modelling. 149(1-2): 41-51.

Williamson, G.B.; Wiemann, W.C. 2010. Measuring 

wood specific gravity...correctly. American 

Journal of Botany. 97(3): 519-524.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 396

FIA’S VOLUME-TO-BIOMASS CONVERSION METHOD (CRM) 

GENERALLY UNDERESTIMATES BIOMASS 

IN COMPARISON TO PUBLISHED EQUATIONS

David C. Chojnacky1

Abstract.—An update (Chojnacky et al. in preparation) of the Jenkins et al. (2003) 

biomass estimation equations for North American tree species resulted in 35 generalized 

equations developed from published equations. These 35 equations, which predict 

aboveground biomass of individual species grouped according to a taxa classification 

(based on genus or family and sometimes specific gravity), generally predicted higher 

biomass than estimates from the U.S. Department of Agriculture, Forest Service Forest 

Inventory and Analysis Program (FIA). FIA uses a conversion approach called the 

component ratio method (CRM) (Woodall et al. 2011) to generate biomass estimates. 

This method converts cubic volume estimates to biomass using constant specific gravity 

values and auxiliary information for branches, bark, and stumps. FIA tree biomass data 

were grouped by the same taxa as used for the 35 equations, biomass for the same trees 

was also predicted with the equations, and then diameter-class averaged values were 

compared. FIA estimates excluded foliage, but the amount of biomass by which the 

equation predictions exceeded FIA’s estimates generally suggested more than a foliage 

discrepancy. The equations predicted 2 to 28 percent higher biomass (at 30-cm d.b.h.) 

for most conifer and hardwood taxa. Exceptions were Larix and western Tsuga genera 

which predicted 10 to 12 percent lower for trees at 30-cm d.b.h. Equations for woodland 

taxa predicted biomass 45 to 53 percent higher than FIA estimates (at 30-cm d.r.c.) but 

FIA’s woodland biomass definition may have confounded comparison. In a similar study, 

Zhou et al. (2011) found that a volume-to-biomass conversion method (resembling FIA’s 

approach) underestimated biomass by 6.3 to16.6 percent—supporting the idea that CRM 

may inherently underestimate biomass. 

1

 Adjunct Faculty, Virginia Tech University, 144 Rees Place, 

Falls Church, VA 22046. To contact, call 703-237-8620 or 

email at dchojnac@vt.edu.

INTRODUCTION

The generalized Jenkins et al. (2003) biomass 

equations came from an effort to produce 

standardized, consistent, and well-documented tree 

estimation equations on a national scale, through 

compilation and synthesis of equations published in 

the literature, for use in the forest sector (Heath et 

al. 2011) of the Inventory of U.S. Greenhouse Gas 

Emissions and Sinks (EPA 2012). A meta-analysis 

was used to develop 10 generalized species-groupspecific equations for estimating biomass from only 

diameter measurements using regression and logtransformation.

Current work (Chojnacky et al. in preparation) updated 

the Jenkins et al. (2004) database and refined biomass 

modeling. Generalized equations were developed 

based on allometric scaling theory (Chojnacky 

2002); taxonomic groupings (genus or family) and 

wood specific gravity were used as surrogates for 

scaling parameters that could not be estimated. The 

update resulted in 35 biomass equations for a taxa 

classification described below. The purpose of this 

paper is to compare the biomass predictions from the 

new equations to estimates of live-tree biomass from 

the U.S. Department Agriculture, Forest Service’s 

Forest Inventory and Analysis Program (FIA).
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 397

UPDATED EQUATIONS

The initial database for Jenkins et al. (2003) included 

2,626 total biomass and component equations; the 

new study brought the total to 3,464 equations for 

North American tree species from 206 source studies. 

These included published equations up to May 

2011 developed in the United States or Canada that 

estimated total bone-dry biomass for individual trees 

and/or components thereof, based on diameter alone or 

on diameter and height. The published equations were 

used (as in Jenkins et al. [2003]) to generate biomass 

values (pseudodata) for diameters at equally spaced, 

approximately 5-cm intervals within the diameter 

range of the trees used for each original equation.

The pseudodata from the published equations were 

classified into what we defined as “taxa” based on 

genus or family and sometimes specific gravity for 

finer separation (Table 1). The classification was 

mostly genus-based for conifer species, but familybased for hardwood and woodland species.

Biomass equations were developed from pseudodata 

by using logarithmic regression for a 2-parameter 

model [ln(biomass) = β0 + β1 ln(diameter), where 

diameter = diameter at 1.37 m (d.b.h.) for conifer/

hardwood species and diameter near root collar (d.r.c.) 

for woodland species]. Parameters for the 13 conifer, 

18 hardwood, and 4 woodland taxa are listed in our 

more detailed manuscript in process of publication 

(Chojnacky et al. in preparation).

COMPARISON TO FIA DATA

FIA generates biomass estimates with a biomass 

expansion factor approach called the component 

ratio method (CRM) (Heath et al. 2009, Woodall et 

al. 2011). Cubic volume estimates are converted to 

biomass using constant wood and bark specific gravity 

values and auxiliary information for branches, bark, 

and stumps (Miles and Smith 2009). We expected 

the new equation estimates to exceed FIA estimates 

because FIA excluded foliage, but the magnitude 

of the differences found suggested more than just a 

foliage discrepancy.

The comparison was complicated both by having 

to sort through FIA definitions to delineate a 

reasonable biomass without excessive deductions 

and by FIA’s exclusion of foliage, which particularly 

underrepresents total biomass for small coniferous 

trees. We considered using our database to devise 

an adjustment for foliage but this seemed to further 

confound comparison. Instead, we defined an estimate 

of FIA biomass as follows, using USDA Forest Service 

(2010) variables as listed in uppercase: Live trees 

(STATUSCD=1) were defined as “growing stock” if 

measured at d.b.h. (TREECLCD Eq 2 and DIAHTCD 

Eq 1) or defined as “rough cull” if measured at 

d.r.c. [TREECLCD In(2,3) and DIAHTCD Eq 2] 

with no additional CULL coded. From this subset 

of 2.2 million trees (≤50 cm diameter) for the entire 

United States, total bone-dry biomass (excluding 

foliage) was calculated for trees ≥12.7 cm diameter 

(biomass=DRYBIO_BOLE + DRYBIO_TOP + 

DRYBIO_STUMP), and selected for saplings 

(DRYBIO_SAPLING) and for trees measured at d.r.c. 

(DRYBIO_WDLD_SPP). FIA biomass data for these 

trees (between 2.5 and 50 cm diameter) were then 

grouped by our taxa and averaged into 2-cm diameter 

classes generally based on about 100 to more than 

1,000 trees per diameter class. For completeness, FIA 

data for families (excluding alien species) not included 

in our 35 taxa classes were grouped as follows: 

Taxaceae grouped with Pseudotsuga; Aquifoliaceae, 

Ebenaceae, Lauraceae, Moraceae, Styracaceae and 

Theaceae grouped with the mixed hardwood group 

(except a few species exceeding specific gravity 0.60 

grouped with deciduous Fagaceae taxa); and woodland 

families Boraginaceae, Rhamnaceae, and Ericaceae 

grouped with Fabaceae/Rosaceae taxa. (Although 

we suggest woodland Aceraceae be estimated from 

Aceraceae <50 hardwood taxon, it was not included in 

this comparison). We also predicted biomass with the 

35 equations for the same FIA trees, averaged them 

within 2-cm diameter classes, and then subtracted FIA 

biomass for comparison.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 398

Table 1.—North American tree species grouped into 35 taxa for biomass equation development. Taxa 

derivation and further description included in Chojnacky et al. (in preparation).

Group Taxa name Description

Conifer Abies <35 Abies species with specific gravity <0.35, eastern species & A. lasiocarpa

Abies ≥35 Abies species with specific gravity ≥0.35, western species

Cupressaceae <30 Cupressaceae family with specific gravity <0.30, eastern Thuja species

Cupressaceae 30-39 Cupressaceae family with specific gravity 0.30-0.39, western Calocedrus, 

 Sequoiadendron, Thuja species 

Cupressaceae ≥40 Cupressaceae family with specific gravity ≥0.40, Chamaecyparis species 

 & Juniperus virginia

Larix Larix species

Picea <35 Picea species with specific gravity <0.35, western species

Picea ≥35 Picea species with specific gravity ≥0.35, eastern species & P. abies

Pinus <45 Pinus species with specific gravity <0.45, western & northeastern species

Pinus ≥45 Pinus species with specific gravity >=0.45, southern species

Pseudotsuga Pseudotsuga species

Tsuga <40 Tsuga species with specific gravity <0.40, eastern species

Tsuga ≥40 Tsuga species with specific gravity ≥0.40, western species

Hardwood Aceraceae <50 Acer species with specific gravity <0.50 

Aceraceae ≥50 Acer species with specific gravity ≥0.50 

Betulaceae 39<40 Betulaceae genera with specific gravity <0.40, primarily Alnus species

Betulaceae 40-49 Betulaceae genera with specific gravity 0.40-49, primarily Betula species

Betulaceae 50-59 Betulaceae genera with specific gravity 0.50-59, primarily Betula species

Betulaceae ≥60 Betulaceae genera with specific gravity ≥0.60, including Betula & Ostrya

 species

Fabaceae/Juglanaceae, Carya Carya species only

Fabaceae/Juglandaceae, other Fabaceae & Juglandaceae genera except Carya, including Robinia, Juglans

 species

Fagaceae, deciduous Deciduous Fagaceae genera, including Fagus, Quercus, Castanea species

Fagaceae, evergreen Evergreen Fagaceae genera, including Quercus, Chrysolepis, Lithocarpus

 species

Hamamelidaceae Hamamelidaceae genera, primarily Liquidambar styraciflua

Hippocastanaceae/Tiliaceae Hippocastanaceae &Tiliaceae genera, primarily Aesculus & Tilia species

Magnoliaceae Magnoliaceae family, primarily Liriodendron tulipifera

Oleaceae <55 Oleaceae genera with specific gravity 0.55, primarily Fraxinus species

Oleaceae ≥55 Oleaceae genera with specific gravity ≥0.55, primarily Fraxinus species

Salicaceae <35 Saliaceae genera with specific gravity <0.35, primarily Populus species

Salicaceae ≥35 Saliaceae genera with specific gravity ≥0.35, primarily Populus & Salix

 species

Mixed hardwoods* Cornaceae, Ericaceae, Lauraceace, Platanaceae, Rosaceae, Ulmaceae 

 famlies or other hardwood families not listed in this table with specific 

 gravity between 0.45 and 0.65

Woodland Cupressaceae Cupressaceae genera, primarily Juniperus & Cupressus species 

Fabaceae/Rosaceae Fabaceae & Rosaceae genera, primarily Cercidium, Prosopis, Cercocarpus

 species

Fagaceae Woodland Fagaceae genera, primarily evergreen Quercus species

Pinaceae Pinyon pine species

*Mixed hardwood equation also appropriate for species not included in table, unless specific gravity of the species more closely related to 

another taxon.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 399

RESULTS

For conifers (Fig. 1), the new equations predicted 5 

to 24 percent higher biomass (at 30-cm d.b.h.) than 

FIA estimates for most taxa, and predicted even 

higher for saplings. Exceptions were Larix and Tsuga

≥0.40 predicting 10 to 12 percent lower at 30-cm 

d.b.h. The small trees showed an interesting biomass 

pattern with a peak (or mode) between 10 and 15 cm 

d.b.h. This peak corresponds to a discontinuity in FIA 

methodology where tree (d.b.h. ≥12.5 cm) biomass is 

estimated from volume conversion but sapling (d.b.h. 

<12.5 cm) biomass is actually estimated from Jenkins 

et al. (2003) equations (Woudenberg et al. 2010) with 

some additional adjustment (JENKINS_SAPLING_

ADJUSTMENT). Because foliage can be quite large 

for small trees—ranging from 13 percent (median) 

to more than 30 percent (90th percentile) of total 

biomass for our conifer pseudodata for trees ≤12.5-

cm d.b.h. (Chojnacky et al. in preparation), it is not 

surprising that our equations overpredict FIA biomass 

(with foliage excluded) for small trees. However, the 

percentage of conifer foliage to total biomass in our 

pseudodata drops to 4 to 12 percent (depending on 

species) for trees larger than 12.5-cm d.b.h., indicating 

some other explanation for the overall 5 to 24 percent 

larger biomass estimates from our conifer equations.

Comparison of the woodland equations (Figs. 1 and 

2) to FIA data (Fig. 3) revealed a pattern similar to 

that for conifer and hardwood except differences were 

much greater—45 to 53 percent for trees at 30-cm 

d.r.c. However, FIA’s definition of woodland biomass 

(DRYBIO_WDLD_SPP) could be excluding much 

branch material less than 3.8 cm in diameter. Although 

the definition of DRYBIO_WDLD_SPP (Woudenberg 

et al. 2010) mentions exclusion of tree top above 

1.5 inches diameter (3.8 cm) in addition to foliage 

exclusion, this could mean all branch biomass smaller 

than 3.8 cm in diameter is excluded, as is typical for 

estimating woodland volume for these bushy multistemmed species (Chojnacky 1994). Otherwise, FIA 

exclusion of only a single top branch less than 3.8 cm 

in diameter—and not the rest—makes little sense.

DISCUSSION

Why did the updated Jenkins et al. (2003) equations 

(Chojnacky et al. in preparation)—based on all 

biomass equations in the literature—generally 

produce estimates higher than those generated by 

the FIA CRM method? One possibility is volume-tobiomass methods simply underestimate. For example, 

Zhou (2011) demonstrated for green ash (Fraxinus

pennsylvanica), ponderosa pine (Pinus ponderosa), 

and eastern redcedar (Juniperus virginiana) that 

volume-to-biomass conversion (using specific gravity 

similar to FIA’s CRM method) consistently and 

significantly underestimates biomass from 6.3 to 16.6 

percent. However, we cannot determine whether the 

pseudodata are accurate biomass estimates nor whether 

volume-to-biomass conversion approaches (as utilized 

by the FIA) inherently underestimate. These questions 

can only be answered from measuring new biomass 

data. We simply offer this comparison that FIA 

biomass estimates are generally 2 to 28 percent lower 

(at 30-cm d.b.h.) for most conifer and hardwood taxa 

than results from a meta-analysis of published biomass 

equations.

ACKNOWLEDGMENTS

The author thanks Linda Heath of the U.S. Forest 

Service and Phil Radtke of Virginia Tech for helpful 

manuscript reviews. This researchwas supported in 

part by the Joint Fire Sciences Program and the U.S. 

Forest Service, Northern Research Station through 

Joint Venture Agreement 09-JV-11242305-128.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 400

Figure 1.—Biomass equations for 13 conifer taxa compared to FIA data for 1,209,140 trees. Difference is equation prediction 

minus FIA biomass estimate, each first averaged within 2-cm diameter class. Legend order corresponds to curves at 30-cm d.b.h.

Figure 2.—Biomass equations for 18 hardwood taxa compared to FIA data for 1,192,774 trees. Difference is equation 

prediction minus FIA biomass estimate, each first averaged within 2-cm diameter class. Legend order corresponds to 

curves at 30-cm d.b.h.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 401

Figure 3.—Biomass equations for four woodland taxa compared to FIA data for 150,167 trees. Difference is equation 

prediction minus FIA biomass estimate, each first averaged within 2-cm diameter class. Legend order corresponds to 

curves at 30-cm d.r.c.

LITERATURE CITED

Chojnacky, D.C. 1994. Volume equations for New 

Mexico’s pinyon-juniper dryland forests. Res. 

Pap. INT-471. Ogden, UT: U.S. Department 

of Agriculture, Forest Service, Intermountain 

Research Station. 10 p.

Chojnacky, D.C. 2002. Allometric scaling theory 

applied to FIA biomass estimation. In: 

McRoberts, R.E.; Reams, G.A.; Van Deusen, P.C.; 

Moser, J.W., eds. Proceedings of Third Annual 

Forest Inventory and Analysis Symposium; 2001 

October 17-19; Traverse City, MI. Gen. Tech. 

Rep. NC-230. St. Paul, MN: U.S. Department of 

Agriculture, Forest Service, North Central Research 

Station: 96-102.

Chojnacky, D.C.; Jenkins, J.C.; Heath, L.S. [In 

preparation]. Updated national-scale biomass 

estimators for North American tree species.

(D.C. Chojnacky may be contacted at 

dchojnac@vt.edu for draft copy.)

U.S. Environmental Protection Agency [EPA]. 2012. 

Inventory of U.S. greenhouse gas emissions and 

sinks: 1990-2010. Hockstad, L.; Cook, B., comps. 

Report No. EPA 430-R-12-001. Washington, DC: 

U.S. Environmental Protection Agency. 481 p. 

Available at http://www.epa.gov/climatechange/

Downloads/ghgemissions/US-GHG-Inventory2012-Main-Text.pdf. (Accessed August 2012).
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Heath, L.S.; Hansen, M.H.; Smith, J.E.; Miles, P.D.; 

Smith, B.W. 2009. Investigation into calculating 

tree biomass and carbon in the FIADB using 

a biomass expansion factor approach. In: 

McWilliams, W.; Moisen, G.; Czaplewski, R., 

comps. Forest Inventory and Analysis (FIA) 

Symposium 2008; 2008 October 21-23; Park 

City, UT. RMRS-P-56CD. Fort Collins, CO: U.S. 

Department of Agriculture, Forest Service, Rocky 

Mountain Research Station [1 CD]: 26 p.

Heath, L.S.; Smith, J.E.; Skog, K.E.; Nowak, D.J.; 

Woodall, C.W. 2011. Managed forest carbon 

estimates for the US greenhouse gas inventory, 

1990-2008. Journal of Forestry. 109(3): 167-173.

Jenkins, J.C.; Chojnacky, D.C.; Heath, L.S.; Birdsey, 

R.A. 2003. National-scale biomass estimators for 

United States tree species. Forest Science. 49(1): 

12-30.

Jenkins, J.C.; Chojnacky, D.C.; Heath, L.S.; Birdsey, 

R.A. 2004. A comprehensive database of 

diameter-based biomass regressions for 

North American tree species. Gen. Tech. Rep. 

NE-319. Newtown Square, PA: U.S. Department of 

Agriculture, Forest Service, Northeastern Research 

Station. 45 p.

Miles, P.D.; Smith, W.B. 2009. Specific gravity 

and other properties of wood and bark for 156 

tree species found in North America. Res. Note 

NRS-38. Newtown Square, PA: U.S. Department 

of Agriculture, Forest Service, Northern Research 

Station. 35 p.

USDA Forest Service. 2010. FIA data mart: 

download files, FIADB4.0. Washington, DC: 

U.S. Department of Agriculture, Forest Service. 

Accessed online at http://199.128.173.17/fiadb4-

downloads/datamart.html/. (Accessed November 

2010). [No longer available; current version 

FIADB5.1 is available at http://apps.fs.fed.us/fiadbdownloads/datamart.html/. (Accessed April 6, 

2012).]

Woodall, C.W.; Heath, L.S.; Domke, G.M.; Nichols, 

M.C. 2011. Methods and equations for 

estimating aboveground volume, biomass, and 

carbon for trees in the U.S. forest inventory, 

2010. Gen. Tech. Rep. NRS-88. Newtown Square, 

PA: U.S. Department of Agriculture, Forest Service, 

Northern Research Station. 30 p.

Woudenberg, S.W.; Conkling, B.L.; O’Connell, B.M.; 

LaPoint, E.B.; Turner, J.A.; Waddell, K.L. 2010. 

The Forest Inventory and Analysis database: 

database description and users manual version 

4.0 for phase 2. Gen. Tech. Rep. RMRS-245. 

Fort Collins, CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research Station. 

336 p.

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M.M.; Martin, D.L.; Xin, Y.; Tang, Z. 2011. The 

use of forest-derived specific gravity for the 

conversion of volume to biomass for opengrown trees on agricultural land. Biomass and 

Bioenergy. 35(5): 1721-1731.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
LiDAR
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 404

APPLYING INVENTORY METHODS TO ESTIMATE ABOVEGROUND 

BIOMASS FROM SATELLITE LIGHT DETECTION AND RANGING 

(LiDAR) FOREST HEIGHT DATA 

Sean P. Healey, Paul L. Patterson, Sassan Saatchi, Michael A. Lefsky, Andrew J. Lister, 

Elizabeth A. Freeman, and Gretchen G. Moisen1

Abstract.—Light Detection and Ranging (LiDAR) returns from the spaceborne 

Geoscience Laser Altimeter (GLAS) sensor may offer an alternative to solely field-based 

forest biomass sampling. Such an approach would rely upon model-based inference, 

which can account for the uncertainty associated with using modeled, instead of fieldcollected, measurements. Model-based methods have been thoroughly described in the 

statistical literature, and an increasing number of model-based forestry applications 

use tactically acquired airborne LiDAR. Adapting these methods to GLAS’s irregular 

acquisition pattern requires a strategy for identifying a subset of GLAS “shots” that 

can be considered a simple random sample. We have developed a flexible method of 

dividing the landscape into equal-area polygons from which a GLAS shot can be chosen 

at random as a member of the sample. This process bears similarities to the approach 

used by the Forest Inventory and Analysis (FIA) Program as it moved toward its current 

hexagonal sample grid. 

Although the ultimate application of this approach would be production of consistent 

biomass estimates across different countries, well-calibrated FIA estimates over the 

United States provide a convenient testing ground. Applied to California, this approach 

produced almost exactly the same estimate of biomass density (Mg/ha) as the FIA 

sample. The GLAS-based estimate had a considerably higher standard error than FIA’s 

estimate, but it comes at a much lower cost and is based upon globally available GLAS 

measurements. 

1

 Ecologist (SH) and Statistician (PLP), U.S. Forest Service, 

Rocky Mountain Research Station, 507 25th St., Ogden, 

UT 84401; Senior Scientist (SS), California Institute of 

Technology; Assistant Professor (MAL), Colorado State 

University; Research Forester (AJL), U.S. Forest Service, 

Northern Research Station; Ecologist (EAF) and Forester 

(GCM), U.S. Forest Service, Rocky Mountain Research 

Station. SH is corresponding author: to contact, call 

801-625-5770 or email at seanhealey@fs.fed.us.

INTRODUCTION AND METHODS

Most official national and international forest carbon 

reporting mechanisms have traditionally relied upon 

information from field-based inventories. However, 

many countries do not have such inventories, and even 

among those that do, significant discrepancies exist 

in methods and definitions. A potential alternative 

to purely field-based sampling may lie in the 70-

m circular waveform Light Detection and Ranging 

(LiDAR) returns from the spaceborne Geoscience 

Laser Altimeter System (GLAS) sensor. Such returns 

have been shown to be sensitive to biomass, and there 

may be a chance to use a smaller subset of co-located 

field plots to create modeled biomass “samples” over 

the areas sampled by GLAS.

In this paper, we describe a method for identifying 

a subset of GLAS shots which can be treated as a 

simple random sample for the purposes of a forest 

biomass inventory. This process is necessary because 

GLAS acquisition patterns are irregular and, in 

aggregate, cannot strictly be considered either random 

or systematic. We then demonstrate the use of such a 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 405

sample over California with a model-based estimator 

similar to that used by Stähl et al. (2011) to estimate 

aboveground biomass. Model-based estimation 

allows us to predict, instead of measure, biomass at 

each sample point using relationships derived from 

a separate set of co-located ground and LiDAR 

measurements. Variance estimators used in this process 

take into account the uncertainty associated with the 

models used. For full methodological detail of this 

estimation process, consult Healey et al. (in press).

The sample design we describe is similar to that used 

by the Forest Inventory and Analysis (FIA) Program. 

Prior to a move toward a national sampling framework 

in the late 1990s, FIA plots were distributed and 

measured in slightly different ways in different regions 

of the country. The move to a nationally coherent 

sampling frame was accomplished by superimposing a 

hexagonal grid over the entire country, with the area of 

each grid cell equal to the nominal area represented by 

each FIA sample (Reams et al. 2005). In cells where 

one existing plot fell, that plot was kept. In those with 

more than one plot, only one was selected at random 

for retention. In those with no existing plots, a plot was 

established in a random location.

Establishment of this semi-systematic, equal-area 

sample frame allowed FIA to accommodate existing 

plot locations while drawing a sample which was 

spatially balanced across the country but was random 

with respect to forest conditions (Reams et al. 2005). 

The sample design we propose for GLAS follows a 

similar approach. One and only one GLAS shot is 

retained in each cell of an equal-area (but not equalshape) tessellation of the area labeled as “forest” in 

a global land cover map. This tessellation is created 

following a fractal-based approach, using simple 

geometric rules to create equal-area clusters (Lister 

and Scott 2009). Since retroactively “adding” GLAS 

measurements (the last of which were collected in 

2008) is not possible, tessellation cell size (and, 

inversely, sample number) is limited by the constraint 

that each equal-area cell must contain at least one 

GLAS shot.

Given the elimination of all GLAS shots except one 

in every tessellation cell under this approach, it is of 

practical interest to know the precision of resulting 

biomass estimates. The precision (i.e., standard error) 

of model-based estimates of biomass in California 

using the GLAS sample will be compared to designbased estimates derived from FIA’s sample of more 

than 5,500 field measurements in the state. 

RESULTS

In view of the constraint that there be at least one 

GLAS shot in each tessellation cell, the maximum 

number of cells in California is 182, or one per 48,000 

ha. While the minimum number of GLAS shots in a 

single cell was one, the average was 560, from which 

a single shot was chosen at random. These randomly 

selected shots constitute the “S1” model-building 

sample (Fig. 1). The average distance between each 

point in the S1 population and its closest neighbor is 

19.6 km (median = 13.5 km). The minimum overall 

distance (i.e., closest pair of neighbors) is 2.4 km.

Thirty-five co-located GLAS/FIA plots were available 

for use in determining the relationship between a 

GLAS derivative called Lorey’s height (described as 

“basal area-weighted height”) and biomass (i.e., the 

S2 sample; Fig. 2). The most parsimonious applicable 

model for this relationship was considered to be a 

model with a single quadratic term and no intercept 

(biomass = 0.3717 Lorey’s height2). A no-intercept 

model was used because of our assumption that 

forested plots with no biomass should return no 

Lorey’s height. Significance tests indicated negligible 

gain from including a linear term in the model. 

It should be noted that seven values (less than 4 

percent) of the S1 sample exceeded the largest value 

in the model-building S2 data set shown in Figure 2 

(specifically, these values were 45, 46, 48, 50, 52, 54, 

and 60 m). Ideally, the model-building data set should 

span the entire range of the values to be modeled. 

Given the small percentage of Lorey’s heights in S1 

not represented in S2, however, we assume that the 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 406

Figure 1.—The 182 GLAS shots selected for inclusion in the S1 sample of California forests. This sample has properties 

similar to the sample used in the U.S. NFI and is treated here as a simple random sample. A National Land Cover Database 

cover map is shown for context.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 407

model is valid for the entire population. We likewise 

assume no spatial autocorrelation among S1 samples. 

Our GLAS-based estimate of biomass density in 

California’s forests was 211.11 Mg/ha, which was 

within standard error bounds (±2.88) of the FIA 

estimate of 208.95 Mg/ha (Miles 2011). The FIA 

estimate was derived through a 10-year ground sample 

of 5,261 forested plots. The standard error of the 

GLAS-based estimate was 20.70 Mg/ha (Fig. 3). The 

modeling variance was approximately 0.77 times the 

variance contributed by the sampling process. 

Figure 2.—Relationship between FIA-measured 

aboveground tree biomass and GLAS-based Lorey’s height 

in California. The line is described by: y = 0.3717 x2.

Lorey’s Height (m)

FIA Aboveground Biomass (t/ha)

Figure 3.—Comparison between the FIA carbon density 

estimate for California’s forests and the estimate made here 

using GLAS- and model-based estimation. The estimates 

are nearly identical, although FIA’s estimate has significantly 

less uncertainty (bars indicate standard error). Biomass Density (Mg/ha)

DISCUSSION

Model-based estimation using the sample design we 

describe provides a transparent method for estimating 

biomass for particular spatial domains. This sample 

design, in which one arbitrarily located sample point is 

drawn from equal-area sample units distributed across 

the landscape, is similar to that used by FIA, and 

our estimate of biomass density in California closely 

matched FIA’s design-based estimate. The standard 

error of our estimate (approximately 9.8 percent of 

the estimate) was substantially larger than that of the 

FIA estimate (1.4 percent) and that derived through 

model-based estimation by Andersen et al. (2011) 

using specifically acquired airborne LiDAR data 

(8 percent). However, the cost of the FIA estimate was 

approximately $10.5 million (based on a commonly 

used valuation of $2,000 per plot), and the LiDAR 

acquisition alone in Anderson et al.’s (2001) much 

smaller study area cost $60,000. Future use of GLAS 

data in the process described here represents an almost 

no-cost option for providing consistent, moderateprecision biomass estimates across the globe.

A primary advantage of the model-based inference 

used here is the capacity to apply models developed in 

areas of rich inventory data to GLAS shots, informing 

estimates in ecologically similar areas where field data 

are sparse. For example, Nelson et al. (2009) used 

relationships observed in a limited area of co-located 

biomass/GLAS observations to estimate biomass 

for all of Quebec, following a modified modelbased approach. However, the validity of inference 

in model-based approaches depends upon how well 

the stipulated models accord with the population of 

interest (Gregoire 1998). The question of how well the 

model applies to the population of interest is a critical 

consideration in the application of our approach, 

whether the model was developed in situ or from a 

spatially remote but perhaps ecologically similar area. 

Since our model was created from an arbitrary subset 

of FIA’s presumably unbiased ground sample, there is 

a compelling argument that the model is appropriate 

for the forests of California.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 408

The degree to which this model may apply beyond 

California remains an open question. Saatchi et al. 

(2011) noted regional differences in the relationship 

between biomass and GLAS-based Lorey’s height 

in their pan-tropical study. Data collected to support 

biomass estimation using the global GLAS data set 

would at least have to span major ecological systems. 

The consolidation of ground data needed to support a 

global GLAS-based biomass inventory would require 

significant international cooperation and, as illustrated 

by our results, would likely not improve the precision 

of biomass estimates available in countries with 

established national forest inventories (NFIs). Those 

inventories typically rely upon a denser sample than 

is available from GLAS and do not have to account 

for model variance, which in our example made up 

approximately 44 percent of the total variance. 

However, a GLAS-based biomass inventory would 

represent an internationally coherent basis for 

comparison among countries, especially those without 

established NFIs. Even moderate-precision biomass 

estimates would be an improvement in many countries 

(Gibbs et al. 2007), and consistent sample design 

and estimation methods would remove an important 

source of uncertainty in international monitoring. 

GLAS data were acquired in spatial patterns difficult 

to associate with either a systematic or random 

process. The sample design presented in this paper 

allows identification of a subset of GLAS data which 

may be used as a simple random sample to estimate 

biomass, perhaps globally, with consistent measures 

of uncertainty under a model-based estimation 

framework.

CONCLUSIONS

• The methods presented here constitute a globally 

extensible approach for generating a simple 

random sample from the global GLAS data set. 

The properties of the sample collected by GLAS 

have hitherto not been strictly identifiable with any 

particular design.

• Model-based estimation, following Stähl et al. 

(2011), based upon GLAS data in California 

produced an estimate of biomass density (biomass/

ha) almost identical to the estimate derived from 

the design-based NFI.

• Global application of model-based estimation using 

GLAS, while demanding significant consolidation 

of training data, would improve inter-comparability 

of international biomass estimates by imposing 

consistent methods and a globally coherent sample 

frame.

ACKNOWLEDGMENTS

This work was supported by the NASA Carbon 

Monitoring System and by FIA. The authors 

additionally would like to thank James Menlove for 

expert help in interpreting FIA biomass data as well as 

Karen Waddell and Jock Blackard for their help with 

the FIA database. 

LITERATURE CITED

Andersen, H.-E.; Strunk, J.; Temesgen, H. 2011. 

Using airborne light detection and ranging as 

a sampling tool for estimating forest biomass 

resources in the Upper Tanana Valley of Interior 

Alaska. Western Journal of Applied Forestry. 

26: 157-164.

Gibbs, H.K.; Brown, S.; Niles, J.O.; Foley, J.A. 

2007. Monitoring and estimating tropical 

forest carbon stocks: making REDD a reality.

Environmental Research Letters. 2: 1-13.

Gregoire, T.G. 1998. Design-based and model-based 

inference in survey sampling: appreciating the 

difference. Canadian Journal of Forest Resources. 

28: 1429-1447.

Healey, S.P.; Patterson, P.L., Saatchi, S.; Lefsky, M.A.; 

Lister, A.J.; Freeman, E.A. [In press]. A sample 

design for globally consistent biomass estimation 

using LiDAR data from the Geoscience Laser 

Altimeter System (GLAS). Carbon Balance and 

Management.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 409

Lister, A.; Scott, C. 2009. Use of space-filling curves 

to select sample locations in natural resource 

monitoring studies. Environmental Monitoring 

and Assessment. 149: 71-80.

Miles, P.D. 2011. Forest Inventory EVALIDator 

web-application version 4.01 beta. Available at 

http://fiatools.fs.fed.us/Evalidator4/tmattribute.jsp. 

[Date accessed unknown].

Nelson, R.; Boudreau, J.; Gregoire, T.G.; Margolis, 

H.; Naesset, E.; Gobakken, T.; Stahl, G. 2009. 

Estimating Quebec provincial forest resources 

using ICESat/GLAS. Canadian Journal of Forest 

Resources. 39: 862-881.

Reams, G.A.; Smith, W.D.; Hansen, M.H.; Bechtold, 

W.A.; Roesch, F.A.; Moisen, G.G. 2005. The 

Forest Inventory and Analysis sampling 

frame. In: Bechtold, W.A.; Patterson, P.L., eds. 

The enhanced Forest Inventory and Analysis 

Program—national sampling design and estimation 

procedure. Gen. Tech. Rep. SRS-80. Asheville, NC: 

U.S. Department of Agriculture, Forest Service, 

Southern Research Station: 31-36.

Saatchi, S.S.; Harris, N.L.; Brown, S.; Lefsky, M.; 

Mitchard, E.T.A.; Salas, W.; Zutta, B.R.; Buermann, 

W.; Lewis, S.L.; Hagen, S.; Petrova, S.; White, L.; 

Silman, M.; Morel, A. 2011. Benchmark map of 

forest carbon stocks in tropical regions across 

three continents. Proceedings of the National 

Academy of Sciences. 108: 9899-9904.

Stähl, G.; Holm, S.; Gregoire, T.G.; Gobakken, 

T.; Naesset, E.; Nelson, R. 2011. Model-based 

inference for biomass estimation in a LiDAR 

sample survey in Hedmark County, Norway.

Canadian Journal of Forest Resources. 41: 96-107.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 410

THE UTILITY OF LiDAR 

FOR LARGE AREA FOREST INVENTORY APPLICATIONS

Nicholas S. Skowronski and Andrew J. Lister1

Abstract.—Multi-resource inventory data are used in conjunction with Light Detection 

and Ranging (LiDAR) data from the Pennsylvania Department of Natural Resource’s 

PAMAP Program to assess the utility of extensive LiDAR acquisitions for large area 

forest assessments. Background, justification, and initial study designs are presented. 

The proposed study will involve three phases: 1) characterization of relationships 

between LiDAR cloud metrics and statistical summaries of tree information on forest 

inventory plots, 2) use of the inventory data to calibrate LiDAR-based forest biomass 

models, and 3) use of subsets of the LiDAR dataset as part of a ground-based forest 

inventory. Initial results of the first phase indicate moderate relationships between 

various combinations of ground inventory and LiDAR data.

1

 Research Forester (NSS), U.S. Forest Service, 

Northern Research Station, 180 Canfield St., Morgantown, 

WV 26505; Research Forester (AJL), U.S. Forest Service, 

Northern Research Station. NSS is corresponding author: to 

contact, call 304-285-1507 or email at nskowronski@fs.fed.us.

INTRODUCTION

Large area forest assessments have been of interest 

for many years. Traditionally, these assessments have 

been conducted by ground-based inventories like 

those conducted by the U.S. Forest Service’s Forest 

Inventory and Analysis (FIA) Program (Gillespie 

1999). Since the early 1900s, FIA has conducted 

inventories of the nation’s forest resource through 

a combination of periodic and annual field data 

collection campaigns. Data on tree and site factors 

in forested areas are collected, processed, and 

converted into summary information that is used by 

resource planners, land managers, scientists, and other 

interested parties.

The use of air- and space-borne sensors in forest 

assessments has increased over the last 40 years. In 

the last decade, the use of LiDAR (Light Detection 

and Ranging) technology in particular has increased 

dramatically. Cost considerations previously limited 

the use of LiDAR to relatively small—generally substate—areas. Now, entities such as state governments 

can afford to acquire LiDAR over large areas. For 

example, Pennsylvania’s PAMAP Program funded 

state-level acquisition of LiDAR between 2006 and 

2008 (PA DCNR 2012). The existence of co-occurring, 

large area LiDAR and forest inventory datasets creates 

opportunities for studies assessing the costs and 

benefits of using LiDAR in various ways for forest 

assessments. 

The most common use of LiDAR in forest assessments 

involves the generation of pixel-based estimates 

of forest parameters such as volume, biomass, or 

tree abundance in relatively small study areas (e.g., 

Asner et al. 2011, Lefsky et al. 2003). However, 

recent interest in large area assessments of forest 

carbon stocks as part of United Nations climate 

change agreements, such as those contained in the 

United Nations Framework Convention on Climate 

Change’s program for Reducing Deforestation and 

Degradation (REDD) (Gullison et al. 2007), has 

led to a need for investigations of cost-effective 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 411

strategies for measuring and monitoring forest carbon 

in areas that do not have established, ground-based 

forest inventories. LiDAR is a particularly appealing 

option due to the nature of the information obtained, 

reflectance information generally shows strong 

relationships with forest canopy height and density, 

two attributes closely related to forest biomass and 

thus carbon content. LiDAR has the added benefit of 

targeted acquisitions that can be less susceptible to 

cloud cover which affects space-borne sensors like 

Landsat.

An option that is not often explored, however, is the 

practical use of LiDAR to aid in large area, ground 

plot-based forest inventories. Due to the immense 

data volumes and processing requirements, it can 

be impractical to collect and process LiDAR over 

large areas on a regular basis. However, advances 

in computing technology make this an option worth 

exploring. A promising approach is the use of subsets 

of the LiDAR information for stratification or, in 

another supporting role with ground plots, as the basis 

for the estimate generation. 

The goal of the current study is to address the need 

for methods that use LiDAR to generate estimates of 

forest attributes, particularly tree carbon stocks, over 

large areas in an efficient way. Specific objectives 

of the study are to 1) characterize relationships 

between LiDAR cloud metrics and FIA data from 

various ecosystems around Pennsylvania, 2) assess 

the usefulness of FIA data for calibrating LiDARbased forest biomass models, and 3) compare the 

costs and benefits of using LiDAR-based maps of 

forest attributes with estimates generated from several 

combinations of LiDAR and ground data in a designbased forest inventory framework. Results of these 

three analyses will not only improve our understanding 

of how FIA data can serve as training data for LiDARbased biomass modeling, but also to help inform 

decisions about carbon inventory and monitoring 

strategies both in the United States and in other 

countries considering using LiDAR for this purpose.

STUDY AREA

The study area is the state of Pennsylvania. It is 

located between 74° 43′ and 80° 31′ west longitude, 

and 39° 43′ and 42° north latitude; the state contains 

approximately 44,819 square miles (116,083 km2) 

of land area. Pennsylvania is nearly 60 percent 

forested and is composed of a variety of ecosystems 

including highly urbanized in the east, agricultural in 

the center, and large areas of contiguous forest in the 

mountainous regions in the north and west. 

METHODS 

Each FIA plot consists of four circular 48 ft (14.6 

m) diameter subplots, with one subplot located in 

the center and three equidistant subplots distributed 

symmetrically around and located 120 ft (36.6 m) 

from the center subplot. The subplots occupy 0.17 

acres (0.07 ha), and the subplot array can be subtended 

by a circle of 1.5 acres (0.6 ha) in area. On each 

plot, information for several site factors (including 

ownership, forest type, land use, slope, and others) are 

collected, as well as data on individual trees, including 

species, diameter at breast height, total height, and 

the relative canopy position of each tree (classified 

as dominant, codominant, overtopped, intermediate, 

and open grown). Tree data are collected in the field 

on portions of plots that are classified as “accessible 

forest,” which is defined in part as belonging to a 

group of trees at least 0.4 ha in extent and at least 37 

m wide at its narrowest point, being capable of natural 

tree regeneration, and having a minimum stem count 

(stocking), dependent on species and tree size (USDA 

Forest Service 2011). All data are stored in a relational 

database. 

Using information found in and tools associated 

with the relational database, total volume, total 

aboveground carbon, total basal area, average tree 

height, and average diameter-weighted height were 

computed for each combination of species, canopy 

position class, and forest type. About 1500 single 

condition plots—those that are 100 percent forested 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 412

land use—were used in the analysis. Some plots were 

omitted based on LiDAR data quality (outliers were 

removed with heights three standard deviations above 

the mean height). 

The statewide LiDAR dataset was processed using the 

Toolbox for LiDAR data Filtering and Forest Studies 

(TiFFs) (Chen et al. 2007). The .LAS LiDAR files 

were provided in a preprocessed format with ground 

and canopy returns identified by the PAMAP vendor. 

One-foot (0.3-m) resolution Digital Elevation Models 

(DEMs) were generated using these predefined 

classifications. The LiDAR point cloud was then 

spatially intersected with each FIA plot location and 

clipped to spatial extent of each subplot. Data from 

each subplot were aggregated to the plot-level and 

standard LiDAR-derived statistical parameters (mean 

and quadratic mean, standard deviation, skew, kurtosis, 

and decile heights) and the LiDAR derived Canopy 

Height Profile (CHP) parameters (Skowronski et al. 

2011) were generated using only first returns for each 

plot. 

PROPOSED ANALYSES

To characterize relationships between LiDAR cloud 

metrics and FIA data, exploratory data analysis will 

be performed, including the generation of correlation 

and scatterplot matrices relating the independent 

variables (the LiDAR metrics) to various subsets of 

the FIA data, including subsets of the data by species, 

species group, forest type, geographic area, and 

canopy position class. The goal of these analyses will 

be to gain a better understanding of inter- and intravariable group relationships, and to inform decisions 

for and interpret results of the second phase of the 

project: carbon model development. For this phase, all 

subsets linear regression will be performed to generate 

a suite of carbon models and associated fit statistics 

and error assessments, with the goal of obtaining 

predictive models that can be applied to large areas of 

Pennsylvania. Finally, based on results of the first two 

phases, a sample design study will be performed. FIA 

generates estimates of forest parameters using a poststratification statistical design (Bechtold and Patterson 

2005) using strata created from classified Landsat 

images. We plan to generate a stratum map using 

LiDAR canopy maps instead of Landsat and calculate 

various estimates of forest parameters. We also plan 

to subdivide the LiDAR dataset by generating “strips” 

of LiDAR over the FIA plot locations and over 

several randomly selected areas with no FIA plots, 

to generate estimates using a double sampling (two 

phase sample) design. Finally, we plan to implement 

the regression estimator using the appropriate model(s) 

from phase 2 of the study. We will then compare all of 

the resulting estimates in terms of relative efficiency, 

or the improvement in sampling error relative to that 

achieved by a simple random sample. Of particular 

interest will be an assessment of the relative costs 

and benefits of acquiring and processing the LiDAR 

information (versus standard methods using less costly 

combinations of plots and Landsat imagery) and the 

development of a decision framework for the use of 

LiDAR in large area inventory applications (Kohl 

et al. 2011). 

LITERATURE CITED

Asner, G.P.; Highes, R.F.; Mascaro, J.; Uowolo, A.L.; 

Knapp, D.E.; Jacobson, J.; Kennedy-Bowdoin, 

T.; Clark, J.K. 2011. High-resolution carbon 

mapping on the million-hectare Island of 

Hawaii. Frontiers in Ecology and Environment. 

9: 434-43.

Bechtold, W.A.; Patterson, P.L., eds. 2005. The 

enhanced forest inventory and analysis program 

- national sampling design and estimation 

procedures. Gen. Tech. Rep. SRS-80. Asheville, 

NC: U.S. Department of Agriculture, Forest 

Service, Southern Research Station. 85 p.

Chen, Q. 2007. Airborne LiDAR data processing 

and information extraction. Photogrammetric 

Engineering & Remote Sensing. 73: 109-112.

Gillespie, A. 1999. Rationale for a national annual 

forest inventory program. Journal of Forestry. 

97(12): 16-20.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 413

Goetz, S.; Dubayah, R. 2011. Advances in remote 

sensing technologies and implications for 

measuring and monitoring forest carbon stocks 

and change. Carbon Management. 2: 231-244.

Gullison, R.E.; Frumhoff, P.C.; Canadell, J.G.; Field, 

C.B.; Nepstad, D.C.; Hayhoe, K.; Avissar, R.; 

Curran, L.M.; Friedlingstein, P.; Jones, C.D.; 

Nobre, C. 2007. Tropical forests and climate 

change. Science. 316: 985-986.

Köhl, M.; Lister, A.; Scott, C.T.; Baldauf, T.; Plugge, 

D. 2011. Implications of sampling design and 

sample size for national carbon accounting 

systems. Carbon Balance and Management. 

6(1): 10.

Lefsky, M.A.; Cohen, W.B.; Harding, D.J.; Parker, 

G.G.; Acker, S.A; Gower, S.T. 2002. Lidar remote 

sensing of biomass in three biomes. Global 

Ecology and Biogeography. 11: 393-399. 

PA DCNR. 2012. PAMAP program LAS files 

(LiDAR Data of Pennsylvania). University Park, 

PA: Pennsylvania Department of Conservation and 

Natural Resources. Available at http://www.pasda.

psu.edu/uci/FullMetadataDisplay.aspx?file=pamap_

lidar_LAS.xml. (Accessed July 1, 2012).

Skowronski, N.S.; Clark, K.L.; Duveneck, M.; Hom, 

J.L. 2011. Three-dimensional canopy fuel loading 

predicted using upward and downward sensing 

LiDAR systems. Remote Sensing of Environment. 

115: 703-714. 

USDA Forest Service. 2011. Forest inventory and 

analysis national core field guide, volume 1: field 

data collection procedures for phase 2 plots, 

Version 5.1. Washington, DC: U.S. Department of 

Agriculture, Forest Service, Forest Inventory and 

Analysis. 310 p.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 414

IMPROVING LIDAR BASED PREDICTION OF FOREST BIOMASS 

USING MODELS WITH SPATIALLY VARYING COEFFICIENTS

Chad Babcock, Andrew O. Finley, and John B. Bradford1

Abstract.—Many studies and production inventory systems have shown the utility of 

coupling covariates derived from Light Detection and Ranging (LiDAR) data with forest 

variables measured on geo-referenced inventory plots through regression models. The 

objectives of this study were to propose and assess the use of 1) a new technique for 

LiDAR variable extraction using Singular Value Decomposition to obtain uncorrelated 

covariates; and 2) a Bayesian hierarchical modeling framework that accommodates 

both residual spatial dependence and non-stationarity of model covariates through 

the introduction of spatial random effects. We explore these objectives using three 

forest inventory datasets that are part of the North American Carbon Program each 

comprising point-referenced measures of aboveground forest biomass and discrete 

LiDAR. For each dataset, we considered three LiDAR variable extraction methods and 

three regression model specifications. Models were assessed based on fit criteria and 

predictive performance using a leave-one-out cross-validation. Results showed that 

among the LiDAR variable extraction methods, no single set of covariates offered a 

consistent advantage across the datasets. The addition of spatial random effects to the 

regression model intercept only improved fit and predictive performance in the presence 

of substantial residual spatial dependence. Allowing all regression slope parameters to 

vary spatially, via the addition of spatial random effects, greatly improved model fit and 

predictive performance across all datasets. The proposed Bayesian modeling framework 

also provides access to pixel-level posterior predictive distributions that are useful for 

uncertainty assessment.

1

 Graduate Student (CB), Michigan State University, Department of Geography, East Lansing, MI 

48824; Assistant Professor (AOF), Michigan State University; Research Ecologist (JBB), U.S. 

Geological Survey. AOF is corresponding author: to contact, call 517-432-7219 or email 

at finleya@msu.edu. 

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Alternative Estimation

Techniques for FIA Data
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 416

AN EFFICIENT ESTIMATOR TO MONITOR 

RAPIDLY CHANGING FOREST CONDITIONS

Raymond L. Czaplewski, Michael T. Thompson, and Gretchen G. Moisen1

Abstract.—Extensive expanses of forest often change at a slow pace. In this common 

situation, FIA produces informative estimates of current status with the Moving Average 

(MA) method and post-stratification with a remotely sensed map of forest-nonforest 

cover. However, MA “smoothes out” estimates over time, which confounds analyses of 

temporal trends; and post-stratification limits gains from remote sensing. Time-series 

estimators, like the Kalman Filter (KF), better detect and analyze unexpected or rapid 

changes in dynamic forests. KF is a recursive multivariate model-based estimator that 

separates complex time-series of panel estimates and multi-sensor remotely sensed data 

into a sequence of smaller and more manageable components. Population-level results 

are disaggregated into expansion factors that assure additivity and simplify small area 

and small domain estimation. Other statistics gauge fit of alternative models to annual 

FIA panel data, which permits quantitative rankings among alternative cause-effect 

hypotheses.

1

 Research Mathematical Statistician (RLC), U.S. Forest 

Service, Rocky Mountain Research Station, 507 25th St., 

Ogden, UT 84404; Forester (MTT) and Research Forester 

(GGM), U.S. Forest Service, Rocky Mountain Research 

Station. RLC is corresponding author: to contact, call 

970- 218-5907 or email at rlczaplewski02@fs.fed.us.

INTRODUCTION

The 1998 Report by the Blue Ribbon Panel on 

Forest Inventory and Analysis (FIA) motivated the 

comprehensive redesign of the FIA program (Bechtold 

and Patterson 2005). FIA replaced decadal periodic 

surveys with annual panel surveys to produce more 

timely analyses for every State. However, a single 

annual panel uses only 10 to 20 percent of the field 

plots available to a periodic survey. To improve 

precision, FIA uses a simple Moving Average (MA) 

of five or more annual panels. While design-unbiased 

as an estimator of the average conditions among 

multiple panels, MA is biased for time-series of 

annual estimators (Bechtold and Patterson 2005). 

This bias is acceptably small whenever net change is 

relatively minor, but not when landscapes are affected 

by unusually rapid changes. Concerns with the MA 

for annual trend analyses are escalating. In 2012, the 

National FIA User Group recommended “renewed 

efforts to investigate alternatives to the simple moving 

average for improved trend detection and estimation, 

... including short term projections (5 to 10 years).” 

We describe an estimator designed to satisfy this and 

previous recommendations from the National FIA User 

Group. 

MONITORING ANALYSES 

AND THE NATURE OF CHANGE

To some degree, every acre of every forest changes 

every year through predictable processes of stand 

dynamics, ambient disturbances, timber management, 

and socioeconomic forces. In a static landscape, 

changes in forest conditions and land use are nearly 

at equilibrium, and MA is an acceptable statistical 

estimator. However, other landscapes change more 

rapidly.

Unexpected change can be subtle, spatially ubiquitous, 

and undetected during early onset. An example is 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 417

the growth decline of southern pines during the 

1970s. Causes might have been changes in air 

pollution, climate, land use, and/or distribution of 

stand conditions (Gadbury and Schreuder 2004). 

Such changes are not well observed with remote 

sensing, although geospatial data on stressors (e.g., 

air pollution) contribute valuable circumstantial 

information. Sufficiently precise estimators require a 

large sample of FIA field plots, which implies analyses 

over large geographic areas (e.g., a multi-state 

ecoregion) and long time intervals (e.g., 5 to 20 years). 

Detailed analyses might include the small subsample 

of “Phase 3” Forest Health Monitoring plots (Bechtold 

and Patterson 2005). Cause-effect analyses might use 

model-based inference to compare alternative causeeffect hypotheses (Gadbury and Schreuder 2004).

Other changes are episodic disturbances, which are 

often apparent with the naked eye. An example is 

severe mortality of western pines caused by outbreaks 

of mountain pine beetles. Other examples include 

changes in the extent of wildfires, timber harvest and 

management treatments, conversion and reversion 

among agricultural fields and forestlands, and 

development within the wildland–urban interface. 

Though locally intense, they might affect only 1 to 

5 percent of a forested landscape per year, and they 

are observed with a correspondingly small number of 

FIA plots. Annual remote sensing provides indicators 

that are well correlated with the extent, intensity and 

location of such changes. Remeasurements of FIA 

field plots at 5- to 10-year intervals monitor detailed 

tree-level consequences of stand-level changes.

Models of population processes are arguably essential 

for detailed monitoring. Model-based estimators 

increase precision with small sample sizes. A model 

can capture an analyst’s hypotheses regarding expected 

behavior of a forest population. Residual differences 

between model predictions and panel estimates 

detect deviations from expectations. Different models 

represent alternative sets of cause-effect hypotheses, 

and analyses of residuals compare the fit of each 

alternative to FIA field data. Models forecast future 

conditions based on past processes. 

Sensitivity of a monitoring program depends upon 

a sufficiently large sample size within each annual 

panel. Furthermore, numerous field plots are required 

for statistical methods that empirically compensate 

for systematic measurement errors with remotely 

sensed data. Hence, the target population must cover 

large geographic expanses, perhaps spanning several 

states. However, certain changes tend to “average out” 

as the extent of the population increases, and many 

monitoring questions involve small subpopulations. 

A partial solution is multivariate small area estimation, 

which uses diverse sets of full-coverage geospatial 

data (e.g., Landsat and MODIS) as predictors of field 

observations (e.g., Czaplewski 2010). 

A MODEL-BASED 

TIME-SERIES ESTIMATOR

The sample-survey literature covers a diverse 

collection of estimators for individual pieces of 

a statistical monitoring system. However, the 

multivariate “Kalman filter” (KF) estimator (BarShalom et al. 2001) can integrate all pieces into a 

single cohesive structure. The senior author is using a 

matrix language to develop software that implements 

the following.

KF is a time-series technique. It combines a designbased panel estimator with a model-based estimator 

for expected changes in population parameters 

(Czaplewski and Thompson 2009). KF is a sequential 

recursive method. It starts at time t=1 with the first 

panel of field data and FIA’s design-based estimator. 

The resulting vector estimate of population parameters 

(i.e., “state-vector”) serves as initial conditions in 

a model for changes in the population as posited 

by the analyst. This multivariate linear model 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 418

predicts the state-vector at time t=2, including a 

covariance matrix for random errors propagated from 

time t=1 plus estimated prediction errors between 

times t=1 and t=2. The second panel of field plots 

provides an independent design-based estimate of 

corresponding population parameters for t=2. KF uses 

the multivariate composite estimator to “optimally” 

combine these competing model-based and designbased estimates. The result is a single, more precise 

estimator at time t=2. This “best” estimate at time t=2 

serves as initial conditions for the transition model that 

predicts the state vector at time t=3. This sequential 

recursive technique proceeds for the entire time-series. 

Analyses of residual differences between modelbased predictions and design-based panel estimates 

help improve the estimated covariance matrix for 

model prediction errors, thereby mitigating bias in the 

model-based portion of the KF estimator. The model 

represents analysts’ understanding of population-level 

processes (Czaplewski and Thompson 2012) or a 

population-level aggregation of plot-level processes 

(see for example Healey et al., these proceedings). 

The model can forecast future conditions and the 

associated covariance matrix for random errors.

The state vector has partitions for each year. This 

autoregressive structure improves estimates for many 

time periods with each FIA panel. Its covariance 

matrix provides variance estimates for changes 

between 5- or 10-year intervals, which KF uses with 

corresponding design-based estimates from plot 

remeasurements to improve annual estimates of status 

and changes. 

The state vector may have hundreds of variables, 

and digital “round-off” errors can degrade numerical 

results. However, the engineering literature abounds 

with solutions that use the square root of a covariance 

matrix (e.g., Bar-Shalom et al. 2001). Covariance 

matrices are typically rank-deficient, and feasible 

estimates require thoughtful pivots of state-space.

KF computes a vector of “optimal” weights that 

combines each model- and design-based vector 

estimate at each time-step. Restrictions on those 

weights can impose inequality constraints. For 

example, the estimated annual mortality rate of insect-

infected live trees can exceed 100 percent if sampling 

errors in two independent annual panels are large. 

Inequality constraints force the estimated rate between 

0 and 100 percent. Those same population-level 

weights may be stored in the FIA plot-level database 

as time-series of multivariate expansion factors, one 

for each state variable, at the condition and tree levels 

(Czaplewski 2010). This step assures additivity across 

statistical tables, facilitates certain types of small 

domain and small area estimation, and potentially 

reduces certain difficulties in analyses with mixedcondition plots.

Insufficient sample size causes sampling zeros, which 

can produce implausible estimates for “rare” variables. 

We collapse classification systems so that each 

category has at least 50 plots within each annual panel. 

Assuming no cross-classifications, annual sample size 

within Colorado’s forests would support only seven 

forest type groups, seven ownership categories, and 

seven tree species groups. KF expansion factors permit 

more detailed estimates within the FIA database, but 

the statistical efficiencies of those detailed estimates 

are suboptimal.

Czaplewski (2010) developed KF structures for 

multiple sources of multivariate remotely sensed 

and other geospatial data. Unlike post-stratification, 

geospatial variables may be continuous or categorical, 

with or without cross-classification. KF uses fullcoverage Landsat data or sample surveys with 

LIDAR or high-resolution aerial photography. This 

KF structure accommodates time-series of remotely 

sensed data, including annual change detection. 

Czaplewski (2010) illustrates compatible methods that 

use geospatial data for small-area estimates for special 

studies, which improves the compromise between 

large sample sizes and small analysis areas.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 419

DISCUSSION AND CONCLUSIONS

FIA analysts and user community require defensible 

estimates of trends in forest resources. Estimates at 

one point in time, such as forest area or amount of 

standing live biomass, have limited value. Detailed 

assessments of insect epidemics, such as the mountain 

pine beetle in the West, require reliable estimates 

of annual tree mortality over long timespans. 

Monitoring broad-scale trends in tree growth helps 

better understand effects of climate change. Before 

making major capital investments, a forest products 

company must know trends in timber volume, and 

their causes, within a modestly sized geographic area. 

Trends in tree removals are a substantial component of 

economic assessments, such as the effect of recessions 

on the forest product sector. To serve these analysis 

requirements, FIA requires an easily understood 

statistical estimator that supports diverse analyses of 

time-series with panel data.

Although the MA estimator is easily understood, it can 

have substantial lag-bias. On the other hand, the purely 

design-based estimator for each independent panel is 

unbiased for annual monitoring. Unfortunately, the 

latter is limited by small sample sizes. Annual trends 

can be estimated only through differences among 

estimates from independent panels (e.g., independent 

estimates of lodgepole pine in Montana for 2010, 

2011, and 2012). Sampling error can exceed net annual 

change, however, obscuring major changes in a rapidly 

changing population, or producing statistical estimates 

of change that misleadingly appear large for a truly 

static population. Furthermore, independent panels 

limit the ability to understand the causes of annual 

change. Remeasurements of individual FIA plots at 5- 

or 10-year intervals help better understand long-term 

changes at the plot and tree scales, but this protracted 

remeasurement interval obscures annual trends. 

Regardless, the design-based approach, by itself, 

restricts an analyst’s ability to quantify and interpret 

trends at the annual time scale.

The multivariate Kalman filter is a relatively simple 

alternative in an annual monitoring program. It fully 

utilizes all available remotely sensed data. It stores 

results as condition- and tree-level expansion factors, 

which simplifies analyses. Its structure helps detect 

unexpected changes and rank competing sets of 

cause-effect hypotheses. This model-based approach 

is inherently multidisciplinary, however, and success 

requires teamwork among analysts, modelers, remote 

sensing technologists, computer scientists, and 

statisticians.

ACKNOWLEDGMENTS

The authors thank reviewers Scott Baggett, John 

Stanovick, and Erkki Tomppo. The authors ultimately 

accept full responsibility for the published content.

LITERATURE CITED

Bar-Shalom, Y.; Li, X.-R.; Kirubarajan, T. 2001. 

Estimation with applications to tracking and 

navigation. New York, NY: John Wiley and Sons. 

584 p.

 Bechtold, W.A.; Patterson, P.L. 2005. The enhanced 

Forest Inventory and Analysis program 

- national sampling design and estimation 

procedures. Gen. Tech. Rep. SRS-80. Asheville, 

NC: U.S. Department of Agriculture, Forest 

Service, Southern Research Station. 85 p.

Czaplewski, R.L. 2010. Complex sample survey 

estimation in static state-space. Gen. Tech. 

Rep. RMRS-GTR-239. Fort Collins, CO: U.S. 

Department of Agriculture, Forest Service, Rocky 

Mountain Research Station. 124 p.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 420

Czaplewski, R.L.; Thompson, M.T. 2009. 

Opportunities to improve monitoring of 

temporal trends with FIA panel data. In: 

McWilliams, W.; Moisen, G.; Czaplewski, R., 

comps. Forest Inventory and Analysis (FIA) 

Symposium 2008; 2008 October 21-23; Park City, 

UT. Proc. RMRS-P-56CD. Fort Collins, CO: U.S. 

Department of Agriculture, Forest Service, Rocky 

Mountain Research Station. 55 p.

Czaplewski, R.L.; Thompson, M.T. 2012. Modelbased time-series analysis of FIA panel data 

absent re-measurements. Res. Paper. RMRSRP-xx. Fort Collins, CO: U.S. Department of 

Agriculture, Forest Service, Rocky Mountain 

Research Station.

Gadbury, G.L.; Williams, M.S.; Schreuder, H.T. 

2004. Revisiting the southern pine growth 

decline: Where are we 10 years later? Gen. Tech. 

Rep. RMRS-GTR-124. Fort Collins, CO: U.S. 

Department of Agriculture, Forest Service, Rocky 

Mountain Research Station. 10 p.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 421

THE FOURTH DIMENSION IN FIA

Francis A. Roesch1

Abstract.—In the past, the goal of forest inventory was to determine the extent of the 

timber resource. Predictions of how the resource was changing were made by comparing 

differences between successive inventories. The general view of the associated sample 

design included selection probabilities based on land area observed at a discrete point 

in time. That is, time was not considered part of the sample design because it was 

not considered an element of the sampled population. Over the last few decades, the 

general goal of Forest Inventory and Analysis (FIA) has been changing to monitoring 

the dynamic forest ecosystem. However, much of the literature discussing FIA’s new 

annual monitoring system, its sample design, and estimators is still based on an areal 

probability paradigm. In Roesch (2008; Forest Science 54(4): 455-464), I pointed out 

why it is usually necessary to include the dimension of time when describing the sampled 

population and the sample design for FIA and similar forest inventory systems. Here, I 

further explore the inferential advantages of replacing the areal probability paradigm with 

a three-dimensional probability paradigm with an application.

1

 Mathematical Statistician, U.S. Forest Service, Southern 

Research Station, 200 WT Weaver Blvd., Asheville, NC 

28804. To contact, call 828-257-4871 or email at 

froesch@fs.fed.us.

INTRODUCTION

In the past, the primary goal of most national-scale 

forest inventories had been simply to determine the 

extent of the timber resource. Estimates of how the 

resource had changed, made by comparing differences 

between successive inventories, were merely an 

incidental benefit of these historic inventories. Over 

the last few decades, many national-scale forest 

inventories have morphed into full-fledged efforts to 

monitor many aspects of dynamic forest ecosystems. 

The U.S. Forest Service’s Forest Inventory and 

Analysis Program (FIA) is no exception and is 

concerned with evaluating the dynamic state of the 

Nation’s forest populations. Most inventories at this 

scale rely on a sampling scheme that has historically 

been described as a three-step process. In the first step, 

a set of random points is located in a two-dimensional 

space, specifically the land area of interest. The second 

step concerns the selection of a set of observation 

times while the third step chooses a cluster of trees in 

the vicinity of each sample point at each observation 

time. The first step, and only the first step, was viewed 

as random, leading to a sample design description in 

which the sample frame partitions the two-dimensional 

areal population. In Roesch (2008), I addressed the 

fact that in today’s panelized sample designs, the 

determination of the set of observation times is also 

random, and the sampled population and the sampling 

frame are three-dimensional. 

To make estimates for the target population, the 

sampled population must be identifiably associated 

with the target population. This association requires 

knowledge of the probability of selection for the 

realized set of observations on each tree (or element) 

in the sample over the course of the period of interest. 

Because there are potentially many sets of observation 

times realizable for each element in the population, 

I described the sample unit as a three-dimensional 

jigsaw puzzle piece resulting from partitioning the 

three-dimensional population volume. The description 

meets the requirements for a probability sample: the 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 422

population is divided up into mutually exclusive, 

exhaustive sample units (the three-dimensional puzzle 

pieces) that in toto make up the sample frame. Each 

unit has a definite probability of selection and the total 

of these probabilities is equal to 1. 

AN APPLICATION

Traditionally, many measures associated with forest 

trees have been reported within tree size classes, such 

as tree diameter classes. For instance, basal area or 

volume growth within 2-inch diameter classes for each 

year within a specific period may be of interest. The 

contribution of measurement error to total variance 

is usually large enough to preclude the measurement 

of the same trees more frequently than about every 5 

years. Often the measurement interval and the period 

of interest are long enough for a large number of trees 

in the population to grow through multiple diameter 

classes creating a potentially intractable problem from 

the viewpoint of successively applied two-dimensional 

samples. I show below that estimation under the 

three-dimensional paradigm is both obvious and 

manageable. 

In this application, I use FIA data to estimate annual 

basal area growth of survivor trees within specific size 

classes (Table 1) over a defined area (A) and temporal 

period. FIA conducts a continuous forest inventory 

using a rotating panel design (Bechtold and Patterson 

(2005) and Roesch (2007). The design consists of 

Table 1.—Diameter classes corresponding 

to standard merchantability limits with 

accommodation for differences in hardwood and 

softwood saw log standards

Diameter Lower Limit Upper Limit

Class (Diameter ≥) (Diameter <)

D1 0 5

D2 5 7

D3 7 9

D4 9 11

D5 11 ∞

g mutually exclusive, spatially disjoint temporal 

panels. These panels are measured in sequence for g

consecutive years, after which the sequence reinitiates. 

That is, if panel 1 is measured in year y, it will 

also be measured in years y + g, y + 2g, and so on. 

Panel 2 would then be measured in years y + 1, 

y + 1 + g, y + 1 + 2g, etc. Because FIA adheres to a 

two-dimensional view of this design, the program 

groups these data into evaluation groups of g years 

and then ignores temporal differences in observations 

within an evaluation group. The interested reader 

is referred to the “temporally indifferent method” 

in Patterson and Reams (2005). The temporally 

indifferent method is a smoothing function that has 

the tendency to obfuscate temporal trends and delay 

recognition of those trends. A judicious application of 

the three-dimensional view of this design can negate 

the necessity of the temporal indifference assumption 

and its associated problems. 

To fully exploit the three-dimensional view, we 

must look at the data differently than it has been 

traditionally viewed. With respect to annual 

growth, we note that each plot is not only located 

in a particular place, but that it is also observed at 

particular times, and that the times of observation 

are possibly more important than the place of 

observation, once place is accounted for. Initially, 

we will focus on two observations for each plot. 

Assign to each observation of variable x labels for 

plot i and (adjusted) beginning date t

b

 and ending 

date t

e

, separated by the (adjusted) time span of si

years. Represent each of these observations as xb and 

xe

, respectively. The dates and times are adjusted to 

approximate the time of observation relative to the 

proportion of growing season elapsed within a year. 

Although beyond the scope of this investigation, 

this could be done using data contributing to the 

USDA plant hardiness zone maps (USDA 2012). 

For simplicity, we make two assumptions, both of 

which can be refined by an appropriate model to suit 

a particular investigator or alternative application, 

as needed. The first is that we assume that the 

growing season spans from March 1 to November 30 

i

i

i

i
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 423

everywhere within the area of interest. The second 

assumption is that growth for each plot is uniform 

throughout the growing season. We can then represent 

each observation date as the year of observation 

plus the proportion of the growing season that has 

elapsed (i.e., in the format year.p), and si is simply 

the difference between the two. Because we have no 

observations between xb and xe, we make the further 

assumption that basal area growth for each living 

tree is uniform between the two observations (e.g., 

across si). This assumption could also be refined by 

the application of the appropriate model, such as 

conditioning on xb or on annual precipitation. We then 

allocate the proportion of basal area growth observed 

over si to the proportion of each year spanned by 

si, (thereby accounting for the marginal probability 

of the time dimension). This assumption of linear 

(basal area) growth is an approximation that should 

only be used for relatively short time intervals. Welldeveloped growth models would provide better 

estimates on individual trees, but can be unavailable 

for many of the species and condition classes 

encountered in a wide area forest monitoring effort. 

The assumption that basal area growth is uniform 

between observations allows us to estimate when the 

threshold for each diameter class limit was crossed 

and to allocate growth within diameter classes to the 

years the growth occurred in those diameter classes. 

This method is a major advantage over FIA’s current 

estimation methods, because the latter do not provide 

a mechanism to accomplish this. This development 

leads immediately to two simple estimators for annual 

basal area growth (within diameter class), a probability 

proportional to size estimator (BAGDCPPS):

i i

y

i

where:

ny

 = the number of plots observing growth in year y,

Pi,y = the product of portion of year y growing season 

observed by plot i and the portion of plot i area within 

the area of interest, and 

bagi,y the basal area growth observed on plot i and 

assignable to year y;

[1]

and a ratio estimator (BAGDCRAT): y

[2]

These estimators were used to obtain annual estimates 

from 2006 to 2010 of basal area growth of survivor 

trees per acre from FIA data for South Carolina. The 

results are compared to FIA’s end of period estimator 

(EOP) and an improved diameter class estimator (DC) 

(Sheffield and Turner, 2010), both of which are based 

on the temporal indifference assumption.

RESULTS

Figure 1 gives the results for estimating the annual 

basal area growth of survivor trees by the diameter 

classes given in Table 1 from each of the four 

estimators. The figure shows the results for the PPS 

estimator (eq. 1) (top left), the ratio estimator (eq. 2) 

(top right), the pooled EOP estimator (bottom left), 

and the pooled diameter class (DC) estimator (bottom 

right). Note that under a non-stringent condition, the 

estimators resulting in the top two graphs are unbiased. 

A linear trend for the intervals covering the year of 

interest is sufficient for unbiasedness. Note also that 

these two estimators gave almost the same results, 

which are quite different from the estimators resulting 

in the bottom two graphs. For the EOP estimators to 

be unbiased, a flat line trend (i.e., linear with a slope 

of 0) over all years used in the estimators would have 

to exist. With a 5-year cycle, a flat line trend must 

have been true for the 10 years before any annual EOP 

estimate of growth. From the top graphs, we see that 

a flat line trend is definitely not indicated for three of 

the five diameter classes. Between 2006 and 2010, 

the pooled DC (Sheffield) estimator gave results that 

were at times closer to the results for the estimators in 

the top two graphs than the FIA EOP estimator, in the 

bottom left graph, although the trend through those 

years is not discernible in either of the EOP estimators.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 424

Year Year

Year Year

Basal Area Growth (ft2/ac/yr)Basal Area Growth (ft2/ac/yr)

Basal Area Growth (ft2/ac/yr)Basal Area Growth (ft2/ac/yr)

Figure 1.—Annual basal area growth estimates by the diameter classes given in Table 1 from each of the four estimators, 

clockwise from the top left: the probability proportional to size (PPS) estimator, the ratio estimator, the pooled diameter class 

(Sheffield) estimator, and the pooled EOP (FIA-temporally indifferent) estimator.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 425

CONCLUSIONS

The field of statistics gives us many estimation tools 

to bolster analyses. All four estimators discussed here 

somehow use “outside information” to make annual 

estimates. In the estimators resulting in the top two 

graphs of Figure 1, the “outside information” has a 

clear relationship to the estimates of interest, because 

the observations span the estimates to which the data 

contribute. It is clear in the formulation of the EOP 

estimators and in Figure 2, that much of the outside 

information used in those estimators does not span the 

time estimated. In the results for 2010, for instance, 

Figure 2 shows that 80 percent of the information used 

in the EOP estimators is from “outside” of, and prior 

to, 2010. This prior information is incorporated under 

a model that assumes that the mean of the “outside” 

information is the same as the mean for 2010. It is 

not very helpful to start a search for trend by first 

assuming that there is not any trend. The description of 

continuous forest inventories as a sample of a threedimensional population is uniquely informative. It 

arose from the recognition of the importance of the 

time of observation on the outcome of the sample and 

it is useful for putting temporally ordered observations 

into perspective while formulating intuitively 

appealing model-unbiased estimators of growth and 

trend.

LITERATURE CITED

Bechtold, W.A.; Patterson, P.L., eds. 2005. The 

enhanced Forest Inventory and Analysis 

program-national sampling design and 

estimation procedures. Gen. Tech. Rep. SRS-80. 

Asheville, NC: U.S. Department of Agriculture, 

Forest Service, Southern Research Station.

Figure 2.—The observation sizes of annual growth estimates 

for each year. The actual sample size is the number of plots 

with growth intervals spanning at least part of the year. The 

effective sample size is the sum of plot-year proportions 

covered by the plots making up the actual sample size. The 

evaluation group size is the number of plots used in the FIA 

EOP estimator (temporally indifferent) and the Sheffield DC 

EOP estimator. The majority of the plots used in the two 

pooled estimators (FIA and Sheffield) do not observe any 

part of the growth interval for that year.

Year

Patterson, P.L.; Reams, G.A. 2005. Combining panels 

for Forest Inventory and Analysis estimation. In: 

Bechtold, W.A.; Patterson, P.L., eds. The enhanced 

Forest Inventory and Analysis Program-national 

sampling design and estimation procedures. 

Gen. Tech. Rep. SRS-80. Asheville, NC: U.S. 

Department of Agriculture, Forest Service, 

Southern Research Station: 11-26.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 426

Roesch, F.A. 2007. Compatible estimators of the 

components of change for a rotating panel forest 

inventory design. Forest Science. 53(1): 50-61.

Roesch, F.A. 2008. An alternative view of 

continuous forest inventories. Forest Science. 

54(4): 455-464.

Roesch, F.A., Jr.; Green, E.J.; Scott, C.T. 1993. An 

alternative view of forest sampling. Survey 

Methodology. 19(2): 199-204.

Sheffield, R.M.; Turner, J.A. 2010. Improving FIA 

diameter class growth distributions. Book of 

Abstracts. 2010 Forest Inventory and Analysis 

symposium, Monitoring Across Borders. 2010 

October 5-7; Knoxville, TN.

USDA. 2012. USDA Plant Hardiness Zone 

Map, 2012. Agricultural Research Service, 

U.S. Department of Agriculture. Accessed from 

http://planthardiness.ars.usda.gov. [Date accessed 

unknown]. 

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 427

IMPROVING FIA TREND ANALYSIS THROUGH 

MODEL-BASED ESTIMATION USING LANDSAT DISTURBANCE MAPS 

AND THE FOREST VEGETATION SIMULATOR

Sean P. Healey, Gretchen G. Moisen, and Paul L. Patterson1

Abstract.—The Forest Inventory and Analysis (FIA) Program’s panel system, in which 

10-20 percent of the sample is measured in any given year, is designed to increase 

the currency of FIA reporting and its sensitivity to factors operating at relatively fine 

temporal scales. Now that much of the country has completed at least one measurement 

cycle over all panels, there is an immediate need for estimation strategies which make 

the best use of this sampling schedule. A primary obstacle is that only a fraction of plots 

can be considered current in any particular year. This leaves the analyst with a choice of 

ignoring annual trends or creating estimates one panel at a time and suffering precision 

losses which may render apparent year-to-year differences uninterpretable.

One option for increasing the temporal specificity of estimates is to update plot 

conditions for every year in a time series using the Forest Vegetation Simulator (FVS) 

and to use model-based estimation to create annual estimates using “observations” from 

every plot. The variance estimators used in such an approach would incorporate both 

sample and model uncertainty, the latter of which could be assessed at remeasured FIA 

plots. Disturbance maps created from time series of Landsat (or similar sensor) satellite 

imagery could be used to identify and appropriately alter FVS simulations for those plots 

which have been disturbed. Use of disturbance maps would allow sensitivity to year-toyear variation in the disturbance rate. FIA has recent experience in all of the components 

of the proposed approach including FVS, Landsat disturbance mapping, and model-based 

estimation. Further study to integrate these components into a production estimation 

system is warranted.

1

 Research Ecologist (SPH), Research Forester (GGM), 

and Mathematical Statistician (PLP), U.S. Forest Service, 

Rocky Mountain Research Station. SPH is corresponding 

author: to contact, call 801-625-5770 or email at 

seanhealey@fs.fed.us. 

The Challenge of

Fia Annual Estimation

The Forest Service’s FIA (Forest Inventory and 

Analysis) Program does not survey each of its 

plots every year. The sample is divided into 

random subsamples called panels, and each panel 

is remeasured once every 5 to 10 years (Patterson 

and Reams 2005). Measuring a fraction of the plots 

in a particular sample unit (e.g., a state) every year 

provides some sensitivity to forest changes as they 

occur throughout a measurement cycle. However, 

since the plot data for any cycle are collected 

uniformly over a 5-10 year period, one is left with the 

dilemma of using all plots to estimate conditions over 

a long and undifferentiated period, or making panelwise estimates that provide high temporal specificity 

but low precision due to a low sample size. Once all 

plots have been surveyed, one can calculate a new 

moving average estimate every year (Roesch et al. 

2002), but the period for such estimation is still an 

undifferentiated 5- or 10-year span, which can obscure 

the effects of discrete large-scale events such as fires, 

droughts, or insect outbreaks.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 428

Research is ongoing regarding the possibility of 

enhancing FIA trend analysis through use of the 

Kalman filter, a sequential application of composite 

estimators (Czaplewski and Thompson 2009), and 

mixed estimators (Van Deusen 2008). Building 

on concepts developed during the formative years 

of FIA’s annual inventory (eg., Czaplewski 1999, 

McRoberts and Hansen 1999), this paper suggests 

an approach for identifying within-cycle trends by 

growing all plots (regardless of panel) forward in time 

with the Forest Vegetation Simulator (FVS) growth 

model (Crookston and Dixon 2005) so that there is an 

observed or modeled value for each plot in every year. 

As discussed below, this option would take advantage 

of a number of tools with which FIA has recent 

experience, including FVS, forest change detection 

using Landsat imagery, and model-based estimation.

USE OF FVS AND LANDSAT 

TO UPDATE PLOT VALUES

The FVS is maintained by the Forest Service’s 

Forest Management Service Center and is delivered 

with executable code which automatically converts 

FIA data into an FVS-ready format. It is a distance-

independent growth model that can project a wide 

range of stand characteristics over a 100-year time 

frame while simulating an equally wide range of 

disturbance/management scenarios. FVS is widely 

used by the Forest Service for forest planning in 

conjunction with FIA and other stand monitoring 

data. Because of its importance in the management 

community, FVS benefits from ongoing active 

calibration.

While FVS projections typically function at 5- or 

10-year intervals, stand changes can also be produced 

in 1-year increments. In the approach we propose, 

each plot would be updated with FVS from the time 

of its last measurement to each subsequent point 

in time until it is re-measured (Fig. 1). Scenarios 

of undisturbed growth in FVS should provide a 

reasonable model for updating the majority of plots 

(i.e., growing forward all trees in the tree list, plus any 

changes due to mortality and recruitment functions), 

particularly since the maximum length of projection 

would be only 9 years and as few as 4 years in some 

states. However, a method of identifying disturbed 

(including managed) plots would be needed for 

two reasons: FVS projections for such plots using 

undisturbed dynamics could introduce significant 

error; and, more importantly, the value and point of 

producing within-cycle trend estimates would be 

greatly diminished if the monitoring system were 

insensitive to disturbance.

The Forest Service and FIA have extensive experience 

using the Landsat series of satellites to characterize 

the timing, type, and magnitude of forest disturbance 

(Nelson et al. 2009). Much of this experience has 

been gained through FIA’s association with the 

NASA-funded North American Forest Dynamics 

project (NAFD). The NAFD project is currently 

producing a national wall-to-wall map of disturbance 

timing and type, and the Forest Carbon Management 

Framework (ForCaMF) project is currently producing 

disturbance timing, type, and magnitude maps for 

the entire National Forest System (approximately 75 

million hectares). Furthermore, a follow-on to the 

Monitoring Trends in Burn Severity (MTBS) project 

called the Landscape Change Monitoring System is 

in development, targeting operational national-scale 

disturbance mapping. Results of these or other efforts 

could be used to direct disturbed plots into specialized 

scenarios to increase model accuracy and to introduce 

intra-cycle sensitivity to disturbance.

MODEL-BASED ESTIMATION

Model-based inference depends upon fundamentally 

different assumptions than the design-based methods 

used by most field-based inventories, including FIA’s. 

(For a detailed description of the difference between 

model- and design-based inference, see Gregoire 

1998.) Unlike design-based estimation, model-based 

methods treat observations as realizations of a random 

process (model). Model-based methods are appropriate 

for forest inventory situations where there is one 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 429

Figure 1.— Schematic of the estimation approach.

sample of plots where both the variable of interest and 

one or more predictors are known, and there is another, 

generally larger, sample in which only the predictors 

are known. Models built over the first sample are 

applied to the second sample, which is used to make a 

population estimate. Variance estimators incorporate 

elements of both sample uncertainty and model 

uncertainty. The authors have used this approach with 

FIA biomass measurements and predictor data from 

spaceborne lidar to predict biomass in California 

(Healey et al., in press). 

In the idea we present, the entire FIA grid (all panels) 

constitutes the second type of sample; the current 

tree list is not known (or is known only for the most 

recent panel), but a tree list from some point in the 

past is known. This predictor tree list is used with FVS 

to model the forest condition to be estimated at each 

point in time (Fig. 1). Ongoing validation activities 

associated with the ForCaMF project mentioned above 

are comparing FVS predictions for incremental tree 

list changes with plot remeasurement occurring at 

intervals from 2 to 12 years. This work could be the 

basis for a larger validation effort, functioning as the 

first type of sample mentioned above by supplying 

model error rates to be used in variance estimates 

(Fig. 1). Model-based estimation should allow FVSmodeled updates which allow each plot to be used 

in estimating the forest condition of interest in every 

year.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 430

ANTICIPATED BENEFITS

In general, using modeled observations in modelbased estimation will tend to increase variance 

estimates relative to observations based on field 

measurements. However, in this case, use of modelbased estimates would increase the sample number for 

any given year by a factor of 5 or 10. Tests are needed 

to determine the interaction of these two factors 

as they impact precision of annual estimates. One 

significant advantage of this approach is that repeated 

“observations” for each plot (through FVS models) 

will allow direct estimation of annual change (e.g., 

change in biomass per hectare). Currently, since each 

year’s panel is independent of the previous year’s, 

change must be inferred from differences in sequential 

estimates of a variable’s “state.” Addressing change 

directly, as illustrated in Figure 2, may lead to a better 

ability to compare inventory estimates to ancillary 

drivers such as housing starts or drought patterns. 

It would also allow more timely identification and 

reporting of forest change than would be possible from 

trend-based alternatives, some of which require several 

years of continued data collection before changes are 

confirmed. 

Figure 2.—Hypothetical illustration of possible benefits of estimating change directly. Estimating the difference between a 

forest attribute at two points in time allows ecosystem change to be assessed more directly than comparison of sequential 

state estimates. The incremental observations shown would only be possible because the Forest Vegetation Simulator (FVS) 

provides modeled observations of each plot annually.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 431

CONCLUSIONS

1. Model-based estimation of FVS-derived intra-panel 

change may provide greater temporal specificity 

than other estimation methods.

2. Ongoing FIA work with FVS, Landsat disturbance 

mapping, and model-based estimation form 

a foundation from which future research may 

proceed. 

ACKNOWLEDGMENTS

Thanks are due to Sara Goeking and Mark Nelson for 

substantive reviews.

LITERATURE CITED

Crookston, N.L.; Dixon, G.E. 2005. The forest 

vegetation simulator: a review of its structure, 

content, and applications. Computers and 

Electronics in Agriculture. 49: 60-80.

Czaplewski, R.; Thompson, M.T. 2009. Opportunities 

to improve monitoring of temporal trends with 

FIA panel data. In: McWilliams, W.; Moisen, 

G.G.; Czaplewski, R., comps. Forest Inventory and 

Analysis (FIA) Symposium 2008. Proc. RMRSP-56CD. Fort Collins, CO: U.S. Department of 

Agriculture, Forest Service, Rocky Mountain 

Research Station. 55 p.

Czaplewski, R.L. 1999. Multistage remote sensing: 

toward an annual national inventory. Journal of 

Forestry. 97: 44-48.

Gregoire, T.G. 1998. Design-based and model-based 

inference in survey sampling: appreciating the 

difference. Canadian Journal of Forest Resources. 

28: 1429-1447.

Healey, S.P.; Patterson, P.L.; Saatchi, S.; Lefsky, M.A.; 

Lister, A.J.; Freeman, E.A. [In press]. A sample 

design for globally consistent biomass estimation 

using lidar data from the Geoscience Laser 

Altimeter System (GLAS). Carbon Balance and 

Management.

McRoberts, R.E.; Hansen, M.H. 1999. Annual forest 

inventories for the North Central region of the 

United States. Journal of Agricultural, Biological, 

and Environmental Statistics. 4: 361-371.

Nelson, M.D.; Healey, S.P.; Moser, W.K.; Hansen, 

M.H. 2009. Combining satellite imagery with 

forest inventory data to assess damage severity 

following a major blowdown event in northern 

Minnesota, USA. International Journal of Remote 

Sensing. 30: 5089-5108.

Patterson, P.L.; Reams, G.A. 2005. Combining panels 

for Forest Inventory and Analysis estimation. In: 

Bechtold, W.A.; Patterson, P.L., eds. The enhanced 

Forest Inventory and Analysis Program-National 

sampling design and estimation procedures. 

Gen. Tech. Rep. SRS-80. Asheville, NC: U.S. 

Department of Agriculture, Forest Service, 

Southern Research Station: 69-74.

Roesch, F.A.; Steinman, J.R.; Thompson, M.T. 

2002. Annual forest inventory estimates based 

on the moving average. In: McRoberts, R.E.; 

Reams, G.A.; Van Deusen, P.C.; Moser, J.W., eds. 

Proceedings of the Third Annual Forest Inventory 

and Analysis Symposium. Gen. Tech. Rep. 

NC-230. St. Paul, MN: U.S. Department of 

Agriculture, Forest Service, North Central 

Research Station: 21-30.

Van Deusen, P.C. 2008. Detecting trend in annual 

inventory data with a mixed estimator. In: 

McWilliams, W.; Moisen, G.G.; Czaplewski, 

R., comps. Forest Inventory and Analysis (FIA) 

Symposium 2008. Proc. RMRS-P-56CD. Fort 

Collins, CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research Station. 

55 p.

The content of this paper reflects the views of the 

author(s), who are responsible for the facts and accuracy 

of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 432

ADVANCEMENTS IN LiDAR-BASED REGISTRATION 

OF FIA FIELD PLOTS

Demetrios Gatziolis1

Abstract.—Meaningful integration of National Forest Inventory field plot information 

with spectral imagery acquired from satellite or airborne platforms requires precise 

plot registration. Global positioning system-based plot registration procedures, such 

as the one employed by the Forest Inventory and Analysis (FIA) Program, yield plot 

coordinates that, although adequate for some purposes, often contain substantial error. 

Conversely, the registration of Light Detection and Ranging (LiDAR) data is accurate and 

precise. Considering the proliferation of high density LiDAR data, there is potential to 

substantially improve plot registration. Earlier attempts were not successful because they 

relied solely on the relative location of mapped tree stems and local maxima in vegetation 

surfaces generated from the LiDAR data. In this study, registration is achieved by 

examining the correlation between plot canopy surfaces generated by using the FIA field 

data and modeled tree crowns and the corresponding vegetation surface derived from 

the LiDAR data. With the LiDAR vegetation surface remaining stationary, the modeled 

surface is jittered in two dimensions at regular intervals, and the correlation is computed 

for each moving instance. Assuming that it satisfies a set of consistency criteria, the 

moving instance for which correlation is maximized yields the plot coordinates. Gains 

in computational efficiency are realized via parallelization. Results from eastern Oregon 

show that precise—better than 2 m—registration is achieved for 80 percent of the 

investigated FIA plots.

1

 Research Forester, U.S. Forest Service, Pacific Northwest 

Research Station, 620 SW Main Street, Suite 400, Portland, 

OR 97205. To contact, call 503-808-2038 or email at 

dgatziolis@fs.fed.us.

INTRODUCTION

Meaningful integration of remotely sensed and 

forest inventory field plot data requires precise plot 

registration. Where this requirement is not met, the 

energy returned to the remote sensing instrument 

corresponds poorly to the trees on the plot. Spatial 

discrepancies between remotely sensed data and 

plot locations compromise the strength and validity 

of modeling and diminish the utility of the data. It 

has been shown that the effects of registration issues 

become more pronounced with smaller field plot sizes 

(Fluwelling 2009, Frazer et al. 2011), and shorter 

spatial continuity in the inventory parameter of interest 

(Gobakken and Næsset 2009, Goodchild et al. 1993).

Most National Forest Inventory (NFI) programs, such 

as the Forest Inventory and Analysis (FIA) Program 

of the U.S. Forest Service, have collected field plot 

location primarily to facilitate future navigation to 

permanent plots. Historically, plot coordinates were 

obtained via several methods, including manual 

transfer from orthophotos and digital raster graphics, 

and, since the mid-1990s by using global positioning 

system (GPS) technology. Repeated visits to 

permanent plots generated multiple coordinates for 

each plot. Rather than accepting the latest as the most 

accurate, the Pacific Northwest FIA Program (PNWFIA) designed and implemented a procedure, known 

as plot grid management (PGM), which evaluates all 

coordinates for each plot and selects the one “most 

accurate.” Anecdotal evidence suggests that PGM 

and continuous advancements in GPS technology 

have improved coordinate accuracy but precision 

issues remain. PNW-FIA’s past efforts to improve plot 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 433

coordinates by using high resolution airborne imagery 

from the National Agricultural Imagery Program 

(NAIP) and manual interpretation were limited by the 

NAIP imagery’s poor registration, obliqueness, and 

inadequate contrast.

Unlike NAIP and most satellite imagery, data 

acquired with Light Detection and Ranging (LiDAR) 

technology have negligible registration errors, are 

unaffected by sun angle and shadows, and when 

collected at high density provide detailed, threedimensional (3D) characterizations of tree crowns 

and terrain. Gatziolis (2009) used a variant of the 

world-view algorithm to register fixed-radius, FIAlike plots in the temperate rainforest of Oregon. He 

linked 3D point patterns obtained by processing 

the LiDAR return cloud and corresponding to the 

locations of identified individual tree tops to point 

patterns generated from field plot data. Of 45 plots in 

the study area only 3 (11 percent) were registered with 

submeter precision, although for 18 more (40 percent) 

registration precision ranged between 1 and 5 m. A 

similar approach (Dorigo et al. 2010) was evaluated in 

Austria using lower density LiDAR data and variable 

radius inventory plots. It, too, relied on matching tree 

tops derived from plot data to tops identified on a 

LiDAR-derived vegetation height surface. The plot 

location was determined iteratively as the one that 

minimized the weighted sum of height differences 

between matched trees. It was reported that 67 percent 

of plot centers were registered within 4 m from of 

location identified by an image analyst.

While these approaches improved the registration for 

most plots, registration was precise (<2 m) for very 

few. By matching only tree tops, potentially valuable 

information on the shape and spatial arrangement of 

individual crowns and canopy openings is not utilized. 

Investigations that incorporate crown shape and 

spatial distribution into plot registration techniques 

are therefore warranted, especially when considering 

the recent proliferation of publicly available, high 

density LiDAR data acquisitions that can support these 

techniques. One such effort is explored and evaluated 

in this study. To enhance potential application, 

technique development focused on full automation, 

computational efficiency, quantitative criteria 

that determine plot registration success or failure, 

accommodation of standard and clustered plot designs, 

and dynamic adjustment to local vegetation structure 

conditions. 

STUDY AREA AND METHODS

The Malheur National Forest in eastern Oregon is 

dominated by open, mostly coniferous forests and 

gentle terrain. Discrete return, high density LiDAR 

data of approximately nine returns per square meter 

were acquired over the entire Forest in 2007 and 2008. 

A 0.91-m ground digital elevation model (DEM) was 

provided by the data vendor. A highest-return Canopy 

Height Model (CHM) was derived by subtracting 

DEM values from the elevation value of each return, 

as a 0.5-meter raster. Individual tree crowns were 

delineated via the valley-following algorithm on a 

smoothed version of the CHM and the location of each 

tree top was recorded (Fig. 1). 

For each subplot of the 82 FIA plots within the 

study area field-visited within 2 years of the LiDAR 

acquisition date, 3D models were developed using 

field assessed tree location relative to subplot 

center, height, and species. Each subplot model was 

constructed using species-specific crown shapes 

ranging from conical to ellipsoidal or weighted 

combinations of the two, and modeled estimates 

of crown radius and length (Donnelly 1997, Shaw 

2009). The ensuing 3D subplot representation was 

then rasterized to form a 0.5-m Subplot Height Model 

(SHM); crown portions outside the subplot boundary 

were excluded (Fig. 2). The retrieval of subplot center 

coordinates was based on the correlation between the 

stationary CHM and instances of the corresponding 

SHM moving at one-cell increments within a 40 by 40 

m window centered at the initial coordinates retrieved 

from the production FIA database. 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 434

LIDAR data

DEM

Rasterization CHM Valley

following

Individual tree

top (x, y)

Plot data

FVS/models

3D

subplots

Rasterization

Trim

SHM

FIA database

production

coordinates

Search window

Height

difference

raster

Sigmoidal

adjustment

Adjusted

height

difference

Spatial

kernel k

Spatial

kernel w

D metric

raster

ITERATION

For each cell in search window

Evaluation for trustworthy

local minimum

Success ? N

For

each

subplot

Terminate

Y

Evaluation for fit

to FIA design Terminate

Fail DGPS

coordinates

Registration

precision

Pass

Figure 1.—Flow chart of the registration process. See text for acronym definitions.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 435

Figure 2.—Illustration of the LiDAR data-based registration for an FIA plot. Darker raster tones indicate higher values or 

weights. See text for acronym definitions.

LIDAR CHM and search rectangle

3D subplot model via field

and FVS data SHM

Sigmoidal height-difference adjustment and resulting raster Weights by kernel k

D metric raster for a 40 x 40 m search window exhibiting a pronounced local minimum coincident 

with the center of an FIA subplot

1
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 436

The correlation metric D

across the search area was computed as the sum of 

absolute height differences between CHM and each 

SHM instance scaled by a nonspatial function f and 

two spatial kernels k and w. Note that N in Equation 

(1) denotes the number of cells in a subplot. Function 

f has sigmoidal form. The slope of the sigmoid is 

inversely related to crown height variability (steeper 

for uniform canopies). Kernel k is computed once for 

each SHM as the distance between cells with openings 

(defined as those with SHM <2 m), and vegetation 

cells (Fig. 1). Kernel w assigns low weight to all 

cells in SHM instances corresponding to crowns in 

the CHM with their top outside the boundary of the 

subplot instance. Kernel w neutralizes the effect of 

trees not tallied in the subplot but having a crown 

portion within it. If the two-dimensional representation 

of metric D exhibits a single local minimum that is 

at least one standard deviation from its mean across 

the search area, the coordinates of the cell center at 

the minimum are considered a trusted subplot match. 

The plot coordinates are computed from subplot 

matches, if any, provided that subplot arrangement 

approximates the FIA clustered design. Scripts were 

coded in C with parallelization support provided by the 

OpenMP library. Plot coordinates derived via matching 

were compared to those obtained by post-processed 

differential GPS (DGPS) with internally calibrated 

precision of 1.05 m.

RESULTS AND DISCUSSION

The root mean square discrepancy between 

production and DGPS coordinates was 8.86 m. At 

this (mis)registration level, the mean areal overlap 

between the actual and assumed subplot areas is only 

28 percent. Sixty-six of the plots, over 80 percent, 

were registered within 2 m from the DGPS data. 

For more than half of those plots, all four subplots 

were registered. For six plots, the distance to DGPS 

references was between 2 and 5 m with all but one 

of them having just one subplot registered (Table 1). 

[1]

The process failed for 10 plots (12 percent) primarily 

because no trustworthy match could be found for any 

of the subplots, although in two cases matches were 

found but were rejected as incompatible with the FIA 

design. There were no false positive registrations. 

The results indicate that the four subplots in the 

FIA design compensate for the limited areal extent 

of the individual subplot. The few crowns in most 

FIA subplots do not usually represent a unique tree 

arrangement within the search area but the cluster 

of four subplots allows the often many individual 

subplot matches to be filtered down to a single trusted 

matching solution for the plot. Because the majority 

of registration failures were associated with estimates 

of crown radius or shape substantially different from 

those observed in the LiDAR return cloud, assessing 

crown attributes via regional models might be a 

better choice than using generalized, national models. 

The weighting functions in Equation (1) quantify 

heterogeneity in the canopy and of openings that 

is apparent and prevalent in the study area. In such 

conditions, precisely registered plots can be used 

to assess the f, w, and k parameters and then used 

them to register other plots in the region. Parameter 

optimization will likely yield a pronounced local 

minimum in the distribution of metric D that can be 

automatically detected by using as threshold a moment 

in the distribution of D, such as the one standard 

deviation suggested. In the event that the crown and 

opening arrangement in one or more of the subplots is 

uncommon, plot registration can be achieved within 

search areas much larger than the one used in this 

study. However, where canopies exhibit uniformity 

and gaps are rare, as is typical in the Oregon temperate 

rainforest, consistent registration appears improbable. 

In such conditions, differences in canopy height 

Table 1.—Plot registration results

Distance to DGPS Number of Mean of Per-plot

 Coordinates (m) Plots (percent) Registered Subplots

<2 66 (80.5) 2.89

2-5 6 (7.3) 1.33

Unable to register 10 (12.2) 0.40
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 437

and shape across the search area are comparable in 

magnitude to, if not smaller than, the discrepancies 

between real and modeled subplot crowns, and thus 

they rarely yield a unique and trusted matching 

solution. In open forests, canopy variability dominates 

imprecision in the modeled tree crowns, thereby 

allowing plot to be precisely registered.

ACKNOWLEDGMENTS

The author would like to thank Edward Uebler, 

forester, for providing the precise GPS data for FIA 

plots in Malheur National Forest, and Andrew Gray, 

research forester, for assistance with computing crown 

dimensions.

LITERATURE CITED

Donnelly, D.M. 1997. Pacific northwest coast 

variant of the Forest Vegetation Simulator.

Internal report. Ft Collins, CO: U.S. Department 

of Agriculure, Forest Service, Forest Management 

Service Center. 

Dorigo, W.; Hollaus, M.; Wagner, W.; Schadauer, 

K. 2010. A new automated approach in coregistration of national forest inventory and 

airborne laser scanning data. International 

Journal of Remote Sensing. 31(5): 1133-1153.

Flewelling, J.W. 2009. Plot size, shape, and coregistration error determine expected overlap.

In: Extending forest inventory and monitoring 

over space and time; 2009 May 19-21; Quebec 

City, QC, Canada. [Place of publication 

unknown]: International Union of Forest Research 

Organizations (IUFRO), Division 4. Available at 

http://blue.for.msu.edu/meeting/proc2/Flewelling.

pdf. (Accessed July 29, 2012).

Frazer, G.W.; Magnussen, S.; Wulder, M.A.; Niemann, 

K.O. 2011. Simulated impact of sample plot size 

and co-registration error on the accuracy and 

uncertainty of LiDAR-derived estimates of forest 

stand biomass. Remote Sensing of Environment. 

115: 636-649.

Gatziolis, D. 2009. Precise FIA plot registration 

using field and dense LiDAR data. In: 

McRoberts, R.E.; Reams, G.A.; Van Deusen, P.C.; 

McWilliams, W.H., eds. Proceedings of the eighth 

annual forest inventory and analysis symposium; 

2006 October 16-19; Monterey, CA. Gen. Tech. 

Report WO-79. Washington, DC: U.S. Department 

of Agriculture, Forest Service: 243-249.

Gobakken, T.; Næsset, E. 2009. Assessing effects 

of positioning errors and sample plot size on 

biophysical stand properties derived from 

airborne laser scanner data. Canadian Journal of 

Forest Research. 39: 1036-1052.

Goodchild, M.F.; Parks, B.O.; Staeyert, L.T., eds. 

1993. Environmental modeling with GIS. New 

York: Oxford University Press.

Shaw, J.D. 2009. Using FIA data in the Forest 

Vegetation Simulator. In: McWilliams, W.; 

Moisen, G.; Czaplewski, R., comps. Forest 

Inventory and Analysis (FIA) Symposium 2008; 

2008 October 21-23; Park City, UT. Proc. RMRSP-56CD. Fort Collins, CO: U.S. Department of 

Agriculture, Forest Service, Rocky Mountain 

Research Station.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 438

Hot-deck Matching of Annual Inventory Plots

to Enhance Trends and Projections

Paul C. Van Deusen1

Abstract.—Annual forest inventory plots awaiting remeasurement can be matched with 

plots that can serve as pseudo remeasurements using various hot-deck matching schemes. 

It will be shown that some matching approaches allow for short-term projections that 

reflect a business as usual (BAU) scenario and require few assumptions. These methods 

also allow for creating scenarios that reflect increasing frequency of particular events 

relative to what occurs in the BAU scenario. Scenarios are generated where increasing 

future harvest levels are controlled to demonstrate this capability. The moving average, 

as applied by U.S. Forest Service Forest Inventory and Analysis (FIA), is typically linked 

to an evaluation group. It is noted that this linkage is unnecessary and limits the value 

and flexibility of the method. The combination of short-term projections with an n-year 

moving average is suggested to provide trend estimates that encompass the current year 

and a few years into the future. These methods are not difficult to implement and they 

expand the utility of FIA data. As the number of FIA plots with multiple remeasurements 

increases, estimation methods should be enhanced to take advantage of the increasing 

value of the database. The methods are demonstrated with several operational size 

applications to FIA data.

1

 Principal Research Scientist (PCVD), National Council of Air and Stream Improvement, 

Tewksbury, MA 01876. To contact, email at pvandeusen@ncasi.org. 

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Posters
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 440

a COMPARISON OF FIA PLOT DATA DERIVED FROM IMAGE PIXELS 

AND IMAGE OBJECTS

Charles E. Werstak, Jr.1

Abstract.—The use of Forest Inventory and Analysis (FIA) plot data for producing 

continuous and thematic maps of forest attributes (e.g., forest type, canopy cover, 

volume, and biomass) at the regional level from satellite imagery can be challenging 

due to differences in scale. Specifically, classification errors that may result from 

assumptions made between what the field data represent and what the corresponding 

spectral information of the image pixels depict. This investigation aimed at determining 

whether image objects derived from Landsat TM imagery can be used as an alternative 

to a 3 by 3 neighborhood of pixels for characterizing forested FIA plots. Results showed 

strong positive correlations between the different scales of base map units across all of 

the image derivatives. Further examination of the data using the Wilcoxon signed rank 

test for paired samples indicated that in most cases, finer level image objects were a 

better representation of the 3 by 3 neighborhood of pixels than coarser ones and some 

image derivatives performed better than others. The same tests were applied to a subset 

of plots dominated by quaking aspen (Populus tremuloides Michx.) with similar results. 

Information gained may provide further insight into object based segmentation and 

classification methods using FIA plot data, satellite imagery, and ancillary geospatial 

data.

1

 Biological Scientist, U.S. Forest Service, Rocky Mountain 

Research Station, 507 25th Street, Ogden, Utah 84401. To 

contact, call 801-625-5699 or email at cewerstak@fs.fed.us. 

INTRODUCTION

Several studies have compared image pixel-based 

classification to image object-based segmentation 

and classification for mapping different vegetation 

attributes from remote sensing imagery, many of 

which have shown that using image object-based 

segmentation combined with decision tree image 

classification methods often achieve higher accuracies 

(Chubey et al. 2006, Gao and Mas 2008, Hay et 

al. 2005, Karl and Maurer 2010, Kim et al. 2010, 

Yasumasa et al. 2011). 

Quaking aspen (Populus tremuloides Michx.) was 

selected as the species of interest because it is a 

critical species that supports wildlife and livestock, 

watershed function, the forest products industry, 

landscape diversity, and recreation in the Interior West 

(Bartos and Campbell 1998). Studies have indicated 

that changes in fire regimes, an increase in herbivore 

presence in young aspen stands, and recent drought 

episodes are the main factors for increased mortality 

rates in aspen (Deblander et al. 2010). 

This objective of this investigation was to determine 

whether different scales of image objects derived from 

Landsat TM imagery can be used as an alternative 

to a 3 by 3 neighborhood of pixels for characterizing 

canopy cover of forested Forest Inventory and 

Analysis (FIA) plots and aspen dominated FIA plots. 

FIA PLOT DATA

Multi-condition forested plots having 10 or greater 

percent canopy cover of live trees were queried from 

the FIA database for Utah, inventory years 2000-

2009, resulting in 3,224 plots. Basal area per acre 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 441

was computed by species and summed to the plot 

level. The Interior West core optional variable crown 

cover percent (CRCOVPCT_RMRS) was used in 

conjunction with basal area per acre, to calculate tree 

cover and tree cover by species (absolute cover) for 

the plots (USDA Forest Service 2011). Ultimately, 

these values were used to calculate the percent canopy 

cover by species (relative cover) for the plots.

PREDICTOR DATA

Three different Landsat TM scenes acquired over 

Utah during the summer of 2009 were used in the 

analysis (Table 1). The scenes were converted to 

Top-Of-Atmosphere (TOA) reflectance using standard 

Landsat-specific methods. The following vegetation 

indices and image transformations were calculated for 

each scene:

1. Enhanced vegetation index (EVI)

2. Normalized difference vegetation index (NDVI)

3. Normalized difference moisture index (NDMI)

4. Modified soil adjusted vegetation index 

(MSAVI2)

5. Tasseled cap transformation (TCAP)

6. Principal components analysis (PCA)

Layer stacks were created for each scene using the 

following derivatives: Landsat TM reflectance bands 

1:6, NDVI, PCA first principal component, and 

TCAP bands 1:3. Due to anomalies with the blue 

bands resulting from the TOA conversion, EVI was 

calculated on the Landsat Standard Terrain Correction 

(Level 1T) product.

Table 1.—Acreage summaries for image objects corresponding to forested FIA plots. Image object size is 

a function of the scale parameter.

TM Scene Number of eCognition Average Size Minimum Size Maximum Size

(Path/Row) FIA Plots Scale Parameter (Acres) (Acres) (Acres)

37/32 691 25 161.36 10.23 717.00

37/32 691 15 62.14 2.45 232.40

38/31 153 25 158.28 21.57 695.21

38/31 153 15 60.54 9.56 175.02

38/33 466 25 161.86 12.90 607.58

38/33 466 15 61.66 5.56 248.42

NEIGHBORHOOD PIXELS 

AND IMAGE OBJECTS 

A 3 by 3 neighborhood of pixels was generated for 

each forested FIA plot location to correspond to the 

FIA plot design. To create the 3 by 3 neighborhoods 

around the 3,224 FIA plots, point feature classes 

of the X and Y plots (plot center of subplot one) 

were converted to 30 m thematic raster images, and 

then 3 by 3 neighborhood filters were applied. As a 

Landsat pixel is 30 m by 30 m or 900 m2, a 3 by 3 

neighborhood consisting of nine pixels is 8,100 m2 or a 

little greater than 2 acres. The area of one FIA subplot 

is 168.11 m2, therefore the area of four FIA subplots is 

1809.56 m2. The outermost circumference of four FIA 

subplots is 6052.08 m2

 or just under 1.5 acres, which 

is almost 75 percent of the 3 by 3 neighborhood pixel 

area (Fig. 1A).

Several different scales of image objects were 

generated from the Landsat TM layer stacks using 

Trimble eCognition software (Definiens 2009). Using 

National Agriculture Image Program (NAIP) 1 m 

color-infrared imagery acquired in 2011 as a backdrop, 

the different scales of image objects were visually 

evaluated to determine which scale(s) best delineated 

forest stands (Figs. 1B, 1C, and 1D). Ultimately, two 

different scales of image objects (scale parameter 25 

or “coarse”, and scale parameter 15 or “fine”) were 

identified for use in this analysis. Table 1 is a summary 

of the image object acreages for each TM scene.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 442

Figure 1.—An example of the FIA plot design over a grid of Landsat TM pixels (A) followed by examples of the two different 

scales of image objects used in the analysis overlaid on Landsat TM (RGB 4, 3, 2) and 2011 1 m NAIP (RGB 4, 3, 2) (D). 

Scale factor 25 (B) is coarse, scale factor 15 (C) is fine, and scale factor 15 (D) is fine.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 443

Figure 2A.—An example of scatterplots (NDVI and TCAP Band 3—Wetness) for forested FIA plots showing strong positive 

correlations between the 3 by 3 pixel area values and the image object values.

MAP UNIT COMPARISONS

Zonal statistics were calculated for the predictor 

data using the two different scales of image objects 

and the 3 by 3 neighborhood pixel areas for each 

Landsat TM scene. Simple linear correlation was 

used to examine the relationships between the zonal 

means of the 3 by 3 neighborhood pixel areas and 

the corresponding image objects for the forested FIA 

plots for all of the predictor layers. The scatterplots 

(Fig. 2A) showed strong positive correlations between 

the different scales of base map units across all of 

the image derivatives with the finer scaled image 

objects consistently having higher Pearson’s r values 

(Table 2). This was expected due to eCognition’s 

homogeneity criterion, which is a combination of 

spectral homogeneity and shape homogeneity, used 

to produce image objects. Essentially, the upper 

heterogeneity threshold is determined by the maximum 

standard deviation derived from the weighted input 

image layers and controlled by the scale parameter—

the lower the scale parameter, the lower the threshold, 

the smaller the image objects (Definiens 2009). 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 444

Figure 2B.—An example of scatterplots (NDVI and TCAP Band 3—Wetness) for aspen plots with a relative cover >50 percent 

showing strong positive correlations between the 3 by 3 pixel area values and the image object values.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 445

Table 2.—Results of the 3 by 3 neighborhood pixels compared to image objects for forested FIA plots. 

P values less than α = 0.05 are bold.

TM Scene Pearson’s r Wilcoxon Signed Rank Test

(Path/Row) N Image Derivative Scale 25 Scale 15 Scale 25 Scale 15

37/32 691 EVI 0.9 0.94 0.1551 0.7078

MSAVI2 0.95 0.96 0.0001 0.0311

NDMI 0.91 0.93 0.0041 0.0199

NDVI 0.94 0.96 0.0006 0.0665

PCA(1) 0.95 0.97 0.0290 0.0505

TCAP(1) 0.95 0.97 0.0445 0.0567

TCAP(2) 0.93 0.95 0.0171 0.3549

TCAP(3) 0.93 0.94 0.0059 0.0203

38/31 153 EVI 0.91 0. 95 0.0953 0.0097

MSAVI2 0.94 0.96 0.8931 0.2189

NDMI 0.92 0.95 0.5368 0.9075

NDVI 0.94 0.96 0.9594 0.1984

PCA(1) 0.93 0.96 0.4797 0.7150

TCAP(1) 0.93 0.95 0.5119 0.7993

TCAP(2) 0.92 0.96 0.3960 0.0373

TCAP(3) 0.93 0.95 0.6072 0.8845

38/33 466 EVI 0.93 0.95 0.5420 0.4122

MSAVI2 0.96 0.97 0.1684 0.3393

NDMI 0.94 0.95 0.0048 0.0228

NDVI 0.96 0.97 0.2911 0.4211

PCA(1) 0.97 0.98 0.2528 0.9638

TCAP(1) 0.97 0.98 0.4766 0.7257

TCAP(2) 0.95 0.96 0.4616 0.3658

TCAP(3) 0.94 0.96 0.0009 0.0204

FIA plots with a relative cover greater than 50 percent 

of quaking aspen were filtered from the whole sample 

and simple linear correlations applied. The scatterplots 

again showed strong positive correlations (Fig. 2B) 

and the finer scaled objects consistently had higher 

Pearson’s r values (Table 3). 

To get a further understanding of the relationships 

between the pixel-based and object-based values, a 

Wilcoxon signed rank test for paired samples was 

used to test for differences between the 3 by 3 pixel 

neighborhood and a) the coarser image object values, 

and b) the finer image object values. Results (Table 

2) of the test when applied to the forested FIA plots 

showed that:

1. Sometimes finer scale objects more closely 

represented the 3 by 3 pixel area values.

2. Often the 3 by 3 pixel area values were the same 

as the image object values. 

3. Sometimes the 3 by 3 pixel area values were 

different than the image object values. 

4. Rarely the coarser scale objects more closely 

represented the 3 by 3 pixel areas. 

MSAVI2, NDMI, and TCAP(3) from TM scene 3732 

and NDMI and TCAP(3) from TM scene 3833 had 

very low p values for the 3 by 3 pixel areas when 

compared to both scales of image objects, meaning 

that neither scale of image objects are the same as 

the 3 by 3 pixel areas for those particular image 

derivatives. Applying a Wilcoxon signed rank test for 

paired samples to the subset of quaking aspen plots 

(Table 3) had comparable results to that of the forested 

FIA test where similar derivatives were significantly 

different from the 3 by 3 pixel values at both scales. 

1. Often finer scale objects more closely 

represented the 3 by 3 pixel area values; 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 446

Table 3.—Results of the 3 by 3 neighborhood pixels compared to image objects for aspen plots with a 

relative cover ≥50 percent. P values less than α = 0.05 are bold.

TM Scene Pearson’s r Wilcoxon Signed Rank Test

(Path/Row) N Image Derivative Scale 25 Scale 15 Scale 25 Scale 15

37/32 88 EVI 0.83 0.92 0.0040 0.0745

MSAVI2 0.83 0.89 0.0098 0.2078

NDMI 0.85 0.89 0.3207 0.8183

NDVI 0.84 0.89 0.0118 0.2123

PCA(1) 0.85 0.89 0.4202 0.1062

TCAP(1) 0.85 0.89 0.2479 0.0461

TCAP(2) 0.83 0.9 0.0042 0.1035

TCAP(3) 0.87 0.89 0.5114 0.7704

38/31 34 EVI 0.91 0.92 0.9596 0.1906

MSAVI2 0.91 0.94 0.0249 0.1629

NDMI 0.91 0.94 0.0328 0.2556

NDVI 0.91 0.94 0.0299 0.1682

PCA(1) 0.9 0.95 0.1430 0.0036

TCAP(1) 0.9 0.95 0.1629 0.0038

TCAP(2) 0.92 0.95 0.3880 0.8527

TCAP(3) 0.91 0.94 0.0445 0.2280

38/33 43 EVI 0.89 0.93 0.0066 0.0010

MSAVI2 0.96 0.97 0.0006 0.0002

NDMI 0.9 0.93 0.0005 0.0006

NDVI 0.94 0.96 0.0006 0.0001

PCA(1) 0.99 0.99 0.9762 0.9381

TCAP(1) 0.99 0.99 0.7154 0.6712

TCAP(2) 0.93 0.96 0.0013 0.0007

TCAP(3) 0.9 0.93 0.0019 0.0048

2. Sometimes the 3 by 3 pixel area values were the 

same as the image object values; 

3. Sometimes the 3 by 3 pixel area values were 

different than the image object values and 

4. Rarely the coarser scale objects more closely 

represented the 3 by 3 pixel areas. 

EVI, MSAVI2, NDMI, NDVI, TCAP(2), and TCAP(3) 

from TM scene 3833 all had very low p values for 

the 3 by 3 pixel areas when compared to both scales 

of image objects meaning that neither scale of image 

objects are the same as the 3 by 3 pixel areas for those 

particular image derivatives. 

The very low p values from both the forested FIA 

plots and quaking aspen plots may be attributed to 

complex forest stand characteristics (structure and 

composition) coupled with the local variance structure 

of the imagery and therein, the resulting image objects. 

In other words, the differences in the local variance 

structure related to the forest stand structure and 

composition with respect to the scale of the image 

objects generated for these scenes and for those 

particular derivatives. 

Additionally, the results of the Wilcoxon signed rank 

test for paired samples when applied to the subset of 

quaking aspen plots seems to infer that an even finer 

scale of image objects may be needed to delineate and 

characterize specific forest types more effectively. 

FUTURE WORK

Additional analyses is needed to further understand the 

relationships between forested FIA plots and image 

objects for use in producing continuous and thematic 

maps of forest attributes at the regional level. Finer 

scales of image objects may help to better delineate 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 447

smaller homogeneous forest stands, mixed forest 

stands where the proportions of a particular species 

(e.g., quaking aspen) are less than what is typically 

considered “dominant” (>50 percent relative cover), 

and forest structure. 

ACKNOWLEDGMENTS

The author thanks R. Justin DeRose, Tracey Frescino, 

Sara Goeking, Jim Menlove, Paul Patterson, Alicia 

Peduzzi, Mark Riley, Bonnie Ruefenacht, and John 

Shaw for their insight and reviews which greatly 

improved the content of this work.

LITERATURE CITED

Bartos, D.L.; Campbell, R.B., Jr. 1998. Decline of 

quaking aspen in the Interior West—examples 

from Utah. Rangelands. 20: 17-24.

Chubey, M.S.; Franklin, S.E.; Wulder, M.A. 2006. 

Object-based analysis of Ikonos-2 imagery 

for extraction of forest inventory parameters.

Photogrammetric Engineering and Remote Sensing. 

72(4): 383-394.

DeBlander, L.T.; Shaw, J.D.; Witt, C.; Menlove, J.; 

Thompson, M.T.; Morgan, T.A.; DeRose, R.J.; 

Amacher, M.C. 2010. Utah’s forest resources, 

2000-2005. Resour. Bull. RMRS-RB-10. Fort 

Collins, CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research Station. 

144 p.

Definiens. 2009. eCognition developer 8 reference 

book, version 1.2.0 user guide. Munich, Germany: 

Definiens. 236 p.

Gao, Y.; Mas, J.F. 2008. A comparison of the 

performance of pixel-based and object-based 

classifications over images with various spatial 

resolutions. Archives of International Society for 

Photogrammertry and Remote Sensing. Available 

at http://www.isprs.org/proceedings/XXXVIII/4-

C1/Sessions/Session1/6589_Y_Gao_Proc_pap.pdf. 

(Accessed August 31, 2012).

Hay, G.J.; Castilla, G.; Wulder, M.A.; Ruiz, J.R. 2005. 

An automated object-based approach for the 

multiscale image segmentation of forest scenes.

International Journal of Applied Earth Observation 

and Geoinformation. 7(4): 339-359.

Karl, J.; Maurer, B. 2010. Spatial dependence 

of predictions from image segmentation: 

avariogram-based method to determine 

appropriate scales for producing land 

management information. Ecological 

Informatics. 5: 194-202.

Kim, S.-R.; Lee, W.-K.; Kwak, D.-A.; Biging, G.S.; 

Gong, P.; Lee, J.-H.; Cho, H.-K. Forest cover 

classification by optimal segmentation of high 

resolution satellite imagery. Sensors. 11(2): 

1943-1958.

USDA Forest Service. 2011. Interior West Forest 

Inventory and Analysis P2 field procedures, 

v5.00. Ogden, UT: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research Station. 

398 p. Available at www.fs.fed.us/rm/ogden/datacollection/pdf/iwfia_p2_50.pdf. 

Yasumasa, H.Y.; Tomoaki, T. 2010. Image 

segmentation and classification of Landsat 

thematic mapper data using a sampling 

approach for forest cover assessment. Canadian 

Journal of Forest Research. 41(1): 35-43. 

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 448

IS LODGEPOLE PINE MORTALITY DUE TO MOUNTAIN PINE BEETLE

LINKED TO THE NORTH AMERICAN MONSOON?

Sara A. Goeking and Greg C. Liknes1

Abstract.—Regional precipitation patterns may have influenced the spatial variability of 

tree mortality during the recent mountain pine beetle (Dendroctonus ponderosa) (MPB) 

outbreak in the western United States. Data from the Forest Inventory and Analysis (FIA) 

Program show that the outbreak was especially severe in the state of Colorado where 

over 10 million lodgepole pines (Pinus contorta Dougl. Ex Loud.) succumbed to MPB 

between 2002 and 2009. Aerial detection maps of MPB–related mortality show that 

the infestation was initially widespread and evenly distributed throughout the range of 

lodgepole pine in Colorado, but gradually became more severe in the northern portion of 

the state. Because southern Colorado receives relatively high summer precipitation due 

to the effects of the North American monsoon (NAM), the spatial pattern of MPB-related 

mortality suggests that infestation severity was lower in areas with the higher summer 

precipitation. This study investigated the link between lodgepole pine mortality due to 

MPB and seasonal precipitation patterns associated with the NAM in Colorado. Data 

regarding insect-related tree mortality and damage data were summarized from FIA data 

collected between 2002 and 2009, and gridded precipitation data were acquired from 

the North American Regional Reanalysis Project. Results indicated that while absolute 

NAM-related precipitation was not an important predictor of infestation severity, the 

deviation of a five-year average of summer and fall precipitation relative to climatic 

means was important.

1

 Biological Scientist (SAG), U.S. Forest Service, Rocky 

Mountain Research Station, 507 25th Street, Ogden, UT 

84401; Research Physical Scientist (GCL), U.S. Forest 

Service, Northern Research Station. SAG is corresponding 

author: to contact, call 801-625-5193 or email at 

sgoeking@fs.fed.us.

INTRODUCTION

An outbreak of mountain pine beetle (Dendroctonus 

ponderosae) (MPB) has killed millions of trees 

across the western United States and Canada in the 

past decade. In Colorado alone, over 10 million 

lodgepole pines (Pinus contorta Dougl. Ex Loud.) 

died due to MPB infestation between 2002 and 

2007 (Thompson 2009). Thompson et al. (2010) 

noted a spatial pattern in the distribution of insectrelated mortality of lodgepole pine in Colorado. The 

relatively minor infestation severity in the southern 

part of the state mirrors the distribution of summer 

precipitation related to the North American monsoon 

(NAM). During the months of July, August, and 

September, the NAM alters regional wind patterns 

and introduces tropical moisture to northwestern 

Mexico and parts of the southwestern United States, 

including southern Colorado (Higgins et al. 1997). As 

a consequence, these areas experience relatively high 

seasonal precipitation until wind patterns return to a 

more dry and westerly flow in autumn. Based on this 

geographical phenomenon, it seems possible that MPB 

infestation severity is inversely related to seasonal 

precipitation associated with the NAM. 

The purpose of this study was to identify a subset 

of stand-level and precipitation variables that have 

the strongest influence on infestation severity. Hicke 

and Jenkins (2008) identified several tree-level and 

stand-level variables that affect the susceptibility of 

lodgepole pine to MPB. However, the influence of 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 449

monsoonal precipitation patterns on the severity of 

MPB infestations in the southwestern United States 

has not yet been explored. Based on the apparent 

geographically inverse linkage between NAM-related 

precipitation and lodgepole pine mortality due to 

mountain pine beetle, we expected that cumulative 

precipitation for the months of July, August, and 

September would be among the best predictors of 

infestation severity.

METHODS

Stand-level data were queried from the national Forest 

Inventory and Analysis (FIA) database. Between 2002 

and 2009, a total of 497 FIA plots with at least one 

tallied lodgepole pine were measured in Colorado. 

This analysis included 248 Colorado plots that 

contained a minimum of 20 tallied lodgepole pines that 

were either live or recent mortality trees at the time of 

survey; 185 plots included at least one tree with insectrelated damage or mortality on the plot. Infestation 

severity was calculated as the percentage of tallied 

lodgepole pines that were recorded either as mortality 

trees with insects listed as the mortality agent, or 

as live trees with severe insect damage. Infestation 

severity was negative log-transformed for normality. 

Independent variables included both stand-level 

variables, which were queried from the FIA database, 

and precipitation data (see Table 1). Because field 

Table 1.—Variables considered as potential predictors of insect-caused lodgepole pine damage and 

mortality. Data sources are the National Forest Inventory database (FIADB) and North American Regional 

Reanalysis (NARR) Program. Intervals used to calculate 3-year and 5-year means end in the year equal to 

the median mortality year. 

Variable Type Source

-Log(Percent [damage + mortality]) Continuous FIADB (calculated)

Measurement year Class FIADB

Median mortality year Class FIADB (calculated)

Elevation Continuous FIADB

Stand age Continuous FIADB

Live basal area Continuous FIADB

Number of all trees Count FIADB (calculated)

Number of lodgepole pines Count FIADB (calculated)

Mean diameter at breast height (d.b.h.) Continuous FIADB (calculated)

Standard deviation of d.b.h. Continuous FIADB (calculated)

Mean tree height Continuous FIADB (calculated)

Winter (Jan/Feb/Mar) climatic mean, 1981-2010 Continuous NARR (calculated)

Spring (Apr/May/Jun) climatic mean, 1981-2010 Continuous NARR (calculated)

Summer (Jul/Aug/Sep) climatic mean, 1981-2010 Continuous NARR (calculated)

Fall (Oct/Nov/Dec) climatic mean, 1981-2010 Continuous NARR (calculated)

Winter (Jan/Feb/Mar) 3-yr mean Continuous NARR (calculated)

Spring (Apr/May/Jun) 3-yr mean Continuous NARR (calculated)

Summer (Jul/Aug/Sep) 3-yr mean Continuous NARR (calculated)

Fall (Oct/Nov/Dec) 3-yr mean Continuous NARR (calculated)

Winter (Jan/Feb/Mar) 5-yr mean Continuous NARR (calculated)

Spring (Apr/May/Jun) 5-yr mean Continuous NARR (calculated)

Summer (Jul/Aug/Sep) 5-yr mean Continuous NARR (calculated)

Fall (Oct/Nov/Dec) 5-yr mean Continuous NARR (calculated)

Winter (Jan/Feb/Mar) 3-yr anomaly Continuous NARR (calculated)

Spring (Apr/May/Jun) 3-yr anomaly Continuous NARR (calculated)

Summer (Jul/Aug/Sep) 3-yr anomaly Continuous NARR (calculated)

Fall (Oct/Nov/Dec) 3-yr anomaly Continuous NARR (calculated)

Winter (Jan/Feb/Mar) 5-yr anomaly Continuous NARR (calculated)

Spring (Apr/May/Jun) 5-yr anomaly Continuous NARR (calculated)

Summer (Jul/Aug/Sep) 5-yr anomaly Continuous NARR (calculated)

Fall (Oct/Nov/Dec) 5-yr anomaly Continuous NARR (calculated)
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 450

crews in the Interior West region of the FIA assign 

an estimated mortality year to each mortality tree, 

we used the median mortality year for all mortality 

trees on the plot, rather than measurement year, as the 

year of most severe impact. For insect-damaged live 

trees, which are defined as those already infested and 

unlikely to survive, the mortality year was estimated 

as the year following the measurement year and 

then incorporated into the median mortality year 

calculation. Median mortality years ranged from 1999 

to 2009.

Precipitation data were acquired from the North 

American Regional Reanalysis (NARR) Project 

(Mesinger et al. 2006). The initial dataset consisted of 

gridded monthly precipitation at a resolution of 32 km 

for the period 1993-2009. Seasonal precipitation for 

each year was calculated as the sum of precipitation 

during 3 months, where summer consisted of July, 

August, and September to coincide with the NAM (see 

Table 1). Climatic mean precipitation was calculated 

for each calendar month based on the 30-year period 

1981-2010, and these monthly climatic means were 

summed in 3-month intervals to obtain seasonal 

climatic means. Seasonal 3-year and 5-year interannual 

precipitation means were also calculated for each plot, 

where the average associated with each plot was based 

on the period ending in the median year of lodgepole 

pine mortality for that plot. To calculate 3-year and 

5-year seasonal precipitation anomalies, which are 

defined here as the simple difference between shortterm precipitation and long-term climatic conditions, 

the 30-year climatic means were subtracted from the 

3-year and 5-year interannual means. Therefore 

positive anomalies indicate relatively wet periods, 

while negative values indicate dry periods.

Analysis was done using two iterations of PROC 

GLMSELECT (SAS Institute Inc. 2009). The first 

iteration did not include any interaction terms, while 

the second included interactions between standlevel and precipitation variables as well as among 

precipitation variables. Selection of variables was 

stepwise, with significance levels of 0.15 for both 

entering and staying in the model. Final model 

selection was based on minimization of the Akaike 

information criterion (AIC). Based on our expectation 

that seasonal precipitation related to the NAM has 

an inverse relationship with infestation severity, we 

expected that climatic mean summer precipitation 

would be a component of the final model. Because 

stepwise variable selection methods may overestimate 

the importance of independent variables (Harrell 

2001), correlations were assessed between infestation 

severity and each of the five variables identified from 

the stepwise regression to reinforce the interpretation 

of each variable’s importance. 

RESULTS

The model with the minimum AIC value included the 

following variables, in order of entry into the model: 

5-year fall precipitation anomaly, mean diameter at 

breast height (d.b.h.), live basal area, 5-year summer 

precipitation anomaly, and stand age (Table 2). During 

the second run of the GLMSELECT procedure, some 

Table 2.—Variables included in the model with the minimum Akaike information criterion (AIC), where 

minimum significance level for entering and staying in the model was 0.15. R values are Pearson 

correlation coefficients of each variable against the log-transformed response.

Step Variable AIC ΔAIC F Value Pr > F r

0 Intercept only 285.0214 48.34 0.00 1.0000 N/A

1 Fall (Oct/Nov/Dec) 5-yr anomaly 254.3260 17.64 35.38 <0.0001 0.4025

2 Mean diameter at breast height (d.b.h.) 245.2348 8.55 11.25 0.0010 0.2274

3 Live basal area 239.2317 2.55 8.00 0.0052 -0.2298

4 Summer (Jul/Aug/Sep) 5-yr anomaly 237.6833 1.00 3.49 0.0635 0.3705

5 Stand age 236.6843 0.00 2.93 0.0889 -0.0655
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 451

interaction terms were selected as important variables 

in the final model. These included interactions 

between mean d.b.h. and both summer and fall 5-

year precipitation anomalies. However, because this 

model’s AIC value was comparable to that of the initial 

model, and the selected interaction terms included 

a nearly identical subset of climatic and stand-level 

predictor variables as the initial model, results are 

presented only for the first and simpler model.

Based on values for Pearson’s correlation coefficient 

(Table 2), infestation severity was more strongly 

correlated with the precipitation variables in the final 

model than with the any of the stand-level attributes. 

The 5-year summer and fall precipitation anomalies 

were negatively correlated with infestation severity. 

These negative correlations signify that 5-year periods 

of relative drought during summer and fall are most 

strongly associated with high infestation severity. 

Although stand age emerged as an important predictor 

variable in the AIC-based variable selection, its low 

correlation with infestation severity suggests it is not 

important.

DISCUSSION

Of the five predictor variables in the final model, 

two represented seasonal precipitation anomalies 

and three were based on stand attributes. The three 

stand-level attributes (mean d.b.h., basal area, and 

stand age) are known to affect infestation severity 

of lodgepole pine by mountain pine beetle (Hicke 

and Jenkins 2008, Raffa et al. 2008). Several aspects 

of the two precipitation variables are notable. First, 

both variables represent anomalies from long-term 

climatic conditions, indicating that anomalies are 

more important than absolute precipitation metrics as 

predictors of infestation severity. Because absolute 

summer precipitation did not meet the criteria for 

inclusion in the final model, there is no evidence 

that the absolute quantity of precipitation associated 

with the NAM has an impact on infestation severity. 

However, interannual variability in seasonal 

precipitation, including precipitation associated with 

the complex meteorological dynamics of the NAM, 

may impact infestation severity. Second, the two 

precipitation variables included in the final model 

indicate that seasonal anomalies during summer and 

fall are more important than those during winter 

and spring. Finally, while the 5-year summer and 

fall anomalies were important, no 3-year anomalies 

appeared in the model. Assuming that the inverse 

relationship between seasonal precipitation anomalies 

and infestation severity is caused by increased stress 

during long-term drought, the temporal realm of 

influence of precipitation anomalies on infestation 

severity appears to last longer than 3 years.

Other analyses of weather and climate data may yield 

further insights about the factors affecting infestation 

severity. Future research should investigate whether 

the relationship between infestation severity and 

seasonal precipitation anomalies holds elsewhere, 

and if so, whether the 5-year timescale is similarly 

important. While precipitation is thought to affect the 

susceptibility of trees to insect attacks, temperature 

data may also be useful for modeling insect 

populations and infestation severity. Analyses of 

temperature data, such as monthly and daily maxima, 

minima, and anomalies, may further illuminate the 

complex relationships among climate, weather, MPB 

population dynamics, and lodgepole pine stands.

ACKNOWLEDGMENTS

Shih-Yu Wang of Utah State University provided 

helpful review comments about climate data sources 

and analysis methods, and Mike Thompson of the 

Interior West FIA Program provided valuable insights 

regarding use of FIA data to monitor trends in 

lodgepole pine mortality related to MPB. The authors 

acknowledge the National Oceanic and Atmospheric 

Administration’s National Climatic Data Center for 

making NARR precipitation data available for free 

download. NARR data were processed and exported 

to GIS-compatible format using OpenGrADS climate 

data analysis freeware. 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 452

LITERATURE CITED

Harrell, F.E. 2001. Regression modeling strategies: 

with applications to linear models, logistic 

regression, and survival analysis. New York, NY: 

Springer-Verlag.

Hicke, J.A.; Jenkins, J.C. 2008. Mapping lodgepole 

pine stand structure susceptibility to mountain 

pine beetle attack across the western United 

States. Forest Ecology and Management. 255: 

1536-1547.

Higgins, R.W.; Yao, Y.; Wang, X.L. 1997. Influence of 

the North American monsoon system on the U.S. 

summer precipitation regime. Journal of Climate. 

10: 2600-2622.

Mesinger, F.; DiMego, G.; Kalnay, E.; Mitchell, K.; 

Shafran, P.R.; Ebisuzaki, W.; Jovic, D.; Woollen, 

J.; Rogers, E.; Berbery, E.H.; Ek, M.B.; Fan, 

Y.; Grumbine, R.; Higgins, W.; Li, H.; Lin, Y.; 

Manikin, G.; Parrish, D.; Shi, W. 2006. North 

American regional reanalysis. Bulletin of the 

American Meteorological Society. 87: 343-360.

Raffa, K.F.; Aukema, B.H.; Bentz, B.J.; Carroll, A.L.; 

Hicke, J.A.; Turner, M.G.; Romme, W.H. 2008. 

Cross-scale drivers of natural disturbances 

prone to anthropogenic amplification: the 

dynamics of bark beetle eruptions. Bioscience. 

58(6): 501-517.

SAS Institute Inc. 2009. Base SAS 9.2 procedures 

guide. Cary, NC: SAS Institute Inc.

Thompson, M.T. 2009. Analysis of conifer mortality 

in Colorado using forest inventory and analysis’s 

annual forest inventory. Western Journal of 

Applied Forestry. 24: 193-197.

Thompson, M.T.; Duda, J.A.; DeBlander, L.T.; Shaw, 

J.D.; Witt, C.; Morgan, T.A.; Amacher, M.C. 

2010. Colorado’s forest resources, 2002-2006.

Resource Bulletin RMRS-RB-11. Fort Collins, CO: 

U.S. Dept. of Agriculture, Forest Service, Rocky 

Mountain Research Station. 108 p.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 453

TRENDS IN STANDING BIOMASS IN INTERIOR WEST FORESTS: 

REASSESSING BASELINE DATA FROM PERIODIC INVENTORIES

Sara A. Goeking1

Abstract.—Trends in U.S. forest biomass and carbon are assessed using Forest Inventory 

and Analysis (FIA) data relative to baseline assessments from the 1990s. The integrity of 

baseline data varies by state and depends largely on the comparability of periodic versus 

annual forest inventory data. In most states in the Interior West FIA region, the periodic 

inventory’s sample design, plot configuration, estimation procedures, and definitions 

were different from those for the annual inventory, which are nationally consistent. Direct 

comparisons of periodic versus annual inventory data are therefore tenuous and may 

reflect changing protocols rather than actual changes, yet they comprise the best available 

method of assessing recent trends in some states. This study attempts to clarify trends in 

aboveground tree biomass in the Interior West region by comparing estimates at matched 

plots that were sampled during both periodic and annual inventories. To illustrate the 

ramifications of ignoring changes in inventory protocols, mean trends at paired plots were 

compared to those demonstrated by unpaired comparisons of entire periodic and annual 

inventories. In some states, the results produced by the two methods are contradictory. 

This demonstrates the importance of reassessing the use of estimates based on periodic 

forest inventories as reference conditions. 

1

 Biological Scientist, U.S. Forest Service, Rocky Mountain 

Research Station, 507 25th Street, Ogden, UT 84401. To 

contact, call 801-625-5193 or email at sgoeking@fs.fed.us. 

INTRODUCTION

The Forest Inventory and Analysis (FIA) program 

provides data for monitoring forest biomass at state, 

regional, and national scales. At a national scale, the 

U.S. Greenhouse Gas Inventory monitors trends in 

forest biomass and carbon based on FIA data (Heath 

et al. 2011). However, in the Interior West FIA region, 

the sample designs used in the historical forest 

inventories of the 1980s and 1990s, including those 

used for Resource Planning Act assessments (Smith 

et al. 2009), were neither internally consistent nor 

spatially balanced (Fig. 1). In 2000, the national FIA 

program implemented the spatially and temporally 

balanced sample design referred to as the annual 

inventory. Implementation in Interior West states 

occurred between 2000 and 2009, and due to a 10-year 

cycle length, most states have not yet completed a 

full annual inventory cycle. This constrains temporal 

monitoring to periodic inventory data coupled with a 

single measurement from the annual inventory. 

The forest inventories conducted in the state of Idaho 

between 1980 and 2002 exemplify the quandary 

presented by comparing periodic and annual forest 

inventory data (see Witt et al. 2012). Idaho’s most 

recent periodic inventories relied partially on aerial 

photograph interpretation to assess changes since the 

1981 Idaho woodland inventory, and if no change was 

observed, then the 1981 data were merged with the 

1990s inventory dataset. Each inventory also targeted 

specific ownership groups. For example, Idaho 

periodic inventories prior to 1992 did not include 

national forest lands. In contrast, periodic inventories 

conducted from 1993 to 2002 consisted almost solely 

of national forest lands. Each national forest was 

responsible for conducting its own periodic inventory, 

so inventory methods, sample grids, and the actual 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 454

Figure 1.—Maps showing the spatial distribution of a) time 1, b) time 2, and c) time 1/time 2 matched plots in the Interior West 

FIA region.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 455

inventory year(s) varied among forests, and vast areas 

within some national forests were completely omitted 

from the periodic inventory’s sample grid. Due to 

these spatial and temporal inconsistencies in Idaho’s 

periodic inventory, direct comparisons between 

periodic and annual inventory estimates are somewhat 

incongruous at both the statewide and the ownership 

group levels.

Despite their incompatibilities, comparisons of 

periodic versus annual inventories are the primary 

method of assessing recent trends in forest resources 

in the Interior West region. Due to the phased 

implementation of the annual inventory as well as the 

10-year cycle length, annual-to-annual remeasurement 

data are only available for a small percentage of plots. 

The purposes of this study were: 1) to assess trends in 

mean aboveground tree biomass per plot by comparing 

Table 1.—Measurement years of the periodic and annual inventories included in this study, by state, in the 

Interior West FIA region. For each state, only the most recent periodic inventory cycle was included. Total 

plots measured (n) and the number of paired plots between the two inventories is indicated. Asterisks 

indicate annual inventory cycles where the first measurement year indicates initiation of the annual 

inventory.

State Cycle n Measurement Years Plot Design

ARIZONA 2 1,966 1995-1999 4-subplot fixed-radius

3* 3,291 2001-2009 4-subplot fixed-radius

Paired plots 1,600

COLORADO 1 388 1997 4-subplot fixed-radius

2* 3,315 2002-2009 4-subplot fixed-radius

Paired plots 270

IDAHO 1 1,215 1998-2002 4-subplot fixed-radius

2* 2,319 2004-2009 4-subplot fixed-radius

Paired plots 664

MONTANA 1 2,368 1993-1999 variable radius

2* 3,217 2003-2009 4-subplot fixed-radius

Paired plots 1,522

NEVADA 1 588 1994-1997 variable radius

2* 465 2004-2005 4-subplot fixed-radius

Paired plots 101

NEW MEXICO 1 1,287 1985-1987 variable radius

2 1,741 1996-2000 mixed

Paired plots 343

UTAH 1 2,686 1991-1995 variable radius

2* 3,526 2000-2009 4-subplot fixed-radius

Paired plots 1,838

WYOMING 1 501 1983 variable radius

2 1,981 1998-2002 4-subplot fixed-radius

Paired plots 398

only plots that were measured during the two most 

recent forest inventories (i.e., paired measurements); 

and 2) to identify cases where these trends contradict 

those demonstrated by comparisons of unpaired plot 

measurements.

METHODS

The study area consisted of eight Interior West states: 

Arizona, Colorado, Idaho, Montana, Nevada, New 

Mexico, Utah, and Wyoming. Each state was evaluated 

separately due to temporal inconsistencies in the 

timing of both periodic inventories and the initiation of 

the annual inventory. For most states, time 1 consisted 

of the most recent periodic inventory while time 2 

represents the annual inventory (Table 1). In New 

Mexico and Wyoming, the annual inventory was not 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 456

implemented until 2008 and 2009, respectively, but 

periodic inventories were conducted in both states in 

the late 1990s through early 2000s. Due to a paucity 

of annual inventory data in these two states, the 

time 2 measurements consisted of the most recent 

periodic inventories and an earlier periodic inventory 

was treated as time 1. Figure 1 portrays the spatial 

distribution of all time 1 plots, all time 2 plots, and the 

paired time 1 and time 2 plots.

The tree-level variable DRYBIOT (total gross biomass 

ovendry weight per tree in pounds) was queried for all 

live and standing dead trees from the national forest 

inventory database (Miles et al. 2001); trees measured 

under obsolete protocols (e.g., down dead trees) or 

with diameter at breast height (d.b.h.) of less than 

5.0 inches were omitted. For periodic plots where 

data collection protocols did not include condition 

mapping, individual tree biomass was multiplied by 

a tree-level expansion factor and a unit adjustment to 

obtain total plot biomass in oven-dry tons per acre. For 

plots where conditions may have been delineated (i.e., 

annual inventory plots and periodic fixed-radius plots), 

this expansion was performed at the condition level 

first, and then condition-level biomass was aggregated 

to the plot level. Plot-level biomass was aggregated 

separately for live and dead trees. 

The periodic and annual inventory datasets were then 

merged to identify plots that were measured during 

both inventories. Plots were assumed to be colocated, 

or measured more than once, if they had the same 

combination of state, county, and plot identifiers. 

Interior West FIA protocols stipulate that annual 

inventory plots should be colocated with periodic plots 

with the same state, county, and plot identifiers. At a 

small proportion of these plots, the periodic plot center 

could not be located so it was assumed that even plots 

that were not colocated sampled the same or a similar 

condition as the original measurement. Comparisons 

based on this subset of “matched” periodic-to-annual 

plots allowed for paired comparisons. 

Trends were assessed by comparing mean tree biomass 

per plot, first using comparisons of all plots in each 

inventory and then using only paired plots that were 

measured during both time 1 and time 2 inventories. 

Cases where the paired-plot trends conflicted with 

unpaired comparisons were identified.

RESULTS

In every state analyzed in this study, comparisons 

of paired plots that were measured during both 

inventories yielded different trends than those 

identified from comparisons of all plots (Fig. 2). In six 

states (Arizona, Colorado, Idaho, Montana, Nevada, 

and Utah), comparisons of all periodic and annual 

inventory plot data demonstrated declines in both 

mean live tree biomass and mean total (live plus dead) 

tree biomass per plot. However, comparisons of paired 

plots showed very little change in live tree biomass 

and increases in total tree biomass. Therefore, the two 

comparisons produced conflicting trends not only in 

magnitude but also in direction of change. Changes 

in dead tree biomass were similar among the two 

comparisons.

The two states that did not fit the pattern previously 

described were New Mexico and Wyoming, where 

unpaired comparisons showed increases in total, live, 

and dead tree biomass while paired comparisons 

demonstrated very little change in any tree biomass 

component. This is likely due to the fact that the time 2 

measurement consisted of a second periodic inventory. 

As previously mentioned, periodic inventories 

typically targeted specific ownership groups. The time 

1 periodic inventories in both states were completed in 

the early to mid-1980s and sampled a disproportionate 

amount of private and non-national forest public lands. 

The time 2 periodic inventories in both states were 

conducted in the late 1990s and sampled a relatively 

large amount of national forest lands. Therefore, the 

apparent increases in biomass, based on unpaired 

analyses, were primarily due to differences in 

sampling. Comparison of paired plots in both states 

showed very little change between the 1980s and late 

1990s.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 457

Figure 2.—Graphs showing mean aboveground biomass of live (dark gray) and dead (light gray) trees by state, in tons 

per acre.

COLORADO

60
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 458

Figure 3 shows the change in live, dead, and total 

aboveground mean biomass per plot from time 1 to 

time 2, by ecoregion subsection. Specific trends in 

live and dead biomass varied geographically, where 

Figure 3.—Maps showing the magnitude of a) live, b) dead, and c) total aboveground tree biomass change at paired plots 

between time 1 and time 2, in tons per acre, averaged by ecoregion subsections. Blank subsections indicate either absence of 

paired plots or change less than 1 ton per acre.

areas of greatest decrease in live biomass presumably 

represent the occurrence of wildfires and/or 

disturbances such as insects that affect specific forest 

types. 
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 459

DISCUSSION

Comparing periodic and annual inventory data is 

problematic due to numerous differences in definitions 

and protocols between the two inventories. Direct 

comparisons may produce apparent trends that differ 

from those found when comparing only paired plots, 

as demonstrated using aboveground tree biomass 

data from eight Interior West states. The unpaired 

comparisons for all states except New Mexico and 

Wyoming showed superficial decreases in standing 

tree biomass. This is likely due to the fact that most 

periodic inventories targeted specific ownership 

groups, which may correspond to oversampling of 

highly productive versus relatively unproductive 

forest lands. In contrast, the spatial configuration of 

the annual inventory represents ownership groups 

and productivity levels proportional to their existence 

across the landscape. As might be expected, results of 

the paired-plot comparisons often conflicted with those 

from the unpaired comparisons. In this case, using 

paired plot data versus spatially disparate samples 

represents the difference between trees in some states 

acting as a forest carbon source versus sink. 

One caveat of this analysis is that conclusions about 

forest trends may characterize areas that were heavily 

sampled during the periodic inventory, and may not 

represent actual trends on a statewide basis. In other 

words, if areas were oversampled previously, the 

sample design of the annual inventory constrains them 

from continuing to be oversampled; yet using paired 

measurements cannot compensate for areas that were 

under-represented previously. Therefore, this analysis 

could not account for the fact that large areas were 

omitted from some periodic inventories, nor could it 

account for different plot configurations (i.e., variableradius versus mapped-plot designs). Using paired-plot 

comparisons to infer statewide trends cannot account 

for small-scale disturbances that may have affected 

trends in under-sampled areas. Instead, it assumes 

that temporally variable processes operating at those 

plots (e.g., precipitation, temperature, and disturbances 

such as fire or insects) exhibit trends that are spatially 

representative of the state as a whole.

In terms of the absolute quantity of mean biomass 

per plot, the spatially balanced design of the annual 

inventory qualifies it as the most representative metric 

of the state as a whole. This means that the unpaired 

time 2 estimate (Fig. 2) is likely more accurate than 

the paired time 2 estimate. The two quantities differ in 

most states, suggesting that the paired plots used for 

trend assessment are not representative of actual mean 

conditions statewide. Note that this interpretation of 

absolute mean biomass per plot does not hold true in 

New Mexico and Wyoming because time 2 data in 

those states were collected during periodic inventories 

that targeted a narrow range of ownership groups and 

regions (see Fig. 1). 

Finally, the sometimes conflicting trends produced 

by comparing paired plots versus entire statewide 

inventories confirm the need to account for 

discrepancies between the periodic and annual 

inventory estimates. Because many Interior West states 

are still in their first annual forest inventory cycle, 

additional plots can be incorporated into paired-plot 

comparisons as additional panels are completed and 

new annual plots are colocated with existing periodic 

plot locations. In some states, statistical modeling 

efforts may produce a more representative pre-2000 

baseline of forest metrics, and thus enable more 

reliable trend assessment.

LITERATURE CITED

Heath, L.S.; Smith, J.E.; Skog, K.E.; Nowak, D.J.; 

Woodall, C.W. 2011. Managed forest carbon 

estimates for the greenhouse gas inventory, 

1990-2008. Journal of Forestry. 109(3): 167-173.

Miles, P.D.; Brand, G.J.; Alerich, C.L.; Bednar, L.F.; 

Woudenberg, S.W.; Glover, J.F.; Ezell, E.N. 2001. 

The forest inventory and analysis database 

description and users manual version 1.0.

Gen. Tech. Rep. NC-218. St. Paul, MN: U.S. 

Dept. of Agriculture, Forest Service, North Central 

Research Station. 130 p.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 460

Smith, W.B.; Miles, P.D.; Perry, C.H.; Pugh, S.A. 

2009. Forest resources of the United States, 

2007. Gen. Tech. Rep. WO-78. Washington, DC: 

U.S. Department of Agriculture, Forest Service, 

Washington Office. 336 p.

Witt, C.; Shaw, J.D.; Thompson, M.T.; Goeking, 

S.A.; Menlove, J.; Amacher, M.C.; Morgan, T.A.; 

Werstak, C. 2012. Idaho’s forest resources, 

2004-2009. Resource Bulletin RMRS-RB-14. Fort 

Collins, CO: U.S. Dept. of Agriculture, Forest 

Service, Rocky Mountain Research Station. 129 p.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
pOSTER aBSTRACTS
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 462

TAMARACK IN MINNESOTA: INVESTIGATING MORTALITY

FROM EASTERN LARCH BEETLE USING FIA DATA

Susan J. Crocker, Jana Albers, Fraser R. McKee, Brian Aukema, and Greg C. Liknes1

Abstract.—Prior to European settlement, tamarack dominated the bogs, peatlands, and 

uplands of Minnesota’s North Woods. Still a major component of Minnesota’s forests, 

the extent and volume of tamarack has since waned. Mortality of tamarack has increased 

over the past decade. The majority of this mortality has been attributed to the activity of 

the eastern larch beetle (Dendroctonus simplex LeConte, Coleoptera, Scolytidae; ELB), 

a pest native to North America. Outbreaks of ELB have been documented in Minnesota 

since 1938. Largely separated by decades, the current outbreak of ELB has been ongoing 

since 2000. ELB frequently colonizes trees weakened by defoliators, however, within 

the current outbreak, it appears to be acting as the primary cause of mortality. While 

conditions that predispose stands to ELB attack are not well understood, physiological 

stress is often associated with infestation (Seybold et al. 2002). Factors related to the 

current outbreak are undetermined. However, drought, which has been a fixture in 9 

of the past 10 years, could be playing an important role. Using data from the Forest 

Inventory Analysis program of the U.S. Forest Service, we analyzed trends in tamarack 

area and mortality over time. Additionally, tamarack mortality was aggregated by climate 

division to examine the relationship between mortality and drought. Future work will 

attempt to quantify the relative contribution of predisposing factors to tree mortality. 

Literature Cited

Seybold, S.J.; Albers, M.A.; Katovich, S.A. 2002. Eastern larch beetle. Forest Insect 

and Disease Leaflet 175. Washington, DC: U.S. Department of Agriculture, Forest 

Service.

1

 Research Forester (SJC), U.S. Forest Service, Northern Research Station, 1992 Folwell 

Avenue, St. Paul, MN 55108; Forest Health Specialist (JA), Minnesota Department of Natural 

Resources; Ph.D. Student (FRM), University of Minnesota; Assistant Professor (BA), University 

of Minnesota; Research Physical Scientist (GCL), U.S. Forest Service, Northern Research Station. 

SJC is corresponding author: to contact, call 651-649-5136 or email at scrocker@fs.fed.us.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 463

AGENT-SPECIFIC TREE MORTALITY RATES 

IN THE EASTERN UNITED STATES FROM FIA DATA

Alan V. Di Vittorio and Jeffrey Q. Chambers1

Abstract.—Forest tree mortality plays an important role in the global carbon budget 

through so-called “background” mortality rates and larger, less frequent mortality events. 

The actual mortality turnover rates of forest biomass are not well understood and can 

vary with forest type, stand characteristics, and environmental conditions. Different 

agents, such as fire, insects, disease, and weather, operate on different time scales with 

effects varying across different ecosystems. This variability makes it difficult, but 

important, to determine patterns of agent-specific mortality for model projections of 

forest carbon balance. However, many regional and global ecosystem models assume 

a single, nonfire mortality rate for all forests, which introduces bias to projections of 

forest carbon balance. Using the U.S. Forest Service Forest Inventory Analysis database 

(FIADB), we estimate annual average mortality rates, on a per-tree basis, for eastern 

U.S. forests between 2000 and 2010 (except for 1974-1984 Louisiana estimates). We 

present spatially explicit estimates of total mortality and of agent-specific mortality due 

to animals, disease, insects, fire, harvest, weather, vegetation, and unknown agent. These 

estimates include all trees greater than or equal to 1 inch in diameter in remeasured 

forest- or timberland plots, and exclude plots with annual average harvest rates greater 

than 3.5 percent. Estimated annual average mortality rates vary from 0.2 percent to 7.5 

percent across the eastern United States. Removing fire and harvest effects limits this 

range to 0.2 percent to 4.9 percent. The unweighted regional average is 3.3 percent for 

total annual average mortality (30 states), and removing fire and harvest effects lowers 

this average to 2.4 percent. Unknown agents dominate the northern state estimates and 

vegetation encroachment dominates southern state estimates. Weather mortality estimates 

can be up to 98 percent, but are generally on the order of disease and fire estimates. These 

estimates indicate that uniform mortality rates in ecosystem models would be improved 

by spatially explicit values.

1

 Postdoctoral Fellow (AVD), Lawrence Berkeley National Laboratory, Earth Sciences Division, 

One Cyclotron Road, MS 84R0171, Berkeley, CA 94709; Staff Scientist (JQC), Lawrence 

Berkeley National Laboratory. AVD is corresponding author: to contact, call 510-486-7798 

or email at avdivittorio@lbl.gov.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 464

REMOTE SENSING DATA AS A MONITORING TOOL: 

TRENDS IN WILDFIRE ACTIVITY FOR THE PAST QUARTER CENTURY

AND THE RELATIONSHIP TO OTHER BIOPHYSICAL

AND ENVIRONMENTAL VARIABLES.

Mark Finco, Brad Quayle, Kevin A. Megown, C. Kenneth Brewer, and Jennifer Lecker1

Abstract.—The Monitoring Trends in Burn Severity (MTBS, www.mtbs.gov) project is 

mapping extent, size, and severity of all large wildland fires greater than 1000 acres in 

the west and 500 acres in the east over the conterminous United States (CONUS), Alaska, 

and Hawaii. In 2012 the project reached a milestone, completing the mapping for all fires 

between 1984 and 2010. The MTBS project produces geospatial and tabular data using 

a consistent protocol for fire trend analysis at a range of spatial, temporal, and thematic 

scales.

Our poster presents some of the more important trends observed by intersecting the 

MTBS geospatial data with data layers related to other biophysical and environmental 

landscape characteristics. Many of these trends were stated in the initial chartering of 

the MTBS project by the Wildland Fire Leadership Council, including understanding 

the trends in burn severity by vegetation type, how burned area and severity differ by 

administrative ownership, and whether there is any trend in the proximity of fires to the 

wildland-urban interface. 

1

 Contract Leader (MF), Program Leaders (BQ and KAM), and Group Leader (JL), U.S. 

Forest Service, Remote Sensing Applications Center, 2222 West 2300 South, Salt Lake City, UT 

84119; Remote Sensing Program Manager (KB), U.S. Forest Service, Washington, DC. 

MF is corresponding author: to contact, call 801-975-3767 or email at mfinco@fs.fed.us.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 465

DETAILED MAPS OF TROPICAL FOREST TYPES ARE WITHIN REACH: 

FOREST TREE COMMUNITIES FOR TRINIDAD AND TOBAGO

MAPPED WITH MULTISEASON LANDSAT AND GOOGLE EARTH

Eileen H. Helmer, Thomas S. Ruzycki, Jay Benner, Shannon M. Voggesser, Barbara P. Scobie, 

Courtenay Park, David W. Fanning, and Seepersad Ramnarine1

Abstract.—Tropical forest managers need detailed maps of forest types for REDD+, but 

spectral similarity among forest types, cloud and scan-line gaps, and scarce vegetation 

ground plots complicate producing such maps from satellite imagery. How can these 

challenges be overcome? We describe a case study of mapping tropical forests to 

floristic classes for Trinidad and Tobago with gap-filled Landsat imagery by judicious 

combination of field and remote sensing work (Helmer et al. 2012). Recent and 

forthcoming developments are making such mapping with Landsat imagery far more 

accessible to nonspecialists. We highlight some key steps to mapping tropical forest 

habitats with cloudy Landsat and related insights from this study. 

In the study area, class characteristics like “deciduousness” allowed discrimination of 

floristic classes. We also discovered that the extensive training data needed for mapping 

tropical forest types with “noisy” gap-filled imagery can be collected by learning to 

identify tree communities in 1) imagery with fine spatial resolution of ≤1 m; 2) multiseason fine resolution imagery (usually only viewable on Google EarthTM); or 3) 

Landsat imagery from different dates, particularly imagery from drought years, even if 

decades old. Further, we show that gap-filled, synthetic multi-season Landsat imagery 

significantly improves class-level accuracy for several seasonal forest associations (by 

14 to 21 percent for deciduous, 7 to 36 percent for semi-evergreen, and 3 to11 percent for 

seasonal evergreen associations, and by 5 to 8 percent for secondary forest and woody 

agriculture). Moreover, in some cases the seasonal spectral patterns in multiseason 

Landsat imagery have much more spatial detail than available ancillary maps of 

environmental variables, making them more useful when mapping tropical forest tree 

communities with Landsat. These detailed mapping efforts can lead to new views of 

tropical forest landscapes. Here we learned that the xerophytic rain forest of Tobago is 

closely associated with ultramaphic geology, helping to explain its unique physiognomy. 

LITERATURE CITED

Helmer, E.H.; Ruzycki, T.S.; Benner, J.; Voggesser, S.M.; Scobie, B.P.; Park, C.; 

Fanning, D.W.; Ramnarine, S. 2012. Detailed maps of tropical forest types are 

within reach: forest tree communities for Trinidad and Tobago mapped with 

multiseason Landsat and multiseason fine-resolution imagery. Forest Ecology and 

Management. 279: 147-166.

1

 Research Ecologist (EHH), U.S. Forest Service, International Institute of Tropical Forestry, 

1201 Calle Ceiba, Jardín Botánico Sur, Río Piedras, PR 00926; Colorado State University, Fort 

Collins, CO (TSR, JB, and SMV); Republic of Trinidad and Tobago, Trinidad and Tobago Forestry 

Division (BPS, CP, and SR); Fanning Software Consulting, Fort Collins, CO (DWF). EHH is 

corresponding author: to contact, call 787-766-5335 or email at ehelmer@fs.fed.us.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 466

DOTS AND PLOTS

Dennis M. Jacobs and Joseph M. McCollum1

Abstract.—Fixed-radius circles provide fixed-area plots for sampling. An array of dots 

within circles provides secondary sampling points within fixed-area plot circles. The 

hexagonal grid offers an elegant equidistant array of points that will deliver a balanced 

association between the circular area and number of points. A chosen target is 100 points 

within the circular plot and no less than 100, to approximate the mental process that one 

dot is about 1 percent. Equidistant integer spacing provides a minimal 109 hexagonal 

points with one dot on the plot center, but the visual weight is not well balanced along the 

circle perimeter. However, the centers of 102 hexagons can be strategically placed inside 

the circle by using the inscribed and circumscribed radii of hexagons, and will provide 

equal weighting between the dot spacing and each dot’s representative area. Dividing the 

area of the circle by 102 gives the area of each small hexagon. We can then determine the 

non-integer spacing for the equidistant dot grid, which has no point at the center of the 

circular area, but with the first ring of three dots balanced around the plot center placed 

upon the vertex of the three central hexagons. By the use of Cartesian coordinates and the 

Pythagorean Theorem, we present the numerical balance of the points bounding the circle 

perimeter. Solutions of 104 and 100 dots may be obtained by balancing upon the bisector 

of two hexagon centers.

1

 Research Forester (DMJ) and Information Technology Specialist (JMM), U.S. Forest Service, 

Southern Research Station, 4700 Old Kingston Pike, Knoxville, TN 37919; DMJ is corresponding 

author: to contact, call 865-862-2060 or email at djacobs@fs.fed.us. 

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 467

PROGRESSION OF THE INVENTORY AND MONITORING 

OF NONFOREST LANDS WITH TREES

Dacia M. Meneguzzo, Greg C. Liknes, and Charles H. Perry1

Abstract.—Since its inception more than 80 years ago, the Forest Inventory and Analysis 

(FIA) Program has evolved from a timber-based inventory to an enhanced inventory 

that includes all forest land. However, FIA’s definition of forest land requires areas of 

tree cover to be 120 feet wide and 1 acre in size. As a result, small scattered patches and 

linear plantings of trees are excluded from the inventory yet they are of ecological and 

economic importance. In the Great Plains region, it is these types of nonforest lands with 

trees that make up much of the total tree cover. In Nebraska, for example, past inventory 

reports have contained information only about the extent of nonforest tree cover but it 

has not been included consistently and explicit spatial information is lacking. Moving to 

an all-tree inventory would be ideal but ground-based data collection is cost prohibitive. 

Advances in remote sensing offer a promising solution to this problem. Our poster 

presents a timeline of past methodologies and area estimates of nonforest lands with trees 

as well as a new methodology for an image-based inventory of all tree cover using freely 

available, digital aerial photography from the National Agriculture Imagery Program 

(NAIP). Furthermore, the repeat availablity of NAIP imagery will make it possible to 

continuously monitor tree cover in the Great Plains.

1

 Research Forester (DMM), Research Physical Scientist (GCL), and Research Soil Scientist 

(CHP), U.S. Forest Service, Northern Research Station, 1992 Folwell Avenue, St. Paul, MN 

55108. DMM is corresponding author: to contact, call 651-649-5129 or email at 

dmeneguzzo@fs.fed.us. 

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 468

FOREST ATLAS OF THE UNITED STATES

Charles H. Perry, Linda R. Smith, Mary A. Carr, Randy Vreeke, and others1

Abstract.—The United States has a tremendous forest resource—more than 750 million 

acres of native and planted forests managed by public and private landowners for forest 

products, recreation, wilderness, wildlife habitat, and many other purposes. Over the 

past 150 years, basic surveys of United States forests have evolved into a rigorous 

inventory program that is used to share information about the value of these forests 

and the challenges that confront them. More recent technological and methodological 

advancements make it possible to create spatial products (maps) from the inventory 

data and other spatial data, such as digital elevation models and satellite imagery. The 

Forest Atlas of the United States uses these maps to highlight the value of our nation’s 

forest in a graphic and novel manner. In the Forest Atlas of the United States, we explore 

these questions and many more: Where do forests grow? What else lives in forests? 

What shapes forests? What benefits do forests provide? What is in the future for our 

forests? This project represents a strategic partnership between several parts of the Forest 

Service, integrating FIA inventory data with remote sensing and GIS applications. Our 

poster provides a sample of the content that will be included in the forthcoming atlas and 

highlight the use of maps, graphics, accessible text, and images to communicate forest 

monitoring information with the public.

1

 Research Soil Scientist (CHP), U.S. Forest Service, Northern Research Station, 1992 Folwell 

Avenue, St. Paul, MN 55108; Graphic Designer (LRS), U.S. Forest Service, Remote Sensing 

Applications Center, Salt Lake City, UT; Technical Publications Editor (MAC), U.S. Forest 

Service, Olympia, WA; Cartographer (RV), U.S. Forest Service, Geospatial Service and 

Technology Center, Salt Lake City, UT. CHP is corresponding author: to contact, call 

651-649-5191 or email at charleshperry@fs.fed.us.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 469

ASSESSING CHANGES IN VEGETATION COMPOSITION 

AND STRUCTURE: WHAT CAN WE LEARN FROM 500 PLOTS?

Bethany K. Schulz and W. Keith Moser1

Abstract.—Using remeasurement data from more than 500 plots measured by the 

Northern Research Station’s Forest Inventory and Analysis Program, we assess changes 

in vegetation indicator estimates, including species richness, vegetation composition, 

and structure. We highlight changes in the frequency of introduced species at the plot, 

subplot, and quadrat levels. Most introduced species are increasing in constancy, with a 

few exceptions. 

1

 Research Ecologist/Vegetation Indicator Advisor (BKS), U.S. Forest Service, Pacific Northwest 

Research Station, Resource Monitoring and Analysis, 161 East 1st Ave., Anchorage, AK 99501; 

Research Forester (WKM), U.S. Forest Service, Northern Research Station. BKS is corresponding 

author: to contact, call 907-743-9424 or email at bschulz@fs.fed.us.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 470

LOGGING RESIDUE UTILIZATION IN THE STATE OF IDAHO

2008 AND 2011

Eric A. Simmons, Erik C. Berg, Todd A. Morgan, Charles B. Gale, 

Stanley J. Zarnoch, and Steven W. Hayes1

Abstract.—The purpose of this study was to respond to land managers’ need for better 

information on growing-stock removals, utilization of trees, and logging residues as a 

result of harvesting timber. 

A two-stage sampling design was used to select felled trees for measurement within 

active Idaho logging sites in 2008 and 2011. Fifty percent of the harvested trees were 

≤12 inches diameter at breast height (d.b.h.) and accounted for 18 percent of the total 

growing-stock volume removed and 19 percent of the mill-delivered (utilized) volume. 

Trees in this range produced 20 percent of the logging residue. About 49 percent of the 

harvested trees were between 12.1 and 27 inches d.b.h. and accounted for 80 percent of 

the total growing-stock volume removed and 80 percent of the mill-delivered volume. 

Trees in this range produced 78 percent of the logging residue. Removal factors 

quantifying impacts on growing stock revealed that harvesting efforts removed 1,011 

cubic feet of timber volume from growing stock for every thousand cubic feet delivered 

to the mill, with just 24 cubic feet left in the forest as logging residue.

Weight estimates in green tons for the tops and limbs were added to the bole residues to 

obtain a total tree residue factor to be used as a biomass estimation tool. This tool can 

provide forest planners and managers the ability to predict potential feasibility of utilizing 

residues, and to gauge the impact on air quality or fire behavior if the residues burned.

1

 Research Associate (EAS), Research Forester (ECB), Program Director (TAM), and Research 

Assistant (CBG), University of Montana, Forest Industry Research Program, Gallagher Business 

Building, Missoula, MT, 59812; Mathematical Statistician (SJZ), U.S. Forest Service, Southern 

Research Station; Research Forester (SWH), University of Montana. EAS is corresponding author: 

to contact call 406-243-4517 or email at eric.simmons@business.umt.edu. 

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 471

MINING HISTORICAL FIA REPORTS TO DEVELOP ESTIMATES 

OF FOREST LAND THROUGH TIME IN THE NORTH CENTRAL REGION 

OF THE UNITED STATES

Paul A. Sowers1

Abstract.—The Forest Inventory and Analysis (FIA) Program began collecting inventory 

data in the early 1930s. While contemporary data (from approximately the last decade) 

is actively managed in a relational database system and readily accessible with a variety 

of software tools, older data was previously available only in printed reports for many 

parts of the United States. For 11 states in the North Central United States, printed reports 

spanning the 1940s to the 1980s were scanned and made available on demand as a series 

of CD-ROMs. These scanned reports have now been manually converted to data files and 

assembled as county-level, GIS compatible datasets. 

Data from the first annual FIA inventory were acquired using FIA’s online EVALIDator 

tool and combined with the historical data. A series of county-level choropleth maps 

are presented showing forest land area change across the 11 state region. The maps 

are portrayed in a matrix depicting the pairwise changes across the different inventory 

combinations. 

1

 Natural Resource Specialist (PAS), U.S. Forest Service, Northern Research Station, 1992 Folwell 

Avenue, St. Paul, MN 55108. To contact, call 651-649-5101 or email at psowers@fs.fed.us. 

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 472

MAPPING ASPEN IN THE INTERIOR WEST

Charles E. Werstak, Jr.1

Abstract.—Quaking aspen (Populus tremuloides Michx.) is a critical species that 

supports wildlife and livestock, watershed function, the forest products industry, 

landscape diversity, and recreation opportunities in the Interior West (Bartos and 

Campbell 1998). Studies have indicated that changes in fire regimes, an increase in 

herbivore presence in young aspen stands, and recent drought episodes have been 

the main factors for increased mortality rates in aspen stands (Deblander et al. 2010). 

Forest Inventory and Analysis (FIA) plot data are a consistent source of ground-based 

information that if used appropriately, can be extremely valuable for mapping and 

modeling forest attributes such as forest type and canopy cover. GEO-object based 

image analysis, or GEOBIA, is a relatively new subdiscipline of geographic information 

systems (GIS) focused on developing automated techniques for partitioning remotely 

sensed imagery into image objects and accessing them for use in a variety of mapping 

applications (Hay and Castilla 2008). Spatial data mining is an automatic or semiautomatic exploration to identify patterns in data that have a geographic component 

(Shekhar et. al. 2005). Random ForestsTM is an ensemble classifier that uses multiple 

decision trees to predict target variables from input variables (Breiman and Cutler 2003). 

To help understand the current status and extent of quaking aspen across the Interior 

West, efficient and repeatable mapping and modeling techniques need to be further 

established. This investigation aims at exploring viable methods for creating canopy 

cover maps of quaking aspen for several different locations across Utah. FIA plot data 

for inventory years 2000-2009 that correspond to image objects derived from Landsat 

TM imagery will be analyzed along with other ancillary geospatial data using spatial data 

mining and Random ForestsTM. Information gained from this investigation may provide 

further insight into object based segmentation and classification techniques using FIA 

plot data, satellite imagery, and ancillary geospatial data.

LITERATURE CITED

Bartos, D.L.; Campbell, R.B., Jr. 1998. Decline of quaking aspen in the Interior 

West—examples from Utah. Rangelands. 20: 17-24.

Breiman, L.; Cutler, A. 2003. Random forests. Version 4.0. Berkeley, CA: University 

of California at Berkeley. Available at www.stat.berkeley.edu/users/breiman/

RandomForests/. (Accessed September 13, 2012).

DeBlander, L.T.; Shaw, J.D.; Witt, C.; Menlove, J.; Thompson, M.T.; Morgan, T.A.; 

DeRose, R.J.; Amacher, M.C. 2010. Utah’s forest resources, 2000-2005. Resour. 

Bull. RMRS-RB-10. Fort Collins, CO: U.S. Department of Agriculture, Forest 

Service, Rocky Mountain Research Station. 144 p.

1

 Biological Scientist, U.S. Forest Service, Rocky Mountain Research Station, 507 25th Street, 

Ogden, Utah 84401. To contact, call 801-625-5699 or email at cewerstak@fs.fed.us.

(Continued on next page)
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 473

Hay, G.J.; Castilla, G. 2008. Geographic object-based image analysis (GEOBIA): A 

new name for a new discipline? Section 1.4. In: Blaschke, T.; Lang, S.; Hay, G.J., 

eds. Object-based image analysis – spatial concepts for knowledge-driven remote 

sensing applications. New York, NY: Springer-Verlag: 81-92.

Shekhar, S.; Zhang, P.; Huang, Y. Spatial data mining. In: Maimon, O.; Rokach, L. The 

data mining and knowledge discovery handbook. New York, NY: Springer-Verlag: 

833-851.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 474

SUSTAINABILITy OF OAKS IN WEST VIRGINIA

Richard H. Widmann1

Abstract.—There is growing concern for the sustainability of the oak resource in West 

Virginia. A look at the U.S. Forest Service’s Forest Inventory and Analysis data over the 

12 million acres of timberland in West Virginia shows that oak volume has continued to 

increase, but all of this increase has been due to growth on large-diameter trees. High 

mortality in the lower diameter classes and low recruitment has resulted in oaks being 

underrepresented in the lower diameter class. Oak species now represent 46 percent of 

trees more than 20 inches in diameter, but only 7 percent of the trees less than 9.0 inches 

in diameter. In 2- and 4-inch diameter classes, oaks represent 5 and 6 percent of trees in 

these classes, respectively. Because of this disparity, volumes of oak will likely decrease 

across the State as large trees are harvested or die and recruitment into large-diameter 

classes decreases. Loss of this keystone species will affect wildlife populations and 

wood-using industries that now depend on oak.

1

 Resource Analyst, U.S. Forest Service, Northern Research Station, 11 Campus Blvd., Suite 200, 

Newtown Square, PA 19073. To contact, call 610-557-4051 or email at rwidmann@fs.fed.us.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 475

DOES STANDING WATER OR SNOW PACK 

BIAS ASSESSMENTS OF TREE REGENERATION 

WITHIN LARGE-SCALE FOREST INVENTORIES?

Christopher W. Woodall, James A. Westfall, Brian F. Walters, Daniel J. Johnson, and Kai Zhu1

Abstract.—A critical component of large-scale assessments of forest ecosystem 

sustainability and function is that of tree regeneration. As forest inventory measurements 

may occur year round at high latitudes, winter snow banks and subsequent spring floods 

may impede measurement of tree seedlings (<1 inch diameter at breast height [d.b.h.]), 

especially at high latitudes/elevations. Using FIA’s measurements of seedlings across 

eastern states, potential biases of tree seedling measurements as affected by snow 

depth and water obstruction was assessed. It was found that there is a general trend of a 

decrease in average annual seedling density across time as stand density increases across 

the eastern United States―a trend that is potentially exacerbated within plots where there 

is substantial snow/water obstruction (>10 cm) to seedling measurement. Assessments 

of seedling surveys should not be biased if sufficient temporal and spatial scales are 

used relying on the unbiased spatial and temporal allocation of field plot measurement to 

eliminate potential bias. However, seedling assessments may be biased if they occur at 

the plot-level with snow/water present on the plot with the greatest potential bias found 

on plots with no obstruction at time one but with substantial snow/water obstructions at 

time two.

1

 Research Forester (CWW), U.S. Forest Service, Northern Research Station, 1992 Folwell Ave., 

St. Paul, MN 55108; Research Forester (JAW), U.S. Forest Service, Northern Research Station, 

Newtown Square, PA; Forester (BFW), U.S. Forest Service, Northern Research Station, St. Paul, 

MN; Ph.D. candidate (DJJ), Indiana University, Bloomington, IN; Ph.D. candidate (KZ), Duke 

University, Durham, NC. CWW is corresponding author: to contact, call 651-649-5141 or email at 

cwoodall@fs.fed.us.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 476

RATES OF COARSE WOODY DEBRIS BIOMASS LOSS 

AND CARBON DEBT IMPLICATIONS IN EASTERN U.S. FORESTS

Christopher W. Woodall, Matthew B. Russell, Anthony W. D’Amato, Shawn Fraver, and Brian F. Walters1

Abstract.—Emerging questions from bioenergy policy debates have highlighted 

knowledge gaps regarding the carbon and biomass dynamics of individual pieces of 

coarse woody debris (CWD) across the diverse forest ecosystems of the United States. 

Using a subset of CWD pieces remeasured across eastern U.S. forests, the rate of biomass 

loss was estimated over time using decay class transition models coupled with volume 

and wood density loss trajectories. Results indicate that biomass loss is related to the 

genera of the species considered, its size, and location within the broad climatic regions 

of the eastern United States. This biomass loss may be broadly summarized as CWD 

“half-life’s” across the eastern U.S. FIA’s inventory of CWD may provide carbon debt 

policy discussions with objective assessments of CWD biomass/carbon loss.

1

 Research Forester (CWW), U.S. Forest Service, Northern Research Station, 1992 Folwell Ave., 

St. Paul, MN 55108; Post-Doctoral Scientist (MBR) and Associate Professor (AWD), University 

of Minnesota; Research Forester (SF) and Forester (BFW), U.S. Forest Service, Northern 

Research Station. CWW is corresponding author: to contact, call 651-649-5141 or email at 

cwoodall@fs.fed.us. 

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 477

FRAMEWORK FOR ASSESSING CLIMATE CHANGE RISKS 

TO FOREST CARBON STOCKS

Christopher W. Woodall, Grant M. Domke, Karin L. Riley, Christopher M. Oswalt, 

Susan J. Crocker, and Gary W. Yohe1

Abstract.—Efforts to negotiate the role of forest carbon stocks in global efforts to 

mitigate potential climate change effects has highlighted the need to quantify risks to 

forest carbon stocks such as massive disturbance events. As risk may be conceptualized 

around the magnitude of an event and its associated probability, this study examined 

potential changes to forest carbon stocks following major disturbance (e.g., hurricane) 

and proposed a framework for assessing the probability of climate change risks to these 

stocks. Results suggest that a valid framework for conceptualizing risk may be centered 

on the various forest carbon pools (e.g., forest floor and belowground), the variability 

of the associated stocks across large scales, and the magnitude of the stocks themselves. 

Furthermore, given the diversity of the forest pools involved, the nature of massive 

disturbances themselves (e.g., insects versus wildfires) can have divergent effects of 

forest carbon stocks resulting in major research unknowns.

1

 Research Forester (CWW), U.S. Forest Service, Northern Research Station, 1992 Folwell 

Ave., St. Paul, MN 55108; Research Foresters (GMB and SJC), U.S. Forest Service, Northern 

Research Station; Geoscientist (KLR), U.S. Forest Service, Rocky Mountain Research Station; 

Research Forester (CMO), U.S. Forest Service, Southern Research Station; Huffington Foundation 

Professor of Economics and Environmental Studies (GWY), Wesleyan University, Middletown, 

CT. CWW is corresponding author: to contact, call 651-649-5141 or email at cwoodall@fs.fed.us.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Moving from Status to Trends: Forest Inventory and Analysis Symposium 2012 GTR-NRS-P-105 478

BIOMASS AND CARBON ATTRIBUTES OF DOWN WOODY MATERIALS 

ACROSS FORESTS OF THE UNITED STATES

Christopher W. Woodall, Brian F. Walters, Grant M. Domke, Chris Toney, Andrew N. Gray, 

Sonja N. Oswalt, and James E. Smith1

Abstract.—In past decades, down woody material (DWM) has emerged as central 

to wildlife habitat, a controlling factor of forest nutrient cycles, facilitator of tree 

regeneration, a carbon store, and fire hazard. Using the first ever national empirical 

inventory of DWM across forests of the United States, the biomass and carbon attributes 

of DWM were assessed. Results indicated that DWM are ubiquitous in forests; 

however, they are only found in large amounts in certain specific ecosystems subject 

to unique climatic or disturbance attributes (e.g., slow decay or recent tree mortality). 

It is suggested that the national empirical inventory of DWM carbon stocks replace the 

simulated stocks used in past national greenhouse gas inventories.

1

 Research Forester (CWW), U.S. Forest Service, Northern Research Station, 1992 Folwell Ave., 

St. Paul, MN 55108; Forester (BFW), Research Forester (GMB), and Research Physiologist 

(JES), U.S. Forest Service, Northern Research Station; Forester (CT), U.S. Forest Service, Rocky 

Mountain Research Station; Research Forester (ANG), U.S. Forest Service, Pacific Northwest 

Research Station; Forester (SNO), U.S. Forest Service, Southern Research Station. CWW is 

corresponding author: to contact, call 651-649-5141 or email at cwoodall@fs.fed.us.

The content of this paper reflects the views of the author(s), who are 

responsible for the facts and accuracy of the information presented herein.
Printed on recycled paper

Morin, Randall S.; Liknes, Greg C., comps. 2012. Moving from Status to Trends: 

Forest Inventory and Analysis (FIA) Symposium 2012; 2012 December 4-6; 

Baltimore, MD. Gen. Tech. Rep. NRS-P-105. Newtown Square, PA: 

U.S. Department of Agriculture, Forest Service, Northern Research Station. 

[CD-ROM]. 478 p.

These proceedings report invited presentations and contributions to the 2012 

biennial Forest Inventory and Analysis (FIA) Symposium, which was hosted by 

the Research and Development branch of the U.S. Forest Service. As the only 

comprehensive and continuous census of the forests in the United States, FIA 

provides strategic information needed to evaluate sustainability of current forest 

management practices across all ownerships. Papers and abstracts included in the 

publication have been sorted into topic areas that match the sessions presented 

during the meeting. Symposium papers cover high priority and timely issue-based 

topics including climate change, wildlife, fire, bioenergy, geo-spatial extensions, 

monitoring over time, integrating remote sensing and GIS applications, statistical 

and related quantitative solutions to emerging needs, and many others.

Key words: statistics, estimation, sampling, modeling, remote sensing, 

 forest health, data integrity, environmental monitoring, 

 cover estimation, international forest monitoring

The U.S. Department of Agriculture (USDA) prohibits discrimination in all its programs and activities 

on the basis of race, color, national origin, age, disability, and where applicable, sex, marital status, 

familial status, parental status, religion, sexual orientation, genetic information, political beliefs, reprisal, 

or because all or part of an individual’s income is derived from any public assistance program. (Not 

all prohibited bases apply to all programs.) Persons with disabilities who require alternate means for 

communication of program information (Braille, large print, audiotape, etc.) should contact USDA’s 

TARGET Center at 202-720-2600 (voice and TDD). To file a complaint of discrimination, write to USDA, 

Director, Office of Civil Rights, 1400 Independence Avenue, S.W., Washington, DC 20250-9410, or call 

800-795-3272 (voice) or 202-720-6382 (TDD). USDA is an equal opportunity provider and employer.

Northern Research Station

www.nrs.fs.fed.us