United States

Department

of Agriculture

Forest Service

Rocky Mountain

Research Station

General Technical

Report RMRS-GTR-175

September 2006

The LANDFIRE 

Prototype Project: 

Nationally Consistent and Locally 

Relevant Geospatial Data for 

Wildland Fire Management
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

Abstract _______________________________________________

The Landscape Fire and Resource Management Planning Tools Prototype Project, or LANDFIRE Prototype 

Project, began in April of 2002 and ended in April of 2005. The project was funded by the U.S. Department 

of Agriculture Forest Service and U.S. Department of the Interior. The objectives of the LANDFIRE Prototype 

Project were to develop the methods, tools, and protocols for producing consistent and comprehensive digital 

maps of current vegetation composition and structure, wildland fuel, historical fire regimes, and fire regime 

condition class (FRCC) to be applied across the entire United States at a 30-meter spatial resolution. The 

LANDFIRE Prototype Project was conducted in two large study areas: the first in the highlands of central Utah 

and the second in the northern Rocky Mountains of Idaho and Montana. The LANDFIRE Prototype Project 

involved the compilation of a large field-referenced database to serve as training data for developing predictive landscape models; the development of Landsat image catalogs and biophysical gradient layers to serve 

as spatial predictors for mapping vegetation and wildland fuel characteristics; the development of vegetation 

and fuel map unit classifications; the development of a suite of vegetation dynamics models for simulating 

vegetation development over time; the implementation of a landscape succession model (LANDSUMv4) for 

simulating historical fire regimes and vegetation reference conditions; and the development of maps of surface 

and canopy fuel and fire effects fuel models for application in wildland fire management planning. This report 

describes the scientific foundations of LANDFIRE and provides details on the methods and results of the 

LANDFIRE Prototype Project.

Key words and phrases: Mapping wildland fuel, Mapping fire regimes, Geographic Information Systems, 

Remote sensing, Fire ecology, Fire behavior, Wildland fire management

You may order additional copies of this publication by sending your 

mailing information in label form through one of the following media. 

Please specify the publication title and number.

Telephone (970) 498-1392

FAX (970) 498-1122

E-mail rschneider@fs.fed.us

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

Mailing Address Publications Distribution

Rocky Mountain Research Station

240 West Prospect Road

Fort Collins, CO 80526

Rollins, Matthew G.; Frame, Christine K., tech. eds. 2006. The LANDFIRE Prototype Project: nationally 

consistent and locally relevant geospatial data for wildland fire management. Gen. Tech. Rep. 

RMRS-GTR-175. Fort Collins: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research 

Station. 416 p.
Contents_______________________________________

 Page

Chapter 1 .

Executive Summary

Chapter 2 .

An Overview of the LANDFIRE Prototype Project

Matthew G. Rollins, Robert E. Keane, Zhiliang Zhu, and 

James P. Menakis

Chapter 3 . 

The Scientific Foundation of the LANDFIRE Prototype Project

Robert E. Keane and Matthew Rollins

Chapter 4 . 

The LANDFIRE Prototype Project Reference Database

John F. Caratti

Chapter 5 . 

Development of Biophysical Gradient Layers for the LANDFIRE

 Prototype Project

Lisa Holsinger, Robert E. Keane, Russell Parsons, and Eva Karau

Chapter 6 . .

Developing the LANDFIRE Vegetation and Biophysical Settings Map 

 Unit Classifications for the LANDFIRE Prototype Project

Jennifer L. Long, Melanie Miller, James P. Menakis, and 

Robert E. Keane

Chapter 7 . .

Mapping Potential Vegetation Type for the LANDFIRE Prototype Project

Tracey S. Frescino and Matthew G. Rollins

Chapter 8 . .

Mapping Existing Vegetation Composition and Structure for the 

 LANDFIRE Prototype Project

Zhiliang Zhu, James Vogelmann, Donald Ohlen, Jay Kost, 

Xuexia Chen, and Brian Tolk

Chapter 9 . .

Vegetation Succession Modeling for the LANDFIRE Prototype Project

Donald Long, B. John (Jack) Losensky, and Donald Bedunah

Chapter 10 . .

Using Simulation Modeling to Assess Historical Reference Conditions 

 for Vegetation and Fire Regimes for the LANDFIRE Prototype Project

Sarah Pratt, Lisa Holsinger, and Robert E. Keane
Federal Recycling Program Printed on Recycled Paper

Chapter 11 . .

Using Historical Simulations of Vegetation to Assess Departure of

 Current Vegetation Conditions across Large Landscapes

Lisa Holsinger, Robert E. Keane, Brian Steele, Matthew C. Reeves,

and Sarah Pratt

Chapter 12 . .

Mapping Wildland Fuel across Large Regions for the LANDFIRE 

 Prototype Project

Robert E. Keane, Tracey Frescino, Matthew C. Reeves, and 

Jennifer L. Long

Chapter 13 . .

Perspectives on LANDFIRE Prototype Project Accuracy Assessment

James Vogelmann, Zhiliang Zhu, Jay Kost, Brian Tolk, and

Donald Ohlen

Chapter 14 . .

Dissemination of LANDFIRE Prototype Project Data

Jeff Eidenshink
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 

Chapter 1—Executive Summar

Chapter

1

Executive Summary

Matthew G. Rollins, Robert E. Keane, and Zhiliang Zhu

In: Rollins, M.G.; Frame, C.K., tech. eds. 2006. The LANDFIRE 

Prototype Project: nationally consistent and locally relevant 

geospatial data for wildland fire management. Gen. Tech. Rep. 

RMRS-GTR-175. Fort Collins: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research Station.

Geospatial data describing wildland fuel and current 

as well as historical vegetation conditions are essential 

for planning, implementing, and monitoring projects supported by the National Fire Plan and the Healthy Forests 

Restoration Act. Scientifically credible, consistent, and 

standardized spatial data allow fire and land managers 

to accurately identify the amount and locations of lands 

or communities with hazardous fuel build-up or extreme 

departure from historical conditions. These data also 

facilitate the prioritization of ecosystem restoration and 

hazardous fuel reduction treatments to protect ecosystems, property, and people. Moreover, these data may be 

used during specific wildland fire incidents to maximize 

firefighter safety, pre-position resources, and evaluate 

fire behavior under a variety of weather conditions.

The Landscape Fire and Resource Management Planning Tools Prototype Project, or “LANDFIRE Prototype 

Project,” was a three-year project that began April 1st, 

2002 and ended April 1st, 2005. The project was funded 

by the United States Department of Agriculture Forest 

Service and United States Department of the Interior, 

with an annual cost of approximately $2 million. The 

objectives of the LANDFIRE Prototype Project were 

to develop the methods, tools, and protocols for producing consistent and comprehensive digital maps of 

current vegetation composition and condition, wildland 

fuel, historical fire regimes, and fire regime condition 

class (FRCC) for the entire U.S. at a 30-meter spatial 

resolution. The LANDFIRE Prototype Project was conducted in two large study areas: the first in the central 

Utah highlands and the second in the northern Rocky 

Mountains of Idaho and Montana.

The LANDFIRE Prototype Project involved various 

government agencies, universities, and private institutions. The two principal partners in the effort were the 

U.S. Department of Agriculture Forest Service, Rocky 

Mountain Research Station, Missoula Fire Sciences 

Laboratory (MFSL) in Missoula, Montana and the U.S. 

Department of the Interior Geological Survey, USGS 

Center for Earth Resources Observation and Science 

(EROS) in Sioux Falls, South Dakota. Additional partners included the University of Montana (Missoula) 

Numeric Terradynamic Simulation Group (NTSG) 

and Systems for Environmental Management (SEM), 

also in Missoula, Montana. MFSL mapped biophysical settings, wildland fuel, historical fire regimes, and 

historical vegetation composition and structure. MSFL 

also conducted most ecosystem and landscape modeling. EROS mapped existing vegetation composition 

and structure; developed a quality assurance, quality 

control, and accuracy assessment system; and developed 

the LANDFIRE data-dissemination system. NTSG developed the daily weather DAYMET database used as 

a foundation for mapping biophysical gradient layers, 

and SEM created the LANDFIRE reference database 

and provided valuable expertise on the classification, 

mapping, and modeling of vegetation and fuel.

As mentioned above, LANDFIRE methods were 

tested in the central Utah highlands and revised based 

on problems and limitations encountered. These revised methods were then tested on the second study 

area in the northern Rocky Mountains of Montana and 

Idaho. In April 2004, one year prior to completion of 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 200

Chapter 1—Executive Summar

the LANDFIRE Prototype Project, the Wildland Fire 

Leadership Council chartered the national implementation of the LANDFIRE Project (LANDFIRE National). 

It is important to emphasize here that the information 

presented in this report refers specifically to methods, 

procedures, and results from the LANDFIRE Prototype Project. LANDFIRE National is an ongoing effort 

involving most of the original collaborators and with 

the addition of The Nature Conservancy, NatureServe, 

and the Student Conservation Association to assist in 

vegetation modeling, vegetation map unit classification 

development, and field data collection, respectively. 

Lessons learned during the prototype process provide 

the foundation for the current mapping of vegetation, 

fuel, and fire regimes across the United States through 

LANDFIRE National. Visit www.landfire.gov for a detailed description of LANDFIRE National and to learn 

about and access the various LANDFIRE National data 

products.

The design criteria for the LANDFIRE Prototype 

Project resulted from several organizational meetings 

between the LANDFIRE team and USDA and USDOI 

fire management staffs. Agency fire management imposed abbreviated timelines and solid deadlines, required 

that LANDFIRE methodology be based on a strong 

scientific foundation, and required that the products be 

scalable to local applications with minimal modification. 

These conditions contributed to the following design 

criteria:

The LANDFIRE Prototype Project must be:

•	 based on the best available science,

•	 able to be implemented consistently across the 

nation,

•	 mapped at a 30-meter pixel size,

•	 reliant on no new field data collection,

•	 repeatable in time and space,

•	 scalable in application,

•	 developed within a three-year development timeline, 

and

•	 tested for accuracy.

Given these design specifications, we then developed 

a set of guiding principles that we used to direct the 

development of all LANDFIRE data layers. These principles allowed us to set boundaries for the development 

of every LANDFIRE product. These guidelines were 

as follows:

•	 Development should be targeted at mid-level map 

classifications.

•	 All themes must be identifiable, scalable, mappable, 

and model-able.

•	 Mapping applications must incorporate the biophysical gradients that determine the distribution 

of vegetation and disturbance regimes across 

landscapes.

•	 The primary development tool should be simulation 

modeling.

•	 The timespan for historical reference conditions 

would be 1600 AD to 1900 AD.

Many databases and computer models were developed 

to create the geospatial data layers describing vegetation 

and wildland fuel for the LANDFIRE Prototype Project 

to demonstrate that the methods and protocols could be 

applied nationwide. These products can be summarized 

by the following categories:

•	 Databases: LANDFIRE reference database, wildland fuel inventory database, vegetation development database, and map attribute tables

•	 Computer models: LANDFIRE-BGC, WXFIRE, 

LANDSUMv4, and HRVStat

•	 Biophysical gradient maps: 38 variables describing 

direct and indirect gradients affecting the distribution of vegetation and historical fire regimes

•	 Vegetation maps: existing vegetation, potential 

vegetation, canopy height, and canopy cover

•	 Wildland fuel maps: fire behavior fuel models, 

fire effects fuel models, and canopy fuel

•	 Fire regime maps: simulated historical fire return 

interval and severity, fire regime condition class, and 

indices of departure from historical conditions

The 24 core LANDFIRE Prototype Project spatial data 

layer products comprise a comprehensive set of data that 

may be implemented in broad- or mid-level prioritization 

of hazardous fuel mitigation efforts or in project-level 

planning of specific land and wildland fire management 

projects. See Chapter 2, table 1 for a detailed list of the 

LANDFIRE Prototype Project products.

The primary goal of the LANDFIRE Prototype Project 

was to develop a high-resolution map of FRCC for the 

entire nation. The computation of FRCC requires a comparison of current conditions with historical conditions 

to quantify departure. Current conditions are described 

using maps of vegetation composition (cover types) and 

structure (structural stages) derived from a supervised 

classification of Landsat imagery using continuous maps 

of biophysical gradients and an extensive collection of 

geo-referenced plot data called the LANDFIRE reference 

database. Historical conditions were described using a 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 

Chapter 1—Executive Summar

spatially explicit landscape fire succession model called 

LANDSUMv4. Vegetation composition and structure 

were described using a cover type and structural stage 

classification developed specifically for LANDFIRE. 

The biophysical gradient layers were created from two 

ecosystem simulation models that compute variables describing the fundamental physical processes that govern 

vegetation dynamics. These models used the DAYMET 

national weather database, which has a 1-km2 spatial 

resolution and daily temporal resolution spanning 18 

years. Biophysical gradient layers created from these 

models were also used to construct a data layer describing biophysical settings (potential vegetation types) that 

was then used to facilitate Landsat imagery classification 

and as an input layer for simulating historical reference 

conditions.

Historical reference conditions were simulated over a 

timespan of thousands of years using a spatially explicit 

landscape fire succession model, called LANDSUMv4, 

which simulates vegetation development using deterministic succession pathways for different biophysical 

settings and simulates fire using a pixel-to-pixel cell 

percolation or spread approach. The time series of simulated historical vegetation conditions was compared with 

imagery-derived current conditions using the area occupied by biophysical setting/cover type/structural stage 

combinations within square (0.81 km2) spatial reporting 

units. Fire regime condition class was mapped using 

methods consistent with the Interagency Fire Regime 

Condition Class Guidebook. In addition, we created a 

statistical program called HRVStat that computes another, more statistically robust index of departure with 

a corresponding measure of statistical significance. Fire 

severity and fire return interval information simulated 

over time was summarized over the entire simulation 

period to create simulated historical fire regime maps.

Several additional data layers were developed as part of 

the LANDFIRE Prototype Project to provide critical information to wildland fire management for planning and 

implementing hazardous fuel mitigation and ecosystem 

restoration projects. We developed a refined set of fire 

behavior fuel models specifically for LANDFIRE that 

was better suited for distinguishing subtle differences in 

fuel characteristics resulting from fuel treatments, and 

we mapped the original 13 fuel models in addition to the 

refined set based on an expert system and an integrated 

vegetation database describing cover type, structural 

stage, and biophysical settings. Maps of canopy fuel, 

including canopy height, canopy cover, canopy bulk 

density, and canopy base height, were mapped based on 

statistical landscape modeling (specifically, regression 

trees) using Landsat imagery and the biophysical gradient layers. Fire effects fuel models were developed and 

mapped using the same approach as that used for fire 

behavior fuel model mapping. Maps of fire behavior fuel 

models and canopy fuel can be used in real-time wildfire 

applications, such as modeling fire growth or quantifying fire and fuel hazard using the suite of available fire 

behavior models, which include FARSITE, FlamMap, 

and NEXUS. Fire effects fuel models represent actual 

estimations of loading by fuel category for fire effects 

applications, such as estimating fuel consumption and 

smoke production using CONSUME or FOFEM.

The main strengths of the LANDFIRE Prototype 

Project included:

•	 a standardized, open source, repeatable method for 

developing consistent and comprehensive maps for 

use in wildland fire management;

•	 use of existing databases from government and 

non-government sources;

•	 the combination of remote sensing, ecosystem 

simulation, and biophysical gradient modeling to 

accurately map vegetation, fuel and fire regimes;

•	 a robust, straightforward, biophysically-driven 

statistical approach;

•	 a quantitative accuracy assessment;

•	 the automation of individual LANDIFRE tasks and 

processing steps;

•	 comprehensive coverage across all administrative 

boundaries and ownerships; and

•	 a seamless, Internet-based database dissemination 

tool.

The comprehensive, consistent, and automated methods developed through the LANDFIRE Prototype 

Project complement an integrated approach to wildland 

fire management and facilitate comparison of potential 

treatment areas using equivalent databases across the 

entire United States. The LANDFIRE system does 

not preclude the use of locally developed fire and fuel 

information in planning and management activities.

Please refer to the individual chapters of this report 

for a detailed description of each aspect of the LANDFIRE Prototype Project, information on the scientific 

background, and specific details regarding the individual procedures for creating the LANDFIRE data 

products.

For further project information, please visit the LANDFIRE website at www.landfire.gov.
 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 200

Chapter 1—Executive Summar

The Authors ____________________

Matthew G. Rollins is a Landscape Fire Ecologist at 

the USDA Forest Service, Rocky Mountain Research 

Station, Missoula Fire Sciences Laboratory (MFSL).

His research emphases have included assessing changes 

in fire and landscape patterns under different wildland 

fire management scenarios in large western wilderness 

areas; relating fire regimes to landscape-scale biophysical 

gradients and climate variability; and developing predictive landscape models of fire frequency, fire effects, 

and fuel characteristics. Rollins is currently science lead 

for the LANDFIRE Project, a national interagency fire 

ecology and fuel assessment being conducted at MFSL 

and the USGS Center for Earth Resources Observation 

and Science (EROS) in Sioux Falls, South Dakota. He 

earned a B.S. in Wildlife Biology in 1993 and an M.S. 

in Forestry in 1995 from the University of Montana 

in Missoula, Montana. His Ph.D. was awarded by the 

University of Arizona in 2000, where he worked at the 

Laboratory of Tree-Ring Research.

Robert E. Keane is a Research Ecologist with the USDA 

Forest Service, Rocky Mountain Research Station, Missoula Fire Sciences Laboratory. Since 1985, Keane has 

developed various ecological computer models for the 

Fire Effects Project for research and management applications. His most recent research includes the development of a first-order fire effects model, construction 

of mechanistic ecosystem process models that integrate 

fire behavior and fire effects into succession simulation, 

restoration of whitebark pine in the Northern Rocky 

Mountains, spatial simulation of successional communities on landscapes using GIS and satellite imagery, 

and the mapping of fuel for fire behavior prediction. 

He received his B.S. degree in Forest Engineering in 

1978 from the University of Maine, Orono, his M.S. 

degree in Forest Ecology in 1985 from the University 

of Montana, and his Ph.D. degree in Forest Ecology in 

1994 from the University of Idaho.

Zhiliang Zhu is a Research Physical Scientist with the 

USDOI Geological Survey Center for Earth Resource 

Observation and Science (EROS). Zhu’s research work 

focuses on mapping and characterizing large-area land 

and vegetation cover, studying land cover and land use 

change, and developing remote sensing methods for the 

characterization of fire fuel and burn severity. His role 

in the LANDFIRE Prototype Project has been to design 

and test a methodology for the mapping of existing 

vegetation cover types and vegetation structure and to 

direct research and problem-solving for all aspects of the 

methodology. He received his B.S. degree in Forestry in 

1982 from the Nanjing Forestry University in China, his 

M.S. degree in Remote Sensing in 1985, and his Ph.D. 

degree in Natural Resources Management in 1989, both 

from the University of Michigan.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 200

Chapter 2—An Overview of the LANDFIRE Prototype Project

Chapter

2

In: Rollins, M.G.; Frame, C.K., tech. eds. 2006. The LANDFIRE 

Prototype Project: nationally consistent and locally relevant 

geospatial data for wildland fire management. Gen. Tech. Rep. 

RMRS-GTR-175. Fort Collins: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research Station.

Introduction ____________________

This chapter describes the background and design of 

the Landscape Fire and Resource Management Planning Tools Prototype Project, or LANDFIRE Prototype 

Project, which was a sub-regional, proof-of-concept 

effort designed to develop methods and applications for 

providing the high-resolution data (30-m pixel) needed 

to support wildland fire management and to implement 

the National Fire Plan and Healthy Forests Restoration 

Act. In addition, this chapter provides synopses of the 

many interrelated procedures necessary for development 

of the 24 LANDFIRE Prototype products (see table 1 

and appendix 2-A). Throughout this chapter, direction 

is provided for where, in this report and elsewhere, additional detailed information is available.

It is important to emphasize that the information 

presented in this report refers specifically to methods, 

procedures, and results from the LANDFIRE Prototype Project. National implementation of the methods 

developed during the LANDFIRE Prototype Project 

An Overview of the LANDFIRE 

Prototype Project

Matthew G. Rollins, Robert E. Keane, Zhiliang Zhu, and James P. Menakis

(LANDFIRE National) was chartered by the Wildland 

Fire Leadership Council in April of 2004 and continues 

on schedule. Approaches and methods for the national 

implementation of LANDFIRE differ slightly from 

those detailed in this report because the LANDFIRE 

National team used the wealth of knowledge gained from 

developing the LANDFIRE products for the two prototype study areas to improve the processes for national 

implementation. With the exception of the first three 

chapters, each chapter in this report describes in detail 

a major procedure required for successful creation of 

the LANDFIRE Prototype products. The final section of 

each chapter (again, with the exception of the first three 

chapters) contains the LANDFIRE Prototype technical 

team’s recommendations for national implementation, 

which have been incorporated into the procedures and 

methods for LANDIFRE National.

Background ____________________

Status of Wildland Fire in the United 

States

A history of fire suppression and land use practices 

has altered fire regimes and associated wildland fuel 

loading, landscape composition, structure, and function 

across the United States over the last century (Brown 

1995; Covington and others 1994; Frost 1998; Hann and 

others 2003; Leenhouts 1998; Pyne 1982; Rollins and 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 200

Chapter 2—An Overview of the LANDFIRE Prototype Project

Table 1—LANDFIRE Prototype products created for mapping zones 16 and 1

Relevant chapter

Mapped products Description in this report

 1 Map Attribute Tables Plot locations and associated themes used to develop training sites f

mapping vegetation and wildland fuel.

	 2	 Potential Vegetation	 A map that identifies unique biophysical settings across landscapes.	 7

 Type Used to link the process of succession to the landscape.

 3 Existing Vegetation Mapped existing vegetation

 4 Structural Stage Percent canopy closure by life form of existing vegetation

 Density/Cover

5 Structural Stage Height Height by life form (in meters) of existing vegetation. 8

	 6	 Fire Interval	 The mean interval between wildland fires (simulated using LANDSUMv4).	 10

	 7	 Fire Severity 1	 Probability of an area to experience non-lethal wildland fire (simulated using	 10

LANDSUMv4)

	 8	 Fire Severity 2	 Probability of an area to experience mixed-severity wildland fire (simulated	 10

using LANDSUMv4)

	 9	 Fire Severity 3	 Probability of an area to experience stand-replacing wildland fire (simulated	 10

using LANDSUMv4)

 10 FRCC Vegetation-Fuel Mapped combinations of vegetation and structure that represent sera

 Class stages of vegetation communities.

 11 Fire Regime Condition Discrete index (1-3) describing departure of existing veget

 Class (FRCC) conditions from those of the simulated historical reference period.

 Developed using Interagency FRCC Guidebook methods.

 12 FRCC Departure Index Index (1-100) describing the difference between existing vegetat

and historical vegetation conditions. Developed using Interagency 

FRCC guidebook methods.

 13 HRVStat Departure Index (0-1) describing the difference between existing vegetation

simulated historical vegetation conditions. Developed using the HRVStat 

multivariate time series analysis program.

	 14	 HRVStat Significance	 Index (0-1) describing the confidence in the HRVStat departure index.	 11

 Index Developed using the HRVStat multivariate time series analysis program.

 15 Fire Behavior Fuel Wildland fuel models for modeling the rate of spread, intensity, size, and

	 	 Models	 shape of wildland fires. Serves as FARSITE/FLAMMAP input.

	 16	 Canopy Base Height	 Height from the ground to the bottom of the vegetation canopy. Required	 12

for predicting the conversion of surface fires to crown fires. Serves as

FARSITE/FLAMMAP input.

	 17	 Canopy Bulk Density	 Metric that describes the density of crown fuels. Required to model the	 12

spread of crown fires. Serves as FARSITE/FLAMMAP input.

 18 Canopy Height Height of the dominant existing vegetation. Serves as FARSITE/FLAMMAP input.

 19 Canopy Cover Density of the dominant existing vegetation. Serves as FARSITE/FLAMMAP input.

20 Slope Slope in percent. Serves as FARSITE/FLAMMAP input. 5

21 Aspect Aspect in degrees. Serves as FARSITE/FLAMMAP input.

22 Elevation Elevation in meters. Serves as FARSITE/FLAMMAP input. 5

	 23	 Fuel Loading Models	 Classification based on fuel loading that provides inputs to models that predict	 12

the effects of wildland fires (including smoke production).

	 24	 FCCS Fuelbeds	 Classification based on fuel loading across several fuel strata. Provides inputs	 12

to models that predict fire effects (including smoke production).	
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 200

Chapter 2—An Overview of the LANDFIRE Prototype Project

others 2001). As a result, the number, size, and severity of wildfires have departed significantly from that 

of historical conditions, sometimes with catastrophic 

consequences (Allen and others 1998; Leenhouts 1998; 

U.S. GAO 1999; U.S. GAO 2002b). Recent examples 

of increasing wildland fire size and uncharacteristic 

severity in the United States include the 2000 Cerro 

Grande fire near Los Alamos, New Mexico that burned 

19,200 hectares and 239 homes; the 2000 fire season 

in the northwestern United States during which over 2 

million hectares burned; and the 2002 Biscuit (Oregon), 

Rodeo-Chediski (Arizona), and Hayman (Colorado) 

fires that burned over one-half million hectares and 

cost nearly $250 million to suppress (U.S. GAO 2002b). 

More recently, the 2003 fire season was distinguished 

by catastrophic wildland fires that began in early summer with the Aspen Fire north of Tucson, Arizona in 

which 322 homes were burned. This was followed by 

large, severe fires in the northern Rocky Mountains of 

western Montana and northern Idaho and arson-caused 

wildland fires that burned over 304,000 hectares and 

3,640 homes in southern California.

The National Fire Plan and Healthy 

Forests Restoration Act

In response to increasing severity of wildland fire 

effects across the United States over the last decade, 

the secretaries of Agriculture and Interior developed a 

National Fire Plan for responding to severe wildland fires, 

reducing hazardous fuel buildup, reducing wildland fire 

threats to rural communities, and maximizing wildland 

firefighting efficiency and safety for the future (USDA 

and USDOI 2001; U.S. GAO 2001; U.S. GAO 2002a; 

U.S. GAO 2002b; www.fireplan.org). To implement this 

plan, the United States Department of Agriculture Forest 

Service (USFS) and Department of Interior (USDOI) 

developed both independent as well as interagency 

management strategies, with the primary objectives 

focused on hazardous fuel reduction and restoration of 

ecosystem integrity in fire-adapted landscapes through 

prioritization, adaptive planning, land management, 

and maintenance (USDA and USDOI 2001). In 2003, 

President George W. Bush signed the Healthy Forests 

Restoration Act into law. The main goals of the act are 

to reduce the threat of destructive wildfires while upholding environmental standards and encouraging early 

public input during review and planning processes for 

forest management projects (www.healthyforests.gov).

Importance of Nationally Consistent 

Spatial Data for Wildland Fire Management

The factors that affect wildland fire behavior and 

effects are inherently complex, being dynamic in both 

space and time. The likelihood that a particular area of 

a landscape will burn is often unrelated to the probability that a wildland fire will ignite in that area because 

wildland fires most often spread into one area based on 

the complex spatial arrangement and condition of fuel 

across landscapes. Spatial contagion in the process of 

wildland fire highlights the critical need for data that 

provide a comprehensive spatial context for planning and 

monitoring wildland fire management and hazardous 

fuel reduction projects. Furthermore, nationwide, comprehensive, consistent, and accurate geospatial data are 

critical for implementation of the National Fire Plan and 

the Healthy Forests Restoration Act (U.S. GAO 2002a; 

U.S. GAO 2002b; U.S. GAO 2003; U.S. GAO 2005). 

Specifically, consistent and comprehensive geospatial 

data are necessary for the following:

•	 planning wildland fire management with a landscape 

perspective,

•	 allocating resources across administrative 

boundaries,

•	 strategic planning for hazardous fuel reduction,

•	 tactical planning for specific wildland fire incidents, 

and

•	 monitoring the geographic consequences of wildland fire management.

The LANDFIRE process provides standardized, 

comprehensive mapped wildland fuel and fire regime 

information to address the objectives listed above. 

LANDFIRE maps were created using consistent methods 

over all ecosystems and geographic areas, which allows 

for reliable representation of “wall-to-wall” wildland 

fire hazard across entire regions and administrative 

areas. The spatial components of LANDFIRE ensure 

that individual areas within landscapes may be considered with a spatial context and therefore analyses can 

incorporate the potential influence of adjacent areas 

where wildland fires may occur more frequently or with 

different ecological or socioeconomic effects.

History of LANDFIRE ____________

In 2000, the United States Department of Agriculture 

Forest Service Missoula Fire Sciences Laboratory developed coarse-scale (1-km grid cells) nationwide maps of 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 200

Chapter 2—An Overview of the LANDFIRE Prototype Project

simulated historical fire regimes and current departure 

from these historical conditions (Hardy and others 2001; 

Schmidt and others 2002). These data were designed to 

assist landscape and wildland fire management at national 

levels (for example, 10,000,000s – 100,000,000s km2) 

and to facilitate comparison of wildland fire hazard 

between regions and states (Schmidt and others 2002). 

These coarse-scale, nationwide data layers include 

mapped potential natural vegetation groups, existing 

vegetation, historical fire regimes, departure from historical fire regimes, fire regime condition class (FRCC), 

national fire occurrence histories, and wildland fire risk 

to structures (Schmidt and others 2002). These data 

layers rapidly became the foundation for national-level, 

strategic wildland fire planning and for responding to 

national- and state-level concerns regarding the risk of 

catastrophic fire. Specifically, FRCC became a key metric 

for assessing fire threats to both people and ecosystems 

across the United States (U.S. GAO 2004).

While well-accepted and valuable for comparative 

analyses at the national level, the coarse-scale FRCC 

data lacked the necessary spatial resolution and detail 

for regional planning and for prioritization and guidance 

of specific local projects. In addition, the coarse-scale 

FRCC maps relied heavily on expert opinion, which led 

to inconsistent classification of vegetation across regional 

boundaries. Further, the low resolution and scale incompatibilities in underlying data resulted in overestimates 

of the number of areas with highly departed conditions 

(Aplet and Wilmer 2003).

As a result of the coarse-scale FRCC data’s shortcomings, U.S. Government Accountability Office (formerly 

General Accounting Office) reports stated that federal 

land management agencies lacked adequate information 

for making decisions about and measuring progress 

in hazardous fuel reduction (U.S. GAO 2002b). The 

U.S. GAO (2002b) stated, “The infusion of hundreds 

of millions of dollars of new money for hazardous fuel 

reduction activities for fiscal years 2001 and 2002 and 

the expectation of sustained similar funding for these 

activities in future fiscal years accentuate the need for 

accurate, complete, and comparable data.” United States 

GAO reports (U.S. GAO 1999, U.S. GAO 2002a, U.S. 

GAO 2002b, U.S. GAO 2003) have pointed to three 

main information gaps in wildland fire management 

planning:

•	 Federal land agencies lack information for identifying and prioritizing wildland-urban interface 

communities within the vicinity of federal lands 

that are at high risk of wildland fires.

•	 Federal land agencies lack adequate field-based 

reference data for expediting the project planning 

process, which requires complying with numerous 

environmental statutes that address individual resources, such as endangered and threatened species, 

clean water, and clean air.

•	 Federal agencies require consistent monitoring 

approaches for measuring the effectiveness of efforts to dispose of the large amount of brush, small 

trees, and other vegetation that must be removed to 

reduce the risk of severe wildland fire.

The LANDFIRE Prototype 

Project ________________________

The LANDFIRE Prototype Project started in 2001 

and was funded by the United States Department of 

Agriculture Forest Service and Department of the Interior, with an annual cost of approximately $2 million. 

The project’s purpose was to develop methods and tools 

for creating the baseline data needed to implement the 

National Fire Plan and to address the concerns of the 

GAO. LANDFIRE was designed specifically to provide 

the spatial data required to implement the National Fire 

Plan at regional levels and to fill critical knowledge gaps 

in wildland fire management planning. To achieve these 

objectives, LANDFIRE integrates information from 

extensive field-referenced databases, remote sensing, 

ecosystem simulation, and biophysical modeling to create 

maps of wildland fuel and fire regime condition class 

across the United States (Rollins and others 2004).

The main strengths of the LANDFIRE Prototype 

Project approach included:

•	 a standardized, repeatable method for developing 

comprehensive fuel and fire regime maps (see appendix 2-A for an outline of the procedures followed 

in developing the data products of the LANDFIRE 

Prototype Project);

•	 a combination of remote sensing, ecosystem simulation, and biophysical gradient modeling to map 

fuel and fire regimes;

•	 a robust, straightforward, statistical framework and 

quantitative accuracy assessment; and

•	 a seamless, Internet-based data-dissemination 

system.

In addition to the strengths of the approach, the main 

strengths of the LANDFIRE Prototype data included:

•	 a resolution fine enough (30-m pixel) for wildland 

fire managers to evaluate and prioritize specific 

landscapes within their administrative units;
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 200

Chapter 2—An Overview of the LANDFIRE Prototype Project

•	 national coverage, ensuring that the data may be 

used for regional and national applications;

•	 comprehensive and consistent methods, allowing 

for both an integrated approach to wildland fire 

management and the ability to compare potential 

treatment areas across the entire United States 

through equivalent databases; and

•	 the ability to monitor the efficacy of hazardous 

fuel treatments as LANDFIRE updates become 

available over time.

Study Areas

The LANDFIRE Prototype Project was implemented 

within two large areas in the western United States: the 

central Utah highlands and the northern Rocky Mountains (fig. 1). These prototype landscapes were chosen 

because each represents a wide variety of vegetation 

assemblages that are common in the western U.S. They 

were chosen also because pre-processing of the Landsat 

satellite imagery had already been completed as part 

of the USGS Multi-Resolution Land Characteristics 

(MRLC) 2001 project (Homer and others 2002; landcover.usgs.gov/index.asp).

As the LANDFIRE Prototype depended on imagery 

solely from the MRLC 2001 project, LANDFIRE adopted the use of MRLC mapping zones to divide the 

United States into workable spatial areas. Use of these 

delineation units ensured that the LANDFIRE timetable requirements would be met by the MRLC image 

processing schedule.

Central Utah Highlands mapping zone — The 

69,907 km2 Central Utah Highlands mapping zone begins 

at the northern tip of the Wasatch Mountains in southern 

Idaho and extends through central Utah to the southern 

border of the state (fig. 1). Elevations range from 980 m 

to 3,750 m. Vegetation communities range from alpine 

forb communities in the Uinta and Wasatch Mountains 

in the northern portion of the mapping zone to desert 

shrub communities in the southern deserts. Extensive 

areas of pinyon-juniper/mountain big sagebrush and both 

evergreen and deciduous shrub communities are found at 

mid-elevations throughout the Central Utah Highlands 

mapping zone. The climate of the this mapping zone 

is highly variable. Thirty-year average temperatures 

range from –4°C in the high Uinta Mountains to 15°C 

in the southern deserts. Average annual precipitation 

varies from 10 cm in the southwestern deserts to nearly 

2 meters in the northern mountains (Bradley 1992).

Northern Rocky Mountains mapping zone — The 

117,976 km2 Northern Rocky Mountains mapping zone 

begins at the Canadian border in northern Montana 

and extends south into eastern Idaho (fig. 1). Elevations 

range from 760 m to 3,400 m. Vegetation communities range from alpine forbs in the highest mountain 

ranges to prairie grasslands east of the Rocky Mountain 

front. Forest communities are prevalent, with spruce-fir 

communities found near the timberline and extensive 

forests of lodgepole pine, western larch, Douglas-fir, 

and ponderosa pine at middle elevations. Thirty-year 

average annual temperatures range from –5°C in the 

Figure 1—The study areas for the LANDFIRE Prototype Project were located in the central Utah highlands (Mapping Zone 

16) and the Northern Rocky Mountains (Mapping Zone 19)

LANDFIRE used mapping zones delineated for the Multiple 

Resolution Land Cover (MRLC) 2001 Project (http://landcover

usgs.gov/index.asp). All of the 24 core LANDFIRE Prototype

products were produced for each zone. Lessons learned in 

the central Utah study area resulted in refinements that were

applied in the northern Rocky Mountains.
10 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 20

Chapter 2—An Overview of the LANDFIRE Prototype Project

high mountains of Glacier National Park to 15°C in the 

valley bottoms. Average precipitation varies from 14 cm 

in the valley bottoms to approximately 3.5 meters in the 

northern mountains (Arno 1980).

Methods _______________________

Many interrelated and mutually dependent tasks had 

to be completed to create the suite of databases, data 

layers, and models needed to develop scientifically credible, comprehensive, and accurate maps of fuel and fire 

regimes (see fig. 2, table 2, and appendix 2-A). After a 

brief introduction, each of these tasks is detailed below. 

First, the LANDFIRE reference database (LFRDB) was 

compiled from existing, georeferenced, ground-based 

databases from both government and non-government 

sources. Second, mapped biophysical gradients, potential vegetation types (PVTs), cover types (CT) (existing 

vegetation composition), and structural stages (SS) (existing vegetation structure) were mapped using the LFRDB, 

existing geospatial data, ecological simulation, Landsat 

imagery, and statistical landscape modeling at 30-meter 

pixel resolution to describe the existing vegetation and 

biophysical environment of each prototype study area. 

Third, succession pathway models and disturbance frequencies were entered into the LANDSUMv4 landscape 

fire succession model (described in the LANDFIRE Fire 

Regime Modeling section below) to simulate disturbance 

dynamics and vegetation development over time. These 

simulations served to quantify both the historical reference conditions and the range and variation of fire 

regime characteristics critical for determining current 

departure from historical conditions. Fourth, wildland 

fuel characteristics were mapped using field-referenced 

data, biophysical data, Landsat imagery, and LANDFIRE 

vegetation products.

Figure 2—An overview of the LANDFIRE Prototype Project procedures. The LANDFIRE mapping processes began with the 

creation of the LANDFIRE reference database, which is comprised of a set of all available georeferenced plot information from 

within each mapping zone. The reference and spatial databases were used in a classification and regression tree-based machine

learning framework for creating maps of biophysical settings (potential vegetation types), existing vegetation composition (cover 

type), and vegetation structure (canopy height and density). These core vegetation maps formed the foundation for the simulation 

of historical fire regimes and the subsequent calculation of current departure from historical vegetation conditions. In addition, the

vegetation maps served as the basis for mapping wildland fuel for simulation of fire behavior and effects.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006

Chapter 2—An Overview of the LANDFIRE Prototype Project

Table 2—Tasks essential for the creation of the LANDFIRE Prototype products. The first column directs the reader to the appropriate chapter sections containing general descriptions of the project’s individual tasks. The second column directs the reader to the appropriate chapter in this GTR 

containing detailed background and procedural information about the project’s individual tasks. Corresponding inputs/dependencies, methods for 

completion, and outputs/products are also listed for each task.

 Chapter

Task / chapter in this

section heading report Inputs/dependencies Methods for completion Outputs/products

Compiling the  4 •	 Existing georeferenced •	 Compiled data from existing •	 Map attribute tables used

LANDFIRE Reference	 	 	 field databases	 	 field databases.	 training data for mapping

Database 	 •	 Automated conversion •	 Re-projected and reformatted biophysical settings, 

 utilities data from native format vegetation, and fuel.

 into LFRDB format. •	 Data for accuracy assessment, 

 •	 Produced attribute tables for all	 	 quality control, and product

 LANDFIRE mapping applications.	 	 evaluation.

Developing the 5 •	 Topographic data from •	 Derived simulation units. •	 Thirty-eight biophysical gradient

Physiography and USGS •	 Implemented WXFIRE. layers used for mapping

Biophysical Gradient 	 •	 STATSGO soils data •	 Evaluated and processed output. biophysical settings, vegetation,

Layers •	 DAYMET daily weather and fuel.

 data •	 Data for comparing mapped

	 •	 WXFIRE ecosystem themes using biophysical 

 simulator information consistently across

 mapzones.

Developing the 6 •	 LFRDB •	 Synthesized existing •	 Custom LANDFIRE 

LANDFIRE Vegetation •	 LANDFIRE design	 	 classifications describing	 	 classifications meeting design

Map Unit criteria potential vegetation, existing criteria that vegetation classes

Classifications	 •	 Existing national	 	 vegetation, and structure.	 	 are identifiable, scaleable,

	 	 classification systems	 •	 Compiled hierarchical mappable, and model-able.

•	 Literature review	 	 classifications	 •	 Rules/keys for implementing 

. •	 Developed keys/queries to	 	 classifications in LFRDB.

	 	 	 	 implement classifications in	 •	 Lists of vegetation types to be 

 LFRDB to produce map used in vegetation mapping and 

 attribute tables. modeling and fuel mapping.

Mapping Potential 7 •	 Biophysical gradient •	 Developed training sites based •	 Maps of PVT.

Vegetation layers on map attribute tables from •	 Maps of probabilities of CT by 

	 •	 PVT classification	 	 LFRDB.	 	 PVT.

	 •	 LFRDB •	 Compiled biophysical gradient •	 Stratification for vegetation

 layers for use as spatial succession modeling.

 independent variables. •	 Simulation units for 

	 	 	 •	 Developed classification trees	 	 LANDSUMv4.

 predicting PVT. •	 Basis for stratification for

	 	 	 •	 Created final map in	 	 mapping wildland fuel.

 ERDAS/Imagine.

Mapping Existing 8 •	 MRLC 2001 Landsat •	 Developed training sites based •	 Maps of existing vegetation

Vegetation Image Catalog on map attribute tables from composition and structure.

	 •	 Biophysical gradient LFRDB. •	 Current baseline for comparison 

	 	 layers •	 Compiled  dates of Landsat with reference conditions to

	 •	 Existing vegetation imagery and biophysical determine ecological departure.

	 	 classifications	 	 gradient layers for use as	 •	 Description of the current 

	 •	 LFRDB mapped predictor variables. successional status of 

 •	 Developed classification and	 	 landscapes across mapping

 regression trees predicting CT zones.

 and SS. •	 Foundation for wildland fuel 

	 	 	 •	 Created final map in ERDAS	 	 mapping.

 imagine.

Developing 9 •	 VDDT model •	 Conducted workshops for •	 Evaluation and refinement of

Succession Pathway •	 LFRDB	 	 developing modeling	 	 classification systems.

Models •	 Vegetation frameworks. •	 Set of vegetation development 

	 	 classifications	 •	 Evaluated disturbance models for each mapped PVT.

	 •	 PVT and existing probabilities and transition •	 Parameters used to simulate 

	 	 vegetation maps	 	 times from literature.	 	 fire effects and post vegetation

	 •	 Vegetation •	 Assigned local ecologists to recovery in LANDSUMv4

	 	 development	 	 derive and refine models using	 •	 Historical reference conditions 

 workshops VDDT. for evaluation of ecological 

	 	 	 •	 Compiled models in the departure.

 vegetation and disturbance

 development database. (continued)
12 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 20

Chapter 2—An Overview of the LANDFIRE Prototype Project

Simulating Historical 10 •	 LFRDB •	 Divided landscape into •	 Time series of historica

Landscape 	 •	 Succession pathway simulation units and vegetation conditions for

Composition models (VADDD) landscape reporting units. simulation period.

•	 Parameter database •	 Parameterized LANDSUMv4 •	 Maps of simulated historical fi

 (VADDD) with information from VADDD. intervals.

	 •	 PVT and existing •	 Ran LANDSUMv4. •	 Probability maps of fire severity

 vegetation maps •	 Compiled and summarized •	 Reference conditions for 

	 •	 LANDSUMv4 model results. comparison with current

 conditions to evaluate ecological

 departure.

Estimating Departure 11 •	 Cover type map •	 Implemented Interagency •	 Consistently mapped F

using Interagency 	 •	 PVT Map FRCC Guidebook methods across each map zone.

RCC Guidebook 	 •	 SS map adapted to LANDFIRE map •	 Consistent baseline information 

Methods •	 Reference conditions	 	 classification systems.	 	 for determining relative levels of

 from LANDSUMv4	 •	 Quantified reference conditions	 	 ecological departure across broa

 based on LANDSUMv4 output. regions

 •	 Calculated departure and 

 created discrete FRCC classes.

Estimating Departure 11 •	 HRVStat statistical •	 Compiled input analysis •	 Multivariate, statistically rob

Using HRVStat software database for HRVStat measure of ecological departure.

	 •	 Cover type map including reference conditions •	 Measure of the significance of the

	 •	 PVT Map and current CT and SS. measurement of ecological

	 •	 SS map •	 Determined departure from departure (p-value).

	 •	 Reference conditions reference conditions. •	 Ecological departure mapped as 

 •	 Determined frequency	 	 a continuous variable.

 distribution of departure •	 Consistently mapped ecological 

 estimates using time series of departure across each map zone.

 historical vegetation conditions. 

	 	 	 •	 Compiled final HRVStat

	 	 	 	 departure and confidence

 maps.

Mapping Surface Fuel 12 •	 LFRDB •	 Compiled fuel mapping •	 Maps of fire behavior fuel mode

	 •	 Vegetation	 	 database as a subset of the	 	 for simulating potential fire spread

	 	 classifications	 	 LFRDB.	 	 and intensity.

	 •	 PVT map •	 Created look-up tables and •	 Maps of fire effects models for

	 •	 CT and SS maps	 	 rule sets to link fuel models	 	 simulating the effects of fires on

	 •	 Look-up tables and rule to biophysical settings and vegetation.

 sets for assigning fuel vegetation composition and

 models structure.

 •	 Compiled final maps in ArcGIS.

Mapping Canopy Fuel 12 •	 LFRDB •	 Populated fuel mapping •	 Maps of canopy fuels f

	 •	 FUELCALC database with FUELCALC simulating the initiation and

	 	 model	 	 output.	 	 behavior of crown fires.

	 •	 biophysical gradient •	 Developed training sites from

 layers fuel database.

	 •	 MRLC 2001 Landsat •	 Compiled Landsat imagery

 Image Catalog biophysical gradients, and

	 •	 PVT maps LANDFIRE vegetation maps

	 •	 Existing vegetation for use as mapped predictor

 maps variables.

 •	 Developed regression trees

 predicting CBH and CBD.

	 	 	 •	 Created final maps in ERDAS

 imagine.

Table 2—(Continued)

 Chapter

Task / chapter in this

section heading report Inputs/dependencies Methods for completion Outputs/products
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006

Chapter 2—An Overview of the LANDFIRE Prototype Project

Compiling the LANDFIRE Reference 

Database

The LFRDB comprised a compilation of all existing 

georeferenced field data available for the prototype mapping zones (fig. 3). This database of georeferenced plot 

information formed the foundation for most phases of 

the LANDFIRE Prototype Project. The database was 

designed in Microsoft ACCESS and had a three-tiered 

hierarchical structure (Caratti, Ch. 4). Existing data 

were entered into the database and incorporated into the 

FIREMON database structure (Lutes and others 2002). 

The data were then further summarized and reformatted 

to ensure consistency across the entire database. This 

involved steps such as converting geographic coordinates to the LANDFIRE map projection, converting 

measurement units to metric, ensuring that all vegetation 

cover estimates represent absolute cover as opposed to 

relative cover, and populating fields that can be used for 

quality assurance and quality control (Caratti, Ch. 4). 

The final step in developing the LFRDB was classifying 

each plot to the appropriate CT, PVT, and SS using the 

LANDFIRE map unit classification systems (Long and 

others, Ch. 6) and assigning appropriate fuel characteristics using the LANDFIRE fuel map unit classification 

systems (Keane and others, Ch. 12). LANDFIRE map 

attribute tables describing georeferenced vegetation 

and fuel types were then used as training databases for 

developing most LANDFIRE products.

For inclusion in the LFRDB, all field data needed to 

be georeferenced and quantify at least one LANDFIRE 

mapping attribute (for example, CT or SS). All field data 

were evaluated for suitability and assigned quality control 

indices based on summary image overlay, logic checking, and associated metadata (Caratti, Ch. 4). Sources 

of data for the LFRDB include but are not limited to 

the following:

•	 Forest Inventory and Analysis (Gillespie 1999)

•	 Forest Health Monitoring (USDA Forest Service 

2003)

•	 Landscape Ecosystem Inventory Systems (Keane 

and others 2002a)

•	 ECODATA (Jensen and others 1993)

•	 FIREMON fire monitoring data (Lutes and others 

2002)

•	 Interior Columbia River Ecosystem Management 

Project (Quigley and others 1996)

•	 Natural Resources Conservation Service (USDA 

2002)

•	 National Park Service fire monitoring database 

(USDI 2001)

Figure 3—The procedure for developing the LANDFIRE reference database. Existing georeferenced plot data were acquired

from numerous sources, including USDA Forest Service Forest Inventory and Analysis data, State GAP programs, and additional 

government and non-government sources. These data were processed through automated quality control and re-projection

procedures and compiled in the FIREMON database architecture. The custom LANDFIRE vegetation classes (cover type, PVT, 

structural stage, and surface fuel models) were determined for each plot using sets of dichotomous sequence tables. The final

stage of compiling the reference database was the development of map attribute tables that are implemented as training databases 

in the LANDFIRE mapping processes.
14 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2

Chapter 2—An Overview of the LANDFIRE Prototype Project

The LFRDB was used to classify, map, and evaluate 

each of the LANDFIRE products. For example, the 

LFRDB was used to classify existing vegetation communities and biophysical settings (Long and others, 

Ch. 6), to map PVTs (Frescino and others, Ch. 7), to 

map cover types (Zhu and others, Ch. 8), to evaluate 

and quantify succession model parameters (Long and 

others, Ch. 9 and Pratt and others, Ch. 10), to develop 

maps of wildland fuel (Keane and others, Ch. 12), and to 

evaluate the quality of LANDFIRE products(Vogelmann 

and others, Ch. 13).

Developing the Physiography and 

Biophysical Gradient Layers

Several spatial data layers provided baseline information for the LANDFIRE Prototype Project and served 

mainly as independent spatial predictor variables in 

the LANDFIRE mapping processes (fig. 4). We used 

topographic data from the National Elevation Database 

(NED) to represent or derive gradients of elevation, slope, 

aspect, topographic curvature, and other topographic 

characteristics (Holsinger and others, Ch. 5). The National Elevation Database, developed by the USGS Center 

for Earth Resources Observation and Science (EROS), 

was compiled by merging the highest-resolution, bestquality elevation data available across the United States 

into a seamless raster format. More information about 

the NED may be found at http://ned.usgs.gov/.

Topographic variables represent indirect biophysical 

gradients, which have no direct physiological influence 

on vegetation dynamics (Müller 1998); however, the 

addition of even indirect gradients has been shown to 

improve the accuracy of maps of vegetation (Franklin 

1995). We used an ecosystem simulation approach to 

create geospatial data layers that describe important 

environmental gradients that directly influence the 

distribution of vegetation, fire, and wildland fuel across 

landscapes (Rollins and others 2004). The simulation 

model WXFIRE was developed for the purpose of 

employing standardized and repeatable modeling methods to derive landscape-level weather and ecological 

gradients for predictions of landscape characteristics 

such as vegetation and fuel (Keane and others 2002a; 

Keane and Holsinger 2006). WXFIRE was designed to 

simulate biophysical gradients using spatially interpolated daily weather information in addition to mapped 

soils and terrain data. The spatial resolution is defined 

by a user-specified set of spatial simulation units. The 

WXFIRE model computes biophysical gradients - up to 

50 - for each simulation unit, where the size and shape 

of simulation units are determined by the user.

The implementation of WXFIRE requires the three 

following steps: 1) develop simulation units (the smallest 

unit of resolution in WXFIRE), 2) compile mapped daily 

weather, and 3) execute the model (Holsinger and others, Ch. 5). Using the DAYMET daily weather database, 

WXFIRE was executed over 10 million simulation units 

Figure 4—The procedure for developing the 

LANDFIRE base geospatial data layers. Topographic information from the National Elevation 

Database, soils information from the NRCS 

STATSGO database, and data from the DAYMET 

daily weather database were input into the WXFIRE weather and ecosystem model. WXFIRE 

was used to develop 38 gradients describing the

factors that define the distribution of vegetation

across landscapes. These gradients were incorporated into the LANDFIRE mapping processes 

to increase the overall accuracy of mapped 

products. Three dates of Landsat imagery from 

the MRLC 2001 project were used as the basis

for mapping existing vegetation composition 

and structure. All information included in the 

LANDFIRE spatial database was developed 

using strict design criteria to ensure that these 

data could be developed consistently across 

the entire United States.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 

Chapter 2—An Overview of the LANDFIRE Prototype Project

for Zone 16 and over 26 million for Zone 19 (Thornton 

and others 1997). Thirty-eight output variables from 

WXFIRE describing average annual weather and average 

annual rates of ecosystem processes (such as potential 

evapotranspiration) were then compiled as raster grids 

and used in developing the final LANDFIRE products 

(Holsinger and others, Ch. 5). Specifically, these layers were used as a basis for mapping PVT, CT, and SS 

(Frescino and Rollins, Ch. 7 and Zhu and others, Ch. 

8) and for mapping both surface and canopy wildland 

fuel (Keane and others, Ch. 12). Additionally, biophysical gradient layers facilitated comparison of map units 

across mapping zones during the map unit development. 

For example, an equivalent CT in two different mapping 

zones should have similar biophysical characteristics. 

Vast differences in biophysical characteristics may 

indicate that a new CT should be developed.

Developing the LANDFIRE Vegetation Map 

Unit Classifications

The LANDFIRE Prototype Project developed vegetation 

map unit classifications that, combined with rule sets (keys), 

allowed the linkage of LFRDB plot data to geospatial data 

layers in a systematic, hierarchal, and scaleable framework. 

These hierarchal classification systems were directly related 

to the predictive landscape modeling of PVT, CT, and SS 

(Frescino and Rollins, Ch. 7 and Zhu and others, Ch. 8) 

for defining the developmental stages within succession 

models for landscape fire regime modeling (Long and others, Ch. 9 and Pratt and others, Ch. 10) and for mapping 

surface and canopy fuel (Keane and others, Ch. 12). In 

order for LANDFIRE to be successful, the LANDFIRE 

vegetation map units need to be:

•	 identifiable – Map units must be easily identifiable 

in the field, and the process for assigning map units 

based on existing plot data (such as FIA) needs to 

be efficient and straightforward.

•	 scalable – Map unit classifications must have a 

hierarchy that is flexible for addressing the spatial 

scales used in landscape- to national-level assessments (for example, 100,000s to 1,000,000s km2

). 

This flexibility in spatial scale also facilitates links 

with existing classifications.

•	 mappable – Only map units that can be delineated 

using remote sensing and biophysical modeling will 

be mapped.

•	 model-able – Map units must fit into the logical 

frameworks of the vegetation and landscape simulation models that are essential for the creation of 

many LANDFIRE products.

The LANDFIRE Prototype Project vegetation map unit 

classifications were based on combinations of extensive 

literature review, existing national vegetation classifications and mapping guidelines, development of vegetation 

succession models, summaries from the LFRDB, and 

classifications from other existing fuel and fire regime 

mapping projects (Long and others, Ch. 6). Each of the 

classifications is composed of two types of units (map and 

taxonomic) with several different nested levels possible 

(Long and others, Ch. 6). Map units are collections of 

areas defined in terms of component taxonomic and/or 

technical group characteristics. Map units may exist at 

any level of a hierarchical map unit classification based 

on physiognomic or taxonomic units or technical groups 

(Brohman and Bryant 2005). Taxonomic units were used 

to define and develop map units from the LFRDB and may 

also be used by land managers to scale the LANDFIRE 

CT map unit classification to floristically finer scales. 

Hierarchically nested, taxonomically defined map units 

allowed the vegetation map units to be aggregated or 

disaggregated to suit multiple purposes (such as vegetation modeling or fuel mapping). Taxonomic information 

was also used to link the LANDFIRE classifications to 

other existing vegetation classification systems (Long 

and others, Ch. 6). The individual classifications are 

described below.

Cover type map unit classification — The LANDFIRE cover type (CT) map unit classifications described 

existing vegetation composition in each mapping zone 

(Long and others, Ch. 6). Generally, CT map units 

were distinguished by dominant species or species assemblages. Records in the LFRDB were classified to 

CT based on indicator types with the highest relative 

canopy cover. The LANDFIRE Prototype CT map unit 

classification was based on the National Vegetation Classification System (NVCS) and the USDA Forest Service 

Existing Vegetation Classification and Mapping Guide 

(Brohman and Bryant 2005; Grossman and others 1998; 

Long and others, Ch. 6) but was modified to meet the 

LANDFIRE classification criteria listed above. By using 

NVCS and the Forest Service Existing Vegetation Classification and Mapping Technical Guide (Brohman and 

Bryant 2005), the LANDFIRE CT map unit classification 

quantitatively combined both physiognomic and floristic 

systems and adhered to important Federal Geographic 

Data Committee classification standards (FGDC 1997). 

The LANDFIRE vegetation map units were used for 

mapping existing vegetation and vegetation structure 

(Zhu and others, Ch. 8), modeling succession (Long and 

others, Ch. 9), parameterizing the LANDSUMv4 model 

(Pratt and others, Ch. 10), quantifying departure from 
16 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 20

Chapter 2—An Overview of the LANDFIRE Prototype Project

historical conditions (Holsinger and others, Ch. 11), and 

for mapping wildland fuel (Keane and others, Ch. 12).

Structural Stage Map Unit Classification — The 

LANDFIRE structural stage (SS) map unit classification was based on summary analyses of vegetation 

characteristics contained within the LFRDB. The two 

main criteria for developing custom SS map units were 

that these map units had to be useful for describing 

vegetation developmental stages in succession models 

and they needed to be relevant for describing vegetation structure for mapping wildland fuel. In addition, 

LANDFIRE SS map units had to be distinguishable using Landsat imagery. The structural stages of forest CTs 

were broken into four SS map units based on a matrix 

of independently mapped canopy cover (CC) and height 

class (HC) map units (Long and others, Ch. 6). Structure 

classification of non-forest CTs was composed of only 

two map units describing canopy density. Height status 

was not included in these SS map units because most 

growth in non-forest areas occurs relatively swiftly in 

the first couple of years after regeneration and then levels 

out over time; therefore, height status is less relevant to 

vegetation succession in non-forest types than in forest 

types (Long and others, Ch. 6). LANDFIRE structural 

stages were used to develop models of vegetation development (Long and others, Ch. 9) and for mapping 

wildland fuel (Keane and others, Ch. 12).

Potential Vegetation Type Map Unit Classi­fication — Potential vegetation type (PVT) is a site classification based on environmental gradients such a

temperature, moisture, and soils (Pfister and others 

1977). Potential vegetation types are analogous to aggregated habitat types or vegetation associations and are 

usually named for the late successional species presumed 

to dominate a specific site in the absence of disturbance 

(Cooper and others 1991; Daubenmire 1968; Frescino 

and Rollins, Ch. 7; Keane and Rollins, Ch. 3; Pfister 

and Arno 1980).

The LANDFIRE PVT map unit classification was created based on summaries from the LFRDB, extensive 

literature reviews, and existing PVT classifications (Long 

and others, Ch. 6). We began with PVT classifications 

that already existed for each of the prototype mapping 

zones (such as the USDA Forest Service regional classifications) and then refined these PVT classifications 

through expert opinion and data from the LFRDB. 

The resultant map unit classification was based on the 

presence of indicator types across gradients of shade 

tolerance, plant water relationships, and ecological 

amplitude. The LANDFIRE Prototype Project PVT 

map units were used to link the ecological process of 

succession to landscapes (Long and others, Ch. 9), to 

guide the parameterization and calibration of the landscape fire succession model LANDSUMv4 (Pratt and 

others, Ch. 10), and to stratify vegetation communities 

for wildland fuel mapping (Keane and others, Ch. 12).

Mapping Potential Vegetation

We mapped PVT using a predictive landscape modeling 

approach (fig. 5). This approach employs spatially explicit 

independent or predictor variables and georeferenced 

training data to create thematic maps (Franklin 1995; 

Keane and others 2002a; Rollins and others 2004). For 

the LANDFIRE Prototype, the training data were created by implementing the PVT map unit classification 

as a set of automated queries that access the LFRDB 

and classify each plot to a LANDFIRE PVT based on 

vegetation composition and condition (Long and others, Ch. 6). Each georeferenced plot and its assigned 

PVT were overlaid with the 38 biophysical gradients 

using GIS software. This resulted in a PVT modeling 

database where PVT was the dependent variable and the 

biophysical gradient layers were the predictor variables 

(Frescino and Rollins, Ch, 7).

In the LANDFIRE Prototype, we used classification 

trees (also known as decision trees) along with the PVT 

modeling database to create a spatially explicit model 

for mapping PVT within GIS applications. Classification trees, used as an analog for regression, develop 

rules for each category of a dependent variable (in this 

case, PVT). Classification trees for mapping PVTs were 

developed using the See5 machine-learning algorithm 

(Quinlan 1993; www.rulequest.com) and were applied 

within an ERDAS Imagine interface (ERDAS 2004; 

Frescino and Rollins, Ch. 7). See5 uses a classification 

and regression tree (CART) approach for generating 

a tree with high complexity and pruning it back to a 

simpler tree by merging classes (Breiman and others 

1984; Friedl and Bradley 1997; Quinlan 1993).

Maps of PVT are a principal LANDFIRE Prototype 

product (table 1). In addition, the LANDFIRE PVT 

maps were used with the LFRDB to create layers that 

represent the probability of a particular CT to exist in 

a specific area (Frescino and Rollins, Ch, 7), used in 

the mapping of CT (Zhu and others, Ch. 8). The PVT 

map also facilitated linkage of the ecological process 

of vegetation succession to the simulation landscapes 

used in modeling historical reference conditions. In the 

LANDFIRE Prototype, vegetation ecologists created 

succession pathway models for individual PVTs that 
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Chapter 2—An Overview of the LANDFIRE Prototype Project

served as input to the LANDSUMv4 landscape fire succession model used to simulate historical fire regimes 

and reference conditions (Long and others, Ch. 9; Pratt 

and others, Ch. 10). Maps of PVT were also used for 

stratification purposes in wildland fuel mapping (Keane 

and others, Ch. 12).

Mapping Existing Vegetation

Maps of existing vegetation composition and structure 

at spatial resolutions appropriate for wildland fire management are principal LANDFIRE products (table 1). 

Maps of existing vegetation serve as a benchmark for 

determining departure from historical vegetation and for 

creating maps of wildland fuel composition and condition. Satellite imagery was integrated with biophysical 

gradient layers and the LFRDB to create maps of CT, 

canopy closure (CC), and height class (HC) map units 

(HC) (fig. 5). Structural stage (SS) is an integration of 

CC and HC as described above in the Structural Stage 

Map Unit Classification section.

Mapping Cover Type

Many mapping algorithms have been developed for 

deriving vegetation maps from satellite imagery (Cihlar 

2000; Foody and Hill 1996; Homer and others 1997; 

Knick and others 1997 ). For the LANDFIRE Prototype 

Project, we created maps of CT using a training database 

developed from the LFRDB, Landsat imagery, biophysical gradient layers (described above in Developing the 

Physiography and Biophysical Gradient Layers), the PVT 

map (for limiting the types of vegetation that may exist 

in any area) and classification tree algorithms similar 

to those described above for mapping PVT (Zhu and 

others, Ch. 8).

The LANDFIRE team developed maps of CT using 

a hierarchical and iterative set of classification models, 

with the first model separating more general land cover 

types (for example, life form) and subsequent models 

separating more detailed levels of the vegetation map 

unit classification until a final map of CT map units 

resulted. Specifically, life form information from the 

Figure 5—The LANDFIRE vegetation mapping 

process. Information from the LANDFIRE reference and spatial databases was used in a classificationandregressiontreeframeworkandthen

implemented within ERDAS IMAGINETM mapping 

software to create all mapped vegetation products. 

The mapping of potential vegetation was based 

purely on biophysical gradients including weather, 

topography, and soil information. Landsat imagery 

was used to create all maps of existing vegetation 

composition and structure.
18 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 20

Chapter 2—An Overview of the LANDFIRE Prototype Project

MRLC 2001 program (Homer and others 2002) was 

used as a stratification to create separate models for 

mapping CT. An iterative approach was implemented 

where mapping models were developed using a “topdown” approach for successively finer floristic levels in 

the LANDIRE vegetation map unit classification (Long 

and others Ch. 6). The LFRDB, biophysical settings 

layers, and ancillary data layers were incorporated to 

guide the mapping process.

Mapping Structural Stage

We used empirical models for estimating canopy 

closure (CC) using satellite imagery and biophysical 

gradients. Though often considered unsophisticated and 

criticized for lack of focus on mechanistic processes, 

empirical models have proved more successful than other 

types of models in applications involving large areas 

(Iverson and others 1994; Zhu and Evans 1994). We 

used regression trees, applied through a Cubist/ERDAS 

Imagine interface, to map CC and HC separately. Model 

inputs included elevation data and derivatives, spectral 

information from Landsat imagery, and the 38 biophysical gradient layers. Similar to PVT and CT mapping, a 

training database was developed using the LFRDB that 

contained georeferenced values for CC and HC for each 

plot. The resultant maps represented these two structure 

variables continuously across each prototype mapping 

zone. Prior to the LANDFIRE Prototype Project, CC 

and HC had been modeled successfully using CART 

for Zone 16 as well as several additional areas (Huang 

and others 2001).

The final SS layer was developed by combining CC 

and HC map units into SS map units and assigning SS 

map units to combinations of PVT and CT. Structural 

stage assignments were based on the SS map unit classification (Long and others, Ch. 6). This integrated 

height and density information was used as an important 

determinant of wildland fuel characteristics and successional status of existing landscapes. The SS map units 

also formed the structural framework for the vegetation 

modeling described in the next section and in detail in 

Long and others, Ch. 9.

Modeling Fire Regimes

In the LANDFIRE Prototype Project, historical and 

current vegetation composition and structure were compared to estimate departure from historical conditions. 

To characterize historical conditions, we used the PVT 

map and succession pathway modeling as key input to the 

LANDSUMv4 landscape fire succession model (fig. 6). We 

then used two separate methods for estimating departure from historical conditions: the Interagency FRCC 

Guidebook method (Hann and others 2004) and the 

HRVStat spatial/temporal statistics software (Holsinger 

and others Ch. 11; Steele and others, in preparation).

The Interagency FRCC Guidebook provides detailed 

methods for estimating departure from historical conditions based on estimation of historical vegetation condition and disturbance regimes. The FRCC classification, 

established by Hann and Bunnell (2001), is defined as: a 

descriptor of the amount of “departure from the historical natural regimes, possibly resulting in alterations of 

key ecosystem components such as species composition, structural stage, stand age, canopy closure, and 

fuel loadings.” Fire regime condition class is a metric 

for reporting the number of acres in need of hazardous 

fuel reduction and is identified in the National Fire Plan 

and Healthy Forests Restoration Act as a measure for 

evaluating the level of efficacy of wildland fuel treatment 

projects. In the FRCC Guidebook approach, low departure (FRCC 1) describes fire regimes and successional 

status considered to be within the historical range of 

variability, while moderate and high departures (FRCC 

2 and 3, respectively) characterize conditions outside of 

this historical range (Hann and Bunnell 2001; Schmidt 

and others. 2002). Detailed description of how the Interagency FRCC Guidebook methods were implemented in 

the LANDFIRE Prototype Project follow in the below 

section titled Estimating Departure using Interagency 

FRCC Guidebook Methods.

Developing Succession Pathway Models

Succession pathway models were created using the 

multiple pathway approach of Kessell and Fischer (1981) 

in which succession classes are linked along pathways 

defined by stand development and disturbance probabilities within a PVT. Succession pathways describe 

the seral status of vegetation communities in the context 

of disturbances such as wildland fire, forest pathogens, 

and land use (Arno and others 1985). These pathways 

link seral vegetation communities or succession classes 

(described by combinations of PVT-CT-SS) over time. 

Each succession class is parameterized with disturbance 

probabilities and transition times. Transition times required to move from one seral succession class to another 

define the development of vegetation across landscapes 

over time. Disturbance probabilities determine the type 

and severity of disturbance. Pathways associated with 

disturbances determine where the post disturbance 

vegetation community trends over time.
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Chapter 2—An Overview of the LANDFIRE Prototype Project

We used a computer model called the Vegetation 

Dynamics Development Tool (VDDT; Beukema and 

others 2003) to build succession pathway models for each 

PVT defined in the LANDFIRE Prototype PVT map 

unit classifications. Specialists in forest and rangeland 

ecology facilitated this succession pathway modeling 

process (Long and others, Ch. 9). Based on the list of 

PVTs mapped for each zone, the specialists used VDDT 

to construct succession models. The existing vegetation 

map legends that describe both dominant species and 

structural stage were used to define the stages of vegetation development over time, called succession classes. 

Summaries from the LFRDB provided a list indicating 

which succession classes were most likely to occur in 

each PVT. An extensive literature search formed the 

basis for the input parameters (primarily transition 

times and disturbance occurrence probabilities) for each 

model. Each specialist used the VDDT software to both 

construct succession models and evaluate the behavior 

of each model. Final models were then reformatted and 

loaded into a relational database called the Vegetation 

and Disturbance Dynamics Database (VADDD) (Long 

and others, Ch. 9; Pratt and others, Ch. 10). This database 

was constructed specifically to facilitate the compilation 

and conversion of the succession pathway models into 

the proper format for LANDSUMv4.

Simulating Historical Landscape 

Composition

The fourth version of the Landscape Succession Model 

(LANDSUMv4) is a spatially explicit application where 

vegetation succession is modeled deterministically 

and disturbances are modeled stochastically over long 

simulation periods. LANDSUMv4 output is summarized 

for user-defined landscape reporting units to spatially 

describe simulated historical vegetation composition and 

structure and fire regimes (Keane and others 2002b). 

Figure 6—The LANDFIRE fire regime and ecological departure mapping procedure. Succession models

were developed for each mapped PVT. These models, along with the PVT map and a suite of disturbance and weather parameters developed from empirical modeling or acquired from expert opinion, were

implemented within the LANDSUMv4 landscape fire succession model to simulate spatial time series of

vegetation characteristics and wildland fire. This information was summarized using the Interagency Fire

Regime Condition Class Guidebook methods and the HRVStat software application to create maps of 

simulated historical fire regimes and departure from historical vegetation conditions.
20 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 200

Chapter 2—An Overview of the LANDFIRE Prototype Project

LANDSUMv4 simulates succession using the LANDFIRE succession models described above. In LANDSUMv4, ignition of wildland fires is simulated based on 

separate probabilities by succession class and defined as a 

part of initial model parameterization. Simulated fires then 

spread across the landscape based on simple topographic 

and wind factors.

LANDSUMv4, stochastically simulates fire effects based 

on the distribution of fire severity types as specified during model parameterization. These effects are determined 

based on the information contained in VADDD. Finally, 

LANDSUMv4 outputs the amount of area in each succession class in each landscape reporting unit every 50 

years over the simulation period. Landscape reporting 

units for the LANDFIRE Prototype Project were fixed at 

900-m by 900-m to register with the 30-m grid cell size 

of the other LANDFIRE layers and to be comparable with 

the coarse-scale maps produced by Schmidt and others 

(2002). For detailed information on the background and 

implementation of LANDSUMv4 in the LANDFIRE 

Prototype Project, see Keane and Rollins, Ch. 3 and 

Pratt and others, Ch. 10.

Estimating Departure using Interagency 

FRCC Guidebook Methods

Comparison of current vegetation condition with that 

of the historical or reference period forms the foundation of FRCC calculation. Calculating FRCC using 

the Guidebook approach involves four distinct steps: 

1) evaluate current vegetation conditions, 2) compute 

reference vegetation conditions, 3) calculate departure, 

and 4) estimate FRCC. For the LANDFIRE Prototype, 

current vegetation conditions were assessed by landscape reporting unit using the PVT, CT, and SS maps. 

Reference conditions for this analysis were estimated 

by executing the LANDSUMv4 model for a simulation period of several thousand years and reporting the 

area of each succession class every 50 years during the 

simulation period.

Calculation of FRCC begins with determining similarity, a concept addressed in depth by Hann and others 

(2004) and at www.frcc.gov. For the prototype, this 

simple metric was calculated by comparing current 

conditions with those of the reference period in the 

same reporting unit for each individual PVT. Percent 

composition of each PVT-CT-SS combination in the 

FRCC vegetation-fuel class map was compared with that 

of the reference conditions for a given landscape reporting unit. The lesser of the two percentages is defined 

as the similarity. That is, if a reporting unit currently 

has a smaller percent composition of a PVT-CT-SS 

combination than the reference conditions (modeled by 

LANDSUMv4) then the similarity equals the percent 

composition of the current PVT-CT-SS combination. 

Conversely, if the percent composition of a PVT-CT-SS 

combination in the reference conditions is less than that 

of the current conditions, the similarity value equals the 

percent composition of the reference conditions. For 

each PVT in a reporting unit, the similarity values were 

totaled and departure was calculated by subtracting the 

aggregate similarity from 100. For details regarding the 

scientific background of and specific methods for FRCC 

calculation, see Holsinger and others, Ch. 11 and visit 

www.frcc.gov

Estimating Departure using HRVStat

HRVStat is a multivariate statistical approach to 

rigorously evaluate patterns of succession classes (PVTCT-SS) over the LANDSUMv4 simulation period – the 

estimated historical conditions – for comparison with 

those of current conditions. One important aspect of 

HRVStat that distinguishes it from the FRCC Guidebook 

approach is that it evaluates the variance structure of 

all PVT-CT-SS combinations as they fluctuate across 

landscapes through time to compute departure (Holsinger 

and others, Ch. 11; Keane and others 2006; Steele and 

others, in preparation).

The LANDSUMv4 output and current conditions 

based on the CT and SS maps were compiled into 

a custom HRVStat analysis database. This database 

consisted of the area in each succession class in each 

landscape reporting unit over the LANDSUMv4 simulation period. The HRVStat method involved a two-step 

process (Holsinger and others, Ch. 11). First, HRVStat 

determined the extent to which current vegetation in a 

reporting unit differed from simulated reference conditions. In addition, the amount of area in each succession 

class for each reporting unit was compared with the same 

for every other reporting unit. This process provided 

information on the variance structure from the entire 

simulation period to estimate a pixel based confidence 

measure for departure across the entire mapping zone. 

Secondly, for each reporting unit, a frequency distribution 

of departure measurements was derived, and the proportion of values in the departure distribution greater than 

or equal to the current departure estimate formed the 

basis for determining the significance level, or p-value. 

This significance level served as a measure of confidence 

in the departure estimate for each reporting unit. From 

this information we then created maps of departure (as 

a continuous variable) and significance (Holsinger and 

others, Ch. 11; Steele and others, in preparation).
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Chapter 2—An Overview of the LANDFIRE Prototype Project

Mapping Wildland Fuel

The various wildland fuel layers developed through the 

LANDFIRE Prototype Project were selected for development because they provide critical input to existing fire 

modeling software used for strategic and tactical planning, 

such as FOFEM (Reinhardt and Keane 1998), BEHAVE 

(Andrews and Bevins 1999), NEXUS (Scott 1999), and 

FARSITE (Finney 1998) (fig. 7). When implemented 

within these existing models, these fuel layers may be 

used to simulate fire intensity, spread rate, and severity 

for current conditions or (with slight modifications based 

on treatment level) used to predict fire behavior of fuel 

characteristics that result as a consequence of fuel treatment activities.

Mapping Surface Fuel

Surface fuel classifications represent biomass components that occur on the ground (less than 2 meters above) 

and integrate all factors that contribute to the behavior 

and effects of fires burning near the ground’s surface. 

For the LANDFIRE Prototype Project, we mapped 

four surface fuel model classifications to provide the 

inputs essential for implementing the fire behavior and 

fire effects applications used in wildland fire management planning (Keane and others, Ch. 12). The 13 fire 

behavior fuel models described by Anderson (1982) and 

the additional new 40 Scott and Burgan fire behavior 

fuel models (Scott and Burgan 2005) were mapped to 

facilitate the modeling of fire behavior variables such 

Figure 7—The LANDFIRE fuel mapping procedure. Surface fuel was mapped using a rule-based 

approach in which combinations of LANDFIRE map classes were matched with both fire behavior

fuel models and fire effects models. These look-up tables and the LANDFIRE vegetation maps were

used to create the final LANDFIRE surface fuel maps. Canopy fuel (crown base height and crown bulk

density) was mapped with a predictive landscape modeling approach using Landsat imagery and a 

suite of biophysical gradient layers.
22 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 200

Chapter 2—An Overview of the LANDFIRE Prototype Project

as fire intensity, spread rate, and size using models such 

as FARSITE and NEXUS (Finney 1998; Scott 1999). 

The Fuel Characterization Classification System fuel 

beds (Sandberg and others 2001) and the fuel loading 

models (Lutes and others, in preparation) were mapped 

to facilitate the spatially explicit modeling of fire effects 

such as vegetation mortality, fuel consumption, and 

smoke production (Keane and others, Ch. 12).

The following is a general description of procedures 

that were used for mapping surface fuel during the 

LANDFIRE Prototype Project; see Keane and others, 

Ch. 12 for detailed descriptions of these procedures. 

First, the LANDFIRE fuel database was compiled 

from the LFRDB by summarizing all georeferenced 

fuel data to the PVT-CT-SS combinations. Each PVTCT-SS combination was assigned to each of the four 

surface fuel model classification systems based on data 

contained within the LFRDB. Information gaps resulting 

from lack of fuel data in the LFRDB were filled using 

either information from the literature or estimates from 

local fire behavior experts. Next, the LANDFIRE fuel 

database was converted to a rule set and implemented 

within a GIS to create the four surface fuel maps.

All surface fuel maps were created using similar classification protocols in which a fuel model category was 

directly assigned to a PVT-CT-SS combination. The rule 

set approach allowed the inclusion of additional detail 

by augmenting the PVT-CT-SS stratification with other 

biophysical and vegetation spatial data. For example, a 

rule set might assign the Anderson Fuel Model 8 to a 

specific PVT-CT-SS combination on slopes less than 50 

percent and the Anderson Fuel Model 10 to the same 

combination on slopes greater than 50 percent (Keane 

and others, Ch. 12).

Mapping Canopy Fuel

Canopy fuel represents the amount and arrangement 

of live and dead biomass in the canopy of the vegetation. 

Characteristics of canopy fuel are important for estimating the probability and characteristics of crown fires, and 

the spatial representation of canopy fuel is important for 

assessing fire hazard on forested landscapes (Chuvieco 

and Congalton 1989; Keane and others 1998; Keane and 

others 2001). Spatially explicit maps of canopy fuel provide 

the critical input to simulation models of wildland fire 

required to simulate the initiation, spread, and intensity 

of crown fires across landscapes (Finney 1998).

Maps of canopy height (CH), canopy cover (CC), canopy 

bulk density (CBD), and canopy base height (CBH) were 

produced through the LANDFIRE Prototype Project. 

These layers are required input (along with maps of 

elevation, aspect, slope, and surface fuel models) for the 

FARSITE fire behavior model to simulate wildland fire 

behavior (Finney 1998). FARSITE is currently used by 

many fire managers to plan prescribed burns as well as 

to manage wildland fires. It is designed to model fire 

behavior over a continuous surface. These same canopy 

characteristics may also be used in NEXUS to calculate 

the critical wind threshold for propagating a crown fire 

(Scott 1999).

Canopy height and canopy cover map layers were 

developed from the stand height and canopy closure 

layers created by the EROS team using satellite imagery 

and statistical modeling (Zhu and others, Ch. 8). We 

calculated CBD and CBH for each georeferenced plot 

in the LFRDB using FUELCALC, a prototype program 

developed by Reinhardt and others at the Missoula Fire 

Sciences Laboratory in Missoula, Montana (Reinhardt 

and Crookston 2003). FUELCALC computes a number 

of canopy fuel characteristics for each field referenced 

plot based on allometric equations relating individual tree 

characteristics to crown biomass. Georeferenced values 

of CBD and CBH were implemented along with Landsat 

imagery and biophysical gradient layers within CART to 

create mapped CBD and CBH using an approach identical to that used in the mapping of existing vegetation 

composition and structure (Keane and others, Ch. 12).

Conclusion_____________________

Throughout the course of the LANDFIRE Prototype 

Project – from fall of 2001 to spring of 2005 – many 

lessons were learned that have proved valuable for 

the successful implementation of LANDFIRE mapping methods and procedures across the entire United 

States. The LANDFIRE team has refined the prototype 

processes and applications to ensure that LANDFIRE 

National will meet its objective of creating nationally 

comprehensive and consistent data for wildland fire management. In addition, LANDFIRE Prototype products 

have been successfully used in fire management applications, including hazard analyses for communities 

in the Color Country area of southern Utah and fire 

behavior analyses at the regional to local levels during 

the 2003 fire season in the northern Rocky Mountains. 

LANDFIRE National products will be available for the 

western U.S. in 2006, for the eastern U.S. in 2008, and 

for Alaska and Hawaii in 2009.

For further project information, please visit the LANDFIRE website at www.landfire.gov.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 

Chapter 2—An Overview of the LANDFIRE Prototype Project

The Authors ____________________

Matthew G. Rollins is a Landscape Fire Ecologist at 

the USDA Forest Service, Rocky Mountain Research 

Station, Missoula Fire Sciences Laboratory (MFSL). 

His research emphases have included assessing changes 

in fire and landscape patterns under different wildland 

fire management scenarios in large western wilderness 

areas; relating fire regimes to landscape-scale biophysical 

gradients and climate variability; and developing predictive landscape models of fire frequency, fire effects, 

and fuel characteristics. Rollins is currently science lead 

for the LANDFIRE Project, a national interagency fire 

ecology and fuel assessment being conducted at MFSL 

and the USGS Center for Earth Resources Observation 

and Science (EROS) in Sioux Falls, South Dakota. He 

earned a B.S. in Wildlife Biology in 1993 and an M.S. 

in Forestry in 1995 from the University of Montana 

in Missoula, Montana. His Ph.D. was awarded by the 

University of Arizona in 2000, where he worked at the 

Laboratory of Tree-Ring Research.

Robert E. Keane is a Research Ecologist with the 

USDA Forest Service, Rocky Mountain Research Station, 

Missoula Fire Sciences Laboratory. Since 1985, Keane 

has developed various ecological computer models for 

the Fire Effects Project for research and management 

applications. His most recent research includes the development of a first-order fire effects model, construction 

of mechanistic ecosystem process models that integrate 

fire behavior and fire effects into succession simulation, 

restoration of whitebark pine in the Northern Rocky 

Mountains, spatial simulation of successional communities on landscapes using GIS and satellite imagery, 

and the mapping of fuel for fire behavior prediction. 

He received his B.S. degree in Forest Engineering in 

1978 from the University of Maine, Orono, his M.S. 

degree in Forest Ecology in 1985 from the University 

of Montana, and his Ph.D. degree in Forest Ecology in 

1994 from the University of Idaho.

Zhiliang Zhu is a Research Physical Scientist with the 

USDOI Geological Survey Center for Earth Resource 

Observation and Science (EROS). Zhu’s research work 

focuses on mapping and characterizing large-area land 

and vegetation cover, studying land cover and land use 

change, and developing remote sensing methods for 

the characterization of fire fuel and burn severity. His 

role in the LANDFIRE Prototype Project has been to 

design and test a methodology for the mapping of existing vegetation cover types and vegetation structure and 

to direct research and problem-solving for all aspects 

of the methodology. He received his B.S. degree in 

Forestry in 1982 from the Nanjing Forestry University 

in China, his M.S. degree in Remote Sensing in 1985, 

and his Ph.D. degree in Natural Resources Management 

in 1989, both from the University of Michigan.

James P. Menakis is a Forester with the USDA Forest 

Service, Rocky Mountain Research Station, Missoula 

Fire Sciences Laboratory (MFSL). Since 1990,Menakis 

has worked on various research projects related to fire 

ecology at the community and landscape levels for the 

Fire Ecology and Fuels Project. Currently, he is working 

on the Rapid Assessment, which is part of the LANDFIRE Project. Menakis has recently worked on mapping 

historical natural fire regimes, fire regime condition 

classes (FRCC), wildland fire risk to flammable structures for the conterminous United States, and relative 

FRCC for the western United States. Before that, he was 

the GIS Coordinator of the Landscape Ecology Team 

for the Interior Columbia River Basin Scientific Assessment Project and was involved with mapping FARSITE 

layers for the Gila Wilderness and the Selway-Bitterroot 

Wilderness. Menakis earned his B.S. degree in Forestry 

in 1985 and his M.S. degree in Environmental Studies in 

1994, both from the University of Montana, Missoula.

Acknowledgments_______________

We thank the USDA Forest Service Office of Fire 

and Aviation Management and the U.S. Department 

of Interior Office of Wildland Fire Coordination for 

administrative and financial support. Thomas Swetnam 

of the University of Arizona and Penelope Morgan of 

the University of Idaho provided valuable technical 

reviews of this chapter. The LANDFIRE Prototype 

Project technical team included Lisa Holsinger, Alisa 

Keyser, Eva Karau, Russ Parsons, Don Long, Jennifer 

Long, Melanie Miller, Jim Menakis, Maureen Mislivets, 

and Sarah Pratt of the USDA Forest Service Rocky 

Mountain Research Station in Missoula, Montana; John 

Caratti of Systems for Environmental Management in 

Missoula, Montana; and Zhiliang Zhu, Don Ohlen, 

Jim Vogelmann, Chengquan Huang, Brian Tolk, 

Michelle Knuppe, and Jay Kost of the National Center 

for Earth Resources and Observation Science in Sioux 

Falls, South Dakota. We commend each member of 

the team for exceptional work. Further, we thank June 

Thormodsgard of the USGS National Center for Earth 

Resources and Observation and Science; Kevin Ryan, 

Cam Johnston, Karen Iverson, Dennis Simmerman, 

Colin Hardy, and Elizabeth Reinhardt of the USDA 

Forest Service Rocky Mountain Research Station for 

technical and administrative support; Wendel Hann, 
24 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 20

Chapter 2—An Overview of the LANDFIRE Prototype Project

Mike Hilbruner, Mike Funston, and Tim Sexton of the 

USDA Forest Service Fire and Aviation Management for 

technical and administrative support; Lindon Wiebe of 

the USDA Forest Service Rocky Mountain Region State 

and Private Forestry for guidance during the completion 

phase of the LANDFIRE Prototype Project; Ayn Shlisky 

and Kelly Pohl of The Nature Conservancy for technical 

guidance in map unit design and vegetation modeling; 

and Colin Bevins and Christine Frame of Systems for 

Environmental Management for administrative support 

and technical editing.

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USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 2

Chapter 2—An Overview of the LANDFIRE Prototype Project

Appendix 2-A – LANDFIRE Prototype Project procedure table ____________

1. LANDFIRE reference database (LFRDB)

1.1. Determine the geographic extent of existing plot data.

1.1.1. Acquire existing plot data from mapping zone for mapping LANDFIRE attributes. 

1.1.2. Extract plot locations and convert coordinates to LANDFIRE map projection.

1.1.3. Conduct cursory quality assurance / quality control (QA/QC) on data to eliminate data with 

irreconcilable geospatial or information content errors.

1.1.4. Plot locations of useful data on LANDFIRE mapping zones to determine spatial gaps in coverage of 

the reference data.

1.1.5. Acquire additional field data in areas of mapping zones lacking sufficient field-referenced data.

1.2. Convert existing plot data into the relevant FIREMON and LANDFIRE attribute tables.

1.2.1. Create a separate directory for each data set.

1.2.2. Build an Access database for each data set.

1.2.3. Import empty LANDFIRE attribute tables into the Access database.

1.2.4. Develop data conversion queries to populate each LANDFIRE attribute table.

1.2.5. Develop data append queries to insert data into each FIREMON and LANDFIRE attribute table.

1.2.6. Document the data conversion process and populate the FIREMON Metadata (MD) table.

1.2.7. Create a subdirectory for all digital plot photos.

1.3. Conduct QA/QC procedures for all plot data.

1.3.1. Check again for geospatial errors in the data. Examples include plots located well outside the known 

study area for a particular database and plots located in bodies of water. 

1.3.2. Check for information content errors. Examples include null values in required fields such as plot 

locations, duplicate records and/or plot locations, and erroneous plant species heights.

1.3.3. Visually inspect LANDFIRE cover types (CTs) with the Multi-Resolution Land Characteristics 

(MRLC) 2001 Landsat data and the 1992 National Land Cover Dataset (NLCD) Landsat imagery.

1.3.4. Visually inspect plots for gross differences in LANDFIRE CTs and NLCD land cover types.

1.3.5. Difference the Normalized Difference Vegetation Index (NDVI) values from the MRLC 2001 and 

the MRLC 1992 Landsat data. 

1.3.6. Determine appropriate thresholds that suggest major land cover change. 

1.3.7. Examine reference data plots that have values above these thresholds by overlaying them on the 

MRLC 2001 imagery.
28 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 200

Chapter 2—An Overview of the LANDFIRE Prototype Project

1.4. Populate the combined LFRDB

1.4.1. Create an Access database with empty FIREMON and LANDFIRE attribute tables as the LFRDB 

for each mapping zone.

1.4.2. Link FIREMON and LANDFIRE attribute tables from each data set to the LFRDB for each 

mapping zone.

1.4.3. Write append queries to add data from each linked table to its associated table in the LFRDB for 

each mapping zone.

1.4.4. Assign a LANDFIRE CT to each plot using the LANDFIRE CT map unit classification for each 

mapping zone.

1.4.5. Assign a LANDFIRE potential vegetation type (PVT) to each plot using the LANDFIRE PVT tables 

and queries for each mapping zone.

1.4.6. Assign a LANDFIRE structural stage (SS) to each plot using the LANDFIRE SS classification for 

each mapping zone.

1.4.7. Create the LANDFIRE map attribute table (MAT) with the PVT, CT, and SS assignments for each 

plot.

1.4.8. Develop data summary queries used in subsequent LANDFIRE tasks. Examples include plot counts 

by PVT/CT/SS, constancy cover tables by PVT/CT/SS, and fuel loading by PVT/CT/SS.

1.4.9. Place all digital plot photos and metadata documents for the LFRDB into one photo directory and 

one documents directory.

1.4.10. Connect the FIREMON database application to the LFRDB to hyperlink plot photos and metadata 

documents. 

2. Mapping biophysical gradients

2.1. Acquire data to develop input layers for WXFIRE.

2.1.1. Acquire Digital Elevation Model (DEM) layer to create elevation, aspect, slope, and topographic 

shading layers. 

2.1.2. Acquire STATSGO soils coverage and associated tabular data.

2.1.3. Acquire NLCD layer to create Ecophysiological Site layer. 

2.1.4. Acquire DAYMET weather database.

2.1.5. Acquire Landsat imagery for leaf-on reflectance date to create Leaf Area Index (LAI) layer.

2.2. Create terrain-related layers.

2.2.1. Create Slope layer using Arc/Info SLOPE command with PERCENTRISE as units of slope.

2.2.2. Create Aspect layer using Arc/Info ASPECT command.

Appendix 2-A — (Continued)
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 2

Chapter 2—An Overview of the LANDFIRE Prototype Project

2.2.3. Create Topographic Shading layer using Arc/Info HILLSHADE command. (Azimuth and altitude 

data were developed using NOAA Solar Position calculator, assuming summer solstice as the date 

and using center coordinates for each zone.)

2.3. Create Soil Texture layers (percent sand, percent silt, percent clay).

2.3.1. Using STATSGO database, compute four soil textures (percent sand, percent silt, percent clay, and 

coarse fragment). 

2.3.2. Weight each soil texture by the layers’ depths and spatial extent for each of soil sequences within 

STATSGO polygons.

2.3.3. Remove coarse fragment proportion from the composition of soil textures and rescale sand, silt, 

and clay components to comprise 100 percent of soil texture estimates.

2.3.4. Calculate average slope in STATSGO database from high and low values for each STATSGO polygon and associated sequences and classify average slope into 4 classes: 

(1) ≤ 4 percent; (2) >4 percent and ≤8 percent; (3) > 8 percent and ≤ 15 percent; and 

(4) >15 percent.

2.3.5. Calculate average soil texture using data from step 2.3.3 for each slope class within each 

STATSGO polygon.

2.3.6. Classify Slope layer (from 2.2.1) into same 4 slope classes.

2.3.7. Partition STATSGO polygon coverage by Classified Slope layer and link this spatial layer with the 

STATSGO variables of soil texture by polygon and slope class (from 2.3.5).

2.4. Create Soil Depth layer.

2.4.1. Extract data on maximum depth per soil sequence from the STATSGO database.

2.4.2. Weight maximum depth per soil sequence by areal extent of sequences to calculate maximum soil 

depth per polygon.

2.4.3. Calculate Topographic Soil Index (TSI) for each pixel using the following relationship:

 

TSI

a

B = 



 



 ln

tan

where a is upslope area (m2) draining past a certain point per unit width of slope calculated using 

Arc/Info FLOWACCUMULATION and FLOWDIRECTION commands and B is local surface 

slope angle (degrees) calculated using Arc/Info SLOPE command with DEGREE as units of slope.

2.4.4. Integrate STATSGO Maximum Depth layer and TSI to calculate soil depth value for each pixel 

using scalars to adjust for skewed TSI distributions in the equation:

 Soil Depth = {M1, M2} *TSI.

where M1 is scalar used if pixel’s TSI is ≤ mean across a mapping zone, and M2 is used if TSI 

value is > mapping zone’s mean. 

Appendix 2-A — (Continued)
30 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 20

Chapter 2—An Overview of the LANDFIRE Prototype Project

Calculate M1 and M2 by the formulas:

 

M

LN LN

M

mo me

1 = 2 ∗ + = Ave.	Max.	Depth

0.5 (

	and	 Ma

)

x.	Depth

LNmax

where ave. max. depth is mean value of the STATSGO Maximum Depth layer across each zone, 

and LNmo and LNme are the mode and mean of the natural log of TSI for each STATSGO polygon 

calculated using Arc/Info’s ZONALMAJORITY and ZONALMEAN commands, respectively.

2.4.5. For Zone 19: increase data resolution using slope data from STATSGO database and Classified 

Slope layer.

2.4.5.1. Use slope classes calculated from STATSGO database in step 2.3.1.

2.4.5.2. Calculate average maximum depth for each slope class within each STATSGO polygon 

using data from step 2.3.2.

2.4.5.3. Link STATSGO polygon coverage partitioned by Classified Slope layer from step 2.3.7 

with STATSGO average maximum depth by polygon and slope class data calculated in 

2.4.5.2.

2.5. Create LAI layer.

2.5.1. Calculate corrected Normalized Difference Vegetation Index (NDVI) using LANDSAT leaf-on 

reflectance imagery and the equation:

 NDVI NIR RED

NIR RED

MIR MIR

MIN c = −+





 



 ∗ − − 1 min

max − min









 MIR 

where NIR is near infrared (band 4), RED is infrared (band 3), and MIR is mid-infrared (band 5); 

MIRmin is minimum value in mid-infrared band in an open canopy; and MIRmax is maximum value in 

the mid-infrared band in a closed canopy.

2.5.2. Convert NDVIc layer to LAI using the equation:

 

LAI NDVIc = −

−

1 0 7

0 7

n( .

.

2.6. Create Weather layer.

2.6.1. Using any one of the DAYMET layers (for example, daily temperature), clip DAYMET layer to 

zonal boundary using Arc/Info GRIDCLIP command.

2.6.2. Use clipped DAYMET layer to obtain center coordinates for each 1-km pixel.

2.7. Create Ecophysiological Site layer.

2.7.1. For Zone 16, partition landscape by 4 elevational breaks using DEM: Site 1 – 0 to 4,000 ft mean sea level 

(MSL); Site 2 – 4,000 to 6,000 ft MSL; Site 3 – 6,000 to 9,000 ft MSL; and Site 4 – 9,000+ ft MSL. 

Appendix 2-A — (Continued)
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006

Chapter 2—An Overview of the LANDFIRE Prototype Project

2.7.2. For Zone 19, reassign 21 broad CTs from NLCD to 4 general plant functional types and one nonvegetated class: water/barren. Reassign developed land CTs to plant functional types based on 

surrounding pixels using FOCALMAJORITY command.

2.8. Classify WXFIRE input layers.

2.8.1. Classify Elevation layer into 100-m ranges.

2.8.2. Classify Slope layer (from 2.2.1) into low (0-10%), moderate (10-30%), and high (>30%) slope 

classes.

2.8.3. Classify Aspect layer into SW (165° to 255°), NW (255° to 345°), NE (345° to 75°), and SE (75° 

to 165°) classes.

2.8.4. Classify Topographic Shading Index layer into 0.25 intervals.

2.8.5. Classify Soil Depth layer into 0.5-m intervals.

2.8.6. Classify LAI layer into 1.0 intervals. 

2.9. Create simulation units for running WXFIRE model.

2.9.1. Combine classified input layers (terrain, soil depth, and LAI), and ecophysiological site and weather layers such that each unique combination forms one simulation unit using Arc/Info’s COMBINE 

command.

2.9.2. Associate values from each input layer to each simulation unit.

2.9.3. Create ASCII file for input to WXFIRE model that lists all the simulation units in a mapping zone 

with their associated site, terrain, weather-coordinates, soils, and LAI values.

2.10. Run WXFIRE simulations and develop biophysical gradient layers.

2.10.1. Input ASCII file to WXFIRE model.

2.10.2. Link each record in ASCII output file from WXFIRE model to its geo-referenced simulation unit 

(from step 2.9).

2.10.3. Create individual biophysical gradient layers for each simulation unit.

3. Mapping potential vegetation type (PVT)

3.1. Prepare data for model building.

3.1.1. Prepare spatially explicit predictor layers (biophysical and topographic gradients).

3.1.1.1. Acquire biophysical and topographic gradients for 3-km buffered zone. 

3.1.1.2. Scale all layers to unsigned 8-bit or 16-bit integers and output summary statistics for each 

layer.

3.1.1.3. Convert layer to unsigned 8-bit or 16-bit integer images.

Appendix 2-A — (Continued)
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Chapter 2—An Overview of the LANDFIRE Prototype Project

3.1.1.4. Quality-check all predictor layers.

3.1.1.4.1. Check projections and row / column numbers for consistency.

3.1.1.4.2. Check all images for erroneous numbers or patterns. 

3.1.2. Prepare response data (PVT classes).

3.1.2.1. Acquire LFRDB MAT with uniqueID, spatial reference, and PVT assignments for plots 

within zone boundary.

3.1.2.2. Examine data spatially and non-spatially, looking for outliers or unusual spatial distributions.

3.1.2.3. Evaluate number of available plots by PVT class to see if classes need to be collapsed or 

dropped.

3.1.2.4. Label each PVT plot as forest or non-forest type using values 1 and 2, respectively.

3.1.3. Perform data extraction.

3.1.3.1. Extract values from each predictor gradient for each X and Y plot coordinate and link to 

the LFRDB MAT.

3.1.4. Perform data exploratory exercises.

3.1.4.1. View data spatially, looking for unusual spatial patterns or outliers.

3.1.4.2. Import data into a statistical package (in other words, R) and examine data for outliers or 

unusual features.

3.1.4.2.1. Examine summary statistics of response (box plots, etc.).

3.1.4.2.2. Examine summary statistics of predictors (distributions, scatter plots, correlation 

matrices, and principal components).

3.2. Generate PVT life form (forest / non-forest) model and map.

3.2.1. Set up input files for the See5 application.

3.2.1.1. Generate an ERDAS Imagine image (dependent variable) of training plots using forest / 

non-forest values.

3.2.1.2. Use NLCD Mapping Tool and Sampling Tool to generate See5 .names input file.

3.2.1.3. Delete .data and .test files that are output from the NLCD Sampling Tool.

3.2.1.4. Export refined training data set to a comma-delimited file (.data) including the uniqueID, 

the predictor gradient values (in the same order as listed in the .names file) and dependent 

(forest / non-forest) value.

Appendix 2-A — (Continued)
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Chapter 2—An Overview of the LANDFIRE Prototype Project

3.2.2. Use See5 to build forest / non-forest model.

3.2.2.1. From See5, open input files (.data and .names).

3.2.2.2. Specify options (such as winnow, boosting, and misclassification cost).

3.2.2.3. Run model with 10-fold cross-validation (for accuracy assessment).

3.2.2.4. Run model without cross-validation (for generating .tree file for prediction).

3.2.3. Apply model across buffered zone.

3.2.3.1. Use NLCD Mapping Tool to generate a Forest / Non-forest map with an associated map of 

confidence.

3.3. Extract value from predicted map of forest / non-forest and link to LFRDB MAT.

3.4. Generate 2 mask images of PVT life form (forest / non-forest).

3.4.1. Create a new image by recoding forest / non-forest image to forest – 1; non-forest – 0.

3.4.2. Create a new image by recoding forest / non-forest image to forest – 0; non-forest – 1.

3.5. Generate forest PVT model.

3.5.1. Set up input files for the See5 application.

3.5.1.1. Query data for forest PVTs, where predicted PVT life form is forest (life form = 1).

3.5.1.2. Generate an ERDAS Imagine image (dependent variable) of training plots using forest 

PVT values from query.

3.5.1.3. Use NLCD Mapping Tool and Sampling Tool to generate See5 .names file.

3.5.1.4. Delete .data and .test files that are output from the NLCD Sampling Tool.

3.5.1.5. Export a randomly selected 10% of the data set to a comma-delimited *.test file.

3.5.1.6. Export remaining 90% of the data set to a comma-delimited *.data file.

3.5.2. Use See5 to build forest PVT classification tree.

3.5.2.1. From See5, open input files (.data and .names).

3.5.2.2. Specify options (such as winnow, boosting, and misclassification cost).

3.5.2.3. Run model (no cross-validation) to generate .tree file for prediction.

3.5.3. Apply model across buffered zone.

3.5.3.1. Use NLCD Mapping Tool and Classifier Tool to generate a map of forest PVTs with an 

associated map of confidence using the forest mask to limit prediction extent.

Appendix 2-A — (Continued)
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Chapter 2—An Overview of the LANDFIRE Prototype Project

3.6. Generate non-forest (shrub and herbaceous) PVT model.

3.6.1. Set up input files for the See5 application.

3.6.1.1. Query database for non-forest PVTs, where predicted PVT life form is forest (life form = 2).

3.6.1.2. Generate an ERDAS Imagine image (dependent variable) of training plots using non-forest 

PVT values from query.

3.6.1.3. Use NLCD Mapping Tool and Sampling Tool to generate See5 .names file.

3.6.1.4. Delete .data and .test files that are output from the NLCD Sampling Tool.

3.6.1.5. Export a randomly selected 10% of the data set to a comma-delimited *.test file.

3.6.1.6. Export remaining 90% of the data set to a comma-delimited *.data file.

3.6.2. Use See5 to build non-forest PVT classification tree.

3.6.2.1. From See5, open input files (.data and .names).

3.6.2.2. Specify options (such as winnow, boosting, and misclassification cost).

3.6.2.3. Run model (no cross-validation) to generate .tree file for prediction.

3.6.3. Apply model across buffered zone.

3.6.3.1. Use NLCD Mapping Tool to generate a map of non-forest PVTs with an associated map of 

confidence using the non-forest mask to limit prediction extent.

3.7. Make final maps and assess accuracy.

3.7.1. Combine forest and non-forest maps.

3.7.2. Combine forest and non-forest error matrices.

3.7.3. Calculate accuracy measures (for example, percent correctly classified, user and producer 

accuracy, and Kappa statistic). 

4. Mapping existing vegetation

4.1. Conduct spatial QA/QC of field plot data

4.1.1. Conduct QA/QC for non-Forest Inventory Analysis (FIA) data point identification.

4.1.1.1. Convert map attribute coordinate data to point attribute (vector) data.

4.1.1.2. Intersect vector coverage with NDVI Change layer

4.1.1.3. Populate table with NDVI difference values. Large differences in NDVI values are likely 

to represent plots without recent major vegetation change. (such as ± 2 std dev. from 

mean NDVI value for table).

Appendix 2-A — (Continued)
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Chapter 2—An Overview of the LANDFIRE Prototype Project

4.1.1.4. Identify plots with a “distance to road” of > 30m.

4.1.1.5. If NLCD data for 2001 is available, compare CTs to NLCD classes to check for matches. 

If NLCD 2001 data is not available, try NLCD 1992 data (provided in LFRDB).

4.1.1.6. Flag values in MAT that require attention based on analyses performed in 4.1.

4.1.2. Identify questionable plots.

4.1.2.1. Overlay points onto imagery stratified by CTs.

4.1.2.1.1. Identify and flag points on roads or other similar types of locations (such as urban 

or agriculture) that should not be used for training. 

4.1.2.1.2. Identify and flag those points that indicate change has occurred since the field 

data were obtained. 

4.1.2.1.3. Identify plots with forest CTs located in relatively intact non-forest locations (and 

vice versa). 

4.1.2.1.4. Identify plots typed as conifer located in relatively intact deciduous forest (and 

vice versa).

4.1.2.2. Flag questionable plots in MAT and omit from future analyses. 

4.1.3. Develop a modified MAT storing only field plots that pass the QA/QC process in 4.1.2. 

4.1.4. Conduct QA/QC for FIA data (same general process as in 4.1.1 but requires FIA analyst).

4.1.5. Isolate 2% of the sample points to be used for accuracy assessment using the 3x3 km, 2% block 

design.

4.2. Preprocess imagery. 

4.2.1. Ensure that Landsat imagery used for LANDFIRE mapping is processed to the following specifications:

4.2.1.1. For each path/row, acquire and process 3 seasonally separate dates (spring, summer, and 

autumn) of Landsat scenes 

4.2.1.2. Conduct geometric rectification to terrain precision correction level, resulting in less than 

± 15m root mean square error (RMSE) spatial accuracy.

4.2.1.3. Conduct radiometric normalization to calibrate radiance values to at-satellite reflectance 

values.

4.2.1.4. Calculate NDVI and tasseled cap transformation values for each of the three dates of the 

data.

4.2.1.5. Develop preliminary maps of forest, shrub, and herbaceous CTs using methods listed in 

3 (potential vegetation mapping) Provide the preliminary maps to the PVT mappers and 

vegetation modelers for internal use.

Appendix 2-A — (Continued)
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Chapter 2—An Overview of the LANDFIRE Prototype Project

4.2.2. Ensure that the PVT map and PVT probability layers are stored in data library

4.2.3. Conduct visual quality check on the PVT layers to ensure no obvious seam lines, dropped pixels, 

or other quality problems exist.

4.2.4. Assemble imagery, topographic data, biophysical gradient layers, PVT probability layers, and 

riparian-wetland mask (if available).

4.3. Map life form-specific CT 

4.3.1. Extract digital values from the spatial layers (4.2.4) using field plots that have passed the visual 

QA/QC inspection process (4.1.3 and 4.1.4).

4.3.2. Determine if a “hierarchical approach” (mapping by high-level stratifications) is needed: if there 

are strong environmental differences between life form-specific CT classes, consider taking the 

hierarchical approach. For example, stratify desert shrub CTs from upland and riparian shrub CTs. 

If the hierarchical approach is needed, go to 4.3.2.1; otherwise, go to 4.3.3.

4.3.2.1. Recode field plot data to high-level CT groups and run decision tree model for high-level 

CT groups.

4.3.2.2. Model CTs with decision tree model under each of the high-level CT groups.

4.3.2.3. Calculate overall cross-validation accuracy by weighting and summarizing all CT groups 

4.3.2.4. If weighted cross-validation is satisfactory, merge all CT groups into one CT map by major 

life form.

4.3.2.5. If weighted cross-validation is not satisfactory, consider rearranging high level groups or 

abandoning the approach.

4.3.3. Run decision tree model separately for forest, shrub, and herbaceous life forms.

4.3.4. Generate life form-specific cross-validation error matrices.

4.3.5. Generate life form-specific CT layers by applying decision tree models (create separate tree, shrub, 

and herbaceous layers).

4.3.6. Check for any visual and information content problems by examining CT maps and interpreting 

error matrices

4.3.7. Determine if there are any rare classes (< 30 field plots) and decide how to treat such rare classes.

4.3.7.1. Option 1: drop rare classes and re-run decision tree models.

4.3.7.2. Option 2: re-run decision tree models without the rare classes and then “burn” rare class 

field plots onto the map.

4.3.7.3. Option 3: merge rare classes with floristically similar classes (solicit feedback from Vegetation Working Group).

4.3.7.4. Option 4: retain the rare classes in the map.

Appendix 2-A — (Continued)
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Chapter 2—An Overview of the LANDFIRE Prototype Project

4.3.8. Determine if other major mapping errors exist and correct by altering input parameters (if possible) 

as well as field-referenced data. 

4.3.9. Apply water, urban, and agriculture masks to life form-specific CT maps.

4.3.10. Merge the 3 life form-specific CT layers to form one CT layer.

4.4. Map life form-specific canopy height (CH) 

4.4.1. Assign life form-specific CH classes to plots in modified MAT (4.1.3 and 4.1.4).

4.4.2. Extract digital values from the spatial layers, including life form-specific CTs (4.3.10), and use 

field plots classified to CH class values from 4.4.1 above.

4.4.3. Run decision tree models separately for the three life forms (tree, shrub, and herbaceous).

4.4.4. Generate life form-specific cross-validation error matrices for CH classes.

4.4.5. Generate life form-specific CH class maps using decision trees.

4.4.6. Check for errors in the three life form-specific CH maps, ensuring ranges of CH values are logical 

for their corresponding CTs.

4.4.7. Mask each CH map with water, urban, and agriculture masks.

4.5. Map life form-specific canopy cover (CC)

4.5.1. Map tree CC

4.5.1.1. Create training set of forest CC using 1-m digital ortho-photography quadrangles or 1-m 

satellite imagery.

4.5.1.2. Establish the relationship between Landsat data and plot data using regression trees.

4.5.1.3. Apply the regression-tree relationship to generate a spatial per-pixel estimate of tree 

canopy for all pixels.

4.5.1.4. Generate cross-validation error matrices, evaluate error and R2 values, and determine effectiveness of the regression tree models.

4.5.1.5. Recode continuous tree CC data to CC classes defined by the Vegetation Working Group.

4.5.1.6. Apply land cover masks: water, urban, and agriculture.

4.5.2. Map shrub and herbaceous CC, option 1:

4.5.2.1. Extract digital values from the spatial layers using field plots that have shrub or herbaceous 

CC associated with them. Use the modified MAT (4.1.3 and 4.1.4).

4.5.2.2. Stratify digital values based upon dominant life form and run regression models.

4.5.2.3. Generate life form-specific error assessments based on cross-validation analysis.

Appendix 2-A — (Continued)
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Chapter 2—An Overview of the LANDFIRE Prototype Project

4.5.2.4. Determine effectiveness of the regression tree models based on error analysis and determine whether changes need to be made to both field data and independent spatial layers.

4.5.2.5. Generate life form-specific CC maps by applying the regression tree models. 

4.5.2.6. Recode continuous variables to CC classes defined by the Vegetation Working Group.

4.5.2.7. Apply land cover masks: water, urban, and agriculture.

4.5.3. Map shrub and herbaceous CC, option 2:

4.5.3.1. Recode plot CC values in modified MAT (4.1.3 and 4.1.4) into CC classes defined by 

Vegetation Working Group.

4.5.3.2. Extract digital values from the spatial layers (4.2.4) using binned shrub or herbaceous field 

plots from step 4.5.3.1.

4.5.3.3. Stratify digital values based upon dominant life form (shrub and herbaceous vegetation) 

and run decision tree models.

4.5.3.4. Generate life form-specific error assessments based on cross-validation analysis.

4.5.3.5. Determine effectiveness of the decision tree models based on error analysis and determine 

whether changes need to be made to both field data and independent spatial layers.

4.5.3.6. Generate life form-specific CC layers by applying the decision tree models.

4.5.3.7. Apply land cover masks: water, urban, and agriculture.

4.5.4. Map shrub and herbaceous CC, option 3:

4.5.4.1. Measure field spectral bands (corresponding to Landsat red and NIR bands) from multiple 

shrub and grass sites and derive field NDVI values.

4.5.4.2. Estimate percent shrub and herbaceous CC for sites where field spectral data has been 

acquired (1-m2).

4.5.4.3. Determine relationship between field percent CC estimates and field-measured NDVI 

values. 

4.5.4.4. Estimate continuous shrub and grass CC through application of relationship described in 

step 4.5.4.3 to Landsat NDVI to standardize Landsat CC estimates (stratified by life form 

using NLCD 2000 data and/or LANDFIRE CT data). 

4.5.4.5. Recode continuous shrub or herbaceous variables to CT classes defined by the Vegetation 

Working Group.

4.5.4.6. Apply land cover masks: water, urban, and agriculture.

4.5.5. Refine and normalize CC estimates.

Appendix 2-A — (Continued)
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Chapter 2—An Overview of the LANDFIRE Prototype Project

4.5.5.1. Normalize individual tree, shrub, and herbaceous CC values such that tree, shrub, and 

herbaceous CC values combined do not exceed 100% per pixel.

4.5.5.2. Locate zones of low confidence using confidence layers and other sources of information.

4.5.5.3. Mask out zones of low confidence for shrub and grass CTs where forest is the dominant CT.

4.6. Generate merged CT and SS maps

4.6.1. Revisit, and revise if necessary, the merged CT map (4.3.10) by using forest, shrub, and herbaceous percent CC as reference. Ensure that CTs match life form CC maps. 

4.6.2. Produce a Federal Geographic Data Committee (FGDC) -compatible metadata file for the final 

merged CT map (4.6.1) using a mapping zone metadata template for CT.

4.6.3. Generate a single CH layer using the CT data layer (4.6.1) for life form masking stratification.

4.6.4. Produce an FGDC-compatible metadata file for the final merged Canopy Height layer (4.6.3) using 

a mapping zone metadata template for CH.

4.6.5. Generate a single CC layer using the CT layer (4.6.1) for life form masking stratification.

4.6.6. Produce an FGDC-compatible metadata file for the final merged Canopy Cover layer (4.6.5) using 

a mapping zone metadata template for CC.

4.7. Conduct cross-validation and accuracy assessments

4.7.1. Summarize all cross-validation errors and accuracy tables for the mapping zone; provide information to the Accuracy Working Team.

4.7.2. Extract the final CT, CC, and CH class values and labels (from 4.6.1, 4.6.3, and 4.6.5) using 

withheld plot locations (4.1.5). Provide extracted data to the Accuracy Working Team.

4.7.3. Evaluate error matrices, overall accuracy, and user and producer accuracy.

5. Mapping ecological departure

5.1. Acquire and develop input layers.

5.1.1. Acquire vegetation layers: PVT, CT, and SS. 

5.1.2. Create Landscape Reporting Unit (LRU) layer by building grid of 900-m x 900-m squares.

5.1.3. Acquire polygon coverage that partitions zone into smaller units – termed simulation landscapes – 

used in LANDSUMv4 simulations.

5.2. Calculate ecological departure and index of significance using HRVStat approach.

5.2.1. Acquire data for historical reference conditions of vegetation patterns, developed using LANDSUMv4 model, including year, LRU, PVT, succession class, and area (m2

).

Appendix 2-A — (Continued)
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Chapter 2—An Overview of the LANDFIRE Prototype Project

5.2.1.1. Partition data into series of files, which function as LANDSUMv4 output for each simulation landscape within a zone.

5.2.1.2. Remove agriculture and urban CTs from reference conditions database.

5.2.2. Combine CT and SS layers to represent current succession classes using ArcInfo’s COMBINE 

command.

5.2.3. Combine Succession Class layer with LRU and simulation landscape layers.

5.2.4. Join historical reference conditions with current succession class data for each unique LRU within 

each simulation landscape of zone data. Create series of ASCII files with these data for each simulation landscape.

5.2.5. Convert ASCII files from text to binary format.

5.2.6. Run the HRVStat program. The HRVStat program outputs ASCII text files containing calculations 

of departure, observed significance level, and classified HRVStat departure for each LRU within 

each simulation landscape file. 

5.2.7. Link HRVStat ASCII output files to LRU layer to develop individual layers of ecological departure, observed significance level, and classified HRVStat departure.

5.3. Calculate ecological departure using FRCC Guidebook approach.

5.3.1. Isolate analysis to individual simulation landscapes.

5.3.2. Combine values for LRU, PVT, CT, and SS. Combined CT and SS information form succession 

classes.

5.3.3. Concatenate the LRU, PVT, CT, and SS fields to create unique IDs for LRU/PVT/succession class 

and LRU/PVT combinations. 

5.3.4. Calculate current fire regime (CFR) by dividing the area of each succession class within an LRU/

PVT combination into the total area (m2) of the LRU/PVT combination. 

5.3.5. Access the LANDSUMv4 files for each simulation landscape. 

5.3.5.1. Create unique IDs for the LANDSUMv4 data corresponding to those of the CFR data.

5.3.5.2. Calculate the 90th percentile for each LRU/PVT/succession class combination and then 

export to historical fire regime (HFR) database.

5.3.6. Join HFR database records for the current simulation landscape with CFR database records using 

the LRU/PVT/succession class field.

5.3.7. Compute similarity, which is the smaller of CFR or HFR.

5.3.8. Total the similarity values across each LRU/PVT combination.

5.3.9. Compute departure as 100 – similarity. This represents the estimated ecological departure for a 

PVT in an LRU.

Appendix 2-A — (Continued)
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Chapter 2—An Overview of the LANDFIRE Prototype Project

5.3.10. Compute departure for entire LRU on an area-weighted basis. Weighting factors are derived 

by dividing the area (m2) of each individual PVT into the area of each LRU (constant at 81 ha or 

900 x 900 meters).

5.3.11. Merge all individual simulation landscapes together to create map for entire zone.

6. Mapping surface fuel models

6.1. Acquire rectified CT, PVT, and SS layers.

6.2. Combine these layers in a GIS format.

6.3. Export combined vegetation data and import into Access.

6.3.1. Assign CT, PVT, and SS names to the coded information from GIS layers.

6.4. Build rule sets for Anderson’s (1982)13 fire behavior fuel models and Scott & Burgan’s (2005) 40 fire 

behavior fuel models.

6.4.1. Use Forest Vegetation Simulator-Fire and Fuels Extension (FVS-FFE) documentation on variant 

fuel rules from Reinhardt and Crookston (2003).

6.4.2. Use additional information, such as local fire and fuel plans, fire behavior studies, other fuel research.

6.4.3. When necessary, consult local experts.

6.4.4. Compare rate of spread and flame length for each fuel model to ensure that fuel models are not illogically assigned to a specific vegetation combination. For example, we would not assign a FBFM 

3 in grass systems that are only 1 foot tall.

6.4.5. Construct logical crosswalks between combined vegetation layers and fuel models.

6.4.5.1. Timber-dominated systems are usually assigned timber FBFMs.

6.4.5.2. Herbaceous systems are usually assigned grass FBFMs. Shrub systems can be assigned 

timber, shrub, or grass models, depending on composition and structure.

6.5. Apply rule set to vegetation combinations and assign surface fuel models in Access table.

6.5.1. Use key to assign fuel models to each combination of vegetation attributes.

6.5.2. Map fire behavior fuel models by linking the combination database to a GIS layer. 

7. Mapping canopy fuel

7.1. Prepare data for model building.

7.1.1. Prepare spatially explicit predictor layers (biophysical and topographic gradients and Landsat 

satellite imagery).

7.1.1.1. Acquire biophysical and topographic gradients for 3-km buffered zone (as unsigned 16-bit 

images). 

Appendix 2-A — (Continued)
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Chapter 2—An Overview of the LANDFIRE Prototype Project

7.1.1.2. Acquire Landsat imagery.

7.1.1.3. Quality-check all predictor layers.

7.1.1.3.1. Ensure all layers are unsigned 8-bit or 16-bit integers.

7.1.1.3.2. Check projections and row/column numbers for consistency.

7.1.1.3.3. Check all images for erroneous numbers or patterns. 

7.1.2. Prepare response data (canopy bulk density [CBD] and canopy base height [CBH]).

7.1.2.1. Set up input table for FUELCALC program, including field-referenced tree attributes from 

LFRDB.

7.1.2.2. Run the FUELCALC program.

7.1.2.3. Link FUELCALC output with LFRDB table (or FIA table).

7.1.3. Perform data extraction.

7.1.3.1. Extract values from each predictor gradient for each X and Y plot coordinate and link to 

the LFRDB MAT.

7.1.4. Perform data exploratory exercises.

7.1.4.1. Import coordinates into ArcMap and view data spatially, looking for unusual spatial 

patterns or outliers.

7.1.4.2. Import all data into a statistical package (in other words, R) and examine data for outliers 

or unusual features.

7.1.4.2.1. Examine summary statistics of response (histograms, box plots, etc.).

7.1.4.2.2. Examine summary statistics of predictors (distributions, scatter plots, correlation 

matrices, and principal components).

7.1.4.3. Create another variable, CBDx, in database with value: CBD * 100.

7.1.4.4. Create another variable, CBHx, in database with value: CBH * (0.3048 * 10).

7.2. Use NLCD Mapping Tool to set up input files for Cubist application.

7.2.1. Generate an ERDAS Imagine image (dependent variable) of training plots using CBDx/CBHx 

values.

7.2.1.1. Import Access table with X/Y coordinates (Albers) and CBDx/CBHx data into Arcmap.

7.2.1.2. Define extent identical to that of predictor layers.

Appendix 2-A — (Continued)
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Chapter 2—An Overview of the LANDFIRE Prototype Project

7.2.2. Use NLCD Mapping Tool and Sampling Tool to generate Cubist .names input file.

7.2.2.1. Set dependent (response) variable as the CBDx image.

7.2.2.2. Set independent (predictor) variable as the list of imagery, gradient, and topographic layers 

used for modeling (in the refined data set).

7.2.2.3. Specify sampling process: set sample to random and set number of samples to 99% 

training and 1% validation.

7.2.2.4. Set name and location of output files.

7.2.2.5. Select model as Cubist.

7.2.2.6. Review .names file to make sure all variables are specified and all discrete variables have 

codes.

7.2.3. Export .data input file from Access to Cubist.

7.2.3.1. Delete .data and .test files that are output from the NLCD Sampling tool.

7.2.3.2. Export refined training data set from Access to a comma-delimited file (.data), including 

the predictor gradient values and dependent (CBD) value.

7.3. Use Cubist to build model.

7.3.1. From Cubist, open input files (.data, .names).

7.3.2. Specify options

7.3.3. Run cubist model with test data set (generating a .rules file for prediction).

7.3.4. Run multiple models with different options and select the model with the highest accuracy.

7.4. Apply model across buffered zone.

7.4.1. Use NLCD Mapping Tool to generate a map of CBDx/CBHx with associated map of confidence.

7.4.2. Analyze output maps.

7.4.3. Check accuracy and run diagnostics.

Appendix 2-A — (Continued)

USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 

Chapter 3—The Scientific Foundation of the LANDFIRE Prototype Project

Chapter

3

In: Rollins, M.G.; Frame, C.K., tech. eds. 2006. The LANDFIRE 

Prototype Project: nationally consistent and locally relevant 

geospatial data for wildland fire management. Gen. Tech. Rep. 

RMRS-GTR-175. Fort Collins: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research Station.

Introduction ____________________

The Landscape Fire and Resource Management Planning Tools Prototype Project, or LANDFIRE Prototype 

Project, originated from a recent mapping project that 

developed a set of coarse-scale spatial data layers for 

wildland fire management describing fire hazard and 

ecological status for the conterminous United States 

(Hardy and others 2001; Schmidt and others 2002; www.

fs.fed.us/fire/fuelman). Schmidt and others (2002) used 

linear succession transition pathways to estimate vegetation conditions that occurred on historical landscapes 

represented by combinations of land cover types and 

structural stages from existing vegetation databases. The 

comparison of current landscape conditions with these 

historical successional sequences provided a means for 

assessing departure and for creating an index, called 

Fire Regime Condition Class (FRCC), that reflects the 

magnitude of departure (see Hann 2004 and www.frcc.

gov).

Although maps generated from this coarse-scale mapping project provided fire management with a first-ever 

The Scientific Foundation of the LANDFIRE 

Prototype Project

Robert E. Keane and Matthew Rollins

picture of ecosystem conditions across the lower 48 

states or conterminous U.S., they contained problems 

that limited their use at finer resolutions and for smaller 

areas. First, the maps were developed with a large grain 

size (pixel size) of 1 km square. Very few field-referenced 

data sets are compatible with this large pixel size, making it difficult to develop empirical predictive models 

to increase or assess accuracy. Second, the project was 

limited to existing spatial data layers, including the land 

cover type and stand structure maps, to describe current 

and historical conditions. The legends of these layers 

tended to have categories that were broad and therefore 

difficult to describe uniquely with any degree of accuracy 

(Schmidt and others 2002). Moreover, the existing map 

layers used in this project were developed at different 

map scales and with varied resolution and detail because 

they were created from various independent efforts that 

had disparate objectives. As a result, it was often difficult 

to rectify the broad and sometimes inconsistent map 

categories between maps. Last, the majority of the data 

layers were based on models that were parameterized 

using ecologists’ and managers’ estimates rather than 

with data collected in the field. Because of these limitations, the coarse-scale mapping products could be used 

only for large, regional assessments and were essentially 

useless for finer-scale applications such as national forest 

management plan revision and implementation or local 

wildland fire hazard assessments.

The inability of the coarse-scale mapping project to 

aid in finer applications spurred the U.S. Department 

of Agriculture (USDA) Forest Service (FS) Fire and 

Aviation Management (FAM) to explore the possibility 
46 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 20

Chapter 3—The Scientific Foundation of the LANDFIRE Prototype Project

of developing a set of data layers that could be used at 

multiple scales, from national planning to local fuel treatment design. To make regional and local comparisons 

possible, it was essential that each layer be constructed 

in a manner ensuring that all areas of the country receive 

the same rigor and detail in the mapping effort.

In December of 1999, FAM met with scientists at the 

USDA FS Rocky Mountain Research Station (RMRS) 

Missoula Fire Sciences Laboratory (MFSL) to request 

the design and implementation of this national, fine-scale 

mapping project. The MFSL scientists wrote a proposal 

and study plan for this project and submitted both to 

FAM in March of 2000. In 2002, the project was funded 

by the United States Department of Agriculture Forest 

Service and Department of the Interior, with an annual 

cost of approximately $2 million. The MSFL scientists 

started the project in April of 2002 and called it the 

“LANDFIRE Prototype Project” (Rollins and others, 

Ch. 2). These methods and tools were tested in two large 

geographic areas (called prototype areas) during 2003 

and 2004 and revisions to the process occurred during 

2004 and 2005. The LANDFIRE Prototype Project was 

completed in April of 2005 and the results are currently 

being implemented across the nation. Meanwhile, the 

General Accounting Office (GAO) published two reports 

on the increasing threat of intense wildland fires across 

the nation (GAO 2002) and fire management’s inability 

to cope with this emerging threat (GAO 2004), and GAO 

identified LANDFIRE as a viable project for generating 

the spatial data needed to plan and implement a national 

fire program (Rollins and others, Ch. 2).

This chapter documents the history, concepts, theory, 

and scientific foundation of the development of LANDFIRE products as they were applied to the two prototype 

areas. First, the background of the project is presented 

to detail the project’s design criteria and guidelines. 

Then, the theory and development of each integrated 

LANDFIRE task is discussed in the context of the overall 

project. This chapter does not describe how each phase 

of LANDFIRE was accomplished (for this information, 

see Rollins and others, Ch. 2); rather, it describes the 

scientific background and concepts behind the design of 

LANDFIRE and the theory and ideas behind the development of the project’s tasks. Although the methods and 

protocols presented here were implemented and refined 

for two prototype areas only (Rollins and others, Ch. 

2), this report is written with the perspective that these 

methods will eventually be applied throughout the entire 

United States.

Background ____________________

Project Objectives

The primary objective of the LANDFIRE Prototype 

Project was to develop a fine-scale (30 meter) digital 

map (spatial data layer) of Fire Regime Condition Class 

(FRCC). FRCC is an ordinal index with three categories 

indicating how far the current landscape has departed 

from historical conditions (Hann 2004) (see www.frcc.

gov for complete details). This data layer can be used 

to identify those areas that are in need of treatment to 

reduce wildland fire hazard and protect homes and lives. 

Fire management can then use this information to allocate funding, fire fighting resources, and personnel to 

these areas for implementation of restoration activities 

and fuel reduction treatments (Laverty and Williams 

2000) (see www.fireplan.gov for details).

The FRCC layer’s primary use lies in fire management planning and resource allocation, with limited 

use in designing and implementing possible treatments. 

We therefore identified several secondary objectives of 

the LANDFIRE Prototype Project aimed at creating 

spatial and software products to aid fire management 

in implementing the National Fire Plan’s Cohesive 

Strategy (Laverty and Williams 2000). For example, 

we specified the development digital maps of wildland 

fuel (surface and canopy) and of vegetation composition and structure as important data for designing fuel 

treatments. We eventually identified 24 core data layers 

to be developed as LANDFIRE Prototype products (see 

table 1 in Rollins and others, Ch. 2).

Design Specifications

A set of design criteria was developed during the initial 

LANDFIRE organizational meetings with USDA FS 

MSFL scientists and FAM. Most of these criteria resulted from the abbreviated timelines and solid deadlines 

imposed by FAM and other fire management agencies. 

Other criteria were imposed as a result of FAM’s condition that LANDFIRE be based on a strong scientific 

foundation and be scalable to local applications with 

minimal modification.

The design criteria for the LANDFIRE Prototype are 

listed below and are detailed in the following paragraphs. 

According to the design criteria, the LANDFIRE Prototype Project must be:

•	 based on the best available science,

•	 able to be implemented consistently across the 

nation,
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Chapter 3—The Scientific Foundation of the LANDFIRE Prototype Project

•	 mapped at a 30-meter pixel size,

•	 reliant on no new field data collection,

•	 repeatable in time and space,

•	 scalable in application,

•	 developed within a three-year development timeline, 

and

•	 tested for accuracy.

Fire and Aviation Management’s overriding design 

specification was that the products generated from 

LANDFIRE must be based on the best available science. 

This meant that the many tools, methods, programs, 

models, and protocols used in LANDFIRE to produce 

the FRCC maps needed to have a publication record, 

preferably in peer-reviewed journals, or in some way 

demonstrate acceptance by the scientific community. 

This limited the methods and procedures, especially 

those involving spatial analyses, to those that are citable 

in the literature. This specification implied that LANDFIRE development should have a minimal amount of 

subjectivity. We interpreted this to mean that each data 

layer and model should be developed using methods that 

are repeatable and objective. Some tasks in LANDFIRE 

required the creation of new methods that are not in the 

literature. In these cases, we developed the new methods from scientifically credible sources and prepared 

manuscripts for publication describing these methods.

To build LANDFIRE on a scientifically credible 

foundation, we based the development of all products in 

LANDFIRE on an extensive, plot-level database, called 

the LANDFIRE reference database (LFRDB) (fig. 1) 

(Caratti, Ch. 4). Every product generated by LANDFIRE was developed from legacy field data collected 

by resource professionals and screened for quality and 

consistency (the LANDFIRE Prototype Design section 

below gives additional detail on the database). This data 

compilation process ensured that each data layer could 

be easily recreated (repeatable), an especially important 

feature when additional data become available in the 

future. In addition, this process allowed for accuracy 

assessment, and the reference plot data could be used 

to scale LANDFIRE products for local applications by 

facilitating the development of additional, more detailed 

classifications of cover types, structural stages, and fuel 

models. A major complication arose from the design 

specification that, to reduce the costs of and time for 

project development, no additional data be collected. 

This meant no new data could be added to the database 

to supplement the ecosystems or geographic areas where 

data were lacking. This criterion was relaxed, however, 

when we found significant gaps in data coverage for the 

mapping of current vegetation conditions.

The second most important design standard was that 

LANDFIRE products must have the potential for national implementation. This meant that all ecosystems 

had to be mapped and modeled with the same degree 

of complexity and detail, ensuring that the FRCC categories would have the same meaning across the entire 

conterminous U.S. In other words, a “red” pixel in 

Maine would mean the same as a “red” pixel in Arizona. 

For LANDFIRE, we used the terms “consistent” and 

“comprehensive” to define this national implementation. 

Consistent means that we used the same methods of 

mapping and modeling for every pixel in the conterminous U.S., and comprehensive means that we used 

the same rigor (a thorough and wide-ranging approach) 

for all LANDFIRE tasks, regardless of ecosystem or 

geographic area.

The 30-meter pixel size condition specified by FAM 

was an important criterion because they wanted finescale applications to be possible for all LANDFIRE 

map products. This posed a significant scale problem 

because it is difficult to match map category and simulation model resolution for national implementation to 

the small spatial resolution of 30 meters; however, this 

pixel size matched the grain of the satellite imagery and 

digital elevation models that were used to map current 

vegetation conditions.

Another important criterion was that the project had 

to be developed so that it could be repeated every five 

to ten years to assess and monitor the efficacy of the 

National Fire Plan and the Cohesive Strategy (www.

fireplan.gov) across the U.S. in a standardized format. 

By repeating the LANDFIRE process every five to ten 

years, fire management would have a vehicle by which 

to monitor the success of the National Fire Plan and 

Cohesive Strategy across multiple spatial scales. Subjective and arbitrary methods and protocols had no place 

in the creation of any LANDFIRE product because they 

are difficult to consistently replicate.

The specification that LANDFIRE include tools and 

methods to scale maps and models both upwards (coarser) 

and downwards (finer) presented a major challenge in 

the creation of many LANDFIRE products. Although 

important, this turned out to be a time-consuming design 

requirement because it required that all products have the 

ability to be scaled both in geospatial and management 

applications. For example, the vegetation data layers 

needed to be developed so that refinement and additional 

detail in the vegetation classification categories could 

be remapped on smaller landscapes with relative ease. 

This stipulation also meant that tools were needed that 

integrate the LANDFIRE products into value-added 
48 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 20

Chapter 3—The Scientific Foundation of the LANDFIRE Prototype Project

Figure 1—Flow chart of all LANDFIRE software and products culminating in the creation of the FRCC layers. All 

squares in the figure are digital maps, circles are analysis tasks involving software, and ovals are data sources.

The acronyms are defined as follows: DEM - digital elevation model, PVT - potential vegetation type, PET - potential

evapotranspiration, NPP - net primary productivity, and GIS AML - geographic information systems macro language. 

All other acronyms are program names that are detailed in this report.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 

Chapter 3—The Scientific Foundation of the LANDFIRE Prototype Project

products that have more specific meaning and utility 

for fire management, especially at local scales. For 

example, the LANDFIRE fuel layers can be used in 

the Fire Hazard Rating Model (FIREHARM), to create 

spatial data layers of expected fire behavior and effects. 

Moreover, all of LANDFIRE analysis programs and 

models are open source so that they can be modified 

for local situations and applications.

As specified by FAM, the entire LANDFIRE Prototype Project needed to be completed in three years. This 

included the final 24 map layers, the analysis software, 

and the final draft manuscripts of all chapters presented 

in this report. This timeline required that the entire 

process had to be developed within the first 18 months 

and implemented within the first prototype mapping 

zone during the following six months. Revisions and 

refinements needed to be made, and the second prototype 

area had to be mapped during the following six months. 

The chapters in this report were written during the last 

six months. This accelerated timeline dictated the detail 

of analysis for many LANDFIRE tasks and prevented 

a more thorough investigation of a few aspects of the 

project.

The last design requirement specified that every data 

layer be assessed for accuracy to ensure and document 

the quality and precision of LANDFIRE products. As 

mentioned, we created the LFRDB to serve as a foundation for meeting the accuracy assessment requirement. 

There is, however, a wide range of accuracy assessments: from thematic accuracy (Is this pixel mapped 

correctly?) to classification accuracy (How often does 

this classification fit real field data?) to model accuracy 

(How often does the statistical mapping model predict 

the correct answer?). FAM identified thematic accuracy 

as the primary target. The estimation of this measure 

has many problems, however, including the possibility 

of field data having 1) inaccurately georeferenced plot 

coordinates, 2) errors in data measurement and entry, 

and 3) inexperience and inconsistency within field crews 

(Vogelmann and others, Ch. 13). We therefore attempted 

to report as many types of accuracy measures as possible, using the reference database and data from other 

modeled sources, to provide a more complete assessment of accuracy. The main three measures of accuracy 

that we calculated for most LANDFIRE products were 

1) thematic accuracy or the accuracy of a pixel being 

mapped correctly, 2) landscape accuracy or the accuracy 

of pixel summaries across large areas, and 3) model 

accuracy or the accuracy of the statistical models used 

to build the maps (Vogelmann and others, Ch. 13).

Guiding Principles

Given these design specifications, we then developed 

a set of principles that we used to guide the development 

of all LANDFIRE data layers. These principles were a 

direct outgrowth of the design specifications, and they 

allowed us to set boundaries for the development of 

every LANDFIRE product. These guidelines were as 

follows:

•	 Development should be targeted at mid-scale map 

classifications.

•	 All themes must be identifiable, scalable, mappable, 

and model-able.

•	 Mapping applications must incorporate the biophysical gradients that determine the distribution 

of vegetation and disturbance regimes across landscapes.

•	 The primary development tool should be simulation 

modeling.

•	 The timespan for historical reference conditions 

would be 1600 AD to 1900 AD.

Development should be targeted at mid-scale – The 

mid-scale development target was chosen because of 

the national implementation design specification. It 

was unrealistic to develop and map classifications of 

vegetation at finer scales for several reasons. First, 

national vegetation classifications that are scalable to 

local applications are nonexistent or, at best, incomplete, 

especially for mapping purposes (Anderson and others 

1998; Grossman and others 1998). Next, most existing 

classifications do not provide for the shared dominance 

of two or more species in a consistent manner for the 

entire nation (Long and others, Ch. 6). In addition, many 

rare vegetation types that are important to management 

have not been sampled in the field and were therefore 

under-represented in the LANDFIRE reference database, making it difficult to map rare vegetation types 

at a national scale. Last, there was not enough time to 

develop the detail in our simulation models required 

for fine-scale mapping. For these reasons, we decided 

to aim for a mid-scale target in the development of all 

LANDFIRE intermediate and final products, including 

map categories, model elements, and fuel classes.

In keeping with this mid-scale development, we chose 

to use the USGS Center for Earth Resources Observation and Science (EROS) mapping zones (Rollins and 

others, Ch. 2) to define the spatial extent which guided 

the design of all vegetation classifications and simulation models. To that end, we decided that a vegetation 

class must occur in at least five percent of the mapping 
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Chapter 3—The Scientific Foundation of the LANDFIRE Prototype Project

zone to warrant its mapping. This restriction eliminated 

from consideration many vegetation types which, although important for local applications, are uncommon 

across an entire mapping zone. It is doubtful that we 

could have mapped these types anyway as there were 

so few representative data for these in the LFRDB. It is 

important to note that the mid-scale development target 

concerned mainly the vegetation and fuel classifications 

that were mapped across the nation, not the spatial and 

temporal resolution of the maps; we still had to meet 

the fine-scale design specification of a 30-meter spatial 

resolution.

All themes must be identifiable, scalable, mappable, 

and model-able — The categories within the vegetation map unit classification mapped by the LANDFIRE 

Prototype team served as the building blocks used to 

construct all LANDFIRE Prototype maps and models. 

It was essential that the design of the classification 

match the detail provided by the 1) imagery used to map 

existing conditions, 2) empirical models used to assign 

attributes to vegetation categories, and 3) simulation 

models used to determine historical conditions (Long 

and others, Ch. 6; Zhu and others, Ch. 8; Pratt and others, 

Ch. 10). For LANDFIRE, we specified that classification categories must meet four criteria to warrant their 

mapping. First, the classification categories had to be 

identifiable on the ground and in the LFRDB, meaning that we had to be able to construct a key that would 

uniquely identify the classes from commonly sampled 

vegetation characteristics, especially those present in the 

reference database. Moreover, field crews needed to be 

able to use the LANDFIRE key to accurately identify 

the vegetation classification type on the ground. Second, 

the classifications had to be scalable; similar categories 

needed to have the ability to “collapse” to form a coarser 

classification, or the ability to expand so that additional, 

finer-scale categories could be added with little effort. 

Third, map categories had to be mappable, meaning 

that the methods used to map entities had to be able to 

detect this classification category. For example, we did 

not map upland willow shrubland types if the Landsat 

imagery (EOSAT 1992; http://landsat.gsfc.nasa.gov/) 

could not distinguish them from other shrubland types. 

Lastly, the vegetation categories needed to be consistent 

in scale and detail with the simulation model entities 

used to estimate historical conditions (model-able). 

Categories that included both shade-tolerant and shadeintolerant tree species, for example, were not included 

in the classification because they could not be used to 

simulate seral states in models of vegetation succession 

(Long and others, Ch. 6; Long and others, Ch. 9).

Methods must stress those ecosystem processes 

that govern mapped entities — LANDFIRE National 

ultimately depends on a set of core data layers that are 

consistent and comprehensive across the entire nation. 

We employed these layers as the raw material to construct other data layers used to create the LANDFIRE 

mapping products. It was especially important that these 

core layers represent those fundamental biophysical 

processes that govern vegetation, fuel, and fire dynamics. In addition, we wanted to incorporate all possible 

existing national 30-meter scale layers into the LANDFIRE Project to ensure that our GIS library had the 

latest mapping technology; however, upon researching 

the availability, extent, and quality of possible core data 

layers, we found very few that had national coverage 

and fine-scale resolution (30-meter). The only two sets 

of nationally consistent data layers that fit LANDFIRE’s 

needs were the USGS National Elevation Database (ned.

usgs.gov/), which served as the LANDFIRE Digital 

Elevation Model (DEM), and the extensive catalog of 

Landsat data from the USGS Multi-Resolution Land 

Characteristics (MRLC) 2001 project (landcover.usgs.

gov/index.asp).

Given the lack of comprehensive, nation-wide GIS 

data layers, we decided to create our own set of base 

layers, called the biophysical gradient layers, because 

we wanted these layers to be consistent in design, extent, 

and detail so that we could use them in complex analyses and minimize error resulting from independent and 

sometimes incompatible sources. Creating our own layers 

also allowed us the opportunity to build a GIS library 

of consistent data layers describing those fundamental 

processes that govern vegetation, fuel, and fire dynamics. The LANDFIRE Prototype’s core layers were data 

we obtained from elsewhere and the base layers were 

those we built to describe basic ecosystem processes.

The biophysical gradient layers were developed using 

a process-based approach that emphasized the creation 

of layers describing the fundamental biophysical drivers 

influencing the vegetation, fuel, and fire characteristics 

that we were to map. Having previously used this approach to map vegetation and fuel characteristics on the 

Kootenai National Forest, we found that it increased 

map accuracies from 10 to 30 percent (Keane and others 

2002a; Rollins and others 2004). In this approach, we 

used spatially explicit biophysical gradients to augment 

the mapping of vegetation and fuel composition and 

structure. Incorporating these gradients in our mapping 

process allowed for increased discrimination between 

mapped categories because the gradients represent 

the causal mechanisms that physically determine the 
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Chapter 3—The Scientific Foundation of the LANDFIRE Prototype Project

distribution of vegetation and fuel across landscapes. 

For example, average annual soil water availability 

is often a better indicator of growing conditions than 

average annual precipitation, especially for fine-scale 

applications.

Several databases were needed to build this biophysical 

gradient base layer library. First, we needed the DEM 

(ned.usgs.gov/) to represent topography across the 

nation. Next, we needed a daily weather database for 

the entire nation with a temporally deep record. The 

DAYMET national weather database (www.daymet.

org) was selected because of its national coverage at 

1-km2 resolution and 18-year daily resolution weather 

record (Running and Thornton 1996; Thornton and others 1997; Thornton and White 1996). The STATSGO 

layer was used to represent soil because of its national 

scope; however, because it was developed at very coarse 

scales, we performed several procedures to compile the 

layers for LANDFIRE analyses (Holsinger and others, 

Ch. 11). Lastly, we needed a set of simulation models 

that could take the DEM, DAYMET weather data, and 

STATSGO information and produce mechanistic maps 

that best discriminate vegetation dynamics. We used the 

LANDFIRE Biogeochemical Cycles (LFBGC) model 

(Thornton and others 2002; Thornton and White 1996; 

White and others 2000) to simulate ecosystem processes, 

the WXFIRE model to simulate weather and climatic 

processes (Keane and Holsinger 2006), and a landscape 

succession model called LANDSUMv4 to generate historical chronosequences (Keane and others 2006; Pratt 

and others, Ch.10; Keane and others 2002b). We found 

no other existing layers that met our design criteria and 

development guidelines.

The primary development tool should be simulation modeling — The use of simulation modeling as 

the primary development tool was an important guiding 

principle in LANDFIRE because it was used to create 

the biophysical layers, as previously discussed, and also 

to quantify historical landscape conditions. Simulation 

modeling provides a comprehensive and consistent 

method to create fine-scale data layers across large 

domains such as the conterminous U.S. The LFBGC 

and WXFIRE models were used to map basic ecosystem processes. In addition, we used a spatial model 

that simulates fire and vegetation dynamics to generate 

chronosequences of historical landscape conditions. The 

LANDSUMv4 model, recreates historical landscapes 

using fire and succession parameters quantified from 

the LFRDB (Keane and others 2002b; Keane and others 

2006; Pratt and others, Ch. 10).

The timespan for historical reference conditions 

would be 1600 AD to 1900 AD — Determining the 

benchmark years within which to frame the historical 

reference conditions posed a special challenge. The 

calculation of FRCC requires that current conditions be 

compared with historical reference conditions, yet these 

historical conditions differ according to the time span 

used to demarcate the historical temporal reference. The 

reference time span needed to be long enough to contain 

ample variation in fire and vegetation dynamics but short 

enough to ensure that the span is fully documented with 

field data and historical observations. We determined 

recent history to be a better reference for current conditions because climate, soil, and vegetation distributions 

are roughly similar and because recent history is the 

only era that contains field data of the resolution useful 

in LANDFIRE. We selected the year 1900 A.D. as the 

end of the historical period because it best represents 

the start of significant Euro-American influences on 

western U.S. landscape characteristics (Keane and others 

2002c). The year 1900 marks the approximate start of 

the industrial revolution, the settlement of the West, the 

fire exclusion era, and active land management (Baron 

2002). We used the findings of fire history studies to 

guide our determination of the start of the historical 

period, and we found most studies had recorded fires 

dating back to at least 1600 A.D., although some went 

back much further (Heyerdahl and others 1995; www.

ngdc.noaa.gov/paleo/impd). This date of 1600 seemed 

the best compromise for all ecosystems and fire history 

findings. We concluded that this span of years best represented the historical reference conditions for use in 

managing today’s landscapes. The historical reference 

years may need to be revisited when mapping the eastern 

portion of the U.S.

LANDFIRE Prototype Design ______

General Description

This chapter summarizes the scientific foundation 

underlying all phases of the LANDFIRE Prototype 

Project, and other chapters in this report detail each 

individual phase. A complete description of the procedures for creating the entire suite of LANDFIRE 

Prototype products is given in Rollins and others, Ch. 

2. This chapter focuses on the concepts that were used 

to create the LANDFIRE products, rather than detail 

actual methods or processes.

The general flow of logic, product development, and 

analysis for the LANDFIRE Prototype can be followed 
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Chapter 3—The Scientific Foundation of the LANDFIRE Prototype Project

in figure 1, and it illustrates the organization of the content in the following sections. In short, the computation 

of FRCC requires a comparison of current conditions 

to historical conditions. In the LANDFIRE Prototype 

Project, current conditions are described using maps 

of vegetation composition (cover types) and structure 

(structural stages) derived from a supervised classification of Landsat imagery and using continuous maps of 

biophysical gradients and Landsat imagery (fig. 1; Zhu 

and others, Ch. 8; Holsinger and others, Ch. 11). Vegetation composition and structure were described using 

cover type and structural stage classifications developed 

specifically for LANDFIRE. The biophysical gradient 

layers were created from two ecosystem simulation 

models that compute variables describing fundamental 

physical processes that govern vegetation dynamics 

(Holsinger and others, Ch. 5). These models used the 

DAYMET national weather database that, as mentioned 

above, has a 1-km2 spatial resolution and daily temporal 

resolution spanning 18 years. Biophysical gradient layers 

created from these models were also used to construct a 

data layer describing potential vegetation types (PVTs) 

that was then used to aid in vegetation mapping and also 

used as an input layer for simulating historical reference 

conditions.

Historical reference conditions were simulated using 

LANDSUMv4, a spatially explicit landscape fire succession model that simulates vegetation development 

using deterministic succession pathways by potential 

vegetation type (PVT; described below) and simulates 

fire using a pixel-to-pixel cell percolation or spread approach (Keane and others 2001; Pratt and others, Ch. 10). 

This simulated, spatially explicit cover type/structural 

stage time series was compared with imagery-derived 

cover type/structural stage data layers describing current 

conditions using a GIS program that computes departure 

and ultimately FRCC (fig. 1) (Holsinger and others, 

Ch. 11). We also created a statistical program called 

the Historical Range and Variation Statistical Analysis 

Program (HRVStat; Steele and others, in preparation) 

that computes another index of departure with a corresponding measure of statistical significance. This 

departure index can be collapsed into the three classes 

to define FRCC.

Several secondary data layers – considered secondary because they were not used to compute the primary 

LANDFIRE product of FRCC – were developed using the intermediate layers created during this FRCC 

mapping process to aid fire management in planning 

and implementing ecosystem restoration and designing fuel treatments. We developed a new set of surface 

fire behavior fuel models specifically for LANDFIRE 

to distinguish subtle differences in fuelbeds resulting 

from fuel treatments (Scott and Burgan 2005). These 

represented a significant improvement over the original 

thirteen fire behavior fuel models of Anderson’s (1982). 

Maps of these fuel models were created from the cover 

type, structural stage, and PVT maps (Keane and others, 

Ch. 12). The canopy fuel characteristics of bulk density 

and canopy base height were mapped using statistical 

landscape modeling (specifically, regression trees) using the biophysical layers, whereas canopy height and 

canopy closure maps were created using Landsat imagery 

(Quinlan 1993; Zhu and others, Ch. 8; Keane and others, 

Ch. 12). We also mapped fuel models representing actual 

estimations of loading (live and dead biomass per unit 

area) by fuel category for fire effects simulations using 

complex statistical modeling using the biophysical 

layers. Two classifications of fire effects fuel models 

were used: the Fuel Characterization Class System 

(FCCS; Cushon and others 2003; Sandberg and others 

2001) and the Fuel Loading Models (FLMs) created 

by Lutes and others (in preparation). The FLMs were 

not yet developed at the conclusion of the prototype 

mapping effort but will be available for the national 

LANDFIRE implementation.

Creating the LANDFIRE Reference 

Database

As previously mentioned, we based all products in 

the LANDFIRE Prototype on an extensive, plot-level 

database called the LANDFIRE reference database 

(LFRDB) (fig. 1) (Caratti, Ch. 4). This ensured that each 

data layer could be recreated and revised, an especially 

important feature when additional data become available 

in the future. This data compilation process provided a 

source for accuracy assessment, and the reference plot 

data could be used to scale LANDFIRE products to 

finer scales by facilitating the development of additional, 

more detailed classifications of cover types, structural 

stages, and fuel models. Every product generated by 

LANDFIRE was developed from this legacy field data 

collected by diverse resource professionals and screened 

for quality and consistency.

The creation of the LFRDB was the most expensive 

task in LANDFIRE, but once created, the database 

became the foundation of nearly every task in the prototype effort. We used the database for many purposes, 

including:

•	 developing training sites for imagery classification;
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Chapter 3—The Scientific Foundation of the LANDFIRE Prototype Project

•	 parameterizing, validating, and testing simulation 

models;

•	 developing vegetation classifications;

•	 creating empirical models;

•	 determining data layer attributes;

•	 describing mapped categories; and

•	 assessing accuracy of maps, models, and 

classifications.

The LANDFIRE reference database was actually 

composed of data from two separate sources: Forest 

Inventory and Analysis (FIA) and legacy ecological 

data. Through its Forest Service programs, the FIA has 

been collecting valuable tree data on fixed and variable 

radius plots for several decades and provided access to 

these valuable data for LANDFIRE tasks. The legacy 

data source was created by locating all possible georeferenced ecological data collected for any purpose and 

by any organization and then reformatting the data into 

LANDFIRE database structure (Caratti, Ch. 4).

Mapping Current Vegetation Composition 

and Structure

The creation of a national 30-m map of existing vegetation composition and structure presented substantial 

challenges. We quickly recognized that there were neither 

enough expertise nor computer resources to create these 

maps at MFSL. The EROS team had been collaborating 

with scientists at MFSL since 1980, researching fire-oriented remote sensing such as the development of national 

fuel and fire danger digital maps (Burgan 1984; Burgan 

and others 1998). Because the remote sensing scientists 

at EROS are internationally renowned, we asked them 

to collaborate, with the specific task of creating the current vegetation maps. We agreed that the MFSL team 

would develop all the fire, fuel, and biophysical layers, 

whereas the EROS team would develop the vegetation 

maps describing current or existing conditions. These 

vegetation layers were compared to historical reference 

conditions to determine FRCC. However, before any area 

was mapped, historical or current, a comprehensive and 

consistent vegetation classification was needed to define 

the map elements that describe vegetation composition 

and structure.

Developing vegetation classifications — The first 

and most important step in this collaborative effort 

was to develop classifications for vegetation composition and structure. In our previous attempts at large, 

regional classifications (Keane and others 1996a; Keane 

and others 1996b; Hann and others 1997; Schmidt and 

others 2002), we found that, for several reasons, it was 

difficult to use existing vegetation classifications to 

map vegetation across large areas. The most significant 

problem lay in the fact that existing classifications rarely 

match the resolution and detail of the entities that can 

be mapped using Landsat imagery. For example, some 

rangeland classifications stratify cover types by sagebrush species, but EROS scientists found it difficult 

to differentiate between these types using the Landsat 

imagery. Second, most existing classifications are limited to particular ecosystems or geographic areas. The 

Society of American Foresters’ (SAF) classification 

of forest types (Eyre 1980) does not include types for 

rangeland species, for example. Further, most existing 

national vegetation classifications were developed for 

description rather than for mapping; the one exception 

is the National Vegetation Classification System (NVCS) 

(Anderson and others 1998; Grossman and others 1998); 

however, we found that this classification was difficult 

to scale to the detail required by LANDFIRE, and it 

was difficult to key plots from the LFRDB to the NVCS 

types.

We decided to create our own vegetation classification process using a top-down approach in which our 

categories were designed to be:

•	 important to management. The classification categories had to be useful to land management for 

mid-scale analyses.

•	 conducive to modeling. The categories needed to adequately represent stages of vegetation development 

to be compatible with our modeling approach.

•	 distinguishable from satellite imagery. The categories needed to represent cover types that can 

be easily detected with remotely sensed data.

•	 identifiable using plot data. The classification must 

have a key that will uniquely identify each category 

from data in the LFRDB (see LANDFIRE guidelines above).

Rather than construct or use a system that classified 

all vegetation types within the conterminous U.S., we 

decided to first identify those vegetation types that we 

could successfully distinguish through satellite imagery 

and identify in the reference database. We developed 

classification categories for forests and rangelands based 

on a blend of many national efforts (Holdridge 1947; 

Kuchler 1975; Eyre 1980; Loveland and others 1991; 

Running and others 1994; Shiflet 1994; Bailey 1995; 

Grossman and others 1998) and synthesized lists of categories within the context of the two prototype mapping 

zones. We based the vegetation composition on dominant 

plant cover and called the classification categories cover 
54 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 20

Chapter 3—The Scientific Foundation of the LANDFIRE Prototype Project

types. We then used the cover type categories to name 

the potential vegetation types (discussed below); therefore, these categories needed to discriminate between 

successional stages based on relative shade tolerance 

and ecological amplitude (Long and others, Ch. 9).

We generated a list of cover types for the central Utah 

prototype area (Zone 16) by conducting a series of informal workshops with ecologists and members of the 

LANDFIRE team (Long and others, Ch 6). First, we 

reviewed the national classifications and devised a list 

of possible categories, keeping in mind our mid-scale 

objective. We determined that the selected cover types 

had to meet the minimum area guideline for mid-scale 

mapping (occupying at least five percent of a mapping 

zone) as well as meet the above mentioned guideline 

that “all themes must be identifiable, scalable, mappable, and model-able.” We then created a set of keys 

to identify the cover type of each plot in the LFRDB 

based on the predominance of plant cover by species. 

Next, we inspected the plot distribution by cover type 

and merged or deleted cover types that were represented 

by less than 50 plots. We also modified the key to reduce 

the number of plots that were unclassified and misclassified (Caratti and others, Ch. 4). Categories were also 

eliminated or merged to distribute plots evenly across 

all classification categories.

We then submitted this preliminary list of cover types 

and the keyed reference database plots to the EROS team 

for evaluation. They assessed whether the imagery could 

successfully distinguish between cover type categories 

and sent suggestions and a modified list back to MFSL 

to evaluate whether the suggestions and modified list fit 

within the modeling framework. This process continued 

until we mutually agreed upon on a final cover type list 

for the prototype areas. We then cross-referenced the 

LANDFIRE vegetation classification with all other national classifications to provide linkages to other mapping 

efforts (Long and others, Ch. 6). In the end, we were 

satisfied that the final list represented those types that 

we could successfully identify, scale, map, and model 

using the reference database and Landsat imagery.

For the northern Rockies prototype area (Zone 19), 

we decided to use a bottom-up approach in which we 

aggregated classes in the NVCS using the classification’s 

inherent hierarchy. We based the final level of aggregation on the above mentioned guideline that “all themes 

must be identifiable, scalable, mappable, and model-able.” 

We sent this aggregated list to the EROS scientists for 

their review, and both teams mutually agreed upon a 

final set of cover types (Long and others, Ch. 6). Both 

approaches (top-down and bottom-up) have advantages 

and disadvantages, and the choice for the national effort 

will depend on the ability of NVCS to meet the needs 

of the LANDFIRE Project.

The structural stage classification proved easier to 

develop because it contained far less detail than the 

cover type classifications (Long and others, Ch. 6). After 

much discussion with the scientists at EROS, we were 

convinced that there was little chance of successfully 

using commonly accepted structural stage classifications 

because Landsat imagery has difficulty detecting subtle 

changes in forest strata (see Lavigne 1992, O’Hara and 

others 1996a, O’Hara and others 1996b, Oliver and 

Larson 1990, and Pfister 1981 for examples of structural 

stage classifications). We therefore decided to base our 

structural stage classification on two structural components that the EROS team had some success in mapping: 

canopy cover and canopy height. These two attributes 

are mapped using a statistical modeling approach in 

which cover and height are regressed against Landsat 

imagery spectral values and other ancillary data layers 

(Zhu and others, Ch 8). We constructed a four-category 

forest structure classification with two categories of 

height (short and tall) and two categories of cover (low 

and high), and the thresholds delineating categories were 

based on cover type (Long and others, Ch. 6). We used 

a similar approach for shrublands and grasslands. We 

then constructed mock succession pathways using these 

structure categories and found that they were consistent 

with our modeling approach for creating the historical 

reference time series.

Describing biophysical settings — Many studies have 

shown that augmenting a satellite imagery classification 

with a quantitative description of the biophysical environment (temperature, elevation, and precipitation, for 

example) improves the mapping of ecological characteristics such as vegetation and fuel (Keane and others 

2002a; Rollins and others 2004). Moreover, our landscape 

modeling approach for quantifying historical reference 

conditions was predicated on the assumption that the 

landscape is stratified into settings that represent broad 

environmental categories and that these categories also 

represent similar succession and disturbance responses, 

such as plant associations and habitat types. We needed 

to invent a classification of biophysical settings that would 

be useful to both the mapping and modeling efforts, and 

this biophysical classification must also be useful for 

scaling LANDFIRE products to finer scales for local 

land management applications. In addition, a biophysical classification was needed for simulation of historical 

vegetation reference conditions. This classification had to 

correlate with the potential vegetation types that would 
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Chapter 3—The Scientific Foundation of the LANDFIRE Prototype Project

eventually inhabit the area in the absence of disturbance 

because this is the premise for simulating succession 

in LANDSUMv4. Most importantly, each plot in our 

LFRDB needed to be keyed to a specific biophysical 

setting category. This last criterion ultimately guided 

the biophysical classification.

We wanted the biophysical settings classification to be 

based on fundamental, process-oriented environmental 

gradients. Austin and Smith (1989) define these as direct 

gradients and resource gradients. Direct gradients, such 

as temperature and humidity, have a direct physiological impact on vegetation, but these gradients are not 

consumed by vegetation. On the other hand, the energy 

and matter used or consumed by plants, such as light, 

water, and nutrients, define resource gradients. Direct and 

resource gradients are important for mapping vegetation 

and ecosystem characteristics because they can fundamentally define the potential species’ niches. Indirect 

gradients, such as slope, aspect, and elevation, have no 

direct physiological influence on plant dynamics, yet we 

included them in the biophysical mapping effort because 

they can be proxies for other unknown or immeasurable 

gradients. Of course, these biophysical gradients needed 

to be spatially explicit, and this requirement involved 

identifying and creating a suite of environmental spatial 

data layers that describe major climate and ecosystem 

variables, such as temperature and evapotranspiration, 

that might be useful for mapping and modeling. The 

layers that represent major biophysical gradients are 

used for a variety of tasks in LANDFIRE including 1) 

the creation of the potential vegetation type layer used 

by the LANDSUMv4 model (Frescino and others, Ch. 

7) and in fuel mapping (Keane and others, Ch. 12), 2) 

the mapping of existing vegetation composition and 

structure (Zhu and others, Ch. 8), and 3) the statistical 

mapping of canopy fuel and fuel models (Frescino and 

others, Ch. 7).

As mentioned above, we found very few existing, nationally consistent data layers that met the LANDFIRE 

design criteria. The few biophysical data layers that 

have a national extent often have pixel sizes of 1 km or 

greater (Hargrove and Luxmore 1998). Finer resolution 

data were available but highly localized and variable in 

quality and consistency. It was evident that there were 

not enough independently developed, 30-meter national 

gradient databases to create the set of biophysical layers 

required by LANDFIRE. We then turned to simulation 

modeling as the primary tool for creating a comprehensive and consistent set of useful biophysical gradient 

layers.

In the mid 1990s, we created a prototype system 

called Landscape Ecosystem Inventory System (LEIS) 

that used data from local weather stations with various 

input data layers to simulate and summarize a wide 

variety of climate and ecosystem processes (Keane 

and others 1997a; Keane and others 2002a; Rollins and 

others 2004). This system used a collection of software, 

including the mechanistic ecosystem process model 

Biome-BGC (Running and Coughlan 1988; Running 

and Gower 1991; Thornton and White 1996; White and 

others 2000), to create biophysical layers that were then 

used to map fuel and vegetation. The addition of biophysical layers to predict ecosystem attributes increased 

mapping accuracies by over 20 percent. Although the 

accuracy of the estimates of the actual values of the 

biophysical variables was sometimes low, the precision was high because relative differences between the 

variables across landscapes were spatially consistent. 

This spatial representation of the relative differences 

was highly important because the biophysical gradients 

were used as spatial predictors in the mapping process, 

rather than an accurate portrayal of the actual values 

of the biophysical variables. However, the LEIS system 

was built for regional – not national – applications, so 

we needed to modify several pieces of software to meet 

LANDFIRE needs.

We redesigned two of the major programs in LEIS 

to access the 1-km national DAYMET weather database built by Thornton and others (1997) so that the 

simulated environmental gradient data layers would be 

nationally consistent and comprehensive. For LANDFIRE purposes, we modified the WXGMRS program 

in LEIS to create the WXFIRE program and modified 

the Biome-BGC model to create the LFBGC model 

(fig. 1). The WXFIRE program scales the DAYMET 

weather data to 30 meters using physical principles 

and then summarizes weather into climate descriptors 

over the 18-year record (for complete documentation, 

see Keane and Holsinger 2006). It also simulates some 

ecosystem processes, such as soil water fraction and 

evapotranspiration, and computes annual summaries. 

The LFBGC program simulates ecosystem processes to 

compute carbon, water, and nitrogen fluxes over time, 

and then outputs summaries of these fluxes for the 18-

year DAYMET daily record (Running and Coughlan 

1988; Thornton 1998). These two programs (WXFIRE 

and LF-BGC) provided us with the ability to create 

consistent and comprehensive spatial data layers for 

LANDFIRE mapping and modeling tasks, especially 

regarding the biophysical settings.
56 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 200

Chapter 3—The Scientific Foundation of the LANDFIRE Prototype Project

We encountered several issues when designing a system 

for classifying biophysical settings. Our first approach 

was to stratify landscapes into biophysical units based 

on simulated weather, climate, and ecosystem process 

variables (outputs from WXFIRE and LFBGC) using 

an unsupervised approach (Hargrove and Luxmore 

1998; Hessburg and others 1999; Hessburg and others 2000). This stratification involved creating a suite 

of environmental gradient spatial data layers, such as 

temperature and evapotranspiration, using the WXFIRE 

and LF-BGC software. We performed an unsupervised 

clustering classification on every pixel in the biophysical 

layers for the prototype mapping zones and mapped the 

resultant clusters as unique biophysical settings. This 

approach created a landscape stratified by categories of 

similar biophysical conditions. That is, this map had the 

advantage of dividing the landscape into areas that reflect 

unique climate, soil, and topographic conditions

We found the results from the unsupervised biophysical 

classification somewhat disappointing because we could 

not find significant correlations between the biophysical 

clusters and a vegetation-based classification, as required 

for the LANDSUMv4 succession modeling effort to 

produce simulated historical conditions. We found the 

connection between the unsupervised clusters of similar 

environmental conditions and the vegetation that these 

biophysical characteristics support was either missing 

or highly variable when we correlated with the field data 

from the reference database to potential vegetation classification categories (for example, contingency statistics 

between biophysical clusters and LANDFIRE potential 

vegetation type classifications were low and variable). 

These results were frustrating as we had planned to use 

the unsupervised biophysical settings layer to scale the 

LANDFIRE vegetation maps and models to local applications. For example, a new, more detailed potential 

vegetation type map could be created from the biophysical 

setting clusters by reassigning the plots in the LFRDB 

a new potential vegetation type category using a more 

complex and detailed key. These results caused us to 

switch from an unsupervised to a supervised approach 

for mapping biophysical settings.

The supervised approach required that we develop 

complex statistical models directly from the field data 

and then implement these models in space using the 

simulated environmental gradient layers (Fresino and 

Rollins, Ch. 7). The plot data in the LFRDB had to be 

keyed to a biophysical classification that is useful for 

both the LANDFIRE mapping and modeling efforts. 

We reviewed a number of biophysical classifications, 

such as terrain models, habitat types, plant associations, 

and fire groups, but found few existing classifications 

that had complete coverage across the conterminous 

U.S. (Pfister 1980; Pfister 1989). Moreover, very few 

of the plots in the LFRDB could be keyed to existing 

biophysical categories, which would facilitate national 

implementation. The biophysical settings classification 

needed to be keyed from consistently collected plot data 

attributes to satisfy the design criteria that LANDFIRE 

be based on the best available science, be repeatable, 

and be objective.

Creating the potential vegetation type classification — We decided to develop our own biophysical 

classification based on the potential vegetation approach 

used in habitat type classifications (Pfister 1989; Pfister 

and Arno 1980; Pfister and others 1977) and other site 

classifications based on climax vegetation (Daubenmire 

1962, 1966; Ferguson and others 1989). In concept, the 

PVT approach assumes that the climax vegetation that 

would eventually develop on a stand in the absence of 

disturbance and this climax community can be used 

to uniquely identify environmental conditions and can 

therefore provide the foundation for a biophysical classification. This approach has a long history in vegetation 

mapping, and potential vegetation type classifications 

have been developed for most of the forests of the 

western U.S. (Ferguson and others 1989; Pfister 1981; 

Pfister and Arno 1980). The approach has had limited 

success in non-forested environments because extensive 

disturbance histories in rangelands has eliminated many 

climax indicator species; yet overall, this approach was 

fitting for LANDFIRE because it provided a vegetation-based classification of potential plant communities 

needed for simulation modeling.

We modified this approach extensively to match the 

scale and limitations of the LFRDB and mapping efforts. First, we assumed that the potential vegetation for 

forested ecosystems could be keyed from plot data based 

on the shade tolerance of tree species according to the 

hypothesis that the tree species with the highest shade 

tolerance will, without disturbance, eventually become 

dominant on a given plot. Following Daubenmire’s theory 

(1966), the tree species with the highest shade tolerance 

will have a high fidelity to unique biophysical settings. 

The potential natural vegetation (PNV) approach to 

classifying vegetation integrates disturbance into succession dynamics as classification criteria and, as such, 

was not useful for the LANDFIRE effort because it was 

incompatible with the LANDSUMv4 modeling structure. 

We made no inference that the shade-tolerant species 

will become the climax species since the term climax 

has many limitations and is often misconstrued. Climax 
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Chapter 3—The Scientific Foundation of the LANDFIRE Prototype Project

is often associated with plant communities rather than 

species, and many ecologists have noted that climax 

vegetation is an unrealistic endpoint since climate, 

genetics, and exotic migrations, among other factors, 

are in constant flux and thus making a stable climax 

community impossible (Hironaka 1987; Huschle and 

Hironaka 1980). For this reason, we do not assume that 

the shade-tolerant species will become climax; rather, 

we believe that the most shade-tolerant species is an 

indicator of distinctive environmental conditions.

For lack of a better term, we named our biophysical 

classification after PVTs, assuming that these shadetolerant species best indicate potential vegetation under 

the current climate regime, not an ultimate climax 

community (fig. 1). This PVT approach facilitates the 

mapping of unique biophysical settings and also allows 

these settings to be directly linked to the succession 

pathways that we use in our simulation modeling to 

determine historical reference conditions. A common 

misconception is that the PVT classification is a vegetation classification; however, the PVT classification is a 

biophysical classification that uses plant species’ names 

as category names because the most shade-tolerant species are indicators of site conditions. This classification 

was useful because, in the LANDSUMv4 succession 

pathway protocols, the PVT classification categories had 

to match the existing cover type classification categories. 

For example, the Douglas-fir cover type category is keyed 

based on the dominance of Douglas-fir (Caratti, Ch. 4), 

but the Douglas-fir PVT is keyed from the presence of 

Douglas-fir on the plot. This is somewhat confusing 

for the user but is absolutely critical for LANDFIRE 

integration.

Each forested plot in the reference database was assigned 

a PVT based on the coverage or tree density data collected 

for that plot. We sorted all tree species present on a plot 

by shade tolerance using information in the literature 

(Burns and Honkala 1990; Fowells 1965; Minore 1979). 

We then identified the most shade-tolerant species on 

the plot that exceeded a designated abundance threshold 

(Caratti, Ch. 4) and matched this species with the associated cover type category used in the mapping of existing 

vegetation (Long and others, Ch. 6). For example, a plot 

having 50 percent canopy cover of ponderosa pine and 5 

percent cover of Douglas-fir would have an existing cover 

type category of ponderosa pine but a PVT category of 

Douglas-fir. The matching of PVT and existing vegetation 

legends ensured logical combinations between maps and 

a consistent linkage for the development of the LANDSUMv4 modeling pathways used in simulating historical 

reference conditions.

As mentioned above, the rangeland ecosystems presented a special problem in the PVT determination since 

late successional species are rarely observed because of 

high frequency of disturbance such as grazing and fire 

(Bunting 1994; Sieg 1997; Westoby 1980). We therefore 

arranged the rangeland cover type categories along a 

moisture gradient from xeric to mesic communities, 

and this arrangement was used as the key criterion for 

classifying plots in the reference database (Long and 

others, Ch. 6). Presence, rather than dominance, however, 

was used to determine the rangeland PVTs in the key. 

There were some inconsistencies in this approach, so we 

used dominant species autecological characteristics to 

further refine the rangeland types (Long and others, Ch. 

6). Although this approach had a number of additional 

flaws, most importantly the inability to consistently 

model successional development, it proved to be the best 

considering the limited resources and data available.

The mapping of PVT was performed by mapping 

zone, so it was important to ensure that PVTs were 

mapped consistently across zones (fig. 1). We did this 

by conducting a study that generated the set of WXFIRE and LF-BGC simulated biophysical variables for 

14 million 1-km pixels across the conterminous U.S. 

These pixel values were then used in complex clustering 

algorithms to create a national set of biophysical settings 

using a supervised approach (Hargrove and Luxmore 

1998; Hessburg and others 1998). The resulting cluster 

map was used to guide the mapping of PVTs using 

the assumption that similar clusters across mapping 

zones should identify similar PVTs. This coarse-scale 

biophysical settings map was not finished by the end 

of the LANDFIRE Prototype but will be available for 

national LANDFIRE implementation.

Mapping existing cover type and structural stage 

with satellite imagery — Using all the classifications, 

digital map layers, and tools detailed above, we were 

able to map current vegetation conditions using the cover 

type and structural stage vegetation classifications. This 

complex task was performed by the EROS team using 

Landsat imagery (Zhu and others, Ch. 8; fig. 1). The 

team used the set of simulated biophysical data layers 

as covariates in the mapping process and also used 

landscape metrics and the PVT layer to guide cover 

type and structural stage mapping. Another aid that the 

EROS team found very useful was a summary from the 

LFRDB containing the distribution of plots across all 

cover type and structural stage categories, which was 

used to guide the selection of vegetation classes that 

were difficult to distinguish.
58 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 200

Chapter 3—The Scientific Foundation of the LANDFIRE Prototype Project

For the two prototype mapping zones, Landsat images were acquired on three different dates over the 

time period between 1999 and 2001 to capture several 

annual growth phases, such as growing season and 

dormant season, to maximize the ability to distinguish 

cover types. All images were corrected for terrain using NED, and the raw satellite digital numbers were 

converted to at-satellite reflectance for the six Landsat 

bands (Markham and Barker 1986). We used at-satellite 

reflectance-based coefficients to calculate values for 

tasseled-cap brightness, greenness, and wetness, which 

have been found useful for vegetation characterization

In the LANDFIRE Prototype Project, we created 

maps of cover types using a training database developed 

from the LFRDB, satellite imagery from Landsat, the 

biophysical gradient layers, the PVT map (for limiting 

the types of vegetation that may exist in any area) and 

classification and regression tree (CART; Breiman and 

others 1984; Quinlan 1993) algorithms similar to those 

used to map PVT. We selected CART-based classification methods for the following reasons. First, as a nonparametric alternative for regression, CART is more 

appropriate for broad-scale mapping than parametric 

methods (such as maximum likelihood estimation or 

discriminant analysis). Second, CART-based models 

may be trained hundreds of times faster than some 

other non-parametric classifiers like neural networks or 

support vector machines. Yet, CART is comparable to 

and performs similarly with regard to accuracy to these 

methods (Franklin 2003; Rollins and others 2004). Third, 

the CART framework explicitly represents logics and 

rules that may be interpreted in and incorporated into 

expert systems for further analysis. Neural networks 

and support vector machines work like “black boxes,” 

with classification logics difficult to interpret or simply 

“invisible”. Lastly, CART has been successfully used 

recently for modeling and mapping vegetation at broad 

scales, including the MRLC 2001 project (Zhu and others, Ch. 8).

The EROS team mapped cover types using a hierarchical set of classification models, with the first model 

separating broader land cover types (such as life form) 

and subsequent models separating more detailed levels 

of the vegetation classification. The LFRDB, biophysical settings layers, and other ancillary data layers were 

incorporated to guide the classification.

Regression trees in a CART framework and applied 

through a custom See5/ERDAS IMAGINE interface 

(ERDAS 2004; Quinlan 1993; Rulequest 1997) were 

used to map canopy closure and canopy height using 

spectral information from Landsat imagery and the 38 

biophysical gradient layers. Again, a training database 

was developed using the LFRDB. The resultant maps represented canopy closure and canopy height continuously 

across each prototype mapping zone. When compared 

to standard, parametric linear regression, regression 

trees developed using CART have the advantage of 

being able to approximate complex nonlinear relationships (Quinlan 1993). The final structural stage layer 

was developed by assigning structural stage classes to 

combinations of PVT, cover type, and the continuous 

canopy closure and height class layers. Structural stage 

assignments were based on the classification described 

above (Long and others, Ch. 6).

A major problem arose when the MFSL scientists 

received the vegetation maps from EROS. The cover 

type and structural stage maps did not match the PVT 

maps for approximately 20 percent of the pixels. For 

example, there were mesic cover types such as Grand 

Fir being mapped in xeric PVTs such as Douglas-fir, 

which is ecologically impossible under our PVT approach and inconsistent with the succession pathways 

used in the LANDSUMv4 modeling effort. These 

inconsistencies occurred because the EROS scientists 

felt confident that their mapping of cover type, based 

on a set of computed statistics, was correct, whereas 

they had low confidence in the PVT maps. Finally, both 

EROS and MFSL members agreed that we should create 

a map of confidence using indices from zero to one for 

the mapping of cover type, structural stage, and PVT. 

These indices were derived from our statistical modeling and analysis results. Any pixel mapped to PVT 

or cover type with a confidence lower than 0.5 would 

be changed to the most appropriate category according to a set of rules built from a statistical analysis of 

LFRDB. This iterative process eventually created a set 

of consistent vegetation maps useful in LANDSUMv4 

modeling and in the ultimate mapping of FRCC.

Many additional layers were developed by the EROS 

team for specific uses in the LANDFIRE Prototype 

Project. As mentioned, the cover type, structural stage, 

and corresponding confidence layers were used to 

revise the PVT map and used in the computation of 

FRCC. The scientists at EROS also created maps of 

canopy height from regression analyses of Lidar data 

and Landsat imagery, as well as maps of canopy cover 

from the imagery and reference database information 

(Zhu and others, 8). These layers were used to map 

canopy fuel and are described in Keane and others, 

Ch. 12).
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 5

Chapter 3—The Scientific Foundation of the LANDFIRE Prototype Project

Describing Historical Conditions

The calculation of FRCC requires that current vegetation conditions be compared with the range and variation of conditions that existed during the historical era 

identified in the LANDFIRE guidelines (1600-1900 

A.D.) to calculate a departure statistic (Hann 2004). 

This historical range and variation (HRV) has several 

forms. Historical range and variation can refer to ecosystem characteristics such as landscape composition 

described by the cover type, the structural stage, or 

the fuel model. It can also refer to landscape structure 

characteristics such as pattern, patch distribution, and 

contagion. In the LANDFIRE Prototype, we used only 

landscape composition as described by the combination 

of PVT, cover type, and structural stage to describe HRV. 

Maps of PVT, cover type, and structural stage were used 

because the combination provided the finest resolution 

for describing landscape dynamics in LANDFIRE. We 

did not address any historical variation in landscape pattern or patch dynamics because of time and computer 

resource limitations.

The only way to quantify HRV over space and time 

is by developing a sequential set of data layers that 

spatially describe the vegetation conditions over many 

points in time. These sequential data layers are referred 

to as spatially explicit historical chronosequences. The 

challenge in the LANDFIRE Prototype was to find a 

set of existing maps of historical vegetation conditions 

that were directly comparable to the maps of current 

conditions created from Landsat imagery and biophysical 

gradients as part of the LANDFIRE Prototype. These 

historical reference layers needed to be consistent and 

comprehensive across the conterminous U.S. at a 30-

meter pixel resolution and created using the same map 

categories, resolution, and accuracy.

We faced a major dilemna when it came to deciding 

which source to use for the representation of historical 

landscape composition. Temporally deep chronosequences that are spatially explicit across the entire nation 

are rare, and we found no historical spatial data that fit 

our design specifications. There were many fire history 

studies and some historical vegetation maps, but many 

ecosystems and geographic areas of the country were 

inconsistently sampled or not sampled at all (Heyerdahl 

and others 1995; Heyerdahl and others 2001). We found 

no national map sequences of historical vegetation. 

Most historical maps were highly localized to small 

geographic areas and limited to one point in time, usually circa 1900. It became clear that previously derived 

historical time series for the conterminous U.S. were 

not available for the computation of FRCC.

Using landscape simulation modeling – This deficit 

left simulation modeling as the only tool for creating a 

spatial historical record that would be consistent with 

the other elements of the LANDFIRE Prototype. For 

the last decade, MSFL scientists have been developing 

spatially explicit landscape models that simulate fire and 

vegetation dynamics (Keane 2000; Keane and Finney 

2003; Keane and Hann 1998; Keane and Long 1997; 

Keane and others 1996b; Keane and others 2002b) and 

therefore have had a great deal of experience with these 

complex computer programs.

Simulation provides several advantages for quantifying HRV (Keane and Finney 2003; Keane and others 

2004a). Simulation models can generate temporally deep 

chronosequences that are limited only by the amount of 

computing resources available to run the model. These 

sequential data layers can be designed to be consistent with 

the data layers developed for describing current conditions 

(PVT, cover type, and structural stage), and they can be 

reported over long intervals so temporal autocorrelation 

is minimized. Modeling also allows the integration of 

spatially and temporally limited field data, which are 

mostly point data, into the simulation framework as parameterization or validation sources for the creation of 

spatially explicit predictions. For example, data from local 

fire history studies can be used to quantify fire frequency 

and severity parameters in the LANDSUMv4 simulation 

model. Moreover, spatial models can be designed so that 

disturbance and succession dynamics are consistently 

modeled across the entire simulation area. These same 

models can also be used to generate chronosequences for 

other applications such as climate change research or a 

national fire management policy.

Simulation models also have their limitations. Most 

importantly, models are simplifications of reality, and 

it is easy to oversimplify model design such that the 

simulation results are meaningless. In addition, simulation models are difficult to test and validate, especially 

spatially explicit landscape models, because of the scarcity of field data that are appropriate in scale, accuracy, 

and detail for comparisons with model predictions. 

Complex simulation models also have difficulty computing accurate predictions because the varied algorithms 

implemented in models come from disparate sources and 

resolutions. The real strength of modeling is precision 

rather than accuracy because prediction errors tend to 

be the same across large spatial and temporal extents. 

Because mapping efforts such as LANDFIRE are more 

concerned with relative differences across large areas, 

we identified simulation modeling as an appropriate 

vehicle for generating the HRV spatial data layers.
60 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 200

Chapter 3—The Scientific Foundation of the LANDFIRE Prototype Project

The LANDSUMv4 model used in the LANDFIRE 

effort is in a class of models called landscape fire succession models (LFSMs) because they spatially simulate 

the complex processes of fire and vegetation succession 

across landscapes. We evaluated many LFSMs that were 

deemed appropriate for LANDFIRE implementation 

(Keane and others 2004a) and found most of them to be 

inadequate for various reasons, mainly because of the 

extensive input data required by most models. In addition, 

many complex LFSMs are difficult to parameterize and 

initialize because the suite of data layers required for 

these tasks are unavailable across the nation. Complex 

biogeochemical models for fire and vegetation dynamics, such as the Fire-BGC model (Keane and others 

1996c), not only require extensive parameterization and 

initialization but also require vast amounts of computing 

resources to execute the program. When we identified 

the data sources available for parameterization and initialization (in essence, the LFRDB and map products) 

and evaluated the LFSMs in this context, we found 

that the LANDSUMv4 model best met the needs of 

the LANDFIRE Prototype (see Keane and others 1996b, 

Keane and others 1997c, and Keane and others 2002b for 

details).

LANDSUMv4 is the fourth version of the LANDSUMv4 

model and was developed specifically for the LANDFIRE 

Prototype (Keane and others 2006) (fig. 1). It contains a 

deterministic simulation of vegetation dynamics in which 

successional communities are linked along multiple 

pathways of development that converge in an end-point 

community. Disturbances, except fire, are stochastically 

modeled at the stand level from probabilities specified by 

the user. Fire ignition is computed from fire frequency 

probabilities specified by PVT, cover type, and structural 

stage categories in model input files (Keane and others 

2001). Fire is then spread across the landscape based on 

simplistic slope and wind factors. LANDSUMv4 does 

not mechanistically simulate fire growth because of the 

lack of fuel inputs, daily weather records, and computing 

resources. The effects of fires are stochastically simulated 

based on the fire severity types as specified in the input 

files. Finally, LANDSUMv4 outputs the historical composition by PVT, cover type, and structural stage for areas 

called landscape reporting units. We selected the size of 

the reporting unit to be 900 meters by 900 meters so that 

it was compatible with the 30-meter pixel size of the base 

maps and approximated the resolution of the coarse-scale 

maps produced by Schmidt and others (2002).

A significant LANDFIRE Prototype Project task involved 

estimating the succession and disturbance parameters 

needed to execute LANDSUMv4. Previous attempts at 

parameter quantification used the “Delphi” method in which 

local and regional experts (ecologists and land managers) 

collaborated through a series of workshops to build succession pathways and estimate pathway disturbance and 

development parameters using local knowledge rather than 

field data (Hann and others 1997). This approach proved 

productive because most of the experts were familiar with 

the literature and able to make realistic approximations. 

However, these workshops were difficult and expensive to 

stage, and the final results were often inconsistent between 

succession pathways and were incompatible with LANDFIRE modeling and mapping goals. For these reasons, we 

employed several vegetation, fire, and landscape ecologists 

to construct and parameterize the LANDSUMv4 succession 

pathways so that the entire modeling effort was consistent 

and comprehensive. Each ecologist constructed and parameterized succession pathways and disturbance parameters 

for an assigned a set of PVTs (see Keane and others 2006 

for details on succession and disturbance parameters). 

These professionals used information from the literature, 

the LFRDB, and local area knowledge to perform this task 

and thoroughly documented their work (Long and others, 

Ch. 9).

An important end product of the LANDSUMv4 simulations was the creation of a set of four fire regime maps 

(fig. 1; Keane and others 2003; Keane and others 2004b). 

The fire frequency map details the number of fires over 

the entire landscape, whereas the three fire severity maps 

represent the probabilities of stand-replacing fires, mixed 

severity fires, and non-lethal surface fires, respectively. 

These maps provide a useful reference for locating those 

areas that tend to experience frequent fire so that fuel treatments can be located and scheduled. They also represent 

an important scaling tool for fire management in that they 

can be used to plan the frequency and severity of proposed 

burn treatments.

Calculating FRCC — The computation of FRCC from 

the comparison current conditions to historical reference 

conditions presented a unique set of challenges. In the 

LANDFIRE Prototype Project, we coded the field methods 

of Hann (2004) into a GIS program that uses the LANDSUMv4 simulated historical chronosequences to determine 

the range of landscape conditions and then compares this 

range to current conditions to compute an index of departure 

from historical conditions (fig. 1). This departure index 

ranges from zero to 100 in which an index of 100 represents the current landscape as being totally departed from 

simulated historical conditions. FRCC is then computed by 

grouping the index into classes (see Holsinger and others, Ch. 

11). FRCC was computed for divisions of the LANDFIRE 

mapping zones called landscape reporting units. These 
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Chapter 3—The Scientific Foundation of the LANDFIRE Prototype Project

900-meter by 900-meter landscape delineations served as 

the smallest units for evaluating departure (Holsinger and 

others, Ch. 11). Hann’s (2004) methodology is the only approach officially accepted by FAM for computing FRCC 

and so, to be consistent with FAM’s protocol, the final 

FRCC map created by the LANDFIRE Prototype Project 

used these methods. However, because of the many limitations to Hann’s approach, we determined that a complete 

exploration of the computation of departure from historical 

conditions was warranted to ensure a scientifically credible 

protocol.

The idea that historical ecological data can be used as a 

reference to inform land management’s decisions regarding 

today’s landscapes is somewhat new and has only recently 

been put into practice (Hessburg and others 1999; Keane 

and others 2002b; Landres and others 1999; Reed and others 1998; Swetnam and others 1999). Although the theory 

is sound and well-founded, its implementation in land 

management is still in its infancy. There are few standardized methods and protocols for quantitatively comparing 

historical reference conditions with current conditions and 

evaluating their differences. Standard parametric statistics 

are not always appropriate for historical time series since 

the chronosequences are autocorrelated in time and in 

space. Also, HRV has to be evaluated across all elements 

or mapping entities that comprise the landscape; in LANDFIRE, the elements evaluated over time are cover type and 

structural stage combinations by PVT. These combinations 

are correlated to each other because a decrease in the area 

of one combination will result in increases in the areas of 

other combinations on the simulation landscape. In addition, these combinations are correlated in space because 

simulated fire spread will tend to burn contagious pixels. 

It is also difficult to ascertain the thresholds of departure 

that determine whether human intervention via fuel treatments is warranted. We therefore explored an entirely new, 

alternative, statistically based method for comparing the 

simulated historical chronosequences with the imagery 

derived from existing conditions using indices of departure 

and a measure of statistical significance. In addition, these 

new methods could be compared with Hann’s (2004) FRCC 

field method for computing departure so that either or both 

methods could be improved at a later date.

A set of complex statistical methods for computing departure from historical conditions were developed by Steele 

and others (in preparation) specifically for the LANDFIRE 

Project (fig. 1). They created a statistical technique that is 

sensitive to the autocorrelation of simulated data over time, 

space, and vegetation categories. This technique computes 

an index of departure that varies from zero (no departure) 

to one (most departed). In addition, a measure of statistical 

significance is computed as a probability value (p-value), also 

varying from zero to one. Steele and others (in preparation) implemented this statistical technique in the HRVStat 

program that compares summaries of simulated historical 

chronosequences with current conditions by reporting unit 

within the mapping zone and outputs a departure value 

and a p-value for each landscape unit or strata. By design, 

Steele and others (in preparation) created this program 

to work with any historical chronosequence, not only 

simulated time series, so that local land management offices can replace the simulated data with actual historical 

observations, if they exist. Moreover, this program can 

be used for reference sequences other than historical that 

are more germane to land management, such as climate 

change chronosequences’ effects on exotics.

Another potentially fruitful area of HRV research exists 

in the determination of the size and shape of the reporting 

unit used as the context analysis landscape for calculating 

departure. The most perplexing problem we encountered 

while developing these protocols was deciding the size 

of the landscape for evaluation. Simulation results from 

landscapes that were too small did not adequately capture 

the full range of conditions within the area, whereas results from landscapes too large were not sensitive to subtle 

changes in cover type and structural stages, such as those 

caused by fuel treatments. We decided to use a 900-meter 

by 900-meter analysis landscape reporting unit because it 

closely matched the coarse-scale project by Schmidt and 

others (2002) and could thereby facilitate (limited) comparisons, and it fit well with the 30-meter grid cell resolution 

of the input layers. We are currently conducting a study to 

determine the characteristic landscape size appropriate for 

computing FRCC and departure statistics.

Ancillary LANDFIRE Prototype 

Products_______________________

The previously discussed LANDFIRE biophysical, 

vegetation, and historical data layers constitute a remarkable set of spatial data sources for describing vegetation 

characteristics and dynamics across the entire nation. 

However, the utility of these data layers is seriously 

limited because there are few applications that use these 

layers directly as inputs. For example, extensive expertise 

and funding would be required to create a map of fire 

hazard across a region from merely the mapped vegetation 

characteristics. Similarly, whereas the FRCC map may 

be used as a reference for the distribution of funding and 

resources to land management agencies and regions, it 

has little application in terms of designing, implementing, and monitoring fuel treatments. Additional layers 
62 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 200

Chapter 3—The Scientific Foundation of the LANDFIRE Prototype Project

and software tools were needed to provide local land 

management with applications that were consistent with 

all other LANDFIRE products. We therefore generated 

a set of fuel data layers and companion analysis models 

to aid fire managers in the design, implementation and 

monitoring of fuel treatments for their respective areas. 

These fuel layers were developed as ancillary products 

in the LANDFIRE process because they were not central to the computation of FRCC, but these products 

were critically important as they provided a means by 

which to use LANDFIRE products to more efficiently 

plan and design ecological restoration projects and fuel 

treatments for local applications.

Creating the LANDFIRE Fuel Layers

It was essential that the entire array of fuel characteristics be mapped in the LANDFIRE Prototype Project 

to ensure that fire hazard analyses be germane to fire 

management. Both surface and canopy fuel had to be 

mapped so that they could be used in fire behavior and 

fire effects predictive models. Since fuel is highly variable 

and complex, many fire applications use classifications 

of fuel as inputs instead of using actual fuel loading 

sampled in the field. Fuel classifications contain fuel 

classes with representative fuel loading for a set of fuel 

components, and these fuel classes are often referred 

to as “fuel models”. To complicate matters, most fire 

behavior simulation models require fuel models that 

are actually abstract representations of expected fire 

behavior and therefore cannot be used to simulate fire 

effects (Anderson 1982). Moreover, existing fire behavior 

fuel models are quite broad and do not match the resolution needed to detect changes in fuel characteristics 

after fuel treatments (Anderson 1982). Because our 

design criteria specified that, with the implementation 

of the National Fire Plan, the LANDFIRE layers must 

be able to identify changes in FRCC and fire hazard, 

we needed to overcome these limitations. We therefore 

developed a new set of fire behavior fuel models and a 

new classification of fire effects fuel models to ensure 

that the fuel layers could be used for local to regional 

assessments and analyses.

A new set of fire behavior fuel models was created 

by Scott and Burgan (2005) using funding from – but 

developed independently of – the LANDFIRE effort. This suite of 40 models represents a significant 

improvement in detail and resolution over Anderson’s 

original 13 (1982). The new fire behavior fuel models 

were developed independently of LANDFIRE because 

we wanted to ensure their use in accepted fire behavior 

applications such as BEHAVE (Andrews 1986; Andrews 

and Bevins 1999) and FARSITE (Finney 1998). Each 

model has a complete description and includes graphs 

showing fire behavior under different fuel moisture and 

weather conditions (www.fire.org).

Fire effects fuel models differ from fire behavior fuel 

models in that they represent real fuel loading by fuel 

category, not abstract representations of expected fire 

behavior. This loading information by fuel component 

is used to calculate important fire effects such as fuel 

consumption, soil heating, smoke, and tree mortality. 

Because there were no national classifications of fuel 

loading at the time of LANDFIRE’s inception, we created our own. Our classification of Fuel Loading Models 

(FLMs) attempts to identify unique fuel loading models 

from an analysis of the variance of fuel loading by fuel 

category (Lutes and others, in preparation). Fuel loading 

categories include four downed woody size classes, live 

and dead shrub and herbaceous biomass, duff and litter 

depth, and crown fuel characteristics. Many studies have 

noted that high variation of fuel loading across temporal and spatial scales often precludes correlations with 

vegetation characteristics (see Brown and Bevins 1986 

and Brown and See 1981). We examined this variance 

across and between fuel categories to identify clusters of 

fuel loading that might facilitate fire effects fuel model 

mapping. We gathered fuel loading data sets from past 

field efforts across the nation and reformatted them in 

the LFRDB and then performed complex clustering 

analyses to identify unique fuel loading clusters or 

models (Lutes and others, in preparation). Resultant fuel 

classes matched the mid-scale development framework 

specified in the project guidelines. The FLM maps were 

not available for the prototype effort because the FLM 

classification was not completed; however, the national 

LANDFIRE effort will map FLMs for use in fire effects 

models such as FOFEM (Reinhardt and others 1997; 

Reinhardt and Keane 1998) and CONSUME (Ottmar 

and others 1993).

Another classification of fuel loading was needed for 

finer-scale applications because the FLM classification 

is quite broad and meant for mid-scale analyses. We 

used the national Fuel Characterization Class (FCC) 

classification developed by Sandberg and others (2001) 

and Cushon and others (2003). This classification was 

developed through sampling fuel extensively in various 

vegetation, structure, and stand history combinations in 

forest and rangeland settings across the nation and then 

assigning the computed fuel loading to the categories 

within the classifications of vegetation and stand condition called default fuelbeds. We mapped these default 

fuelbeds from information collected in the intensive 
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Chapter 3—The Scientific Foundation of the LANDFIRE Prototype Project

sampling effort using the PVT, cover type, and structural 

stage maps (Long and others, Ch. 6). Using software 

developed by the FCC effort, land managers can modify 

fuel loading for their default FCC fuel model to create 

a more site-specific, locally realistic fuel model for use 

in fire effects computer models.

Most fire behavior and effects predictive models require a quantification of several canopy characteristics 

to simulate crown fire initiation and propagation (Albini 1999; Rothermel 1991; van Wagner 1993). These 

characteristics include canopy bulk density, top height, 

base height, and closure. Fortunately, the EROS team 

mapped canopy top height and canopy closure using 

satellite imagery (Zhu and others, Ch. 8), but canopy 

bulk density and canopy base height had to be mapped 

at MFSL by calculating the two canopy attributes from 

tree inventory information in the reference database using the Fuel Calculation system (FUELCALC) program 

(Reinhardt and Crookston 2003). This program uses 

tree measurements of species, height, and diameter to 

compute the vertical distribution of crown biomass from 

a set of biomass equations. The program also contains 

an algorithm that computes the canopy base height from 

the vertical distribution of crown biomass (Reinhardt 

and Crookston 2003). These two canopy characteristics 

are then statistically modeled using complex statistical 

analyses from variables spatially represented in all 

LANDFIRE biophysical layers (Keane and others, Ch. 

12). The statistical models are then implemented across 

the mapping zones using the biophysical layers. FUELCALC is still under development but will be released 

before the LANDFIRE Prototype Project is finished.

Creating analysis tools — Maps of fuel characteristics have limited use until they are linked with an 

appropriate application. An existing application that 

can be used directly with LANDFIRE products is the 

FARSITE fire growth model that simulates the spread 

of fire as it moves across landscapes (Finney 1998). The 

sister program to FARSITE, called FLAMMAP, is used 

for fire hazard analysis and computes fire behavior for 

every pixel based on user-defined weather scenarios. A 

drawback to these programs, however, is that FLAMMAP and FARSITE compute fire behavior from weather 

data with no temporal distribution. For example, crown 

fires are more important to fire management if they occur frequently due to a dry weather record, as in dense 

ponderosa pine stands, than if infrequently, as in crown 

fires of lodgepole pine forests found at high elevations. A 

tool was needed to analyze fire behavior and fire effects 

across a landscape over a temporal domain so that fire 

hazard could be assessed in time and space.

We initiated the development of the FIREHARM program to compute fire hazard across large landscapes over 

long time periods. This program accepts the fuel map 

inputs from LANDFIRE and then uses the DAYMET 

weather database to calculate the probability that a fire 

event could happen on any given day in the 18-year 

weather record. A fire event can be defined from fire 

danger (such as burning index, spread index, and energy 

release component), fire behavior (such as flame length, 

spread rate, and fire intensity), and fire effects (such 

as fuel consumption, soil heating, and tree mortality) 

variables. For example, a user might define a fire event 

as those days when fuel consumption is greater than 

50 percent. The resultant maps of fire event probability 

can be used to target treatment locations, or they can be 

used with other layers to perform other estimations of 

fire hazard. The FIREHARM model is still in development and will be released sometime after LANDFIRE 

Prototype Project completion.

A last application that is still in development is the 

LANDFIRE decision support tool that uses LANDFIRE 

data layers as inputs to an expert system that uses fuzzy 

logic and decision uncertainty to prioritize the selection 

of areas for fuel treatment and restoration activities, 

depending on management objectives (Reynolds and 

Hessburg 2005).

Discussion _____________________

Despite our best efforts, some elements of subjectivity 

crept into LANDFIRE methods, and perhaps the task 

infused with the highest bias was the development of the 

vegetation map classification. It was difficult to avoid subjectivity in creating a comprehensive list of cover types 

that matched the sensitivity of the Landsat imagery and 

simulation models because of the wide variety of cover 

type classifications and vegetation communities possible 

for each individual mapping zone and the inherent variability of vegetation across the mapping landscape. One 

possible solution would be to use an existing cover type 

classification for the entire U.S. so that bias is minimized 

and the list of cover types remains constant across mapping zones. Yet this option poses problems. Often, the 

composition of a cover type can change across large 

regions, making it difficult to develop one classification 

key that fits all areas. Perhaps the best method would 

be to use an existing classification, determine the list 

of cover types to map according to each mapping zone, 

and then vary the keys to these cover types to reflect the 

subtle differences across zones.
64 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 20

Chapter 3—The Scientific Foundation of the LANDFIRE Prototype Project

The LANDFIRE task of assigning the new fire behavior 

fuel models to combinations of cover type, structural stage, 

and PVT also contained some subjectivity (Keane and 

others, Ch. 12). This was primarily the result of the lack 

of a comprehensive and consistent key, which precluded 

our ability to assign a fuel model to each plot in the 

LFRDB. In addition, since the fire behavior fuel model 

classification had been recently created, none of the plots 

in the reference database contained information about the 

new fuel models. Furthermore, the new fuel models could 

not be assigned to any plots in the reference database 

because of a lack of consistent key criteria. Without a 

link to field data, we had to assign a fuel model to each 

PVT-cover type-structural stage combination based on 

the best estimate by the authors of the fuel model classification (Scott and Burgan 2005) and by specialists on 

the LANDFIRE team (Keane and others, Ch. 12). Future 

efforts should attempt to derive a key that objectively assigns fuel models based on the data collected in the field 

or present in the reference database.

LANDFIRE products can be scaled to local applications 

using a variety of approaches. The biophysical layers can 

be used in concert with the field-referenced data to refine 

local vegetation maps and with the PVT maps to reflect 

environmental conditions. For example, precipitation can 

be used to create two Ponderosa Pine cover types, one 

including larch and one without larch, or elevation could 

be used to separate a LANDFIRE Sagebrush cover type 

into Mountain Sage and Black Sage LANDFIRE cover 

types. LANDFIRE fuel assignments to combinations of 

PVT, cover type, and structural stage can be linked to 

local digital maps of vegetation and biophysical settings 

to create a fine-scale fuel layer. In addition, local maps of 

vegetation conditions, such as basal area and tree density, 

can be augmented with the suite of LANDFIRE maps to 

improve fuel model assignments.

In addition to a host of other land management applications, LANDFIRE products can also be used to help 

prioritize, plan, and implement fuel treatments. Maps of 

vegetation and fuel can be used as inputs to a number of 

existing fine-scale computer models, such as SIMMPPLE 

(Chew 1997) or FVS-FFE (Reinhardt and Crookston 

2003), to evaluate alternative fuel treatments and their 

effects on project landscapes. In addition, scenarios can 

be developed for the LANDSUMv4 model to simulate 

the effects of alternative management strategies. The 

FIREHARM program can use the LANDFIRE maps 

and DAYMET database to create data layers that show 

the probability of fire events, such as a crown fire or 

lethal soil heating, across small analysis landscapes. The 

set of LANDFIRE fuel layers can be used in FARSITE 

and FLAMMAP to simulate fire growth or predict fire 

hazard for local landscapes. Furthermore, fire regime 

maps can be used to prioritize, plan, locate, and schedule 

ecological landscape restoration and fuel treatments 

across small landscapes by identifying those areas that 

burn frequently and therefore needing treatment more 

urgently.

Conclusion_____________________

The LANDFIRE Prototype Project has created a 

process for mapping fire, fuel, and vegetation conditions across the entire United States by integrating 

satellite imagery, statistical analyses, and simulation 

modeling using scientifically credible methods. This 

process is currently being implemented through the 

national LANDFIRE effort with the complete set of 

LANDFIRE products scheduled to be delivered by 

2008. The methods, procedures, and protocols implemented within this process ensure consistency across 

all regions, ecosystems, and land types and allow for 

future replications of the process to monitor the efficacy 

of the National Fire Plan and Healthy Forest Restoration 

Act. The LANDFIRE products can be scaled from local 

applications to national assessments and will facilitate 

detailed fuel and fire hazard analyses.

For further project information, please visit the LANDFIRE website at www.landfire.gov

The Authors ____________________

Robert E. Keane is a Research Ecologist with the 

USDA Forest Service, Rocky Mountain Research Station, Missoula Fire Sciences Laboratory, 5775 U.S. 

Highway 10 W., Missoula, MT 59808; phone: (406) 

329-4846; fax: (406) 329-4877; email: rkeane@fs.fed.

us. Since 1985, Keane has developed various ecological 

computer models for the Fire Effects Project for research 

and management applications. His most recent research 

includes the development of a first-order fire effects 

model, construction of mechanistic ecosystem process 

models that integrate fire behavior and fire effects into 

succession simulation, restoration of whitebark pine 

in the Northern Rocky Mountains, spatial simulation 

of successional communities on landscapes using GIS 

and satellite imagery, and the mapping of fuel for fire 

behavior prediction. He received his B.S. degree in Forest Engineering in 1978 from the University of Maine, 

Orono, his M.S. degree in Forest Ecology in 1985 from 

the University of Montana, and his Ph.D. degree in Forest Ecology in 1994 from the University of Idaho.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 6

Chapter 3—The Scientific Foundation of the LANDFIRE Prototype Project

Matthew Rollins is a Landscape Fire Ecologist at the 

USDA Forest Service, Rocky Mountain Research Station, 

Missoula Fire Sciences Laboratory (MFSL), 5775 U.S. 

Highway 10 W., Missoula, MT 59808; phone: (406) 329-

4960; fax: (406) 329-4877; email: mrollins@fs.fed.us. 

His research emphases have included assessing changes 

in fire and landscape patterns under different wildland 

fire management scenarios in large western wilderness 

areas, relating fire regimes to landscape-scale biophysical 

gradients and climate variability, and developing predictive landscape models of fire frequency, fire effects, and 

fuel characteristics. Rollins is currently the lead scientist 

of the LANDFIRE Project, a national interagency fire 

ecology and fuel assessment being conducted at MFSL 

and the USGS Center for Earth Resources Observation 

and Science (EROS) in Sioux Falls, South Dakota. He 

earned a B.S. in Wildlife Biology in 1993 and an M.S. 

in Forestry in 1995 from the University of Montana 

in Missoula, Montana. His Ph.D. was awarded by the 

University of Arizona in 2000, where he worked at the 

Laboratory of Tree-Ring Research.

Acknowledgments_______________

We thank Denny Simmerman of the USDA Forest 

Service Rocky Mountain Research Station Missoula 

Fire Sciences Laboratory (MFSL) for graphics support, 

Kevin Ryan of MFSL for administrative support, Wendel 

Hann of the USDA Forest Service Fire and Aviation 

Management for technical support, and Christine Frame 

of Systems for Environmental Management for editorial 

and technical review. We thank the entire LANDFIRE 

Prototype Project team members for their excellent 

work.

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Chapter 4—The LANDFIRE Prototype Project Reference Databas

Chapter

4

In: Rollins, M.G.; Frame, C.K., tech. eds. 2006. The LANDFIRE 

Prototype Project: nationally consistent and locally relevant 

geospatial data for wildland fire management. Gen. Tech. Rep. 

RMRS-GTR-175. Fort Collins: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research Station.

Introduction ____________________

This chapter describes the data compilation process for 

the Landscape Fire and Resource Management Planning 

Tools Prototype Project (LANDFIRE Prototype Project) 

reference database (LFRDB) and explains the reference 

data applications for LANDFIRE Prototype maps and 

models. The reference database formed the foundation 

for all LANDFIRE tasks. All products generated by the 

LANDFIRE Prototype Project were developed from 

field-referenced data collected by resource professionals 

and screened for quality and consistency by the LFRDB 

team (Keane and Rollins, Chapter 3), ensuring that each 

LANDFIRE data layer could be recreated and improved 

upon with the availability of new data. Field-referenced 

data provided a means of assessing the accuracy of many 

LANDFIRE Prototype products. The LFRDB integrated 

field-referenced data collected for many separate projects 

that used different sampling methods and had varying 

sampling intensities. See appendix 2-A in Rollins and 

others, Ch. 2 for a table outlining, in part, the procedure 

used to build the LFRDB.

Field-referenced data play a critical role in any mapping 

project involving remotely sensed data (Congalton and 

Biging 1992). Field-referenced data are used to generate, 

test, and validate maps and models. Field-referenced 

data provide important field-referenced information 

The LANDFIRE Prototype Project 

Reference Database

John F. Caratti

that accurately describes that which is being remotely 

sensed or mapped, and field-referenced data points 

function as training sites in satellite imagery classifications (Jensen 1998; Verbyla 1995). In the LANDFIRE 

Prototype Project, we used field-referenced data coupled 

with Landsat imagery to model and map cover types 

(CT), canopy closure, and height (Zhu and others, Ch. 

8). Field-referenced data were also used to model and 

map biophysical settings and wildland fuel (Frescino 

and Rollins, Ch. 7; Keane and others, Ch. 12).

In addition, field-referenced data serve another important purpose in the development of vegetation and fuel 

classifications. The distribution and occurrence frequency of cover types across landscapes – both summarized 

in the LFRDB – were used in the LANDFIRE Prototype 

Project to refine the CT classification (Long and others, 

Ch. 6). The PVT classification was also refined based 

upon field-referenced data and the relationship between 

vegetation occurrence and biophysical settings (Long and 

others, Ch. 6). Furthermore, reference data were used 

to develop database tables relating PVT, CT, and SS to 

attributes relevant to wildland fire management, such as 

fire behavior fuel models, fire effects fuel models, and 

canopy fuel characteristics (Keane and others, Ch. 12). 

Reference data were also integral in the development 

of succession pathway models (Long and others, Ch. 9), 

which ultimately served as the foundation for creating 

maps of simulated historical fire regimes and fire regime 

condition class (Pratt and others, Ch. 10; Holsinger and 

others, Ch. 11).

Finally, field-referenced data aid in both the quantitative 

and qualitative interpretation of maps and mapping classifications and in identifying and explaining the associated 

inaccuracies or inconsistencies. A common way to assess 
70 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 200

Chapter 4—The LANDFIRE Prototype Project Reference Databas

the accuracy of classified images and maps is to employ 

the reference data as a measure by which to assess the 

amount of the area that was classified correctly (Story and 

Congalton 1986). Reference data provided three measures 

of accuracy for most LANDFIRE data layers: 1) locational 

accuracy (the probability of a pixel being mapped correctly), 

2) landscape accuracy (the accuracy of pixel summaries 

across large areas), and 3) model accuracy (the accuracy 

of statistical models used to build the maps) (Keane and 

Rollins, Ch. 3; Vogelmann, Ch. 13).

Reference Data Requirements

All field data were georeferenced within reasonable 

locational accuracy, preferably 30 meters or less. To be 

useful in the LANDFIRE Prototype, reference data were 

required to have a minimum set of fields applicable to 

at least one of the various LANDFIRE mapping tasks. 

Field-referenced data suitable for the LANDFIRE Prototype could, for example, include species composition 

data needed to map cover type and structure, have fuel 

attributes for developing fuel models, or contain tree 

data needed to map canopy fuel. Table 1 lists some of 

the variables required for the LFRDB.

A critical element in acquiring LANDFIRE data was 

obtaining these from field plots that were sufficiently 

representative of all land ownerships and ecosystems. 

We acquired the data through three separate yet coordinated efforts. First, with cooperation and support from 

the Forest Inventory and Analysis (FIA) program, we 

obtained forest vegetation data collected on permanent 

FIA inventory plots (Gillespie 1999). We selected only 

FIA plots which occurred within one condition and 

used all subplot data (Interior West Forest Inventory and 

Analysis 2004). FIA plot variables used by the LFRDB 

included tree species, diameter at breast height (DBH), 

tree height, crown ratio, height to live crown base, and 

crown class. FIA data were stored in the database without 

geographic coordinates to preserve the confidentiality 

of the plot locations (Interior West Forest Inventory and 

Analysis 2004). Analyses requiring coordinates were 

conducted by FIA personnel stationed at the Missoula 

Fire Sciences Laboratory (Missoula, MT).

A second data acquisition effort involved the circulation 

of a formal request letter to federal agencies, soliciting 

additional reference data from the LANDFIRE Prototype mapping zones. In conjunction, the LFRDB team 

conducted an exhaustive search for existing field-referenced data relevant to LANDFIRE, relying heavily on 

cooperation from federal, state, and non-governmental 

organizations. Due to the diverse nature of field sampling 

methods and data storage procedures, the reference 

database team spent much time and effort converting 

these data into a common database structure.

Third, even after obtaining FIA data and other existing 

field-referenced data, large areas of the prototype mapping 

zones were under-represented, particularly those in the 

shrublands and grasslands. In response, the LFRDB team 

collaborated with the Southwest Regional Gap Analysis 

Project (Southwest ReGAP) (Utah State University 2004) 

to collect additional field-referenced data in under sampled 

areas of the prototype mapping zones.

Methods _______________________

The LANDFIRE Prototype Project involved many 

sequential steps, intermediate products, and interdependent processes. Please see appendix 2-A in Rollins and 

others, Ch. 2 for a detailed outline of the procedures 

followed to create the entire suite of LANDFIRE Prototype products. This chapter focuses specifically on 

the procedures followed to compile the LFRDB, which 

formed the foundation for nearly all modeling and mapping tasks in the LANDFIRE Prototype Project.

Data Acquisition

Zone 16—The three data sources for Zone 16 were 

FIA, Southwest ReGAP for the state of Utah, and the 

Table 1—Examples of variables included in the LANDFIRE 

reference database.

Data type Attributes

Plot Georeferenced plot location

Sampling date

Digital plot photos

Metadata

Vegetation Species list 

Cover by species

Cover by life form (tree, shrub, herb)

Height by species

Heights of individual trees 

Crown ratios (individual trees)

Crown classes (individual trees)

Diameters (individual trees)

Tree density

Fuel Fine (1-, 10-, and 100-ho

Coarse (1000-hour

Cover of live and dead shrubs

Cover of live and dead herbs

Base height of canopy fuel

Height of shrubs

Height of herbaceous vegetation
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Chapter 4—The LANDFIRE Prototype Project Reference Databas

Southern Utah Fuels Management Demonstration Project (Southern Utah Project). Utah field-referenced data 

from Southwest ReGAP included both plant species 

cover estimates and height measurements for each plot. 

The Southern Utah Project data included plant species 

cover estimates, height measurements, downed woody 

fuel transects, and individual tree measurements for 

each plot. .FIA data included individual tree measurements for each plot and were acquired from the national 

FIA Database (Miles and others 2001) and the Interior 

West FIA program (Interior West Forest Inventory and 

Analysis 2004).

Once all existing field-referenced data were acquired, 

we displayed the non-FIA plot locations for Zone 16 to 

identify obvious gaps in the reference data acquisition. 

Substantial portions of the central and northern areas of 

Zone 16 contained little or no reference data (fig. 1A). 

We contracted with Utah State field crews who, using 

the Southwest ReGAP field data collection protocols, 

collected additional field-referenced data in these areas 

(Utah State University 2004). These efforts helped fill 

gaps in data coverage, especially in non-forest vegetation types.

Zone 19—We drew from several sources for Zone 19 

data acquisition. These data sets included information 

from the FIA Database; United States Department of 

Agriculture (USDA) Forest Service Northern Region 

ecosystem inventory database (ECODATA) (Jensen and 

others 1993); Bureau of Land Management (BLM) Fuels 

database from the Salmon and Challis, Idaho field offices 

(Gollnick-Waid 2001); United States Geological Survey 

(USGS) ECODATA database from Glacier National 

Park; and the USDA Forest Service, Rocky Mountain 

Research Station, Missoula Fire Sciences Laboratory, 

Missoula, Montana (MFSL) ECODATA database. All 

ECODATA plot databases included plant species cover 

estimates and height measurements, downed woody fuel 

transects, and individual tree measurements for each 

plot. The BLM Fuels Survey data included information 

on dominant and codominant plant species by life form, 

life form cover, fuel loading, and fire hazard.

Once all existing field-referenced data were acquired, 

we displayed the non-FIA plot locations within Zone 19 

to determine deficiencies in the reference data acquisition 

for non-forested areas. Areas in the southern, eastern, 

and west-central portions of Zone 19 contained little or 

Figure 1—Field plot locations for non-FIA reference data within zones 16 (A) and 19 (B)
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Chapter 4—The LANDFIRE Prototype Project Reference Databas

no reference data (fig. 1B). Again, we contracted with 

Utah State University field crews who, using the Southwest ReGAP field data collection protocols, collected 

additional field-referenced data in these areas (Utah State 

University 2004). As with Zone 16, these additional plot 

data filled in the data gaps, especially for non-forest 

vegetation types.

LANDFIRE Reference Database Structure

The LFRDB was composed of three levels (fig. 2), 

each containing data processed at the preceding level. 

Each data set was stored in a separate directory in its 

original data format (Level 3). Each directory contained 

the original data, metadata documentation, and any existing data-conversion queries or programs. We designed 

the data directory structure to follow the Fire Effects 

Monitoring and Inventory Protocol (FIREMON) plot 

identifier structure (Lutes and others, 2006; table 2). This 

enabled us to track summary data in the LANDFIRE 

attribute tables back to the original data and identify 

problems in the data-conversion process.

We then developed data-conversion routines for each 

data set that transformed all Level 3 data into a common 

database structure (Level 2) using the FIREMON database. FIREMON is a complete fire effects monitoring 

and inventory package that includes many field sampling 

methods designed to measure and describe vegetation 

and fuel. Most FIREMON field sampling methods are 

well-suited to conducting ecological inventory as well 

as monitoring fire effects. The FIREMON database 

contains a suite of tables that store collected plot data 

using a variety of standard vegetation and fuel sampling 

protocols. We used the database tables associated with 

the subset of FIREMON sampling methods relevant to 

LANDFIRE to store the LANDFIRE reference data (table 

3). See appendix 4-A for a complete list of FIREMON 

tables and field descriptions used in the LFRDB.

We then populated the LANDFIRE attribute tables 

(Level 1) with data from the FIREMON database using 

a set of database queries (fig. 2). The LANDFIRE attribute tables were linked to the FIREMON tables and 

stored plot data and summary data used to develop, test, 

model, and map many of the LANDFIRE data layers. 

Level 1 attribute data included the LANDFIRE map 

table, the LANDFIRE fuel table, and the LANDFIRE 

canopy fuel table (table 4). We used the LANDFIRE 

map table data to model and map cover type, potential vegetation type, and structural stage; we used the 

LANDFIRE fuel table data to model and map fuel; and 

we used the LANDFIRE canopy fuel table data as input 

to the FUELCALC program (Keane and others, Ch. 

12; Reinhardt and Crookston 2003). FUELCALC uses 

tree measurements and biomass equations to compute 

the vertical distribution of canopy biomass and uses an 

algorithm to calculate canopy base height (Reinhardt 

and Crookston 2003). FUELCALC output was used to Figure 2—LANDFIRE reference database structure.

Table 2—LANDFIRE reference database data directory 

structure.

Directory name Contents

FIREMON Registration ID Subdirectories of data by major 

categories (such as agency) 

FIREMON Project ID Original data tables, FIREMON 

and LANDFIRE attribute tables

Metadata Data conversion documents and 

sampling protocol documents
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Chapter 4—The LANDFIRE Prototype Project Reference Databas

model and map canopy bulk density and canopy base 

height. See appendix 4-B for a complete list of LANDFIRE map attribute tables and field descriptions.

Data-Conversion Process

We compiled data collected from many different 

sampling methods into a common database structure 

so that vegetation and fuel data could be queried in a 

consistent manner. We acquired plant species data that 

had estimates of either relative or absolute cover. Some 

plant species data contained entire species lists, with 

cover and height values for each species, whereas other 

data recorded cover estimates for two to four dominant 

species by life form. We obtained tree data sampled on 

variable radius and fixed radius plots. Some tree data 

contained data only for mature trees, whereas other 

data included seedling and sapling measurements. All 

of the surface fuel data we acquired were sampled using 

variations of the planar intersect method (Brown 1974). 

Although different sampling protocols were used for 

each data set, we distilled this information into basic 

vegetation and fuel data within a common database 

structure.

The data-conversion process involved converting 

data from their original format into the formats of the 

FIREMON database tables and LANDFIRE attribute 

tables. This process involved four steps: 1) storing 

the original data sets in the LFRDB Level 3 directory 

structure; 2) populating a Microsoft Access database 

containing the original data tables plus the FIREMON 

and LANDFIRE attribute tables; 3) building a set of 

crosswalk tables, data format queries, and data append 

queries to convert the data and populate the FIREMON 

and LANDFIRE attribute tables; and 4) documenting 

the data-conversion process and populating the FIREMON metadata tables.

Data directories—We created a directory for each 

data set to store the original data, any existing documentation and metadata, the FIREMON data-conversion queries and tables, and the populated FIREMON 

tables. The naming convention for the directories and 

subdirectories followed the naming convention for the 

FIREMON plot identification key. The FIREMON plot 

identification key consists of four fields: Registration 

ID, Project ID, Plot ID, and Date. The Registration ID 

Table 3—FIREMON sampling methods used in the LANDFIRE reference database.

FIREMON sampling method Type of data

Plot description (PD) Geographic coordinates

Life form canopy cover (tree, shrub, herbaceous)

Hyperlinked digital plot photos 

Plant species codes (NRCS plants)

Species composition (SC) Plant species cover

Plant species height

Individual tree height

Individual tree DBH

Tree data (TD) Individual tree live crown percent

Individual tree crown class

 1-hr fu

 10-hr fu

 100-hr fu

Fuel load (FL)  1000-hr fu

Live/dead shrub cover and height

Live/dead herbaceous cover and height

Metadata (MD) Metadata and comments

Hyperlinked metadata documents

Table 4—LANDFIRE attribute tables.

Attribute table name Contents

LANDFIRE map Potential vegetation type, cover 

type, and structure

LANDFIRE fuel Fuel loading

LANDFIRE canopy fuel Canopy bulk density and canopy 

base height
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Chapter 4—The LANDFIRE Prototype Project Reference Databas

and Project ID fields are used to define a directory and 

subdirectory for each data set. For example, the directory 

“mfsl” contains all data acquired from the Missoula Fire 

Sciences Laboratory. The subdirectories “sutah00” and 

“sutah01” contain field-referenced data collected for the 

Southern Utah Fuels Demonstration Project during the 

2000 and 2001 field seasons.

Original data tables—If the original data were not 

delivered in an Access database, we opened an empty 

Access database and imported the data tables, spreadsheets, or ASCII files. We then joined the tables within 

Access to maintain any relationships that existed between 

the data in their original format.

We then imported empty FIREMON and LANDFIRE 

attribute tables into the Access database containing the 

original data set. All FIREMON tables were prefixed 

with “_FM_” to distinguish them from the original data 

tables and to organize them for easier data management 

(fig. 3). LANDFIRE attribute table names were prefixed 

with a “_” for the same reasons (fig. 3). Only FIREMON 

tables compatible with the sampling methods used in 

the original data set were added to the Access database. 

The imported tables retained their relationships and 

maintained the referential integrity of the FIREMON 

database. This allowed for the identification of errors 

associated with invalid or duplicate plot keys and allowed for cascading deletions from and updates to the 

FIREMON and LANDFIRE attribute tables. These 

tables were then populated via data-conversion queries 

and subsequently used to error-check the data-conversion 

Figure 3—FIREMON tables within the Access database containing the Southern Utah Project year 2000 field data.
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Chapter 4—The LANDFIRE Prototype Project Reference Databas

process. If errors were discovered, we emptied the 

FIREMON and/or LANDFIRE attribute tables, corrected the data-conversion queries, and repopulated the 

tables. Once the FIREMON and LANDFIRE attribute 

tables were loaded correctly, the data were added to the 

main LFRDB containing data from all data sets for that 

specific mapping zone.

Data-conversion queries—We created crosswalk tables 

to convert local codes in the original data set to standard 

FIREMON codes. We generated these tables by querying 

each applicable field in the original tables for all unique 

codes, creating a table from the results, and then adding a 

column for the FIREMON code. We populated FIREMON 

codes in each data-conversion table through queries or 

data entry. We then linked these data-conversion tables 

with database queries to format data for each FIREMON 

table. All conversion tables were prefixed with “_cnv” 

for easy identification in the Access database. Figure 4 

illustrates a landform data-conversion table that converts 

the landform codes in the Southern Utah Project year 

2000 data to the FIREMON landform codes. The field 

names containing FIREMON codes were prefixed with 

“FM” to distinguish these field names from field names 

in the original data tables (fig. 4).

Next we designed data format queries to format the 

original data for each FIREMON table. Each data format query was prefixed with “_format” to easily locate 

and identify these queries in the Access database. All 

necessary tables from the original data were joined 

with the appropriate data crosswalk tables so that the 

query results contained the standard FIREMON codes 

(fig. 5). Any numerical computations within fields, such 

as converting units or calculating new values, were 

incorporated as equations in the data format queries. 

Each data format query generated the FIREMON plot 

identifier fields plus any other relevant fields needed to 

populate the corresponding FIREMON table. The data 

format query results were then used to populate the 

corresponding FIREMON table (fig. 6).

FIREMON and LANDFIRE attribute tables—We 

developed a set of append queries to add data from the 

data format queries into the appropriate FIREMON tables. 

We prefixed these query names with “_insert” to facilitate 

identification in the Access database. These queries used 

the Access Structured Query Language (SQL) INSERT 

INTO statement to append data from the data format 

query into the corresponding FIREMON table.

Specifically, within each Access database, we designed a form with two command buttons. This form 

facilitated the deletion and addition of records from and 

to the FIREMON tables when changes were made to 

the data format queries and the FIREMON tables had 

to be updated. One button deleted all of the records in 

the FIREMON tables and one button added all of the 

Figure 4—Data conversion table for landform codes in the Southern Utah 

Project year 2000 database.
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Chapter 4—The LANDFIRE Prototype Project Reference Databas

Figure 5—Data format query used to populate the FIREMON tree data (TDTree) table in the Southern Utah

Project year 2000 database.

Figure 6—Query results for the data format query used to populate the TDTree table in the Southern Utah Project year

2000 database. 
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Chapter 4—The LANDFIRE Prototype Project Reference Databas

records to all of the FIREMON tables. The Delete Records button deleted all records from the FIREMON PD 

table, which then deleted all FIREMON records through 

cascading deletes. The Add Records button executed all 

the append queries in a sequence compatible with the 

referential integrity of the FIREMON tables.

Documentation and Metadata

We documented the data-conversion process for each 

data set using a table that mapped all the applicable 

FIREMON table names and field names to the original 

table names and field names. This data mapping table 

keeps a permanent record of the data-conversion process 

and facilitates the writing of data format queries for 

the FIREMON database structure. This table contains 

columns for the FIREMON table and field names, the 

original data table and field names, and any data-conversion algorithm or constant values applied to the field. 

Appendix 4-C contains the data mapping table for the 

Southern Utah Project year 2000 data set. Instances in 

which data-conversion algorithms or constants were 

applied to this data set include times at which we added 

MFSL and SUtah00 to the FIREMON Registration ID 

and Project ID fields, converting log diameters and decay 

classes from a row format to a column format, using 

a data-conversion table to convert local plant species 

codes to the Natural Resources Conservation Service 

(NRCS) plant codes (NRCS PLANTS Database 2005), 

and calculating absolute canopy cover from relative 

canopy cover.

Documents describing the field sampling protocols and 

the database structure for each data set were compiled 

as metadata for the LFRDB. We stored these documents 

in a metadata subdirectory for each data set. We also 

recorded any metadata pertaining to the data-conversion process in the FIREMON metadata tables (table 3). 

Metadata documents that were supplied with each data 

set were also stored in a documents directory associated 

with the main LFRDB

Quality Assurance and Quality Control 

(QA/QC)

Inaccurate reference data may introduce systematic or 

random bias into any type of mapping effort (Congalton 

1992). We therefore implemented a set of systematic 

quality control measures to assure a high confidence 

level in the maintenance of data quality and accuracy 

standards. We developed QA/QC procedures to test for 

potential errors inherent within the reference data and to 

assess the quality of the reference data. We targeted three 

specific categories of QA/QC: 1) geospatial, 2) information content, and 3) land cover change detection.

Geospatial—We recognized that many incorrect plot 

coordinates result from human data entry errors and from 

samplers, who, in order to save time, record geographic 

coordinates along a road instead of from the actual plot 

center. Such locational inaccuracies were identified and 

corrected before the field-referenced data were used. We 

displayed data points using ArcGIS to visually inspect 

the distribution of plot locations for obvious problems 

resulting from coordinate errors, such as points occurring well outside the known perimeter of a particular 

data set. Data points were displayed with road network 

and hydrology coverages to identify problems such as 

sample points recorded on roads and in bodies of water. 

We also calculated the distance of field plots from the 

nearest road and noted plots positioned within 90 meters 

of a road.

Information content—We also identified errors related to the information collected at each sample point 

as numerous errors occur within the plot data, such 

as fields left empty and data recorded incorrectly. We 

sorted data records and scanned fields for null values 

in required fields to ensure that all plot identifiers and 

plot locations were unique. In addition, we assessed the 

quality of plant species data and determined the level of 

information provided, such as whether the data contained 

full or partial plant species lists. We also noted whether 

heights were provided for each species, flagging plots 

with species heights exceeding the normal range.

Land cover change detection—Plot data used for 

LANDFIRE Prototype Project were collected over a 

variety of dates ranging back to the late 1980s. Over 

time, land cover changes rendered some plot information obsolete. We therefore evaluated older data sets 

for land cover changes resulting from either natural 

or human causes. We used Landsat imagery from the 

Multi-Resolution Land Characteristics (MRLC) 1992 

and 2001 projects and the 1992 National Land Cover 

Data Set (NLCD) (Vogelmann and others 2001) in this 

evaluation.

MRLC 1992 and MRLC 2001 data consist of a collection of terrain-corrected Landsat imagery covering 

all of the United States. The 1992 NLCD is a classified 

Landsat product delineating land cover types across 

the conterminous United States. MRLC 2001 Landsat 

imagery and NLCD 1992 land cover data were used 

for the LANDFIRE vegetation classification, providing 

a means to visually evaluate field-referenced data for 

errors. Each reference point was selected by life form 
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Chapter 4—The LANDFIRE Prototype Project Reference Databas

(for example, forest, shrub, or herbaceous), overlaid onto 

both the 2001 Landsat imagery and the 1992 NLCD land 

cover data, and then visually interpreted for validity. This 

subjective point evaluation was adequate for identifying 

obvious plot errors, such as lodgepole pine-dominated 

plots situated in the middle of a large, homogeneous 

herbaceous area.

To detect reference data errors due to land cover change, 

we compared the difference between the Normalized 

Difference Vegetation Index (NDVI) of the MRLC 

1992 and the MRLC 2001 image data sets. NDVI is 

a widely used vegetation index calculated as a ratio 

between the measured reflectance in the red and near 

infrared spectral bands of the Landsat imagery. This 

index has been shown to be highly effective in providing reliable quantification of vegetation cover (Goward 

and others 1991). The differencing of these NDVI data 

over time provides an excellent spatial representation of 

vegetation change. We extracted data values from the 

resultant images of the NDVI of MRLC 2001 Landsat 

data minus the NDVI of MRLC 1992 Landsat imagery. 

Changed areas within the differenced images assume 

strongly positive or negative values, especially when 

the changes are great (for example, the clear-cutting of 

a forest, mortality caused by insect defoliation, or forest regeneration). The differenced images were evaluated to determine the appropriate threshold values that 

depicted major change. The differenced NDVI values 

were extracted for each reference data point, and those 

points with values above the defined thresholds were 

flagged as areas of possible change. These points were 

then examined in greater detail by overlaying on the 

MRLC 2001 imagery, and a decision was made as to 

whether these points should be used in LANDFIRE 

analyses.

LANDFIRE Reference Database Content

The LFRDB included a subset of tables from the 

FIREMON database plus the LANDFIRE attribute 

tables. The plot description (PD), tree data (TD), fuel 

load (FL), species composition (SC) and metadata (MD) 

tables comprise the FIREMON subset in the LFRDB 

(table 3). Figure 7 shows the relationships between 

the FIREMON tables and the LANDFIRE attribute 

tables.

Figure 7—LANDFIRE reference database relationships view showing FIREMON tables and LANDFIRE attribute tables.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 7

Chapter 4—The LANDFIRE Prototype Project Reference Databas

We linked FIREMON tables from each data set 

separately to the LFRDB. We then developed a set of 

append and update queries for data management purposes. Append queries insert the data from each linked 

FIREMON table into the appropriate LFRDB table. 

Each append query is prefixed with “_insert” followed 

by the table name to which it adds data. Update queries 

modify the LANDFIRE map table by assigning a PVT, 

CT, and SS to each plot. All update queries are prefixed 

with “_update” followed by the table and field in which 

they update. These queries are run only after the cover 

type program and PVT queries are executed (see below 

sections on cover type and potential vegetation type). 

Linked tables were deleted after all required append 

queries were run and the data were successfully appended to the reference database tables. This procedure 

was repeated for each data set within a mapping zone 

until all plot data for the mapping zone were added.

Cover Type

We assigned a cover type (for example, Ponderosa 

Pine, Douglas-fir, etc.) to each plot in the reference 

database using differing methods for FIA and non-FIA 

data. Plot data including plant species cover and height 

information were processed with a computer program 

to assign a LANDFIRE cover type based on the dominant overstory plant species. FIA plots were assigned a 

cover type based on the dominant tree species by basal 

area.

Plant species cover and height data—A LANDFIRE 

cover type classification program was developed to assign a LANDFIRE cover type to each plot having plant 

species cover and height data. This program consists 

of a Windows graphical user interface (GUI) front-end 

(fig. 8) called covertypewin.exe and a console application called covertype.exe. These two programs are .NET 

Figure 8—Cover type classification program graphical user interface (GUI) – front end.
80 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 200

Chapter 4—The LANDFIRE Prototype Project Reference Databas

executables and run under the Microsoft .NET Framework 

version 1.0 (Microsoft .NET 2005). Covertypewin.exe is 

the GUI portion of the package and invokes the console 

application covertype.exe, which then determines the 

cover type assignment for each plot.

There are several user inputs to this program (fig. 8). 

Layer heights are used to specify the lower height of 

up to six vegetation strata (for example, <1 m, 1 to 3 m, 

etc.). Minimum relative cover by life form (RCLF) for 

dominant canopy layer specifies the minimum RCLF 

value, determining which stratum or combination of 

strata occupies the dominant canopy. For example, if 

this parameter is set to .20 (20 percent) and tree species in the upper stratum (>3 m) occupy only 10 percent 

RCLF, the next lower stratum is added to the dominant 

canopy layer. If tree species in these two strata occupy 

at least 20 percent RCLF, then these strata are considered to occupy the dominant canopy (in other words, 

>1 m). This parameter is used primarily to determine 

the dominant canopy layer on forested plots where 

overstory and understory species coexist. Minimum 

absolute cover for dominant and codominant species

specifies the cover threshold for determining dominant 

and codominant species. Maximum relative cover for 

mixed types specifies the relative cover value used to 

determine whether a plot falls in a mixed cover type 

(such as Pinyon – Juniper) or a single species cover type 

(such as Juniper). The Initialize output database tables 

checkbox indicates whether the program will delete 

the current contents of the output table before program 

execution.

This program requires one configuration file, three 

lookup tables, two input data tables, and generates one 

output table. Two of the three lookup tables associate 

dominant and codominant plant species with a LANDFIRE cover type. The other lookup table determines 

“tie-breakers,” based on successional development, 

when two or more cover types have equal cover. For 

example, a tie between Douglas-fir and Ponderosa Pine 

cover types went to the later successional cover type of 

Douglas-fir. The input data tables contain plot life form 

cover values (tree, shrub, and herbaceous) and individual 

plant species’ canopy cover and height values. The output table, cover type classification, is populated with 

the LANDFIRE cover type, dominant and codominant 

plant species, and their associated cover values. The 

configuration file sets the database paths for the input 

and output data tables.

The LANDFIRE cover type classification program 

first determined the dominant life form on the plot 

based on the cover type classification rule set (Long 

and others, Ch. 6). Next, the program determined the 

dominant canopy layer. We stratified canopy layers using the FIREMON defaults of 0 to 1 m, 1 to 3 m, and 

greater than 3 m. Relative cover for each species in the 

dominant life form was totaled for each canopy layer until 

a minimum value was reached. We set this minimum 

value at 20 percent. The cover type was determined by 

totaling the canopy cover values for each species in the 

dominant layer by cover type and selecting the cover 

type that has the greatest canopy cover. Plots having 

cover types with equal cover in the dominant canopy 

layer were assigned the later successional cover type.

For cover types based on codominance, such as Pinyon – Juniper, the program calculated the relative cover 

of each codominant species to determine if both were 

greater than or equal to 25 percent. For example, in Zone 

16, the Pinyon – Juniper cover type was distinguished 

from the Juniper cover type based on each species having at least 25 percent cover.

This program also determined the dominant and codominant plant species on a plot. These plant species 

were within the dominant life form, typically within 

the assigned cover type and dominant canopy layer, 

and had the greatest absolute cover values. If there was 

only one species on the plot in the assigned cover type, 

the codominant species was determined as the species 

with the next greatest canopy cover within the dominant 

life form. Ties went to the later successional species. 

We set the minimum cover value at zero and reported a 

dominant and codominant plant species, when possible, 

and their associated cover values.

FIA tree data—FIA tree data include tree species 

codes, DBH, and trees per acre. We developed Access 

queries to calculate the basal area for each species on 

a plot, calculated the basal area by LANDFIRE cover 

type, and assigned the cover type having the greatest 

basal area. Plots having cover types with equal basal 

area were assigned the later successional cover type.

For cover types based on codominance, such as Pinyon-Juniper, we calculated the relative basal area of the 

codominant species to determine if it is greater than or 

equal to 25 percent. For example, in Zone 16, mixed 

pinyon-juniper plots were distinguished from juniperdominated plots. The criterion for distinguishing the 

Pinyon – Juniper cover type from the Juniper cover type 

was that pinyon and juniper must each have at least 25 

percent relative cover.

We then identified the dominant and codominant tree 

species as those having the greatest basal area within the 

LANDFIRE cover type. If there was only one species 

on the plot in the assigned cover type, the codominant 
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Chapter 4—The LANDFIRE Prototype Project Reference Databas

species was selected as the species with the next greatest 

basal area. Ties went to the later successional species 

for reasons described above.

Potential Vegetation Type

The LANDFIRE potential vegetation type (PVT) classifications (Long and others, Ch. 6) were based on two 

levels of indicator species, one associating plant species 

with each Level 2 PVT and one associating plant species with each Level 3 PVT. The indicator species were 

ordered in these tables by shade tolerance (more to less) 

within forest PVTs or by a moisture gradient (xeric to 

mesic) within shrubland or herbaceous PVTs (Long and 

others, Ch. 6). We developed a set of database queries 

for assigning LANDFIRE PVTs to plots based on the 

presence of these indicator species.

The first set of database queries assigned a Level 2 PVT 

sequence number to each plot based on the PVT Level 

2 indicator species list (Long and others, Ch. 6). First, 

each plant species that was in the PVT classification and 

present on a plot was assigned a Level 2 PVT sequence 

number. Then, the lowest sequence number on the plot 

determined the Level 2 PVT for the plot. A minimum cover 

percentage level for shrub indicator species (greater than 

or equal to 10 percent) was used to prevent plots with an 

herbaceous cover type from being assigned a shrubland 

PVT, such as in plots having shrub species present yet a 

total shrub cover of less than 10 percent.

The second set of database queries assigned a Level 

3 PVT sequence number to each plot based on the PVT 

Level 3 indicator species list (Long and others, Ch. 6). 

Again, each plant species that was in the PVT classification and present on a plot was first assigned a Level 3 

PVT sequence number. Then the lowest sequence number 

on the plot determined the Level 3 PVT for the plot. We 

then concatenated the Level 2 and Level 3 PVT labels 

to generate the proper PVT label.

Data Management and Summary Queries

Data management queries involved appending data to 

the reference database tables and updating the LANDFIRE map table with PVT, CT, and SS assignments for 

all plots. We created data summary queries to assist in 

the development of the potential vegetation classification, 

the cover type classification, succession pathway models, 

and fuel model classifications. These data summary 

queries included plot counts by CT, PVT / CT, and PVT 

/ CT / SS; constancy cover tables by CT, PVT / CT, and 

PVT / CT / SS; and fuel loading by CT, PVT / CT, and 

PVT / CT / SS. We built a form within the LFRDB to 

automate the data management and data summary tasks 

(fig. 9). Automating these tasks facilitated database plot 

record updates as modifications were made to the PVT, 

CT or SS classifications.

Figure 9—Form for automating data management and data summary tasks.
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Chapter 4—The LANDFIRE Prototype Project Reference Databas

FIREMON Database Application

The FIREMON database application has two features 

that were applicable to the LFRDB: plot photo and 

metadata document hyperlinks. The FIREMON database application was connected to the LFRDB using 

the Configuration and Settings menu. This menu also 

set the directory path for the FIREMON documents 

and photos directories. The FIREMON plot description 

(PD) data entry form allowed the LANDFIRE team to 

view hyperlinked photos (fig. 10). For each mapping 

zone, we placed all acquired digital plot photos into a 

photo directory. The digital image (photo) file names 

were added to the FIREMON PD photo fields during 

the data-conversion process. These photos could then 

be viewed via the plot photo hyperlink on the PD form. 

The FIREMON MD data entry form allowed the team 

to view hyperlinked documents (fig. 11). For each mapping zone, we placed all associated metadata documents 

into a documents directory. The document file names 

were added to the MD table document link field in the 

LFRDB for each mapping zone. These documents could 

then be viewed via the document hyperlink.

Figure 10—FIREMON plot description form illustrating the plot photo hyperlink.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 

Chapter 4—The LANDFIRE Prototype Project Reference Databas

Figure 11—FIREMON metadata form illustrating the document hyperlink.

Results and Discussion __________

Reference Database Implementation

The FIREMON database was designed to be flexible, 

and it proved to be fully capable of storing the primary 

data elements required for the LFRDB. One accommodating feature of FIREMON is that many of the fire 

monitoring and ecosystem inventory sampling methods 

employ standard field sampling techniques The flexibility 

of the FIREMON database allowed customization of the 

core data tables to store data from a diverse set of fieldreferenced databases. For example, the FIA, ECODATA, 

and Southern Utah Project tree data were easily added to 

the FIREMON TD tables; The ECODATA, Utah fieldreferenced data from Southwest ReGAP, and Southern 

Utah Project plant species composition and cover data 

were easily added to the FIREMON SC tables; The 

FIREMON PD table accommodated all locational information and site data; and the FIREMON MD tables 

stored all metadata on field sampling protocols and the 

data-conversion process.

Additional LANDFIRE attribute tables were easily 

integrated into the FIREMON database and linked to 

the FIREMON PD table. These tables were developed 

specifically for LANDFIRE modeling and mapping 

tasks and facilitated reference data dissemination to 

specific LANDFIRE teams. For example, data from 

the LANDFIRE map attribute table were used to model 

and map cover type and vegetation structure (Zhu and 

others, Ch. 8) and to model and map potential vegetation 

(Frescino and Rollins, Ch. 7). In addition, these data 
84 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 20

Chapter 4—The LANDFIRE Prototype Project Reference Databas

were used to refine the existing and potential vegetation classifications (Long and others, Ch. 6). Data in 

the LANDFIRE map attribute and fuel attribute tables 

were used to map fire behavior fuel models (Anderson 

1982; Scott and Burgan 2005), fire effects fuel models 

(Keane and Rollins, Ch. 3; Keane and others, Ch 12; 

Lutes and others, in preparation; Sandberg and others 

2004), and canopy fuel. These tables were used to map 

fire behavior and effects fuel models based on spatially 

explicit layers of PVT, CT, and SS. (Keane and others, 

Chapter 12). The LANDFIRE canopy fuel attribute table 

was also used as input to the FUELCALC program, and 

FUELCALC output was used to model and map canopy 

bulk density and canopy base height (Keane and others, 

Chapter 12).

In addition to using the FIREMON database to store 

field-referenced data, we developed a simple and efficient 

data-conversion process. The Access Query Design Window provided a relatively easy way for reference database 

team members to build the SQL statements required for 

the data-conversion process. This process was easy to 

teach resource personnel with limited database skills 

and was effective as a data migration and data cleaning 

tool. Our method proved adaptable enough to convert a 

wide range of data into a common database structure. In 

addition, our process provided an easy way to track plot 

records from the LANDFIRE attribute tables back to 

the original data stored in the data directories for each 

data set.

Reference Data Acquisition and 

Applications

Zone 16—Approximately 11,000 plots were compiled 

in the LFRDB for Zone 16. The initial acquisition of 

existing data sets for Zone 16 produced only three viable 

data sets: FIA tree data, Southern Utah Project data, and 

Utah field-referenced data from Southwest ReGAP. Since 

FIA data is fairly extensive in forested areas, the limiting factor was the acquisition of non-forest vegetation 

data. The Southern Utah Project data adequately covered 

the southern portion of Zone 16. The initial Utah data 

acquired from Southwest ReGAP contained very little 

reference data for the northern and central portions of 

Zone 16. A strategic partnership between the Southwest 

ReGAP project and the LANDFIRE Prototype Project 

was crucial in obtaining additional field-referenced data 

for Zone 16.

Concerning applications, all data sets acquired for 

Zone 16 included vegetation data used to model and map 

potential vegetation, cover type, and structure. In addition 

to use in mapping vegetation cover and structure, all FIA 

tree data were used for modeling and mapping canopy 

fuel (canopy bulk density and canopy base height). A 

limited number of plots in the Southern Utah Project 

data set had tree data used for modeling and mapping 

canopy fuel and downed woody fuel transect data used 

for mapping fire behavior and effects fuel models.

Zone 19—Approximately 12,500 plots were compiled 

in the reference database for Zone 19. The initial data 

acquisition for Zone 19 produced several viable data 

sets in addition to the FIA tree data, due in part to 

existing ECODATA plots from the USDA Forest Service Northern Region ecology program and USGS and 

MFSL research in and around Glacier National Park. 

We acquired data for thousands of BLM fuel inventory 

plots, as well; however, these were heavily concentrated 

around Salmon and Challis, Idaho. Moreover, many of the 

ECODATA plots were concentrated around the Flathead 

National Forest and Glacier National Park. We lacked 

field-referenced data for many non-forested areas in the 

southern and eastern portions of Zone 19. Therefore, it 

was crucial to contract with the Utah field crews from 

Southwest ReGAP for additional field-referenced data 

collection.

All data sets for Zone 19 included vegetation data used 

to model and map potential vegetation, cover type, and 

structure. In addition to use in mapping vegetation and 

structure, all FIA tree data were used for modeling and 

mapping canopy fuel. All ECODATA data sets had tree 

data for modeling and mapping canopy fuel and downed 

woody fuel transect data for mapping fire behavior and 

effects fuel models.

Limitations of Acquired Data

Some of the existing data sets obtained for the LANDFIRE Prototype proved limited in utility. For example, 

the data sets acquired from research projects -- MFSL 

and USGS ECODATA plots for Glacier National Park -- 

provided the most accurate data for mapping existing and 

potential vegetation and fuel models, yet these data were 

geographically limited. Second, FIA tree data provided 

valuable plot data for forest ecosystems, including individual tree measurements used for modeling and mapping 

potential vegetation, cover type, canopy height, canopy 

cover, and canopy fuel. In order to maintain nationally 

consistent FIA data, however, we used only the FIADB 

data tables that are available nationally; we did not use 

FIADB seedling and understory vegetation data as these 

are not collected across the country. Third, the Utah data 

collected for Southwest ReGAP and for additional Zone 

19 reference plots were useful for modeling and mapping 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 8

Chapter 4—The LANDFIRE Prototype Project Reference Databas

potential vegetation, cover type and structure. However, 

because height data were not always collected for every 

plant species recorded, only a subset of data collected 

using the Southwest ReGAP sampling protocols could 

be used to model and map structure. Lastly, although 

there were approximately 4,000 data points in the BLM 

dominant and co-dominant species data sets, most of 

the data were tightly clustered geographically, limiting 

their utility for mapping vegetation and fuel across the 

entire extent of Zone 19.

Recommendations for National 

Implementation _________________

Cooperation with Outside Agencies

Projects requiring extensive field-referenced data, 

such as LANDFIRE, must rely heavily on existing data 

sources since there is not enough time or money to collect new reference data. We initially solicited data via 

a formal data request letter sent to agency personnel, 

but this effort yielded only a few small and limited data 

sets. Nonetheless, a formal data request letter will be 

necessary for the national implementation of LANDFIRE 

and will likely generate some high-quality data sets. 

We do, however, caution against relying primarily on 

such a letter to generate the quantity and quality of data 

required for a project the size and scope of LANDFIRE. 

We discovered that the most efficient and effective way 

to obtain high-quality, well-distributed data is through 

cooperation with agencies that have already collected 

similar reference data. All major field-referenced data 

contributions for the LANDFIRE Prototype Project 

were obtained through new or existing relationships 

with outside agencies and research projects.

It is of particular importance to have cooperation 

from FIA in light of the confidential nature of FIA plot 

locations. The Interior West FIA program provided 

the LANDFIRE Prototype Project with one part-time 

FIA employee to facilitate data acquisition and perform 

tasks requiring the use of FIA plot coordinates. In addition, through this partnership we were able to acquire 

plot data other than those available from the FIADB. 

Furthermore, collaboration between LANDFIRE and 

the Southwest ReGAP Project gained the LFRDB 

team access to a wealth of data for Zone 16. Once we 

established a relationship with the Utah State Southwest 

ReGAP team, we were able to obtain all existing plot 

data as well as contract with their Utah field crews to 

acquire additional field-referenced data for zones 16 and 

19. Contracting with other agencies’ field crews capitalizes on their resources and expertise in field sampling, 

thereby saving investment in additional resources and 

training.

Data Triage

Data triage is critical to the timely delivery of reference 

data. It is easy to become overwhelmed by too much 

data and too little time for processing. For this reason, 

it is essential to concentrate on large, high-quality data 

sets with wide geographic coverage containing data elements pertinent to the LANDFIRE mapping tasks. We 

do not recommend spending much time on small data 

sets unless they have high quality data (such as certain 

data sets from intensive research projects) or until after 

the large data sets have been processed. Next, prior to 

data conversion, we recommend identifying all plot 

locations mistakenly recorded on roads, in water, or 

outside the known study area. Assessing the locations 

of existing reference data will also highlight areas in 

need of additional field data collection. Identifying these 

areas early in the process allows ample time to contract 

with field crews prior to the up-coming field season.

Keep it Simple

Most of the data sets we acquired for the LANDFIRE 

Prototype Project contained many more variables than 

were necessary for our purposes. We initially converted 

all original data that could populate the FIREMON 

database fields and recommend this as the first step in 

data conversion since all data elements of the original 

data set exist in a common database for use in the prototype effort. When time is a limiting factor, however, 

it is practical to first convert only the fields required to 

populate the LANDFIRE attribute tables. If time allows, 

the data-conversion queries may be modified and plot 

records updated in the LFRDB. We did spend a substantial amount of time converting data that were never used 

for the LANDFIRE Prototype, yet the prototype nature 

of the project required that we had all data available 

while the LANDFIRE mapping processes were being 

developed.

Because of the large quantity of field-referenced data 

and necessary distribution across LANDFIRE mapping 

teams, the LANDFIRE National will require a true multiuser relational database management system capable of 

accommodating more data than Microsoft Access and 

operating efficiently under a distributed computer network. Although a database with greater flexibility than 

that of Access should ultimately be used for LANDFIRE 

National, the Access reference database used in the 
86 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 200

Chapter 4—The LANDFIRE Prototype Project Reference Databas

prototype effort proved satisfactory for our purposes. 

If, for LANDFIRE National, a separate LFRDB were 

built for each mapping zone, Access would be able to 

accommodate the large amount of plot data and the 

entire data-conversion process described in this chapter 

could be applied.

Conclusion_____________________

In conclusion, the use of existing databases to meet 

the broad-scale mapping objectives of the LANDFIRE 

Prototype Project worked well. Existing databases from 

both government and non-government sources provided 

excellent information for successfully completing the 

LANDFIRE mapped products. In this prototype effort, however, we found that field data for non-forested 

areas were rare relative to those of forested areas. This 

scarcity of data for non-forested areas may pose more 

of a limitation in other areas of the United States than 

it did in the two study areas of the LANDFIRE Prototype Project. For this reason, it may be necessary to 

collect additional data in non-forested areas for national 

implementation of the LANDFIRE methods.

For further project information, please visit the LANDFIRE website at www.landfire.gov.

The Author _____________________

John F. Caratti is a Research Scientist with Systems 

for Environmental Management. Since 1988, Caratti 

has developed computer software for ecological data 

analysis and wildlife population modeling. His most 

recent work includes developing database applications 

for classifying and mapping vegetation and fuel and for 

monitoring fire effects. He received his B.A. degree in 

Ecology from the University of California, San Diego 

in 1988 and his M.S. degree in Wildlife Biology from 

the University of Montana in 1993. Caratti has worked 

as an Ecologist for the USDA Forest Service – Northern 

Region, The Nature Conservancy, and as an independent 

contractor.

Acknowledgments_______________

I would like to acknowledge Larry Gangi and Aimee 

Vitateau of Systems for Environmental Management for 

their valuable help in developing the reference database. I 

also wish to thank the individuals of Axiom IT Solutions 

in Missoula, Montana (www.axiom4.com) for their roles 

in developing the LANDFIRE cover type classification 

program and the FIREMON database application. Finally, I would like to recognize Tracy Frescino of the 

USDA Forest Service Rocky Mountain Research Station, 

Karen Short of Systems for Environmental Management, 

and Bradley G. Smith of the USDA Forest Service for 

their thoughtful reviews of this manuscript.

References _____________________

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Department of Agriculture, Forest Service, Intermountain Forest 

and Range Experiment Station.

Brown, J. K. 1974. Handbook for inventorying downed woody 

material. Gen. Tech. Rep. GTR-INT-16. Ogden, UT: U.S. Department of Agriculture, Forest Service, Intermountain Forest and 

Range Experiment Station. 34 p.

Congalton, R. G.; Biging, G. S. 1992. A pilot study evaluating 

ground reference data collection efforts for use in forest inventory. Photogrammetric Engineering and Remote Sensing. 58(12): 

1669-1671.

Gillespie, A. J. R. 1999. Rationale for a National Annual Forest 

Inventory Program. Journal of Forestry. 97(12): 16-20.

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Dictionary. Unpublished paper on file at: U.S. Department of 

the Interior, Bureau of Land Management, Idaho State Office, 

Boise, ID. 43 p.

Goward, S. N; Markham, B.; Dye, D.G; Dulaney, W.; Yang, J. 1991. 

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update). Homepage of Interior West Forest Inventory and Analysis, 

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

Research Station, [Online]. Available: http://www.fs.fed.us/rm/

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ecosystem description and evaluation. In: Jensen, M. E.;Bourgeron, 

P. S., eds. Eastside Forest Ecosystem Health Assessment, Volume 

II, Ecosystem Management: Principles and Applications. Gen. 

Tech. Rep. PNW-GTR-318. Portland, OR: U.S. Department of 

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Pp. 249-265.

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landscape inventories. Gen. Tech. Rep. RMRS-GTR-92. Fort 

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Rocky Mountain Research Station. Chapter 8.

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Reinhardt, E. D.; Crookston, N. L., tech. eds. 2003. The Fire and 

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Rep. RMRS-GTR-116. Fort Collins, CO: U.S. Department of 

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218 p.

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Fire.10:381-387.

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a comprehensive set for use with Rothermel’s surface fire spread 

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Department of Agriculture, Forest Service, Rocky Mountain 

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Chapter 4—The LANDFIRE Prototype Project Reference Databas

Plot description (PD)

Field name Description

RegID	 Unique 4 character code for each FIREMON user

ProjID	 Unique 8 character code for each project

PlotID	 Unique number for each plot

Date Sampling date

OrgCode1 Organization cod

OrgCode2 Organization code 2

OrgCode3 Organization cod

OrgCode4 Organization cod

Examiner Name of FireMon crew boss or lead examiner

Units Units of measurement (english or metric)

Radius Radius/length of the macroplot in feet (meters)

Width Width of macroplot in feet (meters)

PlotType Type of plot: C=Control, M=Measured

SEvent Sampling event - reason why plot is being measured at this time

FireID Fire behavior database key-id

MdId Metadata Key-Id

LocType Type of Location L=Lat/Long, U=UTM

Lat Latitude of plot center

Long Longitude of plot center

Northing UTM Northing of plot center

Easting UTM Easting of plot center

Datum GPS datum

GPS_Error GPS error (meters or feet)

GPS_Err_Units Units for GPS Error: ft = feet, m=meters

UTM_Zone UTM zone

Elev Elevation above mean sea level - feet (meters)

Aspect Aspect of plot in azimuth - degrees

Slope Average slope (rise/run)*100 - percen

Landform Landform code

VShape Shape of plot perpendicular to contour

HShape Shape of plot parallel to contour

Geol1	 Primary surficial geology code

Geol2	 Secondary surficial geology code

SoilTex Soil texture

EType Erosion type

ESev Erosion severity

TreeC Total tree cover - percent

SeedC Seedling cover - percent

SapC Sapling cover- percent

PoleC Pole cover - percent

MedC Medium tree cover - percent

LTreeC Tree cover - percent

VLTreeC Very large tree cover - percent

ShrubC Total shrub cover - percent

LShrubC Low shrub cover - percent

MShrubC Medium shrub cover - percent

TShrubC Tall shrub cover - percent

GramC Graminoid cover - percent

ForbC Forb cover - percent

FernC Fern cover - percent

MossC Moss and lichen cover - percent

USpp1 Most dominant species in upper lay

USpp2 Second most dominant species in upper layer

MSpp1 Most dominant species in middle lay

MSpp2 Second most dominant species in middle layer

Appendix 4-A—FIREMON tables and fields in the 

landfire reference database ______________________
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 8

Chapter 4—The LANDFIRE Prototype Project Reference Databas

LSpp1 Most dominant species in lower lay

LSpp2 Second most dominant species in lower layer

PVTId Potential vegetation type code

PotForm Potential life form code

BSoilGC Bare soil ground cover - percent

GravelGC Gravel ground cover - percent

RockGC Rock ground cover - percent

DuffGC Duff and litter ground cover - percent

WoodGC Wood ground cover - percent

MossGC Moss and lichen ground cover - percent

CharGC Charred ground cover - percent

AshGC Ash ground cover - percent

BVegGC Basal vegetation ground cover - percent

WaterGC Water ground cover - percent

FModel Fire behavior fuel model (Anderson 1982

PhotoID Fuel photo series

SHT Stand height: height of highest stratum which contains at least 10% of canopy cover - feet (meters

CBH Canopy fuel base height - feet (meters)

CanopyC Percent canopy cover of forest canopy >6.5 feet - feet (meters)

FLength	 Average flame length - feet (meters)

SRate	 Spread rate; average speed of fire - feet/min (meters/min)

FBevPic	 Picture code for fire behavior picture

FSC Fire severity code

NorthPic Code for plot photo taken in direction of due north

EastPic Code for plot photo taken in direction of due east

Photo1 Code for plot phot

Photo2 Code for plot photo 2

Local1 Local cod

Local2 Local code 2

Comments Comments about plot

 Species composition – macroplot (SCMacro)

Field name Description

RegID	 Unique 4 character code for each FIREMON user

ProjID	 Unique 8 character code for each project

PlotID	 Unique number for each plot

Date Sampling date

SppIDLevel	 Plant species ID level; minimum cover recorded – percent

RegID

 Species composition (SCCover)

Field name Description

RegID	 Unique 4 character code for each FIREMON user

ProjID	 Unique 8 character code for each project

PlotID	 Unique number for each plot

Date Sampling date

Item Item code

Status Heath of species - (live or dead)

SizeCl Size class

Cover Canopy cover - percent

Height Average height - feet (m)

Local1	 Optional field 1

Local2	 Optional field 2

 Tree data – macroplot (TDMacro)

Field name Description

RegID	 Unique 4 character code for each FIREMON user

ProjID	 Unique 8 character code for each project

PlotID	 Unique number for each plot

Date Sampling date

MacroPlotSize	 Macroplot size - acres (square meters)

MicroPlotSize	 Microplot size - acres (square meters)

SnagPlotSize	 Snagplot size - acres (suqare meters)

BreakPntDia Break point diameter - inches (cm)

Appendix 4-A — (Continued)
90 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 200

Chapter 4—The LANDFIRE Prototype Project Reference Databas

 Tree data – mature trees (TDTree)

Field name Description

RegID	 Unique 4 character code for each FIREMON user

ProjID	 Unique 8 character code for each project

PlotID	 Unique number for each plot

Date Sampling date

TagNo Tree tag number

Species Species code

TreeStat Health of tree (live or dead)

DBH Diameter at breast height - inches (cm)

Height Tree Height - feet (m)

LiCrPct Live crown percent

LiCrBHt Live crown base height feet (m)

CrwnCl Crown position class

Age Tree age - years

GrwthRt Tree growth rate (last 10 yrs radial growth) - inches (mm

DecayCl Decay class

Mort Cause of mortality

DamCd1 Damage cod

DamSev1 Severity cod

DamCd2 Damage code 2

DamSev2 Severity code 2

CharHt Bole char height - feet (m)

CrScPct Crown scorch percent

Local1 Optional cod

 Tree data – saplings (TDSap)

Field name Description

RegID	 Unique 4 character code for each FIREMON user

ProjID	 Unique 8 character code for each project

PlotID	 Unique number for each plot

Date Sampling date

SizeCl_Dia Size class

Species Species code

TreeStat Tree status

Count Count number of trees by species, size class, and status

AvgHt Average height - feet (m)

AvgLiCr Average live crown percent

Local1	 Local field 1

 Tree data – seedlings (TDSeed)

Field Name Description

RegID	 Unique 4 character code for each FIREMON user

ProjID	 Unique 8 character code for each project

PlotID	 Unique number for each plot

Date Sampling date

SizeCl_Ht Size class

Species Species code

TreeStat General health condition of sample tree

Count Number of trees by species, size class, and status

Local1	 Local field 1

 Fuel load – macroplot (FLMacro)

Field name Description

RegID	 Unique 4 character code for each FIREMON user

ProjID	 Unique 8 character code for each project

PlotID	 Unique number for each plot

Date Sampling date

1HRTranLen 1-hr transect length - feet 

10HRTranLen 10-hr transect length - feet 

100HRTranLen 100-hr transect length - feet 

1000HRTranLen 1000-hr transect length - feet 

NumTran Number of transects

Appendix 4-A — (Continued)
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 

Chapter 4—The LANDFIRE Prototype Project Reference Databas

 Fuel load – fine fuel, duff, and litter (FLFineDL)

Field name Description

RegID	 Unique 4 character code for each FIREMON user

ProjID	 Unique 8 character code for each project

PlotID	 Unique number for each plot

Date Sampling date

Transect Line transect number

Slope Slope of transect (rise/run)*100 - percen

1hr Number of pieces 0 - 0.25 in. (0 - 0.635 cm) in diamet

10hr Number of pieces 0.25 - 1.0 in. (0.635 - 2.54 cm) in diam

100hr Number of pieces 1 3 in. (2.54 and 7.62 cm) in diam

D/LDep1	 Depth of duff/litter profile - inches (cm)

LitterPct1	 Proportion of total profile depth that is litter- percent

D/LDep2	 Depth of duff/litter profile - inches (cm)

LitterPct2	 Proportion of total profile depth that is litter - percent

Local1	 Local field 2

 Fuel load – 1000-hr fuel (FL1000hr)

Field name Description

RegID	 Unique 4 character code for each FIREMON user

ProjID	 Unique 8 character code for each project

PlotID	 Unique number for each plot

Date Sampling date

Transect Line transect number

LogNum Log number

Dia Diameter of log at line intersection - inches (cm)

DecayCl Log decay class

Local1	 Local field 1

 Fuel load – vegetation (FLVeg)

Field name Description

RegID	 Unique 4 character code for each FIREMON user

ProjID	 Unique 8 character code for each project

PlotID	 Unique number for each plot

Date Sampling date

Transect Line transect number

LiShC1 Live woody cover at poin

DdShC1 Dead woody cover at poin

ShHt1 Woody height at poin

LiHeC1 Live non-woody cover at poin

DdHeC1 Dead non- woody cover at poin

HeHt1 Non-woody height at poin

LiShC2 Live woody cover at point 2

DdShC2 Dead woody cover at point 2

ShHt2 Woody height at point 2

LiHeC2 Live non-woody cover at point 2

DdHeC2 Dead non- woody cover at point 2

HeHt2 Non-woody height at point 2

 Metadata ID (MDID)

Field name Description

MdId Metadata ID

 Metadata (MDComm)

MDID Metadata ID

Subject Metadata subject

DocLink Hyperlink for metadata documen

Appendix 4-A — (Continued)
92 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 200

Chapter 4—The LANDFIRE Prototype Project Reference Databas

Appendix 4-B—LANDFIRE attribute tables __________

LANDFIRE map

Field name Description

RegID	 Unique 4 character code for each FIREMON user

ProjID	 Unique 8 character code for each project

PlotID	 Unique number for each plot

Date Sampling date

Albers_y Albers Northing of plot center

Albers_x Albers Easting of plot center

Datum Datum of coordinate system

LF_Zone LANDFIRE zone - MRLC zone in which plot is located

Orig_CoverType Cover type assigned in original data set

LF_CoverType LANDFIRE cover type

LF_CoverTypeCode LANDFIRE cover type code

DomSpp Dominant plant species

DomSppC Dominant plant species cover - percent

CoDomSpp Codominant plant species

CoDomSppC Codominant plant species cover - percent

TreeC Tree cover - percent

ShrubC Shrub cover - percent

HerbC Herbaceous cover - percent

TreeHt Average tree height weighted by canopy cover (ft.)

ShrubHt Average shrub height weighted by canopy cover (ft.)

HerbHt Average herbaceous height weighted by canopy cover (ft.)

Orig_StrucStage Structural stage assigned in original data set

LF_StrucStage LANDFIRE structural stage

LF_StrucStageCode LANDFIRE structural stage code

Orig_PVT Potential vegetation type assigned in original data set

LF_PVT LANDFIRE potential vegetation type

LF_PVTCode LANDFIRE potential vegetation type code

SoilTex Soil texture

PhotoID	 Plot photo filename identifier

Reference Data reference

Loc_Meth Method for determining plot location

Loc_Acc Plot location accuracy (meters)

DistToRoad Distance to nearest road (meters)

NLCD_Code NLCD code

NLCD_Desc NLCD description

LifeFormCover_QAQC Codes for life form cover QA/QC

SppCover_QAQC Codes for plant species data QA/QC

SppHeight_QAQC Codes for plant species height QA/QC

 LANDFIRE fuel

Field name Description

RegID	 Unique 4 character code for each FIREMON user

ProjID	 Unique 8 character code for each project

PlotID	 Unique number for each plot

Date Sampling date

ContactSource Contact person for data

DataSource Source of data

DataSourceInfo Information about source of data

DataComments Data comments

ID1 Organization cod

ID2 Organization code 2

ID3 Organization cod

ID4 Organization cod

Units Measurement units (E or M)

Albers_y Albers Northing of plot center

Albers_x Albers Easting of plot center

Datum Datum of coordinate system
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 

Chapter 4—The LANDFIRE Prototype Project Reference Databas

LF_Zone LANDFIRE zone - MRLC zone in which plot is located

Slope Elevation above mean sea level - feet (meters)

Aspect Aspect of plot in azimuth - degrees

Elevation Average slope (rise/run)*100 - percen

LocationComments Comments on plot location

LandfirePVT	 LANDFIRE potential vegetation type

CurrentLifeForm Current life form on plot

PotLifeForm Potential life form on plot

CoverType Land cover type

HabitatType Habitat type

BaileyProvince Bailey Province

ICRBStructure ICRB stand structure

VegDataFile	 Vegetation data file

OverSpecies1 Dominant overstory speci

OverSpecies2 Codominant overstory species

UnderSpecies1 Dominant understory speci

UnderSpecies2 Codominant understory species

Graminoid1 Dominant graminoid speci

Graminoid2 Codominant graminoid species

Shrub1 Dominant shrub speci

Shrub2 Codominant shrub species

Forb1 Dominant forb speci

Forb2 Codominant forb species

FuelbedDepth Fuelbed depth

Carrier1	 Primary fire carrier

Carrier2	 Secondary fire carrier

VegComments Vegetation comments

Event Fire event

Pre/post	 Pre/post fire effects

ActivityFuel Activity fuel

EventComments Event comments

1-hrBiomass  1-hr fuel biom

10-hrBiomass  10-hr fuel biom

100-hrBiomass  100-hr fuel biom

3”<TotalBiomass  1-100-hr total fuel bio

3”>TotalBiomass  1000-hr total fuel biom

3”>SndBiomass  1000-hr sound fuel biom

3”>RotBiomass  1000-hr rotten fuel biom

3”-9”SndBiomass  3”-9” sound fuel biom

3”-9”RotBiomass  3”-9” rotten fuel biom

9”-20”SndBiomass 9”-20” sound fuel biomass

9”-20”RotBiomass 9”-20” rotten fuel biomass

20”+SndBiomass 20” and larger sound fuel biomass

20”+RotBiomass 20” and larger rotten fuel biomass

DWMethod Downed woody sampling method

DWSource Downed woody sampling method source

DWComments Downed woody comments

DuffBiomass Duff biomass

DuffBulkDen Duff bulk density

DuffMethod Duff method

DuffSource Duff source

DuffComments Duff comments

LitterBiomass Litter biomass

LitterBulkDen Litter bulk density

LitterMethod Litter method

LitterSource Litter source

LitterComments Litter comments

LiveHerbBiomass Live herbaceous biomass

DeadHerbBiomass Dead herbaceous biomass

Appendix 4-B — (Continued)
94 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 20

Chapter 4—The LANDFIRE Prototype Project Reference Databas

HerbMethod Herbaceous sampling method

HerbSource Herbaceous sampling method source

HerbComments Herbaceous comments

LiveShrubBiomass Live shrub biomass

DeadShrubBiomass Dead shrub biomass

ShrubMethod Shrub sampling method

ShrubSource Shrub sampling method source

ShrubComments Shrub comments

OtherBiomass Other biomass

OtherMethod Other biomass sampling method

OtherSource Other biomass sampling method source

OtherComments Other comments

#ofTrans Number of transects

1-hrLen  1-hr transect len

10-hrLen  10-hr transect len

100-hrLen  100-hr transect len

1000-hrLen  1000-hr transect len

1-hrQMD	 1-hr fuel quadratic mean diameter

10-hrQMD	 10-hr fuel quadratic mean diameter

100-hrQMD	 100-hr fuel quadratic mean diameter

TransectComments Transect comments

DecayCl1Den Decay class1 densi

DecayCl2Den Decay class 2 density

DecayCl3Den Decay class3 densi

DecayCl4Den Decay class4 densi

DecayCl5Den Decay class 5 density

DecayClassComments Decay class comments

 LANDFIRE canopy fuel

Field name Description

RegID	 Unique code for each FIREMON user (up to 4 characters)

ProjID	 Unique code for each project (up to 8 characters)

PlotID	 Unique code for each plot

Date Sampling date

StandNum Stand number

Tag Tree ID number

Spe Tree ID - 2 character code

Dia Diameter at breast height (inches)

Hgt Tree height (feet)

C-HBC Height to base of live crown (feet)

CC Crown class (code = D, C, I, S, E, G)

TPA Tree density (trees/acre)

Appendix 4-B — (Continued)
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 9

Chapter 4—The LANDFIRE Prototype Project Reference Databas

FIREMON table FIREMON field Sutah00 table Sutah00 field Conversion

FL1000hr RegID MFS

FL1000hr ProjID Sutah0

FL1000hr PlotID Down Wood 2000 Plot

FL1000hr Date Veg data 2000 Dat

FL1000hr Transect Down Wood 2000 Transec

FL1000hr	 LogNum	 Log1-17	 Horiz – vert

FL1000hr	 Dia	 Log1-17 dia	 Horiz – vert

FL1000hr	 DecayCl	 Log1-17 dc	 Horiz – vert

FL1000hr Loca

FLFineDL RegID MFSL

FLFineDL ProjID Sutah00

FLFineDL PlotID Down Wood 2000 Plot#

FLFineDL Date Veg data 2000 Date

FLFineDL Transect Down Wood 2000 Transect

FLFineDL Slope Down Wood 2000 Slope

FLFineDL  1hr Down Wood 2000 1

FLFineDL  10hr Down Wood 2000 10

FLFineDL  100hr Down Wood 2000 100

FLFineDL D/LDep1 Down Wood 2000 DuffLittDept

FLFineDL LitterPct

FLFineDL D/LDep2 Down Wood 2000 DuffLittDepth60

FLFineDL LitterPct2

FLFineDL

Local

FLMacro RegID MFSL

FLMacro ProjID Sutah00

FLMacro PlotID Down Wood 2000 Plot#

FLMacro Date Veg data 2000 Date

FLMacro  1HrTranLen 

FLMacro  10HrTranLen 

FLMacro  100HrTranLe  

FLMacro  1000HrTranLe  

FLMacro NumTran 7

PD RegID MFSL

PD ProjID Sutah00

PD PlotID Site2000 Plot#

PD Date Veg data 2000 Date

PD OrgCode

PD OrgCode2

PD OrgCode

PD OrgCode

PD Examiner

PD Units E

PD Radius    

PD Width

PD PlotType

PD SEvent

PD FireID

PD MdId

PD LocType

PD Lat

PD Long

PD Northing Location Data 2000 Northing

Appendix 4-C—FIREMON data conversion table for Southern Utah Project 

year 2000 data, which shows how fields from the Sutah Access database are 

mapped and converted to fields in the FIREMON access database_________
96 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 200

Chapter 4—The LANDFIRE Prototype Project Reference Databas

PD Easting Location Data 2000 Easting

PD Datum

PD GPS_Error

PD GPS_Err_Units

PD UTM_Zone  

PD Elev Location Data 2000 Elevation

PD Aspect Site2000 Aspect

PD Slope Site2000 Slope

PD Landform Site2000 Landform Table: _cnvLandform

PD VShape

PD HShape

PD Geol

PD Geol2

PD SoilTex Site2000 Soil

PD EType

PD ESev

PD TreeC Veg data 2000 Tot tree cover

PD SeedC

PD SapC

PD PoleC

PD MedC

PD LTreeC

PD VLTreeC

PD ShrubC Veg data 2000 Shrub cover

PD LShrubC

PD MShrubC

PD TShrubC

PD GramC Veg data 2000 Gramm cover

PD ForbC Veg data 2000 Forb cover

PD FernC

PD MossC

PD USpp

PD USpp2

PD MSpp

PD MSpp2

PD LSpp

PD LSpp2

PD PVTId Veg data 2000 PVT Sp1, 2, 3 PVT Sp1 + PVT Sp

PVT Sp

PD PotForm

PD BSoilGC

PD GravelGC

PD RockGC

PD DuffGC

PD WoodGC

PD MossGC

PD CharGC

PD AshGC

PD BVegGC

PD WaterGC

PD FModel Site 2000 FBFM nor

PD PhotoID

PD SHT Site 2000 Stand ht

PD CBH Site 2000 Cbh

PD CanopyC Site 2000 FARSITE cc

PD FLength

PD SRate

PD FBevPic

FIREMON table FIREMON field Sutah00 table Sutah00 field Conversion

Appendix 4-C — (Continued)
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 9

Chapter 4—The LANDFIRE Prototype Project Reference Databas

PD FSC

PD NorthPic

PD EastPic

PD Photo

PD Photo2

PD Local

PD Local2

PD Comments

SCCover RegID MFSL

SCCover ProjID Sutah00

SCCover PlotID Species 2000 Plot#

SCCover Date Veg data 2000 date

SCCover Item Species 2000 Species Table: _cnvSpp

SCCover SizeCl

SCCover Cover Species 2000 Rc Rc * Lifeform CC fraction

SCCover Height Species 2000 Ht

SCCover Local

SCCover Local2

SCMacro RegID MFSL

SCMacro ProjID Sutah00

SCMacro PlotID Species 2000 Plot#

SCMacro Date Veg data 2000 Date

SCMacro SppIDLevel

TDMacro RegID MFSL

TDMacro ProjID Sutah00

TDMacro PlotID Tree data 2000 Plot

TDMacro Date Veg data 2000 Date

TDMacro MacroPlotSize

TDMacro MicroPlotSize

TDMacro BreakPntDia

TDSeed RegID MFSL

TDSeed ProjID Sutah00

TDSeed PlotID Seedling Data 2000 Plot#

TDSeed Date Veg data 2000 Date

TDSeed SizeCl_Ht Seedling Data 2000 SizeClass

TDSeed Species Seedling Data 2000 Species

TDSeed TreeStat

TDSeed Count Seedling Data 2000 Number

TDSeed Local

TDTree RegID MFSL

TDTree ProjID Sutah00

TDTree PlotID Tree data 2000

PJ Data 2000 Plot

Plot No

TDTree Date Veg data 2000 Date

TDTree TagNo Tree data 2000

PJ Data 2000 Tree

PJ Number

TDTree Species Tree data 2000

PJ Data 2000 Spp

Spp

TDTree TreeStat Tree data 2000

FIREMON table FIREMON field Sutah00 table Sutah00 field Conversion

Appendix 4-C — (Continued)
98 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 200

Chapter 4—The LANDFIRE Prototype Project Reference Databas

PJ Data 2000 Tree status

Status

TDTree DBH Tree data 2000

PJ Data 2000 Dbh

Dbh (in)

TDTree Height Tree data 2000

PJ Data 2000 Height

Height (ft)

TDTree LiCrPct Tree data 2000

PJ Data 2000 Crown ratio

Crnratio

TDTree LiCrBHt

TDTree CrwnCl

TDTree Age Tree data 2000 Age

TDTree GrwthRt

TDTree DecayCl

TDTree Mort

TDTree DamCd1 Tree data 20

PJ Data 2000 Damcode

Damcode

TDTree SevCd1 Tree data 20

PJ Data 2000 Sevcode

Sevcode

TDTree DamCd2 Tree data 2000 Damcode2

TDTree SevCd2 Tree data 2000 Sevcode2

TDTree CharHt

TDTree CrScPct

TDTree Local

FIREMON table FIREMON field Sutah00 table Sutah00 field Conversion

Appendix 4-C — (Continued)
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 9

Chapter 5—Development of Biophysical Gradient Layers for the LANDFIRE Prototype Project

Chapter

5

In: Rollins, M.G.; Frame, C.K., tech. eds. 2006. The LANDFIRE 

Prototype Project: nationally consistent and locally relevant 

geospatial data for wildland fire management. Gen. Tech. Rep. 

RMRS-GTR-175. Fort Collins: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research Station.

Introduction ____________________

Distributions of plant species are generally continuous, gradually changing across landscapes and blending 

into each other due to the influence of, and interactions 

between, a complex array of biophysical gradients 

(Whittaker 1967; 1975). Key biophysical gradients for 

understanding vegetation distributions include moisture, 

temperature, evaporative demand, nutrient availability, 

and solar radiation. Models to predict plant community distributions across landscapes can be developed 

by identifying the unique set of biophysical gradients 

that drive the physiological responses of plant species 

across landscapes (Guissan and Zimmerman 2000). This 

method of incorporating information about ecological 

characteristics into analyses of vegetation distribution, 

termed gradient modeling, is a standard technique for 

describing ecosystem composition, structure, and function (Gosz 1992; Kessell 1976; Kessell 1979; Whittaker 

1973) and has been applied extensively at varying scales, 

from local to regional (see Keane and others 2002 for a 

review of gradient modeling applications). The modeling process essentially involves developing empirical 

relationships between vegetation distributions and geospatial data describing biophysical gradients to enable 

extrapolation over space. Modeling accuracy becomes 

substantially improved by incorporating those biophysical gradients that directly affect vegetation dynamics 

Development of Biophysical Gradient Layers for 

the LANDFIRE Prototype Project

Lisa Holsinger, Robert E. Keane, Russell Parsons, and Eva Karau

such as temperature, light, and water (Austin 1980, 1985; 

Austin and Smith 1989; Franklin 1995). Recent efforts 

have further demonstrated that the accuracy of mapping 

vegetation and ecological characteristics using remote 

sensing techniques is greatly improved through the inclusion of biophysical gradient data as predictive variables 

(Franklin 1995; Keane and others 2002; Ohmann and 

Gregory 2002; Rollins and others 2004).

The Landscape Fire and Resource Management Planning Tools Prototype Project, or LANDFIRE Prototype 

Project, was conceived, in part, with the objective of 

developing methods and procedures for mapping vegetation composition and structure, wildland fuel, and 

historical conditions at a fine spatial grain (30-m) across 

the entire United States. This information will facilitate 

the identification of areas where current vegetation conditions are markedly different from simulated historical 

conditions (Rollins and others, Ch. 2; Keane and Rollins, 

Ch. 3). We used a gradient modeling approach to describe vegetation conditions by their potential vegetation 

type, existing cover type, and existing structural stage 

(Frescino and Rollins, Ch. 7; Zhu and others, Ch. 8). 

The overall framework was to use geospatial data representing biophysical gradient variables combined with 

field-referenced data describing vegetation composition 

in a classification and regression tree-based approach 

to map potential vegetation type (Frescino and Rollins, 

Ch.7) and then incorporate Landsat imagery to map 

existing vegetation composition, density, and height 

(Zhu and others, Ch. 8).

We assumed that our accuracy in modeling these 

vegetation characteristics would be optimized by using biophysical gradient information, which included 

climatically derived variables related to physiological 
100 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 20

Chapter 5—Development of Biophysical Gradient Layers for the LANDFIRE Prototype Project

responses of vegetation composition and structure 

(Austin and Smith 1989). Although geospatial data 

describing biophysical gradients may exist in certain 

specific locations, we could not rely on the availability 

of these data across the nation at spatial scales that met 

the LANDFIRE design criteria of national consistency 

(Keane and Rollins, Ch. 3); therefore, we relied on 

simulation of biophysical gradient data. A number of 

biogeochemical simulation models and statistical techniques were available to estimate biophysical gradients 

across spatial domains (Keane and Holsinger 2006; 

Kessell 1979; Thornton and White 2000; Thornton 1998; 

White and others 1997). We chose to use the simulation 

model WXFIRE to develop biophysical gradient data 

because the model represents a balance of sophistication and computational efficiency. WXFIRE simulates 

a suite of gradients proven to describe both biotic and 

abiotic characteristics and processes that directly influence ecosystem composition, structure, and function 

(Keane and others 2002; Keane and Holsinger 2006; 

Rollins and others 2004).

We implemented the LANDFIRE methods in two 

large prototype areas to test the feasibility of national 

application of the LANDFIRE design criteria and 

guidelines. The study areas were based on mapping 

zones developed for the USGS Multi-Resolution Land 

Characteristics (MRLC) 2001 project (landcover.usgs.

gov/index.asp). We first applied our methods to Zone 

16, located in central Utah, and then, based on lessons 

learned, applied refined methods in Zone 19, located 

in the northern Rocky Mountains (see fig. 1 in Rollins 

and others, Ch. 2). Most of the biophysical gradient layers were derived using the WXFIRE simulation model 

implemented with data from the DAYMET weather 

database, which comprises daily weather data across 

the conterminous United States (Thornton and others 

1997; Thornton and others 2002; Thornton and Running 

1999). We also acquired or derived ancillary geospatial 

data for use as predictors in vegetation gradient modeling 

(for example, topography from the National Elevation 

Database). In this chapter, we describe our methods 

for creating the biophysical gradient layers, including 

the development of WXFIRE input and simulation 

procedures. We also describe the resulting biophysical 

gradient layers used for mapping potential vegetation 

type, existing vegetation, structural stage, and canopy 

fuel. (Frescino and Rollins, Ch. 7; Zhu and others, Ch. 

8; Keane and others, Ch. 12). Further, the LANDFIRE 

biophysical gradient layers could potentially be applied 

in other land management purposes, such as hydrological 

studies or quantification of thermal cover for wildlife.

In the process of developing these protocols for the 

LANDFIRE Prototype Project, we identified numerous improvements that could be made to our methods, 

and we outlined a set of recommendations for future 

development of biophysical gradient layers. Hence, the 

methods described here do not necessarily reflect the 

protocols followed by LANDFIRE National (Rollins 

and others, Ch. 2).

Methods _______________________

The LANDFIRE Prototype Project involved many 

sequential steps, intermediate products, and interdependent processes. Please see appendix 2-A in Rollins 

and others, Ch. 2 for a detailed outline of the procedures followed to create the entire suite of LANDFIRE 

Prototype products. This chapter focuses specifically 

on the procedure followed in developing biophysical 

gradients, which served as spatial predictors in mapping 

models for nearly all mapping tasks in the LANDFIRE 

Prototype Project.

Figure 1—The flow and component diagram for the WXFIRE

program.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 

Chapter 5—Development of Biophysical Gradient Layers for the LANDFIRE Prototype Project

Spatial Units used in Modeling

We applied numerous spatial units in creating biophysical gradients, ranging in spatial extent from large, 

regional mapping zones to simulation units of intermediate size to 30-m pixels. These various spatial units used 

in the WXFIRE modeling process require some initial 

explanation here for clarity. Detailed descriptions are 

provided in the following sections. At the broadest scale, 

we divided the U.S. into regional mapping zones ranging 

in size from five to fifteen million ha, and we applied 

our protocols to mapping zones 16 and 19, which were 

six and ten million ha, respectively. Next, the mapping 

zones were divided into simulation units representing 

unique environmental conditions for the purposes of 

estimating biophysical gradients using the WXFIRE 

simulation model. Simulation units were derived by 

combining the key spatial WXFIRE inputs such as soils 

data and topography. Simulation units ranged in pixel 

size from 0.09-ha to 575-ha in Zone 16 and 0.09-ha to 

144-ha in Zone 19.

Another spatial unit was developed for describing 

biophysical settings. The WXFIRE model required a set 

of data representing specific ecophysiological parameters 

for landscapes (table 1). These parameters could have 

been included in the development of simulation units 

because they describe unique environmental conditions. 

However, WXFIRE requires so many parameters (45) 

that, for expediency in model simulations, those ecophysiological parameters are simply assumed to be relatively 

homogenous over fairly broad areas or across spatial units 

termed ecophysiological ‘sites’ (Keane and Holsinger 

2006). For example, albedo is an ecophysiological parameter required by the WXFIRE model, and it should 

be relatively constant across many simulation units in 

a landscape for many days of the year. WXFIRE runs 

far more efficiently by assigning albedo (along with the 

other 44 ecophysiological parameters) to a site, rather 

than determining unique parameter values for every 

simulation unit. Typically, one site encompasses many 

simulation units. As such, we identified ecophysiological 

sites across our mapping zones and then assigned unique 

values to all ecophysiological parameters for each site. 

The sites ranged in size from 6.25 to 4.6 million ha in 

Zone 16 and 6.25 to 2.2 million ha in Zone 19.

Overview of the Modeling Process

We developed biophysical gradient layers in several 

steps for each mapping zone (fig. 1). First, we collected 

and modified various topographic, soil, weather, and 

vegetation-related layers and grouped the values in 

these layers into classes to improve the computational 

efficiency of the model. We then partitioned each mapping zone into simulation units by spatially combining 

all of the classified input layers (fig. 1; table 2). That is, 

each unique spatial combination of the values for the 

input layers identified a distinct simulation unit. Next, 

we assembled the three input files needed to run WXFIRE, including: 1) the simulation unit file containing 

soil, topographic, weather, and vegetation-related data 

for all the simulation units in a mapping zone (simulation unit list file) (fig. 2); 2) a file specifying general 

simulation options (driver file), such as time frames for 

summarizing data; and 3) a parameter file describing 

the ecophysiological site conditions (site file). We then 

ran WXFIRE simulations and produced tabular data of 

biophysical gradients for each simulation unit. Finally, we 

linked the tabular data to the spatial layer of simulation 

units to create geospatial biophysical gradient layers for 

each mapping zone. In the following sections, we briefly 

discuss the WXFIRE and DAYMET computer models 

used to generate biophysical gradients and then cover 

in detail our process for developing those layers.

Computer Models for Developing 

Biophysical Gradient Layers

The WXFIRE model was developed with the goal of 

employing standardized and repeatable modeling methods to derive spatially explicit, climate-based biophysical 

gradients for predictions of landscape characteristics 

related to ecosystem management (Keane and others 

2002; Rollins and others 2004). Keane and others’ (2002) 

first model, WXGMRS, was built for a spatially explicit 

gradient modeling application called the Landscape 

Ecosystem Inventory System. The next generation of 

this model, WXFIRE, was used for creating biophysical 

gradient layers in the LANDFIRE Prototype Project 

(Keane and Holsinger 2006). The WXFIRE model is 

designed for simulating biophysical gradient data at 

any geographic extent or spatial resolution using spatial 

data layers of daily weather, soils, topography, leaf area 

index, and a suite of ecophysiological parameters. The 

WXFIRE model produces a broad array of biophysical gradients that can be categorized into two general 

types: 1) weather and climate variables and 2) ecosystem 

variables. The weather variables describe daily weather 

conditions (maximum daily temperature), whereas the 

climate variables summarize weather conditions over 

broader temporal periods (decades) to describe the 

climatic regime of the study area (for example, solar 

radiation flux to the ground). The ecosystem variables 

describe how climate variables interact with vegetation. 
102 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 20

Chapter 5—Development of Biophysical Gradient Layers for the LANDFIRE Prototype Project

Table 1—Parameters requiredintheecophysiological siteinput fileforWXFIRE(Keane

and Holsinger 2006) for a montane site (1,800 – 2,700 m) in Zone 16.

Ecophysiological parameter Units Value

Julian date of start of pre-greenup period Jday  

Julian date of initiation of greenup period Jday  

Julian date of end of greenup period Jday  1

Julian date indicating live fuels are frozen Jday  3

LAI of the site in m2/m2 m2

/m2 2.5

LAI conversion factor index  3

Extinction coefficient	 index	 0.48

Rainfall interception coefficient	 index	 0.0005

Average site albedo (dim) for climax stand index 0.1

Leaf water potential at stomatal opening -MPa -0.5

Leaf water potential at stomatal closure -MPa -1.6

Min vapor pressure deficit stomatal opening	 Pa	 500.0

Max vapor pressure deficit stomatal closure	 Pa	 4100.0

Maximum canopy conductance m sec–1 0.0065

Leaf boundary layer conductance m sec–1 0.0865

Leaf cuticular conductance m sec–1 0.0000

Maximum live foliar moisture content percent 200.0

Minimum live foliar moisture conten percentt 80.0

DBH of reference tree cm 50.0

Bark conversion factor cm bark/cm dbh 0.05

Live crown ratio percent 50.0

Tree height meters 25.0

Initial fuel moisture content - 1-hr woody percent 20.

Initial fuel moisture content - 10-hr woody percent 20.

Initial fuel moisture content - 100-hr woody percent 25.

Initial fuel moisture content - 1000-hr woody percent  3

Initial fuel moisture content - live foliage percent  120

Initial fuel moisture content - litter percent  100

Initial fuel moisture content - duf percent  150

Initial fuel moisture content - shrub percent  100

Initial fuel moisture content - herb percent  14

FOFEM cover type ID number code 

NFDRS fuel model number (a=1...z=26) code 

FBFM ID number from Anderson et al. (1982) code 

FLC fuel loading model ID number code  1

Elevation of site meters 2500.0

Aspect of site degrees  180

Slope of site percent  10

Latitude of site decimal-deg 45.

Longitude of site decimal-deg 120.

Depth of soil defining free rooting zone	 meters	 1.0

Percent sand in soil profile in FRZ	 percent	 50.0

Percent silt in soil profile in FRZ	 percent	 30.0

Percent clay in soil profile in FRZ	 percent	 20.0

Average wind speed m sec–1  10

Topographic shading reduction factor m sec–1  1.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 

Chapter 5—Development of Biophysical Gradient Layers for the LANDFIRE Prototype Project

Table 2—Spatial input data for developing simulation units for the WXFIRE simulation model.

Data scale 

Layer name Description or resolution Source

Aspect Direction of exposure in azimuths 30-m pixel Derived (ESRI 200

DAYMET Daily weather data  1-km pixel Derived (Nemani et al. 1

Elevation Digital Elevation Model (DEM) (m) 30-m pixel Thornton et al. 1

LAI Leaf area index  30-m pixel USGS 20

Percent sand Percent of sand in soil  1:250,000 SCS 

Percent silt Percent of silt in soil  1:250,000 SCS 

Percent clay Percent of clay in soil  1:250,000 SCS 

Shading Ecophysiological site conditions 30-m pixel Derived (ESRI 200

Site Topographic shade index  30-m pixel DEM & USGS NL

Slope Slope derived from DEM in percent 30-m pixel Derived (ESRI 200

Soil depth Soil depth to bedrock (cm)  30-m pixel Derived (Zheng et al. 19

Figure 2—Example of WXFIRE simulation units for a small landscape in the Uinta mountains of Zone 16 and associated WXFIRE

tabularinput, including: simulation unit identifier(POLYID), ecophysiological site (SITE), elevation (ELEV), aspect, slope, geographic

coordinates for weather, LAI, soil depth (SDEPTH), percents sand, silt and clay, and topographic shading (RSHD).
104 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2

Chapter 5—Development of Biophysical Gradient Layers for the LANDFIRE Prototype Project

For example, actual evapotranspiration can describe the 

moisture available for vegetation development much better than average annual precipitation because it integrates 

phenology, temperature, and soil water dynamics. Keane 

and Holsinger (2006) provide extensive documentation of 

WXFIRE, including structure and formats of all input and 

output files, complete descriptions of all model algorithms, 

and guides on preparing and executing the program.

DAYMET is a computer model that extrapolates daily 

spatial surfaces of temperature, precipitation, radiation, 

and vapor pressure deficit across large regions (Thornton 

and others 1997, Thornton and others 2002; Thornton and 

Running 1999). The DAYMET model requires digital 

elevation data, minimum and maximum temperature, 

and precipitation from ground-based meteorological 

stations. The DAYMET model extrapolates stationbased weather data across broad regions using a spatial 

convolution method with a truncated Gaussian weighting filter (Thornton and others 2002). The DAYMET 

weather database was compiled for the entire nation 

using over 1,500 weather stations and served as a key 

input to the WXFIRE model. The DAYMET weather 

database contains gridded 1-km resolution daily data for 

daily minimum and maximum temperature (°C), precipitation (cm), solar radiation (W m–2), and vapor pressure 

deficit (percent) from 1980 to 1997. At this time, the 

DAYMET model is unique in its ability to provide data 

at a temporal (18 years of daily data) and spatial (1-km) 

resolution across the conterminous U.S.

Input Layers for Developing WXFIRE 

Simulation Units

This section details the process used to create the 

spatial data input layers required by WXFIRE (fig. 1). 

Specifically, we describe the procedures used to synthesize information from existing spatial data layers, 

including a suite of terrain-related layers and layers of 

soils, leaf area index, weather and ecophysiological site. 

These input layers were subsequently used to develop 

simulation units and to compute the attributes for each 

simulation unit required as input into the WXFIRE 

model (table 2).

Developing terrain-related input layers—We classified continuous data describing slope, aspect, and 

topographic shading as input to the WXFIRE model. 

Each layer was derived using digital elevation models 

(DEM) from the National Elevation Database (http://edc.

usgs.gov/products/elevation/ned.html) and standard 

algorithms for deriving topographic derivatives. We 

calculated slope as the rate of maximum change in a 

DEM from each cell relative to its neighbors using a 

3x3 grid cell neighborhood and an average maximum 

technique (Burrough 1986; ESRI 2002). Aspect was 

calculated by identifying the direction of maximum rate 

of change in a DEM between each cell and its neighbors 

(ESRI 2002). The topographic shading layer represented 

how direct radiation to a landscape area was attenuated 

by the surrounding high topography. The topographic 

shading layer was created by developing a shaded relief 

grid from a DEM, projecting an artificial light source 

onto the surface, and determining reflectance values. 

Solar azimuth and altitude for the sun’s position were 

required inputs for this process. We calculated azimuth 

and altitude using the National Oceanographic and 

Atmospheric Association Solar Position calculator 

(http://www.srrb.noaa.gov/highlights/sunrise/azel.html, 

assuming the summer solstice as the date and using the 

center coordinates for each mapping zone.

Developing soil-related input layers—WXFIRE required soil texture (percent) and soil depth (m) as input 

for each simulation unit. Soil texture was derived using 

the State Soil Geographic (STATSGO) geo-spatial data, 

which is composed of digitized polygons from 1:250,000 

scale state soil maps (Natural Resources Conservation 

Service or NRCS 1995a). We explored the finer-scale 

Soil Survey Geographic (SSURGO) data but found 

that SSURGO has incomplete coverage across the two 

prototype regions and would not provide sufficient soils 

information for the national LANDFIRE mapping effort 

(NRCS 1995b). The STATSGO data structure consists 

of soil polygons, where each polygon has associated 

descriptions of soil sequence and soil layers in tabular 

format. Soil sequence represents the dominant kinds 

of soils (up to three taxonomic classes) contained in a 

polygon. Geographic locations for these soil sequences 

are not available but are instead represented as percents 

for each soil polygon. Soil information for the STATSGO 

polygons includes vertical composition (soil horizons) 

(up to six layers) for each soil sequence (soil taxonomic 

class).

The WXFIRE model required that soil texture be 

described in terms of percent sand, silt, and clay (Keane 

and Holsinger 2006). These data are not directly defined 

in the STATSGO attribute list but can be extracted from 

the database based on variables describing the percent 

by weight of particles passing through various sieve 

sizes and percent clay content (Thornton and White 

2000). We first calculated four soil textures from the 

STATSGO database, including coarse fragment content and percent sand, silt, and clay. We computed the 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 1

Chapter 5—Development of Biophysical Gradient Layers for the LANDFIRE Prototype Project

four soil textures according to criteria for soil particle 

variables, described in table 3, using a script for SAS 

software (SAS System for Windows 2001). STATSGO 

data provides only high and low values for these attributes 

by soil sequence and layer. We calculated an average for 

each of the soil textures, for example, percent sand = 

(No. 10 sieve high + No. 10 sieve low) / 2 – (No. 200 

sieve high + No. 200 sieve low) / 2, and weighted the 

STATSGO variables by the layers’ depths and by the 

aerial extent of sequences within STATSGO polygons. 

Since the WXFIRE model requires measures of percent 

sand, silt, and clay only, we removed the coarse fragment proportion from the composition of soil textures and 

rescaled the sand, silt, and clay components to comprise 

100 percent of soil texture estimates. Our final results from 

this analysis were estimates of percent sand, silt, and clay 

for each of the STATSGO polygons in a mapping zone.

The soil depth layer was also derived using STATSGO 

data, but we modeled soil depth to a higher resolution using DEM data and hydrologic modeling. For this process, 

we first extracted the maximum depth per soil sequence 

from the STATSGO database and weighted these values 

by their aerial extent to calculate a maximum soil depth 

per polygon. We then calculated a topographic convergence index (TCI) for each pixel using the following 

relationship provided by Beven and Kirkby (1979):

TCI

a

B = 



 



 ln

tan

where a is the upslope area (m2) draining past a certain 

point per unit width of slope and is calculated by accumulating the weight for all cells that flow into each 

down-slope cell (ESRI 2002; Jenson and Domingue 

1988; Tarboton and others 1991) and B is the local 

surface slope angle (degrees) calculated from a 3x3 

grid cell neighborhood using an average maximum 

technique (Burrough 1986; ESRI 2002). Using methods 

developed by Zheng and others (1996), we integrated 

the STATSGO maximum depth layer (STATGO Max 

Depth) and TCI data to calculate a soil depth value for 

each pixel using scalars to adjust for skewed TCI distributions as follows:

Soil	depth = ∗ { , M M1 2} TCI

where M1 is the scalar used if a pixel’s TCI value was 

less than or equal to its mean across a mapping zone 

and was calculated by:

M

LNmo LNme

1	

Ave.	Max.	Depth = 0 5. ( ∗ + )

where Ave. Max. Depth is the mean value of the 

STATSGO maximum depth layer across each mapping 

zone, and LNmo and LNme are mode and mean values for 

the natural log of TCI. M2 is the scalar used if a pixel’s 

TCI value is greater than or equal to its mean across a 

mapping zone and is calculated by:

M

LN

2 = Max.	Depth

max

where Max Depth is the STATSGO maximum depth 

layer for each polygon and LNmax is the maximum 

natural log of TCI.

For Zone 19, we revised this process to improve 

data resolution by including slope in calculations of 

soil texture and depth. The STATSGO database provides high and low slope values for each STATSGO 

polygon. We calculated an average slope and classified 

the average slope into four classes: (1) ≤4 percent; (2) >4 

percent and ≤8 percent; (3) >8 percent and ≤15 percent; 

and (4) >15 percent (N. Bliss, personal communication). 

We extracted the soil texture and soil depth variables by 

these four slope classes from the STATSGO database. 

We used the slope geospatial layer (percent) previously 

described and then classified slope into the above four 

classes. We partitioned the STATSGO polygons by the 

classified slope layer and linked this spatial layer with 

the STATSGO variables of soil texture and depth by 

slope. For the final soil depth layer, we followed with 

the process described above for integrating STATSGO 

Table 3—Soil texture calculations based on STATSGO attributes that describe the percent by weight of particles 

passing through various sieve sizes and percent clay content (Thornton and White 1999)

Soil texture Equation using STATSGO attributes

Coarse fragment content Percent passing No. 10 siev

Percent sand	 Percent passing No. 10 sieve – Percent passing No. 200 sieve

Percent clay Percent clay weighted by percent passing No. 200 sieve

Percent silt	 Percent passing No. 200 sieve – (percent clay – percent passing No. 10 sieve)
106 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 20

Chapter 5—Development of Biophysical Gradient Layers for the LANDFIRE Prototype Project

maximum soil depth with TCI to obtain soil depth values 

for each pixel. The final products were soil textures and 

soil depth—with improved resolution by incorporating 

slope into calculations. Note, improving the soil layers’ 

resolution also contributed to a large increase in the 

number of records in the simulation unit file for Zone 

19 from that of Zone 16.

Developing leaf area index and weather input 

layers—We generated leaf area index (LAI) from Landsat 

imagery (30-m pixel resolution) for leaf-on reflectance 

based on methods developed by Nemani and others 

(1993). We first calculated a corrected normalized difference vegetation index (NDVI) as follows:

NDVI NIR RED

NIR RED

MIR MIR

MIR c = −+





 



 − − * min

m

1 ax− min





 



 MIR

where NIR is near infrared (band 4), RED is infrared 

(band 3), MIR is mid-infrared (band 5), MIRmin is the 

minimum value in mid-infrared band in an open canopy, 

and MIRmax is the maximum value in the mid-infrared 

band in a closed canopy. We then converted the NDVIc

layer to LAI according to the following equation:

LAI NDVIc = −

−

ln( . )

.

0 7

0 7

Developing the ecophysiological site input layer—

For Zone 16, we delineated ecophysiological sites 

by partitioning the landscape by elevational breaks 

corresponding to major vegetation changes (for example, 

landscapes dominated by pinyon pine vs. Douglas-fir). 

The four sites in Zone 16 included:

Site 1 – Mohave (0 to 1,200 m mean sea level),

Site 2 – Sagebrush (1,200 to 1,800 m MSL),

Site 3 – Montane (1,800 to 2,700 m MSL), and

Site 4 – Subalpine (2,700+ m MSL).

We assigned values to the sets of ecophysiological 

variables for each site based on previous synthesis efforts 

(Korol 2001; Hessl and others 2004; White and others 

2000). Table 1 shows the ecophysiological parameters 

and associated values for the Montane site in Zone 16.

For Zone 19, we used a less subjective approach where 

we developed sites using the U.S. Geological Service/

U.S. Environmental Protection Agency National Land 

Cover Database (http://edcwww.cr.usgs.gov/programs/

lccp/natllandcover) and biome types described for national-level ecosystem simulation (Thornton 1998). The 

National Land Cover Database contains 21 broad cover 

types, and we summarized these cover types into five 

general plant functional types and one non-vegetated 

class: water/barren (table 4). Each of these plant functional types represented a site, and we assigned a set 

Table 4—Changes made to National Land Cover Database (NLCD)land cover class definitions

for the LANDFIRE site map.

NLCD land cover class LANDFIRE plant functional types

Open water Water/barren

Perennial ice/snow Water/barren

Bare rock/sand/clay Water/barren

Quarries/strip mines/gravel pits Water/barren

Transitional Closest natural vegetation

Low intensity residential Closest natural vegetation

High intensity residential Closest natural vegetation

Commercial/industrial/transportation Closest natural vegetation

Deciduous forest Deciduous broadleaf forest 

Evergreen forest Evergreen needleleaf forest

Mixed forest Majority of surrounding deciduous broadleaf 

 forest or evergreen needleleaf forest

Orchards/vineyards/other Deciduous broadleaf forest

Shrubland Shrub

Grasslands/herbaceous Grass

Pasture/hay Grass

Row crops Grass

Small grains Grass

Fallow Grass

Urban/recreational grasses Grass

Woody wetlands Deciduous broadleaf forest

Emergent herbaceous wetlands Grass
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Chapter 5—Development of Biophysical Gradient Layers for the LANDFIRE Prototype Project

of ecophysiological parameters to each site. The five 

main plant functional types were C3-grass, evergreen 

needle leaf forest, deciduous broadleaf forest, shrub, and 

barren/water. Areas classified as human development, 

such as urban and agriculture, were assigned a cover 

type based on the dominant cover type in neighboring 

pixels. Similarly, areas classified as mixed forest were 

recoded to either evergreen needle leaf forest or deciduous broadleaf forest based on the dominant forest type 

in surrounding pixels.

Development of WXFIRE Simulation Units 

and Model Input Files

Simulation units were developed by combining all 

11 spatial data layers described above and detailed in 

table 2. Ideally (given the available data) we would have 

combined all input layers for every 30-m pixel to obtain 

the best resolution possible in the biophysical gradient 

layers. However, the LANDFIRE prototype mapping 

zones were very large and would have required the 

simulation of 284 million records for Zone 16 and 289 

million records for Zone 19. Our computer resources 

were insufficient to process this amount of data in a 

timely manner. Instead, we reduced the data sets to 

10 million and 26 million records for mapping zones 

16 and 19, respectively, by classifying input layers to 

a coarser attribute measurement resolution. That is, 

we classified spatial layers so that their measurement 

increments were in broader ranges. Table 5 shows the 

classification scheme for each of the terrain, soil depth, 

and LAI layers. For example, slope was grouped into 

three classes of low, moderate, and high slope. Note that 

the soil texture layers (sand, silt, and clay) were already 

at a coarse spatial resolution, so we maintained them in 

their original form (1:250,000 scale) and did not summarize to a broader attribute measurement resolution.

To create the simulation units for executing WXFIRE, 

we combined the classified input layers (terrain, soil 

depth, LAI) with ecophysiological site, soil texture 

and weather layers such that each unique combination 

formed one simulation unit. Prior to combining these 

layers, we re-sampled all input layers to a 25-m pixel size 

such that each layer nested within the 1-km DAYMET 

weather data layer. Accordingly, each simulation unit was 

geo-referenced at a 25-m pixel size and had a series of 

associated input data. Figure 2 provides an example of 

WXFIRE simulation units developed for a landscape in 

the Uinta Mountains of Zone 16. As input to the WXFIRE 

model, we created an ASCII simulation unit list file that 

contained records for all the simulation units in a mapping zone along with their associated ecophysiological 

site identifiers, terrain data, geographic coordinates for 

weather (latitude and longitude), LAI, and soil values.

WXFIRE Model Simulation and Development 

of Biophysical Gradient Layers

Using the various input files, the WXFIRE model 

calculated a series of biophysical gradients for each 

simulation unit and output the results to ASCII files. 

We linked each record in the ASCII output files to their 

corresponding simulation units to create geospatial 

data representing the biophysical gradients output from 

WXFIRE. We implemented the WXFIRE model using 

average annual time frames to consistently measure 

biophysical gradients across large regions that may have 

variable growing seasons and to match temporal periods 

commonly used in other gradient modeling analyses 

(Waring and Running 1998).

In the initial WXFIRE runs, we retrieved the 

DAYMET weather data in the model simulations in 

its native 1-km format in an effort to maximize model 

efficiency. However, we discovered a strong gridded 

pattern in the biophysical gradient layers—a direct artifact of the coarsely gridded DAYMET weather maps 

(fig. 3A). We revised the WXFIRE program to scale the 

temperature and precipitation maps to a finer resolution 

using a moving window technique to calculate dynamic 

lapse rates (fig. 3B) (Keane and Holsinger 2006). The 

Table 5—Methods used for classifying input layers to coarser attribute measurement resolution, implemented to reduce 

the number of input records and computer processing time. 

 Number of

Input layer Classification method categories

Elevation  100-m intervals 

Aspect SW (165° to 255°; NW (255° to 345°); NE (345° to 75°); SE (75° to

Slope	 Low (<10%); moderate (10% to 30%) and high (≥ 30%)	 3

Topographic shading index Every 0.25 (index) 

Soil depth 0.5-meter classes 

LAI  1.0 LAI intervals
108 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 20

Chapter 5—Development of Biophysical Gradient Layers for the LANDFIRE Prototype Project

Figure 3—The biophysical gradient soil water fraction for an area in the Uinta mountains where: (A) shows 

results from initial model run using a static lapse rate calculation (the 1-km DAYMET footprint is particularly

evident in southern area); and (B) shows final model run using dynamic lapse rates to scale down weather

maps. Use of the dynamic lapse rate dramatically reduced the 1-km DAYMET footprint
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 1

Chapter 5—Development of Biophysical Gradient Layers for the LANDFIRE Prototype Project

lapse rates were incorporated into linear regressions 

that adjusted the weather based on the difference in 

elevation between the coarse-scale DAYMET DEM 

and the elevation of the simulation unit. Solar radiation 

was also scaled to the simulation unit using geometric 

relationships of aspect and slope to sun zenith and azimuth angles (Keane and Holsinger 2006). We used the 

adiabatic lapse rate correction and the solar radiation 

adjustment in WXFIRE model runs for both mapping 

zones to minimize the DAYMET 1-km footprint pattern 

in output data layers, minimally affecting the efficiency 

of our model simulations and computational capacity.

Additional Topographic Layers for 

Gradient Modeling

Additional terrain-related layers were created as 

predictive layers for developing potential vegetation 

type, cover type, structural stage, and canopy fuel maps 

(Frescino and Rollins, Ch.7; Zhu and others, Ch. 8;Keane 

and others, Ch. 12). Although not part of WXFIRE 

input or output, we mention these layers in this chapter 

because they were important biophysical gradients for 

subsequent mapping applications. One of the terrainrelated layers was topographic position index, which 

describes the exposure of a location in space compared to 

the surrounding terrain. Positive values expressed ridges 

or exposed sites, while negative values described sinks, 

gullies, valleys, or toe slopes. The topographic position 

index layer was developed using a moving window to 

describe relative location on a slope (Z. Zhang, personal 

communication). A topographic relative moisture index 

layer was developed to describe potential moisture 

conditions by combining relative slope position, slope 

configuration, slope steepness, and slope-aspect into a 

single scalar value based on methods defined by Haplin 

(1999) and Parker (1982). Finally, a landform layer was 

created based on reclassifying the topographic relative 

moisture index and slope. The landform layer described 

physiographic features such as valley flats, hills, and 

steep mountain slopes formed by erosion, sedimentation, 

mass movement, or glaciation (Neufeldt and Guralnik 

1988).

Results and Discussion __________

Demonstration of Biophysical Gradient 

Layers

We developed thirty-one biophysical gradient layers 

from WXFIRE simulations to describe weather and 

climate variables and ecosystem variables (table 6). We 

created seven additional biophysical gradient layers describing topographic and soil conditions for use in subsequent 

vegetation and wildland fuel mapping (table 6). The mean, 

standard deviations, and ranges for each of the biophysical 

gradient variables in each mapping zone are presented in 

table 7.

Due to the large number of biophysical gradient layers that can be created by WXFIRE, we present only 

maps of a subset of the two variable types developed 

by WXFIRE: weather/climate variables and ecosystem 

variables. Average annual precipitation (cm) was an 

important weather/climate variable because it directly 

influences plant productivity and limits vegetation 

distributions (fig. 4). Another key weather/climate variable was degree-days, which reflects the heat load to 

a simulation unit (fig. 5). Potential evapotranspiration 

(kg H2O yr–1) is an example of an ecosystem variable 

modeled in WXFIRE (fig. 6). Potential evapotranspiration integrates temperature, precipitation, radiation, and 

relative humidity to estimate the maximum evapotranspiration through a vegetated surface. To better demonstrate 

the spatial patterns in the biophysical gradients, we also 

present a close-up view of important WXFIRE layers 

used in predicting potential vegetation type for forested 

areas of Zone 16 (fig. 7).

Limitations in Developing Biophysical 

Gradient Layers

The suite of biophysical gradient layers developed for 

the LANDFIRE Prototype Project must be considered in 

light of the limitations inherent to the simulation modeling process. Simulation modeling using WXFIRE was 

based on a set of algorithms that simplify and synthesize 

correlative relationships and mechanistic understandings 

of biophysical gradient variables (Keane and Holsinger 

2006). The resulting geospatial data do not reflect direct 

and accurate measurements, but rather approximations of 

environmental conditions and ecosystem characteristics 

as they fluctuate across broad landscapes. Specifically, 

biophysical data developed using simulation modeling 

demonstrate the transition of biophysical gradients, emphasizing relative differences across large areas. In any 

given location, estimates for any one of the WXFIRE 

variables may not be particularly accurate; however, 

estimates will be consistently measured with high precision across landscapes. Care must therefore be taken to 

limit mapping applications to relative comparisons of 

variables across large landscapes and to forego expectation of spatial accuracy at any specific location. Keane 

and Holsinger (2006) present a detailed accuracy assessment of several WXFIRE weather outputs for several 
110 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2

Chapter 5—Development of Biophysical Gradient Layers for the LANDFIRE Prototype Project

Table 6—Biophysical gradients developed for the LANDFIRE Prototype Project, including weather, climate, and ecosystem variables simulated by the WXFIRE model and additional geographic variables. 

Description Biological significance

WXFIRE weather and climate variables

Maximum daily temperature (°C) Affects evapotranspiration and productivity

Minimum daily temperature (°C) Limiting factor for plant tolerance

Precipitation (cm) Directly affects productivity; limiting factor at lowest extreme

Average daily temperature (°C) Affects evapotranspiration and productivity

Daytime daily temperature (°C) Determines daily photosynthesis and respiration

Nighttime daily temperature (°C) Important for dark respiration

Soil temperature (°C) Affects water availability, soil respiration, nutrient availability

Relative humidity (%) Determines photosynthesis and evapotranspiration rates

Total solar radiation (kJ m–2 day–1) Directly affects photosynthesis 

Solar radiation flux to the ground (KW m–2 day–1) Dictates fuel moistures, duff moisture, understory response

Photon flux density in PAR (Umol m–2)	 Incident photon flux density of photosynthetically active radiation

Days since last snow (days) Good index of time that snow is on the ground

Days since last rain (days) Index of precipitation environment

Degree-days (°C)	 Reflects heat load at a stand

WXFIRE ecosystem variables

Potential evapotranspiration (kg H2O yr–1)	 Potential evaporation and transpiration if no deficiency of water in

the soil

Actual evapotranspiration (kg H2O yr–1) Water actually lost from plant surface due to evaporation and 

transpiration

Leaf-scale stomatal conductance (M sec–1) Indicates how often stomates are open during the year

Leaf conductance to sensible heat (M sec–1) Ability of foliage to transpire water

Canopy conductance to sensible heat (M sec–1) Ability of canopy to transpire water

Soil water fraction (index) Indicates amount of water available for plant growth

Water potential of soil and leaves (-MPa) How tightly leaf holds moisture--high value indicates plant may be

water stressed 

Volumetric water content (Scalar) Indicates soil moisture availability

Growing season water stress (-MPa)	 Reflects extent of soil drying during the year

Maximum annual leaf water potential (-MPa) Soil water availability and evapotranspiration

Snowfall (kg H2O m–2 day–1) Amount of snowfall aids in water balance for site

Soil water lost to runoff and ground (kg H2O m–2 day–1) Water that is not stored on-site for plant growth

Soil water transpired by canopy (kg H2O m–2 day–1) Amount of water lost from plants through their stomata by

transpiration

Evaporation (kg H2O m–2 day–1) Indicates loss of water other than evapotranspiration

NFDRS - 1-hr wood moisture content (%)	 Illustrates fine fuel moisture regime

NFDRS - 10-hr wood moisture content (%) Illustrates large fuel moisture regim

Keetch-Byram Drought Index Represents net effect of evapotranspiration and precipitation to produce cumulative moisture deficiency in deep duff and upper

soil layers

Additional terrain and soils data

Elevation (m) Indirectly affects plant response to climate 

Aspect (degrees) Indirectly affects radiation, water, temperature

Slope (percent) Indirectly affects soil water, radiation

Landform Indirectly affects water storage

Topographic relative moisture index	 Index reflecting ability of site to hold water

Topographic position index Index describing topographic setting

Soil depth (cm) Affects soil water availability
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Chapter 5—Development of Biophysical Gradient Layers for the LANDFIRE Prototype Project

Table 7—Mean, standard deviation, minimum, and maximum values for biophysical gradients in mapping zones 16 and 19

Zone 16 Zone 19

Std Std

Parameters Mean dev. Min. Max. Mean dev. Min. Max.

Daily max. temperature (°C)  12.82 3.22 1.76 25.59 10.51Daily min. temperature (°C)	 –2.34	 2.51	 –11.99	 7.95	 –3.24	 2.02	 –12.13	 1.96Daily precipitation (cm) 58.12 20.45 19.49 234.12 64.24 30.32Daily ave. temperature (°C)	 5.24	 2.82	 –5.11	 16.72	 3.64	 2.17	 –5.39	 8.46Daily ave. daytime temperature (°C)	 8.65	 2.99	 –2.02	 20.69	 6.73	 2.28	 –2.80	 11.56Daily ave. nighttime temperature (°C)	 3.15	 2.72	 –7.00	 14.31	 1.75	 2.11	 –7.08	 6.66Daily ave. soil temperature (°C)	 0.48	 0.26	 –0.46	 1.52	 0.33	 0.20	 –0.49	 0.77Relative humidity (%) 58.75 2.46 51.91 94.42 61.86 2.74 

Total solar radiation (kJ m–2 day–1)  1,181.93 232.14 68.12 1,300.00 1,275

Solar radiation flux to the ground (KW

m–2 day–1) 67.57 47.62 1.45 178.41 77.6

Photo flux density (Umol

m–2) 2,426.40 403.88 154.97 2,600.00 2,563.84 197.05 0

Days since snowfall (days) 0.91 0.05 0.77 1.00 0.89 0.05 0.69 

Days since rain (days)  1.43 0.17 0.92 1.86 1.32Degree-days (°C) 2,985.47 819.01 586.67 6,831.58 2,544.67 567.04 

Potential evaporation (kg H2O yr–1)  1,478.25 166.85 434.12 1,997.15 1,120Actual evapotranspiration (kg H2O yr–1) 533.67 175.54 147.75 1,852.90 555.52 1Leaf-scale stomatal conductance (M sec–1)  1,199.61 985.41 258.63 12,000.00 2,673.45 2,858.55Leaf conductance to sensible heat (M sec–1) 9.31 0.36 7.90 10.34 16.43 Canopy conductance to sensible heat (M sec–1) 5.60 5.37 1.16 20.65 19.41 17

Soil water fraction (index) 0.38 0.09 0.04 0.86 0.30 0.16 0.00

Water potential of soil and leaves (-MPa)	 –2.66	 1.32	 –9.82	 –0.01	 –4.57	 2.58	 –9.99	 –0.01Volumetric water content of soil (Scalar) 0.17 0.04 0.02 0.41 0.13 0.0

Growing season water stress (-MPa)	 –165.70	 124.58	 –560.00	 –0.38	 –146.64	 160.54	 –560.00	 –0.19Maximum annual leaf water potential (-MPa)	 –9.62	 1.37	 –10.00	 –0.17	 –9.50	 1.84	 –10.00	 –0.10

Snowfall (kg H2O m–2 day–1) 0.83 0.53 0.00 5.16 0.83 0.70 0Soil water lost to runoff and groundwater (kg H2O m–2 day–1) 0.18 0.30 0.00 5.57 0.38 0.64 0.00Soil water transpired by canopy (kg H2O m–2 day–1)	 0.91	 0.50	 0.04	 4.62	 0.32	 0.28	 –27.69	 3.27Evaporation (kg H2O m–2 day–1) 0.55 0.09 0.06 1.13 1.20 0.22 0

1-hour wood moisture content (%)  16.69 3.50 11.47 100.00 1710-hour wood moisture content (%)  18.37 3.27 12.84 100.00 

Keetch-Byram Drought Index 70.27 48.11 1.83 800.00 45.17 26.8
112 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2

Chapter 5—Development of Biophysical Gradient Layers for the LANDFIRE Prototype Project

Figure 4—Average annual precipitation (cm) as simulated by WXFIRE for mapping zones 16 and 1
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006

Chapter 5—Development of Biophysical Gradient Layers for the LANDFIRE Prototype Project Figure 5—Degree-days (describing heat load of stand) as predicted by WXFIRE for mapping zones 16 and 1
114 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 

Chapter 5—Development of Biophysical Gradient Layers for the LANDFIRE Prototype Project

Figure 6—Potential evapotranspiration as predicted by WXFIRE for mapping zones 16 and 1
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 

Chapter 5—Development of Biophysical Gradient Layers for the LANDFIRE Prototype Project

Figure 7—Higher resolution view of key WXFIRE layers used in predicting PVT for forested areas of Zone 16:

(A) Zone 16 showing watershed of interest; (B) actual evapotranspiration; (C) days since rain; (D) days since

snow; (E) moisture content of 1-hour wood; (F) relative humidity; (G) total solar radiation; and (H) minimum

temperature.
116 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2

Chapter 5—Development of Biophysical Gradient Layers for the LANDFIRE Prototype Project

landscapes in the two prototype mapping zones. They 

found that these biophysical gradients had a high level of 

precision but lacked a high degree of accuracy. WXFIRE 

model predictions were limited most by the difficulty 

of accurately quantifying the simulation units and site 

parameters due to the low quality and consistency of 

available GIS layers (Keane and Holsinger 2006).

The quality of biophysical gradient data was also 

constrained by the interpolation of weather data by the 

DAYMET model and by the integration of that weather 

data into biophysical gradient estimates. The WXFIRE 

model relies strongly on predictions of the DAYMET 

weather model. The DAYMET model offers extensive 

weather data for the conterminous United States at 

a high mapping resolution; however, it has limited 

spatial accuracy and temporal depth (Thornton and 

others 1997). In many parts of the country, weather 

stations are scarce, and this sparse distribution reduces 

the DAYMET model’s ability to accurately interpolate 

weather between stations. In addition, the DAYMET 

data set spans 18 years (1980-1997), which, although 

substantial, is relatively short for generalizing the variability in weather patterns over time. For example, if 

our weather data set extended back one hundred years, 

we would have data from the drought years of the 1930s 

and relatively wet years of the 1950s. If such an extensive 

climate data set were available, spatial variability would 

likely be different in our suite of biophysical gradient 

layers—particularly for parameters calculated using 

non-linear equations, such as actual evapotranspiration 

(Keane and Holsinger 2006). An 18-year period possibly 

captured the range of variability in weather for some 

locations, but in other areas, this narrow time window 

may reflect only a relatively homogenous weather period, 

missing the full magnitude of variability.

Additionally, the WXFIRE model calculates weather 

outputs as annual averages (in other words, data were 

summed across daily values in a year and averaged across 

18 years), which was not the ideal approach for predicting 

plant species distribution. Plant phenology most closely 

corresponds to seasonal or daily changes in weather 

conditions (White and others 2000), not annual time 

periods. However, the appropriate seasonal or daily time 

frames that affect vegetation dynamics will vary across 

regional landscapes. For example, the primary growing 

season of whitebark pine in alpine habitats ranges from 

approximately July to October, while sagebrush communities, on average, range from April to October. We 

used annual time period to capture the potential range 

of all plant species in a mapping zone; however, with 

weather data generalized to the coarser annual time 

frame, the resulting biophysical gradients were less 

robust for predicting vegetation distributions.

Another limitation in the development of the biophysical gradients for the LANDFIRE Prototype Project is 

related to the issue of scale, both in terms of spatial resolution and attribute measurement resolution. Our goal was 

to produce moderate resolution spatial data, which for the 

biophysical data layers, corresponds roughly to a 30-m 

resolution. The biophysical gradient layers produced for 

mapping zones 16 and 19 had a pixel size of 30-m, but 

the actual spatial resolution of the simulated data was 

much coarser. The reduced spatial resolution was a result 

of the limited availability of high resolution input layers 

for WXFIRE and limited computer resources. We chose 

only those geospatial data sources that were complete and 

contiguous across the conterminous United States. The 

base data layers used to create the WXFIRE input files 

had a wide variety of spatial resolutions and mapping 

scales. Weather and soil texture data for the nation were 

available only at spatial resolutions greater than 30-m. 

Other input layers were available at a 30-m resolution 

(for example, topographic data) but could not be fully 

utilized because we lacked the computer resources to 

execute model simulations for the large amount of records created at these fine resolutions. Consequently, we 

classified our finer-scale data layers to broader ranges 

in their measurement increments leading to coarser attribute measurement resolution. We also assumed that 

ecophysicological sites were homogenous across broad 

landscapes and thereby omitted smaller patches in the 

gradient patterns, decreasing spatial resolution. Moreover, WXFIRE was constructed, for lack of alternatives, 

under the assumption that the ecophysiological parameters match the simulation units in spatial and temporal 

scale (Keane and Holsinger 2006), which is rarely true. 

For example, maximum canopy conductance derived 

from the ecophysiological site layer has a significantly 

coarser resolution than terrain-related data.

The true resolution of these biophysical gradient layers 

was difficult to determine for several reasons. First, the 

core data layers that were used as input to the WXFIRE 

model were of varying resolution. Second, the WXFIRE 

model integrated multiple algorithms and equations for 

calculation of each biophysical gradient layer. Third, 

WXFIRE required additional ecophysiological site parameters for simulation—also at varying resolutions. We 

can illustrate the difficulty in assessing resolution from 

these compounding influences with the example of potential 

evapotranspiration (kg H2O yr–1) calculation. Calculations 

of potential evapotranspiration required data from six 

input layers: DAYMET weather, elevation, aspect, slope, 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 

Chapter 5—Development of Biophysical Gradient Layers for the LANDFIRE Prototype Project

ecophysiological site (albedo and others, see Keane and 

Holsinger 2006), and topographic shading (table 8). The 

original resolution of these input layers ranged from 30 

m to 1 km, although the terrain-related layers (elevation, 

aspect, slope, and topographic shading) were classified to 

coarser attribute measurement resolutions (table 5). Actual 

simulation of potential evapotranspiration involved calculating several variables as inputs to a complex algorithm, 

and these variables represent processes which introduce 

additional modifications to data resolution. Specifically, 

weather data were interpolated from stations across the 

mapping zone in the DAYMET weather database. These 

weather data were also scaled down from 1-km data to 

the simulation unit, and the extent of scaling varied 

depending on size of the simulation unit. Finally, the 

actual algorithm to calculate potential evapotranspiration required the input of ecophysiological parameters 

of varying scale. For example, the ecophysiological 

parameter albedo can vary over small distances in real 

landscapes and over short time spans as species change 

due to phenology. However, we mapped albedo over large 

areas for broad categories of vegetation types that were 

considered static instead of dynamic. Overall, the resulting spatial resolution of the potential evapotranspiration 

layer depended on the integration of all the multiple data 

inputs and individual processing steps. Perhaps the best 

method for assessing data resolution would be through 

accuracy assessments using field-based data. However, 

observed data for potential evapotranspiration and other 

ecosystem, weather, and climate WXFIRE variables 

were not available. The effect of data resolution could 

also be assessed using sensitivity analysis such as Monte 

Carlo simulation, and such analyses would be worthwhile 

to perform in the future. In an effort to inform some 

understanding of the scale of the data layers, we have 

presented the data inputs required for modeling each of 

the biophysical gradients (table 8), the associated data 

resolution or scale for these input layers (table 2), and 

the classification scheme of input layers (table 5).

Recommendations for National 

Implementation _________________

Many of the decisions made in the LANDFIRE Prototype Project aimed to minimize computation time while 

maintaining information content. Without constraints, it 

would be ideal to model each 30-m pixel on the landscape. 

For the LANDFIRE Prototype, however, that would have 

been intractable due to limited resources, the number 

of processes required, and narrow time-frames. For 

instance, we classified input layers and grouped pixels 

with similar values to create broad simulation units so 

that the model could be run one time and the outputs 

applied to many pixels. The overall scheme of this classification and creation of unique simulation units was 

sufficient, but the details implemented contained some 

flaws, such as in the soil depth layer’s derivation and the 

DAYMET weather grid inclusion for creating simulation 

units. We suggest making changes to various phases of 

the biophysical gradients creation process to increase 

the quality of output while working within the general 

simulation framework developed during the LANDFIRE 

Prototype Project.

Improving Simulation Models

Early in the LANDFIRE Prototype Project, we explored 

using an ecosystem process model called LF-BGC, in 

addition to the WXFIRE simulation model (Thornton 

1998). LF-BGC is a version of Biome-BGC adapted for 

the LANDFIRE Project that simulates carbon, water, and 

nitrogen fluxes to simulate a set of carbon budget metrics and 

ecophysiological characteristics. The version of LF-BGC 

available for the LANDFIRE Prototype Project, however, 

was designed primarily to produce biophysical gradient 

layers at a 1-km resolution, based on the scale objectives 

of Biome-BGC. We attempted to create higher-resolution biophysical gradient layers (30 m) by using higherresolution inputs but discovered that more sophisticated 

model modifications were needed. A subsequent version 

of the LF-BGC model was developed that successfully 

creates higher-resolution biophysical gradient layers. 

Due to time constraints, however, we were unable to 

develop the layers with this new version. For the national implementation of LANDFIRE, we recommend 

incorporating the LF-BGC model to develop a more 

extensive set of biophysical gradient layers.

The LF-BGC and WXFIRE simulation models have 

many similarities, including file input, processing, and 

biophysical gradient output, and we suggest that the two 

models be combined into a single model executable for 

the national implementation of LANDFIRE. A number 

of factors should make this combination straightforward 

and a logical step towards optimizing model efficiency. 

First, both LF-BGC and WXFIRE are written in the 

C+ programming language and contain many equivalent calculations. Second, LF-BGC and WXFIRE have 

similar required inputs, differing in that WXFIRE 

requires more physical site information: slope, aspect, 

and hillshade. These three variables predominantly 

control the scaling of incoming radiation and its derivatives. Third, both models extract DAYMET weather 

data—the most time-consuming and process-intensive 
118 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2

Chapter 5—Development of Biophysical Gradient Layers for the LANDFIRE Prototype Project Table 8—Data inputs used by WXFIRE to model each of the biophysical gradients. Note: units for each of the biophysical gradient variables are provided in table 7. Diamonds indicate that the input variable was applied in the calculation of the WXFIRE output variable. WXFIRE variable DAYMET Elevation Aspect Slope Soil depth Soil texture LAI Site Topo. shading Soil water transpired by canopy

u

u

u

u

u

u

u

u

Soil water fraction

u

u

u

u

u

u

u

u

Daily maximum temperature

u

u

Daily precipitation

u

u

u

u

Daily minimum temperature

u

u

Relative humidity

u

Daily average temperature

u

u

Daily average soil temperature

u

u

Daily average daytime temperature

u

u

Daily average nighttime temperature

u

u

Corrected average daily radiation

u

u

u

u

u

Shortwave radiation

u

u

u

u

u

u

Photo flux density	

u

u

u

u

u

u

Days since snowfall

u

u

u

u

Days since rain

u

u

u

u

Potential evapotranspiration

u

u

u

u

u

u

Actual evapotranspiration

u

u

u

u

u

u

u

u

u

Degree-days

u

u

Leaf-scale stomatal conductance

u

u

u

u

u

u

u

u

Leaf conductance to sensible heat

u

u

u

u

u

u

u

u

Canopy conductance to sensible heat

u

u

u

u

u

u

u

u

Volumetric water content of soil

u

u

u

u

u

u

u

u

u

Growing season water stress

u

u

u

u

u

u

u

u

u

Water potential of soil and leaves

u

u

u

u

u

u

u

u

u

Maximum annual leaf water potential

u

u

u

u

u

u

u

u

u

Snowfall

u

u

Soil water lost to runoff & groundwater

u

u

u

u

u

u

u

u

u

Evaporation

u

u

u

u

u

u

u

u

u

1-hour wood moisture conten

u

u

u

u

u

10-hour wood moisture conten

u

u

u

u

u

Keetch-Byram Drought Index

u
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 

Chapter 5—Development of Biophysical Gradient Layers for the LANDFIRE Prototype Project

step for executing either model. If the two models were 

combined, this call to DAYMET would only occur once. 

Fourth, the ecosystem variables (see table 6) calculated 

in WXFIRE are dependent on a site file and LAI data 

that both represent current land cover. The same (and 

more) ecosystem variables are output from LF-BGC, 

and, by combining models, we would no longer require 

current land cover information.

The process of combining WXFIRE and LF-BGC 

should be relatively seamless and significantly improve 

model outputs and efficiency. The unique variables output by WXFIRE, namely the climate derivatives, can 

readily be added to the LF-BGC model. This procedure 

can be done with minimal change to the core LF-BGC 

program and without affecting the stand-alone output. 

This proposed combination of the models would remove 

a major source of problems and error and virtually 

cut simulation times and computing resource needs in 

half.

In addition to combining the models, we suggest a 

change in the way that LF-BGC is parameterized and 

executed. During exploratory simulations, LF-BGC was 

run with the evergreen needle leaf forest plant functional 

type (PFT) across simulation units in Zone 16. Our goal 

in using this model was not to calculate the actual net 

primary productivity (NPP), but to represent the relative 

differences in potential NPP across landscapes in order 

to delineate unique biophysical settings. We chose to 

simulate the PFT with the narrowest ecological amplitude to maximize the information content of the output 

and selected only one to remain within our deadlines. 

This logic could be applied for most areas of the U.S.; 

however, some landscapes may exist that cannot sustain 

an evergreen needle leaf PFT. Sole use of the evergreen 

needle leaf PFT for LF-BGC simulations does not provide 

enough information to distinguish unique biophysical 

settings in such landscapes. Assuming that efficiency is 

improved and resources are increased for the national 

effort, we suggest adding the C3 grass (cool season) PFT 

to the simulation protocol. By modeling the two plant 

functional types with the narrowest (evergreen needle 

leaf) and broadest (C3 grass) ecological amplitudes, we 

can achieve a more complete picture of the biophysical 

gradients that exist across the entire United States.

Improving Model Input Layers

To minimize the number of unique simulation units, 

we classified much of the input data to broad categories. 

Classification served to minimize the number of 

simulations while attempting to remain within the bounds 

of model sensitivity. We propose a number of changes to 

the methods for creating the input layers used to create 

simulation units.

We derived soil depth from STATSGO slope groups 

that were further divided with a topographic convergence 

index (TCI). The derived soil depth layer showed a very 

speckled pattern or pixelation and contained a definite 

footprint of the hydrologic modeling that carried through 

to final biophysical gradient layers in the form of linear 

hydrologic features. Further examination determined that 

the pixelation of the soil depth layers was the dominant 

determinate of the size and shape of simulation units. 

This pattern was due largely to characteristics of the 

soil depth layer that were undesirable. The input of 

flow accumulation to the soil depth derivation process 

resulted in long, linear artifacts in the simulation units 

resulting from limitations in the flow direction grid. 

We explored different flow direction layers of varying 

complexity, but all created the same artifacts in the final 

layers. The multiple flow direction layers, in addition 

to the high degree of pixelation in the depth values, 

reduced our confidence in the derived soil depth values. 

We recommend eliminating the TCI soil depth calculation and using the soil depth information taken directly 

from STATSGO but modified by slope group, as with 

the soil texture information. In addition, we recommend 

exploring techniques to modify the soil depth estimation 

according to the coarse fragment proportion.

Reducing the soil depth layer complexity would provide 

the flexibility to increase the number of categories in 

our other model input layers and thereby improve the 

characterization of ecologically important and unique 

physical patches on the landscape for creating simulation 

units. We advocate increasing the number of classes in 

the terrain-related layers as follows:

•	 Aspect—Divide into eight classes: north 338 – 22, 

northeast 23 – 67, east 68 – 112, southeast 113 – 

157, south 158 – 202, southwest 203 – 247, west 

248 – 292, and northwest 293 – 337.

•	 Slope—Divide into five classes: <4 percent, 5 – 14 

percent, 15 – 29 percent, 30 – 79 percent, and 

80 – maximum percent.

•	 Hillshade—Divide (as a value between 0 and 1) 

into ten classes with the maximum class value 

representing each class (in other words, 0.1, 0.2, 

and so forth).

As in the prototype effort, the value assigned to the 

pixel should be the midpoint of the class range for aspect 

and slope.
120 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 20

Chapter 5—Development of Biophysical Gradient Layers for the LANDFIRE Prototype Project

We also propose a change in the extraction and handling of the DAYMET weather data. For the LANDFIRE Prototype Project, the center coordinates of 1-km 

DAYMET pixels were used to create simulation units 

and to extract weather data for each simulation unit 

within a 1-km pixel. A lapse rate was applied based on 

the elevation difference between the simulation unit 

and DAYMET pixel. A significant 1-km artifact of this 

DAYMET grid remained in many output layers, even 

when various smoothing techniques were implemented. 

We propose removing the DAYMET grid from the suite 

of layers used to create simulation units and employing 

a more sophisticated smoothing process to scale the 

weather data down to better represent our 30-m simulation units Specifically, we suggest applying a lapse rate 

based on bilinear interpolation to the four surrounding 

DAYMET pixels closest to the center coordinates for 

each simulation unit.

The ecophysiological site and LAI data layers were 

created based on current land use and land cover data, 

and these layers were used to determine unique simulation units for developing the biophysical gradient layers. When the site and LAI layers were used to create 

simulation units, they not only generated artifacts but 

had no bearing on biophysical attributes that define 

environmental site potential or biophysical setting. For 

these reasons, we recommend omitting these layers in 

the creation of unique simulation units. In combining 

the WXFIRE and LF-BGC models, the ecophysiological 

site and LAI inputs could be eliminated because they 

are inherent to the BGC PFT parameterization and will 

represent more generic potential rather than current land 

use and land cover.

Improving Simulation Unit Development

We propose some changes to the protocol for defining 

the simulation units with respect to reducing data volume 

and increasing simulation efficiency. We identified three 

major limitations in our methods, the removal of which 

would improve the quality of the simulation units. First, 

as stated in the previous section, the ecophysiological 

site, LAI, and the 1-km DAYMET layers should not be 

used to create simulation units. These layers impose 

artificial footprints and are not physical features that 

affect the biophysical potential of a landscape patch. 

Second, by removing the DAYMET grid as a controlling 

factor in creating simulation units, we can maintain the 

data at its native resolution rather than re-sampling all 

data to 25 m. Following these two changes to the input 

data used to create simulation units, we propose changes 

in the delineation methods aimed towards reducing the 

overall number of simulation units through classification 

and combination of the input layers.

In our proposed method for creating simulation units, 

we first assume that the following: First, executing the 

model to create biophysical gradient layers for each 30-

m pixel is computationally problematic. Second, model 

input layers can be classified within the bounds of model 

sensitivity, thereby decreasing data volume without losing 

information. Third, physical limits—regarding the size 

of the simulation units—can be imposed without losing 

desired spatial detail. We suggest first combining the 

following input layers with new class definitions: elevation, slope, aspect, hillshade, soil depth, and soil texture 

(sand, silt, and clay). This combination would assign a 

unique identification number to each unique combination 

of the variables. We then suggest grouping adjacent like 

pixels based on the combined output. This aggregation 

would separate any pixels that have the same biophysical gradient properties but are separated in geographic 

space. We then propose applying a minimum mapping 

unit of ~1 ha to these simulation units to reduce the 

total number. Because the DAYMET weather data is 

interpolated to scale it to the 30-m DEM, a maximum 

size for simulation units should be imposed, such that the 

units are less than 1 km; we propose using a maximum 

axis length of 750 m. This set of steps represents small 

changes, but it would result in a significant reduction in 

the number of simulation units. Additionally, we expect 

that the gain in biophysical information through these 

new methods would significantly improve the quality 

of the output biophysical gradient layers.

Conclusion_____________________

Integration of remote sensing, simulation modeling, 

and gradient analysis proved to be an efficient and successful approach for mapping broad-scale vegetation, 

wildland fuel, and fire regime characteristics in the 

LANDFIRE Prototype Project. The ability of remote 

sensing and ecosystem simulation to portray spatial 

distributions of biophysical gradients enables the efficient construction of reasonably accurate maps that 

are critical for both fire managers and ecologists. While 

there were a variety of limitations encountered during 

the application of the LANDFIRE Prototype Project 

biophysical gradient modeling approach, the lessons 

learned will prove valuable when LANDFIRE methods 

are applied across the entire United States.

For further project information, please visit the LANDFIRE website at www.landfire.gov.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 

Chapter 5—Development of Biophysical Gradient Layers for the LANDFIRE Prototype Project

The Authors ____________________

Lisa Holsinger is a GIS Specialist with the USDA 

Forest Service, Rocky Mountain Research Station, 

Missoula Fire Sciences Laboratory (MFSL). Holsinger 

joined the MFSL in 2002 and has worked on developing and analyzing spatial data for simulation models 

applied to large landscapes. She has developed GIS data 

and input for the WXFIRE and LANDSUM simulation 

models and HRVStat program, run simulations, and 

produced associated spatial data. She has also conducted 

sensitivity analyses for both the WXFIRE and LANDSUM models. Prior to the Fire Sciences Laboratory, 

she worked as a Fisheries Biologist and GIS Specialist 

for the National Marine Fisheries Service conducting 

research and management for the conservation of west 

coast salmon populations. Holsinger received her B.S. 

degree in Biological Sciences from University of California, Davis in 1984 and her M.S. degree in Fisheries 

at the University of Washington in 1988.

Robert E. Keane is a Research Ecologist with the 

USDA Forest Service, Rocky Mountain Research Station, Missoula Fire Sciences Laboratory (MFSL). Since 

1985, Keane has developed various ecological computer 

models for the Fire Effects Project for research and 

management applications. His most recent research 

includes the development of a first-order fire effects 

model, construction of mechanistic ecosystem process 

models that integrate fire behavior and fire effects into 

succession simulation, restoration of whitebark pine 

in the Northern Rocky Mountains, spatial simulation 

of successional communities on landscapes using GIS 

and satellite imagery, and the mapping of fuels for fire 

behavior prediction. He received his B.S. degree in Forest Engineering in 1978 from the University of Maine, 

Orono, his M.S. degree in Forest Ecology in 1985 from 

the University of Montana, and his Ph.D. degree in Forest Ecology in 1994 from the University of Idaho.

Russell Parsons is a GIS Specialist with the USDA 

Forest Service, Rocky Mountain Research Station, 

Missoula Fire Sciences Laboratory (MFSL). Parsons 

has worked in GIS and remote sensing since 1997. He 

received his B.S. degree in Forestry in 1992 from the 

University of California, Berkeley, and his M.S. degree 

in 1999 in Forest Resources from the University of Idaho. 

He is currently earning his Ph.D. at the University of 

Montana’s School of Forestry. Parsons previously worked 

as a fire monitor in California’s Sequoia and Kings 

Canyon national parks, and served as an agroforestry 

extensionist volunteer in the Peace Corps in Ecuador 

from 1995 to 1997.

Eva Karau is a GIS Specialist with the USDA Forest 

Service, Rocky Mountain Research Station, Missoula 

Fire Sciences Laboratory (MFSL). She has worked for 

the Fire Effects Research Unit on various projects involving GIS analysis, remote sensing, field sampling, and 

fire effects modeling. She received her B.A. in Geology 

from the University of Montana in 1995 and her M.S. 

in Forestry from the University of Montana in 2002.

Acknowledgments_______________

We would like to thank the USDA Forest Service 

Fire and Aviation Management Washington Office for 

funding the LANDFIRE Prototype Project. We thank 

Dr. Janet Ohmann of the USDA Forest Service Pacific 

Northwest Research Station, Jeff Jones of the USDA Forest 

Service Flathead National Forest, and Alisa Keyser, Maureen Mislivets, and Dr. Matthew Rollins at the Missoula 

Fire Sciences Laboratory for their technical review and 

thoughtful comments on the manuscript. We also thank 

Sarah Pratt and Don Long at the Missoula Fire Sciences 

Laboratory, Dr. Peter Thornton at Colorado State University, 

and Dr. Norman Bliss, Zheng Zhang, and Dr. Zhiliang Zhu 

at the USGS Center for Earth Resources Observation and 

Science for their technical input and valuable assistance in 

this project. Finally, we thank Christine Frame of Systems 

for Environmental Management for technical editing and 

Dennis Simmerman of the Missoula Fire Sciences Laboratory for graphic design.

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USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 

Chapter 6—Developing the LANDFIRE Vegetation and Biophysical Settings Map Unit Classifications for the LANDFIRE Prototype Project

Chapter

6

In: Rollins, M.G.; Frame, C.K., tech. eds. 2006. The LANDFIRE 

Prototype Project: nationally consistent and locally relevant 

geospatial data for wildland fire management. Gen. Tech. Rep. 

RMRS-GTR-175. Fort Collins: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research Station.

Introduction ____________________

The Landscape Fire and Resource Management Planning Tools Prototype Project, or LANDFIRE Prototype 

Project, required a system for classifying vegetation 

composition, biophysical settings, and vegetation structure 

to facilitate the mapping of vegetation and wildland fuel 

characteristics and the simulation of vegetation dynamics 

using landscape modeling. We developed three separate, 

fully integrated vegetation and biophysical settings map 

unit classifications that quantified, categorized, and 

described vegetation and environmental conditions; 

these include: cover type (CT), potential vegetation type 

(PVT) and structural stage (SS). We used a rule-based 

approach to implement these map unit classifications in 

the LANDFIRE reference database (LFRDB), which is a 

field-based database comprised of existing field data from 

the prototype mapping zones (Caratti, Ch. 4). We used the 

LFRDB to create training databases to develop maps of 

CT, PVT, and SS (Frescino and Rollins, Ch. 7; Zhu and 

others, Ch. 8). These vegetation-based maps formed the 

foundation for the mapping of fire regime condition class 

(FRCC), fire behavior fuel models, fuel loading models, 

fuel characteristic classes, and canopy fuel characteristics 

(Pratt and others, Ch. 10; Holsinger and others, Ch. 11; 

Developing the LANDFIRE Vegetation and 

Biophysical Settings Map Unit Classifications for 

the LANDFIRE Prototype Project

Jennifer L. Long, Melanie Miller, James P. Menakis, and Robert E. Keane

Keane and others, Ch. 12). The map unit classifications 

also formed the building blocks for the development of 

succession pathway models for simulating historical fire 

regimes (Long and others, Ch. 9).

In this chapter, we refer to our process of categorizing the biophysical settings, vegetation composition, 

and vegetation structure as a “classification” process. 

Several design criteria were developed to ensure that 

the LANDFIRE map unit classifications were sufficient 

for successfully completing the LANDFIRE vegetation, 

wildland fuel, and fire regime products. We refer to the 

complete list of units in each classification as a “map 

legend.” We call the results of each classification a 

“map unit” or refer to them by the appropriate mapping 

classification topic such as “cover type” or “potential 

vegetation type” or “structural stage.”

The biophysical and vegetation map unit classifications provided guidelines for many of the LANDFIRE 

Prototype mapping and modeling tasks. The CT classification describes existing vegetation composition 

and was used to describe the dominant species within 

vegetation communities that are differentiated by unique 

species compositions. The PVT classification is a biophysical classification that uses indicator plant species 

to identify the unique biophysical characteristics of a 

site. A biophysical classification describes environmental 

conditions such as water availability, nutrient status, 

and average annual temperature. The SS classification 

describes important stages of canopy development, and 

the classes are often referred to as stand structure types. 

These classifications defined the specific map classes 

that were quantified in LANDFIRE vegetation mapping. 
124 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2

Chapter 6—Developing the LANDFIRE Vegetation and Biophysical Settings Map Unit Classifications for the LANDFIRE Prototype Project

Research has shown that the integration of a biophysical 

classification (PVT) with stand structure (SS) and species 

composition (CT) classifications can uniquely describe 

other ecological characteristics, such as wildland fuel 

characteristics, fire regimes, and wildlife habitat (Keane 

and others 1998). In addition, such integration facilitates 

the modeling of vegetation succession needed to simulate 

the historical landscape composition that may be used 

for determining departure from historical conditions 

(Hardy and others 1998; Keane and others 1998).

We designed the LANDFIRE vegetation map unit classifications to contain a comprehensive list of consistently 

categorized vegetation characteristics that may be used 

beyond the scope of the prototype study areas across 

the entire nation. All lands, federal and non-federal, 

and all vegetative communities, forest, shrubland, and 

herbaceous, within the LANDFIRE Prototype Project 

study areas were classified with the same level of detail 

and consideration.

Each individual CT, PVT, and SS map unit had to 

meet the following LANDFIRE guidelines:

•	 Identifiable – The CT, PVT, and SS classes must 

be able to be identified in the field and from existing field databases (such as the Forest Inventory 

and Analysis [FIA]). Additionally, all classes must 

be able to be identified by nationally standard 

terminology used in vegetation classifications and 

descriptions of vegetation map units.

•	 Scalable – The CT, PVT, and SS classes must be 

hierarchical with regard to floristic and spatial scale. 

The aggregation and disaggregation of classes must 

be straightforward.

•	 Mappable – The CT, PVT, and SS classes must 

be able to be delineated accurately on a map using 

standardized remote sensing techniques combined 

with biophysical gradient modeling.

•	 Model-able – The CT, PVT, and SS classes must fit 

into the framework of the landscape simulation models critical for producing several of the LANDFIRE 

products, including maps of historical fire regimes, 

departure from historical conditions (Holsinger and 

others, Ch. 11), fire behavior fuel models, and fire 

effects fuel models (Keane, Ch. 12).

We used established vegetation classifications, biophysical classifications, extensive literature review, 

vegetation modeling science, classifications from other 

fuel and fire regime mapping projects, and reference 

data contained in the LFRDB (Caratti, Ch. 4) in the 

development of LANDFIRE Prototype Project map 

unit classifications and to guide the development of the 

multi-level hierarchy in which we embedded our classes. 

Multiple levels of CT and PVT allowed us to aggregate 

or disaggregate the classes to support multiple LANDFIRE tasks using a single classification scheme. Multiple 

levels also allowed linkage between the LANDFIRE 

map classifications and existing classifications such as 

the Society of American Foresters (SAF) classification 

(Eyre 1980), the Society of Range Management (SRM) 

classification (Shiflet 1994), and the National Vegetation 

Classification System (NVCS) (Grossman and others 

1998).

We developed an iterative process to ensure that ecologically reasonable combinations, based on literature 

review and expert knowledge, would result when maps 

created with our classes were combined for use in succession pathway development, landscape succession 

simulation, and fuel mapping (see appendix 2-A in 

Rollins and others, Ch. 2 for a LANDFIRE Prototype 

procedure table). We also developed a coding protocol 

for the map legends, which can be found in appendix 

6-A. The individual biophysical and vegetation mapping 

classifications and associated hierarchical structures 

developed for prototype zones 16 and 19 are described 

below.

Methods _______________________

The LANDFIRE Prototype Project involved many 

sequential steps, intermediate products, and interdependent processes. Please see appendix 2-A in Rollins and 

others, Ch. 2 for a detailed outline of the procedures 

followed to create the entire suite of LANDFIRE Prototype products. This chapter focuses specifically on 

the development of vegetation map units, which was a 

critical intermediate step for nearly all mapping tasks 

in the LANDFIRE Prototype Project.

Cover Type

The LANDFIRE Prototype Project required maps of 

cover type (CT) representing existing distinct vegetative 

communities that, when combined with maps of PVT 

and SS, allowed for characterization of the variation 

in wildland fuel and fire regimes across the prototype 

study areas. One intent of the LANDFIRE Prototype 

Project was to develop a standard methodology for the 

development of a LANDFIRE CT classification that 

would be applicable across the nation and repeatable 

(for consistency) by other teams. In addition, field data 

from the LFRDB were classified to CT and used as a 

training database for mapping existing vegetation from 

Landsat imagery.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 1

Chapter 6—Developing the LANDFIRE Vegetation and Biophysical Settings Map Unit Classifications for the LANDFIRE Prototype Project

Although several authors have created classifications 

of existing vegetation (Eyre 1980; Grossman and others 

1998; Shiflet 1994), these classifications would not suffice 

for use in the LANDFIRE Prototype or LANDFIRE 

National effort without modification or customization. 

No single, existing vegetation classification met the 

LANDFIRE design criteria and guidelines (Keane and 

Rollins, Ch. 3). For example, classifications such as the 

NVCS (Grossman and others 1998) rely on the organization of plants by morphological characteristics and 

do not necessarily provide the class divisions required 

to delineate distinct and comprehensive mapping categories. In addition, vegetation classifications based on 

floristics, which can describe vegetation characteristics 

or spatial distribution of species, have many more classes 

than were needed for LANDFIRE maps. Inconsistencies 

were also found within some of the available classifications when they were applied across several states; for 

example, the USGS GAP Analysis Program vegetation 

class mapping methodologies (Merchant and others 

1998) are inconsistent across state boundaries. Finally, 

some of the classifications serve specific purposes and 

therefore exclude many vegetation types; for example, the 

SAF cover types were developed primarily to describe 

forests and woodlands (Eyre 1980). Furthermore, several 

of the existing classifications include types composed of 

two or more species with different physiognomies and 

more importantly, different successional roles, which 

made these problematic for use in vegetation modeling 

or succession pathway development. For example, the 

SRM cover type number 509, “Oak-Juniper Woodland 

and Mahogany-Oak” (Shiflet 1994) is identified by multiple species that have different successional roles. To 

simplify the process of succession pathway development, 

we avoided grouping different seral species within a CT. 

LANDFIRE CT classes were designed to be represented 

with a single dominant species that characterized a 

primary stage in successional development (Long and 

others, Ch. 9).

Despite our reservations with available classifications, 

we attempted to integrate the logic and content of existing classifications into the LANDFIRE classification 

development. At times, we used the current classes as 

they were, sometimes we modified them, and other times 

we used them simply as general guidelines to create 

unique sets of CT map legends specifically suited to 

meet LANDFIRE design criteria and guidelines.

After our review of several CT classifications, we 

approached the development of a LANDFIRE CT classification using two fundamentally different methods. 

The approach used for Mapping Zone 16 in the central 

Utah highlands was a top-down method that partitioned 

general vegetation types (forest, woodland, shrub, and 

herbaceous) into classes based on differences within 

these types. This top-down approach, or divisive method, 

is most aptly used for large areas where relationships 

and patterns are already understood (Brohman and 

Bryant 2005). Because the classes are more conceptual 

in nature, fewer observations are required for their 

development (Brohman and Bryant 2005). As a result, 

Zone 16 plot data was used only to fine-tune map units, 

not direct the classification. The second classification 

methodology, used for Zone 19 in the Northern Rockies, 

focused on groupings based on shared characteristics. 

In this bottom-up approach, we used Zone 19 plot data 

to specify the type to be grouped, which, in our case, 

was the dominant species of the plot. This agglomerative 

method is often used to quantify unknown relationships 

and patterns using empirical data (Brohman and Bryant 

2005). As this was a prototype effort to develop nationally 

consistent maps, we decided to test both methodologies 

to determine which approach, conceptually based or 

data-driven, would prove most useful. The following 

sections describe these two distinct approaches used in 

the development of the LANDFIRE CT classification.

Mapping Zone 16: Central Utah Highlands—The 

general approach for Zone 16 was to construct a list 

of CTs applicable to 11 western states. We expected 

detailed descriptions of these CTs to vary significantly 

between different parts of the West because of regional 

differences in species composition. We assumed at the 

outset that the western U.S. list and associated descriptions of the CTs would be refined once applied to Utah 

and further refined when applied to other parts of the 

West.

Through consultation with vegetation ecologists and 

mapping experts, we established general guidelines for 

the CT classification development. We determined that a 

set of approximately 50 western CTs would be suitable to 

map existing vegetation for the LANDFIRE Prototype. 

These types had to have at least one percent coverage of 

the western U.S. in order to describe a mid- to broadscale vegetative community. We placed emphasis on 

the creation of a CT legend for non-forest vegetation, 

which had been inadequately represented in previous 

national mapping efforts. We represented each CT with 

an individual dominant species, such as ponderosa pine 

or bluebunch wheatgrass, and we attempted to avoid the 

use of mixed life form, phenological, and morphological 

classes when grouping the dominant species into CTs and 

when these CTs were arranged into coarser hierarchical 

levels. Finally, we decided to use CT names that describe 
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Chapter 6—Developing the LANDFIRE Vegetation and Biophysical Settings Map Unit Classifications for the LANDFIRE Prototype Project

the dominant species, as opposed to using generic vegetation terminology. Generic terminology such as chaparral, 

for example, comprises many species, and a term such as 

Pacific comprises many geographical regions.

We developed the original legend of non-forest and 

forest CTs from expert knowledge of western vegetation 

and then improved this legend based on reviews of key 

literature that described similar CTs and on other existing CT classifications. We relied heavily upon the SAF 

cover types (Eyre 1980), the SRM cover types (Shiflet 

1994), and a list of USGS Gap Analysis Program (GAP) 

(Merchant and others 1998) land cover classes that we 

compiled from western GAP state maps and standardized 

classes provided by the University of Idaho and BLM 

National Science Technology Center. Essentially, most 

of the western SAF, SRM, and GAP types were linked to 

the LANDFIRE CT legend to ensure this legend included 

the major vegetation types of the western U.S. A few 

of these were not assigned to LANDFIRE CTs because 

they were either too fine spatially or had wide-ranging 

descriptor species, which meant that the presence of a 

particular species did not indicate a discrete CT useful 

to the LANDFIRE mapping effort. With significant 

assistance from Forest Service Region 4 ecologists, we 

also adjusted sagebrush CTs to be compatible with the 

classification used for the sagebrush map prepared by the 

NatureServe for the USGS (Reid and others 2002).

We followed the Federal Geographic Data Committee 

(FGDC 1997) standards for vegetation classification as 

closely as possible when developing CT legends and the 

classification hierarchy, and we used hierarchical levels 

similar to the NVCS (Grossman and others 1998), such 

as class, subclass, and group, to describe our hierarchy. 

Although the FGDC standards do not include mapping 

applications, we found that FGDC guidelines for vegetation classification were useful in the development of the 

LANDFIRE map unit classification. When necessary, 

however, we altered FGDC vegetation classification 

definitions to better suit the requirements of the LANDFIRE Prototype Project. For example, the LANDFIRE 

Prototype Project defined barren as less than 10 percent 

cover of vegetation, whereas FGDC defined it as less 

than 20 percent vegetation cover. If we had used the 

FGDC definition of barren, we would have classified 

many functioning, arid plant communities that fully 

occupy their sites as essentially devoid of vegetation. 

Furthermore, because some of these communities will 

sustain wildland fire, particularly in years when high 

precipitation causes abundant growth of herbaceous 

fine fuel, we determined they must be included in the 

LANDFIRE CTs as vegetated communities.

To facilitate the creation of the CT maps (Zhu and others, Ch. 8), we developed a classification key or sequence 

table for assigning LANDFIRE CTs to LFRDB plots 

(Caratti, Ch. 4). We assigned “dominant species” to each 

CT according to expert knowledge and the descriptions 

provided with each SRM, SAF, and GAP cover type classification. We used the dominant species to represent the 

CT, following an approach similar to that of Brohman 

and Bryant (2005) and their use of a “dominance type” 

in the Existing Vegetation Classification and Mapping 

Technical Guide. Specifically, we represented the CT 

by one important plant taxa in the uppermost layer of 

vegetation. Species defined as dominant usually had 

the greatest amount of canopy cover in the uppermost 

layer. The identification of a single dominant overstory 

species was adequate to describe the plot and therefore 

allowed us to delineate CTs using satellite image processing (which cannot identify lower strata vegetation). 

However, in the case of some shrub and grassland CTs, 

we employed a second species or species group when 

the important plant species could dominate more than 

one CT as a result of its wide-ranging distribution.

In our final step, we improved the western U.S. CT 

legend, added more dominant species to some CTs, and 

developed criteria for identifying dominant species using plot data from the central Utah mapping zone. We 

assigned each additional dominant species found in 

the plots to the most suitable CT based on distribution, 

occurrence, ecological characteristics, and/or habitat requirements of the species, as described in the Fire Effects 

Information System (http://www.fs.fed.us/database/feis). 

Furthermore, we divided graminoid communities into 

cool-season (C3 or C4) and warm season (C4) CTs according to the dominant photosynthetic pathway of the 

species with highest cover. We required the dominant 

species to be listed by complete scientific name (Poa 

pratensis), not just genera (such as Poa). We also required 

that all big sagebrush species be listed with variety or 

sub-species (for example, Artemisia tridentata ssp. 

wyomingensis). Comprehensive methodology detailing 

how the CTs were assigned to plots in the LFRDB can 

be found in Caratti, Chapter 4.

Mapping Zone 19: Northern Rockies—In contrast 

to the CT classification development for Zone 16, we 

implemented a data-driven approach for the creation of 

the Northern Rockies Zone 19 CTs. This bottom-up approach relied heavily on plot data found in the LFRDB. 

For a national classification, this approach would require 

enormous amounts of data and computing capacity to classify a single field-referenced database for the entire U.S.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 1

Chapter 6—Developing the LANDFIRE Vegetation and Biophysical Settings Map Unit Classifications for the LANDFIRE Prototype Project

We also developed guidelines that promoted 

consistency in CT criteria, even though the plots were 

to be classified independently for each zone. All CT and 

CT hierarchy development followed the same general 

principles, such as consideration of the predominance 

of a CT on the landscape, the ecological significance 

of a CT, and plot data availability. As in Zone 16, the 

objective of the CT map classification was to represent 

the CT with distinct yet nationally applicable criteria 

at a landscape-level. We attempted to avoid the use of 

mixed life form, phenological, and morphological classes 

when grouping the dominant species into CTs and when 

these CTs were arranged into coarser hierarchical levels. 

Mixed classes may have included species with different successional roles, making them difficult to use 

as representatives of single seral stages for succession 

models.

We used LFRDB plot data for Zone 19 to determine 

the set of dominant species that formed the foundation of 

our CT map classification and hierarchy development. To 

establish this set of dominant species, we first assigned 

life forms to plots based on criteria established by the 

LFRDB team (see Caratti, Ch. 4). Next we determined 

the dominant species on the plot to be the species within 

that life form that had the highest percent cover (or basal 

area if the plot was from FIA data). As for Zone 16, a 

complex rule set was developed to distinguish the uppermost dominant tree species from multiple layers in 

certain forest types (see Caratti, Ch. 4). The attributes 

for these dominant species became the starting point 

for the bottom-up CT classification.

We based the Zone 19 dominant species groupings 

on a number of taxonomic, physiognomic, succession, 

and site characteristics. We grouped some of dominant 

species into CTs, and we determined that other dominant 

species were CTs themselves because of their continuous 

and distinct distribution across the landscape. In essence, 

we selected the criteria for developing the CT classes 

based on whether they resulted in CT classes that met 

the four LANDFIRE design requirements. That is, they 

had to be identifiable, scalable, mappable, and modelable. This scalable, hierarchical system facilitated both 

mapping and succession modeling because CTs that 

were most suitable for the particular product could be 

selected. For example, if a CT at one level did not meet 

the needs of a certain LANDFIRE task, a level above 

or below could be used instead. As a result, the CTs 

used in processes described in other chapters (see, for 

example, cover type mapping in Zhu and others, Ch. 8) 

existed in more than one hierarchical level.

Potential Vegetation Type

The potential vegetation type (PVT) map classification was important to several LANDFIRE processes 

and products. Potential vegetation types describe and 

classify environmental site conditions, providing succession modelers with the biophysical settings (areas 

with common environmental site conditions) for which 

they then develop succession pathways describing vegetation development (Long and others, Ch. 9). Much in 

the same way as in the creation of the CT map, plot data 

from the LFRDB were classified to a PVT in order to 

provide a training database for mapping PVTs (Keane 

and Rollins, Ch. 3; Frescino and Rollins, Ch. 7). We 

used the PVT map as one of the predictor layers in the 

mapping of CT and SS, along with Landsat imagery 

and biophysical gradient layers (Zhu and others, Ch. 8). 

Potential vegetation type effectively limited the number 

of CTs that could occur on any site because certain 

existing vegetation types had high fidelity to specific 

PVTs. (Zhu and others, Ch. 8). Mapped PVT formed 

the foundation for the simulation of historical reference 

conditions that served as the baseline for characterizing 

the ecological departure of current systems from historical conditions (Keane and Rollins, Ch. 3; Pratt and 

others, Ch. 10; Holsinger and others Ch. 11). The PVT 

map was also used to spatially parameterize disturbance 

dynamics in the LANDSUMv4 fire-succession model 

(Pratt and others, Ch. 10). Finally, the PVT classes and 

map were used in the development of fuel maps (Keane 

and others, Ch. 12). The following section presents the 

background of the PVT concept, the LANDFIRE PVT 

mapping guidelines, and the development of the PVT 

map classification.

Quantitative descriptions of the biophysical environment can provide a process-oriented context for mapping 

and modeling important biological characteristics. Litter 

fall, for example, is greater on warm, moist sites than on 

cold, dry sites. Studies have shown that incorporating a 

quantitative description of the biophysical environment 

(such as temperature, elevation, and precipitation) with 

satellite imagery improved the mapping of ecological 

characteristics such as vegetation and fuel (Keane and 

others 2002; Rollins and others 2004). We recognized 

the need to develop a biophysical classification that would 

be useful for both LANDFIRE mapping and modeling 

and for scaling LANDFIRE products to finer scales for 

use in local land management applications.

Due to the lack of an existing national-scale PVT classification, we developed our own biophysical classification 

based on a revised habitat type classification approach 
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Chapter 6—Developing the LANDFIRE Vegetation and Biophysical Settings Map Unit Classifications for the LANDFIRE Prototype Project

(Pfister 1989; Pfister and Arno 1980; Pfister and others 

1977) and other site classifications based on climax 

vegetation (Daubenmire 1962, 1966; Ferguson 1989). 

In concept, the PVT approach assumes that a climax 

vegetation community would eventually develop on a 

site in the absence of disturbance). This approach has a 

long history in vegetation mapping, and PVT classifications have been developed for many of the forests of 

the western U.S. (Ferguson 1989; Pfister 1981; Pfister 

and Arno 1980). However, the approach has had limited 

success with non-forested environments because extensive disturbance histories in rangelands have eliminated 

many climax species that are indicators of biophysical 

settings. Also, non-forest systems don’t lend themselves 

to a single climax species, but rather a group of species 

or vegetation communities. This type of classification, 

often based on late seral species and/or gradients of 

shade tolerance, provides the basis for LANDFIRE’s 

biophysical classification.

We modified traditional approaches to PVT classification to match the scope and assumptions of the LFRDB 

development and LANDFIRE mapping tasks. Our objective was to identify the unique biophysical setting, 

not the climax vegetation or endpoint of succession. As 

noted above, the term climax is often associated with 

communities rather than species, and many ecologists 

have noted that climax vegetation is an unrealistic 

endpoint since climate, genetics, exotic migrations, 

and other factors are constantly changing such that a 

stable climax community is impossible (Hironaka 1987; 

Huschle and Hironaka 1980). We assumed that PVTs 

for forest ecosystems could be identified from plot data 

based on the most shade-tolerant tree species on a plot. 

The hypothesis is that the tree species with the highest 

shade tolerance will eventually become dominant in 

the absence of disturbance. Following the theory of 

Daubenmire (1966) (the principle of competitive exclusion), the tree species with the highest shade tolerance 

will also have a high fidelity of occurrence in unique 

biophysical settings. Again, we made no assumption that 

the most shade tolerant species was a climax species in 

our classification. We viewed the most shade-tolerant 

species found on a plot as a suitable indicator of the 

plot’s distinctive environmental condition. We named 

our biophysical classification after PVTs because these 

shade-tolerant species best indicate the biophysical setting under the current climate regime, not the ultimate 

climax community. This approach not only ensured the 

mapping of unique biophysical settings but also allowed 

these settings to be directly linked to succession pathways 

in our simulation of historical reference conditions.

The CT map classification provided the building blocks 

for developing the final list of PVTs for the LANDFIRE 

Prototype Project. The PVTs were named according 

to CTs, and lists of CTs that could exist in each PVT 

were developed so that no inconsistencies or illogical 

combinations existed between the CT and PVT maps 

and so that each PVT could occur on the CT map as an 

existing vegetation type. Therefore, the CT map legend 

provided the resolution for all LANDFIRE PVTs. For 

example, a Dwarf Sagebrush PVT could be created only 

if there was a Dwarf Sagebrush CT. This was especially 

important to the LANDSUMv4 modeling effort for determining the historical range of landscape conditions 

(Pratt and others, Ch. 10).

Potential vegetation types were assigned to forested 

plots in the LFRDB based on the presence of a particular 

tree species as determined from the coverage or tree 

density data collected for that plot. Using the reference 

database, we sorted all tree species present (≥ 1 percent 

cover) on a plot by shade tolerance using autoecological 

information found in the literature (Burns and Honkala 

1990; Fowells 1965; Minore 1979). We then matched the 

most shade-tolerant species with the comparable CT. 

Again, matching PVT and CT ensured logical combinations and a consistent linkage between maps for the 

development of the LANDSUMv4 succession pathways 

for simulating historical reference conditions (Pratt and 

others, Ch. 10)

Rangeland ecosystems presented a special problem 

for the PVT concept since residual late successional 

species are rarely observed in plot databases because of 

high frequency of disturbances such as grazing and fire 

(Bunting 1994; Sieg 1997; Westoby 1980). For this reason, 

we arranged the rangeland CTs along a moisture gradient 

from xeric to mesic communities, and this arrangement 

was used as the key criterion for classifying plots in the 

LFRDB. We had some problems uniquely assigning 

rangeland PVTs to plots because of overlap and limited 

coverage of some indicator species along the moisture 

gradient. To determine the PVT for some of the rangeland plots, we had to consider other ecological species 

characteristics, such as ecological amplitude. Presence 

of an indicator species at greater than ten percent cover, 

rather than dominance of that indicator species (species 

with highest cover on a plot), was used as a criterion for 

classifying the rangeland PVTs in the key. Additionally, 

a threshold of ten percent cover was used in the PVT 

key because when presence alone (greater than zero 

percent cover) was used to implement the key, as was 

initially done, none of the herbaceous rangeland PVTs 

were assigned to plots. Most herbaceous plots had a few 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 1

Chapter 6—Developing the LANDFIRE Vegetation and Biophysical Settings Map Unit Classifications for the LANDFIRE Prototype Project

shrubs on them, and the presence threshold of greater 

than zero percent that was employed initially always led 

to an assignment of shrub PVT, which we knew was not 

always accurate (Caratti, Ch. 4). Although this method 

for assigning PVTs to rangeland communities was based 

on a myriad of assumptions, most importantly the ability to consistently model successional development, it 

proved to be the best approach considering the limited 

resources and data available.

We created a nested hierarchy of the PVT categories 

to aggregate similar PVTs into one type and to facilitate 

the development of finer divisions of biophysical settings 

according to the modelers’ and mappers’ needs (Zhu and 

others, Ch. 8; Long and others, Ch. 9). The order of the 

hierarchical levels was also important as it influenced 

how relevant the classification would be for LANDFIRE 

purposes. For example, if we used a general forest PVT, 

such as Spruce – Fir, as our finest level of the hierarchy, 

we would not be able to divide this type any further to 

represent finer distinctions in the biophysical settings 

of Spruce – Fir forest PVTs.

Structural Stage

Structural stage (SS) map classifications delineate 

developmental stages of vegetative communities based 

on characteristics such as vegetation age, height, canopy 

closure, and canopy structure (Quigley and Arbelbide 

1997). These characteristics are the key components in 

modeling vegetation succession, wildland fire behavior, 

and the effects of wildland fire. Arno and others (1985) 

classified forests based on the following stand characteristics: tree canopy coverage, average diameter at 

breast height of the dominant tree, basal area, and stand 

age. Quigley and Arbelbide (1997) used the processes 

approach, based on growth, development, competition, 

and mortality, to classify SS for the Interior Columbia 

Basin Ecosystem Management Project. Many professional foresters have used size classes (such as diameter 

at breast height) to represent seral stage or age, attributes 

which are primarily used to determine timber volumes. 

Foresters often assume the bigger and taller the stand, 

the older the stand or the later the seral stage. However, 

mapping efforts using diameter-breast-height and size 

classes have met with limited success and may not yield 

even enough information to adequately determine seral 

stage. The USGS Center for Earth Resources Observation 

and Science (EROS) team, responsible for producing the 

LANDFIRE SS maps, found that mapping canopy cover 

and height to indicate seral stage was more successful 

(Keane and Rollins, Ch. 3), and so these two attributes 

were used to create the LANDFIRE SS map.

The LANDFIRE SS map classification was critical for 

almost all phases of the project, especially for developing 

the succession pathway models and for mapping wildland 

fuel. This classification allowed modelers to assign seral 

stages to the various CTs that made up the succession 

pathways (Long and others, Ch 9). Additionally, the SS 

classes quantified the horizontal and vertical configuration of vegetation, enabling a more accurate assignment 

of wildland fire behavior models and fire effects models 

and a better overall representation of wildland fuel characteristics (Keane and others, Ch. 12).

We developed the existing SS map units using similar 

methodologies for both zones 16 and 19. We categorized 

continuous canopy cover (density) and height values 

into classes designed to yield the highest precision 

based on the mid-level resolution of Landsat imagery 

because we did not feel confident that the imagery 

had sufficient resolution to detect a more complex and 

detailed SS resolution. We determined the threshold 

values separately for each life form (forest, woodland, 

shrubland, and herbaceous) based on expert opinion. 

We then combined these two variables into a matrix that 

enabled us to describe both attributes with one value. 

The combination of the two attributes provided sufficient 

characterization of seral stage, which was then used to 

map wildland fuel (Keane and others, Ch. 12) and to 

parameterize and implement LANDSUMv4 (Pratt and 

others, Ch. 10).

Results and Discussion __________

Cover type

Mapping Zone 16: Central Utah Highlands—Fifty 

CT classes were created for the western United States. 

Table 1 provides a legend of these CTs and illustrates 

the hierarchical structure of the CT classification. The 

western U.S. CTs included 24 forest, 4 woodland, 15 

shrubland, and 7 herbaceous types. Eight of the forest 

CTs were refined through examination of Zone 16 plot 

data, in addition to 2 woodland types, 14 shrubland 

types, and all 7 of the herbaceous types. Appendix 6-B 

provides a brief description of each western CT.

We assigned dominant species to each CT to enable 

identification (to meet the LANDFIRE guideline that 

all types be “identifiable”) of a CT in the field or in a 

database. Species are commonly recorded in field data 

sets, especially the dominant species, because species 

are usually easily identified in the field, and the connection between dominant species and CT is a commonly 

understood concept.
130 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2

Chapter 6—Developing the LANDFIRE Vegetation and Biophysical Settings Map Unit Classifications for the LANDFIRE Prototype Project

Table 1—Western U.S. cover type legend. For Zone 16, the LANDFIRE Prototype Project used a “top-down” classification approach

in which vegetation classes were developed for the entire western United States. Classes that were actually mapped for Zone 16

are denoted with a superscript b.

CT#a Cover type Class Subclass Group

1401 Riparian Hardwoodb Forest Deciduous Broad

1405	 Aspen – Birchb Forest Deciduous Broadleaf

1406	 Pacific Deciduous Forest	 Forest	 Deciduous	 Broadleaf

[Other Broadleaf]

1102	 Pacific Broadleaf Evergreen Forest	 Forest	 Evergreen	 Broadleaf

[Other Broadleaf Evergreen]

1501 Larch Forest Deciduous Needlel

1201 Ponderosa Pineb Forest Evergreen Needlel

1208	 Pacific Ponderosa Pine Complex	 Forest	 Evergreen	 Needleleaf

1202 Foothill Pines Forest Evergreen Needlelea

1203 Western White Pine Forest Evergreen Needlel

1204 Lodgepole Pineb Forest Evergreen Needlel

1205	 Douglas-firb Forest Evergreen Needleleaf

1206	 Grand Fir – White Firb Forest Evergreen Needleleaf

1207	 Pacific Silver Fir – Noble Fir 	 Forest	 Evergreen	 Needleleaf

1219 Red Fir Forest Evergreen Needlele

1220 California White Fir Forest Evergreen Needlelea

1209 Western Hemlock Forest Evergreen Needlelea

1210 Mountain Hemlock Forest Evergreen Needlele

1211	 Spruce – Firb Forest Evergreen Needleleaf

1212 Sitka Spruce Forest Evergreen Needlele

1213 Cedar Forest Evergreen Needle

1215 Redwood Forest Evergreen Needlele

1216	 Sequoia	 Forest	 Evergreen	 Needleleaf

1217 Cypress Forest Evergreen Needlele

1801 Timberline Pinesb Forest Mixed Evergreen-Deciduous Needlel

2401 Deciduous Oak Woodland Deciduous Broadl

2101 Evergreen Oak Woodland Evergreen Broadl

2201	 Pinyon – Juniperb Woodland Evergreen Needleleaf

2202 Juniperb Woodland Evergreen Needleleaf

3704 Mountain Deciduous Shrubb Shrubland Deciduous Broadl

3402 Riparian Shrubb Shrubland Deciduous Broadle

3403 Exotic Riparian Shrubb Shrubland Deciduous Broad

3101 Mountain Big Sagebrush Complexb Shrubland Evergreen Broad

3102 Wyoming - Basin Big Sagebrush Shrubland Evergreen Broadle

Complexb

3103 Dwarf Sagebrush Complexb Shrubland Evergreen Broad

3104 Sand Sagebrushb Shrubland Evergreen Broad

3105 Blackbrushb Shrubland Evergreen Broadle

3106 Rabbitbrushb Shrubland Evergreen Broadle

3107 Chaparralb Shrubland Evergreen Broadle

3108 Soft Chaparral [Coastal Sage Scrub] Shrubland Evergreen Broadle

3301 Montane Evergreen Shrubsb Shrubland Evergreen Mixed Broadleaf-Needle

3701 Salt Desert Shrubb Shrubland Mixed Evergreen-Deciduous Broadl

3702 Desert Shrubb Shrubland Mixed Evergreen-Deciduous Broadlea

3703 Dry Deciduous Shrubb Shrubland Mixed Evergreen- Broadl

 Deciduous

4101 Warm Season Grassesb Herbaceous Perennial Graminoid G

4102 Cool Season Grassesb Herbaceous Perennial Graminoid Gra

4201 Native Forbsb Herbaceous Perennial Forb F

4202 Exotic Forbsb Herbaceous Perennial Forb For

4301 Wetland Herbaceousb Herbaceous Mixed Perennial Graminoid/Forb Mixed Grass/

4302 Alpineb Herbaceous Mixed Perennial Graminoid/Forb Mixed Grass/Fo

4401 Annual Grasslandsb Herbaceous Annual Graminoid G

a

Coding protocol can be found in appendix 6-A b

Refined with plot data and mapped in Zone 16.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006

Chapter 6—Developing the LANDFIRE Vegetation and Biophysical Settings Map Unit Classifications for the LANDFIRE Prototype Project

While we adhered to the guideline that the CTs be 

“mappable,” we could not logically follow some of the 

other initial guidelines developed for the Zone 16 CT 

mapping classification. For example, we did not name 

each CT according to an individual dominant species for 

several reasons. First, there are more plant communities 

dominated by individual species than needed for the midscale LANDFIRE Prototype map products. Second, in 

many plant communities, especially non-forest, mixes 

of species commonly dominate. Additionally, the subtle 

spatial patterns in many of these diverse plant communities cannot be mapped using current remote sensing 

technology because satellite technology cannot distinguish these as individual plant communities. Therefore, 

to maintain a mid-scale CT classification and adequately 

describe CT variability, we used generic names such as 

Desert Shrub or Chaparral to identify the CT. Lastly, we 

encountered difficulty in assigning unique CTs to plots 

dominated by non-forest species with broad ecological 

amplitude. To classify these systems, we had to either 

create a map unit with a relatively coarse floristic scale 

or use co-dominants in the classification process.

We recognized that categorizing grasses into two types 

only, warm season and cool season, was quite broad and 

may not be suitable for all LANDFIRE Prototype applications. For example, fire behavior fuel model mapping 

requires knowledge of leaf blade type, fine or coarse, to 

assign a grass fuel model; however, a mixture of both 

kinds of leaf blades may dominate both the warm and 

cool season grass CTs.

Overall, we found that the CTs served well in landscape 

succession models; that is, they met the LANDFIRE 

guideline of being “model-able.” The number of map 

units in each classification was sufficient for modeling 

disturbance processes in each map zone. Although mapping accuracies may have increased had we used fewer 

classes (Vogelmann and others, Ch. 13), we needed to 

balance the need for high map accuracies with the need 

to provide useful types to modelers.

 Allowing more than one dominant species to represent 

a CT did, however, create several problems. First, the 

Timberline Pine CT was composed of evergreen and 

deciduous tree species; we therefore created a mixedleaf phenology map unit, which did not adhere to some 

of our initial classification guidelines (see above). In 

addition, some CTs contained species that play different 

successional roles. For example, the Mountain Deciduous 

Shrub CT includes Gambel oak, a long-lived, mid-seral 

species, in addition to other shrubs that show up early in 

the succession pathway. We did try to limit the number 

of CTs composed of different seral species because a 

single map unit was used to represent several different 

distinct stages in different succession pathways, and we 

did not want to expand individual CT’s definitions beyond 

the LANDFIRE broad-scale mapping target (Brohman 

and Bryant 2005; Keane and Rollins, Ch. 3). Finally, 

some CTs, such as Montane Evergreen Shrubs and 

Mountain Deciduous Shrub, included species (in these 

examples, mountain mahogany and Rocky Mountain 

maple, respectively) that the modelers used so often in 

Zone 16 succession pathways that they should have been 

separate CTs.

We arranged the CTs within a hierarchy to address 

the “scalable” requirement. The hierarchy consists of 

three coarse mapping levels, a landscape-scale level, 

and a species-based level (described in table 2). We 

also tiered the LANDFIRE hierarchical levels to those 

of other classification systems (table 2). We created the 

three coarsest levels by aggregating characteristics of 

the CTs’ dominant species, such as leaf type and leaf 

periodicity. Level 5, the species-based level, allows users 

to scale down the CTs and link them to other published 

and unpublished classifications.

The LANDFIRE fuel team found the map units developed for Zone 16 to be useful. Most of the CTs provided 

sufficient information for describing the fuel and fire 

characteristics of a site because many of the CTs were 

based on dominant species with similar growth forms 

and leaf types. In the cases where dominant species 

were lumped to form general CTs, such as Warm Season 

Grasses, the LANDFIRE fuel mapping team found it 

more difficult to determine the vegetative characteristics. 

For example, the warm-season perennial grassland contains both fine- and coarse-leaved graminoids. (Keane 

and others, Ch.12).

We developed a table (appendix 6-C) to relate LANDFIRE CTs to other classification systems. The most 

closely related SAF, SRM, and western U.S. GAP types 

are linked to corresponding CTs. Additionally, linkages 

of LANDFIRE CTs to the NVCS class, subclass, group, 

and alliance levels are found in appendix 6-D.

Mapping Zone 19: Northern Rockies—The Zone 19 

CT map legend consists of 36 CTs (table 3) and includes 14 

forest types, 15 shrub types, and seven herbaceous CTs.

Use of existing data (a main design criterion for the 

LANDFIRE Prototype) that had incomplete species lists 

or general taxonomic descriptions (for example, “Pinus”) 

limited the level of detail that could be extracted from 

the data for the bottom-up CT classification approach 

used in Zone 19. Many plots simply did not have enough 

information to “identify” the CT. For example, one data 

set, representing approximately one-third of the reference 
132 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2

Chapter 6—Developing the LANDFIRE Vegetation and Biophysical Settings Map Unit Classifications for the LANDFIRE Prototype Project Table 2—LANDFIRE Zone 16 hierarchical structure and comparison with other classification systems. U.S. National Vegetation Classification System (NVCS) Hierarchical LANDFIRE cover (Grossman and FS Ecosystem Management Coordination level type classification FGDC (FGDC 1997) others 1998) Staff (Brohman and Bryant 2003) Class	 Broad-scale map unit	 A level in the classification	 Growth form and	 Same definition as FGDC. Classification classes based on	 hierarchy defined by the	 structure of vegetation	 criteria are based on the following structural vegetation physiognomy relative percent canopy (Forest, Woodland, attributes: (life form, structure, and cover of the tree, shrub, Shrubland, Dwarf- •Tree canopy cover canopy cover). dwarf shrub, herb, and shrubland, Herbaceous, •Shrub height and canopy cover nonvascular life form in Non-vascular, & •Herbaceous vs. non-vascular canopy cover the uppermost strata during Sparse vegetation). the peak of the growing season. SubClass	 Broad-scale map unit	 Leaf phenology (evergreen,	 Growth form characteristics,	 Same definition as FGDC. Classification criteria classes based on deciduous, mixed evergreen e.g., leaf phenology are based on the following structural attributes: vegetation physiognomy, deciduous) and average (e.g., Deciduous Woodland). •Leaf phenology (e.g., evergreen vs. deciduous) leaf phenology of woody height of herbaceous •Gross morphology (e.g., graminoid vs. forb) plants, and leaf type and stratum (tall, medium, short). •Herb periodicity (e.g., annual vs. perennial) periodicity of herbaceous plants Group	 Broad-scale map unit	 Combinations of climate,	 Leaf types, corresponding	 Same definition as FGDC. Classification criteria classes formed by grouping leaf morphology, and leaf to climate (e.g., Cold- are based on the following structural attributes: cover types on broad leaf phenology. deciduous woodland). •Climatic Regime (e.g., temperate, tropical, subpolar) morphology. •Leaf morphology (e.g., extremely xeromorphic) •Leaf phenology (e.g., cold- vs. drought-deciduous) •Presence of a sparse woody layer in grasslands. Subgroup	 N/A

– Definition is	

A level of the hierarchy that	 Relative human impact	 Same definition as FGDC. Classification criteria are

problematic, particularly splits Natural/Semi-Natural (Natural/Semi-natural, based on the following structural attributes:

in forest plantations of vegetation types from the or Cultural). •Natural/Semi-Natural - Areas dominated by native or

native species (Brohman Planted/Cultivated vegetation established vegetation that has not been cultivated or

and others 2003) types. treated with any annual management or manipulatio

regime.

•Planted/Cultivated - Areas dominated with vegetation

that has been planted in its current location by

humans and/or is treated with annual tillage, a

modified conservation tillage, or other intensive

management or manipulation.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006

Chapter 6—Developing the LANDFIRE Vegetation and Biophysical Settings Map Unit Classifications for the LANDFIRE Prototype Project

Formation	 N/A

– Difficult to apply	 Ecological groupings of	 Additional physiognomic	 Same definition as FGDC. Classification criteria are

because the plethora of vegetation units; broadly and environmental factors, based on the following structural attributes:

attributes make many	 defined environmental and	 including hydrology (e.g.,	 •Elevation zone (e.g., alpine, submontane)types that are not mutually physiognomic factors. Temporarily Flooded •Flooding regime (Cowardin 1979)

exclusive, making it Cold-deciduous Woodland). •Leaf morphology (e.g., xeromorphic)

impossible to consistently •Tree crown shape (e.g., cylindrical)assign plot or associations •Presence of sparse tree layer in shrublandsto formations (Brohman •Leaf phenology and morphology of sparse tree layerand others 2003). in shrubland

•Leaf phenology and morphology of sparse woody

layer in grasslands

•Shrub growth form (e.g., suffruticose, cushion, mat)

•Presence of succulents in shrublands

•Leaf phenology of shrubs (e.g., facultative-deciduous)

•Plant height in herbaceous vegetation

•Graminoid rooting habit (e.g., sod-forming vs. bunch)

Cover Type Mid-level map unit classes N/A N/A N/A

that depict the distributionof one to several dominancetypes that cover a largegeographic area. Groupingof dominance types arebasedon combinations offactorsrelating to ecology,physiognomy, andsuccession.

Dominance	 Dominant Species or	 N/A	 N/A	 Dominance Type:

A recurring plant community “defined

Type or Species groups: A by the dominance of one or more species which are

Dominant dominant species or usually the most important ones in the [uppermost]Species a few dominant species layer of the community, but sometimes of a lower layer

that indicate a recurring of higher coverage.” Dominance types are most simplylandscape level (mid-)	 	 defined by the single species with the greatest canopyplant community. It is cover in the uppermost stratum.

Table 2—(Continued)

U.S. National

Vegetation

Classification

System (NVCS)

Hierarchical LANDFIRE cover (Grossman and FS Ecosystem Management Coordination

 level type classification FGDC (FGDC 1997) others 1998) Staff (Brohman and Bryant 2003)
134 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 

Chapter 6—Developing the LANDFIRE Vegetation and Biophysical Settings Map Unit Classifications for the LANDFIRE Prototype Project Table 2—(Continued) U.S. National Vegetation Classification System (NVCS) Hierarchical LANDFIRE cover (Grossman and FS Ecosystem Management Coordination level type classification FGDC (FGDC 1997) others 1998) Staff (Brohman and Bryant 2003) defined by dominance of one or more plant taxa, which are usually the most important ones in the uppermost layer of the community and are usually determined from the plot data. Alliance	 N/A

– Difficult to delineate	 A physiognomically uniform	 Dominant/diagnostic 	 A group of associations with a characteristic

for this project as presently group of Associations species of uppermost hysiognomy and habitat and which share one

defined by FGDC 1997.	 sharing one or more	 or dominant stratum	 or more pdiagnostic species, at least one of which

diagnostic (dominant, (e.g., Populus deltoids is typically found in the uppermost or dominant

differential, indicator, Temporarily Flooded stratum of the vegetation.

or character) species, Woodland Alliance).

which, as a rule, arefound in the uppermoststratum of the vegetation.

Association	 N/A

– Difficult to	

A physiognomically uniform	 Additional dominant/	 A recurring plant community with a characteristic

delineate for this	 group of vegetation	 diagnostic species from	 range in species composition, specific diagnostic

project as presently	 stands that share one	 any strata (e.g., Populus	 species, and a defined range in habitat conditions

defined by FGDC 1997.	 or more diagnostic	 deltoides - [Salix	 and physiognomy.

(dominant, differential, amygdaloides] / Salix

indicator, or character) exigua Woodland).

overstory and understoryspecies.

Brohman, R. J.; Bryant, L. D. 2003. Existing Vegetation Classification and Mapping Technical Guide - REVIEW DRAFT. USDA Forest Service, Washington Office, Ecosystem Management

Coordination Staff. p.

FGDC, ed. [Online]. Available: http://www.fgdc.gov/standards/documents/standards/vegetation/vegclass.pdf. accessed 1997

Grossman, D. H.; Faber-Langendoen, D.; Weakley, A. S.; Anderson, M.; Bourgeron, P.; Crawford, R.; Goodin, K.; Landaal, S.; Metzler, K.; Patterson, K.; Pyne, M.; Reid, M.; Sneddon, L. 1998.

International classification of ecological communities: Terrestrial vegetation of the United States Volume I. The National Vegetation Classification System: development, status, and applications. Arlington VA, USA: The Nature Conservancy. 126 p
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 

Chapter 6—Developing the LANDFIRE Vegetation and Biophysical Settings Map Unit Classifications for the LANDFIRE Prototype Project

Table 3—Zone 19 cover type legend. The taxonomic groups are not listed because, where an individual group was

continuous and had a distinct distribution across the landscape, it was made into a unique cover type and listed

under the cover type column.

Cover type

CT# Forest Subclass Group

1201 Cedar Evergreen Needlel

1202	 Douglas-fir	 Evergreen	 Needleleaf

1203 Grand Fir Evergreen Needlel

1204 Hemlock Evergreen Needlel

1205 Lodgepole Pine Evergreen Needlelea

1206 Juniper Evergreen Needlelea

1207 Ponderosa Pine Evergreen Needlelea

1208	 Spruce – Fir	 Evergreen	 Needleleaf

1209 Limber Pine Evergreen Needlelea

1212 White Pine Evergreen Needlele

1401	 Aspen – Birch	 Deciduous	 Broadleaf

1402 Riparian Hardwood Deciduous Broadle

1403 Western Larch Deciduous Needle

1801 Timberline Forest Mixed Needlel

 Site

Shrub Nativity modifier Leaf type Height

2101 Upland Broadleaf Native Upland Broadleaf Dw

 Dwarf Shrubland

2102 Upland Broadleaf Native Upland Broadleaf Mediu

 Medium Shrubland

2103 Upland Broadleaf Native Upland Broadleaf T

 Tall Shrubland

2202 Upland Microphyllous Native Upland Microphyllous Medium

 Medium Shrubland

2211 Dwarf Sage Native Upland Microphyllous Dw

2212	 Shrubby Cinquefoil	 Native	 Upland	 Microphyllous	 Medium

2213 Threetip Sage Native Upland Microphyllous Med

2218 Mountain Big Sage Native Upland Microphyllous Mediu

2219	 Wyoming – Basin	 Native	 Upland	 Microphyllous	 Medium

 Big Sage

2220 Rabbitbrush Native Upland Microphyllous Medium

2222 Greasewood Native Upland Microphyllous Medium

2223 Mountain Mahogany Native Upland Microphyllous Ta

2300 Upland Needlelea

Shrubland Native Upland Needleleaf Medium

2400 Upland Sclerophyllou

 Shrubland Native Upland Sclerophyllous Dwarf

2600 Riparian Broadleaf Native Riparian Broadleaf Tall

 Shrubland

 Site

Herbaceous Modifier Lifeform Growth Form Nativity

3110 Annual Forb Upland Annual Forb Na Nat

3120 Annual Graminoid Upland Annual Gram. Bunch Exot

3130 Perennial Forb Upland Perennial F

3141 Perennial Exotic Bunch Upland Perennial Graminoid 

 Graminoid

3142 Perennial Native Bunch Upland Perennial Graminoid Bu

 Graminoid

3151 Perennial Exotic Upland Perennial Graminoid Rhizoma

 Fhizomatous Graminoid

3152 Perennial Native Upland Perennial Graminoid Rhizomato

 Rhizomatous Graminoid

3200 Wetland Herbaceous Riparian Perennial Gram. Rhizomatous Nativ
136 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2

Chapter 6—Developing the LANDFIRE Vegetation and Biophysical Settings Map Unit Classifications for the LANDFIRE Prototype Project

plots in Zone 19, had so few species listed that it did 

not contain sufficient information to classify plots using 

more than one plant taxa. Usually, the dominant species 

on the plot was named at the species level, but other 

taxonomic levels were sometimes used. A generic level 

(for example, Purshia) was used when it was specific 

enough to identify a CT, and a sub-species level was 

used sometimes when a species level was not detailed 

enough to classify the CT, for example, mountain big 

sagebrush (Artemisia tridentata ssp. vaseyana). Most 

often, however, generic level dominant species were 

not distinctive enough for LANDFIRE CTs in Zone 19. 

For example, when Acer or Abies were described as the 

dominant species on a plot, they were considered too 

taxonomically coarse for LANDFIRE map unit purposes 

and were not used in the classification process.

Many forested plots in Zone 19 were dominated 

typically by one or two taxa, and the classification of 

these species into CTs was relatively simple, as was the 

arrangement of the CTs into a hierarchy. Forest CTs 

were easily identified from plot data as only two plots 

of 6,532 forested plots were not classified to a CT. These 

two plots listed “Pinus” as the dominant tree species, 

which was not sufficient for classification. However, 

most of the forest plot data listed the full species name, 

and the dominant species (or group of dominant species) determined the CT. For example, ponderosa pine, 

Douglas-fir, and lodgepole pine typically form single 

species-dominated stands that occupy vast areas of the 

West. In such instances, the CT was simply the dominant species. In other instances, a few dominant species 

were grouped into a single CT, such as in the case of 

the Timberline Pine CT. These CTs were grouped into 

coarser hierarchical levels by leaf type and then leaf 

phenology. Species mixtures in other areas, such as the 

Sierra Nevada or the eastern U.S., where many species 

could potentially define the dominant species on a plot, 

may require different approaches to classification. The 

Zone 19 CT hierarchy can be found in table 4.

Shrubs presented unique challenges to the development 

of the LANDFIRE mapping classification due to the 

number of taxa, mixes in species composition, and the 

generally broad ecological amplitude of shrub species. 

The process of assigning dominant species to shrub 

plot data was the same as for forested plots; they were 

assigned according to the single taxa with the highest 

cover on the plot. Fifty-two of 3,352 plots (1.5%) remained 

unclassified because the plot data did not describe the 

species sufficiently. As with forest types, the dominant 

types were then grouped into taxonomic and physiognomic categories. However, the criteria for assigning 

the categories to shrub types were different from the 

criteria used to assign categories to forest types, and the 

resulting hierarchy had five levels above the dominant 

species because these different life forms have different 

criteria by which to group them (table 5).

We considered using the NVCS classification criteria 

(Grossman and others 1998) for the shrub classification 

but discovered that certain criteria did not meet LANDFIRE design criteria and guidelines. For example, we 

chose to exclude the xeromorphic leaf type (adapted to 

drought) since it is not always distinguishable (from a 

remote sensing or mapping standpoint) from the microphyllous (small) or sclerophyllous (small and leathery, 

Table 4—Zone 19 forest cover type hierarchy structure and definitions.

Levels Descriptions Categories/examples

Subclass Coarse classes based on leaf phenology. Evergreen, Deciduous, Mixed

Evergreen-Deciduous

Group Classes based general leaf type. Broadleaf, Needleleaf

Site modifier	 Classes based primarily on similar physiognomy,	 Ponderosa Pine, Timberline Pine,

successional ecology, and site characteristics. 

We also considered the “mappability” of similar

vegetation types from other projects and advice

given by remote sensing experts.

Dominant species	 A species in the uppermost vegetation layer that	 Douglas-fir

indicates a recurring plant community as determined

from the plot data.
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Chapter 6—Developing the LANDFIRE Vegetation and Biophysical Settings Map Unit Classifications for the LANDFIRE Prototype Project

drought adapted) leaf types. The terms evergreen and 

deciduous were also discarded due to confusion in applying the terms to specific taxa and the fact that two 

taxa that are similar morphologically may be different in 

leaf phenology. Distinguishing among drought deciduous 

shrubs that typically occur in arid environments, cold 

deciduous shrubs, and evergreen shrubs was problematic because it is difficult to know, based simply on leaf 

morphology, the phenology of a plant, whether a plant 

is evergreen or deciduous, and what causes it to drop 

its leaves.

Herbaceous CTs differed from forest and shrub CTs 

in the vast number of species within a zone and across 

the U.S. and because of the introduction and dominance 

of many exotic species – which made it difficult to use 

a single species to determine a unique CT. Only 30 of 

the 731 (4%) herbaceous plots were not classified to a 

CT. Unlike the forested plots, most of the dominant 

species were grouped in order to result in a reasonable 

number of CTs for LANDFIRE mapping purposes. Herbaceous-dominated plots were grouped into CTs based 

on a small number of criteria that can be consistently 

applied across the country. The hierarchical categories 

include site characteristics, growth characteristics, and 

nativity of the dominant taxa (table 6). The classification 

does not identify systems such as desert grassland, mixed 

grass prairie, tall grass prairie, and short grass prairie; 

however, these types can be delineated using geographic 

and ecological criteria, if necessary. Descriptions of all 

the Zone 19 CTs are found in appendix 6-E.

For the prototype effort, we required that any CT generated for Zone 19 must describe a western community 

at the landscape level; that is, it had to cover at least one 

percent of the western landscape. The amount of cover 

defining a landscape-level community may differ in other 

regions of the U.S. This criterion applied mainly to CTs 

Table 5—Zone 19 shrub cover type hierarchy structure and definitions.

Level Descriptions Categories

Nativity Categories refer to whether the dominant Native, Exotic

species occurred in North America prior to 

western settlement or was introduced to 

North America and is growing naturally in

wild areas without cultivation.

Site modifier	 Cover type level based on site characteristics.	 Facultative Upland, Riparian

Specifically, dominant species may occur in

upland and riparian-wetland areas or are

obligate riparian-wetland.

Leaf type Map units based on leaf type. Broadleaf, Microphyllous, Needleleaf

(scale-leaf), Sclerophyllous, Succulents

Height Broad, mature height categories of the Dwarf (<1 ft)

dominance types. Medium (1-8 ft

Tall (>8 ft)

Taxonomic group Grouping of dominant species based on 

shared taxonomic and morphologic

characteristics. The taxonomic level on

which the grouping is based may occur

at the specific, generic, or family level

depending on the taxonomic level of the

dominance type. We also considered the

“mappability” of similar vegetation types

from other projects and advice given by

remote sensing experts.

Dominant species A species in the uppermost vegetation layer Big sagebrush

that indicates a recurring plant community

as determined from the plot data.
138 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2

Chapter 6—Developing the LANDFIRE Vegetation and Biophysical Settings Map Unit Classifications for the LANDFIRE Prototype Project

that were also dominant species. For example, we could 

have grouped mountain big sage, rabbitbrush, shrubby 

cinquefoil, threetip sage, or Wyoming big sagebrush 

under the Upland Microphyllous Medium Shrublands 

CT. Instead, we considered these dominant species 

individually as CTs because of their abundance across 

the western U.S., their ecological importance, and/or the 

large total number of plots available within each type 

in the Zone 19 reference data. However, we grouped 

bitterbrush, horsebrush, shrubby chenopods, silver sage, 

and snakeweed into the Upland Microphyllous Medium 

Shrublands CT because the number of plots classified to 

the individual dominant species was few, ranging from 

10 to 16 plots each.

If a CT was assigned to less than 20 to 30 plots, the 

CT was either unused or grouped with a similar type, 

if one existed. For example, only one plot (dominated 

by Yucca glauca) fell within the succulent leaf type. 

Due to its minor importance and single plot number, 

succulent was not used as a CT.

The data-driven nature of the bottom-up classification 

approach was the main strength of the LANDFIRE 

classification approach used for Zone 19. This approach 

enabled us to classify all plot data that had detailed 

species lists. However, there are drawbacks to this datadriven approach. The bottom-up approach is completely 

dependent upon reference plot data quality and quantity. 

Cover types that are represented by too few plots within 

a zone were not mapped because the Landsat-based 

mapping process requires a minimum number of plots 

from which to develop training sites (Zhu and others, 

Ch. 8). Moreover, it was difficult to build a hierarchy 

with data from a single zone that would encompass 

all of the CTs that would be encountered across the 

entire United States and allow for incorporation of new 

classes as they were identified. Finally, the data driven 

approach requires that the plot data be available before 

the classification can begin, which may or may not be 

realistic.

Modelers (Long and others, Ch. 9) found the 15 shrub 

types identified in Zone 19 too numerous; as a result, 

even though they were “model-able,” the number of succession classes found in some of the pathways became 

inflated. It was our intention that LANDFIRE vegetation modelers would have more choice in determining 

what scale of CT to use; they could collapse or expand 

the definition of the CT depending upon their needs. It 

was a “scalable” system. However, the modelers did not 

take advantage of the scalability of the CTs primarily 

because of a misunderstanding surrounding this design. 

In general, vegetation modelers (Long and others, Ch. 9) 

found it confusing to use CTs from different hierarchical 

levels throughout the succession pathway creation.

In addition, the LANDFIRE vegetation mapping team 

did not want flexibility in regards to which CTs they 

would map. They requested that we simply give them a 

Table 6—Zone 19 herbaceous cover type hierarchy structure and definitions.

Levels Descriptions Categories

Site modifier	 Map unit level based on site characteristics.	 Upland

Specifically, dominant species may occur in

upland and riparian-wetland areas or are

obligate riparian-wetland.

Life form Map unit based on leaf type and periodicity Annual Forb, Perennial Forb, Annual

of herbaceous plants Graminoid, Perennial Graminoid

Growth form Map unit based on the growing habits of Bunch-forming, Rhizomatous

graminiods (not applicable to forbs).

Nativity Categories refer to whether the dominant Native, Exotic

species occurred in North America prior to

western settlement or was introduced to

North America and is growing naturally in

wild areas without cultivation.

Dominant species A species in the uppermost vegetation layer Cheatgrass

that indicates a recurring plant community as

determined from the plot data.
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Chapter 6—Developing the LANDFIRE Vegetation and Biophysical Settings Map Unit Classifications for the LANDFIRE Prototype Project

CT legend for Zone 19 and they would attempt to map 

those types. They determined that a flexible legend would 

complicate the process greatly. The legend provided was 

considered “mappable.”

As with Zone 16 CTs, the LANDFIRE fuel mapping 

team found the Zone 19 CTs useful. Use of the bottom-up classification approach, in addition to the fact 

that many of the classification criteria were based on 

vegetation characteristics (such as leaf type or growth 

from), facilitated a clear description of the wildland 

fuel characteristics for many of the CTs. Some of the 

graminoid CTs, however, did not adequately distinguish 

between fine and coarse grass sites, which posed the 

same problem encountered in Zone 16 with grass fuel 

models (Keane and others, Ch. 12).

Potential Vegetation Type

We established four hierarchical levels to define the 

potential vegetation types (PVTs) and assigned indicator 

species to each PVT. Species within the PVTs in each 

level share similar site characteristics. Level 1, the top 

level, designates the life form of the PVT as forest, shrubland, or herbaceous. The CTs that would potentially 

dominate the site in the absence of disturbance form 

the next two lower levels of the PVT classification. We 

named level 2 according to either the CT or the species 

that was the most shade-tolerant, such as a “Spruce-Fir 

cover type,” or the species or CT with the narrowest 

ecological amplitude that could occur on a shrub or 

herbaceous site, such as a “Riparian Shrub cover type.” 

Level 3 was named according to the indicator species 

on that site or the geographical setting that differentiates fire regimes of the potential dominant vegetation 

type, an example being “montane.” A fourth level was 

added to discriminate between major seral vegetation 

types of the PVTs because they represented an even finer 

resolution with which to identify unique site conditions. 

Level 4 was named according to the secondary indicator species, CT, or a geographical term such as “north.” 

We identified a PVT by a linking the names in levels 

2 through 4 with forward slashes (/). PVTs could also 

be collapsed back to coarser levels. Finally, a classifier 

key or sequence table was developed to automate the 

linkage of plots in the LFRDB to PVT classes using the 

indicator species (Caratti, Ch. 4).

We calculated the proportions of CTs occurring in 

each PVT using plot data from the LFRDB. The LANDFIRE vegetation mapping team used this information 

to limit the number of specific CTs that could possibly 

occur in each PVT. The probabilities generated from 

reference plot data form the foundation for evaluating 

the probability of CTs existing on sites with specific 

biophysical characteristics. This, in turn, allows a measure of certainty with regard to whether certain CTs can 

occur in specific areas on the map. Incorporating these 

probabilities into the LANDFIRE vegetation mapping 

process distinguishes the LANDFIRE mapping process 

from other broad-scale vegetation mapping efforts. A 

hierarchically organized list of the PVTs developed for 

zones 16 and 19 can be found in tables 7 and 8, respectively. Appendices 6-F and 6-G provide descriptions 

of the PVTs created for zones 16 and 19, respectively. 

Additional information on how the PVT classification 

formed the basis for vegetation modeling may be found 

in Long and others (Ch. 9).

The LANDFIRE fuel mapping team found that the 

number of PVT map classes was adequate to represent 

different site conditions that may influence surface and 

canopy fuel. The scale of the fire behavior fuel models 

and fuel loading models was much coarser than that of 

the PVT classification. To map surface fire behavior 

fuel models and fuel loading models, the LANDFIRE 

fuel mapping team used the upper levels of the PVT 

classification as a stratification to identify unique environmental site conditions. A general description of 

environmental site conditions was adequate for creating 

fire behavior fuel maps because few fuel classes exist 

for the entire United States. However, when mapping 

the Fuel Characteristic Classification System (FCCS) 

national fuelbeds, the LANDFIRE fuel mapping team 

found the levels 2 and 3 PVT classes helpful in determining the crosswalks between PVTs and fuelbeds (Keane 

and others, Ch. 12; Sandberg and others 2001).

Structural Stage

The structural stages (SS) for Zone 16 were composed 

of 16 classes based on a matrix of canopy density classes 

and height classes by life form (table 9). However, as 

the LANDFIRE vegetation modelers combined the 

SS units developed for Zone 16 with CT classes to 

represent seral stages in the succession pathways, they 

found the two height classes per life form insufficient. 

This insufficiency became especially evident when 

the modelers needed to use a mixed CT to represent a 

broad category of vegetation and had to use multiple 

seral stages in multiple pathways; however, the modelers had the use of only two height classes with which to 

describe distinctive seral stages within a CT. To allow 

more flexibility with regard to illustrating the age and 

structure of a CT, we needed a better way to describe 

situations in which the CT was general but potential seral 

stages were more floristically narrow . In response, for 
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Chapter 6—Developing the LANDFIRE Vegetation and Biophysical Settings Map Unit Classifications for the LANDFIRE Prototype Project

Table 7—Zone 16 potential vegetation type partitioned by hierarchical level

PVT# Level 1 Level 2 Level 3 Level 4

1601	 Forest	 Spruce – Fir	 Blue Spruce

1602	 Forest	 Spruce – Fir	 Blue Spruce	 Lodgepole Pine

1603	 Forest	 Spruce – Fir	 Spruce – Fir

1604	 Forest	 Spruce – Fir	 Spruce – Fir	 Lodgepole Pine

1611 Forest Grand Fir White

1612 Forest Grand Fir White Fir Map

1621	 Forest	 Douglas-fir	 Timberline Pine

1622	 Forest	 Douglas-fir	 Douglas-fir

1623	 Forest	 Douglas-fir	 Lodgepole Pine

1631 Forest Timberline 

1632 Forest Ponderosa Pi

1633 Forest Lodgepole 

1634 Forest A

1641	 Forest	 Pinyon – Juniper	 Mountain Big Sagebrush	 North

1642	 Forest	 Pinyon – Juniper	 Mountain Big Sagebrush	 South

1643	 Forest	 Pinyon – Juniper	 Wyoming – Basin Big

 Sagebrush North

1644	 Forest	 Pinyon – Juniper	 Wyoming – Basin Big

 Sagebrush South

1645	 Forest	 Pinyon – Juniper	 Mountain Mahogany

1646	 Forest	 Pinyon – Juniper	 Gambel Oak

1651 Shrubland Blackbr

1652 Shrubland Salt Desert Shru

1653 Herbaceous Warm Herbace

1661 Shrubland Dwarf Sagebr

1662	 Shrubland	 Wyoming – Basin Big

 Sagebrush

1663 Shrubland Mountain 

 Sagebrush

1671 Forest Riparian Hardw

1672 Shrubland Riparian Shru

1673 Herbaceous Wetland Herbace

1680 Herbaceous Alpin

Zone 19, the vegetation modelers were consulted and 

a third height map unit was incorporated for both tree 

and shrub vegetation types (table 10). As a result, the 

LANDFIRE vegetation modelers had more groups with 

which to characterize seral stage, and fewer changes had 

to be made to rectify the SS map with the PVT and CT 

maps. For example, a tree SS would be valid for a forest 

or woodland CT.

The SS threshold breaks deemed adequate for vegetation modeling did not suffice for describing diverse 

wildland fuel characteristics when applied to fuel maps 

in zones 16 and 19. Two classes for vegetation cover, 

while perhaps increasing map accuracy (Vogelman and 

others, Ch. 13), were not sufficient for the derivation of 

fuel characteristics. In addition, the height classes were 

insufficient for portraying surface and canopy fuel. Many 

fire behavior fuel models require specific structural 

thresholds that are often different from those used by the 

LANDFIRE vegetation modelers. For example, whereas 

a five-meter class was sufficient to represent early seral 

forest in the succession models (Long and others, Ch. 

9), this map unit was not fine enough for use in surface 

fuel descriptions where surface fuel height ranges only 

from 0 to 1.8 meters (Keane, Ch. 12).

Recommendations for National 

Implementation _________________

To apply the LANDFIRE mapping approach across 

the United States, we recommend that a vegetation 

working group (VWG) be formed to ensure that the 

LANDFIRE classification systems meet national classification and mapping standards. The VWG should 

consist of members of the LANDFIRE technical teams 
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Chapter 6—Developing the LANDFIRE Vegetation and Biophysical Settings Map Unit Classifications for the LANDFIRE Prototype Project

Table 8—Zone 19 potential vegetation types partitioned by hierarchical levels

PVT# Level 1 Level 2 Level 3 Level 4

1902 Forest Western Redceda

1914	 Forest	 Grand Fir – White Fir

1920	 Forest	 Spruce – Fir	 Montane	 Western Larch

1921	 Forest	 Spruce – Fir	 Montane	 Douglas-fir

1922	 Forest	 Spruce – Fir	 Timberline

1924	 Forest	 Spruce – Fir	 Subalpine

1930	 Forest	 Douglas-fir	 Ponderosa Pine	 Western Larch

1931	 Forest	 Douglas-fir	 Ponderosa Pine	 Douglas-fir

1932	 Forest	 Douglas-fir	 Lodgepole Pine

1934	 Forest	 Douglas-fir	 Timberline Pine

1936	 Forest	 Douglas-fir	 Douglas-fir

1940 Forest Lodgepole Pi

1942 Forest Ponderosa Pi

1944 Forest Timberline Pine Limber 

1946 Forest Timberline Pine Whitebark Pi

1950 Forest Rocky Mountain Junipe

1952 Forest Riparian Hardwoo

1960 Shrubland Riparian Shru

1962 Shrubland Mountain Mahogan

1964 Shrubland Dry Sh

1965 Shrubland Dry Shrub Conife

1970 Shrubland Dwarf Sagebrush Comple

1971 Shrubland Dwarf Sagebrush Complex Coni

1972 Shrubland Mountain Big Sagebrush Comple

1973 Shrubland Mountain Big Sagebrush Complex Coni

1974 Shrubland Threetip Sagebr

1975 Shrubland Threetip Sagebrush Conife

1976	 Shrubland	 Wyoming – Basin Big Sagebrush Complex

1977	 Shrubland	 Wyoming – Basin Big Sagebrush Complex	 Conif

1980 Herbaceous Wetland Herbaceou

1982 Herbaceous Alpin

1984 Herbaceous Fescue Grassla

1985 Herbaceous Fescue Grasslands Conife

1986 Herbaceous Bluebunch Wheatgras

1987 Herbaceous Bluebunch Wheatgrass Conife

Table 9—Zone 16 structural stage list and descriptions

SS# Structural stage name Structural stage description

	11	 Low Cover, Low Height Forest	 Cover ≤ 40% and Height ≤ 10M

	12	 High Cover, Low Height Forest	 Cover > 40% and Height ≤ 10M

 13 High Cover, High Height Forest Cover > 40% and Height

	14	 Low Cover, High Height Forest	 Cover ≤ 40% and Height > 10M

	21	 Low Cover, Low Height Woodland	 Cover ≤ 40% and Height ≤ 10M

	22	 High Cover, Low Height Woodland	 Cover > 40% and Height ≤ 10M

23 High Cover, High Height Woodland Cover > 40% and Height >

	24	 Low Cover, High Height Woodland	 Cover ≤ 40% and Height > 10M

	31	 Low Cover, Low Height Shrubland	 Cover ≤ 40% and Height ≤ 1M

	32	 High Cover, Low Height Shrubland	 Cover > 40% and Height ≤ 1M

 33 High Cover, High Height Shrubland Cover > 40% and Heigh

	34	 Low Cover, High Height Shrubland	 Cover ≤ 40% and Height > 1M

	51	 Low Cover, Low Height Herbaceous	 Cover ≤ 40% and Height ≤ 0.24M

	52	 High Cover, Low Height Herbaceous	 Cover > 40% and Height ≤ 0.24M

53 High Cover, High Height Herbaceous Cover > 40% and Height > 0

	54	 Low Cover, High Height Herbaceous	 Cover ≤ 40% and Height > 0.24M
142 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2

Chapter 6—Developing the LANDFIRE Vegetation and Biophysical Settings Map Unit Classifications for the LANDFIRE Prototype Project

as well as national vegetation classification and mapping 

experts. An informed and involved VWG could have 

addressed and alleviated problems encountered during 

the LANDFIRE Prototype Project. This group should 

oversee all aspects of the biophysical and vegetation map 

classification development and work closely with modeling, vegetation mapping, and wildland fuel mapping 

teams to develop LANDFIRE map legends (ensuring 

standards are followed) for the nation, descriptions of 

the classes in these legends, classification keys linking 

the classes to LFRDB plot data, and cross-walks to 

existing national vegetation classification systems.

We recommend considering the use of an available 

national classification system as a starting point for the 

classification and legend development. New systems 

have been published since the LANDFIRE Prototype 

Project map classification effort, such as the vegetation 

classification developed by NatureServe called “Ecological Systems” (Comer and others 2003), which is an 

existing vegetation classification that uses biophysical 

information to classify types.

While the above recommendation seems to be more 

in concert with the Zone 16 CT classification development approach (a top-down approach initially based 

on other national classifications), plot data should not 

be discounted. Its value was illustrated specifically in 

the Zone 19 CT methodology. Zone 16 CT classes were 

refined from plot data, whereas Zone 19 CT classes were 

developed using plot data. Although existing reference 

data do not support Zone 19’s bottom-up approach 

for the national implementation of LANDFIRE, plot 

information from the reference database should play 

a significant role in the creation, improvement, and 

refinement of the LANDFIRE National’s biophysical 

and vegetation map units. Map units should be assigned 

to the plot data, and an analysis of the results should 

lead to refinements of the classification. In addition, 

these national CT, PVT, and SS map legends should 

be completed at the start of the national effort and 

should then be refined as the national effort moves to 

individual zones in different regions.

Cover types that have been assigned to plot data (via 

either approach) form the foundation for the training 

database that is critical to most of the LANDFIRE 

products. It is imperative that an adequate amount of 

reliable reference data be acquired in a timely fashion 

for CT refinement before the mapping of each new zone 

is initiated. Cooperative arrangements should be in place 

at the beginning of the national effort so that the data 

are available for use within a practical time frame. A 

plan should also exist for the collection of new data in 

areas lacking sufficient amounts.

In addition, as CTs are defined for each zone, it is 

important to ensure that the criteria for distinguishing 

CTs are applicable across the United States and that the 

developers of the CT classification apply these criteria 

in all zones. This will minimize artificial boundaries 

in the maps resulting from inconsistent classification 

efforts.

Table 10—Zone 19 structural stage list and descriptions

SS# Structural stage name Structural stage description

	10	 Low Cover, Low Height Trees 	 Trees - Cover ≤ 40% and Height ≤ 5M

	11	 Low Cover, Low - Mod Height Trees	 Trees - Cover ≤ 40% and Height ≤ 10M

	12	 High Cover, Low - Mod Height Trees	 Trees - Cover > 40% and Height ≤ 10M

	13	 Low Cover, Mod Height Trees	 Trees - Cover ≤ 40% and 5M < Height ≤ 10M

	14	 High Cover, Mod Height Trees	 Trees - Cover > 40% and 5M < Height ≤ 10M

	15	 Low Cover, High Height Trees	 Trees - Cover ≤ 40% and Height > 10M

 16 High Cover, High Height Trees Trees - Cover > 40% and Height >

	21	 Low Cover, Low Height Shrubs	 Shrubs - Cover ≤ 40% and Height ≤ 0.2

	22	 High Cover, Low Height Shrubs	 Shrubs - Cover > 40% and Height ≤ 0.2

	23	 Low Cover, Mod Height Shrubs	 Shrubs - Cover ≤ 40% and 0.24M < Height ≤ 1M

	24	 High Cover, Mod Height Shrubs	 Shrubs - Cover > 40% and 0.24M < Height ≤ 1M

	25	 Low Cover, High Height Shrubs	 Shrubs - Cover ≤ 40% and Height > 

26 High Cover, High Height Shrubs Shrubs - Cover > 40% and Height > 

	31	 Low Cover, Low Height Herbs	 Herbs - Cover ≤ 40% and Height ≤ 0.2

	32	 High Cover, Low Height Herbs	 Herbs - Cover > 40% and Height ≤ 0.2

	35	 Low Cover, High Height Herbs	 Herbs - Cover ≤ 40% and Height > 0.2

 36 High Cover, High Height Herbs Herb  - Cover > 40% and Height > 0
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Chapter 6—Developing the LANDFIRE Vegetation and Biophysical Settings Map Unit Classifications for the LANDFIRE Prototype Project

The CT classification should be developed in concert 

with the PVT and SS classifications. Developers should 

work together to ensure that all classes are ecologically 

consistent between classification systems. We recommend that the developers be the same group for all 

the biophysical and vegetation map classifications. A 

vegetation working group should be the arbitrator of all 

LANDFIRE classification systems to ensure consistency. 

In the LANDFIRE Prototype, the PVT and CT map 

legends had to be adjusted even after the maps were 

created because multiple versions of each classification 

were available and used, resulting in inconsistency between legends. For example, at one point, there was an 

“Herbaceous” PVT, but there was not an “Herbaceous” 

CT. These classifications must be consistent from the 

beginning so that the maps made from them correspond 

ecologically. In addition, LANDSUMv4 (Pratt and others, Ch. 10) requires that the maps be consistent with 

the succession pathway models described in Long and 

others, Ch. 9.

Throughout the development of the LANDFIRE vegetation classifications, we received feedback regarding 

our use of certain terminology and definitions. We found 

that the potential vegetation concept is not uniformly 

accepted among vegetation ecologists, especially range 

scientists. Alternative terminology, such as potential 

natural vegetation group (PNVG), is also not well 

received by some specialists. For national implementation, we recommend that the term biophysical setting

(BpS) be used instead of PVT because this term applies 

to a wide range of environmental conditions in which 

vegetation occurs and does not imply an assumption of 

linear succession processes or the integration (or not) 

of disturbance into the classification system. We also 

recommend that the term cover type (CT) be changed to 

existing vegetation (EV) to more clearly indicate what 

is being represented.

Another problem that affected the PVT development particularly was the numerous personnel changes 

throughout the development process. The instability 

of the personnel resource available to the LANDFIRE 

Prototype Project resulted in inconsistent and sometimes 

conflicting approaches and insufficient documentation. 

For example, some ecologists tended to split biophysical 

characteristics, whereas others tended to lump them; the 

PVT classification therefore went through many phases 

of adjustment and revision. A clearly documented and 

detailed explanation of the purpose of the PVT classification would help developers understand their objectives, 

and documented procedures would help developers avoid 

conflicts in methodologies. Again, the VWG should 

oversee this effort throughout the implementation of 

LANDFIRE National to ensure standards are followed as 

PVTs are classified within and across mapping zones.

As mentioned above, the scalable nature of the PVT 

classification allowed flexibility in representing PVTs, 

but this characteristic was not utilized in the prototype 

effort. By choosing not to employ the scalable nature of 

the classification (not grouping to broader and thus fewer 

classes), the LANDFIRE vegetation modelers ended 

up with succession models that were too numerous and 

complicated, with over 40 CTs in the succession pathway 

for many PVTs in both prototype mapping zones (Long 

and others, Ch. 9). We do not recommend this level of 

complexity in vegetation modeling for LANDFIRE 

National. Various levels of the PVT classes could be 

used to represent different scales and interpretations of 

potential vegetation. For example, level 1 could be used 

to represent major environmental settings, as indicated 

by life form. In another example, level 3 – which differentiates between the historical fire regimes of PVTs 

– could be used as a link to potential natural vegetation 

types, which include natural disturbance in their definitions and descriptions. We recommend that vegetation 

modelers use coarser scale PVTs (and CTs) to simplify 

the models.

Conclusion_____________________

To meet the needs of vegetation and fuel mappers, we 

developed three ecologically consistent vegetation and 

biophysical map unit classifications that were identifiable, scaleable, mappable, and model-able. We found 

that successful implementation of such an endeavor 

requires detailed knowledge of many vegetation systems 

and their succession, fuel, and fire dynamics; awareness 

of differing scientific approaches to vegetation classification; recognition and understanding of the varying 

user needs; and recognition and understanding of the 

varying needs relating to different areas of the country. 

We emphasize the importance of creating a vegetation 

working group for the implementation of LANDFIRE 

National or any similar large scale effort. Lastly, centralized coordination and oversight of the development 

of these map unit classifications is crucial to promote 

the efficiency, consistency, and high scientific standards 

required for this type of project.

For further project information, please visit the LANDFIRE website at www.landfire.gov.
144 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 

Chapter 6—Developing the LANDFIRE Vegetation and Biophysical Settings Map Unit Classifications for the LANDFIRE Prototype Project

The Authors ____________________

Jennifer L. Long is a Research Scientist with Systems 

for Environmental Management working with the USDA 

Forest Service, Rocky Mountain Research Station, Missoula Fire Sciences Laboratory (MFSL). She received a 

B.A degree in Environmental Studies/Geography from 

the University of California, Los Angeles (1994) and 

an M.S. degree in Natural Resources with a Forestry 

option from Humboldt State University (2000). Long’s 

research has focused on fuel classification, fuel mapping, and database development. She began her career 

by serving three seasons as a wildland fire fighter for 

the Forest Service and as a tree researcher for Simpson 

Timber Company. She then moved on to the Fire and 

Environmental Research Applications (FERA) Team 

at the Pacific Northwest (PNW) Research Station to 

work on the Fuel Characteristic Classification System 

(FCCS). She currently works on the LANDFIRE Project 

at MFSL where her responsibilities include the design of 

protocols to classify and map fuel and fire behavior fuel 

models based on vegetation and biophysical variables, 

the development of a national vegetation mapping classification, and the linkage of the FCCS to LANDFIRE 

fuel maps.

Melanie Miller is a Fire Ecologist with the USDOI 

Bureau of Land Management, Office of Fire and Aviation, Boise, Idaho and is stationed at the USDA Forest 

Service, Rocky Mountain Research Station, Missoula 

Fire Sciences Laboratory (MFSL). Since 2001, Miller 

has worked on vegetation mapping for the LANDFIRE 

Prototype Project, co-developed a model that qualitatively predicts understory plant response to fire, and 

recently took responsibility as Steering Group Chair for 

the Third International Fire Ecology and Management 

Congress. Her past work for the Bureau of Land Management includes the development and implementation 

of fire management planning procedures for the western U. S. and Alaska; representation of fire and smoke 

management interests for the Interior Columbia Basin 

Ecosystem Management Project; development of prescribed fire monitoring guidance; participation in course 

development for the national interagency prescribed fire 

curriculum; Steering Group member for Rx510: Applied 

Fire Effects; and the writing and co-authoring of technical 

papers on subjects that include mechanics of vegetation 

recovery after fire, the Fire Effects Information System, 

and fuel moisture sampling. Miller earned a B.S. honors 

degree in Physical Geography from the University of 

Calgary in 1972 and an M.S. degree in Forest Fire Science from the University of Montana in 1976.

James P. Menakis is a Forester with the USDA Forest 

Service, Rocky Mountain Research Station, Missoula 

Fire Sciences Laboratory (MFSL). Since 1990, Menakis has worked on various research projects related to 

fire ecology at the community and landscape levels 

for the Fire Ecology and Fuels Project. Currently, he 

is working on the Rapid Assessment, which is part of 

the LANDFIRE Project. Menakis has recently worked 

on mapping historical natural fire regimes, fire regime 

condition classes (FRCC), wildland fire risk to flammable structures for the conterminous United States, 

and relative FRCC for the western United States. Before 

that, he was the GIS Coordinator of the Landscape 

Ecology Team for the Interior Columbia River Basin 

Scientific Assessment Project and was involved with 

mapping FARSITE layers for the Gila Wilderness and 

the Selway-Bitterroot Wilderness. Menakis earned his 

B.S. degree in Forestry in 1985 and his M.S. degree in 

Environmental Studies in 1994, both from the University 

of Montana, Missoula.

Robert E. Keane is a Research Ecologist with the 

USDA Forest Service, Rocky Mountain Research Station, Missoula Fire Sciences Laboratory (MFSL). Since 

1985, Keane has developed various ecological computer 

models for the Fire Effects Project for research and management applications. His most recent research includes 

the development of a first-order fire effects model, the 

construction of mechanistic ecosystem process models 

that integrate fire behavior and fire effects into succession simulation, the restoration of whitebark pine in 

the Northern Rocky Mountains, the spatial simulation 

of successional communities on landscapes using GIS 

and satellite imagery, and the mapping of fuels for fire 

behavior prediction. He received his B.S. degree in Forest Engineering in 1978 from the University of Maine, 

Orono, his M.S. degree in Forest Ecology in 1985 from 

the University of Montana, and his Ph.D. degree in Forest Ecology in 1994 from the University of Idaho.

Acknowledgments_______________

We thank John Caratti of Systems for Environmental 

Management and Jim Vogelmann of Science Applications International Corporation for their direction on 

map unit classification development. The support of 

Don Long (Missoula Fire Sciences Laboratory), Karen 

Short (Systems for Environmental Management), and 

Mary Manning (U.S. Forest Service – Northern Region) 

has also been invaluable to this map unit classification 

effort. Finally, we would like to recognize Christine 

Frame of Systems for Environmental Management for 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 

Chapter 6—Developing the LANDFIRE Vegetation and Biophysical Settings Map Unit Classifications for the LANDFIRE Prototype Project

her effort in organizing this document (and the authors) 

and her thorough editorial review. Thanks to all for their 

thoughtfulness and time.

References _____________________

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succession within a forest habitat type - an approach designed 

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1998. Mapping historic fire regimes for the western United States: 

integrating remote sensing and biophysical data. In: The seventh 

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proceedings; 1998 April 6-9; Bethesda, Maryland: American 

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mapping protocols used in the GAP Analysis Program - Final Report. Biological Resources Division, U. S. Geological Survey.

Minore, D. 1979. Comparative autecological characteristics of 

northwestern tree species: a literature review. Gen. Tech. Rep. 

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northern Rocky Mountains. In: Successional research and 

environmental pollutant-monitoring associated with biosphere 

reserves. Proceedings Second US-USSR Symposium on Biosphere 

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habitat types of Montana. Gen. Tech. Rep. INT-34. Ogden, UT: 

U.S. Department of Agriculture, Forest Service, Intermountain 

Forest and Range Experiment Station. 174 p.

Pfister, R. D.; Arno, S. F. 1980. Classifying forest habitat types based 

on potential climax vegetation. Forest Science. 26 (1): 52-70.

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components in the Interior Columbia River Basin and portions 

of the Klamath and Great Basins: Volume II. Gen. Tech. Rep. 

PNW-GTR-405. Portland, OR: U.S. Department of Agriculture, 

Forest Service, Pacific Northwest Research Station. 1055 p.

Reid, M.; Comer, P.; Barrett, H.; Caicco, S.; Crawford, R.; Jean, C.; 

Jones, G.; Kagan, J.; Karl, M.; Kittel, G.; Lyon, P.; Manning, M. E.; 

Peterson, E.; Rosentreter, R.; Rust, S.; Tart, D.; Williams, C. K.; 

Winward, A. 2002. International classification of ecological communities: Terrrestrial vegetation of the United States. Sagebrush 

vegetation of the western United States. Final Report for USGS 

Forest and Rangeland Ecosystems Science Center, Boise Idaho. 

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and fire regimes using remote sensing, ecosystem simulation and 

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Denver, CO: Society of Range Management. 151 p.

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on native rangelands of the northern Great Plains. In: Conserving 

Biodiversity on Native Rangelands: Symposium Proceedings. 

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146 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2

Chapter 6—Developing the LANDFIRE Vegetation and Biophysical Settings Map Unit Classifications for the LANDFIRE Prototype Project

Appendix 6-A—Biophysical and vegetation map classification coding 

protocol _________________________________________________________

The purpose of the biophysical and vegetation map classification coding protocol was to create cover type (CT), 

potential vegetation type (PVT), and structural stage (SS) codes that allowed for informed interpretation of the 

vegetation map units. In other words, users of the classification would have access to information about the specific 

CT, PVT, or SS simply by referencing the code definition tables included below.

Cover Type

Zone 16: Central Utah Highlands

The cover type code for Zone 16 is a four-digit, two-level code representing the life form and life form subclass 

of the cover type. The life form is the first digit (app. 6-A: table 1). Note: Here, life form represents the existing

vegetation life form, not the potential.

App. 6-A: Table 1—Zone 16 life form (1-digi

Code Life form

 1 Forest (trees domina

2 Woodland (trees dominate)

 3 Shrubland (shrubs domina

 4 Herbaceous (herbs domina

The second digit (app. 6-A: tables 2 and 3) is the life form subclass (a delineation of leaf phenology and morphology). The herbaceous life form subclass is different from the shrub and forest subclass because leaf phenology and 

morphology in woody species (shrubs and trees) are described with different terms than those used for herbaceous 

or non-woody species. The final two digits represent the dominant species or group of species that indicates that 

type and are found in table 1 of Long and others, Ch. 6. For example, a cover type code of “3101” indicates that it 

is a shrub life form, broadleaf evergreen life form subclass, and the dominant species is mountain big sagebrush. A 

cover type of Warm Season Perennial Grasslands with code 4101 has an herbaceous life form, perennial graminoid 

life form subclass, and the dominant species group is warm season grasses.

App. 6-A: Table 2—Zone 16 life form subclass (1-digit) (excluding herbaceou

Code Life form subclass

 1 Broadleaf evergr

2 Needleleaf evergreen

 3 Mixed broadleaf-needleleaf evergr

 4 Broadleaf decidu

5 Needleleaf deciduous

6 Mixed broadleaf-needleleaf deciduous

7 Broadleaf mixed evergreen-deciduous

8 Needleleaf mixed evergreen-deciduous

9 Mixed broadleaf-needleleaf mixed evergreen-deciduous

App. 6-A: Table 3—Zone 16 herbaceous life form subclass (1-digi

Code Life form subclass

 1 Perennial gramino

2 Perennial forbs

 3 Perennial mixed graminoids-fo

 4 Annual gramin

5 Annual forb

6 Annual mixed graminoids-forbs

7 Mixed perennial-annual graminoid

8 Mixed perennial-annual forb

9 Mixed perennial-annual mixed graminoids-forbs
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 

Chapter 6—Developing the LANDFIRE Vegetation and Biophysical Settings Map Unit Classifications for the LANDFIRE Prototype Project

Zone 19: Northern Rockies

In Zone 19, the cover type code is a 4-digit code representing the life form and the hierarchical mapping level of 

the cover type, which are differentiated by criteria based on this life form. However, in all life forms, the first digit 

(app. 6-A: table 4) represents the life form of the current or existing vegetation. Note that these life form categories 

are different from those used in Zone 16.

App. 6-A: Table 4—Zone 19 life form (1-digi

Code Life form

 1 Forest (trees domina

2 Shrubland (shrubs dominate)

 3 Herbaceous (herbs dominat

In the forest life form, the second digit represents the life form subclass (app. 6-A: table 5) and the third and fourth 

digits represent the dominant species or species groups found within the preceding life form and subclass (see table 

3, Long and others, Ch. 6). For example, a forest cover type code of “1402” represents the Riparian Hardwood cover 

type, where the life form is forest, the life form subclass is broadleaf deciduous, and the dominant species group is 

riparian hardwoods.

App. 6-A: Table 5—Zone 16 forest life form subclass (1-digi

Code Life form subclass

 1 Broadleaf evergr

2 Needleleaf evergreen

 3 Mixed broadleaf-needleleaf evergr

 4 Broadleaf decidu

5 Needleleaf deciduous

6 Mixed broadleaf-needleleaf deciduous

7 Broadleaf mixed evergreen-deciduous

8 Needleleaf mixed evergreen-deciduous

9 Mixed broadleaf-needleleaf mixed evergreen-deciduous

In the shrub life form, the second digit represents the life form subclass (app. 6-A: table 6), which is categorized 

differently than the forest life form subclass. The third and fourth digits (app. 6-A: table 7) represent either the 

height class of the cover type (01-03) or the dominant species groups (beginning with 11). For example, a shrub 

cover type with code “2202” indicates that it is an Upland Microphyllous Medium [Height] Shrubland cover type 

and a cover type with code 2213 is the Threetip Sage cover type where the life form is shrub, the life form subclass 

is facultative-upland microphyllous, and the dominant species group is threetip sagebrush.

App. 6-A: Table 6—Zone 16 shrub life form

subclass (1-digit

Code Life form-subclass

 1 Facultative-upland broadl

2 Facultative-upland microphyllous

 3 Facultative-upland needlel

 4 Facultative-upland sclerophyll

5 Facultative-upland succulent

6 Riparian broadleaf

App. 6-A: Table 7—Zone 16

shrub height class (1-digit

Code Height class

01 Dwa

02 Medium

03 Ta
148 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2

Chapter 6—Developing the LANDFIRE Vegetation and Biophysical Settings Map Unit Classifications for the LANDFIRE Prototype Project

Herbaceous cover types have been coded differently from shrub and forest starting at the second digit, which represents the physiognomy (app. 6-A: table 8), not the life form subclass. The third digit (app. 6-A: table 9) represents 

the life history and growth form. The final and fourth digit (app. 6-A: table 10) represents the nativity of the cover 

type. For example, 3142 indicates that the cover type is herbaceous, facultative-upland, perennial bunch graminoid 

and native. It is called a Perennial Native Bunch Graminoid cover type.

App. 6-A: Table 8—Zone 16 herbaceous

life form subclass (1-digit

Code Life form subclass

 1 Facultative-upla

2 Riparian

App. 6-A: Table 9—Zone 16 life history and

growth form (1-digit

Code Life history and growth form

 1 Annual f

2 Annual graminoid

 3 Perennial f

 4 Perennial bunch grammin

5 Perennial rhizomatous gramminoid

App. 6-A: Table 10—Zone 

16 herbaceous nativity class

(1-digit

Code Nativity class

 1 Exo

2 Native

Potential Vegetation Type __________________________________________

The PVT code is a four-digit, two-level code which includes the zone number in the first and second digits and 

the potentially dominant species or species group in the last two digits (app. 6-A: tables 11 and 12). Exhaustive lists 

of the codes may be found in tables 7 and 8, Long and others, Ch. 6.

App. 6-A: Table 11—Zone 16 potentially dominant species (2-digit

Code Potential species

01-39 Forest-dominated life fo

40-49 Woodland-dominated life fo

50-69 Upland shrub- or herbaceous-dominated life form (non-alpine)

70-79 Riparian shrub- or herbaceous-dominated life form (non-alpine)

80-89 Alpine herbaceous-dominated life form 

App. 6-A: Table 12—Zone 19 potentially

dominant species (2-digit)

Code Potential species

01-59 Tree-dominated life for

60-79 Shrub-dominated life form

80-89 Herbaceous-dominated life form 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 

Chapter 6—Developing the LANDFIRE Vegetation and Biophysical Settings Map Unit Classifications for the LANDFIRE Prototype Project

Structural Stage___________________________________________________

The structural stage codes used in the LANDFIRE Prototype Project are quite simple because they are a two 

digit, two-level numeric code. The first digit is the life form (app. 6-A: tables 13 and 4). The second digit describes 

the cover and height for all life forms in Zone 16. Appendix 6-A: tables 14 and 15 describe the Zone 19 cover and 

height classes by life form.

App. 6-A: Table 13—Zone 16 structural stage life form (1-digi

Code Life form

 1 Forest (trees domina

2 Woodland (trees dominate)

 3 Shrubland (shrubs domina

5 Herbaceous (herbs dominate)

App. 6-A: Table 14—Zone 16

structural stage (1-digit

Code Structural stage

 1 Low Cover, Low Hei

2 High Cover Low Height

 3 High Cover, High Hei

 4 Low Cover, High Hei

App. 6-A: Table 15—Zone 19 structural stage (1-digi

Code Structural stage

0 Low Cover, Low Height Trees

 1 Low Cover, Low Height Shrub and Herbaceous (Low, Moderate Tre

2 High Cover, Low Height Shrub and Herbaceous (High, Low-Moderate Trees)

 3 Low Cover, Moderate Height Trees and Shr

 4 High Cover, Moderate Height Trees and Shr

5 Low Cover, High Height Trees, Shrubs, and Herbaceous

6 High Cover, High Height Trees, Shrubs, and Herbaceous
150 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 20

Chapter 6—Developing the LANDFIRE Vegetation and Biophysical Settings Map Unit Classifications for the LANDFIRE Prototype Project Appendix 6-B—Cover types for the western U.S. _____________________________________________ CT# Cover type Description 1401 Riparian Hardwooda Riparian hardwood forests in the western U.S. can be dominated by many species of deciduous trees, including cot- tonwoods (Populus spp.), which are the most wide ranging group. Other riparian hardwoods include green ash (Fraxi- nus pennsylvanica), and American elm (Ulmus americana) on the Great Plains; in California, California sycamore (P. racemosa), and California walnut (Juglans californica); Arizona sycamore (Platanus wrightii) in the southwest; netleaf hackberry (Celtis laevigata var. reticulata) in the southwest, intermountain west and Great Plains; and white alder (Alnus rhombifolia) and Oregon ash (Fraxinus latifolia) in the Pacific Northwest. 1405 Aspen -- Bircha Extensive areas of upland forests that occur in the West and are dominated by trembling aspen (Populus tremuloides), the most widely distributed North American tree species (Burns and Honkala 1990). 1406	 Pacific Deciduous Forest	 Pure stands dominated by species such as red alder (Alnus rubra) and bigleaf maple (Acer macrophyllum) can occur [Other Broadleaf] in the Northwest, with red alder ranging from central California to southeast Alaska, and bigleaf maple extending only as far north as southwestern British Columbia. These species can co-occur and are often found in mixtures with other conifers and hardwoods (Minore 1979; Harrington 1990; Minore and Zasada 1990). 1101	 Pacific Broadleaf Evergreen Forest 	 Stands of California bay (Umbellularia californica) that are found in California and southwest Oregon (Stein 1990) or [Other Broadleaf Evergreen] stands of tanoak (Lithocarpus densiflorus) (Tappeiner, McDonald et al. 1990). Both species commonly occur in mixes with ot

1501 Larch Stands dominated by western larch (Larix occidentalis) occur in the northern Rockies and east slopes of the Cascade

One of two species of deciduous conifers in the western U.S., western larch is most commonly associated with Douglas-fir, although it can grow with many other species of conifers (Schmidt and Shearer 1990).

1201 Ponderosa Pinea Stands dominated by interior ponderosa pine (Pinus ponderosa var. scopulorum) are found in the interior West a

Rocky Mountains, and Arizona pine (Pinus ponderosa var. arizonica) is found in the Southwestern U.S.

1218	 Pacific Ponderosa Pine	 Mixes of many coniferous species can dominate forests in middle to lower elevations in the Sierra Nevada Range, with

Pacific ponderosa pine (Pinus ponderosa var. pacifica) being “the biological thread that holds the phases of this forest 

together” (Barbour and Minnich 2000). Other species that can dominate a site include sugar pine (Pinus lambertiana), 

incense–cedar (Calocedrus decurrens), Jeffrey pine (Pinus jeffreyi), white fir, and Douglas-fir (Barbour and Minnich

2000).

1202 Foothill Pines The California endemics, Coulter pine (Pinus coulteri), and knobcone pine (Pinus attenuata) of California and Oregon

have some or a high degree of cone serotiny in various populations. Where they occur in pure stands, these species occupy a transition zone between chaparral and coniferous forest or woodland at higher elevations (Howard 1992; Cope1993

1203 Western White Pine Seral stands dominated by western white pine occur in the northern Rockies. It is a minor species in other forest communities in this area and occurs in mixed stands in the Pacific Northwest and Sierra Nevadas (Boyd 198

1204 Lodgepole Pinea Lodgepole pine has four varieties, two of which are assigned to the lodgepole pine map type: Sierra lodgepole pi

(Pinus contorta var. murrayana) and Rocky Mountain lodgepole pine (P. contorta var. latifolia). Extensive pure stands 

occur, as well as in association with many other species (Lotan and Critchfield 1990).
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 

Chapter 6—Developing the LANDFIRE Vegetation and Biophysical Settings Map Unit Classifications for the LANDFIRE Prototype Project

CT# Cover type Description

1205	 Douglas-fira Douglas-fir can be a dominant in large areas of forest in the West, occurring in pure stands, although frequently with a

mix of many other species. Both coast Douglas-fir (Pseudotsuga menziesii var. menziesii) and Rocky Mountain Douglas-fir (P. m. var. glauca) are included in the Douglas-fir cover type.

1206 Grand Fir -- White Fira This type consists of stands dominated by grand fir (Abies grandis) or Rocky Mountain white-fir (Abies concolor var.

concolor), a species and a variety of true fir that occur in somewhat similar habitats in the Interior West.

1207	 Pacific Silver Fir -- Noble fir Stands dominated by the conifers, Pacific silver fir (Abies amabilis) or noble fir (Abies procera), indicate a moist coastal 

site in the Pacific Northwest. These species often co-occur with other species of conifers, most consistently western

hemlock (Tsuga heterophylla), and mountain hemlock (Tsuga mertensiana) (Franklin 1980)

1219	 Red Fir	 Red fir (Abies magnifica and A. magnifica var. shastensis) is a climax, high elevation species that can occur in pure 

stands in the Sierra Nevadas, southern Cascades, and Coast Ranges, although it also intergrades with other conifers (Laacke 1990a)

1220	 California White Fir	 Stands of this type are indicated by California white fir (Abies concolor var. lowiana) and occur in forested stands of 

California and Oregon. It commonly co-occurs in mixture with other conifer species typical of drier sites in the Sierra, Klamath, and Siskiyou Mountains, such as ponderosa pine, red fir, Jeffery pine, sugar pine and Douglas-fir. White fir iscommonly a later seral species (Laacke 1990b)

1209 Western Hemlock Pure or mixed stands dominated by western hemlock (Tsuga heterophylla) occur in mild, humid climatic zones of the

Pacific Northwest, coastal Alaska, and the northern Rockies. It is a major or minor component of at least 20 different forest types (Packee 1990)

1210	 Mountain Hemlock	 In cold, snowy, high altitude forests of the Pacific Northwest, coastal Alaska, the Sierra Nevadas, and the northern

Rocky Mountains, mountain hemlock (Tsuga mertensiana) can be the sole dominant or mixed with other high altitude 

conifers (Means 1990)

1211 Spruce -- Fira In the western U.S., the spruce-fir complex is a high elevation or montane forest community dominated by Engel

spruce (Picea engelmannii) and/or subalpine fir (Abies lasiocarpa). Other species assigned to this complex include blue spruce (Picea pungens), and Brewer’s spruce (Picea breweriana), a species of the mountains of northwest California 

and southwest Oregon (Thornburgh (1990b)

1212 Sitka Spruce Pure stands of Sitka spruce (Picea sitchensis) occur in a narrow band along the Pacific coast, from central California 

coastal Alaska (Harris 1980), although it is often associated with western hemlock (Tsuga heterophlla)

1213 Cedar Western redcedar (Thuja plicata) is the most common western species in this Pacific Northwest type, commonly occurring in moist or wet locations, frequently in association with other moist site conifers (Minore 1990). Alaska c

(Chamaecyparis nootkatensis) can dominate the canopy in Pacific Northwest forests, although it often occurs with many

other tree species (Harris, 1990.). Port Orford cedar (Chamaecyparis lawsoniana) usually occurs in mixed stands in a

small area of coastal southwest Oregon and adjacent northern California (Zobel 1990)

Appendix 6-B—(Continued)
152 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 20

Chapter 6—Developing the LANDFIRE Vegetation and Biophysical Settings Map Unit Classifications for the LANDFIRE Prototype Project CT# Cover type Description

1215 Redwood Forested stands dominated by redwood (Sequoia sempervirens) are found in northern California and extreme southwestern Oregon, occurring in pure stands and in mixtures with other specie

1216	 Sequoia	 Giant sequoia (Sequoiadendron giganteum) occurs in a very limited distribution in the central and southern Sierra Nevada Mountains where it dominates stands where other coniferous species are commonly present.1217 Cypress The cypress cover type consists of stands dominated by one of three species of cypress, generally small trees wit

serotinous cones. Arizona cypress (Cupressus arizonica) (Sullivan 1993) sometimes occurs in pure stands or mixewith other species in riparian and some upland habitats of the southwest. MacNab’s cypress (Cupressus macnabiana) 

(Esser 1994a) and Sargent’s cypress (Cupressus sargentii) both occur in California (Esser 1994b), occurring in p

stands or mixed with at least one other conifer species.

1801 Timberline Pinesa This type consists of species of pines dominating forests and woodlands at upper and lower timberlines, including t

high elevation species foxtail pine (Pinus balfouriana) of California, bristlecone pine (Pinus aristata), Great Basin bristlecone pine (Pinus longaeva), whitebark pine (Pinus albicaulis) and limber pine (Pinus flexilis), a species that can appear at both upper and lower timberline. Alpine larch (Larix lyallii), a deciduous conifer, is also included because it mixes with some of these species in the northern Rockies.

2401 Deciduous Oak These woodlands are open stands dominated by a species of deciduous oak, including the California endemics, bl

oak (Quercus douglasii) or California white oak (Quercus lobata) (Howard 1992b). These species co-occur at lower elevations (McDonald 1990b) Other species of deciduous oak that may each dominate forest or woodland stands includbur oak (Quercus macrocarpa) in the northern plains (Johnson 1990); California black oak (Quercus kelloggii), endemic

to California (McDonald 1990a); and Oregon white oak (Quercus garryana), which ranges from southwestern British

Columbia to central California (Stein 1990a)

2101 Evergreen Oak Evergreen oak woodlands are open stands of generally small stature trees, occurring in the southwest and in Californi

Coast live oak (Quercus agrifolia), Engelmann’s oak (Quercus engelmannii), and interior live oak (Quercus wislizenii) 

are common California woodland species (Finch and McCleery 1990; Tirmenstein 1989). Canyon live oak (Quercu

chrysolepis) is a species of evergreen-leaved oak that dominates forest and woodland stands in Oregon, California, and 

Arizona (Thornburgh 1990a). In the Southwest, these woodlands may be dominated by Emory oak (Quercus emoryi)

(Pavek 1994a), gray oak (Quercus grisea) (Pavek 1994b), Mexican blue oak (Quercus olongifolia) (Pavek 1993

Arizona white oak (Quercus arizonica) (Pavek 1994c). Some of these southwestern woodland species can co-occur, a

well as grow with conifers (Pavek 1994)

2201 Pinyon -- Junipera The pinyon-juniper type contains open stands dominated by a species of tree-like juniper and a species of pinyon pine

with the pinyon at least 20% of the total tree cover. Juniper species include alligator juniper (Juniperus deppeana), California juniper (J. californica), oneseed juniper (J. monosperma), Pinchot’s juniper (J. pinchottii), redberry juniper (J. coahuilensis), Rocky Mountain juniper (J. scopulorum), Utah juniper (J. osteosperma), and western juniper (J. occidentalis). Pinyon species can include singleleaf pinyon (Pinus monophylla), two-needle pinyon (Pinus edulis), Mexican pinyon (Pinus cembroides), papershell pinyon (Pinus remota/cembroides), and Parry pinyon (Pinus quadrifolia).

2202 Junipera Open stands dominated by juniper, with pinyon less than 20% of the total tree cover, or absent. Juniper species are 

those listed for pinyon-juniper.

Appendix 6-B—(Continued)
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 

Chapter 6—Developing the LANDFIRE Vegetation and Biophysical Settings Map Unit Classifications for the LANDFIRE Prototype Project

CT# Cover type Description

3704 Mountain Deciduous Shruba These associations of medium to tall deciduous shrubs are usually in mesic upland sites, generally at lower elevatio

than continuous forest. In the eastern Great Basin, species can include serviceberry (Amelanchier utahensis), gambel oak (Quercus gambelii), chokecherry (Prunus virginiana), and snowberry (Symphoricarpos oreophilus).

3402 Riparian Shruba Riparian zones dominated by tall and medium sized shrubs, with one to several species of willows, occur throughout th

West. In the Intermountain West, riparian willows include Bebb willow (Salix bebbiana), Booth willow (S. boothii), Drummond willow (S. drummondiana), coyote willow (S. exigua), Geyer willow (S. geyeriana), and yellow willow (S. lutea) 

(Welsh et. al. 1987). Thinleaf alder (Alnus incana), water birch (Betula occidentalis), and golden currant (Ribes aureum)

are also common in riparian zones.

3403 Exotic Riparian Shruba Riparian communities can be dominated by pure or hybrid species of tamarisk (Tamarix spp.), a complex of hybridiz

exotic species.

3101 Mountain Big Sagebrush Complexa Common throughout the Intermountain West, this complex is usually dominated by mountain big sagebrush (Artemi

tridentata ssp. vaseyana). Other shrubs of similar ecological requirements that indicate this complex include mountainsilver sagebrush (Artemisia cana ssp. viscidula), snowfield sagebrush (Artemisia tridentata ssp. spiciformis), A. tridentata var. pauciflora, Rothrocks’s artemisia (A. rothrockii), and tall threetip sagebrush (A. tripartita ssp. tripartita)(Tart 

2003). Occurring at higher elevations and more mesic conditions than the other big sagebrush subspecies, it is usually

characterized by the presence of a moderately productive herbaceous understory of both grasses and forbs. No particular herbaceous species are consistently found in this complex.

3102 Wyoming -- Basin Big Sagebrush Occurring at lower elevation and drier sites than mountain big sagebrush, this complex is usually identified by the p

Complexa ence of Wyoming big sagebrush (Artemisia tridentata ssp. wyomingensis), or basin big sagebrush (A. tridentata ssp. 

tridentata). Other shrubs that indicate this complex include plains silver sagebrush (A. cana ssp. cana), Bolander silver sagebrush (A. cana ssp. bolanderi), and Artemisia tridentata ssp. xericensis (Tart 2003). The herbaceous understory isless productive than on mountain big sagebrush sites. No herbaceous species is known consistently occur in this complex.

3103 Dwarf Sagebrush Complexa Communities dominated by any of a group of small sagebrush species, usually with a sparse understory. Species 

include black sagebrush (Artemisia nova), Rigid (stiff) sagebrush (A. rigida), Wyoming threetip sagebrush (A. tripartita ssp. rupicola), birdfoot sagebrush (A.pedatifida), and three subspecies of low sagebrush (A. arbuscula ssp. longiloba, A. a. ssp. thermopopola and A. t. ssp. arbuscula).

3104 Sand Sagebrusha A shrub community common on the Great Plains is that dominated by sand sagebrush (Artemisa filifol

3105 Blackbrusha This desert shrub community common in the arid Southwest is dominated by almost pure stands of blackbrush (Coleogyne ramosissima

3106 Rabbitbrusha Semi-arid to arid shrub communities are found in the Intermountain West that are dominated by any of several specie

of rabbitbrush, including rubber rabbitbrush (Ericameria nauseosa), Greene rabbitbrush (Chrysothamnus greenei), and stickyleaf rabbitbrush (Chrysothamnus viscidiflorus).

Appendix 6-B—(Continued)
154 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2

Chapter 6—Developing the LANDFIRE Vegetation and Biophysical Settings Map Unit Classifications for the LANDFIRE Prototype Project CT# Cover type Description 3107 Chaparrala Chaparral communities have moderate to dense cover of tall, stiffly branched evergreen-leaved shrubs, sometimes dominated by chamise (Adenostoma fasciculatum), and often composed of a mix of many shrub species. Other common chaparral species include: hoaryleaf ceanothus (Ceanothus crassifolius), buckbrush (C. cuneatus), cupleaf ceanothus (C. greggii), hairy ceanothus (C. oliganthus), whitethorn ceanothus (Ceanothus cordulatus), bigberry manza- nita (Arctostaphylos glauca), Eastwood manzanita (A. glandulosa), pointleaf manzanita (A. patula), Nuttall’s scrub oa

(Quercus dumosa), and turbinella oak (Q. turbinella). This type occurs in occurs in California, Arizona, and southern Utah.

3108 Soft Chaparral This is a coastal shrub community of central and southern California, occurring on dry, sometimes rocky slopes, dominated by a mix of species with soft, pliable leaves. Diagnostic species include black sage (Salvia mellifera), purple sag

(Salvia leucophylla), white sage (Salvia apiana), and coastal sagebrush (Artemisia californica).

3301 Other Evergreen Shrubsa These are characteristic, upland, mesic site shrub communities dominated by evergreen-leaved shrubs, such as curl

mountain-mahogany (Cercocarpus ledifolius), littleleaf mountain-mahogany (Cercocarpus intricatus), or snowbrush (Ceanothus velutinus).

3701 Salt Desert Shruba This cover type consists of arid shrub communities that occur throughout the West and are dominated by mixes 

halophytic (salt tolerant) shrub species, such as shadscale (Atriplex confertifolia), iodine bush (Allenrolfea occidentalis), black greasewood (Sarcobatus vermiculatus) and winterfat (Krascheninnikovia lanata) (West and Young 2000).

3702 Desert Shruba Desert shrub communities occur in arid lands of the Southwest, including the Sonoran and Mojave Deserts, often with

a sparse herbaceous understory. Creosotebush (Larrea tridentata), and frequently bursage (Ambrosia dumosa), often dominate, although many other shrub species can be the dominant. Other species include tarbush (Flourensia cernua), paloverde (Cercidium spp.), catclaw acacia (Acacia greggii), and smoke tree (Dalea spinosa).

3703 Dry Deciduous Shruba Dry sites in the Intermountain West that can support a plant community dominated by deciduous leaved shrubs, including bitterbrush (Purshia tridentata), Utah serviceberry (Amelanchier utahensis), and true mountain mahogany (Cercocarpus montanu

4101 Warm Season Grasses a These are communities of grasses that grow during late spring and summer in climates where moisture is not limiting

that time of the year and remain green during the hottest part of the summer (C-4 grasses)

4102 Cool Season Grasses a These plant communities are dominated by species of grasses that grow best during cool and moist periods of the year

They green up early in the growing season, cure during the heat of the summer, and can green up in the fall, depending on species characteristics and weather (C-3 grasses)

4201 Native Forbsa These plant communities can be dominated by a high diversity of perennial forb species, usually occurring on mesic 

moist sites in the mountains. Many different forb species can indicate this type including mountain bluebell (Mertensiana ciliata), northern mule’s-ear (Wyethia amplexicaulis), nettleleaf giant-hyssop (Agastache urticifolia), fernleaf wild lovage (Ligusticum filicinum), Sitka valerian (Valeriana sitchensis), and Arctic lupine (Lupinus arcticus ssp. subalpinus) (Natureserve 2004)

4202 Exotic Forbsa Communities of exotic forbs that can be dominated by weedy species such as spotted knapweed (Centaurea maculosa), leafy spurge (Euphorbia esula), or St. John’s wort (Hypericum perforatum)

Appendix 6-B—(Continued)
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 1

Chapter 6—Developing the LANDFIRE Vegetation and Biophysical Settings Map Unit Classifications for the LANDFIRE Prototype Project

4301 Wetland Herbaceousa Riparian, wet meadows, and seep communities are dominated by graminoids and forbs and usually have soils that 

saturated for at least part of the growing season. Common species of these communities include sedges (e.g. Carex aquatilis, C. nebrascensis and C. rostrata), alkali cordgrass (Spartina gracilis), marsh spikerush (Eleocharis palustris), 

and forbs such as primrose monkey-flower (Mimulus primuloides) and white marsh-marigold (Caltha leptosepala) (Natureserve 2004). Whether a community is considered riparian, wet meadow, or seep depends on the source of its water

4302 Alpinea These plant communities occur above upper treeline and are dominated by herbaceous species, usually mixes o

sedges, grasses, and perennial forbs. Indicator forbs include Ross’ avens (Geum rossii), white coil-beak lousewort (Pedicularis contorta), spreading phlox (Phlox diffusa), Colorado blue columbine (Aquilegia caerulea), and graminoids include shorthair reedgrass (Calamagrostis breweri), and Drummond’s rush (Juncus drummondii) (Natureserve 2004)

4401 Annual Grasslandsa Any extensive area of annual grasslands in the western U.S. that are dominated by exotic species, inclu

cheatgrass (Bromus tectorum), and medusahead (Taeniaetherum caput-medusae).

aIndicates type was refined with Zone 16 plot data.Barbour, M. G. and R. A. Minnich (2000). Californian Upland Forests and Woodlands. North American Terrestrial Vegetation. Cambridge, UK, Cambridge University Press.

Burns, R. M. and B. H. Honkala (1990). Silvics of North America. Volume 1: Conifers. Washington DC, USDA Forest Service: 67

Cope, A. B. (1993). Pinus coulteri. Fire Effects Information System [Online], USDA Forest Service, Rocky Mountain Research Station, Fire Sciences Laboratory (Producer). 200

Harrington, C. A. (1990). Silvics of red alder. Silvics of North America. Volume 2: Hardwoods. R. M. Burns and B. H. H. t. coordinators). Washington D.C., USDA Forest Service: 116

Howard, J. L. (1992). Pinus attenuata. Fire Effects Information System [Online], USDA Forest Service, Rocky Mountain Research Station, Fire Sciences Laboratory (Producer). 2005

Lotan, J. E. and W. B. Critchfield (1990). Pinus contorta ssp. murrayana - lodgepole pine forest. Silvics of North American. Volume 1: Conifers. R. M. Bruns and B. H. H. t. coordinators). Washington D.C., USDA Forest Service: 302

Minore, D. (1979). Comparative autecological characteristics of northwestern tree species: a literature review. Portland, OR., USA, U.S. Department ofAgriculture, Forest Service, Pacific Northwest

Forest and Range Experiment Station: 28.

Minore, D. and J. C. Zasada (1990). Acer macrophyllum Pursh -- Bigleaf maple. Silvics of North America. Volume 2: Hardwoods. R. M. Burns and B. H. H. t. coordinators). Washington D.C.,

USDA Forest Service: 33-

Schmidt, W. C. and R. C. Shearer (1990). Larix occidentalis Nutt -- Western larch. Silvics of North America. Volume 1: Conifers. R. M. Burns and H. B. H. t. coordinators). Washinnton D.C., USD

Forest Service: 160-17

Stein, W. I. (1990). Umbellularia californica (Hook. & Arn.) Nutt., California-laurel. Silvics of North America. Volume 2: Hardwoods. R. M. Burns and B. H. H. t. coordinators). Washington D.C.,

USDA Forest Service: 826-83

Tappeiner, J. C., P. M. McDonald, et al. (1990). Lithocarpus dendiflorus (Hook.

& Arn.) Rehd. Silvics of North America. Volume 2: Hardwoods. R. M. Burns and B. H. H. t. coordinators). Washingtom D.C., USDA Forest Service: 417-4

Tart, D. (2003). Regional Ecologist, USDA Forest Service, Intermountain Region: The shrub species associated with each of the three sagebrush complexes described as zone 16 cover type

are those assigned to these groups during the creation of the map of sagebrush land cover types in Sagebrush mapping, Broadscale stitch project. Prepared by NatureServe and The Nature Conservancy. February 2002.

CT# Cover type Description

Appendix 6-B—(Continued)
156 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 20

Chapter 6—Developing the LANDFIRE Vegetation and Biophysical Settings Map Unit Classifications for the LANDFIRE Prototype Project Appendix 6-C—Crosswalk of western LANDFIRE cover types to Gap Analysis Program (GAP), Society of American Foresters (SAF), and Society for Range Management (SRM) cover types (Eyre 1980, Merchant and others 1998, Shiflet 1994) _____________________________________________________ (Western U.S.) CT# LANDFIRE cover type GAP cover type SRM# SRM cover type SAF# SAF cover type 1401 Riparian Hardwood Maple 203 Riparian Woodland 222 Black Cottonwood-

235 Cottonwood-Willo

1405 Aspen -- Birch Aspen 217 As

Aspen - Conifer    18 Paper B

1406	 Pacific Deciduous Forest

[Other Broadleaf] 221 Red Ald

1101	 Pacific Broadleaf	 California Bay

Evergreen Forest [OtherBroadleaf Evergreen]

1501	 Larch	 Western Larch - Douglas-fir	 109	 Ponderosa Pine-Shrubland	 212	 Western Larch1201 Ponderosa Pine Ponderosa Pine 237 Interior Ponderosa 

Ponderosa Pine/ShrubPonderosa Pine/Oak - Juniper-PinyonPonderosa Pine - Lodgepole PineInterior Ponderosa Pine

	 Pacific Ponderosa Pine	 Jeffrey Pine	 247	 Jeffrey Pine

California Foothill Pine	 245	 Pacific Ponderosa PineMixed Sugar Pine	 244	 Pacific Ponderosa Pine-Douglas-fir

243 Sierra Mixed Coni

1202 Foothill Pine California Foothill Pine 248 Knobcone Pi

Knobcone PineMonterey Pine

1203 Western White Pine Western White Pine 215 Western White 1204 Lodgepole Pine Lodgepole Pine 218 Lodgepole 

Lodgepole Pine - Douglas-fir	Coastal Lodgepole PineSierra Lodgepole Pine

1205	 Douglas-fir	 Inland Douglas-fir	 210	 Interior Douglas-fir

Inland Douglas-fir - Western Red Cedar	 	 230	 Douglas-fir-Western Hemlock

Douglas-fir - Ponderosa Pine - Incense Cedar	 229	 Pacific Douglas-fir

Westside Douglas-fir - Western Hemlock	 	 234	 Douglas-fir-Tanoak-Pacific Madrone

Westside Douglas-firDouglas-fir - Tanoak - Pacific MadroneDouglas-fir - Sugar Pine - Ponderosa PineDouglas-fir - White Fir - Blue Spruce	

1206	 Grand Fir -- White Fir	 Grand Fir - Douglas-fir	 213	 Grand Fir

White Fir - Douglas-fir	 210	 Interior Douglas-firWhite FirWhite Fir - Douglas-fir/Shrub	
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 1

Chapter 6—Developing the LANDFIRE Vegetation and Biophysical Settings Map Unit Classifications for the LANDFIRE Prototype Project

(Western U.S.)

CT# LANDFIRE cover type GAP cover type SRM# SRM cover type SAF# SAF cover type

1207	 Pacific Silver Fir

– Noble Fir	 Pacific Silver Fir	 226	 Coastal True Fir-Hemlock

1219 Red Fir Red Fir 207 Red F1220 California White Fir 211 White1209 Western Hemlock Westside Western Hemlock - 224 Western Heml

Western Red Cedar 225 Western Hemlock-Sitka SpruceInland Western Red Hemlock

1210 Mountain Hemlock Subalpine Spruce - Fir

Mountain Hemlock 205 Mountain Hemlock

1211	 Spruce

– Fir	 Subalpine Spruce Fir - 216	 Blue Spruce

Mountain Hemlock 206 Engelmann Spruce-Subalpine Fir

1212 Sitka Spruce Sitka Spruce 223 Sitka Sp1213 Cedar Inland Western Red Cedar - 231 Port-Orfor

Western Hemlock 228 Western Redcedar

227 Western Redcedar-Western Hemlock

1215 Redwood Coastal Redwood 232 Redw1216	 Sequoia	 Giant Sequoia	 243	 Sierra Mixed Conifer1217 Cypress Cypress 240 Arizona Cypr1801 Timberline Pines Subalpine Pine 209 Bristlecone P

Limber Pine 256 California Mixed Subalpine

219 Limber Pin208 Whitebark Pine

2401 Deciduous Oak California Oak 201 Blue Oak Woodland 250 Blue Oak-Foothills

California Oak - Conifer 246 California Black OaMixed Oak 233 Oregon White Oregon White OakOregon White Oak - Conifer

2101 Evergreen Oak Madrean Oak 202 Coast Live Oak Woodland 241 Western Li

California Oak	 509	 Oak–Juniper Woodland and 	 249	 Canyon Live Oak

Mahogany-Oak 255 California Coast Live Oak

2201	 Pinyon

– Juniper	 Pinyon - Juniper	 412	 Juniper–Pinyon Woodland	 239	 Pinyon-Juniper

Pinyon Juniper 504 Juniper-Pinyon Pin

Woodland

Madrean Pinyon-Juniper	 509	 Transition Oak

– Juniper

Woodland and MahoganyOak Association

2202 Juniper 504 Juniper-Pinyon Pine 239 Pinyon-Juni

Woodland 220 Rocky Mountain Juniper

412	 Juniper–Pinyon Woodland	 238	 Western Juniper   107 Western Juniper-Big 66 Ashe-Redberry (Pinchot) Jun	 Sagebrush–Bluebunch

Wheatgrass

Appendix 6-C—(Continued)
158 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 20

Chapter 6—Developing the LANDFIRE Vegetation and Biophysical Settings Map Unit Classifications for the LANDFIRE Prototype Project (Western U.S.) CT# LANDFIRE cover type GAP cover type SRM# SRM cover type SAF# SAF cover type 509	 Oak–Juniper Woodland and Mahogany-Oak

3704 Mountain Deciduous Mixed oak  421 Chokecherry-Serviceber

Shrub   413 Gamb

3402 Riparian Shrub Riparian/Wetland  422 Ripa3403 Exotic Riparian S3101	 Mountain Big	 Sagebrush 612	 Sagebrush–Grass

Sagebrush Complex Sagebrush/Perennial Grass 404 Threetip Sageb

314	 Big Sagebrush–Bluebunch Wheatgrass	 	315	 Big Sagebrush–Idaho Fescue316	 Big Sagebrush–Rough Fescue   402 Mountain Big Sageb   408 Other Sagebrush T   324 Threetip Sagebrush-Idaho 

3102 Wyoming -- Basin Big Sagebrush  401 Basin Big Sag

Sagebrush Complex Sagebrush/Perennial Grass 403 Wyoming Big Sageb

612	 Sagebrush–Grass   408 Other Sagebrush T314	 Big Sagebrush–Bluebunch Wheatgrass

3103	 Dwarf Sagebrush Complex	 Sagebrush	 320	 Black Sagebrush–Bluebunch Wheatgrass

Sagebrush/Perennial Grass	 321	 Black Sagebrush–Idaho Fescue

   404 Threetip Sag   405 Black Sageb   406 Low Sageb   407 Stiff Sageb

3104 Sand Sagebrush 722 Sand Sagebrush-Mixed Pra3105 Blackbrush Blackbrush 212 Blackbr3106 Rabbitbru3107 Chaparral California Chaparral 206 Chamise Chaparr

Coastal Scrub 208 Ceanothus Mixed ChaparralCoastal Dune Scrub 209 Montane ShrublandInterior Chaparral 207 Scrub Oak Mixed ChaparralSouthern Rockies Oak - 503 Arizona ChaparrManzanita Scrub

Soft Chaparral Coastal sage 205 Coastal Sage Shrub

3301 Montane Evergreen Mountain Mahogany  415 Curlleaf Mountain M

Shrubs   417 Littleleaf Mountain Maho

   420 Snow

3701 Salt Desert Shrub Salt Desert Shrub  414 Salt Dese

Shadscale- Mixed Grass - 501 Saltbush-GreasewoMixed ScrubHopsage

Appendix 6-C—(Continued)
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 1

Chapter 6—Developing the LANDFIRE Vegetation and Biophysical Settings Map Unit Classifications for the LANDFIRE Prototype Project

(Western U.S.)

CT# LANDFIRE cover type GAP cover type SRM# SRM cover type SAF# SAF cover type

GreasewoodGreat Basin Saltbush Scrub

3702 Desert Shrub Creosote-Bursage 506 Creosotebush-Bursag

Mojave Creosotebrush - Yucca 211 Creosotebush ScPaloverde - Mixed Cacti - Scrub 508 Creosotebush-TarbushCatclaw Acacia 507 Palo Verde-CactusSmoketreeChihuahuan Creosotebush Scrub

3703 Dry Deciduous Shrub Bitterbrush 210 Bitterb

Mixed Oak  317 Bitterbrush-Bluebunch WheatgrMountain Mahogany  318 Bitterbrush-Idaho Fes

   319 Bitterbrush-Rough F   416 True Mountain Mah730 Sand Shinnery Oa

4101 Warm Season Grasses Chihuahuan Grassland 502 Grama-Gal

Great Basin Grassland 702 Black Grama-Alkali SacatonSemidesert Mixed Grass 703 Black Grama-Sideoats GraSemidesert Tobosa Grass - Scrub 705 Blue Grama-Galetta

708 Bluestem-Dropseeed

4102 Cool Season Grasses California Native Perennial  302 Bluebunch Wheatgrass-Sandberg Blueg

Grassland  303 Bluebunch Wheatgrass-Western WheatgGreat Basin Grassland  304 Idaho Fescue-Bluebunch Wheatg

   305 Idaho Fescue-Richardson Needleg   306 Idaho Fescue-Slender Wheatg   307 Idaho Fescue-Threadleaf S   309 Idaho Wheatgrass-Western Wheatg614 Crested Wheatgr

4201 Native Forbs   409 Tal4202 Exotic Forb4301 Wetland Herbaceous Riparian/Wetland  422 Ri

Forest Meadow 216 Montane WetlandMeadow 217 Wetland

4302 Alpine Alpine/Subalpine Meadows 410 Alpine Rang

213 Alpine Grassl

4401 Annual Grasslands 215 Valley Gras

Eyre, F. H. E. 1980. Forest cover types of the United States and Canada. Washington DC., USA: Society of American Foresters. 147Merchant, J. W.; Eve, M. D. 1998. A national survey of land cover mapping protocols used in the GAP Analysis Program - Final Report. Biological Resources Division, U.S. Geological Survey. p

Shiflet, T. N. E. 1994. Rangeland cover types of the United States. Denver, CO, USA: Society of Range Management. 151 p.

Appendix 6-C—(Continued)
160 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 20

Chapter 6—Developing the LANDFIRE Vegetation and Biophysical Settings Map Unit Classifications for the LANDFIRE Prototype Project

CT# Cover type

NVCS

class

NVCS

subclass

NVCS

group

1401

Riparian

Hardwooda Forest Deciduous Cold-deciduous

1405 Aspen -- Bircha Forest Deciduous Cold-deciduous

1406

Pacific Deciduous

Forest [Other

Broadleaf]

Forest,

Woodland Deciduous Cold-deciduous woodland

1101

Pacific Broadleaf

Evergreen Forest

[Other Broadleaf

Evergreen] Forest Evergreen

Winter-rain broad-leaved

evergreen sclerophyllous

forest

1501 Larch Forest Deciduous Cold-deciduous

1201 Ponderosa Pinea

Forest,

Woodland,

Herbaceous

Evergreen,

Mixed

evergreendeciduous

forest,

Evergreen

woodland,

Perennial

graminoid

Temperate or subpolar

needle-leaved evergreen

forest, Temperate or subpolar

needle-leaved evergreen

woodland, Mixed needleleaved- evergreen - colddeciduous forest, Mixed

needle-leaved- evergreen -

cold-deciduous woodland,

Temperate or subpolar

grassland with sparse tree

layer

1218

Pacific Ponderosa

Pine

Forest,

Woodland Evergreen

Temperate or subpolar

needle-leaved evergreen

forest; Temperate or subpolar

needle-leaved evergreen

woodland

1202 Foothill Pines

Forest,

Woodland Evergreen

Needle-leaved, Temperate or

subpolar needle-leavedevergreen woodland

1203

Western White

Pine Forest Evergreen

Temperate or subpolar

needle-leaved-evergreen

forest

1204 Lodgepole Pinea Forest,

Woodland

Evergreen,

Mixed

evergreendeciduous

Temperate or subpolar

needle-leaved evergreen

forest, Temperate or subpolar

needle-leaved evergreen

woodland, Mixed needleleaved evergreen - colddeciduous forest

1205 Douglas-fira Forest

Evergreen,

Mixed

evergreendeciduous

Temperate or subpolar

needle-leaved evergreen

forest, Mixed needle-leaved

evergreen - cold-deciduous

forest

Appendix 6-D—Crosswalk of LANDFIRE cover types to the National 

Vegetation Classification System (NVCS) hierarchy (Grossman and 

others 1998) ______________________________________________
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 

Chapter 6—Developing the LANDFIRE Vegetation and Biophysical Settings Map Unit Classifications for the LANDFIRE Prototype Project

1206

Grand Fir -- White

Fira Forest

Evergreen,

Mixed

evergreendeciduous

Temperate or subpolar

needle-leaved evergreen

forest, Mixed needle-leaved

evergreen - cold-deciduous

forest

1207

Pacific Silver Fir --

Noble fir Forest Evergreen

Temperate or subpolar

needle-leaved evergreen

forest

1219 Red Fir Forest Evergreen

Temperate or subpolar

needle-leaved evergreen

forest

1220 California White Fir Forest Evergreen

Temperate or subpolar

needle-leaved evergreen

forest

1209 Western Hemlock Forest Evergreen

Temperate or subpolar

needle-leaved evergreen

forest

1210 Mountain Hemlock Forest Evergreen

1211 Spruce -- Fira Forest,

Woodland

Evergreen,

Mixed

evergreendeciduous

Needle-leaved, Temperate or

subpolar needle-leaved

evergreen forest, Mixed

needle-leaved evergreen -

cold-deciduous forest

1212 Sitka Spruce Forest Evergreen

Temperate or subpolar

needle-leaved evergreen

forest

1213 Cedar Forest Evergreen

Temperate or subpolar

needle-leaved evergreen

forest

1215 Redwood Forest Evergreen

Temperate or subpolar

needle-leaved evergreen

forest

1216 Sequoia Forest Evergreen

Temperate or subpolar

needle-leaved evergreen

forest

1217 Cypress

Forest,

Woodland Evergreen

Temperate or subpolar

needle-leaved evergreen

forest, Temperate or subpolar

needle-leaved evergreen

woodland

1801 Timberline Pinea Forest,

Woodland

Evergreen,

Mixed

evergreendeciduous

forest

Temperate or subpolar

needle-leaved evergreen

forest, Temperate or subpolar

needle-leaved evergreen

woodland, Mixed needleleaved evergreen - colddeciduous forest

 NVCS NVCS NVCS

CT# Cover type class subclass group

Appendix 6-D—(Continued)
162 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 20

Chapter 6—Developing the LANDFIRE Vegetation and Biophysical Settings Map Unit Classifications for the LANDFIRE Prototype Project

2101 Evergreen Oak

Forest,

Woodland,

Shrubland

Evergreen,

Evergreendeciduous

Broad-leaved evergreen

forest,Temperate broadleaf

evergreen forest, Temperate

broadleaf evergreen

woodland, Extremely

xeromorphic evergreen

woodland, Mixed broadleaved evergreen colddeciduous forest, Temperate

broad-leaved evergreen

shrubland

2401 Deciduous Oak

Forest,

Woodland,

Shrubland,

Herbaceous,

Shrubland

Deciduous,

Mixed

evergreendeciduous

Cold-deciduous, Mixed

needle-leaved-evergreencold-deciduous woodland,

Temperate or subpolar

grassland with a sparse tree

layer

2201 Pinyon -- Junipera Woodland Evergreen

Temperate or subpolar

needle-leaved evergreen

2202 Junipera Woodland Evergreen

Temperate or subpolar

needle-leaved evergreen

3704

Mountain

Deciduous Shruba Shrubland Deciduous Cold-deciduous

3402 Riparian Shruba Shrubland Deciduous Cold-deciduous

3403

Exotic Riparian

Shruba Shrubland Deciduous Cold-deciduous

3101

Mountain Big

Sagebrush

Complexa

Shrubland,

Dwarfshrubland,

Herbaceous

Evergreen,

Perennial

graminoid

Microphyllous evergreen,

Extremely xeromorphic

evergreen, Temperate or

subpolar grassland with

sparse shrub layer

3102

Wyoming -- Basin

Big Sagebrush

Complexa Shrubland Evergreen Microphyllous evergreen

3103

Dwarf Sagebrush

Complexa Shrubland,Herbaceous

Evergreen,

Perennial

graminoid,

Deciduous

Microphyllous evergreen,

Temperate or subpolar

grassland with a sparse

shrub layer, Extremely

xeromorphic deciduous

shrubland

3104 Sand Sagebrush Shrubland Evergreen Microphyllous Evergreen

3105 Blackbrusha Shrubland Evergreen

Extremely xeromorphic

evergreen shrubland

3106 Rabbitbrusha Shrubland,Herbaceous

Evergreen,

Perennial

graminoid

Microphyllous evergreen,

Temperate or subpolar

grassland with a sparse

shrub layer

3107 Chaparrala Shrubland Evergreen

Temperate broadleaf

evergreen

 NVCS NVCS NVCS

CT# Cover type class subclass group

Appendix 6-D—(Continued)
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 

Chapter 6—Developing the LANDFIRE Vegetation and Biophysical Settings Map Unit Classifications for the LANDFIRE Prototype Project

3108 Soft Chaparral Shrubland Evergreen

Temperate broad-leaved

evergreen shrubland;

Microphyllous evergreen

shrubland

3301

Montane Evergreen

Shrubsa Shrubland Evergreen

Microphyllous evergreen,

Temperate broadleaf

evergreen

3701 Salt Desert Shruba

Shrubland,

Dwarf

shrubland

Deciduous

shrubland,

Evergreen

dwarf

shrubland,

Evergreen

Extremely xeromorphic

evergreen shrubland,

Extremely xeromorphic

deciduous shrubland

3702 Desert Shruba Shrubland

Evergreen

shrubland,

Deciduous

shrubland

Extremely xeromorphic

evergreen shrubland,

Extremely xeromorphic

deciduous shrubland,

Drought deciduous shrubland

3703

Dry Deciduous

Shruba Shrubland

Evergreen

shrubland,

Deciduous

Microphyllous evergreen

shrubland, Temperate

broadleaf evergreen, Colddeciduous shrubland

4101

Warm Season

Grasses a Herbaceous

Perennial

graminoid

Temperate or subpolar

grassland

4102

Cool Season

Grasses a Herbaceous

Perennial

graminoid

Temperate or subpolar

grassland

4201 Native Forbsa Herbaceous Perennial forbs Forbs

4202 Exotic Forbsa Herbaceous

4301

Wetland

Herbaceousa Herbaceous

Mixed

perennial

graminoid/forb,

Hydromorphic

herbs Mixed Grass/Forbs

4302 Alpinea Herbaceous

Mixed

perennial

graminoid/forbs Mixed Grass/Forbs

4401

Annual

Grasslandsa Herbaceous Annual herbs

a

Refined with Zone 16 plot data.

Grossman, D. H.; Faber-Langendoen, D.; Weakley, A. S.; Anderson, M.; Bourgeron, P.; Crawford, R.; Goodin, K.;

Landaal, S.; Metzler, K.; Patterson, K.; Pyne, M.; Reid, M.; Sneddon, L. 1998. International classification of ecological

communities: Terrestrial vegetation of the United States Volume I. The National Vegetation Classification System:

development, status, and applications. Arlington VA, USA: The Nature Conservancy. 126 p.

 NVCS NVCS NVCS

CT# Cover type class subclass group

Appendix 6-D—(Continued)
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Chapter 6—Developing the LANDFIRE Vegetation and Biophysical Settings Map Unit Classifications for the LANDFIRE Prototype Project

Appendix 6-E—Zone 19 cover type legend and descriptions______________

CT# Cover type Description

1201 Cedar Western redcedar (Thuja plicata) is limited to the northwest corner of Zone 19 where it reaches the eastern limit of its distribution. It is the second most shade-tolerant coniferous spec

in the zone after western hemlock. Cedar commonly occurs in stands with many other conifer 

species including Abies grandis, Larix occidentalis, Tsuga heterophylla, Pinus contorta, 

Pseudotsuga menziesii, Pinus monticola and Picea engelmannii. Understory species may 

be abundant, and common species include Oploplanax horridus, Gymnocarpium dryopteris, 

Tiarella trifoliata and Taxus brevifolia. This is a minor type in the zone and is represented by 

less than  percent of the forested plots in the LFRDB

1202	 Douglas-fir	 This is a major type within Zone 19 and across the western U.S., dominated by Douglas-fir

(Pseudotsuga menziezii) and typically occurring at mid- elevation on a variety of aspects and 

slopes. This cover type mixes with or may be adjacent to many other cover types across the 

zone depending on location and local site factors. Common overstory associates include 

Pinus contorta, Pinus ponderosa, Larix occidentalis and Abies lasiocarpa. Common understory species vary widely depending on local site factors and stand history but may include 

Xerophyllum tenax, Calamagrostis rubescens, Vaccinium membranaceum and Symphoricarpos albus. Cover of Douglas-fir averages 32 percent and ranges from 3 to 90 percent. Thirtythree percent of all forested plots fall into this category and 20 percent of all plots. 

1203	 Grand Fir	 Grand fir (Abies grandis) occurs only in the northern half of the zone and west of the continental divide. It commonly occurs in stands with other conifer species including Pseudotsuga 

menziesii, Abies lasiocarpa, Thuja plicata, Larix occidentalis and Picea engelmannii. Understory species may be abundant and include Taxus brevifolia, Acer glabrum, Arnica spp., 

Linnaea borealis and Amelanchier alnifolia. Cover of grand fir averages 40 percent with a

range of 10 to 90 percent. This is a minor type in the zone and is represented by less than

percent of the plots in the database.

1204 Hemlock This cover type, dominated by western hemlock (Tsuga heterophylla), is restricted to t

northwest corner of the zone and is the most shade-tolerant conifer in the zone. Western 

hemlock cover averages 51 percent with a range of 30 to 90 percent. Common overstor

associates include Thuja plicata, Abies lasiocarpa, Larix occidentalis, Picea engelmannii, 

Pseudotsuga menziesii and Pinus contorta. Understory vegetation may be abundant to nonexistent depending on the overstory canopy and includes Xerophyllum tenax, Taxus brevifolia, Amelanchier alnifolia, Acer glabrum, and Arnica latifolia. Western hemlock reaches its 

eastern range limit within the northwestern portion of the zone and thus is a minor type with 

only 0.3 percent of forested plots occurring here

1205 Lodgepole Pine Lodgepole Pine is a major type within Zone 19, across the middle and northern Rockie

and in portions of the Cascades and Sierra Nevadas. It typically occurs in the montane 

and lower subalpine zones on a variety of aspects and slopes. This cover type commonly 

mixes with or is adjacent to Douglas-fir and Spruce-fir types and is typically seral to those

types. Dominated by lodgepole pine (Pinus contorta), common overstory associates include 

Pinus ponderosa, Larix occidentalis and Abies lasiocarpa. Common understory species vary 

widely depending on local site factors and stand history, but may include Xerophyllum tenax, 

Calamagrostis rubescens, Vaccinium membranaceum and Symphoricarpos albus. Cover of 

lodgepole pine averages 35 percent and ranges from3 to 98 percent. Sixteen percent of al

forested plots fall into this category and 20 percent of all plots.

1206 Juniper Juniper species are wide-ranging though, as cover types, are found primarily east of the

divide in Montana or in the very southern part of Zone 19. Communities are usually open

and dominated by species including Juniperus scopulorum and Juniperus osteosperma, with 

cover averaging 22 percent with a range from  to 50 percent. Common associated species

include Artemisia nova, Artemisia tridentata ssp. vaseyana, Pseudoroegneria spicata, Festuca idahoensis and Koeleria macrantha. This is a minor woodland type in the zone and only 

0.5 percent of forest and woodland plots occur in this type.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 1

Chapter 6—Developing the LANDFIRE Vegetation and Biophysical Settings Map Unit Classifications for the LANDFIRE Prototype Project

1207 Ponderosa Pine Ponderosa pine (Pinus ponderosa) is distributed across large areas of the zone, though it is

absent from several areas including the area south of Salmon, ID. As a cover type, it is limited to some of the lowest elevations and driest sites that are occupied by forest and woodland 

communities in the zone. At higher elevations or on more mesic sites, Pseudotsuga menziesii quickly replaces ponderosa pine. Larix occidentalis and Pinus contorta are other common 

overstory associates. Understory vegetation may be abundant and common species include 

Mahonia repens, Calamagrostis rubescens, Symphoricarpos albus, Arctostaphylos uva-ursi, 

Spiraea betulifolia, Amelanchier alnifolia and Carex geyeri. Cover of ponderosa pine averages 32 percent and ranges from 5 to 70 percent. Only 2 percent of the forested plots are

classified to the cover type.

1208	 Spruce -- Fir	 Spruce-fir is a widespread cover type throughout Zone 19, dominating at high elevations and

often mixing with the Lodgepole Pine, Douglas-fir, and Timberline Pine types. Stands are

usually dominated by Abies lasiocarpa (subalpine fir) and Picea engelmannii (Engelmann 

spruce). Common overstory associates include Pinus albicaulis, Pseudotsuga menziesii and

Pinus contorta. Understory species commonly occurring in this type include Vaccinium membranaceum, Xerophyllum tenax, Menziesia ferruginea, Arnica latifolia, Vaccinium scoparium 

and Luzula glabrata. Approximately 25 percent of forested plots occur in this cover type.

1209 Limber Pine The distribution of this type, dominated by Pinus flexilis, is primarily east of the divide in

Montana and in several mountain ranges in the southern portion of the zone. The Limber 

Pine type occurs at lower elevations where it may co-occur with juniper species and at high 

elevation timberline sites where it may mix with Pinus albicaulis. Common overstory associates are Pseudotsuga menziesii and Juniperus scopulorum. Common understory species 

include Arctostaphylos uva-ursi, Dasiphora floribunda, Pseudoroegneria spicata, Festuca 

idahoensis, Shepherdia canadensis, Juniperus horizontalis and Juniperus communis. Cover 

of limber pine averages 3 percent with a range o 3 to 50 percent. Approximatey 1 perc

of all forested plots occur in this type.

1212 White Pine This cover type’s distribution is primarily to the west of Zone 19, just reaching into t

northwest corner of Zone 19 and, as such, is only represented by3 plots in the LFRDB. It i

dominated by Pinus monticola, western white pine.

1401 Aspen -- Birch The Aspen-Birch type is most common east of the Continental Divide, where it ran

from low elevation riparian areas to the montane and lower subalpine zones and is usually 

dominated by Populus tremuloides (trembling aspen). In the northwest portion of Zone 19,

however, Betula papyrifera (paper birch) as the dominant overstory species is more common 

than aspen. Understory diversity is high and includes many shrub and herbaceous species, 

including Osmorhiza occidentalis, Prunus virginiana, Acer glabrum, Amelanchier alnifolia, 

Symphoricarpus albus, Calamagrostis rubescens, Angelica arguta and Thalictrum occidentale. Cover of Populus tremuloides averages 42 percent with a range of3 to 90 percent

Aspen-birch is much more common in other zones and in Zone 19 is represented by only 1.

percent of the forest plots in the LFRDB.

1402 Riparian The widespread Riparian Hardwood cover type has limited coverage because of its restricte

	 Hardwood	 habitat requirements. It occupies low elevation riparian areas along major drainages where

it often intermingles with the Riparian Broadleaf Shrubland cover type. Stands of riparian 

hardwoods at higher elevations are usually small and isolated. Only two cottonwood species 

occur in riparian hardwoods forests in the zone, Populus angustfolia (narrowleaf cottonwood), which largely occurs east of the Continental Divide in the eastern and northeastern 

part of the zone, and Populus balsamifera ssp. trichocarpa (black cottonwood), which occurs 

throughout the zone. Other deciduous trees such as Acer negundo, and Salix amygdaloides

also occur in riparian hardwood communities as well as Pinus ponderosa, Picea engelmannii,

and Populus tremuloides. Some common understory associated species include Symphoricarpos albus, Salix spp., Poa pratensis, Acer glabrum and Amelanchier alnifolia. Cover of 

cottonwood in these communities averages 30 percent with a range of 10 to 60 percent. Thi

is a minor forest type and is represented in the LFRDB by less than  percent of the forest

plots.

CT# Cover type Description

Appendix 6-E—(Continued)
166 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 20

Chapter 6—Developing the LANDFIRE Vegetation and Biophysical Settings Map Unit Classifications for the LANDFIRE Prototype Project

1403 Western Larch This type occurs in the northern half of the zone, predominantly west of the continental divi

Stands dominated by Larix occidentalis typically occur at mid-elevations and frequently mix

with Douglas-fir and lodgepole pine types. Larch forests are usually seral to Douglas-fir, grand

fir and spruce-fir types. Typical overstory associates are Pseudotsuga menziesii and Pinus 

contorta. Understory species are numerous with some of the most commonly occurring species 

being Vaccinium membranaceum, Paxistima myrsinites, Rubus parviflorus, Xerophyllum tenax 

and Acer glabrum. Approximately  percent of forested plots occur in this cover type

1801 Timberline Forest The Timberline Forest type occurs across the zone and occupies the highest elevations 

any of the forested communities. It is generally dominated by Pinus albicaulis (whitebark 

pine) and can include Larix lyallii (alpine larch). At lower elevations, Timberline Forests typically mix with the Spruce-Fir cover type, and Picea engelmannii and Abies lasiocarpa are 

both common overstory associates. Common understory associated species include Vaccinium scoparium, Xerophyllum tenax, Luzula glabrata and Carex geyeri. Cover of Pinus albicaulis and Larix lyallii averages 19 percent and ranges from3 to 50 percent. Approximatel

percent of forested plots occur in this cover type.

2101 Upland Broadleaf This cover type consists of three main dwarf shrub species, Vaccinium scoparium, Sa

Dwarf Shrubland artica, and Vaccinium caespitosum. It is found from the upper montane region to the alpine 

region. In 77 percent of the plots, the dominant species is Vaccinium scoparium. The remaining plots are dominated by either Vaccinium caespitosum or Salix arctica. Both Vaccinium 

species resprout following fire. Salix arctica occurs in communities that rarely experience fire.

Common associates in Vaccinium communities include Xerophyllum tenax, Carex geyeri, 

Vaccinium membranaceum and Luzula glabrata. This is a minor shrub type with approximately 0.5 percent of the total plots falling into this cover type and 1.5 percent of all shrub

dominated plots occurring here.

2102 Upland Broadleaf This cover type is dominated by numerous species characterized by medium stature

Medium Shrubland (generally  to 8 feet in height) broadleaf shrubs including Symphocarpus spp., Vaccinium

membranaceum, Menziesia ferruginea, Physocarpus malvaceus, Spirea betufolia, Rubus parviflorus, and various Rosa, Ribes, and Lonicera species. Common associated species outside 

of those indicated by the dominant species very widely depending on the dominant species 

and local site factors. Approximately 8 percent of shrub dominated plots occur in this type. 

2103 Upland Broadleaf This cover type consists of several dominant species characterized as tall stature (genera

Tall Shrubland greater than 8 feet in height) broadleaf shrubs. These include Alnus viridus ssp. sinuate, Acer 

glabrum, Amelanchier alnifolia, Sorbus scopulina, and several Prunus species. Common 

associated species outside of those indicated by the dominant species include lower stature 

broadleaf shrubs and a variety of herbaceous species. Approximately 7 percent of shrub 

dominated plots occur in this type. 

2202 Upland Microphyllous This physiognomic grouping is composed of several dominant species characterized as

Medium Shrubland medium stature microphyllous shrubs. These communities are generally on lower elevation arid sites and restricted to the southern portion of the zone. Dominant species include 

Atriplex confertifolia, Purshia tridentata, Artemisia cana, Tetradymia canescens, Gutierrezia 

sarothrae, and Atriplex canescens. Common associated species include Artemisia frigida, 

Hesperostipa comata and Pseudoroegneria spicata. These communities become much more 

common south of Zone 19. This cover type is of minor importance in the zone with approximately1 percent of shrub dominated plots occurring in this typ

2211 Dwarf Sage This cover type is dominated by two morphologically similar species, Artemisia arbusc

and Artemisia nova. Vegetative cover is generally low with only a few commonly occurring 

shrub and grass species. Common associates include Pseudoroegneria spicata, Artemisia 

tridentata ssp. wyomingensis, Artemisia frigida and Heterostipa comata. Cover of sagebrush 

average 17 percent with a range of3 to 50 percent. Occurrence of this cover type in Zon

19 is minor though it is much more abundant in other parts of the western U.S. Very little plot

data exists for dwarf sage communities in the zone with 0.1 percent of the total plots falling

into this cover type and 0.4 percent of all shrub dominated plots occurring here

CT# Cover type Description

Appendix 6-E—(Continued)
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 1

Chapter 6—Developing the LANDFIRE Vegetation and Biophysical Settings Map Unit Classifications for the LANDFIRE Prototype Project

2212	 Shrubby Cinquefoil	 This cover type occurs at mid to upper elevations between 4,500 ft and 8,500 ft. Dasiphora 

floribunda (shrubby cinquefoil), the dominant species in this type, possesses the ability to

resprout following fire depending on fire severity; it is usually killed by high severity fire.

Common associated species include Festuca idahoensis, Koeleria macrantha, Fragaria 

virginiana, Danthonia intermedia and Potentilla gracilis. Cover of shrubby cinquefoil averages

15 percent with a range of3 to 40 percent. This is a minor type in the zone with less th

percent of shrub dominated plots occurring in this cover type. 

2213 Threetip Sage This is a minor type in southwest Montana and becomes more abundant in the Idaho porti

of the zone. Artemisia tripartita (threetip sage) is different from other sagebrush types in the 

zone because of its ability to resprout after fire, though the ability varies among populations.

Common associated species include Chrysothamnus viscidiflorus, Gutierrezia sarothrae, 

Pseudoroegneria spicata. Cover of threetip sage averages 28 percent with a range of 10 to

45 percent. This type is represented by 23 percent of the shrub dominated plots in the zone

An abundance of plot data exists for this type, but it is clustered in a relatively small area of 

the zone so the amount of plot data over-represents its actual occurrence in the zone.

2218 Mountain Big Sage Mountain Big Sage cover type (dominated by Artemisia tridentata ssp. vaseyana) generally

occurs at higher elevations than the Wyoming-Basin Big Sage cover type and ranges to the 

subalpine region. Though present throughout Zone 19, it is most abundant in Idaho and in

Montana generally south and east of Missoula. Common associated species include Festuca 

idahoensis, Pseudoroegneria spicata, Geranium viscosissimum and Lupinus species. Cover of 

Artemisia tridentata ssp. vaseyana averages 29 percent with a range of  to 70 percent. This is

a major shrub type across the zone with 18 percent of shrub-dominated plots occurring here

2219 Wyoming -- Basin This is a major shrub type in the southern half of the zone and a landscape dominant acros

Big Sage vast areas of the West. Dominant species for this type are Artemisia tridentata ssp. tridentate, and Artemisia tridentata ssp. wyomingensis. Other common species include Agropyron 

cristatum, Pseudoroegneria spicata, Poa fendleriana, Artemisia frigida, Achnatherum hymenoides, Heterostipa comata, Chrysothamnus viscidiflorus and Koeleria macrantha. This type 

is represented by 1 percent of the shrub dominated plots in the zone. An abundance of plo

data exists for this type but it is clustered in a relatively small area of the zone so the amount 

of plot data over-represents its actual occurrence in the zone.

2220 Rabbitbrush This cover type is composed of two species of rabbitbrush within the zone, including Chrysothamnus viscidiflorus (yellow rabbitbrush) and Ericameria nauseosa (rubber rabbitbrush). It 

is a minor type in the zone and is usually adjacent to Wyoming-Basin Big Sage, Mountain 

Big Sage or herbaceous dominated cover types. Rabbitbrush may quickly recolonize a site

following fire from sprouts and from seed. Common associated species include Artemisia 

frigida, Pseudoroegneria spicata, Festuca idahoensis, Hesperostipa comata, Artemisia 

tridentata ssp. wyomingensis, Artemisia tridentata ssp. vaseyana, Poa fendleriana and

Agropyron cristatum. Cover of rabbitbrush species averages 8 percent with a range of  to

30 percent. Less than 2 percent of shrub-dominated plots occur in this cover type

2222 Greasewood Sarcobatus vermiculatus is the sole dominant species in this cover type. Though a minor 

type in Zone 19, it is a common species in other areas of the west with a distribution centered

on the Great Basin Floristic Division. Black greasewood communities generally occur below 

the more moist sagebrush or shadscale zones and in Zone 19 are typically found on old

alluvial terraces (Roundy and others 1978). Greasewood commonly grows in pure stands in

high saline areas with little or no understory vegetation, but in less saline areas, other shrubs 

may be common as well as a grass component (McArthur and Plummer 1978). Generally,

greasewood communities suffer little damage from fire and fire occurrence is minimal due to

a lack of fine fuels. However, greasewood communities invaded by cheatgrass may have an

increase in fire occurance. Species diversity is low, but common associates include Agropyron cristatum, Artemisia frigida and Pseudoroegneria spicata. Cover of greasewood averages 

45 percent with a range of 10 to 70 percent. Plot data is almost nonexistent for greasewoo

communities in the zone and less than 0.1 percent of the total plots fall into this cover type

and 0.2 percent of all shrub dominated plots occur here. 

CT# Cover type Description

Appendix 6-E—(Continued)
168 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 20

Chapter 6—Developing the LANDFIRE Vegetation and Biophysical Settings Map Unit Classifications for the LANDFIRE Prototype Project

2223 Mountain Mahogany This cover type is restricted to the south half of the zone where it reaches its northerly rang

limit. Stands of this type typically occur at mid elevations on dry, southerly slopes. Cercocarpus ledifolius (mountain mahogany), the dominant species in this type, is usually killed by fire

and relies on seed to reoccupy a site though regeneration may be slow (Scheldt and Tisdale 

1970). Common associated species include Festuca idahoensis, Pseudoroegneria spicata,

Artemisia tridentata ssp. vaseyana and Artemisia tridentata ssp. wyomingensis. Mountain 

mahogany cover averages 40 percent with a range of4 to 70 percent. This type is relativel

minor across the zone though locally abundant in Idaho. Less than  percent of shrub dominated plots are classified to this type

2300 Upland Needleleaf This physiognomic grouping is composed of dwarf to medium height needle-leaved shru

 that typically form small patches in a variety of sites. On lower elevation dry sites, the dominant Shrubland species are Juniperus communis and Juniperus horizontalis, which account 

for 85 percent of the plots. These sites typically have sparse fuel. The remaining plots are 

dominated by Phyllodoce empetriformis, which occupies sites within the subalpine to lower 

alpine zones and are adjacent to or intermingled with subalpine forest types, herbaceous 

dominated alpine communities, or barren, rocky slopes. This is a very minor shrub type with 

approximately 0.2 percent of the total plots falling into this cover type and 0.8 percent of all 

shrub dominated plots occurring here.

2400 Upland Sclerophyllous This physiognomic grouping is composed of dwarf to medium height sclerophyllous-leave

Shrubland shrubs that typically form small patches mainly within the montane zone. It is comprised of 

three dwarf shrubs, Arctostaphylos uva-ursi, Paxistima myrsinites, and Mahonia repens,

which dominate 72 percent of the plots in this type, and one medium-height shrub species, 

Ceanothus velutinus, on 28 percent of the plots. All species possess the ability to resprout 

following fire and Ceanothus velutinus in particular may recolonize a site after fire from onsite seed sources. This is a minor shrub type with approximately 0.4 percent of the total plots

falling into this cover type and  percent of all shrub dominated plots occurring here

2600 Riparian Broadleaf This cover type is composed of native shrub communities dominated mainly by Alnus incana

Shrubland or by one of several Salix species. This type occupies riparian areas along major drainages. 

Where it is intermingled with the Riparian Hardwood cover type, the shrubs are usually quite

tall and some species may be single-stemmed and tree-like. At lower elevations, these communities usually have a patchy distribution due to flood dynamics and more recently, human

disturbances. At higher elevations, communities may occur as narrow stringers along low 

gradient streams or as broader patches that extend away from streams and into adjacent wet 

meadows where they often form mosaics with herbaceous-dominated communities. Overall, 

this is a minor though important landscape component with approximately 0.6 percent of the 

total plots falling into this cover type and 2 percent of all shrub dominated plots occurring 

here.

3110 Annual Forb The Annual Forb cover type includes forbs that are annual or biennial species. This ty

usually occurs at lower elevation xeric sites across the zone and is composed of mostly 

naturalized species but also includes species that may be the result of seeding for restoration or forage in the cases of Melilotus officianalis or Triticum aestivum. Species composition 

varies widely and includes numerous forbs, natives and exotics, and annual and perennials 

in various mixtures. Approximately 0.2 percent of the total plots fall into this cover type and 

percent of all herbaceous dominated plots occur in this cover type when combined with annual graminoid.

3120 Annual Graminoid Bromus tectorum is the dominant species on the zone 19 plots classified to this cover typ

This type usually occurs at lower elevation xeric sites across the zone and is composed 

of mostly naturalized species. Species composition varies widely and includes numerous 

graminoids, natives, and exotics, and annuals, biennals and perennials in various mixtures. 

Approximately 0.2 percent of the total plots fall into this cover type. 

CT# Cover type Description

Appendix 6-E—(Continued)
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 1

Chapter 6—Developing the LANDFIRE Vegetation and Biophysical Settings Map Unit Classifications for the LANDFIRE Prototype Project

3130 Perennial Forb The Perennial Forb cover type consists of communities dominated mainly by native a

occasionally exotic forbs. Occurring on xeric to mesic sites and ranging from the lowest 

elevations in the zone to the alpine region, species composition may vary widely. Artemisia 

frigida is the dominant species on almost 30 percent of the plots, and no other species dominant on more than 5 percent. The vertical structure of this type ranges from tall forbs such as

Chamerion angustifolium to cushion plants such as Phlox hoodii. Approximately 2 percent of 

the total plots fall into this cover type and 24 percent of all herbaceous dominated plots

3141 Perennial Exotic Fifty-eight percent of the plots in this type are dominated by Phleum pratense (timothy). 

Bunch Gramminoid occur on a variety of sites, ranging from low elevation xeric to mesic montane sites. Areas 

dominated by these grasses may be the result of seeding for restoration or pasture or at least 

have been subject to moderate to heavy disturbance in the past. Approximately 0.2 percent 

of the total plots fall into this cover type and  percent of all herbaceous dominated plots are

classified to this type.

3142 Perennial Native This cover type is mainly composed of low to moderate elevation communities dominated

Bunch Gramminoid Festuca idahoensis, Festuca altaica, and Pseudoroegneria spicata. These dominant species 

account for 82 percent of the plot data in this type and are the dominant grassland communities in Zone 19. These plots may occur at any elevation and on xeric to mesic sites. These

species usually have a clumped or bunched growth form but may possess short rhizomes in 

some cases. Approximately  percent of the total plots fall into this cover type and 49 percen

of all herbaceous dominated plots are classified to this cover type.

3151 Perennial Exotic Fifty percent of the plots in this cover type are dominated by Poa pratensis. The pl

Rhizomatous typically occur on low elevation xeric to mesic montane sites. Areas dominated by these

Gramminoid grasses may be the result of seeding for restoration or pasture or at least have been subject 

to moderate to heavy disturbance in the past. Approximately 0.2 percent of the total plots 

fall into this cover type and 3 percent of all herbaceous dominated plots are classified to this

type.

3152 Perennial Native Calamagrostis rubescens and Carex geyeri dominate 80 percent of the plots in this cov

Rhizomatous type. The remaining plots are dominated by a variety of species. Plots are found in areas

Gramminoid ranging from low elevation xeric sites to mesic montane or subalpine sites. This cover type is 

composed of species that typically have a rhizomatous, stoloniferous, or sod-forming growth 

form, but may be clumped or bunched in some cases. Approximately 0.4 percent of the total

plots fall into this cover type and 5 percent of all herbaceous dominated plots are classified to

this cover type.

3200 Wetland Herbaceous This cover type is dominated by perennial native rhizomatous gramminoids, including Carex

and Juncus species, and several native perennial forb dominated communities would also 

be classified here. This minor type is scattered across the zone from low to high elevations.

Approximately 0.4 percent of the total plots fall into this cover type and 5 percent of all herbaceous dominated plots are classified to this cover type

McArthur, E. D.; Plummer, A. P. 1978. Biography and management of native western shrubs: a case study, section Tridentatae of Artemisia. Great

Basin Naturalist Memoirs (2): 229-24

Roundy, B. A.; Blackburn, W. H.; Jr., R. E. E. 1978. Influence of prescribed burning on infiltration and sediment production in the pinyon-juniper

woodland. Nevada Journal of Range Mangement (31): 250-2

Scheldt, R. S.; Tisdale, E. W. 1970. Ecology and utilization of curl-leaf mountain mahagany in Idaho. Station Note 15. Moscow, ID: University o

Idaho College of Forestry, Wildlife and Range Sciences. p.

CT# Cover type Description

Appendix 6-E—(Continued)
170 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 20

Chapter 6—Developing the LANDFIRE Vegetation and Biophysical Settings Map Unit Classifications for the LANDFIRE Prototype Project

Appendix 6-F—Zone 16 potential vegetation type legend and 

descriptions ______________________________________________________

PVT# Potential vegetation type Description

1601 Spruce -- Fir / Blue Spruce This type is dominated by Picea pungens along with Abi

lasiocarpa as the climax species. Picea engelmannii may be a 

co-climax species in some areas. Common associates include 

Pseudotsuga menziesii, Abies lasiocarpa, Pinus contorta, and

Populus tremuloides. Elevational ranges are generally between 

7,600 and 9,000 feet. Sites tend to be relatively dry and generally 

occur on the warmer portion of the area where Spruce-Fir types 

are found. Understories are varied with Juniperus communis 

common on many sites. Berberis repens and Carex geyeri are 

common along with a wide variety of lesser shrubs and forbs.

1602 Spruce -- Fir / Blue Spruce / Lodgepole Pine This type is dominated by Picea pungens along with Abies

lasiocarpa as the climax species. Picea engelmannii may be a 

co-climax species in some areas. Common associates include 

Pseudotsuga menziesii, Abies lasiocarpa, Pinus contorta, and

Populus tremuloides. Elevational ranges are generally between 

7,600 and 9,000 feet. Sites tend to be relatively dry and generally 

occur on the warmer portion of the area where Spruce-Fir types 

are found. Understories are varied with Juniperus communis 

common on many sites. Berberis repens and Carex geyeri are 

common along with a wide variety of lesser shrubs and forbs. 

This PVT occurs in the northern portion of the zone where Pinus 

contorta occurs as a common seral species.

1603 Spruce -- Fir / Spruce -- Fir This is a major type found throughout the zone. The major indicators for this type are Abies lasiocarpa and/or Picea engelmanni

Common associates include Pseudotsuga menziesii and Populus 

tremuloides. Abies concolor is locally present. Elevations range 

from 8,000 feet to above 11,000 feet and sites are cool to cold an

moist to moderately dry. Understories are highly variable, ranging from shrub dominated to grasses to forbs. Common species 

include Berberis repens, Juniperus communis, Ribes montigenum, 

Symphoricarpos oreophilus, Pachistima myrsinites, Vaccinium 

scoparium, Carex geyeri, and Arnica spp.

1604 Spruce -- Fir / Spruce -- Fir / Lodgepole Pine This is a major type found throughout the zone. The major indicators for this type are Abies lasiocarpa and/or Picea engelmanni

Common associates include Pseudotsuga menziesii and Populus 

tremuloides. This PVT occurs in the northern portion of the zone 

where Pinus contorta occurs as a common seral species. Abies 

concolor is locally present. Elevations range from 8,000 feet to 

above 11,000 feet and sites are cool to cold and moist to moderately dry. Understories are highly variable ranging from shru

dominated to grasses to forbs. Common species include Berberis 

repens, Juniperus communis, Ribes montigenum, Symphoricarpos 

oreophilus, Pachistima myrsinites, Vaccinium scoparium, Carex 

geyeri, and Arnica spp.

1611 Grand Fir -- White Fir This type is represented by Abies concolor within the zone. Common associates include Pseudotsuga menziesii and Popu

tremuloides. Pinus ponderosa and lesser amounts of Pinus flexilis 

may be found on the southern portion of the type. Sites range from 

about 6,200 feet up to 9,600 feet and are usually cool and dry, 

northerly aspects. Major understory associates include Symphoricarpos oreophilus, Berberis repens, Juniperus communis, and 

Carex geyeri.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 

Chapter 6—Developing the LANDFIRE Vegetation and Biophysical Settings Map Unit Classifications for the LANDFIRE Prototype Project

1612 Grand Fir -- White Fir / Maple This PVT is indiciated by Acer grandidentatum and generally occur

in relatively pure stands or interspersed with Quercus, Artemisia, 

Pseudotsuge menziezii, and Abies concolor communities and is 

usually found in canyon bottoms and on portions of side slopes with 

deep, well developed modal soils. In settings where it is at the edge 

of its ecological range, it normally occurs more shrublike.

1621	 Douglas-fir / Timberline Pine	 Pseudotsuga menziesii, in conjunction with either Pinus flexilis or 

Pinus longaeva, are indicators for this PVT. Other species commonly found include Pinus ponderosa, Juniperus scopulorum and 

Populus tremuloides. Minor amounts of Pinus edulis may also 

be encountered. The PVT is generally found on steep southerly 

aspects where windy conditions are common resulting in very dry 

sites. Elevations range from 6,500 to 9,000 feet and the site represents the very dry end of Pseudotsuga menziesii sites. Understories 

are usually sparse, shrubby and composed of various mixtures of 

Symphoricarpos oreophilus, Juniperus communis, Cercocarpus 

ledifolius, Pachistima myrsinites, Artemisia spp., and Amelanchier 

alnifolia. The type occurs sporadically throughout the zone.

1622	 Douglas-fir / Douglas-fir	 Pseudotsuga menziesii is the sole indicator of this type. Pinus 

ponderosa and Populus tremuloides are common associates. Juniperus scopulorum may be a minor associate. The type is found 

on a variety of site conditions ranging in elevation from 5,000 to 

9,500 feet. Sites range from warm and dry to cool, moderately 

moist conditions. Understories are a mixture of shrubs and grasses including Physocarpus malvaceus, Acer glabrum, Amelanchier 

alnifolia, Berberis repens, Arnica cordifolia, and Carex geyeri.

1623	 Douglas-fir / Lodgepole Pine	 This type is indicated by the combination of Pseudotsuga menziesii and Pinus contorta. While sites may be relatively warm, they 

represent the cooler portion of the Pseudotsuga menziesii environment and vary from moist to dry. Other common species include

Pinus ponderosa and Populus tremuloides. Elevations range from 

about 5,500 to 7,500 feet. The type is found only in the northern 

half of the zone. Understories tend to be shrubby and include 

Symphoricarpos oreophilus, Berberis repens, and Juniperus communis along with some taller shrubs such as Amelanchier alnifolia.

1631 Timberline Pine Pinus flexilis and/or Pinus longaeva are the indicators of t

type. Juniperus scopulorum and minor amounts of Pseudotsuga 

menziesii or Populus tremuloides may be present on some sites. 

Stands are frequently found on very steep south or southwest

aspects. Conditions are generally the most adverse for tree growth 

and the type often represents the lower timberline. Elevations 

range from 7,000 to 10,200 feet. Understories are shrubby and

composed of various mixtures of Artemisia tridentata, Symphoricarpos oreophilus, Berberis repens, Cercocarpus ledifolius, 

Pachistima myrsinites, and Juniperus communis.

1632 Ponderosa Pine Pinus ponderosa is the only indicator species for this type. Othe

trees commonly found are Juniperus scopulorum and Populus 

tremuloides. Occasionally Pinus edulis and Juniperus osteosperma may be found. Sites range in elevation from 6,800 to 9,000 

feet. Sites are typically gentle to moderate usually on southerly 

exposures. Understories are varied and vary from shrubby to 

grass dominated. Common species include Amelanchier alnifolia, 

Artemisia tridentata, Quercus gambelii, Symphoricarpos oreophilus, Carex geyeri, Festuca idahoensis, and Sitanion hystrix.

PVT# Potential vegetation type Description

Appendix 6-F—(Continued)
172 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 20

Chapter 6—Developing the LANDFIRE Vegetation and Biophysical Settings Map Unit Classifications for the LANDFIRE Prototype Project

1633 Lodgepole Pine Pinus contort,a in the absence of other shade tolerant conifers,

the sole indicator of this type. Populus tremuloides may occupy 

some sites. The type is found on a variety of landforms, which are 

mostly warm and droughty although it is also found on seasonally moist sites. Elevations range from about 7,600 to 10,000

feet. Understories are commonly sparse and variable. The most 

commonly found species include Juniperus communis, Vaccinium 

scoparium, Vaccinium caespitosum, Arctostaphylos uva-ursi, Berberis repens, and Calamagrostis canadensis. The type is confined

to the northern half of the zone.

1634 Aspen The PVT is characterized by Populus tremuloides that freque

makes up pure stands. The type spans a broad range of environments ranging from high-elevation cool, moist spruce-fir forests to

the relatively dry, low-elevation sagebrush steppes. As a result of 

this wide environmental span, the understory vegetation is highly 

variable. Symphoricarpos oreophilus is a common shrub along 

with varying amounts of Berberis repens, Juniperus communis, 

Rosa woodsii, and Amelanchier alnifolia. Bromus carinatus and 

Elymus glaucus are common grasses and Geranium viscosissimum, Rudbeckia occidentalis, Lathyrus leucanthus, and Lathyrus 

lanszwertii are common forbs.

1641 Pinyon -- Juniper / Mountain Big This type is indicated by the presence of Pinus edulis an

Sagebrush / North Juniperus osteosperma in conjuction with Artemisia tridentata var. 

vaseyana. Sites are at moderate elevations and occupy the upper 

reaches of the Pinyon-Juniper PVTs. Slopes may be gradual to 

steep. This type occurs mostly in the northern part of the zone 

where pinyon pine is less prevalent.

1642 Pinyon -- Juniper / Mountain Big This type is indicated by the presence of Pinus edulis and/

Sagebrush / South Juniperus osteosperma in conjuction with Artemisia tridentata var. 

vaseyana. Sites are at moderate elevations and occupy the upper 

reaches of the Pinyon-Juniper PVTs. Slopes may be gradual to 

steep. This type occurs mostly in the southern part of the zone 

where pinyon pine is more prevalent..

1643 Pinyon -- Juniper / Wyoming -- This type is indicated by the presence of Pinus edulis an

Basin Big Sagebrush / North Juniperus osteosperma in conjuction with Artemisia tridentata

var. wyomingensis or var. tridentata. Sites are at low to moderate 

elevations and occupy the lower reaches of the Pinyon-Juniper 

PVTs. Slopes may be gradual to steep. This PVT commonly intermixes with the Wyoming-Basin Big Sagebrush PVT at the lower 

end. This type is very common in the northern part of the zone 

where pinyon pine is less prevalent.

1644 Pinyon -- Juniper / Wyoming -- This type is indicated by the presence of Pinus edulis an

Basin Big Sagebrush / South Juniperus osteosperma in conjuction with Artemisia tridentata

var. wyomingensis or var. tridentata. Sites are at low to moderate 

elevations and occupy the lower reaches of the Pinyon-Juniper 

PVTs. Slopes may be gradual to steep. This PVT commonly intermixes with the Wyoming-Basin Big Sagebrush PVT at the lower 

end. This type is very common in the southern part of the zone 

where pinyon pine is more prevalent.

1645 Pinyon -- Juniper / Mountain Mahogany Cercocarpus ledifolius is the indicator species for this PVT. Site

are typically on mid elevation, steep slopes and are usually interspersed with Pinyon-Juniper, Douglas-fir, or White Fir PVTs. This

PVT# Potential vegetation type Description

Appendix 6-F—(Continued)
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 

Chapter 6—Developing the LANDFIRE Vegetation and Biophysical Settings Map Unit Classifications for the LANDFIRE Prototype Project

type may occur throughout most of the zone. However, it usually 

occurs in relatively small patches and is of minor importance since 

most sites that are dominated by Cercocarpus ledifolius are probably seral to other PVTs.

1646 Pinyon -- Juniper / Gambel Oak The indicator for this PVT is Quercus gambelii. Sites are typicall

on mid elevation slopes (5,500 ft. to 7,800 ft.) and are frequently

bordered by Pinyon-Juniper PVTs on lower slopes and Douglas-fir

or White Fir PVTs on the upper end. This type may occur throughout most of the zone.

1651 Blackbrush Coleogyne ramosissima is the sole indicator species in this PV

Sites occur in a transition zone between the Mohave and Great 

Basin Deserts and in the Colorado River Drainage in the southern 

portion of the zone. The Salt Desert Shrub PVT commonly intermixes with Blackbrush at the lower end of the PVT.

1652 Salt Desert Shrub These sites are indicated by the presence of various shrub species, mostly in the Chenopodiaceae family. Species representative

of this PVT include Atriplex confertifolia, Atriplex corrugata, Kochia 

americana, Sarcobatus vermiculatus, Sueda torreyana, and/or 

Artemisia spinescens. Sites are low elevation and usually occupy 

basin bottoms that have accumulations of saline or alkaline deposits. Sites may also occur on slopes with fine textured soils derived

from formations such as the Mancos Shale and Tropic Shale. Total 

vegetation cover is usually relatively sparse though may be dense 

in some communities such as black greasewood.

1653 Warm Herbaceous This PVT is represented by mid to low elevation grassland type

generally intermixed with Wyoming-Basin Big Sagebrush PVT and 

the Salt Desert Shrub PVT.

1654 Cool Herbaceous This PVT is represented by mid to high elevation grassland type

generally intermixed with the Mountain Big Sagebrush PVT and 

the Alpine PVT.

1661 Dwarf Sagebrush This PVT includes sites occupied by either Artemisia nova 

Artemisia arbuscula. Sites are harsher than adjacent Artemisia tridentata PVT’s and typically have shallow soil development. These 

communities are mostly at low elevations but may occur much 

higher in limited areas.

1662 Wyoming -- Basin Big Sagebrush Artemisia tridentata var. wyomingensis or var. tridentata are the

indicators of this type. Sites are low elevation and are commonly 

on flat to gradual slopes. These sites commonly intermix with the

Pinyon-Juniper/Wyoming-Basin Big Sagebrush and Mountain Big 

Sagebrush PVTs on the upper end of the type. On lower elevations, it commonly intermixes with the Dwarf Sagebrush and the 

Salt Desert Shrub PVTs. This is a dominant PVT thoughout the 

zone in valley locations.

1663 Mountain Big Sagebrush Artemisia tridentata var. vaseyana is the indicator of this typ

Sites are at moderate to high elevations and are common on unforested areas on the central plateaus. Slopes may be almost flat

to relatively steep. Many other PVTs may border this one depending on elevation, soils and local topographic features.

PVT# Potential vegetation type Description

Appendix 6-F—(Continued)
174 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2

Chapter 6—Developing the LANDFIRE Vegetation and Biophysical Settings Map Unit Classifications for the LANDFIRE Prototype Project

PVT# Potential vegetation type Description

1671 Riparian Hardwood This PVT is indicated by the presence of broadleaf trees such 

Populus angustifolia and Acer negundo. Varying amounts of Acer 

grandidentatum, Betula occidentalis, Populus acuminate, and 

Populus fremontii are also present. Juniperus scopulorum may be 

present in limited amounts. Sites are usually low elevations along 

major drainages though they may extend into the mountains as 

narrow stringers along streams. Understories are highly variable. 

Rosa spp. is the most common shrub along with Cornus serice.

Smilacina stellata is a common forb and Poa pratensis is the 

major grass.

1672 Riparian Shrub This type is found adjacent to major drainages throughout the

zone. A number of species of Salix plus Alnus incana, Betula occidentalis, Lonicera involucrate, Cornus stolonifera, Ribes lacustre, 

and Rhus aromatica var. trilobata are the major types found in the 

community.

1673 Wetland Herbaceous This community is composed of mixtures of wetland forbs a

grasses usually found in high mountain basins. Soils are seasonally saturated. Common species include Calamagrostis canadensis, Streptopus amplexifolius, Senecio triangularis, and Equisetum 

arvense.

1680 Alpine These sites include all vegetated areas above treeline. Sites are

generally above 11,000 ft. in elevation and occur in the Tushar

Uinta, and Wasatch Mtn Ranges. Grasses, sedges, forbs, and/or 

dwarf willows may dominate areas.

Appendix 6-F—(Continued)
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 1

Chapter 6—Developing the LANDFIRE Vegetation and Biophysical Settings Map Unit Classifications for the LANDFIRE Prototype Project

Appendix 6-G – Zone 19 potential vegetation type legend and 

descriptions ______________________________________________________

PVT# Potential vegetation type Description

1902 Western Redcedar This is a small PVT found only in the northwest corner of the zone. Along with Thuja

plicata, other common tree associates are Pseudotsuga menziesii, Picea engelmannii, Larix occidentalis, and Tsuga heterophylla plus lesser amounts of Pinus monticola, Pinus contorta, and Abies grandis. Sites are typically very moist and warm 

bottomland or northerly exposures and range in elevation from 2,000 to 5,000 feet. 

Understories are dominated by a variety of forbs including Clintonia uniflora with the 

shrubs Menziesia ferruginea and Oplopanax horridum found on some sites. Under 

dense stand conditions, understories may be very limited.

1914 Grand Fir -- White Fir This type is represented by Abies grandis within the zone. Common associates 

Pseudotsuga menziesii, Picea engelmannii, and Pinus contorta. Minor amounts of 

Populus tremuloides and Pinus ponderosa may also be present. In the northwestern 

portion of the zone, Larix occidentalis is a major component and Pinus monticola

can be found in the extreme northwest corner in minor amounts. It is found on 

warm, moist sites between 2,500 and 5,500 feet elevation. Vaccinium spp., Calamagrostis rubescens, and Xerophyllum tenax, along with a wide variety of forbs and 

shrubs, may be found in a relatively dense understory.

1920 Spruce -- Fir / Montane / This PVT is represented by Picea engelmannii and Abies lasiocarpa. Pseudotsug

Western Larch menziesii is a major component along with Pinus contorta and in the northwest corner. Larix occidentalis may be common. This type represents the lower elevations 

where Abies lasiocarpa is found. Sites are generally moist and cool however they 

are warm enough to support Pseudotsuga menziesii. Elevations range from 4,500 to

6,500 feet. Understories are dominated by Vaccinium globulare, Xerophyllum tenax, 

and Arnica latifolia with Menziesia ferruginea common on some sites.

1921 Spruce -- Fir / Montane / This PVT is represented by Picea engelmannii and Abies lasiocarpa. Pseudots

Douglas-fir	 menziesii is a major component along with Pinus contorta. This type represents the 

lower elevations where Abies lasiocarpa is found. Sites are generally moist and cool 

however they are warm enough to support Pseudotsuga menziesii. Elevations range 

from 4,500 to 6,500 feet. Understories are dominated by Vaccinium globulare, Xerophyllum tenax, and Arnica latifolia, with Menziesia ferruginea common on some sites

1922 Spruce -- Fir / Timberline These areas represent the highest elevations of the subalpine area where a closed

forest can develop. Picea engelmannii, Abies lasiocarpa, and Pinus albicaulis are 

all major species in the PVT, along with lesser amounts of Pinus contorta. The PVT 

is found along the major ridges above 7,000 feet throughout the zone. Understories 

are dominated by Vaccinium scoparium along with Luzula hitchcockii and lesser 

amounts of Xerophyllum tenax with Menziesia ferruginea on some sites. On some 

sites, this understory may be very sparse.

1924 Spruce -- Fir / Subalpine This PVT is found on wet sites above the limits of Pseudotsuga menziesii. Picea engelmannii is the major tree species along with Abies lasiocarpa and Pinus contort

Minor amounts of Pinus albicaulis may also be present. Elevations range from 6,000 

to 8,000 feet and stands commonly are adjacent to wet meadows. Understories are 

mixtures of Calamagrostis canadensis and Vaccinium scoparium along with Arnica 

latifolia and a variety of other forbs and shrubs.

1930	 Douglas-fir / Ponderosa Pine / 	 This type is found on warm, dry sites where Pseudotsuga menziesii is the 

Western Larch indicated climax; however, while Pseudotsuga menziesii may be present, the stand 

is dominated by fire maintained Pinus ponderosa. With the lack of disturbance, 

Pseudotsuga menziesii may eventually dominate the site. Minor amounts of Larix 

occidentalis may be found in the northwestern portion of the zone and pockets of 

Pinus contorta on the cooler, moister sites. Elevations range from about 2,700 to 

6,400 feet. Understories are about equally divided between forb or grassy sites and

shrub communities with Calamagrostis rubescens, Pseudoroegneria spicata, Carex 

geyeri, Balsamorhiza sagittata, Arctostaphylos uva-ursi, and Symphoricarpos albus

as major species.
176 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 20

Chapter 6—Developing the LANDFIRE Vegetation and Biophysical Settings Map Unit Classifications for the LANDFIRE Prototype Project

1931	 Douglas-fir / Ponderosa 	 This type is found on warm, dry sites where Pseudotsuga menziesii is the 

	 Pine / Douglas-fir	 indicated climax; however, while Pseudotsuga menziesii may be present, the stand

	 	 is dominated by fire maintained Pinus ponderosa. With the lack of disturbance, Pseudotsuga menziesii may eventually dominate the site. Minor amounts of Pinus contorta

are found on the cooler, moister sites. Elevations range from about 2,700 to 6,400

feet. Understories are about equally divided between forb or grassy sites and shrub

communities with Calamagrostis rubescens, Pseudoroegneria spicata, Carex geyeri, 

Balsamorhiza sagittata, Arctostaphylos uva-ursi, and Symphoricarpos albus as major 

species.

1932	 Douglas-fir / Lodgepole Pine	 This type is indicated by the combination of Pseudotsuga menziesii and Pinus 

contorta. Minor amounts of Larix occidentalis may be found in the northwestern 

portion of the zone. The type is found on relatively cold sites at the upper elevations 

of Pseudotsuga menziesii occurrence (4,800 to 7,000 feet). Calamagrostis rubescens and Arnica spp., along with some Linnaea borealis, Vaccinium globulare, and

Xerophyllum tenax typically dominate understories.

1934	 Douglas-fir / Timberline Pine	 This PVT is found on dry sites that are to cold for Pinus ponderosa. Pseudotsuga 

menziesii dominates most sites. East of the Continental Divide it may share dominance with Pinus flexilis on dry, wind-exposed slopes. Juniperus scopulorum is a 

minor component in some stands. Sites are typically cool and dry and range from 

4,800 to 8,200 feet in elevation. Understory vegetation may be sparse and frequently dominated by bunchgrasses including Pseudoroegneria spicata and Festuca idahoensis or scattered forbs. Shrubs such as Artemisia spp. and Juniperus communis

may be common on some sites.

1936	 Douglas-fir / Douglas-fir	 Pseudotsuga menziesii is the sole indicator of this type. Minor amounts of Pinus 

contorta may be present and occasionally Larix occidentalis or Pinus ponderosa. 

Sites are normally at the moist, cool end for Pseudotsuga menziesii and located on 

benches or north slopes ranging from 2,500 feet to about 6,000 feet. A minor amount 

may be found at elevations up to 6,700 ft. on southerly aspects. Shrubby understories 

composed of Physocarpus malvaceus, Symphoricarpos albus, and Linnaea borealis

are common along with Calamagrostis rubescens, Carex geyeri ,and Arnica cordifolia.

1940 Lodgepole Pine Pinus contorta is the only indicator of this fire maintained type. During long fire fr

periods, Picea engelmannii and Abies lasiocarpa will generally become abundant. 

Minor amounts of Pseudotsuga menziesii may also be present. Stands are typically 

found between 6,000 to 7,200 feet on cool to cold sites with moderate moisture. 

Understories composed of the low shrubs Vaccinium scoparium; Vaccinium caespitosum, and Linnaea borealis are common along with Calamagrostis rubescens and 

Carex geyeri.

1942 Ponderosa Pine Pinus ponderosa is the only indicator species for this type. The only other conife

commonly represented is Juniperus scopulorum. Pinus flexilis may be found on 

some sites as an accidental. Sites range in elevation from the lower timberline, 

which is from about 2,600 feet, up to 5,000 feet in warm, dry environments associated with the larger valleys in the zone. Isolated stands may be found at higher 

elevations on steep southerly slopes. Understories are usually open and dominated 

by bunchgrasses including Pseudoroegneria spicata, Festuca idahoensis, and Festuca scabrella. Shrubs such as Symphoricarpos albus, Amelanchier alnifolia, and

Purshia tridentata are common on some sites.

1944 Timberline Pine / Limber Pine This PVT represents the lower elevation timberline where conditions become 

dry to support tree growth. Pinus flexilis is the major overstory species along with 

some Juniperus scopulorum and scattered Pseudotsuga menziesii. Sites are 

generally marginal for tree growth and trees are short and open-grown. The type 

is generally confined to the east side of the Continental Divide between 4,000 and

8,000 feet. Lower elevation sites are dominated by Pseudoroegneria spicata while 

the higher elevations tend to be dominated by Juniperus communis. Artemisia spp.

may be common on some sites.

PVT# Potential vegetation type Description

Appendix 6-G—(Continued)
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 1

Chapter 6—Developing the LANDFIRE Vegetation and Biophysical Settings Map Unit Classifications for the LANDFIRE Prototype Project

1946 Timberline Pine / Whitebark Pine This PVT is characterized by stands that are open and wind stunted. Pinus albicaulis is the principle species along with varying amounts of Picea engelmannii. Abie

lasiocarpa may be present but normally very stunted and growing in the protection 

of the other two species. Site conditions are cold and dry and stands are usually 

found above about 7,800 feet. Understories may be depauperate and composed of 

a mixture of Vaccinium scoparium, Juniperus communis, Phyllodoce glanduliflora or

empetriformis, Festuca idahoensis, and Luzula hitchcockii.

1950 Rocky Mountain Juniper Juniperus scopulorum is the main indicator species in this PVT, although Juniperus

osteosperma may also indicate this type. The Rocky Mountain Juniper PVT may be 

wide-ranging found on both sides of the divide in Montana. Communities are usually 

open with Juniperus cover averaging around 25 percent. Common associated species include: Artemisia nova, Artemisia tridentata ssp. vaseyana, Pseudoroegneria 

spicata, Festuca idahoensis ,and Koeleria macrantha. This is a minor woodland 

type in the zone.

1952 Riparian Hardwood This is the only PVT where broadleaf trees are the major component. It is limited

to the riparian area along the major rivers in the zone and dominated by Populus 

trichocarpa and some Populus tremuloides. Minor amounts of Pinus ponderosa, 

Pseudotsuga menziesii, and Pinus contorta may also be present. This type generally represents the lowest elevations in the zone and is rarely found outside of the 

major river valleys. Understories appear to be highly variable with Cornus stolonifera, Rosa spp., Salix spp., and Juniperus spp. common with a wide variety of forbs 

and grasses also present.

1960 Riparian Shrub This type is found adjacent to major drainages throughout the zone. A number of

species of Salix plus Alnus incana, Betula occidentalis, Lonicera involucrate, Cornus 

stolonifera, Ribes lacustre, and Rhus aromatica var trilobata are the major types 

found in the community.

1962 Mountain Mahogany Cercocarpus ledifolius is the indicator species for this PVT. Sites are typically on mid

elevation, steep slopes and are usually interspersed with Rocky Mountain Juniper 

and Douglas-fir PVTs. This type may occur throughout most of the zone. However, it

usually occurs in relatively small patches.

1964 Dry Shrub Dasiphora floribunda is an indicator of this PVT on moderately moist Monta

grassland and shrub foothill communities east of the Continental Divide. This is a 

productive mountain shrub type found under relatively mesic to dry site conditions 

with limited occurrence in the zone. It occurs at mid to upper elevations between 

4,500 ft and 8,500 ft

1965 Dry Shrub / Conifer Dasiphora floribunda is an indicator of this PVT on moderately moist Montana

grassland and shrub foothill communities east of the Continental Divide usually 

along with Pseudotsuga menziesii, which indicate conifer encroachment in this PVT. 

This is a productive mountain shrub type found under relatively mesic to dry site 

conditions with limited occurrence in the zone. It occurs at mid to upper elevations 

between 4,500 ft and 8,500 ft. This PVT has a conifer encroachment succession

pathway.

1970 Dwarf Sagebrush Complex This PVT is associated with nearly pure stands or mixtures of “low sagebrush” species. The indicator species are Artemisia arbuscula and A. nova and are usually

associated with areas having little soil profile development in desert valleys and

on west and south exposures along the lower slopes of the high desert foothills. It 

occurs most abundantly at elevations between 4,900 to 7,000 feet where annual

precipitation ranges between 7 and 18 inches

PVT# Potential vegetation type Description

Appendix 6-G—(Continued)
178 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 20

Chapter 6—Developing the LANDFIRE Vegetation and Biophysical Settings Map Unit Classifications for the LANDFIRE Prototype Project

PVT# Potential vegetation type Description

1971 Dwarf Sage Complex / Conifer This PVT is associated with nearly pure stands or mixtures of “low sagebrus

species and possible conifer encroachment. The indicator of this type are Artemisia 

arbuscula and A. nova and are usually associated with areas having little soil profile

development in desert valleys and on west and south exposures along the lower 

slopes of the high desert foothills. It occurs most abundantly at elevations between 

4,900 to 7,000 feet where annual precipitation ranges between 7 and 18 inches

This PVT has a conifer encroachment succession pathway.

1972 Mountain Big Sagebrush Artemisia tridentata ssp. vaseyana is a major indicator species of this PVT in th

Complex zone. It is one of the more productive grassland sites. Mountain Big Sagebrush PVT 

extends from generally above Wyoming Big Sagebrush to forest edges and at times 

borders the subalpine area. Though present throughout the zone, it is most abundant in Idaho and in Montana generally south and east of Missoula.

1973 Mountain Big Sagebrush Artemisia tridentata ssp. vaseyana is a major indicator species of this PVT in 

Complex / Conifer zone along with conifer encroachment. It is one of the more productive grassland 

sites. Mountain Big Sagebrush PVT extends from generally above Wyoming Big 

Sagebrush to forest edges and at times borders the subalpine area. Though present 

throughout the zone, it is most abundant in Idaho and in Montana generally south 

and east of Missoula. This PVT has a conifer encroachment succession pathway.

1974 Threetip Sagebrush Artemisia tripartita is the indicator of this PVT. It is a minor type in southwest Montana but becomes more abundant in the Idaho portion of the zone. It generally occurs on gentle, alluvial slopes or benches with moderately deep soils. This speci

is set apart by other sagebrush types in the zone by its ability to resprout after fire.

1975 Threetip Sagebrush / Conifer The Threetip Sagebrush is the indicator of this PVT. It is a minor type in southwest

Montana, but becomes more abundant in the Idaho portion of the zone. It generally 

occurs on gentle, alluvial slopes or benches with moderately deep soils. This species is set apart by other sagebrush types in the zone by its ability to resprout after 

fire. . This PVT has a conifer encroachment succession pathway.

1976 Wyoming -- Basin Big The Wyoming-Basin Big Sagebrush PVT is a major type in the southern half of th

Sagebrush zone. Both Artemisia tridentata ssp. tridentata and Artemisia tridentata ssp. wyomingensis are represented in this PVT.

1977 Wyoming -- Basin Big The Wyoming-Basin Big Sagebrush PVT is a major shrub type in the southern hal

Sagebrush / Conifer of the zone. Both Artemisia tridentata ssp. tridentata and Artemisia tridentata ssp. 

wyomingensis are represented in this PVT. This PVT has a conifer encroachment 

succession pathway.

1980	 Wetland Herbaceous	 This type is confined to riparian stream areas and high mountain basins. Soils are

seasonally saturated. Common species include Calamagrostis canadensis, Streptopus amplexifolius, Senecio triangularis, and Equisetum arvense.

1982 Alpine These sites include all vegetated areas above treeline. Sites are generally above

9000 ft. in elevation and occur in small patches in various mountain ranges throughout the zone. Grasses, sedges, forbs, and/or dwarf willows may dominate areas.

1984 Fescue Grasslands The Fescue Grassland PVT is indicated by Festuca idahoensis and Festuca altaic

Pseudoroegneria spicata is another major component as are a number of other cool 

season grasses depending on soil and moisture conditions. In general, this PVT occurs at low to moderate elevations.

1985 Fescue Grasslands / Conifer The Fescue Grassland PVT is indicated by Festuca idahoensis and Festuca altaica.

Pseudoroegneria spicata is another major component as are a number of other cool 

season grasses depending on soil and moisture conditions. In general, this PVT 

occurs at low to moderate elevations. This PVT has a conifer encroachment succession pathway.

Appendix 6-G—(Continued)
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 1

Chapter 6—Developing the LANDFIRE Vegetation and Biophysical Settings Map Unit Classifications for the LANDFIRE Prototype Project

1986 Bluebunch Wheatgrass The Bluebuch Wheatgrass PVT is represented by grassland communities including

Pseudoroegneria spicata/Bouteloua gracilis, Pseudoroegneria spicata/Pascopyrum 

smithii, and Pseudoroegneria spicata/Poa secunda along with Festuca altaica/Pseudoroegneria spicata. It is generally found east of the continental divide on toe-slopes 

of the foothills and steeper slopes and primarily occurs on southern slopes.

1987 Bluebunch Wheatgrass / The Bluebuch Wheatgrass PVT is represented by grassland communitie

Conifer including Pseudoroegneria spicata/Bouteloua gracilis, Pseudoroegneria spicata/

Pascopyrum smithii, and Pseudoroegneria spicata/Poa secunda along with Festuca 

altaica/Pseudoroegneria spicata. It is generally found east of the continental divide 

on toe-slopes of the foothills and steeper slopes and primarily occurs on southern 

slopes. This PVT has a conifer encroachment succession pathway.

PVT# Potential vegetation type Description

Appendix 6-G—(Continued)

USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 

Chapter 7—Mapping Potential Vegetation Type for the LANDFIRE Prototype Project

Chapter

7

In: Rollins, M.G.; Frame, C.K., tech. eds. 2006. The LANDFIRE 

Prototype Project: nationally consistent and locally relevant 

geospatial data for wildland fire management. Gen. Tech. Rep. 

RMRS-GTR-175. Fort Collins: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research Station.

Introduction ____________________

Mapped potential vegetation functioned as a key 

component in the Landscape Fire and Resource Management Planning Tools Prototype Project (LANDFIRE 

Prototype Project). Disturbance regimes, vegetation 

response and succession, and wildland fuel dynamics 

across landscapes are controlled by patterns of the environmental factors (biophysical settings) that entrain 

the physiology and distribution of vegetation. These 

biophysical characteristics of landscapes are linked to 

stable vegetation communities that occur in the absence 

of disturbance (Arno and others 1985; Cooper and 

others 1991; Ferguson 1989; Pfister and Arno 1980; 

Pfister and others 1977). In the LANDFIRE Prototype 

Project, these stable vegetation community types were 

referred to as potential vegetation types (PVTs). Further, 

the concept of potential vegetation was used as a basis 

for developing biophysical map units that were critical 

for developing the LANDFIRE wildland fuel and fire 

regime products. In the LANDFIRE Prototype Project, 

maps of potential vegetation facilitated linkage of the 

ecological process of succession to simulation landscapes 

used as input the LANDSUMv4 landscape fire succession model for modeling historical vegetation reference 

conditions and historical fire regimes (Long and others, 

Mapping Potential Vegetation Type for the 

LANDFIRE Prototype Project

Tracey S. Frescino and Matthew G. Rollins

Ch. 9). In addition, maps of PVT were used to guide the 

parameterization and calibration of the landscape fire 

succession model LANDSUMv4 (Pratt and others, Ch. 

10) and to stratify vegetation communities for mapping 

current vegetation and wildland fuel mapping (Zhu and 

others, Ch, 8; Keane and others, Ch. 12).

Analysis of the biophysical characteristics of landscapes is commonly used to quantify distributions of 

vegetation along biophysical gradients (Bray and Curtis 

1957; Gleason 1926; Whittaker 1967). Previous research 

has employed cluster analysis and ordination techniques 

to delineate biophysical gradients and link them to corresponding potential vegetation (Galiván and others 

1998). Other research has used supervised classification 

methods or predictive vegetation mapping techniques 

(Franklin 1995) to link potential natural vegetation with 

biophysical gradients (Keane and others 2000; Keane 

and others 2001; Lenihan and Neilson 1993; Rollins and 

others 2004) and gradients of climate, topography, and 

soils (Brzeziecki and others 1993; Jensen and others 

2000).

We developed PVT map unit classifications based on 

species’ shade tolerance and moisture tolerance to link 

LANDFIRE reference plot data to unique environmental 

conditions or biophysical settings. Here, we define biophysical setting as the suite of biotic and abiotic factors 

that affect the composition, structure, and function of 

vegetation. Our main assumption was that the shade 

tolerant species would serve as unique indicators of 

biophysical conditions (Daubenmire 1967). Because of 

dynamic climate and ecosystem complexities, we did 

not assume that a stable climax community would exist 

without the influence of disturbance (Keane and Rollins, 

Chapter 3).
182 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 20

Chapter 7—Mapping Potential Vegetation Type for the LANDFIRE Prototype Project

Initially, we investigated an unsupervised clustering 

approach to stratify the landscape using a series of indirect biophysical gradients (Hargrove and Luxmore 1998; 

Hessburg and others 2000a, Hessburg and others 2000b). 

This approach successfully delineated unique biophysical settings, but the categories were not significantly 

correlated to patterns of vegetation. Alternatively, we 

used a supervised predictive modeling approach based 

on ground-referenced data to explicitly link biophysical 

gradients to potential vegetation. This approach provided 

an objective and repeatable method that could be linked 

directly to vegetation patterns identifiable in the field. 

This chapter describes the process used for mapping potential vegetation for the LANDFIRE Prototype Project 

and provides recommendations for generating maps of 

potential vegetation for the national implementation of 

LANDFIRE.

Methods _______________________

The LANDFIRE Prototype Project involved many 

sequential steps, intermediate products, and interdependent processes. Please see appendix 2-A in Rollins 

and others, Ch. 2 for a detailed outline of the procedures followed to create the entire suite of LANDFIRE 

Prototype products. This chapter focuses specifically 

on the procedure followed in developing the potential 

vegetation maps, which served as spatial templates for 

nearly all mapping tasks in the LANDFIRE Prototype 

Project.

Field-referenced Data

Comprehensive field-based reference data are critical 

for implementing a supervised mapping application, and 

these “training data” must be a statistically robust sample 

of the population. The LANDFIRE reference database 

(LFRDB) was designed to meet these criteria and provided an excellent source of consistent, comprehensive 

reference data from which to develop training sites for 

our predictive landscape models (Caratti, Chapter 4). 

Georeferenced field locations were obtained from the 

LFRDB and assigned PVTs based on hierarchical, floristic keys organized along gradients of shade tolerance 

and moisture tolerance developed a priori (Long and 

others, Chapter 6). The development of the keys began 

with existing national classifications (Kuchler 1975) and 

was then revised by regional (Quigley and others 1996) 

and local (Pfister and others 1977) classifications. The 

keys were further revised using the LFRDB, an extensive literature review, and review by regional ecological 

experts. To qualify as a separate class, individual PVTs 

had to fit the criteria of being identifiable in the field, 

scalable, mappable, and model-able (See Keane and 

Rollins, Ch. 3 and Long and others, Ch. 6).

The keys divided PVTs into three physiological life 

forms, forest, shrub, and herbaceous, with forest PVTs 

following a shade tolerance gradient and shrub and 

herbaceous PVTs following moisture gradients. Initially, 

Zone 16 had 13 classes of forest PVTs, 10 classes of shrub 

PVTs, and 3 classes of herbaceous PVTs. Distinguishing 

between classes requires a sufficient number of training 

plots for each class. We grouped classes having fewer 

than 20 training plots with other classes, resulting in 10 

forest classes, 8 shrub classes, and 3 herbaceous classes 

(table 1). To minimize the number of classes in Zone 19 

and in an effort to increase overall map accuracy, we 

implemented the classification key for this zone under 

the criterion that a minimum of 30 training plots were 

necessary for a PVT to form a unique class. Table 2 

shows Zone 19 PVT classes and the number of training 

plots from the database assigned to each class.

Spatial Data

The biophysical gradient layers included variables 

created using WXFIRE, an ecosystem simulation model 

developed by R.E. Keane at the USDA Forest Service, 

Rocky Mountain Research Station, Missoula Fire Sciences Laboratory in Missoula, Montana (Keane and 

others 2006; Keane and Rollins, Ch. 3) and variables 

from the National Elevation Database (http://ned.usgs.

gov ). The WXFIRE model integrates DAYMET climate data (Running and Thornton 1996; Thornton and 

others 1997; Thornton and others 2000) with landscape 

data and site specific parameters (for example, soils 

and topography) and generates spatially explicit maps 

of climate and ecosystem variables that integrate landscape-weather interactions (See Holsinger and others, 

Ch. 5 for details about these variables and how they were 

derived). For topographic gradients, we used variables 

from the National Elevation Database, including elevation, derivatives of slope, aspect, a classified landform 

variable, and a topographic position index. This process 

resulted in a total of 38 biophysical gradients available for 

use as independent variables in our predictive landscape 

models of PVT. We reviewed correlation matrices and 

principle component analyses to reduce (winnow) this 

list of variables used in the modeling process. For Zone 

16, we used 21 variables (table 3) and for Zone 19, 22 

variables (table 4).
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 

Chapter 7—Mapping Potential Vegetation Type for the LANDFIRE Prototype Project

Table 1—Zone 16 codes, life forms, names, and the number of training sites and test sites by PVT. Life form

categories include F (forest), S (shrub), and H (herbaceous).

Life Number of Number of

Code form Name training sites test sites

 1 F Spruce - Fir / Blue Spruce 

2 F Spruce - Fir / Spruce - Fir  1188

	 3	 F	 Grand fir - White Fir	 439	 40

	 4	 F	 Douglas-fir / Lodgepole Pine - Timberline Pine	 65	 3

	 5	 F	 Douglas-fir / Douglas-fir	 263	 19

6 F Lodgepole Pine - Timberline Pine  1

7 F Ponderosa Pine 205  

8 F Pinyon - Juniper / Mountain Big Sagebrush 

9 F Pinyon - Juniper / Wyoming - Basin Big Sagebrush 1052 

 10 F Riparian Hardwood  

 11 S Riparian Sh

 12 S Blackbrush - Chaparral - Dry Deciduous Shrub 2

 13 S Dwarf Sagebrush

 14 S Salt Desert Shrub

 15 S Mountain Mahogany 6

 16 S Gambel Oak  

 17 S Wyoming - Basin Big Sagebrush  

 18 S Mountain Big Sagebrush 

 19 H Wetland Herbaceous 57

20 H Alpine  47

21 H Herbaceous  1

Table 2—Zone 19 codes, life forms, names, and the number of training sites and test sites by

PVT. Life form categories include F (forest), S (shrub), and H (herbaceous).

Life Number of Number of

Code form Name training sites test sites

 1 F Western Redcedar  

2 F Grand Fir - White Fir  

 3 F Spruce - Fir / Montane  

 4 F Spruce - Fir / Timberline

5 F Spruce - Fir / Subalpine  11

	 6	 F	 Douglas-fir / Ponderosa Pine	 363	 56

	 7	 F	 Douglas-fir / Lodgepole Pine	 546	 88

	 8	 F	 Douglas-fir / Timberline Pine	 161	 26

	 9	 F	 Douglas-fir / Douglas-fir	 947	 125

 10 F Lodgepole Pine  46

 11 F Ponderosa Pine 

 12 F Timberline Pine / Limber Pine 

 13 F Timberline Pine / Whitebark Pine

 14 F Rocky Mountain Juni

 15 F Riparian Hardwood 28

 16 S Riparian Shrub 

 17 S Mountain Mahogany 

 18 S Dry Shrub

 19 S Dwarf Sagebrush Complex 68

20 S Mountain Big Sagebrush Complex 249

21 S Threetip Sagebrush  18

22 S Wyoming - Basin Big Sagebrush Complex 514

23 H Wetland Herbaceous  

24 H Alpine 

25 H Fescue Grasslands  17

26 H Bluebunch Wheatgrass  
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Chapter 7—Mapping Potential Vegetation Type for the LANDFIRE Prototype Project

Table 3—Zone 16 PVT predictor layers. See Holsinger and others, Ch. 5,

table 6 for biological significance of each layer.

Code Units Description

aet kg H20 yr–1 Actual evapotranspiration

dsr days Days since last rain

dss days Days since last snow

gsws -MPa Growing season water stress

mc1	 %	 NFDRS – 1-hr wood moisture content

outflow	 kg H20 m–2 day–1 Soil water lost to runoff and ground

pet kg H20 yr–1 Potential evapotranspiration

ppt cm Precipitation

psi -MPa Water potential of soil and leaves

psi.max -MPa Maximum annual leaf water potential

rh % Relative humidity

srad.tg kJ m–2 day–1 Total solar radiation

tmin °C Minimum daily temperature

vmc Scalar Volumetric water content

sdepth cm Soil depth

elev m Elevation

aspect 8 classes Aspect class*

slope % Slope

lndfrm 10 classes Landform

trmi Index (0-1) Topographic relative moisture inde

posidx Index (0-1) Topographic position inde

*Aspect classes – 0:Level; 1:North; 2:North-East; 3:East; 4:South-East; 5:South; 6:

South-West; 7:West; 8:North-West

**Landform classes – 1:Vally flats; 2:Toe slopes; 3:Gently sloping ridges and hills; 4:

Nearly level plateaus and hills; 5:Very moist steep slopes; 6:Moderately moist steep 

slopes; 7:Moderately dry slopes; 8:Very dry steep slopes; 9:Cool aspect cliffs, canyons; 

10:Hot aspect cliffs, canyons

Table 4—Zone 19 PVT predictor layers. See Holsinger and others, Ch. 5,

table 6 for biological significance of each layer.

Code Units Description

aet kg H20 yr–1 Actual evapotranspiration

dday °C Degree-days

dss days Days since last snow

evap kg H20 m–2 day–1 Evaporation

g.sh M sec–1 Leaf-scale stomatal conductance

gsws -MPa Growing season water stress

outflow	 kg H20 m–2 day–1 Soil water lost to runoff and ground

pet kg H20 yr–1 Potential evapotranspiration

ppfd Umol m–2	 Photon flux density

ppt cm Precipitation

psi -MPa Water potential of soil and leaves

snowfall kg H20 m–2 day–1 Snowfall

srad.fg KW m–2 day–1	 Solar radiation flux to the ground

tmax °C Maximum daily temperature

tmin °C Minimum daily temperature

tnight °C Nighttime daily temperature

trans kg H20 m–2 day–1 Soil water transpired by canopy

vmc Scalar Volumetric water content

sdepth cm Soil depth

elev m Elevation

posidx index (0-1) Topographic position inde

slope % Slope

*Aspect classes – 0:Level; 1:North; 2:North-East; 3:East; 4:South-East; 5:South;

6:South-West; 7:West; 8:North-West
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 1

Chapter 7—Mapping Potential Vegetation Type for the LANDFIRE Prototype Project

Modeling and Mapping Process

Classification trees, also known as decision trees, have 

been widely applied in landscape mapping applications 

(Brown de Colstoun and others 2003; Friedl and Brodley 

1997; Hansen and others 2000; Joy and others 2003; 

Moisen and others 2003, Moore and others 1991; Rollins 

and others 2004). Classification trees were originally 

developed for artificial intelligence research to identify 

patterns and recognize these patterns in similar situations using a hierarchical structure of rules (Quinlan 

1986). The rules are constructed from available training data where observations are delineated into smaller 

subsets of more homogenous classes. Specifically, the 

classification tree algorithm considers each predictor 

variable and examines all n-1 ways to split the data into 

two clusters. For every possible split of each predictor 

variable, the within-cluster impurity is calculated. The 

first split in the tree is that which yields the smallest 

overall within-cluster impurity. This process is repeated 

for each branch defined by the previous split (Breiman 

and others 1984).

Classification trees are well-suited to vegetation mapping because they accommodate common conceptions 

that vegetation has a nonlinear, non-normal response 

to environmental gradients (Austin and others 1984). 

In addition, they are nonparametric models, meaning 

they make no underlying assumptions about the distribution of the data, and they are adaptable for nonlinear 

relationships between the predictors and the response 

(Friedl and Brodley 1997). Classification trees are also 

valuable because they are robust, are able to incorporate 

both categorical and continuous variables, and are relatively insensitive to outliers (Breiman and others 1984). 

Furthermore, for a large project such as LANDFIRE, 

classification trees offer the advantage that models are 

generated and executed quickly.

The classification trees for modeling PVTs were generated using the commercially available See5 machinelearning algorithm (Quinlan 1986, 1993; Rulequest 

Research 2004) and were applied within an ERDAS 

Imagine (ERDAS, Inc. 2001) interface. See5 uses a 

classification and regression tree (CART) approach 

for constructing a tree, generating a tree with high 

complexity, and pruning it back to a more simple tree 

by merging classes (Breiman and others 1984). This 

pruning process was found to improve the efficiency of 

the model and minimize the classification error (Breiman and others 1984). We used the boosting feature of 

See5 to improve the accuracy of the model (Friedl and 

others 1999; Quinlan 1986). In the boosting procedure, 

multiple trees are built in an iterative process and, each 

tree “learns” from the misclassification errors of the 

previously built tree (Bauer and Kohavi 1999). The final 

tree is selected from all the trees based on a weighted 

vote of the predictions. We also employed other features 

of See5 including winnowing, which excludes variables 

that are not relevant in the model, and differential misclassification cost weighting, which assigns more weight 

to classes with more costly classification errors.

Although not fully automated, the process for mapping 

PVTs was simplified using a suite of tools developed 

by Earth Satellite Corporation (2003) in support of the 

National Land Cover Database (NLCD 2000). These 

tools were developed to integrate the Rulequest See5/

C5.0 software package with the ERDAS Imagine imageprocessing software. For mapping PVTs, we used the 

sampling tool to set up See5 input files and the classifier 

tool to generate the final map and a coinciding map of 

error or confidence. The sampling tool allows a user to 

input a spatially explicit layer of field-referenced training data as the dependent variable and multiple spatially 

explicit gradient layers as the independent variables and 

then outputs the input files needed to run See5. The 

classifier tool applies the output tree model from See5 

over the specified spatial extent or a specified masked 

extent.

To meet the input requirements of See5 and to improve 

the efficiency of the model-making process, we followed 

three pre-processing rules: (1) all layers must be ERDAS Imagine images, (2) all layers must have the same 

number of rows and columns, and (3) all layers must be 

size 16-bit or smaller, with positive values. A few data 

preparation steps were necessary to follow these rules. 

The biophysical gradient layers are output from WXFIRE as Arc/Info grids with float data values. We ran an 

Arc/Info AML (Arc Macro Language) to translate and 

dilate or “stretch” the grids to an unsigned, 16-bit integer 

format; converted the grids to ERDAS Imagine images 

using a batch setup in ArcGis 8.0 (ESRI Inc. 2001), and 

masked the images in Imagine using a buffered mask of 

the zone region (the zone boundaries). Through the entire 

LANDFIRE process, we used a 3-km buffer around the 

zone boundary. This buffer facilitated edge matching 

and reduced the edge effects in modeling historical fire 

regimes (Pratt and others, Ch. 10) The topographic and 

soil gradient layers were also converted to images and 

masked with the buffered zone region. We generated 

the spatially explicit dependent layer within ArcGis 8.0 

using the spatial analyst tool to convert a data table to an 

image and set the extent to match the gradient images. 

Prior to creating this layer, we performed exploratory 

data analyses, both spatial and non-spatial, to look for 
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Chapter 7—Mapping Potential Vegetation Type for the LANDFIRE Prototype Project

and remove any major outliers or unusual patterns in the 

data. The output from the sampling tool includes a “data 

file,” which contains values from the model response and 

the corresponding value of the model predictor layers 

for each georeferenced training site, and a “names file” 

identifying the model input names and data types.

For each prototype mapping zone, we built three different See5 classification trees and generated three different 

maps. The first classification tree was generated using a 

binary response variable describing forest and non-forest 

PVTs. The resulting map was used to stratify the zones 

to improve the performance of the PVT models. The 

other two classification trees were generated and applied 

to forest PVTs within the predicted forested areas and 

non-forest PVTs within the predicted non-forested areas. 

The final map was a combined product of the forest PVT 

predictions and the non-forest PVT predictions from 

each zone. For Zone 16, classes of agriculture, barren, 

open water, and urban/developed were masked from the 

Zone 16 cover type map and were considered non-forest 

types. For Zone 19, we masked only classes of barren, 

open water, and snow/ice following the assumption there 

is a potential for vegetation to grow on agricultural and 

urban lands. These classes had not been mapped for 

Zone 19 at this stage of the mapping process and were 

masked after the final PVT map was generated.

For the forested and non-forested stratification map, 

all training plots classified as forest PVTs were grouped 

into one class and the training plots classified as shrub or 

herbaceous PVTs into another class. There were a total 

of 4,032 training sites for Zone 16 with 4,032 forested 

plots and 929 non-forested plots (table 1). For Zone 19, 

there were a total of 8,264 training sites, 6,609 forested 

plots and 1,655 non-forested plots (table 2). Multiple 

models were executed exploring the different features 

of See5, including winnowing, boosting, and analyzing differential misclassification costs. We selected the 

model having the lowest error. The final PVT maps for 

each zone were created using the classifier tool and represented an integration of the forest/non-forest models 

defined by the masking strategy described above.

Accuracy Assessment

We used a 10-fold cross-validation routine performed 

by See5 to assess the accuracy of the binary forested and 

non-forested stratification map and used an independent 

test set to assess the accuracy of the forest and non-forest 

PVT predictions. We determined that a 10-fold crossvalidation measure would be sufficient for assessing the 

accuracy of the stratification map and would maximize 

the number of plots used for developing the model. The 

independent test set would, in turn, assess the accuracy 

of the final map product. To perform the 10-fold crossvalidation routine, the training data set was divided 

into 10 blocks of approximately the same size and class 

distribution. A classification tree was built ten times, and 

each time, one block was withheld for testing purposes. 

The error rate was averaged from the total number of 

errors and the total number of training sites. See5 output 

an error matrix generated from the sum of all errors 

and calculated the percent of the predictions that were 

correctly classified.

From the LANDFIRE reference database, we randomly reserved ten percent of the training sites. These 

sites were withheld from the modeling process and were 

used to independently evaluate the accuracy of the final 

map. There were a total of 421 test sites for Zone 16 

and 1194 test sites for Zone 19 (tables 1 and 2). See5 

automatically tested the model predictions at these sites 

and output an error matrix and a percentage measure 

of PVTs that were correctly classified. We brought the 

error matrix results into R statistical software (Ihaka 

and Gentleman 1996) and calculated user and producer 

accuracy measures and a kappa statistic to see if the 

model could achieve above-random accuracy (Cohen 

1960; Congalton and Green 1999).

Error matrices provide a global summary of the accuracy of the map but do not show the range and variability of the accuracies across the map (Congalton 

1988). The classifier tool provides the ability to generate 

a coinciding map of confidence. This map displays the 

prediction errors and thereby presents a spatial, visual 

representation of map accuracy. We generated a map of 

confidence for Zone 19 to examine this feature.

Results ________________________

The forest and non-forest stratification maps for zones 

16 and 19 are displayed in figure 1. The classification 

model selected for Zone 16 used 12 boosting trials and a 

misclassification cost of 2, meaning the cost of misclassifying a non-forested plot as forested was doubled. This 

weighting compensated for the potential inaccuracies 

resulting from the fewer non-forested shrub and herbaceous training sites relative to the forested training sites. 

No variables were excluded from the model using the 

winnowing feature. The percent of plots correctly classified, according to the 10-fold cross-validation routine 

performed by See5, was 82.5 percent. For Zone 19, we also 

selected a classification tree using 12 boosting routines 

with a misclassification cost of 2. The 10-fold validation 

procedure identified the accuracy at 91.6 percent.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 1

Chapter 7—Mapping Potential Vegetation Type for the LANDFIRE Prototype Project

The classification tree selected for Zone 16 forest 

PVTs used 10 boosting trials, and ten variables (gsws, 

outflow, pet, psi, psi.max, vmc, sdepth, aspect, slope, 

posidx) were winnowed from the model (table 3). The 

non-forest PVT classification tree for Zone 16 also used 

10 boosting trials, and eleven variables (dsr, dss, gsws, 

mc1, outflow, posidx, psi.max, vmc, sdepth, aspect, lndfrm) were winnowed (table 3). The classification tree we 

selected for Zone 19 forest PVTs used 14 boosting trials 

and used all the variables in the model. The non-forest 

PVT classification tree for Zone 19 used 16 boosting 

trials with two variables (tnight, srad.fg) winnowed 

(table 4). The variables that explain the most variance 

in the models are usually at the top of the classification 

tree, defining the initial breaks. For the Zone 19 forest 

classification tree, no variables were winnowed, and the 

variables that most often appeared at the top of the trees 

were snowfall, gl.sh, dday, dss, evap, pet, and tmin (table 

4). For the Zone 19 nonforest classification tree, tnight 

and srad.fg were winnowed, and the prominent variables 

were gl.sh, ppt, pet, aspect, and dday (table 4).

The total percent of plots correctly classified for 

Zone 16 was 61 percent, with a kappa coefficient of 

0.55 (table 7). For Zone 19, the total percent of plots 

correctly classified was 58 percent with a kappa coefficient of 0.54 (table 7). The error matrices for forest and 

non-forest PVTs in Zone 16 are shown in tables 5 and 6, 

respectively. The number of plots correctly classified is 

represented by the diagonal values in bold font. The total 

percent of plots correctly classified for the forested lands 

was 65 percent with a kappa coefficient of 0.55 (table 7). 

The percent of plots correctly classified for shrub and 

herbaceous lands was 48 percent, with a kappa coefficient of 4.0 (table 7). The user and producer accuracies 

for each class in Zone 16 is provided in table 8. User 

accuracies range from 0 percent for the Douglas-fir / 

Lodgepole Pine - Timberline Pine type to 89 percent for 

the Spruce – Fir / Spruce – Fir type. Producer accuracies 

range from 0 percent for the Douglas-fir / Timberline 

Pine type to 100 percent for the Blackbrush and Salt 

Desert Shrub types. Zero percent values are the result 

of having no test sites occurring within a particular 

class. Most of the lower user accuracies are within 

PVT subgroups. The Spruce – Fir / Blue – Spruce 

PVT has a user accuracy of only 15 percent (table 8). 

From the error matrix, we can see that 54 percent of 

Figure 1—Forest and non-forest stratification maps. A, Zone 16; B, Zone 19.
188 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 20

Chapter 7—Mapping Potential Vegetation Type for the LANDFIRE Prototype Project

Table 5—Error matrix for Zone 16 forest PVTs. PVT codes are listed in

table 1. The number of test sites correctly classified is shown in bold.

PVT PVT Code

Code 1 2 3 4 5 6 7 8 9 10

 1 2 7 1 0 2 

2 1 82 2 0 2 1 2

 3 1 7 23 

 4 0 1 1 0 

5 0 8 3 0 3 0 2 

6 0 4 3 0 0

7 0 1 1 0 

8 0 0 4 0 0 

9 0 2 1 0 0 2 

 10 0 1 2 0 2 0 0 

Table 6—Error matrix for Zone 16 non-forest PVTs. PVT codes are listed in

table 1. The number of test sites correctly classified is shown in bold.

PVT PVT Code

Code 11 12 13 14 15 16 17 18 19 20 21

 11 2 0 0 0 0 0 0 0 0

 12 0 2 0 0 0 0 0 1 

 13 0 0 8 0 0 0 

 14 0 0 0 1 0 1 0 

 15 0 0 0 0 3 1 0 0 

 16 0 0 0 0 1

 17 0 0 0 0 0 2 5 5 0 0

 18 0 0 1 0 0

 19 1 0 0 0 0 0 0 2 

20 0 0 0 0 0 0 0 1 2 

21 1 0 0 0 0 2 0 

Table 7—Overall accuracies and kappa coefficients for

Zone 16 a nd Zone 1

Overall

Zone Category accuracy Kappa

 16 Total 61.2 0

 Forest 64.8 0.5

 Shrub and herbaceous 47.8 0

 19 Total 58.4

 Forest 56.5 0.4

 Shrub and herbaceous 66.4 0.
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Chapter 7—Mapping Potential Vegetation Type for the LANDFIRE Prototype Project

Table 8—Zone 16 user and producer accuracy measures

PVT User Producer

code PVT name accuracy accuracy

	 1	 Spruce – Fir / Blue Spruce	 15.4	 50.0

	 2	 Spruce – Fir / Spruce - Fir	 89.1	 72.6

 3 Grand Fir - White Fir 57.5 6

	 4	 Douglas-fir / Lodgepole Pine - Timberline Pine	 0.0	 0.0

	 5	 Douglas-fir / Douglas-fir	 15.8	 23.1

6 Lodgepole Pine - Timberline Pine  14.3

7 Ponderosa Pine  3

8 Pinyon - Juniper / Mountain Big Sagebrush  19.4 

9 Pinyon - Juniper / Wyoming - Basin Big Sagebrush 88.4 

 10 Riparian Hardwood 54.6

 11 Riparian Shrub 50.0 

 12 Blackbrush - Chaparral - Dry Deciduous Shrub 66.7 1

 13 Dwarf Sagebrush 57.

 14 Salt Desert Shrub 50.0

 15 Mountain Mahogany 75.0 75

 16 Gambel Oak 61.

 17 Wyoming - Basin Big Sagebrush  41

 18 Mountain Big Sagebrush 52.9

 19 Wetland Herbaceous  1

20 Alpine  16.7 50

21 Herbaceous  3

the test sites classified as Spruce – Fir / Blue Spruce 

were predicted as Spruce – Fir / Spruce – Fir (table 5). 

The Pinyon – Juniper / Mountain Big Sagebrush type 

had similar results. The user accuracy was 19 percent, 

but 61 percent of the Pinyon – Juniper / Mountain Big 

Sagebrush test sites were predicted as Pinyon – Juniper / 

Wyoming – Basin Big Sagebrush (table 6).

Error matrices for Zone 19 forest and non-forest PVTs 

are presented in tables 9 and 10, respectively. The total 

percent of plots correctly classified for forest PVTs was 57 

percent, with a kappa coefficient of 0.49 and 66 percent 

for shrub and herbaceous PVTs, with a kappa coefficient 

of 0.58 (table 7). Table 11 shows the user and producer 

accuracies for each class in Zone 19. For Zone 19, the 

Table 9—Error matrix for Zone 19 forest PVTs. PVT codes are listed in table 2. The number of

test sites correctly classified is shown in bold.

PVT PVT Code

Code 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

 1 17 2 3 0 1 0 0 0 0 0 

2 3 19 7 0 2 2 0 0 0 0 0 0 0 

 3 7 3 147 17 4

 4 0 0 7 107 16 0 0 0 0 1 0

5 1 3 42 35 75 4 6 0 0 

6 1 3 5 0 1 27 3

7 0 0 20 4 3 2 29 1 22 7 0

8 0 0 1 3 2 0 1 8 1

9 0 2 11 1 4 10

 10 0 0 2 3 6 0 4 0 6 3

 11 0 0 0 0 0 2 0 0 1 0 5 

 12 0 0 1 0 0 0 0 0 3 0 0

 13 0 0 0 2 0 0 0 0 0 2 0 0 2

 14 0 0 0 0 0 0 0 0 0 0 0 0 0

 15 0 0 0 0 2 0 0 0 0 0 0 0 0 0
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Chapter 7—Mapping Potential Vegetation Type for the LANDFIRE Prototype Project

Table 11—Zone 19 user and producer accuracy measures

PVT User Producer

Code PVT name accuracy accuracy

 1 Western Redcedar 73.9 

2 Grand Fir / White Fir 57.6 59.

 3 Spruce - Fir / Montane 62.6 5

	 4	 Spruce - fir / Timberline	 80.5	 62.2

5 Spruce - Fir / Subalpine  43.9

	 6	 Douglas-fir / Ponderosa Pine	 48.2	 51.9

	 7	 Douglas-fir / Lodgepole Pine	 33.0	 46.8

	 8	 Douglas-fir / Timberline Pine	 30.8	 57.1

	 9	 Douglas-fir / Douglas-fir	 59.2	 55.2

 10 Lodgepole Pine 60.0 60

 11 Ponderosa Pine 62.5 

 12 Timberline Pine / Limber Pine  42.9 

 13 Timberline Pine / Whitebark Pine  

 14 Rocky Mountain Juniper  100

 15 Riparian Hardwood 0.0 0

 16 Riparian Shrub  40.0 

 17 Mountain Mahogany 

 18 Dry Shrub 75.0 60

 19 Dwarf Sagebrush Complex  1

20 Mountain Big Sagebrush Complex 65.1 66

21 Threetip Sagebrush 69.2 5

22 Wyoming - Basin Big Sagebrush Complex 81.3 

23 Wetland Herbaceous 66.7 66

24 Alpine  33

25 Fescue Grasslands 68.2 62.5

26 Bluebunch Wheatgrass 52.2 85.7

Table 10—Error matrix for Zone 19 non-forest PVTs. PVT codes are listed in table 2.

The number of test sites correctly classified is shown in bold.

PVT PVT Code

Code 16 17 18 19 20 21 22 23 24 25 26

 16 2 0 0 0 2 0 0 1 

 17 0 1 0 0 0 0 2 0 0 0

 18 0 0 3 0 0 0 0 0 

 19 0 0 0 1 1 0 8 0 

20 0 1 0 0 28 

21 0 0 0 0 1 18 6

22 0 0 0 2 7 5 61 0 0 0 0

23 1 0 0 0 1 0

24 0 0 0 0 0 0 0 0 1 2

25 0 1 0 0 1

26 0 0 2 0 1 1 6 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 

Chapter 7—Mapping Potential Vegetation Type for the LANDFIRE Prototype Project

user accuracies range from 0 percent for the Riparian 

Hardwood type to 100 percent for the Rocky Mountain 

Juniper type. Again, we see similar patterns in the error 

matrices of within-subgroup inaccuracies. Forty-five 

percent of the Spruce – Fir / Subalpine test sites were 

misclassified as Spruce – Fir / Montane (Western Larch 

or Douglas-fir) or Spruce – Fir / Timberline, and 25 

percent of the Spruce – Fir / Montane (Western Larch or 

Douglas-fir) test sites were misclassified as Spruce – Fir 

/ Timberline or Spruce – Fir / Subalpine (table 9). Similarly, 19 percent of the Mountain Big Sagebrush test sites 

were misclassified as Threetip Sagebrush or Wyoming 

– Basin Big Sagebrush Complex, and 16 percent of the 

Wyoming – Basin Big Sagebrush Complex test sites were 

misclassified as Mountain Big Sagebrush or Threetip 

Sagebrush types (table 10). Thirty-six Douglas-fir sites 

were misclassified as Spruce – Fir / Montane (Western 

Larch or Douglas-fir) (table 10). The final PVT maps for 

zones 16 and 19 are presented in figure 2. The spatial 

estimate confidence for Zone 19 is shown in figure 3.

Discussion _____________________

The LANDFIRE PVT Mapping Approach

The LANDFIRE PVT mapping process represents an 

innovative framework for linking vegetation dynamics, 

such as post-disturbance recovery and succession, to 

landscape patterns represented by the biophysical variables compiled from the National Elevation Database 

and modeled using the WXFIRE model. The variables 

that were most important (defined by the first few splits 

of the tree) for the successful mapping of forest PVTs 

included: actual and potential evapotranspiration, days 

since snow, degree days, evaporation, relative humidity, leaf resistance to sensible heat, and minimum temperature. For the non-forest PVTs, the most important 

variables included: actual and potential evapotranspiration, precipitation, degree days, relative humidity, and 

minimum temperature. These gradients are associated 

with plant-water interactions and explain the influence 

of water and temperature derivatives in determining the 

distribution of vegetation across landscapes.

Classification and Regression Trees

Classification tree modeling proved an efficient 

means for identifying relationships between PVTs and 

biophysical variables across broad landscapes. With a 

comprehensive set of training data, classification trees 

can serve as strong predictors of these relationships. 

This predictive power extends across scales and is fully 

repeatable in time and space. Classification trees have 

proven successful in modeling and mapping vegetation 

at regional (Moisen and others 2003), national (Vogelmann and others 2001; Zhu and others, Chapter 8), and 

global scales (Hansen and others 2000). Although some 

research has found the predictive accuracy of classification trees to be inferior to other predictive modeling 

tools (Moisen and Frescino 2002; Pal and Mather 2003), 

the statistical flexibility, speed, and objectivity of the 

trees justify their use for large-scale mapping efforts 

such as LANDFIRE. See5 software adds efficiency to 

classification tree modeling by providing automated 

procedures, flexibility in terms of changing modeling 

functions, and by built-in accuracy measures.

Accuracy Assessment

There are several possible sources of the generally low 

accuracies found in the PVT maps created during the 

LANDFIRE Prototype Project. First, the performance 

of mapping models depends greatly on the quality of 

input data. The training databases for PVT mapping were 

collected and compiled from the LANDFIRE reference 

database (LFRDB), a database that comprised existing 

agency and non-agency field-referenced data sets and 

contained inventory, monitoring, and analysis data that 

originate from a variety of sampling objectives, sizes, and 

designs. (see Caratti, Ch. 4 for details). Data inaccuracies, 

major outliers, and unbalanced or insufficient numbers 

of training sites can have significant negative effects on 

the quality of mapping models (Friedman 2001). While 

the LFRDB was a large, comprehensive database that 

was compiled quickly and economically, the disparate 

sampling objectives, designs, and procedures certainly 

affected the final accuracies of the PVT maps.

A second possible explanation for the low accuracies 

is related to the model building characteristics of classification trees. As See5 builds classification trees, map 

units are divided using hard breaks, making it difficult to 

discriminate between vegetation types that have similar 

responses to the biophysical predictor variables. Most 

of the lower accuracies found during the LANDFIRE 

Prototype Project were within groups of similar PVTs, 

suggesting that these PVTs occur on overlapping biophysical settings, as represented by the predictor variables. The distributions of the three spruce-fir PVTs and 

the four Douglas-fir PVTs over a gradient of potential 

evapotranspiration are quite similar in Zone 19 (fig. 4). 

The error matrices reflect these similarities as well, 

indicating that the See5 classification tree algorithms 

had difficulty in discriminating these PVT subgroups. 

In any mapping application, this overlap between classes 
192 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 20

Chapter 7—Mapping Potential Vegetation Type for the LANDFIRE Prototype Project

Figure 2—Final potential vegetation type (PVT) maps. A, Zone 16; B, Zone 1
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 

Chapter 7—Mapping Potential Vegetation Type for the LANDFIRE Prototype Project

Figure 4—Boxplot distributions of 

potential vegetation types (PVTs) by 

potential evapotranspiration gradient. 

See table  for code descriptions.

Codes 3 to 5 are Douglas-fir PVT variants and codes 6 to 10 are spruce – fir

PVT variants.

Figure 3—Potential Vegetation Type (PVT) Confidence map for Zone 19.
194 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2

Chapter 7—Mapping Potential Vegetation Type for the LANDFIRE Prototype Project

negatively affects overall accuracy. Although accuracies 

may have been higher if we had grouped these PVT 

subgroups into single map units, we determined that 

the resulting loss in resolution in fire regime modeling 

would limit the utility of the final LANDFIRE fuel and 

fire regime products.

A third possible reason for overall low PVT map accuracies relates to the limited set of predictor variables 

used in PVT mapping. We did not include Landsat imagery in the mapping process because we did not want 

current land patterns influencing the final PVT maps; 

we relied completely on the affinity of individual PVT 

map units to specific distributions and combinations of 

biophysical variables. Further, because of technical difficulties, output from the LANDFIRE Biogeochemical 

Cycles model (LFBGC) (Holsinger and others, Ch. 5), 

which spatially represents the rates of the hydrologic, 

carbon, and nitrogen cycles, was not available in time to 

be used in the LANDFIRE Prototype Project and was 

therefore not included in the final mapping models. These 

ecophysiological gradients have proven to be highly 

useful in discriminating between potential vegetation 

types in other research (Keane and others 2001; Rollins 

and others 2004).

A fourth potential reason for low accuracies in the 

PVT maps lies in the possibility that the validation 

procedure we used did not represent true accuracy. The 

validation procedure used in the LANDFIRE Prototype 

Project included a cross-validation routine and a test 

set comparison using a randomly selected set of data 

withheld from classification tree building. Although, in 

both cases, the test sites were randomly selected from a 

probability sample, sampling was conducted at different 

intensities within different sub-populations. Therefore, 

more test sites are drawn from heavily sampled areas and 

fewer from less intensively sampled areas. Other possible 

sources of error include positional inaccuracies in the 

LANDFIRE reference database and errors imbedded in 

the biophysical predictor variables. It should be noted 

that quality control and assurance measures and methods 

for generating the biophysical gradient layers have been 

refined for national implementation (See Holsinger, Ch. 

5 for details).

Recommendations for National 

Implementation _________________

For mapping PVT at the national scale, we recommend 

employing the approach and methods described in this 

chapter. The efficiency and nonparametric flexibility of 

classification trees make them the optimal method for 

implementing LANDFIRE nationally, and the ease of 

implementation of the mapping models created using 

See5 software in ERDAS Imagine facilitate the broadscale implementation of classification trees. We suggest 

conducting more structured quality control and assurance 

in the LANDFIRE reference database. In addition, we 

recommend detailed exploration of the relationships 

between response and predictors in the mapping database using correlation matrices and principle component 

analyses to reduce the number of gradient predictors 

and to remove major outliers or unusual patterns in the 

training data.

In addition, alternative validation sampling schemes 

should be considered for national implementation to 

ensure that the test sites are independent and representative of the population. For example, accuracy assessment 

sites developed solely from the systematically sampled 

Forest Inventory and Analysis data would ensure independent and representative test sites and therefore be a 

possible alternative as an equal probability sampling 

design. A similar procedure would be needed for shrub 

and herbaceous lands.

To compensate for positional errors in the training 

data set, we suggest employing alternative methods for 

calculating map accuracy when implementing LANDFIRE nationally. The agreement between each test site 

and its neighborhood of pixels (for example, 3 by 3) 

should be assessed. If the test site class matches any of 

the pixels, it correctly classifies the prediction. This kind 

of assessment is appropriate for plots in the LANDFIRE 

reference database that were not measured specifically 

for 30-meter pixel accuracy assessments.

Conclusion_____________________

In conclusion, maps of potential vegetation were 

valuable for supporting the broad-scale mapping of 

wildland fuel and also as a foundation for modeling 

fire regimes. The LANDFIRE process of generating 

biophysical gradients from topographic information 

and from the WXFIRE model served as an innovative 

framework for linking vegetation dynamics, such as 

post-disturbance recovery and succession, to landscape 

patterns represented by maps of potential vegetation. 

Although we found that the quality of field data for use 

as training data and of input spatial data layers can be 

limiting to the process of potential vegetation mapping, 

the LANDFIRE Prototype Project illustrated that the 

added effort involved in developing maps of potential 

vegetation results in higher quality data products representing fuel and fire regime characteristics.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 1

Chapter 7—Mapping Potential Vegetation Type for the LANDFIRE Prototype Project

For further project information, please visit the LANDFIRE website at www.landfire.gov.

The Authors ____________________

Tracey S. Frescino is a Forester with the USDA Forest 

Service, Rocky Mountain Research Station (RMRS), Interior West Forest Inventory and Analysis (FIA) Program. 

Frescino received a B.S. degree in Environmental Studies from SUNY’s Environmental Science and Forestry 

program in 1991 and an M.S. degree in Fisheries and 

Wildlife from Utah State University in 1998. She has 

been with FIA since 1992 working as a field technician 

and a reporting analyst, and she is currently serving as 

a specialist in the FIA’s techniques group. From spring 

of 2003 to spring of 2005, Frescino worked as an FIA 

collaborator for the LANDFIRE Prototype Project at the 

RMRS Missoula Fire Sciences Laboratory in Missoula, 

Montana. She was responsible for mapping potential 

vegetation and canopy fuel, in addition to facilitating 

access to and interpretation of FIA data for the LANDFIRE effort.

Matthew G. Rollins is a Landscape Fire Ecologist at 

the USDA Forest Service, Rocky Mountain Research 

Station, Missoula Fire Sciences Laboratory (MFSL). 

Rollins’ research emphases have included assessing 

changes in fire and landscape patterns under different 

wildland fire management scenarios in large western 

wilderness areas, relating fire regimes to landscapescale biophysical gradients and climate variability, and 

developing predictive landscape models of fire frequency, 

fire effects, and fuel characteristics. Rollins is currently 

the lead scientist of the LANDFIRE Project, a national 

interagency fire ecology and fuel assessment being 

conducted at MFSL and the USGS Center for Earth 

Resources Observation and Science (EROS) in Sioux 

Falls, South Dakota. He earned a B.S. degree in Wildlife 

Biology in 1993 and an M.S. degree in Forestry in 1995 

from the University of Montana in Missoula, Montana. 

His Ph.D. was awarded by the University of Arizona in 

2000, where he worked at the Laboratory of Tree-Ring 

Research.

Acknowledgments_______________

Special thanks to Alisa Keyser and Gretchen Moisen 

of the USDA Forest Service, Rocky Mountain Research 

Station, Forest Inventory and Analysis for map design 

and technical review, respectively, and to John Caratti 

of Systems for Environmental Management for technical 

review.

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estimation of daily solar radiation and humidity from observed 

temperature and precipitation: an application over complex terrain 

in Austria. Agriculture and Forest Meteorology. 104:225-271. 

Vogelmann, J.E.; Howard, S.M.; Yang, L., Larson, C.R.; Wylie, 

B.K.; Driel, N.V. 2001. Completion of the 1990s national land 

cover data set for the conterminous United States from Landsat 

Thematic Mapper data and ancillary data sources. Photogrammetric Engineering and Remote Sensing. 67(6):650-662.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 1

Chapter 8—Mapping Existing Vegetation Composition and Structure for the LANDFIRE Prototype Project

Chapter

8

In: Rollins, M.G.; Frame, C.K., tech. eds. 2006. The LANDFIRE 

Prototype Project: nationally consistent and locally relevant 

geospatial data for wildland fire management. Gen. Tech. Rep. 

RMRS-GTR-175. Fort Collins: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research Station.

Introduction ____________________

Overview

The Landscape Fire and Resource Management Planning Tools Prototype Project, or LANDFIRE Prototype 

Project, required the mapping of existing vegetation 

composition (cover type) and structural stages at a 

30-m spatial resolution to provide baseline vegetation 

data for the development of wildland fuel maps and for 

comparison to simulated historical vegetation reference 

conditions to develop indices of ecological departure. 

For the LANDFIRE Prototype Project, research was 

conducted to develop a vegetation mapping methodology 

that could meet the following general requirements:

•	 Cover types (species composition) must be characterized at a scale suitable for subsequent mapping 

of wildland fuel and fire regime condition class 

(FRCC). The vegetation map unit classification used 

for mapping cover types must be based on existing 

national systems, such as the United States National 

Vegetation Classification System (NVCS; Grossman

and others 1998). The alliance (a community with 

multiple dominant species) or association (a community with a single dominant species) levels of 

this standard must provide a clearly defined list of 

map units that can be used as a basis for mapping 

vegetation classes that are both scaleable and representative of suitable units for modeling historical 

fire regimes (see Long and others, Ch. 6 for details 

on the LANDFIRE vegetation map units).

•	 The mapping of existing vegetation structure must 

be based on the relative composition of forest, shrub, 

and herbaceous canopy cover and average forest, 

shrub, and herbaceous canopy height. Although 

structural stages are discrete map units describing 

unique combinations of canopy cover and canopy 

height by life form, mapping individual canopy 

cover and height variables as continuous variables 

is desired to provide additional information for 

mapping and modeling vegetation and flexibility 

for setting threshold values.

The task of mapping existing vegetation is interconnected with several major tasks performed in the 

LANDFIRE Prototype Project. The mapping of existing vegetation requires attribute tables developed from 

the LANDFIRE reference database (LFRDB) (Caratti, 

Ch. 4), satellite imagery acquisition and processing, 

the development of a vegetation map unit classification 

system (see Long and others, Ch. 6), the development of 

a biophysical settings stratification (Frescino and Rollins, Ch. 7), and the modeling of environmental gradient 

layers (Holsinger and others, Ch. 5). The design and 

testing of the vegetation mapping methodology have 

substantial influences on the outcome of the overall 

project because accuracies of subsequent products (such 

as maps of wildland fuel) are a function of the accuracy 

of mapped vegetation types and structure. In this chapter, 

we discuss the design features of the existing vegetation mapping component of LANDFIRE and present 

Mapping Existing Vegetation Composition and 

Structure for the LANDFIRE Prototype Project

Zhiliang Zhu, James Vogelmann, Donald Ohlen, 

Jay Kost, Xuexia Chen, and Brian Tolk
198 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 20

Chapter 8—Mapping Existing Vegetation Composition and Structure for the LANDFIRE Prototype Project

results of the prototype. We conclude the chapter with 

recommendations for the national implementation of a 

consistent vegetation mapping effort.

Technical Problems

Significant technical limitations exist regarding 

achieving desired accuracies in the mapping of vegetation types and structure variables over broad areas. In 

the LANDFIRE Prototype, accuracies were affected 

by the spatial resolution, geographic extent, and information content defined by the project’s objectives. The 

U.S. Geological Survey (USGS) Gap Analysis Program 

demonstrated the feasibility of mapping many existing 

vegetation cover types at the regional scale; however, 

methodologies have been inconsistent between regions 

(Eve and Merchant 1998). In addition, the mapping of 

forest canopy cover using imagery and regression techniques has been routinely performed for the operational 

mapping of vegetation structure variables (Huang and 

others 2001). Beyond that, however, literature reporting 

success stories regarding the mapping of vegetation 

structure using imagery is scant.

We conducted a prototype study to test a methodology for mapping vegetation cover types and structure 

variables. The three central objectives of the study were 

to:

•	 test an adaptable approach for mapping existing 

vegetation types and canopy structure at a 30-m 

resolution for the entire prototype area;

•	 develop digital maps of existing vegetation types 

and structural stages and conduct an accuracy assessment for the vegetation deliverables; and

•	 document research findings and limitations to the 

consistent mapping of existing vegetation composition and structure.

Specifically, this study tested a vegetation mapping 

protocol that met the design criteria and guidelines of 

the LANDFIRE Project (Keane and Rollins, Ch. 3). 

Further, this study investigated the limitations of using 

data contained within the LANDFIRE reference database 

(Carrati, Ch. 4) as training data and the applicability 

of satellite and ancillary data in meeting LANDFIRE’s 

objectives. For vegetation modeling and wildland fuel 

mapping, the LANDFIRE Prototype Project required a 

structural stage map classified on the basis of mapped 

canopy cover (closed and open) and canopy height (high 

and low) by forest, shrub, and herbaceous life forms. 

We attempted to generate continuous maps of vegetation height and cover to maximize the utility of these 

products in a variety of applications.

As described in Rollins and others (Ch. 2), the LANDFIRE Prototype Project was conducted in two mapping 

zones: Zone 16, located in the central highlands of 

Utah and covering approximately 4 million ha of forest ecosystems (57 percent of the total land cover) and 

2.5 million ha of shrub and herbaceous ecosystems (35 

percent of the total land cover); and Zone 19, located 

in the northern Rocky Mountains of western Montana 

and northern Idaho and covering approximately 5.4 

million ha of forest ecosystems (47 percent of the total 

land cover) and 5 million ha of shrub and herbaceous 

ecosystems (44 percent of the total land cover).

Literature Review of Vegetation Mapping

Similar to other natural science problems, the regionalscale mapping of vegetation types and structure variables 

carries unique technical and organizational challenges 

(Gemmell 1995). Spatial variations of vegetation types 

and structure are generally not characterized by unique 

spectral signatures, as captured by conventional broadband optical sensors (Kalliola and Syrjanen 1991; Keane 

and others 2001). Although significant improvements 

can be made by using specialized sensors, such as 

hyperspectral spectrometer or canopy lidar, data from 

such sensors having desired spatial resolutions are not 

available at national or regional scales. The associated 

enormous data volumes and high costs (in time and labor) make these technologies impractical for large-area 

applications at the present time.

Various techniques exist for modeling and estimating 

vegetation type and canopy structure (particularly percent forest cover); these include physics-based canopy 

reflectance models, empirical models linking groundreferenced data to satellite imagery, spectral mixture 

analysis, neural networks, and direct measurement using 

lidar and interferometric synthetic aperture radar. Each 

of these approaches has limitations in large-area applications, such as those related to cost and consistency. 

However, recent applications using the classification and 

regression tree (CART) approach (Breiman and others 

1984) have been found to overcome many such limitations, provided sufficient amounts of field and geospatial 

data are available. Recent studies (Friedl and others 2002; 

Huang and Townshend 2003; Mahesh and Mather 2003; 

Yang and others 2003) have demonstrated the utility of 

CART techniques in mapping land cover, estimating 

species distribution, modeling percent forest canopy 

cover, and computing imperviousness at a 30-m grid 

resolution for large areas and even for the United States. 

Although CART techniques require relatively little 

human decision-making during algorithm executions, 
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Chapter 8—Mapping Existing Vegetation Composition and Structure for the LANDFIRE Prototype Project

it is important to note that, ultimately, the knowledge 

scientists have acquired through studying vegetation patterns and attributes enhances the development mapping 

models to produce the most accurate results possible. 

Computer classifiers, regardless of their sophistication, 

are no substitute for scientists’ understanding of the patterns, attributes, and conditions of existing vegetation 

and associated ecological processes.

Environmental data layers (such as elevation) are important predictor variables for characterizing vegetation 

patterns and attributes and for stratifying the distribution of vegetation along environmental gradient lines 

(Balice and others 2000). The use of spectral bands 

in combination with topographic data (for example, 

digital elevation models (DEM), slope, and aspect) is 

common in many land cover and vegetation mapping 

applications. However, topographic data capture only a 

part of the overall environmental factors that determine 

the establishment, growth, distribution, and succession 

of plant species and associations. The incorporation of 

a more complete set of environmental gradient layers 

into the mapping of existing vegetation should lead to 

increased predictive power and thematic accuracy (Keane 

and others 2002; Rollins and others 2004). Keane and 

others (2002) discuss techniques for deriving an entire 

set of climate, soil, and ecological gradient layers using interpolated weather observations in conjunction 

with topographic and soil databases and also describe 

the advantages of using such biophysical gradients in 

combination with remote sensing and field data to map 

vegetation, wildland fuel, and general ecosystem conditions.

In addition to the development and use of gradient 

variables, Keane and others (2001, 2002), Keane and 

Rollins, Ch. 3, and Rollins and others (2004) also suggest an approach for developing site-specific biophysical 

settings maps by mapping stable, late-seral communities 

as a function of certain climate, topographic, soil, and 

ecological gradients. This mapped “potential” vegetation 

can be used as a stratification tool in mapping actual 

vegetation distribution by constraining the distribution 

of cover types to those geographic strata where growth 

of the cover types’ dominant species is ecologically 

possible.

Methods _______________________

The LANDFIRE Prototype Project involved many 

sequential steps, intermediate products, and interdependent processes. Please see appendix 2-A in Rollins and 

others, Ch. 2 for a detailed outline of the procedures 

followed to create the entire suite of LANDFIRE Prototype products. This chapter focuses specifically on 

maps of vegetation composition and structure, which 

served as important precursors to maps of wildland fuel 

and ecological departure in the LANDFIRE Prototype 

Project. Figure 1 outlines the technical approach used 

in LANDFIRE Prototype vegetation mapping and 

illustrates the data flow between several technically 

challenging tasks. Details of these tasks are described 

below.

Satellite Data Acquisition and Processing

The LANDFIRE Project partnered with the MultiResolution Land Characterization (MRLC) Consortium 

(Homer and others 2004) to facilitate the acquisition 

and processing of Landsat imagery. The consortium has 

completed the acquisition and processing of a full set of 

Landsat imagery for the United States with a minimum 

of three cloud-cover dates (circa 2001) for each pixel 

corresponding to phenological cycles of leaf-on, leaf-off, 

and spring green-up. Huang and others (2002) describe 

the steps involved in processing the MRLC satellite imagery, including terrain-corrected geometric registration 

and radiometric calibration using at-satellite reflectance 

models, calculations of normalized difference of vegetation index (NDVI), and tasseled cap transformations. 

The MRLC Consortium-sponsored development of the 

National Land Cover Dataset (NLCD) includes general 

land cover map units such as forest, agriculture, water, 

and urban areas mapped at a 30-m resolution (Homer 

and others 2004). The acquisition and processing of 

satellite imagery and the mapping of NLCD land cover 

map units were conducted for mapping zones, which 

were loosely delineated along major ecological regions. 

The LANDFIRE central Utah highlands and northern 

Rockies prototype areas were examples of these MRLC 

map zones.

The LANDFIRE Prototype Project had access to the 

following data layers from the MRLC catalogue for the 

Utah and northern Rockies prototype areas: 10 spectral 

bands for each of the 3 Landsat seasonal acquisitions (6 

original spectral bands excluding the thermal band, 3 

tasseled cap transformation bands, and 1 NDVI band) 

and land cover classes mapped to Anderson’s Level 1 

land cover classification (Anderson and others 1976). 

Using these data as a starting point, we mapped forest, 

shrub, and herbaceous cover types and structure attributes. These maps formed the foundation for mapping 

wildland fuel and fire regime characteristics (Holsinger 

and others, Ch. 11; Keane and others Ch. 12).
200 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 200

Chapter 8—Mapping Existing Vegetation Composition and Structure for the LANDFIRE Prototype Project

Figure 1—Flow diagram of the methodology used for mapping cover type and vegetation structure in the LANDFIRE 

Prototype Project.

Use of Biophysical Gradient Variables and 

Potential Vegetation Maps

In addition to the spectral predictor variables discussed above, the LANDFIRE existing vegetation 

mapping task incorporated two ancillary data sets that 

functioned differently in the mapping process. One was 

a suite of biophysical gradient layers developed as a set 

of intermediate LANDFIRE products with input from 

weather, topographic, and soil databases (Holsinger 

and others, Ch. 5: table 6). Table 1 lists the biophysical 

gradient variables used in the prototype for mapping 

existing vegetation; these represent a winnowed set of 

the entire suite of variables produced for the LANDFIRE Prototype. Biophysical gradients were used in 

the mapping process to provide a geographic context 

for the ecological processes that control establishment, 

growth, and distribution of vegetation communities.

The second data set was a potential vegetation type 

(PVT) map with attributes describing the probability of 

specific cover types existing in each PVT. This database 

was derived by calculating the distribution of cover 

types within individual PVTs by intersecting the plots 

contained in the LFRDB with the PVT map (Keane 

and Rollins, Ch. 3; Frescino and Rollins, Ch. 7). Conceptually, by using the PVT and cover type probability 
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Chapter 8—Mapping Existing Vegetation Composition and Structure for the LANDFIRE Prototype Project

information in the mapping of vegetation cover types, 

we implemented a stratification that constrained cover 

types to the geographic areas where cover types were 

ecologically possible. Sites (pixels) where certain cover 

types were not likely to occur would have low probabilities; therefore, these cover types were less likely to be 

predicted for these pixels. Each cover type was associated with a probability distribution map. The probability 

layers were implemented in the mapping process much 

in the same way as the biophysical gradient layers and 

satellite imagery.

Vegetation Map Unit Classification

Two different approaches were used in the development 

of the vegetation map unit classification systems for the 

prototype mapping zones. For the central Utah mapping 

zone, we formulated the map unit classificaton based on 

an overall understanding of the presence of vegetation 

alliances and associations (Long and others, Ch. 6). 

For the northern Rocky Mountains prototype area, we 

examined and summarized the LFRDB to form the basis 

for the vegetation map unit classification. Brohman and 

Bryant (2005) have described these approaches as the 

“top-down” and the “bottom-up” approaches, respectively. Long and others (Ch. 6) discuss the criteria and 

factors used in developing the LANDFIRE vegetation 

map unit classification systems, the lessons learned in 

applying them, and recommendations for a national 

approach to vegetation map unit development.

We were concerned with two technical issues when 

evaluating the map unit classifications of existing cover 

types for the prototype: 1) whether each cover type was 

sufficiently represented by an adequate number of fieldreferenced data from the LFRDB and, if not, how such 

“rare map units” should be treated and 2) whether some 

cover types (such as the Juniper cover type versus the 

Pinyon – Juniper cover type) would be floristically or 

ecologically difficult to separate in spectral, biophysical, 

and geographical domains. The technical issues were 

considered in the context of four guidelines defined at 

the beginning of the LANDFIRE Prototype Project: 

a map unit, whether it is a cover type or a fuel model, 

must be identifiable, scalable, mappable, and modelable (Keane and Rollins, Ch. 3). Because the prototype 

study areas were the first mapping zones to be mapped 

under the LANDFIRE design criteria and guidelines, we 

were unsure whether the map unit classification systems 

could perform consistently across different geographic 

areas.

Table 1—Biophysical and topographic layers used in the LANDFIRE vegetation mapping process.

Symbol Description Unit Source data

SRAD	 Daily solar radiation flux	 KW/m2

/Day Weather and topographic data

Tmin Daily minimum temperature C° Weather and topographic data

Tmax Daily maximum temperature C° Weather and topographic data

Tnight Daily average nighttime temperature C° Weather and topographic data

Dday Degree days C° Weather and topographic data

PPT Daily precipitation cm Weather and topographic data

RH Relative humidity % Weather and topographic data

PET Potential evapotranspiration kgH2O/yr Weather and topographic data

AET Actual evapotranspiration kgH2O/yr Weather, topographic, and soil data

GSWS Growing season water stress -Mpa Weather, topographic, and soil data

PSI Soil water potential -Mpa Weather, topographic, and soil data

KDBI Keetch-Byram drought index Index Weather database

SWF Soil water fraction % Weather, topographic, and soil data

Sdepth Soil depth to bedrock cm Soil and topographic data

LAI Potential leaf area index Index Landsat spectral data

DEM Digital elevation model m National Elevation Database

Slope Slope % National Elevation Database

Aspect Aspect Azimuth National Elevation Database

POSIDX Topographic position index Index National Elevation Database
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Chapter 8—Mapping Existing Vegetation Composition and Structure for the LANDFIRE Prototype Project

Reference Data

Caratti (Ch. 4) describes in detail the compilation of 

the LFRDB for the prototype. The compilation of the 

LANDFIRE reference database relied on the coordination of three separate and independent efforts: 1) the 

cooperation and support from the U.S. Forest Service 

(USFS) Forest Inventory and Analysis (FIA) database 

collected nationwide on permanent inventory plots 

(Smith 2002); 2) the collection and processing of existing field data from all land management units such as 

Bureau of Land Management districts or national parks; 

and 3) the acquisition of new, supplementary field data 

from areas where there were no or not enough existing 

data (for example, various western rangelands in the 

United States do not currently have adequate field data 

collection programs).

Because the LFRDB was compiled from various 

sources collected for different purposes, information 

gleaned from the LFRDB was highly variable in terms 

of sampling design. The FIA data represented the most 

consistent information for forest cover types and canopy 

height. Rangeland field data usually contained cover 

type labels, but structure information was rare. In addition, reference data for mapping forest canopy cover 

were generated by calculating the number of forest cells 

within a 30-m cell using either high-resolution satellite data (spatial resolution of 1-m or better) or digital 

orthophotographs (Homer and others 2004).

Quality-control procedures were conducted as a part 

of the existing vegetation mapping process to detect 

problems and errors inherent in field-referenced data 

derived from disparate sources. We assumed that these 

procedures would identify most existing data problems 

but would not identify and eliminate all problems. These 

procedures were as follows:

Detecting outdated field data—Many field plots 

measured in years past were considered useful if the 

dominant species had not changed. A substantial number 

of plots, however, had undergone major disturbances such 

as fire or logging. We therefore computed the differences 

between the 1992 and 2001 Landsat NDVI values to flag 

field plots with conditions that had potentially changed 

during that 10-year period.

Detecting field data with erroneous geographic 

coordinates—We identified major geo-coding problems 

such as coordinates located on roads or located out of 

mapping areas. We visually examined plot locations 

overlaid with road networks and general land cover 

maps (such as NLCD maps).

Detecting field data with major coding errors—We 

detected such problems by overlaying field data on raw 

satellite imagery and by sorting variables according to 

major cover types. For example, if a field plot coded 

as sagebrush was located in the center of an otherwise 

intact forest polygon, or if a shrub plot had a height value 

taller than that of forest plots, such plots were flagged.

Reducing spatially clumped field plots—The LFRDB 

contains field data that come from different sources and 

are collected with different objectives, which occasionally results in spatially clumped plot information. In order 

to produce a spatially well-distributed and balanced data 

sample, we sub-sampled clumps of the available data to 

result in a more even distribution of field data.

The use of these quality-control procedures resulted 

in the exclusion of a number of available field plots from 

either the mapping or validation processes. This led to 

a total of 6,177 field plots (1,809 FIA forest plots and 

4,368 non-FIA forest and rangeland plots) for Zone 16 

and 7,735 field plots (1,993 FIA forest plots and 5,742 

non-FIA forest and rangeland plots) for Zone 19 to be 

used for subsequent training or accuracy assessment. 

These numbers differ slightly from other applications 

of the LFRDB in LANDFRIE mapping because, based 

on objectives, each mapping effort implemented its own 

quality control procedure. Although all of the plots contained LANDFIRE cover type labels, only subsets of 

plots from the LFRDB had attributes of canopy height 

and canopy cover (table 2). In addition, ten percent of 

the field data points available for each of the cover type 

and structure mapping tasks were withheld from the 

mapping process for the purpose of accuracy assessment 

(Vogelmann and others, Ch. 13).

Mapping Algorithms

Classification and regression tree algorithms have 

demonstrated robust and consistent performance and 

advantages in integrating field data with geospatial data 

layers (Brown de Colstoun and others 2003; Friedl and 

Brodley 1997; Hansen and others 2000; Joy and others 2003; Moisen and others 2003, Moore and others 

1991; Rollins and others 2004). Nonparametric CART 

approaches recursively divide feature space into many 

subsets in a hierarchical fashion to achieve the best 

overall model performance (lowest error and highest 

R2, derived using a cross-validation technique). For 

this study, we adopted the classification tree algorithm 

to map vegetation types as discrete map units and the 

regression tree algorithm to map canopy cover and 

canopy height as continuous variables using two related 
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Chapter 8—Mapping Existing Vegetation Composition and Structure for the LANDFIRE Prototype Project

commercial applications: See5 (classification trees) and 

Cubist (regression trees) developed by Quinlan (1993). 

The mapping models were trained on the compiled data 

set of spectral bands and biophysical ancillary variables 

listed in table 1 and cover type and structure variables 

from the LFRDB.

Vegetation Database Development

Training vegetation mapping models—The creation 

of the CART-based algorithms for mapping existing vegetation involved several steps: 1) exploration of general data 

such as correlation analyses and plotting of cover types 

from the LFRDB against predictor layers, 2) iterations 

of CART algorithm runs to determine the adequacy 

of training data and other biophysical layers, 3) visual 

evaluation of classification and regression trees and final 

output maps, 4) generation of cross-validation statistics 

as an initial indicator of map accuracies, and 5) development of vegetation maps by applying the final mapping 

models. As mentioned above, we withheld data from 10 

percent of available field reference plots for accuracy 

assessment and used the rest of the field plots for training the CART algorithms. We ran classification tree 

or regression tree classifiers, depending on whether 

the mapped theme was categorical or continuous, and 

generated 10-fold cross-validation statistics. Results of 

the cross-validation were used to determine the quality 

of training data and the performance of the predictor 

layers, but not to assess the final accuracy of resulting 

maps.

Determination of rare and similar map units—Although the LANDFIRE Prototype Project vegetation 

map unit classifications were developed to meet specific design criteria and guidelines (Keane and Rollins, 

Ch. 3; Long and others, Ch. 6), two technical questions 

arose during the mapping of existing vegetation: how 

to treat 1) rare cover types and 2) spectrally and biophysically similar cover types. We considered a cover 

type to be rare if it was supported with fewer than 30 

reference plots, and those plots were not concentrated 

in one general location. We retained a rare map unit 

in the overall mapping process if the resulting spatial 

pattern made sense (such as when a riparian cover type 

followed river patterns) and if retaining the map unit did 

not result in a significant drop in accuracy. Otherwise, 

the rare map unit would be omitted. Additionally, we 

decided, based on differences in historical disturbance 

regimes, to keep cover types that were biophysically and 

spectrally similar (such as Pinyon – Juniper) separate, 

even though merging the cover types would significantly 

improve overall map accuracy.

Stratifications by life form—During the mapping 

of these vegetation attributes, the question arose as to 

whether the cover types and structural stages should 

be constrained by their respective forest, shrub, and 

herbaceous life forms; that is, we questioned whether a 

given pixel could be assigned more than one life form 

for cover type, height, and canopy designations. Multiple life form assignments provided flexibility for the 

characterization of wildland fuel. Such flexibility would 

also benefit other potential applications of LANDFIRE 

data, such as insect and disease or biomass studies. In 

the process of LANDFIRE vegetation mapping, we 

therefore modeled each pixel independently for each of 

the three life forms (forest, shrub, and herbaceous; fig. 1).

Product Validation Plan and Accuracy 

Assessment

The LANDFIRE accuracy assessment is described in 

detail in Vogelmann and others (Ch. 13). We tested the 

Table 2—Numbers of field reference plots in each mapping zone used in either mapping or accuracy assessment and corresponding to various map products. Forest canopy cover mapping relied on imagery of high 

spatial resolution instead of field reference plots.

 Mapping Number of Cover Canopy Canopy 

 zone cover types type plots cover plots height plots

Forest  16  10  1,809 N/

 19  14 1,9

Shrub  16  14 1,595 2,

 19  15  1,788  1

Herbaceous 16 7  300 2

 19 8 597 597 2
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Chapter 8—Mapping Existing Vegetation Composition and Structure for the LANDFIRE Prototype Project

approach in which ten percent of the field data points 

available for cover type mapping were withheld from the 

mapping process for the purpose of accuracy assessment 

but found that the approach did not work well because 

of the uneven availability of field data in support of 

different cover types in the map unit classification. For 

several cover types in each of the mapping zones, the 

amount of data withheld in the 10 percent sample was 

too low to be statistically meaningful. As the result, 

we reported overall accuracies for cover types using 

the results of 10-fold cross-validations. For structure 

variables, we used a set of independent plots to assess 

statistical accuracy using regression techniques. This 

afforded us the opportunity to examine the behaviors of 

mapping structure variables versus those of categorical 

variables. Forest canopy cover, mapped with fine-resolution imagery as training data, would be assessed with 

both a sample of withheld reference points generated 

from the fine-resolution imagery as well as field estimates 

obtained from the use of digital cameras equipped with 

fisheye lenses.

Results ________________________

Maps of Cover Type and Structural Stage

We applied the vegetation mapping approach described 

above to the central Utah and northern Rockies prototype 

areas. Spectral imagery, biophysical gradients, PVTs, and 

probabilities were used together with field plot data to produce maps of forest, shrub, and herbaceous cover types, as 

well as canopy cover and canopy height by life form.

Accuracy of LANDFIRE Prototype 

Vegetation Mapping

We reported accuracy assessments using a crossvalidation approach for cover types by life form (table 3) 

and by withholding field data for the structure variables 

by life form (table 4). For cover types, only overall accuracies were reported. For structural stages, R2

 values 

were variable and ranged from relatively consistent (for 

forest canopy cover and height) to relatively inconsistent 

(for shrub and herbaceous canopy cover and height). 

This variability indicates that forest structure may be 

mapped reasonably as a continuous variable, whereas 

consistency and accuracy would be questionable when 

mapping shrub and herbaceous structure as continuous variables. However, when evaluated as two-class 

variables (either as closed and open canopy cover or 

high and low canopy height), results showed that the 

same shrub and herbaceous structure can perform as 

consistently and accurately as categorical variables.

Discussion _____________________

Analysis of Mapping Consistency for 

Vegetation Types and Structure

In general, we found that the approach described 

above for mapping existing vegetation characteristics 

effectively met LANDFIRE requirements, which was a 

difficult objective to achieve due to the large number of 

vegetation map units, reliance on existing field-referenced 

data, the task of characterizing vegetation structure, and 

the requirement for a nationally consistent methodology. 

For the moderately detailed vegetation map unit classification, mapping accuracies of 60 percent or better 

were achieved at a 30-m spatial resolution.

We explored the mapping of more than two map units 

for structure variables. For example, we mapped herbaceous height to three map units (0 to 0.5 m, >0.5 to 1 

m, and >1 m), shrub height to four map units (0 to 0.5 

m, >0.5 to 1 m, >1 to 3 m, and >3 m), and forest height 

to four map units (0 to 5 m, >5 to 10 m, >10 to 25 m, 

and >25 m). The tests yielded independent overall accuracies of 73, 61, and 82 percent for herbaceous, shrub, 

and forest height, respectively. From these results, we 

concluded that grouping continuous values of the structure variables into several discrete map units would be 

an acceptable and rational alternative methodology for 

national implementation of the LANDFIRE methods. 

Use of this alternative methodology would require the 

development of a consistent national structural stage 

map unit classification.

Table 3—Cross validations (10 percent withheld, ten-fold repetitions) conducted separately by mapping zones and by forest,

shrub, and herbaceous life forms.

Mapping Number of Cross

Life form zone classes validation

Forest  16  1

 19

Shrub  16 

 19  1

Herbaceous 16 7 

 19 8 
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Chapter 8—Mapping Existing Vegetation Composition and Structure for the LANDFIRE Prototype Project

Consistency in field sampling and data collection affects the consistency of mapping vegetation characteristics. Of the three types of reference data used in mapping 

existing vegetation, cover type and canopy height values 

can generally be identified or measured consistently in 

the field. Canopy cover, on the other hand, can be difficult to measure in the field. This issue does not affect 

the measurement of forest canopy cover values because 

training data are derived from high-resolution (1 m or 

better) imagery by calculating numbers of high-resolution forest pixels within each 30-m Landsat pixel. The 

use of inconsistently estimated canopy cover values as 

training data, however, can potentially affect the mapping of shrub and herbaceous canopy percent cover (as 

happened during the prototype). Shrub and herbaceous 

canopy results from the two prototype mapping zones 

were reasonable (table 3), but difficulties in consistently 

estimating canopy cover in the field indicated that we 

needed to further research new or alternative methods 

for mapping shrub and herbaceous canopy cover.

The results of this study may be attributed, in part, to 

the use of ecologically significant ancillary data layers, 

which accounts for a moderate but nonetheless significant 

increase in accuracy (ranging from 1 to 9 percent). The 

development of biophysical gradient layers and PVT 

probabilities follows a standardized process for all 

mapping zones. However, for any given area, satellite 

reflectance can vary significantly for the same cover type 

with different canopy cover percentages (either due to 

land management practices or regeneration stages) or 

appear similar for different vegetation types or different structural stages during certain seasonal periods. 

Different cover types or structural stages, however, 

should respond consistently to the effects of biophysical gradient variables such as soil depth or potential 

evapotranspiration (PET); this addition of information 

from the biophysical gradient variables increases the 

likelihood that these map units will be discriminated 

by mapping algorithms. For example, one might expect 

Engelmann spruce (picea engelmannii) to grow in relatively deep soil on cool, north-facing sites with low PET, 

regardless of whether it is found in Zone 16 or Zone 19. 

Therefore, the incorporation of biophysical and PVT data 

in the mapping process should contribute to enhanced 

consistency and thematic accuracy in mapped existing 

vegetation across the United States.

Even though the existing vegetation maps shown in 

figures 2 and 3 characterize the vegetation composition of all life forms, it should be noted that each life 

form was mapped independently, by design, for cover 

type and structure. Modeling life forms independently 

preserves the possibility of more than one mapped life 

form per pixel (in other words, allows for probabilities 

of multiple canopy layers within a pixel) to improve fuel 

mapping and enhance the range of the data’s ecological 

applications. However, mapping approaches should be 

carefully considered when comparing or merging these 

separate data sets. For example, a final map of cover 

types may look different depending on the order of 

precedence between forest, shrub, and herbaceous cover 

and the threshold values used in defining the life forms 

(for example, a pixel with 10 percent or greater forest 

canopy cover may be considered as forested land). It is 

important that precedence and thresholds be applied 

uniformly between mapping zones for consistency.

Table 4—Accuracy assessments conducted separately for two structure variables by life forms and map zones. 

Overall accuracy (OA) was obtained by using holdout withheld field plots (n) that were set aside based on

quality and distribution of the total available field plot data (N). Structure variables are treated as both continuous variables measured with the R2 statistic and two-class categorical variables for overall accuracy (OA)

The Ttwo canopy cover classes of canopy cover are closed (≥40%) and open (<40%); for canopy height they

classes are high (≥10m, 1m, 0.24m) and low (<10m, 1m, 0.24m) for forest, shrub, and herbaceous life forms,

respectively.

Map Canopy cover Canopy height

Life form zone n/N R2 Overall accuracy n/N R2 Overall accuracy

Forest 16 1,272/20,000 0.78 0.92 220/2204 0.5

 19 1,200/20,000 0.88 0.89  127/5,541 

Shrub 16 125/1,253 0.41 0.74 10

 19 119/1,788 0.59 0.79 81/989 0.

Herbaceous 16 18/182 0.37 0.71 15/28

 19 126/597 0.58 0.69 75/182 0.
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Chapter 8—Mapping Existing Vegetation Composition and Structure for the LANDFIRE Prototype Project

Figure 2—LANDFIRE Prototype cover type (top) and structural stage (bottom) maps for Zone 16. The cover type map is compiled

from separate forest, shrub, and herbaceous cover type maps, whereas the structural stage map is grouped from continuous maps 

of height and cover for display purposes. 

Factors that Affect Mapping Accuracies

Several factors should be considered when examining the accuracy estimates for maps of cover types and 

structure. First, the mapping and accuracy assessment 

of cover type and structure variables by life form were 

conducted based on field-referenced databases of different sizes and data collected throughout the study 

areas using a variety of sampling strategies. As would 

be expected, vegetation mapping was sensitive to the 

availability of field data. Test results showed that the 

number of field-referenced plots used for mapping and 

accuracy assessment affected not only the level but also 

the consistency of mapping accuracies, with fewer plots 

related to greater variability in accuracy estimates and 

more plots to more robust accuracy estimates (fig. 4). Data 

for herbaceous vegetation were limited in availability 

relative to the overall size of the field-referenced data 

set and hence affected herbaceous mapping accuracy. To 

improve uncertainties related to shrub and herbaceous 

cover and height, we determined that these variables 

should be mapped as categorical map units.
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Chapter 8—Mapping Existing Vegetation Composition and Structure for the LANDFIRE Prototype Project

Figure 3—LANDFIRE Prototype cover type (top) and structural stage (bottom) maps for Zone 19. The cover type map is compiled

from separate forest, shrub, and herbaceous cover type maps, whereas the structural stage map is grouped from continuous maps 

of height and cover for display purposes. 

Second, field-referenced data, with which mapping 

models were trained and accuracy assessed, were collected from different sources, for different objectives, 

and with different techniques. Even though these plot 

data were quality-screened and standardized through 

an extensive effort (Caratti and others, Ch. 4), it was 

inevitable that the differences and errors in field data carried over into map quality and accuracy assessment. For 

example, certain reference data for forest canopy cover 

were derived using digital ortho-photographs, viewing 

forest cover synoptically from above the canopy. On the 

other hand, field estimates for shrub and herbaceous 

canopy cover were made using visual estimation from 

close-range, oblique positions that limited objectivity 

and consistency. We did not experience these problems 

when determining forest, shrub, and herbaceous height, 

which was usually directly measured and had a high 

degree of user-confidence.
208 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 200

Chapter 8—Mapping Existing Vegetation Composition and Structure for the LANDFIRE Prototype Project

Figure 4—Cross-validation accuracy estimates obtained for the mapping of forest 

cover types as a function of the number of forest field plots. More plots contributed to

better accuracy and consistency (smaller standard deviation) to a certain point, after 

which the relationship became flat.

Third, as discussed above, rare map units and ecologically and biophysically similar map units affected mapping accuracies. For example, if the Juniper cover type 

was merged with the combined Pinyon – Juniper cover 

type, forest cover type accuracy increased by more than 

10 percent. The rationale for keeping such similar cover 

types separate is that, even though they occupy similar 

ecological niches and have similar site characteristics, 

separating them increases the utility of the LANDFIRE 

wildland fuel and fire regime products.

Utility of Biophysical Gradient Data for 

Vegetation Mapping

Although the use of DEM data for improving mapping 

results has been widely documented, the effects of a whole 

host of biophysical gradient layers and PVT-probability 

data layers is largely untested at the scale and scope of 

this study. These data layers provide information that 

supplements satellite imagery. Plant distribution patterns and conditions are strongly linked to a multitude 

of environmental factors (for example, temperature, 

soil, weather patterns, day length, soil properties, and 

rainfall), and the accurate characterization of these 

variables should, at least in theory, improve mapping 

results. In addition, spatial information that indicates 

where particular vegetation types can and cannot exist across a wide region (that is, PVT-probability data 

layers) should be similarly useful. Figure 5 compares 

cross-validation results using mapping models with and 

without the additional biophysical gradients listed in table 

1 and using PVT-probabilities as predictor variables. 

Figure 6 displays mean and standard deviation values 

of a subset of the biophysical variables intersected with 

vegetation cover types from field plot data collected in 

the central Utah prototype area. These figures show 

that the incorporation of certain biophysical gradients 

and PVT-probabilities in mapping models contributes 

to increased mapping accuracy and consistency. These 

results are consistent with the findings of Keane and 

others (2002) and Rollins and others (2004).

Vegetation Patterns in Areas of Major 

Disturbances

Wildfires, insect and disease outbreaks, and forest 

clear cuts are some of the major disturbances to ecosystems captured by the satellite sensor in terms of their 

spectral properties. How well did our mapping capture 

and reflect these changes in vegetation conditions? We 

evaluated our mapping methods’ effectiveness in this 

regard by looking at known areas of wildland fire, bark 

beetle infestation, and clear-cuts in the prototype mapping zones.

We evaluated two wildland fires areas that burned 

in Bryce (summer 2001) and Zion (fall 2001) national 

parks to determine what differences might exist between 

pre-fire and post-fire vegetation maps when mapped 

with the same pre-fire models. Pre- and post-fire map 

comparisons showed distinct differences between both 
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Chapter 8—Mapping Existing Vegetation Composition and Structure for the LANDFIRE Prototype Project

Figure 5—Cross-validation accuracy estimates 

obtained in the Zone 16 prototype area, by

life form, with and without the 15 biophysical

gradients and PVT-probabilities in the mapping models. An average of 8 percent increase 

in cross-validation accuracy was obtained by 

incorporating the selected biophysical gradients 

and PVT-probabilities that together describe the 

habitats of the cover types to be mapped.

Figure 6—Mean and standard deviation values of selected biophysical variables found effective in 

mapping cover type against various forest (top), shrub (middle), and herbaceous (bottom) cover types 

of field-reference data. Most of the biophysical variables were divided or multiplied by a constant

for display purposes. Refer to table 1 for definitions and descriptions of the biophysical variables.

Refer to Long and others, Ch. 6 appendix 6-A for vegetation cover type coding protocol.
210 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 20

Chapter 8—Mapping Existing Vegetation Composition and Structure for the LANDFIRE Prototype Project

vegetation cover types and structural stages. The Bryce 

fire, a prescribed fire, showed general shifts from forest 

to shrub map units and, regarding structure, showed a 

shift toward increased low-height shrubs. The Zion fire, 

a wildfire, revealed a shift from predominately deciduous forest types to low shrubs.

Using bark beetle survey data obtained from the Dixie 

National Forest, we conducted simple zonal statistical 

analyses. Results indicated that the mapped species 

composition corresponded fairly well to that of those 

species identified in the survey data for the years 1998-

2000. (Note that the level of actual disturbance varied 

within the survey data and was not differentiated in 

this study.) Structure information was not available in 

the survey data, but mapped structure data indicated 

that most bark beetle infestations occurred in areas 

identified as high forest cover (greater than 40 percent 

canopy cover) and height (greater than 10 m), indicating 

old-growth forest.

Similarly, we compared clear-cut areas, identified using 

modeling and masking methods, with mapped vegetation 

cover type and structure variables. Shrubs and a high 

percentage of grasses were dominant in clear-cut areas. 

Structural stages indicated a trend from forests with high 

canopy cover and canopy height to a high percentage of 

low cover (less than 40 percent), low height (less than 1 

m) shrubs. Herbaceous cover was identified as being high 

cover (greater than 40 percent) with mixed heights.

Field Data Quality and Quantity 

Requirements

The acquisition of field-referenced data posed a significant challenge to the LANDFIRE Prototype effort, 

both logistically and technically. Caratti (Ch. 4) describes 

the logistical efforts and complications associated with 

conducting a national field data campaign. Specifically, 

technical challenges encountered during the mapping 

process, such as uneven amounts and disparate quality 

of field data used to meet various vegetation mapping 

objectives, were tied to the fact that the LFRDB was 

based on data from varying sources and collected with 

different objectives. As discussed above, such issues 

necessitated the careful implementation of a qualitycontrol and quality-assurance (QA/QC) process prior 

to the training of the mapping algorithms for existing 

vegetation types and structure. “Lessons learned” from 

the QA/QC process follow:

•	 Accuracy and consistency are a function of the 

amount of available field-referenced data. Greater 

amounts of field-referenced data contribute to 

enhanced confidence in mapping accuracy (fig. 4), 

whereas limited field-referenced data are correlated 

to reduced confidence in mapping accuracies of 

affected cover types.

•	 The use of data from different sources requires that 

special attention be given to those cover type map 

units that are not supported with sufficient numbers 

of field plots. Both prototype mapping zones had map 

units with only a few field reference data points for 

training. As discussed above, the question of how 

to define and treat rare map units arose during the 

prototype, and we defined rare map units as those 

having less than 30 field reference plots scattered 

spatially within a mapping zone. Options for the 

treatment of these rare map units included keeping 

the map units in maps, omitting them, or omitting 

them and then “burning” the few field plots to the 

map in a post-process and merging them with floristically similar cover types. For the prototype, we 

chose to retain the rare map units in the models and 

resulting map products to inform the development of 

the LANDFIRE vegetation map unit classification 

system. For national implementation, rare map units 

that cannot be supported with a sufficient number 

of field plots will not attain target-level accuracies. 

We recommend omitting such map units from the 

mapping of existing vegetation cover types.

•	 Spatial distribution and a valid probability-based 

sampling design increase the consistency and 

accuracy of the map products. Compared with 

field-referenced data from various agency sources, 

the use of FIA forest inventory plots for mapping 

forest cover types and structure produced more 

consistent and accurate mapping results because 

the sampling design for FIA data produced training 

data that were spatially well-distributed across the 

landscape. Further, FIA data required very little 

additional processing time and were easy to use; 

in contrast, non-FIA field data required extensive 

processing time, related to QA/QC and re-selecting/re-sampling, to derive suitable data sets (in 

terms of spatial distribution and data quality) from 

available data points. For example, in Zone 19, a 

Bureau of Land Management study produced more 

than 4,800 field plots, mostly describing sagebrush, 

Douglas-fir, and lodgepole pine vegetation communities, in a relatively small area of approximately 

1,152 km2, near Salmon, ID. Spatially, this data 

set equated to approximately one plot for every 24 

ha, versus a mapping zone average of one plot for 

every 835 ha. The inclusion of this data set in the 
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Chapter 8—Mapping Existing Vegetation Composition and Structure for the LANDFIRE Prototype Project

training process overwhelmed the mapping models 

and overrode areas with sparse plot coverage of 

different cover types. We therefore determined that 

the application of locally limited or concentrated 

data collected using various sampling designs to 

an entire mapping zone could have adverse effects 

on the accuracy of final products. For this reason, 

forest mapping in LANDFIRE National should 

employ FIA data exclusively. Rangeland mapping 

in LANDFIRE National, however, will require 

extensive QA/QC processing steps to transform 

available field-referenced data to a more suitable 

data set.

•	 As noted above, the following critical steps should 

be taken prior to the development of the mapping 

models: 1) examine field-referenced data, 2) conduct QA/QC procedures to detect spatial errors 

as well as information content-related errors, 3) 

correct these errors if necessary, and 4) derive a 

final, refined, error-free data set for training and 

accuracy assessment. This is a time-consuming yet 

necessary process that will contribute to increased 

consistency and confidence of map products.

Effects of the Vegetation Map Unit 

Classification System

Determining accuracy objectives and the appropriate 

extent of mapping areas are among the factors that need 

to be considered when defining a workable national 

vegetation map unit classification system. If floristically 

or ecologically overlapping cover types (such as Juniper 

and Pinyon -- Juniper or Upland Microphyllous and 

Upland Sclerophyllous) are to be mapped for LANDFIRE National, then guidelines must be developed for 

defining how the mapping accuracy of such overlapping 

map units is to be assessed.

Next, although our use of the NVCS was a reasonable 

starting point for vegetation map unit classification and 

the approach worked fine for each individual mapping 

zone, vegetation cover types were not always comparable 

between the two prototype mapping areas, however, 

as is evidenced by the legends in figures 2 and 3. As a 

result, accuracy estimates for the two prototype mapping zones could not be compared in a straightforward 

fashion, particularly for shrub cover types.

As discussed above, another challenge encountered 

during the application of the two vegetation map unit 

classification approaches (as discussed above in the 

Vegetation Map Unit Classification section) was answering the question of how to treat rare map units. 

There were no guidelines for consistently defining and 

treating rare map units. Moreover, there was no answer 

as to whether dropping rare map units, instead of using 

the alternative options discussed above, might affect the 

utility of LANDFIRE vegetation maps in other future 

natural resource management projects.

Recommendations for National 

Implementation _________________

Because of the size and complexity of this research effort, many questions concerning LANDFIRE’s national 

implementation are as of yet unanswered. The field data 

compilation effort will be an expensive and time consuming task, and a pressing need exists regarding the study 

of links between mapping performance, resource expenditure, and methods of field data collection. Ecological 

relationships between mapped potential vegetation and 

existing vegetation need to be investigated. Further 

research must be conducted to quantify the relative 

contributions of the different approaches and data sets 

used in the prototype. Performance consistency must 

be tested between adjacent western mapping zones, as 

well as in one or more prototype areas located in the 

eastern United States. Repeatability of the methods used 

in the prototype, both temporally and spatially, must 

also be evaluated. Furthermore, it is not clear whether 

the LANDFIRE Prototype methodology will suffice 

for other vegetation metrics, such as quantifying woody 

or non-woody biomass; a study in this area could yield 

information leading to enhanced applications of LANDFIRE vegetation maps. Nevertheless, the LANDFIRE 

Prototype Project provides sufficient information on 

which to base several recommendations regarding the 

national implementation of LANDFIRE.

Ways to Ensure Consistent National 

Vegetation Mapping

As noted above, several tasks related to existing 

vegetation mapping for the prototype effort may be 

standardized and potentially automated to facilitate 

LANDFIRE’s national implementation. These tasks 

include: 1) the creation of a national vegetation map 

unit classification system that is mappable using spectral and biophysical/ecological data and is supported 

with adequate field-referenced data; 2) the consistent 

acquisition and processing of a multi-seasonal Landsat 

database; 3) the application of QA/QC procedures to 

the LFRDB to ensure a robust field-referenced database 

that can be used for a wide variety of applications; 4) 

the consistent modeling of biophysical data layers and 

probabilities of existing vegetation species or types 
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Chapter 8—Mapping Existing Vegetation Composition and Structure for the LANDFIRE Prototype Project

associated with potential vegetation types; and 5) the 

continued application of CART as the primary mapping 

algorithms to ensure objectivity and flexibility when using high volumes of field data and predictor variables. 

We discuss these points in detail below.

Need for a Mappable Vegetation Map Unit 

Classification System

The vegetation map unit classification system used for 

the national implementation of LANDFIRE must meet 

a number of key criteria including the following: 1) the 

system must be nationally consistent, ecologically logical and hierarchical, acceptable to a wide array of users 

and groups, and must meet existing Federal Geographic 

Data Committee (FGDC) standards; 2) vegetation map 

units must be mappable using operational methodology to 

achieve reasonable accuracies; and 3) the map unit classification system must include vegetation map units that 

have high relevance with respect to the core LANDFIRE 

products. The Ecological Systems classification (Comer 

and others 2003) developed by NatureServe meets these 

objectives. This system represents the hierarchical merging of NVCS alliances into a nationally available suite 

of vegetation map units. Unlike alliances, which have 

proved exceedingly difficult to map accurately, most 

Ecological Systems classes are mappable, assuming an 

adequate number of field plots exist for training purposes. 

In addition, the Ecological Systems classification was 

developed by plant ecologists, lending credibility to the 

approach and resulting in a greater level of acceptance 

throughout the user community. We anticipate that a few 

additional “target alliance” map units will be added to 

the LANDFIRE National map unit classification legend 

on a case-by-case basis. These will be added only when 

it is determined that a particular map unit not specifically identified by the Ecological Systems classification 

has special relevance to LANDFIRE.

Need for National Field-referenced Data 

Collection and Processing

Many LANDFIRE tasks rely on a comprehensive, 

consistent, and extensive field-referenced database. The 

database serves as a reference for the development, testing, and accuracy assessment of all LANDFIRE vegetation, biophysical settings, and wildland fuel data layers 

and of all vegetation and fire regime simulation models. 

Field data from existing projects should be incorporated 

into this database whenever available and should include 

but not be limited to data sets such as FIREMON fire 

monitoring databases, USFS Landscape Ecosystem 

Inventory Systems databases, and the National Park 

Service fire monitoring databases. In addition, the USFS 

FIA Program’s forest inventory plot database proved 

a useful source for the majority of forest data. Where 

data are lacking, supplemental field data collection is 

required to fill informational needs on rangeland map 

units. This assortment of field-referenced data should be 

collectively scrutinized for quality assurance, regularly 

updated, and maintained as a comprehensive LANDFIRE field-referenced database.

Need for Nationally Consistent Imagery 

Database

The availability of a quality Landsat imagery catalog 

is a key prerequisite for national implementation of the 

approaches developed for the LANDFIRE Prototype 

Project. Among all predictor variables, it is satellite 

imagery that usually captures the most current vegetation conditions, and, when used repeatedly over time, 

identifies changes in vegetation conditions and distributions. Thus, we recommend that LANDFIRE National 

continue to play an active role in the MRLC Consortium. 

This membership ensures the continued development of 

suitable multi-seasonal Landsat image catalogs, optimal 

levels of image processing (geometric, radiometric, 

and atmospheric rectification and calibration) for the 

rest of the country, and mapping zone-based image 

compilation for national vegetation mapping. In addition, LANDFIRE National should support studies that 

examine and compare the characteristics of other midresolution sensors with those of Landsat. Even though 

the LANDFIRE Project does not currently require any 

additional Landsat imagery, the potential benefits of 

using different satellite data for future updating should 

be considered.

Need for Nationally Consistent Set of 

Biophysical Gradient Layers

Biophysical gradients have effects similar to that of 

Landsat imagery on the spatial and information integrity 

of existing vegetation maps. Many of the biophysical 

layers are physiologically and ecologically related to the 

establishment, distribution, and conditions of plant species, and the incorporation of these gradient layers into 

the mapping process contributes to increased accuracies. 

For the national implementation of LANDFIRE, we recommend that a set of biophysical gradient layers similar 

to those listed in table 1 be used to map vegetation in all 

mapping zones. In addition, we recommend that further 

research be conducted to quantify the contribution of the 
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Chapter 8—Mapping Existing Vegetation Composition and Structure for the LANDFIRE Prototype Project

individual biophysical variables to mapping accuracy. 

Furthermore, research should be conducted to minimize 

residual coarse-resolution imprints in 30-m biophysical 

data resulting from the coarser resolution weather and soil 

databases used to produce these data. The development 

of standard minimum mapping units in modeling simulations has shown promise in standardizing the process 

and eliminating coarse imprints.

The Need to Continue with Research and 

Improvements

Although results of the LANDFIRE Prototype Project 

indicate that the general approach should effectively 

meet target accuracy and consistency requirements for 

national implementation, there are areas where continued 

research and improvements are needed. One ongoing 

research effort involves the development of a new and 

more consistent approach to mapping shrub and herbaceous canopy cover. Current research is testing ways 

to effectively correlate calibrated Landsat-based NDVI 

to shrub and herbaceous canopy cover (Liu and others 

2004). Other research areas include more efficient use 

of the individual biophysical gradient layers, more effective mapping of riparian vegetation, and a national 

accuracy assessment strategy.

Conclusion_____________________

The mapping of existing vegetation with complete 

national coverage at a 30-m spatial resolution is a core 

requirement of the LANDFIRE Project. National data at 

this 30-m resolution do not currently exist. As a result, 

the prototype research was needed to answer questions 

related to the mapping and characterizing of cover 

types and structure variables. LANDFIRE’s existing 

vegetation products are expected to provide data not 

only for use in wildland fire management, but also for 

use in many other natural resource and environmental 

applications. Findings from the LANDFIRE Prototype 

effort are summarized as follows:

If supported with an adequate amount of field-referenced data, target accuracies of 60 percent or better are 

achievable for a mid-level vegetation map unit classification at the regional scale. The addition or subtraction 

of floristically or ecologically similar cover types has 

significant effects on resulting accuracies. Of the three 

major life forms, herbaceous cover types are the most 

difficult to map because these species adapt to many 

general biophysical characteristics and have few unique 

spectral signatures. Relationships between the floristic 

complexity of the vegetation map unit classification and 

mapping accuracies indicate that the national vegetation 

map unit classification will need to be designed carefully 

to include adequate flexibility.

For LANDIFRE, vegetation structure is defined by 

canopy cover and canopy height of forest, shrub, and 

herbaceous life forms. These structure attributes can be 

mapped consistently as categorical variables. Mapping 

these attributes as continuous variables, particularly for 

shrub and herbaceous height and cover, is inconsistent 

and, thus, is not recommended for national implementation of the LANDFIRE prototype methods.

Field data collection and processing are the most critical 

factors in ensuring that LANDFIRE maps of existing 

vegetation are objective and accurate. The detection and 

correction of errors existing in field-referenced data are 

time-consuming but absolutely necessary tasks, particularly for field data from sources other than FIA (as 

these other data sets tend to be locally limited and have 

various sampling designs). The objective of repeated 

field data processing and quality control is to derive a 

refined, high-quality field data set.

The incorporation of LANDFIRE biophysical gradient layers and cover-type probabilities associated with 

potential vegetation types into the mapping models 

contributes to a significant increase in mapping accuracy. 

In addition, the use of the biophysical and ecological 

stratifications that describe the environmental effects 

on species establishment and growth also contributes 

to enhanced mapping consistency.

For further project information, please visit the LANDFIRE website at www.landfire.gov.

The Authors ____________________

Zhiliang Zhu is a Research Physical Scientist with 

the DOI USGS Center for Earth Resources Observation and Science (EROS). Zhu’s research work focuses 

on mapping and characterizing large-area land and 

vegetation cover, studying land cover and land use 

change, and developing remote sensing methods for the 

characterization of fuel and burn severity. His role in 

the LANDFIRE Prototype Project has been to design 

and test a methodology for the mapping of existing 

vegetation cover types and vegetation structure and to 

direct research and problem-solving for all aspects of the 

methodology. He received his B.S. degree in Forestry in 

1982 from the Nanjing Forestry University in China, his 

M.S. degree in Remote Sensing in 1985, and his Ph.D. 

degree in Natural Resources Management in 1989, both 

from the University of Michigan.
214 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2

Chapter 8—Mapping Existing Vegetation Composition and Structure for the LANDFIRE Prototype Project

James Vogelmann is a Principal Scientist with the 

Science Applications International Corporation (SAIC), 

contracting with the DOI USGS Center for Earth Resources Observation and Science (EROS). Vogelmann’s 

research work focuses on large-region land cover characterization and change assessment using remote sensing 

and ancillary sources of spatial data. His roles in the 

LANDFIRE Prototype Project have been to assess different methods for mapping vegetation types, to serve 

on the LANDFIRE Vegetation Working Group, and to 

help direct project research activities. He received his 

B.A. degree in Botany from the University of Vermont 

in 1978 and his Ph.D. degree in Plant Sciences from 

Indiana University in 1983.

Donald Ohlen is a Environmental Scientist for the 

Science Applications International Corporation (SAIC) at 

the DOI USGS Center for Earth Resources Observation 

and Science (EROS), 47914 252nd Street, Sioux Falls, SD 

57198; phone: (60. Ohlen’s research work and interest 

focus on land cover mapping for fire science applications, including the characterization of satellite data for 

fuel mapping and post-fire burn mapping. He earned 

his B.S. (1976) and M.S. (2000) degrees in Geography 

from South Dakota State University.

Jay Kost is a Research Physical Scientist with the 

Science Applications International Corporation (SAIC), 

contracting with the DOI USGS Center for Earth Resources Observation and Science (EROS). Kost’s work 

focuses primarily on mapping existing vegetation and 

vegetation structure (percent canopy and height) for the 

LANDFIRE Project using decision and regression tree 

models. Optimization of these models and high map accuracy results are paramount in his work and improvements 

in methodology and results are continually pursued. He 

received his B.S. in Electronic Engineering Technology 

in 1987 from Minnesota State-Mankato and his M.S. 

degree in Space Studies from the University of North 

Dakota, Grand Forks. In addition, Kost has completed 

four years of post-graduate study in the Atmospheric, 

Environmental, and Water Resources Ph.D. program at 

South Dakota State University, Brookings.

Xuexia (Sherry) Chen is an Environmental Scientist 

with the Science Applications International Corporation (SAIC), contracting with the DOI USGS Center 

for Earth Resources Observation and Science (EROS). 

Chen’s research work focuses on vegetation mapping 

of canopy cover and canopy structure. Her role in the 

LANDFIRE Prototype Project has been to develop a 

hierarchical methodology for the mapping of existing 

vegetation cover types and vegetation structure and to 

explore new technologies for accuracy and efficiency 

improvements in LANDFIRE products. She received her 

B.S. degree in Geography in 1997 and her M.S. degree 

in Environmental Sciences in 2000, both from Peking 

University, China. Chen received her Ph.D. degree in 

Atmospheric, Environmental, and Water Resources 

in 2004 from the South Dakota School of Mines and 

Technology.

Brian Tolk is a Research Scientist with the Science 

Applications International Corporation (SAIC), contracting with the DOI USGS Center for Earth Resources 

Observation and Science (EROS). Tolk’s research work 

focuses on the mapping and characterization of large-area 

land and vegetation cover and on the use of close-range 

remote sensing methods to aid and improve LANDFIRE 

mapping techniques. His role in LANDFIRE has been to 

map land cover and structure variables for the prototype 

zones, implement a data management scheme, and produce promotional products for the project. He received 

his B.A. degree in Geography from Augustana College, 

Sioux Falls in 1990 and his M.A. degree in Geography 

from the University of Nebraska, Lincoln in 1996.

Acknowledgments_______________

We acknowledge the funding provided by the USDOI 

Office of Wildland Fire Coordination and the USFS Fire 

and Aviation Management. In addition, the cooperation 

of the USFS FIA office in Ogden, Utah, is gratefully 

acknowledged. We also wish to acknowledge the help 

and support provided by the staff at the USDA Forest 

Service Fire Sciences Laboratory in Missoula, Montana 

and by the staff at Utah State University in Logan, 

Utah. The research was performed in part by the Science Application International Corporation under U.S. 

Geological Survey Contract 1434-CR-97-40274 and 

03CRCN0001.

References _____________________

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USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 2

Chapter 9—Vegetation Succession Modeling for the LANDFIRE Prototype Project

Chapter

9

In: Rollins, M.G.; Frame, C.K., tech. eds. 2006. The LANDFIRE 

Prototype Project: nationally consistent and locally relevant 

geospatial data for wildland fire management. Gen. Tech. Rep. 

RMRS-GTR-175. Fort Collins: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research Station.

Introduction ____________________

One of the main objectives of the Landscape Fire and 

Resource Management Planning Tools Prototype Project, 

or LANDFIRE Prototype Project, was to determine 

departure of current vegetation conditions from the 

range and variation of conditions that existed during the 

historical era identified in the LANDFIRE guidelines 

as 1600-1900 A.D. (Keane and Rollins, Ch. 3). In order 

to approximate this range and variation, we simulated 

a series of historical vegetation conditions using the 

landscape succession model LANDSUMv4, the fourth 

version of the LANDSUM model, developed specifically 

for the LANDFIRE Project (Keane and Rollins, Ch. 3).

LANDSUMv4 deterministically simulates vegetation 

dynamics based on successional communities called succession classes. Succession classes are characterized by 

cover types, which describe the species composition of 

the dominant vegetation, and structural stages, which describe the height and cover of the dominant vegetation. The 

combination of these two descriptors captures vegetation 

growth and development through time. These succession 

classes, linked by multiple pathways, transition between seral 

stages after a set number of years and eventually converge 

in an end-point community called a potential vegetation 

type or PVT. Disturbances occur probabilistically within 

the model and alter the successional status of vegetation 

Vegetation Succession Modeling for the LANDFIRE 

Prototype Project

Donald Long, B. John (Jack) Losensky, and Donald Bedunah

communities, often setting succession back a number of 

time-steps (Pratt and others, Ch. 10).

At the end of a user-defined reporting period, LANDSUMv4 outputs a vegetation map. Synthesis of this chronosequence of vegetation maps over the simulation period 

reflects the net result of these successional transitions and 

disturbances. The modeling process results in an estimate 

of the distribution of succession classes through time for 

a particular PVT, which may be thought of as simulated 

historical reference conditions. (For a detailed description of the role played by LANDSUMv4 simulations in 

the LANDFIRE Prototype, see Pratt and others, Ch. 10 

and Holsinger and others, Ch. 11)

To parameterize LANDSUMv4, we had to define all 

succession pathways and their associated transition times 

for each PVT. We estimated transition times between 

succession classes based on a number of factors, such as 

site productivity and species adaptations to disturbance. 

In addition, we had to define all disturbance pathways 

along with the probabilities of their occurrence, requiring that we convert knowledge of historical disturbance 

intervals into yearly probabilities. More importantly, we 

had to test these inputs before they could be used for 

modeling purposes. To test the inputs we created for the 

model, we used a computer model called the Vegetation 

Dynamics Development Tool (VDDT) (Beukema and 

others 2003).

The VDDT modeling framework is almost identical to 

that of LANDSUMv4 (Keane and others 2002), except 

that in VDDT, the modeling environment is “aspatial” 

and uses pixels to track succession classes. These pixels 

are independent of adjacent pixels because VDDT does 

not simulate the contagion of ecosystem processes (such 

as wildland fire) through space or over time (Beukema 
218 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 20

Chapter 9—Vegetation Succession Modeling for the LANDFIRE Prototype Project

and others 2003). This simpler approach allows for nearinstantaneous model execution as well as for rapid model 

building and rapid testing of the model’s sensitivity to 

a wide range of inputs.

The objective of the LANDFIRE Prototype vegetation 

modeling was to provide the myriad of LANDSUMv4 inputs 

as well as to document both the processes used to derive 

these inputs and the assumptions involved in constructing 

the succession models. The following sections describe the 

general process we used to create the succession models in 

addition to all input parameters for LANDSUMv4. This 

process included the initial steps of deciding which PVTs to 

model, which cover types should be included in each PVT, 

and which structural stages should be used in combination 

with these cover types to represent the various succession 

classes within each PVT. We then defined pathways for each 

of the succession classes in each PVT. These pathways took 

two forms. One set described succession and the associated number of time-steps required to transition from one 

succession class to another without disturbance. The other 

set described disturbance, both in terms of the succession 

class that is the result of that disturbance and the associated 

probability of that disturbance occurring for that particular 

succession class. Also included are general descriptions of 

all of the models built as input into LANDSUMv4 along 

with recommendations for modifying this process in the 

context of national implementation.

Methods _______________________

The LANDFIRE Prototype Project involved many 

sequential steps, intermediate products, and interdependent processes. Please see appendix 2-A in Rollins and 

others, Ch. 2 for a detailed outline of the procedures 

followed to create the entire suite of LANDFIRE Prototype products. This chapter focuses specifically on the 

procedure followed in developing the models of vegetation dynamics (including disturbance probabilities and 

transition times) which were an important precursor to 

the modeling of historical vegetation conditions and fire 

regimes.

PVTs and Succession Classes

Succession classes for each PVT were represented by 

combinations of cover types and structural stages (Zhu 

and others, Ch. 8). An example of a succession class in 

the Spruce – Fir/Blue Spruce PVT would be “Douglasfir, High Cover, High Height Forest,” each succession 

class being described by a combination of one cover 

type and one structural stage. Thus, for each PVT, we 

first decided which cover types and which structural 

stages would be used to represent the various stages 

of succession for that PVT. The list of these PVTs developed for LANDFIRE mapping purposes, shown in 

tables 1 and 2, contains the PVTs used for succession 

modeling purposes. The cover type list (tables 3 and 4), 

which describes dominant species, and the structural 

stage list (table 5), which describes dominant vegetation cover and height, were used to limit the number 

of succession classes that could occur within a PVT. 

(For detailed information on the cover types, potential 

vegetation types, and structural stages mapped for the 

LANDFIRE Prototype, see Long and others, Ch. 6.) 

Tabular summaries from the LANDFIRE reference 

Table 1—Potential vegetation types (PVTs) used for succession 

modeling in Zone 16.

PVT# Potential vegetation type

1601	 Spruce – Fir / Blue Spruce

1602	 Spruce – Fir / Blue Spruce / Lodgepole Pine

1603	 Spruce – Fir / Spruce – Fir

1604	 Spruce – Fir / Spruce – Fir / Lodgepole Pi

1611	 Grand Fir – White Fir

1612	 Grand Fir – White Fir / Maple

1621	 Douglas-fir / Timberline Pine

1622	 Douglas-fir / Douglas-fir

1623	 Douglas-fir / Lodgepole Pine

1631 Timberline 

1632 Ponderosa Pi

1633 Lodgepole 

1634 A

1641	 Pinyon – Juniper / Mountain Big Sagebrush / North

1642	 Pinyon – Juniper / Mountain Big Sagebrush / South

1643	 Pinyon – Juniper / Wyoming – Basin B

Sagebrush / North

1644	 Pinyon – Juniper / Wyoming – Basin B

Sagebrush / South

1645	 Pinyon – Juniper / Mountain Mahogany

1646	 Pinyon – Juniper / Gambel Oak

1651 Blackbr

1652 Salt Desert Shru

1653 Warm Herbace

1654 Cool Herbace

1661 Dwarf Sagebr

1662	 Wyoming – Basin Big Sagebrush

1663 Mountain Big Sagebr

1671 Riparian Hardw

1672 Riparian Shru

1673 Wetland Herbace

1680 Alpin
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 2

Chapter 9—Vegetation Succession Modeling for the LANDFIRE Prototype Project

database (Caratti and others, Ch. 4) provided a list of 

the cover types and structural stages that, based on 

plot data, occurred in each PVT. This list provided the 

building blocks for constructing the various succession 

models used to simulate historical reference conditions 

for the LANDFIRE Prototype.

Potential vegetation types represent specific biophysical environments and associated suites of successionally 

dominant species or species complexes (Keane and 

Rollins, Ch. 3; Long and others, Ch. 6) and, as such, are 

Table 2 — Potential vegetation types (PVTs) used for succession modeling in Zone 19.

PVT# Potential vegetation type

1902 Western Redceda

1914	 Grand Fir – White Fir

1920	 Spruce – Fir / Montane / Western Larch

1921	 Spruce – Fir / Montane / Douglas-fir

1922	 Spruce – Fir / Timberline

1924	 Spruce – Fir / Subalpine

1930	 Douglas-fir / Ponderosa Pine / Western Larch

1931	 Douglas-fir / Ponderosa Pine / Douglas-fir

1932	 Douglas-fir / Lodgepole Pine

1934	 Douglas-fir / Timberline Pine

1936	 Douglas-fir / Douglas-fir

1940 Lodgepole Pi

1942 Ponderosa Pi

1944 Timberline Pine / Limber 

1946 Timberline Pine / Whitebark Pi

1950 Rocky Mountain Junipe

1952 Riparian Hardwoo

1960 Riparian Shru

1962 Mountain Mahogan

1964 Dry Sh

1965 Dry Shrub / Conife

1970 Dwarf Sagebrush Comple

1971 Dwarf Sagebrush Complex / Coni

1972 Mountain Big Sagebrush Comple

1973 Mountain Big Sagebrush Complex / Coni

1974 Threetip Sagebr

1975 Threetip Sagebrush / Conife

1976	 Wyoming – Basin Big Sagebrush Complex

1977	 Wyoming – Basin Big Sagebrush Complex / Conifer

1980 Wetland Herbaceou

1982 Alpin

1984 Fescue Grassla

1985 Fescue Grasslands / Conife

1986 Bluebunch Wheatgras

1987 Bluebunch Wheatgrass / Conife

Table 3—Cover types (CTs) used for succession modeling in 

Zone 16

CT# Cover type

1401 Riparian Hard

1405	 Aspen – Birch

1201 [Interior] Ponderosa P

1204 Lodgepole P

1205	 Douglas-fir

1206	 Grand Fir – White Fir

1211	 Spruce – Fir

1801 Timberline Pi

2201	 Pinyon – Juniper

2202 Juniper

3704 Mountain Deciduous Sh

3402 Riparian Shr

3403 Exotic Riparian S

3101 Mountain Big Sagebrush Com

3102	 Wyoming – Basin Big Sagebrush Complex

3103 Dwarf Sagebrush Com

3104 Sand Sageb

3105 Blackbru

3106 Rabbitbru

3107 Chaparr

3301 Montane Evergreen Shr

3701 Salt Desert Sh

3702 Desert Shru

3703 Dry Deciduous Sh

4101 Warm Season Gras

4102 Cool Season Grasse

4201 Native Fo

4202 Exotic Forb

4301 Wetland Herbac

4302 Alpi

4401 Annual Grassl

very similar in concept to “habitat types” (Daubenmire 

1968). A number of habitat type classifications were 

available for the two prototype mapping zones, and we 

used data from these classifications to refine the lists 

of cover types that could exist in each PVT. For forest 

vegetation, habitat classifications for Zone 16 included 

those by Mauk and Henderson 1984; Muegler and 

Campbell 1986; Padgett and others 1989; Pfister 1972; 

Steele and others 1981; Youngblood and Mauk 1985; and 

Youngblood and others 1985. Habitat type classifications 

for Zone 19 included those by Hansen and others 1987; 

Hansen and others 1988; Pierce 1986; and Pfister and 

others 1977.
220 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 200

Chapter 9—Vegetation Succession Modeling for the LANDFIRE Prototype Project

Table 5—Structural stages used for succession modeling in 

zones 16 and 1

Structural Structural Structural stage

stage # stage name abbreviation

Zone 16

11 Low Cover, Low Height Forest 

12 High Cover, Low Height Forest HL

13 High Cover, High Height Forest 

14 Low Cover, High Height Forest 

21 Low Cover, Low Height Woodland L

22 High Cover, Low Height Woodland HLW

23 High Cover, High Height Woodland H

24 Low Cover, High Height Woodland L

31 Low Cover, Low Height Shrubland 

32 High Cover, Low Height Shrubland HL

33 High Cover, High Height Shrubland 

34 Low Cover, High Height Shrubland 

51 Low Cover, Low Height Herbaceous L

52 High Cover, Low Height Herbaceous HLH

53 High Cover, High Height Herbaceous H

54 Low Cover, High Height Herbaceous L

Zone 19 

10 Low Cover, Low Height Trees LL

11 Low Cover, Low -Mod Height Trees L

12 High Cover, Low - Mod Height Trees HLM

13 Low Cover, Mod Height Trees 

14 High Cover, Mod Height Trees 

15 Low Cover, High Height Trees LH

16 High Cover, High Height Trees HH

21 Low Cover, Low Height Shrubs L

22 High Cover, Low Height Shrubs HLS

23 Low Cover, Mod Height Shrubs L

24 High Cover, Mod Height Shrubs H

25 Low Cover, High Height Shrubs LHS

26 High Cover, High Height Shrubs HHS

31 Low Cover, Low Height Herbs 

32 High Cover, Low Height Herbs HL

35 Low Cover, High Height Herbs LH

36 High Cover, High Height Herbs HH

Table 4—Cover types (CTs) used for succession modeling 

in Zone 19

CT# Cover type

1201 Ce

1202	 Douglas-fir

1203 Grand 

1204 Heml

1205 Lodgepole Pin

1206 Junipe

1207 Ponderosa Pine

1208	 Spruce – Fir

1209 Limber Pin

1212 White Pi

1401	 Aspen – Birch

1402 Riparian Hardwo

1403 Western L

1801 Timberline For

2101 Upland Broadleaf Dwarf Shrubl

2102 Upland Broadleaf Medium Shrublan

2103 Upland Broadleaf Tall Shrubl

2202 Upland Microphyllous Medium Shrubland

2211 Dwarf S

2212	 Shrubby Cinquefoil

2213 Threetip Sa

2218 Mountain Big Sag

2219	 Wyoming – Basin Big Sa

2220 Rabbitbrush

2222 Greasewood

2223 Mountain Mahoga

2300 Upland Needleleaf Shrublan

2400 Upland Sclerophyllous Shrublan

2600 Riparian Broadleaf Shrubland

3110 Annual F

3120 Annual Gramino

3130 Perennial F

3141 Perennial Exotic Bunch Grammi

3142 Perennial Native Bunch Grammin

3151 Perennial Exotic Rhizomatous Grammin

3152 Perennial Native Rhizomatous Grammino

3200 Wetland Herbaceou

In cases where several habitat types from a particular 

classification – each having different species compositions – were associated with one PVT, we used a weighting 

process to predict the average cover type composition. 

We assigned weights based on the number of plots recorded for each habitat type. If a cover type was listed 

as a major seral or climax species in a particular habitat 

type, we assumed that it could dominate the site and 

should therefore be included in the succession model. 

Using the weights assigned from data describing each 

habitat type within a PVT, we developed a list of cover 

types and associated expected percent composition for 

each PVT.

Regarding rangeland vegetation, we found no existing habitat type classifications for Zone 16. This lack of 

previously established rangeland habitat classifications 

led us to rely almost entirely on tabular summaries from 

the LANDFIRE reference database (Caratti and others, 

Ch. 4) for the assignment of cover types to rangeland 

PVTs. In Zone 16, the plot data were well distributed 

across PVTs and there were enough data to effectively 

describe the cover types within each PVT. Habitat types 

as defined by Mueggler and Stewart (1980) served as the 

source for nearly all the information used to describe 

cover types found in specific PVTs in Zone 19.
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Chapter 9—Vegetation Succession Modeling for the LANDFIRE Prototype Project

Once all possible cover types had been assigned to each 

PVT, we began defining structural stages for each cover 

type for each PVT. For forest PVTs, each cover type was 

represented by a standard set of structural stages (Long 

and others, Ch. 6). These structural stages consisted of 

one or more shrub or herbaceous cover types (used to 

describe early seral conditions), which generally result 

from a stand-replacing disturbance. Four structural 

stages, defined by two categories of tree height and two 

categories of crown cover, were used to describe each 

forest succession class. Modeled succession for each PVT 

began in the various early seral types and then flowed 

through the three structural stages for that particular 

cover type: “Low Cover, Low Height Forest,” “High 

Cover, Low Height Forest,” and “High Cover, High 

Height Forest.” A fourth structural stage, “Low Cover, 

High Height Forest,” was used to represent stands that 

resulted only from mixed-severity, non-stand-replacing 

disturbances (see Pratt and others, Ch. 10 for details on 

the fire regime classification used in the LANDFIRE 

Prototype).

The development of rangeland pathways was predicated 

on the theory that rangeland vegetation exhibits multiple 

states and transitions (Stringham and others 2003). The 

changes in structural stages generally represented transitions from a grass-dominated state (generally resulting 

from a stand-replacing disturbance, such as fire) to a 

shrub state or, depending on the PVT, a forest state. 

In addition, to capture more subtle transitions between 

these states, we included additional succession classes 

by incorporating two and sometimes three cover and 

height breaks for each cover type.

Succession and Disturbance Modeling

For forest PVTs, we estimated transition times between 

succession classes by forest cover type using site index 

data from a number of sources. Site index is a measure 

often used to describe the height of a free-growing tree 

after a certain number of years, generally between 50 to 

100 years. We then interpolated these data to the height 

classes defined in the structural stages. Transition times 

for rangeland PVTs were gleaned from a wide variety 

of rangeland vegetation studies. Information from these 

studies often characterized the response of rangeland 

plant communities to fire and other stand-replacing 

disturbances and was applied on a case-by-case basis 

to the appropriate PVT.

For Zone 16, we obtained site index data from Alexander 1966; Brickell 1966; Mauk and Henderson 1984; 

Mueggler and Stewart 1980; Padgett and others 1989; 

Pfister 1972; Youngblood and Mauk 1985; Youngblood 

and others 1985; and, for adjacent areas, from studies by 

Pfister and others 1977 and Steele and others 1975. We 

based the expected longevity of various tree species on 

Alexander 1974; Burns and Honkala 1990; Jones 1974; 

and McCaughey and Schmidt 1982.

 For Zone 19, we obtained site index data from Brickell 

1966; Burns and Honkala 1990; Pfister and others 1977; 

and Seidel 1982. We based the expected longevity of 

various tree species on Burns and Honkala 1990 and 

Ferguson and others 1986. We then adjusted the life 

expectancy to reflect the environmental conditions found 

in the PVT.

We used an extensive literature search to define disturbance pathways for each PVT. Disturbance pathway 

parameters were based primarily on the way each succession class responds to disturbance. These parameters were generally based on vegetation studies that 

addressed an individual species’ response to fire. We 

supplemented the results of the literature search with 

information provided by local scientists as well as with 

online sources of information on plant communities’ 

responses to fire, including the Fire Effects Information 

System (FEIS) database (USDA Forest Service 2005) 

and the National Resource Conservation Service and 

its associated descriptions of rangeland ecological site 

data (USDA NRCS 2005).

For Zone 16, information pertinent to defining disturbance pathways was gleaned from studies by Bradley 

and others 1992; Brown and Debyle 1989; and Yanish 

2002. For Zone 19, these data were taken from studies 

by Fisher and Bradley 1987; Zlatnik and others 1999; 

Arno and Gruell 1983, 1986; Fiedler (no date); Ferguson 

and others 1986; and Oliver 1979.

We obtained information on fire intervals from literature searches and from personal communication 

with local scientists, as well as from online sources of 

information on plant communities’ responses to fire, 

including the FEIS database (USDA Forest Service 

2005) and the National Resource Conservation Service 

and its associated descriptions of rangeland ecological 

site data (USDA NRCS 2005).

For Zone 19, historical fire intervals for each succession 

class were derived from Arno 1976; Arno and others 

2000; Barrett 1988, 1995, 2002; Losensky 1989, 1992, 

1993, and 1995; and Pierce 1982.

Model Evaluation

We ran each of our models for a 1000-year simulation period and examined the distribution of succession 

classes for each PVT. We assumed that the proportion 

of succession classes at the end of the simulation period 
222 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 200

Chapter 9—Vegetation Succession Modeling for the LANDFIRE Prototype Project

would represent the natural conditions found on the 

landscape at the time of Euro-American settlement. 

These values were largely dependent on the assignment 

of pixels to various succession classes as they moved 

from initiation communities to tree-dominated communities. In addition, changes in succession class could 

result from wildland fire. Evaluation of the proportion of 

succession classes associated with each PVT is highly 

important in the parameterization of each model. We 

reviewed the models to determine if the proportion of 

succession classes within a PVT, the modeled fire intervals, and the modeled severities were similar to findings 

in the literature or as expected according to the known 

information about the plant communities.

Model Descriptions ______________

The next two sections describe VDDT succession 

modeling results. These results relate to groups of 

PVTs with similar succession dynamics and similar 

fire return intervals. The objective of the discussion is 

to highlight the important succession and disturbance 

regimes of each PVT and connect them to the resulting 

succession class distributions. Detailed results from the 

simulations are presented in appendices 9-A through 

9-P and include summaries of transition times between 

succession classes, fire return intervals, and succession 

class distributions -- by succession class for each PVT. 

(Note: PVT legends and descriptions can be found in 

Long and others, Ch. 6: appendices 6-F and 6-G.)

Zone 16 Models

Spruce – Fir Forests—Spruce – Fir forests in Zone 

16 were represented by the Spruce – Fir/Blue Spruce 

and Spruce – Fir/Spruce Fir PVTs in Zone 16 (appendix 9-A). Two variants were modeled for both of these 

PVTs to reflect the distribution of the Lodgepole Pine 

cover type in the northern sections of Zone 16 and the 

lack of the Lodgepole Pine cover type in the southern 

part of Zone 16 (table 1). All PVTs had fairly long 

fire return intervals between stand-replacing fires and 

moderately long intervals between mixed-severity fires 

and non-lethal fires (appendix 9-A: table 2). Dominant 

cover types were Douglas-fir, Spruce – Fir, Lodgepole 

Pine (restricted to northern portions of the zone) 

and Aspen – Birch. Each cover type was consistently 

dominated by late seral structural stages, with a slightly 

higher proportion of the open cover class. Spruce – Fir 

was the successional endpoint in all of these models, 

but Douglas-fir is a long-lived seral dominant.

White Fir/Douglas-fir Forests—White Fir/Douglasfir forests in Zone 16 were represented by one Grand

Fir/White Fir PVT and three Douglas-fir PVTs (appendix 9-B). All of these PVTs support the Douglas-fir, 

Ponderosa Pine, and Aspen – Birch cover types but 

differ from each other in the unique combinations of 

other seral species they also support. Non-lethal fires 

with short return intervals characterize nearly all of 

this group’s PVTs (appendix 9-B: table 2). Late seral 

Douglas-fir cover types dominate nearly all PVTs in this 

group, with the exception of late seral Ponderosa Pine 

cover types in the Grand Fir/White Fir PVT (appendix 

9-B: table 3).

Pine Forests—Pine forests in Zone 16 were represented 

by three PVTs, each of which occupies a fairly distinct 

landscape setting that generally favored the dominance 

of a single cover type (appendix 9-C). The Lodgepole 

Pine PVT occurred primarily in an upper montane and 

subalpine setting, while the Ponderosa Pine PVT occupied a lower montane setting. The Timberline Pine 

PVT occupied unique sites where species composition 

was purely limber pine or bristlecone pine. Fire intervals 

were modeled to be moderately long or very long for 

stand-replacing and mixed-severity fires, but short to 

moderate for non-lethal fires (appendix 9-C: table 2). 

Modeling results under these fire intervals produced a 

mixture of all structural stages of the dominant cover 

type, except where the Aspen – Birch cover type codominates with the Lodgepole Pine cover type in the 

Lodgepole Pine PVT.

Broadleaf Forests—Broadleaf forest PVTs in Zone 

16 were represented with the Riparian Hardwood PVT 

and the Aspen PVT (appendix 9-D). The Juniper cover 

type played a mid-seral role in the Riparian Hardwood 

PVT and eventually succeeded to the Riparian Hardwood 

cover type, which is dominated mostly by cottonwood, 

the endpoint of succession for this PVT (appendix 9-D: 

table 3). The fire regime of this PVT was stand-replacing 

fires with moderate to long return intervals (appendix 

9-D: table 2). The Aspen PVT occurred on sites where 

the Aspen – Birch cover type, dominated by aspen, is 

the “stable” climax community. The fire regime of this 

PVT was stand-replacing fires with moderate to long 

return intervals as well (appendix 9-D: table 2).

Pinyon – Juniper Woodlands—Pinyon – Juniper woodlands in Zone 16 were composed of the Pinyon – Juniper/

Mountain Big Sagebrush PVT and the Pinyon – Juniper/

Wyoming – Basin Big Sagebrush PVT (appendix 9-E). 

The Pinyon – Juniper/Mountain Big Sagebrush PVT was 

divided into two succession models: a northern variant 
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Chapter 9—Vegetation Succession Modeling for the LANDFIRE Prototype Project

and a southern variant. Fires were always stand-replacing and had fairly short intervals (appendix 9-E: table 2). 

The major differences between the northern and southern 

succession models were associated with the amount of 

Juniper cover type on the landscape. The Juniper cover 

type is dominant in the northern model, whereas the 

Pinyon – Juniper cover type is dominant in the southern 

model.

The Pinyon – Juniper/Wyoming – Basin Big Sagebrush 

PVT was also divided geographically into two succession models (northern and southern). They are identical 

with the exception of the time spent in the Cool Season 

Grasses cover type, which reflects site productivity differences across the PVT. We varied the fire intervals 

in this PVT from 40 to 60 years, depending on the 

succession class (appendix 9-E: table 2). This range in 

fire frequency reflected the biophysical variation in this 

PVT, with dryer sites of the PVT having a longer fire 

return interval. The resulting distribution of succession 

classes varied between the northern and southern zones. 

The Pinyon – Juniper cover type dominates more in the 

south, while the Wyoming – Basin Big Sagebrush cover 

type has a much larger component in the north.

Mountain Shrublands—Mountain shrubland PVTs 

in Zone 16 consisted of the Pinyon – Juniper/Mountain 

Mahogany PVT, the Pinyon – Juniper/Gambel Oak PVT, 

and the Grand Fir – White Fir/Maple PVT (appendix 

9-F). The Mountain Mahogany PVT has a moderate fire 

return interval, which allowed Mountain Mahogany to 

escape fires and form relatively mature stands of treelike shrubs. The Pinyon – Juniper/Gambel Oak PVT was 

designed to have two successional endpoints: one in the 

Pinyon – Juniper cover type and one in the Mountain 

Deciduous Shrub cover type, which is dominated by 

Gambel oak. On somewhat drier sites in this PVT, the 

successional endpoint leads to the Pinyon – Juniper 

cover type; however, on more mesic sites, dominance of 

pure Gambel oak is more common, and the successional 

endpoint is the Mountain Deciduous Shrub cover type. 

Stand-replacing fires with fairly short return intervals 

were modeled in this PVT (appendix 9-F: table 2). We 

considered the Bigtooth Maple PVT to be a moister, 

northern variant of the Pinyon – Juniper/Gambel Oak 

PVT. This PVT was found in northern parts of Zone 

16 where bigtooth maple, contained within the Riparian Hardwood cover type, occurred in relatively pure 

stands. The results of the VDDT modeling show a fairly 

significant component of white fir sharing dominance 

with bigtooth maple (appendix 9-F: table 3). Moderately 

short fire return intervals were modeled in the Bigtooth 

Maple PVT (appendix 9-F: table 2).

Sagebrush Shrublands—We modeled three individual sagebrush PVTs for Zone 16 (appendix 9-G). 

The Mountain Big Sagebrush PVT represented the 

upper elevation ranges that support big sagebrush. Fire 

intervals in the Mountain Big Sagebrush PVT were fairly 

short (appendix 9-G: table 2). This fire regime resulted 

in the dominance of Low Cover, Low Height Shrubland 

structural stages of the Mountain Big Sagebrush cover 

type. The Dwarf Sage PVT represented lower elevations 

with drier, warmer conditions and nearly pure stands of 

“low sagebrush” species or mixtures of low sagebrush 

and black sagebrush. This PVT was modeled with a 

moderately long fire return interval (appendix 9-G: 

table 2). High Cover, Low Height Shrubland structural 

stages of the Dwarf Sagebrush Complex cover type 

almost completely dominated the landscape (appendix 

9-G: table 3). More mesic sites at lower elevations with 

deeper soils were represented by the Wyoming – Basin 

Big Sagebrush PVT. Moderately short fire return intervals were used in this PVT (appendix 9-G: table 2), 

resulting in a mixture of High Cover, Low Height and 

Low Cover, Low Height Shrubland structural stages of 

the Wyoming – Basin Big Sagebrush cover type and a 

substantial component of the Cool Season Grasses cover 

type (appendix 9-G: table 3).

Desert Shrublands—The Blackbrush PVT and the 

Salt Desert Shrub PVT were modeled to represent desert 

shrubland conditions in Zone 16 (appendix 9-H). The 

Blackbrush PVT had low productivity, and fire intervals 

were modeled to be fairly low (appendix 9-H: table 

2). Much of the landscape in the Blackbrush PVT was 

dominated by the High Cover, High Height Shrublands 

structural stage of the Blackbrush cover type along 

with a significant component of both High Cover, Low 

Height Shrubland and Low Cover, Low Height Shrubland structural stages of the Desert Shrub cover type. 

(appendix 9-H: table 3). The Salt Desert Shrub PVT had 

a limited distribution in Zone 16. Moderately low fire 

return intervals were modeled for this PVT (appendix 

9-H: table 2). The Wyoming – Basin Big Sagebrush 

cover type dominated much of this PVT -- both as a 

High Cover, Low Height Shrubland and Low Cover, Low 

Height Shrubland -- along with a significant proportion 

of the Salt Desert Shrub cover type.

Zone 19 Models

Western Redcedar and Grand Fir Forests—Cedar 

and Grand Fir forest PVTs in Zone 19 were comprised 

of the Western Redcedar PVT and the Grand Fir/White 

Fir PVT (appendix 9-I). We used a diverse array of 
224 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 20

Chapter 9—Vegetation Succession Modeling for the LANDFIRE Prototype Project

succession classes for each of these two PVTs (appendix 

9-I: table 1). We modeled very long fire intervals for most 

stand-replacing fires in the Western Redcedar PVT and 

moderate to long intervals for the Grand Fir/White Fir 

PVT (appendix 9-I: table 2). Intervals for mixed-severity 

fires were generally moderate for both types, and nonlethal fires were also modeled at moderate intervals. For 

both PVTs, the results of the modeling (appendix 9-I: 

table 3) featured the dominance of long-lived seral species 

including the Douglas-fir cover type and the Western 

Larch cover type, in addition to smaller amounts of the 

White Pine cover type. The main difference between 

the two PVTs is the substantial amounts of the Cedar, 

Hemlock, and Spruce – Fir cover types in the Western 

Redcedar PVT.

Spruce – Fir Forests—Spruce – Fir forests in Zone 19 

(appendix 9-J) were divided into two groups: those that 

occurred in a montane or mid-elevation landscape setting and those occurring in a higher elevation, subalpine 

or timberline landscape setting. Montane settings were 

represented by the Spruce – Fir/Montane PVT, which 

had the most floristically diverse succession classes (appendix 9-J: table 1). The Spruce – Fir/Subalpine PVT 

and Spruce – Fir/Timberline PVT were less productive 

PVTs and were modeled with fewer cover types (appendix 9-J: table 1). Moderately long return interval, 

mixed-severity fires played a significant role in the Spruce 

– Fir/Subalpine PVT, whereas stand-replacing fires occurred in these systems infrequently (appendix 9-J: table 

2). VDDT modeling results (appendix 9-J: table 3) show 

that, with the exception of the Douglas-fir cover type in 

the Spruce – Fir/Montane PVT, the Spruce – Fir cover 

type dominated these sites historically. Lodgepole Pine 

was the next most dominant cover type in the Spruce 

– Fir/Subalpine PVT, while Timberline Forest, which 

consisted of whitebark pine, was the next most dominant 

cover type in the Spruce – Fir/Timberline PVT.

Douglas-fir Forests—A wide array of Douglas-fir

PVTs was modeled to represent the historical dynamics 

of Douglas-fir forests in Zone 19 (appendix 9-K). Succession classes for each PVT are shown in appendix 9-K: 

table 1. The Western larch cover type was modeled in the 

Douglas-fir/Ponderosa Pine PVT and played minor roles 

in the Douglas-fir/Douglas-fir PVT and in the higher, 

colder Douglas-fir/Lodgepole Pine PVT. In all cases, 

the cover type was restricted to the northwest corner of 

the zone. The Ponderosa pine cover type played a major 

role in the Douglas-fir/Ponderosa Pine PVT and a minor 

role in the Douglas-fir/Douglas-fir PVT. Both PVTs had 

the Lodgepole Pine cover type as well. The driest of the 

Douglas-fir forests was the Douglas-fir/Timberline PVT. 

This PVT had a distinctive array of cover types, including the Limber Pine and Juniper cover types, in addition 

to the Douglas-fir cover type. Many of the succession 

classes in these PVTs historically had short to moderately short fire intervals in mixed-severity and non-lethal 

regimes (appendix 9-K: table 2). Stand-replacing fires 

were rare, except in younger age classes for all of these 

PVTs. With the exception of the Douglas-fir/Ponderosa 

Pine PVT, which was dominated by the Ponderosa Pine 

cover type, cover types were dominated by Douglas-fir 

in nearly all of these PVTs (appendix 9-K: table 3).

Pine Forests—Pine forest PVTs represented areas 

generally out of the range of distribution of either the 

Spruce – Fir cover type or the Douglas-fir cover type. 

These PVTs included the Ponderosa Pine PVT, the 

Timberline Pine/Limber Pine PVT, the Lodgepole Pine 

PVT, and the Timberline Pine/Whitebark Pine PVT 

(appendix 9-L). The Ponderosa Pine PVT occurred at 

the lowest elevations and was characterized by very 

short fire return intervals (appendix 9-L: table 2). This 

regime maintained both High Cover, High Height and 

Low Cover, High Height Forest structural stages of the 

Ponderosa Pine cover type in high proportions (appendix 

9-L: table 3). The remaining PVTs were characterized 

by fairly long fire return intervals, which maintained a 

variety of structural stages in each of the cover types 

that were modeled in the PVT.

Broadleaf Forests—Broadleaf forests were represented by the Riparian Hardwood PVT, which was the 

only PVT where broadleaf trees were the chief component 

(appendix 9-M). Appendix 9-M: table 1 shows the list of 

succession classes used for the VDDT modeling of the 

Riparian Hardwood PVT. This PVT had a mix of fire 

regimes but tended to be dominated by stand-replacing 

fire with a long return interval (although, unlike other 

PVTs, the influence of surrounding PVTs’ fire regimes 

seemed to affect this PVT more than its own). The result 

of this PVT’s fire regime was dominance of the Riparian Hardwood cover type, dominated by cottonwood, 

with small and dispersed amounts of the Aspen – Birch 

cover type (appendix 9-M: table 3).

Woodlands—Woodland vegetation in Zone 19 was 

represented by the Rocky Mountain Juniper PVT and 

the Mountain Mahogany PVT (appendix 9-N). The 

Rocky Mountain Juniper PVT featured the Juniper cover 

type – with Rocky Mountain juniper as the dominant 
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Chapter 9—Vegetation Succession Modeling for the LANDFIRE Prototype Project

species – in addition to a significant component of the 

Perennial Native Bunch Graminoids cover type (appendix 

9-N: table 3). Fire intervals used in the VDDT modeling 

process were fairly long (appendix 9-N: table 2). The 

Mountain Mahogany PVT represented somewhat rare 

sites around the zone that were located adjacent to ridge 

tops and on rock outcrops that support the Mountain 

Mahogany cover type. Our succession model used fairly 

long fire return intervals (appendix 9-N: table 2), resulting 

in the dominance of the Mountain Mahogany cover type 

and a wide array of structural stages, along with lesser 

amounts of the Wyoming – Basin Big Sagebrush cover 

type.

Sagebrush and Other Dry Shrublands—Sagebrush 

and other shrub types in Zone 19 were represented by 

four different PVTs (appendix 9-O). All of these PVTs 

featured a model including conifer succession classes 

and a model excluding conifer succession. Models with 

conifer succession classes represented areas generally 

adjacent to conifer PVTs where conifer encroachment 

is most likely to occur due to proximity to seed source 

and site conditions. The Mountain Big Sagebrush PVT 

and the Threetip Sagebrush PVT were modeled with 

fairly short fire return intervals (appendix 9-O: table 2). 

In both cases, a substantial proportion of the PVT was 

maintained in the Perennial Native Bunch Graminoid 

cover type (appendix 9-O: table 3). The remainder of 

the PVT was dominated by each respective sagebrush 

species cover type. The Wyoming – Basin Big Sagebrush 

PVT had somewhat longer fire return intervals and was 

maintained historically in a higher proportion of the 

Wyoming – Basin Big Sagebrush cover type; however, 

this PVT also had a significant proportion of the Perennial Native Bunch Graminoid cover type (appendix 

9-O: table 3). The Dwarf Sagebrush PVT was modeled 

to represent fairly dry and less productive sites. With 

an available seed source, conifer encroachment will 

occur without fire; however, the encroachment will be 

very slow as these sites have soils with high salinity, or 

a caliche layer exists. Fire return intervals were moderately long (appendix 9-O: table 2), and most of the 

PVT was dominated by various structural stages of the 

Dwarf Sagebrush cover type (appendix 9-O: table 3).

The Dry Shrub PVT was modeled to represent a wide 

variety of shrub cover types found across a number of 

landscape settings (appendix 9-O: table 4). These cover 

types were relatively common in Zone 19 but did not 

necessarily grow adjacent to each other. Similar to the 

sagebrushes, this PVT had two succession pathway models, one associated with conifer encroachment and one 

not. We assumed a long fire return interval for this PVT 

and, like the sagebrushes, results showed a substantial 

proportion of the PVT dominated by the Perennial Native 

Bunch Graminoid cover type (appendix 9-O: table 6). 

The dominant shrub cover was the Shrubby Cinquefoil 

cover type.

Grasslands—Grassland PVTs for Zone 19 consisted 

of the Fescue Grassland PVT and the Bluebunch Wheatgrass PVT (appendix 9-P). The Fescue Grassland PVT 

was represented by Idaho fescue and rough fescue. We 

modeled two fescue grasslands that differ only in inclusion of a conifer component. Conifers, predominantly 

Douglas-fir, are often adjacent to fescue grassland PVTs, 

and if a seed source is available, conifer encroachment 

will occur over time without fire. We modeled these 

types of sites with the Fescue Grassland/Conifer PVT. 

On sites where grasses are competitive, especially on 

finer-textured soils, large areas of the landscape presently show very little conifer encroachment. These 

types of sites were modeled with a moderately short 

fire return interval (appendix 9-P: table 2) which, over 

time, maintained the PVT with an even distribution of 

the Perennial Native Bunch Graminoid and shrub cover 

types (appendix 9-P: table 3).

The Bluebunch Wheatgrass PVT represents some of 

the drier grasslands in Zone 19, and conifer invasion 

occurred slowly. The potential and degree of conifer 

invasion depended on the soils, surrounding landscape, 

and past disturbances. In the southern portion of the 

zone, Utah juniper and Rocky Mountain juniper were 

the conifer species most likely to encroach into these 

grasslands. In the central and northern parts of Zone 19, 

Rocky Mountain juniper was common, as were Douglas-fir, limber pine and ponderosa pine. Fire intervals 

in this PVT were fairly short (appendix 9-P: table 2). 

A large proportion of the PVT was maintained in the 

Perennial Native Bunch Graminoid cover type, attesting 

to the drier nature of these sites.

Recommendations for National 

Implementation _________________

PVT Classification

The PVT classification formed the foundation for all 

succession modeling in the two prototype areas (Long 

and others, Ch. 6). A number of existing western U.S. 

habitat type classifications, which could be linked directly to the LANDFIRE PVT classification, proved 

to be immensely helpful. The modeling of succession 

and the effects of disturbance would have been, at best, 

conjectural without these baseline, floristically detailed 
226 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 200

Chapter 9—Vegetation Succession Modeling for the LANDFIRE Prototype Project

classifications embedded within the PVT classification. 

This classification provided the framework for understanding the interactions between the succession classes 

found within each PVT. As noted, much of the western 

U.S. has existing habitat classifications in place, at least 

for forest vegetation; however, in other portions of the 

country, such classifications do not exist. Furthermore, 

the development of a climax vegetation-based PVT 

classification and subsequent succession modeling become problematic due to the historical land use of these 

non-western areas and the more subtle and complicated 

species interactions therein. The modeling of vegetation response in the Midwest and East should therefore 

be based on concepts other than the climax vegetation 

theory to properly evaluate succession and disturbance 

processes.

Cover Type Classification

The vegetation models were generally designed to 

simulate vegetation dynamics at the mid-level, but small 

inclusions of other PVTs or cover types were often evident 

in the plot data. These inclusions resulted in a number 

of illogical cover type combinations for some PVTs. 

Unfortunately, there was no process in place to address 

this issue, and, in some cases, these combinations were 

carried forward into the succession modeling process. 

Similarly, we encountered situations where, within a 

zone, a cover type occurred in only a particular geographic region of the PVT. In these situations, it became 

necessary to develop rules by which to subdivide the 

mapping zone and apply different succession models to 

these geographic variants. We recommend developing 

succession classes based on a more generalized and 

robust characterization of cover types so these situations 

can be avoided.

In addition, because there is a wide diversity of understory vegetation that may dominate during the early seral 

stages of forest development, we had to use a number 

of cover types to represent these stages of many PVTs. 

We used four succession classes to describe the early 

seral stages of forest development in Zone 16 PVTs and, 

on average, over seven succession classes to describe 

the early seral stages of forest development in Zone 

19. At any given time, these early stages represented 

l0 percent or less of the total amount of all succession classes. Consequently, at any point in time in the 

modeling, a particular succession class in these early 

seral stages may have represented less than one percent 

of the vegetation. For this reason, we recommend that 

the number of cover types used to describe early seral 

stages of forest development be kept to a minimum and 

represent broad categories of vegetation.

For Zone 19, we employed a cover type classification 

that relied more on physiognomic characteristics in 

an attempt to provide a more systematic methodology 

to the classification process (Long and others, Ch. 6). 

However, this classification resulted in a number of cover 

types that were difficult to use for succession modeling 

purposes. For example, the Upland Broadleaf Medium 

Shrubland cover type included both mountain snowberry and menziesia shrubs. In one case, the cover type 

occurs in very dry conditions while, in the other case, 

it occurs in a moist, cool environment. This resulted 

in two very different fire intervals for the same cover 

type. We recommend using a cover type classification 

more closely aligned with the classification employed 

for Zone 16, which categorizes the cover types based 

on their response to environmental conditions and fire 

intervals, rather than on a physiognomic classification 

(Long and others, Ch. 6). It should be noted that the 

development of such a classification requires the input 

of expert opinion.

Structural Stage Classification

Structural stages, as defined by the LANDFIRE 

structural stage classification, served as the main characteristic to describe forest development in the modeling 

process. It was assumed that as forests age, they become 

taller and denser. In addition, it was assumed that the 

height and cover classes would represent meaningful 

differences in seral stages and effectively describe 

early, mid, and late seral communities associated with 

the forest development process. The structural stage 

classification was built around four combinations of 

two height and two cover classes for each life form, and 

these classes were defined prior to the model building 

process. Thresholds used to define low height and high 

height as well as low cover and high cover had a great 

bearing on the modelers’ ability to describe the forest 

development process.

For many of the cover types, the height thresholds used 

to define low height structural stages created succession 

classes that existed for too short of a time period and 

did not capture the entire age range of the mid-seral 

stage of forest development. This caused these classes 

to be insensitive to changes made in many of the model 

parameters, and they consequently had very little effect 

on the final results of the model. Conversely, height 

thresholds used define high height structural stages 

created succession classes that existed for too long of 

a time period and subsequently affected the model results greatly. We recommend defining structural stage 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 22

Chapter 9—Vegetation Succession Modeling for the LANDFIRE Prototype Project

categories that use height breaks that more concisely 

bracket age ranges within the succession classes and 

tier more to early, mid, and late seral stage concepts.

Disturbance Modeling

The overall disturbance modeling process became 

somewhat problematic because of the inherent differences between the ways VDDT and LANDSUMv4 

model disturbance. The VDDT model is designed to 

treat each pixel independently of its neighbors, whereas 

LANDSUMv4 models fire spread across landscapes, 

incorporating landscape context into mapped model 

output. In other words, a simulated fire will spread to adjacent pixels in the LANDSUMv4 model, whereas pixels 

are modeled independently in the VDDT model. Thus 

fire intervals modeled in LANDSUMv4 for particular 

places on the landscape may not match those modeled 

in VDDT. We recommend use of LANDSUMv4 to test 

and verify the succession model input parameters. There 

may also be value in allowing the modelers to review 

the LANDSUMv4 output as a final assessment of the 

input parameters used in the modeling process and to 

evaluate the spatial aspects that LANDSUMv4 uses in 

the disturbance simulation process.

Another issue related to disturbance modeling encountered in the LANDFIRE Prototype Project involved 

species that followed stand-replacing disturbances. No 

preference was given to cover types that aggressively 

colonize following a fire event, such as Lodgepole Pine. 

Similarly, no advantage was given to cover types better-adapted to regeneration under the tree canopy conditions that usually develop after moderate disturbances, 

in types such as Grand Fir – White Fir. This approach 

may have underestimated the amount of Lodgepole 

Pine cover type resulting from stand-replacing fire as 

well as the amount of Grand Fir – White Fir cover type 

resulting from an insect outbreak. This situation should 

be evaluated in future modeling efforts. We recommend 

that, when estimating proportions of these outcomes, 

fire adapted species and their inherent survival strategies be considered in this process with less reliance on 

proportions from habitat type classifications.

One of the most difficult tasks in the vegetation 

modeling for the LANDFIRE Prototype was estimating the fire intervals and fire severities for the various 

succession classes within each PVT. Although estimates 

were available in the literature for the average fire return 

interval and fire severity of a particular cover type, little 

information was available regarding the ways return 

intervals or severities varied with the age of the cover 

type. In addition, there is very little information available 

regarding the return intervals of post-disturbance early 

seral stages of many cover types. We recommend that a 

wider array of experts, who specialize in a wide array of 

ecological conditions found around the country, develop 

such estimates for use in future modeling efforts.

Although we adjusted fire intervals by the structural 

stage of the cover type, no attempt was made to adjust 

fire intervals following events in the life of a stand that 

affect fuel loading or fuel conditions. One example of 

such an event would be an outbreak of mountain pine 

beetle in a lodgepole pine stand, which generally increases the risk of stand-replacing fire. We recommend 

that these types of interactions be explored in future 

modeling studies.

Model Evaluation

Historical vegetation studies may be used as guidelines 

to evaluate the results of each model; however, conclusive 

evaluation of the results from the various succession 

models is uncertain at best. Even in areas with good 

fire history studies, the model evaluation is subjective. 

In areas with limited data available on natural fire frequencies, the process will be even more difficult. We 

recommend developing guidelines, according to expert 

opinion, prior to model development to determine which 

criteria will be used to evaluate model results.

Conclusion_____________________

We executed each of our models for a 1000-year 

simulation period and assumed that the proportion of 

succession classes for each PVT at the end of the period 

would represent the historical conditions found on the 

landscape at the time of Euro-American settlement. In 

the succession model development process, we made 

every effort to simulate the historical succession and 

disturbance processes for each PVT. However, the 

variation and complexity of these processes is such 

that we should not imply that these results are the only 

representation of historical conditions for each PVT. 

The models reflect only our best understanding of these 

historical processes. The results of these models should 

be thought of as portraying a range of conditions, with 

a great deal of variation from one time period to the 

next.

For further project information, please visit the LANDFIRE website at www.landfire.gov.
228 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 200

Chapter 9—Vegetation Succession Modeling for the LANDFIRE Prototype Project

The Authors ____________________

Donald Long is a Fire Ecologist currently working on 

many of the technical aspects of the vegetation mapping 

and modeling effort for the LANDFIRE Proj^p ^p^tect 

at the USDA Forest Service, Rocky Mountain Research 

Station, Missoula Fire Sciences Laboratory (MFS). Long 

has worked on a variety of research projects concerned 

with ecosystem dynamics, fuel and vegetation inventory 

and mapping, and fire behavior and effects. He earned 

his B.S. degree in Forest Science from the University 

of Montana in 1981 and his M.S. degree in Forest Resources from the University of Idaho in 1998.

B. John (Jack) Losensky is a graduate of Pennsylvania 

State University (B.S. in Forest Management, 1959, and 

M.S. in Forest Ecology, 1961) and conducted post-graduate work at the University of Montana. He spent 35 years 

working for the USDA Forest Service in regions 1 and 6, 

holding various positions in forest management, forest 

planning, and forest ecology. He specializes in historical 

fire effects and stand structure. He currently provides 

consulting services through Ecological Services.

Donald Bedunah is a professor of Range Resource 

Management with the Department of Forest Management at the University of Montana, Missoula. His major 

research interests lie in restoration ecology – specifically, 

the role played by fire and other disturbances in ecosystems – and in international rangeland management. 

He received a B.S. in Range Science from Texas A&M 

University in 1975, an M.S. in Range Science from Colorado State University in 1977, and a Ph.D. in Rangeland 

Ecology in 1982 from Texas Tech University.

Acknowledgments_______________

We wish to thank those individuals whose efforts 

contributed substantially to the successful completion 

of this project; they include: Alisa Keyser, who initiated 

the succession modeling process and trained the modelers; Scott Mincemoyer, who helped interpret various 

aspects of the vegetation classification; John Caratti, 

who provided numerous LANDFIRE reference database 

summaries; and Lisa Holsinger and Brendan Ward, who 

imported the final data into the LANDSUMv4 model.

References _____________________

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RM-24, Fort Collins, CO: U.S. Department of Agriculture, Forest 

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Alexander R.R. May 1974. Silviculture of subalpine forests in 

the central and southern Rocky Mountains: The status of our 

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Arno, S.F. 1976. The historical role of fire on the Bitterroot National 

Forest. Res. Pap. INT-187. Ogden, UT: U.S. Department of Agriculture, Forest Service, Intermountain Research Station. 29 p.

Arno, S.F.; Gruell, G.E. 1983. Fire history at the forest-grassland 

ecotone in southwestern Montana. Journal of Range Management. 36(3):332-336.

Arno, S.F.; Gruell, G.E. 1986. Douglas-fir encroachment into 

mountain grasslands in southwestern Montana. Journal of Range 

Management. 39(3) 272-278.

Arno, S.F.; Parsons, D.J.; Keane, R.E. 2000. Mixed-severity fire 

regimes in the Northern Rocky Mountains: Consequences of fire 

exclusion and options for the future. RMRS-P-VOL-5. Ogden, 

Utah: U.S. Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station. Pp. 225-232.

Barrett, S.W. 1988. Fire regimes classification for coniferous forests 

of the Northwestern United States. Unpublished report on file at 

United States Department of Agriculture, Forest Service, Missoula 

Fire Sciences Laboratory, Missoula, Montana. 74 p.

Barrett, S.W. 1995. Fire regimes assessment for the Beaverhead 

National Forest, Montana. Unpublished report on file at U.S. 

Forest Service, Beaverhead National Forest, Wise River Ranger 

District, Wise River, Montana..

Barrett, Stephen W. 2002. A Fire Regimes Classification for Northern 

Rocky Mountain Forests: Results from Three Decades of Fire History Research. Contract report on file, Planning Division, USDA 

Forest Service Flathead National Forest, Kalispell MT. 61 p.

Beukema, S.J.; Kurz, W.A.; Pinkham, C.B.; Milosheva, K.; Frid, 

L. 2003. Vegetation Dynamics Development Tool, User’s Guide, 

Version 4.4c. ESSA Technologies Ltd. Vancouver, BC.

Bradley A.F.; Noste, N.V.; Fischer, W.C. 1992. Fire ecology of forests 

and woodlands in Utah. Gen. Tech. Rep. INT-287. Ogden, UT: 

U.S. Department of Agriculture, Forest Service, Intermountain 

Forest and Range Experiment Station. 128p.

Brickell J.E. 1966. Site index curves for Engelmann spruce in the 

northern and central Rocky Mountains. Research Note INT-42. 

Ogden, UT: U.S. Department of Agriculture, Forest Service, 

Intermountain Research Station. 8 p.

Brown J.A.; DeByle, N.V. 1989. Effects of prescribed fire on biomass and plant succession in western aspen. Res. Pap. INT-412. 

Ogden, UT: U.S. Department of Agriculture, Forest Service, 

Intermountain Research Station. 16 p.

Burns, R.M.; Honkala, B.H., tech. 1990. Silvics of North America: 

1 Conifers. Agriculture Handbook 654. Washington, D.C.: United 

States Department of Agriculture, Forest Service, Headquarters. 

675 p.

Daubenmire, R. 1968. Plant communities: a textbook of plant synecology. New York, NY: Harper and Row. 300 p.

Ferguson, D.E.; Stage, A.R.; Boyd, R.J. 1986. Predicting regeneration in the grand fir-cedar-hemlock ecosystem on the Northern 

Rocky Mountains. Supplement to Forest Science. Vol. 32, No. 

1, Monograph 26. 42 p.

Fiedler C.E. 1982. Regeneration of clearcuts within four habitat types 

in western Montana. In: Site preparation and fuels management 

on steep terrain: proceedings; Pullman, WA: Washington State 

University Cooperative Extension: 139-147.

Fisher W.C.; Bradley, A.F. 1987. Fire ecology of western Montana 

forest habitat types. Gen. Tech. Rep. INT-223. Ogden, UT: U.S. 

Department of Agriculture, Forest Service, Intermountain Research Station. 95p.

Hansen P.L.; Chadde, S.W.; Pfister, R.D. 1987. Riverine wetlands of 

southwestern Montana. Missoula, Montana: Montana Riparian 

Association, School of Forestry, University of Montana. 34 p.
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Hansen P.L.; Chadde, S.W.; Pfister, R.D. 1988. Riparian dominance 

types of Montana. Missoula, MT: Montana Forest and Conservation Experiment Station, School of Forestry, University of 

Montana. 411 p.

Jones, J.R. 1974. Silviculture of southwestern mixed conifers and 

aspen: The status of our knowledge. Res. Pap. RM-122, Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky 

Mountain Forest and Range Experiment Station. 44p.

Keane, R.E.; Parsons, R.A.; Hessburg, P.F. 2002. Estimating historical range and variation of landscape patch dynamics: limitations 

of the simulation approach. Ecological Modeling. 151. 29-49.

Losensky, B. J. 1989. Canyon Creek Report. Unpublished report. 

Missoula, Montana: U.S. Department of Agriculture, Forest 

Service, Lolo National Forest.

Losensky, B. J. 1992. Fire history for Finnegan Ridge, Montana. 

Unpublished report. Missoula, Montana: U.S. Department of 

Agriculture, Forest Service, Lolo National Forest.

Losensky, B.J. 1993. Fire history for the Doolittle Creek Drainage, 

Wisdom District, Beaverhead Forest. Unpublished report. Wisdom, Montana: U.S. Department of Agriculture, Forest Service, 

Beaverhead National Forest, Wisdom Ranger District, 8 p.

Losensky, B. J. 1995. Historical vegetation types of the Interior 

Columbia River Basin. Final Report. INT-94951-RJVA. Missoula, Montana: U.S. Department of Agriculture Forest Service, 

Rocky Mountain Research Station, Missoula Fire Sciences 

Laboratory. 90 p.

Mauk, R.; Henderson, J.A. 1984. Coniferous forest habitat types 

of northern Utah. Gen. Tech. Rep. INT-170. Ogden, UT: U.S. 

Department of Agriculture, Forest Service, Intermountain Research Station. 89 p.

McCaughey, W.W.; Schmidt, W.C. 1982. Understory tree release 

following harvest cutting in spruce-fir forests of the Intermountain 

West. Res, Pap, INT-285. Ogden, UT: U.S. Department of Agriculture, Forest Service, Intermountain Research Station. 19 p.

Mueggler W.F.; Stewart, W.L.1980. Grassland an shrubland habitat 

types of Western Montana. Gen. Tech. Rep. INT-66. Ogden, UT: 

U.S. Department of Agriculture, Forest Service, Intermountain 

Research Station. 154 p.

Mueggler W.F.; Campbell Jr., R.B. 1986. Aspen community types 

of Utah. Gen. Tech. Rep. INT-362. Ogden, UT: U.S. Department of Agriculture, Forest Service, Intermountain Research 

Station. 69 p.

Oliver, W.W. 1979. Early response of ponderosa pine to spacing 

and brush: Observations on a 12-year-old plantation. Res. Note 

PSW-341. Berkeley, CA: U.S. Department of Agriculture, Forest Service, Pacific Southwest Forest and Range Experiment 

Station. 7 p.

Padgett W.G.; Youngblood, A.P.; Winward, A.H. 1989. Riparian 

community type classification of Utah and southeastern Idaho. 

National Forest report, R4-Ecol-89-01. Ogden, UT: U.S. Department of Agriculture, Forest Service, Region 4 Headquarters. 

191 p.

Pierce, J.R. 1982. A floristic study of the Big Hole National Battlefield. Missoula, MT: University of Montana. Thesis.

Pierce, J. 1986. Wetland community type classification for westcentral Montana: Review draft. Missoula, Montana: Ecosystem 

Management Program, U.S. Department of Agriculture, Forest 

Service, Northern Region Headquarters.158 p.

Pfister, R.D. Vegetation and soils in the subalpine forests of Utah. 

1972. Pullman, WA: Washington State University. 98 p. Thesis.

Pfister, R.D.; Kovalchik, B.L.; Arno, S.F.; Presby, R.C. 1977. Forest 

habitat types of Montana. Gen. Tech. Rep. INT-34. Ogden, UT: 

U.S. Department of Agriculture, Forest Service, Intermountain 

Research Station. 174 p.

Seidel, K.W., 1982. Growth and yield of western larch: 15-year results of a levels-of-growing-stock study. Research Note PNW-398. 

Portland, OR: U.S. Department of Agriculture, Forest Service, 

Pacific Northwest Forest and Range Experiment Station. 14 p.

Steele, R.; Pfister, R.D.; Ryker, R.A.; Kittams, J.A. 1981. Forest 

habitat types of Central Idaho. Gen. Tech. Rep. INT-114. Ogden, 

UT: U.S. Department of Agriculture, Forest Service, Intermountain Research Station. 138 p.

Steele R.; Pfister, R.D.; Ryker, R.A.; Kittams, J.A. June 1975. 

Forest habitat types of central Idaho. Review draft. Ogden, UT: 

U.S. Department of Agriculture, Forest Service, Intermountain 

Research Station. 191 p.

Stringham, T.K.; Kreuger, W.C.; Shaver, P.L. 2003. State and transition modeling: an ecological process approach. Journal of Range 

Management. 56:106-113.

USDA Forest Service 2005. Fire Effects Information System. 

[Online]. Available: http://www.fs.fed.us/database/feis [May 

15, 2006].

USDA NRCS 2005. Ecological Site Descriptions. [Online]. Available: http://efotg.nrcs.usda.gov [May 16, 2006].

Yanish C.R. December 2002. Western juniper succession: Changing fuels and fire behavior. Moscow, ID: University of Idaho. 

85 p. Thesis.

Youngblood A.P.; Mauk, R.L. 1985. Coniferous forest habitat types 

of central and southern Utah. Gen. Tech. Rep. INT-187. Ogden, 

UT: U.S. Department of Agriculture, Forest Service, Intermountain Research Station. 89 p.

Youngblood A.P.; Padgett, W.G.; Winward, A.H. 1985. Riparian community type classification of eastern Idaho – western 

Wyoming. National Forest report, R4-Ecol-85-01. Ogden, UT: 

U.S. Department of Agriculture, Forest Service, Region 4 Headquarters. 78 p.

Zlatnik, E.J.; DeLuca, T.H.; Milner, K.S.; Potts, D.F. 1999. Site 

productivity and soil conditions on terraced ponderosa pine 

sites in western Montana. Western Journal of Applied Forestry. 

14(1): 35-40.
230 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 20

Chapter 9—Vegetation Succession Modeling for the LANDFIRE Prototype Project

Appendix 9-A—Spruce – Fir Forest PVTs________________________________________

Appendix 9-A: Table 1—Transition times between succession classes, in years, used in 

succession modeling for Zone 16 Spruce – Fir PVTs.

Succession class Spruce – Fir/Blue Spruce Spruce – Fir/Spruce – Fir

Cool Season Grasses  15

Dry Deciduous Shrub  15

Montane Evergreen Shrubs  15

Mountain Deciduous Shrub  

Native Forbs  10

Wetland Herbaceous  15

Aspen – Birch-HHF*	 115	 120

Aspen – Birch-HLF	 25	 22

Aspen – Birch-LHF	 40	 35

Aspen – Birch-LLF	 10	 8

Douglas-fir-HHF	 255	 260

Douglas-fir-HLF	 30	 27

Douglas-fir-LHF	 45	 45

Douglas-fir-LLF	 15	 13

Grand Fir – White Fir-HHF	 2

Grand Fir – White Fir-HLF	 

Grand Fir – White Fir-LHF	 

Grand Fir – White Fir-LLF	 

Lodgepole Pine-HHF  170 

Lodgepole Pine-HLF  1

Lodgepole Pine-LHF  35

Lodgepole Pine-LLF  15

Ponderosa Pine-HHF  4

Ponderosa Pine-HLF  

Ponderosa Pine-LHF  

Ponderosa Pine-LLF 

Spruce – Fir-HHF	 395	 400

Spruce – Fir-HLF	 30	 30

Spruce – Fir-LHF	 55	 50

Spruce – Fir-LLF	 25	 20

Timberline Pine-HHF 225

Timberline Pine-HLF  

Timberline Pine-LHF  

Timberline Pine-LLF  

* For complete structural stage names, refer to table 5: Structural stages used for succession modeling in zones 16 and 19
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 

Chapter 9—Vegetation Succession Modeling for the LANDFIRE Prototype Project

Appendix 9-A: Table 2—Fire frequencies, in years and by severity type, used in succession modeling for Zone 16 Spruce – Fir PVTs.

Succession class Spruce – Fir/Blue Spruce Spruce – Fir/Spruce – Fir

SR* MS NL SR MS NL

Cool Season Perennial Grass 200 200

Native Forb  35

Wetland Herbaceous 750 750

Mountain Deciduous Shrub 600

Dry Deciduous Shrub  300

Montane Evergreen Shrub 250  

Aspen – Birch-LLF**	 250	 	 	 300

Aspen – Birch-HLF	 200	 	 	 200

Aspen – Birch-LHF	 150	 	 50	 150	 	 50

Aspen – Birch-HHF	 125	 85	 60	 125	 100	 60

Douglas-fir-LLF	 75	 	 	 100

Douglas-fir-HLF	 150	 	 50	 75

Douglas-fir-LHF	 300	 100	 40	 400	 100	 40

Douglas-fir-HHF	 200	 100	 50	 300	 100	 50

Ponderosa Pine-LLF 75

Ponderosa Pine-HLF  15  

Ponderosa Pine-LHF 250 15

Ponderosa Pine-HHF 200 12

Lodgepole Pine-LLF  300

Lodgepole Pine-HLF  150

Lodgepole Pine-LHF 200 125 80 200 15

Lodgepole Pine-HHF  175 100 75 17

Timberline Pine-LLF  3

Timberline Pine-HLF  3

Timberline Pine-LHF  300

Timberline Pine-HHF  300 200 

Spruce – Fir-LLF	 400	 	 	 400

Spruce – Fir-HLF	 300	 	 	 300

Spruce – Fir-LHF	 400	 200	 75	 400	 200	 75

Spruce – Fir-HHF	 300	 200	 75	 300	 200	 100

Grand Fir – White Fir-LLF	 100

Grand Fir – White Fir-HLF	 150	 	 50

Grand Fir – White Fir-LHF	 300	 125	 40

Grand Fir – White Fir-HHF	 200	 125	 50

*SR = stand-replacing fire

MS = mixed-severity fire

NL = non-lethal fire

** For complete structural stage names, refer to table 5: Structural stages used for succession modeling in 

zones 16 and 19
232 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 20

Chapter 9—Vegetation Succession Modeling for the LANDFIRE Prototype Project

Appendix 9-A: Table 3—Succession modeling results in percent 

composition of each of the Zone 16 Spruce – Fir PVTs.

Spruce – Fir/ Spruce – Fir/

Succession class Blue Spruce Spruce – Fir

Dry Deciduous Shrub  1

Mountain Deciduous Shrub 0.9

Montane Evergreen Shrub 1.5 

Riparian Shrub

Cool Season Perennial Grass 0.9 0.

Native Forb 0.9  1

Wetland Herbaceous 0.1 

Douglas-fir-LLF*	 1	 1.2

Douglas-fir-HLF	 1	 1.3

Douglas-fir-LHF	 9.6	 10.2

Douglas-fir-HHF	 14.8	 16.1

Ponderosa Pine-LLF 0.2

Ponderosa Pine-HLF 0.

Ponderosa Pine-LHF 

Ponderosa Pine-HHF 2.

Grand Fir – White Fir-LLF	 0.4

Grand Fir – White Fir-HLF	 0.6

Grand Fir – White Fir-LHF	 1.3

Grand Fir – White Fir-HHF	 1.3

Lodgepole Pine-LLF 0.

Lodgepole Pine-HLF 0.4 

Lodgepole Pine-LHF 6.2  4

Lodgepole Pine-HHF  3.7 6

Spruce – Fir-LLF	 1.1	 2.8

Spruce – Fir-HLF	 2	 3.1

Spruce – Fir-LHF	 9.6	 11.3

Spruce – Fir-HHF	 10.3	 14.7

Aspen – Birch-LLF	 2.1	 1.6

Aspen – Birch-HLF	 4.1	 5.5

Aspen – Birch-LHF	 7.7	 5.3

Aspen – Birch-HHF	 7.6	 9.4

Timberline Pine-LLF  

Timberline Pine-HLF 0.5

Timberline Pine-LHF 0.5

Timberline Pine-HHF  3

* For complete structural stage names, refer to table 5: Structural stages 

used for succession modeling in zones 16 and 19
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 

Chapter 9—Vegetation Succession Modeling for the LANDFIRE Prototype Project

Appendix 9-B—White Fir / Douglas-fir Forest PVTs _______________________________

Appendix 9-B: Table 1—Transition times between succession classes, in years, used in succession modeling 

for Zone 16 White Fir / Douglas-fir PVTs.

Grand Fir – Douglas-fir/ Douglas-fir/ Douglas-fir/

Succession class White Fir Timberline Pine Douglas-fir Lodgepole Pine

Cool Season Grasses-HLH*  

Dry Deciduous Shrub-HLS  12

Montane Evergreen Shrub-LLS

Mountain Deciduous Shrub-LHS 14

Native Forb-HLH 

Aspen – Birch-HHF	 110	 125	 110	 110

Aspen – Birch-HLF	 30	 35	 30	 30

Aspen – Birch-LHF	 45	 50	 45	 45

Aspen – Birch-LLF	 12	 15	 12	 12

Douglas-fir-HHF	 255	 300	 305	 305

Douglas-fir-HLF	 25	 20	 25	 25

Douglas-fir-LHF	 45	 60	 45	 45

Douglas-fir-LLF	 20	 30	 20	 20

Grand Fir – White Fir-HHF	 250

Grand Fir – White Fir-HLF	 30

Grand Fir – White Fir-LHF	 50

Grand Fir – White Fir-LLF	 20

Juniper-HHF   3

Juniper-HLF   150

Juniper-LHF 75 75

Juniper-LLF   4

Lodgepole Pine-HHF   

Lodgepole Pine-HLF 

Lodgepole Pine-LHF  

Lodgepole Pine-LLF  

Pinyon – Juniper-HHF	 225

Pinyon – Juniper-HLF	 35

Pinyon – Juniper-LHF	 75

Pinyon – Juniper-LLF	 40

Ponderosa Pine-HHF  470  460  4

Ponderosa Pine-HLF  15 20 20

Ponderosa Pine-LHF  35  45

Ponderosa Pine-LLF  15 20 20

Timberline Pine-HHF 225 225

Timberline Pine-HLF  45

Timberline Pine-LHF 50 75

Timberline Pine-LLF  30

* For complete structural stage names, refer to table 5: Structural stages used for succession modeling in zones 16 and 19
234 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2

Chapter 9—Vegetation Succession Modeling for the LANDFIRE Prototype Project

Appendix 9-B: Table 2—Fire frequencies, in years and by severity type, used in succession modeling for Zone 16 White Fir and Douglas-fir PVTs.

Grand Fir – White Fir Douglas-fi/Lodgepole Pine

 Succession class SR* MS NL SR MS NL

Cool Season Grasses-HLH**

Dry Deciduous Shrub-HLS 500 200

Montane Evergreen Shrub-LLS 200 200

Mountain Deciduous Shrub-LHS 1

Native Forb-HLH  

Aspen – Birch-HHF	 125	 75	 50	 125	 75	 50

Aspen – Birch-HLF	 150	 	 	 150

Aspen – Birch-LHF	 200	 	 50	 175	 	 50

Aspen – Birch-LLF	 200	 	 	 200

Douglas-fir-HHF	 200	 125	 40	 150	 125	 75

Douglas-fir-HLF	 150	 	 40	 100	 	 60

Douglas-fir-LHF	 300	 100	 40	 300	 75	 50

Douglas-fir-LLF	 50	 	 	 75

Grand Fir – White Fir-HHF	 200	 125	 40

Grand Fir – White Fir-HLF	 150	 	 40

Grand Fir – White Fir-LHF	 300	 150	 40

Grand Fir – White Fir-LLF	 75

Lodgepole Pine-HHF    15

Lodgepole Pine-HLF  

Lodgepole Pine-LHF 200 150 

Lodgepole Pine-LLF  

Ponderosa Pine-HHF 250 100 35 

Ponderosa Pine-HLF  15  50 50

Ponderosa Pine-LHF 250 150 30 

Ponderosa Pine-LLF 75 75

Timberline Pine-HHF  300 200 

Timberline Pine-HLF 250

Timberline Pine-LHF  300

Timberline Pine-LLF 250

*SR = stand-replacing fire

MS = mixed-severity fire

NL = non-lethal fire

** For complete structural stage names, refer to table 5: Structural stages used for succession modeling in 

zones 16 and 19
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 2

Chapter 9—Vegetation Succession Modeling for the LANDFIRE Prototype Project

Appendix 9-B: Table 3—Fire frequencies, in years and by severity type, used in succession

modeling for Zone 16 White Fir and Douglas-fir PVTs.

Douglas-fir/Timberline Pine Douglas-fir/Douglas-fir

Succession class SR* MS NL SR MS NL

Cool Season Grasses-HLH** 

Dry Deciduous Shrub-HLS 

Montane Evergreen Shrub-LLS

Mountain Deciduous Shrub-LHS 50 

Native Forb-HLH

Aspen – Birch-HHF	 125	 85	 59	 125	 85	 50

Aspen – Birch-HLF	 150	 	 	 150

Aspen – Birch-LHF	 175	 	 75	 175	 	 50

Aspen – Birch-LLF	 200	 	 	 200

Douglas-fir-HHF	 300	 150	 59	 150	 100	 35

Douglas-fir-HLF	 150	 	 59	 125	 	 35

Douglas-fir-LHF	 350	 	 50	 250	 100	 35

Douglas-fir-LLF	 100	 	 	 50

Juniper-HHF  300 20  250 2

Juniper-HLF 200 

Juniper-LHF  300 20  200

Juniper-LLF 200 75

Lodgepole Pine-HHF 75 

Lodgepole Pine-HLF

Lodgepole Pine-LHF  150

Lodgepole Pine-LLF

Pinyon – Juniper-HHF	 250	 150	 100

Pinyon – Juniper-HLF	 200	 	 100

Pinyon – Juniper-LHF	 250	 150	 75

Pinyon – Juniper-LLF	 200	 	 	 300	 200	 15

Ponderosa Pine-HHF  30  25  00

Ponderosa Pine-HLF  150  30  4

Ponderosa Pine-LHF  400 150 

Ponderosa Pine-LLF 50

Timberline Pine-HHF  300 200

Timberline Pine-HLF  1

Timberline Pine-LHF  30  

Timberline Pine-LLF 200

*SR = stand-replacing fire

MS = mixed-severity fire

NL = non-lethal fire

** For complete structural stage names, refer to table 5: Structural stages used for succession modeling in 

zones 16 and 19
236 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 20

Chapter 9—Vegetation Succession Modeling for the LANDFIRE Prototype Project

Appendix 9-B: Table 4—Succession modeling results in percent composition of each of the Zone 16 White

Fir / Douglas-fir PVTs.

Grand Fir – Douglas-fir/ Douglas-fir/ Douglas-fir/

Succession class White Fir Timberline Pine Douglas-fir Lodgepole Pine

Cool Season Grasses-HLH* 0.2

Dry Deciduous Shrub-HLS 5.1 1

Montane Evergreen Shrub-LLS 0.8  

Mountain Deciduous Shrub-LHS 0.7 7.8

Native Forb-HLH 2.8

Aspen – Birch-HHF	 4.1	 1.8	 9.8	 3.7

Aspen – Birch-HLF	 4	 1.3	 5.6	 2.8

Aspen – Birch-LHF	 3.1	 2.2	 11.1	 3.5

Aspen – Birch-LLF	 1.8	 1.1	 2.7	 1.2

Douglas-fir-HHF	 19.3	 30	 21.5	 38.7

Douglas-fir-HLF	 1.3	 1.8	 3.2	 2.3

Douglas-fir-LHF	 11.3	 15.6	 15.6	 17.7

Douglas-fir-LLF	 2.1	 2.9	 4.7	 4.2

Grand Fir – White Fir-HHF	 4.4

Grand Fir – White Fir-HLF	 0.8

Grand Fir – White Fir-LHF	 4.7

Grand Fir – White Fir-LLF	 2.8

Juniper-HHF   1.8

Juniper-HLF   1.

Juniper-LHF   1

Juniper-LLF 0.8  

Lodgepole Pine-HHF   

Lodgepole Pine-HLF 0.5

Lodgepole Pine-LHF 2.8

Lodgepole Pine-LLF  

Pinyon – Juniper-HHF	 2

Pinyon – Juniper-HLF	 0.4

Pinyon – Juniper-LHF	 1.5

Pinyon – Juniper-LLF	 1

Ponderosa Pine-HHF  14.1 7.

Ponderosa Pine-HLF  1.1 0.4 0

Ponderosa Pine-LHF 6.8  4 3.

Ponderosa Pine-LLF  1.3 0.5 0.5

Timberline Pine-HHF  4.8 5

Timberline Pine-HLF  1.2 

Timberline Pine-LHF 0.9  1

Timberline Pine-LLF 0.4 

* For complete structural stage names, refer to table 5: Structural stages used for succession modeling in zones 16 and 19
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 2

Chapter 9—Vegetation Succession Modeling for the LANDFIRE Prototype Project

Appendix 9-C: Table 1—Transition times between succession classes, in years, 

used in succession modeling for Zone 16 Pine PVTs

Lodgepole Ponderosa Timberline

Succession class Pine Pine Pine

Cool Season Grasses-HLH* 12 

Mountain Deciduous Shrub-LHS 12  3

Montane Evergreen Shrub-LLS 12 

Native Forb-HLH  

Wetland Herbaceous-LHH  

Aspen – Birch-HHF	 110	 100

Aspen – Birch-HLF	 30	 35

Aspen – Birch-LHF	 45	 50

Aspen – Birch-LLF	 12	 15

Juniper-HHF  

Juniper-HLF   

Juniper-LHF 75

Juniper-LLF  

Lodgepole Pine-HHF 270

Lodgepole Pine-HLF  

Lodgepole Pine-LHF  

Lodgepole Pine-LLF  

Pinyon – Juniper-HHF	 225

Pinyon – Juniper-HLF	 35

Pinyon – Juniper-LHF	 75

Pinyon – Juniper-LLF	 40

Ponderosa Pine-HHF   

Ponderosa Pine-HLF 20

Ponderosa Pine-LHF  

Ponderosa Pine-LLF 20

Timberline Pine-HHF 925

Timberline Pine-HLF  

Timberline Pine-LHF 75

Timberline Pine-LLF  

* For complete structural stage names, refer to table 5: Structural stages used for succession 

modeling in zones 16 and 19

Appendix 9-C—Pine Forest PVTs ___________________________________
238 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 20

Chapter 9—Vegetation Succession Modeling for the LANDFIRE Prototype Project

Appendix 9-C: Table 2—Fire frequencies, in years and by severity type, used in succession modeling for Zone

16 Pine PVTs

Lodgepole Pine Ponderosa Pine Timberline Pine

Succession class SR* MS NL SR MS NL SR MS NL

Cool Season Grasses-HLH** 1

Mountain Deciduous Shrub-LHS 200 50 200

Montane Evergreen Shrub-LLS 200 75

Native Forb-HLH  3

Wetland Herbaceous-LHH 500

Aspen – Birch-HHF	 150	 100	 60	 100	 75	 40

Aspen – Birch-HLF	 150	 	 	 75

Aspen – Birch-LHF	 175	 	 60	 125	 	 45

Aspen – Birch-LLF	 300	 	 	 100

Juniper-HHF 200 10

Juniper-HLF   

Juniper-LHF    300

Juniper-LLF 50

Lodgepole Pine-HHF  15

Lodgepole Pine-HLF  1

Lodgepole Pine-LHF 200 100 

Lodgepole Pine-LLF  3

Pinyon – Juniper-HHF	 	 	 	 200	 100	 30

Pinyon – Juniper-HLF	 	 	 	 100	 	 30

Pinyon – Juniper-LHF	 	 	 	 300	 200	 25

Pinyon – Juniper-LLF	 	 	 	 40

Ponderosa Pine-HHF    3

Ponderosa Pine-HLF   10

Ponderosa Pine-LHF    400

Ponderosa Pine-LLF 

Timberline Pine-HHF 

Timberline Pine-HLF 

Timberline Pine-LHF 

Timberline Pine-LLF 200

*SR = stand-replacing fire

MS = mixed-severity fire

NL = non-lethal fire

** For complete structural stage names, refer to table 5: Structural stages used for succession modeling in zones 16 and 19
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 2

Chapter 9—Vegetation Succession Modeling for the LANDFIRE Prototype Project

Appendix 9-C: Table 3—Succession modeling results in percent composition of 

each of the Zone 16 Pine PVTs

Lodgepole Ponderosa Timberline

Succession class Pine Pine Pine

Cool Season Grasses-HLH* 0.4 

Mountain Deciduous Shrub-LHS 1.7 6.8

Montane Evergreen Shrub-LLS 0.8 2.5

Native Forb-HLH 0.2

Wetland Herbaceous-LHH 0.6

Aspen – Birch-HHF	 17.7	 2.7

Aspen – Birch-HLF	 9.5	 3

Aspen – Birch-LHF	 16.6	 2.8

Aspen – Birch-LLF	 4.5	 1

Juniper-HHF 0.5

Juniper-HLF  

Juniper-LHF 0.

Juniper-LLF 0.9

Lodgepole Pine-HHF  19

Lodgepole Pine-HLF 5.7

Lodgepole Pine-LHF  16

Lodgepole Pine-LLF 5.9

Pinyon – Juniper-HHF	 0.8

Pinyon – Juniper-HLF	 0.5

Pinyon – Juniper-LHF	 0.2

Pinyon – Juniper-LLF	 0.5

Ponderosa Pine-HHF   3

Ponderosa Pine-HLF   

Ponderosa Pine-LHF 28

Ponderosa Pine-LLF 

Timberline Pine-HHF   

Timberline Pine-HLF 7.2

Timberline Pine-LHF  

Timberline Pine-LLF   

* For complete structural stage names, refer to table 5: Structural stages used for succession modeling in zones 16 and 19.
240 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 20

Chapter 9—Vegetation Succession Modeling for the LANDFIRE Prototype Project

Appendix 9-D: Table 1—Transition times between succession classes, in 

years, used in succession modeling for Zone 16 Broadleaf PVTs

Succession class Riparian Hardwood Aspen

Cool Season Grasses-HLH*  10

Dry Deciduous Shrub-HLS  15

Montane Evergreen Shrub-LLS  

Mountain Deciduous Shrub-LHS  

Native Forb-HLH 8

Wetland Herbaceous-LHH  

Aspen – Birch-HHF	 120

Aspen – Birch-HLF	 22

Aspen – Birch-LHF	 35

Aspen – Birch-LLF	 8

Juniper-HHF  1

Juniper-HLF 70

Juniper-LHF 50

Juniper-LLF  

Mountain Deciduous Shrub -HHF 80

Mountain Deciduous Shrub -HLF  

Mountain Deciduous Shrub -LHF 25

Mountain Deciduous Shrub -LLF  

Riparian Hardwood-HHF 200

Riparian Hardwood-HLF 22

Riparian Hardwood-LHF 50

Riparian Hardwood-LLF 8

* For complete structural stage names, refer to table 5: Structural stages used for succession modeling in zones 16 and 19

Appendix 9-D—Broadleaf Forest PVTs ________________________________
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 

Chapter 9—Vegetation Succession Modeling for the LANDFIRE Prototype Project

Appendix 9-D: Table 3—Succession modeling results in percent composition of each of the Zone 16 Broadleaf PVTs

Succession class Riparian Hardwood Aspen

Cool Season Grasses-HLH* 2.5 0.5

Dry Deciduous Shrub-HLS 2.3 2

Montane Evergreen Shrub-LLS 0.9

Mountain Deciduous Shrub-LHS 1

Native Forb-HLH 0.8

Wetland Herbaceous-LHH 0

Aspen – Birch-HHF	 55.4

Aspen – Birch-HLF	 14.8

Aspen – Birch-LHF	 17.7

Aspen – Birch-LLF	 7.4

Juniper-HHF 2.

Juniper-HLF 2.

Juniper-LHF  3

Juniper-LLF 2.9

Mountain Deciduous Shrub -HHF 3

Mountain Deciduous Shrub -HLF 0.8

Mountain Deciduous Shrub -LHF 0.9

Mountain Deciduous Shrub -LLF 0.6

Riparian Hardwood-HHF  39

Riparian Hardwood-HLF 8.7

Riparian Hardwood-LHF 23

Riparian Hardwood-LLF  3

* For complete structural stage names, refer to table 5: Structural stages used 

for succession modeling in zones 16 and 19

Appendix 9-D: Table 2—Fire frequencies, in years and by severity type, used in succession modeling for Zone 16 Broadleaf PVTs

Riparian Hardwood Aspen

Succession class SR* MS NL SR MS NL

Cool Season Grasses-HLH** 250 200

Dry Deciduous Shrub-HLS 300

Montane Evergreen Shrub-LLS  

Mountain Deciduous Shrub-LHS 4

Native Forb-HLH  

Wetland Herbaceous-LHH 750

Aspen – Birch-HHF	 	 	 	 100	 	 75

Aspen – Birch-HLF	 	 	 	 150

Aspen – Birch-LHF	 	 	 	 200	 	 50

Aspen – Birch-LLF	 	 	 	 200

Juniper-HHF 150  10

Juniper-HLF 150 

Juniper-LHF 150  10

Juniper-LLF 200

Mountain Deciduous Shrub-HHF 15  

Mountain Deciduous Shrub-HLF 200

Mountain Deciduous Shrub-LHF 15  

Mountain Deciduous Shrub-LLF 200

Riparian Hardwood-HHF 200  

Riparian Hardwood-HLF 200

Riparian Hardwood-LHF 200  

Riparian Hardwood-LLF 200

*SR = stand-replacing fire

MS = mixed-severity fire

NL = non-lethal fire

* *For complete structural stage names, refer to table 5: Structural stages used for succession modeling in zones 16 and 19
242 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 20

Chapter 9—Vegetation Succession Modeling for the LANDFIRE Prototype Project

Appendix 9-E—Pinyon – Juniper Woodland PVTs_______________________

Appendix 9-E: Table 1—Transition times between succession classes, in years, used in succession modeling 

for Zone 16 Pinyon – Juniper PVTs.

Pinyon – Juniper/Wyoming – Pinyon – Juniper/

Basin Big Sagebrush Mountain Big Sagebrush

Northern Southern Northern Southern

Succession class variant variant variant variant

Cool Season Grasses-LHH* 20 5  

Cool Season Grasses-LLH 25 25

Desert Shrub-LHS 25 25

Desert Shrub-LLS 20 20

Dry Deciduous Shrub-LHS 25 25 25

Dry Deciduous Shrub-LLS 20 20 20

Dwarf Sagebrush Complex-LHS 25 25

Dwarf Sagebrush Complex-LLS 20 20

Juniper-HHW 250 250  300

Juniper-LHW  100 100 

Juniper-LLW 50 50 50 5

Mountain Big Sagebrush Complex-HLS    

Mountain Big Sagebrush Complex-LLS    

Mountain Deciduous Shrub-LHS 25 25

Mountain Deciduous Shrub-LLS 20 20

Pinyon – Juniper HHW	 250	 250	 300	 300

Pinyon – Juniper-LHW	 100	 100	 100	 100

Pinyon – Juniper-LLW	 50	 50	 50	 50

Wyoming – Basin Big Sagebrush Complex-HLS	 50	 50

Wyoming – Basin Big Sagebrush Complex-LLS	 25	 25

* For complete structural stage names, refer to table 5: Structural stages used for succession modeling in zones 16 and 19
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 

Chapter 9—Vegetation Succession Modeling for the LANDFIRE Prototype Project

Appendix 9-E: Table 2—Fire frequencies, in years, used in succession modeling for Zone 16 Pinyon – Juniper

PVTs. All fires were modeled as stand-replacing.

Pinyon – Juniper/Wyoming – Pinyon – Juniper/

Basin Big Sagebrush Mountain Big Sagebrush

Northern Southern Northern Southern

Succession class variant variant variant variant

Cool Season Grasses-LHH* 50 50  3

Cool Sseason Grasses-LLH 50 50  3

Desert Shrub-HLS 60 60

Desert Shrub-LLS 60 60

Dry Deciduous Shrub-HLS  40

Dry Deciduous Shrub-LLS 50 50

Dwarf Sagebrush Complex-HLS 60 60

Dwarf Sagebrush Complex-LLS 60 60

Juniper-HHW 60 60  3

Juniper-HLW 50 50  3

Mountain Big Sagebrush Complex –LLS	 30	 30

Mountain Big Sagebrush Complex-HLS   

Mountain Deciduous Shrub-HLS   

Mountain Deciduous Shrub-LLS   

Pinyon – Juniper-HHW 50	 50	 30	 30

Pinyon – Juniper-HLW 60	 60	 30	 30

Wyoming – Basin Big Sagebrush Complex-HLS	 50	 50

Wyoming – Basin Big Sagebrush Complex–LLS	 50	 50

* For complete structural stage names, refer to table 5: Structural stages used for succession modeling in zones 16 and 19

Appendix 9-E: Table 3—Succession modeling results in percent composition of each of the Zone 16 Pinyon –

Juniper PVTs. PVTs. All fires were modeled as stand-replacing.

Pinyon – Juniper/Wyoming – Pinyon – Juniper/

Basin Big Sagebrush Mountain Big Sagebrush

Northern Southern Northern Southern

Succession class variant variant variant variant

Cool Season Grasses-LHH* 5

Cool Season Grasses-LLH 2 

Desert Shrub-LHS 2 2

Desert Shrub-LLS 2 2

Dry Deciduous Shrub-LHS 2

Dry Deciduous Shrub-LLS 

Dwarf Sagebrush Complex-LHS 

Dwarf Sagebrush Complex-LLS 2

Juniper-HHW 

Juniper-LHW  

Juniper-LLW  

Mountain Big Sagebrush Complex-HLS 8 7

Mountain Big Sagebrush Complex-LLS   

Mountain Deciduous Shrub-LHS 9

Mountain Deciduous ShrubLLS 

Pinyon – Juniper High-HHW	 5	 10

Pinyon – Juniper-LHW	 24	 35	 	 16

Pinyon – Juniper-LLW	 33	 32	 	 44

Wyoming – Basin Big Sagebrush Complex-HLS	 4	 1

Wyoming – Basin Big Sagebrush Complex-LLS	 23	 2

* For complete structural stage names, refer to table 5: Structural stages used for succession modeling in zones 16 and 19
244 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2

Chapter 9—Vegetation Succession Modeling for the LANDFIRE Prototype Project

Appendix 9-F: Table 2—Fire frequencies, in years and by severity type, used in succession modeling for

Zone 16 Mountain PVTs

Pinyon – Juniper/ Pinyon – Juniper/ Grand Fir – 

Succession class Mountain Mahogany Gambel Oak White Fir/Maple

Cool season perennial grass-HLH*  

Cool season perennial grass-LLH 60 50  1

Dry Deciduous Shrub-LHS 50  

Dry Deciduous Shrub_LLS 60 50

Grand Fir – White Fir-HHF	 35

Grand Fir – White Fir-LHF	 50

Mountain Deciduous Shrub-HLW 50  

Mountain Deciduous Shrub-LHW 50 50

Mountain Deciduous Shrub-LLW  

Mountain Deciduous Shrub-HHW  

Mountain Mahogany-HHW 60

Mountain Mahogany-LHW 60

Pinyon – Juniper-HHW	 60	 35

Pinyon – Juniper-LLW	 60	 35

Riparian Hardwood-HHF

Riparian Hardwood-LHF 50

Riparian Hardwood-LLF 50

* For complete structural stage names, refer to table 5: Structural stages used for succession modeling in zones 16

and 19.

Appendix 9-F—Mountain Shrubland PVTs _____________________________

Appendix 9-F: Table 1—Transition times between succession classes, in years, used in succession modeling for Zone 16 Mountain PVTs

Pinyon – Juniper/ Pinyon – Juniper/ Grand Fir – 

Succession class Mountain Mahogany Gambel Oak White Fir/Maple

Cool Season Grasses-HLH* 2

Cool Season Grasses-LLH  1

Dry Deciduous Shrub-LHS 25  

Dry Deciduous Shrub-LLS 25  

Grand Fir – White Fir-HHF	 50

Grand Fir – White Fir-LHF	 50

Mountain Deciduous Shrub-HLW 20 20 

Mountain Deciduous Shrub-LHW   

Mountain Deciduous Shrub-LLW  16 2

Mountain Mahogany-HHW  

Mountain Mahogany-LHW 255

Pinyon – Juniper -LHW	 65	 100

Pinyon – Juniper-HHW	 100	 100

Pinyon – Juniper-LLW	 25	 50

Riparian Hardwood-HHF   

Riparian Hardwood-LHF 65

Riparian Hardwood-LLF  

* For complete structural stage names, refer to table 5: Structural stages used for succession modeling in zones 16

and 19.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 2

Chapter 9—Vegetation Succession Modeling for the LANDFIRE Prototype Project

Appendix 9-F: Table 3—Succession modeling results in percent composition of each of the Zone 16

Mountain PVTs.

Pinyon – Juniper/ Pinyon – Juniper/ Grand Fir – 

Succession class Mountain Mahogany Gambel Oak White Fir/Maple

Cool season perennial grass-HLH* 

Cool season perennial grass-LLH 2 

Dry Deciduous Shrub-LHS 8 

Dry Deciduous Shrub-LLS 

Grand Fir – White Fir-HHF	 49

Grand Fir – White Fir-LHF	 19

Mountain Deciduous Shrub-HLW 2

Mountain Deciduous Shrub-LHW 0

Mountain Deciduous Shrub-LLW 2 

Mountain Mahogany-HHW 

Mountain Mahogany-LHW 5

Pinyon – Juniper-HHW	 2

Pinyon – Juniper-HLW	 1	 14

Pinyon – Juniper-LLW	 31

Riparian Hardwood-HHF 0

Riparian Hardwood-LHF 

Riparian Hardwood-LLF 0

* For complete structural stage names, refer to table 5: Structural stages used for succession modeling in zones 16

and 19.
246 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 20

Chapter 9—Vegetation Succession Modeling for the LANDFIRE Prototype Project

Appendix 9-G—Sagebrush Shrubland PVTs ___________________________

Appendix 9-G: Table 1—Transition times between succession classes, in years, used in succession modeling for Zone 16

Sagebrush PVTs.

Dwarf Sagebrush Wyoming – Basin Big Mountain Big

Succession class Complex Sagebrush Complex Sagebrush Complex

Cool Season Grasses-HLH*   4

Cool Season Grasses-LLH 20 

Dry Deciduous Shrub-HLS   3

Dry Deciduous Shrub-LLS   

Dwarf Sagebrush Complex-HLS  1

Dwarf Sagebrush Complex-LLS  

Mountain Deciduous Shrub-HHS   1

Mountain Deciduous Shrub-LHS   1

Mountain Big Sagebrush Complex-HLS 56

Mountain Big Sagebrush Complex-LLS  

Rabbitbrush-HLS  30 

Rabbitbrush-LLS  17  

Salt Desert Shrub-HLS  180 

Salt Desert Shrub-LLS  17 

Wyoming – Basin Big Sagebrush-HLS	 100	 100

Wyoming – Basin Big Sagebrush-LLS	 50	 55

* For complete structural stage names, refer to table 5: Structural stages used for succession modeling in zones 16 and 19

Appendix 9-G: Table 2—Fire frequencies, in years, used in succession modeling for Zone 16 Sagebrush PVTs. All fires were

modeled as stand-replacing.

Dwarf Sagebrush Wyoming – Basin Big Mountain Big

Succession class Complex Sagebrush Complex Sagebrush Complex

Cool Season Grasses-HLH* 80 20

Cool Season Grasses-LLH 80  100 

Dry Deciduous Shrub-HLS 60 20

Dry Deciduous Shrub-LLS 80 20

Dwarf Sagebrush Complex-HLS  1

Dwarf Sagebrush Complex-LLS  1

Mountain Deciduous Shrub-HHS 60 20

Mountain Deciduous Shrub-LHS 80 20

Mountain Big Sagebrush Complex-HLS 20

Mountain Big Sagebrush Complex-LLS 20

Rabbitbrush-HLS 60 60 20

Rabbitbrush-LLS 80 80 20

Salt Desert Shrub-HLS  100 

Salt Desert Shrub-LLS  120 

Wyoming – Basin Big Sagebrush-HLS	 80	 80

Wyoming – Basin Big Sagebrush-LLS	 80	 80

* For complete structural stage names, refer to table 5: Structural stages used for succession modeling in zones 16 and 19
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 2

Chapter 9—Vegetation Succession Modeling for the LANDFIRE Prototype Project

Appendix 9-G: Table 3—Succession modeling results in percent composition of each of the Zone 16 Sagebrush PVTs

Dwarf Sagebrush Wyoming – Basin Big Mountain Big

Succession class Complex Sagebrush Complex Sagebrush Complex

Cool Season Grasses-HLH* 23

Cool Season Grasses-LLH  

Dry Deciduous Shrub-HLS  

Dry Deciduous Shrub-LLS  

Dwarf Sagebrush Complex-HLS 80

Dwarf Sagebrush Complex-LLS

Mountain Deciduous Shrub-HHS  

Mountain Deciduous Shrub-LHS 7 8

Mountain Big Sagebrush Complex-HLS 22

Mountain Big Sagebrush Complex-LLS  

Rabbitbrush-HLS 

Rabbitbrush-LLS 5 2

Salt Desert Shrub-HLS 

Salt Desert Shrub-LLS  

Wyoming – Basin Big Sagebrush-HLS	 27

Wyoming – Basin Big Sagebrush-LLS	 1	 28

* For complete structural stage names, refer to table 5: Structural stages used for succession modeling in zones 16 and 19
248 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 20

Chapter 9—Vegetation Succession Modeling for the LANDFIRE Prototype Project

Appendix 9-H: Table 3—Succession modeling results in percent composition of each of 

the Zone 16 Desert PVTs

Succession class Blackbrush Salt Desert Shrub

Blackbrush-HLS* 6

Cool Season Grasses-LLH 

Warm Season Grasses-LLH 

Desert Shrub-HLS  

Desert Shrub-LLS 25 0

Rabbitbrush-LLS 

Salt Desert Shrub-HLS  

Salt Desert Shrub-LLS 

Wyoming – Basin Big Sagebrush Complex-HLS	 	 29

* For complete structural stage names, refer to table 5: Structural stages used for succession modeling in zones 16 and 19

Appendix 9-H—Desert Shrubland PVTs _______________________________

Appendix 9-H: Table 1—Transition times between succession classes, in years, used in 

succession modeling for Zone 16 Desert PVTs

Succession class Blackbrush Salt Desert Shrub

Blackbrush-HLS* 1

Cool Season Grasses-LLH  

Warm Season Grasses-LLH 

Desert Shrub-HLS 27  1

Desert Shrub-LLS 71

Rabbitbrush-LLS  

Salt Desert Shrub-HLS   

Salt Desert Shrub-LLS  

Wyoming – Basin Big Sagebrush Complex-HLS	 	 100

* For complete structural stage names, refer to table 5: Structural stages used for succession modeling in zones 16 and 19

Appendix 9-H: Table 2—Fire frequencies, in years, used in succession modeling for Zone

16 Desert PVTs. All fires were modeled as stand-replacing.

Succession class Blackbrush Salt Desert Shrub

Blackbrush-HLS* 200

Cool Season Grasses-LLH   

Warm Season Grasses-LLH 200

Desert Shrub-HLS 200  1

Desert Shrub-LLS 200  1

Rabbitbrush-LLS   

Salt Desert Shrub-HLS   

Salt Desert Shrub-LLS   

Wyoming – Basin Big Sagebrush Complex-HLS	 	 85

* For complete structural stage names, refer to table 5: Structural stages used for succession modeling in zones 16 and 19
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 2

Chapter 9—Vegetation Succession Modeling for the LANDFIRE Prototype Project

Appendix 9-I: Table 1—Transition times between succession classes, in years, used in 

succession modeling for Zone 19 Western Redcedar and Grand Fir PVTs

Succession class Western Redcedar Grand Fir

Aspen – Birch-HHT*	 120

Aspen – Birch-HLMT	 18

Aspen – Birch-LHT	 35

Aspen – Birch-LLMT	 12

Cedar-HHT 550

Cedar-HLMT 20

Cedar-LHT 55

Cedar-LLMT  

Douglas-fir-HHT	 375	 375

Douglas-fir-HLMT	 10	 10

Douglas-fir-LHT	 30	 30

Douglas-fir-LLMT	 15	 15

Grand fir-HHT	 265	 945

Grand fir-HLMT	 15	 15

Grand fir-LHT	 40	 40

Grand fir-LLMT	 20	 20

Hemlock-HHT  4

Hemlock-HLMT  

Hemlock-LHT  

Hemlock-LLMT 25

Larch-HHT  330

Larch-HLMT 8  

Larch-LHT 25  

Larch-LLMT  12

Lodgepole Pine-HHT  125 

Lodgepole Pine-HLMT  10

Lodgepole Pine-LHT  30

Lodgepole Pine-LLMT  15

Ponderosa Pine-HHT   

Ponderosa Pine-HLMT  

Ponderosa Pine-LHT  

Ponderosa Pine-LLMT  

Spruce – Fir-HHT	 365	 310

Spruce – Fir-HLMT	 15	 15

Spruce – Fir-LHT	 40	 45

Spruce – Fir-LLMT	 20	 25

White Pine-HHT  420 

White Pine-HLMT  

White Pine-LHT  35

White Pine-LLMT  15

Perennial Forb-HLHB 9 

Perennial Native Bunch Gramminoid-HHHB 

Perennial Native Rhizomatous Gramminoid-HLHB 9 

Riparian Broadleaf Shrubland-HHSH 

Upland Broadleaf Dwarf Shrubland-HLSH 

Upland Broadleaf Medium Shrubland-HMSH 

Upland Broadleaf Tall Shrubland-HHSH  

Wetland Herbaceous-HHHB 

* For complete structural stage names, refer to table 5: Structural stages used for succession 

modeling in zones 16 and 19

Appendix 9-I—Western Redcedar and Grand Fir Forest PVTs _____________
250 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 200

Chapter 9—Vegetation Succession Modeling for the LANDFIRE Prototype Project

Appendix 9-I: Table 2—Fire frequencies, in years and by severity type, used in succession modeling for Zone

19 Western Redcedar and Grand Fir PVTs

Western Redcedar Grand Fir

Succession class SR* MS NF SR MS NL

Aspen – Birch-HHT**	 	 	 	 200	 95

Aspen – Birch-HLMT	 	 	 	 150

Aspen – Birch-LHT	 	 	 	 200	 	 65

Aspen – Birch-LLMT	 	 	 	 200

Cedar-HHT 5

Cedar-HLMT 

Cedar-LHT 00 30

Cedar-LLMT 

Douglas-fir-HHT	 350	 250	 200	 300	 125	 90

Douglas-fir-HLMT	 300	 	 	 50

Douglas-fir-LHT	 400	 200	 170	 350	 150	 65

Douglas-fir-LLMT	 300	 	 	 75

Grand Fir-HHT 00125  25

Grand Fir-HLMT  30  

Grand Fir-LHT 00 200 170 3

Grand Fir-LLMT  30  

Hemlock-HHT 5

Hemlock-HLMT 

Hemlock-LHT 00 30

Hemlock-LLMT 

Larch-HHT 200 100  1

Larch-HLMT 250 75

Larch-LHT 00 200 85 200 

Larch-LLMT    

Lodgepole Pine-HHT    30

Lodgepole Pine-HLMT    

Lodgepole Pine-LHT    350

Lodgepole Pine-LLMT 75

Ponderosa Pine-HHT  350 50  

Ponderosa Pine-HLMT  35  2

Ponderosa Pine-LHT  400 300 250 3

Ponderosa Pine-LLMT  35  2

Spruce – Fir-HHT	 300	 200	 135	 300	 200	 60

Spruce – Fir-HLMT	 500	 500	 	 100	 100

Spruce – Fir-LHT	 350	 200	 100	 350	 200	 48

Spruce – Fir-LLMT	 250	 	 	 75

White Pine-HHT  350 150 400 300

White Pine-HLMT  350

White Pine-LHT  400 250 150 300

White Pine-LLMT  350

Perennial Forb-HLHB  3

Perennial Native Bunch Gramminoid-HHHB 200  

Perennial Native Rhizomatous Gramminoid-HLHB 225  

Riparian Broadleaf Shrubland-HHSH  

Upland Broadleaf Dwarf Shrubland-HLSH 250  

Upland Broadleaf Medium Shrubland-HMSH 250  

Upland Broadleaf Tall Shrubland-HHSH  450

Wetland Herbaceous-HHHB 500

*SR = stand-replacing fire

MS = mixed-severity fire

NL = non-lethal fire

** For complete structural stage names, refer to table 5: Structural stages used for succession modeling in zones 16 and 19
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 2

Chapter 9—Vegetation Succession Modeling for the LANDFIRE Prototype Project

Appendix 9-I: Table 3—Succession modeling results in percent composition of each of 

the Zone 19 Western Redcedar and Grand Fir PVTs

Succession class Western Redcedar Grand Fir

Aspen – Birch-HHT*	 0.9

Aspen – Birch-HLMT	 0.7

Aspen – Birch-LHT	 0.3

Aspen – Birch-LLMT	 0.4

Cedar-HHT  15

Cedar-HLMT 0.9

Cedar-LHT 0.9

Cedar-LLMT  1

Douglas-fir-HHT	 20.3	 20.7

Douglas-fir-HLMT	 0.5	 0.4

Douglas-fir-LHT	 4.5	 9.8

Douglas-fir-LLMT	 1.7	 2.7

Grand Fir-HHT 0.7 5.8

Grand Fir-HLMT 0.2 0.5

Grand Fir-LHT 0.1 

Grand Fir-LLMT 0.5  1

Hemlock-HHT 9.2

Hemlock-HLMT 0.5

Hemlock-LHT 0

Hemlock-LLMT  1

Larch-HHT 0.9 0.6

Larch-HLMT 0.1 0

Larch-LHT 0 0.6

Larch-LLMT 0.3 0

Lodgepole Pine-HHT   

Lodgepole Pine-HLMT 0.

Lodgepole Pine-LHT  

Lodgepole Pine-LLMT 0.

Ponderosa Pine-HHT 7.1 

Ponderosa Pine-HLMT 0.4 0

Ponderosa Pine-LHT 0.9  1

Ponderosa Pine-LLMT 0.9  1

Spruce – Fir-HHT	 16.6	 21.6

Spruce – Fir-HLMT	 0.5	 0.4

Spruce – Fir-LHT	 1.6	 8.7

Spruce – Fir-LLMT	 1	 0.7

White Pine-HHT 5.5 2.5

White Pine-HLMT 0.1 

White Pine-LHT 2.1 

White Pine-LLMT 0 0.2

Perennial Forb-HLHB  1.5

Perennial Native Bunch Gramminoid-HHHB 0.

Perennial Native Rhizomatous Gramminoid-HLHB 0 0.

Riparian Broadleaf Shrubland-HHSH 0.5

Upland Broadleaf Dwarf Shrubland-HLSH 0.7 2.5

Upland Broadleaf Medium Shrubland-HMSH 0.6 0.5

Upland Broadleaf Tall Shrubland-HHSH 0.6

Wetland Herbaceous-HHHB 0 0.5

* For complete structural stage names, refer to table 5: Structural stages used for succession 

modeling in zones 16 and 19
252 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 200

Chapter 9—Vegetation Succession Modeling for the LANDFIRE Prototype Project

Appendix 9-J—Spruce – Fir Forest PVTs ______________________________

Appendix 9-J: Table 1—Transition times between succession classes, in years, used in succession 

modeling for Zone 19 Spruce – Fir PVTs.

Spruce – Fir/ Spruce – Fir/ Spruce – Fir/

Succession class Montane Subalpine Timberline

Douglas-fir-HHTR*	 370

Douglas-fir-HLMT	 15

Douglas-fir-LHT	 35

Douglas-fir-LLMT	 15

Lodgepole Pine-HHT  175 220 

Lodgepole Pine-HLMT  10  1

Lodgepole Pine-LHT  30  3

Lodgepole Pine-LLMT  15  

Spruce – Fir-HHT	 302	 300	 290

Spruce – Fir-HLMT	 20	 20	 20

Spruce – Fir-LHT	 55	 55	 65

Spruce – Fir-LLMT	 28	 30	 40

Timberline Forest-HHT  

Timberline Forest-HLMT  

Timberline Forest-LHT   

Timberline Forest-LLMT 50

Western Larch-HHT  3

Western Larch-HLMT  

Western Larch-LHT  

Western Larch-LLMT  

Perennial Forb-HLHB  12  

Perennial Native Bunch Gramminoid-HHHB  15  1

Perennial Native Rhizomatous Gramminoid-HLHB 12  

Riparian Broadleaf Shrub-HHSH  18 

Shrubby Cinquefoil-HMSH	 40

Upland Broadleaf Dwarf Shrub-HLSH  15  

Upland Broadleaf Medium Shrub-HMSH  15  

Upland Broadleaf Tall Shrub-HHSH  18 

Upland Needleleaf Shrub-LLSH  

Upland Sclerophyllous Shrub-HLSH  15

Wetland Herbaceous-HHHB  15

* For complete structural stage names, refer to table 5: Structural stages used for succession modeling in zones 

16 and 19
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 2

Chapter 9—Vegetation Succession Modeling for the LANDFIRE Prototype Project

Appendix 9-J: Table 2—Fire frequencies, in years and by severity type, used in succession modeling for Zone 19

Spruce – Fir PVTs.

Spruce – Fir/ Spruce – Fir/ Spruce – Fir/

Montane Subalpine Timberline

Succession class SR* MS NL SR MS NL SR MS NL

Douglas-fir-HHTR**	 300	 200	 100

Douglas-fir-HLMT	 200

Douglas-fir-LHT	 400	 200	 75

Douglas-fir-LLMT	 250

Lodgepole Pine-HHT 250 100  300 20

Lodgepole Pine-HLMT  100 300

Lodgepole Pine-LHT  300 150 150 00 

Lodgepole Pine-LLMT  300  

Spruce – Fir-HHT	 250	 270	 	 350	 350	 	 300	 400

Spruce – Fir-HLMT	 300	 	 	 400	 	 	 350

Spruce – Fir-LHT	 350	 180	 70	 400	 400	 	 400	 300

Spruce – Fir-LLMT	 300	 	 	 400	 	 	 350

Timberline Forest-HHT    

Timberline Forest-HLMT  

Timberline Forest-LHT    

Timberline Forest-LLMT  

Western Larch-HHT  300 200 

Western Larch-HLMT  400

Western Larch-LHT 500 

Western Larch-LLMT 250

Perennial Forb-HLHB  300  

Perennial Native Bunch Gramminoid-HHHB 175 150

Perennial Native Rhizomatous Gramminoid-HLHB 200 200  

Riparian Broadleaf Shrub-HHSH  400

Shrubby Cinquefoil-HMSH	 	 	 	 	 	 	 300

Upland Broadleaf Dwarf Shrub-HLSH 200   1

Upland Broadleaf Medium Shrub-HMSH 200 250 200

Upland Broadleaf Tall Shrub-HHSH  150

Upland Needleleaf Shrub-LLSH  1

Upland Ssclerophyllous Shrub-HLSH 250 200

Wetland Herbaceous-HHHB 500

*SR = stand-replacing fire

MS = mixed-severity fire

NL = non-lethal fire

**For complete structural stage names, refer to table 5: Structural stages used for succession modeling in zones 16 and 1
254 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 20

Chapter 9—Vegetation Succession Modeling for the LANDFIRE Prototype Project

Appendix 9-J: Table 3—Succession modeling results in percent composition of each of the Zone 19

Spruce – Fir PVTs.

Spruce – Fir/ Spruce – Fir/ Spruce – Fir/

Succession class Montane Subalpine Timberline

Douglas-fir-HHTR*	 24.1

Douglas-fir-HLMT	 1.1

Douglas-fir-LHT	 7.7

Douglas-fir-LLMT	 1.9

Lodgepole Pine-HHT 6.1 1

Lodgepole Pine-HLMT 0.7 0.8 0.8

Lodgepole Pine-LHT  3.2 6.1

Lodgepole Pine-LLMT  1.5  1.

Spruce – Fir-HHT	 21.3	 50.2	 38.3

Spruce – Fir-HLMT	 2.2	 4.6	 2.8

Spruce – Fir-LHT	 6.7	 14.2	 10.5

Spruce – Fir-LLMT	 3.9	 6.5	 5.1

Timberline Forest-HHT    

Timberline Forest-HLMT 

Timberline Forest-LHT 7.2

Timberline Forest-LLMT   

Western Larch-HHT 9.5

Western Larch-HLMT 0.5

Western Larch-LHT  

Western Larch-LLMT 0.6

Perennial Forb-HLHB  1.3

Perennial Native Bunch Gramminoid-HHHB 0.1 0.

Perennial Native Rhizomatous Gramminoid-HLHB 0.3 0.

Riparian Broadleaf Shrub-HHSH 0.1 

Shrubby Cinquefoil-HMSH	 0.4

Upland Broadleaf Dwarf Shrub-HLSH 0.5 0.6  4

Upland Broadleaf Medium Shrub-HMSH  1.6 0.

Upland Broadleaf Tall Shrub-HHSH 0.2 0

Upland Needleleaf Shrub-LLSH 0.

Upland Sclerophyllous Shrub-HLSH 0.2 0.8

Wetland Herbaceous-HHHB 0.2 

* For complete structural stage names, refer to table 5: Structural stages used for succession modeling in zones 

16 and 19
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 25

Chapter 9—Vegetation Succession Modeling for the LANDFIRE Prototype Project

Appendix 9-K—Douglas-fir Forest PVTs_______________________________

Appendix 9-K: Table 1—Transition times between succession classes, in years, used in succession modeling for Zone 19

Douglas-fir PVTs.

Douglas-fir/ Douglas-fir/ Douglas-fir/ Douglas-fir/

Succession class Timber-line Pine Ponderosa Pine Douglas-fir Lodge-pole Pine

Douglas-fir-HHT*	 350	 415	 375	 365

Douglas-fir-HLMT	 20	 15	 10	 15

Douglas-fir-LHT	 60	 40	 30	 40

Douglas-fir-LLMT	 30	 20	 15	 20

Juniper-HHT 250

Juniper-HLMT  

Juniper-LHT  

Juniper-LLMT 60

Limber Pine-HHT 620

Limber Pine-HLMT  

Limber Pine-LHT 90

Limber Pine-LLMT 50

Lodgepole Pine-HHT   175  1

Lodgepole Pine-HLMT   12 

Lodgepole Pine-LHT   35 

Lodgepole Pine-LLMT   15 

Ponderosa Pine-HHT   470

Ponderosa Pine-HLMT   

Ponderosa Pine-LHT   3

Ponderosa Pine-LLMT   1

Western Larch-HHT   475  

Western Larch-HLMT   12

Western Larch-LHT   35 

Western Larch-LLMT   1

Mountain Big Sage-HMSH  45

Perennial Forb-HLHB  35 20  

Perennial Native Bunch Gramminoid-HHHB 50 25 20 20

Perennial Native Rhizomatous Gramminoid-HLHB 25 20  

Upland Broadleaf Dwarf Shrub-HLSH    

Upland Broadleaf Medium Shrub-HMSH  40 20  

Upland Broadleaf Tall Shrub-HHSH 20

Upland Needleleaf Shrub-LMSH 50 20

Upland Sclerophyllous Shrub-HLSH  40 20

Wyoming – Basin Big Sage-HMSH	 45	 30	 35	 25

* For complete structural stage names, refer to table 5: Structural stages used for succession modeling in zones 16 and 1
256 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 200

Chapter 9—Vegetation Succession Modeling for the LANDFIRE Prototype Project

Appendix 9-K: Table 2—Fire frequencies, in years and by severity type, used in succession modeling for Zone 19

Douglas-fir / Timberline Pine and Douglas-fir / Ponderosa Pine PVTs.

Douglas-fir/Timberline Pine Douglas-fir/Ponderosa Pine

Succession class SR* MS NL SR MS NL

Douglas-fir-HHT**	 200	 150	 120	 150	 75	 50

Douglas-fir-HLMT	 75	 	 	 40

Douglas-fir-LHT	 200	 	 58	 300	 150	 25

Douglas-fir-LLMT	 75	 	 	 45

Juniper-HHT200  1

Juniper-HLMT 200

Juniper-LHT 300 

Juniper-LLMT200

Limber Pine-HHT 200 1

Limber Pine-HLMT 200

Limber Pine-LHT  30  

Limber Pine-LLMT 200

Lodgepole Pine-HHT 5

Lodgepole Pine-HLMT 

Lodgepole Pine-LHT 200 125 

Lodgepole Pine-LLMT 50

Ponderosa Pine-HHT 00

Ponderosa Pine-HLMT 70 70

Ponderosa Pine-LHT 

Ponderosa Pine-LLMT 

Western Larch-HHT 

Western Larch-HLMT 80 60

Western Larch-LHT 500 250 2

Western Larch-LLMT 

Mountain Big Sage-HMSH 60

Perennial Forb-HLHB 75 50

Perennial Native Bunch Gramminoid-HHHB 3  

Perennial Native Rhizomatous Gramminoid-HLHB 

Upland Broadleaf Dwarf Shrub-HLSH

Upland Broadleaf Medium Shrub-HMSH  15  

Upland Broadleaf Tall Shrub-HHSH

Upland Needleleaf Shrub-LMSH 60

Upland Sclerophyllous Shrub-HLSH 50 50

Wyoming – Basin Big Sage-HMSH	 60	 	 	 25

*SR = stand-replacing fire

MS = mixed-severity fire

NL = non-lethal fire

**For complete structural stage names, refer to table 5: Structural stages used for succession modeling in zones 16 and 19
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 25

Chapter 9—Vegetation Succession Modeling for the LANDFIRE Prototype Project

Appendix 9-K: Table 3—Fire frequencies, in years and by severity type, used in succession modeling for Zone 19

Douglas-fir / Lodgepole Pine and Douglas-fir / Douglas-fir PVTs.

Douglas-fir/Lodgepole Pine Douglas-fir/Douglas-fir

Succession class SR* MS NL SR MS NL

Douglas-fir-HHT**	 250	 125	 93	 300	 100	 120

Douglas-fir-HLMT	 50	 	 	 50

Douglas-fir-LHT	 300	 200	 55	 350	 100	 60

Douglas-fir-LLMT	 75	 	 	 75

Lodgepole Pine-HHT  150 65 5

Lodgepole Pine-HLMT 50 50

Lodgepole Pine-LHT 200 150 75 200 15

Lodgepole Pine-LLMT 75 75

Ponderosa Pine-HHT 00

Ponderosa Pine-HLMT 67 200

Ponderosa Pine-LHT 

Ponderosa Pine-LLMT 75

Western Larch-HHT  400 175 75  300 

Western Larch-HLMT  100 100

Western Larch-LHT 500  38 

Western Larch-LLMT 75 75

Mountain Big Sage-HMSH 

Perennial Forb-HLHB  10  

Perennial Native Bunch Gramminoid-HHHB 50 

Perennial Native Rhizomatous Gramminoid-HLHB 75 75

Upland Broadleaf Dwarf Shrub-HLSH  100

Upland Broadleaf Medium Shrub-HMSH 75 75

Upland Broadleaf Tall Shrub-HHSH 

Upland Needleleaf Shrub-LMSH 75

Upland Sclerophyllous Shrub-HLSH  1

Wyoming – Basin Big Sage-HMSH	 40	 	 	 35

*SR = stand-replacing fire

MS = mixed-severity fire

NL = non-lethal fire

**For complete structural stage names, refer to table 5: Structural stages used for succession modeling in zones 16 and 19
258 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 200

Chapter 9—Vegetation Succession Modeling for the LANDFIRE Prototype Project

Appendix 9-K: Table 4—Succession modeling results in percent composition of each of the Zone 19 Douglas-fir PVTs.

Douglas-fir/ Douglas-fir/ Douglas-fir/ Douglas-fir/

Succession class Timber-line Pine Ponderosa Pine Douglas-fir Lodge-pole Pine

Douglas-fir-HHT*	 25.2	 8.6	 43.7	 32.9

Douglas-fir-HLMT	 4	 1.9	 1.7	 2.1

Douglas-fir-LHT	 13.2	 10.4	 17.7	 19.4

Douglas-fir-LLMT	 9.2	 3.3	 6	 5

Juniper-HHT 

Juniper-HLMT 0.

Juniper-LHT 0.7

Juniper-LLMT 0.

Limber Pine-HHT  1

Limber Pine-HLMT 2.5

Limber Pine-LHT  4

Limber Pine-LLMT  4

Lodgepole Pine-HHT 0.5  1.9 

Lodgepole Pine-HLMT 0.1 0.

Lodgepole Pine-LHT 0.3 0.7 6

Lodgepole Pine-LLMT 0.1 0.2

Ponderosa Pine-HHT 24.7 2.

Ponderosa Pine-HLMT   

Ponderosa Pine-LHT 25.7  1

Ponderosa Pine-LLMT   

Western Larch-HHT   4.7  

Western Larch-HLMT 0 0.2 0.

Western Larch-LHT   3 2.8

Western Larch-LLMT 0 0.1 0

Mountain Big Sage-HMSH 0.1  

Perennial Forb-HLHB 6.5  1.5 

Perennial Native Bunch Gramminoid-HHHB  12.5  3.6

Perennial Native Rhizomatous Gramminoid-HLHB .8  

Upland Broadleaf Dwarf Shrub-HLSH 0  1

Upland Broadleaf Medium Shrub-HMSH  1.6  1

Upland Broadleaf Tall Shrub-HHSH 0.2

Upland Needleleaf Shrub-LMSH  1.6

Upland Sclerophyllous Shrub-HLSH 0.3 1.5

Wyoming – Basin Big Sage-HMSH	 0.8	 0.4	 0.6	 0.6

* For complete structural stage names, refer to table 5: Structural stages used for succession modeling in zones 16 and 19
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 25

Chapter 9—Vegetation Succession Modeling for the LANDFIRE Prototype Project

Appendix 9-L—Pine Forest PVTs_____________________________________

Appendix 9-L: Table1—Transition times between succession classes, in years, used in succession modeling for Zone 19 Pine

PVTs.

Succession class Lodgepole Pine Whitebark Pine Limber Pine Ponderosa Pine

Douglas-fir-HHT*	 365	 875

Douglas-fir-HLMT	 15	 25

Douglas-fir-LHT	 40	 70

Douglas-fir-LLMT	 20	 40

Juniper-HLT    

Juniper-HMT 245 27

Juniper-LHT    1

Juniper-LLT 60  

Juniper-LMT    10

Limber pine-HLT  

Limber pine-HMT 61

Limber pine-LHT   

Limber pine-LLT 50

Limber pine-LMT 50

Lodgepole Pine-HHT  3

Lodgepole Pine-HLMT  

Lodgepole Pine-LHT  

Lodgepole Pine-LLMT 20

Ponderosa Pine-HHT 555

Ponderosa Pine-HLMT 25

Ponderosa Pine-LHT 50

Ponderosa Pine-LLMT 20

Spruce – Fir-HHT	 930	 850

Spruce – Fir-HLMT	 15	 40

Spruce – Fir-LHT	 50	 100

Spruce – Fir-LLMT	 30	 50

Timberline forest-HHT 54

Timberline forest-HLMT 50

Timberline forest-LHT  

Timberline forest-LLMT 60

Mountain Big Sage-HMSH 24

Upland Broadleaf Dwarf Shrubland-HLSH 

Upland Broadleaf Medium Shrubland-HMSH 1

Upland Broadleaf Tall Shrubland-LHSH 29

Upland Microphyllous Medium Shrubland-HMSH

Upland Needle-leaf Shrubland-LMSH 59 5

Upland Sclerophyllous Shrubland-HLSH 

Upland Sclerophyllous Shrubland-LLSH 

Wyoming-Basin Big Sage-HMSH 59  

Perennial Frb-HLHB 9  49

Perennial Forb-LLHB  

Perennial Native Bunch Gramminoid-HHHB 14 

Perennial Native Rhizomatous Gramminoid-HLHB 1

Wetland Herbaceous-HHHB  

* For complete structural stage names, refer to table 5: Structural stages used for succession modeling in zones 16 and 19
260 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 200

Chapter 9—Vegetation Succession Modeling for the LANDFIRE Prototype Project

Appendix 9-L: Table 2—Fire frequencies, in years and by severity type, used in succession modeling for Zone

19 Ponderosa Pine and Timberline Pine PVTs

Ponderosa Pine Limber Pine

Succession class SR* MS NL SR MS NL

Douglas-fir-HHT**	 	 	 278	 244

Douglas-fir-HLMT	 	 	 150

Douglas-fir-LHT	 	 	 300	 	 200

Douglas-fir-LLMT	 	 	 300

Juniper-HLT 50   300

Juniper-HLMT   400 1

Juniper-LLT 50   

Juniper-LLMT   300 150

Limber Pine-HLT    

Limber Pine-HMT  

Limber Pine-LLT   40

Limber Pine-LMT  

Lodgepole Pine-HHT

Lodgepole Pine-HLMT

Lodgepole Pine-LHT

Lodgepole Pine-LLMT

Ponderosa Pine-HHT   300

Ponderosa Pine-HLMT   100

Ponderosa Pine-LHT   400 2

Ponderosa Pine-LLMT  

Mountain Big Sage-HMSH

Upland Broadleaf Dwarf Shrub-HLSH

Upland Broadleaf Medium Shrub-HMSH 

Upland Broadleaf Tall Shrub-LHSH 

Upland Microphyllous Medium Shrub-LMSH 

Upland Needleleaf Shrub-LMSH 

Upland Sclerophyllous Shrub-LLSH 

Wyoming – Basin Big Sage-HMSH	 	 25	 	 	 100

Perennial Forb-HLHB 50  

Perennial Native Bunch Graminoid-HHHB 20  

Perennial Native Rhizomatous Graminoid-HLHB 200

Wetland Herbaceous-HHHB

*SR = stand-replacing fire

MS = mixed-severity fire

NL = non-lethal fire

** For complete structural stage names, refer to table 5: Structural stages used for succession modeling in zones 16 and 19
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 2

Chapter 9—Vegetation Succession Modeling for the LANDFIRE Prototype Project

Appendix 9-L: Table 3—Fire frequencies, in years and by severity type, used in succession modeling for Zone

19 Lodgepole Pine and Whitebark Pine PVTs

Lodgepole Pine Whitebark Pine

Succession class SR* MS NL SR MS NL

Douglas-fir-HHT**	 200	 125	 95

Douglas-fir-HLMT	 75

Douglas-fir-LHT	 300	 200	 60

Douglas-fir-LLMT	 200

Lodgepole Pine-HHT  150 

Lodgepole Pine-HLMT 75

Lodgepole Pine-LHT 200 150

Lodgepole Pine-LLMT 200

Spruce – Fir-HHT	 150	 150	 	 150	 350

Spruce – Fir-HLMT	 150	 	 	 400

Spruce – Fir-LHT	 300	 200	 150	 200	 400

Spruce – Fir-LLMT	 300	 	 	 400

Timberline Forest-HHT    

Timberline Forest-HLMT  

Timberline Forest-LHT    

Timberline Forest-LLMT  

Mountain Big Sage-HMSH  100

Upland Broadleaf Dwarf Shrub-HLSH  150

Upland Broadleaf Medium Shrub-HMSH 200

Upland Needleleaf Shrub-HLSH  

Upland Sclerophyllous Shrub-HLSH 200

Perennial Forb-HLHB 200  

Perennial Native Bunch Graminoid-HHHB 100

Perennial Native Rhizomatous Graminoid-HLHB 200  

Wetland Herbaceous-HHHB 500

*SR = stand-replacing fire

MS = mixed-severity fire

NL = non-lethal fire

** For complete structural stage names, refer to table 5: Structural stages used for succession modeling in zones 16 and 19
262 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 200

Chapter 9—Vegetation Succession Modeling for the LANDFIRE Prototype Project

Appendix 9-L: Table 4—Succession modeling results in percent composition of each of the Zone 19 Pine PVTs

Succession class Ponderosa Pine Timberline Pine Lodgepole Pine Whitebark Pine

Douglas-fir-HHT*	 0.9	 0.7

Douglas-fir-HLMT	 0	 0.3

Douglas-fir-LHT	 0.1	 0.9

Douglas-fir-LLMT	 0.4	 0.3

Juniper-HHT 0.3 

Juniper-HLMT 0.4

Juniper-LHT 0.2 2.

Juniper-LLMT 0.7 2.8

Limber Pine-HHT   

Limber Pine-HLMT 8.6

Limber Pine-LHT  

Limber Pine-LLMT   

Lodgepole Pine-HHT    

Lodgepole Pine-HLMT 6.9

Lodgepole Pine-LHT  

Lodgepole Pine-LLMT   

Ponderosa Pine-HHT  40

Ponderosa Pine-HLMT  

Ponderosa Pine-LHT  

Ponderosa Pine-LLMT 7

Spruce – Fir-HHT	 11.1	 8

Spruce – Fir-HLMT	 2.5	 2.6

Spruce – Fir-LHT	 1	 7.2

Spruce – Fir-LLMT	 4.5	 2.7

Timberline Forest-HHT 25.2

Timberline Forest-HLMT 8.8

Timberline Forest-LHT   

Timberline Forest-LLMT  

Mountain Big Sage-HMSH 0  1

Upland Broadleaf Dwarf Shrub-HLSH    3

Upland Broadleaf Medium Shrub-HMSH  1.6 0

Upland Broadleaf Tall Shrub-LHSH  1

Upland Microphyllous Medium Shrub-HMSH 1

Upland Needleleaf shrub-LMSH 5.7   

Upland Sclerophyllous shrub-LMSH 0.9 0.5

Wyoming – Basin Big Sage-HMSH	 0.6	 1.6

Perennial Forb-HLHB  1.8 0.8 0.4

Perennial Native Bunch Graminoid-HHHB 9.3 7.5 0.9 

Perennial Native Rhizomatous Graminoid-HLHB 0.8

Wetland Herbaceous-HHHB 0.5

* For complete structural stage names, refer to table 5: Structural stages used for succession modeling in zones 16 and 19
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 2

Chapter 9—Vegetation Succession Modeling for the LANDFIRE Prototype Project

Appendix 9-M—Broadleaf Forest PVTs_______________________________

Appendix 9-M: Table 1—Transition times between succession classes, in years, used in succession modeling for Zone 

19 Broadleaf PVTs

 Riparian

Succession class Hardwood

Aspen – Birch-HHT*	 135

Aspen – Birch-HLMT	 7

Aspen – Birch-LHT	 20

Aspen – Birch-LLMT	 8

Douglas-fir-HHT	 365

Douglas-fir-HLMT	 15

Douglas-fir-LHT	 40

Douglas-fir-LLMT	 20

Lodgepole Pine-HLMT  

Lodgepole Pine-LHT  

Lodgepole Pine-LHT  1

Lodgepole Pine-LLMT  

Ponderosa Pine-HHT  4

Ponderosa Pine-HLMT  

Ponderosa Pine-LHT  

Ponderosa Pine-LLMT  

Riparian Hardwood-HHT  1

Riparian Hardwood-HLMT 5

Riparian Hardwood-LHT  

Riparian Hardwood-LLMT 5

Perennial Forb  

Perennial Native Bunch Gramminoid  

Perennial Native Rhizomatous Gramminoid 

Riparian Broadleaf Shrub  

Upland Broadleaf Medium Shrub  

Upland Broadleaf Tall Shrub  

Upland Needleleaf Shrub  

Wetland Herbaceous  

* For complete structural stage names, refer to table 5: Structural stages 

used for succession modeling in zones 16 and 19
264 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 20

Chapter 9—Vegetation Succession Modeling for the LANDFIRE Prototype Project

Appendix 9-M: Table 2—Fire frequencies, in years and by severity, used

in succession modeling for zone 19 Broadleaf PVTs

Riparian Hardwood

Succession class SR* MS NL

Aspen – Birch-HHT**	 250	 150

Aspen – Birch-HLMT	 250

Aspen – Birch-LHT	 300	 	 200

Aspen – Birch-LLMT	 350

Douglas-fir-HHT	 300	 175	 60

Douglas-fir-HLMT	 75

Douglas-fir-LHT	 300	 250	 50

Douglas-fir-LLMT	 75

Lodgepole Pine-HHT 250 

Lodgepole Pine-HLMT  1

Lodgepole Pine-LHT  300

Lodgepole Pine-LLMT 250

Ponderosa Pine-HHT  300 

Ponderosa Pine-HLMT 67 200

Ponderosa Pine-LHT  300 20

Ponderosa Pine-LLMT 75

Riparian Hardwood-HHT 250 250 500

Riparian Hardwood-HLMT 250

Riparian Hardwood-LHT 200  

Riparian Hardwood-LLMT  3

Perennial Forb  3

Perennial Native Bunch Gramminoid 200

Perennial Native Rhizomatous Gramminoid 225

Riparian Broadleaf Shrub  4

Upland Broadleaf Medium Shrub 250

Upland Broadleaf Tall Shrub 250

Upland Needleleaf Shrub 250

Wetland Herbaceous 500

*SR = stand-replacing fire

MS = mixed-severity fire

NL = non-lethal fire

**For complete structural stage names, refer to table 5: Structural stages used for 

succession modeling in zones 16 and 19
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 26

Chapter 9—Vegetation Succession Modeling for the LANDFIRE Prototype Project

Appendix 9-M: Table 3—Succession modeling results in percent composition of each of the Zone 19 Broadleaf PVTs

 Riparian

Succession class Hardwood

Aspen – Birch-HHT*	 12.3

Aspen – Birch-HLMT	 0.9

Aspen – Birch-LHT	 1.2

Aspen – Birch-LLMT	 1.2

Douglas-fir-HHT	 6

Douglas-fir-HLMT	 0.1

Douglas-fir-LHT	 4.4

Douglas-fir-LLMT	 0.2

Lodgepole Pine-HHT 0.5

Lodgepole Pine-HLMT 0.

Lodgepole Pine-LHT 0

Lodgepole Pine-LLMT 0.

Ponderosa Pine-HHT 5

Ponderosa Pine-HLMT 0.

Ponderosa Pine-LHT 5.5

Ponderosa Pine-LLMT 0.6

Riparian Hardwood-HHT  49

Riparian Hardwood-HLMT  1

Riparian Hardwood-LHT  3

Riparian Hardwood-LLMT  1

Perennial Forb  

Perennial Native Bunch Gramminoid 0.

Perennial Native Rhizomatous Gramminoid 0.2

Riparian Broadleaf Shrub  1

Upland Broadleaf Medium Shrub 0.2

Upland Broadleaf Tall Shrub 

Upland Needleleaf Shrub 0.

Wetland Herbaceous 0.

* For complete structural stage names, refer to table 5: Structural 

stages used for succession modeling in zones 16 and 19
266 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 200

Chapter 9—Vegetation Succession Modeling for the LANDFIRE Prototype Project

Appendix 9-N: Table 1—Transition times between succession classes, in years, used in succession 

modeling for Zone 19 Woodland PVTs

Succession class Rocky Mountain Juniper Mountain Mahogany

Douglas-fir-HHT*	 99

Douglas-fir-LHT	 84

Douglas-fir-LLT	 29

Douglas-fir-LMT	 39

Mountain Mahogany-HHS 255

Mountain Mahogany-HMS 

Mountain Mahogany-LHS 

Mountain Mahogany-LLS  

Mountain Mahogany-LMT 

Mountain Big Sage-HMS 

Mountain Big Sage-LLS  12

Mountain Big Sage-LMS 

Perennial Native Bunch Graminoid-HHH  1

Perennial Native Bunch Graminoid-HLH 

Perennial Native Bunch Graminoid-LHH  12

Perennial Native Bunch Graminoid-LLH 2 

Perennial Forb-LHH 29

Perennial Forb-LLH  12

Rabbitbrush-LLH  12

Rabbitbrush-LMH 29 29

Juniper-HHH 200

Juniper-LHH 200

Juniper-LLH 

Juniper-LMH 5

Upland Microphyllous Medium Shrubland-HMS

Upland Microphyllous Medium Shrubland-LLS 12

Upland Microphyllous Medium Shrubland-LMS

Wyoming – Basin Big Sage-HMS	 200	 200

Wyoming – Basin Big Sage-LLS	 14	 14

Wyoming – Basin Big Sage-LMS	 54	 54

* For complete structural stage names, refer to table 5: Structural stages used for succession modeling in zones 

16 and 19

Appendix 9-N—Woodland PVTs _____________________________________
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 26

Chapter 9—Vegetation Succession Modeling for the LANDFIRE Prototype Project

Appendix 9-N: Table 2—Fire frequencies, in years and by severity type, used in succession modeling

for Zone 19 Woodland PVTs

Succession class Rocky Mountain Juniper Mountain Mahogany

Douglas-fir-HHT*

Douglas-fir-LHT

Douglas-fir-LLT

Douglas-fir- LMT

Mountain Mahogany-HHS 

Mountain Mahogany-HMS  

Mountain Mahogany-LHS 50

Mountain Mahogany-LLS 50

Mountain Mahogany-LMT  

Mountain Big Sage-HMS 22 22

Mountain Big Sage-LLS 29 29

Mountain Big Sage-LMS 25 25

Perennial Native Bunch Graminoid-HHH 25 25

Perennial Native Bunch Graminoid-HLH 50 50

Perennial Native Bunch Graminoid-LHH  40

Perennial Native Bunch Graminoid-LLH 50 50

Perennial Forb-LHH 29

Perennial Forb-LLH 29  

Rabbitbrush-LLH 

Rabbitbrush-LMH 29 29

Juniper-HHH 25

Juniper-LHH 

Juniper-LLH 295

Juniper-LMH 25

Upland Microphyllous Medium Shrubland-HMS 25 29

Upland Microphyllous Medium Shrubland-LLS  

Upland Microphyllous Medium Shrubland-LMS 29 

Wyoming – Basin Big Sage-HMS	 29	 33

Wyoming – Basin Big Sage-LLS	 40	 40

Wyoming – Basin Big Sage-LMS	 33	 50

* For complete structural stage names, refer to table 5: Structural stages used for succession modeling in zones 

16 and 19 1 In this class, approximately 20 percent of fires were estimated as mixed stand-replacing fir
268 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 200

Chapter 9—Vegetation Succession Modeling for the LANDFIRE Prototype Project

Appendix 9-N: Table 3—Succession modeling results in percent composition of each of the Zone 19

Woodland PVTs.

Succession class Rocky Mountain Juniper Mountain Mahogany

Douglas-fir-HHT*	 0

Douglas-fir-LHT	 0

Douglas-fir-LLT	 0

Douglas-fir- LMT	 0

Mountain Mahogany-HHS 

Mountain Mahogany-HMS 

Mountain Mahogany-LHS 

Mountain Mahogany-LLS  

Mountain Mahogany-LMT 9

Mountain Big Sage-HMS  

Mountain Big Sage-LLS  

Mountain Big Sage-LMS  

Perennial Native Bunch Graminoid-HHH  

Perennial Native Bunch Graminoid-HLH  

Perennial Native Bunch Graminoid-LHH  18

Perennial Native Bunch Graminoid-LLH  

Perennial Forb-LHH 2

Perennial Forb-LLH 2 0

Rabbitbrush-LLH  

Rabbitbrush-LMH 2 0

Juniper-HHH 0

Juniper-LHH 

Juniper-LLH  

Juniper-LMH 20

Upland Microphyllous Medium Shrubland-HMS 2 0

Upland Microphyllous Medium Shrubland-LLS 5 0

Upland Microphyllous Medium Shrubland-LMS  

Wyoming – Basin Big Sage-HMS	 2	 6

Wyoming – Basin Big Sage-LLS	 0	 9

Wyoming – Basin Big Sage-LMS	 2	 18

* For complete structural stage names, refer to table 5: Structural stages used for succession modeling in zones 

16 and 19
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 26

Chapter 9—Vegetation Succession Modeling for the LANDFIRE Prototype Project

Appendix 9-O: Table 1—Transition times between succession classes, in years, used in succession 

modeling for Zone 19 Sagebrush and Other Dry Shrubland PVTs

 Dwarf Mountain Threetip Wyoming

Succession class Sage Big Sage Sage Sage

Douglas-fir-HHT*	 200	 200	 200	 200

Douglas-fir-LHT	 69	 84	 84	 79

Douglas-fir-LLT	 34	 29	 29	 29

Douglas-fir-LMT	 49	 39	 39	 39

Dwarf Sage-HMS 270

Dwarf Sage-LLS  

Dwarf Sage-LMS 

Mountain Big Sage-HMS  19  19

Mountain Big Sage-LLS  12 8 8

Mountain Big Sage-LMS  

Perennial Forb-HHH 9

Perennial Forb-LHH  

Perennial Forb-LLH 8 8

Perennial Native Bunch Graminoid-HHH  

Perennial Native Bunch Graminoid-HLH 

Perennial Native Bunch Graminoid-LHH 27 8 8  

Perennial Native Bunch Graminoid-LLH

Rabbitbrush-HMH  19   

Rabbitbrush-LLH  12 8 8

Rabbitbrush-LMH  

Three-tip Sage-HMS  19  19

Three-tip Sage-LLS  12 8

Three-tip Sage-LMS 

Upland Microphyllous Medium Shrubland-HMS 

Upland Microphyllous Medium Shrubland-LLS 8 8  

Upland Microphyllous Medium Shrubland-LMS 

Wyoming – Basin Big Sage-HHS	 	 199

Wyoming – Basin Big Sage-HMS	 125	 200	 200	 250

Wyoming – Basin Big Sage-LHS	 49

Wyoming – Basin Big Sage-LLS	 19	 19	 19	 9

Wyoming – Basin Big Sage-LMS	 24	 54	 54	 14

* For complete structural stage names, refer to table 5: Structural stages used for succession modeling in zones 16

and 19.

Appendix 9-O—Sagebrush and Other Dry Shrubland PVTs_______________
270 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 200

Chapter 9—Vegetation Succession Modeling for the LANDFIRE Prototype Project

Appendix 9-O: Table 2—Fire frequencies, in years and by severity type, used in succession modeling

for Zone 19 Sagebrush and Other Dry Shrubland PVTs. All fires are stand-replacing unless otherwise

noted.

 Dwarf Mountain Threetip Wyoming

Succession class Sage Big Sage Sage Sage

Douglas-fir-HHT*	 25	 20	 20	 40

Douglas-fir-LHT	 401	 291	 291	 50

Douglas-fir-LLT	 40	 25	 25	 40

Douglas-fir-LMT	 33	 22	 22	 40

Dwarf Sage-HMS  1

Dwarf Sage-LLS  

Dwarf Sage-LMS  1

Mountain Big Sage-HMS 25 20 20 25

Mountain Big Sage-LLS  33 29 

Mountain Big Sage-LMS 29 25 25 29

Perennial Forb-HHH   40

Perennial Forb-LHH   40  

Perennial Forb-LLH 50 50

Perennial Native Bunch Graminoid-HHH 29 29 50

Perennial Native Bunch Graminoid-HLH 50 50

Perennial Native Bunch Graminoid-LHH 75  40  4

Perennial Native Bunch Graminoid-LLH 149 50 5

Rabbitbrush-HMH  

Rabbitbrush-LLH 60  40  4

Rabbitbrush-LMH 50  

Three-tip Sage-HMS 29 22 22

Three-tip Sage-LLS 60 295 29

Three-tip Sage-LMS  33 2

Upland Microphyllous Medium Shrubland-HMS 

Upland Microphyllous Medium Shrubland-LLS 0 50

Upland Microphyllous Medium Shrubland-LMS 0  

Wyoming – Basin Big Sage-HHS	 40

Wyoming – Basin Big Sage-HMS	 50	 33	 33	 50

Wyoming – Basin Big Sage-LHS	 50

Wyoming – Basin Big Sage-LLS	 60	 40	 40	 50

Wyoming – Basin Big Sage-LMS	 50	 40	 40	 50

* For complete structural stage names, refer to table 5: Structural stages used for succession modeling in zones 16

and 19.

In this class, approximately 20 percent of fires were estimated as mixed stand-replacing fires.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 2

Chapter 9—Vegetation Succession Modeling for the LANDFIRE Prototype Project

Appendix 9-O: Table 3—Succession modeling results in percent composition of each of the Zone 19

Sagebrush and Other Dry Shrubland PVTs.

 Dwarf Mountain Threetip Wyoming

Succession class Sage Big Sage Sage Sage

Douglas-fir-HHT*	 0	 0	 0	 1

Douglas-fir-LHT	 2	 1	 1	 2

Douglas-fir-LLT	 1	 2	 2	 1

Douglas-fir-LMT	 2	 1	 2	 2

Dwarf Sage-HMS 

Dwarf Sage-LLS 7

Dwarf Sage-LMS  

Mountain Big Sage-HMS  1 

Mountain Big Sage-LLS  1 6

Mountain Big Sage-LMS 2 0 0

Perennial Forb-HHH 0 0 

Perennial Forb-LHH 0 0 

Perennial Forb-LLH 0 0 2

Perennial Native Bunch Graminoid-HHH 29 29  

Perennial Native Bunch Graminoid-HLH 10 0 

Perennial Native Bunch Graminoid-LHH 0 0 

Perennial Native Bunch Graminoid-LLH 1

Rabbitbrush-HMH  1 0 

Rabbitbrush-LLH 2 0 0 

Rabbitbrush-LMH  1 0

Three-tip Sage-HMS 2 0  

Three-tip Sage-LLS 2 0 

Three-tip Sage-LMS 2 0 

Upland Microphyllous Medium Shrubland-HMS 0 0 2

Upland Microphyllous Medium Shrubland-LLS 0 0 2

Upland Microphyllous Medium Shrubland-LMS 0 0 

Wyoming – Basin Big Sage-HHS	 0

Wyoming – Basin Big Sage-HMS	 4	 0	 0	 34

Wyoming – Basin Big Sage-LHS	 1

Wyoming – Basin Big Sage-LLS	 4	 0	 0	 8

Wyoming – Basin Big Sage-LMS	 3	 0	 0	 11

* For complete structural stage names, refer to table 5: Structural stages used for succession modeling in zones 16

and 19.
272 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 200

Chapter 9—Vegetation Succession Modeling for the LANDFIRE Prototype Project

Appendix 9-O: Table 4—Transition times between succession 

classes, in years, used in succession modeling for the Zone 

19 Sagebrush and Other Dry Shrubland PVTs

Succession class Dry shrub

Douglas-fir-HHT*	 299

Douglas-fir-LHT	 84

Douglas-fir-LLT	 29

Douglas-fir-LMT	 39

Mountain Big Sage-HMS 

Mountain Big Sage-LLS 

Mountain Big Sage-LMS 

Perennial Forb-LHH 

Perennial Forb-LLH  

Perennial Naive Bunch Graminoid-HHH  

Perennial Native Bunch Graminoid-HLH 27

Perennial Native Bunch Graminoid-LLH 2

Shrubby cinquefoil-HMS	 14

Shrubby cinquefoil-LLS	 12

Shrubby cinquefoil-LMS	 14

Upland Broadleaf Medium Shrubland-LLS 

Upland Broadleaf Medium Shrubland-LMS

Upland Microphyllous Medium Shrubland-HMS

Upland Microphyllous Medium Shrubland-LLS 

Upland Microphyllous Medium Shrubland-LMS

Upland Needleleaf Shrubland–LLS	 12

Wyoming – Basin Big Sage-HMS	 199

Wyoming – Basin Big Sage-LLS	 14

Wyoming – Basin Big Sage-LMS	 54

* For complete structural stage names, refer to table 5: Structural stages 

used for succession modeling in zones 16 and 19

Appendix 9-O: Table 5—Fire frequencies, in years and by

severity type, used in succession modeling for Zone 19 Sagebrush and Other Dry Shrubland PVTs

Succession class Dry shrub

Douglas-fir-HHT*	 20

Douglas-fir-LHT	 25

Douglas-fir-LLT	 29

Douglas-fir-LMT	 29

Mountain Big Sage-HMS 25

Mountain Big Sage-LLS 

Mountain Big Sage-LMS 29

Perennial Forb-LHH 

Perennial Forb-LLH  

Perennial Naive Bunch Graminoid-HHH 25

Perennial Native Bunch Graminoid-HLH 29

Perennial Native Bunch Graminoid-LLH  

Shrubby cinquefoil-HMS	 25

Shrubby cinquefoil-LLS	 25

Shrubby cinquefoil-LMS	 29

Upland Broadleaf Medium Shrubland-LLS

Upland Broadleaf Medium Shrubland-LMS 29

Upland Microphyllous Medium Shrubland-HMS 25

Upland Microphyllous Medium Shrubland-LLS

Upland Microphyllous Medium Shrubland-LMS 29

Upland Needleleaf Shrubland–LLS	 33

Wyoming – Basin Big Sage-HMS	 50

Wyoming – Basin Big Sage-LLS	 50

Wyoming – Basin Big Sage-LMS	 40

* For complete structural stage names, refer to table 5: Structural stages 

used for succession modeling in zones 16 and 19 1 In this class, approximately 20 percent of fires were estimated

mixed stand-replacing fires.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 2

Chapter 9—Vegetation Succession Modeling for the LANDFIRE Prototype Project

Appendix 9-O: Table 6—Succession modeling results in 

percent composition of each of the Zone 19 Sagebrush and

Other Dry Shrubland PVTs.

Succession class Dry shrub

Douglas-fir-HHT*	 0

Douglas-fir-LHT	 0

Douglas-fir-LLT	 0

Douglas-fir-LMT	 0

Mountain Big Sage-HMS 

Mountain Big Sage-LLS 2

Mountain Big Sage-LMS 2

Perennial Forb-LHH 2

Perennial Forb-LLH 2

Perennial Naive Bunch Graminoid-HHH 

Perennial Native Bunch Graminoid-HLH 

Perennial Native Bunch Graminoid-LLH 

Shrubby cinquefoil-HMS	 5

Shrubby cinquefoil-LLS	 7

Shrubby cinquefoil-LMS	 36

Upland Broadleaf Medium Shrubland-LLS 0

Upland Broadleaf Medium Shrubland-LMS

Upland Microphyllous Medium Shrubland-HMS 0

Upland Microphyllous Medium Shrubland-LLS

Upland Microphyllous Medium Shrubland-LMS

Upland Needleleaf Shrubland–LLS	 1

Wyoming – Basin Big Sage-HMS	 4

Wyoming – Basin Big Sage-LLS	 2

Wyoming – Basin Big Sage-LMS	 1

* For complete structural stage names, refer to table 5: Structural stages 

used for succession modeling in zones 16 and 19
274 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 20

Chapter 9—Vegetation Succession Modeling for the LANDFIRE Prototype Project

Appendix 9-P—Grassland PVTs _____________________________________

Appendix 9-P: Table 1—Transition times between succession classes, in years, 

used in succession modeling for Zone 19 Grassland PVTs

Fescue Bluebunch

Succession class Grassland Wheatgrass

Douglas-fir-HHT*	 300	 400

Douglas-fir-LHT	 84	 59

Douglas-fir-LLT	 29	 14

Douglas-fir-LMT	 39	 14

Juniper-HHT 200

Juniper-LHT 220

Juniper-LLT 

Juniper-LMT  

Mountain Big Sage-HMS  

Mountain Big Sage-LLS  1

Mountain Big Sage-LMS  1

Perennial Forb-LHH 9

Perennial Forb-LLH 8

Perennial Native Bunch Graminoid-HHH 

Perennial Native Bunch Graminoid-LHH 28 8

Perennial Native Bunch Graminoid-LLH 

Rabbitbrush-LLS 8

Rabbitbrush-LMS 9

Shrubby Cinquefoil-HMS	 14

Shrubby Cinquefoil-LLS	 14

Shrubby Cinquefoil-LMS	 12

Upland Broadleaf Medium Shrubland-HLS 12

Upland Broadleaf Medium Shrubland-HMS 1

Upland Broadleaf Medium Shrubland-LMS 

Upland Microphyllous Medium Shrubland-LLS 8

Upland Microphyllous Medium Shrubland-LMS 9

Wyoming – Basin Big Sage-HMS	 200	 59

Wyoming – Basin Big Sage-LLS	 14	 14

Wyoming – Basin Big Sage-LMS	 54	 14

* For complete structural stage names, refer to table 5: Structural stages used for

succession modeling in zones 16 and 19
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 27

Chapter 9—Vegetation Succession Modeling for the LANDFIRE Prototype Project

Appendix 9-P: Table 2—Fire frequencies, in years and by severity type, used

in succession modeling for Zone 19 Grassland PVTs

Fescue Bluebunch

Succession class Grassland Wheatgrass

Douglas-fir-HHT*	 20	 20

Douglas-fir-LHT	 331	 25

Douglas-fir-LLT	 20	 29

Douglas-fir-LMT	 20	 29

Juniper-HHT 22

Juniper-LHT 25

Juniper-LLT 29

Juniper-LMT 29

Mountain Big Sage-HMS 50 20

Mountain Big Sage-LLS 29 29

Mountain Big Sage-LMS 29 29

Perennial Forb-LHH 

Perennial Forb-LLH  

Perennial Native Bunch Graminoid-HHH  3

Perennial Native Bunch Graminoid-LHH  3

Perennial Native Bunch Graminoid-LLH  40

Rabbitbrush-LLS 25

Rabbitbrush-LMS 22

Shrubby Cinquefoil-HMS	 25

Shrubby Cinquefoil-LLS	 22

Shrubby Cinquefoil-LMS	 25

Upland Broadleaf Medium Shrubland-HLS 10

Upland Broadleaf Medium Shrubland-HMS 25 25

Upland Broadleaf Medium Shrubland-LMS 3

Upland Microphyllous Medium Shrubland-LLS

Upland Microphyllous Medium Shrubland-LMS 25

Wyoming – Basin Big Sage-HMS	 50	 50

Wyoming – Basin Big Sage-LLS	 50	 50

Wyoming – Basin Big Sage-LMS	 40	 40

* For complete structural stage names, refer to table 5: Structural stages used for succession modeling in zones 16 and 19

Frequency of mixed-severity fire.
276 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 200

Chapter 9—Vegetation Succession Modeling for the LANDFIRE Prototype Project

Appendix 9-P: Table 3—Succession modeling results in percent composition 

of each of the Zone 19 Grassland PVTs

Fescue Bluebunch

Succession class Grassland Wheatgrass

Douglas-fir-HHT*	 1	 0

Douglas-fir-LHT	 8	 0

Douglas-fir-LLT	 15	 0

Douglas-fir-LMT	 8	 0

Juniper-HHT 0

Juniper-LHT 0

Juniper-LLT 0

Juniper-LMT 

Mountain Big Sage-HMS 0 2

Mountain Big Sage-LLS 

Mountain Big Sage-LMS  

Perennial Forb-LHH 0

Perennial Forb-LLH 0

Perennial Native Bunch Graminoid-HHH 6 5

Perennial Native Bunch Graminoid-LHH 16

Perennial Native Bunch Graminoid-LLH 2 2

Rabbitbrush-LLS 0

Rabbitbrush-LMS 0

Shrubby Cinquefoil-HMS	 3

Shrubby Cinquefoil-LLS	 17

Shrubby Cinquefoil-LMS	 19

Upland Broadleaf Medium Shrubland-HLS

Upland Broadleaf Medium Shrubland-HMS

Upland Broadleaf Medium Shrubland-LMS 0 

Upland Microphyllous Medium Shrubland-LLS 2

Upland Microphyllous Medium Shrubland-LMS 2

Wyoming – Basin Big Sage-HMS	 1	 14

Wyoming – Basin Big Sage-LLS	 0	 6

Wyoming – Basin Big Sage-LMS	 0	 5

* For complete structural stage names, refer to table 5: Structural stages used for succession modeling in zones 16 and 19
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 27

Chapter 10—Using Simulation Modeling to Assess Historical Reference Conditions for Vegetation and Fire Regimes for the LANDFIRE Prototype Projec

Chapter

10

In: Rollins, M.G.; Frame, C.K., tech. eds. 2006. The LANDFIRE 

Prototype Project: nationally consistent and locally relevant 

geospatial data for wildland fire management. Gen. Tech. Rep. 

RMRS-GTR-175. Fort Collins: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research Station.

Introduction ____________________

A critical component of the Landscape Fire and Resource Management Planning Tools Prototype Project, 

or LANDFIRE Prototype Project, was the development 

of a nationally consistent method for estimating historical reference conditions for vegetation composition and 

structure and wildland fire regimes. These estimates of 

past vegetation composition and condition are used as a 

baseline for evaluating current landscape conditions in 

terms of ecological departure from historical conditions 

(Landres and others 1999). Simulated historical fire regime characteristics provide managers with information 

for designing and evaluating hazardous fuel treatments 

in which the objective is to restore landscapes to nearhistorical reference conditions (Keane and Rollins, Ch. 

3). In LANDFIRE, simulated historical conditions are 

used to characterize the departure of current landscapes 

using Fire Regime Condition Class (FRCC) calculations 

(Hann and Bunnell 2001) and other measures of ecological departure (Holsinger and others, Ch. 11). Previously, 

Schmidt and others (2002) produced fire regime and 

departure information on a nationwide basis at a 1-km 

resolution; this effort used existing broad-scale spatial 

data and a rule-based approach to assign fire regimes 

Using Simulation Modeling to Assess Historical 

Reference Conditions for Vegetation and Fire 

Regimes for the LANDFIRE Prototype Project

Sarah Pratt, Lisa Holsinger, and Robert E. Keane

and FRCC to mapped biophysical settings across the 

United States. The LANDFIRE Prototype methods used 

the Landscape Succession Model version 4.0 (LANDSUMv4), a spatially explicit fire and vegetation dynamics 

simulation model, to simulate disturbance and succession 

dynamics over a simulation period of thousands of years 

(Keane and others 2006). The model uses pathways of 

successional transitions and disturbance effects stratified 

by unique biophysical settings, called potential vegetation types (PVTs), across the simulation landscape to 

produce estimates of historical reference conditions for 

fire frequency, fire severity, and vegetation conditions. 

This chapter describes the model and how it was used to 

generate historical reference conditions of vegetation and 

fire regimes for the LANDFIRE Prototype Project.

Background

Estimating reference conditions for vegetation 

and fire regimes—The non-equilibrium paradigm of 

disturbance ecology maintains that ecosystems are not 

static, and natural disturbance regimes create temporal 

and spatial variability in the structure and composition 

of most ecosystems (Picket and White 1985). Within 

this framework, therefore, reference conditions should 

be defined in terms of a range of conditions over space 

and time rather than in terms of a static set of conditions. For the LANDFIRE Prototype, we described 

reference conditions for vegetation by the quantification 

of the temporal fluctuations in vegetation characteristics 

(defined by dominant species and stand structure) prior 

to Euro-American settlement, specifically from 1600 

to 1900 A.D. (Keane and Rollins, Ch. 3). Fire regimes 
278 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 200

Chapter 10—Using Simulation Modeling to Assess Historical Reference Conditions for Vegetation and Fire Regimes for the LANDFIRE Prototype Projec

describe fire characteristics over time (Baker 1992) 

and are often defined in terms of fire frequency, size, 

pattern, seasonality, intensity, and severity (Agee 1993; 

Heinselman 1981). In the LANDFIRE Prototype, we 

described fire regimes by their frequency and severity 

because these metrics influence vegetation composition 

and structure across landscapes and have been used in 

the majority of fire regime studies (Barrett and others 

1991; Brown and others 1994; Keane and others 2003; 

Morgan and others 2001; Rollins and others 2004) and 

management planning activities. We used the point-based 

mean fire return interval (MFRI; in years) to quantify 

fire frequency. Fire severity is defined as the effect on 

overstory vegetation, and we use three categories to 

describe fire severity: 1) non-lethal surface fires, 2) 

mixed-severity fires, and 3) stand-replacing fires. A nonlethal surface fire kills few individuals (< 10 percent) 

in the overstory (Schimdt and others 2002), whereas 

stand-replacing fires kill the majority (> 90 percent) of 

the dominant vegetation (Brown 1995; Schmidt and others 2002). Mixed-severity fire regimes contain elements 

of both non-lethal surface and stand-replacing fires and 

may be used to describe an area of patchy burn patterns 

created during one fire event; however, mixed-severity 

fire regimes can also be used to describe a mix of fire 

severities occurring over time (Shinneman and Baker 

1997). For the LANDFIRE Prototype simulation modeling effort, we used mixed-severity fires to describe 

single fire events that cause mixed mortality and have 

moderate effects on overstory vegetation (Schmidt and 

others 2002).

Since the turn of the twentieth century, human activities 

such as fire suppression, logging, and grazing of domestic 

livestock have altered fire regimes and vegetation structure and dynamics in ecosystems across the United States 

(Baker 1992; Ferry and others 1995; Heinselman 1973; 

Herron 2001; Keane and others 2002b). In recent years, 

there has been increasing recognition that information 

about ecosystem processes and characteristics prior to 

intensive Euro-American settlement may offer the best 

reference conditions for managing complex ecosystems 

to maintain diversity and sustainability (Kaufmann and 

others 1998; Landres and others 1999; Swanson and 

others 1994). Furthermore, the increasing occurrence of 

large, ecosystem-altering fires and escalating fire suppression costs in recent years have shown that managing 

ecosystems outside of their natural range can be difficult, 

costly, and devastating to important ecosystem elements. 

Although historical reference conditions of vegetation 

and fire dynamics can serve as effective tools for fire 

planning and management, fire managers in many areas 

of the country lack the necessary information to develop 

baseline historical information (Keane and others 2002b; 

Landres and others 1999; Schmidt and others 2002).

The importance and challenges of estimating historical reference conditions for vegetation have been 

widely recognized by the scientific and land management communities (Kaufmann and others 1998; Landres 

and others 1999; Moore and others 1999; Swanson and 

others 1994; Veblen 2003). There are several types of 

data that may be used to estimate historical fire regimes 

and vegetation conditions including 1) time series data, 

2) spatial series data, and 3) simulated data. Time series data are based on actual data from one location 

over a long period of time. Time series data used in 

estimating historical vegetation characteristics may 

come from historical imagery, historical documents, 

or dendroecological data. Information about historical 

fire regimes, on the other hand, is often gathered from 

fire scars, charcoal sediments in lakes, bogs, or soils, or 

post-fire tree establishment dates from tree ring analysis 

(Kaufmann and others 1994, 1998; Keane and others 

2004; Swanson and others 1994). Time series data have 

the benefit of being based on the evidence of actual fires, 

but are often difficult to obtain. Furthermore, the data 

generally describe fire regimes or vegetation over small 

spatial extents or short temporal spans and cannot be 

extrapolated consistently across broad areas. Historical vegetation conditions and fire regimes may also be 

estimated for a geographic region from spatial series of 

data (imagery or otherwise) collected on several similar 

landscapes within a geographic region (Hessburg and 

others 1999). Inferences about conditions for the larger 

region are predicated on the assumption that the sampled 

landscapes represent the range of possible conditions 

over time for the entire region. This assumption may be 

tenuous, however, as variations in disturbance histories 

between sites may obscure historical patterns when 

extrapolated over the entire region. Furthermore, an 

adequate amount of data to fully describe the range of 

possible conditions may be difficult to obtain, and data 

may not be equally available for all ecosystems and regions. Simulation modeling provides an alternative for 

estimating historical fire regimes and vegetation conditions. This approach substitutes data modeled over long 

simulation periods for actual historical data. Although 

the parameters used in the models are based on available 

data, simulation modeling is necessarily a simplification of the actual processes occurring in ecosystems. 

Simulated results represent only an estimate of actual 

historical conditions. Nevertheless, simulation modeling has several advantages: 1) models can integrate the 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 27

Chapter 10—Using Simulation Modeling to Assess Historical Reference Conditions for Vegetation and Fire Regimes for the LANDFIRE Prototype Projec

limited data available and extrapolate over larger areas, 

2) simulation modeling can be applied over large areas 

consistently and comprehensively, and 3) models can be 

used to simulate a broad range of potential conditions 

rather than merely the actual conditions experienced, 

which represent only one possible scenario.

Simulation modeling—Given the LANDFIRE objective of creating a robust methodology for estimating 

historical reference conditions consistently and comprehensively across the nation, simulation modeling, 

despite limitations, proves the best source for estimating 

historical fire regimes and vegetation conditions. A class 

of simulation models, called landscape fire succession 

models (LFSMs), simulate fire and vegetation dynamics in the spatial domain (Keane and others 2004), and 

several existing LFSMs have the potential to generate 

estimates of historical fire regimes and vegetation conditions over time (for reviews of existing models see 

Baker 1989, Gardner and others 1999, Keane and others 

2004, and Mladenoff and Baker 1999). Keane and others (2004) identified four separate components essential 

for simulating fire and vegetation dynamics in LFSMs: 

1) vegetation succession, 2) fire ignition, 3) fire spread, 

and 4) fire effects. For each of these components, the 

complexity of the approach and the scale of application 

may differ from model to model and must be considered 

when selecting a model for a particular use (Keane and 

others 2004).

LANDSUMv4—We selected LANDSUMv4 as the 

landscape fire succession model for LANDFIRE because of the minimal number of inputs required and its 

generalized structure, which allowed it to be portable, 

flexible, and robust with respect to geographic area, 

ecosystem, and disturbance regime (Keane and others 

2002a; Keane and others 2006). More complex models, 

such as Fire-BGC (Keane and others 1996) and LANDIS (Mladenoff and others 1996), would likely have 

generated more realistic landscape simulations, but 

the required extensive parameterization would likely 

have been difficult to implement for every ecosystem 

and landscape in the United States. Also, to generate 

sufficient time series (especially for landscapes with 

infrequent fires), complex models such as these would 

have required prohibitively long execution times. Less 

complex models, such as TELSA (Kurz and others1999) 

or SIMPPLLE (Chew and others 2004), would have 

been easy to parameterize, but these models do not 

adequately simulate the spatial dynamics of fire spread 

and effects so that variation in landscape structure can 

be assessed. LANDSUMv4 provided a good balance 

between the realism of more complex models and the 

simplicity of less complex models.

LANDSUMv4 is a spatial state-and-transition patchlevel succession model combined with a spatially explicit 

disturbance model that simulates fire growth using a 

cell-to-cell spread method (Keane and others 2002a). 

The model is based on vegetation pathways developed for 

each PVT on the simulation landscape with user-defined 

transition times for succession events and user-defined 

probabilities for disturbance events and their effects (see 

Long and others, Ch. 9 for details on pathway development). A PVT identifies a distinct biophysical setting that 

supports a unique and stable climax plant community 

under a constant climate regime (see Keane and Rollins, 

Ch. 3; Long and others, Ch. 6; and Frescino and Rollins, 

Ch. 7 for detailed information on the role of PVT in the 

LANDFIRE Prototype). Succession classes, which are 

defined by the combination of the dominant species, or 

cover type (CT), and the stand structure, or structural 

stage (SS) (see Long and others, Ch. 6; Zhu and others, 

Ch. 8; and Long and others, Ch. 9 for descriptions of the 

CTs and SSs used in the LANDFIRE Prototype), serve 

as discrete stages along the pathways. The pathways are 

applied across the landscape to mapped patches and their 

associated PVTs and succession classes. In the model, 

patches (also referred to as polygons or stands) are spatially contiguous areas having homogenous attributes of 

PVT, CT, SS, and age (Keane and others 2006).

LANDUSMv4 operates at an annual time–step, and, 

for each year, the model first simulates disturbance 

(fig. 1). The model iterates through all the patches in 

the landscape and, for each patch, cycles through all 

possible disturbances for the current PVT/succession 

class for that patch and stochastically determines if a 

disturbance occurs. Once a disturbance is modeled for 

a particular patch, the simulation year is over for that 

patch and no further disturbances or succession can 

occur.

There are two different disturbance categories in 

LANDSUMv4: non-fire, or aspatial, and fire, or spatial. 

Non-fire disturbances are simulated in two steps: initiation and effects. Initiation is based on probabilities 

defined in the vegetation pathways. Effects are then 

modeled as a change in succession class based on a 

second set of probabilities, also defined in the vegetation 

pathways, unique to the succession class/disturbance 

combination. A disturbance effect can have multiple 

pathways; in other words, a patch can transition to any 

one of several succession classes following a disturbance. 

The probabilities for all pathways from a particular succession class/disturbance combination must total one.
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Chapter 10—Using Simulation Modeling to Assess Historical Reference Conditions for Vegetation and Fire Regimes for the LANDFIRE Prototype Projec

Figure 1—Flow diagram showing the LANDSUMv4 modeling process. Information from vegetation pathway models, parameters

estimated from literature and preliminary testing, an elevation map, and a potential vegetation type (PVT) map are used to make 

the input files and input maps for the model. Each model year, LANDSUMv4 begins by simulating disturbances. Fire is simulated

in three steps (ignition, spread, and effects) and results in the creation of new patches on the landscape. Other disturbances are 

simulated at the stand level and are simulated in two steps (initiation and effects). For stands where no disturbances are modeled, the stand ages one year and a succession event is modeled based on transition times. Summaries of the area occupied by 

each PVT/succession class combination are output to a tabular file every reporting year, and fire regime characteristics (mean

fire return interval [MFRI], non-lethal surface fires [NLSF], mixed-severity fires [MSF], and stand-replacing fires [SRF]) are output

to maps at the end of the simulation. 

Fire disturbances are modeled in three steps: ignition, 

spread, and effects. Like non-fire disturbances, fire ignitions are based on probabilities in the pathways, but the 

probabilities are adjusted to account for fuel build-up 

(probability increases with increased time since last fire) 

and a no-burn period following a fire (Keane and others 

2006). The probability is then further adjusted using a 

fire weather multiplier and a scaling factor based on 

patch size and average fire size (Keane and others 2006). 

Once a fire has ignited, the model calculates the size 

of the fire and then, based on wind and slope vectors, 

spreads this burned area over the landscape until it has 

reached the calculated fire size or reaches an unburnable 

boundary (Keane and others 2002a; Keane and others 

2006). The model limits the fire size estimate using a 

minimum fire size of 1 ha and a maximum fire size equal 
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Chapter 10—Using Simulation Modeling to Assess Historical Reference Conditions for Vegetation and Fire Regimes for the LANDFIRE Prototype Projec

to the size of the simulation landscape, which, for the 

LANDFIRE Prototype, was 20,000 ha (see the Methods

section below for details on the LANDFIRE simulation 

landscape) (Keane and others 2006). The fire spread is 

independent of patch boundaries, thus fires can divide 

patches and create new patches. Fire effects are then 

simulated at the patch level for each of the new patches 

created by the fire. The model stochastically determines 

the fire severity based on the probabilities for each fire 

severity type (non-lethal surface, mixed-severity, and 

stand-replacing fire) in the vegetation pathways. Finally, 

the model determines the post-fire succession class 

based on probabilities assigned to the pre-fire succession 

class/severity combination.

For those patches where no disturbances occurred, 

the model simulates succession. LANDSUMv4 implements a multiple pathway succession approach using 

unique sets of succession pathways for each PVT. This 

approach assumes that all pathways of successional development will eventually converge to a stable or climax 

plant community (PVT) in the absence of disturbance 

(Arno and others 1985; Cattelino and others 1979; Davis 

and others 1980; Kessell and Fischer 1981; Noble and 

Slatyer 1977; Steele 1984). Each simulation year, all 

undisturbed patches advance one year in age, and when 

a patch reaches the final age for the current succession 

class (defined in the pathways), the patch transitions to 

a new succession class. A succession event can result 

in the patch transitioning to any one of several classes 

defined in the vegetation pathways (Long and others, 

Ch. 9), and the model stochastically determines which 

pathway succession follows.

The LANDFIRE Prototype was conducted for two 

broad study areas or mapping zones: one in the central 

Utah highlands and the other in the northern Rocky 

Mountains (referred to as Zone 16 and Zone 19, respectively; see Rollins and others, Ch. 2 for description of 

the two study areas). In this chapter, we describe the 

use of the LANDSUMV4 simulation model to generate 

estimates of historical reference conditions for vegetation 

composition and structure and wildland fire regimes for 

the LANDFIRE Prototype (for more information on the 

use of models in simulating historical fire regimes and 

vegetation and the LANDSUMv4 model, see Keane and 

others 2004 and Keane and others 2006). Whereas the 

fire regime data form a final LANDFIRE product (Rollins and others, Ch. 2), the data on reference condition 

vegetation served as an interim product used to determine 

ecological departure (this chapter describes only the 

LANDSUMv4 simulation process; for information on 

how departure was estimated using the LANDSUMv4 

output, see Holsinger and others, Ch. 11). In this chapter, 

we outline the preliminary testing and analyses used to 

parameterize and initialize LANDSUMv4 in addition to 

the final methods used to simulate historical chronosequences (time series) and fire regimes for each mapping 

zone. We present the results from each mapping zone, 

a discussion of the benefits and limitations of our approach, and recommendations for estimating vegetation 

and fire regime reference conditions for LANDFIRE’s 

national implementation.

Methods _______________________

The LANDFIRE Prototype Project involved many 

sequential steps, intermediate products, and interdependent processes. Please see appendix 2-A in Rollins and 

others, Ch. 2 for a detailed outline of the procedures 

followed to create the entire suite of LANDFIRE Prototype products. This chapter focuses specifically on 

the procedure for modeling historical vegetation and 

fire regimes in the LANDFIRE Prototype Project.

We prepared the inputs for LANDSUMv4 from several key pieces of data, including a PVT map, a digital 

elevation model (DEM), succession pathways containing probabilities for disturbance events and effects, 

and times and transitions for succession events (fig. 1). 

Frescino and Rollins (Ch. 7) developed the PVT map 

using a suite of biophysical gradient layers (Holsinger 

and others, Ch. 5) and predictive landscape modeling. 

We obtained the DEM from the National Elevation 

Database (NED; ned.usgs.gov). Local experts developed 

the succession pathways for each PVT based on extensive literature review and experience (Long and others, 

Ch. 9). They prepared and tested these pathways with 

the aid of a simple aspatial state-and-transition model, 

the Vegetation Dynamics Development Tool or VDDT 

(Beukema and others 2003; Kurz and others 1999), 

before converting them for use in LANDSUMv4. We 

determined the model parameters through a combination of literature review, expert opinion, and exploratory 

analysis. We partitioned each zone into a series of 20,000 

ha simulation landscapes and divided the landscapes 

into discrete 81 ha reporting units for summarizing 

statistics (fig. 2). Succession and disturbance were then 

simulated for each landscape using LANDSUMv4. The 

total simulation time for Zone 16 was 4,500 years, but 

because of temporal autocorrelation in the vegetation 

output, the simulation time for Zone 19 was extended to 

10,500 years. The model produced maps of fire severity 

and mean fire return interval, which were processed in 

a global information system (GIS) to create the final fire 
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Chapter 10—Using Simulation Modeling to Assess Historical Reference Conditions for Vegetation and Fire Regimes for the LANDFIRE Prototype Projec

Figure 2—The simulation landscapes for LANDFIRE consisted of a 20,000-ha context area and a 3-km buffer. Each mapping zone

was divided into a series of simulation landscapes, and each simulation landscape was further divided into reporting units. Here, 

Zone 16 is divided into 427 context landscapes. One context landscape (boundary shown in red) with buffer (boundary show

in blue) is displayed with a PVT background and the 900-m by 900-m reporting units. Stand boundaries for the initial map were 

determined by a spatial overlay of the PVT and reporting unit boundaries. 
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Chapter 10—Using Simulation Modeling to Assess Historical Reference Conditions for Vegetation and Fire Regimes for the LANDFIRE Prototype Projec

regime maps for each zone. Statistical summaries of 

vegetation composition by PVT were computed for each 

reporting unit for every reporting year. These summaries 

comprised the reference conditions that Holsinger and 

others (Ch. 11) then used in subsequent LANDFIRE 

processes to calculate ecological departure and FRCC. 

The following sections detail methods for 1) designing 

the simulation landscape, 2) preparing the input maps, 

3) preparing the input files, 4) parameterizing the model, 

and 5) processing the output data.

Designing the Simulation Landscape

Before preparing the LANDSUMv4 input files, we 

had to determine the most appropriate size and shape 

of the simulation area and the landscape reporting units. 

In designing the simulation landscape, we aimed to balance the need for model efficiency with that of obtaining 

realistic simulation results. The main factor influencing 

simulation time is the number of patches on the simulation landscape, and the number of patches increases 

exponentially throughout the simulation period because 

fire creates new patches throughout the simulation. In 

general, use of smaller simulation landscapes increases 

model efficiency, whereas use of larger landscapes 

better represents fire’s long-term effects on vegetation 

composition (Keane and others 2002a).

Size and shape of the simulation landscape – The simulation landscape for the LANDFIRE Prototype consisted 

of the area of interest — or context area — for which 

vegetation conditions were summarized and a simulation 

buffer surrounding this area, which was not included 

in the summaries. The LANDFIRE Prototype required 

a simulation landscape (context area surrounded by a 

simulation buffer) that allowed for realistic simulations 

of fire and vegetation while at the same time minimized 

1) the edge effect, 2) the amount of simulation time, 3) 

the area of overlap between simulations (thus minimizing total computing time for the entire zone), and 4) 

the total number of simulations required to complete a 

mapping zone. Previous analysis of the model showed 

that the effect of both landscape shape and size on fire 

spread and patch dynamics (Keane and others 2002a) 

was significant. Circular or square landscapes resulted 

in the most realistic simulation of fire spread, while 

narrow, linear landscapes tended to underestimate fire 

spread. We selected square-shaped simulation landscapes 

over circular shapes for the LANDFIRE Prototype to 

simplify GIS processing and to decrease overlap between 

simulation landscapes.

Keane and others (2002a) also found that use of smaller 

landscapes led to the overestimation of mean fire return 

intervals. This overestimation results from the inability 

of fires to immigrate from outside the landscape into the 

edges of the simulation area; therefore, fewer fires than 

expected occur near the edges. With smaller landscapes, 

this “edge effect” impacts a greater proportion of the 

simulation landscape than with larger landscapes. The 

edge effect can also be decreased by adding a buffer 

zone around the context area to provide a place from 

which fires can spread into the context area. The ideal 

simulation area size may depend on both the size of 

the context area and the size of fires. Keane and others 

(2002a) recommend a total simulation area of 8 to 10 

times the size of the context area for a 2,500-ha context landscape; however, the appropriate size is highly 

dependent on landscape complexity. Although larger 

landscapes and larger buffers minimized edge effects, 

they increased simulation times. We determined that 

a 20,000-ha context box with a 3-km buffer offered a 

reasonable compromise between minimal edge effects 

and manageable simulation times. In addition, all other 

LANDFIRE maps (such as DEM and PVT) were produced with a 3-km buffer around the zone boundary; 

therefore, the maximum buffer size that was available 

at the edges of the zone was only 3 km. We used a 

14,400-m x 14,400-m (20,736 ha) context area to allow 

the 30-m pixels to nest within the landscape and simplify 

GIS processing. With the 3-km buffer on all sides, the 

simulation landscape was 20,400 m x 20,400 m (41,616 

ha). We divided the entire mapping zone into a series 

of adjacent 20,000-ha boxes with 3-km buffers where 

buffer areas overlapped adjacent context areas (fig. 3).

Size and shape of the landscape reporting units—

Landscape reporting units define the area on the ground 

into which the vegetation conditions are summarized and 

are used in subsequent modeling to calculate departure 

and FRCC (Holsinger and others, Ch. 11). For LANDSUMv4, the simulation landscape must be stratified 

into reporting units because ecological departure and 

FRCC are spatial and not point measurements. Although 

reporting units have no impact on the LANDSUMv4 

simulations, the size and shape of these reporting units 

is important when summarizing the LANDSUMv4 output for use in departure calculations (see Holsinger and 

others, Ch.11 for a complete discussion of the considerations relevant to selecting landscape reporting units for 

departure calculations). For LANDFIRE, the selection 

of an appropriate landscape reporting unit must include 
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Chapter 10—Using Simulation Modeling to Assess Historical Reference Conditions for Vegetation and Fire Regimes for the LANDFIRE Prototype Projec

Figure 3—A series of nine 20,000-ha context areas in Zone 16 shown with potential vegetation type in the background. The 

km buffer area (in blue) is shown for the center context area (in red). Notice that the 3-km buffer area overlaps adjacent context

areas. The total simulation landscape (context area + buffer) is 40,000 ha
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Chapter 10—Using Simulation Modeling to Assess Historical Reference Conditions for Vegetation and Fire Regimes for the LANDFIRE Prototype Projec

consideration of scale from both an ecological standpoint 

and a management standpoint. The most desirable landscape extent is one that is small enough to detect subtle 

changes resulting from land management actions (such 

as fuel treatments) but large enough to capture important 

ecological patterns and processes (such as fire, migration, and climate) in the correct spatial context. Other 

studies have used watersheds or ownerships to delineate 

reporting units. These units are highly variable in size, 

however, which complicates the comparability of departure estimates across reporting units. Furthermore, 4th

and 5th order Hydrologic Unit Codes (HUCs) were too 

large for our purposes, and the spatial data for 6th order 

HUCs were at that time incomplete for both prototype 

zones as well as for many areas across the nation. We 

chose reasonably small, uniform squares of 900 m by 

900 m (or 81 ha) as reporting units (fig. 2) based on the 

need for ecological and managerial relevance, national 

consistency, and computational efficiency. Furthermore, 

we chose the 900-m box so that the 30-m pixels of the 

LANDFIRE vegetation layers would nest perfectly inside 

and reporting unit boundaries would therefore not split 

pixels.

Preparing Input Maps

A map of patch boundaries and a map of elevation 

are the only spatial inputs required for the model. The 

elevation map was derived directly from the DEM map 

obtained from NED and required no additional processing, whereas the map of patch boundaries had to be 

created. As mentioned above, patches in LANDSUMv4 

are spatially contiguous areas that have identical PVT, 

CT, and structure. In addition, each patch can belong 

to only a single reporting unit. Succession and disturbance input parameters are stratified by PVT, and PVT 

distributions remain constant for any given landscape 

throughout the simulation.

In LANDSUMv4 simulations, overall processing time 

increases dramatically with the number of patches on the 

initial landscape. To minimize the number of patches on 

the initial landscape, we used only PVT and reporting 

units to determine our initial patch boundaries (fig. 2) 

and assigned a single value for each of the other patch 

attributes. Before the PVT map could be used to determine patch boundaries, however, we had to rectify the 

PVT, CT, and SS maps.

In the LANDFIRE Prototype, vegetation conditions, 

whether current or reference, were defined by PVT, CT, 

and SS. The reference conditions were generated by 

LANDSUMv4, and the current conditions were defined 

by mapped existing vegetation composition and structure (Frescino and Rollins, Ch. 7; Zhu and others, Ch. 8; 

Holsinger and others, Ch. 11). The succession pathways 

in LANDSUMv4 were based on the assumption that 

there is a unique set of succession classes (CT and SS 

combinations) that can occur naturally within a PVT. 

All of these succession classes were included as modeled 

states and thus had some probability of occurring in the 

historical reference conditions. When LANDSUMv4 

output is compared to current conditions, succession 

classes that cannot occur in the reference conditions 

will tend to increase departure when calculating FRCC. 

These succession classes may appear in the current 

conditions because of exotic or invasive species or as a 

result of mapping errors in the independently created 

PVT, cover, or structure maps. Mismatches that are 

the result of mapping errors and not the result of exotic 

species must be corrected so that there is ecological 

consistency between a given PVT and the CTs and SSs 

that occur therein.

To ensure this ecological consistency, we performed 

a spatial overlay of the PVT, cover, and structure maps 

along with confidence layers to identify and correct such 

mismatches. The confidence layers (associated with 

each LANDFIRE vegetation layer) report a percentage 

of confidence in the predicted vegetation attribute for 

a pixel based on the rules that were used to classify it 

(Earth Satellite Corporation 2003; Frescino and Rollins, 

Ch. 7). A value of zero represents the lowest confidence 

whereas a value of 100 represents the highest confidence. 

For Zone 16, the only confidence layer available was 

that associated with the CT map (Zhu and others, Ch. 

8). Succession classes that did not occur in the vegetation pathways and that included a CT not considered an 

exotic species were assumed to result from errors in the 

PVT or CT maps. The confidence layer information was 

then used to determine whether the PVT or CT should 

be changed. If the CT confidence was 50 percent or 

greater, the PVT was changed; otherwise, the CT was 

changed. For Zone 19, we had confidence layers for both 

the PVT and CT maps (Frescino and others, Ch. 7; Zhu 

and others, Ch. 8), so if the PVT confidence was greater 

than the CT confidence, the CT was changed; otherwise, 

the PVT was changed. Following all reassignments, 

the PVT, CT, and SS maps were recoded to reflect the 

changes. We then used the rectified PVT map as input 

for LANDSUMv4 and the rectified PVT, CT, and SS 

maps to define current conditions.
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Chapter 10—Using Simulation Modeling to Assess Historical Reference Conditions for Vegetation and Fire Regimes for the LANDFIRE Prototype Projec

Non-vegetated areas (urban, barren, water, snow/ice, 

and agriculture) presented a special case regarding the 

rectification of the PVT and CT maps. For Zone 16, we 

used a mask of all non-vegetated types in making both the 

PVT and CT maps, ensuring that there was 100 percent 

agreement between the maps for all non-vegetated types. 

LANDSUMv4 will not simulate fire occurrence and 

spread through areas mapped as non-vegetated PVTs. In 

terms of historical reference conditions, however, some 

of these non-vegetated PVTs, particularly agriculture 

and urban, would have historically been vegetation CTs 

and thus would have experienced wildland fire. Agriculture and urban areas, therefore, serve as unnatural fire 

breaks and will lower the fire frequency of surrounding 

pixels, creating an effect similar to that on the edges 

of the simulation landscape where fire immigration is 

limited. To remedy this problem for Zone 19, Frescino 

and others (Ch. 7) predicted a PVT for every pixel on 

the map rather than masking non-vegetated types before 

predicting PVTs. In the rectification process described 

above, we reassigned the water, barren, and snow/ice 

land covers in the PVT layer to agree with the CT layer 

because these non-vegetated types occurred historically. 

The mapped PVTs in areas where the CT was agriculture 

or urban were not changed; thus, these areas did not act 

as fire breaks and LANDSUMv4 could simulate fire 

occurrence and spread according to the vegetation and 

topography.

Preparing Input Files from the Vegetation 

Pathways

Many of the key inputs into the LANDSUMv4 model 

come from the succession pathways developed by the 

vegetation modelers (Long and others, Ch 9). The 

modelers used the VDDT model to develop and test the 

vegetation pathways and stored the results of the VDDT 

modeling process in the Vegetation and Disturbance 

Dynamics Database (VADDD; Long and others, Ch. 

9). This database was structured to store all vegetation 

and disturbance dynamics information used as input to 

LANDSUMv4 (see appendix 10-A for a description of 

VADDD, VADDD tables, and the fields within). The 

VADDD served as the primary reference for the codes 

and labels for all map unit classifications developed in 

LANDFIRE, including PVT, CT, and SS. In addition, 

this database was designed to efficiently check for errors 

in the succession and disturbance information prior to 

input into simulation modeling and to provide a standardized set of LANDSUMv4 parameters for subsequent 

applications of the model in different settings.

We converted the vegetation pathways developed in 

VDDT to the appropriate LANDSUMv4 files through 

the use of a custom software program, V2L. In addition to the spatial quality of LANDSUMV4, there are 

subtle differences between the way VDDT and LANDSUMv4 simulate succession and disturbance, including 

1) partitioning disturbance and effects probabilities, 

2) implementing multiple pathway succession, and 3) 

tracking patch age (Keane and others 2006; Kurz and 

others 1999). Most of these differences are rectified by 

the V2L program, but careful scrutiny was required to 

ensure that the pathways developed in VDDT functioned 

as intended in LANDSUMv4. 

Parameterizing the Model

Many parameters must be set by the user prior to running LANDSUMv4. Previous research using the model 

has shown that simulation time, reporting interval, and 

fire spread parameters are important factors affecting 

simulation results (Keane and others 2002a; Keane and 

others 2003). In this section, we discuss the preliminary 

testing conducted to determine several key parameters, 

including simulation time parameters and fire ignition 

and spread parameters (for a list of all model parameters 

and associated values for the LANDFIRE Prototype, 

see appendix 10-B).

Simulation time parameters—Simulation time 

parameters for the model include 1) reporting interval, 

2) total simulation time, and 3) initialization time. The 

reporting interval determines how often (in years) 

LANDSUMv4 reports vegetation conditions and fire 

characteristics across the landscape over the simulation 

period and the number of samples available for quantifying historical reference conditions. The selection of 

the reporting interval was largely driven by the need to 

reduce temporal autocorrelation in the LANDSUMv4 

output so that each reporting interval represented an 

independent observation for calculating departure 

(Holsinger and others, Ch. 11; Steele and others, in 

preparation). After preliminary analysis, we selected a 

20-year reporting interval for Zone 16. Following the 

Zone 16 runs, however, we determined that a 20-year 

reporting interval resulted in autocorrelation between 

observations, so we extended the reporting interval to 

50 years for Zone 19 (see Holsinger and others, Ch. 11 

for details on temporal autocorrelation in the reference 

conditions data).

The initialization period represents the number of 

years it takes the initial simulation landscape to reach 

equilibrium, and this initial simulation period is excluded 
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Chapter 10—Using Simulation Modeling to Assess Historical Reference Conditions for Vegetation and Fire Regimes for the LANDFIRE Prototype Projec

from subsequent analysis. Keane and others (2002a) 

found that it took approximately 200 years for succession class distributions to reach equilibrium, whereas 

Keane and others (2003) found that the landscape MFRI 

stabilized after the first 200 – 400 years of simulation. 

In our exploratory analysis for Zone 16, we found that 

successional development had a distinct trend for the 

first 250 to 500 years and then stabilized around a mean 

with expected variations (Keane and others 2002a). As 

a result, we specified a 500-year initialization period 

for both Zone 16 and Zone 19.

The total simulation period needed to be long enough 

to adequately capture the full range of vegetation conditions and fire regime characteristics and produce an 

adequate chronosequence to quantify historical vegetation conditions for subsequent departure calculations. 

In addition, extreme fire events, though rare, have a 

disproportionately large impact on vegetation patterns 

across the landscape (Moritz 1997; Strauss and others 

1989), so it is important that the simulation period is 

long enough to allow sufficient opportunity for these 

rare events to occur. The fire return interval influences 

the simulation length appropriate for capturing fire 

and vegetation characteristics, and thus the appropriate 

simulation length varies across landscapes. Our selection 

of the total simulation period was largely informed by 

the number of samples needed for departure analysis. 

Holsinger and others (Ch. 11) found that a minimum 

of 200 independent observations from LANDSUMv4 

were required to develop a time series reasonable for 

quantifying historical landscape conditions for departure calculations. Based on this criterion, we calculated 

simulation periods of 4,500 years and 10,500 years (each 

including a 500-year initialization period) for Zone 16 

and Zone 19, respectively.

Fire ignition and fire spread parameters—In 

LANDSUMv4, wildland fire is modeled in three steps: 

ignition, spread, and effects. Fire effects are determined 

stochastically by the probabilities defined in the vegetation pathways. Fire ignition uses a three-parameter 

Weibull hazard function (Johnson and Gutsell 1994; 

Johnson and Van Wagner 1985) based on the fire probabilities in the vegetation pathways, a shape parameter, 

and a years-until-reburn parameter to adjust the probability in the pathways to account for fuel build-up. The 

fire probabilities from the vegetation pathways reflect 

point estimates of fire return interval and describe the 

probability that a fire will burn a point on the landscape. 

The probability is adjusted using the relationship of 

average fire size (ignition average fire size parameter) 

to the pixel area (90 m2) and patch size to pixel area to 

scale point-level fire probabilities to stand-level ignition 

probabilities. The ignition probability is also adjusted 

for yearly weather variations based on a fire weather 

parameter that establishes the number of dry, normal, 

and wet fire years in a decade (for a thorough discussion 

of the equations and parameters used LANDSUMv4 

fire simulations, see Keane and others 2002a and Keane 

and others 2006). The fire spread algorithm calculates 

a fire size from a heavy-tailed exponential distribution 

that is defined by a shape and a scale parameter (Keane 

and others 2002a; Keane and others 2006). Fire is then 

spread cell-to-cell based on the modified equations of 

Rothermel (1991) and wind and slope vectors until it 

reaches the calculated fire size or an unburnable boundary. In this section, we will discuss the ignition average 

fire size parameter, the spread scale parameter, and the 

ignition fire weather parameter.

Both the ignition average fire size parameter and the 

spread scale parameter are related to average fire size. 

The spread scale parameter influences the average fire 

size simulated by LANDSUMv4 by controlling the distribution from which fire sizes are drawn. As the scale 

parameter increases, the simulated average fire size 

also increases (fig. 4). The ignition fire size parameter 

should approximate the estimated average fire size for 

the landscape and is used in scaling fire probabilities to 

ignition probabilities. As the ignition fire size parameter 

increases, the number of fires (number of ignitions) 

decreases (fig. 4). Estimating both of these parameters 

correctly requires information about historical fire 

sizes; however, reliable data on historical average fire 

sizes are difficult to obtain. We calculated an average 

fire size from the National Integrated Fire Management 

Interagency Database (NIFMID; USDA Forest Service 

1993) for each mapping zone. Although this database 

records recent fires only and is therefore likely a poor 

representation of historical fire sizes, it was the only 

source available for estimating fire size across the entire United States. We used the NIFMID estimate as a 

starting point for the ignition average fire size and the 

spread scale parameter estimates. Together, ignition and 

spread will determine the mean fire return interval over 

the simulation. In an attempt to refine these parameters, 

we tried various combinations of the ignition average fire 

size and spread scale parameters within a test area and 

evaluated the mean fire return interval. The vegetation 

pathways define fire probabilities based on estimates of 

historical fire frequencies from literature review and fire 

history studies (Long and others, Ch. 9). We averaged 

these probabilities and treated these mean probabilities 
288 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 200

Chapter 10—Using Simulation Modeling to Assess Historical Reference Conditions for Vegetation and Fire Regimes for the LANDFIRE Prototype Projec

Figure 4—Average fire size and average number of fires as a function of fire size parameter pairs (ignition fire size parameter

and spread scale parameter) for three test landscapes in a) Zone 16 and b) Zone 19. The average fire size and the number of

fires were calculated for each landscape and then averaged for a single parameter pair.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 28

Chapter 10—Using Simulation Modeling to Assess Historical Reference Conditions for Vegetation and Fire Regimes for the LANDFIRE Prototype Projec

as the expected fire occurrence and the inverse of these 

probabilities as the expected mean fire return interval. 

Fire occurrence in LANDSUMv4 is based, in part, on 

the probabilities of fire in the succession pathways, so we 

operated under the assumption that the MFRI modeled 

by LANDSUMv4 should be reasonably close (within 25 

percent) to the expected MFRI calculated from the vegetation pathways. LANDSUMv4 calculates and reports 

a simulated MFRI and an expected MFRI (calculated 

from the pathway-defined fire probabilities) for the entire simulation landscape. For Zone 16, we found that 

an ignition average fire size parameter that was three 

times the spread scale parameter resulted in simulated 

MFRIs for the simulation landscape that were similar 

to the expected MFRI as calculated by LANDSUMv4. 

We set the spread scale parameter to the 30-ha NIFMID 

estimate and the ignition average fire size parameter to 

90 ha for Zone 16.

After simulating the entire zone, the MFRI for many 

PVTs in Zone 16 were much longer than expected (see 

the Results section below for details). We therefore 

modified our methods for setting the ignition average 

fire size and spread scale parameters for Zone 19. We 

started with an average fire size estimate of 30 ha from 

the NIFMID database. We then varied the two fire size 

distribution parameters and ran simulations for each 

parameter pair on three test landscapes of varying 

topography and vegetation (fig. 5). We calculated the 

simulated mean fire return interval for each PVT and 

calculated a similarity value (expected MFRI divided by 

simulated MFRI) to compare the simulated MFRI and 

expected MFRI for each PVT (fig. 5b). For the Zone 19 

simulations, we selected 30 ha for the ignition average 

fire size parameter based on the 30 ha NIFMID estimate 

and 15 ha for the spread scale parameter.

The ignition fire weather multiplier does not affect 

the long-term fire probabilities, but rather affects the 

year-to-year probability of fire occurrence. To estimate 

the weather parameter (the number of dry, normal, and 

wet years in a decade), we used reconstructions of the 

Palmer Drought Severity Index (PDSI; Palmer 1965) 

from various sources. PDSI is an index of soil moisture 

based on precipitation, temperature, and available water 

content (AWC) of the soil. The index varies roughly 

between -6.0 and +6.0, with positive numbers representing wetter conditions and negative numbers drier 

conditions. For Zone 16, we relied on the Northeastern 

Utah Palmer Drought Severity Index Reconstruction, 

which is based on tree ring chronologies in and around 

the Uinta Basin (Gray and others 2003), to estimate 

historical climate conditions from 1405 to 2000 A.D. 

We assumed that wet years were those with a PDSI 

greater than 1.0, normal years were those with a PDSI 

between –1.99 and 0.99, and severe drought years were 

those with a PDSI of less than –1.99. Accordingly, we 

estimated the number of dry, normal, and wet years over 

the course of a decade as 3, 5, and 2, respectively. For 

Zone 19, we extracted data from the Alternative Method 

USA Summer PDSI Reconstruction (Zhang and others 

2004). This data set is based on tree ring chronologies 

from 1700 to 1894 and instrumental data from 1895 to 

1978. Using the same breaks for wet, normal, and dry 

years as described above, we estimated the number of 

dry, normal, and wet years as 1, 6, and 3, respectively, 

for Zone 19.

Model Output

Although there are many maps and tabular files output 

from LANDSUM, we produced only two types of data 

for the LANDFIRE Prototype: (1) vegetation chronosequence data and 2) fire regime maps. The time series 

data that define reference conditions for vegetation are 

summarized in a tabular file that summarizes the area 

(m2) within each reporting unit that is occupied by each 

PVT-succession class combination for each reporting 

year. Holsinger and others (Ch. 11) used these data to 

calculate FRCC and departure values for each reporting 

unit. While there are many important characteristics 

of fire regimes, we mapped and evaluated only fire 

frequency and fire severity values. Other characteristics 

such as fire size and pattern can be evaluated using output 

from LANDSUMv4 but, because of limited computer 

resources, we chose not to create these files for the entire mapping zone. The fire frequency and fire severity 

maps were processed to create the final LANDFIRE 

Prototype fire regime map products. 

Fire regime maps—LANDSUMv4 outputs four different fire regime maps — three severity maps and one 

frequency map — which we then processed to create 

the final LANDFIRE Prototype fire regime maps. Fire 

effects in LANDSUMv4 are defined as one of three 

severity types: non-lethal surface, mixed-severity, and 

stand-replacing fires. LANDFIRE produces maps for 

each of these severity types that display the percentage of fires of the given severity type experienced by a 

particular pixel. Fire severity is calculated as the total 

number of fires of the given severity type divided by 

the total number of fires experienced by that cell times 

100. Values for each map range from 0 - 100 and, for 

any cell, the sum of the three maps should equal 100. 

The fire frequency map simply reports the fire return 
290 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 200

Chapter 10—Using Simulation Modeling to Assess Historical Reference Conditions for Vegetation and Fire Regimes for the LANDFIRE Prototype Projec

Figure 5—Box and whisker plots for the similarity index calculated for each of the nine fire parameter pairs (ignition fire size parameter and spread scale parameter) for three test landscapes in a) Zone 16 and b) Zone 19. The similarity index is a measur

of the similarity between the simulated mean fire return intervals and the mean fire return intervals based on the fire probabilities

set in the vegetation pathways. A similarity value of  indicates that the two MFRIs are the same. A similarity above1 indicate

that the simulated MFRI is shorter than the scenario MFRI, and a similarity less than  indicates that the simulated MFRI is longer

than the scenario MFRI. 
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Chapter 10—Using Simulation Modeling to Assess Historical Reference Conditions for Vegetation and Fire Regimes for the LANDFIRE Prototype Projec

interval (in years) and is calculated as the total number 

of simulation years divided by the total number of fires 

occurring in that cell.

When the fire frequency maps from the individual 

simulations were tiled together to create a composite 

map for the entire zone, the resulting map contained a 

heavy imprint from the individual simulation landscapes 

(fig. 6a). We used the simulation buffer areas to smooth 

the edges between adjacent simulation landscapes and 

reduce the imprint to create a seamless map for the zone. 

The buffer area for one simulation landscape overlapped 

the context area of the adjacent simulation landscape 

(fig. 3). As noted above, these buffer regions tended to 

yield underestimations of fire frequencies. However, 

since fires tend to burn primarily in the direction of the 

prevailing winds (WSW in our simulation areas), buffer 

areas along the north and east edges still had many fires 

burning in from the context area; thus, fire was usually 

realistically simulated in these areas. We clipped the 

south and west buffer areas off of each context area and 

left the north and east buffer areas intact. We then used 

the mosaic command in ARC/INFO to overlay the individual simulation landscapes and smooth the overlapping 

areas (fig. 6b). Although this process led us to change 

some pixel values from their original simulated value, 

values were changed only in areas where fires tended 

to be underestimated by the model (along the western 

and southern borders of the context area). Following the 

smoothing process for Zone 16, 26 percent of the pixel 

values on the map changed from the simulated values 

and the mean change for all pixels was three fires.

Results ________________________

Fire Regime Maps

Fire frequency—Figure 7 shows the mean fire return 

interval (MFRI) in years for both mapping zones. The 

MFRIs for Zone 16 were relatively short: 58 percent of 

the zone had a fire return interval of 35 years or less, 

94 percent of the zone had a fire return interval of 100 

years or less, and fire return intervals of more than 100 

years occurred in less than 0.5 percent of the zone. Six 

percent of the zone never burned during the 4,500-year 

simulation period. The MFRIs in Zone 19 tended to be 

longer: 31 percent of the zone had a fire return interval 

of 35 years or less, 93 percent had fire return intervals 

of 100 years or less, and nearly 5 percent of the zone 

had fire return intervals between 100 and 200 years. 

Less than 3 percent of the zone was unburned during 

the 10,500-year simulation period. In both zones, most 

of the pixels that did not burn (99 percent) belonged 

to PVTs that were defined as unburnable in the model 

(water, rock, snow/ice, agriculture, and urban for Zone 

16 and water, rock, and snow/ice for Zone 19). There 

were a few vegetated areas with the potential to burn 

that never burned; these pixels were imbedded in a 

matrix of unburnable pixels, thereby preventing fires 

from spreading to those pixels.

To get a sense of how well the model simulated fire for 

different vegetation types, we compared the expected 

MFRI calculated from the fire probabilities in the 

vegetation pathways to the simulated MFRIs averaged 

for each PVT (tables 1 and 2). As noted previously, the 

simulation of fire in LANDSUMv4 is based in part on 

the fire probabilities in the vegetation pathways. Fire 

spread, however, operates independent of these probabilities. Furthermore, adjacency of PVTs with different 

fire probabilities may cause the simulated MFRI to be 

different from the expected MFRI in some PVTs. We 

determined, however, that when averaged by PVT across 

the zone, the simulated MFRI should be within 25 percent of the expected MFRI for most PVTs. Two-thirds 

of the PVTs in Zone 16 had more than a 30-percent 

difference between the simulated MFRI and the expected MFRI (table 1). Many of these PVTs represented 

only a small portion of the zone, but together, these 20 

PVTs represented over 70 percent of Zone 16. In all but 

three of these PVTs, the simulated MFRI was shorter 

than the expected MFRI. For example, the Spruce Fir 

/ Spruce Fir PVT had an MFRI in the pathways of 53 

years, whereas the MFRI simulated by LANDSUMv4 

for this PVT was 32 years. For some PVTs, however, the 

simulated MFRI was fairly close to the expected MFRI 

(see Pinyon Juniper / Mountain Big Sagebrush / South, 

Douglas-fir / Douglas-fir, and Grand Fir – White Fir in 

table 1). In Zone 19, the simulated MFRIs corresponded 

somewhat better to the expected MFRIs (table 2). Just 

over 50 percent of the PVTs in Zone 19 had more than 

a 30-percent difference between the simulated MFRI 

and the expected MFRI; however, these PVTs comprised 

only 38 percent of Zone 19. Again, the simulated MFRIs 

tended to be shorter than the expected MFRIs for these 

PVTs (see Spruce-Fir / Timberline in table 2, for example), yet six PVTs did have a simulated MFRI that 

was longer than the pathway MFRI (see Douglas-fir / 

Ponderosa Pine / Douglas-fir in table 2, for example).

Fire severity—The model produced three fire severity 

maps, one for each severity type (stand-replacing, mixedseverity, and non-lethal surface fires). Each map displayed 

the percentage of total fires that were of a particular 

severity (see Model Output in the Methods section for 
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Chapter 10—Using Simulation Modeling to Assess Historical Reference Conditions for Vegetation and Fire Regimes for the LANDFIRE Prototype Projec

Figure 6—Fire frequency maps for two adjacent simulation landscapes in Zone 16 that were processed by a) removing all

3-km buffer areas and merging the context areas and b) removing only the south and west 3-km buffer areas and mosaickin

the context areas and overlapping buffers.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 2

Chapter 10—Using Simulation Modeling to Assess Historical Reference Conditions for Vegetation and Fire Regimes for the LANDFIRE Prototype Project Figure 7—Mean fire return interval (MFRI) in years for a) Zone 16 and b) Zone 19. MFRI was calculated for each pixel as the total simulation time divided by the total number of fires that occurred within the pixel during the simulation period. Non-vegetated pixels are those areas with no natural vegetation, such as water or rock, and therefore cannot burn. Unburned pixels are vegetated pixels that never burned during the simulation period
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Chapter 10—Using Simulation Modeling to Assess Historical Reference Conditions for Vegetation and Fire Regimes for the LANDFIRE Prototype Projec

Table 1—Mean fire return interval (MFRI) values summarized for each PVT in Zone 16. MFRIs are reported as “Pathway MFR

(the mean of the fire probabilities for all succession classes in a PVT weighted by the percent area of the PVT occupied by each

succession class in 200 reporting years), “Classified MFRI” (percent of the PVT in each of the five MFRI classes used by Schmidt

and others 2002 after the 4500-year simulation), and “Raw MFRI” (mean MFRI for the PVT after the 4,500-year simulation)  Th

percent of Zone 16 that each PVT occupies is also given

Pathway Raw simulated

PVT MFRI (yrs) MFRI (yrs) Classified simulated MFRI (% of PVT)

% of 1-35 36-100 101-200 200+

Name zone Mean Mean Std. dev. 0 yrs yrs yrs yrs

Alpine 0.33 244 71 290 0.6 39.6

Aspen 2.07 63 33 28 0.0 78.4 21.6

Blackbrush 0.36 200 65 142 0.2 9.1 88.

Cool Herbaceous 0.14 25 47 153 0.2 54.6

Douglas-fir / Douglas-fir	 3.17	 29	 32	 17	 0.0	 80.5	 19.5	 0.0	 0.0

Douglas-fir / Lodgepole Pine	 0.19	 41	 33	 4	 0.0	 79.8	 20.2	 0.0	 0.0

Douglas-fir / Timberline Pine	 0.47	 44	 31	 29	 0.0	 89.4	 10.6	 0.0	 0.0

Dwarf Sagebrush  4.48 97 46 73 0.0 23.7 75.

Grand Fir – White fir	 7.02	 36	 31	 12	 0.0	 90.3	 9.7	 0.0	 0.0

Grand Fir – White Fir / Maple	 2.61	 34	 42	 133	 0.1	 57.7	 41.2	 0.6	 0.3

Lodgepole Pine 0.81 60 33 4 0.0 71.6 2

Mountain Big Sagebrush 8.67 20 32 44 0.0 83.1

Pinyon – Juniper / Gambel Oak	 1.44	 41	 39	 112	 0.1	 65.8	 33.4	 0.5	 0.3

Pinyon – Juniper / Mountain Big	 1.16	 24	 34	 42	 0.0	 78.4	 21.3	 0.2	 0.0

 Sagebrush / North

Pinyon – Juniper / Mountain Big	 5.22	 30	 33	 19	 0.0	 79.3	 20.6	 0.0	 0.0

 Sagebrush / South

Pinyon – Juniper / Mountain Mahogany	 1.06	 58	 34	 38	 0.0	 73.7	 26.2	 0.1	 0.0

Pinyon – Juniper / Wyoming – Basin Big	 2.43	 66	 48	 53	 0.0	 4.9	 94.6	 0.4	 0.1

 Sagebrush / North

Pinyon – Juniper / Wyoming – Basin Big	 16.47	 56	 39	 34	 0.0	 36.3	 63.5	 0.1	 0.0

 Sagebrush / South

Ponderosa Pine 2.48 15 29 19 0.9 93.4 5.7

Riparian Hardwood 2.11 72 37 66 0.1 5

Riparian Shrub  1.00 64 72 316 1.1

Salt Desert Shrub 2.13 119 69 197 0.2 2.6 

Spruce – Fir / Blue Spruce	 1.15	 41	 32	 34	 0.0	 84.0	 15.9	 0.0	 0.0

Spruce – Fir / Blue Spruce / Lodgepole	 0.54	 43	 34	 13	 0.0	 68.2	 31.8	 0.0	 0.0

 Pine

Spruce – Fir / Spruce – Fir	 7.97	 53	 32	 24	 0.0	 86.2	 13.7	 0.0	 0.0

Spruce – Fir / Spruce – Fir / Lodgepole	 13.25	 53	 34	 12	 0.0	 72.0	 27.9	 0.0	 0.0

 Pine

Timberline Pine 0.03 186 32 30 0.0 86.6 

Warm Herbaceous 0.73 40 45 151 0.1 56.

Wetland Herbaceous 0.27 183 48 186 0.3 57.8 4

Wyoming – Basin Big Sagebrush	 4.45	 81	 54	 177	 0.2	 27.1	 70.8	 1.2	 0.7
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Chapter 10—Using Simulation Modeling to Assess Historical Reference Conditions for Vegetation and Fire Regimes for the LANDFIRE Prototype Projec

Table 2—Mean fire return interval (MFRI) values summarized for each PVT in Zone 19. MFRIs are reported as “Pathway MFR

(the mean of the fire probabilities for all succession classes in a PVT weighted by the percent area of the PVT occupied by each

succession class in 200 reporting years), “Classified MFRI” (percent of the PVT in each of the five MFRI classes used by Schmidt

and others 2002 after the 4500-year simulation), and “Raw MFRI” (mean MFRI for the PVT after the 10,500-year simulation)  Th

percent of Zone 19 that each PVT occupies is also given

Pathway Raw simulated

PVT MFRI (yrs) MFRI (yrs) Classified simulated MFRI (% of PVT)

% of 1-35 36-100 101-200 200+

Name zone Mean Mean Std. dev. 0 yrs yrs yrs yrs

Alpine 0.03 245 39 15 0.00 50.56 48.6

Bluebunch Wheatgrass 9.46 26 32 27 0.00 77.27 22.70 0.02 

Bluebunch Wheatgrass / Conifer 0.26 26 33 68 0.01 74.07 25.7

Douglas-fir / Douglas-fir	 10.52	 52	 39	 13	 0.00	 42.05	 57.66	 0.29	 0.00

Douglas-fir / Lodgepole Pine	 4.19	 53	 45	 15	 0.00	 24.65	 74.71	 0.64	 0.01

Douglas-fir / Ponderosa Pine/	 3.03	 20	 32	 7	 0.00	 72.00	 28.00	 0.00	 0.00

 Douglas Fir

Douglas-fir / Timberline Pine	 1.11	 48	 39	 16	 0.00	 45.79	 53.92	 0.29	 0.01

Douglas-fir / Ponderosa Pine/	 2.43	 21	 32	 14	 0.00	 77.27	 22.49	 0.21	 0.03

 Western Larch/

Dry Shrub  1.28 29 40 108 0.01 58.50 

Dry Shrub / Conifer 0.21 26 61 169 0.04 19.73 7

Dwarf Sagebrush Complex / Conifer 0.00 67 36 7 0.00 61.32 38.68 0.0

Dwarf Sagebrush Complex 0.26 32 40 6 0.00 18.81 81.

Fescue Grasslands 2.02 25 36 36 0.00 60.90 38.84

Fescue Grasslands / Conifer 0.46 34 42 29 0.00 36.68 62.1

Grand Fir - White Fir 0.82 55 55 37 0.00 12.51 83.79

Lodgepole Pine  3.65 85 53 18 0.00 9.62 88.76 1

Mountain Mahogany 0.28 32 37 21 0.00 46.31 53.

Mountain Big Sagebrush 0.76 33 41 40 0.00 33.68 65

 Complex/Conifer

Mountain Big Sagebrush Complex 5.20 34 37 14 0.00 48.06

Ponderosa Pine  1.14 13 28 10 0.00 89.55 10

Riparian Hardwood  1.43 122 46 64 0.00 26.99

Riparian Shrub 0.92 62 48 107 0.02 21.08 77.94 0

Rocky Mountain Juniper 0.12 31 36 32 0.00 52.28 47

Spruce - Fir / Montane / Douglas-fir	 4.01	 97	 50	 23	 0.00	 17.74	 80.29	 1.96	 0.01

Spruce - Fir / Subalpine 6.69 209 81 48 0.00 2.50 73.28 23.8

Spruce – Fir / Timberline	 8.88	 261	 81	 86	 0.01	 2.02	 77.41	 20.43	 0.13

Spruce - Fir / Montane/ Western Larch 8.21 73 66 28 0.00 7.50 82.51 9

Threetip Sagebrush / Conifer 0.02 31 38 9 0.00 42.44 5

Threetip Sagebrush 0.54 32 36 6 0.00 53.15 46.8

Timberline Pine / Limber Pine 0.13 211 40 98 0.02 48.08 51

Timberline Pine / Whitebark Pine 0.44 368 70 250 0.08 6.85 88.65 4.

Western Redcedar 0.55 107 65 25 0.00 6.41 86.67 6.6

Wetland Herbaceous  1.32 180 52 101 0.02 20.84 76

Wyoming – Basin Big Sagebrush	 0.50	 54	 39	 32	 0.00	 41.49	 58.31	 0.17	 0.03

 Complex / Conifer

Wyoming – Basin Big Sagebrush	 16.72	 54	 42	 28	 0.00	 19.23	 80.70	 0.06	 0.01

 Complex
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Chapter 10—Using Simulation Modeling to Assess Historical Reference Conditions for Vegetation and Fire Regimes for the LANDFIRE Prototype Projec

a more detailed explanation). The three severity maps 

for each zone are shown in figure 8. Both zones were 

dominated by stand-replacing fires. The mean percentage 

of stand-replacing fires was 69.8 percent in Zone 16 and 

72.5 percent in Zone 19. The mean percentage of nonlethal surface fires was 17.1 percent and 14.5 percent, 

and that of mixed-severity fires was 6.5 percent and 

12.2 percent for zones 16 and 19, respectively. We also 

examined severity in relation to PVT (tables 3 and 4). 

In Zone 16, most of the forest PVTs had roughly equal 

amounts of stand-replacing and non-lethal surface fires, 

with very few (15 percent or less) mixed-severity fires. A 

few forest PVTs had almost exclusively stand-replacing 

fires. Mixed-severity fires were more common in Zone 

19, where several PVTs had a mean greater than 20 

percent. Timberline pine PVTs had almost exclusively 

stand-replacing fires, with mean probabilities from 78 

percent to 97 percent. In both zones, almost all of the 

rangeland types had predominantly stand-replacing fires 

(80 to 100 percent). In Zone 19, only the Riparian Shrub 

PVT had less than 99 percent stand-replacing fires. Zone 

16 shrub and herbaceous PVTs were slightly more diverse 

in terms of fire severity. In Zone 16, a few PVTs were 

equally divided between having stand-replacing and 

non-lethal surface fires and one PVT almost equally 

divided between stand-replacing and mixed-severity 

fires. There was fairly close correspondence between 

the mean percentages for simulated fires and the mean 

percentages calculated from the pathway probabilities 

for both zones.

Historical Vegetation Reference 

Conditions

The simulated historical reference conditions for vegetation were summarized as the area occupied by each 

succession class in each PVT for every reporting unit 

across each reporting year. There were 200 reporting 

years in a simulation with up to 33 succession classes 

(with an average of 13) per PVT and as many as 26 PVTs 

(with an average of 11) per reporting unit for Zone 16. For 

demonstration purposes, figures 9 and 10 show a sample 

of this data set for the top six succession classes for one 

forest (Lodgepole Pine) and one rangeland (Mountain 

Big Sagebrush) PVT in a single simulation landscape 

(20,000 ha). These figures illustrate the large range of 

vegetation conditions experienced over the simulation 

period. In the Mountain Big Sagebrush PVT, for example, 

Low Cover, Low Height Cool Season Perennial Grass 

occupied anywhere from zero to almost 90 percent of 

the PVT for this landscape throughout the 4,000 years of 

simulation. Other succession classes had less amplitude. 

Low Cover, Low Height Dry Deciduous Shrub occupied 

only between zero and 10 percent of the landscape. In 

general, the Mountain Big Sagebrush PVT appeared to 

be dominated alternately by Mountain Big Sagebrush 

and Cool Season Perennial Grass succession classes, 

with other shrub types at lower levels throughout the 

simulation. The Lodgepole Pine PVT was generally 

dominated by Aspen-Birch succession classes, with 

Lodgepole Pine succession classes dominating for short 

periods and generally at moderate levels.

Discussion _____________________

Limitations of the Simulation Approach for 

Describing Historical Conditions

Estimating fire parameters—One of the main difficulties in realistically simulating historical fire regimes 

lies in the estimation of model parameters. Fire history 

studies remain the primary source for estimating fire 

probabilities in modeling efforts, particularly fire return 

interval. However, the data collected in fire history 

studies pose problems of scale and analysis. First, many 

fire history studies are conducted in small areas within 

highly complex landscapes. Topographical features 

and their orientation, coupled with predominant wind 

patterns, can influence fire history within a small study 

area. Second, in a fire history chronology, there are 

often years in which only a single scar exists and years 

where nearly every tree in the area is scarred. To be 

considered in the calculation of mean fire return interval 

for the study area, a fire year is often determined by a 

threshold number of trees that show evidence of fires 

(for example, a “fire year” is one where 10 percent or 

more of the trees in the area are scarred). The number 

of fire years incorporated into the calculation of mean 

fire return interval is highly sensitive to this threshold. 

Furthermore, if a tree shows no scar, it is difficult to 

determine whether this is because the tree was not in 

the area burned by the fire or because the fuel located 

directly around the tree was insufficient to generate the 

intensity required to create a scar. Thus, the computed 

fire return interval is dependent upon not only the 

number of trees used to identify a fire year, but also 

upon the number of fire-scarred trees sampled within 

the study area. To complicate matters further, fire scars 

are point measures of fire history and do not integrate 

the complex spatial interactions of fire spread over a 

study area or landscape. Finally, fire history studies are 

spatially limited to several key ecosystems (mostly forest) of the United States and document fire events over 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 29

Chapter 10—Using Simulation Modeling to Assess Historical Reference Conditions for Vegetation and Fire Regimes for the LANDFIRE Prototype Project Figure 8—Percentage of simulated fires that were a) stand-replacing, b) mixed-severity, and c) non-lethal surface fires in Zone 16 and d) stand-replacing, e) mixed-severity, and f) non-lethal surface fires in Zone 19. Non-vegetated pixels are those areas with no natural vegetation, such as water or rock, and therefore cannot burn. Unburned pixels are vegetated pixels that never burned during the simulation period
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Chapter 10—Using Simulation Modeling to Assess Historical Reference Conditions for Vegetation and Fire Regimes for the LANDFIRE Prototype Projec

Table 3—Mean percentage of stand-replacing fire (SRF), mixed-severity fire (MSF), and non-lethal surface fire (NLSF) for each

potential vegetation type (PVT) for Zone 16 calculated from the simulated fire severity maps (SIM MEAN) and the vegetation

pathways (PATH MEAN). For each severity type, the simulated mean was calculated by taking the mean of the percentage value 

reported on the severity map for each pixel across each PVT. The pathway mean is the mean of the probabilities for the severity 

type for each succession class in each PVT weighted by the percent area of the PVT occupied by each succession class over all 

200 reporting intervals.

SRF MSF NLSF

 PATH SIM PATH SIM PATH SIM

PVT NAME MEAN MEAN MEAN MEAN MEAN MEAN

Alpine 88.0 99.4 0.0 0.0 0.0 0

Aspen 61.1 57.4 0.0 0.0

Blackbrush 00.0 99.8 0.0 0.0 0.0 

Cool Herbaceous  100.0 99.8 0.0 0.0 0.0 0

Douglas-fir / Douglas-fir	 42.2	 39.3	 10.8	 11.3	 47.0	 47.9

Douglas-fir / Lodgepole Pine	 47.0	 43.8	 15.9	 16.0	 37.2	 38.7

Douglas-fir / Timberline Pine	 60.2	 59.6	 4.0	 4.0	 35.8	 34.9

Dwarf Sagebrush  100.0 100.0 0.0 0.0 0.0

Grand Fir – White fir	 40.6	 36.8	 13.1	 13.5	 46.3	 48.2

Grand Fir – White Fir / Maple	 93.5	 91.0	 6.5	 7.9	 0.0	 0.0

Lodgepole Pine 60.7 56.8 10.7 12.6 28.6 

Mountain Big Sagebrush  100.0 100.0 0.0 0.0 0.0

Pinyon – Juniper / Gambel Oak	 98.2	 97.4	 1.8	 1.7	 0.0	 0.0

Pinyon – Juniper / Mountain Big Sagebrush / North	 86.5	 83.7	 13.5	 15.4	 0.0	 0.0

Pinyon – Juniper / Mountain Big Sagebrush / South	 97.2	 95.9	 2.8	 3.2	 0.0	 0.0

Pinyon – Juniper / Mountain Mahogany	 99.6	 99.2	 0.4	 0.4	 0.0	 0.0

Pinyon – Juniper / Wyoming – Basin Big Sagebrush / North	 98.5	 97.5	 1.5	 1.8	 0.0	 0.0

Pinyon – Juniper / Wyoming – Basin Big Sagebrush / South	 96.1	 95.7	 3.9	 3.4	 0.0	 0.0

Ponderosa Pine  37.4 37.4 5.0 4.6 

Riparian Hardwood 63.7 59.4 2.1 1

Riparian Shrub 68.5 56.5 31.5 41.7 0

Salt Desert Shrub  100.0 99.8 0.0 0.0 0.0 0

Spruce – Fir / Blue Spruce	 41.4	 37.5	 14.2	 15.3	 44.4	 45.8

Spruce – Fir / Blue Spruce / Lodgepole Pine	 42.0	 38.1	 12.4	 13.7	 45.5	 46.8

Spruce – Fir / Spruce – Fir	 54.4	 48.5	 11.0	 13.1	 34.7	 36.9

Spruce – Fir / Spruce – Fir / Lodgepole Pine	 52.4	 46.8	 12.6	 14.6	 35.0	 37.1

Timberline Pine 97.8 97.8 0.3 0.1 

Warm Herbaceous  100.0 99.9 0.0 0.0 0.0 0

Wetland Herbaceous  100.0 99.7 0.0 0.0 0.0 0

Wyoming – Basin Big Sagebrush	 100.0 99.8 0.0 0.0 0.0 0.0

a very short time period of approximately three to five 

centuries. The lack of fire history data for large areas 

of the United States and the limited temporal depth of 

evidence of past fires are perhaps the largest obstacles 

to simulating historical conditions across the nation.

The fire size parameters required by LANDSUMv4 

are even more difficult to estimate because sound historical data do not exist at broad scales. While fire scars or 

pollen records can be used to reconstruct historical fire 

frequencies, they cannot be used to reconstruct historical 

fire perimeters or fire sizes. Atlases of fires over time 

have been compiled for some areas, but the temporal 

scale is relatively short (Rollins and others 2001). Fire 

size data from recent decades are available for most of 

the nation, but these data reflect fire size distributions 

during an era where fire suppression was common, and 

thus these fire sizes may not be representative of historical 

conditions. Moreover, NIFMID data – the only source 

for nationwide fire data – further compound this problem by including double reports of fires and excluding 

reports of small fires. Further complicating the attempt 

to compare historical fire size estimates with fire sizes 

simulated by LANDSUMv4 is the fact that the model 

does not simulate small fires. Due to the spatial scale of 

input data and model efficiency, no fires smaller than 1 

ha are simulated. This exclusion removes a portion of the 
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Chapter 10—Using Simulation Modeling to Assess Historical Reference Conditions for Vegetation and Fire Regimes for the LANDFIRE Prototype Projec

Table 4—Mean percentage of stand-replacing fire (SRF), mixed-severity fire (MSF), and non-lethal surface fire (NLSF) for each

potential vegetation type (PVT) for Zone 19 calculated from the simulated fire severity maps (SIM MEAN) and the vegetation

pathways (PATH MEAN). For each severity type, the simulated mean was calculated by taking the mean of the percentage value 

reported on the severity map for each pixel across each PVT. The pathway mean is the mean of the probabilities for the severity 

type for each succession class in each PVT weighted by the percent area of the PVT occupied by each succession class over all 

200 reporting intervals. 

SRF MSF NLSF

 PATH SIM PATH SIM PATH SIM

PVT NAME MEAN MEAN MEAN MEAN MEAN MEAN

Alpine 86.9 100.0 0.0 0.0 0.0 0

Bluebunch Wheatgrass  100.0 100.0 0.0 0.0 0.0

Bluebunch Wheatgrass / Conifer  100.0 99.9 0.0 0.0 0.0 0

Douglas-fir / Douglas-fir	 32.0	 46.4	 24.2	 24.5	 28.3	 27.6

Douglas-fir / Lodgepole Pine	 33.0	 45.7	 20.7	 21.5	 32.4	 31.3

Douglas-fir / Ponderosa Pine/ Douglas Fir	 21.4	 29.2	 7.1	 7.0	 64.6	 62.4

Douglas-fir / Timberline Pine	 71.3	 84.1	 3.1	 2.9	 12.2	 11.5

Douglas-fir / Ponderosa Pine/ Western Larch	 26.1	 34.8	 7.2	 7.2	 58.5	 56.6

Dry Shrub 00.0 100.0 0.0 0.0 0.

Dry Shrub / Conifer 99.2 98.6 0.8 0.7 0.0 0.0

Dwarf Sagebrush Complex / Conifer  100.0 99.9 0.0 0.0 0.0 0

Dwarf Sagebrush Complex  100.0 100.0 0.0 0.0 0.0

Fescue Grasslands  100.0 100.0 0.0 0.0 0.0

Fescue Grasslands / Conifer  100.0 99.9 0.0 0.0 0.0 0

Grand Fir - White Fir 26.6 34.2 23.1 25.

Lodgepole Pine  37.2 62.2 24.0 25.4 1

Mountain Mahogany 99.7 99.3 0.3 0.1 0

Mountain Big Sagebrush Complex/Conifer 84.8 99.0 0.5 0.3 0.0 

Mountain Big Sagebrush Complex  100.0 100.0 0.0 0.0 0.0

Ponderosa Pine 28.8 33.4 6.7 6.6 59.7

Riparian Hardwood  47.1 50.0 0.0 0.0 4

Riparian Shrub 66.5 60.7 33.5 38.3 

Rocky Mountain Juniper 99.4 98.9 0.6 0.4 0.0

Spruce – Fir / Montane/ Douglas-fir	 37.3	 53.8	 24.1	 24.6	 20.7	 20.1

Spruce – Fir / Subalpine	 47.1	 67.7	 22.1	 24.7	 7.3	 6.1

Spruce – Fir / Timberline Pine	 59.0	 78.3	 13.0	 14.1	 7.0	 6.1

Spruce – Fir / Montane/ Western Larch	 31.1	 38.9	 27.4	 29.0	 31.7	 30.7

Threetip Sagebrush / Conifer 99.6 99.3 0.4 0.2 0.0

Threetip Sagebrush  100.0 100.0 0.0 0.0 0.0

Timberline Pine / Limber Pine 82.3 97.1 0.7 0.5

Timberline Pine / Whitebark Pine 71.8 94.3 2.6 2.5 2

Western Redcedar  34.3 44.2 25.3 28.5 

Wetland Herbaceous  100.0 100.0 0.0 0.0 0.0

Wyoming – Basin Big Sagebrush Complex/Conifer	 100.0	 99.8	 0.0	 0.0	 0.0	 0.0

Wyoming – Basin Big Sagebrush Complex 100.0 100.0 0.0 0.0 0.0 

left end (small fires) of the fire size distribution curve, 

which is where the largest numbers of fires occur. This 

effectively increases the simulated mean fire size.

Even though the fire probabilities are estimates only 

and have inherent problems, we can assume that the 

simulated MFRIs approach the fire frequencies in the 

vegetation pathways that were used to parameterize 

the model (if the model is functioning properly). There 

are several possible reasons for the differences between the expected MFRIs and the simulated MFRIs, 

including 1) the role of the pathway fire probabilities 

in ignition and spread, 2) the spread of fire from PVTs 

with different fire probabilities, and 3) incorrect model 

parameterization.

First, the fire probabilities in the pathways represent 

the likelihood that a point on the landscape will burn 

given a particular PVT/succession class combination. 

These probabilities consider both ignition and spread. 

In LANDSUMv4, ignition and spread are simulated 

separately, and the fire probabilities influence only fire 
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Chapter 10—Using Simulation Modeling to Assess Historical Reference Conditions for Vegetation and Fire Regimes for the LANDFIRE Prototype Projec

Figure 9—Percent area occupied for each of the top six succession classes in the Lodepole Pine PVT by reporting year for Zone 

16. Succession classes shown are a) Aspen - Birch Low Cover, Low Height Forest; b) Aspen - Birch High Cover, Low Height Forest;

c) Lodgepole Pine Low Cover, High Height Forest; d) Aspen - Birch Low Cover, High Height Forest; e) Aspen - Birch High Cover, 

High Height Forest; and f) Lodgepole Pine High Cover, High Height Forest.
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Chapter 10—Using Simulation Modeling to Assess Historical Reference Conditions for Vegetation and Fire Regimes for the LANDFIRE Prototype Projec

Figure 10—Percent area occupied for each of the top six succession classes in the Mountain Big Sagebrush PVT by reporting 

year for Zone 16. Succession classes shown are a) Mountain Big Sagebrush Low Cover, Low Height Shrubland; b) Cool Season

Grasses Low Cover, Low Height Herbaceous; c) Cool Season Grasses High Cover, Low Height Herbaceous; d) Mountain Big 

Sagebrush High Cover, Low Height Shrubland; e) Mountain Deciduous Shrub Low Cover, High Height Shrubland; and f) Dry Deciduous Shrub High Cover, Low Height Shrubland.
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Chapter 10—Using Simulation Modeling to Assess Historical Reference Conditions for Vegetation and Fire Regimes for the LANDFIRE Prototype Projec

ignition. Once ignited, fire spread is controlled by wind 

and slope and can spread equally to all PVTs. Neither 

vegetation nor associated fire probabilities (estimated 

from the literature and fire history studies) influence 

where a fire burns. A PVT with a low fire probability 

could therefore have many pixels that burn as a result 

of fire spread, which could cause the simulated MFRI 

to be quite different than the expected MFRI.

The effect of spatial adjacency on the simulated MFRIs 

is also related to how fire spread is modeled in LANDSUMv4. Because fire can spread to any adjacent pixel 

that is burnable, areas adjacent to those with high fire 

probabilities will tend to burn more often than expected 

based on the fire probabilities in the pathways. In addition, topography influences fire spread in that areas 

upslope will burn more often than areas downslope. If 

MFRI were determined only by fire probabilities for 

each PVT, we would expect the map in figure 11b to 

look very similar to the map in 11a. In figure 11a, there 

are areas with high fire probabilities at the southern end 

of many of the drainages. In figure 11b, this high fire 

probability continues up most of the drainage as a result 

of fire spread, causing shorter MFRIs than expected in 

vegetation with lower fire probabilities. The fact that 

so much of the area in figure 11b has shorter MFRIs 

than expected suggests that fire spread is overwhelming the underlying fire probabilities. This supposition 

is supported by the observation that the mean fire size 

simulated by LANDUSMv4 in our test areas using the 

model parameters for both Zone 16 and Zone 19 was 

considerably larger than the NIFMID estimate (fig. 4).

In addition, incorrect estimations of the parameters 

that control fire ignition and fire spread could result in 

shorter than expected MFRIs. The fact that the simulated 

MFRIs were shorter than the pathway MFRIs for most 

PVTs in Zone 16 and many PVTs in Zone 19, combined 

with the fact that the average fire size was so much larger 

than any estimates we have for this area, indicates that 

the fire parameters we used resulted in too much fire on 

the simulation landscape. Although the fire parameters 

used in Zone 19 resulted in general correspondence between the simulated MFRIs and the expected MFRIs in 

our test landscapes, the average simulated fire sizes for 

these landscapes using these parameters were still much 

larger than the NIFMID estimate (fig. 4). The spread 

scale parameter will likely have a stronger influence on 

the overall fire frequencies because far more pixels will 

burn as a result of fire spread than fire ignitions. When 

the simulated fire sizes are too large, the spread of fire 

and the effect of PVT adjacency may have a disproportionate influence on simulated MFRIs. Keane and others 

(2003) found that an error of 20 percent in estimating the 

spread scale parameter could result in an error of more 

than 50 percent in the fire return interval. The average 

fire size for all the test simulations in both zones varied 

with the spread scale parameter (fig. 4). The relationship 

of the spread scale parameter to the simulated average 

fire size is directly related to the shape parameter for the 

maximum fire size equation of the fire spread algorithm 

(3.0 for LANDFIRE simulations). We did not examine 

the effect of the shape parameter on the simulated average fire size or on the correspondence of simulated and 

pathway MFRIs. Although our test simulations were 

informative, more research is needed as to the effect 

of these crucial parameters and how they can best be 

estimated for a particular landscape.

Scale and complexity in fire simulation—Even if 

sound historical data for estimating fire parameters were 

abundant, differences between simulated fire regimes 

and actual historical fire regimes are to be expected. 

Fire, like many natural processes, is complex. Any attempt to model it is a simplified abstraction of the actual 

process. Fire operates at many different spatial and 

temporal scales. Its occurrence is influenced by many 

factors, such as vegetation, weather, wind, topography, 

and climate, which also operate at different spatial and 

temporal scales. As a result of this complexity, it is 

difficult to realistically simulate fire without building 

overly complex models that would be difficult to parameterize and inefficient to execute for large landscapes 

and over long simulation periods. Fire simulation in 

LANDSUMv4 incorporates mainly large-scale processes. The weather parameters in the model function 

at a yearly time-step and are generalized for the entire 

zone. Wind speed and direction are also parameterized 

for the entire zone and then varied by time–step; they 

are not varied locally for each fire event (Keane and 

others 2006). The model does not incorporate daily or 

localized weather information. Fire ignition is tied only 

to vegetation in terms of succession class changes, which 

operate at a coarser grain than fuel build up, and ignition 

probabilities are constant within a succession class. The 

most important limitation of the LANDSUMv4 model is 

absence of the close linkage between fuel, weather, and 

topography when determining fire effects and pattern 

(Keane and others 2006). Fire is spread solely on the 

basis of wind and topography. Because LANDSUMv4 

does not integrate the spatial distribution of fuel loading, fuel moisture, and daily weather, the pattern and 

severity of fire may not be entirely accurate. Integrating 

fine-scale processes of weather or fuel into the model 

would make the model computationally intensive and 
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Chapter 10—Using Simulation Modeling to Assess Historical Reference Conditions for Vegetation and Fire Regimes for the LANDFIRE Prototype Projec

Figure 11—Comparison of mean fire return intervals a) calculated from the probabilities in the vegetation pathways and mapped

to the potential vegetation type and b) simulated by LANDSUMv4 during the 4,500-year simulation period for Zone 16. Fire w

much more frequent in fire regime maps derived from the spatial simulation of fire in LANDSUMv4 than in maps based solely on

the aspatial fire regime information contained in the vegetation pathways.
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Chapter 10—Using Simulation Modeling to Assess Historical Reference Conditions for Vegetation and Fire Regimes for the LANDFIRE Prototype Projec

would dramatically increase the complexity and length of 

the model executions. Furthermore, estimating even the 

most generalized fire parameters is difficult, as discussed 

above. Parameterizing the model for fine-scale processes 

would be even more difficult, and the problems inherent in estimating these values for historical conditions 

(on which we have limited data) would result in limited 

returns for considerable time and effort. It is important 

to understand that the fire regime results from the model 

are general and not intended to be applied at the pixel 

level or yearly time-step at which the model operates.

Fire spread and the simulation landscape—As noted 

above, we had to simulate each of the mapping zones 

as a series of smaller simulation landscapes for more 

efficient processing of the model. One of the problems 

in defining simulation landscapes lies in the fact that the 

simulation landscape edges create artificial boundaries 

that fires cannot traverse. In actual landscapes, water, 

rock, and topography create real boundaries and influence 

fire spread, but our simulation landscapes did not follow 

natural boundaries and sometimes even divided areas of 

homogeneous vegetation or topography through which 

fire would naturally spread. This problem is obvious if 

the simulation landscape is not large enough relative to 

the size of the larger fires as more fires will tend to run 

into the arbitrary boundaries of the simulation landscape, and fire will thus not be realistically simulated. 

Knight (1987) recommends the simulation area be 5 

to 10 times the size of the largest fire, whereas Baker 

(1992) suggests 50 to 100 times the average fire size. 

Based on estimates of average fire size from NIFIMID, 

the simulation landscape for both zones should be about 

3000 ha. However, using maximum fire size estimates 

from NIFMID, the simulation landscape should be about 

35,000 ha for Zone 16 and 100,000 ha for Zone 19.

Another problem arose from the fact that areas near 

the edge of the simulation landscape have a limited 

number of surrounding pixels from which a fire can 

spread. This problem was exacerbated by wind direction. 

A single wind direction (60 degrees, randomly varied 

±45 degrees) was used for the entire simulation and, 

because fire is spread by wind and slope, all fires tended 

to spread from the west-southwest to the east-northeast. 

As a result, pixels near the south and west edges had the 

lowest probability of burning, while those near the north 

and east edges had the highest probability of burning.

As noted above, a 3-km buffer area was placed around 

each 20,000 ha context area to create the simulation 

landscape and to decrease the edge effect in the context 

area or the area from which the reference conditions are 

defined (fig. 3). If the buffer area is large enough, the 

position of pixels within the landscape relative to the 

edge will not influence the chance of burning, and fire 

frequency will be determined by the input fire probabilities and topography. Areas that were simulated 

twice, once as part of the buffer and once as part of the 

context area, proved useful for examining the function 

of the buffers. Fire probabilities and topography were 

constant between the two runs, but the position of the 

pixels relative to the landscape edges changed (fig. 12). 

Fire should be realistically simulated in the eastern 

buffer region because most fires burned from the direction of the southwest. If the buffer is large enough, 

fire should also be realistically simulated in the context 

area as the buffer should provide an adequate source 

for fires to spread from the west. If the buffer width is 

adequate, therefore, the fire frequency in the eastern 

buffer region should not be substantially different from 

the fire frequency for this same region when it is part of 

the context area. However, as shown in figure 12, there 

still tended to be differences –sometimes by 20 or more 

fires – between these two areas. This indicates that the 

3-km buffer may not be large enough to eliminate the 

edge effect. We are conducting a more detailed analysis 

of model behavior with respect to the sizes of the context 

and buffer areas, which will be used to inform future 

decisions regarding the simulation landscape.

Selecting simulation time parameters—Another 

challenge lies in the selection of simulation time parameters that result in efficient model execution over 

mapping zones while allowing sufficient initialization 

periods to reduce significant trends in the vegetation. 

We allowed for a 500-year initialization period before 

actually collecting data from the model for each mapping 

zone. In evaluating the vegetation chronosequences for 

both zones, we found that some succession classes in 

some PVTs continued to have noticeable trends beyond 

this 500-year period (fig. 13). Some succession classes 

had distinct upward trends over thousands of years, 

others trended downward, and yet other succession 

classes varied around a mean, as expected. Use of an 

initialization time that is long enough to allow all succession classes to reach equilibrium is not logistically 

possible because it would extend total simulation times 

beyond a reasonable length. Assigning the dominant 

succession class from the current conditions to each 

PVT for the initial landscape may have increased the 

time required to reach equilibrium for some PVTs. In 

addition, the complexity of the vegetation pathway may 

also impact initialization time as pathways with more 

succession classes may take longer to reach equilibrium. 

More research is being conducted to study the effect of 
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Chapter 10—Using Simulation Modeling to Assess Historical Reference Conditions for Vegetation and Fire Regimes for the LANDFIRE Prototype Projec

Figure 13—Succession class time series data 

smoothed using a 20-year lag for succession classes 

in the Spruce – Fir / Spruce – Fir PVT in Zone 16.

Succession classes shown are A) Aspen - Birch High 

Cover, Low Height Forest; B) Aspen - Birch High Cover, 

High Height Forest; and C) Douglas-fir High Cover,

High Height Forest.

Figure 12—Mean fire return intervals for two adjacent context areas and their north and east buffer areas. The area inside the

dashed rectangle is simulated in both landscapes and then smoothed using the mosaic command in ArcInfo. Differences in fire

return intervals in the area simulated twice are due to the difference in the amount of the simulation landscape to the south and 

west from which fires can immigrate.
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autocorrelation and succession class trends on LANDSUMv4 output and the subsequent departure and FRCC 

calculations. These results will be used to mitigate the 

effects of these issues in LANDFIRE National.

Benefits of the Simulation Approach for 

Describing Historical Conditions

Although simulation models in general and the LANDSUMv4 model in particular have limitations, simulation 

modeling is still a useful, and perhaps the most viable, 

approach for generating historical reference conditions 

of vegetation and fire regimes across the entire nation. 

While data on actual historical conditions are limited, 

simulation modeling can estimate the reference conditions efficiently and in a consistent manner across the 

United States. Simulated historical reference conditions 

are not intended to replace actual historical data where 

they exist, but they can provide information where it is 

currently lacking.

In addition to effectively increasing the spatial extent 

of empirically derived historical reference conditions, 

simulation modeling can increase the temporal depth. 

Although the fire and climate data used to parameterize 

LANDSUMv4 are taken from historical databases and 

represent a narrow time frame, data can be simulated for 

much longer time spans (Keane and others 2003; Keane 

and others 2002b). It may seem problematic to simulate 

fire and landscape dynamics over millennial simulation 

periods while holding climate and fire regimes constant. 

This would be true if the objective of LANDSUMv4 

modeling was to replicate historical fire events. However, the primary purpose of LANDSUMv4 modeling 

efforts is to document the historical variability of vegetation conditions and fire regimes across landscapes. 

This documentation of the entire range and variation of 

landscape conditions and processes serves the important 

purpose of allowing current conditions to be compared 

against a realistic and comprehensive reference database. 

Wherever possible, the modeling effort uses the results 

of fire history studies to parameterize the models, even 

though these represent a small duration of time. We 

selected the last three to five centuries as our reference 

time span because it is an era for which fire history data 

are available and is likely the most climatically similar 

to the present and near future. However, the fire events 

that actually occurred during this time frame represent 

only one unique sequence of fire occurrences, and the 

timing of these events created the unique landscapes 

observed today. This sampled fire history represents 

only one record of events. If these events had occurred 

according to a different timetable or in different locations, 

an entirely new set of landscape conditions may have 

resulted. It follows then that documentation of landscape 

conditions derived solely from historical records would 

tend to underestimate the variability of conditions that 

a particular landscape could have experienced and will 

experience in the future. We therefore attempted to 

quantify the entire range of conditions by simulating 

the static historical fire regime for thousands of years. 

We assume that 3,000 to 5,000 years is a long enough 

span from which to approximate all the conditions this 

historical landscape would have experienced. We determined that this is the best way to estimate historical 

reference conditions because it allows future landscapes 

to have variable fire ignitions and fire patterns. Despite 

its limitations, LANDSUMv4 creates the ability to generate a consistent and comprehensive set of data from 

which to estimate historical reference conditions across 

the entire nation.

Recommendations for National 

Implementation _________________

Simulation Landscape Size and Shape

Much of the LANDSUMv4 modeling effort for the 

LANDFIRE Prototype involved balancing the need for 

realistic simulations of fire and vegetation dynamics 

with the often conflicting goal of computational and 

logistical efficiency, and balancing these two goals will 

present an even greater challenge as methods are applied 

for national implementation. We have found that larger 

simulation landscapes are logistically simpler and produce better simulation results overall, but there is likely 

some specific landscape size at which the model becomes 

inefficient and overall processing time to complete a 

zone increases dramatically. The 20,000 ha landscapes 

used in the prototype simulations were likely too small, 

given the expected size of large fires for these regions. 

We recommend that simulation landscapes larger than 

20,000 ha be used for national implementation. Larger 

landscapes may result in more realistic simulations by 

reducing the impact of the edge effect and increasing 

the simulation landscape size relative to the size of the 

larger fires. At the same time, such landscapes may 

decrease processing time by reducing areas of overlap 

and simplifying the logistics involved in a large number 

of model executions. Tests on 100,000-ha landscapes for 

Zone 19 demonstrated that these landscapes are, given 

our current computing resources, too large to run the 

model efficiently with landscape and pathway complexities similar to those of the prototype effort. Simplified 
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Chapter 10—Using Simulation Modeling to Assess Historical Reference Conditions for Vegetation and Fire Regimes for the LANDFIRE Prototype Projec

pathways and an improved computing platform could 

alleviate this problem and allow the simulation of sufficiently large landscapes.

Square simulation landscapes worked well, reducing 

overlap and simplifying processing. While square landscapes create unnatural landscape edges and fire breaks, 

the use of buffers mitigates this effect. Although the 3-km 

buffer we used did help to reduce the edge effect, this 

buffer may be too small; however, a larger buffer will 

increase overall processing time as the area of overlap 

increases (leading to more areas of the landscape that 

will be simulated twice). Overall, we recommend continuing with square simulation landscapes and using a 

5-km buffer to better allow for simulation of fire spread 

without substantially impacting the total processing 

time.

Simulation time is another important aspect of the 

simulation design that impacts the balance between 

efficiency and realism. The 10,000-year simulations 

used for Zone 19 appear sufficiently long although the 

500-year initialization period may not be adequate for 

some systems. We recommend simulations of 10,000 

years and recommend increasing the initialization period 

beyond 500 years to the extent possible given computing limitations. In addition, we recommend assigning 

the historically dominant succession class (rather than 

the current dominant class) to each PVT for the initial 

landscape in an attempt to reduce the time required to 

reach equilibrium.

Determining Model Parameters

In addition to the LANDFIRE Prototype research, 

past sensitivity analyses indicate that there are several 

parameters of particular importance to the simulation 

results that must be accurately quantified for the LANDFIRE National. The ignition average fire size parameter 

– especially the spread scale parameter – strongly influences fire frequencies and, in-turn, vegetation dynamics. 

As discussed above, the historical data available for 

estimating these parameters are limited. A consistent 

methodology is required for setting these parameters 

to achieve appropriate fire frequencies. We recommend 

more research be conducted on the role of the spread 

shape parameter in these relationships. The NIFMID 

database contains only information on recent fires, and 

therefore the fire size estimates based on these data have 

been affected by fire suppression. Taking this limitation 

into consideration, we nevertheless recommend starting 

with the NIFMID estimate as a target average fire size 

because it represents the only source of recorded data on 

fire size for the entire United States. Vegetation modelers and ecologists who develop the succession pathway 

models for LANDSUMv4 present another source of 

information for estimating historical fire sizes and fire 

regime parameters. These individuals are presumably 

familiar with local ecosystems and offer estimates of 

fire sizes based on their extensive experience in addition 

to a literature review conducted when developing the 

pathways. These estimates could be used in combination with the NIFMID information to establish a target 

average fire size. Until further research is completed, 

we recommend executing the model over several test 

landscapes and varying both parameters until the 

average simulated fire size approaches the estimated 

average fire size for the zone and the fire frequencies 

simulated for each PVT approach the probabilities set 

in the pathways.

Error-checking

Another key to efficient and accurate simulations lies in 

the development of a consistent methodology for testing 

the pathways and parameters established for a mapping 

zone to check for errors and problems before the entire 

zone is simulated. We found two main sources for problems related to LANDSUMv4 executions: inconsistencies or errors in the input data and problems or “bugs” 

in the code. LANDSUMv4’s extensive error-checking 

routine scans the input data for inconsistencies between 

the various input files, which can cause problems during 

simulation. However, there are problems with the input 

data that the error-checking routine does not recognize 

as inconsistencies but may still lead to unexpected 

results. In addition, although the LANDSUMv4 model 

underwent an extensive de-bugging process, it is always 

possible that some new, unique circumstance will arise 

in a new mapping zone that will cause unexpected results. We recommend performing a thorough quality 

assurance and quality control (QA/QC) process for the 

pathways once they are developed by the vegetation 

modelers. Once the pathways have passed this QA/QC, 

three to five landscapes – distributed throughout the 

mapping zone and containing a variety of topography 

and PVTs – should be simulated. Vegetation and fire 

regime information should be summarized for these 

test simulations and given to the vegetation modelers 

(or other experts) to check for any unexpected results. If 

there are any suspicious results, further analysis should 

be performed to determine the source of the unexpected 

results before proceeding with simulations for the full 

zone. Results from the test landscapes do not always 

reveal problems that can develop when the entire zone 

is simulated. Because simulating an entire zone may 
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Chapter 10—Using Simulation Modeling to Assess Historical Reference Conditions for Vegetation and Fire Regimes for the LANDFIRE Prototype Projec

take six weeks or more, we recommend developing a 

systematic way to periodically check the output from 

the individual simulation landscapes within the zone as 

they are completed.

Further Study

Finally, although the LANDSUMv4 model has been 

tested (Keane and others 2002a; Keane and others 2003) 

this testing was conducted with earlier model versions, 

on smaller landscapes, and using parameters different 

from those being used in LANDFIRE. And while the 

prototype effort has contributed much information 

concerning the parameterization of the model for the 

national implementation of LANDFIRE, it has also 

raised many questions. Extensive testing and revision 

of the LANDSUMv4 model has begun, focusing on 

the application of the model for LANDFIRE purposes 

and examining the effects and interactions of several 

simulation parameters, including simulation time, context and buffer sizes, and fire size parameters. Separate 

studies are also planned to evaluate issues of scale in 

summarizing the data and the effect of autocorrelation 

and trends in the vegetation output of LANDSUMv4 

on the computation of departure and FRCC. It is our 

recommendation that the national implementation of 

LANDFIRE incorporate, to the extent possible, the 

information from these studies as it becomes available. 

Furthermore, the model and the key parameters should 

be tested as they are applied to different regions of the 

country.

Conclusion_____________________

The methods outlined in this chapter were successful 

in producing estimates of historical reference conditions for vegetation and fire regimes in a manner that 

can be applied consistently across the nation. While the 

methodology used for simulating reference conditions 

for the LANDFIRE Prototype was generally sound, 

we recommend that more research be conducted to 

facilitate a more thorough understanding of the effect 

of various parameters on model behavior. Furthermore, 

we propose that development of consistent methods 

for setting appropriate values for key parameters, particularly the fire spread parameters, before proceeding 

with national implementation be a top priority. Finally, 

it is extremely important to consider the assumptions 

and limitations of the simulation approach and LANDSUMv4 when applying the results of the model. When 

the strengths and limitations are carefully considered, 

the use of simulation modeling, and LANDSUMv4 in 

particular, appears to be an effective and feasible way 

to generate estimates of historical reference conditions 

for vegetation composition and structure and wildland 

fire regimes for large landscapes on a national scale.

For further project information, please visit the LANDFIRE website at www.landfire.gov.

The Authors ____________________

Sarah Pratt is a GIS Specialist with the USDA Forest 

Service, Rocky Mountain Research Station, Missoula 

Fire Sciences Laboratory (MFSL). Sarah joined the Fire 

Sciences Laboratory in 2003, where she has prepared 

and analyzed spatial data for the simulation models 

LANDSUMv4 and FIREHARM. Pratt received her B.A. 

from Kenyon College (Gambier, Ohio) in 1992 and her 

M.S. in Biological Sciences from Northern Michigan 

University (Marquette, Michigan) in 2003.

Lisa Holsinger is a GIS Specialist with the USDA 

Forest Service, Rocky Mountain Research Station, 

Missoula Fire Sciences Laboratory (MFSL). Holsinger 

joined MFSL in 2002 and has worked on developing 

and analyzing spatial data for simulation models applied 

to large landscapes. She has developed GIS data and 

input for the WXFIRE and LANDSUMv4 simulation 

models and HRVStat program, run simulations, and 

produced associated spatial data. She has also conducted 

sensitivity analyses for both the WXFIRE and LANDSUMv4 models. Prior to the Fire Sciences Laboratory, 

she worked as a Fisheries Biologist and GIS Specialist 

for the National Marine Fisheries Service conducting 

research and management for the conservation of west 

coast salmon populations. Holsinger received her B.S. 

degree in Biological Sciences from the University of 

California, Davis in 1984 and her M.S. degree in Fisheries at the University of Washington (Seattle) in 1988.

Robert E. Keane is a Research Ecologist with the 

USDA Forest Service, Rocky Mountain Research Station, Missoula Fire Sciences Laboratory (MFSL). Since 

1985, Keane has developed various ecological computer 

models for the Fire Effects Project for research and 

management applications. His most recent research 

includes the development of a first order fire effects 

model, construction of mechanistic ecosystem process 

models that integrate fire behavior and fire effects into 

succession simulation, restoration of whitebark pine 

in the Northern Rocky Mountains, spatial simulation 

of successional communities on landscapes using GIS 

and satellite imagery, and the mapping of fuels for fire 

behavior prediction. He received his B.S. degree in Forest Engineering in 1978 from the University of Maine, 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 3

Chapter 10—Using Simulation Modeling to Assess Historical Reference Conditions for Vegetation and Fire Regimes for the LANDFIRE Prototype Projec

Orono, his M.S. degree in Forest Ecology in 1985 from 

the University of Montana (Missoula), and his Ph.D. 

degree in Forest Ecology in 1994 from the University 

of Idaho (Moscow).

Acknowledgments_______________

We thank Maureen Mislivets, Brendan Ward, and Don 

Long of the USDA Forest Service Rocky Mountain Research Station for technical assistance with this project. 

We also thank Dennis Simmerman, also of the USDA 

Forest Service Rocky Mountain Research Station, for 

graphical design assistance. Finally, we thank Doug 

Havlina of the Bureau of Land Management, Russell 

Parsons of the USDA Forest Service Rocky Mountain 

Research Station, Randy Swaty of The Nature Conservancy, and Christine Frame of Systems for Environmental 

Management for technical and editorial review.

References _____________________

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Chapter 10—Using Simulation Modeling to Assess Historical Reference Conditions for Vegetation and Fire Regimes for the LANDFIRE Prototype Projec

Appendix 10-A—Vegetation and Disturbance Dynamics Database ________

The Vegetation and Disturbance Dynamics Database (VADDD) is structured to store all vegetation and disturbance 

dynamics information used as input to LANDSUMv4. VADDD served as the primary reference for the codes and 

labels for all map unit classifications developed in LANDFIRE, including PVT, cover type, and structural stage. In 

addition, this database was designed to efficiently check for errors in the succession and disturbance information 

prior to input into simulation modeling and to provide a standardized set of LANDSUMv4 parameters for subsequent 

applications of the model in different settings.

The database is composed of two general components. The first set of tables is primarily for reference only and 

serves as lookup tables for the PVT, cover type, structural stage, succession class, and disturbance codes used in 

the modeling process. The second set of tables is used for creating the LANDSUMv4 inputs and describes succession and disturbance dynamics for each succession class, including subsequent effects and the probability of those 

effects occurring. The “VEGDEV” table contains important vegetation development information about every succession class by PVT, including successional development parameters and descriptors that quantify the pathways 

of successional development without disturbance. The “DISTURB_PARM” table quantifies the consequences of 

disturbance for a PVT/succession class combination and the “SCENARIO_PARM” table contains data regarding 

the probabilities of a particular disturbance occurring in a PVT/succession class combination.

App. 10-A: Table 1—Field names and descriptions for the potential vegetation

type table in VADDD.

Field name Field description

PROJECT Project code

REGION Region ID number

PVT	Unique	 PVT ID number

PVTLABEL	 Unique header to use as labels on graphs and tables

PVTNAME	 Unique name to use for report writing and data file building

PVTDESC Brief description of this type

App. 10-A: Table 2—Field names and descriptions for the cover type table in 

VADDD.

Field name Field description

PROJECT Project code

REGION Region code

COVTYPE	 Unique cover type ID number

CTLABEL	 Unique header to use as labels on graphs and tables

CTNAME	 Unique name to use for report writing and data file building

CTDESC Brief description of this type
312 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2

Chapter 10—Using Simulation Modeling to Assess Historical Reference Conditions for Vegetation and Fire Regimes for the LANDFIRE Prototype Projec

App. 10-A: Table 4— Field names and descriptions for the succession class 

table in VADDD.

Field name Field description

PROJECT Project code

REGION Region code

SCLASS	 Unique succession class ID number

COVTYPE	 Unique cover type ID number

SSTAGE	 Unique structural stage ID information

SCLABEL	 Unique header to use as labels on graphs and tables

SCNAME	 Unique name to use for report writing

SCDESC Brief description of this type

App. 10-A: Table 5—Field names and descriptions for the disturbance type table 

in VADDD. 

Field name Field description

PROJECT Project code

REGION Region code

DISTURB	 Unique disturbance type ID number

DISTLABEL	 Unique header to use as labels on graphs and tables

DISTNAME	 Unique name to use for report writing and data file building

DISTDESC Brief description of this type

App. 10-A: Table 3—Field names and descriptions for the structural stage table 

in VADDD.

Field name Field description

PROJECT Project code

REGION Region code

SSTAGE	 Unique structural stage ID information

SSLABEL	 Unique header to use as labels on graphs and tables

SSNAME	 Unique name to use for report writing and data file building

SSDESC Brief description of this type
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006

Chapter 10—Using Simulation Modeling to Assess Historical Reference Conditions for Vegetation and Fire Regimes for the LANDFIRE Prototype Projec

App. 10-A: Table 8—Field names and descriptions for the scenario parameter (SCENERIO_PARM) table in VADDD.

Field name Field description

PROJECT	 A unique ID naming the project application of these data

REGION	 A unique ID naming a geographic management region to apply data

SCENARIO The ID code of the scenario

PVT	 Unique PVT ID for this disturbance information

SCLASS	 Unique succession class ID for this disturbance information

DIST	 Unique ID number of disturbance or management action in question

PROB Probability of occurrence for this disturbance 

App. 10-A: Table 6—Field names and descriptions for the vegetation development 

(VEGDEV) table in VADDD.

Field name Field description

PROJECT Project ID number

REGION ID number for geographical region

PVT PVT ID number

SCLASS	 Unique succession class ID number

BYEAR Beginning year of this succession class

EYEAR Ending year of this succession class

NEXT_SCLASS Next succession class this class goes to once AGE > EYEAR

PROB Probability that this transition will occur

App. 10-A: Table 7—Field names and descriptions for the disturbance parameter (DISTURB_

PARM) table in VADDD.

Field name Field description

PROJECT Project ID number

REGION ID number for geographical region

PVT	 Unique PVT ID for this disturbance information

SCLASS	 Unique succession class ID for this disturbance information

DIST	 Unique ID number of disturbance or management action in question

GOTO_SCLASS Ensuing structural stage resulting from this disturbance

PROB Probability of this disturbance transition

NEXT_AGE Successional age to set resultant succession class after this disturbance

AGE_INC Number of years to add/subtract from pixel age 
314 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 

Chapter 10—Using Simulation Modeling to Assess Historical Reference Conditions for Vegetation and Fire Regimes for the LANDFIRE Prototype Projec

Values for LANDSUMv4 model parameters used in the Zone 16 and Zone 19 simulations and the information source used f

estimating the parameters. In the source column, “Preliminary Testing” refers to parameters estimated from simulations done on 

several test landscapes (see Methods section for more information) and “LANDFIRE” refers to model execution parameters set to 

meet the needs of the LANDFIRE Project. 

Zone Zone

Parameter Description 16 19 Source

Simulation time Number of years to simulate  4520 10500 Prelimi

 Testing

Age initialization Controls how age is assigned to initial stands (0 - use 1 1 Keane

age in stand info.,  - random, 2 - midpoint of stage, others 2002

 3 - entered beg. age in PV

Reporting interval Interval (yrs) to print results to output maps and 20 50 Preliminary

tabular files	 	 	 Testing,

 Keane and

 others 2002a

Initialization time Year to start recording output results to maps and 520 550 Preliminary

tabular files	 	 	 Testing,

 Keane and 

 others 2002a

Disturbance exclusion	 Specifies which disturbance to exclude (0 - include	 0	 0	 LANDFIRE

all dist., 1 - exclude all dist., 2 - exclude all but fire)

Random number scheme 0 = different every time,  = repeatable random 0 0 LANDFIR

Random number generator 0 = system,  = Ran1, 2 = Ran2 0 0 Keane a

 others 2006

Fire spread model Model of spatial spread simulation (1 - cell automata, 3 3 Kean

2 - cookie-cut shapes,  - cell spread percolation) others 2002

Fire weather multiplier Number of years that a dry, normal and wet year  3/5/2 1/6/3 Palmer D

occurs in a decade (must sum to 10) Severity Inde

 Reconstructions

Average fire size - ignition	 Average fire size (ha) for ignition equation	 90	 30	 NIFMID,

 Preliminary Testing

Wind simulation Mode of wind simulation: 0 - same wind speed/dir.  3 3 LAN

every year, 1 - vary by year, 2 - vary by fire, 3 -

vary by time-step,  - vary by cell

Wind speed Average wind speed in meters per second 5 5 Expert Opinion

Wind direction	 Average wind direction for a fire event	 60	 60	 Expert Opinion

(azimuths true north)

Fire ignition equation	 Equation for computing probability of ignition (1 - Weibull )	 1	 1	 Keane and others

 2002a

Years since burn	 Years since burn parameter for the ignition equation	 3	 3	 Keane and others

 2002a

Shape parameter - ignition	 Shape parameter for the ignition equation 2	 2	 Keane and others

 2002a

Fire size equation	 Equation for computing fire size (1 - Pareto, 2 - lognormal,	 7	 7	 Keane and others

 3 - exponential 4 - uniform, 5 - normal, 6 - extrem   )20

7 - negative exponential, 8 - logistic, 9 – let burn

Fire size distribution magnitude	 Magnitude parameter for the fire size equation	 30	 10	 NIFMID,

 preliminary testing

Fire size distribution shape Shape parameter for the fire size equation	 3	 3	 Keane and others

 2002a

Appendix 10-B ___________________________________________________
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 

Chapter 11—Using Historical Simulations of Vegetation to Assess Departure of Current Vegetation Conditions across Large Landscap

Chapter

11

In: Rollins, M.G.; Frame, C.K., tech. eds. 2006. The LANDFIRE 

Prototype Project: nationally consistent and locally relevant 

geospatial data for wildland fire management. Gen. Tech. Rep. 

RMRS-GTR-175. Fort Collins: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research Station.

Introduction ____________________

Background

The Landscape Fire and Resource Management Planning Tools Prototype Project, or LANDFIRE Prototype 

Project, was conceived, in part, to identify areas across 

the nation where existing landscape conditions are 

markedly different from historical conditions (Keane 

and Rollins, Ch. 3). This objective arose from the recognition that over 100 years of land use and wildland 

fire suppression have dramatically affected wildfire 

characteristics and associated landscape composition, 

structure, and function (Turner and others 2001). Metrics were needed to describe the extent and distribution 

of highly departed landscapes to protect communities, 

ecosystems, firefighters, and public safety, as outlined 

in the National Fire Plan (USDA and USDI 2002; U.S. 

GAO 1999; http://www.fireplan.gov). Accordingly, the 

Departments of Agriculture and Interior were directed 

by Congress to develop a cohesive strategy for implementing the National Fire Plan (Laverty and Williams 

2000), which resulted in the development of the “Fire 

Regime Condition Class” (FRCC) classification system 

for use as a key implementation measure. The FRCC 

classification is based on the concepts of historical ecology and is intended to represent the departure of current 

landscapes from the range of variability of historical 

conditions. Fire Regime Condition Class is defined as: a 

descriptor of the amount of departure from the historical 

natural regimes, possibly resulting in alterations of key 

ecosystem components such as species composition, 

structural stage, stand age, canopy closure, and fuel 

loadings (Hann and Bunnell 2001).

The U.S. GAO (2002) further recommended the development of consistent and comprehensive spatial data 

to identify landscapes at high risk of wildfires. Previous FRCC mapping efforts created coarse-scale (1-km) 

spatial data layers describing fire hazard and ecological 

status for the conterminous United States (Hardy and 

others 2001; Schmidt and others 2002; http://www.fs.fed.

us/fire/fuelman). However, the coarse spatial resolution 

made these maps useful only for national-scale assessments. In addition, these maps were largely a product 

of expert systems, which limited the repeatability of 

the process for monitoring purposes. Finer-scale maps, 

compiled using consistent, quantitative methods, were 

needed for applications such as national forest plan revision and implementation and assessments related to 

wildland fire management plans (Rollins and others, Ch. 

2). Our challenge in the LANDFIRE Prototype Project 

was to develop methods, applicable in a systematic and 

consistent manner across the U.S., which identify and 

Using Historical Simulations of Vegetation 

to Assess Departure of Current Vegetation 

Conditions across Large Landscapes

Lisa Holsinger, Robert E. Keane, Brian Steele, 

Matthew C. Reeves, and Sarah Pratt 
316 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2

Chapter 11—Using Historical Simulations of Vegetation to Assess Departure of Current Vegetation Conditions across Large Landscap

map – at a mid-scale spatial resolution – landscapes that 

have diverged substantially from historical conditions.

Overview ______________________

Broad-scale changes have occurred in many landscapes of the U.S., particularly over the last century, 

due to land management practices and other forms of 

human intervention. Fire exclusion and grazing have 

increased tree density and fuel accumulations in many 

forest communities, especially those adapted to frequent 

surface fires (Covington and others 1994; Leenhouts 

1998). These conditions favor diseases, insects, and 

high-intensity crown fires, which can kill old-growth 

trees and alter community structure (Moore and others 

1999). Invasions by non-native plant species have also 

profoundly altered many native plant communities and 

ecosystems (Gurevitch and Padilla 2004). Exotic insects 

and pathogens have accelerated the succession cycle by 

killing important seral tree species, converting mid-successional stands to late-successional (Keane and others 

2002). In addition, increasing emissions of atmospheric 

concentrations of carbon dioxide and nitrogen have 

altered photosynthetic processes, species richness, and 

ecosystem function (Farquhar 1997; Jones and others 

1998; Stevens and others 2004).

We developed our methods for estimating such departure based on a set of ecological concepts used to detect 

changes in ecosystem properties and processes across 

landscapes at multiple scales. Species, ecological communities, and ecosystems vary naturally across spatial 

and temporal scales in response to disturbances, biotic 

processes, and environmental constraints (Levin 1978). 

These biotic and abiotic agents of pattern formation 

interact over time to produce a range and variability in 

ecological structures and processes (Morgan and others 

1994; Swanson and others 1994). When the mechanisms 

driving ecological systems, such as disturbance, change 

dramatically, ecological processes and structures respond 

and shift that range and variability (Barnes and others 

1998). Landscapes experiencing extensive changes may 

become altered to the point that their ecological properties are well beyond their historical range and variability, 

especially in their species composition and structure.

The extent of change in any particular ecosystem may 

be assessed by comparing current vegetation conditions 

to the range and variability in historical compositions 

and structures of vegetation communities, or simply their 

“natural variability.” The focus of describing natural 

variability is not on a single condition, but rather on 

a range of conditions and the variability under which 

ecosystems were sustained in the past (Swetnam and 

others 1999). Characterization of these past ecosystems 

has been referred to as the “historical range of variability” (Kaufmann and others 1994) or simply “reference 

conditions” (Moore and others 1999). Ideally, characterization of reference conditions considers all ecosystem 

components (organisms, structures, biogeochemical 

cycles, disturbance processes, and abiotic factors) and 

includes the appropriate time depth and spatial scales for 

the ecosystem components included in the assessment 

(Moore and others 1999). However, many of these factors 

are poorly understood or are difficult to measure (Moore 

and others 1999). Holling (1992) suggests that a small 

group of “keystone” or highly interactive organisms and 

abiotic processes may control ecological thresholds at 

certain scales. The potential list of important keystone 

variables may still be relatively long (Aronson and 

others 1993; Keddy and Drummond 1996), but experience and practical considerations have led researchers 

to select certain variables that reflect the evolutionary 

environment (Moore and others 1999; Swetnam and 

others 1999). For example, fire and autotrophic organisms (trees, shrubs, and herbaceous plants) are used to 

describe ponderosa pine and sequoia ecosystems (Fulé 

and others 1999; Moore and others 1999; Stephenson 

1999). Other considerations include identifying the 

historical time period for describing natural variability, 

including the point in time when ecological systems 

were considered relatively unaffected by Euro-American 

settlement (Hunter 1996; Schrader-Frechette and McCoy 

1995). Moreover, characterization of past ecosystems 

should specify whether Native American influences on 

ecosystems are regarded as natural (Landres and others 

1999).

We adopted the natural variability concept to guide 

our methods for estimating landscape changes from 

past to present. Specifically, our goal was to describe 

deviations of current landscape conditions from conditions between the years 1600 A.D. and 1900 A.D. and 

to describe them at a regional level with a mid-scale 

spatial resolution to help planning efforts address ecological issues (Keane and Rollins, Ch. 3). We selected 

this time frame for our reference conditions as the appropriate range to represent recent history because fire 

history reconstructions typically date back to at least 

1600 and because we determined that 1900 A.D. best 

approximates the start of significant Euro-American 

influences on western U.S. landscapes (Keane and others 2002; Keane and Rollins, Ch. 3). Also, we assumed 

that the influence of Native Americans on landscapes 

was inherent in our depiction of reference conditions.
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Chapter 11—Using Historical Simulations of Vegetation to Assess Departure of Current Vegetation Conditions across Large Landscap

To develop methods that estimate landscape changes 

from this historical time period, we needed to address a 

number of questions. What were the historical dynamics of plant communities across the diverse ecosystems 

of the nation? How do we measure plant community 

change? At what point does the magnitude of change 

drive an ecosystem beyond its historical boundaries to 

some uncharacteristic condition and warrant ecosystem 

restoration? In this chapter, we outline several approaches 

to 1) addressing each of these questions within the 

ecological framework of plant community function in 

fire-adapted environments and 2) creating maps of ecological departure over large regions. Our chief premise 

for the LANDFIRE Prototype Project was that fire and 

other disturbances regulate succession by regeneration, 

reproduction, and maintenance of plant species and 

assemblages. That is, fires kill existing stands and set 

the process of regeneration in motion for the next forest; fire as a selective force elicits asexual and sexual 

reproduction; and periodic surface fires reduce vegetation 

encroachment and competition for light, soil water, and 

nutrition and kill understory-tolerant seedlings (Barnes 

and others 1998). The frequency, intensity, extent, and 

timing of fires (in other words, fire regimes) are characteristic of different regional and local ecosystems 

(Barnes and others 1998). We assumed that under each 

unique fire regime, plant communities approach some 

dynamic equilibrium in their composition and structure. 

When fire regime characteristics change, the character 

of a plant community shifts and distributions of pioneer, mid-successional, and late-successional species 

or assemblages become altered. The magnitude of this 

change can be used to prioritize, plan, and implement 

restorative treatments (Hann and others 2004).

Our general approach involved developing a historical 

spatial database that describes the natural variability 

of vegetation across landscapes over time and quantitatively compares that historical distribution to the 

current vegetation patterns for two large study areas in 

the western United States. For example, the current 

vegetation pattern on a landscape might have changed 

by 70 percent from vegetation distributions observed 

in historical records, indicating a strong divergence 

(fig. 1). To quantify landscape patterns, we chose 

the metric of landscape composition and delineated 

composition by classifying landscapes according 

to their potential vegetation type (PVT), cover type, 

and structural stage (Frescino and others, Ch. 7; Zhu 

and others, Ch. 8). The PVT map identified areas with 

similar climate, landform, and geomorphic processes 

(biophysical settings) where distinct plant communities 

are assumed to develop in the absence of disturbance 

(Arno and others 1985; Steele and Geier-Hayes 1989). 

The cover type map depicted the existing dominant plant 

species or assemblages, and the structural stage map 

approximated the stages of vegetation development for 

the various cover types, ranging from stand initiation 

to old-growth, as described by height and percent cover 

(Zhu and others, Ch. 8). We integrated the cover type and 

structural stage maps such that each unique combination 

described a discrete stage along succession pathways, 

which we call a “succession class” (Long and others, Ch. 

9; see also Long and others, Ch. 6 and Zhu and others, 

Ch. 8 for descriptions of the cover types and structural 

stages used in the LANDFIRE Prototype Project). We 

then combined the succession class and PVT maps to 

describe landscape composition in a spatial context.

The collective area of each succession class in a PVT 

functions as our measure of the conditions of a landscape, which we refer to as the “vegetation composition.” 

We chose to use the combination of PVT and succession class as a descriptor of vegetation composition 

because it provided the finest classification resolution 

possible for evaluating landscape dynamics. That is, 

the PVT-succession class classification integrates the 

biophysical environment with existing vegetation, which 

discriminates between major site types. For example, 

we can differentiate ponderosa pine types occurring in 

a Douglas-fir PVT from ponderosa pines growing in 

a Ponderosa Pine PVT. Other landscape composition 

classifications are available, such as fuel models, cover 

types, and structural stages. However, we felt that classifying landscapes by PVT – succession class would 

be the most meaningful and useful depiction for the 

purposes of land managers, who typically use similar 

classification schemes for depicting landscape condition. 

It should be noted that landscape composition can also 

be described by measures other than area by vegetation 

class, including relative richness, diversity, dominance, 

and connectivity (Turner and others 2001). Similarly, 

landscape pattern can be described by landscape configuration instead of landscape composition using measures 

such as contagion, patch-based metrics, and fractals. We 

chose not to use these landscape metrics and measures 

because they are not yet widely used in management or 

would have required prohibitively expensive computer 

resources. Vegetation composition, on the other hand, 

was far more feasible to map, comprehend, and implement in management applications.

Comprehensive and consistent spatial estimates of 

historical vegetation composition were used in the 

LANDFIRE Prototype to identify natural variability. We 
318 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2

Chapter 11—Using Historical Simulations of Vegetation to Assess Departure of Current Vegetation Conditions across Large Landscap

Figure 1—Example of general approach for estimating departure in a 6th hydrologic unit code (HUC). Vegetation patterns 

(described here by succession classes) of historical sequences are quantitatively compared to those of the current landscape

to estimate departure. See table 7 for explanation of succession class codes.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 

Chapter 11—Using Historical Simulations of Vegetation to Assess Departure of Current Vegetation Conditions across Large Landscap

relied on simulation modeling to generate a time series of 

data for estimating the potential range and variability of 

historical vegetation for our large regional areas (Keane 

and Rollins, Ch. 3; Pratt and others, Ch. 10). We chose 

to use a moderately detailed simulation model, called 

LANDSUMv4, because it balanced the simulation of 

complex ecosystem processes with computational efficiency and thereby allowed for the acquisition of time 

series estimating historical conditions for large regions 

in a timely manner (Keane and others 2003; Keane and 

others 2006). The justification for simulation modeling 

and implementation of LANDSUMv4 to develop time 

series representing reference conditions are described in 

detail by Pratt and others, Ch. 10. Results from LANDSUMv4 simulation modeling do not represent actual 

historical records, but they are our best approximation 

of how vegetation responded to fire disturbances in the 

past.

We developed two methods to measure the extent of 

change, which we refer to as “departure,” between the 

simulated time series estimating historical vegetation 

composition and the current vegetation composition 

on landscapes. The purpose of the first method was to 

implement a field-based procedure, developed by Hann 

and others (2004), within a digital mapping context. 

Hann and others’ (2004) Interagency FRCC Guidebook 

(http://www.frcc.gov) details field-based protocols 

by which land managers can assess the departure of 

current vegetation composition from that of historical 

conditions to meet the objectives of the National Fire 

Plan and Healthy Forests Restoration Act (http://www.

fireplan.gov; http://www.healthyforests.gov) and also for 

reporting purposes, such as to the National Fire Plan 

Operations and Reporting System (http://www.nfpors.

gov). Our implementation of this method, which we 

refer to as the “FRCC Guidebook approach,” compares 

the current vegetation composition to the simulated 

historical time series; we do not, however, compare fire 

frequency and fire severity, as outlined in the FRCC 

Guidebook field procedures (Hann and others 2004), 

because contemporary conditions can be difficult to 

define, quantify, and depict spatially.

Calculations based on vegetation composition using the 

FRCC Guidebook approach require that the simulated 

historical time series data be summarized and distilled 

to represent one state or observation. As such, the FRCC 

Guidebook approach is very limited in its ability to 

characterize the full range and variability of vegetation 

reference conditions within the simulated historical 

data. We determined that a more statistically sound 

approach was needed to comprehensively account for 

patterns of temporal variation in the simulated historical 

landscapes (Steele and others, in preparation). Hence, 

we implemented a statistical method, which we refer to 

as the “Historical Range and Variability–Statistical” or 

“HRVStat” approach, to evaluate all states observed in 

the simulated historical time series and compares them to 

the current landscape to provide a complete assessment 

of departure. The HRVStat approach also measures the 

strength of evidence for the estimated departure value, 

which we call the “observed significance level” (Steele 

and others, in preparation).

Both the FRCC Guidebook and HRVStat approaches 

for describing vegetation change estimate departure on 

a continuous scale with values ranging from 0 to 100. 

However, the previous coarse-scale (1-km) map of FRCC 

(Hardy and others 2001; Schmidt and others 2002) and 

the FRCC Guidebook field procedures (Hann and others 

2004) describe departure simply in terms of three classes, 

including: FRCC 1 – minimal departure from the central tendency of the natural disturbance regime, FRCC 

2 – moderate departure, and FRCC 3 – high departure. 

Using the FRCC Guidebook and HRVStat approaches, 

we likewise classified our departure estimates into three 

categories to be consistent with the FRCC Guidebook 

field procedures and to facilitate comparisons with the 

coarse-scale map of FRCC from Schmidt and others 

2002.

The development of methods for estimating departure was one of the most important objectives of the 

LANDFIRE Prototype Project. Documentation of these 

procedures is the purpose of this chapter and is presented 

below in detail. These procedures can serve as the 

foundation for estimating departure as the LANDFIRE 

Project is implemented across the entire United States 

(Keane and Rollins, Ch. 3). In the process of developing 

these protocols, we identified various areas in need of 

improvement and further research, which we outline 

as recommendations for developing departure indices 

at the national level. The methods described here may 

not necessarily reflect protocols followed when the 

LANDFIRE Project is implemented nationally, and 

results and specific findings may change as protocols are 

improved. We present results from our current methods 

to demonstrate their implementation and to compare 

procedures.

Methods _______________________

The LANDFIRE Prototype Project involved many 

sequential steps, intermediate products, and interdependent processes. Please see appendix 2-A in Rollins and 
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Chapter 11—Using Historical Simulations of Vegetation to Assess Departure of Current Vegetation Conditions across Large Landscap

others, Ch. 2 for a detailed outline of the procedures 

followed to create the entire suite of LANDFIRE Prototype products. This chapter focuses specifically on the 

procedure followed in developing maps describing the 

departure of current from historical landscape conditions, which served as important core data products of 

the LANDFIRE Prototype Project.

In this chapter, we describe: 1) the key spatial layers 

used for estimating departure and preliminary considerations for identifying cover types for analyses; 2) the 

data sets for estimating departure; 3) the FRCC Guidebook approach; 4) the HRVStat approach; 5) a detailed 

demonstration for estimating departure using these two 

approaches; and 6) a comparison of departure between 

areas with different simulated fire return intervals. We 

implemented the FRCC Guidebook and HRVStat approaches across two large regions in the western United 

States: one in the central Utah highlands and a second 

in the northern Rocky Mountains of Idaho and Montana 

(LANDFIRE mapping zones 16 and 19, respectively; 

see fig. 1 in Rollins and others, Ch. 2).

Key Spatial Layers and Preliminary 

Considerations

Key spatial layers—Of the four maps essential for 

estimating departure in both the FRCC Guidebook and 

HRVStat approaches, three described vegetation: the 

PVT, existing cover type, and existing structural stage maps 

(Frescino and others, Ch. 7; Zhu and others, Ch. 8), and 

the fourth partitioned each zone into smaller map areas 

or “landscape reporting units” (LRUs) so that departure 

could be estimated for each LRU (Pratt and others, Ch. 

10). The determination of appropriate units held great 

importance because, measurements of landscape change 

being scale-dependent, departure estimates vary with 

landscape size. (Gardner 1998).

In general, the most appropriate ecological scale for 

detecting change matches the scale at which key processes affecting ecosystems (such as fire and succession) 

interact to limit landscape dynamics at a point in time 

(Parker and Pickett 1998). Identifying that scale is a 

challenging problem (Gardner 1998) but may be accomplished by evaluating the change in variance in a 

landscape metric with changes in spatial extent (Levin 

and Buttel 1986; O’Neill and others 1991) or by using 

more mathematically complex methods, such as the gliding-box method (Gardner 1998). Due to time constraints, 

we did not conduct such analyses, but we expected that 

the appropriate ecological scales in Zones 16 and 19 

would vary depending on the dominant landscape fire 

and succession processes. For example, in landscapes 

subject to small, low intensity disturbances that kill 

vegetation in patches of only a few trees, the stand scale 

(about 1-10 ha) (Urban and others 1999) would likely be 

the most appropriate for measuring departure; however, 

departure may be better estimated at the landscape 

scale (103 to 106 ha) (Mladenoff and others 1993, 1994; 

Spies and others 1994) in areas subject to large, intense, 

stand-replacing disturbances that kill vegetation in big 

patches. Another consideration was the scale that would 

be most useful to management, which is often at a smaller 

spatial extent approaching the stand scale and at which 

subtle changes to cover type and structural stages, such 

as those caused by fuel treatment, can be detected (Keane 

and Rollins, Ch. 3).

Ultimately, we balanced our selection of LRU-scale 

based on both ecological and management considerations 

(see Pratt and others, Ch. 10 for additional details). We 

chose as reporting units uniform squares of 900-m by 

900-m (81 ha) but coded these squares so that they could 

be grouped and summarized at the sub-watershed level 

(average area of 6,450 ha). We determined that summarizing departure to these 900-m by 900-m squares would 

capture stand-level processes and provide land managers 

with a sufficient data resolution. If a landscape-level 

measurement was desired, we included information that 

facilitates the aggregation of data over 6th level Hydrologic Unit Codes (HUCs). We are currently conducting 

additional analyses to systematically evaluate the appropriate reporting unit size for estimating departure.

Identifying cover types for analyses—Certain cover 

types would skew departure estimates and provide little 

useful information for conservation and restoration of 

landscapes. Specifically, the cover types water, barren, and ice/snow change little over time and always 

contribute to low departure. Conversely, agriculture 

and urban areas always contribute to high departure 

since current conditions such as these did not exist in 

the majority of the U.S. during the reference period. If 

a large proportion of any of these five cover types occur within an LRU, they can overwhelm the departure 

estimate and mask the condition of vegetation types 

present. For example, suppose that an LRU composed 

almost entirely of barren rock contains a small amount 

of vegetation that historically was perennial grasslands 

but is now teeming with exotic weeds. If we included the 

barren cover type in our departure measurements, we 

would calculate a very low departure, and this LRU may 

go unnoticed by land managers. Alternatively, consider 

an LRU that is predominately urbanized but contains 

vegetation uncharacteristic of historical conditions having missed numerous fire intervals. If we included the 
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Chapter 11—Using Historical Simulations of Vegetation to Assess Departure of Current Vegetation Conditions across Large Landscap

urban cover type in our departure estimate, we would 

not know whether the high departure estimate was due 

to the urban or the vegetation component. Hence, land 

managers would have potentially ambiguous information 

for assessing that landscape.

The main purpose of assessing departure is to prioritize 

areas for management and to allow for assessments of 

management efforts aimed at lowering departure values. 

Because agriculture and urban areas typically cannot 

be managed nor departure in these areas reversed, we 

decided that it was best to exclude these land cover 

types from departure calculations. Similarly, since water, barren, and snow/ice types may obscure the need 

for management in surrounding vegetation types, these 

also were excluded from departure estimates. It is important to note, however, that we made these decisions 

after completing the Zone 16 maps. Time constraints 

prevented us from rectifying this error, and water, agriculture, barren, snow/ice, agriculture, and urban cover 

types were included in Zone 16 departure estimates. 

For Zone 19, on the other hand, we treated these cover 

types as effectively immutable and removed them from 

departure estimates.

Data Sets for Estimating Departure

Simulating historical reference conditions—Using the LANDSUMv4 simulation model, we created 

a historical reference data set to describe succession 

patterns continuously across broad regions and with 

temporal depth (Pratt and others, Ch. 10). The LANDSUMv4 model simulates disturbances (primarily fire, 

but also insect and disease infestations) spatially across 

landscapes and predicts the resulting effects of fire on 

vegetation using a framework of succession pathways 

(Keane and others 2006). The LANDSUMv4 output 

provided a time series describing vegetation dynamics 

in terms of succession classes within PVTs for all 30-m 

pixels in a mapping zone (see Pratt and others, Ch. 10). 

Specifically, the LANDSUMv4 output file described 

the total area (m2) for each of the succession classes 

occurring within the PVTs in each LRU across a zone 

at every time interval over the simulation period. Details 

of the LANDSUMv4 simulations pertinent to departure 

estimates are described here, additional information 

can be found in Pratt and others, Ch. 10, and a detailed 

description of succession pathway development is available in Long and others, Ch. 9.

Ideally, one simulation would have been conducted for 

an entire zone; however, because of computer limitations, 

we partitioned the zones into smaller units of 20,000-

ha and ran LANDSUMv4 separately for each of these 

landscapes, which we called “simulation landscapes.” 

Figure 2 shows Zone 16 (6-million ha) divided into 427 

simulation landscapes (20,000-ha each). Within each 

simulation landscape, we again partitioned the area 

into 256 LRUs of 81 ha each (fig. 2). LANDSUMv4 

simulated succession and disturbance across the entire 

20,000-ha landscape but reported only the composition 

of succession classes by PVTs contained within each 

LRU. For example, in figure 2, there are five PVTs 

distributed across the LRU. For each of the five PVTs 

and at every reporting interval, LANDSUMv4 reported 

the composition of succession classes summarized collectively across all stands of the same PVT.

A key requirement for measuring departure through the 

HRVStat approach was the acquisition of a statistically 

valid number of temporally uncorrelated observations 

from the LANDSUMv4 time series. Early testing of 

HRVStat indicated that a minimum of 200 observations 

from a LANDSUMv4 time series at reporting intervals 

long enough to minimize temporal autocorrelation was 

needed. Because fire disturbance dynamics tend to occur 

at longer frequencies, short annual reporting intervals 

result in correlated observations, but succession class 

distributions become less correlated with longer reporting intervals (Pratt and others, Ch. 10). Initial tests 

indicated that intervals of 20-years or more showed 

relatively little autocorrelation, and, using this interval, we executed the model for a 4,000-year simulation 

period to obtain 200 observations for Zone 16. Further 

examination, however, revealed that PVTs in Zone 16 

(except Aspen, Wetland Herbaceous, Cool Herbaceous, 

and Alpine) showed notable autocorrelation (fig. 3a). 

Based on the autocorrelation in Zone 16, we extended the 

reporting interval to 50 years for Zone 19 and executed 

the model for a 10,000-year simulation period to obtain 

200 observations. Although the autocorrelation in Zone 

19 was not as pervasive as that for Zone 16, it was still 

present to some degree for most PVTs. Several PVTs, 

particularly forest PVTs, had moderately high correlation 

coefficients, even with a 50-year time lag (fig. 3b). It was 

logistically impractical to further increase the reporting 

interval and the simulation time to the length necessary 

to minimize autocorrelation in all PVTs because the 

total simulation time would become prohibitively long, 

given our computing resources. Further research is being conducted to study the effect of autocorrelation and 

succession class trends on LANDSUMv4 output and 

the subsequent departure calculations. It is important 

to note that the FRCC Guidebook approach had less 

rigorous requirements for representing historical conditions, requiring only one observation that describes the 
322 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 20

Chapter 11—Using Historical Simulations of Vegetation to Assess Departure of Current Vegetation Conditions across Large Landscap

Figure 2—Example of the hierarchical configuration of spatial reporting units used to estimate reference conditions from LANDSUMv4. The broadest extent is the zone; followed by the 20,000-ha simulation landscape; and lastly, the landscape reporting

unit which displays the spatial distribution of PVTs for this example in Zone 16.

central tendency of long-term natural dynamics (Hann 

and others 2004).

Depicting current vegetation conditions—Whereas 

succession class distributions by PVT were simulated 

by LANDSUMv4 to estimate reference conditions, current vegetation was described using existing cover type 

and structural stage maps derived from recent satellite 

imagery (Zhu and others, Ch. 8). Excepting classes with 

non-native or exotic cover types (Long and others, Ch. 9) 

established over the last (twentieth) century — which we 

considered a recent invasion to plant communities — we 

classified existing cover type and structural stage maps 

into the same set of succession classes used for the historical simulation modeling. The dominance of an exotic 

species in current succession class maps represented a 

distinct change from the simulated historical conditions. 

We then spatially combined the PVT, existing cover 

type, existing structural stage, and LRU layers such that 

all unique combinations of spatial input variables were 

tabulated. The result of this process depicted the areal 

extent for each of the succession classes within each 

PVT occurring within every LRU across the zone.

Compiling the final data sets for estimating 

departure—We combined the data set for existing 

vegetation with the time series from LANDSUMv4 

to develop data sets for estimating departure. That is, 

the departure data sets from the LANDSUMv4 output 

summarized the total area in each PVT-succession class 

combination within an LRU for the current time period 

and for each reporting interval (20 or 50 years, in this 

effort). In these data sets, current vegetation was depicted 

by only one instance in time for each PVT-succession 

class, whereas the simulated historical conditions were 

represented by 200 observations sampled from the 

LANDSUMv4 simulations for each PVT-succession 

class.

Departure was estimated by comparing the succession class distributions for the five PVTs contained in 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 

Chapter 11—Using Historical Simulations of Vegetation to Assess Departure of Current Vegetation Conditions across Large Landscap

Figure 3—Box and whiskers plots showing the mean autocorrelation coefficient across PVTs in (A) Zone 16 with

a 20-year lag and (B) Zone 19 with a 50-year lag. A correlation coefficient of zero represents no autocorrelation.

See appendix 11-A for key to PVT code
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Chapter 11—Using Historical Simulations of Vegetation to Assess Departure of Current Vegetation Conditions across Large Landscap

the example LRU as simulated by the LANDSUMv4 

model (with 200 observations possible for each unique 

succession class within each PVT) to succession class 

distributions for the same five PVTs in the current landscape (with only one observation for each PVT-succession 

class) (see, for example, figure 2). Put another way, the 

spatial arrangement of PVTs did not change between 

LANDSUMv4 simulations and the current landscape, but 

succession class distributions within PVTs did vary – and 

that change was the basis for measuring departure.

Implementing the FRCC Guidebook 

Approach

In implementing the FRCC Guidebook approach, we 

explored a number of options for representing reference 

conditions and estimating departure and applied them 

to Zone 16, as will be described below. Based on these 

and additional analyses, we developed a final method, 

which was applied to Zone 19.

One of the main challenges for this approach was 

distilling the time series of LANDSUMv4 model output with 200 observations for each PVT-succession 

class (within an LRU) down to a single observation for 

that PVT-succession class, as required by the FRCC 

Guidebook field procedures (Hann and others 2004). 

That is, the calculations of departure outlined in the 

FRCC Guidebook field procedures (described below) 

require a single value for each succession class within 

a PVT (within an LRU) for comparisons to the current 

conditions. In Zone 16, we evaluated two ways to reduce 

the LANDSUMv4 model output. In the first method, 

termed the “temporal snapshot,” we elected simply to 

use conditions from one reporting interval across the 

entire time series of the LANDSUMv4 output, and we 

chose year-1,000. This approach provided a “snapshot” 

of the simulated historical landscape. In other words, the 

total area that a succession class occupied within a given 

PVT (of a LRU) in year-1,000 of the LANDSUMv4 time 

series was used to represent its reference conditions.

In the second method, termed “multi–temporal,” we 

aimed to capture temporal variation in the simulated 

historical succession class distributions but with the 

inherent constraint of using a single value for each 

PVT-succession class combination (within an LRU) 

among the 200 values in the LANDSUMv4 time series. 

Various metrics were possible — such as the maximum, 

median, mean, and the minimum — with a succession class distribution (n = 200) showing values for 

the median and various percentiles of the percent area 

observed for that succession class across the simulated 

time series (fig. 4). Metrics emphasizing the maximum 

or minimum ranges of succession class distribution 

can capture variability to some extent. For example, 

consider two succession class distributions with the 

same mean, but one has low variability and the other 

high variability. The maximum value for each of these 

distributions will be different, and the distribution with 

low variability will have a smaller maximum value than 

the distribution with high variability. For Zone 16, we 

chose to use 90 percent of the maximum area for each 

succession class within a PVT (within an LRU) in an 

effort to portray the variability in the upper end of the 

distributions for succession classes; for simplicity, we 

term this metric the “90 percent of maximum.” For 

Zone 19, we chose to use the 90th percentile of the area 

(in other words, the value that is as large as 90 percent 

of all values in the data set and smaller than 10 percent 

of all values) for each PVT-succession class (within an 

LRU) to, again, capture the upper range of the succession class distributions. But using the 90th percentile, we 

more effectively eliminated inordinately high outliers. 

We term this metric the “90th percentile.”

Determining the 90percent of maximum and 90th percentiles from the LANDSUMv4 output was a straightforward process. We searched the LANDSUMv4 

output and extracted the appropriate value for each 

PVT-­succession class found in each LRU. For example

if the 90th percentile was used to represent reference 

conditions, the value that was as large as 90 percent of 

all values in the pool of LANDSUMv4 observations was 

chosen to represent reference conditions. The extracted 

values for each succession class were then converted 

from area to percent of the PVT that they occupied.

After choosing the metric to represent reference conditions, a second key decision involved determining the 

appropriate extent or spatial domain for summarizing 

the LANDSUMv4 time series data. Choosing the correct spatial domain was problematic because different 

spatial domains leads to different estimates of reference 

conditions for any given succession class. For example, 

if reference conditions were summarized across watersheds, then each PVT-succession class combination 

would be assigned the same reference conditions across 

an entire watershed, regardless of any spatial variability 

within that landscape.

In Zone 16, we evaluated three spatial domains for 

calculating reference conditions to describe the 90 percent of maximum for each PVT-succession class over 

the time series: 1) mapping zones (6 to 10 million ha), 

2) simulation landscapes (approximately 20,000 ha), 

and 3) individual LRUs (81 ha) (fig. 3). For Zone 19, we 

evaluated only the LRU-level to focus the spatial domain 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 3

Chapter 11—Using Historical Simulations of Vegetation to Assess Departure of Current Vegetation Conditions across Large Landscap

Figure 4—Empirical probability density distributions fitted to the frequency of observed percent area occupied by a slightly

departed succession class (Aspen – Birch High Cover, High Height) in the LANDSUMv4 time series data (n=200). The 75th an

95th percentiles (in the percent area observed for that succession class across the simulated time series) occur in 14.1 perce

and 15.8 percent of the PVT; the current conditions (CC) data point is at 12.7 percent and corresponds reasonably well to th

median of the simulated reference conditions distribution but is relatively distant from the 75th and 95th percentile measurements. Data for each succession class was taken from a 20,000-ha spatial unit within Zone 16 dominated by the Spruce – F

/ Spruce – Fir / Lodgepole Pine PVT containing that succession class.

on local variability (and used the 90th percentile metric). 

The process of aggregating the LANDSUMv4 time series 

to the various spatial domains was a straightforward 

process. First the LANDSUMv4 output was examined 

across a given spatial domain, and all instances of a 

given PVT-succession class were identified. Second, for 

each occurrence of a PVT-succession class combination 

across the spatial domain, the desired reference conditions (such as the 90th percentile) were identified from 

the pool (n = 200) of LANDSUMv4 output.

Current conditions were easily calculated by converting area to the percent that a succession class occupied 

for each PVT across an LRU. For example, table 1 

demonstrates that the PVT-succession class combination of the Pinyon–Juniper / Mountain Big Sagebrush / 

South PVT with the Juniper–High Cover, High Height 

succession class occupied 38.71 percent of the total area 

in the example LRU.

Comparing current to simulated historical vegetation 

conditions enabled the calculation of departure, which 
326 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 20

Chapter 11—Using Historical Simulations of Vegetation to Assess Departure of Current Vegetation Conditions across Large Landscapes Table 1—Example of computation of current conditions for a landscape reporting unit (LRU) showing relative distributions of succession classes expressed as the percent area occupied within each potential vegetation type (PVT) contained in the LRU  See table 7 for structural stage codes. Structural Current conditions PVT stage Cover type (% of area occupied) Piny

– Juniper / Mountain Big Sagebrush / South	 HHW	 Juniper	 38.71

Pinyon

– Juniper / Mountain Big Sagebrush / South	 LLW	 Juniper	 11.52

Pinyon

– Juniper / Mountain Big Sagebrush / South	 LHW	 Juniper	 1.23

Pinyon

– Juniper / Mountain Big Sagebrush / South	 LLW	 Pinyon

– Juniper	 19.20

Pinyon

– Juniper / Mountain Big Sagebrush / South	 HHW	 Pinyon

– Juniper	 17.05

Pinyon

– Juniper / Mountain Big Sagebrush / South	 LHW	 Pinyon

– Juniper	 0.77

Pinyon

– Juniper / Mountain Big Sagebrush / South	 LHS	 Mountain Deciduous Shrub	 9.37

Pinyon

– Juniper / Mountain Big Sagebrush / South	 HHS	 Mountain Deciduous Shrub	 2.00

Pinyon

– Juniper / Mountain Big Sagebrush / South	 HLS	 Mountain Big Sagebrush Complex	 0.15

Total Percent Area 100Pinyon

– Juniper / Gambel Oak	 LLW	 Pinyon

– Juniper	 44.76

Pinyon

– Juniper / Gambel Oak	 HHW	 Pinyon

– Juniper	 41.26

Pinyon

– Juniper / Gambel Oak	 LHS	 Mountain Deciduous Shrub	 7.69

Pinyon

– Juniper / Gambel Oak	 HLS	 Mountain Deciduous Shrub	 3.50

Pinyon

– Juniper / Gambel Oak	 HLS	 Mountain Deciduous Shrub	 2.80

Total Percent Area 100Wyoming

– Basin Big Sagebrush	 HLS	 Wyoming

– Basin Big Sagebrush Complex	 85.71

Wyoming

– Basin Big Sagebrush	 LLS	 Wyoming

– Basin Big Sagebrush Complex	 10.71

Wyoming

– Basin Big Sagebrush	 LLS	 Rabbitbrush	 3.57

Total Percent Area 100Pinyon

– Juniper / Wyoming

– Basin Big Sagebrush / South	 LLW	 Juniper	 50.00

Pinyon

– Juniper / Wyoming

– Basin Big Sagebrush / South	 HLS	 Wyoming

– Basin Big Sagebrush Complex	 29.41

Pinyon

– Juniper / Wyoming

– Basin Big Sagebrush / South	 LLS	 Wyoming

– Basin Big Sagebrush Complex	 2.94

Pinyon

– Juniper / Wyoming

– Basin Big Sagebrush / South	 LLW	 Pinyon

– Juniper	 14.71

Pinyon

– Juniper / Wyoming

– Basin Big Sagebrush / South	 HLS	 Dwarf Sagebrush Complex	 2.94

Total Percent Area 100Ponderosa Pine LLF Ponderosa Pine 58.82Ponderosa Pine LHF Ponderosa Pine 23.Ponderosa Pine LLF Juniper 5.88Ponderosa Pine HHF Ponderosa Pine 5.88Ponderosa Pine	 LLF	 Pinyon

– Juniper	 5.88

Total Percent Area 100
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 3

Chapter 11—Using Historical Simulations of Vegetation to Assess Departure of Current Vegetation Conditions across Large Landscap

was then classified to represent FRCC. The measure 

of departure relied on the computation of “similarity,” 

discussed in depth by Hann and others (2004). We calculated this simple metric by comparing current and 

reference conditions in the same LRU for a given PVT. 

The percent composition of each succession class in the 

current condition map was compared with that of the 

reference conditions for each PVT within an LRU, and 

the lesser of the two was termed “similarity.” Across 

each PVT, the similarity values were totaled throughout 

the entire LRU. Departure was subsequently calculated 

for each PVT as:

Departure = 100 – Similarity (1)

where Similarity is the summation of individual similarity values for each of the PVTs across an entire LRU, 

given as:

Similarity i

i

SClasses

( )

=

∑

0

(2)

where Similarity is computed as the smaller area of either 

current vegetation or that of the reference conditions for 

each succession class encountered in a PVT. Aggregation 

of estimated Similarity values from individual PVTs 

to the LRU was performed on an area-weighted basis. 

We conducted this process in two steps. First, the area 

and departure of each PVT within a given LRU were 

computed (table 2). In the second step, we computed 

the final departure estimate by weighting each PVTbased departure by its respective area and summing 

these values across the entire LRU (table 3). For visual 

simplification and to allow for identification of areas 

with low, moderate, or high departure, we classified departure for Zone 16 using the following threshold values 

from the FRCC Guidebook field procedures (Hann and 

others 2004): departure < 33; 33 ≤ departure < 67; and 

departure ≥ 67, which correspond to FRCC 1, 2, and 3, 

respectively. For Zone 19, we used FRCC classification 

thresholds that were different from those used for Zone 

16 to match values subsequently modified by managers 

implementing the FRCC Guidebook procedures in the 

field; these were: departure < 5, 5 ≤ departure < 52.5, 

and departure ≥ 52.5, which correspond to FRCC 1, 2, 

and 3, respectively (Hann, personal communication).

Implementing the HRVStat Approach

In developing and implementing the HRVStat approach, we wanted to employ a statistical test that could 

detect whether a single observation of current vegetation 

was unusual compared to a set of observations representing historical vegetation composition. That is, we 

wanted to consider every observation in the simulated 

historical record for all succession classes in a PVT and 

compare this set to the current conditions. This approach 

was fundamentally different from the FRCC Guidebook 

method, which measures departure using only one value 

to represent the time series of simulated historical conditions for any PVT-succession class combination.

To estimate departure using a range of historical conditions, Steele and others (in preparation) developed a 

new statistical technique based on measuring the extent 

that a suspected outlier (in our case, the current observation) can be estimated from the simulated historical 

observations. This multivariate statistical approach 

uses concepts from matrix algebra to compute linear 

approximations and measurements of approximation 

error (Leon 2002). Essentially, this method computes the 

best possible approximation of the current observation 

that can be formed as a linear function of the simulated 

historical data. Usually, there is some error in the approximation, and the square root of that error is the 

estimated departure value using the HRVStat method. 

More specifically, departure is calculated as the square 

root of the error sum-of-squares after normalizing the 

current observation vector. If the measured error (that 

is, departure) is small, the current observation is similar 

to the simulated historical data. Conversely, a current 

observation inconsistent with historical patterns will be 

poorly approximated, and the error will be relatively 

large, as will the estimated departure value. Steele and 

others (in preparation) considered other approaches to 

identifying whether an observation is dissimilar from 

other observations in a data set. Some of these methods 

are based on the measure of the distance of a single 

observation from measures of central tendency (for example, the mean), such as Mahalanobis distance. More 

commonly, these methods concentrate on measuring 

distance along particular eigenvector axes extracted from 

the sample variance matrix. A simulation study showed 

that the HRVStat approach is far better at detecting unusual observations and particularly effective for use with 

our highly-dimensional (in other words, having numerous categories of PVT–succession class combinations) 

data sets comprised of count data (Steele and others, 

in preparation). We adopted this new method, termed 

herein as the “best linear approximation,” to measure 

the extent to which current vegetation composition in an 

LRU differs from simulated historical vegetation composition – which we call the “observed departure.”

We also wanted a measure that expressed the strength 

of evidence for a given observed departure estimate, or 
328 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 20

Chapter 11—Using Historical Simulations of Vegetation to Assess Departure of Current Vegetation Conditions across Large Landscapes Table 2—First step in computing departure in example LRU using the FRCC Guidebook method  This LRU has 12 PVTs, but only 5 are shown here for brevit.  See table 7 for structural stage codes. Note: current conditions are also shown in table 1. Structural Current Reference Sum of PVT stage Cover type conditions conditions Similarity similarity Departure Pi

– Juniper / Mt. Big Sagebrush / South	 HHW	 Juniper	 38.71	 0.04	 0.04	 18.52	 81.48

Pinyon

– Juniper / Mt. Big Sagebrush / South	 LLW	 Pinyon

– Juniper	 19.20	 10.00	 10.00

Pinyon

– Juniper / Mt. Big Sagebrush / South	 HHW	 Pinyon

– Juniper	 17.05	 0.11	 0.11

Pinyon

– Juniper / Mt. Big Sagebrush / South	 LLW	 Juniper	 11.52	 2.51	 2.51

Pinyon

– Juniper / Mt. Big Sagebrush / South	 LHS	 Mt. Deciduous Shrub	 9.37	 2.31	 2.31

Pinyon

– Juniper / Mt. Big Sagebrush / South	 HHS	 Mt. Deciduous Shrub	 2.00	 4.71	 2.00

Pinyon

– Juniper / Mt. Big Sagebrush / South	 LHW	 Juniper	 1.23	 0.62	 0.62

Pinyon

– Juniper / Mt. Big Sagebrush / South	 LHW	 Pinyon

– Juniper	 0.77	 2.47	 0.77

Pinyon

– Juniper / Mt. Big Sagebrush / South	 HLS	 Mtn. Big Sagebrush Complex	 0.15	 25.30	 0.15

Pinyon

– Juniper / Gambel Oak	 LLW	 Pinyon

– Juniper	 44.76	 7.12	 7.12	 21.55	 78.45

Pinyon

– Juniper / Gambel Oak	 HHW	 Pinyon

– Juniper	 41.26	 0.44	 0.44

Pinyon

– Juniper / Gambel Oak	 LHS	 Mt. Deciduous Shrub	 7.69	 16.70	 7.69

Pinyon

– Juniper / Gambel Oak	 HHS	 Mt. Deciduous Shrub	 3.50	 40.20	 3.50

Pinyon

– Juniper / Gambel Oak	 HLS	 Mt. Deciduous Shrub	 2.80	 7.61	 2.80

Wyoming

– Basin Big Sagebrush	 HLS	 Wy.–Basin Big Sagebrush Complex	 85.71	 6.98	 6.98	 21.02	 78.98

Wyoming

– Basin Big Sagebrush	 LLS	 Wy.–Basin Big Sagebrush Complex	 10.71	 17.60	 10.70

Wyoming

– Basin Big Sagebrush	 LLS	 Rabbitbrush	 3.57	 3.33	 3.33

Pinyon

– Juniper / Wy.– Basin Big Sagebrush / S	 LLW	 Juniper	 50.00	 1.12	 1.12	 25.70	 74.30

Pinyon

– Juniper / Wy.– Basin Big Sagebrush / S	 HLS	 Wy.–Basin Big Sagebrush Complex	 29.41	 14.60	 14.60

Pinyon

– Juniper / Wy.– Basin Big Sagebrush / S	 LLW	 Pinyon

– Juniper	 14.71	 4.60	 4.60

Pinyon

– Juniper / Wy.– Basin Big Sagebrush / S	 HLS	 Dwarf Sagebrush Complex	 2.94	 2.39	 2.39

Pinyon

– Juniper / Wy.– Basin Big Sagebrush / S	 LLS	 Wy.–Basin Big Sagebrush Complex	 2.94	 16.70	 2.94

Ponderosa Pine LLF Ponderosa Pine 58.82 2.24 2.24 32.

Ponderosa Pine LHF Ponderosa Pine 23.53 37.60 Ponderosa Pine LLW Juniper 5.88 0.50 0.50Ponderosa Pine	 LLW	 Pinyon

– Juniper	 5.88	 0.36	 0.36

Ponderosa Pine HHF Ponderosa Pine 5.88 23.00 5.8
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 3

Chapter 11—Using Historical Simulations of Vegetation to Assess Departure of Current Vegetation Conditions across Large Landscap

Table 3—Second step in computing departure for example landscape reporting unit using the FRCC Guidebook method. Departure 

is calculated as 100 – similarity. The final estimate of departure is computed by weighting the departure for the 12 PVTs acro

the entire landscape reporting unit (LRU) by their respective areas. All 12 PVTs are shown here

Sum of similarity Departure for Weighted

PVT for PVT PVT Area of PVT departure

Douglas-fir / Douglas-fir	 14.20	 85.80	 1.44	 1.24

Grand Fir – White Fir	 16.72	 83.28	 0.67	 0.56

Grand Fir – White fir / Maple	 0.82	 99.18	 0.11	 0.11

Mountain Big Sagebrush  13.86 86.14

Pinyon – Juniper / Gambel Oak	 21.55	 78.45	 15.89	 12.47

Pinyon – Juniper / Mountain Big Sagebrush / South	 18.52	 81.48	 72.33	 58.94

Pinyon – Juniper / Mountain Mahogany	 1.12	 98.88	 0.22	 0.22

Pinyon – Juniper / Wy.– Basin Big Sagebrush / South	 25.70	 74.30	 3.78	 2.81

Ponderosa Pine  32.51 67.49  1

Riparian Hardwood 20.57 79.43 0.22 

Spruce – Fir / Blue Spruce / Lodgepole Pine	 5.94	 94.06	 0.22	 0.21

Wyoming – Basin Big Sagebrush	 21.02	 78.98	 3.11	 2.46

Departure for entire LRU 80.55

an “observed significance level.” The observed significance level is similar to a p-value measurement, which 

estimates the probability that a type I error (rejecting 

a true null hypothesis) occurred. However, this formal 

interpretation requires independent observations from 

LANDSUMv4 simulations within an LRU – a condition which could not necessarily be met due to possible 

autocorrelation across time and space. As previously 

discussed, we observed evidence of temporal autocorrelation using a 20-year reporting interval and, to some 

extent, a 50-year reporting interval. LRUs may also be 

spatially correlated because areas close in space tend to 

have similar vegetation and fire disturbances. Moreover, 

if we conducted formal tests for each of the tens of thousands of LRUs across a mapping zone, we may obtain 

a significant result by chance alone, and adjustments 

would be needed to avoid such type I errors. Hence, 

the observed significance values reported here are used 

only to provide a quantitative measure of the evidence 

of departure for comparisons between LRUs, and not 

for formal testing (Steele and others, in preparation).

To determine the observed significance level of a 

departure estimate, we first constructed an empirical 

distribution using the current and simulated historical 

observations for each LRU (Steele and others, in preparation). That is, after calculating the observed departure, 

as described above, we next calculated the departure for 

each observation within the simulated historical data of 

an LRU, using the best linear approximation calculations. We used the term “divergence” to describe the 

best linear approximations of the simulated historical 

time series – to avoid confusion with the “observed 

departure” term used to estimate differences between 

the current and simulated historical time series. We 

calculated divergence within the simulated historical 

data set by removing each observation from the data 

set and computing its divergence from the remaining 

data (for example, we measured the divergence between 

year-20 and years 40 to 4,000 in the LANDSUMv4 

data set for Zone 16). We then combined the divergence 

estimates (n = 200) with the observed departure (n = 

1) to produce the empirical distribution for each LRU. 

Finally, we computed the observed significance level 

by calculating the proportion of divergence values in 

the distribution that are at least as large as the observed 

departure (Steele and others, in preparation).

To help illustrate the statistical procedures in the 

HRVStat approach, we provide a simplified example in 

figure 5. Consider two LRUs that contain only one PVT 

with one succession class, the same mean area over time 

for reference conditions (percent of the area is 0.2 in 

fig. 5A), and the same observed area for current conditions (percent of the area is 0.8 in fig. 5A). However, 

LRU-A has lower variability in the percent areas of the 

succession class than LRU-B. In figure 5B, we show the 

distribution of the divergence estimates and the observed 

departure. The divergence estimates similarly show that 

LRU-A has less variability for divergence estimates and 

a lower mean divergence than LRU-B, whereas observed 

departures are the same for both LRUs (fig. 5B). In 

figure 5C’s empirical distributions of the divergence, 

we show the observed significance level for each LRU 

as the proportion of values greater than or equal to the 
330 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2

Chapter 11—Using Historical Simulations of Vegetation to Assess Departure of Current Vegetation Conditions across Large Landscap

Figure 5—Hypothetical, simplified example demonstrating HRVStat for two landscape reporting units (LRUA and LRU-B) that contain only one PVT with one succession class under current and reference conditions. 

Set A shows that LRU-A and LRU-B have the same current percent area for that succession class (current 

succession class) and that the reference conditions have similar mean percent areas ( X ), but LRU-A has less 

variability than LRU-B. Set B shows estimates for observed departure from and divergence within reference 

conditions. LRU-A and LRU-B have identical observed departures, but LRU-A has a lower mean divergence 

( X ) and variability than LRU-B for reference conditions. Set C shows the probability distributions of divergence, where the area under the curve above the observed departure represents the observed significance

level. Observed significance level is less in LRU-A than LRU-B because LRU-A has less variability in t

reference conditions.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006

Chapter 11—Using Historical Simulations of Vegetation to Assess Departure of Current Vegetation Conditions across Large Landscap

observed departure, where the observed significance 

value is higher in LRU-B than LRU-A. In summary, we 

have the same estimate of observed departure for LRUA and LRU-B, but evidence for that observed departure 

is greater in LRU-A because of less variability in the 

distribution of the succession class in the historical 

time series. In reality, calculations in an actual LRU 

from Zone 16 or Zone 19 would be far more intensive 

because of the large number of PVT-succession class 

combinations (up to 220).

Creating maps for comparing departure estimates—Our last task was classifying HRVStat results 

to make comparisons with the 1-km coarse-scale 

FRCC maps (Schmidt and others 2002) and the FRCC 

Guidebook approach maps, which classify departure as 

low (FRCC 1), moderate (FRCC 2) and high departure 

(FRCC 3). We determined that the most informative 

classification scheme integrates both the departure and 

observed significance level estimates from HRVStat 

to describe not only the degree of departure in a landscape but also the evidence supporting the departure 

estimate.

Both departure and observed significance level values 

ranged from 0 to 1.0. We classified these parameters 

into three groupings by choosing two thresholds for 

partitioning values (table 4). For observed significance 

level, we chose thresholds of 0.01 and 0.1 to describe 

high, moderate, and low observed significance within 

our departure estimate, based on value limits commonly 

used in statistics to assess significance. To partition departure into classes, we chose threshold values of 0.33 

and 0.67, as recommended in the FRCC Guidebook field 

methods (Hann and others 2004) and also used in the 

FRCC Guidebook approach for Zone 16. We call the 

three classes “classified HRVStat departure” estimates, 

instead of FRCC, to avoid confusion with the classified 

departure values from the FRCC Guidebook approach. 

Accordingly, the classified HRVStat departure values of 

Class 1, Class 2, and Class 3 correspond to the categories of FRCC 1, FRCC 2, and FRCC 3. We determined 

that managers would be most interested in areas where 

the strength of evidence (observed significance) for a 

departure estimate was highest, and we assigned those 

areas relatively higher classification values. For example, 

an LRU may have a relatively low departure estimate 

(less than 0.33), but if the observed significance value 

was less than <0.01, we assigned a Class 2 value to the 

unit. Conversely, we gave lower classification values to 

areas where evidence in the departure estimate was low; 

for example, an LRU with a high departure estimate 

(≥ 0.67) and a high observed significance (≥ 0.1) would 

be assigned a Class 1 value.

Table 4—Classified HRVStat departure as assigned to each departure/observed significance

grouping and the percent of each zone in these categories for zones 16 and 1

obs. sign. < 0.01 0.01 ≤ obs. sign. < 0.1 obs. sign. ≥ 0.1

Classified HRVStat departure: 

d < 0.33 2 

0.33 ≤ d < 0.67  3

d ≥ 0.67  

Percent area of zone:

Zone 16

d < 0.33 60.34% 22.27% 9.9

0.33 ≤ d < 0.67 5.69% 0.01% 

d ≥ 0.67  1.73% 

Zone 19

d < 0.33 61.42% 6.88% 6.

0.33 ≤ d < 0.67  12.67% 0.03% 0.

d ≥ 0.67  11.99% 0.90% 0.
332 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2

Chapter 11—Using Historical Simulations of Vegetation to Assess Departure of Current Vegetation Conditions across Large Landscap

Operational process for HRVStat—The steps for 

developing the departure, observed significance level, and 

classified HRVStat departure map layers were three-fold 

(fig. 6). First, we extracted the relevant fields from the 

LANDSUMv4 database, including reporting interval, 

LRU, PVT, succession class, and area, and combined 

these data with the associated current landscape data to 

create the input file. Next, we ran the HRVStat program 

using GAUSS software (Aptech Systems, Inc. 2004) in 

addition to an independent platform of the HRVStat 

program. The HRVStat program produced output files 

containing departure, observed significance level, and 

classified HRVStat departure values for each LRU. We 

then linked the HRVStat output files to the LRU map, 

and created maps of departure, observed significance 

level, and classified HRVStat departure.

Detailed Demonstration of Departure 

Estimates using the HRVStat and FRCC 

Guidebook Approaches

For illustration purposes, we provide a detailed demonstration of departure calculations for a selection of 

LRUs using both the FRCC Guidebook and HRVStat 

approaches. We chose three LRUs in Zone 16 with classified HRVStat departure and FRCC estimates of 1, 2 and 

3. To demonstrate the FRCC Guidebook approach, we 

present a detailed description of estimation procedures 

for only one LRU (FRCC 3) because we determined 

one example was sufficient, given the simplicity of the 

calculations. Because the HRVStat approach is less easily 

comprehended, we provide examples for all three LRUs. 

Specifically, we present the distributions of succession 

Figure 6—The flow diagram for

developing the departure, observed 

significance level, and classified

HRVStat departure maps using the 

HRVStat method. Data from LANDSUMv4 simulations are combined

with current landscape data to build 

input files containing the attributes

of landscape reporting unit (LRU), 

PVT, succession class, and area 

(m2). HRVStat calculates departure 

statistics for each LRU and produces 

an output file with departure, observed significance, and classified

HRVStat departure estimates for 

each LRU. Departure statistics are 

linked to the LRU spatial layer to 

build maps of departure, observed 

significance level, and classified

HRVStat departure.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006

Chapter 11—Using Historical Simulations of Vegetation to Assess Departure of Current Vegetation Conditions across Large Landscap

classes by PVT and qualitatively describe the succession 

classes contributing most to departure for individual 

LRUs. We also present the empirical distributions of 

divergence and observed departure estimates, from 

which observed significance values are derived.

Departure Estimates by Fire Return 

Intervals

The national coarse-scale project evaluated the relationship of estimated FRCCs on current landscapes 

to estimates of historical fire return intervals (Schmidt 

and others 2002), and the LANDFIRE Prototype Project conducted similar analyses for comparison. One 

of the map layers created by the LANDSUMv4 model 

described fire return intervals (the number of years 

between successive fires for each pixel in the mapping 

zone), which was classified into four categories to be 

compatible with the fire regime maps developed for the 

national coarse-scale project, including: 0-35 year frequency, 36-100 year frequency, 101-200 year frequency, 

and 201+ year frequency (Pratt and others, Ch. 10). We 

compared the departure indices estimated by the FRCC 

Guidebook and HRVStat approaches to the classified 

fire return interval layer to evaluate whether departure 

becomes higher in areas where more fire is observed 

under simulated historical conditions.

Results ________________________

We present maps and other results from the exploratory stages in our method development for Zone 16; 

for Zone 19, we present our resultant recommended 

method for estimating departure. However, even for 

Zone 19, results reflect a work in progress, and more 

analysis and research is needed to further improve the 

FRCC Guidebook and HRVStat approaches. Hence, the 

specific findings presented here should be considered 

primarily as a demonstration of method development and 

as a comparison of approaches to estimating departure. 

Maps and computed values for departure statistics may 

change as these procedures are refined for the national 

implementation of the LANDFIRE Project.

FRCC Guidebook Approach

A comparison of the temporal snapshot and multitemporal methods for deriving reference conditions 

revealed that the snapshot approach produced the highest 

zone-wide estimates of departure, with a zonal mean of 73 

(fig. 7A) compared to means ranging from 23 to 63 using 

the multi-temporal method (fig. 7B-7D). We expected this 

result because many of the currently present succession 

classes did not exist in the year-1,000 LANDSUMv4 

output. Simply stated, if reference conditions for a succession class are 0 (the succession class, by chance, did 

not occur in year-1,000 LANDSUMv4 output) then there 

would be no similarity and thus, complete departure. 

Once we recognized the ineffectiveness of the snapshot 

method for deriving reference conditions, we focused 

our efforts on the multi-temporal approach.

Using the multi-temporal approach for deriving reference conditions, we discovered that for Zone 16, each 

progressively smaller spatial domain (fig. 2) produced 

noticeably lower estimates of departure (fig. 7B-7D), with 

a zonal mean of 63 using the zone as the spatial domain, 

45 using the simulation landscape, and 23 using the LRU 

as the spatial domain; furthermore, the proportions of 

the zone belonging to FRCC 3 were highest using the 

zonal-spatial domain (41 percent) and lowest using the 

LRU-spatial domain (1 percent) (table 5). For all three 

spatial domains, departure and FRCC were higher in 

the area surrounding the Uinta Mountains and in the 

southern portions of the mapping zone (figs. 7B-7D and 

8). Examining the LRU-spatial domain alone, the departure estimates for Zone 16 ranged from 0 to 96 with 

a mode of 11 (fig. 9A). For Zone 19, we evaluated only 

the LRU-spatial domain to derive reference conditions 

and observed a zonal departure mean of 42, mode of 

43, and a range of 0 to 100 (fig. 9B); in addition, most 

(74 percent) of Zone 19 belonged to FRCC 2 (table 

5). Departure and FRCC were generally higher in the 

northern portions of the zone and in scattered clusters in 

the central and eastern portions of the zone (fig. 10).

To assess how the various vegetation types contributed 

to departure, we also evaluated the mean departures for 

each PVT across the mapping zones by constructing 

simple spatial overlays of departure and PVT maps for 

each zone (appendices 11-B and 11-C). For Zone 16, 

using all three spatial domains for computing reference 

conditions, the highest departure was estimated to occur in the Douglas-fir / Timberline Pine PVT (appendix 

11-B), but this PVT occupied only a small fraction (0.47 

percent) of the zone. The Pinyon-Juniper / Mountain Big 

Sagebrush / South PVT had the second highest estimate 

of departure across all three spatial domains (appendix 

11-B) and was the most abundant PVT (17 percent) in 

the zone. In Zone 19, the Bluebunch Wheatgrass PVT 

had the highest estimated departure (62), followed by 

the Dry Shrub PVT (58) (appendix 11-C), but they occupied relatively small portions of the zone (six and one 

percent, respectively). The most abundant PVTs were 

Wyoming – Basin Big Sagebrush Complex (15 percent 
334 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 

Chapter 11—Using Historical Simulations of Vegetation to Assess Departure of Current Vegetation Conditions across Large Landscap

Figure 7—Departure estimates using FRCC Guidebook approach for Zone 16 based on reference conditions derived

from: (A) LANDSUMv4 output at simulation year-1000 and 90 percent of the maximum percent area observed fo

each succession class in a PVT across the simulated time series for three spatial domains (the areal extent for 

summarizing the LANDSUMv4 time series data) including: (B) the entire zone, (C) individual simulation landscapes,

and (D) individual landscape reporting units. 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 

Chapter 11—Using Historical Simulations of Vegetation to Assess Departure of Current Vegetation Conditions across Large Landscap

Table 5—Proportion of each mapping zone in the three classes describing (1) classified HRVStat departure, (2) FRCC using the

FRCC Guidebook approach for each of the three spatial domains of mapping zone, simulation landscape (SL), and landscape 

reporting unit (LRU) in Zone 16 and for the LRU spatial domain only in Zone 19, and (3) FRCC from Schmidt and others 200

Classified FRCC Guidebook FRCC Guidebook FRCC Guidebook

HRVStat approach using approach using approach using Schmidt and

Class departure zonal spatial domain SL spatial domain LRU spatial domain others 2002

Zone 16

 1 32% 5% 24% 77

2 60% 54% 67% 22% 

 3 8%  41% 

Zone 19

 1 13% n/a n/a

2 70% n/a n/a 74%  

 3 17% n/a n/a 25

Figure 8—Fire regime condition class (FRCC) for Zone 16 using the FRCC Guidebook approach and based on reference conditions derived from 90 percent of the maximum percent area observed for each succession class in a PVT across the simulated

time series for three spatial domains (the areal extent for summarizing the LANDSUMv4 time series data) including: (A) the entire

zone, (B) individual simulation landscapes, and (C) individual landscape reporting units. 
336 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2

Chapter 11—Using Historical Simulations of Vegetation to Assess Departure of Current Vegetation Conditions across Large Landscap

Figure 9—Frequency distribution of departure estimates using the HRVStat and FRCC Guidebook approaches for (A) Zone

16 and (B) Zone 19  Departure values from HRVStat were rescaled from 0-1.0 to 0-100 to match the scale of the F

Guidebook values. FRCC Guidebook estimates use the LRU as the spatial domain.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 

Chapter 11—Using Historical Simulations of Vegetation to Assess Departure of Current Vegetation Conditions across Large Landscapes Figure 10—FRCC Guidebook approach results for (A) departure and (B) FRCC for Zone 19 based on reference conditions derived from the 90th percentile of percent area observed for each succession class in PVTs across the simulated time series using the landscape reporting unit as the spatial domain (the areal extent for sum- marizing the LANDSUMv4 time series da
338 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2

Chapter 11—Using Historical Simulations of Vegetation to Assess Departure of Current Vegetation Conditions across Large Landscap

of zone) and Douglas-fir / Douglas-fir (11 percent), 

and they had relatively low departures of 39 and 34, 

respectively.

It should be recognized that these results do not precisely estimate the degree to which each vegetation class 

contributed to departure. That is, the results presented 

were produced through simple overlays (for example, a 

departure layer overlaid on a PVT layer), and the amount 

that any of these vegetation classes contributed to the 

measured departure within LRUs could not be precisely 

measured. For example, an LRU with a high departure 

estimate may be composed of 90 percent Douglas-fir 

PVT and 10 percent Cool Herbaceous PVT. In these 

simple spatial overlays, both PVTs would be reported 

as having high departure. However, the Douglas-fir PVT 

would likely be the dominant contributor to the high 

departure estimate. The Cool Herbaceous PVT may 

be only slightly departed, but we would not observe its 

true value given our overlay methods. More extensive 

programming and analysis are needed to more precisely 

describe departure by vegetation class.

HRVStat Approach

The final products using the HRVStat method were 

sets of three maps representing departure, observed 

significance level, and classified HRVStat departure 

(figs. 11 and 12). In Zone 16, departure estimates were 

generally higher in the southern portion of the mapping zone, and correspondingly, most occurrences of 

high classified HRVStat departure (Class 3) were in the 

southern area, as well. Departure and classified HRVStat 

departure in Zone 19 were generally higher in the central 

and eastern portions of the zone. Overall, estimates of 

departure were lower in Zone 16 (0.086) than in Zone 

19 (0.285), but observed significance values were similar 

(0.039 and 0.014 in zones 16 and 19, respectively). Most 

departure estimates for individual LRUs were less than 

0.1 in Zone 16 and less than 0.3 in Zone 19 (fig. 9), and 

observed significance levels for individual LRUs were 

generally less than 0.1 in both zones. The proportions 

of each mapping zone belonging to the three classified 

HRVStat departure categories were similar between 

zones, with the majority (60-70 percent) belonging to 

Class 2 (table 5).

To obtain an overall sense of the extent to which each 

of the vegetation classes contributed to estimated departure, we also evaluated departure estimates by PVT 

using simple spatial overlays of departure, observed significance, and classified HRVStat departure layers with 

the PVT layer (appendices 11-D and 11-E). The highest 

departure (mean values greater than 0.1) observed in Zone 

16 occurred primarily in non-forest PVTs (pinyon-juniper 

types and the Salt Desert Shrub PVT), had relatively 

low observed significance (mean values of 0.01 to 0.03), 

and comprised 28 percent of the mapping zone; some 

forest PVTs (Ponderosa Pine, Douglas-fir / Timberline 

Pine, and Douglas-fir / Douglas-fir) had similarly high 

departure and low observed significance and comprised 

6 percent of the mapping zone (appendix 11-D). Results 

for the distributions of classified HRVStat departure by 

PVT were similar. Of those areas in Zone 16 categorized 

as Class 3, the most prevalent were non-forest PVTs, 

including: Pinyon – Juniper / Mountain Big Sagebrush 

/ South PVT (comprising approximately 26 percent of 

the zone), Pinyon – Juniper / Wyoming – Basin Big 

Sagebrush / South (24 percent), and Salt Desert Shrub 

(9 percent).

In Zone 19, the PVTs with the relatively highest departure estimates (>0.5) and low observed significance 

(<0.01) were also non-forest (bluebunch wheatgrass types, 

the Dry Shrub PVT, and Fescue Grasslands PVT) and 

encompassed about 10 percent of the mapping zone. 

PVTs with relatively moderate departure (<0.5 and 

>0.3) were a mixture of forest and non-forest types and 

comprised approximately 6 percent of the zone (appendix 11-E). The lowest departure (<0.3) observed in the 

mapping zone occurred mainly in forest PVTs (about 

55 percent of the zone) but also occurred in sagebrushrelated PVTs (about 20 percent). It should be noted that 

approximately 10 percent of Zone 19 was composed 

of water, agriculture, and non-vegetated areas, which 

were omitted from departure estimates. Of the areas 

in Zone 19 categorized as Class 3, the most abundant 

PVTs were a mixture of grass, shrub, and forest: Bluebunch Wheatgrass (14 percent of the mapping zone), 

Wyoming – Basin Big Sagebrush Complex (19 percent), 

and Douglas-fir / Douglas–fir (8 percent).

When evaluating the extent to which a landscape 

has diverged from simulated historical conditions, we 

can also examine which current succession classes 

within a given PVT contribute the most to departure. 

A complete presentation of departure estimates by all 

PVTs and succession classes for each mapping zone is 

beyond the scope of this report; however, we present an 

example from Zone 16 for illustration purposes (table 

6). Succession classes within the Pinyon – Juniper / 

Mountain Big Sagebrush / South PVT with the highest 

mean departure indices across Zone 16 were Juniper 

High Cover, High Height (0.45) and Pinyon – Juniper 

High Cover, High Height (0.37) (table 6). Both of these 

succession classes are late-seral classes that were less 

prevalent under reference conditions perhaps because 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 

Chapter 11—Using Historical Simulations of Vegetation to Assess Departure of Current Vegetation Conditions across Large Landscapes Figure 11—Zone 16 HRVStat results for (A) departure, (B) observed significance level, and (C) classified HRVStat departure, as categorized from departure and observed significance leve
340 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2

Chapter 11—Using Historical Simulations of Vegetation to Assess Departure of Current Vegetation Conditions across Large Landscap

Figure 12—Zone 19 HRVStat results for (A) departure, (B) observed significance level, and (C) classified HRVStat departure, as categorized from departure and observed

significance level.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006

Chapter 11—Using Historical Simulations of Vegetation to Assess Departure of Current Vegetation Conditions across Large Landscap

fires were simulated with higher frequencies than occurs in current landscapes. Exotic Forbs (High Cover, 

Low Height) within this PVT also had relatively high 

mean departure estimates (0.17) (table 6). Users of the 

spatial data layers can easily perform similar analyses 

with the HRVStat data sets for estimating departure.

Detailed Demonstration of Departure 

Estimates using the HRVStat and FRCC 

Guidebook Approaches

We demonstrate the process for computing FRCC 

through the FRCC Guidebook method using an LRU 

with an estimated FRCC of 3 that was located in the 

southern portion of Zone 16 (tables 2 and 3; fig. 13). 

We provide just one example because it is a simple 

calculation relative to that required using the HRVStat 

approach. We estimated departure for the example LRU 

in two steps. First, we computed the departure for each 

PVT within the LRU of interest (table 2). Although there 

are 12 PVTs in the LRU examined in table 2, for brevity, we present only the calculations for the five most 

dominant PVTs in the LRU (table 10). Note that there is 

one overall departure calculation for a given PVT, but 

each succession class within a PVT also has a unique 

departure value. This reflects the fact that the similarity 

values for each succession class are summed across the 

PVT in which they reside. Once the overall departure 

is calculated for each PVT, the first step is complete. In 

the second step, we computed the area of each PVT in 

the LRU (table 3). These data were then used to scale 

departure measures for every PVT on an area-weighted 

basis (table 3). This process is accomplished by multiplying the proportion of each PVT within an LRU by its 

respective departure estimate and summing these values 

for the entire LRU. In our example calculation (tables 2 

and 3), the Pinyon – Juniper / Mountain Big Sagebrush / 

South PVT had the highest departure (81.48). This high 

departure greatly affected the final departure estimate for 

the entire LRU because the Pinyon – Juniper / Mountain 

Big Sagebrush / South PVT also occupied the largest 

area (72%).

To demonstrate the characteristics of LRUs with departure estimates derived through the HRVStat approach, we 

present three examples of LRUs with classified HRVStat 

departures of 1, 2, and 3. Our example of a Class 3 LRU 

was composed primarily of two PVTs: the Pinyon – Juniper / Mountain Big Sagebrush / South PVT (72 percent) 

and the Pinyon – Juniper / Gambel Oak PVT (16 percent) (fig. 13). For the Pinyon – Juniper / Mountain Big 

Sagebrush / South PVT, current distributions of succession classes were very different from those of simulated 

historical conditions, which lead to a high departure 

estimate. The dominant succession classes under simulated historical conditions were low shrub and grassland 

types, whereas the pinyon and juniper succession classes 

dominated the current vegetation (fig. 14A; table 7). The 

second and more minor component of this LRU was the 

Table 6—Mean departure estimates and observed significance, using HRVStat methods, for each succession

class in the Pinyon – Juniper / Mountain Big Sagebrush / South PVT, as well as the percent by area of each

succession class for the PVT in Zone 16. See table 5 for definitions of structural stage codes in succession

class names.

Mean observed

Succession class Mean departure significance Percent of PVT in zone

Juniper HHW 0.45 0.004 1

Pinyon – Juniper HHW	 0.37	 0.004	 12.74%

Juniper LHW 0.33 0.004

Pinyon – Juniper LHW	 0.31	 0.005	 1.03%

Juniper LLW 0.26 0.006 26.92%

Mountain Deciduous Shrub LHW 0.26 0.006 9.15

Pinyon – Juniper LLW	 0.22	 0.006	 24.42%

Cool Season Grasses HLH 0.21 0.006 0.0

Dry Deciduous Shrub HLS 0.19 0.007 0.1

Mountain Deciduous Shrub HHS 0.18 0.008 5.8

Exotic Forbs HLH 0.17 0.006 0.0

Mountain Big Sagebrush Complex HLS 0.14 0.010 

Mountain Big Sagebrush Complex LLS 0.12 0.012 0.

Dry Deciduous Shrub LLS 0.07 0.019 0.1
342 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2

Chapter 11—Using Historical Simulations of Vegetation to Assess Departure of Current Vegetation Conditions across Large Landscap

Figure 13—Three LRUs in Zone 16 assigned by the FRCC Guidebook and HRVStat methods to three classes (described here

as FRCC 1, 2, and 3) and the spatial distribution of PVTs within each of these LRUs

Pinyon – Juniper / Gambel Oak PVT, which also showed 

considerable differences between historical and current 

succession class distributions. Under simulated historical 

conditions, this PVT was composed mainly of low shrub 

and grassland-related succession classes, whereas the current landscape contains mainly pinyon and juniper type 

succession classes (fig. 14B; table 7). Figure 15 shows that 

the observed departure estimate (0.74) is much greater 

than the distribution of divergence estimates, resulting 

from the dissimilarities in succession class distributions 

between current and reference conditions; moreover, these 

dissimilarities lead to a low estimate for the observed 

significance level (0.005).

To illustrate the characteristics of a Class 2 LRU 

derived using the HRVStat approach, we chose a unit 

with an estimated moderately low departure index (0.25) 

but high observed significance value (0.005) (table 4). 

The LRU contained mostly the Grand Fir – White Fir 

PVT (53 percent) but also a substantial amount of the 

Pinyon – Juniper / Mountain Big Sagebrush / South 

PVT (32 percent) (fig. 13). For the more abundant Grand 

Fir – White Fir PVT, succession class distributions were 

only moderately dissimilar between current and simulated historical conditions, contributing to a relatively 

low departure estimate. Simulated historical conditions 

contained numerous succession classes (27), which 

consisted primarily of the Douglas-fir and Aspen-Birch 

types. The current landscape also contains a substantial amount of Aspen-Birch in addition to a dominant 

Grand Fir succession class and various others (fig. 16A, 

table 7). The second PVT revealed greater differences 

in succession class distributions between current and 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006

Chapter 11—Using Historical Simulations of Vegetation to Assess Departure of Current Vegetation Conditions across Large Landscap

Figure 14—Distribution of succession classes in the two dominant PVTs, for reference and current conditions, in a landscape reporting unit with a classified HRVStat departure of 3 (departure index = 0.74 and

observed significance level = 0.005) in Zone 16: (A) Pinyon – Juniper / Mountain Big Sagebrush / Sou

covers 72 percent of the landscape reporting unit and (B) Pinyon – Juniper / Gambel Oak covers 16 percent

of the landscape reporting units. See table 7 for explanation of succession class codes. 
344 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 

Chapter 11—Using Historical Simulations of Vegetation to Assess Departure of Current Vegetation Conditions across Large Landscap

Table 7—Succession classes for PVTs in landscape reporting units 

demonstrating classified HRVStat departure in figures 14, 16, 18 and

22 – using the HRVStat approach. Codes defining succession class

are described first by cover type and then by structural stage.

Cover type code Cover type

Forest types

AB	 Aspen − Birch

DF	 Douglas-fir

GF	 Grand Fir−White Fir

LP Lodgepole Pine

PP Ponderosa Pine

SF	 Spruce − Fir

TP Timberline Pines

WH Western Hemlock

Woodland types

J Juniper

PJ	 Pinyon – Juniper

Shrub & Grassland types

CG Cool Season Grasses

DDS Dry Deciduous Shrub

ES Montane Evergreen Shrubs

MBS Mountain Big Sagebrush Complex

MDS Mountain Deciduous Shrub

NF Native Forbs

Structural stage code Structural stage

LLF Low Cover, Low Height Forest

HLF High Cover, Low Height Forest

LHF Low Cover, High Height Forest

HHF High Cover, High Height Forest

LHW Low Cover, High Height Woodland

HHW High Cover, High Height Woodland

LHW Low Cover, High Height Woodland

LLS Low Cover, Low Height Shrubland

HLS High Cover, Low Height Shrubland

LHS Low Cover, High Height Shrubland

HHS High Cover, High Height Shrubland

LLH Low Cover, Low Height Herbaceous

HLH High Cover, Low Height Herbaceous

simulated historical conditions. Specifically, the most 

frequent succession classes under simulated historical 

conditions were rare to absent under current conditions, 

and vice versa (fig. 16B). Figure 17 demonstrates that 

the observed departure estimate (0.25) is greater than 

the distribution of divergence estimates for the simulated historical conditions, resulting in a low observed 

significance value (0.005).

The unit chosen to demonstrate the characteristics of 

a Class 1 LRU had a low departure estimate (0.002), 

a relatively high observed significance (0.2475), and 

was composed almost entirely of one PVT: 99 percent 

Spruce-Fir / Spruce-Fir / Lodgepole Pine (fig. 13). 

Most of the succession classes were relatively rare in 

the simulated historical data sets (fig. 18; table 7), and 

the current conditions’ dominant class of Aspen-Birch 

Low Cover / High Height was relatively abundant in 

the historical data. Figure 19 shows that the observed 

departure estimate (0.002) was similar to the median 

(0.001) of the divergence estimates from the simulated 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 

Chapter 11—Using Historical Simulations of Vegetation to Assess Departure of Current Vegetation Conditions across Large Landscap

Figure 15—Frequency distributions for divergence estimates in the example landscape reporting unit with a classified HRVStat departure of -3, the normalized probability distribution (dashed line), and an observed departure for current conditions of

0.74. The proportion of area under the probability distribution above the observed departure is 0.005, which is the estimated

observed significance level.

historical data set, leading to a relatively high observed 

significance (0.2475).

Departure Estimates by Fire Return 

Interval

Evaluation of departure results by fire return interval 

classes suggested that, under simulated historical conditions, areas with short simulated fire return intervals 

tended to have higher departure estimates (table 8). In 

Zone 16, HRVStat departure estimates were highest 

(9.42 on a scale of 1 to 100) in areas with short fire 

return intervals (0-35 years) under reference conditions. 

In contrast, areas with long fire return intervals (201+ 

years) had the lowest departures (3.16 on a scale of 1 to 

100) under reference conditions. The FRCC Guidebook 

departure estimates showed a less clear but somewhat 

similar trend, with higher departure (mean value of 

35.22) in areas with short simulated fire return intervals 

(0-35 years) and somewhat lower departure (mean value 

of 27.27) in areas where long fire return intervals (201+ 

years) were simulated (table 8).

Results from Zone 19 showed similar patterns (table 8). 

HRVStat departure estimates were highest (mean value 

of 38.13 on a scale of 1 to 100) in areas with short fire 
346 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2

Chapter 11—Using Historical Simulations of Vegetation to Assess Departure of Current Vegetation Conditions across Large Landscap

Figure 16—Distribution of succession classes in the two dominant PVTs, for reference and current conditions, 

in a landscape reporting unit with a classified HRVStat departure of -2 (departure index = 0.25 and observed

significance level = 0.005) in Zone 16: (A) Grand Fir – White Fir covers 53 percent of this landscape reporti

unit and (B) Pinyon – Juniper / Mountain Big Sagebrush / South covers 32 percent of the landscape reporting

units. See table 7 for explanation of succession class codes.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 

Chapter 11—Using Historical Simulations of Vegetation to Assess Departure of Current Vegetation Conditions across Large Landscap

Figure 18—Distribution of 

succession classes by percent area, under reference 

and current conditions, for 

the Spruce – Fir / Spruce

– Fir / Lodgepole Pine PVT

that dominates a landscape 

reporting unit with a classified HRVStat departure of

-1 (departure index = 0.002

and observed significance

level = 0.25) in Zone 16. See

table 7 for explanation of 

succession class codes.

Figure 17—Frequency

distributions for divergence 

estimates in the example 

landscape reporting unit 

with a classified HRVStat

departure of -2, the normalized probability distribution (dashed line), and 

an observed departure for 

current conditions of 0.25. 

The proportion of area 

under the probability distribution above the observed 

departure is 0.005, which 

is the estimated observed 

significance level.
348 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2

Chapter 11—Using Historical Simulations of Vegetation to Assess Departure of Current Vegetation Conditions across Large Landscap

Table 8—Summary of departure estimates (area weighted mean) by historical fire

return interval classes using the HRVStat and FRCC Guidebook methods. In the 

FRCC Guidebook method, the spatial domains were the individual landscape reporting 

units. Note: the HRVStat departure index was rescaled from 0-1 to 0-100 to matc

the FRCC Guidebook departure range.

Fire Departure using Departure using

frequency class HRVStat approach FRCC Guidebook approach

Zone 16

1 - 35 years 9.42  

36 – 100 years	 7.78	 31.50

101 - 200 years 7.92 22.

201+ years  3.16 2

Zone 19

1 - 35 years  38.

36 – 100 years	 16.08	 16.59

101 - 200 years  4.

201+ years  14.77 

Figure 19—Frequency distributions for divergence estimates in the example landscape reporting unit with

a classified HRVStat departure of -1, the normalized probability distribution (dashed line), and an observed

departure index for current conditions of 0.002. The proportion of area under the probability distribution above 

the observed departure index is 0.2475, which is the estimated observed significance level. 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 

Chapter 11—Using Historical Simulations of Vegetation to Assess Departure of Current Vegetation Conditions across Large Landscap

return intervals (0-35 years) under reference conditions. 

Average departure decreased as fire return intervals 

increased (with a mean value of 16.08 for the 36 to 

100 year group and 4.37 for the 101-200 year group); 

however, in areas with the longest fire return intervals 

(201+ years), the mean departure estimate was intermediate (14.77). The FRCC Guidebook departure indices 

in Zone 19 showed the most departure (20.15) in areas 

with short simulated fire return intervals (0-35 years) 

and somewhat lower departure in the areas with longer 

return intervals (17.06 for 201+-years).

Discussion _____________________

We developed two methods for creating maps describing the departure of current landscape conditions from 

simulated historical conditions. Both methods are standardized, both can be applied across large, continuous 

landscapes, and both can be automated to produce maps 

efficiently. The methods were designed for different applications: the FRCC Guidebook approach was intended 

to implement field-based procedures in a GIS environment, whereas the HRVStat approach was intended for 

use in the development of a statistically rigorous method 

that incorporates the temporal variability in a complex, 

spatial database describing historical conditions. Correspondingly, each method produced divergent results 

in the maps of departure and their classifications (figs. 

7, 8, 10, 11 and 12; table 5). To interpret these disparities and ranges, we need to recognize the strengths and 

weaknesses of each approach and understand their differences and commonalities.

Strengths and Limitations of the FRCC 

Guidebook Approach

The FRCC Guidebook method is simple, flexible, and 

designed to be easily understood by managers. This approach does not require advanced statistical techniques 

or large data sets, and the metrics used to represent the 

simulated historical conditions in the departure calculation can be easily changed. For Zone 16, we used the 90 

percent of the maximum percent area observed for each 

succession class in a PVT as the metric through which 

to summarize the reference conditions’ time-series; for 

Zone 19, we used the 90th percentile of the percent area 

observed for each succession class in a PVT. However, 

median, mean, minimum, or other metrics characterizing 

the reference conditions data set could also be substituted. 

The FRCC Guidebook approach does not require the 

simulated historical data set to have a minimum temporal 

depth (as does the HRVStat approach) and can use a data 

set describing PVT-succession class distributions that is 

limited to one observation, such as is demonstrated in 

figure 7A where only one reporting interval was used. 

Since the departure estimate is based on a procedure currently being applied by federal managers to characterize 

ecosystems with regard to hazardous fuel accumulation 

(Hann and others 2004), many managers should already 

be familiar with the calculations and readily understand 

the process by which spatial data are produced using 

the FRCC Guidebook approach.

Despite these merits, the FRCC Guidebook approach 

fails to provide a consistent and comprehensive measure of departure. Specifically, this approach requires 

that reference conditions are represented by a single 

observation, but departure estimates are very sensitive 

to the metric chosen to represent that point statistic. To 

compare the PVT-succession class distributions of current and reference conditions, we must choose a metric 

to reduce the 200 observations from the simulated time 

series to one observation. However, the choice of vegetation metric (median, mean, minimum, 90 percent of 

maximum, or 90th percentile) for summarizing reference 

conditions cannot aptly represent all cases of vegetation 

composition distributions, and the chosen metric will bias 

departure estimates in different ways, depending on the 

composition of an LRU. This shortcoming is illustrated 

in table 9 where departure ranged from 0 using the 90 

percent of maximum and 90th percentile metrics to 32 

using the median as the metric.

We chose metrics for describing reference conditions 

(90 percent of maximum and 90th percentile) that would 

be more sensitive to variation on the upper end of succession class distributions (Hann, personal communication). 

We used the 90 percent of maximum metric for estimating 

reference conditions in Zone 16, but determined that the 

metric over-emphasized the upper end of distributions 

and consequently over-predicted departure – particularly 

in cases where an extreme, rare event triggers large but 

brief fluctuations in vegetation composition. In other 

words, departure would be measured only by evaluating 

the reference conditions for a PVT-succession class combination at its widest ranges, even though the remaining 

time series may be primarily in a narrower range. The 

90th percentile metric proved more suitable because it 

filtered the rare, large oscillations in vegetation conditions 

and better represented the upper range of variation in 

vegetation classes. However, the 90th percentile metric 

represented the simulated time series poorly towards the 

median and lower end of succession class distributions. 

For example, the current vegetation composition may 

be very similar to the dominant vegetation composition 
350 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 20

Chapter 11—Using Historical Simulations of Vegetation to Assess Departure of Current Vegetation Conditions across Large Landscapes Table 9—Example departure calculations using three vegetation metrics to characterize reference conditions. Note that, using the logic of the FRCC Guide- book field procedures (Hann and others 2004), reference conditions are non-orthogonal (the summation of their area exceeds 100 percent) with respect to the total area that the succession classes occupy within a PVT. See table 7 for codes to structural stages within succession class categories. Current Sum of PVT Succession class Reference condition condition Similarity similarity 90th percentile Ponderosa Pine Ponderosa Pine / LLF 60  15  15  100 Ponderosa Pine Ponderosa Pine / LHF 90 85 8

– Juniper / Mountain Big Sagebrush / South	 Juniper / HHW	 85	 65	 65	 100

Pinyon

– Juniper / Mountain Big Sagebrush / South	 Pinyon-Juniper / LLW	 75	 35	 30

Pinyon

– Juniper / Mountain Big Sagebrush / South	 Pinyon-Juniper / HHW	 35	 0	 0

Departure estimate 0

90 % of max.

Ponderosa Pine Ponderosa Pine / LLF 70  15  1Ponderosa Pine Ponderosa Pine / LHF 95 85 85Pinyon

– Juniper / Mountain Big Sagebrush / South	 Juniper / HHW	 90	 65	 65	 100

Pinyon

– Juniper / Mountain Big Sagebrush / South	 Pinyon-Juniper / LLW	 85	 35	 35

Pinyon

– Juniper / Mountain Big Sagebrush / South	 Pinyon-Juniper / HHW	 45	 0	 0

Departure Estimate 0

Mean

Ponderosa Pine Ponderosa Pine / LLF  3 1Ponderosa Pine Ponderosa Pine / LHF 53 85Pinyon

– Juniper / Mountain Big Sagebrush / South	 Juniper / HHW	 45	 65	 45	 80

Pinyon

– Juniper / Mountain Big Sagebrush / South	 Pinyon-Juniper / LLW	 53	 35	 35

Pinyon

– Juniper / Mountain Big Sagebrush / South	 Pinyon-Juniper / HHW	 28	 0	 0

Departure estimate 32
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Chapter 11—Using Historical Simulations of Vegetation to Assess Departure of Current Vegetation Conditions across Large Landscap

observed in the simulated time series, as shown in figure 

4, with an Aspen – Birch High Cover, High Height succession class in a Spruce-Fir / Spruce–Fir / Lodgepole 

Pine PVT. Using the 90th percentile for our metric, we 

would estimate a relatively high departure – a seemingly 

incongruous result since current conditions are similar 

to the historical conditions.

We avoided using metrics that evaluate central tendencies (in other words the mean or median) because 

they can overlook or understate variation. For example, 

succession classes may have the same mean occurrence 

in the simulated historical record regardless of whether 

variability around the mean was low or high, and the 

resulting departure estimates would also be the same – even 

though the succession class occurrence for the current 

conditions might fall well within the simulated historical 

range when the data contain high variability. In short, 

the similarity calculation in the FRCC Guidebook approach is very sensitive to the metric used to represent 

reference condition time series data, and we expect that 

departure estimates for Zones 16 and 19 would look 

very different if other metrics were used.

An additional limitation to spatial application of the 

FRCC Guidebook approach is that illogical calculations 

can result. Specifically, we must select a single value to 

represent the percent of each succession class occupying 

a PVT across a time series. Because these selected values 

are not likely to occur at the same time interval (as in 

the temporal snapshot approach illustrated in fig. 7A), 

the sum of the area for all succession classes in a PVT 

will not equal 100 percent. In a simple hypothetical example, an LRU consists entirely of one PVT with three 

succession classes occurring across the simulated time 

series such that their 90th percentiles are: 65 for succession class A in year 150; 40 for class B in year 4,550; 

and 20 for class C in year 7,050. The total percent area 

for the reference conditions sums to 125 percent — an 

area greater than the possible size of an LRU. Table 9 

also demonstrates that, for a given PVT, the sum of the 

succession class areas does not necessarily total 100 

percent, such as in the case of the Ponderosa Pine PVT 

where, using the 90th percentile as the reference condition metric, the sum of the succession classes totals 150 

percent. Such nonsensical results in the total summed 

area occur regardless of the metric used. For example, in 

table 9, the sum of succession classes for the Ponderosa 

Pine PVT, using the mean as the reference condition, 

was 56 percent. These incongruities do not occur in 

the field implementation of the FRCC Guidebook field 

procedures (Hann and others 2004) because reference 

conditions are estimated for a discrete point in time. In 

the simulation environment, however, we encountered 

the problem because we applied the FRCC field-based 

procedures to a time series of data. Despite the illogical 

calculations in our FRCC Guidebook implementation, 

departure estimates were constrained to values from 

0 to 100 because the calculation does not rely on total 

area, but instead uses the value for the least abundant 

succession (the smaller of reference or current conditions) 

to determine similarity and ultimately the departure 

estimate.

Another problem — stemming from the requirement 

of using a point statistic to represent reference conditions — is that departure estimates are sensitive to 

stochasticity in the LANDSUMv4 simulations. Because 

the LANDSUMv4 model includes stochastic processes 

in the simulation of fire and vegetation processes, 

the likelihood of a rare event increases with greater 

simulation time. Consequently, departure estimates 

could be inconsistent between LANDSUMv4 runs 

and, potentially, substantially different, depending on 

simulation length. For example, a catastrophic fire may 

not occur during a 4,000-year simulation but is more 

likely to occur during a 5,000-year simulation. Use of 

the 90th percentile to represent reference conditions for 

Zone 19 helped minimize the potential effects of rare 

disturbances on the upper range of succession class 

distributions. Because point statistics cannot completely 

represent the full range and variability of a data set, the 

other possible metrics (median, mean, 90th percentile, 

etc.) would also be limited in their ability to moderate 

the effects of extreme stochastic events and would only 

impose different biases.

A final limitation of the FRCC Guidebook approach 

relates to the fact that the number of succession classes 

in a PVT affected the calculation of the departure index. 

That is, greater complexity in the succession models 

used to model reference conditions for each PVT led to 

an increase in the FRCC departure index. As the succession pathway complexity increased in the simulated 

historical data, the percent area occupied by any one 

succession class became dispersed across more classes, 

leading to a lower percent area for each individual class 

over the simulation period. Lower area values for each 

succession class leads to lower similarity indices when 

comparing reference conditions to the current, and 

consequently higher, departure estimates (see table 2 

for examples of similarity and departure calculations). 

The effect of PVT succession pathway complexity on 

departure estimates is also demonstrated in the departure 

maps for Zone 16, in which the three spatial domains 

of mapping zone, simulation landscapes, and LRUs 
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Chapter 11—Using Historical Simulations of Vegetation to Assess Departure of Current Vegetation Conditions across Large Landscap

were explored (figs. 7B-7D). The number of succession 

classes per PVT in the reference conditions was highest 

in the zonal spatial domain, followed by the simulation 

landscapes domain, and lowest in the LRU domain; 

correspondingly, the departure estimates were highest 

in the largest spatial domain (zone) and lowest in the 

smallest (LRU). The effect of PVT succession pathway 

complexity on departure estimates highlights the need 

to identify ways to standardize succession pathways 

across all areas of consideration.

Strengths and Limitations of the HRVStat 

Approach

The HRVStat approach statistically compares current 

succession class distributions to simulated historical 

distributions, integrating every observation of PVT and 

succession class as they fluctuate across landscapes in 

the time-series. The data sets for these analyses were 

complex: the number of PVTs and succession classes 

occurring within any one LRU ranged from 1 to approximately 220, and each of these classes varied in 

percent area for each landscape reporting interval across 

the time series. HRVStat was able to incorporate the 

variance structure within the entire time-series of this 

highly dimensional data set to produce a measure of 

departure using the best linear approximation approach 

and estimate an observed significance for measuring the 

evidence for departure estimates (Steele and others, in 

preparation).

One limitation of this approach was the requirement 

by HRVStat for certain characteristics in the simulated 

historical data set. As discussed above, an adequate reference time series for HRVStat requires minimal temporal 

autocorrelation, stationary processes in vegetation class 

distributions, and a sufficiently long record. These requirements led to longer simulation times, larger data sets, 

and higher computational demands. However, the overall 

quality of the resulting data sets was enhanced, enabling 

the HRVStat approach to provide results with reasonable 

statistical rigor (Steele and others, in preparation).

In turn, statistics for HRVStat were developed that 

were specific to the simulated historical data set and that 

explicitly recognized the stochasticity that was intrinsic 

to LANDSUMv4. As discussed above, the LANDSUMv4 

model simulates disturbance stochastically such that the 

resulting vegetation composition in a landscape will 

vary somewhat between repeated simulations and with 

longer simulation periods as the likelihood increases 

for rare and extreme disturbance events (such as a catastrophically large fire). The HRVStat departure statistic 

accommodates such stochasticity because it incorporates 

every observation in the historical record. Specifically, 

stochastic differences between simulation runs will 

produce different vegetation distributions, possibly including unusual vegetation compositions resulting from 

extreme disturbances. However, because the departure 

statistic emphasizes dominant patterns by integrating all 

observations, departure estimates should be relatively 

robust to rare, extreme disturbance events.

We suspect a limitation in the statistical calculations 

of HRVStat because the number of PVT and succession 

class combinations within any LRU affects departure 

estimates – a problem also apparent with the FRCC 

Guidebook approach but with opposite effects. As the 

number of PVT-succession class combinations increased 

within LRUs, estimates of departure by HRVStat tended 

to decrease. This observation is based on a cursory 

examination of an assortment of LRUs and a general 

comparison of forested versus non-forested areas. In 

evaluating various LRUs in zones 16 and 19, we generally found lower departure in LRUs with few PVTs and 

simple succession pathways, as opposed to the higher 

departure found in LRUs with more complex vegetation 

composition. Comparisons of forested to non-forested 

areas showed that forest PVTs, which generally had 

more classes in their succession pathways, also had 

lower departure estimates. However, the lower departure 

estimates in forest PVTs may also be attributed to the 

generally longer fire return intervals because the effects of 

fire exclusion would be less evident since fewer intervals 

would have been missed over the last century and the 

process of departure from historical vegetation composition would be slower than in those PVTs with more rapid 

succession and disturbance processes. Alternatively, we 

may simply be better able to model forested systems 

than non-forested systems because more information 

exists describing vegetation and disturbance (particularly 

fire) processes in forests (Long and others, Ch. 9). As 

with the FRCC Guidebook approach, the influence of 

succession pathway complexity on HRVStat departure 

estimates warrants further evaluation and should include 

exploration of techniques to standardize the succession 

pathways or parameters in the multivariate statistics of 

the HRVStat approach.

Comparison of Approaches

We categorized our FRCC and HRVStat departure 

estimates into three classes to compare results with the 

earlier nationwide coarse-scale (1-km) mapping project 

(Hardy and others 2001; Schmidt and others 2002). For 

Zone 16, the HRVStat map showed little correspondence to the coarse-scale map, both in terms of patterns 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 

Chapter 11—Using Historical Simulations of Vegetation to Assess Departure of Current Vegetation Conditions across Large Landscap

observed and relative area in each class (fig. 20; table 

5). Among the three FRCC Guidebook maps produced 

for Zone 16, the FRCC map based on the LRU spatial 

domain corresponded best to the coarse-scale map and 

showed similar patterns of low and moderate departures 

(FRCC 1 and 2) throughout the zone (figs. 7D and 20). 

For Zone 19, overall patterns of both the HRVStat and 

FRCC Guidebook maps were relatively different from 

those of the coarse-scale map (fig. 21). However, some 

correspondences existed between the coarse-scale and 

FRCC Guidebook maps, with high departure observed 

in the northern region of Zone 19. We did not compare 

these maps in any quantitative detail because the coarsescale map was intended for use at the regional level. 

Furthermore, the accuracy in any of the maps was not 

known, and an extensive field campaign would be needed 

to evaluate the accuracy of maps such as these. Moreover, 

the thresholds used to categorize departure estimates into 

three classes are arbitrary, and if different thresholds were 

used, completely different patterns in the maps of FRCC 

and classified HRVStat departure would result.

To avoid the classes imposed by the FRCC Guidebook method, we examined the unclassified departure 

maps produced by the HRVStat and FRCC Guidebook 

methods and compared the distributions of continuous 

departure estimates. Figure 9 shows frequency distributions of departure estimates using the HRVStat and 

FRCC Guidebook methods (with LRUs as the spatial 

domain), and figures 7, 10A, 11A and 12A show maps 

of departure for Zones 16 and 19. Generally speaking, 

compared to the HRVStat approach, distributions of 

departure estimates for each zone were dominated by 

higher values using the FRCC Guidebook approach (fig. 

9). In terms of spatial pattern, some similarities were 

apparent in the southerly portions of Zone 16 (figs. 7D 

AND 11A). Zone 19 showed more spatial pattern correspondences in that higher departure estimates were 

observed in the northern portions of the zone (in the 

North Fork Flathead River Valley and along the Rocky 

Mountains’ eastern front) and in the central part of the 

zone (in the Clark Fork River Valley and eastern prairie 

of Montana) (figs. 10A and 12A). We suspect that the 

greater correspondence in Zone 19 resulted from using the 90th percentile metric instead of 90 percent of 

maximum to represent reference conditions in the FRCC 

Guidebook approach. As in the case of the FRCC / classified HRVStat departure maps, we do not have field 

validation data with which to assess which departure 

index is most effective. At best, we can qualitatively look 

for consistency between map results to obtain an overall 

picture of vegetation conditions across broad regions. 

Consistent evidence of ecosystem change from these two 

departure maps can serve to highlight landscapes that 

have potentially undergone extensive ecological change. 

Additional information, such as site-specific field data 

and expert opinions, could supplement the departure 

estimates to evaluate how to best manage an ecological 

system (Landres and others 1999). It is important to 

note, however, that similar measures from the different 

approaches could also signify that the measures are 

simply both equally erroneous.

Dissimilarity between results from the two departure 

methods reveals potential weaknesses in both approaches. For example, departure estimates were different in 

Zone 16’s Uinta Mountains in areas dominated by the 

Spruce-Fir / Spruce-Fir / Lodgepole Pine PVT – a PVT 

with a complex succession pathway. Figure 22 shows 

succession class distributions in an LRU dominated by 

that PVT, which is typical for this area of the Uintas. 

In the reference conditions, LRUs were occupied by as 

many as 22 succession classes, whereas only a few classes 

dominated the landscape under current conditions. The 

HRVStat method tended to produce low departure estimates for LRUs in the Uintas, resulting potentially from 

the abundance of succession classes in the reference 

conditions. Conversely, the FRCC Guidebook approach 

tended to produce high departure estimates in these LRUs 

because the percent area in any one succession class of 

these complex PVTs was reduced since the total area was 

spread across many succession classes. As previously 

mentioned, both methods require further refinement to 

ensure that departure estimates are not biased by the 

complexity of the succession class pathways.

Departure Estimates by Fire Return 

Intervals

We compared departure estimates by fire return interval 

classes in a manner similar to that used in the evaluations 

conducted in the coarse-scale project (Schmidt and others 

2002). For both zones 16 and 19, we found that departure 

estimates were higher in areas with shorter fire return 

intervals. Similarly, Schmidt and others (2002) generally 

found that landscapes with estimated historical fire return 

intervals of 100-years or less had higher proportions of 

landscapes categorized as FRCC 3 (analogous to classified HRVStat departure-3), whereas areas with fire return 

intervals of 200-years or more had the least proportion of 

highly departed landscapes. We expect that the impacts of 

fire exclusion during the past 100 years are more evident 

in areas that historically had more frequent fires because 

such ecosystems have missed more fire return intervals 
354 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2

Chapter 11—Using Historical Simulations of Vegetation to Assess Departure of Current Vegetation Conditions across Large Landscap

Figure 20—Comparisons between (A) FRCC estimated by Schmidt and others 2002, (B) FRCC calculated using the FRCC Guidebook method, and (C) classifiedHRVStat departure for Zone 16
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 3

Chapter 11—Using Historical Simulations of Vegetation to Assess Departure of Current Vegetation Conditions across Large Landscapes Figure 21—Comparisons between (A) FRCC estimated by Schmidt and others 2002, (B) FRCC calculated using the FRCC Guidebook method, and (C) classified HRVStat departure for Zone 
356 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 20

Chapter 11—Using Historical Simulations of Vegetation to Assess Departure of Current Vegetation Conditions across Large Landscap

and, as a result, have become more highly departed in 

their vegetation composition than systems with longer 

fire return intervals.

However, this assessment is predicated on the assumption that our understanding and simulation of historical 

fire return intervals is reasonably accurate. Indeed, fire 

history in different vegetation types may be poorly 

understood, and considerable uncertainty may exist for 

describing fire frequencies in some ecosystems (Baker 

and Ehle 2001; Baker and Shinneman 2004). For example, pinyon-juniper woodlands have been described 

by some as having a frequent, low severity fire regime 

(Brown and others 2001; Gottfried and others 1995; 

West 1999;), while others identify the vegetation type 

as having a high-severity (Floyd and others 2000) and 

less frequent fire regime (Baker and Shinneman 2004). 

Another example can be found in ponderosa pine forests, 

where fire return intervals were typically reported as 2-25 

years, but recent work suggests that return intervals may 

range between 22 and 308 years (Baker and Ehle 2001). 

Such uncertainty in fire history information emphasizes 

the need for thorough evaluations of the best available 

data to determine the appropriate data for inclusion in 

LANDSUMv4 simulations. If we assume that fire return 

intervals that are shorter than their actual intervals, we 

are likely to overestimate departure; conversely, if the 

assumed fire return intervals are longer than the actual, 

departure will likely be underestimated.

Figure 22 – Distribution of succession classes by percent area under reference and current vegetation conditions for the Spruce –

Fir / Spruce – Fir / Lodgepole Pine PVT that dominates a landscape reporting unit in the Uinta Mountain Range of Zone 16. S

table 7 for explanation of succession class codes.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 3

Chapter 11—Using Historical Simulations of Vegetation to Assess Departure of Current Vegetation Conditions across Large Landscap

Recommendations for National 

Implementation _________________

Before departure indices are computed for the national implementation of the LANDFIRE Project, the 

limitations of each approach need to be addressed, or 

at least recognized. Specifically, the effect of succession pathway complexity on departure estimates in both 

approaches should be evaluated. To ensure consistency 

across the nation, we propose using a simple, standardized model with a limited number of succession stages 

(for example, only five succession classes per PVT). 

Such a set structure may more closely characterize 

information on vegetation structure and fire regimes 

available across the nation. Also, limitations inherent to 

the FRCC Guidebook approach need to be addressed; 

these include: 1) the inability to capture the range and 

variability across all succession classes, 2) inconsistencies in area comparisons between current and reference 

landscapes, and 3) potential inconsistencies in departure 

estimates with longer or repeated LANDSUMv4 simulations. Additionally, if multiple departure maps are 

developed using different approaches, instructions must 

be provided for the interpretation and application of the 

maps both on a nationwide basis and at the local level. 

Furthermore, prior to national implementation, a policy 

decision must be made regarding the treatment of certain 

land cover types for modeling reference conditions and 

estimating departure. As discussed above, inclusion of 

immutable land cover types (urban, agriculture, water, 

barren, and snow/ice) in departure estimates can mask 

ground conditions and conceal the need to restore departed landscapes or to conserve healthy landscapes.

Another consideration for national implementation is 

the use of other ways to measure landscape condition 

that describe landscape configuration instead of landscape composition. Landscape configuration depicts 

the physical distribution or spatial character of patches 

within the landscape (McGarigal and Marks 1995). Such 

patch-based metrics can be used to identify landscapes 

that have become highly fragmented and possibly beyond 

their historical distributions of patches within landscapes 

(Spies and others 1994; Wallin and others 1996), and 

these metrics include patch size, shape, density, and 

relative location (Farina 2000). We did not use these 

metrics in the LANDFIRE Prototype Project because 

they are computationally intensive, but if better computer 

resources become available for national implementation, 

such metrics may be informative and preferable.

Research Recommendations

In developing methods for the LANDFIRE Prototype, 

a number of questions emerged that require further 

research before implementing LANDFIRE across the 

nation. We first describe recommended research pertaining to the HRVStat approach and then propose a series 

of other lines of research.

Adequate simulation of historical reference conditions 

is a key factor in developing HRVStat statistics. Two 

main factors determine the adequacy of the LANDSUMv4 simulations for generating historical reference 

conditions: (1) sufficient number of sampling observations and (2) adequate spatial representation. Prior to 

executing LANDSUMv4 simulations for zones 16 and 

19, we conducted analyses to address these two factors. 

We focused on developing the best data sets of simulated 

historical reference conditions, given our time constraints 

and computer resources. Other alternatives for producing simulated historical data sets should be explored as 

described below.

Our initial evaluations of sample size found that 200 

observations from LANDSUMv4 simulations would 

adequately describe simulated historical landscape dynamics. However, it is important to ensure that enough 

sampling observations are reported to estimate the effects of periodic rare and large fire events. For example, 

infrequent catastrophic fires maintained large patches 

of old-growth ponderosa pine in a non-equilibrium 

state in the Black Hills of South Dakota and Wyoming 

(Shinneman and Baker 1997). If we under-sample 

LANDSUMv4 simulations with such fire regimes, 

we may observe only one dominant vegetation condition and miss a different state that was an important 

component of historical conditions. If that unobserved 

state is now the current condition of the landscape, we 

would overestimate departure and underestimate the 

observed significance level (that is, describe evidence 

for departure as higher than its true value). We suggest 

further analysis that examines the sensitivity of departure 

statistics to the number of sampling observations, given 

a fixed current landscape.

An additional issue related to sample size involves 

the way observations are sampled in simulations and 

the influence on temporal autocorrelation. We sampled 

data within one simulation at reporting intervals that 

were determined long enough to minimize temporal 

autocorrelation. An alternative approach is to run multiple simulations at shorter time periods, sampling only 

once from each simulation. These separate executions 
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Chapter 11—Using Historical Simulations of Vegetation to Assess Departure of Current Vegetation Conditions across Large Landscap

with different random number streams would ensure 

independence in sampling observations and eliminate 

any chance of temporal autocorrelation. We recommend 

comparing departure statistics from one long simulation 

to many shorter simulations (for example, a 10,000-year 

simulation with 50-year reporting intervals versus 200 

simulations of 1,000-year length) to evaluate the influence of temporal autocorrelation. We anticipate that any 

differences detected would primarily affect the observed 

significance level. That is, the observed significance from 

one long simulation would be smaller than that produced 

from many shorter simulations because the number 

of independent observations and therefore degrees of 

freedom will be less where autocorrelation exists. If 

temporal autocorrelation within reporting intervals 

does substantially influence observed significance level 

estimates, we suggest exploring additional statistical 

techniques to minimize the effects before implementing 

the approach of multiple simulations. Multiple simulations, although potentially preferable, would prove costly 

because of the need for substantially longer simulation 

periods and could therefore be prohibitive to national 

implementation of the LANDFIRE Project.

Temporal autocorrelation may also affect departure 

statistics for landscapes that express a periodicity in 

their vegetation characteristics matching the reporting 

interval. For example, if a PVT alternates between two 

succession classes at 50-year intervals and we sample at 

50-year intervals, we may only observe one of those two 

succession classes. In this case, the observed departure 

would be underestimated and observed significance level 

would be overestimated. We suspect that the likelihood 

of coinciding vegetation and reporting intervals is low, 

but recommend exploring the use of a restricted randomization scheme to avoid this possibility. For example, 

we might choose random numbers between 50 and 10 

to sample the simulation data and generate reporting 

intervals such as 50, 75, 90, and so on.

Besides the adequacy of sampling observations, satisfactory spatial representation of vegetation composition 

is vital to estimating departure and is influenced by two 

main components: 1) LANDSUMv4 simulations and 

2) the spatial grain of LRUs. In simulating historical 

vegetation conditions using LANDSUMv4, the main 

concern regarding spatial representation is simulating 

the full distribution of succession classes within a PVT 

on the landscape and, specifically, ensuring detection 

of all states that may currently exist on the landscape. 

Some of the primary LANDSUMv4 input components 

are fire regime parameters, fire size, and landscape 

simulation size (Pratt and others, Ch. 10). Accordingly, 

a given fire regime and fire size will have an optimum 

landscape simulation size for simulating fire spread and 

the corresponding effects on succession. Sensitivity 

analyses on the scale of LRUs are needed to identify the 

appropriate sizes for estimating departure. As the size 

of LRUs decreases, temporal variability may increase 

in fire and vegetation characteristics. If LRUs are too 

small, we may observe a limited and highly variable 

distribution of succession classes within a stand than 

would be expected at the optimal size for describing its 

vegetation characteristics. Conversely, if LRUs are too 

large, important components within LRUs needing restoration or conservation could be missed. In performing 

these various sensitivity analyses, we expect to identify 

thresholds at a relatively narrow range of sizes, which 

will give the most effective estimates of departure. 

We also expect that these optimal sizes will vary from 

ecosystem to ecosystem, especially if fire regimes are 

very different, and this source of variation should be 

included in sensitivity analyses.

Other kinds of research are also critically needed 

to fully investigate the implications of calculating departure and determining FRCC. One line of research 

should focus on the implications of calculating departure from an array of possible methods, including the 

two presented here (FRCC Guidebook and HRVStat). 

Numerous limitations were noted, particularly in 

the FRCC Guidebook approach, in the methods for 

computing ecological departure, prompting the exploration of other departure methods. We have developed 

a computer program, called DEPART, that computes 

various other ecological indices of similarity, including 

Sorenson’s Index, Jaccard’s Index, and Similarity Ratio 

(Mueller-Dombois and Ellenberg 1974). Departure 

estimates determined from all these indices should 

be evaluated and compared for their sensitivity to and 

consistency in detecting vegetation change. A second 

line of research should pursue the development of field 

methods that can be used to validate the departure and 

FRCC maps created by the LANDFIRE Project. A 

final research track should investigate the implications 

of collapsing departure measures into an ordinal classification, as with the FRCC and classified HRVStat 

departure groupings. Specifically, identification of appropriate breakpoints should be based on standardized 

methods to best represent departure estimates and avoid 

arbitrary classifications, which may skew assessments 

of landscapes.
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Chapter 11—Using Historical Simulations of Vegetation to Assess Departure of Current Vegetation Conditions across Large Landscap

Conclusion_____________________

Spatial data describing historical and existing conditions were successfully implemented in both the Interagency FRCC Guidebook approach and the HRVStat 

approach to quantify ecological departure across two 

large western study areas. Each method for quantifying ecological departure had strengths and limitations. 

The FRCC Guidebook approach proved relatively easy 

to use, but was not statistically rigorous. Conversely, 

the HRVStat approach was computationally complex, 

but provided a statistically sound measurement of the 

departure of current conditions from simulated historical conditions. While each approach has promise, 

more research related to the application of fire history 

information and the spatial units for measuring departure is needed to develop a consistent map of ecological 

departure across the United States.

For further project information, please visit the LANDFIRE website at www.landfire.gov.

The Authors ____________________

Lisa Holsinger is a GIS Specialist with the USDA 

Forest Service, Rocky Mountain Research Station, 

Missoula Fire Sciences Laboratory (MFSL). Holsinger 

joined MFSL in 2002 and has worked on developing and 

analyzing spatial data for simulation models applied to 

large landscapes. She has developed GIS data and input 

for the WXFIRE and LANDSUMv4 simulation models 

and HRVStat program, run simulations, and produced 

associated spatial data. She has also conducted sensitivity analyses for both the WXFIRE and LANDSUMv4 

models. Prior to working at MFSL, she worked as a 

Fisheries Biologist and GIS Specialist for the National 

Marine Fisheries Service conducting research and 

management for the conservation of west coast salmon 

populations. Holsinger received her B.S. degree in 

Biological Sciences from the University of California, 

Davis in 1984 and her M.S. degree in Fisheries at the 

University of Washington in 1988.

Robert E. Keane is a Research Ecologist with the 

USDA Forest Service, Rocky Mountain Research Station, Missoula Fire Sciences Laboratory (MFSL). Since 

1985, Keane has developed various ecological computer 

models for the Fire Effects Project for research and 

management applications. His most recent research 

includes the development of a first-order fire effects 

model, construction of mechanistic ecosystem process 

models that integrate fire behavior and fire effects into 

succession simulation, restoration of whitebark pine 

in the Northern Rocky Mountains, spatial simulation 

of successional communities on landscapes using GIS 

and satellite imagery, and the mapping of fuel for fire 

behavior prediction. He received his B.S. degree in Forest Engineering in 1978 from the University of Maine, 

Orono, his M.S. degree in Forest Ecology in 1985 from 

the University of Montana, and his Ph.D. degree in Forest Ecology in 1994 from the University of Idaho.

Brian Steele is an Associate Professor in the Department of Mathematical Sciences at the University of 

Montana, where he has been on the faculty since 1998. 

Steele earned a B.S. degree in Resource Management 

from Cornell University in 1978 and an M.S. degree in 

Statistics from Oregon State University in 1987. In 1995, 

Steele received his Ph.D. in Mathematics, specializing 

in Statistics, from the University of Montana. He has 

worked on statistical applications in ecology, resource 

management, and biology since 1997. Much of this effort has involved multivariate statistical methods and 

statistical methods for analyzing dependent data.

Matthew C. Reeves is a GIS Specialist with the 

LANDFIRE Project at the USDA Forest Service, Rocky 

Mountain Research Station, Missoula Fire Sciences 

Laboratory (MFSL). He earned a B.S. degree in Range 

Management (minoring in Wildlife Management) from 

Washington State University in 1995. In 1999, Reeves 

received his M.S. degree from Arizona State University’s 

Environmental Resources program, where he focused 

on the remote sensing of desert vegetation and the 

development of GIS-based wildlife habitat suitability 

models. In 2004, he earned a Ph.D. from the University 

of Montana’s School of Forestry, where he developed 

automated wheat yield simulation models and rangeland 

biomass estimators from remotely sensed data in a GIS 

framework.

Sarah Pratt is a GIS Specialist with the USDA Forest 

Service, Rocky Mountain Research Station, Missoula 

Fire Sciences Laboratory (MFSL). Sarah joined MFSL 

in 2003, where she has prepared and analyzed spatial 

data for the simulation models LANDSUMv4 and FIREHARM. Pratt received her B.A. from Kenyon College 

(Gambier, Ohio) in 1992 and her M.S. in Biological 

Sciences from Northern Michigan University in 2003.

Acknowledgments_______________

We would like to thank the USDA Forest Service 

Fire and Aviation Management Washington Office for 

funding the LANDFIRE Project. We thank Dr. Ayn 

Shlisky of The Nature Conservancy for her review of 
360 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 20

Chapter 11—Using Historical Simulations of Vegetation to Assess Departure of Current Vegetation Conditions across Large Landscap

and insightful comments on the manuscript. We also 

thank Brendan Ward, Don Long, and Dr. Matthew 

Rollins at the USDA Forest Service, Rocky Mountain 

Research Station, Missoula Fire Sciences Laboratory 

and Dr. Swarna Reddy at the University of Montana 

for their valuable help during this effort. We also thank 

Christine Frame and Dennis Simmerman of Systems 

for Environmental Management for their assistance in 

technical editing and graphic design.

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362 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 20

Chapter 11—Using Historical Simulations of Vegetation to Assess Departure of Current Vegetation Conditions across Large Landscap

 Zone 16 Zone 19

PVT code Zone 16 PVT name PVT code Zone 19 PVT name

	 1601	 Spruce Fir – Blue Spruce	 1902	 Western Redcedar

	 1602	 Spruce Fir – Blue Spruce – Lodgepole Pine	 1914	 Grand Fir – Rocky Mtn. White Fir

	 1603	 Spruce Fir – Spruce Fir	 1920	 Spruce – Fir / Western Larch

	 1604	 Spruce Fir – Spruce Fir – Lodgepole Pine	 1921	 Spruce – Fir / Douglas-fir

	 1611	 Grand Fir – White Fir	 1922	 Spruce – Fir / Timberline Pine

	 1612	 Grand Fir – White Fir - Maple	 1924	 Spruce – Fir / Lodgepole Pine

	 1621	 Douglas-fir – Timberline Pine	 1930	 Douglas-fir / Western Larch

	 1622	 Douglas-fir – Douglas-fir	 1931	 Douglas-fir / Ponderosa pine

	 1623	 Douglas-fir – Lodgepole Pine	 1932	 Douglas-fir / Lodgepole Pine

	 1631	 Timberline Pine	 1934	 Douglas-fir / Timberline Pine

	 1632	 Ponderosa Pine	 1936	 Douglas-fir / Douglas-fir

 1633 Lodgepole Pine  1940 Lodgep

 1634 Aspen  1942 Ponder

	 1641	 Pinyon Juniper–Mtn. Big Sagebrush - North	 1944	 Timberline Pine / Limber Pine

	 1642	 Pinyon Juniper–Mtn. Big Sagebrush - South	 1946	 Timberline Pine / Whitebark Pine

 1643 Pinon Juniper-Wyoming-Basin Big Sagebrush-North 1950 Rocky Mountain 

 1644 Pinyon Juniper-Wyoming-Basin Big Sagebrush-South 1952 Riparian H

	 1645	 Pinyon Juniper – Mountain Mahogany	 1960	 Riparian Shrub

	 1646	 Pinyon Juniper – Gambel Oak	 1962	 Mountain Mahogany

 1651 Blackbrush  1964 D

 1652 Salt Desert Shrub  1965 Dry Shrub/Con

 1653 Warm Herbaceous  1970 Dwarf Sagebrush C

 1654 Cool Herbaceous  1971 Dwarf Sage

 1661 Dwarf Sagebrush  1972 Mtn. Big Sagebrush C

 1662 Wyoming-Basin Big Sagebrush  1973 Mtn. Big Sage/C

 1663 Mountain Big Sagebrush  1974 Threetip S

 1671 Riparian Hardwood  1975 Threetip Sage/C

 1672 Riparian Shrub  1976 Wyoming/Basin Big Sagebrush Com

 1673 Wetland Herbaceous  1977 Wyoming/Basin Big Sage/C

 1680 Alpine  1980 Wetland Herbac

 1690 Open Water  1982 Al

 1691 Urban - Developed  1984 Fescue Gr

 1692 Barren  1985 Fescue Grasslands/Con

 1693 Agricultural  1986 Bluebunch Whea

 1694 Snow - Ice  1987 Bluebunch Wheatgrass/C

990 Open W

991 Urban - Dev

992 Ba

993 Agricu

994 Snow

Appendix 11-A—Names of PVT codes for zones 16 and 19 _______________
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 

Chapter 11—Using Historical Simulations of Vegetation to Assess Departure of Current Vegetation Conditions across Large Landscap

Mean and standard deviation (SD) of departure estimates and a ranking of the five PVTs with the greatest departure across each of

the three spatial domains of mapping zone (Zone), simulation landscape (SL), and landscape reporting unit (LRU) across Zone 16

using the FRCC Guidebook method. Overall, the PVTs with the highest departure were similar across the three spatial domains, 

but departure values generally decreased as the size of the spatial domain decreased (from Zone to SL to LRU). For the smallest 

spatial domain of LRU, departure values for each PVT were generally higher using the FRCC methods.

Zone Zone Zone SL- SL- SL- LRU- LRU- LRUlevel level level level level level level level level

PVT mean ank SD mean rank SD mean rank SD

Agricultural 28  15 

Alpine  37  1

Aspen 54 

Barren  41 1

Blackbrush 54  1 

Cool Herbaceous 61  13 5

Douglas-fir / Douglas-fir	 64	 	 9	 44	 	 13	 19	 	 14

Douglas-fir / Lodgepole Pine	 69	 3	 7	 53	 3	 12	 33	 4	 15

Douglas-fir / Timberline Pine	 77	 1	 15	 65	 1	 18	 42	 1	 18

Dwarf Sagebrush 55  12

Grand Fir – White Fir	 65	 	 9	 47	 	 12	 25	 	 13

Grand Fir – White Fir / Maple	 54	 	 11	 31	 	 12	 12	 	 8

Lodgepole Pine 65  10 

Mountain Big Sagebrush 63

Open Water  33  20

Pinyon – Juniper / Gambel Oak	 62	 	 12	 39	 	 15	 21	 	 13

Pinyon – Juniper / Mountain Big Sagebrush / North	 62	 	 8	 42	 	 10	 23	 	 11

Pinyon – Juniper / Mountain Big Sagebrush / South	 75	 2	 8	 57	 2	 11	 36	 2	 15

Pinyon – Juniper / Mountain Mahogany	 62	 	 11	 39	 	 15	 19	 	 13

Pinyon – Juniper / Wy. – Basin Big Sagebrush / N	 66	 	 13	 44	 	 17	 19	 	 15

Pinyon – Juniper / Wy. – Basin Big Sagebrush / S	 71	 5	 12	 52	 5	 15	 28	 	 15

Ponderosa Pine 62  1

Riparian Hardwood 54  10 9

Riparian Shrub 56  17 

Salt Desert Shrub 63  1

Spruce – Fir / Blue Spruce	 66	 	 9	 48	 	 12	 26	 	 13

Agricultural 28  15 

Alpine  37  1

Aspen 54 

Barren  41 1

Blackbrush 54  1 

Spruce – Fir / Blue Spruce / Lodgepole Pine	 68	 4	 12	 53	 4	 17	 31	 5	 17

Spruce – Fir / Spruce – Fir 67	 	 9	 51	 	 13	 24	 	 14

Spruce – Fir / Spruce – Fir / Lodgepole Pine	 67	 	 11	 49	 	 14	 23	 	 17

Urban–Developed	 24	 	 13	 17	 	 8	 13	 	 6

Warm Herbaceous 56  10

Wetland Herbaceous 56  13 3

Wyoming – Basin Big Sagebrush	 63	 	 13	 38	 	 14	 17	 	 13

Appendix 11-B ____________________________________________________
364 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2

Chapter 11—Using Historical Simulations of Vegetation to Assess Departure of Current Vegetation Conditions across Large Landscap

Mean and standard deviation (SD) of departure estimates and ranking (up to 5) for each PVT for the 

landscape reporting unit (LRU) spatial domain across Zone 19 using the FRCC Guidebook method. Departure values for each PVT were generally higher than those estimated using HRVStat methods

PVT LRU-level mean LRU-level rank LRU-level SD

Alpine  43.94

Bluebunch Wheatgrass 62.42  1 

Bluebunch Wheatgrass / Conifer  47.71

Douglas-fir / Douglas-fir	 33.96	 13.80

Douglas-fir / Lodgepole Pine	 36.57	 14.09

Douglas-fir / Ponderosa Pine	 40.51	 13.88

Douglas-fir / Timberline Pine	 35.38	 13.86

Douglas-fir / Western Larch	 42.35	 12.29

Dry Shrub 57.61 2 20.

Dry Shrub / Conifer  41.69 

Dwarf Sage / Conifer  40.28 

Dwarf Sagebrush Complex  48.32 5 2

Fescue Grasslands  47.97   

Fescue Grasslands / Conifer  41.4

Grand Fir - White Fir  43.62 

Lodgepole Pine  43

Mountain Mahogany  41.

Mountain Big Sage Complex / Conifer 39.18  

Mountain Big Sagebrush Complex 31.65 

Ponderosa Pine  45.44

Riparian Hardwood  48.97  

Riparian Shrub  44.01

Rocky Mountain Juniper  43.52  

Spruce – Fir / Douglas-fir	 36.87	 14.03

Spruce – Fir / Lodgepole Pine	 47.81	 15.80

Spruce – Fir / Timberline Pine	 42.42	 15.72

Spruce – Fir / Western Larch	 39.60	 17.27

Threetip Sagebrush / Conifer  35.63

Threetip Sagebrush  30.74

Timberline Pine / Limber Pine  36.30 

Timberline Pine / Whitebark Pine  34.95 

Western Redcedar 52.22  3 

Wetland Herbaceous  45.84

Wy. – Basin Big Sage Complex / Conifer	 40.57	 14.91

Wy. – Basin Big Sagebrush Complex	 38.66	 17.12

Appendix 11-C ____________________________________________________
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 3

Chapter 11—Using Historical Simulations of Vegetation to Assess Departure of Current Vegetation Conditions across Large Landscap

Average and standard deviation for departure and observed significance by PVT for Zone 16, estimated using the HRVStat method, percent of classifiedHRVStat departure groups by PVT across the zone, and percent of zone composed of that PVT. Departure values for each PVT were generally lower than those estimated using FRCC methods.

Departure Observed significance Classified HRVStat departure % of

PVT Mean Std. dev. Mean Std. dev. 1 2 3 zone

Pinyon

– Juniper / Mountain Big Sagebrush / South	 0.28	 0.24	 0.01	 0.01	 1.00%	 4.98%	 25.77%	 5.22%

Pinyon

– Juniper / Wy.

– Basin Big Sagebrush / S.	 0.12	 0.16	 0.01	 0.03	 9.47%	 19.34%	 23.66%	 16.47%

Salt Desert Shrub 0.29 0.36 0.03 0.10 1.47% 1.57%

%

Grand Fir

– White Fir	 0.09	 0.13	 0.02	 0.05	 4.17%	 8.68%	 5.87%	 7.02%

Ponderosa Pine 0.15 0.17 0.01 0.02 0.78% 3.12% Douglas-fir / Douglas-fir	 0.12	 0.16	 0.02	 0.06	 1.78%	 3.76%	 4.42%	 3.17%Pinyon

– Juniper / Wyo.

– Basin Big Sagebrush / N.	 0.13	 0.17	 0.02	 0.07	 1.39%	 2.82%	 3.82%	 2.43%

Pinyon

– Juniper / Gambel Oak	 0.17	 0.20	 0.01	 0.04	 0.81%	 1.51%	 3.63%	 1.44%

Mountain Big Sagebrush 0.05 0.09 0.03 0.07 8.66% 9.39% 2.76% 8.Wyoming

– Basin Big Sagebrush	 0.06	 0.13	 0.04	 0.09	 4.88%	 4.45%	 2.63%	 4.45%

Dwarf Sagebrush 0.06 0.12 0.06 0.14 5.95% 3.95% 2.Pinyon

– Juniper / Mountain Mahogany	 0.17	 0.17	 0.01	 0.02	 0.32%	 1.30%	 2.26%	 1.06%

Pinyon

– Juniper / Mountain Big Sagebrush / North	 0.15	 0.17	 0.01	 0.01	 0.31%	 1.50%	 2.08%	 1.16%

Riparian Hardwood 0.06 0.11 0.02 0.06 1.

% 0.98% 2.

%

Grand Fir

– White Fir / Maple	 0.06	 0.09	 0.01	 0.03	 1.39%	 3.49%	 0.75%	 2.61%

Urban–Developed	 0.08	 0.16	 0.02	 0.06	 0.51%	 0.87%	 0.64%	 0.74%Agricultural 0.04 0.10 0.02 0.07 1.36% 2.29% 0.6Douglas-fir / Timberline Pine	 0.13	 0.14	 0.01	 0.02	 0.18%	 0.61%	 0.60%	 0.47%Aspen 0.04 0.08 0.04 0.09 2.37% 2.09% 0.55% Barren 0.04 0.07 0.04 0.10 3.19% 2.80% 0.Spruce

– Fir / Blue Spruce	 0.05	 0.09	 0.05	 0.10	 1.51%	 1.05%	 0.41%	 1.15%

Spruce

– Fir / Spruce

– Fir / Lodgepole Pine	 0.01	 0.03	 0.09	 0.15	 24.51%	 8.81%	 0.33%	 13.25%

Spruce

– Fir / Spruce

– Fir	 0.02	 0.03	 0.09	 0.14	 15.04%	 5.14%	 0.22%	 7.97%

Riparian Shrub 0.03 0.08 0.07 0.13 1.62% 0.77% 0.2Warm Herbaceous 0.04 0.08 0.04 0.09 0.95% 0.69% 0.17%Spruce

– Fir / Blue Spruce / Lodgepole Pine	 0.03	 0.07	 0.05	 0.11	 0.63%	 0.54%	 0.11%	 0.54%

Open Water 0.03 0.09 0.19 0.35 0.67% 0.35% 0.

%

Wetland Herbaceous 0.04 0.10 0.05 0.1

% 0.25% 0.10% 0.27

Blackbrush 0.03 0.07 0.10 0.15 0.68% 0.22% 0.06% Douglas-fir / Lodgepole Pine	 0.02	 0.05	 0.07	 0.12	 0.31%	 0.14%	 0.02%	 0.19%Timberline Pine 0.06 0.07 0.02 0.04 0.03% 0.04% 0.01%Lodgepole Pine 0.02 0.02 0.05 0.10 1.08% 0.75% 0.00% 0Cool Herbaceous 0.01 0.02 0.06 0.11 0.

% 0.00% 0.

%

Snow

– Ice	 0.01	 0.05	 0.12	 0.20	 0.00%	 0.00%	 0.00%	 0.00%

Alpine 0.01 0.02 0.11 0.15 0.66% 0.19% 0

%

Appendix 11-D _______________________________________________________________
366 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 20

Chapter 11—Using Historical Simulations of Vegetation to Assess Departure of Current Vegetation Conditions across Large Landscapes Average and standard deviation for departure and observed significance by PVT for Zone 19, estimated using the HRVStat method, percent of classified HRVStat departure group by PVT across the zone, and percent of zone composed of that PVT  Departure values for each PVT were generally lower than those estimated using FRCC methods. Departure Observed significance Classified HRVStat departure % of PVT Mean Std. dev. Mean Std. dev. 1 2 3 zone Bluebunch Wheatgrass 0.67 0.31 0.00 0.00 0.03% 4.16% 18.65% 6.

– Basin Big Sagebrush Complex	 0.22	 0.29	 0.02	 0.10	 9.28%	 15.89%	 13.80%	 14.64%

Douglas-fir / Douglas-fir	 0.17	 0.22	 0.01	 0.04	 5.65%	 12.09%	 8.17%	 10.56%

Fescue Grasslands 0.53 0.31 0.01 0.00 0.02% 1.65% 5

Douglas-fir / Ponderosa Pine	 0.23	 0.26	 0.01	 0.02	 0.49%	 3.29%	 4.04%	 3.05%

Spruce

– Fir / Western Larch	 0.11	 0.17	 0.03	 0.08	 13.78%	 8.45%	 3.29%	 8.25%

Ponderosa Pine 0.44 0.30 0.01 0.01 0.03% 

%

Mountain Big Sagebrush Complex 0.15 0.22 0.01 0.05 2.40% 6.16% 2.83%

Riparian Hardwood 0.43 0.31 0.01 0

% 2.81% 1

Dry Shrub 0.59 0.33 0.01 0.00 

% 2.79% 1.2

Douglas-fir / Lodgepole Pine	 0.12	 0.20	 0.03	 0.09	 6.37%	 4.27%	 2.37%	 4.21%

Spruce

– Fir / Lodgepole Pine	 0.09	 0.17	 0.06	 0.11	 17.97%	 5.78%	 2.03%	 6.72%

Spruce

– Fir / Douglas-fir	 0.11	 0.18	 0.03	 0.09	 6.47%	 4.10%	 1.86%	 4.02%

Riparian Shrub 0.34 0.30 0.01 0.01 0.07% 0.84%

Lodgepole Pine 0.10 0.17 0.05 0.12 7.16% 3.56% 

Douglas-fir / Western Larch	 0.15	 0.19	 0.01	 0.02	 0.42%	 3.07%	 1.44%	 2.44%

Wetland Herbaceous 0.41 0.31 0.01 0.02 0.06

% 0.71

Douglas-fir / Timberline Pine	 0.23	 0.24	 0.01	 0.02	 0.27%	 1.25%	 1.22%	 1.12%

Mountain Big Sage Complex / Conifer 0.28 0.26 0.01 0.02 0.12% 0.81% 1.03

Spruce

– Fir / Timberline Pine	 0.05	 0.10	 0.07	 0.14	 26.14%	 7.66%	 0.96%	 8.91%

Western Redcedar 0.37 0.28 0.01 0.00 0.01% 0.56% 0.94% 

Wy.

– Basin Big Sage Complex / Conifer	 0.32	 0.28	 0.01	 0.01	 0.05%	 0.49%	 0.92%	 0.50%

Fescue Grasslands / Conifer 0.38 0.29 0.01 0.00 0.02%

% 0.87% 0.46

Bluebunch Wheatgrass / Conifer 0.49 0.29 0.01 0.00 0.00% 0.19% 0.77% 0

Grand Fir- White Fir 0.19 0.19 0.01 0.00 0.01% 1.02% 0.65

Mountain Mahogany 0.31 0.23 0.01 0.00 0.01% 0.29% 0.

Dwarf Sagebrush Complex 0.39 0.35 0.01 0.02 0.02% 0.29% 0.30% 

Dry Shrub / Conifer 0.25 0.23 0.01 0.01 0.03% 0.23% 0.

Rocky Mountain Juniper 0.42 0.28 0.01 0.00 0.01

% 0.24% 0.1

Threetip Sagebrush 0.13 0.21 0.01 0.05 0.27% 0.66% 0.

Timberline Pine / Limber Pine 0.26 0.23 0.01 0.01 0.

% 0.18% 0

%

Timberline Pine / Whitebark Pine 0.11 0.13 0.01

% 0.59% 0.

% 0.45

Alpine 0.40 0.30 0.01 0.01 0.00% 0.03% 0.0

Threetip Sagebrush / Conifer 0.17 0.19 0.01 0.01 0.00% 0.03% 0.0

Dwarf Sagebrush Complex / Conifer 0.34 0.26 0.01 0.00 0.00% 0.00% 0.00% 

Appendix 11-E _______________________________________________________________
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 3

Chapter 12—Mapping Wildland Fuel across Large Regions for the LANDFIRE Prototype Projec

Chapter

12

In: Rollins, M.G.; Frame, C.K., tech. eds. 2006. The LANDFIRE 

Prototype Project: nationally consistent and locally relevant 

geospatial data for wildland fire management. Gen. Tech. Rep. 

RMRS-GTR-175. Fort Collins: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research Station.

Introduction ____________________

The Landscape Fire and Resource Management Planning Tools Prototype Project, or LANDFIRE Prototype 

Project, required that the entire array of wildland fuel 

characteristics be mapped to provide fire and landscape 

managers with consistent baseline geo-spatial information to plan projects for hazardous fuel mitigation 

and to improve public and firefighter safety. Fuel maps 

were some of the core deliverables of the LANDFIRE 

Prototype Project. The LANDFIRE approach for mapping fuel combined information from the LANDFIRE 

reference database (LFRDB) (Caratti and others, Ch. 4), 

biophysical gradient layers (Holsinger and others, Ch. 5), 

maps of potential vegetation (Frescino and Rollins, Ch. 

7), and maps of vegetation composition and vegetation 

structure (Zhu and others, Ch. 8) to produce the entire 

suite of geo-spatial data for predicting the behavior and 

effects of wildland fires across the United States.

The fuel layers developed for the LANDFIRE effort 

were selected on the basis that they provide input to 

software commonly used in fire management planning. 

All LANDFIRE fuel layers can be directly used in one 

or more fire analysis tools, including the FARSITE fire 

growth model (Finney 1998). Moreover, these fuel layers 

may also be used for many other applications. Surface 

fuel layers provide comprehensive inventories of dead 

biomass that can be used to calculate carbon pools for 

estimating particulate production and modeling smoke 

dispersal. Canopy fuel layers provide important information on canopy characteristics that can be used to 

calculate leaf area index (LAI), shading, rain and snowfall interception, and surface roughness for ecosystem 

and hydrological modeling. Still other layers provide 

data on critical stand characteristics that may be used 

to quantify hiding and thermal cover for wildlife.

Background

Fuel is defined for the LANDFIRE Prototype as any 

material that can burn in a wildland fire. More specifically, wildland fuel is defined by characteristics of live 

and dead biomass pools that contribute to the spread, 

intensity, and severity of wildland fire (Burgan and 

Rothermel 1984). The primary characteristic used to 

describe fuel is “loading,” which is defined as mass per 

unit area, or more specifically, the dry weight of a fuel 

component per unit area (kg m–2). Other characteristics 

include particle density, surface area-to-volume ratio, 

packing ratio, and heat content. Fine fuel, such as twigs, 

grass, and foliage, primarily contributes to the spread 

of wildland fire, whereas coarse fuel, such as branches 

and logs, contributes mostly to post-frontal combustion 

and fire intensity.

Perhaps the most confounding property of fuel is its 

high variability in space and time (Brown and Bevins 

1986). Fuel tends to have a clumped distribution within a 

stand that is related to the interaction between exogenous 

disturbance factors, such as windthrow, snowbreak, and 

Mapping Wildland Fuel across Large Regions for 

the LANDFIRE Prototype Project

Robert E. Keane, Tracey Frescino, Matthew C. Reeves, and Jennifer L. Long
368 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 20

Chapter 12—Mapping Wildland Fuel across Large Regions for the LANDFIRE Prototype Projec

insects, and the endogenous stand characteristics, such 

as tree distribution, density, and tree species. Moreover, 

the spatial distribution of fuel can vary by fuel size class 

and fuel type (grass, shrub, or woody, for example). Fine 

fuel (such as foliage or small twigs) tends to fall and 

accumulate uniformly over time, but the coarser fuel, 

such as branches and logs, tends to accumulate after 

episodic events such as windstorms, spring snowfalls, 

and insect epidemics. These factors contribute to the 

difficulty in describing, modeling, and mapping fuel 

(Keane and others 2001).

Two major categories of fuel were mapped in the 

LANDFIRE Prototype Project: surface fuel and canopy 

fuel. Surface fuel is composed of those dead and live 

biomass components that occur on the ground (under 2 m) 

and is the fuel that contributes to the spread and intensity 

of surface fire. This type of fuel is typically described 

by the following fuel components: herbaceous (live or 

dead), shrub (live or dead), downed/dead woody, litter, 

and duff. There were five size classes of downed/dead 

woody fuel used in the LANDFIRE Prototype: 1-, 10-, 

100-, 1000-, and 10,000-hour fuel (1-, 2.5-, 8-, and 50-

cm upper diameter thresholds). Litter is freshly fallen 

organic material, and duff is the decomposed organic 

material. For most fire behavior and effects applications, 

surface fuel is represented by a set of characteristics, 

with fuel loading being the most dynamic over time 

and space. Other characteristics include surface areato-volume ratios, bulk density, and heat content (Albini 

1976; Anderson 1982; Rothermel 1972). Because of the 

high diversity and variability of surface fuel components 

(Brown and Bevins 1986), surface fuel characteristics 

are usually quantified using “fuel models” that are composed of summaries of fuel loading by fuel component 

for unique ecological or fire behavior conditions (see 

Anderson 1982 for examples). For LANDFIRE purposes, 

all surface fuel is represented by fuel models that classify 

fuel loading by component. For example, a fuel model, 

as used in this paper, might represent the actual loading 

of each fuel component (such as litter, duff, and canopy 

fuel), or a fuel model might represent loadings by fuel 

component calculated to achieve a desired outcome 

when simulating fire behavior or effects.

Canopy fuel comprises those aerial biomass components higher than 2 m above the ground that can carry a 

crown fire and is typically consumed in the crown fire. 

This fuel is usually the foliage and small branchwood 

(<2.5 cm diameter) in a tree’s crown (Scott and Reinhardt 

2002). Unlike surface fuel, which is often described 

using categorical variables such as fuel component, 

canopy fuel was described in the LANDFIRE Prototype 

by four continuous variables: bulk density (kg m–3), 

canopy cover (%), canopy height (m), and canopy base 

height (m). These four characteristics are essential for 

modeling crown fire initiation and propagation in the 

various fire management software tools (Finney 1998; 

Scott 1999).

We developed eight layers to describe both surface 

and canopy fuel for the LANDFIRE Prototype. As 

mentioned above, these layers were selected because 

they are essential for predicting fire behavior and effects so that fire hazard analyses and fire management 

planning may be performed in a spatial domain (Salas 

and Chevico 1994). These eight layers are:

•	 Anderson’s (1982) 13 fire behavior fuel models 

(FBFM13).

•	 Scott’s and Burgan’s (2005) 40 fire behavior fuel 

models (FBFM40)

•	 Fuel characterization classes (FCCs) (Sandberg and 

Ottmar 2001)

•	 Fuel loading models (FLM) (Lutes and others, in 

preparation)

•	 Canopy bulk density (CBD)

•	 Canopy cover (CC)

•	 Canopy height (CH)

•	 Canopy base height (CBH)

Each classification or continuous variable that was 

mapped for each layer will be described in detail in the 

following sections.

The development of the LANDFIFRE fuel spatial data 

layers was a complex task that required the integration 

of diverse spatial analyses. For example, the surface fuel 

maps were created using a classification or rule-based 

approach, whereas the canopy layers were created using 

an integration of statistical modeling, classification, and 

ecosystem simulation. We have therefore stratified the 

sections of this chapter by surface fuel and canopy fuel 

for simplicity.

Surface Fuel Layers

In the LANDFIRE Prototype, we mapped four surface fuel model classifications to represent the gamut 

of surface fuel inputs needed to run commonly used 

models that simulate both fire behavior and effects. Two 

of these fuel classifications (fire behavior fuel models) 

are used to calculate fire behavior variables, such as 

fire intensity and spread rate, and the remaining two 

(fire effects fuel models) are used for computing fire 

effects, such as fuel consumption and smoke production. 

For the LANDFIRE Prototype, two new classifications 

describing fire behavior and effects were developed to 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 3

Chapter 12—Mapping Wildland Fuel across Large Regions for the LANDFIRE Prototype Projec

complement two existing classifications and to match 

the scale and resolution of the LANDFIRE process. In 

this chapter, the term “fuel model” is used to represent 

a unique category in the fuel classification. These categories are unique sets of fuel characteristics (primarily 

amount of biomass) by fuel component that are linked 

to vegetation composition and structure. For example, 

an open ponderosa pine stand would likely be assigned 

an FBFM13 model 2 and a dense spruce-fir stand would 

be assigned an FBFM13 model 10.

Fire behavior fuel models—Eleven of the 13 fire behavior fuel models (FBFM13) were originally developed 

by Rothermel (1972) as input into his spread model for 

predicting fire behavior (spread and intensity) (table 1). 

Albini (1976) added two other models to this classification (dormant brush and southern rough) to create 

the standard 13 fire models used in fire management 

today (see Rothermel 1983). Anderson (1982) provided 

vegetation descriptions, stylized pictures, and a key to 

aid managers in determining fire behavior fuel models. 

These 13 fire behavior fuel models represent distinct distributions of fuel loading among surface fuel types (live 

and dead), size classes, and fuel components. They are 

described by the fuel type or carrier (grass, brush, litter, 

or slash) most commonly responsible for fire spread and 

are represented by a variety of characteristics, including biomass loading, surface area-to-volume ratio by 

size class and component, fuelbed depth, and moisture 

of extinction. Extensive early fire modeling research 

revealed that prediction of fire behavior with real-world 

fuel loading is problematic (Albini and Anderson 1982; 

Andrews 1980; Rothermel 1983). Therefore, fire scientists 

Table 1—The 13 standard fire behavior fuel models developed by Rothermel (1972) and Albini (1976) and described by

Anderson (1982)

Fuel Model Group Description

	 1	 Grass	 Surface fires that burn fine herbaceous fuels, cured and curing fuels, little shrub or timber

present, primarily grasslands and savanna

	 2	 Grass	 Burns fine, herbaceous fuels, stand is curing or dead, may produce fire brands in oak or pine

stands

	 3	 Grass	 Most intense fire of grass group, spreads quickly with wind, one third of stand dead or cured,

stands average  ft tal

	 4	 Shrub	 Fast spreading fire, continuous overstory, flammable foliage and dead woody material, deep

litter layer can inhibit suppression

	 5	 Shrub	 Low intensity fires, young, green shrubs with little dead material, fuels consist of litter from

understory

	 6	 Shrub	 Broad range of shrubs, fire requires moderate winds to maintain flame at shrub height, or will

drop to the ground with low winds

	 7	 Shrub	 Foliage highly flammable, allowing fire to reach shrub strata levels, shrubs generally 2 to 6 feet

high 

	 8	 Timber	 Slow, ground burning fires, closed canopy stands with short needle conifers or hardwoods, litter

consist mainly of needles and leaves, with little undergrowth, occasional flares with concentrated

fuels

	 9	 Timber	 Longer flames, quicker surface fires, closed canopy stands of long-needles or hardwoods,

rolling leaves in fall can cause spotting, dead-down material can cause occasional crowning

	 10	 Timber	 Surface and ground fire more intense, dead-down fuels more abundant, frequent crowning and

spotting causing fire control to be more difficult

	 11	 Logging Slash	 Fairly active fire, fuels consist of slash and herbaceous materials, slash originates from light

partial cuts or thinning projects, fire is limited by spacing of fuel load and shade from overstory

	 12	 Logging Slash	 Rapid spreading and high intensity fires, dominated by slash resulting from heavy thinning

projects and clearcuts, slash is mostly 3 inches or less in diameter, fire is usually sustained until

there is a fuel break or a change in fuel type

	 13	 Logging Slash	 Fire spreads quickly through smaller material and intensity builds slowly as large material

 ignites, continuous layer of slash larger than  inches in diameter predominates, resultin

	 	 	 from clearcuts and heavy partial cuts, active flames sustained for long periods of time, fire i

 susceptible to spotting and weather conditions
370 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 20

Chapter 12—Mapping Wildland Fuel across Large Regions for the LANDFIRE Prototype Projec

created synthetic representations of wildland fuel that, 

when input into the fire model, would simulate realistic 

fire behavior under known temperature, moisture, and 

wind conditions. Although the fuel loading used in these 

models did not represent actual amounts measured in the 

field, the simulated fire behavior using these artificial 

amounts was found to approximate reality, especially 

with respect to the resolution of the fire behavior model. 

Since their development, FBFM13 have served as the 

foundation for fire behavior prediction (Andrews and 

Bevins 1999).

Despite FBFM13’s advantages, the resolution of these 

13 fuel model categories is so coarse that subtle changes 

in fuelbed conditions, such as those incurred by fuel 

treatment activities, often cannot be detected using the 

FBFM13 categories. In addition, since the FBFM13 

models were developed for application during severe 

fire weather (Anderson 1982), they had limited abilities 

to predict fire behavior for purposes of prescribed fire 

and wildland fire use. Additionally, these models had 

limited abilities for simulating and comparing different fuel treatments’ effects on fire behavior. Many fuel 

treatments do not produce sufficient fuel modification by 

which to reclassify a stand to a different FBFM13 fuel 

model category. Scott and Burgan (2005) also mention 

that new fuel models were needed to better represent 

fuel types in high humidity areas and forests with litter, 

grass, and shrub understories. We therefore determined 

that a finer resolution fire behavior fuel model would be 

necessary for guiding fuel treatments at a national scale 

(Keane and Rollins, Chapter 3).

In 2003, the LANDFIRE Prototype Project funded an 

extensive fuel modeling study, led by the Fire Behavior 

Research Unit (RWU-4401) of the Rocky Mountain 

Research Station and Systems for Environmental Management, to create the next generation of fire behavior 

fuel models. In 2005, Scott and Burgan created a new 

set of 40 fire behavior fuel models (FBFM40) that are 

hierarchically organized by fuel strata and fuel loading 

(table 2). This set of fuel models provides a better tool 

for fire behavior prediction because it balances the resolution of fuel conditions with the algorithms contained 

in widely accepted fire behavior models. These 40 fire 

behavior fuel models have already been implemented 

in the BehavePlus fire modeling system (Andrews 1986; 

Andrews and Bevins 1999; Andrews and others 2003) 

and the FARSITE fire growth model (Finney 1998). 

Unlike Anderson’s (1982) FBFM13 descriptions, subtle 

modifications in vegetation composition and structure 

resulting from fuel treatment activities may be detected 

using FBFM40 under most circumstances.

One limitation of fire behavior fuel model classifications relates to the difficultly in accurately and consistently determining which fire behavior fuel model best 

describes fuel conditions in a particular stand. Since 

fire behavior fuel models are assigned according to expected fire behavior, extensive experience in evaluating 

potential fire behavior under particular fuel conditions 

is required to assign fire behavior fuel models. Even the 

most experienced fire modeling specialists have difficulty 

agreeing on a common fuel model for certain stand and 

weather conditions. This limitation is exacerbated by 

the high variability of fuel by component across spatial 

scales (Keane and others 2001). It is therefore common 

for a stand to be described by two or more fire behavior 

fuel models. As a consequence, spatially explicit field 

data containing estimates of fire behavior fuel models 

are rare. Another limitation of fire behavior fuel models 

is that they do not quantify all dead and live biomass 

pools at a stand level, thus they are not useful for other 

fire applications such as predicting smoke production 

and vegetation mortality (Keane and others 1998a; 

Leenhouts 1998).

Fire effects fuel models—The many fire effects 

prediction models, such as FOFEM (Reinhardt and 

Keane 1998; Reinhardt and others 1997) and CONSUME (Ottmar and others 1993), require actual fuel 

loading estimates by fuel component to simulate fire 

effects-related processes, such as fuel consumption and 

smoke generation. However, because fuel loadings for 

the FBFM13 and FBFM40 classifications were modified to predict realistic fire behavior, the fire behavior 

fuel models are not useful for computing fire effects. 

Simulation of fire effects requires classifications of fuel 

loading across all biomass components that accurately 

describe real fuel across large landscapes.

There are two main fire effects fuel model classification systems used in the LANDFIRE Prototype mapping 

effort. The first, called the Fuel Characterization Classification System (FCCS), was developed by Sandberg 

and others (2001). This system summarizes fuel loading 

by component using canopy, shrub, surface, and ground 

fuel stratifications (www.fs.fed.us/pnw/fera/research). 

Several fuelbed categories that describe unique combustion environments form the foundation of FCCS. These 

categories were selected based on general characteristics, 

such as region, stand structure, and stand history. Fuel 

component loadings for these fuelbeds were summarized 

into a set of fuel models referred to as the “national 

default fuelbeds,” which we will refer to here as default 

fuel characterization classes, or FCCs for brevity. These 

default FCCs can then be modified using specialized 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 

Chapter 12—Mapping Wildland Fuel across Large Regions for the LANDFIRE Prototype Projec

Table 2—Description of the 40 fire behavior fuel models developed by Scott and Burgan (2005).

Fuel Fuel

Model Model

Number Code Name Description

GRASS

	 101	 GR1	 Short, sparse dry climate	 Grass is short naturally or heavy grazing, predicted rate of fire

	 	 	 grass	 spread and flame length is low

	 102	 GR2	 Low load, dry climate grass	 Primarily grass with some small amounts of fine, dead fuel, any

shrubs do not affect fire behavior

 103 GR3 Low load, very coarse, Continuous, coarse humid climate grass, any shrubs do not 

	 	 	 humid climate grass	 fire behavior

 104 GR4 Moderate load, dry Continuous, dry climate grass, fuelbed depth about 

 climate grass

 105 GR5 Low load, humid climate Humid climate grass, fuelbed depth is about 1-2 f

 grass

 106 GR6 Moderate load, humid Continuous humid climate grass, not so coarse as G

 climate grass

 107 GR7 High load, dry climate Continuous dry climate grass, grass is abou 3 feet h

 grass

	 108	 GR8	 High load, very coarse,	 Continuous, coarse humid climate grass, spread rate and flame

 humid climate grass length may be extreme if grass is fully cured

 109 GR9 Very high load, humid Dense, tall, humid climate grass, about 6 feet tall, spread rate a

	 	 	 climate grass	 flame length can be extreme if grass is fully cured

GRASS-SHRUB

 121 GS1 Low load, dry climate Shrubs are bout 1 foot high, grass load is low, spre

	 	 	 grass-shrub	 moderate and flame length is low

 122 GS2 Moderate load, dry climate Shrubs are 1-3 feet high, grass load is moder

	 	 	 grass-shrub	 spread rate high and flame length is moderate

 123 GS3 Moderate load, humid Moderate grass/shrub load, grass/shrub depth i

	 	 	 climate grass-shrub	 than 2 feet, spread rate is high and flame length is moderate

 124 GS4 High load, humid climate Heavy grass/shrub load, depth is greater than 2

	 	 	 grass-shrub	 spread rate is high and flame length very high

SHRUB

 141 SH1 Low load dry climate shrub Woody shrubs and shrub litter, fuelbed depthabout 1 foot,

some grass, spread rate and flame low

 142 SH2 Moderate load dry climate Woody shrubs and shrub litter, fuelbed depth abot 1 foo

	 	 	 shrub	 grass, spread rate and flame low

 143 SH3 Moderate load, humid Woody shrubs and shrub litter, possible pine ove

	 	 	 climate shrub	 fuelbed depth 2-3 feet, spread rate and flame low

 144 SH4 Low load, humid climate Woody shrubs and shrub litter, low to moderate load, possib

	 	 	 timber shrub	 overstory, fuelbed depth about 3 feet, spread rate high and flame

moderate

 145 SH5 High load, humid climate Grass and shrubs combined, heavy load with de

	 	 	 grass-shrub	 greater than 2 feet, spread rate and flame very high

 146 SH6 Low load, humid climate Woody shrubs and shrub litter, dense shrubs, little or no herbace

	 	 	 shrub	 fuel, depth about 2 feet, spread rate and flame high

 147 SH7 Very high load, dry climate Woody shrubs and shrub litter, very heavy shrub load, depth

	 	 	 shrub	 feet, spread rate somewhat lower than SH6 and flame very high

(continued)
372 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 20

Chapter 12—Mapping Wildland Fuel across Large Regions for the LANDFIRE Prototype Projec

 148 SH8 High load, humid climate Woody shrubs and shrub litter, dense shrubs, little or no herbace

	 	 	 shrub	 fuel, depth about 3 feet, spread rate and flame high

	 149	 SH9	 Very high load, humid	 Woody shrubs and shrub litter, dense finely branched shrubs with

	 	 	 climate shrub	 fine dead fuel, 4-6 feet tall, herbaceous may be present, spread rate

and flame high

TIMBER-UNDERSTORY

	 161	 TU1	 Low load dry climate	 Low load of grass and/or shrub with litter, spread rate and flame low

 timber grass shrub

 162 TU2 Moderate load, humid Moderate litter load with some shrub, spread rate moderate a

	 	 	 climate timber-shrub	 flame low

 163 TU3 Moderate load, humid Moderate forest litter with some grass and shrub, spread rat

	 	 	 climate timber grass shrub	 and flame moderate

 164 TU4 Dwarf conifer with Short conifer trees with grass or moss understory, spread ra

	 	 	 understory	 flame moderate

 165 TU5 Very high load, dry Heavy forest litter with shrub or small tree understory, spread ra

	 	 	 climate shrub	 and flame moderate

TIMBER LITTER

 181 TL1 Low load compact Compact forest litter, light to moderate load, 1-2 inches de

	 	 	 conifer litter	 represent a recent burn, spread rate and flame low

	 182	 TL2	 Low load broadleaf litter	 Broadleaf, hardwood litter, spread rate and flame low

 183 TL3 Moderate load conifer Moderate load conifer litter, light load of coarse fuels, spread ra

	 	 	 litter	 flame low

	 184	 TL4	 Small downed logs	 Moderate load of fine litter and coarse fuels, small diameter downed

	 	 	 	 logs, spread rate and flame low

	 185	 TL5	 High load conifer litter	 High load conifer litter, light slash or dead fuel, spread rate and flame

low

	 186	 TL6	 Moderate load broadleaf	 Moderate load broadleaf litter, spread rate and flame moderate

 litter

 187 TL7 Large downed logs Heavy load forest litter, larger diameter downed logs, spread rate a

	 	 	 	 flame low

 188 TL8 Long needle litter Moderate load long needle pine litter, may have small amounts 

	 	 	 	 herbaceous fuel, spread rate moderate and flame low

	 189	 TL9	 Very high load broadleaf	 Very high load fluffy broadleaf litter, may be heavy needle drape,

	 	 	 litter	 spread rate and flame moderate

SLASH-BLOWDOWN

	 201	 SB1	 Low load activity fuel	 Light dead and down activity fuel, fine fuel is 10-20 t/ac, 1-3 inches in

	 	 	 	 diameter, depth less than 1 foot, spread rate moderate and flame low

202 SB2 Moderate load activity fuel Moderate dead down activity fuel or light blowdown, 7-12 t/ac

 or low load blowdown 0-3 inch diameter class, depth about1 foot, blowdown scattered wi

	 	 	 	 many still standing, spread rate and flame low

203 SB3 High load activity fuel or Heavy dead down activity fuel or moderate blowdown, 7-12

 moderate load blowdown 0-.25 inch diameter class, depth greater than  foot, blowdow

	 	 	 	 moderate, spread rate and flame high

	 204	 SB4	 High load blowdown	 Heavy blowdown fuel, blowdown total, foliage and fine fuel still

	 	 	 	 attached to blowdown, spread rate and flame very high

Table 2 (Continued)

Fuel Fuel

Model Model

Number Code Name Description

SHRUB
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 

Chapter 12—Mapping Wildland Fuel across Large Regions for the LANDFIRE Prototype Projec

software (see http://www.fs.fed.us/pnw/fera/research/ 

for details) to create new, finer-scale FCCs to represent 

local conditions. In the LANDFIRE Prototype, we 

mapped the default FCCs, allowing managers to then, 

through the software, modify these default FCCs to 

reflect finer-scale, local fuel conditions for project-level 

fuel treatment planning. Over 200 default FCCs were 

used in the LANDFIRE prototype effort, thus a table 

describing each would be prohibitively long and not 

appropriate for this report.

The default FCCs can be keyed only from vegetation 

characteristics observed in the field or from variables 

contained in existing databases. However, there is often 

a low degree of fidelity between FCCs and LANDFIRE 

vegetation classes (Long and others, Ch. 6) because of 

the high variability in fuel loadings within and between 

fuel components (Keane and others 2001). There are no 

key criteria in the FCCS that use fuelbed characteristics 

to uniquely identify default FCCs; that is, it is difficult, 

if not impossible, to consistently determine FCCs based 

on fuel data alone.

The high fuel loading variability and large number of 

default FCCs presented a special scale problem in the 

LANDFIRE Prototype. We found the default FCCs to 

be useful at fine spatial scales, but it was difficult to accurately map the default FCCs across large regions with 

diverse ecosystems because the classification resolution 

of the FCCS did not match the resolution needed to 

describe fuel across entire regions. For example, there 

was an insufficient number of default FCCs in the FCCS 

to link to all the vegetation conditions quantified by the 

LANDFIRE mapping process. We therefore created a 

second, companion fire effects fuel model classification that accounted for the high variability across fuel 

components and matched the resolution of LANDFIRE 

mapping process as well as the resolution of the models 

used to predict fire effects.

The fuel loading model (FLM) classification was 

developed for the LANDFIRE Prototype by Lutes and 

others (in preparation) to specifically match the scale 

of LANDFIRE mapping with the scale of fire effects 

modeling. They developed a broad classification of fuelbeds based on fuel loading by component that accounts 

for the high variability of loading within and between 

fuel components. Instead of assigning fire effects fuel 

models to vegetation characteristics, Lutes and others 

(in preparation) analyzed the loading of seven surface 

fuel components in over 4,000 fuelbeds measured in 

the field and grouped them using an unsupervised agglomerative clustering approach based on component 

loading. They then calculated the fire effects of smoke 

production and soil temperature for each fuelbed and 

used these outputs in a cluster analysis to obtain an 

FLM classification, which accounts for the variability 

of fuel loading and related potential fire effects across 

the seven surface fuel components. Moreover, a rule set 

was developed in addition to the FLM classification that 

can be used to determine the appropriate FLM in the 

field or from existing field databases containing fuel 

information. A comprehensive description of each FLM 

is detailed in Lutes and others (in preparation) and is 

summarized in table 3.

Table 3—Fuel loading models (FLMs) are combinations of duff/litter and coarse woody debris (CWD) biomass that lead to unique fire

effects, as measured by soil heating and PM2.5 emissions. Multiple combinations of duff/litter and CWD may point to one FLM.

Fuel combination 1 Fuel combination 2 Fuel combination 3

FLM Duff/litter CWD Duff/litter CWD Duff/litter CWD Associated cover types

- - - - - - - - - - - - - - - - - - - - - - - - Tons/acre - - - - - - - - - - - - - - - - - - - - - - - -

 1 <8 <13 >5, <8 <13   Her

2 >8, <15 <13     Shrub, 

	 4	 >20, <40	 <13	 <8	 >17, <35	 	 	 Tall shrub, low – mid density forest

	 5	 >8, <20	 >13, <35	 	 	 Low – mid density forest

	 6	 >40, <60	 <35	 	 Mid – high density forest

	 7	 >20, <40	 >13, <35	 <40	 >35, <90	 	 	 Mid – high density forest

8 >60, <80 <35 >40, <80 >35 <40 >90 High density forest, large t

99 Agricultural, barren, unburnable
374 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2

Chapter 12—Mapping Wildland Fuel across Large Regions for the LANDFIRE Prototype Projec

Canopy Fuel Layers

The spatial representation of canopy fuel is important for assessing the probability and simulating the 

characteristics of crown fire across forested landscapes 

(Chuvieco and Congalton 1989; Finney 1998; Keane 

and others 1998a; Keane and others 2001). Four main 

variables describing canopy fuel characteristics are 

commonly applied in wildland fire simulation and fire 

management planning; these include canopy bulk density 

(CBD, kg m–3), canopy base height (CBH, m), canopy 

height (CH, m), and canopy cover (CC, percent).

Canopy bulk density (CBD) describes the mass of 

available canopy fuel per unit volume of canopy in a 

stand (Scott and Reinhardt 2005); it is the dry weight 

of available canopy fuel per unit volume of the canopy 

including the spaces between the tree crowns (Scott 

and Reinhardt 2001). Canopy fuel is typically defined 

by all foliage and branchwood material less than 1 cm 

in diameter because this is the fuel that is typically 

consumed in a crown fire (Keane and others 2005). 

The bulk density of the canopy determines the initiation of a crown fire and the subsequent rate at which a 

fire spreads through the canopy (Cruz and others 2003; 

Finney 1998; Van Wagner 1977; Van Wagner 1993).

Canopy base height (CBH) describes the level above 

the ground at which there is enough aerial fuel to carry 

the fire vertically into the canopy. This measurement 

is commonly thought of as the height from the ground 

to the bottom of the live canopy (Scott and Reinhardt 

2001) but may also include dense, dead crown material 

that can carry a fire. The CBH determines the likelihood 

of a crown fire and the interaction between the ground, 

surface, and canopy fuel layers (Cruz and others 2003).

Canopy height (CH) is the height of the top of the 

canopy, and canopy cover (CC) is the vertically projected 

percent cover of the live canopy layer for a specific area. 

Spatially explicit canopy information combined with 

topographic and weather data are used to determine 

when and where the transition from a surface fire to a 

crown fire may occur (Finney 1998).

Maps of these four canopy characteristics, in conjunction with maps of elevation, aspect, slope, and 

fire behavior fuel models, are required as input to the 

FARSITE model to simulate fire growth under various 

weather and wind scenarios (Finney 1998). FARSITE is 

currently used by many fire managers to plan prescribed 

burns and to manage wildland fires. It is designed to 

model fire behavior over a continuous surface at fine 

time-steps. These canopy characteristics can also be 

used in NEXUS to calculate the critical wind threshold 

for propagating a crown fire (Scott 1999).

The CH and CC map layers were mapped by the 

USGS Center for Earth Resources Observation and 

Science (EROS) using field-referenced data, satellite 

imagery, and statistical modeling (Zhu and others, 

Ch. 8); however, we describe only the development of 

the CBD and CBH canopy fuel layers in this chapter. 

These layers were mapped at the USDA Forest Service, 

Rocky Mountain Research Station, Missoula Fire Sciences Laboratory (MFSL) in Missoula, Montana using 

a complex statistical modeling procedure that employs 

biophysical gradients and satellite imagery to predict 

these variables across landscapes.

Fuel Mapping

Because of recent advances in fire modeling software 

and GIS analysis packages, maps of wildland fuel have 

become essential in wildland fire management and in 

planning and implementing fuel treatments (Finney 1998; 

Keane and others 2001). As mentioned, these spatial layers 

provide critical input to the numerous fire models currently 

available for fire management (see www.frames.nbii.gov). 

However, the mapping of wildland fuel is a difficult and 

costly task for two main reasons.

First, many of the remotely sensed data used in 

mapping, such as aerial photos and satellite images, 

are unable to detect surface fuel because the ground 

is often obscured by the forest canopy (Asner 1998; 

Elvidge 1988; Lachowski and others 1995). Even if 

sensors were able to view the ground, the resolution of 

the imagery makes it difficult to distinguish between 

fuel components on the ground and between surface 

fuel and fuel suspended in the canopy (Keane and others 2001). Second, high variability in fuel loading and 

other vegetation characteristics across time and space 

is a confounding property of fuel that prevents it from 

being accurately mapped (Agee and Huff 1987; Brown 

and See 1981; Harmon and others 1986). Fuel variability 

within a stand can often equal or be greater than the 

variability of fuel across the landscape (Brown and 

Bevins 1986; Brown and See 1981; Jeske and Bevins 

1979). Brown and Bevins (1986) found few statistically 

significant differences in fuel loadings between vegetation types and biophysical settings because of the vast 

differences in stand histories between areas with similar 

environments. This finding indicates that fuel is not 

always related to mapped vegetation categories. Keane 

and others (2001) summarized four general strategies 

commonly used to map fuel: 1) field reconnaissance, 2) 

indirect mapping with remote sensing, 3) direct mapping with remote sensing, and 4) biophysical gradient 

modeling. The indirect mapping with remote sensing 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 3

Chapter 12—Mapping Wildland Fuel across Large Regions for the LANDFIRE Prototype Projec

approach recognizes the inability of imagery to directly 

map fuel; thus, other, more easily mapped ecosystem 

characteristics are used instead as surrogates for fuel. 

This approach assumes certain biological properties can 

be accurately classified from remotely sensed imagery, 

and these attributes, most often related to the vegetation, 

correlate well with fuel characteristics or fuel models. 

Field reconnaissance methods map fuel through direct 

observation, whereas remote sensing methods assign 

fuel characteristics using imagery data. (Verbyla 1995). 

Lastly, the biophysical gradient modeling approach uses 

environmental gradients and biophysical modeling to 

create fuel maps. Environmental gradients are those 

biogeochemical processes, such as climate, topography, 

and disturbance that directly influence vegetation and 

fuel dynamics. In the LANDFIRE Prototype fuel mapping effort, we integrated the indirect remote sensing 

approach with the biophysical gradient modeling approach to map surface fuel, and integrated the direct 

remote sensing mapping approach with the biophysical 

gradient modeling approach to map canopy fuel.

Methods _______________________

The LANDFIRE Prototype Project involved many 

sequential steps, intermediate products, and interdependent processes. Please see appendix 2-A in Rollins and 

others, Ch. 2 for a detailed outline of the procedures 

followed to create the entire suite of LANDFIRE Prototype products. This chapter focuses specifically on the 

procedure followed in developing maps of surface and 

canopy fuel characteristics, which served as important 

core products of the LANDFIRE Prototype Project.

Creating the LANDFIRE Fuel Database

The LANDFIRE fuel database was derived from the 

LANDFIRE reference database (LFRDB) (Caratti, Ch. 

4) and compiled so that fuel layers could be directly 

created based on other LANDFIRE vegetation and 

biophysical data layers – specifically, the potential 

vegetation type (PVT) layer (Frescino and Rollins, Ch. 

7), the cover type (CT) layer, and the structural stage 

(SS) layer (Zhu and others, Ch. 8). This database was 

designed such that each PVT-CT-SS combination was 

assigned a set of fuel attributes. These fuel attributes 

were quantified in the following order of priority: 1) 

from field data, 2) from published literature, and 3) from 

estimates of experienced wildland fuel professionals.

The LANDFIRE fuel database was used for several 

purposes: First, it was used to create the surface fuel 

layers that did not have field data represented in the 

LFRDB. For example, the FBFM13 and FBFM40 values were rarely recorded in the LFRDB, so these layers 

were impossible to create using standard mapping and 

spatial modeling procedures. We had to therefore assign 

fuel model classification categories to each PVT-CT-SS 

combination using the myriad of variables describing 

vegetation composition and condition contained in the 

LANDFIRE fuel database. This fuel database was also 

used to assign values to the map where mapping models 

were in error. Moreover, it could also be used as a quasivalidation or data-check to ensure map consistency. And 

lastly, it could be used as a reference and guide to step 

down LANDFIRE fuel assignments to local applications. 

For example, managers may decide to change assigned 

fuel models to reflect local conditions.

The database was designed with the following 

fields:

1. Mapping zone – EROS mapping zone identification 

number

2. PVT – Potential vegetation type code

3. SCLASS – Succession class code, which represents 

a combination of cover type and structural stage

4. FBFM13 – Albini (1976) standard 13 fire behavior 

fuel models (see Anderson 1982) including additional models for water and rock

5. FBFM40 – Scott and Burgan (2005) 40 fire behavior 

fuel models

6. Default FCCs – Default fuel characterization classes 

from Sandberg and others (2001)

7. FLMs – Fuel loading models from Lutes and others 

(in preparation)

8. Canopy height (m) – Uppermost height of the canopy 

layer

9. Canopy base height (m) — Height at which crown 

bulk density exceeds 0.011 kg m–3

10. Canopy cover (%) – Percentage of vertically projected tree cover

11. Canopy bulk density (kg m–3) – Maximum bulk 

density of all vertical layers comprising the forest 

canopy

Creating Maps of Surface Fuel

The methods used to develop the surface fuel layers 

were distinctly different from the approach used for the 

canopy layers. We used a classification or rule-based 

approach in which fuel model categories from each 

classification (FBFM13, FBFM40, default FCCs, and 

FLMs) were assigned to combinations of mapped attributes from other LANDFIRE products using generalized rule sets. A rule set is a hierarchically nested set of 
376 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 20

Chapter 12—Mapping Wildland Fuel across Large Regions for the LANDFIRE Prototype Projec

rules that assigns surface fuel models to combinations 

of LANDFIRE data layers using information from the 

LANDFIRE fuel database (see appendix 12-A). This 

approach has been used successfully in several recent 

fuel mapping efforts and fit the design criteria for the 

LANDFIRE Prototype (Keane and others 1998a; Keane 

and others 1998b; Keane and Rollins, Ch. 3; Menakis 

and others 2000). The procedure for the surface fuel 

mapping process is detailed in Ch. 2: appendix 2-A.

For the LANDFIRE Prototype, the rule-based approach to the mapping of surface fuel was the only 

available technique for two main reasons: First, statistical 

modeling approaches could not be used because only 

a small fraction of the LFRDB contained information 

about fuel models. This meant that we could not use the 

classification and regression tree (CART) (Breiman and 

others 1984) analysis techniques that were applied in 

other LANDFIRE mapping tasks because there were 

insufficient reference data to build the statistical functions for spatially predicting surface fuel models. This 

lack of data was especially a problem in the case of 

the two new fuel classifications developed during the 

LANDFIRE Prototype – FBFM40 and FLMs – because 

they had never been used in the field. Second, there 

were no existing field and database keys with which to 

consistently identify fuel models from variables commonly included in field reference databases, such as 

canopy cover, vegetation type, fuel loading, and tree 

density. Efforts are currently underway to create field 

and database keys for each fuel model classification so 

that fuel models can be assigned to individual plots.

All surface fuel maps were created using rule sets 

where, in the most simple cases, surface fuel models 

were assigned to solely PVT-CT-SS combinations (Ch. 2: 

appendix 2-A). This rule-based approach also allowed 

for the inclusion of additional data when surface fuel 

models could not be uniquely described by a PVT-CT-SS 

combination. In these cases, the PVT-CT-SS stratification was augmented with other data that determine the 

distribution of surface fuel across landscapes, such as 

topography or geographic location. For example, a rule 

set might assign a FBFM13 fuel model to a PVT-CT-SS 

combination on slopes greater than 50 percent in the 

northern part of a mapping zone.

We gave confidence rankings to each of the default FCC 

assignments based on which attributes from the FCCS 

fuelbed database (Sandberg and Ottmar 2001) were applied. We gave the highest ranking (1) to fuelbeds that 

had species lists identical to the LANDFIRE PVT and 

CT species lists. We assigned a confidence ranking of 2 

if we needed to associate fuelbeds with unique vegetation 

classes based on vegetation characteristics and actual and 

inferred (through expert knowledge) site characteristics. 

In other words, the default FCCs’ vegetation description 

was similar to but not exactly the same as the LANDFIRE 

vegetation map units. Finally, using expert knowledge, 

we assigned a confidence ranking of 3 to the remaining 

fuelbeds where the information in the FCCs’ vegetation 

description was not represented by the LANDFIRE vegetation map units. For fuelbeds given a confidence ranking 

of 3, we determined the most appropriate fuelbed based 

on species composition and structure. In certain cases, 

we were unable to associate some of the fuelbeds with a 

unique vegetation class combination because there was 

not an appropriate fuelbed to represent this situation, even 

with an expanded definition.

Creating Maps of Canopy Fuel

We developed the two canopy fuel maps (CBD, CBH) 

for the forested lands of Zone 16 and Zone 19 using a 

predictive landscape modeling approach (Franklin 1995). 

This approach integrates remote sensing, biophysical 

gradients, and field-referenced data to generate maps 

of canopy bulk density and canopy base height. These 

canopy fuel characteristics were calculated for numerous plots in the LFRDB and then augmented with a set 

of mapped predictor variables in a classification and 

regression tree (CART) approach to predict crown fuel 

attributes across the two prototype mapping zones.

Calculating canopy fuel characteristics—The first 

step was to calculate CBD and CBH using FUELCALC, a prototype program developed by Reinhardt 

and Crookston (2003). FUELCALC computed several 

canopy fuel characteristics for each field reference plot 

from the LFRDB based on allometric equations relating 

individual tree size, canopy, and species characteristics 

to crown biomass. The canopy characteristics for a stand 

are computed from a list that specifies the tree species, 

density (trees per unit area), diameter at breast height 

(DBH), height, crown base height, and crown class. 

FUELCALC computes vertical canopy fuel distribution 

using algorithms that evenly distribute crown biomass 

over the live crown for each tree. For each plot, the 

program then divides the canopy into horizontal layers 

of a user-specified width and reports the CBD value of 

the layer with the greatest bulk density. The CBH value 

for each plot is reported as the height of the lowest layer 

of the canopy that has a bulk density value greater than 

0.011 kg m–3. FUELCALC estimates for CH and CC 

were not used in the mapping process since these maps 

were created by Zhu and others (Ch. 8).
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Chapter 12—Mapping Wildland Fuel across Large Regions for the LANDFIRE Prototype Projec

For the LANDFIRE Prototype FUELCALC canopy 

fuel calculations we used the Forest Inventory and Analysis (FIA; Gillespie 1999) data from the LFRDB because 

they provided consistent information and FUELCALC 

input values across both prototype zones. There were 

a total of 1,806 FIA plots that fell within the Zone 16 

boundary. This included over 32,000 individual tree 

records. Zone 19 encompassed a total of 1,988 FIA 

forested plots with over 44,600 individual tree records. 

We derived crown depth (a FUELCALC input) from 

the FIA compacted crown ratio attribute, defined as the 

percentage of the total height of a tree that supports live 

foliage (Miles and others 2001). We did not attempt to 

deconstruct the live crown ratio.

Mapped predictor data—Our hypothesis was that 

plot-level estimates of canopy fuel are correlated with 

spectral (from satellite imagery) and biophysical (from 

LANDFIRE computer models) gradients. As a basis for 

developing a database of mapped predictor variables, 

we used data from a leaf-on (June 2000) Landsat image and a leaf-off (October 2000) Landsat image. We 

included three visible bands, three infrared bands, a 

thermal band, three tasseled-cap transformation bands 

(brightness, greenness, and wetness) (Huang and others 

2001), and a normalized difference vegetation index for 

each image – totaling 22 variables derived from spectral 

information (table 4).

The database of mapped predictor variables also included a suite of biophysical gradient layers that were 

created using WXFIRE, an ecosystem simulation model 

(Keane and Holsinger 2006; Keane and Rollins, Ch. 3), 

and four topographic gradient layers. The WXFIRE 

model integrates DAYMET (Running and Thornton 

1996; Thornton and others 1997; Thornton and others 

2000) climate data with landscape data and site specific 

parameters (such as soils and topography) and interpolates 1-km grid DAYMET climate variables to a 30-m 

grid cell resolution, thereby generating spatially explicit 

maps of climate and ecosystem variables at fine spatial 

resolutions. WXFIRE outputs a total of 33 variables (See 

Holsinger and others, Ch. 5 for detailed information 

about WXFIRE and biophysical gradient modeling in 

the LANDFIRE Prototype). However, this exhaustive 

list was reduced (winnowed) to 16 variables for Zone 

16 (table 5) and 18 variables for Zone 19 (table 6) using exploratory analyses of principle components and 

correlation matrices.

The topographic gradients included four variables 

derived from the National Elevation Dataset (NED): 

elevation, percent slope, classified aspect, and a topographic position index. The topographic position index 

is a metric scaled from 0 to 1 defining the position on a 

slope, with 0 being the bottom of a valley and 1 the top 

of a ridge (table 7). In addition to the mapped predictor 

variables described above, we included four LANDFIRE 

products as additional predictors: maps of CT, SS, CH, 

and CC (Long and others Chapter 6; Zhu and others, 

Chapter 8).

Table 4—Zone 16 and Zone 19 satellite imagery predictor layers for canop

bulk density and canopy base height models.

Variable Description

	 onb1	 Landsat Leaf-on – band 1 (visible blue)

	 onb2	 Landsat Leaf-on – band 2 (visible green)

	 onb3	 Landsat Leaf-on – band 3 (visible red)

	 onb4	 Landsat Leaf-on – band 4 (near infrared)

	 onb5	 Landsat Leaf-on – band 5 (mid infrared)

	 onb6	 Landsat Leaf-on – band 7 (mid infrared)

	 onb9	 Landsat Leaf-on – band 9 (thermal)

	 onndvi	 Landsat Leaf-on – normalized difference vegetation index

	 offb1	 Landsat Leaf-off – band 1 (visible blue)

	 offb2	 Landsat Leaf-off – band 2 (visible green)

	 offb3	 Landsat Leaf-off – band 3 (visible red)

	 offb4	 Landsat Leaf-off – band 4 (near infrared)

	 offb5	 Landsat Leaf-off – band 5 (mid infrared)

	 offb6	 Landsat Leaf-off – band 7 (mid infrared)

	 offb9	 Landsat Leaf-off – band 9 (thermal)

	 offtc1	 Landsat Leaf-off – tassel-cap transformation (brightness)

	 offtc2	 Landsat Leaf-off – tassel-cap transformation (greenness)

	 offtc3	 Landsat Leaf-off – tassel-cap transformation (wetness)

	 offndvi	 Landsat Leaf-off – normalized difference vegetation index
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Chapter 12—Mapping Wildland Fuel across Large Regions for the LANDFIRE Prototype Projec

Table 5—Zone 16 biophysical gradient predictor layers, produced using the

WXFIRE model, for canopy bulk density and canopy base height regression 

tree models.

Variable Units Description

aet kgH20/yr Actual evapotranspiration

dday degree C Degree-days

dsr days Days since last rain

evap kgH20 m–2 day–1 Evaporation

gc_sh s m–1 Canopy conductance to sensible heat

gl_sh s m–1 Leaf-scale stomatal conductance

	 outflow	 kgH20m–2 day–1 Soil water lost to runoff and ground

pet kgH20 yr–1 Potential evapotranspiration

ppt cm Precipitation

psi -Mpa Water potential of soil and leaves

rh % Relative humidity

snow cm Amount of snowfall

srad.fg w m–2 Shortwave radiation for the site

tmin degree C Minimum daily temperature

trans kgH20 m–2 day–1 Soil water transpired by canopy

vmc scalar Volumetric water content

Table 6—Zone 19 biophysical gradient predictor layers, produced using the

WXFIRE model, for canopy bulk density and canopy base height regression 

tree models.

Variable Units Description

aet kgH20 yr–1 Actual evapotranspiration

dday degree C Degree-days

dsr days Days since last rain

evap kgH20 m–2 day–1 Evaporation

gc_sh s m–1 Canopy conductance to sensible heat

	 outflow	 kgH20 m–2 day–1 Soil water lost to runoff and ground

pet kgH20 yr–1 Potential evapotranspiration

ppfd umol m–2 Photon flux density

ppt cm Precipitation

psi -Mpa Water potential of soil and leaves

rh % Relative humidity

snow cm Amount of snowfall

srad.fg w m–2 Shortwave radiation for the site

swf dimension Soil water fraction

tave degree C Average daily temperature

tmin degree C Minimum daily temperature

trans kgH20 m–2 day–1 Soil water transpired by canopy

vmc scalar Volumetric water content

Table 7—Zone 16 and Zone 19 topographic gradient predicto

layers for canopy bulk density and canopy base height regression tree models.

Variable Units Description

elev meters Elevation

asp 8 classes Aspect class

slp % Slope

posidx index (0-1) Topographic position inde
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Chapter 12—Mapping Wildland Fuel across Large Regions for the LANDFIRE Prototype Projec

Classification and regression trees (CART)—As 

in the mapping of PVT, CT, and SS (Frescino and 

Rollins, Chapter 7; Zhu and others, Chapter 8), we used 

regression trees (Breiman and others 1984) to model 

and map canopy fuel across zones 16 and 19. Regression tree models are rule-based predictive models in 

which continuous data values are recursively divided 

into smaller subsets based on a set of rules. The rules 

are constructed from available training data in which 

observations are delineated into smaller subsets of more 

homogenous classes. For every possible split of each 

predictor variable, the within-cluster sum of squares 

about the mean of the cluster on the response variable 

(the theme being mapped) is calculated. The predictor 

defines a split at the point that yields the smallest overall within-cluster sum of squares (Breiman and others 

1984). For a detailed description of the use of CART in 

the LANDFIRE Prototype, see Frescino and Rollins, 

Chapter 7. We tried other statistical approaches, such as 

nearest neighbor, discriminant analysis, and generalized 

linear modeling but decided to employ the regression 

tree approach because it consistently generated valid 

models that created realistic maps.

The regression trees for modeling canopy fuel were 

generated using the commercially available machinelearning algorithm, Cubist (Quinlan 1986; Quinlan 

1993; Rulequest Research 2004). Cubist offers a fast 

and efficient means for building regression tree models 

and applying these models to large areas (Homer and 

others 2002; Huang and others 2001; Moisen and others 

2004; Xian and others 2002; Yang and others 2003). Cubist generates rule-based models with one or more rules 

defining the conditions in which a linear regression model 

is established. Cubist can also build “composite models,” 

where a rule-based model is combined with an instancebased (nearest-neighbor) model (Quinlan 1993).

Other features of Cubist include 1) generation of 

committee models, 2) simplification of (pruning) the 

models, and 3) extrapolation of the model predictions. 

First, the committee models are made up of multiple 

rule-based models where each model “learns” from the 

prediction errors of the previously built model. The final 

model’s predictions are an average of the predictions 

of the previously built models. Second, to simplify or 

prune a model in Cubist, you can specify the percentage 

of cases that meet the conditions of a rule or explicitly 

define the maximum number of rules allowed. Third, 

the extrapolation feature defines the percentage factor 

in which model predictions can occur outside the range 

of values determined by the training data (Rulequest 

Research 2004).

Although not fully automated, the process for modeling and mapping canopy fuel was simplified using a 

suite of tools developed by Earth Satellite Corporation 

(2003) in support of the National Land Cover Database 

(NLCD) project (Vogelmann and others 2001). These 

tools were developed to integrate the Rulequest Cubist software package (Rulequest Research 2004) with 

ERDAS Imagine image-processing software (ERDAS, 

Inc. 2001). We used the NLCD Sampling Tool to set up 

the input files needed to build the models in Cubist and 

the NLCD Classifier Tool to generate the final map. The 

Sampling Tool allows a user to input a spatially explicit 

layer of field-referenced training data as a dependent 

variable and multiple spatially explicit gradient layers 

as independent variables; the tool then outputs the files 

needed to execute Cubist. The Classifier Tool applies 

the regression tree model output from Cubist across the 

specified spatial extent or a specified masked extent.

To meet the input requirements of the NLCD mapping tool and to improve the efficiency of the modeling 

process, we followed three pre-processing rules: 1) all 

layers must be ERDAS Imagine images, 2) all layers 

must have the same number of rows and columns, and 

3) all layers must be scaled to size 16-bit or smaller and 

have positive values. The output from the Sampling Tool 

includes a data text file, which contains values from 

the model response and the corresponding value of the 

model predictor layers for each georeferenced training 

site, and a file identifying the model input names and 

data types. We built multiple Cubist models for CBD 

and CBH for each prototype mapping zone – exploring the different features of Cubist – and selected the 

model having the lowest error as the model to use for 

prediction. The final maps of both CBD and CBH were 

created using the Classifier Tool based on the predictor 

variables listed in table 8.

Performing QA/QC Procedures

The LANDFIRE fuel layers (both crown and surface 

fuel) needed to be not only congruent across all fuel 

layers, but also consistent with all other LANDFIRE 

layers. It was essential that all pixels in the LANDFIRE 

data layers have logical combinations of vegetation, fuel, 

fire, and biophysical parameters. The process used to 

ensure that pixels across layers were assigned logical map 

categories was called the LANDFIRE Quality Assurance/Quality Control (QA/QC) procedure. This process 

was designed for the Zone 16 and Zone 19 fuel maps but 

not implemented because of administrative problems. 

In addition, late completion of other LANDFIRE tasks 

precluded a comprehensive comparison with the fuel 
380 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 20

Chapter 12—Mapping Wildland Fuel across Large Regions for the LANDFIRE Prototype Projec

layers. The LANDFIRE Prototype effort performed 

only minor logic checks and data scans for inconsistent or abnormal data values. A more comprehensive 

QA/QC procedure is currently being implemented for 

LANDFIRE National.

Performing the Accuracy Assessment

Surface fuel layers—Accuracy assessment of the 

mapped fire behavior fuel models (FBFM13 and 

FBFM40) proved problematic in the LANDFIRE Prototype for two reasons: 1) there can be more than one 

correct FBFM assignment in the field and 2) a lack of 

sufficient geo-referenced field data where FBFM13 and 

FBFM40 categories were recorded in the field. The 

FBFM maps were developed to serve as inputs to the 

fire behavior prediction software (BEHAVE and FARSITE); however, there are numerous other weather, wind, 

and fuel moisture variables that influence fire behavior 

simulations. Consequently, more than one FBFM can 

lead to the same fire behavior characteristics if other 

environmental variables are adjusted. Accuracy can only 

be truly tested during specific wildland fires because the 

primary purpose of the FBFM is to predict fire behavior, not describe fuel characteristics. In such cases, the 

expected fire behavior can be compared to the observed 

behavior and the accuracy assessed for that specific 

situation. The lack of comprehensive field-referenced 

data prevented a conventional accuracy assessment of 

all the surface fuel layers. The FBFM40 data were new 

and therefore hadn’t yet been used by field personnel.

The surface fire behavior fuel models (FBFMs), fuel 

loading models (FLMs), and default FCCS fuelbeds 

were mapped based on rule sets that were used to link 

FCCs to unique combinations of PVT, CT, and SS. To 

test the accuracy of the rule sets, we assigned surface 

fuel attributes to the PVT-CT-SS combinations for each 

plot in the LFRDB using the map rule sets. We then 

compared the assigned surface fuel attribute value of the 

plot to the corresponding pixel location for that plot on 

the fuel model maps. We assumed the plot assignment 

was correct and determined our accuracy based on this 

surface fuel attribute value.

Canopy fuel layers—We randomly withheld a percentage of the total number of training sites from the 

LFRDB for each mapping zone for independent accuracy 

assessment of the final maps. For Zone 16, we withheld 

20 percent of the total plots, leaving 1,304 plots for 

modeling CBD and 325 plots for assessing the accuracy 

of CBD predictions; we had 1,098 for modeling CBH 

and 275 for assessing the accuracy of CBH predictions 

(table 9). Regression trees were pruned and modified 

based on the cross-validation accuracy assessment.

After analyzing error distribution for Zone 16, we 

determined for Zone 19 that a subset of only 10 percent 

of the total plots would be sufficient for assessing accuracy, and the consequent increase in the number of 

plots used for modeling improved the performance of 

the model. This resulted in 1,768 plots for modeling 

CBD, 184 plots for testing CBD predictions, 674 plots 

for modeling CBH, and 198 for testing CBH predictions (table 9). The data distributions of the model data 

sets for CBD and CBH (zones 16 and 19) are shown in 

figure 1. The test data sets had identical distributions. 

Table 8—Zone 16 and Zone 19 modeled predictor layers fo

canopy bulk density and canopy base height regression tree 

models.

Variable Units Description

	 evtr	 class	 	Forest - cover type (rectified)

	 ssr	 class	 Forest - structure stage (rectified)

forht m Forest - average dominant height

forcov % Forest - canopy cover

Table 9—Number of plots used for modeling and accuracy assessment. Error 

estimates for canopy bulk density (CBD) are in kg m–3 and for canopy base 

height (CBH) in meters.

Model Test Average Relative Correlation

Zone Model plots plots error error coefficient

Z16 CBD 1304 325 0.03 

CBH 1098 275 1.9 0.65

Z19 CBD 1768 184 0.05 0.

CBH 1674 198 1.9
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 

Chapter 12—Mapping Wildland Fuel across Large Regions for the LANDFIRE Prototype Project Figure 1—Distribution of plots (frequency) across canopy bulk density (CBD) and canopy base height (CBH) values across the two prototype zones. They are arranged in the following order a) CBD model data set for Zone 16, b) CBH model data set for Zone 16, c) CBD model data set for Zone 19, and d) CBH model data set for Zone19.  The test data sets for CBD and CBH used to validate and assess accuracy of the statistical models had identical distributions for both z
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Chapter 12—Mapping Wildland Fuel across Large Regions for the LANDFIRE Prototype Projec

Cubist automatically tests the model predictions at each 

test site and outputs three measures of error: an average 

error, a relative error, and a correlation coefficient. The 

average error represents the magnitude of the errors 

defined by the predicted value compared to the actual 

value. The relative error is the ratio of the average error 

to the error that would result from always predicting the 

mean value. The correlation coefficient measures the 

agreement between the predicted values and the actual 

values. Cubist also outputs a scatter plot of the predicted 

values against the actual values. This is used for visual 

evaluation of the regression models (Rulequest Research 

2004).

Results ________________________

Surface Fuel

The FBFM40 and FLM classifications were not completed in time for the mapping of surface fuel models 

for Zone 16, so only maps of FBFM13 and default 

FCCs are presented in figure 2. These classifications 

were completed prior to the mapping of Zone 19, but, 

unfortunately, the FCCS fuelbeds for this region were 

unavailable. Therefore, only three surface fuel maps are 

shown in figure 3 for Zone 19. A summary of the area 

in each mapping zone by the four fuel classifications is 

shown in table 10.

Canopy Fuel

Mapping Zone 16—The maps of CBD and CBH for 

Zone 16 are shown in figure 4. We selected a Cubist 

model built using a composite model as the best model 

for predicting CBD across the zone. This model was 

produced by combining a rule-based model with an 

instance-based model and adjusting the results based 

on the seven nearest (most similar) neighbors. Five 

committee models were built to improve the predictive 

ability of the model. We set the minimum rule cover at 

one percent, under the premise that that the conditions 

associated with any rule should be satisfied by at least 

one percent of the training cases, and we allowed a ten 

percent extrapolation of values across the total range of 

values. Main predictor layers are shown in table 8.

A comparison of the predicted values with our independent test set for Zone 16 revealed an average error 

of 0.026 kg m–3, a relative error of 0.55 kg m–3, and a 

correlation coefficient of 0.76 (table 9). The scatter plot 

is displayed in figure 5a. For CBH, the model selected 

as having the best predictive power was also built using a composite model. This model was produced by 

a rule-based model adjusted by six nearest neighbors 

and four committee models. According to our accuracy 

measures, the average error was 0.39 m, the relative 

error was 0.65 m, and the correlation coefficient was 

0.63 (table 9). The corresponding scatter plot is shown 

in figure 5b.

The CBD model included imagery variables that are 

typical for distinguishing vegetative characteristics 

(Campbell 1987), biophysical gradients that explain 

vegetation-water interactions, and topographic gradients 

explaining the local variation across the zone. The CBH 

model included transformed imagery variables (offtc1, 

onb3, and onb5 in table 4), water-related biophysical 

gradients (aet, pet, ppt, and psi in table 5), and elevation 

(table 7). The inclusion of transformed imagery variables 

in the CBH model suggests that there are no direct relationships between CBH and any one band signature. 

The inclusion of spectral and biophysical gradients as 

relevant predictors in the models indicates a strong correlation between canopy fuel characteristics and both 

vegetation and ecological site characteristics.

Mapping Zone 19—The maps of CBD and CBH for 

Zone 19 are shown in figure 6. For CBD, we created ten 

committee models, set the minimum rule cover to four 

percent of the training cases, and allowed ten percent 

extrapolation. For CBH, we created seven committee 

models, set a four percent minimum rule, and allowed 

extrapolation of ten percent.

The accuracy assessment for the CBD model revealed 

a 0.05 kg m–3 average error, a 0.72 kg m–3 relative error, and a correlation coefficient of 0.66. For CBH, the 

errors were quite low, with an average error of 1.9 m, a 

relative error of 0.91 m, and a correlation coefficient of 

0.38 (table 9). The relative error of 0.91 suggests that the 

model for predicting CBH in Zone 19 did not achieve 

accuracy any higher than the pure mapping of a mean 

CBH across the zone. The scatter plot of predicted versus 

real values, shown in figures 5c and 5d, demonstrates 

that the model over-predicted low values of CBH and 

under-predicted high values of CBH.

The set of variables that were important for CBD and 

CBH discrimination showed patterns similar to those 

in Zone 16. Imagery variables of onb3, onb5, and offtc1 

(table 6) were the prominent variables that defined the 

splits for the canopy bulk density model; followed by 

biophysical gradients of pet, ppt, dsr, tmin, rh, elev, 

and posidx (table 7); and modeled variables of evtr and 

forht. For Zone 19 CBH, the imagery transformations of 

offtc1 and offtc2 were prominent again,; with gradients 

of dday, ppt, evap, srad_fg, dsr, elev, and slp; and modeled variables of evtr and forth (table 7).
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Chapter 12—Mapping Wildland Fuel across Large Regions for the LANDFIRE Prototype Projec

Table 10—A summary of the area (km2) within Zone 16 and Zone 19 occupied by the ten most frequent fuel models from each of the four fuel

model classifications stratified by potential vegetation type (PVT). The acronyms are defined as follows: FBFM13 = Anderson (1982) 13 standard

fire behavior fuel models; FBFM40 = Scott and Burgan (2005) 40 fire behavior fuel models; Default FCCs = default fuel characterization classes

(Sandberg and others 2001); and FLMs = fuel loading models by Lutes and others (in preparation). Note: default FCCs were not mapped for Zone

19 because of an insufficient number of fuelbeds and FLMs and FBFM40 were not mapped for Zone 16 because the classifications were not

finished in time to map fuel models for that zone.

FBFM13 FBFM40 Default FCCs FLM

Model km2 Model km2 Classes km2 Model km2

Zone 16

- - Not Available - - - - Not Available - - 

6 25,333.   Big Sagebrush Steppe 9,4

8  15,088.   Pinyon-Juniper Woodland 7,00

5  12,374.84   Western Juniper/S

 Shrubland 6,731

2 6,047.90 Subalpine Fir-Engelman

 Spruce-Lodgepole Pine

 Forest 5,979.98

 10  4,0.47   No Fuelbed Assigned 5

Barren 1,9442   Quaking Aspen Forest 

 mixed conifer understory 5,434

Agriculture 1,877.33   Quaking Aspen Fore

 1 1,5.12   Big Sagebrush Step

Urban 923.55 Western Juniper/Sagebrush

 Bitterbrush  3,196

9  439.63   Montane Bigtooth 

 Gambel Oak / Ponderosa

 Pine Mixed Forest 2,304

	 Water	 348.17	 	 	 Douglas fir (dominated) /

	 	 	 	 Pacific Ponderosa Pine

 Mixed Conifer Forest w/

 shrub 2,130.

 3 219.84   White Fir / Ga

 Mixed Forest  1,966.

Snow/Ice  1.54   Barren

 Black Cottonwood-Alder-

 Ash Riparian Forest  1,932

 Agriculture  1,87

 Overmature Lodgepole 

 Pine Forest  1,721

 Ponderosa Pine-Pinyon-

 Juniper  1,19

 Perennial Grass Savanna 975.75

 Urban 923.5

 Gambel Oak - Sagebrush 

 Shrubland 852.86

Zone 19

- - - - - - - - - - - - Not Available - - - - - - - - - - - -

8  39,958.60 TL3 36,098.67  

2 28,087.17 GS2 21,196.08 7 25,01

 1 13,458.08 SH2 12,13

5  13,209.15 TU5 7,466.23 No Fue

Agriculture 7,397.57 NB3 7,397.57 2 9,2

6 6,359.86 GR4 7,306.96 8 7,

 10  1,884.77 GR2 6,528.90 5 3

Barren 1,456.84 TL1 2,795.22 

Water 1,184.94 GR1

9 787.52 GS1 2,7

Urban  362.41 TU1

Snow/Ice  13.88 NB9 1

 NB8 1,1

 SH7 701.

 TL6 670.3

 NB1 3

 TL9 35

 SH1 277.

 TL2 106

 NB2 13
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Chapter 12—Mapping Wildland Fuel across Large Regions for the LANDFIRE Prototype Projec

Figure 2—Zone 16 surface fuel maps.

Surface fuel model maps for a) the 

Anderson (1982) 13 standard fire behavior fuel models (FBFM13) and th

b) default fuel characterization classes 

(FCCs). The fuel loading model and 40

fire behavior fuel model classifications

were not developed when fuel for Zone 

16 was mapped

A

B
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Chapter 12—Mapping Wildland Fuel across Large Regions for the LANDFIRE Prototype Projec

Figure 3—Zone 19 surface fuel maps. Surface fuel model maps for a) the Anderson (1982) 13 standard fire behavior fuel mode

(FBFM13), b) Scott and Burgan (2005) 40 fire behavior fuel models (FBFM40), and c) the default fuel characterization classes

(default FCCs).

A

B

C
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Chapter 12—Mapping Wildland Fuel across Large Regions for the LANDFIRE Prototype Projec

Figure 4—Zone 16 canopy fuel layers. Shown are maps of a) canopy bulk density (CBD) and b) canopy base height (CBH) for Zone 1A

B
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Chapter 12—Mapping Wildland Fuel across Large Regions for the LANDFIRE Prototype Projec

Figure 5—Accuracy assessment of the predicted versus real (observed) values for canopy bulk density (CBD) and canopy base 

height (CBH) for both mapping zones. The diagonal line indicates full agreement between the model and field data. The scatterplots are: a) Zone 16 canopy bulk density, b) Zone 16 canopy base height, c) Zone 19 canopy bulk density, and d) 

canopy base height.

A B

C D
388 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 20

Chapter 12—Mapping Wildland Fuel across Large Regions for the LANDFIRE Prototype Projec

Figure 6—Zone 19 canopy fuel layers. Shown are maps of a) canopy bulk density (CBD) and b) canopy base height (CBH) for Zone 1A

B
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Chapter 12—Mapping Wildland Fuel across Large Regions for the LANDFIRE Prototype Projec

Discussion _____________________

Surface Fuel Maps

Rule-based approach to fuel mapping—In the 

LANDFIRE Prototype, we used the classification or 

rule-based approach to assign fuel models to combinations of vegetation and biophysical settings. Despite 

limitations, this approach was the most appropriate given 

the project guidelines, design criteria, and available data 

(Keane and Rollins, Ch. 3). The fuel maps described in 

this chapter are important products of the LANDFIRE 

Prototype Project because they provide critical inputs 

to fire behavior and effects models commonly used to 

explore alternative management strategies for implementation of the National Fire Plan. However, it would 

have been preferable to use the same gradient-based 

predictive landscape modeling approaches to surface 

fuel mapping as those used for the mapping of vegetation and canopy fuel (see Frescino and Rollins, Ch. 7 

and Zhu and others, Ch. 8) because:

1) the mapping resolution (30 m pixel) would more 

closely match the resolution of fuel variability as 

compared with the mapping resolution of PVTCT-SS combinations (usually mapped as groups 

of pixels), and this would eventually result in more 

accurate fire behavior predictions;

2) the resolution of the fuel model classification categories would more closely match the spatial resolution than the PVT-CT-SS resolution (for example, 

you could have more than one fuel model within 

a PVT-CT-SS combination using the gradient approach); and

3) the fuel models would be mapped based on the ecological processes and gradients, such as productivity, species composition, and decomposition, that 

govern the distribution and condition of wildland 

fuel across landscapes.

Overall, the lack of consistent and accurate field data 

on fuel in the LANDFIRE Prototype prevented a statistical modeling strategy for fuel mapping. Therefore, if 

possible, a comprehensive empirical approach should be 

employed, as in other LANDFIRE tasks, for fuel mapping in the national implementation of LANDFIRE.

The LANDFIRE Prototype did not deliver all surface fuel map products for a number of reasons. First, 

the fuel classifications (FBFM40, default FCCs, and 

FLMs) were not completed in time for the LANDFIRE 

Prototype mapping effort. For Zone 16, we mapped 

the default national fuelbeds provided to us by the Fire 

and Environmental Effects Research Team (FERA) in 

December of 2003. However, these default FCCs were 

still in draft format. We found that while the default 

fuelbed set seemed to apply to a wide variety of vegetation and fuel types throughout Zone 16, over 20 percent 

of the map area – especially the herbaceous and shrub 

types – was not well-represented by the default FCCS 

categories. In addition, we found that the fuelbed list was 

also missing detailed information for large sections of 

the country. The default FCCs provided with the FCCS 

software were developed to represent major fuelbeds of 

concern to fire managers. Many of these defaults were 

selected through workshops held throughout the country 

by fire managers and ecologists who were focusing on 

the problem fuel types in their respective areas. Less 

hazardous vegetation and fuel types were not emphasized in the development of the FCCS. However, the 

LANDFIRE Prototype needed to map all vegetation 

and fuel conditions found within the mapping zones. We 

anticipated a new and more comprehensive version of 

the default FCCs prior to mapping Zone 19 since more 

than a year had passed since the release of the previous 

version. Unfortunately, we did not receive the new set 

in time to map Zone 19. However, during this time we 

determined through discussions with the FERA team 

that a better way to create a LANDFIRE fuelbed map 

would be to modify the default fuelbeds to reflect the 

vegetation and fuel conditions described in the LFRDB, 

thereby creating custom LANDFIRE fuelbed classes. 

These would be more meaningful to the LANDFIRE 

National Project, and custom development is encouraged 

by FERA. This approach is currently being evaluated for 

national LANDFIRE implementation. Lastly, because 

the FLM classification was not completed in time, it was 

not extensively tested and validated in the LANDFIRE 

Prototype effort.

Canopy Fuel Maps

Again, training data were the main limiting factor for 

the statistical regression tree modeling approached used 

to map CBH and CBD across both zones. However, this 

limitation was not as severe as during the surface fuel 

modeling phase. Low accuracies may also have resulted 

from the quality of the training data used to build and 

test the models because regression tree performance 

depends greatly on the quality of the field data used. We 

conducted our analysis under the assumption that the 

data perfectly represented ground conditions; however, 

the fuel database used to estimate canopy fuel may 

not have been free of errors. The accuracy of the CBD 

and CBH calculations is dependent upon the accuracy 

of the tree measurements on the ground as well as the 
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Chapter 12—Mapping Wildland Fuel across Large Regions for the LANDFIRE Prototype Projec

accuracy of the allometric equations used to derive CBD 

and CBH. Another assumption was that the positional 

accuracy of the training data was within spatial tolerances (Vogelmann and others, Ch. 13). In other words, 

each georeferenced location was assumed to match the 

corresponding pixel value of each predictor. Another 

source of error in the fuel database was that the FIA 

tree data crown dimensions were sampled by visually 

compacting the crown length to eliminate gaps in the 

canopy, which subjectively and falsely raises the tree 

crown base height and results in overestimations of 

CBH. Lastly, the variable plot sampling used in FIA 

inventory may tend to oversimplify canopy conditions 

because not all trees that contribute to canopy fuel are 

measured in the same area.

Another possible reason for the fuel maps’ low accuracies were the scale and resolution difficulties encountered when computing CBD and CBH at the plot 

level using the FUELCALC program; both values are 

difficult to assess at a stand or pixel level because they 

have highly variable distributions in the horizontal and 

vertical dimensions (Keane and others 2005). The limited 

information gathered for each tree requires that several 

assumptions be made in order to compute CBD and CBH 

at the stand level. For example, the CBH was computed 

as the lowest layer with greater than 0.037 kg m–3 bulk 

density. This layer’s bulk density might have been based 

on data from only one tree or from a seedling/sapling 

layer that is well below the overstory canopy. Moreover, 

the threshold of 0.037 kg m–3 is a somewhat arbitrary 

number suggested by Alexander (1988).

The FUELCALC program served as a critical tool 

in the calculation of canopy characteristics, but this 

software presented some major limitations. We used a 

beta version of the program that contained crown biomass algorithms and crown fuel adjustment factors for 

only 14 Rocky Mountain conifer species. However, this 

program was preferable over other biomass calculation 

software packages, such as BIOPAK (Means and others 1994), because it computes crown biomass by fuel 

component and integrates the results of an extensive 

canopy fuel sampling effort into biomass components 

(Scott and Reinhardt 2002, 2005). More tree species 

must be included in the software so it can be applied to 

other ecosystems in the U.S. This program needs to be 

revised to incorporate a user-friendly interface and an 

extensive users’ manual so that it can be used to compare 

and contrast LANDFIRE map values at local scales. 

Lastly, FUELCALC output has not been compared 

with measured canopy characteristics in many areas of 

the United States and subsequently refined. This model 

refinement must be completed to ensure credible canopy 

fuel estimates are being calculated.

Low accuracies of the CBD and CBH regression tree 

models posed a major problem in canopy fuel mapping. These low accuracies may indicate that plot-level 

estimates of canopy fuel are not directly or closely correlated with spectral imagery information and gradients 

of biophysical and topographic characteristics, resulting 

primarily from the fact that CBH and CBD are canopy 

characteristics that are hidden from view. Canopy base 

height is nearly impossible to detect using passive sensors such as Landsat because it is at the bottom of an 

obstructing canopy. Only active remote sensing techniques, such as Lidar or Radar, have the ability to detect 

vertical canopy dimensions (Keane and others 2001). 

Since the passive sensors do not detect canopy depth, 

it is difficult to determine which canopy layer has the 

greatest CBD. This is the main reason our statistical models 

underestimate both CBD and CBH (figs. 5a and 5c).

The difficulty we experienced predicting canopy fuel 

characteristics indicates that we may have had an inappropriate or insufficient number of predictor gradients. 

Satellite imagery is an excellent source for describing 

vegetation patterns but is limited to dominant overstory 

features. The modeling of forest structure attributes, 

particularly that of CBH, requires additional predictors 

to discriminate patterns. Outputs from the BGC process 

model (Holsinger and others, Ch. 5), which spatially 

represents the rates of the hydrologic, carbon, and nitrogen cycles, were not available for the LANDFIRE 

Prototype and were therefore not included in the models. 

These ecophysiological gradients have proven to be highly 

useful in discriminating vegetation characteristics (Keane 

and others 2001; Rollins and others 2004). It should also 

be noted that the methods for generating the biophysical 

gradient layers were still under development during the 

LANDFIRE Prototype and met with limited success 

(Holsinger and others, Ch. 5). In the national implementation of LANDFIRE, refined, consistent methods for 

generating these layers will maximize their utility for all 

LANDFIRE mapping tasks.

Lastly, we encountered a problem upon combining the 

surface and canopy fuel layers into the landscape format 

required by the FARSITE model (Finney 1998). We found 

many inconsistencies between CBD, CBH, CC, and CH 

values that should have been detected during the analysis 

and QA/QC phase. For example, canopy height values 

were lower than canopy base height values for the same 

pixel. In addition, many CBD estimates were too low to 

be useful for FARSITE simulation. These inconsistencies were not errors, but rather resulted from problems 
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Chapter 12—Mapping Wildland Fuel across Large Regions for the LANDFIRE Prototype Projec

with the way canopy characteristics were computed 

in FUELCALC and the way they were independently 

mapped using biophysical statistical modeling. A more 

comprehensive QA/QC procedure for these layers will 

correct many – but not all – of these inconsistencies. 

To fix these problems in the LANDFIRE Prototype, 

we directly assigned CBD and CBH values from the 

LANDFIRE fuel reference database for each PVT-CTSS combination.

Recommendations for National 

Implementation _________________

Above all, we recommend obtaining and/or collecting 

as many georeferenced field fuel and tree data as possible across the nation to create the six fuel layers for 

the LANDFIRE National effort. It would be beneficial 

if these data contained assessments of surface fuel 

model categories for each plot, but fuel loading data 

can also be used once the field keys for all fuel model 

classifications have been developed. These data are 

critical for all modeling, mapping, accuracy assessment, 

and parameterization tasks in the LANDFIRE Project 

(Keane and Rollins, Ch. 3). In addition, the field keys, 

essential for assessing fuel model categories in the field, 

must be comprehensive, consistent, and accurate. We 

highly recommend that simple, easy-to-use field keys 

be created for all surface fuel model classifications. 

These keys must be tested and validated before they 

are released for nationwide use.

We also recommend the implementation of extensive 

QA/QC procedures that rigorously test the fuel layers 

to detect any inconsistencies and errors, especially in 

the context of other LANDFIRE digital maps. This 

procedure should include all LANDFIRE layers and 

extensive evaluation based on data contained in the 

LFRDB. This procedure should be flexible so that it can 

be adjusted according to the specific vegetation types 

and fire management strategies of the various mapping 

zones across different regions of the United States.

In addition, we recommend employing the regression 

tree-based process described above for creating the 

canopy fuel layers. The efficiency and nonparametric 

flexibility make regression trees the optimal model 

for the national implementation of LANDFIRE. The 

NLCD mapping tool and Cubist software facilitate the 

implementation of the trees and offer several features 

for developing the best model. Moreover, the regression 

tree model’s performance will improve with refined 

predictor layers and training data, which involves the 

refinement and integration of the BGC and WXFIRE 

simulation models.

The LANDFIRE vegetation mapping processes must 

be integrated so that maps combine logically with regard 

to vegetation ecology and fuel characteristics. In Zone 

16’s forested areas, for example, approximately 2.6 

percent of the total pixels had a predicted CBH value of 

greater than or equal to the predicted value of CH (fig. 6). 

Although this percentage is fairly low, some rectification 

must occur before these layers are used effectively in 

programs such as FARSITE. If there is no rectification 

process for “fixing” these pixels, an alternative method 

will be necessary.

Furthermore, we strongly recommend that all surface 

and canopy fuel layers be reviewed by local experts 

to ensure realistic, accurate mapping products. This 

includes a review of the LANDFIRE fuel database and 

the rule set used to assign surface fuel model categories 

to PVT-CT-SS combinations to identify inconsistencies 

and errors. In addition, a comprehensive documentation 

of layer properties in the LANDFIRE metadata record 

will be critical to a thorough technical review of the 

fuel layers.

Conclusion_____________________

In conclusion, the fuel mapping effort described in 

this chapter was the last in a series of complex tasks 

performed in the LANDFIRE Prototype effort. As a 

result, the time allotted to fuel map development and 

implementation was much less than that allotted to all 

other tasks. The resulting fuel map products are only first 

approximations and do not contain the level of detail and 

investigation incorporated in the other LANDFIRE Prototype products. The results reported here are therefore 

not as extensive and conclusive as in most of the other 

LANDFIRE chapters. We plan to fully investigate and 

explore the process and products of the fuel mapping 

effort as LANDFIRE National proceeds.

For further project information, please visit the LANDFIRE website at www.landfire.gov.

The Authors ____________________

Robert E. Keane is a Research Ecologist with the 

USDA Forest Service, Rocky Mountain Research Station, Missoula Fire Sciences Laboratory. Since 1985, 

Keane has developed various ecological computer 

models for the Fire Effects Project for research and 

management applications. His most recent research 
392 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 20

Chapter 12—Mapping Wildland Fuel across Large Regions for the LANDFIRE Prototype Projec

includes the development of a first order fire effects 

model, construction of mechanistic ecosystem process 

models that integrate fire behavior and fire effects into 

succession simulation, restoration of whitebark pine 

in the Northern Rocky Mountains, spatial simulation 

of successional communities on landscapes using GIS 

and satellite imagery, and the mapping of fuel for fire 

behavior prediction. He received his B.S. degree in Forest Engineering in 1978 from the University of Maine, 

Orono, his M.S. degree in Forest Ecology in 1985 from 

the University of Montana, and his Ph.D. degree in Forest Ecology in 1994 from the University of Idaho.

Tracey Frescino is a Forester with the USDA Forest 

Service, Rocky Mountain Research Station (RMRS), 

Interior West Forest Inventory and Analysis (FIA) 

Program. She received a B.S. degree in Environmental 

Studies from SUNY’s Environmental Science and Forestry program in 1991 and an M.S. degree in Fisheries 

and Wildlife from Utah State University in 1998. She has 

been with FIA since 1992 working as a field technician 

and a reporting analyst, and she is currently serving as 

a specialist in the FIA’s techniques group. From spring 

of 2003 to spring of 2005, Frescino worked as an FIA 

collaborator for the LANDFIRE Prototype Project at the 

RMRS Missoula Fire Sciences Laboratory in Missoula, 

Montana. She was responsible for mapping potential 

vegetation and canopy fuel, in addition to facilitating 

access to and interpretation of FIA data for the LANDFIRE effort. 

Matthew C. Reeves is a GIS Specialist with the 

LANDFIRE Project at the USDA Forest Service, Rocky 

Mountain Research Station, Missoula Fire Sciences 

Laboratory (MFSL). He earned a B.S. degree in Range 

Management (minoring in Wildlife Management) from 

Washington State University in 1995. In 1999, Reeves 

received his M.S. degree from Arizona State University’s 

Environmental Resources program, where he focused 

on the remote sensing of desert vegetation and the 

development of GIS-based wildlife habitat suitability 

models. In 2004, he earned a Ph.D. from the University 

of Montana’s School of Forestry, where he developed 

automated wheat yield simulation models and rangeland 

biomass estimators from satellite remotely sensed data 

in a GIS framework. 

Jennifer L. Long is a Research Scientist with Systems 

for Environmental Management working with the USDA 

Forest Service, Rocky Mountain Research Station, Missoula Fire Sciences Laboratory (MFSL). She received a 

B.A degree in Environmental Studies/Geography from 

the University of California, Los Angeles (1994) and 

an M.S. degree in Natural Resources with a Forestry 

option from Humboldt State University (2000). Long’s 

research has focused on fuel classification, fuel mapping, and database development. She began her career 

by serving three seasons as a wildland fire fighter for 

the Forest Service and as a tree researcher for Simpson 

Timber Company. She then moved onto the Fire and 

Environmental Research Applications (FERA) Team 

at the Pacific Northwest (PNW) Research Station to 

work on the Fuel Characteristic Classification System 

(FCCS). She currently works on the LANDFIRE Project 

at MFSL where her responsibilities include the design of 

protocols to classify and map fuel and fire behavior fuel 

models based on vegetation and biophysical variables, 

the development of a national vegetation mapping unit 

classification, and the linkage of the FCCS to LANDFIRE fuel maps. 

Acknowledgments_______________

We thank Mark Finney and Chuck McHugh of the 

USDA Forest Service Rocky Mountain Research Station 

Fire Behavior Work Unit; Don Long of the USDA Forest 

Service Rocky Mountain Research Station LANDFIRE 

Project; Elizabeth Reinhardt of the USDA Forest Service Rocky Mountain Research Station Fire Effects 

Work Unit; Joe Scott, Rick Stratton, and Larry Gangi 

of Systems for Environmental Management; and Don 

McKenzie and Ken Pierce of the USDA Forest Service 

Pacific Northwest Research Station.

References _____________________

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Chapter 12—Mapping Wildland Fuel across Large Regions for the LANDFIRE Prototype Projec

Vegetation combination assignment rule set for Mapping Zone 16 fire behavior fuel

models (FBFMs) (Anderson 1982). See Long and others, Ch. 6 for descriptions of

cover types, life forms, and structural stages

If cover type is an Herbaceous life form, then the primary fire carrier is grass.

If cover type is Wetland Herbaceous, then FBFM 1; els

If Low Height, then FBFM 

If High Height, then FBFM 

If cover type is a Shrubland life form and structural stage is a Shrubland type,

then the primary fire carrier is grass or shrub.

If cover type is Blackbrush, then FBFM 6

If cover type is Desert Shrub, then FBFM 6

If cover type is Dry Deciduous Shrub and High Cover, Low Height and the

If PVT is a Forest type (except Riparian Hardwood cover types) then

FBFM 5

If cover type is Dry Deciduous Shrub and PVT is Riparian Hardwood or a

non-forest type,

then FBFM 6

If cover type is Dwarf Sagebrush Complex, then FBFM 6

If cover type is Mtn. Deciduous Shrub where Gambel oak is not the

dominant shrub and the structural stage is a Shrubland type, then FBFM 6

If cover type is Mtn. Big Sagebrush, then FBFM 2

If cover type is Montane Evergreen Shrub where mountain mahogany is

the dominant shrub and is High Cover, High Height, then FBFM 6 else

Montane Evergreen Shrub is FBFM 5.

If cover type is Rabbitbrush, then FBFM 6

If cover type is Riparian Shrub, then FBFM 6

If cover type is Salt Desert Shrub, then FBFM 6

	 If cover type is Wyoming – Basin Big Sagebrush Complex and Low Cover,

then FBFM 5

	 If cover type is Wyoming – Basin Big Sagebrush Complex and High Cover,

then FBFM 6

If cover type is a Woodland life form or structural stage is a Woodland type, then

the primary fire carrier is grass, shrub, or timber litter.

	 If cover type is Pinyon – Juniper or Juniper and Low Height, then FBFM 6

Appendix 12-A ____________________________________________________
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Chapter 12—Mapping Wildland Fuel across Large Regions for the LANDFIRE Prototype Projec

	 If cover type is Pinyon – Juniper or Juniper and High Cover, High Height,

then FBFM 8

	 If cover type is Pinyon – Juniper or Juniper and Low Cover, High Height,

then FBFM 2

If cover type is Mtn. Deciduous Shrub where Gambel oak is the dominant

shrub and Low Height, then FBFM 6

If cover type is Mtn. Deciduous Shrub where Gambel oak is the dominant

shrub and High Height, then FBFM 

If cover type is a Forest life form, then primary fire carrier is grass, shrub, or

timber litter.

If any Forest type and Low Cover, High Height, then FBFM 2

If conifer and Low Cover, Low Height, then FBFM 2

If hardwood and Low Cover, Low Height, then check early growth form.

 If bushy or sprouter, then FBFM 6

 If single stem, then FBFM 2

If long needle conifer and High Cover, High Height and PVT is

moderate/dry, then FBFM 9; (optional) If moist PVT, then FBFM 1

If short needle conifer and High Cover, High Height and PVT is

moderate/dry, then FBFM 8; (optional) If moist PVT, then FBFM 1

If hardwood and High Cover, High Height, then FBFM 8

	 If cover type is Grand Fir or Spruce – Fir and High Cover, Low Height and

PVT is moist, then FBFM 8

	 If cover type is Ponderosa Pine, Lodgepole Pine, Douglas-fir, or

Timberline Pine and High Cover, Low Height, then FBFM 6

If hardwood and High Cover, Low Height, then check young growth form.

 If bushy or sprouter, then FBFM 6

 If single stem, then FBFM 8

Assuming non-drought conditions, we adjusted the rules above after the lookup-table was created based on the logic for modeling fire at the low fuel loading

levels found in the Utah Forest Vegetation Simulator – Fire Fuels Extension (FVSFFE) variant in Reinhardt and Crookston (2003). For example, we adjusted some

of the juniper cover types from FBFM 6 to FBFM 5. For mixed-conifer cover types 

(such as Douglas-fir and Grand Fir – White Fir) we assumed that Low Height

meant a stand density >1000 stems/acre

A moist forest PVT was considered a subalpine forest type and included all 

Spruce – Fir and Lodgepole Pine PVTs. A moderate/dry forest PVT was considered a montane forest type and included all Grand Fir, Douglas-fir, Timberline

Pine, and Ponderosa Pine PVTs.

Appendix 12-A — (Continued)
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 3

Chapter 13—Perspectives on LANDFIRE Prototype Project Accuracy Assessme

Chapter

13

In: Rollins, M.G.; Frame, C.K., tech. eds. 2006. The LANDFIRE 

Prototype Project: nationally consistent and locally relevant 

geospatial data for wildland fire management. Gen. Tech. Rep. 

RMRS-GTR-175. Fort Collins: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research Station.

Introduction ____________________

The purpose of this chapter is to provide a general 

overview of the many aspects of accuracy assessment 

pertinent to the Landscape Fire and Resource Management Planning Tools Prototype Project (LANDFIRE 

Prototype Project). The LANDFIRE Prototype formed 

a large and complex research and development project 

with many broad-scale data sets and products developed 

throughout its various stages. The scope of the project 

was defined as mapping and modeling vegetation, 

wildland fuel, and fire regime characteristics (Rollins and others, Ch. 2). Because of the breadth of the 

investigation, it is important to base our expectations 

for accuracy on a clear understanding of the intricacies, 

interdependencies, and scope of mapping and modeling 

LANDFIRE products. Our goals in this chapter are to: 

1) provide relevant background information regarding 

accuracies and what was realistically achievable in the 

LANDFIRE Prototype, 2) provide background regarding 

our strategies for LANDFIRE National, 3) describe our 

actual LANDFIRE Prototype accuracy results in broad 

terms, and 4) provide recommendations for the national 

Perspectives on LANDFIRE Prototype Project 

Accuracy Assessment

James Vogelmann, Zhiliang Zhu, Jay Kost, Brian Tolk, and Donald Ohlen

implementation of LANDFIRE. This chapter is not intended to provide an exhaustive list and description of 

all of the various accuracy-related issues and conclusions 

resulting from the LANDFIRE Prototype (for specific 

details, the reader will be referred to the appropriate 

chapters). Rather, this chapter is intended to be broad 

in scope and to place the many accuracy components 

within the context of the LANDFIRE Prototype and 

LANDFIRE National projects. Please note that Lunetta 

and Lyon (2004) provide an in-depth discussion of the 

current state of accuracy assessment within the science 

community.

Background ____________________

General Accuracy Tenets and Philosophy

First we will provide the reader with several broad 

tenets used in defining accuracy assessment for the 

LANDFIRE Prototype Project and thereby lay the foundation for the more in-depth discussion following.

Tenet 1: Assuming that thematic detail and spatial 

scale are constant, product accuracy is generally inversely 

correlated with the size of the region being assessed.

Within the remote sensing literature, there are many 

references to accuracy levels, and many of the reported 

values are quite high. These high levels may lead to 

inflated expectations regarding what types of accuracies 

will be achievable from LANDFIRE. Many previous 

studies were conducted within relatively small study 
398 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 20

Chapter 13—Perspectives on LANDFIRE Prototype Project Accuracy Assessme

areas, often aided by high levels of “hand crafting” 

during the mapping process and/or in-depth knowledge 

of the particular study area. We do not have the luxury 

of spending a great amount of time and effort on any 

one particular region mapped through the LANDFIRE 

Project, and the mapping and modeling tasks need to 

be accomplished through largely automated processes. 

These limitations do not by any means reduce the value 

of the products being created through LANDFIRE; 

however, it should be stated that LANDFIRE products 

will likely have lower overall accuracies than do data 

sets derived from more localized studies characterized 

by large amounts of field data, increased processing effort that may include on-screen digitizing and recoding, 

and/or iterative refinement of modeled results.

Tenet 2: The higher the thematic detail, the lower the 

accuracy.

A relatively large number of vegetation classes were 

mapped for the LANDFIRE Prototype (Long and others, Ch. 6). While the chosen map unit classification 

system made sense on many levels for the LANDFIRE 

Prototype, it must be recognized that the proliferation of 

classes in this or similarly complex systems will imply 

a relative decrease in accuracy levels. This does not in 

any way diminish the value of the vegetation products, 

but is rather simply a result of a more complex map unit 

classification design. For example, a two-category classification of water and uplands is likely to result in high 

accuracy, with expected accuracies above 99 percent. 

This high accuracy does not mean that the value of the 

product is particularly high, but simply reflects that the 

accuracy for depicting these two classes is high. Additionally, there are difficulties that arise when categorizing 

continuous phenomena into rigid and discrete classes. For 

instance, a more detailed map unit classification system 

might treat juniper and pinyon – juniper ecosystems 

as several discrete classes even though the boundaries 

between them are relatively arbitrary and difficult to 

delineate both in the field as well as within the imagery. 

With complex vegetation map unit legends, such as that 

used in the LANDFIRE Prototype, vegetation class 

accuracy levels can be expected to drop. Nevertheless, 

LANDFIRE products reliably and consistently describe 

the distribution of vegetation composition, condition, and 

structure and associated wildland fuel and fire regimes 

across broad landscapes. These mapped data are useful 

for hazardous fuel reduction projects, for a variety of 

resource management projects, and for both strategic 

and tactical wildland fire management.

Tenet 3: Field information used for assessing accuracy 

is not perfect.

As mentioned under Tenet 2, the LANDFIRE Prototype vegetation map unit legends are relatively complex 

(Long and others, Ch. 6). The map unit classifications 

are developed using large quantities of field data, and 

all of the field plots are assigned to one of the many 

possible classes. Most of these plots are used to generate maps, but some are reserved for use in the accuracy 

assessment phase of the investigation. We recognize 

four major potential sources of error associated with 

field plot data:

•	 Errors occur frequently in the identification of species and measurement of vegetation structure in the 

field (for example, in the data for one prototype field 

plot, a misplaced decimal point indicated a shrub 

height of 60 feet).

•	 The vegetation on some field plots has undoubtedly 

changed between the time the field data were collected and when the imagery was acquired.

•	 Geo-location errors in plot and imagery data result 

in inaccurate characterization of some imagery 

pixels.

•	 The assignment of plots to specific vegetation 

classes will have errors associated with the wide 

array of opinions among professional field ecologists regarding the field classification of any given 

field plot.

Tenet 4: The modeled results of complex ecological systems will be characterized by ambiguity and 

controversy.

The products generated from the LANDFIRE Prototype represent our best approximations in depicting the 

current status of very complex natural phenomena. The 

information used in our modeling efforts is based on 

the best available input data and assumptions. However, 

although our output products represent reasonable and 

robust depictions of current conditions, we recognize 

that, due to lack of baseline research, our knowledge 

of certain ecological systems is imprecise. Use of 

such information in the modeling process may result 

in potential flaws in the products, and hence not all of 

the core LANDFIRE deliverables will be free of error 

and ambiguity. Nevertheless, the LANDFIRE Project 

represents an integration of the best available science 

in remote sensing, ecosystem simulation, landscape fire 

and succession modeling, predictive landscape mapping, and wildland fire behavior and effects prediction. 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 3

Chapter 13—Perspectives on LANDFIRE Prototype Project Accuracy Assessme

We are therefore confident that the products generated 

represent the best current assessments of the status of 

these ecosystems with regard to wildland fire and will 

be of great value to natural resource managers.

Accuracy Assessment Considerations for 

LANDFIRE

The need for conducting accuracy assessments of 

the spatial products created from mapping projects has 

been well documented (Congalton 1991; Foody 2002). 

Factors that influence map accuracy include (but are 

not limited to) the remote sensing platform, the quality 

of ancillary sources of information, the quality of field 

data, the floristic complexity of the map unit classification system used, and the sampling design. Traditional 

first-order map accuracy estimates involve generating 

an error matrix, computing overall accuracy, and estimating “producer’s accuracy” and “user’s accuracy” 

(Congalton 1991). In the past, assessment of map accuracy has involved much post-mapping fieldwork in 

order to develop error matrices. These formal, traditional 

accuracy assessments involving field campaigns can be 

labor-intensive, time-consuming, and cost-prohibitive, 

especially when dealing with projects that cover large 

regions of diverse and overlapping vegetation composition and conditions (Stehman and others 2000). For 

this reason, only a few efforts have conducted accuracy 

assessments across broad expanses such as the entire 

United States (Stehman and others 2003; Wickham and 

others 2004).

Techniques that worked well in assessing mapping 

accuracy across large regions for the 1990s National 

Land Cover Database (NLCD; Vogelmann and others 

2001) employed modifications of traditional accuracy 

assessment methodologies (Stehman and others 2003; 

Wickham and others 2004). As background, the 1990s 

NLCD database was developed using Landsat satellite 

imagery acquired for the Multi-Resolution Land Characteristics (MRLC) 2001 consortium using methods 

previously described (Vogelmann and others 1998). 

During development of the database, it was determined 

that an accuracy assessment for the large area product 

was required, and that such an effort would have to 

be modified from more traditional assessments. The 

modifications were necessary in part due to the scarcity 

of field data across the mapped regions, the large size 

of the area being assessed (and associated high costs 

of collecting data from a statistically valid number of 

field locations across the entire conterminous United 

States), difficulties in assigning unambiguous map unit 

labels to many field plots, and geolocational errors 

associated with field plot and satellite-derived mapping 

information.

Three important lessons learned from the accuracy 

assessments of the 1990s NLCD effort pertain directly 

to the accuracy assessment methods used during the 

LANDFIRE Prototype Project:

•	 Collecting data for and compiling custom field 

databases is time consuming and expensive. Similarly, combining data from disparate sources and 

distilling them into a training database for mapping 

purposes is time consuming, expensive, and can 

result in data inconsistencies unless special effort 

is made to crosswalk and/or standardize input data. 

On the other hand, using existing field data, rather 

than collecting custom field data, saves both time 

and money. In short, for large-area projects, it makes 

sense to use existing field data for conducting accuracy assessments.

•	 Determining accuracy values for different subregions is acceptable when mapping large regions. 

Accuracies are likely to vary across large mapped 

areas due to region-specific heterogeneity in landscape composition and structure, and it was advantageous to derive an understanding of the geographic 

variability of accuracies of the products developed 

for LANDFIRE. To this end, use of a systematic 

random sampling design can provide optimal results. 

Such a design ensures that all geographic regions 

are adequately sampled and thereby ensures that at 

least some estimates of accuracies exist throughout 

the entire study region.

•	 Some errors are more “wrong” than others. For instance, for the LANDFIRE effort, misclassification 

of a pinyon – juniper stand as a riparian woodland 

stand will likely have a greater negative impact on 

the predicted fire behavior than misclassification 

of a pinyon – juniper stand as a juniper stand. Furthermore, some vegetation types are spectrally and 

biogeographically very similar to other vegetation 

types, and even with “perfect” source material, it 

is difficult to adequately distinguish some of these 

classes. For example, Douglas-fir and white fir 

are spectrally very close (fig. 1), and both species 

inhabit similar ecological niches. In regions where 

both Douglas-fir and white fir occur, we can expect 

significant confusion between the two classes. For 

instance, in central Utah, cross validation accuracies 

for these two classes were quite low, as anticipated. 

Nonetheless, we suspect that the errors related to 

misclassifying similar vegetation types will only 

minimally impact predicted fire behavior, whereas 
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Figure 1—Seasonal normalized difference vegetation index (NDVI) spectral profiles for Douglas-fir

and White Fir cover types.

errors related to misclassifications of more dissimilar vegetation types lead to greater negative 

impact. For this reason, both ecologists and image 

analysts need to critically analyze error matrices 

in order to fully understand and characterize the 

ways in which product errors may affect project 

objectives.

 We took these lessons into consideration in the 

design of our LANDFIRE accuracy assessment 

protocol:

•	 Because LANDFIRE is a large-region project, we 

tapped into a variety of data sources and made 

use of existing field data to assess the accuracy of 

LANDFIRE Prototype products (rather than wasting time and money collecting data for and compiling a custom field database). See Caratti, Ch. 4 for 

details on the acquisition of data for and compilation 

of the LANDFIRE reference database.

•	 Cross-validation error matrices were generated 

and examined separately for both LANDFIRE 

Prototype regions.

•	 For the LANDFIRE Prototype, mappers, ecologists, 

and wildland fire scientists critically evaluated errors at several stages in prototype product development. These evaluations resulted in aggregation and 

disaggregation of classes based on the “mappability” 

and “model-ability” of the vegetation classes. See 

Keane and Rollins, Ch. 3 and Long and others, 

Ch. 6 for detailed descriptions of the creation of 

the final vegetation legends for the LANDFIRE 

Prototype. This expert-based process for map unit 

classification refinement is built into the accuracy 

assessment system for LANDFIRE National.

Overview of Accuracy Assessment 

Conducted for the LANDFIRE 

Prototype Project________________

The LANDFIRE Prototype Project involved many sequential steps, intermediate products, and interdependent 

processes, each involving evaluations of the accuracy 
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of intermediate and final products. Please see appendix 

2-A in Rollins and others, Ch. 2 for a detailed outline 

of the procedures followed to create the entire suite of 

LANDFIRE Prototype products.

Role of Input Data

Field data accuracy issues—Field data played a critical role in many stages of the LANDFIRE Prototype. 

These data were essential inputs for developing the 

vegetation products, percent canopy cover and height 

data layers, and potential vegetation data layers. See 

Caratti, Ch. 4 for detailed information on data acquisition for and compilation of the LANDFIRE reference 

database.

Described below are a number of data quality issues that needed to be addressed in the LANDFIRE 

Prototype.

•	 Number of field plots: For the LANDFIRE Prototype 

accuracy assessment, we used all field plot data that 

met the stringent quality-control criteria (Caratti, 

Ch. 4) and represented the large number of classes 

mapped during the vegetation mapping tasks (for 

details about the vegetation mapping procedures, 

see Frescino and Rollins, Ch. 7 and Zhu and others, 

Ch. 8) We used literally thousands of points for each 

of the two prototype regions. During this process, 

we recognized that some vegetation classes had 

limited numbers of field plots. Short of gathering 

additional plot information (see Keane and Rollins, 

Ch. 3 for LANDFIRE Prototype design criteria), 

there was no obvious solution to this problem. We 

attempted to map these rarely sampled vegetation 

types, even when we had limited numbers of field 

plots for those classes. We believe that most of these 

rare classes were under-represented in the resultant 

products.

•	 Field plot geolocational accuracy: Field plots 

must have accurate geolocational coordinates to 

geographically rectify with the many spatial databases involved in the LANDFIRE process. This 

was especially important during the vegetation 

cover and structure characterization phase of the 

LANDFIRE Prototype, wherein each field plot was 

matched with a single Landsat pixel and used in 

the mapping process. Any significant error in the 

field location coordinates has the potential to match 

the wrong spectral information with that particular 

field plot, thereby resulting in mapping error. For 

the prototype effort, we overlaid plot locations onto 

satellite imagery to determine whether there were 

plots that obviously did not match the imagery. 

While most plot locations appeared to be reasonable, 

we observed that many plots representing natural 

vegetation were actually located on major roads. 

When plot information was originally acquired for 

these sites, the actual Global Positioning System 

(GPS) measurements were apparently made at the 

road locations adjacent to the field plots, rather 

than within the field plots. Thus, the GPS locations 

did not exactly match the locations where the field 

measurements were made. For these sites in the 

LANDFIRE Prototype, a new set of geolocations 

was derived to better represent actual field plot 

locations.

 In another case, we noted (also based upon 

imagery assessment) that many putative shrub 

sites were located in obviously forested areas. We 

later discovered that those plots corresponded to 

a particular project in which the main focus was 

to describe shrub vegetation regardless of whether 

or not it represented the dominant vegetation type. 

These plots were consequently discarded from the 

prototype accuracy assessment. Both cases illustrate the need for assessing field plot information 

in conjunction with satellite imagery to ensure that 

the field information is accurately recorded.

 Moreover, it should be recognized that satellite 

imagery can have georeferencing errors as well. 

As a general rule, the coordinates of most pixels 

in the imagery used for the LANDFIRE Prototype 

are within 30 meters of the actual location – but 

exceptions occur. Even in the case where a pixel 

has slightly greater than a 15-meter error associated with it, this may be large enough to create a 

slight yet definite mismatch between the imagery 

and field information. While there is little that 

we can do about this problem, we at least need to 

recognize that some of the error term associated 

with the products generated will be attributable to 

this issue.

•	 Assignment of field data into discrete vegetation classes: One of the challenges in generating 

land cover maps is the stratification into discrete 

classes of a very complex natural world composed 

of multiple continuums. Regardless of which vegetation map unit system is used, many vegetation 

plots will represent elements of two or even more 

classes, and thus some plots will defy unambiguous 

categorization. As an example of one such problem, 

we mapped Juniper and Pinyon – Juniper (PJ) as 

two distinct classes. In nature, pinyon pine and 
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juniper often coexist, but sometimes juniper occurs 

as more-or-less pure stands. We used 25 percent 

juniper composition as the threshold separating 

Juniper from Pinyon – Juniper (in other words, if 

a stand had 75 percent or greater basal area juniper in a stand comprised of both pinyon pine and 

juniper, it was called “Juniper”; whereas, if it had 

less than 75 percent juniper, it was called “PJ”). 

Analysis of seasonal spectral data indicated that 

many juniper stands were spectrally distinct from 

many of the PJ stands (fig. 2); however, significant 

spectral overlap existed between the two classes, as 

well. After decision tree classification, cross-validation accuracies indicated significant error in the 

classification of these two cover types (fig. 3). We 

believe that much of this error is attributable to the 

artificial boundaries imposed by the classification 

of a continuum.

•	 Temporal correlation between field data and satellite imagery: Disturbance such as that caused by 

fire, insects, or logging can alter the sites enough 

to cause the temporal mismatches between field 

data and satellite imagery that result in classification problems. For the prototype, we made use of 

a large volume of existing field data acquired from 

disparate sources (Caratti, Ch. 4), and much of the 

field information was acquired over a long period 

of time. Although information from many plots 

was relatively old (for example, field data acquired 

over a 10-year time period prior to imagery acquisition), we determined that many of these plots still 

contained information that was useful and relevant 

to the LANDFIRE Prototype. For example, plots 

located within reasonably intact and undisturbed 

forests or sagebrush lands, under normal circumstances, do not change much over a 10-year span. 

After completing the first prototype study in Utah, 

we recognized the importance of using a changedetection approach and employed such an approach 

in the northern Rockies prototype region to discard 

plot information derived from areas that changed 

between the times when the field information was 

obtained and when the imagery was acquired.

Geospatial data issues—Landsat imagery data from 

the MRLC 2001 consortium served as the primary 

source of spatial data for developing the vegetation and 

structure products (Homer and others 2004) (refer to 

Figure 2—Seasonal normalized difference vegetation index (NDVI) spectral profiles for Douglas-fir,

Pinyon – Juniper, and Juniper cover types.
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Chapter 13—Perspectives on LANDFIRE Prototype Project Accuracy Assessme

Zhu and others, Ch. 8 for further discussion regarding 

the imagery and ancillary data sources used for vegetation mapping in the LANDFIRE Prototype). In general, 

the images used for the prototype effort were the best 

data available during the LANDFIRE Prototype and 

represented three seasonal time periods (leaf-off spring, 

leaf-on summer, and leaf-on fall). Although the MRLC 

2001 data used are of high quality, problems can arise 

when using any source of remotely sensed information. 

The foremost imagery-related problems affecting the 

LANDFIRE Prototype included atmospheric issues, 

disparate imagery acquisition dates, and geolocational 

problems.

•	 Atmospheric issues: Most of the acquired image 

scenes used in the prototype effort were of excellent quality. Even the best scenes, however, have 

occasional cloud and/or haze problems, which can 

either totally obstruct the view of portions of landscape or change the digital values enough to impact 

the mapping process. While not a large problem in 

the prototype areas, there were a few locations for 

which imagery quality was sub-par. These issues 

are inevitable and are likely to be a bigger problem 

in cloudier locations of the country such as the 

eastern United States and the upper Midwest.

•	 Disparate imagery acquisition dates: We attempted to use imagery from similar time periods 

as much as possible; however, due to cloud issues, 

optimal imagery data were not always available. 

Using scenes from different dates of the same year, 

such as using July and September data in the same 

“leaf-on” mosaic, resulted in problems resulting 

from phenological differences. Using scenes from 

different years, such as using one scene from 

2002 and an adjacent scene from 2003, resulted 

in problems related to different weather patterns 

(for example, vegetation spectral response can be 

very different during wet versus dry years) and 

to occasional land cover changes that occurred 

between years. For the LANDFIRE Prototype, 

we attempted to minimize these problems through 

careful selection of scenes and use of spatial “date 

of acquisition” information in our decision tree 

and regression tree classifications.

Figure 3—Cross-validation errors for forest types in the Zone 16 prototype study area as a function of different

amounts of input source material. Black bars depict the effects of merging the Pinyon – Juniper and Junip

classes.
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Chapter 13—Perspectives on LANDFIRE Prototype Project Accuracy Assessme

•	 Geolocational problems: Images used in this 

investigation were processed using the National 

Landsat Archive Production System methods 

(USGS Landsat Website 2004). Data were corrected 

for terrain and projected to a standard projection 

(Albers Equal Area) using automated software 

processing. Individual pixel coordinate information was approximately 30 meters from actuality. 

Thus, even when field information had precise GPS 

coordinates, the field data were sometimes linked 

to the wrong pixel due to imagery registration errors. Because of technological, time, and budget 

constraints, we could not circumvent this problem. 

Registration methods needed to be consistent and 

automated to ensure that the process was feasible 

for application over the entire United States. We 

simply had to assume that the field data adequately 

characterized an area broader than the precise location of the plot and that the image pixel used was 

spectrally representative of its surrounding pixels. 

Note that in many cases, the quality-control checks 

performed on the field data mitigated some of these 

problems.

Ancillary data issues—Other sources of input information for the LANDFIRE Prototype included Digital 

Elevation Model (DEM) data and derivative products, 

1990s NLCD land cover data (Vogelmann and others 

2001), 2000s NLCD land cover data (Homer and others 2004), a suite of biophysical gradient data layers 

(Holsinger and others, Ch. 11; Keane and others 2001; 

Rollins and others 2004), and potential vegetation 

information (Frescino and Rollins, Ch. 7). Error terms 

are associated with each data type. While it is beyond 

the scope of this chapter to describe in detail all of the 

sources of errors associated with the many data layers, 

a few specific points should be made:

•	 Although not flawless, each data source used in 

the LANDFIRE Prototype represented the best 

available science and data quality.

•	 The source of the DEM data was the National Elevation Dataset (NED) (Gesch and others 2002). 

Although NED is an excellent source of digital 

elevation data, it came to our attention during the 

final stages of the prototype effort that another 

data source would have been more appropriate: 

the Elevation Derivatives for National Applications (EDNA) data set (http://edna.usgs.gov). The 

EDNA data represent a set of data layers derived 

from an earlier version of the NED. To create the 

EDNA data layers, the NED data were “smoothed” 

so that they would be better suited for hydrological 

modeling purposes. It should also be noted that, 

regardless of the source of the digital elevation 

model information, there are horizontal and vertical 

error terms associated with these data sets tracing 

back to the original source material. These digital 

elevation model data sets are regularly improved 

and updated.

•	 The 1990s and 2000s NLCD data sets were used 

for stratification purposes at various stages in the 

prototype effort, and both data sets have known error 

terms associated with them. See Yang and others 

(2001) and Homer and others (2004) for details 

regarding the accuracies of these products.

Accuracy of Thematic Maps

Cross-validation and points for independent 

validation—Accuracy assessment is an integral component of land cover mapping work. When a large number 

of field points are available, a reasonable alternative to 

generating traditional first-order accuracy estimates (see 

the above section Accuracy Assessment Considerations 

for LANDFIRE) is cross-validation. To create the LANDFIRE vegetation products, we employed decision tree 

analysis implemented within the See5 program (Quinlan 

1993) using Landsat, DEM, slope, aspect, biophysical 

gradient, and potential vegetation data layers. The program enables cross-validation, which consists of repeated 

experiments in which a subset of the sample is used to 

train a classification model and an unseen subset is used 

to evaluate the model. In model runs for the prototype 

effort, we found that a five-fold cross-validation was appropriate. In each model run, the original field point data 

sets were divided into five subsets of equal size, and each 

subset was used to evaluate the algorithm trained using 

the remaining four subsets. Theoretically, this approach 

is not as thorough as a rigorous, statistically designed 

post-mapping field accuracy assessment campaign. It has 

been shown, however, that cross-validation can provide 

accuracy estimates comparable to these time-consuming 

and expensive methods (Huang and others 2003). See 

Frescino and Rollins, Ch. 7 and Zhu and others, Ch. 8 

for actual accuracy results and cross-validation error 

matrices for the vegetation products derived for the 

LANDFIRE Prototype. For LANDFIRE National, we 

recommend reserving a set percentage of plots from the 

decision and regression tree analyses for independent 

accuracy assessment. See the Recommendations for 

National Implementation section below for details.
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Chapter 13—Perspectives on LANDFIRE Prototype Project Accuracy Assessme

Field verification—Although it is not always feasible 

to conduct a detailed field verification and validation 

campaign, when possible, field visits at various stages of 

product development can be highly useful. Field visits, 

both during and after the product generation phase, provide the technical teams conducting the mapping work 

with a good basic understanding of the natural vegetation 

and ecology of the regions in which they are working. 

Further, field checks of particular sites to determine if 

they match the modeled results can be very instructive 

and useful for improving mapping accuracies. For the 

LANDFIRE Prototype, we made three separate field 

visits of approximately five days each. We traveled to 

the central Utah highlands region twice (once before 

mapping and once after the products were created), 

and we traveled once to the western Montana region 

(post-mapping). In all cases, images and/or maps were 

evaluated in the field, and actual plot measurements were 

made. Although not statistically rigorous, such efforts 

provided a better understanding of potential problem 

areas for future methods improvement. For example, an 

area of western hemlock was overestimated in the map 

products, and we were able to trace the overestimation 

back to problems in the original field sampling methods 

used to help generate the training data in the mapping 

process. Although no obvious solution to the problem 

was apparent, the case illustrates the importance of field 

visits in methods improvement. In another field activity, 

spectral measurements of shrub and herbaceous vegetation 

density were made by one team in the western Montana 

region to help refine shrub and herbaceous canopy cover 

methodology. This activity was undertaken in an attempt 

to improve canopy cover mapping and is being considered 

for the National Implementation of LANDFIRE.

Consistency checks with data from other sources—

Related data sets, generated by other projects and for 

other applications, are often available and can be used for 

comparison purposes. The USGS Gap Analysis Program 

(GAP), for example, generates detailed vegetation maps 

for conservation management and planning (http://www.

gap.uidaho.edu). We compared the GAP products created 

for the central Utah highlands prototype area with the 

cover type maps created for the LANDFIRE Prototype. 

The two sources of data compared reasonably well in 

some cases and less so in others (see figs. 4 and 5). It 

should be noted that the GAP products were created 

using different field databases than those used for the 

LANDFIRE Prototype. In addition, the vegetation map 

unit classification systems used were different, which 

limited the utility of direct, parallel comparison between 

the GAP products and LANDFIRE products. Although 

such comparisons may lack statistical rigor, they indicate 

where major qualitative similarities and differences exist 

between products and in turn may indicate which classes 

and regions are the most suspect. In addition, vegetation 

and structure products should be reviewed by regional 

experts whenever possible to determine whether noteworthy mapping problems exist and whether additional 

work is warranted. Such a review is recommended for 

national implementation of LANDFIRE.

Accuracy of Potential Vegetation Type and 

Canopy Fuel Maps

We generated potential vegetation type (PVT) data sets 

using decision tree software and cross-validation routines 

very similar to those used for generating vegetation 

maps. We also produced coinciding maps of confidence, 

which depict the relative prediction errors representing a 

spatial and visual representation of PVT map accuracy. 

See Frescino and Rollins, Ch. 7 for detailed descriptions 

and results of these activities. We estimated the accuracy 

of canopy fuel layers using regression tree procedures in 

which correlation coefficients were generated to measure 

the agreement between the predicted values and actual 

values. Additionally, we compared with predicted values 

a set of points randomly selected from the LANDFIRE 

reference database from each prototype zone. As in 

the case of PVT, we also produced coinciding maps of 

confidence. See Keane and others, Ch. 12 for a detailed 

description of canopy fuel accuracy.

Accuracy of Maps Based on Landscape 

Simulation Models

Accuracy evaluation of vegetation maps created from 

satellite imagery and ancillary data is straightforward and 

is based on a foundation of scientific literature (Foody 

2002; Lunetta and Lyon 2004). In contrast, it is often 

conceptually very difficult to ascertain the quantitative 

accuracy of many of the products that are generated 

through complex modeling efforts, such as those employed to create the historical reference conditions for 

quantifying ecological departure in LANDFIRE. Moreover, it is difficult — if not impossible — to assign an 

absolute measure of accuracy to an ecological departure 

product because such a product represents deviation from 

conditions modeled under a variety of limitations in terms 

of baseline ecological data. Modeling assumptions, while 

based on the best available disturbance ecology science, 

may or may not be completely valid. Without the luxury 

of time-travel, it is very difficult to validate what the 

“normal” or historical vegetation condition actually was. 
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Chapter 13—Perspectives on LANDFIRE Prototype Project Accuracy Assessme

Figure 4—Comparison between a LANDFIRE vegetation type product and a product developed by the Southwest GAP Project in 

southern Utah. Multiple thematic classes have been combined to facilitate visual comparisons.
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Chapter 13—Perspectives on LANDFIRE Prototype Project Accuracy Assessme

Figure 5—Additional comparison between a LANDFIRE vegetation type product and a product developed by the Southwest GAP 

Project in southern Utah. Multiple thematic classes have been combined to facilitate visual comparisons. Major differences between shrub and broadleaf forest classes can be traced back to differences in classification systems (Gambel oak and bigtooth

maple were categorized as trees in the LANDFIRE map unit classification and as shrubs by GAP).
408 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 20

Chapter 13—Perspectives on LANDFIRE Prototype Project Accuracy Assessme

For accuracy assessment approaches used to evaluate 

LANDFIRE products based on landscape simulation 

models, see Pratt and others, Ch. 10 and Holsinger and 

others, Ch. 11. In addition, see the Recommendations for 

National Implementation section below for suggestions 

on improving the accuracy assessment of LANDFIRE 

products based on landscape simulation models.

Recommendations for National 

Implementation _________________

Source Data

All source data need to be inspected carefully. This is 

especially true for field data and imagery, which form important foundations for much of the ensuing LANDFIRE 

tasks. As a matter of course, if field data used for training 

are inaccurate, then the resulting products will likely have 

lower levels of accuracy. Imagery quality can also greatly 

affect accuracy levels of derived products. Although optimal imagery data sets are not always available for a given 

location, there are usually several excellent options. It is 

important to ensure that the best possible imagery data 

sets are used. Below are some specific recommendations 

regarding the selection of source data.

Number of field plots—As general rule, the more 

field reference plots, the better. For each LANDFIRE 

National mapping zone, we anticipate using literally 

thousands of field plots in order to develop adequate 

characterizations. These must represent the entire range 

of conditions that occur throughout the mapping zones. 

For vegetation map unit classification development, for 

example, we have a target number of at least 100 plots 

per class. Fewer plots per class would diminish our 

confidence in our ability to map that class accurately 

and would likely result in the inadequate mapping of 

that particular feature. Rare classes (land cover features 

limited in occurrence across the landscape) are notoriously difficult to map accurately, largely because there 

are relatively few field plots representing these classes 

that can be used for training data. For national implementation of LANDFIRE, we recommend 1) generating vegetation products using all plots, 2) evaluating 

results, 3) determining which vegetation classes were 

represented by too few plots, and 4) re-running the map 

unit classification without these rare classes.

Field plot geolocational accuracy—Field plots with 

inaccurate coordinates have the potential to cause significant error in mapping results. We recommend that 

field plot locations be overlaid onto the imagery and that 

the plot locations be visually inspected to determine if 

attribute data for each plot are consistent with the imagery. Points located on roads or other locations clearly 

not characterized by the reference plot should be either 

omitted or shifted to the appropriate location.

Field data temporal issues—Much of the field information available for the national implementation of 

LANDFIRE is likely to have been acquired by various 

organizations over a relatively long period of time. As 

discussed above, inclusion of plots located in areas where 

the vegetation has changed between the time the field 

information was collected and when the imagery was 

acquired can cause significant mapping problems. The 

ideal situation is for field data and imagery to be acquired 

at approximately the same time, but this is impractical 

due to the large volume of field data necessary for product 

generation. One option is to discard plots with relatively 

old information (by imposing an arbitrary cutoff of five 

or more years); however, including as many plots as possible, even if some include older information, is preferable 

because even old plots can contain useful information. 

For this reason, for national implementation, we recommend using the change-detection approach developed 

for the western Montana prototype area. We recommend 

using normalized difference vegetation index (NDVI) 

change between 1990s and 2000s NLCD imagery to 

locate and isolate plots that have changed markedly 

over the last 10 years. If a plot is located within a region 

of high spectral change (based upon imagery analysis) 

and if the change appears to be related to a land cover 

change event (such as fire, logging, or insect disease) 

as opposed to a cloud or cloud shadow, the plot should 

be flagged and omitted from further analyses.

Imagery data—Imagery acquired by Landsat will 

likely continue to be the primary source of spatial data 

for developing vegetation and structure products for 

LANDFIRE National. The MRLC 2001 consortium, of 

which the LANDFIRE Project is a partner, is the best 

source for imagery in part because it is readily obtained 

and has been consistently pre-processed. Although 

this imagery represents the best data available, we do 

anticipate some issues that will need to be addressed. 

As with the prototype effort, we anticipate the primary 

imagery-related problems impacting LANDFIRE National to include atmospheric issues, disparate imagery 

acquisition dates, and geolocational issues (see above 

section Geospatial data issues). It is anticipated that 

haze and cloud problems will be especially prevalent 

in the eastern U.S., upper Midwest, and in the Pacific 

Northwest. Imagery differences related to phenological 
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Chapter 13—Perspectives on LANDFIRE Prototype Project Accuracy Assessme

variables are also likely to impact mapping on a grander 

scale than was experienced in the prototype effort. 

When current MRLC data are deemed insufficient for 

LANDFIRE purposes (based upon visual inspection), 

additional scenes should be purchased and processed 

and incorporated into the mosaicking process.

Ancillary data—LANDFIRE will continue to use the 

best available source data for national implementation. 

One change that we recommend is using the EDNA data 

set (USGS EDNA website 2004) as the primary source 

of digital elevation data. These data are more refined than 

the data used in the prototype effort. The 1990s and 2000s 

NLCD data sets will continue to be used for stratification 

purposes at various stages of LANDFIRE National.

Accuracy of Output Products

Output product inspection—All LANDFIRE 

products must initially undergo an inspection phase 

during which the following question is asked: “Do these 

products make sense?” Although admittedly subjective, 

many errors will be caught early in the process through 

such inspections. If performed properly, such an initial 

evaluation provides a valuable safeguard that can save 

time and prevent the need to recreate the products.

Cross-validation and error matrices—As in the 

LANDFIRE Prototype, we recommend the use of crossvalidation for approximating accuracies, especially for 

existing vegetation type and potential vegetation type. 

Correlation coefficients derived from regression tree 

analyses should be used when generating continuous 

variable data sets. Error matrices should be evaluated 

to facilitate better understanding of the strengths and 

weaknesses of the vegetation products. Regarding 

creation of the mapping models, we recommend using 

5- or 10-fold cross-validation for each of the individual 

LANDFIRE mapping zones.

Points for independent validation—For national 

implementation of LANDFIRE, we recommend reserving a set percentage of plots from the decision tree and 

regression tree analyses solely for assessing accuracy. 

Note, however, that the field-referenced data used as 

input are collected from various projects and agencies, 

and thus the original source of field data cannot be 

considered a “random” sample of plots. Any sample of 

plots selected from a non-random set of points cannot 

be considered statistically random. Nonetheless, we have 

determined that withholding a limited number of points 

for validation purposes provides worthwhile accuracy 

information.

Nevertheless, we determined that it’s better to produce 

a more accurate set of products with imperfect accuracy 

information than a less accurate set of products with better 

known accuracy estimates. We do not want to withhold 

plots that would best be used for model and product 

development. As a compromise, we recommend that 

two percent of the plots be withheld from the modeling 

activities. These plots will then be used to estimate accuracies for aggregations of LANDFIRE mapping zones 

or “superzones”. We plan to merge data sets from three 

to four adjacent mapping zones and conduct validation 

activities for these regions. A target of at least 50 plots 

for each vegetation class per superzone provides useful 

information for estimating accuracies.

Stratification of accuracy assessment—In addition to providing general accuracy information at 

the superzone and individual mapping zone levels, we 

recommend providing more local estimates of accuracy 

nested within these other levels. This will be accomplished through spatial stratification of broad areas using 

biophysical gradient modeling information and other 

sources of spatial data and through thematic aggregation of similar vegetation types for localized regions. 

The process of stratifying mapping zones into zones 

based on the biophysical gradient layers developed for 

LANDFIRE (see Holsinger and others, Ch. 5) will be 

used as a basis to further our understanding of product 

errors, which in turn will enable refinement of future 

mapping procedures. This stratification process may 

facilitate the discrimination of different vegetation types 

with similar spectral signatures that occupy sites having 

very different environmental characteristics.

Field verification—As discussed above, we recommend 

conducting a modest level of field verification throughout 

LANDFIRE National. Field visits provide the technical 

teams with a basic understanding of the natural vegetation 

and ecology of the regions in which they are working, and 

field visits to particular sites serve to verify (or invalidate) 

the modeled results. Ideally, a field visit should take place 

at the beginning of each zone’s mapping activities for 

familiarization purposes, and an additional field visit 

should occur near the end of the mapping process to 

verify and refine the mapping process.

Consistency checks with data from other sources—

Whenever possible, products should be compared with 

existing independently produced data sets. In some 

cases, products unrelated to LANDFIRE have been 

generated for certain local areas, and these can be 

used to help assess accuracies of LANDFIRE products. 

Spatial and tabular data potentially provide good 
410 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2

Chapter 13—Perspectives on LANDFIRE Prototype Project Accuracy Assessme

general information. In addition, we recommend that 

LANDFIRE support the generation of local validation 

data sets, where appropriate.

Accuracy of Maps Based on Landscape 

Simulation Models

As discussed above, it is generally very difficult to 

ascertain the quantitative accuracy of products generated through complex landscape modeling efforts. Even 

so, there are some approaches suitable for assessing the 

validity of certain LANDFIRE modeled products, such 

as modeled historical fire regimes.

Although as of yet there are no examples of complete 

data sets representing historical vegetation conditions 

for the entire United States at the spatial grain of the 

LANDFIRE products, there are local historical data 

sets that can be used to “spot check” the validity of the 

products generated. For instance, historical aerial photographs and field-based data sets may provide useful 

information for assessing modeled historical fire regime 

products. Although not a true quantitative analysis, 

comparisons with historical data will likely provide 

information regarding the validity of the products.

As described above, it is important that the outputs 

from complex modeling activities be scrutinized carefully and checked for obvious flaws or deviations from 

expected results. As obvious as this seems, we are aware 

of numerous investigations in which this avenue has been 

neglected and in which spatial products were produced but 

not carefully examined. Although this type of evaluation 

does not yield quantitative error estimates, it can provide 

valuable insight regarding probable accuracies.

Finally, users of the LANDFIRE data sets should 

recognize that the inputs to the modeling process, while 

not always perfect, reflect the most accurate and current 

information available and are based upon ecologically 

sound assumptions. For these reasons, LANDFIRE 

products represent state-of-the-art modeling and technology and thus a significant improvement over other 

current options.

Conclusion_____________________

There is no single recommended procedure for deriving accuracy estimates for LANDFIRE products. 

Because time- and cost-related constraints, it will not 

be possible to conduct traditional accuracy assessments 

for the LANDFIRE mapping region (the entire U.S.). 

Yet at the same time, we recognize that evaluations 

of quality and accuracy increase the credibility of the 

final LANDFIRE products. Additionally, we can learn 

much by assessing error terms in the products, and this 

knowledge can be invaluable for future mapping and 

modeling endeavors. We suggest conducting a suite of 

accuracy assessment methods for LANDFIRE National, 

ranging from mostly qualitative assessments (such as 

the critical inspection of products, consultation with 

regional experts, and comparisons with existing data 

sets) to more quantitative analyses (such as cross-validation assessments, traditional accuracy assessments at the 

superzone level, and select evaluations at local levels). 

These combined approaches will provide LANDFIRE 

data users with the accuracy information necessary to 

facilitate the appropriate use of the data.

For further project information, please visit the LANDFIRE website at www.landfire.gov.

The Authors ____________________

James Vogelmann is a Principal Scientist with the 

Science Applications International Corporation (SAIC), 

contracting with the USDOI Geological Survey Center 

for Earth Resource Observation and Science (EROS). 

Vogelmann’s research focuses on large-region land cover 

characterization and change assessment using remote 

sensing and ancillary sources of spatial data. His role 

in the LANDFIRE Prototype Project has been to assess 

different methods for mapping vegetation types, serve 

on the LANDFIRE Vegetation Working Group, and 

help direct project research activities. He received his 

B.A. degree in Botany from the University of Vermont 

in 1978 and his Ph.D. degree in Plant Sciences from 

Indiana University in 1983.

Zhiliang Zhu is a Research Physical Scientist with the 

USDOI Geological Survey Center for Earth Resource 

Observation and Science (EROS). Zhu’s research focuses on mapping and characterizing large-area land 

and vegetation cover, studying land cover and land use 

change, and developing remote sensing methods for 

the characterization of fire fuel and burn severity. His 

role in the LANDFIRE Prototype Project has been to 

design and test a methodology for the mapping of existing vegetation cover types and vegetation structure and 

to direct research and problem-solving for all aspects 

of the methodology. He received his B.S. degree in 

Forestry in 1982 from the Nanjing Forestry University 

in China, his M.S. degree in Remote Sensing in 1985, 

and his Ph.D. degree in Natural Resources Management 

in 1989, both from the University of Michigan.

Jay Kost is a Research Physical Scientist with the 

Science Applications International Corporation (SAIC), 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006

Chapter 13—Perspectives on LANDFIRE Prototype Project Accuracy Assessme

contracting with the USDOI Geological Survey Center 

for Earth Resource Observation and Science (EROS). 

Kost’s work focuses primarily on mapping existing 

vegetation and vegetation structure (percent canopy 

and height) for the LANDFIRE Project using decision and regression tree models. Optimization of these 

models and high map accuracy results are paramount 

in his work and improvements in methodology and 

results are continually pursued. He received his B.S. 

in Electronic Engineering Technology in 1987 from 

Minnesota State-Mankato and his M.S. degree in Space 

Studies from the University of North Dakota, Grand 

Forks. In addition, Kost has completed four years of 

post-graduate study in the Atmospheric, Environmental, 

and Water Resources Ph.D. program at South Dakota 

State University, Brookings.

Brian Tolk is a Research Scientist with the Science 

Applications International Corporation (SAIC), contracting with the USDOI Geological Survey Center for Earth 

Resources Observation and Science (EROS). Tolk’s 

research focuses on the mapping and characterization 

of large-area land and vegetation cover, as well as on 

the use of close-range remote sensing methods to aid 

and improve LANDFIRE mapping techniques. His 

role in LANDFIRE has been to map land cover and 

structure variables for the prototype zones, implement 

a data management scheme, and produce promotional 

products for the project. He received his B.A. degree in 

Geography from Augustana College, Sioux Falls in 1990 

and his M.A. degree in Geography from the University 

of Nebraska, Lincoln in 1996.

Donald Ohlen is an Environmental Scientist for the 

Science Applications International Corporation (SAIC) at 

the USDOI Geological Survey Center for Earth Resource 

Observation and Science (EROS). Ohlen’s research and 

interest focus on land cover mapping for fire science applications, including the characterization of satellite data 

for fuel mapping and post-fire burn mapping. He earned 

his B.S. (1976) and M.S. (2000) degrees in Geography 

from South Dakota State University.

Acknowledgments_______________

We wish to acknowledge the funding provided by the 

USDOI Office of Wildland Fire Coordination and the 

USFS Fire and Aviation Management. The cooperation 

of the USFS Forest Inventory Analysis office in Ogden, 

Utah, and the help and support provided by the staff 

at the USFS Fire Sciences Laboratory in Missoula, 

Montana, are gratefully acknowledged. We also thank 

Terry Sohl, Sherry Chen, Matt Rollins, and Christine 

Frame for helpful technical and editorial review through 

the creation of this manuscript. The research was performed, in part, by the Science Application International 

Corporation under U.S. Geological Survey Contract 

1434-CR-97-40274 and 03CRCN0001.

References _____________________

Congalton, R.G. 1991. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment. 37: 35-46.

Foody, G.M. 2002. Status of land cover classification accuracy 

assessment. Remote Sensing of Environment. 80: 185-201.

Gesch, D.; Oimoen, M.; Greenlee, S.; Nelson, C.; Steuck, M.; Tyler, 

D. 2002. The National Elevation Dataset. Photogrammetric 

Engineering & Remote Sensing. 68: 5-12.

Homer, C.; Huang, C.; Yang, L.; Wylie, B.; Coan, M. 2004. Development of a 2001 National land-cover database for the United 

States. Photogrammetric Engineering & Remote Sensing. 70: 

829-840.

Huang, C.; Homer, C.G.; Yang, L. 2003. Regional forest land cover 

characterization using medium spatial resolution satellite data. 

In: Wulder, M.A.; Franklin, S.E., eds. Remote sensing of forest 

environments: concepts and case studies. Amsterdam, Netherlands: Kluwer Academic Publishers. Pp. 389-410.

Keane, R.E.; Burgan, R.; van Wagtendonk, J.V. 2001. Mapping wildland fuels for fire management across multiple scales: integrating 

remote sensing, GIS, and biophysical modeling. International 

Journal of Wildland Fire. 10: 301-319.

Lunetta, R.S.; Lyon, J.G. 2004. Remote Sensing and GIS Accuracy 

Assessment. Boca Raton, FL: CRC Press. 320 p.

Quinlan, J.R. 1993. C4.5: programs for machine learning. San Mateo, 

CA: Morgan Kaufman Publishers. 302 p.

Rollins, M.G.; Keane, R.E.; Parsons, R.A. 2004. Mapping Fuels and 

Fire Regimes Using Remote Sensing, Ecosystem Simulation, and 

Gradient Modeling. Ecological Applications. 14: 75-95.

Stehman, S.V.; Zhu, Z.; Czaplewski, R.L.; Nusser, S.M.; Yang, L. 

2000. Combining accuracy assessment of land-cover maps with 

environmental monitoring programs, Environmental Monitoring 

and Assessment. 64: 115-126.

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: 500-516.

USGS EDNA Website, [Online]. (2004). Available: http://edna.

usgs.gov

USGS Landsat Website, [Online]. (2004). Available: http://landsat.

usgs.gov

Vogelmann, J.E.; Sohl, T.; Howard, S.M. 1998. Regional characterization of land cover using multiple sources of data. Photogrammetric Engineering & Remote Sensing. 64: 45-57.

Vogelmann, J.E.; Howard, S.M.; Yang, L.; Larson, C.R.; Wylie, 

B.K.; Van Driel, N. 2001. Completion of the 1990’s National Land 

Cover Data set for the conterminous United States from Landsat 

Thematic Mapper data and ancillary data sources. Photogrammetric Engineering & Remote Sensing. 67: 650-662.

Wickham, J.D.; Yang, L.; Stehman, S.V.; Smith, J.H. 2004. Thematic 

accuracy of the 1992 National Land-Cover Data for the western 

United States. Remote Sensing of Environment. 91: 452-468.

Yang, L.; Stehman, S.V.; Smith, J.H.; Wickham, J.D. 2001. Thematic 

accuracy of MRLC land cover for the eastern United States. 

Remote Sensing of Environment 76: 418-422.

USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006

Chapter 14—Dissemination of LANDFIRE Prototype Project Da

Chapter

14

In: Rollins, M.G.; Frame, C.K., tech. eds. 2006. The LANDFIRE 

Prototype Project: nationally consistent and locally relevant 

geospatial data for wildland fire management. Gen. Tech. Rep. 

RMRS-GTR-175. Fort Collins: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research Station.

Introduction ____________________

The transfer of LANDFIRE data to users is the most 

important aspect of the Landscape Fire and Resource 

Management Planning Tools Project (LANDFIRE Prototype Project). The creation of an accurate, consistent, 

nationwide data set provides the foundation for a successful project. The final step is to make the data readily 

available to the user community. User capabilities and 

needs vary widely. Many users require LANDFIRE 

data to solve day-to-day wildfire management problems such as planning fuel treatments or managing 

active wildfires. Others use LANDFIRE data to gather 

information over large geographic areas for strategic 

planning and analyses. The diversity of users and the 

variety of applications of LANDFIRE data present an 

interesting challenge: to develop a data dissemination 

system that is comprehensive, user-friendly, and flexible. 

The system must be functional across many levels of 

technology, ranging from powerful computing capability 

to support national-scale strategic planning to field-level 

tactical wildfire operations support. The system must 

be sustainable, dependable, affordable, and adaptable 

to various levels of and changes in technology.

Dissemination of LANDFIRE Prototype 

Project Data

Jeff Eidenshink

The Technical Problem ___________

Most of the LANDFIRE products are developed at a 

30-meter grid resolution. Because the overall volume of 

LANDFIRE data is expected to exceed four terabytes, 

an effective and efficient distribution mechanism is 

required to allow for seamless data download for any 

given polygon area.

Access to LANDFIRE data must be supported on 

several levels. First, users must be able to view the core 

deliverables produced by the LANDFIRE Project. The 

U.S. Geological Survey (USGS), via The National Map, 

uses Internet map services (IMS) as the primary mechanism for viewing geospatial data layers. With an IMS, a 

view of selected data layers is rendered as an image for 

a selected map scale and extent. The LANDFIRE data 

and information from The National Map are provided 

through an IMS (fig. 1) that is supported by fully compliant Open Geospatial Consortium (OGC) web map service 

(WMS) connectors and ArcIMS interfaces. The IMS can 

also be directly accessed through Geographic Information System (GIS) tools, such as ArcMap. Second, users 

must be able to download data to their own computers 

for local application. An interface is needed that enables 

users to select a segment of data for a geographic region 

and download the data in a timely manner and standard 

format. Third, users must be able to perform applications 

via the Internet, without having to download the data 

or the application. Examples include running models 

interactively or producing summary reports from the 

geospatial data.
414 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 

Chapter 14—Dissemination of LANDFIRE Prototype Project Da

Figure 1—A portion of the LANDFIRE Zone 16 (central Utah) Fire behavior Fuel Models layer from The National Map. The

colors represent different fire behavior fuel models. (Please visit the Data Products section of www.landfire.gov for details

on the fire behavior fuel models.)
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2006 

Chapter 14—Dissemination of LANDFIRE Prototype Project Da

Objectives _____________________

The plan for distributing LANDFIRE data capitalizes on the capabilities of The National Map to provide 

integrated geospatial data within the context of the 

wildland fire community’s data requirements. This 

approach is logical from the standpoint that the USGS 

has the necessary technical experience, a long history 

of delivering geospatial and remotely sensed data, and 

the required software and hardware resources.

The objectives of the LANDFIRE data distribution 

plan are as follows:

•	 to develop a LANDFIRE data access interface 

based on the technical capabilities of The National 

Map,

•	 to develop a document that articulates the requirements for LANDFIRE data delivery, and

•	 to integrate LANDFIRE products with other geospatial data.

LANDFIRE Prototype Project Data 

Distribution Approach____________

The Internet is the enabling technology that will increase the public and private sectors’ and individuals’ 

awareness and use of spatial data. Having evolved from 

a means to view content and deliver products, the Internet encourages the development of more holistic spatial 

services, stand-alone devices, specialized applications, 

customized spatial queries, and interactive capabilities. 

The USGS, via The National Map, is currently employing 

the Internet as the primary mechanism for improving 

the availability of geospatial data.

A functional prototype IMS has been developed for 

LANDFIRE data dissemination (see www.landfire.

gov). The two LANDFIRE Prototype mapping zones 

are used as the foundation for the prototype IMS. As 

they are mapped, other zones are added to the IMS. 

The availability of the LANDFIRE IMS will enable 

users of LANDFIRE products to integrate current GIS 

capability with LANDFIRE data.

In circumstances where bandwidth capability is limited 

or where in-house applications require complex data 

analysis, field practitioners may wish to download the data 

as opposed to simply viewing the data through an IMS. 

LANDFIRE data are distributed through the LANDFIRE IMS following the model of the USGS Seamless 

Data Distribution System (SDDS) (http://seamless.usgs.

gov/). The SDDS includes an area of interest selection 

tool and online direct data downloads. Initially, these 

data sets can be retrieved interactively by drawing a box 

around the area of interest. The next level of capability 

will support the use of a template defining the desired 

area, such as a county or watershed. The template may 

be derived from an existing LANDFIRE geospatial 

layer or from The National Map. Ultimately, the data 

distribution system will need to accept user-defined 

templates such as the perimeter of an active wildfire or 

an administrative unit.

Recommendations for National 

Implementation _________________

Scientists from the USGS Center for Earth Resources 

Observation and Science, The Nature Conservancy, the 

USDA Rock Mountain Research Station Missoula Fire 

Sciences Laboratory, and the National Interagency Fire 

Center are cooperating with field personnel to develop 

the IMS in addition to the data download and delivery 

capabilities. Recommendations for national implementation address issues affecting the integration of the IMS 

with GIS technology and existing models. Four main 

points for refinement have been identified.

“Third-party” Fire and Land Management 

Models

A major requirement of the LANDFIRE Prototype 

Project is that it must facilitate the implementation 

of existing models. Specifically, it must support four 

standardized analytical fire models: FARSITE, a fire 

behavior simulation model; FlamMap, a fire potential 

model; and FOFEM and CONSUME, both fire effects 

models. Each of these models uses several LANDFIRE 

layers combined with ancillary data. The models also 

produce derived layers that need to be catalogued as part 

of the LANDFIRE database and also made accessible 

for other users and applications.

The capability to perform interactive modeling using 

IMS is in the early stages of development. Research will 

determine how these models will function as part of the 

analytical tools provided within an IMS. The capability 

to execute these models through an Internet-based interface would substantially reduce the need to download a 

large volume of data from remote locations using slow 

Internet connections. Instead, model results, having a 

considerably smaller data volume than the input data itself, 

could be delivered to a field center in a timely manner. 

The modeling results will be enhanced using ancillary 

layers (such as those depicting transportation, hydrology, 

terrain, and/or structures) from The National Map.
416 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-175. 2

Chapter 14—Dissemination of LANDFIRE Prototype Project Da

Scaling of LANDFIRE Products and 

Geospatial Data

LANDFIRE products and geospatial data must be 

useable at spatial scales ranging from that of a watershed 

to that of the entire nation. LANDFIRE data will be used 

in decision support systems where emphasis could range 

from real-time requirements for wildland fire fighting 

to national planning requirements for allocation of fuel 

treatment resources. For effective use of LANDFIRE 

data, a robust scaling capability will be needed to enable 

land managers to scale LANDFIRE data layers from 

local to national applications. Users will identify the most 

appropriate scales for applying LANDFIRE products. 

Techniques for display of multi-scale data using IMS, 

at scales that meet the needs of decision support and 

modeling, are under development.

Updating LANDFIRE Geospatial Data

A LANDFIRE requirement is that data be routinely 

updated in order to capture varying patterns of fire and 

fuel in terms of both time and geographic scale. This 

updating process presents challenges for the LANDFIRE 

team, the greatest of which will be solving the administrative and technical problems regarding processes for 

collecting and collating updates from the field. Ideally, 

every fuel treatment, such as prescribed burns or the 

mechanical removal of fuel, and all wildfires will be 

mapped by a land management agency; however, the 

collection and collation of this information at a national 

scale across agencies and programs remains a difficult 

task. Data will be updated through GIS capabilities that 

integrate with the data dissemination system.

Developing the Technical Infrastructure

Implementing a national LANDFIRE data distribution 

system can be viewed simply as expanding the prototype 

effort to the national scale. The data distribution infrastructure, such as data servers, network bandwidth, and 

data storage, can be improved by incremental expansion 

of the hardware capability and through routine replacement of old technology. Meeting the technological needs 

of the users, however, is much more complex. Processes 

such as formatting data, merging data from disparate 

sources, and analyzing data become increasingly complex as more users find more ways to employ the data.

Conclusion_____________________

The LANDFIRE data dissemination system is built on 

existing and emerging Internet technology. The LANDFIRE data layers are available for viewing and download 

using the functional capability of Internet map services. 

The LANDFIRE data dissemination framework is modeled after The National Map and other geospatial data 

delivery systems. Unique LANDFIRE requirements will 

be identified through cooperation with users. The goal 

is to meet the needs of field-level management as well 

as those of national strategic planning. The technology 

of the LANDFIRE data dissemination system will be 

an effective and affordable solution to the challenge of 

wildfire management information delivery.

For further project information, please visit the 

LANDFIRE website at www.landfire.gov.

The Author _____________________

Jeff Eidenshink is a Remote Sensing Scientist 

with the USDOI Geological Survey Center for Earth 

Resources Observation and Science (EROS). He received B.S and M.S. degrees in Geography from South 

Dakota State University (1973 and 1979) and his Ph.D. 

in Atmospheric, Environmental, and Water Resources 

from South Dakota State University in 2000. He has 

been involved in remote sensing and GIS research for 

over 30 year. Eidenshink has researched and developed 

environmental data sets from satellite observations 

and applications of remote sensing technology in fire 

science. Pertinent projects include the development 

and leadership of the USGS Conterminous United 

States Greenness Mapping Project and application of 

vegetation indices for ecological assessment and fire 

danger monitoring. His research activities include the 

development and distribution of fire information from 

satellite information.

Acknowledgments_______________

I wish to thank the members of the LANDFIRE team 

that provided the data layers for the prototype mapping 

zones. I also wish to acknowledge the efforts of the 

Information Technology Services group at EROS for 

implementing the LANDFIRE Prototype Internet map 

service.
NOTES


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The Rocky Mountain Research Station develops scientific information and technology to improve management, protection, and use of 

the forests and rangelands. Research is designed to meet the needs 

of National Forest managers, Federal and State agencies, public and 

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Research Locations

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RMRS

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