United States FDA a Department 

of Agriculture 

Forest Service 

Rocky Mountain 

Research Station 

General Technical 

Report 

RM RS-GTR-3 

March 1998 

Development of Input Data 

Layersfor the FARSITE 

Fire Growth Model for the 

Selway-Bitterroot Wilderness 

Complex, USA 

Robert E. Keane 

Janice L. Garner 

Kirsten M. Schmidt 

Donald G. Long 

James P. Menakis 

Mark A. Finney 

This file was created by scanning the printed publication.

Errors identified by the software have been corrected;

however, some errors may remain.
The Authors 

Robert E. Keane is a Research Ecologist with the USDA 

Forest Service, Rocky Mountain Research Station at the 

lntermountain Fire Sciences Laboratory, P.O. Box 8089, 

Missoula, MT 59807, Phone 406-329-4846, FAX 406- 

329-4877, IBM: bkeane/rmrs,missoula. Since 1 985, Keane 

has developed various ecological computer models for 

the Fire Effects Project to study both ecosystem management and research. His most recent research includes 

the synthesis of a First Order Fire Effects Model, construction of mechanistic ecosystem process models that 

integrate fire behavior and effects into succession simulation, status of whitebark pine in the Northern Rocky 

Mountains, and spatial simulation of successional communities on the landscape using GIs and satellite imagery. He received his B.S. degree in forest engineering 

from the University of Maine, Orono; his M.S. degree in 

forest ecology from the University of Montana, Missoula; 

and his Ph.D. degree in forest ecology from the University of Idaho, Moscow. 

Janice L. Garner is a Resource lnformation Specialist 

with the USDA Forest Service at the lntermountain Fire 

Sciences Laboratory, P.O. Box 8089, Missoula, MT 

59807, Phone 406-329-4958, FAX 406-329-4877, IBM: 

jgarner/rmrs,missoula. Garner has worked on various 

GIs and field research projects for ecosystem management. She has assisted with the GIs analysis and map 

production in projects focused on gradient modeling and 

ecosystem dynamics. She received a B.S. degree in 

ecology and evolutionary biology from the University of 

Arizona, Tucson; and an M.S. degree in organismal 

biology and ecology from the University of Montana, 

Missoula. 

Kirsten M. Schmidt is a Resource lnformation Technician with the USDA Forest Service at the Intermountain Fire Sciences Laboratory, P.O. Box 8089, Missoula, 

MT 59807, Phone 406-329-4958, FAX 406-329-4877, 

IBM: kschmidt/rmrs,missoula. Schmidt has been working on several research projects for the Fire Effects 

Project including the development of input datalayers for 

the Fire-BGC model using GIs and satellite imagery, field 

sampling for the Gradient Modeling and Remote Sensing 

Project, and the compilation of maps for the Interior 

Columbia River Basin Scientific Assessment Project. 

Schmidt received her B.A. degree in biology from the 

University of Montana, Missoula. 

Donald G. Long is a Forester with the USDA Forest 

Service at the lntermountain Fire Sciences Laboratory, 

P.O. Box 8089, Missoula, MT 59807, Phone 406-329- 

4706, FAX 406-329-4877, IBM: dlong/rmrs,missoula. 

Long is currently working on GIs related projects for 

ecosystem management using remote sensing and ecological modeling. He has worked on various research 

projects concerned with ecosystem dynamics, vegetation inventory, and fire regimes. He earned a B.S. degree 

in forest science from the University of Montana, Missoula 

in 1981 and is completing an M.S. degree in forest 

resources at the University of Idaho, Moscow. 

James P. Menakis is a Forester with the USDA Forest 

Service at the lntermountain Fire Sciences Laboratory, 

P.O. Box 8089, Missoula, MT 59807, Phone 406-329- 

4846, FAX 406-329-4877, IBM: jmenakis/rmrs,missoula. 

Since 1990, Menakis has worked on various research 

projects relating to fire ecology on a community and 

landscape level for the Fire Effects Project. He is currently the GIs Coordinator on the Landscape Ecology 

Team for the lnterior Columbia River Basin Scientific 

Assessment Project. His most recent work includes the 

development of GIs themes for the Ecosystem Management Program of the USDA Forest Service's Northern 

Region, the evaluation of whitebark pine status in the 

Northern Rocky Mountains using ecosystem sampling 

and satellite imagery, and a comparison of the SelwayBitterroot Wilderness prescribed natural fire program to 

presettlement fire regimes. Menakis received his B.S. 

degree in forestry and M.S. degree in environmental 

studies from the University of Montana, Missoula. 

Mark A. Finney is a Research Scientist with Systems for 

Environmental Management, P.O. Box 8868, Missoula, MT 

59807, Phone 406-329-4832, IBM: mfinney/rmrs,missoula, 

e-mail: mfinney@montana.com. Finney has been involved in cooperative fire research between Systems 

for Environmental Management and the lntermountain 

Fire Sciences Laboratory since 1993. Most recently, 

he has concentrated on developing and testing the 

FARSITE fire growth simulation model. Before that he 

spent 2 years in fire research as an Ecologist with 

Sequoia National Park. He received his B.S.degree in 

forestry from Colorado State University, an M.S. degree 

in forestry from University of Washington, and a Ph.D. 

degree in wildland resource science from the University 

of California, Berkeley. 

Rocky Mountain Research Station 

(formerly lntermountain Research Station) 

324 25th Street 

Ogden, UT 84401 
Research Summary 

Fuel and vegetation spatial layers needed for the 

execution of the FARSITE fire model were created for all 

lands in and around the Selway-Bitterroot Wilderness 

Area in Montana and ldaho using satellite imagery and 

potential vegetation type modeling . FARSITE is a spatially explicit fire growth model used to predict the size. 

intensity. spread. and rate of wildland fires . FARSITE 

requires eight data layers as input; fire behavior fuel 

model. crown closure. crown height. stand height. crown 

bulk density. elevation. aspect. and slope . These input 

layers were created from a digital terrain model and the 

base vegetation layers of potential vegetation type. cover 

type. and structural stage . This methodology was designed to be easily replicated by others . Potential vegetation types were modeled from topographic and geographic data layers based on criteria developed at 

modeling workshops . Cover type and structural stage 

layers were taken from a comprehensive satellite imagery classification of Montana and ldaho lands . Fuel 

models were assigned to each potential vegetation type. 

cover type and structural stage combination by area 

ecologists and fire managers . An accuracy assessment 

of the layers showed the potential vegetation type layer 

is 60 percent accurate and the fuel model layer is 59 

percent accurate . 

Acknowledgments 

This project was a joint effort between the USDA Forest 

Service lntermountain Fire Sciences Laboratory and the 

Bitterroot, Nez Perce. and Clearwater National Forests . We thank all who helped with the successful completion 

of this project including Leslie Anderson. Byron Bonney. 

Tim Brickell. Gary Brown. Lee Clark. Randy Doman. Pat 

Green. Jack Kirkendall. Curt McChesney. Jim Mital. 

Larry Russell. Chuck Stanich. Cathy Stewart of the 

USDA Forest Service. Bitterroot. Nez Perce. and 

Clearwater National Forests; Mark Woods of the Northern Region Aerial Fire Depot. Missoula. MT; Pat Andrews. 

Steve Arno. Todd Carlson. Wayne Cook. Cameron 

Johnston. Scott Mincemoyer. Kevin Ryan. and Dennis 

Simmerman of USDA Forest Service. Rocky Mountain 

Research Station. lntermountain Fire Sciences Laboratory. Missoula. MT . 

Contents 

Page 

Introduction ................................................................ I 

Background ................................................................ 2 

FARSITE Description .............................................. 3 

Development Tools ............................................... 4 

Study Approach ...................................................... 5 

Study Area .............................................................. 7 

Methods ..................................................................... 9 

Ground-Truth Data ............................................... 10 

Historical Plots ................................................ 10 

SBWC Ground-Truth Effort ................................ 10 

Spatial Data Layers .............................................. I I 

Digital Elevation Model (DEM) ........................... 12 

SlLC Cover Type .............................................. 13 

Ancillary Vector Layers ...................................... 14 

Potential Vegetation Type (PVT) ....................... 14 

SBWC Cover Type ............................................ 17 

Structural Stage ................................................ 18 

Fire Behavior Fuel Models (FBFM) ................... 18 

Carropy Cover .................................................... 19 

Stand Height ...................................................... 20 

Crown Height ..................................................... 20 

Crown Bulk Density ........................................... 20 

Accuracy Assessment ........................................... 20 

Results ................................................................... 22 

Accuracy Assessment ........................................... 26 

Final GIs and FARSITE Input Layers ................... 29 

Discussion ................................................................ 30 

Data Layer Development ...................................... 30 

PVT Map ........................................................... 30 

Fuels Maps ........................................................ 31 

Map Accuracy ...................................................... -31 

Recommendations ............................................... 34 

Conclusions .............................................................. 37 

References ............................................................... 38 

Appendix A: SBWC ECODATA Ground-Truth 

Plot Forms ............................................................ 42 

Appendix B: DEM Filtering Procedure ..................... 51 

Appendix C: Cover Type Cross Reference Table .... 52 

Appendix D: Terrain Models ..................................... 54 

Appendix E: Fuels Mapping Worksheet Example .... 57 

Appendix F: Fire Behavior Fuel Model 

Assignments ......................................................... 58 

Appendix G: Crown Bulk Density Reference Data ... 64 

Appendix H: Accuracy Assessment Matrices of 

PVT and Structural Stage ..................................... 65 

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

imply endorsement by the US . Deparfment of Agriculture of any product or service . 

Development of Input Data 

Layers for the FARSITE 

Fire Growth Model for the 

Selway-Bitterroot Wilderness 

Complex, USA 

Robert E, Keane 

Janice L. Garner 

Kirsten M. Schmidt 

Donald G. Long 

James P. Menakis 

Mark A. Finney 

Introduction 

Fire managers need to quickly evaluate the potential size, rate, and 

intensity of a wildland fire under current and future conditions to aid in the 

planning and allocation of resources used to directly or indirectly manage the 

fire. Recent advances in 6omputer software and hardware technology have 

allowed the development of several spatially explicit fire behavior simulation 

models that predict the spread and intensity offire as it progresses across the 

landscape (Anderson and others 1982; Andrews 1989; Baker 1992; Ball and 

Guertin 1992; McAlpine and Wotton 1993; Turner and Dale 1991). Some of 

these computer programs have the ability to project future fire growth and 

compute maximum perimeters of wildland fires for planning or for real-time 

simulations. One of the best spatially explicit fire growth models is Finney's 

(1994) computer program FARSITE (Fire ARea Simulator) available for 

most IBM-compatible personal computers. FARSITE is currently being used 

by many wildland fire mangers in the United States and other countries to 

predict the characteristics of prescribed natural fires. 

FARSITE propagates fire across landscape using the fire behavior routines found in the one-dimensional fire model BEHAVE (Albini 1976; 

Andrews 1986; Andrews and Chase 1989; Rothermel 1972). FARSITE 

requires eight spatial data layers for a comprehensive evaluation of surface 

and crown fire behavior. A spatial data layer, also called a "raster" layer, is 

defined as a georeferenced grid of squares called pixels that have values that 

describe certain characteristics of the associated piece of ground. The first 

raster layer needed by FARSITE is called a Digital Elevation Model (DEM) 

where each pixel is assigned an elevation. Slope and aspect are also required 

FARSITE input layers, and they can be derived from the DEM layer using 

elevation values from surrounding pixels. The fourth layer is a Fire Behavior 

Fuel Model (FBFM) map. Pixels in this layer are assigned the fire behavior 

fuel model (Anderson 1982) that best represents the fuel complex that would 

produce expected fire behavior for the corresponding piece of ground. Average canopy cover, in percent, is needed as a map to compute hourly fuel 

moistures. FARSITE can compute crown fire behavior if three other data 
layers are present (Rothermel and others 1986). Average stand height and 

average crown base height are data layers that FARSITE needs to compute 

crown fire initiation using the Van Wagner (1977, 1993) model and spread 

from Rothermel(1991). A crown bulk density raster map is used to compute 

crown fire spread, along with the previously mentioned crown cover and 

stand height map. 

An important factor in accurately predicting spatial fire behavior using 

FARSITE is the quality of the input spatial data layers. These data layers 

must be accurately and consistently derived across all lands and ecosystems 

in the analysis area, and more important, the layers must agree with all other 

thematic layers in the Geographic Information System (GIs). Unfortunately, 

many land management agencies do not have the FARSITE input layers 

available to compute spatial fire behavior and growth. Moreover, the development of these data layers for large geographic areas with diverse ecosystems requires a high level of expertise, an advanced computer technology, 

'and an abundance of computer resources. An inexpensive and repeatable 

method of creating these input raster data layers is needed so the layers can 

be efficiently developed and used by many agencies to spatially model fire 

behavior and manage wildland fire. 

In this paper we describe the development ofthe suite of data layers needed 

by FARSITE for the simulation ofwildland fires that occur in and around the 

Selway-Bitterroot Wilderness Complex (SBWC) of Montana and Idaho. We 

developed these input layers from three base vegetation data layers of 

potential vegetation type, cover type, and structural stage. A major goal of 

this effort was to create these layers using a methodology that could be 

repeated by other land management organizations to create their own 

FARSITE data layers. This project used a combination of GIs, satellite 

imagery, ecological and fire management knowledge, and management 

participation to generate the FARSITE layers. We will detail the generalized 

methods used to create these maps and then present the results of our fuels 

mapping effort. 

Background 

In fall 1995, fire managers.from the Nez Perce, Bitterroot, and Clearwater 

National Forests of Idaho and Montana approached scientists at the Intermountain Fire Sciences Laboratory (IFSL) about developing the set of 

FARSITE input spatial data layers they would need to simulate prescribed 

natural fires and wildfires in the Selway-Bitterroot Wilderness Area. The 

fire managers had tried to create these layers but did not have remotesensing expertise and adequate computer equipment. Coincidently, many 

IFSL scientists and support personnel had been intimately involved in the 

Interior Columbia Basin Ecosystem Management Project (ICBEMP) scientific assessment (Quigley and others 1996) and, as a result, had the needed 

computer and data resources. These scientists undertook the project under 

the auspices of a newly formed technology transfer organization called the 

Fire Modeling Institute managed at the IFSL. The Institute is a new concept 

for applying technical knowledge and specialized skills to complex issues 

facing resource managers. Land managers bring potentially complex management analysis projects to the Fire Modeling Institute and participate in 

the project's ~uccessful completion. The IFSL has the latest in computer 

hardware and software technology, extensive scientific expertise (especially 
in wildland fire issues), and highly trained personnel available to use on the 

project. 

FARSITE Description 

The fire growth model FARSITE (Finney 1995) computes fire intensities 

and spread rates for numerous points across the landscape using the fire 

behavior algorithms of Albini (1976), Rothermel (1972), and Rothermel 

(1991). The fire is then propagated across the landscape from these points 

using a series of ellipses generated from Huygens' principle, which is a wavetype model. Huygens' principle essentially states that a wave can be propagated from points on its edge that serve as independent sources of smaller 

waves (Richards 1990). FARSITE then connects all points at the end of the 

smaller waves to delineate the fire front. The fuels, weather, and topography 

of areas within the fire line dictate fire intensity and spread rates. A detailed 

discussion of the FARSITE program is given in a user guide (Finney 1995). 

FARSITE was developed primarily to be used as a tool in the management 

of prescribed natural fires, although it could be used in wildfire situations. 

The FARSITE model uses spatial data layers of topography, surface fuels, 

crown fuels, and weather to predict fire behavior. Topography is described as 

elevation, aspect, and slope from a Digital Elevation Model (DEM) raster 

layer (USGS 1987). Surface fuels can be input as either a standard FBFM 

(Anderson 1982) or customized FBFM (Burgan and Rothermel1984). Crown 

fuels are described from four stand characteristics. Stand height (m) is the 

average height of the dominant tree layer. Crown height (m) is the average 

height of the bottom of the tree crowns in the stand. This parameter is 

important for predicting the transition of a surface fire to a crown fire. 

Canopy closure (percent) is the average vertically projected tree crown cover 

in the stand. Only trees taller than the shrub layer are used to compute this 

parameter. Last, crown bulk density (kg m-3) is the density of the crown 

biomass above the shrub layer. 

FARSITE weather data are not input as a spatial data layer, but rather as 

a set of generalized weather text (ASCII) files composed of a stream of hourly 

or daily temperatures, precipitation, and relative humidities (Finney 1995): 

Each weather file is assigned to a point on the ground, and FARSITE 

extrapolates this weather across the landscape using adiabatic lapse rates. 

Wind is treated differently than other weather parameters in FARSITE. 

Wind speeds and directions are specified by time of day in a separate set of 

wind ASCII files. Each wind file is assigned to a portion of the simulation 

landscape using FARSITE protocols. A complete discussion of input layers 

and data files is presented in the FARSITE user manual (Finney 1995). 

FARSITE creates several raster and vector spatial data layers of computed 

fire intensity (kW m-I), spread rates, and flame lengths stratified in space 

and time. Fire growth and intensity patterns are displayed on the computer 

screen overlaid on topography and fuels layers. All FARSITE output layers 

can be exported to a GIs for additional analysis and display. Keane and 

others (199513) successfully linked FARSITE to a forest succession model to 

evaluate the effects of fire across a large landscape in Glacier National Park 

under a climate change scenario. 

FARSITE has specific data needs for the simulation of wildland fire. First, 

spatial fire behavior predictions require a fine spatial resolution of the input 

data layers todelineate the complexity of the landscape (dissected or flat, 

for example). Finney (1995) mentions that a 30 m pixel size for all data layers 
Development Tools 

is probably the optimal resolution for accurate assessments of fire spread in 

heterogeneous conditions considering the slowness of computer simulations 

with finer grids. Second, all data layers and data files must be congruent. 

This means that data values assigned to a pixel must agree within and across 

all data layers. For example, crown height must not exceed stand height for 

any pixel. Next, FARSITE requires a specific format for all data layers 

and input files (Finney 1995). These formats are available in many current 

GIs export programs. Last, the speed of FARSITE displays and simulations 

can be improved if the simulation landscape is divided into blocks that are 

approximately 375 by 550 pixels, or roughly the coverage of a standard 7.5 

minute US. Geological Survey quadrangle map. 

Remote sensing is often the primary tool used for development of the 

FARSITE input layers, especially for large land areas (greater than 100,000 ha). 

Aerial photo interpretation and classification is a proven technique for 

spatially defining areas of homogeneous vegetation and fuels (Jensen 1986; 

Lillesand and Kiefer 1994; Schowengerdt 1983). However, photo delineation 

is often costly and time-consuming for large geographic areas, and the 

interpretation of fuels is often limited to the minor textural differences in a 

photograph and interpreter subjectivity (Jensen 1986). Moreover, describing 

fuel characteristics from aerial photos is problematic because it is often 

difficult to see the forest floor through the forest canopy. Most photo 

interpretation projects assign fuel models to vegetation type or topographic 

zones (Greer 1994). 

Satellite imagery is an inexpensive method to generate fuels and vegetation data layers over large areas (Bain 1990; Greer 1994). Most imagery 

consists of an assessment of light reflectance measured across various 

wavelengths in the thermal magnetic spectrum (Jensen 1986; Lillesand and 

Kiefer 1994; Schowengerdt 1983). An advantage of satellite imagery is that 

it is frequently, consistently, and comprehensively collected across vast 

geographical regions. However, many image classifications are often inexact 

because of confusing spectral signatures, and most classifications only 

accurately identify broad vegetation communities on the landscape because 

these types have the greatest difference in light reflectance (Bain 1990; 

Bolstad and Lillesand 1992; Schowengerdt 1983). Moreover, satellite image 

classifications are often incompatible with the land classification systems 

used by land managers. Shadow, atmospheric phenomenon, haze, smoke, 

topography, and georectification errors can also contribute to potential 

errors in the final classified image (Foody and Curran 1994; Leprieur and 

others 1988). 

Fuels characteristics are rarely assessed from remotely sensed imagery 

because forest floor properties are not discriminated well by the satellite 

sensors because of forest canopy interference (Elvidge 1988). Another problem with using remotely sensed digital imagery to map fuel and vegetation 

characteristics is the fixed-resolution of the pixels (Jensen 1986). Common 

satellite imagery products of SPOT, Thematic Mapper (TM), or MultiSpectral Scanner (MSS) scenes have 10,30, and 80 m pixel widths, respectively. Unfortunately, many fuel characteristics are not confined to these 

scales. Surface fuels often have a higher spatial variability than crown fuels. 

Small pixel sizes usually require more computer resources and result in 

slower computing and display time depending on the computer system. 
Large pixel sizes usually result in loss of spatial information because mapped 

characteristics must be averaged over large areas. Also, it is difficult to 

discriminate between woody fuel size classes using current satellite imagery 

because of the coarse resolution and inadequate wavelengths of available 

sensors. 

Study Approach 

All SBWC FARSITE fuels layers were created from three primary vegetation layers-potential vegetation type, cover type, and structural 

stage. This three-tiered classification strategy was successfully used to 

describe ICBEMP ecosystems and vegetation communities at the coarse and 

midscale (Quigley and others 1996). We assumed a myriad of ecosystem 

characteristics could be quantified from this classification triplet based on 

past succession and ecological research (Arno and others 1985; Kessell and 

Fischer 1981; Steele and Geier-Hayes 1989). Maps of these ecological 

characteristics are created by assigning values to classification triplet 

combinations and then entering these summaries into a data base linked 

to the three vegetation GIs layers. 

The advantages of this approach are many. First, the concept can be used 

across many spatial scales because the categories in each of the three 

classifications can be scaled to the appropriate level of application. For 

instance, a cover type category at a coarse scale may be "needleleaf conifer" 

whereas the same cover type at a mid or fine scale might be "ponderosa pine." 

Second, most land management agencies already use these three classifications to some extent in their analyses, and these classifications can be 

easily developed if they do not exist for some areas. Resource professionals 

also use some form of these classifications to formally or informally describe 

stands or watersheds in the field or office. Last, there is a large body of 

research available on these types of classifications and their mapping (for 

example, Erye 1980; Shiflet 1994). Many National Forests have existing 

classifications for these three attributes, but few have accurate maps of 

these attributes across large land areas. 

The first major task was to select the remote sensing medium to create the 

three base vegetation layers. Our first impulse was to purchase a wide 

variety of remotely sensed data products, mostly from satellite platforms, 

and then classify these imagery scenes directly to fuels categories using 

the latest image processing software. However, it would have been difficult 

for other land and fire management organizations to repeat the image 

classification procedure because satellite image processing is as much an art 

as it is a science, and the final classifications usually have abundant 

subjective elements. So we decided to use a landscape classification procedure that could be generally applied to any fuels or vegetation mapping 

project using base vegetation spatial data layers that are commonly available to land management agencies. If these vegetation layers are unavailable, they are probably the layers easiest to create for many geographic areas 

because they are the ecological attributes most often used in land management planning. We also selected these spatial data layers because they can 

be used for purposes than other than fuels mapping such as wildlife habitat 

analysis. Keane and others (1996b) used these same vegetation layers to 

simulate landscape successional changes across the Interior Columbia 

River Basin using the model CRBSUM. 
The Potential Vegetation Type (PVT) layer, developed specifically for this 

project, is a spatial map that describes the distribution of "climax" vegetation 

types across a land area ( Kessell 1979; Pfister and others 1977). Climax 

vegetation types provide an indirect characterization of site type or biophysical setting (Pfister and others 1977). Habitat types and habitat type phases 

(Pfister and others 1977) are roughly equivalent to PVT's at fine spatial 

scales, while habitat type groups, fire groups (Fisher and Bradley 19871, or 

topographic settings (Keane and others 199513) can be used as PVT's at 

midscales, which is the scale of reference for this study. The ICBEMP effort 

used a biophysical model of temperature and moisture to classify and 

delineate coarse scale PVT's across the Interior Columbia River Basin (Reid 

and others 1995). The methods used to create the PVT map for this fuels 

mapping project are discussed in detail in later sections. 

The SBWC cover type layer was taken from a comprehensive Satellite 

Imagery Land Cover (SILC) map developed by the Wildlife Spatial Analysis 

Laboratory, Montana Cooperative in Wildlife Research Unit at the University of Montana (Redmond and Prather 1996). This project used TM satellite 

imagery to map vegetation cover types across the entire Northern Region of 

the USDA Forest Service on both public and private lands. The SILC map is 

a raster map of polygons with a minimum map size of 2 ha (5 acres). Each 

polygon is assigned several attributes including cover type, structural stage, 

and canopy closure. Over 10,000 ground-truth plots were used to validate 

and refine this extensive map and data base. Several ecological attributes, 

including structural stage, were assessed at each ground-truth plot, and 

these data were summarized and assigned to classified polygons. The 

structural stage layer used in this study was created from the structural 

stage assignments to SILC polygons that were in turn based on the groundtruth data (Redmond and Prather 1996). This paper does not detail the 

classification methods needed to create the vegetation cover type and 

structural stage maps used for fuels mapping because these procedures are 

quite complex and they have been presented by many other authors (Jensen 

1986; Lachowski and others 1995; Redmond and Prather 1996). It is therefore assumed that the vegetation cover type map and structural stage map 

already exist for any fuels mapping project. These maps can be taken from 

many sources including timber stand maps, past vegetation mapping projects, 

and satellite imagery. 

Two fire behavior fuel model (FBFM) GIs layers were developed for this 

project. The SBWC fire managers wanted one FBFM map that would 

represent fuel characteristics under normal burning conditions and another 

that would represent severe or extreme burning conditions. Apparently, r 

some plant communities can exhibit drastically different fire behavior after 

prolonged drought and with high winds. For example, SBWC montane 

shrub communities are often assigned the Anderson (1982) FBFM 5 (live 

shrub, low fire intensity fuel model) because of their high summer moisture 

contents. However, these same communities can exhibit the fire behavior 

typical of the FBFM 6 (xeric shrub, high fire intensity fuel model) under 

extreme drought conditions and moderate to high winds because their live 

fuel moisture contents get quite low. The normal FBFM map will probably 

be used for many prescribed natural fire plans and real-time simulations. 

However, the extreme FBFM map is available to simulate wildfires for 

"worst-case" ~cenarios. Table 1 presents the fire behavior fuel models used in 

this study. 
Table 1-Fire behavior fuel models (FBFM) used in this project. All fuel models are discussed in detail in 

Anderson (1 982). 

Fuel modela 

- -- -- - -- - -- - 

Fire behavior 

Description Rate of spread Flame length 

Short grass(0.3 m) 

Timber (with grass and understory) 

Brush (shrubs and conifer regeneration, 0.8 m) 

Dormant brush 

Closed timber litter 

Ponderosa pine duff 

Timber (litter and understory) 

Water 

Nonvegetation (rock, ice, snow) 

"From Anderson (1 982). 

bFire behavior under the following conditions: windspeed 8 kmlhr, dead fuel moisture 8 percent, and live fuel moisture 100 

percent. 

Another type of fuel model was also mapped for this project. A Fire Effects 

Fuel Model (FEFM) was assigned to each PVT, cover type, structural stage 

combination in the SBWC. The FEFM's are different from FBFM fuel models. 

Whereas FBFM's are used to characterize expected fire behavior, 

FEFM's characterize actual fuel loadings by size class. The FEFM's are 

primarily used to quantify the amount of fuel present within polygon 

boundaries so that fire effects such as fuel consumption and smoke 

generation can be predicted using computer models such as FOFEM (Keane 

and others 1995a; Reinhardt and others 1997). The FEFM's used in this 

study were taken from the three fuel photo series guides developed by 

Fischer (1981a,b,c). Each fuel photo within the guides was considered an 

FEFM, and the fuel loading data describing the photo were used to quantify 

that fuel model. The page number of the photo series guide served as an 

identification number. The FEFM's were assigned to each PVT, cover type, 

structural stage combination based on the ground-truth data collected for 

this project and on fire manager assessments. Fuel loading data from Brown 

and Bevins (1986) and Brown and See (1981) were used to adjust some fire 

effects fuel models to agree with the PVT, cover type, structural stage 

descriptions. 

Study Area 

The Selway-Bitterroot Wilderness Complex (SBWC) consists of 1.15 million ha in and around the Selway-Bitterroot Wilderness Area of Montana and 

Idaho, USA (fig. 1). This rugged country contains great topographic contrasts 

with elevational relief from valleys to mountain tops often exceeding 1,500 

to 1,800 m (Habeck 1972). Geologically, most of the SBWC consists of 

granites, gneiss, and schists belonging to the Idaho Batholith. The Bitterroot 

Mountain crest resulted from a great flat fault that was covered with a 

continuous ice sheet during the Pleistocene. Elevations range from 550 m 

along the Selway River to over 3,000 m along the Bitterroot Crest. SBWC 

climate is generally transitional between a north-Pacific coastal type and a 

continental type (Finklin 1983). The SBWC has a wide variety of potential 

and existing vegetation types (Cooper and others 1991; Pfister and others 

1977). 
Selway Bitterroot Wilderness Complex 

Elk 

Figure 1-The Selway-Bitterroot Wilderness Complex (SBWC) study area. 

8 
The SBWC was divided into three zones because of the great diversity in 

vegetation, topography, and climate (fig. 1). These zones were delineated 

based on several coarse scale data layers developed by the ICBEMP scientific 

assessment (Quigley and others 1996). We used the biophysical settings 

(Reid and others 1996) and climate maps (Thornton and others 1997) to 

delineate the broad geographic zones based on similarity in climate, potential vegetation, and topography. The Montana zone (MT) consists of all lands 

from the crest of the Bitterroot Mountain range down to the Bitterroot River 

(fig. 1). The North West zone (NW) consists of all lands north of the Selway 

River to the Lochsa River. The West Central zone (WC) are all remaining 

lands that are east of Elk City, ID, all the way to the Bitterroot crest. 

The MT zone is somewhat drier that the other zones because of the rainshadow effect of the Bitterroot Mountains. This zone can have grand fir 

(Abies grandis) on north slopes and in valley bottoms below 1,370 m 

elevation. Douglas-fir (Pseudotsuga mensiezia) and ponderosa pine (Pinus 

ponderosa) are usually dominant on the south-facing, lower elevation slopes. 

Western larch (Larix occidentalis) and Douglas-fir grow on the northerly 

aspects at moderate altitudes. Lodgepole pine (Pinus contorts), subalpine fir 

(Abies lasiocarpa), and Engelmann spruce (Picea engelmannii) forests are 

common at the subalpine environments, while upper subalpine and timberline forests consist primarily of subalpine fir, Engelmann spruce, alpine 

larch (Larix lyallii), and whitebark pine (Pinus albicaulis). Persistent 

shrublands are found throughout the MT zone where soils are skeletal, 

where fire was common, and where climate prevents tree growth mainly 

above timberline (Habeck 1972). 

The NW and WC zones have a more maritime climate with higher rainfall 

and moderate temperatures (Finklin 1983). Western redcedar (Thujaplicata) 

forests dominate low elevation north slapes and riparian settings, while 

grand fir and Douglas-fir are the dominant mid-elevation tree species. 

Subalpine and upper subalpine forests are similar to those in the MT zone. 

The NW zone has extensive areas that are maintained in shrub dominated 

communities by repeated fire. There are also extensive alder (Alnus sinuata) 

shrubfields in wet, seep areas (Habeck 1972). Persistent herb lands are also 

common in these zones. 

Methods 

The methodology used to develop the FARSITE input layers is based on the 

premise that most ecological characteristics, including fuels, can be described from three commonly used ecological descriptors-potential vegetation type (PVT), cover type, and structural stage. Keane and others (199613) 

used this framework to simulate landscape succession at coarse (for example, 

ICBEMP), mid- and fine scales. Many hydrologic, wildlife, fire, and fuels 

characteristics were also mapped for the ICBEMP scientific assessment 

using this approach (Quigley and others 1996). Shao and others (1996) used 

potential vegetation types to refine a cover type classification from satellite 

imagery for a natural reserve in China. 

This methods section will detail the data and procedures used to create 

these three vegetation data layers, and describe how they were then used to 

develop the fuels layers. 

Development of the FARSITE fuels layers was accomplished in two phases 

(draft and final). In the first phase (started in January 1996), we created a 
draft version of the FARSITE data layers by June 1996 because fire managers needed them for the 1996 fire season. This meant we could only use 

historical field data for testing and validation of developed GIs layers. In the 

second phase, we collected additional field data in summer 1996 to refine and 

again test the vegetation and fuels maps. The final set of maps were passed 

to fire managers in spring 1997, with the entire process completed in just over 

1 year. 

Ground-Truth Data 

The extensive field data sets used to modify, test, and refine the developed 

and acquired SBWC spatial data layers came from both historical plot 

information and.ground-truth plots established specifically for this study 

(table 2). Ground-truth data are georeferenced plot information used to 

develop and verify maps and algorithms for this project. These data were 

essential in the construction and refinement of all FARSITE layers prepared 

for this study. Additionally, ground-truth data were integral in assessing 

the accuracy of acquired data layers and the derived heuristic rules bases 

used to construct PVT and fuel model maps (see later sections). They were 

also necessary for understanding complexities of the derived classifications 

as they related to other ecosystem components and processes. 

Historical Plots-Three criteria were used to select the historical plots to 

be included as SBWC ground-truth data. First, each plot needed to be georeferenced to at least a 30 m accuracy and occur in the study area. Second, 

each plot must have had an estimation of fire behavior fuel model, stand 

height, and canopy closure. Crown height and crown bulk density were 

considered optional but desirable measurements. Third, the three base 

vegetation descriptors (PVT, cover type, structural stage) must have been 

evaluated for each plot. Only 1,500 plots satisfactorily met these criteria 

after querying over 100,000 plots from various vegetation data bases including the Northern Region's vegetation data base (Hann and others 1988) 

(table 2). All selected plots were established and sampled using ECODATA 

sampling methodologies (Jensen and others 1993, Keane and others 1990). 

SBWC Ground-Truth Effort-Ground-truth information collected 

for this study was obtained using two hierarchically nested sampling 

Table 2-Data sources used as ground-truth for all developed spatial data layers. 

Name of 

data set Reference Area or zone Number plots Purpose 

GMRS Keane and WC 1 74 Gradient modeling 

others (1 996a) and remote 

sensing study 

SlLC Redmond and WC, NW 325 

others (1 990) 

GRlZ Davis and NW, WC 974. 

Butterfield 

(1 991) 

Ground-truth of 

the SlLC map 

Grizzly bear 

habitat study 

SBWC This study NW, MT 200 plots, Ground-truth 

C 600 validated SBWC 

polygons FARSITE maps 
methodologies. The less intensive sampling method, called polygon validation, was used to quickly assess five major polygon attributes: PVT, cover 

type, structural stage, normal FBFM, and extreme FBFM. This was accomplished by first printing a set of color maps for the entire SBWC study area 

at the same size and scale of the U.S.Geologica1 Survey 7.5 minute quadrangle maps using GIs software. These paper maps contained color-coded 

SILC polygon boundaries, streams, roads, and trails overlaid on 120 m 

contours. Each polygon was coded as to its PVT, cover type, structural stage, 

and both types of FBFM's (normal and extreme). We used the maps to 

navigate to various polygons in the field and then recorded, directly onto the 

maps, the actual (observed) PVT, cover type, structural stage, and fire 

behavior fuel models of located polygons. This information was then entered 

into a data file along with the polygon number for reference in the GIs. We 

validated only those polygons whose locations were absolutely known because they were adjacent to geographic features that were easily recognizable on the map, such as a stream confluence or road junction. 

The second type of ground-truth sampling involved the use of fixed-area 

(0.04 ha circular) plots to extensively sample ecological attributes of the plant 

community delineated by the polygon. These data were not only used to 

validate polygons and quantify stand characteristics, but more important, 

they were used to understand and interpret ecological relationships that 

influence fuel models. Plots were established in a representative portion of 

the polygon, and ecological characteristics were sampled using a modification of the ECODATA methods (Hann and others 1988; Jensen and others 

1993; Keane and others 1990). Site information, general vegetation characteristics, fuels descriptions, and ground cover were measured at each plot 

using the General ECODATA plot form (Mueller-Dombois and Ellensburg 

1974). We modified this form to include fields for crown height, crown bulk 

density, fire effects fuel model (FEFM, page number of fuel photo series; 

see Fischer 1981a, b, c), extreme fire behavior fuel model, and structural 

stage, which were not standard ECODATA fields. Species cover, average 

height, and composition were recorded on the Plant Composition ECODATA 

form. Appendix A presents the plot forms we used for recording SBWC 

ground-truth measurements. (We include them in this book in a format in 

appendix A that we hope might prove useful for the reader.) All plots were 

georeferenced to UTM coordinates with a Global Positioning System (GPS) 

receiver and then differentially corrected using base station data from 

Missoula, MT. These plot locations and their ground-truth attributes (such 

as cover type, FBFM, PVT) were included in the SBWC GIs as point data. 

Spatial Data Layers 

Many spatial data layers were either acquired or created for the development of the eight FARSITE input layers. All layers, including the FARSITE 

input layers, were developed at a 30 m pixel size. A raster layer for the entire 

analysis area has the dimensions of 5,200 by 4,200 pixels and comprises 

over 10,000 km2 (fig. 1). The ARC/INFO and GRASS (USA CERL 1990) GIs 

software packages were used to perform the majority of spatial analysis on 

IBM C10 and SUN Sparc 10 UNIX workstations. The SAS and SYSTAT 

statistical packages were used to perform initial analyses, to compute 

accuracy assessments, and to construct fuel and vegetation keys. The next 

sections describe the data layers that were used or created specifically for 

this project. 
Digital Elevation Model (DEM)-The SBWC elevation layer was 

essential to vegetation mapping, fuel layer development, and FARSITE 

modeling. It was constructed by edge-matching or tiling 113, 7.5 minute 

quadrangle (quad) DEM's, each with a 30 m pixel size. A DEM is a raster 

map with each pixel assigned an elevation value measured above mean sea 

level. Quadrangle DEM's are distinguished as either Level 1 or Level 2, 

depending on method of production. Level 1 DEM's sometimes exhibit a 

horizontal banding pattern, an inherent side-effect of the scanning procedures used in their production (USGS 1987). Level 2 quad DEM's are 

currently produced from Digital Line Graphs and processed for errors so 

they do not contain systematic errors that create the banding problem 

(USGS 1987). Horizontal banding occurred in most Level 1 SBWC DEM 

quads and needed to be removed because it influenced FARSITE simulations. All Level 2 DEM's currently available, about 67 percent of the total 

number of SBWC quads, were ordered from Geometronics Service Center in 

Salt Lake City, UT. The remaining areas were filled using Level 1 quad 

DEM's obtained from the Northern Region and the Nez Perce National 

Forest, which contained the banding. The comprehensive SBWC elevation 

layer was created by merging all DEM quads using a patchwork of Level 1 

and Level 2 sources. Merging or splicing quad DEM's sometimes created 

slivers of missing data along quad edges. These slivers were replaced with 

the average value of surrounding pixels across DEM quads. 

We developed a procedure to correct the horizontal banding in Level 1 

DEM's and to maintain the quality and relief of the Level 2 DEM's (see 

appendix B). The most common method to remove banding is to run a "filter" 

across the layer (Brown and Bara 1994; Stitt 1990; White and others 1995). 

A filter is a smoothing tool that adjusts a pixel value based on the surrounding pixels' values using user-specified weights (Jensen 1986; Lachowski and 

others 1995). We tested several filter sizes and types and concluded an equalweight, directional filter (1 pixel wide by at least 7 pixels high) removed a 

majority of banding error. The height of the filter depended mostly on the 

width of the bands. Since filtering ultimately reduces relief and definition, 

the directional filter was only applied to Level 1 DEM quads. This was 

achieved by running the filter twice over the entire tiled layer, then overlaying the original Level 2 DEM's with the final filtered layer, using the filtered 

tiled layer to fill in areas where no Level 2 DEM's existed. The final steps in 

DEM processing involved filtering the edges between Level 1 and Level 2 

quads, to smooth the transition in elevation from filtered to unfiltered quads 

(appendix B). 

A different problem was encountered in two Level 1 DEM quads along the 

Bitterroot River. These quads did not exhibit any horizontal banding but 

rather appeared to have irregular vertical bands. The area is basically flat 

terrain along the river. The rest of the DEM quads along the Bitterroot River 

were of Level 2 quality and did not exhibit any vertical banding. In addition, 

aspect layers derived from these two DEM quads had a scattered, "salt and 

pepper" appearance caused by small changes in elevation (Stitt 1990). To 

reduce both effects, these two quads were filtered once with a square (5 x 5 

pixel) filter, instead of the directional filter, which would only enhance the 

vertical banding. Out of several filters tested, the 5 x 5 filter seemed to 

produce the best visual results, although the vertical banding was still 

slightly visibl~. These two DEM's were filtered separately, then merged with 

the final layer prior to the edge-smoothing process. 
Slope and aspect maps were generated from the final SBWC DEM layer 

using the slope and aspect functions in ARCIGRID. We created the same 

maps using the r.slope.aspect command in GRASS but found major inconsistencies in the derivation of aspect using this algorithm, so we decided to 

use the ARC/GRID results. We also developed algorithms to compute slope 

and aspect, but these were inferior to ARCIGRID results as well. Slope and 

especially aspect computations are sensitive to minor errors in the DEM 

layer, so it was essential these layers be validated for accuracy. 

SILC Cover Type-The SBWC PVT, cover type, and structural stage data 

layers were partially or entirely derived from the SILC map developed by 

Redmond and Prather (1996). Broad cover type classes, structural stage, 

canopy cover, and other ecological attributes were assigned to mapped 

polygons by Redmond and Prather (1996) following criteria based on 

ECODATA sampling methods (Hann and others 1988; Jensen and others 

1993; Keane and others 1990). A polygon is a delineation of land area 

containing pixels that have similar spectral reflectance across several 

LANDSAT Thematic Mapper (TM) wavelengths (that is, a unique spectral 

class). The SBWC lies entirely within one TM scene with a 30 m pixel size. 

Redmond and Prather (1996) processed these polygons in raster format to a 

minimum polygon size of 2 ha using image processing and GIs software. 

Initial polygon delineations were validated by ground-truthing field crews 

from 1994 to 1996 and these data were used to refine the image classification. 

The field data were also used to assign each polygon over 25 attributes 

including structural stage, canopy closure, and average spectral reflectance 

(Redmond and Prather 1996). These attributes assignments were entered 

into a data base that was linked to the SILC GIs raster layer by polygon 

identification numbers. 

All SILC cover types found in the SBWC study area are presented in 

appendix C along with the reference to the final SBWC cover type categories. 

The final set of structural stage categories are shown in table 3 with some size 

class descriptions. Structural stage categories were created for water, urban, 

Table 3-List of structural stages used in the Selway-Bitterroot 

Wilderness Complex study. 

Code Name Description 

Grasslands 

1 

Shrub 

2 

3 

4 

Tree 

5 

6 

7 

8 

Grasslands - 

Low shrub 

Medium shrub 

Tall shrub 

Seedlinglsapling c5.0 inch d.b.h. 

Pole 5.0-8.9 inch d.b.h. 

Medium 9.0-21.0 inch d.b.h. 

Largelvery large >21.0 inch d.b.h. 

Other 

9 Water - 10 Urban - 11 Agriculture _ 12 Roc Walpinelperennial - 

snowfields 
agriculture, and rock (table 3) to fill in missing values on the map. It is 

important to note that we did not change any polygon boundary delineated 

in the SILC map. All polygons in the vegetation maps (PVT, cover type, 

structural stage) created by this project are the same as the original SILC 

map polygons. We only changed the vegetation attributes assigned to the 

SILC map polygons. We found the polygon delineations agreed quite well 

with community boundaries on the landscape. 

SILC cover type codes are stratified as follows: developed lands, grasslands, shrublands, forests, riparian and wetland areas, water, barren lands, 

alpine meadows, and snow or ice (appendix C). This cover type classification 

was dependent on canopy cover, both absolute and relative. For instance, to 

be classified as forested, a polygon must have at least 15 percent total canopy 

cover oftrees. Further, to be classified as a Douglas-fir cover type, the polygon 

must have at least 66 percent relative cover of Douglas-fir. Details of the 

SILC classifications are presented in Redmond and Prather (1996). We 

imported the classified SILC layer into ARCIGRID and then extracted cover 

types from the comprehensive list of attributes to form a separate layer and 

clipped the new cover type layer to the analysis area. We maintained the 

original cover type codes and categories of the SILC map. 

Ancillary Vector Layers-Many GIs layers describing SBWC geophysical attributes were available for this area from the Grizzly Bear Evaluation 

project based out of the Clearwater National Forest (Davis and Butterfield 

1991). These vector layers included streams, roads, trails, and the Selway 

Bitterroot Wilderness Boundary. Streams, roads, and trails were generated 

at 1:100,000 map scale from USGS digital line graph files. Administrative 

boundaries were digitized at 1:24,000 map scale from USGS 7.5 minute 

quads. We also obtained the layers of ownership boundaries, subwatersheds, 

and biophysical settings from the ICBEMP effort (Quigley and others 1996). 

A raster map of land type associations was acquired from the Northern 

Region's Ecology Program. We generated a 100 m vector contour layer from 

the DEM elevation layer in ARCIINFO. We also created maps that portray 

soil depth and water-holding capacity for the SBWC using methodologies 

presented in White (1996). 

The SBWC analysis area boundary vector layer was created from several 

of the above coverages (fig. 1). Because wildland fire can burn across 

wilderness boundaries, we decided to make the study area boundary at least 

2 km larger than the boundary of the Selway-Bitterroot Wilderness Area. 

However, we did not want to impose a standard buffer width on the entire 

Wilderness Boundary because of the diversity in topography, vegetation, and 

fuels in the adjacent watersheds. So, we used vectors from an assortment of 

GIs layers to form the SBWC study area boundaries. The Bitterroot River 

defines the eastern edge ofthe analysis area. The western edge was restricted 

by the limits of our DEM layer. The rest of the boundary is a compilation of 

vectors from rivers, administrative boundaries, and watersheds. We used 

three GIs layers in this process to ensure a sufficient buffer around the entire 

Selway Bitterroot Wilderness Area for fire modeling purposes. Hopefully, we 

have delineated sufficient land to adequately predict fire growth for any 

prescribed natural fire in this complex. 

Potential Vegetation Type (PVT)-No suitable PVT map was available for this SBWC fuels mapping project. Brown and others (1994) had 

constructed aVhe regimes map for the wilderness area using a topographic 
rulebase, but these regimes did not adequately stratify the landscape to 

reflect major changes in biophysical settings, fuel characteristics and distribution. In addition, a large portion of the landscape (40 percent) was 

classified to only one fire regime type. Reid and others (1995) developed a 

biophysical settings layer for the ICBEMP effort that was later crossreferenced to a coarse scale PVT layer, but the spatial and organizational 

scale of this layer was too coarse to spatially predict fuel models for 

FARSITE. Most other layers that mapped SBWC potential vegetation (for 

example, habitat type maps) did not cover the entire analysis area, or the 

PVT classification was not suitable for fuels predictions. Therefore, we 

decided to create our own PVT map for this FARSITE project. 

The SBWC PVT map was created from geographic and topographic settings 

using a heuristic, rule-based approach. First, a list of midscale PVT's were 

compiled for the SBWC based on the opinions of local ecologists, research 

literature, and available field data (table 4) (Habeck 1972; Pfister and others 

1977). Four GIs layers were used as primary references in the rule-based 

Table 4-List of potential vegetation types (PVT) used in this project. 

PVT code Description Habitat typesa 

1 Developed lands (occur only along the Eastern Front of the No reference 

Bitterroots and the West Fork of the Bitterroot River in Montana) 

2 THPL - Thuja plicata (western redcedar) habitat types All THPL 

3 ABGR - Abies grandis (grand fir) series ABGRIASCA ABGRILIBO 

ABGRKLUN ABGRIXETE 

ABGRISETR 

4 PSME - Pseudotsuga menziesii (Douglas-fir) habitat types, dry All PSME ABGRIPHMA 

ABGR series, and Pinus ponderosa (ponderosa pine-PIPO) All PIP0 ABGRBPBE 

habitat types ABG RIVAG L 

5 Lower subalpine-moist - Abies lasiocarpa (subalpine fir- ABLNALSI ABLNLIBO 

ABLA) series ABLNCACA ABLNMEFE 

ABLNCLUN ABLNOPHO 

ABLAlGATR ABLNSTAM 

Lower subalpine-dry - Abies lasiocarpa (subalpine fir-ABLA) ABLNCARU ABLNVAGL 

series ABLANACA ABLANASC 

ABLNSYAL ABLNXETE 

Upper subalpine-moist - Abies lasiocarpa (subalpine fir- ABLNLUHI-MEFE TSMEIXETE 

ABLA) and Tsuga mertensiana (mountain hemlock-TSME) TSMEILUHI 

series 

Upper subalpine-dry - Abies lasiocarpa (subalpine fir-ABLA) ABLNLUHI-VASC LALY-ABLA 

series, Larix lyallii (subalpine larch-LALY) habitat types, PIAL-ABLA LALY-PIAL 

and Pinus albicaulis (whitebark pine-PIAL) habitat types ABLA-PIAL 

9 Persistent herblands No reference 

10 RocMalpineIperennial snowfields No reference 

11 Water No reference 

aPfister and others (1 977), Cooper and others (1991). 

15 
terrain model-geographic zone, elevation, slope, and aspect. Ancillary data 

layers of average annual precipitation, land type associations (Bailey 1988, 

1995), average annual temperature, watercourses, roads, and trails were 

prepared for the SBWC analysis area but were never used in the PVT terrain 

modeling because it was difficult to match the PVT list to the map classification categories. 

Rules were developed from slope, aspect, and elevation criteria to predict 

PVTfor each zone. These rules were formulated from a March 1996 workshop 

sponsored by the IFSL and attended by area ecologists and scientists. We 

refined these rules based on vegetation plot data collected by past research 

studies and by this project (see "Ground-Truth Data" section). Final sets of 

PVT rules by geographic zone (appendix D) were a result of three extensive 

revisions. These rules were mapped onto a PVT raster layer using GIs 

techniques, and after extensive analysis and revision, a PVT category was 

assigned to each SILC polygon from this raster layer using methods detailed 

below. 

The first draft PVT map delineated only forest PVT's. It was created by 

running the GRASS command r.infer using the rules compiled from the 

March 1996 workshop. We generated a buffer in ARCIGRID around all 

major rivers in the SBWC to delineate streamside western redcedar and 

grand fir PVT's. The width of the buffer was 90 m for narrow stream 

bottoms and 120 m in wide valleys where slopes were less than 30 percent. 

Elevation and aspect constraints were also imposed on the streamside 

buffer. For example, streamside areas with aspects of 315" to 90" (NW to 

NE), elevations less than 1,463 m, and slopes less than 30 percent were 

assigned a western redcedar PVT in the northwestern and west-central 

regions of the SBWC (appendix D). 

The SILC cover type map was used to delineate rock, alpine, and barren 

PVT's because these features could not be predicted from topography and 

geographic zone. This assumes that the satellite image classification of 

nonforest cover types is accurate and adequately portrays nonforest site 

types (Jensen 1986; Redmond and Prather 1996). Persistent herblands on 

the SILC map also defined associated PVT's using topographical constraints 

(appendix D). For instance, the persistent herblands PVT was defined as 

foothill and disturbed grasslands from the SILC map on south aspects 

(120" to 270" azimuths) at elevations less than 2,256 m and slopes greater 

than 50 percent in the northwestern zone. Developed and urban lands were 

considered a separate PVT and were delineated from the ownership layer 

produced for the ICBEMP (Quigley and others 1996) scientific assessment. 

We did this because it was difficult to determine habitat type on lands that 

are intensively managed such as agricultural fields and urban areas. 

The PVT map was modified to spatially correspond (overlay) directly with 

the polygons in the SILC map because all input layers needed to be spatially 

congruent for FARSITE simulations. We accomplished this by using the 

zonalmajority function in ARCIGRID, which assigns the modal PVT value to 

the SILC delineated polygon. We assumed that the delineated SILC polygon 

boundaries more accuratelyrepresented coarse and fine scale changes across 

the landscape and therefore would be the preferred layer to use as the 

polygon delineator. However, many SILC polygons exceeded the topographic 

bounds of some PVT's. We attempted to divide these large polygons along the 

topographic limits (elevation range, for example) but this created many 
slivers and small polygons that were well below the 2 ha minimum mapping 

threshold of the SILC map. As a result, we kept the integrity of the original 

SILC polygons and allowed large polygons to traverse PVT limits in the final 

map by assigning the predominant PVT to the polygon attribute data base. 

We improved the accuracy of the PVT layer by intensively examining the 

1500-plus reference ECODATA plots for topographical characteristics and 

for PVT and cover type combinations to detect major misclassifications of 

PVT, cover type, or both. Western redcedar and grand fir PVT's were 

especially dificult to delineate from topographic criteria because our midscale 

terrain model often did not detect fine scale PVT boundaries that occur in 

small microsites often occupied by cedar and grand fir. Based on plot data, 

80 percent of observed grand fir cover types occurred within mapped grand 

fir PVT's, and 57 percent of the observed redcedar cover types occurred 

within redcedar PVT's. We reclassified areas having grand fir cover types to 

the grand fir PVT and western redcedar cover types to the western redcedar 

PVT because we felt the SILC map more accurately represented these types 

based on ground-truth data. The western redcedadgrand fir cover type 

(appendix C) was similarly reassigned to its respective PVT and cover type 

using a cover type-aspect combination, with all but the most northerly and 

flat aspects being reclassified to grand fir cover type and grand fir PVT. 

We created another PVT raster layer independent of the terrain-modeled 

PVT layer using the statistical analysis techniques of discriminant analysis 

in the SAS software package. This statistical PVT raster map was created so 

it could be compared to the terrain-modeled PVT map to evaluate the two 

methodologies and perhaps fill in some areas that were inappropriately 

mapped using the terrain modeled approach. This was accomplished by 

creating a data file from the plot data by extracting field-measured data 

variables for which we either had a SBWC GIs map or could create a GIs map 

using modeling techniques. These data included PVT, geographic zone, 

elevation, aspect, slope, effective soil depth, soil water-holding capacity, 

slope curvature, topographic slope index, and solar radiation index (based on 

shading) (Lapen and Martz 1993). The PVT was used as the dependent 

(predicted) variable and the remaining were considered independent (predictor) variables. Keane and others (1996a) use environmental gradients such 

as those mentioned here to predict many ecosystem variables including PVT. 

Many independent variables included in the discriminant analysis were 

modeled from the DEM layer and the STATSGO soil layer (SCS 1991) using 

methods discussed in Thornton and White (1996). Soil depth was taken 

directly from STATSGO and adjusted using slope and upslope drainage. 

Water-holding capacity was estimated from the STATSGO layer using soil 

textures (percent clay, sand, and silt) to compute soil pressure and volume 

characteristics (Cosby and others 1984) that can then be used to calculate 

maximum soil water content. Topographic slope index is based on potential 

saturation deficit, which is computed from upslope drainage to a down slope 

point and the topographic slope of that point (Thornton and White 1996). 

Solar radiation index estimates shading potential and is computed using the 

sun angle from a midsummer's day as a reference point. 

SBWC Cover Type-A SBWC cover type map was created from the 

combination of the SILC cover type map and the PVT map. Many polygons 

in the SILC map had cover type assignments that did not agree with the 

classified PVT of that polygon. For instance, there were some polygons in the 

SILC map that were assigned a cover type of western redcedar but were 
classified as a Douglas-fir PVT. It is highly unlikely that the mesic western 

redcedar would flourish on a dry Douglas-fir site because of limited moisture 

(Pfister and others 1977). All incompatible polygon combinations were 

reassigned a new cover type on a case-by-case basis. Cover types were 

changed only if the polygon was assigned a PVT that was mapped with a high 

degree of confidence, and vice versa. The degree of confidence we had in each 

layer depended on the zone, PVT, cover type, topographic setting, and 

ecological knowledge of the SBWC. About 10 percent of the polygons were 

modified in this phase. 

The draft PVT raster layer was compared to the SILC map using the 

GRASS command r.report that produces a summary of polygon assignments by zone, cover type and PVT across the two maps. SILC cover types 

that were not compatible with PVT assignments were reclassified to a cover 

type more likely to occur in the PVT based on ecological relationships of plant 

species as defined by the ground-truth data and the habitat type constancy 

tables in Pfister and others (1977), Cooper and others (1991), and Habeck 

(1972). An example of such a reclassification would be a mixed high elevation 

subalpine forest cover type in the low elevation Douglas-fir (Pseudotsuga 

menziesii) PVT. This cover type would be reclassified to the mixed xeric forest 

cover type (see appendix D) based on an evaluation of the plot data and 

literature. The SILC map was refined more often than the PVT map because 

it was developed for the entire Northern Region and its categories were too 

general for the smaller SBWC geographic region. We copied all other polygon 

attributes from the SILC map to the SBWC cover type map. The final map 

created from SILC modifications was called the SBWC cover type map. 

The SBWC cover type map was also modified so that polygons assigned a 

cloud or cloud/shadow cover type were reclassified to the appropriate vegetation cover type. We used the ARCIGRID function nibble (nearest neighbor 

algorithm) to assign a value to a polygon that has missing or erroneous data 

based on surrounding polygon values. Many missing data in the SILC map 

were filled using the nibble command to create the SBWC cover type maps. 

This command uses Euclidean distance to assign the value of the nearest 

polygon. 

Structural Stage-The structural stage map was created from the SILC 

map using the structural stage attribute assigned to each polygon by 

Redmond and Prather (1996). Some structural stage attributes were modified to account for impossible PVT, cover type, structural stage combinations, 

such as pole size trees occurring on a grassland, but these changes were few 

(less than 5 percent). We also added structural stage values for grasslands, 

water, urban, agriculture, rock, and snow because the original SILC map did 

not have structural stages for these types. The initial SBWC structural stage 

map was also modified by performing nibble function with the new cover type 

assignments from the SILC map to fill in cloud and shadow polygons. 

Fire Behavior Fuel Models (FBFM)-The three base vegetation layers 

(PVT, SBWC cover type, and SBWC structural stage) were overlaid to create 

a worksheet of all possible combinations to assign FBFM's (appendix E). 

Various ecologists and fire managers assigned the FBFM's to each combination of PVT, cover type, structural stage for normal and severe fire conditions 

(Anderson 1982). They also estimated the average crown height (height to 

live crown base) and a fire effects fuel model to each combination. Crown 

height was estjmated by the managers rather than taken from the groundtruth data computations because it is more an index of the transition of 
surface fires to the crown and may be better characterized by qualitative 

assessment than a summary of tree structure data. The fire effects model is 

the page number of the fuel photo series (Fischer 1981a,b,c) that best depicts 

the fuel loadings of a particular vegetation combination. 

We built the fuel model layers by first creating a GIs layer that contained 

PVT, cover type, and structural stage assignments to each polygon using 

overlay techniques. We applied a simple map algebra equation in ARCIGRID 

to accomplish this: 

VEGMAP = (PVT * 1,000,000) + (COVER * 100) + (STRUCTURE) 

where VEGMAP is the value of a pixel in the final map that portrays all 

combinations, PVT is the identification number of a pixel in the PVT map 

(table 4), COVER is the cover type number of a pixel in the cover type map 

(appendix C), and STRUCTURE is the pixel value of structural stage number 

assignment (appendix C). This equation resulted in values such as 1420605 

(1 = PVT Developed Lands, 4206 = COVER TYPE Pinus ponderosa, and 05 

= STRUCTURAL STAGE SeedlingISapling). We then built a reclassification 

table that cross-references the values in this new map to the fuel model 

assignment mentioned above. This table was then linked to the VEGMAP 

layer to produce fuel model layers for normal and severe conditions. For 

example, the value 1420605 was assigned a normal FBFM 5 based on the 

worksheet assignments for normal conditions made by the fire managers 

(appendix F). We changed some FBFM assignments made by fire managers 

because of discrepancies with field data. A complete list of FBFM assignments to PVT, cover type, structural stage combinations are presented in 

appendix F. 

Canopy Cover-The canopy cover layer was also taken from the original 

SILC polygon attributes data base, which had a canopy closure code 

category. Canopy cover or closure is defined as the vertically projected 

canopy cover (percent) of all tall shrub and tree species. SILC canopy closure 

(percent) values were categorized, in accordance with ECODATA sampling 

methods, into the four classes, presented in table 5. However, the SILC 

maps had assignments of canopy cover to shrub and forested cover types, 

but the FARSITE program only accepts canopy cover estimates for forested 

cover types. Therefore, all shrublands were assigned a canopy cover of zero. 

In addition, the ranges of canopy cover classes in the SILC layer did not 

conform to those used by FARSITE. Without the actual percent canopy 

closure, we could not create a perfect fit; therefore, we had to adjust SILC 

canopy code values to approximate those in FARSITE. Both of the above 

modifications were done in ARC/GRID by using the combine function on the 

Table 5-Canopy cover classes present in the SILC 

map and used by FARSITE. 

SlLC FARSITE 

Cover class cover ranges cover ranges 

- - - - - - - - - - Percent - - - - - - - - - 

0 0 to 9 1 to 20 

1 10 to39 21 to 50 

2 40 to 69 51 to 80 

vL 3 70 to 100 81 to 100 
cover type and canopy cover layers, and then reassigning new canopy cover 

estimates based on cover type using the reclass function. 

Stand Height-Stand height (ft) was generated using the results of a 

project conducted by Ottmar and others (1994) as a part of the midscale 

analysis for the ICBEMP effort (Quigley and others 1996). They compiled 

data from the Northern Region ECODATA data base (Jensen and others 

1993) to compute total crown biomass by cover type and structural stage. In 

the process, Ottmar and others (1994) generated average values of stand 

height and other stand characteristics for various cover type and structural 

stage combinations. These values were entered into a data base structured 

by ICBEMP cover types and structural stages. We linked our SBWC cover 

types and structural stages to the most suitable counterpart in the ICBEMP 

data base on a case-by-case basis, and then we assigned our polygons the 

corresponding stand height from the linked data base. The layer was then 

created by building a classification table in ARCIGRID. We adjusted some 

stand heights based on available field data summaries. 

Crown Height-Crown height (ft) was assigned to PVT, cover type, 

structural stage combinations by the attendees of the April 1996 workshop 

(appendix E). Crown height is defined as an effective height to live crown base 

for the stand, and it represents the flame height necessary to enable the 

transition from surface to crown fire, accounting for the effects ofladder fuels. 

This layer was created using simple reclassification tables in ARCIGRID, 

separately assigning the values given to each PVT, cover type, structural 

stage combination by workshop attendees across each geographic zone. 

Crown Bulk Density-Crown bulk density values (kg m-3) were assigned 

by cover type and structural stage and then reduced by canopy cover class 

based on the crown bulk density table shown in appendix G. Pole, medium, 

and large tree stands were assigned a crown bulk density by cover type 

for high canopy cover values. This value was reduced proportionally for 

medium and low canopy cover classes (30 and 60 percent, respectively). 

Seedlinglsapling stands were assigned a crown bulk density for the low cover 

class and increased by 15 percent for the medium cover class. There were no 

seedlinglsapling stands with high canopy cover. The final bulk density 

assignments were made from polygon attributes using a look-up table that 

linked the canopy cover, cover type, structural stage values of a polygon to the 

bulk density values (appendix G). These assignments were multipled by 100 

and converted to integers to save computer disk space. 

Accuracy Assessment 

Testing, validation, and verification of existing and developed data layers 

involved overlaying the layer in question with the plot layer where each plot 

was georeferenced as a point. Each point referenced a GIs data base that 

contained the sampled field values. The point data were compared to the 

values of the polygon where it resided, and reports were created that 

compared point values with corresponding map values. We designed several 

GIs routines to display and report the overlap between the plot layer and the 

layer in question. These reports were summarized into contingency tables 

and into data files used by other programs written specifically for this project. 

Accuracy assessments procedures differed by the type of map being 

compared with ground data. Categorical GIs maps are those maps that 
portray discrete, nominal classification categories such as cover type and 

structural stage layers. Continuous maps have polygon values that are 

measured using continuous data scales such as elevation (m) and slope 

(percent). 

We assessed the accuracy of all categorical SBWC maps using the methodologies presented in Congalton (1991) where contingency tables are constructed comparing the reference (ground-truth) data values to the classified 

(maphalues. In addition, we used the same process to compare ground-truth 

data to the FBFM assignments from workshop participants (appendix E), 

and to the PVT assignments by local scientists and ecologists (appendix B). 

Omission and commission errors were computed for each map category, and 

a final accuracy was estimated using the KHAT statistic (Congalton 1991). 

The KHAT statistic adjusts overall accuracy to account for the uneven 

distribution of plot data across classification categories. 

The accuracy of continuous SBWC maps such as elevation, aspect, and 

slope was computed using a regression approach. The observed values at 

each polygon (plot data) were regressed with the predicted values from the 

maps using a linear, least-squares regression (Sokal and Rohlf 1969). Three 

regression statistics were recorded. The coefficient of determination (R2) 

provides an index on how tightly correlated the predicted data are to the 

observed data. Values of R2 close to 1.0 indicate the data are perfectly 

correlated (accurate), whereas values near zero mean the data are totally 

unrelated or inaccurate. The slope of the regression line (alpha) can be used 

to evaluate trends in the accuracy of predictions. Slopes greater than 1.0 

usually indicate map overestimation when observed values (plot data) are 

high and underestimation when observedvalues are low. The opposite is true 

when slopes are less than 1.0 but greater than zero. Ideally, the slope should 

be 1.0 if the observed values perfectly match the predicted values. Thirdly, 

the intercept of the regression line (beta) is used to evaluate general 

overestimation (beta greater than 0.0) or underestimation (beta less than 

0.0) of the spatial model (map) predictions to the reference (plot) data. 

Another statistic called the mean error (ME) was also computed to quantify 

the error of map predictions. It is defined as: 

where 0 is the observed (plot) value, P is the predicted (map) value for that 

plot, and N is the number of plots. This statistic is useful for evaluating the 

magnitude of accuracy error. 

An assessment of aspect accuracy presented some special problems because of its circular scale. Northern aspects are especially difficult to 

estimate because they traverse the beginning and end of the azimuth scale. 

A plot with a measured aspect of 10 degrees and a predicted aspect of 350 

degrees are only 20 degrees different ecologically but 340 degrees numerically. We used the following transformation to scale aspects along a more 

ecologically oriented gradient. 

ASP = 1 + COS (ASPECT) 
Where ASP is the ecological representation of aspect (number between 0 and 

2), COS is the cosine function, and ASPECT is the aspect in degrees azimuth 

(0 to 360). This assumes that equal weight is given to east and west aspects. 

Results 

Three people completed this project in approximately 1 year. Most of the 

effort was in the preparation of the GIs data layers used for analysis (30 

percent), summarizing workshop results into heuristic rule sets and look-up 

tables (25 percent), and collecting and analyzing ground-truth information 

(20 percent). The remaining time was spent creating the FARSITE input 

maps, checking all data layers for inconsistencies, refining vegetation and 

fuels maps, and quality control. The entire project cost around $100,000 or 

about 9 cents per ha (4 cents per acre), which includes software and hardware 

maintenance, salaries, transportation, and supplies. 

The SBWC study area encompasses over 1.15 million ha across three 

National Forests and two States (fig. 1). The distribution of area by PVT and 

cover type is presented in table 6 where the lower subalpine forests of mixed 

and pure lodgepole pine and subalpine fir compose the majority of the SBWC 

landscape (over 34 percent). Douglas-fir, shrublands, and mixed mesic 

forests are next in importance with 10, 11, and 14 percent coverage, respectively. Rockland also is predominantly featured in the SBWC (5 percent) 

from the SILC polygons. Structurally, the majority of the landscape is in pole 

and medium tree size classes (20 and 30 percent coverage) while old growth 

or large tree forests account for only 5 percent of the land area (table 7). 

Shrubs are tall in lower elevation sites and somewhat low in high elevation 

stands. Grand fir and western redcedar cover type stands are predominately 

old growth (table 7). 

Timber FBFM's dominate the SBWC landscape under normal conditions 

(40 percent cover) with closed timber low (FBFM 8) and high (FBFM 10) 

intensity fuel models the most common (table 8). Low elevation, moist timber 

sites tended to have FBFM 10, while the high, dry sites often keyed to the low 

intensity FBFM 8 because of the limited amount of fuels. The closed timber 

FBFM 8 and shrub FBFM 5 models accounted for over 80 percent of the 

change from normal to extreme weather conditions (tables 8 and 9). The 

shrub model (FBFM 5) could shift to all but FBFM 9 depending on the cover 

type and PVT. All stands classified to the grass FBFM 1 stayed the same from 

normal to extreme conditions. 

Development of a PVT map using discriminant analysis provided a good 

reference to the terrain-modeled PVT map. We used a stepwise discriminant 

analysis to create discriminant functions for the entire SBWC and for each 

zone to produce the final statistically generated PVT map. Many independent variables were evaluated for inclusion in the final discriminant functions (table lo), but only a few seemed to explain the majority of variation. 

The best statistical PVT map was created using the variables of elevation, 

aspect, and slope (table ll), which closely matches the variables used to 

create the terrain PVT map. 

We made many refinements to maps and rules after the draft phase of this 

study because of the additional field data we collected over summer 1996. 

Based on these data, we merged some SILC cover type categories that were 

difficult to discriminate on the ground and to map in the GIs. These merged 

cover types did not seem to have a high fidelity to any one fuel model. Second, 
Table 6-Distribution of area (hectares) by potential vegetation type and cover type. 

Potential vegetation type 

Lower Lower Upper Upper 

Developed Western Douglas- subalpine subalpine subalpine subalpine Persistent 

Cover type lands redcedar Grand fir fir moist dry moist dry herblands 

Urban 

Agriculture 

Grasslands 

Montane parkland1 

subalpine meadow 

Warm mesic shrubland 

Cold mesic shrubland 

Broadleaf forest 

Englemann spruce 

Lodgepole pine 

Ponderosa pine 

Grand fir 

Subalpine fir 

Western redcedar 

Douglas-fir 

Western larch 

Mixed alpine forest 

Mixed subalpine forest 

Mixed mesic forest 

Mixed xeric forest 

Douglas-firllodgepole pine 

Douglas-firlgrand fir 

Western redcedarlgrand fir 

Western larch/lodgepole pine 

Western larch/Douglas-fir 

Standing burnt and dead timber 

Water 

Exposed rock 

Mixed barren land 

Shoreline and stream gravel bars 

Alpine meadow 

Snow or ice 

Totals 
Table 7-Distribution of area (hectares) by cover type and size class. 

Size class 

Cover type 

Medium Tall Seedling1 Pole Medium Largelvery 

Herbland Low shrub shrub shrub sapling tree tree large tree Water Urban 

Urban 202 

Agriculture 

Grasslands 28,169 

Montane parkland1 

subalpine meadow 63,243 

Warm mesic shrubland 

Cold mesic shrubland 

Broadleaf forest 

Englemann spruce 

Lodgepole pine 

Ponderosa pine 

Grand fir 

Subalpine fir 

Western redcedar 

N Douglas-fir 

P Western larch 

Mixed alpine forest 

Mixed subalpine forest 

Mixed mesic forest 

Mixed xeric forest 

Douglas-firllodge pole pine 

Douglas-firlgrand fir 

Western redcedarlgrand fir 

Western larchllodgepole pine 

Western larch/Douglas-fir 

Standing burnt and dead timber 802 

Water 

Exposed rock 

Mixed barren land 

Shoreline and stream gravel bars 

Alpine meadow 

Snow or ice 

Totals 92,214 24,518 15,385 43,197 94,006 199,022 524,430 89,596 3,181 202 
Table 8-Spatial distribution (hectares) of potential vegetation type by normal and severe fire behavior fuel model. 

Potential vegetation type 

Fire Lower Lower Upper Upper 

Behavior Developed Western Grand Douglas- subalpine subalpine subalpine subalpine Persistent RocW 

Model lands redcedar fir fir : moist dry moist dry herblands alpine Water 

Normal 

conditions 

1 

2 

5 

8 

9 

10 

98 

99 

Severe 

conditions 

1 

2 

5 

6 

8 

9 

10 

98 

99 

Table 9--Change in Fire Behavior Model from normal to severe conditions (hectares). 

Normal Severe conditions 

conditions 1 2 5 6 8 9 10 98 99 

Table 10-Variables used to predict potential vegetation type for the discriminant analysis with 

associated partial correlation coefficient (R-square) and the F statistics to assess the 

relative importance of each variable in predicting potential vegetation type. 

Variable Partial R-square F statistic Prob >F 

Elevation (m) 0.55 

Slope (percent) .09 

Waterholding capacitya .06 

Topographic slope indexa .05 

Shading indexa .03 

Soil depth index (m)" .02 

Aspect (degrees) .02 - -- 

"These indices were created from routines discussed by Thornton and White (1 996). 

I 
Table 11-Overall and KHAT accuracy (percent correct) of categorical GIs layers used in the Selway-Bitterroot Wilderness Complex 

fuels mapping study. 

Draft version--June1 996 

Historical plots 1996 plots 

Categorical Overall KHAT Overall KHAT 

map accuracy accuracy accuracy accuracy 

PVTa - Terrian model - - - 

rulebase 

PVTb - Terrain model 41 3 1 47 37 

raster layer 

PVTc - Terrain model 40 30 42 3 1 

polygon layer 

PVTd - Terrain model 46 35 49 36 

polygons excluding rock 

PVTe - Discriminant - - - - 

analysis raster layer 

PVTf - Discriminant - - - - analysis polygon layer 

SlLC cover type 

Sl LC structural stage 

SBWC cover type 

SBWC structural stage 

Normal FBFM - rulebase 

Normal FBFM layer 

Severe FBFM layer 

Canopy coveri 

Final version-March 1997 

Historical plots 1996 plots 

Overall KHAT Overall KHAT 

accuracy accuracy accuracy accuracy 

47 37 53 45 

"Validation of the rulesets used to create the PVT raster map. 

bAccuracy of PVT raster layer created directly from the rulebase and 30 m raster DEM data. 

cPolygon PVT layer created by assigning PVT to SlLC polygons from the PVT raster layer. 

dPolygon PVT layer with only forested PVT's mapped from terrain model. PVT's mapped from SlLC map are excluded. 

"PVT raster layer created from discriminant analysis. 

'PVT polygon layer created by assigning PVT to SlLC polygons from PVT raster map generated from discriminant analysis. 

glncludes 1996 ECODATA plot and map validation polygon data. 

hGRIZ Plots not included in this accuracy because of incompatible structural stages. 

'Canopy cover was classified into three categories in SlLC map - 0 to 30 percent, 30 to 70 percent, 70 to 100 percent. 

the topographic criteria designed in the original workshop were revised 

based on a thorough statistical analysis of the new and historical plot data. 

We graphed frequency distributions of the topographic variables for each 

PVT by zone and determined ranges of elevation, aspect, and slope that best 

fit the data. We also intensively examined the field data to determine if 

any additional site variables could improve our predictions of PVT, and we 

found none. Last, some PVT categories were merged because an analysis of 

the ground-truth data showed there were no significant differences between the categories. For example, the original PVT workshop list included 

an alder shrub field PVT, but after analyzing the field data, we found no 

evidence that this type could be successfully mapped in the GIs because our 

topographical criteria were too coarse for accurate spatial delineation of the 

alder PVT, and site factors other than topography were more important in 

alder shrub field development. 

Accuracy Assessment 

An extensive accuracy assessment was completed for all derived and 

existing FARSITE data layers using the ground-truth information discussed 

in the "Methods" section. Most ground-truth plots and polygons were along 
major travel routes (roads or trails) because of the inaccessibility and 

remoteness of the SBWC (fig. 2). The SBWC ground-truth data base included 

over 1,500 historical plots and 767 plots collected from this effort. We 

stratified our accuracy assessments by four categories-phase of study (draft 

or final), ground-truth data source (historical or 1996 plots), type of accuracy 

analysis (categorical or continuous), and accuracy measure (overall and 

KHAT). 

Data Layer Validation & Plot Locations 

Selwzy Bitterroot Wilderness Complex 

LEGEND 

Volidatcd Polygons 

1906 Ground Truth Plo~s A 

PI7A9% P~ou: A 

ID/MT State Line .. .. . ..... .. ... 

Major Rivers -- 

Figure 2-Ground-truthed plots and polygons used in this study. This includes 

historical plots, plots established during 1996, and validated polygons during 

this effort. 
The first phase of the study released a draft of the fuels data layers in 

early summer 1996 for immediate use by the National Forest System 

prescribed fire program. The second and final version of the data layers was 

released late winter 1997. Accuracies were stratified by data source because 

we suspected some historical data were inaccurate and probably not appropriate for this study. We extensively used the 1996 SBWC data set to create 

the final version of fuels and vegetation GIs layers. We also stratified our 

assessment by two types of accuracies-overall and KHAT. Overall accuracy 

is simply the percent correct classification of ground-truth data (reference) 

to predicted polygon attributes (classified). The KHAT accuracy adjusts 

the overall accuracy to account for the uneven distribution of ground-truth 

data across map categories (Congalton 1991). 

Contingency tables were prepared for every categorical map used in this 

project to evaluate and refine map components. However, only tables for the 

final PVT and structural stage maps are presented in appendix H because of 

space limitations. The cover type contingency table was not included in 

appendix H because of its large size (over 25 map categories). Once all 

accuracy assessments were complete, we refined the polygon attributes to 

reflect actual ground-truth information to create maps that totally agreed 

with the ground-truth data set. Overall, the ground-truth plots were NOT 

distributed across most PVT and cover type categories at the same 

proportion as these attributes are spatially distributed on the landscape 

(appendix H and table 6). Additionally, we did not have adequate distribution plots across many cover type categories and geographic regions, and 

many remote portions of the SBWC, such as the west-central zone, were not 

visited during any of the sampling efforts (fig. 2). 

A summary of the overall and KHAT accuracy for all SBWC categorical 

maps is shown in table 11 for the first (draft) and second (final) project 

phases. Overall, the revision of the draft layers with the additional plot data 

increased accuracy over 10 percent. Historical plot data consistently produced approximately 5 to 15 percent lower map accuracies as compared to 

ground-truth data specifically collected for this project. The modification of 

SILC cover types based on PVT map values increased SBWC cover type map 

accuracies by about 10 percent. There was a minor loss of about 10 percent 

accuracy when PVT and FBFM rulebases were spatially mapped into the 

associated GIs layers (table 11). Error rates for the PVT map created using 

discriminant analysis are somewhat higher than the errors in the terrainbased PVT map (52 versus 60 percent accurate). There was generally a loss 

of 5 percent accuracy when raster maps were converted to polygon maps, 

except for the 1996 plot data in the final version. Accuracy of the terrainmodeled PVT map increased by 5 percent (60 to 65 percent) when SILC 

polygons classified as rock were removed from the accuracy assessment 

(table 11). 

Continuous SBWC GIs map accuracies were computed using the regression approach (table 12). All topographic layers had errors inherent in map 

creation, including the primary layer of elevation (DEM). Layers depicting 

FARSITE stand and crown characteristics had errors well within the 

acceptable margin of 20 percent (table 12). Predicted crown height had a 

poor correlation to plot data because of the way it was assessed. Crown 

height was estimated by fire managers as an index of crown fire transition 

potential from\PVT, cover type, and structural stage (appendix E). Field 
Table 12-Accuracy assessment results for Selway-Bitterroot Wilderness Complex continuous GIs layers using regression 

analysis. 

Draft revision Final 

Mean Mean 

n R2 Slope Intercept error R2 Slope Intercept error 

Elevation 

(m) 1,622 - - - - 0.98 0.99 18.03 4.82 

Aspect 

(degrees) 1,614 - - - - .54 .68 76.0 -1.35 

Slope 

(percent) 1,600 - - - - .56 .67 9.58 2.43 

Stand height 

(m) 655 0.21 0.43 7.82 0.59 .21 .43 7.74 .65 

Crown 

height (m) 1 98 .03 .06 1.37 1.03 .01 .05 1.59 .85 

Crown bulk 

density (kg m-3) 198 .30 .55 .063 -.006 .28 -50 .065 -.003 

crews estimated crown height directly from stand structure on the plot, not 

as an index of the vertical fuel ladder. Nearly all errors were positive, and 

regression slopes were below 1.0, indicating maps were underpredicting 

crown and stand characteristics. Crown bulk density has a negative error 

because of the large intercept value (.065 kg m3). We increased the aspect 

R2 by about 0.2 when we performed the accuracy assessment using the 

aspect transformation mentioned in the "Methods" section rather than using 

the raw aspect degree values (table 12). The slope of the line also increased 

by 35 percent with the transformation. 

Final GIs and FARSITE Input Layers 

The final FARSITE input layers were exported from the SBWC GIs 

into FARSITE landscape format files by individual USGS quads and then 

written onto a compact disk (CD). This disk was formatted using a directory 

structure recommended by Finney (1995) where input data layers were 

stored by 7.5 minute quad to improve FARSITE simulation and display 

time. Included on the disk were the eight FARSITE input layers (DEM, slope, 

aspect, normal FBFM, stand height, crown height, crown cover, and crown 

bulk density) and several ancillary data layers including extreme FBFM, 

streams, roads, trails, elevation contours, and wilderness boundary. Copies 

of the final revision were sent to all the SBWC fire managers for use on 

laptop personal computers. An electronic version of this document and a 

file containing data storage details were also included on the CD. 

The entire SBWC GIs (all spatial data layers) created for this project was 

also made available to the personnel at each National Forest within the 

study area on the CD and on the computer network. This spatial data base 

contains all vegetation layers (PVT, cover type, structural stage) and the 

FARSITE layers mentioned above in ARC/INFO format. Also included in the 

GIs are the cartographic features of ownership boundaries, transportation 

routes, streams, rivers, soils, and weather. In addition, the GIs data base 

that links ecological data to mapped polygons contained the assignments of 

FEFM's to polygons so that fire effects can be calculated. " 
Discussion 

Data Layer Development 

The primary limitation of this project was using cover type and structural 

stage maps that were inaccurate and not developed in concert with the 

PVT map. This resulted in many inconsistencies among the three maps. 

The assignment of PVT to the SILC polygon attributes from the terrainmodeled PVT raster layer proved problematic. Many polygons (about 15 

percent) traversed the topographic criteria used to create the raster PVT 

layer, and when these polygons were split along the topographic limits, 

over 500,000 small slivers (smaller than the minimum mapping specification) were created. This compromised map integrity. We tried to merge the 

slivers in the surrounding larger polygons, but this required over 4 weeks of 

computer time and abundant computer resources. Conversely, we accepted 

a great deal of error in the final PVT map when we allowed original polygon 

boundaries to remain intact and cross the topographic thresholds. In fact, 

the overall accuracy of the PVT rulebase was 47 percent, while the 

accuracy of the draft PVT map created from polygons was less than 42 

percent (table 11). This problem can be easily be remedied if the PVT and 

cover type maps are developed together to ensure polygons remain above 

minimum map sizes and do not cross topographic limits. In fact, a list of 

possible cover types should be specified for each PVT to avoid ecologically 

impossible combinations such as western redcedar occurring on a Douglasfir PVT. Polygon boundaries should not exceed PVT topographic constraints, and polygon cover types should be constrained by modeled PVT. 

The most important GIs layer used in this project was the SBWC cover type 

map. Unfortunately, this layer was the most inaccurate for several reasons 

(table 11). First, there were many instances of confusing spectral signatures 

in the classified imagery of the base SILC map. For instance, the SILC map 

classified many spruce communities in the Montana zone to Douglas-fir 

cover types, probably because the two cover types have similar spectral 

reflectances. As a result, the fire behavior fuel models were often inaccurate 

because the cover type, structural stage, or both were wrong. Second, over 

25 cover type categories described vegetation dominance on the SILC map. 

When classification units increase, the accuracy of spatial data layers 

constructed from imagery classifications often decreases because of inadequate ground sampling. Third, the SILC cover type map was developed 

for use at a slightly coarser scale than the scale required by the FARSITE 

model. The SILC map was intended to depict midscale differences in 

vegetation across large land areas that are sometimes in excess of 2 million 

ha. As a result, the cover type classification categories were sometimes 

difficult to identify on the plot because the sampling was done at a stand level. 

PVT Map-The terrain-modeling approach for producing a midscale PVT 

data layer proved somewhat successful for this project. The PVT topographic criteria were composed quickly (less than 6 hours) with an acceptable accuracy (60 to 65 percent) (table 11). The topographic rulebase was 

easily coded into the GIs to create the final PVT raster layer. Terrain 

modeling is recommended when ground-truth data are scarce and there is a 

reliable source of expert knowledge for the land area in question. The design 

of PVT categories is perhaps the most important part of PVT terrain 

modeling. These categories must effectively discriminate a set of ecosystem 

properties and site conditions for a specific objective (fuel mapping, for 
example). They must also have characteristics that can be quantitatively 

described from existing GIs data layers and from field data. Category design 

must also match the scale of application and the resolution of associated GIs 

layers. 

There was no significant advantage in developing the PVT layer using a 

statistical approach rather than a terrain-modeling approach for this 

project. The PVT map created using discriminant analysis was marginally 

less accurate (about 5 percent) than the terrain-based PVT map, mainly 

because of the limited and incomplete ground-truth data base (table 11). 

Data used to create the discriminant functions were not evenly distributed 

across all PVT categories, and the data set did not include process-based 

variables that directly influence site conditions such as weather (radiation, 

temperature, precipitation), soils (texture, nutrient availability), and productivity. A discriminant PVT mapping approach is recommended when 

the ground-truth data set is extensive and comprehensive, and when there 

are accurate GIs data layers that match the independent variables in the 

data set. The PVT map created from discriminant analysis did have some 

unexpected and beneficial characteristics. First, fine-scale PVT delineations 

along riparian corridors and other linear features were mapped successfully 

using this statistical procedure. Also, the discriminant functions predicted 

PVT values for every map pixel with no "holes" in the data layer. However, 

the discriminant model did not accurately predict the distribution of the 

upland PVT's, which composed a majority of the SBWC landscape. Interestingly, both PVT polygon maps looked quite similar after their raster 

originals were merged with the SILC polygon layer. 

Fuels Maps-Overall, the workshop approach for assigning fire behavior 

fuel models to vegetation attributes was successful. Many models assigned 

by the workshop participants tended to be representative of the FBFM 

sampled in the field with an overall accuracy of 64 percent (table 11). In 

several instances, workshop FBFM assignments needed additional explanations before we went to the field to verify them. This was especially true for 

stands of ponderosa pine that had dense understories of Douglas-fir. The 

workshop participants assigned FBFM 10 to many of these cover types 

because of the high amount of conifer regeneration, while field crews were 

assessing FBFM 8 at the stands because of the sparce woody fuel on the 

ground. Determination of the FBFM by fire managers on the ground is 

subjective because it relies on a comprehensive knowledge of expected fire 

behavior across a wide range of weather and fuel conditions. Therefore, a 

great deal of discrepancy in FBFM estimates often emerges between fire 

personnel. However, the fire managers involved with this project seemed to 

agree on most fuel model assignments for each PVT, cover type, structural 

stage combination. This was also true for the ecologist's and scientist's 

design of PVT topographic criteria (table 11) although that rulebase had a 

substantially lower accuracy (53 percent). It was apparent that many of 

these resource specialists knew a great deal about fuel and ecological 

relationships in this area at a meso-scale level and their knowledge could be 

somewhat captured in a GIs layer using this approach. 

Map Accuracy 

Of the many sources of error in map design and development, probably 

the most important were spatial and organizational scale inconsistencies. 

Base vegetation maps used to create the FARSITE input maps were developed for midscale applications, whereas the FARSITE simulation model 
must have data that accurately characterize fine scale spatial fuel distribution and dynamics. This factor was probably most important during the 

construction of PVT and fuel model rulebases. Midscale classification categories for the vegetation maps (PVT, cover type, structural stage) were 

sometimes too broad to uniquely identify an appropriate FBFM. For instance, three FBFM fuel models (5,8, and 10) were sampled for the grandfir PVT, Douglas-fir cover type, and medium tree structural stage combination in the field. Finer delineations of structural stages in this PVT could 

have reduced the number of possible fuel models. The differentiation of 

structural stage in the field was often subjective, inexact, and difficult 

because many managed forest stands have a diverse distribution of tree 

diameter classes. Last, some cover type categories did not mesh well with 

our PVT classification. For example, it was difficult to decide if mixed xeric 

forest as defined by Redmond and Prather (1996) was restricted to the 

Douglas-fir PVT. 

Probably the next important factor contributing to low map accuracies is 

inadequate and incomplete ground-truth information. The historical field 

data used in this project were not an ideal ground-truthing source because 

these data were collected for diverse studies with specific objectives. Keane 

and others (1996a) gradient modeling data set (table 2) was accurate 

and extensive, but many of their plots were established in stands that were 

smaller than the SILC minimum mapping size because their primary 

objective was to sample environmental gradients rather than to map vegetation attributes. As a result, many small stands (less than 2 ha) sampled 

by Keane and others (1996a) were not represented on the original SILC 

map because they had been "smoothed" into larger polygons. The FBFM's 

and PVT estimates sampled by Davis and Butterfield (1991) did not correspond well to our PVT, cover type, structural stage assignments. This is 

probably because of inadequate habitat type and fire behavior fuel model 

training for their ground crews, inaccurate plot locations, and limited spatial 

coverage. However, the accuracy of some maps increased only marginally 

when the historical plot data were removed from the accuracy assessment 

(table 11). 

The clumpy spatial distribution of ground-truth information did not 

provide an adequate representation of all topographic and vegetation settings in the SBWC (fig. 2). Most ground-truth plots were established along 

roads or trails because of the logistical problems sampling inaccessible areas. 

This lack of sampling was especially true in the remote interior and westcentral portions of the SBWC. We did not extensively sample some cover 

types on the SBWC landscape because they either were rare or confined to 

the remote, untrailed portions of the wilderness. A large number of groundtruth samples is usually recommended for comprehensive satellite imagery 

analyses and accuracy assessments (sometimes over 100 plots per category depending on size of land area) to ensure statistical validity 

(Congalton 1991; Mowrer and others 1996). However, sampling intensities 

of remote wilderness settings must reflect a compromise between logistics, 

costs, statistical significance, and desired map accuracies. 

The initial topographically based PVT rules and FBFM assignments 

contained errors that lowered map accuracy. Fire managers were unfamiliar 

with some PVT, cover type, structural stage combinations because they are 

either rare or in less frequented areas (timberline and upper subalpine, 

for example) in the SBWC, and therefore the assignment of FBFM was 
difficult. Some areas had cover types or PVT's that were not represented in 

the SBWC cover type or PVTlist. For example, bogs, seeps, fens, and marshes 

were not represented in the SBWC PVT list, but they were present on the 

SBWC landscape (2 percent of total). Ceanothus shrubfields are common on 

the SBWC landscape and have distinctive fuel characteristics (FBFM 6) 

compared with other upland shrubs, but this cover type is lumped into the 

SILC "mesic shrub" cover type (appendix C). Refinements to the PVT rule set 

based on the plot data may have improved the classification across the entire 

landscape, but this was difficult to assess because less than 1 percent of the 

polygons were ground-truthed. Changes in the PVT or FBFM rules resulted 

in major changes across the SBWC, but often these changes did not affect 

sampled polygons. Often our refinements would improve one PVT category 

but weaken the predictive value of several others. Moreover, it was difficult 

to assess the value of a PVT or FBFM refinement because of the limited 

ground-truth information for that category. The only solution is to increase 

the ground-truth sample to represent at least 5 percent of the area, and this 

may be logistically and financially impossible for some projects. 

The FBFM layers had a low accuracy (approximately 59 percent) for a 

number of reasons. First, this accuracy was difficult to evaluate because fuel 

models on many of the historical plots were wrong or they were evaluated at 

the wrong scale. FBFM's were assigned to PVT, cover type, structural stage 

combinations, and because the cover type and structural stage layers were 

imprecise, the FBFM layer was also inaccurate (Baker 1989). In addition, 

FBFM's can be variable within the minimum 2 ha mapping limit used in the 

SILC effort. As a result, the FBFM was often wrong when the cover type was 

wrong. Accuracies of the historical data were low (under 37 percent) primarily because they were wrongly assessed at the sample site. However, it is 

interesting that the fuels maps had higher accuracies than cover type or 

structural stage maps. This is probably because of the close relationship of 

fuels to site (PVT) and the small number of fuel categories. 

We were surprised at the low accuracy of the acquired continuous SBWC 

GIs layers (table 12). Over 10 percent of the DEM pixels had elevation errors 

in excess of 50 m based on a comparison with elevations measured at the plots 

with a GPS receiver (table 13). Minimum and maximum DEM errors were 

quite large with a range that sometimes exceeded 2,200 m (table 13a). Slope 

and aspect errors (table 13b,c) were high because their field measurement 

has a high degree of variability across a polygon, and the DEM layer had 

elevational errors. The distribution of errors across all sampled SBWC 

pixels for elevation, aspect, and slope are shown in figure 3. A majority of 

validated DEM pixels (83 percent) were within 30 m (about 100 ft) of the 

observed value. This error was acceptable for PVT elevation constraints but 

unsatisfactory for slope and aspect computation. The wide spread of errors 

for aspect and slope is probably a result of the errors in the DEM compounded 

with errors generated by the ARC/INFO GIs commands used to create these 

layers and also errors and limitations in field measurement (table 14). 

The crown-based fuel layers of stand height, crown height, and crown bulk 

density also had low accuracies, which is probably an artifact of the mapping 

procedure rather than the assignments by PVT, cover type, and structural 

stage. We assessed the accuracy of these assignment keys using the groundtruth data and found substantially higher percent accuracies and higher 

correlations to observed data (table 15). These assignments were entered into 

the GIs datahase as a reference or "look-up" table that attributes each polygon 
Table 13a-Percentage of plots where field-measured elevation deviates from the 

DEM by more than 30,50, and 100 m. 

>30 m error >50 m error >I00 m error 

Percent of all plots 17.6 10.3 

n = 1,622 

Percent of 1996 plots 13.5 1.4 

n= 141 

Table 13b--Percentage of plots where field-measured slope deviates from DEMderived slope by more than 1 O and 20 percent. 

>10 percent error >20 percent error 

Percent of all plots 35.7 14.2 

n = 1,600 

Percent of 1996 plots 35.7 11.4 

n= 140 

Table 13c-Percentage of plots where field-measured aspect deviates from DEMderived aspect by more than 45 and 90 degrees. ---- -- - 

>45 degree error >90 degree error 

Percent of all plots 

n = 1,614 

Percent of 1996 plots 

n= 141 

with the crown characteristic based on PVT, cover type, and structural 

stage. The accuracy of some crown characteristics (such as crown height in 

table 15) varied greatly by geographic zone. Most of the error in the 

assignments is due to the inherent ambiguity in the broad PVT, cover type, 

and structural stage categories. Any fine-scale stand characteristic such as 

stand height can have a highvariation across the mapped midscale polygons. 

These assignments can be improved by using finer scale PVT, cover type, and 

structural stage classifications, or by not using a plot-level approach but 

rather a sampling methodology that assesses vegetation characteristics 

across the entire polygon. 

Recommendations 

Development of the FARSITE fuels data layers should be considered a 

process and not a product by many land management agencies. These maps 

should be constantly updated and improved as more ground-truth data are 

gathered and as better vegetation maps are created. It is a costly and timeconsuming task to gather the vast amounts of data needed to adequately 

validate an image classification from remote sensing, especially if the 

mapping project only lasts 1 or 2 years. So fire managers shouldn't expect 

high FARSITE input map accuracies at first, given most land management , 
Elevation Error (m) 

Slope Error (Percent) 

Aspect Error (Degree) 

Figure 3-Distribution of error for the continuous GIs layers of elevation, aspect, and slope. 
Table 14-Error statistics for elevation, slope, and aspect (all plots). 

Mean Mean 

signed unsigned Minimum Maximum Standard 

error error error error error 

Elevation (m) 4181 6 25.227 -1,531.730 763.850 1.647 

n = 1,622 

Slope (percent) 2.425 10.945 -86.0 76.0 .298 

n = 1,600 

Aspect (degrees) -1.349 49.485 -356.0 358.0 1.994 

n = 1,614 

Table 15-Accuracy analysis of the assignment keys of the vegetation-based fuels characteristics 

of stand height, crown height, and crown bulk density from PVT, cover type, and 

structural stage. Predicted value obtained from look-up tables; only forested cover 

types included. 

Key n R2 Slope Intercept Mean error 

Stand height 60 0.65 0.89 2.05 -0.1 4 

Crown heightMontana 37 .08 -23 2.28 -.I 7 

Crown heightnorthwest/ 

west-central 45 .44 .21 1.04 3.59 

Crown bulk 

density 63 .60 .88 .037 -1.78 

agencies do not have and probably will never be able to collect the amount of 

georeferenced field data required for high accuracy map development. 

However, map accuracy can be improved over time as additional field data 

are gathered and as remote sensing platforms and data analysis techniques 

improve in technology resolution, and quality. The methodology presented in 

this paper allows for future revisions as new vegetation layers and FBFM 

keys are created. 

The importance of the ground-truth data to map development cannot be 

overemphasized. These data provide the reference for classification and 

mapping, and the limitations of these data will surface in the resultant 

maps. Inadequate plot coverage for important cover types or PVT's will result 

in an inadequate spatial representation of that type on developed maps. 

Because of the high cost of georeferenced ground-based information, it is 

often necessary to evaluate other sources of existing ground-truth data. 

Stand maps delineated from aerial photos can be used for ground-truth and 

may be available for some portions of a project area. Although stand maps 

have some inaccuracies (70 to 90 percent accurate), they can be used to assess 

the success of developed classifications and mapping algorithms. Other 

sources of ground-truth data are continuous forest inventory plots, stand 

examination plots, and other research and management ground-truth efforts. Current university, government, and private research and management projects within the study area may also be potential field data sources. 

However, it is important that the objectives and methodologies used to collect 

historical data be consistent with objectives of fuel mapping. For instance, 

data collectedvfrom plots that represent stands that are smaller than the 

minimum mapping size may not be appropriate for midscale mapping. 
Sampling efforts concerned with quantifying fine-scale characteristics such 

as fuel loading or tree densities may not produce data that are compatible 

with coarser scale characterizations. It is critical to address the problem of 

scale in any sampling effort. 

It is important that any ground-truth sampling effort require the sampling 

of more than just the map categories in the field. A complete site and 

vegetation description is often necessary to understand the processes that 

created the designed map classification or why a sampled stand keys to a 

particular map category. Moreover, maps are more useful if they can be 

expanded for resource applications other than fire. Additional ecosystem 

characteristics sampled at each plot can be summarized and linked to the 

vegetation maps for use in other management projects such as the FEFM 

data base we linked to thevegetation maps. The Northern Region's ECODATA 

sampling package (Jensen and others 1993; Keane and others 1990) provides 

the methodology, plot forms, and data base needed for most ground-truthing 

efforts. This system can be modified in various ways to include new fields or 

improve existing fields. In addition, it contains standardized methods to 

extensively describe site characteristics so that other data can be augmented 

with data collected for a specific ground-truthing effort. 

We received some positive feedback from fire managers during summer 

and fall 1996 concerning the accuracies and value of the SBWC FARSITE 

fuels maps. Most people were pleased with the maps, but they have not yet 

identified areas of possible improvement. Most comments submitted by fire 

mangers concerned the operation and application of the FARSITE program 

rather than the quality of the fuels maps. Comments submitted by fire 

managers will be integrated into the next version of the fuels maps. 

We are currently refining and modifying the methods used in this study to 

map fuels and vegetation on the Gila National Forest in New Mexico. A 

gradient modeling technique is being used to predict PVT's, cover types, and 

fuel models on this southwestern United States landscape (Keane and others 

1996b; Kessell 1979). We use expressions of primary ecological process 

mapped across the landscape to drive the development of vegetation layers. 

For example, PVT's are being predicted from maps of average annual 

precipitation, net primary productivity, solar radiation, and temperature. 

Conclusions 

This project successfully created the FARSITE fuels input maps from three 

vegetation layers of potential vegetation type, cover type, and structural 

stage. These vegetation layers provide the context in which to understand 

fire behavior fuel model distributions across the landscape. Moreover, the 

intermediate products used to create the fuels maps can be easily revised and 

refined to create better fuel maps or to extend fuel mapping to other areas. 

However, the quality of these maps could be improved if several items are 

accomplished. First, the three vegetation layers must be developed together 

to ensure consistency and quality. Second, an extensive and comprehensive 

ground-truth data base is essential for map development and accuracy 

assessment. This data base should contain plot data that are evenly distributed across all geographic regions, map categories, and fuel attributes. 

Third, the input maps should be continually revised and modified as new 

data are collected and better base vegetation maps become available. 

Development of the PVT map can be accomplished using either of the two 

approaches discussed here. The terrain-modeling, rule-base approach is best 
when little ground-truth data are available for extensive statistical analysis 

and there are resource personnel that know and understand the area's 

ecological relationships. The statistical approach is best when abundant 

data are available to derive accurate predictive models for PVT assignments. 

Perhaps a third approach is to meld the two approaches so that a rule-based 

model can confine the discriminant analysis to only those areas where 

substantial data have been gathered or only those areas where a particular 

PVT can occur. 

The inherent errors in all GIs layers can be propagated as new maps are 

created from base data layers. We found error in the DEM base layer, and this 

error was compounded in the secondary slope and aspect layers and in the 

tertiary PVT and fuels layers. The SILC cover type map had low accuracies 

because spectral reflectance did not adequately discriminate many forest 

cover types and structural stages. The PVT map was inaccurate because of 

scale discrepancies in the heuristic rulebase. The only way to improve map 

accuracies is to refine map categories, gather additional ground-truth information, and use better GIs base layers. 

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moisture content of fine dead wildland fuels: Input to the BEHAVE fire prediction system. 

Res. Pap. INT-359. Ogden, UT: U.S. Department ofAgriculture, Forest Service, Intermountain 

Research Station. 62 p. 

Schowengerdt, R. A. 1983. Techniques for image processing and classification in remote 

sensing. New York: Academic Press. 289 p. 

Shao, Guofan; Zhao, Guang; Zhao, Shidong; Shugart, Herman H.; [and others]. 1996. Forest 

cover types derived from Landsat Thematic Mapper imagery for Changbai Mountain area 

of China. Canadian Journal of Forest Research. 26: 206-216. 

Shiflet, T.N., ed. 1994. Rangeland cover types of the United States. Denver, CO: Society of 

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Soil Conservation Service (SCS). 1991. State soil geographic data base (STATSGO): data 

user's guide. Misc. Publ. 1492. Washington, DC: U.S. Department of Agriculture, Soil 

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Sokal, Robert R.; Rohlf, F. James. 1969. Biometry. San Francisco, CA: W. H. Freeman and 

Company. 776 p. 

Steele, Robert; Geier-Hayes, Kathleen. 1989. The Douglas-firhinebark habitat type in central 

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Appendix A: SBWC ECODATA Ground-Truth Plot Forms 

The following four pages contain the forms we used in gathering the field data for this study. We hope 

the forms, as presented here, might prove useful for the reader in similar data-gathering ventures. See 

Methods section for further explanation. 
GENERAL FIELD DATA FORM (GF) 

FII-30: Sample Forms - - - - - - - - - - - F31: Unit E F32 Plot Radius (ft): - - . - F33: PW 000 

Potential Vegetation 

Form Author Yr Ind Spp 1 Ind Spp 2 Ind Spp 3 

PVT: - _ F37-41: - - ---- -- -------- -------- -------- 

Existing Vegetation 

ICRB GAP 

F43-46: Lifeform: - - LSC: - - DSC: - - CC: - Stand Structure: - - Stand Structure: - - 

Live Tree Dead Tree 

F86-89: BAF: - - BAN: - - BA: - - DBH: - - Ht: - - - F90-92: BAF: - - BAN: - - BA: - - DBH: - - Ht: - - 

Tree Cover (%) F93-99: Tot: - - Se: - - Sa: - - PT: - - MT: - - LT: - - VL: - 

shrub Cover (%) Herb Cover (%) 

Fl00-103: Tot: - - LS: - - MS: - - TS: - - F104-107: Gram: - - Forb: - - Fern: - - Moss: - - 

Site Data 

Spec Ftr Landform Par Mat Position Vert Pos Hor Pos 

F53: - - F54-56: - - - - - - F57-59: - - - - - - F60-61: - - - - F62: - F63: - 

Map Elev(ft) Aspect (deg) Slope (%) Horizons (%) 

F64: - - - - - F65: - - - F66: - - - F67-69: East: - - - South: - - - West: - - - 

Ground Cover (%) 

F72-79: BS: - - Gr: - - Ro: - - LD: - - Wo: - - ML: - - BV: - - Wa: - - 

Fuels Data 

Fire Behavior Fire Behavior Fuel Depth (ft) 

Model Normal(F80): - Model Severe: - - F81: -.- 

Down Log Diam (in) Dom Layer Ht (ft) 

F84: - - F85: - - - Ht to Crown (ft): - - . - 

DLDepth (in) D WCover (%) 

F82: - -. - F83: - - 

Fischer Photo Series: 

GTR No: - - Page No: - - 

Crown Bulk Density: - . - - 

Form LL: LocationILinkage Data 

Stand ID: DICo 

Quad ID: ID Quad Name* Photo ID: 

I F25-26: --------- ------------------- F27-28": 

Meth Accuracy Sec HSec 

F34-35: Plt Loc I I I F36-39: Lat (N) - ---- - 1 "" I "i 1 - - 1 - - 1 tm40-43: Long(W) 

F54: Purp I - -1 F55*-Project I - - - - - - I F56*-Picture I - ( F57*-Pic ID 1- - - I F58: Sta

"Optional Fields 

F72*-0therType2 1- - - F74-79"-LSI Info: I F73*-OtherType21D I - - - - - - - - - - I 

I Subsection 

I LT Association Landtype 

I LTPhase I VBLT 

--------- ............................ ...........

Plant Composition Data Form (PC) 

F10 

Plant Code 

F13: Size Classes (%) Notes 

Comments Data Form (CD) 

ID: F8: Comments: (Always enter your sampling method ID in the first 2 characters, i.e., PC for Plant Composition.) 

1. 

Appendix B: DEM Filtering Procedure 

I. The following procedure was used to remove horizontal banding in the Level 1 DEMs yet retain the quality of the Level 

2 DEMs 

1. DEM files converted to lattices using the ARC commands 

DEMREAD and DEMLATTICE. 

2. All Level 1 DEMs were merged using LATTICEMERGE in ARC. 

3. All Level 2 DEMs were merged with LATTICEMERGE. 

4. Both the Level 1 and Level 2 layers were converted to integers in GRID: 

GRID: levldem-int = int(lev1Jattice) 

5. The Level 1 layer and Level 2 layer were merged in GRID: 

GRID: DEMsbw-1st = merge(lev2dem~int,levldem~int) 

6. Remove NODATA slivers resulting from the merge: 

GRID: DEMsbw-sliv = con(isnull(DEMsbw~lst),int(focalmean(DEMsbw~lst)),DEMsbw~lst) 

7. The above grid was filtered twice with a vertical filter (1 cell wide x 7 cells high): 

GRID: DEMsbw-filt 1 = int(focalmean(DEMsbw~sliv,rectangle, l,7)) 

GRID: DEMsbw-filt2 = int(focalmean(DEMsbw-filt l,rectangle, 1,7)) 

8. The Level 2 dem layer was merged with the filtered layer, giving preference to the Level 2 DEMs: 

GRID: dem-2-filtmrg = merge(lev2dem-int,DEMsbw-filt2) 

9. Two quads along the Bitterroot River did not have horizontal banding, but rather seemed to have irregular vertical 

banding. In addition, aspect grids derived from these two DEMs had a scattered "salt and pepper" appearance caused 

by small changes in elevation (see Stitt, 1990). To reduce both effects, these two DEMs were filtered differently, with 

one pass of a square 5 cell x 5 cell filter, then merged with the above grid (dem-2-filtmrg). 

GRID: btr-filt5x5 = int(focalmean(levl~btrflats,rectangle,5,5)) 

GRID: btr-2-fil$mrg = merge(btr-filt5~5,dem-2-filtmrg) 

11. Edge smoothing process: the final "merged" layer from the above steps did not appear to have smooth elevational 

transitions between Level 2 and filtered DEMs. Several edges were apparent in shaded relief and aspect grids as single 

horizontal or vertical bands. The following procedure was used to smooth these edges. 

1. Create a line coverage with arcs only where edges between Level 2 and Level 1 DEMs occur. This was created in Arcedit 

from a minute quad coverage by selecting and deleting arcs. Separate coverages were made for horizontal edges 

and vertical edges. Resulting coverages: hzedge-line and vredge-line 

2. Create 30 m grids from the above line coverages: 

GRID: setwindow btr-2-filtmrg 

GRID: setcell 30 

GRID: edgegrd-hz = linegrid(hzedge-line) 

GRID: edgegrd-vr = linegrid(vredge-line) 

3. Create mask grids: 

GRID: edgemsk-hz = edgegrd-hz / edgegrd-hz 

GRID: edgemsk-vr = edgegrd-vr / edgegrd-vr 

4. Increase the width of the mask grids from 1 cell to 11 cells: 

GRID: hz-1 lcell = int(focalmax(edgemsk~hz,rectangle, 1, 11,data)) 

GRID: vr-llcell = int(focalmax(edgemsk~vr,rectangle, 11, 1,data)) 

5. Using hz-llcell and vr-llcell as masks, filter the edges with a 7 cell directional filter: 

GRID: setmask hz-llcell 

GRID: dem-hzedge = int(focalmean(btr~2-filtmrg,rectangle,l,7,NODATA**)) 

GRID: setmask vr-1 lcell 

GRID: dem-vredge = int(focalmean(btr~2-filtmrg,rectangle,7,1,NODATA**)) 

*" The NODATA option ensures that if any cell in the neighborhood (in this case a 7 cell strip) has a value of nodata, then the 

output for the processing cell will be nodata. Therefore, of the 11 cell wide mask, only the 5 central cells will receive a value. 

6. Merge the filtered edges with the DEM layer 

GRID: dem-final = merge(dem-hzedge,dem-vredge,btr-2-filtmrg) 

*" MOST of the edges were smoothed nicely with this process. However, because the mask grids were derived from a line 

coverage not related to the dem layer, some areas of the mask grid did not precisely overlay the edges in the dem layer. 
Appendix C: Cover Type Cross Reference Table 

SlLC codea SlLC cover type PVT~ SBWC code SBWC cover type 

Urban or developed land 

Agricultural 

Foothills grasslands 

Disturbed grasslands 

Montane parklands and 

subalpine meadows 

Warm mesic shrubland 

Cold mesic shrubland 

Curlleaf mountain mahogany 

Bitterbrush 

Mountain big sagebrush 

Wyoming big sagebrush steppe 

Black sagebrush steppe 

Broadleaf forest 

Engelmann spruce 

Lodgepole pine 

Ponderosa pine 

Grand fir 

Subalpine fir 

Western redcedar 

1 

1 

1-4 

5,6 

1-4 

5-8 

1-4 

5-8 

1 -4 

576 

1-4 

5-8 

1-4 

5-8 

1-4 

5-8 

1-4 

5-8 

1-4 

5-8 

1-4 

5-8 

1-3, 5-7 

4 

3 

4 

5-7 

All 

1,394 

2, 5-7 1 

1-3 

4-7 

4 

5-8 

2 

193 

,4 

Urban or developed land 

Agricultural 

Grasslands 

Montane parklands and subalpine meadows 

Grasslands 

Montane parklands and subalpine meadows 

Grasslands 

Montane parklands and subalpine meadows 

Warm mesic shrubland 

Cold mesic shrubland 

Warm rnesic shrubland 

Cold mesic shrubland 

Warm mesic shrubland 

Cold mesic shrubland 

Warm mesic shrubland 

Cold mesic shrubland 

Warm mesic shrubland 

Cold mesic shrubland 

Warm mesic shrubland 

Cold mesic shrubland 

Warm mesic shrubland 

Cold mesic shrubland 

Broadleaf forest 

Warm mesic shrubland 

Grand fir 

Douglas-fir 

Engelmann spruce 

Lodgepole pine 

Ponderosa pine 

Lodgepole pine 

Grand fir 

Douglas-fir 

Douglas-fir 

Subalpine fir 

Western redcedar 

Grand fir 

Douglas-fir 

(con.) 
Appendix C (Con.) 

SlLC codea SlLC cover type PVT~ SBWC code SBWC cover type 

Western larch 

Mixed alpine forest 

Mixed subalpine forest 

Mixed mesic forest 

Mixed xeric forest 

Douglas-fir1 

lodgepole pine 

Standing burnt and dead timber 

Douglas-firlgrand fir 

Western redcedarlgrand fir 

Western larchllodgepole pine 

Western larch1Douglas-fir 

Mixed needleleaflbroadleaf 

forest 

Water 

Exposed rock 

Mixed barren land 

Shorleline and stream gravel 

bars 

Alpine meadow 

Perennial snowfields 

1-6 

718 

1-6 

7 

4 

5-7 

8 

1,314 

5-8 

1-4 

5-8 

12 

213 

5-7 

1-6 

73 

All 

213 

415 

617 

2 

3 

1,4 

5 

All 

All 

A1 l 

11 

10 

1 

2-4 

5-8 

10 

10 

10 

Douglas-fir 

Subalpine fir 

Western larch 

Mixed subalpine forest 

Mixed xeric forest 

Mixed subalpine forest 

Mixed timberline forest 

Mixed mesic forest 

Mixed subalpine forest 

Mixed mesic forest 

Mixed subalpine forest 

Mixed xeric forest 

Mixed mesic forest 

Mixed subalpine forest 

Douglas-f irllodgepole pine 

Mixed subalpine forest 

Standing burnt and dead timber 

Douglas-firlgrand fir 

Douglas-fir 

Mixed subalpine forest 

Western redcedarlgrand fir 

Grand fir 

Mixed mesic forest 

Mixed subalpine forest 

Western larchllodgepole pine 

Western larch1Douglas-fir 

Broadleaf forest 

Water 

Exposed rock 

Agriculture 

Grasslands 

Montane parklands and subalpine meadows 

Shorleline and stream gravel bars 

Alpine meadow 

Perennial snowfields 

"See Redmond and others (1 996) for detail description'of SlLC cover types. 

bThis number references the PVT in which the cover type occurs. See table 3 for PVT description. 

53 
Appendix D: Terrain Models 

Terrain models of potential vegetation in three zones in the Selway-Bitterroot Wilderness of Idaho 

and Montana, USA. 

West-Central Zone 

Series and Elevation 

Number PVT group habitat types Aspects range Slope Commentsa 

Developed1 

agricultural1 

urban lands 

THPL 

ABGR 

PSME 

Lower 

subalpinemoist 

Lower 

subalpinedry 

Upper 

subalpinemoist 

Upper 

subalpinedry 

Persistent 

herblands 

Rocklalpine/ 

perennial 

snowfields 

Water 

Dry and moist PSME, 

PIPO, 

dry ABGR 

ABLNCLUN, LIBO, ALSI, 

MEFE, CACA 

ABLNXETE 

ABLNLUHI-MEFE 

ABLNLUHI-VASC, 

PIAL-ABLA, 

LALY-ABLA 

Azimuths 

31 5-90 

Meters (ft) 

1 <I ,463 (4,800) 

THPL buffer1,524 (5,000) 

THPL buffer1,524 (5,000) 

31 5-90 

3 1 5-90 

90-31 5 

31 5-90 

All 

90-31 5 

Percent 

Not in WC 

<30 Streamside 

buffer 

Not THPL 

Not PSME 

Not rock 

or alpine 

>50 From SlLC 

map 

From SlLC 

map 

From SlLC 

map 

(con.) 
Appendix D (Con.) 

Northwest Zone 

Series and , Elevation 

Number PVT group habitat types Aspects range Slope Commentsa 

Azimuths Meters (ft) Percent 

None in 

NW 

1 Developed1 

agricultural1 

urban lands 

2 THPL THPUCLUN 

3 ABGR ABGRICLUN, LIBO, Not PSME 

XETE 

PSME 

Lower 

subalpinemoist 

Lower 

subalpinedry 

Upper 

subalpinemoist 

Upper 

subalpinedry 

Persistent 

herblands 

Rockfalpinel 

perennial 

snowfields 

Dry and moist PSME, 

dry ABGR 

ABLNCLUN, LIBO, ALSI, 

MEFE, CACA 

ABLNXETE 

ABLNLUHI-VAGL 

ABLNLUHI-VASC, 

PIAL-ABLA, 

LALY-ABLA 

Not rock 

or alpine 

From SlLC 

map 

From SlLC 

map 

Water From SlLC 

map 

(con.) 
Appendix D (Con.) 

--- - - 

Montana Zone 

Series and Elevation 

Number PVT group habitat types Aspects range Slope commentsa 

Developed1 

agricultural1 

urban lands 

THPL 

ABGR 

THPUCLUN 

ABGRICLUN, LIBO, 

XETE 

Azimuths 

All 

All 

4 PSME Dry and moist PSME, 

PIPO, dry ABGR 

Lower 

subalpinemoist 

Lower 

subalpinedry 

Upper 

suba1pin.emoist 

Upper 

subalpinedry 

Persistent 

herblands 

Rock/alpinel 

perennial 

snowfields 

Water 

ABLNCLUN, LIBO, ALSI, 

MEFE, GATR, CACA 

ABLNXETE 

ABLNLUHI-MEFE 

ABLNLUHI-VASC, 

PIAL-ABLA, 

LALY-ABLA 

Meters (ft) Percent 

From ICB 

ownership 

map 

Not in MT 

Streamside 

buffer 

Not lower,, 

subalpinemoist 

Not ABGR * 

not lower 

subalpinemoist 

Not rock 

or alpine 

Not in MT 

From SlLC 

map 

From SlLC 

map 

"SBWC = Selway-Bitterroot Wilderness Complex Assessment Area; NW = Northwest section of SBWC; WC = West-central section of SBWC; 

Mi = Montana section of SBWC; SlLC Map is the Satellite Imagery Land Cover spatial classification developed by the Wildlife Spatial Analysis 

Laboratory, University of Montana, Missoula. 
Appendix E: Fuels Mapping Worksheet Example 

Worksheet directions: Fill in FBFM Normala. FBFM Severeb. HLCBc. and FEFMd. 

PVTe, COVER TYPE, STRUCTURAL'STAGE I FBFM 1 FBFM I HLCB I FEFM I 

Normal Severe 

( PVT CODE, NAME I - --- 

Cover Type Code, Cover Type Name 

Structural Stage Code, Structural Stage 

Description 

Example: 

PVT, COVER PIPE, STRUCTURAL STAGE 

6 Pole Tree 5.0-8.9" d.b.h. 

a FBFM Normal = Fire Behavior Fuel Model under normal conditions, Anderson (1982): Number 1-10 (1 1-13 are slash fuels) 

FBFM Severe = Fire Behavior Fuel Model under severe conditions, Anderson (1 982): Number 1-1 0 (1 1-1 3 are slash fuels) 

" HLCB = Height to live crown base 

FEFM = Fire Effects Model (Fischer Photo Series, 1981a-c), only natural fuels 

' PVT = Potential Vegetation Type ' d.b.h. = Diameter at breast height 

1-1 0 

(11-13) 

7 Medium Tree 9.0-21 .On d.b.h. 

8 LargeNery Large Tree >21 .ON d.b. h. 

1 Grand Fir, Abies grandis 

FBFM 

Normal 

8 

1-10 

(11-13) 

8 

10 

FBFM 

Severe 

8 

To 

nearest 

foot 

10 

I0 

Book No. 

PageNo. 

HLCB 

4 

FEFM 

98,14 

6 

4 

98,18 

98,38 
Appendix F: Fire Behavior Fuel Model Assignments 

Fire behavior fuel model assignments to PVT, cover type, structural stage combinations. 

Montana West-Central and Northwest 

FBFM FBFM FBFM FBFM 

PVT Cover Stg Norm Sev PVT Cover St r Norm Sev 

1 

5 

5 

6 

8 

8 

8 

10 

8 

10 

10 

8 

8 

10 

10 

5 

9 

9 

10 

5 

8 

10 

10 

8 

8 

8 

8 

10 

5 

8 

10 

10 

5 

8 

10 

10 

5 

8 

10 

10 

5 

8 

10 

10 

5 

8 

10 

10 

8 

8 

8 

9 

9 

1 

2 

(con.) 
Appendix F (Con.) 

Montana 

FBFM FBFM 

PVT Cover Sta Norm Sev 

West-Central and Northwest 

FBFM FBFM 

PVT Cover Str Norm Sev 

6 

5 

6 

8 

9 

9 

5 

8 

10 

10 

8 

8 

10 

10 

5 

9 

9 

10 

5 

8 

10 

10 

5 

8 

8 

10 

5 

8 

10 

10 

5 

8 

10 

10 

8 

8 

10 

10 

5 

8 

10 

10 

5 

8 

10 

10 

5 

8 

10 

10 

5 

8 

10 

10 

8 

8 

(con.) 
Appendix F (Con.) 

Montana 

FBFM FBFM 

PVT Cover Stg Norm Sev 

3 4223 06 8 8 

3 4223 07 8 10 

3 4223 08 10 10 

3 4224 0 1 10 10 

3 4225 05 5 6 

3 4225 06 8 8 

3 4225 07 10 10 

3 4225 08 10 10 

3 4226 05 5 6 

3 4226 06 8 8 

3 4226 07 10 10 

3 4226 08 10 10 

3 4228 05 5 6 

3 4228 06 8 8 

3 4228 07 8 ! 0 

3 4228 08 10 10 

3 4229 05 5 6 

3 4229 06 8 8 

3 4229 07 8 10 

3 4229 08 10 10 

3 430 1 05 5 6 

3 430 1 06 8 8 

3 430 1 08 8 10 

3 770 1 12 99 99 

4 1000 10 99 99 

4 2000 11 1 1 

4 31 00 0 1 1 1 

4 3104 0 1 2 2 

4 3202 02 5 5 

4 3202 03 5 6 

4 3202 04 5 6 

4 41 02 05 5 5 

4 41 02 06 8 8 

4 41 02 07 8 8 

4 41 02 08 8 10 

4 4203 05 5 6 

4 4203 , 06 8 8 

4 4203 07 8 10 

4 4203 08 10 10 

4 4206 05 5 5 

4 4206 06 8 8 

4 4206 07 2 2 

4 4206 08 10 10 

4 421 2 05 5 ,6 

4 421 2 06 8 8 

4 421 2 07 8 8 

4 421 2 08 10 10 

4 421 5 05 5 5 

4 421 5 06 8 8 

4 421 5 07 8 8 

4 421 5 08 10 10 

4 422 1 05 5 6 

4 422 1 06 8 8 

4 422 1 07 8 10 

4 422 1 08 10 10 

4 4222 05 5 6 

4 4222 06 8 ., 8 

West-Central and Northwest 

FBFM FBFM 

PVT Cover Str Norm Sev 

8 

99 

1 

2 

6 

5 

2 

8 

8 

10 

10 

5 

9 

9 

9 

5 

8 

8 

10 

5 

8 

10 

10 

5 

8 

10 

10 

8 

8 

10 

10 

5 

8 

10 

10 

10 

5 

8 

10 

10 

5 

8 

10 

10 

99 

99 

99 

2 

1 

6 

6 

6 

6 

6 

2 

8 

9 

(con.) 
Appendix F (Con.) 

Montana 

FBFM FBFM 

PVT Cover Sta Norm Sev 

West-Central and Northwest 

FBFM FBFM 

PVT Cover Str Norm Sev 

9 

9 

5 

8 

10 

10 

8 

8 

10 

10 

5 

5 

10 

10 

5 

5 

10 

10 

5 

8 

10 

10 

8 

8 

10 

10 

5 

8 

10 

10 

10 

5 

8 

10 

10 

5 

8 

10 

10 

99 

2 

6 

6 

6 

6 

6 

6 

8 

9 

9 

5 

8 

10 

10 

8 

8 

8 

10 

5 

5 

(con.) 
Appendix F (Con.) 

PVT Cover 

Montana 

FBFM FBFM 

Stg Norm Seu 

08 10 10 

0 1 10 10 

05 5 6 

06 8 8 

07 8 10 

08 10 10 

05 5 6 

06 8 8 

07 8 10 

08 10 10 

05 5 6 

12 99 99 

0 1 2 2 

02 5 5 

03 5 6 

04 5 6 

02 5 5 

03 5 6 

04 5 6 

05 5 5 

06 8 8 

07 8 10 

05 5 6 

06 8 8 

07 10 10 

08 10 10 

05 5 6 

06 8 8 

07 8 10 

08 10 10 

05 5 6 

06 8 8 

07 10 10 

08 10 10 

05 5 6 

06 8 8 

07 8 10 

08 8 10 

05 5 5 

06 8 8 

07 8 10 

08 10 10 

05 5 6 

06 8 8 

07 8 10 

08 10 10 

05 5 6 

06 8 8 

07 8 8 

08 10 10 

0 1 10 10 

05 5 6 

06 8 8 

07 10 10 

06 8 8 

07 8 10 

08 10 10 

05 5 6 

06 8 8 

07 8 ..I 0 

PVT 

West-Central and Northwest 

FBFM 

Cover St r Norm 

FBFM 

Sev 

10 

10 

5 

5 

10 

10 

5 

8 

10 

10 

5 

8 

8 

10 

5 

8 

10 

10 

10 

8 

10 

10 

5 

8 

10 

2 

6 

6 

6 

8 

8 

5 

8 

8 

10 

10 

5 

5 

10 

10 

5 

5 

10 

10 

8 

10 

10 

10 

10 

99 

99 

2 

6 

6 

6 

5 

8 

8 

8 

8 

(con.) 
Appendix F (Con.) 

Montana West-Central and Northwest 

FBFM FBFM FBFM FBFM 

PVT Cover Stn Norm Sev PVT Cover St r Norm Sev 
Appendix G: Crown Bulk Density Reference Data 

Canopy cover 

Species Low cover Medium cover High cover 

Ponderosa pine 

Seedlinglsapling 

Polelmediumllarge 

Douglas-fir 

Seedlinglsapling 

Polelmediumllarge 

Lodgepole pine 

Seedlinglsapling 

Pole/medium/large 

Western larch 

Seedlinglsapling 

Pole/medium/large 

Western redcedar 

Seedlinglsapling 

Pole/medium/large 

Englemann spruce 

Seedlinglsapling 

Pole/medium/large 

Grand fir 

Seedlinglsapling .15 

Pole/medium/large .12 

Subalpine fir 

Seedlinglsapling 

Pole/medium/large 

Whitebark pine 

Seedlinglsapling .10 

Pole/medium/large .09 
Appendix H: Accuracy Assessment Matrices of PVT and Structural 

Stage 

PVT Categories 

CLASSIFIED 6 

PVTs 

From 7 

Terrain Model 

8 

TOTAL 

OMISSION 

ERROR (%) 

' REFERENCE PVTs PIot Data 

2 3 4 5 6 7 8 9 10 11 

COMMISSION 

TOTAL ERROR (96) 

OVERALL ACCURACY = 37 % 

KHAT ACCURACY = 25% 

(con.) 
Appendix H (Con.) 

STRUCTURAL STAGE 

CLASSIFIED 5 

STAGES 

from 6 

SBWC 

PO~Y~O~ 7 

TOTAL 

OMISSION 

ERROR (%) 

REFERENCE STAGE (Plot data) 

1 2 3 4 5 6 7 8 11 12 TOTAL 

52 

20 

14 

20 

54 

58 

151 

48 

1 

5 

423 

COMMISSION 

ERROR (%) 

7 1 

80 

79 

70 

94 

84 

50 

69 

100 

OVERALL ACCURACY = 31 % 

KHAT ACCURACY = 12% 
Keane, Robert E.; Garner, Janice L.; Schmidt, Kirsten M.; Long, Donald G.; Menakis, 

James P.; Finney, Mark A. 1998. Development of input data layers for the FARSITE fire 

growth model forthe Selway-Bitterroot Wilderness Complex, USA. Gen. Tech. Rep. RMRSGTR-3. Ogden, UT: U.S. Department of Agriculture, Forest Service, Rocky Mountain 

Research Station. 66 p. 

Fuel and vegetation spatial data layers required by the spatially explicit fire growth model 

FARSITE were developed for all langs in and around the Selway-Bitterroot Wilderness Area 

in Idaho and Montana. Satellite imagery and terrain modeling were used to create the three 

base vegetation spatial data layers of potential vegetation, cover type, and structural stage. 

Fire behavior fuel models and crown characteristics were assigned to combinations of base 

layer categories on these maps by local fire managers, ecologists, and existing data. 

FARSITE fuels maps are used to simulate growth of prescribed natural fires in the wilderness 

area, aiding managers in the planning and allocation of resources. An extensive accuracy 

assessment of all maps indicated fuels layers are about 60 percent accurate. This methodology was designed to be replicated for other areas. 

Keywords: fuels mapping, fire behavior fuel model, GIs, terrain modeling, satellite imagery 

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