MAPPING VEGETATION AND FUELS

FOR FIRE MANAGEMENT ON THE GILA NATIONAL FOREST

COMPLEX, NEW MEXICO

Robert E. Keane

Scott A. Mincemoyer

Kirsten M. Schmidt

Donald G. Long

Janice L. Garner

USDA Forest Service

RMRS-GTR-46-CD

USDA Forest Service

Rocky Mountain Research Station

The Fire Sciences Laboratory

Missoula, MT 59807

Phone: 406-329-4846, FAX: 406-329-4877

email: bkeane/rmrs_missoula@fs.fed.us
Keane, Robert E.; Mincemoyer, Scott A.; Schmidt, Kirsten M.; Long, Donald G.;

Garner, Janice L. 2000. Mapping vegetation and fuels for fire management

on the Gila National Forest Complex, New Mexico, [CD-ROM]. Gen. Tech. Rep.

RMRS-GTR-46-CD. Ogden, UT: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, 126 p.

Fuels and vegetation spatial data layers required by the spatially explicit fire growth

model FARSITE were developed for all lands in and around the Gila National Forest in

New Mexico. Satellite imagery, terrain modeling, and biophysical simulation were used

to create the three vegetation spatial data layers of biophysical settings, cover type, and

structural stage. Fire behavior fuel models and vegetation characteristics needed by

FARSITE were assigned to combinations of categories on maps developed from sampled

field data and also from estimates by local fire managers, ecologists, and resource specialists. FARSITE fuels maps will be used to simulate growth of fires on the Gila National Forest aiding managers in the planning and allocation of resources for managing

fire. An extensive accuracy assessment of all maps indicated surface and crown fuels

layers are about 30 to 40 percent accurate. This methodology was designed to be replicated for other areas of the western United States.

Keywords: Gila National Forest, FARSITE modeling, fuels mapping, fire behavior fuel

model, GIS, terrain modeling, satellite imagery classification, vegetation

mapping, biophysical classification

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

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

Acknowledgments

This project was a cooperative effort between the USDA Forest Service Rocky Mountain Research Station Fire Sciences Laboratory, the Gila National Forest, and the Fire

Management and Engineering staffs of Region 3. We thank all who helped with the

successful completion of this project including Lindon Weibe, Gary Benavidez,

Abel Camarena, Paul Boucher, and Bruce Williams of the Gila National Forest; Bill

Krausmann, Martin Martinez, Dan Winter, and Ron Moody of Southwestern Regional

Office; Wayne Cook, Cameron Johnston, Kevin Ryan, Dennis Lemond, and Dennis

Simmerman of Fire Sciences Laboratory, Missoula, MT; and Mark Finney, Systems

for Environmental Management, Missoula, MT. We would especially like to acknowledge the superior efforts of Owen Williams and Lawrence Garcia of the Gila National

Forest for their help in field sampling and logistics.

Abstract
The Authors

Robert E. Keane is a research ecologist with the USDA Forest Service, Rocky Mountain Research Station at The Fire Sciences Laboratory, P.O. Box 8089, Missoula, MT 59807. Phone

406-329-4846, fax 406-329-4877, email: bkeane/rmrs_missoula@fs.fed.us. Since 1985, 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 mapping of fuels for fire

growth and fire effects; synthesis of a First Order Fire Effects Model; construction of mechanistic ecosystem process models that integrate fire behavior and effects into succession simulation;

restoration 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.

Scott A. Mincemoyer is a biological science technician with the USDA Forest Service, Rocky

Mountain Research Station, Fire Sciences Laboratory, P.O. Box 8089, Missoula, MT 59807.

Phone 406-329-4844, email: smincemoyer/rmrs_missoula@fs.fed.us. Mincemoyer has worked

on several research projects for the Fire Effects Project since 1995, including field sampling for

the Gradient Modeling and Remote Sensing Project, whitebark pine restoration, and characterization of fuels and vegetation on the Tenderfoot Creek Experimental Forest. Mincemoyer received

his B.S. degree in forest resource management from the University of Montana, Missoula.

Kirsten M. Schmidt is a resource information specialist with the USDA Forest Service at the

Rocky Mountain Research Station, Fire Sciences Laboratory, P.O. Box 8089, Missoula, MT

59807. Phone 406-329-4957, fax 406-329-4877, email: kschmidt/rmrs_missoula@fs.fed.us.

Schmidt has worked on several research projects for the Fire Effects Unit involving GIS analysis

and field sampling for ecosystem management, gradient modeling, and fire behavior and effects

modeling. 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 Intermountain Fire Sciences

Laboratory, P.O. Box 8089, Missoula, MT 59807. Phone 406-329-4846, fax 406-329-4877,

email: dlong/rmrs_missoula@fs.fed.us. 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 an M.S. degree in forest resources at the University of Idaho, Moscow.

Janice L. Garner is a resource information specialist 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, email: jgarner/rmrs_missoula@fs.fed.us. 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.
CONTENTS

INTRODUCTION .................................................................................................................... 1

Background................................................................................................................... 1

FARSITE Description .................................................................................................. 4

Fuel Mapping Studies ................................................................................................... 7

Study Area .................................................................................................................. 11

METHODS ............................................................................................................................. 13

Framework .................................................................................................................. 13

Mapping Approach ..................................................................................................... 19

Field Sampling............................................................................................................ 20

Sampling Strategy .......................................................................................... 22

Sampling Stratification ................................................................................... 23

Plot Measurement Detail ................................................................................ 25

Vegetation Classifications .......................................................................................... 28

PVT Classification.......................................................................................... 29

Cover Type Classification .............................................................................. 30

Structural Stage Classification........................................................................ 32

Fuels Classification ........................................................................................ 32

Ancillary Layer Development .................................................................................... 34

Vegetation Base Layer Development ......................................................................... 36

Gradient Biophysical Settings Map ................................................................ 38

PVT Map ........................................................................................................ 40

Cover Type, Structural Stage, and Canopy Cover Maps................................ 41

Base Layer Refinement .................................................................................. 43

FARSITE Input Layer Development.......................................................................... 43

Accuracy Assessment ................................................................................................. 45

RESULTS ............................................................................................................................... 48

Field Sampling............................................................................................................ 48

Draft Layer Development ........................................................................................... 50

Final Vegetation Classifications ................................................................................. 50

Vegetation Base Layer Mapping ................................................................................ 51

FARSITE Input Layer Mapping ................................................................................. 53

Accuracy Assessment ................................................................................................. 54

GNFC FARSITE CD .................................................................................................. 56

DISCUSSION......................................................................................................................... 57

Layer Accuracy........................................................................................................... 57

Field Sampling............................................................................................................ 62

Vegetation and Fuel Layer Development ................................................................... 65

CONCLUSIONS .................................................................................................................... 66

REFERENCES ....................................................................................................................... 66

GLOSSARY ........................................................................................................................... 78

APPENDICES ........................................................................................................................ 81
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000 1

INTRODUCTION

Background

The movement toward holistic ecosystem management, coupled

with the prolonged effects of 70 years of fire exclusion in the

western United States, has necessitated the use of fire for returning ecosystem health and preventing disastrous wildfires (Mutch

1994, Mutch and others 1993). Fire managers must evaluate the

potential size, rate, and intensity of a wildland fire to aid in shortand long-term wildland fire planning and resource allocation. Recent advances in computer software and hardware technology

have enabled development of several spatially explicit fire behavior simulation models that predict the spread and intensity of fire

as it progresses across the landscape (see Andrews 1989). Some

of these computer programs have the ability to project future fire

growth and compute possible parameters of wildland fires for

planning applications or for real-time simulations (Campbell and

others 1995, Richards 1990). One of the best spatially explicit

fire growth models is the computer program FARSITE (Fire Area

Simulator) available for most IBM-compatible personal computers (Finney 1995, 1998). FARSITE is currently used by many

wildland fire managers in the United States and other countries

to simulate characteristics of prescribed natural fires and wildfires (Finney 1998, Grupe 1998, Keane and others 1998a).

Realistic predictions of fire growth ultimately depend on the consistency and accuracy of the input data layers needed to execute

spatially explicit fire behavior models (Keane and others 1998a,

Finney 1998). FARSITE requires eight data layers for surface and

crown fire simulations (Finney 1995). These data layers must be

both precise and consistent for all lands and ecosystems across the

analysis area. More importantly, the layers must agree with all other

Geographical Information Systems (GIS) layers (i.e., spatially congruent). It is also helpful if these layers describe large land areas

(e.g., greater than one million acres) so simulated fires will not encounter missing data at layer boundaries (Grupe 1998). Comprehensive development of these input data layers requires a high level of

expertise in GIS methods, fire and fuel dynamics, field ecology, and

advanced computer technology. It also requires abundant computer

resources and field data. Unfortunately, many land management

agencies do not have the computer resources or expertise to develop

these complex spatial data layers.

So the FARSITE model, which is available for free to anyone,

requires fuels layers that are quite costly and difficult to build

(Keane and others 1998a). Since FARSITE has been selected by
2 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000

many federal land management agencies as the best model for predicting fire growth, many fire managers across the country are now

learning how to use this tool and trying to obtain the input data layers for their land areas (Grupe 1998, Campbell and others 1995).

Unfortunately, most fire and land managers do not have the fuels

maps, or even base maps from which they could create the fuels

maps, needed to run the FARSITE model for their area. Most existing vegetation layers and databases do not quantify fuels information to the level of detail or resolution needed by FARSITE.

Moreover, some attempts to create FARSITE layers from existing

maps have failed because of inexperience with fuels and vegetation

modeling and mapping in the context of fire behavior. And those

projects where suitable FARSITE layers were created, such as the

Selway-Bitterroot Wilderness Complex, have been expensive and

time-consuming, costing upwards of $0.10 per hectare and taking

as long as one or two years (Keane and others 1998a).

Fire managers from the Gila National Forest and the Southwestern

Regional Office of the USDA Forest Service had some unique fire

management challenges. Areas in and around the Gila National

Forest in southwestern New Mexico have a rich history of frequent

fires, especially in the montane dry forests and grassland ecosystems (Abolt 1997, Boucher and Moody 1998, Gonzales and Maus

1992). However, effective fire suppression during the last 70 years

coupled with intensive grazing has resulted in increased surface

and crown fuels, thereby creating the potential for uncontrollable

wildfires (Covington and others 1994). Moreover, intensive grazing in pinyon-juniper woodlands and grasslands had reduced fuel

loads so much that conifer encroachment has proceeded unchecked

by fires (Boucher and Moody 1998, Szaro 1989). Fire managers

wanted to use the FARSITE computer program to simulate current

and future fires for planning and real-time fire management, but

they did not have the resources to construct the detailed FARSITE

input layers needed for such a large area. Moreover, they wanted

to develop spatially explicit, digital fuels maps useful for other fire

management concerns, such as smoke generation and fuel consumption, to include in the fire planning process. We had just completed

development of FARSITE input layers for 1 million ha (2.3 million

acres) in the Selway-Bitterroot Wilderness Complex (SBWC) and

had refined several new methods for mapping fuels and vegetation

in mountainous terrain (Keane and others 1998a, 1998b). The Gila

National Forest managers asked us to develop FARSITE data layers for their area using these new methods. They would then take

the methods learned for their area to show other Forests in the Region how to map fuels on their areas.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000 3

This Gila fuels mapping project presented some challenging opportunities. First, there were no georeferenced ecological plot data for

any part of the study area that were useful for mapping fuels and

vegetation. In addition, we found very few GIS layers or paper maps

available that were useful for FARSITE input mapping. To us, this

meant that the development of vegetation and fuels classifications

was unencumbered by existing classifications and data. Next, the

fire managers wanted to use the layers for more than FARSITE fire

behavior predictions. They needed spatially explicit data layers to

predict smoke production, plan prescribed fire activities, and prioritize treatment areas. In addition, other resource groups besides

fire management on the Gila National Forest needed vegetation

layers to plan other management activities in wildlife and ecosystem restoration. This lack of spatial data and the inclusion of these

additional objectives meant that we essentially had to start from

“scratch” and could design every needed data layer around specific

management objectives and FARSITE requirements, thereby minimizing translation problems with historical data and classifications.

Moreover, the lack of field and spatial data meant that we did not

have to follow existing classifications but could design efficient

sampling methodologies and new vegetation and fuel classifications

that would be meaningful to all mapping objectives. This was vastly

different from our SBWC mapping effort where we encountered

problems with translating existing georeferenced plot data to the

vegetation classifications useful to fuel mapping, and incompatible

existing and potential vegetation classifications that were rarely in

agreement because they were developed independently (Keane and

others 1998b).

The primary objective of this mapping project was to develop all

input spatial data layers required by FARSITE to spatially simulate fire behavior on lands in and surrounding the Gila National

Forest. In addition, we agreed to develop several other vegetation

and biophysical layers and relational databases useful for other

phases of fire and natural resource management. In fact, the vegetation base layers developed for the primary objective of creating

FARSITE input data layers provided a context to develop layers

for the secondary objectives.

Some relevant terminology must be defined to avoid confusion for

the reader. First, the term polygon describes a delineated area of

similar environmental and vegetation conditions (Jensen 1986). In

this paper, the terms “stand” and “polygon” have nearly identical

meanings. Spatial data layers are either raster or vector layers. A

raster layer is a grid of pixels over the geographic region of concern. Every pixel is square and its size defines the resolution of the

layer. For the GNFC project, all pixels are 30 meters in width or
4 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000

900 m2 in area. A raster layer is defined as a georeferenced grid

of pixels with each pixel assigned a value that describes a certain

characteristics of the associated piece of ground. A vector layer is

composed of georeferenced lines that define spatial objects—in

this case, stands or polygons. Georeferenced plot data are field

data collected within a fixed area plot whose center has been spatially referenced using a geographic coordinate system, often estimated using a Geographic Positioning System (GPS). In this study

we used Universal Transverse Metacor (UTM) coordinates in

UTM zone 12 with NAD 27 projection. Fuels are defined as the

live and dead biomass that either contribute to the advancement of

the fire front or are consumed after the flaming front has passed.

Fuels are usually categorized as live or dead foliage and wood

(Anderson 1982). Woody fuels are further stratified by four size

classes based on their drying rates (Fosberg 1970). Two types of

classifications are commonly discussed throughout this paper. A

vegetation classification is a hierarchical list of categories that describe some characteristic of the vegetation referenced by a corresponding key to these categories. The three vegetation classifications used in this project are cover type, structural stage, and

potential vegetation type. An image classification is the grouping

of pixels based on similar spectral reflectance characteristics to

map categories for a vegetation classification. A Glossary has been

provided for terminology related to this project.

FARSITE Description

FARSITE requires eight spatial data layers for a comprehensive

evaluation of surface and crown fire behavior. The first layer 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 easily derived from the DEM layer in

a GIS using elevation values from surrounding pixels. The fourth

layer is a Fire Behavior Fuel Model (FBFM) map. Pixels in this

layer are assigned the Anderson (1982) fire behavior fuel model

that best represents the surface fuel complex for the corresponding

piece of ground. Pixels are assigned one of the 13 models of

Anderson (1982) or assigned a customized fuel model (Finney

1995). We used seven of Anderson’s (1982) 13 FBFMs and then

built two customized fuel models for some unique conditions in

the GNFC (Table 1). Average canopy cover is needed to compute

hourly fuel moistures and reduce wind under the forest canopy.

Canopy cover (percent) is the average vertically projected tree

crown cover in the stand. These are the layers needed to simulate

surface fire behavior and growth.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000 5

FARSITE can compute crown fire behavior if three other vegetation

data layers are present. Average stand height (m) and average

crown base height (m) data layers are needed to compute crown

fire initiation based upon the Van Wagner (1977, 1993) crown fire

model. Stand height is the average height of the dominant tree layer.

Crown base height is the average height to the bottom of the tree

crowns in the stand. A crown bulk density raster layer is used to

compute crown fire spread, along with the previously mentioned

crown cover map. Crown bulk density (kg m–3) is the density of

the combustible tree crown biomass above the shrub layer. We

used vegetation characteristics based on cover types to guide our

estimations of crown bulk density in the field since it is a difficult

parameter to directly sample (Table 2).

FARSITE spreads fire across a landscape using the fire behavior

routines found in the one-dimensional fire model BEHAVE

(Andrews and Chase 1989, Andrews 1986, Burgan and Rothermal

1984, Rothermal 1972). FARSITE computes fire intensities and

spread rates for numerous points along the existing fire line using

the fire behavior algorithms of Albini (1976) and Rothermal (1972).

Fire is then propagated across the landscape from these points using

a series of eclipses based on Huygen’s principle (Anderson and

others 1982), which is a wave-type model (Richards 1990). Huygen’s

principle essentially states that a wave can be propagated from

points on its edge that serve as independent sources of smaller

waves (Richards 1990). Dimensions of the ellipses are computed

from the fire behavior predictions. FARSITE then connects all

points at the end of the smaller waves using topological algorithms

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

in Anderson (1982) except for the pinyon-juniper (number 50), which we built specifically for this

project.

Fire behavior2

Fuel model1 Description Rate of spread Flame length

(m/sec) (m)

1 Short grass (0.3

 m) 0.436 1.22

2 Timber (with grass and understory) 0.196 1.83

5 Brush (shrubs and conifer regeneration, 0.8 m) 0.10 1.22

6 Dormant brush 0.179 1.83

8 Closed timber litter 0.0089 0.31

9 Ponderosa Pine duff 0.042 0.79

10 Timber (litter and understory) 0.044 1.46

50 Pinyon-juniper 0.004 0.15

98 Water — —

99 Non-vegetation (rock, mines, barren) — —

1

 From Anderson (1982).

2

 Fire behavior under the following conditions: windspeed 8 km/hr, dead fuel moisture 8%, and live fuel moisture 100%.
6 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000

and this delineates a fire line at a given time. The fuels, weather

and topography of areas within the fire line dictate fire intensity

and spread rates. A complete discussion of FARSITE algorithms

is presented in Finney (1998) and it is recommended that this

document be read before FARSITE is used.

Table 2—Crown Bulk Density Assignments after Brown (1978) and Pollard (1971).

Crown Bulk Density (kg/m3)

Canopy cover

Species Low cover Medium cover High cover

Ponderosa Pine

Small 0.10 0.12 —

Medium/Large 0.09 0.14 0.20

Douglas-fir

Small 0.10 0.12 —

Medium/Large 0.10 0.18 0.25

Aspen

Small 0.10 0.01 —

Medium/Large 0.01 0.01 0.01

Gambel Oak

Small 0.01 0.01 —

Medium/Large 0.01 0.01 0.01

Juniper

Open Woodland 0.03 — —

Closed Woodland — 0.05 —

Piñon

Open Woodland 0.03 — —

Closed Woodland — 0.05 —

Evergreen Oak

Open Woodland 0.03 — —

Closed Woodland — 0.06 —

Subalpine Fir - Spruce

Small 0.12 0.14 —

Medium/Large 0.12 0.20 0.27

Ponderosa Pine - Douglas-fir

Small 0.10 0.12 —

Medium/Large 0.10 0.16 0.22

Pinyon - Juniper

Open Woodland 0.03 — —

Closed Woodland — 0.04 —

Broadleaf Riparian Forest

Small 0.01 0.01 —

Medium/Large 0.01 0.01 0.01

Broadleaf - Conifer Mix

Small 0.12 0.14 —

Medium/Large 0.12 0.20 0.27

Conifer - Gambel Oak

Small 0.10 0.12 —

Medium/Large 0.09 0.14 0.20

Conifer - Woodland Mix

Small 0.10 0.12 —

Medium/Large 0.09 0.14 0.20

Mixed Woodland

Open Woodland 0.03 — —

Closed Woodland — 0.04 —

Mixed Conifer - Mesic

Small 0.10 0.12 —

Medium/Large 0.10 0.18 0.25

Mixed Conifer - Xeric

Small 0.10 0.12 —

Canopy Cover Descriptions: Low = 21-50 percent overstory canopy cover; Medium = 51-80 percent overstory canopy cover; High = 81-100 percent overstory canopy cover
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000 7

Weather data are not input as a spatial data layer, but rather as a

set of generalized ASCII files composed of a stream (i.e., list) 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 and other algorithms. 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 present in the FARSITE users manual

(Finney 1995). We included several weather files in the Gila

FARSITE database for this project so fire managers wouldn’t have

to obtain these data from complex sources.

FARSITE creates many raster and vector spatial data layers and

tabular ASCII databases such as maps and summaries of computed

fire intensity (kW m–1), spread rates, and flame lengths stratified

by space and time variables. Fire growth and intensity patterns can

be interactively displayed on the computer screen overlaid on top

of topography and fuels layers. FARSITE was developed primarily

to be used as a tool in the management of prescribed natural fires

so that maximum allowable perimeters could be predicted. However, its use has grown to many other phases of fire management

including wildfire planning, prediction and real-time management.

All FARSITE output layers can be imported into a GIS for additional analysis and display. Keane and others (1996b) linked

FARSITE to a the forest succession model Fire-BGC to evaluate

the effects of fire across a large landscape in the Bob Marshall

Wilderness Complex, Montana.

Fuel Mapping Studies

There have been few studies where the sole objective was to map

fuels for the prediction or description of fire behavior. Most studies

map vegetation, then assign fuel models to the vegetation classification. However, Grupe (1998) used the Terrestrial Ecosystem

Survey (TES) to quantify and map FARSITE input requirements

for the Cibola National Forest, New Mexico, but found that, although TES contained sufficient data to quantify crown fuel information, there was not enough information to create or assign

FBFM to land areas. Moreover, he found that small variations in

fuel models significantly affected fire behavior predictions. De

Vasconcelos and others (1998) mapped the Anderson (1982) fuel

models over a 192,000 ha region in north-central Portugal using

neural network pattern searching on elevation, land use, and satellite imagery layers. They found this method strongly differentiated
8 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000

between grassland and shrubland fuel models with accuracies between 33 to 75 percent depending on land cover type. Their study

emphasized the importance of ground data to train, test, and validate the neural networks. And of course, Keane and others (1998a)

specifically mapped fuels for FARSITE use and their work is referenced throughout this paper.

Most studies mapped vegetation first and then developed fuels layers from the vegetation layers for fire modeling. Jain and others

(1996) intensively sampled fuels for all categories of a forest type

map created from IRS LISS II (Linear Image Self Scanning) imagery to create a fuel map for Rajaji National Park in India. Fire fuel

model maps of the North Cascades National Park were developed

by Root and others (1985) from plant community maps created

from 1979 Landsat MSS (Multi Spectral Scanner) imagery and environmental gradients. They assigned both the NFDRS (Deeming

and others 1978) and the Northern Forest Fire Laboratory (NFFL)

(Albini 1976) fuel models to each classified vegetation type. A

similar approach was used by Miller and Johnston (1985) where

they assigned NFDRS fuel models to vegetation classifications of

MSS and AVHRR imagery. Mark and others (1995) assigned

Anderson (1982) fuel models to combinations of timber size class,

stocking level, crown density, crown texture, and vegetation type

that were sampled or extrapolated attributes of photo-interpreted

polygons in their timber stand atlases.

In Canada, Canadian Forest Fire Behaviour Prediction System fuel

types were assigned to vegetation categories on maps created from

Landsat MSS data for Wood Buffalo National Park (Wilson and

others 1994), Quebec (Kourtz 1977), and Manitoba (Dixon and

others 1985). Hawkes and others (1995) used an expert systems

approach to assign Canadian Fire System fuel types to combinations of stand structure and composition information obtained from

forest surveys. Taylor and others (1998) used a similar method to

simulate the changes in fuel characteristics from stand conditions.

In Taiwan, SPOT imagery and GIS were used to create land use

types that were linked to NDVI greenness estimates to predict spatial

changes in vegetation phenology (Hsieh 1996). Roberts and others

(1998) used AVIRIS (Airborne Visible Infra-Red Imaging Spectrometer) satellite sensor imagery and spectral mixture analysis to

classify vegetation fraction, cover, and water content which were

then related to fuel loadings directly sampled on the ground. Yool

and others (1985) used TM imagery to describe brushy fuels in

southern California while Hardwick and others (1996) assigned

Anderson (1982) fuel models to vegetation categories from the

TM-derived CALVEG vegetation map to create a fuel map for the

Lassen National Forest.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000 9

A major disadvantage of this approach is fuels are not always correlated with existing vegetation characteristics or land-use categories because stand history, biophysical setting, and vegetation

structure are also significant factors governing fuel characteristics.

They need to be incorporated into the fuel model assignment protocols. Another disadvantage is that vegetation layers are often composed of stands or polygons that may be too coarse for fine scale

fire spread prediction. Homogeneity of the fine scale fuel mosaic

may generate “smoothed” fire spread predictions which may not be

realistic (Finney 1998). This indirect approach is often the easiest

and quickest because many vegetation classifications and maps are

available and most people can identify vegetation types with little

trouble (Eyre 1980). The greatest benefit of this approach is the

creation of a vegetation map that can be useful for other land management applications. Other attributes can be assigned to land use

categories to create other useful maps. The ICBEMP (Interior

Columbia River Basin Ecosystem Management Project) effort assigned wildlife habitat levels to the coarse scale cover type map to

estimate historical to current declines in habitat value (Quigley and

others 1996).

Projects where fuels were directly mapped from remotely sensed

products such as aerial photos and satellite imagery have the highest success when estimating total living and dead biomass in grasslands and shrublands (Friedl and others 1994, Millington and

others 1994), and have limited use for assessing surface fuels in

forested ecosystems because of canopy obstruction of the forest

floor. Principal components and NDVI calculated from AVHRR

(Advanced Very High Resolution Radiometer) imagery composites

of the western United States were classified directly to fuel classes

that were based on vegetation for input to a Initial Attack Management System (McKinley and others 1985). The three images generated from the tasseled cap transformation on Thematic Mapper

(TM) multispectral data have been used to classify chaparral shrub

fuel characteristics across mid-scale landscapes in California

(Cohen 1989, Stow and others 1993). Merrill and others (1993)

estimated living grassland biomass in Yellowstone National Park

using regression models on bands 4, 6, and 7 from MSS data.

Salas and Chuvieco (1994) classified TM imagery directly to 11

of Anderson’s (1982) fuel models, then assigned vegetation categories to each fuel model to compute fire risk on a large landscape in

Spain. An Anderson (1982) fuel model map was directly classified

from TM imagery of Camp Lejeune, North Carolina, for simulating prescribed fires with FARSITE (Campbell and others 1995).

A special kriging technique called isarithmic analysis was used to

interpolate sagebrush fuel loadings across a small Colorado landscape from field data (Kalabokidis and Omi 1995). At very fine
10 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000

scales, large scale aerial photography has been successfully used to

estimate natural and slash fuel distributions in a variety of forested

settings in Canada (Belfort 1988, Dendron Resource Surveys 1981,

Morris 1970, Muraro 1970).

The use of environmental gradients to predict fuel characteristics

has had mixed success. These gradients can be topographical

(elevation, aspect, slope), vegetational (successional stages), biophysical (soils, landform), or biogeochemical (evapotranspiration,

productivity, nutrient availability). Kessell (1979) used seven gradients based on topography and vegetation to predict fuel models

and loadings in Glacier National Park, Montana. Habeck (1976)

sampled fuels and vegetation in the Selway-Bitterroot Wilderness

Area of Idaho and related fuel loadings to stand age and moisturetemperature gradients. Keane and others (1997b) developed an untested protocol for mapping fuels from several biogeochemical and

biophysical variables using an extensive network of field plots.

Kessell and Catellino (1978) used a form of gradient modeling to

predict chaparral fuels in California.

One advantage of the gradient approach is that an expression of

the surrounding environment provides a context in which to understand and predict fuel dynamics (Whittaker 1967). For example,

low fuel loadings in a stand may be explained by low precipitation,

high evapotranspiration, and shallow soils. Furthermore, environmental gradients that describe important ecosystem processes, such

as biogeochemical cycles, correlate well with fuels dynamics and

therefore provide a temporal and spatial framework for creating fuels maps. Climate change effects on spatial fuel loadings can be

easily created by recomputing the environmental gradients under

the new climate (Keane and others 1996b). Most environmental

gradients are scale-independent which means that the same gradients may be used to predict fuel characteristics across many spatial

scales regardless of pixel size (Kessell 1979, Whittaker 1967). A

problem with this approach is that gradients do not provide a spatial description of existing conditions and remotely sensed data are

often needed to portray vegetation-based gradients such as succession classes or cover types. Gradient information is best used to describe the potential of a landscape or stand rather than to compute

existing conditions (Keane and others 1997b, Kessell 1979). Some

fuel and vegetation mapping projects have merged combinations of

the above approaches to map fuels. Keane and others (1998a) used

terrain modeling to differentiate environmental gradients using potential vegetation types (Pfister and others 1977) and satellite imagery to differentiate vegetation types to create FARSITE fuel

maps for several areas in the Rocky Mountains.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000 11

Study Area

FARSITE input layers were developed for all lands in and around

the Gila National Forest with boundaries defined by the limits of

the satellite imagery and Digital Elevation Model (DEM) coverage

(see bold line in Figure 1). This study area will hereafter be referred

to as the Gila National Forest Complex or GNFC. The Continental Divide winds its way through the GNFC, where elevations

range from 1370 m in the low elevation grasslands to over 3000 m

along the Mogollon Rim in the southwestern GNFC.

Figure 1—Boundaries of the Gila National Forest Complex (GNFC). Boundaries are set by the extent

of the Digital Elevation Models (DEMs) and the extent of the 1993, 1996 Thematic Mapper satellite

imagery.
12 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000

Vegetation in the GNFC ranges from desert grassland and scrub at

the lowest elevations to subalpine forest at the highest elevations.

Mixed woodlands of pinyon (Pinus edulis), juniper (Juniperus

spp.), and oak (Quercus spp.) and forests of ponderosa pine (Pinus

ponderosa) interspersed with plains-mesa grasslands at mid elevations occupy large expanses of the GNFC. Upper elevations are

dominated by montane coniferous forests of white fir (Abies

concolor), blue spruce (Picea pungens), Douglas-fir (Pseudotsuga

menziesii), and southwestern white pine (Pinus strobiformis) with

the highest slopes and ridges dominated by subalpine coniferous

forests of subalpine fir (Abies lasiocarpa) and Engelmann spruce

(Picea engelmannii). Broadleaf forests of quaking aspen (Populus

tremuloides) and gambel oak (Quercus gambelii) also occur interspersed throughout the montane and subalpine forests (Kruse and

others 1996, Dahms and Geils 1997).

GNFC climate is monsoonal with dry, mild winters and hot, dry

summers punctuated by a monsoon season of about two months

starting in mid-July. At Gila Hot Springs (elevation 1740 m), in the

center of the Gila Wilderness area in the GNFC, average daily temperatures range seasonally from 1.7° C (35° F) to 21° C (70° F)

and average daily precipitation ranges seasonally from .02 cm to

.28 cm (McCurdy 1997). At higher elevations (2500 m), temperatures range from –5° C (22° F) to 14° C (57° F) (Dick-Peddie

1993). Precipitation ranges from less than 200 mm per year in the

low elevation scrublands to over 1000 mm per year along the

Mogollon rim.

The geology of the GNFC is regionally simple but locally complex. Marine sediments were deposited during much of the Paleozoic Era as seas covered most of the GNFC (Chronic 1995). As

tension stretched the continental crust to the breaking point during

the middle Cenozoic Era, composite volcanoes spewed lava and

ash, eventually collapsing into large calderas in the western highlands of western New Mexico. More recently, geomorphic processes of erosion and mass wasting have created some unique and

complex landforms that dictate vegetation composition and fuel

dynamics. The GNFC landscape is highly dissected due to locally

intense rainstorms contributing to highly variable erosion processes which create diverse topographic and biophysical settings.

The dissected nature of the GNFC landscape affects the type and

loading of fuels.

The GNFC had a rich and varied fire history (Boucher and Moody

1998). Low severity surface fires occurred every 3 to 27 years

prior to 1900 in the ponderosa pine forests, and fires were more

frequent in the grass and shrublands. Non-lethal surface fires and
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000 13

some stand-replacement fires occurred in the montane ecosystems

at average fire intervals ranging from 10 to 50 years (Abolt 1997).

Historically, the vast majority of GNFC landscape was composed

of six fire behavior fuel models (Anderson 1982); the fast-moving

grassland models 1 and 2, and the shrub models 5 and 6, and the

slower-moving timber models 8 and 9 (Chronic 1995, Rixon 1905).

However, over a century of livestock grazing and 60 years of

active fire exclusion have dramatically altered fire regimes in the

GNFC (Boucher and Moody 1998, Dahm and Geils 1997, Savage

and Swetnam 1990). Stand-replacement fires are now more common because of the buildup of woody fuels and litter (Covington

and Moore 1994). Conifer stands are denser and have more canopy

layers that are more likely to result in crown fires (Covington and

others 1994).

Tree densities have increased exponentially with the lack of fires

because of abundant regeneration, and this has precipitated declines

in tree vigor and forest health that has, in turn, caused increases in

insect and pathogen infections (Abolt 1997, Covington and others

1994, Wilson and Tkacz 1996). Many stands that were historically

described by low severity fire behavior fuel models (FBFM) 8

(closed timber low woody fuel loadings) and 9 (litter understory)

have now succeeded to FBFM 10 (closed timber heavy fuel loading) which predicts severe fire behavior (Boucher and Moody

1998). Grasslands have been invaded by pinyon and juniper, which

can create depauperate understories devoid of fine fuels. Now, fuel

models for historical grasslands have gone from a FBFM 1 (grasslands) to some custom fuel model with much less fine fuels and

less intense fire behaviors (FBFM 50 in Table 1). This anthropogenic modification of landscape fuels has made development of

FARSITE fuels layers all the more imperative because these layers

will be used to plan the prescribed fires that will help restore historical fire regimes and return ecosystem health.

METHODS

Framework

There are many reasons why FARSITE input data layers are difficult and costly to build. First and most important, most remotely

sensed products used for fuel mapping, such as aerial photos and

satellite images, are not particularly useful for discriminating different fuel types because the ground is often obscured from view

by the forest canopy (Elvidge 1988, Lachowski and others 1995).

Second, the most important layer needed by FARSITE, the fire

behavior fuel model (FBFM) layer, is not so much a quantitative
14 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000

characterization of fuel loadings, but rather a description of expected fire behavior for the stand (Anderson 1982, Burgan and

Rothermel 1984). Therefore, many people who do not have experience in fire behavior or fuel model classifications are often unable

to estimate the FBFM accurately and consistently. Next, the characterization of all the types and sizes of fuel in a FBFM is very difficult to discern from remotely sensed imagery. In fact, wildland

fire propagates primarily through the fine fuels (i.e., grasses,

needles, and small woody material less than 1 cm in diameter), and

the loadings of these small fuels are notoriously difficult to classify

from imagery in timber environments (Jensen 1986). Fourth, the

eight data layers needed to simulate fire growth must be developed

and mapped simultaneously so they are spatially congruent. This

means the crown height for a stand must not be taller than the

stand height for the same stand, for example. The three topography

layers (elevation, aspect, slope) are easily derived from DEM’s

(USGS 1987), but the surface fuel layer (FBFM) and the four

crown fuel layers (closure, bulk density, stand height, and crown

height) must be consistently quantified in an ecological context

across large land areas (Finney 1998). Next, fuels are notoriously

variable in time and space, and it is difficult to match their scale

of measurement to the scale of mapping (Brown and Bevins 1986,

Whittaker 1962). Last, fuel maps must be developed at fine resolutions (e.g., 30 meter pixels) for the accurate simulation of fire behavior, and many existing stand and vegetation classifications and

maps are too coarse for use in FARSITE. So, since fuels are difficult to directly map or detect from imagery, we assumed that there

must be a suite of biophysical or biological spatial data layers that

are easy to map and yet correlate well with FBFMs and crown

characteristics.

The methodology we used to develop the GNFC FARSITE input

layers is based on the premise that most ecological characteristics,

especially fuels, can be described from three commonly used ecological classifications of site environment, species composition,

and stand structure. This assumes that many stand characteristics

pertinent to land and fire management can be uniquely represented

from a characterization of that stand’s biophysical environment,

dominant plant species, and vertical structure of the vegetation,

called the vegetation triplet. The site environment is important because critical, site-dependent ecological processes such as productivity, decomposition, and fire regime often govern fuel loadings

and fuel characteristics (Brown and Bevins 1986, Waring and Running 1998, Whittaker 1967). Species composition is important because branchfall and leaffall rates are unique to many forest and

range communities. Moreover, the diverse fuel accumulation rates

and varied species morphology of plant communities can create
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000 15

unique fuelbed characteristics (Brown and See 1981, Brown and

Bevins 1986). Stand structure is critical because it describes the

vertical arrangement of live and dead biomass above the ground

surface (O’Hara and others 1996, Oliver and Larson 1990). The

vertical arrangement of biomass, both on the ground and in the air,

dictates the subsequent intensity and severity of a fire, especially

a crown fire. The mapping protocol presented here is not the only

way to map fuels for fire behavior prediction. There are many

other methods for constructing fuel maps such as timber stand map

assignments, direct satellite image classification, and photo interpretation (Grupe 1998, Mark and others 1995). But, we feel the

methods presented in this paper provide for the most comprehensive and robust map products without exorbitant development

costs.

This vegetation triplet concept has been successfully used to indirectly describe ecological characteristics for many applications.

Arno and others (1985) classified successional plant communities

from a description of habitat type (i.e., biophysical setting), cover

type, and diameter structure. Keane and others (1996a) used this

framework to simulate landscape succession at coarse, mid- and

fine scales (Keane and others 1997a, Keane and Long 1998).

Moreover, many hydrologic, wildlife, fire, and fuels characteristics

were mapped for the Interior Columbia Basin Ecosystem Management Project (ICBEMP) scientific assessment from these three

characteristics (see 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. Keane

and others (1998b) detail four fuel mapping projects across the

western United States where this approach was used.

The problem then is to select the set of three classifications that

best describe environment, composition, and structure. For the

GNFC project, we selected the classifications of biophysical settings to describe site environment, cover type to describe species

composition, and structural stage to describe the vertical stand

structure. Hereafter these three classifications will be referred to

as the base vegetation classifications and maps. We assumed a

myriad of ecosystem characteristics, including surface and crown

fuels, can be quantified from this triplet based on past succession

and ecological research (Arno and others 1985, Steele and GeierHayes 1989, Kessell and Fischer 1981). Classifications similar to

these three were used successfully for the Selway-Bitterroot Wilderness Complex fuel mapping project and several other projects

in the northern Rocky Mountains (Keane and others 1998a, Keane

and others 1998b).
16 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000

The biophysical settings layer was to be created from a biophysical settings classification of the GNFC landscape that integrated

climate, hydrology, evapotranspiration, vegetation, and soils processes to spatially predict changes in the GNFC environment important to fuels mapping. We were going to use the mechanistic

gradient model of Keane and others (1997b) to spatially predict

biophysical settings by coupling topographic variables such as

landform, elevation, and aspect to climate and biogeochemical process variables such as precipitation, evapotranspiration, and productivity to define GNFC biophysical settings categories. However, the simulation and mapping technology and expertise needed

to perform this extensive task were not ready at the time. Instead,

we decided to create the biophysical settings map from topographic

rulebased terrain modeling based on potential vegetation type (see

detailed description below). We did, however, test the applicability

of the Keane and other (1997b) gradient model in creating the biophysical settings layer as presented in later sections.

Biophysical settings are inherently difficult to map because they

represent the complex integration of long-term climatic interactions with vegetation, soils, fauna, and disturbance (Keane and

others 1996a, Deitschman 1973). Moreover, selection of those

biophysical processes critical to fuel dynamics is difficult because

most are unknown or unquantifiable. Biophysical setting categories

are hard to identify in the field because of their temporal aspect;

many are quantified as a rate such as precipitation (mm year–1).

For example, one would need to place a weather station within

each mapped polygon for several years to identify the biophysical

setting categories described by climate. So, a vegetation-based

classification was needed to easily identify biophysical settings on

the ground. The biophysical classification can then be cross-referenced to the vegetation-based potential vegetation classification to

directly identify biophysical settings from a plant key. Plant classifications that accurately identify the potential vegetation able to inhabit a site in the absence of disturbance provided the perfect linkage between biophysical settings and vegetation (Pfister and others

1977, Daubenmire 1966). Therefore, a Potential Vegetation Type

(PVT) classification was used to identify biophysical settings in

the field (Keane and others 1998b).

This linkage of the “bottom-up” PVT classification with the “topdown” biophysical settings classification (gradient model) will

eventually provide a more robust approach to mapping site conditions. This strategy does not require the mapping of both PVT and

biophysical settings, but rather uses each to improve the ultimate

classification of site environment. The great utility of this approach

is that these two site classifications can be used alone or in tandem.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000 17

The biophysical settings layer can be used if field data and ecological expertise are limited, while the PVT settings can be used if

weather, soils, simulation model input data are unavailable. And

the PVT and biophysical settings layers can be linked to improve

the overall site descriptions. For example, the mapping of PVT categories from terrain data (topography) can be enhanced if the data

layers of annual rainfall, solar radiation, and net primary productivity are used to refine the topographic ruleset (Keane and others

1997b).

A PVT describes the composition of near-climax communities at

the endpoint of succession (Daubenmire 1966). Theoretically, a

PVT supports a stable, self-perpetuating plant community in the

absence of disturbance (Pfister and others 1977). This community

exists within a unique set of environmental conditions that can

serve as a surrogate for classifying environmental site conditions

(Arno and others 1985, Pfister and others 1977, Steele and GeierHayes 1989, see Jensen and others 1993). Habitat types and habitat

type phases (Pfister and others 1977) are roughly equivalent to

PVTs at fine spatial scales, while habitat type groups (Reid and

others 1995), fire groups (Fischer and Bradley 1987), or topographic

settings (Barrett and Arno 1992, Brown and others 1994, Keane

and others 1997b) can be used as PVTs at mid scales, which is the

scale of reference for most fuel mapping studies. The ICBEMP

created a biophysical settings layer from temperature and moisture

variables to classify and delineate coarse scale PVTs across the Interior Columbia River Basin (Reid and others 1995). Methods used

to create the biophysical settings layer and the PVT classification

for this GNFC project are discussed in detail in later sections.

Species composition is roughly characterized from cover types

with categories that generally describe the dominant plant species

based on a plurality of basal area and canopy cover for forest types

or based on vertically projected plant cover for rangelands. Examples of coarse and mid scale cover type categories are presented

in Shiflet (1994) for range types and Eyre (1980) for forest types.

Differences in cover types can be successfully discriminated from

satellite imagery and remote sensing but with a limited accuracy

(see Greer 1994, Lachowski and others 1995, Redmond and

Prather 1996, Shao and others 1996). Cover type maps can be

created from a multitude of remotely sensed products including

aerial photo interpretation, digitized stand maps, videography, and

satellite imagery (Lachowski and others 1995, Keane and others

1997b, Kessell 1979). Both cover type and structural stage maps

for the GNFC project were created from Landsat 5 Thematic Mapper (TM) satellite imagery using image processing techniques.
18 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000

Plant community structure is the vertical arrangement of dead and

live plant biomass above the ground and mostly describes the vertical characteristics of canopy layers and stem material. Stand structure was described in the ICBEMP by a process-based classification of structural stage which describes the vertical succession of

tree and rangeland structures during stand development (O’Hara

and others 1996, Oliver and Larsen 1990). However, preliminary

field investigations on the GNFC revealed many process-based

structural stages could not be accurately and consistently assessed

in the field or adequately discriminated with satellite imagery because of past disturbance history. Therefore, we simplified our

forest structural stages categories by relating them to tree diameter

size-classes and the rangeland stages by stratifying them by

lifeforms and site type.

There are many advantages of using this “vegetation triplet” approach to mapping fuels. First, the concept can be used across

many spatial scales because the categories in each of the three

classifications are easily 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, many land management

agencies already use some form of these classifications in their

every day management activities, and these classifications can be

easily developed if they do not exist for some areas. There is also

a large body of research available on these types of classifications

and their mapping (Arno and others 1985, Steele and Geier-Hayes

1989). Many National Forests have existing classifications for

these three attributes, and many of their databases contain fields

for these classifications, but very few have accurate maps of these

attributes across large land areas as yet. Fourth, this vegetation

triplet provides a context in which to interpret fuels maps. For example, it is useful to know that a stand received a closed conifer

FBFM because it is a high elevation, northfacing site with a sprucefir cover type in the pole stage. Next, many types of georeferenced

field data can be used to identify categories of these classifications

in the field. Numerous historical plot data contain plant species lists

that can be keyed to cover type and PVT. These layers can easily

be updated and refined, and new categories can be added as additional field data become available. Lastly, and probably most importantly, these layers can be used to map, not only the FARSITE

input data layers, but also many other ecosystem characteristics

such as hiding cover, coarse woody debris, and erosion potential

useful to wildlife, fuels, and hydrology issues.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000 19

Mapping Approach

This mapping project required many tasks to be done simultaneously so that it could be completed in less than three years with

two years of field sampling. This means that many critical steps

needed to be initiated without critical products from other steps.

For example, we could not afford to wait for the first year’s field

data to be collected to develop the vegetation classifications. Figure 2 generally describes the steps (listed vertically) in each phase

Figure 2—Steps involved in this fuel mapping process. To save time, many steps needed to be simultaneously.

Collect

Field

Data

Develop

Preliminary

Classification

Delineate

Polygon

Boundaries

Develop

Maps for

Sampling

Create

Field

Database

Refine

Classifications

Develop

Draft Maps

Reformat

Maps for

FARSITE

Input

Create CD for

Fire Management

Perform

Accuracy

Assessment

Collect

Additional

Field Data

Finalize

Classifications

Refine

Maps

Finalize

Maps

Create Fuels

Lookup

Database

Create

FARSITE

Fuels Maps

Field Data Classification Maps
20 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000

(listed horizontally) of the project (Field data collection, classification, and mapping). In summary, preliminary vegetation and fuels

classifications were developed and an unsupervised cluster layer of

tentative polygons was created to aid in collection of the first year

of field data and the first draft version of the FARSITE fuels maps

(Figure 2). Extensive field data were collected and organized into

a database that was then used to refine classifications. Polygon

boundaries were delineated for all areas of the GNFC using textural classification algorithms and then a supervised classification

of the same TM imagery was used to assign vegetation categories

to each delineated polygon. Field data were used to iteratively refine and improve both the supervised classification and polygon

assignments. Categories for each base vegetation classification

were finally assigned to each delineated polygon and then a fuels

lookup table was created from the field data to assign FARSITE

fuel parameters to each base vegetation triplet combination. These

maps were then reformatted to FARSITE format and written to a

Compact Disk (CD) for fire management.

Implementation of this vegetation-based strategy to create the

FARSITE layers involved the complex integration of field sampling, image processing, GIS, and fuel modeling tasks as illustrated

in Figure 3. In short, maps of PVT, cover type, and structural stage

were created from terrain modeling, gradient modeling, and satellite imagery using an extensive georeferenced field data set. Then,

the three layers were overlaid to identify all combinations of PVT,

cover type, and structural stage for every polygon. Illogical combinations were discovered and fixed using the field data and ecological knowledge of the area as criteria. Field data were then summarized by every PVT-cover type-structural stage combination to

assign an FBFM, crown height, stand height, crown closure, and

crown bulk density to each combination (right side of Figure 3).

Field data were also used to assign other ecological parameters discussed later to each combination. Lastly, raster maps of the eight

FARSITE layers were constructed and imported into the FARSITE

landscape format along with weather databases and other cartographic raster layers (Finney 1995).

Field Sampling

Collection of field data is the most critical task in the mapping of

fuels, even though it is often the most costly and time-consuming

part of any mapping effort. It would be difficult to overemphasize

the importance of obtaining ground-based data to guide fuel

mapping projects. As mentioned, it is nearly impossible to accurately describe fuels characteristics important to fire behavior

from remotely sensed imagery because the canopy obscures fuels
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000 21

Figure 3—Flow diagram illustrating the general procedure used to create the FARSITE input fuel maps

for the Gila National Forest Complex (GNFC) area.

Field

Data

Field

Data

Field

Data

DEM

Soils

Cartographic

Features

TM images

1993, 1997

Terrain

Modeling

Image

Classification

PVT

Cover

Type

Structural

Stage Crown

Closure

Crown

Bulk Density

Crown

Height

Stand

Height

FBFM

Field

Data

and

Workshop

Summaries

PVT - Potential Vegetation Type

DEM - Digital Elevation Model

FBFM - Fire Behavior Fuel Model

characteristics and the important fine fuels are too small to detect.

Therefore, plot data with georeferenced coordinates are the only

source available to accurately describe fuelbed characteristics for

mapping and relating these characteristics to other mappable entities that correlate closely with fuels. Field sampling is literally the

only way as yet to adequately describe fuel characteristics for fire

modeling for all situations. It would be folly to attempt to map

FARSITE input layers without adequate field sampling.

Georeferenced plot data are important for many reasons. First, field

data provide important ground-truth information or an accurate
22 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000

description of what is being sensed from imagery. This means

sampled polygons can be used as training areas that define classification categories for controlling image classification procedures

and techniques (i.e., supervised approaches) (Jensen 1986). In addition, field data can be used to describe a polygon classified from

unsupervised clustering techniques (Verbyla 1995). Field data also

provide a means for quantifying accuracy and precision of developed spatial classifications. These data are also useful for designing and improving keys for the vegetation classification categories

that are being mapped with imagery. But most importantly, field

data provide a means for interpreting image classifications. Reasons for inaccuracies in an image classification can be explored using detailed plot data. For example, an inaccurate shrub-herb classification category can often be improved if the cover of bare soil

and rock was sampled at each plot.

Sampling Strategy — We used a fixed-area plot sampling technique to describe ecological characteristics within each map unit

(i.e., polygon). Each plot was circular in shape and 405 m2 (1/10th

acre) in size. Plot centers were subjectively located in a representative portion of selected polygons without preconceived bias

(Mueller-Dombois and Ellenburg 1974, Arno and others 1985).

Representativeness was determined from disturbance history,

plant species composition, and site environment (Pfister and

others 1977). It was assumed conditions within the circular plot

were indicative of the stand or polygon as a whole. Each plot was

georeferenced using a Global Positioning System (GPS) and its coordinates were entered into a database that was later imported into

the GNFC GIS. We gathered all GNFC field data during the spring

and summers of 1997 and 1998.

Information collected at each plot was measured using modified

ECODATA methodology (Keane and others 1990, Jensen and others 1993). ECODATA is a set of standardized methodologies designed to measure common ecological characteristics using flexible procedures. ECODATA sampling techniques provided the

desired level of detail 1) to develop vegetation classifications, 2)

to map vegetation and fuels using satellite imagery, and 3) to understand and interpret each classification with ecological parameters. We modified ECODATA forms and methods so that the inputs required by FARSITE were directly measured on all field

plots. Plot forms used in this effort are presented in Appendix A.

ECODATA methods are explained in detail in Hann and others

(1988), Keane and others (1990), and Jensen and others (1993) so

we will not discuss those methodologies here, except to describe

the modifications specifically implemented for this study presented

in later sections.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000 23

Sampling Stratification — A preliminary map of GNFC polygons

was developed from an unsupervised classification of satellite imagery data obtained for this study to temporarily stratify the landscape for field sampling (Jensen 1986, Lachowski and others

1995). These tentative polygons were used to find map units to

sample on the ground. Their boundaries were improved as more refined image classifications were completed later in the study. Paper

copies of these preliminary polygon layers, made at the same scale

as a USGS 7.5 minute quadrangle map, were brought into the field

for navigation to the polygons selected for sampling.

Polygons were selected for sampling based on a geographic and

topographic hierarchical sampling stratification where the GNFC

was divided into ecological regions, then important environmental

gradients within each region dictated the sampling locations

(Dicke-Peddie 1993). The GNFC study area was divided into five

ecological zones based on vegetation, climate, and topographic diversity (Figure 4). The first zone, the Black Range zone, occurs on

Figure 4—The five ecological zones within the Gila National Forest Complex. These zones

geographically stratified major GNFC ecological types for sampling stratification, image classification, and terrain modeling.
24 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000

the eastern border of the GNFC and includes the Black and

Mimbres mountains. The second zone is the Pinos Altos zone,

located north of Silver City, New Mexico, where the absence of

white fir differentiates this mountain range from others with similar elevation ranges within the GNFC. The third zone, the Burro

Mountains Zone, includes the Burro Mountains, located to the

southwest of Silver City, New Mexico, where vegetation ranges

from desert scrub at the lower elevations to ponderosa pine and

gambel oak in the higher elevations. The fourth zone, the San

Mateo zone, includes the San Mateo Mountains, a portion of the

Cibola National Forest, located to the southwest of Socorro, New

Mexico. The fifth zone, the Western zone, includes the Mogollon

Mountains and the remaining lands of the study area and is characterized by woodlands and montane and subalpine coniferous forest

(Dick-Peddie 1993). These zones were partially created from a

classification of watersheds based on soils, topography, and geographical locations performed in the GNFC GIS. We refined the

zones as sampling progressed and as additional plot data became

available for evaluation (Figure 4). A consistent and comparable

sample size was imposed for each zone weighted by area.

We used these ecological zones not only to stratify plot sampling,

but also to refine and constrain the cover type and PVT base layers

needed to map fuels. This zonation allowed us to key certain cover

type and PVT categories to those areas where they occurred. For

instance, white fir does not occur in the Pinos Altos Zone, so we

did not map white fir cover types and white fir PVTs in the Pinos

Altos Zone. This zonation also allowed us to stratify the imagery

classification by broad ecosystem types so that we could constrain

the unique list of possible cover types and structural stages to each

zone and minimize image classification categories by geographical

region.

We originally divided each ecological zone by watershed using the

6th code Hydrologic Unit Code or HUC (Seaber and others 1987,

USGS 1990) to delineate mid-scale areas to sample based on the

following criteria: accessibility, representation of ecological zone,

full elevational range, and watershed size (20,000 to 40,000 ha).

The selected sample watersheds (HUCs) were further divided into

polygons generated from the unsupervised satellite image classification discussed above. However, because of limited time and

sampling crews, we had to abandon the HUC sampling stratification and instead sample based on accessibility, ecological zone,

and elevation range.

Selection of the polygons to sample was based on several factors. First, it was important to establish plots along important
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000 25

environmental gradients within each zone to ensure all combinations of vegetation and environments are represented in the field

data. Since the direct gradients of climate, soils, and disturbance

are often difficult to identify in the field, we needed a set of easily

measured surrogates that correlate well with these direct environmental factors (Keane and others 1997b). We selected the indirect

topographic gradients of elevation, aspect, and slope as important

indicators of environmental gradients to guide polygon selection.

These gradients served as guidelines for plot establishment and

were not absolute requirements. We tried to establish at least one

plot in each major topographic settings within each zone, but this

wasn’t always possible because of inaccessibility and time constraints. We also tried to sample all cover type and structural stage

combinations within a topographic gradient, but this was also difficult because the combinations were not known prior to going into

the field. Obviously, a great deal of field experience was needed to

guide the selection of sample polygons to ensure a robust data set

because of the somewhat subjective nature of polygon selection.

Plot Measurement Detail — It would have been nearly impossible to adequately sample the entire 2.3 million ha GNFC study

area to the appropriate level of intensity and detail needed to

accurately describe fuels for all project objectives because of

time, money, and access constraints. There was not enough time

or money to hire, train, and mobilize the field crews needed to extensively sample all ecosystems and topographical situations in the

GNFC study area. And, there were many private lands where we

could not gain access for sampling. Moreover, the level of detail

needed from each plot could take several hours to measure which

would prohibit large sample sizes needed for image classification

and accuracy assessment. In other words, the wide array of ecological characteristics specified for measurement at each plot

(e.g., tree sizes, plant species coverages, fuel loadings) would

have exhausted the time that could be used to sample more plots.

As a result, we needed to design a sampling strategy that would

balance measurement detail with sampling intensity and available

resources.

Three levels of measurement detail were utilized in this field approach. The most intensive sampling methodology was used to

quantify the myriad of ecological attributes and stand characteristics (e.g., fuel loadings, tree size distributions, plant species compositions) important to understanding the ecological interrelationships that influence vegetation and fuel dynamics in and around the

Gila National Forest. The next level of sampling detail was used to

quantify ecological attributes and stand characteristics used in all
26 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000

map layers and vegetation classifications. The least intensive measurement methodology was used to quickly assess accuracy of developed layers.

The most intensive measurements were taken on the Ecosystem

Characterization (EC) plots that were semi-permanently located

within representative portions of unsupervised polygons using a

wooden stake (See Appendix B for EC plot establishment details).

The primary purpose of this sampling scheme was to collect the

detailed information needed to characterize and describe all ecosystem attributes, especially actual fuel loadings, for assigning important attributes to base vegetation (PVT-cover type-structural

stage) categories. In addition, these plots were used to describe the

context and relationships of the mapped entities with other ecological characteristics such as plant cover, slope position, and ground

cover. EC plots were essential for understanding the interrelationships between fuels, climate, fire and vegetation, and they also provided baseline data for the calculation of many other ecological

characteristics useful to land management including timber biomass, hiding cover, and thermal cover.

General site information including lifeform plant cover, ground

cover, geology, topographic features, fuels descriptions, and fire

behavior models (FBFM) were estimated on the EC plots from the

General ECODATA methodologies described by Hann and others

(1988) and these data were recorded on a modified General Form

(GF) shown in Appendix A. We also recorded the eight FARSITE

input parameters specially added fields on this General form. Plant

composition measurements (species, percent cover, height) were

recorded on the ECODATA Plant Composition (PC) form with

tree and shrub cover stratified by six size classes (Appendix A).

Fuels data were recorded using the line intersect technique of

Brown (1974) described in Appendix C which was taken from

the ECODATA Downed Woody (DW) Fuels method (form in -

Appendix A). Tree health, species, diameter at breast height

(DBH), crown height, and total height were recorded for all trees

within the plot boundaries using the modified ECODATA Tree

Data (TD) methods presented in Appendix D (TD form presented

in Appendix A). The position of each EC plot was georeferenced

to UTM coordinates in UTM zone 12 using Global Positioning

System (GPS) units and recorded on the ECODATA Location

Linkage (LL) form (see Appendix A for LL form, Appendix B

for GPS procedures). Differential correction of locations was completed in the office and recorded on the LL form (see Keane and

others 1998b). Approximately 10 percent of the field plots were

targeted to be EC plots. It took from one to two hours to complete

measurements on the EC plot.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000 27

Estimating the FARSITE input layer values for each plot was difficult because of the high variability observed in each ecosystem.

Stand and crown base heights were ocularly estimated to the nearest meter. Canopy cover was assessed in the same way used to

assess cover for each plant species from the ECODATA PC methods. Crown bulk density values was assessed from the canopy

cover-cover type-structural stage combinations (Table 2). Values

from Table 2 were reduced proportionally for medium and low

canopy cover classes (30 and 60 percent, respectively) (Pollard

1972, Brown 1978). Seedling/sapling stands will be assigned a

crown bulk density for the low cover class and increased by 15

percent for the medium cover class. Bulk densities for woodland

types were calculated from the Leaf Area Index measurement device (LiCor LAI-2000) readings measured on woodland-type plots.

The leaf area was converted to crown biomass using specific leaf

areas (kg m-2).

The next level of measurement detail was captured by the Ground

Truth (GT) plot and this was the primary method used to collect

field data for this study. The purpose of this sampling level was to

collect the data needed to develop biophysical settings, cover type,

and structural stage classifications, and also to collect the georeferenced data needed as training areas and accuracy assessments

for satellite image classifications. This plot sampling method also

gathered all data needed for direct estimation of all FARSITE input

layer variables, for development of all base vegetation map classifications (PVT, cover type, structural stage), and for computation

of all the variables needed to key or compute and validate the

FARSITE and base map classifications. Forms completed at each

GT sample site included modified General Forms (GF), Location

Linkage (LL), and Plant Composition (PC) (Appendix A). The

GT plot center coordinates were also determined from GPS units

(See Appendix B for GT plot establishment details). Approximately

40 percent of all sample plots were targeted to be GT plots for this

study and it took about 40 minutes to complete all GT forms.

The least intensive sample plot was the Polygon Validation (PV)

plot. This sampling method was only used to assess accuracy and

precision of the developed vegetation base layers and the FARSITE

input layers. Only the PVT, cover type, structural stage, FBFMs,

canopy cover, crown base height, stand height, and crown bulk

density were assessed at these plots. Paper maps, identical in size

and scale with USGS quadrangle maps, were created with the

boundaries of the spectrally classified polygons overlaid on roads,

trails, streams, and contour lines. A unique polygon identification

number (ID) was printed inside each polygon on the map. These

maps were taken into the field and used to navigate to selected
28 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000

polygons in the sample watersheds. Most sampled polygons were

along roads and trails because they transacted the topographic gradients, and also, they were more efficient to sample because of accessibility. Once we verified the polygon location with either GPS

readings or map reference points (e.g., confluence of streams, road

intersections, unique topographic settings), we visually estimated

the eight base parameters mentioned above. These data were written onto the PV form shown in Appendix A along with the unique

polygon ID number printed on the paper map. It took less than 10

minutes to sample a polygon using PV plot methods but the information content of these measurements was very limited. This

method was only used during the second sampling season of 1998.

All field data collected for the Gila fuels mapping project were

entered into Paradox databases and double-checked for errors by

scanning all entries and then performing logic checks via database

queries (Jensen and others 1993). These databases were structured

so that they were easily transportable to the GNFC ArcInfo GIS

spatial database and various statistical programs. DBF (database

format) files of all field data are stored on the CD in the directory

CD\DATABASE with each subdirectory named for the two-letter

form name (e.g., GF contains all data recorded on the General

Form shown in Appendix A). The formats of these DBF data sets

are in CD\DATABASE\FORMATS and presented in Appendix G.

Vegetation Classifications

We created robust, comprehensive, and flexible vegetation classifications of PVT, cover type, and structural stage from the data collected in the field. This proved to be one of the most demanding

tasks of the project because the classifications for the vegetation

triplet are the heart of the image classification and fuel mapping

procedure, so the resolution of the categories of each vegetation

map needed to match the resolution of FARSITE input layer categories and the resolution of the digital maps. For example, cover

type categories needed to be fine enough to identify major changes

in surface FBFMs and crown fuel characteristics at a 30 meter pixel

resolution, but broad enough to minimize classification and sampling complexity (Finney 1998). Broad categories could “smooth”

the spatial distribution of fuels, while many fine categories could

overwhelm the satellite image classification process and require

inordinately large field data sets (Jensen 1986, Schowengerdt

1997). And most importantly, we needed to design the vegetation

classification categories so that they would be useful to other facets

of land management, and not only fire management (Keane and

others 1998b). This was difficult since most cover types and structural stages commonly used in land management are difficult to
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000 29

accurately discriminate using satellite imagery (Keane and others

1998b, Lachowski and others 1995). Lastly, the categories of the

base maps needed to be consistent across the three classifications

to allow a unique set of cover types and structural stages for each

PVT. Keys for the base vegetation classifications are presented in

Appendix E.

PVT Classification —The existing plant association classification

for the GNFC (USDA Forest Service 1997) was not useful for this

study for several reasons. First, the level of classification was too

fine for fuel mapping, and it was difficult to aggregate plant associations to the coarser categories needed for this mapping effort.

This was partially because many associations were not true PVTs

but rather plant communities defined by disturbance processes and

therefore did not consistently represent unique site conditions. It

was difficult to hierarchically aggregate associations with the existing classification system and relate the composite types to dynamic

site descriptors such as climate or soils. So instead, we created our

own PVT classification based on the synecology of existing tree

species in the GNFC.

First, the field data were analyzed using database queries to identify similar groups that describe biophysical conditions appropriate

for mapping fuels. We used the existing classification as a starting

point, and then refined, deleted, and added classified types in accordance with our project objectives. We then created a working

list of draft PVTs and an associated key to their classification. Next,

this draft PVT key was refined by reclassifying the field data and

also by soliciting help from regional experts.

A tentative list of PVTs along with a corresponding key was developed after analysis of the 1997 vegetation database of 1000 plots.

Only 1 percent of field plots were not classified to PVT by the tentative key; those plots were assigned the most plausible PVT from

an assessment of the original plot data sheet. This tentative PVT

key was then refined and used for the 1998 field season to determine PVT for the Polygon Validation sampling effort. Following

the 1998 field season, several PVT classes were combined or reclassified because they could not be adequately mapped using the

terrain model methodology even though the key and associated

classes worked well in the field. For example, pinyon, juniper, and

oak PVTs were combined into one class since differences in ecological, vegetation, and fuel characteristics among these types are

better defined at the cover type level and these three PVTs were

notoriously difficult to map using topographical rules because of

the wide environmental ranges of the species (Appendix E). The

final list of PVT categories is presented in Table 3.
30 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000

Cover Type Classification — The cover type classification key

was primarily driven by data collected in the field. Because we

found no adequate cover type classification existed for the project

area, we sampled our first field season without an a priori classification. This allowed us to develop a classification that was ecologically based and also useful to fuel mapping. The cover type

classification key was constructed by first specifying the target

number of cover types we wanted to include in the mapping effort.

This number could not be so great that many plots were needed for

their description, or so few that fuels weren’t adequately differentiated. The cover type classification also had to include all land

cover types not related to vegetation such as rock, soil, water, and

so on. We decided that there should be between 10 to 30 cover

types based on a review of the literature and discussions with National Forest personnel (Eyre 1980, Shiflet 1994, USDA Forest

Service 1997). Next, we specified the list of potentially important

cover types on the GNFC. The first year’s field data, coupled with

our knowledge of the area and its ecosystems, were used as guides

in devising this list (USDA Forest Service 1997). The list of cover

types were developed in a hierarchy where similar cover types

could be aggregated in case the list needed to be reduced later. We

then tried to rectify the number in the cover type list to the target

Table 3—The Potential Vegetation Types (PVTs) created and used in this study.

Mapped percent

PVT code PVT name Site description GNFC landscape (%)

1 Riparian all broadleaf trees, shrubs, 1.4

and herbaceous riparian and

wetland types

2 Spruce-Fir subalpine fir and Engelmann 0.6

spruce habitat types

3 Mixed Conifer white fir and Colorado blue 4.1

spruce habitat types

4 Douglas-fir Douglas-fir habitat types 7.6

5 Ponderosa Pine ponderosa pine and 18.8

Chihuahuan pine habitat

types

6 Pinyon-Oak-Juniper pinyon, Emory oak, gray 39.2

oak, alligator juniper, oneseed juniper, Utah juniper

habitat types

7 Scrub-Grassland all non-riparian shrub and 10.6

grassland types occurring

below the P-O-J zone

8 Nonvegetated rock, tailings, barren or 17.7

developed land, water
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000 31

number by eliminating cover types that could be easily aggregated

with similar types, or cover types that had small aerial extents.

Classification categories for both cover type and structural stage

were created in cooperation with the Gila National Forest resource

personnel (Table 4 and Table 5). These categories were designed

to mesh with current management-oriented landscape analyses

along with FARSITE fuel mapping. However, we mostly used

sampled field data to guide the classification criteria for each

category and to assess the value of each category to the overall

classification and mapping effort in a spatial context. We also

made sure that our classification categories agreed with National

vegetation mapping standards and protocols (Anderson and others

1998, Grossman and others 1998) (Appendix E).

Table 4—The Gila National Forest Complex cover types used in this study.

Cover Mapped percent

type code Cover type name Description GNFC landscape (%)

100 Water lakes, ponds, reservoirs 0.1

201 Rock rock, gravel, scree 17.0

202 Barren Ground dominated by bare soil with <10% vegetation cover 12.3

203 Mine-Quarry mines, quarries, tailing piles 0.4

205 Urban urban and urban-interface areas 0.2

310 Herblands grass and/or forb dominated communities 23.0

321 Xeric Shrublands desert scrub, mountain mahogony or other commuities 6.0

 dominated by sclerophyllous shrubs

322 Mesic Shrublands riparian shrub(willows) or high elevation deciduous 0.9

 shrub communities

401 Broadleaf Riparian cottonwood, alder, boxelder, sycamore or other 1.0

 Forest broadleaf riparian communities

402 Broadleaf/Conifer ponderosa pine mixed with broadleaf riparian trees 0.7

 Forest or aspen mixed with conifers

403 Conifer-Gambel conifers mixed with gambel oak 1.5

 Oak Forest

404 Conifer Forest- ponderosa pine and/or Douglas-fir mixed with pinyon, 2.2

 Woodland Mix juniper and/or evergreen oaks

405 Mixed Woodland pinyon and/or junipers mixed with evergreen oaks 3.9

406 Mesic Mixed Conifer stands dominated by white fir and/or blue spruce usually

 mixed with other conifers 3.4

407 Xeric Mixed Conifer stands dominated by mix of ponderosa pine, douglas-fir 2.6

 and/or southwestern white pine

410 Aspen stands dominated by aspen 0.2

411 Gambel Oak stands dominated by gambel oak 0.3

412 Juniper juniper dominated communities 5.0

413 Pinyon pinyon dominated communities 6.2

414 Evergreen Oak Emory, gray, netleaf and/or silverleaf oaks 3.4

415 Ponderosa Pine stands dominated by ponderosa pine 5.7

416 Douglas-fir stands dominated by douglas-fir 0.5

421 Pinyon-Juniper pinyon mixed with any species of juniper 3.0

424 Spruce-Fir stands dominated by subalpine fir and/or Engelmann spruce 0.3
32 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000

We coded all vegetation classifications into Paradox database

queries that use plant species and canopy cover information along

with other relevant site descriptions to key each plot to PVT, cover

type, and size class. We did this as a test of the classification keys

and because it was easy to update classification criteria to refine

and improve classification accuracy. Moreover, the database queries were used to assign classification categories to each plot for

use in satellite image classifications. The coded plots were then

used to assign the FARSITE fuel and crown characteristics parameters to each layer to finally create the FARSITE input layers.

Structural Stage Classification — The initial structural stage

classes used during the 1997 field season were based on stand

developmental processes and included seven forested types and

eight rangeland types as described by O’Hara and others (1996).

We then added six woodland classes to describe structure of pinyon, juniper, and oak. After the 1998 field season, it was determined that this fine level of structural differentiation was not

needed to describe the changes in fuels as related to vegetation

structure. We found very few stands dominated by seedlings, saplings or large trees within the GNFC, so we lumped the seven forested structure types were into three broad classes based on tree

diameter at breast height (DBH) (Table 5). Woodland, shrub, and

herbaceous communities were each grouped into two structural

stages based on projected canopy cover. These seven structural

stages plus two more for non-vegetated types were used as the

final set of categories for the key to stand structure (Appendix E).

Fuels Classification — FARSITE does not restrict the characterization of fuels to only the 13 FBFMs described by Anderson

Table 5—The structural stage categories used in this GNFC study.

Structural Structural Predominant Mapped percent

stage code stage name Description vegetation GNFC landscape (%)

1 Closed Herbland ≥55 percent herbaceous cover grasses, forbs 8.8

2 Open Herbland <55 percent herbaceous cover grasses, forbs 14.2

3 Closed Shrubland ≥55 percent shrub cover shrubs 1.6

4 Open Shrubland <55 percent shrub cover shrubs 5.3

5 Closed Woodland ≥55 percent woodland cover pinyon, juniper, oak 8.3

6 Open Woodland <55 percent woodland cover pinyon, juniper, oak 13.1

7 Small Tree trees <5.0 inch DBH broadleaf & coniferous forests 0.8

8 Medium Tree trees 5.0 - 21.0 inch DBH broadleaf & coniferous forests 17.6

9 Large Tree trees > 21.0 inch DBH broadleaf & coniferous forests 0.1

96 Urban developed areas — 0.2

98 Water water — 0.1

99 Rock rock, barren or mining related areas — 29.7
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000 33

(1982). In fact, FARSITE has options that allow over 80 userdefined or “custom” FBFMs in a spatial fire simulation. Most

people use the standard 13 FBFMs because it requires a great deal

of expertise to develop a custom FBFM (Burgan and Rothermel

1984). The FBFM includes many other characterizations besides

fuel loading by size class, such as bulk density, surface area-tovolume ratios (cm–1), and heat content (kW kg–1). The inherent

complexity of the mechanistic fire behavior models of Albini

(1976) and Rothermel (1972) make it difficult to predict realistic

fire behavior from actual fuel loadings (Burgan and Rothermel

1984). As a result, a somewhat complicated procedure must be

done each time a fire manager wants to create a new fuel model

for a local situation. This procedure involves altering actual fuel

characteristics to generate realistic fire behavior observed for the

new situation using the fire behavior models (Burgan and Rothermel

1984). Therefore, many people who do not have experience in

fire or fuels modeling find it difficult to accurately and consistently

create new fuel models (Root and others 1985, Hardwick and others 1996).

We developed two new FBFMs for the GNFC project by following

the procedures detailed in Burgan and Rothermel (1984). The first

FBFM was for surface fires in the pinyon-juniper understory.

There are very little fine fuels on the floor of a pinyon or juniper

stand, probably due to excessive droughty conditions, allelopathic

influences by the pine and juniper foliage, heavy grazing pressure,

and excessive root competition (Moody and Boucher 1998). Therefore, fires that occur in these ecosystems have exceptionally low

intensities and do not spread very well, unless there is a wind

strong enough to spread the fire through the crowns. No FBFM in

Anderson (1982) could adequately describe this unique situation,

so we decided to build our own FBFM. We estimated fuel loadings

in the standard size classes for this ecosystem based on measured

fuel data (EC plots) and ocular estimates (GT plots). We then entered these fuel loadings in a custom fuel model option in the fire

behavior program BEHAVE (Andrews 1986). Extensive documentation of fire behavior in pinyon-juniper was obtained from historical fire reports from Gila National Forest fire managers and from

the available literature. Once the pinyon juniper FBFM was quantified, it was then coded into a file for input into FARSITE (see

Table 1). The other new FBFM was for rock, bare soil, and water

where fuel loadings were set to zero so no fire could spread on this

area (Table 1). The details for the custom fuel models are stored on

the CD in the directory CD\CUSTOM for input to FARSITE and in

Appendix G.
34 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000

Only seven of the 13 Anderson (1982) FBFMs were used in this

study (Table 1). Timber harvesting is rare in the GNFC so the

activity fuel models 11, 12, and 13 were deemed unnecessary. The

sawgrass model (FBFM 3) was not applicable to any area in the

GNFC because of the high loadings for perennial grasses. The high

intensity shrub model (FBFM 4) is also rare on the GNFC because

shrub fields rarely become tall and dense enough to generate fire

behavior typical to FBFM 4. A reduced list of possible FBFMs can

often increase layer accuracy and simplify fuels assignments to

vegetation triplets as described later (Keane and others 1998a).

However, a limited number of FBFMs may not adequately describe

fuel variablility in diverse landscapes like the GNFC.An advantage

of using the vegetation-triplet methodology presented here is that

new FBFMs can be added or substituted later as more fire behavior

data become available.

Two fire behavior fuel model (FBFM) GIS layers were developed

for this project. Apparently, some plant communities can exhibit

drastically different fire behavior after prolonged drought or under

severe winds, and the standard fire behavior fuel models do not

account for the contribution of deciduous shrubs to subsequent fire

behavior. For example, montane shrub communities are often assigned the Anderson (1982) FBFM 5 (live shrub 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 behavior model) under extreme drought conditions because of their very low live fuel moisture contents. To

represent these situations in the FARSITE input maps, we developed two FBFM maps. The normal FBFM map describes the most

common distribution of fuel models on the landscape and will

probably be used for many prescribed fire planning and real time

wildfire simulations. The extreme FBFM map describes the spatial arrangement of fuel models under the most severe conditions

(e.g., extreme drought or highest fuel loadings). This map can be

used to simulate wildfires for “worst-case” scenarios.

Ancillary Layer Development

Many GIS raster and vector data layers were needed for the successful completion of this vegetation and fuels mapping effort.

All ancillary spatial data layers mentioned here were obtained,

compiled, or created specifically for this project because they

were not available from the Gila National Forest. The support data

layers spatially describe topography, soils, climate, and ecosystem

dynamics and were needed because they either were direct inputs

to FARSITE (e.g., elevation, aspect, slope) or used in the creation

of vegetation base layers used to map fuels (Keane and others
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000 35

1998b). All GIS raster data layers for the GNFC were developed at

a 30 m resolution and the GIS vector layers were developed with a

minimum mapping polygon size of 1 ha (2.5 acres). Finney (1994)

suggests a 30 m pixel size may be so large that it can over predict

fire spread for some landforms, but is acceptable because smaller

pixel sizes considerably slows FARSITE computer simulations.

The topographic data layers of elevation, aspect, and slope needed

to map PVT’s, constrain cover types and structural stage assignments, and needed as input into FARSITE, were taken from Digital

Elevation Models (DEMs). A DEM is a raster map of a 7.5 minute

USGS quadrangle with each 30 meter pixel assigned an elevation

(m) measured above mean sea level (USGS 1987). All DEMs

within the GNFC study area were acquired from the U.S. Geological Survey in Salt Lake City, Utah. We created one DEM layer for

the entire GNFC by “sewing” or “tiling” together 120 USGS DEM

quads using methods detailed in Stitt (1990) to define the GNFC

study area (Figure 1). We corrected horizontal or vertical banding

in some USGS DEM quads using methods presented in Brown and

Bara (1994), Keane and others (1998a), and Stitt (1990). Slope and

aspect layers were generated from the processed and tiled GNFC

DEM layer using the ARC/INFO command (slope.aspect) that

derives slope and aspect from surrounding elevation pixel values.

Spatial soils information was needed for PVT terrain modeling and

also for input to the biogeochemical simulation modeling effort

used as a test of the Keane and others (1997b) gradient model. The

Soil Conservation Service’s STATSGO (1991) data layers were

used to develop the soil texture and depth layers needed for the

biophysical classification and simulation modeling. We also used

spatial simulation modeling to improve the STATSGO assignments

following methods outlined in White and others (1996) and created

soils inputs to the BGC model from methods in Cosby and others

(1984).

Satellite imagery was used to map existing vegetation (cover and

structure) and to define polygon boundaries. All imagery data

used for this study were obtained from the USDA Forest Service

Southwestern Regional Office Engineering Staff. We used remotely sensed imagery taken by the Thematic Mapper (TM) sensor

on the LANDSAT 5 satellite taken at two dates (September 1993

and June 1997) for two areas (Row 42 Path 32, Row 42 Path 33,

see Figure 1), or four scenes total. A TM scene consists of six

layers of at about 30 meter pixel sizes (approximately 250 km by

250 km). Pixel values in each layer quantify the reflectance of

electromagnetic radiation in six bands that bound six ranges of

wavelengths in the electromagnetic spectrum (Jensen 1986, 1990,
36 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000

Schowengerdt 1997). There is a seventh band in the thermal region

of the spectrum with pixel sizes of 120 meters, but we did not use

this band for our study. Reflectance is quantified by digital numbers (DN) ranging from 0 to 255 in magnitude. The TM imagery

was geo-rectified to less than a half pixel rectification error by

EROS Data Center so there was no need to rectify any of the TM

imagery. However, we performed many rectification accuracy assessments to ensure the TM layers were accurate across the entire

scene, across all bands, and across all dates. These layers were then

included in the GNFC GIS once accuracy was verified.

Many other ancillary layers were integrated into the GNFC GIS

and used extensively in this study. Cartographic features such as

roads, streams, and rivers were compiled from cartographic feature

files provided by the Gila National Forest and the Geometronics

Service Center in Salt Lake City, Utah. Watershed boundaries at

the 4th and 6th Hydrologic Unit Code levels were copied from USGS

(1982). Ownership boundaries for USDA Forest Service and private lands were provided by the Forest Service Southwestern Regional office in Albuquerque, New Mexico. All ancillary layers

can be found on the CD in the directory CD\UNIX_GIS, however,

we created separate ASCII vector boundary files (Gila National

Forest boundary and Wilderness Boundary) to be used as overlay

in FARSITE simulations (CD\BOUNDARY). Lastly, a GNFC

bitmap showing the boundaries of each USGS quad and the quad

name is contained in the CD\GNFC_MAP directory. This map can

be used by any PC-based imaging software to reference quad maps

for use in FARSITE.

Vegetation Base Layer Development

The three vegetation base layers created in this project were the

PVT (biophysical settings), the cover type, and structural stage

layers. Again, the FARSITE input fuel maps were created from

these base layers by assigning the five FARSITE fuel parameters

to each combination of the three base layers. FARSITE fuel assignments were calculated from field data and the opinions of local

fire experts. Therefore, these vegetation base layers represent the

most critical phase in the FARSITE input development process.

These base layers also provide the critical link to mapping other

ecosystem characteristics for other land management needs. Many

of the ancillary data layers listed above and field data described in

the Field Sampling section were used in the development of all

base maps.

The first task was to create a common polygon layer for all base

vegetation layers in this project. Previously, we tried to create a
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000 37

polygon layer suitable for FARSITE simulations from independently created data layers and found a great deal of error occurred

when translating the polygon boundaries from each layer into a

common theme (Keane and others 1998a, 1998b). So, we decided

to create a universal polygon layer that would be used to spatially

describe the distribution of all base vegetation classifications for

fuel map creation. This polygon layer could not have polygons that

were so large that fuel conditions would be homogenous over large

areas. A category from each base vegetation classification would

be assigned to each polygon as an attribute, rather than develop

three different base vegetation maps and then decide the relevant

polygon boundaries after the fact. The universal polygon layer was

created by using a textural analysis called “Segment” found in the

IPW (Image Processing Workbench) software. This technique uses

a moving window to evaluate similar regions based on the values

of the surrounding pixels. We imposed a minimum polygon size of

1 ha and a maximum size of 400 ha in this procedure. The final

layer was called the polygon layer.

Two biophysical settings layers were developed for this study, but

only one was used to map fuels. The PVT map was created using

terrain modeling where topographical rulebases defined potential

vegetation types from attributes derived from the DEM. The PVT

map was the primary layer describing biophysical settings layer

and only it was used to map FARSITE fuels. The second biophysical settings layer, the Gradient Biophysical Settings (GBS) map,

was developed using gradient modeling and simulation modeling

(Keane and others 1997b). The GBS layer was only used to test

and refine the PVT map and was not used to map FARSITE fuels.

In fact, the GBS layer was not essential for the GNFC fuel mapping project but its creation provided an unique application of the

gradient model developed by Keane and others (1997a).

Two biophysical settings layers were created for many reasons.

First, the integration of the two approaches would remove some of

the limitations resulting from using only one approach. For example, PVT terrain models are heavily dependent on field data to

create and validate the topographic rulebase, whereas mechanistic

gradients can be described with minimal field data using simulation modeling (Barrett and Arno 1992, Waring and Running 1998).

Simulated gradients are computed using minimal subjectivity because simulation models objectively compute biogeochemical

characteristics the same for every pixel. However, PVT modeling

requires subjective interpretations of topographic settings based on

highly subjective and variable potential vegetation assignments

(Keane and others 1998b). On the other hand, simulated gradients

are difficult to verify on the ground because of the temporal element
38 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000

(i.e., most are expressed per unit time, e.g., productivity is tons

of carbon per acre per year) and measurement difficulty (e.g.,

average annual precipitation must be measured over many years),

but topographic categories can be easily identified in the field by

identifying the PVT from a plant key. Simulated gradients provide

a mechanistic context in which to interpret or predict vegetation

change while the terrain model is purely an empirical approach

with little cause-and-effect implications. Keane and others (1998b)

found terrain models could be developed in the absence of extensive field data from expert opinion, yet process-related gradients

needed field data to develop statistical predictive functions. Therefore, a completely integrated approach to mapping biophysical settings would utilize all the advantages of each strategy while minimizing the disadvantages. Although this integrated approach is

not yet possible for many regional and National Forest mapping

projects because of inadequate vegetation inventories and incompatible simulation models, it seems to be the next logical step to

accurate and consistently map fuels and fire hazard.

Gradient Biophysical Settings Map — We created the gradientbased biophysical settings layer using a “top-down” approach

where we mapped discrete biophysically based environment categories continuously over the entire GNFC from climate and ecosystem gradients computed from simulation models (Keane and

others 1997b, Milner and others 1996, Brzeziecki and others 1993,

Waring and Running 1998). Since 1995, we have been developing

a spatial gradient model that would allow the mapping of important

land management entities using biophysical gradients that describe

critical ecosystem processes (Keane and others 1997b). This approach has been used in previous studies but with indirect environmental gradients, such as elevation and landform, that act as surrogates to describe the critical biogeochemical processes that govern

landscape dynamics (see Kessell 1979). Instead, our approach

quantifies those mechanistic ecosystem process gradients that directly dictate vegetation biogeography such as precipitation, productivity, and temperature and uses these gradients to compute

vegetation maps useful to landscape management.

Since direct measurement of these mechanistic gradients across an

entire landscape would be a complex and expensive task, we use

simulation modeling to extrapolate climate variables across the

spatial domain and to compute some biogeochemical process variables that relate to vegetation dynamics. This approach to mapping

biophysical settings is highly experimental and we did not expect it

to be our primary mapping vehicle, but rather, a secondary map

refinement tool. Our primary purpose in including the gradient
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000 39

approach was to test its utility in mapping fuels in the southwestern

United States, and to improve the PVT terrain model.

The gradient modeling system used here is an integrated system

of relational databases, analysis software, and simulation models

linked together to compute a list of variables important in ecosystem dynamics (Keane and others 1997b). A set of topographic,

climatic, and mechanistic variables (Table 6) are used to develop

predictive equations that are implemented across a spatial domain.

First, values of all gradient variables in Table 6 were computed for

each polygon sampled using the EC or GT plot methodology (see

Field Sampling section). Then, a correlation analysis was performed to find the biophysical variables important for mapping

Table 6—Environmental variables evaluated for use in the gradient model.

Variable Units Variable Units

Gross primary productivity kgC/m2 Average annual degree days degday

Net primary productivity kgC/m2 Average annual daylength seconds

Maintenance respiration kgC/m2 Average annual snow water depth cm

Growth respiration kgC/m2 Moisture content 1 hr wood fuel Percent

Autotrophic respiration kgC/m2 Moisture content 10 hr wood fuel Percent

Litter respiration kgC/m2 Moisture content 100 hr wood Percent

Soil respiration kgC/m2 Moisture content 1000 hr wood Percent

Heterotrophic respiration kgC/m2 Ketch-Byram Drought index none

Ecosystem respiration kgC/m2 Reaction intensity btu/ft

Net ecosystem production kgC/m2 Spread component ft/min

Evapotranspiration kgH2O/m2 Energy release component btu/ft2

Outflow kgH2O/m2 Burning index none

Leaf carbon kgC/m2 Ignition component Prob

Dead stem carbon kgC/m2 Spread component threshold sum days-ft/min

Canopy conductance sensible heat m/sec Energy release compon thres sum days-btu/ft2

Leaf conductance sensible heat m/sec Burning index threshold sum days-none

Soil water potential Mpa Ignition component thres sum days-Prob

Soil volumetric water content m3/m3 Elevation m

Average annual maximum temp degC Slope Percent

Average annual minimum temp degC Aspect degrees

Average annual dewpoint temp degC Mineral soil cover Percent

Average annual average temp degC Gravel cover Percent

Average annual soil temp degC Rock cover Percent

Average annual daytime temp degC Duff/litter cover Percent

Average annual nighttime temp degC Wood cover Percent

Average annual precipitation cm/yr Moss Lichen cover Percent

Average annual effective ppt cm/yr Basal veg cover Percent

Average annual relative humidity Percent Fuel depth m

Average annual solar radiation kw/m2 Duff/litter depth cm

Average annual days since snow days Live tree DBH cm

Average annual days since rain days Live tree basal area m2/ha

Average annual daily radiation J/m2/day Live tree height m

Average annual corrected rad J/m2/day Latitude dd

Average annual vap press deficit mbar Longitude dd

Average annual absol humidy def ug/m3 Structural stage none

Average annual absorbed SW rad kJ/m2/day Sand percent by volume Percent

Average annual transmit SW rad kJ/m2/day Silt percent by volume Percent

Average annual Potential ET m Clay percent by volume Percent
40 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000

PVTs. Discriminant analysis and other statistical techniques

were used to develop predictive equations that predict PVT from

the selected biophysical variables. Then, the values of those biophysical variables were computed for all remaining polygons. Predictive equations were then used to assign the PVT value to all

unsampled polygons. The polygon validation (PV) plots were used

to validate, test, and refine the cluster assignments and the discriminant functions.

Values for all weather variables were computed from the meteorological simulation model DAYMET (Thornton and others 1997)

which spatially extrapolates daily weather from a set of base stations to the EC, GT, and PV plot locations in mountainous terrain.

We computed daily weather data (see Table 6) for each polygon

from daily observations (maximum temperature, minimum temperature, and precipitation) taken at 125 meteorological stations

in and around the GNFC for a period of 24 years (1970-1993).

DAYMET uses a 1 km2 DEM to aid in the extrapolation. These

weather data were input to the WXGMRS program to compute

some simulated weather variables including the National Fire

Danger Rating System indices (see Table 6) (Deeming and others

1978). Then, the extrapolated weather data, and a host of other

parameters assessed from the sampled field data, were input to

the BGC model (Thornton 1998) to compute ecosystem process

values (see Table 6). Lastly, field data were augmented with the

simulated data to create the gradient database (see Table 6).

PVT Map — The second biophysical settings layer was created

using a “bottom-up” strategy where environmental conditions

were indirectly coded into a terrain model from hierarchically

structured topographic combinations of elevation, aspect, and

slope with each combination assigned a PVT category (Keane and

others 1998a, Brown and others 1994). Field data were summarized to determine plausible ranges in elevation, aspect, and slope

that would consistently and accurately identify these PVTs. This is

a time-consuming process of querying the field database and comparing query results with results found in the literature. Important

ranges in elevation, aspect, and slope were synthesized from the

field data summaries and then coded into an ARC/INFO AML program we created for interactively developing PVT terrain models

for our other fuel mapping efforts (Keane and others 1998a, Keane

and others 1998b). We adjusted the ranges and PVT assignments

to those topographic ranges based on interactive interpretations of

intermediate PVT maps and input from Gila National Forest personnel. The final terrain models by ecological zone are shown in

Appendix F. This terrain model was used to create a PVT terrain
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000 41

model raster layer where each pixel is assigned a PVT category

value.

The final PVT GNFC GIS layer was created by assigning a PVT

category to each polygon delineated by the textural analysis of

the TM imagery (see Cover Type Map section below). This was

accomplished by assigning the modal PVT category value from

all pixels in the PVT terrain model layer within the delineated

polygon as a polygon attribute using the ARC/INFO command

zonalmajority. Polygons having no clear dominant PVT assignment (greater than 50 percent pixels having one PVT category)

were evaluated on a case-by-case basis to ensure the best PVT

was assigned.

Cover Type, Structural Stage, and Canopy Cover Maps — A

combined unsupervised- supervised satellite image classification

was performed using the two different LANDSAT (TM) scenes

for two time periods (1993 and 1997). We used the PCI image

processing software to perform this task on a series of IBM UNIX

workstations. These two maps can be created using any remotely

sensed technology such as aerial photograph interpretation, sketch

mapping, and image classification with other satellite or airborne

scanners. However, the development of these layers can be highly

technical and is best accomplished by experienced professionals.

An initial unsupervised classification created from only one Sept

1993 TM scene was used to divide the GNFC landscape into polygons for field sampling using a clustering algorithm in the PCI

software that creates spectral aggregates based on spectral class

signatures in the TM image. Only one scene was used because

TM scenes for other areas were not yet available. We assumed,

and then later validated, that the spectral cluster classes assigned

to polygons identified unique cover class and structural stage

combinations at a resolution sufficient for plot establishment

(Gonzales and Maus 1992). We also assumed, then validated by

examination on the ground, that stand conditions within the polygon were sufficiently uniform so we could use the entire polygon

as a “training site” in our supervised classification. However,

some polygons proved too large, in which case we constrained

maximum training site size to 50 ha. Training sites are defined as

areas on the ground used to represent a particular cover type or

structural stage category (Lachowski and others 1995, Verbyla

1995). Polygons boundaries were verified for accuracy in the field

using orthophoto quads (i.e., an aerial photographs for an entire

USGS 7.5 minute quadrangle), 1:4000 aerial photos, and walkthrough reconnaissance (Lachowski and others 1995, Keane and

others 1998a, Howard 1991). We assigned unique cover type and
42 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000

structural stage categories to each spectral class in the unsupervised classified layer to create a draft cover type and structural

stage map for GNFC fire management to use during the 1998 fire

season (Gonzales and Maus 1997). However, the final product described here included refinements that significantly increased the

quality, consistency, and accuracy of the draft layers.

The final cover type map was created using a supervised classification approach that utilized the sampled polygons (created from the

unsupervised effort) as training areas to build statistical tables that

describe classification category spectral signatures. Sampled field

attributes for the base vegetation layers were assigned to each draft

polygon (unsupervised clusters) then assigned to the final polygons

(textural analysis) using modal values. We performed the maximum likelihood supervised analysis using only those polygons described by field data and the translation from the draft to final did

not incur significant error (i.e., there was general agreement between the two). The final cover type classification was stratified by

PVT to precisely target specific cover types to the land area where

they could occur. The structural stage and canopy cover class maps

were performed for the whole scene and not stratified by PVT.

Many other remotely sensed data products could have been used

to develop the cover type and structural stage maps. We chose

satellite imagery because it was the best product to consistently

map fuels and vegetation over large areas like the GNFC (over

2 million ha). Aerial photographs could have been digitized to

produce a better product perhaps, but photo interpretation is a

time-consuming task that is costly for large areas (Quigley and

others 1996). Timber stand atlases or maps could have been described by to vegetation and fuels using sample timber stand data,

but rarely do these databases contain the fuels information needed

for fire behavior, and rarely are large landscapes entirely and comprehensively mapped or sampled (Grupe 1998, Mark and others

1995). Active remote sensor data products such as Lidar could

have been used but the data are especially costly and difficult to

obtain for large areas (Dubayah and others 1997, Nelson and

others 1988, Naesset, E. 1997).

Previous vegetation mapping efforts revealed that canopy cover

might be best mapped from satellite imagery rather than from vegetation type assignments (Redmond and Prather 1996, Keane and

others 1998b). Therefore, we used the same supervised approach

to compute a cover class category for each polygon from the TM

satellite imagery. We also computed a cover type class from the
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000 43

field data for each combination of the vegetation triplet and

assigned that cover class to the polygon based on the triplet

(see FARSITE Input Layer Development section).

Base Layer Refinement — Because of the need for congruence

and consistency across all data layers, we used various ancillary

data layers and other base vegetation layers to refine each vegetation base layer and polygon assignments. The PVT map was used

to constrain the cover types to those environments where they are

expected to occur. For example, if a cover type map polygon was

assigned a mixed conifer cover type and this polygon crossed a ravine and occurred on both a north and south-facing slope, then the

PVT layer was used to split the polygon into a mixed conifer cover

type on the cool, moist, north-facing slope and a pinyon-juniper

cover type on the warm, dry, south-facing slope. Conversely, rock,

water, and bare soil PVTs were mapped from the cover type classification of satellite imagery because these PVTs are geological

phenomenon that are difficult to map from topographic criteria but

are easily and more accurately mapped using imagery (Howard

1991, Jensen 1986). Decisions on which PVT-cover type-structural

stage combinations were illogical or impossible, and estimates as

to what these illogical combinations should be, were based on the

summarized field data, information in the literature, and input from

fire and resource managers.

FARSITE Input Layer Development

The FARSITE input layers were created by summarizing the field

database by all possible combinations of categories for all three

vegetation base layers. First, we determined all possible combinations of PVT, cover type, and structural stage attributes for every

polygon across the entire GNFC. Then, a database, called the base

layer combination lookup table, was created as an ASCII file

where each line in the file is a PVT-cover type-structural stage

combination. Also on that line are all summarized ecological characteristics used to create FARSITE input maps and to develop

other maps useful for land management. The format of this lookup

table is shown in Appendix G and the file can be found on the CD

(CD/DATABASE/LUT/PCSLUT.DBF).

The base layer combination lookup table was created from an

extensive analysis of the field data to compute summaries of each

FARSITE fuel input requirements (e.g., stand height, crown

height) stratified by the appropriate vegetation combination. This

summary analysis mostly used modal values were used to assign

FBFMs (i.e. normal and severe fuel models) to each combination.
44 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000

Stand height, crown height, crown bulk density, and canopy closure were computed as averages across every plot that keyed to

the unique triplets. For example, there were four plots in the field

database that were assigned a Douglas fir PVT, a ponderosa pine

cover type, and a pole structural stage. Stand height for this triplet

was computed as an average of the stand height measured on all

four plots. Other categorical variables, such as normal and severe

FBFMs, were also selected as the most frequently occurring category in the four plots. These summaries were then imported into

the GIS database and linked to the polygon layer.

Some combinations of PVT-cover type-structural stage were

poorly represented or highly variable in the field database. It

was very difficult to find some of the vegetation combinations in

the GNFC because they were rare or remote, so they were never

sampled. In these situations, we used values taken from the literature and estimates made by fire managers to quantify missing

cases. In the winter of 1998, we conducted a two day workshop

on the Gila National Forest that was attended by the various Fire

Management Officers employed on that and nearby National Forests. We distributed an empty form to everyone (see Appendix H)

that listed every vegetation base layer combination and asked

workshop attendees to assign a normal and severe FBFM, and

also to assign the crown height, to each combination. For efficiency, the workshop attendees were divided into groups and

given separate sets of combinations stratified by PVT for the three

FARSITE assignments. It took only one day for the attendees to

estimate the three values for all combinations. We used the field

data to validate and assess the accuracy of these assignments. The

final values assigned by managers for all combinations are shown

in Appendix H.

The fuel model assignments from this successful workshop provided the GNFC project with a very important data source. First,

these assignments were used to fill in values for the combinations

with missing or limited field data representation. Second, this form

provided an excellent source of “pseudo-ground truth” for all of

our FARSITE combination assignments by using them as guides

to validate the triplet field data summaries. For example, some

base layer combinations were represented by as many as five

FBFMs sampled on 10 plots making it difficult to select a modal

value with any certainty or reliability. In these cases, we referenced

the assignment made by the fire managers and compared that with

the highly variable field data, and selected the model which best fit

with both data sources. This proved a great help in selecting the final combination assignments and in assessing accuracy and quality

of the base layer combination lookup table.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000 45

The final base layer combination lookup table was then imported

into the GNFC GIS database and then linked to the polygon map

for FARSITE input assignments. The PVT-cover type-structural

stage assignment for each polygon was referenced to the combination GIS lookup table and the FARSITE input values corresponding to that combination were then assigned to the polygon. GIS

raster layers for each FARSITE input requirement were easily created from the polygon map once the combination lookup table was

linked to the polygons in the GIS. We developed an ARC/INFO

AML to reformat the GIS layers into input FARSITE landscape

format required by the program (Finney 1995).

The creation of one huge landscape file for the entire GNFC is

not computationally efficient for FARSITE execution (Keane and

others 1998b). The program would take too long to load, display,

and analyze this extensive spatial layer. So instead, we divided

the GNFC area into 7.5 minute blocks (i.e., size of a USGS quad

map) and created a landscape file for each corresponding quad

and named for that quad. FARSITE landscape files contain other

layers besides the eight base FARSITE layers needed for this

project. There are also three cartographic layers of roads, trails,

and water. FARSITE landscape files were then compressed to

optimize CD space and put in the CD\QUADS directory.

Accuracy Assessment

We performed an intensive, hierarchical assessment of the accuracy and precision of all field data, vegetation classifications, ancillary data layers, base vegetation layers, and the FARSITE fuel

layers using a multitude of ground-truth sources and accuracy assessment methodologies. First, we tested and evaluated the eight

key evaluations made in the field for the GT and PV plots from

the detailed EC plot information. Ocular estimates of PVT, cover

type, structural stage, stand and crown heights, crown closure,

crown bulk density, and fire behavior fuel models made on EC

plots were compared with an analysis of the detailed ecological

data measured on each of these plots (Taylor 1997).

Crown fuel characteristics (stand and crown height, crown bulk

density, and crown closure) were objectively computed from EC

plot information using a computer program developed for this

project called CFCC (Crown Fuel Characteristics Calculator, see

CD/SOFTWARE/CFCC) that calculates crown bulk densities for

successive layers above the ground. Then, a running average is

calculated for each layer based on the seven layers below and the

seven layers above each layer. Crown height is computed in CFCC
46 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000

as the height to the first layer that exceeds an average bulk density

of 0.037 kg m–3. Stand height is computed as the height to the

highest layer where the bulk density is greater than 0.037 kg m–3.

The bulk density used for the entire crown is taken from the layer

with the highest bulk density. Crown closure is computed from

empirical estimates of crown widths of each individual tree. Measured fuel characteristics from the EC fuel transects were compared

to the loadings in the ocularly estimated FBFM. These comparisons

of the computed EC information with the GT and PV plot subjective assessments helped in the interpretation of GIS data layer accuracy assessments. Lastly, we assessed the reliability and consistency of the workshop fuel and crown base height assignments by

comparing them to summarized field data in the base layer combination lookup table.

Accuracy and consistency of developed vegetation classifications

were estimated from queries of the field database on plant species

cover information gathered on the GT and EC plots. All plots were

keyed to PVT, cover type, and structural stage category using database queries on the data that were entered into the GF and PC plot

forms on the GT and EC plots. The keyed categories were compared to the ocularly assessed categories and the agreement was recorded as percent correct using contingency tables (Congalton

1991, Congalton and Green, 1999). This analysis could then be

used to assess the accuracy of the PV plot data.

Testing, validation, and verification of existing and developed

spatial data layers involved overlaying the layer in question with

the georeferenced field plot data and comparing the plot measurement with the polygon assignment. We used accuracy assessment

routines in the PCI image processing software and also designed

several GIS routines of our own to display and report the correspondence between the plot data 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.

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 GNFC maps using the methodologies presented in Congalton (1991), Woodcock

and Gopal (1992), and Gopal and Woodcock (1994), where contingency tables of fuzzy sets are constructed comparing the reference

(ground-truth) data values to the classified (map) values. In addition, we used the same process to compare ground-truth data to the
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000 47

fuel model assignments from workshop participants, and to the

PVT rule assignments designed by local scientists and ecologists.

Omission and commission errors were computed for each map

category, and a final accuracy was estimated using the KHAT

statistic (Congalton 1991, Mowrer and others 1996). The KHAT

statistic adjusts overall accuracy to account for the uneven distribution of plot data across classification categories (Congalton

1991).

Accuracy of continuous GNFC maps such as elevation, aspect,

and slope were computed using a regression approach similar to

that used by Keane and others (1998b). Observed values for each

polygon (i.e., plot data) were regressed with the predicted values

(i.e., polygon assignments) from the maps using a linear, leastsquares 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 observed values 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 (Taylor 1997).

It is defined as:

where O 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 (Taylor

1997).

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

 

ME

O P

N

i

N

=

−

=

∑( )

1
48 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000

transformation to scale aspects along a more ecologically oriented

gradient (Beers and others 1972).

ASP=1+COS(ASPECT)

Where ASP is the ecological aspect index (number between 0 and

2), COS is the cosine function, and ASPECT is the aspect in degrees azimuth (0 to 360). This assumes equal weight is given to

east and west aspects.

RESULTS

Field Sampling

During 1997, we established exactly one thousand plots across

more than two million ha of the Gila National Forest Complex.

Exactly 810 of these plots were Ground Truth plots where data

from the GF, LL, and PC forms were recorded (Appendix A).

Ecosystem Characterization (EC) plots numbered 190 and were

also scattered across the entire GNFC to extensively describe

various ecosystem characteristics for each PVT, cover type, and

structural stage category where we actually measured fuel loadings,

tree density, diameter, height, and all other variables mentioned in

Appendices A, E, and F. All plot locations were georeferenced to

within a 5 meter accuracy using Global Positioning Systems

(GPS). Plots were established along environmental gradients to ensure the sampling of all biophysical and vegetational types across

the GNFC. However, most plots were located adjacent to roads or

trails because of time and cost constraints. All sampled data were

entered into several databases (see Appendix G and

CD\DATABASE) that were linked to the GIS point layer of plot

locations created from the GPS coordinates. This GIS point layer

and database were essential in the development and accuracy assessment of the GIS base layers and FARSITE input spatial data

layers.

We again sampled exactly 1,000 polygons in 1998 using the Polygon Validation (PV) procedures outlined in Appendix A where

ocular estimates of all vegetation classification categories and

FARSITE input parameters were obtained. These data were also

entered into a database (CD\DATABASE\PV) where the polygon

number in the GIS layer referenced the polygon ID entered on the

plot form and into the database (see Appendix G). These data were

only used to assess accuracy of the data layers and to refine the

PVT terrain model.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000 49

Representation of field plots across all base vegetation categories

seemed more than adequate for this project (Table 7). The number

of plots stratified by PVT class and cover type category seems

unbalanced in Table 7, but when adjusted for area (numbers in

parenthesis), there appears to be a somewhat even distribution.

However, several cells were severely under- or over-sampled.

Rock, barren ground, and herblands were not sampled to the level

of their occurrence because we were concentrating on documenting

the variation in forested communities. The most difficult communities to differentiate with TM imagery are forest types, so we spent

the majority of our sampling effort attempting to describe differences in forested communities based on species composition and

fuel characteristics. As a result, many non-forest types were not

sampled to the level of their occurrence on the landscape. This is

Table 7—Distribution of sample plots stratified PVT and cover type categories. Numbers in parentheses indicate the expected sample

size based on the area encompassed by the PVT and CT combinations computed from the final cover type and PVT maps.

Potential vegetation type

Pinyon- NonSpruce- Mixed Douglas- Ponderosa Oak- Scrub- Vegetated/

Cover type Riparian Fir Conifer fir Pine Juniper Grassland Rock Total

Water 3(3) 3

Rock 25(272) 25

Barren Ground 0(3) 0(43) 14(146) 7(86) 7(0) 28

Mine-Quarry 4(10) 4

Herblands 13(0+

) 2(4) 3(25) 62(113) 160(306) 45(54) 285

Xeric Shrublands 0(5) 5(8) 61(45) 37(71) 103

Mesic Shrublands 2(3) 1(3) 6(13) 9

Broadleaf Riparian 23(25) 23

 Forest

Broadleaf/Conifer 4(5) 15(1) 21(3) 14(6) 54

 Forest

Conifer-Gambel Oak 4(1) 34(11) 33(26) 71

 Forest

Conifer Forest- 3(0) 29(43) 157(1) 189

 Woodland Mix

Mixed Woodland 1(0) 44(16) 145(40) 190

Mesic Mixed Conifer 9(3) 87(62) 96

Xeric Mixed Conifer 3(2) 60(8) 147(18) 0(36) 210

Aspen 7(1) 7(2) 5(1) 19

Gambel Oak 3(2) 9(4) 3(1) 15

Juniper 12(20) 83(95) 95

Pinyon 6(24) 44(95) 50

Evergreen Oak 5(14) 23(55) 28

Ponderosa Pine 12(5) 52(27) 254(90) 318

Douglas-fir 2(0) 4(0) 18(12) 24

Pinyon-Juniper 23(5) 157(56) 180

Spruce-Fir 29(9) 29

Total 42 65 204 318 604 687 89 39 2048 ++

+

Expected sample size may be biased by number of training areas (plots) used to classify any particular cover type

++Not all 2048 plots were used in phases of classification due to problems and constraints in sampling methodology and classification requirements.
50 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000

demonstrated by the over sampling of ponderosa pine (254 plots

when only 90 were needed), xeric mixed conifer (147 when only

18 needed), and conifer forest-woodland mix (157 when only one

was needed) cover types. Herbaceous and shrublands were also not

sampled to expected levels (about 50 percent less than occurrence).

It was difficult to compute expected levels prior to field sampling

because no map existed that described extents of major cover types

for the GNFC area.

Draft Layer Development

We created a set of draft FARSITE input layers for the GNFC

fire management because the layers were needed for the 1998 fire

season. These draft layers were ONLY for use in 1998 and did

not constitute the final product. We created these draft FARSITE

layers using the triplet strategy mentioned above but with draft

layers of the vegetation base maps. The draft PVT layer was developed from terrain-based rules derived from field data collected in

1997. The rule parameters were then translated to the GIS using

a menu-driven routine we programmed in ARC/INFO’s macro

language (AML) that allows the user to interactively assign PVT

category values into a matrix of elevation versus slope and aspect

combinations.

This draft PVT map was limited in scope but provided a good

baseline for fuels assignment. The draft cover type, structural

stage, and canopy closure FARSITE layers were created from the

unsupervised classification of the September 26, 1993 LANDSAT

Thematic Mapper (TM) satellite scene with classification types derived from 1997 field data. This was the same map that was used to

guide polygon selection during the field sampling effort. The draft

version of the FARSITE input layers will was given to Gila National Forest personnel in April 1998 to be used for the 1998 fire

season.

Final Vegetation Classifications

The eight potential vegetation types (PVTs) that describe biophysical settings in the GNFC are presented in Table 3 and the key to

these types is detailed in Appendix E. The terrain model or topographic rulebase used to map the PVTs is presented in Appendix F.

We narrowed our list of PVTs from 12 to 8 because when we constructed the 1998 draft vegetation and FARSITE layers, we found

some PVTs did not improve our ability to map fuels and vegetation. We aggregated the lowland oak types with the pinyon-juniper

PVTs because it was difficult to identify them in the field due to
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000 51

past disturbances. In the end, we found our PVTs were very similar

to the life zones described by the General Ecosystem Survey

(Carleton and others 1991, Dahms and Geils 1997). Life zones

are perhaps the appropriate scale for describing aspects of community development such as succession, disturbance regimes, and

most importantly, fuels (Dahms and Geils 1997).

Twenty-four cover types (Table 4) and 12 structural stage categories (Table 5) were used to describe the composition and structure

of existing vegetation on the GNFC. Keys to these existing vegetation classifications are presented in Appendix E. Again, categories

for both classifications were designed to map many ecological

characteristics other than FARSITE fuels. The primary rules

used to design the classification were that the categories needed

to (1) be easily identified in the field, (2) be keyed from the data

collected on the GT and EC plots, (3) have adequate discrimination

between fire behavior fuel models, and (4) uniquely describe existing vegetation. Some draft cover types were aggregated because

their inclusion did not improve the mapping accuracy or contain

information important to map ecological characteristics. For example, a white fir cover type is possible but rare in the GNFC so

it was lumped into the mesic mixed conifer type. Moreover, fuel

characteristics on a white fir cover type are very similar to those in

the Mixed Conifer type. As mentioned, structural stage categories

were modified from the processed based approach of O’Hara and

others (1996) to a diameter size class approach because the processbased categories were difficult to identify in the field and in the

imagery, and the increased detail was not needed for fuel mapping.

Vegetation Base Layer Mapping

Potential vegetation types (PVT) were mapped for over 2 million ha

that comprised the GNFC project area using the terrain modeling

method (Plate 1). A summary of land area by PVT and cover type,

and by cover type and structural stage, is shown in Appendix I.

Mapped PVT categories are unevenly distributed across the GNFC.

Thirty-nine percent of the GNFC landscape was mapped as Pinyon-Oak-Juniper, close to 19 percent was mapped as Ponderosa

Pine, and about 10 percent as Scrub-Grassland (Table 3). Seventy

percent of the area is occupied by just three PVTs because we assumed most herblands (94 percent) were seral to a forest or woodland type (Appendix I). Therefore, the extensive grasslands in the

low lying areas surrounding the Gila National Forest were considered to have a woodland or closed forest potential, thereby increasing the land area in the Pinyon-Oak-Juniper PVT. This assumption
52 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000

was based on the eventual conifer encroachment we observed in

the foothills of the GNFC, and it was also based on documentation

in the literature (Covington and Moore 1994, Dahms and Geils

1997, Dick-Peddie 1993, Savage and Swetnam 1990).

The cover type map is shown in Plate 2. A relatively large portion

of the GNFC was mapped as rock or barren ground cover type

(527,531 ha or 27 percent of the entire study area) from the satellite imagery (Table 5). Much of this occurs outside the Gila National Forest boundaries to the east and west in desert scrub zones

and is partially explained by the TM Scene date (June 1997)

which is before monsoonal rains and subsequent green up of the

scrub grasslands. Many grasslands and shrublands have similar

reflectances to barren ground so the classification often confuses

areas that have the potential to support grasslands with true rock

or barren grounds. Monsoonal greenup of grassland ecosystems

would have helped differentiate these two cover types.

Herblands comprised nearly 25 percent of the GNFC while rock/

barren ground was found on close to 27 percent of the area. This

seemed to agree with other classifications of similar landscapes

(Gonzales and Maus 1992), but points out that over 50 percent

of the GNFC is in nonforest types where confusion between fuel

models is minimal. Only 20 percent of the GNFC is closed forest

and 15 percent is in woodlands. We were surprised by the low

coverage of shrublands (8 percent), but the high cover of shrubs

needed to key to shrublands (15 percent) precluded many herblands

from keying to shrublands. This was done to distinguish between

the grassland (FBFM 1) and shrubland (FBFM 6) fuel models.

The structural stage map had uneven distributions in tree structural

stages (Plate 3). Close to 19 percent of the GNFC landscape was

mapped as Medium Tree structural stage which is over 94 percent

of all mapped forest structural stages (Table 5). This percentage

is relatively high and is primarily a result of lacking an adequate

structural classification scheme prior to field sampling and lacking

prior knowledge of the distribution of structural stages across the

landscape. The diameter classes we used to key to structural stages

(Table 5) were based on ECODATA criteria and proved too broad

to comprehensively describe stand structure for GNFC forests.

GNFC closed forests rarely have diameters greater than 20 inches

DBH which is the upper limit of the Medium Tree category. However, we found these structural stages were more than adequate for

fuel mapping.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000 53

FARSITE Input Layer Mapping

Fuel model assignments to base vegetation classification categories

are documented in Appendix J and in the database on the CD

(CD\DATABASE\LUT\PCSLUT.DBF and

CD\DATABASE\LUT\CSCLUT.DBF).

Final fuels layers are shown in Plates 4 to 8. Woodland fuel

models comprised the majority of the GNFC FBFM map (34 percent in Table 8 and see Plate 4). Stand and crown height (Plate 5

and 6, respectively) was very diverse across the GNFC landscape.

Forested crown bulk densities were surprisingly uniform (Plate 7).

Canopy closure ranged from 0 percent to 99 percent (Plate 8)

with the majority of the GNFC having a canopy closure of 3 to

20 percent.

The FARSITE fuels layers in the GNFC GIS were reformatted

into the FARSITE landscape format geographically stratified by

USGS quadrangle boundaries to create 111 FARSITE landscape

files. This was done to optimize FARSITE execution on a laptop

PC. Simulation landscapes that are too large take a long time to

display and modify in the FARSITE program. On the other hand,

simulation areas that are too small run the risk of a simulated fire

reaching the edge. We found simulation landscapes about the size

of a USGS quadrangle (around 10 km by 10 km) seemed to be the

optimal size for both worlds. Each landscape file has the eight

FARSITE input raster layers and some vector files of cartographic

features (e.g., streams, roads, trails).

Table 8—Spatial summary of crown fuels by surface FBFM Fuel Model (normal conditions).

Mapped

percent Crown fuel averages

of GNFC Stand Crown Crown Crown bulk

Fuel model1 Description landscape height (m) height (m) closure (%) density (kg m–3)

1 Short grass 8.8 ——— —

2 Timber grass 0.6 5.1 0.8 30 0.066

5 Brush 0.7 ——— —

6 Dormant brush 8.5 4.4 0.6 3 0.017

8 Closed timber litter 5.0 14.2 2.5 60 0.122

9 Ponderosa pine 9.1 14.9 3.8 40 0.081

10 Heavy timber 3.5 17.5 2.0 70 0.198

50 Pinyon-juniper 33.6 1.8 0.2 10 0.006

98 Water 0.1 ——— —

99 Non-vegetated (rock, 29.9 ——— —

 mines, urban)

1

 From Anderson (1982).
54 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000

We also reformatted daily weather taken from surrounding National Weather Service weather stations and Remote Automated

Weather Stations (RAWS) and included these weather files so

managers could have default weather conditions to execute

FARSITE. The Weather Service data was obtained from the National Climatic Data Center (NCDC) while the RAWS data was

obtained from the Western Regional Climate Center. We stored

these data in the directory CD\WX\NCDC and CD\WX\RAWS, respectively. In addition, we simulated 24 years (1970 to 1994) of

weather at sites representative of each PVT with the DAYMET

model (Thornton 1998) and included these weather streams in the

weather database (CD\WX\DAYMET). No wind files were created

because of the lack of historical data on wind speeds and duration.

Canopy cover was best described from the satellite imagery rather

than as a assignment from base vegetation classification combinations. Therefore, we assigned the canopy cover class to individual

polygons based on the spectral imagery classification.

Accuracy Assessment

The summary of the entire hierarchical accuracy assessment for

categorical variables is presented in Table 9 where all ocularly

estimated categories in the field data, vegetation classifications,

and map layers were analyzed to estimate their accuracy. The assessment summary for continuous variables is shown in Table 10.

Continuous variable accuracies were estimated using regression

techniques while accuracy for categorical variables were estimated

from contingency table analysis.

Contingency tables and regression analysis of individual maps are

detailed in Appendix K and an example of the PVT classification

accuracy assessment is shown in Table 10. Included in the contingency tables is a fuzzy score that provides additional information

such as the magnitude and source of error (Gopal and Woodcock

1994, Woodcock and Gopal 1992). The fuzzy scores also provide

a more useful way to evaluate a spatial layer that is mapped into

discrete categories but is actually continuous on the landscape.

Fuzzy scores are rated as 1 (absolutely wrong), 2 (understandable

but wrong), 3 (acceptable), 4 (good answer), or 5 (perfect).

Our field sampling crews seemed to maintain a high level of

accuracy and consistency (over 85 percent) in ocularly assessing

variables at the plots (Table 9 and Table 10). For example, when

the PVT field assignments where checked against the ECODATA

PC plant species list sampled for the same plot using the GT and

EC methodologies, an accuracy of over 89 percent resulted.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000 55 Table 9—Accuracy assessment summary for the categorical variables used in the GNFC project. The KHAT statistic adjusts for unequal sampling for each categorical variable. Fuzzy set Number of Accuracy KHAT accuracy, score 3 observations (%) statistic (%) or better (%) Number of Accuracy KHAT (plots and (plots and (plots and (plots and Evaluated Item Number of observations (%) statistic (%) polygon polygon polygon polygon source evaluated categories (plots only)1 (plots only) (plots only) validations)2 validations) validations) validations) PVTField Data 8 1000 89 85 — —— — Structural Stage Field Data 11 1000 77 71 — —— — PVT Classification 8 1000 99 — — —— — Cover Type Classification 23 1000 98 — — —— — Structural Stage Classification 11 1000 98 — — —— — FBFM Normal Assignments 11 —— — 2037 55 45 — FBFM Severe Assignments 12 —— — 2037 49 39 — FBFM Normal Layer 11 981 36 26 1825 35 24 62 FBFM Severe Layer 11 982 38 28 1826 34 24 58 PVT Terrain Model Layer 8 1000 62 48 — —— — PVT (Polygon) Layer 8 982 61 50 1829 57 45 89 Cover Type Layer 23 982 43 39 1829 36 31 65 Structural Stage Layer 12 982 52 42 1829 52 39 64 Crown Closure Layer 11 982 41 26 1829 41 24 — Lifeform Layer 7 982 64 55 1829 61 48 — 1 Plots used in these accuracies were also used as training areas so these results may be biased. 2 Plots were used in the accuracy assessments and as training areas; polygon validations were used solely for the accuracy assessment.
56 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000

Table 10—Accuracy assessment summary for the continuous variables used in the GNFC project. The KHAT statistic adjusts for

unequal sampling for each categorical variable.

Coefficient

Evaluated Item Number determination Standard Mean

source evaluated obs r2 error error Units

Stand Height Field Data 110 0.67 10.51 -8.40 Meters

Crown Height Field Data 110 0.12 10.85 -7.88 Meters

Stand Height Layer 1394 0.37 18.08 10.16 Meters

Crown Base Height Layer 1394 0.21 4.74 1.97 Meters

Crown Bulk Density Layer 917 0.35 0.06 0.04 Kg/m3

Elevation Layer 999 0.99 90.80 19.21 Meters

Aspect Layer 960 0.68 0.41 0.01 Degrees+

Slope Layer 999 0.76 8.99 0.56 Percent

+ Degrees were converted for this accuracy assessment using the equation

 ASPECT = 1 + COS(ASPECT_DEGREES)

Base vegetation classification accuracies were also quite high.

Only 2 percent of the plots sampled with the GT and EC protocols

failed to meet the PVT key criteria (Table 9). This compares well

to other potential vegetation classifications (Daubenmire 1966,

Pfister and others 1977, USDA Forest Service 1997).

GNFC FARSITE CD

All data inputs and products used and created in this study are

contained on the CD that accompanies this report. This CD has a

unique file structure so that all data can be accessed efficiently

and quickly (see Appendix L and the readme.txt file in CD\).

The FARSITE input layers in the landscape format have been

compressed using the specialized software contained in the

CD\SOFTWARE\PKZIP directory. These layers, in the CD\QUAD

directory can be individually loaded onto a laptop or desktop

Personal Computer (PC) hard disk for FARSITE simulation.

The FARSITE computer program is contained in the directory

CD\FARSITE and you can install FARSITE onto a computer

using this program.

Once FARSITE is installed on a PC, individual quads in landscape

format can be copied to the PC’s hard disk as needed. It’s probably

not a good idea to copy all landscape quad files over to the hard

disk unless there is abundant disk space (over one gigabyte). However, all weather and custom fuel model files should be copied to

special directories on the PC hard disk. Refer to the FARSITE

user’s manual (Finney 1995) to create a robust hard disk directory

structure that will facilitate efficient FARSITE simulations.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000 57

DISCUSSION

This mapping project was highly successful for a number of

reasons, despite the apparent low accuracies for some vegetation

and fuels maps. First, a consistent and comprehensive set of

FARSITE input maps was created for a large and diverse area.

This extensive data set can be used to simulate many types of

fires with FARSITE over large areas and across complex terrain

and diverse ecosystems. Although the accuracies of the layers

may appear low, erroneous polygon attribute assignments are

often quite similar ecologically to those observed on the ground.

Usually, fuel model assignments are quite close to observed values

even though the mapped FBFM might be wrong when interpreted

in the context of fire behavior. Forested types are rarely confused

with grassland types, but some woodland types often have the

same reflectance characteristics as forests and grasslands.

The comprehensive data set (layers and files) generated from this

project provides a framework to create other maps useful for land

management. Other non-fuel attributes in the base layer combination lookup table can be used to create maps that might be used for

wildlife planning or forest sampling. The procedure of asking fire

managers to assign three fuels characteristics to each PVT-cover

type-structural stage combination can be used to assign other ecologically relevant attributes to the combination. For example, hiding cover classes for deer and elk can be assigned to the vegetation

base layer category combinations to predict those areas on the

landscape where big game ungulates would go for cover. Spatial

and tabular data sampled, created, simulated, or assigned from this

project can be used for a wide variety of land management activities. Fuel loadings can be linked to the base vegetation triplet for

input into smoke and planning models (Keane and others 1998b).

Diagrammatic succession models can be developed for each PVT,

like Keane and Long (1997) did for the Salmon-Challis National

Forest, to predict landscape changes over time and quantify historical ranges of variation. Moreover, simulated future landscapes of

FARSITE fuels, created from the base vegetation combination

lookup table, can be used to simulate fires of the future using

FARSITE.

Layer Accuracy

The low accuracies of developed maps may cause some to think

perhaps another method of mapping should have been used. While

the accuracies seem low (36 percent to 57 percent), they are comparable to accuracies of other similar vegetation maps generated

from remotely sensed satellite imagery (Brzeziecki and others
58 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000

1993, Deitschman 1973, Grupe 1998, Redmond and Prather 1996).

The primary reason why fuels and vegetation layers appear inaccurate is simple; fuels and vegetation are continuous, not discrete,

ecological characteristics that are highly variable in space and

time. So, while vegetated stands can be differentiated across a

landscape from classified satellite imagery, it is often difficult to

correctly identify those stands as to their composition and structure

(see Greer 1996, 1998, Lachowski and others 1995, Redmond and

Prather 1996, Shao and others 1996). Therefore, their quantification and classification is highly scale dependent (Daubenmire

1966). Vegetation communities consist of many plant species that

vary in abundance, stature, and dominance across environmental

and disturbance continua (Whittaker 1965, Whittaker 1967). Fuels

consist of many sizes of live and dead biomass that are extremely

variable both within stands and across landscapes (Brown and

Bevins 1986, Brown and See 1981). Therefore, mapping fuels and

vegetation communities into discrete polygons using discrete classifications is tactically difficult and complex (Brown and Bevins

1986, Kessell 1979, Whittaker 1967). When fuzzy set accuracies

of the fuels and vegetation layers of score 3 (acceptable) or better

are considered, accuracies improve considerably to 89 percent for

the PVT, 65 percent for the Cover Type, 64 percent for the Structural Stage, and 62 percent for the FBFM Normal layers (Appendix

K). These fuzzy set accuracies provide a more meaningful assessment of continuous landscape attributes mapped as categorical

variables than do the overall accuracies.

The effect of the continuous behavior of fuels and vegetation dynamics across the GNFC on map accuracy is somewhat demonstrated in the results of the intensive accuracy assessment (Table 9

and 10). Errors exist in the sampled field data because it is often

difficult to estimate continuous variables into the required discrete

categorical inherent in the classifications. When the accuracies

of the Fire Behavior Fuel Model field calls are compared to the

Lookup Table Assignments, accuracies range from 49 to 55 percent.

This demonstrates the difficulty inherent in consistently quantifying continuous fuel conditions into discrete categories across a

highly variable landscape. When one bases accuracy assessments

on reference (field) data that is assumed to be 100 percent accurate

and yet the agreement between the reference data and the Lookup

Table Assignments is only 49 to 55 percent, these discrepancies

are carried through to the fuels spatial layer, resulting in low accuracies. Moreover, continuous variables measured on a site have a

high degree of variablility making it difficult to assess GIS layer

accuracy. For example, stand and crown base height are hard to

ocularly estimate for the computation of crown fire spread when
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000 59

different species and different sized trees have a wide range of

observed heights and crown characteristics within the plot.

Because the FARSITE layers are built from the vegetation triplet

of PVT, Cover Type, and Structural Stage, errors present in the

three layers comprising the triplet are carried through to the

FARSITE layers. For instance, if a polygon was classified in the

layer as a Ponderosa Pine PVT, Ponderosa Pine cover type, and

medium tree structural stage, it would be assigned from the Vegetation Characteristics Lookup Table a FBFM Normal 9, FBFM

Severe 9, a Stand Height of 35 ft, and a Crown Base Height of 10

ft. If the reference plot falling within this polygon was sampled as

a Ponderosa Pine PVT, Pinyon pine cover type, open woodland

structural stage, in the field, it could have been assigned an FBFM

Normal 50, FBFM Severe 2, Stand Height 45 ft, and Crown Base

Height 1 ft. Thus, only the PVT layer would have been classified

correctly and all subsequent layers incorrectly. While the methodology of using the vegetation triplet to describe ecological characteristics is sound and creates multi-user data layers, it can cause errors to be propagated throughout the many layers dependent on the

triplet.

With the seemingly low Fire Behavior Fuel Model accuracies,

one has to ask the question, why not directly map fuels from remotely sensed data instead of basing them on vegetation characteristics if using this methodology does not confer better results? The

answer is this; if fuels are mapped directly, the fuel layers would

not be congruent with the other spatial layers necessary to run

FARSITE, namely Stand Height, Crown Base Height, Crown Closure, and Crown Bulk Density. In other words, illogical combinations of the input fuel layers would result in inaccurate fire behavior predictions.

The base vegetation classifications contain inherent error because

the effects of past disturbance history, integrated site environment,

and genetic differences influence the composition and abundance

of indicator species that confounds consistent and precise identification of a vegetation category. For example, a small amount of

white fir in the understory of a stand can drastically alter crown

fire initiation and behavior. Moreover, classification of potential

and existing vegetation types has a high degree of error because

indicator plant species vary in abundance and dominance because

of genotypic variation across complex environments. For example,

a ponderosa pine PVT may not be identifiable on the ground because a previous stand-replacement wildfire killed every ponderosa

pine tree (Deitschman 1973, Steuver and Hayden 1996).
60 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000

There are also errors in the fire management workshop estimates

because fire managers tend to simplify and synthesize fuel dynamics to broad vegetation types and then assign these simplifications to more detailed vegetation classifications with more categories (Keane and others 1996a). There are errors in the fuels and

vegetation GIS maps because of all the compounding errors mentioned above, and because fuels and vegetation are not constantly

arrayed in space (i.e., they vary across a continuum). Probably the

most frequent source of error in image classification and mapping

is that conditions inside the field sample plot do not always represent conditions found throughout the polygon. Therefore, it is

doubtful this method of sampling and mapping fuels will ever

produce highly accurate GIS layers, and it will require exorbitant

amounts of time and money to sample fuels to adequately describe conditions across an entire polygon. Even then, the high

variability of the polygon sampling coupled with scale problems

may always limit map accuracy.

Another reason for low accuracies in the vegetation layers can be

explained by the under-representation of ground truth in common

cover types such as rock, herblands and barren (Table 7). We did

not extensively sample these areas because we were concerned

with describing forest communities. These cover types are easily

identified by image processing because of their unique spectral

signature (Howard 1991, Verbyla 1995) (Table 11). If we had established ground-truth plots in these cover types at the same level

as their occurrence on the landscape, the overall accuracies would

have increased to 83 percent for the PVT map, 63 percent for the

cover type map, and 77 percent for the structural stage map based

on the data in Table 7 and Table 11. Other satellite-based cover

type mapping efforts with higher accuracies than this study

(Gonzales and Maus 1992) have a proportionately higher percentage of ground-truth plots in the cover types that are most clearly

distinguished by satellite imagery such as rock, barren, or water

(Verbyla 1995). Because we concentrated our sampling efforts in

vegetated areas most affected by fire, we under-sampled in the

cover types that would have made our accuracies much higher.

Layer accuracies varied by PVT category ranging from 14 to

92 percent (Table 10). Cover type accuracies were highest in

the non-forest types (100 to 67 percent) and lowest for woodland

types (6 to 51 percent). This is a direct result of the inability of

TM imagery to discriminate between major cover types within

one lifeform. Mesic mixed conifer and xeric mixed conifer have

different ecological characteristics, but the imagery cannot distinguish between the two. However, the imagery can discriminate

between rock and forest quite well (Appendix K).
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000 61 Table 11—Accuracy of the Potential Vegetation Type (PVT) Classification based on PVT field calls. Reference (field) PVTs Spruce- Mixed Douglas- Ponderosa Pinyon-Oak- Scrub- Non- Commission Riparian Fir Conifer fir Pine Juniper Grassland vegetated Totals Error (%) Riparian 21(5) 0(1) 2(1) 9(1) 19(1) 6(1) 1(1) 1(1) 59 64 Spruce-Fir 0(1) 49(5) 15(4) 7(3) 0(2) 0(1) 0(1) 1(1) 72 32 Mixed Conifer 1(1) 9(4) 94(5) 67(4) 11(3) 0(1) 0(1) 0(1) 182 48 Douglas-fir 2(1) 2(3) 33(4) 136(5) 87(4) 3(2) 0(1) 1(1) 264 48 Ponderosa Pine 2(1) 0(2) 3(3) 37(4) 254(5) 100(4) 1(2) 2(1) 399 36 Pinyon-Oak-Juniper 6(1) 0(1) 1(1) 18(2) 147(4) 432(5) 46(4) 9(2) 659 34 Scrub-Grassland 1(1) 0(1) 0(1) 0(1) 0(2) 14(4) 35(5) 1(2) 51 31 Non-vegetated 5(1) 0(1) 7(1) 8(1) 27(1) 70(2) 6(2) 20(5) 143 86 Totals 38 60 155 282 545 625 89 35 1829 Omission Error (%) 45 18 39 52 53 31 61 43 Classified Format: Number of plots (fuzzy score) Overall Accuracy = 57%, KHAT Accuracy = 45%. Fuzzy sets: 89% have a fuzzy score of 3 (acceptable) or better, 87% have a fuzzy score of 4 or better. (layer) PVT
62 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000

There are several ways to increase the accuracies of these maps.

First, the mapped categories for the cover type and structural stage

layers could be designed to describe what the satellite TM sensor

is sensing rather than to comprehensively describe the vegetation.

However, the resultant vegetation categories are rarely useful to

common land management applications, especially fuels mapping,

because many vegetation complexes are missing or aggregated

(Verbyla 1995). For example, ponderosa pine cover types would

probably have been lumped with xeric mixed conifer in our study,

so unique fuel conditions in the ponderosa pine stands would not

be identified. Second, fuels could be directly sensed from the satellite imagery thereby eliminating the vegetation triplet approach.

This has proved successful for some fuel mapping studies (see

Fuel Mapping Studies section) but would probably not be successful for creating FARSITE layers because all five fuel layers

must be created congruently. Since some FARSITE layers are continuous (e.g., stand height) and some are categorical (FBFM), direct image classification for all fuel layers would be very difficult.

Moreover, the independent image classification of each FARSITE

fuel component would probably not be successful because there is

a high chance many polygons would have illogical assignments

such as having stand heights less than crown heights.

The best way to improve accuracy would be to use new remotely

sensed products that comprehensively and consistently distinguish

aerial and surface fuel loadings and characteristics. Fuels may not

correlate well with the vegetation characteristics best detected by

passive sensors such as the Thematic Mapper (Hardwick and others 1996, Kourtz 1977). Aerial photo interpretation may increase

accuracies but perhaps not enough to justify the increased cost and

effort (Root and others 1985). Schetch mapping where maps are

created through field reconnaissance are also costly and require a

great deal of human resources (Dendron Resource Surveys 1981).

Lidar and SAR are new active remote sensing technologies that

show great potential for fuel mapping but have been untested as

yet (Bufton 1989, Dubayah and others 1997, Naessent 1997,

Nelson and others 1988).

Field Sampling

A major shortcoming of this project was the temporary delineation

of polygon boundaries from an unsupervised classification of the

satellite imagery. We found major differences in polygon boundaries when we replaced the draft polygon delineations done in

1997 with the permanent delineations derived from the textural

analysis of TM imagery in 1998. We did not have the newer TM

scene coverages for the field sampling phase so the 1993 TM scene
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000 63

was used to create an unsupervised polygon map of differing spectral clusters as spatial stratification for landscape sampling. This

preliminary classification had many problems including inconsistency in polygon sizes and spectral classifications. We then experimented with various image processing methods to create more

consistent polygon delineations but this was not finished in time to

help in the sampling effort. As a result, it was difficult to assign the

data collected for the area defined by the draft polygons to the appropriate final polygon in the final map. For example, a polygon

created from the unsupervised image classification might be 50 ha,

but the final polygon delineation using textural attributes created

two polygons of 20 and 30 ha within the original 50 ha draft polygon. This problem was especially important in the 1998 field sampling because nearly half of the polygons sampled with the PV

methodology did not have GPS-estimated UTM coordinates due to

time and cost constraints. We had to manually assign the sampled

attributes from the PV effort to appropriate polygons in the final

map on a case-by-case basis. We should have delineated the polygon boundaries from all TM scenes prior to our sampling effort

and these delineations should have been static throughout the entire project. However, this was not possible because we did not

obtain GIS and TM imagery in time. We should have also taken

GPS geo-referenced positions for every plot regardless of sampling

methodology for greater flexibility in using the field data for any

polygon delineation. However, the real strength of the PV methodology is its simplicity and rapid assessment. You really shouldn’t

need to take a GPS location at every PV plot. In fact, the PV

method works the most efficient when you are walking a trail or

driving a road, to evaluate recognizable polygons to sample the

major vegetation and fuel category values.

The success of this fuels mapping project was directly related to

the quality and quantity of the data obtained from the field sampling effort. Our top-down approach where sampling zones were

stratified by geographic area and then by inherent topographic gradients coupled with the hierarchically nested measurement intensities seemed to optimize plot location, data gathering, and available

resources for the amount of time and money available for the

project. It appears most vegetation and fuels combinations were

sampled, even though only 2,000 plots were gathered in this effort

(Table 7). However, additional field data could have improved

map accuracies and strengthened vegetation classifications by filling in major data gaps and providing deeper coverages in important fuel and vegetation types.

We estimate another 2,000 to 3,000 plots would have helped

strengthened our spectral classifications and improved our overall
64 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000

accuracy assessment. The intensive EC method would be used to

collect data on only 5 percent of those plots, the GT method would

be used for 30 percent, while the remainder be sampling with the

quick PV methodology. This means that the majority of plots

(greater than 60 percent) could be measured using an uncomplicated methodology. But, even with the doubled field sample, we

probably would have only improved map accuracies by only 5 to

10 percent because of the great variability in ecosystem properties

over the GNFC landscape.

We ended up spending over 70 percent of our sizeable mapping

budget on field sampling and still did not seem to obtain enough

plots. This is because collecting data using the GT and EC methodologies required a high level of field expertise that was not available from Gila National Forest personnel. We conducted a weeklong EC and GT plot field training session for about 20 people

on Gila National Forest staff in the hopes that they could assist

in the sampling effort, but their busy schedules during the field

season prevented them from providing substantial help. In retrospect, we should have trained these people only in the PV methodology which would have allowed them to quickly sample polygons

at their convenience without requiring vast knowledge of ecological sampling and a predetermined sampling schedule. Since our

crews consisted of highly trained ecologists and botanists, we

could have concentrated on EC and GT sampling in both years and

improved our ecological database considerably, thereby strengthening the study, with the PV data provided by the National Forest.

A high level of data quality is essential for the creation of base

vegetation and FARSITE input layers. Our sampling crews were

well trained and had considerable field experience so the data they

collected tended to be of high quality. We double checked the

ASCII data files for entry mistakes and ran extensive data checking

programs to control error and improve database quality. However,

the combination of extensive fire behavior, fuel modeling, and ecological expertise needed to create a high caliber data base for fuel

mapping may require additional money to be spent on professional

fire ecologists to ensure optimum data and subsequent map quality.

Typically, natural resource agencies develop field inventory techniques so that inexperienced field personnel can be trained in a

short time to measure ecosystem characteristics with simple techniques. However, fuel mapping seems to require extensive expertise in all phases of fire management so more experienced field

crews may be necessary.

Insufficient or low quality field data is usually the major reason

cited for low accuracies and poor map quality in a number of
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000 65

vegetation sampling efforts (Hardwick and others 1996, Howard

1991, Jensen 1986, Verbyla 1995). So, how much field data are

enough? Perhaps the best plan for field sampling is to replicate our

nested sampling intensities and have one experienced field crew

sample the detailed ecosystem attributes using the intensive methodologies. Then, several inexperienced crews can be mobilized to

gather general data with simplified methodologies such as the PV

protocols. It would be optimal if the simplified methods could be

taught in less than a day, and only require one person to take the

measurements. This approach would allow other natural resource

or fire inventory crews to collect data when time allows or collect

the fuel data in addition to the data they are already taking. There

usually is no upper limit on the amount of data needed for a project

because funding, personnel, and access ultimately dictates the size

of the field database. Any increase in these factors will result in

higher quality and more robust data.

Vegetation and Fuel Layer Development

The application of this vegetation-based methodology to map fuels

on the Gila National Forest was highly successful despite the low

map accuracies. First, fuels and vegetation were consistently and

comprehensively classified and mapped over a large and complex

landscape. Second, the vegetation layers provide an ecological

context in which to interpret the surface and crown fuels layers.

Users can easily understand why a particular fuel complex is assigned to a polygon once the vegetation triplet is known. Moreover, this ecological context allows easy modification of fuels

maps if users feel fuel assignments are in error. Next, the vegetation triplet also allows the assignment of other ecological attributes

useful to other land management applications. The entire mapping

project generated maps and databases that will surely be useful to

other phases of land management. Soils and DEM layers could be

used to map erosion potential. The DEM, stream layer, and cover

type classification could be used to map riparian environments.

Fuels were successfully mapped using the three base vegetation

classification maps in concert. We estimate the use of the PVT

map to stratify cover type image classifications resulted in an

increase of between 5 and 15 percent in map accuracy. Structural stage delineations improved fuel model assignments by

about 10 percent.

Linkage of gradient-based biophysical settings with PVTs in this

study was highly experimental and generated mixed results. While

the gradient biophysical settings layer aided in the refinement of

PVT categories and polygons, it certainly was not necessary for the
66 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000

successful completion of this GNFC project. The PVT map refinement using environmental gradients created from current technology could probably increase accuracy by about 5 percent, but the

creation of this biophysical settings layer is time consuming and

requires specialized expertise. The simulation models and algorithms used to quantify and described biophysical parameters are

highly complex and very experimental and do not yet seem to have

the inherent resolution needed for fine scale fuel mapping projects.

This project could have easily been completed with only the PVT

classification and PVT map.

CONCLUSIONS

FARSITE fuel layers were successfully developed for the Gila

National Forest Complex using a vegetation-based approach where

fuels were mapped from there relationship to biophysical setting,

species composition, and stand structure. This approach was also

expanded to map other ecological characteristics. This approach

is not the only way to create the FARSITE input layers, but

rather, it is presented as one of the many tools that can be used to

create these complex layers. Current remote sensors do not provide imagery that is directly useful for fuel mapping. Hopefully,

future satellite platforms will contain sensors that sense the many

characteristics of fuels that influence fire behavior so that accurate, consistent, and useful maps can be used in fire management

and planning.

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78 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000

GLOSSARY

Accuracy assessment — The process of describing the accuracy of

the data layers and field data based on ground truth or more intensive

measurements.

ASCII file — A digital data file stored on a computer in ASCII

(American Standard Coding for Informational Interchange) format.

AVHRR imagery — Advanced Very High Resolution Radiometer

satellite sensor. Pixel sizes from this imagery are usually around 1 km

on a side.

AVIRIS — Airborne Visible Infra-Red Imaging Spectrometer satellite sensor.

Biophysical setting — The site environment that integrates the

influences of climate, soils, topographic setting, and geographical

region. Similar terms are biophysical environment, site, and site

environment. A biophysical setting or biophysical environment

usually creates certain conditions that allow a unique assemblage of

plant species in the absence of disturbance. This unique plant

community is referred to as the potential vegetation type.

Base vegetation classifications — The three vegetation-based classifications used to map fuels. They include a classification of

biophysical settings, cover type, and structural stage. These classifications describe a method to uniquely key plant community types.

These classifications should not be confused with image classifications.

CD — Compact Disk or the storage media used to save all information

and data used in this project
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000 79

Composition — The list of species within a plant community or the

list of community types or stand types on a landscape

Cover type — A vegetation classification depicting the major tree,

shrub or grass species having the plurality of canopy cover.

Data layer — A thematic raster layer describing a section of ground.

DEM — A digital elevation model that is comprised of a grid of

georeferenced pixels assigned to the elevation (m) of the piece of

ground they represent.

Field data — Ecological data directly measured on the ground using

standardized methodologies.

Fire Behavior Fuel Model (FBFM) — This is a set of fuel characteristics by live and dead size classes that define a fuelbed for the

propagation of fire. Most FBFM’s described in this paper are from

Anderson (1982).

Ground truth — Data collected in the field to verify mapped entities.

GIS — Geographical Information System or computerized software

to analyze and summarize spatial data

Image classification — The process of classifying spectral reflectance values from one or more ranges of the electromagnetic spectrum

into discrete land units for mapping.

Layer — A spatially explicit georeferenced digital file that can either

be composed of a grid of pixels (raster layer) or georeferences line

vertices (vectors).

Pixel — A square cell in a georeferenced grid of cells. Pixels are

assigned values to create a raster layer.
80 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000

Potential Vegetation Type (PVT) — An expression of the environmental conditions of a stand based on the vegetation that would

potentially inhabit the site in the absence of disturbance.

Raster — Data that is organized in a grid of columns and rows.

Terrain model — A model that uses topography (slope, aspect,

elevation) to predict some environmental attribute. In this study,

terrain modeling was used to predict potential vegetation type.

Thematic Mapper (TM) imagery — Imagery from LANDSAT 5

satellite taken with the Thematic Mapper scanner. Contains 7 spectral

bands and has a pixel width of 30 meters.

Vector — Data that represents physical forms such as points, lines,

and polygons. In a GIS, vectors usually represent a boundary between

spatial objects.

Vegetation triplet — The naming of a mapped polygon using the

triple vegetation classifications of PVT, cover type, and structural

stage.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000 81

APPENDIX A — ECODATA plot forms

GENERAL FIELD DATA FORM (GF) - 2/97

GILA FUELS MAPPING PROJECT

 Ag R/S NF RD Yr Ex Plt

Key Id Name Mo Day

 F1-7: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ F8: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ F9: _ _ F10: _ _

Plot Radius F32 (ft): _ _ . _

Potential Vegetation

 Form Author Yr Ind Spp 1 Ind Spp 2 Ind Spp 3

 F37-41: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

Existing Vegetation

F43-46: Lifeform: _ _ LSC: _ _ DSC: _ _ CC: _ _ Stand Structure: _ _ _ _

Vegetation Layers F47-52: UL Dom Spp1: _ _ _ _ _ _ _ _ UL Dom Spp2: _ _ _ _ _ _ _ _ (> 6.5 ft tall)

 ML Dom Spp1: _ _ _ _ _ _ _ _ ML Dom Spp2: _ _ _ _ _ _ _ _ (2.5 to 6.5 ft tall)

 LL Dom Spp1: _ _ _ _ _ _ _ _ LL Dom Spp2: _ _ _ _ _ _ _ _ (< 2.5 ft tall)

 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 (%)

 F100-103: Tot: _ _ LS: _ _ MS: _ _ TS: _ _ F104-107: Gram: _ _ Forb: _ _ Fern: _ _ Moss: _ _

Site Data

Spec Ftr Landform Par Mat

 F53: _ _ F54-56: _ _ _ _ _ _ F57-59: _ _ _ _ _ _ Soil Depth (in): _ _ Soil Texture: _ _

 Positon Vertical Pos Horizontal Pos Topo Map Elevation (ft)

F60-61: _ _ _ _ F62: _ F63: _ F64: _ _ _ _ _

 Aspect (degrees) Slope (%) Horizons (%)

 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) DLDepth (in) DWCover (%)

Model Normal(F80): _ _ Model Severe: _ _ F81: _ _ . _ F82: _ _ . _ F83: _ _

 Down Log Diam (in) Dom Layer Ht (ft)

 F84: _ _ F85: _ _ _ Ht to Crown (ft): _ _ . _ Crown Bulk Density: _ . _ _
82 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000

LOCATION LINKAGE DATA FORM (LL) - 2/97

GILA FUELS MAPPING PROJECT

 Map Data

Plot Name __________________________________ (landmarks, drainages, trail no., etc)

 Quad Name F26: _________________________________

Photo Data

Roll: _ _ _ _ _ Exposure: _ _ Dir/Aspect: _ _ Comments: ______________________________

Roll: _ _ _ _ _ Exposure: _ _ Dir/Aspect: _ _ Comments: ______________________________

GPS Data

File Name: _ _ _ _ _ _ _ _ Mode: _ _ _ _ Entered 2D Elevation (ft): _ _ _ _ _

Source(M/A): _ M=Topo Map

Datum: NAD Yr F44: 27 A=Altimeter

Uncorrected Data:

Northing (m): _ _ _ _ _ _ _ UTM Zone: _ _ Elevation (m): _ _ _ _ _

Easting (m): _ _ _ _ _ _

Corrected Data:

Northing F45 (m): _ _ _ _ _ _ _ _ UTM Zone F47: _ _ Elevation (m): _ _ _ _ _

Easting F46 (m): _ _ _ _ _ _ _

Standard Deviation: _ _ _ _ Total Points: _ _ _ 2D Points: _ _ _

 GPS Comments: __________________________________________________________________________

________________________________________________________________________________________

________________________________________________________________________________________

________________________________________________________________________________________
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000 83

Plant Composition Data Form (PC) - 2/97

Gila Fuels Mapping Project

Key Id: Ag R/S NF RD Yr Ex Plt

F1-7: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

F9 F10 F11 F12 F13: Size Classes (%) Notes

LF Plant Code CC Mht 1 2 3 4 5 6

(%) (ft)

1 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _________________________

2 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _________________________

3 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _________________________

4 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _________________________

5 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _________________________

6 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _________________________

7 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _________________________

8 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _________________________

9 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _________________________

10 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _________________________

11 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _________________________

12 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _________________________

13 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _________________________

14 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _________________________

15 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _________________________

16 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _________________________

17 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _________________________

18 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _________________________

19 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _________________________

20 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _________________________

21 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _________________________

22 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _________________________

23 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _________________________

24 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _________________________

25 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _________________________

26 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _________________________

27 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _________________________

28 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _________________________

29 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _________________________

30 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _________________________

31 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _________________________

32 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _________________________

33 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _________________________

34 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _________________________

35 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _________________________

36 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _________________________

37 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _________________________

38 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _________________________

39 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _________________________
84 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000

Tree Data Form (TD) - 3/97

Gila Fuels Mapping Project

Ag R/S NF RD Yr Ex Plt

Key Id: Subplot radius: F12: _ _ . _

 F1-7: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

Individual Tree Data

Tree Spp Tree DBH Height Crown Crown Damage Severity Damage Severity Snag

Num Code Status (in) (ft) Ratio Class Code 1 Code 1 Code 2 Code 2 Code

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000 85

Tree Spp Tree DBH Height Crown Crown Damage Damage Snag

Num Code Status (in) (ft) Ratio Class Code 1 Severity Code 2 Severity Code

33

34

35

36

37

38

39

40

41

42

43

44

45

46

47

48

49

50

Hgt to Hgt to Hgt to

Spp Num Hgt Crown Num Hgt Crown Num Hgt Crown

Seedling and Sapling Data

SC 0 (<4.5 ft tall) SC 1 (0.1-1.0 in DBH) SC 2 (1.1-2.0 in DBH)

Hgt to Hgt to Hgt to

Spp Num Hgt Crown Num Hgt Crown Num Hgt Crown

SC 3 (2.1-3 in DBH) SC 4 (3.1-4.0 in DBH) SC 5 (4.1-4.9 in DBH)
86 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000

Down Wood Data Form (DW) - 2/97

Gila Fuels Mapping Project

Ag R/S NF RD Yr Ex Plt F8-# of Transects: _ F9-Litter & Duff #: 2 F10-Integrated #: 2

Key Id: Transect Lengths(ft): 60

F1-7: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ F11-1 Hour (<1/4 in): 6 (10-16 ft)

F12-10 Hour (1/4 to 1 inch): 6 (10-16 ft)

F13-100 Hour (1 to 3 inches): 10 (10-20 ft)

F14-1000 Hour (>3 inches): 50 (10-60 ft)

Transect Number

1234567

F16-Slope(%): _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

Size Classes Woody Fuel Counts

F17-1 Hour: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

F18-10 Hour: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

F19-100 Hour: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

1000 Hour Log Diameters and Decay Classes(dia to closest inch)

Log ID Dia LDC Dia LDC Dia LDC Dia LDC Dia LDC Dia LDC Dia LDC

1 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

2 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

3 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

4 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

5 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

6 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

7 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

8 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

9 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

 10 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

 11 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

 12 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

 LDC 1 = needles intact(green or brown), recently fallen. 2 = bark and branches present. 3 = bark partially intact, branches gone.

4 = bark and branches gone, partially rotten. 5 = rotten, > half log dia above soil surface.

Duff & Litter Depth Measurements (inches to closest 1/10)

Point #

1234567

1(35 ft) _ _ . _ _ _ . _ _ _ . _ _ _ . _ _ _ . _ _ _ . _ _ _ . _

2(60 ft) _ _ . _ _ _ . _ _ _ . _ _ _ . _ _ _ . _ _ _ . _ _ _ . _

F20-Sum _ _ . _ _ _ . _ _ _ . _ _ _ . _ _ _ . _ _ _ . _ _ _ . _

Integrated Woody Matter Depth Measurements (feet to closest 1/2)

Point #

1(35 ft) _ _ . _ _ _ . _ _ _ . _ _ _ . _ _ _ . _ _ _ . _ _ _ . _

2(60 ft) _ _ . _ _ _ . _ _ _ . _ _ _ . _ _ _ . _ _ _ . _ _ _ . _

F21-Sum _ _ . _ _ _ . _ _ _ . _ _ _ . _ _ _ . _ _ _ . _ _ _ . _
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000 87

 Comments Data Form (CD) - 2/97

Gila Fuels Mapping Project

Key Id: Ag R/S NF RD Yr Ex Plt

F1-7: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

ID:

1. General Location: _________________________________________________________________________________________

___________________________________________________________________________________________________________

___________________________________________________________________________________________________________

2. Weather: _________________________________________________________________________________________________

___________________________________________________________________________________________________________

3. Sampling Problems: _______________________________________________________________________________________

___________________________________________________________________________________________________________

___________________________________________________________________________________________________________

___________________________________________________________________________________________________________

4. Predominant Features: _____________________________________________________________________________________

___________________________________________________________________________________________________________

___________________________________________________________________________________________________________

___________________________________________________________________________________________________________

___________________________________________________________________________________________________________

5. Disturbance Evidence: _____________________________________________________________________________________

___________________________________________________________________________________________________________

___________________________________________________________________________________________________________

___________________________________________________________________________________________________________

6. Insect/Disease Evidence: ____________________________________________________________________________________

___________________________________________________________________________________________________________

___________________________________________________________________________________________________________

___________________________________________________________________________________________________________

7. Ecological Interpretation: ___________________________________________________________________________________

___________________________________________________________________________________________________________

___________________________________________________________________________________________________________

___________________________________________________________________________________________________________

___________________________________________________________________________________________________________

8. Other: ___________________________________________________________________________________________________

___________________________________________________________________________________________________________

___________________________________________________________________________________________________________

___________________________________________________________________________________________________________
88 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000 Gila Fuels Mapping Project Polygon Validation Field Sheet Examiner Name: _______________________________________________________________________________________________________________ Crown Bulk Duff & Soil Bare GPS Plot Position 1 Structural FBFM 3 FBFM 3 Canopy Stand Crown Base Density Litter Depth Textural Soil/ Polygon Number Quad Name Easting Northing Elev. (ft) PVT 2 Cover Type Stage Normal Severe Cover Class Height (ft) Height (ft) (kg/m_) (in) Class Rock _______________________________________________________________________________________________________________ _______________________________________________________________________________________________________________ _______________________________________________________________________________________________________________ _______________________________________________________________________________________________________________ _______________________________________________________________________________________________________________ _______________________________________________________________________________________________________________ _______________________________________________________________________________________________________________ _______________________________________________________________________________________________________________ _______________________________________________________________________________________________________________ _______________________________________________________________________________________________________________ _______________________________________________________________________________________________________________ _______________________________________________________________________________________________________________ _______________________________________________________________________________________________________________ _______________________________________________________________________________________________________________ _______________________________________________________________________________________________________________ _______________________________________________________________________________________________________________ _______________________________________________________________________________________________________________ _______________________________________________________________________________________________________________ _______________________________________________________________________________________________________________ _______________________________________________________________________________________________________________ 1 GPS Plot Position - NAD 1927, UTM Zones 12, 13 2 PVT=Potential Vegetation Type 3 FBFM=Fire Behavior Fuel Model
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000 89

APPENDIX B — Plot establishment procedures

Ecosystem Characterization (EC) Plot Establishment

1. Procede to a GNFC zone and unsupervised polygon based on

accessiblity and the sample levels of PVT, cover type, and structural

stage. Establish plot center at least 75 m (approximately 2 pixels

distance) from topographic boundaries (e.g. ridgetops, ravines) or

polygon edges in a homogeneous area representative of the polygon.

A homogenous area is an area that has consistent patterns throughout.

For instance, choose areas that do not have major differences in cover

types such as Douglas-fir and grassland cover types or major

differences in structural stages.

2. Drive a 1 inch x 2 inch x 3 foot wooden stake, labeled with the

KEYID from the GF form with a sharpie permanent marker, into the

ground until stable. Also pound in an 8 inch nail next to the wooden

stake. The nail will aid in plot relocation when using a metal detector.

This will be plot center.

3. Mark the plot position and KEYID in pencil on a 7.5 min

quadrangle map.

4. Take GPS readings at plot center

5. Flag plot boundaries at 37.2 ft (or Plot Radius, GF Field 32) from

plot center. Flag trees on the side facing plot center if they are outside

plot boundaries and on the opposite side if trees are within the

boundaries.

Ground Truth (GT) Plot Establishment

1. Procede to selected unsupervised polygon (see EC Plot Establishment #1).

2. Mark the plot position and KEYID in pencil on a 7.5 min

quadrangle map.

3. Take GPS readings at plot center

Polygon Validation (PV) Plot Establishment

1. Using the GNFC paper maps developed from the previous field

season, navigate to selected polygons and record on the paper map the

following actual (observed) attributes: 1) Habitat Type, 2) cover

type, 3) structural stage, 4) normal FBFM, 5) extreme FBFM. The
90 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000

GNFC maps will be in the same scale as USGS 7.5 minute quadrangle

maps, overlaid with 120 meter contours, polygon boundaries, roads,

streams, and trails. Only validate those polygons whose locations are

absolutely known because they are adjacent to geographic features

that are easily recognizable such as a stream confluence or road

junction.

GPS Procedures

Plot locations— Because accurate plot locations are critical to the

satellite imagery classification effort, care must be taken to ensure

that all locations be accurately georeferenced. The most accurate

GPS readings are recorded when the GPS unit is in 3-D mode and

all attempts should be made to receive satellite signals in 3-D

mode. For example, if under a dense canopy and no satellites are

being received, move within the plot boundaries to more open

areas in the canopy or try obtaining position at a later time. The

following procedures detail how to use the GEOEXPLORER

GPS unit to obtain positions.

1. Using the GEOEXPLORER GPS

I. Buttons

A. The bottom button turns the unit on and off.

B. ESC button is used to escape out of all selections and to

go back to previous menus.

C. The up and down arrow buttons are used to scroll thru

menus and to change setting numbers.

D. The left and right arrow keys are used to change

numbers in certain fields.

E. The center button is used to select features when they are

highlighted in the middle of the screen.

F. The bent arrow button is not used during any applications.

II. Power

A. The unit uses four AA batteries. To replace batteries turn

unit off (active files may remain open), press tab at bottom of the

unit towards the front and slide battery pack down. Replace

batteries and slide battery pack into place. Be sure cords don’t

interfere with proper connection of battery pack.

B. Replace batteries as soon as possible. If unit is left without

power for more than a few minutes, the memory will be lost.

C. To turn unit on press the bottom button. To turn unit off

press and hold bottom button for 5 seconds.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000 91

III. Checking Configuration Settings

A. Under the Main Menu ( if not on Main Menu, press the

escape button until Main Menu appears at the top of the screen)

select Option 6. Configuration

B. Select 1.Rover Options

C. Check the following settings.

Only the Pos Mode, PDOP Mask and 2D Alt need to be

checked before collecting points as these are the only options

that should ever be changed.

Settings should be as follows:

Elev Mask -15

SNR Mask - 5

PDOP Mask -6

PDOP Switch -9

Antenna Ht. -1.00 meter or 3.28 feet

Log DOP’s - On

Velocity -Off

File Prefix -Depends on unit

Feature Logging

Points -1 second

Line/Area -N.A.

Min Posn -Off

Not in Feature

Rate -All

High Accuracy

Recording -Off

Log Rate -N.A.

Min Time -N.A.

Pt Feature -Off

Dynamics -Land

Pos Mode -May be 3ODS, Manual 3D, or Manual 2D.

Manual 3D is the desired setting.

2D Alt -Need to enter if using Auto 2D/3D or Manual

2D. Elevation should be entered as height above mean sea level

in feet.

D. Escape back to Configuration Menu

E. Check Settings

1. Option 3. Coordinates -UTM

2. Option 4. Datum -N-AM. 1927 Conus

3. Option 5. Units -English

a. Custom Setup

i. Option 4. Altitude Units -feet

ii. Option 5. Alt Reference -Geoid (MSL)

IV. Collecting Points
92 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000

A. From Main Menu, select option 1. Data Capture

B. Under Data Capture, select Option 1. Open Rover File

(Never choose Open Base File)

File name will appear at top of screen

Ex. A061015A

A=unit prefix, different for each unit

06=month

10=day

15=hour, according to Greenwich Mean Time

A=letter differentiates between files opened in the

same hour. A=first file opened in that hour, B=second file opened

in that hour, etc.

1. Number in upper right hand corner = the # of

positions collected.

2. Collect at least 120 positions (preferably 180) at

each location.

C. Under Main Menu, select Option 2. Position

1. This screen shows the coordinates according to the

selected datum, coordinate system and units.

D. Under Main Menu, select Option 3. GPS Status

E. Under GPS Status, select Option 1. Sat. Tracking

1. This screen shows the satellites currently being

tracked by the unit.

2. An arrow beside a satellite number indicates that a

signal is being received from that satellite and is being used to

determine a position. Four satellites are needed to determine a 3D

position, three sat. for a 2D position.

3. The PDOP number in the lower right-hand corner

needs to be below 6.00 when four satellites (3D position) are

being tracked for the unit to start determining positions (this

number is related to the geometry of the satellites in relation to

each other).

F. When the required number of position has been reached

record file name and coordinates from the position screen on the

Location Linkage3 (LL) form..

G. Close File, Option 3 under Data Capture

H. Under Close File, choose Yes.

2. What to do if GPS is not receiving in 3-D mode

If 3-D mode cannot be obtained, follow these procedures:

1. If four satellites are being tracked with the PDOP mask set

at 6 in 3-D mode, but no satellites are received, raise the PDOP
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000 93

to 12 to obtain readings. Set PDOP back to 6 after closing the

file.

2. Enter elevation manually into the GPS and record points

in 2-D only mode. Elevation is determined by an altimeter

whose elevation has been initialized at a known benchmark.

In variable weather, the altimeter must be set throughout the

day. Elevation can also be determined from a 7.5 min quad

map. These two methods can be used in conjunction to

estimate elevation for input into the GPS.

3. If elevation cannot be determined by the above procedures,

consider returning to the plot at a later time. Because plot

locations will be clustered in the sample areas, crews will be

returning to the same location until 90 percent of the area is

sampled, thereby providing the opportunity to return to plots

not sampled.

4. As a last resort, crews can measure distances and azimuths

to known points such as road switchbacks, stream junctions,

or other readily-identified spots on the quad map.
94 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000

APPENDIX C — ECODATA Down Woody (DW)

Methods

Measurement of downed woody fuels using the ECODATA

DW methods

Downed woody fuel will be estimated using the intersect method

describe by Brown (1978) where woody fuel intersecting a

vertical plane originating from ground line will be counted in four

size classes of 0-0.25 inch diameter (1 hour), 0.25-1.0 dia (10

hour), 1-3 in dia (100 hour) and 3+ in dia (1000 hour). The vertical

plane is referenced to a transect that is established by stretching a

cloth tape between two points. Downed woody transects will be

60 feet long but fuel intersections will be counted only on the last

50 feet. A cloth tape will be stretched due EAST from plot center.

Crews will then walk to the 10 foot mark and start counting fuel

intersections. Down woody 1 hour and 10 hour fuels (0-0.25 and

0.25-1 inch diameter particles) will be sampled along the first 6

feet of the transect (10 foot mark to 16 foot mark). Hundred hour

fuels (1-3 inch dia) will be sampled on the first 10 feet (10 foot

mark to 20 foot mark) and 1000 hour fuels (>3 inch dia) will be

sampled on the entire 50 feet (10 foot mark to 60 foot mark). Only

downed, dead woody material that is part of the fuelbed is sampled

along this transect. Dead limbs extending from standing live or

dead trees are not measured with this technique. Stumps are not

measured as 3+ woody in this study. Logs intersecting sampling
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000 95

plane are individually measured to the nearest inch and their log

decay class is estimated using one of five classes on the DW form.

Duff and litter depth and Integrated fuel depth will be measured

at the 35 foot mark and 60 foot mark of the tape. Duff and litter

depth are measured within 1 foot from tape at a point representative of the duff/litter layer of the surrounding area (3 foot radius

circle). If duff/litter depth cannot be measured at the first point,

crews will go 1 foot from the other side of the tape. The RIGHT

side of the tape (looking toward the end or 60 foot mark) is always

the starting side.

The next transect is established at a 60o

 angle from the first

transect at a azimuth of 330o

. An azimuth of 240o is used for the

third transect. If there are less than 100 intersections for all 1, 10,

100 and 1000 hour size classes, then additional transects are

established in the order and direction shown in Figure 5. If 100

transects can be accomplished with just 3 transects, then the 3rd

transect can be directed back to macroplot center for efficiency.

However, if after 2 transects have been measured and it is evident

that many more are needed to get 100 intersections, then the

outside route that surrounds macroplot must be used (i.e., hexagonal sampling route) (Figure). Fuel measurement may stop once

100 intersects are counted, however, the entire transect must be

finished. Crews must measure at least three transects to get 100

intersections.
96 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000

APPENDIX D — Tree Data (TD) Methods

Measurement of tree data (TD) using ECODATA methods.

Conifer and Broadleaf Forests

Live trees will be recorded on the TD form. All live and dead trees

greater than 4.9 inches DBH (Poles and Matures) will be recorded

individually in Table 1 of the TD form if they occur in the tenth acre

circular plot. Live trees less than 4.9 inches DBH (Saplings and

Seedlings) are recorded in Table 2 if they occur within a onehundredth acre plot (11.8 feet radius).

Tree status, DBH, height, crown position and ratio, insect and

disease evidence are recorded for all Poles and Matures as per

ECODATA instructions. No ages will be determined for any trees

in this study. Sapling and seedling trees will be measured for DBH

(to nearest inch) and tallied in Table 2. Then average tree height and

crown height across all trees in that DBH category. Insect and

disease evaluations are a critical part of this inventory. All species

of insects and all pathogens (or symptoms) will be recorded for trees

on the macroplot. These agents can be either recorded in Table 1 of

the TD form or described in the comments (CD form).

The first tree to be measured is the tree due North of plot center.

Trees are measured in a clockwise sequence from the first tree. Tree

heights are only measured on 3-5 trees representative of each tree

layer present on the plot. Heights for remaining trees are estimated

to nearest 3 feet.

Special attention should be given to trees that fork or exist in

clusters. They are treated like any other single stem tree — recorded

on Table 1 if stem is > 4.9 inches in tenth acre plot, or tallied on Table

2 if < 4.9 inches within hundredth acre plot. Forks above DBH are

treated as single stems, forks below DBH are multiple stems.

Woodlands

Tree species on woodland sites will NOT be measured using TD

techniques. Instead, a separate size class canopy cover stratification

was developed to describe woodland species dynamics. This

stratification is implemented on the PC (Plant Composition) form

rather than the TD form. Cover for each woodland tree species will

be estimated for the entire species and for the following height

classes:0 to 6.5 feet, 6.6 to 25.0 feet, greater than 25 feet, entered in

the consecutive columns in the Size Class fields on the PC forms.

This was done to simplify and standardize methods across all

ecosystems and resource desires.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000 97

APPENDIX E —Classification Keys Potential Vegetation Type, Cover Type, and Structural Stage.

Potential Vegetation Type Classification

PVT Key

1. Sites with < 10% vegetation cover...Non-Vegetated(08)

1. Sites with Æ 10% vegetation cover...2

2. Sites with tree or woodland species* present...3

2. Sites with tree and woodland species absent or accidental...11

3. Broadleaf species (Populus spp., Acer negundo, Alnus oblongifolia, Platanus wrightii, Fraxinus spp., Celtis reticulata, or Salix goodingii) other than aspen

(Populus tremuloides) or oaks (Quercus spp.) well-represented... Riparian PVT(01)

3. Broadleaf species not well-represented...4

4. Subalpine fir (Abies lasiocarpa) and/or Engelmann spruce (Picea engelmannii) common and/or reproducing successfully...Spruce-Fir PVT(02)

4. Subalpine fir (A. lasiocarpa) and Engelmann spruce (P. engelmannii) absent or accidental...5

5. White fir (Abies concolor) and/or blue spruce (Picea pungens) common and/or reproducing successfully...Mixed Conifer PVT(03)

5. White fir (A. concolor) and blue spruce (P. pungens) absent or accidental...6

6. Douglas-fir (Pseudotsuga menziesii) common and/or reproducing successfully... Douglas-fir PVT(04)

6. Douglas-fir (P. menziesii) absent or accidental...7

7. Aspen (Populus tremuloides) well-represented...Mixed Conifer PVT(03)

7. Aspen (P. tremuloides) not well-repesented...8

8. Ponderosa pine (Pinus ponderosa) or Chihuahuan pine (P. leiophylla) common and/or reproducing successfully...Ponderosa Pine PVT(05)

8. Ponderosa pine (P. ponderosa) or Chihuahuan pine (P. leiophylla) absent or accidental...9

9. Gambel oak (Quercus gambelii) well-represented...Douglas-fir PVT(04)

9. Gambel oak (Q. gambelii) not well-represented...10

10. Pinyon (Pinus edulis or P. cembroides), Juniper (Juniperus deppeana, J. monosperma, J. osteosperma, J. scopulorum), Gray oak (Quercus grisea)

and/or Emory oak (Q. emoryi) common...P-O-J PVT(06)

10. Pinyon (P. edulis or P. cembroides), Juniper (Juniperus deppeana, J. monosperma, J. osteosperma, J. scopulorum), Gray oak (Q. grisea) and/or

Emory oak (Q. emoryi) not common...11

11. Mountain mahogany (Cercocarpus montanus) well-represented...P-O-J PVT(06)

11. Mountain mahogany (C. montanus) not well-represented...12

12. Willows (Salix spp.) well-represented...Riparian PVT(01)

12. Willows (Salix spp.) not well-represented...13

13. Chihuahuan desert species* * well-represented, apparently below zone dominated by P-O-J...Desert Scrub/Grassland PVT(07)

13. Not as above...14

14. Riparian graminoids and/or forbs well-represented...Riparian PVT(01)

14. Non-Riparian shrubs and/or herbaceous species dominate the site, but surrounding sites indicated climax is coniferous or woodland PVT, then

determine PVT by extrapolating from surrounding sites using key numbers 4-10

Definitions:

Present - Not restricted to microsites.

Reproducing successfully - 01 canopy cover class in either the seedling or sapling size class.

Common - Æ1% (03 canopy cover class).

Well-represented - Æ5% (10 canopy cover class).

Abundant - Æ25% (30 canopy cover class).

* Reference Gila Fuels Mapping Project Vascular Plant List to determine appropriate lifeforms (ex. Tree, woodland, shrub, gramminoid, forb)

**Common Representative Chihuahuan Desert Species

Shrubs

Acacia spp.

Ephedra spp.

Larrea tridentata (creosotebush)

Mimosa spp.

Opuntia spp.

Prosopis glandulosa (mesquite)

Quercus turbinella (shrub live oak)

Grasses

Bouteloua eriopida (black grama)

Bouteloua hirsuta (hairy grama)

Erioneuron pulchellum (fluff grass)

Muhlenbergia porteri (bush muhly)

Muhlenbergia torreyi (ring muhly)
98 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000

Cover Type Classification

Cover Type Key

1. Vegetation cover Æ15%...5

1. Vegetation cover <15%...2

2. Site covered by persistant water...100 Water

2. Not as above...3

3. Site covered by rock exposed by mining or quarry activity...203 Mine/Quarry

3. Not as above...4

4. Rock, scree and/or talus with Æ45% cover...201 Rock

4. Not as above...202 Barren Land

5. Trees with Æ15% cover...6

5. Trees with <15% cover...16

6. Broadleaf trees with Æ15% cover and coniferous trees with <15% cover...7

6. Not as above...9

7. Aspen with Æ15% cover...410 Aspen

7. Not as above...8

8. Gambel oak with Æ15% cover...411 Gambel Oak

8. Other broadleaf trees with Æ15% cover...401 Broadleaf Riparian Forest

9. Broadleaf (excluding gambel oak) and coniferous trees each with >25% of the relative tree cover...

402 Broadleaf/Conifer Forest

9. Not as above...10

10. Coniferous trees with Æ15% cover, but <55% cover and Gambel Oak with Æ15% cover...

403 Conifer/Gambel Oak Forest

10. Not as above...11

11. Coniferous trees with Æ15% cover, but <55% cover and woodland species with Æ15% cover...

404 Conifer Forest/Woodland Mix

11. Not as above...12

12. Ponderosa pine and/or Chihuahuan pine with >66% of the relative tree cover...415 Ponderosa Pine

12. Not as above...13

13. Douglas-fir with >66% of the relative tree cover...416 Douglas-fir

13. Not as above...14

14. Subalpine fir and/or Engelmann spruce with Æ20% of the relative tree cover ...424 Spruce/Fir

14. Not as above...15

15. White fir and/or blue spruce with Æ20% of the relative tree cover...406 Mesic Mixed Conifer

15. Not as above...407 Xeric Mixed Conifer

16. Shrub species with Æ15% cover and woodland species with <15% cover...17

16. Not as above...18

17. Shrub species include Acer glabrum, Amelanchier utahensis, Holodiscus dumosus, Jamesia americana, Physocarpus monogynous, Prunus spp., Quercus gambellii, Rhamnus spp., Ribes spp., Robinia neomexicana, Rubus

 spp., Salix spp., and/or Symphoriocarpus spp...322 Mesic Shrubland

17. Not as above...321 Xeric Shrubland

18. Woodland cover Æ15%...19

18. Woodland cover <15%...23

19. One species (any combination of junipers = 1 species or any combination of evergreen oaks =1 species) with

 >66% of the relative woodland cover...20

19. Not as above...22
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000 99

20. Any combination of juniper species...412 Juniper

20. Not as above...21

21. Pinyon the indicated species...413 Pinyon

21. Any combination of evergreen oaks...414 Evergreen Oak

22. Pinyon and juniper with >80% of the relative woodland cover...421 Pinyon/Juniper

22. Not as above...405 Mixed Woodland

23. Herbaceous cover Æ15%...310 Herbalands

23. Combined woodland and shrub cover Æ15%...405 Mixed Woodland

Representative Species by Cover Type

406 Mesic Mixed Conifer 424 Spruce/Fir

White fir Engelmann spruce

Blue spruce Subalpine fir

-Douglas-fir -Douglas-fir

-Southwestern white pine -Southwestern white pine

-Pondeosa pine -White fir

-Subalpine fir -Blue spruce

-Engelmann spruce

407 Xeric Mixed Conifer 414 Evergreen Oaks

Douglas-fir Quercus grisea

Southwestern white pine Q . emoryi

Ponderosa pine Q. arizonica

-minor amounts of any other conifer Q. hypoleucoides

Q. rugosa
100 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000

Structural Stage Classification Key

1. Cover Type is Water... 98 WATER

1. Not as above...2

2. Cover Type is Rock, Barren, or Mines/Quarries...99 ROCK

2. Not as above...3

3. Cover Type is Herbland...4

3. Cover Type is not Herbland...5

4. Herbaceous cover Æ55%...01 CLOSED HERBLAND

4. Herbaceous cover <55%...02 OPEN HERBLAND

5. Cover Type is Xeric or Mesic Shrubland...6

5. Not as above...7

6. Shrub cover Æ55%...03 CLOSED SHRUBLAND

6. Shrub cover <55%...04 OPEN SHRUBLAND

7. Cover Type is Juniper, Pinyon, Evergreen Oak, Pinyon/Juniper or Mixed Woodland...8

7. Cover Type is Broadleaf Riparian Forest, Broadleaf/Conifer Forest, Conifer/Gambel Oak Forest, Conifer

Forest/Woodland Mix, Mesic Mixed Conifer, Xeric Mixed Conifer, Aspen, Gambel Oak, Ponderosa Pine,

Douglas-fir or Spruce/Fir...9

8. Woodland vegetation plus shrubs in approximately the same height rangeÆ55% cover...

05 CLOSED WOODLAND

8. Woodland vegetation plus shrubs in approximately the same height range <55% cover...

06 OPEN WOODLAND

 9. Trees Æ21.0 inches DBH with Æ15% cover...09 LARGE TREE

 9. Not as above...10

10. Trees Æ5.0 inches DBH withÆ15% cover...08 MEDIUM TREE

10. Trees Æ5.0 inches DBH with <15% cover...07 SMALL TREE
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000 101

APPENDIX F — Terrain models used to construct the PVT layer by ecological zone.

San Mateo Zone

PVT Number PVT Name Aspect(deg) Elevation(m) Slope(%) Comments

1 Riparian — — — Not in SM

2 Spruce-Fir — — — Not in SM

3 Mixed Conifer 315-90 >2600 All

91-314 — —

4 Douglas-fir 315-90 2300-2600 All

91-314 >2400 All

5 Ponderosa Pine 315-90 2100-2299 All

91-314 2150-2400 All

6 P-O-J 315-90 1700-2099 All

91-314 1700-2149 All

7 Desert Scrub/Grassland All <1700 All

8 Non-vegetated — — — From imagery

Burro Mountains Zone

PVT Number PVT Name Aspect(deg) Elevation(m) Slope(%) Comments

1 Riparian — — — Streamside Buffer

2 Spruce-Fir — — — Not in BM

3 Mixed Conifer — — — Not in BM

4 Douglas-fir — — — Not in BM

5 Ponderosa Pine 315-90 >2050 All

91-314 >2300 All

2050-2300 ≤20

6 P-O-J 315-90 1600-2049 All

1450-1599 >30

91-314 2050-2300 >20

1800-2049 All

1450-1799 >10

7 Desert Scrub/Grassland 315-90 1450-1599 ≤30

<1450 All

91-314 1450-1799 ≤10

<1450 All

8 Non-vegetated — — — From imagery

Black Range Zone

PVT Number PVT Name Aspect(deg) Elevation(m) Slope(%) Comments

1 Riparian — — — Streamside Buffer

2 Spruce-Fir 315-90 >3050 All

3 Mixed Conifer 315-90 2550-3049 All

2400-2549 ≥35

91-314 >2900 All

4 Douglas-fir 315-90 2500-2549 <35

2400-2499 10-35

2300-2399 ≥10

91-314 2400-2899 All

5 Ponderosa Pine 315-90 2300-2499 <10

2100-2299 ≥10

91-314 2300-2399 All

2200-2299 <10

6 P-O-J 315-90 1600-2099 All

2100-2299 <10

91-314 1700-2199 All

2200-2299 ≥10

7 Desert Scrub/Grassland 315-90 <1600 All

91-314 <1700 All

8 Non-vegetated — — — From imagery
102 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000

Pinos Altos Zone

PVT Number PVT Name Aspect(deg) Elevation(m) Slope(%) Comments

1 Riparian — — — Streamside Buffer

2 Spruce-Fir — — — Not in PA

3 Mixed Conifer — — — Not in PA

4 Douglas-fir 315-90 >2300 All

2100-2300 >35

91-314 >2350 All

5 Ponderosa Pine 315-90 2100-2300 ≤35

2000-2099 All

91-314 2200-2350 All

6 P-O-J 315-90 1600-1999 All

1450-1599 >35

91-314 1600-2199 All

7 Desert Scrub/Grassland 315-90 1450-1599 ≤35

<1450 All

91-314 <1600 All

8 Non-vegetated — — — From imagery

Western Gila Zone

PVT Number PVT Name Aspect(deg) Elevation(m) Slope(%) Comments

1 Riparian — — — Streamside Buffer

2 Spruce-Fir 301-90 >2750 All

91-300 >3000 All

3 Mixed Conifer 301-90 2550-2750 >10

91-300 2800-3000 All

2600-2799 <10

4 Douglas-fir 301-90 2600-2749 <10

2250-2549 >10

2100-2249 >35

91-300 2600-2799 >10

2500-2599 All

5 Ponderosa Pine 301-90 2200-2549 <10

2100-2249 11-35

2050-2099 >10

91-300 2300-2499 All

6 P-O-J 301-90 2050-2199 <10

1600-2049 All

91-300 1700-2299 All

7 Desert Scrub/Grassland 301-90 <1600 All

91-300 <1700 All

8 Non-vegetated — — — From imagery
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000 103

APPENDIX G —GNFC field database formats

General Field Form Format

FIELD UNITS DESCRIPTION

PLOT NO None Plot number

FORM Alpha code Potential vegetation formation

PVT_SPP11 6 letter species code Overstory indicator species (Series level classification)

PVT_SPP2 6 letter species code Understory indicator species (climate community type,

 association, or habitat type level classification)

PVT_SPP3 6 letter species code Additional understory indicator species (habitat type phase)

LF Alpha code Dominant live life form

LSC Alpha code Live life form size class

DSC Alpha code Dead life form size class

CC Alpha code Live canopy cover class

SS Alpha code Stand Structure

UL DOM SPP1 6 letter species code Dominant species in the upper layer (>6.5 ft)

UL DOM SPP2 6 letter species code Co-dominant species in the upper layer (> 6.5 ft)

ML DOM SPP1 6 letter species code Dominant species in the middle layer (2.5 to 6.5 ft)

ML DOM SPP2 6 letter species code Co-dominant species in the middle layer (2.5 to 6.5 ft)

LL DOM SPP1 6 letter species code Dominant species in the lower layer (<2.5 ft)

LL DOM SPP2 6 letter species code Co-dominant species in the lower layer (<2.5 ft)

LIVE_TREE_BAF None Live tree Basal Area Factor (10, 20, 40, etc)

BAN None Number of live trees counted with the above BAF

BA ft2

/ac. Basal Area estimate (BAF x BAN) of live trees

DBH Inches Mean DBH of live trees counted with BAF

HT Feet Average height of live trees counted with BAF

DEAD_TREE_BAF None Dead tree (no live crown) Basal Area Factor (10, 20, 40, etc)

BAN_D None Number of dead trees counted with the above BAF

BA_D ft2/ac. Basal Area estimate (BAF x BAN) of dead trees

DBH_D Inches Mean DBH of dead trees counted with BAF

HT_D Feet Average height of dead trees counted with BAF

TREE_COV_T Percent code Total tree canopy cover (100% maximum)

SE Percent code Total seedling (<4.5 ft tall) canopy cover

SA Percent code Total sapling (0.1 to 4.9 in dbh) canopy cover

PT Percent code Total pole tree (5.0 to 8.9 in dbh) canopy cover

MT Percent code Total medium tree (9.0 to 20.9 in dbh) canopy cover

LT Percent code Total large tree (21 to 32.9 in dbh) canopy cover

VL Percent code Total very large tree (>=33 in dbh) canopy cover

WOODLAND_COV_TOT Percent code Total woodland canopy cover (100% maximum)

LW Percent code Total low woodland (<10 ft in height) canopy cover

MW Percent code Total medium woodland (10 to 25 ft in height) canopy cover

TW Percent code Total tall woodland (>25 ft in height) canopy cover

SHRUB_COV_ Percent code Total shrub cover

LS Percent code Total low shrub (<2.5 ft) cover

MS Percent code Total medium shrub (2.5 - 6.5 ft) cover

TS Percent code Total tall shrub (>6.5 ft) cover

GRAM_COV_T Percent code Total cover of gramminoids on plot

FORB_COV_T Percent code Total cover of forbs on plot

FERN_COV_T Percent code Total cover of ferns and fern allies on plot

MOSS_COV_T Percent code Total cover of moss and lichen

LANDFORM Alpha code Landform setting of plot

POSITION Alpha code Topographic position of plot

VERT_POS Alpha code Shape of slope perpendicular to the contour

HOR_POS Alpha code Shape of slope parallel to the contour

MAP_ELEV Feet Elevation of plot from topo. map

ASPECT Degrees Average aspect of plot

SLOPE Percent Average slope of plot

BS Percent code Total ground cover of exposed soil on plot

GR Percent code Total ground cover of gravel (0.625 - 3 in) on plot

RO Percent code Total ground cover of rock (> 3 in) on plot
104 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000

LD Percent code Total groung cover of litter and duff on plot

WO Percent code Total ground cover of wood (> 0.25 in diameter) on plot

ML Percent code Total ground cover of moss and lichens on plot

BV Percent code Total ground cover of basal (stems) vegetation on plot

WA Percent code Total ground cover of water on plot

FBFM_NORMAL None Anderson fuel model or custom fuel model number

FBFM_SEVERE None Anderson fuel model or custom fuel model number

FUEL_DEPTH Feet Mean height of surface fuels up to 6.5 ft high

DLDEPTH Inches Total depth of duff and litter

DWCOVER Percent code Total cover of 1000 hr fuels

DOWN_LOG_DIAM Inches Mean diameter of 1000 hr fuels

DOM_LAYER_HT Feet Stand Height(Farsite attribute)

HT_TO_CROWN Feet Crown Base Height(Farsite attribute)

EASTING Meters Plot location from GPS (NAD 27, UTM Zone 12 or 13)

NORTHING Meters Plot location from GPS (NAD 27, UTM Zone 12 or 13)

ELEV Feet Height above mean sea level from GPS

1

Reference Steuver and Hayden 1996 and Szaro 1989

Plant Composition Form Format

Field Units Desciption

PLOT None Plot number

LIFEFORM Alpha Code Lifeform of species (Tree, shrub, grass, forb, etc.)

SPECIES 6 letter species code Individual plant species

CAN_COV Percent code Total canopy cover of species on plot

HEIGHT Feet Mean height of species on plot

SC1 Percent code Total cover of species in size class 1

SC2 Percent code Total cover of species in size class 2

SC3 Percent code Total cover of species in size class 3

SC4 Percent code Total cover of species in size class 4

SC5 Percent code Total cover of species in size class 5

SC6 Percent code Total cover of species in size class 6

Tree Data Form Format

FIELD UNITS DESCIPTION

PLOT NO None Plot number

TNUM None Tree number

SPP Alpha Code Tree species

STAT Numeric Code Health of tree

DBH Inches x 10 Diameter at Breast Height of tree

HT Feet Height of tree

CROWN RATIO Numeric code Percent of tree with live crown

CROWN CLASS Alpha code Position of tree crown in stand

DAM1 Numeric code Damage code agent 1

SEV1 Numeric code Measure of severity of damage code 1

DAM2 Numeric code Damage code agent 2

SEV2 Numeric code Measure of severity of damage code 2

DAM3 Numeric code Damage code agent 3

SEV3 Numeric code Measure of severity of damage code 3

SNAG CODE Numeric code Estimate of the condition of the snag

Seedling/Sapling (TD2) Form Format

FIELD UNITS DESCRIPTION

PLOT NO None Plot number

SPP Alpha code Species of seedling or sapling

SC Numeric code Size class of tallied species

NUMT None Number of seedlings or saplings counted

HEIGHT Feet Mean height of size class

HT TO CRN Feet Mean height to crown for size class
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000 105

Down Wood Data Form Format

FIELD UNITS DESCRIPTION

PLOT NO None Plot number

TRAN NO None Transect line number on plot

SLOPE Percent Slope of transect line

DUFF1 Inches Measured duff and litter thickness at 35 foot mark along

 transect

DUFF2 Inches Measured duff and litter thickness at 60 foot mark along

 transect

DUFF SUM Inches Sum of Duff1 and Duff2 measurements

INT WOODY1 Feet Mean height of surface fuels at 35 foot mark along transect

INT WOODY2 Feet Mean height of surface fuels at 60 foot mark along transect

INTWOODY SUM Feet Sum of INTWOODY1 and INTWOODY2 measurements

1 HR None Count of 1hr fuels

10 HR None Count of 10 hr fuels

100 HR None Count of 100 hr fuels

1000 HR NUM None Number of 1000 hr fuels along transect

LOG1-16 DIAM Inches Diameter of each log along transect, Field repeats for each log

 encountered

LOG1-16 LDC Numeric code Log decay class of each log, Field repeats for each log

 encountered

Polygon Validation (DATA_98) Format

FIELD UNITS DESCRIPTIONS

POLY98_UNQ None Unique ID

POLYNO_ORG None Polygon number (GIS)

POLYNO_COR None Corrected polygon number (GIS)

QUAD_NAME None USGS quad name

EASTING Meters GPS Coodinate (NAD 27, UTM Zone 12 or 13)

NORTHING Meters GPS Coodinate (NAD 27, UTM Zone 12 or 13)

ELEV Feet Height above Mean Sea Level from GPS

PVT_V2 Numeric code Potential Vegetation Type for polygon

CT Numeric code Cover Type for polygon

SS Numeric code Structural stage for polygon

FBFM_NORMAL None Anderson fuel model or custum fuel model under normal fuel

 conditions

FBFM_SEVER None Anderson fuel model or custum fuel model under extreme fuel

 conditions

CNPY_CVR Numeric code Total canopy cover of forest and/or woodland cover (FARSITE

 attribute)

STND_HGHT Feet Mean height of forest or woodland stand (FARSITE attribute)

CBH Feet Crown base height of forest or woodland stand (FARSITE

 attribute)

CBD kg/m3 Crown Bulk Density of stand (FARSITE attribute)

DUFF_LIT Inches Mean depth of litter and duff

SOIL_TEXTU Code Estimate of soil texture

BARE_SOIL Percent Estimate of exposed soil surface

Gila_stand Database Format

FIELD UNITS DESCRIPTIONS

PLOT None Plot number

NO ITEMS None

LIVE BA ft2/ac Live stem basal area

SNAG BA ft2/ac Dead stem basal area

TPA trees/ac Number of trees/acre

SPA saplings/ac Number of saplings/acre

SNAG/AC snags/ac Number of snags/acre

DOMDBH Inches Dominant DBH for Plot

AVEDBH Inches Average DBH for all trees

SNAGDBH Inches Average DBH for snags

DOMHT Feet Height of dominant tree layer
106 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000

AVEHT Feet Average height for all trees

HT(CBD) Feet Height estimated from crown bulk density

CBH(AVE) Feet Average crown base height

CBH(CBD) Feet crown base height estimated from crown bulk density

CBD kg/m3 Crown Buld Density

LAI m2/m2 Leaf area index (all sided)

LAI(PRJCTD) m2

/m2 Leaf area index (projected)

LEAF WT kg/m2 leaf weight

Fuel Loadings (Dw_sum) Database Format

FIELD UNITS DESCRIPTIONS

PLOT_NO None Plot number

1HR Tons/acre 1 hr fuel loadings

10HR Tons/acre 10 hr fuel loadings

100HR Tons/acre 100 hr fuel loadings

DUFF_DEPTH Inches Duff and litter depth

WOODY_DEPT Feet Surface fuel depth

1000HR_SOU Tons/acre 1000 hr fuel loadings (sound logs)

1000HR_ROT Tons/acre 1000 hr fuel loadings (rotten logs)

1000HR_TOT Tons/acre Total 1000 hr fuel loadings

PVT-CT-SS Assignments Database Format

FIELD UNITS DESCRIPTIONS

PLOT_NO None Plot number from 1997 plots

PVT Numeric code Potential vegetation type from key

CT Numeric code Cover type from key

SS Numeric code Structural stage from key

Plantlist Database Format

FIELD UNITS DESCRIPTION

LIFEFORM Alpha code Species lifeform

ACRONYM 6 letter species code 6 letter plant code

PLANT_NAME None Species scientific name

SYNONYM None Accepted synonyms

COMMON_NAME None Common name

Custom Fuel File (*.FMD) Format

FIELD DATA TYPE UNITS DESCRIPTIONS

FMod Interger 14-50 Fuel Model

1H Decimal Tons/acre 1 hr fuel loadings

10H Decimal Tons/acre 10 hr fuel loadings

100H Decimal Tons/acre 100 hr fuel loadings

LiveH Decimal Tons/acre Live herbaceous loadings

LiveW Decimal Tons/acre Live woody loadings

1HSAV Integer 1/ft 1 Hr fuel surface-area-to-volume ratio

LiveHSAV Integer 1/ft Live Herbaceous fuel surface-area-to-volume ratio

LiveWSAV Integer 1/ft Live Woody fuel surface-area-to-volume ration

Depth Decimal Feet Fuel Bed Depth

XtMoist Integer Percent Extinction Moisture

DHt Interger BTU/lb Heat content of dead fuels

LHt Interger BTU/lb Heat content of live fuels

PCSLUT.DBF Format

FIELD UNITS DESCRIPTIONS

PVTFINAL Integer Potential Vegetation Type Code

CTFINAL Integer Cover Type Code

SSFINAL Integer Structural Stage Code

STND_HGTFT Feet, Integer Stand Height

CBHFT Feet, Integer Crown Base Height

PVTCTSSFIN Integer (PVTFINAL * 100,000) + (CTFINAL * 100) + SSFINAL
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000 107

 Use as joinitem for Lookup table

STDHTM Meters, Integer Stand Height

CBHM Meters, Decimal Crown Base Height

CBHM10 Meters, Integer Crown Base Height * 10

FBFMNORM Integer Fire Behavior Fuel Model under Normal Conditions

FBFMSEV Integer Fire Behavior Fuel Model under Severe Conditions

CSCLUT.DBF Format

FIELD UNITS DESCRIPTIONS

VALUE Integer (CTFINAL * 10,000) + (SSFINAL * 100) + CCFINAL

 Use as joinitem for Lookup table

CTFINAL Integer Cover Type Code

SSFINAL Integer Structural Stage Code

CCFINAL Integer Canopy Closure Code

CBD Kg/m3, Decimal Crown Bulk Density

CBD_100 Kg/m3

, Integer Crown Bulk Density * 100

CTSSCCFINA Integer (CTFINAL * 10,000) + (SSFINAL * 100) + CCFINAL

 Use as joinitem for Lookup table, same as VALUE
108 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000

APPENDIX H — Gila Fuels workshop assignment form and results

FIRE BEHAVIOR FUEL MODEL ASSIGNMENTS

Potential

Vegetation CT SS Structural FBFM FBFM HLCB

Type Code Cover Type CODE Stage Normal Severe (ft)

01 401 Broadleaf Riparian Forest 07 Small Tree 50 2 1

Broadleaf 08 Medium Tree 8 8 1

Riparian 09 Large Tree 2 2 10

Forest 402 Broadleaf-Conifer Forest 07 Small Tree 10 10 5

09 Large Tree 10 10 20

02 402 Broadleaf-Conifer Forest 08 Medium Tree 8 8 3

Spruce - Fir 410 Aspen 08 Medium Tree 8 8 30

416 Douglas-fir 08 Medium Tree 10 10 10

09 Large Tree 10 10 20

424 Subalpine Fir-Engelmann Spruce 08 Medium Tree 10 10 1

09 Large Tree 10 10 3

03 322 Mesic Shrubland 04 Open Shrubland 5 5 -

Mixed Conifer 402 Broadleaf-Conifer Forest 08 Medium Tree 8 8 3

403 Conifer-Gambel Oak Forest 07 Small Tree 9 9 1

08 Medium Tree 9 9 2

09 Large Tree 9 9 5

406 Mesic Mixed Conifer 07 Small Tree 8 8 1

08 Medium Tree 8 8 1

09 Large Tree 8 8 1

410 Aspen 08 Medium Tree 8 8 30

411 Gambel Oak 07 Small Tree 9 9 1

08 Medium Tree 9 9 3

415 Ponderosa Pine 08 Medium Tree 9 9 30

416 Douglas-fir 08 Medium Tree 10 10 10

04 202 Barren 99 Rock 99 99 -

Douglas-fir 310 Herbland 01 Closed Herbland 2 2 -

322 Mesic Shrubland 03 Closed Shrubland 5 4 -

04 Open Shrubland 2 2 -

402 Broadleaf-Conifer Forest 07 Small Tree 8 8 1

08 Medium Tree 10 10 5

09 Large Tree 10 10 5

403 Conifer-Gambel Oak Forest 07 Small Tree 2 2 5

08 Medium Tree 8 9 5

09 Large Tree 8 9 10

404 Conifer Forest-Woodland Mix 07 Small Tree 8 6 5

08 Medium Tree 8 9 5

407 Xeric Mixed Conifer 07 Small Tree 2 2 10

08 Medium Tree 9 9 15

09 Large Tree 10 10 20

410 Aspen 08 Medium Tree 8 8 35

411 Gambel Oak 07 Small Tree 6 4 1

08 Medium Tree 42 6 5

415 Ponderosa Pine 07 Small Tree 9 2 10

08 Medium Tree 9 9 20

416 Douglas-fir 07 Small Tree 2 2 5

08 Medium Tree 8 2 10

05 202 Barren 99 Rock 99 99 -

Ponderosa Pine 310 Herbland 01 Closed Herbland 2 2 -

322 Mesic Shrubland 04 Open Shrubland 5 2 -

402 Broadleaf-Conifer Mix 07 Small Tree 8 8 1
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000 109

05 403 Conifer-Gambel Oak Forest 07 Small Tree 2 2 5

Ponderosa Pine 08 Medium Tree 8 9 5

09 Large Tree 8 9 5

404 Conifer Forest -Woodland Mix 07 Small Tree 8 6 5

08 Medium Tree 8 9 5

09 Large Tree 9 9 10

405 Mixed Woodland 05 Closed Woodland 6 6 2

411 Gambel Oak 07 Small Tree 6 6 5

08 Medium Tree 2 6 5

412 Juniper 05 Closed Woodland 6 2 2

06 Open Woodland 2 2 2

413 Pinyon 05 Closed Woodland 50 6 2

06 Open Woodland 2 2 2

414 Evergreen Oak 05 Closed Woodland 5 6 2

06 Open Woodland 50 6 2

415 Ponderosa Pine 07 Small Tree 9 9 10

08 Medium Tree 9 9 15

09 Large Tree 9 9 20

421 Pinyon-Juniper 05 Closed Woodland 6 6 1

06 Open Woodland 2 2 2

06 202 Barren 99 Rock 99 99 -

Pinyon - Oak - 310 Herbland 01 Closed Herbland 1 1 -

 Juniper 02 Open Herbland 50 2 -

321 Xeric Shrubland 03 Closed Shrubland 6 6 -

04 Open Shrubland 50 50 -

322 Mesic Shrubland 03 Closed Shrubland 6 6 -

04 Open Shrubland 50 50 -

405 Mixed Woodland 05 Closed Woodland 50 6 5

06 Open Woodland 50 6 5

412 Juniper 05 Closed Woodland 50 2 5

06 Open Woodland 50 50 5

413 Pinyon 05 Closed Woodland 2 2 10

06 Open Woodland 50 6 2

414 Evergreen Oak 05 Closed Woodland 50 6 -

06 Open Woodland 1 1 -

421 Pinyon-Juniper 05 Closed Woodland 50 6 2

06 Open Woodland 50 6 2

07 202 Barren 99 Rock 50 50 -

 Desert Scrub/ 310 Herbland 01 Closed Herbland 2 2 -

Grassland 02 Open Herbland 1 1 -

321 Xeric Shrubland 03 Closed Shrubland 6 6 -

04 Open Shrubland 50 50 -

322 Mesic Shrubland 03 Closed Shrubland 6 6 -

04 Open Shrubland 50 50 -

08 100 Water 98 Water 98 98 -

Non- 201 Rock 99 Rock 99 99 -

vegetated/Barren 202 Barren 99 Rock 99 99 -

203 Mine/Quarry 99 Rock 99 99 -

Potential

Vegetation CT SS Structural FBFM FBFM HLCB

Type Code Cover Type CODE Stage Normal Severe (ft)
110 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000

APPENDIX I — Land summaries of base vegetation classifications

Distribution of area (hectares) by PVT and Cover Type

Potential Vegetation Type

Pinyon- NonSpruce- Mixed Douglas- Ponderosa Oak- Scrub- vegetated/

Cover Type Riparian Fir Conifer fir Pine Juniper Grassland Rock Totals

Water 2723 2723

Rock 401682 401682

Barren Ground 520 50 3165 40989 148824 97562 291110

Mine-Quarry 9918 9918

Urban 5042 5042

Herblands 158 352 3807 25102 112241 327800 76076 545536

Xeric Shrublands 5217 8400 51564 77274 142455

Mesic Shrublands 3109 298 2941 13692 1339 21379

Broadleaf Riparian Forest 23961 23961

Broadleaf/Conifer Forest 5408 616 3439 6680 16143

Conifer-Gambel Oak Forest 1001 10350 25323 36674

Conifer Forest-

 Woodland Mix 42717 8900 51617

Mixed Woodland 29167 62652 91819

Mesic Mixed Conifer 3267 76553 79820

Xeric Mixed Conifer 473 4677 22078 34650 61878

Aspen 979 2027 1197 4203

Gambel Oak 2073 4688 1409 8170

Juniper 20004 98967 118971

Pinyon 29171 116953 146124

Evergreen Oak 22613 58505 81118

Ponderosa Pine 4456 28426 103135 136017

Douglas-fir 66 565 11750 12381

Pinyon-Juniper 7398 63894 71292

Spruce-Fir 8380 8380

 Totals 33156 14431 101589 175062 444739 929159 250912 419365 2368415
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000 111 Distribution of area (hectares) by Cover Type and Structural Stage. Structural Stage Closed Open Closed Open Closed Open Small Medium Large Cover Type Herbland Herbland Shrubland Shrubland Woodland Woodland Tree Tree Tree Urban Water Rock Totals Water 2723 2723 Rock 401682 401682 Barren Ground 291110 291110 Mine-Quarry 9918 9918 Urban 5042 5042 Herblands 209306 336231 545537 Xeric Shrublands 21245 121210 142455 Mesic Shrublands 17630 3751 21381 Broadleaf Riparian Forest 4544 19375 41 23960 Broadleaf/Conifer Forest 628 15391 123 16142 Conifer-Gambel Oak Forest 3631 32996 47 36674 Conifer Forest- Woodland Mix 189 51402 26 51617 Mixed Woodland 58422 33397 91819 Mesic Mixed Conifer 704 76907 2209 79820 Xeric Mixed Conifer 647 61087 145 61879 Aspen 1229 2974 4203 Gambel Oak 93 7686 391 8170 Juniper 13764 105207 118971 Pinyon 108254 37870 146124 Evergreen Oak 13866 67252 81118 Ponderosa Pine 417 135540 61 136018 Douglas-fir 311 11929 141 12381 Pinyon-Juniper 3639 67653 71292 Spruce-Fir 12 8138 230 8380 Totals 209306 336231 38968 124961 197945 311379 19998 416130 3023 5042 2723 702710 2368415
112 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000

APPENDIX J — Fuel model assignments to base vegetation classification combinations

Codes used in table

Potential Vegetation Type

1 Riparian

2 Spruce-Fir

3 Mixed Conifer

4 Douglas-fir

5 Ponderosa Pine

6 Pinyon-Oak-Juniper

7 Scrub-Grassland

8 Non-vegetated

Cover Type

100 Water

201 Rock

202 Barren Ground

203 Mine-Quarry

205 Urban

310 Herblands

321 Xeric Shrublands

322 Mesic Shrublands

401 Broadleaf Riparian Forest

402 Broadleaf/Conifer Forest

403 Conifer-Gambel Oak Forest

404 Conifer Forest-Woodland Mix

405 Mixed Woodland

406 Mesic Mixed Conifer

407 Xeric Mixed Conifer

410 Aspen

411 Gambel Oak

412 Juniper

413 Pinyon

414 Evergreen Oak

415 Ponderosa Pine

416 Douglas-fir

421 Pinyon-Juniper

424 Spruce-Fir

Structural Stage

1 Closed Herbland

2 Open Herbland

3 Closed Shrubland

4 Open Shrubland

5 Closed Woodland

6 Open Woodland

7 Small Tree

8 Medium Tree

9 Large Tree

96 Urban

98 Water

99 Rock
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000 113

Potential Vegetation Cover Structural Stand Crown Base

Type Type Stage Height (m) Height (m) FBFM Normal FBFM Severe

1 202 99 0 0 99 99

1 310 1 0 0 1 1

1 310 2 0 0 50 1

1 322 3 0 0 5 5

1 401 7 7 0.9 50 2

1 401 8 15 4 9 2

1 401 9 21 8.5 9 2

1 402 7 9 1.2 2 2

1 402 8 12 1.8 2 2

1 402 9 22 7.6 9 9

2 310 1 0 0 1 1

2 322 3 0 0 5 5

2 402 7 12 1.5 8 8

2 402 8 21 2.4 10 10

2 402 9 22 4.6 10 10

2 406 8 21 2.4 10 10

2 406 9 24 4.6 10 10

2 407 7 10 1.5 8 8

2 407 8 20 1.5 8 10

2 407 9 22 3 8 10

2 410 7 9 4.6 8 8

2 410 8 17 9.1 8 8

2 416 7 10 1.5 8 8

2 416 8 18 2.4 8 10

2 416 9 22 3 8 10

2 424 7 10 1.5 8 8

2 424 8 20 3 10 10

2 424 9 24 4.6 10 10

3 202 99 0 0 99 99

3 310 1 0 0 1 1

3 310 2 0 0 50 1

3 322 3 0 0 5 5

3 322 4 0 0 2 2

3 402 7 12 2.4 8 8

3 402 8 18 3 8 10

3 402 9 27 3 8 10

3 403 7 12 0.6 8 2

3 403 8 18 3.7 8 2

3 403 9 19 4.6 8 2

3 406 7 12 2.4 8 8

3 406 8 17 1.8 10 10

3 406 9 21 4.6 8 10

3 407 7 12 2.4 8 8

3 407 8 17 2.7 8 8
114 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000

3 407 9 21 5.5 8 8

3 410 7 9 4.6 8 8

3 410 8 15 7 8 8

3 411 3 0 0 6 6

3 411 7 2 0.6 6 6

3 411 8 6 1.5 2 2

3 415 7 10 3 9 9

3 415 8 20 4.6 9 2

3 415 9 25 6.1 2 2

3 416 7 10 1.8 8 8

3 416 8 14 3 8 10

3 416 9 20 3.7 8 10

4 202 99 0 0 99 99

4 310 1 0 0 1 1

4 310 2 0 0 50 1

4 321 3 0 0 6 6

4 321 4 0 0 2 2

4 322 3 0 0 5 5

4 322 4 0 0 2 2

4 402 7 7 0.9 8 8

4 402 8 15 2.4 8 8

4 402 9 16 3 8 8

4 403 7 10 1.8 8 2

4 403 8 14 2.4 8 2

4 403 9 18 6.1 8 2

4 404 7 9 0.9 8 8

4 404 8 12 1.8 8 8

4 404 9 16 3 8 8

4 407 7 12 2.4 8 8

4 407 8 16 2.4 8 2

4 407 9 19 5.5 8 10

4 410 7 9 4.6 8 8

4 410 8 17 7.9 8 8

4 411 3 0 0 6 6

4 411 7 2 0.3 6 6

4 411 8 6 1.2 2 2

4 415 7 10 3 9 9

4 415 8 16 4.3 9 9

4 415 9 21 7 9 2

4 416 7 12 1.8 8 8

4 416 8 17 2.7 8 8

4 416 9 20 3.7 8 8

5 202 99 0 0 99 99

5 310 1 0 0 1 1

Potential Vegetation Cover Structural Stand Crown Base

Type Type Stage Height (m) Height (m) FBFM Normal FBFM Severe
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000 115

Potential Vegetation Cover Structural Stand Crown Base

Type Type Stage Height (m) Height (m) FBFM Normal FBFM Severe

5 310 2 0 0 50 1

5 321 3 0 0 6 6

5 321 4 0 0 2 2

5 403 7 10 1.5 2 2

5 403 8 13 2.4 9 9

5 403 9 15 3 2 2

5 404 7 8 1.5 8 2

5 404 8 10 1.5 8 8

5 404 9 21 6.1 8 9

5 405 5 4 0.6 50 6

5 405 6 4 0.6 50 2

5 407 7 10 3 9 9

5 407 8 15 4 9 9

5 407 9 21 7 9 9

5 411 7 3 0.8 2 6

5 411 8 8 0.6 2 2

5 412 5 4 0.6 6 6

5 412 6 3 0.3 50 2

5 413 5 5 0.6 6 6

5 413 6 10 0.3 50 2

5 414 5 4 0.3 6 6

5 414 6 3 0.6 50 2

5 415 7 10 3 9 9

5 415 8 15 4 9 9

5 415 9 21 7 9 2

5 421 5 5 0.6 6 6

5 421 6 4 0.6 50 2

6 202 99 0 0 99 99

6 310 1 0 0 1 1

6 310 2 0 0 50 1

6 321 3 0 0 6 6

6 321 4 0 0 50 2

6 405 5 4 0.6 6 6

6 405 6 4 0.6 50 2

6 412 5 5 0.9 6 6

6 412 6 4 0.3 50 2

6 413 5 6 0.9 6 6

6 413 6 6 0.9 50 2

6 414 5 5 0.6 6 6

6 414 6 3 0.3 50 2

6 421 5 4 0.6 6 6

6 421 6 4 0.6 50 2

7 202 99 0 0 99 99

7 310 1 0 0 1 1

7 310 2 0 0 50 1

7 321 3 0 0 6 6

7 321 4 0 0 50 50

8 100 98 0 0 98 98

8 201 99 0 0 99 99

8 203 99 0 0 99 99

8 205 96 0 0 99 99

5 322 3 0 0 5 5

5 402 8 18 3.7 8 8
116 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000 APPENDIX K — Accuracy assessment summaries Accuracy of the Potential Vegetation Type (PVT) Classified Layer based on PVT Field Calls Reference (Field) PVT’s Pinyon Spruce- Mixed Douglas- Ponderosa Oak- Scrub- Non- Commission Riparian Fir Conifer fir Pine Juniper Grassland Vegetated Totals Error(%) Riparian 21(5) 0(1) 2(1) 9(1) 19(1) 6(1) 1(1) 1(1) 59 64 Spruce-Fir 0(1) 49(5) 15(4) 7(3) 0(2) 0(1) 0(1) 1(1) 72 32 Mixed Conifer 1(1) 9(4) 94(5) 67(4) 11(3) 0(1) 0(1) 0(1) 182 48 Douglas-fir 2(1) 2(3) 33(4) 136(5) 87(4) 3(2) 0(1) 1(1) 264 48 Ponderosa Pine 2(1) 0(2) 3(3) 37(4) 254(5) 100(4) 1(2) 2(1) 399 36 Pinyon-Oak-Juniper 6(1) 0(1) 1(1) 18(2) 147(4) 432(5) 46(4) 9(2) 659 34 Scrub-Grassland 1(1) 0(1) 0(1) 0(1) 0(2) 14(4) 35(5) 1(2) 51 31 Non-vegetated 5(1) 0(1) 7(1) 8(1) 27(1) 70(2) 6(2) 20(5) 143 86 Totals 38 60 155 282 545 625 89 35 1829 Omission Error (%) 45 18 39 52 53 31 61 43 Format: Number of plots (fuzzy score) Overall Accuracy = 57%, KHAT Accuracy = 45%. Fuzzy scores are rated as 1 (absolutely wrong), 2 (understandable but wrong), 3 (acceptable), 4 (good answer), or 5 (perfect). Fuzzy sets: 89% have a fuzzy score of 3 or better, 87% have a fuzzy score of 4 or better. Classified (Layer) PVT
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000 117 Classified (Layer) Cover Type Accuracy of the Cover Type Classified Layer based on Cover Type Field Calls Reference (Field) Cover Type Commission Wa Ro Ba MQ He XS MS BR BC CG CW MW MC XC As GO Ju Pi EO PP DF PJ SF Totals Error (%) Water 2(5) 0(1) 0(1) 0(1) 0(1) 0(1) 0(1) 0(1) 0(1) 0(1) 0(1) 0(1) 0(1) 0(1) 0(1) 0(1) 0(1) 0(1) 0(1) 0(1) 0(1) 0(1) 0(1) 2 0 Rock 0(1) 14(5) 4(4) 0(4) 23(2) 9(1) 1(1) 2(1) 0(1) 1(1) 14(1) 16(1) 0(1) 5(1) 2(1) 0(1) 16(1) 3(1) 1(1) 12(1) 0(1) 15(1) 0(1) 138 90 Barren Ground 0(1) 4(4) 18(5) 0(4) 61(2) 14(2) 0(1) 1(1) 0(1) 0(1) 2(1) 7(1) 0(1) 0(1) 0(1) 0(1) 10(1) 0(1) 2(1) 0(1) 0(1) 9(1) 0(1) 128 86 Mine-Quarry 0(1) 0(4) 0(4) 3(5) 0(1) 0(1) 0(1) 0(1) 0(1) 0(1) 0(1) 0(1) 0(1) 0(1) 0(1) 0(1) 0(1) 0(1) 0(1) 0(1) 0(1) 0(1) 0(1) 3 0 Herblands 1(1) 0(2) 1(2) 0(1) 148(5) 9(1) 0(1) 0(1) 0(1) 6(1) 17(1) 16(1) 1(1) 6(1) 0(1) 0(1) 13(1) 2(1) 2(1) 31(1) 4(1) 24(1) 2(1) 283 48 Xeric Shrublands 0(1) 0(1) 2(1) 0(1) 14(1) 40(5) 0(4) 0(1) 0(1) 1(1) 5(1) 14(1) 0(1) 2(1) 0(1) 0(1) 0(1) 2(1) 2(1) 0(1) 0(1) 2(1) 0(1) 84 52 Mesic Shrublands 0(1) 2(1) 0(1) 0(1) 4(1) 2(4) 4(5) 3(3) 1(1) 4(1) 3(1) 0(1) 2(1) 4(1) 0(1) 0(1) 0(1) 0(1) 0(1) 1(1) 0(1) 1(1) 0(1) 31 87 Broadleaf Riparian Forest 0(1) 0(1) 1(1) 0(1) 2(1) 2(1) 0(3) 14(5) 0(4) 1(1) 7(1) 4(1) 0(1) 1(1) 0(1) 0(1) 0(1) 0(1) 2(1) 2(1) 0(1) 1(1) 0(1) 37 62 Broadleaf/Conifer Forest 0(1) 0(1) 0(1) 0(1) 0(1) 0(1) 0(1) 0(4) 12(5) 2(2) 3(2) 1(1) 1(3) 6(3) 1(4) 1(1) 0(1) 0(1) 0(1) 0(4) 0(4) 0(1) 1(4) 28 57 Conifer-Gambel Oak Forest 0(1) 0(1) 0(1) 0(1) 0(1) 1(1) 0(1) 0(1) 0(2) 12(5) 5(3) 3(1) 2(3) 8(3) 0(1) 0(3) 0(1) 1(1) 0(1) 8(4) 0(4) 2(1) 0(2) 42 71 Conifer Forest- Woodland Mix 0(1) 0(1) 0(1) 0(1) 0(1) 0(1) 0(1) 0(1) 0(2) 7(3) 7(5) 3(2) 0(2) 7(3) 0(1) 0(1) 0(4) 1(4) 0(4) 23(4) 0(4) 1(4) 0(2) 49 86 Mixed Woodland 0(1) 0(1) 0(1) 0(1) 1(1) 3(1) 0(1) 0(1) 0(1) 2(1) 6(2) 9(5) 0(1) 6(1) 0(1) 0(1) 2(4) 1(4) 2(4) 6(1) 0(1) 4(4) 0(1) 42 79 Mesic Mixed Conifer 0(1) 0(1) 0(1) 0(1) 0(1) 0(1) 0(1) 0(1) 13(3) 4(3) 1(2) 0(1) 64(5) 49(4) 0(1) 0(1) 0(1) 0(1) 0(1) 6(2) 8(3) 0(1) 6(3) 151 58 Xeric Mixed Conifer 0(1) 1(1) 0(1) 0(1) 0(1) 1(1) 1(1) 0(1) 1(3) 8(3) 11(3) 2(1) 6(4) 38(5) 0(1) 0(1) 0(1) 0(1) 0(1) 17(4) 1(4) 1(1) 0(2) 88 57 Aspen 0(1) 0(1) 0(1) 0(1) 0(1) 0(1) 0(1) 0(1) 5(4) 0(1) 0(1) 0(1) 0(1) 1(1) 15(5) 0(2) 0(1) 0(1) 0(1) 0(1) 1(1) 0(1) 1(1) 23 35 Gambel Oak 0(1) 0(1) 0(1) 0(1) 0(1) 0(1) 1(1) 0(1) 2(1) 4(4) 1(1) 0(1) 1(1) 3(1) 0(2) 13(5) 0(1) 0(1) 0(1) 2(1) 1(1) 0(1) 0(1) 28 54 Juniper 0(1) 0(1) 2(1) 0(1) 3(1) 5(1) 0(1) 0(1) 0(1) 1(1) 10(4) 26(4) 0(1) 2(1) 0(1) 0(1) 39(5) 5(3) 0(3) 15(1) 0(1) 51(4) 0(1) 159 75 Pinyon 0(1) 0(1) 0(1) 0(1) 1(1) 6(1) 0(1) 1(1) 0(1) 4(1) 22(4) 26(4) 0(1) 3(1) 0(1) 0(1) 3(3) 23(5) 4(3) 14(1) 0(1) 19(4) 0(1) 126 82 Evergreen Oak 0(1) 1(1) 0(1) 0(1) 11(1) 7(1) 0(1) 1(1) 1(1) 0(1) 7(4) 18(4) 0(1) 2(1) 0(1) 0(1) 2(3) 0(3) 11(5) 5(1) 0(1) 4(2) 0(1) 70 84 Ponderosa Pine 0(1) 0(1) 0(1) 0(1) 2(1) 0(1) 0(1) 0(1) 1(4) 3(4) 23(4) 3(1) 2(2) 19(4) 0(1) 1(1) 2(1) 1(1) 1(1) 122(5) 1(4) 2(1) 0(2) 183 33 Douglas-fir 0(1) 0(1) 0(1) 0(1) 0(1) 0(1) 0(1) 0(1) 4(4) 2(4) 2(4) 0(1) 2(3) 9(4) 0(1) 0(1) 0(1) 0(1) 0(1) 6(4) 6(5) 0(1) 0(3) 31 81 Pinyon-Juniper 0(1) 0(1) 0(1) 0(1) 1(1) 0(1) 0(1) 0(1) 0(1) 1(1) 20(4) 10(4) 0(1) 0(1) 0(1) 0(1) 3(4) 6(4) 0(2) 10(1) 0(1) 27(5) 0(1) 78 65 Spruce-Fir 0(1) 0(1) 0(1) 0(1) 0(1) 0(1) 0(1) 0(1) 4(4) 0(2) 0(2) 0(1) 1(3) 0(2) 0(1) 0(1) 0(1) 0(1) 0(1) 0(2) 1(3) 0(1) 19(5) 25 24 Totals 3 22 28 3 271 99 7 22 44 63 166 158 82 171 18 15 90 45 27 280 23 163 29 1829 Omission Error (%) 33 36 36 0 45 60 43 36 73 81 96 94 22 78 17 13 57 49 59 56 74 83 34 Format: Number of plots (fuzzy score) Overall Accuracy = 36%, KHAT Accuracy = 31%. Fuzzy scores are rated as 1 (absolutely wrong), 2 (understandable but wrong), 3 (acceptable), 4 (good answer), or 5 (perfect). Fuzzy sets: 65% have a fuzzy score of 3 or better, 59% have a fuzzy score of 4 or better.
118 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000 Accuracy of the Structural Stage Classified Layer based on Structural Stage Field Calls Reference (Field) Structural Stage Closed Open Closed Open Closed Open Small Medium Large Commission Herbland Herbland Shrubland Shrubland Woodland Woodland Tree Tree Tree Water Rock Totals Error (%) Closed Herbland 89(5) 8(4) 1(1) 3(1) 1(1) 28(1) 3(1) 48(1) 4(1) 0(1) 0(1) 185 52 Open Herbland 35(4) 16(5) 0(1) 5(1) 1(1) 27(1) 0(1) 12(1) 0(1) 1(1) 1(1) 98 84 Closed Shrubland 4(1) 0(1) 8(5) 3(4) 1(1) 3(1) 5(1) 19(1) 2(1) 0(1) 2(1) 47 83 Open Shrubland 3(1) 11(1) 3(4) 32(5) 9(1) 8(1) 0(1) 0(1) 0(1) 0(1) 2(1) 68 53 Closed Woodland 5(1) 0(1) 4(1) 3(1) 42(5) 39(4) 3(1) 57(1) 0(1) 0(1) 1(1) 154 73 Open Woodland 10(1) 2(1) 3(1) 11(1) 44(4) 170(5) 1(1) 75(1) 3(1) 0(1) 2(1) 321 47 Small Tree 2(1) 0(1) 1(1) 0(1) 2(1) 0(1) 12(5) 44(4) 2(3) 0(1) 0(1) 63 81 Medium Tree 2(1) 0(1) 4(1) 1(1) 15(1) 14(1) 15(4) 537(5) 19(4) 0(1) 2(1) 609 12 Large Tree 0(1) 0(1) 0(1) 0(1) 0(1) 0(1) 0(3) 10(4) 3(5) 0(1) 0(1) 13 77 Water 0(1) 0(1) 0(1) 0(1) 0(1) 0(1) 0(1) 0(1) 0(1) 2(5) 0(1) 2 0 Rock 18(1) 66(1) 8(1) 16(1) 5(1) 74(1) 5(1) 31(1) 3(1) 0(1) 43(5) 269 84 Totals 168 103 32 74 120 363 44 833 36 3 53 1829 Omission Error (%) 47 84 75 57 65 53 73 36 92 33 19 Classified Format: Number of plots (fuzzy score) Overall Accuracy = 52%, KHAT Accuracy = 39%. Fuzzy scores are rated as 1 (absolutely wrong), 2 (understandable but wrong), 3 (acceptable), 4 (good answer), or 5 (perfect). Fuzzy sets: 64% have a fuzzy score of 3 or better. (Layer) Structural Stage
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000 119 Classified (Layer) Normal FBFM Accuracy of the Fire Behavior Fuel Model (FBFM) Normal Classified Layer based on FBFM Normal Field Calls Reference (Field) Normal FBFM Commission 1 2 5 6 8 9 10 50 98 99 Totals Error (%) 1 98(5) 39(3) 0(1) 0(1) 21(1) 4(1) 2(1) 21(3) 0(1) 0(1) 185 47 2 1(3) 3(5) 0(1) 2(1) 8(1) 1(1) 0(1) 2(3) 0(1) 0(1) 17 82 5 4(1) 4(1) 0(5) 6(3) 7(1) 3(1) 2(1) 1(1) 0(1) 2(1) 29 100 6 12(1) 35(1) 4(3) 24(5) 31(1) 20(1) 1(1) 54(1) 0(1) 1(1) 182 87 8 4(1) 26(1) 1(1) 7(1) 96(5) 46(3) 18(3) 5(1) 0(1) 0(1) 203 53 9 10(1) 40(1) 5(1) 8(1) 82(3) 89(5) 3(3) 23(1) 0(1) 1(1) 261 66 10 0(1) 7(1) 0(1) 0(1) 94(3) 9(3) 61(5) 1(1) 0(1) 0(1) 172 65 50 100(3) 72(3) 2(1) 13(1) 60(1) 24(1) 0(1) 234(5) 1(1) 1(1) 507 54 98 0(1) 0(1) 0(1) 0(1) 0(1) 0(1) 0(1) 0(1) 2(5) 0(1) 2 0 99 56(1) 26(1) 2(1) 3(1) 26(1) 5(1) 0(1) 125(1) 0(1) 24(5) 269 91 Totals 285 252 14 63 425 201 87 466 3 29 1825 Omission Error (%) 66 99 100 62 77 56 30 50 33 17 Format: Number of plots (fuzzy score). Overall Accuracy = 35%, KHAT Accuracy = 24%. Fuzzy scores are rated as 1 (absolutely wrong), 2 (understandable but wrong), 3 (acceptable), 4 (good answer), or 5 (perfect). Fuzzy sets: 62% have a fuzzy score of 3 or better.
120 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000 Accuracy of the Fire Behavior Fuel Model (FBFM) Severe Classified Layer based on FBFM Severe Field Calls Reference (Field) Severe FBFM Commission 1 2 4 5 6 8 9 10 50 98 99 Totals Error (%) 1 139(5) 81(3) 1(1) 0(1) 5(1) 25(1) 3(1) 3(1) 23(3) 1(1) 0(1) 283 51 2 30(3) 144(5) 3(1) 1(1) 78(1) 72(1) 45(1) 14(1) 93(3) 0(1) 2(1) 482 70 4 0(1) 0(1) 0(5) 0(3) 0(3) 0(1) 0(1) 0(1) 0(1) 0(1) 0(1) 0 100 5 3(1) 4(1) 1(3) 0(5) 8(3) 6(1) 2(1) 3(1) 0(1) 0(1) 2(1) 29 100 6 6(1) 43(1) 10(3) 3(3) 63(5) 29(1) 24(1) 2(1) 17(1) 0(1) 1(1) 198 68 8 0(1) 25(1) 0(1) 0(1) 9(1) 31(5) 34(3) 26(3) 2(1) 0(1) 0(1) 127 76 9 4(1) 54(1) 1(1) 0(1) 16(1) 57(3) 85(5) 6(3) 4(1) 0(1) 0(1) 227 63 10 0(1) 6(1) 0(1) 0(1) 2(1) 43(3) 12(3) 122(5) 0(1) 0(1) 0(1) 185 34 50 12(3) 5(3) 0(1) 0(1) 2(1) 0(1) 0(1) 0(1) 7(5) 0(1) 0(1) 26 73 98 0(1) 0(1) 0(1) 0(1) 0(1) 0(1) 0(1) 0(1) 0(1) 2(5) 0(1) 2 0 99 83(1) 53(1) 4(1) 1(1) 16(1) 21(1) 8(1) 0(1) 59(1) 0(1) 24(5) 269 91 Totals 277 415 20 5 199 284 213 176 205 3 29 1826 Omission Error (%) 50 65 100 100 68 89 60 31 97 33 17 Classified Format: Number of plots (fuzzy score) Overall Accuracy = 34%, KHAT Accuracy = 24%. Fuzzy scores are rated as 1 (absolutely wrong), 2 (understandable but wrong), 3 (acceptable), 4 (good answer), or 5 (perfect). Fuzzy sets: 58% have a fuzzy score of 3 or better. (Layer) Severe FBFM
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000 121

Accuracy of the Canopy Closure Classified Layer based on Canopy Closure Class Field Calls

Reference (Field) Canopy Closure

Commission

0 1-15 16-45 46-75 76-99 Totals Error (%)

0 252 134 227 48 8 669 62

1-15 13 14 93 92 2 214 93

16-45 11 13 298 125 3 450 34

46-75 2 2 109 134 11 258 48

76-99 1 0 35 158 44 238 82

Totals 279 163 762 557 68 1829

Omission Error (%) 10 91 61 76 35

Format: Number of plots

Overall Accuracy = 41%, KHAT Accuracy = 24%.

Classified

(Layer)

Canopy

Closure

Accuracy of the Lifeform Classified Layer based on Lifeform Field Calls

Reference (Field) Lifeform

Broadleaf Conifer Commission

Herblands Shrublands Woodlands Forest Forest Water Rock Totals Error (%)

Herblands 148 9 57 67 67 1 1 283 48

Shrublands 18 46 21 22 22 0 4 115 60

Woodlands 17 21 295 136 136 0 3 475 38

Broadleaf Forest 2 3 8 39 39 0 1 116 46

Conifer Forest 2 3 23 516 516 0 1 569 9

Water 0 0 0 0 0 2 0 2 0

Rock 84 24 79 34 34 0 43 269 84

Totals 271 106 483 814 814 3 53 1829

Omission Error (%) 45 57 39 37 37 33 19

Format: Number of plots

Overall Accuracy = 61%, KHAT Accuracy = 48%.

Classified

(Layer)

Lifeform
122 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000

APPENDIX L — The directory structure and description of the CD

FARSITE Input Data Layers for the Gila National Forest Complex,

Version 1.0, May 1999

INTRODUCTION:

This disk contains Version 1.0 of the fuel and vegetation data layers

needed for input to the FARSITE 3.0 (Fire Area Simulator) model for

lands in the Gila National Forest Complex (GNFC), New Mexico,

USA.

FARSITE is a spatially-explicit fire model used to predict the spread,

intensity, direction, size, and pattern of wildland fires. The FARSITE

input maps were created by assigning fire behavior fuel models and

vegetation attributes to unique combinations of potential vegetation

type, cover type, and structural stage data layers.

These input data layers have been converted to the landscape file

format required by FARSITE and stored as USGS quads in a special

directory structure. Also included on this disk are the data layers

developed for FARSITE in Arc/Info raster format, several accessory

vector layers, and a report documenting details of this fuels mapping

effort. Version 1.0 was completed by the Fire Modeling Institute at

the Rocky Mountain Fire Sciences Laboratory, Missoula, Montana,

USA.

DIRECTORY STRUCTURE:

A brief description of the directory structure and data included on this

disk.

FARSITE INPUT LAYERS:

The following directories contain map themes to be used with Farsite.

ARCINFO layers of the map themes required for Farsite were “cut”

into 7.5' USGS quadrangle files, then exported out of ARCINFO

using the GRIDASCII command and converted to DOS format.

Several ARCINFO vector layers that can viewed in FARSITE as an

overlay to the raster layers were also “cut” into quad files, then

exported out of ARCINFO using the UNGENERATE command and

converted to DOS format. Prior to creating this disk, a landscape

file was created for each quad from the 8 ascii raster map themes

required for Farsite. The landscape file and all accessory ascii files for

each quad were zipped together into one zip file labeled with an

abbreviation of the quadrangle’s name. The preferred (and fastest)
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000 123

method of running FARSITE is to first copy the needed quadrangle

map themes to your PC hard disk and then run FARSITE.

 7.5 minute QUADS: Zipped files created on an IBM Unix system

with the pkzip for unix command. They can be unzipped with either

the MSDOS or the Windows 95 version of PKZIP, both of which are

provided on this disk. Each Zip file contains all of the map themes

needed to run FARSITE for a 7.5' quad and is named with an

abbreviation of the quad name. Included in each zip file are the

following files (each prefixed with the abbreviated quad name and

given the extension listed in parentheses):

FARSITE landscape (.LCP) files: Each LCP contains 8 raster themes

(elevation, slope, aspect, surface fuel model — normal, canopy cover,

stand height, crown height, crown bulk density). The surface fuel

theme is for normal burning conditions. The LCP can ONLY be read

by FARSITE.

SEVERE FUEL MODELS (.SEV): Ascii raster surface fuels theme

for severe wildfire conditions. These files can be spliced into normal

LCP files above using utilities in the FARSITE program.

CONTOURS (.CTR): Ascii vector theme showing 100 meter topographic contours. Contours were derived from the 30 meter USGS

Digital Elevation Model (DEM).

ROADS (.RDS): Ascii vector theme showing roads. Roads are a

combination of a roads layer obtained from the Gila National Forest,

Silver City, NM, and a roads layer generated from 1:24,000 USGS

Cartographic Feature Files (CFFs). Roads within the Gila National

Forest boundary were taken from the GNF layer; roads outside the

forest boundary came from a CFF layer generated at the Fire Sciences

Lab. ** NOTE: CFFS for several quads within the GNF are not

currently available.

STREAMS (.HYD): Ascii vector theme showing streams. This streams

layer was obtained from the Gila National Forest, Silver City, NM in

September 1998. It originated from Cartographic Feature Files

(CFFs) and was extensively edited by GNF staff. CFFs for a few quads

on the Cibola NF were added to this layer at the Fire Sciences Lab. **

NOTE: CFFs for several quads within the GNF are not currently

available.

TRAILS (.TRL): Ascii vector theme showing trails. Trails are a

combination of a trails layer obtained from the Gila National Forest,

Silver City, NM, and a trails layer generated from 1:24,000 USGS

Cartographic Feature Files. Trails within the Gila National Forest

boundary were taken from the GNF layer; trails outside of the GNF

came from CFFs. ** NOTE: CFFs for several quads within the GNF

are not currently available.
124 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000

All Zip files should have a Landscape, Severe Fuel Models, and

Contours file, however Roads, Streams, and Trails files may or may

not exist for a particular quad.

\BOUNDARY BOUNDARIES: Two ASCII vector files provided.

These files were NOT clipped to 7.5' quads. They can be viewed as an

additional overlay in FARSITE.

FOREST_BDY.ASC: Vector theme showing the Gila National Forest boundary and the portion of the Cibola National Forest boundary

that is included in the GNFC.

WILDERNESS.ASC: Vector theme showing the Gila Wilderness &

Aldo Leopold Wilderness boundaries, obtained from Gila National

Forest, Silver City, NM.

OTHER DIRECTORIES:

\CUSTOM CUSTOM Fuel Model: This directory contains a custom

fuel model file that quantifies the necessary parameters for Fuel

Model 50. This file will be REQUIRED for FARSITE to run. It must

be copied over to your PC along with the FARSITE input files.

\WEATHER \WEATHER\DAYMET: This directory contains simulated weather data (*.wtr) that are ready to be input directly into

FARSITE. See readme.txt file in directory for more specific information on format.

\WEATHER/NCDC: This directory contains weather data

from the National Climatic Data Center’s EarthInfo Cd. Files

are not in *.wtr fromat required by FARSITE. See readme.txt

file in directory for more specific information on format.

\WEATHER\RAWS: This directory contains weather (*.wtr)

and wind (*.wnd) files summarized from RAWS data that are

ready to be input directly into FARSITE. See readme.txt file in

directory for more specific information on format.

\DATABASE \DATABASE\DATA: This directory contains copies

of the *.dbf ground-truth databases.

\DATABASE\FORMATS: This directory contains a copies of

the formats used in the *.dbf databases.

\DATABASE\PV: This directory contains the *.dbf’s of the

Polygon Validation data.

\DATABASE\LUTS: This directory contains lookup tables in *.dbf

format of fire behavior model assignments and vegetation attributes.

PCSLUT.DBF is the lookup table assigning fuel models, crown base

height, and stand height to PVT-Cover Type-Structural Stage Combinations. CSCLUT.DBF is the lookup table assigning crown bulk

density to Cover Type-Structural Stage-Canopy Cover combinations.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000 125

\DOC DOCumenation: This directory contains the project report

detailing the methods used to develop all the Version 1.0 FARSITE

spatial data layers and the 1998 progress report. The directory also

contains lookup tables in *.dbf format of fire behavior model assignments and vegetation attributes.

\GNFC_MAP Gila National Forest Complex MAP: A map showing

forest boundaries of the Gila NF and the portion of the Cibola NF

included in this project, wilderness boundaries, and 7.5' USGS

quadrangles (including names). The map is provided in three formatsa Bitmap, JPEG, and postscript. The postscript will produce the best

paper copy if so desired. The Bitmap or JPEG file may be loaded into

a Windows graphics program as an on-screen reference to quadrangle

locations and names.

\UNIX_GIS UNIX GIS: This directory contains all the Version 1.0

GIS raster data layers developed for FARSITE and several ancillary

vector layers. The directory is in UNIX ARCINFO format and can

only be read by ARCVIEW 3.0 on a PC/DOS system or ARCVIEW

and ARCINFO on a UNIX system.

\FARSITE FARSITE: This directory contains a self-extracting, selfinstalling copy of FARSITE Version 3.0 that can be loaded onto a PC.

FARSITE 3.0 requires Windows 95 or Windows NT 4.0; it will not

work on Windows 3.1. FARSITE will NOT run from this directory.

\SOFTWARE \SOFTWARE\PKZIP\MSDOS: This directory contains a copy of the MSDOS version of PKZIP.

\SOFTWARE\PKZIP\WIN95: This directory contains a copy

of the Windows 95 version of PKZIP. Quad files can be

unzipped using either version.

\SOFTWARE\CFCC: This directory contains the Crown Fuel

Characteristics Calculator software used to compute crown

characteristics from stand data. The executable file is CFCC.

DISCLAIMER:

The data and computer programs provided on this CDROM are

available with the understanding that the Fire Science Laboratory —

Rocky Mountain Research Station — USDA Forest Service cannot

assure their accuracy, completeness, reliability, or suitability for any

particular purpose. Spatial information may not meet National Map

Accuracy Standards. The use of trade or firm names in any documentation is for reader information only; it does not imply endorsement of

any product or service. This data will not be maintained by the Rocky

Mountain Fire Science Laboratory, but may be updated without

further notification.
126 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-46-CD. 2000

PRODUCED BY:

 Robert E. Keane, Kirsten M. Schmidt, Scott Mincemoyer,

Janice L. Garner, and Mark A. Finney.

CONTACTS:

 Data Layers: Fire Effects Project — (406) 329-4800. FARSITE:

Fire Behavior Project — (406) 329-4820.

 FAX: (406) 329-4877.

 Fire Modeling Institute

 Fire Science Laboratory

 Rocky Mountain Research Station

 USDA Forest Service

 P.O. Box 8089

 Missoula, Montana 59807
The U.S. Department of Agriculture (USDA) prohibits discrimination in all its

programs and activities on the basis of race, color, national origin, sex, religion,

age, disability, political beliefs, sexual orientation, or marital or family status. (Not

all prohibited bases apply to all programs.) Persons with disabilities who require

alternative means for communication of program information (Braille, large print,

audiotape, etc.) should contact USDA’s TARGET Center at (202) 720-2600 (voice

and TDD).

To file a complaint of discrimination, write USDA, Director, Office of Civil Rights,

Room 326-W, Whitten Building, 1400 Independence Avenue, SW, Washington,

DC 20250-9410 or call (202) 720-5964 (voice or TDD). USDA is an equal

opportunity provider and employer.

ROCKY MOUNTAIN RESEARCH STATION

RMRS

The Rocky Mountain Research Station develops scientific information and technology to improve management, protection, and use of

the forests and rangelands. Research is designed to meet the needs

of National Forest managers, Federal and State agencies, public and

private organizations, academic institutions, industry, and individuals.

Studies accelerate solutions to problems involving ecosystems,

range, forests, water, recreation, fire, resource inventory, land reclamation, community sustainability, forest engineering technology,

multiple use economics, wildlife and fish habitat, and forest insects

and diseases. Studies are conducted cooperatively, and applications

may be found worldwide.

Research Locations

Flagstaff, Arizona Reno, Nevada

Fort Collins, Colorado* Albuquerque, New Mexico

Boise, Idaho Rapid City, South Dakota

Moscow, Idaho Logan, Utah

Bozeman, Montana Ogden, Utah

Missoula, Montana Provo, Utah

Lincoln, Nebraska Laramie, Wyoming

*Station Headquarters, Natural Resources Research Center,

2150 Centre Avenue, Building A, Fort Collins, CO 80526