United States

Department

of Agriculture

Forest Service

Rocky Mountain

Research Station

General Technical

Report RMRS-GTR-183

December 2006

Guidance on Spatial 

Wildland Fire Analysis:

Models, Tools, and Techniques

Richard D. Stratton
The Author ______________________________________

Richard D. Stratton is a fire modeling analyst with Systems for Environmental 

Management (SEM), a nonprofit research and educational corporation in Missoula, 

Montana. His professional interests include fire modeling, wildland fire use, fuel treatment 

design, the wildland-urban fire problem, and wildland fire decision support systems. 

Rick received his MS at Utah State University in forestry (fire and GIS emphasis) and 

his BS in conservation biology (wildlife emphasis) from Brigham Young University. 

Previous to SEM he worked eight seasons with the U.S. Forest Service/Park Service 

in varying biological and fire suppression and monitoring capacities.

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Fort Collins, CO 80526

Stratton, Richard D. 2006. Guidance on spatial wildland fire analysis: models, tools, 

and techniques. Gen. Tech. Rep. RMRS-GTR-183. Fort Collins, CO: U.S. Department of 

Agriculture, Forest Service, Rocky Mountain Research Station. 15 p.

Abstract ________________________________________

There is an increasing need for spatial wildland fire analysis in support of incident management, 

fuel treatment planning, wildland-urban assessment, and land management plan development. 

However, little guidance has been provided to the field in the form of training, support, or research 

examples. This paper provides guidance to fire managers, planners, specialists, and analysts 

in the use of “models” (FARSITE, FlamMap, RERAP-Term), tools/programs (KCFAST, RAWS, 

FireFamily Plus, WindWizard), and procedures for spatial fire analysis. The approach includes a 

brief discussion about models and their assumptions and limitations, historical fire and weather 

analysis, landscape file data acquisition and development, landscape file and model output critique, 

and model calibration.

Keywords: Calibration, FARSITE, FireFamily Plus, Fire History, Fire Modeling, Fire Weather, 

FlamMap, Fuel Treatment, Gridded Wind, Hazard/Risk Assessment, Landscape 

Analysis, RAWS, RERAP, WindWizard.

Acknowledgments________________________________

This document was supported by the USDA Forest Service, Rocky Mountain 

Research Station, Missoula Fire Sciences Laboratory and the Fire Modeling Institute. I 

am grateful for the review comments of Mark Finney, Larry Bradshaw, Chuck McHugh, 

Robert Seli, Louisa Evers, and Pat Stephen and for discussions with Joe Scott and 

Elizabeth Reinhardt. I also express my thanks to Kristi Coughlon and Nancy Chadwick 

for editorial assistance and publication design.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-183. 2006 

Introduction_____________________

	 Large fires are a landscape-level phenomenon because

they affect multiple stands over a large area. They are

also a spatial phenomenon because the movement and

behavior of fire depends on where and what things it

encounters and their spatial arrangement to one another. To adequately perform both a landscape-level

and spatial fire analysis, one must conduct a mid-scale

assessment of expected fire behavior and movement

across multiple stands under specific weather and fuel

moisture conditions.

	 Arno and Brown (1989) stated, “An impressive body

of scientific evidence...makes it clear that much of

North America is a ‘fire environment’ where wildfire

or a substitute recycling mechanism is inevitable.” This

ecological certainty has been exacerbated by threequarters of a century of fire exclusion in many parts

of the United States, often resulting in uncharacteristic

large fire growth, behavior, and its undesirable effects

(Arno and Brown 1991).

	 Policymakers see destructive and costly fire seasons

such as 2000 or 2002 and react with legislation and

increased scrutiny (for example, National Fire Plan,

Healthy Forest Initiative, General Accounting Office

Reports). Accordingly, land managers and fire specialists are required to adhere to Federal and state mandates and guidelines. With compliance comes reports,

many of which can benefit from or require spatial fire

analysis, such as land management plans; wildland fire

situation analysis and implementation plans; hazard/risk

assessments; community protection plans; and National

Environmental Policy Act (NEPA) compliance, documentation, and outreach.

Unfortunately, little training or instruction has been

given to the field to adequately conductspatial wildland

fire analyses. Irrespective of the project, plan, or assessment, all landscape-level spatial fire analyses should

share a common foundation of procedures and analysis

Guidance on Spatial

Wildland Fire Analysis:

Models, Tools, and Techniques

Richard D. Stratton

of the fire environment (weather, fuel, topography).

This paper describes and provides examples of several

common models, tools, and methods for spatial fire

growth and behavior modeling and weather and fire

analysis for use in research, wildland fire decision

support, and land management planning.

A Note About Models and Their

Assumptions and Limitations_ _____

	 “Amodel is a simplification or approximation ofreality

and hence will not reflect all of reality” (Burnham and

Anderson1998).GeorgeBox(1979)stated,“Allmodelsare

wrong, but some are useful.” It is the task of the modeler

to select the appropriate model, produce usable output,

and interpret model findings given model assumptions

and limitations. However, it is the client that ultimately

determines the usefulness of the model. Modeling is an

art as well as a science and one’sfield experience enables

the art of the modeler. Be mindful that a model is a decision support tool, not a tool that makes decisions.

	 A variety of programs and toolssupport wildland fire

management. For example, there are systems to predict

fire growth and behavior, tools and information on fire

effects, and smoke models for dispersion and emission

estimates. Firemodels,such as FARSITE(Finney 1998)

and BehavePlus(Andrews and others 2005) are actually

fire modeling systems that link multiple empirical and

deterministic models or set(s) of mathematical equations to predict fire growth and behavior. Each model

(for example, surface spread model [Rothermel 1972]

or spotting model [Albini 1979]) has assumptions and

limitations, and can be applied differently in the modeling systems. It isimportant that users understand model

limitations and assumptions and knowhowthesemodels

are used in the fire modeling systems.

	 For example, among our greatest challenges in fire

science is predicting crown fire occurrence and behavior.

To do this we use models by Van Wagner (1977, 1993)
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-183. 2006

for crown fire initiation and Rothermel (1991) for rate of

spread calculations. However, modeling systems such

as NEXUS (Scott 1999), FVS/FFE (Reinhardt and

Crookston 2003),BehavePlus, and FARSITE/FlamMap

(Finney 2006) are implemented differently and can

produce differing results. The different interpretations

stem primarily from our poor understanding of crown

fire—a field of research still in its infancy. The various

models ofsurface and crown fire spread were developed

independently, and the current understanding of crown

fire behavior is insufficient to suggest only one way of

integrating them. This incongruity is compounded by

model assumptions and limitations. A major assumption is that of a uniform tree canopy—which we know

is not the case, since a canopy is comprised of crowns

of individual trees separated by gaps. Likewise, crown

base height is not a uniform value for an area of land,

yet the models assume a single value can be applied to

an entire stand.

	 Sowhatthen isthe analystleftto do?Until an improved

crown fire model is developed and it is applied consistently among fire programs, users must (1) know model

assumptions, limitations, and design, (2) determine the

usefulness of model output through adequate data development and critique, and (3) calibrate the model to

be consistentwith field observations.Remembermodels

are “...artful applications of existing knowledge. They

do not attempt to explain the physics or mechanics... As

the science ofsurface and crown fire behavior advances,

so too will our fire management applications” (Scott

2006).

Modeling Systems and Tools for

Landscape Wildland Fire Analysis___

FARSITE [1]

	 FARSITE (Fire Area Simulator) (version 4.1) is a

two-dimensional program for spatially and temporally

simulating the spread and behavior of fires under heterogeneous conditions. FARSITE incorporates existing

fire behaviormodels ofsurface fire spread (Albini 1976,

Rothermel 1972), crown fire spread (Rothermel 1991,

Van Wagner 1977, 1993), spotting (Albini 1979), pointsource fire acceleration (Forestry Canada Fire Danger

Group 1992), and fuel moisture (Nelson 2000) with

spatial information on fuels, weather, and topography

(the FARSITE landscape file [LCP]). Simulation output

is in tabular, vector, and raster formats. FARSITE is

often used to help answer the questions: Where will the

fire go? How large will the fire get? When might the

fire reach a particular location?

FlamMap [2]

	 FlamMap (version 3.0) is a spatial fire behavior

mapping and analysis program that requires an LCP

and fuel moisture and weather data. Unlike FARSITE,

FlamMap makesindependentfire behavior calculations

(for example, fireline intensity, flame length) for each

location ofthe rasterlandscape (cell), independent of one

another. That is, there is no predictor of fire movement

acrossthe landscape, and weather and wind information

are held constant. FlamMap output lends itself well to

landscape comparisons(for example, pre- and post-treatment effectiveness) and to identifying hazardousfuel and

topographic combinations, thus aiding in prioritization

and assessment (Stratton 2004).

FireFamily Plus [3]

	 FireFamily Plus (FFP) (Bradshaw and McCormick

2000) (version 3.0.5) is a fire climatology and occurrence program that combines the functionality of the

PCFIRDAT (Cohen and others 1994, Main and others

1990), PCSEASON (Cohen and others 1994, Main and

others 1990), FIRES (Andrews and Bradshaw 1997),

and CLIMATOLOGY (Bradshaw and Fischer 1984)

programs into a single package with a graphical user

interface. It allows the user to summarize and analyze

weather observations, associate weather with local fire

occurrence data, and compute fire danger indices based

on the National Fire Danger Rating System (NFDRS)

(Deeming and others 1977) and the Canadian Forest

Fire Danger Rating System (Canadian Forestry Service

1987).

Rare Event Risk Assessment Process [4]

	 Rare Event Risk Assessment Process (RERAP)

(USDA 2000, Wiitala and Carlton 1993) (version 7.0)

is a programused to estimate the risk that a fire willreach

a particular point of concern before a fire-ending event

occurs(Term).Itincorporatesweather,fuels,topography,

and Rothermel’s surface spread (1972) and crown fire

(1991) models with two waiting-time distributions—one

forthe fire-endingevent andtheotherfor “critical” spread

events—toproduceprobabilities alonga straight-line transect. RERAP consists of three modules: Term, Spread,

andRisk. Forthe purpose of this paper, the Term Module

is used to develop a Weibull waiting-time distribution for

a user defined fire- and season-ending event.

[1/2/3] FARSITE, FlamMap, and FireFamily Plus: www.fire.org

[4] RERAP: www.fs.fed.us/fire/rerap/indexz.htm
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-183. 2006

WindWizard [5]

	 WindWizard is an interface to a computational fluid

dynamics model (CFD) (FLUENT®) to simulate the

effect terrain has on wind (Butler and others 2004).CFD

technology wasinitially developed by the aerospace and

automotive industriesto simulate fluid flow around and

through aircraft, automobiles, pipes, etc. Output is wind

velocity and direction in raster and vector format with

a default resolution of 100 meters. Gridded wind has

two principle functions: visualization and model input.

Shapefiles can be plotted to help managers visualize

the channeling and checking effect topography has on

wind flow for operational, planning, and educational

purposes. Output can be imported into FARSITE/

FlamMap and may improve fire growth and behavior

simulations, particularly at a finer scale (Butler and

others 2004, 2006).

The Spatial Wildland Fire Analysis

Process_________________________

	 Above I addressed the need for fire specialists to

perform both landscape-level and spatial wildland

fire analyses. I defined frequently used terms, briefly

discussed model assumptions and limitations, and

mentioned a few key modeling systems and tools. This

paper will now shift from a purely informational style

to an instructional approach. Each subsequent topic is

prefaced by a short introduction followed by specific

instructions intermingled with a few thought-directing

questions.

Project Design and the Management

Question

	 A research or management question(s) should be at

the core of any wildland fire analysis. Development,

execution, and implementation of the project should

be centered on answering the question. The formulation of a clear question will guide data acquisition,

model selection, the analysis process, application of

the results, and reporting and archival (the “four As”

as taught in S-492 [RERAP]). Sometimes a project can

be structured as a series of questions and the findings

of the initial inquiry are needed to answer subsequent

questions. For example, where are our ecological and

socio-economic values? Which of these values are at

risk from a wildfire? What can be done to mitigate this

risk? It is important that the question be answerable,

roles and responsibilities identified, funding secured,

and a reasonable timeline prepared.

Historical Fire Analysis: Understanding

Fire Occurrence and Movement

	 Historical fire records can be useful in understanding the size, behavior, frequency, seasonality, and

spatial distribution and orientation of fires. They help

managers conceptualize the fireshed—the possible or

expected area influenced by a single fire start as constrained by natural barriers, fuel, terrain, and weather

within a given period of time. Fire records are either

hardcopy or electronic—in a Geographic Information

System(GIS)/spreadsheetformat orin a central database

(for example, National Interagency Fire Management

Integrated Database [NIFMID] [USDA Forest Service

1993]). Information is typically categorized as a fire

occurrence or ignition history, or a fire area history

(for example, fire atlas). A few areas may contain fire

historiesreconstructed from crosssections of fire-scarred

trees or establishment dates of post-fire tree cohorts[6]. In

most cases, the longer the fire record, the easier it is to

ascertain the fireshed and see patterns on the landscape.

Fire ignition history—Common among federal

and some state agencies are GIS point ignition layers.

Federal fire occurrence information is available for

download at the National Fire and Aviation Management Web Applications Site (FAMWEB) or Kansas

City Fire Access Software (KCFAST) (USDA Forest

Service 1996) [7]—Forest Service only (Fire > Standard

Extract) (.RAW file). If electronic data are not available,

a GIS layer can be generated from local fire reports

or associated records—include attribute data such as

fire name, date, size, and cause. Create a density grid

from fire locations to identify high fire frequency areas

(fig. 1). Where do most fires occur? Are they human or

natural-caused? Display the fires by size; where do most

large fires occur? Display the fires by date; are there

any noticeable trends? This information should give

a manager an adequate feel for where ignitions could

occur and when the fire season begins and ends.

[5] WindWizard: www.firelab.org [6] NOAA Paleoclimatology: www.ncdc.noaa.gov/paleo

[7]FAMWEB(KCFASTandUSFederalWildlandFireManagement

Data): http://famweb.nwcg.gov/
 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-183. 2006

Figure 1—Fire ignition density, both human and lightning caused, on the Boise National Forest (ID) (1955 to 2002). One to three 

fires within the 1-mile search radius are in blue, 4 to 6 fires (green), 7 fires (yellow), 8 fires (orange), and 9 to 10 fires (red).

	 An ignition density only indicates where fires have

started, not where or under what conditions fires will

spread or what impacts will occur. Thus, fire occurrence

data alone are of limited value to risk assessment. The

components of fire risk include the probability of burning and the impacts that fires have when they do burn.

This is different than hazard—the physical setting (fire

environment) adjacent to the value (Bachmann and Allgower 2000). Most area is burned by the rare, large fires

(Finney 2005) that are not necessarily related to the locations of frequent ignitions. So use the ignition information

cautiously for where a fire may originate.

Fire area history—Develop a fire atlas or fire area

history using Global Positioning System (GPS) perimeters, remote sensing, aerial photographs, historical

documents, fire scars, dates of post-fire tree cohorts,

and so forth (fig. 2). Pay particular attention to the large

spread events and consider the following: What was

the size, behavior, and orientation of these large spread

events? What did the fuel complex look like when these

large fires occurred? What was the weather, wind, and

fuel moisture? What was the energy release component

(ERC) or other fire danger indices when the fire made

significant advances? To obtain this information, talk
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-183. 2006

Figure 2—Large fire history (>100 ac) on the Coconino National Forest, north of Flagstaff, AZ (1950 to 2001) (courtesy of 

Charles W. McHugh).

with past fire managers and obtain fire packages and

related materials from the host unit.

	 Answering the above questions about large fire growth

providesthe frameworkforunderstandinganddeveloping

the problem fire—a possible fire event, as evidenced

by the historical fire record, whose occurrence on the

landscape would be undesirable. Information from a

thorough historical fire analysis will enable managers

to understand the fire threat and visualize the fireshed,

resulting in an appropriate management response and

improved project development and implementation.

Environmental Analysis: RAWS, FireFamily

Plus, RERAP, WRCC, and WindWizard

	 FARSITE and FlamMap require weather and fuel

moisturevariablesformodeling(Finney1998).Historical

and currentweatherinformation—obtained fromremote

automated weather stations (RAWS) (NWCG 2000;

Zachariassen and others 2003)—can be downloaded

from NIFMID using KCFAST, and imported into FFP.

If RAWS stations are unavailable for your analysis area,

consider stations administered by the National Weather
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-183. 2006

Service, Federal Aviation Administration, and Natural

Resource Conservation Service (SNOTEL sites). [8]

Remote Automated Weather Station retrieval—

Locate the nearest RAWS(s) for your project area [9].

It is important to select a RAWS at a similar elevation

and topographic setting, keeping in mind the influence

the terrain and vegetation may have on the station.

Occasionally,localtopographic influencesmask prevailing wind patterns that influence large fire growth. In

this case, one station may be used for the wind direction

and another for the weather (Stratton 2004).

	 Use KCFAST Weather Information Retrieval [10] to

(1) find the Weather Information Management System

(WIMS) station number (KCFAST: Weather > Station

Catalog > Station Information), (2) obtain a year count

(KCFAST:Weather>DataExtract>Utilities),(3) obtain

the station catalog, and (4) retrieve the raw historical

weather data file(s). The station year count is important

asitshows how long the station has been operable. Generally, the longer the history the better. The station year

count also shows the number of observations per year.

Years with few observations or large gaps in the data

(KCFAST: Weather > Data Extract > Utilities > Missing

Data) should be omitted. Region and unit numbers can

be obtained using FFP (Fire > Associations).

Climatology and fires analysis in FireFamily 

Plus—Using FFP, import the station catalog (.TXT),

historical fire (.RAW or .FPL), and weather files (1972

data format [.FWX]) (Data > Import). Hourly data are

also available fromKCFAST(1998 data format[.FW9]),

but is only accessible for the previous 18 months. View

the import error log(s) after bringing in station and

weather information. Errors in these files can significantly affect your analyses. List all weather observations (Weather > View Observations > All). Sort each

weather variable in ascending or descending order—right

mouse click—looking for errors including missing data

and excessive values (for example, precipitation, wind,

relative humidity).

	 Associate fire data with the weatherstation(s)(Fires >

Associations). Determine when fires start, peak, and

end (FFP: Fires > Summary). What was the range of

ERC values or other fire danger indices when the large

fires occurred (FFP: Fires > Fire Analysis)? Remember

that fire size is associated with the discovery date, so

be careful when interpreting the data.

	 To obtain climatology and moisture information, users

can generate a daily listing of customized variables (for

example, relative humidity, fuel moisture) relative to the

number of fires or size of the fire (Weather > Seasonal

Reports > Daily Listing). This listing can be imported

into a spreadsheet. Or, users can generate a percentile

weatherreport(Weather > SeasonalReports > Percentile

Weather) using an NFDRS index (for example, ERC) to

produce the required inputsformodeling. Using the daily

listing feature is preferred in some cases because fires

resulted from these conditions and agency personnel

and the interested public can remember and relate to

that particular event.

RERAP—Use RERAP, Term Module, to develop

waiting-time distributions for fire- and season-ending

events (fig. 3). A fire-ending or fire-stopping weather

event occurs when sufficient moisture results in termination of fire spread. A season-ending weather event

consists of a fire-ending event followed by a persistent

combination of environmental factors that end the fire

season (USDA 2000).

	 Develop Term event criteria by combining local

knowledge with climatology and fires data in FFP. A

fire-ending event is usually a result of a certain amount

[8] RAWS and other Station Information: http://raws.wrh.noaa.

gov/­roman/index.html

[9] RAWS Locations: www.raws.dri.edu/index.html

[10] KCFAST/Fire Weather Information Retrieval: http://famweb.

nwcg.gov/kcfast/html/wxmenu.htm

Figure 3—Weibull waiting-time distribution from RERAP given 

the likelihood of a fire-stopping event (>2 inches of rain over a 

3-day period) on the White Mountain National Forest (NH).
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-183. 2006

of precipitation over a specified timeframe. A seasonending event combines the fire-stopping event with a

predictor of fire behavior (for example, ERC). In FFP,

graph ERC and overlay a series of years with historical

fires to identify a threshold ERC value when fire occurrence and large fires cease and resurgence is no longer

a threat. Use the Event Locator in FFP to identify the

occurrence of the fire-stopping events and the threshold

ERCvalue.Thisinformation, alongwith the season start

date, is used in RERAP to develop the season-ending

event.Itis not uncommon to havemultiple season-ending

events—a bimodalfire season. Term information is useful for incident management, risk assessment, planning

of prescribed fires, and fine-tuning season start and end

dates.

Wind speed and direction from RAWS/­FireFamily 

Plus—Wind is one of the primary environmental variables influencing wildland fire growth and intensity

(Catchpole and others 1998, Rothermel 1972). For this

reason, it is critical that representative wind speed and

direction information are used in any landscape fire

analysis. When obtaining and using wind information,

consider the following questions: What is the prevailing wind direction during the fire season? Is the wind

direction consistent with whatfire managers perceive as

their predominant wind direction? At what wind speed

do fires torch and actively crown? What causes high

wind events and how often and when do they occur?

	 Be cautious when using FWX filesfor wind speed and

direction information.First,mostRAWSwind speed and

direction information is a 10-minute average priorto data

transmission, typically 1300 local standard time. In the

case of wind speed, this value is an average composed

oflulls and gusts.Averaging the gustinessis problematic

when modeling fire behavior. Often these gusts or peak

winds affectfire growth, intensity, and spotting (Crosby

and Chandler 2004). By using the 10-minute average,

even at the 99th percentile, the wind speed is often underrepresented. Adjust this value to be more characteristic

of target conditions. Consider using the maximum

1-minute wind speed from the “wind gust estimating

table” by Crosby and Chandler. Second, because FWX

files are only a single snapshot in time, no other wind

information is provided. This may be of concern for

mid-morning activity, late afternoon runs, and active

burning in the evening. In FFP, wind direction information can be obtained by a wind speed vs. wind

direction report (Weather > Winds), as specified by

analysis period (days) and summarized by direction.

Information from FFP can also be exported into FARSITE/FlamMap format.

	 Wind speed and direction from WRCC—An excellentsupplement to the KCFAST database is weather and

wind information from the Western Regional Climate

Center (WRCC). WRCC stores historical data from all

RAWS in the U.S., including the Caribbean Islands [11].

WRCC data is hourly, may include gusts, and viewable

via their website or downloadable in FW9 or FWX

format for import into FFP (Select your RAWS [see 9]/

Data Lister > Data Format). Wind speed and direction

information isreported as a 10-minute average. A useful

feature is the selection of sub intervals by month, day,

and starting and ending hours. This allows the user to

get more detailed wind information for the fire season

and burning period displayed in a wind rose (fig. 4).

WindWizard—WindWizard requires synoptic

(general or upper level) wind speed and direction, and

a digital elevation model (DEM) (USGS 1990). This

input speed differs from the 20-ft wind speed. Usually, the speed needs to be two to three times that of

the observed 20-ft ridgetop wind to produce the desired

results. WindWizard is not a forecast; it simulates

topographically modified flow for an instant in time.

Multiple simulations of wind speed and direction can

be created for use in FARSITE/FlamMap.

	 The DEM must include a buffer area of approximately

20 percent of the horizontal and vertical distance to

incorporate surrounding terrain that may influence

the area of interest. If DEMs are inaccessible for your

area of interest, the United States Geological Survey

(USGS) Fire Data Ordering System [12] provides a quick

and efficient way to obtain a DEM and other ancillary

layers.

	 Output from the WindWizard is wind velocity and

direction in vector(ArcVieworArcMap; one file)(fig. 5)

and raster (ASCII/Raster; two files) format. To view

vector format, download the instructional briefs from

the WindWizard website [see 5]. Import the raster data

for fire modeling in FlamMap by selecting New Run >

Inputs > Wind Grid. In FARSITE,select Input > Project

Inputs > Select Wind File (.ATM). The atmosphere file

(ATM) is a text file specifying month, day, hour, wind

speed, direction, and cloud cover. It must be precisely

formatted to function in FARSITE (refer to FARSITE

Help).

[11] Western Regional Climate Center: www.wrcc.dri.edu/index.

html

[12] USGS, Fire Data Ordering System: http://firedata.cr.usgs.gov/
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-183. 2006

Figure 4—Wind rose from the Western Regional Climate Center for Horton Peak RAWS (ID), 

July 1 to Oct. 31, 1985 to 2004. Observations are hourly from 1100 to 1800.

Spatial Data Acquisition and Development:

The FARSITE Landscape File

	 At the beginning of any landscape project, one should

develop a spatial needs analysis—a list of required and

ancillary GIS information requisite for completion of

the task. Table 1 provides an extensive list of GIS data

for landscape wildland fire analysis. Central to this

type of work is the accurate parameterization of the

fire environment. The most common file structure for

spatialfire analysisistheFARSITE/FlamMap landscape

file or LCP, a single file consisting of elevation, slope,

aspect, fuel model (Anderson 1982, Scott and Burgan

2005), and canopy cover(CC)—required inputs—aswell

as optional themes of stand height (SH), crown base

height (CBH), crown bulk density (CBD), duff loading,

and coarse woody debris (Finney 1998). The standard

practice is to create an LCP using all of the required

files and the three canopy characteristic files. Development of the optional themes may not be necessary in

areas where crown fire spread is of little concern (for

example, hardwood forests). Each GIS layer needs to be

exported as an ASCII Raster to create the LCP file.

	 Data required to create an LCP come from a DEM

(elevation,slope, aspect) andsatellite imageryorremotely

sensed data that are usually classified by vegetation

type and attributed. DEMs are easily obtained from

the USGS; fuel model, canopy cover, and canopy characteristic information is more problematic. Presently,

several Federal lands have FARSITE data accessible by

contacting the host agency. LANDFIRE, a nationwide

fire, fuel, and vegetation mapping project, will begin

rollout ofwestern productsin 2006, including FARSITE

data, with final coverage of the United States in 2009

(Alaska). [13] Irrespective of the source of the FARSITE

data, LCPsshould be thoroughly critiqued and mapping

assumptions and limitations understood to ensure proper

use and to produce realistic results.

[13] LANDFIRE: www.landfire.gov
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-183. 2006

Critique of the Landscape File

Errors in creating the LCP file—There are several

common errors in the creation of the FARSITE landscape file. To identify these errors, load the LCP in

FARSITE or FlamMap. Critique the LCP by querying

the landscape and viewing the dialogue box looking for

common oversights (listed below). View the LCP file

generation box looking for errors including (1) a “0”

latitude, (2) the loading of incorrect ASCII Raster data,

and(3)distance andASCIIRasterthemeunitsincorrectly

specified. If creating the LCP from individual ASCII

Rasterfiles, an additional check is done by opening each

file in WordPad© and checking the extent, resolution,

and grid values.

Common oversights in ASCII Raster data—There

are several common missteps in FARSITE layer development. Often, CC values are too high and CBD

and CBH are inaccurate. CC rarely exceeds 70 percent

even in so-called closed-canopy forests (see Scott and

Reinhardt [2005]). If excessive CC values exist within

the modeling domain, rate of spread will be reduced

due to the sheltering effect of the tree canopy.

Figure 5—Gridded wind (100 m) derived from WindWizard based on 30+ mph ridgetop winds from the northeast. Wind vectors 

are colored by speed with 0 to 7 mph (blue), 8 to 14 (green), 15 to 22 (yellow), 23 to 29 (orange), and 30+ (red). The Blossom 

Fire (14,908 acres) (Aug. 12, 2005; Agness, OR) perimeter is overlaid on a shaded relief with trails (black) and the Rogue River 

to the south (blue).
10 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-183. 2006

Table 1—GIS data requirements and considerations for spatial wildland fire 

analysis.

FARSITE/FlamMap Landscape File

Canopy cover

Crown base height

Crown bulk density

Digital elevation model (DEM)

Fuel model

Stand height

Wildland Fire History

Fire atlas (perimeters)

Fire occurrence (points)

Fire progression layers

Fuel treatments and other stand modifications

Prescribed fire history

Ecological Considerations

Areas of critical environmental concern (ACEC)

Erosive soils

Fire regime condition class (FRCC)

Historical range of variability (HRV)

Invasive weeds

Old growth

Proper functioning condition (PFC)

Research sites (for example, research natural areas [RNA])

Rivers and streams

Sensitive or critical wildlife habitat (for example, winter range, riparian)

Threatened, endangered, and sensitive (TES) flora and fauna habitat

Timber products

Water bodies

Watersheds of concern (6th code HUC particularly useful)

Socio-economic Considerations

Bridges

Camps

Communication sites

Concentrated use areas (CUA)

Cultural resources

Grazing allotments

Historic and recreational sites

Mine and mineral claims

Municipal water supplies and infrastructure

Primary and secondary residences

Railroads

Remote automated weather stations (RAWS)

Roads

Scenic integrity

Ski areas

Structures - other (for example, highway maintenance buildings, rest areas)

Trails and trailheads

Utility corridors

Wildland-urban interface areas

Other Useful Base Layers

Aerial photos

Coordinate system (for example, UTM, Lat/Long)

Digital orthophoto quads (DOQ) or quarter quads (DOQQ)

Digital raster graph (DRG)

Ownership and jurisdictions (for example, private lands)

Public land survey system (PLSS)

Satellite imagery

Shaded/painted relief

Vegetation or cover-type classification
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-183. 2006 11

	 Field measurements of CBD rarely exceed 0.25 kg m

–3

(for example,spruce-fir)(Scott andReinhardt2005),butto

yieldrealisticcrownfireestimatesinFARSITE/FlamMap,

these upper values often need to be increased to 0.4+ if

the default crown fire calculation method (Finney 1998)

is used. If CBD values are generated from FVS/FFE or

FMA Plus(Carlton 2005),select the Scott and Reinhardt

(2001) crownfire calculationmethod.Particular attention

shouldbegiventoCBH.Ifvalues are toohigh,particularly

when coupled with a modest fuel model (for example,

FM 8), crown fires will seldom initiate.

Critique of Fire Behavior Outputs

	 An excellent way to critique fire behavior outputs is

by using FlamMap. Considersimulating low, moderate,

and severe conditions using both crown fire calculation

methods(Finney 1998, Scott andReinhardt 2001).Compare output from each simulation and perform sensitivity analyses by asking questions such as: Is the flame

length, rate ofspread, and intensity consistent with field

observations? Are areas of crown fire initiation appropriate given fuel and weather conditions? Are passive

(torching) and active crown fire areasreasonable?When

you see things that are not quite right, remember “Don’t

Be DUM”—data, user, model. We are often too quick

to accuse the model when the data are the source of the

problem (as taught in S-493 [FARSITE]). In general, if

modifications of the spatial data are needed, adjust the

fuel model layer first, followed by CBH and CC. The

FARSITE landscape calculator is an excellent way to

make changes to an LCP without GIS assistance.

Flame length and fireline intensity—Flame length

is calculated from fireline intensity—the product of the

rate ofspread and heat generated from the available fuel

during flaming combustion (Byram1959).InFARSITE/

FlamMap, Byram’s equation is used to predict surface

fire flame length; Thomas’ (1963) equation is used to

predict flame length for passive and active crown fire

(Rothermel 1991).

	 Since flame length is defined and measured forshallow

surface fires,flame dimensionsin crown fires or deep fuel

beds may appear to an observer to be greater than the

calculated values. Flames generated from aerial fuels are

viewed relative to the ground but originate well above the

surface. So keep in mind that “flame length is an elusive

parameter that existsin the eye of the beholder. It is a poor

quantity to use in a scientific or engineering sense, but it

is so readily apparent to fireline personnel and so readily

conveys a sense of fire intensity that it is worth featuring

as a primary fire variable” (Rothermel 1991).

Crown fire occurrence—Crown fire initiation

is dependent on several factors, including surface

fireline intensity, canopy foliar moisture, and CBH

(Van Wagner 1977). CBH is used to determine if

torching occurs. CBD affects transition to an active

crown fire. If modeled output is inconsistent with

field observations (for example, an under-prediction

of active crown fire) (Cruz and others 2003, Fulé and

others 2001), modifications to fuel model, CBH, CBD,

or windspeed may be necessary. Also, lowering the

foliar moisture content—a landscape wide adjustment

in FARSITE/FlamMap—will increase the occurrence

of crown fire.

Model Calibration

	 Calibration is critical to any landscape analysis. To

produce fire growth and behavior outputs consistent

with observations, model checking, modifications, and

comparisons are done with known fire perimeters and

weather conditions (Finney 2000). If the model has not

beenproperlycalibratedtolocalfires,howcananalystsor

managers have any confidence in its output?Conversely,

when fire behavior outputs have been critiqued and the

model calibrated to previous large fires, one can have a

higher degree of confidence in future simulations. For

the purposes of this paper, calibration of the FARSITE

model is discussed. However, a similar approach can be

usedtocalibrateothermodelingsystems,suchasRERAP

(Spread Module) and FSPro (Finney, in preparation).

	 Rate ofspread isthe most common fire behavior variable calibrated. Where appropriate, calibration of fire

type may be important for fire effects modeling and to

accurately differentiate between surface fire spread rate

(includes torching) and active crown fire spread. Field

observations, still and video photography from aerial

reconnaissance, and the National Park Service/USGS

National Burn Severity Mapping Project can be used

to calibrate fire type [14]. Consider selecting one fire or

run to calibrate the model under moderate conditions

and another forsevere conditions(fig. 6) with and without a high wind event. In order of importance, criteria

fundamental to any calibration exercise include:

	 •	 a carefully developed and critiqued LCP;

	 •	 progression layer or detailed field observations

that identify the position and time of the fire (for

example, field notes, dispatch logs), including a

precise starting location;

[14] Burn Severity Mapping Project: http://burnseverity.cr.usgs.gov
12 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-183. 2006

Figure 6—Calibration of the FARSITE model (in red; 16,686 

acres) to a 1-day crown fire run on the northeast head of the 

Sanford Fire (June 8, 2002; Panguitch, UT). Overlaid on slope 

are trails (green), roads (black), and the fire perimeter (blue).

	 •	 a fire of a sufficient size and/or includes several

burn periods;

	 •	 ample and representative weather and wind information (for example, RAWS[s] nearby);

	 •	 a fire that burned in several different fuel models

and varying terrain;

	 •	 accurate fuel moisture information;

	 •	 a fire with minimal suppression or knowledge of

suppression tactics;

	 •	 a fire that burned under a variety of weather and

wind conditions; and

	 •	 a fire that resulted in both surface and crown fire runs.

	 Sufficient calibration takes time and patience. Do

not expect perfection given model assumptions and

limitations, data inaccuracies, fire suppression,

variability in the weather and wind, etc. Sometimes,

FARSITE will adequately predict the shape of the fire,

but not the timing of fire arrival. Every fire and LCP

is different, hence every calibration exercise will vary.

The following calibration tips usually hold true:

	 •	 Wind, weather, and fuel moisture files are crucial

so get these inputs as close to reality as possible.

Spending time here will isolate several variables

and streamline your calibration process. Make sure

the station elevation is within the fire area and fuels

are conditioned (if needed).

	 •	 Accurately define the burn period by the starting

and ending time of daily fire movement.

	 •	 Select the appropriate model parameters—usually

a coarser resolution at the start of your simulations

moving to a finer resolution as you get closer to

completion.

	 •	 If spotting contributed to the growth of the fire,

enable spot fire growth early on, but at a low

frequency (0.5 to 1 percent). If fire spread is predominately through spotting, adjustments to CBH

will likely be necessary.

	 •	 Make one modification at a time and then rerun the

simulation.

	 •	 Do not try to calibrate the entire fire at once. Start

with the first few hours or burn period and then

build from there. If the initial progressions are off,

it islikely the entire simulation willfollow—a result

of compounding error.

	 •	 For large fires, or where the origin or progressions

are lacking, use a reliable perimeter and begin

your calibration process there—watch for errors in

perimeter dates and times.

	 •	 A substantial change (~0.3 to 0.4) in the adjustment

file indicates a different fuel model may be needed.

Try using a conversion file, or as a last resort, a

custom fuel model.

	 •	 Adjust for the lack of extinction of the fire perimeter

after nightfall orrain.Thisisimportantin lightfuels,

where the fire will resume when the fuel moisture

drops.

	 •	 Personnelonthefirecanbeausefulresourceasmultiple

perspectives lead to corroboration of key events.

	 •	 Keepadetailedlogthroughoutthe calibrationprocess

of model settings, parameters, adjustments, etc.

Summary_ ______________________

	 This paper has been written to provide direction

to fire specialists in spatial wildland fire analysis. It

is a product of seven years of experience in support

of incident management, fuel treatment projects, risk

assessments, and reviews/investigations. Figure 7 is a

flowchart that tiesthe tools and programsto the analysis

process. Although “applications” are listed on the right

(the end), the research or management question should

dictate model and tool selection. When a modeler is

familiarwithmodel assumptions,limitations, anddesign;

completes a comprehensive fire and weather analyses;
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-

183. 2006

13

Figure 7—Flow chart for spatial wildland fire analysis: design, tools, and application.
14 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-183. 2006

carefully critiques the LCP and resultant fire behavior

output; calibratesthemodel; and performsthe necessary

analyses, then line officers, incident commanders, and

fire specialists have com­pelling and useful analyses for

improved decision making, documentation, and public

outreach and education.

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