Wildfire Risk and Hazard: 

Procedures for the First 

Approximation

United States

Department

of Agriculture

Forest Service

Rocky Mountain

Research Station

General Technical

Report RMRS-GTR-235

March 2010
Calkin, David E.; Ager, Alan A.; Gilbertson-Day, Julie, eds. 2010. Wildfire risk and hazard: procedures 

for the first approximation. Gen. Tech. Rep. RMRS-GTR-235. Fort Collins, CO: U.S. Department 

of Agriculture, Forest Service, Rocky Mountain Research Station. 62 p.

Abstract

This report was designed to meet three broad goals: (1) evaluate wildfire hazard on Federal lands; 

(2) develop information useful in prioritizing where fuels treatments and mitigation measures might be 

proposed to address significant fire hazard and risk; and (3) develop risk-based performance measures to document the effectiveness of fire management programs. The research effort described in 

this report is designed to develop, from a strategic view, a first approximation of how fire likelihood 

and fire intensity influence risk to social, economic, and ecological values at the national scale. The 

approach uses a quantitative risk framework that approximates expected losses and benefits to highly 

valued resources from wildfire. Specifically, burn probabilities and intensities are estimated with a fire 

simulation model and are coupled with spatially explicit data on human and ecological values and 

fire-effects response functions to estimate the percent loss or benefit. This report describes the main 

components of the risk framework, including the burn probability models, highly valued resource 

data, and development of response functions, and illustrates the application to the State of Oregon. 

The State of Oregon was selected for prototype due to the wide range of variability in ecoregions 

represented in the state. All of the highly valued resource themes were represented in the mix of 

developed and natural resources present in the state. National risk and hazard approximation results 

for the Continental United States are available at the following location: www.fs.fed.us/wwetac/wflc/.

Keywords: wildland fire, risk management, wildfire hazard, highly valued resources, net value change

Acknowledgments

We would like to thank the following Federal agency fire and fuels program management officials 

for their recommendations on the assignment of response functions and valuation of highly valued 

resource layers: Tim Sexton (FS), Rick Prausa (FS), Rich Lasko (FS), Peter Teensma (DOI), John 

Segar (FWS), Fred Wetzel (FWS), Erik Christiansen (FWS), Roy Johnson (BLM), Dan Buckley (NPS), 

and Dennis Dupuis (BIA). Additionally, we thank Claire Montgomery (Oregon State), Miles Hemstrom 

(FS), and James Menakis (FS) for providing science review and Donna Scholz (BLM) and Fred Wetzel 

(FWS) for their fire management-perspective review. Their insightful and thorough comments greatly 

improved this work. We also thank Jack Waide (USGS) for his review of many recovery plans and 

critical habitat designations to identify fire-susceptible species and Allison Reger (FS) for her help with 

the document graphics.

You may order additional copies of this publication by sending your 

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

Please specify the publication title and number.

Publishing Services

Telephone (970) 498-1392

FAX (970) 498-1122

E-mail rschneider@fs.fed.us

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

Mailing Address Publications Distribution

Rocky Mountain Research Station

240 West Prospect Road

Fort Collins, CO 80526
i

Co-Authors

David E. Calkin is a Research Forester in the Human 

Dimensions Program located at the Rocky Mountain 

Research Station in Missoula, MT. He develops processes 

and tools for assessing economic effects of wildland fire 

and fuel management activities.

Alan A. Ager is an Operations Research Analyst at the 

Western Wildlands Environmental Threat Assessment 

Center, Pacific Northwest Research Station in Prineville, 

OR. He develops landscape tools for fuel treatment planning and wildfire risk issues.

Julie Gilbertson-Day is a GIS Analyst/Research 

Specialist for the College of Forestry and Conservation 

at the University of Montana. She is a cooperator with the 

National Fire Decision Support Center and researches 

data issues related to wildland fire and fuel management 

activities.

Joe H. Scott is a fire modeling specialist with Pyrologix, 

a fire management consultancy based in Missoula, MT. 

He specializes in the characterization of wildland fuel 

complexes, simulation of fire growth and behavior, and 

quantitative assessment of hazard and risk.

Mark A. Finney is a Research Forester at the Fire 

Sciences Laboratory, Rocky Mountain Research Station 

in Missoula, MT. His research interests are fire behavior, 

fire growth modeling, and landscape fuel management.

Charlie Schrader-Patton is a Remote Sensing/GIS 

Analyst and Applications Developer for RedCastle 

Resources, Inc., in Bend, OR. He provides geospatial support to the FS Remote Sensing Applications Center and 

the FS Western Wildlands Environmental Threat Center.

Thomas M. Quigley is a Senior Advisor for Natural 

Resource Management and Science, Management and 

Engineering Technologies International, Inc., in Saratoga 

Springs, UT. He primarily works at the policy interface 

between science and management with emphasis on 

fire related issues.

James R. Strittholt is President of the Conservation 

Biology Institute, a 501(c)3 non-profit based in Corvallis, 

OR, dedicated to applied conservation science. His expertise includes landscape ecology, conservation planning, 

and computer mapping technologies.

Jeffrey D. Kaiden is a GIS Analyst employed by the 

College of Forestry and Conservation at the University of 

Montana assigned as a cooperator for the Rocky Mountain 

Research Station in Missoula, MT. He is a cooperator with 

the National Fire Decision Support Center and conducts 

research on the Wildland-Urban Interface.
ii

Contents

Introduction....................................................................................................1

Relationship to Other Fire Management Planning Efforts...................2

Methods...........................................................................................................2

Overview..............................................................................................2

Estimating Wildfire Likelihood and Intensity......................................3

Highly Valued Resource Data Layers Acquisition...............................5

Energy Infrastructure ...........................................................................6

Power Transmission Lines.......................................................6

Oil and Gas Pipelines...............................................................7

Power Plant Locations .............................................................7

Cellular Tower Point Locations ...............................................7

Federal Recreation and Recreation Infrastructure ...............................7

United States Forest Service Campgrounds.............................7

Ranger Stations........................................................................7

Bureau of Land Management (BLM) Recreation Sites 

and Campgrounds.............................................................7

National Park Service Visitor Services and Campgrounds......7

FWS Recreation Assets............................................................8

National Scenic and Historic Trails .........................................8

National Alpine Ski Area Locations ........................................8

Fire-Susceptible Species......................................................................8

Designated Critical Habitat......................................................8

National Sage-Grouse Key Habitat..........................................9

Air Quality ...........................................................................................9

Municipal Watersheds..........................................................................9

Fire-Adapted Ecosystems ....................................................................9

Residential Structure Location.............................................................9

Other Biological Values Considered for Inclusion ............................10

Categorizing Highly Valued Resources.............................................11

Response Functions ...............................................................12

Generating Risk Maps and Calculating Outputs................................16
iii

Results...........................................................................................................18

Area of Highly Valued Resources by FPU in Oregon .......................18

Burnable Area by FPU.......................................................................18

Area in HVR Categories by FPU.......................................................18

Response Functions Assignment and Initial Value Categories..........18

Burn Probability Outputs from Wildfire Modeling............................23

Wildfire Hazard..................................................................................23

Wildfire Risk Calculations.................................................................24

Risk Calculations Without Sage-Grouse............................................29

Discussion......................................................................................................29

Wildfire Hazard..................................................................................29

Wildfire Risk......................................................................................30

Resource Value Issues........................................................................35

Data Direction and Roles...................................................................36

Understanding Fire Effects on Highly Valued Resources..................37

Conclusion ....................................................................................................37

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

Appendix A ...................................................................................................40

Appendix B...................................................................................................61

Appendix C...................................................................................................62 

USDA Forest Service Gen. Tech. Rep. RMRS-GTR-235. 2010 1

Introduction ____________________

Reviews have been conducted by Federal oversight 

agencies and blue ribbon panels to identify causal factors 

of the unprecedented fire suppression costs and to suggest 

possible modifications to Federal fire management policy 

and strategies (USDOI, USDA 2004; USDAOIG 2006; 

GAO 2007, 2009). Agency and panel member reviews 

have found that Federal agencies with wildland fire 

responsibilities are not able to quantify the value of fire 

management activities in terms of reducing wildfire risk 

to social, economic, and ecological values. In response, the 

Wildland Fire Leadership Council’s (WFLC) monitoring 

strategy asked: What are the trends and changes in fire 

hazard on Federal lands? Fire risk assessment requires an 

understanding of the likelihood of wildfire by intensity 

level and the potential beneficial and negative effects to 

valued resources from fire at different intensity levels.

This monitoring study was conducted to meet three 

broad goals: (1) address the WFLC monitoring question regarding fire hazard on Federal lands; (2) develop 

information useful in prioritizing where fuels treatments 

and mitigation measures might be proposed to address 

significant fire hazard and risk; and (3) respond to critiques by Office of Management and Budget, General 

Accounting Office, and Congress that call for risk-based 

performance measures to document the effectiveness of 

fire management programs. The results of this monitoring study are useful for project planning to quantify the 

potential effects of proposed actions in terms of reducing 

risk to specific resources of concern.

Developing decision support tools that utilize an 

appropriate risk management framework would 

address many of the issues identified within government 

oversight reports. Specifically, the Office of Inspector 

General (USDAOIG 2006) reviewed USDA Forest 

Service (FS) large fire costs and directed that the “FS 

must determine what types of data it needs to track in 

order to evaluate its cost effectiveness in relationship to its 

accomplishments. At a minimum, FS needs to quantify 

and track the number and type of isolated residences and 

other privately owned structures affected by the fire, the 

number and type of natural/cultural resources threatened, 

and the communities and critical infrastructure placed 

at risk.”

The application of fire risk and fire hazard analyses has 

been demonstrated at the watershed and National Forest 

scales (Ager and others 2007). There, specific details 

regarding probabilities of fire and fire intensity are linked 

with specific resource benefit and loss functions (Ager and 

others 2007). Expanding these detailed analyses to regional 

and national scales to provide consistent risk assessment 

processes is complicated by the required data specificity 

and difficulty in developing loss-benefit functions for 

the range of human and ecological values. The research 

effort described in this report is designed to develop, from 

a strategic view, a first approximation of how both fire 

likelihood and intensity influence risk to social, economic, 

and ecological values at the national scale. The approach 

uses a quantitative risk framework that approximates 

expected losses and benefits from wildfire to highly valued 

resources (HVR).

The information gathered in this study can be summarized in tabular and map formats at many different scales 

using administrative boundaries or delineations of HVR 

such as built structure density. The overall purpose of 

the analysis is to provide a base line of current conditions 

for monitoring trends in wildfire risk over time. Future 

analyses would be used to determine trends and changes 

in response to fuel reduction investments, climate shifts, 

and natural disturbance events (e.g., bark beetles) between 

the timeframes analyzed. Monitoring data could be used 

to address national and regional questions regarding 

changes in fire risk and hazard as a result of investment 

strategies or changing conditions. While similar analyses 

could be conducted for alternative scenarios, this work is 

designed to develop the base line hazard and risk situation.

Wildfire Risk and Hazard: 

Procedures for the First Approximation

David E. Calkin, Alan A. Ager, Julie Gilbertson-Day, Joe H. Scott, Mark A. Finney, 

Charles Schrader-Patton, Thomas M. Quigley, James R. Strittholt, and Jeffrey D. Kaiden
2 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-235. 2010

Relationship to Other Fire Management 

Planning Efforts

This study is closely related to two large interagency 

national fire management planning and decision support 

efforts: Fire Program Analysis (FPA) and the Wildland 

Fire Decision Support System (WFDSS).

The FPA system is a common interagency strategic 

decision support tool for wildland fire planning and 

budgeting (www.fpa.nifc.gov). The research described in 

this report uses wildfire simulation outputs from FPA to 

quantify wildfire likelihood and intensity. FPA wildfire 

simulations include geospatial data, which provide the 

means to map levels of wildfire risk on analyzed lands 

within the United States. FPA provides managers with 

tradeoff analysis tools for strategic planning and budgeting 

to support comprehensive, interagency fire management 

programs. FPA is tasked to evaluate the effectiveness 

of fire management strategies for meeting fire and land 

management goals based on the following five performance metrics:

(1) Reducing the probability of costly fires

(2) Reducing the probability of costly fires within the 

Wildland-Urban Interface

(3) Increasing the proportion of land meeting or trending toward attaining fire and fuels management 

objectives

(4) Protecting HVR areas from unwanted fire

(5) Maintaining a high initial attack success rate

The initial FPA model used a weighting system called 

Expert Opinion Weighted Elicitation Process (EOWEP) to 

guide resource allocation decisions (Rideout and Ziesler 

2004). However, the EOWEP value process was dropped 

and subsequent efforts on understanding the effects of 

fire management resource allocation on the protection of 

HVR have been limited.

This first approximation is designed to offer data and 

preliminary categorical ranking of HVR for evaluation of 

FPA’s performance metric (4): Protecting HVR areas from 

unwanted fire. Additionally, this effort may provide a 

more accurate spatial representation of human population and development than available in the Silvis WUI 

layer (http://silvis.forest.wisc.edu/projects/WUI_Main.

asp) currently used to evaluate FPA’s metric (2): Reducing 

the probability of costly fires within the Wildland-Urban 

Interface.

The National Fire and Aviation Executive Board 

chartered the WFDSS project in June of 2005 “to 

develop a scalable decision support system for agency 

administrators that utilizes appropriate fire behavior 

modeling, economic principles and information technology to support effective wildland fire decisions 

consistent with Resource and Fire Management Plans.”

The economic effects module of WFDSS is known 

as the Rapid Assessment of Values At Risk (RAVAR) 

(www.fs.fed.us/rm/wfdss_ravar/). Critical infrastructure 

(residential structures, major power lines, communication towers, etc.) and highly valued natural and cultural 

resources (endangered species habitat, critical watersheds, 

etc.) are spatially identified and linked to output from 

the Fire Spread Probability (FSPro) model that identifies 

the probability of a fire reaching a given point on the 

landscape over a period of time (typically 7 to 14 days) 

in the absence of fire suppression. Because RAVAR was 

developed to respond to the individual event, national data 

consistency is desirable but not required. A primary data 

set within the critical infrastructure is the Structures layer, 

which is developed from county level cadastre data. The 

Structures layer was developed in coordination with the 

Federal Geographic Data Committee’s National Cadastral 

Subcommittee. Committee members intended to acquire 

spatial tax record data from each county in the western 

United States. To date, approximately three-quarters of 

those counties have provided data to the WFDSS team. 

However, full national coverage does not yet exist because 

many counties, some of which are rural and have low total 

county income, have not defined their tax records in geospatial format. In the absence of these records, structure 

data for RAVAR are acquired in real time for a given event 

from remotely sensed image interpretation conducted by 

the U.S. Geological Survey’s Rocky Mountain Geographic 

Science Center.

Fire intensity, an important predictor of fire effects, is 

currently calculated within FSPro; however, due to the 

stochastic nature of the model, interpretation is not obvious 

and appropriate use of these data is being studied. Within 

RAVAR, only resource presence is identified, not information regarding potential benefits or losses. Therefore, 

the effect of wildfire on the identified resources is left to 

local interpretation.

Methods _______________________

Overview

Three main components were combined to generate 

wildfire risk outputs, namely (1) burn probability generated from wildfire simulations, (2) spatially identified 

HVR, and (3) response functions that describe the impact 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-235. 2010 3

of fire on the HVR. The components were combined in a 

risk framework modified from Finney 20051 to calculate 

Expected Net Value Change:

[formula 1]

where

p(Fi

) = probability of a fire at intensity i, and

RFij= “response function” measuring the net change to 

value j from fire intensity i.

Thus, risk is the product of the burn probability at a 

given fire intensity (p(Fi

)) and the resulting change in 

value (RFij) summed over all possible fire intensities and 

1

The original specification developed by Finney represented benefits 

and losses separately at individual flame lengths to the specified 

resource values; whereas we evaluate net change in aggregate using 

a “response function” RFij.

values. Calculating risk at a given location (according 

to formula 1) requires spatially defined estimates of the 

likelihood and intensity of fire interacted with identified 

values. This interaction is quantified through the use of 

response functions that estimate expected benefits and 

losses to resources at the specified intensities. This monitoring study has been designed for analysis on a national 

scale; however, protocols were tested and reported here 

as a prototype only for the State of Oregon. Oregon was 

selected because of its wide range of variability in ecoregions and diversity of HVR.

Estimating Wildfire Likelihood and Intensity

The risk assessment was conducted on a pixilated 

landscape made up of 886- by 886-ft (approx. 270- by 

270-m) pixels, consistent with methodology developed 

for the FPA project. Analyses were conducted at the 

individual Fire Planning Unit (FPU) and results were 

developed for the 11 FPUs contained in Oregon (figure 1).

Figure 1—Map of FPUs in Oregon and adjacent states. This study covers only FPUs that are assigned to Oregon, as denoted 

in the FPU numeric codes. 
4 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-235. 2010

Fire simulation was used to produce estimates of burn 

probabilities and fire intensity distributions (the first 

component of this hazard and risk model) following 

methods developed for the FPA project. Specifically, the 

wildfire simulation model FSim (Finney 2007) was used to 

estimate annual burn probability for each 886- by 886‐ft 

(or approx. 18-acre) pixel. The FSim program features:

(1) sensitivity to seasonal and daily weather and ignition variability by geographic area;

(2) effect of fuels and topography on large fire spread, 

which are the primary drivers of wildfire threat in 

much of the United States;

(3) spatial effects of fuel treatments on local and offsite 

values; and

(4) fire intensity distributions that reflect both weather 

and fire spread variations from multiple ignition 

sources, which are essential to estimating fire 

effects.

Results can be compared to historical average burn 

probabilities and fire size distributions. Such comparisons provide a means to check that simulation outputs 

are realistic.

Fire simulations with FSim were performed individually for each of the 139 FPUs within the United States. 

Simulations for each FPU were parameterized to reflect 

differences in historical fire management activities as well 

as climatic and ignition variability. The FSim simulation 

process was conducted as follows (Finney 2007):

(1) A representative Remote Automated Weather 

Station (RAWS) was selected for each FPU. Daily 

historical weather was processed to obtain Energy 

Release Component (ERC) as implemented in the 

National Fire Danger Rating System. Selected 

weather stations had a minimum of 20 years of 

weather records and were judged to best reflect fire 

weather and seasonal and daily climatology for the 

FPU. We recognize there is considerable heterogeneity in weather conditions within many FPUs, and 

more robust modeling results would probably be 

obtained if the larger FPUs were further subdivided 

or if multiple weather stations were incorporated 

into the wildfire modeling. Neither of these options 

was feasible in this study.

(2) Weather data were analyzed to generate 20,000 

to 50,000 artificial fire seasons (more artificial 

seasons were required for FPUs with historically 

smaller fires or lower fire frequency). Specifically, 

daily ERC values for 20 years of weather data were 

subjected to a Time Series Analysis that derived 

estimates of the seasonal trends, the auto correlation 

(dependency of a day’s ERC value on previous 

days), and the daily standard deviation. Wind data 

(speed by direction) were also derived and tabulated 

by month as a joint probability distribution, which 

was used to stochastically assign wind data to each 

day in each of the simulated fire seasons.

(3) A logistic regression was developed for each FPU 

that predicted the probability of a large fire based 

on the historical ERC-large fire relationship.

(4) The daily occurrence of large fires was then 

simulated using ERC-large fire relationship and the 

simulated fire season data (step (2)). We assumed 

random ignition location. While wildfire ignitions 

are not a random spatial process, in general there 

is a weak relationship between burn probability 

and ignition location, mostly because the bulk of 

the area burned is by relatively few large fires that 

spread over long distances.

(5) A fire suppression model was developed and 

employed to determine containment probability 

based on historic large fires and their recorded 

sequence of daily activity (Finney and others 2009). 

This model was developed by analyzing change in 

daily fire size from 306 fires occurring from 2001 

to 2005. The analysis identified intervals of high 

spread and low spread for each fire. The containment probability model was found to be positively 

related to periods of low fire spread. The model was 

tested against independent data from 140 fires in 

2006 (Finney and others 2009).

(6) Each fire’s growth and behavior was simulated 

from its ignition day through the remainder of 

the season or until containment was achieved as 

predicted by the suppression model (step (5)).

(7) Each pixel’s annual burn probability and the marginal probability of a fire at specific flame length 

categories were then calculated. The flame length 

categories were 0 to 2, greater than 2 to 6, greater 

than 6 to 12, and greater than 12 ft.

The FSim program uses the minimum travel time fire 

spread algorithm (Finney 2002), which is optimized for 

processing large numbers of fires. Simulations were completed on a bank of computers located at the EROS Data 

Center in Sioux Falls, SD. Results were assembled by FPU 

and by geographic area (aggregates of FPUs).

A number of validation exercises were completed as part 

of the FPA project, including a comparison of predicted fire 

size distributions with historic data from selected FPUs 

(Finney 2009). Initial outputs revealed sharp transitions 

between burn probabilities on the boundaries of adjacent 

FPUs, which were possibly caused by limitations of this 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-235. 2010 5

initial attempt to generate FPU-specific fire size distributions from historic weather and perhaps also from highly 

variable RAWS data among adjacent FPUs. To smooth 

inter-FPU differences, the burn probability estimates were 

normalized using historic burn probability data for each 

FPU calculated from historic fire size data. The normalization involved multiplying the simulated burn probability 

by the ratio of the simulated to historical burn probability.

The FSim probability and flame length model results 

were then used in risk calculations (formula 1) to calculate 

the expected net value change to HVR.

Highly Valued Resource Data Layers 

Acquisition

The second component of the risk assessment model 

involved identification of highly valued resource (HVR) 

data layers. Five HVR categories were originally proposed by the FPA Executive Oversight Group, including 

natural and cultural resources and critical infrastructure. 

These categories were initially critical habitat (refined to 

fire-susceptible species), Federal recreation infrastructure, energy infrastructure, air quality, and municipal 

watersheds. Within these categories, data layers were 

chosen based on availability at a national scale and HVR 

representation upon which fire management decisions 

are made. The categories proposed were not intended to 

represent the full suite of resource layers considered to be 

of high value. Instead, categories were chosen based on 

available data to make a first attempt at approximating 

national HVR data sets. Fire-adapted ecosystems and built 

structures were later identified as nationally consistent data 

sets and were included as two additional data categories, 

making seven total for this exercise.

Despite wide interest in these specific data for many 

other wildland fire assessment projects, there remain significant challenges to acquire, assemble, and reconcile these data 

for national wildfire risk analyses. The “Discussion” section 

(p. 29) of this study addresses the challenges and issues 

associated with HVR data acquisition at the national scale.

Initial efforts to locate data sets included exploring 

national Internet portals such as Geospatial One Stop 

and the National Atlas project (www.nationalatlas.gov). 

However, many of the data layers requested or proposed 

as HVR themes were not available from these and other 

enterprise sources. Generally, this was either because the 

data release required a measure of security not offered in 

a Web portal or because data are managed by a Federal 

land management agency that has not mapped the 

resource to a national extent.

Much of the available enterprise data lacks the attribution necessary to distinguish between resources of high 

value in relative scarcity versus those of low value found 

in abundance. This issue is particularly common among 

such data sets as recreation infrastructure and Federal 

structures. For example, highly developed campgrounds 

on Federal lands hold value both in their developed 

infrastructure and in their attraction to visitors, thereby 

providing recreation income to the managing agency and 

neighboring communities. In the data sets acquired for these 

resources, attribution was limited or did not exist to help 

distinguish between lightly used primitive backcountry 

campsites and those with high visitation and significant 

infrastructure investments.

Attribution and data completion consistency often vary 

geographically. For example, within the National Park 

Service (NPS) Visitor Services data, Federal buildings 

and visitor centers are missing from certain National 

Parks where they are known to exist. However, other Parks 

appear to have complete representation. Additionally, in the 

Visitor Services data set, some Parks distinguish between 

developed and primitive campsites, while others do not. 

The National Scenic and Historic Trails data set available 

from the NPS GIS Data Store is not a complete representation of all trails designated National Scenic or National 

Historic (www.nps.gov/nts/nts_trails.html) because many 

are missing from the available GIS data set. Multiple 

data contacts, including the publishers of these maps 

and brochures documenting the complete National Trails 

System, indicated that some mapped trails are cartographic 

renderings that do not exist in geospatial form.

Some data sources became available only after 

we obtained additional access and security clearance. Databases such as the Homeland Security 

Infrastructure Program (HSIP), a large database of 

national infrastructure GIS data sets, are available 

only by granted access. Some additional data sources 

not accessible through Web sites and Web portals 

are the NPS Facility Maintenance Software System 

that contains NPS building information and locations, the Natural Resources Conservation Service 

that provides access to sixth order Hydrologic Unit 

Codes, and the National Pipeline Mapping System that 

contains national oil and gas pipelines information.

Despite the challenges of using national enterprise data 

sets, this remains the best approach for a first approximation of a national monitoring exercise. Data collected 

for this exercise were obtained from a combination 

of sources, including enterprise databases, data clearinghouses and servers, and local data aggregated to the 

national scale. Many of the data sets required augmentation from other sources, while others appeared to require 

relatively little, if any, processing.
6 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-235. 2010

Table 1 displays the list of HVR layers included in this 

monitoring exercise, and the following discussion addresses 

data set sources and any known errors or limitations for 

using the data in a project of this nature.

Energy Infrastructure

The energy infrastructure category is made up of four 

data layers: power lines, oil and gas pipelines, power 

plants, and cell communication towers. Each sub-category 

is reviewed in detail below.

Power Transmission Lines—This layer is comprised 

of power line data from both the HSIP database and the 

Cartographic Feature Files (CFF) from the FSGeodata 

Clearinghouse. Global Energy Decisions created the power 

transmission line data set from HSIP for use by agencies, offices, and contractors of the Federal government. 

The FSGeodata Clearinghouse (http://svinetfc4.fs.fed.

us/clearinghouse/index.html) provides access to CFF 

line files, from which records labeled “power lines” were 

extracted. The CFF are point and line features digitized 

from FS Primary Base Series maps. Their coverage is 

primarily limited to FS lands.

Table 1—Data layers acquired for first approximation of HVR.

HVR theme Sub-layer within theme Source

Energy Infrastructure

Power Transmission Lines Homeland Security Infrastructure Program 

Oil and Gas Pipelines National Pipeline Mapping System

Power Plant Locations Homeland Security Infrastructure Program 

Cellular Tower Point Locations Federal Communication Commission 

http://wireless.fcc.gov/geographic/index.htm

Federal Recreation 

and Recreation 

Infrastructure

FS Campgrounds USDA Forest Service (FS), FSGeodata Clearinghouse-Vector 

Data Gateway http://svinetfc4.fs.fed.us/vectorgateway/index.html

Ranger Stations ESRI Data and Maps 9.3

BLM Recreation Sites and CampgroundsGeoCommunicator 

http://www.geocommunicator.gov/GeoComm/index.shtm

NPS Visitor Services and CampgroundsNational Park Service (NPS) Data Store 

http://www.nps.gov/gis/data_info

FWS Recreation Assets USDI Fish and Wildlife Service (FWS)

National Scenic and Historic Trails NPS Data Store 

http://www.nps.gov/gis/data_info

National Alpine Ski Area LocationsNational Operational Hydrologic Remote Sensing Center 

http://www.nohrsc.noaa.gov/gisdatasets/

Fire-Susceptible 

Species

Designated Critical Habitat U.S. Fish and Wildlife Service Critical Habitat Portal 

http://crithab.fws.gov/

National Sage-Grouse Key 

Habitat Bureau of Land Management (BLM)

Air Quality

Class I Airsheds NPS Air Resources Division

http://www.nature.nps.gov/air/maps/receptors/index.cfm

Non-Attainment Areas for PM 2.5 

and Ozone

Environmental Protection Agency

downloaded from www.myfirecommunity.net

Municipal Watersheds Sixth Order Hydrologic Unit 

Codes Natural Resource Conservation Service 

Fire-Adapted 

Ecosystems Fire-Adapted Regimes LANDFIRE map products http://www.landfire.gov/

Residential Structure 

Location

Pixels Identified as Containing 

Built Structures LandScan USA
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-235. 2010 7

Because the CFF layer contained power lines on Federal 

lands that were not mapped in the HSIP data layer, both data 

sets were included in the final HVR data layer. Proposed 

power lines were eliminated from the HSIP data layer.

Oil and Gas Pipelines—Oil and gas pipelines are 

mapped by the National Pipeline Mapping System (NPMS). 

This data layer was obtained from the Defense Threat 

Reduction Agency’s Incident Command Water Modeling 

Tool (ICWater) database. The NPMS is a GIS data layer 

created by the U.S. Department of Transportation, Pipeline 

and Hazardous Materials Safety Administration Office 

of Pipeline Safety in cooperation with other Federal and 

State governmental agencies and the pipeline industry 

(USDOT 2007). The pipeline layer consists of line 

segments representing hazardous liquid transmission, 

liquefied natural gas plants, and hazardous liquid breakout 

tanks under the jurisdiction of the Pipeline and Hazardous 

Materials Safety Administration.

Power Plant Locations—Power plant locations were 

obtained from the HSIP database, and records for proposed plants were eliminated from the final HVR data 

set. This data set was created by Global Energy Decisions 

(for HSIP) for use by agencies, offices, and contractors of 

the Federal government. Records detailed generating 

stations and power plants producing hydroelectricity, 

geothermal, solar, and nuclear energy.

Cellular Tower Point Locations—Tower locations were 

obtained from the Federal Communication Commission’s 

Geographic Information Systems Web site (http://wireless.

fcc.gov/geographic/index.htm, accessed March 31, 

2009) for all towers for which the Commission collects 

information.

Federal Recreation and Recreation 

Infrastructure

The Federal Recreation and Recreation Infrastructure 

data layer consists of six sub-category data layers: FS 

Campgrounds and Ranger Stations, BLM Recreation 

Sites and Campgrounds, NPS Campgrounds and Visitor 

Centers, FWS Campgrounds, National Scenic and 

Historic Trails, and National Ski Areas. Each sub-category 

is reviewed in detail below.

United States Forest Service Campgrounds—These 

data were downloaded from the Geospatial Service and 

Technology Center, FSGeodata Clearinghouse’s Vector 

Data Gateway. The Miscellaneous Points layer was downloaded from this clearinghouse, and using the Cartographic 

Feature File metadata, points labeled with the FS campground feature number were extracted. Attribute records 

for FS campgrounds do not distinguish between developed 

and primitive campgrounds; therefore, all campground 

records are included in this HVR layer.

Ranger Stations—Though a national GIS data set of 

primarily FS ranger stations was desired as one sub-layer 

of the Federal Recreation HVR data layer, this data set 

could not be acquired from FS at the national extent. Yet 

the Environmental Systems Research Institute (ESRI) 

Data and Maps 9.3 database contains a data layer titled 

“glocale,” from which records containing the text “ranger 

stations” were extracted and added to the final Federal 

Recreation HVR layer. This data set contains ranger stations located primarily on NPS and FS lands. Records 

indicate both operational ranger stations and historic 

stations no longer in use.

Bureau of Land Management (BLM) Recreation 

Sites and Campgrounds—These data were obtained 

from GeoCommunicator’s National Integrated Land 

System (NILS) GIS Web service (www.geocommunicator.

gov, accessed August 1, 2008). Recreation Sites and 

Campgrounds are two separate data layers that were 

combined to create the BLM Recreation HVR layer. 

Attribute records defined certain BLM campgrounds 

as being unimproved, developed, or semi-developed. 

Because this attribution is absent from some agencies’ 

data sets, all records in the BLM Recreation Sites and 

Campgrounds data layers were included in the final HVR 

layer for consistency.

National Park Service Visitor Services and 

Campgrounds—NPS data were downloaded from the 

NPS Data Store (www.nps.gov/gis/data_info, accessed 

August 1, 2008). Selected HVR attributes from this data 

layer include Campgrounds, Headquarters, Lodges, 

Museums, Ranger Stations, and Visitor Centers. For this 

study, it was desired that campgrounds be limited to front 

country and developed campsites only. The attribution was 

not available in all Parks and Units; therefore, all records 

for the themes listed above are included in the final HVR 

data layer.

Additional limitations of this data layer occur in areas 

where resources are known to exist but are not included 

in the data set. For example, Glacier National Park lodges 

were not included in the original Visitor Services data layer. 

To address this issue, hotels and lodges were extracted 

from a separate data set provided by the NPS. The NPS 

Facility Maintenance Software System contains NPS 
8 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-235. 2010

building locations, facility names, and assigned dollar 

values. Using building names, all hotels and lodges were 

extracted and then matched with the original NPS Visitor 

Services data layer to identify hotels and lodges missing 

from the HVR data set. This data augmentation enabled 

a more accurate value category placement.

FWS Recreation Assets—USDI Fish and Wildlife 

Service (FWS) provided recreation asset data for all its 

Regions. Campgrounds were extracted from the data set 

provided, but there was no distinction between developed 

and undeveloped campgrounds. All records labeled campgrounds were included in the final HVR layer. Additional 

latitude and longitude points were provided for known 

FWS Visitor Centers and Environmental Education Centers 

in existence in 2007. According to data documentation, 

the coordinates approximate the location of the Visitor 

Center parking lot visible in Google Earth. FWS assumes 

no liability or responsibility for the accuracy of this data 

as these data sets are still in development. An estimated 

50 percent of the data set approximates structure location (within approx. 0.5 miles) visible from Google Earth 

imagery; however, the authors were unable to assess the 

completeness of the data set.

National Scenic and Historic Trails—A spatial trails 

data set was acquired from the NPS Data Store (http://

science.nature.nps.gov/nrdata, accessed August 1, 2008). 

This data set contains 12 trails of National Scenic and 

Historic designation: the Appalachian Trail, Trail of 

Tears, Pony Express, Oregon Trail, Mormon Pioneer, 

Lewis and Clark Trail, El Camino Real de Tierra Adentro, 

California Trail, Iditarod Trail, North Country Trail, Ice 

Age Trail, and the Juan Bautista De Anza. According to 

the associated metadata, the intended use of these data is 

to “support diverse planning activities including planning, 

management, maintenance, research, and interpretation.” However, this data set is not a full representation 

of all National Scenic and Historic trails. Compared to 

the National Trails System Web site (www.nps.gov/nts/

nts_trails.html), which provides a comprehensive list of 

all National and Historic Trails, the NPS Data Store data 

set contains only 12 of the 26 trails with National Scenic 

or Historic designation. An additional four data sets have 

been located to represent trails not included in the NPS 

Data Store layer: Continental Divide Trail, Pacific Crest 

Trail, Florida Trail, and Natchez Trace Trail. Although the 

data set has known gaps, this final layer contains 16 of the 

26 trails included in the National Trails system.

National Alpine Ski Area Locations—A complete 

spatial data set of national alpine ski areas could not be 

located for this study. However, the National Operational 

Hydrologic Remote Sensing Center (NOHRSC) hosts 

access to a data set of “Skiing Locations” in the lower 

48 states (www.nohrsc.noaa.gov/gisdatasets, accessed 

October 20, 2008). Disclaimers on the site indicate that 

inclusion of a ski location does not imply endorsement 

and the locations included are those that were known in 

2007, at the time of publication.

After performing an attribute selection of “ski area” and 

“ski basin” on the NOHRSC data set and removing any 

records labeled “cross country,” 499 records remained. 

According to the National Ski Area Association (www.

nsaa.org/nsaa/press/sa-per-state.pdf), the total number of 

ski areas is 481. Using a combination of visual confirmation 

in Google Earth, comparison with a Google Earth .kml 

file titled “Geotagged Ski Areas U.S.” and Web searches 

on the status of specific ski areas, the NOHRSC data set 

was modified to eliminate ski areas that no longer exist 

and those whose locations were incorrectly reported to 

NOHRSC. The final data layer in the Recreation HVR layer 

includes 469 downhill ski area points approximating the 

ski area’s main lodge—three-quarters of which was edited 

to correct original latitude and longitude assignment. In 

order to represent more of the ski area features potentially 

at risk of wildfire, these points were buffered by 1 mile.

Fire-Susceptible Species

The HVR layer representing fire-susceptible species is 

made up of two data sets discussed below. One data set 

represents Federally designated critical plant and wildlife habitat, and the other represents greater sage-grouse 

(Centrocercus urophasianus) key habitat as defined by 

the BLM.

Designated Critical Habitat—Designated critical 

habitat data were obtained from the Conservation Biology 

Institute, and the national layer was built by combining 

several hundred individual layers downloaded from the 

U.S. Fish and Wildlife Service Critical Habitat Portal 

(http://crithab.fws.gov) with nine newer data sets from 

agency staff. The Federal database contains 1,357 endangered species. Of these, 485 have had designated critical 

habitat mapped as part of their recovery plans. Jack Waide 

(Research Coordinator for Conservation Science, National 

Wetlands Research Center, USGS) identified 64 vertebrate, 

invertebrate, and plant species as fire-susceptible species 

(or species most likely to be negatively impacted by fire) 

through review of many recovery plans and critical habitat designations. This included 23 species of vertebrates 

(four mammals, eight birds, three reptiles, five amphibians, and three fishes), seven species of invertebrates, 

and 34 species of plants. Many listed species have very 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-235. 2010 9

small geographic distributions but are scattered across the 

country with concentrations in several biological hotspots 

(e.g., southern California and the Florida panhandle). The 

64 species included in the model broadly represented the 

general geographic distribution of listed taxa. Eight of the 

64 fire-susceptible species were located in Oregon and were 

included in the prototype run. These species are bull trout 

(Salvelinus confluentus), Fender’s blue butterfly (Icaricia 

icarioides fender), greater sage-grouse (Centrocercus 

urophasianus), Kincaid’s lupine (Lupinus sulphureus), 

marbled murrelet (Brachyramphus marmoratus), northern 

spotted owl (Strix occidentalis caurina), Oregon silverspot 

butterfly (Speyeria zerene hippolyta), and Willamette daisy 

(Erigeron decumbens var. decumbens).

National Sage-Grouse Key Habitat—Although the 

sage-grouse is not a Federally listed threatened or endangered species (USFWS 2009), a National Sage-Grouse 

Key Habitat layer is included in the Fire-Susceptible 

Species HVR layer. The key habitat layer was compiled 

by the BLM National Sage-Grouse Mapping Team and 

was provided to this group for the purpose of informing 

wildfire decisionmaking.

Air Quality

Air quality data from the Environmental Protection 

Agency (EPA) was recommended for use by the 

wildland fire community and was acquired from 

www.myfirecommunity.net. Data sets consist of nonattainment areas for particulate matter (PM) 2.5 and 

Ozone. Class I Airsheds data were downloaded from the 

NPS Air Resources Division (www.nature.nps.gov/air/

maps/receptors/index.cfm, accessed October 23, 2008).

Class I Airshed data delineate 215 airsheds representing 

NPS, FWS, and FS jurisdiction. Bureau of Indian Affairs 

(BIA) and Tribal land are not represented. According to 

Western Regional Air Partnership (www.wrapair.org/

tribal/index.htm), Class I designation on Tribal land is not 

mandatory but is encouraged in 309 Tribal Implementation 

Plans under the Regional Haze Rule. The EPA states, 

“Mandatory Class I Federal areas include international 

parks, national wilderness areas, and national memorial 

parks greater than five thousand acres in size, and national 

parks greater than six thousand acres in size, as described 

in section 162(a)” (www.epa.gov/EPA-AIR/1997/January/

Day-16/1043.htm). Under the Clean Air Act(PL 108-201), 

Federal land managers are required to protect the air quality and associated values (including visibility) of areas 

designated as Class I lands.

Municipal Watersheds

A comprehensive national GIS data set for municipal 

watersheds was not available for this study. A spatial data 

layer was developed for this resource by obtaining the 

Hydrologic Unit Code (HUC) level six watersheds from 

the Natural Resource Conservation Service and selecting 

watersheds that contain water supply intakes. An additional 

buffer was applied to the watersheds to further obscure the 

location of the water supply intakes for national security 

purposes. Water intake sources were obtained from the 

ICWater database (http://eh2o.saic.com/SectionProjects/

Transport/Surface/ICWater/ICWater.aspx).

Fire-Adapted Ecosystems

Many of the concepts regarding a first approximation 

of the layer labeled “Fire-Adapted Ecosystem” for use 

in fire risk analysis came from Robert Keane (Research 

Ecologist, Rocky Mountain Research Station, FS). 

The data represents fire-adapted ecosystems where the 

management goal is to re-introduce fire. The intent was 

to obtain a layer that would represent landscapes where 

fire historically played an important role in a non-lethal 

way to maintain the ecosystem. The data are derived using 

fire regime and fire return interval data products from 

LANDFIRE (www.landfire.org). LANDFIREis an interagency (USDA, USDI) project that has generated consistent 

spatial data describing vegetation, wildland fuel, and fire 

regimes across the United States at an approximately 98-ft 

(30-m) grid spatial resolution.

The LANDFIRE fire regime data products were resampled to 0.62-mile (1-km) data sets. Fire-Adapted 

Ecosystems were defined as cells that had fire regime 

groups 1 or 3 (fire return interval of less than 200 years 

and low to mixed severity) and the percent of low severity 

fire was greater than 50 percent (codes 11 to 61).

Residential Structure Location

National-level building or structure location data sets 

do not exist, although the U.S. Department of Homeland 

Security (DHS) is working with various local, State, 

Federal, and Tribal government agencies to initiate 

development of these data (DHS 2008). Because of 

inconsistent spatial data for structures, recent studies have 

used alternatives such as parcel data (USDAFS 2008) and 

census population data (Dobson and others 2000; Radeloff 

and others 2005; Theobald and Romme 2007) to estimate 

potential wildland fire impacts.
10 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-235. 2010

Studies that employ the U.S. census data compute housing density by dividing the total number of people counted 

within each census block by the area of the respective 

census block to identify where housing density exceeds 

one housing unit per 40 acres (Stewart and others 2009). 

Generally, census blocks are small in area; however, blocks 

in sparsely settled areas may contain many square miles 

of territory (Census 2001). This can result in large census 

blocks with a small cluster of homes in one area but with 

large uninhabited regions in the remaining area. This 

can either create an average density too low to meet the 

Wildland-Urban Interface (WUI) criteria set forth in the 

Federal Register (Stewart and others 2009), or it can result 

in the designation of an entire large census block as WUI 

where only a small portion of the block contains housing 

units (Leonard 2007). To work around this problem, studies 

have used ancillary data in dasymetric (or thematic) mapping to modify the boundaries of census blocks omitting 

areas where people typically do not reside, such as public 

lands. However, extensive private inholdings, typical in 

eastern U.S. forests, would not be accounted for by masking public lands (Stewart and others 2009).

Researchers at the U.S. Department of Energy’s Oak 

Ridge National Laboratory have used geographic information systems and remote sensing to develop, refine, and 

update a U.S. population database known as LandScan 

USA. LandScan produces a high-resolution (approx. 295-ft2 

[90-m2] cell) assessment of the number of people present 

in a given area during the night (also known as residential 

population) and the day (Krause 2002). The database was 

derived from the best available census counts that were 

redistributed to spatial cells from probability coefficients 

related to slope, road proximity, land cover, nighttime 

lights, and other information including an urban density 

factor (Dobson and others 2000).

Although LandScan does not attempt to represent structure location, a strong positive correlation is expected to 

exist between population count and structure location. 

In the absence of a national structure location data set, 

LandScan USA population estimates were categorized 

to represent built structure locations. Incorporating the 

LandScan USA data with the output from the fire simulation model required aligning the geographic coordinate 

systems, including the datum, projection, and raster cellsize, while assuring cell alignment. The LandScan data 

was transformed to conform to the FSim burn probability 

grid of approximately 886- by 886-ft (270- by 270-m) cells. 

In order to do this, we needed to categorize the population 

density to represent built structures. Therefore, it was determined for the prototype that nighttime population density 

should be separated into two categories: (1) low density 

(cells with two or fewer occupants approximating at least 

one structure per 18-acre pixel), and (2) moderate or high 

density (cells with three or more occupants approximating 

more than one structure per 18-acre pixel).

Other Biological Values Considered for 

Inclusion

We evaluated numerous other spatial data sets for 

describing biological values in the wildfire risk model 

(Appendix A). These included data from a wide range of 

biological assessments and other studies. While future 

risk assessments could leverage these data for wildfire 

risk assessments, they were excluded from this study for 

the following reasons: (1) the spatial resolution was too 

coarse relative to other data sets; (2) the spatial extent 

was incomplete (did not include the entire nation); or 

(3) response function assignment was not possible given 

the framework used in this study (table 2). A subset of the 

data sets would best be described by a fourth category as 

potentially valuable for future assessments given more 

time and resources to modify the data to fit the assessment approach.

Additional information for each data set considered, 

including description, citation, Web site, and sample 

map(s), are provided in Appendix A. Of the biological 

value data sets examined, 12 fell into the category of being 

too coarse for inclusion in the risk model, four showed 

some promise but did not cover the entire country, and 

three data sets were inappropriate as their relationships 

with wildfire were part of their inherent value.

An aggregation of the Natural Heritage data sets 

maintained at the state level would potentially improve 

this risk model. These data contain thousands of records 

for hundreds of species; however, the data sets (usually 

a combination of points and polygons) would need to be 

purchased and standardized. Secondly, the data sets would 

need to be reviewed and fire-susceptible species extracted 

for meaningful response functions to be assigned—not all 

fire-susceptible species respond to fire the same way. It 

may be possible to “bundle” species into response function 

groups (e.g., group six species that share the same response 

to fire); however, doing so would require an additional 

post-processing step. See Appendix A for more detail.

We examined the Conservation Biology Institute 

Protected Areas Database (version 4.5) with the idea of 

identifying specific protected areas as biological values 

that might be negatively impacted by fire. We also considered looking at Gap Analysis Program (GAP) Status 

1 and 2 lands, but this proved problematic (Appendix A). 

For example, GAP Status 1 lands are defined as “lands 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-235. 2010 11

having permanent protection from conversion of natural 

land cover and mandated management plans to maintain 

a natural state and maintenance of natural disturbance 

events,” which makes these lands difficult to include in 

the model. However, regardless of the protection status, 

the fact that these lands are protected does not easily 

translate into specific response functions in a wildfire 

risk model. Risk is a function of what is on-the-ground, 

not of its particular management designation. This data 

set may provide some information about rare or vulnerable ecological values (e.g., Botanic Areas and Research 

Natural Areas), but these designated places are small and 

difficult to assess without additional information. Not all 

Botanic Areas, for example, contain the same plant communities characterized by the same response functions.

The new United States Protected Areas Database 

(PAD-US) will likely expand its attribute table to describe 

more than just management intent expressed through 

GAP codes. Discussions are underway to provide 

additional information about various ecological values 

contained in each mapped polygon. In the not too distant 

future, the new PAD-US database may provide important 

inputs to future wildfire risk modeling.

We considered including data on ecological systems 

mapping for the country (a joint LANDFIRE-NatureServe 

effort) in the fire risk model. Ecological systems are 

medium resolution vegetation community types comprised of several association/alliance level communities. 

The associations (more than 400 types) that comprise 

each system are fully described, but their locations are 

not mapped; therefore, the detailed spatial extent of each 

system is unknown. Those ecological systems that are most 

rare in the country (as defined by covering less than 0.05 

percent of the United States land cover) might provide a 

starting point for mapping these communities, but these 

data must be more refined before they can be logically 

incorporated into the fire risk model (Appendix A).

Categorizing Highly Valued Resources

Integrating multiple assets and resource values into 

a general risk assessment framework is facilitated by 

quantifying values in a common unit of measurement 

(formula 1). Venn and Calkin (2009) reviewed the state of 

economic research in regard to the monetary quantification 

of value change to non-market natural resource values due 

to wildfire. They found that the ability to assign monetary 

value change to natural resources due to wildfire was constrained by the following challenges: (1) gaps in scientific 

understanding about how the spatial and temporal provision 

of non-market values are affected by wildfire; (2) a lack of 

studies that have estimated marginal willingness-to-pay 

Table 2—List of biological data sets considered, accompanied by their sources and reason for 

exclusion.

Name Source

1. Resolution too coarse

International Biological Hotspots Conservation International

Global 200 Most Biologically Valuable Ecoregions World Wildlife Fund

Global Wetlands UNEP-WCMC

Ecoregions of High Species Richness World Wildlife Fund

Ecoregions of High Species Endemism World Wildlife Fund

Ecoregions of High Species Rarity World Wildlife Fund

Centers of High Plant Diversity World Wildlife Fund / UNEP

Forest Intactness Conservation Biology Institute

Critical Watersheds for Conserving Biodiversity NatureServe

Watersheds of High Crayfish Rarity NatureServe

Watersheds of High Mussel Rarity NatureServe

Imperiled Species by Equal Area Hexagons U.S. Environmental Protection Agency

2. Map extent incomplete

Priority Conservation Areas The Nature Conservancy

National Wetlands Inventory U.S. Fish and Wildlife Service

GAP Species Richness U.S. Geological Survey

Underrepresented GAP Plant Communities U.S. Geological Survey

3. Inappropriate to assign loss functions

Frontier Forests World Resources Institute 

Last of the Wild Wildlife Conservation Society

Top 1 Percent Wild Areas Wildlife Conservation Society
12 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-235. 2010

Figure 2—Graphic depiction of the response functions 1 through 14 used in this first approximation. Bars indicate a range of 

relative net value change; however, only the midpoint of the bar (table 4) is used in the calculations.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-235. 2010 13

Figure 2 (CONTINUED)

to conserve non-market values; (3) violation of consumer 

budget constraints; and (4) the infeasibility of valuing 

indigenous cultural heritage. Given these challenges, 

Boyd (2004) recommended evaluating important natural 

resource values in their natural units rather than by using 

a common unit of measurement.

Despite the difficulties in quantifying net fire-caused 

change in value across multiple HVR, a general aggregation of HVR may be informative. To that end, we 

classified each HVR by initial value—Moderate, High, 

and Very High. Because this project addresses only highly 

valued resources, a “low value” category was not included, 

thereby excluding all areas not identified as having an 

HVR. Categorizing the HVR themes implies that HVR 

in the same category presumably have values of a similar 

magnitude. Therefore, the relative risk values are additive 

within each value category. However, we made no attempt 

to weight the relative value or importance of the three 

value categories to produce an overall weighted wildfire 

risk. As a result, formula 1 must be calculated separately 

for each HVR category. Estimation of formula 1 across all 

values would require that each HVR category be given a 
14 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-235. 2010

relative weight. Efforts to integrate the separate wildfire risk values or value categories into a common unit 

for tradeoff assessment provides potential opportunities; 

however, caution is recommended given the challenges of 

non-market valuation.

We consulted fire and fuels program management 

officials from the Federal agencies with fuels and fire 

management responsibility before assigning HVR value 

categories. Discussions were held with 10 national leaders 

from the FS, NPS, BLM, FWS, and BIA. Though support 

was not unanimous on all initial value assignments, there 

was considerable agreement. Most disagreement was centered on the need to assign a single initial value to national 

level data when there was desire to assign a different value 

to assets within a national aggregate class. For instance, 

there was a desire to assign well-developed campground 

sites in major National Parks a High initial value compared 

to small campgrounds with few amenities that would be 

assigned a Moderate initial value. However, as discussed 

above, the attribution for all Federal campgrounds was 

not equally descriptive, and further separation by value 

classes is not feasible at this time.

Response Functions—Response functions are the third 

component in the risk framework employed in this study. 

These functions translate fire effects into net value change 

(NVC) to the described resource. In each response function, 

NVC is based on the flame length of the fire and represents 

both beneficial and adverse effects to the resource. The 

approach used here quantified NVC to a given resource as 

the percentage change in the initial resource value resulting from a fire at a given flame length. Specifically, risk 

was quantified as the expected annual relative NVC on a 

pixel-by-pixel basis. The value of the resource represents 

the value derived from the resource in this and all future 

periods; therefore, NVC was inclusive of future resource 

value changes including potential recovery or deterioration over time. For example, if a low flame length fire 

occurred in an area that reduced the immediate values of 

critical habitat but resulted in less adverse affects (or even 

positive effects) in subsequent years, the overall response 

may be less adverse than the initial response because it 

integrates the future outcomes of the fire.

The response functions are mathematical or tabular 

relationships between fire characteristic and fire outcome. Although fire outcomes could be related to any 

fire characteristic, response is typically related to some 

measure of fire intensity (e.g., flame length) (Ager and 

others 2007; Finney 2005). Fire intensity is a robust fire 

characteristic because it integrates two important fire 

characteristics—fuel consumption and spread rate. In 

this first approximation, we related relative NVC to flame 

length. The fire modeling results described in the previous 

section produced burn probability by flame length class 

for each 886- by 886-ft (approx. 270- by 270‐m) pixel. 

Accordingly, we developed response functions to correspond to these same flame length classes: Low = 0 to 

2 ft, Moderate = greater than 2 to 6 ft, High = greater than 

6 to 12 ft, and Very High = greater than 12 ft.

In detailed risk analyses conducted at smaller scales, 

tradeoffs may be thoroughly evaluated using a weighted 

prioritization model such as Analytical Hierarchical 

Process (Schmoldt and others 2001). In some cases, it 

may be possible for outcomes to be expressed as absolute 

benefits and losses in a common currency, such as dollars. 

However, such detail is not possible in this large-scale first 

approximation. Rather than developing response functions 

that directly address absolute change in resource or asset 

value, this first approximation of fire risk relies on generalized, relative response functions that can be applied 

to any number of resources or assets. The generalized 

response functions indicate a range of relative NVC as 

a percentage of initial resource value for the four flame 

length classes. Although response range is shown for the 

response functions (figure 2), only the midpoint value is 

used in calculations.

The stylized response functions used in the first approximation of fire risk are shown in figure 2 and described in 

table 3. The 14 functions (RF1 to RF14) were developed 

after considering the different ways in which the various 

HVR under consideration might respond to fire of different intensities. The response functions were selected 

in an attempt to adequately cover the range of responses 

expected for the resources and assets analyzed in this 

project. However, these functions do not necessarily represent the complete set that could be applied in other risk 

assessment projects.

As was the case when assigning HVR value categories, 

although national leaders were mostly in agreement, 

opinions differed on assigning response functions to 

non-attainment areas, Class I airsheds, and fire-adapted 

ecosystems. Differences largely centered on the desire to 

alter the response function through different time horizons. 

For example, smoke from current wildfires is initially 

harmful; however, smoke from future fires in the area 

will likely be lessened due to the reduction in fuels. The 

authors rectified any disagreements among managers and 

selected a single response function for each HVR after 

carefully considering the managers’ rationale.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-235. 2010 15

Table 3—Summary characteristics of the 14 response functions used in the first approximation. 

Values indicate the midpoint (+/– 20 percent) expected net value change (percent of 

initial value) for the four flame length classes.

Response 

function Description

Midpoint of relative net 

value change by flame 

length class (percent)

L M H VH

1

All fire is beneficial; strong benefit at low and 

moderate fire intensities and moderate benefit at 

high and very high intensity.

+80 +80 +40 +40

2 All fire is beneficial; moderate benefit at low fire 

intensity and mild benefit at higher intensity. +50 +20 +20 +20

3 Strong benefit at low fire intensity decreasing to a 

strong loss at very high fire intensity. +60 +20 –20 –60

4 Moderate benefit at low fire intensity decreasing to 

a moderate loss at very high fire intensity. +30 +10 –10 –30

5 Slight benefit or loss at all fire intensities. 0 0 0 0

6

Mild increasing loss from slight benefit or loss 

at low intensity to a moderate loss at very high 

intensity.

0 –10 –20 –30

7 Moderate increasing loss from mild loss at low 

intensity to a strong loss at very high intensity. –10 –30 –50 –80

8 Slight benefit or loss at all fire intensities except a 

moderate loss at very high intensity. 0 0 0 –50

9

Slight benefit or loss at low and moderate fire 

intensities and a mild loss at high and very high 

intensities.

0 0 –20 –20

10 Mild loss at all fire intensities. –20 –20 –20 –20

11 Moderate loss from fire at all fire intensities. –50 –50 –50 –50

12 Strong loss from fire at all fire intensities. –80 –80 –80 –80

13 Loss increases from slight loss at low intensity to 

strong loss at very high intensity. –10 –60 –70 –80

14

Slight benefit or loss from fire at low and moderate 

intensities and a strong loss from fire at high and 

very high intensities.

0 0 –80 –80
16 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-235. 2010

Generating Risk Maps and Calculating 

Outputs

The following simple example illustrates the risk calculation. Assume hypothetical burn probabilities by flame 

length class for a single pixel illustrated in table 4. These 

burn probabilities describe the likelihood of a fire at a 

given flame length. The pixel of interest contains one 

HVR that has been assigned response function 4 (RF4).

The total annual burn probability for the pixel is 0.01 

(1 percent), and the expected fire behavior leans toward 

the lower flame length classes (that is, the probabilities of 

the lower flame length classes are greater than the higher 

flame length classes). The percentage change in initial 

value for RF4 is displayed on the second row of the table. 

Note that RF4 exhibits moderate benefit at low intensity, 

moderate loss at very high intensity, and mostly neutral 

effects at moderate and high intensities. Multiplying the 

burn probability by the percent value change for each flame 

length class and then adding those four values generates 

the risk output—the annual average percent change—for 

the pixel. In this example, the result is +0.001 percent, 

or an expected net increase in value of one-thousandth 

percent per year.

The computations for calculating risk from the fire 

simulation flame length probability files, the HVR layers, and the response functions were automated in Python 

programming language within ArcMapTM. The process 

was written in three sequential modules that batch-process 

the data for each FPU. All processing was conducted on a 

PC with ESRI ArcGIS 9.3 software with Spatial Analyst 

extension. The first two modules, Import FPA 2 Raster

and FLC_HVR, prepare the data for processing by the 

third module, BL_Calc, which calculates the percent 

change in value for each HVR pixel (figures 3 and 4). 

All input data were in the Albers USGS NAD83 projection. Appendix B summarizes the function of each module.

Figure 3—Screen capture of the 

BL_Calc python script modules for 

calculating wildfire risk using FPA 

wildfire simulation data (FSim), 

HVR, and loss/benefit (response) 

functions.

Table 4—Example burn calculation of value change to a hypothetical pixel. The example uses response 

function RF4. 

Flame length class

L M H VH Total

Annual burn probability 0.004 0.003 0.002 0.001 0.010

Response (RF4), percent value change +30 +10 –10 –30 0

Annual average percent change (APC) +0.0012 +0.0003 –0.0002 –0.0003 +0.001
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-235. 2010 17

Figure 4—Analysis flow for calculating risk.

Import FPA 2 

Raster

(Python module)

FPA.txt files 

obtained from Fire 

Program Analysis 

model run – one 

file for each FPU

One file for each 

flame length 

category (FLC):

<FPU>_L.img

<FPU>_M.img

<FPU>_H.img

<FPU>_VH.img

FLC_HVR

(Python module)

Highly Valued Resource 

binary raster files (HVR)

BL_Calc

(Python module)

Response 

Function 

number 

(user input)

Benefit/Loss LU 

Table

Output 1

% change in value raster 

for each FLC/HVR 

combination

FLC raster files masked 

with the HVRs and FPU. 

Output 2

Total expected change for 

the HVR – sum of % 

change (Output 1 values) 

for the HVR

Output 3

Sum of total expected 

change (Output 2) for 

HVRs in each initial value 

class 

Initial value of the 

HVR – L, M, H, or 

VH (user input) 
18 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-235. 2010

Results ________________________

Area of Highly Valued Resources by FPU 

in Oregon

Highly valued resources (HVR) are described by area 

and FPU, recognizing that some themes have layers in 

multiple HVR value categories (e.g., Air Quality—ClassI 

Airsheds (Moderate value) and NAA (Very High value)). 

Fire-susceptible species comprised the largest area 

(15.7 million acres) among the HVR themes, primarily 

due to sage-grouse habitat within FPUs 006, 007, and 

008 and because of northern spotted owl and marbled 

murrelet habitat in FPU 001 (tables 5 and 5a). Fireadapted ecosystems comprised 9.4 million acres, mostly 

distributed east of the Cascade Mountains. Municipal 

watersheds and built structure density were next largest 

at 5 and 2.1million acres, respectively. The air quality 

theme made up 1.6 million acres, energy related utilities 

covered more than 1.2 million acres, and the recreation 

theme comprised the lowest total area at approximately 

217,000 acres. It should be noted that all HVR were represented as 886- by 886-ft (approx. 270- by 270-m) pixels 

so the area of linear features like trails and transmission 

lines were represented by a buffer and probably cover less 

area than estimated. Ski area locations were represented 

as 1-mile (1.6-km) buffered points because actual area 

polygons were unavailable.

Burnable Area by FPU

Variation existed in the burnable area among the 

FPUs due to different proportions of non-burnable fuels 

(table6). Non-burnable fuels resulted in burn probabilities 

of zero in the FSim data. In particular, large areas of the 

Northwest Oregon FPU are considered urban and nonburnable (27 percent) in a wildfire context. Non-burnable 

area covered 25 percent of the Northeast Oregon FPU 

and 29 percent of the Coos Bay-Roseburg FPU because 

each contained large agricultural areas, rock, and other 

non-burnable land types.

Area in HVR Categories by FPU

The percent area covered by at least one HVR on 

burnable area varied from 19 to 91 percent among the 

11 FPUs and averaged 58 percent over the entire study 

area (table 7). Southeast and Eastern Oregon FPUs had 

the highest percentage of HVR within their boundaries 

primarily due to sage-grouse key habitat. The Coos Bay/

Roseburg and Northwest Oregon FPUs had the highest 

percentage of area in the Very High category (17 and 21 

percent, respectively) primarily due to human population 

as indicated by the built structure data. It is important to 

note that there is considerable overlap among the HVR 

value categories (figures 5a and 5b). The majority of the 

overlap exists between two initial value categories, while 

relatively little overlap occurs among all three initial value 

categories.

Response Functions Assignment and 

Initial Value Categories

The assignment of each HVR to a response function 

and HVR initial value category is described by table 8.

All critical plant and wildlife habitat layers were placed 

in the High value category due to the rarity of critical 

habitat and the inability or length of time required to 

replace them if lost or damaged by fire. Critical habitat 

of fire-susceptible species was assigned to RF13 based on 

the assumption that low flame length fire results in mild 

loss, but greater flame length fire results in substantial 

net reduction of habitat value.

Municipal watersheds were placed in the Very High 

value category due to the direct implication to human 

health and welfare. It was assumed that low flame length 

fire generally corresponds to lower fuel consumption and 

lower severity and would, therefore, have a less adverse 

impact on the watershed and water quality value. This 

was assigned to RF7, indicating a small value reduction 

at low flame length, which would increase to substantial 

reduction of value at high flame length.

All energy infrastructure HVR were placed in the High 

value category because of their importance to the function 

of modern society. Although these assets are replaceable if damaged or destroyed, loss can cause significant 

disruption over the short- and medium-term. All energy 

infrastructure HVR were assigned to RF14 based on the 

assumption that only high to very high flame length causes 

damage to these assets, but that damage, when it occurs, 

is generally substantial.

For recreation infrastructure, the recreation sites and 

national trails HVR were placed in the Moderate value 

category (the lowest value category used in this project, 

which addresses only highly valued resources). Ski areas 

were placed in the High value category due to their relative 

scarcity and to the difficulty of replacing them because of 

site requirements. Recreation sites were assigned to RF6 

on the assumption that their value reduction is proportional 

to flame length; however, sites may remain functional 

even after high severity fire. Ski areas were assigned to 

RF4 on the assumption that low flame length fire may 

confer a net benefit by accomplishing routine vegetation 

management and reducing the likelihood of a future, more 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-235. 2010 19 Table 5—HVR area by Oregon FPU, including all burnable and non-burnable acres. Energy Energy Energy Energy Rec Rec Rec Fire-sus. Class I Fire Built FPU (Plants) (Gas) (ElecTrans) (Cell) (Camp) (Trails) (Ski) species a NAA Airsheds Watershed ecos. structures 1 Northwest Oregon 685 104,283 318,181 1,873 2,918 51,592 3,170 2,543,574 13,024 318,487 2,595,995 189,021 1,284,271 2 Central Coast Range 144 0 11,367 36 793 3,152 0 269,886 0 5,927 117,235 42,765 2,702 3 Southwest Oregon 216 8,809 74,182 432 2,450 27,021 1,225 1,309,220 0 181,293 333,745 59,140 178,519 4 Central Oregon 252 28,984 174,249 739 4,053 24,643 2,882 863,698 0 175,024 392,056 1,985,231 195,812 5 Northeast Oregon 234 33,956 84,792 612 721 27,327 3,999 0 0 0 287,629 1,039,407 111,471 6 Southeast Oregon 108 21,473 41,306 288 955 17,798 0 4,322,888 0 0 92,214 2,439,742 61,590 7 Eastern Oregon 0 0 47,323 54 576 0 0 3,188,061 0 0 0 1,414,494 15,150 8 Southeast/South 126 21,653 122,207 414 1,549 15,258 2,054 2,442,750 63,607 219,987 0 1,587,789 79,298 9 Wallowa-Whitman 54 5,728 50,908 144 1,279 4,936 2,000 7,494 0 571,187 254,466 397,911 46,908 10 Malheur 18 0 15,294 72 468 0 0 4,918 0 68,543 73,317 204,909 11,997 11 Coos Bay/Roseburg 54 10,034 86,089 630 883 13,312 0 790,021 0 18 901,204 33,830 163,657 Total 1,891 234,920 1,025,898 5,294 16,645 185,039 15,330 15,742,510 76,631 1,540,466 5,047,861 9,394,239 2,151,374 a Fire-susceptible species acres are for all species represented in the Oregon prototype. Table 5a presents acres by FPU for individual species. Table 5a—Area of fire-susceptible species by Oregon FPU, including all burnable and non-burnable acres. FPU Bull Trout Fenders Blue Bfly Sage-Grouse Kincaids Lupine Marbled Murrelet N. Spotted Owl Oregon Silverspot Bfly Willamette Daisy 1 Northwest Oregon 15,726 2,954 0 504 891,494 1,647,739 180 703 2 Central Coast Range 0 0 0 0 0 269,886 0 0 3 Southwest Oregon 0 0 0 0 420,014 889,206 0 0 4 Central Oregon 9,295 0 765,180 0 0 95,888 0 0 5 Northeast Oregon 38,262 0 0 0 0 0 0 0 6 Southeast Oregon 8,881 0 4,322,888 0 0 0 0 0 7 Eastern Oregon 3,585 0 3,188,061 0 0 0 0 0 8 Southeast/South 31,777 0 2,333,784 0 0 84,287 0 0 9 Wallowa-Whitman 61,067 0 7,494 0 0 0 0 0 10 Malheur 1,261 0 4,918 0 0 0 0 0 11 Coos Bay/Roseburg 0 0 0 0 226,652 563,369 0 0 Total 169,854 2,954 10,622,324 504 1,538,160 3,550,376 180 703
20 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-235. 2010

Table 6—Area by FPU classified as burnable for Oregon.

Acres Percent

FPU Burnable Non-burnable Total burnable

1 Northwest Oregon 7,377,573 2,767,921 10,145,494 73

2 Central Coast Range 1,024,816 15,438 1,040,254 99

3 Southwest Oregon 3,775,911 164,828 3,940,739 96

4 Central Oregon 8,355,444 553,047 8,908,491 94

5 Northeast Oregon 4,273,782 1,455,224 5,729,005 75

6 Southeast Oregon 7,201,991 382,455 7,584,446 95

7 Eastern Oregon 5,476,575 409,998 5,886,574 93

8 Southeast/South 8,992,274 803,009 9,795,284 92

9 Wallowa-Whitman 3,916,078 271,543 4,187,621 94

10 Malheur 1,917,426 11,493 1,928,919 99

11 Coos Bay/Roseburg 2,337,945 969,567 3,307,512 71

Total 54,649,814 7,804,523 62,454,337 87

Table 7—Area by HVR category for the 11 Oregon FPUs. M = moderate, H w/ SG = high with sage-grouse habitat, H w/o SG = 

high without sage-grouse habitat, and VH = very high. Only burnable lands were included in the calculations and area 

reported includes all HVR present in a pixel (i.e., acres reported account for overlapping HVR within a value category).

HVR category Percent of total FPU

FPU M H w/ SG H w/o SG VH M H w/ SG H w/o SG VH

1 Northwest Oregon 479,496 2,809,515 2,809,515 2,120,228 5 28 28 21

2 Central Coast Range 52,241 282,603 282,603 119,072 5 27 27 11

3 Southwest Oregon 262,464 1,407,397 1,407,397 357,560 7 36 36 9

4 Central Oregon 2,033,473 1,108,833 344,175 439,865 23 12 4 5

5 Northeast Oregon 686,675 96,375 96,375 163,153 12 2 2 3

6 Southeast Oregon 2,393,572 4,385,306 71,660 24,301 32 58 1 0

7 Eastern Oregon 1,325,289 3,209,029 52,565 2,828 23 55 1 0

8 Southeast/South 1,726,280 2,597,779 265,184 52,475 18 27 3 1

9 Wallowa-Whitman 919,416 119,451 111,957 240,433 22 3 3 6

10 Malheur 270,408 26,589 21,671 78,001 14 1 1 4

11 Coos Bay/Roseburg 31,020 738,699 738,699 575,2951 22 22 17

damaging fire. We also assumed that high flame length 

fire would significantly, but not completely, reduce the 

value of the ski area. National trails were assigned to RF8 

on the assumption that only very high fire severity would 

adversely affect the value of these trails; lower flame length 

fires would have little effect on trail value. Furthermore, 

the net value reduction at the highest fire flame length 

class is not 100 percent because these trails can typically 

be reconstructed as needed.

Non-attainment areas were placed in the Very High value 

category due to the direct implication to human health 

and welfare. Non-attainment areas were assigned to RF13 

because they have the potential for slight loss from low 

flame length fires, but would likely experience substantial 

loss at all flame lengths above low. Class I Airsheds were 

assigned to RF6 and the Moderate value category, which 

assumes that fires of increasing flame length class would 

be increasingly damaging to the visibility and air quality 

within the airshed.

Fire-adapted ecosystems were placed in the Moderate 

value category because fire is important in these ecosystems and is necessary to maintain healthy and functioning 

environments. Fire-adapted ecosystems were assigned to 

RF3 on the assumption that low flame length fire would 

confer a substantial net benefit, but higher flame length 

fires would result in increasing loss.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-235. 2010 21 Figure 5a—Map of Oregon FPUs that demonstrate overlapping pixels among HVR categories, with sage-grouse habitat. Blue pixels represent areas of overlap between two HVR value categories, and yellow pixels represent overlapping pixels among three HVR categories. The black box and inset map highlight an area of high overlap (among three HVR categories). Counts of overlapping pixels by FPU are shown in the table inset above.
22 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-235. 2010 Figure 5b—Map of Oregon FPUs that demonstrate overlapping pixels among HVR categories, without sage-grouse habitat. Blue pixels represent areas of overlap between two HVR value categories, and yellow pixels represent overlapping pixels among HVR categories. The black box and inset map highlight an area of high overlap (among three categories). Counts of overlapping pixels by FPU are shown in the table inset above.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-235. 2010 23

Finally, pixels identified as containing built structures 

were assigned to one of two categories: (1) High initial 

value for low density cells that were estimated to contain 

only one built structure (1 or 2 projected persons per 

cell), and (2) Very High for cells that were estimated to 

contain more than one built structure (3 or more persons 

per cell). Both built structure categories were assigned to 

RF12, assuming that any fire had the potential to result 

in a substantial loss.

Burn Probability Outputs from Wildfire 

Modeling

Burn probability (BP) varied both among and within 

FPUs (tables 9 and 10, figures 6 and 7). Burn probabilities were arbitrarily categorized for tabular and display 

purposes only as noted in table 9. The highest BP values 

were observed for the Southwest FPU (3 percent in burn 

probability class 5). In contrast, BP for the Northwest, and 

Table 8—Assignment of response functions to the HVR included in the study and the initial value category 

assigned to each HVR.

HVR type HVR

Response function

(% value change by intensity)

Initial HVR 

category

Fire-susceptible species

Bull trout 13 (–10,–60,–70,–80) H

Fender’s blue butterfly 13 (–10,–60,–70,–80) H

Greater sage-grouse 13 (–10,–60,–70,–80) H

Kincaid’s lupine 13 (–10,–60,–70,–80) H

Marbled murrelet 13 (–10,–60,–70,–80) H

Northern spotted owl 13 (–10,–60,–70,–80) H

Oregon silverspot butterfly 13 (–10,–60,–70,–80) H

Willamette daisy 13 (–10,–60,–70,–80) H

Watershed Municipal watershed 7 (–10,–30,–50,–80) VH

Energy infrastructure

Communication towers 14 (0, 0,–80,–80) H

Electric transmission lines 14 (0, 0,–80,–80) H

Power plants 14 (0, 0,–80,–80) H

Oil and gas transmission 14 (0, 0,–80,–80) H

Recreation infrastructure

Rec. sites and campgrounds 6 (0,–10,–20,–30) M

Ski areas 4 (+30,+10,–10,–30) H

National trails 8 (0, 0, 0, –50) M

Air quality

Non-attainment areas 13 (–10,–60,–70,–80) VH

Class I Airsheds 6 (0, –10,–20,–30) M

Ecosystem Fire-adapted ecosystems 3 (+60,+20,–20,–60) M

Built structures Low density 12 (–80,–80,–80,–80) H

Moderate and high density 12 (–80,–80,–80,–80) VH

to a lesser extent, Coos Bay/Roseburg FPUs exhibited the 

lowest burn probabilities, and values for the Northwest 

Oregon FPU were almost entirely within BP class 1. On 

both a percentage and absolute basis, burn probabilities for 

the Southeast Oregon FPU were concentrated in BP class 3.

Wildfire Hazard

Wildfire hazard was defined in this report as the average flame length of all simulated fires that burned a given 

pixel. Hazard was calculated as the probability weighted 

flame length among the flame length intervals output 

from the FSim program. The outputs were then placed 

into categories corresponding with the response function 

flame length categories of Low (L), Moderate (M), High 

(H), and Very High (VH). Variation in flame length among 

simulated fires is caused by a number of factors, including 

wind speed, fuel moisture, and the direction of fire arrival 

relative to the maximum spread direction.
24 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-235. 2010

Table 9—Area in BP classes by FPU, excluding non-burnable land types.

Area in burn probability classes (acres)

1 2 3 4 5

0.000019 - 0.0024 - 0.0055 - 0.0097 - 0.019 - 

FPU 0.0024 0.0055 0.0097 0.019 0.041 Total

1 Northwest Oregon 7,006,035 299,897 80,180 6,017 0 7,392,128

2 Central Coast Range 232,182 525,630 263,653 14,898 0 1,036,362

3 Southwest Oregon 165,783 1,071,418 1,658,980 768,423 112,155 3,776,758

4 Central Oregon 1,886,659 3,512,619 2,951,159 6,161 0 8,356,597

5 Northeast Oregon 1,724,893 1,659,772 788,472 105,580 0 4,278,717

6 Southeast Oregon 60,455 833,561 6,271,208 40,117 0 7,205,341

7 Eastern Oregon 154,506 2,015,368 3,303,747 3,765 0 5,477,386

8 Southeast/South 3,002,589 5,318,431 712,345 198 0 9,033,562

9 Wallowa-Whitman 485,315 1,542,664 1,408,639 483,711 0 3,920,329

10 Malheur 304,202 1,364,001 249,350 108 0 1,917,661

11 Coos Bay/Roseburg 1,823,898 355,254 374,529 192,660 919 2,747,259

Table 10—Percent area in BP classes by FPU, excluding non-burnable land types.

Percent area in burn probability classes

1 2 3 4 5

0.000019 - 0.0024 - 0.0055 - 0.0097 - 0.019 - 

FPU 0.0024 0.0055 0.0097 0.019 0.041

1 Northwest Oregon 95 4 1 0 0

2 Central Coast Range 22 51 25 1 0

3 Southwest Oregon 4 28 44 20 3

4 Central Oregon 23 42 35 0 0

5 Northeast Oregon 40 39 18 2 0

6 Southeast Oregon 1 12 87 1 0

7 Eastern Oregon 3 37 60 0 0

8 Southeast/South 33 59 8 0 0

9 Wallowa-Whitman 12 39 36 12 0

10 Malheur 16 71 13 0 0

11 Coos Bay/Roseburg 66 13 14 7 0

In general, high hazard values were observed within all 

FPUs and were associated with higher elevation mixed 

conifer forests distributed among the various mountain 

ranges in Oregon (figure 8). Averaged across all FPUs, 

13 percent of the total burnable area was assigned to the 

H hazard category (greater than 6 to 12 ft flame length), 

62 percent to the M category, and 24 percent to the L 

category. None of the burnable area in Oregon was in 

the VH hazard category. The Southwest Oregon FPU 

contained the largest area on both a percentage and total 

area basis in the H category (47 percent, table 11). The 

Central Oregon and Southeast/South FPUs show relatively 

minor area within the H hazard category (4 and 2 percent, 

respectively). Nearly all the burnable area in the Southwest, 

Southeast, and Eastern Oregon FPUs were in the M and 

H categories (table 11). The Northwest and Central Coast 

FPUs had the largest percentage of area in the L hazard 

category, reflecting relatively moderate weather and fuel 

moisture conditions.

Wildfire Risk Calculations

Within each HVR value category, the annual change in 

area (table 4) was calculated by combining the burn probabilities and response functions for each HVR category. 

The resulting risk estimates are represented by the Total 

Change Equivalent (TCE) and Average Percent Change 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-235. 2010 25

Figure 6—Percent area in burn probability classes by FPU. Calculations include burnable land types only.

(APC, table 12). TCE is the equivalent area lost, or gained, 

assuming 100 percent loss, or gain, for a particular HVR 

as measured in acres. APC is TCE divided by the sum of 

burnable area of HVR in a given value category.

These area measures aggregate marginal responses to the 

HVR in order to arrive at a unitary proxy for decisionmaking. The proxy sums over all affected area in each HVR 

class, conditioned on the probability of burning and fire 

intensity. For example, assume that a given 18‐acre pixel 

of critical habitat (RF13) had a probability of burning of 

0.5 percent with a high fire intensity (70 percent loss in 

value) and a 1 percent chance of burning with a very high 

fire intensity (80 percent loss in value). The contribution 

of this pixel to TCE would equal 0.207 acres [(0.005 * 0.7 

* 18 acres) + (0.01 * 0.8 * 18 acres)].

In general, APC estimates were relatively low, reflecting low burn probabilities for individual pixels (table 12). 

Large differences in TCE and APC among the FPUs were 

projected, especially between the coastal and interior 

FPUs. The APCs averaged across all FPUs were 0.097 

(M), –0.258 (H, with sage-grouse), –0.163 (H, without 

sage-grouse), and –0.063 (VH) of the total HVR area 

(table 12, figure 9a). Projected loss was largest for the 

H category whether sage-grouse was included or not 

(figures 9a and 9b). The total HVR area by category had 

a strong influence on the APC and TCE estimates—the 

more HVR area within an FPU the higher the loss estimate 

for TCE given a constant burn probability, flame length, 

and RF (table 7). Spatial patterns in the APC largely 

reflected the distribution of HVR (figures 10, 11a, 

11b, and 12). The largest negative APC was observed 

for the Southwest Oregon FPU for the H category at 

–0.582 percent, which equated to a TCE of –8,192 acres. 
26 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-235. 2010 Figure 7—Map of Oregon FPUs showing burn probability outputs from the FSim model. Values represent estimates of annual burn probability. 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-235. 2010 27Figure 8—Map of Oregon FPUs showing wildland fire hazard results from the FSim program. Fire hazard is defined as the average flame length of the fire.
28 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-235. 2010

Eastern Oregon and Southwest Oregon FPUs exhibited 

the largest negative APCs for the VH category. The large 

negative TCEs for the H (with sage-grouse) category in the 

Eastern Oregon and Southeastern Oregon FPUs resulted 

from a combination of high burn probabilities and large 

areas of key sage-grouse habitat. The large negative TCE 

in the H category in the Southwestern FPU resulted from 

large areas of northern spotted owl and marbled murrelet 

critical habitat combined with high burn probabilities. 

Including sage-grouse habitat in the risk model did not 

affect the Southwestern FPU results.

Positive TCE and APC were realized in the Moderate 

category in many of the FPUs due to the positive benefits 

realized from RF3 for low and moderate intensity fires in 

fire-adapted ecosystems. The Central and Southeast FPUs 

have the largest projected benefit from wildfire at 0.151 

and 0.147 percent, respectively, followed by the Eastern 

Oregon FPU at 0.095 percent. Risk results are mapped in 

figures 10, 11a, 11b, and 12 for the Moderate, High with 

sage-grouse, High without sage-grouse, and Very High 

HVR categories, respectively.

Table 11—Area in the L, M, H, and VH Wildfire hazard categories. Categories defined as L = 0 to 2 ft, M = greater than 2 to 6 ft, 

H = greater than 6 to 12 ft, VH = greater than 12 ft. Hazard is defined as the average flame length of the simulated fires.

Wildfire hazard categories

Wildfire hazard categories (acres) (percent of area)

FPU L M H VH L M H VH

1 Northwest Oregon 3,022,981 3,553,763 800,830 0 41 48 11 0

2 Central Coast Range 445,017 480,523 99,275 0 43 47 10 0

3 Southwest Oregon 281,270 1,705,582 1,789,059 0 7 45 47 0

4 Central Oregon 2,999,671 5,010,427 345,346 0 36 60 4 0

5 Northeast Oregon 1,492,891 2,043,416 737,474 0 35 48 17 0

6 Southeast Oregon 327,818 6,084,241 789,985 0 5 84 11 0

7 Eastern Oregon 295,609 4,411,408 769,557 0 5 81 14 0

8 Southeast/South 2,535,216 6,241,737 215,321 0 28 69 2 0

9 Wallowa-Whitman 891,350 2,135,378 889,350 0 23 55 23 0

10 Malheur 235,101 1,164,838 517,488 0 12 61 27 0

11 Coos Bay/Roseburg 746,878 1,222,177 368,890 0 32 52 16 0

Average 24 62 13 0

Table 12—Estimates of Average Percent Change (APC) and Total Change Equivalent (TCE) by FPU and HVR category. Values 

represent annual change. The M category contains both positive and negative values because some FPUs had beneficial 

effects from fire that outweighed the negative effects—principally due to the response function for fire-adapted ecosystems. 

Results for the H value category are shown with and without sage-grouse habitat for comparison.

TCE (acres) APC

FPU M H (w/ SG) H (w/o SG) VH M H (w/ SG) H (w/o SG) VH

1 Northwest Oregon –2 –500 –500 –161 –0.001 –0.018 –0.018 –0.008

2 Central Coast Range 35 –504 –504 –106 0.066 –0.178 –0.178 –0.089

3 Southwest Oregon –173 –8192 –8192 –1,026 –0.066 –0.582 –0.582 –0.287

4 Central Oregon 3,075 –2105 –224 –510 0.151 –0.190 –0.065 –0.116

5 Northeast Oregon 392 –55 –55 –132 0.057 –0.057 –0.057 –0.081

6 Southeast Oregon 3,507 –16922 –76 –52 0.147 –0.386 –0.106 –0.214

7 Eastern Oregon 1,261 –10120 –56 –11 0.095 –0.315 –0.106 –0.372

8 Southeast/South 1,569 –4492 –158 –40 0.091 –0.173 –0.059 –0.076

9 Wallowa-Whitman 80 –194 –175 –424 0.009 –0.162 –0.156 –0.176

10 Malheur 108 –23 –18 –93 0.040 –0.086 –0.084 –0.119

11 Coos Bay/Roseburg 2 –155 –155 –73 0.008 –0.021 –0.021 –0.013

Total 9,854 –43263 –10113 –2,627

Average 896 –3933 –919 –239 0.097 –0.258 –0.163 –0.063
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-235. 2010 29

Figure 9a—Annual Average Percent Change (APC) by HVR value category for the Oregon 

FPUs. The High HVR category shown here includes sage-grouse key habitat. 

Risk Calculations Without Sage-Grouse

The inclusion of the sage-grouse key habitat layer 

significantly impacted risk calculations in those FPUs 

where habitat is abundant. A full analysis was completed 

comparing the TCE and APC results for all HVR in the 

High value category with and without sage-grouse. The 

results confirm that this layer drives risk results in the 

Southeastern, Eastern, Central, and Southeast/Southern 

FPUs where APC is significantly reduced by removing 

sage-grouse from the calculations (figure 9b). The risk was 

reduced by 0.280 (–0.386 to –0.106 percent), 0.209 (–0.315 

to –0.106 percent), 0.125 (–0.190 to –0.065 percent), 

and 0.114 (–0.173 to –0.059 percent), respectively, in 

these FPUs (table 12).

Discussion _____________________

Wildfire Hazard

Wildfire hazard represents the intensity with which an 

area is likely to burn if fire does occur there. If wildfire 

hazard, not risk, was the main concern, areas of highest 

hazard within individual Fire Planning Units (FPUs) 

could be further refined using map products from this 

analysis. It is important to recognize the difference between 

the hazard values calculated here and various indices of 

fire behavior reported in other publications. Typically 

fire behavior models like FlamMap (Finney 2006) and 

Behave are used to model flame length or fire intensity 

for individual pixels or stands assuming a heading fire. 

Northwest Oregon

Central Coast Ran..

Southwest Oregon

Central Oregon

Northeast Oregon

Southeast Oregon

Eastern Oregon

Southeast/South

Wallowa-Whitman

Malheur

Coos Bay/Roseburg

-0.8

-0.6

-0.4

-0.2

0.0

0.2

Net APC (%)

Moderate HVR

High HVR

Very High HVR
30 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-235. 2010

A spreading fire front will have variable intensity depending on the arrival direction relating to the spreading front. 

Flanking and backing fires have a lower intensity than 

a heading fire. The relative frequency of fires arriving 

from different directions and the resulting intensity is not 

accounted for in analyses that measure fire behavior at the 

pixel or stand level. Thus, the measure of hazard in this 

report represents a more robust estimate of potential fire 

behavior by accounting for these characteristics.

Wildfire Risk

We demonstrated a wildfire risk modeling system 

that incorporates important interactions among wildfire 

spread, flame length, and relative change to highly valued 

resources (HVR). The core wildfire simulation models 

used in this work are being applied daily for operational 

problems throughout the United States and the world 

(www.fpa.nifc.gov/). The USDA Forest Service also 

applies the same core wildfire spread model on a daily 

basis to support wildland fire incidents throughout the 

United States (McDaniel 2007; USGS 2009b). Burn probability modeling is implemented in the operational version 

of FlamMap (Finney 2006) that is widely used for fuel 

planning in Federal land management agencies. The 

modeling framework is well-suited for analyzing a wide 

variety of wildfire risk problems at a range of spatial scales 

ranging from project development (Ager and others 

2007), watershed analyses, National Forest assessments 

(Ager and Finney 2009), to national scales as in this report. 

While some of the methods used in this work remain in 

the research domain, we envision the suite of tools we 

used being made operationally available within the next 

year or two. To accomplish this study, we relied on high 

performance, 64-bit 32 processor computers housed 

at Fire Program Analysis (FPA). Each FPU simulation 

required 3 to 5 hours, depending on the number of fires 

and the size of the FPU. Thus, similar resources will be 

required for implementation at field units.

Northwest OregonCentral Coast RangeSouthwest OregonCentral OregonNortheast OregonSoutheast OregonEastern OregonSoutheast/SouthWallowa-WhitmanMalheurCoos Bay/Roseburg

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0.0

Net APC for High HVRs - with and without sage-grouse (%)

High w/ sage-grouse

High w/o sage-grouse

Figure 9b—Side-by-side comparison of annual APC for the High HVR category 

with sage-grouse and for the High HVR category without sage-grouse for the 

Oregon FPUs. 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-235. 2010 31 Figure 10—APC of HVR in the Moderate category for the Oregon FPUs.
32 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-235. 2010 Figure 11a—APC of HVR in the High category for the Oregon FPUs with sage-grouse.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-235. 2010 33Figure 11b—APC of HVR in the High category for the Oregon FPUs without sage-grouse.
34 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-235. 2010 Figure 12—APC of HVR in the Very High category for the Oregon FPUs.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-235. 2010 35

This approximation quantifies wildfire risk using a 

formal quantitative definition that incorporates wildfire 

likelihood, intensity, and fire effects. Numerous other 

wildfire risk measures have been proposed and published, 

most of which lack one or more of the key components 

of risk. The importance of incorporating wildfire spread 

into risk measures is especially important in the western 

United States where the bulk of the total area burned is 

from a few large fires that spread over large distances. As 

average fire size increases, the utility of risk indices that 

only incorporate local fire behavior diminish, since the 

latter indices do not consider fire spread over large distance.

We summarized wildfire risk as net value change (NVC) 

for individual HVR categories (Very High, High, and 

Moderate). Multiple data layers contributed to each of the 

HVR categories. The bulk of the average percent change 

(APC) occurred in the H HVR category (–0.258 percent 

with sage-grouse and –0.163 percent without sage-grouse) 

compared with 0.097 percent for the M category and 

–0.063 percent for the VH HVR category. The Southwest, 

Southeast, and Eastern FPUs were projected to have the 

largest APC. The higher than average risk in these areas 

is primarily related to the amount of critical habitat and 

key sage-grouse habitat located in these FPUs. On an area 

basis, the bulk of the Total Change Equivalent (TCE) was 

in the H HVR category within the Southeast, Eastern, 

and Southwest FPUs and totaled roughly 35,000 of the 

approximately 43,000 acres. TCE for other FPUs was 

relatively minor for other HVR categories.

The results of the burn probability analyses are consistent 

with recent wildfire activity within Oregon. However, 

data on HVR loss from these fires is not typically available—with the exception of habitat area for the northern 

spotted owl. Hence, comparisons to historic trends with 

outputs like those presented here (APC and TCE) are not 

possible. As demonstrated above, exclusion of key habitat 

for the sage-grouse shifted some of the FPU rankings and 

significantly reduced the projected risk in Southeast and 

Eastern Oregon. Further sensitivity analyses related to 

HVR and response functions assignment will be included 

in subsequent analyses.

Overall, the risk projections calculated here are useful for 

establishing base line information about wildfire threats 

to human and ecological values. These projections can be 

used for ranking relative risk among FPUs and geographic 

areas and for monitoring risk over time. The various risk 

and hazard metrics included in this study (BP, Hazard, 

APC, and TCE) provide a robust set of spatially explicit 

indicators that can be applied at a wide range of scales to 

address management and policy questions pertaining to 

wildfire threats.

Resource Value Issues

Consistent data availability at the national scale is a key 

component of risk assessments and a topic of growing interest to Federal land management agencies, emergency relief 

and rescue agencies, the Department of Homeland Security 

and the Department of Defense. National or centrally 

located enterprise data sets are becoming more readily 

available; however, effort is needed to enhance national 

data sets to reflect recent changes and to maintain data 

accuracy. Various data sets used in this study have been 

developed using data standards to establish consistency 

and accuracy; this is documented in associated metadata. 

Yet with other national data sets, nation-wide coverage 

is achieved through a collection of data compiled at the 

local or state level. Often these data are assembled using 

differing or no data standards, creating attribution and 

scale inconsistencies. For national risk assessment efforts, 

potential data inconsistencies within data sets collated 

from local or regional sources need to be recognized and 

carefully evaluated for potential impacts to the assessment.

Nationally mapped data layers often omit or overlook 

local level resource detail. Such data are frequently developed at a map scale of 1:250,000 to 1:2,000,000, which 

equates to an on-the-ground accuracy of approximately 

4 to 32 miles, respectively (USGS 2009a). Yet often national 

data sets are considered the best available and using them 

is necessary to call attention to needed improvements 

for future data set publications. Frequently, the data set’s 

intended use is not made clear in metadata, or metadata is 

not included with downloaded data sets. If data are intended 

to be used for mapping and analysis, data stewards must 

re-engage with these data to correct known errors and to 

improve accuracy.

Local data constructed at a fine map scale proves to 

be more accurate for local planning and management 

purposes. However, many challenges exist in acquiring 

and using local data for national analysis purposes and 

management applications. Past and current efforts to 

obtain data about resources considered highly valued (for 

example, threatened and endangered species habitat, energy 

resources, and Federal historic structures) have produced 

irregular or often incomplete data sets mapped at the local, 

rather than the national scale. Because data standards, 

types, and completeness vary or may conflict from one 

locale to the next, use and aggregation of local data for a 

national mapping exercise is not yet feasible. These data 

issues are well known among Federal fire management 

agencies and efforts are underway to establish standards 

between FS and DOI agencies to develop, collect, and 

aggregate local data to the national extent (discussed in 

“Data Direction and Roles”).
36 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-235. 2010

Although a great deal of effort was made to collect the best 

available data for this report, several sets were incomplete 

and lacked appropriate detail to accurately distinguish the 

resources of highest value from those of lower value. The 

Sage-Grouse Key Habitat layer is one data set that presented 

challenges when used in this exercise. As indicated in 

table 12, due to the coarseness of the data set, large portions of the southeastern Oregon FPUs in particular are 

indicated at risk because they contain large amounts of 

sage-grouse habitat. According to BLM, this data set was 

the first attempt at a national sage-grouse habitat layer and, 

therefore, is not at a sufficiently fine scale to approximate 

the highest priority sage-grouse habitat. BLM is working 

to produce a data set that includes more detailed mapping 

of critical sage-grouse habitat. This improved data set will 

most likely be more accurate for use in the second and 

subsequent approximations of wildfire risk.

Issues of data scale and attribution also presented 

challenges in assigning resource value to the appropriate 

value category and response function. For example, the 

inconsistent value and campground type attribution of the 

Federal Recreation Sites and Campgrounds HVR layer 

among various agencies’ data sets made it impossible to 

reduce the data set to include only the resources of very 

high value. While the BLM Campgrounds layer and much 

of the NPS Campgrounds layer contained sufficient detail to 

distinguish between developed and unimproved campsites, 

FS and FWS data did not make the distinction. In order to 

prevent overvaluing data with incomplete attribution and 

essentially discounting data sets with comprehensive attribution, all campground records were retained for the final 

HVR data layer. Complete attribution may have resulted 

in some areas being assigned to a higher value category 

than the current Moderate designation (particularly those 

with significant visitation or historic significance), while 

others might not have been included as HVR.

Contrastingly, the Class I Airsheds layer is a data set that 

presented challenges not related to incomplete attribution. 

Class I Airsheds generally represent National Parks and 

National Wilderness Areas (P.L. 108–201)—landscapes 

that often present good opportunities for wildfire use. 

Assigning a value greater than Moderate might overvalue a 

relatively large geographic area and imply that these areas 

are inappropriate candidates for fire use, or worse yet, 

suggest they are appropriate areas for active suppression. 

Additionally, the Class I designation serves to protect air 

quality (including visibility) of designated lands, but smoke 

dispersion affects visibility beyond the boundaries of the 

Class I lands. Using a backward dispersion model like Air 

Quality Impacts Planning Tool (AQUIPT) could potentially 

improve future assessments (www.fs.fed.us/pnw/about/

programs/mdr/airfire-sacc.shtml). The AQUIPT allows 

managers to combine historical weather data and fuels 

characteristics with other burn parameters to map wildfire 

locations that would produce severe impact to each Class 

I Airshed (Larkin, personal communication).

Assigning appropriate response functions was most 

challenging for the Critical Habitat layer. Within the data 

layer, species were identified as fire-tolerant, fire-neutral, 

or fire-susceptible. Because additional data and input from 

the scientific community was needed in order to understand 

the full spectrum of impact to species’ habitat, it was not 

possible to accurately assign response functions to both 

fire-tolerant and fire-neutral species. We decided to include 

only fire-susceptible species for the first approximation 

exercise until further recommendations from the scientific 

community can be acquired.

The data sets acquired for this first approximation of 

risk represent the best available HVR data defined by the 

FPA Executive Oversight Group and the authors of this 

study. As discussed above, most of these data are still 

incomplete with respect to extent and attribution, and we do 

not fully understand the effects of fire on these resources. 

Despite our best efforts to present complete and accurate 

HVR data layers, a bias exists toward data that are readily 

available due to agency priorities for data completion. A 

firm understanding of the limitations and inherent biases 

is important when using these data. While the resources 

presented here do represent some of the HVR impacting 

management of wildfire, they do not provide a comprehensive representation of all possible HVR. Undoubtedly, 

the second approximation of wildfire risk will include data 

set revisions and a modified HVR list—excluding some 

layers presented here and adding data that was unavailable 

at the time of this study.

Data Direction and Roles

One of the purposes of this study was to identify the best 

available, nationally consistent data and to call attention 

to those data sets that need improvement and updating. 

Data challenges similar to those described here exist in 

other areas of wildland fire management planning and 

response. As previously discussed, the WFDSS values-atrisk assessment (RAVAR) requires geospatial data related 

to National Critical Infrastructure (NCI) and natural and 

cultural resources. Many of the NCI data are available from 

sources used in this exercise, such as Homeland Security 

Infrastructure Program (HSIP), ESRI Data and Maps, 

and National Transportation Atlas Database. However, 

natural and cultural resources data are rarely maintained 

by an authoritative national source. Data of this nature are 

inherently different from data managed and created in the 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-235. 2010 37

private sector due to the lack of commercial opportunities 

and incentives. Not only are Federal agencies limited to 

the finances already allocated for data development, but 

catastrophic events have not catalyzed natural resource 

data amelioration in the same way natural disasters and 

terrorist attacks have motivated NCI data development.

Natural resource data are most often collected and compiled at the local scale, within the jurisdictional Federal 

agency. For example, threatened and endangered species 

habitat is often mapped at a local scale and the extent is 

limited to the land management agency’s administrative 

boundaries (for example, National Forest, National Park, 

or Wildlife Refuge), resulting in data sets that do not accurately represent the full habitat extent. In order for these 

data to be used in national mapping exercises, data must 

be compiled from all local units and integrated into larger 

or national data layers. These endeavors are time intensive 

and often result in data sets with gaps where geospatial 

data are unavailable or where data are limited to one land 

management agency. These gaps make it impossible to 

determine whether the species or resource is not present 

in the area of interest or whether the geospatial data are 

simply unavailable.

Much attention and financial resources are being directed 

toward improving national data sets used in wildland 

fire management, planning, and response. Efforts are 

underway to address undoubtedly the greatest challenge 

in this process—acquiring natural resource data managed 

by Federal agencies. The WFDSS Data Coordinator and 

cooperating Federal wildland fire management agencies 

are identifying individuals to lead data collection efforts 

from within geographic areas (generally organized by 

Geographic Area Coordination Center (GACC) boundaries) and between land management agencies. As priority 

resource data are identified from within geographic areas, 

those area leads will coordinate with other agency leads to 

identify priority resource overlap. Although this process 

was initiated in the 2009 fire season, clear direction and 

prioritization of resources from agency leadership is needed 

in order to re-task individuals to address these data issues 

and achieve long-term success (Appendix C).

To effectively support a national monitoring exercise 

or decision support system, data must be consistent in 

scale and extent. Although the wildland fire community 

is aware of the data issues previously discussed, an inherent lag exists between problem identification and data 

solutions development. The practice of employing “best 

available” data must be the interim solution while Federal 

land management agencies work together to establish 

data standards that create consistent resource data across 

management boundaries.

Understanding Fire Effects on Highly 

Valued Resources

Input related to data interpretation is another critical 

component of successful risk analysis of HVR. The 

authors consulted with experts and senior leadership in 

the wildland fire community for assistance in valuing 

resources and interpreting fire effects on resources, in 

order to adequately assign response functions in this report. 

We intend to involve more input from the scientific and 

research communities in subsequent exercises to better 

inform management decisions related to fire effects on 

resources and sensitive species. This input is critical to 

accurately interpret model results.

It is also anticipated that as natural resource data prioritization is determined within geographic areas, data 

stewards and fire management personnel will engage with 

local scientists and resource specialists to determine how 

to interpret these data within the context of wildland fire 

management. Familiarity with data sources and knowledge 

of resource locations within one’s geographic area is an 

added benefit of compiling local data to create coverage 

at a larger geographic extent.

Using the data and response functions developed in this 

study creates significant opportunities to explore tradeoffs 

associated with alternative policies and budgeting mechanisms. However, there remain two primary challenges: (1) 

the quality and availability of resource value data (previously discussed), and (2) the state of economic valuation 

in quantifying change to non-market resources. It appears 

that the existing state of non-market economics is not sufficiently advanced to provide price-based mechanisms to 

guide the prioritization of goal programming exercises to 

explore efficient resource distribution and policy (Venn and 

Calkin 2009). A variety of multi-criteria decision analysis 

models may be applied to provide useful approximations 

that help guide management activity prioritization where 

multiple resource values interact with wildfire threat. 

The application and extension of the HVR data to goal 

programming efforts may be very informative. However, 

due to these challenges it is critical that all efforts evaluate 

the implications of these known limitations to any results.

Conclusion_____________________

This paper presented methods to incorporate wildfire 

spread, fire intensity, and change in value for a range of 

human and ecological values into a risk framework. The 

spatial, temporal, and social dimensions of the wildfire 

risk problem are challenging the Federal land management 

agencies to meet societal needs while maintaining the 
38 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-235. 2010

health of the lands they manage. Recent fire management 

data and modeling developments, such as LANDFIRE, 

Fire Program Analysis (FPA), and Wildland Fire Decision 

Support System (WFDSS), among others, allow a level 

of analysis and assessment that were, until very recently, 

impossible. These developments pose opportunities to analyze, communicate, and implement a more risk-informed 

fire management policy that can reduce Federal fire 

management costs and improve land condition. Extensions 

of these efforts, such as the one described in this report, 

demonstrate the potential of a national risk assessment 

framework. This study is scalable from local project 

planning to national assessments and can accommodate 

a broad range of fire management activities, such as fuel 

treatment scheduling, fire planning, suppression decisions 

support, and fire resource budgeting.

This first approximation established an appropriate 

risk framework to meet the three goals identified in the 

introduction—the framework: (1) addressed the WFLC 

monitoring questions on wildfire hazard by identifying Fire 

Planning Units (FPUs) with high burn probabilities and/or 

high fire intensities; (2) established a base line to inform 

the prioritization of fuels treatments and identified FPUs 

where mitigation measures might most effectively reduce 

wildfire risk (i.e., areas with the potential for high loss of 

highly developed and natural resources); and (3)responded 

to calls from oversight agencies for risk-based performance 

measures of fire management efforts by identifying HVR 

data sets to track in order to evaluate cost effectiveness of 

suppression measures.

Work remains to improve the quality, extent, and consistency of spatial data for highly valued resources (HVR) 

and the economic valuation of the effects of fire on these 

HVR. Of particular concern are spatial data that represent 

consistent resource value such that all characterized areas 

belong in the assigned HVR category. This is a particular 

problem if the distribution of misrepresented HVR differs 

in a way that could affect policy. For example, suppose 

you are trying to allocate treatment dollars between two 

FPUs. In one FPU, all campgrounds are highly developed 

and very high valued; in the other, all the campgrounds 

are dispersed and undeveloped and of low value—the 

composite average value is moderate. Resources could 

be misallocated to protect the low value campgrounds.

The application of annual value change to quantify risk 

to HVR creates additional challenges because some of the 

identified HVR are proxies for the real underlying value 

at risk. For example, consider the Critical Habitat layer. 

Its role in preventing extinction is what is most important. 

If the entire habitat is destroyed in the short-term, it will 

not matter if it recovers quickly because the species that 

depend on it may become extinct in the meantime. If half 

the habitat is destroyed now and then recovers, but the other 

half is destroyed later, the species may survive. These are 

two very different outcomes for the same aggregate value 

change. Given these challenges and recognized limitations, the authors recommend caution in utilizing these 

results to distribute budgets and prioritize large areas for 

fuel treatment and mitigation efforts. This is because of 

the potential biases created by the current state of spatial 

data and challenges in assigning relative value to the 

modeled resources.

However, we believe this first approximation demonstrates that it is now possible to represent and quantify 

risk at the broad scale of the sub-regions of the United 

States using the best available data. We also believe this 

first approximation has identified future efforts that could 

make this framework more applicable to fire management 

resource distribution efforts. Existing models’ analytical 

capabilities could be enhanced by incorporating the effects 

of fire on HVR. The authors are encouraged by the efforts 

that Federal fire management officials and agencies have 

made to date and we believe the potential payoffs from 

this focus far outweigh the costs.

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40 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-235. 2010

Appendix A: Biological Data Not Included in the Model

Ecological Spatial Data Sets Discarded

The following list of ecological spatial data sets were collected, evaluated, and discarded from further consideration in 

this report for a variety of reasons. This summary is organized and presented in four main categories of data problems: 

(1) Spatial resolution is too coarse; 

(2) Map extent is incomplete (not national coverage); 

(3) Data set is inappropriate to assign response functions; and 

(4) Data set might make a viable input with further refinement.

1. Spatial resolution is too coarse

a. Conservation International Hotspots Revisited

The Conservation International Hotspots Revisited data set consists of regions known to hold especially high 

numbers of species found nowhere else, yet their total remaining habitat covers a little more than 2 percent 

of Earth’s land surface (figure A1). To qualify, an area must: (1) contain at least 1,500 endemic, native vascular plant species, and (2) have already lost at least 70 percent of its primary, native vegetation. As evidence 

of their urgent need for global conservation, hotspots also house exceptionally high numbers of threatened 

vertebrates, including 50 percent of threatened mammals, 73 percent of threatened birds, and 79 percent of 

threatened amphibians as endemics (Conservation International 2004; Mittermeier and others 2004). Although 

some areas within the regions would be valuable inputs to the model, the coarse nature of the representation 

makes it inappropriate.

Figure A1—Conservation International Hotspots Revisited data set. Red shaded areas represent 

habitat of unique species.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-235. 2010 41

b. Global 200 Most Biologically Valuable Ecoregions

The Global 200 Most Biologically Valuable Ecoregions are defined as ecoregions whose conservation would 

achieve the goal of saving a broad diversity of the Earth’s ecosystems, including those with exceptional levels 

of biodiversity, such as high species richness or endemism, or those with unusual ecological or evolutionary 

phenomena (figure A2). This data set aims to represent all of the world’s biodiversity by identifying outstanding ecoregions in all of the world’s biomes and biogeographic realms (World Wildlife Fund 2006a; Olson and 

Dinerstein 1998).

Figure A2—Global 200 Most Biologically Valuable Ecoregions.
42 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-235. 2010

c. Global Wetlands

The Global Wetlands dataset represents wetlands in danger (Dungan 1993) as defined by the United Nations 

Environmental Program World Conservation Monitoring Center (figure A3).

Figure A3—Global Wetlands data set. 

d. Species Richness by Taxa

The Species Richness by Taxa data set represents the number of mammal, bird, reptile, and amphibian species known to occur by ecoregion (figures A4, A5, A6, and A7). The dataset is available by WWF Terrestrial 

Ecoregion (World Wildlife Fund 2006b).
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-235. 2010 43

Figure A4—Mammal species richness by WWF Terrestrial Ecoregion.

Figure A5—Bird species richness by WWF Terrestrial Ecoregion.
44 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-235. 2010

Figure A7—Amphibian species richness by WWF Terrestrial Ecoregion.

Figure A6—Reptile species richness by WWF Terrestrial Ecoregion.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-235. 2010 45

e. Species Endemism by Taxa

The Species Endemism by Taxa data set represents the number of endemic mammal, bird, reptile, and amphibian species (figures A8, A9, A10, and A11). The data set is available by WWF Terrestrial Ecoregion. Endemic 

species are of particular concern because conservation options are so geographically limited (World Wildlife 

Fund 2006b).

Figure A8—Mammal 

endemism by WWF 

Terrestrial Ecoregion.

Figure A9—Bird endemism 

by WWF Terrestrial 

Ecoregion.
46 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-235. 2010

Figure A10—Reptile endemism by WWF Terrestrial Ecoregion.

Figure A11—Amphibian endemism by WWF Terrestrial Ecoregion.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-235. 2010 47

f. Species Rarity by Taxa

The Species Rarity by Taxa data set represents the number of mammal, bird, reptile, and amphibian species 

on the IUCN Red List of Threatened Species by WWF Terrestrial Ecoregion (figures A12, A13, A14, and 

A15). The “threatened” designation includes species listed as Critically Endangered (CR), Endangered (EN), 

and Vulnerable (VU) (World Wildlife Fund 2006b).

Figure A12—Number of 

mammal species on the 

IUCN Red List of Threatened 

Species by WWF Terrestrial 

Ecoregion.

Figure A13—Number of bird 

species on the IUCN Red List 

of Threatened Species by 

WWF Terrestrial Ecoregion.
48 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-235. 2010

Figure A14—Number of reptile species on the IUCN Red List of Threatened Species by WWF 

Terrestrial Ecoregion.

Figure A15—Number of amphibian species on the IUCN Red List of Threatened Species by 

WWF Terrestrial Ecoregion.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-235. 2010 49

g. Centers of Plant Diversity 

World Wildlife Fund and International Union for Conservation of Nature identified centers of plant 

diversity (figure A16) using the following criteria. The sites had to be either particularly species-rich or contain a large number of endemic species. Mainland centers had to contain at least 1,000 vascular plant species 

(estimated), with 100 or more endemics, and the island centers had to contain at least 50 endemics or at least 

10 percent of the flora had to be endemic. To be considered, sites also had to contain: an important gene-pool 

of plants having value, or potential value, to humans; a diverse range of habitat types; a significant proportion 

of species adapted to special soil conditions; and must be under threat of large-scale devastation (Davis and 

others 1995).

Figure A16—Plant diversity centers. 

h. Forest Intactness

Relative forest intactness is spatially represented in this data set for 39 forested ecoregions of the coterminous United States (figure A17). Forest intactness was mapped within “landunits” that were defined by 

highways and urban areas that contained more than 50,000 people. For each landunit, road density was calculated as well as a suite of class and landscape level fragmentation metrics, including class area, percentage of 

landscape, total core area index, and mean nearest neighbor, from which an overall relative forest intactness 

score was derived. The data set from the Conservation Biology Institute identifies remaining, relatively intact 

forest and good regional restoration candidates (Heilman and others 2002).
50 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-235. 2010

Figure A17—Forest intactness.

i. Critical Watersheds for Conserving Aquatic Biodiversity 

NatureServe identified the 15 percent of the U.S. watershed areas that will conserve the greatest proportion 

of at-risk freshwater fish and mussel species (www.natureserve.org/publications/preciousHeritageCharts.jsp) 

(figure A18; Stein and others 2000).

Figure A18—Critical 

watersheds for aquatic 

biodiversity conservation.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-235. 2010 51

j. Crayfish Rarity 

NatureServe identified USGS 8-digit Hydrologic Unit Codes (HUC) with known occurrences of G1-G3 

(globally critically imperiled, imperiled, or vulnerable) crayfish species (figure A19). Half of U.S. crayfish 

species are at risk (Stein and others 2000).

k. Mussel Rarity 

NatureServe identified USGS 8-digit HUCs with known occurrences of G1-G3 (globally critically imperiled, 

imperiled, or vulnerable) freshwater mussel species (figure A20). Two-thirds of U.S. fresh water mussel species are at risk (Stein and others 2000).

l. Imperiled Species by Equal Area Hexagon 

NatureServe identified EPA EMAP hexagons (160,000 acres) with known occurrences of G1-G2 (globally 

critically imperiled or imperiled) species (Stein and others 2000). Not only is the resolution of this data set 

too coarse, but the content is outdated (figure A21).

Figure A19—Number of imperiled crayfish species by USGS 8-digit HUC.
52 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-235. 2010

Figure A20—Number of imperiled mussel species by USGS 8-digit HUC.

Figure A21—Number of imperiled species by EPA EMAP hexagon.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-235. 2010 53

2. Map extent is incomplete

a. Priority Conservation Areas

The Nature Conservancy identified conservation interest areas that represent landscapes of mixed land use 

where compatible activities should be integrated properly to allow ecosystems to sustain an adequate level of 

functional stability and to provide connecting habitat corridors for the movement of species and maintenance 

of biological diversity. These areas are a mix of existing public land and private conservation areas, recommended acquisition areas, and areas of conservation interest where less than fee simple techniques should be 

used in addition to purchasing. Data are available for the Western, Northwestern, Northeastern, and Southern 

United States; while CA, HI, AK, and the Midwest are missing (The Nature Conservancy 2007; The Nature 

Conservancy, 2009 n.d.).

b. National Wetlands Inventory 

The National Wetlands Inventory identifies type, size, and location of wetlands and deepwater habitats in the 

United States. Full digital availability exists for only 26 states with sporadic extent (USFWS 2009).

c. Species Richness layer creation from GAP Vertebrate data 

Species richness data are available by EMAP hexagon or 30- by 30-m grid for 26 states. Twenty-three states 

have distribution data for creation of richness grids/hexagons, and data are unavailable for two states (USGS 

n.d.). 

d. Underrepresented Plant Communities from GAP Landcover data 

GAP Landcover regionally updated data sets are available for the Southwest (NV, UT, CO, NM, and AZ), 

Northwest (OR and WA), and Southeast (KY, TN, MS, AL, FL, GA, SC, NC, and VA). Original regional 

data sets are available for the Northeast (VT, NH, CT, MA, RI, MD, DE, and NJ). The remaining 25 states 

must be downloaded individually from original GAP data. Classes differ between all 31 layers, and “underrepresented” plant classes must be defined. All data grids must be reclassed and edges must be matched/

joined. No data exists for AK (USGS n.d.).

3. Inappropriate to assign response functions

The following conservation data sets, by their nature, are inappropriate for the fire model as these lands are valued for 

their lack of human management. Natural disturbance regimes (including fire) are also highly valued in these landscapes 

making them poor candidates for the fire risk modeling.

a. Frontier Forests

Frontier forests, mapped in 1997 by Global Forest Watch and the World Resources Institute, are defined as 

being primarily forested; of sufficient size to support viable populations of the full range of indigenous species 

associated with that particular forest ecosystem given periodic natural disturbance episodes; and exhibiting 

a structure and composition shaped largely by natural events, as well as by limited human disturbance from 

traditional activities (figure A22). Frontier forests are relatively unmanaged, are home to most if not all of 

the species associated with that ecosystem type, are dominated by indigenous tree species associated with 

that ecosystem type, and are characterized by mosaics of forest patches representing a range of seral stages 

in areas where such landscape heterogeneity would be expected to occur under natural conditions (Bryant 

and others 1997). The data set is available from Global Forest Watch (www.globalforestwatch.org).

b. Last of the Wild 

The Last of the Wild data set was derived from the Human Footprint Dataset (figure A23). The ten largest wild 

polygons of more than 5 km2 within each biome by realm are selected and identified (Columbia University 

2002). 
54 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-235. 2010

Figure A22—Frontier Forests as delineated by Global Forest Watch and the World 

Resources Institute.

Figure A23—Wildlife Conservation Society’s Last of the Wild data set.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-235. 2010 55

c. Top 1 Percent Wild Areas 

This data set was derived from the Human Footprint Dataset (figure A24). The top 1 percent of the wild areas 

within each biome by realm are selected and identified (Columbia University 2002).

Figure A24—The top 1 percent of the wild areas as delineated by the Wildlife Conservation 

Society.

4. Viable input with further refinement

a. State Level Natural Heritage Data

Each state has its own Natural Heritage Program that routinely collects field level natural heritage data and 

manages the findings in spatial databases. NatureServe is the umbrella organization that establishes data 

collection and maintenance standards and provides national summary information for a variety of planning 

purposes. Coarse level roll-ups are inappropriate for inclusion in the wildfire risk model; however, the more 

detailed point data collected at the state level may prove far more promising. For example, the following figures show Federally listed species locations collected by the Oregon Natural Heritage Program (figure A25) 

compared to similar data organized by EMAP hexagons (figure A26). The enhanced spatial specificity of 

the former data set provides much greater value for the wildfire risk model; however, several additional considerations need to be examined before proper implementation of the risk model can be achieved.

Natural heritage data sets contain thousands of records for hundreds of species. In order to assign meaningful response 

functions, each species of interest would need to be examined individually. There is no one response function that applies 

to all rare species—responses to fire intensity would need to be addressed on a species-by-species basis. It may be possible to “bundle” species into response function groups (e.g., these six species share the same response to fire), but doing 

so would require an additional post-processing step after each species of interest was assessed. Regardless of whether 

species were treated individually or in groups, a short-list of species would need to be developed.
56 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-235. 2010

Figure A25—Number of Federal status species records in the Oregon Natural Heritage data set per 

270-m grid cell for Oregon.

Figure A26—Number of imperiled species by EPA EMAP hexagon for Oregon.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-235. 2010 57

Another factor to consider is the data format used by each state heritage program. Even though there are data collection 

standards being implemented across the country, there are several different ways individual heritage programs publish 

these data. Some provide actual point locations, others summarize point locations over larger geographic areas (e.g., 

regular grid array of some size), and still others represent their data using different sized circles around point locations 

to denote level of precision. These different approaches would need to be brought together into a single file format, 

which would be somewhat tedious if data were collected on a state-by-state basis and then processed. A far superior 

alternative would be to work directly with NatureServe to provide the service required for this component in the risk 

model. NatureServe was founded to provide the overarching support for state heritage programs and to provide these 

types of services. 

b. Protected Lands 

Using the CBI-PAD version 4.5 (Conservation Biology Institute 2008), GAP Status 1 and 2 lands were considered for inclusion in the wildfire risk model (figure A27; DellaSalla and others 2001). However, after closer 

examination of the attributes currently available, it is difficult to assign response functions to these polygons. 

Complicating this exercise further are the definitions of these two GAP categories: 

• GAP Status 1: lands having permanent protection from conversion of natural land cover and mandated 

management plans to maintain a natural state, and maintenance of natural disturbance events. 

• GAP Status 2: lands having permanent protection from conversion of natural land cover and mandated 

management plans to maintain a primarily natural state, but allowing for management practices that 

degrade the quality of existing natural communities, including suppression of natural disturbances. 

Figure A27—Protected lands (with GAP Status 1 or 2) from the CBI-PAD.
58 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-235. 2010

Regardless of the protection status, the fact that these lands are protected does not easily translate into specific 

response functions in a wildfire risk model. Risk is a function of what is on the ground, not its particular management 

designation. This data set may provide some information about rare or vulnerable ecological values (e.g., botanic areas 

and research natural areas), but these designated places are small and difficult to assess without additional information. 

Not all botanic areas, for example, would be characterized by the same response functions. It would depend upon what 

plant communities are present. 

The new United States Protected Areas Database (PAD-US) will likely expand its attribute table to describe more 

than just management intent expressed through GAP codes. Discussions are underway to provide additional information about various ecological values contained in each mapped polygon. In the not too distant future, the new PAD-US 

database may provide important inputs to future wildfire risk modeling.

c. Rare and Vulnerable Ecological Systems 

Using the existing data on ecological systems mapping for the country (a joint Landfire-NatureServe effort) 

we considered including these data in the wildfire risk model (USGS 2008; www.natureserve.org/explorer/

servlet/NatureServe).

Ecological systems are medium resolution vegetation community types comprised of several association/alliance level 

communities. The associations (greater than 400 types) that comprise each system are fully described, but their locations are not mapped; therefore, the spatial extent of each is unknown. Those ecological systems that are most rare in 

the country (covering less than 0.05 percent of the U.S. landcover) were mapped (figure A28). These systems are either 

naturally rare or rare due to anthropogenic changes.

Figure A28—Rare ecological systems.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-235. 2010 59

We could also query those ecological systems that contained high numbers of rare plant communities, but the exact 

location and extent of a given system could not be delineated. For example, system A has 10 member associations, eight 

of which are G1-G2. System A would be considered to have a high percentage of imperiled associations (80 percent). 

However, the spatial extent of the eight imperiled associations is unknown: they may cover only 10 percent of the system’s mapped extent or 90 percent (figure A29).

By combining these two primary inputs, it may be possible to map areas that cover little area and contain high levels 

of imperiled plant communities. A follow-up step would evaluate the systems of highest concern in terms of response 

functions.

Figure A29—Ecological systems with high percentages of imperiled vegetation associations.
60 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-235. 2010

References _____________________

Bryant, D., Nielsen, D., Tangley, L. 1997. The last frontier forests: 

Ecosystems and economies on the edge. World Resources Institute. 

Washington, DC.

Columbia University. 2002. Last of the wild project, version 1, 

2002 (LWP-1): Last of the wild dataset. Palisades, NY: Columbia 

University, Center for International Earth Science Information 

Network. Available: http://www.ciesin.columbia.edu/wild_areas/ 

[September 30, 2008].

Conservation Biology Institute. 2008. Protected areas database. 

Available:http://www.consbio.org/what-we-do/protected-areasdatabase-pad-version-4/ [February 12, 2009].

Conservation International. 2004. Hotspots revisited 2004 data set. 

Conservation International, Center for Applied Biodiversity Science. 

Available: http://www.biodiversityhotspots.org/xp/hotspots/

resources/Pages/maps.aspx [September 30, 2008].

Davis, S.D., Heywood, V.H., Hamilton, A.C., eds. 1995. Centres of plant 

diversity: A guide and strategy for their conservation. 3 volumes. 

Worldwide Fund for Nature and The World Conservation Union, 

IUCN Publications, University of Cambridge.

DellaSala, D.A., Staus, N.L., Strittholt, J.R., Hackman, A., Iacobelli, 

A. 2001. An updated protected areas database for the United States 

and Canada. Natural Areas Journal. 21:124-135.

Dugan, P., ed. 1993. Wetlands in danger: A world conservation atlas. 

New York, NY: Oxford University Press. 187 p.

Global Forest Watch and World Resources Institute. 1997. Frontier 

forests. Available: http://ims.missouri.edu/gfwmetadataexplorer/ 

[September 30, 2008].

Heilman, G.E., Jr., Strittholt, J.R., Slosser, N.C., DellaSala, D.A. 2002. 

Forest fragmentation of the conterminous United States: Assessing 

forest intactness through road density and spatial characteristics. 

BioScience. 52:411-422. Available: http://www.consbio.org/whatwe-do/forest-intactness-database [September 30, 2008].

Mittermeier, R.A., Robles-Gil, P., Hoffmann, M., Pilgrim, J.D., Brooks, 

T.B., Mittermeier, C.G., Lamoreux, J.L., Fonseca, G.A.B. 2004. 

Hotspots revisited: Earth’s biologically richest and most endangered 

terrestrial ecoregions. Conservation International. 390 p.

The Nature Conservancy. 2007. Priority conservation areas in western 

North America, version 1. Tuscon, AZ: The Nature Conservancy, 

Arizona Field Office. Available: http://azconservation.org/

downloads/multi/category/inter_mountain_west/ [September 19, 

2008].

The Nature Conservancy. (December 9, 2009—last update). 

Washington conservation and science planning, ecological 

assessments. Arlington, VA: The Nature Conservancy. Available: 

http://www.waconservation.org/ecoregionalAssessments.shtml 

[January 13, 2010].

The Nature Conservancy. [n.d.] Conservonline: Eastern conservation 

science, ecoregional plans. Arlington, VA: The Nature Conservancy. 

Available: http://conserveonline.org/workspaces/ecs/plans 

[September 19, 2008].

Olson, D.M., Dinerstein, E. 1998. The Global 200: Priority ecoregions 

for global conservation. Annals of the Missouri Botanical Garden. 

89:125-126.

Stein, B.A., Kutner, L.S., Adams, J.S., eds. 2000. Precious heritage: 

The status of biodiversity in the United States. Oxford University 

Press. Available: http://www.natureserve.org/publications/

preciousHeritageCharts.jsp [September 26, 2008].

U.S. Fish and Wildlife Service [USFWS]. (December 4, 2009—last 

update). National wetlands inventory, wetlands mapper. Washington, 

DC: U.S. Fish and Wildlife Service. Available: http://www.fws.gov/

wetlands/Data/Mapper.html [January 13, 2010].

U.S. Geological Survey [USGS]. 2008. The national map LANDFIRE: 

LANDFIRE national existing vegetation type layer. Available: http://

gisdata.usgs.net/website/landfire/ [November 4, 2008].

U.S. Geological Survey [USGS]. [n.d.] The GAP Analysis Program. 

Reston, VA: U.S. Department of the Interior, U.S. Geological 

Survey. Available: http://www.nbii.gov/portal/community/

GAP_Analysis_Program/Communities/Maps,_Data,_&_Reports/ 

[November 17, 2008].

World Wildlife Fund. 2006a. Global 200 ecoregions database. 

Washington, DC: World Wildlife Fund. Available: http://www.

worldwildlife.org/science/data/item1878.html [September 30, 2008].

World Wildlife Fund. 2006b. WildFinder: Online database of species 

distributions, ver. Jan-06. Washington, DC: World Wildlife Fund. 

Available: http://www.worldwildlife.org/wildfinder/wildFinderDB.

cfm [September 24, 2008].
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-235. 2010 61

Import FPA 2 Raster

This module reads in the text files produced by FSim, 

which contain probabilities of wildland fire by flame lengths 

under specified fuel and weather conditions analyzed at the 

FPU level (see figure 1 in main text for a map of the Oregon 

FPUs, or visit http://www.fpa.nifc.gov/Implementation/

TechInfo/Docs/SizeA_FPU_Basemap_20081231.pdf 

for the national FPU map). The text files contain (X,Y) 

coordinates and six flame lengths—F1 through F6. Point 

locations in the files are on a 270-m grid. These points are 

converted to an intermediate database (dbf) file; in this 

conversion process, the six flame length fields are collapsed 

into four flame length classes (FLCs)—L, M, H, and VH:

L = F1 (less than 4 ft)

M = F2 + F3 (greater than 4 to 8 ft)

H = F4 + F5 (greater than 8 to 12 ft)

VH = F6 (greater than 12 ft)

The database file is converted to an intermediate (X,Y) 

event table. A raster file for FLC for the FPU is then 

created (FPU_FLC). 

FLC_HVR 

The output files from the Import FPA 2 Raster module contain FLC values for all pixels in the FPU; the 

FLC_HVR module masks these input FPU_FLC files so 

that only those pixels corresponding to the highly valued 

resources (HVR) are output. The HVR raster files have 

Appendix B: Modules of the Risk Assessment Model

been processed so they correspond properly with the 

270-m grid of the FPU_FLC file. The input FPA files 

contain pixel values outside the FPU boundary, so an 

additional mask is applied in this module to output only 

pixels inside the FPU boundary.

BL_Calc 

For each HVR, an initial value (IV) of L, M, H, or VH 

was assigned (see Categorizing Highly Valued Resources 

in main text). This final module calculates the percent 

change in IV for the HVR pixels based on a fire event 

of a particular intensity (FLC). This percent change is 

calculated by multiplying a coefficient for each FLC with 

the FLC_HVR pixel value (i.e., the reclassed FPA flame 

length values). These coefficients are obtained from a 

user-defined response function (Benefit/Loss (BL) 

number that the module uses to reference a table with the 

coefficient values see (table 3 in main text). Output from 

this step of the module is a raster file of percent change 

pixels for the HVR for each of the FLCs. These output 

pixels are then summed by HVR, producing a raster file 

of total percent change for the HVR across all FLCs. The 

third output is a sum of these total percent change values 

for all HVR assigned to a particular initial HVR category 

value (see figure 4 in main text). This module was also 

engineered to run as an ArcToolBox Model with a userfriendly interface.
62 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-235. 2010

Identification of Key Data Sets to Inform 

Fire and Fuel Management Decisions 

As previously discussed, efforts by Federal wildland 

fire agencies are underway to identify agency leads, prioritize resource data layers, and in some cases, appropriate financial resources to address the need for nationally 

consistent natural resource data layers. The development 

of the Wildland Fire Decision Support System (WFDSS) 

Geographic Information System (GIS) Team is one step 

toward this end. The GIS Team consists of interagency 

personnel involved in fire-related GIS and/or decision 

support management. Identified geographic area GIS 

personnel and WFDSS Geographic Area Editors will work 

to collect data and coordinate with interagency individuals to incorporate data to a larger geographic extent. As 

this effort begins to unfold, the team will be identifying 

priority data layers to serve as a representative model for 

how this process will take place. The wildland fire community would be well-served by appointment of a GIS or 

Data Management Specialist at each Geographic Area 

Coordination Center (GACC) to facilitate this critical data 

effort and ensure project success.

The first steps following identification of priority data 

layers will be to develop data standards and request data 

input from the appropriate agencies along particular 

Appendix C: Efforts to Improve HVR Data Sets

themes. For example, within the theme of threatened and 

endangered species habitat, certain species will be identified whose terrestrial range encompasses the administrative authority of multiple land management agencies. It 

will be recommended to each agency to collect the data 

of all smaller management units within their management 

jurisdiction or geographic area. From this collection, 

areas of no-data will be identified and polygons will be 

attributed in the corresponding records. Once this level 

of accuracy is assured within a particular geographic area 

and/or agency, these data can be combined with resulting 

layers from neighboring agencies. 

Additionally, Forest Service Fire and Aviation 

Management GIS have developed a contract with ESRI 

to design an enterprise geodatabase that will house 

interagency, nationally consistent GIS data with national 

coverage (where possible). These data will be accessible 

for many projects requiring national geospatial data sets 

that are otherwise unavailable. The specific intent of this 

geodatabase is to avoid future duplication of data efforts 

within Federal agencies and to allow for easy access and 

enhanced development of these data. Although the platform 

for this project has been established, the development will 

be ongoing and data (especially of more challenging data 

sets) may not be available for years. 



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