United States Department of Agriculture

Forest Rocky Mountain General Technical Report

Service Research Station RMRS-GTR-392 May 2019

Cross-Boundary Wildfire and Community Exposure: 

A Framework and Application in the Western U.S.

Alan A. Ager, Michelle A. Day, Palaiologos Palaiologou, 

Rachel M. Houtman, Chris Ringo, and Cody R. Evers
Front cover: A DC10 Air Tanker is seen over the Woolsey Fire in California. (Photo courtesy of Peter Buschmann)

All Rocky Mountain Research Station publications are published by U.S. Forest Service employees and are in the public 

domain and available at no cost. Even though U.S. Forest Service publications are not copyrighted, they are formatted 

according to U.S. Department of Agriculture standards and research findings and formatting cannot be altered in 

reprints. Altering content or formatting, including the cover and title page, is strictly prohibited.

Ager, Alan A.; Day, Michelle A.; Palaiologou, Palaiologos; Houtman, Rachel M.; Ringo, Chris; Evers, Cody R. 2019. Cross-boundary 

wildfire and community exposure: A framework and application in the western U.S. Gen. Tech. Rep. RMRS-GTR-392. Fort Collins, 

CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 36 p. 

Abstract

In this report we provide a framework for assessing cross-boundary wildfire exposure and a case study application in the 

western U.S. The case study provides detailed mapping and tabular decision support materials for prioritizing fuel management 

investments aimed at reducing wildfire exposure to communities located proximal to national forests. The work was motivated by 

a number of factors, including a request from U.S. Department of Agriculture Undersecretary James Hubbard (Natural Resources 

and Environment) to assess community wildfire risk specifically from Forest Service lands, language in the 2018 omnibus bill 

(Public Law 115-141) calling for a national assessment of wildfire risk to communities, and newer shared stewardship initiatives 

(Clavet 2018). We used national FSim simulation outputs to (1) estimate cross-boundary wildfire among major land types 

(Federal, State, private); (2) quantify structure exposure to all western communities; (3) map sources of community wildfire 

exposure (firesheds); (4) characterize firesheds in terms of management opportunity and fuels; and (5) prioritize communities 

based on integration of exposure and fireshed characteristics. The study revealed that 1,812 communities in the western U.S. 

could potentially be significantly impacted by future wildfires (more than 1 structure per year on average). Ignitions on national 

forest lands will most likely affect 516 of these 1,812 communities (more than one structure per year on average). Of the total 

exposure, ignitions on national forest lands will expose an estimated 4,000 structures (21 percent of total) in the western 

U.S. per year on average. Due to administrative restrictions on national forest lands, only about half of the total exposure from 

national forest lands (2,200 structures) originates on lands where mechanical treatments and prescribed fire are either allowed 

or ecologically appropriate. The framework can guide future efforts aimed at quantifying community and other cross-boundary 

exposure situations, and the outputs can be used to help identify shared stewardship projects, and prioritize fuel and other 

management activities within public land management agencies. 

Keywords: firesheds, wildland urban interface, community wildfire risk, wildland fuel management, wildfire transmission
Authors

Alan A. Ager is a Research Forester in the Fire, Fuel and Smoke Research Program, Rocky Mountain Research Station, Missoula, 

Montana.

Michelle A. Day is a Senior Faculty Research Assistant in the Department of Forest Ecosystems & Society in the College of 

Forestry at Oregon State University in Corvallis, Oregon.

Palaiologos Palaiologou was an International Visiting Scholar with the U.S. Forest Service in the College of Forestry at Oregon 

State University in Corvallis, Oregon.

Rachel M. Houtman is a Faculty Research Assistant in the Department of Forest Ecosystems & Society in the College of Forestry 

at Oregon State University in Corvallis, Oregon.

Chris Ringo is a Senior Faculty Research Assistant in the Department of Crop and Soil Science in the College of Agricultural 

Sciences at Oregon State University in Corvallis, Oregon.

Cody R. Evers is a National Science Foundation IGERT Fellow in the Department of Environmental Science and Management at 

Portland State University in Portland, Oregon.

Acknowledgments

Thanks to Ken Bunzel for spatial analysis and Haiganoush Preisler for development of the models for human and natural ignitions.
ii

Contents

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

Cross-Boundary and Community Assessment Framework.........................................2

Wildfire Risk and Exposure............................................................................................2

Measuring Cross-Boundary Fire....................................................................................3

Risk Transmission..........................................................................................................4

Fireshed Mapping ..........................................................................................................5

Integrated Prioritization .................................................................................................6

Case Study...........................................................................................................................9

Assessment Overview....................................................................................................9

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

Study Area .................................................................................................................9

Land Tenures ............................................................................................................9

Mapping WUI and Communities .............................................................................9

Fire Simulation ........................................................................................................10

Modeling Human Versus Natural Ignitions ...........................................................11

Cross-Boundary Fire Transmission Among Ownerships.......................................12

Community Exposure..............................................................................................12

Fireshed Mapping ..................................................................................................12

Fireshed Characterization .....................................................................................13

Integrated Prioritization ..........................................................................................13

Assessment Results ...................................................................................................13

Cross-Boundary Fire Among Ownerships .............................................................13

Community Exposure..............................................................................................17

Fireshed Mapping ...................................................................................................21

Fireshed Characterization ......................................................................................22

Contribution of National Forests to Community Wildfire Exposure......................23

Integrated Prioritization ...............................................................................................24

Community Archetypes ..........................................................................................26

Discussion .........................................................................................................................26

References.........................................................................................................................30
USDA Forest Service RMRS-GTR-392. 2019 1

Introduction

The cause of recent wildfire catastrophes can be traced to multiple factors including the 

expanding urban footprint (Radeloff et al. 2018), increasing human ignitions (Nagy et 

al. 2018), droughts (Littell et al. 2016), and high-wind events (Abatzoglou et al. 2018). 

Plume-driven fires shower developed areas with embers causing a chain reaction of mass 

structure ignitions, burning urban subdivisions (e.g., Carr and Camp Fires of 2018). The 

expanding scale of wildfire risk to communities in the U.S. and elsewhere is recognized 

in several new federal forest management authorities and initiatives that aim to motivate 

larger-scale management activities spanning jurisdictional and landowner boundaries 

(USDA Forest Service 2018). This includes the Good Neighbor Authority (2014), the 

2014 Farm Bill, and the recent Shared Stewardship Strategy (USDA Forest Service 2018). 

Emphasis on cross-boundary management of wildfire issues can benefit from new 

assessment frameworks that are fine-tuned to meet the information needs found in new 

cross-boundary authorizing legislation (Ager et al. in press; Ager et al. 2018; Evers et al. 

2019). For instance, existing risk assessment technologies and frameworks do not explicitly 

examine the cross-boundary problem intrinsic to community wildfire risk from large areas 

of public wildlands (Dillon et al. 2015; WWWRA 2013) because they measure in situ 

risk without specifying a linkage to ignition origin or fire-spread path through parcels 

to developed areas. As fires get larger and larger, linking human and natural values to 

the wildlands that propagate fires becomes increasingly important. In particular, the risk 

assessment process used by State agencies and implemented in Wildfire Risk Assessment 

Portals (WRAPs) for each of the 17 western States (WWWRA 2013) could be enhanced 

with newer methods to consider cross-boundary risk and exposure metrics. These risk 

assessments use pixel-level indices to measure community exposure to wildfire, ignoring 

large fire spread in the surrounding wildlands. By contrast, the newer assessment methods 

developed by Forest Service researchers use large fire simulations (Dillon et al. 2015; 

Scott et al. 2013), but lack frameworks to explicitly measure and map cross-boundary and 

wildland-community wildfire transmission. Neither ad hoc nor published definitions of 

community wildfire protection areas (USDA and USDI 2001) account for the geography 

and scale of risk to communities (Ager et al. 2015). Science reviews (Miller and Ager 

2013) also lack discussion of cross-boundary fire and measurement of community wildfire 

risk. Clearly, in an era where the scale of risk is rapidly expanding with larger and larger 

fires, it is important to understand the topological properties of cross-boundary fire on 

landscapes in the U.S. and elsewhere that are fragmented by ownership type, management 

intent (Charnley et al. 2017), and fire management jurisdictions. 

In this report, we first summarize methods and findings from a series of prior research 

studies on cross-boundary wildfire exposure in the western U.S. with the aim of providing a framework for assessing cross-boundary wildfire issues. We then provide a detailed 

assessment of community cross-boundary wildfire exposure in the western U.S., both 

among major land ownerships (private, public, State, Federal) and from those land types 

to communities. The results can be used to refine a wide spectrum of fire protection 

and management activities, including efforts to prioritize fuel management programs 

on public lands surrounding populated areas, as well as cross-boundary projects as part 

of the shared stewardship initiative. The results from this study are available in ArcGIS 

online (Palaiologou and Aiello, 2019).
2 USDA Forest Service RMRS-GTR-392. 2019

Cross-Boundary and Community Assessment Framework

Cross-boundary or transboundary risk (sensu Lidskog et al. (2010)) has been described 

and documented for a wide range of global environmental problems, especially water 

and air pollution (Lidskog et al. 2010; Lidskog et al. 2011; Linnerooth-Bayer et al. 

2001). A number of wildfire studies have examined cross-boundary wildfire issues, 

but typically do not frame the problem in a cross-boundary governance context. The 

assessment framework described here can be applied to fire exchange across social, 

jurisdictional, legal, administrative, and ecological boundaries. The cross-boundary 

framework includes methods and metrics that quantify numerous aspects of fire exchange and community exposure, including metrics that measure scale of risk, diversity 

of landowners that contribute risk to communities, and other measures that describe 

mitigation opportunities. In the first section of this report, we describe core metrics for 

measuring cross-boundary exposure and community risk. This material is followed by a 

case study that assesses cross-boundary fire in the western U.S. 

Wildfire Risk and Exposure

Risk concerns the prediction of expected loss, calculated as the product of the likelihood 

of a fire at a given intensity and the consequence(s). A formal definition of risk consistent 

with the Society for Risk Analysis was first formulated by Finney (2005). It was first applied by Ager et al. (2007) in studies of the northern spotted owl, followed by application 

to study risk to old growth and carbon storage (Miller and Ager 2013). Risk is composed 

of three components: (1) ignition likelihood, (2) expected fire intensity, and (3) effects 

related to expected fire intensity. Fire can be beneficial or result in loss to social and 

ecological values (Miller and Ager 2013). Early risk studies used the site-specific response 

functions developed using the Forest Vegetation Simulator (Miller and Ager 2013). Largescale risk assessments with multiple values were first conducted by the Cohesive Strategy 

Science Team, (Calkin et al. 2011) where four category response functions were developed 

using expert judgment. A geospatial risk assessment system was integrated into ArcMap 

via the ArcFuels landscape planning system (Ager et al. 2011; Vaillant et al. 2013). This 

latter system provided a robust framework for integrating response functions with burn 

probability outputs from models like FlamMap and FSim for building wildfire risk maps. 

Scott (2006) summarized risk calculations in a technical guide. 

Wildfire exposure, by contrast, only concerns the juxtaposition of threatened values 

in relation to predicted fire occurrence and intensity without estimating potential 

loss (SRA 2006). The framework in this document focuses on wildfire exposure for 

the following reasons. First, it is not possible to estimate structure loss from large fire 

simulations because the variables necessary to perform such estimates are not used in 

large landscape simulation studies (Mell et al. 2010). Second, many other factors besides 

landscape fuels ultimately influence whether or not structures ignite after fire arrives in 

the vicinity. Third, exposure formulation reduces the complexity associated with multiple 

resource risk estimates, which require some form of decomposition to understand 

the driving factors causing risk in the first place, e.g., fire likelihood, fire intensity, or 

susceptibility. Understanding the relative influence of these risk drivers is key to the 

socialization of risk assessments and their adoption by landowners, communities, and 

other affected entities. 
USDA Forest Service RMRS-GTR-392. 2019 3

Measuring Cross-Boundary Fire

From a cross-boundary wildfire perspective, there are three possible sources of 

exposure on a given parcel: (1) self-burning, (2) incoming, and (3) outgoing (fig. 1). 

Again, the parcel boundary can be ecological (e.g. fire regime), jurisdictional (fire 

suppression), administrative (forest plan allocation), ownership (private versus public), 

or aggregates thereof (e.g. a community). Self-burning fire originates within the 

parcel. Incoming fire arrives from other distinct parcels in the landscape. Outgoing fire 

is fire that originates on a given parcel and burns into adjacent parcels. In general, land 

parcels with similar conditions (e.g., fire spread rate, spatial ignition density, and wind 

direction) will have equal amounts of incoming versus outgoing fire. Experiments with 

real landscapes show dramatically different ratios of incoming to outgoing fire (Ager 

et al. 2014a; fig. 4). A number of factors influenced both relative and absolute transmission including fuels, assumed weather for simulated fires, parcel size, geometry, 

and arrangement, but it is difficult to quantify the relative importance of the causal 

factors (Ager et al. 2014a). In general, the ratio of fire exchange (incoming + outgoing) 

to self-burning generally decreases with increasing parcel size and decreasing edge to 

area ratio (Ager et al. 2018). 

BLM NATIONAL FOREST

PRIVATE

STATE

PRIVATE

BLM

URBAN INTERFACE

COMMUNITY

BLM

Figure 1—For each parcel, cross-boundary fire components include self-burning, incoming, and outgoing. In this example an ignition on a national forest (red dot at right) 

spreads to adjacent landowners and into a community where structures are exposed to 

the fire. Within the national forest the fire is considered self-burning, while it is incoming for the other land parcels. Over many fires, incoming and outgoing area burned 

may differ due to ignition density, spread rates, parcel geometry, and other factors. In 

the present study, wildfire exposure to communities was determined by intersecting 

the fire perimeter with the developed area (red boundary within the orange community 

parcel). Exposure to structures was then calculated by multiplying the proportion of the 

community or wildland urban interface polygon that was burned by the count of the 

structures in the parcel.
4 USDA Forest Service RMRS-GTR-392. 2019

Quantifying cross-boundary fire requires an ignition location, the fire perimeter, and a 

spatial definition of parcels. These can be estimated from both empirical and simulated 

fires, although the empirical data are typically too sparse to obtain robust estimates 

of cross-boundary fire, the exception being large (e.g., 106 hectares) study areas (Ager 

et al. 2014b; fig. 8). There are many ways cross-boundary fire can be expressed using 

nonstandardized or standardized measures that correct for size differences among the 

parcels or communities. For instance, fire transmitted from a parcel can be expressed as 

total area of fire transmitted or standardized to the area of the source parcel to measure 

transmission efficiency or expressed as a per fire basis. In the simplest case of estimating 

the amount of outgoing fire (TF) for a fire ignited in land designation i that burned 

across a boundary into j, the calculations can be expressed as: 

 TFij = ABj /Ni (1)

The sum of TFij for designation i (ignition source) over j parcels yields the total amount 

of transmitted fire to other parcels (TF-OUT). The sum of area burned across all ignition sources i for a particular designation j estimates the total incoming fire per ignition 

outside the designation (TF-IN). TFij measures the average area burned in an adjacent 

parcel j given an ignition in i. The area of fire per ignition where i= j is nontransmitted, 

or self-burning (NonTF). 

Risk Transmission

Cross-boundary fire can be viewed as transmission of risk when the recipient of a fire 

event from another parcel incurs loss or benefits. The science of risk transmission has 

many important applications, such as calculating the spread of disease in humans, plants, 

and animal populations (Sander et al. 2002). As with disease transmission, many elements or circumstances influence or affect the causal factors and the eventual outcome. 

Wildfire risk transmission is dependent on spatial heterogeneity in landowner-specific 

vegetation management, wind direction, responsibility for fire suppression, parcel size, 

management practices, and ignition probability. 

We defined risk transmission as occurring when the conditions in one parcel result in 

an amplified expected loss (SRA 2006) in the other. For instance, consider two adjacent 

land parcels, A and B, of similar size, shape, and conditions with respect to fire spread 

rate, intensity, ignition probability, suppression capacity, and potential loss (ecological, 

financial, or other). When wind direction is random, the net expected transmission of 

risk between the two parcels will be equal. A change in these factors creates the potential 

for risk transmission among the parcels. Some of these factors are natural (e.g., wind 

direction), ecological (e.g., fire regime), or anthropogenic (e.g., fuel management, urban 

development, or parcel geometry). Transmitted risk can be quantitatively defined and 

measured with the following formula, modified from Finney (2005), where we include 

both the source parcel (ignition) and the affected parcel where losses occur: 

(2)

where: E(L) is the expected loss (risk), RFij is the loss from fire intensity class i in pixel 

j, A is the set of all pixels of a given land parcel, Pij is the probability of a fire of intensity 

i from an ignition in pixel j located outside A. Local risk (i.e., fires ignited within the 

EE(LL) = ∑ ∑ RF*+,PP*+. / +⁄∈3 *01
USDA Forest Service RMRS-GTR-392. 2019 5

parcel) versus transmitted risk can be calculated by substituting jεA into the first term. 

Removing the response term RFij leads to a measure of wildfire exposure. The key 

difference between risk and exposure is that the former requires intensity information 

for each pixel, while the latter does not. 

Existing simulation methods in models such as FlamMap, Randig, and FSim store 

perimeter footprints and ignition locations for each fire, but pixel-specific intensity 

values are not retained for both computational and storage space reasons. Processing fire 

intensity outputs for more than 100,000 fire perimeters would overwhelm the typical 

geo-processing capabilities of desktop computers. It is possible to obtain estimates of 

intensity by modeling static fire conditions (wind speed, wind direction) for every pixel 

in a landscape (Finney 2006). The marginal benefits of this, over quantifying exposure 

from fire, as in the current study, would be small in our opinion. This approach would 

only be warranted when robust response functions can be developed for particularly high 

value infrastructure or natural resources.

Fireshed Mapping

The first mention of “firesheds” was in the Pacific Southwest Region in the early 2000s 

(Bahro et al. 2007). It was introduced as a concept to advance an integrated interdisciplinary approach to evaluating fuel treatment effectiveness at reducing fire spread across 

landscapes (GAO 2004). The evaluation process recognized the scale of the fire problem 

and was most useful on landscapes where forest land and resource management plans 

resulted in fragmented management opportunity and fire management goals. Thus 

collaborative and integrated solutions were developed for fuel management programs 

that respected parcel level (i.e., forest plan allocation) management goals and restrictions 

while recognizing that all parcels were potentially exposed to the same fire event. The 

fireshed concept was forgotten by the fire community for almost two decades and 

eventually resurfaced as spatial fire planning. 

Many years later, the fireshed concept was redefined as a process for identifying 

the scale of risk, or risk container, around communities or other values. It proved 

particularly useful in developing Community Wildfire Protection Plans because the 

Healthy Forest Restoration Act only specified arbitrary buffers around communities 

(1-1⁄2 miles) (HFRA 2003), whereas fire simulation outputs provided a way to estimate 

the actual risk container (fireshed) around communities. The latter proved to be 

substantially larger (10–50 times) than the planning boundaries based on the fixed 

buffer (Ager et al. 2016), except in the few cases where entire counties were used as 

the boundary. 

Methods for mapping firesheds using wildfire simulation outputs have become widely 

used, but lack consistent methodology (table 1). Recent attempts (Ager et al. in press; 

Ager et al. 2016; Ager et al. 2018; Evers et al. 2019; Palaiologou et al. 2019; Scott et 

al. 2015) have used a variety of approaches. Scott et al. (2015), for example, describe a 

convex hull that circumscribes all ignition points. Concave hulls are similar to convex 

hull firesheds, but allow more flexibility in defining the fireshed perimeter (see Park 

and Oh 2012). Both approaches tend to create large firesheds that are defined by the 

most distant ignition point. While these distant ignitions are notable for the size of the 
6 USDA Forest Service RMRS-GTR-392. 2019

resulting fire, they also tend to be extremely rare, which means that hull-based firesheds 

tend to be defined by outlier events. 

Another set of geospatial techniques relies on interpolation that results in firesheds 

as surfaces. These techniques include inverse-distance weighting, spatial kriging, and 

kernel density estimates. Kriging functions are similar to a logistic regression model, and 

therefore require both ignition points that reached community areas and ignition points 

that did not. Kernel-density estimation applies a Gaussian bandwidth to interpolate 

point density estimates across an entire region. 

Firesheds can be mapped for any feature of interest to define the scale of risk and 

examine fuel and forest management opportunities. Most fireshed applications to date 

are focused on communities and the surrounding wildland-urban interface (WUI), but 

other examples include sensitive habitat (Ager et al. 2007) and historical monuments 

(Palaiologou et al. 2018). If interpolation techniques are used to create the fireshed, 

individual ignition points can be weighted by the degree of exposure. For example, 

some community firesheds are highly skewed in a single geographic direction due to a 

combination of fuels, topography, and development patterns. Weighting can reveal these 

asymmetries. For firesheds in the shape of a hull, the fireshed represents the space within 

which ignitions can reach communities. 

Integrated Prioritization

Once defined, firesheds can be characterized with respect to forest conditions, fuels, 

ownership, fire regimes, fire hazard, and many other variables to shape management 

strategies and priorities (Evers et al. 2019). For instance, the landowner composition 

of the fireshed represents the relative contribution of exposure to communities by 

different owners and creates a clear picture of who owns the risk. Some communities 

will receive the majority of their exposure from surrounding national forests, while 

in other communities, exposure may be spread across a dozen different land tenure 

classes. Information on fire hazard (e.g., fire regime and forest conditions) within 

the fireshed can be used to prioritize forest management activities. Areas available 

for treatment as determined from forest land and resource management plans can be 

examined to understand what role fuel management can play. Firesheds can also be 

Table 1—Geospatial techniques available for describing firesheds.

Technique Description

Convex hull Envelope drawn around all ignition points

Concave hull Similar to convex hulls, but able to capture more nuanced firesheds 

due to concave indents in the hull

IDW Inverse distance weighting (IDW) is a simple deterministic interpolation 

technique based on surrounding points that assumes that each measured point has a local influence that diminishes with distance

Kriging Kriging is an advanced geostatistical interpolation technique based 

on a fitted spatial model (variogram) that allows for more flexibility 

compared to IDW

KDE Kernel density estimation (KDE) is a point process analysis that calculates the density of point features around each output cell using a 

Gaussian bandwidth
USDA Forest Service RMRS-GTR-392. 2019 7

decomposed into individual exposure vectors that connect potential ignition points to 

specific community parcels. These vectors connect source conditions with those where 

exposure occurs. In similar fashion, the fireshed can be decomposed into individual 

exposure pathways, which allows for analysis of conditions along the entire exposure 

continuum. 

All of this information contributes to integrated prioritization schemes that measure 

conditions in both the fireshed and community. A multitude of variables, both social and 

biophysical, can be combined to rank communities and evaluate the type and amount 

of exposure. These variables can combine community characteristics such as fuels, fire 

intensity, and the properties of the fireshed (Evers et al. 2019). Exposure conditions are 

further examined both in terms of how the variables co-vary, but also in terms of the 

primary dimensions of risk management. For instance, Evers et al. (2019) reported three 

biophysical dimensions and five management dimensions of community exposure in the 

western U.S. At the community scale, they found that WUI class varied independently 

of dominant canopy and fuels. 

Statistical clustering is one method used to sort the different types of firesheds or archetypes. Using a combination of eighteen variables, Evers et al. (2019) identified five 

community exposure archetypes (table 2). Each archetype was used to classify communities, both to prioritize them and consider treatment options within the fireshed. In 

this process a workflow was used to identify meaningful clusters that differentiated 

types of community exposure. These variables are scaled and decomposed into 

individual components using factor analysis or principal component analysis. Biplots 

can be used to examine correlation among individual exposure variables in addition to 

identifying how exposure variables for individual communities vary in relation to these 

components. Once values are scaled and compressed, a variety of different clustering 

approaches can be applied to identify distinct groups of communities. 

A key problem in the practical application of firesheds is determining the number of 

archetypes supported by the data. Dendrograms can help reveal where large branches 

in the data begin to splinter into individual leaves. Another technique is to examine the 

amount of variation within each cluster, since ideally, clusters will minimize withincluster variance while maximizing between-cluster variance. A complete discussion 

about determining the correct cluster number is beyond the scope of this report (see 

Evers et al. 2019).
8 USDA Forest Service RMRS-GTR-392. 2019

Table 2—Variables used to distinguish nature of wildfire exposure among threatened communities in the 

western U.S. Variables reflect conditions found within the national forest source area (NF), exposed areas of 

the community (C), or both (NF/C). Table from Evers et al. (2019). 

Variable Zone Description

Canopy cover (%) NF/C Canopy cover can limit spread but also lead to crowning and spotting. Source: 

LANDFIRE

Flame length (m) NF/C Conditional flame length describes the intensity of the fire and can limit suppression. Source: FSIM

Forested fuel types (%) NF/C Fuel models 161–189 contain timber-understory and timber-litter fuels. 

Source: LANDFIRE

Shrub fuel types (%) NF/C Fuel models 141–149 contain woody shrubs and foliage with limited herbaceous fuels. Source: LANDFIRE

Grass/shrub fuel types 

(%)

NF/C Fuel models 101–129 contain mixture of grasses and shrubs, including chaparral fuels in SE California. Source: LANDFIRE

Nonburnable fuel types 

(%)

NF/C Fuel models <100 include urban/developed, agricultural, and bare lands. 

Source: LANDFIRE

Slope (%) NF/C Slope amplifies fire spread, influences local winds, and limits access. Source: 

LANDFIRE

Manageable (%) NF Portion of forest that is manageable, i.e., not a protected status where mechanical thinning might be limited or prohibited. Source: PAD

Vegetation departure 

(%)

NF Percent difference in successional class from historical reference conditions. 

Suppression in fire-adapted forest increases departure. Source: LANDFIRE

Low-severity fire (%) NF Fire regime group 1. Fire occurred at <35-year fire return interval, low and 

mixed severity. Vegetation often fire adapted. Source: LANDFIRE

Mixed-severity fire (%) NF Fire regime group 3. Fire historically occurred at 35–200 year fire return interval, resulted in low and mixed severity. Vegetation often fire adapted. Source: 

LANDFIRE

High-severity fire (%) NF Fire regime group 4. Fire historically occurred at 35–200 year fire return interval, replacement severity. Source: LANDFIRE

Infrequent fire (%) NF Fire regime group 5. Fire historically occurred at >200-year fire return interval, 

any severity. Source: LANDFIRE

Agricultural lands (%) C Percent of WUI classified as agriculture or pasture. Agricultural lands are much 

less likely to carry fire due to intensive management. Source: NLCD

Intermixed WUI (%) C Development (density > 1 hu/6.17 km2) that intersects with wildland vegetation (> 50% cover). Source: SILVIS

Interface WUI (%) C Development where wildland vegetation cover <50% but located <2.4 km from 

heavily vegetated area (> 75% wildland vegetation, >5km2). Source: SILVIS

Non-WUI (%) C Development not classified as either interface or intermix due to lack of structure density, lack of wildland vegetation, or lack of proximity to wildland vegetation. Source: SILVIS

Percent high, medium, 

or low density (%)

C Percent of community exposure from areas with structure density > 741 hu/

km2, density > 49.5 hu/km2 and 6.17 hu/km2 respectively. Source: SILVIS
USDA Forest Service RMRS-GTR-392. 2019 9

Case Study

Assessment Overview

We demonstrate the assessment process described above with a case study in 

the western U.S. The case study synthesizes and condenses several earlier papers; 

additional details can be found in these reports (Ager et al. in press; Evers et al. 2019; 

Palaiologou et al. in press). In the following sections we report: (1) estimates of crossboundary wildfire among major land types (Federal, State, private), (2) structure 

exposure from wildfire to communities, (3) community fireshed maps, (4) fireshed 

characteristics in terms of management opportunity and fuels, and (5) an example of 

ranking communities based on integration of exposure and fireshed characteristics. 

Methods

Study Area

The primary study area included all lands in the 11 western U.S states (fig. 2) and the 

adjacent wildland urban interface as mapped by the SILVIS project (Radeloff et al. 

2005). The secondary study area is comprised of the western national forests, covering 56 

million ha. The Dakota Prairie Grasslands and the Black Hills and Nebraska National 

Forests were excluded from the secondary study area. About 36 million ha of national 

forests (64 percent) are fire-adapted (fire regimes 1 and 3) (LANDFIRE 2009), 27 

million ha are managed (48 percent), and 30.5 million ha are classified as forested fuel 

(Timber-litter, Timber understory and Slash-blowdown; 56 percent) based on 2014 

LANDFIRE data (Rollins 2009). 

Land Tenures 

Land tenures were derived from the Protected Areas Database of the United States 

(PADUS) (USGS 2016). We considered protected areas as lands coded with PAD 

designations 1 and 2. In addition, roadless areas (2001 rule; 36 CFR Part 294) (USDA 

Forest Service 2017a) and wilderness, wild and scenic rivers, and other designated or 

protected areas were excluded from manageable lands (USDA Forest Service 2017b). 

Although management activities can take place in specific roadless areas, we excluded 

them from consideration based on the fact that few are actually managed. The listing of 

land tenures is included in table 3. 

Mapping WUI and Communities 

To delineate discrete communities, we attached SILVIS WUI with the U.S. Census 

Bureau populated places (U.S. Census Bureau 2016) using a travel time estimated with 

the Cost Allocation ArcGIS tool. We used a maximum distance equal to 45 minutes 

driving time (Ager et al. 2018). Using this approach, we organized 98.3 percent of 

WUI polygons into 5,118 communities, representing 65 million people and 25 million 

structures. We removed SILVIS WUI polygons that were smaller than 0.1 ha or had 

a structure density less than two structures per km2. The SILVIS WUI defines WUI 

as the area where houses meet or intermingle with undeveloped wildland vegetation, 

classified according to four density categories, structure density (one structure per 16 ha 

minimum), and distance to wildland vegetation, with further classifications into intermix 
10 USDA Forest Service RMRS-GTR-392. 2019

(housing and vegetation intermingle) and interface (housing in the vicinity of contiguous vegetation) (Radeloff et al. 2005). 

Fire Simulation

Wildfire simulation data from FSim (Finney et al. 2011) were used to predict wildfire 

exposure within and among the national forests and adjacent land tenures. FSim generates daily wildfire scenarios for a large number of wildfire seasons based on observed 

relationships between historical Energy Release Component (ERC) (Bradshaw et al. 

1983) and large fire occurrence. Wildfires are simulated with the minimum travel time 

(MTT) (Finney 2002) algorithm under weather conditions derived from time series 

analysis of historical weather. Fires can burn over several days if ERC remains high. 

Weather data were derived from the network of remote automated weather stations 

located throughout the US (Zachariassen et al. 2003). FSim outputs include the ignition 

location of each fire, fire perimeters, and grids of burn probability and conditional probabilities by flame length category. The data used consisted of 262,368 ignitions simulated 

Sources: Esri, USGS, NOAA

Land Tenure

Tribal

BLM

BOR

City/County

Community

DOD/DOE

FS

FWS

OtherFED

Private

NPS

Public

State

$ 0 125 250 500 km

Figure 2—Map of the major land tenures included in the assessment. The majority of the land is privately 

owned, and found in the States along the eastern edge of the 11 State area, followed by BLM, Forest 

Service, and Community lands. See table 3 for land tenure descriptions. 
USDA Forest Service RMRS-GTR-392. 2019 11

inside USFS administered lands, representing between 20,000 and 50,000 fire season 

replicates depending on the region (Finney et al. 2011).

Modeling Human Versus Natural Ignitions 

Simulated fires were partitioned post hoc into human or natural caused ignitions using 

historic wildfire occurrence data for the 11 western U.S. States for the 1992-2013 period 

(Short 2015). The cause of ignition (natural vs human) was modelled by fire size (acres), 

longitude and latitude (decimal degrees), Geographic Area Coordination Centers 

(GACC), and day of ignition as a General Additive Model (GAM) with a logit link 

function and a binomial error distribution: 

resp ~ te (lon,lat) + te (jday ,bs=“cc”,by= gac) +te (lsize,k =4) (3)

where: 

• resp is the probability of lightning ignition (i.e., 1 minus resp is the probability of a 

human-caused ignition), 

• lon is longitude, lat is latitude, jday is the day-of-year of fire ignition, 

• gac is the GACC, 

• bs = “cc” specifies a cyclic cubic regression spline, lsize is the fire size, 

• the ‘te’ function is a full tensor product smooth, and 

• k = 4 is the dimension of the basis used to represent the smooth term. 

Table 3—Land tenures used in the assessment of cross-boundary wildfire transmission in the western U.S. 

Land tenure Code Comment

Bureau of Land ManagementBLM Extensive land tenure in the west composed of a variety of shrub and 

forest systems. Often checker-boarded.

Bureau of Reclamation BOR Lands typically adjacent to reservoirs or large water bodies. 

City and County City/County Municipal and county lands.

Community Community U.S. Census populated places and wildland urban interface within 

45-minute drive time

U.S. Department of 

Defense

DOD Large land-holdings uses for training and weapons testing. Often includes large undeveloped landscapes.

U.S. Department of 

Energy

DOE Land operated for energy production and transmission.

U.S. Forest Service FS The national forest systems tend towards forested systems at higher elevation, but also includes extensive grasslands. Multiple demands lead 

to gridwork of managed and protected areas

U.S. Fish and Wildfire 

Service

FWS Land primary managed for habitat values. Often located in or near riparian or wetlands.

National Park Service NPS Recreation and conservation locations that often operate under strict 

management constraints.

Other federal OtherFED Other federal land not described above.

Private Private Private lands include large holdings such as industrial forestry and largeacreage grazing operations in addition to smaller family forests, rural 

residential lots, and denser urban development.

Public Public Nongovernmental organizations and public trusts

State State The extent of State lands varies drastically by State. In many States, 

State land occurs adjacent to national forests and forms a buffer between federal and private lands.

Tribal Tribal Tribal lands can exhibit drastically different fire regimes and vegetation 

patterns than neighboring lands.
12 USDA Forest Service RMRS-GTR-392. 2019

The simulated ignition dataset was then partitioned into either natural or humancaused ignitions using the rbinom function in the Binomial stats package in R 

(Kachitvichyanukul and Schmeiser 1988).

Cross-Boundary Fire Transmission Among Ownerships

Cross-boundary wildfire was quantified by intersecting wildfire perimeters with 

major land tenures and communities of the western U.S. Polygons were dissolved 

by the major land tenure to avoid a false fragmentation within the same agency/

landowner, while all polygons and slivers < 1 ha were merged with their largest 

neighbor. Second, all fire perimeters were partitioned into non-transmitting areas 

(i.e., burned areas within the same land tenure as the ignition). The origin of each 

wildfire was assigned based on the point of ignition. Third, total burn area within 

each land tenure was aggregated by incoming fire (TFin, the sum of all fire ignited 

on another land tenure and entering each particular polygon) or outgoing fire 

(TFout, the sum of all fire ignited in a land tenure or community that escapes its 

boundaries) (fig. 1).

Community Exposure

We intersected simulated fire perimeters with the community layer to estimate the 

annual number of structures exposed to wildfire. Structure exposure estimates were 

calculated as the product of the proportion of each community polygon burned and the 

number of structures within a polygon. We also estimated the normalized structure exposure for the entire community, which is the number of structures affected per year, per 

hectare of the exposed polygon. We assumed that structures reported in U.S. census data 

for each WUI polygon are spatially distributed equally, and the percentage of burned 

area from each simulated fire within each polygon was translated into the annualized 

number of structures affected. For each ignition, we summed all the predicted structures 

affected based on the intersection of the associated fire perimeter with the different 

WUI polygons (for more details see Evers et al. 2019). 

Fireshed Mapping 

We mapped community firesheds by creating a continuous smoothed surface of 

predicted structure exposure from all FSim ignitions that resulted in fires that 

intersected SILVIS community polygons. In this instance, we used Empirical 

Bayesian Kriging (EBK) geostatistical interpolation, implemented through the 

ArcGIS geostatistical analyst module (ESRI 2018). Kriging accounts for the error 

introduced by estimating the underlying semi-variogram, with accurate predictions 

of nonstationary data (i.e. wildfire ignitions). EBK was based on the estimation of a 

series of semi-variograms for overlapping subsets of specified size (100 points) that 

capture observed spatial dependence between points (Berman et al. 2015; Pilz and 

Spöck 2008; Zimmerman et al. 1999). We applied a log-empirical transformation 

on the data, and included up to 10 neighbors at a radius of 1.6 km. Then, using the 

EBK raster layer (100 m cell size), we estimated the maximum exposure value of all 

cells that intersect each parcel, and standardized values so that total exposure of all 

fireshed parcel equaled the total simulated exposure of all ignitions in NFs (3,945 

structures yr-1).
USDA Forest Service RMRS-GTR-392. 2019 13

Fireshed Characterization 

We used spatial data on fuels (LANDFIRE 2016) and manageability (USGS 2016) to 

characterize conditions in the fireshed. Fireshed composition was summarized at the 

State level and by national forest. The purpose was to determine the composition of 

fuels and suitability for fuel management. Each community fireshed was characterized 

in terms of fire hazard and fuel model composition using wildfire simulation modeling 

output layers and 2014 LANDFIRE data. We combined the area characterized as high 

or very high wildfire hazard potential (classes 4 and 5) (Dillon 2015) to estimate the 

percentage of each community fireshed with high fire hazard (henceforth termed fire 

hazard).

Integrated Prioritization

We experimented with several approaches for identifying high priority communities in 

terms of fuel treatment investments in the fireshed and community investments in terms 

of Firewise and other preparation and planning processes. There are many methods that 

work for this process based on multi-criteria plots that integrated both exposure in the 

WUI and characteristics of the fireshed. The concept of archetypes can also be used to 

organize variability in exposure and capacity to respond In our example case study, we 

present several approaches for plotting exposure data to obtain integrated measures that 

can be used for ranking and prioritizing communities. 

Assessment Results 

Cross-Boundary Fire Among Ownerships 

The amount of incoming versus outgoing fire averaged for all major land tenures was 

nearly equal (fig. 3). However, ownerships varied substantially by amount of self-burning 

and by the relative amounts in human versus natural ignitions. Forest Service, National 

Park Service (NPS), and tribal lands had the lowest amount of fire exchange as a proportion of total transmission. State lands had substantially higher rates of fire exchange 

with surrounding lands. 

Examining the relative amount of incoming versus self-burning shows locations where 

the majority of area burned is from fires that originate on other land ownerships (fig. 

4a). Variability among States shows that Nevada had the highest rate of self-burning. 

The areas of highest incoming area burned were in central Arizona and western New 

Mexico, southern California, and south-central Wyoming (fig. 4a). Areas of the western 

U.S. with large homogenous polygons with one owner, such as the tribal lands in northern Arizona, national forest lands in central Idaho, BLM lands in southern Nevada, 

and private lands in eastern Colorado, had low (<20 percent) and homogenous areas of 

incoming area burned. When summarized by State (fig. 4b), the differences are much 

less pronounced. Summarized by land tenure (fig. 4c), the percentage of area burned 

by high values of incoming fire varied among land tenures with the highest values for 

transmitted fire observed for the Bureau of Reclamation (BOR), city/county, and minor 

public lands (Public).

Filtering the data to just show where more than 50 percent of the fire is incoming 

highlights transmission zones (fig. 5). For clarity the map is limited to NFS, BLM, and 

private lands (fig. 5), and shows that the majority of national forest boundaries receive a 

high percentage of incoming fire (red), with the exception of some enclaves where land 
14 USDA Forest Service RMRS-GTR-392. 2019

Figure 3—Wildfire transmission (incoming, outgoing, non-transmitted) among major land tenures of 11 western 

U.S. States estimated for (a) natural ignitions; and (b) human ignitions. The amount of incoming versus outgoing 

fire for all land tenures was nearly equal. The amount of self-burning and relative amounts of human versus natural 

ignitions varied widely by ownership. Figure modified from Palaiologou et al. in press.

0

50

100

150

200

250

300

Area burned (thousand ha/year)

Transmission

Self−burning

Outgoing

Incoming

0

25

50

75

100

City County

Public

FWS

DOD/DOE

NPS

Tribal

Community

State

Private

FS

BLM

Land tenure

Area burned per year (%)

A

0

50

100

150

200

250

300

Area burned (thousand ha/year)

0

25

50

75

100

City County

Public

FWS

DOD/DOE

NPS

Tribal

Community

State

Private

FS

BLM

Land tenure

Area burned per year (%)

B
USDA Forest Service RMRS-GTR-392. 2019 15

Utah

Montana

California

Idaho

Nevada

Arizona

Oregon

Colorado

Wyoming

New Mexico

Washington /

0 125 250 500km

Average percentage

of incoming fire

>50%

40% - 50%

30% - 40%

20% - 30%

10% - 20%

<10%

/

0

25

50

75

100

AZ CA CO ID MT NM NV OR UT WA WY

State

Area (%)

0

25

50

75

100

BLM

BOR

City County

Community

DOD

DOE

FS

FWS

NPS

Other FED

Private

Public

State

Tribal

Land Tenure

Area (%)

Figure 4—(a) Map of the percentage 

of area burned by incoming fire. 

High values indicate locations 

where majority of area burned is 

from fires ignited on other land 

ownerships, meaning high levels of 

transmission at landscape scales. 

Large blocks of land with low values 

(high self-burning) were observed 

for several States including Arizona, 

Nevada, Idaho and Washington; 

(b) Distribution of the data in panel 

(a) by State. On a percentage area 

basis, the highest transmission 

(incoming fire) varied little at the 

State scale; and (c) Distribution of 

the data in panel (a) by land tenure. 

The percentage of area burned by 

high values of incoming fire varied 

among land tenures with the highest 

values for transmitted fire observed 

for the Bureau of Reclamation 

(BOR), City /County and minor public 

lands (Public). Figure modified from 

Palaiologou et al. in press.

C

A

B
16 USDA Forest Service RMRS-GTR-392. 2019

/

0 125 250 500

km

State boundaries

High incoming 

fire (>50%)

National Forest

Bureau of Land 

Management

Private lands National Forest (manageable)

/

Figure 5—Locations where incoming fire exceeds 50 percent on the three highest fire transmission land 

tenures (National Forest System, NFS; BLM, Bureau of Land Management and private). The majority of 

national forest boundaries received a high percentage of incoming fire. Figure modified from Palaiologou et 

al. in press.
USDA Forest Service RMRS-GTR-392. 2019 17

Ojai, CA Good Hope, CA French Valley, CA Boise City, ID Temescal Valley, CA Cloudcroft, NM Green Valley, AZ Pinetop Lakeside, AZ Valle Vista, CA Lake Mathews, CA

Jamul, CA Pinetop Country Club, AZ Lake Forest, CA

San Diego, CA Lake Elsinore, CA Eagle Mountain, UT

Lexington Hills, CA Bonsall, CA Moreno Valley, CA Murrieta, CA Mead Valley, CA Show Low, AZ Running Springs, CA San Diego Country Estates, CA Wildomar, CA Leavenworth, WA Escondido, CA

Aguanga, CA

St George, UT Alpine, CA Ruidoso, NM Santa Clarita, CA

Prescott Valley, AZ Hemet, CA Los Angeles, CA emecula, CAT Idyllwild Pine Cove, CA Flagstaff, AZ

Crestline, CA Elko, NV Spriing Creek, NV Banning, CA Lake Arrowhead, CA Ramona, CA

Fallbrook, CA

Yucaipa, CA Anza, CA Beaumont, CA Prescott, AZ

Valley Center, CA

0 25 50 75 100 125 150 175 200

Structures affected from

incoming wildfires (n/yr)

Land

Tenure

FS

BLM

BOR

FWS

NPS

Other FED

DOD/DOE

State

Tribal

Private

Public

City County

Community

Figure 6—Top 50 communities exposed to wildfire in the western U.S. by ignition source. The different colors 

indicate the magnitude of exposure to each community from different land ownerships/agencies. In general 

Forest Service, private, and community lands were the largest ignition sources. Note that the distribution 

changes substantially among the different communities.

tenures are intermixed. Sinks of private and BLM land adjacent to national forest land 

are also evident, showing clear hot spots for collaborative planning to reduce risk. 

Community Exposure

We estimated that a total of 1,812 communities in the western U.S. could be 

significantly impacted by future wildfires ignited on any land tenures (“significant” is 

defined as more than 1 structure per year on average) (figs. 6 and 7 show the top 100 

communities by source of exposure). Ignitions on national forest lands will likely affect 

516 out of the 1,812 communities—more than 1 structure per year on average. We 

estimated that ignitions on all land tenures will expose 19,400 structures to wildfire 

per year on average. Note that these estimates do not predict structure loss, but rather 

exposure to wildfire on a portion or all of the SILVIS parcel where structures are 

located. The modeling estimates account for structure exposure from wildland fires, 

not structure to structure fire within communities. Of the total exposure, ignitions on 

national forest lands will expose an estimated 4,000 structures (21 percent of total) 
18 USDA Forest Service RMRS-GTR-392. 2019

Carlin, NV Colorado Springs, CO

Sedona, AZ

Kingman, AZ

Sierra Vista Southeast, AZ Aspen Park, CO

Hunter Creek, AZ

Robie Creek, ID Dewey Humboldt, AZ

Hamilton, MT

Evergreen, CO

Salmon, ID

Pocatello, ID

Tucson, AZ

Henderson, NV

Hurricane, UT

Williams, AZ

Sahuarita, AZ Doney Park, AZ Saratoga Springs, UT

Phoenix, AZ

Parks, AZ

Munds Park, AZ

Twin Forks, NM

Globe, AZ

Oak Glen, CA

Mesquite, NV Big Bear City, CA

Thousand Oaks, CA CherryValley, CA Payson, AZ

Calimesa, CA Kachina Village, AZ

Castaic, CA

Rainbow, CA

San Jacinto, CA

Lake of the Woods, AZ

Menifee, CA

Chino Hills, CA Lakeland Village, CA

Mountain Home, ID Heber Overgaard, AZ

Pine, AZ

Enterprise, NV

Redlands, CA

Winnemucca, NV

Simi Valley, CA Redding, CA

Calabasas, CA

Lake RIverside, CA

0 10 20 30 40 50

Structures affected from

incoming wildfires (n/yr)

Land

Tenure

FS

BLM

BOR

FWS

NPS

Other FED

DOD/DOE

State

Tribal

Private

Public

City County

Community

Figure 7—The top 51–100 communities exposed to wildfire in the western U.S. by ignition source. The different colors indicate the magnitude of exposure to each community from different land ownerships/agencies. 

Note x-axis scale differs from figure 6. In general, Forest Service, private, and community lands were the 

largest ignition sources. Note exceptions to this, for example Henderson, NV and Lake of the Woods, AZ.

in the western U.S. per year on average. Larger communities experienced higher 

exposure due to their larger populations. However, when exposure was adjusted by 

the exposed area of each community (str. yr-1 ha-1), emphasizing structure density, a 

different suite of communities was ranked in the top 50 for exposure (dashed vertical 

line, fig. 8). Only 15 communities were ranked on both lists, with three communities 

in the top 10 based on both metrics (Crestline, CA; Lake Arrowhead, CA; Fontana, 

CA). Thus, there was not a strong relationship in structure exposure between raw 

and area weighted values (fig. 8). While communities in California still had the 

highest percentage of adjusted exposure (46.8 percent), communities in Arizona 

(26.7 percent), Utah (7.9 percent), and New Mexico (6.4 percent) also showed highly 

exposed structure density. 
USDA Forest Service RMRS-GTR-392. 2019 19

Crestline

Fontana

Lake Arrowhead

Lake Elsinore

Murrieta

Pine Valley

Running Springs

Santa Clarita

Temecula

Temescal Valley

Wildomar

Mead Ranch

Mesa del Caballo

Tobin

Whispering Pines

0

25

50

75

100

125

150

0.000 0.005 0.010 0.015 0.020 0.025

Structure exposure density (n/ha/yr)

Structure exposure (n/yr)

State

AZ

CA

ID

MT

NM

NV

Figure 8—Structure exposure of the top 50 communities in the western U.S. to wildfires ignited on national 

forest land by State, based on number of structures exposed and structure density. The top 50 communities for both metrics are indicated by the dashed horizontal and vertical red lines. Structure exposure is 

measured as the annual predicted structures affected using simulation outputs. Communities in California 

had the highest percentage of exposure. Figure modified from Ager et al. in press.

For each land tenure, we estimated the area burned by ignitions and transmitted to 

communities, and color coded them by the land tenure causing the highest structure 

exposure (fig. 9a). The southern parts of Idaho and Utah, and northwestern Arizona 

and northwestern Nevada are mostly affected by fires ignited on BLM lands, while in 

northern Utah, southwest and northern California, northern Nevada and eastern New 

Mexico, structure exposure fires are mostly a problem caused by private land ignitions. 

National forest ignitions are responsible for most of the community exposure in parts 

of northern and southern California, central Idaho and western Montana, north-central 

Washington, central Arizona, and southwest New Mexico. State land fires are dominant 

in southern Arizona and central Utah, while WUI ignitions prevail in coastal California 

and across the Sierras, north-central Colorado, and northeast and southern Washington. 

Tribal land fires mostly expose communities to fire in central Arizona, but have a lower 

influence in Montana, Washington, and central Oregon. 

When fire transmission is expressed in terms of annual structure exposure (fig. 9b), we 

see big differences between California and Arizona and the majority of other States. 

More than 11,000 and 2,500 structures per year were predicted to be exposed in 

California (59 percent of total exposed structures) and Arizona (14 percent) respectively. 
20 USDA Forest Service RMRS-GTR-392. 2019

Utah

Montana

California

Idaho

Nevada

Arizona

Oregon

Colorado

Wyoming

New Mexico

Washington

/

0 125 250 500km

Land tenure

DOD, DOE, BOR

Public, City/County

Other Federal

Private

NPS

FS

BLM

State

Tribal

Community

WY

OR

MT

WA

CO

UT

NV

NM

ID

AZ

CA

0 2000 4000 6000 8000 10000 12000

Structures exposed (n/yr)

Land

Tenure

FS

BLM

BOR

FWS

NPS

Other FED

DOD

DOE

State Tribal

Private Public

City/County Community Other WY

OR

MT

WA

CO

UT

NV

NM

ID

AZ

CA

0 25 50 75 100

Structures exposed (%)

Figure 9—(a) Land tenures causing 

the highest structure exposure to 

communities in the 11 western U.S. 

States. Data are filtered to show 

exposure greater than or equal to 

1 structure per year as defines the 

fireshed, (b) total annual structure 

exposure, and (c) percentage exposure by land tenure where the wildfire 

was ignited. California and Arizona 

were the only States with more than 

1,000 structures exposed. Half of the 

predicted structure exposure came 

from ignitions on private and community lands. Figure modified from 

Palaiologou et al. in press.

A

B C
USDA Forest Service RMRS-GTR-392. 2019 21

Although total fire activity in Idaho and California was similar, we observed huge differences in terms of structure exposure. All other States had fewer than 1,000 structures 

exposed per year, ranging from a low of 150 in Wyoming (1 percent) to a high of 850 

in Idaho (4.5 percent). In conjunction with our previous findings, half of the predicted 

structure exposure is coming from ignitions on private and community lands, followed 

by national forest (21.5 percent), BLM (6 percent), State (4.5 percent), city/county and 

tribal lands (3.6 percent each) (fig. 9c). 

Fireshed Mapping

We estimate that within the western 11 U.S. States, 86 million acres, or 62 percent of 

the total national forest area, have the potential to contribute wildfire to communities (fig. 10). When including all landowners, firesheds comprise approximately 173 

million acres across the 11 western U.S. States, or 24 percent of all burnable lands 

Reno Provo Denver

Tucson

John Day

Pueblo

Fresno

Kennewick

Spokane

Santa Fe

Helena

Las Vegas

Boise

Albuquerque

Bakers�ield

Salt Lake City

Idaho Falls

Billings

Hayfork

Phoenix

Sacramento

Medford

Casper

Gillette

/

0 125 250 500

km

National Forest

Fireshed

Outside fireshed

Manageable, fire adapted 

and forested fireshed

Figure 10—National forest firesheds in the western U.S. showing where ignitions have 

the potential to cause structure exposure to nearby communities. Eighty-six million 

acres (62 percent) of the total national forest area have the potential to contribute 

wildfire to communities. Areas in blue (19 million acres; 14 percent) are firesheds that 

are manageable according to the PADUS data and have fire-adapted conifer forests. 

Figure modified from Ager et al. in press.
22 USDA Forest Service RMRS-GTR-392. 2019

(NLCD 2011). California has the greatest total area contained in firesheds, followed by 

Arizona, Montana, Colorado, and New Mexico (fig. 11). Firesheds are found in distinct 

geographic clusters, which in turn form regional wildfire risk hot spots. In total, we 

identified approximately twenty hot spots. These hot spots define the regional scale at 

which fire management operates. 

Fireshed Characterization

Five land tenures own or manage 92 percent of the area within community firesheds. 

Half of these lands are under private and community ownership, followed by national 

forest (25 percent), BLM (10 percent) and State (6 percent) (fig. 11a). The distribution 

of land tenure throughout the States shows that percentages of ownerships are relatively 

consistent throughout the western U.S., with the exception of increased tribal ownership 

in Arizona, Montana, and New Mexico, and a higher proportion of BLM lands in 

0

3

6

9

12

15

CA AZ MT CO NM OR ID WA UT WY NV

Area in fireshed (million ha)

Land tenure

FS

BLM

BOR

FWS

NPS

Other FED

DOD

DOE

State

Tribal

Private

Public

City/County

Community

Other

0

25

50

75

100

CA AZ MT CO NM OR ID WA UT WY NV

State

Land tenure (%)

0

25

50

75

100

CA AZ MT CO NM OR ID WA UT WY NV

State

Fuel model group (%)

Timber−Litter Timber Understory Shrub Grass/Shrub Grass

0

25

50

75

100

CA AZ MT CO NM OR ID WA UT WY NV

State

Wildfire Hazard Potential class (%)

Very High High Moderate Low Very Low

Figure 11—State level breakdown for (a) land tenure area, (b) percentage area, (c) fuel model composition class percentage, and (d) wildfire hazard potential (Dillon 2015) for firesheds in the western U.S. Five land tenures own or manage 

92 percent of the fireshed area; ownership percentages are fairly consistent across States. Grass and grass/shrub fuel 

models combined composed more than half of all firesheds. Wildfire hazard potential was very low or low on more than 50 

percent of Wyoming, Washington, and Colorado firesheds. California, Idaho, Utah, Nevada, and Oregon firesheds had high 

or very high hazard potential on at least 40 percent of the fireshed area. Figure modified from Palaiologou et al. in press.

A

B

C

D
USDA Forest Service RMRS-GTR-392. 2019 23

Nevada (fig. 11b). The distribution of surface fuel models within firesheds shows that 

grass and grass/shrub fuel models combined composed more than half of all firesheds, 

although there was variation among the States (fig. 11c). Forested fuel models (timber 

understory and timber-litter) have the lowest share in Nevada (10 percent) and the 

highest in Oregon and Washington (~50 percent) (fig. 11c). The distribution of wildfire 

hazard potential is vital to understanding where community firesheds and high hazard 

converge. Wildfire hazard potential was low or very low on more than 50 percent of 

Wyoming, Washington, and Colorado firesheds, while California, Idaho, Utah, Nevada, 

and Oregon firesheds had high or very high hazard potential in at least 40 percent of the 

fireshed area (fig. 11d). 

Contribution of National Forests to Community Wildfire Exposure

Examining structure exposure in terms of where risk on national forest lands can be mitigated, 

only 7.6 million ha (14 percent of the total NF area) were manageable, fire-adapted, and 

forested, thus limiting the area on national forest land that can be treated to reduce risk (~700 

structures yr-1, 17.5 percent of total exposure) (fig. 12). One-quarter of the total national forest 

fireshed was predicted to have very low exposure to communities (9 million ha with only 1 

percent of total exposure). Approximately 6 percent of the total area burned by fires ignited 

inside the national forests was transmitted to the community core and/or WUI polygons, with 

~25% from 80k ha

~50% from 700k ha

~75% from 4 million ha

90% of total structure exposure from 11 million ha

Manageable, fire adapted and forested fuels

Manageable and forested fuels

Forested fuels

Manageable

Fire adapted

All Forest Service lands

0.00

0.25

0.50

0.75

1.00

1 10 20 30 40

National forest area (million ha)

Cumulative structure exposure

Figure 12—Cumulative structure exposure from wildfires ignited on western U.S. national forests as 

predicted by simulation modeling. The lines represent Forest Service lands by management capacity and 

fuel models. Only 14 percent of the national forest area is manageable, fire-adapted, and forested land, 

limiting the area that can be treated to reduce risk. Figure from Ager et al. in press.
24 USDA Forest Service RMRS-GTR-392. 2019

State-level values ranging from about 1 percent (Colorado, Nevada) to 47 percent (California). 

These numbers are substantially smaller than we have previously reported (Ager, et al. 2014b), 

but are limited to the area burned from fires ignited within national forests that intersected 

with community polygons (simulated fires not reaching communities were excluded from 

the analysis). In terms of structure exposure, of the 19,400 structures yr-1 exposed from all 

lands, 20 percent were exposed from national forest lands only (fig. 13). Due to administrative 

restrictions on national forest lands, 11 percent of structure exposure originated on NFS 

lands where mechanical treatments can be conducted, and 2 percent originated on NFS lands 

available for treatment and in areas of high or very high wildfire potential (fig. 13).

Integrated Prioritization

A heat diagram (fig. 14) shows an example of integrated prioritization of both community and ignition source wildfire hazard for the top 50 communities ranked by structure 

exposure from wildfires ignited on national forest land and percentage contribution to 

total structure density (HU). The diagram also shows the percentage of the ignition 

source area that is manageable, fire-adapted, and located in areas of high wildfire hazard 

(S-WHP), as well as the percentage of the community area that is located in areas of 

high wildfire hazard (C-WHP). Communities are ranked from top to bottom; those 

at the top of the list were the most exposed to wildfire, those at the bottom, the least 

exposed. The darker brown color indicates communities that also had the highest exposure when weighed by community area. Warmer colors indicate a higher percentage in 

high hazard areas or with higher percentages of area available for mechanical treatments 

(Man) and that are fire-adapted (Adapt).

Structures Exposed / Year

From ignitions on all lands

From ignitions on FS lands

From ignitions on FS lands

available for treatment

From ignitions on FS lands

available for treatment that

are forested and at high or

very high risk of wildfire

19,400 4,000 2,200 365

A B C D

Figure 13—The number of structures 

exposed to wildfire by land base. 

Concentric circles indicate relative 

exposure by A) all lands, B) national 

forest land, C) national forest land 

available for treatment, and D) national 

forest land available for treatment and 

forested in areas of high or very high 

wildfire potential.
USDA Forest Service RMRS-GTR-392. 2019 25

Alpine (CA)

HU

ManAdapt

C-WHP

S-WHP

25

50

75

100

2

4

6

8

Area (%)

HU

Density

 (%)

Boulevard (CA)

Sedona (AZ)

Banning (CA)

Phelan (CA)

Rancho Santa Margarita (CA)

Julian (CA)

Stevensville (MT)

Agua Dulce (CA)

Lake Forest (CA)

San Bernardino (CA)

Descanso (CA)

Corona (CA)

Palm Springs (CA)

Payson (AZ)

French Valley (CA)

Palmdale (CA)

Menifee (CA)

Aguanga (CA)

Los Angeles (CA)

Pine Valley (CA)

Prescott Valley (AZ)

Prescott (AZ) Santa Fe (NM)

San Diego Country Estates (CA)

Yucaipa (CA)

Missoula (MT)

Acton (CA)

Big Bear City (CA)

Rancho Cucamonga (CA)

Castaic (CA)

Campo (CA) Idyllwild-Pine Cove (CA)

Ramona (CA)

Hamilton (MT)

Running Springs (CA)

Lakeland Village (CA)

Fallbrook (CA)

Salmon (ID)

Fontana (CA)

Reno (NV)

Anza (CA)

Lake Arrowhead (CA)

Temescal Valley (CA)

Crestline (CA)

Temecula (CA)

Lake Elsinore (CA)

Wildomar (CA)

Santa Clarita (CA)

Murrieta (CA) Figure 14—Top 50 communities 

ranked by structure exposure from 

national forest-ignited wildfires 

and percent contribution to total 

structure density (HU); percentage 

of the ignition source area that is 

manageable (Man), fire-adapted 

(Adapt), and in areas of high wildfire 

hazard (S-WHP); and the percentage 

of the community area that is in 

area of high wildfire hazard (C-WHP). 

Communities at the top of the list 

were the most exposed to wildfire; 

those at the bottom, the least 

exposed. Figure from Ager et al. in 

press.
26 USDA Forest Service RMRS-GTR-392. 2019

Community Archetypes 

We selected the top 20 percent of the communities most exposed to wildfire from 

western U.S. national forest lands and identified five primary exposure archetypes within 

this group. Each exposure archetype reflects a common set of management opportunities 

and constraints based on biophysical and social conditions on both sides of the national 

forest boundary. The five community exposure archetypes are (1) infrequent exposure, 

(2) open-interface, (3) mixed-interface, (4) forested-intermix, and (5) shrub-interface. 

While this classification schema is related to common WUI descriptors, such as the 

SILVIS WUI, it differs in that these archetypes are process-based because they consider 

the pathway of exposure from ignition to household. Further, because this definition 

is process-based, this schema describes the proportional mix of WUI-type threats 

westwide. For instance, the two most common exposure types are open-interface and 

forested-intermix, which roughly correspond to interface and intermixed WUI. Other 

exposure categories are less recognized, including the communities where exposure can 

be extreme, but is highly unlikely, such as the shrub-based interface found along much 

of the Wasatch region of Utah. This area shares many of the characteristics of southern 

California chaparral, yet has received much less attention in terms of management and 

planning.

Discussion

Our multiscale quantitative framework and case study for measuring both cross-boundary wildfire exposure for large landowners and structures within communities, directly 

addresses the specific initiatives that stimulated the development of cross-boundary 

assessment methods and the assessment itself. This assessment of communities in the 

western U.S. that are potentially exposed to wildfires originating on national forest lands 

has underscored the need for Federal agencies to understand their role in community 

risk relative to other public and private land organizations and administrative units. As 

noted in the Introduction of this report, there have been several calls for community 

assessments and cross-boundary planning to help integrate management among State, 

private, and Federal land management agencies. Assessment methods will always 

improve over time with new data and technologies. This assessment provides a wealth of 

information to guide investments by the Forest Service, communities, and other public 

land management agencies to enact a wide spectrum of fire management activities aimed 

at reducing wildfire losses. These activities include management mapping activities, such 

as identifying areas where unplanned ignitions can be used to manage fuels as part of 

spatial community fire planning. 

The methods and data presented here were synthesized from a number of prior papers 

by the authors (Ager et al. in press; Evers et al. 2019; Palaiologou et al. 2019). The 

methods can contribute to improving wildfire mitigation planning by providing a 

process to explicitly measure the scale of risk and identify individual sources of wildfire 

risk to parcels and communities. This type of information is key to providing a quantitative foundation for community risk mitigation programs, including the Community 

Wildfire Protection Plan (CWPP) process ( Jakes et al. 2012). As a result, the framework 

has a potentially important role for prioritizing shared stewardship projects aimed at 

reducing community risk. Realizing the diversity of conditions that lead to exposure, 
USDA Forest Service RMRS-GTR-392. 2019 27

integrated prioritization provides a decision tree to aid in allocating resources among 

risk abatement efforts, including suppression, fuel management, reducing vulnerability 

of dwellings, and ignition prevention programs. For instance, we partition risk within 

firesheds among the major land ownerships according to management capability, and 

then identify locations where opportunities exist for reducing wildfire risk. The methods 

summarized here have been used in several studies outside the U.S. including Greece 

(Palaiologou et al. 2018), Spain (Alcasena et al. 2017), and Argentina (Argañaraz et al. 

2017). 

The assessment process used an array of metrics to describe the scale of fire exposure to 

communities and properties within the firesheds in relation to opportunities for forest 

and fuel management. Some of the broad metrics we presented are relatively insensitive 

to the underlying variability in wildfire activities. For example, large areas of fuels with 

high spread rates and intensity are more fire prone and contribute to the observed spatial 

patterns. Land ownership patterns and the degree to which landscapes are fragmented 

contribute significantly to the exchange of fire. 

The least robust of the metrics we are reporting, however, are probably those that 

describe community exposure. There are multiple reasons for this, including the fact that 

many WUI disasters result from structure to structure ignitions. Further, fuel mapping 

within urban interface and intermix around communities is prone to error, especially 

where dryland and irrigated agriculture create fuels that vary substantially within a fire 

season in terms of loadings and flammability. Thus, the community rankings should be 

viewed as groups of communities with high, medium, or low overall structure exposure. 

Another consideration is that larger communities have higher exposure just because they 

have more people. We did not present statistics showing per area or per capita exposure, 

which would dramatically change the rankings (Ager et al. in press). Our future work 

will consider bootstrap estimates of community rankings to better understand inherent 

variability in fire simulation outputs.

The evaluation of fuel treatment strategies for cross-boundary risk has been largely 

ignored in several recent reviews (Kalies and Yocom Kent 2016; Vaillant and Reinhardt 

2017). The fact that most new initiatives and legislative authorities are attuned to 

cross-boundaries suggests a change in the scale of management investments needed to 

counter the dual effects of urban expansion into wildlands and climate change, both of 

which are increasing the scale of risk. The degree to which risk is in situ versus ex situ 

has substantial bearing on formulating fire protection and restoration goals in areas 

fragmented by jurisdictions, ownerships, and fire regimes. For instance, fire-adapted 

lands near land tenure boundaries where transmission risk is generally high will not be 

maintained with natural ignitions; thus, mechanical treatments and prescribed fire must 

be emphasized in these areas. To get the most out of the combined effect of mechanical 

treatments and restoration wildfires at the landscape scale to meet socioeconomic and 

ecological goals of federal forest restoration programs will require mapping protection 

from versus restoration of wildfire, while also considering the juxtaposition of patch size, 

fire regimes, and socioecological values. 

In the western U.S., about 45 percent of the land area is within designated conservation 

reserves where fuels treatments are either prohibited or highly restricted, potentially 

marginalizing risk reduction efforts (Agee 2002; Finney et al. 2007; Kaufman 2004; 
28 USDA Forest Service RMRS-GTR-392. 2019

North et al. 2015; Williams 2013). We found that about 40 percent of national forest 

land in community firesheds is either not available or not suitable for treatments, even 

with the coarse filter we used for operability. Management restrictions are particularly 

prominent in some regions. For instance, 90 percent of the national forest land in the 

Wasatch region of northern Utah lies in wilderness, roadless, or source-water protection 

areas. Additional economic and operational constraints on lands that can be managed 

will further reduce the area of opportunity. 

Our cross-boundary assessment methods have multiple applications for WUI protection 

planning. By classifying WUI both in terms of the surrounding landscape and community structure, we have provided a functional definition of the WUI that is necessary in 

order to link the biophysical and social processes that together define this space (Moritz 

et al. 2014; Spies et al. 2014). From the perspective of Federal land management agencies, this functional definition of the WUI provides guidance on where to spend Federal 

dollars effectively and provides opportunities for drafting agreements with communities 

and private landowners that better leverage these expenditures. 

A framework that better accounts for the scale and geography of WUI wildfire risk 

would most likely improve federal funding systems that currently allocate assistance 

to communities based on boundaries ( Jakes et al. 2011) that do not include the major 

sources of risk. Thus, identification of community transmission hot spots could dramatically increase the efficiency of building fire-adapted communities and fire-resilient 

landscapes (USDA-USDI 2013). Incorporation of social factors would also help and is 

part of ongoing efforts (Palaiologou et al. 2019). 

The scale of the risk to communities as defined by firesheds vastly exceeds the scale of 

planning as defined in the CWPP process. Ager et al. (2016) found that over half the 

area that contributes wildfire exposure to CWPPs fell outside CWPP boundaries, and 

was not analyzed as part of the planning process. As a result, Firewise and other homeowner mitigation activities implemented as part of CWPP planning (Williams et al. 

2012) can potentially be ineffective without matching mitigation efforts on the adjacent 

wildlands and forests in which fires originate. Transmission networks can be used to 

provide explicit identification of the sources of wildfire exposure and the responsible 

landowners. This approach differs from current community wildfire protection CWPP 

guidelines ( Jakes et al. 2007) where perimeters are typically based on administrative 

boundaries (Williams et al. 2012). The lack of a spatial planning framework for the 

CWPP process has led to a wide range of planning scales (e.g., neighborhoods, towns, 

multiple towns, entire counties) and subsequent boundary delineations that may not 

incorporate the spatial extent of fire transmission to communities. 

This type of scale mismatch between planning boundaries and biophysical disturbances 

has been widely discussed in the literature. Our analysis inherently connects landscapes 

and represents exposure as a process among land parcels rather than being a property 

of the parcel. Since landscape fragmentation within public lands and on private lands is 

at a fraction of the scale of large wildfire events, the importance of the landscape overshadows the properties of individual parcels in terms of risk. While this is incorporated 

in risk assessments that use burn probability, it is not possible to disentangle the spatial 

scale of risk and the relative contributors of different parcels. 
USDA Forest Service RMRS-GTR-392. 2019 29

Our work contributes to building community archetypes that can help organize federal 

wildland fire policy at the community scale in terms of blending goals for promoting 

fire-adapted communities, restoring fire-resilient landscapes, and ensuring safe and effective wildfire response. There is not enough guidance regarding translating these policy 

goals into specific strategies appropriate to each local context. Community exposure archetypes aid in tailoring national risk mitigation policies to local conditions. Differences 

among these communities suggest implementing strategies that emphasize mitigation 

along a spectrum of management options. These strategies may target anything from the 

source of ignitions to the area of fire exposure and potential structure loss. For instance, 

projects may include actions such as restricting development in wildlands (Schoennagel 

et al. 2009), expanding and improving hazardous fuel treatments and prescribed burns 

(North et al. 2015; OIG 2016), reducing flammable vegetation surrounding homes 

(Gibbons et al. 2012), and improving community-based disaster planning and response 

(Calkin et al. 2014; Paveglio et al. 2016). 

This work has several applications and implications for the shared stewardship initiative. 

The data in this assessment can be integrated into cross-boundary planning efforts 

to prioritize management investments in areas of high fire transmission. Specifically, 

combining transmission data with existing State and Forest Service assessments provides 

a spatial planning container within which the respective landowners share wildfire issues 

that will only be solved with collaborative planning efforts. 
30 USDA Forest Service RMRS-GTR-392. 2019

References

Abatzoglou, J.T.; Balch, J.K.; Bradley, B.A.; Kolden, C.A. 2018. Human-related ignitions 

concurrent with high winds promote large wildfires across the USA. International Journal 

of Wildland Fire. 27(6): 377-386.

Agee, J.K. 2002. The fallacy of passive management managing for firesafe forest reserves. 

Conservation in Practice. 3: 18-26.

Ager, A.A.; Day, M.A.; Finney, M.A. [et al.]. 2014a. Analyzing the transmission of wildfire 

exposure on a fire-prone landscape in Oregon, USA. Forest Ecology and Management. 

334: 377-390. doi: 10.1016/j.foreco.2014.09.017.

Ager, A.A.; Day, M.A.; McHugh, C.W. [et al.]. 2014b. Wildfire exposure and fuel management 

on western U.S. national forests. Journal of Environmental Management. 145: 54-70.

Ager, A.A.; Day, M.A.; Short, K.C.; Evers, C.R. 2016. Assessing the impacts of federal forest 

planning on wildfire risk mitigation in the Pacific Northwest, USA. Landscape and Urban 

Planning. 147: 1-17. doi: 10.1016/j.landurbplan.2015.11.007.

Ager, A.A.; Finney, M.A.; Kerns, B.K.; Maffei, H. 2007. Modeling wildfire risk to northern 

spotted owl (Strix occidentalis caurina) habitat in Central Oregon, USA. Forest Ecology and 

Management. 246: 45-56.

Ager, A.A.; Kline, J.; Fischer, A.P. 2015. Coupling the biophysical and social dimensions of 

wildfire risk to improve wildfire mitigation planning. Risk Analysis. 35(8): 1393–1406. doi: 

10.1111/risa.12373.

Ager, A.A.; Palaiologou, P.; Evers, C. [et al.]. In press. Wildfire exposure to the wildland urban 

interface in the western U.S. Applied Geography.

Ager, A.A.; Palaiologou, P.; Evers, C.R. [et al.]. 2018. Assessing transboundary wildfire 

exposure in the southwestern United States. Risk Analysis. 38(10): 2105-2127. doi: 

10.1111/risa.12999.

Ager, A.A.; Vaillant, N.M.; Finney, M.A. 2011. Integrating fire behavior models and geospatial 

analysis for wildland fire risk assessment and fuel management planning. Journal of 

Combustion. 572452: 19. doi: 10.1155/2011/572452. 

Agricultural Act of 2014 (2014 Farm Bill). Pub. L. 113-79. 128 Stat. 649. 10 July 2013. 

Alcasena, F.J.; Salis, M.; Ager, A.A. [et al.]. 2017. Assessing wildland fire risk transmission to 

communities in northern Spain. Forests. 8(2): 27. doi: 10.3390/f8020030.

Argañaraz, J.P.; Radeloff, V.C.; Bar-Massada, A. [et al.]. 2017. Assessing wildfire exposure 

in the wildland-urban interface area of the mountains of central Argentina. Journal of 

Environmental Management. 196: 499-510. doi: 10.1016/j.jenvman.2017.03.058.

Bahro, B; Barber, K.H.; Sherlock, J.W.; Yasuda, D.A. 2007. Stewardship and fireshed 

assessment: A process for designing a landscape fuel treatment strategy. In: Powers, 

Robert F., (tech. editor). 2007. Restoring fire-adapted ecosystems: Proceedings of the 

2005 national silviculture workshop. Gen. Tech. Rep. PSW-GTR-203, Albany, CA: Pacific 

Southwest Research Station, Forest Service, U.S. Department of Agriculture: 41-54.

Berman, J.D.; Breysse, P.N.; White, R.H. [et al.]. 2015. Evaluating methods for spatial 

mapping: Applications for estimating ozone concentrations across the contiguous United 

States. Environmental Technology & Innovation. 3: 1-10.

Bradshaw, L.S.; Deeming, J.E.; Burgan, R.E.; Cohen, J.D. 1983. The 1978 National FireDanger Rating System: Technical documentation. Gen. Tech. Rep. GTR-INT-169. Ogden, 

UT: U.S. Department of Agriculture, Forest Service, Intermountain Forest and Range 

Experiment Station. 44 p. http://www.treesearch.fs.fed.us/pubs/29615

Calkin, D.; Ager, A.A.; Thompson, M. [et al.]. 2011. A comparative risk assessment 

framework for wildland fire management: The 2010 cohesive strategy science report. 

Gen. Tech. Rep. RMRS-GTR-262. Fort Collins, CO: U.S. Department of Agriculture, Forest 

Service, Rocky Mountain Research Station. 63 p.
USDA Forest Service RMRS-GTR-392. 2019 31

Calkin, D.E.; Cohen, J.D.; Finney, M.A.; Thompson, M.P. 2014. How risk management can 

prevent future wildfire disasters in the wildland-urban interface. Proceedings of the 

National Academy of Sciences. 111(2): 746-751. doi: 10.1073/pnas.1315088111.

Charnley, S.; Spies, T.A.; Barros, A.M.G. [et al.]. 2017. Diversity in forest management 

to reduce wildfire losses: Implications for resilience. Ecology and Society. 22(1) doi: 

10.5751/Es-08753-220122.

Clavet, C. 2018. Wildfire funding in the Omnibus Bill: What you need to know. Blog essay, 26 

April 2018. Fire Adapted Communities Learning Network. https://fireadaptednetwork.org/

wildfire-funding-omnibus-bill-need-know/ [Accessed 2019 April 26].

Dillon, G.K. 2015. Wildfire Hazard Potential (WHP) for the conterminous United States 

(270-m GRID), version 2014 continuous. Fort Collins, CO: Forest Service Research Data 

Archive. https://doi.org/10.2737/RDS-2015-0047.

Dillon, G.K.; Menakis, J.; Fay, F. 2015. Wildland fire potential: A tool for assessing wildfire 

risk and fuel management needs. In: Keane et al. (eds). Proceedings of the large wildland 

fire conference. May 19-23, 2014; Missoula, MT. Proc. RMRS-P-73. Fort Collins, CO: U.S. 

Department of Agriculture, Forest Service, Rocky Mountain Research Station: 60-76.

Evers, C.; Ager, A.A.; Nielsen-Pincus, M. [et al.]. 2019. Archetypes of community wildfire 

exposure from national forests in the western U.S. Landscape and Urban Planning. 182: 

55-66. doi: 10.1016/j.landurbplan.2018.10.004.

Finney, M.A. 2002. Fire growth using minimum travel time methods. Canadian Journal of 

Forest Research. 32(8): 1420-1424. doi: 10.1139/x02-068.

Finney, M.A. 2005. The challenge of quantitative risk analysis for wildland fire. Forest Ecology 

and Management. 211(1-2): 97-108.

Finney, M.A. 2006. An overview of FlamMap fire modeling capabilities. In Andrews, P.L.; 

Butler, B.W. (comps). Fuels management—How to measure success; Conference 

Proceedings. 28-30 March 2006; Portland, OR. Proc. RMRS-P-41. Fort Collins, CO: U.S. 

Department of Agriculture, Forest Service, Rocky Mountain Research Station: 213-220. 

http://www.fs.fed.us/rm/pubs/rmrs_p041/rmrs_p041_213_220.pdf

Finney, M.A.; McHugh, C.W.; Grenfell, I.C. [et al.]. 2011. A simulation of probabilistic wildfire 

risk components for the continental United States. Stochastic Environmental Research 

and Risk Assessment. 25: 973–1000. doi: 10.1007/s00477-011-0462-z.

Finney, M.A.; Seli, R.C.; McHugh, C.W. [et al.]. 2007. Simulation of long-term landscapelevel fuel treatment effects on large wildfires. International Journal of Wildland Fire. 16: 

712-727.

General Accounting Office (GAO). 2004. Wildland fires: Forest Service and BLM need better 

information and a systematic approach for assessing the risks of environmental effects. 

Washington, DC: GAO. 88 p.

Gibbons, P.; Van Bommel, L.; Gill, A.M. [et al.]. 2012. Land management practices 

associated with house loss in wildfires. PLoS ONE. 7(1): e29212. doi: 10.1371/journal.

pone.0029212. http://dx.doi.org/10.1371/journal.pone.0029212. 

Good Neighbor Authority. 2014. U.S.Code Title 16 - Conservation Chapter 41 - Cooperative 

Forestry Assistance Sec. 2113a.

Healthy Forests Restoration Act of 2003. Pub. L. 108-148. 117 Stat. 1887, Dec. 3, 2003.

Jakes, P.; Burns, S.; Cheng, A. [et al.]. 2007. Critical elements in the development and 

implementation of community wildfire protection plans (CWPPs). In: Butler, B.W.; Cook, 

W. (comps). The fire environment—Innovations, management and policy; Conference 

proceedings. 26-30 March 2007; Destin, FL. Proc. RMRS-P-46CD. Fort Collins, CO. Fort 

Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research 

Station: 613-625. 

Jakes, P.J.; Esposito, C.; Burns, S. [et al.]. 2012. Best management practices for creating a 

community wildfire protection plan. Gen. Tech. Rep. NRS-GTR-89. Newtown Square, PA: 

U.S. Department of Agriculture, Forest Service, Northern Research Station. 27 p.
32 USDA Forest Service RMRS-GTR-392. 2019

Jakes, P.J.; Nelson, K.C.; Enzler, S.A. [et al.]. 2011. Community wildfire protection plannning: 

Is the Heathly Forests Restoration Act’s vagueness genius? International Journal of 

Wildland Fire. 20: 350-363.

Kachitvichyanukul, V.; Schmeiser, B.W. 1988. Binomial random variate generation. 

Communications of the ACM. 31(2): 216-222. doi: 10.1145/42372.42381.

Kalies, E.L.; Yocom Kent, L.L. 2016. Tamm Review: Are fuel treatments effective at achieving 

ecological and social objectives? A systematic review. Forest Ecology and Management. 

375: 84-95. doi: 10.1016/j.foreco.2016.05.021.

Kaufman, J.B. 2004. Death rides the forest: Perceptions of fire, land use, and ecological 

restoration of western forests. Conservation Biology. 18(4): 878-882.

LANDFIRE. 2016. 40 Scott and Burgan Fire Behavior Fuel Models. U.S. Department of 

Interior, U.S. Geological Survey. https://www.landfire.gov/fbfm40.php. Accessed 20 June 

2018.

Lidskog, R.; Soneryd, L.; Uggla, Y. 2010. Transboundary risk governance. London: Earthscan. 

176 p.

Lidskog, R.; Uggla, Y.; Soneryd, L. 2011. Making transboundary risks governable: Reducing 

complexity, constructing spatial identity, and ascribing capabilities. Ambio. 40(2): 

111-120.

Linnerooth-Bayer, J.; Löfstedt, R.E.; Sjöstedt, G., eds. 2001. Transboundary risk 

management. London/Sterling, VA: Earthscan. 352 p.

Littell, J.S.; Peterson, D.L.; Riley, K.L. [et al.]. 2016. A review of the relationships between 

drought and forest fire in the United States. Global Change Biology. 22(7): 2353-2369. 

doi: 10.1111/gcb.13275.

Mell, W.E.; Manzello, S.L.; Maranghides, A. [et al.]. 2010. The wildland-urban interface fire 

problem: Current approaches and research needs. International Journal of Wildland Fire. 

19: 238-251.

Miller, C.; Ager, A.A. 2013. A review of recent advances in risk analysis for wildfire 

management. International Journal of Wildland Fire. 22(1): 1-14. doi: http://dx.doi.

org/10.1071/WF11114.

Moritz, M.A.; Batllori, E.; Bradstock, R.A. [et al.]. 2014. Learning to coexist with wildfire. 

Nature. 515(7525): 58-66. doi: 10.1038/nature13946.

Nagy, R.; Fusco, E.; Bradley, B. [et al.]. 2018. Human-related ignitions increase the 

number of large wildfires across U.S. ecoregions. Fire. 1(1): 4. https://doi.org/10.3390/

fire1010004. 

North, M.; Brough, A.; Long, J. [et al.]. 2015. Constraints on mechanized treatment 

significantly limit mechanical fuels reduction extent in the Sierra Nevada. Journal of 

Forestry. 113(1): 40-48.

Office of Inspector General (OIG). 2016. Forest Service Wildland Fire Activities – Hazardous 

Fuels Reduction. Washington, DC: USDA Office of Inspector General. https://www.usda.

gov/oig/webdocs/08601-0004-41.pdf.

Palaiologou, P.; Ager, A.A.; Evers, C. [et al.]. In press. Fine scale assessment of crossboundary wildfire events in the Western US. Natural Hazards and Earth System Sciences. 

https://doi.org/10.5194/nhess-2019-56.

Palaiologou, P., Ager, A.A. [et al.]. 2019. Social vulnerability to large wildfires in the western 

USA. Landscape and Urban Planning 189:99-116.

Palaiologou, P.; Ager, A.A.; Nielsen-Pincus, M. [et al.]. 2018. Using transboundary wildfire 

exposure assessments to improve fire management programs: A case study in Greece. 

International Journal of Wildland Fire. 27: 501-513. doi: 10.1071/WF17119.

Palaiologou, P.; Aiello, E. 2019. A map portal for wildfire transmission and community 

exposure for the western U.S. USDA Forest Service. https://usfs.maps.arcgis.com/apps/

MapSeries/index.html?appid=fb8b5561702944e5b467ad0419786107
USDA Forest Service RMRS-GTR-392. 2019 33

Park, J.-S.; Oh, S.-J. 2012. A new concave hull algorithm and concaveness measure for 

n-dimensional datasets. Journal of Information Science and Engineering. 28(3): 587-600.

Paveglio, T.B.; Abrams, J.; Ellison, A. 2016. Developing fire adapted communities: The 

importance of interactions among elements of local context. Society & Natural Resources. 

29(10): 1246-1261. doi: 10.1080/08941920.2015.1132351.

Pilz, J.; Spöck, G. 2008. Why do we need and how should we implement Bayesian kriging 

methods. Stochastic Environmental Research and Risk Assessment. 22(5): 621-632. doi: 

10.1007/s00477-007-0165-7.

Radeloff, V.C.; Hammer, R.B.; Stewart, S.I. [et al.]. 2005. The wildland-urban interface in the 

United States. Ecological Applications. 15: 799-805.

Radeloff, V.C.; Helmers, D.P.; Kramer, H.A. [et al.]. 2018. Rapid growth of the US wildlandurban interface raises wildfire risk. Proceedings of the National Academy of Sciences. 

115(13): 3314-3319.

Sander, L.M.; Warren, C.P.; Sokolov, I.M. [et al.]. 2002. Percolation on heterogeneous 

networks as a model for epidemics. Mathematical Biosciences. 180: 293-305.

Schoennagel, T.; Nelson, C.R.; Theobald, D.M. [et al.]. 2009. Implementation of national 

fire plan treatments near the wildland–urban interface in the western United States. 

Proceedings of the National Academy of Sciences. 106: 10706-10711.

Scott, J.H. 2006. An analytical framework for quantifying wildland fire risk and fuel treatment 

benefit. In: Andrews, P.L. Butler, B.W. (comps). Fuels management-How to measure 

success. Conference Proceedings. 28-30 March 2006; Portland, OR. Proc. RMRS-P-41. 

Fort Collins, CO. U.S. Department of Agriculture, Forest Service, Rocky Mountain Research 

Station: 169-184.

Scott, J.H.; Thompson, M.P.; Calkin, D.E. 2013. A wildfire risk assessment framework for 

land and resource management. Gen. Tech. Rep. RMRS-GTR-315. Fort Collins, CO: U.S. 

Department of Agriculture, Forest Service, Rocky Mountain Research Station. 83 p.

Scott, J.H.; Thompson, M.P.; Gilbertson-Day, J.W. 2015. Exploring how alternative mapping 

approaches influence fireshed assessment and human community exposure to wildfire. 

GeoJournal. 82(1): 201-215. doi: 10.1007/s10708-015-9679-6. 

Short, K.C. 2015. Spatial wildfire occurrence data for the United States, 1992-2013 [FPA_

FOD_20150323]. 3rd edition. Fort Collins, CO: Forest Service Research Data Archive. 

http://dx.doi.org/10.2737/RDS-2013-0009.3. [Date accessed: 27 July 2016].

Spies, T.A.; White, E.M.; Kline, J.D. [et al.]. 2014. Examining fire-prone forest landscapes as 

coupled human and natural systems. Ecology and Society. 19(3): 9. doi: 10.5751/ES06584-190309. http://dx.doi.org/10.5751/ES-06584-190309. 

Society for Risk Analysis (SRA). 2006. Society for Risk Analysis. http://www.sra.org/. [Date 

accessed: 26 November 2013].

The Consolidated Appropriations Act, 2018. Pub. L. 115-141. 132 Stat. 348. (March 23, 

2018). 

U.S. Census Bureau. 2016. USA Census populated places areas. ESRI. http://www.arcgis.

com/home/item.html?id=4e75a4f7daaa4dfa8b9399ea74641895. [Date accessed: 14 

March 2016].

U.S. Department of Agriculture and U.S. Department of the Interior (USDA-USDI). 2013. A 

National Cohesive Wildland Fire Management Strategy: Challenges, opportunities, and 

national priorities. Washington, DC: U.S. Department of Agriculture-U.S. Department of 

Interior. 104 p.

U.S. Department of Agriculture and U.S. Department of the Interior (USDA and USDI). 2001. 

Urban wildland interface communities within the vicinity of federal lands that are at high 

risk from wildfire. Federal Register. 66(3): 751-777.

USDA Forest Service. 2017a. Inventoried Roadless Areas. U.S. Forest Service. https://data.

fs.usda.gov/geodata/edw/edw_resources/meta/S_USA.RoadlessArea_2001.xml
34 USDA Forest Service RMRS-GTR-392. 2019

USDA Forest Service. 2017b. National forest lands with nationally designated management 

or use limitations. https://data.fs.usda.gov/geodata/edw/edw_resources/meta/S_USA.

OtherNationalDesignatedArea.xml. [Date accessed: November 30, 2017].

USDA Forest Service. 2018. Towards shared stewardship across landscapes: An outcomebased investment strategy. Washington, DC: USDA Forest Service. http://www.fs.fed.

us/sites/default/files/media/2014/25/2015-BudgetJustification-030614.pdf [Date 

accessed: 18 May 2015].

U.S. Geological Survey (USGS). 2016. Protected Areas Database of the United States (PADUS) [Vector digital data]. USGS Gap Analysis Program (GAP). https://gapanalysis.usgs.

gov/padus/data/metadata/. [Date accessed: 14 March 2016].

Vaillant, N.M.; Ager, A.A.; Anderson, J. 2013. ArcFuels10 system overview. Gen. Tech. Rep. 

PNW-GTR-875. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific 

Northwest Research Station.65 p.

Vaillant, N.M.; Reinhardt, E.D. 2017. An evaluation of the Forest Service Hazardous Fuels 

Treatment Program—Are we treating enough to promote resiliency or reduce hazard? 

Journal of Forestry. 115(4): 300-308. doi: 10.1007/s00267-016-0791-2.

Williams, D.R.; Jakes, P.J.; Burns, S. [et al.]. 2012. Community wildfire protection planning: 

the importance of framing, scale, and building sustainable capacity. Journal of Forestry. 

110(8): 415-420.

Williams, J. 2013. Exploring the onset of high-impact mega-fires through a forest land 

management prism. Forest Ecology and Management. 294: 4-10.

Oregon Department of Forestry (ODF). 2013. West Wide Wildfire Risk Assessment 

(WWWRA). 2013. West Wide Wildfire Risk Assessment—Final Report. Report prepared 

for: Oregon Department of Forestry, Council of Western State Foresters and the Western 

Forestry Leadership: 105 p. http://www.odf.state.or.us/gis/data/Fire/West_Wide_

Assessment/WWA_FinalReport.pdf

Zachariassen, J.; Zeller, K.F.; Nikolov, N.; McClelland, T. 2003. A review of the Forest Service 

remote automated weather station (RAWS) network. Gen. Tech. Rep. RMRS-GTR-119. Fort 

Collins, CO: USDA Forest Service, Rocky Mountain Research Station. 153 p. + CD. 

Zimmerman, D.; Pavlik, C.; Ruggles, A.; Armstrong, M.P. 1999. An experimental comparison 

of ordinary and universal kriging and inverse distance weighting. Mathematical Geology. 

31(4): 375-390.
In accordance with Federal civil rights law and U.S. Department of Agriculture (USDA) civil rights regulations and policies, the USDA, its Agencies, offices, and employees, and institutions participating in or 

administering USDA programs are prohibited from discriminating based on race, color, national origin, 

religion, sex, gender identity (including gender expression), sexual orientation, disability, age, marital status, family/parental status, income derived from a public assistance program, political beliefs, or reprisal 

or retaliation for prior civil rights activity, in any program or activity conducted or funded by USDA (not 

all bases apply to all programs). Remedies and complaint filing deadlines vary by program or incident.

Persons with disabilities who require alternative means of communication for program information (e.g., 

Braille, large print, audiotape, American Sign Language, etc.) should contact the responsible Agency or 

USDA’s TARGET Center at (202) 720-2600 (voice and TTY) or contact USDA through the Federal Relay 

Service at (800) 877-8339. Additionally, program information may be made available in languages other 

than English.

Back cover: The Woolsey Fire seen from Topanga, California. (Photo courtesy of Peter 

Buschmann)
To learn more about RMRS publications or search our online titles:

RMRS web site at: https://www.fs.fed.us/rmrs/rmrs-publishing-services