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USGS2022

Fuels and vegetation changes in southwestern, unburned portions of Great Smoky Mountains National Park, USA, 2003-2019

Overstory basal area, ericaceous shrub cover ( Kalmia latifolia L. and Rhododendron maximum L.), and fuels (i.e., woody fuel loads and depths and O Horizon thickness) were assessed within Great Smoky Mountains National Park, USA, in 2003 − 2004. Due to recent wildfire activity within the southern Appalachian Mountain region (including Great Smoky Mountains National Park), the potential spread and expansion of ericaceous shrubs, and the impacts of the hemlock woolly adelgid ( Adelges tsugae Annand) on eastern hemlock ( Tsuga canadensis (L.) Carrière), these same ecosystem components were again assessed in 2019. Elevation and moisture regime (xeric, intermediate, and mesic) were included in this assessment as potential influential factors. An evaluation of repeated measurements from 40 plots suggested that O Horizon thickness did not change significantly over the 16-year period, but increased as elevation increased, and moisture regime (xeric O Horizon thickness > mesic O Horizon thickness) was a significant, related factor. The sum of 1-, 10-, and 100-h fuel loads (fuels less < 7.6 cm diameter) increased, whereas woody fuel depth decreased over the 16-year period. No significant changes in 1000-h fuel loads (> 7.6 cm diameter), total woody fuel loads, ericaceous shrub cover, total basal area, or live T. canadensis basal area were observed. Live T. canadensis basal area decreased with increasing elevation. Dead, standing T. canadensis basal area increased from 2003–2019, and that increase was most pronounced as elevation increased on xeric and intermediate sites. Overall, we found that: 1. hypothesized increases in total woody fuel loads and ericaceous shrub cover were not present; and 2. elevation and moisture regime were most related to observed changes in vegetation and fuel condition.

North Carolina, Tennessee↗

USGS2016

Duration of fuels reduction following prescribed fire in coniferous forests of U.S. national parks in California and the Colorado Plateau

Prescribed fire is a widely used forest management tool, yet the long-term effectiveness of prescribed fire in reducing fuels and fire hazards in many vegetation types is not well documented. We assessed the magnitude and duration of reductions in surface fuels and modeled fire hazards in coniferous forests across nine U.S. national parks in California and the Colorado Plateau. We used observations from a prescribed fire effects monitoring program that feature standard forest and surface fuels inventories conducted pre-fire, immediately following an initial (first-entry) prescribed fire and at varying intervals up to >20 years post-fire. A subset of these plots was subjected to prescribed fire again (second-entry) with continued monitoring. Prescribed fire effects were highly variable among plots, but we found on average first-entry fires resulted in a significant post-fire reduction in surface fuels, with litter and duff fuels not returning to pre-fire levels over the length of our observations. Fine and coarse woody fuels often took a decade or longer to return to pre-fire levels. For second-entry fires we found continued fuels reductions, without strong evidence of fuel loads returning to levels observed immediately prior to second-entry fire. Following both first- and second-entry fire there were increases in estimated canopy base heights, along with reductions in estimated canopy bulk density and modeled flame lengths. We did not find evidence of return to pre-fire conditions during our observation intervals for these measures of fire hazard. Our results show that prescribed fire can be a valuable tool to reduce fire hazards and, depending on forest conditions and the measurement used, reductions in fire hazard can last for decades. Second-entry prescribed fire appeared to reinforce the reduction in fuels and fire hazard from first-entry fires.

Forest Ecology and Management↗

USGS2020

Estimating wildfire fuel consumption with multitemporal airborne laser scanning data and demonstrating linkage with MODIS-derived fire radiative energy

Characterizing pre- and post-fire fuels remains a key challenge for estimating biomass consumption and carbon emissions from wildfires. Airborne laser scanning (ALS) data have demonstrated effectiveness for estimating canopy, and to a lesser degree, surface fuel components at fine-scale (i.e., 30 m) across landscapes. Using pre- and post-fire ALS data and corresponding field data, this study estimated consumption of canopy fuel (ΔCF), understory fuel (ΔUF), total fuel (ΔTF), and canopy bulk density (ΔCBD) for the 2012 Pole Creek fire in Oregon, USA (10,760 ha), and portions of the 2011 Las Conchas fire in New Mexico, USA (4,934 ha). Additionally, the feasibility of predicting fuel consumption was tested using separate pre- and post-fire models (PrePost), models combining all pre- and post-fire data (Pooled), and models using all data from both fires (Global). Estimates of ΔTF were then compared to fire radiative energy (FRE, units: MJ) derived from Fire Radiative Power (FRP, units: MW) observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard NASA Terra and Aqua satellites to mechanistically derive a biomass combustion coefficient (BCC, units: kg MJ −1 ). The PrePost and Pooled approaches yielded similar results at Las Conchas, but at Pole Creek insufficient pre-fire field data resulted in erroneous fuel consumption estimates outside the fire perimeter using the PrePost models. These results demonstrated that pre-fire field data were less important for these models than having field data which represent the full range of fuel conditions likely to exist across the landscape. Estimated total biomass consumed for the PrePost, Pooled, and Global models were 226 Gg, 224 Gg, and 224 Gg at Las Conchas, and 581 Gg, 713 Gg, and 552 Gg at Pole Creek. Comparisons between estimated ΔTF and FRE yielded an average BCC for both fires of 0.367 (s.d. ± 0.049) kg MJ −1 based on pixels with at least five MODIS observations. Both higher MODIS observations per pixel and accounting for canopy occlusion of FRE improved the relationship between ΔTF and MODIS-FRE. This study suggested a practical modelling approach for future efforts using only post-fire field observations and quantified a landscape-scale relationship between MODIS-derived FRE and fine-scale fuel consumption consistent with prior experiments.

New Mexico, Oregon↗

USGS2007

Estimation of forest fuel load from radar remote sensing

Understanding fire behavior characteristics and planning for fire management require maps showing the distribution of wildfire fuel loads at medium to fine spatial resolution across large landscapes. Radar sensors from airborne or spaceborne platforms have the potential of providing quantitative information about the forest structure and biomass components that can be readily translated to meaningful fuel load estimates for fire management. In this paper, we used multifrequency polarimetric synthetic aperture radar (SAR) imagery acquired over a large area of the Yellowstone National Park by the Airborne SAR sensor to estimate the distribution of forest biomass and canopy fuel loads. Semiempirical algorithms were developed to estimate crown and stem biomass and three major fuel load parameters, namely: 1) canopy fuel weight; 2) canopy bulk density; and 3) foliage moisture content. These estimates, when compared directly to measurements made at plot and stand levels, provided more than 70% accuracy and, when partitioned into fuel load classes, provided more than 85% accuracy. Specifically, the radar-generated fuel parameters were in good agreement with the field-based fuel measurements, resulting in coefficients of determination of R2 = 85 for the canopy fuel weight, R 2 = 0.84 for canopy bulk density, and R2 =0.78 for the foliage biomass. ?? 2007 IEEE.

IEEE Transactions on Geoscience and Remote Sensing↗

USGS2026

Fueling the fire: Predicting cogongrass (Imperata cylindrica) impacts on fire behavior across environmental and seasonal gradients

Invasive species can fundamentally alter fire regimes by modifying fuel characteristics, yet predicting their impacts on fire behavior remains challenging. Cogongrass ( Imperata cylindrica ), a highly invasive grass spreading across the southeastern US, has been associated with increased fuel loads and fire intensity, but it is unclear how its impacts on fuel load and fire behavior vary across environmental and seasonal gradients. We measured fuel characteristics across 159 cogongrass invaded and non-invaded plots spanning from central Florida to southern Mississippi across multiple seasons. Using these data, we developed custom fuel models in BehavePlus to predict cogongrass effects on surface fire behavior and tree torching across spatial and temporal gradients. Cogongrass invasion increased live fuel loads by up to 1.96 t/ha, with effects strongest in summer and at lower latitudes, and dead fuel loads by up to 1.88 t/ha across all sites. Latitude × invasion interactions indicated that fuel load differences between invaded and non-invaded plots diminished toward northern sites. Cogongrass-driven fuel load alterations elevated surface fire rate of spread (median + 0.75 m/min), and flame length (+ 0.16 m), with the largest increases observed in spring (rate of spread: + 2.60 m/min; flame length: + 0.80 m). Invasion also consistently increased the crown fire transition probability, with magnitudes mirroring those of surface fire effects. Our findings highlight the potential for cogongrass to substantially alter fire behavior across a range of environmental conditions, although the magnitude of these effects varies geographically and tends to be strongest at lower latitudes. These results underscore the importance of targeted management in regional fire planning of invaded landscapes.

Alabama, Florida, Mississippi↗

USGS2024

Distribution of ancient carbon in groundwater and soil gas from degradation of petroleum near the Red Hill Bulk Fuel Storage Facility, O‘ahu, Hawai‘i

The groundwater below the Red Hill Bulk Fuel Storage Facility (the facility) in Oʻahu, Hawaiʻi, contains fuel compounds from past spills. This study used carbon-14 analyses to distinguish fuel-derived carbon from background carbon, along with other biodegradation indicators, to address two goals: (1) determine the extent and migration direction of groundwater affected by residual fuel below the facility and (2) determine if residual fuel locations in the subsurface could be identified by analyzing soil gas at the surface above the facility. Groundwater from 19 wells was sampled between September 2022 and April 2023. Nonvolatile dissolved organic carbon (NVDOC) from a well presumed to be unaffected by past spills contained 38 percent ancient carbon indicating a natural source of ancient carbon in the subsurface. The NVDOC concentrations and ancient carbon percentages indicate fuel biodegradation products are likely present on the north and south of Red Hill with the greatest effects at well RHMW02 near the 2014 spill site. The NVDOC concentrations are almost three times higher than diesel range organic (DRO) concentrations in groundwater from the same sites. Major ion data indicate that iron reduction is an important biodegradation process. Soil probe samples and soil carbon traps were used to determine the carbon-14 content of soil carbon dioxide. Ancient carbon from fuel biodegradation was not detected at any soil probe or carbon trap site in contrast to a 2017 study which reported ancient carbon detections. A reanalysis of the 2017 results using a range of local values for background carbon-14 indicates that ancient carbon from fuel biodegradation was probably only detected in lower tunnel exhaust system samples and not in any soil carbon trap samples. Measurements of carbon dioxide efflux with a dynamic closed chamber were highly variable. The soil gas results indicate that soil gas measurements at land surface were not useful for detecting residual fuel at the facility.

Hawaii↗

USGS2009

Modeling fuel succession

Surface fuels data are of critical importance for supporting fire incident management, risk assessment, and fuel management planning, but the development of surface fuels data can be expensive and time consuming. The data development process is extensive, generally beginning with acquisition of remotely sensed spatial data such as aerial photography or satellite imagery (Keane and others 2001). The spatial vegetation data are then crosswalked to a set of fire behavior fuel models that describe the available fuels (the burnable portions of the vegetation) (Anderson 1982, Scott and Burgan 2005). Finally, spatial fuels data are used as input to tools such as FARSITE and FlamMap to model current and potential fire spread and behavior (Finney 1998, Finney 2006). The capture date of the remotely sensed data defines the period for which the vegetation, and, therefore, fuels, data are most accurate. The more time that passes after the capture date, the less accurate the data become due to vegetation growth and processes such as fire. Subsequently, the results of any fire simulation based on these data become less accurate as the data age. Because of the amount of labor and expense required to develop these data, keeping them updated may prove to be a challenge. In this article, we describe the Sierra Nevada Fuel Succession Model, a modeling tool that can quickly and easily update surface fuel models with a minimum of additional input data. Although it was developed for use by Yosemite, Sequoia, and Kings Canyon National Parks, it is applicable to much of the central and southern Sierra Nevada. Furthermore, the methods used to develop the model have national applicability.

Fire Management Today↗

USGS2023

Future direction of fuels management in sagebrush rangelands

Sagebrush ecosystems in the United States have been declining since EuroAmerican settlement, largely due to agricultural and urban development, invasive species, and altered fire regimes, resulting in loss of biodiversity and wildlife habitat. To combat continued conversion to undesirable ecological states and loss of habitat to invasive species fueled by frequent fire, a variety of fuel treatments, including networks of fuel breaks, are being implemented or proposed in sagebrush ecosystems, particularly in and around the Great Basin. In this forum paper we briefly review current knowledge of common fuel treatment approaches, their intended benefits, potential risks, and limitations. We additionally discuss challenges for fuel treatment strategies in the context of changes in climate, invasive species, wildlife habitat, and human population, and we explore how advances in geospatial technologies, monitoring, and fire behavior modeling, as well as accounting for social context, can improve the efficacy of fuels management in sagebrush ecosystems. Finally, given continued potential for ecosystem transformation, we describe approaches to future fuels management by considering the applicability of the Resist-Accept-Direct (RAD) framework. The intent of the paper is to provide scientists and land managers with key information and a forward-thinking framework for fuels science and adaptive management that can respond to both expected and unexpected changes in sagebrush rangelands.

Rangeland Ecology and Management↗

USGS2021

Variability in wildland fuel patches following high-severity fire and post-fire treatments in the northern Sierra Nevada

Surface fuel loads are highly variable in many wildland settings, which can have many important ecological effects, especially during a wildland fire. This variability is not well described by a single metric (e.g. mean load), so quantifying traits such as variability, continuity and spatial arrangement will help more precisely describe surface fuels. This study measured surface fuel variability in the northern Sierra Nevada of California following a high-severity fire that converted a mixed-conifer forest to shrub-dominant vegetation, both before and after a subsequent shrub removal treatment conducted as site preparation for reforestation. Data were collected on vegetation composition, spatial arrangement and biomass load of the common surface fuel components (1–1000-h woody fuel, litter, duff and shrubs). Mean shrub patch length decreased significantly from 9.25 to 1.0 m and mean dead and down surface fuel load decreased significantly from 131.4 to 73.4 Mg ha −1 . Additionally, probability of encountering a continuous high fuel load segment decreased after treatment. This work demonstrates a method of quantifying important spatial characteristics of surface fuel that could be used in the next generation of fire behaviour models and provides metrics that land managers may consider when designing post-fire reforestation treatments.

California↗

USGS2010

The use of multi-temporal Landsat Normalized Difference Vegetation Index (NDVI) data for mapping fuels in Yosemite National Park, USA

The objective of this study was to test the applicability of using Normalized Difference Vegetation Index (NDVI) values derived from a temporal sequence of six Landsat Thematic Mapper (TM) scenes to map fuel models for Yosemite National Park, USA. An unsupervised classification algorithm was used to define 30 unique spectral-temporal classes of NDVI values. A combination of graphical, statistical and visual techniques was used to characterize the 30 classes and identify those that responded similarly and could be combined into fuel models. The final classification of fuel models included six different types: short annual and perennial grasses, tall perennial grasses, medium brush and evergreen hardwoods, short-needled conifers with no heavy fuels, long-needled conifers and deciduous hardwoods, and short-needled conifers with a component of heavy fuels. The NDVI, when analysed over a season of phenologically distinct periods along with ancillary data, can elicit information necessary to distinguish fuel model types. Fuels information derived from remote sensors has proven to be useful for initial classification of fuels and has been applied to fire management situations on the ground.

International Journal of Remote Sensing↗

USGS2026

Recovery trajectories of surface fuels and forest trees following prescribed fire in low elevation conifer forests of California and southern Oregon

Background Prescribed fire is commonly used to manage surface fuels and stand structure in dry coniferous forests in the western United States. While the effectiveness of prescribed fire to manage fuel loads and live forest structure has been well documented, less is known about the shape of recovery trajectories more than a decade following treatment. We used up to 30 years of longitudinal observations from > 180 monitoring plots treated with a single prescribed fire-only treatment across six national parks in California and Oregon to compare competing recovery trajectory models: generalized linear models (GLM) that presumed exponential trajectories over time, generalized additive models (GAM) that had maximum flexibility to fit responses, and generalized Michaelis–Menten models that describe an asymptotic recovery trajectory. Results The Michaelis–Menten model had the lowest median RMSE of the fuel recovery models, with predicted recovery times and maximal fuel loads tending to increase with increasing fuel particle size. However, predictive performance was similar between model forms. GAM best predicted forest structure, showing that stem density of live overstory trees (> 15 cm stem diameter) declined immediately following prescribed fire without appreciable postfire accumulation. Results for live stem biomass were similar, but with less pronounced reductions. Conclusions We found that reductions in surface fuel biomass from a single prescribed fire-only treatment can last for more than a decade for some fuel types. Our analysis also demonstrates that asymptotic models reasonably describe surface fuel recovery trajectories. However, we lack the evidence to definitively conclude that asymptotic models best describe postfire recovery, even with repeated observations of up to three decades postfire. We also found that stem density and biomass of live trees with stem diameter > 15 cm both declined in the first few years immediately following the fire, after which they remained relatively unchanged for the remainder of the observation period.

California, Oregon↗

USGS2014

Influence of fuels, weather and the built environment on the exposure of property to wildfire

Wildfires can pose a significant risk to people and property. Billions of dollars are spent investing in fire management actions in an attempt to reduce the risk of loss. One of the key areas where money is spent is through fuel treatment – either fuel reduction (prescribed fire) or fuel removal (fuel breaks). Individual treatments can influence fire size and the maximum distance travelled from the ignition and presumably risk, but few studies have examined the landscape level effectiveness of these treatments. Here we use a Bayesian Network model to examine the relative influence of the built and natural environment, weather, fuel and fuel treatments in determining the risk posed from wildfire to the wildland-urban interface. Fire size and distance travelled was influenced most strongly by weather, with exposure to fires most sensitive to changes in the built environment and fire parameters. Natural environment variables and fuel load all had minor influences on fire size, distance travelled and exposure of assets. These results suggest that management of fuels provided minimal reductions in risk to assets and adequate planning of the changes in the built environment to cope with the expansion of human populations is going to be vital for managing risk from fire under future climates.

PLoS ONE↗

USGS2023

A multimodal data fusion and deep learning framework for large-scale wildfire surface fuel mapping

Accurate estimation of fuels is essential for wildland fire simulations as well as decision-making related to land management. Numerous research efforts have leveraged remote sensing and machine learning for classifying land cover and mapping forest vegetation species. In most cases that focused on surface fuel mapping, the spatial scale of interest was smaller than a few hundred square kilometers; thus, many small-scale site-specific models had to be created to cover the landscape at the national scale. The present work aims to develop a large-scale surface fuel identification model using a custom deep learning framework that can ingest multimodal data. Specifically, we use deep learning to extract information from multispectral signatures, high-resolution imagery, and biophysical climate and terrain data in a way that facilitates their end-to-end training on labeled data. A multi-layer neural network is used with spectral and biophysical data, and a convolutional neural network backbone is used to extract the visual features from high-resolution imagery. A Monte Carlo dropout mechanism was also devised to create a stochastic ensemble of models that can capture classification uncertainties while boosting the prediction performance. To train the system as a proof-of-concept, fuel pseudo-labels were created by a random geospatial sampling of existing fuel maps across California. Application results on independent test sets showed promising fuel identification performance with an overall accuracy ranging from 55% to 75%, depending on the level of granularity of the included fuel types. As expected, including the rare—and possibly less consequential—fuel types reduced the accuracy. On the other hand, the addition of high-resolution imagery improved classification performance at all levels.

California↗

USGS1996

Use of a deterministic fire growth model to test fuel treatments

Fuel treatments are necessary in many vegetated areas of the Sierra Nevada to mitigate the effects of decades of fire suppression and land-management activities on fuel accumulations and understory canopies. Treating fuels will reduce the severity of wildfires and, as a result, the threat to human lives, the destruction of property and valuable resources, and the alteration of natural fire regimes. This chapter describes the use of a deterministic fire-modeling approach to obtain information about the relative effectiveness of fuel treatments, including fuel breaks, prescribed burning, biomassing, piling and burning, and cutting and scattering. Wildfire spread was simulated under idealized conditions to see how specific fuel and stand treatments affect fire behavior. It was obvious from the simulations that fuel breaks alone do not halt the spread of wildfire. Prescribed burning appears to be the most effective treatment for reducing a fire’s rate of spread, fireline intensity, flame length, and heat per unit of area. A management scheme that includes a combination of fuel treatments in conjunction with other land-management scenarios should be successful in reducing the size and intensity of wildfires.

Book chapter↗

USGS2017

Evidence of fuels management and fire weather influencing fire severity in an extreme fire event

Following changes in vegetation structure and pattern, along with a changing climate, large wildfire incidence has increased in forests throughout the western U.S. Given this increase there is great interest in whether fuels treatments and previous wildfire can alter fire severity patterns in large wildfires. We assessed the relative influence of previous fuels treatments (including wildfire), fire weather, vegetation and water balance on fire severity in the Rim Fire of 2013. We did this at three different spatial scales to investigate whether the influences on fire severity changed across scales. Both fuels treatments and previous low to moderate severity wildfire reduced the prevalence of high severity fire. In general, areas without recent fuels treatments and areas that previously burned at high severity tended to have a greater proportion of high severity fire in the Rim Fire. Areas treated with prescribed fire, especially when combined with thinning, had the lowest proportions of high severity. Proportion of the landscape burned at high severity was most strongly influenced by fire weather and proportional area previously treated for fuels or burned by low to moderate severity wildfire. The proportion treated needed to effectively reduce the amount of high fire severity fire varied by spatial scale of analysis, with smaller spatial scales requiring a greater proportion treated to see an effect on fire severity. When moderate and high severity fire encountered a previously treated area, fire severity was significantly reduced in the treated area relative to the adjacent untreated area. Our results show that fuels treatments and low to moderate severity wildfire can reduce fire severity in a subsequent wildfire, even when burning under fire growth conditions. These results serve as further evidence that both fuels treatments and lower severity wildfire can increase forest resilience.

California↗

USGS2025

Edge effects along roadside fuel treatments in sagebrush steppe

Increasing wildfire has motivated the construction of fuel breaks on many rangelands to improve prospects for wildfire suppression. However, the linear shape of fuel breaks greatly increases treatment perimeter: area and thus increased potential for edge effects, e.g., invasions by exotic plants. Potential for edge effects are further increased by the disturbances associated with fuel-break implementation. Fire risk in perennial-dominated and sagebrush-steppe rangelands are increased by exotic species, such as cheatgrass and other associated annuals, and therefore invasions after fuel-break implementation are problematic, yet have rarely been evaluated. Abundances of dominant invaders, cheatgrass and Russian thistle, were measured along treated and neighboring untreated edges in 40 paired plots along ∼61 km of 60-m wide fuel breaks. Fuel breaks were constructed using a variety of shrub-cutting and herbicide applications 1–4 yr before measurement. Generalized linear mixed effect models revealed that fractional cover significantly increased in treated compared with untreated areas by 0.02–0.12 for cheatgrass and 0–0.06 for Russian thistle within 9 m of treatment boundaries (on a scale of 0-1). We neither detected increased invasion in adjacent and untreated areas nor gradients of increasing invasion with proximity to treatment boundaries. Although these findings reveal invasions that were otherwise undetected across the entire 60 m width of fuel breaks, invasion levels did not surpass nominal management thresholds for fire behavior or risk of conversion to annual grasslands.

Rangeland Ecology and Management↗

USGS2009

Spatial fuel data products of the LANDFIRE Project

The Landscape Fire and Resource Management Planning Tools (LANDFIRE) Project is mapping wildland fuels, vegetation, and fire regime characteristics across the United States. The LANDFIRE project is unique because of its national scope, creating an integrated product suite at 30-m spatial resolution and complete spatial coverage of all lands within the 50 states. Here we describe development of the LANDFIRE wildland fuels data layers for the conterminous 48 states: surface fire behavior fuel models, canopy bulk density, canopy base height, canopy cover, and canopy height. Surface fire behavior fuel models are mapped by developing crosswalks to vegetation structure and composition created by LANDFIRE. Canopy fuels are mapped using regression trees relating field-referenced estimates of canopy base height and canopy bulk density to satellite imagery, biophysical gradients and vegetation structure and composition data. Here we focus on the methods and data used to create the fuel data products, discuss problems encountered with the data, provide an accuracy assessment, demonstrate recent use of the data during the 2007 fire season, and discuss ideas for updating, maintaining and improving LANDFIRE fuel data products.

International Journal of Wildland Fire↗

USGS2020

Cost-effective fuel treatment planning: A theoretical justification and case-study

Modelling the spatial prioritisation of fuel treatments and their net effect on values at risk is an important area for applied work as economic damages from wildfire continue to grow. We model and demonstrate a cost-effective fuel treatment planning algorithm using two ecosystem services as benefits for which fuel treatments are prioritised. We create a surface of expected fuel treatment costs to incorporate the heterogeneity in factors affecting the revenue and costs of fuel treatments, and then prioritise treatments based on a cost-effectiveness ratio to maximise the averted loss of ecosystem services from fire. We compare treatment scenarios that employ cost-effectiveness with those that do not, and use common tools and models in a case study of the Sisters Ranger District on the Deschutes National Forest in central Oregon, USA. Using cost-effectiveness not only increases the expected averted losses from fuel treatments, but it also allows a larger area to be treated for the same cost, simply by incorporating costs and cost-effectiveness into the prioritisation routine. These results have considerable implications for policymakers and land managers trying to minimise risk. Incorporating costs into the spatial planning of treatments could allow more effective outcomes without increasing fuel treatment budgets.

Oregon↗