Note: This Wildfire Hazard Potential (WHP) image service has been deprecated. Previous versions—including 2014, 2018, 2020, and 2023 continuous and classified datasets—have been replaced by a unified WHP service containing the most current data.For the updated continuous WHP service, visit: https://usfs.maps.arcgis.com/home/item.html?id=c984aeeecfcc4bef887a3f72a5b4e65a For the updated classified WHP service, visit: https://usfs.maps.arcgis.com/home/item.html?id=13004659506b4032bf7998038176f1c3Wildfire hazard potential (WHP) is an index that depicts the relative potential for wildfire that would be difficult for suppression resources to contain, based on wildfire simulation modeling. This dataset produced by the USDA Forest Service, Fire Modeling Institute in 2020 shows WHP at a spatial resolution of 270 meters across the entire conterminous United States, classified into five WHP classes of very low, low, moderate, high, and very high. Areas mapped with higher WHP values represent fuels with a higher probability of experiencing torching, crowning, and other forms of extreme fire behavior under conducive weather conditions, based primarily on 2014 landscape conditions. This WHP dataset is based on outputs of wildfire simulation modeling published in 2020. Starting with the 2020 version, the WHP dataset is integrated with the Wildfire Risk to Communities project. The 2020 dataset is the first version to include Alaska and Hawaii. There is a spatially-refined, 30-m resolution version of the WHP as part of the downloadable Wildfire Risk to Communities data, and related datasets that depict other components of wildfire hazard and risk to homes. This 2020 version supersedes all previous versions of Wildfire Hazard Potential (2018, 2014) or Wildland Fire Potential (2012, 2010, 2007). We generally do not advise direct comparisons between versions because changes can reflect improvements in methodology at all stages of the WHP calculation in addition to actual land cover changes. For more information and to download the raster data, please visit the Wildfire Hazard Potential website. Map author: Greg Dillon, USDA Forest Service, Rocky Mountain Research Station, Fire Modeling InstituteThis record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

The wildfire hazard potential (WHP) is a raster geospatial product at 270-meter resolution covering all lands in the conterminous United States. It can help to inform evaluations of wildfire risk or prioritization of fuels management needs across very large landscapes (millions of acres). Our specific objective with the WHP map is to depict the relative potential for wildfire that would be difficult for suppression resources to contain. For more information, please visit: https://www.firelab.org/project/wildfire-hazard-potential.

This data publication is a second edition. The first edition (https://doi.org/10.2737/RDS-2015-0046) represents WHP mapped in 2014, depicting landscape conditions as of 2010. This second edition is the 2018 version, and depicts landscape conditions as of 2012. (See \Supplements\WHP2014_to_2018_ChangeSummary.pdf for a summary of the changes between the first and second editions of these data.)�To check for the latest version of the WHP geospatial data and map graphics, as well as documentation on the mapping process, see: https://www.firelab.org/project/wildland-fire-potential. Details about the Wildfire Hazard Potential mapping process can be found in Dillon et al. 2015. Steps described in this paper about weighting for crown fire potential have been dropped in the 2018 version due to changes to the FSim modeling products used as the primary inputs to WHP mapping. The FSim products used to create the 2018 version of WHP can be found here in Short et al. 2016. Dillon, Gregory K.; Menakis, James; Fay, Frank. 2015. Wildland fire potential: A tool for assessing wildfire risk and fuels management needs. In: Keane, Robert E.; Jolly, Matt; Parsons, Russell; Riley, Karin. Proceedings of the large wildland fires 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. p. 60-76. https://www.fs.usda.gov/treesearch/pubs/49429 Short, Karen C.; Finney, Mark A.; Scott, Joe H.; Gilbertson-Day, Julie W.; Grenfell, Isaac C. 2016. Spatial dataset of probabilistic wildfire risk components for the conterminous United States. Fort Collins, CO: Forest Service Research Data Archive. This dataset can be downloaded at: https://www.fs.usda.gov/rds/archive/Product/RDS-2015-0046-2This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

This dataset is the 2023 version of wildfire hazard potential (WHP) for the United States. The files included in this data publication represent an update to any previous versions of WHP or wildland fire potential (WFP) published by the USDA Forest Service. WHP is an index that quantifies the relative potential for high-intensity wildfire that may be difficult to manage, used as a measure to help prioritize where fuel treatments may be needed. This 2023 version of WHP was created from updated national wildfire hazard datasets of annual burn probability and fire intensity generated by the USDA Forest Service, Rocky Mountain Research Station with the large fire simulation system (FSim). Vegetation and wildland fuels data from LANDFIRE 2020 (version 2.2.0) were the primary inputs to the updated FSim modeling work and therefore form the foundation for this version of the WHP. As such, the data presented here reflect landscape conditions as of the end of 2020. LANDFIRE 2020 vegetation and fuels data were also used directly in the WHP mapping process, along with updated point locations of fire occurrence ca. 1992-2020. With these datasets as inputs, we produced an index of WHP for all of the conterminous United States at 270-meter resolution. We present the final WHP map in two forms: 1) continuous integer values, and 2) five WHP classes of very low, low, moderate, high, and very high. On its own, WHP is not an explicit map of wildfire threat or risk, but when paired with spatial data depicting highly valued resources and assets such as structures or powerlines, it can approximate relative wildfire risk to those specific resources and assets. WHP is also not a forecast or wildfire outlook for any particular season, as it does not include any information on current or forecasted weather or fuel moisture conditions. It is instead intended for long-term strategic fuels management.These new data represent an update to all previous versions of WHP or WFP published by the USDA Forest Service. On 07/17/2024 this data package was updated to correct a data processing error that caused a very small number of pixels to be Nodata in the initial classified version that should have been Very High WHP. This update also included the addition of summaries tables by management jurisdictions. To check for the latest version of the WHP geospatial data and map graphics, as well as documentation on the mapping process, see: https://www.firelab.org/project/wildfire-hazard- potential. Details about the Wildfire Hazard Potential mapping process can be found in Dillon et al. (2015). Steps described in this paper about weighting for crown fire potential were dropped in the 2018 and subsequent versions due to changes to the FSim modeling products used as the primary inputs to WHP mapping.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

This web map service portrays the Wildfire Hazard Potential (WHP), developed by the U.S. Forest Service (USFS) and Fire Modeling Institute to help inform assessments of wildfire risk or prioritization of fuels management needs across large landscapes. The map service depicts the relative potential for those wildfires that would be difficult for suppression resources to contain. This version of the WHP is based upon spatial estimates of wildfire likelihood & intensity. It is generated from the Large Fire Simulator (FSim) for the Fire Program Analysis system (FPA); spatial fuels and vegetation data from LANDFIRE 2010; and point locations of fire occurrence from FPA (ca. 1992 - 2012). The web map service displays those areas within the continental United States that have different levels of fire potential, categorized by five WHP classes of (very low – very high) and two non-WHP classes (non-burnable and water). Areas with higher WHP values represent fuels with a higher probability of experiencing torching, crowning, and other forms of extreme fire behavior under conducive weather conditions. According to the USFS, the data is not an explicit map of wildfire threat or risk; nor is it a forecast or outlook model for any particular season. When paired with spatial data depicting resources and assets such as communities, structures, or power lines, it can approximate relative wildfire risk to those resources and assets. It is instead intended for long-term strategic planning and fuels management.Wildfire PotentialData currency: 2014For past data versions2012 Wildland Fire Potential2007 Wildland Fire PotentialFor additional information and data, please access: USFS Rocky Mountain Research Station WHPFor feedback please contact: ArcGIScomNationalMaps@esri.comThis story map is part of the Watershed Improvement Program (WIP) and Watershed Information Network (WIN).The Wildfire Hazard Potential web map is a feature service used in the Sierra Nevada Cascade story map; therefore, it should not be altered or deleted under any circumstances while the story map is in use.

Australia is one of the most flammable counties due to fuel accumulation and frequent droughts. The number and size of wildfire incidents have increased during the last decades. Global warming, industrialisation and extensive human activities played an important role in the increase of wildfire incidents. Wildfires are a considerable threat to human lives and properties, especially in populated areas. In addition, wildfires will negatively impact the components of our ecosystem such as vegetation, soil, water and forests. Wildfire susceptibility maps show the areas with different probabilities of fire occurrence. These maps help managers and policymakers to act efficiently and reduce the negative impacts of wildfires. Many models were created by Geospatial Information System (GIS) and Remote Sensing (RS) to predict wildfires. This thesis aims to investigate wildfire susceptibility in Victoria located in south-eastern Australia with an area of 227,444 km2. The elevation in this area ranged between -76 m to 1,986 m. More than a million hectares burned in Victoria in the last bushfire season in 2019-2020. In addition, more than 110 homes or businesses were destroyed during this period. A wildfire susceptibility model could be a useful tool to control and manage the future wildfires by predicting vulnerable areas. This study aims to generate wildfire susceptibility maps for the south-eastern part of Australia. The main research objectives are as follows. 1. To generate a wildfire inventory map from the Moderate Resolution Imaging Spectroradiometer (MODIS) data. 2. To develop the conditioning factors and map layers. 3. To generate wildfire susceptibility maps using statistical methods e.g., Frequency Ratio (FR) and Logistic Regression (LR) and evolutionary algorithms separately. 4. To apply ensemble techniques (statistical methods combined with evolutionary algorithms) to generate wildfire susceptibility maps. 5. To evaluate the performance of the proposed methods by using the Receiver Operating Characteristics (ROC) curve.

This dataset is the Wildfire Hazard Potential (WHP) for the United States. It is part of the Wildfire Risk to Communities: Spatial datasets of landscape-wide wildfire risk components for the United States. WHP is an index that quantifies the relative potential for wildfire that may be difficult to control, used as a measure to help prioritize where fuel treatments may be needed. See Dillon et al. (2015) for a full description, or https://www.firelab.org/project/wildfire-hazard-potential for additional information and companion data for the U.S. at 270-m pixel resolution. Vegetation and wildland fuels data from LANDFIRE 2014 (version 1.4.0) form the foundation for the Wildfire Risk to Communities data. As such, the data presented here reflect landscape conditions as of the end of 2014. National wildfire hazard datasets of annual burn probability and fire intensity were generated from the LANDFIRE 2014 data by the USDA Forest Service, Rocky Mountain Research Station (Short et al. 2020) using the large fire simulation system (FSim). These national datasets produced with FSim have a relatively coarse cell size of 270 meters (m). To bring these datasets down to a finer resolution more useful for assessing hazard and risk to communities, we upsampled them to the native 30 m resolution of the LANDFIRE fuel and vegetation data. In this upsampling process, we also spread values of modeled burn probability and intensity into developed areas represented in LANDFIRE fuels data as non-burnable. Additional methodology documentation is provided with the data publication download. Metadata and Downloads.Note: Pixel values in this image service have been altered from the original raster dataset due to data requirements in web services. The service is intended primarily for data visualization. Relative values and spatial patterns have been largely preserved in the service, but users are encouraged to download the source data for quantitative analysis.Dillon, Gregory K.; Menakis, James; Fay, Frank. 2015. Wildland fire potential: A tool for assessing wildfire risk and fuels management needs. In: Keane, Robert E.; Jolly, Matt; Parsons, Russell; Riley, Karin. Proceedings of the large wildland fires 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. p. 60-76. https://www.fs.usda.gov/treesearch/pubs/49429

The Fireshed Registry is a geospatial dashboard and decision tool built to organize information about wildfire transmission to buildings and monitor progress towards risk reduction for communities from management investments. The concept behind the Fireshed Registry is to identify and map the source of risk rather than what is at risk across all lands in the United States. While the Fireshed Registry was organized around mapping the source of fire risk to communities, the framework does not preclude the assessment of other resource management priorities and trends such as water, fish and aquatic or wildlife habitat, or recreation. The Fireshed Registry is also a multi-scale decision tool for quantifying, prioritizing, and geospatially displaying wildfire transmission to buildings in adjacent or nearby communities. Fireshed areas in the Fireshed Registry are approximately 250,000 acre accounting units that are delineated based on a smoothed building exposure map of the United States. These boundaries were created by dividing up the landscape into regular-sized units that represent similar source levels of community exposure to wildfire risk. Subfiresheds are approximately 25,000 acre accounting units nested within firesheds. Firesheds for the Conterminous U.S., Alaska, and Hawaii were generated in separate research efforts and are published in incremental versions in the Research Data Archive. They are combined here for ease of use.

Wildfire Mapping from Space Market Outlook

According to our latest research, the global wildfire mapping from space market size has reached USD 1.32 billion in 2024, reflecting robust demand for advanced space-based technologies to combat and monitor wildfires worldwide. The market is projected to grow at a CAGR of 11.7% from 2025 to 2033, reaching an estimated USD 3.67 billion by 2033. This impressive growth trajectory is primarily driven by the increasing frequency and severity of wildfires, the rapid advancement in satellite and remote sensing technologies, and the growing emphasis on disaster preparedness and environmental sustainability across the globe.

One of the primary growth factors for the wildfire mapping from space market is the escalating incidence of wildfires due to climate change and prolonged droughts in various regions. Governments and environmental agencies are under mounting pressure to implement proactive prevention and rapid response strategies. Space-based technologies, such as satellite imaging and remote sensing, have become crucial tools for early detection, real-time monitoring, and post-event assessment of wildfires. These technologies enable authorities to track fire spread, assess affected areas, and allocate resources more efficiently. Furthermore, the integration of artificial intelligence and machine learning with satellite data is enhancing the accuracy and speed of wildfire prediction models, which is further fueling market growth.

Another significant driver is the increasing investment by both public and private sectors in space infrastructure and geospatial intelligence. The launch of new constellations of low-earth orbit (LEO) satellites and the deployment of advanced thermal imaging sensors are expanding the capabilities of wildfire mapping systems. These advancements are reducing the latency and increasing the resolution of wildfire data, making it possible to deliver near real-time information to disaster management teams. Additionally, the collaboration between space agencies, technology providers, and research institutions is leading to the development of more sophisticated wildfire monitoring platforms, which are being adopted by a growing number of end-users, including insurance companies and forestry managers.

The growing awareness of the economic and environmental impact of wildfires is also propelling the adoption of space-based mapping solutions. Wildfires not only cause significant loss of life and property but also lead to long-term ecological damage and substantial financial losses for governments and businesses. The insurance industry, in particular, is leveraging satellite data to assess risks, estimate damages, and streamline claims processing. Moreover, the integration of geospatial information systems (GIS) with remote sensing data is enabling more comprehensive environmental monitoring and forestry management, supporting sustainable development goals and regulatory compliance.

From a regional perspective, North America currently dominates the wildfire mapping from space market, accounting for the largest share in 2024. This leadership position can be attributed to the high incidence of wildfires in the United States and Canada, strong government initiatives, and the presence of leading space technology providers. Europe and Asia Pacific are also witnessing significant growth, driven by rising investments in space-based disaster management infrastructure and increasing awareness about climate change. Meanwhile, Latin America and the Middle East & Africa are emerging markets, with growing adoption of wildfire mapping solutions in response to recent wildfire events and international collaborations.

Technology Analysis

The technology segment of the wildfire mapping from space market encompasses satellite imaging, remote sensing, GIS mapping, and thermal imaging, each playing a pivotal role in the detection, monitoring, and management of wildfires. Satellite imaging is at the forefront, providing high-resolution images of large geographic areas, which are essential for early detection and situational awareness during wildfire outbreaks. The continuous improvement in satellite sensor technology has enabled the capture of multispectral and hyperspectral data, allowing for more precise identification of fire hotspots and assessment of vegetation health. This technological evolution is further supported by the proliferation of small satellites and CubeSats, which offer more frequent revisit times and

Fireline GIS Mapping in Vehicles Market Outlook

According to our latest research, the Global Fireline GIS Mapping in Vehicles market size was valued at $1.2 billion in 2024 and is projected to reach $3.4 billion by 2033, expanding at a CAGR of 12.7% during 2024–2033. The primary factor propelling the growth of this market globally is the increasing frequency and intensity of wildfires, which has prompted fire departments, government agencies, and private contractors to adopt advanced Geographic Information System (GIS) solutions for real-time situational awareness and enhanced response capabilities. As wildfires become more unpredictable due to climate change, the integration of GIS mapping technologies in vehicles is rapidly becoming indispensable for efficient wildfire management, emergency response, and asset tracking, driving robust market expansion across multiple regions and industry verticals.

Regional Outlook

North America currently dominates the Fireline GIS Mapping in Vehicles market, accounting for the largest share, with an estimated market value of $540 million in 2024 and expected to reach $1.5 billion by 2033. This region’s leadership position is primarily attributed to its mature emergency response infrastructure, widespread adoption of advanced vehicle telematics, and robust government policies supporting wildfire management. The United States, in particular, has invested heavily in GIS mapping technologies for both public and private firefighting fleets, leveraging real-time data analytics and cloud-based platforms to enhance coordination during wildfire events. Additionally, the region benefits from a high level of technological innovation and a proactive approach to integrating emerging digital solutions into first responder operations, further cementing its market dominance.

Asia Pacific is projected to be the fastest-growing region in the Fireline GIS Mapping in Vehicles market, with a remarkable CAGR of 15.8% from 2024 to 2033. This growth is fueled by increasing wildfire incidents across Australia, Southeast Asia, and parts of China, coupled with rapid urbanization and expanding vehicle fleets dedicated to emergency response. Significant investments in smart city initiatives and government mandates for disaster preparedness are driving the adoption of GIS mapping technologies in emergency vehicles. Furthermore, the proliferation of cloud-based solutions and mobile GIS applications is enabling even smaller municipalities and rural fire departments to access advanced mapping tools, thereby accelerating market penetration and technological advancement in the region.

Emerging economies in Latin America, the Middle East, and Africa are witnessing gradual but steady adoption of Fireline GIS Mapping in Vehicles solutions, although growth is tempered by budgetary constraints, limited technological infrastructure, and inconsistent policy frameworks. In these regions, localized demand is primarily concentrated in countries prone to seasonal wildfires and where international aid or government-backed modernization programs are in place. Challenges such as lack of skilled personnel, inadequate data connectivity, and the need for region-specific customization of GIS platforms persist. However, ongoing efforts to strengthen disaster response capabilities and increasing awareness of the benefits of real-time GIS mapping are expected to create new opportunities for market growth in these emerging markets over the forecast period.

Report Scope

Fireline GIS Mapping in Vehicles Market Outlook

As per our latest research, the global Fireline GIS Mapping in Vehicles market size was valued at USD 1.48 billion in 2024 and is expected to reach USD 4.26 billion by 2033, expanding at a robust CAGR of 12.6% during the forecast period. This remarkable growth is primarily driven by the increasing frequency and intensity of wildfires, the urgent need for real-time situational awareness in emergency response, and the rapid integration of advanced geospatial technologies into first responder vehicles. The adoption of GIS mapping solutions is transforming how fire departments and emergency services coordinate, strategize, and execute high-stakes operations, making these systems indispensable for modern firefighting and disaster response efforts.

One of the pivotal growth factors propelling the Fireline GIS Mapping in Vehicles market is the escalating incidence of wildfires and large-scale urban fires globally. Climate change has led to longer, more severe fire seasons, particularly in regions such as North America, Australia, and Southern Europe. As a result, fire departments and emergency agencies are under immense pressure to deploy resources more efficiently and ensure the safety of both personnel and affected communities. GIS mapping technologies, when integrated into vehicles, provide real-time data visualization, enable dynamic route planning, and facilitate the rapid identification of fire perimeters, hotspots, and safe zones. This capability significantly enhances decision-making and operational effectiveness, fueling increased demand for advanced GIS solutions across public and private sectors.

Another significant driver is the technological advancement in vehicle-mounted hardware and software ecosystems. The proliferation of high-speed mobile internet, robust onboard computing platforms, and sophisticated sensors has made it feasible to deploy high-resolution mapping and analytics tools directly within fire trucks, emergency response vehicles, and utility vehicles. Vendors are increasingly offering interoperable solutions that seamlessly integrate with existing command and control systems, mobile data terminals, and satellite communication devices. These innovations not only improve the accuracy and timeliness of geospatial data but also support advanced features such as predictive modeling, automated resource allocation, and remote collaboration among multiple agencies. Such technological convergence is expected to further accelerate market expansion over the coming years.

In addition, the growing emphasis on inter-agency collaboration and data sharing is catalyzing the adoption of Fireline GIS Mapping in Vehicles. Government mandates and industry standards are evolving to promote the interoperability of GIS platforms, ensuring that fire departments, government agencies, and private contractors can coordinate seamlessly during complex incidents. Cloud-based deployment models and service-oriented architectures are making it easier to scale GIS solutions, provide secure access to critical data, and enable rapid deployment in both urban and remote environments. This trend is particularly pronounced in regions with fragmented emergency response infrastructures, where centralized, real-time mapping is essential for effective disaster management.

From a regional perspective, North America currently dominates the global Fireline GIS Mapping in Vehicles market, accounting for over 38% of the total market share in 2024. This leadership is attributed to substantial investments in wildfire management technologies, a high incidence of catastrophic fires, and strong government support for digital transformation initiatives within public safety agencies. Europe follows closely, driven by stringent regulatory frameworks and the increasing adoption of smart city solutions. The Asia Pacific region is emerging as the fastest-growing market, with a projected CAGR of 14.2% through 2033, fueled by rapid urbanization, rising awareness of disaster preparedness, and expanding government initiatives in countries such as Australia, Japan, and China.

Wildland–Urban Interface (WUI) Risk Assessment via Satellite Market Outlook

According to our latest research, the global Wildland–Urban Interface (WUI) Risk Assessment via Satellite market size reached USD 1.38 billion in 2024, reflecting the increasing urgency to address wildfire risks at the intersection of wildlands and urban areas. The market is projected to expand at a robust CAGR of 12.6% from 2025 to 2033, reaching a forecasted value of USD 4.01 billion by 2033. This sustained growth is primarily propelled by the integration of advanced satellite technologies, real-time data analytics, and the growing need for proactive disaster management strategies in response to escalating wildfire incidents worldwide.

The primary growth factor accelerating the WUI Risk Assessment via Satellite market is the alarming rise in the frequency and intensity of wildfires globally, particularly in regions where urban development encroaches upon natural landscapes. Urban expansion into forested and grassland areas has significantly heightened the vulnerability of human settlements to wildfire hazards, making risk assessment and mitigation a top priority for governments, insurers, and urban planners. The deployment of high-resolution satellite imagery, combined with sophisticated remote sensing technologies, enables the real-time monitoring of vegetation health, fire behavior, and environmental changes, providing critical data for early intervention and resource allocation. As climate change continues to exacerbate fire-prone conditions, the demand for comprehensive WUI risk assessment solutions is expected to surge, fostering innovation and investment in satellite-based risk management tools.

Another significant driver for market growth is the increasing adoption of geospatial information systems (GIS) and advanced data analytics platforms, which have revolutionized the way stakeholders assess and manage wildfire risks. By integrating satellite-derived data with GIS mapping and predictive analytics, agencies can generate highly accurate risk maps, simulate fire spread scenarios, and optimize evacuation plans. These capabilities empower emergency responders, land use planners, and insurance companies to make informed decisions, minimize property loss, and protect lives. The proliferation of cloud-based platforms further enhances accessibility, scalability, and collaboration among stakeholders, facilitating the seamless sharing and analysis of critical information across jurisdictions. This technological synergy is expected to play a pivotal role in shaping the future landscape of WUI risk assessment, driving both market expansion and operational efficiency.

Moreover, the growing emphasis on regulatory compliance and sustainable land management practices is fueling the adoption of WUI risk assessment solutions across diverse end-user segments. Governments and forestry services are increasingly mandated to implement risk reduction strategies, conduct environmental monitoring, and develop resilient urban planning frameworks. Insurance companies are leveraging satellite-based risk assessment tools to refine underwriting processes, assess policyholder exposure, and manage claims more effectively. Urban planners are utilizing these solutions to design fire-resilient communities, optimize land use, and integrate green infrastructure. As stakeholders recognize the economic, social, and environmental benefits of proactive risk management, investment in satellite-enabled WUI risk assessment solutions is expected to accelerate, fostering a culture of resilience and preparedness.

From a regional perspective, North America currently dominates the WUI Risk Assessment via Satellite market, accounting for the largest market share in 2024, driven by substantial investments in wildfire management infrastructure and the prevalence of high-risk WUI zones in the United States and Canada. Europe follows closely, propelled by stringent regulatory frameworks and increasing wildfire incidents in Mediterranean countries. The Asia Pacific region is emerging as a high-growth market, fueled by rapid urbanization, expanding forested areas, and heightened awareness of climate-related risks. Latin America and the Middle East & Africa are also witnessing increased adoption, albeit at a slower pace, as governments prioritize disaster preparedness and environmental sustainability. The regional outlook underscores the global imperative to

Fuels reduction is a forest management tool that has been in practice for the last couple of decades, especially since the development of the Wildfires and Healthy Forest Initiative Act (2003, White House). The goal behind reduction of fuels is to limit and/or prohibit catastrophic wildfires, pest infestation and to help promote healthy forests and wildlife habitat. Fuel reduction management plays an important role for forest managers when trying to minimize impact of wildfire on the wildland urban interface (WUI). One of the concerns with fuels reduction practices is mineral soil exposure in a steep, mountainous environment. These types of conditions are suitable for promoting erosion. Several studies have investigated fuels reduction in the northwestern United States after management practices. Research has explored methods which include but are not limited to; thinning and harvesting biomass and even simulated wildfire.

The Lakeface-Lamb Fuel Reduction Project (LLFRP) site is located in the northern Idaho panhandle. This research site is located within the bounds of Unit 33, which was commercially thinned using a harvester forwarder cut-to-length logging system in May of 2004. Plots were installed shortly thereafter, and then in October of 2004 the unit was slashed and left to dry before piling with an excavator in October of 2005 and jackpot burned in the fall of 2006. Seven hillslope tipping bucket runoff plots were installed: four disturbed plots on the Unit 33 forwarder trails and the other three on an adjacent undisturbed area (control plots). Hillslope runoff data were collected from 2004-2008. Cover data were collected on the disturbed sites yearly (2004-2008), and on the control sites every year, except 2008. Soil samples were gathered at each plot to discern soil texture (particle size analysis) and bulk density. Soil loss samples were collected 2005-2009 from 1) the suspended water in the sediment trap, 2) the settled soil which accumulates in the sediment trap and 3) the gutter. Because the soil loss samples were collected after the events of equipment installation (2004), dates for this file are off by one year. The soil loss samples are designated as: 2005-2009 as they were collected during these years for soil loss that occurred during 2004-2008. This data publication also includes 2004-2008 weather data from the Remote Automatic Weather Station (RAWS)-Priest Lake Ranger District.

Also included for download is a map package of the area (available for viewing in ArcGIS program) or the standalone files associated with the map (for use in ArcGIS or other GIS programs). The following data are included: Digital Elevation Model, topographic raster, and several shapefiles outlining the locations of unit boundaries, control and treated plots and trails color coded for traffic use and one-way and two-way traffic.This study investigates and measures sediment yield and runoff from small hillslope plots on forwarder trails to the sediment yield and runoff from small hillslope plots on an undisturbed (control) site.

The wildland fire potential layer provides information on the relative potential for wildfire that would be difficult for fire crews to contain. Areas with higher wildland fire potential values represent fuels with a higher likelihood of experiencing high-intensity fire with torching, crowning, and other forms of extreme fire behavior.Dataset SummaryThis layer provides access to a 270m cell size raster derived from the Large Fire Simulation System (LFSim) produced as part of the Fire Program Analysis System by the USDA Forest Service’s Fire Modeling Institute. The data covers the contiguous U.S.The layer is useful for analyses of wildfire risk, hazardous fuels prioritization and strategic planning across large landscapes (hundreds of square miles) up through regional and national scales. When paired with spatial data depicting highly valued resources, land managers can use these data to create value-specific risk maps. Examples of published research using these data include:Integrated national-scale assessment of wildfire risk to human and ecological valuesA simulation of probabilistic wildfire risk components for the continental United StatesThis layer is derived from the USA Wildland Fire Potential service produced by the US Forest Service.

Wildfire Suppression Difficulty Index (SDI) is a rating of relative difficulty in performing fire control work. This is a preliminary SDI map for Alaska intended for exploratory uses on fires in 2024. This version includes the 2021 slope adjustment factor used in the current CONUS SDI maps, which adds extra difficulty to steep terrain.SDI (Rodriguez y Silva et al. 2020) factors in topography, fuels, expected fire behavior under typical burning conditions, fireline production rates in various fuel types with and without heavy equipment, and access via roads, trails, or cross-country travel.SDI is currently classified into six categories representing low through extreme difficulty. Extreme SDI zones represented in red are “watch out” situations where engagement is likely to be very challenging given the combination of potential high intensity fire behavior and difficult suppression environment (high resistance fuel types, steep terrain, and low accessibility). Low difficulty zones represented in blue indicate areas where some combination of reduced potential for dangerous fire behavior and ideal suppression environment (low resistance fuel types, mellow terrain, and high accessibility) make suppression activities easier. SDI does not account for standing snags or other overhead hazards to firefighters, so it is not a firefighter hazard map. It is only showing in relative terms where it is harder or easier to perform suppression work.SDI incorporates flame length and heat per unit area from basic FlamMap runs (Finney et al. 2019). This version for Alaska uses fuel moisture settings from the Alaska Fire Analysis Guide (1-h 6%, 10-h 7%, 100-h 8%, herb 85%, woody 100%) with 15 mph uphill winds (@ 20-ft) to represent a consistent worst-case wind scenario. Input fuels data come from LANDFIRE 2022 (v2.3.0). Fuels were updated to represent wildfires from 2023 as represented in the Alaska Fire Perimeters Database by: 1) changing the fire behavior fuel model to TL1 (181) per Alaska Fire Analysis Guide; 2) reducing canopy cover and canopy bulk density 50%; and 3) raising canopy base height 50% or a minimum of 2-m and a maximum of 10-m or 90% of canopy height. Road and trail inputs are developed from a combination of HERE 2020 Roads, USFS, DOI, and State of Alaska road and trails databases. Hand crew and dozer fireline production rates are from FPA 2012 (Dillon et al. 2015). Classification of topography and accessibility thresholds are detailed in Rodriguez et al. (2020).More detail on SDI Methods can be found on the RMA Sharepoint Site (RMA Dashboard Analytics --> Suppression Difficulty Index (SDI) folder.ReferencesDillon, G.K.; Menakis, J.; Fay, F. (2015) Wildland Fire Potential: a tool for assessing wildfire risk and fuels management needs. In: Keane, R.E.; Jolly, M.; Parsons, R.; Riley, K., eds. Proceedings of the large wildland fires 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. 345 p.Finney, M.A.; Brittain, S.; Seli, R.C.; McHugh, C.W.; Gangi, L. (2019) FlamMap:Fire Mapping and Analysis System (Version 6.0) [Software]. Available from https://www.firelab.org/document/flammap-softwareRodriguez y Silva, F.; O'Connor, C.D.; Thompson, M.P.; Molina, J.R.; Calkin, D.E. (2020). Modeling Suppression Difficulty: Current and Future Applications. International Journal of Wildland Fire.

The geospatial products described and distributed here depict the probability of high-severity fire, if a fire were to occur, for several ecoregions in the contiguous western US. The ecological effects of wildland fire – also termed the fire severity – are often highly heterogeneous in space and time. This heterogeneity is a result of spatial variability in factors such as fuel, topography, and climate (e.g. mean annual temperature). However, temporally variable factors such as daily weather and climatic extremes (e.g. an unusually warm year) also may play a key role. Scientists from the US Forest Service Rocky Mountain Research Station and the University of Montana conducted a study in which observed data were used to produce statistical models describing the probability of high severity fire as a function of fuel, topography, climate, and fire weather. Observed data from over 2000 fires (from 2002-2015) were used to build individual models for each of 19 ecoregions in the contiguous US (see Parks et al. 2018, Figure 1). High severity fire was measured using a fire severity metric termed the relativized burn ratio, which uses pre- and post-fire Landsat imagery to measure fire-induced ecological change. Fuel included pre-fire metrics of live fuel amount such as NDVI. Topography included factors such as slope and potential solar radiation. Climate summarized 30-year averages of factors such as mean summer temperature that spatially vary across the study area. Lastly, fire weather incorporated temporally variable factors such as daily and annual temperature. In turn, these statistical models were used to generate "wall-to-wall" maps depicting the probability of high severity fire, if a fire were to occur, for 13 of the 19 ecoregions. Maps were not produced for ecoregions in which model quality was deemed inadequate. All maps use fuel data representing the year 2016 and therefore provide a fairly up-to-date assessment of the potential for high severity fire. For those ecoregions in which the relative influence of fire weather was fairly strong (n=6), two additional maps were produced, one depicting the probability of high severity fire under moderate weather and the other under extreme weather. An important consideration is that only pixels defined as forest were used to build the models; consequently maps exclude pixels considered non-forest.

New Mexico Debris Flow study areas and analysis results.

This page describes an out-of-date version of the WHP data product. Please check out the current version here.Wildfire hazard potential (WHP) is an index that depicts the relative potential for wildfire that would be difficult for suppression resources to contain, based on wildfire simulation modeling. This dataset produced by the USDA Forest Service, Fire Modeling Institute in 2018 shows WHP at a spatial resolution of 270 meters across the entire conterminous United States, classified into five WHP classes of very low, low, moderate, high, and very high. Areas mapped with higher WHP values represent fuels with a higher probability of experiencing torching, crowning, and other forms of extreme fire behavior under conducive weather conditions, based primarily on 2012 landscape conditions. This WHP dataset is based on outputs of wildfire simulation modeling, available in a map service of Probabilistic Wildfire Risk components. This 2018 version supersedes the previous version created in 2014, as well as earlier versions from 2007, 2010, and 2012 known as Wildland Fire Potential. We generally do not advise comparisons between versions because changes can reflect improvements in methodology at all stages of WHP calculation in addition to actual land cover changes.For more information and to download the raster data, please visit the Wildfire Hazard Potential website.Map author: Greg Dillon, USDA Forest Service, Rocky Mountain Research Station, Fire Modeling Institute.

The data included in this publication depict the 2024 version of components of wildfire risk for all lands in the United States that: 1) are landscape-wide (i.e., measurable at every pixel across the landscape); and 2) represent in situ risk - risk at the location where the adverse effects take place on the landscape.National wildfire hazard datasets of annual burn probability and fire intensity, generated by the USDA Forest Service, Rocky Mountain Research Station and Pyrologix LLC, form the foundation of the Wildfire Risk to Communities data. Vegetation and wildland fuels data from LANDFIRE 2020 (version 2.2.0) were used as input to two different but related geospatial fire simulation systems. Annual burn probability was produced with the USFS geospatial fire simulator (FSim) at a relatively coarse cell size of 270 meters (m). To bring the burn probability raster data down to a finer resolution more useful for assessing hazard and risk to communities, we upsampled them to the native 30 m resolution of the LANDFIRE fuel and vegetation data. In this upsampling process, we also spread values of modeled burn probability into developed areas represented in LANDFIRE fuels data as non-burnable. Burn probability rasters represent landscape conditions as of the end of 2020. Fire intensity characteristics were modeled at 30 m resolution using a process that performs a comprehensive set of FlamMap runs spanning the full range of weather-related characteristics that occur during a fire season and then integrates those runs into a variety of results based on the likelihood of those weather types occurring. Before the fire intensity modeling, the LANDFIRE 2020 data were updated to reflect fuels disturbances occurring in 2021 and 2022. As such, the fire intensity datasets represent landscape conditions as of the end of 2022. Additional methodology documentation is provided in a methods document (\Supplements\WRC_V2_Methods_Landscape-wideRisk.pdf) packaged in the data download.The specific raster datasets in this publication include:Risk to Potential Structures (RPS): A measure that integrates wildfire likelihood and intensity with generalized consequences to a home on every pixel. For every place on the landscape, it poses the hypothetical question, "What would be the relative risk to a house if one existed here?" This allows comparison of wildfire risk in places where homes already exist to places where new construction may be proposed. This dataset is referred to as Risk to Homes in the Wildfire Risk to Communities web application.Conditional Risk to Potential Structures (cRPS): The potential consequences of fire to a home at a given location, if a fire occurs there and if a home were located there. Referred to as Wildfire Consequence in the Wildfire Risk to Communities web application.Exposure Type: Exposure is the spatial coincidence of wildfire likelihood and intensity with communities. This layer delineates where homes are directly exposed to wildfire from adjacent wildland vegetation, indirectly exposed to wildfire from indirect sources such as embers and home-to-home ignition, or not exposed to wildfire due to distance from direct and indirect ignition sources.Burn Probability (BP): The annual probability of wildfire burning in a specific location. Referred to as Wildfire Likelihood in the Wildfire Risk to Communities web application.Conditional Flame Length (CFL): The mean flame length for a fire burning in the direction of maximum spread (headfire) at a given location if a fire were to occur; an average measure of wildfire intensity. Flame Length Exceedance Probability - 4 ft (FLEP4): The conditional probability that flame length at a pixel will exceed 4 feet if a fire occurs; indicates the potential for moderate to high wildfire intensity.Flame Length Exceedance Probability - 8 ft (FLEP8): the conditional probability that flame length at a pixel will exceed 8 feet if a fire occurs; indicates the potential for high wildfire intensity. Wildfire Hazard Potential (WHP): An index that quantifies the relative potential for wildfire that may be difficult to manage, used as a measure to help prioritize where fuel treatments may be needed.Additional methodology documentation is provided with the data publication download. https://www.fs.usda.gov/rds/archive/Catalog/RDS-2020-0016-2Note: Pixel values in this image service have been altered from the original raster dataset due to data requirements in web services. The service is intended primarily for data visualization. Relative values and spatial patterns have been largely preserved in the service, but users are encouraged to download the source data for quantitative analysis.

Wildfire Suppression Difficulty Index (SDI) 90th Percentile is a rating of relative difficulty in performing fire control work under regionally appropriate fuel moisture and 15 mph uphill winds (@ 20 ft).SDI (Rodriguez y Silva et al. 2020) factors in topography, fuels, expected fire behavior under prevailing conditions, fireline production rates in various fuel types with and without heavy equipment, and access via roads, trails, or cross-country travel. SDI is currently classified into six categories representing low through extreme difficulty. Extreme SDI zones represented in red are “watch out” situations where engagement is likely to be very challenging given the combination of potential high intensity fire behavior and difficult suppression environment (high resistance fuel types, steep terrain, and low accessibility). Low difficulty zones represented in blue indicate areas where some combination of reduced potential for dangerous fire behavior and ideal suppression environment (low resistance fuel types, mellow terrain, and high accessibility) make suppression activities easier. SDI does not account for standing snags or other overhead hazards to firefighters, so it is not a firefighter hazard map. It is only showing in relative terms where it is harder or easier to perform suppression work. SDI incorporates flame length and heat per unit area from basic FlamMap runs (Finney et al. 2019). SDI is based on fire behavior modeled using regionally appropriate percentile fuel moisture conditions and uphill winds. This product uses the wind blowing uphill option to represent a consistent worst-case scenario. Input fuels data are updated to the most recent fire year using a crosswalk for surface and canopy fuel modifications for fires and fuel treatments that occurred after the most recent LANDFIRE version. For example, LANDFIRE 2016 model inputs are modified to incorporate fires (Monitoring Trends in Burn Severity (MTBS), Geospatial Multi- Agency Coordination (GeoMac), and Wildland Fire Interagency Geospatial Services (WFIGS) and fuel treatments (USFS Forest Activity Tracking System (FACTS) and DOI National Fire Plan Operations and Reporting System (NFPORS) hazardous fuels reduction treatments) from 2017-present. Road and trail inputs are developed from a combination of HERE 2020 Roads, USFS, and DOI road and trails databases. Hand crew and dozer fireline production rates are from FPA 2012 (Dillon et al. 2015). Classification of topography and accessibility thresholds are detailed in Rodriguez et al. (2020). Dillon, G.K.; Menakis, J.; Fay, F. (2015) Wildland Fire Potential: a tool for assessing wildfire risk and fuels management needs. In: Keane, R.E.; Jolly, M.; Parsons, R.; Riley, K., eds. Proceedings of the large wildland fires 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. 345 p. Finney, M.A.; Brittain, S.; Seli, R.C.; McHugh, C.W.; Gangi, L. (2019) FlamMap:Fire Mapping and Analysis System (Version 6.0) [Software]. Available from https://www.firelab.org/document/flammap-software Rodriguez y Silva, F.; O'Connor, C.D.; Thompson, M.P.; Molina, J.R.; Calkin, D.E. (2020). Modeling Suppression Difficulty: Current and Future Applications. International Journal of Wildland Fire.More detail on SDI Methods can be found on the RMA Sharepoint Site (RMA Dashboard Analytics --> Suppression Difficulty Index (SDI) folder.