The data included in this publication depict components of wildfire risk specifically for populated areas in the United States. These datasets represent where people live in the United States and the in situ risk from wildfire, i.e., the risk at the _location where the adverse effects take place.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. The data products in this publication that represent where people live, reflect 2021 estimates of housing unit and population counts from the U.S. Census Bureau, combined with building footprint data from Onegeo and USA Structures, both reflecting 2022 conditions.The specific raster datasets included in this publication include:Building Count: Building Count is a 30-m raster representing the count of buildings in the building footprint dataset located within each 30-m pixel.Building Density: Building Density is a 30-m raster representing the density of buildings in the building footprint dataset (buildings per square kilometer [km²]).Building Coverage: Building Coverage is a 30-m raster depicting the percentage of habitable land area covered by building footprints.Population Count (PopCount): PopCount is a 30-m raster with pixel values representing residential population count (persons) in each pixel.Population Density (PopDen): PopDen is a 30-m raster of residential population density (people/km²).Housing Unit Count (HUCount): HUCount is a 30-m raster representing the number of housing units in each pixel.Housing Unit Density (HUDen): HUDen is a 30-m raster of housing-unit density (housing units/km²).Housing Unit Exposure (HUExposure): HUExposure is a 30-m raster that represents the expected number of housing units within a pixel potentially exposed to wildfire in a year. This is a long-term annual average and not intended to represent the actual number of housing units exposed in any specific year.Housing Unit Impact (HUImpact): HUImpact is a 30-m raster that represents the relative potential impact of fire to housing units at any pixel, if a fire were to occur. It is an index that incorporates the general consequences of fire on a home as a function of fire intensity and uses flame length probabilities from wildfire modeling to capture likely intensity of fire.Housing Unit Risk (HURisk): HURisk is a 30-m raster that integrates all four primary elements of wildfire risk - likelihood, intensity, susceptibility, and exposure - on pixels where housing unit density is greater than zero.

The data included in this publication depict components of wildfire risk specifically for populated areas in the United States. These datasets represent where people live in the United States and the in situ risk from wildfire, i.e., the risk at the location where the adverse effects take place.

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. The data products in this publication that represent where people live, reflect 2020 estimates of housing units and 2021 estimates of population counts from the U.S. Census Bureau, combined with building footprint data from Onegeo and USA Structures, both reflecting 2022 conditions.

The specific raster datasets included in this publication include:

Building Count: Building Count is a 30-m raster representing the count of buildings in the building footprint dataset located within each 30-m pixel.

Building Density: Building Density is a 30-m raster representing the density of buildings in the building footprint dataset (buildings per square kilometer [km²]).

Building Coverage: Building Coverage is a 30-m raster depicting the percentage of habitable land area covered by building footprints.

Population Count (PopCount): PopCount is a 30-m raster with pixel values representing residential population count (persons) in each pixel.

Population Density (PopDen): PopDen is a 30-m raster of residential population density (people/km²).

Housing Unit Count (HUCount): HUCount is a 30-m raster representing the number of housing units in each pixel.

Housing Unit Density (HUDen): HUDen is a 30-m raster of housing-unit density (housing units/km²).

Housing Unit Exposure (HUExposure): HUExposure is a 30-m raster that represents the expected number of housing units within a pixel potentially exposed to wildfire in a year. This is a long-term annual average and not intended to represent the actual number of housing units exposed in any specific year.

Housing Unit Impact (HUImpact): HUImpact is a 30-m raster that represents the relative potential impact of fire to housing units at any pixel, if a fire were to occur. It is an index that incorporates the general consequences of fire on a home as a function of fire intensity and uses flame length probabilities from wildfire modeling to capture likely intensity of fire.

Housing Unit Risk (HURisk): HURisk is a 30-m raster that integrates all four primary elements of wildfire risk - likelihood, intensity, susceptibility, and exposure - on pixels where housing unit density is greater than zero.The geospatial data products described and distributed here are part of the Wildfire Risk to Communities project. This project was directed by Congress in the 2018 Consolidated Appropriations Act (i.e., 2018 Omnibus Act, H.R. 1625, Section 210: Wildfire Hazard Severity Mapping) to help U.S. communities understand components of their relative wildfire risk profile, the nature and effects of wildfire risk, and actions communities can take to mitigate risk. The first edition of these data represented the first time wildfire risk to communities had been mapped nationally with consistent methodology. They provided foundational information for comparing the relative wildfire risk among populated communities in the United States. In this version, the 2nd edition, we use improved modeling and mapping methodology and updated input data to generate the current suite of products.See the Wildfire Risk to Communities website at https://www.wildfirerisk.org for complete project information and an interactive web application for exploring some of the datasets published here. We deliver the data here as zip files by U.S. state (including AK and HI), and for the full extent of the continental U.S.

This data publication is a second edition and represents an update to any previous versions of Wildfire Risk to Communities risk datasets published by the USDA Forest Service. This second edition was originally published on 06/03/2024. On 09/10/2024, a minor correction was made to the abstract in this overall metadata document as well as the individual metadata documents associated with each raster dataset. The supplemental file containing data product descriptions was also updated. In addition, we separated the large CONUS download into a series of smaller zip files (one for each layer).

There are two companion data publications that are part of the WRC 2.0 data update: one that characterizes landscape-wide wildfire hazard and risk for the nation (Scott et al. 2024, https://doi.org/10.2737/RDS-2020-0016-2), and one that delineates wildfire risk reduction zones and provides tabular summaries of wildfire hazard and risk raster datasets (Dillon et al. 2024, https://doi.org/10.2737/RDS-2024-0030).

Knowing the historical variation in fire regimes is instrumental in managing forests today and in predicting what may happen in the future. By cross-dating 745 fire scars in 378 samples of remnant Scots pines, we delineated 254 individual forest fires during the past 700 years in a 74-km2 section of Trillemarka-Rollagsfjell Nature Reserve in south-central Norway. Fire sizes, numbers, burn rates, and frequencies were compared with historical climate proxies, vegetation maps, and written sources. The results revealed patterns consistent with a predominantly climate-driven fire regime up to 1625, followed by periods of strong anthropogenic influence that increased fire frequency during 1600–1700s and diminished fires during 1800–1900s. This was documented by an abrupt increase in number of small fires from the early 1600s that markedly shortened fire intervals from a median of 73 to 37 yr. This shift in fire frequency coincided with a sudden appearance of early-season fires from 1625 and onward. Whereas late-season burn rate increased with summer temperature, no such relationship was found for early-season fires. These results were corroborated by written sources that describe anthropogenic forest fires and slash-and-burn cultivation expanding with the increasing population from the late 1500s and subsequently diminishing due to increasing timber values during 1700–1800s. Whereas human activity strongly influenced the fire regime at multidecadal to centennial scales, it was the interannual variability in climate that triggered large fire events, especially during the pre-1625 period. Prior to 1625, the percentage of years with fire tripled from 7% during cold summers (10–12°C) to 21% during warm summers (14–16°C). Burn rate increased even more, from 0.01% to 1.3% for the same temperature intervals. Ecologically, the post-1625 period is remarkable in such a way that human activity, first by greatly increasing fire frequency and subsequently almost eradicating fires, possibly influenced the fire regime to such an extent that it may be unprecedented for millennia.

Higuera, P.E., M.C. Cook, J.K. Balch, E.N. Stavros, A.L. Mahood, and L.A. St. Denis. 2023. Shifting social-ecological fire regimes explain increasing structure loss from Western wildfires. PNAS Nexus 2: In Press. Structure loss is an acute, costly impact of the wildfire crisis in the western United States (“West”), motivating the need to understand recent trends and causes. We document a 246% rise in West-wide structure loss from wildfires between 1999–2009 and 2010–2020, driven strongly by events in 2017, 2018, and 2020. Increased structure loss was not due to increased area burned alone. Wildfires became significantly more destructive, with a 160% higher structure loss rate (loss/kha burned) over the past decade. Structure loss was driven primarily by wildfires from unplanned human-related ignitions (e.g. backyard burning, power lines, etc.), which accounted for 76% of all structure loss and resulted in 10 times more structures destroyed per unit area burned compared to lightning-ignited fires. Annual structure loss was well explained by area burned from human-related ignitions, while decadal structure loss was explained by state-level structure abundance in flammable vegetation. Both predictors increased over recent decades and likely interacted with increased fuel aridity to drive structure-loss trends. While states are diverse in patterns and trends, nearly all experienced more burning from human-related ignitions and/or higher structure loss rates, particularly California, Washington, and Oregon. Our findings highlight how fire regimes – characteristics of fire over space and time – are fundamentally social-ecological phenomena. By resolving the diversity of Western fire regimes, our work informs regionally appropriate mitigation and adaptation strategies. With millions of structures with high fire risk, reducing human-related ignitions and rethinking how we build are critical for preventing future wildfire disasters. Methods See associated paper.

Fire, land-cover, weather dataR data file containing data about fire counts, land cover and weather. Spatial resolution of the data: 10 km. Data span from 2000 to 2010 and over 200,000 km2.DATA_agg.RData

Wildfires are part of the Mediterranean ecosystem, however, in Israel all wildfires are human caused, either intentionally or un-intentionally. In this study we aimed to develop and test a new method for mapping fire scars from MODIS imagery, to examine the temporal and spatial patterns of wildfires in Israel in the 2000s and to examine the factors controlling Israel's wildfire regime. To map the fires we used two 'off-the-shelf' MODIS fire products as our basis—the 1 km MODIS Collection 5 fire hotspots, the 500 m MCD45A1 burnt areas—and we created a new set of fire scar maps from the 250 m MOD13Q1 product. We carried out a cross comparison of the three MODIS based wildfire scar maps and evaluated them independently against the wild fire scars mapped from 30 m Landsat TM imagery. To examine the factors controlling wildfires we used GIS layers of rainfall, land use, and a Landsat-based national vegetation map. Wildfires occurred in areas where annual rainfall was above 250 mm, mostly in areas with herbaceous vegetation. Wildfire frequency was especially high in the Golan Heights and in the foothills of the Judean mountains, and a high correspondence was found between military training zones and the spatial distribution of fire scars. The use of MODIS satellite images enabled us to map wildfires at a national scale due to the high temporal resolution of the sensor. Our MOD13Q1 based mapping of fire scars adequately mapped large (>1 km**2) fires with accuracies above 80%. Such large fires account for a large proportion of all fires, and pose the greatest threats. This database can aid managers in determining wildfire risks in space and in time.

This data publication contains tabular data with repeat measurements of tree and shrub data for a set of transects located in Stanislaus National Forest (STF) and Yosemite National Park (YOSE) in California. These transects represent part of a systematic timber inventory collected across a large mixed-conifer dominated landscape by the U.S. Forest Service in 1911. Trees were tallied by species, diameter and height within 40 x 400 meter (m) strips that spanned the center of quarter-quarter sections (QQs) delineated by the Public Land Survey System. Shrub cover was determined using an ocular estimate. Repeat data were collected in either 2005, 2007 or 2013 in three to four 0.1 hectare circular plots (radius 17.8 m) per transect, centered at random, non-overlapping distances along the historical transect centerline. This data publication therefore contains measurements such as the percentage cover of shrubs for multiple species, basal area of dead and live conifer trees, and density of live conifer trees with various diameters at breast height for both STF and YOSE in 1911 and the remeasurement year of 2005, 2007, or 2013.These data were used to compare forest change due to fire exclusion and logging over the 20th century between Yosemite National Park, which was protected from timber harvesting, and Stanislaus National Forest.

The wildfire hazard models are a set of 12 raster geospatial products produced by the USDA Forest Service, Northern Research Station that are intended to be used in analyses of wildfire hazards in the region of New Jersey, Ohio, and Pennsylvania. Each raster represents the monthly hazard of a wildfire occurring within a 30 meter pixel as a probability. A statistical model for each month was parameterized with an integrated moisture index, a cumulative drought severity index for the month during the period 2000 to 2009, percent forest cover, and wildland-urban interface classifications to predict the probability of wildfire occurrence based on reported wildfires. Each model included 10 iterations and the raster products of average, minimum, maximum, median, and standard deviation of the predicted probability of a wildfire occurrence is provided. All raster values were converted to integers by multiplying by 10 to reduce file sizes. Therefore, the range of probabilities is 0 to 1000 for the modeled occurrence of a wildfire.These products are intended to provide managers and planners with information related to the wildfire hazard based on reported incidents from 2000 to 2009. Local and daily weather conditions should be monitored to determine site specific burn susceptibility. Our monthly wildfire hazard data is intended to provide long-term trends of potential environmental conditions that coincided with reported wildfires.

The wildfire hazard models are a set of 12 raster geospatial products produced by the USDA Forest Service, Northern Research Station that are intended to be used in analyses of wildfire hazards in the region of New Jersey, Ohio, and Pennsylvania. Each raster represents the monthly hazard of a wildfire occurring within a 30 meter pixel as a probability. A statistical model for each month was parameterized with an integrated moisture index, a cumulative drought severity index for the month during the period 2000 to 2009, percent forest cover, and wildland-urban interface classifications to predict the probability of wildfire occurrence based on reported wildfires. Each model included 10 iterations and the raster products of average, minimum, maximum, median, and standard deviation of the predicted probability of a wildfire occurrence is provided. All raster values were converted to integers by multiplying by 10 to reduce file sizes. Therefore, the range of probabilities is 0 to 1000 for the modeled occurrence of a wildfire.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.

The data included in this publication depict components of wildfire risk specifically for populated areas in the United States. These datasets represent where people live in the United States and the in situ risk from wildfire, i.e., the risk at the location where the adverse effects take place.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. The data products in this publication that represent where people live, reflect 2021 estimates of housing unit and population counts from the U.S. Census Bureau, combined with building footprint data from Onegeo and USA Structures, both reflecting 2022 conditions.The specific raster datasets included in this publication include:Building Count: Building Count is a 30-m raster representing the count of buildings in the building footprint dataset located within each 30-m pixel.Building Density: Building Density is a 30-m raster representing the density of buildings in the building footprint dataset (buildings per square kilometer [km²]).Building Coverage: Building Coverage is a 30-m raster depicting the percentage of habitable land area covered by building footprints.Population Count (PopCount): PopCount is a 30-m raster with pixel values representing residential population count (persons) in each pixel.Population Density (PopDen): PopDen is a 30-m raster of residential population density (people/km²).Housing Unit Count (HUCount): HUCount is a 30-m raster representing the number of housing units in each pixel.Housing Unit Density (HUDen): HUDen is a 30-m raster of housing-unit density (housing units/km²).Housing Unit Exposure (HUExposure): HUExposure is a 30-m raster that represents the expected number of housing units within a pixel potentially exposed to wildfire in a year. This is a long-term annual average and not intended to represent the actual number of housing units exposed in any specific year.Housing Unit Impact (HUImpact): HUImpact is a 30-m raster that represents the relative potential impact of fire to housing units at any pixel, if a fire were to occur. It is an index that incorporates the general consequences of fire on a home as a function of fire intensity and uses flame length probabilities from wildfire modeling to capture likely intensity of fire.Housing Unit Risk (HURisk): HURisk is a 30-m raster that integrates all four primary elements of wildfire risk - likelihood, intensity, susceptibility, and exposure - on pixels where housing unit density is greater than zero.Funding for this project provided by: USDA Forest Service, Fire and Aviation Management. Funding also provided by USDA Forest Service, Fire Modeling Institute, which is part of the Rocky Mountain Research Station, Fire, Fuel and Smoke Science Program. Work on dataset development was primarily completed by the USDA Forest Service, Fire Modeling Institute. Some salary was provided by FMI through an ORISE agreement under the U.S. Department of Energy (DE-SC0014664).Author information:Melissa R. JaffePyrologix, LLChttps://orcid.org/0009-0002-8623-407XJoe H. ScottPyrologix, LLChttps://orcid.org/0009-0008-3246-1190Michael N. CallahanPyrologix, LLChttps://orcid.org/0009-0009-4937-5405Gregory K. DillonUSDA Forest Service, Rocky Mountain Research Stationhttps://orcid.org/0009-0006-6304-650XEva C. KarauUSDA Forest Service, Rocky Mountain Research Stationhttps://orcid.org/0009-0009-6776-9387Mitchell T. LazarzUSDA Forest Service, Rocky Mountain Research Stationhttps://orcid.org/0000-0002-4558-4949