This layer contains wildfire hazard potential (WHP) data for the conterminous United States aggregated from states to block groups and 50 km hex bins then enriched with demographic data. The data is from the USDA Forest Service Fire Modeling Institute providing an index of WHP at a 270 meter resolution. Wildfire hazard potential provides information on the relative potential for wildfire that would be difficult for fire crews to contain. "Areas with higher wildfire potential values represent fuels with a higher likelihood of experiencing high-intensity fire with torching, crowning, and other forms of extreme fire behavior." - Fire Modeling Institute. A score of 5 is very high risk and a score between 0-1 is likely non-burnable area such as water or asphalt. "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 communities, structures, or powerlines, it can approximate relative wildfire risk to those 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 planning and fuels management."Each layer has been enriched with 2020 Esri demographic attributes to better approximate wildfire hazard risk relating to the human population. This layer is available in a ready to use web map. A hosted imagery layer of this data is available in ArcGIS Living Atlas for additional analysis.Data notes:Zonal Statistics as Table were run against a local copy of the WHP data using US standard geographies as the feature zone input for the analysis. Geographies included are: State, County, Congressional District, ZIP Code, Tract, and Block Group. Statistical tables were joined to geographies. To learn more about zonal statistics, view the documentation here. 50 km hex bins were created using Generate Tessellation and then joined to zonal statistics as described above (step 1).Data was enriched with 2020 Esri Demographics. Attributes include population, households & housing units, growth rate, and calculated variables such as population change over time. To create the population-weighted attributes on the state, congressional district, and county layers, the hex value population values were used to create the weighting. Within each hex bin, the total population figure and average WHP were multiplied.The hex bins were converted into centroids and summarized within the state, congressional district, and county boundaries.The summation of these values were then divided by the total population of each respective geography.

Financial overview and grant giving statistics of International Association of Wildland Fire

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.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.

Using a cross-sectional design, we surveyed participants in the program, Support and Aid for Everyone (SAFE) operated by Mariposa County Health and Human Services Agency (MC), which assists persons who self-identify as having special needs in an emergency, e.g., use wheelchairs or electrical medical equipment. A questionnaire was distributed to SAFE participants on behalf of California Department of Public Health (CDPH) by MC staff allowed for anonymous participation. Following a modified Dillman method, the survey was mailed, with a phone interview option. Reminders were made by postcard and follow-up calls by MC staff. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Data analysis files are available from the corresponding author upon reasonable request to Sumi Hoshiko sumi.hoshiko@cdph.ca.gov. Format: Raw data from this study will not be made publicly available to protect confidentiality. Data analysis files are available from the corresponding author upon reasonable request to Sumi Hoshiko sumi.hoshiko@cdph.ca.gov. This dataset is associated with the following publication: Rappold, A., S. Hoshiko, J. Buckman, C.G. Jones, K. Yeomans, A. Mello, R. Thilakaratne, E. Sergienko, MD, K. Allen, and L. Bello. Responses to wildfire and prescribed fire smoke: A survey of a medically vulnerable adult population in the wildland-urban interface, Mariposa County, California. International Journal of Environmental Research and Public Health. Molecular Diversity Preservation International, Basel, SWITZERLAND, 20(2): 1, (2023).

This layer contains wildfire hazard potential (WHP) data for the conterminous United States aggregated from states to block groups and 50 km hex bins then enriched with demographic data. The data is from the USDA Forest Service Fire Modeling Institute providing an index of WHP at a 270 meter resolution. Wildfire hazard potential provides information on the relative potential for wildfire that would be difficult for fire crews to contain. "Areas with higher wildfire potential values represent fuels with a higher likelihood of experiencing high-intensity fire with torching, crowning, and other forms of extreme fire behavior." - Fire Modeling Institute. A score of 5 is very high risk and a score between 0-1 is likely non-burnable area such as water or asphalt. "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 communities, structures, or powerlines, it can approximate relative wildfire risk to those 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 planning and fuels management."Each layer has been enriched with 2020 Esri demographic attributes to better approximate wildfire hazard risk relating to the human population. This layer is available in a ready to use web map. A hosted imagery layer of this data is available in ArcGIS Living Atlas for additional analysis.Data notes:Zonal Statistics as Table were run against a local copy of the WHP data using US standard geographies as the feature zone input for the analysis. Geographies included are: State, County, Congressional District, ZIP Code, Tract, and Block Group. Statistical tables were joined to geographies. To learn more about zonal statistics, view the documentation here. 50 km hex bins were created using Generate Tessellation and then joined to zonal statistics as described above (step 1).Data was enriched with 2020 Esri Demographics. Attributes include population, households & housing units, growth rate, and calculated variables such as population change over time. To create the population-weighted attributes on the state, congressional district, and county layers, the hex value population values were used to create the weighting. Within each hex bin, the total population figure and average WHP were multiplied.The hex bins were converted into centroids and summarized within the state, congressional district, and county boundaries.The summation of these values were then divided by the total population of each respective geography.

Financial overview and grant giving statistics of Eric Marsh Foundation for Wildland Firefighters

This map shows the average risk for wildfires in comparison to the total population. This is an assessment of fire risk and human impact, and can be seen at states, congressional districts, counties, ZIP Codes, tracts, and block groups. Optionally, you can turn on a hex bin pattern to see non-administrative patterns. *Map contains a subscriber layer, so an ArcGIS Online login is required. This layer contains wildfire hazard potential (WHP) data for the conterminous United States aggregated from states to block groups and 50 km hex bins then enriched with demographic data. The data is from the USDA Forest Service Fire Modeling Institute providing an index of WHP at a 270 meter resolution. Wildfire hazard potential provides information on the relative potential for wildfire that would be difficult for fire crews to contain. Areas with higher wildfire potential values represent fuels with a higher likelihood of experiencing high-intensity fire with torching, crowning, and other forms of extreme fire behavior. A score of 5 is very high risk and a score between 0-1 is non-burnable area such as water or asphalt. 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 communities, structures, or powerlines, it can approximate relative wildfire risk to those 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 planning and fuels management.For more information and metadata about the data and analysis, visit the USA Wildfire Hazard Potential layer.

The FirePerimeter polygon layer represents daily and final mapped wildland fire perimeters. Incidents of 10 acres or greater in size are expected. Incidents smaller than 10 acres in size may also be included. Data are maintained at the Forest/District level, or their equivalent, to track the area affected by wildland fire. Records in FirePerimeter include perimeters for wildland fires that have corresponding records in FIRESTAT, which is the authoritative data source for all wildland fire reports. FIRESTAT, the Fire Statistics System computer application, required by the USFS for all wildland fire occurrences on National Forest System Lands or National Forest-protected lands, is used to enter and maintain information from the Individual Fire Report (FS-5100-29).National USFS fire occurrence final fire perimeters where wildland fires have historically occurred on National Forest System Lands and/or where protection is the responsibility of the US Forest Service. Knowing where wildland fire events have happened in the past is critical to land management efforts in the future.This data is utilized by fire & aviation staffs, land managers, land planners, and resource specialists on and around National Forest System Lands.*This data has been updated to match 2021 National GIS Data Dictionary Standards.Metadata and DownloadsThis 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 CSV Shapefile GeoJSON KML For complete information, please visit https://data.gov.

Global human exposure to wildland fires dataset: 2002-2021

Seyd Teymoor Seydi¹, John T. Abatzoglou², Matthew W. Jones³, Mojtaba Sadegh^1,4

¹Department of Civil Engineering, Boise State University, Boise, ID, USA

²Management of Complex Systems Department, University of California, Merced, Merced, CA, USA

³Tyndall Centre for Climate Change Research, School of Environmental Sciences, University of East Anglia (UEA), Norwich, UK

⁴United Nations University Institute for Water, Environment and Health, Richmond Hills, ON, Canada

1. Dataset Overview

This dataset contains comprehensive information on global fire events from 2002 to 2021, including fire characteristics, environmental variables, land cover properties, and detailed population exposure estimates separated by age group and gender. The data is organized into two separate dataset series:

1. Fire Events Series (df_Fire_Events_2002 to df_Fire_Events_2021): Contains fire characteristics, environmental variables, land cover properties, and summary exposure information for each fire event. Each file includes global fire events for one year, as indicated in the file name.

2. Age Groups Series (df_Fire_Age_Groups_2002 to df_Fire_Age_Groups_2021): Contains detailed demographic breakdowns of populations exposed to individual fire events by age group and gender. Each file includes global fire events for one year, as indicated in the file name.

2. Data Structure

Each annual dataset contains the following information:

• Temporal Coverage: Individual years from 2002 to 2021 (20 datasets total x 2 [environmental variables and summary exposure + exposures broken down by age and gender structures])
• Spatial Coverage: Global
• Unit of Analysis: Individual fire events identified by unique fire_ID
• Fire Metrics: Location, size, duration, spread characteristics, active fire days, and fire radiative power
• Environmental Variables: Vegetation indices (EVI, NDVI) and land cover fractions
• Population Metrics: Total exposure and detailed breakdowns by 5-year age groups and gender
• Geographic Context: Country and continent information

Column Definitions

Fire Characteristics

• fire_ID: Unique identifier for each fire event
• Latitude: Latitude coordinate of fire ignition point (decimal degrees)
• Longitude: Longitude coordinate of fire ignition point (decimal degrees)
• size: Fire area
• perimeter: Fire perimeter length
• start_date: Fire start date
• duration: Fire duration (days)
• spread: Fire spread rate
• speed: Fire speed rate
• Active_Fire_Days: Number of active fire days in each pixel within fire polygon, averaged for each fire polygon (Source: MOD14A1)
• total_frp: Total fire radiative power summed from start to end date of fire, aggregated by sum for each fire polygon (Source: MOD14A1)

3. Geographic Information

• Country: Name of country where fire occurred
• Continent: Name of continent where fire occurred

4. Land Cover Characteristics (Source: MCD12Q1)

• Agriculture-Fraction: Fraction of agricultural land cover within fire polygon
• Urban-Fraction: Fraction of urban areas within fire polygon

5. Vegetation Indices (Source: MOD13A1)

• EVI: Enhanced Vegetation Index - maximum annual value for each pixel averaged across fire polygon
• NDVI: Normalized Difference Vegetation Index - maximum annual value for each pixel averaged across fire polygon

6. Population Exposure (Source: WorldPop)

• Exposure: Total population exposure to fire, aggregated by sum for each individual fire event using contemporary population data for each year
• Scenario-Exposure: Total population exposure to fire using constant 2002 population data across all years, with dynamic fire data for each year, aggregated by sum for each individual fire event. This is also referred to as counterfactual exposure.

6.1. Female Population Exposure (SUM_f_*)

• SUM_f_0: Number of females aged <1 year exposed to fire
• SUM_f_1: Number of females aged ≥1 to <5 years exposed to fire
• SUM_f_5: Number of females aged ≥5 to <10 years exposed to fire
• SUM_f_10: Number of females aged ≥10 to <15 years exposed to fire
• SUM_f_15: Number of females aged ≥15 to <20 years exposed to fire
• SUM_f_20: Number of females aged ≥20 to <25 years exposed to fire
• SUM_f_25: Number of females aged ≥25 to <30 years exposed to fire
• SUM_f_30: Number of females aged ≥30 to <35 years exposed to fire
• SUM_f_35: Number of females aged ≥35 to <40 years exposed to fire
• SUM_f_40: Number of females aged ≥40 to <45 years exposed to fire
• SUM_f_45: Number of females aged ≥45 to <50 years exposed to fire
• SUM_f_50: Number of females aged ≥50 to <55 years exposed to fire
• SUM_f_55: Number of females aged ≥55 to <60 years exposed to fire
• SUM_f_60: Number of females aged ≥60 to <65 years exposed to fire
• SUM_f_65: Number of females aged ≥65 to <70 years exposed to fire
• SUM_f_70: Number of females aged ≥70 to <75 years exposed to fire
• SUM_f_75: Number of females aged ≥75 to <80 years exposed to fire
• SUM_f_80: Number of females aged ≥80 years exposed to fire

6.2. Male Population Exposure (SUM_m_*)

• SUM_m_0: Number of males aged <1 year exposed to fire
• SUM_m_1: Number of males aged ≥1 to <5 years exposed to fire
• SUM_m_5: Number of males aged ≥5 to <10 years exposed to fire
• SUM_m_10: Number of males aged ≥10 to <15 years exposed to fire
• SUM_m_15: Number of males aged ≥15 to <20 years exposed to fire
• SUM_m_20: Number of males aged ≥20 to <25 years exposed to fire
• SUM_m_25: Number of males aged ≥25 to <30 years exposed to fire
• SUM_m_30: Number of males aged ≥30 to <35 years exposed to fire
• SUM_m_35: Number of males aged ≥35 to <40 years exposed to fire
• SUM_m_40: Number of males aged ≥40 to <45 years exposed to fire
• SUM_m_45: Number of males aged ≥45 to <50 years exposed to fire
• SUM_m_50: Number of males aged ≥50 to <55 years exposed to fire
• SUM_m_55: Number of males aged ≥55 to <60 years exposed to fire
• SUM_m_60: Number of males aged ≥60 to <65 years exposed to fire
• SUM_m_65: Number of males aged ≥65 to <70 years exposed to fire
• SUM_m_70: Number of males aged ≥70 to <75 years exposed to fire
• SUM_m_75: Number of males aged ≥75 to <80 years exposed to fire
• SUM_m_80: Number of males aged ≥80 years exposed to fire

7. Data Sources

• Population Data: WorldPop Project demographic datasets

o Source URL: https://hub.worldpop.org/project/categories?id=8

o Population estimates are provided in 5-year age groups by gender at high spatial resolution

• Land Cover Data: MODIS MCD12Q1 Land Cover Type product

o Used to calculate agricultural and urban land cover fractions within fire polygons

• Vegetation Indices: MODIS MOD13A1 Vegetation Indices product

o Used to derive maximum annual EVI and NDVI values, averaged across fire polygons

• Fire Activity Data: MODIS MOD14A1 Thermal Anomalies and Fire Daily product

o Used to calculate active fire days and total fire radiative power within fire polygons

• Fire Data: Global fire atlas is available at: https://doi.org/10.5281/zenodo.11400062" href="https://doi.org/10.5281/zenodo.11400062" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.11400062

8. Usage Notes

1. Exposure Definition: Exposure is defined as population within fire polygon boundaries
2. Exposure Scenarios:

o Exposure: Uses contemporary population data for each year, allowing analysis of changing demographics over time

o Scenario-Exposure: Uses constant 2002 population data across all years to isolate the effect of changing fire patterns while holding population constant

1. Age Group Structure: Age groups follow standard demographic categories with the first year of life separate (0)

This web map shows the wildfire hazard potential (WHP) for the conterminous United States aggregated from states to block groups and 50 km hex bins. The data is from the USDA Forest Service Fire Modeling Institute providing an index of WHP at a 270 meter resolution. Wildfire hazard potential provides information on the relative potential for wildfire that would be difficult for fire crews to contain. Areas with higher wildfire potential values represent fuels with a higher likelihood of experiencing high-intensity fire with torching, crowning, and other forms of extreme fire behavior. A score of 5 is very high risk and a score between 0-1 is non-burnable area such as water or asphalt. 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 communities, structures, or powerlines, it can approximate relative wildfire risk to those 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 planning and fuels management.Each layer has been enriched with 2020 Esri demographic attributes to better approximate wildfire hazard risk. A hosted imagery layer of this data is available in ArcGIS Living Atlas for additional analysis.Data notes:Zonal Statistics as Table were run against a local copy of the WHP data using US standard geographies as the feature zone input for the analysis. Geographies included are: State, County, Congressional District, ZIP Code, Tract, and Block Group. Statistical tables were joined to geographies. To learn more about zonal statistics, view the documentation here. 50 km hex bins were created using Generate Tessellation and then joined to zonal statistics as described above (step 1).Data was enriched with 2020 Esri Demographics. Attributes include population, households & housing units, growth rate, and calculated variables such as population change over time. To create the population-weighted attributes on the state, congressional district, and county layers, the hex value population values were used to create the weighting. Within each hex bin, the total population figure and average WHP were multiplied.The hex bins were converted into centroids and summarized within the state, congressional district, and county boundaries.The summation of these values were then divided by the total population of each respective geography.

The FinalFirePerimeter polygon layer represents final mapped wildland fire perimeters. This feature class is a subset of the FirePerimeters feature class. Incidents of 10 acres or greater in size are expected. Incidents smaller than 10 acres in size may also be included. Data are maintained at the Forest/District level, or their equivalent, to track the area affected by wildland fire. Records in FirePerimeter include perimeters for wildland fires that have corresponding records in FIRESTAT, which is the authoritative data source for all wildland fire reports. FIRESTAT, the Fire Statistics System computer application, required by the USFS for all wildland fire occurrences on National Forest System Lands or National Forest-protected lands, is used to enter and maintain information from the Individual Fire Report (FS-5100-29).National USFS fire occurrence final fire perimeters where wildland fires have historically occurred on National Forest System Lands and/or where protection is the responsibility of the US Forest Service. Knowing where wildland fire events have happened in the past is critical to land management efforts in the future.This data is utilized by fire & aviation staffs, land managers, land planners, and resource specialists on and around National Forest System Lands.*This data has been updated to match 2021 National GIS Data Dictionary Standards.Metadata and Downloads

In 2024, there were a total of 64,897 wildland fires recorded in the United States. This represents an increase of roughly 14 percent from the previous year. That year, California was the state with the highest number of wildfires in the United States.

The FIRESTAT (Fire Statistics System) Fire Occurrence point layer represents ignition points, or points of origin, from which individual wildland fires started on National Forest System lands. The source is the FIRESTAT database, which contains records of fire occurrence, related fire behavior conditions, and the suppression actions taken by management taken from the Individual Wildland Fire Report. This publicly available dataset is updated annually for all years previous to January 1 on or after February 16th.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 OGC WMS CSV Shapefile GeoJSON KML For complete information, please visit https://data.gov.

Data during wildfire seasons (May 1 - October 31) over the years 2008 - 2012 in the contiguous U.S. used for spatial causal analysis of wildland fire-contributed PM2.5. The two sources of PM2.5 data are monitor data from the EPA’s Air Quality System (AQS) and simulated PM2.5 from the CMAQ model. This dataset is associated with the following publication: Larsen, A., S. Yang, B. Reich, and A. Rappold. A spatial causal analysis of wildland fire-contributed PM2:5 using numerical model output. Annals of Applied Statistics. Institute of Mathematical Statistics, Beachwood, OH, USA, 16(4): 2714-2731, (2022).

Financial overview and grant giving statistics of Wildland Fire Litigation Conference

The Wildland Firefighting Clothing market plays a crucial role in ensuring the safety and efficiency of firefighters engaged in battling wildfires across the globe. This specialized clothing is designed to provide protection against extreme heat, flames, and hazardous conditions encountered during wildland firefight

Wildland fire has a major impact on the sustainability of many Canadian forests. Fire policies attempt to balance suppression costs with values at risk while recognizing the natural role of fire in managing the landscape. There are three aspects of wildland fire in Canada: fire regimes, fire management, and fire research.

Based on the FireTracks Scientific Dataset, the global wildfire exposure dataset implements space-time alignment between fire clusters and climate as well as socioeconomic data across the globe from 2002 to 2020. The global wildfire exposure dataset is produced to meet the demand for wildfire exposure and its analysis of global inequity.

The data structure is shown in the following:

• fire_clusters.csv:
  • Contains detailed records of global wildfire clusters, including fire characteristics, geographic distribution, and population exposure.

  • Column	Description	Units	Valid Range	Data Type
    cp	fire component label	-	>=0	int64
    duration	fire duration	days	>= 1	uint16
    maxFRP_sum	sum of maximum fire radiative powers	MW*10	>= 0	float64
    area	total burned area	km^2	>= 0.86 (1 MODIS pixel)	float64
    country	country of occurrence	-	-	string
    continent	continent of occurrence	-	-	string
    dtime_min	ignition date (YYYY-MM-DD)	-	>= 2002-01-01	datetime64
    lat_mean	mean location latitude	degrees	[-180, 180]	float64
    lon_mean	mean location longitude	degrees	[-90, 90]	float64
    exposure	primary population exposure to wildfire	persontime	>=0	int64
    5km_band_pop	secondary population exposure to wildfire	persontime	>=0	int64
    year	year of occurrence	-	[2002, 2020]	int64
    month	month of occurrence	-	[1, 12]	int64

• exposure_four_covariate.csv:
  • Aggregates population exposure and covariates for equity-focused analyses, linking wildfire impacts to socioeconomic and climatic drivers.

  • Column	Description	Units	Valid Range	Data Type
    exposure	population exposed to wildfire	persontime	>=0	int64
    year	year of occurrence	-	[2002, 2020]	int64
    country	country of occurrence	-	-	string
    continent	continent of occurrence	-	-	string
    gdp	National Gross Domestic Product for the corresponding year	million U.S. dollars	>=0	float64
    country_pop	national population of the corresponding year	persontime	>=0	int64
    fwi	Fire Weather Index	-	>=0	float32
    vpd	Vapor Pressure Deficit	kPa	[0, 10]	float32
    ndvi	Normalized Difference Vegetation Index	-	[0, 1]	float64
    dnbr	Delta Normalized Burn Ratio	-	[-2, 2]	float64
    iso3	standardized country code for cross-dataset integration	-	-	string
    cluster	results from K-means clustering	-	[0, 2]	int64

Get data on forest fires, compiled annually for the National Forestry Database

The National Forestry Database includes national forest data and forest management statistics to seve as a credible, accurate and reliable source of information on forest management and its impact on the forest resource.

Forest fire data is grouped into eight categories, which are further broken down by geographic location. These include:

• number of fires by cause class and response category
• area burned by cause class and response category
• number of fires by month and response category
• area burned by month and response category
• number of fires by fire size class and response category
• area burned by fire size class and response category
• area burned by productivity class, stocking class, maturity class and response category
• other fire statistics, such as property losses

The Geospatial Multi-Agency Coordination Group, or GeoMAC, is an internet-based mapping tool originally designed for fire managers to access online maps of current fire locations and perimeters in the continental United States, including Alaska.Active wildfires can be viewed in the USA Wildfire Activity Layer. Additional information about how to use fire perimeter data can be found in several blog posts:Learning about the Thomas Fire using ArcGIS Online and Living Atlas by Bern Szukalski Mapping the Inferno by Dan PisutFurther information about this data can be found here. All of these layers can be found in a corresponding web map which can be copied for customization. The layers in this map can be geoenriched with demographics or used in spatial analysis.Disclaimer: Wildland fire perimeters are submitted to GeoMAC by the incidents and then posted to the GeoMAC site for downloading. While every effort is made to provide accurate and complete information, there may be gaps in daily coverage. Please note: Files only contain perimeter data as they are submitted by the incidents. Files do not contain all fires. This data are not the authoritative fire perimeter data and should not be used as such.