Fire history metrics enable rapidly increasing amounts of burned area data to be collapsed into a handful of data layers that can be used efficiently by diverse stakeholders. In this effort, the U.S. Geological Survey's Landsat Burned Area product was used to identify burned area across CONUS over a 41-year period (1984-2024). The Landsat BA product was consolidated into a suite of annual BA products, which in-turn were used to calculate a series of contemporary fire history metrics (30 m resolution). Fire history metrics included: (1) fire frequency (FRQ), (2) time since last burn (TSLB) and (3) year of last burn (YLB), (4) longest fire-free interval (LFFI), and (5) average fire interval length (FIL). All metrics were reported using years as the unit. The FRQ, TSLB and YLB metrics are useful across a wide range of fire regimes, and can be used to inform risk of wildfire, answer fire-management questions, or support fire model parameterization. The FIL and LFFI, alternatively, provide data on the distribution of fire events across the period of record and can help guide land management in regions with frequent fire, such as the Midwest and Southeast.

Fire history metrics enable rapidly increasing amounts of burned area data to be collapsed into a handful of data layers that can be used efficiently by diverse stakeholders. In this effort, the U.S. Geological Survey’s Landsat Burned Area product was used to identify burned area across CONUS over a 39-year period (1984-2022). The Landsat BA product was consolidated into a suite of annual BA products, which in-turn were used to calculate a series of contemporary fire history metrics (30 m resolution). Fire history metrics included: (1) fire frequency (FRQ), (2) time since last burn (TSLB) and (3) year of last burn (YLB), (4) longest fire-free interval (LFFI), and (5) average fire interval length (FIL). All metrics were reported using years as the unit. The FRQ, TSLB and YLB metrics are useful across a wide range of fire regimes, and can be used to inform risk of wildfire, answer fire-management questions, or support fire model parameterization. The FIL and LFFI, alternatively, provide data on the distribution of fire events across the period of record and can help guide land management in regions with frequent fire, such as the Midwest and Southeast.

LANDFIRE (LF) disturbance products are developed to provide temporal and spatial information related to landscape change. Historical Disturbance (HDist) is developed from the base LF disturbance products, and attribute code system, to represent the history of disturbance for a 10-year span. Each year's disturbance scenarios are checked against time relevant LF vegetation products to check for logical inconsistencies. Errant codes are flagged and updated to a discard code with the remaining disturbance codes cross-walked to Fuel Disturbance (FDist) codes. HDist development involves a comprehensive review of fuel and disturbance attributes. Starting with LF Remap, HDist replaces Vegetation Disturbance (VDist) from previous LF versions.

LANDFIRE (LF) disturbance products are developed to provide temporal and spatial information related to landscape change. Historical Disturbance (HDist) is developed from the base LF disturbance products, and attribute code system, to represent the history of disturbance for a 10-year span. Each year's disturbance scenarios are checked against time relevant LF vegetation products to check for logical inconsistencies. Errant codes are flagged and updated to a discard code with the remaining disturbance codes cross-walked to Fuel Disturbance (FDist) codes. HDist development involves a comprehensive review of fuel and disturbance attributes. Starting with LF Remap, HDist replaces Vegetation Disturbance (VDist) from previous LF versions.

First, we would like to thank the wildland fire advisory group. Their wisdom and guidance helped us build the dataset as it currently exists. Currently, there are multiple, freely available wildland fire datasets that identify wildfire and prescribed fire areas across the United States. However, these datasets are all limited in some way. Time periods, spatial extents, attributes, and maintenance for these datasets are highly variable, and none of the existing datasets provide a comprehensive picture of wildfires that have burned since the 1800s. Utilizing a series of both manual processes and ArcGIS Python (arcpy) scripts, we merged 40 of these disparate datasets into a single dataset that encompasses the known wildfires within the United States from the 1800s to the present. These datasets were ranked by order of observed quality, and overlapping polygons in the same year were used individually or dissolved together with other polygons based on ranked quality (see individual steps in the polygon metadata for full details). The fire polygons were turned into 30 meter rasters representing various summary counts: (a) count of all wildland fires that burned a pixel, (b) count of wildfires that burned a pixel, (c) the first year a wildfire burned a pixel, (d) the most recent year a wildfire burned a pixel, and (e) count of prescribed fires that burned a pixel.

First, we would like to thank the wildland fire advisory group. Their wisdom and guidance helped us build the dataset as it currently exists. Currently, there are multiple, freely available wildland fire datasets that identify wildfire and prescribed fire areas across the United States. However, these datasets are all limited in some way. Time periods, spatial extents, attributes, and maintenance for these datasets are highly variable, and none of the existing datasets provide a comprehensive picture of wildfires that have burned since the 1800s. Utilizing a series of both manual processes and ArcGIS Python (arcpy) scripts, we merged 40 of these disparate datasets into a single dataset that encompasses the known wildfires within the United States from the 1800s to the present. These datasets were ranked by order of observed quality, and overlapping polygons in the same year were used individually or dissolved together with other polygons based on ranked quality (see individual steps in the polygon metadata for full details). The fire polygons were turned into 30 meter rasters representing various summary counts: (a) count of all wildland fires that burned a pixel, (b) count of wildfires that burned a pixel, (c) the first year a wildfire burned a pixel, (d) the most recent year a wildfire burned a pixel, and (e) count of prescribed fires that burned a pixel.

연도별 화재경계지구 현황(2008~2017)

전국 화재통계에 대한 데이터로 최근 5년간(2019년 부터 2023년 까지) 연도별 전체 화재건수, 인명피해(명), 재산피해(천원) 현황입니다.

LANDFIRE (LF) disturbance products are developed to provide temporal and spatial information related to landscape change. Historical Disturbance (HDist) is developed from the base annual LF disturbance products, and attribute code system, to represent the history of disturbance for a 10-year span. Each year's disturbance scenarios are checked against time relevant LF vegetation products to check for logical inconsistencies. Errant codes are flagged and updated to a discard code with the remaining disturbance types cross-walked/aggregated to Fuel Disturbance (FDist) types. HDist includes the year of disturbance that is recorded for that pixel. In LF 2022, the time since disturbance code is the same for both HDist and FDist. Starting with LF 2016 Remap, HDist replaced Vegetation Disturbance (VDist) from previous LF versions.

First, we would like to thank the wildland fire advisory group. Their wisdom and guidance helped us build the dataset as it currently exists. This dataset is comprised of two different zip files. Zip File 1: The data within this zip file are composed of two wildland fire datasets. (1) A merged dataset consisting of 40 different wildfire and prescribed fire layers. The original 40 layers were all freely obtained from the internet or provided to the authors free of charge with permission to use them. The merged layers were altered to contain a consistent set of attributes including names, IDs, and dates. This raw merged dataset contains all original polygons many of which are duplicates of the same fire. This dataset also contains all the errors, inconsistencies, and other issues that caused some of the data to be excluded from the combined dataset. Care should be used when working with this dataset as individual records may contain errors that can be more easily identified in the combined dataset. (2) A combined wildland fire polygon dataset composed of both wildfires and prescribed fires ranging in years from mid 1800s to the present that was created by merging and dissolving fire information from 40 different original wildfire datasets to create one of the most comprehensive wildfire datasets available. Attributes describing fires that were reported in the various sources are also merged, including fire names, fire codes, fire IDs, fire dates, fire causes. Zip File 2: The fire polygons were turned into 30 meter rasters representing various summary counts: (a) count of all wildland fires that burned a pixel, (b) count of wildfires that burned a pixel, (c) the first year a wildfire burned a pixel, (d) the most recent year a wildfire burned a pixel, and (e) count of prescribed fires that burned a pixel.