Earth Resources Observation and Science (EROS) Center
Email:
gschmidt@contractor.usgs.gov
Office Phone:
605-593-2722
ORCID:
0000-0002-9684-8158
Location
47914 252nd Street
Sioux Falls
, SD
57198-9801
US
Supervisor:
Frederick M Badke
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The U.S. Geological Survey (USGS) has developed and implemented an algorithm that identifies burned areas in dense time series of Landsat image stacks to produce the Landsat Burned Area Essential Climate Variable (BAECV) products. The algorithm makes use of predictors derived from individual Landsat scenes, lagged reference conditions, and change metrics between the scene and reference conditions. Outputs of the BAECV algorithm consist of pixel-level burn probabilities for each Landsat scene, and annual burn probability, burn classification, and burn date composites. These products were generated for the conterminous United States for 1984 through 2015. These data are also available for download at https://rmgsc.cr.usgs.gov/outgoing/baecv/BAECV_CONUS_v1.1_2017/...
Categories: Data Release - Revised;
Tags: Alabama,
Arizona,
Arkansas,
BAECV,
Burned area, All tags...
California,
Colorado,
Connecticut,
Delaware,
Florida,
Georgia,
Idaho,
Illinois,
Indiana,
Iowa,
Kansas,
Kentucky,
Landsat,
Louisiana,
Maine,
Maryland,
Massachusetts,
Michigan,
Minnesota,
Mississippi,
Missouri,
Montana,
Nebraska,
Nevada,
New Hampshire,
New Jersey,
New Mexico,
New York,
North Carolina,
North Dakota,
Ohio,
Oklahoma,
Oregon,
Pennsylvania,
Prescribed fire,
Rhode Island,
South Carolina,
South Dakota,
Tennessee,
Texas,
USGS Science Data Catalog (SDC),
United States,
Utah,
Vermont,
Virginia,
Washington,
West Virginia,
Wildfire,
Wildland fire,
Wisconsin,
Wyoming, Fewer tags
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The U.S. Geological Survey (USGS) has developed and implemented an algorithm that identifies burned areas in temporally dense time series of Landsat Analysis Ready Data (ARD) scenes to produce the Landsat Burned Area Products. The algorithm uses predictors derived from individual ARD Landsat scenes, lagged reference conditions, and change metrics between the scene and reference conditions. Scene-level products include pixel-level burn probability (BP) and burn classification (BC) images corresponding to each Landsat image in the ARD time series. Annual composite products are also available by summarizing the scene-level products. Prior to generating annual composites, individual scenes that had > 0.010 burned proportion...
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Starting in 2022, processing switched to the Collection 2 Landsat ARD data. Landsat Burned Area Products for 2022 based on Landsat Collection 2 data are available at: Hawbaker, T.J., Vanderhoof, M.K., Schimdt, G.L., and Picotte, J.P., 2023. The Landsat Collection 2 Burned Area Products for the conterminous United States, U.S. Geological Survey Data Release, https://doi.org/10.5066/P9F26LY6 The U.S. Geological Survey (USGS) has developed and implemented an algorithm that identifies burned areas in temporally-dense time series of Landsat Analysis Ready Data (ARD) scenes to produce the Landsat Burned Area Products. The algorithm makes use of predictors derived from individual ARD Landsat scenes, lagged reference...
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