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U.S Geological Survey (USGS) scientists conducted field data collection efforts during the time periods of September 5 - 14, 2018, November 8 - 13, 2018, June 18 - 27, 2019, July 30 - August 8, 2019, September 13 - 19, 2019, and June 23 - July 1, 2020. These efforts used a combination of technologies to map twenty burned and twelve unburned forest plots at eleven sites in the Black Hills of South Dakota. Twelve burned plots at five sites and nine unburned plots at two sites are located within Custer State Park, five burned plots are located on private land adjacent to Custer State Park at two sites, three unburned plots are located at one site near Hazelrodt Picnic Area in the Black Hills National Forest, and three...
Tags: Black Hills,
Black Hills National Forest,
Burn Severity,
Custer County,
Custer State Park, All tags...
Differenced Normalized Burn Ratio,
EROS,
Earth Resources Observation and Science Center,
Ecology,
Fire Science,
Forest Structure,
Forestry,
Fuels,
GBL,
GNSS,
Geography,
Global Navigational Satellite System,
Ground Based Lidar,
Land Use Change,
Legion Lake,
Legion Lake Fire,
Lidar,
Light Detection and Ranging,
MTBS,
Monitoring Trends in Burn Severity,
NBR,
Normalized Burn Ratio,
Remote Sensing,
State of South Dakota,
TLS,
Terrestrial Laser Scanner,
Total Station,
U.S. Geological Survey,
USGS,
USGS Science Data Catalog (SDC),
Vegetation Structure,
Wildland Fire,
Wind Cave National Park,
biota,
dNBR,
elevation,
geoscientificInformation,
location, 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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These data provide on-the-ground estimates of burn severity as estimated by the Composite Burn Index (CBI) for fires that burned between 1994 and 2018. Landsat imagery was subsequently used to develop regression relationships between the Normalized Burn Ratio (NBR) and differenced NBR (dNBR).
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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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These data provide on-the-ground estimates of burn severity as estimated by the Composite Burn Index (CBI) for fires that burned between 1994 and 2018. Landsat imagery was subsequently used to develop regression relationships between the Normalized Burn Ratio (NBR) and differenced NBR (dNBR).
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