Filters: Contacts: Joshua J Picotte (X)
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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...
Categories: Data,
Data Release - Revised;
Tags: Burned area,
Landsat,
Prescribed fire,
USGS Science Data Catalog (SDC),
United States,
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...
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).
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...
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).
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).
Wildfires and prescribed fires are frequent but under-mapped across wetlands of the southeastern United States . High annual precipitation supports rapid post-fire recovery of wetland vegetation, while associated cloud cover limits clear-sky observations. In addition, the low burn severity of prescribed fires and spectral confusion between fluctuating water levels and burned areas have resulted in wetland burned area being chronically under-estimated across the region. In this analysis, we first quantify the increase in clear-sky observations by using Sentinel-2 in addition to Landsat 8. We then present an approach using the Sentinel-2 archive (2016-2019) to train a wetland burned area algorithm at 20 m resolution....
These data provide on the ground estimates of burn severity as estimated by the Composite Burn Index (CBI). Data were collected between 1996 and 2018 for fires that burned during this time period. Landsat imagery was used to develop regression relationships between the Normalized Burn Ratio (NBR) and differenced NBR (dNBR).
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