Filters: partyWithName: U.S. Geological Survey - ScienceBase (X) > partyWithName: Geosciences and Environmental Change Science Center (X) > partyWithName: Todd J Hawbaker (X)
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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/...
Geospatial data were developed to characterize pre-fire biomass, burn severity, and biomass consumed for the Black Dragon Fire that burned in northern China in 1987. Pre-fire aboveground tree biomass (Mh/ha) raster data were derived by relating plot-level forest inventory data with pre-fire Landsat imagery from 1986 and 1987. Biomass data were generated for individual species: Dahurian larch (Larix gmelinii Rupr. Kuzen), white birch (Betula platyphylla Suk), aspen (Populus davidiana Dode and Populus suaveolens Fischer), and Mongolian Scots pine (Pinus sylvestris var. mongolica Litvinov). A raster layer of total aboveground tree biomass was also generated. Burned area was manually delineated using the normalized...
Plot-level field data were collected in the summer of 2014 to estimate aboveground and belowground biomass in the Great Dismal Swamp National Wildlife Refuge and Dismal Swamp State Park in North Carolina and Virginia. Data were collected at 85 plots. The location of the center of each plot was recorded with a Trimble ProXH global positioning system (GPS) and differentially corrected. Data files included 1: GDS_plots.csv, 2. GDS_FWD.csv, 3. GDS_LWD.csv, 4. GDS_Shrubs.csv, 5. GDS_Trees.csv, and 6. GDS_plot_summaries.csv. The data contained in GDS_plot_summaries.csv were calculated from the GDS_plots.csv, GDS_FWD.csv, GDS_LWD.csv, GDS_Shrubs.csv, GDS_Trees.csv files using the R statistical software environment (R Core...
Categories: Data;
Types: Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: Biomass,
Botany,
Cover,
Duff,
Ecology,
Interpretations of post-fire condition and rates of vegetation recovery can influence management priorities, actions, and perception of latent risks from landslides and floods. In this study, we used the Waldo Canyon fire (2012, Colorado Springs, Colorado, USA) as a case study to explore how a time series (2011-2016) of high-resolution images can be used to delineate burn extent and severity, as well as quantify post-fire vegetation recovery. We applied an object-based approach to map burn severity and vegetation recovery using Worldview-2, 3, and QuickBird-2 imagery. The burned area was classified as 51% high, 20% moderate and 29% low burn-severity. Across the burn extent, the shrub cover class showed a rapid recovery,...
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,
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 40-year period (1984-2023). 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...
Categories: Data;
Types: Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: Landsat,
USGS Science Data Catalog (SDC),
United States,
burned area,
fire frequency,
This data release provides output produced by a statistical, aridity threshold fire model for 11 extensively forested ecoregions in the western United States. We identified thresholds in fire-season climate water deficit (FSCWD) that distinguish years with limited, moderate, and extensive area burned for each ecoregion. We developed a new area burned model using these relationships and used it to simulate annual area burned using historical climate from 1980 - 2020 and output from global climate models (GCMs) from 1980 - 2099. The data release includes a comparison of mean annual FSCWD for 13 GCMs that we used to select five GCMs that bracket the range of conditions projected for the RCP 8.5 emissions scenario....
Landscape carbon (C) flux estimates are necessary for assessing the ability of terrestrial ecosystems to buffer further increases in anthropogenic carbon dioxide (CO2) emissions. Advances in remote sensing have allowed for coarse-scale estimates of gross primary productivity (GPP) (e.g., MODIS 17), yet efforts to assess spatial patterns in respiration lag behind those of GPP. Here, we demonstrate a method to predict growing season soil respiration at a regional scale in a forested ecosystem. We related field measurements (n=144) of growing season soil respiration across subalpine forests in the Southern Rocky Mountains ecoregion to a suite of biophysical predictors with a Random Forest model (30 m pixel size). We...
Post-fire shifts in vegetation composition will have broad ecological impacts. However, information characterizing post-fire recovery patterns and their drivers are lacking over large spatial extents. In this analysis we used Landsat imagery collected when snow cover (SCS) was present, in combination with growing season (GS) imagery, to distinguish evergreen vegetation from deciduous vegetation. We sought to (1) characterize patterns in the rate of post-fire, dual season Normalized Difference Vegetation Index (NDVI) across the region, (2) relate remotely sensed patterns to field-measured patterns of re-vegetation, and (3) identify seasonally-specific drivers of post-fire rates of NDVI recovery. Rates of post-fire...
This data release provides inputs needed to run the LANDIS-II landscape change model, NECN and Base Fire extensions for the Greater Yellowstone Ecosystem (GYE), USA, and simulation results that underlie figures and analysis in the accompanying publication. We ran LANDIS-II simulations for 112 years, from 1988-2100, using interpolated weather station data for 1988-2015 and downscaled output from 5 general circulation models (GCMs) for 2016-2100. We also included a control future scenario with years drawn from interpolated weather station data from 1980-2015. Model inputs include raster maps (250 × 250 m grid cells) of climate regions and tables of monthly temperature and precipitation for each climate region. We...
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...
Wildland-urban interface (WUI) maps identify areas with wildfire risk, but they are often outdated due to the lack of building data. Convolutional neural networks (CNNs) can extract building locations from remote sensing data, but their accuracy in WUI areas is unknown. Additionally, CNNs are computationally intensive and technically complex making it challenging for end-users, such as those who use or create WUI maps, to apply. We identified buildings pre- and post-wildfire and estimated building destruction for three California wildfires: Camp, Tubbs, and Woolsey. We used a CNN model from Esri to detect buildings from high-resolution imagery. This dataset represents the state-of-the-art of what is readily available...
Categories: Data;
Tags: Building footprint,
California,
Convolutional neural network,
Ecology,
Geography,
The wildland-urban interface (WUI) is the area where urban development occurs in close proximity to wildland vegetation. We generated WUI maps for the conterminous U.S. using building point locations (Carlson et al. 2022), offering higher spatial resolution compared to previously developed WUI maps based on U.S. Census Bureau housing density data (Radeloff et al., 2017). Building point locations were obtained from a Microsoft product released in 2018, which classified building footprints based on high-resolution satellite imagery. Maps were also based on wildland vegetation mapped by the 2016 National Land Cover Dataset (Yang et al., 2018). The mapping algorithm utilized definitions of the WUI from the U.S. Federal...
Categories: Data;
Tags: USGS Science Data Catalog (SDC),
United States,
habitat alteration and disturbance,
habitat fragmentation,
human impacts,
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....
Complete and accurate burned area map data are needed to document spatial and temporal patterns of fires, to quantify their drivers, and to assess the impacts on human and natural systems. In this study, we developed the Landsat Burned Area (BA) algorithm, an update from the Landsat Burned Area Essential Climate Variable (BAECV) algorithm. We present the BA algorithm and products, changes relative to the BAECV algorithm and products, and updated validation metrics. We also present spatial and temporal patterns of burned area across the conterminous U.S. and a comparison with other burned area datasets. The BA algorithm identifies burned areas in analysis ready data (ARD) time-series of Landsat imagery from 1984...
Categories: Data,
Data Release - Revised;
Tags: CONUS,
Landsat,
North America,
USGS Science Data Catalog (SDC),
United States,
Landsat Normalised Difference Vegetation Index (NDVI) is commonly used to monitor post-fire green-up; however, most studies do not distinguish new growth of conifer from deciduous or herbaceous species, despite potential consequences for local climate, carbon and wildlife. We found that dual season (growing and snow cover) NDVI improved our ability to distinguish conifer tree presence and density. We then examined the post-fire pattern (1984–2017) in Landsat NDVI for fires that occurred a minimum of 20 years ago (1986–1997). Points were classified into four categories depending on whether NDVI, 20 years post-fire, had returned to pre-fire values in only the growing season, only under snow cover, in both seasons...
The Department of the Interior (DOI) Office of Wildland Fire and USGS created the The Wildfire Hazard and Risk Assessment Inventory to meet the Monitoring, Maintenance, and Treatment Plan requirements under the Bipartisan Infrastructure Law (BIL). It provides an inventory of key national, regional, and state wildfire risk and fire hazard assessments useful for understanding different characterizations of fire risk. Some of the assessments may be useful for communicating contributions toward risk reduction of treatments funded by DOI, including investments under BIL. For each assessment, the inventory provides a description and information about the spatial extent, resolution, fire modeling approach, values considered...
This data release provides inputs needed to run the LANDIS PRO forest landscape model and the LINKAGES 3.0 ecosystem process model for the area burned by the Black Dragon Fire in northeast China in 1987, and simulation results that underlie figures and analysis in the accompanying publication. The data release includes the fire perimeter of Great Dragon Fire; input data for LINKAGES including soils, landtype, and climate data; initial conditions of stands in the study area before the Great Dragon Fire; and maps of LANDIS PRO output for each model grid cell including total trees, total biomass (Mg/ha), and tree density (trees/ha) in two-year timesteps.
Plot-level field data were collected in the summer of 2014 to estimate aboveground and belowground biomass in the Great Dismal Swamp National Wildlife Refuge and Dismal Swamp State Park in North Carolina and Virginia. Data were collected at 85 plots. The location of the center of each plot was recorded with a Trimble ProXH global positioning system (GPS) and differentially corrected. Data files included 1: GDS_plots.csv, 2. GDS_FWD.csv, 3. GDS_LWD.csv, 4. GDS_Shrubs.csv, 5. GDS_Trees.csv, and 6. GDS_plot_summaries.csv. The data contained in GDS_plot_summaries.csv were calculated from the GDS_plots.csv, GDS_FWD.csv, GDS_LWD.csv, GDS_Shrubs.csv, GDS_Trees.csv files using the R statistical software environment (R Core...
Categories: Data;
Types: Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: Biomass,
Botany,
Cover,
Duff,
Ecology,
This data release provides inputs needed to run the LANDIS PRO forest landscape model and the LINKAGES 3.0 ecosystem process model for the temperate-boreal ecotone Great Xing’an Mountains of northeastern China, and simulation results that underlie figures and analysis in the accompanying publication. The study compared the impacts of small and large fires on vegetation dynamics. The data release includes input data for LINKAGES including soils, landtype, and climate data; initial conditions of stands in the study area for LANDIS PRO; and maps of LANDIS PRO output for each model grid cell including total trees, total biomass (Mg/ha), and tree density (trees/ha) in ten-year timesteps. Output for four climate and fire...
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