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These data were used to quantify land area change in a wetlands possible zone of coastal wetlands during a 1985-2020 observation period. The datasets presented in this data release represent annual median estimates of the fractional amount of land, floating aquatic vegetation, submerged aquatic vegetation, and water per Landsat pixel. These data are intended for coarse-scale analysis of wetland change area. The datasets are summarized by 10-digit Hydrologic Unit Code (HUC10), and land area change through time is fit using a penalized regression smooth spline. The trends are therefore generalized in time and are intended to present coarse scale observations of trends in wetland area change.
These data were used to quantify land area change in a wetlands possible zone of coastal wetlands during a 1985-2020 observation period. The datasets presented in this data release represent annual median estimates of the fractional amount of land, floating aquatic vegetation, submerged aquatic vegetation, and water per Landsat pixel. These data are intended for coarse-scale analysis of wetland change area. The datasets are summarized by 10-digit Hydrologic Unit Code (HUC10), and land area change through time is fit using a penalized regression smooth spline. The trends are therefore generalized in time and are intended to present coarse scale observations of trends in wetland area change.
These data were used to quantify land area change in a wetlands possible zone of coastal wetlands during a 1985-2020 observation period. The datasets presented in this data release represent annual median estimates of the fractional amount of land, floating aquatic vegetation, submerged aquatic vegetation, and water per Landsat pixel. These data are intended for coarse-scale analysis of wetland change area. The datasets are summarized by 10-digit Hydrologic Unit Code (HUC10), and land area change through time is fit using a penalized regression smooth spline. The trends are therefore generalized in time and are intended to present coarse scale observations of trends in wetland area change.
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Prescribed burning is a critical tool for managing wildfire risks and meeting ecological objectives, but its safe and effective application requires that specific meteorological criteria are met. This dataset contains results from a study examining the potential impacts of projected climatic change on prescribed burning in the southeastern United States. A set of burn window criteria (suitable weather conditions within which burning may occur based on maximum daily temperature, daily average relative humidity, and daily average wind speed), were applied to projections from an ensemble of Global Climate Models (GCM) under two greenhouse gas emission scenarios, as well as past observations for comparison. Data are...
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Prescribed burning is a critical tool for managing wildfire risks and meeting ecological objectives, but its safe and effective application requires that specific meteorological criteria are met. This dataset contains results from a study examining the potential impacts of projected climatic change on prescribed burning in the southeastern United States. A set of burn window criteria (suitable weather conditions within which burning may occur based on maximum daily temperature, daily average relative humidity, and daily average wind speed), were applied to projections from an ensemble of Global Climate Models (GCM) under two greenhouse gas emission scenarios, as well as past observations for comparison. Data are...
These datasets were created from high-resolution (1-m) datasets representing median conditions during a 2014-2019 time period. These datasets used National Agricultural Inventory Program (NAIP) imagery, as well as Sentinel-2 satellite imagery, to estimate the fractional composition of unvegetated, vegetated, and water in each pixel. Random samples from these high resolution datasets were used to inform calibration and validation of the moderate resolution (30-m) Landsat datasets. To facilitate comparability with the Landsat datasets, these data were aggregated up to 30-m resolution.
These data were used to quantify land area change in a wetlands possible zone of coastal wetlands during a 1985-2020 observation period. The datasets presented in this data release represent annual median estimates of the fractional amount of land, floating aquatic vegetation, submerged aquatic vegetation, and water per Landsat pixel. These data are intended for coarse-scale analysis of wetland change area. The datasets are summarized by 10-digit Hydrologic Unit Code (HUC10), and land area change through time is fit using a penalized regression smooth spline. The trends are therefore generalized in time and are intended to present coarse scale observations of trends in wetland area change.
These data were used to quantify land area change in a wetlands possible zone of coastal wetlands during a 1985-2020 observation period. The datasets presented in this data release represent annual median estimates of the fractional amount of land, floating aquatic vegetation, submerged aquatic vegetation, and water per Landsat pixel. These data are intended for coarse-scale analysis of wetland change area. The datasets are summarized by 10-digit Hydrologic Unit Code (HUC10), and land area change through time is fit using a penalized regression smooth spline. The trends are therefore generalized in time and are intended to present coarse scale observations of trends in wetland area change.
These data were used to quantify land area change in a wetlands possible zone of coastal wetlands during a 1985-2020 observation period. The datasets presented in this data release represent annual median estimates of the fractional amount of land, floating aquatic vegetation, submerged aquatic vegetation, and water per Landsat pixel. These data are intended for coarse-scale analysis of wetland change area. The datasets are summarized by 10-digit Hydrologic Unit Code (HUC10), and land area change through time is fit using a penalized regression smooth spline. The trends are therefore generalized in time and are intended to present coarse scale observations of trends in wetland area change.
This U.S. Geological Survey (USGS) data release represents geospatial data that are the beach mouse presence outputs from the Biological Objectives for the Gulf Coast Project’s Beach Mice Bayesian network model. The USGS partnered with the U.S. Fish and Wildlife Service (USFWS), the Florida Fish and Wildlife Conservation Commission, and their conservation partners to develop a Bayesian Network model that predicts the annual probability of beach mouse presence at a local (30-m) scale. The model was used to predict the annual probability of presence across a portion of the USFWS's Central Gulf and Florida Panhandle Coast Biological Planning Unit. This spatial extent included critical habitat for three endangered sub-species...
Clean water is important for a variety of uses, including drinking, recreation, and as habitat for aquatic species. Nonpoint-source pollution, such as nutrients, sediment, and pesticides from agricultural runoff, is a major cause of impaired water quality in the United States . Vegetation and soil in natural land cover help to remove pollutants from runoff water before it reaches streams and other waterways by slowing water flow and physically trapping sediment. To assess the spatial distribution of water purification potential in the southeastern United States, we mapped the demand for purification as the total area of agricultural land.
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Prescribed burning is a critical tool for managing wildfire risks and meeting ecological objectives, but its safe and effective application requires that specific meteorological criteria are met. This dataset contains results from a study examining the potential impacts of projected climatic change on prescribed burning in the southeastern United States. A set of burn window criteria (suitable weather conditions within which burning may occur based on maximum daily temperature, daily average relative humidity, and daily average wind speed), were applied to projections from an ensemble of Global Climate Models (GCM) under two greenhouse gas emission scenarios, as well as past observations for comparison. Data are...
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Prescribed burning is a critical tool for managing wildfire risks and meeting ecological objectives, but its safe and effective application requires that specific meteorological criteria are met. This dataset contains results from a study examining the potential impacts of projected climatic change on prescribed burning in the southeastern United States. A set of burn window criteria (suitable weather conditions within which burning may occur based on maximum daily temperature, daily average relative humidity, and daily average wind speed), were applied to projections from an ensemble of Global Climate Models (GCM) under two greenhouse gas emission scenarios, as well as past observations for comparison. Data are...
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Prescribed burning is a critical tool for managing wildfire risks and meeting ecological objectives, but its safe and effective application requires that specific meteorological criteria are met. This dataset contains results from a study examining the potential impacts of projected climatic change on prescribed burning in the southeastern United States. A set of burn window criteria (suitable weather conditions within which burning may occur based on maximum daily temperature, daily average relative humidity, and daily average wind speed), were applied to projections from an ensemble of Global Climate Models (GCM) under two greenhouse gas emission scenarios, as well as past observations for comparison. Data are...
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Prescribed burning is a critical tool for managing wildfire risks and meeting ecological objectives, but its safe and effective application requires that specific meteorological criteria are met. This dataset contains results from a study examining the potential impacts of projected climatic change on prescribed burning in the southeastern United States. A set of burn window criteria (suitable weather conditions within which burning may occur based on maximum daily temperature, daily average relative humidity, and daily average wind speed), were applied to projections from an ensemble of Global Climate Models (GCM) under two greenhouse gas emission scenarios, as well as past observations for comparison. Data are...
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Prescribed burning is a critical tool for managing wildfire risks and meeting ecological objectives, but its safe and effective application requires that specific meteorological criteria are met. This dataset contains results from a study examining the potential impacts of projected climatic change on prescribed burning in the southeastern United States. A set of burn window criteria (suitable weather conditions within which burning may occur based on maximum daily temperature, daily average relative humidity, and daily average wind speed), were applied to projections from an ensemble of Global Climate Models (GCM) under two greenhouse gas emission scenarios, as well as past observations for comparison. Data are...
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Prescribed burning is a critical tool for managing wildfire risks and meeting ecological objectives, but its safe and effective application requires that specific meteorological criteria are met. This dataset contains results from a study examining the potential impacts of projected climatic change on prescribed burning in the southeastern United States. A set of burn window criteria (suitable weather conditions within which burning may occur based on maximum daily temperature, daily average relative humidity, and daily average wind speed), were applied to projections from an ensemble of Global Climate Models (GCM) under two greenhouse gas emission scenarios, as well as past observations for comparison. Data are...
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Prescribed burning is a critical tool for managing wildfire risks and meeting ecological objectives, but its safe and effective application requires that specific meteorological criteria are met. This dataset contains results from a study examining the potential impacts of projected climatic change on prescribed burning in the southeastern United States. A set of burn window criteria (suitable weather conditions within which burning may occur based on maximum daily temperature, daily average relative humidity, and daily average wind speed), were applied to projections from an ensemble of Global Climate Models (GCM) under two greenhouse gas emission scenarios, as well as past observations for comparison. Data are...
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Prescribed burning is a critical tool for managing wildfire risks and meeting ecological objectives, but its safe and effective application requires that specific meteorological criteria are met. This dataset contains results from a study examining the potential impacts of projected climatic change on prescribed burning in the southeastern United States. A set of burn window criteria (suitable weather conditions within which burning may occur based on maximum daily temperature, daily average relative humidity, and daily average wind speed), were applied to projections from an ensemble of Global Climate Models (GCM) under two greenhouse gas emission scenarios, as well as past observations for comparison. Data are...
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Prescribed burning is a critical tool for managing wildfire risks and meeting ecological objectives, but its safe and effective application requires that specific meteorological criteria are met. This dataset contains results from a study examining the potential impacts of projected climatic change on prescribed burning in the southeastern United States. A set of burn window criteria (suitable weather conditions within which burning may occur based on maximum daily temperature, daily average relative humidity, and daily average wind speed), were applied to projections from an ensemble of Global Climate Models (GCM) under two greenhouse gas emission scenarios, as well as past observations for comparison. Data are...


map background search result map search result map A NAIP and Sentinel-2 based quantification of fractional composition of unvegetated, vegetated, and water in the Gulf of Mexico Coast, 2014-2019 used for calibration and validation of Landsat based datasets L5_1989_GOM_Fractional_Land_FAV_SAV_Water L5_1995_GOM_Fractional_Land_FAV_SAV_Water L5_1999_GOM_Fractional_Land_FAV_SAV_Water L5_2000_GOM_Fractional_Land_FAV_SAV_Water L8_2013_GOM_Fractional_Land_FAV_SAV_Water L8_2016_GOM_Fractional_Land_FAV_SAV_Water Seasonal Future Prescribed Burn Windows for the Southeast United States - June-August 2010-2099 RCP 4.5 BNU Monthly Future Prescribed Burn Windows for the Southeast United States 2010-2099 RCP 4.5 CNRM Monthly Future Prescribed Burn Windows for the Southeast United States 2010-2099 RCP 4.5 GFDLM Monthly Future Prescribed Burn Windows for the Southeast United States 2010-2099 RCP 4.5 MRI Monthly Future Prescribed Burn Windows for the Southeast United States 2010-2099 RCP 4.5 CSIRO Monthly Future Prescribed Burn Windows for the Southeast United States 2010-2099 RCP 8.5 IPSL-CM5A-MR Monthly Future Prescribed Burn Windows for the Southeast United States 2010-2099 RCP 8.5 BNU Historical Prescribed Burn Windows for the Southeast United States 1950-1999 HADSE Historical Prescribed Burn Windows for the Southeast United States 1950-1999 MIROCESM Historical Prescribed Burn Windows for the Southeast United States 1950-1999 Seasonal Future Prescribed Burn Windows for the Southeast United States - December-February 2010-2099 RCP 8.5 L5_1989_GOM_Fractional_Land_FAV_SAV_Water L5_1995_GOM_Fractional_Land_FAV_SAV_Water L5_1999_GOM_Fractional_Land_FAV_SAV_Water L5_2000_GOM_Fractional_Land_FAV_SAV_Water L8_2013_GOM_Fractional_Land_FAV_SAV_Water L8_2016_GOM_Fractional_Land_FAV_SAV_Water A NAIP and Sentinel-2 based quantification of fractional composition of unvegetated, vegetated, and water in the Gulf of Mexico Coast, 2014-2019 used for calibration and validation of Landsat based datasets Seasonal Future Prescribed Burn Windows for the Southeast United States - June-August 2010-2099 RCP 4.5 BNU Monthly Future Prescribed Burn Windows for the Southeast United States 2010-2099 RCP 4.5 CNRM Monthly Future Prescribed Burn Windows for the Southeast United States 2010-2099 RCP 4.5 GFDLM Monthly Future Prescribed Burn Windows for the Southeast United States 2010-2099 RCP 4.5 MRI Monthly Future Prescribed Burn Windows for the Southeast United States 2010-2099 RCP 4.5 CSIRO Monthly Future Prescribed Burn Windows for the Southeast United States 2010-2099 RCP 8.5 IPSL-CM5A-MR Monthly Future Prescribed Burn Windows for the Southeast United States 2010-2099 RCP 8.5 BNU Historical Prescribed Burn Windows for the Southeast United States 1950-1999 HADSE Historical Prescribed Burn Windows for the Southeast United States 1950-1999 MIROCESM Historical Prescribed Burn Windows for the Southeast United States 1950-1999 Seasonal Future Prescribed Burn Windows for the Southeast United States - December-February 2010-2099 RCP 8.5