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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...
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.
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.
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...
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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...
thumbnail
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 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.


map background search result map search result map L5_1986_GOM_Fractional_Land_FAV_SAV_Water L5_1987_GOM_Fractional_Land_FAV_SAV_Water L5_1988_GOM_Fractional_Land_FAV_SAV_Water L5_1994_GOM_Fractional_Land_FAV_SAV_Water L5_1998_GOM_Fractional_Land_FAV_SAV_Water L5_2005_GOM_Fractional_Land_FAV_SAV_Water_pre_Hurricanes_Katrina_Rita L5_2005_GOM_Fractional_Land_FAV_SAV_Water_post_Hurricanes_Katrina_Rita L5_2009_GOM_Fractional_Land_FAV_SAV_Water L8_2019_GOM_Fractional_Land_FAV_SAV_Water L5_2006_GOM_Fractional_Land_FAV_SAV_Water Seasonal Future Prescribed Burn Windows for the Southeast United States - September-November 2010-2099 RCP 4.5 INMCM Monthly Future Prescribed Burn Windows for the Southeast United States 2010-2099 RCP 4.5 MIROC Monthly Future Prescribed Burn Windows for the Southeast United States 2010-2099 RCP 8.5 MIROCESMCHM Monthly Future Prescribed Burn Windows for the Southeast United States 2010-2099 RCP 8.5 HADCC Historical Prescribed Burn Windows for the Southeast United States 1950-1999 INMCM Historical Prescribed Burn Windows for the Southeast United States 1950-1999 IPSL-CM5A-MR Historical Prescribed Burn Windows for the Southeast United States 1950-1999 MRI Historical Prescribed Burn Windows for the Southeast United States 1950-1999 Seasonal Future Prescribed Burn Windows for the Southeast United States - June-August 2010-2099 RCP 8.5 Seasonal Future Prescribed Burn Windows for the Southeast United States - September-November 2010-2099 RCP 8.5 L5_1986_GOM_Fractional_Land_FAV_SAV_Water L5_1987_GOM_Fractional_Land_FAV_SAV_Water L5_1988_GOM_Fractional_Land_FAV_SAV_Water L5_1994_GOM_Fractional_Land_FAV_SAV_Water L5_1998_GOM_Fractional_Land_FAV_SAV_Water L5_2005_GOM_Fractional_Land_FAV_SAV_Water_pre_Hurricanes_Katrina_Rita L5_2005_GOM_Fractional_Land_FAV_SAV_Water_post_Hurricanes_Katrina_Rita L5_2009_GOM_Fractional_Land_FAV_SAV_Water L8_2019_GOM_Fractional_Land_FAV_SAV_Water L5_2006_GOM_Fractional_Land_FAV_SAV_Water Seasonal Future Prescribed Burn Windows for the Southeast United States - September-November 2010-2099 RCP 4.5 INMCM Monthly Future Prescribed Burn Windows for the Southeast United States 2010-2099 RCP 4.5 MIROC Monthly Future Prescribed Burn Windows for the Southeast United States 2010-2099 RCP 8.5 MIROCESMCHM Monthly Future Prescribed Burn Windows for the Southeast United States 2010-2099 RCP 8.5 HADCC Historical Prescribed Burn Windows for the Southeast United States 1950-1999 INMCM Historical Prescribed Burn Windows for the Southeast United States 1950-1999 IPSL-CM5A-MR Historical Prescribed Burn Windows for the Southeast United States 1950-1999 MRI Historical Prescribed Burn Windows for the Southeast United States 1950-1999 Seasonal Future Prescribed Burn Windows for the Southeast United States - June-August 2010-2099 RCP 8.5 Seasonal Future Prescribed Burn Windows for the Southeast United States - September-November 2010-2099 RCP 8.5