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This dataset is a raster of predicted suitable bioclimate using statistical correlations between known habitat and current climate (1950-1999 average) , and then projecting that niche into the future. The future timeslices used are 2020's, which is an average of 2020-2029, and 2050's which is 2050-2059. The Values 1-6 show the degree of model agreement (For example: areas with a value of 1 is where only 1 GCM predicted suitability; pixels with a value of 6 are where 6 GCMs predicted suitability, ect). *see Maxent output pdfs for more details about model inputs and settings.
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This dataset is a raster of predicted suitable bioclimate using statistical correlations between known habitat and current climate (1950-1999 average) , and then projecting that niche into the future. The future timeslices used are 2020's, which is an average of 2020-2029, and 2050's which is 2050-2059. The Values 1-6 show the degree of model agreement (For example: areas with a value of 1 is where only 1 GCM predicted suitability; pixels with a value of 6 are where 6 GCMs predicted suitability, ect). *see Maxent output pdfs for more details about model inputs and settings.
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This dataset is a raster of predicted suitable bioclimate using statistical correlations between known habitat and current climate (1950-1999 average) , and then projecting that niche into the future. The future timeslices used are 2020's, which is an average of 2020-2029, and 2050's which is 2050-2059. The Values 1-6 show the degree of model agreement (For example: areas with a value of 1 is where only 1 GCM predicted suitability; pixels with a value of 6 are where 6 GCMs predicted suitability, ect). *see Maxent output pdfs for more details about model inputs and settings.
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This dataset is a raster of predicted suitable bioclimate using statistical correlations between known habitat and current climate (1950-1999 average) , and then projecting that niche into the future. The future timeslices used are 2020's, which is an average of 2020-2029, and 2050's which is 2050-2059. The Values 1-6 show the degree of model agreement (For example: areas with a value of 1 is where only 1 GCM predicted suitability; pixels with a value of 6 are where 6 GCMs predicted suitability, ect). *see Maxent output pdfs for more details about model inputs and settings.
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Rocky Mountain Research Station scientists initiated a study in the 1990s on avian distribution and habitat associations within the Sky Islands. By re-measuring vegetation and bird populations following wildfires and applying climate change models, they will assess the singular and synergistic effects of climate change and wildfire and provide strategies for managing resilient forests and conserving the avian community structure. They will also continue and expand citizen science efforts to develop a long term avian monitoring plan, as well as simulation studies to provide optimal monitoring designs for avian species to detect changes from large-scale stressors.
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 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. The scene-level products are available through https://earthexplorer.usgs.gov. Annual composite products were derived from the scene level products. Prior to generating...
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This Data Release summarizes measurements of hydraulic and physical properties of soils and ash at sites in the area impacted by the 2017 Thomas Fire, USA. Physical properties include dry bulk density, loss on ignition, and saturated soil water content. Hydraulic properties include field-saturated hydraulic conductivity, sorptivity, Green-Ampt wetting front potential, and soil water retention. These measurements provide a foundation to reduce uncertainty of parameters in hydrologic models used to predict water-related hazards, water quality, and water quantity. Note that all methods of data acquisition and processing, column headings, and data annotations are explained in the metadata files.


map background search result map search result map Assessing Large-Scale Effects of Wildfire and Climate Change on Avian Communities and Habitats in the Sky Islands, Arizona van Genuchten parameters for soil in the area impacted by the 2017 Thomas Fire in California, USA BLM REA CBR 2010 Modeled Future Bioclimate 2050 - Rocky Mountain Aspen Forest Woodland BLM REA CBR 2010 Modeled Future Bioclimate 2050 - Inter-Mountain Basins Sagebrush Shrubland BLM REA CBR 2010 Modeled Future Bioclimate 2050 - Mojave Mid Elevation Mixed Desert Scrub BLM REA CBR 2010 Modeled Future Bioclimate 2050 - Greater Sage Grouse Landsat Burned Area Products Data Release - Landsat 8 OLI/TIRS products van Genuchten parameters for soil in the area impacted by the 2017 Thomas Fire in California, USA Assessing Large-Scale Effects of Wildfire and Climate Change on Avian Communities and Habitats in the Sky Islands, Arizona BLM REA CBR 2010 Modeled Future Bioclimate 2050 - Rocky Mountain Aspen Forest Woodland BLM REA CBR 2010 Modeled Future Bioclimate 2050 - Inter-Mountain Basins Sagebrush Shrubland BLM REA CBR 2010 Modeled Future Bioclimate 2050 - Mojave Mid Elevation Mixed Desert Scrub BLM REA CBR 2010 Modeled Future Bioclimate 2050 - Greater Sage Grouse Landsat Burned Area Products Data Release - Landsat 8 OLI/TIRS products