Filters: partyWithName: U.S. Geological Survey (X) > Extensions: Raster (X)
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The whooping crane (Grus americana) is a listed, endangered species in North America, protected under federal legislation in the United States and Canada. The only self-sustaining and wild population of Whooping Cranes nests at and near Wood Buffalo National Park near the provincial border of Northwest Territories and Alberta, Canada. Birds from this population migrate through the Great Plains of North America and spend a nonbreeding period along the Gulf Coast of Texas at Aransas National Wildlife Refuge and surrounding lands. These data represent predictions from a resource selection function using GPS locations between 2010 and 2016 during migration. This surface is a composite of drought and non-drought conditions...
Categories: Data;
Types: Downloadable,
GeoTIFF,
Map Service,
Raster;
Tags: Kansas,
Migratory birds,
Montana,
Nebraska,
North Dakota,
The whooping crane is a listed endangered species in North America, protected under federal legislation in the United States and Canada. The only self-sustaining and wild population of Whooping Cranes nests at and near Wood Buffalo National Park near the provincial border of Northwest Territories and Alberta, Canada. Birds from this population migrate through the Great Plains of North America and winter along the Gulf Coast of Texas at Aransas National Wildlife Refuge and surrounding lands. These data represent predictions from a resource selection function using GPS locations between 2010 and 2016 during migration. This surface represents predictions under drought conditions across the study area. Pixel values...
Categories: Data;
Types: Downloadable,
GeoTIFF,
Map Service,
Raster;
Tags: Kansas,
Migratory birds,
Montana,
Nebraska,
North Dakota,
The whooping crane (Grus americana) is a listed, endangered species in North America, protected under federal legislation in the United States and Canada. The only self-sustaining and wild population of Whooping Cranes nests at and near Wood Buffalo National Park near the provincial border of Northwest Territories and Alberta, Canada. Birds from this population migrate through the Great Plains of North America and spend a nonbreeding period along the Gulf Coast of Texas at Aransas National Wildlife Refuge and surrounding lands. These data represent predictions from a resource selection function using GPS locations between 2010 and 2016 during migration. This surface is a composite of drought and non-drought conditions...
Categories: Data;
Types: Downloadable,
GeoTIFF,
Map Service,
Raster;
Tags: Kansas,
Migratory birds,
Montana,
Nebraska,
North Dakota,
Management and disturbances have significant effects on grassland forage production. When using satellite remote sensing to monitor climate impacts such as drought stress on annual forage production, minimizing these effects provides a clearer climate signal in the productivity data. The use of an ecosystem performance approach for assessment of seasonal and interannual climate impacts on forage production in semi-arid grasslands proved to be a successful method in a case study covering the Nebraska Sandhills. In this study we developed a time series (2000-2018) of the Expected Ecosystem Performance (EEP), which serves as a proxy for annual forage production after accounting for non-climatic influences, while minimizing...
Categories: Data;
Types: Downloadable,
GeoTIFF,
Map Service,
Raster;
Tags: Land Use Change,
NDVI,
USGS Science Data Catalog (SDC),
United States,
biomass,
These data were generated with MAXENT 3.3.3k freeware (Phillips et al. 2011) using climate data and fire probability data for for three time periods: reference (1900-1929), mid-century (2040-2069) and late century (2070-2099), and community occurrence point data extracted from LANDFIRE Environmental Site Potential (ESP). Future time period data are from three global climate models (GCMs): CGCM, GFDL, and HadCM3. In MAXENT, we used the logistic output format (generating presence probabilities between 0 and 1), a random test percentage of 30 (using 70 % of the occurrence points to generate the suitability model and 30 % of the occurrence points to validate it), and a jackknife test to measure variable importance....
Categories: Data;
Types: Downloadable,
GeoTIFF,
Map Service,
Raster;
Tags: Climate Change,
Drought, Fire and Extreme Weather,
Environmental Suitability Models,
Fire,
LANDFIRE,
These data were generated with MAXENT 3.3.3k freeware (Phillips et al. 2011) using climate data and fire probability data for for three time periods: reference (1900-1929), mid-century (2040-2069) and late century (2070-2099), and community occurrence point data extracted from LANDFIRE Environmental Site Potential (ESP). Future time period data are from three global climate models (GCMs): CGCM, GFDL, and HadCM3. In MAXENT, we used the logistic output format (generating presence probabilities between 0 and 1), a random test percentage of 30 (using 70 % of the occurrence points to generate the suitability model and 30 % of the occurrence points to validate it), and a jackknife test to measure variable importance....
Categories: Data;
Types: Downloadable,
GeoTIFF,
Map Service,
Raster;
Tags: Climate Change,
Drought, Fire and Extreme Weather,
Environmental Suitability Models,
Fire,
LANDFIRE,
These data were generated with MAXENT 3.3.3k freeware (Phillips et al. 2011) using climate data and fire probability data for for three time periods: reference (1900-1929), mid-century (2040-2069) and late century (2070-2099), and community occurrence point data extracted from LANDFIRE Environmental Site Potential (ESP). Future time period data are from three global climate models (GCMs): CGCM, GFDL, and HadCM3. In MAXENT, we used the logistic output format (generating presence probabilities between 0 and 1), a random test percentage of 30 (using 70 % of the occurrence points to generate the suitability model and 30 % of the occurrence points to validate it), and a jackknife test to measure variable importance....
Categories: Data;
Types: Downloadable,
GeoTIFF,
Map Service,
Raster;
Tags: Climate Change,
Drought, Fire and Extreme Weather,
Environmental Suitability Models,
Fire,
LANDFIRE,
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