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Folders: ROOT > ScienceBase Catalog > National and Regional Climate Adaptation Science Centers > North Central CASC > FY 2012 Projects > Integrating Climate and Biological Data into Management Decisions for the Greater Sage-Grouse and their Habitats ( Show direct descendants )
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ROOT _ScienceBase Catalog __National and Regional Climate Adaptation Science Centers ___North Central CASC ____FY 2012 Projects _____Integrating Climate and Biological Data into Management Decisions for the Greater Sage-Grouse and their Habitats Filters
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This landcover raster was generated through a Random Forest predictive model developed in R using a combination of image-derived and ancillary variables, and field-derived training points grouped into 18 classes. Overall accuracy, generated internally through bootstrapping, was 75.5%. A series of post-modeling steps brought the final number of land cover classes to 28.
Categories: Data;
Types: Citation;
Tags: Birds,
CMR,
Charles M. Russell National Wildlife Refuge,
Data Visualization & Tools,
Landcover,
Training points collected in the field between 2012 and 2013 were grouped into 18 classes: Forested Burn (66), Foothill Woodland Steppe Transition (73), Greasewood Flat (73), Greasewood Steppe (239), Greasewood Sage Steppe (277), Great Plains Badlands (166), Great Plains Riparian (255), Low Density Sage Steppe (776), Medium Density Sage Steppe (783), Mixed Grass Prairie (555), Mixed Grass Prairie Burned (278), Ponderosa Pine Woodland and Shrubland (512), Riparian Floodplain (223), Semi-Desert Grassland (103), Sparsely Vegetated Mixed Shrub (252), Silver Sage Flat (70) , Silver Sage Steppe (64), and Water (246). When insufficient field data were available for a class, we augmented it through photointerpretation of...
Categories: Data;
Types: Citation;
Tags: Birds,
Charles M. Russel Wildlife Refuge,
Data Visualization & Tools,
North Central CASC,
Science Tools For Managers,
This landcover raster was generated through a Random Forest predictive model developed in R using a combination of image-derived and ancillary variables, and field-derived training points grouped into 18 classes. Overall accuracy, generated internally through bootstrapping, was 72.7%. A series of post-modeling steps brought the final number of land cover classes to 28.
Categories: Data;
Types: Citation;
Tags: Birds,
CMR,
Charles M. Russell National Wildlife Refuge,
Data Visualization & Tools,
Landcover,
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