These raster images represent continuous surfaces of estimated land cover types for the western Kenai Peninsula circa 1973, circa 2002, and circa 2017. This raster image represents the coded series of estimated land cover types for each pixel over the three distinct time periods. The estimated land cover types (Needleleaf Forest, Mixed Forest, Broadleaf Forest, Herbaceous, Wetland, Alpine, Barren, Shrub, Water) were originally derived from a random forest classifier executed in R (version 3.5.0). Predictor variables from training data included known landcover types deduced from high resolution aerial imagery, summer and winter spectral indices obtained from historical Landsat scenes, and topographic parameters derived from a digital elevation model. For each era (c. 1973, c. 2002, and c. 2017) 3,600 training points (400 points for each land cover type) were randomly distributed within training areas and training areas were opportunistically distributed to capture the regional and geomorphic extent of each land cover type to the extent possible given availability of aerial imagery. Each training point was assigned feature list values from the Landsat mosaics and digital elevation model while land cover was manually interpreted using high-resolution areal imagery. Model output included predicted landcover type and a corresponding probability score and were rasterized for each era – one raster image featuring land cover types and one raster image featuring land cover type probability. This raster image characterizes a coded series that represents the unique combinations (or series) of vegetation types that pixels experienced over time. Each cell's series value corresponds to a series of numbers (#_#_#) where the first number is the vegetation code from 1973, the middle number is the vegetation code from 2002 and the last number is the vegetation code from 2017. Raster values (1-687) can be translated into series using the vegetation_code_series_table.csv. Series numbers can be translated into vegetation types using the vegetation_class_table.csv. The code series raster image allows for the graphical depiction of certain landcover change types such as deforestation (e.g. Forest_Forest_Shrub) and has 30 meter resolution.

These raster images represent continuous surfaces of estimated land cover types for the western Kenai Peninsula circa 1973, circa 2002, and circa 2017. This raster image represents the coded series of estimated land cover types for each pixel over the three distinct time periods. The estimated land cover types (Needleleaf Forest, Mixed Forest, Broadleaf Forest, Herbaceous, Wetland, Alpine, Barren, Shrub, Water) were originally derived from a random forest classifier executed in R (version 3.5.0). Predictor variables from training data included known landcover types deduced from high resolution aerial imagery, summer and winter spectral indices obtained from historical Landsat scenes, and topographic parameters derived from a digital elevation model. For each era (c. 1973, c. 2002, and c. 2017) 3,600 training points (400 points for each land cover type) were randomly distributed within training areas and training areas were opportunistically distributed to capture the regional and geomorphic extent of each land cover type to the extent possible given availability of aerial imagery. Each training point was assigned feature list values from the Landsat mosaics and digital elevation model while land cover was manually interpreted using high-resolution areal imagery. Model output included predicted landcover type and a corresponding probability score and were rasterized for each era – one raster image featuring land cover types and one raster image featuring land cover type probability. This raster image characterizes a coded series that represents the unique combinations (or series) of vegetation types that pixels experienced over time. Each cell's series value corresponds to a series of numbers (#_#_#) where the first number is the vegetation code from 1973, the middle number is the vegetation code from 2002 and the last number is the vegetation code from 2017. Raster values (1-687) can be translated into series using the vegetation_code_series_table.csv. Series numbers can be translated into vegetation types using the vegetation_class_table.csv. The code series raster image allows for the graphical depiction of certain landcover change types such as deforestation (e.g. Forest_Forest_Shrub) and has 30 meter resolution.

This geodatabase contains three (3) rasters, two (2) of which represent landcover for Selawik National Wildlife Refuge and surrounding areas. The third raster contains plot-based ground characteristics for pixels classified with high confidence. The two landcover rasters contain attribute information for soils, vegetation, and ecotypes; and differ slightly in their classifications because one encompasses a broader geographic area (lc_arcn), and therefore some classes are more generalized than in the other (lc_nokose). The classification of local-scale ecosystems (ecotypes) combines physiography (e.g., riverine, coastal), topography (DEM), geology and vegetation from the landcover spectral database derived from the satellite image processing. These layers are used to model ecotypes in a way that best partitions geomorphic, hydrologic, pedologic, and vegetative characteristics. Map projection: Albers Alaska, NAD 83, meters. ***NOTE*** The lc_nokose raster was used for the landcover classifications in the final report, as it is more specific to Selawik National Wildlife Refuge than the other landcover raster (lc_arcn), which includes some surrounding National Park Service lands and differs slightly in its classifications at the pixel level. PDF maps are provided here for reference to help visualize what the data look like before downloading. Full resolution maps can be viewed in the final report (ServCat #49603).

This geodatabase contains three (3) rasters, two (2) of which represent landcover for Selawik National Wildlife Refuge and surrounding areas. The third raster contains plot-based ground characteristics for pixels classified with high confidence. The two landcover rasters contain attribute information for soils, vegetation, and ecotypes; and differ slightly in their classifications because one encompasses a broader geographic area (lc_arcn), and therefore some classes are more generalized than in the other (lc_nokose). The classification of local-scale ecosystems (ecotypes) combines physiography (e.g., riverine, coastal), topography (DEM), geology and vegetation from the landcover spectral database derived from the satellite image processing. These layers are used to model ecotypes in a way that best partitions geomorphic, hydrologic, pedologic, and vegetative characteristics. Map projection: Albers Alaska, NAD 83, meters. ***NOTE*** The lc_nokose raster was used for the landcover classifications in the final report, as it is more specific to Selawik National Wildlife Refuge than the other landcover raster (lc_arcn), which includes some surrounding National Park Service lands and differs slightly in its classifications at the pixel level. PDF maps are provided here for reference to help visualize what the data look like before downloading. Full resolution maps can be viewed in the final report (ServCat #49603).

These raster images represent continuous surfaces of estimated land cover types for the western Kenai Peninsula circa 1973, circa 2002, and circa 2017. The estimated land cover types (Needleleaf Forest, Mixed Forest, Broadleaf Forest, Herbaceous, Wetland, Alpine, Barren, Shrub, Water) were derived from a random forest classifier executed in R (version 3.5.0). Predictor variables from training data included known landcover types deduced from high resolution aerial imagery, summer and winter spectral indices obtained from historical Landsat scenes, and topographic parameters derived from a digital elevation model. For each era (c. 1973, c. 2002, and c. 2017) 3,600 training points (400 points for each land cover type) were randomly distributed within training areas and training areas were opportunistically distributed to capture the regional and geomorphic extent of each land cover type to the extent possible given availability of aerial imagery. Each training point was assigned feature list values from the Landsat mosaics and digital elevation model while land cover was manually interpreted using high-resolution areal imagery. Model output includes predicted landcover types and was rasterized for each era to illustrate the classification of landcover.

These raster images represent continuous surfaces of estimated land cover types for the western Kenai Peninsula circa 1973, circa 2002, and circa 2017. The estimated land cover types (Needleleaf Forest, Mixed Forest, Broadleaf Forest, Herbaceous, Wetland, Alpine, Barren, Shrub, Water) were derived from a random forest classifier executed in R (version 3.5.0). Predictor variables from training data included known landcover types deduced from high resolution aerial imagery, summer and winter spectral indices obtained from historical Landsat scenes, and topographic parameters derived from a digital elevation model. For each era (c. 1973, c. 2002, and c. 2017) 3,600 training points (400 points for each land cover type) were randomly distributed within training areas and training areas were opportunistically distributed to capture the regional and geomorphic extent of each land cover type to the extent possible given availability of aerial imagery. Each training point was assigned feature list values from the Landsat mosaics and digital elevation model while land cover was manually interpreted using high-resolution areal imagery. Model output included predicted landcover types and was rasterized for each era to illustrate the classification of landcover. Raster values (0-9) for each era can be translated into vegetation type using the vegetation_class_table.csv. For the 1973 era, these raster images are at a 60 meter resolution. For the 2002 and 2017 era, raster image resolution is 30 meters.

This dataset contains annual land use/cover (LUC) maps at 30 m resolution across Mawas, Central Kalimantan, Indonesia. There are six files, each representing a five-year interval over the period 1994-2019. An additional file for 2015 was created for accuracy assessment. A high-quality and low-cloud coverage image from Landsat 5 or Landsat 8 over each 5-year period was selected or composited for the January-August timeframe. Investigators used their knowledge to manually identify training polygons in these images for five LUC classes: peat swamp forest, tall shrubs/ secondary forest, low shrubs/ferns/grass, urban/bare land/open flooded areas, and river. Pixel values of Landsat Tier 1 surface reflectance products and selected indices were extracted for each LUC and used to predict LUC classes across the Mawas study area using the Classification and Regression Trees (CART) method. These data can be used to evaluate the relationship between fire occurrence and land cover type in the study site.

Landcover classes and ecological systems were delineated at an eight ha minimum mapping unit on 1:36,000 and 1:40,000 2004 and 2005 color infrared air photos using a stereoscope. The ecological systems correlate directly with aggregated or single landcover classes. See associated technical report NPS/KEFJ/NRTR—2008/136 for details.

This data set is a 1-kilometer resolution land cover map for the land area of the Primor'ye and Southern Khabarovsk Regions, in the Russian Far East, based on 1990 NOAA AVHRR data. Labeling of land cover classes depended upon the Russian 1990 Forest Cover Map (Garsia, 1990), the analyst's experience with AVHRR data, and Russian data sources. There are eight classes distinguished in this dataset, of which 5 are forest cover classes.The objective of this work was to create a 1-km resolution land cover map of the region of the Far Eastern Siberia based on NOAA AVHRR data which might be used by World Wildlife Fund researchers to aid in the definition of remaining habitats and range for threatened animal species (Stone and Schlesinger, 1996).