This dataset represents an estimate of the day of year (i.e. , "Julian Day") of the onset of the seasonal snowpack. Specifically these are estimates of the last day of bare ground derived from long-term time-series of Landsat TM, ETM, and OLI imagery starting in 1993. To facilitate computation of sn

This dataset represents an estimate of the day of year (i.e. , "Julian Day") of the persistence of the seasonal snowpack. Specifically these are estimates of the first day of bare ground derived from long-term time-series of Landsat TM, ETM, and OLI imagery starting in 1993. These maps combine month

This map is a soil moisture proxy derived from analysis of the UG 1m hydrologically corrected digital elevation model. The intuition behind this map is that areas that have flat topography and are near the elevation of local streams are likely to have high soil moisture. This index builds on the wor

This is a 1m resolution aerial imagery orthomosaic resampled from 0.6m 4-band orthoimagery collected on September 14th 2019 as part of the USDA National Aerial Imagery Program. The original NAIP tiles were reprojected, mosaiced and bilinearly resampled to the standard UG 1m grid, and compressed usin

This is a 1m resolution map of Normalized Differential Vegetation Index (NDVI) derived from resampled 0.6m 4-band orthoimagery collected as part of the USDA National Aerial Imagery Program. The NAIP tiles were mosaiced and bilinearly resampled to the standard UG 1m grid before calculating NDVI as (N

This is a 1m resolution map of Normalized Differential Vegetation Index (NDVI) derived from resampled 0.6m 4-band orthoimagery collected as part of the USDA National Aerial Imagery Program. The NAIP tiles were mosaiced and bilinearly resampled to the standard UG 1m grid before calculating NDVI as (N

This is a 1m resolution map of Normalized Differential Vegetation Index (NDVI) derived from resampled 0.6m 4-band orthoimagery collected as part of the USDA National Aerial Imagery Program. The NAIP tiles were mosaiced and bilinearly resampled to the standard UG 1m grid before calculating NDVI as (N

This is 1 meter resolution landcover map developed for the RMBL Spatial Data Platform. Source datasets include 2017 and 2019 4-band imagery from the National Aerial Imagery Program, and 2019 LiDAR data collected by Quantum Geospatial for the Colorado Hazard Mapping Program. The numeric codes for the

This is a 1 m resolution map of topographic slope (measured in degrees) computed using a 3*3 pixel kernel and Horn's formula. It is derived from a 1m resolution Digital Elevation Model (DEM) for the Upper Gunnison River derived from public LiDAR datasets. The primary data source was a 2019 LiDAR col

This is a 1 m resolution map of the relative "westness" of topographic aspect, computed from the cosine of the topographic aspect using the equation: cos(aspect_radians) * -1 Where the aspect is computed clockwise with east having a value of 0. In this map, east-facing aspects have a value of -1 and

This is a 1 m resolution map of the relative "southness" of topographic aspect, computed from the cosine of the topographic aspect using the equation: cos(aspect_radians) * -1 North-facing aspects have a value of -1 and south-facing aspects have a value of 1. East and west-facing aspects have a valu

This is a map of 20th percentile canopy height above the ground for the Upper Gunnison River Basin based on 2015 and 2019 LiDAR data. Height is measured in meters. This dataset was generated from the terrain-normalized point clouds using functions in the R package lidR. The ground-classified points

This is a map of 20th percentile canopy height above the ground for the Upper Gunnison River Basin based on 2015 and 2019 LiDAR data. Height is measured in meters. This dataset was generated from the terrain-normalized point clouds using functions in the R package lidR. The ground-classified points

This is a map of vegetation canopy height above the ground for the Upper Gunnison River Basin based on 2015 and 2019 LiDAR data. Height is measured in meters. This dataset was generated from the original point clouds using a pit-free algorithm implemented in the R package lidR. The ground-classified

This is a map of vegetation understory cover or density for the Upper Gunnison River Basin based on 2015 and 2019 LiDAR data. Cover is measured as a proportion (0 representing no cover, and 1 representing complete cover). This dataset was generated from the normalized, elevation-corrected LiDAR poin

This dataset represents potential clear-sky incident solar radiation (in w/m^2) for day of year 355 (winter solstice), taking into account shading from topography, buildings, and vegetation greater than 1m in height. This map was generated with the GRASS GIS program r.sun and a subcanopy solar radia

This dataset represents potential clear-sky incident solar radiation (in w/m^2) for day of year 265 (fall equinox), taking into account shading from topography, buildings, and vegetation greater than 1m in height. This map was generated with the GRASS GIS program r.sun and a subcanopy solar radiatio

This dataset represents potential clear-sky incident solar radiation (in w/m^2) for day of year 172 (summer solstice), taking into account shading from topography, buildings, and vegetation greater than 1m in height. This map was generated with the GRASS GIS program r.sun and a subcanopy solar radia

This dataset represents potential clear-sky incident solar radiation (in w/m^2) for day of year 265 (fall equinox), taking into account shading from topography, buildings, and vegetation greater than 1m in height. This map was generated with the GRASS GIS program r.sun and a subcanopy solar radiatio

This dataset represents potential clear-sky incident solar radiation (in w/m^2) for day of year 355 (winter solstice), taking into account shading from topography and buildings, but not vegetation. This map was generated with the GRASS GIS program r.sun.