Data for the vignette associated with the geodiv R package. Data may be used to analyze patterns in landscape heterogeneity over Oregon, USA.
Data included are as follows:
(var)_img_450m.tif: texture images (gradient metric applied over moving windows), for the specified metric, applied over a small portion of southwestern Oregon state. The window size used is 450x450m.
oregon_elevation_SRTM_2000.tif: Shuttle Radar Topography Mission (SRTM) elevation data covering Oregon state for the year 2000. Data were prepared and downloaded in Google Earth Engine in Fall 2019, and are at 240m resolution.
oregon_max_growing_season_EVI_2000_2019.tif: Mean maximum growing season MODIS Enhanced Vegetation Index (EVI) data from 2000-2019. Data were prepared and exported from Google Earth Engine in Fall 2019, and are at 250m resolution.
elevation_variables.csv: All gradient surface metrics included in the geodiv R package, calculated for 30km moving windows over Oregon state, using a raster of SRTM elevation (above) aggregated to ~2km resolution.
evi_variables.csv: All gradient surface metrics included in the geodiv R package, calculated for 30km moving windows over Oregon state, using a raster of MODIS EVI (above) aggregated to 2km resolution.
evi_variables_1km.csv: All gradient surface metrics included in the geodiv R package, calculated for 15km moving windows over Oregon state, using a raster of MODIS EVI (above) aggregated to 1km resolution.
naip_ndvi(year).tif: NAIP NDVI covering the 2017 Jolly Mountain fire in Washington State. The 2015 image is pre-fire, and the 2017 image is post-fire.
Funding
BIODIVERSITY IS ESSENTIAL FOR ECOSYSTEM FUNCTIONING AND THE PROVISIONING OF ECOSYSTEM SERVICES. CURRENTLY, GLOBAL CHANGE IS THREATENING BIODIVERSITY IN MANY PARTS OF THE GLOBE. THUS, A MAJOR GOAL FOR SOCIETY IS TO EXPLAIN AND FORECAST PATTERNS OF BIODIVER
MONITORING DIMENSIONS OF BIODIVERSITY IN A MEGA-DIVERSE REGION OF SOUTHERN AFRICA: FROM TRAITS TO COMMUNITIES TO ECOSYSTEMS.\NWE PROPOSE TO DEVELOP PLANS FOR A NASA FIELD CAMPAIGN INCORPORATING HYPERSPECTRAL, LIDAR, AND FIELD OBSERVATIONS ACROSS THE GREAT