The EarthEnv project is a collaborative project of biodiversity scientists and remote sensing experts to develop near-global standardized, 1km resolution layers for monitoring and modeling biodiversity, ecosystems, and climate. The work is supported by NCEAS, NASA, NSF, and Yale University.
A standardized delineation of the worlds’ mountains has many applications in research, education, and the science-policy interface. Here we provide a new inventory of 8616 mountain ranges developed under the auspices of the Global Mountain Biodiversity Assessment (GMBA). Building on an earlier compilation, the presented geospatial database uses a further advanced and generalized mountain definition and a semi-automated method to enable globally standardized, transparent delineations of mountain ranges worldwide. The inventory is presented on EarthEnv at various hierarchical levels, allowing users to select their preferred level of regional aggregation from continents to small subranges according to their needs and the scale of their analyses. The clearly defined, globally consistent and hierarchical nature of the presented mountain inventory offers a standardized resource for referencing and addressing mountains across basic and applied natural, social sciences research and a range of other uses in science communication and education.
Topographic variation underpins a myriad of patterns and processes in hydrology, climatology, geography and ecology and is key to understanding the variation of life on the planet. We provide a fully standardized and global multivariate product of different terrain features has the potential to support many large-scale research applications. The product is based on the digital elevation model products of global 250 m GMTED2010 and near-global 90 m SRTM4.1dev. We provide the following topographic variables: elevation, slope, aspect, eastness, northness, roughness, terrain roughness index, topographic position index, vector ruggedness measure, profile/tangential curvature, first/second order partial derivative, and 10 geomorphological landform classes. Each variable is provided at different aggregations from 1, 5, 10, 50 to 100 km spatial grains.
14 metrics quantifying spatial heterogeneity of global habitat at multiple resolutions based on the textural features of Enhanced Vegetation Index (EVI) imagery acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS).
Multiple global remote sensing-derived land-cover products and consensus information on the prevalence of 12 land-cover classes at 1-km resolution.
Twice-daily remote sensing-derived cloud observations at 1-km resolution integrated over a 15-year period.
Derived from the CGIAR-CSI SRTM v4.1 and ASTER GDEM v2 data products, these data have been processed and merged to provide continuous coverage of ~91% of the globe. Processing and merging methodology are available in Robinson et al. (2014).
Near-global, spatially continuous, and freshwater-specific environmental variables in a standardized 1km grid. We delineated the sub-catchment for each grid cell along the HydroSHEDS river network and summarized the upstream environment (climate, topography, land cover, surface geology and soil) to each grid cell using various metrics (average, minimum, maximum, range, sum, inverse distance-weighted average and sum).