<?xml version='1.0' encoding='UTF-8'?><metadata xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcterms="http://purl.org/dc/terms/" xmlns="http://dublincore.org/documents/dcmi-terms/"><dcterms:title>Global Maps of Potential Natural Vegetation at 1 km resolution</dcterms:title><dcterms:identifier>https://doi.org/10.7910/DVN/QQHCIK</dcterms:identifier><dcterms:creator>Hengl, Tomislav</dcterms:creator><dcterms:publisher>Harvard Dataverse</dcterms:publisher><dcterms:issued>2018-03-27</dcterms:issued><dcterms:modified>2018-04-04T06:33:58Z</dcterms:modified><dcterms:description>Potential Natural Vegetation (PNV) is the vegetation cover in equilibrium with climate, that would exist at a given location non-impacted by human activities. PNV is useful for raising public awareness about land degradation and for estimating land potential. Here you can download results of predictions of (1) global distribution of biomes based on the BIOME 6000 data set (8057 modern pollen-based site reconstructions), (2) distribution of forest tree species in Europe based on detailed occurrence records (1,546,435 ground observations), and (3) global monthly Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) values (30,301 randomly-sampled points). To report an issue or artifact in maps, please use https://github.com/envirometrix/PNVmaps/issues</dcterms:description><dcterms:subject>Computer and Information Science</dcterms:subject><dcterms:subject>Earth and Environmental Sciences</dcterms:subject><dcterms:subject>random forest</dcterms:subject><dcterms:subject>biomes</dcterms:subject><dcterms:subject>forest species</dcterms:subject><dcterms:subject>photosynthesis</dcterms:subject><dcterms:isReferencedBy>Hengl T, Walsh MG, Sanderman J, Wheeler I, Harrison SP, Prentice IC. (2018) Global mapping of potential natural vegetation: an assessment of Machine Learning algorithms for estimating land potential. PeerJ Preprints 6:e26811v1, doi, 10.7287/peerj.preprints.26811v1, https://doi.org/10.7287/peerj.preprints.26811v1</dcterms:isReferencedBy><dcterms:date>2018-03-27</dcterms:date><dcterms:contributor>Hengl, Tomislav</dcterms:contributor><dcterms:dateSubmitted>2018-03-27</dcterms:dateSubmitted><dcterms:type>GeoTIFFs spatial data</dcterms:type><dcterms:source>- an expanded version of the BIOME 6000 DB data set representing site-based reconstructions from surface pollen samples of major vegetation types or biomes (http://dx.doi.org/10.17864/1947.99),&#xd;
- EU Forest (Mauri et al., 2017) and GBIF (Global Biodiversity Information Facilities) occurrence records of occurrences for the 76 main forest tree taxa in Europe (http://dx.doi.org/10.15468/dl.fhucwx),&#xd;
- Long-term Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) monthly images derived using a time-series of Copernicus Global Land products (https://land.copernicus.eu),</dcterms:source><dcterms:rights>This dataset is made available under a Creative Commons CC0 license with the following additional/modified terms and conditions: </dcterms:rights></metadata>