Global Maps of Potential Natural Vegetation at 1 km resolutiondoi:10.7910/DVN/QQHCIKHarvard Dataverse2018-03-274Hengl, Tomislav, 2018, "Global Maps of Potential Natural Vegetation at 1 km resolution", https://doi.org/10.7910/DVN/QQHCIK, Harvard Dataverse, V4Global Maps of Potential Natural Vegetation at 1 km resolutiondoi:10.7910/DVN/QQHCIKHengl, TomislavRHarvard DataverseHengl, TomislavHengl, Tomislav2018-03-27PNV1kmComputer and Information ScienceEarth and Environmental Sciencesrandom forestbiomesforest speciesphotosynthesisPotential 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/issuesGeoTIFFs spatial dataTo view data please use: https://www.arcgis.com/apps/MapJournal/index.html?appid=1856322400844a7cab348bccfa4bee76- 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), - 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), - 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),All maps are available for download under the Open Database License (ODbl) v1.0 (https://opendatacommons.org/licenses/odbl/)This is the v0.1 of the Global Potential Natural Vegetation maps at 1 km. This data is preliminary and errors and artifacts are still possible. Training data sets BIOME 6000 and EU Forest are constantly being updated and could still contain erroneously geolocated points. Predictions of FAPAR are based on randomly simulated points and not on ground observations of FAPAR and classification of sites. Predictions of EU forest tree species are presented for experimental purposes only.This dataset is made available under a Creative Commons CC0 license with the following additional/modified terms and conditions: 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:e26811v110.7287/peerj.preprints.26811v1Hengl 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 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