DOI: 10.5281/zenodo.3631254
title: Potential distribution of land cover classes (Potential Natural Vegetation) at 250 m spatial resolution

description: Potential distribution of land cover classes (Potential Natural Vegetation) at 250 m spatial resolution based on a compilation of data sets (Biome6000k, Geo-Wiki, LandPKS, mangroves soil database, and from various literature sources; total of about 65,000 training points). We used a comparable thematic legend used to produce the Dynamic Land Cover 100m: Version 2. Copernicus Global Land Operations product (Buchhorn et al. 2019), which is based on the UN FAO Land Cover Classification System (LCCS), so that users can compare actual (https://lcviewer.vito.be/) vs potential (this data set) land cover. Two classes not available in the LCCS were added: "subtropical/tropical mangrove vegetation" and "sub-polar or polar barren-lichen-moss, grassland". The map was created using relief and climate variables representing conditions the climate for the last 20+ years and predicted at 250 m globally using an Ensemble Machine Learning approach as implemented in the mlr package for R. Processing steps are described in detail <strong>here</strong>. Maps with "_sd_" contain estimated model errors per class. Antarctica is not included. Produced for the needs of the <strong>NatureMap</strong> which is project run by the <strong>International Institute for Applied Systems Analysis</strong> (IIASA), the <strong>International Institute for Sustainability</strong> (IIS), the <strong>UN Environment Programme World Conservation Monitoring Centre</strong> (UNEP-WCMC), and the <strong>UN Sustainable Development Solutions Network</strong> (SDSN). NatureMap is funded by Norway’s International Climate Initiative (NICFI). Maps will also be made available via: OpenLandMap.org. These are initial predictions for testing purposes only. A publication explaining all processing steps is pending. If you discover a bug, artifact or inconsistency in the predictions, or if you have a question please use some of the following channels: Technical issues and questions about the code: https://github.com/Envirometrix/PNVmaps/issues All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention: pnv = theme: potential natural vegetation, potential.landcover = variable: potential land cover type (e.g. "open forest, evergreen needleleaf"), probav.lc100 = classification model: ProbaV-based land cover mapping legend (LCCS), c = factor, 250m = spatial resolution / block support: 250 m, b0..0cm = vertical reference: land surface, 2000..2017 = time reference: period 2000-2017, v0.1 = version number: 0.1,
description: {"references": ["Buchhorn M, Smets B, Van De R, Lesiv M, Fritz S, Herold M, et al. (2019) Moderate Dynamic Land Cover 100m: Version 2. Copernicus Global Land Operations; available from: https://land.copernicus.eu/global/products/lc.", "Di Gregorio A, (2005) Land Cover Classification System: Classification Concepts and User Manual : LCCS. No. v. 2 in Environ Nat Res Management Series. Food and Agriculture Organization of the United Nations, Rome.", "Fritz S, See L, Perger C, McCallum I, Schill C, Schepaschenko D, et al. (2017) A global dataset of crowdsourced land cover and land use reference data. Scientific Data. 4:170075. doi:10.1038/sdata.2017.75.", "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 6:e5457 https://doi.org/10.7717/peerj.5457", "Herrick JE, Beh A, Barrios E, Bouvier I, Coetzee M, Dent D, et al. (2016) The Land-Potential Knowledge System (LandPKS): mobile apps and collaboration for optimizing climate change investments. Ecosystem Health and Sustainability, 2(3):e01209.", "Sanderman J, Hengl T, Fiske G, Solvik K, Adame MF, Benson L, et al. (2018) A global map of mangrove forest soil carbon at 30 m spatial resolution. Environmental Research Letters, 13(5):055002."]}