Potential distribution of land cover classes (Potential Natural Vegetation) at 250 m spatial resolution

• Main Authors: Hengl, Tomislav, Jung, Martin, Visconti, Piero
• Format: Dataset
• Language: English
• Published: Zenodo 2020
• Subjects: OpenLandMap; NatureMap; potential vegetation; Barrios; Bouvier; Dent; McCallum; Antarc*; Antarctica
• Online Access: https://dx.doi.org/10.5281/zenodo.3631253; https://zenodo.org/record/3631253

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 here . Maps with "_sd_" contain estimated model errors per class. Antarctica is not included. Produced for the needs of the NatureMap which is project run by the International Institute for Applied Systems Analysis (IIASA), the International Institute for Sustainability (IIS), the UN Environment Programme World Conservation Monitoring Centre (UNEP-WCMC), and the UN Sustainable Development Solutions Network (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, : {"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."]}