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

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

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(Redirected from Dataset:6688821)



DOI10.5281/zenodo.3631254Zenodo3631254MaRDI QIDQ6688821

Dataset published at Zenodo repository.

Author name not available (Why is that?)

Publication date: 30 January 2020

Copyright license: No records found.



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 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 Norways 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,






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