Dataset Open Access

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

Hengl, Tomislav; Jung, Martin; Visconti, Piero

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:

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,
Files (23.0 GB)
Name Size
001_pnv_predictions_glc100.png
md5:06db84a60480f994f07ba5ca4a181339
203.4 kB Download
pnv_potential.landcover_probav.lc100.bare.sparse.vegetation_p_250m_s0..0cm_2017_v0.1.tif
md5:7bbb508de731d09f58817682541630c9
705.4 MB Download
pnv_potential.landcover_probav.lc100.bare.sparse.vegetation_sd_250m_s0..0cm_2017_v0.1.tif
md5:653c8cb7c3cc091d8f7e2f9b6e86c94c
875.8 MB Download
pnv_potential.landcover_probav.lc100.closed.forest..deciduous.broadleaf_p_250m_s0..0cm_2017_v0.1.tif
md5:676a21256f0225c1924e23a5b1c85196
1.3 GB Download
pnv_potential.landcover_probav.lc100.closed.forest..deciduous.broadleaf_sd_250m_s0..0cm_2017_v0.1.tif
md5:472f89acce4ff98019121b3f23d41772
958.3 MB Download
pnv_potential.landcover_probav.lc100.closed.forest..deciduous.needleleaf_p_250m_s0..0cm_2017_v0.1.tif
md5:b812b349bc5daa5fab34dd9218d0e9df
469.7 MB Download
pnv_potential.landcover_probav.lc100.closed.forest..deciduous.needleleaf_sd_250m_s0..0cm_2017_v0.1.tif
md5:b3c971d5168fd28f98a48f2b8143f8c5
635.8 MB Download
pnv_potential.landcover_probav.lc100.closed.forest..evergreen.broadleaf_p_250m_s0..0cm_2017_v0.1.tif
md5:7cdacee9a8af71bf519aa80a80eea4ad
1.0 GB Download
pnv_potential.landcover_probav.lc100.closed.forest..evergreen.broadleaf_sd_250m_s0..0cm_2017_v0.1.tif
md5:9d3c636e6d419902bb506b6a02c3a2b5
966.9 MB Download
pnv_potential.landcover_probav.lc100.closed.forest..evergreen.needleleaf_p_250m_s0..0cm_2017_v0.1.tif
md5:5d1d34b04e2aab9858b135d1e8745e0b
1.3 GB Download
pnv_potential.landcover_probav.lc100.closed.forest..evergreen.needleleaf_sd_250m_s0..0cm_2017_v0.1.tif
md5:d769011b462e00b00550f7db185a1327
1.1 GB Download
pnv_potential.landcover_probav.lc100.closed.forest..mixed_p_250m_s0..0cm_2017_v0.1.tif
md5:1bfb076870b0b2f8e12ecdba8e95e441
727.2 MB Download
pnv_potential.landcover_probav.lc100.closed.forest..mixed_sd_250m_s0..0cm_2017_v0.1.tif
md5:2495bfbf04796a9cc6ffe420952ec2c7
740.4 MB Download
pnv_potential.landcover_probav.lc100.herbaceous.vegetation_p_250m_s0..0cm_2017_v0.1.tif
md5:03367f0ca02576587dcb9fbf572d8939
1.1 GB Download
pnv_potential.landcover_probav.lc100.herbaceous.vegetation_sd_250m_s0..0cm_2017_v0.1.tif
md5:b59ae19d549d8c0d65e47c8fd6492fcf
985.7 MB Download
pnv_potential.landcover_probav.lc100.herbaceous.wetland_p_250m_s0..0cm_2017_v0.1.tif
md5:930beaad3ddb12bcfeef50bd93f6573a
646.7 MB Download
pnv_potential.landcover_probav.lc100.herbaceous.wetland_sd_250m_s0..0cm_2017_v0.1.tif
md5:66a2c47abbee380577e23046173875c0
800.3 MB Download
pnv_potential.landcover_probav.lc100.moss.and.lichen_p_250m_s0..0cm_2017_v0.1.tif
md5:5dcd0b2fea7c9ca6030bec4353b07bcf
109.6 MB Download
pnv_potential.landcover_probav.lc100.moss.and.lichen_sd_250m_s0..0cm_2017_v0.1.tif
md5:7cc42eb77a1fb101cb92e0bf373acc75
193.9 MB Download
pnv_potential.landcover_probav.lc100.open.forest..deciduous.broadleaf_p_250m_s0..0cm_2017_v0.1.tif
md5:130076363221cc7092b56ff7afcb4ce9
453.7 MB Download
pnv_potential.landcover_probav.lc100.open.forest..deciduous.broadleaf_sd_250m_s0..0cm_2017_v0.1.tif
md5:a8cb05499795608c40bbc1415d8901fe
531.4 MB Download
pnv_potential.landcover_probav.lc100.open.forest..deciduous.needleleaf_p_250m_s0..0cm_2017_v0.1.tif
md5:5dcd0b2fea7c9ca6030bec4353b07bcf
109.6 MB Download
pnv_potential.landcover_probav.lc100.open.forest..deciduous.needleleaf_sd_250m_s0..0cm_2017_v0.1.tif
md5:56f8fd61c964d8dcc391a5992e32713a
147.9 MB Download
pnv_potential.landcover_probav.lc100.open.forest..evergreen.broadleaf_p_250m_s0..0cm_2017_v0.1.tif
md5:3301b2c0fda6595ffcd6a6bc037fe454
267.9 MB Download
pnv_potential.landcover_probav.lc100.open.forest..evergreen.broadleaf_sd_250m_s0..0cm_2017_v0.1.tif
md5:b91c3331e0bb9dbc1de3237341edca88
437.7 MB Download
pnv_potential.landcover_probav.lc100.open.forest..evergreen.needleleaf_p_250m_s0..0cm_2017_v0.1.tif
md5:ea325410490b13558ba14cb2bbf79efa
369.3 MB Download
pnv_potential.landcover_probav.lc100.open.forest..evergreen.needleleaf_sd_250m_s0..0cm_2017_v0.1.tif
md5:b2eb75e900de36b1bc1e610894577865
366.1 MB Download
pnv_potential.landcover_probav.lc100.open.forest..mixed_p_250m_s0..0cm_2017_v0.1.tif
md5:0594b75a665bab499073b9b57e9b5439
125.9 MB Download
pnv_potential.landcover_probav.lc100.open.forest..mixed_sd_250m_s0..0cm_2017_v0.1.tif
md5:7b7eb4a770bb0da3c27be1445a10f58e
176.4 MB Download
pnv_potential.landcover_probav.lc100.shrubs_p_250m_s0..0cm_2017_v0.1.tif
md5:52915db78b7145e76168f63f4f1d7712
1.7 GB Download
pnv_potential.landcover_probav.lc100.shrubs_sd_250m_s0..0cm_2017_v0.1.tif
md5:da293e36fbc23e8f28015a5f70346d8c
1.4 GB Download
pnv_potential.landcover_probav.lc100.sub.polar.or.polar.barren.lichen.moss..grassland_p_250m_s0..0cm_2017_v0.1.tif
md5:7f23ccc5192df6514dd28f45725c63cf
746.7 MB Download
pnv_potential.landcover_probav.lc100.sub.polar.or.polar.barren.lichen.moss..grassland_sd_250m_s0..0cm_2017_v0.1.tif
md5:258a8f627fd4e51743bb8ea12b2de14a
726.1 MB Download
pnv_potential.landcover_probav.lc100.subtropical.tropical.mangrove.vegetation_p_250m_s0..0cm_2017_v0.1.tif
md5:45f0ceb833064cce4d7859eebb4a00dd
185.1 MB Download
pnv_potential.landcover_probav.lc100.subtropical.tropical.mangrove.vegetation_sd_250m_s0..0cm_2017_v0.1.tif
md5:eb57398aefdbe5af33c5381b9911a581
414.5 MB Download
pnv_potential.landcover_probav.lc100_c.qml
md5:42f75fba3fee3c44c01702fd009375a6
20.6 kB Download
pnv_potential.landcover_probav.lc100_c_250m_s0..0cm_2017_v0.1.tif
md5:5c0cc48ba8789ef579ecab3b8466e99f
233.0 MB Download
pnv_potential.landcover_probav.lc100_c_250m_s0..0cm_2017_v0.1.tif.csv
md5:c0666858215cf99bd7b81a90179c2475
1.3 kB Download
  • 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.

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