OpenLandMap Potential Distribution of Biomes

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envirometrix opengeohub openlandmap potential


Potential Natural Vegetation biomes global predictions of classes (based on predictions using the BIOMES 6000 dataset's 'current biomes' category.)

Potential 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. This dataset contains 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).

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1000 meters


Name Description

Potential distribution of biomes

biome_type Class Table

Value Color Description
1 #1c5510 tropical evergreen broadleaf forest
2 #659208 tropical semi-evergreen broadleaf forest
3 #ae7d20 tropical deciduous broadleaf forest and woodland
4 #000065 warm-temperate evergreen broadleaf and mixed forest
7 #bbcb35 cool-temperate rainforest
8 #009a18 cool evergreen needleleaf forest
9 #caffca cool mixed forest
13 #55eb49 temperate deciduous broadleaf forest
14 #65b2ff cold deciduous forest
15 #0020ca cold evergreen needleleaf forest
16 #8ea228 temperate sclerophyll woodland and shrubland
17 #ff9adf temperate evergreen needleleaf open woodland
18 #baff35 tropical savanna
20 #ffba9a xerophytic woods/scrub
22 #ffba35 steppe
27 #f7ffca desert
28 #e7e718 graminoid and forb tundra
30 #798649 erect dwarf shrub tundra
31 #65ff9a low and high shrub tundra
32 #d29e96 prostrate dwarf shrub tundra

Terms of Use

Terms of Use

This is a human-readable summary of (and not a substitute for) the license.

You are free to - Share - copy and redistribute the material in any medium or format Adapt - remix, transform, and build upon the material for any purpose, even commercially.

This license is acceptable for Free Cultural Works. The licensor cannot revoke these freedoms as long as you follow the license terms.

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  • 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. 10.7287/peerj.preprints.26811v1


Explore with Earth Engine

Code Editor (JavaScript)

var dataset = ee.Image('OpenLandMap/PNV/PNV_BIOME-TYPE_BIOME00K_C/v01');

var visualization = {
  bands: ['biome_type'],
  min: 1.0,
  max: 32.0,
  palette: [


Map.addLayer(dataset, visualization, 'Potential distribution of biomes');
Open in Code Editor