20210711
19531600
1.0
FALSE
arcgis_geodatabase.sde.IIASA_001_NatureMap_PotNatVegetation
Server=10.2.1.241; Service=sde:postgresql:10.2.1.241; Database=arcgis_geodatabase; User=sde; Version=sde.DEFAULT
ArcSDE Connection
002
-180.000000
179.999942
-62.000809
87.370000
1
Geographic
GCS_WGS_1984
Angular Unit: Degree (0.017453)
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CopyRaster O:\f01_projects_active\Global\p08425_naturemap\outputs\Sept_2020_outputs_FINAL\Corinna\IIASA_001_NatureMap_PotNatVegetation\pnv_potential.landcover_probav.lc100_c_250m_s0..0cm_2017_v0.1.tif "O:\f01_projects_active\Global\p08425_naturemap\outputs\Sept_2020_outputs_FINAL\Corinna\carbon, biodiversity, water, NatureMap\InternalPortal.sde\arcgis_geodatabase.sde.pnv_potential_landcover_probav_lc100_c_250m_s0__0cm_2017_v0_1" # # 255 NONE NONE # NONE NONE "Esri Grid" NONE CURRENT_SLICE NO_TRANSPOSE
Rename "O:\f01_projects_active\Global\p08425_naturemap\outputs\Sept_2020_outputs_FINAL\Corinna\carbon, biodiversity, water, NatureMap\InternalPortal.sde\arcgis_geodatabase.sde.pnv_potential_landcover_probav_lc100_c_250m_s0__0cm_2017_v0_1" "O:\f01_projects_active\Global\p08425_naturemap\outputs\Sept_2020_outputs_FINAL\Corinna\carbon, biodiversity, water, NatureMap\InternalPortal.sde\arcgis_geodatabase.sde.IIASA_001_NatureMap_PotNatVegetation" RasterDataset
20210728
13331500
20210728
13331500
150000000
5000
Microsoft Windows 10 Version 10.0 (Build 19043) ; Esri ArcGIS 12.4.2.19948
NatureMap_PotNatVegetation
0.1
20200130
1
-180.000000
179.999942
87.370000
-62.000809
This is one of a number of datasets to download. This dataset named pnv_potential.landcover_probav.lc100_c_250m_s0..0cm_2017_v0.1.tif , showing 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). Datasets for individual landcover types are also available on the website.
These are initial predictions for testing purposes only. A publication explaining all processing steps is pending.
<div style='text-align:Left;'><div><div><p><span>A </span><span>comparable thematic legend</span><span> was</span><span> 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 </span><a href='https://github.com:443/Envirometrix/PNVmaps' target='_blank' style='text-decoration:underline;'><span>here</span></a><span>. Maps with "_sd_" contain estimated model errors per class. Antarctica is not included</span><span>.</span></p><p><span>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).</span></p></div></div></div>
Hengl, Tomislav, Jung, Martin, & Visconti, Piero. (2020). Potential distribution of land cover classes (Potential Natural Vegetation) at 250 m spatial resolution (Version v0.1) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3631254
potential natural vegetation
potential landcover
NatureMap
250
external
119302e09c02b27f8ce40cc1f241c28af2ae5374
Martin Jung
IIASA
jung@iiasa.ac.at
True
Martin Jung
potential landcover
global
<div style='text-align:Left;'><div><div><p><span>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.</span></p></div></div></div>
This dataset was downloaded from http://doi.org/10.5281/zenodo.3631254. It was named pnv_potential.landcover_probav.lc100_c_250m_s0..0cm_2017_v0.1.tif
The Value in the raster dataset refers to the map_code in the information below. This information was contained in an accompanying csv file can be downloaded from the website.
"map_code","land_cover_class","pv_mapping","land_cover_class_agg"
,0,"unknown",0,"unknown"
111,"closed forest, evergreen needleleaf",1,"forests"
113,"closed forest, deciduous needleleaf",1,"forests"
112,"closed forest, evergreen broadleaf",1,"forests"
114,"closed forest, deciduous broadleaf",1,"forests"
115,"closed forest, mixed",1,"forests"
116,"closed forest, unknown",0,"forests"
121,"open forest, evergreen needleleaf",1,"forests"
123,"open forest, deciduous needleleaf",1,"forests"
122,"open forest, evergreen broadleaf",1,"forests"
124,"open forest, deciduous broadleaf",1,"forests"
125,"open forest, mixed",1,"forests"
126,"open forest, unknown",0,"forests"
127,"subtropical/tropical mangrove vegetation",1,"forests"
21,"sub-polar or polar barren-lichen-moss, grassland",1,"shrubs"
20,"shrubs",1,"shrubs"
30,"herbaceous vegetation",1,"herbaceous vegetation"
90,"herbaceous wetland",1,"herbaceous wetland"
100,"moss and lichen",1,"moss and lichen"
60,"bare/sparse vegetation",1,"bare/sparse vegetation"
40,"cropland",0,"cropland"
50,"urban/built-up",0,"urban/built-up"
70,"snow and ice",0,"snow and ice"
80,"permanent water bodies",0,"permanent water bodies"
200,"open sea",0,"open sea"
Enterprise Geodatabase Raster Dataset
dataset
EPSG
6.14(3.0.1)
2
1
0
-180.000000 -62.000809
-180.000000 87.370000
179.999942 87.370000
179.999942 -62.000809
-0.000029 12.684595
172800
0.002083
71698
0.002083
arcgis_geodatabase.sde.SDE_VAT_112
Table
63
objectid
objectid
OID
4
10
0
Internal feature number.
Esri
Sequential unique whole numbers that are automatically generated.
Value
value
Integer
4
10
0
Count
count
Double
8
38
0
Band_1
8
20210728
external
5d156fac6c56de7bdd6f74d66dac3fbd0ada0468
UNEP-WCMC
Data Manager
spatialanalysis@unep-wcmc.org
Corinna Ravilious
True
UNEP-WCMC
external
119302e09c02b27f8ce40cc1f241c28af2ae5374
Martin Jung
IIASA
jung@iiasa.ac.at
True
Martin Jung
Pixel
71698
172800
0.002083
0.002083
8
1
Upper Left
FALSE
LZ77
1
pixel codes
SDR
TRUE
row and column
0.002083
0.002083