Catalogue INRAE
An Unexpectedly Large Count of Trees in the West African Sahara and Sahel - Bassin Arachidier au Sénégal
This dataset provides georeferenced polygon vectors of individual tree canopy geometries for dryland areas in West African Sahara and Sahel that were derived using deep learning applied to 50 cm resolution satellite imagery. More than 1.8 billion non-forest trees (i.e., woody plants with a crown size over 3 m2) over about 1.3 million km2 were identified from panchromatic and pansharpened normalized difference vegetation index (NVDI) images at 0.5 m spatial resolution using an automatic tree detection framework based on supervised deep-learning techniques. Combined with existing and future fieldwork, these data lay the foundation for a comprehensive database that contains information on all individual trees outside of forests and could provide accurate estimates of woody carbon in arid and semi-arid areas throughout the Earth for the first time.
Simple
- Date (Revision)
- 2022-05-17T19:08:39
- Edition
-
1.0
- Edition date
- 2015-01-01
- Citation identifier
- https://doi.org/10.3334/ORNLDAAC/1832
- Cited responsible party
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Organisation name Individual name Electronic mail address Role NASA ORNL DAAC
NASA
Principal investigator
- Status
- Completed
- Point of contact
-
Organisation name Individual name Electronic mail address Role NASA ORNL DAAC
NASA
Principal investigator
- Maintenance and update frequency
- As needed
-
General
-
-
remote sensing
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vegetation map
-
deep learning
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Very High spatial resolution optical imagery
-
- Use limitation
-
Credits: Brandt, M., C.J. Tucker, A. Kariryaa, K. Rasmussen, C. Abel, J.L. Small, J. Chave, L.V. Rasmussen, P. Hiernaux, A.A. Diouf, L. Kergoat, O. Mertz, C. Igel, F. Gieseke, J. Schöning, S. Li, K.A. Melocik, J.R. Meyer, S. Sinno, E. Romero, E.N. Glennie, A. Montagu, M. Dendoncker, and R. Fensholt. 2020. An unexpectedly large count of trees in the West African Sahara and Sahel. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1832. This dataset is openly shared, without restriction, in accordance with the EOSDIS Data Use Policy: https://earthdata.nasa.gov/earth-observation-data/data-use-policy?_ga=2.213474524.955659520.1604914682-676515214.1576510456
- Access constraints
- unrestricted
- Use constraints
- unrestricted
- Classification
- Unclassified
- User note
-
unclassified
- Classification system
-
no classification in particular
- Handling description
-
description
- Spatial representation type
- Vector
- Distance
- 50 cm
- Language
- English
- Character set
- UTF8
- Topic category
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- Environment
- Imagery base maps earth cover
- Biota
))
- Begin date
- 2005-11-01T00:00:00Z
- End date
- 2018-03-31T00:00:00Z
- Supplemental Information
-
some additional information
- Reference system identifier
- EPSG / 32628
- Distribution format
-
Name Version GeoPackage, ESRI Shapefile
1.0
- OnLine resource
-
Protocol Linkage Name file for download
http://idg-tetis.teledetection.fr//data/ArbresAfricainsNASA/BassinArachidierSenegal/BassinArachidienSenegal.zip Vector Layer WARNING: 15Gb!!
file for download
http://idg-tetis.teledetection.fr//data/ArbresAfricainsNASA/Senegal_BA.gpkg Geopackage
file for download
http://idg-tetis.teledetection.fr//tests/ArbresAfricainsNASA/tilemap.gpkg NASA African trees tilemap
OGC:WMS
http://193.48.189.79:8080/geoserver/tree_west_africa_nasa/wms/utm_28_tiles utm_28_tiles
OGC:WMS
http://193.48.189.79:8080/geoserver/tree_west_africa_nasa/wms/utm_29_tiles utm_29_tiles
- Hierarchy level
- Dataset
Conformance result
- Alternate title
-
This is is some data quality check report
- Date (Publication)
- 2022-05-17T19:08:39
- Explanation
-
some explanation about the conformance
- Pass
- Yes
Conformance result
- Date (Publication)
- 2010-12-08T12:00:00
- Explanation
-
See the referenced specification
- Pass
- Yes
Conformance result
- Date (Publication)
- 2008-12-04T12:00:00
- Explanation
-
See the referenced specification
- Pass
- Yes
- Statement
-
The mapping of woody plants at the level of single trees was achieved by the use of satellite data at very high spatial resolution (0.5 m) from DigitalGlobe satellites, combined with modern machine-learning techniques. More than 50,000 DigitalGlobe multispectral images from the QuickBird-2, GeoEye-1, WorldView-2 and WorldView-3 satellites, were collected from 2005–2018 (in November to March) from 12° to 24° N latitude within Universal Transverse Mercator zones 28 and 29 (provided under the NextView license from the National Geospatial Intelligence). Normalized difference vegetation index (NDVI) images were used to distinguish tree crowns from the non-vegetated background because the images were taken from a period during which only woody plants are photosynthetically active in the study area. A set of decision rules was applied to select images for the mosaic, consisting of 25 × 25 km tiles. This resulted in 11,128 images that were used for the study. The neural network model (UNet; publicly available at https://doi.org/10.5281/zenodo.397818 5) was used to automatically segment the tree crowns—that is, to detect tree crowns in the input images. The segmented areas were then converted to polygons for counting the trees and measuring their crown size. Using machine learning coupled to training data of 89,899 manually delineated and annotated trees, the location of individual trees over 1,300,000 km2 and their crown area were determined from the input images. Every tree with a crown area >3 m2 was enumerated resulting in 1,837,565,501 trees.
Metadata
- File identifier
- 51c7c1a1-33e9-4f5f-8da8-f5b7c9ebb75c XML
- Metadata language
- English
- Character set
- UTF8
- Hierarchy level
- Dataset
- Date stamp
- 2022-05-17T19:20:40
- Metadata standard name
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ISO 19115:2003/19139
- Metadata standard version
-
1.0
- Metadata author
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Organisation name Individual name Electronic mail address Role CNRS UMR TETIS
Claudia Lavalley
Point of contact NASA ORNL DAAC
NASA
Principal investigator NASA ORNL DAAC
NASA
Publisher
Overviews
Spatial extent
Provided by
