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.
 
Citation proposal
. An Unexpectedly Large Count of Trees in the West African Sahara and Sahel - Bassin Arachidier au Sénégal. https://idg-tetis.teledetection.fr/geonetwork/srv/api/records/51c7c1a1-33e9-4f5f-8da8-f5b7c9ebb75c
 

Simple

Date ( Revision )
2022-05-17T19:08:39
Edition
1.0
Edition date
2015-01-01
Identifier
https://doi.org/10.3334/ORNLDAAC/1832

  Principal investigator

Status
Completed

  Principal investigator

Maintenance and update frequency
As needed
General ( Theme )
  • remote sensing
  • vegetation map
  • deep learning
  • Very High spatial resolution optical imagery
GEMET - INSPIRE themes, version 1.0 ( Theme )
GEMET - Concepts ( Theme )
GCMD Keywords viewer ( Theme )
TETIS Thesaurus, version 1.0 21112019 ( Theme )
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
Metadata language
English
Character set
UTF8
Topic category
  • Environment
  • Imagery base maps earth cover
  • Biota
N
S
E
W


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
  • GeoPackage, ESRI Shapefile (1.0 )

OnLine resource
Vector Layer WARNING: 15Gb!!  
OnLine resource
Geopackage  
OnLine resource
NASA African trees tilemap  
OnLine resource
utm_28_tiles  

WMS Service

OnLine resource
utm_29_tiles  

WMS Service

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
true

Conformance result

Date ( Publication )
2010-12-08T12:00:00
Explanation
See the referenced specification
Pass
true

Conformance result

Date ( Publication )
2008-12-04T12:00:00
Explanation
See the referenced specification
Pass
true
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.3978185 ) 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.

gmd:MD_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
ISO 19115:2003/19139
Metadata standard version
1.0

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