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 ( Révision )
2022-05-17T19:08:39
Edition
1.0
Date d'édition
2015-01-01
Identificateur
https://doi.org/10.3334/ORNLDAAC/1832
Point de recherche
NASA ORNL DAAC - NASA

Etat
Finalisé
Point de recherche
NASA ORNL DAAC - NASA

Fréquence de mise à jour
Lorsque nécessaire
General ( Thème )
  • remote sensing
  • vegetation map
  • deep learning
  • Very High spatial resolution optical imagery
GEMET - INSPIRE themes, version 1.0 ( Thème )
GEMET - Concepts ( Thème )
GCMD Keywords viewer ( Thème )
TETIS Thesaurus, version 1.0 21112019 ( Thème )
Limitation d'utilisation
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
Contraintes d'accès
unrestricted
Contraintes d'utilisation
unrestricted
Restrictions de manipulation
Non classifié
Explications sur les restrictions
unclassified
Système de classification
no classification in particular
Description de manipulation
description
Type de représentation spatiale
Vecteur
Distance de résolution
50 cm
Langue
en
Jeu de caractères
Utf8
Catégorie ISO
  • Environnement
  • Imagerie/Cartes de base/Occupation des terres
  • Biote
N
S
E
W


Date de début
2005-11-01T00:00:00Z
Date de fin
2018-03-31T00:00:00Z
Informations supplémentaires
some additional information
Nom du système de référence
EPSG / 32628
Format (encodage)
  • GeoPackage, ESRI Shapefile ( 1.0 )

Ressource en ligne
Vector Layer WARNING: 15Gb!! ( file for download )
Ressource en ligne
Geopackage ( file for download )
Ressource en ligne
NASA African trees tilemap ( file for download )
Ressource en ligne
utm_28_tiles ( OGC:WMS )

WMS Service

Ressource en ligne
utm_29_tiles ( OGC:WMS )

WMS Service

Niveau
Jeu de données

Résultat de conformité

Autres appellations ou acronymes
This is is some data quality check report
Date ( Publication )
2022-05-17T19:08:39
Explication
some explanation about the conformance
Degré de conformité
Oui

Résultat de conformité

Date ( Publication )
2010-12-08T12:00:00
Explication
See the referenced specification
Degré de conformité
Oui

Résultat de conformité

Date ( Publication )
2008-12-04T12:00:00
Explication
See the referenced specification
Degré de conformité
Oui
Généralités sur la provenance
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

Identifiant de la fiche
51c7c1a1-33e9-4f5f-8da8-f5b7c9ebb75c XML
Langue
en
Jeu de caractères
Utf8
Type de ressource
Jeu de données
Date des métadonnées
2022-05-17T19:20:40
Nom du standard de métadonnées
ISO 19115:2003/19139
Version du standard de métadonnées
1.0
Point de contact
CNRS UMR TETIS - Claudia Lavalley

Point de recherche
NASA ORNL DAAC - NASA

Editeur (publication)
NASA ORNL DAAC - NASA

Proposition de citation


. An Unexpectedly Large Count of Trees in the West African Sahara and Sahel - Bassin Arachidier au Sénégal.
http://147.100.164.43:8080/geonetwork/srv/api/records/51c7c1a1-33e9-4f5f-8da8-f5b7c9ebb75c

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