51c7c1a1-33e9-4f5f-8da8-f5b7c9ebb75c Claudia Lavalley CNRS UMR TETIS claudia.lavalley@cnrs.fr NASA NASA ORNL DAAC uso@daac.ornl.gov NASA NASA ORNL DAAC uso@daac.ornl.gov 2022-05-17T19:20:40 ISO 19115:2003/19139 1.0 32628 EPSG An Unexpectedly Large Count of Trees in the West African Sahara and Sahel - Bassin Arachidier au Sénégal 2022-05-17T19:08:39 1.0 2015-01-01 https://doi.org/10.3334/ORNLDAAC/1832 NASA NASA ORNL DAAC uso@daac.ornl.gov 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. NASA NASA ORNL DAAC uso@daac.ornl.gov http://idg-tetis.teledetection.fr/data/vignettes/BandeauNASA.png Bandeau NASA image/png remote sensing vegetation map deep learning Very High spatial resolution optical imagery General 2015-01-01T01:00:00 Land cover GEMET - INSPIRE themes, version 1.0 2008-06-01 environment land forestry GEMET - Concepts 2010-01-13 VEGETATION COVER FOREST COMPOSITION/VEGETATION STRUCTURE SHRUBLAND/SCRUB SAVANNAS GCMD Keywords viewer 2019-04-24 Biodiversité TETIS Thesaurus, version 1.0 21112019 2019-11-21 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 unclassified no classification in particular description 50 environment imageryBaseMapsEarthCover biota -17.14375 -13.91848 13.49101 15.35907 2005-11-01T00:00:00Z 2018-03-31T00:00:00Z some additional information GeoPackage, ESRI Shapefile 1.0 http://idg-tetis.teledetection.fr//data/ArbresAfricainsNASA/BassinArachidierSenegal/BassinArachidienSenegal.zip file for download Vector Layer WARNING: 15Gb!! http://idg-tetis.teledetection.fr//data/ArbresAfricainsNASA/Senegal_BA.gpkg file for download Geopackage http://idg-tetis.teledetection.fr//tests/ArbresAfricainsNASA/tilemap.gpkg file for download NASA African trees tilemap http://193.48.189.79:8080/geoserver/tree_west_africa_nasa/wms/utm_28_tiles OGC:WMS utm_28_tiles WMS Service http://193.48.189.79:8080/geoserver/tree_west_africa_nasa/wms/utm_29_tiles OGC:WMS utm_29_tiles WMS Service Data Quality check This is is some data quality check report 2022-05-17T19:08:39 some explanation about the conformance true Commission Regulation (EU) No 1089/2010 of 23 November 2010 implementing Directive 2007/2/EC of the European Parliament and of the Council as regards interoperability of spatial data sets and services 2010-12-08T12:00:00 See the referenced specification true COMMISSION REGULATION (EC) No 1205/2008 of 3 December 2008 implementing Directive 2007/2/EC of the European Parliament and of the Council as regards metadata 2008-12-04T12:00:00 See the referenced specification true 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.