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 (NDVI) 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.
N: 24.03 S: 11.35 E: -5.49 W: -18.0
|Temporal Extent:||Platform(s):||Environmental Modeling|
|Project Short Name||Campaigns||Project Dates|
|Vegetation||No campaigns listed.||No dates provided.|
|Coverage Type||Zone Identifier||Geometry||Granule Representation|
ORNL DAAC User Services Office, P.O. Box 2008, MS 6407, Oak Ridge National Laboratory
Oak Ridge, Tennessee 37831-6407
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Format Type: Native