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.
Science Keywords: |
|
||
Spatial Extent: | Bounding Rectangle: N: 24.03 S: 11.35 E: -5.49 W: -18.0 |
Data Format(s): | Distribution: Binary |
Temporal Extent: | Platform(s): | Environmental Modeling | |
Data Center(s): | ORNL_DAAC | Instrument(s): | Computer |
Version: | 1 |
10.3334/ORNLDAAC/1832
https://doi.org
Creation | |
Last Revision |
4
model products
Complete
Project Short Name | Campaigns | Project Dates |
---|---|---|
Vegetation | No campaigns listed. | No dates provided. |
Coverage Type | Zone Identifier | Geometry | Granule Representation |
---|---|---|---|
HORIZONTAL | CARTESIAN | CARTESIAN |
ORNL DAAC User Services Office, P.O. Box 2008, MS 6407, Oak Ridge National Laboratory
Oak Ridge,
Tennessee
37831-6407
(865) 241-3952
There are no listed data contacts for this collection.
Format: Binary
Format Type: Native
Fees: 0