Description
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.
Resources
| Name | Format | Description | Link |
|---|---|---|---|
| Download this dataset | HTML | This link allows direct data access via Earthdata login | Open resource |
| Get a related visualization | PNG | The tree density per hectare is shown for different crown size classes: (a) 3-15 m2, (b) 15-50 m2, (c) 50-200 m2, (d) >200 m2. Source: Brandt et al. (2020) | Open resource |
| View documentation related to this dataset | HTML | ORNL DAAC Data Set Documentation | Open resource |
| View documentation related to this dataset | HTML | An Unexpectedly Large Count of Trees in the West African Sahara and Sahel: gpkg_to_esri.py | Open resource |
| View documentation related to this dataset | An Unexpectedly Large Count of Trees in the West African Sahara and Sahel: Non-Forest_Trees_Sahara_Sahel.pdf | Open resource | |
| This dataset's landing page | HTML | Data set Landing Page DOI URL | Open resource |
| Google Scholar search results | HTML | Search results for publications that cite this dataset by its DOI. | Open resource |