This dataset provides individual tree canopy geometries as georeferenced polygon vectors for dryland areas in West African Sahara and Sahel. Derived using deep learning applied to very fine (50 cm) resolution satellite imagery, more than 1.8 billion non-forest trees (i.e., woody plants with a crown size over 3 square meters) over about 1.3 million square km were identified from panchromatic and pansharpened normalized difference vegetation index (NVDI) images. The classification used an automatic tree detection framework based on a supervised deep-learning technique. 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.
Data Citation: 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