Mapping Continental African Tree Cover at Individual Tree Level With Planet Nanosatellites

Trees Outside Forests (TOF) play a vital role in African ecosystems. In addition to stabilising local climates and acting as a carbon stock, TOF provide key ecological services, serving as a foundation for local livelihoods through the use of tree products for food, fodder, construction, medicinal use and increased agricultural productivity. Despite their essential role, the extent of TOF has not been assessed at continental scale. Existing tree cover maps primarily quantify forest cover and do not include isolated trees, as these are not discernible in lower resolution satellite images. Here we use high-resolution images from the PlanetScope nanosatellite constellation to produce a 1 m map of tree cover in 2019 for all African drylands. We composite 175505 4-band images into 1x1° mosaics and apply a Convolutional Neural Network to segment canopy cover of all trees and shrubs. By upsampling the source images from 3 m to 1 m and training the network with manually annotated 1 m labels, we achieve a model that can segment closed canopies in forest areas and individual trees in savannah areas, across the entire drylands of the continent. We show that for many dryland countries TOF are the dominant component of tree cover, and that most of this tree cover is not considered in existing forest cover maps. Our analysis demonstrates the new opportunities emerging from the combination of machine learning with commercially-available and low cost nanosatellite imagery, and lays the groundwork towards global scale studies of tree cover at individual tree level and annual temporal scale. This will be crucial for managing woody resources, monitoring TOF in relation to tree planting and restoration efforts, as well as detecting illegal removal of trees.