An African-wide map of tree cover at individual tree level

The consistent monitoring of trees both inside and outside of forests is key to mitigating climate change. However existing large-scale tree cover maps primarily quantify forest cover and do not include isolated trees, as these are not discernible in lower resolution satellite images. In many dryland countries these non-forest trees constitute the main form of tree cover, and play a vital role in ecological stability, local economies, livelihoods, and food security.

Here we make use of the PlanetScope nanosatellite constellation, which delivers global very high-resolution daily imagery, to map both forest and non-forest tree cover for continental Africa using images from a single year. We composite 232,053 4-band scenes from 2019 into 1x1° mosaics and apply a Convolutional Neural Network to segment canopy cover of all trees and shrubs. To train the network we use a combination of manually annotated 1 m labels and source images upsampled from 3 m to 1 m, resulting in a final model that maps tree cover at 1 m across the continent, segmenting closed canopies in forest areas and individual scattered trees in savannah areas.

Our prototype map demonstrates that a precise assessment of all tree-based ecosystems is possible at continental scale, and reveals that 29% of tree cover is found outside areas previously classified as tree cover in state-of-the-art maps, such as in croplands and grassland. This analysis lays the groundwork towards global scale studies of tree cover at individual tree level and annual temporal scale, which is crucial for improved managing of woody resources, monitoring of TOF in relation to agroforestry, tree planting and restoration efforts, and assessing land use impacts in non-forest landscapes.