Forest mapping and monitoring in Africa using Sentinel-2 data and deep learning

We propose and investigate a method for creating large scale forest height maps at 10 m resolution from Sentinel 2 data using deep neural networks. In addition, we demonstrate how clear-cutting events can be detected in a time series of the resulting forest height maps. The network architecture is a convolutional neural network based on the U-Net architecture. The 13 Sentinel-2 spectral bands are resampled to 10 m spatial resolution and input to the U-Net, which outputs a map with per-pixel forest height estimates. The network is trained with ground truth data acquired from airborne lidar scanning surveys from three different geographical regions. They cover different types of forests: lowland tropical rainforest in the Democratic Republic of Congo, Miombo woodlands (dry forest) in Liwale, Tanzania, and submontane tropical rainforest in Amani, Tanzania. We demonstrate that the trained network generalizes to new geographical regions within the African continent with a mean average error of 4.6 m. This is on-par with a previously published method's ability to generalize to new geographical regions within the same country. Clear-cutting events are detected using a t-test. The null-hypothesis of the t-test is that the forest height has not changed after any given point in time in the forest height time-series.