Publicações

 Tree canopy height is one of the most important indicators of forest biomass, productivity, and ecosystem structure, but it is challenging to measure accurately from the ground and from space. Here, we used a U-Net model adapted for regression to map the 2020 tree canopy height in the Amazon forest from Planet Nicfi images at ~4.78 m of spatial resolution. The U-Net model was trained using canopy height models computed from aerial LiDAR data as a reference, along with their corresponding Planet Nicfi images. Our predictions of tree heights exhibited a mean error of 3.68 m and showed relatively low systematic bias across the entire range of tree heights present in the Amazon forest. Our model successfully estimated canopy heights up to 40-50 m without much saturation, outperforming existing canopy height products from global models in this region. Furthermore, events such as logging or deforestation can be detected from the changes in tree height, and our approach also enables the monitoring of the height of regenerating forests. These findings demonstrate the potential for large-scale mapping and monitoring of tree height, as well as potential biomass estimation of old and regenerating Amazon forests, using Planet Nicfi imagery.