Wall-to-wall Amazon forest height mapping with planet NICFI, Aerial LiDAR, and a U-Net regression model

Tree canopy height is a key indicator of forest biomass, productivity and structure, yet measuring it accurately at regional or larger scales, whether from the ground or remotely, remains challenging. The objective of this study is to generate the first complete canopy height map of the Amazon forest at ~4.78 m resolution using Planet NICFI imagery and deep learning. Specifically, we (i) trained a U-Net regression model with canopy height models (CHMs) derived from tropical airborne LiDAR and their corresponding Planet NICFI images to estimate canopy height, (ii) evaluated the accuracy of our map against existing global products based on Sentinel-2/1 and Maxar Vivid2 imagery and (iii) assessed its capacity to capture small-scale canopy height changes. Tree height predictions on the validation sample had a mean absolute error of 3.68 m, with minimal systematic bias across the full range of tree heights in the Amazon forest. The main biases are a slight overestimation (up to 5 m) for heights of 5–15 m and an underestimation for most trees above 50 m. Outperforming existing global model-based canopy height products in this region, the model accurately estimated canopy heights up to 40–50 m with minimal saturation. We determined that the Amazon forest has an average canopy height of ~22 m (standard deviation ~5.3 m) and exhibits large-scale patterns, ranging from the tallest forests of the Guiana Shield to shorter forests along wetlands, rivers, rocky outcrops, savannas and high elevations. Events such as logging or deforestation could be detected from changes in tree height, and the results demonstrated a first success in monitoring the height of regenerating forests. Finally, the map of the Amazon forest canopy height is displayed.