High Resolution Tree Height Mapping of the Amazon Forest using Planet NICFI Images and LiDAR-Informed U-Net Model

TL;DR

This study developed a U-Net regression model trained on LiDAR data to accurately map and monitor tree canopy heights in the Amazon using high-resolution satellite imagery, enabling large-scale forest assessment.

Contribution

The paper introduces a novel U-Net model for regression that estimates Amazon tree heights from satellite images, outperforming existing global models and enabling detailed forest monitoring.

Findings

Mean error of 3.68 m in height predictions

Successfully mapped Amazon forest with an average height of ~22 m

Detected changes due to logging and regeneration processes

Abstract

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 mean tree canopy height in the Amazon forest from Planet NICFI images at ~4.78 m spatial resolution for the period 2020-2024. 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. Predictions of tree heights on the validation sample 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. We determined that the Amazon forest has an average canopy height of ~22 m. Events such as logging or deforestation could be detected from changes in tree height, and encouraging results were obtained to monitor the height of regenerating forests. These findings demonstrate the potential for large-scale mapping and monitoring of tree height for old and regenerating Amazon forests using Planet NICFI imagery.