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Tree canopy height is one of the most important indicators of forest biomass, productivity, and ecosystemstructure, but it is challenging to measure accurately from the ground and from space. Here, we used a U-Netmodel adapted for regression to map the canopy height of all trees in the state of California with very highresolution aerial imagery 0.6 m from the USDA-NAIP program. The U-Net model was trained using canopyheight models computed from aerial LiDAR data as a reference, along with corresponding RGB-NIR NAIP imagescollected in 2020. We evaluated the performance of the deep-learning model using 42 independent 1 km2 areasacross various forest types and landscape variations in California. Our predictions of tree heights exhibited amean error of 2.9 m and showed relatively low systematic bias across the entire range of tree heights present inCalifornia. In 2020, trees taller than 5 m covered ∼ 19.3% of California. Our model successfully estimatedcanopy heights up to 50 m without saturation, outperforming existing canopy height products from globalmodels. The approach we used allowed for the reconstruction of the three-dimensional structure of individualtrees as observed from nadir-looking optical airborne imagery, suggesting a relatively robust estimation andmapping capability, even in the presence of image distortion. These findings demonstrate the potential of largescale mapping and monitoring of tree height, as well as potential biomass estimation, using NAIP imagery., 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 canopy height of all trees in the state of California with very high-resolution aerial imagery 0.6 m from the USDA-NAIP program. The U-Net model was trained using canopy height models computed from aerial LiDAR data as a reference, along with corresponding RGB-NIR NAIP images collected in 2020. We evaluated the performance of the deep-learning model using 42 independent 1 km2 areas across various forest types and landscape variations in California. Our predictions of tree heights exhibited a mean error of 2.9 m and showed relatively low systematic bias across the entire range of tree heights present in California. In 2020, trees taller than 5 m covered ∼ 19.3% of California. Our model successfully estimated canopy heights up to 50 m without saturation, outperforming existing canopy height products from global models. The approach we used allowed for the reconstruction of the three-dimensional structure of individual trees as observed from nadir-looking optical airborne imagery, suggesting a relatively robust estimation and mapping capability, even in the presence of image distortion. These findings demonstrate the potential of large-scale mapping and monitoring of tree height, as well as potential biomass estimation, using NAIP imagery.

Monitoring changes in tree cover for assessment of deforestation is a premise for policies to reduce carbon emission in the tropics. Here, a U-net deep learning model was used to map monthly tropical tree cover in the Brazilian state of Mato Grosso between 2015 and 2021 using 5 m spatial resolution Planet NICFI satellite images. The accuracy of the tree cover model was extremely high, with an F1-score >0.98, further confirmed by an independent LiDAR validation showing that 95% of tree cover pixels had a height >5 m while 98% of non-tree cover pixels had a height <5 m. The biannual map of deforestation was then built from the monthly tree cover map. The deforestation map showed relatively consistent agreement with the official deforestation map from Brazil (67.2%) but deviated significantly from Global Forest Change (GFC)’s year of forest loss, showing that our product is closest to the product made by visual interpretation. Finally, we estimated that 14.8% of Mato Grosso’s total area had undergone clear-cut logging between 2015 and 2021, and that deforestation was increasing, with December 2021, the last date, being the highest. High-resolution imagery from Planet NICFI in conjunction with deep learning techniques can significantly improve the mapping of deforestation extent in tropical regions.