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Tree canopy height is one of the most important indicators of forest biomass,productivity, and species diversity, but it is challenging to measureaccurately from the ground and from space. Here, we used a U-Net model adaptedfor regression to map the canopy height of all trees in the state of Californiawith very high-resolution aerial imagery (60 cm) from the USDA-NAIP program.The U-Net model was trained using canopy height models computed from aerialLiDAR data as a reference, along with corresponding RGB-NIR NAIP imagescollected in 2020. We evaluated the performance of the deep-learning modelusing 42 independent 1 km$^2$ sites across various forest types and landscapevariations in California. Our predictions of tree heights exhibited a meanerror of 2.9 m and showed relatively low systematic bias across the entirerange of tree heights present in California. In 2020, trees taller than 5 mcovered ~ 19.3% of California. Our model successfully estimated canopy heightsup to 50 m without saturation, outperforming existing canopy height productsfrom global models. The approach we used allowed for the reconstruction of thethree-dimensional structure of individual trees as observed from nadir-lookingoptical airborne imagery, suggesting a relatively robust estimation and mappingcapability, even in the presence of image distortion. These findingsdemonstrate the potential of large-scale mapping and monitoring of tree height,as well as potential biomass estimation, using NAIP imagery.

Sub-Meter Tree Height Mapping of California using Aerial Images and LiDAR-Informed U-Net Model