Large-scale, high-resolution forest canopy height mapping plays a crucial role in understanding regional and global carbon and water cycles. Spaceborne LiDAR missions, including the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) and the Global Ecosystem Dynamics Investigation (GEDI), provide global observations of forest structure but are spatially sparse and subject to inherent uncertainties. In contrast, near-surface LiDAR platforms, such as airborne and unmanned aerial vehicle (UAV) LiDAR systems, offer much finer measurements of forest canopy structure, and a growing number of countries have made these datasets openly available. In this study, a state-of-the-art monocular depth estimation model, Depth Anything V2, was trained using approximately 16,000 km2 of canopy height models (CHMs) derived from publicly available airborne LiDAR point clouds and related products across multiple countries, together with 3 m resolution PlanetScope and airborne RGB imagery. The trained model, referred to as Depth2CHM, enables the estimation of spatially continuous CHMs directly from PlanetScope RGB imagery. Independent validation was conducted at sites in China (approximately 1 km2) and the United States (approximately 116 km2). The results showed that Depth2CHM could accurately estimate canopy height, with biases of 0.59 m and 0.41 m and root mean square errors (RMSEs) of 2.54 m and 5.75 m for these two sites, respectively. Compared with an existing global meter-resolution CHM product, the mean absolute error is reduced by approximately 1.5 m and the RMSE by approximately 2 m. These results demonstrated that monocular depth estimation networks trained with large-scale airborne LiDAR-derived canopy height data provide a promising and scalable pathway for high-resolution, spatially continuous forest canopy height estimation from satellite RGB imagery.

翻译：大规模、高分辨率的森林冠层高度制图对于理解区域及全球碳循环与水循环具有至关重要的作用。星载激光雷达任务，包括冰、云和陆地高程卫星-2（ICESat-2）与全球生态系统动力学调查（GEDI），提供了全球森林结构的观测数据，但其空间分布稀疏且存在固有的不确定性。相比之下，近地表激光雷达平台，如机载和无人机激光雷达系统，能够提供更为精细的森林冠层结构测量数据，并且越来越多的国家已公开提供这些数据集。在本研究中，我们利用来自多个国家公开可用的机载激光雷达点云及相关产品衍生的约16,000 km²的冠层高度模型，以及3米分辨率的PlanetScope和机载RGB影像，训练了一个先进的单目深度估计模型Depth Anything V2。训练后的模型，称为Depth2CHM，能够直接从PlanetScope RGB影像估算空间连续的冠层高度模型。研究在中国（约1 km²）和美国（约116 km²）的站点进行了独立验证。结果表明，Depth2CHM能够准确估算冠层高度，在这两个站点的偏差分别为0.59米和0.41米，均方根误差分别为2.54米和5.75米。与现有的全球米级分辨率冠层高度模型产品相比，平均绝对误差降低了约1.5米，均方根误差降低了约2米。这些结果表明，利用大规模机载激光雷达衍生的冠层高度数据训练的单目深度估计网络，为从卫星RGB影像进行高分辨率、空间连续的森林冠层高度估算提供了一条前景广阔且可扩展的途径。