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 km 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.

中文翻译：

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使用航空图像和基于 LiDAR 的 U-Net 模型绘制加利福尼亚州亚米级树高地图

树冠高度是森林生物量、生产力和生态系统结构最重要的指标之一，但从地面和太空精确测量具有挑战性。在这里，我们使用适用于回归的 U-Net 模型，通过来自 USDA-NAIP 计划的 0.6 m 高分辨率航空图像来绘制加利福尼亚州所有树木的树冠高度。 U-Net 模型使用根据航空 LiDAR 数据计算出的冠层高度模型作为参考，以及 2020 年收集的相应 RGB-NIR NAIP 图像进行训练。我们使用跨不同区域的 42 个独立的 1 公里区域来评估深度学习模型的性能。加利福尼亚州的森林类型和景观变化。我们对树高的预测平均误差为 2.9 m，并且在加州整个树高范围内显示出相对较低的系统偏差。 2020年，加州5m以上树木覆盖率达到19.3%。我们的模型在不饱和的情况下成功估计了高达 50 m 的树冠高度，优于全球模型中现有的树冠高度产品。我们使用的方法可以重建从最低点光学机载图像观察到的单棵树的三维结构，这表明即使存在图像失真，也具有相对强大的估计和绘图能力。这些发现证明了使用 NAIP 图像进行大规模绘图和树高监测以及潜在生物量估计的潜力。