用卫星影像与激光雷达训练模型，实现亚马逊森林高精度树高制图。
High Resolution Tree Height Mapping of the Amazon Forest using Planet NICFI Images and LiDAR-Informed U-Net Model
- 用激光雷达数据训练U-Net回归模型，映射树冠高度。
- 预测误差仅3.68米，最高可测50米树高且无明显饱和。
- 适合监测森林砍伐与再生，支持大范围生态追踪。
树冠高度是森林生物量、生产力和生态系统结构的重要指标，但地面和遥感测量均具挑战性。本文利用2020至2024年期间空间分辨率为约4.78米的Planet NICFI影像，结合航空激光雷达（LiDAR）生成的冠层高程模型作为参考，训练了一个用于回归的U-Net模型，对亚马逊森林的平均树冠高度进行制图。模型在验证样本上的平均误差为3.68米，且在整个亚马逊树高范围内表现出较低的系统性偏差。该模型成功估计了高达40–50米的树高，未出现明显饱和现象，优于现有全球树高产品。研究发现亚马逊森林平均冠层高度约为22米。通过树高变化可识别采伐或毁林事件，且在再生林监测中已获得良好效果。结果表明，利用Planet NICFI影像可实现对老林与再生林的大范围树高动态监测。
原文摘要 · Abstract (English)
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.
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