基于新一代星载LiDAR数据的中国30m分辨率森林高度反演研究

高分辨率森林高度产品对陆地生态系统碳循环研究具有重要意义。相比第一代星载激光雷达ICESat-1/GLAS，新一代星载激光雷达ICESat-2/ATLAS和GEDI的激光点密度大幅提高，为大区域高分辨率森林高度制图提供了可靠的数据源。本项目拟开展波形分解、光子去噪等星载LiDAR数据处理方法研究；建立兼顾激光特性与地形因子影响的光斑尺度森林高度几何物理改正模型，结合星载光子LiDAR数据特点提取特征参数并构建光斑尺度森林高度反演模型；分析和评价两种不同模态星载LiDAR反演的森林高度精度一致性和可靠性；充分挖掘长时间序列光学遥感影像的森林垂直结构信息，利用主被动遥感协同构建不同生态地理分区和森林类型对应的森林高度外推模型，实现中国30m分辨率森林高度制图。本研究将为星载LiDAR大区域森林高度制图提供理论和方法支撑，同时为我国森林碳汇估算、应对全球气候变化与生态环境治理提供科学数据支持。

Mapping forest height with high spatial resolution is of great significance for better understanding terrestrial ecosystem carbon cycle. Compared with ICESat/GLAS, the new space-borne LiDAR systems (ICESat-2/ATLAS and GEDI) have denser data sampling, providing a reliable data source for mapping forest height with high spatial resolution at large scale. This project aims to explore the possibility of ICESat-2/ATLAS and GEDI data in estimating forest height with 30m spatial resolution over China. Firstly, we will conduct the research on the processing methods of space-borne LiDAR data, including waveform decomposition and noise photon removal. Secondly, a physical correction model will be developed to take into account the influence of laser sensor characteristics and surface topography on the retrieval of footprint-scale forest height; Besides, some feature parameters will be designed to establish the footprint-scale forest height models from the photon-counting ICESat-2/ATLAS data. Thirdly, we will conduct the research about the consistency analysis between the footprint-scale forest heights from the two space-borne LiDAR datasets, ensuring the consistency and reliability of LiDAR-estimated forest height accuracy. Finally, the time-series optical images will be used to obtain the information of canopy vertical structure, then active and passive remote sensing data will be combined to construct forest height extrapolation models corresponding to different ecological geographic divisions and forest types for mapping forest height with 30m spatial resolution over China. The study will provide theoretical basis and methodological support for forest height mapping over large areas, and also provide scientific data for estimating forest carbon sink in China, coping with global climate change and ecological environment.