红树林结构参数的准确获取能为红树林资源有效管理提供决策依据，对红树林资源科学保护工作的顺利开展具有重要意义。然而现有红树林遥感研究多侧重区域尺度，且未充分考虑潮汐淹没及野外采样数据与遥感数据尺度不匹配的影响，而且针对单木尺度的研究较少，难以满足红树林科学保护工作的数据需求。基于此，本项目拟从以下三个方面入手：1）基于UAV LiDAR系统采集高密度LiDAR数据，实现红树林单木结构参数提取；2）基于Sentinel-2时间序列影像及其他辅助数据校正潮汐淹没影响，实现红树林空间分布精确提取；3）基于UAV LiDAR数据实现红树林采样数据与Sentinel-2影像数据空间尺度的无差异匹配，构建时间序列红树林结构参数反演模型，并对模型进行区域外推，实现结构参数区域无缝提取。本项目的顺利实施不仅能推动我国单木及区域尺度红树林结构参数反演研究进展，而且能为红树林科学保护提供高精度基础数据。

The accurate acquisition of mangrove structural information can provide information for the decision-making of effective management of mangrove resources and is significant for scientific protection of mangrove resources. However, most the existing studies focus on mangrove structural parameters retrieval on regional scale and without considering the effects of tidal submergence and scale mismatch between field sampling data and remote sensing data on the results. Furthermore, there are few studies on individual tree scale. Therefore, the existing results are difficult to meet the data requirement of mangrove scientific protection. To solve the above problem, this project focuses on the following three aspects: 1) The individual tree structural parameters are extracted based on the high-density LiDAR data acquired with the UAV LiDAR system. 2) The high-precision extraction of mangrove spatial distribution is achieved by correcting the effect of tidal inundation with Sentinel-2 time series images and other auxiliary data. 3) Based on UAV LiDAR data, the scale effect of spatial sampling is researched and the spatial sampling of mangrove sampling data and Sentinel-2 time series data is matched without difference. The regional structural parameters retrieval models are constructed with different time series images and the influence of time series on the results is explored. Then the regional extrapolation of the model is carried out to realize the seamless extraction of mangrove structural parameters. The implementation of the project can not only promote the research progress of mangrove structural parameters retrieval on individual tree scale and regional scale in China, but also provide high-precision foundation data for the scientific protection of mangrove resources.