单一垂直观测获取的LAI（叶面积指数）未能量化出各林木冠层垂直结构在减缓降雨侵蚀的功能差异！在前期青年基金研究基础上，通过⑴地面激光扫描+无人机多角度遥感的信息处理，获取林冠层的LAI、LAD（叶面积密度）作为林冠层垂直结构参数；⑵设置林下植被覆盖模式+隐含指示因子量化表达比较，从差异性、互补性出发，构建可体现林下植被覆盖遥感复合判定因子，替代判定林下植被覆盖状况；⑶借助①现场测定、揭示林木的LAI、LAD的降雨减蚀过程作为林冠层降雨减蚀规律，②径流小区测定的林地覆盖物的降雨减蚀规律，系统阐明林分垂直结构降雨减蚀规律；⑷利用遥感反演出林分LAI、LAD和林下植被覆盖物，根据阐明的林分垂直结构降雨减蚀规律，反演出区域植被覆盖的降雨减蚀功能，并通过室内降雨模拟+区域实测验证分析，实现林分垂直结构降雨减蚀功能的遥感量化模拟，为遥感的尺度性水土流失的预测预报提供理论基础和技术借鉴。

Leaf area index (LAI) of single vertical observations obtained cannot be able to quantify functional differences of trees canopy vertical structure in the rainfall reducing cavitation. On the basis of National Natural Science Foundation of China (41401385), LAI and leaf area density (LAD) of trees canopy obtained by information processing of terrestrial laser scanning and Unmanned Aerial Vehicle (UAV) multi-angle remote sensing are as tree canopy vertical structure parameters. Set up undergrowth vegetation cover patterns and hidden indicators to quantify expression and comparation, and then construct the remote sensing composite determining factors that can reflect the undergrowth vegetation cover to be instead of determining undergrowth vegetation cover condition. In addition, by measuring in place, revealing rainfall reducing cavitation process of forestry LAI and LAD are as canopy rainfall reducing cavitation rules. Also, rainfall erosion reduction rules of stand vertical structure can be systematacially clarified according to forestry cover rainfall reducing cavitation rules measured in the runoff plot. Therefore, based on the above, rainfall reducing cavitation function can be inversed by stand LAI, LAD and undergrowth vegetation cover according to clarified rainfall erosion reduction rules of stand vertical structure; meanwhile, remote sensing quantitative simulation of rainfall reducing cavitation function of stand vertical structure can be achieved by simulating laboratory rainfall and regional test validation analysis, that can provide the theoretical foundation and technical reference for scalability soil erosion forecast of remote sensing.