集成高分辨率UAV影像与激光雷达点云的亚热带森林林分特征反演

Inversion of subtropical forest stand characteristics by integrating very high resolution imagery acquired from UAV and Li DAR point-cloud

该研究集成高分辨率无人机(UAV)影像和激光雷达(Li DAR)点云数据估算亚热带天然次生林林分基本特征变量。首先,基于Li DAR点云和反距离加权插值法构建林下高精度数字高程模型(DEM);然后利用UAV影像对序列构建植被冠层上层三维点云,并借助DEM进行高度信息归一化,提取高度和冠层点云密度相关的特征变量;最后,构建预测模型并估算Lorey’s高、林分密度、胸高断面积、蓄积量。结果表明:联合提取的特征变量与Lorey’s高的敏感性最高,蓄积量次之,林分密度和胸高断面积最低;利用UAV灵活快速的手段获取森林冠层信息,辅以高精度Li DAR数据获取的地形信息,两者互补实现一种可重复的快速、廉价和灵活的林分特征的反演方式。

Aims We applied the integrated very high resolution imagery acquired from Unmanned Aerial Vehicles （UAV） and Light Detection and Ranging （LiDAR） point-loud data to estimate the stand characteristics of a naturally- regenerated forest in a subtropical area. Methods The high precision digital elevation model （DEM） of the forest was constructed base on LiDAR point-cloud and the inverse distance weighted interpolation method. The 3D point-cloud of forest canopy layer was constructed from UAV image pairs, with information from DEM height information normalization, for can- opy height and density. With the above effort, we developed a prediction model to estimate Lorey＇s height, stand density, basal area, and volume. Important findings The quantitative metrics generated from this study appeared very sensitive to Lorey＇s height, followed by volume and basal area. Using UAV as a flexible and rapid method for generating forest can- opy characteristics, combined with topographic information from high precision LiDAR data, seems a viable, rapid, inexoensive, and flexible method in canopy research.