基于无人机激光雷达的森林冠层高度分析

快速准确获取森林结构参数对森林资源调查管理及全球碳汇研究具有重要意义。以祁连山东、中部青海云杉林为研究对象，利用16个无人机激光雷达（LiDAR）点云数据、正射影像数据结合实地样方观测数据，提取样方内青海云杉的单木树高并准确验证树木分割精度；结合实测数据和地形数据，依据统计指标验证提取树高精度并分析原因；基于点云数据提取的各样方树高分析祁连山青海云杉冠层高度在空间上的变化。结果表明：在祁连山山地森林，冠层高度平均值估算精度最高，R²为0.93，RMSE为1.39 m（P<0.05）；地形影响基于点云数据的树高提取，坡度较小的青海云杉树高提取效果更好；从东到西，青海云杉平均树高呈下降趋势；随着海拔高度上升，青海云杉的平均树高先上升后下降，这与祁连山东西水热条件差异和不同海拔树木年龄分布有关。

Analysis of Forest Canopy Height based on UAV LiDAR: A Case Study of Picea crassifolia in the East and Central of the Qilian Mountains

Rapid and accurate acquisition of forest structural parameters has been significant for forest resource investigation. In this study， photogrammetric and field-based tree height measurement of the Picea crassifolia were validated in the east and central of the Qilian Mountains. The individual segmentation algorithm using Canopy Height Model was applied to identify the position and height of the Picea crassifolia within each plot. The extraction accuracy of the average tree height was recognized the highest among the four indexes of maximum value， minimum value， mean value and standard deviation， with Root Mean Square Error （RMSE） values of 1.39 m and R² values of 0.93（P<0.05）. Tree heights extracted from LiDAR data of Picea crassifolia were used to analyze the spatial distribution of tree height in the Qilian Mountains. There was a downward trend of the average forest canopy height from east to west in the Qilian Mountains. As the altitude rises， the forest canopy height showed a “unimodal” change， which peaked at change an altitude of 2 900 m. This study shown that UAV photogrammetric tree height measurements was a viable option for intensive forest monitoring plots. Additionally， it was shown that underestimated evident in field-based and UAV laser scanning tree height measurements could potentially lead to misinterpretation of results when field-based measurements are used as validation.