近地面激光雷达点云密度对森林冠层结构参数提取准确性的影响

Effects of point cloud density on the accuracy of forest canopy structure parameters extracted from near-surface light detection and ranging data

基于激光雷达技术获取冠层结构为森林生态学研究增加了新的维度。搭载于多旋翼无人机的近地面激光雷达相比于固定翼有人机的机载激光雷达,能够更加灵活高效地获取森林群落样地高密度点云。但在实际操作中,往往出现局部低密度点云数据,影响了冠层结构参数提取的准确性。使用4块森林动态样地的近地面激光雷达点云数据;利用航带分解方法分析各样地低密度样方成因;采用点云抽稀模拟算法计算并拟合偏差曲线,对比不同样地、参数和取样尺度间的点云密度对冠层结构参数提取准确性的影响;根据偏差曲线计算各条件下保证参数提取准确性的最低点云密度。结果发现:1)低密度区域主要受地形或(和)近地面遥感设计规划的影响。地形复杂的测区(西双版纳和古田山样地),遥感规划难度大,整体难以获取高密度点云(在30点/m^(2)左右),容易在沟谷和高海拔处出现低密度样方。平坦测区(长白山两块样地)虽可获取高密度点云(均超过150点/m^(2)),但欠佳的遥感规划设计导致长白山1测区北部出现1hm^(2)低密度区域。2)冠层参数提取准确性随点云密度减少而迅速降低,呈负指数幂函数关系。这一变化趋势在不同参数和尺度间差异较大,但在各样地间无显著差异。3)根据偏差曲线拟合结果,点云密度达到16点/m^(2)可以实现5—20 m取样尺度下冠层结构参数提取准确度达95%。综上,为更好的将近地面激光雷达技术应用在森林生态学研究中,应注重外业遥感作业方案的合理性,充分考虑地形的影响,在源头把控数据质量;如果数据存在低密度点云的情况,应适当放宽取样尺度并慎重提取对点云密度较为敏感的冠层结构参数。

Forest canopy structure parameters extracted and derived from light detection and ranging(LiDAR)technology could be regard as a noble view and new dimension to the traditional forest ecology research.The near-surface light detection and ranging on small multiple-rotors drone is more flexible and more efficient to gain local-scale and community-scale forest plots′high-density point cloud than airborne light detection and ranging on lager fixed wing aircraft.However,unexpected low-density sample was spotted in the relative high-density target region,which affects the accuracy of canopy structure parameters calculating.This project was based on point cloud collected by near-surface light detection and ranging from 4 large long-tern morning forest dynamic plots.Firstly,strip decomposition method was applied to analyze the reason of low-density samples in each plot.Secondly,point cloud thinning simulation algorithm were utilized to fit deviation curve.We compared deviation curve among plots,parameters and sampling scales to demonstrate the effect of low density on extracting forest canopy structure parameter accuracy.Finally,the necessary point cloud density which could guarantee adequate accuracy of parameter extraction was calculated under each condition.The results showed that:1)the landform or(and)the ill-considered near-surface remote sensing programing and design were main reasons of occurrence of low-density sample in forests dynamic plots.It is relative hard to design and gather high-density point cloud data in rugged and complex dynamic forest plots(around 30 points/m^(2)),such as Gutianshan and Xishuangbanna plot.Low-density sample was often spotted in the deep valley and high elevation samples in such plots.In the flat forest region such as Changbaishan 1&2 plot,it is easier to gain high-density point cloud data(more than 150 points/m^(2)).However,the imperfect design result to 1 hm^(2)low-density area in the north part of Changbaishan 1 plot.2)As point could density dropping,the accuracy of parameters was also accelerated dropping as well,which showed a negative exponent power function.The deviation curve showed clearly difference between parameters and sample scales other than plots.3)We could gain 95%extracting accuracy at 5—20 sample scales with a point cloud density of 16points/m^(2)according to the deviation curve.In conclusion,in order to better apply near-surface light detection and ranging technology in forest ecology research,we should pay more attention to the rationality of remote sensing design and programing.Researchers should fully understand the impact of terrain and control the point cloud data quality from the source.If low-density point cloud sample already existed in the target region,ecologist could coarse the sampling scale appropriately to gain a satisfied accuracy of canopy structure parameters and extracted point cloud density-sensitive canopy structure parameter at fine sample scale cautiously.