点云密度对无人机激光雷达森林参数估测精度的影响  

Effect of UAV-LiDAR Point Density on Estimation Accuracy of Forest Inventory Attributes

作　　者：周梅[1] 李春干[2] 李振 余铸 ZHOU Mei;LI Chun-gan;LI Zhen;YU zhu(School of Computer,Electronic and Information in Guangxi University,Nanning 530004,Guangxi,China;Forestry College of Guangxi University,Nanning530004,Guangx,China;Guangxi Forest Inventory and Planning Institute,Nanning 530011,Guangxi,China)

机构地区：[1]广西大学计算机与电子信息学院,广西南宁530004 [2]广西大学林学院,广西南宁530004 [3]广西林业勘测设计院,广西南宁530011

出　　处：《林业科学研究》2024年第2期39-47,共9页Forest Research

基　　金：广西林业科技推广示范项目“桉树人工林全林分生长无人机精准快速监测与调控技术推广与示范(GL2020KT02)”;广西壮族自治区林业勘测设计院科研业务费专项“无人机森林调查监测”(GXLKYKJ202201)。

摘　　要：[目的]点云密度是影响无人机激光雷达数据获取和预处理成本和效率的关键因素,探明点云密度对林分尺度无人机激光雷达森林参数估测精度的影响,有助于优化无人机激光雷达森林应用技术方案。[方法]以马尾松、桉树人工林为研究对象,采用百分比重采样方法,对密度为247点·m^(-2)的原始点云按40%、20%、8%、4%和2%的比例降低点云密度,得到1个全密度原始点云数据集和5个稀疏密度点云数据集;每个数据集独立进行点云分类、地面点滤波和数字高程模型生成、点云高度归一化等预处理并提取激光雷达变量;对于同一森林类型的同一个森林参数(林分蓄积量、断面积、平均高和平均直径)的估测,各个数据集都采用相同的乘幂模型结构式进行模型拟合,然后比较分析模型优度统计指标的差异,包括:决定系数(R^(2)),相对根方根误差(rRMSE)和平均预报误差(MPE);采用配对样本t检验方法对各个数据集的森林参数估测结果和激光变量的差异进行统计分析。[结果]当点云密度分别稀疏至100、50、…、5点·m^(-2)时,各个森林参数估测模型的精度保持基本一致;各个稀疏密度点云数据集的森林参数估测值的均值与原始点云数据集的估测值的均值不存在显著性差异(p≥0.05);各个稀疏密度点云数据集激光变量的均值和原始点云数据集激光变量的均值基本上不存在显著性差异(p>0.05)。[结论]在无人机激光雷达森林资源调查监测应用中,点云密度可低至5点·m^(-2)。然而,本试验结果仍需通过不同飞行高度获取不同密度点云数据予以验证。[Objective]Point density is a key factor affecting the cost and efficiency of data acquisition and pre-processing of unmanned aerial vehicle(UAV)-based light detection and ranging(LiDAR),and it is helpful to explore the effects of point density on the estimation accuracy of UAV-LiDAR-based forest inventory attributes to optimize the technical schemes for UAV-LiDAR forest applications.[Methods]This study focused on the planted Masson pine and Eucalyptus forests.The original UAV-LiDAR point cloud with a density of 247 points·m^(-2) was reduced by 40%,20%,8%,4%,and 2% according to the percentage of the total point reduction algorithm to obtain six plot-level UAV-LiDAR datasets,including five sets of reduced point densities.Each dataset was pre-processed separately,including point cloud classification,ground point filtering,digital elevation model(DEM)generation,point cloud height normalization,and UAV-LiDARderived metric extractions.The same multiplicative power formula was used for estimating the same forest parameters(stand volume,basal area,mean height,and average diameter at breast height)for the same forest type,and each dataset was used to calibrate the model.Then,the differences in the goodness-of-fit statistics of the models were compared and analyzed based oncoefficient of determination(R^(2)),relative root square error(rRMSE),mean prediction error(MPE),and the differences in the mean of the estimates and the UAV-LiDAR-derived metrics between the reduced point density datasets and the original point density dataset were statistically analyzed.[Results]The results indicated that the model accuracy remained essentially the same when the original point density was reduced to 100,50,...,5 points·m^(-2),and there were no statistically significant differences(p≥0.05)in the estimates of forest inventory attributes between the reduced point density datasets and the original point density dataset.There were basically no statistically significant differences(p≥0.05)in UAV-LiDAR-derived metrics between the reduc

关 键 词：林分蓄积量 断面积 平均高 平均直径 UAV-LiDAR变量 乘幂模型 

分 类 号：S757.2[农业科学—森林经理学]