点云密度对机载激光雷达大区域亚热带森林参数估测精度的影响  

Effects of Point Cloud Density on the Estimation Accuracy of Large-Area Subtropical Forest Inventory Attributes Using Airborne LiDAR Data

作　　者：周相贝 李春干[1] 代华兵 余铸 李振 苏凯 Zhou Xiangbei;Li Chungan;Dai Huabing;Yu Zhu;Li Zhen;Su Kai(Forestry College of Guangxi University,Nanning 530004;Guangxi Natural Resources Vocational and Technical College,Fusui 532199;Guangxi Forest Inventory and Planning Institute,Nanning 530011)

机构地区：[1]广西大学林学院,南宁530004 [2]广西自然资源职业技术学院,扶绥532199 [3]广西壮族自治区林业勘测设计院,南宁530011

出　　处：《林业科学》2023年第9期23-33,共11页Scientia Silvae Sinicae

基　　金：广西林业科技推广示范项目(GL2020KT02);广西壮族自治区林业勘测设计院科研业务费项目(GXLKYKJ201601)。

摘　　要：【目的】点云密度是影响机载激光雷达数据获取和预处理成本的关键因素,探明点云密度对森林参数估测精度的影响,为机载激光雷达大区域森林调查监测应用技术方案的优化提供参考依据。【方法】基于我国广西一个亚热带山地丘陵区域获取的机载激光雷达和样地数据,通过系统稀疏方法,将全密度点云(4.35点·m^(-2))分别稀疏至4.0、3.5、3.0、2.5、2.0、1.5、1.0、0.5、0.2和0.1点·m^(-2),得到11个样地尺度的点云数据集,包括1个全密度和10个稀疏密度点云数据集;应用配对样本t检验方法,分析4种森林类型(杉木林、松树林、桉树林和阔叶林)中稀疏密度点云和全密度点云之间12个激光雷达变量的差异;通过变量和结构固定的多元乘幂模型式,分别采用不同密度点云数据集对林分蓄积量(VOL)和断面积(BA)进行估测,比较模型优度统计指标决定系数(R^(-2))、相对均方根误差(rRMSE)和平均预估误差(MPE)的差异,并应用t检验方法分析稀疏密度点云VOL和BA估测值均值和全密度点云相应估测值均值的差异。【结果】1)点云密度较低时,稀疏密度点云分位数高度(ph25、ph50和ph75)的均值与全密度点云相应变量的均值存在显著性差异,但不同森林类型、不同变量出现显著性差异时的点云密度不同,各森林类型中稀疏密度点云平均高(H_(mean))和点云高变动系数(H_(cv))的均值与全密度点云相应变量的均值基本不存在显著性差异,但点云最大高(Hmax)的均值存在显著性差异;2)各森林类型中,稀疏密度点云冠层覆盖度(CC)和中下层分位数密度(dh25)的均值与全密度点云相应变量的均值差异不显著(阔叶林dh25除外),但中上层分位数密度(dh50和dh75)存在显著性差异;3)各森林类型中,稀疏密度点云平均叶面积密度(LAD_(mean))的均值与全密度点云LAD_(mean)的均值存在显著性差异,当点云密度较低时,稀疏密度点云叶面积�【Objective】Point cloud density is a critical factor affecting the cost of airborne LiDAR data acquisition and preprocessing.Therefore,exploring the influence of point cloud density on the estimation accuracy of forest inventory attributes can provide a reference for optimizing technical schemes for airborne LiDAR-based large-area forest inventory and monitoring.【Method】In this study,we used airborne LiDAR data and field plot data collected in a subtropical mountainous and hilly region in Guangxi,China.Firstly,the original point clouds with a density of 4.35 points·m^(-2)were reduced to 4.0,3.5,3.0,2.5,2.0,1.5,1.0,0.5,0.2,and 0.1 points·m^(-2)using a systematic thinning method,respectively,resulting in 11 plot-level point cloud datasets,including one full-density point cloud dataset and ten reduced-density point cloud datasets.Secondly,a paired sample t-test was used to analyze the differences in 12 LiDAR-derived metrics between reduced-density point clouds and full-density point clouds in four forest types(Chinese fir,pine,eucalyptus,and broad-leaved).Thirdly,using a multiplicative power model formulation with fixed variables and stable structure,the stand volume(VOL)and basal area(BA)were estimated using various density datasets of point clouds,respectively,and their goodness-of-fit statistics,including coefficient of determination(R^(-2)),relative root square error(rRMSE),and mean prediction error(MPE),were compared.Finally,a t-test was used to analyze the differences in the means of the estimates between the reduced-density point clouds and full-density point clouds.【Result】1)When the point cloud density was low,the means of the 25th,50th,and 75th height percentiles(ph25,ph50,and ph75)of the reduced-density point clouds showed statistically significant differences from those of the corresponding variables of the full-density point clouds.However,when statistically significant differences were found for different variables in various forest types,the point cloud densities differed.There were no st

关 键 词：机载激光雷达 LiDAR变量 林分蓄积量 断面积 模型 

分 类 号：S757[农业科学—森林经理学]