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点云密度和体素大小对单木LAI反演的影响 被引量:1

Effects of Point Cloud Density and Voxel Size on Individual Tree LAI Inversion
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摘要 为了提高立体像素法对单木叶面积指数(leaf area index,LAI)的反演精度,探讨了点云密度和体素大小对单木LAI反演结果的影响。获取滇朴和雪松2种具有典型代表性的单木地面激光雷达点云数据和实测LAI数据,分别对单木点云进行0.02~0.1和0.2~1倍不同程度抽稀,以点云平均最邻近距离表征点云密度,探讨了在不同点云密度下估测LAI随体素大小变化的关系。结果表明:(1)点云密度和体素大小对单木LAI的反演精度影响较大。相同体素大小下,反演的LAI值随点云平均最邻近距离的减小而增大,即点云密度越大,估测LAI越大;相同点云平均最邻近距离即同一点云密度下,反演的LAI值随体素的增大而增大。(2)以反演精度最高的体素大小为最优体素,不同点云密度下最优体素值不同,应根据点云密度选取体素大小以提高精度。 In order to improve the inversion accuracy of the leaf area index(LAI)of the individual tree by the voxel-based method,the effects of the point cloud density and voxel size on the inversion result of the individual tree LAI are discussed.Obtain the terrestrial laser scanner point cloud data of two typical representative individual trees which are Celtis tetrandra Roxb.and Cedrus deodara G.Don,as well as its measured LAI data.The individual tree point cloud is diluted 0.02-0.1 and 0.2-1 times respectively.The point cloud density is characterized by the average nearest distance of the point cloud,and the relationship between the estimated LAI and the voxel size under different point cloud density is discussed.The results show that:(1)The point cloud density and voxel size have a great influence on the accuracy of LAI inversion.Under the same voxel size,the inversion LAI value increases with the decrease of the average nearest distance of the point cloud,that is,the greater the point cloud density,the larger the estimated LAI;under the same point cloud average closest distance,the inversion LAI value increases with the increase of voxel.(2)The voxel size with the highest inversion accuracy is taken as the optimal voxel.The optimal voxel value is different under different point cloud densities.The voxel size should be selected according to the point cloud density to improve the accuracy.
作者 张建鹏 王成 王金亮 ZHANG Jianpeng;WANG Cheng;WANG Jinliang(Faculty of Geography,Yunnan Normal University,Kunming 650500,China;Key Laboratory of Resources and Environmental Remote Sensing for Universities in Yunnan,Kunming 650500,China;Center for Geospatial Information Engineering and Technology of Yunnan Province,Kunming 650500,China;Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100094,China)
出处 《遥感信息》 CSCD 北大核心 2021年第1期112-119,共8页 Remote Sensing Information
基金 国家自然科学基金项目(41961060) 国家重点研发计划政府间国际科技创新合作重点专项项目(2018YFE0184300) 云南省高校创新团队项目(IRTSTYN) 云南师范大学研究生科研创新基金项目(ysdyjs2019142)。
关键词 立体像素法 单木 叶面积指数 地面激光雷达 点云密度 体素大小 voxel-based method individual tree leaf area index terrestrial laser scanner point cloud density voxel size
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