文献类型：期刊文献

中文题名：地基与无人机LiDAR融合点云的人工林蓄积量估算

英文题名：Estimation of Volume based on Fusion Point Cloud of Terrestrial and UAV LiDAR

作者：朱俊峰[1] 刘清旺[2,3] 崔希民[1] 张文博[1]

第一作者：朱俊峰

机构：[1]中国矿业大学(北京)地球科学与测绘工程学院,北京100083;[2]中国林业科学研究院资源信息研究所,北京100091;[3]国家林业和草原局林业遥感与信息技术重点实验室,北京100091

年份：2024

卷号：39

期号：1

起止页码：45-54

中文期刊名：遥感技术与应用

外文期刊名：Remote Sensing Technology and Application

收录：CSTPCD;;北大核心:【北大核心2023】；CSCD:【CSCD2023_2024】；

基金：国家重点研发计划项目“森林资源激光雷达遥感动态监测与蓄积量估测技术联合研发”(2020YFE0200800);高分辨率对地观测系统国家重大专项“高分共性产品真实性检验关键技术研究与标准规范编制”(21-Y20B01-9001-19/22)。

语种：中文

中文关键词：点云;融合;单木;材积

外文关键词：Point cloud;Fusion;Individual tree;Volume

分类号：TP79

摘要：激光雷达(Light Detection and Ranging, LiDAR)在林业调查中应用广泛,但单独利用地基或无人机LiDAR难以完整描述复杂的森林垂直结构,地基和无人机的结合可以获取更完整的森林空间结构信息。对地基与无人机点云进行配准融合并提取单木主干,使用随机Hough变换分段拟合树干点云,由分段树干直径拟合削度方程并使用区分求积法计算单木材积,累加单木材积得到样地蓄积量。与二元材积模型计算值进行对比,结果表明:基于融合点云计算单木材积的精度优于地基点云,R2可提升2%以上,RMSE降低0.01 m^(3);削度方程结合区分求积法计算出样地蓄积量R2=0.98,RMSE为0.87 m^(3),达到较高精度,其中杉木材积计算结果的R2为0.96、RMSE为0.07 m^(3),桉树材积的R2为0.93,RMSE为0.07 m^(3);简单、中等、复杂3类样地中,简单和中等样地杉木和桉树材积的R2均在0.94以上,RMSE在0.07 m^(3)左右,复杂样地中材积结果的R2在0.9以下;地基和无人机LiDAR融合点云可以更精细地测量森林空间结构,更好地满足森林资源调查业务化应用的需求。
The Light Detection and Ranging(LiDAR)has been widely used in forest inventory.It is quite diffi-culty to describe the complex vertical structures of forest using the terrestrial or Unmanned Aerial Vehicle(UAV)LiDAR or laser scanning,individually.The complete spatial structure of forest can be obtained by combing the Terrestrial Laser Scanning(TLS)and UAV Laser Scanning(ULS).The TLS and ULS point cloud were registered and fused to extract the trunks of individual trees.The random Hough transform was used to fit the point cloud of the trunk in segments.The taper equation was fitted using the diameters of trunk seg-ments and the differential quadrature method was used to calculate the volumes of individual trees.The volumes of individual trees were accumulate to get plot volume.Compared with the calculated value of the binary volume model,the results showed that the accuracy of calculating the volume of individual tree based on the fusion point cloud was better than that of the terrestrial point cloud,the R2 can be increased by more than 2%,and the RMSE can be reduced by 0.01 m^(3).The R2 and RMSE were 0.98 and 0.87m^(3) for the plot volume,which calcu-lated by the combination of taper equation and differential quadrature method.Among them,the R2 and RMSE of Cunninghamia lanceolata volume were 0.96 and 0.07 m^(3),for Eucalyptus,the R2 and RMSE were 0.93 and 0.07 m^(3).Among the three types of plots:easu,medium,and difficult,the volume R2 of Cunninghamia lanceo-lata and Eucalyptus in easy and medium plots were all above 0.94,the RMSE was about 0.07 m^(3),but the R2 of the volume results in difficult plot was below 0.9.The TLS and ULS fusion point cloud can more finely mea-sure the forest spatial structure,and better meet the needs of forest resource survey applications.