文献类型：期刊文献

英文题名：Analysis of the Spatial Differences in Canopy Height Models from UAV LiDAR and Photogrammetry

作者：Liu, Qingwang[1,2] Fu, Liyong[1,3] Chen, Qiao[1] Wang, Guangxing[4] Luo, Peng[1] Sharma, Ram P.[5] He, Peng[6] Li, Mei[1] Wang, Mengxi[1] Duan, Guangshuang[1,7]

第一作者：Liu, Qingwang;刘清旺

通信作者：Wang, GX[1]

机构：[1]Chinese Acad Forestry, Res Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China;[2]Natl Forestry & Grassland Adm, Key Lab Forestry Remote Sensing & Informat Syst, Beijing 100091, Peoples R China;[3]Natl Forestry & Grassland Adm, Key Lab Forest Management & Growth Modeling, Beijing 100091, Peoples R China;[4]Southern Illinois Univ, Sch Earth Syst & Sustainabil, Carbondale, IL 62901 USA;[5]Tribhuwan Univ, Inst Forestry, Kathmandu 44600, Nepal;[6]Natl Forestry & Grassland Adm, Cent South Inventory & Planning Inst, Changsha 410014, Peoples R China;[7]Xinyang Normal Univ, Coll Math & Stat, Xinyang 464000, Peoples R China

年份：2020

卷号：12

期号：18

外文期刊名：REMOTE SENSING

收录：;EI(收录号：20204009301104);Scopus(收录号：2-s2.0-85091839717);WOS:【SCI-EXPANDED(收录号:WOS:000580391600001)】；

基金：This research was partially funded by the National Key R&D Program of China under grant number 2017YFD0600904, the Central Public-interest Scientific Institution Basal Research Fund (Grant No. CAFYBB2019QD003 and CAFYBB2016SZ003).

语种：英文

外文关键词：digital surface model; digital terrain model; canopy height model; constrained neighbor interpolation; ordinary neighbor interpolation; point cloud density; stereo imagery

摘要：Forest canopy height is one of the most important spatial characteristics for forest resource inventories and forest ecosystem modeling. Light detection and ranging (LiDAR) can be used to accurately detect canopy surface and terrain information from the backscattering signals of laser pulses, while photogrammetry tends to accurately depict the canopy surface envelope. The spatial differences between the canopy surfaces estimated by LiDAR and photogrammetry have not been investigated in depth. Thus, this study aims to assess LiDAR and photogrammetry point clouds and analyze the spatial differences in canopy heights. The study site is located in the Jigongshan National Nature Reserve of Henan Province, Central China. Six data sets, including one LiDAR data set and five photogrammetry data sets captured from an unmanned aerial vehicle (UAV), were used to estimate the forest canopy heights. Three spatial distribution descriptors, namely, the effective cell ratio (ECR), point cloud homogeneity (PCH) and point cloud redundancy (PCR), were developed to assess the LiDAR and photogrammetry point clouds in the grid. The ordinary neighbor (ON) and constrained neighbor (CN) interpolation algorithms were used to fill void cells in digital surface models (DSMs) and canopy height models (CHMs). The CN algorithm could be used to distinguish small and large holes in the CHMs. The optimal spatial resolution was analyzed according to the ECR changes of DSMs or CHMs resulting from the CN algorithms. Large negative and positive variations were observed between the LiDAR and photogrammetry canopy heights. The stratified mean difference in canopy heights increased gradually from negative to positive when the canopy heights were greater than 3 m, which means that photogrammetry tends to overestimate low canopy heights and underestimate high canopy heights. The CN interpolation algorithm achieved smaller relative root mean square errors than the ON interpolation algorithm. This article provides an operational method for the spatial assessment of point clouds and suggests that the variations between LiDAR and photogrammetry CHMs should be considered when modeling forest parameters.