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Applying a Portable Backpack Lidar to Measure and Locate Trees in a Nature Forest Plot: Accuracy and Error Analyses
Xie, Yuyang; Yang, Tao1; Wang, Xiaofeng1; Chen, Xi1; Pang, Shuxin2; Hu, Juan3; Wang, Anxian3; Chen, Ling3; Shen, Zehao
2022
发表期刊REMOTE SENSING
卷号14期号:8
摘要Accurate tree positioning and measurement of structural parameters are the basis of forest inventory and mapping, which are important for forest biomass calculation and community dynamics analyses. Portable backpack lidar that integrates the simultaneous localization and mapping (SLAM) technique with a global navigation satellite system receiver has greater flexibility for tree inventory than terrestrial laser scanning, but it has never been used to measure and map forest structure in a large area (>10(1) hectares) with high tree density. In the present study, we used the LiBackpack DG50 backpack lidar system to obtain the point cloud data of a 10 ha plot of subtropical evergreen broadleaved forest, and applied these data to quantify errors and related factors in the diameter at breast height (DBH) measurements and positioning for more than 1900 individual trees. We found an average error of 4.19 cm in the DBH measurements obtained by lidar, compared with manual field measurements. The incompleteness of the tree stem point clouds was the main factor that caused the DBH measurement errors, and the field DBH measurements and density of the point clouds also had significant impacts. The average tree positioning error was 4.64 m, and it was significantly affected by the distance and route length from the measured trees to the data acquisition start position, whereas it was affected little by the habitat complexity and characteristics of tree stems. The tree positioning measurement error led to increases in the mean value and variability of paired-tree distance error as the sample plot scale increased. We corrected the errors based on the estimates of predictive models. After correction, the DBH measurement error decreased by 31.3%, the tree positioning error decreased by 44.3%, and the paired-tree distance error decreased by 56.3%. As the sample plot scale increased, the accumulated paired-tree distance error stabilized gradually.
关键词backpack lidar closed forest SLAM stem positioning accuracy paired-tree distance
学科领域Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
DOI10.3390/rs14081806
收录类别SCI
语种英语
WOS关键词INDIVIDUAL TREES ; POINT CLOUDS ; TERRESTRIAL ; HEIGHT ; UAV ; QUANTIFICATION ; REGISTRATION ; CLIMATE
WOS研究方向Science Citation Index Expanded (SCI-EXPANDED)
WOS记录号WOS:000787398300001
出版者MDPI
文献子类Article
出版地BASEL
EISSN2072-4292
资助机构Zhujiangyuan Provincial Natural Reserve ; Natural Science Foundation of China [41790425]
作者邮箱xie_yy@pku.edu.cn ; yt941112@ynu.edu.cn ; wangxiaofeng@mail.ynu.edu.cn ; chenxi@mail.ynu.edu.cn ; pangshuxin@ibcas.ac.cn ; zjybhqhj@163.com ; a2571181108@163.com ; cl13988994441@163.com ; shzh@urban.pku.edu.cn
作品OA属性gold
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被引频次:16[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ibcas.ac.cn/handle/2S10CLM1/28725
专题植被与环境变化国家重点实验室
作者单位1.Peking Univ, Coll Urban & Environm Sci, Minist Educ Key Lab Earth Surface Proc, Beijing 100871, Peoples R China
2.Yunnan Univ, Sch Ecol & Environm Sci, Kunming 650091, Yunnan, Peoples R China
3.Chinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing 100093, Peoples R China
4.Zhanyi Branch Zhujiangyuan Prov Nat Reserve Adm &, Qujing 655331, Peoples R China
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GB/T 7714
Xie, Yuyang,Yang, Tao,Wang, Xiaofeng,et al. Applying a Portable Backpack Lidar to Measure and Locate Trees in a Nature Forest Plot: Accuracy and Error Analyses[J]. REMOTE SENSING,2022,14(8).
APA Xie, Yuyang.,Yang, Tao.,Wang, Xiaofeng.,Chen, Xi.,Pang, Shuxin.,...&Shen, Zehao.(2022).Applying a Portable Backpack Lidar to Measure and Locate Trees in a Nature Forest Plot: Accuracy and Error Analyses.REMOTE SENSING,14(8).
MLA Xie, Yuyang,et al."Applying a Portable Backpack Lidar to Measure and Locate Trees in a Nature Forest Plot: Accuracy and Error Analyses".REMOTE SENSING 14.8(2022).
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