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A Robust Algorithm for Constructing Pit-Free Canopy Height Model
Chen, Chuanfa1,2; Wang, Yifu3; Li, Yanyan2; Yue, Tianxiang3
2018-04-01
Source PublicationJOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING
ISSN0255-660X
Volume46Issue:4Pages:491-499
Corresponding AuthorChen, Chuanfa(chencf@lreis.ac.cn)
AbstractDue to the penetration ability of airborne light detection and ranging (lidar) into tree crowns, data pits commonly appear in lidar-derived canopy height models (CHMs). They have a seriously negative effect on the quality of tree detection and subsequent biophysical measurements. To construct a pit-free CHM, an algorithm based on robust locally weighted regression and robust z-score was presented to remove data pits. The significant advantage of the new algorithm is parameter-free, which makes it efficient and robust for practical applications. A numerical test and a real-world example were respectively employed to assess the performance of our method for CHM construction, and its results were compared with those of three classical methods including natural neighbor interpolation of the highest point method, mean and median filters. The numerical test demonstrates that our algorithm is more accurate than the other methods for generating pit-free CHMs under the presence of data pits. The real-world example shows that compared with the classical methods, our method has a better ability of data pit removal. Moreover, our method performs better than the other methods for deriving plot-level maximum tree height from CHMs. In a word, the new method shows high potential for pit-free CHM construction.
KeywordCanopy height model Data pit Tree crown Robust
DOI10.1007/s12524-017-0710-x
WOS KeywordLOCALLY WEIGHTED REGRESSION ; INDIVIDUAL TREE CROWNS ; AIRBORNE LASER SCANNER ; VARIABLE WINDOW SIZE ; FOOTPRINT LIDAR DATA ; DENSITY LIDAR ; FOREST ; RESOLUTION ; OUTLIERS ; BIOMASS
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[41371367] ; National Natural Science Foundation of China[41101433] ; SDUST Research Fund ; Joint Innovative Center for Safe and Effective Mining Technology and Equipment of Coal Resources ; Special Project Fund of Taishan Scholars of Shandong Province
Funding OrganizationNational Natural Science Foundation of China ; SDUST Research Fund ; Joint Innovative Center for Safe and Effective Mining Technology and Equipment of Coal Resources ; Special Project Fund of Taishan Scholars of Shandong Province
WOS Research AreaEnvironmental Sciences & Ecology ; Remote Sensing
WOS SubjectEnvironmental Sciences ; Remote Sensing
WOS IDWOS:000433003500001
PublisherSPRINGER
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/54765
Collection中国科学院地理科学与资源研究所
Corresponding AuthorChen, Chuanfa
Affiliation1.Shandong Univ Sci & Technol, Minist Sci & Technol, State Key Lab Min Disaster Prevent & Control Cofo, Qingdao 266590, Peoples R China
2.Univ Sci & Technol, Shandong Prov Key Lab Geomat & Digital Technol Sh, Qingdao 266590, Peoples R China
3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, 11A Datun Rd, Beijing 100101, Peoples R China
Recommended Citation
GB/T 7714
Chen, Chuanfa,Wang, Yifu,Li, Yanyan,et al. A Robust Algorithm for Constructing Pit-Free Canopy Height Model[J]. JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING,2018,46(4):491-499.
APA Chen, Chuanfa,Wang, Yifu,Li, Yanyan,&Yue, Tianxiang.(2018).A Robust Algorithm for Constructing Pit-Free Canopy Height Model.JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING,46(4),491-499.
MLA Chen, Chuanfa,et al."A Robust Algorithm for Constructing Pit-Free Canopy Height Model".JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING 46.4(2018):491-499.
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