IGSNRR OpenIR
Robust and Parameter-Free Algorithm for Constructing Pit-Free Canopy Height Models
Chen, Chuanfa1,2; Wang, Yifu3; Li, Yanyan2; Yue, Tianxiang3; Wang, Xin2
2017-07-01
Source PublicationISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
ISSN2220-9964
Volume6Issue:7Pages:13
Corresponding AuthorChen, Chuanfa(chencf@lreis.ac.cn)
AbstractData pits commonly appear in lidar-derived canopy height models (CHMs) owing to the penetration ability of airborne light detection and ranging (lidar) into tree crowns. They have a seriously negative effect on the quality of tree detection and subsequent biophysical measurements. In this study, we propose an algorithm based on robust locally weighted regression and robust z-scores for the construction of a pit-free CHM. A significant advantage of the new algorithm is that it is parameter free, which makes it efficient and robust for practical applications. Simulated and airborne lidar-derived data sets are employed to assess the performance of the new method for CHM construction, and its results are compared to those of three classical methods, namely the natural neighbor (NN) interpolation of the highest point method (HPM), mean filter, and median filter. The results from the simulated data set demonstrate that our algorithm is more accurate compared to the three classical methods for generating pit-free CHMs in the presence of data pits. CHM construction using the lidar-derived data set shows that, compared to the classical methods, the new method has a better ability to remove data pits as well as preserving the edges, shapes, and structures of canopy gaps and crowns. Moreover, the proposed method performs better compared to the classical methods in deriving plot-level maximum tree heights from CHMs. Thus, the new method shows high potential for pit-free CHM construction.
Keywordcanopy height model data pit tree crown robust fitting
DOI10.3390/ijgi6070219
WOS KeywordLOCALLY WEIGHTED REGRESSION ; INDIVIDUAL TREE CROWNS ; AIRBORNE LASER SCANNER ; VARIABLE WINDOW SIZE ; FOOTPRINT LIDAR DATA ; DENSITY LIDAR ; FOREST ; RESOLUTION ; BIOMASS ; IMAGERY
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[41371367] ; SDUST Research Fund ; Joint Innovative Center for Safe and Effective Mining Technology and Equipment of Coal Resources ; State Key Laboratory of Resources and Environmental Information System
Funding OrganizationNational Natural Science Foundation of China ; SDUST Research Fund ; Joint Innovative Center for Safe and Effective Mining Technology and Equipment of Coal Resources ; State Key Laboratory of Resources and Environmental Information System
WOS Research AreaPhysical Geography ; Remote Sensing
WOS SubjectGeography, Physical ; Remote Sensing
WOS IDWOS:000407506900036
PublisherMDPI AG
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/61402
Collection中国科学院地理科学与资源研究所
Corresponding AuthorChen, Chuanfa
Affiliation1.Shandong Univ Sci & Technol, State Key Lab Min Disaster Prevent & Control, Shandong Prov & Minist Sci & Technol, Qingdao 266590, Peoples R China
2.Shandong Univ Sci & Technol, Shandong Prov Key Lab Geomat & Digital Technol Sh, Qingdao 266590, Peoples R China
3.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, 11A,Datun Rd, Beijing 100101, Peoples R China
Recommended Citation
GB/T 7714
Chen, Chuanfa,Wang, Yifu,Li, Yanyan,et al. Robust and Parameter-Free Algorithm for Constructing Pit-Free Canopy Height Models[J]. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,2017,6(7):13.
APA Chen, Chuanfa,Wang, Yifu,Li, Yanyan,Yue, Tianxiang,&Wang, Xin.(2017).Robust and Parameter-Free Algorithm for Constructing Pit-Free Canopy Height Models.ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,6(7),13.
MLA Chen, Chuanfa,et al."Robust and Parameter-Free Algorithm for Constructing Pit-Free Canopy Height Models".ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 6.7(2017):13.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Chen, Chuanfa]'s Articles
[Wang, Yifu]'s Articles
[Li, Yanyan]'s Articles
Baidu academic
Similar articles in Baidu academic
[Chen, Chuanfa]'s Articles
[Wang, Yifu]'s Articles
[Li, Yanyan]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Chen, Chuanfa]'s Articles
[Wang, Yifu]'s Articles
[Li, Yanyan]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.