A Robust Algorithm for Constructing Pit-Free Canopy Height Model
文献类型：期刊论文
| 作者 | Chen, Chuanfa1,2; Wang, Yifu3; Li, Yanyan2; Yue, Tianxiang3
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| 刊名 | JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING
 |
| 出版日期 | 2018-04-01 |
| 卷号 | 46期号:4页码:491-499 |
| 关键词 | Canopy height model Data pit Tree crown Robust |
| ISSN号 | 0255-660X |
| DOI | 10.1007/s12524-017-0710-x |
| 通讯作者 | Chen, Chuanfa(chencf@lreis.ac.cn) |
| 英文摘要 | Due 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. |
| WOS关键词 | LOCALLY WEIGHTED REGRESSION ; INDIVIDUAL TREE CROWNS ; AIRBORNE LASER SCANNER ; VARIABLE WINDOW SIZE ; FOOTPRINT LIDAR DATA ; DENSITY LIDAR ; FOREST ; RESOLUTION ; OUTLIERS ; BIOMASS |
| 资助项目 | National 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 |
| WOS研究方向 | Environmental Sciences & Ecology ; Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:000433003500001 |
| 出版者 | SPRINGER |
| 资助机构 | National 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 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/54764]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Chen, Chuanfa |
| 作者单位 | 1.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 |
| 推荐引用方式 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. |
入库方式： OAI收割
来源：地理科学与资源研究所
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