Robust and Parameter-Free Algorithm for Constructing Pit-Free Canopy Height Models
文献类型：期刊论文
| 作者 | Chen, Chuanfa1,2; Wang, Yifu3; Li, Yanyan2; Yue, Tianxiang3 ; Wang, Xin2
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| 刊名 | ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
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| 出版日期 | 2017-07-01 |
| 卷号 | 6期号:7页码:13 |
| 关键词 | canopy height model data pit tree crown robust fitting |
| ISSN号 | 2220-9964 |
| DOI | 10.3390/ijgi6070219 |
| 通讯作者 | Chen, Chuanfa(chencf@lreis.ac.cn) |
| 英文摘要 | Data 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. |
| WOS关键词 | LOCALLY WEIGHTED REGRESSION ; INDIVIDUAL TREE CROWNS ; AIRBORNE LASER SCANNER ; VARIABLE WINDOW SIZE ; FOOTPRINT LIDAR DATA ; DENSITY LIDAR ; FOREST ; RESOLUTION ; BIOMASS ; IMAGERY |
| 资助项目 | National 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 |
| WOS研究方向 | Physical Geography ; Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:000407506900036 |
| 出版者 | MDPI AG |
| 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 ; State Key Laboratory of Resources and Environmental Information System |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/61401]  |
| 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, 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 |
| 推荐引用方式 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. |
入库方式： OAI收割
来源：地理科学与资源研究所
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