A bottom-up approach to segment individual deciduous trees using leaf-off lidar point cloud data
文献类型：期刊论文
| 作者 | Lu, Xingcheng1; Guo, Qinghua1 ; Li, Wenkai1; Flanagan, Jacob1
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| 刊名 | ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
 |
| 出版日期 | 2014 |
| 卷号 | 94页码:1-12 |
| 关键词 | Lidar Deciduous forest Tree segmentation Intensity 3-D structure Bottom-up |
| ISSN号 | 0924-2716 |
| DOI | 10.1016/j.isprsjprs.2014.03.014 |
| 文献子类 | Article |
| 英文摘要 | Light Detection and Ranging (Lidar) can generate three-dimensional (3D) point cloud which can be used to characterize horizontal and vertical forest structure, so it has become a popular tool for forest research. Recently, various methods based on top-down scheme have been developed to segment individual tree from lidar data. Some of these methods, such as the one developed by Li et al. (2012), can obtain the accuracy up to 90% when applied in coniferous forests. However, the accuracy will decrease when they are applied in deciduous forest because the interlacing tree branches can increase the difficulty to determine the tree top. In order to solve challenges of the tree segmentation in deciduous forests, we develop a new bottom-up method based on the intensity and 3D structure of leaf-off lidar point cloud data in this study. We applied our algorithm to segment trees in a forest at the Shavers Creek Watershed in Pennsylvania. Three indices were used to assess the accuracy of our method: recall, precision and F-score. The results show that the algorithm can detect 84% of the tree (recall), 97% of the segmented trees are correct (precision) and the overall F-score is 90%. The result implies that our method has good potential for segmenting individual trees in deciduous broadleaf forest. (C) 2014 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) Published by Elsevier B.V. All rights reserved. |
| 学科主题 | Geography, Physical ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology |
| 出版地 | AMSTERDAM |
| 电子版国际标准刊号 | 1872-8235 |
| WOS关键词 | WAVE-FORM LIDAR ; SMALL-FOOTPRINT ; STEM VOLUME ; SAMPLING DENSITY ; FOREST STRUCTURE ; F-SCORE ; BIOMASS ; HEIGHT ; CROWNS ; AREA |
| WOS研究方向 | Physical Geography ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000339134000001 |
| 出版者 | ELSEVIER |
| 资助机构 | Directorate For Geosciences [1043051, 1339015] Funding Source: National Science Foundation |
| 源URL | [http://ir.ibcas.ac.cn/handle/2S10CLM1/27027]  |
| 专题 | 植被与环境变化国家重点实验室 |
| 作者单位 | 1.Chinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing 100093, Peoples R China 2.Univ Calif Merced, Sch Engn, Merced, CA 95343 USA |
| 推荐引用方式 GB/T 7714 | Lu, Xingcheng,Guo, Qinghua,Li, Wenkai,et al. A bottom-up approach to segment individual deciduous trees using leaf-off lidar point cloud data[J]. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING,2014,94:1-12. |
| APA | Lu, Xingcheng,Guo, Qinghua,Li, Wenkai,&Flanagan, Jacob.(2014).A bottom-up approach to segment individual deciduous trees using leaf-off lidar point cloud data.ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING,94,1-12. |
| MLA | Lu, Xingcheng,et al."A bottom-up approach to segment individual deciduous trees using leaf-off lidar point cloud data".ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 94(2014):1-12. |
入库方式： OAI收割
来源：植物研究所
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