A Comparison of LiDAR Filtering Algorithms in Vegetated Mountain Areas
Filtering of airborne light detection and ranging (LiDAR) data is a challenging task in vegetated mountain areas. Environmental features and LiDAR data characteristics have significant impacts on the performance of filtering algorithms. This study aims to determine the effects of topographic and env...
| Main Authors: | , , , , , |
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| Format: | Article |
| Jezik: | English |
| Izdano: |
Taylor & Francis Group
2018-07-01
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| Serija: | Canadian Journal of Remote Sensing |
| Online dostop: | http://dx.doi.org/10.1080/07038992.2018.1481738 |
| _version_ | 1827794903426400256 |
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| author | Xiaoqian Zhao Yanjun Su WenKai Li Tianyu Hu Jin Liu Qinghua Guo |
| author_facet | Xiaoqian Zhao Yanjun Su WenKai Li Tianyu Hu Jin Liu Qinghua Guo |
| author_sort | Xiaoqian Zhao |
| collection | DOAJ |
| description | Filtering of airborne light detection and ranging (LiDAR) data is a challenging task in vegetated mountain areas. Environmental features and LiDAR data characteristics have significant impacts on the performance of filtering algorithms. This study aims to determine the effects of topographic and environmental features such as slope, canopy cover, elevation variability, and LiDAR point density on five widely used filtering algorithms, including multi-scale curvature classification (MCC), interpolation-based filtering (IBF) algorithm, morphological filtering (MF) algorithm, progressive triangulated irregular network densification filtering (PTDF) algorithm, and slope-based filtering (SBF). The results show that the performances of these filtering algorithms are all significantly influenced by the chosen factors, but the dominant influential factor varies with algorithms. The MCC works well in steep and dense forests; IBF and MCC outperform the rest of filtering algorithms in areas with steep terrain but low vegetation coverage; and PTDF is more reliable for low-density LiDAR data. Our results can provide guidance for choosing the appropriate filtering algorithm based on the specific topographic and environmental features of a study area. |
| first_indexed | 2024-03-11T18:40:47Z |
| format | Article |
| id | doaj.art-1b4d97b691a543078b33934bcc874b04 |
| institution | Directory Open Access Journal |
| issn | 1712-7971 |
| language | English |
| last_indexed | 2024-03-11T18:40:47Z |
| publishDate | 2018-07-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Canadian Journal of Remote Sensing |
| spelling | doaj.art-1b4d97b691a543078b33934bcc874b042023-10-12T13:36:22ZengTaylor & Francis GroupCanadian Journal of Remote Sensing1712-79712018-07-0144428729810.1080/07038992.2018.14817381481738A Comparison of LiDAR Filtering Algorithms in Vegetated Mountain AreasXiaoqian Zhao0Yanjun Su1WenKai Li2Tianyu Hu3Jin Liu4Qinghua Guo5Institute of Botany, Chinese Academy of SciencesInstitute of Botany, Chinese Academy of SciencesSun Yat-Sen UniversityInstitute of Botany, Chinese Academy of SciencesInstitute of Botany, Chinese Academy of SciencesInstitute of Botany, Chinese Academy of SciencesFiltering of airborne light detection and ranging (LiDAR) data is a challenging task in vegetated mountain areas. Environmental features and LiDAR data characteristics have significant impacts on the performance of filtering algorithms. This study aims to determine the effects of topographic and environmental features such as slope, canopy cover, elevation variability, and LiDAR point density on five widely used filtering algorithms, including multi-scale curvature classification (MCC), interpolation-based filtering (IBF) algorithm, morphological filtering (MF) algorithm, progressive triangulated irregular network densification filtering (PTDF) algorithm, and slope-based filtering (SBF). The results show that the performances of these filtering algorithms are all significantly influenced by the chosen factors, but the dominant influential factor varies with algorithms. The MCC works well in steep and dense forests; IBF and MCC outperform the rest of filtering algorithms in areas with steep terrain but low vegetation coverage; and PTDF is more reliable for low-density LiDAR data. Our results can provide guidance for choosing the appropriate filtering algorithm based on the specific topographic and environmental features of a study area.http://dx.doi.org/10.1080/07038992.2018.1481738 |
| spellingShingle | Xiaoqian Zhao Yanjun Su WenKai Li Tianyu Hu Jin Liu Qinghua Guo A Comparison of LiDAR Filtering Algorithms in Vegetated Mountain Areas Canadian Journal of Remote Sensing |
| title | A Comparison of LiDAR Filtering Algorithms in Vegetated Mountain Areas |
| title_full | A Comparison of LiDAR Filtering Algorithms in Vegetated Mountain Areas |
| title_fullStr | A Comparison of LiDAR Filtering Algorithms in Vegetated Mountain Areas |
| title_full_unstemmed | A Comparison of LiDAR Filtering Algorithms in Vegetated Mountain Areas |
| title_short | A Comparison of LiDAR Filtering Algorithms in Vegetated Mountain Areas |
| title_sort | comparison of lidar filtering algorithms in vegetated mountain areas |
| url | http://dx.doi.org/10.1080/07038992.2018.1481738 |
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