A novel method for cliff vegetation estimation based on the unmanned aerial vehicle 3D modeling
The cliff ecosystem is one of the least human-disturbed ecosystems in nature, and its inaccessible and often extreme habitats are home to many ancient and unique plant species. Because of the harshness of cliff habitats, their high elevation, steepness of slopes, and inaccessibility to humans, surve...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-09-01
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| Series: | Frontiers in Plant Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2022.1006795/full |
| _version_ | 1811265791366004736 |
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| author | Minghui Li Minghui Li Minghui Li Enping Yan Enping Yan Enping Yan Hui Zhou Hui Zhou Hui Zhou Hui Zhou Jiaxing Zhu Jiaxing Zhu Jiaxing Zhu Jiaxing Zhu Jiawei Jiang Jiawei Jiang Jiawei Jiang Dengkui Mo Dengkui Mo Dengkui Mo |
| author_facet | Minghui Li Minghui Li Minghui Li Enping Yan Enping Yan Enping Yan Hui Zhou Hui Zhou Hui Zhou Hui Zhou Jiaxing Zhu Jiaxing Zhu Jiaxing Zhu Jiaxing Zhu Jiawei Jiang Jiawei Jiang Jiawei Jiang Dengkui Mo Dengkui Mo Dengkui Mo |
| author_sort | Minghui Li |
| collection | DOAJ |
| description | The cliff ecosystem is one of the least human-disturbed ecosystems in nature, and its inaccessible and often extreme habitats are home to many ancient and unique plant species. Because of the harshness of cliff habitats, their high elevation, steepness of slopes, and inaccessibility to humans, surveying cliffs is incredibly challenging. Comprehensive and systematic information on cliff vegetation cover is not unavailable but obtaining such information on these cliffs is fundamentally important and of high priority for environmentalists. Traditional coverage survey methods—such as large-area normalized difference vegetation index (NDVI) statistics and small-area quadratic sampling surveys—are not suitable for cliffs that are close to vertical. This paper presents a semi-automatic systematic investigation and a three-dimensional reconstruction of karst cliffs for vegetation cover evaluation. High-resolution imagery with structure from motion (SFM) was captured by a smart unmanned aerial vehicle (UAV). Using approximately 13,000 records retrieved from high-resolution images of 16 cliffs in the karst region Guilin, China, 16 models of cliffs were reconstructed. The results show that this optimized UAV photogrammetry method greatly improves modeling efficiency and the vegetation cover from the bottom to the top of cliffs is high-low-high, and very few cliffs have high-low cover at the top. This study highlights the unique vegetation cover of karst cliffs, which warrants further research on the use of SFM to retrieve cliff vegetation cover at large and global scales. |
| first_indexed | 2024-04-12T20:30:19Z |
| format | Article |
| id | doaj.art-33e5f7050ebf44ada95a357d4048a63f |
| institution | Directory Open Access Journal |
| issn | 1664-462X |
| language | English |
| last_indexed | 2024-04-12T20:30:19Z |
| publishDate | 2022-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj.art-33e5f7050ebf44ada95a357d4048a63f2022-12-22T03:17:44ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2022-09-011310.3389/fpls.2022.10067951006795A novel method for cliff vegetation estimation based on the unmanned aerial vehicle 3D modelingMinghui Li0Minghui Li1Minghui Li2Enping Yan3Enping Yan4Enping Yan5Hui Zhou6Hui Zhou7Hui Zhou8Hui Zhou9Jiaxing Zhu10Jiaxing Zhu11Jiaxing Zhu12Jiaxing Zhu13Jiawei Jiang14Jiawei Jiang15Jiawei Jiang16Dengkui Mo17Dengkui Mo18Dengkui Mo19Key Laboratory of Forestry Remote Sensing Based Big Data and Ecological Security for Hunan Province, Changsha, ChinaKey Laboratory of State Forestry Administration on Forest Resources Management and Monitoring in Southern Area, Changsha, ChinaCollege of Forestry, Central South University of Forestry and Technology, Changsha, ChinaKey Laboratory of Forestry Remote Sensing Based Big Data and Ecological Security for Hunan Province, Changsha, ChinaKey Laboratory of State Forestry Administration on Forest Resources Management and Monitoring in Southern Area, Changsha, ChinaCollege of Forestry, Central South University of Forestry and Technology, Changsha, ChinaKey Laboratory of Forestry Remote Sensing Based Big Data and Ecological Security for Hunan Province, Changsha, ChinaKey Laboratory of State Forestry Administration on Forest Resources Management and Monitoring in Southern Area, Changsha, ChinaCollege of Forestry, Central South University of Forestry and Technology, Changsha, ChinaGuangxi Forest Inventory and Planning Institution, Nanning, ChinaKey Laboratory of Forestry Remote Sensing Based Big Data and Ecological Security for Hunan Province, Changsha, ChinaKey Laboratory of State Forestry Administration on Forest Resources Management and Monitoring in Southern Area, Changsha, ChinaCollege of Forestry, Central South University of Forestry and Technology, Changsha, ChinaHunan Maoyuan Forestry Co., Ltd., Yueyang, ChinaKey Laboratory of Forestry Remote Sensing Based Big Data and Ecological Security for Hunan Province, Changsha, ChinaKey Laboratory of State Forestry Administration on Forest Resources Management and Monitoring in Southern Area, Changsha, ChinaCollege of Forestry, Central South University of Forestry and Technology, Changsha, ChinaKey Laboratory of Forestry Remote Sensing Based Big Data and Ecological Security for Hunan Province, Changsha, ChinaKey Laboratory of State Forestry Administration on Forest Resources Management and Monitoring in Southern Area, Changsha, ChinaCollege of Forestry, Central South University of Forestry and Technology, Changsha, ChinaThe cliff ecosystem is one of the least human-disturbed ecosystems in nature, and its inaccessible and often extreme habitats are home to many ancient and unique plant species. Because of the harshness of cliff habitats, their high elevation, steepness of slopes, and inaccessibility to humans, surveying cliffs is incredibly challenging. Comprehensive and systematic information on cliff vegetation cover is not unavailable but obtaining such information on these cliffs is fundamentally important and of high priority for environmentalists. Traditional coverage survey methods—such as large-area normalized difference vegetation index (NDVI) statistics and small-area quadratic sampling surveys—are not suitable for cliffs that are close to vertical. This paper presents a semi-automatic systematic investigation and a three-dimensional reconstruction of karst cliffs for vegetation cover evaluation. High-resolution imagery with structure from motion (SFM) was captured by a smart unmanned aerial vehicle (UAV). Using approximately 13,000 records retrieved from high-resolution images of 16 cliffs in the karst region Guilin, China, 16 models of cliffs were reconstructed. The results show that this optimized UAV photogrammetry method greatly improves modeling efficiency and the vegetation cover from the bottom to the top of cliffs is high-low-high, and very few cliffs have high-low cover at the top. This study highlights the unique vegetation cover of karst cliffs, which warrants further research on the use of SFM to retrieve cliff vegetation cover at large and global scales.https://www.frontiersin.org/articles/10.3389/fpls.2022.1006795/fullcliffvegetation coverstructure from motionunmanned aerial vehicleclose-range photogrammetry |
| spellingShingle | Minghui Li Minghui Li Minghui Li Enping Yan Enping Yan Enping Yan Hui Zhou Hui Zhou Hui Zhou Hui Zhou Jiaxing Zhu Jiaxing Zhu Jiaxing Zhu Jiaxing Zhu Jiawei Jiang Jiawei Jiang Jiawei Jiang Dengkui Mo Dengkui Mo Dengkui Mo A novel method for cliff vegetation estimation based on the unmanned aerial vehicle 3D modeling Frontiers in Plant Science cliff vegetation cover structure from motion unmanned aerial vehicle close-range photogrammetry |
| title | A novel method for cliff vegetation estimation based on the unmanned aerial vehicle 3D modeling |
| title_full | A novel method for cliff vegetation estimation based on the unmanned aerial vehicle 3D modeling |
| title_fullStr | A novel method for cliff vegetation estimation based on the unmanned aerial vehicle 3D modeling |
| title_full_unstemmed | A novel method for cliff vegetation estimation based on the unmanned aerial vehicle 3D modeling |
| title_short | A novel method for cliff vegetation estimation based on the unmanned aerial vehicle 3D modeling |
| title_sort | novel method for cliff vegetation estimation based on the unmanned aerial vehicle 3d modeling |
| topic | cliff vegetation cover structure from motion unmanned aerial vehicle close-range photogrammetry |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2022.1006795/full |
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