Slope Surface Deformation Monitoring Based on Close-Range Photogrammetry: Laboratory Insights and Field Applications
Slope surface deformation monitoring plays an important role in landslide risk assessment and early warning. Currently, the mainstream GNSS, as a point-measurement technique, is expensive to deploy, resulting in information on only a few points of displacement being obtained on a target slope in pra...
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MDPI AG
2024-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/23/4380 |
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| author | Tianxin Lu Peng Han Wei Gong Shuangshuang Li Shuangling Mo Kaiyan Hu Yihua Zhang Chunyu Mo Yuyan Li Ning An Fangjun Li BingBing Han Baofeng Wan Ruidong Li |
| author_facet | Tianxin Lu Peng Han Wei Gong Shuangshuang Li Shuangling Mo Kaiyan Hu Yihua Zhang Chunyu Mo Yuyan Li Ning An Fangjun Li BingBing Han Baofeng Wan Ruidong Li |
| author_sort | Tianxin Lu |
| collection | DOAJ |
| description | Slope surface deformation monitoring plays an important role in landslide risk assessment and early warning. Currently, the mainstream GNSS, as a point-measurement technique, is expensive to deploy, resulting in information on only a few points of displacement being obtained on a target slope in practical applications. In contrast, optical images can contain more information on slope displacement at a much lower cost. Therefore, a low-cost, high-spatial-resolution and easy-to-implement landslide surface deformation monitoring system based on close-range photogrammetry is developed in this paper. The proposed system leverages multiple image processing methods and monocular visual localization, combined with machine learning, to ensure accurate monitoring under time series. The results of several laboratory landslide experiments show that the proposed system achieved millimeter-level monitoring accuracy in laboratory landslide experiments. Moreover, the proposed system could capture slow displacement precursors of 5 mm to 10 mm before significant landslide failure occurred, which provides favorable surface deformation evidence for landslide monitoring and early warning. In addition, the system was deployed on a natural slope in Lanzhou, yielding preliminary effective monitoring results. The laboratory experimental results demonstrated the system’s effectiveness and high accuracy in monitoring landslide surface deformation, particularly its significant application value in early warning. The field deployment results indicated that the system could also effectively provide data support in natural environments, offering practical evidence for landslide monitoring and warning. |
| format | Article |
| id | doaj-art-237c270da05847338fabf78f89e46488 |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-237c270da05847338fabf78f89e464882025-08-20T01:55:45ZengMDPI AGRemote Sensing2072-42922024-11-011623438010.3390/rs16234380Slope Surface Deformation Monitoring Based on Close-Range Photogrammetry: Laboratory Insights and Field ApplicationsTianxin Lu0Peng Han1Wei Gong2Shuangshuang Li3Shuangling Mo4Kaiyan Hu5Yihua Zhang6Chunyu Mo7Yuyan Li8Ning An9Fangjun Li10BingBing Han11Baofeng Wan12Ruidong Li13Department of Earth and Space Sciences, Southern University of Science and Technology, Shenzhen 518055, ChinaDepartment of Earth and Space Sciences, Southern University of Science and Technology, Shenzhen 518055, ChinaDepartment of Earth and Space Sciences, Southern University of Science and Technology, Shenzhen 518055, ChinaDepartment of Earth and Space Sciences, Southern University of Science and Technology, Shenzhen 518055, ChinaDepartment of Earth and Space Sciences, Southern University of Science and Technology, Shenzhen 518055, ChinaSchool of Geophysics and Geomatics, China University of Geosciences (Wuhan), Wuhan 430074, ChinaDepartment of Earth and Space Sciences, Southern University of Science and Technology, Shenzhen 518055, ChinaDepartment of Earth and Space Sciences, Southern University of Science and Technology, Shenzhen 518055, ChinaDepartment of Earth and Space Sciences, Southern University of Science and Technology, Shenzhen 518055, ChinaGansu Institute of Engineering Geology, Lanzhou 730099, ChinaGansu Institute of Engineering Geology, Lanzhou 730099, ChinaGansu Institute of Engineering Geology, Lanzhou 730099, ChinaGansu Institute of Engineering Geology, Lanzhou 730099, ChinaGansu Institute of Engineering Geology, Lanzhou 730099, ChinaSlope surface deformation monitoring plays an important role in landslide risk assessment and early warning. Currently, the mainstream GNSS, as a point-measurement technique, is expensive to deploy, resulting in information on only a few points of displacement being obtained on a target slope in practical applications. In contrast, optical images can contain more information on slope displacement at a much lower cost. Therefore, a low-cost, high-spatial-resolution and easy-to-implement landslide surface deformation monitoring system based on close-range photogrammetry is developed in this paper. The proposed system leverages multiple image processing methods and monocular visual localization, combined with machine learning, to ensure accurate monitoring under time series. The results of several laboratory landslide experiments show that the proposed system achieved millimeter-level monitoring accuracy in laboratory landslide experiments. Moreover, the proposed system could capture slow displacement precursors of 5 mm to 10 mm before significant landslide failure occurred, which provides favorable surface deformation evidence for landslide monitoring and early warning. In addition, the system was deployed on a natural slope in Lanzhou, yielding preliminary effective monitoring results. The laboratory experimental results demonstrated the system’s effectiveness and high accuracy in monitoring landslide surface deformation, particularly its significant application value in early warning. The field deployment results indicated that the system could also effectively provide data support in natural environments, offering practical evidence for landslide monitoring and warning.https://www.mdpi.com/2072-4292/16/23/4380landslide monitoringphotogrammetrymachine learningimage processinglaboratory experiments |
| spellingShingle | Tianxin Lu Peng Han Wei Gong Shuangshuang Li Shuangling Mo Kaiyan Hu Yihua Zhang Chunyu Mo Yuyan Li Ning An Fangjun Li BingBing Han Baofeng Wan Ruidong Li Slope Surface Deformation Monitoring Based on Close-Range Photogrammetry: Laboratory Insights and Field Applications Remote Sensing landslide monitoring photogrammetry machine learning image processing laboratory experiments |
| title | Slope Surface Deformation Monitoring Based on Close-Range Photogrammetry: Laboratory Insights and Field Applications |
| title_full | Slope Surface Deformation Monitoring Based on Close-Range Photogrammetry: Laboratory Insights and Field Applications |
| title_fullStr | Slope Surface Deformation Monitoring Based on Close-Range Photogrammetry: Laboratory Insights and Field Applications |
| title_full_unstemmed | Slope Surface Deformation Monitoring Based on Close-Range Photogrammetry: Laboratory Insights and Field Applications |
| title_short | Slope Surface Deformation Monitoring Based on Close-Range Photogrammetry: Laboratory Insights and Field Applications |
| title_sort | slope surface deformation monitoring based on close range photogrammetry laboratory insights and field applications |
| topic | landslide monitoring photogrammetry machine learning image processing laboratory experiments |
| url | https://www.mdpi.com/2072-4292/16/23/4380 |
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