Integration of Sentinel-1A, ALOS-2 and GF-1 Datasets for Identifying Landslides in the Three Parallel Rivers Region, China
In the process of using InSAR technology to identify active landslides, phenomena such as steep terrain, dense vegetation, and complex clouds may lead to the missed identification of some landslides. In this paper, an active landslide identification method combining InSAR technology and optical sate...
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| Format: | Article |
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MDPI AG
2022-10-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/19/5031 |
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| author | Cong Zhao Jingtao Liang Su Zhang Jihong Dong Shengwu Yan Lei Yang Bin Liu Xiaobo Ma Weile Li |
| author_facet | Cong Zhao Jingtao Liang Su Zhang Jihong Dong Shengwu Yan Lei Yang Bin Liu Xiaobo Ma Weile Li |
| author_sort | Cong Zhao |
| collection | DOAJ |
| description | In the process of using InSAR technology to identify active landslides, phenomena such as steep terrain, dense vegetation, and complex clouds may lead to the missed identification of some landslides. In this paper, an active landslide identification method combining InSAR technology and optical satellite remote sensing technology is proposed, and the method is successfully applied to the Three Parallel Rivers Region (TPRR) in the northwest of Yunnan Province, China. The results show that there are 442 landslides identified in the TPRR, and the fault zone is one of the important factors affecting the distribution of landslides in this region. In addition, 70% of the active landslides are distributed within 1 km on both sides of the fault zone. The larger the scale of the landslide, the closer the relationship between landslides and the fault zone. In this identification method, the overall landslide identification rate based on InSAR technology is 51.36%. The combination of Sentinel-1 and ALOS-2 data is beneficial to improve the active landslide identification rate of InSAR. In the northern region with large undulating terrain, shadows and overlaps occur easily. The southern area with gentle terrain is prone to the phenomenon where the scale of landslides is too small. Both phenomena are not conducive to the application of InSAR. Thus, in the central region, with moderate terrain and slope, the identification rate of active landslides based on InSAR is highest. The active landslide identification method proposed in this paper, which combines InSAR and optical satellite remote sensing technology, can integrate the respective advantages of the two technical methods, complement each other’s limitations and deficiencies, reduce the missed identification of landslides, and improve the accuracy of active landslide inventory maps. |
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| language | English |
| last_indexed | 2024-03-09T21:12:11Z |
| publishDate | 2022-10-01 |
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| series | Remote Sensing |
| spelling | doaj.art-807fd39272fc44f1a697aacc249408602023-11-23T21:42:48ZengMDPI AGRemote Sensing2072-42922022-10-011419503110.3390/rs14195031Integration of Sentinel-1A, ALOS-2 and GF-1 Datasets for Identifying Landslides in the Three Parallel Rivers Region, ChinaCong Zhao0Jingtao Liang1Su Zhang2Jihong Dong3Shengwu Yan4Lei Yang5Bin Liu6Xiaobo Ma7Weile Li8Evaluation and Utilization of Strategic Rare Metals and Rare Earth Resource Key Laboratory of Sichuan Province, Sichuan Geological Survey, Chengdu 610081, ChinaEvaluation and Utilization of Strategic Rare Metals and Rare Earth Resource Key Laboratory of Sichuan Province, Sichuan Geological Survey, Chengdu 610081, ChinaEvaluation and Utilization of Strategic Rare Metals and Rare Earth Resource Key Laboratory of Sichuan Province, Sichuan Geological Survey, Chengdu 610081, ChinaEvaluation and Utilization of Strategic Rare Metals and Rare Earth Resource Key Laboratory of Sichuan Province, Sichuan Geological Survey, Chengdu 610081, ChinaEvaluation and Utilization of Strategic Rare Metals and Rare Earth Resource Key Laboratory of Sichuan Province, Sichuan Geological Survey, Chengdu 610081, ChinaEvaluation and Utilization of Strategic Rare Metals and Rare Earth Resource Key Laboratory of Sichuan Province, Sichuan Geological Survey, Chengdu 610081, ChinaEvaluation and Utilization of Strategic Rare Metals and Rare Earth Resource Key Laboratory of Sichuan Province, Sichuan Geological Survey, Chengdu 610081, ChinaEvaluation and Utilization of Strategic Rare Metals and Rare Earth Resource Key Laboratory of Sichuan Province, Sichuan Geological Survey, Chengdu 610081, ChinaState Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, ChinaIn the process of using InSAR technology to identify active landslides, phenomena such as steep terrain, dense vegetation, and complex clouds may lead to the missed identification of some landslides. In this paper, an active landslide identification method combining InSAR technology and optical satellite remote sensing technology is proposed, and the method is successfully applied to the Three Parallel Rivers Region (TPRR) in the northwest of Yunnan Province, China. The results show that there are 442 landslides identified in the TPRR, and the fault zone is one of the important factors affecting the distribution of landslides in this region. In addition, 70% of the active landslides are distributed within 1 km on both sides of the fault zone. The larger the scale of the landslide, the closer the relationship between landslides and the fault zone. In this identification method, the overall landslide identification rate based on InSAR technology is 51.36%. The combination of Sentinel-1 and ALOS-2 data is beneficial to improve the active landslide identification rate of InSAR. In the northern region with large undulating terrain, shadows and overlaps occur easily. The southern area with gentle terrain is prone to the phenomenon where the scale of landslides is too small. Both phenomena are not conducive to the application of InSAR. Thus, in the central region, with moderate terrain and slope, the identification rate of active landslides based on InSAR is highest. The active landslide identification method proposed in this paper, which combines InSAR and optical satellite remote sensing technology, can integrate the respective advantages of the two technical methods, complement each other’s limitations and deficiencies, reduce the missed identification of landslides, and improve the accuracy of active landslide inventory maps.https://www.mdpi.com/2072-4292/14/19/5031Three Parallel Rivers Region (TPRR)landslidesInSARoptical satellitefault zone |
| spellingShingle | Cong Zhao Jingtao Liang Su Zhang Jihong Dong Shengwu Yan Lei Yang Bin Liu Xiaobo Ma Weile Li Integration of Sentinel-1A, ALOS-2 and GF-1 Datasets for Identifying Landslides in the Three Parallel Rivers Region, China Remote Sensing Three Parallel Rivers Region (TPRR) landslides InSAR optical satellite fault zone |
| title | Integration of Sentinel-1A, ALOS-2 and GF-1 Datasets for Identifying Landslides in the Three Parallel Rivers Region, China |
| title_full | Integration of Sentinel-1A, ALOS-2 and GF-1 Datasets for Identifying Landslides in the Three Parallel Rivers Region, China |
| title_fullStr | Integration of Sentinel-1A, ALOS-2 and GF-1 Datasets for Identifying Landslides in the Three Parallel Rivers Region, China |
| title_full_unstemmed | Integration of Sentinel-1A, ALOS-2 and GF-1 Datasets for Identifying Landslides in the Three Parallel Rivers Region, China |
| title_short | Integration of Sentinel-1A, ALOS-2 and GF-1 Datasets for Identifying Landslides in the Three Parallel Rivers Region, China |
| title_sort | integration of sentinel 1a alos 2 and gf 1 datasets for identifying landslides in the three parallel rivers region china |
| topic | Three Parallel Rivers Region (TPRR) landslides InSAR optical satellite fault zone |
| url | https://www.mdpi.com/2072-4292/14/19/5031 |
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