Risk Assessment of Geological Landslide Hazards Using D-InSAR and Remote Sensing
Landslide geological disasters, occurring globally, often result in significant loss of life and extensive economic damage. In recent years, the severity of these disasters has increased, likely due to the frequent occurrence of extreme rainstorms associated with global warming. This escalating tren...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/2/345 |
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| author | Jiaxin Zhong Qiaomin Li Jia Zhang Pingping Luo Wei Zhu |
| author_facet | Jiaxin Zhong Qiaomin Li Jia Zhang Pingping Luo Wei Zhu |
| author_sort | Jiaxin Zhong |
| collection | DOAJ |
| description | Landslide geological disasters, occurring globally, often result in significant loss of life and extensive economic damage. In recent years, the severity of these disasters has increased, likely due to the frequent occurrence of extreme rainstorms associated with global warming. This escalating trend emphasizes the urgent need for a simple and efficient method to identify hidden dangers related to landslide geological disasters. Areas experiencing seasonal heavy rainfall are particularly susceptible to such disasters, posing a serious threat to the lives and property of local residents. In response to the challenging characteristics of landslide geological hazards, such as their strong concealment and the high vegetation coverage in the Liupan Mountain area of the Loess Plateau, this study focuses on the integrated remote sensing identification and research of hidden landslide dangers in Longde County. The methodology combines differential interferometric synthetic aperture radar technology (D-InSAR) and high-resolution optical remote sensing. Surface deformation information of Longde County was obtained by analyzing 85 Sentinel-1A data from 2019 to mid-2020 using Stacking-InSAR, in conjunction with high-resolution optical remote sensing image data from GF-2 in 2019. Furthermore, the study conducted integrated remote sensing identification and field verification of landslide hazards throughout the entire county. This involved interpreting the shape and deformation marks of landslide hazards, identifying the disaster-bearing bodies, and expertly interpreting the environmental factors contributing to the hazards. As a result, 47 suspected landslide hazards and 21 field investigation points were identified, with 16 hazards verified with an accuracy of 76.19%. This outcome directly confirms the applicability and accuracy of the integrated remote sensing identification technology in the study area. The research results presented in this paper provide an effective scientific and theoretical basis for the monitoring and treatment of landslide geological disasters in the future stages. They also play a pivotal role in the prevention of such disasters. |
| first_indexed | 2024-03-08T10:35:49Z |
| format | Article |
| id | doaj.art-d2b5e7da1286467eb2e4162f473625c1 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-08T10:35:49Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-d2b5e7da1286467eb2e4162f473625c12024-01-26T18:18:50ZengMDPI AGRemote Sensing2072-42922024-01-0116234510.3390/rs16020345Risk Assessment of Geological Landslide Hazards Using D-InSAR and Remote SensingJiaxin Zhong0Qiaomin Li1Jia Zhang2Pingping Luo3Wei Zhu4College of Geological Engineering and Geomatics, Chang’an University, Xi’an 710054, ChinaRemote Sensing Investigation Institute of Ningxia Hui Autonomous Region, Yinchuan 750021, ChinaNingxia Institute of Survey and Monitoring of Land and Resources, Yinchuan 750002, ChinaSchool of Water and Environment, Chang’an University, Xi’an 710054, ChinaSchool of Water and Environment, Chang’an University, Xi’an 710054, ChinaLandslide geological disasters, occurring globally, often result in significant loss of life and extensive economic damage. In recent years, the severity of these disasters has increased, likely due to the frequent occurrence of extreme rainstorms associated with global warming. This escalating trend emphasizes the urgent need for a simple and efficient method to identify hidden dangers related to landslide geological disasters. Areas experiencing seasonal heavy rainfall are particularly susceptible to such disasters, posing a serious threat to the lives and property of local residents. In response to the challenging characteristics of landslide geological hazards, such as their strong concealment and the high vegetation coverage in the Liupan Mountain area of the Loess Plateau, this study focuses on the integrated remote sensing identification and research of hidden landslide dangers in Longde County. The methodology combines differential interferometric synthetic aperture radar technology (D-InSAR) and high-resolution optical remote sensing. Surface deformation information of Longde County was obtained by analyzing 85 Sentinel-1A data from 2019 to mid-2020 using Stacking-InSAR, in conjunction with high-resolution optical remote sensing image data from GF-2 in 2019. Furthermore, the study conducted integrated remote sensing identification and field verification of landslide hazards throughout the entire county. This involved interpreting the shape and deformation marks of landslide hazards, identifying the disaster-bearing bodies, and expertly interpreting the environmental factors contributing to the hazards. As a result, 47 suspected landslide hazards and 21 field investigation points were identified, with 16 hazards verified with an accuracy of 76.19%. This outcome directly confirms the applicability and accuracy of the integrated remote sensing identification technology in the study area. The research results presented in this paper provide an effective scientific and theoretical basis for the monitoring and treatment of landslide geological disasters in the future stages. They also play a pivotal role in the prevention of such disasters.https://www.mdpi.com/2072-4292/16/2/345geological hazardsidentification of landslide hazardsintegrated remote sensingdifferential interferometry (D-InSAR)high-resolution optical remote sensing |
| spellingShingle | Jiaxin Zhong Qiaomin Li Jia Zhang Pingping Luo Wei Zhu Risk Assessment of Geological Landslide Hazards Using D-InSAR and Remote Sensing Remote Sensing geological hazards identification of landslide hazards integrated remote sensing differential interferometry (D-InSAR) high-resolution optical remote sensing |
| title | Risk Assessment of Geological Landslide Hazards Using D-InSAR and Remote Sensing |
| title_full | Risk Assessment of Geological Landslide Hazards Using D-InSAR and Remote Sensing |
| title_fullStr | Risk Assessment of Geological Landslide Hazards Using D-InSAR and Remote Sensing |
| title_full_unstemmed | Risk Assessment of Geological Landslide Hazards Using D-InSAR and Remote Sensing |
| title_short | Risk Assessment of Geological Landslide Hazards Using D-InSAR and Remote Sensing |
| title_sort | risk assessment of geological landslide hazards using d insar and remote sensing |
| topic | geological hazards identification of landslide hazards integrated remote sensing differential interferometry (D-InSAR) high-resolution optical remote sensing |
| url | https://www.mdpi.com/2072-4292/16/2/345 |
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