Landslide monitoring using PS-InSAR: a cost-effective approach for the Himalayas
Abstract Landslides cause a significant impact on human lives, infrastructure, and the environment in the Himalaya, making reliable monitoring of landslides essential to reduce losses. To overcome the high cost and limited spatial coverage of in-situ instrumentation, this study applies Sentinel-1 C...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-07-01
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| Series: | Discover Applied Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s42452-025-07343-0 |
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| Summary: | Abstract Landslides cause a significant impact on human lives, infrastructure, and the environment in the Himalaya, making reliable monitoring of landslides essential to reduce losses. To overcome the high cost and limited spatial coverage of in-situ instrumentation, this study applies Sentinel-1 C band imagery and Interferometric Synthetic Aperture Radar (InSAR) for landslide monitoring and evaluation of the slow-moving Guthitar landslide along the Dharan-Dhankuta road in Nepal. The Persistent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR) technique is used to scrutinize the landslide by analyzing the permanent scatterers (PS) observed from temporal C-band Sentinel-1A datasets from both ascending and descending satellite tracks from 2015 to 2024. The findings indicate an escalation in land deformation of approximately − 76 mm from the onset in 2020 to 2024. Moreover, the 2D analyses using the combined dataset showed the range of vertical velocity as − 11.5 to 4.5 mm/year and that of east–west velocity as − 6.2 to 12.4 mm/year in the timeframe 2020–2024. The deformation estimated from PSInSAR technique is verified by deformation monitored using inclinometers. Further, the deformation from PSInSAR was correlated with the precipitation data, showing the correlation of the movement of the landslide following the rainy season of 2020. These findings demonstrate that Persistent Scatterer InSAR provides an accurate, cost-effective approach for monitoring slow-moving landslides and can support early warning and risk mitigation efforts along critical infrastructure corridors in the Nepal Himalayas. |
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| ISSN: | 3004-9261 |