Landslide Identification and Monitoring along the Jinsha River Catchment (Wudongde Reservoir Area), China, Using the InSAR Method

Chaoying Zhao, Ya Kang, Qin Zhang, Zhong Lu, Bin Li

Remote Sensing · 2018 · 142 citations · 52 references

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TL;DR

Landslide identification, monitoring, and mode deduction are critical for hazard prevention, with surface deformation at varying scales informing analysis. The study uses L‑band SAR with IPTA to detect landslides across the Wudongde reservoir catchment, then applies SBAS InSAR and compares results with georobot data to analyze the spatiotemporal deformation of the Jinpingzi landslide. Twenty‑two active landslides were mapped over more than 2500 km², and the Jinpingzi landslide’s deformation pattern revealed section‑specific stability, pull‑type motion governed by boundaries and geology, and a strong correlation with rainfall, soil moisture, and water level, indicating heavy precipitation as the main trigger with a 1–2 month lag.

Abstract

Landslide identification and monitoring are two significant research aspects for landslide analysis. In addition, landslide mode deduction is key for the prevention of landslide hazards. Surface deformation results with different scales can serve for different landslide analysis. L-band synthetic aperture radar (SAR) data calculated with Interferometric Point Target Analysis (IPTA) are first employed to detect potential landslides at the catchment-scale Wudongde reservoir area. Twenty-two active landslides are identified and mapped over more than 2500 square kilometers. Then, for one typical landslide, Jinpingzi landslide, its spatiotemporal deformation characteristics are analyzed with the small baseline subsets (SBAS) interferometric synthetic aperture radar (InSAR) technique. High-precision surface deformation results are obtained by comparing with in-situ georobot measurements. The spatial deformation pattern reveals the different stabilities among five different sections of Jinpingzi landslide. InSAR results for Section II of Jinpingzi landslide show that this active landslide is controlled by two boundaries and geological structure, and its different landslide deformation magnitudes at different sections on the surface companying with borehole deformation reveals the pull-type landslide mode. Correlation between time series landslide motion and monthly precipitation, soil moisture inverted from SAR intensity images and water level fluctuations suggests that heavy rainfall is the main trigger factor, and the maximum deformation of the landslide was highly consistent with the peak precipitation with a time lag of about 1 to 2 months, which gives us important guidelines to mitigate and prevent this kind of hazard.

References

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