Landslide Identification and Monitoring along the Jinsha River Catchment (Wudongde Reservoir Area), China, Using the InSAR Method
1
School of Geology Engineering and Geomatics, Chang’an University, Xi’an 710054, China
2
National Administration of Surveying, Mapping and Geoinformation, Engineering Research Center of National Geographic Conditions Monitoring, Xi’an 710054, China
3
Roy M. Huffington Department of Earth Sciences, Southern Methodist University, Dallas, TX 75275, USA
4
Institute of Geomechanics, Chinese Academy of Geological Sciences, Beijing 100081, China
*
Authors to whom correspondence should be addressed.
Remote Sens. 2018, 10(7), 993; https://doi.org/10.3390/rs10070993
Received: 30 April 2018
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Revised: 17 June 2018
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Accepted: 20 June 2018
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Published: 22 June 2018
(This article belongs to the Special Issue Radar Interferometry for Geohazards)
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.
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Keywords:
InSAR; landslide identification; landslide monitoring; landslide type inversion; interferometric point target analysis (IPTA)
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MDPI and ACS Style
Zhao, C.; Kang, Y.; Zhang, Q.; Lu, Z.; Li, B. Landslide Identification and Monitoring along the Jinsha River Catchment (Wudongde Reservoir Area), China, Using the InSAR Method. Remote Sens. 2018, 10, 993. https://doi.org/10.3390/rs10070993
AMA Style
Zhao C, Kang Y, Zhang Q, Lu Z, Li B. Landslide Identification and Monitoring along the Jinsha River Catchment (Wudongde Reservoir Area), China, Using the InSAR Method. Remote Sensing. 2018; 10(7):993. https://doi.org/10.3390/rs10070993
Chicago/Turabian StyleZhao, Chaoying, Ya Kang, Qin Zhang, Zhong Lu, and Bin Li. 2018. "Landslide Identification and Monitoring along the Jinsha River Catchment (Wudongde Reservoir Area), China, Using the InSAR Method" Remote Sensing 10, no. 7: 993. https://doi.org/10.3390/rs10070993
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