Identification and monitoring of unstable slopes across wide regions using Synthetic Aperture Radar Interferometry (InSAR) can further help to prevent and mitigate geological hazards. However, the low spatial density of measurement points (MPs) extracted using the traditional time-series InSAR method in topographically complex mountains and vegetation-covered slopes makes the final result unreliable. In this study, a method of time-series InSAR analysis using single- and multi-look phases were adopted to solve this problem, which exploited single- and multi-look phases to increase the number of MPs in the natural environment. Archived ascending and descending Sentinel-1 datasets covering Zhouqu County were processed. The results revealed that nine landslides could be quickly identified from the average phase rate maps using the Stacking method. Then, the time-series InSAR analysis with single- and multi-look phases could be used to effectively monitor the deformation of these landslides and to quantitatively analyze the magnitude and dynamic evolution of the deformation in various parts of the landslides. The reliability of the InSAR results was further verified by field investigations and Unmanned Aerial Vehicle (UAV) surveys. In addition, the precursory movements and causative factors of the recent Yahuokou landslide were analyzed in detail, and the application of the time-series InSAR method in landslide investigations was discussed and summarized. Therefore, this study has practical significance for early warning of landslides and risk mitigation.

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Investigating landslide deformation patterns in different evolution stages is important for understanding landslide movement. Translational landslides generally slide along a relatively straight surface of rupture. Whether the post-failure spatiotemporal deformation for certain translational landslides follows the pre-failure pattern remains untested. Here, the pre- and post-failure spatiotemporal deformations of the Simencun landslide along the Yellow River in 2018 were analyzed through multi-temporal remote sensing image analysis, Interferometric Synthetic Aperture Radar (InSAR) deformation monitoring and intensive field investigations. The results show that the pre- and post-failure spatial deformations both follow a retrogressive failure pattern. The long time series of the displacement before and after failure is characterized by obvious seasonal and periodic stage acceleration movements. Effective rainfall played an important role in the increase of the displacement acceleration, and the change in temperature might have accelerated the displacement. Finally, there is a possibility that the post-failure spatiotemporal deformation pattern of translational landslides does follow the pre-failure pattern when certain conditions are satisfied. The results are of great significance to improving our understanding of the spatiotemporal deformation pattern of landslides and to post-failure risk prevention and control.

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