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.

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.

The deformation characteristics and instability patterns of rotational landslides are complicated. Such landslides are large and occur continuously, seriously threatening people’s lives. We used interferometry synthetic aperture radar (InSAR), digital elevation models of difference (DODs), numerical simulations, and other techniques for analyzing the topographic changes, surface deformation and movement process before, during and after a landslide. Based on the high-resolution terrain data before and after the landslide, the topographic changes were analyzed, and the active zone of the landslide was identified. The areas of the topographic changes were mainly located on the main scarp, toe and secondary landslides. The topographic changes were influenced by rainfall and rill erosion. The geomorphologically-guided InSAR interpretation method was applied to explore the displacement pattern. The deformation area in the middle of the landslide coincided with the secondary landslides. A time-series InSAR analysis revealed the dynamic evolution of the deformation before and after the landslide. Based on its evolution, the simulated landslide process included the main landslide and three secondary landslides. Based on the displacement of the longitudinal ground surface profiles, the displacement characteristics and kinematic behavior were summarized and compared with those of a single rotational landslide and multiple rotational landslides. The single rotational landslide had obvious secondary and progressive characteristics, developing into multiple rotational landslides triggered by conditions such as rainfall.

Information about the long-term spatiotemporal evolution of landslides can improve the understanding of landslides. However, since landslide deformation characteristics differ it is difficult to monitor the entire movement of a landslide using a single method. The Interferometric Synthetic Aperture Radar (InSAR) and pixel offset tracking (POT) method can complement each other when monitoring deformation at different landslide stages. Therefore, the InSAR and improved POT method were adapted to study the pre- and post-failure surface deformation characteristics of the Gaojiawan landslide to deepen understanding of the long-term spatiotemporal evolution characteristics of landslides. The results show that the deformation displacement gradient of the Gaojiawan landslide exhibited rapid movement that exceeded the measurable limit of InSAR during the first disaster. Moreover, the Gaojiawan landslide has experienced long-term creep, and while studying the post-second landslide’s failure stability, the acceleration trend was identified via time series analysis, which can be used as a precursor signal for landslide disaster warning. Our study aims to provide scientific reference for local governments to help prevent and mitigate geological disasters in this region.