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Slow-moving landslides can be found worldwide, exhibiting gradual movement over years to decades, with velocities ranging from millimeters to meters per year. However, slow-moving landslides can undergo transformation into rapid and catastrophic events, leading to severe damage to human society. Given the presence of >2500 pre-existing slow-moving landslides identified and cataloged using LiDAR bare ground data in Taiwan, it is crucial to efficiently assess their activity prior to the typhoon and heavy rainfall seasons. Therefore, a key research objective is to enhance the ability to detect landslide velocities and movements over large areas spanning tens of thousands of square kilometers. This study aims to establish a workflow that employs the multitemporal interferometric synthetic aperture radar (MTInSAR) technique for the detection and monitoring of multiple slow-moving landslides across wide regions. Specifically, we propose an optimized analysis package called "multi-snap2stamps," based on the existing snap2stamps method, to generate interferograms for multiple regions simultaneously. Focusing on the analysis of nine pre-existing slow-moving landslides, with velocities <1.6 m/year (classified as very slow or extremely slow landslides according to established criteria), distributed across Taiwan with diverse terrain and geological conditions, this study investigates the characteristics of these deep-seated landslides using the proposed MTInSAR analysis workflow in both spatial and temporal domains. The results reveal that two slow-moving landslide sites exhibit significant seasonal signals in the time series, while seven sites do not display such signals. Additionally, the MTInSAR time series effectively captures accelerated movement in the Shiding-T001 landslide, which corresponds well with in situ inclinometer monitoring data. This study demonstrates the effective analysis of velocity distribution and movement characteristics of slow-moving landslides using the proposed MTInSAR workflow. Furthermore, it highlights the potential of this workflow for improving landslide motion detection over vast areas of interest in the future. • Simultaneous generation of SAR interferograms from multiple regions of landslides. • Optimizing an MTInSAR workflow for analysis of multiple slow-moving landslides. • Detecting accelerated movements of slow-moving landslides by time series analysis. • Implication of seasonal fluctuation signals from the MTInSAR time series. [ABSTRACT FROM AUTHOR]

A slow-moving landslide on the Huafan University campus, which is located on a dip slope in northern Taiwan, has been observed since 1990. However, reliable monitoring data are difficult to acquire after 2018 due to the lack of continuous maintenance of the field measurement equipment. In this study, the multitemporal interferometry (MTI) technique is applied with Sentinel-1 SAR images to monitor the slow-moving landslide from 2014–2019. The slow-moving areas detected by persistent scatterer (PS) pixels are consistent with the range of previous studies, which are based on in situ monitoring data and field surveys. According to the time series of the PS pixels, a long period gravity-induced deformation of the slow-moving landslide can be clearly observed. Moreover, a short period seasonal surface fluctuation of the slow-moving landslide, which has seldom been discussed before, can also be detected in this study. The seasonal surface fluctuation is in-phase with precipitation, which is inferred to be related to the geological and hydrological conditions of the study area. The MTI technique can compensate for the lack of surface displacement data, in this case, the Huafan University campus, and provide information for evaluating and monitoring slow-moving landslides for possible landslide early warning in the future. [ABSTRACT FROM AUTHOR]

A natural hillslope developing into a landslide shows ground cracks and topographic deformation. Geomorphological and subsurface investigations using appropriate methodology are essential to understand the failure mechanisms and stability of a hillslope. Huafan University campus located on a dip slope in northern Taiwan is facing a potential landslide hazard. Slope movement was detected through the development of ground cracks and persistent deformation of campus buildings and facilities. To monitor the sliding behavior of the dip slope, a nail network consisting of 144 ground monitoring points was set in 2001, and its coordinates were measured using conventional traverse surveying twice a year until 2017. The 17-year surficial surveying results were presented as a time series of displacements with constraints of geometry and distribution of ground cracks and underground observations. The long-term surveying results reveal multiple potential sliding blocks within the Huafan University campus. A model of landslide movement with a listric sliding surface is proposed. Additionally, from the velocity field derived from the monitoring points, the horizontal strain rates of the slope are estimated. The pattern of strain rates indicates that a plausible fault passing through the campus may have affected the movement of the dip slope. The long-term surface monitoring of a potential landslide slope in this study provides a reliable and economical way to understand the mechanism of movement behavior of the slope and evaluate slope stability. • 17-year surficial monitoring distinguishes movement behavior of different parts of dip-slope landslides. • A model of movement process of landslides having listric sliding surface(s) is proposed to reveal intermittent movements. • A strain pattern obtained of the slope surface evidences a tectonic fault passing through the landslide block. • Economic and reliable study procedures are applicable to unstable slopes for research and disaster prevention. [ABSTRACT FROM AUTHOR]