A new method to detect changes in displacement rates of slow-moving landslides using InSAR time series

Abstract

Slow-moving landslides move downslope at velocities that range from mm year−1 to m year−1. Such deformations can be measured using satellite-based synthetic aperture radar interferometry (InSAR). We developed a new method to systematically detect and quantify accelerations and decelerations of slowly deforming areas using InSAR displacement time series. The displacement time series are filtered using an outlier detector and subsequently piecewise linear functions are fitted to identify changes in the displacement rate (i.e., accelerations or decelerations). Grouped accelerations and decelerations are inventoried as indicators of potential unstable areas. We tested and refined our new method using a high-quality dataset from the Mud Creek landslide, CA, USA. Our method detects accelerations and decelerations that coincide with those previously detected by manual examination. Second, we tested our method in the region around the Mazar dam and reservoir in Southeast Ecuador, where the time series data were of considerably lower quality. We detected accelerations and decelerations occurring during the entire study period near and upslope of the reservoir. Application of our method results in a wealth of information on the dynamics of the surface displacement of hillslopes and provides an objective way to identify changes in displacement rates. The displacement rates, their spatial variation, and the timing of accelerations and decelerations can be used to study the physical behavior of a slow-moving slope or for regional hazard assessment by linking the timing of changes in displacement rates to landslide causal and triggering factors.

A Clustering Approach for the Analysis of InSAR Time Series: Application to the Bandung Basin (Indonesia)

“…The average subsidence rate of MPs within this cluster was −4.9 cm/y, with a maximum rate of −10 cm/y. This coincides with previous studies, which observed an extensive subsidence feature encompassing the area of Margaarish Town, Bandung City with displacement rates of −10 cm/yr to −2 cm/yr between 2014 to 2017 [62]. Another study, which used Sentinel-1 and ALOS-2 images acquired from September 2014 to July 2017, found that the mean velocity of this subsidence centre was −11.2 cm/y, covering an area of 14.3 km 2 [67].…”

“…The re-referenced displacement pattern remains similar to the un-corrected map (Figure 5c); however, the range of velocity values have changed and now reflect more realistic displacement estimates with a maximum subsidence rate of −18.7 cm/y and an uplift rate of 2.3 cm/y, which is more or less stable given that the standard deviation for the re-referenced InSAR velocity results is 3.1 cm/y. These values are more in line with previous InSAR results [62][63][64].…”

“…This study allows us to infer that changes in subsidence rates identified by decomposing the cluster barycenters with piecewise functions may be induced by groundwater abstraction within industrial areas, or the of soft soils to infrastructural loading. Previous studies on subsidence processes in the Bandung Basin using InSAR analysis either relied on the average annual deformation [42,62,76] or analyzed a small number of InSAR MPs to examine the displacement behaviour over time [44,70]. Both methods imply linear movement, which we have shown to be an inaccurate assumption.…”

A Low-Cost and Fast Operational Procedure to Identify Potential Slope Instabilities in Cultural Heritage Sites

“…A-DInSAR, on the other hand, made it possible to analyze and monitor the areas in proximity to the cliffs and the potential presence of slow-moving phenomena in the gently dipping slopes on the northwest side of the towns. The A-DInSAR technique is a well-known approach used to detect and monitor slow-moving landslides [57][58][59]. However, rockfalls are characterized by rapid and very rapid velocities [60], decreasing the applicability of this technique to these phenomena.…”

Critical area identification and dynamic process simulation for landslide hazard chain formation in the upstream Jinsha River

“…Moreover, Landsat, ALOS, Quick Bird, and other satellites with high-resolution images have been widely used for landslide identification; however, they only identify landslide hazards with clear deformation (McDonald and Grubbs, 1975;Sato and Harp, 2009;Youssef et al, 2009;García-Davalillo et al, 2014). InSAR with centimeter-high precision and all-weather surface observation technology can perform large-scale landslide identification and monitoring to build a landslide hazard inventory (Woods et al, 2020;Urgilez Vinueza et al, 2022;Zhang et al, 2022). Because of the satellite lateral imaging mode, the terrain may cause geometric distortion of SAR images, including perspective shortening, overlap, and shadow (Jie et al, 2018).…”