Using Satellite Interferometry to Infer Landslide Sliding Surface Depth and Geometry

Abstract

Information regarding the shape and depth of a landslide sliding surface (LSS) is fundamental for the estimation of the volume of the unstable masses, which in turn is of primary importance for the assessment of landslide magnitude and risk scenarios as well as in refining stability analyses. To assess an LSS is not an easy task and is generally time-consuming and expensive. In this work, a method existing in the literature, based on the inclination of movement vectors along a cross-section to estimate the depth and geometry LSSs, is used for the first time while exploiting satellite interferometric data. Given the advent of satellite interferometric data and the related increasing availability of spatially dense and accurate measurements, we test the effectiveness of this method—here named the vector inclination method (VIM)—to four case landslides located in Italy characterized by different types of movement, kinematics and volume. Geotechnical and geophysical information of the LSS is used to validate the method. Our results show that each of the presented cases provides useful insight into the validity of VIM using satellite interferometric data. The main advantages of VIM applied to satellite interferometry are that it enables estimation of the LSS with a theoretical worldwide coverage, as well as with no need for onsite instrumentation or even direct access; however, a good density of measurement points in both ascending and descending geometry is necessary. The combined use of VIM and traditional investigations can provide a more accurate LSS model.

Persistent Scatterer Interferometry and Statistical Analysis of Time-Series for Landslide Monitoring: Application to Santo Stefano d’Aveto (Liguria, NW Italy)

“…The comparison between each PS position with the values of slope and aspect shown in Figure 1 did not highlight any relationship, suggesting a subdivision of the landslide likely not driven by topographical effects but presumably due to geomorphological factors and/or to the tectonic setting of the bedrock over which the landslide lays. This assumption is in accordance with previous studies [18,29] that identify at least two sectors within the landslide: an upper one near the crown at Rocca d'Aveto, characterized by an 8-to-10 meter-deep sliding surface that showed a moderate velocity of 40-50 mm/y as reported in other researches and technical reports [17,27], and a central-lower one, roughly corresponding to Roncolungo and Santo Stefano d'Aveto villages, where regional studies [26][27][28][29][30] suggest the presence of a double system of slow sliding surfaces below the ground level with a depth that varies from -22 m to -54 m. This latter value corresponds to the interface between the bottom of the incoherent deposits of the landslide and the fractured silty-sandy bedrock (as derived from the borehole SSA3, Figure 11). The WSA results allowed us to identify one group of PSs within the uppermost sector (Family 1) and two within the central-lower sector (Family 2 and Family 3).…”

“…In this study, we applied PS Interferometry to the Santo Stefano d'Aveto landslide (Liguria, NW Italy), one of the most investigated and monitored active landslides of eastern Liguria. Many studies dealt with its characterization using different investigation techniques, such as PS or common geotechnical monitoring methodologies (e.g., inclinometric data), highlighting a complex and potentially dangerous geological and geomorphological scenario [17][18][19][20][21]. In order to achieve an ever better comprehension on the evolution and on the dynamics of this area, we analysed the period 2015-2021 by processing descending and ascending acquisitions from the Sentinel-1A satellite and obtaining maps of PS with time-series of movements registered along the LOS.…”

Three-dimensional and long-term landslide displacement estimation by fusing C- and L-band SAR observations: A case study in Gongjue County, Tibet, China

“…This has been widely used to determine the location of landslides over large areas and to monitor the temporal activities of 3 of 43 landslides in specific regions (Dong et al, 2018;Herrera et al, 2013;Hu et al, 2020;Shi et al, 2019). In particular, InSAR-derived displacement information can be used to investigate the mechanisms of landslides, including landslide types (Burrows et al, 2019), triggering factors , failure modes (Eriksen et al, 2017;Kang et al, 2017), depth and volume estimation, and risk assessment (Hu et al, 2016(Hu et al, , 2018Intrieri et al, 2020).…”

A Robust SAR Speckle Tracking Workflow for Measuring and Interpreting the 3D Surface Displacement of Landslides

“…The simplest method, referred to as the vector inclination method (VIM), assumes that the trend and plunge of displacements reflect the local orientation of the sliding surface [54,55], which with this method can then be graphically reconstructed along user-defined sections. The development of remote sensing techniques, such as InSAR and ALS, capable of monitoring wide ground surface areas allows researchers to use the VIM method to quickly estimate the thickness of active rock slides [56]. A more advanced approach exploits the mass conservation equation to estimate differences in depth of the sliding surface over large areas.…”