Persistent Scatterer Interferometry (PSI) Technique for the Identification and Monitoring of Critical Landslide Areas in a Regional and Mountainous Road Network

Abstract

A reliable road network is a vital local asset, connecting communities and unlocking economic growth. Every year landslides cause serious damage and, in some cases, the full disruption of many road networks, which can last from a few days to even months. The identification and monitoring of landslides with conventional methods on an extended and complex road network can be a rather difficult process, as it requires a significant amount of time and resources. The road network of the Chania regional unit on the island of Crete in Greece is a typical example, as it connects, over long distances, many remote mountainous villages with other local communities, as well as with the main urban centers, which are mainly located across the shore. Persistent scatterer interferometry (PSI) is a remote-sensing technique that can provide a reliable and cost-effective solution, as it can be used to identify and monitor slow-moving and ongoing landslides over large and complex areas such as those of the mountainous road networks. This study applied PSI in the Chania regional unit, using the novel parallelized PSI (P-PSI) processing chain, developed by the Operational Unit Center for Earth Observation Research and Satellite Remote Sensing BEYOND of the Institute of Astronomy and Astrophysics, Space Applications and Remote Sensing of the National Observatory of Athens (BEYOND) for the rapid identification of the areas, most critical to landslide in a local road network. The application of P-PSI speeded up the total required processing time by a factor of five and led to the rapid identification and monitoring of 235 new slow-moving landslides. The identified landslides were correlated with a pre-existing landslide inventory and open access visual data to create a complete landslide inventory and a relative landslide inventory map, thus offering a valuable tool to local stakeholders.

Creating a Comprehensive Landslides Inventory Using Remote Sensing Techniques and Open Access Data

“…In the first step, which is symbolized with number 1 in Figure 1, the spatial characteristics of the landslides, such as their location, orientation, and extent, are determined by employing the Parallelized Persistent Scatterer Interferometry (P-PSI) method, which can speed up the whole process by a factor of five [8]. A detailed analysis of the processing of Sentinel-1 images with the P-PSI method for landslide evaluation and monitoring is provided by [8,9]. The results from the P-PSI method are then further combined with open access data, specifically focusing on each identified landslide.…”

“…In some countries, a national landslides inventory does not exist, despite the frequent occurrence of intense landslides that result in many fatalities and the destruction of critical infrastructure [1]. In such cases, scientists often attempt to create a landslides inventory based on open access landslides geodatabases, such as the Global Landslide Catalogue (GLC) [7], and remote sensing techniques, such as differential interferometric synthetic aperture radar (DInSAR) methods [8]. However, the level of detail and completeness of these inventories is often low due to two main reasons.…”

UAV, GNSS, and InSAR Data Analyses for Landslide Monitoring in a Mountainous Village in Western Greece

“…It has been proven that PSI appears to be slightly more accurate compared to the SBAS approach [23]. In more detail, some researchers have utilized PSI measurements derived from the Sentinel-1 constellation to evaluate landslide kinematics and subsequently understand the sliding mechanisms [24], while others focused on creating methodologies for the continuous assessment or update of landslide inventories through PSI measurements [25,26]. Moreover, a post-processing PSI procedure has been proposed for the identification of slowly moving regions in the French Alps and the subsequent extraction of areas of active deformation with velocities lower than 1.6 cm/y [27].…”

Retrieving the Kinematic Process of Repeated-Mining-Induced Landslides by Fusing SAR/InSAR Displacement, Logistic Model, and Probability Integral Method

“…Among them, the typical small baseline subsets (SBASs) approach can suppress the decorrelation effect based on optimizing the combination of interferometric pairs with a short spatiotemporal baseline [11]. These advanced InSAR methods have made great contributions to monitor the mining subsidence or landslide displacement [12][13][14][15], detect the potential active landslides [16,17], or dynamically estimate the deformation parameters [18,19]. However, mining operation in hilly regions can cause significant landslide events characterized by steep displacement gradients [2,5].…”