PSI Spatially Constrained Clustering: The Sibari and Metaponto Coastal Plains

Abstract

PSI data are extremely useful for monitoring on-ground displacements. In many cases, clustering algorithms are adopted to highlight the presence of homogeneous patterns; however, clustering algorithms can fail to consider spatial constraints and be poorly specific in revealing patterns at lower scales or possible anomalies. Hence, we proposed a novel framework which combines a spatially-constrained clustering algorithm (SKATER) with a hypothesis testing procedure which evaluates and establishes the presence of significant local spatial correlations, namely the LISA method. The designed workflow ensures the retrieval of homogeneous clusters and a reliable anomaly detection; to validate this workflow, we collected Sentinel-1 time series from the Sibari and Metaponto coastal plains in Italy, ranging from 2015 to 2021. This particular study area is interesting due to the presence of important industrial and agricultural settlements. The proposed workflow effectively outlines the presence of both subsidence and uplifting that deserve to be focused and continuous monitoring, both for environmental and infrastructural purposes.

Refined Landslide Susceptibility Mapping by Integrating the SHAP-CatBoost Model and InSAR Observations: A Case Study of Lishui, Southern China

“…This makes it well-suited for monitoring urban areas with permanent scatterers, as demonstrated in recent years. For example, Amoroso et al [31] employed PS-InSAR to assess surface deformations in the Sibari and Metaponto coastal plains and Chen et al [32] completed the deformation monitoring of Eboling Mountain using PS-InSAR. SBAS-InSAR, capable of capturing long-term surface deformations like PS-InSAR, has lower image requirements.…”