Open-source InSAR data for the characterization of geomorphic processes at different scales

Abstract

Ground deformation, such as landslides and land subsidence, is a prevalent and potentially hazardous phenomenon in a multitude of landscapes. It is imperative to comprehend the nature and extent of ground movement to ensure the safety of individuals, infrastructure, and natural environments. The advent of satellite-based radar technology and related services has facilitated the observation of ground motion across extensive regions with exceptional precision and continuity. Among these, the European Ground Motion Service (EGMS) provides free and standardized displacement measurements that are increasingly utilised in geomorphological studies. The present paper includes an automated methodology to extract and classify ground deformation patterns from EGMS data. The objective is to support large-scale geomorphological analysis and hazard assessment. A fully automated procedure has been developed and applied to identify clusters of ground motion across Italy. Two different velocity thresholds (±10 mm/year and ±5 mm/year) were used to test their influence on detection and classification. The application of the higher threshold resulted in the identification of over 2,000 clusters, while the lower threshold led to the identification of almost 10,000 clusters. The results obtained demonstrate that steeper slopes exhibiting horizontal movement are commonly associated with landslides, while vertical motion in flat areas is indicative of subsidence. Local case studies confirmed the ability of the method to detect both strong and subtle deformation signals, even in complex or urbanized environments and its capability to support characterization of deformation pattern. The findings demonstrate that open satellite data, when combined with automated tools, can improve our ability to map and interpret surface deformation. The method is scalable and adaptable and may be applied to other regions to support land management, early warning, and long-term monitoring strategies.