Work

Investigating the Evolution of Active Deformation Areas (ADAs) in the Veneto-Friulian Plain Using Multi-Platform SAR Data

Year: 2026

Type: article

Abstract: Coastal alluvial plains underlain by unconsolidated deposits are prone to land subsidence, a geohazard that can damage infrastructure and alter drainage patterns. One such example is the Venetian–Friulian coastal plain (NE Italy), where natural sediment compaction and anthropogenic activities have led to ground deformation across multiple zones. From this perspective, this study presents a 30-year analysis of land subsidence across the Venetian–Friulian plain, particularly highlighting municipalities such as Portogruaro, Concordia Sagittaria, San Stino di Livenza, Eraclea, and Caorle. The dataset comprises multi-source SAR data from ERS, Envisat, COSMO-SkyMed (CSK), Sentinel-1, and the European Ground Motion Service (EGMS), covering the period from 1992 to 2021. The study integrates multi-platform SAR observations with ADAFinder-based extraction of Active Deformation Areas (ADAs), data quality evaluation using the Quality Index (QI), building-scale analysis based on LOS-derived vertical displacement time series, and orthophotos to confirm the building’s presence and evolution. By using the adopted extraction thresholds, a total of 57, 16, 83, 33, and 72 ADAs were identified from the ERS, ENVISAT, COSMO-SkyMed, Sentinel-1, and EGMS datasets, respectively. The result suggests that the strongest deformation occurred during the earlier observation periods in Zones 1 to 3, then progressively stabilized, whereas some parts of Zone 4 remained active and showed renewed deformation during the later periods. The research highlights the importance of conducting long-term analysis using multi-platform interferometric datasets to refine and personalize outcomes in geohazard monitoring. The findings from this research offer invaluable insights into the ongoing surveillance of geohazards, which are progressively related to urban development and planning.

Source: Remote Sensing

DOI: 10.3390/rs18081252