Date Presented:
2-6 Feb.
Abstract:
The Amynteo mega-landslide in northern Greece represents one of the most significant mass-wasting events in southeastern Europe in recent decades, with substantial geomorphological, geotechnical, and socio-economic impacts, causing the relocation of an entire village (Anargiri). Understanding such large-scale slope failure requires a multi-scale and multitemporal approach that captures both the surface dynamics and underlying controlling processes. This study investigates pre- and post-failure surface motion associated with the event by integrating Earth Observation (EO) data and Unmanned Aerial System (UAS) surveys. Surface motion gradients extending from the mine toward the nearby village of Anargyri were assessed using multi-temporal SAR interferometry (MT-InSAR) and offset tracking techniques, together with high-resolution UAS-derived Digital Surface Models (DSMs) and orthophotos. We examined the limits of each technique in measuring surface motion and exploited their complementarities across multiple spatial and temporal scales. Multi-sensor SAR datasets, including Copernicus Sentinel-1 and TerraSAR-X, were processed using MT-InSAR, supported by Copernicus EGMS products and the SNAPPING online service. Offset tracking contributed insights in areas with high displacement gradients where phase decorrelation limited interferometric methods. Repeated UAS campaigns further enhanced spatial interpretation and deformation quantification. Our analysis indicated persistent ground deformation in the pre-failure phase, spatial variability in displacement rates, and post-failure reactivation zones. The integration of InSAR and UAS photogrammetry links geomorphic process domains, such as headscarp retreat, lateral spreading, and toe bulging, with slope kinematics. We also investigate how hydrological forcing, mining-related disturbances, and lithological controls contribute to the triggering of the landslide. The study highlights the spatial and temporal dynamics of the Amynteo landslide and demonstrates the value of integrating diverse EO and UAS techniques for advancing landslide geomorphology and hazard assessment in complex slope systems. This multi-scale, multitemporal monitoring approach enhances the early detection of instability and supports the development of early warning strategies in mining environments.
