- 1University of Florence, Earth Sciences Department, Firenze, Italy
- 2National Institute of Oceanography and Applied Geophysics (OGS), Seismological Research Centre, Udine, Italy
Landslides represent a significant natural hazard on a global scale, resulting in considerable economic losses and indirect social impacts. Italy is one of the European countries most affected by landslides, with more than 500,000 mapped events, of which approximately 100,000 are located in Tuscany. Remote sensing has emerged as a powerful tool for the investigation and monitoring of ground deformation. Earth observation techniques, particularly the Interferometric Synthetic Aperture Radar (InSAR) analysis and optical imagery enable ground deformation measurement with millimetric to centimetric precision and high temporal frequency.
The present study focuses on the municipality of Zeri, in the province of Massa-Carrara (Tuscany), specifically on the hamlets of Patigno and Coloretta affected by quiescent and active landslides. These areas have been selected considering the displacement recorded by the Interferometric Synthetic Aperture Radar (InSAR) data from Sentinel-1 for the period 2019-2023. Data is made available from the European Ground Motion Service (EGMS), enabling precise measurements of ground deformation over time with millimetric accuracy through the time-series analysis. For this reason, Zeri municipality was chosen as a case study for exploring the interplay between optical and radar satellite-derived deformation data and in-situ information on buildings impacted by landslides. A multi-temporal analysis integrating advanced remote sensing techniques employs optical imagery, acquired from high-resolution sensors such as WorldView-2/3, SPOT-7, and other oldest aerial optical datasets, to provide long-term information on surface changes, vegetation displacement, and impact of the landslides on the structures and infrastructure.
The integration of these datasets allows for a comprehensive assessment of the spatial and temporal evolution of ground movements, highlighting areas of active deformation and their direct impact on built structures. By correlating both radar and optical satellite-derived deformation trends with detailed in-situ surveys of buildings, the study aims to identify patterns of structural vulnerability and the progression of damage linked to ongoing ground instability. This dual approach leverages the strengths of optical and SAR data to enhance the understanding of landslide dynamics in this geologically complex area, providing a robust basis for further risk assessment and mitigation planning.
The research is part of the PRIN-PNRR project SMILE: Statistical Machine Learning for Exposure development, funded by the European Union- Next Generation EU, Mission 4 Component 1 (CUP F53D23010780001), which aims to investigate how Machine Learning (ML) can be used to assemble or update exposure layers by combining crowdsourced data gathered by trained citizens, ancillary data (such as national census data), and remote sensing images.
How to cite: Nardini, O., Poggi, F., Del Soldato, M., Bianchini, S., and Scaini, C.: Long term ground deformation analysis of landslide integrating remote sensed and in-situ data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15819, https://doi.org/10.5194/egusphere-egu25-15819, 2025.