Beyond Static Analysis: Importance of Multi-Temporal Inventories in Alpine Environments for Dynamic Landslide Assessment in Belluno Province (Veneto Region, NE, Italy). 

Landslides are complex and dynamic natural hazards that require a comprehensive understanding of their temporal changes for effective assessment and management. Traditional landslide inventories often focus on static analysis, providing a snapshot of landslide occurrences at a specific point in time. However, to capture the dynamic nature of landslides and assess their evolution over time, multi-temporal inventories are essential. This study aims to go beyond static analysis by proposing the use of multi-temporal inventories for dynamic landslide assessment. The approach involves the integration of remote sensing data, advanced modeling techniques, and deep learning algorithms to analyze and map landslides over multiple time periods. By considering the temporal dimension, the proposed method enables the identification of changes in landslide patterns, movements, and susceptibility over time. We used orthophotos retrieved from WMS and WMTS services provided by the Italian national portal, covering the period from 1989 to 2021, for a study conducted in the Cordevole and Alpago areas (Belluno province, NE Italian Alps). These areas were impacted by two extreme meteorological events (return period > 100 years) in 2018 (October 27th–30th) and 2020 (December 4th–6th). The first, known as windstorm VAIA, has induced severe damage to the forest cover. The generated multi-temporal inventories provide valuable information for understanding the temporal dynamics of landslides, which is crucial for accurate landslide hazard assessment and risk management. The findings of this study highlight the importance of incorporating multi-temporal inventories into landslide assessment methodologies to enhance our understanding of landslide behavior and improve decision-making processes.Acknowledgement:This study was carried out within the PNRR research activities of the consortium iNEST (Interconnected North-Est Innovation Ecosystem) funded by the European Union Next-Generation EU (Piano Nazionale diRipresa e Resilienza (PNRR) – Missione 4 Componente 2, Investimento 1.5 – D.D. 1058 23/06/2022, ECS_00000043). This manuscript reflects only the Authors’ views and opinions; neither the European Union nor the European Commission can be considered responsible for them.