Phd in EARTH SCIENCES

Thesis title: Multi-Sensor Monitoring for the analyses and prediction of rockfalls at the Poggio Baldi Landslide Natural Lab

Rockfall processes pose a significant natural hazard, but their rapid and sudden nature makes prediction and the development of early warning systems a complex and unresolved challenge. This research aims to fill this critical knowledge gap by establishing the empirical and methodological basis necessary to achieve a quantitative understanding of kinematic precursors. To overcome the limitations of episodic measurements, the first step involved the creation of the Poggio Baldi Natural Laboratory (PBL), a permanent multisensory platform that allowed for a transition to continuous and automated observation. The Characterization phase integrated traditional geomechanical analysis with advanced remote sensing techniques (TLS, UAV-SfM, and optical monitoring), quantifying spatial susceptibility models and temporal risk variations. The Detection objective was achieved by developing automated workflows that produced the first high-resolution daily rockfall inventory for the site, documenting 171 events. This database provided the essential reference data for validating risk models (such as the Q-SIF model) and for correlating rockfall activity with triggering factors. In this context, high-resolution optical systems proved to be the key technology for identifying micro-displacements. Finally, the Prediction work aimed to identify and quantify the kinematic precursors (displacement and dilatancy) that precede detachment. While a complete prediction system remains a future goal, this research establishes the methodological pathway for the transition from reactive to proactive monitoring, making the aim of an early warning system for rockfalls concretely more achievable.