This study introduces a Hybrid Interferometric Approach (HIA) tailored for the detection, mapping, and measurement of landslides using Sentinel-1 satellite data. The HIA is specifically designed to identify ground displacements that exceed the detection thresholds of the European Ground Motion Service (EGMS), offering an enhanced capacity for monitoring faster-moving landslides. The methodology integrates multi-baseline interferometric analysis, utilizing backscattered signals from both point-like and distributed radar targets at full spatial resolution. The approach utilizes ten interferometric datasets acquired between 2017 and 2021 from both ascending and descending orbits. Each annual dataset is restricted to a six-month observation window to reduce temporal decorrelation effects. The HIA was implemented in a landslide-prone sector of the Dolomites, a UNESCO World Heritage Site located in the Eastern Italian Alps. Comparative evaluation against EGMS ground motion products demonstrates that the HIA significantly broadens the range of detectable slope instabilities, thus providing a valuable supplement to existing ground motion monitoring services and contributing meaningfully to landslide hazard assessment and risk reduction efforts.

In recent years, global warming has been associated with an increasing frequency and intensity of extreme meteorological events causing severe damage to land and infrastructure, and potential harm to people. For this reason, it is crucial to develop predictive tools to support land management. In May 2023, two extreme meteorological events struck the Emilia-Romagna region of Italy in succession. The first rainfall event was associated with a limited number of landslides while the following one triggered widespread landslides of various types, leading to extensive damage and forcing the closure of hundreds of roads. The extent of the damage in the affected areas was recorded by satellite imagery captured by Sentinel constellations of the European Space Agency (ESA). By looking for cloud-free pre-event, between-events and post-events images, two study areas were chosen for the development and calibration of a physically-based Bayesian network model, incorporating prior rainfall data and soil spatial variability. The primary objective is to calibrate the model using these training images and subsequently validate it across the entire affected regions utilizing available open-source data on landslide event inventories. With a given weather forecast, the resulting output aims to pinpoint the most hazardous areas in a timely manner. This research stems from a collaboration funded through the MSCA-SE UPGRADE project (GA 101131146).

Monitoring systems are essential tools for landslide risk mitigation. Monitoring reduces vulnerability by providing the data required for slope instability characterization and hazard assessment. Moreover, alert and alarm systems rely on the ability of monitoring networks to timely deliver reliable and precise measurements. Remote monitoring systems are not mature enough to ensure reliable early-warning capabilities; therefore, information redundancy is achieved by deploying a set of sensors directly on the landslide body. Commonly used devices include total stations, GPS receivers, automatic inclinometers, and ground-based radar systems. However, the harsh environmental conditions typical of unstable slopes frequently affect the instruments performance and the data availability. The lack of a stable power supply is one of the main limitations of these systems, often preventing their operation precisely during the most critical situations, such as thunderstorms. In addition, adverse atmospheric conditions, including fog or low cloud cover, can compromise the visibility of topographic benchmarks, reducing data availability when it is most needed. To address these limitations a nonlinear parametric model for forecasting landslide displacements based on rainfall input has been developed. The model is trained and continuously updated using displacement data acquired by the monitoring system. In the event of system failure, the model is able to simulate landslide kinematics by means of “virtual sensors”, forecasting displacements and detecting sudden accelerations of the landslide, thereby preserving the early-warning functionality. The approach was successfully tested using data from the monitoring system installed at the Rotolon landslide, located in the municipality of Recoaro Terme (Vicenza, Italy).