Shifting from traditional landslide occurrence modeling to scenario estimation with a "glass-box" machine learning.

Extreme rainfall events represent one of the main triggers of landslides. As climate change continues to reshape global weather patterns, the frequency and intensity of such events are increasing, amplifying landslide occurrences and associated threats to communities. In this contribution, we analyze relationships between landslide occurrence and extreme rainfall events by using a "glass-box" machine learning model, namely Explainable Boosting Machine.

InSAR data for detection and modelling of overexploitation-induced subsidence: application in the industrial area of Prato (Italy).

Spaceborne-based monitoring for environmental purposes has become a well-established practice. The recent progress of synthetic aperture radar (SAR) sensors, including through the European Space Agency's (ESA) Sentinel-1 constellation, has enabled the scientific community to identify and monitor several geohazards, including subsidence ground deformations. A case study in the Tuscany Region, Italy, highlights the effectiveness of interferometric synthetic aperture radar (InSAR) in detecting abrupt increases in ground deformation rates in an industrial area of Montemurlo municipality.

A methodological approach of QRA for slow-moving landslides at a regional scale.

Landslides represent a serious worldwide hazard, especially in Italy, where exposure to hydrogeological risk is very high; for this reason, a landslide quantitative risk assessment (QRA) is crucial for risk management and for planning mitigation measures. In this study, we present and describe a novel methodological approach of QRA for slow-moving landslides, aiming at national replicability. This procedure has been applied at the basin scale in the Arno River basin (9100 km, Central Italy), where most landslides are slow-moving.

Vulnerability Assessment of Buildings due to Land Subsidence using InSAR Data in the Ancient Historical City of Pistoia (Italy).

The launch of the medium resolution Synthetic Aperture Radar (SAR) Sentinel-1 constellation in 2014 has allowed public and private organizations to introduce SAR interferometry (InSAR) products as a valuable option in their monitoring systems. The massive stacks of displacement data resulting from the processing of large C-B and radar images can be used to highlight temporal and spatial deformation anomalies, and their detailed analysis and postprocessing to generate operative products for final users. In this work, the wide-area mapping capability of Sentinel-1 was used in synergy with the COSMO-SkyMed high resolution SAR data to characterize ground subsidence affecting the urban fabric of the city of Pistoia (Tuscany Region, central Italy).

Author Correction: Perspectives on the prediction of catastrophic slope failures from satellite InSAR.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Perspectives on the prediction of catastrophic slope failures from satellite InSAR.

We demonstrate the potential of satellite Interferometric Synthetic Aperture Radar (InSAR) to identify precursors to catastrophic slope failures. To date, early-warning has mostly relied on the availability of detailed, high-frequency data from sensors installed in situ. The same purpose could not be chased through spaceborne monitoring applications, as these could not yield information acquired in sufficiently systematic fashion.

Continuous, semi-automatic monitoring of ground deformation using Sentinel-1 satellites.

We present the continuous monitoring of ground deformation at regional scale using ESA (European Space Agency) Sentinel-1constellation of satellites. We discuss this operational monitoring service through the case study of the Tuscany Region (Central Italy), selected due to its peculiar geological setting prone to ground instability phenomena. We set up a systematic processing chain of Sentinel-1 acquisitions to create continuously updated ground deformation data to mark the transition from static satellite analysis, based on the analysis of archive images, to dynamic monitoring of ground displacement.