A multi-scale methodological approach for slow-moving landslide risk mitigation in urban areas, southern Italy

Abstract

Several urban areas in Euro-Mediterranean countries are affected by slow-moving landslides that, even if rarely associated with the loss of human life, can cause damage to structures and infrastructure. In such contexts, the progressive decay of the built environment can bring along a generalized increase of the physical vulnerability and, as a result, slow-moving landslide risk increases over the time. Under these conditions, as long as suitable risk mitigation measures are lacking, the level of risk (also related to earthquakes) could turn out to be no longer acceptable within an a priori unknown time interval. This problem has a relevant social-economic impact, thus requiring the adoption of risk mitigation strategies that need to be effective and, at the same time, sustainable for the involved stakeholders. In this regard, this paper proposes a multiscale methodological approach-based on the joint use of satellite-derived displacement monitoring data and the results of building damage surveys-whose applicability is tested with reference to urban areas affected by slow-moving landslides in Calabria region (southern Italy).

Quantitative analysis of the risk to road networks exposed to slow-moving landslides: a case study in the Campania region (southern Italy)

“…In this regard, the processing of synthetic aperture radar (SAR) images via differential interferometric algorithms (DInSAR) represents a well-established cost-effective non-invasive technique capable of providing displacement time series of affected areas (Antronico et al 2013;Bianchini et al 2012;Cascini et al 2010;Colesanti and Wasowski 2006;Crosetto et al 2018;Herrera et al 2013;Tofani et al 2013;Wasowski and Bovenga 2014), in turn useful to characterize the monitored slow-moving landslides from both geometric and kinematic points of view (Bianchini et al 2013;Calvello et al 2017;Cascini et al 2013;Castaldo et al 2015;Cigna et al 2013;Di Maio et al 2018;Frattini et al 2018;Gullà et al 2017;Journault et al 2018;Raspini et al 2013Raspini et al , 2017Rosi et al 2018). More recently, thanks to the increased availability of very high-resolution sensor datasets (i.e., COSMO-SkyMed and TerraSAR-X), the DInSAR data started to be used within procedures aimed at analyzing the consequences induced by slow-moving landslides on both buildings (Bianchini et al 2015;Ferlisi et al 2015Ferlisi et al , 2019aLu et al 2014;Nicodemo et al 2017Nicodemo et al , 2020Peduto et al 2016Peduto et al , 2017Peduto et al , 2018Peduto et al , 2019 and road networks (Infante et al 2018(Infante et al , 2019Nappo et al 2019;North et al 2017;Bovenga 2014, 2015). In the latter case, road damage data can be profitably collected by filling ad hoc predisposed fact-sheet...…”

Landslides behavior spatial modeling by using evidential belief function model, Promethean II model, and index of entropy in Tabriz, Iran

“…Mitigation is the final stage of the risk management process (Ferlisi et al 2019 ). One of the mitigation methods is to compensate for the shortcomings.…”

Preliminary Modeling of Rockfall Runout: Definition of the Input Parameters for the QGIS Plugin QPROTO

“…These models are generally used on large scales (i.e., site-specific) and local scales, following the recommendations of Corominas et al (2014) [3] for detailed risk analyses or to design protection works. Even more simplified models are used on small scales for territorial planning and preliminary study purposes [2][3][4]. Necessarily, in these cases, the runout model should be both simple and reliable in order to overcome the limited availability of data and their epistemic uncertainties.…”