Use of Remote Sensing techniques for monitoring the impacts of ground movements on structures and infrastructure

“The substantial reduction of disaster risk and losses in lives, livelihoods and health and in the economic, physical, social, cultural and environmental assets of persons, businesses, communities and countries” is the purpose of the Sendai Framework for Disaster Risk Reduction 2015-2030 adopted by the United Nations in March 2015 (UNDRR, 2021). The priority of the Sendai Framework is “understanding disaster risk in all its dimensions of vulnerability, capacity, exposure of persons and assets, hazard characteristics and environment” (UNDRR, 2021). Therefore, it is fundamental to prevent that (natural or anthropogenic) hazards of moderate intensity (e.g., subsidence, landslides) may turn into unforeseen disasters with severe consequences for the community. In this perspective, this Ph.D. Thesis aims at investigating the impacts of ground movements originated from subsidence and landslides on (historic) buildings and road networks by using remote sensing techniques as satellite-based InSAR and UAV-based photogrammetry, thus enhancing the current knowledge about the cause-effect relationships between the considered hazards and building/road damage. A standard framework for multi-scale monitoring of subsidence and analysis of related consequences on (historic) buildings is proposed here using multi-temporal and multi-platform InSAR data and local geological and stratigraphic setting. A large-scale multivariate regression model is adopted for mapping areas subjected to ground movements generated by subsidence, where a small-scale building vulnerability analysis can be then performed using damage records and InSAR-derived descriptors of building foundation motions due to subsidence. The proposed framework is applied to the city of Como (northern Italy), thus providing local Governments with an integrated tool for multi-scale monitoring of subsidence-affected areas and preserving the local architectural heritage. Moreover, this research proposes a semi-automatic procedure combining UAV-based 3D and 2D photogrammetry products to characterize longitudinal and transverse cracks on asphalt road pavements in landslide-affected areas, and rate the road damage severity using quantitative descriptors of pavement quality, such as the International Roughness Index (IRI). This procedure is applied to two road sections selected within the Province of Como (northern Italy) to contribute to the landslide risk management of this territory. Finally, the challenges arising from the use of remote sensing techniques, as satellite-based InSAR and UAV-based photogrammetry, for monitoring the impacts of subsidence and landslides respectively on buildings and road networks are discussed by presenting two additional case studies: a residential area in the city of Rome (Italy) affected by subsidence, and an asphalt-paved highway interchange in Panagia (Greece) subjected to landslide motions. This research aims at supporting local Governments or road practitioners in the management of risks related to subsidence and landslides using remote sensing technologies, thus fostering the demand of safe and resilient constructions to mitigate (potential) disasters. The obtained results enhance our understanding of the cause-effect relationships between hazards and constructions (i.e., buildings and roads) and provide ready-to-use tools for building vulnerability analysis and road damage assessment. The research activities were performed at the University of Insubria (Como, Italy) with secondments at the University of Twente (Enschede, The Netherlands) as part of the Erasmus for Traineeship Programme and the Horizon 2020 PANOPTIS Project (WP5) and at Sapienza University of Rome (Italy) as part of the DPC-ReLUIS 2019-2021 Project (WP6).