Abstract
The uncertainty related to disasters generated by climate change and anthropogenic modifications of the environment adds yet another challenge for the decision makers, in terms of strategies, regulations and technologies adopted for protecting the communities without limiting their development and increasing their resilience to natural hazards. In mountain regions the choice of appropriate sites for infrastructure such as roads, railways, cable cars or hydropower dams is often very limited. In parallel, the increasing demand for supply infrastructure induces a continuous transformation of the territory. The new role played by the precautionary monitoring in the risk governance becomes fundamental and may overcome the modeling of future events, which represented so far the predominant approach to these sort of issues. The monitoring carried out by radar satellite systems represents, for practicability, resolution and cover, a good solution for keeping under observation extensive areas affected by hydrogeological instability. On the other hand, satellite remote sensing needs to be corroborated by terrestrial measurements particularly in the case they are used to evaluate an increasing of the deformation trend for community protection. Basing on the experience of European Projects as SAFER (http://www.ecmwf.int/research/EU_projects/SAFER) and SAFELAND (http://www.safeland-fp7.eu), the Interreg funded project SloMove aims to structure and consolidate methodologies in order to integrate differential interferometry technique with most common terrestrial technologies as D-GPS and Terrestrial Laser Scanning for monitoring slow mass movements. Within the project, the partnership has identified two test sites located between 2,500 and 3,000 m of altitude, in South Tyrol, Italy, and Grisons Canton, Switzerland.
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Co-funded by the Interreg IV Italia-Svizzera Program, European Regional Development Fund.
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Chinellato, G. et al. (2015). Remote and Terrestrial Ground Monitoring Techniques Integration for Hazard Assessment and Prediction in Densely Populated Mountain Areas. In: Lollino, G., et al. Engineering Geology for Society and Territory - Volume 2. Springer, Cham. https://doi.org/10.1007/978-3-319-09057-3_59
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DOI: https://doi.org/10.1007/978-3-319-09057-3_59
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