Abstract
Natural and man-made disasters have become an issue of growing concern throughout the world. The frequency and magnitude of disasters threatening large populations living in diverse environments, is rapidly increasing in recent years across the world due to demographic growth, inducing to urban sprawls into hazardous areas. These disasters also have far-reaching implications on sustainable development through social, economic and environmental impact. This chapter summarises three scientific contributions from relevant experiences of the British Geological Survey and the Federico II University of Naples, where remote sensing sensors have been playing a crucial role to potentially support disaster management studies in areas affected by natural hazards. The three cases are: the landslide inventory map of St. Lucia island, tsunami-induced damage along the Sendai coast (Japan) and the landslide geotechnical characterization in Papanice (Italy). For each case study we report the main issue, datasets available and results achieved. Finally, we analyse how recent developments and improved satellite and sensor technologies can support in overcoming the current limitations of using remotely sensed data in disaster management so to fully utilize the capabilities of remote sensing in disaster management and strength cooperation and collaboration between relevant stakeholders including end users.
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Acknowledgements
This publication benefited from inputs and contributions from the following: D. Calcaterra, D. Di Martire, M. Ramondini (University of Naples) and Hussain E. (British Geological Survey). Thanks also to the editors of the special issue in which this chapter is published, for their guidance and patience. Data for the St. Lucia case study have been obtained through the ESA eoworld2 initiative.
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Novellino, A., Jordan, C., Ager, G., Bateson, L., Fleming, C., Confuorto, P. (2019). Remote Sensing for Natural or Man-Made Disasters and Environmental Changes. In: Durrani, T., Wang, W., Forbes, S. (eds) Geological Disaster Monitoring Based on Sensor Networks. Springer Natural Hazards. Springer, Singapore. https://doi.org/10.1007/978-981-13-0992-2_3
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DOI: https://doi.org/10.1007/978-981-13-0992-2_3
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