High-resolution remote sensing data to monitor active volcanic areas: an application to the 2011-2015 eruptive activity of Mount Etna (Italy) (Conference Presentation)

Maria Marsella

doi:10.1117/12.2245084

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8 December 2016 High-resolution remote sensing data to monitor active volcanic areas: an application to the 2011-2015 eruptive activity of Mount Etna (Italy) (Conference Presentation)

Maria Marsella

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Proceedings Volume 10003, SAR Image Analysis, Modeling, and Techniques XVI; 100030C (2016) https://doi.org/10.1117/12.2245084
Event: SPIE Remote Sensing, 2016, Edinburgh, United Kingdom

Abstract

In volcanic areas, where it could be difficult to gain access to the most critical zones for carrying out direct surveys, remote sensing proved to have remarkable potentialities to follow the evolution of lava flow, as well as to detect slope instability processes induced by volcanic activity. By exploiting SAR and optical data a methodology for observing and quantifying eruptive processes was developed. The approach integrates HR optical images and SAR interferometric products and can optimize the observational capability of standard surveillance activities based on in-situ video camera network. A dedicated tool for mapping the evolution of the lava field, using both ground-based and satellite data, was developed and tested to map lava flows during the 2011-2015 eruptive activities. Ground based data were collected using the permanent ground NEtwork of Thermal and VIsible Sensors located on Mt. Etna (Etna_NETVIS) and allowed to downscale the information derived from satellite data and to integrate the satellite datasets in case of incomplete coverage or missing acquisitions. This work was developed in the framework of the EU-FP7 project “MED-SUV” (MEDiterranean SUpersite Volcanoes).

Conference Presentation

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Maria Marsella "High-resolution remote sensing data to monitor active volcanic areas: an application to the 2011-2015 eruptive activity of Mount Etna (Italy) (Conference Presentation)", Proc. SPIE 10003, SAR Image Analysis, Modeling, and Techniques XVI, 100030C (8 December 2016); https://doi.org/10.1117/12.2245084

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