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Your search keyword '"Avezzano, Ruggero"' showing total 9 results
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9 results on '"Avezzano, Ruggero"'

2. COSMO-SkyMed potential to detect and monitor Mediterranean maquis fires and regrowth: a pilot study in Capo Figari, Sardinia, Italy.

Author

Laurin, Gaia Vaglio, Avezzano, Ruggero, Bacciu, Valentina, Del Frate, Fabio, Papale, Dario, and Virelli, Maria

Subjects
*WILDFIRES, *FOREST fires, *FORESTS & forestry, *FOREST ecology, *CLIMATE change, *ECOSYSTEM dynamics
Abstract

Mediterranean maquis is a complex and widespread ecosystem in the region, intrinsically prone to fire. Many species have developed specific adaptation traits to cope with fire, ensuring resistance and resilience. Due to the recent changes in socio-economy and land uses, fires are more and more frequent in the urban-rural fringe and in the coastlines, both now densely populated. The detection of fires and the monitoring of vegetation regrowth is thus of primary interest for local management and for understanding the ecosystem dynamics and processes, also in the light of the recurrent droughts induced by climate change. Among the main objectives of the COSMO-SkyMed radar constellation mission there is the monitoring of environmental hazards; the very high revisiting time of this mission is optimal for post-hazard response activities. However, very few studies exploited such data for fire and vegetation monitoring. In this research, Cosmo-SkyMed is used in a Mediterranean protected area covered by maquis to detect the burnt area extension and to conduct a mid-term assessment of vegetation regrowth. The positive results obtained in this research highlight the importance of the very high-resolution continuous acquisitions and the multi-polarization information provided by COSMO-SkyMed for monitoring fire impacts on vegetation. [ABSTRACT FROM AUTHOR]

Published
2018
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9. Neural networks for oil spill detection using TerraSAR-X data

Author

Avezzano, Ruggero G., Velotto, Domenico, Soccorsi, Matteo, Del Frate, Fabio, and Lehner, Susanne

Abstract

The increased amount of available Synthetic Aperture Radar (SAR) images involves a growing workload on the operators at analysis centers. In addition, even if the operators go through extensive training to learn manual oil spill detection, they can provide different and subjective responses. Hence, the upgrade and improvements of algorithms for automatic detection that can help in screening the images and prioritizing the alarms are of great benefit. In this paper we present the potentialities of TerraSAR-X (TS-X) data and Neural Network algorithms for oil spills detection. The radar on board satellite TS-X provides X-band images with a resolution of up to 1m. Such resolution can be very effective in the monitoring of coastal areas to prevent sea oil pollution. The network input is a vector containing the values of a set of features characterizing an oil spill candidate. The network output gives the probability for the candidate to be a real oil spill. Candidates with a probability less than 50% are classified as look-alikes. The overall classification performances have been evaluated on a data set of 50 TS-X images containing more than 150 examples of certified oil spills and well-known look-alikes (e.g. low wind areas, wind shadows, biogenic films). The preliminary classification results are satisfactory with an overall detection accuracy above 80%.

Published
2011
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