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Your search keyword '"Revellino, Paola"' showing total 25 results
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25 results on '"Revellino, Paola"'

2. Debris flows and debris avalanches initiation and runout susceptibility assessment in Campania region (Italy)

Author

Tufano Rita, Mazza Davide, Guadagno Francesco Maria, De Vita Pantaleone, Russo Giacomo, and Revellino Paola

Subjects
Environmental sciences, GE1-350
Abstract

Sectors of the Campania region (southern Italy), where pyroclastic airfall deposits cover carbonate slopes, have frequently experienced debris avalanches and debris flows. These landslides are triggered by heavy rainfall and in the last century caused severe destruction and hundreds of deaths. Their occurrence is controlled by the interaction of several factors, both natural and human induced. The initial failure is linked with the presence of morphological discontinuities such as rocky scarps and road cuts, which interrupt the stratigraphical continuity of an “infinite slope”. Here the main criteria for susceptibility analysis are defined trying to develop a procedure for identify highly-susceptible sources, runout and invasion areas.

Published
2023
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25. Multi-Method Tracking of Monsoon Floods Using Sentinel-1 Imagery.

Author

Ruzza, Giuseppe, Guerriero, Luigi, Grelle, Gerardo, Guadagno, Francesco Maria, and Revellino, Paola

Subjects
TYPHOONS, SYNTHETIC aperture radar, FLOODS, K-means clustering, MONSOONS
Abstract

Floods cause great losses in terms of human life and damages to settlements. Since the exposure is a proxy of the risk, it is essential to track flood evolution. The increasing availability of Synthetic Aperture Radar (SAR) imagery extends flood tracking capabilities because of its all-water and day/night acquisition. In this paper, in order to contribute to a better evaluation of the potential of Sentinel-1 SAR imagery to track floods, we analyzed a multi-pulse flood caused by a typhoon in the Camarines Sur Province of Philippines between the end of 2018 and the beginning of 2019. Multiple simple classification methods were used to track the spatial and temporal evolution of the flooded area. Our analysis indicates that Valley Emphasis based manual threshold identification, Otsu methodology, and K-Means Clustering have the potential to be used for tracking large and long-lasting floods, providing similar results. Because of its simplicity, the K-Means Clustering algorithm has the potential to be used in fully automated operational flood monitoring, also because of its good performance in terms of computation time. [ABSTRACT FROM AUTHOR]

Published
2019
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