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Combining remote sensing analysis with machine learning to evaluate short-term coastal evolution trend in the shoreline of Venice.
Fogarin, S; Zanetti, M; Dal Barco, M K; Zennaro, F; Furlan, E; Torresan, S; Pham, H V; Critto, A.
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  • Fogarin S; Fondazione Centro-Euro-Mediterraneo sui Cambiamenti Climatici, I-73100 Lecce, Italy; Department of Environmental Sciences, Informatics and Statistic, University Ca' Foscari Venice, I-30170 Venice, Italy.
  • Zanetti M; Fondazione Centro-Euro-Mediterraneo sui Cambiamenti Climatici, I-73100 Lecce, Italy; Department of Environmental Sciences, Informatics and Statistic, University Ca' Foscari Venice, I-30170 Venice, Italy.
  • Dal Barco MK; Fondazione Centro-Euro-Mediterraneo sui Cambiamenti Climatici, I-73100 Lecce, Italy; Department of Environmental Sciences, Informatics and Statistic, University Ca' Foscari Venice, I-30170 Venice, Italy.
  • Zennaro F; Fondazione Centro-Euro-Mediterraneo sui Cambiamenti Climatici, I-73100 Lecce, Italy; Department of Environmental Sciences, Informatics and Statistic, University Ca' Foscari Venice, I-30170 Venice, Italy.
  • Furlan E; Fondazione Centro-Euro-Mediterraneo sui Cambiamenti Climatici, I-73100 Lecce, Italy; Department of Environmental Sciences, Informatics and Statistic, University Ca' Foscari Venice, I-30170 Venice, Italy.
  • Torresan S; Fondazione Centro-Euro-Mediterraneo sui Cambiamenti Climatici, I-73100 Lecce, Italy; Department of Environmental Sciences, Informatics and Statistic, University Ca' Foscari Venice, I-30170 Venice, Italy.
  • Pham HV; Fondazione Centro-Euro-Mediterraneo sui Cambiamenti Climatici, I-73100 Lecce, Italy; Department of Environmental Sciences, Informatics and Statistic, University Ca' Foscari Venice, I-30170 Venice, Italy.
  • Critto A; Fondazione Centro-Euro-Mediterraneo sui Cambiamenti Climatici, I-73100 Lecce, Italy; Department of Environmental Sciences, Informatics and Statistic, University Ca' Foscari Venice, I-30170 Venice, Italy. Electronic address: critto@unive.it.
Sci Total Environ ; 859(Pt 1): 160293, 2023 Feb 10.
Article en En | MEDLINE | ID: mdl-36403828
With increasing storminess and incessant sea-level rise, coastal erosion is becoming a primary issue along many littorals in the world. To cope with present and future climate change scenarios, it is important to map the shoreline position over years and assess the coastal erosion trends to select the best risk management solutions and guarantee a sustainable management of communities, structures, and ecosystems. However, this objective is particularly challenging on gentle-sloping sandy coasts, where also small sea-level changes trigger significant morphological evolutions. This study presents a multidisciplinary study combining satellite images with Machine Learning and GIS-based spatial tools to analyze short-term shoreline evolution trends and detect erosion hot-spots on the Venice coast over the period 2015-2019. Firstly, advanced image preprocessing, which is not frequently adopted in coastal erosion studies, was performed on satellite images downloaded within the same tidal range. Secondly, different Machine Learning classification methods were tested to accurately define shoreline position by recognizing the land-sea interface in each image. Finally, the application of the Digital Shoreline Analysis System tool was performed to evaluate and visualize coastal changes over the years. Overall, the case study littoral reveals to be stable or mainly subjected to accretion. This is probably due to the high presence of coastal protection structures that stabilize the beaches, enhancing deposition processes. In detail, with respect to the total length of the considered shoreline (about 83 km), 5 % of the coast is eroding, 36 % is stable, 52 % is accreting and 7 % is not evaluable. Despite a significant coastal erosion risk was not recognized within this region, well-delimited erosion hot-spots were mapped in correspondence of Caorle, Jesolo and Cavallino-Treporti municipalities. These areas deserve higher attention for territorial planning and prioritization of adaptation measures, facing climate change scenarios and sea-level rise emergencies in the context of Integrated Coastal Zone Management.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Cambio Climático / Ecosistema Idioma: En Revista: Sci Total Environ Año: 2023 Tipo del documento: Article País de afiliación: Italia Pais de publicación: Países Bajos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Cambio Climático / Ecosistema Idioma: En Revista: Sci Total Environ Año: 2023 Tipo del documento: Article País de afiliación: Italia Pais de publicación: Países Bajos