Shoreline detection by applying semiautomatic algorithms for hyperspectral and multispectral satellite imagery on the beaches of the Gulf of Oristano (Sardinia, Italy) Sabrina Terracciano University of Ferrara, Italy Juan Montes-Pérez University of Cadiz, Spain Riccardo Brunetta University of Ferrara, Italy Paulo Cabrita University of Ferrara, Italy Enrico Duo University of Ferrara, Italy Paolo Ciavola University of Ferrara, Italy Clara Armaroli University of Bologna, Italy This is a section of Tenth InternationaSymposium Monitoring of Mediterranean Coastal Areas: Problems and Measurement Techniques(DOI: 10.36253/979-12-215-0556-6) by Laura Bonora, Marcantonio Catelani, Matteo De Vincenzi, Giorgio Matteucci Firenze University Press Florence 2024 https://doi.org/10.36253/979-12-215-0556-6.71

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Coastal areas are influenced by natural processes and human activities and require effective monitoring tools to understand their dynamic evolution. This study utilizes satellite imagery and semi-automatic shoreline extraction algorithms (CoastSat and SAET) to assess coastal erosion in the Gulf of Oristano over long-term and short-term periods. The algorithms' performance was validated using PRISMA and Sentinel-2 imagery, alongside RTK-GNSS surveys acquired during ASI OVERSEE project fieldwork. The field site beaches vary in exposure due to their positions, and their morphologies are influenced by the presence of Posidonia oceanica banquettes. A one-year analysis focused on Arborea Beach, where periodic Posidonia banquettes affect the accuracy of Satellite-Derived Shorelines (SDSs). The study shows the beach's susceptibility to storm surges, evidenced by significant erosion after a major storm in March. Despite algorithmic limitations, automated shoreline extraction allows efficient temporal analysis. This research evidences the role of advanced algorithms in precise coastal monitoring, offering crucial insights into erosion dynamics and supporting effective mitigation strategies.

 Coastal dynamics CoastSat SAET

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