Chapter Shoreline detection by applying semiautomatic algorithms for hyperspectral and multispectral satellite imagery on the beaches of the Gulf of Oristano (Sardinia, Italy)
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 O...
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| 主要な著者: | , , , , , , |
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| フォーマット: | Online |
| 言語: | 英語 |
| 出版事項: |
Firenze University Press
2025
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| 主題: | |
| オンライン･アクセス: | ONIX_20250801T173835_9791221505566_292 |
| タグ: |
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| 要約: | 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. |
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