AI-driven remote sensing enhances Mediterranean seagrass monitoring and conservation to combat climate change and anthropogenic impacts
Abstract Seagrasses are undergoing widespread loss due to anthropogenic pressure and climate change. Since 1960, the Mediterranean seascape lost 13–50% of the areal extent of its dominant and endemic seagrass-Posidonia oceanica, which regulates its ecosystem. Many conservation and restoration projec...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-04-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-59091-7 |
| _version_ | 1797209401939984384 |
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| author | Masuma Chowdhury Alejo Martínez-Sansigre Maruška Mole Eduardo Alonso-Peleato Nadiia Basos Jose Manuel Blanco Maria Ramirez-Nicolas Isabel Caballero Ignacio de la Calle |
| author_facet | Masuma Chowdhury Alejo Martínez-Sansigre Maruška Mole Eduardo Alonso-Peleato Nadiia Basos Jose Manuel Blanco Maria Ramirez-Nicolas Isabel Caballero Ignacio de la Calle |
| author_sort | Masuma Chowdhury |
| collection | DOAJ |
| description | Abstract Seagrasses are undergoing widespread loss due to anthropogenic pressure and climate change. Since 1960, the Mediterranean seascape lost 13–50% of the areal extent of its dominant and endemic seagrass-Posidonia oceanica, which regulates its ecosystem. Many conservation and restoration projects failed due to poor site selection and lack of long-term monitoring. Here, we present a fast and efficient operational approach based on a deep-learning artificial intelligence model using Sentinel-2 data to map the spatial extent of the meadows, enabling short and long-term monitoring, and identifying the impacts of natural and human-induced stressors and changes at different timescales. We apply ACOLITE atmospheric correction to the satellite data and use the output to train the model along with the ancillary data and therefore, map the extent of the meadows. We apply noise-removing filters to enhance the map quality. We obtain 74–92% of overall accuracy, 72–91% of user’s accuracy, and 81–92% of producer’s accuracy, where high accuracies are observed at 0–25 m depth. Our model is easily adaptable to other regions and can produce maps in in-situ data-scarce regions, providing a first-hand overview. Our approach can be a support to the Mediterranean Posidonia Network, which brings together different stakeholders such as authorities, scientists, international environmental organizations, professionals including yachting agents and marinas from the Mediterranean countries to protect all P. oceanica meadows in the Mediterranean Sea by 2030 and increase each country’s capability to protect these meadows by providing accurate and up-to-date maps to prevent its future degradation. |
| first_indexed | 2024-04-24T09:54:07Z |
| format | Article |
| id | doaj.art-4334c540c044416eadeb7ad77d74212d |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-24T09:54:07Z |
| publishDate | 2024-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-4334c540c044416eadeb7ad77d74212d2024-04-14T11:12:25ZengNature PortfolioScientific Reports2045-23222024-04-0114111210.1038/s41598-024-59091-7AI-driven remote sensing enhances Mediterranean seagrass monitoring and conservation to combat climate change and anthropogenic impactsMasuma Chowdhury0Alejo Martínez-Sansigre1Maruška Mole2Eduardo Alonso-Peleato3Nadiia Basos4Jose Manuel Blanco5Maria Ramirez-Nicolas6Isabel Caballero7Ignacio de la Calle8Quasar Science ResourcesQuasar Science ResourcesQuasar Science ResourcesQuasar Science ResourcesQuasar Science ResourcesQuasar Science ResourcesQuasar Science ResourcesInstituto de Ciencias Marinas de Andalucía (ICMAN), Consejo Superior de Investigaciones Científicas (CSIC)Quasar Science ResourcesAbstract Seagrasses are undergoing widespread loss due to anthropogenic pressure and climate change. Since 1960, the Mediterranean seascape lost 13–50% of the areal extent of its dominant and endemic seagrass-Posidonia oceanica, which regulates its ecosystem. Many conservation and restoration projects failed due to poor site selection and lack of long-term monitoring. Here, we present a fast and efficient operational approach based on a deep-learning artificial intelligence model using Sentinel-2 data to map the spatial extent of the meadows, enabling short and long-term monitoring, and identifying the impacts of natural and human-induced stressors and changes at different timescales. We apply ACOLITE atmospheric correction to the satellite data and use the output to train the model along with the ancillary data and therefore, map the extent of the meadows. We apply noise-removing filters to enhance the map quality. We obtain 74–92% of overall accuracy, 72–91% of user’s accuracy, and 81–92% of producer’s accuracy, where high accuracies are observed at 0–25 m depth. Our model is easily adaptable to other regions and can produce maps in in-situ data-scarce regions, providing a first-hand overview. Our approach can be a support to the Mediterranean Posidonia Network, which brings together different stakeholders such as authorities, scientists, international environmental organizations, professionals including yachting agents and marinas from the Mediterranean countries to protect all P. oceanica meadows in the Mediterranean Sea by 2030 and increase each country’s capability to protect these meadows by providing accurate and up-to-date maps to prevent its future degradation.https://doi.org/10.1038/s41598-024-59091-7 |
| spellingShingle | Masuma Chowdhury Alejo Martínez-Sansigre Maruška Mole Eduardo Alonso-Peleato Nadiia Basos Jose Manuel Blanco Maria Ramirez-Nicolas Isabel Caballero Ignacio de la Calle AI-driven remote sensing enhances Mediterranean seagrass monitoring and conservation to combat climate change and anthropogenic impacts Scientific Reports |
| title | AI-driven remote sensing enhances Mediterranean seagrass monitoring and conservation to combat climate change and anthropogenic impacts |
| title_full | AI-driven remote sensing enhances Mediterranean seagrass monitoring and conservation to combat climate change and anthropogenic impacts |
| title_fullStr | AI-driven remote sensing enhances Mediterranean seagrass monitoring and conservation to combat climate change and anthropogenic impacts |
| title_full_unstemmed | AI-driven remote sensing enhances Mediterranean seagrass monitoring and conservation to combat climate change and anthropogenic impacts |
| title_short | AI-driven remote sensing enhances Mediterranean seagrass monitoring and conservation to combat climate change and anthropogenic impacts |
| title_sort | ai driven remote sensing enhances mediterranean seagrass monitoring and conservation to combat climate change and anthropogenic impacts |
| url | https://doi.org/10.1038/s41598-024-59091-7 |
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