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Articles

Mapping coastal marine habitats and delineating the deep limits of the Neptune’s seagrass meadows using very high resolution Earth observation data

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Pages 8670-8687 | Received 06 Jun 2017, Accepted 24 May 2018, Published online: 05 Jul 2018
 

ABSTRACT

Seagrass meadows are one of the most important coastal habitats across the globe. These are mainly constituted by the marine plants of the genus Posidonia and Thalassia. In the Mediterranean Sea, Posidonia oceanica is the dominant endemic plant that affects physical, biogeochemical, and biological processes. The decline in the spatial distribution has been attributed to excessive anthropic pressures and other large-scale environmental changes. The monitoring of the spatial distribution requires an update and accurate seagrass meadows delineation, i.e. meadow edge marking with a replicable method. The present study aims to present an approach to support the coastal marine habitat mapping, under the scheme of the Natura 2000 network using very high resolution Earth observation data and to prove that satellite images can be used for the mapping of the deep limits of the seagrass meadows. Pixel-based classification and object-oriented image analysis have been implemented for the image classification. Pixel-based Support Vector Machines and object-based Nearest Neighbor classifiers provided the best results with an overall accuracy of more than 90%, while deep limits have been successfully identified and separated from the deep waters.

Acknowledgements

This project was partially funded by EU H2020 project number 641762 ‘Improving Future Ecosystem Benefits through Earth Observationsʼ (ECOPOTENTIAL). We would like to thank the Samaria National Park for the support of their staff and for accommodating our every need during fieldwork. Special thanks are given to Antonios Barnias and Petros Lymberakis.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the H2020 Environment: [Grant Number 641762].

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