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  4. Seagrass Mapping in Cyprus Using Earth Observation Advances
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Seagrass Mapping in Cyprus Using Earth Observation Advances

Journal
Remote Sensing
Date Issued
October 31, 2025
Author(s)
Makri, Despoina  
Christofilakos, Spyridon  
Poursanidis, Dimitris  
Traganos, Dimosthenis  
Mettas, Christodoulos  
Stylianou, Neophytos  
Hadjimitsis, Diofantos G.  
DOI
10.3390/rs17213610
Abstract
Highlights: What are the main findings? Scalable workflow was developed for local-scale seagrass mapping in Cyprus using Sentinel-2 imagery, cloud computing and machine learning. The workflow maps key Natura 2000 habitats—soft bottoms, hard bottoms, and Posidonia beds—along the Cypriot coastline. The method estimated 10-17 km<sup>2</sup> of seagrass with approximately 19,000 Mg C stored in Posidonia oceanica meadows. What is the implication of the main finding? The approach addresses a knowledge gap in the Eastern Mediterranean, providing a replicable, consistent methodology for local- and country-scale mapping. The integration of open-access satellite data and cloud computing supports sustainable blue-carbon management and conservation planning. Seagrass meadows are vital for biodiversity and provide a plethora of ecosystem services, but significant losses due to human activity and climate change have been observed in recent decades. This study aims to evaluate whether the integration of Sentinel-2 composites, cloud computing (Google Earth Engine, GEE), and machine learning (ML) classifiers can produce accurate, scalable maps of seagrass habitats, enabling reliable estimates of associated carbon stocks. In this case study, we developed a methodological workflow for local-scale seagrass mapping in Cyprus, covering a total area of 310 km<sup>2</sup>. ML techniques, specifically the Random Forest (RF) classifier and Classification And Regression Tree (CART), were employed in the main processing stage. The RF classifier achieved an overall accuracy of 73.5%, with a seagrass-specific F1-score of 69.4%. Class-specific F1-scores ranged from 63.2% for hard bottoms to 98.2% for deep water, accounting for variability in habitat separability. The workflow is designed to be scalable across Cyprus and potentially the broader EMMENA region (Eastern Mediterranean, Middle East, and North Africa). Based on the mapped extent of Posidonia oceanica meadows, preliminary estimates suggest a carbon stock of approximately 19,000 Mg C in Cyprus.
Subjects

carbon stock

conservation

Google Earth Engine

protection

remote sensing

seagrass mapping

Sentinel-2

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