Seagrass Mapping in Cyprus Using Earth Observation Advances - Makri, Despoina; Christofilakos, Spyridon; Poursanidis, Dimitris; Traganos, Dimosthenis; Mettas, Christodoulos; Stylianou, Neophytos; Hadjimitsis, Diofantos G. (My University, 2025)
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 km2 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 km2. 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.
Keywords: carbon stock; conservation; Google Earth Engine; protection; remote sensing; seagrass mapping; Sentinel-2