Remote Sensing for Seagrass Mapping and Monitoring: Methods, Sensors, and Applications
DOI:
https://doi.org/10.67710/jomafish.v1i1.29Keywords:
seagrass mapping, remote sensing, entinel-2, machine learning, coastal monitoringAbstract
Seagrass meadows are highly productive coastal ecosystems, yet their submerged position, patchy distribution, seasonal variability, and exposure to turbidity make consistent mapping difficult. Remote sensing provides a practical means to extend field observations across space and time, but mapping performance depends strongly on sensor characteristics, water-column conditions, image preprocessing, classification design, and validation quality. This review synthesizes recent progress in remote sensing for seagrass mapping and monitoring, emphasizing studies published from 2017 to August 2026 and applications relevant to tropical and Indonesian waters. The literature was evaluated according to sensor type, preprocessing strategy, classification or regression method, ecological variable, and accuracy assessment. Landsat remains important for multi-decadal change detection, while Sentinel-2 has become the principal freely available sensor for contemporary mapping because its 10-m visible bands and frequent revisit provide a useful compromise between spatial detail and temporal coverage. PlanetScope and WorldView imagery improve delineation of small or fragmented meadows, whereas UAV and hyperspectral systems enable centimetre-scale mapping, species discrimination, and detailed biomass assessment. Machine-learning approaches, particularly Random Forest, Support Vector Machine, and gradient-boosting methods, are increasingly used, but classifier choice cannot compensate for poor atmospheric correction, sun-glint contamination, depth effects, mixed pixels, or weak reference data. Recent Indonesian studies demonstrate applications ranging from seagrass extent and benthic habitat mapping to aboveground carbon estimation. A robust operational workflow should therefore combine aquatic atmospheric correction, tidal and cloud screening, representative field data, depth-aware feature engineering, independent spatial validation, and explicit uncertainty reporting. Such standardization is essential for reproducible monitoring, restoration assessment, fisheries habitat management, and blue-carbon accounting.
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