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This study presents an attempt to utilize seagrass data acquired from field surveys to compare classification models for mapping seagrasses using Sentinel-2A satellite data. Out of three models tested, viz. Random Forest, Support Vector Machine and K-Nearest Neighbor; Random Forest classification model proved most effective in the given scenario with 0.99 model accuracy. Seagrasses present as deep as 21 m were detected post water column correction, presenting the capability of Sentinel-2A satellite in detecting submerged benthic habitat. [ABSTRACT FROM AUTHOR]

We analysed gut samples of stranded dugongs from Tamil Nadu and Gujarat, India, to understand their dietary preferences. We quantified seagrass fragments from the gut as leaf, stem and rhizome, and identified leaf fragments up to genera level by their morphological features and epidermal cell characteristics using an inverted microscope. The overall abundance of aboveground fragments (leaf, stem) was higher in all samples, which may suggest the dugongs use a cropping mechanism to forage. The ingested seagrass generic diversity was higher in Tamil Nadu (n = 5) dugong individuals than those in Gujarat (n = 2). A total of five genera were recorded from all samples, viz. Halophila spp., Halodule spp., Cymodocea spp., Enhalus sp. and Syringodium spp. In Tamil Nadu, Cymodocea spp. (46.24%) was the most dominant, followed by Halophila spp. (26.49%), Syringodium spp. (14.83%) and Halodule spp. (12.16%), with a low occurrence of Enhalus spp. (0.19%). In Gujarat, Halodule spp. (61.48%) was the most dominant, followed by Halophila spp. (30.20%). The recorded plastic and wood fragments suggest fine spatial scale threat mapping in dugong habitats. [ABSTRACT FROM AUTHOR]