Detection and Mapping of Seagrass Meadows at Ritchie’s Archipelago using Sentinel 2A Satellite Imagery

Abstract

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.