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Benthic habitat mapping on different coral reef types using random forest and support vector machine algorithm

Machine learning classification in remote sensing imagery is considered capable of producing classification results with high accuracy in short processing times. This research was conducted with the aim of mapping the spatial distribution of benthic habitat on different types of coral reefs in the waters of Flores Island, NTT using PlanetScope image using Random Forest (RF) and Support Vector Machine (SVM) classification algorithm. Benthic habitat information from field surveys were used to train the RF and SVM algorithm and validate the classification results. The classification results indicated that Mesa Island, the Northern and the Western side of Labuan Bajo are dominated by seagrass beds, and on Bangkau Island is dominated by coral reefs and bare substratum. The highest overall accuracy of the RF classification results is 71.88% from West Labuan Bajo (fringing reef) result. Meanwhile, the highest overall accuracy of the SVM classification is 76.74% from Bangkau Island (patch reef) result.