Comparative Performance Evaluation Of Machine Learning Algorithms For Sentinel-2 Benthic Habitat Classification Using Google Earth Engine - Adhitya Nugroho; Muhammad Abdul Ghofur Al Hakim (Department of Marine Sciences, Trunojoyo University of Madura, Indonesia, 2025)
This study evaluates the optimal classification methodology and analyzes temporal changes over five years across four benthic habitat classes (Seagrass, Coral Reef, Rubble, and Sand) in the shallow waters of Ohoidertawun, Southeast Maluku, using Sentinel-2 imagery and the Google Earth Engine (GEE) platform. A comparative assessment of Machine Learning (ML) algorithms revealed that Random Forest (RF) demonstrated the best classification performance compared to Support Vector Machine (SVM), Classification and Regression Tree (CART), K-Nearest Neighbors (KNN), and Minimum Distance (MD) in benthic habitat mapping, achieving an Overall Accuracy of 0.856 and a Kappa Coefficient of 0.870. The classification results and accuracy assessment using the best-performing ML model from the 2025 Sentinel-2 imagery were used to analyze temporal changes relative to the 2020 Sentinel-2 data. Temporal analysis indicated a significant ecosystem shift, marked by a 52.41% increase in seagrass cover and a 31.46% decrease in coral reef area. These findings can serve as a recommendation for conservation site selection and urge stakeholders to help mitigate coral reef loss by utilizing the results of this research. The resulting benthic habitat map can serve as a reference for effective coastal resource management and blue carbon initiatives. Based on these findings, the Random Forest ML algorithm can be considered an optimal methodology for tropical benthic habitat mapping in the study area.Keywords: Benthic Habitat, Sentinel-2, Machine Learning, Google Earth Engine