Comparative Performance Evaluation Of Machine Learning Algorithms For Sentinel-2 Benthic Habitat Classification Using Google Earth Engine
Abstract
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
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Alifatri, L. O., Prayudha, B., Anggraini, K. (2022). Klasifikasi Habitat Bentik Berdasarkan Citra Sentinel 2 di Kepulauan Kei, Maluku Tenggara. Jurnal Ilmu Pertanian Indonesia (JIPI), 27(3), 372-384. https://doi.org/10.18343/jipi.27.3.372
Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5-32. https://doi.org/10.1023/A:1010933404324
Burns, C., Bollard, B., & Narayanan, A. (2022). Machine-Learning for Mapping and Monitoring Shallow Coral Reef Habitats. Remote Sensing, 14(11), 2666. https://doi.org/10.3390/rs14112666
Congalton, R. G., & Green, K. (2009). Assessing the Accuracy of Remotely Sensed Data Principles and Practices (Second Edition). CRC Taylor & Francis Group. France. 183.
Ghayour, L., Neshat, A., Paryani, S., Shahabi, H., Shirzadi, A., Chen, W., Al-Ansari, N., Geertsema, M., Amiri, M. P., Gholamnia, M., Dou, J., Ahmad, A. (2021). Performance Evaluatioin of Sentinel-2 and Landsat OLI Data for Land Cover/Use Classification Using a Comparison Between Machine Learning Algorithms. Remote Sensing, 13, 1349. https://doi.org/10.3390/rs13071349
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., Moore, R. (2017). Google Earth Engine: Planetary-scale Geospatial Analysis For Evereyone. Remote Sensing of Environment, 202, 18-27. https://doi.org/10.1016/j.rse.2017.06.031
Green, E. P., Mumby, P. J., Edwards, A. J., & Clark, C. D. (2000). Remote Sensing Handbook For Tropical Coastal Management. UNESCO Publishing.
Hartoni., Siregar, V. P., Wouthuyzen, S., Agus, S. B. (2022). Object Based Classification of Benthic Habitat Using Sentinel 2 Imagery by Applying With Support Vector Machine and Random Forest Algorithms in Shallow Waters of Kepulauan Seribu, Indonesia. Biodiversitas, 23(1), 514-520. https://doi.org/10.13057/biodiv/d230155
Kurniawati, E., Apdillah, D., Safitri, J. (2025). Pemetaan Habitat Bentik Menggunakan Citra Sentinel-2a Dengan Algoritma Maximum Likelihood Classification di Desa Pengudang. Journal of Marine Research, 14(2), 263-276. https://doi.org/10.14710/jmr.v14i2.48946
Landis, J. R., & Koch, G. G. (1977). The Measurement of Observer Agreement For Categorical Data. Biometrics, 33(1), 159-174. https://doi.org/10.2307/2529310
Misiuk, B., Brown, C. J. (2024). Benthic Habitat Mapping: A Review of Three Decades of Mapping Biological Patterns on The Seafloor. Estuarine, Coastal and Shelf Science, 296, 108599. https://doi.org/10.1016/j.ecss.2023.108599
Mountrakis, G., Im, J., & Ogole, C. (2011). Support Vector Machine in Remote Sensing: A Review. ISPR Journal of Photogrammetry and Remote Sensing, 66(2), 247-259. https://doi.org/10.1016/j.isprsjprs.2010.11.001
Nugroho, A., Nababan, B., Panjaitan, J. P., Agus, S. B. (2024). Pemetaan Habitat Bentik Berbasir Objek Menggunakan Drone di Perairan Gili Labak, Sumenep. Jurnal Kelautan, 17(1), 29-42. http://doi.org/10.21107/jk.v17i1.24518
Patil, A. & Panhalkar, S. (2023). A Comparative Analysis of Machine Learning Algorithms For Land Use and Land Cover Classification Using Google Earth Engine Platform. Journal of Geomatic, 17(2), 226-233. https://doi.org/10.58825/jog.2023.17.2.96
Pal, M. (2005). Random Forest Classifier For Remote Sensing Classification. International Journal of Remote Sensing, 26(1), 217-227. https://doi.org/10.1080/01431160412331269698
Richards, J. A., Jia, X. (2006). Remote Sensing Digital Image Analysis: An Introduction (4th ed.). Springer-Verlag
Short, F., Carruthers, T., Dennison, W., Waycott, M. (2007). Global Seagrass Distrubution and Diversity: A Bioregional Model. Journal of Experimental Marine Biology and Ecology, 350, 3-20. https://doi.org/10.1016/j.jembe.2007.06.012
Siregar, V. P., Agus, S. B., Subarno, T., Prabowo, N. W. (2018). Mapping Shallow Waters Habitats Using OBIA by Applying Several Approches of Depth Invariant Index in North Kepulauan Seribu. IOP Conf. Series: Earth and Environmental Science, 149, 012052. https://doi.org/10.1088/1755-1315/149/1/012052
Thomasberger, A., Nielsen, M. M., Flindt, M. R., Pawar, S., Svane, N. (2023). Comparative Assessment of Five Machine Learning Algorithm For Supervised Object-Based Classification of Submerged Seagrass Beds Using High-Resolution UAS Imagery. Remote Sensing, 15, 3600. https://doi.org/10.3390/rs15143600
Traganos, D., Poursanidis, D., Aggarwal, B., Chrysoulakis, N., Reinartz, P. (2018). Estimating Satellite-Derived Bathymetry (SDB) with the Google Earth Engine and Sentinel-2. Remote Sensing, 10, 859. https://doi.org/10.3390/rs10060859
Vahtmäe, E., Kutser, T., Paavel, B. (2020). Performance and Applicability of Water Column Correction Models in Optically Comples Coastal Waters. Remote Sensing, 12, 1861. https://doi.org/10.3390/rs12111861
Van An, N., Quang, N. H., Son, T. P. H., Thi An, T. (2023). High-resolution Benthic Habitat Mapping From Machine Learning on PlanetScope Imagery and ICESat-2 Data. Geocarto International, 38(1), 2184875. https://doi.org/10.1080/10106049.2023.2184875
Wahidin, N., Siregar, V. P., Nababan, B., Jaya, I., Wouthuyzen, S. (2015). Object-based Image Analysis for Coral Reef Benthic Habitat Mapping with Severeal Classificatioin Algorithms. Procedia Environmental Sciences, 24, 222-227. https://doi.org/10.1016/j.proenv.2015.03.029
Wicaksono, P. (2016). Improving the Accuracy of Multispectral-Based Benthic Habitats Mapping Using image Rotations: The Application of Principle Component Analysis and Independent Conponent Analysis. European Journal of Remote Sensing, 49, 433-463. https://doi.org/10.5721/EuJRS20164924
Wicaksono, P., Aryaguna, P. A., Lazuardi, W. (2019). Benthic Habitat Mapping Model and Cross Validation Using Maching-Learning Classification Algorithms. Remote Sensing, 11, 1279. https://doi.org/10.3390/rs11111279
Yeong, W. Y., Chong, W. S., Hue, C., Nius, I. (2024). Enhancing Benthic Habitat Mapping: A Review of Integrating Satellite and Side Scan Sonar Data For Improved Classification Accuracy. Transactions on Science and Technology, 11(4), 257-284.
Zibar, Z., Saputra, R., Raynaldo, A., Supriyanto, S. (2024). Web GIS Based Benthic Habitat Mapping Update Supports Smart Island Lemukutan. Journal of Applied Geospatial Information, 8(2), 133-139. https://doi.org/10.30871/jagi.v8i2.8705
DOI: https://doi.org/10.21107/jk.v18i3.32389
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Published by: Department of Marine Sciences, Trunojoyo University of Madura