Mapping Shallow Water Benthic Habitats Using High Resolution Multispectral UAV Data: A Case Study of Panggang Island

Devi Aliyah Sari S.Si (1) , Prof. Dr.Ir. Vincentius P. Siregar, DEA (2) , Dr. Syamsul Bahri Agus, S.Pi, M.Si (2)
(1) Department of Marine Technology , Faculty of Fisheries and Marine Sciences, IPB University, 16680, Indonesia, Indonesia,
(2) Department of Marine Science and Technology, Faculty of Fishery and Marine Science, IPB University, Dramaga Campus, Bogor 16680, Indonesia, Indonesia

Abstract

The use of Unmanned Aerial Vehicles (UAVs) has become increasingly important for high-resolution remote sensing applications, particularly for mapping coastal and shallow-water environments. Benthic habitats in shallow marine environments include seagrass meadows, macroalgae, live coral reefs, and degraded coral communities associated with sandy, muddy, and coral rubble substrates. This study mapped benthic habitats on Panggang Island, Indonesia, using multispectral imagery acquired by a DJI Phantom 4 Multispectral UAV with a spatial resolution of 6 cm per pixel. Seven benthic habitat classes were identified; sand , seagrass, live coral, dead coral with algae, coral with algae, rubble, and macroalgae. Habitat classification was performed using a pixel-based Support Vector Machine (SVM) algorithm. Classification accuracy was evaluated using a confusion matrix, yielding an overall accuracy of 87% and a Kappa coefficient of 0.84 The results demonstrate that integrating high-resolution UAV multispectral imagery with pixel-based SVM classification provides an effective approach for detailed benthic habitat mapping in small-island shallow-water environments.

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References

Belfiore, S., Cicin-Sain, B., & Ehler, C.N. (2004). Incorporating marine protected areas into integrated coastal and ocean management : principles and guidelines. Gland, Switzerland: IUCN.

Cervantes, J., Garcia-Lamont, F., Rodríguez-Mazahua, L., & Lopez, A. (2020). A comprehensive survey on support vector machine classification: Applications, challenges and trends. Neurocomputing, 408, 189-215. https://doi.org/10.1016/j.neucom.2019.10.118 DOI: https://doi.org/10.1016/j.neucom.2019.10.118

Elma, E., Gaulton, R., Chudley, T.R., Scott, C.L., East, H.K., Westoby, H., & Fitzsimmons, C. (2024). Evaluating UAV-based multispectral imagery for mapping an intertidal seagrass environment. Aquat. Conserv. Mar. Freshw. Ecosyst, 34(8), 1-14. https://doi.org/10.1002/aqc.4230 DOI: https://doi.org/10.1002/aqc.4230

Feng, C., Ye, G., Jiangning, Z., Jian, Z., Jiang, Q., He, L., Zhang, Y., & Xu, Z. (2023). Sustainably developing global blue carbon for climate change mitigation and economic benefits through international cooperation. Nat Commun., 14(1), 1-10. https://doi.org/10.1038/S41467-023-41870-X DOI: https://doi.org/10.1038/s41467-023-41870-x

Gohari, A., Ahmad, A.B., Rabiu, L., Rahim, R.B.A., Supa’At, A.S.M., Elamin, N.I.M., Gismalla, M.S.M., Al-Dharrab, S.I., Rashid, R.A., Nawawi, S.W., et al. (2024). A systematic review of the uav technology usage in ASEAN. IEEE Open J. Veh. Technol, 5(June), 1036-1058. https://doi.org/10.1109/OJVT.2024.3436065 DOI: https://doi.org/10.1109/OJVT.2024.3436065

Gunathilaka, M.D.K.L., & Fernando, S.L.J.. (2022). Accuracy assessment of unsupervised land use and land cover classification using remote sensing and geographical information systems. Int. J. Environ. Eng. Educ., 4(3), 76-82. https://doi.org/10.55151/ijeedu.v4i3.73 DOI: https://doi.org/10.55151/ijeedu.v4i3.73

Hamidah, M., Pasaribu, R.A., & Aditama, F.A. (2021). Benthic habitat mapping using Object-Based Image Analysis (OBIA) on Tidung Island, Kepulauan Seribu, DKI Jakarta. IOP Conf. Ser. Earth Environ. Sci., 944(1). https://doi.org/10.1088/1755-1315/944/1/012035 DOI: https://doi.org/10.1088/1755-1315/944/1/012035

Hamuna, B., Pujiyati, S., Gaol, J.L., & Hestirianoto, T. (2024). Classification and prediction of benthic habitat based on scientific echosounder data: application of machine learning algorithms. Appl. Comput. Sci., 20(4), 100-116. https://doi.org/10.35784/acs-2024-42 DOI: https://doi.org/10.35784/acs-2024-42

Joyce, K.E., Fickas, K.C., & Kalamandeen, M. (2023). The unique value proposition for using drones to map coastal ecosystems. Cambridge Prism. Coast. Futur. 1. https://doi.org/10.35784/acs-2024-42 DOI: https://doi.org/10.1017/cft.2022.7

Kurniawati, E., Siregar, V., & Nurjaya, I.W. (2020). Classification of shallow water habitat based on object using Worldview 2 and Sentinel 2B images in Kepulauan Seribu waters. Ilmu dan Teknol. Kelaut. Trop., 12(2), 421-435. https://doi.org/10.29244/jitkt.v12i2.26089 DOI: https://doi.org/10.29244/jitkt.v12i2.26089

Lillesand, T.M., Kiefer, R.W., & Chipman, J.W. (2015). Remote sensing and image interpretation (7th ed.). Hoboken, New Jersey: John Wiley & Sons. https://books.google.com/books/about/Remote_Sensing_and_Image_Interpretation.html?id=AFHDCAAAQBAJ

Mansor, N.S., Awang, H., Malami, S.T.S., Zolkafli, A., Taiye, M.A., & Maulana, H. (2024). Support Vector Machine for Satellite Images Classification Using Radial Basis Function Kernel Method. Commun. Comput. Inf. Sci., 2001, 301-312. https://doi.org/10.1007/978-981-99-9589-9_23 DOI: https://doi.org/10.1007/978-981-99-9589-9_23

Mohamed, H., Nadaoka, K., & Nakamura, T. (2020). Semiautomated mapping of benthic habitats and seagrass species using a convolutional neural network framework in shallow water environments. Remote Sens., 12(23), 1-18. https://doi.org/10.3390/rs12234002 DOI: https://doi.org/10.3390/rs12234002

Prabowo, N.W., Siregar, V.P., & Agus, S.B. (2018). Classification of benthic habitat based on object with support vector machines and decision tree algorithm using Spot-7 Multispectral Imagery in Harapan and Kelapa Island. J. Ilmu dan Teknol. Kelaut. Trop., 10(1), 123-134. https://doi.org/10.29244/jitkt.v10i1.21670v DOI: https://doi.org/10.29244/jitkt.v10i1.21670

Prentice, R.M., Peciña, M.V., Ward, R.D., Bergamo, T.F., Joyce, C.B., & Sepp, K. (2021). Machine learning classification and accuracy assessment from high-resolution images of coastal wetlands. Remote Sens., 13(18), 1-27. https://doi.org/10.3390/rs13183669 DOI: https://doi.org/10.3390/rs13183669

Roelfsema, C., Kovacs, E., Ortiz, J.C., Wolff, N.H., Callaghan, D., Wettle, M., Ronan, M., Hamylton, S.M., Mumby, P.J., & Phinn, S. (2018). Coral reef habitat mapping: A combination of object-based image analysis and ecological modelling. Remote Sens. Environ., 208, 27-41. https://doi.org/10.1016/j.rse.2018.02.005 DOI: https://doi.org/10.1016/j.rse.2018.02.005

Sugara, A., Suryanita, A., Maulana, A., Anggoro, A., & Siregar, V.P. (2023). Aplikasi teknologi drone sebagai pelengkap data survei lapang untuk pemetaan ekosistem terumbu karang menggunakan citra Worldview-2. J. Mar. Aquat. Sci., 8(2), 202-209. https://doi.org/10.24843/jmas.2022.v08.i02.p05 DOI: https://doi.org/10.24843/jmas.2022.v08.i02.p05

Utama, P.W., Siregar, V., & Nababan, B. (2023). Klasifikasi habitat dasar berbasis objek di perairan dangkal Karang Lebar dan Pulau Lancang. J. Ilmu dan Teknol. Kelaut. Trop., 15(2), 167-184. https://doi.org/10.29244/jitkt.v15i2.36036 DOI: https://doi.org/10.29244/jitkt.v15i2.36036

Ventura, D., Grosso, L., Pensa, D., Casoli, E., Mancini, G., Valente, T., Scardi, M., & Rakaj, A. (2023). Coastal benthic habitat mapping and monitoring by integrating aerial and water surface low-cost drones. Front. Mar. Sci., 9, 1-15. https://doi.org/10.3389/fmars.2022.1096594 DOI: https://doi.org/10.3389/fmars.2022.1096594

Authors

Devi Aliyah Sari S.Si
devialiyah29@gmail.com (Primary Contact)
Prof. Dr.Ir. Vincentius P. Siregar, DEA
Dr. Syamsul Bahri Agus, S.Pi, M.Si
Author Biography

Devi Aliyah Sari S.Si, Department of Marine Technology , Faculty of Fisheries and Marine Sciences, IPB University, 16680, Indonesia

Devi is a master’s student in the Department of Marine Technology, IPB University, Indonesia.

Mapping Shallow Water Benthic Habitats Using High Resolution Multispectral UAV Data: A Case Study of Panggang Island. (2026). Jurnal Ilmu Dan Teknologi Kelautan Tropis, 18(1), 97-105. https://doi.org/10.29244/jitkt.18.1.70650

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How to Cite

Mapping Shallow Water Benthic Habitats Using High Resolution Multispectral UAV Data: A Case Study of Panggang Island. (2026). Jurnal Ilmu Dan Teknologi Kelautan Tropis, 18(1), 97-105. https://doi.org/10.29244/jitkt.18.1.70650
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