CLASSIFICATION OF BENTHIC HABITAT BASED ON OBJECT IN SHALLOW WATERS OF KARANG LEBAR AND LANCANG ISLAND
Article Sidebar
Accepted
25 August 2023
Published
31 August 2023
Keywords:
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benthic habitat, Karang Lebar, Lancang Island, mapping, OBIA
Abstract
The object-based classification technique (OBIA) is one of the benthic habitat mapping techniques besides the conventional (pixel-based) method. The mapping of the OBIA method using machine learning algorithms is limited to the waters of Karang Lebar and Lancang Island. This study aims to determine the performance of machine learning algorithms (support vector machine (SVM), decision tree (DT), random forest (RF), and k-nearest neighbor (KNN)) in classifying shallow water benthic habitats based on objects using Sentinel satellite data. -2. The classification method used is the OBIA method with two levels of analysis. A total of 6 benthic habitat classes were obtained from field observations and Agglomerative Hierarchial Clustering analysis, namely coral, rubble, seagrass, rubble sand, and sand. The results obtained include the first level separating land, shallow sea and deeper sea. The second level is classification using a machine learning algorithm, the results of the classification show that the SVM algorithm gets a higher accuracy value than other algorithms with an accuracy of 84% in Karang Lebar waters, then in Lancang Island waters it gets an accuracy of 80% with the SVM algorithm. The bottom habitat of the shallow waters of Karang Lebar and Lancang Island can be well mapped using the OBIA method. The difference in the level of accuracy between the waters of Karang Lebar and Pulau Lancang is caused by the level of turbidity of the waters.
References
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Prabowo, N.W., V.P. Siregar, & S.B. Agus. 2018. Klasifikasi habitat bentik berbasis objek dengan algoritma support vector machine dan decision tree menggunakan citra multispektral spot-7 di pulau Harapan dan pulau Tunda. J. Ilmu dan Teknologi Kelautan Tropis, 10(1): 123-124. https://doi.org/10.29244/jitkt.v10i1.21670
Phinn, S.R., C.M. Roeflsema, & P.J. Mumby. 2012. Multi-scale, object-based image analysis for mapping geomorphic and ecological zones on coral reefs. International Journal of Remote Sensing, 33(12): 3768-3787. https://doi.org/10.1080/01431161.2011.633122
Rastner, P., T. Bolch, C. Notarnicola, & F. Paul. 2014. A comparison of pixel- and object-based glacier classification with optical satellite image. IEEE Journal of Selected Topics in Applied Science Earth Observations and Remote Sensing, 7(3): 853-862. https://doi.org/10.1109/JSTARS.2013.2274668
Sangadji, M.S., V.P. Siregar, & H.M. Manik. 2018. Klasifikasi habitat perairan dangkal menggunakan logika fuzzy dan maximum likelihood. J. Ilmu dan Teknologi Kelautan Tropis, 11(3): 667-681. https://doi.org/10.29244/jitkt.v10i3.22859
Sarianto, D., D. Simbolon, & B. Wiryawan. 2016. Dampak Pertambangan Nikel Terhadap Daerah Penangkapan Ikan di Perairan Kabupaten Halmahera Timur. Jurnal Ilmu Pertanian Indonesia, 21(2): 104-113. https://doi.org/10.18343/jipi.21.2.104
Sartika, D. & D.I. Sensuse. 2017. Perbandingan algoritma klasifikasi naive bayes, nearest neighbour, dan decision tree pada studi kasus pengambilan keputusan pemilihan pola pakaian. Jatisi, 1(2): 151-161. https://doi.org/10.35957/jatisi.v3i2.78
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Siregar, V.P., S.B. Agus, A. Sunuddin, R.A. Pasaribu, M.S. Sangadji, A. Sugara A, & E. Kurniawati. 2020. Benthic habitat classification using high resolution satellite imagery in Sebaru Besar Island, Kepulauan Seribu. IOP Conference Series: Earth and Environmental Science, 429(1): 01240-01248. https://doi.org/10.1088/1755-1315/429/1/012040
Suhendar, D.T., S.I. Sachoemar, & A.B. Zaidy. 2020. Hubungan kekeruhan terhadap materi partikulat tersuspensi (MPT) dan kekeruhan terhadap klorofil dalam tambak udang. Journal of Fisheries and Marine Research, 4(3): 332-338. https://doi.org/10.21776/ub.jfmr.2020.004.03.3
Susilo, S.B. 2017. Penginderaan Jarak Jauh “Ocean Color”. PT Penerbit IPB Press. Bogor, 2 p.
Qian, Y., W. Zhou, J. Yan, W. Li, & L. Han. 2015. Comparing machine learning classifiers for object-based land cover classification using very high resolution imagery. Remote Sensing, 7(1): 153–168. https://doi.org/10.3390/rs70100153
Wahiddin, N., V.P. Siregar, B. Nababan, I. Jaya, & S. Wouthuyzen. 2015. Object-based image analysis for coral reef benthic habitat mapping with several classification algorithms. Proceedings of The 1st International Symposium on LAPAN-IPB Satellite for Food Security and Environmental Monitoring, Bogor City, Indonesia. 24-26 November 2014, 222-227 pp. https://doi.org/10.1016/j.proenv.2015.03.029
Wilson, K. L., M.A. Skinner, & H.K. Lotze. 2019. Eelgrass (Zostera marina) and benthic habitat mapping in Atlantic Canada using high-resolution SPOT 6/7 satellite imagery. Estuarine, Coastal and Shelf Science, 226: 106292. https://doi.org/10.1016/j.ecss.2019.106292
Wu, Z., W. Lin, Z. Zhang, A. Wen, & L. Lin. 2017. An Ensemble Random Forest Algorithm for Insurance Big Data Analysis. Proceedings - 2017 IEEE International Conference on Computational Science and Engineering and IEEE/IFIP International Conference on Embedded and Ubiquitous Computing, CSE and EUC 2017, 531–536 pp. https://doi.org/10.1109/CSE-EUC.2017.99
Yusuf, M., Y. Koniyo, & C. Panigoro. 2013. Keanekaragaman lamun di Perairan sekitar Pulau Dudepo Kecamatan Anggrek Kabupaten Gorontalo Utara. Jurnal Ilmiah Perikanan dan Kelautan, 1(1): 18-23. https://ejurnal.ung.ac.id/index.php/nike/article/viewFile/1212/962
Zhang, C. 2015. Applying data fusion techniques for benthic habitat mapping and monitoring in a coral reef ecosystem. ISPRS Journal of Photogrammetry and Remote Sensing, 104: 213-223. https://doi.org/10.1016/j.isprsjprs.2014.06.005
Zhang, Z. 2016. Introduction to machine learning : k-nearest neighbors. Annals and Translational Medicine, 4(11): 1-7. https://doi.org/10.21037/atm.2016.03.37
Benfield, S.L., H.M. Guzman, J.M. Mair, & J.A.T. Young. 2007. Mapping the distribution of coral reefs and associated sublittoral habitats in Pacific Panama: a comparison of optical satellite sensors and classification methodologies. International J. of Remote Sensing, 28(22): 5047-5070. https://doi.org/10.1080/01431160701258062
Ben-Romdhane, H., P.R. Marpu, T.B.M.J. Ouarda, & H. Ghedira. 2016. Corals & benthic habitat mapping using DubaiSat-2: A spectral-spatial approach applied to Dalma Island, UAE (Arabian Gulf). Remote Sensing Letters, 7(8): 781–789. https://doi.org/10.1080/2150704X.2016.1187317
Biau, G. & E. Scornet. 2016. A random forest guided tour. TEST, 25(2): 197-227. https://doi.org/10.1007/s11749-016-0481-7
Blaschke, T. 2010. Object based image analysis for remote sensing. J. of Photogrammery and Remote Sensing, 65: 2-16. https://doi.org/10.1016/j.isprsjprs.2009.06.004
Bohn, V.Y., F. Carmona, R. Rivas, L. Lagomarsino, N. Diovisalvi, & H.E. Zagarese. 2018. Development of an empirical model for chlorophyll-a and Secchi Disk Depth estimation for a Pampean shallow lake (Argentina). Egyptian Journal of Remote Sensing and Space Science, 21(2): 183–191. https://doi.org/10.1016/j.ejrs.2017.04.005
Cai, S. & D. Liu. 2013. A comparison of object-based and contextual pixel-based classification using high and medium spatial resolution images. Remote Sensing Letters, 4(10): 998-1007. https://doi.org/10.1080/2150704X.2013.828180
Congalton, R.G. & K. Green. 2009. Assessing the accuracy of remotely sensed data—principles and practices. 2nd ed. CRC Press. Boca Raton, 183 p.
Cortes, C. & V. Vapnik. 1995. Support-vector networks. Machine Learning, 20: 273-297. https://doi.org/10.1007/BF00994018
Deng, Z., X. Zhu, D. Cheng, M. Zong, & S. Zhang. 2016. Efficient kNN classification algorithm for big data. Neurocomputing, 195: 143–148. https://doi.org/10.1016/j.neucom.2015.08.112
Green, E.P., P.J. Mumby, A.J. Edwards, & C.D. Clark. 2000. Remote sensing handbook for tropical coastal management: UNESCO, 109 p.
Hedley, J.D., C.M. Roelfsema, I. Chollet, A.R. Harborne, S.F. Heron, S.J. Weeks, W.J. Skirving, A.E. Strong, C.M. Eakin, T.R.L. Christensen, V. Ticzon, S. Bejarano, & P.J. Mumby. 2016. Remote sensing of coral reefs for monitoring and management: a review. J. Remote Sensing, 8: 118-168. https://doi.org/10.3390/rs8020118
Hossain, M.D. & D. Chen. 2019. Segmentation for Object-Based Image Analysis (OBIA): A review of algorithms and challenges from remote sensing perspective. ISPRS Journal of Photogrammetry and Remote Sensing, 150: 115–134. https://doi.org/10.1016/j.isprsjprs.2019.02.009
Janowski, L., K. Trzcinska, J. Tegowski, A. Kruss, M. Rucinska-Zjadacz, & P. Pocwiardwoski. 2018. Nearshore Benthic Habitat Mapping Based on Multi-Frequency, Multibeam Echosounder Data Using a Combined Object-Based Approach: A Case Study from the Rowy Site in the Southern Baltic Sea. Remote Sensing, 10(12): 1983-2003. https://doi.org/10.3390/rs10121983
Kementerian Kelautan dan Perikana (KKP). 2015. Profil Kawasan Konservasi Provinsi DKI Jakarta. KKP. Jakarta, 8 p.
Kurniawati, E., V.P. Siregar, & I.W. Nurjaya. 2020. Klasifikasi habitat perairan dangkal berbasis objek menggunakan citra worldview 2 dan sentinel 2b di perairan kepulauan Seribu. J. Ilmu dan Teknologi Kelautan Tropis, 12(2): 421-435. https://doi.org/10.29244/jitkt.v12i2.26089
Lazuardi, W., P. Wicaksono, & M.A. Marfai. 2021. Remote sensing for coral reef and seagrass cover mapping to support coastal management of smalls islands. IOP Conference Series: Earth and Environmental Science, 686(1): 012031-012040. https://doi.org/10.3390/rs10121983
Liu, T., J. Im. & L.J. Quackenbush. 2015. A novel transfelable individual tree crown deliniation model based on Fishing Net Dragging and boundary classification. Journal of Photogrammetry and Remote Sensing, 110(1): 34-47. https://10.1016/j.isprsjprs.2015.10.002
Mastu, L.O.K., B. Nababan, & J.P. Panjaitan. 2018. Pemetaan habitat bentik berbasis objek menggunakan citra sentinel-2 di perairan pulau Wangi-Wangi kabupaten Wakatobi. J. Ilmu dan Teknologi Kelautan Tropis, 10(2): 381-396. https://doi.org/10.29244/jitkt.v10i2.21039
McGarigal, K., H.Y. Wan, K.A. Zeller, B.C. Timm, & S.A. Cushman. 2016. Multi-scale habitat selection modeling: a review and outlook. Landscape Ecol, 31: 1161-1175. https:// doi.org/10.1007/s10980-016-0374-x
Mohamed, H., K. Nadaoka, & T. Nakamura. 2020. Towards Benthic Habitat 3D Mapping Using Machine Learning Algortihms and Structures from Motion Photogrammetry. Remote Sensing, 12(1): 127-143. https://doi.org/10.3390/rs12010127
Nababan, B., L.O.K. Mastu, N.H. Idris, & J.P. Panjaitan. 2021. Shallow-water benthic habitat mapping using drone with object based image analyses. Remote Sensing, 13(21): 4452-4475. https://doi.org/10.3390/rs13214452
Noi, P.T. & M. Kappas. 2017. Comparison of random forest, k-nearest neighbor, and support vector machine classifiers for land cover classification using sentinel-2 imagery. Sensors, 18(1): 18-38. https://doi.org/10.3390/s18010018
Osisanwo, F.Y., J.E.T. Akinsola, O. Awodele, J.O. Hinmikaiye, O. Olakanmi, & J. Akinjobi. 2017. Supervised Machine Learning Algorithms : Classification and Comparison. International Journal of Computer Trends and Technology, 48(3): 128–138. https://doi.org/10.14445/22312803/IJCTT-V48P126
Prabowo, N.W., V.P. Siregar, & S.B. Agus. 2018. Klasifikasi habitat bentik berbasis objek dengan algoritma support vector machine dan decision tree menggunakan citra multispektral spot-7 di pulau Harapan dan pulau Tunda. J. Ilmu dan Teknologi Kelautan Tropis, 10(1): 123-124. https://doi.org/10.29244/jitkt.v10i1.21670
Phinn, S.R., C.M. Roeflsema, & P.J. Mumby. 2012. Multi-scale, object-based image analysis for mapping geomorphic and ecological zones on coral reefs. International Journal of Remote Sensing, 33(12): 3768-3787. https://doi.org/10.1080/01431161.2011.633122
Rastner, P., T. Bolch, C. Notarnicola, & F. Paul. 2014. A comparison of pixel- and object-based glacier classification with optical satellite image. IEEE Journal of Selected Topics in Applied Science Earth Observations and Remote Sensing, 7(3): 853-862. https://doi.org/10.1109/JSTARS.2013.2274668
Sangadji, M.S., V.P. Siregar, & H.M. Manik. 2018. Klasifikasi habitat perairan dangkal menggunakan logika fuzzy dan maximum likelihood. J. Ilmu dan Teknologi Kelautan Tropis, 11(3): 667-681. https://doi.org/10.29244/jitkt.v10i3.22859
Sarianto, D., D. Simbolon, & B. Wiryawan. 2016. Dampak Pertambangan Nikel Terhadap Daerah Penangkapan Ikan di Perairan Kabupaten Halmahera Timur. Jurnal Ilmu Pertanian Indonesia, 21(2): 104-113. https://doi.org/10.18343/jipi.21.2.104
Sartika, D. & D.I. Sensuse. 2017. Perbandingan algoritma klasifikasi naive bayes, nearest neighbour, dan decision tree pada studi kasus pengambilan keputusan pemilihan pola pakaian. Jatisi, 1(2): 151-161. https://doi.org/10.35957/jatisi.v3i2.78
Statnikov, A., L. Wang, & C.F. Aliferis. 2008. A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification. BMC Bioinformatics, 9(1): 319-329. https://doi.org/10.1186/1471-2105-9-319
Siregar, V.P., S.B. Agus, A. Sunuddin, R.A. Pasaribu, M.S. Sangadji, A. Sugara A, & E. Kurniawati. 2020. Benthic habitat classification using high resolution satellite imagery in Sebaru Besar Island, Kepulauan Seribu. IOP Conference Series: Earth and Environmental Science, 429(1): 01240-01248. https://doi.org/10.1088/1755-1315/429/1/012040
Suhendar, D.T., S.I. Sachoemar, & A.B. Zaidy. 2020. Hubungan kekeruhan terhadap materi partikulat tersuspensi (MPT) dan kekeruhan terhadap klorofil dalam tambak udang. Journal of Fisheries and Marine Research, 4(3): 332-338. https://doi.org/10.21776/ub.jfmr.2020.004.03.3
Susilo, S.B. 2017. Penginderaan Jarak Jauh “Ocean Color”. PT Penerbit IPB Press. Bogor, 2 p.
Qian, Y., W. Zhou, J. Yan, W. Li, & L. Han. 2015. Comparing machine learning classifiers for object-based land cover classification using very high resolution imagery. Remote Sensing, 7(1): 153–168. https://doi.org/10.3390/rs70100153
Wahiddin, N., V.P. Siregar, B. Nababan, I. Jaya, & S. Wouthuyzen. 2015. Object-based image analysis for coral reef benthic habitat mapping with several classification algorithms. Proceedings of The 1st International Symposium on LAPAN-IPB Satellite for Food Security and Environmental Monitoring, Bogor City, Indonesia. 24-26 November 2014, 222-227 pp. https://doi.org/10.1016/j.proenv.2015.03.029
Wilson, K. L., M.A. Skinner, & H.K. Lotze. 2019. Eelgrass (Zostera marina) and benthic habitat mapping in Atlantic Canada using high-resolution SPOT 6/7 satellite imagery. Estuarine, Coastal and Shelf Science, 226: 106292. https://doi.org/10.1016/j.ecss.2019.106292
Wu, Z., W. Lin, Z. Zhang, A. Wen, & L. Lin. 2017. An Ensemble Random Forest Algorithm for Insurance Big Data Analysis. Proceedings - 2017 IEEE International Conference on Computational Science and Engineering and IEEE/IFIP International Conference on Embedded and Ubiquitous Computing, CSE and EUC 2017, 531–536 pp. https://doi.org/10.1109/CSE-EUC.2017.99
Yusuf, M., Y. Koniyo, & C. Panigoro. 2013. Keanekaragaman lamun di Perairan sekitar Pulau Dudepo Kecamatan Anggrek Kabupaten Gorontalo Utara. Jurnal Ilmiah Perikanan dan Kelautan, 1(1): 18-23. https://ejurnal.ung.ac.id/index.php/nike/article/viewFile/1212/962
Zhang, C. 2015. Applying data fusion techniques for benthic habitat mapping and monitoring in a coral reef ecosystem. ISPRS Journal of Photogrammetry and Remote Sensing, 104: 213-223. https://doi.org/10.1016/j.isprsjprs.2014.06.005
Zhang, Z. 2016. Introduction to machine learning : k-nearest neighbors. Annals and Translational Medicine, 4(11): 1-7. https://doi.org/10.21037/atm.2016.03.37
Authors
UtamaP. W., SiregarV., & NababanB. (2023). CLASSIFICATION OF BENTHIC HABITAT BASED ON OBJECT IN SHALLOW WATERS OF KARANG LEBAR AND LANCANG ISLAND. Jurnal Ilmu Dan Teknologi Kelautan Tropis, 15(2), 167-184. https://doi.org/10.29244/jitkt.v15i2.36036
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