• Esty Kurniawati Fakultas Perikanan dan Ilmu Kelautan Departemen Ilmu dan Teknologi Kelautan
  • Vincentius Siregar
  • I Wayan Nurjaya
Keywords: benthic habitats, DT, Kepulauan Seribu Waters, KNN, OBIA, SVM


The benthic habitats of shallow waters of Sebaru Island and Lancang Island have different water characteristics from geographical location. Data and information about benthic habitat are needed to maintain and preserve ecosystems in the waters. This study aims is to know the effect of different satellite image resolution, different algorithms and water quality e.g chlorophyll-a (Chl-a) and total suspended solid (TSS) on the accuracy of shallow-water benthic habitats mapping on Sebaru Besar Island and Lancang Island. The accuracy (OA) of the application of different  classification algorithms showed a good results. The highest OA results in shallow waters of Sebaru Island with Wordview 2 imagery were obtained from the SVM and DT algorithms with the same value of 76.24%, while the Sentinel 2B image with the DT algorithm obtained results (OA) of 68.08%. In Lancang Island the highest OA value of Wordview 2 imagery was obtained by DT algorithm with a value of 74.44%, while Sentinel 2B imagery was obtained from KNN algorithm with a value of 59.0%. High concentrations of Chl-a and TSS cannot yet be said to affect the low accuracy in mapping shallow water benthic habitats.


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American Public Health Association (APHA). 2012. Standard methods for the examination of water and waste water. Washington DC. 1496 p.

Andréfouët, S., F.E.M. Kargera, E.J. Hochberg, C. Hua, & K.L. Cardera. 2001. Change detection in shallow coral reef environments using Landsat 7 ETM+ data. Remote Sens. Environ., 79: 150–162.

Atkinson, P.M. & D.K. Naser. 2010. A Geostatistically Weighted K-NN Classifier for Remotely Sensed Imagery. Geogr. Anal., 42(2): 204−225.

Blaschke, T., G.J. Hay, M. Kelly, S. Lang, P. Hofmann, E. Addink, R.Q. Feitosa, F. Meer, H. Werff, F. Coillie, & D. Tiede. 2014. Geographic Object-Based Image Analysis – Towards a new paradigm. ISPRS J. of Photogrammetry and Remote Sensing, 87: 180-191.

Chen, J., T. Cui, J. Tang, & Q. Songe. 2014. Remote sensing of diffuse attenuation coefficient using MODIS imagery of turbid coastal waters: A case study in Bohai Sea. Remote Sens. Environ., 140: 78-93.

Congalton, R.G. & Green K. 2008. Assessing the accuracy of remotely sensed data: principles and practices. CRC Press: Taylor & Francis Group. France. 27 p.

Firmansyah, I., E. Riani, & R. Kurnia. 2012. Model pengendalian pencemaran laut untuk meningkatkan daya dukung lingkungan Teluk Jakarta. J. of Natural Resources and Environmental Management, 2(1): 22-28.

Green, E.P., P.J. Mumby, A.J. Edwards, & C.D. Clark. 2000. Remote sensing handbook for tropical coastal management. United Nations Educational, Scientific and Cultural Organization (UNESCO). Paris. 325 p.

Groetsch, P. 2011. Optimization and verification of a new analytical radiative transfer model. Deutschen Luft- und Raumfahrtzentrum. Munchen (Germany). 89 p.

Guifen, W., W. Cao, D. Yang, & D. Xu. 2008. Variation in downwelling diffuse attenuation coefficient in the northern South China Sea. Chin. J. Oceanol. Limn., 26: 323-333.

Gupta, N., M. Tech, & P. Garhwal. 2014. Object based information extraction from high resolution satellite imagery using eCognition. International J. of Computer Science Issues, 11: 139-144.

Hochberg, E.J. & M.J. Atkinson. 2003. Capabilities of remote sensors to classify coral, Algaee, and sand as pure and mixed spectra. Remote Sens. Environ., 85: 174–189.

Jamu, D.M., Z. Lu, & R.H. Piedrahita. 1999. Relationship between Secchi disk visibility and chlorophyll a in aquaculture ponds. Aquac., 170: 205-214.

Keller, B.D., B.D. Keller, D.F. Gleason, E. McLeod, C.M. Woodley, S. Airame, B.D. Causey, A.M. Friedlander, R.G. Dunsmore, J.E. Johnson, S.L. Miller, & R.S. Steneck. 2009. Climate change, coral reef ecosystems, and management options for marine protected areas. Environmental Management, 44: 1069–1088.

Kirk, J.T.O. 1994. Light and photosynthesis in aquatic ecosystems. Cambridge university press. Australia. 471 p.

Liao, H. & W. Sun. 2010. Forecasting and evaluating water quality of chao lake based on an improved decision tree method. Procedia Environ. Sci., 2: 970-979.

Mastu, L.O.K, B. Nababan, & P.J. 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: 381-396.

Ouillon, S., P. Douillet, A. Petrenko, J. Neveux, C. Dupouy, J.M. Froidefond, S. Andréfouët, & A.M. Caravaca. 2008. Optical algorithms at satellite wavelengths for total suspended matter in tropical coastal waters Sensors. Sensors (Basel), 8(7): 4165-4185.

Pena, J.M., J.T. Sánchez, A.I. Castro, M. Kelly, & F.L. Granados. 2013. Weed mapping in early-season maize fields using object-based analysis of unmanned aerial vehicle (UAV) images. Plos One, 8: 1-11.

Peng, S., R. Zhou, X. Qin, H. Shi, & D. Ding. 2013. Application of macrobenthos functional groups to estimate the ecosystem health in a semienclosed bay. Mar. Pollut., 74: 302-310.

Prabowo, N.W., V.P. Siregar, & S.B. Agus. 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 Teknologi Kelautan Tropis, 10(1): 123-134.

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 Sens., 7: 153-168.

Ricotta, C. & J. Podani. 2017. On some properties of the Bray-Curtis dissimilarity and their ecological meaning. Ecological Complexity, 31: 201-205.

Roelfsema, C., S. Phinn, S. Jupiter, J. Comley, M. Beger, & E. Paterson. 2010. The application of object based analysis of high spatial resolution imagery for mapping large coral reef systems in The West Pacific at Geomorphic and Benthic Community Spatial Scales. Editor Proceedings of 2010 IEEE International Geoscience and Remote Sensing Symposium, 4346-4349 pp.

Sugiyono. 2008. Metode Penelitian kuantitatif kualitatif dan R&D. Alfabeta. Bandung. 213 p.

Tzotsos, A. 2006. A support vector machine approach for object based image analysis. Proceedings of OBIA. 1-7 pp.

Verhulp, J. & A.V. Niekerk. 2017. Transferability of decision trees for land cover classification in a heterogeneous area. South African J. of Geomatics, 6: 30-46.

Wahidin, N., V.P. Siregar, B. Nababan, I. Jaya, & S. Wouthuyzend. 2015. Object-based image analysis for coral reef benthic habitat mapping with several classification algorithms. Procedia Environ. Sci., 24: 222-227.

Wei, W., X. Chen, & A. Ma. 2005. Objectoriented information extraction and application in high-resolution remote sensing image. IEEE International Geoscience and Remote Sensing Symposium, 8: 3803-3807.