K, The BENTHIC HABITAT CLASSIFICATION OF ATOL KELEDUPA WAKATOBI NATIONAL PARK USING SUPPORT VECTOR MACHINE ALGORITHM
Article Sidebar
Accepted
28 January 2023
Published
31 January 2023
Keywords:
-
benthic habitat, Kaledupa Atoll, sentinel-2 satellite, Wakatobi
Abstract
Kaledupa Atoll is one of the areas designated as a marine protection zone and local use zone in Wakatobi National Park. Spatial information on the benthic habitat of Kaledupa Atoll is very limited so that this information is expected to be a support in strategies and efforts to conserve marine biodiversity. This study aims to map the benthic habitat of Kaledupa Atoll using a pixel-based and object-based guided classification method/OBIA with a support vector machine (SVM) algorithm. The data used is the Sentinel-2 satellite image with a spatial resolution of 10 x10 m which was acquired on November 4, 2019. Observations of benthic habitats were carried out directly at the study site by placing quadrant transects and taking points on the dominant or homogeneous habitat area. The transect used is 100 x 100 cm2. Image classification uses thematic layer input from field data. The results of the classification of benthic habitats are grouped into six classes. Based on the OBIA method, benthic habitats can be mapped with an accuracy rate of 78.1%, while the pixel-based classification has an overall accuracy of 61.8%. Classification of benthic habitats with the SVM algorithm using the OBIA method provides better information than the pixel-based method.
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Fourqurean, J.W., C.M. Duarte, H. Kennedy, N. Marbà, M. Holmer, M.A. Mateo, E.T. Apostolaki, G.A. Kendrick, D. Krause-Jensen & K.J. Mcglathery. 2012. Seagrass ecosystems as a globally significant carbon stock. Nature Geoscience, 5: 505-509. https://doi.org/10.1038/ngeo1477
Galparsoro, I., A. Borja, & M.C. Uyarra. 2014. Mapping ecosystem services provided by benthic habitats in the European North Atlantic Ocean. Frontiers in Marine Science, 1: 1-14. https://doi.org/10.3389/fmars.2014.00023
Gao, J. 2009. Bathymetric mapping by means of remote sensing: methods, accuracy and limitations. Progress in Physical Geography, 33(1): 103-116. https://doi.org/10.1177/0309133309105657
Green, E., P. Mumby, A. Edwards, & C. Clark. 2000. Remote sensing: handbook for tropical coastal management. United Nations Educational, Scientific and Cultural Organization (UNESCO).
Hafizt, M., M.D.M. Manessa, N.S. Adi, & B. Prayudha. 2017. Benthic habitat mapping by combining lyzenga’s optical model and relative water depth model in Lintea Island, Southeast Sulawesi. Earth and Environmental Sciences, (98): 012037. https://doi.org/10.1088/1755-1315/98/1/012037
Harmel, T., M. Chami, T. Tormos, N. Reynaud, & P.A. Danis. 2018. Sunglint correction of the Multi-Spectral Instrument (MSI)-SENTINEL-2 imagery over inland and sea waters from SWIR bands. Remote Sensing of Environment, 204: 308-321. https://doi.org/10.1016/j.rse.2017.10.022
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Kux, H.J.H. & C.M.D. Pinho, 2006. Objek-oriented analysis of high-resolution satellite image for intra-urban land cover classification: case study in São José Dos Campos, São Paulo State, Brazil. Brazil : Instituto Nacional de Pesquisas Espaciais.
Li, W. & Q. Guo. 2013. A new accuracy assessment method for one-class remote sensing classification. IEEE transactions on geoscience and remote sensing, 52(8): 4621-4632. https://doi.org/10.1109/TGRS.2013.2283082
Lillesand, T., R.W. Kiefer, & J. Chipman, 2015. Remote sensing and image interpretation. John Wiley & Sons.
Lyzenga, D.R. 1978. Passive remote sensing techniques for mapping water depth and bottom features. Applied optics, 17(3): 379-383. https://doi.org/10.1364/AO.17.000379
Lyzenga, D.R. 1981. Remote sensing of bottom reflectance and water attenuation parameters in shallow water using aircraft and landsat data. International Journal of Remote Sensing, 2(1): 71–82. http://doi.org/10.1080/01431168108948342
Madanguit, C.J.G., J.P.L. Oñez, H.G. Tan, M.D. Villanueva, J.E. Ordaneza, & A.U. Novero. 2017. Application of support vector machine (SVM) and quick unbiased efficient statistical tree (QUEST) algorithms on mangrove and agricultural resource mapping using lidar data sets. International Journal of Applied Environmental Sciences, 12(10): 1821-1830.
Manalu, R.J., Sutanto, A. & Trisakti, B. 2016. Perbandingan metode klasifikasi penutup lahan berbasis piksel dan berbasis obyek menggunakan data pisar-L2. Jurnal Penginderaan Jauh Dan Pengolahan Data Citra Digital, 13(1): 49-60. https://doi.org/10.30536/j.pjpdcd.2016.v13.a2936
Mastu, L.O.K., B. Nababan, & J.P. Panjaitan. 2018. Pemetaan habitat bentik berbasis objek menggunakan citra unmanned aerial vehicle (UAV) dan satelit 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
McCarthy, M.J., K.E. Colna, M.M. El-Mezayen, A.E. Laureano-Rosario, P. Méndez-Lázaro, D.B. Otis, ... & F.E. Muller-Karger. 2017. Satellite remote sensing for coastal management: A review of successful applications. Environmental Management, 60(2): 323-339. https://doi.org/10.1007/s00267-017-0880-x
McCormick, M.I., D.P. Chivers, B.J. Allan, & M.C. Ferrari. 2017. Habitat degradation disrupts neophobia in juvenile coral reef fish. Global change biology, 23(2): 719-72. https://doi.org/10.1111/gcb.13393
Mellin, C., D. Mouillot, M. Kulbicki, T.R. Mcclanahan, L. Vigliola, C.J.A. Bradshaw,... & M.J. Caley. 2016. Humans and seasonal climate variability threaten large-bodied coral reef fish with small ranges. Nature Communications, 7(1): 1-9. https://doi.org/10.1038/ncomms10491
Mobley, C.D. 1994. Light and water radiative transfer in natural waters. California: Academic Press, lnc. 579p.
Mora, C. 2008. A clear human footprint in the coral reefs of the Caribbean. Proceedings of the Royal Society B: Biological Sciences, 275(1636): 767-773. https://doi.org/10.1098/rspb.2007.1472
Mumby, P.J., E.P. Green, A.J. Edwards, & C.D. Clark. 1997. Coral reef habitat mapping: how much detail can remote sensing provide?. Marine Biology, 130(2): 193-202. https://doi.org/10.1007/s002270050238
Mumby, P.J., C.D. Clark, E.P. Green, & A.J. Edwards. 1998. Benefits of water column correction and contextual editing for mapping coral reefs. International Journal of Remote Sensing, 19(1): 203-210. https://doi.org/10.1080/014311698216521
Navulur, K. 2007. Multispektral image analysis using the object-oriented paradigm Taylor & Francis Group. LLC. 171 p.
Ouellette, W. & W. Getinet. 2016. Remote sensing for marine spatial planning and integrated coastal areas management: achievements, challenges, opportunities and future prospects. Remote Sensing Applications: Society and Environment, 4: 138-157. https://doi.org/10.1016/j.rsase.2016.07.003
Phinn, S.R., C.M. Roelfsema, & 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): 3768-3797. https://doi.org/10.1080/01431161.2011.633122
Pragunanti, T., B. Nababan, H. Madduppa, & D. Kushardono. 2020. Accuracy assessment of several classification algorithms with and without hue saturation intensity input features on object analyses on benthic habitat mapping in the Pajenekang Island Waters, South Sulawesi. In IOP conference series: Earth and environmental science. IOP Publishing. 429: 012044. https://doi.org/10.1088/1755-1315/429/1/012044
Randin, C.F., M.B. Ashcroft, J. Bolliger, J. Cavender-Bares, N.C. Coops, S. Dullinger,... & G. Giuliani. 2020. Monitoring biodiversity in the Anthropocene using remote sensing in species distribution models. Remote sensing of environment, 239: 111626. https://doi.org/10.1016/j.rse.2019.111626
Rogers, A., J.L. Blanchard, & P.J. Mumby. 2018. Fisheries productivity under progressive coral reef degradation. Journal of applied ecology, 55(3): 1041-1049. https://doi.org/10.1111/1365-2664.13051
Roth, F., F. Saalmann, T. Thomson, D.J. Coker, R. Villalobos, B.H. Jones,… & S. Carvalho. 2018. Coral reef degradation affects the potential for reef recovery after disturbance. Marine Environmental Research, 142: 48-58. https://doi.org/10.1016/j.marenvres.2018.09.022
Siregar, V.P., M.S. Sangadji, S.B. Agus, A. Sunuddin, R.A. Pasaribu, & E. Kurniawati. 2020. Klasifikasi habitat perairan dangkal dari citra multispasial di Perairan Pulau Kapota dan Pulau Kompoone, Kepulauan Wakatobi. Jurnal Ilmu dan Teknologi Kelautan Tropis, 12(3): 791-803. https://doi.org/10.29244/jitkt.v12i3.32013
Sugara, A., V.P. Siregar, & S.B. Agus. 2020. Klasifikasi habitat bentik perairan dangkal dari citra worldview-2 menggunakan data in-situ dan drone. Jurnal Ilmu dan Teknologi Kelautan Tropis, 12(1): 135-150. https://doi.org/10.29244/jitkt.v12i1.26448
Vahtmäe, E., T. Kutser, & B. Paavel. 2020. Performance and applicability of water column correction models in optically complex coastal waters. Remote Sensing, 12(11): 1861. https://doi.org/10.3390/rs12111861
Vapnik, V. 1982. Estimation of Dependences Based on Empirical Data [in Russian]. Nauka, Moscow. English translation, Springer Verlag, New York. 211-222.
Vidya, N.A., M.I. Fanany, & I. Budi. 2015. Twitter sentiment to analyze net brand reputation of mobile phone providers. Procedia Computer Science, 72: 519-526. https://doi.org/10.1016/j.procs.2015.12.159
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Authors
SetiawanA., SiregarV. P., SusiloS. B., MardiastutiA., & AgusS. B. (2023). K, The BENTHIC HABITAT CLASSIFICATION OF ATOL KELEDUPA WAKATOBI NATIONAL PARK USING SUPPORT VECTOR MACHINE ALGORITHM. Jurnal Ilmu Dan Teknologi Kelautan Tropis, 14(3), 427-438. https://doi.org/10.29244/jitkt.v14i3.35315
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