SEAGRASS ECOSYSTEM MAPPING WITH AND WITHOUT WATER COLUMN CORRECTION IN PAJENEKANG ISLAND WATERS, SOUTH SULAWESI
Article Sidebar
Accepted
12 March 2020
Published
27 April 2020
Keywords:
-
algorithm, OBIA classification, Pajenekeng Island, seagrass, water column
Abstract
Previous studies showed that water column correction in habitat benthic mapping using remote sensing data can increase the accuracy of the information produced. This study aims to look at the distribution of seagrasses with and without water column correction using object-based classification (OBIA) on the Pajanekang Island. Field data on the distribution of seagrass and non-seagrass of a total of 347 points were taken in July-August 2018 with a transect 1x1 m2. The satellite data used was SPOT-7 imagery acquired on March 27, 2017, with a spatial resolution of 6×6 m2. Within the OBIA classification method, we used several algorithms such as Support Vector Machine (SVM), Bayes, K-Nearest Neighbor (KNN), and Decision Tree (DT) to map benthic and seagrass habitats. The results showed that the treatment of with and without water column correction in mapping benthic and seagrass ecosystem habitats using several classification algorithms produced no significant difference in the accuracy of classification image product. However, from the four algorithms used, the Bayes algorithm without water column correction produced the highest accuracy value for benthic habitat mapping of 70.36% and seagrass habitat of 66.47%. The results showed that water column correction did not provide better results in the classification of benthic and seagrass habitats of digital satellite imagery than that of without water column correction.
References
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Putri, R.E., Suparti, & R. Rahmawati. 2014. Perbandingan metode klasifikasi Naibe Bayes dan K-Nearest Neighbour pada analisis data status kerja di Kabupaten Demak Tahun 2012. J. Gaussian, 3(4): 831-838.
Prabowo, N.W., V.P. Siregar, & S.B. Agus. 2018. Klasifikasi habitat bentik berbasis objek dengan algoritma support vector machines dan decision tree menggunakan citra multispectral SPOT-7 di Pulau Harapan dan Pulau Kelapa. J. Ilmu dan Teknologi Kelautan Tropis, 10(1): 123-134. http://doi.org/10.29244/jitkt.v10i1.21670
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Suwargana, N. 2014. Analisis citra ALOS AVNIR-2 untuk pemetaan terumbu karang (studi kasus: Banyuputih, Kab. Situbondo). Prosiding Deteksi Parameter Geobiofisik Dan Diseminasi Penginderaan Jauh Seminar Nasional Penginderaan Jauh 2014. 588–596 pp.
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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 alghorithms. Procedia Environmental Sciences, 24: 222-227. https://doi.org/10.1016/j.proenv.2015.03.029
Waycott, M., K. Mcmahon, J. Mellors, A. Calladine, & A.D. Kleine. 2004. A guide to tropical seagrasses of the Indo-West Pacific. James Cook University. Townsville. 72 p.
Wei, W., X. Chen, & A. Ma. 2005. Object-oriented information extraction and application in high-resolution remote sensing image. IEEE International Geoscience and Remote Sensing Symposium, 8: 3803-3807. https://doi.org/10.1109/IGARSS.2005.1525737
Whiteside, T.G., G.S. Boggs, & S.W. Maier. 2011. Comparing object-based and pixelbased classifications for mapping savannas. International J. of Applied Earth Observation and Geoinformation, 13(6): 884-893. https://doi.org/10.1016/j.jag.2011.06.008
Yang, D. & C. Yang. 2009. Detection of seagrass distribution changes from 1991 to 2006 in Xincun Bay, Hainan, with satellite remote sensing. Sensors, 9: 830-844. https://doi.org/10.3390/s90200830
Zhang, C. 2015. Applying data fusion techniques for benthic habitat mapping. ISPRS J. of Photogrammetry and Remote Sensing, 104: 213-223. https://doi.org/10.1016/j.isprs jprs.2014.06.005
Zoffoli, M.L., R. Frouin, & M. Kampel. 2014. Water Column for Coral Reef Studies by Remote Sensing. Sensors J., 14(9): 16881–16931. https://doi.org/10.3390/s140916881
Aziizah, N.N., V.P. Siregar, & S.B. Agus. 2016. Penerapan algoritma spectral angle mapper (SAM) untuk klasifikasi lamun menggunakan citra satelit Worldview-2. J. Penginderaan Jauh, 13(2): 61-72. http://doi.org/10.30536/j.pjpdcd.2016.v13.a2205
Bradski, G. & A. Kaehler. 2008. Learning opencv: Computer vision with the opencv library. O’Reilly Media, Inc. Sebastopol, CA, USA. 555 p.
Budhiman, S., G. Winarso, & W. Asriningrum. 2013. Pengaruh pengambilan training sample substrat dasar berbeda pada koreksi kolom air menggunakan data penginderaan jauh. J. Penginderaan Jauh, 10(2): 83-92.
Bukata, R.P., J.H. Jerome, A.S. Kondratyev, & D.V. Pozdnyakov. 2018. Optical properties and remote sensing of inland and coastal waters. CRC Press. Boca Raton. 384 p.
Congalton, R.G. & K. Green 2009. Assessing the accuracy of remotely sensed data principles and practices (Second Edition). CRC Taylor & Francis Group. France. 183 p.
COREMAP CTI. 2007. Lamun. http://www.coremap.or.id/datin/seagrass. [diunduh 23 Februari 2019].
Dekker, A.G., V.E. Brando, & J.M. Anstee. 2005. Retrospective seagrass change detection in a shallow coastal tidal Australian lake. Remote Sensing of Environment, 97: 415-433. https://doi.org/10.1016/j.rse.2005.02.017
Fukunaga, K. 1990. Introduction to statistical pattern recognition (2nd ed.). Academic Press. New York. 591 p.
Green, E., A.J. Edwards, & C. Clark. 2000. Remote sensing handbook for tropical coastal management. Unesco Pub. Paris (FR). 316 p.
Hafitz, M. & P. Danoedoro. 2015. Kajian pengaruh koreksi kolom air pada citra multispektral Worldview-2 untuk pemetaan habitat bentik di Pulau Kemujan Kepulauan Karimunjawa Kabupaten Jepara. Jogjakarta. Prosiding Pertemuan Ilmiah Tahunan XX 2015, Jogjakarta, Februari 2015. 566-575 pp.
Han, J. & M. Kamber. 2006. Data Mining Concepts and Techniques, second edition. Morgan Kaufman. California. 135 p.
Hedley, J., C. Roelfsema, & S.R. Phinn, 2009. Efficient radiative transfer model in- version for remote sensing applications. Remote Sensing of Environment, 113: 2527-2532. https://doi.org/10.1016/j.rse.2009.07.008
Hossain, M.S., J.S. Bujang, M.H. Zakaria, & M. Hashim. 2015. Application of Landsat images to seagrass areal cover change analysis for Lawas, Terengganu and Kelantan of Malaysia. Continental Shel Research, 110: 124-148. https://doi.org/10.1016/j.csr.2015.10.009
Kayabol, K. & S. Kutluk. 2016. Bayesian classification of hyperspectral image using spatially-varying Gaussian mixture model. Digital Signal Processing, 59: 106-114. https://doi.org/10.1016/j.dsp.2016.08.010
Kushardono, D. 2017. Klasifikasi digital pada penginderaan jauh. IPB Press. Bogor. 76 p.
Lyzenga, D.R. 1981. Remote sensing of bottom reflectance and water attenuation parameters in shallow water using aircraft and landsat data. International J. Remote Sensing, 2: 171-172. https://doi.org/10.1080/01431168108948342
Macreadie, P.I., M.E. Baird, S.M. Trevathan, A.W.D. Larkum, & P.J. Ralph, 2014. Quantifying and modelling the carbon sequestration capacity of seagrass meadows – a critical assessment. Mar. Pollut. Bull., 83: 430–439. https://doi.org/10.1016/j.marpolbul.2013.07.038
Mastu, L.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. http://doi.org/10.29244/jitkt.v10i2.21039
McKenzie, L.J., S.J. Campbell, & C.A. Roder. 2003. Seagrass-watch: manual for mapping & monitoring seagrass resources by community (citizen) volunteers. 2nd Edition. (QFS, NFC, Cairns). 100 p.
Phinn, S.R., C.M. Roelfsema, & P.J. Mumby. 2011. Multi scale object based image analysis for mapping geomorphic and ecological zones on coral reefs. International J. Remote Sensing, 33: 3768-3797. https://doi.org/10.1080/01431161.2011.633122
Putri, R.E., Suparti, & R. Rahmawati. 2014. Perbandingan metode klasifikasi Naibe Bayes dan K-Nearest Neighbour pada analisis data status kerja di Kabupaten Demak Tahun 2012. J. Gaussian, 3(4): 831-838.
Prabowo, N.W., V.P. Siregar, & S.B. Agus. 2018. Klasifikasi habitat bentik berbasis objek dengan algoritma support vector machines dan decision tree menggunakan citra multispectral SPOT-7 di Pulau Harapan dan Pulau Kelapa. J. Ilmu dan Teknologi Kelautan Tropis, 10(1): 123-134. http://doi.org/10.29244/jitkt.v10i1.21670
Roelfsema, C., S. Phinn, S. Jupiter, J. Comley, & S. Albert. 2013. Mapping coral reefs at reef to reef-system scales, 10s-1000s km2, using object-based image analysis. International J. Remote Sensing, 34(18): 6367-6388. https://doi.org/10.1080/01431161.2013.800660
Romimohtarto, K. & S. Juwana. 2001. Biologi Laut: Ilmu Pengetahuan Tentang Biota Laut. Djambatan. Jakarta. 540 p.
Sartika, D. & D.I. Sensuse. 2017. Perbandingan algoritma klasifikasi naive bayes, nearest neighbour, dan decision tree pada studi kasus pengambilan keputusan pemilihan. Jatisi, 1(2): 151-161. https://doi.org/10.35957/jatisi.v3i2.78
Setiawan, K.T., M.D.M. Manessa, G. Winarso, N. Anggraini, G. Giarrastowo, W. Asriningrum, Herianto, S. Rosid, & A.H. Supardjo. 2019. Estimasi batimetri dari data spot 7 studi kasus perairan Gili Matra Nusa Tenggara Barat. J. Penginderaan Jauh, 15(2): 69-82. http://doi.org/10.30536/j.pjpdcd.2018.v15.a3008
Sjafrie, N.D.M., U.E. Hernawan, B. Prayudha, I.H. Supriyadi, M.Y. Iswari, Rahmat, K. Anggraini, S. Rahmawati, & Suyarso. 2018. Status padang lamun indonesia Ver. 02. LIPI. Jakarta. 40 p.
Suwargana, N. 2014. Analisis citra ALOS AVNIR-2 untuk pemetaan terumbu karang (studi kasus: Banyuputih, Kab. Situbondo). Prosiding Deteksi Parameter Geobiofisik Dan Diseminasi Penginderaan Jauh Seminar Nasional Penginderaan Jauh 2014. 588–596 pp.
Thalib, M.S., N. Nurdin, & A. Aris. 2018. The ability of lyzenga’s algorithm for seagrass mapping using sentinel-2a imagery on Small Island, Spermonde Archipelago, Indonesia. Proceeding of IOP Conference Series: Earth and Environmental Science, 165(1): 012028. https://doi.org/10.1088/1755-1315/165/1/012028
Traganos, D., B. Anggarwal, D. Poursanidis, K. Topouzelis, N. Chrysoulakis, & P. Reinartz. 2018. Towards global-scale seagrass mapping and monitoring using sentinel-2 on google earth engine: the case of the Aegean and Ionian Seas. MDPI J., 10(8): 1227. https://doi.org/10.3390/rs10081227
Trimble. 2014. Ecognition developer: user guide. Trimble Germany GmbH. Munchen, Germany. 262 p.
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 alghorithms. Procedia Environmental Sciences, 24: 222-227. https://doi.org/10.1016/j.proenv.2015.03.029
Waycott, M., K. Mcmahon, J. Mellors, A. Calladine, & A.D. Kleine. 2004. A guide to tropical seagrasses of the Indo-West Pacific. James Cook University. Townsville. 72 p.
Wei, W., X. Chen, & A. Ma. 2005. Object-oriented information extraction and application in high-resolution remote sensing image. IEEE International Geoscience and Remote Sensing Symposium, 8: 3803-3807. https://doi.org/10.1109/IGARSS.2005.1525737
Whiteside, T.G., G.S. Boggs, & S.W. Maier. 2011. Comparing object-based and pixelbased classifications for mapping savannas. International J. of Applied Earth Observation and Geoinformation, 13(6): 884-893. https://doi.org/10.1016/j.jag.2011.06.008
Yang, D. & C. Yang. 2009. Detection of seagrass distribution changes from 1991 to 2006 in Xincun Bay, Hainan, with satellite remote sensing. Sensors, 9: 830-844. https://doi.org/10.3390/s90200830
Zhang, C. 2015. Applying data fusion techniques for benthic habitat mapping. ISPRS J. of Photogrammetry and Remote Sensing, 104: 213-223. https://doi.org/10.1016/j.isprs jprs.2014.06.005
Zoffoli, M.L., R. Frouin, & M. Kampel. 2014. Water Column for Coral Reef Studies by Remote Sensing. Sensors J., 14(9): 16881–16931. https://doi.org/10.3390/s140916881
Authors
IlyasT. P., Bisman Nababan, Hawis Madduppa, & Dony Kushardono. (2020). SEAGRASS ECOSYSTEM MAPPING WITH AND WITHOUT WATER COLUMN CORRECTION IN PAJENEKANG ISLAND WATERS, SOUTH SULAWESI. Jurnal Ilmu Dan Teknologi Kelautan Tropis, 12(1), 9-23. https://doi.org/10.29244/jitkt.v12i1.26598
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