STUDY OF MANGROVE COVERAGE CHANGE BASED ON OBJECT (OBIA) USING SATELLITE IMAGERY IN DOMPAK ISLAND PROVINCE OF KEPULAUAN RIAU
Article Sidebar
Accepted
10 March 2021
Published
30 April 2021
Keywords:
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classification, Dompak Island, mangrove, OBIA, sentinel 2B, SPOT 4
Abstract
The threats on mangrove forest, either naturally such as climate change or human activities such as landfill, land-use change, and deforestation, can increase the vulnerability of this ecosystem itself. Remote sensing is an effective method to use as mangrove monitoring activity because it can be done periodically and can reach a large area. This research aims to analyse mangrove coverage changes in Dompak Island, Kepulauan Riau Province. The method that was used is satellite imagery classification based on object (OBIA) with support vector machine (SVM) algorithm. Satellite imagery data that was used are SPOT 4 in 2007 and Sentinel 2B in 2018 with spatial resolution of 10 x 10 m. Ground check was conducted on September-October 2018 using random sampling method. The classification results of OBIA with SVM algorithm showed 89% accuracy level, 0.86 kappa values with optimum segmentation value of 3. Based on coverage land analysis, there was degradation of 34.19% mangrove area, or about 46.61 ha, since 2007 to 2018.
References
Abd-El Monsef, H. & S.E. Smith. 2017. A new approach for estimating mangrove canopy cover using Landsat 8 imagery. Comput. Electron. Agr., 135: 183–194. https://doi.org/10.1016/j.compag.2017.02.007
Alongi, D.M. 2002. Present state and future of the world’s mangrove forests. Environ. Conserv., 29(3): 331-349. https://doi.org/10.1017/S0376892902000231
Ahmad, S. 2009. Recreational values of mangrove forest in Larut Matang, Perak. J. Trop. For. Sci., 21(2): 81-87. https://www.jstor.org/stable/23616638
Alimudi, S., S.B. Susilo, & J.P. Panjaitan. 2017. Deteksi perubahan luasan mangrove menggunakan citra Landsat berdasarkan metode OBIA di Teluk Valentine Pulau Buano Seram bagian barat. J. Teknol. Perikanan Kelautan, 8(1): 139-146. https://doi.org/10.24319/jtpk.8.139-146
Barati, S., B. Rayegani, M. Saati, A. Sharifi, & M. Nasri. 2011. Comparison the accuracies of different spectral indices for estimation of vegetation cover fraction in sparse vegetated areas. Egyp. J. Remote Sens. Space Sci., 14: 49–56. https://doi.org/10.1016/j.ejrs.2011.06.001
Blaschke, T. 2010. Object based image analysis for remote sensing. ISPRS. J. Photogramm, 65: 2-16. https://doi.org/10.1016/j.isprsjprs.2009.06.004
Blaschke, T., G.J. Hay, M. Kelly, S. Lang, P. Hofmann, E. Addink, R.Q. Feitosa, F. van der Meer, H. van der Werff, F. van Coillie, et al. 2014. Geographic object-based image analysis-towards a new paradigm. ISPRS. J. Photogramm, 87: 180-191. https://doi.org/10.1016/j.isprsjprs.2013.09.014
Cannicci, S., D. Burrows, S. Fratini, T.J. Smith III, J. Offenberg, & F. Dahdouh-Guebas. 2008. Faunal impact on vegetation structure and ecosystem function in mangrove forests: A review. Aquat. Bot., 89: 186-200. https://doi.org/10.1016/j.aquabot.2008.01.009
Cao, J., W. Leng, K. Liu K, L. Liu, Z. He, & Y. Zhu. 2018. Object-Based mangrove species classification using unmanned aerial vehicle hyperspectral images and digital surface models. Remote Sens, 10(89): 1–20. https://doi.org/10.3390/rs10010089
Carrasquilla-Henao, M., H.A.G. Ocampo, A.L. Gonzalez, & G.R. Quiroz. 2013. Mangrove forest and artisanal fishery in the southern part of the Gulf of California, Mexico. Ocean Coast. Manage, 83: 75-80. https://doi.org/10.1016/j.ocecoaman.2013.02.019
Conchedda, G., D. Lauren, & M. Phileppe. 2008. An object-based method for mapping and change analysis in mangrove ecosystem. ISPRS. J. Photogramm, 63: 578-589. https://doi.org/10.1016/j.isprsjprs.2008.04.002
Chianucci, F., U. Chiavetta, & A. Cutini. 2014. The estimation of canopy attributes from digital cover photography by two different image analysis methods. iForest, 7: 255–259. https://doi.org/10.3832/ifor0939-007
Congalton, R.G. & K. Green. 2009. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. 2nd ed. CRC Press. Boca Raton. 183 p.
Datta, D., R.N. Chattopadhyay, & P. Guha. 2012. Community based mangrove management: A review on status and sustainability. J. Environ. Manage., 107: 84-95. https://doi.org/10.1016/j.jenvman.2012.04.013
Danoedoro, P. 2012. Pengantar Penginderaan Jauh Digital. Andi Offset. Yogyakarta. 398 hlm.
Donato, D.C., J.B. Kauffman, D. Murdiyarso, S. Kurnianto, M. Stidham, & M. Kanninen. 2011. Mangroves among the most carbon-rich forests in the tropics. Nat. Geosci., 4: 293-297. https://doi.org/10.1038/ngeo1123
Dwiputra, A.J., R. Suharyadi, & P. Danoedoro. 2016. Pengaruh jumlah kelas dan skema klasifikasi terhadap akurasi informasi penggunaan lahan hasil klasifkasi berbasis objek dengan teknik support vector machine di sebagian Kabupaten Kebumen Provinsi Jawa Tengah. Majalah Geografi Indonesia, 30(2): 120–133. https://doi.org/10.22146/mgi.15632
Fajri, P. Subardjo, & R. Pribadi. 2012. Studi perubahan luasan vegetasi mangrove mengunakan citra Landsat TM dan Landsat 7 ETM+ tahun 1998–2010 di pesisir kabupaten Mimika Papua. J. Mar. Res., 1(1): 146-145. https://doi.org/10.14710/jmr.v1i1.2002
Food and Agriculture Organization (FAO). 2007. The World's Mangrove 1980-2005. FAO. Rome. FAO. 89 p.
Field, C.D. 1995. Impact of expected climate change on mangroves. In: Wong, Y.S. & N.F.Y. Tam. (ed.). Asia Pacific Symposium on Mangrove Ecosystems. Springer. Kowloon, 75-81 pp. https://doi.org/10.1007/978-94-011-0289-6_10
Firmansyah, S., J.L. Gaol, & S.B. Susilo. 2019. Perbandingan klasifikasi SVM dan Decision Tree untuk pemetaan mangrove berbasis objek menggunakan citra satelit Sentinel-2B di Gili Sulat, Lombok Timur. J. Pengelolaan Sumber Daya Alam dan Lingkungan, 9(3): 746-757. https://doi.org/10.29244/jpsl.9.3.746-757
Gilman, E.L., J. Ellison, V. Jungblut, H. van Lavieren, L. Wilson, F. Areki, G. Brighouse, J. Bungitak, E. Dus, M. Henry, et al. 2006. Adapting to Pacific Island mangrove responses to sea level rise and climate change. Clim. Res., 32: 161-176. https://doi:10.3354/cr032161
Giri, C., B. Pengra, Z. Zhu, A. Singh, & L.L. Tieszen. 2007. Monitoring mangrove forest dynamics of the Sundarbans in Bangladesh and India using multi-temporal satellite data from 1973 to 2000. Estuar. Coast. Shelf Sci., 73: 91-100. https://doi.org/10.1016/j.ecss.2006.12.019
Giri, C., E. Ochieng, L.L. Tieszen, Z. Zhu, A. Singh, T. Loveland, J. Masek, & N. Duke. 2011. Status and distribution of mangrove forests of the world using earth observation satellite data. Global Ecol. Biogeogr., 20: 154-159. https://doi.org/10.1111/j.1466-8238.2010.00584.x
Hastiana, Y., F. Sjarkowi, D.D.A. Putranto, & R. Ridho. 2010. Analisis perubahan kawasan mangrove berdasarkan interpretasi data spasial di Tn. Sembilang, Pantai Timur Sumatera, Banyuasin, Sumsel. Eksakta, 11(2): 53-62. https://journal.uii.ac.id/Eksakta/article/view/2392
Hendrawan, J.L. Gaol, & S.B. Susilo. 2018. Studi kerapatan dan perubahan tutupan mangrove menggunakan citra satelit di Pulau Sebatik Kalimantan Utara. J. Ilmu dan Teknologi Kelautan Tropis, 10(1): 99–110. https://doi.org/10.29244/jitkt.v10i1.18595
Heumann, B.W. 2011. Satellite remote sensing of mangrove forests: recent advances and future opportunities. Progress in Physical Geography, 35(1): 87-108. https://doi.org/10.1177/0309133310385371
Jhonnerie, R., V.P. Siregar, B. Nababan, L.B. Prasetyo, & S. Wouthuyzen. 2014. Deteksi perubahan tutupan mangrove menggunakan citra Landsat berdasarkan klasifikasi hibrida di Sungai Kembung Pulau Bengkalis Provinsi Riau. J. Ilmu dan Teknologi Kelautan Tropis, 6(2): 491-506. https://doi.org/10.29244/jitkt.v6i2
Karyono, A., R. Pribadi, & M. Helmi. 2013. Analisis perubahan luas mangrove berdasarkan citra satelit IKONOS tahun 2004 dan 2010 di kecamatan Mlonggo, Tahunan dan Kedung Kabupaten Jepara Jawa Tengah. J. Mar. Res., 2(3): 129-137. https://doi.org/10.14710/jmr.v2i3.3141
Kuenzer, C., A. Bluemel, S. Gebhardt, T. vo Quoc, & S. Dech. 2011. Remote sensing of mangrove ecosystems: A review. Remote Sens., 3: 878-928. https://doi.org/10.3390/rs3050878
Lembaga Ilmu Pengetahuan Indonesia (LIPI). 2014. Panduan monitoring status ekosistem mangrove. PT. Sarana Komunikasi Utama. Jakarta. 35 hlm.
Liu, D. & F. Xia. 2010. Assessing object-based classification: advantages and limitations. Remote Sens. Lett, 1(4): 187–194. https://doi.org/10.1080/01431161003743173
Madanguit, C.J.G., J.P.L. Onez, H.G. Tan, M.D. Villanueva, J.E. Ordaneza, R.M. Aurelio Jr., & 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. Int. J. Appl. Environ. Sci., 12(10): 1821–1830. https://www.ripublication.com/ijaes17/ijaesv12n10_06.pdf
Mallinis, G., I. Mitsopoulos, & I. Chrysafi. 2018. Evaluating and comparing Sentinel 2A and Landsat-8 Operational Land Imager (OLI) spectral indices for estimating fire severity in a Mediterranean pine ecosystem of Greece. GISci. Remote Sens., 55(1): 1–18. https://doi.org/10.1080/15481603.2017.1354803
Mazda, Y., M. Magi, H. Nanao, M. Kogo, T. Miyagi, N. Kanazawa, & D. Kobashi. 2002. Coastal erosion due to long-term human impact on mangrove forest. Wetl. Ecol. Manag., 10: 1-9. https://doi.org/10.1023/A:1014343017416
Myint, S.W., P. Gober, A. Brazel, S. Grossman-Clarke, & Q. Weng. 2011. Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery. Remote Sens. Environ., 115: 1145–1161. https://doi.org/10.1016/j.rse.2010.12.017
Nagelkerken, I., S.J.M. Blaber, S. Bouillon, P. Green, M. Haywood, L.G. Kirton, J.O. Meynecke, J. Pawlik, H.M. Penrose, A. Sasekumar, et al. 2008. The habitat function of mangroves for terrestrial and marine fauna: A review. Aquat. Bot., 89: 155-185. https://doi.org/10.1016/j.aquabot.2007.12.007
Noor, Y.R., M. Khazali, & I.N.N. Suryadiputra. 2012. Panduan pengenalan mangrove di Indonesia. PHKA/WI-IP. Bogor. 220 hlm.
Nugroho, A.S., A.B. Witarto, & D. Handoko. 2003. Application of support vector machine in bioinformatics. In: Proceeding Indonesia Scientific Meeting, Central Japan, Gifu, 1–11 pp. https://asnugroho.net/papers/ikcsvm.pdf
Ostling, J.L., D.R. Butler, & R.W. Dixon. 2009. The biogeomorphology of mangroves and their role in natural hazards mitigation. Geogr. Compass, 3(5): 1607-1624. https://doi.org/10.1111/j.1749-8198.2009.00265.x
Pujiono, E., D.A. Kwak, W.K. Lee, Sulistyanto, S.R. Kim, J.Y. Lee, S.H. Lee, T. Park, & M.I. Kim. 2013. RGB-NDVI color composites for monitoring the change in mangrove area at the Maubesi Nature Reserve, Indonesia. Forest Sci. Technol., 9(4): 171–179. https://doi.org/10.1080/21580103.2013.842327
Rosmasita, V.P. Siregar & S.B. Agus. 2018. Klasifikasi mangrove berbasis objek dan piksel menggunakan citra Sentinel-2B di Sungai Liong, Bengkalis, Provinsi Riau. J. Ilmu dan Teknologi Kelautan Tropis, 10(3): 601-615. https://doi.org/10.29244/jitkt.v10i3.22182
Sarkar, S.K. & A.K. Bhattacharya. 2003. Conservation of biodiversity of the coastal resources of Sundarbans, Northeast India: an integrated approach through environmental education. Mar. Pollut. Bul., 47: 260-264. https://doi.org/10.1016/S0025-326X(02)00475-7
Shafri, H.Z.M. & F.S.H. Ramie. 2009. A comparison of support vector machine and decision tree classifications using satellite data of Langkawi Island. Inf. Technol. J., 8(1): 64–70. https://doi.org/10.3923/itj.2009.64.70
Stow, D., Y. Hamada, L. Coulter, & Z. Anguelova. 2008. Monitoring shrubland habitat changes through object-based change identification with airborne multi-spectral imagery. Remote Sens. Environ., 112: 1051-1061. https://doi.org/10.1016/j.rse.2007.07.011
Thampanya, U., J.E. Vermaat, S. Sinsakul, & N. Panapitukkul. 2006. Coastal erosion and mangrove progradation of Southern Thailand. Estuar. Coast. Shelf Sci., 86(1): 75-85. https://doi.org/10.1016/j.ecss.2006.01.011
Tzotzos, A. 2006. A support vector machine approach for object based image analysis. 2006. In: 1st International Conference on Object-based Image Analysis, OBIA, Salzburg, Austria, 4-5 Juli. 6 p. https://www.isprs.org/proceedings/xxxvi/4-C42/Papers/09_Automated%20classification%20Generic%20aspects/OBIA2006_Tzotsos.pdf
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 Environmental Sciences, 24: 222-227. https://doi.org/10.1016/j.proenv.2015.03.029
Zhu, G. & D.G. Blumberg. 2002. Classification using ASTER data and SVM algorithms; The case study of Beer Sheva, Israel. Remote Sens. Environ., 80: 233–240. https://doi.org/10.1016/S0034-4257(01)00305-4
Alongi, D.M. 2002. Present state and future of the world’s mangrove forests. Environ. Conserv., 29(3): 331-349. https://doi.org/10.1017/S0376892902000231
Ahmad, S. 2009. Recreational values of mangrove forest in Larut Matang, Perak. J. Trop. For. Sci., 21(2): 81-87. https://www.jstor.org/stable/23616638
Alimudi, S., S.B. Susilo, & J.P. Panjaitan. 2017. Deteksi perubahan luasan mangrove menggunakan citra Landsat berdasarkan metode OBIA di Teluk Valentine Pulau Buano Seram bagian barat. J. Teknol. Perikanan Kelautan, 8(1): 139-146. https://doi.org/10.24319/jtpk.8.139-146
Barati, S., B. Rayegani, M. Saati, A. Sharifi, & M. Nasri. 2011. Comparison the accuracies of different spectral indices for estimation of vegetation cover fraction in sparse vegetated areas. Egyp. J. Remote Sens. Space Sci., 14: 49–56. https://doi.org/10.1016/j.ejrs.2011.06.001
Blaschke, T. 2010. Object based image analysis for remote sensing. ISPRS. J. Photogramm, 65: 2-16. https://doi.org/10.1016/j.isprsjprs.2009.06.004
Blaschke, T., G.J. Hay, M. Kelly, S. Lang, P. Hofmann, E. Addink, R.Q. Feitosa, F. van der Meer, H. van der Werff, F. van Coillie, et al. 2014. Geographic object-based image analysis-towards a new paradigm. ISPRS. J. Photogramm, 87: 180-191. https://doi.org/10.1016/j.isprsjprs.2013.09.014
Cannicci, S., D. Burrows, S. Fratini, T.J. Smith III, J. Offenberg, & F. Dahdouh-Guebas. 2008. Faunal impact on vegetation structure and ecosystem function in mangrove forests: A review. Aquat. Bot., 89: 186-200. https://doi.org/10.1016/j.aquabot.2008.01.009
Cao, J., W. Leng, K. Liu K, L. Liu, Z. He, & Y. Zhu. 2018. Object-Based mangrove species classification using unmanned aerial vehicle hyperspectral images and digital surface models. Remote Sens, 10(89): 1–20. https://doi.org/10.3390/rs10010089
Carrasquilla-Henao, M., H.A.G. Ocampo, A.L. Gonzalez, & G.R. Quiroz. 2013. Mangrove forest and artisanal fishery in the southern part of the Gulf of California, Mexico. Ocean Coast. Manage, 83: 75-80. https://doi.org/10.1016/j.ocecoaman.2013.02.019
Conchedda, G., D. Lauren, & M. Phileppe. 2008. An object-based method for mapping and change analysis in mangrove ecosystem. ISPRS. J. Photogramm, 63: 578-589. https://doi.org/10.1016/j.isprsjprs.2008.04.002
Chianucci, F., U. Chiavetta, & A. Cutini. 2014. The estimation of canopy attributes from digital cover photography by two different image analysis methods. iForest, 7: 255–259. https://doi.org/10.3832/ifor0939-007
Congalton, R.G. & K. Green. 2009. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. 2nd ed. CRC Press. Boca Raton. 183 p.
Datta, D., R.N. Chattopadhyay, & P. Guha. 2012. Community based mangrove management: A review on status and sustainability. J. Environ. Manage., 107: 84-95. https://doi.org/10.1016/j.jenvman.2012.04.013
Danoedoro, P. 2012. Pengantar Penginderaan Jauh Digital. Andi Offset. Yogyakarta. 398 hlm.
Donato, D.C., J.B. Kauffman, D. Murdiyarso, S. Kurnianto, M. Stidham, & M. Kanninen. 2011. Mangroves among the most carbon-rich forests in the tropics. Nat. Geosci., 4: 293-297. https://doi.org/10.1038/ngeo1123
Dwiputra, A.J., R. Suharyadi, & P. Danoedoro. 2016. Pengaruh jumlah kelas dan skema klasifikasi terhadap akurasi informasi penggunaan lahan hasil klasifkasi berbasis objek dengan teknik support vector machine di sebagian Kabupaten Kebumen Provinsi Jawa Tengah. Majalah Geografi Indonesia, 30(2): 120–133. https://doi.org/10.22146/mgi.15632
Fajri, P. Subardjo, & R. Pribadi. 2012. Studi perubahan luasan vegetasi mangrove mengunakan citra Landsat TM dan Landsat 7 ETM+ tahun 1998–2010 di pesisir kabupaten Mimika Papua. J. Mar. Res., 1(1): 146-145. https://doi.org/10.14710/jmr.v1i1.2002
Food and Agriculture Organization (FAO). 2007. The World's Mangrove 1980-2005. FAO. Rome. FAO. 89 p.
Field, C.D. 1995. Impact of expected climate change on mangroves. In: Wong, Y.S. & N.F.Y. Tam. (ed.). Asia Pacific Symposium on Mangrove Ecosystems. Springer. Kowloon, 75-81 pp. https://doi.org/10.1007/978-94-011-0289-6_10
Firmansyah, S., J.L. Gaol, & S.B. Susilo. 2019. Perbandingan klasifikasi SVM dan Decision Tree untuk pemetaan mangrove berbasis objek menggunakan citra satelit Sentinel-2B di Gili Sulat, Lombok Timur. J. Pengelolaan Sumber Daya Alam dan Lingkungan, 9(3): 746-757. https://doi.org/10.29244/jpsl.9.3.746-757
Gilman, E.L., J. Ellison, V. Jungblut, H. van Lavieren, L. Wilson, F. Areki, G. Brighouse, J. Bungitak, E. Dus, M. Henry, et al. 2006. Adapting to Pacific Island mangrove responses to sea level rise and climate change. Clim. Res., 32: 161-176. https://doi:10.3354/cr032161
Giri, C., B. Pengra, Z. Zhu, A. Singh, & L.L. Tieszen. 2007. Monitoring mangrove forest dynamics of the Sundarbans in Bangladesh and India using multi-temporal satellite data from 1973 to 2000. Estuar. Coast. Shelf Sci., 73: 91-100. https://doi.org/10.1016/j.ecss.2006.12.019
Giri, C., E. Ochieng, L.L. Tieszen, Z. Zhu, A. Singh, T. Loveland, J. Masek, & N. Duke. 2011. Status and distribution of mangrove forests of the world using earth observation satellite data. Global Ecol. Biogeogr., 20: 154-159. https://doi.org/10.1111/j.1466-8238.2010.00584.x
Hastiana, Y., F. Sjarkowi, D.D.A. Putranto, & R. Ridho. 2010. Analisis perubahan kawasan mangrove berdasarkan interpretasi data spasial di Tn. Sembilang, Pantai Timur Sumatera, Banyuasin, Sumsel. Eksakta, 11(2): 53-62. https://journal.uii.ac.id/Eksakta/article/view/2392
Hendrawan, J.L. Gaol, & S.B. Susilo. 2018. Studi kerapatan dan perubahan tutupan mangrove menggunakan citra satelit di Pulau Sebatik Kalimantan Utara. J. Ilmu dan Teknologi Kelautan Tropis, 10(1): 99–110. https://doi.org/10.29244/jitkt.v10i1.18595
Heumann, B.W. 2011. Satellite remote sensing of mangrove forests: recent advances and future opportunities. Progress in Physical Geography, 35(1): 87-108. https://doi.org/10.1177/0309133310385371
Jhonnerie, R., V.P. Siregar, B. Nababan, L.B. Prasetyo, & S. Wouthuyzen. 2014. Deteksi perubahan tutupan mangrove menggunakan citra Landsat berdasarkan klasifikasi hibrida di Sungai Kembung Pulau Bengkalis Provinsi Riau. J. Ilmu dan Teknologi Kelautan Tropis, 6(2): 491-506. https://doi.org/10.29244/jitkt.v6i2
Karyono, A., R. Pribadi, & M. Helmi. 2013. Analisis perubahan luas mangrove berdasarkan citra satelit IKONOS tahun 2004 dan 2010 di kecamatan Mlonggo, Tahunan dan Kedung Kabupaten Jepara Jawa Tengah. J. Mar. Res., 2(3): 129-137. https://doi.org/10.14710/jmr.v2i3.3141
Kuenzer, C., A. Bluemel, S. Gebhardt, T. vo Quoc, & S. Dech. 2011. Remote sensing of mangrove ecosystems: A review. Remote Sens., 3: 878-928. https://doi.org/10.3390/rs3050878
Lembaga Ilmu Pengetahuan Indonesia (LIPI). 2014. Panduan monitoring status ekosistem mangrove. PT. Sarana Komunikasi Utama. Jakarta. 35 hlm.
Liu, D. & F. Xia. 2010. Assessing object-based classification: advantages and limitations. Remote Sens. Lett, 1(4): 187–194. https://doi.org/10.1080/01431161003743173
Madanguit, C.J.G., J.P.L. Onez, H.G. Tan, M.D. Villanueva, J.E. Ordaneza, R.M. Aurelio Jr., & 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. Int. J. Appl. Environ. Sci., 12(10): 1821–1830. https://www.ripublication.com/ijaes17/ijaesv12n10_06.pdf
Mallinis, G., I. Mitsopoulos, & I. Chrysafi. 2018. Evaluating and comparing Sentinel 2A and Landsat-8 Operational Land Imager (OLI) spectral indices for estimating fire severity in a Mediterranean pine ecosystem of Greece. GISci. Remote Sens., 55(1): 1–18. https://doi.org/10.1080/15481603.2017.1354803
Mazda, Y., M. Magi, H. Nanao, M. Kogo, T. Miyagi, N. Kanazawa, & D. Kobashi. 2002. Coastal erosion due to long-term human impact on mangrove forest. Wetl. Ecol. Manag., 10: 1-9. https://doi.org/10.1023/A:1014343017416
Myint, S.W., P. Gober, A. Brazel, S. Grossman-Clarke, & Q. Weng. 2011. Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery. Remote Sens. Environ., 115: 1145–1161. https://doi.org/10.1016/j.rse.2010.12.017
Nagelkerken, I., S.J.M. Blaber, S. Bouillon, P. Green, M. Haywood, L.G. Kirton, J.O. Meynecke, J. Pawlik, H.M. Penrose, A. Sasekumar, et al. 2008. The habitat function of mangroves for terrestrial and marine fauna: A review. Aquat. Bot., 89: 155-185. https://doi.org/10.1016/j.aquabot.2007.12.007
Noor, Y.R., M. Khazali, & I.N.N. Suryadiputra. 2012. Panduan pengenalan mangrove di Indonesia. PHKA/WI-IP. Bogor. 220 hlm.
Nugroho, A.S., A.B. Witarto, & D. Handoko. 2003. Application of support vector machine in bioinformatics. In: Proceeding Indonesia Scientific Meeting, Central Japan, Gifu, 1–11 pp. https://asnugroho.net/papers/ikcsvm.pdf
Ostling, J.L., D.R. Butler, & R.W. Dixon. 2009. The biogeomorphology of mangroves and their role in natural hazards mitigation. Geogr. Compass, 3(5): 1607-1624. https://doi.org/10.1111/j.1749-8198.2009.00265.x
Pujiono, E., D.A. Kwak, W.K. Lee, Sulistyanto, S.R. Kim, J.Y. Lee, S.H. Lee, T. Park, & M.I. Kim. 2013. RGB-NDVI color composites for monitoring the change in mangrove area at the Maubesi Nature Reserve, Indonesia. Forest Sci. Technol., 9(4): 171–179. https://doi.org/10.1080/21580103.2013.842327
Rosmasita, V.P. Siregar & S.B. Agus. 2018. Klasifikasi mangrove berbasis objek dan piksel menggunakan citra Sentinel-2B di Sungai Liong, Bengkalis, Provinsi Riau. J. Ilmu dan Teknologi Kelautan Tropis, 10(3): 601-615. https://doi.org/10.29244/jitkt.v10i3.22182
Sarkar, S.K. & A.K. Bhattacharya. 2003. Conservation of biodiversity of the coastal resources of Sundarbans, Northeast India: an integrated approach through environmental education. Mar. Pollut. Bul., 47: 260-264. https://doi.org/10.1016/S0025-326X(02)00475-7
Shafri, H.Z.M. & F.S.H. Ramie. 2009. A comparison of support vector machine and decision tree classifications using satellite data of Langkawi Island. Inf. Technol. J., 8(1): 64–70. https://doi.org/10.3923/itj.2009.64.70
Stow, D., Y. Hamada, L. Coulter, & Z. Anguelova. 2008. Monitoring shrubland habitat changes through object-based change identification with airborne multi-spectral imagery. Remote Sens. Environ., 112: 1051-1061. https://doi.org/10.1016/j.rse.2007.07.011
Thampanya, U., J.E. Vermaat, S. Sinsakul, & N. Panapitukkul. 2006. Coastal erosion and mangrove progradation of Southern Thailand. Estuar. Coast. Shelf Sci., 86(1): 75-85. https://doi.org/10.1016/j.ecss.2006.01.011
Tzotzos, A. 2006. A support vector machine approach for object based image analysis. 2006. In: 1st International Conference on Object-based Image Analysis, OBIA, Salzburg, Austria, 4-5 Juli. 6 p. https://www.isprs.org/proceedings/xxxvi/4-C42/Papers/09_Automated%20classification%20Generic%20aspects/OBIA2006_Tzotsos.pdf
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 Environmental Sciences, 24: 222-227. https://doi.org/10.1016/j.proenv.2015.03.029
Zhu, G. & D.G. Blumberg. 2002. Classification using ASTER data and SVM algorithms; The case study of Beer Sheva, Israel. Remote Sens. Environ., 80: 233–240. https://doi.org/10.1016/S0034-4257(01)00305-4
Authors
SaputraR., GaolJ. L., & AgusS. B. (2021). STUDY OF MANGROVE COVERAGE CHANGE BASED ON OBJECT (OBIA) USING SATELLITE IMAGERY IN DOMPAK ISLAND PROVINCE OF KEPULAUAN RIAU. Jurnal Ilmu Dan Teknologi Kelautan Tropis, 13(1), 39-55. https://doi.org/10.29244/jitkt.v13i1.27886
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