BENTHIC HABITAT CLASSIFICATION ON MULTISPATIAL SATELLITE IMAGERY IN KAPOTA AND KOMPOONE ISLANDS, WAKATOBI
Article Sidebar
-
multi-scales mapping, satellite imagery, shallow water habitat
Abstract
Shallow water habitat mapping is important to do because: (1) it can support the planning, management, and decision making of government spatial; (2) it can support and design a Marine Protected Area (MPA); (3) it can conduct a scientific research program to determine a knowledge about benthic ecosystem and seabed geology; (4) it can do seabed resource valuation, both biotic and abiotic, for economic and management goals. Nowadays, the standardization of thematic map details level in coastal ecosystem has not determined, especially in shallow water habitat based on coastal management needs in certain scale. The study aims to compare map accuracy level between SPOT 6, Sentinel 2A, and Landsat 8 classification results using support vector machine algorithm. The study site is in Wakatobi Island, including Kapota Island and Kompoone Island. The in-situ data took on July 2019. The 347 ground truth and transect images in the field analyzed using Coral Point Count with Excel Extension (CPCe). The classification scheme that was gotten is 8 habitat benthic classes, then conducted classification with classify them to be 6 and 5 classes. The result from SPOT 6 for 5 habitat classes has the highest overall accuracy. The differences between pixel (spatial resolution) and the amount of classification scheme influence accuracy results.
References
Capolsini, P., S. Andrefouet, C. Rion, C. Payri. 2003. A Comparison of Landsat ETM+, SPOT HRV, Ikonos, ASTER, and Airbone Master Data for Coral Reef Habitat Mapping in South Pasific Islands. J. Remote Sens., 29(2): 187-200. https://doi.org/10.5589/m02-088
Congalton, R.G. & K. Green. 2008. Assessing the accuracy of remotely sensed data: principles and practices. CRC Taylor & Francis Group. 183 p.
Doxani, G., M. Papadopulou, P. Lafaxani, & M. Tsakiri-Strati. 2012. Shallow-water bathymetry over variable bottom types using multispectral worldview-2 image. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Science, ISPRS, 39(8): 329-340. https://doi.org/10.5194/isprsarchives-XXXIX-B8-159-2012
Eugenio, F., J. Marcello, & J. Martin. 2015. High-resolution maps of bathymetry and benthic habitats in shallow-water environments using multispectral remote sensing imagery. IEEE Transactions on Geoscience and Remote Sensing, 53(7): 3539-3549. https://doi.org/10.1109/TGRS.2014.2377300
Green, E.P., P.J. Mumby, A.J. Edwards, & C.D. Clark. 2000. Remote sensing handbook for tropical coastal management. coastal management sourcebooks 3. In A.J. Edwards (Ed.) (p. x + 316). Paris (FR). 316 p.
Harris, P. & E.K. Baker. 2012. Seafloor Geomorphology as Benthic Habitat. GeoHAB Atlas of Seafloor Geo-morphic Features and Benthic Habitats. Elsevier Inc. 888 p.
Hedley, J., C. Roelfsema, B. Koetz, & S. Phinn. 2012. Capability of the sentinel mission for tropical coral reef mapping and coral bleaching detection. Remote Sens. of Environment, 120(2012): 145-155. https://doi.org/10.1016/jrse.2011.06.028
Laffoley, D. & G.D. Grimsditch. 2009. The Management of Natural Coastal Carbon Sinks. IUCN Press. Gland Switzerland. 53 p.
Lyons, M., S. Phinn, & C. Roelfsema. 2011. Integrating quickbird multispectral satellite satelitte and field data: mapping bathymetry, seagrass cover, seagrass species and change in Moreton Bay, Australia in 2004 and 2007. Remote Sens., 3: 42-64. https://doi.org/10.3390/rs3010042
Mandanici, E. & G. Bitelli. 2016. Pre-liminary comparison of sentinel 2A and Landsat 8 Imagery for a combined use. Remote Sens., 8(1014): 1-10. https://doi.org/10.3390/rs8121014
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
Mountrakis, G., J. Im, & C. Ogole. 2011. Support vector machines in remote sensing: A review. ISPRS J. Photogramm. Remote Sens., 66(3): 247–259. https://doi.org/10.1016/j.isprsjprs.2010.11.001
Mumby, P.J., W. Skirving, A.E. Strong, J.T. Hardy, E.F. LeDrew, E.J. Hochberg, R.P. Stumpf, & L.T. David. 2003. Review remote sensing of coral reefs and their physical environment. Marine Pollution Bulletin, 48: 210-228. https://doi.org/10.1016/j.marpolbul.2003.10.031
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 Kelapa. J. Ilmu dan Teknologi Kelautan Tropis, 10(1): 123-134. http://doi.org/10.29244/jitkt.v10il.21670
Pu, R., S. Bell, & C. Meyer. 2014. Mapping and assessing seagrass bed changes in Central Florida’s West Coast using Multitemporal Landsat TM Imagery. Estuarine, Coastal and Shelf Science, 149: 68-79. https://doi.org/10.1016/j.ecss.2014.07.014
Sangadji, M.S., V.P. Siregar, & H.M. Manik. 2018. Klasifikasi habitat perairan dangkal menggunakan logika fuzzy dan maximum likelihood pada citra satelit multispektral. J. Ilmu dan Teknologi Kelautan Tropis, 10(3): 639-649. http://doi.org/10.29244/jitkt.v10i3.22859
Siregar, V.P., S.B. Agus, A. Sunuddin, T. Subarno, & N.N. Aziizah. 2020. Analisis perubahan habitat dasar perairan dangkal menggunakan citra satelit resolusi tinggi di Karang Lebar, Kepulauan Seribu. J. Ilmu dan Teknologi Kelautan Tropis, 12(1): 37-51. http://doi.org/10.29244/jitkt.v12il.25528
Traganos, D. & P. Reinartz. 2017. Mapping mediterranean seagrass with sentinel-2 Imagery. Marine Pollution Bulletin, 134: 197-209. http://doi.org/10.1016/j.marpolbul.2017.06.075
Wahidin, 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. Procedia Environmental Sciences, 24: 222-227. https://doi.org/10.1016/j.proenv.2015.03.029
Wei, C., J. Huang, L.R. Mansaray, Z. Li, W. Liu, & J. Han. 2017. Estimation and mapping of winter oilseed rape lai from high spatial resolution satellite data based on a hybrid method. Remote Sens., 9(5): 1-16. https://doi.org/10.3390/rs9050488
Authors
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
The author submitting the manuscript must understand and agree that the copyright of the article manuscript must be submitted/transferred to the Jurnal Ilmu dan Teknologi Kelautan Tropis. This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 (CC BY-SA) International License in which the Author and Reader can copy and redistribute the material in any media or format, and remix, modify and build material for any purpose, but they must provide appropriate credit (citing articles or content), provide a link to the license, and indicate whether there is a change. If you mix, change, or create material, you must distribute your contribution under the same license as the original.