• Anisa Aulia Sabilah Study Program of Marine Technology, Graduate School, IPB University, Bogor
  • Vincentius Paulus Siregar Department of Marine Science and Technology, Faculty of Fisheries and Marine Science, IPB University, Bogor
  • Muhammad Anshar Amran Department of Marine Science, Faculty of Marine Science and Fisheries, Hasanuddin University, Makassar
Keywords: accuracy, mapping, seagrass condition, sentinel-2, worldview-2


Seagrass beds play an ecological role in the shallow marine environment, such as a habitat for biota, primary producers, and sediment traps. They also act as nutrient recyclers. Since they have such an important role, this natural resource needs to be preserved. Therefore, continuous monitoring and mapping of seagrass beds, especially by remote sensing methods, is paramount. The current rapid development of satellite sensor technology, especially its spatial and spectral resolutions, has improved the quality of the seagrass distribution map. The use of proper classification methods and schemes in the classification of seagrass distribution based on satellite imagery can affect the accuracy of the map, which is why various alternative algorithm studies are required. In this study, the Support Vector Machine and Fuzzy Logic algorithms were used to classify the WorldView-2 and Sentinel-2 satellite imageries on Kodingareng Lompo Island with four classes of seagrass cover, sparse (0–25%), moderate (26–50%), dense (51–75%), and very dense (76–100%). The result showed that the Fuzzy Logic algorithm applied to WorldView-2 imagery has the best overall accuracy of 78.60% seagrass cover classification.


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Author Biography

Anisa Aulia Sabilah, Study Program of Marine Technology, Graduate School, IPB University, Bogor

Departement of Marine Science and Technology


Ampou, E.E., S. Ouillon, & S. Andrѐfouёt. 2017. Challenges in rendering coral triangle habitat richness in remotely sensed habitat maps: The case of Bunaken Island (Indonesia). Marine Pollution Bulletin, 131: 72-82.

Amran, M.A. 2017. Mapping seagrass condition using google earth imagery. J. of Engineering Science and Technology Review, 10(1): 18-23.

Aziizah, N.N., V.P. Siregar, & S.B. Agus. 2016. Analysis of the spectral reflectance of seagrass using a spectrophotometer in Tunda Serang Island, Banten. J. of Fisheries and Marine Technology, 6(2): 199-208.

Baumstark, R., R. Duffey, & R. Pu. 2016. Mapping seagrass and colonized hard bottom in springs coast Florida using Worldview-2 satellite imagery. Estuarine, Coastal and Shelf Science, 181: 83-92.

Bayyana, S., S.P. Pawar, S. Gole, S. Dudhat, A. Pande, D. Mitra, J.A. Johnson, & P. Sivakumar. 2020. Detection and mapping of seagrass meadows at Ritchie’s archipelago using Sentinel 2A satellite imagery. Current Science, 118(8): 1275-1282.

Blaschke, T. 2010. Object-based image analysis for remote sensing. J. of Photogrammetry and Remote Sensing, 65: 2-16.

Congalton, R.G. & K. Green. 2009. Assessing the accuracy of remotely sensed data principles and practices. Second Edition. Crc Press. New York. 210 p.

Da Silva G.C.M., F.E.S. De Souza, & E. Marinho-Soriano. 2016. Application of Alos Avnir-2 for the detection of seaweed and seagrass beds on the Northeast of Brazil.

International J. of Remote Sensing, 38(3): 662-678.

DigitalGlobe. 2010. The benefits of the 8 spectral bands of Worldview-2. Longmont. Inc. 12 p.

Eastman, J.R. 2012. Idrisi Selva Manual-Version 17. Worcester. Clark University. 10 p.

European Space Agency (ESA). 2015. Sentinel-2 ESA’s optical high-resolution mission for GMES operational services. ESA Bulletin. 78 p.

Fauzan, M.A., I.S.W. Kumara, R. Yogyantoro, S. Suwardana, N. Fadhilah, I. Nurmalasari, S. Apriyani, & P. Wicaksono. 2017. Assessing the capability of Sentinel-2A data for mapping seagrass percent cover in Jerowaru, East Lombok. Indonesian J. of Geography, 49(2): 195-203.

Fauzan, M.A., Hartono, & P. Wicaksono. 2018. Monitoring of changes in seagrass cover using the Sentinel-2 MSI time-series imagery in the coastal area of Derawan Island. Marxiv, 11 p.

Goodman, J.A., S.J. Purkis, & S.R. Phinn. 2013. Coral reef remote sensing: A guide for mapping, monitoring and management. Springer, 436 p.

Green, E.P., P.J. Mumby, A.J. Edwards, C.D. Clark. 2000. Remote sensing handbook for tropical coastal management. The United Nations Educational, Scientific, and Cultural Organization. Paris. Unesco. 328 p.

Huang, Z., B.P. Brooke, & P.T. Harris. 2011. A new approach to mapping marine benthic habitats using physical environmental data. Continental Shelf Research, 31: 4-16.

Kamal, M., S. Phinn, & K. Johansen. 2014. Characterizing the spatial structure of mangrove features for optimizing image-based mangrove mapping. Remote Sensing, 6: 984-1006.

Larkum, A.W.D. & R.J. West. 1990. Long-Term changes of seagrass meadows in Botany Bay, Australia, 37: 55-70.

Lizarazo, I. & P. Elsner. 2009. Fuzzy segmentation for object-based image classification. International J. of Remote Sensing, 30(6): 1643-1649.

Lyzenga, D.R. 1981. Remote Sensing of bottom reflectance and water attenuation parameters in shallow water using Aircraft and Landsat data. International J. of Remote Sensing, 2: 71-82.

Maksum, Z.U., Y. Prasetyo, & Haniah. 2016. Comparison of land cover classification using object-based classification methods and pixel-based classification in high and medium resolution images. J. of Geodesy UNDIP, 5(2): 97-107.

Ministry of State and Environment (KLH). 2004. Kriteria Penentuan Status Kerusakan Padang Lamun. KLH. Jakarta. 1513 p.

Murmu, S. & S. Biswas. 2015. Application of Fuzzy Logic and neural network in crop classification: A review. Aquatic Bulletin, 48: 210-228.

Nedeljkovic, I. 2004. Image classification based on Fuzzy Logic. Remote Sensing and Spatial Information Sciences, 34(30): 1-6.

Ni, T.N.K., H.C. Tin, V.T. Thach, C. Jamet, & I. Saizen. 2020. mapping submerged aquatic vegetation along the central vietnamese coast using multi-source remote sensing. International J. of Geo-Information, 9: 1-27.

Patty, S.I. 2016. Mapping conditions of seagrass beds in Ternate-Tidore waters, and surrounding areas. Platax Scientific J., 4(1): 2302-3589.

Phinn, S.R., C.M. Roelfsema, V. Brando, & J. Anstee. 2008. Mapping seagrass species, cover, and biomass in shallow waters: An assessment of satellite multi-spectral and Airborne hyper-spectral imaging systems in Moreton Bay (Australia). Remote Sensing of Environment, 112: 3413–3425.

Poursanidis, D., K. Topouzelis, & N. Chrysoulakis. 2018. Mapping coastal marine habitats and delineating the deep limits of the neptune’s seagrass meadows using very high-resolution earth observation data. International J. of Remote Sensing, 39(23): 8670-8687.

Puspitasari, A.M., D.E. Ratnawati, & A.W. Widodo. 2018. The classification of dental and oral diseases uses the support vector machine method. J. of Information Technology Development and Computer Science, 2(2): 802-810.

Rahmawati, I.H. Supriyadi, M.H. Azkab, & W. Kiswara. 2014. Panduan Monitoring Padang Lamun. Coremap-CTI LIPI. Jakarta. 45 p.

Richards, J.A. 2013. Remote sensing digital image analysis: An introduction. Springer, 404 p.

Sangadji, M.S., V.P. Siregar, & H.M. Manik. 2018. Shallow water habitat classification using Fuzzy Logic and maximum likelihood on multispectral satellite imagery. J. of Tropical Marine Science and Technology, 10(3): 667-681.

Saputro, N.D. 2015. Application of a support vector machine algorithm for prediction of gold prices. J. of Informatics UPGRIS, 1: 1-19.

Short, F., T. Carruthers, W. Dennison, & M. Waycott. 2007. Global seagrass distribution and diversity: A bioregional model. J. of Experimental Marine Biology and Ecology, 350: 3-20.

Siregar, V.P., S.B. Agus, & T. Subarno. 2018a. Mapping shallow-water habitats using OBIA by applying several approaches of depth invariant index in North Kepulauan Seribu. Proceeding The 4th International Symposium on LAPAN-IPB Satellite for Food Security and Environmental Monitoring, Bogor, Indonesia, 9–11 October 2017. 1-9 pp.

Siregar, V.P., N.W. Prabowo, S.B. Agus, & T. Subarno. 2018b. The effect of atmospheric correction on object based image classification using SPOT-7 imagery: a case study in the Harapan and Kelapa Islands. Proceeding The 2nd International Conference on Marine Science: Better Insight for the Healthy Ocean, Bogor, Indonesia, 6-7 September 2017. 1-11 pp.

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 di Indonesia Ver. 02. LIPI. Jakarta. 40 p.

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 The 3rd International Conference of Indonesia Society for Remote Sensing, Semarang, Indonesia, 31 October-1 November 2017. 1-13 pp.

Thendean, H. & M. Sugiarto. 2008. Application of fuzzy if-then rules to increase contrast in mammographic images. J. of Informatics, 9(1): 1-7.

Topouzelis, K., D. Makri, N. Stoupas, A. Papakonstantinou, & S. Katsanevakis. 2018. Seagrass mapping in Greek erritorial waters using Landsat-8 satellite images. International J. of Applied Earth Observation and Geoinformation, 67: 98-113.

Traganos, D., B. Aggarwal, D. Poursanidis, K. Topouzelis, N. Chrysoulakis, & P. Reinartz. 2018. Towards global-scale seagrass mapping and monitoring uuing Sentinel-2 on Google Earth Engine: The case study of the Aegen and Ionian Seas. Remote Sensing, 10(1227): 1-14.

Traganos, D. & P. Reinartz. 2017. Mapping mediterranean seagrasses with Sentinel-2 imagery. Marine Pollution Bulletin, 30(40): 197-209.

Urbanski, J.A. & M. Szymelfenig. 2003. GIS-based mapping of benthic habitats. Estuarine Coastal and Shelf Science, 56: 99-109.

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.

Wang, D., B. Wan, P. Qiu, Y. Su, Q. Guo, R. Wang, F. Sun, & W.U. Xincai. 2018. Evaluating the performance of Sentinel-2, Landsat-8 and Pleiades-1 in mapping mangrove extent and species. Remote Sensing, 10(9): 1-27.

Wicaksono, P., I.S.W. Kumara, M. Kamal, M.A. Fauzan, Z. Zhafarina, D.A. Nurswanto, & R.N. Yogyantoro. 2017. Multispectral resampling of seagrass species spectra: WorldView-2. Quickbird, Sentinel-2A, Aster Vnir, and Landsat 8 OLI. Proceeding The 5th Geoinformation Science Symposium, Yogyakarta, Indonesia, 27–28 September 2017. 1-11 pp.

Wicaksono, P. & M. Hafizt. 2013. Mapping seagrass from space: Addressing the complexity of seagrass mapping. European J. of Remote Sensing, 46: 18-39.

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