ANALYSIS OF BENTHIC HABITAT CHANGE BY USING HIGH RESOLUTION SATELLITE IMAGERY IN KARANG LEBAR, KEPULAUAN SERIBU

  • Vincentius P. Siregar Departemen Ilmu dan Teknologi Kelautan, FPIK- IPB
  • Syamsul B. Agus Departemen Ilmu dan Teknologi Kelautan, FPIK- IPB
  • Adriani Sunuddin Departemen Ilmu dan Teknologi Kelautan, FPIK- IPB
  • Tarlan Subarno Rare Indonesia, Bogor http://orcid.org/0000-0002-0979-1027
  • Nunung Noer Aziizah Departemen Ilmu dan Teknologi Kelautan, FPIK- IPB
Keywords: benthic habitat, change detection, Kepulauan Seribu, multispectral imagery

Abstract

The need for data and information about benthic habitat is very necessary to maintain and conserve the ecosystems that exist in the waters. Damage to benthic habitats can occur due to anthropogenic activities and natural disasters that will impact on the surrounding biota and ecosystem, therefore to know and monitor the condition of waters and shallow water habitats it is necessary to do mapping. This study aims to detect the change of ​​benthic habitats in Karang Lebar, Kepulauan Seribu. This study utilized high resolution multispectral imagery QuickBird (2008) and WordView-2 (2018) to detect changes in the distribution and the area of the benthic habitat coverage at the study site. The classification of multispectral imagery was carried out with two approaches, namely the application of the Support Vector Machine (SVM) algorithm and Depth Invariant Index (DII) transformation on both satellite imageries. The number of benthic habitat classes produced was five classes, namely live coral, dead coral, seagrass beds, sand, and rubble. The results of the analysis showed an overall accuracy of 58.18% and 70.9% in the classification with multispectral input bands for the 2008 and 2018 imagery, and 60% and 80% for the DII transformation on 2008 and 2018 imageries respectively. The results of change detection showed the rubble class to sand had the largest area of 81.46 ha.

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References

Ampou, E.E.S., C. Ouillon, & S. Andréfouët. 2018. Change detection of Bunaken Island coral reefs using 15 years of very high resolution satellite images: a kaleidoscope of habitat trajectories. Marine Pollution Bulletin, 131: 83-95. https://doi.org/10.1016/j.marpolbul.2017.10.026

Anderson, G.P., G.W. Felde, M.L. Hoke, A.J. Ratkowski, T.W. Cooley, J.H. Chetwynd, J. Gardner, S.M. Adler-Golden, M.W. Matthew, & A. Berk. 2002. Modtran4-based atmospheric correction algorithm: flaash (fast line-of-sight atmospheric analysis of spectral hypercubes). In, Algorithms and technologies for multispectral, hyperspectral, and ultraspectral imagery VIII. International Society for Optics and Photonics, 4725: 65-71.

Aziizah, N.N., V.P. Siregar, & S.B. Agus. 2017. Penerapan algoritma spectral angle mapper (sam) untuk klasifikasi lamun menggunakan citra satelit worldview-2. J. Penginderaan Jauh dan Pengolahan Data Citra Digital, 13(2): 61-72. http://doi.org/10.30536/j.pjpdcd.2016.v13.a2205

Congalton, R.G. & K. Green. 2019. Assessing the accuracy of remotely sensed data: principles and practices (Third Edition). CRC press Taylor & Francis Group. France. 159 p.

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

Green, E.P., P.J. Mumby, A.J. Edwards, & C.D. Clark. 2000. Remote sensing: handbook for tropical coastal management. United Nations Educational, Scientific and Cultural Organization (UNESCO). Paris. 316 p.

Hernández-Cruz, L.R., S.J. Purkis, & B.M. Riegl. 2006. Documenting decadal spatial changes in seagrass and acropora palmata cover by aerial photography analysis In Vieques, Puerto Rico: 1937–2000. Bulletin of Marine Science, 79(2): 401-414.

Holden, H., E. Ledrew, C. Derksen, & M. Wilder. 2000. Coral reef ecosystem change detection based on spatial autocorrelation of multispectral satellite data. Proceedings of the second international asia pacific symposium on remote sensing of the atmosphere, environment, and space, October 9-12, 2000, Sendai, Japan. Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA. 5524. Asian J. Geoinform, 1(3): 45-51.

Jhonnerie, R. 2015. Random forest classification for mangrove land cover mapping using Landsat 5 TM and ALOS PALSAR imageries. Procedia Environ. Sci., 24: 215-221. https://doi.org/10.1016/j.proenv.2015.03.028

Khrisnamurti, K.H. Utami, & R. Darmawan. 2017. Dampak pariwisata terhadap lingkungan di Pulau Tidung Kepulauan Seribu. Kajian, 21: 257-273. https://doi.org/10.22212/kajian.v21i3.779

Laffoley, D. & G.D. Grimsditch. 2009. The management of natural coastal carbon sinks. IUCN Press. Gland Switzerland. 53 p.

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. https://doi.org/10.1080/01431168108948342

Manessa, M.D.M., A. Kanno, M. Sekine, E.E. Ampou, N. Widagti, & A.R. As-Syakur. 2014. Shallow-water benthic identification using multispectral satellite imagery: investigation on the effects of improving noise correction method and spectral cover. Remote Sensing, 6: 4454-4472. https://doi.org/10.3390/rs6054454

Mumby, P.J. 2006. Connectivity of reef fish between mangroves and coral reefs: algorithms for the design of marine reserves at seascape scales. Biological Conservation, 128: 215-222. https://doi.org/10.1016/j.biocon.2005.09.042

Pahlevan, N., M. Valadanzouj, & A. Alimohammadi. 2006. A quantitative comparison to water column correction techniques for benthic mapping using high spatial resolution data. In, Proceedings of Isprs Commission VII Mid-Term Symposium on Remote Sensing: From Pixels to Processes, Enschede, The Netherlands: Citeseer, Mei 2006. 21-28 pp.

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 J. of Remote Sensing, 33: 3768-3797. https://doi.org/10.1080/01431161.2011.633122

Prabowo, N.W., V.P. Siregar, & S.B. Agus. 2018. Classification of benthic habitat based on object with support vector machines and decision tree algorithm using spot-7 multispectral imagery in Harapan and Kelapa Island. J. Ilmu dan Teknologi Kelautan Tropis, 10(1): 123-134. https://doi.org/10.29244/jitkt.v10i1.21670

Roelfsema, C.M., M. Lyons, E.M. Kovacs, P. Maxwell, M.I. Saunders, J. Samper-Villarreal, & S.R. Phinn. 2014. Multi-temporal mapping of seagrass cover, species and biomass: a semi-automated object based image analysis approach. Remote Sensing of Environment, 150: 172-187. https://doi.org/10.1016/j.rse.2014.05.001

Selamat, M.B., I. Jaya, V.P. Siregar, & T. Hestirianoto. 2012a. Akurasi tematik peta substrat dasar dari citra quickbird (studi kasus gusung karang lebar, Kepulauan Seribu, jakarta)(thematic accuracy of bottom substrate map from quickbrid imagery (Case Study: Gusung Karang Lebar, Kepulauan Seribu, Jakarta)). Ilmu Kelautan: Indonesian J. of Marine Sciences, 17: 132-140. https://doi.org/10.14710/ik.ijms.17.3.132-140

Selamat, M.B., I. Jaya, V.P. Siregar, & T. Hestirianoto. 2012b. Aplikasi citra quickbird untuk pemetaan 3d substrat dasar di Gusung Karang. J. Ilmiah Geomatika, 18(2): 95-106.

Setyawan, I.E., V.P. Siregar, G.H. Pramono, & D.M. Yuwono. 2014. Pemetaan profil habitat dasar perairan dangkal berdasarkan bentuk topografi: studi kasus Pulau Panggang, Kepulauan Seribu Jakarta. Majalah Ilmiah Globe, 16(2): 125-132.

Siregar, V. 2010. Pemetaan substrat dasar perairan dangkal karang congkak dan lebar Kepulauan Seribu menggunakan citra satelit quickbird. J. Ilmu dan Teknologi Kelautan Tropis, 2(1): 19-30. https://doi.org/10.29244/jitkt.v2i1.7860

Vapnik, V. 1982. Estimation of dependencies based on empirical data, Translated By S. Kotz. In: New York: Springer-Verlag. 124 p.

Wicaksono, P. & M. Hafizt. 2013. Mapping seagrass from space: addressing the complexity of seagrass lai mapping. European J. of Remote Sensing, 46: 18-39. https://doi.org/10.5721/EuJRS20134602

Kovacs, E., C. Roelfsema, M. Lyons, S. Zhao, & S. Phinn. 2018. Seagrass habitat mapping: how do Landsat 8 OLI, Sentinel-2, ZY-3A, and Worldview-3 perform?. Remote Sensing Letters, 9: 686-695. https://doi.org/10.1080/2150704X.2018.1468101.

Urbański, J., A. Mazur, & U. Janas. 2009. Object-oriented classification of QuickBird data for mapping seagrass spatial structure. Oceanological and Hydrobiological Studies, 38(1): 27-43. https://doi.org/10.2478/v10009-009-0013-9

Published
2020-04-27