Land Cover Classification of Kei Kecil Island in 2019 Based on Multispectral Image Analysis

Karolina Wael; Willem A Siahaya

doi:10.30598/jbdp.2022.18.1.18

Karolina Wael Jurusan Budidaya Pertanian, Fakultas Pertanian, Universitas Pattimura, Jl. Ir. M. Putuhena, Kampus Poka, Ambon 97233, Indonesia
Willem A Siahaya

DOI: https://doi.org/10.30598/jbdp.2022.18.1.18

Keywords: Kei Kecil island, land coverage, Landsat 8 (OLI), multispectral classification

Abstract

Land coverage of an island can be determined based on a multispectral image analysis. This research was carried out in Kei Kecil Island, Southeast Maluku Regency. The research aimed to determine land cover based on multispectral analysis of Landsat 8 (OLI) record on 27 November 2019. This research was carried out through several stages, namely pre-processing of image data (radiometric correction, correction geometric and image cutting), digital analysis of Landsat Image (Image Processing) and Accuracy Test. The classification method used was the Maximum Likelihood (MCL) by considering the prior probability factor, namely the chance of a pixel to be explained into a certain class. The results of Landsat 8 (OLI) image classification showed that there were 7 classes of land cover, with the coverage area of each land cover: settlements 34.73%, secondary forests 10.54%, water bodies 0.05%, shrubs 34.77%, mixed gardens 14.57%, open land 1.91%, and cloud 3.43%. The land cover of the multispectral image of 543 was dominated by shrubs, which was 34.8%, and the smallest was water body, which was 0.1%. In the multispectral image of 654, settlements dominated the land cover of the research area, which was 31.5% and the narrowest was open land, which was 0.9%. The accuracy was shown with an overall accuracy value of 88% and a Kappa score of 0.85%. This showed that the level of accuracy of classification results obtained through Landsat 8 multispectral image analysis (OLI) in 2019 had a very high level of accuracy (very good). These results met the requirements applied by USGS (United States Geological Survey).

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References

Anderson, J. R., Hardy, E. E., Roach, J.T., and Witmer, R. E. 1983. A Land Use and Land Cover Classification System for Use with Remote Sensor Data. USGS Professional Paper 964. p. 28. The Fourth Printing. Washington, DC: United States Government Printing Office.

Andrew, M. E., M. A. Wulder, and T. A. Nelson. 2014. Potential Contributions of Remote Sensing to Ecosystem Service Assessments. Progress in Physical Geography 38 (3): 328–353. DOI:10.1177/ 0309133314528942.

Bounoua, L., De Fries, R., Collatz, G.J., Sellers, P., Khan, H., 2002. Effects of land cover conversion on surface climate. Climatic Change 52:29–64

De Fries, R. S., Hansen, M., Townshend, J. R. G., and Sohlberg, R., 1998. Global Land Cover Classifications at 8 Km Spatial Resolution: the use of Training Data Derived from Landsat Imagery in Decision Tree Classifiers. International Journal of Remote Sensing 19(16): 3141-3168

De Fries, R. S., Houghton, R. A., Hansen, M. C., Field, C. B., Skole, D., and Townshend, J.R.G. 2002. Carbon Emissions from Tropical Deforestation and Regrowth Based on Satellite Observations for the 1980s and 1990s. Proceedings of the National Academy of Sciences of the United States of America 99 (22): 14256–14261. DOI:10.1073/pnas.182560099.

Djurdjani dan C.N. Kartini C. N. 2004. Pengolahan Citra Digital. Teknik Geodesi UGM, Yogyakarta

Franklin, S. E., and Wulder, M. A. 2002. Remote Sensing Methods in Medium Spatial Resolution Satellite Data Land Cover Classification of Large Areas. Progress in Physical Geography 26(2): 173–205. DOI:10.1191/0309133302pp332ra.

Foody, G.M., 2008. GIS: biodiversity applications. SAGE Publications. Progress in Physical Geography 32(2): 223-235. DOI: 10.1177/0309133308094656

Gevana, D., Camacho, L., Carandang, A., Camacho, S., & Im, S. 2015. Land use characterization and change detection of a small mangrove area in Banacon Island, Bohol, Philippines using a maximum likelihood classification method. Forest Science and Technology 11(4): 97–205. https://doi.org/10.1080/21580103.2014.996611

Gong, P., Wang, J., Yu, L., Zhao, Y., Zhao, Y., Liang, L and Li, C. 2013. Finer Resolution Observation and Monitoring of Global Land Cover: First Mapping Results with Landsat TM and ETM+ Data. International Journal of Remote Sensing 34(7): 2607-2654. DOI:10.1080/01431161.2012.748992.

Jia, K., Liang, S., Zhang, N., Wei, X., Gu, X., Zhao, X., Yao, Y., Xie, X.. 2014. Land cover classification of finer resolution remote sensing data integrating temporal features from time series coarser resolution data. ISPRS Journal of Photogrammetry and Remote Sensing 93: 49–55. DOI:10.1016/j.isprsjprs.2014.04.004

Jung, M., Henkel, K., Herold, M., Churkina, G., 2006. Exploiting synergies of global land cover products for carbon cycle modeling. Remote Sens. Environ. 101: 534– 553

Knorn, J., Rabe, A., Radeloff, V. C., Kuemmerle, T., Kozak, J., and Hostert, P., 2009. Land cover mapping of large areas using chain classification of neighboring Landsat satellite images. Remote Sens. Environ. 113(5): 957–964.

Liang, D.; Zuo, Y.; Huang, L.; Zhao, J.; Teng, L.; Yang, F., 2015. Evaluation of the consistency of MODIS land cover product (MCD12Q1) based on Chinese 30 m globeland 30 datasets: A case study in Anhui Province, China. ISPRS Int. J. Geo-Inf. 4: 2519–2541. https://doi.org/10.3390/ijgi4042519

Liang, S. 2008. Advances in Land Remote Sensing System, Modeling Inversion and Application. Springer, Dordrecht

Lopez-Lopez, P., Garcia-Ripolles, C., Aguilar, J., Garcia-Lopez, F. and Verdejo, J. 2006. Modelling breeding habitat preferences of Bonelli’s eagle (Hieraaetus fasciatus) in relation to topography, disturbance, climate and land use at different spatial scales. Journal of Ornithology 147: 97–106. ISSN: 2193-7192.

Lunetta, R. S., Knight, J. F., Ediriwickrema, J., Lyon, J. G., & Worthy, L. D. 2006. Land-Cover Change Detection Using Multi-Temporal MODIS NDVI Data. Remote Sensing of Environment 105(2): 142-154. https://doi.org/10.1016/j.rse.2006.06.018

Mahmon, N. A., Ya’acob N., Yusof, A. L., Jaafar, J. 2015. Classification Methods for Remotely Sensed Data: Land Use and Land Cover Classification Using Various Combinations of Bands. Jurnal Teknologi 74: 89–96.

Miller, S.N., Guertin, D.P., Goodrich, D.C. 2007. Hydrologic modeling uncertainty resulting from land cover misclassification. J. Am. Water Resour. Assoc. 43: 1065–1075

Mora A., Santos, T.M.A., Tukasik, S., Silva, J.M.N., Falcão, A.J., Fonseca J.M., and Ribeiro, R.A. 2017. Land Cover Classification from Multispectral Data Using Computational Intelligence Tools: A Comparative Study. Information 2017, 8, 147. MDPI Open Access Journal.

Noss R.F., 1996. Ecosystems as conservation targets. Trends Ecol Evol. 11(8):351. DOI: 10.1016/0169-5347(96)20058-8. PMID: 21237874.

Qadri, S., Khan, D.M., Ahmad, F., Qadri S. F., Babar, M. E., Shahid, M., Ul-Rehman, M., Razzaq, A., Syed S. M., Fahad, M., Sarfraz, A., Pervez, M. T., Naveed, N., Aslam, N., Jamil, M., Rehmani, E, A. Ahmad, N., Khan, N.A., 2016. A Comparative Study of Land Cover Classification by Using Multispectral and Texture Data. Hindawi Publishing Corporation BioMed Research International Volume 2016, Article ID 8797438, 12 pages. http://dx.doi.org/10.1155/2016/8797438.

Rundquist, B. C., 2000. Fine-scale spatial and temporal variation in the relationship between spectral reflectance and a prairie vegetation canopy. Ph.D. Thesis. Kansas State University, Manhattan, Kan, USA.

Running, S.W., 2008. Ecosystem disturbance, carbon, and climate. Science 321:652– 653.

Sampurno. R. M. dan A.Thoriq. 2016. Klasifikasi Tutupan Lahan Menggunakan Citra Landsat 8(OLI) Di Kabupaten Sumedang. Jurnal Teknotan. 10:61-69. E-ISSN: 2528-6285

Satyanarayana, B.; Thierry, B.; Seen, D.L.; Raman, A.V.; Muthusankar, G., 2001. Remote sensing in mangrove research-relationship between vegetation indices and dendrometric parameters: A case for Coringa, east coast of India. In proceeding of the 22nd Asian Conference on Remote Sensing, Singapore, 5–9 November; Volume 5, p. 9.

Seetha, M., Muralikrishna, I. V., 2008. Artificial Neural Network and Other Methods of Image Classification. Journal of theoretical and Applied Information Technology

Shelestov, A., Lavreniuk, M., Kussul, N., Novikov, A., and Skakun, S., 2017. Exploring Google Earth Engine Platform for Big Data Processing: Classification of Multi-Temporal Satellite Imagery for Crop Mapping, Front. Earth Sci. 5: 1–10. https://doi.org/10.3389/feart.2017.00017

Siahaya, W.A. 2016. Pengaruh Perubahan Tutupan Lahan Terhadap Perubahan Tutupan Bentik Dasar Perairan Pesisir Pulau Kecil Berdasarkan Citra Satelit Resolusi Menengah (studi kasus empat belas DAS yang bermuara di Teluk Ambon). Disertasi S3 Program Pascasarjana UGM, Yokyakarta.

SK Bupati Malra No. 221 tahun 2015 tentang Rencana Pengelolaan dan Zonasi Kawasan Konservasi Pesisir dan Pulau-Pulau Kecil Kabupaten Maluku Tenggara (2015-2035)

Stehman, S. V. 2009. Sampling Designs for Accuracy Assessment of Land Cover. International Journal of Remote Sensing 30 (20): 5243–5272.

Sutanto, 1994. Penginderaan Jauh. Jilid I. Cetakan ketiga. Gadjah Mada University Press. Yoyakarta.

Tamouk, J. 2012. Satellite Image Classification Methods and Landsat 5TM bands. Department of Computer Engineering. EMU University, Famagusta, North CyprusSatellite Image Classification Methods and Landsat 5TM bands. Department of Computer Engineering. EMU University, Famagusta, North Cyprus.

Thenkabail, P. S., Schull, M., & Turral, H., 2005. Ganges and Indus River Basin Land Use/Land Cover (LULC) and Irrigated Area Mapping Using Continuous Streams of MODIS Data. Remote Sensing of Environment, 95 (3): 317-341.

Townshend, J. R. G., 1992. Land Cover. International Journal of Remote Sensing 13, 6-7, 13191328. DOI: 10.1080/01431169208904193.

Townshend, J. R.G., Masek, J. G., Huang, C., Vermote, E. F., Gao, F., Channan, S., Sexton, J. O., Feng, M., Narasimhan, R., Kim, D. Song, K., Song, D., Song, X.-P., Noojipady, P., Tan, B., Hansen, M. C., Li, M., and Wolfe. R. E. 2012. Global Characterization and Monitoring of Forest Cover Using Landsat Data: Opportunities and Challenges. International Journal of Digital Earth 5:373–397. DOI:10.1080/17538947.2012.713190

[USGS]. 2019. United States Geological Survey. https://glovis.usgs.gov/app5.

Vogiatzakis, I.N., Mannion, A.M. and Griffi ths, G.H. 2006. Mediterranean ecosystems: problems and tools for conservation. Progress in Physical Geography 30:175–200.

Zhang, J.T., Pennington, D.D. and Liu, X.H. 2007: GBD-Explorer: extending open source java GIS for exploring ecoregion-based biodiversity data, Ecological Informatics 2:94–102.