THE IDENTIFICATION OF SEASONAL COASTLINE CHANGES FROM LANDSAT 8 SATELLITE DATA USING ARTIFICIAL NEURAL NETWORKS AND K-NEAREST NEIGHBOR

Year 2020, Volume: 4 Issue: 1, 47 - 56, 01.01.2020

Mustafa Hayri Kesikoğlu , Sevim Yasemin Çiçekli , Tolga Kaynak

https://doi.org/10.31127/tuje.599359

Cited By: 4

Abstract

Coastline boundaries are constantly changing due to natural or human-induced events that take place in the world. Therefore it is necessary to correctly observe coastline boundaries. Remote sensing is one of the most frequently used methods to monitor the changes in coastal areas. In this study, it is aimed to solve the problem of choosing the right method for coastal change observation. This paper introduces a spatial pixel-based and object-based image classification approach to recognize changing areas in coastline. The coastline boundary changes occurred in a part of Yamula Dam Lake in Kayseri province were examined using three multispectral Landsat 8 satellite images of March, August and November 2016. Firstly, imageto-image registration processing was performed to register the three satellite images. Then, each satellite image was classified into two information classes either ‘Lake’ and ‘Other Field’ by using pixel-based Artificial Neural Networks (ANNs) and object-based K-Nearest Neighbor (KNN) method. Classification accuracies for ANNs method were obtained 99.97%, 99.90% and 99.80% respectively in March, August and November. As for the accuracies of the classification for the KNN method, in March, August and November were obtained 99.99%, 99.93% and 99.92% respectively. The change images were formed for March-August and August-November pairs by using the obtained classification images. The post classification comparison method was used to determine the changes in coastline boundaries. At the end of the study, seasonal changes from water to land and from land to water were detected. According to the result of the changes there is a 5,67 km² increase from March to August and a 3,14 km² decrease from August to November in Yamula Dam Lake. 

Keywords

Artificial Neural Networks, k-nearest Neighbor, Change Detection, Landsat 8

References

• A. M. Al Fugara, A., M., B. Pradhan, B., and T. A. Mohamed, T., A., (2009). “Improvement of land-use classification using object-oriented and fuzzy logic approach.” Applied Geomatics, 1(4), 111.
• Baek, S., and Sung, K. M. (2000). “Fast K-nearestneighbour search algorithm for nonparametric classification.” Electronics Letters,36(21), 1821-1822.
• Chebud, Y., Naja, G. M., Rivero, R. G., and Melesse, A. M. (2012). “Water quality monitoring using remote sensing and an artificial neural network.” Water, Air, and Soil Pollution, 223(8), 4875-4887.
• De Giglio, M., Greggio, N., Goffo, F., Merloni, N., Dubbini, M., and Barbarella, M. (2019). “Comparison of Pixel-and Object-Based Classification Methods of Unmanned Aerial Vehicle Data Applied to Coastal Dune Vegetation Communities: Casal Borsetti Case Study.” Remote Sensing, 11(12), 1416.
• Denoeux, T. (1995). “A k-nearest neighbor classification rule based on Dempster-Shafer theory. ”IEEE Transactions on Systems, Man, and Cybernetics,25(5), 804-813.
• Duro, D. C., Franklin, S. E., and Dubé, M. G. (2012). “A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT5 HRG imagery”. Remote sensing of environment, 118, 259-272.
• Erener, A., and Yakar, M. (2012). “Monitoring Coastline Change Using Remote Sensing and GIS Technologies. ”Lecture Notes in Information Technology, 30, 310-314.
• Franco-Lopez, H., Ek, A. R., and Bauer, M. E. (2001). “Estimation and mapping of forest stand density, volume, and cover type using the k-nearest neighbors method. ”Remote Sensing of Environment, 77(3), 251-274.
• Güney, Y., and Polat, S. (2015). “Coastline Change Detection Using Remote Sensing Data: The Case of Aliaga and Candarli. ”Journal of Aeronautics and Space Technologies/ Havacılık ve Uzay Teknolojileri Dergisi, 8(1).
• Hassan-Esfahani, L., Torres-Rua, A., Jensen, A., and McKee, M. (2015). “Assessment of surface soil moisture using high-resolution multi-spectral imagery and artificial neural networks. ”Remote Sensing,7(3), 2627-2646.
• Haykin, S. (1994). Neural networks: A comprehensive foundation, Macmillan College Publishing Company, New York, USA.
• Iklim Kayseri, https://tr.climatedata.org/asya/tuerkiye/kayseri/kayseri-250/ [Accessed 25 June 2017]
• Iklim Sivas, https://tr.climatedata.org/asya/tuerkiye/sivas/sivas-255/ [Accessed 25 June 2017]
• Im, J., & Jensen, J. R. (2005). A change detection model based on neighborhood correlation image analysis and decision tree classification. Remote Sensing of Environment, 99(3), 326-340.
• Isa N. A. M., Mashor M. Y, Othman N. H., Zamli K.Z., (2005). “Application of artificial neural networks in the classification of cervical cells based on the Bethesda system.” Journal of JICT, 4, 77-97.
• Karaburun, A., and Demirci, A. (2010). “Coastline changes in Istanbul between 1987 and 2007.”Scientific Research and Essays, 5(19), 3009-3017.
• Kesikoglu, M. H., Atasever, U. H., Dadaser-Celik, F., & Ozkan, C. (2019). Performance of ANN, SVM and MLH techniques for land use/cover change detection at Sultan Marshes Wetland, Turkey. Water Science and Technology.
• Kesikoğlu, M. H., Atasever, Ü. H., and Özkan, C. (2013). “Uzaktan Algılamada Kontrolsüz Değişim Belirleme.”Tmmob Harita Ve Kadastro Mühendisleri Odası, 14. Türkiye Harita Bilimsel Ve Teknik Kurultayı, Ankara.
• Kesikoğlu, M. H. (2013). Research Of Coastal Change In Sultan Marshes National Park And Ramsar Site With Satellite Image Analysis, MSc. Thesis, University of Erciyes, Kayseri, Turkey.
• Kesikoğlu M.H., Çiçekli S.Y., Kaynak T., Özkan C. (2017). “The determination of coastline changes using artificial neural networks in Yamula Dam Lake, Turkey. ”The 8th International Conference on Information Technology, Amman, Ürdün, 1-4.
• Kuleli, T., Guneroglu, A., Karsli, F., and Dihkan, M. (2011). “Automatic detection of shoreline change on coastal Ramsar wetlands of Turkey. ”Ocean Engineering,38(10), 1141-1149.
• Lillesand, T., Kiefer, R. W., and Chipman, J. (2014). Remote sensing and image interpretation. John Wiley and Sons.
• McFeeters, S. K. (1996). “The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. ”International Journal of Remote Sensing,17(7), 1425-1432.
• McRoberts, R. E., Nelson, M. D., and Wendt, D. G. (2002). “Stratified estimation of forest area using satellite imagery, inventory data, and the k-Nearest Neighbors technique. ”Remote Sensing of Environment, 82(2), 457-468.
• Ozpolat, E. and Demir, T. (2014). “Coğrafi Bilgi Sistemleri Ve Uzaktan Algılama Yöntemleriyle Kıyı Çizgisi Değişimi Belirleme : Seyhan Deltası.”Xvi., Akademik Bilişim, Mersin Üniversitesi ,Mersin.
• Prabaharan, S., Raju, K. S., Lakshumanan, C., and Ramalingam, M. (2010). “Remote sensing and GIS applications on change detection study in coastal zone using multi temporal satellite data.” International Journal of Geomatics and Geosciences, 1(2), 159.
• Pradhan, B., and Lee, S. (2010). “Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling” Environmental Modelling and Software, 25(6), 747-759.
• Rodriguez-Galiano, V. F., Ghimire, B., Rogan, J., ChicaOlmo, M., & Rigol-Sanchez, J. P. (2012). An assessment of the effectiveness of a random forest classifier for landcover classification. ISPRS Journal of Photogrammetry and Remote Sensing, 67, 93-104.
• Sang, L., Xu, Y., Cao, R., Chen, Y., Guo, Y., and Xu, R. (2011). “Modelling of GaN HEMT by using an improved k-nearest neighbors algorithm. ”Journal of Electromagnetic Waves and Applications, 25(7), 949-959.
• Sarle, W. S. (1994). “Neural networks and statistical models. ”Proceedings Of The Nineteenth Annual Sas Users Group International Conference.
• Shetty, A., Jayappa, K. S., and Mitra, D. (2015). “Shoreline change analysis of Mangalore coast and morphometric analysis of Netravathi-Gurupur and Mulky-Pavanje spits.” Aquatic Procedia, 4, 182-189.
• Tabarroni, A. (2010). Remote sensing and image interpretation change detection analysis: case study of borgo panigale and reno districts, MSc. Thesis, University of Florence, Florence, Italy.
• Wang, X. Z., Zhang, H. G., Fu, B., and Shi, A. Q. (2013). “Analysis on the coastline change and erosion-accretion evolution of the Pearl River Estuary, China, based on remote-sensing images and nautical charts.” Journal of Applied Remote Sensing, 7(1), 073519-073519.
• What are the band designations for the Landsat satellites?,http://landsat.usgs.gov/band_designations_landsat_satellites.php[Accessed 22 June 2017].
• Yamula Barajı (n.d.), http://www.kayseri.gov.tr/yamulabaraji [Accessed 8 March 2017]
• Yu, Q., Gong, P., Clinton, N., Biging, G., Kelly, M., and Schirokauer, D. (2006). “Object-based detailed vegetation classification with airborne high spatial resolution remote sensing imagery.” Photogrammetric Engineering and Remote Sensing, 72(7), 799-811.
• Zhang, H., Li, Q Liu, J., Du, X., Dong, T., McNairn, H., Champagne, C., Liu, M., and Shang, J.(2017). “Object based crop classification using multi-temporal SPOT-5 imagery and textural features with a random forest classifier.”Geocarto International, 1-19.