Water-body Segmentation in Heterogeneous Hydrodynamic and Morphodynamic Structured Coastal Areas by Machine Learning

Yıl 2023, Cilt: 10 Sayı: 1, 100 - 110, 19.03.2023

İrem Gümüşçü , Furkan Altaş , Beril Türkekul , Hasan Alper Kaya , Fırat Erdem , Tolga Bakırman , Bülent Bayram

https://doi.org/10.30897/ijegeo.1119096

Cited By: 1

Öz

Coastal areas constitute the most important part of the world when considered in terms of their socio-economic and natural values. Measuring and monitoring the coastal areas accurately is an important issue for coastal management. Compared to ground-based studies, remote sensing applications enriched with machine learning algorithms such as Random Forest (RF) and Support Vector Machine (SVM) provide significant benefits in terms of cost, time, and size of the study area. Within the scope of this study, Sentinel-2 images for five coastal areas located in Turkey with different morphological and hydrodynamic properties were classified as land and water-bodies using SVM and RF algorithms. Water-body segmentation results of the SVM and RF classification for the different band combinations of Sentinel-2 images have been compared. The reasons affecting the results of the accuracy analysis were examined in accordance with the geography of each area. Experimental results show that the utilized machine learning methods provide satisfactory results for combinations involving the NIR band in all study areas.

Anahtar Kelimeler

Sentinel-2, shoreline extraction, Support Vector Machines, Random Forest, water-body classification

Kaynakça

• Ai, B., Zhang, R., Zhang, H., Ma, C., & Gu, F. (2019). Dynamic process and artificial mechanism of coastline change in the Pearl River Estuary. Regional Studies in Marine Science, 30, 100715.
• Aksu A. E., Piper D. J. W. & Konak T. (1987). Quaternary Growth Patterns of the Büyük Menderes and Küçük Mederes Deltas, Western Turkey. Marine Geology, 76, 89-104.
• Ataol, M., Kale, M. M., & Tekkanat, İ. S. (2019). Assessment of the changes in shoreline using digital shoreline analysis system: a case study of Kızılırmak Delta in northern Turkey from 1951 to 2017. Environmental Earth Sciences, 78(19), 579.
• Balázs, B., Bíró, T., Dyke, G., Singh, S. K., & Szabó, S. (2018). Extracting water-related features using reflectance data and principal component analysis of Landsat images. Hydrological sciences journal, 63(2), 269-284.
• Bamdadinejad, M., Ketabdari, M. J., & Chavooshi, S. M. H. (2021). Shoreline Extraction Using Image Processing of Satellite Imageries. Journal of the Indian Society of Remote Sensing, 1-11.
• Bangira, T., Alfieri, S. M., Menenti, M., & Van Niekerk, A. (2019). Comparing thresholding with machine learning classifiers for mapping complex water. Remote Sensing, 11(11), 1351.
• Belgiu, M., & Drăguţ, L. (2016). Random forest in remote sensing: A review of applications and future directions. ISPRS journal of photogrammetry and remote sensing, 114, 24-31.
• Berkün, M., Anılan, T., & Aras, E. (2010). Doğu Karadeniz Bölgesi’nde Sediment Taşınması ve Kıyı Erozyonu Etkileşimleri. Türkiye Mühendislik Haberleri (TMH), 3(4), 461-462.
• Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32.
• Cao, W., & Wong, M. H. (2007). Current status of coastal zone issues and management in China: a review. Environment International, 33(7), 985-992.
• Choung, Y. J., & Jo, M. H. (2017). Comparison between a machine-learning-based method and a water-index-based method for shoreline mapping using a high-resolution satellite image acquired in Hwado Island, South Korea. Journal of Sensors, 2017.
• Coiman, A. (2020). Shoreline evolution of Valencia lake and land use and land cover changes in Zamora municipality, Aragua state, Venezuela, period 1986-2016. International Journal of Environment and Geoinformatics, 7(3), 305-318.
• Çiftçi, N. S. (2011). Türkiye Denizleri Açık Sularının Ekim 2000'deki Fitoplankton Kompozisyonu. Süleyman Demirel Üniversitesi Eğirdir Su Ürünleri Fakültesi Dergisi, 7(2), 23-36.
• Demir, N., Oy, S., Erdem, F., Şeker, D. Z., & Bayram, B. (2017). Integrated shoreline extraction approach with use of Rasat MS and SENTINEL-1A SAR Images. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 4, 445.
• Demir, N., Bayram, B., Şeker, D. Z., Oy, S., & Erdem, F. (2019). A nonparametric fuzzy shoreline extraction approach from Sentinel-1A by integration of RASAT pan-sharpened imagery. Geo-Marine Letters, 39(5), 401-415.
• Dervisoglu, A., Bilgilioğlu, B. B., & Yağmur, N. (2020). Comparison of Pixel-Based and Object-Based Classification Methods in Determination of Wetland Coastline. International Journal of Environment and Geoinformatics, 7(2), 213-220.
• Dixon, B., & Candade, N. (2008). Multispectral landuse classification using neural networks and support vector machines: one or the other, or both?. International Journal of Remote Sensing, 29(4), 1185-1206.
• Gislason, P. O., Benediktsson, J. A., & Sveinsson, J. R. (2006). Random forests for land cover classification. Pattern Recognition Letters, 27(4), 294-300.
• Guney, Y., & Polat, S. (2015). Coastline Change Detection Using Remote Sensing Data: The Case Of Aliağa And Çandarli. Journal of Aeronautics and Space Technologies, 8(1), 11-17.
• Haslett, S. (2008). Coastal systems. Routledge.
• Isik, S., Sasal, M., & Dogan, E. (2006). Sakarya Nehrinde Barajların Mansap Etkisinin Araştırılması. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 21(3), 401-408.
• Jamil, A., & Bayram, B. (2017). Tree species extraction and land use/cover classification from high-resolution digital orthophoto maps. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(1), 89-94.
• Knerr, S., Personnaz, L., & Dreyfus, G. (1990). Single-layer learning revisited: a stepwise procedure for building and training a neural network. In Neurocomputing (pp. 41-50). Springer, Berlin, Heidelberg.
• Korkmaz, H., Gecen, R., & Kuscu, V. (2016). Asi Deltasi (Samandağ) Kiyi Kenar Çizgisi. Fırat Üniversitesi Sosyal Bilimler Dergisi, 26(1).
• Kumar, L., Afzal, M. S., & Afzal, M. M. (2020). Mapping shoreline change using machine learning: a case study from the eastern Indian coast. Acta Geophysica, 68(4), 1127-1143.
• Lin, Y. C., Cheng, Y. T., Zhou, T., Ravi, R., Hasheminasab, S. M., Flatt, J. E., … Habib, A. (2019). Evaluation of UAV LiDAR for mapping coastal environments. Remote Sensing, 11(24), 1–32. https://doi.org/10.3390/rs11242893.
• Maglione, P., Parente, C., & Vallario, A. (2014). Coastline extraction using high resolution WorldView-2 satellite imagery. European Journal of Remote Sensing, 47(1), 685-699.
• Mahboubi, P., Parkes, M., Stephen, C., & Chan, H. M. (2015). Using expert informed GIS to locate important marine social-ecological hotspots. Journal of Environmental Management, 160, 342-352.
• 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.
• Minghelli, A., Spagnoli, J., Lei, M., Chami, M., & Charmasson, S. (2020). Shoreline Extraction from WorldView2 Satellite Data in the Presence of Foam Pixels Using Multispectral Classification Method. Remote Sensing, 12(16), 2664.
• Mountrakis, G., Im, J., & Ogole, C. (2011). Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 66(3), 247-259.
• Neumann, J. E., Emanuel, K., Ravela, S., Ludwig, L., Kirshen, P., Bosma, K., & Martinich, J. (2015). Joint effects of storm surge and sea-level rise on US Coasts: new economic estimates of impacts, adaptation, and benefits of mitigation policy. Climatic Change, 129(1), 337-349.
• Ngowo, R. G., Ribeiro, M. C., & Pereira, M. J. (2021). Quantifying 28-year (1991–2019) shoreline change trends along the Mnazi Bay–Ruvuma Estuary Marine Park, Tanzania. Remote Sensing Applications: Society and Environment, 23, 100607.
• Nowozin, S. (2014). Optimal decisions from probabilistic models: the intersection-over-union case. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 548-555).
• Öner, E. (2008). Alluvial geomorphology and paleogeographical studieson the Asi (Orontes) delta plain (Antakya/HATAY). Aegean Geograp. J, 17, 1-25.
• Osborne, B. P., Osborne, V. J., & Kruger, M. L. (2012). Comparison of satellite surveying to traditional surveying methods for the resources industry. Journal of the British Interplanetary Society, 65(2), 98.
• Ozturk, D., & Sesli, F. A. (2015). Shoreline change analysis of the Kizilirmak Lagoon Series. Ocean & Coastal Management, 118, 290-308.
• Ozturk, D., Beyazit, I., & Kilic, F. (2015). Spatiotemporal analysis of shoreline changes of the Kizilirmak Delta. Journal of Coastal Research, 31(6), 1389-1402.
• Patil, R. G., & Deo, M. C. (2020). Sea Level Rise and Shoreline Change under Changing Climate Along the Indian Coastline. Journal of Waterway, Port, Coastal, and Ocean Engineering, 146(5), 05020002.
• Paul, A., Tripathi, D., & Dutta, D. (2018). Application and comparison of advanced supervised classifiers in extraction of water bodies from remote sensing images. Sustainable Water Resources Management, 4(4), 905-919.
• Sahin, C. (2020). Sakarya Nehri Deltası’nda Uzun Süreli Rüzgar ve Dalga İklimi Değişimleri. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi, 22(65), 353-368.
• Samsun Investment Support Office, (2018). Bafra İlçesi Tarım Sektörü Raporu. Samsun, Türkiye.
• Scholkopf, B., Herbrich, R., & Smola, A. J. (2001). A generalized representer theorem. In International conference on computational learning theory (pp. 416-426). Springer, Berlin, Heidelberg.
• Sekovski, I., Stecchi, F., Mancini, F. ve Del Rio, L. (2014). Image classification methods applied to shoreline extraction on very high-resolution multispectral imagery, International Journal of Remote Sensing, 35(10): 3556-3578.
• Small, C., & Nicholls, R. J. (2003). A global analysis of human settlement in coastal zones. Journal of coastal research, 584-599.
• TMMOB (Union of Chambers of Turkish Engineers and Architects). (2012). Karasu Kıyı Alanı Kıyı Daralması Raporu.
• Toure, S., Diop, O., Kpalma, K., & Maiga, A. S. (2019). Shoreline detection using optical remote sensing: A review. ISPRS International Journal of Geo-Information, 8(2), 75.
• Turgut, Ü. (2007). Doğu Karadeniz Bölgesinde Sel Felaketine Neden Olan Sinoptik Modellerin Tahmin Tekniği Açısından İncelenmesine Dönük Karşılaştırmalı Bir Araştırma. In TMMOB Disaster Symp (pp. 387-394).
• Turkish State Meteorological Service (2021). The Latest Weather in | MiddleEast. https://www.mgm.gov.tr/eng/forecast-cities.aspx. Erişim Tarihi: 14.3.2021.
• Uzun, M., & Özcan, S. (2016). Solakli Dere–Iyidere Arasinda (Trabzon/Of) Kiyi Kullaniminin Zamansal Değişimi Ve Sürdürülebilir Yönetimi-The Sustainable Management Of Usage Of Coastal And Temporal Change In Between Solaklı Creek-Iyidere (Trabzon-Of). Doğu Coğrafya Dergisi, 21(35), 175-196.
• Zhang, F., Tiyip, T., Johnson, V. C., Wang, J., & Nurmemet, I. (2016). Improved water extraction using Landsat TM/ETM+ images in Ebinur Lake, Xinjiang, China. Remote Sensing Applications: Society and Environment, 4, 109-118.
• Zhang, T., Yang, X., Hu, S. ve Su, F. (2013). Extraction of coastline in aquaculture coast from multispectral remote sensing images: Object-based region growing integrating edge detection, Remote Sensing, 5(9): 4470-4487.