Determination and Estimation of Water Surface Change With Landsat Data and Machine Learning Algorithms; A Case Study in Lake Marmara

Yıl 2022, Cilt: 4 Sayı: 2, 43 - 52, 30.12.2022

Can Cezayirlioğlu , Ramazan Çelik , Dilek Küçük Matcı

https://doi.org/10.51489/tuzal.1134783

Öz

Water resources play an important role in the continuity of life. Therefore, it is necessary to map water resources and monitor changes. Remote sensing technologies provide important data in the monitoring, control and protection studies of water resources. These data are important for planners in studies related to water bodies. In this study, the change of the water surface of Marmara Lake, located in Gölmarmara district, 70 km from Manisa, was determined. In addition, an estimation study of the future spatial change of Marmara Lake was carried out. In this direction, the surface areas were obtained as a result of the analysis of the Landsat 7 images of the study area for the years 2002-2021 with the unsupervised classification method. In addition, precipitation, temperature and LST data of the area were obtained with the help of Google Earth Engine. RBF Regressor, Linear Regression, Additive Regression and MultiLayer Perceptron CS methods were used to make the most accurate estimation using the data obtained. Using the data between 2002 and 2012, the change between 2013 and 2021 was determined. When the results were examined, it was observed that the best estimation was obtained with MultiLayer Perceptron CS with R2= 0.91. As a result of the estimation study carried out for the years 2022 and 2026 with this method, it is predicted that the lake will shrink much more and reach 1.56 km2.

Anahtar Kelimeler

Remote Sensing, Marmara Lake, Environment, Changes in Water Surface Area

Landsat Verileri ve Makine Öğrenme Algoritmaları ile Su Yüzeyi Değişiminin Belirlenmesi Ve Tahmini; Marmara Gölü Örneği

Öz

Su kaynakları yaşamın devamlılığında önemli bir rol oynamaktadır. Bu nedenle su kaynaklarının haritalanması ve değişikliklerin izlenmesi gerekmektedir. Su kaynaklarının izlenmesi, kontrolü ve koruma çalışmalarında uzaktan algılama teknolojileri önemli veriler sağlamaktadır. Bu veriler, su kütleleri ile ilgili çalışmalarda planlayıcılar için önemlidir. Bu çalışmada Manisa’ya 70 km uzaklıkta bulunan Gölmarmara ilçesinde yer alan Marmara Gölü su yüzeyinin değişim analizi gerçekleştirilmiştir. Ek olarak Marmara Gölünün gelecekteki alansal değişimine ait tahminleme çalışması gerçekleştirilmiştir. Bu doğrultuda yüzey alanları, çalışma alanına ait 2002-2021 yıllarına ait Landsat 7 görüntülerinin kontrolsüz sınıflandırma yöntemi ile analizi sonucunda elde edilmiştir. Bunun yanında alana ait yağış, sıcaklık ve arazi yüzey sıcaklığı (LST) verileri Google Earth Engine yardımıyla elde edilmiştir. Elde edilen veriler kullanılarak en doğru tahminlemeyi yapabilmek amacıyla Radyal Tabanlı Fonksiyon (RBF Regressor), Doğrusal Regresyon (Lineer Regression), Toplamsal Regresyon (Additive Regression) ve Çok Katmanlı Perceptron Sınıflandırıcı (MultiLayer Perceptron Classifier) yöntemleri kullanılmıştır. 2002-2012 yılları arasındaki veriler kullanılarak 2013 ve 2021 yılları arasındaki değişim belirlenmiştir. Sonuçlar incelendiğinde en iyi tahminin R2= 0.91 ile Çok Katmanlı Perceptron CS ile elde edildiği gözlemlenmiştir. Bu yöntem ile 2022 ve 2026 yılları için gerçekleştirilen tahmin çalışması sonucunda gölün çok daha fazla küçüleceği ve 1.56 km2’ ye ulaşacağı öngörülmüştür.

Anahtar Kelimeler

Uzaktan Algılama, Marmara Gölü, Su Yüzey Alanı Değişimi, Çevre.

Kaynakça

• Bununu, Y. A. (2017). Integration of Markov chain analysis and similarity-weighted instance-based machine learning algorithm (SimWeight) to simulate urban expansion. International Journal of Urban Sciences, 21(2), 217-237.
• Celik, T. (2009). Unsupervised change detection in satellite images using principal component analysis and $ k $-means clustering. IEEE Geoscience and Remote Sensing Letters, 6(4), 772-776.
• Chen, J., Du, P., Wu, C., Xia, J., & Chanussot, J. (2018). Mapping urban land cover of a large area using multiple sensors multiple features. Remote Sensing, 10(6), 872.
• DeFries, R. S., & Townshend, J. (1994). NDVI-derived land cover classifications at a global scale. International Journal of Remote Sensing, 15(17), 3567-3586.
• Demir, N. (2020). NDVI Analysis of Australian Bushfires with Cloud Computing. Türk Uzaktan Algılama ve CBS Dergisi, 1(2), 78-84.
• Fernandez, F. G., Los Santos, I. S., Martinez, J. L., Izquierdo, S. I., & Redondo, F. L. (2013). Use of artificial neural networks to predict the business success or failure of start-up firms. Artificial neural networks-architecture and applications, 245-56.
• Feyisa, G. L., Meilby, H., Fensholt, R., & Proud, S. R. (2014). Automated Water Extraction Index: A new technique for surface water mapping using Landsat imagery. Remote Sensing of Environment, 140, 23-35.
• Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., . . . Hoell, A. (2015). The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. Scientific data, 2(1), 1-21.
• Gao, B.-C. (1996). NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58(3), 257-266.
• Gopi, A. P., Jyothi, R., Narayana, V. L., & Sandeep, K. S. (2020). Classification of tweets data based on polarity using improved RBF kernel of SVM. International Journal of Information Technology, 1-16.
• Guha, S., Govil, H., Dey, A., & Gill, N. (2018). Analytical study of land surface temperature with NDVI and NDBI using Landsat 8 OLI and TIRS data in Florence and Naples city, Italy. European Journal of Remote Sensing, 51(1), 667-678.
• Gülci, S., Gülci, N., & YÜKSEL, K. (2019). Aslantaş Baraj Gölü ve çevresinin su yüzey alanı ve arazi örtüsü değişiminin Landsat uydu görüntüleri kullanılarak izlenmesi. Journal of the Institute of Science and Technology, 9(1), 100-110.
• Hu, Z., & Lo, C. (2007). Modeling urban growth in Atlanta using logistic regression. Computers, Environment and Urban Systems, 31(6), 667-688.
• Huang, B., Xie, C., & Tay, R. (2010). Support vector machines for urban growth modeling. Geoinformatica, 14(1), 83.
• Huang, F., Yu, Y., & Feng, T. (2019). Automatic extraction of urban impervious surfaces based on deep learning and multi-source remote sensing data. Journal of Visual Communication and Image Representation, 60, 16-27.
• Ismail, M. A., Waqas, M., Ali, A., Muzzamil, M. M., Abid, U., & Zia, T. (2021). Enhanced index for water body delineation and area calculation using Google Earth Engine: a case study of the Manchar Lake. Journal of Water and Climate Change, 13 (2): 557–573.
• Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L., . . . Woollen, J. (1996). The NCEP/NCAR 40-year reanalysis project. Bulletin of the American Meteorological Society, 77(3), 437-472.
• Kanungo, T., Mount, D. M., Netanyahu, N. S., Piatko, C. D., Silverman, R., & Wu, A. Y. (2002). An efficient k-means clustering algorithm: Analysis and implementation. IEEE transactions on pattern analysis and machine intelligence, 24(7), 881-892.
• Koonsanit, K., Jaruskulchai, C., & Eiumnoh, A. (2012). Parameter-free K-means clustering algorithm for satellite imagery application. Information Science and Applications (ICISA), 2012 International Conference on,
• Logan, B. R., Sparapani, R., McCulloch, R. E., & Laud, P. W. (2019). Decision making and uncertainty quantification for individualized treatments using Bayesian Additive Regression Trees. Statistical Methods in Medical Research, 28(4), 1079-1093.
• Maithani, S. (2009). A neural network based urban growth model of an Indian city. Journal of the Indian Society of Remote Sensing, 37(3), 363-376.
• 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.
• McFeeters, S. K. (2013). Using the Normalized Difference Water Index (NDWI) within a Geographic Information System to Detect Swimming Pools for Mosquito Abatement: A Practical Approach. Remote Sensing, 5(7), 3544-3561.
• Papa, F., Prigent, C., & Rossow, W. B. (2008). Monitoring flood and discharge variations in the large Siberian rivers from a multi-satellite technique. Surveys in Geophysics, 29(4), 297-317.
• Pijanowski, B. C., Pithadia, S., Shellito, B. A., & Alexandridis, K. (2005). Calibrating a neural network‐based urban change model for two metropolitan areas of the Upper Midwest of the United States. International Journal of Geographical Information Science, 19(2), 197-215.
• Ridd, M. K., & Liu, J. (1998). A comparison of four algorithms for change detection in an urban environment. Remote Sensing of Environment, 63(2), 95-100.
• Rojas, I., Pomares, H., Bernier, J. L., Ortega, J., Pino, B., Pelayo, F. J., & Prieto, A. (2002). Time series analysis using normalized PG-RBF network with regression weights. Neurocomputing, 42(1-4), 267-285.
• Rokni, K., Ahmad, A., Selamat, A., & Hazini, S. (2014). Water feature extraction and change detection using multitemporal Landsat imagery. Remote Sensing, 6(5), 4173-4189.
• Seto, K. C., & Kaufmann, R. K. (2003). Modeling the drivers of urban land use change in the Pearl River Delta, China: integrating remote sensing with socioeconomic data. Land Economics, 79(1), 106-121.
• Sreehari, E., & Srivastava, S. (2018). Prediction of climate variable using multiple linear regression. 2018 4th International Conference on Computing Communication and Automation (ICCCA), 1-4.
• Sun, F., Sun, W., Chen, J., & Gong, P. (2012). Comparison and improvement of methods for identifying waterbodies in remotely sensed imagery. International Journal of Remote Sensing, 33(21), 6854-6875.
• Tarkan, A. S., Sarı, H. M., İlhan, A., Kurtul, I., & Vilizzi, L. (2017). Risk screening of non-native and translocated freshwater fish species in a Mediterranean-type shallow lake: Lake Marmara (West Anatolia). Zoology in the Middle East, 63(1), 48-57.
• Tatar, A., Shokrollahi, A., Mesbah, M., Rashid, S., Arabloo, M., & Bahadori, A. (2013). Implementing radial basis function networks for modeling CO2-reservoir oil minimum miscibility pressure. Journal of natural gas science and engineering, 15, 82-92.
• Torun, A. T., Ekercin, S., & Gezgin, C. (2017). Ysa ile optimize edilmiş yapay arı koloni algoritmasının landsat uydu görüntülerinin sınıflandırılmasında kullanılabilirliğinin araştırılması. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi, 17(4), 86-93.
• Tucker, C. J., Townshend, J. R. G., & Goff, T. E. (1985). African Land-Cover Classification Using Satellite Data. Science, 227(4685), 369-375.
• Usman, B. (2013). Satellite imagery land cover classification using k-means clustering algorithm computer vision for environmental information extraction. Elixir Computer Science and Engineering, 63, 18671-18675.
• Wan, Z. (2007). Collection-5 MODIS land surface temperature products users’ guide. ICESS, University of California, Santa Barbara.
• Wang, J., Ding, J., Li, G., Liang, J., Yu, D., Aishan, T., Zhang, F., Yang, J., Abulimiti, A., Liu, J. (2019). Dynamic detection of water surface area of Ebinur Lake using multi-source satellite data (Landsat and Sentinel-1A) and its responses to changing environment. Catena, 177, 189-201.
• Wang, X., Xie, S., Zhang, X., Chen, C., Guo, H., Du, J., & Duan, Z. (2018). A robust Multi-Band Water Index (MBWI) for automated extraction of surface water from Landsat 8 OLI imagery. International Journal of Applied Earth Observation and Geoinformation, 68, 73-91.
• Xie, H., Luo, X., Xu, X., Pan, H., & Tong, X. (2016). Evaluation of Landsat 8 OLI imagery for unsupervised inland water extraction. International Journal of Remote Sensing, 37(8), 1826-1844.
• Xiong, L., Deng, R., Li, J., Liu, X., Qin, Y., Liang, Y., & Liu, Y. (2018). Subpixel surface water extraction (SSWE) using Landsat 8 OLI data. Water, 10(5), 653.
• Xu, H. (2006). Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing, 27(14), 3025-3033.
• Yang, X., Wang, N., He, J., Hua, T., & Qie, Y. (2020). Changes in area and water volume of the Aral Sea in the arid Central Asia over the period of 1960–2018 and their causes. Catena, 191, 104566. Yildiz, Ş., Altindağ, A., & Ergönül, M. B. (2007). Seasonal fluctuations in the zooplankton composition of a eutrophic lake: Lake Marmara (Manisa, Turkey). Turkish Journal of Zoology, 31(2), 121-126.
• Yu, H., Xie, T., Paszczyñski, S., & Wilamowski, B. M. (2011). Advantages of radial basis function networks for dynamic system design. IEEE Transactions on Industrial Electronics, 58(12), 5438-5450.
• Zha, Y., Gao, J., & Ni, S. (2003). Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. International Journal of Remote Sensing, 24(3), 583-594.