Determining Land Cover and Land Use Changes Using Aster Satellite Images at Different Time Points: The Case of Arnavutköy, Istanbul

Year 2026, Volume: 8 Issue: 1 , 56 - 64 , 30.03.2026

Cansu Çelik , Hasan Bilgehan Makineci

https://izlik.org/JA32BF25PS

Abstract

The use of satellite systems and remote sensing (RS) technologies is rapidly growing. RS is frequently utilized for classifying land use and detecting land cover changes, with resulting thematic maps providing valuable data sources. This study investigates land cover changes in Arnavutköy, a district in northwest Istanbul, from 2000 to 2009. The results, supported by existing literature, show that Arnavutköy has experienced rapid urbanization, particularly since the early 2000s. The evaluation of land cover types in the study focused on urban areas, vegetation/forests, bare soil, and wetlands. Findings indicate a consistent increase in urban areas over the period, while agricultural lands, natural vegetation, and habitats declined. The main factors driving these changes are identified as population growth, transportation and infrastructure development, land speculation, and planning pressures.

Keywords

Arnavutköy , Change Detection , Land Cover , Land Use , SVM

Ethical Statement

In the study, the author(s) declare that there is no violation of research and publication ethics.

Thanks

This article includes excerpts from the master's thesis of Cansu ÇELİK. We express our sincere appreciation to MipMap Technologies for their valuable contributions.

References

• Atay Kaya, İ., & Kut Görgün, E. (2020). Land use and land cover change monitoring in Bandırma (Turkey) using remote sensing and geographic information systems. Environmental Monitoring and Assessment, 192, 430.
• Congalton, R. G. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37(1), 35–46.
• Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297.
• Cover, T., & Hart, P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), 21–27.
• Foley, J. A., DeFries, R., Asner, G. P., Barford, C., Bonan, G., Carpenter, S. R., … Snyder, P. K. (2005). Global consequences of land use. Science, 309(5734), 570–574.
• Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
• Gülersoy, A. E. (2013). Farklı uzaktan algılama teknikleri kullanılarak arazi örtüsü/kullanımında meydana gelen değişimlerin incelenmesi: Manisa merkez ilçesi örneği (1986–2010). Turkish Studies, 8, 1915–1934.
• Güvercin, B., & Makineci, H. B. (2025). Suğla Gölü için 2017-2023 Yılları Arasında Multispektral PlanetScope Uydu Verileri ile Tarımsal Arazi Uygunluk Analizi (ALSA). Türkiye Fotogrametri Dergisi, 7(1), 28-36.
• Houghton, R. A., Lawrence, K. T., Hackler, J. L., & Brown, S. (2001). The spatial distribution of forest biomass in the Brazilian Amazon: A comparison of estimates. Global Change Biology, 7(7), 731–746.
• Huang, C., Davis, L. S., & Townshend, J. R. G. (2002). An assessment of support vector machines for land cover classification. International Journal of Remote Sensing, 23(4), 725–749.
• Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 25, 1097–1105.
• Lambin, E. F., Geist, H. J., & Lepers, E. (2003). Dynamics of land-use and land-cover change in tropical regions. Annual Review of Environment and Resources, 28, 205–241.
• LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.
• Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91–110.
• Makineci, H. B. (2023). Spatio-temporal change detection of built-up areas with Sentinel-1 SAR data using random forest classification for Arnavutköy Istanbul. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, 12(2), 626-636.
• Mather, P. M., & Koch, M. (2011). Computer processing of remotely-sensed images: An introduction (4th ed.). Wiley-Blackwell.
• Orhan, O., Makineci, H.B. (2023). Agricultural Land Suitability Analysis. In: Zhang, Q. (eds) Encyclopedia of Digital Agricultural Technologies. Springer, Cham. https://doi.org/10.1007/978-3-031-24861-0_270
• Pal, M., & Mather, P. M. (2005). Support vector machines for classification in remote sensing. International Journal of Remote Sensing, 26(5), 1007–1011.
• Pielke, R. A., Marland, G., Betts, R. A., Chase, T. N., Eastman, J. L., Niles, J. O., ... & Running, S. W. (2002). The influence of land-use change and landscape dynamics on the climate system. Climate Change, 54(3), 189–210.
• Pimm, S. L., Russell, G. J., Gittleman, J. L., & Brooks, T. M. (1995). The future of biodiversity. Science, 269(5222), 347–350.
• Richards, J. A., & Jia, X. (2006). Remote sensing digital image analysis (4th ed.). Springer.
• Sala, O. E., Chapin, F. S., Armesto, J. J., Berlow, E., Bloomfield, J., Dirzo, R., ... & Wall, D. H. (2000). Global biodiversity scenarios for the year 2100. Science, 287(5459), 1770–1774.
• Shorten, C., & Khoshgoftaar, T. M. (2019). A survey on image data augmentation for deep learning. Journal of Big Data, 6(1), 60.
• Zou, K. H., Warfield, S. K., Bharatha, A., Tempany, C. M., Kaus, M. R., Haker, S. J., … Kikinis, R. (2004). Statistical validation of image segmentation quality based on a spatial overlap index. Academic Radiology, 11(2), 178–189.