Analyzing and Predicting Spatiotemporal Urban Sprawl in Eskişehir Using Remote Sensing Data

Abstract

Urban areas of major cities in developing countries are expanding rapidly due to rapid population growth and industrialization. Determining the reasons for rapid urbanization and the amount of urban area that will be needed in the future is important for the planned growth of these cities. In this study, the LU/LC change of Eskisehir city center that has a rapid rate of urbanization and industrialization in Turkey between 1984 and 2020 was determined. Then, the area needed for urban and industrial areas in 2030 was investigated using the Cullullar Automata–Markov Chain (CA–MC) hybrid model simulation. Each year's LU/LC map was produced with over 80% kappa accuracies performing the pixel-based Random Forest (RF) algorithm for change analysis. In the change analysis made between 1984 and 2020, it was determined that there was a change of 117% in the urban area and 977% in the industrial area. The prediction was made for the validation of the CA–MC model from the period 2000–2010 to 2020. When compared with the 2020 LU/LC map, the model success was obtained as 0.84, 0.87, and 0.87 in the Kstandard, Klocation, and Kno Kappa metrics, respectively. By using the 2010–2020 periods in the estimation of 2030, it has been observed that the urban and industrial area will increase by 22.95%; therefore, there will be decreases in agriculture and other natural areas as in previous years. © 2022, Indian Society of Remote Sensing.