Effects of Urban Sprawl on Natural Land Cover in Batman City (1993–2024): Remote Sensing and GIS-Based Assessment and Trend-Based Projections for 2074

This study examines the impacts of urban expansion on the natural land-use pattern in Batman City between 1993 and 2024 and aims to reveal potential land-use changes for the year 2074 through the integration of the CA–Markov model, trend analysis, and linear projection methods. By evaluating the influence of past land-use changes on future spatial distributions, the direction and intensity of urban growth were analyzed. Landsat satellite imagery with a spatial resolution of 30 m was utilized to determine land-use/land-cover (LULC) changes, and the data were subjected to radiometric, geometric, and atmospheric corrections within a GIS environment. The images obtained for 1993 and 2024 were processed using different band combinations to generate both natural and false-color composites. Subsequently, artificial and natural land classes were distinguished through a supervised classification method. The classification accuracy was evaluated by comparison with various reference data, and the overall accuracy was calculated as 96% in the accuracy assessment. While future-oriented projections were generated through trend analysis, the CA–Markov model was implemented using the QGIS MOLUSCE module. Transition probabilities between land-use classes and spatial neighborhood relationships were jointly evaluated to produce possible land-use scenarios for 2074. The model results indicate that urban areas are expected to expand predominantly along transportation corridors and around existing settlements, whereas agricultural lands are likely to exhibit a significant decline. The findings demonstrate that urban sprawl in Batman exerts substantial pressure on the natural land cover and has led to a remarkable reduction in agricultural areas. This transformation is considered likely to increase environmental risks such as the urban heat island effect, increased surface runoff, and ecological fragmentation due to the expansion of impervious surfaces. In conclusion, the study emphasizes the necessity of developing sustainable land management policies for Batman City and recommends the protection of agricultural lands, the planning of green infrastructure, and the strengthening of GIS- and QGIS-based monitoring systems. Furthermore, it is suggested that integrating artificial intelligence-supported spatial modeling approaches with the CA–Markov model in future studies may provide more accurate and multidimensional analyses.