Simulating urban expansion dynamics in Tehran through satellite imagery and cellular automata Markov chain modelling

Urban expansion in rapidly growing cities presents significant challenges for sustainable development, particularly in regions like Tehran, where urban growth is occurring rapidly and is expected to continue in the coming decades. This study employs advanced techniques, including satellite imagery analysis and Cellular Automata Markov Chain (CA-Markov) modelling, to forecast urban expansion in Tehran, Iran, up to the year 2035. While the study is geographically focused on Tehran, the methodologies and insights can be adapted to other rapidly urbanizing regions facing similar challenges. Leveraging satellite imagery (2000-2023), the research delineates Tehran's historical urban growth and projects future land use change. The study uses supervised machine learning algorithms for classification and CA-Markov modeling to simulate future urban dynamics. The study generates a simulated map for 2035, offering insights into the potential spatial distribution of built-up areas, bare land, water bodies, and vegetation. The findings reveal significant urban expansion, driven by population growth, economic development, and infrastructure expansion. The built-up area is projected to grow significantly due to rapid urbanization, while bare land and vegetation cover will decrease, highlighting the environmental pressures of urban expansion. The study also observes minor effects on water bodies, reflecting the subtle impact of urbanization on aquatic ecosystems. This analysis enhances the understanding of the socio-economic, policy, and environmental factors shaping Tehran's urban landscape. It offers valuable insights for developing sustainable development strategies and provides a model for assessing urban growth dynamics, contributing to global urban sustainability discussions and offering tools for urban planners worldwide.