Tracking dynamic land-use change using spatially explicit Markov Chain based on cellular automata: the case of Tehran.

The main objective of this research is to, first, monitor urban sprawl in the metropolis of Tehran and, second, to assess the CA–Markov model in the simulation of land-use change. Land-use changes generally occur in developing countries through new building construction. The rapid pace of this development has brought forward a number of research activities involving new applications for modelling this phenomenon. In this research, urban sprawl is examined in the urban fringe of Tehran and, subsequently, the CA–Markov model is implemented in order to evaluate the model. The CA–Markov model is a spatially explicit model for land-change modelling, which has not been implemented very frequently to track urban expansion. This model, however, which is an integrated module of both cellular automata and Markov Chain models, predicts forthcoming changes over time, based on the past use of land in the research area. Urban expansion between 1986 and 2006 was simulated through employing the model in the metropolis of Tehran in order to calibrate and customise the model through a training phase. Consequently, the optimal rules to achieve the most accurate results were retrieved and input to simulate future land-use situations, i.e. 2016 and 2026. The achieved results represent a big conversion of green areas and open lands to built-up areas. [ABSTRACT FROM AUTHOR]