Predicting the Spatial Growth and Urban Sprawl in Sari, Iran Using Markov Cellular Automata Model and Shannon Entropy

Constant urban development in today’s world has turned urban growth management into a main challenge in the 21st century. Obtaining spatiotemporal information about the pattern and rate of urban growth is critical to a better understanding of the urban growth process and practicing appropriate management policies. The present study investigated the trend of urban sprawl in Sari, Iran. First, land use and cover maps of the study area were prepared by processing multitemporal Landsat images from 1992, 2002, and 2010. Moreover, the urban growth of the city in 2010 was predicted by combining the Markov-cellular automata model with multi-criteria evaluation (overall Kappa = 83.80% area under the receiver operating characteristic curve ≈ 0.69). Afterward, the same model was applied to simulate urban development in 2021 and 2031. Built-up area per capita and Shannon entropy were then measured to assess urban sprawl in Sari. According to the results of change detection and simulation of urban growth of the study area, the built-up area had increased proportional to population growth since 1992. The same trend is expected to continue until 2031 when the urban area will exceed 2800 hectares. In addition, based on the relative values of Shannon entropy, although Sari has not yet faced the urban sprawl phenomenon, the absence of physical barriers around the city necessitates comprehensive urban management approaches to control urban sprawl and prevent future environmental problems