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Prediction of Land Cover Change Using Markov and Cellular Automata Models: Case of Al-Ain, UAE, 1992-2030

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Abstract

The UAE has witnessed rapid urban development and economic growth in recent years. With its ambitious vision to become one of the advanced nations by 2021, planners and policy-makers need to know the most likely direction of future urban development. In this study, remotely sensed imagery coupled with cellular automata models were used to predict land cover in Al Ain, the second largest city in the Emirate of Abu Dhabi. Markov and cellular automata models were used for 1992 and 2006 to predict land cover in 2012. Land Use and Land Cover maps for the study area were derived from 1992, 2006, and 2012 Landsat satellite images (TM, ETM+). The models achieved an overall accuracy of approximately 80 %. A Markov model was applied for 2006 and 2012 to predict land cover in 2030. The results conformed to the general trend of the Al Ain Master Plan 2030. This study demonstrates that remote sensing, with the availability of free Landsat data, is a viable technology that could be used to help in the prediction process especially in developing countries, where data availability is a problem.

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Acknowledgments

Thanks are due to Dr. Nazmi Saleous and Mr. Ahmad Nassar for their valuable support and assistance. Gratitude is extended to Dr. Gerald George Garland and Professor Ian MacLachlan for their efforts in editing the paper. The Editor of the Journal of the Indian Society of Remote Sensing and the anonymous reviewers are acknowledged for their constructive suggestions and comments.

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Authors and Affiliations

  1. Department of Geography, Faculty of Humanities and Social Sciences, United Arab Emirates University, P. O. Box 17771, Al Ain, United Arab Emirates

    M. M. Yagoub & Abdallah Ahmad Al Bizreh

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  1. M. M. Yagoub
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  2. Abdallah Ahmad Al Bizreh
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Correspondence to M. M. Yagoub.

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Yagoub, M.M., Al Bizreh, A.A. Prediction of Land Cover Change Using Markov and Cellular Automata Models: Case of Al-Ain, UAE, 1992-2030. J Indian Soc Remote Sens 42, 665–671 (2014). https://doi.org/10.1007/s12524-013-0353-5

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  • DOI: https://doi.org/10.1007/s12524-013-0353-5

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