Monitoring and predicting land use change in Tripoli Metropolitan City using an integrated Markov chain and cellular automata models in GIS

The rapid development of cities in developing countries results in deteriorating of agricultural lands. The majority of these agricultural lands are converted to urban areas, which affects the ecosystems. In this research, an integrated model of Markov chain and cellular automata models was applied to simulate urban land use changes and to predict their spatial patterns in Tripoli metropolitan area, Libya. It is worth mentioning that there is not much research has been done about land use/cover change in Libyan cities. In this study, the performance of integrated CA–Markov model was assessed. Firstly, the Markov chain model was used to simulate and predict the land use change quantitatively; then, the CA model was applied to simulate the dynamic spatial patterns of changes explicitly. The urban land use change from 1984 to 2010 was modelled using the CA–Markov model for calibration to compute optimal transition rules and to predict future land use change. In validation process, the model was validated using Kappa index statistics which resulted in overall accuracy more than 85 %. Finally, based on transition rules and transition area matrix produced from calibration process, the future land use changes of 2020 and 2025 were predicted and mapped. The findings of this research showed reasonably good performance of employed model. The model results demonstrate that the study area is growing very rapidly especially in the recent decade. Furthermore, this rapid urban expansion results in remarkable continuous decrease of agriculture lands.

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