Modeling Land Use/Cover Changes by the Combination of Markov Chain and Cellular Automata Markov (CA-Markov) Models

Land use/cover changes modeling is essential for land use planning and management. Arak is one of several cities in Iran which have undergone swift urban expansion during recent decades due to rapid industrialization and population growth. In this study, aerial photos and Landsat TM and IRS-P6 LISS-III images were used to predict land use/cover changes in Arak. Land use/cover maps were generated with four classes from visual interpretation of aerial photos and an artificial neural network classification method for satellite images. Both classification methods resulted in land use/cover maps with overall accuracy over 95 %. In order to predict changes, Markov chain and Cellular Automata Markov models were applied and a land use/cover map for 2025 was simulated. The results showed that the combination of satellite remote sensing, GIS and Markov models provides useful information on land use/cover dynamics in future which could be consequently used for land use planning

Keywords:

CA-Markov model , land use/cover , neural network classification , remote sensing