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Modeling urban land use change by the integration of cellular automaton and Markov model

DongJie Guan, HaiFeng Li, Takuro Inohae, Weici Su, Tadashi Nagaie and Kazunori Hokao

Ecological Modelling, 2011, vol. 222, issue 20, 3761-3772

Abstract: Spatially land use models are indispensable for sustainable land use planning. This study demonstrates a combined Markov–Cellular Automata model to analyze temporal change and spatial distribution of land use stressed by natural and socioeconomic factors in Saga, Japan. Firstly, area change and spatial distribution of land use are calculated using GIS technology, and then the transition among different land use types is analyzed to obtain the transformation matrices during a period of 1976–2006. Meanwhile, an integration evaluation procedure with natural and socioeconomic data is used to generate the transition potential maps. Secondly, using the transition potential maps and transition matrices, a Markov–Cellular Automata model is established to simulate spatial distribution of land use in 2006. Finally, we use this Markov–Cellular Automata model to forecast the future land use changes during the period of 2015–2042. As a consequence, area change simulation predicts a continuing downward trend in agriculture land and forestland areas, as well as an upward trend in built-up areas; spatial distribution simulation indicates that built-up land will expand toward suburban regions, and land use of urban center is at the decline stage. Hence, if the current trends keep constant without holistic sustainable development measures, severe land use decline will ensue. The study is anticipated to help local authorities better understand and address a complex land use system, and develop the improved land use management strategies that can better balance urban expansion and ecological conservation.

Keywords: Land use change; GIS; Markov model; Cellular Automata model; Saga in Japan (search for similar items in EconPapers)
Date: 2011
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Handle: RePEc:eee:ecomod:v:222:y:2011:i:20:p:3761-3772