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Research Article

Analysis of Landscape Pattern Based on the CA-Markov Model

Y.H. Zhao, S. Fang, X.F. Wang and X. Huang

Based on the remote sensing images of Landsat Thematic Mapper (TM), China-Brazil Earth Resources Satellite (CBERS) and Environment and Disaster Monitoring and Forecasting (SSMFDE), this study analyzed the landscape characteristics and spatial pattern of Xi’an City, predicted its future landscape changes and proposed data conversion methods for the landscape pattern prediction. These analyses were by the ENVI, ARCGIS and IDRISI software. The results showed that the study area had a composite landscape matrix consisted of woodland and farmland from 2000 to 2020. The areas of the farmland and grassland will continue to decrease and those of the woodland, construction land, waters and unused land will increase until 2020. The vegetation coverage in the study area would remain high in 2020, corresponding to an excellent ecological environment that would not restrict social and economic development. The difference causes between the simulated landscape pattern with CA-Markov model and the interpreted landscape pattern from remote sensing images were discussed. A major issue needed to be improved for the CA-Markov model was proposed. The data processing and simulating procedures used in this study may significantly streamline the workload and boost efficiency.

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  How to cite this article:

Y.H. Zhao, S. Fang, X.F. Wang and X. Huang, 2013. Analysis of Landscape Pattern Based on the CA-Markov Model. Journal of Applied Sciences, 13: 1889-1894.

DOI: 10.3923/jas.2013.1889.1894


Adhikari, S. and J. Southworth, 2012. Simulating forest cover changes of bannerghatta national park based on a CA-markov model: A remote sensing approach. Remote Sens, 4: 3215-3243.
CrossRef  |  

Aitkenhead, M.J. and I.H. Aalders, 2009. Predicting land cover using GIS, Bayesian and evolutionary algorithm methods. J. Environ. Manage., 90: 236-250.
CrossRef  |  Direct Link  |  

Arsanjani, J.J., M. Helbich, W. Kainz and A.D. Boloorani, 2012. Integration of logistic regression, Markov chain and cellular automata models to simulate urban expansion. Int. J. Applied Earth Obs. Geoinform., 21: 265-275.
CrossRef  |  Direct Link  |  

Arsanjani, J.J., W. Kainz and A.J. Mousivand, 2011. Tracking dynamic land-use change using spatially explicit Markov Chain based on cellular automata: The case of Tehran. Int. J. Image Data Fusion, 2: 329-345.
CrossRef  |  

Auch, R., J. Taylor and W. Acevedo, 2004. Urban growth in American Cities: Glimpses of U.S. urbanization. U.S. Geological Survey Circular.

Eastman, J.R., 2006. IDRISI andes guide to GIS and image processing. Clark University, Clark Labs, IDRISI Productions, Worcester, MA.

Guan, D., H. Li, T. Inohae, W. Su, T. Nagaie and K. Hokao, 2011. Modeling urban land use change by the integration of cellular automaton and Markov model. Ecol. Modell., 222: 3761-3772.
CrossRef  |  Direct Link  |  

Han, W.Q. and Y. Chang, 2004. The Markov model analysis of landscape dynamic: A case researches in Changbai Mountain Natural Reserve. Acta Ecologica Sinica, 24: 1958-1965.

Hu, Z. and C.P. Lo, 2007. Modeling urban growth in Atlanta using logistic regression. Comput. Environ. Urban Syst., 31: 667-688.
CrossRef  |  Direct Link  |  

Kamusoko, C., M. Aniya, B. Adi and M. Manjoro, 2009. Rural sustainability under threat in Zimbabwe-simulation of future land use/cover changes in the Bindura district based on the Markov-cellular automata model. Applied Geogr., 29: 435-447.
CrossRef  |  Direct Link  |  

Kaplan, D.H., J.O. Wheeler and S.R. Holloway, 2008. Urban Geography. 1st Edn., John Wiley, New York.

Luo, J. and Y.H.D. Wei, 2009. Modeling spatial variations of urban growth patterns in Chinese cities: The case of Nanjing. Landscape Urban Plann., 91: 51-64.
CrossRef  |  Direct Link  |  

Mondal, P. and J. Southworth, 2010. Evaluation of conservation interventions using a cellular automata-Markov model. Forest Ecol. Manage., 260: 1716-1725.
CrossRef  |  Direct Link  |  

Sang, L., C. Zhang, J. Yang, D. Zhu and W. Yun, 2011. Simulation of land use spatial pattern of towns and villages based on CA-Markov model. Math. Comput. Modell., 54: 938-943.
CrossRef  |  Direct Link  |  

Torrens, P.M., 2006. Geosimulation and its Application to Urban Growth Modeling. Springer-Verlag, London, pp: 119-134.

Wang, S.Q., X.Q. Zheng and X.B. Zang, 2012. Accuracy assessments of land use change simulation based on Markov-cellular automata model. Procedia Environ. Sci., 13: 1238-1245.
CrossRef  |  Direct Link  |  

Yang, X. and Z. Liu, 2005. Use of satellite-derived landscape imperviousness index to characterize urban spatial growth. Comput. Environ. Urban Syst., 29: 524-540.
CrossRef  |  Direct Link  |  

Zhou, D., Z. Lin and L. Liu, 2012. Regional land salinization assessment and simulation through cellular automaton-Markov modeling and spatial pattern analysis. Sci. Total Environ., 439: 260-274.
CrossRef  |  

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