Simulation of Landuse Changes and Urban Dynamics using CA-Markov Hybrid Model Case Study: Maragheh City

Subject Areas : Urban and Regional Planning Studies

Hoshang  sarvar ¹ (Assistant Professor Dep. of Geography and Urban Planning, University of Maragheh, Maragheh, Iran)

Received: 2018-04-30 Accepted : 2019-01-04 Published : 2020-02-20

Keywords: cellular automata, Markov chain, geographic information systems (GIS), Urban dynamics, Maragheh city,

Abstract :

In recent decades, along with urbanization, various models have been used to urban growth prediction. In this regard, Urban models based on the automata technique have emerged under the paradigm of a self-organizing system, with cellular automata (CA) being the simplest but most popular in action What happens to each grid cell is defined by a transition rule or transition rules.If the transition rule requires that the state of a grid cell is only dependent on its state at a previous time step, such a model is called a Markov model, and is not considered a CA model. Cellular automata models have one additional feature: the transition rules operate on cells based on the local neighborhood of those cells. In this research, the spatial expansion of Maragheh city was simulated using Cellular automata- Markov chain hybrid model. Satellite images (Landsat) were used for land cover mapping, urban growth monitoring, and modeling land cover changes. Results represent high efficiency of Cellular automata- Markov chain in the urban spatial growth simulation. In the past three decades,development trend of Maragheh city has been more towards barren lands.According to the output of the model,this trend will continue over the next 17 years. So that, the city will be expended due to the transition of barren lands cells state to urban cells state,and 774 hectares from surrounding barren lands will be converted to urban lands. However, with continue of the previous trend, nearly 417 hectares of good agricultural lands will also change to urban lands.

References:

Ahadnejad, M., Zolfi, A., & Shokripor Dizaj, H. (2011). Estimation and prediction of physical expansion of cities using multi-temporal satellite imagery and geographic information system (Case study of Ardabil city 1363-1400). Environmental planning, 4(15),107-124. (in Persian)

Amanpour, S., Alizadeh, H, & Ghorari, H. (1392). An Analysis of Finding Optimal Directions of Physical Development in Ardabil City Using the AHP Model. Regional Planning, 4(10), 83- 96. (in Persian)

Anderson, B.N., Howarth, R.W., & Walker, L.R. (2008). Ecology, planning, and management of urban forests: international perspectives. Berlin: Springer

As-syakur, A. R., Adnyana, I. W. S., Arthana I. W., & Nuarsa, I. W. (2012). Enhanced built-up and bareness index (EBBI) for mapping built-up and bare land in an urban area. Remote sensing, 4, 2957-2970.

Azizi Galati, S., Rangzan, K., Sadidi, J., Heidarian, P., & Taghizadeh, A. (1395). Predicting locational trend of land use changes using CA-Markov model (Case study: Kohmare Sorkhi, Fars province). Remote Sensing and Geographic Information System in Natural Resources, 7(1), 59-71. (in Persian)

Babaei Aghdam, F., & Ebrahimzadeh Asmin, H. (2013). Modeling land use changes and Bayer levels built in Ardebil Municipality model CLUE-S. Geography and Development, 10(26), 21-34. (in Persian)

Bagan, H., & Yamagata, Y. (2012). Landsat analysis of urban growth: How Tokyo became the world's largest megacity during the last 40 years. Remote Sensing of Environment, 127, 210–222.

Benenson, I., Torrens, P. M. (2004). Geosimulation: Object-based modeling of urban phenomena. Computers. Environment and Urban Systems, 28, 1–8.

Chen, X. L., Zhao, H. M., Li, P. X., & Yin, Z. Y. (2006). Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes. Remote Sensing of Environment, 104, 133–146.

Coulthard, T.J. (1999). Modelling upland catchment response to Holocene environmental change, school of geography, university of Leeds.

Dongjie, G., Weijun, G., Kazuyuki, W., & Hidetoshi, F. (2008). Land use change of Kitakyushu based on landscape ecology and Markov model. Journal of Geographical Sciences, 18, 455-468.

Eastman, J. R (a). (2012). IDRISI Selva Tutorial, Manual Version 17, Clark Labs, Clark University.

Eastman, J. R (b). (2012). IDRISI Selva Manual, Manual Version 17, Clark Labs, Clark University.

Fonstad, M. A. (2006). Cellular automata as analysis and synthesis engines at the geomorphology–ecology interface. Geomorphology, 77, 217-234.

Garcia, A. M., Sante, I. Crecente, R. & Miranda, D. (2011). An analysis of the effect of the stochastic component of urban cellular automata models. Computers, Environment and Urban Systems, 35, 289–296.

Guan, D., Li, H. Inohae, T. W. Nagaie, T. & Hokao, N. (2011). Modeling urban land use change by the integration of cellular automaton and Markov model. Ecological Modelling, 222, 3761-3772.

Gutman, G., Janetos, A. C. Justice, C. O. Moran, E. F. Mustard J. F. Rindfuss, R. R. Skole, D. Turner B. L. & Cochrane, M. A. (2004). Remote sensing and digital image processing. Springer.

Lee, J. A., Lee, S. S., & Chi, K. H. (2010). Development of an urban classification method using a built-up index. Selected topics in power systems and remote sensing. Iwate Prefectural University.

Lei, S., Jinghai, Z.,Shaohong, R., Yuanman, H., & Miao, L. (2012). Landscape pattern change prediction of Jinhu coastal area based on Logistic-CA-Markov model. Advances in information Sciences and Service Sciences, 4(11), 1-10.

Li, X., & Yeh, A. G. (2002). Integration of principal components analysis and cellular automata for spatial decision making and urban simulation. Science in China, 45(6), 521–529.

Liu, Y. (2009). Modelling urban development with geographical information systems and cellular automata, Taylor & Francis Group: CRC Press.

Mabodi, M., & Hakimi, H. (2015). An Analysis of Land Use Land Changes and Simulation of Urban Expansion in Central Midlands City (Case Study: Khoy City) Geographical research of urban planning, 3(2), 211-226. (in Persian) 

Menard, A., & Marceau, D. J. (2007). Simulating the impact of forest management scenarios in an agricultural landscape of Southern Quebec, Canada, using a geographic cellular automata. Landscape and Urban Planning, 79(3–4), 253–265.

Ministry of Roads and Urban Development, Department of Roads and Urban Development East Azerbaijan province, in 1390, the master plan of the city of Maragheh, Volume I: assessment of the city. (in Persian)

Mitsova, D., Shuster, W., & Wang, X. (2011). A cellular automata model of land cover change to integrate urban growth with open space conservation. Landscape and Urban Planning, 99, 141–153.

Moreno, N., Wang, F., & Marceau, D. J. (2009). Implementation of a dynamic neighborhood in a land-use vector-based cellular automata model. Computers, Environment and Urban Systems, 33(1), 44–54.

Movahed, A., Mostafavi, S., & Soran Ahmadi, M. (1393). Model for Open space-physical Saqez approach to sustainable urban form.Urban Planning Studies, 2 (5), 55-75. (in Persian)

Netzband, M., Stefanov, W. L., & Redman, C. (2007). Applied Remote Sensing for Urban Planning, Governance and Sustainability, Berlin: Springer.

Pan, Y., Roth, A. Yu, Z., & Doluschitz, R. (2010). The impact of variation in scale on the behavior of a cellular automata used for land use change modeling. Computers, Environment and Urban Systems, 34, 400–408.

Sante, I., Garcia, A., Miranda, M. D., & Crecente, R. (2010). Cellular automata models for the simulation of real-world urban processes: A review and analysis. Landscape and Urban Planning, 96, 108–122.

Van Vliet, J., White, R., & Dragicevic, S. (2009). Modeling urban growth using a variable grid cellular automata. Computers, Environment and Urban Systems, 33(1), 35–43.

Wang, F., Hasbani, J. G., Wang, X., & Marceau, D. J. (2011). Identifying dominant factors for the calibration of a land-use cellular automata model using Rough Set Theory. Computers, Environment and Urban Systems, 35, 116–125.

White, R., Engelen, G., & Uljee, I. (1997). The use of constrained cellular automata for high-resolution modelling of urban land-use dynamics. Environment and Planning B: Planning and Design, 24(3), 323–343.

Wolfram, S. (1984). Computation theory of cellular automata. Communications in Mathematical Physics, 96, 15–57.

Wu, F. (2002). Calibration of stochastic cellular automata: the application to rural-urban land conversions. International Journal of Geographical Information Science, 16(8), 795-818.

Xin, Y., Xin-Qi, Z., & Li-Na, L. (2012). A  spatiotemporal  model  of  land  use change  based  on  ant  colony  optimization, Markov  chain  and  cellular  automata. Ecological Modelling, 233, 11-19.

Xu, H. (2007). Extraction of urban built-up land features from Landsat imagery using a thematic- oriented index combination technique. Photogrammetric engineering & remote sensing, 73(12), 1381-1391.

Yang, X., & Li, J. (2013). Advances in mapping from remote sensor imagery: techniques and applications, Taylor & Francis Group: CRC Press.