Land Use Change Simulation Model Based on MCDM and CA and Its Application

  • Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education, Spatial Information Research Center of Fujian Province, Fuzhou University, Fuzhou 350002, China

Received date: 2006-08-21

  Revised date: 2007-11-26

  Online published: 2008-02-25

Supported by

Key Project of Science and Technology of Fujian Province, No. 2006Y0019, No.2007I0016, No.2005I011; Foundation Project of Fujian Province, No. D0710011; International Cooperation Project, No.2007DFA21600


A macro-micro integrated land use change model, Grey-Cellular automata (CA) -Multi-Criterion Decision-Making (MCDM)-Geographic Information System (GIS) based model (GCMG for short) which can simulate human decision making process was proposed. The GCMG model borrows the theoretical hypothesis of CLUE-S model which supposes that regional land use change is driven by its land use requirement and the land use distribution is in dynamic balance with land use demands and regional natural resources and socio-economic conditions. The GCMG model consists of both non-spatial and spatial part. The non-spatial part, so called macro model, calculates the changes of land use demand in the future based on experiential relationship of land use and its dominating drivers using the grey model. The spatial part, also called micro model, completes the land use allocation process whose total quantity is calculated by the non-spatial part with a combined method of MCDM, GIS and CA model. In the spatial part, firstly MCDM method was used to simulate the human decision making process for land use change considering socio-economic and bio-physical conditions; the results of which was brought into conversion rule of CA model; and the integration was finally implemented in GIS to model the land use allocation. To illustrate the functioning of GCMG model and its validation, it is applied in Longhai County to simulate land use change in 2010. As one of the typical counties at coastal area of Fujian Province, great changes in land use have taken place in Longhai County over the past decades, including the garden plots expansion, town land for urban and rural housing, and land for industrial and mining purpose. Firstly the GCMG simulation results are compared with map of the actual distribution of land use in 2000 for validation. The Kappa equals to 0.93 in the simulation at 10 m×10 m grid level and has gained satisfactory results. Then the validated model is applied to simulate the land use conversion probabilities under different decision-making scenarios. The results show that the basic farmland protection policy will determine the future land use change pattern. The application of GCMG model indicated that it can both simulate the land use demand at macro level and land suitability at micro level, thus possessing the ability of studying the multi-level land use system.

Cite this article

QIU Bingwen, CHEN Chongcheng . Land Use Change Simulation Model Based on MCDM and CA and Its Application[J]. Acta Geographica Sinica, 2008 , 63(2) : 165 -174 . DOI: 10.11821/xb200802006


[1] He Chunyang, Shi Peijun, Chen Jin. Research of scenarios simulation model based on dynamic model and CA. Science in China (Series D), 2005, 35(5): 464-473.
[何春阳, 史培军, 陈晋等. 基于系统动力学模型和元胞自动机模型的土地 利用情景模型研究. 中国科学(D 辑), 2005, 35(5): 464-473.]

[2] Li Xia, Yeh Anthony Gar-On. Neural-network-based cellular automata for realistic and idealized urban simulation. Acta Geographica Sinica, 2002, 57(2): 159-166.
[黎夏, 叶嘉安. 基于神经网络的单元自动机CA 及在真实和优化的城市模 拟. 地理学报, 2002, 57(2): 159-166.]

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

[4] Veldkamp A, Fresco L O. CLUE-CR: An integrated multi-scale model to simulate land use change scenarios in Costa Rica. Ecological Modeling, 1996, 91: 231-218.

[5] Veldkamp A, Fresco L O. CLUE: A conceptual model to study the conversion of land use and its effects. Ecological modeling, 1996, 85: 253-270.

[6] Koen P Overmars, Verburg P H. Multilevel modelling of land use from field to village level in the Philippines, Agricultural Systems, 2006(2/3): 435-456.

[7] Veldkamp A, Fresco L O. Reconstructing land use drivers and their spatial scale dependence for Costa Rica. Agricultural Systems, 1997, 55: 19-43.

[8] Hubacek K, Sun L. A scenario analysis of China's land use and land cover change: Incorporating biophysical information into input-output modeling. Structural Change and Economic Dynamics, 2001, 12: 367-397.

[9] Lambin E F, Rounsevell M D A, Geist H J. Are agricultural land-use models able to predict changes in land-use intensity? Agriculture, Ecosystems and Environment, 2000, 82: 321-331.

[10] Briassoulis H. Analysis of land use change: Theoretical and modeling approaches. Loveridege S (ed.). In the Web Book of Regional Science. West Virginia University, Morgantown, 2000.

[11] Zhou Chenghu, Sun Zhanli, Xie Yichun. The Research of Geography Cellular Automata. Beijing: Science Press, 1999. 1-163.
[ 周成虎, 孙战利, 谢一春. 地理元胞自动机研究. 北京: 科学出版社, 1999. 1-163.]

[12] Batty M, Xie Y. From cells to cities. Environment and Planning B: Planning and Design, 1994, 21: 531-548.

[13] Batty M, Yichun Xi, Zhanli Sun. Modeling urban dynamics through GIS-based cellular automata. Computers, Environment and Urban Systems, 1999, 23: 205-233.

[14] Almeida C M D, Michael Batty, Antonio Miguel Vieira Monteiro et al. Stochastic cellular automata modeling of urban land use dynamics. Computers, Environment and Urban Systems, 2003, 27: 481-509.

[15] Fang S, Gertner G Z, Sun Zhanli et al. The impact of interactions in spatial simulation of the dynamics of urban sprawl. Landscape and Urban Planning, 2005, 73(4): 294-306.

[16] Ward D P, Murray A T, Phinn S R. A stochastically constrained cellular model of urban growth. Computer, Environment and Urban System, 2000, 24: 539-558.

[17] Yeh Anthony Gar-On, Li Xia. Errors and uncertainties in urban cellular automata. Computers, Environment and Urban Systems, 2006, 30(1): 10-21.

[18] White R, Engelen G. High-resolution integrated modelling of the spatial dynamics of urban and regional systems. Computers, Environment and Urban Systems, 2000, 24: 383-400.

[19] White R, Engelen G, Uljee I. The use of constrained cellular automata for high-resolution modelling of urban land-use dynamics. Environment and Planning B, 1997, 24: 323-343.

[20] Wu F, Webster C J. Simulating artificial cities in a GIS environment: Urban growth under alternative regulative regimes. International Journal of Geographical Information Science, 2000, 14(7): 625-648.

[21] R Gil Pontius Jr, Joseph D Cornell, Charles A S Hall. Modeling the spatial pattern of land-use change with GEOMOD2: Application and validation for Costa Rica. Agriculture, Ecosystems and Environment, 2001, 85: 191-203.

[22] Douglas P Ward, Alan T Murray, Stuart R Phinn. Integrating spatial optimization and cellular automata for evaluating urban change. Ann. Reg. Sci., 2003, 37: 131-148.

[23] Deng Julong. Grey Control System. Wuhan: Huazhong University of Science and Technology Press, 1985.
[ 邓聚龙. 灰 色控制系统. 武汉: 华中工学院出版社, 1985.]

[24] Hopkins Lewis D. Methods for generating land suitability maps: A comparative evaluation. Journal of the American Institute of Planners, 1977, 43(4): 386-400.

[25] Michael J Hill, Robert Braaten, Simon M Veitch et al. Multi-criteria decision analysis in spatial decision support: The ASSESS analytic hierarchy process and the role of quantitative methods and spatially explicit analysis. Environmental Modelling & Software, 2005, 20: 955-976.

[26] Wu F, Webster C J. Simulation of land development through the integration of cellular automata and multicriteria evaluation. Environment and Planning B: Planning and Design, 1998, 25: 103-126.

[27] Eastman J R, Weigen Jin, Peter A K Kyem et al. Raster procedure for mulit-criteria decisions. American Society for Photogrammetry and Remote Sensing, 1995, 61(5): 530-547.

[28] Qiu Bingwen. Scale effect analysis of driving forces of land-use patterns of Longhai County in Fujian Province. Journal of Natural Resources, 2007, 22(1): 70-78.
[ 邱炳文. 福建省龙海市土地利用空间分布影响因子的尺度效应分析, 自 然资源学报, 2007, 22(1): 70-78.]

[29] Zheng Daoxi. Based on the new century, make new leap forward and try to press forward Zhangzhou urbanization process.
[郑道溪. 立足新世纪, 实现新跨越, 努力推进漳州城市 化进程.]

[30] Pontius R G. Quantification error versus location error in comparison of categorical maps. Photogrammetric Engineering & Remote Sensing, 2000, 66(8): 1011-1016.