In recent years

In recent years, global change research has been one of the most active areas of research internationally. Human activities are continually changing the environment on an unprecedented scale and speed. Land use change is one of the important research subjects on understanding of global environmental change and sustainable development of industries, agriculture and so on. The concentration of land use in response to world population growth and its expenditure for the environment warrant in-depth studies of these transformations (Wu et al., 2006). Several international interdisciplinary research projects have been initiated during the past two decades for this purpose. Both of these projects indicated the need to construct an updated and accurate database concerning these changes, their meaning, their pace and the explanatory factors prompting their appearance (Mather, 1999). IGBP and IHDB have launched a plan of “Land use /Cover Change (LUCC)” in 1995,sincw then LUCC has been an advanced and hot subject in global environment change research (Meyer and Turner, 1996; Verburg et al., 1999; Dai et al., 2001; Geist and Lambin, 2001; Veldkamp and Lambin, 2001; Susanna and Chen, 2002; Honnay et al., 2003; Quan et al., 2006; Luciana et al., 2007; Ge et al., 2007; Fikir et al., 2009). The studies on LUCC can be shortened as three issues: dynamic analysis, driving forces, and global and regional models of LUCC (Henk and Latesteijn, 1995; Fischer and Sun, 2001; Pijanowski et al., 2002; Gautam et al., 2003; Kline, 2003; Aspinall, 2004; Patma et al., 2004; Erika et al., 2005; Shao et al., 2005; Guan et al., 2008). In recent years, the LUCC community has produced a large set of operational models used to predict and explore land use change trajectories (Verburg et al., 2006). The models cannot support only the exploration of future land use changes under different conditions but also support land use planning and policy. All these models can divide into: empirical and statistical models such as Markov chains and Regression model, dynamic models such as Cellular Automata (CA) model, Agent-based model and System dynamic model, integrated model such as CLUE (Conversion of Land Use and its Effects) model. Empirical and statistical model can complete dynamic simulation. Dynamic models appear to be better to be suited to predict land use changes in the future. Integrated model that is based on multidisciplinary and combining elements of different modelling techniques.
A Markov-CA model included with geographic information system (GIS) data is claimed to be proper approach to model the temporal and spatial change of land use (Myint and Wang, 2006; Courage et al., 2009). In the Markov-CA model, Markov chain process controls temporal change among the land use types based on transition matrices (L√≥peza et al., 2001). Since LUCC has a direct and indirect impact on a number of factors of natural environment, as well as the regional and global sustainable development, the land change modelling has attracted increasing attention in the perspective global climate change (Li, 1996; Wijesekara et al., 2012). The continuous evaluation and transformation of land surface has resulted in serious consequence to the physical system at multiple scales, and raised a number of change in the ecological processes, such as surface runoff, soil erosion and agricultural non-point source pollution (Wijesekara et al., 2012; Li et al., 2010; Ouyang et al., 2010). The Markov model can quantitatively predict the dynamic changes of landscape pattern, while it can’t deal with the spatial pattern of landscape change (Balzter et al., 1998). The accuracy of CA-Markov decreased when the model tried to predict for a longer period of time, possibly due to the fact that a uniform transition rule was used by the model throughout the simulation period (Samat et al. (2011)). In general, the LUCC spatial modelling objectives are: 1). to measure the LUCC and explain its dynamics; 2). to identify the spatial pattern of LUCC and urban expansion rate; 3). to model the spatial relationship between LUCC and its driving factors; and 4). to predict the LUCC sensitivity in the future.