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Land use and land cover change (LUCC) has been proving itself as an important phenomenon with significant impacts in environment, soil consumption, population health, and life quality. Understanding studying this observable fact can help policy makers and land planners to take better decisions. Even though spatial data infrastructures and users are growing, the application of knowledge to support spatial decisions has not met an equivalent increase (Murgante et al. 2009). Thus, studies to support spatial planning decisions are needed.
Markov chains are one way of analyzing and projecting land cover changes and have been successfully applied in many studies. (Turner 1987) compared the results of a Markov chain model with others from spatial simulation models to project land cover changes in Georgia, USA. (Muller and Middleton 1994) used first-order Markov chains to investigate the dynamics of land cover changes in Niagara, Canada, between 1935 and 1981. (Cabral and Zamyatin 2009) evaluated, using remotely sensed Landsat images, the influence of the Natural Park of Sintra-Cascais, in the land cover dynamics of the municipalities of Sintra and Cascais, Portugal, between 1989 and 2001, with Markov chains. (Iacono et al. 2012) used a Markov model with land cover data between 1958 and 2005 to estimate the fraction of available land for transportation in Minneapolis, USA. (Chen et al. 2013) investigated and projected future land cover changes using Markov chains in the mangrove forest of Honduras using Landsat images obtained between 1985 and 2013.
The Markov chains do not predict the changes of land use in a spatially explicit way which may limit the usefulness of the results obtained with these models. One way to overcome this limitation is to combine Markov chains with cellular automata. This combination of models has been used in studies related with LUCC modelling (Ahmed et al. 2013; Kityuttachai et al. 2013; Martins et al. 2012; Pontius JR and Malanson 2005; Tewolde and Cabral 2011).
This study is developed within the Landyn's research project which extends the period of analysis of previous studies of LUCC in continental Portugal back to the 80ies (DGT 2013). Landyn’s main objectives are (DGT 2013): (i) to provide a good understanding of the LUCC changes; (ii) to identify and understand the major driving forces of changes; (iii) to use a spatial model to build alternative scenarios of LUCC and; (iv) to study energy demand and Greenhouse Gases (GHG) emissions and removals.
In this paper, we report the results of the third objective of Landyn project. We start by investigating whether the LUCC in continental Portugal depend on the changes occurred in the previous time moment. If this hypothesis is proved, then the projection for future land cover using Markov chains is legitimate, considering that the past land change matrixes are stationary in time. Subsequently, a spatial model based on these land change matrixes is developed with cellular automata (CA) to reflect hypothetical scenarios of LUCC in the study area.