Quantitative analysis of scale sensitivity in geographic cellular automata

Geographical Cellular Automata (GCA) approach is based on complexity theory and is widely used in geospatial modeling. A reason for the increasing attention given to GCA models is that they can easily be integrated with rasterbased GIS environment. However, the behavior of the GCA models is affected by uncertainties arising from the interaction between model elements, structures, and the quality of data sources used as model input. The objective of this study is to examine the impacts of model elements on the generated outputs of a GIS-based GCA land-use growth model using sensitivity analysis (SA) approach. The proposed SA method consists of KAPPA index with different spatial metrics. A stochastic GCA model was built to model land use change in the changsha region (Hunan,China). The transition rules were empirically derived from four Landsat-TM (30m resolution) images taken in 1996,1999, 2002 and 2005 that have been resampled to four resolutions (30, 60, 90, 120m). Five different neighbourhood configurations were considered (Moore, Von Neumann, and circular approximations of 2, 3 and 4 cell radii). Simulations were performed for each of the twenty spatial scale scenarios. Results show that spatial scale has a considerable impact on simulation dynamics in terms of both land use area and spatial structure. The spatial scale domains present in the results reveal the nonlinear relationships that link the spatial scale components to the simulation results.