Simulating land use change by integrating ANN-CA model and landscape pattern indices

Artificial neural network–cellular automata model has been applied successfully in land use change simulation. However, it has rarely been integrated with landscape pattern indices (LPIs), the embodiment of the spatial heterogeneity of land use. This paper proposed to integrate LPIs as the parameters of artificial neural network–cellular automata model. Subsequent to a description of the principles and implementation of the model, a case study was presented in Changping district. In the case study, two LPIs, the landscape similarity index and patch density, along with 10 other spatial variables, were selected as the influencing factors of land use change. Based on land use maps in years 1988 and 1998, a land use map in 2008 was simulated by the proposed model. Comparing with the actual land use map in 2008 and the simulated result of artificial neural network–cellular automata model, the results showed that the proposed model is more applicable for simulating land use change in the study area; the limitation of this model is the spatial scale and calculation method of LPIs