SPATIAL DEFORESTATION MODELILNG USING CELLULAR AUTOMATA (CASE STUDY: CENTRAL ZAGROS FORESTS)

Abstract

Abstract. Forests have been highly exploited in recent decades in Iran and deforestation is going to be the major environmental concern due to its role in destruction of natural ecosystem and soil cover. Therefore, finding the effective parameters in deforestation and simulation of this process can help the management and preservation of forests. It helps predicting areas of deforestation in near future which is a useful tool for making socioeconomic disciplines in order to prevent deforestation in the area. Recently, GIS technologies are widely employed to support public policies in order to preserve ecosystems from undesirable human activities. The aim of this study is modelling the distribution of forest destruction in part of Central Zagros Mountains and predicting its process in future. In this paper we developed a Cellular Automata (CA) model for deforestation process due to its high performance in spatial modelling, land cover change prediction and its compatibility with GIS. This model is going to determine areas with deforestation risk in the future. Land cover maps were explored using high spatial resolution satellite imageries and the forest land cover was extracted. In order to investigate the deforestation modelling, major elements of forest destruction relating to human activity and also physiographic parameters was explored and the suitability map was produced. Then the suitability map in combination with neighbourhood parameter was used to develop the CA model. Moreover, neighbourhood, suitability and stochastic disturbance term were calibrated in order to improve the simulation results. Regarding this, several neighbourhood configurations and different temporal intervals were tested. The accuracy of model was evaluated using satellite image. The results showed that the developed CA model in this research has proper performance in simulation of deforestation process. This model also predicted the areas with high potential for future deforestation.