Dominant transition probability: combining CA-Markov model to simulate land use change

Land use change models have been widely used to predict the changes in the future and provide different scenarios under different policies. However, most models commonly compare the transition proportions of change relying on the gross change, which generally fails to represent the inherent dominant transition of land categories. In this paper, Markov chain was used to obtain the dominant transition as global transition probability of each land use cell by separating the systematic information from random information. The dominant transition is transformed into transition probability and then combined with WLC (weighted linear combination) to calculate the new suitability map for cellular automata (CA) simulation. Thus, an improved CA model coupled with dominant transition and Markov chain was designed to improve the performance of simulating land use changes. Choosing Changping District of Beijing, China as the study area, this paper mined the driving factors and conversion relationship revealed by land use patterns in 1988 and 1995. Some land use types have the large area and result in large transition, but the expected transition is not exceptionally large, which used to revise the probability to simulate the land use status in 2000. The predictive power of the model was evaluated by Kappa index and fractal dimension, which reached the higher simulation accuracy and landscape index similarity. The improved model provides an effective and useful method to extract dominant transition to overcome some limitation of CA–Markov model. And the dominant transition information provides a possibility for ecologically risk identification.