Evaluation of Spatiotemporal Dynamics of Simulated Land Use/Cover in China Using a Probabilistic Cellular Automata-Markov Model

Using the fuzzy rule-based classification method, normalized difference vegetation index (NDVI) images acquired from 1982 to 1998 were classified into seventeen phases. Based on these classification images, a probabilistic cellular automata-Markov Chain model was developed and used to simulate a land cover scenario of China for the year 2014. Spatiotemporal dynamics of land use/cover in China from 1982 to 2014 were then analyzed and evaluated. The results showed that the change trends of land cover type from 1998 to 2014 would be contrary to those from 1982 to 1998. In particular, forestland and grassland areas decreased by 1.56% and 1.46%, respectively, from 1982 to 1998, and should increase by 1.5% and 2.3% from 1998 to 2014, respectively.