Land use and landscape pattern changes in coastal areas of Shandong province, China

In this paper, the Spatial-Markov model, which was based on the theory of Markov process and spatial analysis techniques, was proposed to simulate land use change and landscape dynamics. By the Spatial-Markov model, the study area could be divided into numerous lattices and land use change in each lattices was simulated separately by the Markov process model. The outputs of the model include a set of ratio scale images and a nominal scale image. The whole process of the model was fulfilled by compiling programs with AML in ArcGIS 9.3. The coastal area of Shandong province was selected as the case study area. Land use maps were extracted based on Landsat TM/ETM+ images captured in 2000, 2005, and 2010 respectively. Firstly, characteristics of land use change and landscape dynamics were analyzed. It showed that, from 2000 to 2010, urban area and rural settlement expanded dramatically by massively occupying farmland, which, in turn, drove grassland reclaimed to farmland. At the landscape level, the landscape fragmentation increased, and both the diversity and evenness of the landscape increased. Secondly, using land use maps in 2000 and 2005, the Spatial-Markov model was developed to simulate the land use map in 2010 at a spatial scale of 500 m. At the same time, the CA-Markov model was selected for model comparison, in specific, eleven driving factors were selected and the Logistic regression method was used to create the transitional maps for CA. Both Kappa coefficient and landscape indices were introduced to evaluate and compare the two models. It showed that the Spatial-Markov model not only achieved much higher Kappa coefficient, but also much better landscape indices than the CA-Markov model. Therefore, the Spatial-Markov model was applied to predict land use change and landscape dynamics in the next decade. Moreover, the prediction result shows that, from 2010 to 2020, areas of urban area and rural settlement will go on increasing, while areas of farmland will continue to decline. At the landscape level, all the landscape indices will follow their historical trend except for fractal dimension. As to the landscape indices at the class level, all landscape types will follow the same trend as before except for water and unused land.