Scenario simulation and forecast of land use/cover in northern China

Modeling land use/cover scenario changes and its potential impacts on structure and functions of ecosystem in typical regions are helpful to understanding the interactive mechanism between land use/cover system and ecosystem. Based on the analysis of the existing land use/cover simulation and forecast models, a land use/cover scenario dynamics model by the integration of System Dynamics (SD) model, Back Propagation Neural Network (BPNN) and Cellular Automata (CA) model is developed with land use/cover scenario changes in northern China in the next 30 years and simulated in this paper. The model is to simulate the land use/cover scenario demands by using a SD model at first, and then allocating the land use scenario patterns at the local scale with the considerations of land use/cover suitability, inheritance ability and neighborhood effect by using BPNN-CA model to satisfy the balance between land use/cover scenario demands and supplies. It integrates the advantages of SD, BPNN and CA. Macro-driving factors and the micro-spatial pattern are also fully taken into account. The BPNN simplifies the identification of the factors’ weights used in CA model and improves the reliability of the simulation results. The simulation accuracy of the model developed in this paper was found to be about 74%. It suggests that the model has the ability to reflect the complexity of land use/cover system at different scales to some extent and it is a useful tool for assessing the potential impacts of land use system on ecosystem. The simulated results also indicate that the urban land, water area and forest will increase significantly, and farmland and unable land will decrease gradually. Obvious land use/cover changes will take place in the farming-pastoral zone and the southeast area of northern China. Supported by the Science & Technology Research Project of Chongqing Municipal Education Commission (Grant No. KJ070811), the Doctor Startup Fund of Chongqing Normal University (Grant No. 06XLB004) and the National Key Project for Basic Sciences of China (Grant No. 2006CB400505)