Simulation model of land use dynamics and application: Progress and prospects

Land Use and Land Cover change (LUCC) is a main research subject of global environmental change and sustainable development. LUCC is a complex and dynamic process that involves both natural and human systems. Land use simulation models are powerful tools for understanding the driving forces of LUCC, for supporting urban planning and decision making, and for providing important information to evaluate the ecosystem effects of LUCC. Land use dynamic systems are difficult to predict through traditional "top-down" models. The Cellular Automata (CA) model and Agent-Based Model (ABM) are "bottom-up" approaches that have attracted increasing attention as powerful modeling tools in simulating land use patterns and evolution processes. In a CA model, each cell has a finite number of states, which can represent land use or land cover types. Changes in individual cells are defined by transition rules and generate the macro pattern of land use changes. The CA model has outstanding advantages in simulating the natural driving factors of land use dynamics. However, the influences of human factors are difficult to represent in a CA model. ABM, by contrast, can reflect the decisions and behaviors of individuals, such as government, investors, and residents. In an ABM, agents can move and interact with each other and with the environment. These local interactions generate certain land use patterns on the global scale. Thus, the two models have distinct advantages in modeling land use dynamics. Currently, the development of the CA and ABM models has achieved several important breakthroughs. This paper summarizes the recent progress in land use simulation models from the perspective of theory and methodology, including scale sensitivity, CA transition and ABM behavior rules, and coupled CA and ABM models. This paper also outlines the applications of these models in virtual city simulation and theoretical verification, realistic city simulation and scenario prediction, multitype land use and cover simulation, and decision support. However, the current land use models have obvious limitations on some crucial issues, such as fine and large-scale simulation. Hence, this paper discusses these problems and proposes the inclusion of three-dimensional modeling, big data, and rule-mining for fine simulation, as well as large-scale simulation and knowledge transfer, in future studies.