Delineating early warning zones in rapidly growing metropolitan areas by integrating a multiscale urban growth model with biogeography-based optimization. (January 2020) - Xia, Chang; Zhang, Anqi; Wang, Haijun; Liu, Jiafeng
Highlights: A framework was established for delineating early warning zones in metropolitan areas. A multiscale urban growth model and biogeography-based optimization were integrated. Different economic development strategies and spatial planning policies were considered. Early warning zones and suggestions for future urban growth in the YRMRM were put forward. The proposed framework can facilitate large-scale land supervision of illegal development. Abstract: Urban agglomerations have become the core areas of new urbanization in China, leading to major structural adjustments in regional development patterns and thus threatening the ecosystem. Predicting future urban growth and delineating early warning zones can provide scientific guidance for land use planning and ecological protection. However, limited research has targeted fast-urbanizing metropolitan areas, which can be particularly difficult compared with existing approaches conducted in a single city. Another concern is that the adjustable demand of urban land in the future can invalidate simple and rigid zoning schemes. This study proposed a framework of delineating hierarchical early warning zones to represent different probabilities of potential development in vulnerable areas. Specifically, a multiscale urban growth model and biogeography-based optimization (BBO) were integrated for calibrating transition rules and simulating historical urban evolution (2005–2015) in the rapidly growing Yangtze River middle reaches megalopolis (YRMRM) in China. Then, the future urban land demand in 2025 was predicted under different economic development strategies, and multiple urban growth scenarios were simulated by coupling urban demand and spatial planning policies. Finally, simulation results of different scenarios were overlaid in a GIS environment to provide three levels of zones for the early warning of illegal development in the protected areas. Results indicate that BBO performed better in calibrating parameters of the urban growth model than did particle swarm optimization and genetic algorithm. The prediction of future urban dynamics suggests that urbanization will continue in this region until 2025, and these early warning areas should be given attention for farmland protection and ecological security in the YRMRM.