区域尺度城市增长动态模拟(Reg-UGM) 是城市化影响下的土地利用/覆盖变化以及全球 环境变化研究的重要手段之一。针对单个城市增长未来情景模拟难以在区域尺度刻画与解释 城市化影响下的土地利用/覆盖变化以及与全球环境变化交互过程与作用机理主要问题，本研 究集成人工神经网络模型(ANN) 与元胞自动机模型(CA) 构建适合不同情景的区域尺度城市 增长动态模型，通过1979 年以来高分辨率遥感信息探测获取的4 个时段京津唐都市圈城市增 长时空特征分析提取先验知识规则，模拟京津唐都市圈在基准模式、经济模式、政策模式与 结构调整模式未来不同情景模式下城市增长过程。研究表明，Reg-UGM在模拟区域尺度受国 家宏观政策等影响城市非线性增长过程具有较好的可靠性，为进一步研究城市化影响下的宏 观生态效应提供前提保障。模拟结果显示京津唐都市圈在不同情景模式下城市增长呈现较大 的时空格局差异，未来城市增长结构调整模式更符合区域城市化的实际情况，未来区域城市 增长将由特大城市向中小城市转移，滨海沿线城镇发展带将经历更快的城市增长过程。
匡文慧, 刘纪远, 邵全琴, 何剑锋, 孙朝阳, 田汉勤, 班艺舫
. 区域尺度城市增长时空动态模型及其应用[J]. 地理学报, 2011
, 66(2)
: 178
-188
.
DOI: 10.11821/xb201102004
Dynamic urban expansion simulation at regional scale is one of the important research methodologies in Land Use/Cover Change (LUCC) and global environmental change influenced by urbanization. However, previous studies indicate that the single urban expansion simulation for future scenarios at local scale cannot meet the requirements for characterizing and interpreting the interactive mechanisms of regional urbanization and global environmental change. This study constructed a regional Dynamic Urban Expansion Model(Reg-DUEM) suitable for different scenarios by integrating the Artificial Neural Network (ANN) and Cellular Automaton (CA) model. Firstly we analyzed the temporal and spatial characteristics of urban expansion and acquired a prior knowledge rules using land use/cover change datasets of Beijing-Tianjin-Tangshan metropolitan area. The future urban expansion under different scenarios is then simulated based on a baseline model, economic models, policy models and the structural adjustment model. The results indicate that Reg-DUEM has good reliability for a non-linear expansion simulation at regional scale influenced by macro-policies. The simulating results show that future urban expansion patterns from different scenarios of the metropolitan area have the tremendous spatio-temporal differences. Future urban expansion will shift quickly from Beijing metropolis to the periphery of Tianjin and Tangshan city along coastal belt.
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