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Assessment of landscape changes under different urban dynamics based on a multiple-scenario modeling approach
Environment and Planning B: Urban Analytics and City Science ( IF 3.511 ) Pub Date : 2020-03-11 , DOI: 10.1177/2399808320910161
Chao Xu 1 , Dagmar Haase 2 , Meirong Su 3 , Yutao Wang 4 , Stephan Pauleit 5
Affiliation  

In the context of rapid urbanization, it remains unclear how urban landscape patterns shift under different urban dynamics, in particular taking different influencing factors of urban dynamics into consideration. In the present study, three key influencing factors were considered, namely, housing demand, spatial structure, and growth form. On this basis, multiple urban dynamic scenarios were constructed and then calculated using either an autologistic regression–Markov chain–based cellular automata model or an integer programming-based urban green space optimization model. A battery of landscape metrics was employed to characterize and quantitatively assess the landscape pattern changes, among which the redundancy was pre-tested and reduced using principal component analysis. The case study of the Munich region, a fast-growing urban region in southern Germany, demonstrated that the changes of the patch complexity index and the landscape aggregation index were largely similar at sub- and regional scales. Specifically, low housing demand, monocentric and compact growth scenarios showed higher levels of patch complexity but lower levels of landscape aggregation, compared to high housing demand, polycentric and sprawl growth scenarios, respectively. In contrast, the changes in the landscape diversity index under different scenarios showed contrasting trends between different sub-regional zones. The findings of this study provide planners and policymakers with a more in-depth understanding of urban landscape pattern changes under different urban planning strategies and its implications for landscape functions and services.

中文翻译:

基于多场景建模方法的不同城市动态下的景观变化评估

在快速城市化的背景下,城市景观格局如何在不同的城市动态下发生变化尚不清楚,特别是考虑到城市动态的不同影响因素。在本研究中,考虑了三个关键影响因素,即住房需求、空间结构和增长形式。在此基础上,构建多个城市动态场景,然后使用基于自回归-马尔可夫链的元胞自动机模型或基于整数规划的城市绿地优化模型进行计算。采用一系列景观指标来表征和定量评估景观格局变化,其中使用主成分分析对冗余进行了预测试和减少。慕尼黑地区的案例研究,德国南部一个快速增长的城市地区,表明斑块复杂性指数和景观聚集指数的变化在子和区域尺度上基本相似。具体而言,与高住房需求、多中心和蔓延增长情景相比,低住房需求、单中心和紧凑增长情景分别显示出更高水平的斑块复杂性但较低水平的景观聚合。相比之下,不同情景下景观多样性指数的变化在不同次区域之间呈现出对比鲜明的趋势。本研究的结果让规划者和政策制定者更深入地了解不同城市规划策略下的城市景观格局变化及其对景观功能和服务的影响。表明斑块复杂性指数和景观聚集指数的变化在子和区域尺度上基本相似。具体而言,与高住房需求、多中心和蔓延增长情景相比,低住房需求、单中心和紧凑增长情景分别显示出更高水平的斑块复杂性但较低水平的景观聚合。相比之下,不同情景下景观多样性指数的变化在不同次区域之间呈现出对比鲜明的趋势。本研究的结果让规划者和政策制定者更深入地了解不同城市规划策略下的城市景观格局变化及其对景观功能和服务的影响。表明斑块复杂性指数和景观聚集指数的变化在子和区域尺度上基本相似。具体而言,与高住房需求、多中心和蔓延增长情景相比,低住房需求、单中心和紧凑增长情景分别显示出更高水平的斑块复杂性但较低水平的景观聚合。相比之下,不同情景下景观多样性指数的变化在不同次区域之间呈现出对比鲜明的趋势。本研究的结果让规划者和政策制定者更深入地了解不同城市规划策略下的城市景观格局变化及其对景观功能和服务的影响。
更新日期:2020-03-11
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