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基于多尺度斑块分析(MSPA)的SLEUTH城市增长模型用于渥太华的绿地保护

MSPA-informed SLEUTH urban growth modeling for green space protection in Ottawa.

作者信息

Salmanmahiny Abdolrassoul, Mitchell Scott W, Bennett Joseph R

机构信息

Department of Geography and Environmental Studies, Carleton University, Ottawa, Canada.

Geomatics and Landscape Ecology Research Laboratory, Carleton University, Ottawa, Canada.

出版信息

PLoS One. 2025 Aug 8;20(8):e0328656. doi: 10.1371/journal.pone.0328656. eCollection 2025.

Abstract

We created optimal urban expansion scenarios that also safeguard green spaces using SLEUTH-3r in the National Capital Region, Ottawa, Ontario. The scenarios were based on using two exclusion layers in SLEUTH-3r modeling, adjustments to the model's calibrated growth coefficients for a compact city scenario and applying green space social equity weights to urban zones in model's prediction results. The first exclusion layer contained common restricted areas for urban growth, while the second additionally incorporated cores of green spaces defined through Morphological Spatial Pattern Analysis (MSPA), core importance and their corridors for connectivity. For each scenario, we selected 23,850 hectares as the required urban growth by the year 2050 and only 10% of this amount (2385 ha), to encourage more compact growth. We compared the scenarios based on the affected green space cores and urban growth polygons using Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). In most cases, scenarios incorporating MSPA were the favored ones. As the first attempt integrating MSPA definition of green space cores, their importance and connectivity into SLEUTH-3r model, we showed that MSPA-informed SLEUTH-3r modeling affects prediction results and provides a useful platform for generating scenarios. Incorporating MSPA information into SLEUTH-3r modeling enhanced the protection of green space cores and their connectivity. However, it also led to the selection of smaller urbanization polygons for the year 2050, distributed across the study area. Focusing on the preferred options, social equity weights and the selected polygons, provides city planners and stakeholders with valuable assistance and flexibility in designing urban growth scenarios while protecting green spaces.

摘要

我们利用SLEUTH - 3r模型在安大略省渥太华的国家首都地区创建了既能保障绿地又能实现最优城市扩张的情景。这些情景基于在SLEUTH - 3r建模中使用两个排除层,针对紧凑型城市情景调整模型的校准增长系数,并将绿地社会公平权重应用于模型预测结果中的城市区域。第一个排除层包含城市增长的常见限制区域,而第二个排除层额外纳入了通过形态空间格局分析(MSPA)定义的绿地核心、核心重要性及其连通走廊。对于每个情景,我们选择到2050年所需的城市增长面积为23,850公顷,且仅选取该面积的10%(2385公顷),以鼓励更紧凑的增长。我们使用逼近理想解排序法(TOPSIS),基于受影响的绿地核心和城市增长多边形对这些情景进行比较。在大多数情况下,纳入MSPA的情景更受青睐。作为首次将MSPA对绿地核心的定义、其重要性和连通性整合到SLEUTH - 3r模型中的尝试,我们表明基于MSPA的SLEUTH - 3r建模会影响预测结果,并为生成情景提供了一个有用的平台。将MSPA信息纳入SLEUTH - 3r建模增强了对绿地核心及其连通性的保护。然而,这也导致在2050年选择了较小的城市化多边形,分布在整个研究区域。关注首选方案、社会公平权重和选定的多边形,为城市规划者和利益相关者在设计城市增长情景同时保护绿地时提供了有价值的帮助和灵活性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/13fd/12334021/78b99159118d/pone.0328656.g001.jpg

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