ORIGINAL RESEARCH article
Front. Environ. Sci.
Sec. Social-Ecological Urban Systems
Volume 13 - 2025 | doi: 10.3389/fenvs.2025.1642739
Green Space System Planning and Optimization Coupling Landscape Pattern Analysis with Spatial Models: A Case Study of Fuzhou, China
Provisionally accepted
- 1Jimei University, Xiamen, China
- 2Huazhong University of Science and Technology, Wuhan, China
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The network structure and connectivity of green spaces play a crucial role in ecosystem functionality. However, there are still many challenges in improving the structure of urban green space systems (GSS) through quantitative scientific methods.In particular, there is an obvious lack of how to integrate quantitative landscape pattern analysis with multi-scenario network analysis, which leads to insufficient scientific and operationalization of green space system optimization. This paper aims to present a methodological framework for planning and constructing green networks within urban green space system planning (GSSP), using the GSSP of Fuzhou as a case study. The methods include using Fragstats to assess the current landscape pattern, providing guidance for planning directions; employing Conefor to classify Green Protection Areas (GPA); and identifying minimum resistance ecological corridors and strategic nodes through ArcGIS. Additionally, the gravity model was applied to classify the ecological corridors of Fuzhou. The analysis demonstrates that these planning methods, combined with manual identification and scenario network analysis, offer enhanced visualization and robust support for constructing green networks within urban GSSP. This research establishes a structured GSSP approach that not only addresses urban ecological continuity issues but also provides a replicable model for enhancing biodiversity and ecological health in urban settings, offering insights and implications for achieving sustainable development goals in future regions.
Keywords: Ecological corridor, green infrastructure planning, green network, Minimum resistance, Gravity model, Network analysis
Received: 07 Jun 2025; Accepted: 11 Aug 2025.
Copyright: © 2025 Chen, Zhou and Wu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Qingqing Zhou, Huazhong University of Science and Technology, Wuhan, China
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