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Optimizing regional ecological governance through landscape pattern analysis: Insights from the “Pattern-Process-Service” framework
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DOI：10.1016/j.ecoleng.2026.108015.png)
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Abstract
En 中文
Intensifying urbanization profoundly alters landscape patterns, degrading ecosystem services (ES) and challenging regional sustainability. While optimizing landscape structure is a primary policy objective, effective governance is often hampered by an insufficient quantitative understanding of how multi-dimensional landscape patterns influence ES. To bridge this gap, this study develops a spatially explicit analytical framework guided by the “pattern–process–service” paradigm. In a case study of Zhejiang Province (2005–2020), four key ESs and three dimensions of landscape patterns, namely spatial aggregation features (SAF), shape-edge features (SEF), and diversity features (DF), were quantified. Subsequently, Geographically Weighted Regression (GWR) was utilized to dissect the response mechanisms and spatial heterogeneity at both the overall landscape and key ecological class levels, including forests, grasslands, and water bodies. The results reveal distinct structural dependencies. At the landscape level, ES exhibited a significant positive correlation with SAF but negative correlations with SEF and DF. However, class-level responses were more complex, indicating that the ecological benefits of specific land covers are shaped by their distinct morphological characteristics. Ultimately, these GWR findings were translated into spatially explicit ecological zoning strategies. These findings underscore the necessity of transitioning from single-objective land management to a “pattern-process-oriented” integrated spatial governance model. Consequently, targeted optimization strategies tailored to specific landscape and class-level characteristics are proposed, providing a scientific foundation for enhancing regional ecological security and aligning land use policy with sustainable development goals in rapidly urbanizing regions.
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