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昆明市蓝绿空间格局变化对热环境的影响

Impacts of Blue-Green Space Pattern Changes on the Urban Thermal Environment in Kunming

  • 摘要: 以昆明主城区为例,基于Landsat 8和Landsat 5遥感影像,采用单窗算法反演地表辐射温度,并利用标准差椭圆、景观指数等方法分析昆明市蓝绿空间时空演变特征及对热环境的缓解作用;同时,采用局部空间自相关研究蓝绿空间分布和地表温度之间的相互作用,揭示其内在影响机制。在此基础上,对比普通最小二乘回归、地理加权回归和多尺度地理加权回归模型在揭示蓝绿空间格局指数对地表温度变化影响机制方面的拟合性能。结果表明:蓝绿空间重心向东南方向偏移,近20 a昆明市蓝绿空间整体规模呈减少趋势,面积共减少138.23 km2,变化较均匀,其中前9 a动态度为−0.49%,后10 a动态度为−0.56%。对地表温度等级变化影响最大的因素是蓝绿空间中景观边缘指数和绿色空间的形状指数、斑块密度,合理配置景观形态,有助于提升城市的生态韧性。多尺度地理加权回归模型对LST等级变化的拟合效果最好(R2=0.66),该模型在空间维度上提供了更多样化的信息,为城市规划提供参考。

     

    Abstract: Using the central urban area of Kunming as a case study, this research retrieved land surface radiant temperature based on Landsat 5 and Landsat 8 imagery using the single-window algorithm. The spatiotemporal evolution of blue-green spaces and their mitigating effects on the urban thermal environment were analyzed through methods such as the standard deviation ellipse and landscape metrics. Furthermore, local spatial autocorrelation was employed to investigate the interactions between the spatial distribution of blue-green spaces and gradients in land surface temperature(LST), thereby uncovering the underlying influence mechanisms.On this basis, the performance of Ordinary Least Squares(OLS), Geographically Weighted Regression(GWR), and Multiscale Geographically Weighted Regression(MGWR) models was compared in assessing the impact of blue-green spatial pattern indices on LST variations. The results reveal a southeastward shift in the spatial centroid of blue-green spaces, accompanied by a declining trend in their overall area—decreasing by 138.23 km2 over the past 20 years. The dynamic degrees were −0.49% during the first nine years and −0.56% in the subsequent decade, indicating a relatively uniform rate of change.Among all landscape metrics, the landscape edge index, shape index, and patch density of green spaces exerted the most significant influence on LST variations. Proper configuration of landscape morphology can thus enhance urban ecological resilience. The MGWR model demonstrated the highest explanatory power for LST level variations(R2 = 0.66), providing more detailed spatial insights and offering valuable guidance for refined urban planning and climate adaptation strategies.

     

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