Effects of patterns of blue-green spatial landscape on land surface temperature under different environmental contexts in Wuhan City

XIE Qijiao; XIAO Shengyong

doi:10.13300/j.cnki.hnlkxb.2026.01.003

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Effects of patterns of blue-green spatial landscape on land surface temperature under different environmental contexts in Wuhan City

High-quality development of urban-rural with green integration | 更新时间：2026-11-05

- Effects of patterns of blue-green spatial landscape on land surface temperature under different environmental contexts in Wuhan City
- Journal of Huazhong Agricultural University Vol. 45, Issue 1, Pages: 24-36(2026)
- 作者机构：
  
  湖北大学旅游学院，武汉 430062
- 作者简介：
- 基金信息：
- DOI：10.13300/j.cnki.hnlkxb.2026.01.003
  CLC： TU984
- Received：14 October 2025，
  
  Published：15 January 2026
- 稿件说明：
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谢启姣，肖声勇.不同环境基质下武汉蓝绿空间景观格局对降温效应的影响［J］.华中农业大学学报，2026，45（1）：24‐36.

XIE Qijiao,XIAO Shengyong.Effects of patterns of blue-green spatial landscape on land surface temperature under different environmental contexts in Wuhan City[J].Journal of Huazhong Agricultural University,2026,45(01):24-36.
谢启姣，肖声勇.不同环境基质下武汉蓝绿空间景观格局对降温效应的影响［J］.华中农业大学学报，2026，45（1）：24‐36. DOI： 10.13300/j.cnki.hnlkxb.2026.01.003.

XIE Qijiao,XIAO Shengyong.Effects of patterns of blue-green spatial landscape on land surface temperature under different environmental contexts in Wuhan City[J].Journal of Huazhong Agricultural University,2026,45(01):24-36. DOI： 10.13300/j.cnki.hnlkxb.2026.01.003.

摘要

为探究蓝绿空间景观结构特征对地表温度（land surface temperature， LST）的影响，更好地发挥蓝绿基础设施的热环境调节功能，基于Landsat 8-9遥感数据对武汉市地表温度进行反演，分析主城区、都市发展区和市域3个基质范围所有常用景观指数与LST之间的定量关系；采用主成分回归分析寻求不同基质条件下影响LST的主导因子，揭示其影响机制。结果显示：水体和绿地表现出明显的“冷岛效应”，水体的冷岛强度（8.96~9.34 ℃）明显大于绿地（4.44~5.47 ℃）；总体上各景观指数对LST变化的独立解释能力呈现出水体>绿地、景观组成>空间构形、斑块类型水平>景观水平>斑块水平、主城区>都市发展区>市域的规律。不同基质范围蓝绿空间影响地表温度的主导因子不同，主城区依次为水体斑块所占景观面积比例（percentage of landscape， PLAND_W）、水体斑块密度（patch density， PD_W）、绿地有效粒度面积（effective mesh size，MESH_G）和绿地边缘密度（edge density，ED_G）；都市发展区依次为水体对比度加权边缘密度（contrast-weighted edge density， CWED_W）、水体斑块所占景观面积比例（contrast-weighted edge density， PLAND_W）、绿地相似度均值（mean similarity index，SIMI_MN_G）和绿地斑块所占景观面积比例（PLAND_G）；市域蓝绿空间5个主要景观指数仅能解释35%的LST变化。综合考虑蓝绿空间之外其他景观要素的叠加影响时，水体和建设用地对热环境变化解释能力较强，绿地的降温作用被明显弱化或平抑。蓝绿基础设施影响热环境调节作用的主要因素及其贡献呈现出基质效应，针对不同基质环境进行蓝绿景观空间配置及结构优化，如通过保护主城区内大型水体、保证水系覆盖面积、增加小型水体间的连接性，丰富都市发展区和市域范围蓝绿空间的形态特征、加强其与周围环境的交互频率等，可切实提升蓝绿基础设施的降温效果。

Abstract

The quantitative relationship between all commonly used landscape metrics and land surface temperature （LST） across three spatial contexts including the main urban area， the urban development area， and the entire municipality of Wuhan City was analyzed by deriving the LST values and classifying land cover categories of Wuhan city based on Landsat 8-9 remote sensing data acquired on September 18 and 19， 2022 to study the effects of the patterns of blue-green spatial landscape on the LST to better utilize the thermal environment regulation function of blue-green infrastructure. The principal component regression analysis was used to identify the dominant factors affecting LST under different spatial contexts and reveal their underlying mechanisms. The results showed that water bodies and green spaces had a significant “cooling island effect”， with the cooling intensity of water bodies （8.96-9.34 ℃） significantly greater than that of green spaces （4.44-5.47 ℃）. Overall， the independent explanatory power of the landscape metrics for LST changes followed in the order of water bodies > green spaces， landscape composition > spatial configuration， patch-level > landscape-level > class-level， and the main urban area > the urban development area > the administrative area. The dominant factors affecting LST varied across spatial contexts. The four key factors in the main urban area were the percentage of water body area （PLAND_W）， water body patch density （PD_W）， effective mesh size of green spaces （MESH_G）， and edge density of green spaces （ED_G）， collectively explaining 82.4% of the LST variation. The dominant factors in the metropolitan development area were contrast-weighted edge density of water bodies （CWED_W）， percentage of water body area （PLAND_W）， mean proximity index of green spaces （SIMI_MN_G）， and percentage of green space area （PLAND_G）， collectively explaining 59.2% of the LST variation. The five dominant landscape metrics related to blue-green spaces in the entire municipality， only explained 35% of the LST variation. Water bodies and construction land had a strong explanatory power for changes in the thermal environment， and the cooling effect of green spaces was significantly weakened or suppressed when considering the combined effects of other landscape elements outside of blue-green spaces. It is indicated that the regulation function of blue-green infrastructure in the thermal environment has a distinct context effect. Measures for optimizing the spatial allocation and structural configuration of blue-green landscapes according to different environmental matrices including preserving large water bodies in central urban areas， ensuring adequate water surface coverage， enhancing connectivity between smaller water bodies， enriching the morphological complexity of blue-green spaces in urban development zones and metropolitan regions， and strengthening their interactive frequency with surrounding environments can effectively enhance the cooling performance of blue-green infrastructure.

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references

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