基于遥感反演的江南水网地区城镇蓝绿空间格局对夏季降温的影响与尺度效应分析
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作者:
作者单位:

1.高密度人居环境生态与节能教育部重点实验室(同济大学)水绿生态智能分实验中心,上海 200092;2.广州市城市规划勘测设计研究院,广州 510030;3.同济大学建筑与城市规划学院,上海 200092

作者简介:

朱雯,E-mail:15301891151@163.com

通讯作者:

王敏,E-mail:wmin@tongji.edu.cn

中图分类号:

TU985

基金项目:

国家自然科学基金面上项目(52178053)


Impact and scale effect of urban blue-green spatial pattern on summer cooling in Jiangnan water network areas based on remote sensing retrieval
Author:
  • ZHU Wen 1,2

    ZHU Wen

    Eco-Smart Lab Attached to Ministry of Education Key Laboratory of Ecology and Energy-Saving Study of High Density Habitat (Tongji University), Shanghai 200092, China;Guangzhou Institute of Urban Planning & Design Survey (GZPI), Guangzhou 510030, China
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  • WANG Min 1,3

    WANG Min

    Eco-Smart Lab Attached to Ministry of Education Key Laboratory of Ecology and Energy-Saving Study of High Density Habitat (Tongji University), Shanghai 200092, China;College of Architecture and Urban Planning (CAUP),Tongji University, Shanghai 200092, China
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Affiliation:

1.Eco-Smart Lab Attached to Ministry of Education Key Laboratory of Ecology and Energy-Saving Study of High Density Habitat (Tongji University), Shanghai 200092, China;2.Guangzhou Institute of Urban Planning & Design Survey (GZPI), Guangzhou 510030, China;3.College of Architecture and Urban Planning (CAUP),Tongji University, Shanghai 200092, China

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    摘要:

    为探索提升江南水网地区城镇蓝绿空间格局降温效应的空间尺度差异性,提升城镇蓝绿空间的降温效应。以昆山市为例,利用遥感数据和辐射传输方程法进行地表温度反演,在市域和城区2个尺度下分别构建城镇蓝绿空间格局与夏季降温效应度量指标的相关性分析与增强回归树模型,揭示了江南水网地区影响夏季降温效应的城镇蓝绿空间格局关键因子及重要性排序,并对比研究其降温效应的空间尺度差异性。结果显示:市域尺度下,蓝绿空间格局对夏季最高地表温度影响的相对贡献率高于10%的指标有4个,影响程度依次为:水体形状指数>蓝绿空间占比>滨水绿地宽度>水体聚集度;蓝绿空间格局对冷岛强度影响的相对贡献率高于10%的指标有3个,影响程度依次为:滨水绿地宽度>水体形状指数>蓝绿空间占比。城区尺度下,蓝绿空间格局对夏季最高地表温度影响的相对贡献率高于10%的指标有5个,影响程度依次为:水体聚集度>水体平均斑块面积指数>绿地率>水面率>水体形状指数;蓝绿空间格局对冷岛强度影响的相对贡献率高于10%的指标有2个,影响程度为:绿地聚集度>蓝绿空间占比。研究表明,蓝绿空间格局特征与夏季降温的相关程度和贡献率水平均具有显著的尺度效应。

    Abstract:

    Global climate change has led to a gradual intensification of heat island effects and a significant increase in events of extreme high temperature at summer in urbans. How to effectively improve the cooling effect of urban blue-green spaces is an important way to adapt to the climate change and build a living environment with high-quality. The Landsat-8 data and radiative transfer equation was used to retrieve land surface temperature in Kunshan City. The correlation analysis and boosted regression tree (BRT) model of the urban blue-green spatial pattern and the measurement index of summer cooling effect were established constructed at the scale of urban and block to identify the key factors and their importance ranking of urban blue-green spatial patterns that affect summer cooling effects in the Jiangnan water network area and to explore the differences in spatial scale of cooling effect with comparative study. Results showed that there were four indicators with a relative contribution rate of over 10% for the impact of the blue-green spatial pattern on the summer maximum surface temperature at the scale of urban. The decreasing order of impact degree was as follows: index of water body shape > proportion of blue-green space > width of waterfront green space > aggregation index of water body. There were three indicators with a relative contribution rate of over 10% for the impact of the blue-green spatial pattern on the average intensity of cold island at the scale of urban. The decreasing order of impact degree was as follows: width of waterfront green space > index of water body shape > proportion of blue-green space. At the scale of block, there were five indicators with a relative contribution rate of over 10% for the impact of the blue-green spatial pattern on the summer maximum surface temperature at the scale of urban. The decreasing order of impact degree was as follows: index of water aggregation > average patch index of water > ratio of green space > ratio of water surface > index of water body shape. There were two indicators with a relative contribution rate of over 10% for the impact of the blue-green spatial pattern on the average intensity of cold island at the scale of urban. The decreasing order of impact degree was as follows: aggregation index of green space > proportion of blue-green space. It is indicated that the spatial pattern characteristics of urban blue-green space have significant scale effects on the correlation degree and contribution level of summer cooling. The optimization strategies of planning and design were proposed. It will provide a practical reference for creating a comfortable urban living environment at different scales.

    图1 市域尺度(A)和城区尺度(B)的研究范围、蓝绿空间分布与研究单元划分Fig.1 Research scope, blue-green spatial distribution and division of research units at city scale(A) and urban scale(B)
    图2 技术路径示意图Fig.2 Technical path diagram
    图3 市域尺度整体地表温度(A)、最高地表温度(B)、平均冷岛强度(C)分布图Fig.3 Distribution of global surface temperature (A), maximum surface temperature (B) and mean cold island intensity (C) at city scale
    图4 城区尺度整体地表温度(A)、最高地表温度(B)、平均冷岛强度(C)分布图Fig.4 Distribution of global surface temperature(A), maximum surface temperature(B) and mean cold island intensity(C) at urban scale
    图5 城镇蓝绿空间格局影响夏季最高地表温度的相对贡献率Fig.5 Relative contribution rate of urban blue-green spatial pattern to summer maximum land surface temperature
    图6 城镇蓝绿空间格局影响夏季平均冷岛强度的相对贡献率Fig.6 Relative contribution rate of urban blue-green spatial pattern to average cold island intensity in summer
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朱雯,王敏.基于遥感反演的江南水网地区城镇蓝绿空间格局对夏季降温的影响与尺度效应分析[J].华中农业大学学报,2023,42(4):86-97

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  • 收稿日期:2022-10-27
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