山地高密度城市热岛效应的多约束因子格局分析与定量探测——重庆都市区案例研究

doi:10.11821/dlyj020190975

地理研究 ›› 2021, Vol. 40 ›› Issue (3): 856-868.doi: 10.11821/dlyj020190975

山地高密度城市热岛效应的多约束因子格局分析与定量探测——重庆都市区案例研究

汪洋¹^,²^,³(), 杨丹¹, 闵婕¹^,²^,³(), 翟非同¹, 王雨¹, 吴晓娇¹, 张洪睿¹

1.重庆师范大学地理与旅游学院,重庆 401331
2.GIS应用研究重庆市高校重点实验室,重庆 401331
3.三峡库区地表过程与环境遥感重庆市重点实验室,重庆401331

收稿日期:2019-11-11 接受日期:2020-07-01 出版日期:2021-03-10 发布日期:2021-05-10
通讯作者: 闵婕
作者简介:汪洋（1978-）,男,重庆北碚人,博士,教授,硕士生导师,主要研究方向为城乡空间规划与GIS应用研究。E-mail: cqwangyang@foxmail.com
基金资助:
国家社会科学基金项目(19XGL027);国家自然科学基金项目(42071277);重庆市教委科学技术研究项目(KJQN202000515)

Spatial pattern analysis and quantitative detection of multi-factor influence for urban heat island effect in a mountainous city: A case study of Chongqing metropolitan circle

WANG Yang¹^,²^,³(), YANG Dan¹, MIN Jie¹^,²^,³(), ZHAI Feitong¹, WANG Yu¹, WU Xiaojiao¹, ZHANG Hongrui¹

1. School of Geography and Tourism, Chongqing Normal University, Chongqing 401331, China
2. Key Laboratory of GIS Application, Chongqing Municipal Education Commission, Chongqing 401331, China
3. The Three Gorges Reservoir Area Surface Processes and Remote Sensing Municipal Laboratory, Chongqing 401331, China

Received:2019-11-11 Accepted:2020-07-01 Online:2021-03-10 Published:2021-05-10
Contact: MIN Jie

摘要/Abstract

摘要：

城市热岛受城市地表多因素影响,山地城市的情况则更为复杂。本文以典型山地城市重庆为例,基于Landsat8 OLI/TIRS影像、高精度矢量建筑等多源空间数据,采用城市地温反演、归一化植被指数、天空开阔度建模等方法,重点针对城市建成区,通过地理探测器分析各因子对城市热岛效应的约束力。研究表明：① 在都市区全域,城市热场空间异质性显著,植被覆盖、城市表面高程和天空开阔度因子具有全局性约束力表现,建筑密度、容积率和路网距离具有局部约束力表现。② 在城市建成区内部,对城市热岛效应约束性排前三位因子分别为：植被覆盖（q=0.782）、城市表面高程（q=0.499）、建筑密度（q=0.496）;局部约束因子建筑密度、容积率和道路距离的约束力均较强,但三者差异小;天空开阔度对城市热岛的全局性影响相对较小（q<0.1）。③ 在城市建成区内部,各约束因子对城市热岛具有叠加约束效应,叠加解释度q（X_i∩X_j）均强于独立解释度,叠加q值介于0.50~0.82之间。

关键词: 山地城市热岛效应, 多约束因子探测, 空间格局分析, 地理探测器, 重庆

Abstract:

Urban heat island effect (UHI) is affected by multiple factors on the urban surface, while the situation of a mountainous city is more complicated. To detect the cause of UHI, this article takes a typical mountainous city of Chongqing as an example. Firstly, we collected multi-source spatial data based on Landsat8 OLI/TIRS images, high-precision vector building, etc. as basic dataset. Then, the model method of urban land surface temperature retrieval (LST), normalized vegetation index (NDVI) and the sky view of factor (SVF) etc. are applied to obtain spatial pattern of each factor. Finally, with focus on the urban built-up area, the binding force of each factor on the UHI is analyzed by the method of geographic detector. Through the above steps, this study found that: (1) Around the whole metropolitan area, the spatial heterogeneity of urban thermal field is significant. From the spatial pattern of each factor, we can see that some factors, like vegetation coverage (NDVI), urban surface elevation (CSE) and the sky view of factor (SVF), have global binding performance for UHI, while others like building density (BD), building volume rate (BVR) and road network distance (RD) have local binding performance for UHI. (2) Within the urban built-up area, the top three factors influencing the spatial pattern of UHI separately are vegetation coverage (q=0.782), urban surface elevation (q=0.499) and building density (q=0.496). Besides, the local constraint factors, like building density (BD), building volume rate (BVR) and road network distance (RD), performance strong binding on UHI, yet among them show little difference. In addition, for there is no significant spatial difference in urban sky horizon within the high-density built-up area, the overall effect of the sky view factor (SVF) on urban heat island is relatively small (q<0.1). (3) Through interactive detection and analysis, the results suggested that each influencing factor shows overlapping constraints on the spatial distribution of UHI within the built-up area of the city. From the criterion q value of the factor binding force, we can see that interaction of two factors will increase the interpretation of the UHI. In other words, the superposition explanatory degree (q(X_i∩X_j) is stronger than the independent interpretation degree, whose superposition interpretation degree q value is between 0.50 and 0.82.

Key words: mountainous urban heat island effect, multi constraint factor detection, spatial pattern analysis, geographic detector, Chongqing

汪洋, 杨丹, 闵婕, 翟非同, 王雨, 吴晓娇, 张洪睿. 山地高密度城市热岛效应的多约束因子格局分析与定量探测——重庆都市区案例研究[J]. 地理研究, 2021, 40(3): 856-868.

WANG Yang, YANG Dan, MIN Jie, ZHAI Feitong, WANG Yu, WU Xiaojiao, ZHANG Hongrui. Spatial pattern analysis and quantitative detection of multi-factor influence for urban heat island effect in a mountainous city: A case study of Chongqing metropolitan circle[J]. GEOGRAPHICAL RESEARCH, 2021, 40(3): 856-868.

图/表 7

图1

图2

表1

各因子数量建模方法汇总表"

因子	数据与建模方法	参数含义
城市表面高程（CSE）	建筑矢量面数据转栅格（1 m×1 m）,重采样后叠加DEM数据,得到城市表面高层模型（CSE）	建筑图层先转换为1 m分辨率网格数据,叠加DEM数据,再重采样为30 m网格,以此表达城市自然与人工建筑的叠加高度
建筑密度（BD）	基于已有经验^[22],计算100 m×100 m格网范围内,建筑基底正射投影面积所占比例,重采样为30m网格,得到建筑密度（BD） $BD = BA / 10000$	式中：BD为格网内建筑密度;BA为格网内的建筑基底面积
建筑容积率（BVR）	设定建筑层高3 m,乘以楼层数,得到建筑近似高度;计算100 m×100 m格网范围内,总建筑面积与建筑基底正射投影面积的比值,重采样为30 m网格,得到容积率（BVR） $BVR = (BA × F) / 10000$	式中：BVR为格网内建筑容积率;BA为建筑基底正射投影面积;F为建筑楼层数
归一化植被指数（NDVI）	$NDVI = (Band 5 - Band 4) / (Band 5 + Band 4)$	式中：基于OLI数据,Band5为近红外波段反射值（NIR）;Band4为红光波段反射值(R)^[12]
天空开阔度（SVF）	基于CSE计算结果,采用光线追踪法^[21],模拟光源次数432次^[17]。通过ArcGIS叠置分析、阴影建模、重分类等过程,统计不在阴影里的次数占总次数的比值^[21],得到天空开阔度指数（SVF） $SVF = 1 n ∑ i = 0 int 2 n Δ α ∑ j = 0 int 2 n Δ β P α i β j$	式中：SVF为每个栅格单元的天空开阔度指数;n为总模拟次数; $P α i β j$ 为栅格单元间被遮挡的情况;α_i、β_j分别为给定方位角和高度角积分步长
距城市主干道距离（RD）	基于ArcGIS空间分析功能,计算每栋建筑几何中心至主次干道的最近欧式距离	道路类型主要包括城市快速路、都市区高速公路、城市主干道和次干道
反演地温指数（LST）	基于Landsat8 OLI/TIRS数据产品,计算亮温、地表比辐射率、大气透过率;应用劈窗算法^[11,20]计算地表温度（K）,计算多年夏季同期均值,得到反演地温指数（LST） $T s = A 0 + A 1 × T 10 + A 2 × T 11$	式中：T_s为地表温度（K）;T₁₀、T₁₁分别为Landsat8第10、11波段的亮温值（K）;A₀、A₁、A₂为大气透过率和地表比辐射率确定参数

表1

图3

图4

表2

表3

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q(A),q(B)	q(A∩B)	q(A)+q(B)	判据	交互作用
q(X₁)=0.499,q(X₂)=0.782	q(X₁∩X₂)=0.797	q(X₁)+q(X₂)=1.281	Max(q(A),q(B))<q(A∩B)	双因子增强
q(X₁)=0.499,q(X₃)=0.093	q(X₁∩X₃)=0.596	q(X₁)+q(X₃)=0.592	q(A)+q(B)<q(A∩B)	非线性增强
q(X₁)=0.499,q(X₄)=0.496	q(X₁∩X₄)=0.531	q(X₁)+q(X₄)=0.995	Max(q(A),q(B))<q(A∩B)	双因子增强
q(X₁)=0.499,q(X₅)=0.495	q(X₁∩X₅)=0.526	q(X₁)+q(X₅)=0.994	Max(q(A),q(B))<q(A∩B)	双因子增强
q(X₁)=0.499,q(X₆)=0.490	q(X₁∩X₆)=0.524	q(X₁)+q(X₆)=0.989	Max(q(A),q(B))<q(A∩B)	双因子增强
q(X₂)=0.782,q(X₃)=0.093	q(X₂∩X₃)=0.796	q(X₂)+q(X₃)=0.875	Max(q(A),q(B))<q(A∩B))	双因子增强
q(X₂)=0.782,q(X₄)=0.496	q(X₂∩X₄)=0.793	q(X₂)+q(X₄)=1.278	Max(q(A),q(B))<q(A∩B)	双因子增强
q(X₂)=0.782,q(X₅)=0.495	q(X₂∩X₅)=0.792	q(X₂)+q(X₅)=1.277	Max(q(A),q(B))<q(A∩B)	双因子增强
q(X₂)=0.782,q(X₆)=0.490	q(X₂∩X₆)=0.820	q(X₂)+q(X₆)=1.272	Max(q(A),q(B))<q(A∩B)	双因子增强
q(X₃)=0.093,q(X₄)=0.496	q(X₃∩X₄)=0.525	q(X₃)+q(X₄)=0.589	Max(q(A),q(B))<q(A∩B)	双因子增强
q(X₃)=0.093,q(X₅)=0.495	q(X₃∩X₅)=0.520	q(X₃)+q(X₅)=0.588	Max(q(A),q(B))<q(A∩B)	双因子增强
q(X₃)=0.093,q(X₆)=0.490	q(X₃∩X₆)=0.548	q(X₃)+q(X₆)=0.583	Max(q(A),q(B))<q(A∩B)	双因子增强
q(X₄)=0.496,q(X₅)=0.495	q(X₄∩X₅)=0.504	q(X₄)+q(X₅)=0.991	Max(q(A),q(B))<q(A∩B)	双因子增强
q(X₄)=0.496,q(X₆)=0.490	q(X₄∩X₆)=0.506	q(X₄)+q(X₆)=0.986	Max(q(A),q(B))<q(A∩B)	双因子增强
q(X₅)=0.495,q(X₆)=0.490	q(X₅∩X₆)=0.505	q(X₅)+q(X₆)=0.985	Max(q(A),q(B))<q(A∩B)	双因子增强

山地高密度城市热岛效应的多约束因子格局分析与定量探测——重庆都市区案例研究

Spatial pattern analysis and quantitative detection of multi-factor influence for urban heat island effect in a mountainous city: A case study of Chongqing metropolitan circle

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