城市地表温度空间分异及驱动因子差异性分析——以合肥市为例

doi:10.16258/j.cnki.1674-5906.2023.01.012

生态环境学报 ›› 2023, Vol. 32 ›› Issue (1): 110-122.DOI: 10.16258/j.cnki.1674-5906.2023.01.012

城市地表温度空间分异及驱动因子差异性分析——以合肥市为例

付蓉¹(), 武新梅², 陈斌³^,^*

1.合肥工业大学资源与环境工程学院，安徽合肥 230009
2.武汉大学资源与环境科学学院，湖北武汉 430079
3.核工业二九〇研究所/广东省环境保护核辐射追踪研究重点实验室，广东韶关 512029

收稿日期:2022-09-19 出版日期:2023-01-18 发布日期:2023-04-06
通讯作者: *
作者简介:付蓉（1992年生），女，硕士研究生，研究方向为资源环境遥感与GIS分析。E-mail: 1025780235@qq.com
基金资助:
广东省海洋遥感重点实验室（中国科学院南海海洋研究所）基金项目(2017B030301005-LORS2009);中核集团核工业二九〇研究所科研创新项目(202003)

Analysis on the Spatial Stratified Heterogeneity and Driving Factors Differences of the Urban Land Surface Temperature: A Case Study of Hefei

FU Rong¹(), WU Xinmei², CHEN Bin³^,^*

1. School of Resources and Environmental Engineering, Hefei University of Technology, Hefei 230009, P. R. China
2. School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, P. R. China
3. Research Institute No.290, CNNC, Guangdong Provincial Key Laboratory of Environmental Protection and Nuclear Radiation Tracking Research, Shaoguan 512029, P. R. China

Received:2022-09-19 Online:2023-01-18 Published:2023-04-06

摘要/Abstract

摘要：

近年来合肥市发展迅速，不透水面、建筑密度的迅速增加加剧了城市热岛效应。研究城市地表温度的空间分异性及驱动因素对城市规划和生态环境的改善有重要意义。然而目前对城市地表温度的研究仍存在空间分异探讨较少、单一因子解释力度不够、驱动因子交互影响研究不多等问题。利用2020年9月MOD11A2遥感影像，选取气压、相对湿度、短波辐射、DEM、NDVI、人口密度、夜间灯光为驱动因子，对合肥市地表温度的空间分异性及地表温度驱动因子的差异性进行分析。首先将合肥市划分为1 km×1 km的单元网格并提取地表温度和各驱动因子的值，然后利用地理探测器确定该区域地表温度的空间分异性，最后结合广义可加模型对地表温度进行驱动因子分析。结果表明，（1）合肥市地表温度空间分异q值为0.575，各驱动因子空间分异q值均大于0且均通过P<0.01的显著性检验，地表温度和各因子的空间分异性显著。地表温度大致呈现中心高、四周低的空间格局，因此，将研究区域分为高温区和低温区。（2）利用GAM拟合地表温度与驱动因子，得到高温区驱动因子显著性排序为夜间灯光>NDVI>短波辐射>人口密度>相对湿度>DEM>气压；低温区驱动因子显著性排序为NDVI>夜间灯光>短波辐射>人口密度>气压>相对湿度>DEM。（3）高温区和低温区因子交互均在P<0.001水平下显著影响地表温度分布，模型调整后均优于单因素拟合模型，说明因子交互对地表温度影响更大。合肥市高温区和低温区的地表温度的主控因素不同，因子间交互对地表温度的影响更显著，可根据主控因素差异调节热环境，为今后城市发展规划提供参考依据。

关键词: 地表温度, 空间分异, 地理探测器, 广义可加模型, 驱动因子, 合肥市

Abstract:

Recently, the urban heat island effect (UHI) has been exacerbated by impervious surfaces and rapid growth in building density. It is of great significance to study the spatial stratified heterogeneity and driving factors of urban surface temperature (LST) for urban planning and ecological environment improvement. However, there are few studies of LST such as the spatial stratified heterogeneity, the explanation of single factor and the interaction of driving factors. In this paper, using the MOD11A2 remote sensing images in September 2020, air pressure (AP), relative humidity (RH), shortwave radiation (SR), digital elevation model (DEM), normalized difference vegetation index (NDVI), density of population (DP), night-time light (NL) were selected as driving factors to analyze the spatial stratified heterogeneity and driving factors differences of the urban land surface temperature. Firstly, the values of the LST and driving factors were extracted by the divided 1km square cell grids of Hefei area; then the spatial stratified heterogeneity was estimated by the GeoDetector model, and finally the driving factors of land surface temperature was analyzed by generalized additive model (GAM). The experimental results showed that: (1) The spatial differentiation q value was 0.575 and the q value of each driving factor was greater than 0 and all pass the significance test of P<0.01, which indicated that the spatial stratified heterogeneity of LST in Hefei was significant and the pattern was characterized by the spatial pattern of high center and low surrounding area. Therefore, Hefei could be divided into the high temperature area and the low temperature area. (2) Using Generalized Additive Model (GAM) to fit the LST and driving factors, the significance order of the driving factors in the high temperature area was as follows: NL>NDVI>SR>DP>RH>DEM>AP; while the corresponding order in the low temperature area was NDVI>NL>SR>DP>AP>RH>DEM. (3) The driving factors interaction in the high temperature region and the low temperature region were both significantly affected by the surface temperature distribution with level of P<0.001. Furthermore, the improved models had better performance than the single-factor fitting models, which indicated that the interaction of the driving factors had greater impact on the LST. To summarize, the major driving factors of LST in the high temperature and the low temperature area in Hefei were different, and the interactions between driving factors had more significant impact on LST than its single counterpart. The above results can provide reference basis for future urban planning and environmental improvement.

Key words: land surface temperature, spatial stratified heterogeneity, GeoDetector, GAM, driving factors, Hefei

中图分类号:

付蓉, 武新梅, 陈斌. 城市地表温度空间分异及驱动因子差异性分析——以合肥市为例[J]. 生态环境学报, 2023, 32(1): 110-122.

FU Rong, WU Xinmei, CHEN Bin. Analysis on the Spatial Stratified Heterogeneity and Driving Factors Differences of the Urban Land Surface Temperature: A Case Study of Hefei[J]. Ecology and Environment, 2023, 32(1): 110-122.

图/表 15

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数据	数据简记	时间分辨率	空间分辨率	数据来源
地表温度	LST	8 d	1 km	NASA (https://earthdata.nasa.gov/) MOD11A2
归一化植被指数	NDVI	16 d	1 km	NASA (https://earthdata.nasa.gov/) MOD13A2
数字高程模型	DEM	—	30 m	USGS (https://earthexplorer.usgs.gov) ASTER GDEM
人口密度	DP	—	1 km	美国能源部橡树岭国家实验室 (https://landscan.ornl.gov/)
夜间灯光	NL	1 m	0.5 km	国家青藏高原科学数据中心 (http://data.tpdc.ac.cn)
相对湿度	RH	1 m	1 km	国家科技基础条件平台—国家地球系统科学数据中心 (http://www.geodata.cn)
气压	AP	1 d	0.0625(°)	国家气象科学数据中心 (http://www.nmic.cn/) CLDAS-V2.0数据集
短波辐射	SR	1 d	0.0625(°)	国家气象科学数据中心 (http://www.nmic.cn/) CLDAS-V2.0数据集

数据	数据简记	时间分辨率	空间分辨率	数据来源
地表温度	LST	8 d	1 km	NASA (https://earthdata.nasa.gov/) MOD11A2
归一化植被指数	NDVI	16 d	1 km	NASA (https://earthdata.nasa.gov/) MOD13A2
数字高程模型	DEM	—	30 m	USGS (https://earthexplorer.usgs.gov) ASTER GDEM
人口密度	DP	—	1 km	美国能源部橡树岭国家实验室 (https://landscan.ornl.gov/)
夜间灯光	NL	1 m	0.5 km	国家青藏高原科学数据中心 (http://data.tpdc.ac.cn)
相对湿度	RH	1 m	1 km	国家科技基础条件平台—国家地球系统科学数据中心 (http://www.geodata.cn)
气压	AP	1 d	0.0625(°)	国家气象科学数据中心 (http://www.nmic.cn/) CLDAS-V2.0数据集
短波辐射	SR	1 d	0.0625(°)	国家气象科学数据中心 (http://www.nmic.cn/) CLDAS-V2.0数据集

指标	地表温度	气压	相对湿度	短波辐射	夜间灯光	人口密度	归一化植被指数	高程
q	0.575	0.554	0.397	0.758	0.605	0.32	0.65	0.337
P	<0.01

指标	地表温度	气压	相对湿度	短波辐射	夜间灯光	人口密度	归一化植被指数	高程
q	0.575	0.554	0.397	0.758	0.605	0.32	0.65	0.337
P	<0.01

模型	修正决定r²	偏差解释率	GCV值	AIC值
g(x)	0.772	77.7%	0.739	6198.62
g₁(x)	0.771	77.4%	0.742	6208.95
g₂(x)	0.759	76.3%	0.782	6335.66
g₃(x)	0.770	77.4%	0.746	6222.04
g₄(x)	0.763	76.7%	0.768	6290.55
g₅(x)	0.688	69.3%	1.011	6962.92
g₆(x)	0.769	77.3%	0.748	6227.12
g₇(x)	0.737	74.2%	0.852	6545.90

城市地表温度空间分异及驱动因子差异性分析——以合肥市为例

Analysis on the Spatial Stratified Heterogeneity and Driving Factors Differences of the Urban Land Surface Temperature: A Case Study of Hefei

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平滑效应项	估计自由度	参考自由度	F值	P值	显著性排序
AP	8.058	8.771	2.725	0.00696	7
SR	8.181	8.809	17.772	<2×10^-16	3
RH	3.129	4.000	6.415	0.00004	5
DP	7.023	8.100	12.481	<2×10^-16	4
NL	6.029	7.213	129.351	<2×10^-16	1
DEM	6.67	7.664	4.700	0.00002	6
NDVI	5.691	6.902	55.814	<2×10^-16	2

交互项	估计自由度	参考自由度	F值	P值
AP×DP	8.007	9.146	19.13	<2×10^-16
AP×SR	10.441	11.923	13.39	<2×10^-16
AP×NL	10.844	12.114	8.332	<2×10^-16
AP×RH	12.333	13.342	40.6	<2×10^-16
AP×DEM	10.505	11.654	16.97	<2×10^-16
AP×NDVI	9.788	11.417	11.42	<2×10^-16
SR×DP	7.795	9.116	27.66	<2×10^-16
SR×NL	10.183	11.929	7.047	<2×10^-16
SR×RH	6.865	8.346	5.536	<0.00001
SR×DEM	9.610	11.07	17.22	<2×10^-16
SR×NDVI	7.459	9.117	25.1	<2×10^-16
DP×NL	9.313	10.723	14.9	<2×10^-16
DP×RH	5.958	7.254	11.05	<2×10^-16
DP×DEM	7.052	15	11.4	<2×10^-16
DP×NDVI	6.364	7.756	16.78	<2×10^-16
NL×RH	8.992	11.045	4.528	<0.00001
NL×DEM	8.564	10.221	7.949	<2×10^-16
NL×NDVI	8.707	10.852	8.717	<2×10^-16
RH×DEM	8.438	10.03	6.041	<2×10^-16
RH×NDVI	9.634	11.406	7.525	<2×10^-16
DEM×NDVI	7.715	9.19	16.3	<2×10^-16

平滑效应项	估计自由度	参考自由度	F值	P值	显著性排序
AP	8.498	8.920	22.069	<2×10^-16	5
SR	8.418	8.902	48.090	<2×10^-16	3
RH	4.812	6.001	16.972	<2×10^-16	6
DP	8.728	8.976	34.661	<2×10^-16	4
NL	6.954	7.972	67.440	<2×10^-16	2
DEM	6.787	7.816	4.903	<0.0001	7
NDVI	7.781	8.617	166.857	<2×10^-16	1

交互项	估计自由度	参考自由度	F值	P值
AP×DP	11.829	12.766	44.76	<2×10^-16
AP×SR	15.328	15.615	30.88	<2×10^-16
AP×NL	15.873	15.992	18.91	<2×10^-16
AP×RH	15.873	15.992	18.91	<2×10^-16
AP×DEM	13.997	14.273	3.251	0.0000836
AP×NDVI	14.689	15.436	7.433	<2×10^-16
SR×DP	12.726	13.683	15.95	<2×10^-16
SR×NL	13.134	13.724	41.44	<2×10^-16
SR×RH	14.677	15.632	34.17	<2×10^-16
SR×DEM	11.411	12.27	28	<2×10^-16
SR×NDVI	15.948	15.998	12.57	<2×10^-16
DP×NL	14.949	15.52	15.45	<2×10^-16
DP×RH	9.632	10.709	18.84	<2×10^-16
DP×DEM	7.966	15.000	56.14	<2×10^-16
DP×NDVI	6.973	8.653	9.933	<2×10^-16
NL×RH	13.927	14.825	16.02	<2×10^-16
NL×DEM	11.08	12.148	43.48	<2×10^-16
NL×NDVI	11.938	13.475	5.941	<2×10^-16
RH×DEM	12.245	13.153	22.559	<2×10^-16
RH×NDVI	12.988	14.064	6.376	<2×10^-16
DEM×NDVI	6.062	7.44	21.5	<2×10^-16

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