Effect of human settlements on urban thermal environment and factor analysis based on multi-source data: A case study of Changsha city

doi:10.1007/s11442-021-1873-5

Abstract

Abstract:

In view of the lack of comprehensive evaluation and analysis from the combination of natural and human multi-dimensional factors, the urban surface temperature patterns of Changsha in 2000, 2009 and 2016 are retrieved based on multi-source spatial data (Landsat 5 and Landsat 8 satellite image data, POI spatial big data, digital elevation model, etc.), and 12 natural and human factors closely related to urban thermal environment are quickly obtained. The standard deviation ellipse and spatial principal component analysis (PCA) methods are used to analyze the effect of urban human residential thermal environment and its influencing factors. The results showed that the heat island area increased by 547 km² and the maximum surface temperature difference reached 10.1℃ during the period 2000-2016. The spatial distribution of urban heat island was mainly concentrated in urban built-up areas, such as industrial and commercial agglomerations and densely populated urban centers. The spatial distribution pattern of heat island is gradually decreasing from the urban center to the suburbs. There were multiple high-temperature centers, such as Wuyi square business circle, Xingsha economic and technological development zone in Changsha County, Wangcheng industrial zone, Yuelu industrial agglomeration, and Tianxin industrial zone. From 2000 to 2016, the main axis of spatial development of heat island remained in the northeast-southwest direction. The center of gravity of heat island shifted 2.7 km to the southwest with the deflection angle of 54.9° in 2000-2009. The center of gravity of heat island shifted to the northeast by 4.8 km with the deflection angle of 60.9° in 2009-2016. On the whole, the change of spatial pattern of thermal environment in Changsha was related to the change of urban construction intensity. Through the PCA method, it was concluded that landscape pattern, urban construction intensity and topographic landforms were the main factors affecting the spatial pattern of urban thermal environment of Changsha. The promotion effect of human factors on the formation of heat island effect was obviously greater than that of natural factors. The temperature would rise by 0.293℃ under the synthetic effect of human and natural factors. Due to the complexity of factors influencing the urban thermal environment of human settlements, the utilization of multi-source data could help to reveal the spatial pattern and evolution law of urban thermal environment, deepen the understanding of the causes of urban heat island effect, and clarify the correlation between human and natural factors, so as to provide scientific supports for the improvement of the quality of urban human settlements.

Key words: thermal environment, natural-human factor, multi-source data, spatial PCA, Changsha city

XIONG Ying, ZHANG Fang. Effect of human settlements on urban thermal environment and factor analysis based on multi-source data: A case study of Changsha city[J].Journal of Geographical Sciences, 2021, 31(6): 819-838.

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Data series	Name of the data	Data source	Use	Time
Remote sensing data	Landsat 5 TM, Landsat 8 OLI TIRS	http://www.gscloud	Inversion of surface temperature and extraction of surface information	2000.7; 2009.8; 2016.7
Remote sensing data	DEM	http://www.gscloud	Calculation of elevation and slope	2016
Vector data	Administrative boundary vector data of Changsha city	China Earth System Science Data Sharing Network	Extraction of boundary of the study area	2016
Big spatial data	POI data of Changsha city	Scott map	Extraction of index of economic activity	2016

Year	Extremely low		Low		Relatively low		Medium		Relatively high		High		Extremely high
Year	Area	Proportion	Area	Proportion	Area	Proportion	Area	Proportion	Area	Proportion	Area	Proportion	Area	Proportion
2000	76.04	1.93	456.42	11.59	2346.89	59.57	781.76	19.84	180.58	4.58	64.75	1.64	33.07	0.84
2009	11.67	0.30	1003.43	25.47	1443.27	36.64	871.28	22.12	280.12	7.11	227.68	5.78	102.07	2.59
2016	149.68	3.80	908.47	23.06	1274.13	32.34	852.05	21.63	371.64	9.43	278.21	7.06	105.34	2.67

Category (temperature zone)	Furong			Tianxin			Yuelu			Kaifu
Category (temperature zone)	2000	2009	2016	2000	2009	2016	2000	2009	2016	2000	2009	2016
Extremely low	0.15	0.00	0.49	0.00	0.00	6.12	0.00	0.00	17.67	6.85	0.09	7.86
Low	0.79	0.39	0.95	7.42	6.15	1.85	27.41	43.35	160.79	28.27	16.24	15.77
Relatively low	5.53	1.40	0.80	23.41	4.12	3.64	304.21	194.52	145.28	105.07	40.88	36.57
Medium	8.91	4.45	2.30	17.06	11.68	9.72	139.86	162.65	80.28	28.03	62.51	50.41
Relatively high	13.39	9.15	8.78	14.02	16.26	16.52	34.94	55.74	62.31	13.59	37.60	42.48
High	8.98	16.99	16.29	9.85	26.46	28.14	10.31	47.48	45.48	6.77	27.05	32.25
Extremely high	5.93	11.30	14.06	3.75	10.86	9.53	3.33	16.33	8.26	7.61	11.83	10.86
Category (temperature zone)	Yuhua			Wangcheng			Changsha
Category (temperature zone)	2000	2009	2016	2000	2009	2016	2000	2009	2016
Extremely low	1.63	0.00	0.24	1.04	0.02	65.80	66.37	11.55	51.51
Low	7.96	0.30	1.30	83.00	213.84	249.37	301.57	723.16	478.44
Relatively low	38.20	3.20	3.70	582.09	361.73	347.29	1288.38	837.42	736.84
Medium	24.50	13.72	9.03	275.08	304.85	211.57	288.31	311.42	488.74
Relatively high	18.44	21.57	24.15	35.62	68.61	69.31	50.59	71.20	148.09
High	12.87	42.28	51.56	6.04	27.05	32.25	9.92	40.38	72.24
Extremely high	7.48	30.00	21.09	2.61	9.39	9.89	2.35	12.38	31.64

First level indicators	Second level indicators	Third level indicators
Natural factors	Vegetation and water body	FVC
	Vegetation and water body	MNDWI
	Topographic features	DEM
	Topographic features	Slope
Human factors	Landscape pattern index	CONTAG
		SHDI
		DIVISION
		COHESION
	Intensity of urban construction	NDISI
		NDBBI
		Albedo
	Socio-economic activities	POI

Index	NDISI	FVC	MNDWI	NDBBI	POI	DEM
Pearson correlation coefficient	0.369**	-0.508**	-0.326**	0.815**	0.519**	-0.514**
Correlation results	significant	significant	significant	significant	significant	significant
Index	Slope	DIVISION	SHDI	CONTAG	COHESION	Albedo
Pearson correlation coefficient	-0.270**	-0.276**	-0.373**	-0.338*	0.433**	0.395**
Correlation results	significant	significant	significant	significant	significant	significant