基于多源数据的长沙市人居热环境效应及其影响因素分析

doi:10.11821/dlxb202011013

地理学报 ›› 2020, Vol. 75 ›› Issue (11): 2443-2458.doi: 10.11821/dlxb202011013

• 生态文明与可持续发展 • 上一篇下一篇

基于多源数据的长沙市人居热环境效应及其影响因素分析

熊鹰¹^,²^,³(), 章芳¹^,²

1. 长沙理工大学建筑学院,长沙 410076
2. 国土资源评价与利用湖南省重点实验室,长沙 410007
3. 长沙理工大学资源环境与城乡规划研究中心,长沙 410114

收稿日期:2019-10-12 修回日期:2020-07-13 出版日期:2020-11-25 发布日期:2021-01-25
作者简介:熊鹰(1977-), 男, 湖南汉寿人, 博士, 教授, 主要从事城市人居环境、土地利用/覆盖变化与城市扩张等方面的研究。E-mail: csustxy@126.com
基金资助:
国家社会科学基金项目(15BJY051);国土资源评价与利用湖南省重点实验室开放课题(SYS-ZX-202002);湖南省社会科学成果评审委员会课题(XSP18ZDI031)

Thermal environment effects of urban human settlements and influencing factors based on multi-source data: A case study of Changsha city

XIONG Ying¹^,²^,³(), ZHANG Fang¹^,²

1. College of Architecture, Changsha University of Science and Technology, Changsha 410076, China
2. Hunan Key Lab of Land & Resource Evaluation & Utilization, Changsha 410007, China
3. Research Center of Resource Environment and Urban Planning, Changsha 410114, China

Received:2019-10-12 Revised:2020-07-13 Online:2020-11-25 Published:2021-01-25
Supported by:
National Social Science Foundation of China(15BJY051);Open Topic of Hunan Key Laboratory of Land Resources Evaluation and Utilization(SYS-ZX-202002);Research Project of Appraisement Committee of Social Sciences Research Achievements of Hunan Province(XSP18ZDI031)

摘要/Abstract

摘要：

针对城市人居热环境效应研究缺乏从自然—人文多维因子组合综合评价分析的现状,本文基于多源空间数据（Landsat 5、Landsat 8卫星影像数据、POI空间大数据、数字高程模型等）反演长沙市2000年、2009年和2016年城市地表温度格局,并快速获取与城市热环境密切相关的自然和人文因素共12个影响因子。应用标准差椭圆、空间主成分分析（PCA）等方法多角度分析了城市人居热环境效应及影响因素的联动关系。结果表明：① 2000—2016年共16年间热岛面积共增加547 km²、地表最高温度差达到10.1 ℃。城市热岛区的空间分布主要集中在城市建成区,如工商业集中和人口密集的城市中心地区,并呈现出地表温度从城市中心向郊区逐渐降低的热岛的空间分布格局,出现多个高温中心点,如五一广场商圈、长沙县星沙经济技术开发区、望城工业区、岳麓工业集中区、天心工业区等;② 2000—2016年热岛空间发展主轴保持在东北—西南方向,2000—2009年热岛重心向西南偏移了2.7 km,偏转角度为54.9°,2009—2016年热岛重心向东北偏移了4.8 km,偏转角度为60.9°。整体上,长沙市热环境空间格局的变化和城市的建设强度的变化存在一定的关联;③ 通过主成分分析得出影响长沙城市热环境格局的因子为景观格局、城市建设强度、地形地貌3个主因子;④ 人文因素对于当前加剧热岛效应形成产生的促进作用明显大于自然因素的抑制作用,综合作用下地区将升温0.293 ℃。影响城市人居热环境的因素众多,多源数据有助于揭示城市热环境空间格局及演变规律,深化对城市热岛效应成因分析认知,明确人文和自然影响因素间相关关系和相关程度,以此为改善城市人居环境质量提供科学依据。

关键词: 热环境, 自然—人文影响因素, 多源数据, 空间主成分分析（PCA）, 长沙市

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, 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 method and spatial principal component analysis (PCA) method are used to analyze the effect of urban human residential thermal environment and the linkage of its influencing factors. The results show that: (1) During the 16 years from 2000 to 2016, the heat island area increased by 547 km², and the maximum surface temperature difference reached 10.1 ℃. The spatial distribution of urban heat island was mainly concentrated in urban built-up areas, such as industrial and commercial areas and densely populated urban centers. The spatial distribution pattern of heat island is gradually decreasing from the urban center to the suburbs. There were many high-temperature centers, such as Wuyi square business circle, Xingsha economic and technological development zone in Changsha County, Wangcheng industrial zone, Yuelu industrial concentration zone, and Tianxin industrial zone. (2) From 2000 to 2016, the main axis of spatial development of heat island maintained in the northeast-southwest direction. From 2000 to 2009, the center of gravity of heat island shifted 2.7 km to the southwest, and the deflection angle was 54.9°; the center of gravity of heat island shifted to the northeast by 4.8 km, and the deflection angle was 60.9°. On the whole, the change of spatial pattern of thermal environment in Changsha was related to the change of urban construction intensity. (3) Through the principal component analysis method, it was concluded that the factors affecting the urban thermal environment pattern of Changsha were landscape pattern, urban construction intensity and landform. (4) The promotion effect of human factors on the formation of heat island effect was obviously greater than that of natural factors. Under the comprehensive effect, the temperature would rise by 0.293 units. There are many factors influencing the urban human settlements thermal environment. 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 and degree 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 principal component analysis, Changsha city

熊鹰, 章芳. 基于多源数据的长沙市人居热环境效应及其影响因素分析[J]. 地理学报, 2020, 75(11): 2443-2458.

XIONG Ying, ZHANG Fang. Thermal environment effects of urban human settlements and influencing factors based on multi-source data: A case study of Changsha city[J]. Acta Geographica Sinica, 2020, 75(11): 2443-2458.

图/表 17

图1

图2

表1

表2

长沙市地表温度等级划分"

温度等级	极低温区	低温区	较低温区	中温区	较高温区	高温区	极高温区
温度区间	$T < u - 2.5 std$	$u - 2.5 std ≤ T < u - 1.5 std$	$u - 1.5 std ≤ T < u - 0.5 std$	$u - 0.5 std ≤ T < u + 0.5 std$	$u + 0.5 std ≤ T < u + 1.5 std$	$u + 1.5 std ≤ T < u + 2.5 std$	$T ≥ u + 2.5 std$

表2

图3

图4

表3

表4

图5

表5

表6

图6

表7

表8

图7

表9

表10

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数据系列	数据名称	数据来源	用途	时间
遥感数据	Landsat 5 TM、Landsat 8 OLI TIRS	http://www.gscloud	反演地表温度、提取地表信息	2000年7月; 2009年8月; 2016年7月
遥感数据	DEM数据	http://www.gscloud	计算高程、坡度	2016年
矢量数据	长沙市行政边界矢量数据	中国地球系统科学数据共享网	提取研究区边界	2016年
空间大数据	长沙市POI数据	高德地图	提取经济活动指数	2016

年份	极低温区		低温区		较低温区		中温区		较高温区		高温区		极高温区
年份	面积(km)	比例(%)	面积(km)	比例(%)	面积(km)	比例(%)	面积(km)	比例(%)	面积(km)	比例(%)	面积(km)	比例(%)	面积(km)	比例(%)
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

等级区	芙蓉区			天心区			岳麓区			开福区
等级区	2000年	2009年	2016年	2000年	2009年	2016年	2000年	2009年	2016年	2000年	2009年	2016年
极低温区	0.15	0.00	0.49	0.00	0.00	6.12	0.00	0.00	17.67	6.85	0.09	7.86
低温区	0.79	0.39	0.95	7.42	6.15	1.85	27.41	43.35	160.79	28.27	16.24	15.77
较低温区	5.53	1.40	0.80	23.41	4.12	3.64	304.21	194.52	145.28	105.07	40.88	36.57
中温区	8.91	4.45	2.30	17.06	11.68	9.72	139.86	162.65	80.28	28.03	62.51	50.41
较高温区	13.39	9.15	8.78	14.02	16.26	16.52	34.94	55.74	62.31	13.59	37.60	42.48
高温区	8.98	16.99	16.29	9.85	26.46	28.14	10.31	47.48	45.48	6.77	27.05	32.25
极高温区	5.93	11.30	14.06	3.75	10.86	9.53	3.33	16.33	8.26	7.61	11.83	10.86
等级区	雨花区			望城区			长沙县
等级区	2000年	2009年	2016年	2000年	2009年	2016年	2000年	2009年	2016年
极低温区	1.63	0.00	0.24	1.04	0.02	65.80	66.37	11.55	51.51
低温区	7.96	0.30	1.30	83.00	213.84	249.37	301.57	723.16	478.44
较低温区	38.20	3.20	3.70	582.09	361.73	347.29	1288.38	837.42	736.84
中温区	24.50	13.72	9.03	275.08	304.85	211.57	288.31	311.42	488.74
较高温区	18.44	21.57	24.15	35.62	68.61	69.31	50.59	71.20	148.09
高温区	12.87	42.28	51.56	6.04	27.05	32.25	9.92	40.38	72.24
极高温区	7.48	30.00	21.09	2.61	9.39	9.89	2.35	12.38	31.64

一级指标	二级指标	三级指标
自然因素	植被与水体	植被覆盖度(FVC)
	植被与水体	改进归一化差异水体指数(MNDWI)
	地形地貌	数字地表高程(DEM)
	地形地貌	坡度
人文因素	景观格局指数	散布指数(CONTAG)
		香农多样性指数(SHDI)
		离散度(DIVISION)
		斑块内聚力指数(COHESION)
	城市建设强度	归一化差值不透水面指数(NDISI)
		归一化差值裸地与建筑指数(NDBBI)
		地表反照率(Albedo)
	社会经济活动	兴趣点密度指数(POI)

成分	初始特征值			提取载荷平方和
成分	特征值	贡献率(%)	累积贡献率(%)	特征值	方差百分比(%)	累积贡献率(%)
1	3.557	29.644	29.644	3.557	29.644	29.644
2	2.637	21.978	51.622	2.637	21.978	51.622
3	1.893	15.778	67.400	1.893	15.778	67.400
4	0.991	8.254	75.654
5	0.786	6.554	82.208
6	0.762	6.354	88.562
7	0.514	4.286	92.848
8	0.428	3.569	96.417
9	0.259	2.159	98.575
10	0.134	1.114	99.689
11	0.030	0.246	99.935
12	0.008	0.065	100.000

基于多源数据的长沙市人居热环境效应及其影响因素分析

Thermal environment effects of urban human settlements and influencing factors based on multi-source data: A case study of Changsha city

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图/表 17

参考文献 24

相关文章 2

Metrics

本文评价

影响因子	第1主成分	第2主成分	第3主成分
POI密度指数	0.085	0.207	-0.295
归一化水体指数	-0.506	-0.470	0.136
植被覆盖指数	0.395	-0.457	0.284
离散度	0.930	-0.175	-0.219
香农多样性	0.936	-0.188	-0.227
散布指数	0.511	0.054	-0.180
斑块内聚力指数	-0.918	0.193	0.004
归一化不透水指数	0.431	0.687	0.243
坡度	-0.206	-0.600	0.432
高程	0.086	-0.269	0.762
地表反照率	-0.144	0.817	-0.125
归一化差值裸地与建筑指数	0.192	0.699	-0.331

影响因子	未标准化系数		标准化系数 Beta	t	显著性	共线性统计
影响因子	B	标准误差	标准化系数 Beta	t	显著性	容差	VIF
常量	0.361	0.010	-	35.175	0.000	-	-
第1主成分	0.116	0.007	0.327	15.442	0.000	0.625	1.601
第2主成分	0.012	0.008	0.031	1.471	0.000	0.644	1.553
第3主成分	-0.289	0.011	-0.640	-26.032	0.006	0.463	2.159

人文因素		自然因素		温度 (℃)
作用程度	变化比例(%)	作用程度	变化比例(%)	温度 (℃)
1.754	100	-1.460	100	0.293
1.5786	90	-1.606	110	-0.0274
1.4032	80	-1.752	120	-0.3488

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