闽南三市城镇发展与地表温度的空间关系

doi:10.11821/dlxb202103006

摘要/Abstract

摘要：

基于厦门、漳州、泉州三市1996—2017年的Landsat遥感数据与夜间灯光数据,应用总体耦合态势模型及协调性模型探讨了城镇发展与地表温度的时空耦合规律,并运用标准差椭圆、双变量空间自相关及景观指数分析两者之间的空间响应规律,结果表明：1996—2017年,闽南三市表征城镇发展变化的夜间灯光与地表温度的空间分布呈现出与地形、区位等因素的空间耦合性。三市夜间灯光与地表温度的总体耦合态势不断加强,协调类型逐渐增多;城镇发展早期,夜间灯光对地表温度的影响存在滞后性,在城镇发展后期,夜间灯光则对地表温度的影响呈超前性;夜间灯光与地表温度呈正相关关系,空间溢出效应明显。夜间灯光对地表温度的正向影响逐渐加强,其与地表温度的HH、LL聚集不断扩张;HH集聚连片趋势明显。厦门的城镇发展对地表温度的影响更显著,而在泉州、漳州则较弱。本文可为闽南三市热环境优化提供科学指导。

关键词: 地表温度, 热环境, 城市发展, 夜间灯光, 空间自相关, 闽南三市

Abstract:

Exploring the influence mechanism of expansion for urban areas on thermal environment is significant for improving urban ecological environment. In this study, nighttime light (NTL) can be regarded as an evidence of urban development. Based on Landsat remote sensing data, DMSP/OLS nighttime light data and NPP/VIIRS nighttime light data of Xiamen, Zhangzhou and Quanzhou in southern Fujian during 1996-2017, this study applied the overall coupling model and coordination model to discuss the spatio-temporal coupling and coordination relationship between urban development and land surface temperature (LST) distribution. Additionally, the spatial response law was analyzed by standard deviation ellipse, bivariate spatial autocorrelation and landscape index. The results show that during 1996-2017, the spatial distribution patterns of LST and NTL show that urban development is closely related with factors such as geographical location and terrain. In the western inland areas with high elevations, forests are obviously concentrated with relatively low LST, while in the eastern plain areas, more urban areas are distributed with relatively high LST. In the three cities, the overall coupling situation of NTL and LST is constantly strengthening, and the proportion of coordination for NTL and LST is gradually increasing. In the early stage of urban development, the influence of NTL on LST is hysteretic. In the late stage, the influence of NTL on LST is in advance. There is a positive correlation between NTL and LST, and a spatial spillover effect is obvious. The correlation coefficient and bivariate spatial autocorrelation Moran's I value gradually increase, indicating that NTL has an increasing influence on the change of LST. The HH (High-High)-type and LL (Low-Low)-type agglomeration areas continue to expand. Influenced by the trend of integrated development of the three study cities, HH-type agglomeration area is gradually concentrated in regions connecting the cities, such as Xiamen, Jinjiang, Shishi and central urban area of Zhangzhou. The influence of urban development on LST is related to the development condition of itself. Compared with Zhangzhou and Quanzhou, NTL has a more significant influence on the LST in Xiamen. The study results provide a scientific guidance for the optimization of thermal environment in the three cities of southern Fujian.

Key words: land surface temperature, thermal environment, urban development, nighttime light, spatial autocorrelation, the three cities of southern Fujian

沈中健, 曾坚. 闽南三市城镇发展与地表温度的空间关系[J]. 地理学报, 2021, 76(3): 566-583.

SHEN Zhongjian, ZENG Jian. Spatial relationship of urban development to land surface temperature in three cities of southern Fujian[J]. Acta Geographica Sinica, 2021, 76(3): 566-583.

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年份	加权中心距离(km)			时段	加权中心移动方向夹角(º)
年份	厦门	漳州	泉州	时段	厦门	漳州	泉州
1996	26.160	26.371	41.259	1996—2002	178.393	153.699	47.076
2002	11.136	11.193	17.521	2002—2007	148.105	55.592	40.772
2007	5.792	12.913	17.843	2007—2012	11.625	18.155	49.796
2012	3.666	15.213	19.256	2012—2017	21.862	24.213	40.802
2017	3.223	12.879	11.441	1996—2017	178.337	17.430	25.995

类型	形成条件	意义
协调增进型	0.8 < O ≤ 1.0, UR ≈ TR >0	NTL、LST协调增长, 城镇发展与LST呈协同增进状态
拮抗升温超前型	0 ≤ O < 0.5, TR > UR	NTL与LST变化协调度较弱, LST上升超前于城镇发展
磨合升温超前型	0.5 ≤ O < 0.8, TR > UR	NTL与LST变化处于勉强协调, LST上升超前于城镇发展
磨合升温滞后型	0.5 ≤ O < 0.8, TR < UR	NTL与LST变化处于勉强协调, LST上升滞后于城镇发展
拮抗升温滞后型	0 ≤ O < 0.5, TR < UR	NTL与LST变化协调度较弱, LST上升滞后于城镇发展
协调减退型	0.8 <O ≤ 1.0, UR ≈ TR < 0	NTL、LST协调下降, 城镇发展与LST呈协同减退状态

地区	年份	相关系数	标准差椭圆参数
地区	年份	相关系数	中心坐标(NTL, LST)	方向性	离散性	方位角(º)
厦门	1996	0.093^***	(0.239, 0.695)	1.705	0.587	85.187
	2002	0.545^***	(0.283, 0.565)	2.285	0.438	67.655
	2007	0.762^***	(0.396, 0.435)	3.090	0.324	60.893
	2012	0.824^***	(0.590, 0.439)	3.572	0.280	59.003
	2017	0.859^***	(0.590, 0.481)	4.388	0.228	63.135
漳州	1996	0.149^***	(0.083, 0.501)	1.526	0.615	8.685
	2002	0.321^***	(0.058, 0.533)	1.694	0.590	18.936
	2007	0.423^***	(0.091, 0.459)	1.620	0.617	34.274
	2012	0.661^***	(0.168, 0.537)	2.925	0.342	68.511
	2017	0.665^***	(0.168, 0.531)	2.493	0.401	66.010
泉州	1996	0.386^***	(0.120, 0.531)	1.511	0.662	40.253
	2002	0.643^***	(0.128, 0.513)	2.179	0.459	51.330
	2007	0.696^***	(0.168, 0.498)	2.464	0.406	54.827
	2012	0.736^***	(0.285, 0.504)	3.430	0.292	68.086
	2017	0.806^***	(0.285, 0.534)	3.913	0.256	65.969

年份	厦门	漳州	泉州
1996	0.084(7.573)^***	0.141(35.654)^***	0.373(82.939)^***
2002	0.541(45.293)^***	0.310(80.156)^***	0.636(128.881)^***
2007	0.746(55.677)^***	0.409(103.164)^***	0.687(131.404)^***
2012	0.809(58.262)^***	0.641(135.030)^***	0.721(135.594)^***
2017	0.844(61.455)^***	0.594(145.593)^***	0.759(154.359)^***