Investigating the Spatiotemporal Variability and Driving Factors of Artificial Lighting in the Beijing-Tianjin-Hebei Region Using Remote Sensing Imagery and Socioeconomic Data

被引:9
|
作者
Leng, Wanchun [1 ,2 ]
He, Guojin [1 ,3 ]
Jiang, Wei [1 ,2 ,4 ]
机构
[1] Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100094, Peoples R China
[2] Univ Chinese Acad Sci, Beijing 100049, Peoples R China
[3] Key Lab Earth Observat Hainan Prov, Sanya 572029, Peoples R China
[4] China Inst Water Resources & Hydropower Res IWHR, Beijing 100038, Peoples R China
基金
中国国家自然科学基金;
关键词
artificial lighting; DMSP; OLS; spatio-temporal patterns; driving factors; socioeconomic data; nighttime light pollution; Beijing-Tianjin-Hebei region; remote sensing; ELECTRIC-POWER CONSUMPTION; POPULATION-DENSITY; URBAN EXPANSION; CHINA; DYNAMICS; POLLUTION; TRENDS;
D O I
10.3390/ijerph16111950
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
With rapid urbanization and economic development, artificial lighting at night brings convenience to human life but also causes a considerable urban environmental pollution issue. This study employed the Mann-Kendall non-parametric test, nighttime light indices, and the standard deviation method to investigate the spatio-temporal characteristics of artificial lighting in the Beijing-Tianjin-Hebei region. Moreover, nighttime light imagery from the Defense Meteorological Satellite Program Operational Linescan System, socioeconomic data, and high-resolution satellite images were combined to comprehensively explore the driving factors of urban artificial lighting change. The results showed the following: (1) Overall, there was an increasing trend in artificial lighting in the Beijing-Tianjin-Hebei region, which accounted for approximately 56.87% of the total study area. (2) The change in artificial lighting in the entire area was relatively stable. The artificial lighting in the northwest area changed faster than that in the southeast area, and the areas where artificial lighting changed the most were Beijing, Tianjin and Tangshan. (3) The fastest growth of artificial lighting was in Chengde and Zhangjiakou, where the rates of increase were 334% and 251%, respectively. The spatial heterogeneity of artificial lighting in economically developed cities was higher than that in economically underdeveloped cities such as Chengde and Zhangjiakou. (4) Multi-source data were combined to analyse the driving factors of urban artificial lighting in the entire area. The Average Population of Districts under City (R-2 = 0.77) had the strongest effect on artificial lighting. Total Passenger Traffic (R-2 = 0.54) had the most non-obvious effect. At different city levels, driving factors varied with differences of economy, geographical location, and the industrial structures of cities. Urban expansion, transportation hubs, and industries were the major reasons for the significant change in nighttime light. Urban artificial lighting represents a trend of overuse closely related to nighttime light pollution. This study of artificial lighting contributes to the rational planning of urban lighting systems, the prevention and control of nighttime light pollution, and the creation of liveable and ecologically green cities.
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页数:20
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