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Remote Sensing for Land & Resources    2018, Vol. 30 Issue (3) : 213-223     DOI: 10.6046/gtzyyg.2018.03.29
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Remote sensing analysis of temporal-spatial variations of urban heat island effect over Beijing
Min YANG1,2,3, Guijun YANG2,3,4(), Yanjie WANG2,3,4, Yongfeng ZHANG5, Zhihong ZHANG6, Chenhong SUN7
1. Shaanxi Earthquake Agency, Xi’an 710068, China;
2. National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
3. Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing 100097, China
4. Beijing Engineering Research Center of Agricultural Internet of Things, Beijing 100097, China
5. Xi’an Zhongtianweidi Surveying & Mapping Technology Co.,Ltd., Xi’an 710054, China;
6. College of Geomatics, Xi’an University of Science and Technology, Xi’an 710054, China;
7. Xi’an Aerospace Tian Painted Data Technology Co., Ltd., Xi’an 710054, China;
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Abstract  

In order to study the temporal-spatial variation of the urban heat island effect over Beijing since 1985, the authors utilized the 7 phases of Landsat TM/ETM+/TIRS images in summer to perform retrieval of the land surface brightness temperature so as to replace the true land surface temperature(LST). And the LST data were used for a series of qualitative and quantitative analysis of urban heat island effect to reveal Beijing heat distribution and the characteristics of urban heat island effect. Some conclusions have been reached: ① The high-temperature regions and sub-high temperature regions are continuously centralized to the urban area, but the high-temperature regions in Dongcheng District and Xicheng District show a significant downward trend, and the large scale of heat island is replaced by the small heat islands;② The influence of industrial estate on the urban heat island effect in Beijing is much higher than that of the residential district in Beijing;③ The temperature of the areas with low-rise and dense buildings and low vegetation coverage are much higher than the temperature of the areas with tall and sparse buildings and high vegetation coverage. The results of the study would play an important role in urban planning in that they provide the reference frame for the government departments to reduce the impact of urban heat island effect based on rational planning of the distribution of water, green land,industrial estate and residential areas.
Keywords land surface brightness temperature      land surface temperature(LST)      urban heat island      spatial distribution      temporal and spatial variation      Beijing     
:  TP79  
Corresponding Authors: Guijun YANG     E-mail: yanggj@nercita.org.cn
Issue Date: 10 September 2018
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Min YANG
Guijun YANG
Yanjie WANG
Yongfeng ZHANG
Zhihong ZHANG
Chenhong SUN
Cite this article:   
Min YANG,Guijun YANG,Yanjie WANG, et al. Remote sensing analysis of temporal-spatial variations of urban heat island effect over Beijing[J]. Remote Sensing for Land & Resources, 2018, 30(3): 213-223.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2018.03.29     OR     https://www.gtzyyg.com/EN/Y2018/V30/I3/213
Fig.1  Location of study area
Fig.2  Ideal time of Landsat data acquired
传感器类型 获取日期 空间分辨率/m 时间分
辨率/d
可将光和近
红外波段		 热红外
波段
Landsat5 TM 19850718 30 120 16
19910617
19950916
20050725
20110726
Landsat7 ETM+ 20010924 30 60
Landsat8 TIRS 20150824 30 100
Tab.1  Parameters of Landsat data acquired
覆盖类别 LST 1985年 1991年 1995年 2001年 2005年 2011年 2015年 7期平均
水体 最低/K 287.87 290.99 284.80 283.63 293.55 296.20 296.86 290.56
最高/K 302.09 305.36 298.92 300.11 304.34 312.18 307.72 304.39
平均/K 295.19 295.95 292.02 292.07 297.91 299.34 299.75 296.03
面积比例/% 0.57 0.74 0.62 0.73 0.75 0.75 0.68 0.69
高大植被 最低/K 286.45 290.61 284.62 282.90 292.94 293.76 293.74 289.29
最高/K 303.09 308.85 303.76 301.84 308.67 310.90 310.81 306.85
平均/K 295.70 297.69 293.60 293.86 298.73 300.58 301.55 297.39
面积比例/% 22.91 23.79 33.16 27.57 24.45 24.87 6.38 23.30
低矮植被 最低/K 286.50 290.25 287.60 282.38 291.35 294.75 293.10 289.42
最高/K 303.14 308.46 299.34 302.55 305.80 310.59 312.73 306.09
平均/K 295.62 297.08 292.91 293.11 298.29 300.23 302.04 297.04
面积比例/% 30.72 8.06 31.09 18.62 30.88 27.50 35.22 26.01
裸土 最低/K 281.98 291.87 289.32 282.37 293.25 294.69 295.34 289.83
最高/K 305.98 310.11 301.18 306.51 310.28 314.42 315.11 309.08
平均/K 296.84 299.07 294.23 294.68 300.25 302.40 304.25 298.82
面积比例/% 34.67 46.59 14.90 24.13 14.91 11.01 16.09 23.19
不透水面 最低/K 277.24 290.72 283.12 275.15 289.90 292.51 293.86 286.07
最高/K 308.72 315.46 311.19 309.09 313.24 317.51 316.18 313.05
平均/K 298.16 300.65 294.93 295.02 301.22 304.06 305.47 299.93
面积比例/% 11.13 20.82 20.23 28.95 29.02 35.87 41.64 26.81
分类精度 OA/% 96.04 95.98 95.26 92.25 94.07 92.02 93.46 94.16
Kappa 0.95 0.95 0.94 0.90 0.92 0.90 0.92 0.93
Tab.2  Minimum / maximum / average of LST and area ratio for classes of coverage in Landsat images acquired in seven periods
温度分区 所占比例/% 分区范围/%
低温区 24.0 [0,24)
亚低温区 26.0 [24,50)
中温区 23.2 [50,73.2)
亚高温区 25.5 [73.2,98.7)
高温区 1.3 [98.7,100]
Tab.3  Classification of temperature grades based on RPGS method
Fig.3  Distribution of LST grades
Fig.4-1  Evolution of average LST of Landsat image in different stages
Fig.4-2  Evolution of average LST of Landsat image in different stages
Fig.5  Changes in distribution of large shopping malls
Fig.6  Relationship between thermal distribution and factory area as well as residential area
Fig.7  Total energy consumption in 2014
Fig.8  Temperature grades of low-vegetation-covered areas with low-dense of buildings(A)and high-vegetation-covered areas with tall- sparse of buildings(B)
Fig.9  Areas for quantitative study of surface urban heat island effect in 1985
Fig.10  Beijing heat aggregation indicators from 1985 to 2015
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