Spatial-temporal change features of impervious surface and its effect on land surface temperature from 1984 to 2014 in Beijing

doi:10.12006/j.issn.1673-1719.2019.084

Abstract

Abstract:

It is of great scientific significance to study the relationship between urban surface cover and land surface temperature (LST) for improving urban ecological environment. Based on the Landsat TM data, this paper uses the linear spectral hybrid analysis model to extract the impervious surface land information. Combined with the LST and surface heat flux, the spatial-temporal variation characteristics and inter relationships of the impervious surface area (ISA) and the LST are analyzed. The results show that the ISA of Beijing increased rapidly from 1984 to 2014. The medium coverage of ISA decreased, while high coverage increased remarkably. The LST decreased from the city center to the suburbs, and the high temperature area was expanding continuously. There was a significant positive correlation between the LST and ISA, and the rate of the LST rise was the fastest when ISA was at 0.6-0.9. So reducing ISA can alleviate the phenomenon of high temperature area concentration.

Key words: Impervious surface area (ISA), Coverage, Land surface temperature (LST), Spatial-temporal features, Beijing

Jia-Le ZHANG,Zhi-Hua PAN,Wen-Hui KUANG,Yu-Ying PAN,Guo-Lin HAN,Jia-Lin WANG,Na HUANG,Zi-Yuan ZHANG,Wen-Juan YIN. Spatial-temporal change features of impervious surface and its effect on land surface temperature from 1984 to 2014 in Beijing[J]. Climate Change Research, 2020, 16(3): 296-305.

Figures/Tables 13

Table 1 Urban land use indexes in 1984, 1994, 2004 and 2014 in Beijing

Fig. 1 Spatial distribution of impervious surface area (ISA) in Beijing in 1984, 1994 , 2004 and 2014

Fig. 2 Area and proportion of different types of surface in Beijing in 1984, 1994, 2004 and 2014

Fig. 3 Different ISA surface area proportions in Beijing in 1984, 1994, 2004 and 2014

Fig. 4 Zonal statistic of the area of impervious surface in 1984, 1994, 2004 and 2014

Table 2 Statistics of surface temperature in Beijing based on Landsat TM data

Fig. 5 Comparison of surface temperature in main city, near suburb and far suburb of Beijing in 1984, 1994, 2004 and 2014

Fig. 6 Spatial distribution of surface temperature in Beijing in 1984 (a), 1994 (b), 2004 (c) and 2014 (d)

Table 3 Zonal statistic of the surface temperature in different rings of Beijing in 1984, 1994, 2004 and 2014

Fig. 7 Relationship between ISA and normalized surface temperature in Beijing

Table 4 Regression analysis of surface temperature and ISAat different levels

Fig. 8 Sensible heat flux of underlying surface in Beijing in 1984 (a), 1994 (b), 2004 (c) and 2014 (d)

Fig. 9 Urban and rural gradient bowen score in Beijing in 1984, 1994, 2004 and 2014

References

[1]	周玄德, 郭华东, 孜比布拉, 等. 城市扩张过程中不透水面空间格局演变以及对地表温度的影响: 以乌鲁木齐市为例[J]. 生态学报, 2018,38(20):7336-7347. DOI: 10.5846/stxb201711122022.
[1]	Zhou X D, Guo H D, Zibibula S, et al. Spatial pattern evolution of impervious surfaces and its influence on surface temperature in the process of urban expansion: a case study of Urumqi[J]. Acta Ecologica Sinica, 2018,38(20):7336-7347. DOI: 10.5846/stxb201711122022 (in Chinese)
[2]	Roth M. Review of urban climate research in (sub) tropical regions[J]. International Journal of Climatology, 2007,27:1859-1873. DOI: 10.1002/joc.1591
[3]	Akio O, Cao X, Ito T, et al. Evaluating the potential for urban heat-island mitigation by greening parking lots[J]. Urban Forestry & Urban Greening, 2010,9:323-332. DOI: 10.1016/j.ufug.2010.06.002
[4]	谢启娇. 武汉市城市热岛特征及其影响因素分析[J]. 长江流域资源与环境, 2016,25(3):462-469. DOI: 10.11870/cjlyzyyhj201603013.
[4]	Xie Q J. Analysis on characteristics and influence factors of urban heat island effect in Wuhan[J]. Resources and Environment in the Yangtze Basin, 2016,25(3):462-469. DOI: 10.11870/cjlyzyyhj201603013 (in Chinese)
[5]	尹昌应, 石忆邵, 王贺封, 等. 城市地表形态对热环境的影响: 以上海市为例[J]. 长江流域资源与环境, 2015,24(1):97-105. DOI: 10.11870/cjlyzyyhj201501013.
[5]	Yin C Y, Shi Y S, Wang H F, et al. Impacts of urban landscape form on thermal environment at multi-spatial levels[J]. Resources and Environment in the Yangtze Basin, 2015,24(1):97-105. DOI: 10.11870/cjlyzyyhj201501013 (in Chinese)
[6]	杨可明, 周玉洁, 齐建伟, 等. 城市不透水面及地表温度的遥感估算[J]. 国土资源遥感, 2014,26(2):134-139. DOI: 10.6046/gtzyyg.2014.02.22.
[6]	Yang K M, Zhou Y J, Qi J W, et al. Remote sensing estimating of urban impervious surface area and land surface temperature[J]. Remote Sensing for Land & Resources, 2014,26(2):134-139. DOI: 10.6046/gtzyyg.2014.02.22 (in Chinese)
[7]	北京市统计局. 北京统计年鉴2015 [M]. 北京: 中国统计出版社, 2015.
[7]	Beijing Statistics Bureau. Beijing statistical year book 2015 [M]. Beijing: China Statistical Press, 2015 ( in Chinese)
[8]	牟雪洁, 赵昕奕. 珠三角地区地表温度与土地利用类型关系[J]. 地理研究, 2012,31(9):1589-1597. DOI: 10.11821/yj2012090005.
[8]	Mou X J, Zhao X Y. Study on the relationship between surface temperature and land use in Pearl River Delta[J]. Geographical Research, 2012,31(9):1589-1597. DOI: 10.11821/yj2012090005 (in Chinese)
[9]	郑祚芳, 郑艳, 李青春. 近30年来城市化进程对北京区域气温的影响[J]. 中国生态农业学报, 2007,15(4):26-29. DOI: 10.3724/SP.J.1011.
[9]	Zheng Z F, Zheng Y, Li Q C. Effect of urbanization on the temperature of Beijing metropolis in recent 30 years[J]. Chinese Journal of Eco-Agriculture, 2007,15(4):26-29. DOI: 10.3724/SP.J.1011 (in Chinese)
[10]	张光智, 徐祥德, 王继志, 等. 北京及周边地区城市尺度热岛特征及其演变[J]. 应用气象学报, 2002 ( S1):43-50. DOI: 10.3969/j.issn.1001-7313.2002.z1.005.
[10]	Zhang G Z, Xu X D, Wang J Z, et al. A study of characteristics and evolution of urban heat island over Beijing and its surrounding area[J]. Journal of Applied Meteorological Science, 2002 ( S1):43-50. DOI: 10.3969/j.issn.1001-7313.2002.z1.005 (in Chinese)
[11]	宋艳玲, 张尚印. 北京市近40年城市热岛效应研究[J]. 中国生态农业学报, 2003,11(4):126-129. DOI: 10.3724/SP.J.1011.
[11]	Song Y L, Zhang S Y. The study on heat island effect in Beijing during last 40 years[J]. Chinese Journal of Eco-Agriculture, 2003,11(4):126-129. DOI: 10.3724/SP.J.1011 (in Chinese)
[12]	屈益挺, 孟丹, 李小娟. 北京市城市扩张及其对城市增温效应的影响[J]. 首都师范大学学报:自然科学版, 2017,38(2):77-83. DOI: 10.3969/j.issn.1004-9398.2017.02.016.
[12]	Qu Y T, Meng D, Li X J. Study on urban expansion and its influence on urban warming effect in Beijing[J]. Journal of Capital Normal University: Natural Science Edition, 2017,38(2):77-83. DOI: 10.3969/j.issn.1004-9398.2017.02.016 (in Chinese)
[13]	Qiao K, Zhu W Q, Hu D Y, et al. Examining the distribution and dynamics of impervious surface in different function zones in Beijing[J]. Journal of Geographical Sciences, 2018,28(5):669-684. DOI: 10.1007/s11442-018-1498-5
[14]	匡文慧, 杨天荣, 刘爱琳, 等. 城市地表覆盖结构组分与热环境调控模型(EcoCity)研究: 以北京城市为例[J]. 中国科学:地球科学, 2017,47(7):847-859. DOI: 10.1360/N072016-00273.
[14]	Kuang W H, Yang T R, Liu A L, et al. An Ecocity model for regulating urban land cover structure and thermal environment: taking Beijing as an example[J]. Scientia Sinica: Terrae, 2017,47(7):847-859. DOI: 10.1360/N072016-00273 (in Chinese)
[15]	Elvidge C D, Tuttle B T, Sutton P C, et al. Global distribution and density of constructed impervious surfaces[J]. Sensors, 2007,7:1962-1979. DOI: 10.3390/s7091962
[16]	徐涵秋, 王美雅. 地表不透水面信息遥感的主要方法分析[J]. 遥感学报, 2016,20(5):1270-1289. DOI: 10.11834/jrs.20166210.
[16]	Xu H Q, Wang M Y. Remote sensing-based retrieval of ground impervious surfaces[J]. Journal of Remote Sensing, 2016,20(5):1270-1289. DOI: 10.11834/jrs.20166210 (in Chinese)
[17]	Wu C S, Murray A T. Estimating impervious surface distribution by spectral mixture analysis[J]. Remote Sensing of Environment, 2003,84(4):493-505. DOI: 10.1016/S0034-4257(02)00136-0
[18]	Kuang W H, Chi W F, Lu D S, et al. A comparative analysis of megacity expansions in China and the U.S.: patterns, rates and driving forces[J]. Landscape Urban Plan, 2014,13(2):121-135. DOI: 10.1016/j.landurbplan.2014.08.015
[19]	Carlson T N, Arthur S T. The impact of land use-land cover changes due to urbanization on surface microclimate and hydrology: a satellite perspective[J]. Global and Planetary Change, 2000,25(1/2):49-65. DOI: 10.1016/S0921-8181(00)00021-7
[20]	覃志豪, Zhang M H, Arnon K, et al. 用陆地卫星TM6数据演算地表温度的单窗算法[J]. 地理学报, 2001,56(4):456-466. DOI: 10.1142/S0252959901000401.
[20]	Qin Z H, Zhang M H, Arnon K, et al. Mono-window algorithm for retrieving land surface temperature from Landsat TM6 data[J]. Acta Geographica Sinica, 2001,56(4):456-466. DOI: 10.1142/S0252959901000401 (in Chinese)
[21]	覃志豪, 李文娟, 徐斌, 等. 陆地卫星TM6波段范围内地表比辐射率的估计[J]. 国土资源遥感, 2004,61(3):28-30. DOI: 10.6046/gtzyyg.2004.03.07.
[21]	Qin Z H, Li W J, Xu B, et al. The estimation of land surface emissivity for Landsat TM6[J]. Remote Sensing for Land & Resources, 2004,61(3):28-30. DOI: 10.6046/gtzyyg.2004.03.07 (in Chinese)
[22]	Kuang W H, Dou Y Y, Zhang C, et al. Quantifying the heat flux regulation of metropolitan land use/land cover components by coupling remote sensing modeling with in situ measurement[J]. Journal of Geophysical Research: Atmospheres, 2015,120(1):113-130. DOI: 10.1002/2014JD022249
[23]	张仁华. 定量热红外遥感模型及地面实验基础[M]. 北京: 科学出版社, 2009.
[23]	Zhang R H. Quantitative thermal infrared remote sensing model and ground experiment basis [M]. Beijing: Science Press, 2009 ( in Chinese)
[24]	陈松林, 王天星. 等间距法和均值标准差法界定城市热岛的对比研究[J]. 地球信息科学学报, 2009,11(2):145-150. DOI: 10.3969/j.issn.1560-8999.
[24]	Chen S L, Wang T X. Comparison analyses of equal interval method and mean standard deviation method used to delimitate urban heat island[J]. Journal of Geo-Information Science, 2009,11(2):145-150. DOI: 10.3969/j.issn.1560-8999 (in Chinese)
[25]	宋小宁, 赵英时, 冯晓明. 基于卫星遥感数据改进区域尺度的显热通量模型[J]. 地理与地理信息科学, 2007,23(1):36-38. DOI: 10.3969/j.issn.1672-0504.2007.01.009.
[25]	Song X N, Zhao Y S, Feng X M. An improved regional scale sensible heat flux model using remote sensing[J]. Geography and Geo-Information Science, 2007,23(1):36-38. DOI: 10.3969/j.issn.1672-0504.2007.01.009 (in Chinese)