سابِق|تال

Response relationship between urban thermal environment and impervious surface distribution density at multi-spatial scale： A case study of Beijing

  • role： First author第一作者
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

    University of Chinese Academy of Sciences, Beijing 100049, China

    Sanya Institute of Remote Sensing, Chinese Academy of Sciences, Sanya 572029, China

    Aerospace Information Hainan Research Institute, Chinese Academy of Sciences, Sanya 572029, China

  • Email：mengqy@radi.ac.cn
  • Introduction：孟庆岩，研究方向为城市陆表环境遥感。 E-mail： mengqy@radi.ac.cn
MENG Qingyan1234,  
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

    University of Chinese Academy of Sciences, Beijing 100049, China

    Sanya Institute of Remote Sensing, Chinese Academy of Sciences, Sanya 572029, China

    Aerospace Information Hainan Research Institute, Chinese Academy of Sciences, Sanya 572029, China

WANG Zi’an1234,  
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

    University of Chinese Academy of Sciences, Beijing 100049, China

    Sanya Institute of Remote Sensing, Chinese Academy of Sciences, Sanya 572029, China

    Aerospace Information Hainan Research Institute, Chinese Academy of Sciences, Sanya 572029, China

ZHANG Linlin1234,  
  • Affiliation：

    Sanya Institute of Remote Sensing, Chinese Academy of Sciences, Sanya 572029, China

    School of Geology and Geomatics, Tianjin Chengjian University, Tianjin 300384, China

SUN Zhenhui35,  
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

    University of Chinese Academy of Sciences, Beijing 100049, China

    Sanya Institute of Remote Sensing, Chinese Academy of Sciences, Sanya 572029, China

    Aerospace Information Hainan Research Institute, Chinese Academy of Sciences, Sanya 572029, China

HU Die1234,  
  • Affiliation：

    Sanya Institute of Remote Sensing, Chinese Academy of Sciences, Sanya 572029, China

    Aerospace Information Hainan Research Institute, Chinese Academy of Sciences, Sanya 572029, China

YANG Tianliang34

ملخص

In view of the insufficient research on the response law of urban thermal environment and the Impervious Surface Distribution Density (ISDD), this paper selected Beijing as a case study. On the basis of the MODIS data, the average temperature difference between the urban construction and marginal areas was calculated as the UHI intensity (UHII). In combination with the characteristics of the UHII level, six typical regions (ⅠXierqi, Xisanqi, Huilongguan, ⅡDongsi, Xi’anmen, Xinjiekou, Ⅲthe middle of Fengtai District, ⅣSihui, Dingfuzhuang, Dongba, ⅤShijingshan District, Ⅵnear the Sijiqing bridge on the North Fourth Ring Road) with a high expected UHII level were selected to study the response relationship between the UHII level and the ISDD. Finally, Landsat data was used to invert the surface temperature to study the critical point and optimal scale of surface temperature response to the ISDD in urban construction areas and typical regions. Results show that the following. (1) The frequency of occurrence of UHII levels, their variation degree during the day and at night have a certain correlation with the ISDD, and the variation characteristics of the UHII under similar ISDDs are the same. (2) The distribution characteristics of the surface mean temperature in urban construction areas and the ISDD are obviously consistent. As the scale radius r increases, the response is more obvious. Besides, there are critical points for the response of the surface temperature to ISDD. The response of critical points gradually increases with the decrease of scale radius r. When scale radius r=1000 m, the ISDD reaches 60%, and its influence tends to weaken; the critical points of r=500 m and r=300 m are 69% and 83%, respectively. (3) The optimal scales of different typical regions have differences. The optimal scale of Xierqi, Xisanqi, and Huilongguan is 150 m, and that of Dongsi, Di’anmen, Xinjiekou, and Qingqiao near the fourth ring is 60 m. However, seasonal changes have little impact on the optimal scale. The optimal scale can measure the degree of fragmentation of the impervious surface distribution in different regions and the single degree of surface type to some extent. This study can provide a reference for urban planning and UHI governance.

مفهوم

urban heat island effect;impervious surface distribution density;urban construction area;critical point;response law

References

  1. 1.
    Amiri R, Weng Q H, Alimohammadi A and Alavipanah S K. 2009. Spatial–temporal dynamics of land surface temperature in relation to fractional vegetation cover and land use/cover in the Tabriz urban area, Iran. Remote Sensing of Environment, 113(12): 2606-2617
  2. 2.
    Bauer M E, Heinert N J, Doyle J K and Yuan F. 2004. Impervious surface mapping and change monitoring using Landsat remote sensing, ASPRS Annual Conference Proceedings, May 23—28, Denver, Colorado. Bethesda, MD: American Society for Photogrammetry and Remote Sensing.
  3. 3.
    Beck B R. 1990. Measurement of the Magnetic and Temperature Dependence of the Electron-electron Anisotropic Temperature Relaxation Rate. San Diego: University of California.
  4. 4.
    Carlson T N and Arthur S T. 2000. The impact of land use — land cover changes due to urbanization on surface microclimate and hydrology: a satellite perspective. Global and Planetary Change, 25(1/2): 49-65
  5. 5.
    Deng C B and Wu C S. 2012. BCI: a biophysical composition index for remote sensing of urban environments. Remote Sensing of Environment, 127: 247-259
  6. 6.
    Ding H Y and Shi W Z. 2013. Land-use/land-cover change and its influence on surface temperature: a case study in Beijing city. International Journal of Remote Sensing, 34(15): 5503-5517
  7. 7.
    Estoque R C, Murayama Y and Myint S W. 2017. Effects of landscape composition and pattern on land surface temperature: an urban heat island study in the megacities of Southeast Asia. Science of the Total Environment, 577: 349-359
  8. 8.
    Gaffin S R, Rosenzweig C, Khanbilvardi R, Parshall L, Mahani S, Glickman H, Goldberg R, Blake R, Slosberg R B and Hillel D. 2008. Variations in New York city’s urban heat island strength over time and space. Theoretical and Applied Climatology, 94(1/2): 1-11
  9. 9.
    Imhoff M L, Zhang P, Wolfe R E and Bounoua L. 2010. Remote sensing of the urban heat island effect across biomes in the continental USA. Remote Sensing of Environment, 114(3): 504-513
  10. 10.
    Ji C P, Liu W D and Xuan C Y. 2006. Impact of urban growth on the heat island in Beijing. Chinese Journal of Geophysics, 49(1): 69-77
  11. 11.
    Li W F, Ouyang Z Y, Chen Q W and Mao J Q. 2008. Study on the spatial pattern of impervious surface using remote sensing data within the urban area of Beijing. Journal of Remote Sensing, 12(4): 603-612
  12. 12.
    Lu D S and Weng Q H. 2006. Spectral mixture analysis of ASTER images for examining the relationship between urban thermal features and biophysical descriptors in Indianapolis, Indiana, USA. Remote Sensing of Environment, 104(2): 157-167
  13. 13.
    Mathew A, Khandelwal S and Kaul N. 2016. Spatial and temporal variations of urban heat island effect and the effect of percentage impervious surface area and elevation on land surface temperature: study of Chandigarh city, India. Sustainable Cities and Society, 26: 264-277
  14. 14.
    Qiao Z and Tian G J. 2015. Dynamic monitoring of the footprint and capacity for urban heat island in Beijing between 2001 and 2012 based on MODIS. Journal of Remote Sensing, 19(3): 476-484
  15. 15.
    Qin Z H, Li W J, Xu B and Zhang W C. 2003. Estimation method of land surface emissivity for retrieving land surface temperature from Landsat TM6 data//The 14th National Remote Sensing Technology Academic Exchange Meeting. Qingdao, China: Chinese Society for Oceanography
  16. 16.
    Quattrochi D, Luvall J C, Rickman D L, Estes Jr M G, Laymon C A and Howell B F. 2000. A decision support information system for urban landscape management using thermal infrared data. Photogrammetric Engineering and Remote Sensing, 66(10): 1195-1207
  17. 17.
    Ridd M K. 1995. Exploring a V-I-S (vegetation-impervious surface-soil) model for urban ecosystem analysis through remote sensing: comparative anatomy for cities. International Journal of Remote Sensing, 16(12): 2165-2185
  18. 18.
    Sobrino J A, Jiménez-Muñoz J C, Sòria G, Romaguera M, Guanter L, Moreno J, Plaza A and Martinez P. 2008. Land surface emissivity retrieval from different VNIR and TIR sensors. IEEE Transactions on Geoscience and Remote Sensing, 46(2): 316-327
  19. 19.
    Tang F and Xu H Q. 2013. Quantitative relationship between impervious surface and land surface temperature based on remote sensing technology. Journal of Jilin University (Earth Science Edition), 43(6): 1987-1996, 2017
  20. 20.
    Tran H, Uchihama D, Ochi S and Yasuoka Y. 2006. Assessment with satellite data of the urban heat island effects in Asian mega cities. International Journal of Applied Earth Observation and Geoinformation, 8(1): 34-48
  21. 21.
    Unger J, Gál T, Rakonczai J, Mucsi L, Szatmári J, Tobak Z, Van Leeuwen B and Fiala K. 2009. Air temperature versus surface temperature in urban environment//The seventh International Conference on Urban Climate. Yokohama, Japan
  22. 22.
    Wang L, Meng Q Y, Wu J, Zhang J H and Zhang L L. 2015. Monitoring and analyzing spatio-temporal changes of heat island intensity in Beijing main urban construction area from 2005 to 2014. Journal of Geo-Information Science, 17(9): 1047-1054
  23. 23.
    Wang X K, Meng Q Y, Li J., Hu X., Zhang L., 2021. Spatiotemporal evolution and thermal environmental effects of impervious surfaces in the main urban area of Beijing. Ecological Science, 40(1): 169–181
  24. 24.
    Xian G. 2008. Satellite remotely-sensed land surface parameters and their climatic effects for three metropolitan regions. Advances in Space Research, 41(11): 1861-1869
  25. 25.
    Xian G and Crane M. 2006. An analysis of urban thermal characteristics and associated land cover in Tampa Bay and Las Vegas using Landsat satellite data. Remote Sensing of Environment, 104(2): 147-156
  26. 26.
    Xiao R B, Ouyang Z Y, Zheng H, Li W F, Schienke E W and Wang X K. 2007. Spatial pattern of impervious surfaces and their impacts on land surface temperature in Beijing, China. Journal of Environmental Sciences, 19(2): 250-256
  27. 27.
    Xu H Q. 2009. Quantitative analysis on the relationship of urban impervious surface with other components of the urban ecosystem. Acta Ecologica Sinica, 29(5): 2456-2462
  28. 28.
    Yang S B, Zhao X Y, Shen S H, Hai Y L and Fang Y X. 2010. Characteristics of urban heat island seasonal pattern in Beijing based on Landsat TM/ETM+ imagery. Transactions of Atmospheric Sciences, 33(4): 427-435
  29. 29.
    Yang Y B and Pan P. 2011. Research on the impact of impervious surface area on urban heat island in Jiangsu Province//Proceedings of the SPIE 8286 International Symposium on Lidar and Radar Mapping. Nanjing: SPIE, 82861P
  30. 30.
    Ye Z W. 2009. Relation between neighborhood with dense high-rises and thermal landscape using remote sensing. Beijing: Capital Normal University
  31. 31.
    Yuan F and Bauer M E. 2007. Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery. Remote Sensing of Environment, 106(3): 375-386
  32. 32.
    Zhang J H, Hou Y Y, Li G C, Yan H and Yang L M. 2005. The diurnal and seasonal characteristics of urban heat island variation in Beijing city and surrounding areas and impact factors based on remote sensing satellite data. Science in China (Series D: Earth Sciences), 48(S2): 220-229
  33. 33.
    Zhang X L, Zhang S W, Li Y, Xie Y F and Kuang W H. 2008. Correlation between urban thermal environment effect and land use pattern in Changchun. Resources Science, 30(10): 1564-1570
  34. 34.
    Zhang Y, Bao W J, Yu Q and Ma W C. 2012. Study on seasonal variations of the urban heat island and its interannual changes in a typical Chinese megacity. Chinese Journal of Geophysics, 55(4): 1121-1128
  35. 35.
    Zhou J, Chen Y H, Li J, Weng Q H and Yi W B. 2008. A volume model for urban heat island based on remote sensing imagery and its application: a case study in Beijing. Journal of Remote Sensing, 12(5): 734-742
  36. 36.
    Zhou W Q, Huang G L and Cadenasso M L. 2011. Does spatial configuration matter? Understanding the effects of land cover pattern on land surface temperature in urban landscapes. Landscape and Urban Planning, 102(1): 54-63

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