Skip to main content

Advertisement

Log in

Impacts of Large-Area Impervious Surfaces on Regional Land Surface Temperature in the Great Pearl River Delta, China

  • Research Article
  • Published:
Journal of the Indian Society of Remote Sensing Aims and scope Submit manuscript

Abstract

Rapid urbanization has led to an increase in urban land surface temperature (LST). In contrast to individual cities or megacity scale, urban agglomeration can increase LST in a continuous area due to decreasing or disappearing distance between cities. Thus, the impact of ISA on LST needs further understanding in the large scale of urban agglomerations. This study investigated the impacts of impervious surface area (ISA) on LST in urban agglomeration region. The distribution of ISA and LST of the Greater Pearl River Delta in 2015 was extracted using the Landsat 8 OLI and Aqua MODIS images. Next, the standard deviational ellipse methods were used to systematically analyze the spatial correlation of ISA and LST. Subsequently, the influences of ISA density and landscape pattern of ISA on LST were analyzed by various methods. The results showed that when the ISA density increased 10%, the daytime LST increased 0.46 °C at the density level lower than 70% and 0.55 °C at the density level higher than 70%, respectively. Likewise, when the ISA density increased 10%, the nighttime LST increased 0.285 °C at the density level lower than 70% and 0.39 °C at the density level higher than 70%, respectively. In addition, the results of correlation analysis indicated that landscape metrics of ISA and the density of ISA had significant correlation with the LST. However, the correlation was higher at daytime than at nighttime, due to the large terrain, complex environment and diverse surface cover types in the study area.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  • Alberti, M. (2005). The effects of urban patterns on ecosystem function. International Regional Science Review, 28(2), 168–192.

    Google Scholar 

  • Chen, L., & Sun, R. (2013). Eco-environmental effects of urban landscape pattern changes: Progresses, problems, and perspectives. Acta Ecologica Sinica, 33(4), 1042–1050.

    Google Scholar 

  • Chen, S., & Wang, T. (2009). Comparison analyses of equal interval method and mean-standard deviation method used to delimitate urban heat island. Journal of Geo-information Science, 2, 001.

    Google Scholar 

  • Du, H., Wang, D., Wang, Y., Zhao, X., Qin, F., Jiang, H., et al. (2016). Influences of land cover types, meteorological conditions, anthropogenic heat and urban area on surface urban heat island in the Yangtze River Delta Urban Agglomeration. Science of the Total Environment, 571, 461–470.

    Google Scholar 

  • Estoque, R. C., Murayama, Y., & 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.

    Google Scholar 

  • Fang, C. (2015). Important progress and future direction of studies on China’s urban agglomerations. Journal of Geographical Sciences, 25(8), 1003–1024.

    Google Scholar 

  • Fischer, M. M. (2010). Handbook of Applied Spatial Analysis. Journal of Geographical Systems, 10(2), 109–139.

    Google Scholar 

  • Gong, J. (2002). Clarifying the standard deviational ellipse. Geographical Analysis, 34(2), 155–167.

    Google Scholar 

  • Gong, A. D., Chen, Y. H., Jing, L. I., & Hua-Lang, H. U. (2007). Study on relationship between urban heat island and urban land use and cover change in Beijing. Journal of Image and Graphics, 12, 1476–1482.

    Google Scholar 

  • Gustafson, E. J. (1998). Quantifying landscape spatial pattern: What is the state of the art? Ecosystems, 1(2), 143–156.

    Google Scholar 

  • Huang, J., Zhao, X., Tang, L., & Qiu, Q. (2012). Analysis on spatiotemporal changes of urban thermal landscape pattern in the context of urbanisation: A case study of Xiamen City. Shengtai Xuebao/Acta Ecologica Sinica, 32(2), 622–631.

    Google Scholar 

  • Jiang, X. D. (2007). Spatial characteristics and dynamic simulations of urban heat environment of cities in Pearl River Delta. Acta Ecologica Sinica, 27(4), 1461–1470.

    Google Scholar 

  • Jun Xiang, L. I., Wang, Y. J., Shen, X. H., & Song, Y. C. (2004). Landscape pattern analysis along an urban-rural gradient in the Shanghai metropolitan region. Acta Ecologica Sinica, 24(9), 1973–1980.

    Google Scholar 

  • Kaloustian, N., & Diab, Y. (2015). Effects of urbanization on the urban heat island in Beirut. Urban Climate, 14, 154–165.

    Google Scholar 

  • Kittler, J., & Illingworth, J. (1986). Minimum error thresholding. Pattern Recognition, 19(1), 41–47.

    Google Scholar 

  • Lefever, D. W. (1926). Measuring geographic concentration by means of the standard deviational ellipse. American Journal of Sociology, 32(1), 88–94.

    Google Scholar 

  • Li, J., Song, C., Cao, L., Zhu, F., Meng, X., & Wu, J. (2011). Impacts of landscape structure on surface urban heat islands: A case study of Shanghai. China. Remote Sensing of Environment, 115(12), 3249–3263.

    Google Scholar 

  • Li, X., Zhou, W., Ouyang, Z., & Zheng, H. (2012). Spatial pattern of greenspace affects land surface temperature: Evidence from the heavily urbanized Beijing metropolitan area, China. Landscape Ecology, 27(6), 887–898.

    Google Scholar 

  • Li, X., Zhou, W., & Ouyang, Z. (2013). Relationship between land surface temperature and spatial pattern of greenspace: What are the effects of spatial resolution? Landscape and Urban Planning, 114(8), 1–8.

    Google Scholar 

  • Liu, Y., Peng, J., & Wang, Y. (2017). Relationship between urban heat island and landscape patterns: From city size and landscape composition to spatial configuration. Acta Ecologica Sinica, 37(23), 1–12.

    Google Scholar 

  • Lo, C. P., Quattrochi, D. A., & Luvall, J. C. (1997). Application of high-resolution thermal infrared remote sensing and GIS to assess the urban heat island effect. International Journal of Remote Sensing, 18(2), 287–304.

    Google Scholar 

  • McGarigal, K., & Marks, B. J. (1995). FRAGSTATS: Spatial pattern analysis program for quantifying landscape structure. In Genetics Technical Report PNW-GTR-351. Portland, OR: US Department of Agriculture, Forest Service, Pacific Northwest Research Station.

  • McGarigal, K., Cushman, S. A., Neel, M. C., & Ene, E. (2002). Fragstats: spatial pattern analysis program for categorical maps. https://www.umass.edu/landeco/research/fragstats/fragstats.html.

  • Morabito, M., Crisci, A., Messeri, A., Orlandini, S., Raschi, A., Maracchi, G., et al. (2016). The impact of built-up surfaces on land surface temperatures in Italian urban areas. Science of the Total Environment, 551–552, 317–326.

    Google Scholar 

  • Noro, M., & Lazzarin, R. (2015). Urban heat island in Padua, Italy: Simulation analysis and mitigation strategies. Urban Climate, 14(2), 187–196.

    Google Scholar 

  • Peng, J., Liu, Y., Shen, H., Xie, P., Xiaoxu, H. U., & Wang, Y. (2016a). Using impervious surfaces to detect urban expansion in Beijing of China in 2000s. Chinese Geographical Science, 26(2), 229–243.

    Google Scholar 

  • Peng, J., Xie, P., Liu, Y., & Ma, J. (2016b). Urban thermal environment dynamics and associated landscape pattern factors: A case study in the Beijing metropolitan region. Remote Sensing of Environment, 173, 145–155.

    Google Scholar 

  • Rao, S., Zhang, H. Y., Jin, T. T., & Dou, H. Y. (2010). The spatial character of regional heat island in Pearl River Delta using MODIS remote sensing data. Geographical Research, 29(1), 127–136.

    Google Scholar 

  • Rondeaux, G., Steven, M., & Baret, F. (1996). Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment, 55(2), 95–107.

    Google Scholar 

  • Roy, D. P., Wulder, M. A., Loveland, T. R., Woodcock, C. E., Allen, R. G., Anderson, M. C., et al. (2014). Landsat-8: Science and product vision for terrestrial global change research. Remote Sensing of Environment, 145(145), 154–172.

    Google Scholar 

  • Runpeng, Z. (2013). Zhujiang delta spatial reorganization for new urbanization development. Planners, 29(4), 27–31.

    Google Scholar 

  • Science (2016). Rise of the City. Science, 352(6288), 906. https://doi.org/10.1126/science.352.6288.906.

  • Sun, R. (2012). How can urban water bodies be designed for climate adaptation? Landscape and Urban Planning, 105(1–2), 27–33.

    Google Scholar 

  • Sungzoon, C., Robert, H., & Seungku, Y. (1989). Improvement of kittler and illingworth’s minimum error thresholding. Pattern Recognition, 22(5), 609–617.

    Google Scholar 

  • Turner, M. G. (2005). Landscape ecology: What is the state of the science? Annual Review of Ecology Evolution and Systematics, 36(36), 319–344.

    Google Scholar 

  • Urban Agglomeration in the Pearl River Delta Yearbook (2016). (Vol. 2016). Guangzhou: Fangzhi Publishing House.

  • Wan, Z. (2008). New refinements and validation of the MODIS land-surface temperature/emissivity products. Remote Sensing of Environment, 112(1), 59–74.

    Google Scholar 

  • Wan, Z., & Dozier, J. (1996). A generalized split-window algorithm for retrieving land-surface temperature from space. IEEE Transactions on Geoscience and Remote Sensing, 34(4), 892–905.

    Google Scholar 

  • Weng, Q., & Lu, D. (2008). A sub-pixel analysis of urbanization effect on land surface temperature and its interplay with impervious surface and vegetation coverage in Indianapolis, United States. International Journal of Applied Earth Observation and Geoinformation, 10(1), 68–83.

    Google Scholar 

  • Weng, Q., Lu, D., & Schubring, J. (2004). Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies. Remote Sensing of Environment, 89(4), 467–483.

    Google Scholar 

  • Weng, Q., Liu, H., & Lu, D. (2007). Assessing the effects of land use and land cover patterns on thermal conditions using landscape metrics in city of Indianapolis. United States. Urban Ecosystems, 10(2), 203–219.

    Google Scholar 

  • Wong, D. S. (1999). Several fundamentals in implementing spatial statistics in GIS: Using centrographic measures as examples. Geographic Information Sciences, 5(2), 163–174.

    Google Scholar 

  • Wu, C. (2004). Normalized spectral mixture analysis for monitoring urban composition using ETM + imagery. Remote Sensing of Environment, 93(4), 480–492.

    Google Scholar 

  • Xian, G., & 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.

    Google Scholar 

  • Xu, H. (2009). Quantitative analysis on the relationship of urban impervious surface with other components of the urban ecosystem. Acta Ecologica Sinica, 29(5), 2456–2462.

    Google Scholar 

  • Xu, H. Q. (2010). Analysis of impervious surface and its impact on urban heat environment using the normalized difference impervious surface index (NDISI). Photogrammetric Engineering and Remote Sensing, 76(5), 557–565.

    Google Scholar 

  • Xu, H., & Chen, B. (2003). An image processing technique for the study of urban heat island changes using different seasonal remote sensing data. Remote Sensing Technology and Application, 18(3), 129–133.

    Google Scholar 

  • Xu, S., Li, F. X., & Zhang, L. (2015). Spatiotemporal change of thermal environment landscape pattern in Changsha. Acta Ecologica Sinica, 35, 3743–3754.

    Google Scholar 

  • Yue, W. Z., & Li-Hua, X. U. (2007). Thermal environment effect of urban land use type and pattern—A case study of central area of Shanghai City. Scientia Geographica Sinica, 27(2), 243–248.

    Google Scholar 

  • Zhang, J. (2006). Thermal environment detection in the Pearl River Delta Area by remote sensing and analysis of its spatial and temporal evolutions. Ph. D. Dissertation, Guangzhou: Guangzhou Institute of Geochemistry, Chinese Academy of Sciences.

  • Zhang, Y., Yu, T., Gu, X., Zhang, Y., & Chen, L. (2006). Land surface temperature retrieval from CBERS-02 IRMSS thermal infrared data and its applications in quantitative analysis of urban heat island effect. Journal of Remote Sensing, 10(5), 789.

    Google Scholar 

  • Zhang, L., Weng, Q., & Shao, Z. (2017a). An evaluation of monthly impervious surface dynamics by fusing Landsat and MODIS time series in the Pearl River Delta, China, from 2000 to 2015. Remote Sensing of Environment, 201(11), 99–114.

    Google Scholar 

  • Zhang, S., Liu, Y., & Huang, H. (2017b). Research on quantitative evaluations and spatial and temporal distribution of heat islands for the Pearl River Delta agglomeration. Ecology and Environmental Sciences, 26(7), 1157–1166.

    Google Scholar 

  • Zhang, S., Yang, K., Li, M., Ma, Y., & Sun, M. (2018). Combinational Biophysical Composition Index (CBCI) for effective mapping biophysical composition in urban areas. IEEE Access, 6, 41224–41237.

    Google Scholar 

  • Zhao, Z.-Q., He, B.-J., Li, L.-G., Wang, H.-B., & Darko, A. (2017). Profile and concentric zonal analysis of relationships between land use/land cover and land surface temperature: Case study of Shenyang, China. Energy and Buildings, 155, 282–295.

    Google Scholar 

  • Zheng, T., Lau, K. L., & Ng, E. (2016). Urban tree design approaches for mitigating daytime urban heat island effects in a high-density urban environment. Energy and Buildings, 114, 265–274.

    Google Scholar 

  • Zhou, W., Huang, G., & 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.

    Google Scholar 

  • Zhou, D., Dan, L., Ge, S., Zhang, L., Liu, Y., & Lu, H. (2016). Contrasting effects of urbanization and agriculture on surface temperature in eastern China. Journal of Geophysical Research Atmospheres, 121(16), 9597–9606.

    Google Scholar 

  • Zhou, D., Bonafoni, S., Zhang, L., & Wang, R. (2018). Remote sensing of the urban heat island effect in a highly populated urban agglomeration area in East China. Science of the Total Environment, 628–629, 415–429.

    Google Scholar 

Download references

Acknowledgements

The authors would like to thank the anonymous reviewers and editor for constructive comments and suggestions.

Funding

This work was supported by the Yunnan Normal University Postgraduate Innovation Fund [Grant Number yjs201680], Yunnan Provincial Department of Education Research Fund [Grant Number 2011Y307], National Natural Science Foundation of China [Grant Number 41461038], Yunnan Provincial Science and Technology Project [Grant Number 2011XX2005] and Specialized Research Fund for the Doctoral Program of Higher Education [Grant Number 20115303110002].

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Kun Yang or Shaohua Zhang.

Ethics declarations

Conflict of interest

No potential conflict of interest was reported by the authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ma, Y., Yang, K., Zhang, S. et al. Impacts of Large-Area Impervious Surfaces on Regional Land Surface Temperature in the Great Pearl River Delta, China. J Indian Soc Remote Sens 47, 1831–1845 (2019). https://doi.org/10.1007/s12524-019-01023-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12524-019-01023-4

Keywords

Navigation