Driving forces of impervious surface in a world metropolitan area, Shanghai: threshold and scale effect
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Shanghai is one of the largest metropolitan areas in the world, during the rapid urbanization of the past decades, impervious surface expanded dramatically and became a main factor influencing surface water quality. Thus, exploring the driving forces of impervious surface has great implications in such metropolitan area. In this study, an impervious surface coefficient method (ISC) was used to measure the percentage of total impervious area (PTIA) of Shanghai; regression analysis was conducted to define the relationship between PTIA and three socio-economic factors, population density, unit area gross domestic product, and unit area industrial output at the city and district scale. Results showed that the industrial land use generated the highest ISC value, followed by high-density residential. Strong correlations were showed between PTIA and socio-economic indicators, in which population density was the most significant. Threshold effect was presented that when population density was higher than 15000 per/km2, this relationship would become less significant and PTIA remained stable. Similar effects were found when unit area gross domestic product exceeded 125 million yuan/km2. Scale effect was also discussed that the relationship was more significant at city scale than district. An improved understanding of the threshold effect and scale effect will help guide future urban planning and design new urban ecosystem policies.
KeywordsImpervious surface coefficient Driving forces Population density Threshold
We thank the Key Laboratory of Geographic Information Science, Ministry of Education of East China Normal University, for providing land use data of Shanghai.
The study was funded by the National Natural Science Foundation of China (41101550).
- Azar, D., Graesser, J., Engstrom, R., Comenetz, J., Leddy, R. M., Schechtman, N. G., & Andrews, T. (2010). Spatial refinement of census population distribution using remotely sensed estimates of impervious surfaces in Haiti. International Journal of Remote Sensing, 31(21), 5635–5655. https://doi.org/10.1080/01431161.2010.496799.CrossRefGoogle Scholar
- Barbara, W., Katie, Y., et al. (2010). User’s guide for the California impervious surface coefficients. California Environment Protection Agency: United States.Google Scholar
- Chabaeva, A., Civco, D. L., & Hurd, J. D. (2009). Assessment of impervious surface estimation techniques. Journal of Hydrologic Engineering, 14(4), 377–387. https://doi.org/10.1061/(ASCE)1084-0699(2009)14:4(377.CrossRefGoogle Scholar
- Chen, S., Zhang, X. Y., & Peng, L. H. (2006). Impervious surface coverage in urban land use based on high resolution satellite images. Resources Science, 28(2), 41–46.Google Scholar
- Dhorde, A. A., Dhorde, A., & Joshi, G. (2012). Population calibrated land cover impervious surface coefficients for Upper Bhima basin. International Journal of Geomatics & Geosciences, 4, 7–1047.Google Scholar
- Hafsi, R., Ouerdachi, L., Kriker, A. E., & Boutaghane, H. (2016). Assessment of urbanization/impervious effects on water quality in the urban river Annaba (Eastern Algeria) using physicochemical parameters. Water Science & Technology, 74(9), 2051–2059. https://doi.org/10.2166/wst.2016.350.CrossRefGoogle Scholar
- Prisloe, S., Lei, Y., & Hurd, J. (2001). Interactive GIS-based impervious surface model. Proceedings of the 2001 ASPRS Annual Convention, St. Louis, MO. CD-ROM. American Society for Photogrammetry & Remote Sensing.Google Scholar
- Rashed, T., Weeks, J. R., Gadalla, M. S., & Hill, A. G. (2001). Revealing the anatomy of cities through spectral mixture analysis of multispectral satellite imagery: a case study of the greater Cairo region, Egypt. Geocarto International, 16(4), 7–18. https://doi.org/10.1080/10106040108542210.CrossRefGoogle Scholar
- Sekertekin, A., Abdikan, S., Marangoz, A. M. (2018) The acquisition of impervious surface area from LANDSAT 8 satellite sensor data using urban indices: a comparative analysis. Environmental Monitoring and Assessment, 190, (7).Google Scholar
- Shanghai Statistical Bureau. (2007). Shanghai Statistical Yearbook. Beijing: China Statistics PressGoogle Scholar
- Sleavin, W. J., Civco, D. L., Prisloe, S., Educator, A., Giannotti, L., & Coordinator, N. P. (2000). Measuring impervious surfaces for non-point source pollution modeling. Proceedings Asprs Annual Convention.Google Scholar
- Yan, Z. G., Teng, M. J., He, W., Liu, A. Q., Li, Y. R., Wang, P. C. (2019). Impervious surface area is a key predictor for urban plant diversity in a city undergone rapid urbanization. Science of The Total Environment, 650, 335–342. https://doi.org/10.1016/j.scitotenv.2018.09.025.CrossRefGoogle Scholar
- Zhao, J. (2008). Landscape pattern change and its environmental response across multiple spatial scales in tidal plainGoogle Scholar