Human-Environment Interactions

Evaluating impervious surface growth and its impacts on water environment in Beijing- Tianjin-Tangshan Metropolitan Area

  • Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
Kuang Wenhui (1978-), Ph.D and Assistant Professor, specialized in land use/cover change, remote sensing and GIS. E-mail:

Received date: 2011-08-16

  Revised date: 2011-11-02

  Online published: 2012-05-04

Supported by

The Young Scientist Fund of National Natural Science Foundation of China, No.40901224; National Basic Research Program of China, No.2010CB950900; Open Fund of State Key Laboratory of Remote Sensing Science, No.2009KFJJ005; Open Fund of State Key Lab of Resources and Environmental Information System, No.A0725


The impervious surface area (ISA) at the regional scale is one of the important environmental factors for examining the interaction and mechanism of Land Use/Cover Change (LUCC)-ecosystem processes-climate change under the interactions of urbanization and global environmental change. Timely and accurate extraction of ISA from remotely sensed data at the regional scale is challenging. This study explored the ISA extraction based on MODIS and DMSP-OLS data and the incorporation of China's land use/cover data. ISA datasets in Beijing-Tianjin-Tangshan Metropolitan Area (BTTMA) in 2000 and 2008 at a spatial resolution of 250 m were developed, their spatiotemporal changes were analyzed, and their impacts on water quality were then evaluated. The results indicated that ISA in BTTMA increased rapidly along urban fringe, transportation corridors and coastal belt both in intensity and extents from 2000 to 2008. Three cities (Tangshan, Langfang and Qinhuangdao) in Hebei Province had higher ISA growth rates than Beijing due to the pressure of population-resources- environments in the city resulting in increasingly transferring industries to the nearby areas. The dense ISA distribution in BTTMA has serious impacts on water quality in the Haihe River watershed. Meanwhile, the proportion of ISA in sub-watersheds has significantly linear relationships with the densities of river COD and NH3-N.

Cite this article

KUANG Wenhui . Evaluating impervious surface growth and its impacts on water environment in Beijing- Tianjin-Tangshan Metropolitan Area[J]. Journal of Geographical Sciences, 2012 , 22(3) : 535 -547 . DOI: 10.1007/s11442-012-0945-y


Ahn J H, Grant S B, Surbeck C Q et al., 2005. Coastal water quality impact of storm water runoff from an urbanwatershed in southern California. Environmental Science & Technology, 39: 5940-5953.
Alberti M, 2009. Advances in urban ecology integrating humans and ecological processes in urban ecosystems. New York: Springer science + business media.
Arnold C L, Gibbons C J, 1996. Impervious surface coverage: The emergence of a key environmental indicator. Journal of American Planning Association, 2: 243-258.
Bierwagen B G, Theobald D M, Pyke C R et al., 2010. National housing and impervious surface scenarios forintegrated climate impact assessments. PNAS, 107(49): 20887-20892.
Booth D B, Jackson C R, 1997. Urbanization of aquatic systems: Degradation thresholds, stormwater detection,and the limits of mitigation. Journal of the American Water Resource Association, 33(5): 1077-1089
Brabec E, Schulte S, Richards P L, 2002. Impervious surface and water quality: A review of current literature andits implications for watershed planning. Journal of Planning Literature, 16: 499-514.
Elvidge C D, Tuttle B T, Sutton P C et al., 2007. Global distribution and density of constructed impervious surfaces. Sensors, 7: 1962-1979.
Grimm N B, Faeth S H, Golubiewski N E et al., 2008. Global change and the ecology of cities. Science, 319:756-760.
Hodgson M E, Jensen J R, Schmidt L, et al., 2003. An evaluation of LIDAR- and IFSAR-derived digital elevationmodels in leaf-on conditions with USGS Level 1 and Level 2 DEMs. Remote Sensing of Environment, 84:295-308.
Hu X F, Weng Q H, 2009. Estimating impervious surfaces from medium spatial resolution imagery using the self-organizing map and multi-layer perception neural networks. Remote Sensing of Environment, 113:2089-2102.
IHDP Report, 2005. Urbanization and global environmental change. International Human Dimensions Programmeon Global Environmental Change, 15: 1-60.
Kuang W H, 2011. Simulating dynamic urban expansion at regional scale in Beijing-Tianjin-Tangshan Metropolitan Area. Journal of Geographical Science, 21(1): 317-330.
Liu J Y, Liu M L, Zhuang D F et al., 2003a. Study on spatial pattern of land-use change in China during1995-2000. Science in China (Series D), 46(4): 373-384.
Liu Jiyuan, Zhang Zengxiang, Zhuang Dafang, 2003. A study on the spatial-temporal dynamic changes ofland-use and driving forces analyses of China in the 1990s. Geographical Research, 22(1): 1-12. (in Chinese)
Lu Dadao, 2007. Urbanization process and spatial sprawl in China. Urban Planning Forum, 170(4): 47-52. (in Chinese)
Lu D S, Tian H Q, Zhou G M, 2008. Regional mapping of human settlements in southeastern China with multisensoryremotely sensed data. Remote Sensing of Environment, 112: 3668-3679.
Lu D S, Weng Q H, 2006a. Spectral mixture analysis of ASTER images for examining the relationship betweenurban thermal features and biophysical descriptors in Indianapolis, Indiana, USA. Remote Sensing of Environment,104: 157-167.
Lu D S, Weng Q H, 2006b. Use of impervious surface in urban land use classification. Remote Sensing of Environment,102: 146-160.
Lu D S, Weng Q H, 2007. A survey of image classification methods and techniques for improving classificationperformance. International Journal of Remote Sensing, 28: 823-870.
Lu D S, Weng Q H, Li G Y, 2006. Residential population estimation using a remote sensing derived impervioussurface approach. International Journal of Remote Sensing, 27: 3553-3570.
Madhavan B B, Kubo S, Kurisaki N, 2001. Appraising the anatomy and spatial growth of the Bangkok metropolitanarea using a vegetation-impervious-soil model through remote sensing. International Journal of Remote Sensing, 22(5): 789-806.
Ministry of Environmental Protection of the People's Republic of China (MEP of PRC), 2011. National surfacewater quality automatic monitoring and real-time publishing system. (in Chinese)
NOAA, 2011. Global dmsp-ols nighttime lights time series 1992-2009 (version 4).( sensors/ols.html)
Pickett S A, Cadenasso M L, Grove J M et al., 2011. Urban ecological systems: Scientific foundations and a decadeof progress. Journal of Environmental Management, 92: 331-362.
Ridd M K, 1995. Exploring a V-I-S (Vegetation-Impervious Surface-Soil) model for urban ecosystem analysisthrough remote sensing: Comparative anatomy for cities. International Journal of Remote Sensing, 16(12):2165-2185.
U.S. Geological Survey, 2010. HYDRO1k Elevation Derivative Database.( and_Data_Available/gtopo30/hydro/asia)
U.S. Geological Survey, 2011. MODIS NDVI production (
Wang Hao, Wu Binfang, Li Xiaosong et al., 2011. Extraction of impervious surface in Hai Basin using remotesensing. Journal of Remote Sensing, 15(2): 388-400. (in Chinese)
Wu C S, Murray A T, 2005. A cokriging method for estimating population density in urban areas. Computers Environmentand Urban Systems, 29: 558-579.
Yang L M, Huang C, Homer C G et al., 2003. An approach for mapping large-area impervious surfaces: Synergisticuse of Landsat 7 ETM+ and high spatial resolution imagery. Canadian Journal of Remote Sensing, 2:230-240.
Yao Shimou, Wang Chen, Zhang Luocheng et al., 2008. The influencing factors of resources and environments inthe process of urbanization of China. Progress in Geography, 27(3): 94-100. (in Chinese)