PENG Jian, LIU Yanxu, SHEN Hong, XIE Pan, HU Xiaoxu, WANG Yanglin. Using Impervious Surfaces to Detect Urban Expansion in Beijing of China in 2000s[J]. Chinese Geographical Science, 2016, 26(2): 229-243. doi: 10.1007/s11769-016-0802-5
Citation: PENG Jian, LIU Yanxu, SHEN Hong, XIE Pan, HU Xiaoxu, WANG Yanglin. Using Impervious Surfaces to Detect Urban Expansion in Beijing of China in 2000s[J]. Chinese Geographical Science, 2016, 26(2): 229-243. doi: 10.1007/s11769-016-0802-5

Using Impervious Surfaces to Detect Urban Expansion in Beijing of China in 2000s

doi: 10.1007/s11769-016-0802-5
Funds:  Under the auspices of Key Project of National Natural Science Foundation of China (No. 41130534)
More Information
  • Corresponding author: PENG Jian
  • Received Date: 2015-05-08
  • Rev Recd Date: 2015-08-05
  • Publish Date: 2016-02-27
  • The change of impervious surface area (ISA) can effectively reveal the gradual process of urbanization and act as a key index for monitoring urban expansion. Experiencing rapid growth of the built environment in the 2000s, urban expansion of Beijing has not been fully characterized through ISA. In this study, Landsat TM images of Beijing in 2001 and 2009 were obtained, and the eight-year urban expansion process in Beijing was analyzed using the ISA extracted by means of the vegetation-imperious surface-soil (V-I-S) model. From the spatial variation in ISA, the ring structure of urban expansion in Beijing was significant during the study period, with decreasing urban density from the city center to the periphery. In the ring road analysis, the most dramatic changes of ISA were found between the fifth ring and the sixth ring. This area has experienced the most new residential development, and is currently the main source of urban expansion. The typical profile lines revealed the directional characteristics of urban expansion. The east-west profile was the most urbanized axes in Beijing, while ISA change in the east-north profile was more significant than in the other five profiles. Moreover, the transition matrix of ISA levels revealed an increase in urban density in the low density built areas; the Moran's I index showed a clear expansion of the central urban area, which spread contiguously; and the standard deviational ellipse indicated the northeast was the dominant direction of urban expansion. These findings can provide important spatial control guidelines in the next round of national economic and social development planning, overall urban and rural planning, and land use planning.
  • [1] Brabec E, 2002. Impervious surfaces and water quality: a review of current literature and its implications for watershed planning. Journal of Planning Literature, 16: 499-514. doi: 10. 1177/088541202400903563
    [2] Cai Yuanbin, Zhang Hao, Pan Weibin et al., 2012. Urban expansion and its influencing factors in natural wetland distribution area in Fuzhou City, China. Chinese Geographical Science, 22(5): 568-577. doi:  10.1007/s11769-012-0564-7
    [3] Ding Feng, 2009. A new method for fast information extraction of water bodies using remotely sensed data. Remote Sensing Technology and Application, 24(2): 167-171. (in Chinese)
    [4] Emma U, Susan U, Deanne D, 2003. Mapping nonnative plants using hyperspectral imagery. Remote Sensing of Environment, 86: 150-161. doi:  10.1016/S0034-4257(03)00096-8
    [5] Ichoku C, Karnieli A, 1996. A review of mixture modeling techniques for sub-pixel land cover estimation. Remote Sensing Reviews, 13: 161-186. doi:  10.1080/02757259609532303
    [6] Jantz P, Goetz S, Jantz C, 2005. Urbanization and the loss of resource lands in the Chesapeake Bay Watershed. Environmental Management, 36: 808-825. doi:  10.1007/s00267-004-0315-3
    [7] King R S, Baker M E, Whigham D F et al., 2005. Spatial considerations for linking watershed land cover to ecological indicators in streams. Ecological Applications, 15(1): 137-153. doi:  10.1890/04-0481
    [8] Kuang Wenhui, Liu Jiyuan, Lu Dengsheng et al., 2011. Pattern of impervious surface change and its effect on water environment in the Beijing-Tianjin-Tangshan Metropolitan Area. Acta Geographica Sinica, 66(11): 1486-1496. (in Chinese)
    [9] Li W F, Ouyang Z Y, Zhou W Q et al., 2011. Effects of spatial resolution of remotely sensed data on estimating urban impervious surfaces. Journal of Environmental Sciences, 23(8): 1375-1383. doi:  10.1016/S1001-0742(10)60541-4
    [10] Li X M, Zhou W Q, Ouyang Z Y et al., 2012. Spatial pattern of greenspace affects land surface temperature: evidence from the heavily urbanized Beijing metropolitan area, China. Landscape Ecology, 27(6): 887-898. doi:  10.1007/s10980-012-9731-6
    [11] Li X M, Zhou W Q, Ouyang Z Y, 2013. Forty years of urban expansion in Beijing: what is the relative importance of physical, socioeconomic, and neighborhood factors? Applied Geography, 38: 1-10. doi:  10.1016/j.apgeog.2012.11.004
    [12] Liu Jiyuan, Zhang Qian, Hu Yunfeng, 2012. Regional differences of China's urban expansion from late 20th to early 21st century based on remote sensing information. Chinese Geographical Science, 22(1): 1-14. doi:  10.1007/s11769-012-0510-8
    [13] Liu Z H, Wang Y L Li Z G et al., 2013. Impervious surface impact on water quality in the process of rapid urbanization in Shenzhen, China. Environmental Earth Sciences, 68(8): 2365-2373. doi:  10.1007/s12665-012-1918-2
    [14] Liu Zhenhuan, Wang Yanglin, Peng Jian et al., 2011. Using ISA to analyze the spatial pattern of urban land cover change: a case study in Shenzhen. Acta Geographica Sinica, 66(7): 961-971. (in Chinese)
    [15] Liu Zhenhuan, Li You, Peng Jian, 2010. Remote sensing of impervious surface and its applications: a review. Progress in Geography, 29(9): 1143-1152. (in Chinese)
    [16] Lu D S, Weng Q H, 2006. Use of impervious surface in urban land use classification. Remote Sensing of Environment, 102: 146-160. doi:  10.1016/j.rse.2006.02.010
    [17] Lu D S, Weng Q H, Li G Y, 2006. Residential population estimation using remote sensing derived impervious surface. International Journal of Remote Sensing, 27: 3553-3570. doi:  10.1080/01431160600617202
    [18] Michishita R, Jiang Z, Xu B, 2012. Monitoring two decades of urbanization in the Poyang Lake area, China through spectral unmixing. Remote Sensing of Environment, 117: 3-18. doi:  10.1016/j.rse.2011.06.021
    [19] Peng Jian, WangYanglin, Zhang Yuan et al., 2006. Research on the influence of land use classification on landscape metrics. Acta Geographica Sinica, 61(2): 157-168. (in Chinese)
    [20] Phinn S, Stanford M, Murray A T, 2002. Monitoring the composition of urban environments based on the vegetation impervious surface-soil model by sub-pixel analysis techniques. International Journal of Remote Sensing, 23: 4131-4153. doi:  10.1080/01431160110114998
    [21] Qian Yuguo, Zhou Weiqi, Yu Wenjuan et al., 2015. Quantifying spatiotemporal pattern of urban greenspace: new insights from high resolution data. Landscape Ecology, 30(7): 1165-1173. doi:  10.1007/s10980-015-0195-3
    [22] Qiao Z, Tian G J, Xiao L, 2013. Diurnal and seasonal impacts of urbanization on the urban thermal environment: a case study of Beijing using MODIS data. ISPRS Journal of Photogrammetry and Remote Sensing, 85: 93-101. doi: 10.1016/j.isprsjprs. 2013.08.010
    [23] Quan J L, Chen Y H, Zhan W F et al., 2014. Multi-temporal trajectory of the urban heat island centroid in Beijing, China based on a Gaussian volume model. Remote Sensing of Environment, 149: 33-46. doi:  10.1016/j.rse.2014.03.037
    [24] Rashed T, Weeks J R, Gadalla M S et al., 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. doi:  10.1080/10106040108542210
    [25] Ridd M K, 1995. Exploring a V-I-S (vegetation-imperious surface-soil) model for urban ecosystem analysis through remote sensing: comparative anatomy for cities. International Journal of Remote Sensing, 16: 2165-2185. doi: 10.1080/014311695 08954549
    [26] Small C, 2001. Estimation of urban vegetation abundance by spectral mixture analysis. International Journal of Remote Sensing, 22: 1305-1334. doi:  10.1080/01431160151144369
    [27] Small C, 2002. Multi temporal analysis of urban reflectance. Remote Sensing of Environment, 81: 427-442. doi:  10.1016/S0034-4257(02)00019-6
    [28] Small C, 2004. The Landsat ETM+ spectral mixing space. Remote Sensing of Environment, 93: 1-17. doi: 10.1016/j.rse. 2004.06.007
    [29] Wang Hao, Wu Bingfang, Li Xiaosong et al., 2011. Extraction of impervious surface in Hai Basin using remote sensing. Journal of Remote Sensing, 15(2): 394-400. (in Chinese)
    [30] Wu C S, 2004. Normalized spectral mixture analysis for monitoring urban composition using ETM+ imagery. Remote Sensing of Environment, 93(4): 480-492. doi: 10.1016/j.rse.2004. 08.003
    [31] Wu C S, Murray A T, 2003. Estimating impervious surface distribution by spectral mixture analysis. Remote Sensing of Environment, 84: 493-505. doi:  10.1016/S0034-4257(02)00136-0
    [32] Wu C S, Murray A T, 2005. A cokriging method for estimating population density in urban areas. Remote Sensing for Urban Analysis, 29: 558-579. doi: 10.1016/j.compenvurbsys.2005. 01.006
    [33] Xiao R B, Ouyang Z Y, Zheng H et al., 2007. Spatial pattern of impervious surfaces and their impacts on land surface temperature in Beijing, China. Journal of Environmental Sciences, 19(2): 250-256. doi:  10.1016/S1001-0742(07)60041-2
    [34] Xie Miaomiao, Wang Yanglin, Fu Meichen et al., 2013. Pattern dynamics of thermal-environment effect during urbanization: a case study in Shenzhen City, China. Chinese Geographical Science, 23(1): 101-112. doi:  10.1007/s11769-012-0580-7
    [35] Xie Miaomiao, Wang Yanglin, Li Guicai et al., 2009. Thermal environment effect dynamic of landscape changes in different urbanization phases: a case study of western Shenzhen. Geographical Research, 28(4): 1085-1094. (in Chinese)
    [36] Yuan Chao, Wu Bingfang, Luo Xingshun et al., 2009. Estimating urban impervious surface distribution with RS. Engineering of Surveying and Mapping, 18(3): 32-39. (in Chinese)
    [37] Yue Wenze, 2009. Improvement of urban impervious surface estimation in Shanghai using Landsat7 ETM+ data. Chinese Geographical Science, 19(3): 283-290. doi:  10.1007/s11769-009-0283-x
    [38] Zhang M H, Qin Z H, Liu X et al., 2003. Detection of stress in tomatoes induced by late blight disease in California, USA, using hyperspectral remote sensing. International Journal of Applied Earth Observation and Geoinformation, 4: 295-310. doi:  10.1016/S0303-2434(03)00008-4
    [39] Zhou Cunlin, Xu Hanqiu, 2007. A spectral mixture analysis and mapping of impervious surfaces in built-up land of Fuzhou City. Journal of Image and Graphics, 5: 875-881. (in Chinese)
    [40] Zhou Jianhua, Zhou Yifan, GuoXiaohua et al., 2011. Methods of extracting distribution information of plants at urban. Journal of East China Normal University (Natural Science), 6: 1-9. (in Chinese)
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Using Impervious Surfaces to Detect Urban Expansion in Beijing of China in 2000s

doi: 10.1007/s11769-016-0802-5
Funds:  Under the auspices of Key Project of National Natural Science Foundation of China (No. 41130534)
    Corresponding author: PENG Jian

Abstract: The change of impervious surface area (ISA) can effectively reveal the gradual process of urbanization and act as a key index for monitoring urban expansion. Experiencing rapid growth of the built environment in the 2000s, urban expansion of Beijing has not been fully characterized through ISA. In this study, Landsat TM images of Beijing in 2001 and 2009 were obtained, and the eight-year urban expansion process in Beijing was analyzed using the ISA extracted by means of the vegetation-imperious surface-soil (V-I-S) model. From the spatial variation in ISA, the ring structure of urban expansion in Beijing was significant during the study period, with decreasing urban density from the city center to the periphery. In the ring road analysis, the most dramatic changes of ISA were found between the fifth ring and the sixth ring. This area has experienced the most new residential development, and is currently the main source of urban expansion. The typical profile lines revealed the directional characteristics of urban expansion. The east-west profile was the most urbanized axes in Beijing, while ISA change in the east-north profile was more significant than in the other five profiles. Moreover, the transition matrix of ISA levels revealed an increase in urban density in the low density built areas; the Moran's I index showed a clear expansion of the central urban area, which spread contiguously; and the standard deviational ellipse indicated the northeast was the dominant direction of urban expansion. These findings can provide important spatial control guidelines in the next round of national economic and social development planning, overall urban and rural planning, and land use planning.

PENG Jian, LIU Yanxu, SHEN Hong, XIE Pan, HU Xiaoxu, WANG Yanglin. Using Impervious Surfaces to Detect Urban Expansion in Beijing of China in 2000s[J]. Chinese Geographical Science, 2016, 26(2): 229-243. doi: 10.1007/s11769-016-0802-5
Citation: PENG Jian, LIU Yanxu, SHEN Hong, XIE Pan, HU Xiaoxu, WANG Yanglin. Using Impervious Surfaces to Detect Urban Expansion in Beijing of China in 2000s[J]. Chinese Geographical Science, 2016, 26(2): 229-243. doi: 10.1007/s11769-016-0802-5
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