京津唐城市群不透水地表增长格局以及水环境效应

doi:10.11821/xb201111005

地理学报 ›› 2011, Vol. 66 ›› Issue (11): 1486-1496.doi: 10.11821/xb201111005

京津唐城市群不透水地表增长格局以及水环境效应

匡文慧¹, 刘纪远¹, 陆灯盛²

1. 中国科学院地理科学与资源研究所, 北京 100101;
2. Indiana University, Indianapolis 47405, USA

收稿日期:2011-01-27 修回日期:2011-03-16 出版日期:2011-11-20 发布日期:2011-11-20
作者简介:匡文慧(1978-), 男, 博士, 助研。目前主要从事土地利用/覆盖变化、城市遥感应用研究工作。E-mail: kuangwh1978@sina.com
基金资助:
国家“973”计划项目(2010CB950900); 国家自然科学青年基金项目(40901224); 遥感科学国家重点实验室开放基金项目(2009KFJJ005)

Pattern of Impervious Surface Change and Its Effect on Water Environment in the Beijing-Tianjin-Tangshan Metropolitan Area

KUANG Wenhui¹, LIU Jiyuan¹, LU Dengsheng²

1. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China;
2. Indiana University, Indiana 47405, USA

Received:2011-01-27 Revised:2011-03-16 Online:2011-11-20 Published:2011-11-20
Supported by:
National Basic Research Program of China, No.2010CB950900;National Nature Science Foundation of China, No.40901224; Opening Foundation of State Key Laboratory ofRemote Sensing Science, No.2009KFJJ005

摘要/Abstract

摘要： 不透水地表(Impervious Surface Area,ISA) 时空格局是城市化与全球环境变化交互影响下的土地利用/覆盖变化—生态系统过程—区域气候变化相互作用机理以及导致的环境效应研究的重要内容。如何快速准确地实现区域尺度不透水地表遥感信息的获取是上述研究面临的重要瓶颈问题。本研究嵌入中国LUCC 信息改进了基于MODIS NDVI 与夜间灯光指数(DMSP-OLS) 提取不透水地表信息的方法,反演了2000 年与2008 年京津唐城市群2 期250 m不透水地表信息,进而分析其变化的时空格局,结合子流域单元与河流污染物监测数据评价其对海河流域地表水环境的影响。结论表明：受环渤海经济区,天津滨海新区开发等政策的影响,京津唐城市群在21 世纪初8 年不透水地表沿着城—乡梯度带、城市交通廊道及海岸带高速增长。城乡建设用地不透水地表增长呈现显著的时空差异特征,由于北京市受人口-资源-环境的压力,产业发展不断向周边地区转移,天津与河北唐山、廊坊、秦皇岛三市具有更快的不透水地表增长速度。京津唐城市群城市高密度的不透水地表分布对于海河流域地表水环境产生严重影响,不透水地表的增长加剧了流域河流水质的污染程度。同时也发现,子流域不透水地表面积比例与COD、NH₃-N浓度均值呈现显著的线性关系。

关键词: 不透水地表, 水环境, 城市, 遥感, 京津唐城市群

Abstract: The impervious surface area (ISA) at the regional scale is one of important environmental factors for examining the interaction and mechanism of Land Use/Cover Change (LUCC) - ecosystem processes - regional climate change under the interactions of urbanization and global environment change. The timely and accurate extraction of ISA from remotely sensed data at the regional scale is becoming a bottle-neck issue. This study improved the MODIS NDVI and DMSP-OLS based ISA extraction method by incorporating LULC information of China. ISA datasets in Beijing-Tianjin-Tangshan Metropolitan Area (BTTMA) in 2000 and 2008 at a spatial resolution of 250 m were developed, their spatial-temporal changes were analyzed, and their impacts on water quality were evaluated. The results indicated that ISA in BTTMA has rapidly increased along urban fringe and urban transportation corridors and coastal belt both in intensity and extents from 2000 to 2008. The growth rates of ISA in Tianjin and three cities (Tanshan, Langfang, and Qinhuangdao) in Hebei Province were greater than that in Beijing. The ISA increase has great impacts on water quality; particularly, the water pollution in the Haihe River has deteriorated due to the increase and intensification of impervious surface areas.

Key words: impervious surface, water environment, urban area, remote sensing, Beijing-Tianjin-Tangshan Metropolitan Area

匡文慧, 刘纪远, 陆灯盛. 京津唐城市群不透水地表增长格局以及水环境效应[J]. 地理学报, 2011, 66(11): 1486-1496.

KUANG Wenhui, LIU Jiyuan, LU Dengsheng. Pattern of Impervious Surface Change and Its Effect on Water Environment in the Beijing-Tianjin-Tangshan Metropolitan Area[J]. Acta Geographica Sinica, 2011, 66(11): 1486-1496.

参考文献

[1] Grimm N B, Faeth S H, Golubiewski N E et al. Global change and the ecology of cities. Science, 2008, 319: 756-760.

[2] IHDP Report. Urbanize and global environment change. International Human Dimensions Programme on GlobalEnvironmental Change, 2005, 15: 1-60.

[3] Alberti M. Advances in urban Ecology integrating humans and ecological processes in urban ecosystems. New York:Springer science + business media, 2009.

[4] Pickett S A, Cadenasso M L, Grove J M et al. Urban ecological systems: Scientific foundations and a decade ofprogress. Journal of Environmental Management, 2011, 92: 331-362.

[5] Arnold C L, Gibbons C J. Impervious surface coverage: The emergence of a key environmental indicator. Journal of American Planning Association, 1996, 2: 243-258.

[6] Elvidge C D, Tuttle B T, Sutton P C et al. Global distribution and density of constructed impervious surfaces. Sensors,2007, 7: 1962-1979.

[7] Bierwagen B G, Theobald D M, Pyke C R et al. National housing and impervious surface scenarios for integratedclimate impact assessments. PNAS, 107(49): 20887-20892.

[8] Madhavan B B, Kubo S, Kurisaki N. Appraising the anatomy and spatial growth of the Bangkok metropolitan areausing a vegetation-impervious-soil model through remote sensing. International Journal of Remote Sensing, 2001, 22(5):789-806.

[9] Lu D S, Weng Q H. Use of impervious surface in urban land use classification. Remote Sensing of Environment, 2006,102: 146-160.

[10] Wu C S, Murray A T. A cokriging method for estimating population density in urban areas. Computers Environmentand Urban Systems, 2005, 29: 558-579

[11] Lu D S, Weng Q H, Li G Y. Residential population estimation using a remote sensing derived impervious surfaceapproach. International Journal of Remote Sensing, 2006, 27: 3553-3570.

[12] Brabec E, Schulte S, Richards P L. Impervious surface and water quality: A review of current literature and itsimplications for watershed planning. Journal of Planning Literature, 2002, 16: 499-514.

[13] Ahn J H, Grant S B, Surbeck C Q et al. Coastal water quality impact of storm water runoff from an urban watershedin southern California. Environmental Science & Technology, 2005, 39: 5940-5953.

[14] Hodgson M E, Jensen J R, Schmidt L et al. An evaluation of LIDAR- and IFSAR-derived digital elevation models inleaf-on conditions with USGS Level 1 and Level 2 DEMs. Remote Sensing of Environment, 2003, 84: 295-308.

[15] Yang L M, Huang C, Homer C G et al. An approach for mapping large-area impervious surfaces: Synergistic use ofLandsat 7 ETM+ and high spatial resolution imagery. Canadian Journal of Remote Sensing, 2003, 2: 230-240.

[16] Ridd M K. Exploring a V-I-S (Vegetation-Impervious Surface-Soil) model for urban ecosystem analysis throughremote sensing: Comparative anatomy for cities. International Journal of Remote Sensing, 1995, 16(12): 2165-2185.

[17] Lu D S, Weng Q H. A survey of image classification methods and techniques for improving classificationperformance. International Journal of Remote Sensing, 2007, 28: 823-870.

[18] Lu D S, Weng Q H. Spectral mixture analysis of ASTER images for examining the relationship between urbanthermal features and biophysical descriptors in Indianapolis, Indiana, USA. Remote Sensing of Environment, 2006,104: 157-167.

[19] Lu D S, Weng Q H. Use of impervious surface in urban land use classification. Remote Sensing of Environment,2006, 102: 146-160.

[20] Hu X F, Weng Q H. Estimating impervious surfaces from medium spatial resolution imagery using the self-organizingmap and multi-layer perception neural networks. Remote Sensing of Environment, 2009. 113: 2089-2102.

[21] Wang Hao, Wu Bingfang, Li Xiaosong et al. Extraction of impervious surface in Hai Basin using remote sensing.Journal of Remote Sensing, 2011, 15(2): 388-400. [王浩, 吴炳方, 李晓松等. 流域尺度的不透水面遥感提取. 遥感学报,2011, 15(2): 388-400.]

[22] Booth D B, Jackson C R. Urbanization of aquatic systems: degradation thresholds, stormwater detection, and the limitsof mitigation. Journal of the American Water Resource Association, 1997, 33(5): 1077-1089.

[23] Ren W, Zhong Y, Meligrana J et al. Urbanization, land use, and water quality in Shanghai 1947-1996. EnvironmentInternational, 2003, 29: 649-659.

[24] Yin Z, Walcott S, Kaplan B et al. An analysis of the relationship between spatial patterns of water quality and urbandevelopment in Shanghai, China. Computers, Environment and Urban Systems, 2005, 29: 197-221.

[25] Lu Dadao. Urbanization process and spatial sprawl in China. Urban Planning Forum, 2007, (4): 47-52. [陆大道.我国的城镇化进程与空间扩张. 城市规划学刊, 2007, (4): 47-52.]

[26] Yao Shimou, Wang Chen, Zhang Luocheng et al. The influencing factors of resources and environments in the processof urbanization of China. Progress in Geography, 2008, 27(3): 94-100. [姚士谋, 王辰, 张落成等. 我国资源环境对城镇化问题的影响因素. 地理科学进展, 2008, 27(3): 94-100.]

[27] Kuang Wenhui. Simulating dynamic urban expansion at regional scale in Beijing-Tianjin-Tangshan Metropolitan Area.Journal of Geographical Sciences, 2011, 21(1): 317-330.

[28] Liu J Y, Liu M L, Zhuang D F et al. Study on spatial pattern of land-use change in China during 1995-2000. Sciencein China: Series D, 2003, 46(4): 373-384.

[29] Liu Jiyuan, Zhang Zengxiang, Zhuang Dafang et al. A study on the spatial-temporal dynamic changes of land-use anddriving forces analyses of China in the 1990s. Geographical Research, 2003, 22(1): 1-12. [刘纪远, 张增祥, 庄大方等. 20 世纪90 年代中国土地利用变化时空特征及其成因分析. 地理研究, 2003, 22(1): 1-12.]

[30] U.S. Geological Survey. HYDRO1k Elevation Derivative Database. http://eros.usgs.gov/#Find_Data/Products_and_Data_Available/gtopo30/hydro/asia.

[31] U.S. Geological Survey. MODIS NDVI production. http://glovis.usgs.gov.

[32] NOAA. Global dmsp-ols nighttime lights time series 1992 - 2009 (version 4) (http://www.ngdc.noaa.gov/dmsp/sensors/ols.html)

[33] Lu D S, Tian H Q, Zhou G M. Regional mapping of human settlements in southeastern China withmultisensory remotely sensed data. Remote Sensing of Environment. 2008, 112, 3668-3679.

[34] Ministry of Environmental Protection of the People's Republic of China. National surface water quality automaticmonitoring and real-time publishing system. http://58.68.130.147/#. 2011. [中国环境保护部. 国家地表水水质自动监测实时数据发布系统, 2011.]

京津唐城市群不透水地表增长格局以及水环境效应

Pattern of Impervious Surface Change and Its Effect on Water Environment in the Beijing-Tianjin-Tangshan Metropolitan Area

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