Original Articles

Remote Sensing of Impervious Surface and Its Applications: A Review

Expand
  • 1. Key Laboratory for Urban Habitat Environmental Science and Technology, School of Urban Planning and Design, Shenzhen Graduate School of Peking University, Shenzhen 518055;
    2. Key Laboratory for Earth Suuace Processes of Ministry of Education, and College of Urban and Environmental Sciences, Peking University, Beijing 100871, China

Received date: 2010-01-01

  Revised date: 2010-05-01

  Online published: 2010-09-25

Abstract

Impervious surface can be defined as any materials that prevent the infiltration of water into the soil. Principally, roads and rooftops in the urban are the most prevalent and easily identified types of impervious surfaces. Other types include sidewalks, patios, bedrock outcrops, and compacted soils in the urban areas. Impervious surface not only indicates urbanization, but also is a major contrihutor to the environmental impacts of urbanization. Impervious surface area (ISA) is the index of impervious surface landscape components, which uses the percentage in a pixel for representation. It is an index to monitor the urban ecological system and environmental change, and is an important indicator of the ecological and environmental model, which can affect urban hydrological cycle, surface runoff, water quality, local climate, and biological diversity. This paper reviews the development of remote sensing technology of impervious surface, and summarizes how it impacts urban ecosystem and urban environmental system. From the aspect of remote sensing technology, sub-pixel decomposition including spectral mixture analysis and regression analysis, and other new methods for interpreting image, will be the trend of the application of remote sensing research to urban natural resources and environmental studies in the future. From the aspect of remote sensing data source, the data of medium-resolution (10-100 m) image and high-resolution (0.3-5 m) image, which are used to estimate the index of impervious surface in multi-temporal and large-spatial area, can provide a reliable basis to monitor urban land use/cover change and environmental response. As an environmental indicator, impervious surface area (ISA) can be used to monitor urban land cover change and simulate future urban development, providing a basis for the decision making of urban planning and management. Referring to the fact that impervious surface has an important relation to the hydrological cycle, non-point source pollution, land surface temperature, vegetation variation and biological diversity, if we can understand the relationship between impervious surface area and the environmental or ecological indicators, we(an better understand urban landscape pattern and ecological processes. Impervious surface area plays an important role in studying the eco-environmental effects of urbanization.

Cite this article

LIU Zhenhuan, WANG Yanglin, PENG Jian . Remote Sensing of Impervious Surface and Its Applications: A Review[J]. PROGRESS IN GEOGRAPHY, 2010 , 29(9) : 1143 -1152 . DOI: 10.11820/dlkxjz.2010.09.018

References


[1] United Nations. World Urbanization Prospects: The 2005 Revision. Database. New York: Department of Economic and Social Affairs, Population Division, 2006.

[2] Sui D Z, Zeng H. Modeling the dynamics of landscape stm] cture in Asia's emerging desakota regions: a case study in Shenzhen. Landscape and Urban Planning, 2001, 53(1-4): 37-52.

[3] Fu B, Lu Y, Chen L. Expanding the bridging capability of landscape ecology. Landscape Ecology, 2008, 23(4): 375-376.

[4] Weng Q. Remote Sensing of Impervious Surfaces. Taylor & Francis: London: CRC Press, 2008: 12-49.

[5] Espy W H, Morgan WC, Masch F D. A study of some ef-fects of urbanization on storm muroff fiom a small water-shed. Report 23. Austin: Texas Water Development Board, 1966: 7-9.

[6] Stankowski S J. Population density as an indirect indicator or urban and suburban land-surface modifications. U.S. Geological Survey Professional Paper 800-B. Washington, DC: U.S. Geological Survey, 1972: 25-40.

[7] Arnold C L, Gibbons C J. Impervious surface coverage: E-mergence of a key environmental factor. Journal of the American Planning Association, 1996, 62(2): 243-258.

[8] Schueler T R. The importance of imperviousness. Water-shed Protection Techniques, 1994, 1(3): 100-111.

[9] Brabec E, Schulte S, Richards P L. Impervious surfaces and water quality: A review of current literature and its implications for watershed planning. Journal of planning literature, 2002, 16(4): 499-514.

[10] Wissmar R C, Tinnn R K, Logsdon M G. Effects of chang-ing forest and impervious land covers on discharge char-acteristics of watershed. Environmental Management, 2004, 34(1): 91-98.

[11] Weng Q, Lu D. A sub-pixel analysis of urbanization effect on land surface temperature and its interplay with imper-vious surface and vegetation coverage in Indianapolis, U-nited States. International Journal of Applied Earth Obser-vation and Geoinformation, 2008, 10(1): 68-83.

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

[13] Slonecker E T, Jennings D, Garofalo D. Remote sensing of impervious surface: A review. Remote Sensing Reviews, 2001, 20(3): 227-255.

[14] Yang L, Xian G, Klaver J M, et al. Urban land一。OVPT change detection through sub-pixel imperviousness map-ping using remotely sensed data. Photogrammetric Engi-neering and Remote Sensing, 2003, 69(9): 1003一1010.

[15] Dougherty M, Dymond R L, Goetz S J. et al. Evaluation of impervious surface estimates in a rapidly urbanizing wa-tershed. Photogrammetric Engineering&Remote Sensing, 2004, 70(11): 1275-1284.

[16] Dare P M. Shadow analysis in high-resolution satellite im-ageiy of urban areas. Photogrammetric Engineering and Remote Sensing, 2005, 71(2):169-177.

[17] Wu C, Murray A T. Estimating impervious surface distri-bution by spectral mixture analysis. Remote Sensing of Environment, 2003, 84(4): 493-505.

[18] Ward D, Phinn S R, Murray A T. Monitoring growth in rapidly urbanizing areas using remotely sensed data. Pro-fessional Geographer, 2000, 52(3): 371-386.

[19] Phinn S, Stanford M, Scarth P, et al. Monitoring the com-position and form of urban environments based on the veg-etation-impervious surface-soil (VIS) model by sub-pixel analysis techniques. International Journal of Remote Sens-ing, 2002, 23(20): 4131-4153.

[20] Flanagan M. Subpixel impervious surface mapping, ASPRS 2001 Annual Convention, 2001.

[21] Madhavan B B, Kubo S, Kurisaki N, et al. Appraising the anatomy and spatial growth of the Bangkok metropolitan area using a vegetation-impemious-soil model through re-mote sensing. International Journal of Remote Sensing, 2001, 22(5): 789-806.

[22] Rashed T, Weeks J R, Gadalla M S. Revealing the anato-my of cities through spectral mixture analysis of multi-spectral satellite imagery: a case study of the greater Cairo region, Egypt. Geocarto International, 2001, 16(4): 5一15.

[23] Adams J B, Sabol D E, Kapos V, et al. Classification of multiple images based on fiactions of endmembeis: appli-canon to land-cover change in the Brazilian Amazon. Re-mote Sensing of Environment, 1995, 52(2): 137一154.

[24] Robeits D A, Gaidnei M, Church R, et al. Mapping cha-parial in the Santa Monica mountains using multiple end-member spectral mixture models. Remote Sensing of En-viionment, 1998, 65(3): 267-279.

[25] Setiawan H, Mathieu R, Thompson-Fawcett M. Assessing the applicability of the V-I-S model to map urban land use in the developing world: Case study of Yogyakaita, Indonesia. Computers, Environment and Urban Systems, 2006, 30(4): 503-522.

[26] Lu D, Weng Q. Use of impervious surface in urban land-use classification. Remote Sensing of Environment, 2006, 102(1-2): 146-160.

[27] 岳文泽,徐建华,武佳卫,等.基于线性光谱分析的城市 旧城改造空间格局遥感研究:以1997-2000年上海中 心城区为例.科学通报,2007, 51(8): 966-974.

[28] Powell S L, Cohen WB, Yang Z, et al. Quantification of impervious surface in the Snohomish Water Resources In-ventoiy Area of Western Washington fiom 1972-2006. Remote Sensing of Environment, 2008, 112(4):1895-1908.

[29] Weng Q, Lu D, Schubiing J. Estimation of land surface temperature-vegetation abundance relationship for urban heat island studies. Remote Sensing of Environment, 2004, 89(4): 467-483.

[30] 肖荣波,欧阳志云,蔡运楠,等.基于亚像元估测的城市 硬化地表景观格局分析.生态学报,2007, 27 (8): 3189-3197.

[31] Xiao R, Ouyang Z, Zheng H, et al. Spatial pattern of im-pervious surfaces and their impact on land surface temper-atuie in Beijing, China. Journal of Environmental Sci-ences, 2007, 19(2): 250-256.

[32] Xian G, Crane M. Assessments of urban growth in the Tampa Bay watershed using remote sensing data. Remote Sensing of Environment, 2005, 97(2): 203-215.

[33] Xian G, Crane M. An analysis of urban thermal character-istics and associated land cover in Tampa Bay and Las Vegas using Landsat satellite data. Remote Sensing of En-viionment, 2006, 104(2): 147-156.

[34] 徐涵秋.城市不透水面与相关城市生态要素关系的定量 分析.生态学报,2009, 29(5): 2456-2462.

[35] Hu X, Weng Q. Estimating impervious surfaces fiom medi-um spatial resolution imagery using the self-organizing map and multi-layer perception neural networks. Remote Sensing of Environment, 2009, 113: 2089-2102.

[36] Bauei M E, Heineit N J, Doyle J K,et al. Impervious suiface mapping and change monitoring using Landsat remote sensing. ASPRS anm] al conference proceedings, May 23-28, 2004, Denver, Colorado. Bethesda, MD: American So-ciety for Photogrammetiy and Remote Sensing, 2004.

[37] Dillies R R, Box J B, Symanzik J, et al. Effects of urban-ization on the aquatic fauna of the Line Creek watershed, Atlanta-a satellite perspective. Remote Sensing of Envi-ronment, 2003, 86(3): 411-422.

[38] Lu D, Weng Q. Mapping urban impervious surfaces fiom medium and high spatial resolution multispectral imagery// Weng Q. Remote Sensing of Impervious Surfaces. London: CRC Press Tavlor&Francis, 2008: 59-75.

[39] Van der Linden S, Hostert P. The influence of urban stm] ctures on impervious surface maps fiom airborne hy-perspectral data. Remote Sensing of Environment, 2009, 113(11): 2298-2305.

[40] Esch T, Himmler V, Schorcht G, et al. Large-area assess-ment of impervious surface based on integrated analysis of single-date Landsat-7 images and geospatial vector data. Remote Sensing of Environment, 2009, 113(8):1678-1690.

[41] Theobald D M, Goetz S J, Norman J B, et al. Watersheds at Risk to Increased Impervious Surface Cover in the Conterminous United States. Journal of Hydrologic Engi-neering, 2009, 14(4): 362-368.

[42] Zhou G. Urban 3D Building Model from LiDAR Data and Digital Aerial Images//Weng Q. Remote Sensing of hnper-vious Surfaces. London: CRC Press Taylor&Francis, 2008: 251-268.

[43] Gruen A. Building Extraction fiom Aerial Imagery//Weng Q. Remote Sensing of Impervious Surfaces. London: CRC Press Taylor&Francis, 2008: 269-296.

[44] Quackenbush L J. Separating Types of Impervious Land Cover Using Fractals//Weng Q. Remote Sensing of Imper vious Surfaces. London: CRC Press Taylor&Francis, 2008:119-142.

[45] Chabaeva A, Civco D L, Hurd J D. Assessment of hnper-vious Surface Estimation Techniques. Journal of Hydro-logical Engineering. 2009,14(4): 377-387.

[46] Wu C. Normalized spectral mixture analysis for monitoring urban composition using ETM' imagery. Remote Sensing of Environment, 2004, 93(4): 480-492.

[47] Jantz P, Goetz S, Jantz C. Urbanization and the Loss of Resource Lands in the Chesapeake Bay Watershed. Envi-ronmental Management, 2005, 36(6): 808-825.

[48] Rashed T, Weeks J R, Stow D, et al. Measuring temporal compositions of urban morphology through spectral mix-tore analysis: Toward a soft approach to change analysis in crowded cities. International Journal of Remote Sensing, 2005, 26(4): 699-718.

[49] 李伟峰,欧阳志云,陈求稳,等.基于遥感信息的北京硬化 地表格局特征研究.遥感学报,2008, 12(4): 603-612.

[50] Relly J, Maggio P, Kaip S. A model to predict impervious surface for regional and municipal land use planning pur-poses. Environmental Impact Assessment Review, 2004, 24(3): 363-382.

[51] Rashed T. Remote sensing of within-class change in urban neighborhood structures. Computers, Environment and Ur-ban Systems, 2008, 32(5): 343-354.

[52] Clapham W B. Quantitative classification as a tool to show change in an urbanizing watershed. International Journal of Remote Sensing, 2005, 26(22): 4923-4939.

[53] Davis C, Schaub T. A transboundaiy study of urban sprawl in the Pacific Coast region of North America: The benefits of multiple measurement methods. International Journal of Applied Earth Observation and Geoinformation, 2005, 7 (4): 268-283.

[54] Yuan F, Sawaya K E, Loeffelholz B C, et al. Land cover mapping and change analysis in the Twin Cities Metropolitan Area with Landsat remote sensing. Remote Sensing of Environment, 2005, 98(2-3): 317-328.

[55] Conway T M. Impervious surface as indicator of pH and specific conductance in the urbanizing coastal zone of New Jersey, USA. Journal of Environmental Management, 2007, 85(2): 308-316.

[56] Brun S E, Band L E. Simulating runoff behavior in an ur-banizing watershed. Computers, Environment and Urban System, 2000, 24(1): 2-5.

[57] 谢苗苗,王仰麟,李贵刁一基于亚像元分解的不透水表 面与植被盖度空间分异测度.资源科学,2009, 31(2): 257-264.

[58] Powell R L, Roberts D A, Dennison P E, et al. Sub-pixel mapping of urban land cover using multiple endmember spectral mixture analysis: Manaus, Brazil. Remote Sensing of Environment, 2007, 106(2): 253-267.

[59] Soil Conservation Service. Urban hydrology for small wa-tersheds. SCS Technical Release No. 55. Washington D C: U.S. Department of Agriculture, 1975.

[60] Kalnay E, Cai M. Impact of urbanization and land-use change on climate. Nature, 2003, 423: 528-531.

[61] Xian G. Analysis of impacts of urban land use and land cover on air quality in the Las Vegas region using remote sensing information and ground observations. International Journal of Remote Sensing, 2007, 28 (24): 5427-5445.

[62] Yuan F, Bauer M E. Comparison of impervious surface area and normalized difference vegetation index as indica-toes of surface urban heat island effects in Landsat im-ageiy. Remote Sensing of Environment, 2006, 106 (3): 375-386.

[63] Weng Q, Liu H, Lu D. Assessing the effects of land use and land cover patterns on thermal conditions using land-scape metrics in city of Indianapolis, United States. Urban Ecosystem, 2007, 10(2): 203-219.

[64] Yang X, Liu Z. Use of satellite-derived landscape imper-viousness index to characterize urban spatial growth. Computers, Environment and Urban Systems, 2005, 29(5): 524-540.

[65] Zhang Y S, Odeh I 0 A, Han C F. Bi-temporal character-ization of land surface temperature in relation to impemi-ous surface area, NDVI and NDBI, using a sub-pixel im-age analysis. International Journal of Applied Observation and Geoinformation, 2009, 11(4): 256-264.

[66] 岳文泽.基于遥感影像的城市景观格局及其热环境效应 研究「Dl.上海:华东师范大学,2005: 35-70.

[67] Xian G, Crane M, Su J. An analysis of urban development and its environmental impact on the Tampa Bay water-shed. Journal of Environmental Management, 2007, 85(4): 965-976.

[68] 郭旭东,陈利顶,傅伯杰.上地利用/上地覆被变化对区 域生态环境的影响.环境科学进展,1999,7(6): 66-75.

[69] USEPA. National Water Quality Inventory, Report to Congress Executive Summary. Washington DC: USEPA, 1995.

[70] Jensen J R, Schill S R. Environmental resources evaluation of the inland coastal region of the South Carolina lower Savannah Salkehatchie watershed. Final Report to NASA, Visiting Investigator Program, University of South Carolina Extension, 1996.

[71] Neitsch S L, Arnold J G, Kiniiy J R, et al. Soil and water assessment tool theoretical documentation version 2005. Grassland, Soil and Water Research Laboratory, Agricul-tural Research Service. Temple, Texas, 2005.

[72] Rossman L A. Storm water management model user's manual version 5.0. USEPA, National risk management re-search laboratory office of research and development. Cincinnati, 0 H. EPA/600/R-05/040, 2008.

[73] Shaw S B, Walter M T, Steenhuis T S. A physical model of particulate wash-off fiom rough impervious surfaces. Jour-nal of Hydrology, 2006, 327(3-4): 618-626.

[74] Pappas E A, Smith D R, Huang C, et al. Impervious sur-face impacts to runoff and sediment discharge under labo-ratoiy rainfall simulation. CATENA, 2008, 72(1):146-152.

[75] Bonta J V, Glick R H. Impacts of impervious cover and other factors on storm-water quality in Austin, Tex. Jour-nal of Hydrologic Engineering, 2009, 14(4): 316-323.

[76] Moglen G E. Hydrology and impervious areas. Journal of Hydrologic Engineering, 2009, 14(4): 303-304.

[77] 金卫斌,李百炼.流域尺度的景观一水质模型研究进展. 科技导报,2008, 26(7): 72-77.

[78] Schueler T R, Fraley-Mc Neal L, Cappiella K. Is impemi-ous cover still important? Review of recent research. Jour-nal of Hydrologic Engineering, 2009, 14(4): 309-315.

[79] Kromroy K, Ward K, Castillo P, et al. Relationships be-tween urbanization and the oak resource of the Minneapo-lis/St. Paul Metropolitan area fiom 1991 to 1998. Land-scape and Urban Planning, 2007, 80(4): 375-385.

[80] Alberti M, Booth D, Hill K, et al. The impact of urban patterns on aquatic ecosystems: An empirical analysis in Puget lowland sub-basins. Landscape and urban planning, 2007, 80(4): 345-361.

[81] Booth D B, Jackson C R. Urbanization of aquatic systems: Degradation thresholds, stormwater detection and the lim-its of mitigation. Journal of the American Water Resources Association, 1997, 33(5): 1077-1090.

[82] Miltner R J. Urbanization influences on biotic integrity in the Cuyahoga River Basin. Paper presented at the 59th Midwest Fish and Wildlife Conference, Milwaukee, WI, 1997.

[83] Homer R R, Booth D B, Azous A, et al. Watershed deter-minants of ecosystem functioning//Roesner L A. Effects of Watershed Development and Management on Aquatic E-cosystems. New York: American Society of Civil Engi-neers, 1997.

[84] 岳文泽,徐丽华,徐建华,等.城市多光谱遥感像元分解 技术改进研究.浙江大学学报:工学版,2006, 40(4): 719-723.

[85] Brabec E A. Imperviousness and land-use policy: Toward an effective approach to watershed planning. Journal of Hydrologic Engineering, 2009,14(4): 425-433.

Outlines

/