Advances in Remote Sensing Research on Urban Impervious Surface
REN Jin-hua1, WU Shao-hua1,2, ZHOU Sheng-lu1, LIN Chen2,3
1. School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210093, China;
2. State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Science, Nanjing 210008, China;
3. Nanjing Institute of Geography and Limnology, Chinese Academy of Science, Nanjing 210008, China
Abstract：Impervious surface, as an important indicator to measure the urbanization degree and environmental quality, has attracted more and more attention. The magnitude, location, geometry, spatial pattern of impervious surfaces and the ratio of perviousness-imperviousness significantly affect regional eco-environment changes. Extracting and mapping impervious surface by means of multiple remote sensing data and analytical methods have constituted a hot topic in these research directions. In this paper, impervious surface extraction methods are summarized from traditional method of remote sensing, extraction based on spectrum and geometrical features and artificial intelligence algorithms, then the principles, characteristics, application fields are described, and finally the future prospects are pointed out.
[1] Slonecker E T,Jennings D,Garofalo D.Remote Sensing of Impervious Surfaces:A Review[J].Remote Sensing Reviews,2001,20(3):227-255.[2] 刘珍环,王仰麟,彭建.不透水表面遥感监测及其应用研究进展[J].地理科学进展,2010,29(9):1143-1152.Liu Z H,Wang Y L,Peng J.Remote Sensing of Impervious Surface and Its Applications:A Review[J].Progress in Geography,2010,29(9):1143-1152(in Chinese with English Abstract).[3] Schueler T R.The Importance of Imperviousness[J].Watershed Protection Techniques,1994,1(3):100-111.[4] Arnold C L,Gibbons C J.Impervious Surface Coverage:The Emergence of a Key Environmental Indicator[J].Journal of the American Planning Association,1996,62(2):243-258.[5] Ridd M K.Exploring a V-I-S (Vegetation-impervious Surface-soil) Model for Urban Ecosystem Analysis Through Remote Sensing:Comparative Anatomy for Cities[J].International Journal of Remote Sensing,1995,16(12):2165-2185.[6] Phinn S,Stanford M,Scarth P,et al.Monitoring the Composition of Urban Environments Based on the Vegetation-impervious Surface-soil (VIS) Model by Subpixel Analysis Techniques[J].International Journal of Remote Sensing,2002,23(20):4131-4153.[7] 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[J].遥感学报,2008,12(4):603-612.Li W F,Ouyang Z Y,Chen Q W,et al.Study on the Spatial Pattern of Impervious Surface Using Remote Sensing Data Within the Urban Area of Beijing[J].Journal of Remote Sensing,2008,12(4):603-612(in Chinese with English Abstract).[8] Mohapatra R P,Wu C S.High Resolution Impervious Surface Estimation:An Integration of IKONOS and Landsat-7 ETM Imagery[J].Photogrammetric Engineering and Remote Sensing,2010,76(12):1329-1341.[9] Bauer M E,Loffelholz B C,Wilson B.Estimating and Mapping Impervious Surface Area by Regression Analysis of Landsat Imagery[M] //Weng Q.Remote Sensing of Impervious Surfaces.Boca Raton,FL:CRC Press,2007.[10] Sawaya K E,Olmanson L G,Heinert N J,et al.Extending Satellite Remote Sensing to Local Scales:Land and Water Resource Monitoring Using High-resolution Imagery[J].Remote Sensing of Environment,2003,88(1/2):144-156.[11] Lu D S,Tian H Q,Zhou G M,et al.Regional Mapping of Human Settlements in Southeastern China with Multisensor Remotely Sensed Data[J].Remote Sensing of Environment,2008,112(9):3668-3679.[12] 张路,高志宏,廖明生,等.利用多源遥感数据进行城市不透水面覆盖度估算[J].武汉大学学报,2010,35(10):1212-1216.Zhang L,Gao Z H,Liao M S,et al.Estimating Urban Impervious Surface Percentage with Multi-source Remote Sensing Data[J].Geomatics and Information Science of Wuhan University,2010,35(10):1212-1216(in Chinese with English Abstract).[13] Yang L M,Huang C Q,Homer C G,et al.An Approach for Mapping Large-area Impervious Surfaces:Synergistic Use of Landsat-7 ETM+ and High Spatial Resolution Imagery[J].Canadian Journal of Remote Sensing,2003,29(2):230-240.[14] 李倩,李彩丽,芮菡艺,等.基于遥感影像不同亮度的CART方法估算不透水率研究[J].水电能源科学,2010,28(12):45-48.Li Q,Li C L,Rui H Y,et al.Estimate of Impervious Surface Percent Based on Different Brightness of CART Method with Remote Sensing Images[J].Water Resources and Power,2010,28(12):45-48(in Chinese with English Abstract).[15] Xian G.Mapping Impervious Surfaces Using Classification and Regression Tree Algorithm[M].Weng Q.Remote Sensing of Impervious Surfaces.Boca Raton,FL:CRC Press,2007.[16] 江利明,廖明生,林珲,等.利用雷达干涉数据进行城市不透水层百分比估算[J].遥感学报,2008,12(1):176-185.Jiang L M,Liao M S,Lin H,et al.Estimating Urban Impervious Surface Percentage with ERS-1/2 InSAR Data[J].Journal of Remote Sensing,2008,12(1):176-185(in Chinese with English Abstract).[17] 廖明生,江利明,林珲,等.基于CART集成学习的城市不透水层百分比遥感估算[J].武汉大学学报,2007,32(12):1099-1106.Liao M S,Jiang L M,Lin H,et al.Estimating Urban Impervious Surface Percent Using Boosting as a Refinement of CART Analysis[J].Geomatics and Information Science of Wuhan University,2007,32(12):1099-1106(in Chinese with English Abstract).[18] Im J,Lu Z Y,Rhee J,et al.Impervious Surface Quantification Using a Synthesis of Artificial Immune Networks and Decision/Regression Trees from Multi-sensor Data[J].Remote Sensing of Environment,2011,117:101-113.doi:10.1016/j.rse.2011.06.024.[19] 赵英时.遥感应用分析原理与方法[M].北京:科学出版社,2003:330-331.Zhao Y S.The Principles and Methods of Remote Sensing Applications and Analysis[M].Beijing:Science Press,2003:330-331(in Chinese).[20] Wu C S,Murray A T.Estimating Impervious Surface Distribution by Spectral Mixture Analysis[J].Remote Sensing of Environment,2003,84(4):493-505.[21] 谢苗苗,王仰麟,李贵才.基于亚像元分解的不透水表面与植被覆盖空间分异测度——以深圳市为例[J].资源科学,2009,31(2):257-264.Xie M M,Wang Y L,Li G C.Spatial Variation of Impervious Surface Area and Vegetation Cover Based on Subpixel Model in Shenzhen[J].Resources Science,2009,31(2):257-264(in Chinese with English Abstract).[22] 潘竟虎,李晓雪,刘春雨.兰州市中心城区不透水面覆盖度的遥感估算[J].西北师范大学学报:自然科学版,2009,45(4):95-100.Pan J H,Li X X,Liu C Y.Urban Impervious Surface Abundance Estimation in Lanzhou City Based on Remote Sensing[J].Journal of Northwest Normal University:Natural Science,2009,45(4):95-100(in Chinese with English Abstract).[23] 岳文泽,吴次芳.基于混合光谱分解的城市不透水面分布估算[J].遥感学报,2007,11(6):914-922.Yue W Z,Wu C F.Urban Impervious Surface Distribution Estimation by Spectral Mixture Analysis[J].Journal of Remote Sensing,2007,11(6):914-922(in Chinese with English Abstract).[24] 袁超,吴炳方,罗兴顺,等.城市不透水表面遥感估算研究[J].测绘工程,2009,18(3):32-36.Yuan C,Wu B F,Luo X S,et al.Estimating Urban Impervious Surface Distribution with RS[J].Engineering of Surveying and Mapping,2009,18(3):32-36(in Chinese with English Abstract).[25] 周纪,陈云浩,张锦水,等.北京城市不透水层覆盖度遥感估算[J].国土资源遥感,2007(3):13-17.Zhou J,Chen Y H,Zhang J S,et al.Urban Impervious Surface Abundance Estimation in Beijing Based on Remote Sensing[J].Remote Sensing for Land and Resources,2007(3):13-17(in Chinese with English Abstract).[26] Roberts D A,Gardner M,Church R,et al.Mapping Chaparral in the Santa Monica Mountains Using Multiple Endmember Spectral Mixture Models[J].Remote Sensing of Environment,1998,65(3):267-279.[27] Rashed T,Weeks J R,Roberts D,et al.Measuring the Physical Composition of Urban Morphology Using Multiple Endmember Spectral Mixture Models[J].Photogrammetric Engineering and Remote Sensing,2003,69(9):1011-1020.[28] Weng Q,Hu X,Lu D.Extracting Impervious Surface from Hyperspectral Imagery with Linear Spectral Mixture Analysis[M] //Weng Q.Remote Sensing of Impervious Surfaces.Boca Raton,FL:CRC Press,2007.[29] Lu D,Weng Q H.Spectral Mixture Analysis of the Urban Landscape in Indianapolis with Landsat ETM+ Imagery[J].Photogrammetric Engineering and Remote Sensing,2004,70(9):1053-1062.[30] 李彩丽,都金康,左天惠.基于高分辨率遥感影像的不透水面信息提取方法研究[J].遥感信息,2009(5):36-40.Li C L,Du J K,Zuo T H.The Study of Extracting Impervious Surface Information Based on High Resolution Remote Sensing Image[J].Remote Sensing Information,2009(5):36-40(in Chinese with English Abstract).[31] 孙志英,赵彦锋,陈杰,等.面向对象分类在城市地表不可透水度提取中的应用[J].地理科学,2007,27(6):837-842.Sun Z Y,Zhao Y F,Chen J,et al.Application of Object-oriented Classification in Extraction of Impervious Degree of Urban Surface[J].Scientia Geographica Sinica,2007,27(6):837-842(in Chinese with English Abstract).[32] 黄慧萍,吴炳方.地物大小、对象尺度、影像分辨率的关系分析[J].遥感技术与应用,2006,21(3):243-248.Huang H P,Wu B F.Analysis to the Relationship of Feature Size,Objects Scales,Image Resolution[J].Remote Sensing Technology and Application,2006,21(3):243-248(in Chinese with English Abstract).[33] Zhou Y,Wang Y Q.Extraction of Impervious Surface Areas from High Spatial Resolution Imagery by Multiple Agent Segmentation and Classification[J].Photogrammetric Engineering and Remote Sensing,2008,74(7):857-868.[34] Hu X F,Weng Q H.Impervious Surface Area Extraction from IKONOS Imagery Using an Object-based Fuzzy Method[J].Geocarto International,2011,26(1):3-20.[35] Yu B,Liu H,Zhang L,et al.An Object-based Two-stage Method for a Detailed Classification of Urban Landscape Components by Integrating Airborne LiDAR and Color Infrared Image Data:A Case Study of Downtown Houston[C] //Urban Remote Sensing Joint Event.Shanghai,China:IEEE,2009:1-8.[36] Verbeeck K,Hermy M,Van Orshoven J.An Hierarchical Object Based Image Analysis Approach to Extract Impervious Surfaces Within the Domestic Garden[J].Joint Urban Remote Sensing Event,2011:397-400.[37] Walton J T.Subpixel Urban Land Cover Estimation:Comparing Cubist,Random Forests,and Support Vector Regression[J].Photogrammetric Engineering and Remote Sensing,2008,74(10):1213-1222.[38] Plumb A P,Rowe R C,York P,et al.Optimisation of the Predictive Ability of Artificial Neural Network(ANN) Models:A Comparison of Three ANN Programs and Four Classes of Training Algorithm[J].European Journal of Pharmaceutical Sciences,2005,25(4/5):395-405.[39] Pal M,Mather P M.An Assessment of the Effectiveness of Decision Tree Methods for Land Cover Classification[J].Remote Sensing of Environment,2003,86(4):554-565.[40] Weng Q H,Hu X F.Medium Spatial Resolution Satellite Imagery for Estimating and Mapping Urban Impervious Surfaces Using LSMA and ANN[J].Transactions on Geoscience and Remote Sensing,2008,46(8):2397-2406.[41] Mohapatra R P,Wu C.Subpixel Imperviousness Estimation with IKONOS Imagery:An Artificial Neural Network Approach[M] //Weng Q.Remote Sensing of Impervious Surfaces.Boca Raton,FL:CRC Press,2007.[42] 骆成凤.遗传算法优化的BP神经网络城市不透水层百分比估算[J].测绘科学,2011,36(1):48-50.Luo C F.Estimating Urban Impervious Surface Percentage with BP Neural Network Based on Genetic Algorithm[J].Science of Surveying and Mapping,2011,36(1):48-50(in Chinese with English Abstract).[43] Chormanski J,Vande Voorde T,Roeck T D,et al.Improving Distributed Runoff Prediction in Urbanized Catchments with Remote Sensing Based Estimates of Impervious Surface Cover[J].Sensors,2008,8:910-932.[44] Hu X F,Weng Q H.Estimating Impervious Surfaces from Medium Spatial Resolution Imagery Using the Self-organizing Map and Multi-layer Perceptron Neural Networks[J].Remote Sensing of Environment,2009,113(10):2089-2102[45] Heermann P D,Khazenie N.Classification of Multispectral Remote Sensing Data Using a Back-propagation Neural Network[J].IEEE Transactions on Geoscience and Remote Sensing,1992,30(1):81-88.[46] Filippi A,Dobreva L,Klein A G,et al.Self-organizing Map-based Applications in Remote Sensing[C] //Matsopoulos G K.Self-organizing Maps.2010:231-248.[47] Hasi B,Ma J M,Li Q Q,et al.Self-organizing Feature Map Neural Network Classification of the ASTER Data Based on Wavelet Fusion[J].Science in China Series D:Earth Sciences,2004,47(7):651-658.[48] Ji C Y.Land-use Classification of Remotely Sensed Data Using Kohonen Self-organizing Feature Map Neural Networks[J].Photogrammetric Engineering and Remote Sensing,2000,66(12):1451-1460.[49] Lee S,Lathrop R G.Subpixel Analysis of Landsat ETM+ Using Self-organizing Map (SOM) Neural Networks for Urban Land Cover Characterization[J].Transactions on Geoscience and Remote Sensing,2006,44(6):1642-1654.[50] 张学工.关于统计学习理论与支持向量机[J].自动化学报,2000,26(1):32-42.Zhang X G.Introduction to Statistical Learning Theory and Support Vector Machines[J].Acta Automatica Sinica,2000,26(1):32-42(in Chinese with English Abstract).[51] 程熙,沈占锋,骆剑承,等.利用混合光谱分解与SVM估算不透水面覆盖率[J].遥感学报,2011,15(6):1228-1241.Cheng X,Shen Z F,Luo J C,et al.Estimating Impervious Surface Base on Comparison of Spectral Mixture Analysis and Support Vector Machine Methods[J].Journal of Remote Sensing,2011,15(6):1228-1241(in Chinese with English Abstract).[52] Zhang Y J,Chen L,He C.Estimating Urban Impervious Surfaces Using LS-SVM with Multi-scale Texture[C] //Urban Remote Sensing Joint Event Shanghai,China:IEEE,2009:1-6.[53] Wald L.Some Terms of Reference in Data Fusion[J].IEEE Transactions on Geoscience and Remote Sensing,1999,37(3):1190-1193.[54] Zhang Y,Maxwell T.Roof Mapping Using Fused Multiresolution Optical Satellite Images[M] //Weng Q.Remote Sensing of Impervious Surfaces.Boca Raton,FL:CRC Press,2007.[55] Lu D,Moran E,Hetrick S.Detection of Impervious Surface Change with Multitemporal Landsat Images in an Urban-rural Frontier[J].ISPRS Journal of Photogrammetry and Remote Sensing,2011,66(3):298-306.[56] 肖荣波,欧阳志云,蔡云楠,等.基于亚像元估测的城市硬化地表景观格局分析[J].生态学报,2007,27(8):3189-3197.Xiao R B,Ouyang Z Y,Cai Y N,et al.Urban Landscape Pattern Study Based on Sub-pixel Estimation of Impervious Surface[J].Acta Ecologica Sinica,2007,27(8):3189-3197(in Chinese with English Abstract).[57] Yu B L,Liu H X,Wu J P,et al.Automated Derivation of Urban Building Density Information Using Airborne LiDAR Data and Object-based Method[J].Landscape and Urban Planning,2010,98(3/4):210-219.[58] Hodgson M E,Jensen J R,Tullis J A,et al.Synergistic Use of Lidar and Color Aerial Photography for Mapping Urban Parcel Imperviousness[J].Photogrammetric Engineering and Remote Sensing,2003,69(9):973-980.[59] Lu D S,Li G Y,Moran E,et al.Mapping Impervious Surfaces with the Integrated Use of Landsat Thematic Mapper and Radar Data:A Case Study in an Urban-rural Landscape in the Brazilian Amazon[J].ISPRS Journal of Photogrammetry and Remote Sensing,2011,66(6):798-808.
2012, Vol. 24 
