Methods to extract impervious surface areas from satellite images

Impervious surface area (ISA) is an important parameter for many environmental or socioeconomic relevant studies. The unique characteristics of remote sensing data made it the primary data source for ISA mapping at various scales. This paper summarizes general ISA mapping procedure and major techniques and discusses impacts of scale issues on selection of remote sensing data and corresponding algorithms. Previous studies have indicated that ISA mapping remains a challenge, especially in urban–rural frontiers and in covering a large area. Effectively employing rich spatial information in high spatial resolution imagery through texture and object-based methods is valuable. Data fusion of multi-resolution images and spectral mixture analysis are common approaches to reduce the mixed pixel problem in medium spatial resolution images such as Landsat. Coarse spatial resolution images such as MODIS and DMSP-OLS are valuable for national and global ISA mapping but more research is needed to effectively integrate multisource/scale data for improving mapping performance. Development of an optimal procedure corresponding to specific study areas and purposes is required to generate accurate ISA mapping results.

[1]  Austin Troy,et al.  Object-based Land Cover Classification and Change Analysis in the Baltimore Metropolitan Area Using Multitemporal High Resolution Remote Sensing Data , 2008, Sensors.

[2]  A. Rango,et al.  Object-oriented image analysis for mapping shrub encroachment from 1937 to 2003 in southern New Mexico , 2004 .

[3]  Qihao Weng,et al.  A survey of image classification methods and techniques for improving classification performance , 2007 .

[4]  Yunhao Chen,et al.  The utility of texture analysis to improve per-pixel classification for CBERS02's CCD image , 2006, Geoinformatics.

[5]  Changshan Wu,et al.  Quantifying high‐resolution impervious surfaces using spectral mixture analysis , 2009 .

[6]  George Xian,et al.  Satellite remotely-sensed land surface parameters and their climatic effects for three metropolitan regions , 2008 .

[7]  Martin Herold,et al.  Some challenges in global land cover mapping : An assessment of agreement and accuracy in existing 1 km datasets , 2008 .

[8]  Anne Puissant,et al.  The utility of texture analysis to improve per‐pixel classification for high to very high spatial resolution imagery , 2005 .

[9]  Corina da Costa Freitas,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 , 2011 .

[10]  M. Bauer,et al.  Estimating and Mapping Impervious Surface Area by Regression Analysis of Landsat Imagery , 2007 .

[11]  Joseph F. Knight,et al.  Mapping Impervious Cover Using Multi-Temporal MODIS NDVI Data , 2011, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[12]  P. Gong,et al.  Comparison of IKONOS and QuickBird images for mapping mangrove species on the Caribbean coast of Panama , 2004 .

[13]  Russell G. Congalton,et al.  Assessing the accuracy of remotely sensed data : principles and practices , 1998 .

[14]  Thomas Blaschke,et al.  Object based image analysis for remote sensing , 2010 .

[15]  K. Seto,et al.  Mapping urbanization dynamics at regional and global scales using multi-temporal DMSP/OLS nighttime light data , 2011 .

[16]  D. Lu,et al.  Estimation of land surface temperature-vegetation abundance relationship for urban heat island studies , 2004 .

[17]  John B. Vogler,et al.  LiDAR-Landsat data fusion for large-area assessment of urban land cover: Balancing spatial resolution, data volume and mapping accuracy , 2012 .

[18]  T. Esch,et al.  Settlement detection and impervious surface estimation in the Mekong Delta using optical and SAR remote sensing data , 2011 .

[19]  Elizabeth Brabec,et al.  Impervious Surfaces and Water Quality: A Review of Current Literature and Its Implications for Watershed Planning , 2002 .

[20]  T. Esch,et al.  Large-area assessment of impervious surface based on integrated analysis of single-date Landsat-7 images and geospatial vector data , 2009 .

[21]  Dengsheng Lu,et al.  Detection of impervious surface change with multitemporal Landsat images in an urban-rural frontier. , 2011, ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing.

[22]  Giorgos Mountrakis,et al.  Converting local spectral and spatial information from a priori classifiers into contextual knowledge for impervious surface classification , 2011 .

[23]  D. Lu,et al.  Spectral mixture analysis of ASTER images for examining the relationship between urban thermal features and biophysical descriptors in Indianapolis, Indiana, USA , 2006 .

[24]  C. Deng,et al.  A spatially adaptive spectral mixture analysis for mapping subpixel urban impervious surface distribution , 2013 .

[25]  Dengsheng Lu,et al.  Impervious surface mapping with Quickbird imagery , 2011, International journal of remote sensing.

[26]  Xuefei Hu,et al.  Estimating impervious surfaces using linear spectral mixture analysis with multitemporal ASTER images , 2009 .

[27]  Wang Hao,et al.  Advances in Remote Sensing of Impervious Surfaces Extraction and Its Applications , 2012 .

[28]  Luciano Vieira Dutra,et al.  A comparative analysis of ALOS PALSAR L-band and RADARSAT-2 C-band data for land-cover classification in a tropical moist region , 2012 .

[29]  C. Homer,et al.  Updating the 2001 National Land Cover Database Impervious Surface Products to 2006 using Landsat Imagery Change Detection Methods , 2010 .

[30]  R. Nemani,et al.  Global Distribution and Density of Constructed Impervious Surfaces , 2007, Sensors.

[31]  T. Schueler.  The importance of imperviousness , 1995 .

[32]  Zhifeng Liu,et al.  Extracting the dynamics of urban expansion in China using DMSP-OLS nighttime light data from 1992 to 2008 , 2012 .

[33]  Uwe Stilla,et al.  Remote Sensing of Impervious Surfaces , 2007 .

[34]  D. Quattrochi,et al.  Thermal remote sensing of urban areas: An introduction to the special issue , 2006 .

[35]  George Xian,et al.  Quantifying Multi-temporal Urban Development Characteristics in Las Vegas from Landsat and ASTER Data , 2008 .

[36]  D. B. Jennings,et al.  Changes in anthropogenic impervious surfaces, precipitation and daily streamflow discharge: a historical perspective in a mid-atlantic subwatershed , 2002, Landscape Ecology.

[37]  K. Seto,et al.  The Vegetation Adjusted NTL Urban Index: A new approach to reduce saturation and increase variation in nighttime luminosity , 2013 .

[38]  D. Lu,et al.  Extraction of urban impervious surfaces from an IKONOS image , 2009 .

[39]  Alan T. Murray,et al.  Monitoring the composition of urban environments based on the vegetation-impervious surface-soil (VIS) model by subpixel analysis techniques , 2002 .

[40]  Alan T. Murray,et al.  Estimating impervious surface distribution by spectral mixture analysis , 2003 .

[41]  Dengsheng Lu,et al.  Regional mapping of human settlements in southeastern China with multisensor remotely sensed data , 2008 .

[42]  P. Sutton,et al.  Paving the planet: impervious surface as proxy measure of the human ecological footprint , 2009 .

[43]  T. Pei,et al.  Quantitative estimation of urbanization dynamics using time series of DMSP/OLS nighttime light data: A comparative case study from China's cities , 2012 .

[44]  Changshan Wu,et al.  Normalized spectral mixture analysis for monitoring urban composition using ETM+ imagery , 2004 .

[45]  M. Bauer,et al.  Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery , 2007 .

[46]  J. Townshend,et al.  Urban growth of the Washington, D.C.–Baltimore, MD metropolitan region from 1984 to 2010 by annual, Landsat-based estimates of impervious cover , 2013 .

[47]  Toby N. Carlson,et al.  The impact of land use — land cover changes due to urbanization on surface microclimate and hydrology: a satellite perspective , 2000 .

[48]  Dengsheng Lu,et al.  Mapping impervious surface area in the Brazilian Amazon using Landsat Imagery , 2013, GIScience & remote sensing.

[49]  D. Lu,et al.  Spectral Mixture Analysis of the Urban Landscape in Indianapolis with Landsat ETM+ Imagery , 2004 .

[50]  Qihao Weng,et al.  Medium Spatial Resolution Satellite Imagery for Estimating and Mapping Urban Impervious Surfaces Using LSMA and ANN , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[51]  J. A. Tullis,et al.  Synergistic Use of Lidar and Color Aerial Photography for Mapping Urban Parcel Imperviousness , 2003 .

[52]  M. Friedl,et al.  Mapping global urban areas using MODIS 500-m data: new methods and datasets based on 'urban ecoregions'. , 2010 .

[53]  M. Friedl,et al.  A new map of global urban extent from MODIS satellite data , 2009 .

[54]  Jürgen Symanzik,et al.  Effects of urbanization on the aquatic fauna of the Line Creek watershed, Atlanta—a satellite perspective , 2003 .

[55]  Limin Yang,et al.  An approach for mapping large-area impervious surfaces: synergistic use of Landsat-7 ETM+ and high spatial resolution imagery , 2003 .

[56]  M. Zug,et al.  Pollution wash-off modelling on impervious surfaces : Calibration, validation, transposition , 1999 .

[57]  L. Bounoua,et al.  Remote sensing of the urban heat island effect across biomes in the continental USA , 2010 .

[58]  E. Terrence Slonecker,et al.  Remote sensing of impervious surfaces: A review , 2001 .

[59]  William J. Emery,et al.  A neural network approach using multi-scale textural metrics from very high-resolution panchromatic imagery for urban land-use classification , 2009 .

[60]  Chad Hendrix,et al.  A Comparison of Urban Mapping Methods Using High-Resolution Digital Imagery , 2003 .

[61]  Juan Carlos Duque,et al.  A review of regional science applications of satellite remote sensing in urban settings , 2013, Comput. Environ. Urban Syst..

[62]  Alan H. Strahler,et al.  On the nature of models in remote sensing , 1986 .

[63]  Bunkei Matsushita,et al.  A pre-screened and normalized multiple endmember spectral mixture analysis for mapping impervious surface area in Lake Kasumigaura Basin, Japan , 2010 .

[64]  F. Canters,et al.  Mapping form and function in urban areas: An approach based on urban metrics and continuous impervious surface data , 2011 .

[65]  A. Cracknell.  Review article Synergy in remote sensing-what's in a pixel? , 1998 .

[66]  O. Dikshit,et al.  Improvement of classification in urban areas by the use of textural features: The case study of Lucknow city, Uttar Pradesh , 2001 .

[67]  Chunyang He,et al.  Timely and accurate national-scale mapping of urban land in China using Defense Meteorological Satellite Program’s Operational Linescan System nighttime stable light data , 2013 .

[68]  Bunkei Matsushita,et al.  Temporal mixture analysis for estimating impervious surface area from multi-temporal MODIS NDVI data in Japan , 2012 .

[69]  Michael A. Wulder,et al.  An accuracy assessment framework for large‐area land cover classification products derived from medium‐resolution satellite data , 2006 .

[70]  J. Weeks,et al.  Revealing the Anatomy of Cities through Spectral Mixture Analysis of Multispectral Satellite Imagery: A Case Study of the Greater Cairo Region, Egypt. , 2001 .

[71]  Lindi J. Quackenbush,et al.  Impervious surface quantification using a synthesis of artificial immune networks and decision/regression trees from multi-sensor data , 2012 .

[72]  J. Chan,et al.  Mapping impervious surfaces from superresolution enhanced CHRIS/Proba imagery using multiple endmember unmixing , 2012 .

[73]  Hanqiu Xu,et al.  Analysis of Impervious Surface and its Impact on Urban Heat Environment using the Normalized Difference Impervious Surface Index (NDISI) , 2010 .

[74]  Frank Canters,et al.  Mapping impervious surface change from remote sensing for hydrological modeling , 2013 .

[75]  M. A. Aguilar,et al.  Using texture analysis to improve per-pixel classification of very high resolution images for mapping plastic greenhouses , 2008 .

[76]  John R. Jensen,et al.  Introductory Digital Image Processing: A Remote Sensing Perspective , 1986 .

[77]  D. Roberts,et al.  Sub-pixel mapping of urban land cover using multiple endmember spectral mixture analysis: Manaus, Brazil , 2007 .

[78]  Nikos Koutsias,et al.  Object-based classification using Quickbird imagery for delineating forest vegetation polygons in a Mediterranean test site , 2008 .

[79]  Guiying Li,et al.  Comparative analysis of classification algorithms and multiple sensor data for land use/land cover classification in the Brazilian Amazon , 2012 .

[80]  Vinod K. Lohani,et al.  CONSTRUCTING A PROBLEM SOLVING ENVIRONMENT TOOL FOR HYDROLOGIC ASSESSMENT OF LAND USE CHANGE , 2002 .

[81]  Hanqiu Xu,et al.  Remote sensing of the urban heat island and its changes in Xiamen City of SE China. , 2004, Journal of environmental sciences.

[82]  Xuefei Hu,et al.  Estimating impervious surfaces from medium spatial resolution imagery using the self-organizing map and multi-layer perceptron neural networks. , 2009 .

[83]  Simon J. Hook,et al.  Synergies Between VSWIR and TIR Data for the Urban Environment: An Evaluation of the Potential for the Hyperspectral Infrared Imager (HyspIRI) , 2012 .

[84]  S. Goetz,et al.  IKONOS imagery for resource management: Tree cover, impervious surfaces, and riparian buffer analyses in the mid-Atlantic region , 2003 .

[85]  Xuefei Hu,et al.  Impervious surface area extraction from IKONOS imagery using an object-based fuzzy method , 2011 .

[86]  Huadong Guo,et al.  Earth observation satellite data receiving, processing system and data sharing , 2012, Int. J. Digit. Earth.

[87]  S. Taylor Jarnagin,et al.  A Modeling Approach for Estimating Watershed Impervious Surface Area from National Land Cover Data 92 , 2004 .

[88]  F. Canters,et al.  A comparison of two spectral mixture modelling approaches for impervious surface mapping in urban areas , 2009 .

[89]  Jixian Zhang.  Multi-source remote sensing data fusion: status and trends , 2010 .

[90]  D. Lu,et al.  Residential population estimation using a remote sensing derived impervious surface approach , 2006 .

[91]  Christine Pohl,et al.  Multisensor image fusion in remote sensing: concepts, methods and applications , 1998 .

[92]  Qihao Weng,et al.  Remote sensing of impervious surfaces in the urban areas: Requirements, methods, and trends , 2012 .

[93]  M. Ridd.  Exploring a V-I-S (vegetation-impervious surface-soil) model for urban ecosystem analysis through remote sensing: comparative anatomy for cities , 1995 .

[94]  D. Lu,et al.  Use of impervious surface in urban land-use classification , 2006 .

[95]  C. Jacobson.  Identification and quantification of the hydrological impacts of imperviousness in urban catchments: a review. , 2011, Journal of environmental management.

[96]  Liming Jiang,et al.  Quantifying Sub-pixel Urban Impervious Surface through Fusion of Optical and InSAR Imagery , 2009 .

[97]  C. Arnold,et al.  IMPERVIOUS SURFACE COVERAGE: THE EMERGENCE OF A KEY ENVIRONMENTAL INDICATOR , 1996 .

[98]  J. R. Jensen,et al.  Effectiveness of Subpixel Analysis in Detecting and Quantifying Urban Imperviousness from Landsat Thematic Mapper Imagery , 1999 .

[99]  S. Linden,et al.  The influence of urban structures on impervious surface maps from airborne hyperspectral data. , 2009 .

[100]  Dengsheng Lu,et al.  Spatiotemporal dynamics of impervious surface areas across China during the early 21st century , 2013 .

[101]  George Xian,et al.  Assessments of urban growth in the Tampa Bay watershed using remote sensing data , 2005 .

[102]  Scott L. Powell,et al.  Quantification of impervious surface in the Snohomish Water Resources Inventory Area of Western Washington from 1972–2006 , 2007 .

[103]  Dengsheng Lu,et al.  Mapping Impervious Surface Distribution with the Integration of Landsat TM and QuickBird Images in a Complex Urban–Rural Frontier in Brazil , 2012 .

[104]  M. Alberti,et al.  The impact of urban patterns on aquatic ecosystems: An empirical analysis in Puget lowland sub-basins , 2007 .

[105]  Michael A. Wulder,et al.  Opening the archive: How free data has enabled the science and monitoring promise of Landsat , 2012 .

[106]  Osamu Higashi,et al.  A SVM-based method to extract urban areas from DMSP-OLS and SPOT VGT data , 2009 .

[107]  C. Elvidge,et al.  A Technique for Using Composite DMSP/OLS "City Lights"Satellite Data to Map Urban Area , 1997 .

[108]  G. Asner,et al.  Cloud cover in Landsat observations of the Brazilian Amazon , 2001 .

[109]  John R. Weeks,et al.  Measuring the Physical Composition of Urban Morphology Using Multiple Endmember Spectral Mixture Models , 2003 .