Landscape as a continuum: an examination of the urban landscape structures and dynamics of Indianapolis City, 1991–2000, by using satellite images

References

• Adams , J. B. , Sabol , D. E. , Kapos , V. , Filho , R. A. , Roberts , D. A. , Smith , M. O. and Gillespie , A. R. 1995 . Classification of multispectral images based on fractions of endmembers: application to land cover change in the Brazilian Amazon. . Remote Sensing of Environment , 52 : 137 – 154 .
   Web of Science ®Google Scholar
• Adams , J. B. , Smith , M. O. and Johnson , P. E. 1986 . Spectral mixture modeling: a new analysis of rock and soil types at the Viking Lander site. . Journal of Geophysical Research , 91 : 8098 – 8112 .
   Web of Science ®Google Scholar
• Aguiar , A. P. D. , Shimabukuro , Y. E. and Mascarenhas , N. D. A. 1999 . Use of synthetic bands derived from mixing models in the multispectral classification of remote sensing images. . International Journal of Remote Sensing , 20 : 647 – 657 .
   Web of Science ®Google Scholar
• American Planning Association . 2004 . Land‐Based Classification System , Washington, DC : American Planning Association . Available online at: www.planning.org/lbcs/ (accessed 20 January 2007)
   Google Scholar
• Anderson , J. R. , Hardy , E. E. , Roach , J. T. and Witmer , R. E. 1976 . A Land Use and Land Cover Classification System for Use with Remote Sensor Data , Washington, D.C. : US Government Printing Office . US Geological Survey Professional Paper 964
   Google Scholar
• Arnold , C. L., Jr. and Gibbons , C. J. 1996 . Impervious surface coverage: the emergence of a key environmental indicator. . Journal of the American Planning Association , 62 : 243 – 258 .
   Web of Science ®Google Scholar
• Asner , G. P. and Lobell , D. B. 2000 . A biogeophysical approach for automated SWIR unmixing of soils and vegetation. . Remote Sensing of Environment , 74 : 99 – 112 .
   Web of Science ®Google Scholar
• Auch , R. , Taylor , J. and Acevedo , W. 2004 . Urban Growth in American Cities: Glimpses of U.S. Urbanization U.S. Geological Survey Circular 1252, US Department of the Interior
   Google Scholar
• Betty , M. and Longley , P. 1994 . Fractal Cities , San Diego, CA : Academic Press .
   Google Scholar
• Boardman , J. W. 1993 . “ Automated spectral unmixing of AVIRIS data using convex geometry concepts. ” . In Summaries of the Fourth JPL Airborne Geoscience Workshop , 11 – 14 . Pasadena, CA : NASA Jet Propulsion Laboratory . JPL Publication 93‐26
   Google Scholar
• Boardman , J. W. and Kruse , F. A. 1994 . “ Automated spectral analysis: a geological example using AVIRIS data, north Grapevine Mountains, Nevada. ” . In Proceedings, ERIM Tenth Thematic Conference on Geologic Remote Sensing , 407 – 418 . Ann Arbor, , MI : Enviromental Research Institute of Michigan . 9–12 May 1994, Ann Arbor, MI
   Google Scholar
• Boardman , J. M. , Kruse , F. A. and Green , R. O. 1995 . “ Mapping target signature via partial unmixing of AVIRIS data. ” . In Summaries of the Fifth JPL Airborne Earth Science Workshop 23 – 26 . JPL Publication 95‐1, NASA Jet Propulsion Laboratory, Pasadena, CA
   Google Scholar
• Campbell , J. B. 1983 . Mapping the Land: Aerial Imagery for Land Use Information , Washington, DC : Association of American Geographers .
   Google Scholar
• Chavez , P. S., Jr. 1996 . Image‐based atmospheric corrections – revisited and improved. . Photogrammetric Engineering and Remote Sensing , 62 : 1025 – 1036 .
   Web of Science ®Google Scholar
• Clapham , W. B., Jr. 2003 . Continuum‐based classification of remotely sensed imagery to describe urban sprawl on a watershed scale. . Remote Sensing of Environment , 86 : 322 – 340 .
   Web of Science ®Google Scholar
• Clarke , K. C. and Gaydos , L. J. 1998 . Loose‐coupling a cellular automaton model and GIS: long‐term urban growth prediction for San Francisco and Washing/Baltimore. . International Journal of Geographic Information Science , 12 : 699 – 714 .
   PubMed Web of Science ®Google Scholar
• Cochrane , M. A. and Souza , C. M., Jr. 1998 . Linear mixture model classification of burned forests in the eastern Amazon. . International Journal of Remote Sensing , 19 : 3433 – 3440 .
   Web of Science ®Google Scholar
• Cracknell , A. P. 1998 . Synergy in remote sensing – what's in a pixel? . International Journal of Remote Sensing , 19 : 2025 – 2047 .
   Web of Science ®Google Scholar
• ENVI . 2000 . ENVI User's Guide , Boulder, CO : Research Systems Inc. .
   Google Scholar
• Epstein , J. , Payne , K. and Kramer , E. 2002 . Techniques for mapping suburban sprawl. . Photogrammetric Engineering and Remote Sensing , 63 : 913 – 918 .
   Google Scholar
• Fisher , P. 1997 . The pixel: a snare and a delusion. . International Journal of Remote Sensing , 18 : 679 – 685 .
   Web of Science ®Google Scholar
• Foody , G. M. 2002 . Status of land cover classification accuracy assessment. . Remote Sensing of Environment , 80 : 185 – 201 .
   Web of Science ®Google Scholar
• Forman , R. T. T. 1995 . Land Mosaics: The Ecology of Landscapes and Regions , Cambridge : Cambridge University Press .
   Google Scholar
• Garcia‐Haro , F. J. , Gilabert , M. A. and Melia , J. 1996 . Linear spectral mixture modeling to estimate vegetation amount from optical spectral data. . International Journal of Remote Sensing , 17 : 3373 – 3400 .
   Web of Science ®Google Scholar
• Gong , P. and Howarth , P. J. 1990 . The use of structure information for improving land‐cover classification accuracies at the rural–urban fringe. . Photogrammetric Engineering and Remote Sensing , 56 : 67 – 73 .
   Web of Science ®Google Scholar
• Gong , P. and Howarth , P. J. 1992 . Frequency‐based contextual classification and gray‐level vector reduction for land‐use identification. . Photogrammetric Engineering and Remote Sensing , 58 : 423 – 437 .
   Web of Science ®Google Scholar
• Green , A. A. , Berman , M. , Switzer , P. and Craig , M. D. 1988 . A transformation for ordering multispectral data in terms of image quality with implications for noise removal. . IEEE Transactions on Geoscience and Remote Sensing , 26 : 65 – 74 .
   Web of Science ®Google Scholar
• Herold , M. , Liu , X. and Clark , K. C. 2003 . Spatial metrics and image texture for mapping urban land use. . Photogrammetric Engineering and Remote Sensing , 69 : 991 – 1001 .
   Web of Science ®Google Scholar
• Herold , M. , Schiefer , S. , Hostert , P. and Roberts , D. A. 2006 . “ Applying imaging spectrometry in urban areas. ” . In Urban Remote Sensing , Edited by: Weng , Q and Quattrochi , D . 137 – 161 . Boca Raton, FL : CRC/Taylor & Francis .
   Google Scholar
• Homer , C. , Huang , C. , Yang , L. , Wylie , B. and Coan , M. 2004 . Development of a 2001 national land‐cover database for the United States. . Photogrammetric Engineering and Remote Sensing , 70 : 829 – 840 .
   Web of Science ®Google Scholar
• Jensen , J. R. 2005 . Introductory Digital Image Processing: A Remote Sensing Perspective , Upper Saddle River, NJ : Pearson Prentice Hall .
   Google Scholar
• Jensen , J. R. and Cowen , D. C. 1999 . Remote sensing of urban/suburban infrastructure and socio‐economic attributes. . Photogrammetric Engineering and Remote Sensing , 65 : 611 – 622 .
   Web of Science ®Google Scholar
• Ju , J. , Gopal , S. and Kolaczyk , E. D. 2005 . On the choice of spatial and categorical scale in remote sensing land cover classification. . Remote Sensing of Environment , 96 : 62 – 77 .
   Web of Science ®Google Scholar
• Lee , S. and Lathrop , R. G., Jr. 2005 . Sub‐pixel estimation of urban land cover components with linear mixture model analysis and Landsat Thematic Mapper imagery. . International Journal of Remote Sensing , 26 : 4885 – 4905 .
   Web of Science ®Google Scholar
• Lo , C. P. 1986 . Applied Remote Sensing , New York : Longman Inc .
   Google Scholar
• Lu , D. , Mausel , P. , Brondizio , E. and Moran , E. 2002 . Assessment of atmospheric correction methods for Landsat TM data applicable to Amazon basin LBA research. . International Journal of Remote Sensing , 23 : 2651 – 2671 .
   Web of Science ®Google Scholar
• Lu , D. and Weng , Q. 2004 . Spectral mixture analysis of the urban landscapes in Indianapolis with Landsat ETM+imagery. . Photogrammetric Engineering and Remote Sensing , 70 : 1053 – 1062 .
   Web of Science ®Google Scholar
• Lu , D. and Weng , Q. 2006a . Use of impervious surface in urban land use classification. . Remote Sensing of Environment , 102 : 146 – 160 .
   Web of Science ®Google Scholar
• Lu , D. and Weng , Q. 2006b . Spectral mixture analysis of ASTER imagery for examining the relationship between thermal features and biophysical descriptors in Indianapolis, Indiana. . Remote Sensing of Environment , 104 : 157 – 167 .
   Web of Science ®Google Scholar
• Madhavan , B. B. , Kubo , S. , Kurisaki , N. and Sivakumar , T. V. L. N. 2001 . Appraising the anatomy and spatial growth of the Bangkok Metropolitan area using a vegetation‐impervious‐soil model through remote sensing. . International Journal of Remote Sensing , 22 : 789 – 806 .
   Web of Science ®Google Scholar
• Mather , A. S. 1986 . Land Use , London : Longman .
   Google Scholar
• Mather , P. M. 1999 . “ Land cover classification revisited. ” . In Advances in Remote Sensing and GIS , Edited by: Atkinson , P. M and Tate , N. J . 7 – 16 . New York : John Wiley & Sons .
   Google Scholar
• McGwire , K. , Minor , T. and Fenstermaker , L. 2000 . Hyperspectral mixture modeling for quantifying sparse vegetation cover in arid environments. . Remote Sensing of Environment , 72 : 360 – 374 .
   Web of Science ®Google Scholar
• Myint , S. W. 2001 . A robust texture analysis and classification approach for urban land‐use and land‐cover feature discrimination. . Geocarto International , 16 : 27 – 38 .
   Google Scholar
• Oke , T. R. 1982 . The energetic basis of the urban heat island. . Quarterly Journal of the Royal Meteorological Society , 108 : 1 – 24 .
   Web of Science ®Google Scholar
• Okin , G. S. , Roberts , D. A. , Murray , B. and Okin , W. J. 2001 . Practical limits on hyperspectral vegetation discrimination in arid and semiarid environments. . Remote Sensing of Environment , 77 : 212 – 225 .
   Web of Science ®Google Scholar
• Phinn , S. , Stanford , M. , Scarth , P. , Murray , A. T. and Shyy , P. T. 2002 . Monitoring the composition of urban environments based on the vegetation‐impervious surface‐soil (VIS) model by subpixel analysis techniques. . International Journal of Remote Sensing , 23 : 4131 – 4153 .
   Web of Science ®Google Scholar
• Powell , R. L. , Roberts , D. A. , Dennison , P. E. and Hess , L. L. 2007 . Sub‐pixel mapping of urban land cover using multiple endmember spectral mixture analysis: Manaus, Brazil. . Remote Sensing of Environment , 106 : 253 – 267 .
   Web of Science ®Google Scholar
• Raptis , V. S. , Vaughan , R. A. and Wright , G. G. 2003 . The effect of scaling on land cover classification from satellite data. . Computers and Geosciences , 29 : 705 – 714 .
   Web of Science ®Google Scholar
• Rashed , T. , Weeks , J. R. , Roberts , D. A. , Rogan , J. and Powell , R. 2003 . Measuring the physical composition of urban morphology using multiple endmember spectral mixture models. . Photogrammetric Engineering and Remote Sensing , 69 : 1011 – 1020 .
   Web of Science ®Google Scholar
• Rashed , T. , Weeks , J. R. , Stow , D. and Fugate , D. 2005 . Measuring temporal compositions of urban morphology through spectral mixture analysis: toward a soft approach to change analysis in crowded cities. . International Journal of Remote Sensing , 26 : 699 – 718 .
   Web of Science ®Google Scholar
• Ridd , M. K. 1995 . Exploring a V‐I‐S (Vegetation‐Impervious Surface‐Soil) model for urban ecosystem analysis through remote sensing: comparative anatomy for cities. . International Journal of Remote Sensing , 16 : 2165 – 2185 .
   Web of Science ®Google Scholar
• Roberts , D. A. , Batista , G. T. , Pereira , J. L. G. , Waller , E. K. and Nelson , B. W. 1998 . “ Change identification using multitemporal spectral mixture analysis: applications in eastern Amazonia. ” . In Remote Sensing Change Detection: Environmental Monitoring Methods and Applications , Edited by: Lunetta , R. S and Elvidge , C. D . 137 – 161 . Chelsea, MI : Ann Arbor Press .
   Google Scholar
• Setiawan , H. , Mathieu , R. and Thompson‐Fawcett , M. 2006 . Assessing the applicability of the V‐I‐S model to map urban land use in the developing world: case study of Yogyakarta, Indonesia. . Computers, Environment and Urban Systems , 30 : 503 – 522 .
   Web of Science ®Google Scholar
• Shaban , M. A. and Dikshit , O. 2001 . Improvement of classification in urban areas by the use of textural features: the case study of Lucknow city, Uttar Pradesh. . International Journal of Remote Sensing , 22 : 565 – 593 .
   Web of Science ®Google Scholar
• Small , C. 2001 . Estimation of urban vegetation abundance by spectral mixture analysis. . International Journal of Remote Sensing , 22 : 1305 – 1334 .
   Web of Science ®Google Scholar
• Small , C. 2002 . Multitemporal analysis of urban reflectance. . Remote Sensing of Environment , 81 : 427 – 442 .
   Web of Science ®Google Scholar
• Small , C. 2004 . The Landsat ETM+spectral mixing space. . Remote Sensing of Environment , 93 : 1 – 17 .
   Web of Science ®Google Scholar
• Small , C. 2005 . A global analysis of urban reflectance. . International Journal of Remote Sensing , 26 : 661 – 681 .
   Web of Science ®Google Scholar
• Soil Conservation Service . 1975 . Urban Hydrology for Small Watersheds , Washington, DC : USDA Soil Conservation Service . Technical Release No. 55
   Google Scholar
• Song , C. 2005 . Spectral mixture analysis for subpixel vegetation fractions in the urban environment: how to incorporate endmember variability? . Remote Sensing of Environment , 95 : 248 – 263 .
   Web of Science ®Google Scholar
• Strahler , A. H. , Woodcock , C. E. and Smith , J. A. 1986 . On the nature of models in remote sensing. . Remote Sensing of Environment , 70 : 121 – 139 .
   Web of Science ®Google Scholar
• Stuckens , J. , Coppin , P. R. and Bauer , M. E. 2000 . Integrating contextual information with per‐pixel classification for improved land cover classification. . Remote Sensing of Environment , 71 : 282 – 296 .
   Web of Science ®Google Scholar
• Turner , B. L., II. , Skole , D. , Sanderson , S. , Fisher , G. , Fresco , L. and Leemans , R. 1995 . Land‐Use and Land‐Cover Change: Science and Research Plan , Stockholm and Geneva : International Geosphere‐Bioshere Program and the Human Dimensions of Global Environmental Change Program . IGBP Report No. 35 and HDP Report No. 7
   Google Scholar
• van der Meer , F. and de Jong , S. M. 2000 . Improving the results of spectral unmixing of Landsat Thematic Mapper imagery by enhancing the orthogonality of end‐members. . International Journal of Remote Sensing , 21 : 2781 – 2797 .
   Web of Science ®Google Scholar
• Wang , F. 1990 . Fuzzy supervised classification of remote sensing images. . IEEE Transactions on Geoscience and Remote Sensing , 28 : 194 – 201 .
   Web of Science ®Google Scholar
• Ward , D. , Phinn , S. R. and Murray , A. T. 2000 . Monitoring growth in rapidly urbanizing areas using remotely sensed data. . The Professional Geographer , 53 : 371 – 386 .
   Web of Science ®Google Scholar
• Welch , R. A. 1982 . Spatial resolution requirements for urban studies. . International Journal of Remote Sensing , 3 : 139 – 146 .
   Google Scholar
• Weng , Q. 1999 . Environmental Impacts of Land Use and Land Cover Change in the Zhujiang Delta, China: An Analysis Using an Integrated Approach of GIS, Remote Sensing, and Spatial Modeling PhD dissertation, University of Georgia, Athens, Georgia
   Google Scholar
• Weng , Q. , Lu , D. and Schubring , J. 2004 . Estimation of land surface temperature‐vegetation abundance relationship for urban heat island studies. . Remote Sensing of Environment , 89 : 467 – 483 .
   Web of Science ®Google Scholar
• Wilson , E. H. , Hurd , J. D. , Civco , D. L. , Prisloe , M. P. and Arnold , C. 2003 . Development of a geospatial model to quantify, describe and map urban growth. . Remote Sensing of Environment , 86 : 275 – 285 .
   Web of Science ®Google Scholar
• Wu , C. and Murray , A. T. 2003 . Estimating impervious surface distribution by spectral mixture analysis. . Remote Sensing of Environment , 84 : 93 – 505 .
   Web of Science ®Google Scholar
• Wu , J. G. and David , J. L. 2002 . A spatially explicit hierarchical approach to modeling complex ecological systems: theory and application. . Ecological Modelling , 153 : 7 – 26 .
   Web of Science ®Google Scholar
• Wu , J. G. , Jelinski , D. E. , Luck , M. and Tueller , P. T. 2000 . Multiscale analysis of landscape heterogeneity: scale variance and pattern metrics. . Geographic Information Sciences , 6 : 6 – 19 .
   Google Scholar
• Wu , J. W. , Xu , J. H. and Yue , W. Z. 2005 . V‐I‐S model for cities that are experiencing rapid urbanization and development. . Geoscience and Remote Sensing Symposium, IGARSS'05. Proceedings , 3 : 1503 – 1506 .
   Google Scholar