Residential population estimation using a remote sensing derived impervious surface approach

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
• Barnsley , M. J. , Steel , A. M. and Barr , S. L. 2003 . “ Determining urban land use through an analysis of the spatial composition of buildings identified in LIDAR and multispectral image data. ” . In Remotely Sensed Cities , Edited by: Mesev , V . 83 – 108 . London : Taylor & Francis .
   Google Scholar
• Bateson , A. and Curtiss , B. 1996 . A method for manual endmember selection and spectral unmixing. . Remote Sensing of Environment , 55 : 229 – 243 .
   Web of Science ®Google Scholar
• Bauer , M. E. , Heinert , N. J. , Doyle , J. K. and Yuan , F. Impervious surface mapping and change monitoring using Landsat remote sensing. ASPRS Annual Conference Proceedings ,
   Google Scholar
• Dennison , P. E. and Roberts , D. A. 2003 . Endmember selection for multiple endmember spectral mixture analysis using endmember average RMSE. . Remote Sensing of Environment , 87 : 123 – 135 .
   Web of Science ®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
• Garcia‐Haro , F. J. , Gilabert , M. A. and Melia , J. 1999 . Extraction of endmembers from spectral mixtures. . Remote Sensing of Environment , 68 : 237 – 253 .
   Web of Science ®Google Scholar
• Harvey , J. T. 2002a . Estimating census district populations from satellite imagery: some approaches and limitations. . International Journal of Remote Sensing , 23 : 2071 – 2095 .
   Web of Science ®Google Scholar
• Harvey , J. T. 2002b . Population estimation models based on individual TM pixels. . Photogrammetric Engineering and Remote Sensing , 68 : 1181 – 1192 .
   Web of Science ®Google Scholar
• Harvey , J. T. 2003 . “ Population estimation at the pixel level: developing the expectation maximization technique. ” . In Remotely Sensed Cities , Edited by: Mesev , V . 181 – 205 . London : Taylor & Francis .
   Google Scholar
• Langford , M. , Maguire , D. J. and Unwin , D. J. 1991 . “ The areal interpolation problem: estimating population using remote sensing in a GIS framework. ” . In Handing Geographical Information: Methodology and Potential Applications , Edited by: Masser , L and Blakemore , M . 55 – 77 . New York : Longman Scientific & Technical/John Wiley & Sons, Inc. .
   Google Scholar
• Li , G. and Weng , Q. 2005 . Using Landsat ETM+ imagery to measure population density in Indianapolis, Indiana, USA. . Photogrammetric Engineering and Remote Sensing , 71 : 947 – 958 .
   Web of Science ®Google Scholar
• Lo , C. P. 1986a . Accuracy of population estimation from medium‐scale aerial photography. . Photogrammetric Engineering and Remote Sensing , 52 : 1859 – 1869 .
   Web of Science ®Google Scholar
• Lo , C. P. 1986b . Applied Remote Sensing , New York : Longman .
   Google Scholar
• Lo , C. P. 1995 . Automated population and dwelling unit estimation from high resolution satellite images: a GIS approach. . International Journal of Remote Sensing , 16 : 17 – 34 .
   Web of Science ®Google Scholar
• Lo , C. P. 2001 . Modeling the population of China using DMSP operational linescan system nighttime data. . Photogrammetric Engineering and Remote Sensing , 67 : 1037 – 1047 .
   Web of Science ®Google Scholar
• Lo , C. P. 2003 . “ Zone‐based estimation of population and housing units from satellite‐generated land use/land cover maps. ” . In Remotely Sensed Cities , Edited by: Mesev , V . 157 – 180 . London : Taylor & Francis .
   Google Scholar
• Lo , C. P. and Welch , R. 1977 . Chinese urban population estimation. . Annals of the Association of American Geographers , 67 : 246 – 253 .
   Web of Science ®Google Scholar
• Lu , D. , Mausel , P. , Brondízio , 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
• Maselli , F. 2001 . Definition of spatially variable spectral endmembers by locally calibrated multivariate regression analysis. . Remote Sensing of Environment , 75 : 29 – 38 .
   Web of Science ®Google Scholar
• Maas , H.‐G. and Vosselman , G. 1999 . Two algorithms for extracting building models from raw laser altimetry data. . ISPRS Journal of Photogrammetry and Remote Sensing , 54 : 153 – 163 .
   Web of Science ®Google Scholar
• Mustard , J. F. and Sunshine , J. M. 1999 . “ Spectral analysis for earth science: investigations using remote sensing data. ” . In Remote Sensing for the Earth Sciences: Manual of Remote Sensing, , 3 , Edited by: Rencz , A. N . Vol. 3 , 251 – 307 . New York : John Wiley & Sons Inc. .
   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
• Painter , T. H. , Roberts , D. A. , Green , R. O. and Dozier , J. 1998 . The effects of grain size on spectral mixture analysis of snow‐covered area from AVIRIS data. . Remote Sensing of Environment , 65 : 320 – 332 .
   Web of Science ®Google Scholar
• Quarmby , N. A. , Townshend , J. R. G. , Settle , J. J. and White , K. H. 1992 . Linear mixture modeling applied to AVHRR data for crop area estimation. . International Journal of Remote Sensing , 13 : 415 – 425 .
   Web of Science ®Google Scholar
• Rashed , T. , Weeks , J. R. , Gadalla , M. S. and Hill , A. G. 2001 . Revealing the anatomy of cities through spectral mixture analysis of multispectral satellite imagery: a case study of the Greater Cairo region, Egypt. . Geocarto International , 16 : 5 – 15 .
   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. 1998a . “ 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 . Ann Arbor, MI : Ann Arbor Press .
   Google Scholar
• Roberts , D. A. , Gardner , M. , Church , R. , Ustin , S. , Scheer , G. and Green , R. O. 1998b . Mapping chaparral in the Santa Monica mountains using multiple endmember spectral mixture models. . Remote Sensing of Environment , 65 : 267 – 279 .
   Web of Science ®Google Scholar
• Settle , J. J. and Drake , N. A. 1993 . Linear mixing and the estimation of ground cover proportions. . International Journal of Remote Sensing , 14 : 1159 – 1177 .
   Web of Science ®Google Scholar
• Shimabukuro , Y. E. and Smith , J. A. 1991 . The least‐squares mixing models to generate fraction images derived from remote sensing multispectral data. . IEEE Transactions on Geoscience and Remote Sensing , 29 : 16 – 20 .
   Web of Science ®Google Scholar
• Smith , M. O. , Ustin , S. L. , Adams , J. B. and Gillespie , A. R. 1990 . Vegetation in Deserts: I. A regional measure of abundance from multispectral images. . Remote Sensing of Environment , 31 : 1 – 26 .
   Web of Science ®Google Scholar
• Sutton , P. , Roberts , D. , Elvidge , C. D. and Baugh , K. 2001 . Census from heaven: an estimate of the global human population using night‐time satellite imagery. . International Journal of Remote Sensing , 22 : 3061 – 3076 .
   Web of Science ®Google Scholar
• Sutton , P. , Roberts , D. , Elvidge , C. D. and Meij , H. 1997 . A comparison of nighttime satellite imagery and population density for the continental United States. . Photogrammetric Engineering and Remote Sensing , 63 : 1303 – 1313 .
   Web of Science ®Google Scholar
• Theseira , M. A. , Thomas , G. , Taylor , J. C. , Gemmell , F. and Varjo , J. 2003 . Sensitivity of mixture modeling to endmember selection. . International Journal of Remote Sensing , 24 : 1559 – 1575 .
   Web of Science ®Google Scholar
• Tompkins , S. , Mustard , J. F. , Pieters , C. M. and Forsyth , D. W. 1997 . Optimization of endmembers for spectral mixture analysis. . Remote Sensing of Environment , 59 : 472 – 489 .
   Web of Science ®Google Scholar
• Van der Meer , F. 1999 . Iterative spectral unmixing (ISU). . International Journal of Remote Sensing , 20 : 3431 – 3436 .
   Web of Science ®Google Scholar
• Welch , R. and Zupko , S. 1980 . Urbanized area energy utilization patterns from DMSP data. . Photogrammetric Engineering and Remote Sensing , 46 : 1107 – 1121 .
   Web of Science ®Google Scholar
• Wu , C. 2004 . Normalized spectral mixture analysis for monitoring urban composition using ETM+ imagery. . Remote Sensing of Environment , 93 : 480 – 492 .
   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 : 493 – 505 .
   Web of Science ®Google Scholar