Analysis of population dynamics using satellite remote sensing and US census data

References

• Chander , G. and Markham , B. 2003 . Revised landsat-5 TM radiometric calibration procedures and postcalibration dynamic ranges . IEEE Transactions on Geoscience and Remote Sensing , 41 : 2674 – 2677 .
   Web of Science ®Google Scholar
• Dobson , J. E. 2000 . LandScan: a global population database for estimating populations at risk . Photogrammetric Engineering and Remote Sensing , 66 : 849 – 857 .
   Web of Science ®Google Scholar
• Dougherty , M. 2004 . Evaluation of impervious surface estimates in a rapidly urbanizing watershed . Photogrammetric Engineering and Remote Sensing , 70 : 1275 – 1284 .
   Web of Science ®Google Scholar
• Harris , R. J. and Longley , P. A. 2000 . New data and approaches for urban analysis, modelling residential densities . Transactions in GIS , 3 : 217 – 234 .
   Google Scholar
• Harvey , J. T. 2002a . Population estimation models based on individual TM pixels . Photogrammetric Engineering and Remote Sensing , 68 : 1181 – 1192 .
   Web of Science ®Google Scholar
• Harvey , J. T. 2002b . 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. 2003 . “ Population estimation at the pixel level: developing the expectation maximisation technique ” . In Remotely sensed cities , Edited by: Mesev , V. 181 – 205 . London : Taylor & Francis .
   Google Scholar
• Iisaka , J. and Hegedus , E. 1982 . Population estimation from landsat imagery . Remote sensing of environment , 12 : 259 – 272 .
   Web of Science ®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 .
   Google Scholar
• Lee , S. and Lathrop , R. G. 2006 . Subpixel analysis of landsat ETM+ using self-organising map (SOM) neural networks for urban land cover characterisation . IEEE Transactions on Geoscience and Remote Sensing , 44 : 1642 – 1654 .
   Web of Science ®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. 1986 . Applied remote sensing , 393 London : 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 . Modelling 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
• Lu , D. and Weng , Q. 2004 . Spectral mixture analysis of the urban landscape in Indianapolis city with landsat ETM+ imagery . Photogrammetric Engineering and Remote Sensing , 70 : 1053 – 1062 .
   Web of Science ®Google Scholar
• Lu , D. , Weng , Q. and Li , G. 2006 . Residential population estimation using remote sensing derived impervious surface . International Journal of Remote Sensing , 27 : 3553 – 3570 .
   Web of Science ®Google Scholar
• Martin , D. 1998 . 2001 Census output areas: from concept to prototype . Population Trends , 94 : 19 – 24 .
   PubMedGoogle Scholar
• Mennis , J. 2003 . Generating surface models of population using dasymetric mapping . Professional Geographer , 55 : 31 – 42 .
   Web of Science ®Google Scholar
• Metropolitan Council . 2004 . 2030 Regional development framework , Saint Paul, MN : Metropolitan Council .
   Google Scholar
• Metropolitan Council . 2006 . Generalised land use–historical 1984, 1990, 1997, 2000 and 2005, for the twin cities metropolitan area [online] Available from: http://www.datafinder.org/metadata/landuse_hist.htm [Accessed 13 October 2007]
   Google Scholar
• Phinn , S. 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
• Ridd , M. K. 1995 . Exploring a V-I-S (vegetation-impervious surface-soil) model for urban ecosystems analysis through remote sensing: comparative anatomy for cities . International Journal of Remote Sensing , 16 : 2165 – 2186 .
   Web of Science ®Google Scholar
• Roberts , D. A. 1998 . 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
• Tobler , W. R. 1969 . Satellite confirmation of settlement size coefficients . Area , 1 : 30 – 34 .
   Google Scholar
• U.S. Bureau of the Census . 1990 . State and local agencies preparing population and housing estimates Current population reports, series P-25, No. 1063. Washington, DC: U.S. Government Printing Office
   Google Scholar
• U.S. Bureau of the Census . 2001 . Census block groups cartographic boundary files descriptions and metadata [online] Available from: http://www.census.gov/geo/www/cob/bg_metadata.html [Accessed 10 June 2007]
   Google Scholar
• U.S. Bureau of the Census . 2005 . Estimates and projections area documentation state and county total population estimates [online] Available from: http://www.census.gov/popest/topics/methodology/2005_st_co_meth.html [Accessed 22 Dec 2007]
   Google Scholar
• U.S. Bureau of the Census . 2006 . US census press releases: nation's population to reach 300 million on October 17 [online] Available from: http://www.census.gov/Press-Release/www/releases/archives/population/007616.html [Accessed 22 Dec 2007]
   Google Scholar
• Weng Q. Remote sensing of impervious surfaces CRC Boca Ratan, FL 2007 454
   Google Scholar
• Wu , C. 2004 . Normalised 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
• Wu , C. and Murray , A. T. 2007 . Population estimation using landsat enhanced thematic mapper imagery . Geographical Analysis , 39 : 26 – 43 .
   Web of Science ®Google Scholar
• Wu , C. and Yuan , F. 2007 . Seasonal sensitivity analysis of impervious surface estimation with satellite imagery . Photogrammetric Engineering and Remote Sensing , 73 : 1393 – 1401 .
   Web of Science ®Google Scholar
• Wu , S. , Qiu , X. and Wang , L. 2005 . Population estimation methods in GIS and remote sensing: a review . GIScience and Remote Sensing , 42 : 58 – 74 .
   Google Scholar
• Yang , L. 2003 . An approach for mapping large-area impervious surfaces: synergistic use of landsat-7 ETM+ and high spatial resolution imagery . Canadian Journal of Remote Sensing , 29 : 230 – 240 .
   Web of Science ®Google Scholar
• Yuan , F. 2005 . Multi-level land cover mapping of the twin cities (Minnesota) metropolitan area with multi-seasonal landsat TM/ETM+ data . Geocarto International , 20 : 5 – 14 .
   Google Scholar
• Yuan , F. , Wu , C. and Bauer , M. E. 2008 . Comparison of various spectral analytical techniques for impervious surface estimation using landsat imagery . Photogrammetric Engineering and Remote Sensing , 74 : 1045 – 1055 .
   Web of Science ®Google Scholar
• Yuan , Y. , Smith , R. M. and Limp , W. F. 1997 . Remodeling census population with spatial information from landsat TM imagery . Computers, Environment and Urban Systems , 21 : 245 – 258 .
   Google Scholar