Using DMSP OLS Imagery to Characterize Urban Populations in Developed and Developing Countries

  • Paul C. Sutton
  • Matthew J. Taylor
  • Christopher D. Elvidge
Part of the Remote Sensing and Digital Image Processing book series (RDIP, volume 10)


Nighttime Satellite imagery shows great potential for mapping and monitoring many human activities including: (1) population size, distribution, and growth, (2) urban extent and rates of urbanization, (3) impervious surface, (4) energy consumption, and (5) CO2 emissions. Surprisingly the relatively coarse spectral, spatial, and temporal resolution of the imagery proves to be an advantage rather than a disadvantage for these applications.


Impervious Surface Landsat Imagery Modifiable Areal Unit Problem Urban Cluster Guatemala City 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Aker HR (1969) A typology of ecological fallacies. In: Rokkam S, Dogan M (eds) Quantitative ecological analysis in the social sciences. MIT Press, Cambridge, MA, pp 69–86Google Scholar
  2. Cova TJ, Sutton PC, Theobald DM (2004) Exurban change detection in fire-prone areas with nighttime satellite imagery. Photogramm Eng Remote Sens 70(11):1249–1257Google Scholar
  3. Dickinson LC, Boselly SE, Burgmann WW (1974) Defense Meteorological Satellite Program User’s Guide, Air Weather Service (MAC), U.S. Air ForceGoogle Scholar
  4. Dobson JE, Bright EA, Coleman PR, Durfee RC, Worley BA (2000) LandScan: a global population database for estimating populations at risk. Photogramm Eng Remote Sens 66(7):849–857Google Scholar
  5. Doll C, Muller JP, Elvidge CD (2000) Night-time imagery as a tool for global mapping of socio-economic parameters and greenhouse gas emissions. Ambio 29(3):159–174Google Scholar
  6. Elvidge CD, Baugh K, Kihn E, Kroehl H, Davis E, Davis C (1996) Relation between satellite observed visible–near infrared emissions, population, economic activity, and electric power consumption. Int J Remote Sens 18:1373–1379CrossRefGoogle Scholar
  7. Elvidge CD, Baugh KE, Kihn K, Kroehl H, Davis E (1997) Mapping city lights with nighttime data from the DMSP operational linescan system. Photogramm Eng Remote Sens 63(June):727–734Google Scholar
  8. Elvidge CD, Baugh K, Dietz JB, Bland T, Sutton PC, Kroehl H (1998) Radiance Calibration of DMSP-OLS low-light imaging data of human settlements. Remote Sens Environ 68(1):77–88CrossRefGoogle Scholar
  9. Elvidge CD, Milesi C, Dietz JB, Tuttle BT, Sutton PC, Nemani R, Vogelmann JE (2004) U.S. constructed area approaches the size of Ohio. EOS Trans Am Geophys Union 85:2333CrossRefGoogle Scholar
  10. Foster JL (1983) Observations of the Earth using nighttime visible imagery. Int J Remote Sens 4:785–791CrossRefGoogle Scholar
  11. Imhoff ML, Lawrence WT, Stutzer DC, Elvidge CD (1997) A technique for using composite DMSP/OLS city lights satellite data to map urban area. Remote Sens Environ 61(3):361–370CrossRefGoogle Scholar
  12. Lo CP (2001) Modeling the population of China using DMSP OLS nighttime data. Photogramm Eng Remote Sens 67:1037–1047Google Scholar
  13. Lo CP (2002) Urban indicators of China from radiance calibrated digital DMSP-OLS nighttime images. Ann Assoc Am Geogr 92(2):225–240CrossRefGoogle Scholar
  14. Long L, Nucci A (1997) The clean break revisited: is U.S. population again deconcentrating? Environ Plann A 29(8):1355–1366CrossRefGoogle Scholar
  15. Nelson AC, Sanchez TW (1999) Debunking the exurban myth: a comparison of suburban households. Hous Policy Debate 10(3):689–709Google Scholar
  16. Stewart J, Warntz W (1958) Physics of population distribution. J Reg Sci 1:99–123CrossRefGoogle Scholar
  17. Sutton PC (1997) Modeling population density with nighttime satellite imagery and GIS. Comput Environ Urban Syst 21(3/4):227–244CrossRefGoogle Scholar
  18. Sutton PC (1999) Census from heaven: estimation of human population parameters using nighttime satellite imagery. Int J Remote Sens 22:3061–3076CrossRefGoogle Scholar
  19. Sutton PC (2003) A scale-adjusted measure of Urban Sprawl using nighttime satellite imagery. Remote Sens Environ 86(3):353–363CrossRefGoogle Scholar
  20. Sutton PC, Costanza R (2002) Global estimates of market and non-market values derived from nighttime satellite imagery, land cover, and ecosystem service valuation. Ecol Econ 41:509–527CrossRefGoogle Scholar
  21. Sutton PC, Roberts D, Elvidge CD, Meij H (1997) A comparison of nighttime satellite imagery and population density for the continental United States. Photogramm Eng Remote Sens 63(11):1303–1313Google Scholar
  22. Sutton PC, Elvidge CD, Obremski T (2003) Building and evaluating models to estimate ambient population density. Photgramm Eng Remote Sens 69(5):545–553Google Scholar
  23. Taylor MJ (2005) Electrifying rural Guatemala: central policy and local reality. Environ Plann C 23(2):173–189CrossRefGoogle Scholar
  24. Tobler W (1969) Satellite confirmation of settlement size coefficients. Area 1:30–34Google Scholar
  25. Todaro M (1994) Economic development, 5th edn. Longman, New YorkGoogle Scholar
  26. Vogelmann JE, Limin Y, Larson CR, Wylie BK, Van DN (2001) Completion of the 1990s national land cover data set for the conterminous United States from Landsat thematic mapper data and ancillary data sources. Photogramm Eng Remote Sens 67(6):650–662Google Scholar
  27. Welch R (1980a) Monitoring urban population and energy utilization patterns from satellite data. Remote Sens Environ 9(1):1–9CrossRefGoogle Scholar
  28. Welch R (1980b) Urbanized area energy utilization patterns from DMSP data. Photogramm Eng Remote Sens 46(2):201–207Google Scholar

Copyright information

© Springer Netherlands 2010

Authors and Affiliations

  • Paul C. Sutton
    • 1
  • Matthew J. Taylor
    • 2
  • Christopher D. Elvidge
    • 3
  1. 1.Department of GeographyUniversity of DenverDenverUSA
  2. 2.Department of GeographyUniversity of DenverDenverUSA
  3. 3.Earth Observation GroupNOAA National Geophysical Data CenterBoulderUSA

Personalised recommendations