The V-I-S Model: Quantifying the Urban Environment

Part of the Remote Sensing and Digital Image Processing book series (RDIP, volume 10)


This chapter emphasizes the ecological nature of urban places and introduces the V-I-S (Vegetation-Impervious surface-Soil) model for use by remote sensing to characterize, map, and quantify the ecological composition of urban/peri-urban environments. The model serves not only as a basis for biophysical and human system analysis, but also serves as a basis for detecting and measuring morphological/environmental change of urban places over time.


Land Cover Cover Type Thematic Mapper Land Cover Type Impervious Surface 
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. Burgess EW (1925) The growth of the city. In: Park RE, Burgess EW, McKenzie RD (eds) The city. University of Chicago Press, Chicago, ILGoogle Scholar
  2. Card DH (1993) Examination of a simple surface composition model of the urban environment using remote sensing. Doctoral dissertation, The Department of Geography, The University of Utah, Salt Lake City, UtahGoogle Scholar
  3. Chen J (1996) Satellite image processing for urban land cover composition analysis and runoff estimation. Doctoral dissertation, The Department of Geography, University of Utah, Salt Lake City, UtahGoogle Scholar
  4. Chung JM (1989) SPOT Pixel analysis for urban ecosystem study in Salt Lake City, Utah. Master’s thesis, The Department of Geography, The University of Utah, Salt Lake City, UtahGoogle Scholar
  5. Dorney RS, McLellan PW (1984) The urban ecosystem: its spatial structure, its scale relationships, and its subsystem attributes. Environment 16:9–20Google Scholar
  6. Douglas I (1983) The urban environment. Edward Arnold, LondonGoogle Scholar
  7. Forster BC (1985) An examination of some problems and solutions in monitoring urban areas from satellite platforms. Int J Remote Sens 6:139–151CrossRefGoogle Scholar
  8. Gluch RM, Quattrochi DA, Luvall JC (2006) A multi-scale approach to urban thermal analysis. Remote Sens Environ 104:123–132CrossRefGoogle Scholar
  9. Harris CO, Ullman EL (1945) The nature of cities. Ann Am Acad Pol Soc Sci 242:7–17CrossRefGoogle Scholar
  10. Herold M, Gardner ME, Roberts DA (2003) Spectral resolution requirements for mapping urban areas. IEEE Trans Geosci Remote Sens 41:1907–1919CrossRefGoogle Scholar
  11. Hoyt H (1939) The structure and growth of residential neighborhoods in American cities. Federal Housing Administration, Washington, DCGoogle Scholar
  12. Hung MC (2003) Remote sensing and GIS for urban environmental modeling monitoring and visualization. Doctoral dissertation, Department of Geography, University of Utah, Salt Lake City, UtahGoogle Scholar
  13. Hung MC, Ridd MK (2002) A subpixel classifier for urban land-cover mapping based on a maximum-likelihood approach and expert system rules. Photogramm Eng Remote Sens 68:1173–1180Google Scholar
  14. Kaya, S, Llewellyn G, Curran PJ (2004) Displaying earthquake damage in urban area using a vegetation-impervious-soil model and remotely sensed data. In: Proceedings of the XX congress international society of photogrammetry and remote sensing, Istanbul, Turkey, 12–23 July 2004Google Scholar
  15. Lwin T, Maritz JS (1980) Note on the problem of statistical calibration. J R Stat Soc Ser C 29:135–141Google Scholar
  16. Madhaven BB, Kubo S, Kurisaki N, Sivakumar TVLN (2001) Appraising the anatomy and spatial growth of the Bangkok Metropolitan area using a vegetation-impervious-soil model through remote sensing. Int J Remote Sens 22:789–806CrossRefGoogle Scholar
  17. Phinn SR, Stanford M, Scarth PF, Shyy T, Murray A (2002) Monitoring the composition and form of urban environments based on the vegetation-impervious surface-soil (VIS) model by sub-pixel analysis techniques. Int J Remote Sens 23(20):4131–4153CrossRefGoogle Scholar
  18. Quattrochi DA (1996) Cities as urban ecosystems: a remote sensing perspective. In: Proceedings, Pecora Thirteen, Sioux Falls, SD, 20–22 Aug 1996, pp 470–479 (on CD)Google Scholar
  19. Rashed T, Weeks JR, Gadalla MA, Hill AG (2001) Revealing the anatomy of cities through spectral mixture analysis of multispectral satellite imagery: a case study of the greater Cairo region, Egypt. Geocarto Int 16(4):5–15CrossRefGoogle Scholar
  20. Ridd MK (1995) Exploring a V-I-S (Vegetation-Impervious Surface-Soil) model for urban ecosystem analysis through remote sensing: comparative anatomy for cities. Int J Remote Sens 16:2165–2185CrossRefGoogle Scholar
  21. Ridd MK, Hipple J (eds) (2006) Remote sensing of human settlements. Manual for remote sensing, 3rd edn. ASPRS, Washington DCGoogle Scholar
  22. Ridd MK, Merola JA, Jaynes RA (1983) Detecting agricultural to urban land use change from multispectral MSS digital data. In: Proceedings of the ASP-ACSM fall convention, Salt Lake City, Utah, pp 473–482Google Scholar
  23. Ridd MK, Ritter ND, Green RO (1997) Neural network analysis of urban environments with airborne AVIRIS data. In: Proceedings of the 3rd international airborne remote sensing conference and exhibition, Copenhagen, Denmark, 7–10 July 1997Google Scholar
  24. UNFPA (2001) The state of world population 2001. United Nations Population Fund, United Nations Publications, New York. Available at
  25. Ward D, Phinn SR, Murray AT (2000) Monitoring growth in rapidly urbanizing areas using remotely sensed data. Prof Geographer 52:371–386CrossRefGoogle Scholar
  26. Wu CS, Murray AT (2003) Estimating impervious surface distribution by spectral mixture analysis. Remote Sens Environ 84:493–505CrossRefGoogle Scholar

Copyright information

© Springer Netherlands 2010

Authors and Affiliations

  1. 1.Department of GeographyBrigham Young UniversityProvoUSA
  2. 2.Department of GeographyUniversity of UtahSalt Lake CityUSA

Personalised recommendations