Arabian Journal for Science and Engineering

, Volume 37, Issue 1, pp 101–114 | Cite as

Assessment of Urban Sprawl of Islamabad Metropolitan Area Using Multi-Sensor and Multi-Temporal Satellite Data

  • Mohsin Jamil ButtEmail author
  • Ahmad Waqas
  • Muhammad Farooq Iqbal
  • Gul Muhammad
  • M. A. K. Lodhi
Research Article – Earth Sciences


The present research relates to the exploitation of remote sensing and geographic data to study the metropolitan expansion, land use patterns and land cover classification of Islamabad. The metropolitan development based on growth direction and expansion trends from the city centre was observed for a period of 38 years. Landsat satellite data from three sensors, that is, multi-spectral sensor, thematic mapper and enhanced thematic mapper plus, were used in this study. The classification of the complex spatial assemblage of urban environment and its expanding precincts was done using temporal data collected during the period 1972–2009 and geo-referenced to a common coordinate system. The information regarding agriculture farms, bare land, dense forest, mixed vegetation, shadowy regime, sparse residential areas, thickly populated areas and water bodies was extracted. The extent of urban conurbation growth was computed and analyzed. Our results show that the urban development is expanded by 87.31 km2. In addition, the urban growth is accompanied by the loss of forest and other natural habitat in the city and has influenced the spatial patterns and structure of urban development. It is concluded that the remote sensing and GIS techniques supplemented with census data are very efficient and effective for studying the metropolitan growth patterns.


Pakistan Islamabad Landsat Remote sensing GIS 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Tapiador F., Casanova J.L.: Land cover mapping methodology using remote sensing for the regional planning directives in Segovia, Spain. Landsc. Urban Plan 62, 103–115 (2003)CrossRefGoogle Scholar
  2. 2.
    Yang X.: Satellite monitoring of urban spatial growth in the Atlanta metropolitan area. Photogramm. Eng. Remote Sens 68, 725–734 (2002)Google Scholar
  3. 3.
    Madhavan B.B., Kubo S., Kurisaki N., Sivakumar N.: 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, 789–806 (2001)CrossRefGoogle Scholar
  4. 4.
    Ridd M.K., Liu J.: A comparison of four algorithms for change detection in an urban environment—a remote sensing perspective. Remote Sens. Environ. 63, 95–100 (1998)CrossRefGoogle Scholar
  5. 5.
    Jensen, J.R.: Digital Change Detection. Introductory Digital Image Processing: A Remote Sensing Perspective, pp. 467–494. Prentice-Hall, New Jersey (2004)Google Scholar
  6. 6.
    Jensen, J.R.: Introductory Digital Image Processing: A Remote Sensing Perspective. Prentice Hall, Englewood Cliffs (1996)Google Scholar
  7. 7.
    Ehlers M., Jadkowski M.A., Howard R.R., Brostuen D.E.: Application of SPOT data for regional growth analysis and local planning. Photogramm. Eng. Remote Sens 56, 175–180 (1990)Google Scholar
  8. 8.
    Epstein J., Payne K., Kramer E.: Techniques for mapping suburban sprawl. Photogramm. Eng. Remote Sens 63, 913–918 (2002)Google Scholar
  9. 9.
    Treitz P.M., Howard P.J., Gong P.: Application of satellite and GIS technologies for land-cover and land-use mapping at the rural-urban fringe: a case study. Photogramm. Eng. Remote Sens. 58, 439–448 (1992)Google Scholar
  10. 10.
    Harris P.M., Ventura S.J.: The integration of geographic data with remotely sensed imagery to improve classification in an urban area. Photogramm. Eng. Remote Sens. 61, 993–998 (1995)Google Scholar
  11. 11.
    Sudhira H.S., Ramachandra T.V., Jagadish K.S.: Urban sprawl: metrics, dynamics and modeling using GIS. Int. J. Appl. Earth Obs. Geoinf. 5, 29–39 (2004)CrossRefGoogle Scholar
  12. 12.
    Yang X., Liu Z.: Use of satellite derived landscape imperviousness index to characterize urban spatial growth. Comput. Environ. Urban Syst. 29, 524–540 (2005)CrossRefGoogle Scholar
  13. 13.
    Haack B.N., Rafter A.: Urban growth analysis and modeling in the Kathmandu valley, Nepal. Habitat Int 30, 1056–1065 (2006)CrossRefGoogle Scholar
  14. 14.
    Kaya S., Curran P.J.: Monitoring urban growth on the European side of the Istanbul metropolitan area: a case study. Int. J. Appl. Earth Obs. Geoinf 8, 18–25 (2006)CrossRefGoogle Scholar
  15. 15.
    Weng Q.: A remote sensing-GIS evaluation of urban expansion and its impact on surface temperature in the Zhujiang Delta, China. Int. J. Remote Sens. 22, 1999–2014 (2001)Google Scholar
  16. 16.
    Bhattarai K., Conway D.: Urban vulnerabilities in the Kathmandu valley, Nepal: visualizations of human/hazard interactions. J. Geogr. Inf. Syst. 2, 63–84 (2010)Google Scholar
  17. 17.
    Haack B.: A history and analysis of mapping urban expansion in the Kathmandu valley, Nepal. Cartogr. J 46, 233–241 (2009)CrossRefGoogle Scholar
  18. 18.
    Thapa, R.B.: Spatial process of urbanization in Kathmandu valley, Nepal. PhD Dissertation. Graduate School of Life and Environmental Sciences, University of Tsukuba, Ibaraki (2009)Google Scholar
  19. 19.
    Thapa, R.B.; Murayama, Y.: Urban growth modeling of Kathmandu metropolitan region, Nepal. Comput. Environ. Urban Syst. doi: 10.1016/j.compenvurbsys.2010.07.005 (2010)
  20. 20.
    Lata K.M., Prasad V.K., Badrinath K.V.S., Raghavaswamy V., Rao C.H.S.: Measuring urban sprawl: a case study of Hyderabad. GIS Dev 5, 26–29 (2001)Google Scholar
  21. 21.
    Kamal A.S.M.M., Midorikawa S.: GIS-based geomorphological mapping using remote sensing data and supplementary geoinformation a case study of the Dhaka City area, Bangladesh. Int. J. Appl. Earth Obs. Geoinf 6, 111–125 (2004)CrossRefGoogle Scholar
  22. 22.
    Taubenbock H., Wegmann M., Roth A., Mehl H., Dech S.: Urbanization in India—spatiotemporal analysis using remote sensing data. Comput. Environ. Urban Syst. 33, 179–188 (2009)CrossRefGoogle Scholar
  23. 23.
    Jat M.K., Grag P.K., Khare D.: Monitoring and modeling of urban sprawl using remote sensing and GIS techniques. Int. J. Appl. Earth Obs. Geoinf 10, 26–43 (2008)CrossRefGoogle Scholar
  24. 24.
    Dewan A.M., Yamaguchi Y.: Land use and land cover change in Greater Dhaka, Bangladesh: using remote sensing to promote sustainable urbanization. Appl. Geogr 29, 390–401 (2009)CrossRefGoogle Scholar
  25. 25.
    Thapa R.B., Murayama Y.: Examining spatiotemporal urbanization patterns in Kathmandu valley,Nepal: remote sensing and spatial metrics approaches. Remote Sens 1, 534–556 (2009)CrossRefGoogle Scholar
  26. 26.
    Doxiadis C.: Islamabad, the creation of a New Capital. Town Plan. Rev 36, 1–28 (1965)Google Scholar
  27. 27.
    Population Census Organization, Statistics Division, Government of Pakistan, 1998, District Census Report of Islamabad, Population Census Organization Press IslamabadGoogle Scholar
  28. 28.
    Fuller R.M., Groom G.B., Jones A.R.: The land cover map of Great Britain: an automated classification of Landsat Thematic Mapper data. Photogramm. Eng. Remote Sens 60, 553–562 (1994)Google Scholar
  29. 29.
    Townshend, J.R.G.: Effects of Spatial Resolution on the Classification of Land Cover Type. In: Fuller, R.M. (ed.) Ecological Mapping from Ground, Air And Space. Institute of Terrestrial Ecology, Cambridge (1983)Google Scholar
  30. 30.
    Vogelmann J.E., Helderb D., Morfitta R., Choatea M.J., Merchantc J.W., Bulley H.: Effects of Landsat 5 thematic mapper and Landsat 7 enhanced thematic mapper plus radiometric and geometric calibrations and corrections on landscape characterization. Remote Sens. Environ. 78, 55–70 (2001)CrossRefGoogle Scholar
  31. 31.
    Woodcock C.E., Macomber S.A., Pax-Lenney M., Cohen W.C.: Monitoring large areas for forestchange using Landsat: generalization across space, time and Landsat sensors. Remote Sens. Environ. 78, 194–203 (2001)CrossRefGoogle Scholar
  32. 32.
    Cohen W.B., Goward S.N.: Landsat’s role in ecological applications of remote sensing. BioScience 54, 535–545 (2004)CrossRefGoogle Scholar
  33. 33.
    Masek J.G., Vermote H.C., Wolfe R., Cohen W., Hall F., Kutler J., Nelson P.: North American forest disturbance mapped from a decadal Landsat record. Remote Sens. Environ 112, 2914–2926 (2008)CrossRefGoogle Scholar
  34. 34.
    Wulder M.A., White J.C., Goward S.N., Masek J.G., Irons J.R., Herold M., Cohen W.B., Loveland T.R., Woodcock C.E: Landsat continuity: issues and opportunities for land cover monitoring. Remote Sens. Environ. 112, 955–969 (2008)CrossRefGoogle Scholar
  35. 35.
    Woodcock, C.E.; Allen, A.A.; Anderson, M.; Belward, A.S.; Bindschadler, R.; Cohen, W.B.; Gao, F.; Goward, S.N.; Helder, D.; Helmer, E.; Nemani, R.; Oreapoulos, L.; Schott, J.; Thenkabail, P.S.; Vermote, E.F.; Vogelmann, J.; Wulder, M.A.; Wynne, R.: Free access to Landsat imagery. Science, 320–1011 (2008)Google Scholar
  36. 36.
    Chander G., Markham B.L., Helder D.L.: Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+ and EO-1 ALI sensors. Remote Sens. Environ 113, 893–903 (2009)CrossRefGoogle Scholar
  37. 37.
    Baumont C., Ertur C., Le Gallo J.: Spatial analysis of employment and population density: the case of the agglomeration of Dijon 1999. Geogr. Anal 36(2), 146–176 (2004)CrossRefGoogle Scholar
  38. 38.
    Erdas Imagine® 9.1, Tour Guide, 2006, GIS & Mapping, LLC Atlanta, GeorgiaGoogle Scholar
  39. 39.
    Esri. Arc GIS Desktop Developer Guide 9.1, pp. 1–340. New York street Redlands, California (2004)Google Scholar
  40. 40.
    Weng F.: A knowledge-based vision system for detecting land changes at urban fringes. IEEE Transm. Geosci. Remote Sens 31, 136–145 (1993)CrossRefGoogle Scholar
  41. 41.
    Singh A., Harrison A.: Standardized principal components. Int. J. Remote Sens. 6, 883–896 (1985)CrossRefGoogle Scholar
  42. 42.
    Vogelmann J.E., Howard S.M., Yang L., Larson C.R., Wylie B.K., Van Driel J.N.: Completion of the 1990’s National Land Cover Data Set for the conterminous United States. Photogramm. Eng. Remote Sens 67, 650–662 (2001)Google Scholar
  43. 43.
    Stuckens J., Coppin P.R., Bauer M.E.: Integrating contextual information with per-pixel classification for improved land cover classification. Remote Sens. Environ. 71, 282–296 (2000)CrossRefGoogle Scholar
  44. 44.
    Stefanov W.L., Christensen M.S.: Monitoring urban land cover change: an expert system approach to land cover classification of semiarid to arid urban centers. Remote Sens. Environ. 77, 173–185 (2001)CrossRefGoogle Scholar
  45. 45.
    Sugumaran R., Pavuluri M.K., Zerr D.: The use of high resolution imagery for identification of urban climax forest species using traditional and rule based classification approach. IEEE Transm. Geosci. Remote Sens. 41, 1933–1939 (2003)CrossRefGoogle Scholar
  46. 46.
    Lu D., Weng Q.: Urban classification using full spectral information of Landsat ETM+ imagery in Marion County, Indiana. Photogramm. Eng. Remote Sens. 71, 1275–1284 (2005)Google Scholar
  47. 47.
    Mundia C.N., Aniya M.: Analysis of land use/cover changes and urban expansion of Nairobi city using remote sensing and GIS. Int. J. Remote Sens. 26, 2831–2849 (2005)CrossRefGoogle Scholar
  48. 48.
    Berberoglu S., Evrendilek F., Ozkan C., Donmez C.: Modeling forest productivity using Envisat MERIS data. Sensors 7, 2115–2127 (2007)CrossRefGoogle Scholar
  49. 49.
    Xiaoling C., Xiaobin C., Hui L.: Expert classification method based on patch-based neighborhood searching algorithm. Geo Spatial Inf. Sci. 10, 37–43 (2007)CrossRefGoogle Scholar
  50. 50.
    Yuan, D.; Elvidge, C.D.; Lunetta, R.S.: Survey of multispectral methods for land cover change analysis. Remote Sensing Change Detection: Environmental Monitoring Methods and Applications, pp. 21–39. Ann Arbor Press, Michigan (1998)Google Scholar
  51. 51.
    Anderson, R.; Hardy, E.E.; Roach, J.T.; Witmer, R.E.: A land use and land cover classification system for use with remote sensor data. USGS Professional Paper 964, Sioux Falls (1976)Google Scholar
  52. 52.
    Koutsias, N.; Karteris, M.: Classification analyses of vegetation for delineating forest fire fuel complexes in a Mediterranean test site using satellite remote sensing and GIS. Int. J. Remote Sens. 24, 3093–3104 (2003)Google Scholar
  53. 53.
    Federal Bureau of Statistics. Compendium on environmental statistics of Pakistan. Islamabad. Pakistan, Federal Bureau of Statistics (2004)Google Scholar
  54. 54.
    Population Census Organization, Statistics Division, Government of Pakistan. District Census Report of Islamabad, Population Census Organization Press, Islamabad (1981)Google Scholar
  55. 55.
    National Institute of Public Administration. A Report in Urban Growth in Pakistan (1989)Google Scholar
  56. 56.
    Agriculture Census Organization, Statistics Division, Government of Pakistan. Agricultural Census 1990. Agriculture Census Organization Press, Islamabad (1990)Google Scholar
  57. 57.
    SURVEY OF PAKISTAN. Islamabad/Rawapindi guide map, 2nd edn. (1:30,000). Survey of Pakistan press, Rawalpindi (1982)Google Scholar
  58. 58.
    Lohibehr History Files 1989–90, Forest Department Rawalpindi, PakistanGoogle Scholar
  59. 59.
    Ahmad Z., Ahmad I.: Infiltration galleries—a possible perennial source of water supply in arid/semi-arid regions. Islamabad J. Sci. 13, 28–35 (2003)Google Scholar
  60. 60.
    Shah, Z.U.H.; Ahmad, Z.: Groundwater contamination study of the Islamabad aquifers and their graphical projection using “HC-GRAM” software. J. Virtual Explor. 23 (2006)Google Scholar
  61. 61.
    Zhou W.M., Wang J.D., Liu J.S., Yang J.S.: Influence of different land-use to regional climate in Sanjiang Plain. J. Soil Water Conserv 10, 155–158 (2005)Google Scholar
  62. 62.
    Zhou Q.X.: Advance in the effects of climate change on environment and health. J. Meteorol. Environ 22, 38–44 (2006)Google Scholar

Copyright information

© King Fahd University of Petroleum and Minerals 2011

Authors and Affiliations

  • Mohsin Jamil Butt
    • 1
    Email author
  • Ahmad Waqas
    • 2
  • Muhammad Farooq Iqbal
    • 2
  • Gul Muhammad
    • 2
  • M. A. K. Lodhi
    • 2
    • 3
  1. 1.Department of MeteorologyKing Abdulaziz UniversityJeddahSaudi Arabia
  2. 2.Climate Snow and Hydrology Research Group (CSHRG), Department of MeteorologyCOMSATS Institute of Information Technology (CIIT)IslamabadPakistan
  3. 3.Department of PhysicsTexas Tech UniversityLubbockUSA

Personalised recommendations