Land Use Classification and Change Detection Using Multi-temporal Landsat Imagery in Sulaimaniyah Governorate, Iraq

  • Karwan Alkaradaghi
  • Salahalddin S. Ali
  • Nadhir Al-AnsariEmail author
  • Jan Laue
Conference paper
Part of the Advances in Science, Technology & Innovation book series (ASTI)


Rapid growth in urbanized areas is a worldwide phenomenon. The rate of urban growth is very fast in developing countries like Iraq. This study illustrated urbanized area development in Sulaimaniyah Governorate from 2001 to 2017 using different Landsat imagery, Landsat Thematic Mapper (TM) and Landsat Operational Land Imager (OLI). The Environment for visualizing images ENVI 5.3 and GIS software was utilized for image pre-processing, calibration and classification. The Maximum likelihood method was used in the accurately extracted solution information from geospatial Landsat satellite imagery of different periods. The Landsat images from the study area were categorized into six different classes. These are: forest, vegetation, rock, soil, built up and water body. Land cover variation and land use change detection in the area were calculated for over a 17 year period. The Change detection Analysis shows an explosive demographic shift in the urban area with a record of +8.99% which is equivalent to 51.80 km2 over a 17 years period and the vegetation area increased with 214 km2. On the other hand, soil area was reduced by 257.87 km2. This work will help urban planners in the future development of the city.


Landsat Land use land cover (LULC) Maximum likelihood classification (MLC) Change detection ArcMap 


  1. 1.
    Comber, A.J.: Land use or land cover? J. Land Use Sci. 3(4), 199–201 (2008)CrossRefGoogle Scholar
  2. 2.
    Liu, J., Heiskanen, J., Aynekulu, E., Pellikka, P.K.E.: Seasonal variation of land cover classification accuracy of landsat 8 images in Burkina Faso. Int. Arch. Photogrammetry, Remote Sens. Spat. Info. Sci.—ISPRS Arch. 40(7W3), 455–460Google Scholar
  3. 3.
    Rawat, J.S., Kumar, M.: Monitoring land use/cover change using remote sensing and GIS techniques: a case study of Hawalbagh block, district Almora, Uttarakhand, India. Egypt. J. Remote Sens. Sp. Sci. 18(1), 77–84 (2015)Google Scholar
  4. 4.
    Eklund, L., Abdi, A., Islar, M.: From producers to consumers: the challenges and opportunities of agricultural development in Iraqi Kurdistan. Land 6(3), 44 (2017)CrossRefGoogle Scholar
  5. 5.
    Blaschke, T., Hay, G.J., Weng, Q., Resch, B.: Collective sensing: integrating geospatial technologies to understand urban systems-an overview. Remote Sens. 3(8), 1743–1776 (2011)CrossRefGoogle Scholar
  6. 6.
    Kachhwala, T.S.: Temporal monitoring of forest land for change detection and forest cover mapping through satellite remote sensing. Proc. 6th Asian Conf. Remote Sens. 77–83 (1985)Google Scholar
  7. 7.
    Weil, G., Lensky, I.M., Resheff, Y.S., Levin, N.: Optimizing the timing of unmanned aerial vehicle image acquisition for applied mapping ofwoody vegetation species using feature selection. Remote Sens. 9(11) (2017)Google Scholar
  8. 8.
    Planning, M.O.F., Cooperation, D., Health, M.O.F.: Comprehensive food security and vulnerability analysis in Iraq. Methodology (2008)Google Scholar
  9. 9.
    Seto, K.C., Christensen, P.: Remote sensing science to inform urban climate change mitigation strategies. Urban Clim. 3, 1–6 (2013)CrossRefGoogle Scholar
  10. 10.
    Song, C., Woodcock, C.E., Seto, K.C., Lenney, M.P., Macomber, S.A.: Classification and change detection using Landsat TM data: when and how to correct atmospheric effects? Remote Sens. Environ. 75(2), 230–244 (2001)CrossRefGoogle Scholar
  11. 11.
    Schulz, J.J., Cayuela, L., Echeverria, C., Salas, J., Rey Benayas, J.M.: Monitoring land cover change of the dryland forest landscape of central Chile (1975–2008). Appl. Geogr. 30(3), 436–447 (2010)CrossRefGoogle Scholar
  12. 12.
    Robert, A.S.: Remote Sensing: Models and Methods for Image Processing, vol. 95, no. 1 (2012)Google Scholar
  13. 13.
    Rosenfield, G.H., Fitzpatrick-Lins, K.: A coefficient of agreement as a measure of thematic classification accuracy. Photogramm. Eng. Remote Sens. 52(1979), 223–227 (1986)Google Scholar
  14. 14.
    Wu, C., et al.: Land surface phenology derived from normalized difference vegetation index (NDVI) at global FLUXNET sites. Agric. For. Meteorol. 233, 171–182 (Dec 2016) (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Karwan Alkaradaghi
    • 1
    • 2
    • 3
  • Salahalddin S. Ali
    • 2
    • 4
  • Nadhir Al-Ansari
    • 1
    Email author
  • Jan Laue
    • 1
  1. 1.Lulea University of TechnologyLuleaSweden
  2. 2.Department of GeologyCollege of Science, Sulaimani UniversitySulaimaniyahIraq
  3. 3.Kurdistan Institution for Strategic Studies and Scientific ResearchSulaimaniyahIraq
  4. 4.Komar University of Science and TechnologySulaymaniyahIraq

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