Land Use Classification and Change Detection Using Multi-temporal Landsat Imagery in Sulaimaniyah Governorate, Iraq
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.
KeywordsLandsat Land use land cover (LULC) Maximum likelihood classification (MLC) Change detection ArcMap
- 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
- 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
- 8.Planning, M.O.F., Cooperation, D., Health, M.O.F.: Comprehensive food security and vulnerability analysis in Iraq. Methodology (2008)Google Scholar
- 12.Robert, A.S.: Remote Sensing: Models and Methods for Image Processing, vol. 95, no. 1 (2012)Google Scholar
- 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