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Land Use Detection Using Remote Sensing and GIS (A Case Study of Rawalpindi Division)
American Journal of Remote Sensing
Volume 6, Issue 1, June 2018, Pages: 39-51
Received: Feb. 11, 2018; Accepted: Mar. 8, 2018; Published: Apr. 23, 2018
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Muhammad Zubair Iqbal, University Institute of Information Technology, Peer Mahar Ali Shah Arid Agriculture University, Rawalpindi, Pakistan
Muhammad Javed Iqbal, Department of Geo- Informatics, Peer Mahar Ali Shah Arid Agriculture University, Rawalpindi, Pakistan
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Change detection is a process of identifying variations in the substances or marvels which are supposed in the different time interims. This study takes the spatial-temporal dynamics of land use/cover change in Rawalpindi division Punjab Pakistan, using satellite imageries of two different years 2000, 2008. Supervised classification method was applied to demonstrate the object in a certain period of time. The method represents the vegetation index of differencing through object-based and supervised classification along with expert knowledge of GIS. This method gave different results in terms of land cover area, and it's generally concluded most accurate result from spatial images of medium resolution. The result of this process will be used for agriculture, urban and environmental changes in the various time periods. All information leads to the conclusion that surface under land class tabulation will be generated. The result shows vegetation, forest degradation and increase in built-up area with seasonal urbanization. Maps of the land use/land cover changes available in GIS platform can be used for the enhancement of the available tools for urban planning and environmental factor in the area.
Imagery, Supervised Classification, Land Use Planning, Remote Sensing, Geographical Information System (GIS)
To cite this article
Muhammad Zubair Iqbal, Muhammad Javed Iqbal, Land Use Detection Using Remote Sensing and GIS (A Case Study of Rawalpindi Division), American Journal of Remote Sensing. Vol. 6, No. 1, 2018, pp. 39-51. doi: 10.11648/j.ajrs.20180601.17
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This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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