Evaluation of the Efficiency of Satellite Imagery Classification Approaches in Monitoring of Land Cover Changes (Case Study: Shahrekord Basin, Chaharmahal va Bakhtiari)

Document Type : Research Paper


1 M.Sc in Rangeland Management / Faculty of Natural Resources and Earth Science, Department of Rangeland and Watershed Management, Shahrekord University

2 Ph.D. Student / Shahrekord University, Faculty of Natural Resource and Earth Science/ Department of Range and Watershed Management


Land cover mapping is essential for natural resource management. Satellite imagery can be used for mapping land cover. Several methods are available for land cover mapping whilst choosing the best method is one of the most important issue in this context. To compare pros and cons of three methods of classification including maximum likelihood, object-based segmentation and artificial neural network, first, the efficiency of these three methods were evaluated. Then the trend of land cover changes in Shahrekord basin was investigated for 1999, 2009 and 2015 using Landsat images of TM, ETM+ and OLI sensors, respectively. After geometric and radiometric corrections, the land cover map of 2015 was prepared based on the three methods. The results of the validation mapping methods revealed that object-based method was more promising than the others with 93 and 90% for total accuracy and Kappa coefficients of agreement, respectively. So, the object-based segmentation method is recommended for monitoring of land cover changes. The results of the land cover change indicated that residential areas increased from 1.727% in 1999 to 2.98% in 2015 and agricultural lands increased from 5.73% to 12.60% but rangelands were decreased by 9.05 in total. Moreover, bare-lands were increased from 1999 to 2009 by 6.19% but decreased from 2009 to 2015 by 5.27%. The result of this study showed that the object-based method is superior to pixel based method of Maximum-liklihood and neural network. So, object-based segmentation is recommended for estimating land cover changes.


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Volume 71, Issue 3
December 2018
Pages 699-714
  • Receive Date: 19 October 2017
  • Revise Date: 09 December 2018
  • Accept Date: 10 June 2018
  • First Publish Date: 22 November 2018