Journal of Arid Regions Geographic Studies

Journal of Arid Regions Geographic Studies

Evaluating the Effectiveness of Supervised and Unsupervised Classification Methods in Monitoring Regs (Case Study: Jazmourian Reg)

Authors
Abstract
Due to its mobility and ability to move and its direct impact on residential areas and various developmental activities, the Ergs are of major importance in the desert areas, so monitoring of those is very important. Considering that the use of supervised and unguarded methods is considered as one of the most common methods in determining and monitoring land uses, in this research, the accuracy of different classification methods in the monitoring of Jazmourian regs was investigated. In this research, in order to evaluate the accuracy of different classification methods in determining the type of usage of the study area, especially the regs, satellite image of the Landsat 8, OLI sensor in 2017 has been used. First, the area of ​​the regs prepared manually using Google Earth and Topography Map of the region, and then in the ENVI software, the land use type of ​​the study area was determined by different supervised (Maximum Probability, Minimum Distance to Mean, equilibrium, Parallelepiped) and unsupervised methods (K-mean) and then the plotted area using two point and surface methods were compared with the area determined by different classification methods, and the accuracy of each method was measured. Due to the similarity of the reflection type of Landsat images in desert areas, the results are of little precision, so that the evaluation results indicate that in the point and surface method, maximum probability classification, with a total accuracy of 64.9 % and 53% has the highest accuracy and the k-mean method with a total accuracy of 15.5% and 17% has the lowest accuracy, respectively. So, in order to monitor the type of land use, including desert areas regs, a different kind of satellite imagery or other classification algorithms should be used.
Keywords

Abd Al-Razzaq, H., Alnajjar. H. A. (2013). Maximum Likelihood for Land-Use/Land-Cover Mapping and Change Detection Using Landsat Satellite Images: A Case Study “South Of Johor”, International Journal of Computational Engineering Research, Vol 03, Issue 6 Alkaradaghi, K., Ali, S., Al-Ansari, N., Laue, J. (2018). Evaluation of Land Use & Land Cover Change Using Multi-Temporal Landsat Imagery: A Case Study Sulaimaniyah Governorate, Iraq, Earth & Environmental Sciences, JGIS, Vol.10 No.3, Eastman, J. R. (2006). IDRISI Andes. Guide to GIS and Image Processing. Clark Labs, Clark University, Worcester, MA. Islam. K., Jashimuddi, M., Nath, B., Nath, K. (2018). Land use classification and change detection by using multi-temporal remotely sensed imagery: The case of Chunati wildlife sanctuary, Bangladesh, The Egyptian Journal of Remote Sensing and Space Science, Volume 21, Issue 1, Pages 37-47 Khoi, D.D., Murayama, Y. (2010). Forecasting Areas Vulnerable to Forest Conversion in the Tam Dao National Park Region, Vietnam. Remote Sensing 2 (5), 1249–1272 Lillesand, T., M., Kiefer, R., W., Chipman, J., W. (2004). Remote Sensing and Image Interpretation. John Wiley and Sons. New York. Reynolds J. F. (2008). Cutting through the confusion: Desertification, an old problem viewed through the lens of a new framework, the Dry lands Development Paradigm (DDP), Dry lands, Deserts & Desertification Conference December 24-21. SedeBoque Campus, Israel Richards, J A, and Jia, X. (2006). Remote Sensing Digital Image Analysis an Introduction; 4th Edition, Springer, Germany, Berlin, Heidelberg, 439 p. Tso, B, Mather, P M. (‌2009). Classification Methods for Remotely Sensed Data, 2nd edition, Taylor and Francis Pub, America. 376 p. Warner. A., Blonski. S., Gasser. G., Royan. R., Zanoni. V. (2001). An approach to application validation of multispectral sensors using AVIRIS data, 9 PP.

  • Receive Date 23 November 2022
  • Publish Date 22 June 2018