Comparing Different Methods of Land use Classification Using the Thermal Band (Case study: Southern Khorasan province)

Document Type : Original Article

Authors

1 Department of Natural Resources Engineering, University of Hormozgan, Bandar-Abbas, Hormozgan, Iran

2 Department of Surveying, university of Zabol, Zabol, Sistan and Baloochestan, Iran

3 School of Geography, Earth and Environmental Sciences, Plymouth University, Plymouth, Devon, PL4 8AA , UK

4 Department of Natural Resources Engineering, University of Hormozgan, Bandar-Abbas, Hormozgan, Iran.

Abstract

In the present study, four supervised classification algorithms including Maximum Likelihood, Mahalanobis Distance, Minimum Distance and Neural Network with and without TIR1 were used to mapping land use of southern Khorasan province. Based on the results, the highest of overall accuracy and Kappa coefficient were calculated for the Maximum Likelihood algorithm with and without of TIR1. Using of TIR1 increased classification accuracy by Maximum Likelihood and Mahalanobis Distance algorithms; but using of TIR1 decreased classification accuracy by Minimum Distance and Neural Network algorithms, remarkably. Using of thermal data along with other spectral bands caused facilitation of discriminating classes with similar spectral characteristics. According to the land use map, bare land covered about 60% area of southern Khorasan province, generally more than 90% of the area of the province is involved by sparse land or weak vegetation cover which is prone to wind erosion.

Keywords

Desert Management
Volume 6, Issue 11 - Serial Number 11
8 Articles
March 2018
Pages 65-81

Files

History

  • Receive Date: 02 March 2018
  • Revise Date: 24 April 2018
  • Accept Date: 25 April 2018

Share

How to cite

Statistics