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.
