Using remote sensing data due to providing updated information, cover repetitive, low-cost assessment of natural resources have a special place. Also change detection in the management and evaluation of natural resources is one of the basic needs. Thus the value of change of land use /land cover (LULC) is the result of the change detection process can obtain on multi-temporal remote sensing images. Therefore, in this study, both of the Landsat satellite images 8 (OLI&TIRS) the year 2013 and 7(ETM⁺) the year 2000 were used as input data for land cover/ use mapping level 1 and 2. In the meantime, because of the new images OLI, radiometric corrections was formulation with existing equation with using in Erdas software model maker.also from Normalize Difference Vegetation Index (NDVI), Bare Soil Index (BI) and three main components from Principal Component Analyze (PCA) as input alongside other bands were used to increase the accuracy of classification. The polynomial 5 degree from SVM method compared with artificial neural network (ANN) and maximum likelihood classification (MLC). Results showed that support vector machine method using Polynomial kernel and degree 5 (accuracy 92%) gives overall accuracy higher than artificial neural network method (accuracy 89% ) and maximum likelihood method (accuracy 91.8%) . Also SVM method shows better performance where classes exhibit similar spectral behavior. Post classification method used for detect changes in the timeframe of 13 years. The results show large changes in (LULC) was occurred thus need monitoring and proper management is needed for this watershed.