Detecting Land cover (LC) change at local scale is needed for a wide range of applications, including deforestation, land degradation and desertification. LC changes based on human activities, negatively impact the patterns of climate and socio-economic dynamics at both local and global scales. In this study, LC changes investigated using satellite remote sensing imagery and Geographic Information System (GIS) in West Kordofan Region of Sudan. For this purpose, firstly supervised classification is performed to Landsat images acquired in 1994 and 2015. Image classification of six reflective bands of two Landsat images is carried out by using maximum likelihood techniques with the aid of ground truth data obtained from detailed field survey across the study area in 2015. The second step focused on LC changes by applying change detection comparison (pixel by pixel). The results showed that massive LC changes have occurred in bareland (50%), Sand dunes (28.7%), Acacia trees and Shrubs (-13.9%) and dense forest (-7.5%), areas between 1994 and 2015. It was seen that the LC changes mostly occurred in bareland.