Accurate and timely data on land cover change are not only important for global change study, but also provide significant foundation for decision-making, management and monitoring in resources sustainable application. As the modern remote sensing systems have provided a huge amount of data for land cover change study, remote sensing technology has become the most economical and effective way to acquire land cover change information. With the rapid development of earth observation technology, image resolution has been improved gradually, and remote sensing change detection algorithms have been remarkably developed. Remote sensing change detection methods are changing from traditional pix-level detection to object-oriented detection. In order to explore the validity and applicability of object-oriented change detection methods, we compared and evaluated object-oriented change detection methods and traditional change detection methods using Landsat TM/ETM+ images in the grassland area of Baotou and Ordos in Inner Mongolia. The results showed that object-oriented change detection methods had significant advantages both in overall accuracy and kappa coefficient. The overall accuracies obtained by object-oriented change detection methods were above 87.42%. The object-oriented change vector analysis got the highest accuracy, with overall accuracy of 91.56%. Besides, principal component differencing also had good detection result with overall accuracy of 87.83%. The three methods with the highest overall accuracy were further compared over different land cover change types. The results showed that the object-oriented change vector analysis was better than object-oriented spectral vector similar method, and the difference was most obvious for those change types related to construction land and dry land. The object-oriented change vector analysis provided good detection for all land cover change types in the study area with the average accuracy of 85%. However, there was a big difference in the detection results of principal component differencing between different land cover change types. Its accuracy reached as 93% for four land cover change types. But for detection of the transformation between bare land and grassland, the accuracy was low to 8.69%. While detecting the change types related to industrial and mining sites, object-oriented change vector analysis was more accurate than principal component differencing. But principal component differencing showed its superiority in detecting the change types related to construction land.