Dongting Lake is the second largest freshwater lake of China, and has an important ecological function and economic value for its abundant wetland resources. The fast-growing poplar trees have grown fast on the beach land in Dongting Lake during the past 20 years, especially in West Dongting Lake, where a dramatic wetland vegetation distribution change is observed with a high ecological risk. In this study, two fast growing poplar tree change extraction methods, which are the classification method and change detection method, are proposed using remote sensed data (Landsat ETM+ images and HJ-1A/1B CCD images, both with a resolution of 30 m) in two periods in West Dongting Lake. And comparisons of the results obtained by these two methods were analyzed. In the classification method, an object-oriented hierarchical information extraction approach was applied to extract the woods beach land information. Then the regions within a certain distance from the dike were classified as windbreaks fields. The change area of fast-growing poplar trees was extracted to be the difference between the results of the two periods. In the change detection method, the change area was firstly calculated based on the changed pixels. Then the change poplar trees were detected as the expansion information for the poplar trees according to the sample information and priori knowledge. The two methods were examined using experimental data, and the result shows that both methods are feasible to extract poplar tree change information. The spatial distributions of changed poplar trees are similar in the two results, though some differences in the location are observed. The overall accuracy and Kappa coefficient of the classification method are slightly higher than that of the change detection method. The overall accuracy is 84.00% and the Kappa coefficient is 0.67 for the classification method. The overall accuracy is 83.00% and the Kappa coefficient is 0.65 for the change detection method. More large polygon patches are observed in the classification result with low degree of fragmentation. By contrast, more small and isolated patches are observed in the change detection result with high degree of fragmentation. The classification method tends to obtain lower omission errors but higher misclassification errors from the accuracy statistic. The change detection method gains lower misclassification errors but higher omission error. This study on extracting change areas of fast-growing poplar trees in Dongting Lake provides a way to dynamically monitor changing areas of fast-growing poplar trees, and offers basic research for other further studies such as the research on the expansion of fast-growing poplar trees and ecological effects that poplar expansion brought about.