Abstract: Abstract: Rice is the most important food crop in China and its production ranks first in three major grain crops which are wheat, rice and corn. Therefore, it is very important to understand planting area and growth of rice. Because change detection is time-sensitive, remote sensing data is often used as a data source for it. Most of widely-used change detection methods using TM image are at pixel level, or only use image of single time phase. Based on multi-temporal Landsat TM images, this research extracted change area of rice planting in Pingle County. The process explored the methods of calculating changes in intensity and determining the threshold. First, the paper selected images of heading stage and harvest stage as data sources, and except for crops, there are no significant changes of ground objects in a few months. Since the spectral characteristics of late rice in the two periods are different from those of other crops, the changes of the images at two stages in the same year could be used to extract the change area of rice planting. In the process, change vector analysis method, correlation coefficient method and vector similarity method were used to calculate the change intensity. Otsu method, minimum error rate method and the method based on double window with variable step size were used to determine the threshold of the change intensity map. The change area maps of rice planting extracted by nine combinations of the methods were compared. Comprehensive utilization of three methods for extracting changes could get change area of rice planting with higher accuracy. This paper chose minimum error rate method based on histogram curvature or the method based on double window with variable step size to determine the threshold. The intersection of change area based on change vector analysis method and change area based on correlation coefficient method could inhibit the pseudo change of river beach and mountain shadow. Then the rice area was extracted exactly according to the difference between NDVI values and the object-oriented method. In order to refine the change detection of rice planting areas, the change areas of all ground objects and the change area of rice planting in two years were intersected to get the final variation for rice planting area. The results showed that the inhibitory effect of change vector analysis method for river beach change was better than correlation coefficient method, and yet the inhibition effect of correlation coefficient method for mountain shadow was better than change vector analysis method. Besides, vector similarity method was sensitive to mountain shadow and had low sensitivity to paddy field change. Among the three threshold determination methods, minimum error rate method was more accurate than Otsu method and more stable than the method based on double window with variable step size. Finally, the overall accuracy of change detection for late rice's planting area in Pingle County reached 96.8% in confusion matrix for verification. The error of the change area was 2.85% compared with the statistical data. This method could effectively extract the change of rice planting area in Pingle County.