After Wenchuan earthquake disaster, landslide disaster becomes a common concern, and remote sensing becomes more and more important in the application of landslide monitoring. Now, the method of interpretation and recognition for landslides using remote sensing is visual interpretation mostly. Automatic recognition of landslide is a new and difficult but significative job. For the purpose of seeking a more effective method to recognize landslide automatically, this project analyzes the current methods for the recognition of landslide disasters, and their applicability to the practice of landslide monitoring. Landslide is a phenomenon and disaster triggered by natural and artificial reasons that a part of slope comprised of rock, soil and other fragmental materials slide alone a certain weak structural surface under the gravitation. Consequently, according to the geo-science principle of landslide, there is an obvious change in the sliding region between the pre-landslide and post-landslide, and it can be described in remote sensing imagery, so we develop the new approach to identify landslides, which uses change detection based on texture analysis in multi-temporal imageries. Preprocessing the remote sensing data including the following aspects of image enhancement and filtering, smoothing and cutting, image mosaics, registration and merge, geometric correction and radiation calibration, this paper does change detection base on texture characteristics in multi-temporal images to recognize landslide automatically. After change detection of multi-temporal remote sensing images based on texture analysis, if there is no change in remote sensing image, the image detected is relatively homogeneous, the image detected shows some clustering characteristics; if there is part change in image, the image detected will show two or more clustering centers; if there is complete change in remote sensing image, the image detected will show disorderly and unsystematic. At last, this paper takes some landslides at the Parry Lake as a case to implement the effectiveness of the new method in the application of landslide identification, which takes SPOT-5(Oct 10, 2003) and ALOS-AVNIR2(Sep 19, 2007) as the respective data sources of pre-landslide and post-landslide. The result shows that the method based on change detection is available of landslide information in arid area and other area where there is not obvious spectral difference between landslide mass and the background. Certainly, it will be more available of such area where there is obvious spectrum difference between landslide region and the background.© (2009) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.