Surface mining will completely change the original landscape pattern of land use, directly destroy the local ecological environment, and even affect the production and life of the nearby residents; therefore, more and more scholars have begun to pay attention to mining disturbance. Previous studies on extracting mining disturbances from temporal multispectral images focused on forest areas with single disturbance form. However, most surface mines in China are concentrated in grassland areas, and in the grassland mining areas in northeastern China, mining disturbances are more difficult to be identified because of their fragile ecological environment and the existence of various other forms of disturbance. In order to clarify the mining disturbance of grassland open stope in ecologically fragile areas in northeastern China, the authors taking Shengli mining area as an example, firstly employs 27 Landsat multi-spectral remote sensing images from 1986 to 2017, and bases the study on the long time series trajectory change characteristics of NDVI (normalized difference vegetation index). (In order to remove the effects of phenology, cloud and shadow on time series multispectral images, BISE-WT filter is used to filter the original NDVI time series to effectively remove the noise in the time series NDVI data and retain the effective information at the same time). After sample point training, CV threshold (Coefficient of Variation) and Max vegetation threshold are obtained. The Max vegetation threshold (vegetation threshold) is then used to construct the CV-Max disturbance recognition model and extract the disturbance distribution in the study area. Furthermore, using vegetation threshold, NDVI time series trajectory is analyzed to obtain disturbance interannual information and reconstruct disturbance history map. Then, by analyzing the spectral characteristics of typical terrain in the study area, bare coal extraction rules are constructed to extract the distribution of bare coal in the study area. Finally, the topological relationship between bare coal and disturbance area is constructed and a spatial topological overlay analysis is conducted to obtain mining disturbance information. The accuracy verification reveals the extraction accuracy of mining disturbance is 93.17% (Kappa coefficient=0.85) and the extraction accuracy of disturbance interannual information is 83.35% (Kappa coefficient=0.81) respectively. The results show that during the study period, the mining disturbance area accounts for 8.90% of the total area of the study area in space; in terms of time, the occurrence of mining disturbance concentrated in 2000—2009, during which the mining disturbance pixels accounted for 76.70% of the total mining disturbance pixels; the years from 1988 to 1998 witness the initial period of land destruction, and in 2000—2005, land destruction increased in the mining area, and in 2006—2009, the land destruction the mining area reached the peak. The proportion trend of mining disturbance pixels in 2010—2017 is relatively flat and continues to be at a low level, and the scope of land damage in mining area is basically stable. In view of the ecologically fragile grassland mining area in northeastern China, the method of extracting mining disturbance information by using NDVI and bare coal spectral features based on time series multispectral images is feasible. The research results can provide data and theoretical method support for the sustainable development of arid and semi-arid grassland surface mining area.