Vegetation resilience is the ability of vegetation to recover from disturbances without shifting to an alternative state or losing function and services, which is critical to maintain ecosystem quality and stability. Assessing vegetation resilience has become an urgent requirement to deal with ecosystem degradation under the climate change and influence of anthropogenic. However, large scale vegetation resilience measurement is fraught with difficulty, since the lack of remote sensing production and limitation of measurement model. Here, we used GLASS LAI and critical slowing down model to monitor the pattern of vegetation and vegetation resilience in the Three Gorges Reservoir Area (TGRA). We also analyzed the accuracy of critical slowing down model using case model to discuss the feasibility in remote sensing vegetation resilience monitoring. In general, LAI autocorrelation as an indicator monitored the vegetation resilience of each district in the TGRA. We found that the average LAI was 3.4 and LAI showed an increasing trend during 2000-2018 in the TGRA. LAI of Chongqing section showed a decrease trend while that of Hubei section showed an increase trend. Spatially, the area of significant decline accounted for 21.75% of the Chongqing section and the significantly increased area accounted for 21.22% of the area of Hubei section. For vegetation resilience, Hubei section showed stronger resilience than Chongqing section. Within TGRA, Beibei, Dadukou and Yubei exhibited low vegetation resilience while Xingshan, Yiling and Dianjun exhibited high vegetation resilience. In terms of model accuracy, the results of the case model and the critical slowing down model were with a high consistency in two geohazard disturbances. Overall, when assessing vegetation resilience in a large scale using long-term remote sensing data, the critical slowing down was able to offer reasonable indicators. Furthermore, our results indicated that anthropogenic factors had the negative effects on vegetation resilience. Confirmation with ground data will be needed to validate these results and to better understand the biological processes determining vegetation restoration ability.