Forest growth is mainly currently monitored using in-situ measurements in northeast of China.To effectively monitor forest growth disturbance at large scale,we attempted to use remote sensing technique,particularly,time series MODIS data from 2004 to 2006.The annual time series of 8-day enhanced vegetation index(EVI) dataset was generated and smoothed using a Savitzky-Golay filter.The EVI trajectory during growth season was simulated using a logistic model. From the simulated trajectory,the EVI area of growth season and annual EVI entropy were calculated.These two factors were combined to map the disturbance regions of forest growth. Finally,the disturbance regions were verified using a set of random samples.The result indicates that the disturbance points have distinctively higher entropy and lower peak.Some of these points also show abrupt EVI decline during the midseason of the peak phases or double peaks.This approach is demonstrated to be feasible for disturbance monitoring of forest growth. Forest growth is mainly currently monitored using in situ measurements in northeast of China. To effectively monitor forest growth disturbance at large scale, we attempted to use remote sensing technique, particularly, time series MODIS data from 2004 to 2006. 8-day enhanced vegetation index (EVI) dataset was generated and smoothed using a Savitzky-Golay filter. EVI trajectory during growth season was simulated using a logistic model. From the simulated trajectory, the EVI area of ​​growth season and annual EVI entropy were calculated. these two factors were combined to map the the disturb regions of forest growth. Finally, the disturbance regions were verified using a set of random samples. the result indicates that the disturbance points have distinctively higher entropy and lower peak.Some of these points also also show abrupt EVI decline during the midseason of the peak phases or double peaks.This approach is demonstrated to be feasible for disturbance monitoring of for est growth.