Research on forest dynamic changes has a significant meaning for revealing changes in ecosystem

overall arrangement

and vegetation recovery. Time series remote sensing image provides abundant data for forest monitoring. This paper presents a novel method for forest cover change detection with time series images.（ 1） On the basis of the statistical properties of a forest area

a modified dark object identification method

which added NDVI to filter other dark objects

was used to acquire forest samples.（ 2）A new Integrated Forest Z-score（ IFZ） that added NDVI was constructed on the basis of the forest samples to indicate forest characteristic.（ 3） A time series IFZ value was used to identify forest cover changes. The research area was in Three Gorges Dam.Peripheral area and Thematic Mapper images in every growing season（ May-October） from 2001 to 2012 in this area were acquired. The accuracy of the change detection results was evaluated by t-test using Quick Bird images in the growing season（ JulySeptember） in 2002

2006

and 2012. The overall precision was 96. 53%

and the Kappa coefficient was 0. 9512. The commission and omission errors of this class were 2. 74% and 3. 64% respectively. The accuracy of the disturbance-restoration class was also improved but not as significant as that of the deforestation class; its commission and omission errors were 10. 79% and10. 51% respectively. The modified dark object method could improve the efficiency of sampling

and the new IFZ that added NDVI could identify forest effectively. In addition

the novel method not only identified forest quality changes

but also detected the time and degree of disturbance quantitatively.