Abstract: 【Objective】ZY-3 remotely sensed images from 2014 and 2015 were used to assess annual forest cover change in Eucalyptus plantations. 【Method】The normalized difference vegetation index(NDVI)was computed for each image(NDVI₂₀₁₄ and NDVI₂₀₁₅), then a difference image(NDVI_d)was developed based on the two NDVI images. Then, NDVI₂₀₁₄, NDVI₂₀₁₅ and NDVI_d were used to generate a color-composite image. Three methods: rule-based object-oriented classification(object RGB-NDVI), unsupervised classification(unsupervised RGB-NDVI)of the RGB-NDVI image, and NDVI image differencing(NDVI-DIFF)were implemented to extract forest cover change information on clear-cut and regrowth areas. 【Result】The object RGB-NDVI method had the highest overall accuracy of 98.4%, followed by the NDVI-DIFF method(97.3%)and the unsupervised RGB-NDVI method(96.1%); their corresponding Kappa coefficients were 0.906 1, 0.817 4 and 0.790 4, respectively. 【Conclusion】The RGB-NDVI image has high readability, represents the magnitude and direction of forest cover change, and can be used to monitor annual forest cover change in rapidly changing forest areas.