The accurate forest inforrnation extraction through remote sensing technology is an important content of remote sensing applications. The land cover maps of Zhejiang province in 2000, 2005 and 2010 are generated based on object-oriented segmentation and multi-level decision tree classification technologies. Landsat TM/ETM+ images of variance times are used in this process. The forest in this area mainly contains evergreen coniferous forest, evergreen broad-leaf forest, deciduous coniferous forest, deciduous broad-leaf forest, mixed broadleaf-conifer forest and shrubbery. The forest information extraction for the studied area is carried out through a multiple level scheme, which is the main innovation of this work. The multi-scale segmentation technology is used to construct a 3-level segmentation system to get different scale objects in different classification stages. The multi-level decision tree is emploied as the classification tool. The first level objects will be classified as vegetation or non-vegetation. The second level objects within vegetation will be classified as evergreen forest or deciduoud forest. The third level objects within the evergreen forest and the deciduoud forest will be respectively classified as the relavent sub-type forests. Particularly, some features are computed and used as the input of the decision tree for the sub-type forest classification, including LBV(Level Balance Variance) transform from the 7 TM bands, NDVI(Normalized Difference Vegetation Index) from infrared and red bands of TM, and Tessal transform. Through the decision tree training, we find that in different stage there is a particular feature which plays the key role. For example, NDVI is a typical index to distinguish the vegetation and non-vegetation. NDVI of winter image is also a key index to differentiate evergreen forest and deciduous forest.V derived from LBV transform of the summer TM data is proved to be the best index for classifying the evergreen broad-leaf forest and evergreen coniferous forest. It is also used for deciduous coniferous forest and deciduous broad-leaved forest. In a nother hand, the field data is used to evaluate the mapping result. 1216 samples validate that the classification result of 2010 is highly precise with the overall accuracy of 92.76% and the Kappa of 0.893. The evergreen forest has higher classification accuracy than the deciduous forest. The evergreen coniferous forest and evergreen broad-leaf forest are usually confused with the mixed broadleaf-conifer which leads to the main errors.The results indicate that the decision tree classification combined with object-oriented segmentation provides an effective method for the extraction of forest information. Finally, the dynamic spatial distribution of forest resources is implemented through the spatial overlay analysis for the woodland thematic maps of the three periods. The results show that the forest increasing mostly accur in the central and the southern mountainous areas of zhejiang province, and the deforestation mainly accur in the coastal zones.The categories and reasons for the forest cover change are analyzed as well which can be helpful for the decision-making for the relevant departments. It should be noticed that there are dramatic land cover changes of forest in Zhejiang province. A large number of cultivated lands have been changed into evergreen coniferous forest or evergreen broad-leaf forest which is believed to be a response of the relevant policy.