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鄄 oriented segmentation and multi鄄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鄄leaf forest, deciduous coniferous forest, deciduous broad鄄leaf forest, mixed broadleaf鄄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鄄scale segmentation technology is used to construct a 3鄄level segmentation system to get different scale objects in different classification stages. The multi鄄level decision tree is emploied as the classification tool. The first level objects will be classified as vegetation or non鄄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鄄type forests. Particularly, some features are computed and used as the input of the decision tree for the sub鄄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,
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