Trees are important objects in our living environment. Modeling of living tree in our environment is hard work in computer vision and pattern recognition, since trees are related to large shape diversity and geometry complexity. In this paper, we present a range image analysis based approach to model a 3D tree from a single range image data. Range image pixels are thought of as 3D discrete points. Points from leaves and points from branches are segmented based on a new metrics on the convergence of local directions. A region growing method is then adopted to classify points from different branches. Skeletons of main branches are then computed by clustering each branch segment into small bins. The shape patterns of visible branches are used to predict those of obscured branches. Experiments show that this approach is applicable to modeling living trees.