Trees in 3D images obtained from lidar were automatically extracted in the presence of other objects that were not trees. We proposed a method combining 3D image processing and machine learning techniques for this automatic detection. Consequently, tree detection could be done with 95% accuracy. First, the objects in the 3D images were segmented one by one; then, each of the segmented objects was projected onto 2D images. Finally, the 2D image was classified into "tree" and "not tree" using a one-class support vector machine, and trees in the 3D image were successfully extracted.