Traditionally, field-based investigation is the main method to investigate greenbelt in urban environment, which is costly and low updating frequency. In higher resolution image, the imagery structure and texture of tree canopy has great similarity in statistics despite the great difference in configurations of tree canopy, and their surface structures and textures of tree crown are very different from the other types. In this paper, we present an automatic method to detect tree crowns using high resolution image in urban environment without any apriori knowledge. Our method catches unique structure and texture of tree crown surface, use variance and mathematical expectation of defined image window to position the candidate canopy blocks coarsely, then analysis their inner structure and texture to refine these candidate blocks. The possible spans of all the feature parameters used in our method automatically generate from the small number of samples, and HOLE and its distribution as an important characteristics are introduced into refining processing. Also the isotropy of candidate image block and holes' distribution is integrated in our method. After introduction the theory of our method, aerial imageries were used (with a resolution about 0.3m) to test our method, and the results indicate that our method is an effective approach to automatically detect tree crown in urban environment.