Olive tree geometric features such as height, diameter, area and volume serve for monitoring crop status and are input variables in crop production models. Traditionally, these variables are estimated after an intensive field work and applying equations that treat the trees as geometric solids, which may produce inconsistent results. As an alternative, this work present an innovative automatic method for olive tree characterization based on two phases: 1) close range photogrammetry from Unmanned Aerial Vehicles (UAV) and 2) use of object based image analysis (OBIA) techniques. The presented methodology was tested in two olive orchards in southern Spain. 100% of the olive crowns were reconstructed by the photogrammetric software, and 100% of the olives trees were detected by the OBIA algorithm. This method could be adapted to other similar woody crops allowing a notably reduction in time and labor of measuring tasks.