The measurement of geometric tree attributes is a major part of forest inventories. In practice respective attributes are mostly measured by terrestrial surveys, less often by laser scanning. These methods are relatively time consuming or costly. The presented study was conducted in order to evaluate photogrammetric point clouds, based on Unmanned Aerial Vehicles (UAV) photo flights, as a cost effective and fast additional instrument for single tree detection and tree height measurements within forest stands. A coniferous and a deciduous forest site near Freiburg im Breisgau, Germany were surveyed during the leaf-on state. For each site point clouds were generated photogrammetrically from UAV-based aerial photographs. The point clouds were processed with software developed for analyzing Light Detection And Ranging (LiDAR) data for forestry applications, to generate virtual tree models. Multiple models were calculated, by varying processing parameters, and analyzed for their accuracy by comparing the models to reference data acquired by Terrestrial Laser Scanning (TLS). Through evaluating the models by their number of detected trees and height accuracy, conclusions could be drawn for the various software parameters’ impact on the results.