Forest modelling is important for the forest management and forest ecosystem prediction. Growth simulators are essential tool for forest modelling, among which distance-dependent tree models are included. This group of growth simulators requires input data at the level of individual trees. One possible way to obtain them is to use UAV to take imagines of forest stand and further process them using photogrammetry algorithms. In this thesis, we focused on the estimated of tree heights and tree crown diameters using UAV photogrammetry in combination with an aerial laser scanner. The aim of the thesis was to determine the influence of tree species, flight height and bio-sociological position trees status on the accuracy of estimating the tree height and tree crown diameter in the conditions of forest cover. We chose two deciduous and two coniferous tree species: European beech (Fagus sylvatica L.), sessile oak (Quercus petraea (Matt.) Liebl.), Norway spruce (Picea abies (L.) H. Karst.), and European silver fir (Abies alba Mill.). Each of eight research plots we imagined using UAV SenseFly eBee Plus RTK / PPK fixed-wing at two different flight altitudes: 70 m and 200 m ALG. In the ArcGIS software environment, we estimated tree heights using the Local maxima (LM) algorithm and tree crown diameters using Inverse watershed segmentation (IWS) algorithm. We achieved a tree detection rate of 58% to 95% on individual research plots. We derived tree heights with a mean square error in the range of 2.48 - 5.71 m (RMSE% 9.33 - 25.05%). We estimated crown diameters with a mean square error of 0.59 - 1.35 m (RMSE% 26.28 - 65.46%). Based on the analysis of variance, it was shown that the tree species and bio-sociological position has statistically significant influence on the accuracy of estimation of tree heights and tree crown diameters. The statistically significant influence of flight altitude on the accuracy of estimation of tree heights and crown diameters has not been confirmed. We have thus demonstrated the potential of UAV photogrammetry to capture well the tops and crown parts of trees belonging to the bio-sociological categories of pre-grown and shaded. Linking the data obtained by UAV photogrammetry with the data obtained by terrestrial scanning could in the future serve as a base for estimating the input data of growth simulators.