Forests that are dynamic ecosystems often change due to continuous growth, expansion, and natural and human-induced external influences. Thus, an accurate and up-to-date inventory of forests is one of the most important features of establishing a forest management policy. The most appropriate methods should be chosen after a detailed investigation for eliminating the complexity of the ecosystem, developing forest management, and shaping the environmental policy. In this study, photogrammetry and Light Detection and Ranging (LIDAR) data were evaluated both separately and together to obtain the most appropriate results related to forest inventories. Digital Elevation Models (DEM), Digital Terrain Models (DTM), Digital Surface Models (DSM), Normalized Digital Surface Models (NDSM), and Canopy Height Models (CHM) were produced using point cloud data obtained separately from each technique. Then, DEMs were produced through the classification of point clouds by means of 4 different methods in the determined study area. Although LIDAR data is more expensive and laborious than the photogrammetric method, it is the most successful method to produce forest inventory by producing more accurate results. In the first model, the full inventory of the forest could not have been realized. In the second method, better performance was realized than the first method, but even worse results were obtained than the fourth method. Unexpectedly several counted trees were obtained in the third method than the second one. The main reason for the differences obtained with various methods was evaluated as the 5 years differences between obtaining LIDAR and photogrammetric data.