Digital Elevation Models are topographical products which provide important information about the surface of the Earth and are used in many different applications such as modelling water flow (Jain and Singh, 2003), flood simulation and management (Honghai and Altinakar, 2011) and (Ramlal and Baban, 2008),terrain visualization and mapping (Spark and Williams, 1996). The accuracy of such applications depends on the accuracy of the produced Digital Elevation Model (Januchowski et al., 2010). Therefore, Digital Elevation Models with various accuracies can be prepared using different techniques depending on the desired accuracy. The most prominent techniques to prepare Digital Elevation Models are photogrammetric methods with stereo data (Hohle, 2009) and (Kraus, 2007), airborne laser scanning (Vosselman and Maas, 2010), radar interferometry (Arun, 2013) and land surveying (Wilson and Gallant, 2000). Due to land surveys, traditional methods to prepare Digital Elevation Models have high cost and is time consuming (Uysal et al., 2015). The use of photogrammetric methods has therefore become very common due to the advantages they provide in terms of time, accuracy and costs when preparing Digital Elevation Models. One of these methods, LIDAR systems, has become a preferred method for Digital Elevation Model preparation thanks to its three dimensional information gathering capability, which is very effective for wide areas. However, the biggest disadvantage of these platforms is their high cost, especially in small areas of study (Remondino et al., 2011). Thus, there emerged a need for different techniques to produce data in applications in small study areas. In the last ten years, with the increase in the number of firms producing them, the costs of Unmanned Aerial Vehicles have decreased substantially, thus eliminating the problematic cost issue related to these platforms. The use of these vehicles is becoming more and more common due to decreased costs of UAVs for different applications, eg. meteorological studies, natural disaster management, forest fire detection and control, agricultural product monitoring, mapping and threedimensional city and terrain modelling (Ruzgienė, 2015), (Mesas-Carrascosa, 2014 ) and (Austin, 2010). It is believed that Unmanned Aerial Vehicles will be a significant alternative to the traditional mapping methods by allowing the production of maps at low costs while providing high spatial and temporal resolution (Colomina and Molina, 2014) and (Sauerbier and Eisenbeiss, 2010). In scientific literature, it is accepted that the accuracy of the Digital Elevation Model depends on many factors such as topographic variance, sampling density, interpolation techniques and spatial resolution (Aguilar et al., 2005), (Gong et al., 2000) and (Kienzle, 2004). In the study conducted, using the point clouds obtained from UAV GatewingX100 images, the impact of the topographical variance and the different interpolation methods on the accuracy of the prepared Digital Elevation Model is investigated. ; The main objective of the study was to examine accuracies of DEMs (Digital Elevation Models) with different topographical structures generated by using the Unmanned Aerial Vehicle (UAV) point clouds. Two different terrains with flat and sloping topographical structures were selected for the study, and DEMs of these terrains were generated using eight interpolation techniques (Kriging, Natural Neighbor, Radial Basis Function Triangulation with Linear interpolation, Nearest Neighbor, Invers Distance to a Power, Local Polynomial and Minimum Curvature). The accuracies of DEMs were tested by calculating the statistic methods with the help of the control points obtained by land surveying techniques. At the end of the study, it was observed that in DEMs prepared for both flat (study area 1) and sloping (study area 2) terrains, Kriging interpolation method yields the best results as study area 1 and 2, respectively. In addition, the results were examined using Shapiro-Wilk and ANOVA: Friedman tests. After observing with the Shapiro- Wilk test that the data has a normal distribution, it was statistically determined through the parametric ANOVA: Friedman test that there is no difference between the variables.