## 1 Introduction
### Related Work  
The assessment of the accuracy of UAV-based laser scanning systems can typically be divided into theoretical and empirical studies. One theoretical approach is described by [PERSON] et al. [13], who focus on the propagation of errors in the UAV's position and orientation on the point cloud. In addition, [PERSON] [14] also considers the errors introduced by the calibration of the system with bore-sight angles and lever arm. Further studies about the error budget for airborne or UAV-based laser scanning systems are [7; 12; 15]. The critical aspect for the theoretical investigations is mainly the consideration of all errors that are included in any estimation process for mobile mapping systems with laser scanner [16]. For this reason, most of the quality assessments so far have been done on the empirical approach based on the resulting product, which is usually a georeferenced 3D point cloud with optional RGB information coming from a camera. Examples for the empirical assessment of point cloud accuracy can be found in [PERSON] et al. [17] or investigations with additional optimization strategies by [PERSON] et al. [18].  
For the empirical evaluation of point clouds different approaches are recommended, which can be divided into (1) point-based, (2) area-based and (3) parameter-based strategies [11]. Point-based evaluation requires the identification of identical points with a given reference. These reference points are determined by traditional surveying methods such as total stations, GNSS, leveling or TLS. Typical objects for point-based evaluation can be natural objects (e.g., building corners, road markings or manholes) or artificial objects (e.g., targets or checkerboard patterns) [19; 20; 21; 22; 23; 24; 25]. These enable the assessment of the absolute accuracy and the separation of errors into horizontal and vertical components. By measuring points multiple times with the same system, the precision can also be quantified. One challenge with point-based evaluation methods and UAV-based laser scanning is due to the comparatively low point density with increasing flight height and speed, which has to be considered during flight planning.  
Besides the point-based evaluation, the area-based strategies are often used for comparison with a known reference point cloud (e.g., TLS) [26; 27; 28; 23]. Also, 3D city models have been used as a reference for evaluation [29]. For the calculation of differences, various algorithms such as the cloud-to-cloud distance or the Multiscale Model to Model Cloud Comparison (M3C2) method are used, which are integrated into common software tools. Since UAV-based laser scanning measurements are typically made with overlapping flight strips to correct the estimated UAV position and orientation [30], area-based comparisons between flight strips can indicate the precision during one flight [31].  
The third possibility of the above-mentioned evaluation strategies is the parameter-based evaluation, in which derived parameters from the point cloud are used for the comparison [1; 23]. Potential parameters are typically estimated from objects with a defined geometric shape. [PERSON] et al. [32] for example analyzed the accuracy of estimated DEM models with different flight parameters. Furthermore, several studies investigated the quality for the calculation of forest structural attributes (e.g., stem diameter, volume) in forestry [33; 34; 35]. In addition, [PERSON] et al. [36] analyzed the accuracy of point clouds in vegetated terrain with a focus in the vertical direction. Similar to the previous evaluation methods, parameter-based evaluation can also serve for the analysis of precision.