## 4 Discussion  
Using direct georeferencing can significantly improve the efficiency of UAV measurement. This study evaluated the geospatial accuracy of photogrammetric products obtained by direct georeferencing, and the impact of the configuration of GCPs on the quality of results. The _RMSE_, the M3C2 distance of the point cloud, the GCP distribution, and measures to improve the accuracy of the results when direct georeferencing are discussed below. 1. Error analysis based on the _RMSE_ shows that adding one GCP can help to reduce the deviation, but there may still be dome error, as shown in the study of [PERSON] and [PERSON] [27; 38]. When two GCPs were used, the mean vertical difference was reduced to 0.003 m, and the horizontal _RMSE_ was 0.0198 m, approximately 1.1 GSD, when 3 GCPs were used. From the work in some natural environments to the investigation of infrastructure, such as modelling water runoff during rain, different projects have different requirements for the fineness of ground features, so the required accuracy depends on the purpose of generating DSM. Therefore, using only one GCP may not meet the high accuracy standards; two to three GCPs are recommended for a trade-off between accuracy and work efficiency. The influence of each error on _RMSE_ is directly proportional to the size of the square error, and therefore _RMSE_ is sensitive to large differences and does not reflect terrain changes. Because of scale differences, errors that do not occur in flat areas may also occur in sloped areas [15]. The natural environment presents a series of complexities, including changing vegetation cover, strong topographic relief, and changes in texture. Future studies will need to assess the impact of these complexities on the accuracy of the results. Calculating _RMSE_ is a common error assessment method when the actual dataset of the ground surface is a set of distribution points rather than a continuous, real surface. Error evaluation benefits from a larger number and more evenly distributed checkpoints. [PERSON] et al. arranged 270 vertical checkpoints in an area of about 0.22 km\({}^{2}\), with a density of 1227 checkpoints per km\({}^{2}\)[39]. In the study of [PERSON], the density of checkpoints at the three sites was approximately 11363,  
Figure 10: Relationship between local DSM accuracy and nearest GCP. (**a**) Differential DSM; (**b**) the scatterplot and nonlinear curve fit obtained by sampling from the differential DSM. 3674 and 2749 per km\({}^{2}\)[8]. Thus, even if the number of GCP measurements on the ground is minimized, the role of checkpoints in the error assessment is critical. In the future, the plan is to deploy as many checkpoints as possible in the study area, build an accurate error surface, analyze the spatial distribution characteristics of errors, and better verify the results; these tasks will help to understand and reduce the potential error sources in the UAV SfM workflow [36];
2. The M3C2 algorithm eliminates the error introduced by the interpolation process. Lower error measurements with M3C2 are comparable to point-to-point or point-to-mesh; the method has been widely used in research based on point cloud change detection [40; 41; 42; 43]. Due to the high point density of UAV matching point clouds, an intercomparison can actually be regarded as continuous [44]. At zero GCP, the M3C2 distance error shows randomness in the study area. The standard deviation range of the comparison between the reference point cloud and the point cloud of different projects is 0.021-0.028 m. The change is relatively small, and the point cloud only deviates in the vertical direction, which is similar to the study of [PERSON] and [PERSON] et al. [6; 44]. Standard deviation is an indicator of precision. In some applications of UAV SfM, the accuracy of geolocation is not as important as the repeatability (precision) of data.