# Quality Assessment of DSMs Produced from UAV Flights Georeferenced with On-Board RTK Positioning
## 4 Discussion
### DSM Repeatability and Accuracy  
With traditional ground control (**12 GCP**), the ranges of DSM elevation discrepancies within each DSM cell look rather stable over the area, with an average of about 6 cm, and a standard deviation of 3 cm. In gently varying and smooth surfaces, this is true almost independently of the surface characteristics, provided there is some texture. However, if texture is not ideal (though not necessarily uniform), there might be discrepancies in the order of several decimeters, which is all due to dense matching failures. In **RTK** configurations, the range of variability is higher, in the order of 10-12 cm, but still with a standard deviation of about 3 cm. Therefore, as found for checkpoints on targets, this larger range is unlikely to depend on a larger noise in the GNSS observations; it is rather due to an elevation bias introduced by the self-calibration. Using at least one GCP (**RTK+1 GCP**) removes most of the bias, and though less precise than GCPs, GNSS observations of the camera stations spread the control over the block fairly homogeneously. This empirical result is in agreement with the simulations of [PERSON] ([PERSON], 2015) and [PERSON] et al. ([PERSON], 2016), where georeferencing with GCPs or AAT was found to yield comparable ground point vertical precisions. The well-known rule of photogrammetry, stating that plotting is safe only inside the polygon enclosing the GCPs, somehow applies to the projection centers as well, and is reinforced to some extent by the larger overlaps adopted in UAV photogrammetry. Indeed, the only noticeable exception to the irrelevance of surface type in Figure 6 is the standard deviation in ploughed fields for **12 GCP**, which is slightly larger than in other cases. As can be seen from Figure 3, the check areas of the ploughed field class all fall outside the polygon enclosing the GCP. With AAT, to the contrary, the block control from the projection centers is still effective in the area. From Figures 11-14 in the Appendix, it can be seen that with **12 GCP** and **RTK+1 GCP**, the discrepancies in elevations are fairly homogeneous inside the block, apart from along step edges or steep slopes, or where texture is not ideal for image matching. Inconsistencies up to 30 cm can be found along the block borders and at the strip ends in the **12 GCP** and **RTK** cases. In the former case, the likely reason is poor control outside the GCP polygon, in the latter, the likely reason is one of the flights with a bias in the principal distance. Figure 12, which refers to the **RTK** configuration for the RTK1 to RTK4 flights, shows that in the right-hand side of the block, the discrepancies are higher than those in the left-hand side. Discrepancies in Figure 15, which refer also to the **RTK** configuration for the NRTK1 to NRTK4 flights, are on the other hand more homogenous over the area. This suggest that the bias in elevation due to estimation errors in the principal distance is not the only reason for the discrepancies in the elevation ranges. In the first pair of terrain profiles in Figure 6, the RTK2 DSM is offset with respect to the other three in the **RTK** case (a1). The coherence of the four DSMs is improved to some extent in the **RTK+1 GCP** case (a2). A better profile coherence is apparent with the four NRTK DSM of the second graph pair (b1, b2), where the profile is close to the location of the GCP constrained in the **RTK+1 GCP** case. This might suggest that the location of the GCPs is relevant in order to enforce to remove the elevation bias. Previous investigations in Benassi ([PERSON], 2015) hinted that this should not be the case, though this is worth testing more. This is to some extent confirmed by a comparison of Figures 13 and 14: at the block corners, the improvement in DSM coherence from the **RTK** to the **RTK+1 GCP** configuration is different for the RTK and NRTK flights, despite the GCP enforced being the same. The irrelevance of surface type on the DSM repeatability might be surprising.