# Quality Assessment of DSMs Produced from UAV Flights Georeferenced with On-Board RTK Positioning
## Materials and Methods
### DSM Generation  
To measure the repeatability of DSM generation, the elevation range in each cell of the four DSMs based on the same block control configuration and RTK positioning mode was computed. The spatial distribution, the mean value, and the standard deviation of these ranges over the surveyed area were taken as variability indicators. Finally, though unrelated to block orientation configuration, cell-wise elevation ranges were also computed in areas of homogeneous ground surface characteristics, to find out whether different surfaces showed different variabilities.  
To this aim, for each block and each block control configuration, a dense point cloud (average density 138 pts/m\({}^{2}\)) was generated with PhotoScan, setting the Quality parameter to Medium, and the Depth filtering parameter to Aggressive (Figure 1); no classification or filtering of the point cloud was performed with PhotoScan. This parameter setting was chosen to remove as much image-matching noise as possible from the DSM comparison, though at the expense of details, in order to focus on the effect of block orientation configuration only. No filtering or manual cleaning of the point cloud generated with PhotoScan was performed to remove vegetation, cars, and other disturbances at this stage. From each point cloud, a raster DSM with a cell size of 10 cm (about 4.3 GSD) was generated in PhotoScan, with the Interpolation processing parameter set to Enabled. The raster area with elevation data covers about 180.000 m\({}^{2}\). Though the flight plan was always the same, the actual ground coverage varied slightly from flight to flight, mainly due to wind and air turbulence, especially along strips.  
For the comparisons, the 24 DSMs were grouped into six different sets, each coming from the processing of the four blocks flown with the same block control configuration and with the same RTK mode (from the local master or a NRTK).  
Map algebra was used in QGIS to produce a raster with the elevation ranges among the four DSMs of each set. A minDSM raster was computed, where each pixel contained the minimum elevation value at that pixel among the four DSMs; likewise, a MAXDSM raster was generated that contained the maximum elevation at that pixel among the four-DSM set. Finally, the raster difference between MAXDSM and minDSM was computed, obtaining the pixel-wise elevation range for the four DSM.  
The influence, if any, of the surface type on the DSM cell ranges was investigated. To this aim, three ground-level surface classes were selected: ploughed field, meadow, and asphalt (car parks and roads, concrete on pavements); in addition, another class of hard surfaces (roof) was considered. Areas belonging to each of the above-mentioned classes were manually contoured with polylines. Disturbances such as trees and bushes, cars, fences, and any other object with step edges or sharp discontinuities were left out; roof edges as well as heating and air conditioning equipment, pipes, or skylights were also excluded from the roof control areas. The rationale for selecting only smooth or gently varying surfaces is that it is well known [16] that image matching is not accurate along sharp discontinuities. Therefore, large discrepancies in elevation might originate along edges, due to perspective differences of vertical or sub-vertical surfaces in the images of different flights.  
The size of the disturbance-free areas extracted for each surface class, shown in Table 1, amount to about 37% of the total surveyed area. Figure 3 shows the areas contoured for the repeatability analysis, marked with different colours, according to surface class.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Surface Type** & **Asphalt** & **Roof** & **Meadow** & **Ploughed Field** & **Total** \\ \hline size (m\({}^{2}\)) & 12883 & 8626 & 28504 & 17838 & 67851 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Size in square meters of the surface classes considered in the evaluation of DSM variability.