# Quality Assessment of Photogrammetric Methods--A Workflow for Reproducible UAS Orthomosaics
## Materials and Methods
### Orthomosaic Reproducibility  
Since the orthomosaics are the result of complex photogrammetric methods, its reproducibility has to be assessed. In this context, reproducibility is a measure of how identical individual orthomosaics are, if they are computed from the same image source and with identical photogrammetric processing parameters. This way, the reproducibility of the photogrammetric process itself is evaluated without the influence of changes in the surveyed environment. For this purpose, a set amount of orthomosaics is computed with identical settings (Figure 2). To quantify the reproducibility, the pixel-wise correlation coefficient of the RGB values is calculated between each pair of the computations (_raster_ R package, corLocal function). Pixels with a correlation coefficient of 0.95 or higher are considered identical between two orthomosaics. This leads to a binary layer for all pair-wise correlations marking reproducible and non-reproducible pixels. By summing up the binary layers, regions of high and low reproducibility can be identified (Figure 2c). High values then denote a high level of reproducibility of a pixel.  
The more orthomosaics are computed, the more correlation layers can be calculated (Equation (2)) and the more likely a layer is to receive a non-correlating pair of pixels. Therefore, a preliminary test with an arbitrarily high number of 25 orthomosaics (x = 25) was done, which leads to n = 300 correlation layers.  
\[n_{correlations}=\frac{x!}{2!*(x-2)!} \tag{2}\]  
In practice, computing 25 orthomosaics is, in most cases, unreasonable regarding the computation time and processing resources. Therefore, the reproducibility analysis was also done with only 5 identical orthomosaic computations (i.e., 10 pairwise correlations). The comparison of both reproducibility layers revealed that summing up 10 correlation layers (x = 5) is sufficient to identify most pixels which are also denoted as non-reproducible when using 300 binary layers. The analysis of the time series and the full forest set were therefore done with only 5 identical orthomosaic computations.  
In addition, the edges of the orthomosaic are heavily distorted and have a lower positional accuracy due to less image overlap [37]. Therefore, the orthomosaic should be cropped to the central area with a sufficient overlap. In the R package uavRmp provided with this study, this crop mask is automatically generated from spatial polygons defined by the seamless (i.e., the outline of the individual image parts) of the mosaic. The outermost polygons are identified using a concave hull of the seamless and are discarded from the orthomosaic.  
All computations were done in R (Version 4.0.2; [38]). All presented methods are provided as the R-package uavRmp ([[https://gisma.github.io/uavRmp/](https://gisma.github.io/uavRmp/)]([https://gisma.github.io/uavRmp/](https://gisma.github.io/uavRmp/))) and the Metashape Python Scripts ([[https://github.com/envima/MetashapeTools](https://github.com/envima/MetashapeTools)]([https://github.com/envima/MetashapeTools](https://github.com/envima/MetashapeTools))).