# Quality Assessment of Photogrammetric Methods--A Workflow for Reproducible UAS Orthomosaics
The basic image processing workflow starts with the alignment of the individual images, which results in a projected 3D point cloud. Usually, this point cloud is georeferenced through the individual image coordinates or via the use of measured ground control points (GCPs) [22; 23]. The point cloud is the basis for the generation of a surface model and the orthorectification and mosaicing of the individual images based on this surface. Commercial software such as Metashape (Aigsoft LLC, St. Petersburg, Russia; formerly known as Photoscan) makes these complex photogrammetric methods accessible to a broad range of users and has been utilized in numerous studies [3; 13; 24]. The quality of photogrammetrically compiled orthomosaics is commonly expressed as their georeference accuracy [25; 26; 27; 28] or statistical error metrics derived from the image alignment (e.g., the amount of points in the point cloud or the reprojection error [8; 29]). However, these measures alone provide no comprehensive information about the quality of the orthomosaic since the subsequent steps of orthorectification and mosaicing are not taken into account. Image artifacts and distortions can occur during these processing steps that are not reflected in the georeference accuracy [30]. Especially in forest ecosystems, the complex and diverse structures and similar image patterns in the canopy can lead to erroneous imagery [20]. In addition, wind exposure leads to changes in the structure of the tree canopy and consequently causes problems in the alignment of individual images [31]. The quality assessment becomes even more important when time series are analyzed since actual changes of the observed environmental variables have to be separated from deviations which stem from the image processing itself. In addition, here low georeferencing errors are even more important since the errors of the individual time steps can accumulate. Studies utilizing time series relied on georeferencing errors below 10 cm of the individual time steps [26; 32]. To evaluate the reproducibility and validity of orthomosaic time series, it is therefore necessary to: (1) optimize the positional accuracy of the individual orthophoto mosaics, (2) to evaluate the reproducibility of the photogrammetric processing of these orthophoto mosaics and (3) to evaluate the positional accuracy between features in the individual time steps of the series. This study proposes (i) an optimization for the orthorectification of UAS images and (ii) an additional quality criterion for UAS orthomosaics that focuses on the reproducibility of the photogrammetric processing. The orthorectification is improved by an automated optimization of the checkpoint error based on an iteration of point cloud filters. The reproducibility of orthomosaics is quantified by the repeated processing of the same scene and a pixel-wise correlation analysis between the resulting orthophoto mosaics. We illustrate the importance of both methods using different orthorectification surfaces and two time series in a grassland and a forest area, respectively. To foster an error and reproducibility optimized orthomosaic processing, we incorporate the new methods into an improved UAS workflow.