## 3 Data and Methods
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**July 2020 Survey**} & \multicolumn{2}{c}{**October 2020 Survey**} \\ \hline Flying altitude & & 375m & & 383m & \\ Number of images & & 595 & & 451 & \\ Overlap & & 80\% & & 80\% \\ Dense point cloud & \multicolumn{2}{c}{\(\sim\)77 million points} & \multicolumn{2}{c}{\(\sim\)54 million points} \\  & X(m) & Y(m) & Z(m) & X(m) & Y(m) & Z(m) \\ Camera location RMS error & 1.19 & 1.98 & 1.57 & 0.003 & 0.004 & 0.007 \\ GCPs RMS error & X(m) & Y(m) & Z(m) & X(m) & Y(m) & Z(m) \\ GCPs RMS error & 0.03 & 0.07 & 0.07 & 0.03 & 0.07 & 0.07 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Details of the photogrammetric processing of the two surveys. Figure 8: Overview of the geomorphic changes at the popular Myrtos beach, through comparison of the constructed orthoimages before (**a**) and after (**b**) the Medicare Ianos disaster. was 6-8 cm through an effort to keep the ground sampling distance (GSD) as low as possible due to the site topography that was characterized by sharp relief. This resolution was rather satisfactory for the specific use of the final products. The images were acquired with the camera set at an off-nadir position, and this 90\({}^{\circ}\) angle was steady due to the UAS built-in gimbal. The flight plan was set to keep an image overlap of 80% forward and 80% side, as this was calculated by the flight mission software. Photogrammetric processing was carried out twice, for the pre-and post-medicane surveys. It was based on the SfM photogrammetrical approach and implemented using the commercial software Agisoft Metashape Professional (1.7.5 build 13229) [77]. The processing required the alignment of the aerial images and the creation of a sparse point cloud followed by a regular mesh generation, which was used in both flight surveys. The differentiation between the two projects comes right afterward at the processing workflow and it has to do with the co-registration of the results of the two periods (before and after the medicane damages) and the need for higher precision of both outcomes. Several techniques, each one with pros and cons, were used for the determination of surface deformation features, as well as for the calculation of changes within the prone area covered by mudflows accompanied by volumetric differences from the multi-temporal DSMs [78]. Therefore, we chose the post-medicane survey as the reference project since the non-destructed areas could be identified in both projects and used as tie-points between them. This is a very important barrier that needs to be overcome to create a successive multi-temporal image after co-registering the final products very accurately [79]. We chose the post-processing kinematics (PPK) approach for georeferencing, by including GNSS permanent station RINEX data within the procedure [80]. The succeeded accuracy was more than satisfactory, reaching horizontal and vertical errors in the order of 0.009 m, yielding a very reliable dataset from the geolocation point of view. The procedure continued with generating a dense point cloud, therefore creating much finer topographic details. At a later step, texturing was also applied before generating the final ortho-image as well as the DSM [81]. Even though the NRTK approach was also chosen for the earliest survey, the need for co-registration led us to also use ground control points (GCPs) within the processing. By applying this technique, we managed to succeed the highest precision between the two projects by picking seven points on the post-disaster ortho-image which remained unchanged after the medicane and using them as pseudo-GCPs [82] during the photogrammetric procedure of the pre-disaster project (Figure 9). The pseudo-GCPs were used in the reference dataset to easily bring the target dataset to the exact same position with an error of 0.007 m, which is rather satisfactory. The post-medicane orthoimage was used for recording the X and Y coordinates, whilst the interpolated DSM was used for the extraction of every GCP elevation.