## 5 Discussion  
### Effect of Snow Surface Type, Light Conditions, and Image Quality  
Two limitations are known to affect SfM performance in a geoscience context, non-linear deformations and image texture dependence [20; 21]. In our study, we countered the issue of non-linear deformation by ensuring accurate visual observations of the reconstructed snow surfaces, a targeted geometric image pre-processing and the implementation of the Photoscan built-in function _optimization_, which performed well with the accurate GCPs collected for this study [35]. In this work, we are primarily investigating the latter SfM caveat in relation to featureless surfaces, such as snow, that can be further affected by poor lighting conditions. Snow surface reconstruction was demonstrated to be successful for all tested cases employing two opposite extremes of imaging quality, the minimal and advanced set-up. We found image acquisition was critical because SfM performance relies heavily on the quality of the image (Appendixs B and C), especially when the subject lacks natural features for reconstruction or when contrast inducing \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Area Name**} & **Area Covered** & **Snow Depth Map** & \multirow{2}{*}{**VPs**} & **Average Difference** & **Standard** & **RMSE** \\  & **(m\({}^{2}\))** & **Resolution (m)** & & & \(\mathbf{HS_{m}-HS_{uAS}}\) **(m)** & **Deviation (m)** & \(\mathbf{HS_{m}-HS_{uAS}}\) **(m)** \\ \hline
**Swal1** & 6100 & 0.06 & 8 & 0.0379 & 0.0457 & 0.0594 \\
**Swal2** & 1386 & 0.09 & 5 & 0.0156 & 0.0428 & 0.0456 \\
**Green1** & 2738 & 0.1 & 4 & 0.0873 & 0.0985 & 0.1317 \\
**Green2** & 2260 & 0.09 & 11 & 0.161 & 0.0928 & 0.1857 \\
**Green3** & 38,410 & 0.09 & 9 & 0.038 & 0.0862 & 0.0943 \\
**Green4** & 27,687 & 0.09 & 7 & 0.021 & 0.086 & 0.0887 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of the results corresponding to the estimated snow depth maps for all the different surveyed areas. Average difference between the measured snow depth \(HS_{m}\) and the estimated snow depth \(HS_{UAS}\) is calculated for the validation points (VPs). The standard deviation is in regards of the average difference between the VPs. RMSE refers to Root Mean Square Error between measured and estimated snow depth. Figure 5: Results from the snow-free validation procedure over all the surveyed areas. The histograms represent the distribution of pixel samples of estimated snow depth (\(HS_{UAS}\)) in snow-free areas providing an additional validation source. This assessment assesses the overall integrity of the SDEM models correct reconstruction through comparison with a different data source (the TDEMs) at common points away from the GCPs. Resolution refers to the pixel size of the sample in the SDEM orthophotos. Samples refer to the number of pixels extracted from the SDEM having an RGB mean inferior to 0.2 (associated with dark, snow-free pixels). luminance is lacking [23; 24]. It is also optimal for the camera's internal parameters (focal length, aperture, ISO, and shutter speed) to be set according to the camera's capabilities, the snow surface type, and the actual luminance conditions depending on the aimed resolution (or GSD) and the UAS flying parameters (altitude and speed) (see Appendix B for more information). Nevertheless, both luminance and snow surface type were observed to have an impact in the SfM reconstruction of snow surfaces. The effect of luminance availability, as observed in the SDEM production (Table 3 and visual observations), was found to be more influential than the snow surface type for both camera set-ups. This is attributed not only to the lack of contrast and features in the images but also to the fact that, in combination with the faster shutter-speeds required for a moving platform, it can produce dark images characterized by increased noise, leading to incorrect surface interpolations or a complete lack of matched points (Appendix C). This was evident for the inferior optical performance of the minimal set-up.