## 6 Conclusions  
Optical satellite images from SPOT6/7 (tri)stereo, and other similar \(<\)2 m resolution satellites, offer a rich trove of past and ongoing Earth observations from which elevation models can be derived. These DEMs suffer from many errors (both systematic and random), which are concerning for geomorphic applications seeking to analyze fine landscape features such as ridges and valleys. Quality issues can be partly mitigated by carefully chosen processing steps, including appropriate software, pre-processing, stereo-correlation algorithm, and parameters. In this study, we have used the open-source Ames Stereo Pipeline [23; 24; 25] to generate DEMs from SPOT6 tristereo imagery on a smooth, mountainous, vegetation-free study area. Our ASP results extend to other software that uses similar algorithms and parameters. We can summarize our findings in the following:  
1. The unique salar (salt flat) in our study area allowed us to explore the relationship between image texture and DEM uncertainty. Higher image texture (measured by local panchromatic variance) improved DEM quality due to better stereo matching up to a certain point (\(\sim\)10\({}^{3}\) panchromatic variance), beyond which other factors such as satellite view geometry and sensor biases may dominate. 2. Compared to the Block Matching stereo-correlation algorithm, the More-Global Matching algorithm (with the ternary census cost function) preserved smaller scale features using smaller correlation kernels and had improved matching in low-texture areas. 3. Larger correlation kernels improved matching (less holes) at the cost of smoothing the landscape. Increasing the kernel size will lead to more reliable matches and the ability to output a DEM at 2\(\times\) the input image resolution and should be preferred in geomorphic applications to a naive smoothing by downsampling the final DEM to 4\(\times\) the input image resolution. 4. Using only the AC image pair (widest view-angle difference) for stereo-DEM generation produced higher-quality results than using the neighboring (AB, BC) pairs with the nadir scene (B). Mixing them (e.g., by median blending) likely degrades overall quality. However, the neighboring pairs with the nadir image are useful for filling holes (e.g., in steep canyons with occluded views). Images with optimal, wide viewing angles (even if they are from different dates) are therefore recommended for high-quality DEM generation. The higher curvature that we found using a smaller correlation kernel available in the More-Global Matching algorithm (\(9\times 9\) pixels) likely demonstrates the preservation of landscape sharpness. Smaller kernels may also preserve even finer features, but this comes at the expense of more noise on hillslopes and other low-texture areas. Taken together, our findings show a path forward for optical DEM fusion, whereby different parts of the landscape have different processing parameters (e.g., adaptive correlation kernels in areas of low vs. high image texture, use of the nadir image in areas with steep gorges). However, we did not present such a DEM fusion method here (e.g., using several outputs from different parameters), as this is beyond the scope of the current study. Overall, we find that the Ames Stereo Pipeline is a very suitable tool for the generation of optical DEMs for geometric applications and encourage the wider community of topographic data users to adopt it for their research. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/rs15010085/s1](https://www.mdpi.com/article/10.3390/rs15010085/s1)]([https://www.mdpi.com/article/10.3390/rs15010085/s1](https://www.mdpi.com/article/10.3390/rs15010085/s1)). Conceptualization, B.P. and B.B. ; methodology, B.P., B.B. and A.M.; formal analysis and investigation, B.P. ; resources, B.B. ; data curation, B.P. ; writing--original draft preparation, B.P. and A.M.; writing--review and editing, B.P., A.M. and B.B. ; visualization, B.P.