## 4 Discussion
Fortunately, smaller failure volumes (\(<\)0.01 m\({}^{3}\)) correspond to surface erosion (e.g., raveling), which does not pose a significant slope hazard and is typically neglected when assessing rock slope hazards. More rigorous field procedures and dense networks of targets could be implemented to enable improved detection of these failures at smaller individual sites as has been conducted in other studies, if needed; however, these techniques are not practical for long sections such as these because they are laborious to employ and also suffer from significant error propagation over large distances. It also is difficult to isolate all individual rockfall events at highly active sites. For example, at Site C, many rockfall events were clustered together on some of the channel-like portions of the slope (_i.e._, \"chutes,\" Figure 19). To improve these results, surveys must be completed at higher temporal resolutions. Model resolution plays a pivotal role in the volumes calculated as well as the magnitude-frequency relationships. Recall that in this study, resolution is defined in terms of the model resolution, rather than actual sampling resolution. Figures 11 and 16 demonstrate that higher resolutions (lower cell size) are able to account for more erosion; however, the 0.01 m models underestimated the volume (Figures 11a and 16a). Even with hole filling, the volume estimates for the 0.01 m models were markedly low because it is right at the typical acquisition resolution for Sites A and B. Note that although 0.01 m represents a typical sample spacing, there is significant variability in the actual spacing of the points due to sensor noise and topographic effects. Hence, there were insufficient cells with sample points to produce a satisfactory TPS (e.g., more than 50% of the cells in the window were required to have data), unless that criteria was relaxed. The 0.01 m models could also underestimate the volume because holes can be created in the rockfall scars themselves. As the cell size increases, there are fewer holes and the volume estimates converge with those for the models where holes are not filled. Decreased resolution smoothes out smaller features on the surface (Figure 9), but also can help reduce noise in the surface that is present at higher resolutions due to the inability to remove short vegetation. This smoothing at lower resolution then effects the ability to detect change and results in smaller events being undetected (Figures 10a and 15a). In turn, this leads to a lower estimated total volume at each site with decreased resolution (Figures 11a, 16a, and 21). Hence, the minimum detectable volume is primarily a function of resolution. Figures 10, 15 and 20 all show significantly different magnitude-frequency relationships with varied resolution, particularly for detecting smaller volumes. Generally, the relationships are fairly similar for events greater than 0.1 m\({}^{3}\). However, at Site A (Figure 10), the 0.01 m model without hole filling was unable to detect those larger events unless holes were filled. The 0.01 m model with holes filled produced a very similar magnitude-frequency relationship to the lower resolution models for volumes \(>\)0.01 m\({}^{3}\). The 0.01 m model at Site B performed much better. These smaller rockfall events cumulatively can be a significant portion of the total volumes. Sites A (Figure 11a) and C (Figure 21) were not very sensitive to model resolution for overall volume computations (except at high resolutions that resulted in significant data gaps). Site B, in contrast, showed higher dependence on resolution for the erosion volume computations (Figure 16a). This is because Site B contains several rock overlangs, which produce abrupt transitions in the terrain model. At this site, the resolution selected will significantly influence the ability to model those overhangs.