## 4 Discussion  
### UAS and Field Work for Validation  
Considering the similarity in accuracy between UAS and field-based assessments, our conclusion is strongly in favor of validation with UAS where feasible. Primarily, this is due to the increased ability to sample validation points across a larger proportion of the study site, while requiring less time and less intrusion on the study area. For our study, using conventional field-based methods required two 10-h days for three individuals to validate six small transects (214 validation points in 30 h across 186,000 m\({}^{2}\)) and UAS-based methods required five hours of mission planning, five two-hour expeditions of two individuals, and five hours of post-flight analysis to map the entire study area (450 validation points in 15 h across 4,325,000 m\({}^{2}\)). While startup costs of pilot training and platform acquisition may be relatively high, the benefits appear to outweigh the costs in most applications. UAS imagery can be taken much more rapidly than a comparable number of field points, meaning smaller windows in acceptable weather, season, and tidal condition are sufficient for data collection. This should allow for validation data acquisition to be closer in time to the satellite imagery. Aerial surveys also do not require a field team to trek through a fragile wetland environment that may itself influence the study site over time. Moreover, aerial imagery allows for a direct comparison to various satellite pixel sizes, which is more flexible and accurate than bringing quadrats into the field and looking at them from the ground. UAS imagery, combined with properly georectified structure from motion processing, permits geolocation errors <0.05 m ([PERSON] et al., 2017). Co-registering satellite imagery to georectified UAS imagery can result in <1 m geolocation error even over large study sites, compared to typical 3-5 m satellite image geolocation error plus any GPS uncertainty in the field. Given that satellites like WorldView-3 can capture features as small as a single tree, or a tendril of emergent wetland, this precision in geolocation is necessary for accurate training and assessment of very high resolution imagery. Tidal rectification of satellite imagery with UAS is straightforward, as typically the tidal state does not change substantially within the duration of a single flight and can be matched easily. Conversely, it is not typically feasible to constrain traditional in-situ fieldwork to a precise tidal period, especially over large study areas. An additional benefit from UAS, though not done in this study, is to include UAS-based Digital Surface Models (DSMs) to add to training and validation accuracy by incorporating elevation into the workflow ([PERSON] et al., 2018). In addition to startup and training costs, there are a few limitations with UAS that should be considered. For example, shadows and glare can prevent an analyst from visually determining water depth or vegetation type; however, appropriate flight timing can limit this issue. Typically, early morning and late afternoon provide minimum glare due to the low sun angle, which is ideal for flights over water and other reflective surfaces. Mid-day flights are ideal in areas with high relief and tall vegetation for minimal shadows. Wind and weather can also prevent smaller platforms from safely operating even if other conditions are ideal and add to glare on water. Early morning flights often mitigate wind issues in coastal areas. Small differences in our UAS and field validation results can be attributed to tidal offset, georeferencing error, vegetation change from August 2016 to February 2018, and the limited number of field points that were taken due to the high time and resource requirements of field validation. In addition to these logistical challenges, sole reliance on UAS-based methods may lead to further issues, such as remote pilots lacking basic knowledge about the ecosystem that will help better design classification systems and interpret classification issues.