## Conclusions  
This study provides a detailed example for future integrations of UAS sampling and satellite imagery in the study of coastal landscape changes. When deciding between satellite imagery, we suggest considering the spatial extent of a study site, the finest features, spectral similarity of classes, timing specificity requirements, and total budget. For example, the finer resolution of WV imagery may be more useful when classification subjects are smaller and similar in spectral signatures to nearby environments, but the frequency of RE imagery could be more beneficial for capturing temporally short-term changes (e.g., storm impacts). Sensor selection should then drive additional indices and image layers, with higher dimensional multispectral datasets not always needing additional layers, as in our study. When evaluating UAS versus field-based validation, we find that, across the board, increased numbers of sample points over a larger area augment the quality and reliability of the final accuracy results. UAS-based accuracy assessment allows for a greater number of validation points to be collected with marginal impact to processing time, permits field work to be conducted within rapid collection windows, and leaves a minimal environmental footprint.  
The following are available online at [[http://www.mdpi.com/2072-4292/10/8/1257/s1](http://www.mdpi.com/2072-4292/10/8/1257/s1)]([http://www.mdpi.com/2072-4292/10/8/1257/s1](http://www.mdpi.com/2072-4292/10/8/1257/s1)), Table S1: Full confusion matrices for unoccupied aircraft system and field-based validations. Figure S1: Unoccupied Aircraft System (UAS) imagery of the Rachel Carson Reserve. Top shows the outline of the Reserve with all mosaics, UAS validation points, and a base map of satellite imagery for reference. Bottom shows just the outline of the Reserve and the UAS mosaics labeled by flight number. Note: all validation points not covered by UAS imagery were clear subtidal haline points and thus did not need additional resolution beyond that of the satellite imagery to determine class. Table S2: Flight details for unoccupied aircraft system (UAS) imagery of the Rachel Carson Reserve.  
Conceptualization, J.J.S., A.M.S., D.W.J., and P.C.G.; Methodology, J.T.R., S.K.P., A.C.S., J.S., and P.C.G.; Software, A.C.S. and P.C.G.; Data Curation: J.T.R., S.K.P., A.C.S., P.C.G.; Investigation, J.T.R., S.K.P., D.W.J., and P.C.G.; Validation, J.T.R., S.K.P., and P.C.G.; Formal Analysis, J.T.R., S.K.P., A.M.S., and P.C.G.; Funding Acquisition, J.T.R. and S.K.P.; Writing-Original Draft Preparation, P.C.G., J.T.R., S.K.P., A.C.S., A.M.S., J.J.S., D.W.J.; Writing-Review & Editing, P.C.G., J.T.R, S.K.P., A.C.S., A.M.S., J.J.S., D.W.J.  
This research was supported by the North Carolina Sea and Space Grant Graduate Fellowship (Grant # 2017-R/MG-1710) as well as the North Carolina Coastal recreational Fishing License Grants Program (Marine Resources Fund, Grant # 2017-H-068).  
We thank [PERSON] and [PERSON] of the North Carolina Coastal Reserve for sharing their 2004 Habitat Map of the Rachel Carson Reserve and GIS knowledge. We thank [PERSON] for constructive feedback through this research project and [PERSON] for imagery and insight into the Reserve's structure. We also appreciate the satellite imagery generously provided for this project from the DigitalGlobe Foundation and Planet Inc.