Many restoration projects’ success is not evaluated (Roni & Beechie 2013; Nilsson et al. 2016), despite available conventional ecological assessment methods. There is a need for more flexible, affordable, and efficient methods for evaluation, particularly those that take advantage of new remote sensing and geospatial technologies (Hubbart et al. 2017). This study explores the use of illustrative small unmanned aerial system (sUAS) products, made using a simple structure-from-motion photogrammetry workflow, coupled with a visual assessment protocol as a remote evaluation and ecological condition archive approach. Three streams were assessed in the field (“surface assessments”) using the Stream Visual Assessment Protocol Version 2 (SVAP2) and later illustrated in sUAS products. A survey of 10 stream experts was conducted to 1) assess the general utility of the sUAS products (high resolution video, orthomosaics, and 3D models), and 2) test whether the experts could interpret the products and apply the 16 SVAP2 elements remotely. The channel condition, bank condition, riparian area quantity, and canopy cover elements were deemed appropriate for remote assessment, while the riparian area quality, water appearance, fish habitat complexity, and aquatic invertebrate complexity elements were deemed appropriate for remote assessment but with some potential limitations due to the quality of the products and varying site conditions. In general, the survey participants agreed that the illustrative products would be useful in stream ecological assessment and restoration evaluation. Although not a replacement for more quantitative surface assessments when required, this remote visual approach is suitable when more general monitoring is satisfactory.

Note: This is an Author’s Original Manuscript of an article to be published by Wiley in Restoration Ecology, the Accepted Manuscript is currently available online: https://doi.org/10.1111/rec.13228

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We would like to acknowledge the National Science Foundation’s support via the Research Infrastructure Improvement Award (NSF #IIA-1539071). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

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