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The purpose of this project was to evaluate and compare methodologies for using remotely sensed hyperspatial imagery from both Unmanned Aircraft Systems (UAS) and nextgeneration satellite technology to calculate species composition and ecosystem service metrics in a coastal intermediate marsh. Such methods would be important steps towards comprehensive monitoring of wetland landscapes and would provide useful metrics to study wetland condition and response to ecosystem restoration and disturbance events. BACKGROUND: Plant species composition, cover, density, and biomass are structural components of coastal marshes that are commonly used to quantify vegetative characteristics and often serve as indicators of wetland condition (Chamberlain and Ingram 2012; Cretini et al. 2012). Historically, regional and coastwide surveys to map coastal vegetation have consisted of laborious traversing of wetland landscapes, including time consuming and subjective ocular estimates of species type and cover (O'Neil 1949; Chabreck and Linscombe 1978; Sasser et al. 2014). Although recent surveys, like those in coastal Louisiana, enlist the use of helicopters for transport between, and hovering over, sampling sites, they continue to rely on ocular estimates of dominant species and abundance (i.e., Braun-Blanquet cover scale), they are costly, and are typically reproduced approximately every ten years. Rapid species classification (even of dominant plants) using remotely sensed data would provide many advantages over traditional field techniques. NOTE: The contents of this technical note are not to be used for advertising, publication, or promotional purposes. Citation of trade names does not constitute an official endorsement or approval of the use of such products.

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