This research collaboration was the first of its kind in the region and has catalyzed continued collaboration to identify regional management needs and expand UAS-based monitoring to additional coastal habitats (e.g., oyster reefs). Furthermore, the project increased the technical capacity of end users to conduct UAS-based wetland monitoring. This project serves as a critical first step for improving tidal wetland monitoring conducted as part of SWMP. The strength of the relationship between NDVI and biomass was weaker in mixed-species plots (R2 = 0.52). Aboveground biomass in monospecific Spartina alterniflora plots was highly correlated to NDVI (R2 > 0.69), although the relationship was reserve- and sensor-specific. Comparison of field- and image-based estimates of species-specific cover indicated minimal agreement between the two approaches the interquartile ranges of the differences were wide for all species (>40%). Overall accuracy of species-specific classifications among reserves was 74 +/- 6% when using both orthomosaics and surface vegetation models. Average differences in percent cover between approaches was ~5% at one reserve, but >25% at four reserves.
Comparison of field- and image-based estimates of total percent vegetated cover indicated modest agreement between the two approaches, although percent cover was generally overestimated from imagery. Overall accuracy of vegetated and unvegetated classifications among sites was 85 +/- 4%. The average distance between image- and field-based delineations of the wetland-water ecotone was 0.18 +/- 0.01m, whereas differences of the low-high marsh ecotone were 1.25 +/- 0.11m. The accuracy of ecotones delineated from UAS imagery varied among ecotones. Across sites, root mean squared error ranged from 0.25 to 0.59m for bare earth models, 0.15 to 1.58m for vegetation surface models, and 0.33 to 2.1m for canopy height. UAS-derived elevation models and canopy height estimates were generally of insufficient accuracy to be useful when compared to field measures. Using ground-based surveys for validation, we 1) assessed the efficacy of UAS-based imagery for estimating vegetation percent cover, delineating ecotones (e.g., low to high marsh), and generating digital elevation models, and 2) assessed the utility of multispectral sensors for improving products from #1 and developing vegetation indices to estimate aboveground biomass (e.g., normalized difference vegetation index, NDVI). Based on a needs assessment and discussions with NERRS end users, we conducted a regionally coordinated effort, working in salt marshes and mangroves within six reserves in the Southeast and Caribbean to develop, assess and collaboratively refine a UAS-based tidal wetlands monitoring protocol aimed at entry-level UAS users. Uncrewed Aerial Systems (UAS, i.e., drones) can provide high spatial resolution and coverage, with customizable sensors, at user-defined times. While both approaches have strengths, important processes at intermediate spatial (i.e., marsh platform) and finer temporal (i.e., storm events) scales may be missed. This monitoring is conducted in 1m2 permanent plots every 1-3 years via in situ sampling and at reserve-wide scales via airplane imagery every 5-10 years. The National Estuarine Research Reserve System (NERRS) invests heavily in assessing changes in tidal wetlands through the System-wide Monitoring Program (SWMP). Monitoring plays a central role in detecting change in coastal ecosystems. Bridging the Gap between Quadrats and Satellites: Assessing Utility of Drone-based Imagery to Enhance Emergent Vegetation Biomonitoring - NERRS/NSC(NERRS Science Collaborative)
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