Abstract
Sentinel-2 satellite imagery has been successfully used to map submerged seagrasses in clear waters, and surface-canopy forming seaweed habitats in a range of water types. We examined the ability to use Sentinel-2 remote sensing reflectance to classify fully submerged seagrass and seaweed habitats in optically complex, temperate waters within a high priority management region in Atlantic Canada. To do so, we determined the “best” Sentinel-2 image available between 2015 and 2019 based on tidal height, absence of sun glint and clouds, and water transparency. Using the full Sentinel-2 tile, we atmospherically corrected the image using ACOLITE’s dark spectrum fitting method. Our classification goal was a two-class prediction of vegetation presence and absence. Using information obtained from drop-camera surveys, the image was first partially classified using simple band thresholds based on the normalized difference vegetation index (NDVI), red/green ratio and the blue band. A random forest model was built to classify the remaining areas to a maximum depth of 10 m, the maximum depth at which field surveys were performed. The resulting habitat map had an overall accuracy of 79% and ~231 km2 of vegetated habitat were predicted to occur (total area 345.15 km2). As expected, the classification performed best in regions dominated by bright sandy bare substrate, and dense dark vegetated beds. The classification performed less well in regions dominated by dark bare muddy substrate, whose spectra were similar to vegetated habitat, in pixels where vegetation density was low and mixed with other substrates, and in regions impacted by freshwater input. The maximum depth that bottom habitat was detectable also varied across the image. Leveraging the full capacity of the freely available Sentinel-2 satellite series with its high spatial resolution and resampling provides a new opportunity to generate large scale vegetation habitat maps, and examine how vegetation extent changes over time in Atlantic Canada, providing essential data layers to inform monitoring and management of macrophyte dominated habitats and the resulting ecosystem functions and services.
Highlights
Seaweeds and seagrasses are submerged aquatic vegetation found in nearshore coastal environments
We examined changes in the spectral signatures of remote sensing reflectance for pixels extracted from the best Sentinel2 image over four known habitat types: sand, mud, eelgrass, and seaweed
We used the “best” Sentinel-2 image acquired between 2015 and 2019 for a high priority management region and examined to what detail marine macrophyte habitat could be classified in an optically complex costal environment based on empirical image-based classification procedures
Summary
Seaweeds and seagrasses (marine macrophytes) are submerged aquatic vegetation found in nearshore coastal environments. Coastal ecosystems which are dominated by seaweeds and seagrasses are some of the most productive habitats globally and provide several important ecosystem functions and services (Barbier et al, 2011). These include acting as ecosystem engineers to provide biogenic habitat structure, providing coastal protection against erosion, absorbing nutrient runoff, providing carbon storage, supporting biodiversity and fisheries, and generally acting as an indicator of overall ecosystem health (Orth et al, 2006; Schmidt et al, 2011; Duarte et al, 2013; Wong and Dowd, 2016; Teagle et al, 2017; Wong and Kay, 2019). Tools for mapping and monitoring marine macrophyte distribution are important to understand and quantify habitat changes, to inform decision making related to conservation areas for seaweed and eelgrass habitat, and resource management for commercially important seaweeds
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