Pushing the Limits of Seagrass Remote Sensing in the Turbid Waters of Elkhorn Slough, California

Abstract

Remote sensing imagery has been successfully used to map seagrass in clear waters, but here we evaluate the advantages and limitations of different remote sensing techniques to detect eelgrass in the tidal embayment of Elkhorn Slough, CA. Pseudo true-color imagery from Google Earth and broadband satellite imagery from Sentinel-2 allowed for detection of the various beds, but retrievals particularly in the deeper Vierra bed proved unreliable over time due to variable image quality and environmental conditions. Calibrated water-leaving reflectance spectrum from airborne hyperspectral imagery at 1-m resolution from the Portable Remote Imaging SpectroMeter (PRISM) revealed the extent of both shallow and deep eelgrass beds using the HOPE semi-analytical inversion model. The model was able to reveal subtle differences in spectral shape, even when remote sensing reflectance over the Vierra bed was not visibly distinguishable. Empirical methods exploiting the red edge of reflectance to differentiate submerged vegetation only retrieved the extent of shallow alongshore beds. The HOPE model also accurately retrieved the water column absorption properties, chlorophyll-a, and bathymetry but underestimated the particulate backscattering and suspended matter when benthic reflectance was represented as a horizontal eelgrass leaf. More accurate water column backscattering could be achieved by the use of a darker bottom spectrum representing an eelgrass canopy. These results illustrate how high quality atmospherically-corrected hyperspectral imagery can be used to map eelgrass beds, even in regions prone to sediment resuspension, and to quantify bathymetry and water quality.

Branching Algorithm to Identify Bottom Habitat in the Optically Complex Coastal Waters of Atlantic Canada Using Sentinel-2 Satellite Imagery

“…Shallow to moderate depth bare sediment was classified using the Red/Green ratio (Band 4 divided by Band 3). Pixels with a Red/Green ratio ≤ 0.3 were classified as bare sand as in Dierssen et al (2019), and pixels with a Red/Green ratio ≥ 0.9 were classified as mud (i.e., dark sediment) or contaminated by fresh tannic water runoff which is common along beaches in the region. Dashed lines indicate pixels that would be classified using the NDVI or R/G band thresholds.…”

“…Our stepwise approach to image classification is a unique hybrid of other methods of image classification, which generally focus on simple band ratios (e.g., Dierssen et al, 2019;Mora-Soto et al, 2020) or a supervised classifier to quantify bottom habitat (e.g., Traganos and Reinartz, 2018b;Poursanidis et al, 2019), but not both. The ratio approach was applied first to all pixels, and when the threshold was met, pixels were classified with no assumptions made on the other pixels (i.e., the one that did not meet the threshold criteria).…”

“…The band ratios include vegetation indices based on red-edge indices such as NDVI and the Red-Green ratio, which can be effective tools for quantifying intertidal vegetation at low tide or vegetation that floats at, or near, the surface (e.g., Barillé et al, 2010;Dierssen et al, 2019;Mora-Soto et al, 2020). Following Dierssen et al (2019) we found that the Red-Green ratio could successfully classify bare sand at shallow to moderate depths, albeit at a slightly lower threshold value (<0.30 this study, <0.35 Dierssen et al, 2019). While no upper threshold could be determined to classify vegetated habitat, an upper threshold could be defined to exclude shallow muddy substrate (>0.9).…”

“…A more complicated classification question arises for submerged macrophytes in optically complex temperate waters, where high CDOM, suspended particulate matter, and phytoplankton concentration reduce the maximum depth at which the seafloor is visible compared to tropical habitats (3-10 m vs. <40 m). The use of satellite remote sensing in temperate habitats is becoming more common (e.g., Casal et al, 2011;O'Neill and Costa, 2013;Dierssen et al, 2019), and in the process, new methods are being developed to leverage the many benefits of satellite remote sensing to accurately quantify the distribution of marine macrophytes in areas where water transparency still permits bottom habitat mapping with passive sensors.…”

Between a Reef and a Hard Place: Capacity to Map the Next Coral Reef Catastrophe

“…Spatial remote sensing has been viewed as advantageous to environmental monitoring, providing cost-efficient observations across large (≥10 km 2 ) and difficult to access geographical areas at extended temporal scales (decadal) (Hedley et al, 2016). However, the tropical marine environment provides difficulties for traditional aerial remote sensing data acquisition, such as the spatial resolution of satellite data (Kachelriess et al, 2014;Hedley et al, 2016), and water clarity (e.g., turbidity, atmospheric noise including clouds, and ability of spectral wavelengths to penetrate water) (Dierssen et al, 2019). As such these methods have not been able to adequately detect fine-to-moderate scale (≤10 m 2 ) coral reef responses to environmental pressures including temperature variations.…”