Seagrasses provide a multitude of ecosystem services and serve as important organic carbon stores. However, seagrass habitats are declining worldwide, threatened by global climate change and regional shifts in water quality. Acoustical methods have been applied to assess changes in oxygen production of seagrass meadows since sound propagation is sensitive to the presence of bubbles, which exist both within the plant tissue and freely floating the water as byproducts of photosynthesis. This work applies acoustic remote sensing techniques to characterize two different regions of a seagrass meadow: a densely vegetated meadow of Thalassia testudinum and a sandy region sparsely populated by isolated stands of T. testudinum. A Bayesian approach is applied to estimate the posterior probability distributions of the unknown model parameters. The sensitivity of sound to the void fraction of gas present in the seagrass meadow was established by the narrow marginal probability distributions that provided distinct estimates of the void fraction between the two sites. The absolute values of the estimated void fractions are biased by limitations in the forward model, which does not capture the full complexity of the seagrass environment. Nevertheless, the results demonstrate the potential use of acoustical methods to remotely sense seagrass health and density.
Ballard, Megan S., Kevin M. Lee, Jason D. Sagers, Gabriel R. Venegas, Andrew R. McNeese, Preston S. Wilson, and Abdullah F. Rahman. 2020. “Application of Acoustical Remote Sensing Techniques for Ecosystem Monitoring of a Seagrass Meadow.” The Journal of the Acoustical Society of America 147 (3): 2002–19. https://doi.org/10.1121/10.0000954
The Journal of the Acoustical Society of America