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Review

A Blueprint for the Estimation of Seagrass Carbon Stock Using Remote Sensing-Enabled Proxies

1
School of Geosciences, University of Sydney, Madsen Building, Eastern Avenue, Sydney, NSW 2006, Australia
2
Centre for CubeSats, UAVs and Their Applications (CUAVA), University of Sydney, Sydney, NSW 2006, Australia
3
ArborCarbon Pty Ltd., Murdoch University, Rota Trans 1, Murdoch, WA 6150, Australia
4
Centre for Terrestrial Ecosystem Science & Sustainability, Harry Butler Institute, Murdoch University, Murdoch, WA 6150, Australia
*
Author to whom correspondence should be addressed.
Remote Sens. 2022, 14(15), 3572; https://doi.org/10.3390/rs14153572
Submission received: 26 May 2022 / Revised: 18 July 2022 / Accepted: 22 July 2022 / Published: 25 July 2022
(This article belongs to the Special Issue Use of Remote Sensing in Valuation of Blue Carbon and Its Co-benefits)

Abstract

Seagrass ecosystems sequester carbon at disproportionately high rates compared to terrestrial ecosystems and represent a powerful potential contributor to climate change mitigation and adaptation projects. However, at a local scale, rich heterogeneity in seagrass ecosystems may lead to variability in carbon sequestration. Differences in carbon sequestration rates, both within and between seagrass meadows, are related to a wide range of interrelated biophysical and environmental variables that are difficult to measure holistically using traditional field surveys. Improved methods for producing robust, spatially explicit estimates of seagrass carbon storage across large areas would be highly valuable, but must capture complex biophysical heterogeneity and variability to be accurate and useful. Here, we review the current and emerging literature on biophysical processes which shape carbon storage in seagrass beds, alongside studies that map seagrass characteristics using satellite remote sensing data, to create a blueprint for the development of remote sensing-enabled proxies for seagrass carbon stock and sequestration. Applications of satellite remote sensing included measuring seagrass meadow extent, estimating above-ground biomass, mapping species composition, quantifying patchiness and patch connectivity, determining broader landscape environmental contexts, and characterising seagrass life cycles. All of these characteristics may contribute to variability in seagrass carbon storage. As such, remote sensing methods are uniquely placed to enable proxy-based estimates of seagrass carbon stock by capturing their biophysical characteristics, in addition to the spatiotemporal heterogeneity and variability of these characteristics. Though the outlined approach is complex, it is suitable for accurately and efficiently producing a full picture of seagrass carbon stock. This review has drawn links between the processes of seagrass carbon sequestration and the capabilities of remote sensing to detect and characterise these processes. These links will facilitate the development of remote sensing-enabled proxies and support spatially explicit estimates of carbon stock, ensuring climate change mitigation and adaptation projects involving seagrass are accounted for with increased accuracy and reliability.
Keywords: blue carbon; seagrass; carbon stock; proxy blue carbon; seagrass; carbon stock; proxy

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MDPI and ACS Style

Simpson, J.; Bruce, E.; Davies, K.P.; Barber, P. A Blueprint for the Estimation of Seagrass Carbon Stock Using Remote Sensing-Enabled Proxies. Remote Sens. 2022, 14, 3572. https://doi.org/10.3390/rs14153572

AMA Style

Simpson J, Bruce E, Davies KP, Barber P. A Blueprint for the Estimation of Seagrass Carbon Stock Using Remote Sensing-Enabled Proxies. Remote Sensing. 2022; 14(15):3572. https://doi.org/10.3390/rs14153572

Chicago/Turabian Style

Simpson, Jamie, Eleanor Bruce, Kevin P. Davies, and Paul Barber. 2022. "A Blueprint for the Estimation of Seagrass Carbon Stock Using Remote Sensing-Enabled Proxies" Remote Sensing 14, no. 15: 3572. https://doi.org/10.3390/rs14153572

APA Style

Simpson, J., Bruce, E., Davies, K. P., & Barber, P. (2022). A Blueprint for the Estimation of Seagrass Carbon Stock Using Remote Sensing-Enabled Proxies. Remote Sensing, 14(15), 3572. https://doi.org/10.3390/rs14153572

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