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Temporal Stability of Seagrass Extent, Leaf Area, and Carbon Storage in St. Joseph Bay, Florida: a Semi-automated Remote Sensing Analysis

Seagrasses are globally recognized for their contribution to blue carbon sequestration. However, accurate quantification of their carbon storage capacity remains uncertain due, in part, to an incomplete inventory of global seagrass extent and assessment of its temporal variability. Furthermore, seag...

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Autores principales: Lebrasse, Marie Cindy, Schaeffer, Blake A., Coffer, Megan M., Whitman, Peter J., Zimmerman, Richard C., Hill, Victoria J., Islam, Kazi A., Li, Jiang, Osburn, Christopher L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10054859/
https://www.ncbi.nlm.nih.gov/pubmed/37009415
http://dx.doi.org/10.1007/s12237-022-01050-4
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author Lebrasse, Marie Cindy
Schaeffer, Blake A.
Coffer, Megan M.
Whitman, Peter J.
Zimmerman, Richard C.
Hill, Victoria J.
Islam, Kazi A.
Li, Jiang
Osburn, Christopher L.
author_facet Lebrasse, Marie Cindy
Schaeffer, Blake A.
Coffer, Megan M.
Whitman, Peter J.
Zimmerman, Richard C.
Hill, Victoria J.
Islam, Kazi A.
Li, Jiang
Osburn, Christopher L.
author_sort Lebrasse, Marie Cindy
collection PubMed
description Seagrasses are globally recognized for their contribution to blue carbon sequestration. However, accurate quantification of their carbon storage capacity remains uncertain due, in part, to an incomplete inventory of global seagrass extent and assessment of its temporal variability. Furthermore, seagrasses are undergoing significant decline globally, which highlights the urgent need to develop change detection techniques applicable to both the scale of loss and the spatial complexity of coastal environments. This study applied a deep learning algorithmto a 30-year time series of Landsat 5 through 8 imagery to quantify seagrass extent, leaf area index (LAI), and belowground organic carbon (BGC) in St. Joseph Bay, Florida, between 1990 and 2020. Consistent with previous field-based observations regarding stability of seagrass extent throughout St. Joseph Bay, there was no temporal trend in seagrass extent (23 ± 3 km(2), τ = 0.09, p = 0.59, n = 31), LAI (1.6 ± 0.2, τ = −0.13, p = 0.42, n = 31), or BGC (165 ± 19 g C m(−2), τ = − 0.01, p = 0.1, n = 31) over the 30-year study period. There were, however, six brief declines in seagrass extent between the years 2004 and 2019 following tropical cyclones, from which seagrasses recovered rapidly. Fine-scale interannual variability in seagrass extent, LAI, and BGC was unrelated to sea surface temperature or to climate variability associated with the El Niño-Southern Oscillation or the North Atlantic Oscillation. Although our temporal assessment showed that seagrass and its belowground carbon were stable in St. Joseph Bay from 1990 to 2020, forecasts suggest that environmental and climate pressures are ongoing, which highlights the importance of the method and time series presented here as a valuable tool to quantify decadal-scale variability in seagrass dynamics. Perhaps more importantly, our results can serve as a baseline against which we can monitor future change in seagrass communities and their blue carbon.
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spelling pubmed-100548592023-11-01 Temporal Stability of Seagrass Extent, Leaf Area, and Carbon Storage in St. Joseph Bay, Florida: a Semi-automated Remote Sensing Analysis Lebrasse, Marie Cindy Schaeffer, Blake A. Coffer, Megan M. Whitman, Peter J. Zimmerman, Richard C. Hill, Victoria J. Islam, Kazi A. Li, Jiang Osburn, Christopher L. Estuaries Coast Article Seagrasses are globally recognized for their contribution to blue carbon sequestration. However, accurate quantification of their carbon storage capacity remains uncertain due, in part, to an incomplete inventory of global seagrass extent and assessment of its temporal variability. Furthermore, seagrasses are undergoing significant decline globally, which highlights the urgent need to develop change detection techniques applicable to both the scale of loss and the spatial complexity of coastal environments. This study applied a deep learning algorithmto a 30-year time series of Landsat 5 through 8 imagery to quantify seagrass extent, leaf area index (LAI), and belowground organic carbon (BGC) in St. Joseph Bay, Florida, between 1990 and 2020. Consistent with previous field-based observations regarding stability of seagrass extent throughout St. Joseph Bay, there was no temporal trend in seagrass extent (23 ± 3 km(2), τ = 0.09, p = 0.59, n = 31), LAI (1.6 ± 0.2, τ = −0.13, p = 0.42, n = 31), or BGC (165 ± 19 g C m(−2), τ = − 0.01, p = 0.1, n = 31) over the 30-year study period. There were, however, six brief declines in seagrass extent between the years 2004 and 2019 following tropical cyclones, from which seagrasses recovered rapidly. Fine-scale interannual variability in seagrass extent, LAI, and BGC was unrelated to sea surface temperature or to climate variability associated with the El Niño-Southern Oscillation or the North Atlantic Oscillation. Although our temporal assessment showed that seagrass and its belowground carbon were stable in St. Joseph Bay from 1990 to 2020, forecasts suggest that environmental and climate pressures are ongoing, which highlights the importance of the method and time series presented here as a valuable tool to quantify decadal-scale variability in seagrass dynamics. Perhaps more importantly, our results can serve as a baseline against which we can monitor future change in seagrass communities and their blue carbon. 2022-11-01 /pmc/articles/PMC10054859/ /pubmed/37009415 http://dx.doi.org/10.1007/s12237-022-01050-4 Text en https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Lebrasse, Marie Cindy
Schaeffer, Blake A.
Coffer, Megan M.
Whitman, Peter J.
Zimmerman, Richard C.
Hill, Victoria J.
Islam, Kazi A.
Li, Jiang
Osburn, Christopher L.
Temporal Stability of Seagrass Extent, Leaf Area, and Carbon Storage in St. Joseph Bay, Florida: a Semi-automated Remote Sensing Analysis
title Temporal Stability of Seagrass Extent, Leaf Area, and Carbon Storage in St. Joseph Bay, Florida: a Semi-automated Remote Sensing Analysis
title_full Temporal Stability of Seagrass Extent, Leaf Area, and Carbon Storage in St. Joseph Bay, Florida: a Semi-automated Remote Sensing Analysis
title_fullStr Temporal Stability of Seagrass Extent, Leaf Area, and Carbon Storage in St. Joseph Bay, Florida: a Semi-automated Remote Sensing Analysis
title_full_unstemmed Temporal Stability of Seagrass Extent, Leaf Area, and Carbon Storage in St. Joseph Bay, Florida: a Semi-automated Remote Sensing Analysis
title_short Temporal Stability of Seagrass Extent, Leaf Area, and Carbon Storage in St. Joseph Bay, Florida: a Semi-automated Remote Sensing Analysis
title_sort temporal stability of seagrass extent, leaf area, and carbon storage in st. joseph bay, florida: a semi-automated remote sensing analysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10054859/
https://www.ncbi.nlm.nih.gov/pubmed/37009415
http://dx.doi.org/10.1007/s12237-022-01050-4
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