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Upscaling methane fluxes from peatlands across a drainage gradient in Ireland using PlanetScope imagery and machine learning tools

Wetlands are one of the major contributors of methane (CH(4)) emissions to the atmosphere and the intensity of emissions is driven by local environmental variables and spatial heterogeneity. Peatlands are a major wetland class and there are numerous studies that provide estimates of methane emission...

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Autores principales: Ingle, Ruchita, Habib, Wahaj, Connolly, John, McCorry, Mark, Barry, Stephen, Saunders, Matthew
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10368722/
https://www.ncbi.nlm.nih.gov/pubmed/37491422
http://dx.doi.org/10.1038/s41598-023-38470-6
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author Ingle, Ruchita
Habib, Wahaj
Connolly, John
McCorry, Mark
Barry, Stephen
Saunders, Matthew
author_facet Ingle, Ruchita
Habib, Wahaj
Connolly, John
McCorry, Mark
Barry, Stephen
Saunders, Matthew
author_sort Ingle, Ruchita
collection PubMed
description Wetlands are one of the major contributors of methane (CH(4)) emissions to the atmosphere and the intensity of emissions is driven by local environmental variables and spatial heterogeneity. Peatlands are a major wetland class and there are numerous studies that provide estimates of methane emissions at chamber or eddy covariance scales, but these are not often aggregated to the site/ecosystem scale. This study provides a robust approach to map dominant vegetation communities and to use these areas to upscale methane fluxes from chamber to site scale using a simple weighted-area approach. The proposed methodology was tested at three peatlands in Ireland over a duration of 2 years. The annual vegetation maps showed an accuracy ranging from 83 to 99% for near-natural to degraded sites respectively. The upscaled fluxes were highest (2.25 and 3.80 gC m(−2) y(−1)) at the near-natural site and the rehabilitation (0.17 and 0.31 gC m(−2) y(−1)), degraded (0.15 and 0.27 gC m(−2) y(−1)) site emissions were close to net-zero throughout the study duration. Overall, the easy to implement methodology proposed in this study can be applied across various landuse types to assess the impact of peatland rehabilitation on methane emissions by mapping ecological change.
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spelling pubmed-103687222023-07-27 Upscaling methane fluxes from peatlands across a drainage gradient in Ireland using PlanetScope imagery and machine learning tools Ingle, Ruchita Habib, Wahaj Connolly, John McCorry, Mark Barry, Stephen Saunders, Matthew Sci Rep Article Wetlands are one of the major contributors of methane (CH(4)) emissions to the atmosphere and the intensity of emissions is driven by local environmental variables and spatial heterogeneity. Peatlands are a major wetland class and there are numerous studies that provide estimates of methane emissions at chamber or eddy covariance scales, but these are not often aggregated to the site/ecosystem scale. This study provides a robust approach to map dominant vegetation communities and to use these areas to upscale methane fluxes from chamber to site scale using a simple weighted-area approach. The proposed methodology was tested at three peatlands in Ireland over a duration of 2 years. The annual vegetation maps showed an accuracy ranging from 83 to 99% for near-natural to degraded sites respectively. The upscaled fluxes were highest (2.25 and 3.80 gC m(−2) y(−1)) at the near-natural site and the rehabilitation (0.17 and 0.31 gC m(−2) y(−1)), degraded (0.15 and 0.27 gC m(−2) y(−1)) site emissions were close to net-zero throughout the study duration. Overall, the easy to implement methodology proposed in this study can be applied across various landuse types to assess the impact of peatland rehabilitation on methane emissions by mapping ecological change. Nature Publishing Group UK 2023-07-25 /pmc/articles/PMC10368722/ /pubmed/37491422 http://dx.doi.org/10.1038/s41598-023-38470-6 Text en © The Author(s) 2023 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
Ingle, Ruchita
Habib, Wahaj
Connolly, John
McCorry, Mark
Barry, Stephen
Saunders, Matthew
Upscaling methane fluxes from peatlands across a drainage gradient in Ireland using PlanetScope imagery and machine learning tools
title Upscaling methane fluxes from peatlands across a drainage gradient in Ireland using PlanetScope imagery and machine learning tools
title_full Upscaling methane fluxes from peatlands across a drainage gradient in Ireland using PlanetScope imagery and machine learning tools
title_fullStr Upscaling methane fluxes from peatlands across a drainage gradient in Ireland using PlanetScope imagery and machine learning tools
title_full_unstemmed Upscaling methane fluxes from peatlands across a drainage gradient in Ireland using PlanetScope imagery and machine learning tools
title_short Upscaling methane fluxes from peatlands across a drainage gradient in Ireland using PlanetScope imagery and machine learning tools
title_sort upscaling methane fluxes from peatlands across a drainage gradient in ireland using planetscope imagery and machine learning tools
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10368722/
https://www.ncbi.nlm.nih.gov/pubmed/37491422
http://dx.doi.org/10.1038/s41598-023-38470-6
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