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Spatial predictions and uncertainties of forest carbon fluxes for carbon accounting

Countries have pledged to different national and international environmental agreements, most prominently the climate change mitigation targets of the Paris Agreement. Accounting for carbon stocks and flows (fluxes) is essential for countries that have recently adopted the United Nations System of E...

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Autores principales: Araza, Arnan, de Bruin, Sytze, Hein, Lars, Herold, Martin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10404296/
https://www.ncbi.nlm.nih.gov/pubmed/37543683
http://dx.doi.org/10.1038/s41598-023-38935-8
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author Araza, Arnan
de Bruin, Sytze
Hein, Lars
Herold, Martin
author_facet Araza, Arnan
de Bruin, Sytze
Hein, Lars
Herold, Martin
author_sort Araza, Arnan
collection PubMed
description Countries have pledged to different national and international environmental agreements, most prominently the climate change mitigation targets of the Paris Agreement. Accounting for carbon stocks and flows (fluxes) is essential for countries that have recently adopted the United Nations System of Environmental-Economic Accounting - ecosystem accounting framework (UNSEEA) as a global statistical standard. In this paper, we analyze how spatial carbon fluxes can be used in support of the UNSEEA carbon accounts in five case countries with available in-situ data. Using global multi-date biomass map products and other remotely sensed data, we mapped the 2010–2018 carbon fluxes in Brazil, the Netherlands, the Philippines, Sweden and the USA using National Forest Inventory (NFI) and local biomass maps from airborne LiDAR as reference data. We identified areas that are unsupported by the reference data within environmental feature space (6–47% of vegetated country area); cross-validated an ensemble machine learning (RMSE=9–39 Mg C [Formula: see text] and [Formula: see text] =0.16–0.71) used to map carbon fluxes with prediction intervals; and assessed spatially correlated residuals (<5 km) before aggregating carbon fluxes from 1-ha pixels to UNSEEA forest classes. The resulting carbon accounting tables revealed the net carbon sequestration in natural broadleaved forests. Both in plantations and in other woody vegetation ecosystems, emissions exceeded sequestration. Overall, our estimates align with FAO-Forest Resource Assessment and national studies with the largest deviations in Brazil and USA. These two countries used highly clustered reference data, where clustering caused uncertainty given the need to extrapolate to under-sampled areas. We finally provide recommendations to mitigate the effect of under-sampling and to better account for the uncertainties once carbon stocks and flows need to be aggregated in relatively smaller countries. These actions are timely given the global initiatives that aim to upscale UNSEEA carbon accounting.
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spelling pubmed-104042962023-08-07 Spatial predictions and uncertainties of forest carbon fluxes for carbon accounting Araza, Arnan de Bruin, Sytze Hein, Lars Herold, Martin Sci Rep Article Countries have pledged to different national and international environmental agreements, most prominently the climate change mitigation targets of the Paris Agreement. Accounting for carbon stocks and flows (fluxes) is essential for countries that have recently adopted the United Nations System of Environmental-Economic Accounting - ecosystem accounting framework (UNSEEA) as a global statistical standard. In this paper, we analyze how spatial carbon fluxes can be used in support of the UNSEEA carbon accounts in five case countries with available in-situ data. Using global multi-date biomass map products and other remotely sensed data, we mapped the 2010–2018 carbon fluxes in Brazil, the Netherlands, the Philippines, Sweden and the USA using National Forest Inventory (NFI) and local biomass maps from airborne LiDAR as reference data. We identified areas that are unsupported by the reference data within environmental feature space (6–47% of vegetated country area); cross-validated an ensemble machine learning (RMSE=9–39 Mg C [Formula: see text] and [Formula: see text] =0.16–0.71) used to map carbon fluxes with prediction intervals; and assessed spatially correlated residuals (<5 km) before aggregating carbon fluxes from 1-ha pixels to UNSEEA forest classes. The resulting carbon accounting tables revealed the net carbon sequestration in natural broadleaved forests. Both in plantations and in other woody vegetation ecosystems, emissions exceeded sequestration. Overall, our estimates align with FAO-Forest Resource Assessment and national studies with the largest deviations in Brazil and USA. These two countries used highly clustered reference data, where clustering caused uncertainty given the need to extrapolate to under-sampled areas. We finally provide recommendations to mitigate the effect of under-sampling and to better account for the uncertainties once carbon stocks and flows need to be aggregated in relatively smaller countries. These actions are timely given the global initiatives that aim to upscale UNSEEA carbon accounting. Nature Publishing Group UK 2023-08-05 /pmc/articles/PMC10404296/ /pubmed/37543683 http://dx.doi.org/10.1038/s41598-023-38935-8 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
Araza, Arnan
de Bruin, Sytze
Hein, Lars
Herold, Martin
Spatial predictions and uncertainties of forest carbon fluxes for carbon accounting
title Spatial predictions and uncertainties of forest carbon fluxes for carbon accounting
title_full Spatial predictions and uncertainties of forest carbon fluxes for carbon accounting
title_fullStr Spatial predictions and uncertainties of forest carbon fluxes for carbon accounting
title_full_unstemmed Spatial predictions and uncertainties of forest carbon fluxes for carbon accounting
title_short Spatial predictions and uncertainties of forest carbon fluxes for carbon accounting
title_sort spatial predictions and uncertainties of forest carbon fluxes for carbon accounting
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10404296/
https://www.ncbi.nlm.nih.gov/pubmed/37543683
http://dx.doi.org/10.1038/s41598-023-38935-8
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