High-resolution digital mapping of soil organic carbon in permafrost terrain using machine learning: a case study in a sub-Arctic peatland environment

Soil organic carbon (SOC) stored in northern peatlands and permafrost-affected soils are key components in the global carbon cycle. This article quantifies SOC stocks in a sub-Arctic mountainous peatland environment in the discontinuous permafrost zone in Abisko, northern Sweden. Four machine-learni...

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Published in:Biogeosciences
Main Author: M. B. Siewert
Format: Article in Journal/Newspaper
Language:English
Published: Copernicus Publications 2018
Subjects:
Online Access:https://doi.org/10.5194/bg-15-1663-2018
https://doaj.org/article/34b3804717a14a4cab70adca9c19f9da
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spelling ftdoajarticles:oai:doaj.org/article:34b3804717a14a4cab70adca9c19f9da 2023-05-15T12:59:32+02:00 High-resolution digital mapping of soil organic carbon in permafrost terrain using machine learning: a case study in a sub-Arctic peatland environment M. B. Siewert 2018-03-01T00:00:00Z https://doi.org/10.5194/bg-15-1663-2018 https://doaj.org/article/34b3804717a14a4cab70adca9c19f9da EN eng Copernicus Publications https://www.biogeosciences.net/15/1663/2018/bg-15-1663-2018.pdf https://doaj.org/toc/1726-4170 https://doaj.org/toc/1726-4189 doi:10.5194/bg-15-1663-2018 1726-4170 1726-4189 https://doaj.org/article/34b3804717a14a4cab70adca9c19f9da Biogeosciences, Vol 15, Pp 1663-1682 (2018) Ecology QH540-549.5 Life QH501-531 Geology QE1-996.5 article 2018 ftdoajarticles https://doi.org/10.5194/bg-15-1663-2018 2022-12-31T01:55:58Z Soil organic carbon (SOC) stored in northern peatlands and permafrost-affected soils are key components in the global carbon cycle. This article quantifies SOC stocks in a sub-Arctic mountainous peatland environment in the discontinuous permafrost zone in Abisko, northern Sweden. Four machine-learning techniques are evaluated for SOC quantification: multiple linear regression, artificial neural networks, support vector machine and random forest. The random forest model performed best and was used to predict SOC for several depth increments at a spatial resolution of 1 m (1×1 m). A high-resolution (1 m) land cover classification generated for this study is the most relevant predictive variable. The landscape mean SOC storage (0–150 cm) is estimated to be 8.3 ± 8.0 kg C m −2 and the SOC stored in the top meter (0–100 cm) to be 7.7 ± 6.2 kg C m −2 . The predictive modeling highlights the relative importance of wetland areas and in particular peat plateaus for the landscape's SOC storage. The total SOC was also predicted at reduced spatial resolutions of 2, 10, 30, 100, 250 and 1000 m and shows a significant drop in land cover class detail and a tendency to underestimate the SOC at resolutions > 30 m. This is associated with the occurrence of many small-scale wetlands forming local hot-spots of SOC storage that are omitted at coarse resolutions. Sharp transitions in SOC storage associated with land cover and permafrost distribution are the most challenging methodological aspect. However, in this study, at local, regional and circum-Arctic scales, the main factor limiting robust SOC mapping efforts is the scarcity of soil pedon data from across the entire environmental space. For the Abisko region, past SOC and permafrost dynamics indicate that most of the SOC is barely 2000 years old and very dynamic. Future research needs to investigate the geomorphic response of permafrost degradation and the fate of SOC across all landscape compartments in post-permafrost landscapes. Article in Journal/Newspaper Abisko Arctic Northern Sweden Peat permafrost Directory of Open Access Journals: DOAJ Articles Arctic Abisko ENVELOPE(18.829,18.829,68.349,68.349) Biogeosciences 15 6 1663 1682
institution Open Polar
collection Directory of Open Access Journals: DOAJ Articles
op_collection_id ftdoajarticles
language English
topic Ecology
QH540-549.5
Life
QH501-531
Geology
QE1-996.5
spellingShingle Ecology
QH540-549.5
Life
QH501-531
Geology
QE1-996.5
M. B. Siewert
High-resolution digital mapping of soil organic carbon in permafrost terrain using machine learning: a case study in a sub-Arctic peatland environment
topic_facet Ecology
QH540-549.5
Life
QH501-531
Geology
QE1-996.5
description Soil organic carbon (SOC) stored in northern peatlands and permafrost-affected soils are key components in the global carbon cycle. This article quantifies SOC stocks in a sub-Arctic mountainous peatland environment in the discontinuous permafrost zone in Abisko, northern Sweden. Four machine-learning techniques are evaluated for SOC quantification: multiple linear regression, artificial neural networks, support vector machine and random forest. The random forest model performed best and was used to predict SOC for several depth increments at a spatial resolution of 1 m (1×1 m). A high-resolution (1 m) land cover classification generated for this study is the most relevant predictive variable. The landscape mean SOC storage (0–150 cm) is estimated to be 8.3 ± 8.0 kg C m −2 and the SOC stored in the top meter (0–100 cm) to be 7.7 ± 6.2 kg C m −2 . The predictive modeling highlights the relative importance of wetland areas and in particular peat plateaus for the landscape's SOC storage. The total SOC was also predicted at reduced spatial resolutions of 2, 10, 30, 100, 250 and 1000 m and shows a significant drop in land cover class detail and a tendency to underestimate the SOC at resolutions > 30 m. This is associated with the occurrence of many small-scale wetlands forming local hot-spots of SOC storage that are omitted at coarse resolutions. Sharp transitions in SOC storage associated with land cover and permafrost distribution are the most challenging methodological aspect. However, in this study, at local, regional and circum-Arctic scales, the main factor limiting robust SOC mapping efforts is the scarcity of soil pedon data from across the entire environmental space. For the Abisko region, past SOC and permafrost dynamics indicate that most of the SOC is barely 2000 years old and very dynamic. Future research needs to investigate the geomorphic response of permafrost degradation and the fate of SOC across all landscape compartments in post-permafrost landscapes.
format Article in Journal/Newspaper
author M. B. Siewert
author_facet M. B. Siewert
author_sort M. B. Siewert
title High-resolution digital mapping of soil organic carbon in permafrost terrain using machine learning: a case study in a sub-Arctic peatland environment
title_short High-resolution digital mapping of soil organic carbon in permafrost terrain using machine learning: a case study in a sub-Arctic peatland environment
title_full High-resolution digital mapping of soil organic carbon in permafrost terrain using machine learning: a case study in a sub-Arctic peatland environment
title_fullStr High-resolution digital mapping of soil organic carbon in permafrost terrain using machine learning: a case study in a sub-Arctic peatland environment
title_full_unstemmed High-resolution digital mapping of soil organic carbon in permafrost terrain using machine learning: a case study in a sub-Arctic peatland environment
title_sort high-resolution digital mapping of soil organic carbon in permafrost terrain using machine learning: a case study in a sub-arctic peatland environment
publisher Copernicus Publications
publishDate 2018
url https://doi.org/10.5194/bg-15-1663-2018
https://doaj.org/article/34b3804717a14a4cab70adca9c19f9da
long_lat ENVELOPE(18.829,18.829,68.349,68.349)
geographic Arctic
Abisko
geographic_facet Arctic
Abisko
genre Abisko
Arctic
Northern Sweden
Peat
permafrost
genre_facet Abisko
Arctic
Northern Sweden
Peat
permafrost
op_source Biogeosciences, Vol 15, Pp 1663-1682 (2018)
op_relation https://www.biogeosciences.net/15/1663/2018/bg-15-1663-2018.pdf
https://doaj.org/toc/1726-4170
https://doaj.org/toc/1726-4189
doi:10.5194/bg-15-1663-2018
1726-4170
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op_doi https://doi.org/10.5194/bg-15-1663-2018
container_title Biogeosciences
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