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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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 1726-4189 https://doaj.org/article/34b3804717a14a4cab70adca9c19f9da |
op_doi |
https://doi.org/10.5194/bg-15-1663-2018 |
container_title |
Biogeosciences |
container_volume |
15 |
container_issue |
6 |
container_start_page |
1663 |
op_container_end_page |
1682 |
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1766031182679506944 |