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High-resolution digital mapping of soil organic carbon in permafrost terrain using machine-learning: An integrated case study in a sub-Arctic peatland environment
Stockholm University, Faculty of Science, Department of Physical Geography.ORCID iD: 0000-0003-2890-8873
(English)Manuscript (preprint) (Other academic)
Keyword [en]
Soil organic carbon, Digital soil mapping, Machine-learning, Regression, Permafrost, Arctic
National Category
Physical Geography
Research subject
Physical Geography
Identifiers
URN: urn:nbn:se:su:diva-134984OAI: oai:DiVA.org:su-134984DiVA: diva2:1040759
Funder
EU, FP7, Seventh Framework Programme, 282700
Available from: 2016-10-28 Created: 2016-10-28 Last updated: 2016-11-11Bibliographically approved
In thesis
1. High-resolution mapping and spatial variability of soil organic carbon storage in permafrost environments
Open this publication in new window or tab >>High-resolution mapping and spatial variability of soil organic carbon storage in permafrost environments
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Large amounts of carbon are stored in soils of the northern circumpolar permafrost region. High-resolution mapping of this soil organic carbon (SOC) is important to better understand and predict local to global scale carbon dynamics. In this thesis, studies from five different areas across the permafrost region indicate a pattern of generally higher SOC storage in Arctic tundra soils compared to forested sub-Arctic or Boreal taiga soils. However, much of the SOC stored in the top meter of tundra soils is permanently frozen, while the annually thawing active layer is deeper in taiga soils and more SOC may be available for turnover to ecosystem processes. The results show that significantly more carbon is stored in soils compared to vegetation, even in fully forested taiga ecosystems. This indicates that over longer timescales, the SOC potentially released from thawing permafrost cannot be offset by a greening of the Arctic. For all study areas, the SOC distribution is strongly influenced by the geomorphology, i.e. periglacial landforms and processes, at different spatial scales. These span from the cryoturbation of soil horizons, to the formation of palsas, peat plateaus and different generations of ice-wedges, to thermokarst creating kilometer scale macro environments. In study areas that have not been affected by Pleistocene glaciation, SOC distribution is highly influenced by the occurrence of ice-rich and relief-forming Yedoma deposits. This thesis investigates the use of thematic maps from highly resolved satellite imagery (<6.5 m resolution). These maps reveal important information on the local distribution and variability of SOC, but their creation requires advanced classification methods including an object-based approach, modern classifiers and data-fusion. The results of statistical analyses show a clear link of land cover and geomorphology with SOC storage. Peat-formation and cryoturbation are identified as two major mechanisms to accumulate SOC. As an alternative to thematic maps, this thesis demonstrates the advantages of digital soil mapping of SOC in permafrost areas using machine-learning methods, such as support vector machines, artificial neural networks and random forests. Overall, high-resolution satellite imagery and robust spatial prediction methods allow detailed maps of SOC. This thesis significantly increases the amount of soil pedons available for the individual study areas. Yet, this information is still the limiting factor to better understand the SOC distribution in permafrost environments at local and circumpolar scale. Soil pedon information for SOC quantification should at least distinguish the surface organic layer, the mineral subsoil in the active layer compared to the permafrost and further into organic rich cryoturbated and buried soil horizons.

Place, publisher, year, edition, pages
Stockholm: Department of Physical Geography, Stockholm University, 2016. 54 p.
Series
Dissertations from the Department of Physical Geography, ISSN 1653-7211 ; 60
Keyword
carbon, soil organic carbon, permafrost, soil, land cover classification, digital soil mapping, machine-learning, ecosystem, mapping, landscape studies, Siberia, Arctic
National Category
Physical Geography
Research subject
Physical Geography
Identifiers
urn:nbn:se:su:diva-134986 (URN)978-91-7649-529-2 (ISBN)978-91-7649-530-8 (ISBN)
Public defence
2016-12-21, DeGeersalen, Geovetenskapens hus, Svante Arrhenius väg 14, Stockholm, 13:00 (English)
Opponent
Supervisors
Funder
EU, FP7, Seventh Framework Programme, 282700
Note

At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 3: Manuscript. Paper 4: Manuscript.

Available from: 2016-11-28 Created: 2016-10-28 Last updated: 2016-11-22Bibliographically approved

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Siewert, Matthias Benjamin
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