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High-resolution mapping and spatial variability of soil organic carbon storage in permafrost environments
Stockholm University, Faculty of Science, Department of Physical Geography. (Permafrost and Hydrology)ORCID iD: 0000-0003-2890-8873
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 [en]
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: urn:nbn:se:su:diva-134986ISBN: 978-91-7649-529-2ISBN: 978-91-7649-530-8OAI: oai:DiVA.org:su-134986DiVA: diva2:1040947
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
List of papers
1. Comparing carbon storage of Siberian tundra and taiga permafrost ecosystems at very high spatial resolution
Open this publication in new window or tab >>Comparing carbon storage of Siberian tundra and taiga permafrost ecosystems at very high spatial resolution
Show others...
2015 (English)In: Journal of Geophysical Research - Biogeosciences, ISSN 2169-8953, E-ISSN 2169-8961, Vol. 120, no 10, 1973-1994 p.Article in journal (Refereed) Published
Abstract [en]

Permafrost-affected ecosystems are important components in the global carbon (C) cycle that, despite being vulnerable to disturbances under climate change, remain poorly understood. This study investigates ecosystem carbon storage in two contrasting continuous permafrost areas of NE and East Siberia. Detailed partitioning of soil organic carbon (SOC) and phytomass carbon (PC) is analyzed for one tundra (Kytalyk) and one taiga (Spasskaya Pad/Neleger) study area. In total, 57 individual field sites (24 and 33 in the respective areas) have been sampled for PC and SOC, including the upper permafrost. Landscape partitioning of ecosystem C storage was derived from thematic upscaling of field observations using a land cover classification from very high resolution (2x2m) satellite imagery. Nonmetric multidimensional scaling was used to explore patterns in C distribution. In both environments the ecosystem C is mostly stored in the soil (86%). At the landscape scale C stocks are primarily controlled by the presence of thermokarst depressions (alases). In the tundra landscape, site-scale variability of C is controlled by periglacial geomorphological features, while in the taiga, local differences in catenary position, soil texture, and forest successions are more important. Very high resolution remote sensing is highly beneficial to the quantification of C storage. Detailed knowledge of ecosystem C storage and ground ice distribution is needed to predict permafrost landscape vulnerability to projected climatic changes. We argue that vegetation dynamics are unlikely to offset mineralization of thawed permafrost C and that landscape-scale reworking of SOC represents the largest potential changes to C cycling.

Keyword
permafrost, soil organic carbon, phytomass carbon, remote sensing, tundra, taiga
National Category
Earth and Related Environmental Sciences
Research subject
Physical Geography
Identifiers
urn:nbn:se:su:diva-126860 (URN)10.1002/2015JG002999 (DOI)000368730300007 ()
Available from: 2016-02-24 Created: 2016-02-16 Last updated: 2016-11-11Bibliographically approved
2. Landscape controls and vertical variability of soil organic carbon storage in permafrost-affected soils of the Lena River Delta
Open this publication in new window or tab >>Landscape controls and vertical variability of soil organic carbon storage in permafrost-affected soils of the Lena River Delta
2016 (English)In: Catena (Cremlingen. Print), ISSN 0341-8162, E-ISSN 1872-6887, Vol. 147, 725-741 p.Article in journal (Refereed) Published
Abstract [en]

To project the future development of the soil organic carbon (SOC) storage in permafrost environments, the spatial and vertical distribution of key soil properties and their landscape controls needs to be understood. This article reports findings from the Arctic Lena River Delta where we sampled 50 soil pedons. These were classified according to the U.S.D.A. Soil Taxonomy and fall mostly into the Gelisol soil order used for permafrost-affected soils. Soil profiles have been sampled for the active layer (mean depth 58 ± 10 cm) and the upper permafrost to one meter depth. We analyze SOC stocks and key soil properties, i.e. C%, N%, C/N, bulk density, visible ice and water content. These are compared for different landscape groupings of pedons according to geomorphology, soil and land cover and for different vertical depth increments. High vertical resolution plots are used to understand soil development. These show that SOC storage can be highly variable with depth. We recommend the treatment of permafrost-affected soils according to subdivisions into: the surface organic layer, mineral subsoil in the active layer, organic enriched cryoturbated or buried horizons and the mineral subsoil in the permafrost. The major geomorphological units of a subregion of the Lena River Delta were mapped with a land form classification using a data-fusion approach of optical satellite imagery and digital elevation data to upscale SOC storage. Landscape mean SOC storage is estimated to 19.2 ± 2.0 kg C m− 2. Our results show that the geomorphological setting explains more soil variability than soil taxonomy classes or vegetation cover. The soils from the oldest, Pleistocene aged, unit of the delta store the highest amount of SOC per m2 followed by the Holocene river terrace. The Pleistocene terrace affected by thermal-degradation, the recent floodplain and bare alluvial sediments store considerably less SOC in descending order.

Keyword
Soil organic carbon, Soil taxonomy, Permafrost, Thematic mapping, Deltas
National Category
Physical Geography
Research subject
Physical Geography
Identifiers
urn:nbn:se:su:diva-134976 (URN)10.1016/j.catena.2016.07.048 (DOI)000385598800069 ()
Funder
EU, FP7, Seventh Framework Programme, 282700
Available from: 2016-10-28 Created: 2016-10-28 Last updated: 2016-11-28Bibliographically approved
3. Spatial variability of soil organic carbon in tundra terrain at local scale
Open this publication in new window or tab >>Spatial variability of soil organic carbon in tundra terrain at local scale
(English)Manuscript (preprint) (Other academic)
Keyword
Soil organic carbon, tundra, spatial variability, permafrost, ice-wedge, Tobler
National Category
Physical Geography
Research subject
Physical Geography
Identifiers
urn:nbn:se:su:diva-134977 (URN)
Funder
EU, FP7, Seventh Framework Programme, 282700Swedish Research Council
Available from: 2016-10-28 Created: 2016-10-28 Last updated: 2016-11-11Bibliographically approved
4. High-resolution digital mapping of soil organic carbon in permafrost terrain using machine-learning: An integrated case study in a sub-Arctic peatland environment
Open this publication in new window or tab >>High-resolution digital mapping of soil organic carbon in permafrost terrain using machine-learning: An integrated case study in a sub-Arctic peatland environment
(English)Manuscript (preprint) (Other academic)
Keyword
Soil organic carbon, Digital soil mapping, Machine-learning, Regression, Permafrost, Arctic
National Category
Physical Geography
Research subject
Physical Geography
Identifiers
urn:nbn:se:su:diva-134984 (URN)
Funder
EU, FP7, Seventh Framework Programme, 282700
Available from: 2016-10-28 Created: 2016-10-28 Last updated: 2016-11-11Bibliographically approved

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