Abstract

Arctic permafrost-affected soils store large amounts of carbon, and high-quality maps of these soils are needed to model climate feedbacks from permafrost thaw. Ice-rich polygonal tundra is one landscape type that is widespread in the Arctic and rich in carbon. These environments are especially susceptible to climate change as thawing of ground ice causes the irreversible degradation of these landforms. The thawing processes open pathways for release of carbon that has been preserved under frozen conditions over long timescales. This release can occur through gradual thickening of the active layer, which is the upper ground layer that thaws seasonally, but also through abrupt thaw processes, such as thermokarst formation following thaw of ice-rich ground. To better project the future trajectory of permafrost carbon at local to regional scales we need high-resolution information on soil and landscape properties.

This thesis aims to combine field sampling and spatial modeling to investigate the soils and landforms of permafrost landscapes along the Canadian Yukon coast of the Beaufort Sea coast. A major focus of this thesis is on mapping the variability of the landscape on different scales, as most pan-Arctic studies have a coarse resolution and do not capture local variability. It utilizes advanced machine learning methods for digital soil mapping to analyze soil organic carbon and nitrogen stock distributions across multiple scales, while also assessing the associated uncertainties. The availability of high- and medium-resolution (here defined as <10 m and ≥10 m pixels resolution) satellite imagery enables detailed landcover mapping, and this thesis explores various pattern recognition methods for landcover classification.

The results show that parallel analyses at multiple scales is necessary to understand carbon storage and landscape dynamics. For studies beyond the local scale medium-resolution data has the advantage of capturing differences at the landform level, while also being more widely available and accessible compared to high-resolution data. Lower spatial resolution fails to detect local variability and masks subpixel heterogeneity, whereas high-resolution mapping uncovers this variability, revealing distinctions between landforms and regions with varied landscape histories.

Ice wedge polygon landscapes are heterogeneous and carbon storage as well as lateral fluxes are determined by polygon type (high center polygon, low center polygon), but also their sublandform types (troughs, rims, centers). The object-based landcover mapping approach shows that spectral properties allow the differentiation of ice wedge polygon type, but scale properties are important to distinguish between centers, troughs and rims.

This thesis emphasizes that properties and spatial distribution of sampling sites are critical for accurate mapping results; high mapping accuracy requires that available field sites effectively capture the full range of the landscape's variability. This poses significant challenges for synthesis studies that utilize existing soil data. This thesis further highlights that an integrated view on soils and hydrological systems is necessary to understand carbon storage and potential release from ice wedge polygon landscapes.

Link to the thesis
 

Public defence

2024-11-29, De Geersalen, Geovetenskapens hus, Svante Arrhenius väg 14 and online via Zoom: https://stockholmuniversity.zoom.us/j/67728317763, Stockholm, 13:00 (English)

Opponent

Heuvelink, Gerard, Professor
Wageningen University and ISRIC - World Soil Information, Netherlands.

Supervisors

Hugelius, Gustaf, Professor
Department of Physical Geography. Stockholm Universityand Bolin Centre for Climate Research (together with KTH & SMHI).

Brown, Ian, Docent
Department of Physical Geography. Stockholm Universityand Bolin Centre for Climate Research (together with KTH & SMHI).