Vegetation map of Trail Valley Creek, Northwest Territories, Canada

The vegetation map distinguishes between five tundra vegetation types, trees, and open water at the forest–tundra transition north of Inuvik, Northwest Territories, Canada. The area is underlain by continuous permafrost. Vegetation types were distinguished based on vegetation height derived from airborne laser scanning, airborne orthophotos and observations from the field site. A detailed description of the data sources and processing steps is included

Linking tundra vegetation, snow, soil temperature, and permafrost

Connections between vegetation and soil thermal dynamics are critical for estimating the vulnerability of permafrost to thaw with continued climate warming and vegetation changes. The interplay of complex biophysical processes results in a highly heterogeneous soil temperature distribution on small spatial scales. Moreover, the link between topsoil temperature and active layer thickness remains poorly constrained. Sixty-eight temperature loggers were installed at 1-3 cm depth to record the distribution of topsoil temperatures at the Trail Valley Creek study site in the northwestern Canadian Arctic. The measurements were distributed across six different vegetation types characteristic for this landscape. Two years of topsoil temperature data were analysed statistically to identify temporal and spatial characteristics and their relationship to vegetation, snow cover, and active layer thickness. The mean annual topsoil temperature varied between -3.7 and 0.1°C within 0.5 km2. The observed variation can, to a large degree, be explained by variation in snow cover. Differences in snow depth are strongly related with vegetation type and show complex associations with late-summer thaw depth. While cold winter soil temperature is associated with deep active layers in the following summer for lichen and dwarf shrub tundra, we observed the opposite beneath tall shrubs and tussocks. In contrast to winter observations, summer topsoil temperature is similar below all vegetation types with an average summer topsoil temperature difference of less than 1°C. Moreover, there is no significant relationship between summer soil temperature or cumulative positive degree days and active layer thickness. Altogether, our results demonstrate the high spatial variability of topsoil temperature and active layer thickness even within specific vegetation types. Given that vegetation type defines the direction of the relationship between topsoil temperature and active layer thickness in winter and summer, estimates of permafrost vulnerability based on remote sensing or model results will need to incorporate complex local feedback mechanisms of vegetation change and permafrost thaw

Arctic shrub expansion revealed by Landsat-derived multitemporal vegetation cover fractions in the Western Canadian Arctic

Warming induced shifts in tundra vegetation composition and structure, including circumpolar expansion of shrubs, modifies ecosystem structure and functioning with potentially global consequences due to feedback mechanisms between vegetation and climate. Satellite-derived vegetation indices indicate widespread greening of the surface, often associated with regional evidence of shrub expansion obtained from long-term ecological monitoring and repeated orthophotos. However, explicitly quantifying shrub expansion across large scales using satellite observations requires characterising the fine-scale mosaic of Arctic vegetation types beyond index-based approaches. Although previous studies have illustrated the potential of estimating fractional cover of various Plant Functional Types (PFTs) from satellite imagery, limited availability of reference data across space and time has constrained deriving fraction cover time series capable of detecting shrub expansion. We applied regression-based unmixing using synthetic training data to build multitemporal machine learning models in order to estimate fractional cover of shrubs and other surface components in the Mackenzie Delta Region for six time intervals between 1984 and 2020. We trained Kernel Ridge Regression (KRR) and Random Forest Regression (RFR) models using Landsat-derived spectral-temporal-metrics and synthetic training data generated from pure class spectra obtained directly from the imagery. Independent validation using very-high-resolution imagery suggested that KRR outperforms RFR, estimating shrub cover with a MAE of 10.6 and remaining surface components with MAEs between 3.0 and 11.2. Canopy-forming shrubs were well modelled across all cover densities, coniferous tree cover tended to be overestimated and differentiating between herbaceous and lichen cover was challenging. Shrub cover expanded by on average + 2.2 per decade for the entire study area and + 4.2 per decade within the low Arctic tundra, while relative changes were strongest in the northernmost regions. In conjunction with shrub expansion, we observed herbaceous plant and lichen cover decline. Our results corroborate the perception of the replacement and homogenisation of Arctic vegetation communities facilitated by the competitive advantage of shrub species under a warming climate. The proposed method allows for multidecadal quantitative estimates of fractional cover at 30 m resolution, initiating new opportunities for mapping past and present fractional cover of tundra PFTs and can help advance our understanding of Arctic shrub expansion within the vast and heterogeneous tundra biome

Soil temperature and thaw depth differences associated with tundra vegetation types at Trail Valley Creek, NWT, Canada

Climate, vegetation, and permafrost are coupled through various positive and negative feedback loops in the Arctic and Subarctic. Many of these feedback mechanisms are still poorly quantified, in particular with respect to vegetation density or biomass. For instance, climate warming facilitates shrub densification and range expansion. The shrub canopies in-turn shade the ground surface during the summer, keeping permafrost cooler, while during the winter the canopies trap more snow, insulating the surface and keeping the ground (and permafrost) warmer. We investigated the feedback of vegetation change on permafrost conditions and local climate at the Trail Valley Creek study site, near tree-line, in Northwest Canada (133.50 ◦ W, 68.74 ◦ N). In particular, we quantified the effect of vegetation on the soil surface temperature and thaw depth through shading in summer and through snow collection in winter. We combine local field measurements of vegetation, climate, and permafrost with spatially resolved data from repeated aerial surveys of high resolution imagery and laser scanning. Our results show that winter ground surface temperatures below tall shrubs are on average 2 ◦ C warmer than below lichen tundra due to the snow layer being twice as deep. However, delayed spring onset and soil shading in summer result in shallower thaw depths below tall shrubs (47cm on average) as compared to lichen tundra (61cm on average). Our results highlight the complex interactions between vegetation and permafrost involving snow, the surface energy budget and soil properties

Super-high-resolution Earth observation datasets of North American permafrost landscapes

While temperatures are increasing on the global scale, the Arctic regions are especially vulnerable to this changing climate and landscapes underlain by permafrost experience increased thaw and degradation. The enhanced warming of organic-rich frozen ground can have severe consequences on infrastructure and ecosystems and is projected to become a highly relevant driver of greenhouse gas fluxes into the atmosphere. Degrading permafrost landscapes occur extensively in vast areas of the North American Arctic, directly affecting communities and ecosystems. To identify and quantify these widespread degradation phenomena over vast areas, we require highest-resolution Earth observation dataset that we collect during aerial imaging campaigns. We here report on observations and first results from three airborne campaigns in 2018, 2019 and 2021. We performed large-scale monitoring of permafrost-affected areas in northern Canada and Alaska, focusing on sites that experienced disturbances in the past or recently. This included sites with vulnerable settlements, coastal erosion, thaw slumping, lake expansion and drainage, ice-wedge degradation and thaw subsidence, fire scars, pingos, methane seeps, and sites affected by beaver activities. All surveys were flown with the Alfred Wegener Institute's Polar-5 and -6 scientific airplanes at 500-1500 m altitude above terrain. The onboard sensor, the Modular Aerial Camera System (MACS), a very-high-resolution multispectral camera developed by the German Aerospace Center, operated in the visible (RGB) and near-infrared (NIR) domain. From the comprehensive collection of multiple TB of gathered data, super-high-resolution (up to 7 cm/px) RGB+NIR image mosaics and stereophotogrammetric digital surface models were derived. By presenting the data and first analyses, we would like to invite the community to discuss best use for maximized benefit of the data, in order to substantially contribute to our understanding of permafrost thaw dynamics