20 Oct 2025

Tensile Strength of Native Boreal Forest Plant Species

Abstract: Plant roots influence the engineering properties of soil, such as erodibility and strength. Plant roots’ contribution to soil shear strength is of particular importance in Arctic and subarctic environments where the shallow subsurface experiences a decrease in shear strength due to permafrost thaw, subsidence, and wildfires. This paper presents the testing method, sample collection and specimen preparation, and tensile strength testing results for laboratory- and greenhouse-grown boreal forest plants to compare root tensile strengths among plant species and functional groups, including deciduous shrubs and trees, evergreen trees, forbs, graminoids, and grasses using a universal testing machine and a modified triaxial device. The results illustrate that root tensile strength increases as root diameter decreases (as a power function). The root diameters successfully tested ranged from 0.063 mm (grasses) to 8.72 mm (deciduous shrubs) across all functional groups. When compared across functional groups and root diameters for each species, grass roots exhibited the highest tensile strength for root diameters less than 0.8 mm, deciduous tree roots displayed the largest tensile strength for root diameters greater than 0.8 mm, and forbs were consistently the weakest, supporting the conclusion that a diverse spread of functional groups is most effective for slope stabilization.

10 Sep 2025

Snow Depth Measurements from Arctic Tundra and Boreal Forest Collected During NASA SnowEx Alaska Campaign

Abstract: Boreal forest and Arctic tundra environments collectively hold the largest percentage of global terrestrial seasonal snow cover. Тhe in-situ snow measurement network is sparse and costly in these remote northern regions. Here, we complement existing snow depth monitoring in Arctic tundra and boreal forest by presenting an extensive (64°N–70°N) snow depth dataset and description of ground-based snow depth measurements collected during the NASA SnowEx Alaska intensive field campaign, March 7–16, 2023. We also report the accuracy of snow depth measurements in shallow boreal forest and Arctic tundra snowpack and share considerations in developing the consistent and repeatable snow depth data collection procedures. Snow depth measurements and technical validation described in this paper can serve as a robust product for testing snow remote sensing techniques, and for providing a reference dataset for climatological and hydrological studies.

10 Sep 2025

Mapping the Vulnerability of Boreal Permafrost in Central Alaska in Relation to Thaw Rate, Ground Ice, and Thermokarst Development

Abstract: Permafrost roughly affects half the boreal region in Alaska and varies greatly in its thermophysical properties and genesis. In boreal ecosystems, permafrost formation and degradation respond to complex interactions among climate, topography, hydrology, soils, vegetation, and disturbance. We synthesized data on soil thermal conditions and permafrost characteristics to assess current permafrost conditions in central Alaska, and classified and mapped soil landscapes vulnerable to future thaw and thermokarst development. Permafrost soil properties at 160 sites ranged from rocky soils in hillslope colluvium and glacial till, to silty loess, to thick peats on abandoned floodplains and bogs, across 64 geomorphic units. To assess the vulnerability of permafrost to climate variability and disturbance, we differentiated permafrost responses in terms of rate of thaw, potential thaw settlement, and thermokarst development. Using a rule-based model that uses geomorphic units for spatial extrapolation at the landscape scale, we mapped 10 vulnerability classes across three areas ranging from high potential settlement/low thaw rate in extremely ice-rich loess to low potential settlement/high thaw rate in rocky hillslope colluvium. Vulnerability classes corresponded to thermokarst features developed in response to past climates. Differing patterns in permafrost vulnerability have large implications for ecosystem trajectories, land use, and infrastructure damage from thaw.

2 Jul 2025

Remote Detection of Soil Shear Strength in Arctic and Subarctic Environments

Abstract: Soil shear strength affects many military activities and is affected significantly by plant roots. Unfortunately, root contribution to soil shear strength is difficult to measure and predict. In the boreal forest ecosystem, soil and hydrologic dynamics make soil shear strength less predictable, while the need for prediction grows due to the rapid changes occurring in this environment. Our current study objectives are to (1) observe possible aboveground vegetation indicators of soil shear strength variation across soils and other environmental heterogeneity, (2) observe possible image-based indicators of soil shear strength variation, and (3) identify the best remote-sensing data source for predicting soil shear strength variation. A total of 65 sites were sampled from a diversity of soil and vegetation types across interior Alaska and Ontario, Canada. Ground-collected data were analyzed to develop a predictive model, while a similar approach was undertaken with Sentinel-2 imagery. Results indicate that both ground-collected data and satellite imagery can reasonably predict boreal forest soil shear strength, with satellite imagery providing the higher predictive ability. A comparison of 10 m Sentinel-2 and submeter Maxar imagery indicated that Sentinel-2 provides a better prediction of soil shear strength.

21 Jul 2021

Machine Learning Analyses of Remote Sensing Measurements Establish Strong Relationships Between Vegetation and Snow Depth in the Boreal Forest of Interior Alaska

Abstract: The seasonal snowpack plays a critical role in Arctic and boreal hydrologic and ecologic processes. Though snow depth can be different from one season to another there are repeated relationships between ecotype and snowpack depth. Alterations to the seasonal snowpack, which plays a critical role in regulating wintertime soil thermal conditions, have major ramifications for near-surface permafrost. Therefore, relationships between vegetation and snowpack depth are critical for identifying how present and projected future changes in winter season processes or land cover will affect permafrost. Vegetation and snow cover areal extent can be assessed rapidly over large spatial scales with remote sensing methods, however, measuring snow depth remotely has proven difficult. This makes snow depth–vegetation relationships a potential means of assessing snowpack characteristics. In this study, we combined airborne hyperspectral and LiDAR data with machine learning methods to characterize relationships between ecotype and the end of winter snowpack depth. Our results show hyperspectral measurements account for two thirds or more of the variance in the relationship between ecotype and snow depth. An ensemble analysis of model outputs using hyperspectral and LiDAR measurements yields the strongest relationships between ecotype and snow depth. Our results can be applied across the boreal biome to model the coupling effects between vegetation and snowpack depth.