8 Sep 2022

Ground-penetrating Radar Studies of Permafrost, Periglacial, and Near-surface

Abstract: Installations built on ice, permafrost, or seasonal frozen ground require careful design to avoid melting issues. Therefore, efforts to rebuild McMurdo Station, Antarctica, to improve operational efficiency and consolidate energy resources require knowledge of near-surface geology. Both 200 and 400 MHz ground-penetrating radar (GPR) data were collected in McMurdo during January, October, and November of 2015 to detect the active layer, permafrost, excess ice, fill thickness, solid bedrock depth, and buried utilities or construction and waste debris. Our goal was to ultimately improve surficial geology knowledge from a geotechnical perspective. Radar penetration ranged between approximately 3 and 10 m depth for the 400 and 200 MHz antennas, respectively. Both antennas successfully detect buried utilities and near-surface stratified material to ~0.5–3.0 m whereas 200 MHz profiles were more useful for mapping deeper stratified and un-stratified fill over bedrock. Artificially generated excess ice which appears to have been created from runoff, water pooling and refreezing, aspect shading from buildings, and snowpack buried under fill, are prevalent. Results show that McMurdo Station has a complex myriad of ice-rich fill, scoria, fractured volcanic bedrock, permafrost, excess ice, and buried anthropogenically generated debris, each of which must be considered during future construction.

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.