Ground‐penetrating radar, electromagnetic induction, terrain, and vegetation observations coupled with machine learning to map permafrost distribution at Twelvemile Lake, Alaska

• Published in: Permafrost and Periglacial Processes
• Main Authors: Campbell, Seth William, Briggs, Martin, Roy, Samuel G., Douglas, Thomas A., Saari, Stephanie
• Other Authors: National Science Foundation, Strategic Environmental Research and Development Program
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2021
• Subjects: Active layer thickness; permafrost; Permafrost and Periglacial Processes; Alaska; Yukon
• Online Access: http://dx.doi.org/10.1002/ppp.2100; https://onlinelibrary.wiley.com/doi/pdf/10.1002/ppp.2100; https://onlinelibrary.wiley.com/doi/full-xml/10.1002/ppp.2100

Abstract We collected ground‐penetrating radar (GPR) and frequency‐domain electromagnetic induction (FDEM) profiles in 2011 and 2012 to identify the extent of permafrost relative to surface biomass and solar insolation around Twelvemile Lake near Fort Yukon, Alaska. We compared a Landsat‐derived biomass estimate and modeled solar insolation from a digital elevation model to the geophysical measurements. We show correspondence between vegetation type and biomass relative to permafrost extent and seasonal freeze–thaw. Thicker permafrost (≥25 m) was covered by greater biomass, and seasonal thaw depths in these regions were minimal (1 m). Shallow (1–3 m depth) and thin (20–50 cm) newly forming permafrost or frozen layers from the previous winter occurred below northward oriented slopes with thin biomass cover. South‐facing slopes exhibited permafrost when there was enough biomass to shield incoming solar energy. We developed an artificial neural network to predict permafrost extent across the broader region by mapping GPR‐observed instances of permafrost to FDEM, biomass, and terrain observations with 90.2% accuracy. We identified a strong linear correlation ( r = −0.77) between permafrost probability and seasonal thaw depth, indicating that our models may also be used to explore thaw patterns and variability in active layer thickness. This study highlights the combined influence of biomass and terrain on the presence of permafrost and the value of evaluating such parameters via remote sensing to predict permafrost spatial or temporal variability. Incorporating diverse geophysical datasets with in‐situ validation into machine learning models demonstrates a useful approach to upscale estimated permafrost extent across large Arctic expanses.