High‐Resolution Maps of Near‐Surface Permafrost for Three Watersheds on the Seward Peninsula, Alaska Derived From Machine Learning

• Published in: Earth and Space Science
• Main Authors: E. A. Thaler, S. Uhleman, J. C. Rowland, J. Schwenk, C. Wang, B. Dafflon, K. E. Bennett
• Format: Article in Journal/Newspaper
• Language: English
• Published: American Geophysical Union (AGU) 2023
• Subjects: permafrost extent; machine learning; high‐resolution; Astronomy; QB1-991; Geology; QE1-996.5; permafrost; Seward Peninsula; Alaska
• Online Access: https://doi.org/10.1029/2023EA003015; https://doaj.org/article/aefeea54822e4b78b3764d27780f3b3b

Abstract Permafrost soils are a critical component of the global carbon cycle and are locally important because they regulate the hydrologic flux from uplands to rivers. Furthermore, degradation of permafrost soils causes land surface subsidence, damaging infrastructure that is crucial for local communities. Regional and hemispherical maps of permafrost are too coarse to resolve distributions at a scale relevant to assessments of infrastructure stability or to illuminate geomorphic impacts of permafrost thaw. Here we train machine learning models to generate meter‐scale maps of near‐surface permafrost for three watersheds in the discontinuous permafrost region. The models were trained using ground truth determinations of near‐surface permafrost presence from measurements of soil temperature and electrical resistivity. We trained three classifiers: extremely randomized trees (ERTr), support vector machines (SVM), and an artificial neural network (ANN). Model uncertainty was determined using k‐fold cross validation, and the modeled extents of near‐surface permafrost were compared to the observed extents at each site. At‐a‐site near‐surface permafrost distributions predicted by the ERTr produced the highest accuracy (70%–90%). However, the transferability of the ERTr to the sites outside of the training data set was poor, with accuracies ranging from 50% to 77%. The SVM and ANN models had lower accuracies for at‐a‐site prediction (70%–83%), yet they had greater accuracy when transferred to the non‐training site (62%–78%). These models demonstrate the potential for integrating high‐resolution spatial data and machine learning models to develop maps of near‐surface permafrost extent at resolutions fine enough to assess infrastructure vulnerability and landscape morphology influenced by permafrost thaw.