Mapping Localized Permafrost and Seasonal Freezing with Machine Learning

Interior Alaska’s rapidly thawing permafrost poses risks to environmental systems and infrastructure, challenging municipal planning. As part of a larger project examining frozen commons, McGrath, Alaska, officials and tribal council members requested a permafrost map. This paper presents ground thermal monitoring (October 2023 to March 2025) and imagery-derived land cover and permafrost/seasonal freezing maps developed after testing machine learning and contextual feature methods. Over the two years of observation, ground temperature warmed 0.26 °C year−1 at 1.5 m depth. A high-accuracy land cover classification was generated to project ground thermal conditions across the community. Several supervised machine learning algorithms were compared with and without contextual features on a Satellite Pour l’Observation de la Terre (SPOT) scene in ArcGIS Pro. Per-pixel classification performed better given the contiguous spectral features, and contextual features did not improve overall accuracy. Instead, a random forest classifier that yielded the highest overall accuracy was used to generate a 1.5 m resolution permafrost/seasonal freezing map. Maps and thermal data can inform community frozen commons decision-making, and methods can be repeated to monitor regional change. Discussion of results highlights potential permafrost mapping applications, particularly of Gabor and mean contextual features with object segmentation.