Comparing High-Resolution Snow Mapping Approaches in Palsa Mires: UAS LiDAR vs. Machine Learning

Abstract. Snow cover has an important role in permafrost processes and dynamics, creating cooling and warming systems, impacting the aggradation and degradation of frozen soil. Despite theoretical, experimental, and remote sensing-based research, comprehensive understanding of small-scaled snow distribution at palsas remains limited. This study compares two approaches to generate spatially continuous, small-scale snow distribution models in palsa mires in northwestern Finland based on Digital Surface Models: a machine learning approach using the Random Forest algorithm with in-situ measured snow depth data and an Unmanned Aerial System (UAS) equipped with a Light Detection and Ranging (LiDAR) sensor. For the first time, snow distribution was recorded over a palsa using a UAS. The aim is to review which approach is more precise overall and which areas are not represented sufficiently accurate. In comparison to in-situ collected validation data, the machine learning results showed high accuracy, with a RMSE of 6.16 cm and an R² of 0.98, outperforming the LiDAR-based approach, which had an RMSE of 26.73 cm and an R² of 0.59. Random Forest models snow distribution significantly better at steep slopes and in vegetated areas. This considerable difference highlights the ability of machine learning to capture fine-scale snow distribution patterns in detail. However, our results indicate that UAS data enables the study of snow and permafrost interaction at a highly detailed level as well.

Generally, snow accumulation zones especially at steep edges of the palsas and inside cracks are recognizable, while thin snow cover occurs at exposed areas on top of the palsas. Correspondingly, areas with thicker snow cover at the edges and inside cracks act as potential warming spots, possibly leading to heavy degradation including block erosion. In contrast, areas with thinner snow cover on the exposed crown parts can act as cooling spots. They initially stabilize the frozen core under the crown parts, but then form steep edges and expose the frozen core, leading finally to even more block erosion and degradation.