Using Sentinel 1 C-band SAR imagery to Detect Avalanches: An Analysis of Smaller Scale Avalanches and Proposed Algorithm

Abstract

Snow avalanches are a destructive natural hazard that pose a serious threat to humans, ecosystems, and the built environment in mountain regions. There is a lot of human subjectivity involved in the field of avalanche prediction and mitigation. One potential solution is to add objectivity through predictive machine learning models. These models are, however, limited by a lack of training data due to limited, manually recorded avalanche occurrences. Research has been performed into using Sentinel-1 C-band Synthetic Aperture Radar (SAR) imagery to detect avalanches. The detection of avalanches using Sentinel 1 C-band SAR imagery is a problem that has been addressed for large scale avalanches, however, the detection of small-scale avalanches has not been adequately addressed. This investigation looked at using four detection techniques to detect smaller scale avalanches: K-means, DBSCAN, global thresholding and the replication of the Karbou et al. 2022 method. It was found that the global thresholding algorithm performed the best, but the identification of small-scale avalanches is a difficult problem due to the complexity of distinguishing these events from surrounding noise. To reduce errors due to the noise, a mask based off optical Sentinel 2 images using global thresholding was developed. The detection of small-scale avalanches is a difficult and unsolved problem, this thesis attempted to use relatively novel techniques and data in order to detect them, and some leads were detected, though the problem remains difficult.