Snow Avalanche Debris Analysis Using Time Series of Dual-Polarimetric Synthetic Aperture Radar Data

Abstract

Snow avalanches constitute a dangerous natural hazard in snow-clad mountain regions. Spaceborne synthetic aperture radar (SAR) has emerged as an effective tool for monitoring avalanche activity also in remote areas due to its all-weather capabilities and sensitivity to the presence of avalanche debris. The objective of this study was the application of a novel polarimetric change detection approach to the task of identifying avalanche debris candidate areas and characterizing the impact of factors, such as avalanche size and topography, on its performance. We applied polarimetric change vectors (PCVs) for change detection between pairs of Sentinel-1 SAR images acquired over the Swiss Alps during a two-month period characterized by episodes of exceptionally high avalanche activity. PCV were classified using a two-step mixture analysis of their magnitude and direction components. Candidates for avalanche debris were matched against a reference database of $>$ 16 000 avalanche outlines. The detected changes were largely attributed to snow processes, such as transitions from dry to wet snow and vice versa, as well as the occurrence of avalanches. 23% of all reference avalanche polygons could be matched to retrieved avalanche debris patches. The matching rate strongly depended on avalanche size, reaching $>$ 33% for very large (size 4) avalanches and, to a lesser degree, on the orientation of the avalanche relative to the look direction of the sensor. Apart from constituting a standalone approach for detecting avalanche debris candidate areas, the PCV method has potential for integration into automatic avalanche detection workflows based on machine learning methods.

Keywords

• Backscatter
• change detection
• Sentinel-1
• snow avalanches
• snowmelt