Automatic detection of snow avalanche debris in central Svalbard using C-band SAR data

• Published in: Polar Research
• Main Authors: Dieuwertje S. Wesselink, Eirik Malnes, Markus Eckerstorfer, Roderik C. Lindenbergh
• Format: Article in Journal/Newspaper
• Language: English
• Published: Norwegian Polar Institute 2017
• Subjects: Radar remote sensing; Radarsat-2; Sentinel-1; avalanche debris detection; avalanche forecasting; geo; info; Longyearbyen; Nordenskiöld Land; Svalbard; polar night; Polar Research
• Online Access: https://doi.org/10.1080/17518369.2017.1333236; https://doaj.org/article/f030a590a43842b6802f4a192b749f09

Snow avalanches pose a threat to people and infrastructure in and around Svalbard’s main settlement Longyearbyen. Since January 2016, publically available regional avalanche warnings are issued daily for Nordenskiöld Land, the area around Longyearbyen. Avalanche warning services rely on information of when and where avalanches occur. Systematic field observations of avalanche activity are not feasible across all of the vast area (ca. 7200 km2) of Nordenskiöld Land. Svalbard also experiences over four months of polar night per year. However, using synthetic aperture radar (SAR), a weather- and light-independent technique, large areas can be monitored at once. We have developed a SAR-based automatic avalanche debris detection algorithm and tested it on satellite image pairs from Sentinel-1A at medium resolution and from Radarsat-2 at very high resolution. The detection algorithm uses a threshold value that distinguishes avalanche debris with increased backscatter from undisturbed snow with lower backscatter. Depending on the spatial resolution of the SAR image, different post-processing filters are applied. There is a promising level of agreement between automatic detection results and manual identification of avalanche debris, but the algorithm’s drawback is marked overdetection. We envision that further improvements in the form of avalanche debris shape recognition could ultimately lead to the development of operational avalanche activity maps. These frequently updated maps could then assist in regional avalanche forecasting, notably in and around Longyearbyen, Svalbard. The detection algorithm we have developed could eventually have applications in other avalanche-prone regions in the world.