Remote-Sensing Derived Avalanche Inventory Data

Abstract

Every year snow avalanches pose a significant threat to transportation infrastructure. The societal demand to minimize closures of the main transport network while maintaining an acceptable level of personal safety at the same time has dramatically increased over the past decade. In Norway, decisions regarding avalanche warning, including pre-emptive road closure, are based on factors such as snow depth, meteorological conditions and expert opinion. The ability to automatically identify snow avalanches using very-high resolution optical imagery would greatly assist in the development of highly accurate, widespread, detailed maps of zones prone to avalanches. This would provide decision makers with better knowledge of previous events and details regarding the size and extent of historical events. We present the results of a ‘proof-of-concept’ study on the operation of a service providing the Norwegian Public Roads Administration (NPRA) with satellite data derived avalanche inventory data. We have explored the use of imagery from high-resolution and very-high-resolution space-borne satellites by developing and testing automated image segmentation and classification algorithms for the detection and mapping of avalanche deposits.