The utility of forest attribute maps for automated Avalanche Terrain Exposure Scale (ATES) modelling

• Main Authors: Schumacher, Johannes, Larsen, Håvard Toft, McLean, Paul, Hauglin, Marius, Astrup, Rasmus, Breidenbach, Johannes
• Format: Article in Journal/Newspaper
• Language: English
• Published: Zenodo 2022
• Subjects: Avalanches, Airborne laser scanning, remote sensing, National Forest Inventory, SR16; Arctic; Norway
• Online Access: https://dx.doi.org/10.5281/zenodo.5929629; https://zenodo.org/record/5929629

Fatalities associated with recreational activities occur every year as a result of snow avalanches. Terrain classification systems, such as the Avalanche Terrain Exposure Scale (ATES) are designed to provide guidance for safe route finding and this system has been automated (AutoATES). ATES classifies terrain into the three classes simple, challenging, and complex. Forests can provide some protection from avalanches, and these can be incorporated into avalanche hazard models. The objective of this study was to map relevant forest attributes (stem density and canopy cover) based on National Forest Inventory and remote sensing data and, subsequently, use these forest attributes as input to the AutoATES model to improve avalanche hazard maps. We predicted stem density with species-specific mixed-effects models and directly calculated canopy cover using airborne laser scanning data in a 20 Mha study area ranging from the arctic circle to southern Norway. We mapped these forest attributes for 16 m x 16 m pixels, which were used as input for the AutoATES model. The uncertainty of the stem number and canopy cover maps were 30% and 32%, respectively. The overall classification accuracy of 52 ski touring routes in Western Norway with a total length of 282 km increased by up to 12% when utilizing the mapped forest attributes, compared to the model without forest information. The F1 score for the three predicted ATES classes improved by up to 31%, 9%, and 6% for the three classes, respectively, when including a forest attribute in the AutoATES model. We conclude that large-scale fine-resolution forest attribute maps are valuable data in the modelling of avalanche hazards.