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: Airborne laser scanning, National Forest Inventory, SR16, disaster risk reduction, nature based solutions; Arctic; Norway
• Online Access: https://dx.doi.org/10.5281/zenodo.6106389; https://zenodo.org/record/6106389

The number of people affected by snow avalanches during recreational activities has increased considerably over the recent years in Norway. An instrument to reduce these numbers are improved terrain classification systems to provide guidance for safe route finding. One such system is the Avalanche Terrain Exposure Scale (ATES) which classifies terrain into the three classes simple, challenging, and complex. Forests can provide some protection from avalanches, and information on forest attributes can be incorporated into avalanche hazard models such as the automated ATES model (AutoATES). The objectives of this study were to i) map relevant forest attributes (stem density and canopy cover) based on National Forest Inventory and remote sensing data and, ii) 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. The forest attributes were mapped for 16 m x 16 m pixels, which were used as input for the AutoATES model. The uncertainties of the stem number and canopy cover maps were 30% and 31%, respectively. The overall classification accuracy of 52 ski touring routes in Western Norway with a total length of 282 km increased from 55% in the model without forest information to 67% when utilizing canopy cover. The F1 score for the three predicted ATES classes improved by 31%, 9%, and 6%. The use of stem number improved the hazard maps to a slightly smaller degree. We conclude that large-scale fine-resolution forest attribute maps are valuable data in the modelling and mapping of avalanche hazards. Together, these maps may be valuable for precise planning of forest management operations aiming at the utilization of forests as nature-based solutions for avalanche-related disaster risk reduction.