Abstract
Abstract. Snow avalanches are destructive mass movements in mountain regions that continue to claim lives and cause infrastructural damage and traffic detours. Given that avalanches often occur in remote and poorly accessible steep terrain, their detection and mapping is extensive and time consuming. Nonetheless, systematic avalanche detection over large areas could help to generate more complete and up-to-date inventories (cadastres) necessary for validating avalanche forecasting and hazard mapping. In this study, we focused on automatically detecting avalanches and classifying them into release zones, tracks, and run-out zones based on 0.25 m near-infrared (NIR) ADS80-SH92 aerial imagery using an object-based image analysis (OBIA) approach. Our algorithm takes into account the brightness, the normalised difference vegetation index (NDVI), the normalised difference water index (NDWI), and its standard deviation (SDNDWI) to distinguish avalanches from other land-surface elements. Using normalised parameters allows applying this method across large areas. We trained the method by analysing the properties of snow avalanches at three 4 km−2 areas near Davos, Switzerland. We compared the results with manually mapped avalanche polygons and obtained a user's accuracy of > 0.9 and a Cohen's kappa of 0.79–0.85. Testing the method for a larger area of 226.3 km−2, we estimated producer's and user's accuracies of 0.61 and 0.78, respectively, with a Cohen's kappa of 0.67. Detected avalanches that overlapped with reference data by > 80 % occurred randomly throughout the testing area, showing that our method avoids overfitting. Our method has potential for large-scale avalanche mapping, although further investigations into other regions are desirable to verify the robustness of our selected thresholds and the transferability of the method.
Highlights
Snow avalanches are frequent and destructive mountain hazards, during the winter and spring months
We propose an automatic method based on objectbased image analysis (OBIA) for detecting avalanche run-out zones, their tracks, and release areas
We found that object-based image analysis (OBIA) is useful for complex shapes because it allows the implementation of assumptions regarding each different situation
Summary
Snow avalanches are frequent and destructive mountain hazards, during the winter and spring months. They are fast mass movements controlled by weather conditions, snowpack, and topography (Schweizer et al, 2003; Castebrunet et al, 2012). Past research indicates that poor decision-making and forecasting are the main causes of deadly avalanche accidents (Techel et al, 2015; McClung, 2016). Techel et al (2015) stated that most destructive events occur on days when the snow-avalanche risk is very critical and the snowpack layer is weak. 4750 people lost their lives in the European Alps between 1970 and 2015 (Techel et al, 2016); in the past 2 decades, avalanches have killed 461 people in the Swiss Alps alone (Fig. 1). Since 1946, avalanches in Switzerland have had the highest share of vic-
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