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Volume 17, issue 10 | Copyright
Nat. Hazards Earth Syst. Sci., 17, 1823-1836, 2017
https://doi.org/10.5194/nhess-17-1823-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 23 Oct 2017

Research article | 23 Oct 2017

Regional snow-avalanche detection using object-based image analysis of near-infrared aerial imagery

Karolina Korzeniowska1,2, Yves Bühler3, Mauro Marty4, and Oliver Korup2 Karolina Korzeniowska et al.
  • 13D Mapping, BSF Swissphoto GmbH, Schönefeld, 12529, Germany
  • 2Geohazards Research Group, University of Potsdam, Potsdam, 14476, Germany
  • 3WSL Institute for Snow and Avalanche Research SLF, Davos, 7260, Switzerland
  • 4Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, 8903, Switzerland

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 0.25m 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 4km−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 226.3km−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.

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In this study, we have focused on automatically detecting avalanches and classifying them into release zones, tracks, and run-out zones based on aerial imagery using an object-based image analysis (OBIA) approach. 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 km2, we estimated producer’s and user’s accuracies of 0.61 and 0.78, respectively.
In this study, we have focused on automatically detecting avalanches and classifying them into...
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