Articles | Volume 17, issue 10
https://doi.org/10.5194/nhess-17-1823-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/nhess-17-1823-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
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 Korzeniowska
CORRESPONDING AUTHOR
3D Mapping, BSF Swissphoto GmbH, Schönefeld, 12529, Germany
Geohazards Research Group, University of Potsdam, Potsdam, 14476,
Germany
Yves Bühler
WSL Institute for Snow and Avalanche Research SLF, Davos, 7260,
Switzerland
Mauro Marty
Swiss Federal Institute for Forest, Snow and Landscape Research WSL,
Birmensdorf, 8903, Switzerland
Oliver Korup
Geohazards Research Group, University of Potsdam, Potsdam, 14476,
Germany
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Cited
20 citations as recorded by crossref.
- Automatic Detection of Regional Snow Avalanches with Scattering and Interference of C-band SAR Data J. Yang et al. 10.3390/rs12172781
- Mapping avalanches with satellites – evaluation of performance and completeness E. Hafner et al. 10.5194/tc-15-983-2021
- Combining OBIA, CNN, and UAV photogrammetry for automated avalanche deposit detection and characterization S. Dewali et al. 10.1016/j.asr.2023.06.033
- Automating avalanche detection in ground-based photographs with deep learning J. Fox et al. 10.1016/j.coldregions.2024.104179
- Where are the avalanches? Rapid SPOT6 satellite data acquisition to map an extreme avalanche period over the Swiss Alps Y. Bühler et al. 10.5194/tc-13-3225-2019
- Spatially continuous snow depth mapping by aeroplane photogrammetry for annual peak of winter from 2017 to 2021 in open areas L. Bührle et al. 10.5194/tc-17-3383-2023
- Mapping snow avalanche debris by object-based classification in mountainous regions from Sentinel-1 images and causative indices Y. Liu et al. 10.1016/j.catena.2021.105559
- Automated snow avalanche release area delineation – validation of existing algorithms and proposition of a new object-based approach for large-scale hazard indication mapping Y. Bühler et al. 10.5194/nhess-18-3235-2018
- Snow avalanche detection and mapping in multitemporal and multiorbital radar images from TerraSAR-X and Sentinel-1 S. Leinss et al. 10.5194/nhess-20-1783-2020
- Near-Real Time Automatic Snow Avalanche Activity Monitoring System Using Sentinel-1 SAR Data in Norway M. Eckerstorfer et al. 10.3390/rs11232863
- Automated snow avalanche release area delineation in data-sparse, remote, and forested regions J. Sykes et al. 10.5194/nhess-22-3247-2022
- Avalanche size estimation and avalanche outline determination by experts: reliability and implications for practice E. Hafner et al. 10.5194/nhess-23-2895-2023
- Winter–Spring Prediction of Snow Avalanche Susceptibility Using Optimisation Multi-Source Heterogeneous Factors in the Western Tianshan Mountains, China J. Yang et al. 10.3390/rs14061340
- Automated avalanche mapping from SPOT 6/7 satellite imagery with deep learning: results, evaluation, potential and limitations E. Hafner et al. 10.5194/tc-16-3517-2022
- Determining forest parameters for avalanche simulation using remote sensing data N. Brožová et al. 10.1016/j.coldregions.2019.102976
- Automatic Color Detection-Based Method Applied to Sentinel-1 SAR Images for Snow Avalanche Debris Monitoring A. Karas et al. 10.1109/TGRS.2021.3131853
- Review article: Snow and ice avalanches in high mountain Asia – scientific, local and indigenous knowledge A. Acharya et al. 10.5194/nhess-23-2569-2023
- Grammar Guided Genetic Programming for Network Architecture Search and Road Detection on Aerial Orthophotography V. de la Fuente Castillo et al. 10.3390/app10113953
- Identifying groundwater discharge zones in the Central Mackenzie Valley using remotely sensed optical and thermal imagery B. Glass et al. 10.1139/cjes-2019-0169
- Intercomparison of photogrammetric platforms for spatially continuous snow depth mapping L. Eberhard et al. 10.5194/tc-15-69-2021
20 citations as recorded by crossref.
- Automatic Detection of Regional Snow Avalanches with Scattering and Interference of C-band SAR Data J. Yang et al. 10.3390/rs12172781
- Mapping avalanches with satellites – evaluation of performance and completeness E. Hafner et al. 10.5194/tc-15-983-2021
- Combining OBIA, CNN, and UAV photogrammetry for automated avalanche deposit detection and characterization S. Dewali et al. 10.1016/j.asr.2023.06.033
- Automating avalanche detection in ground-based photographs with deep learning J. Fox et al. 10.1016/j.coldregions.2024.104179
- Where are the avalanches? Rapid SPOT6 satellite data acquisition to map an extreme avalanche period over the Swiss Alps Y. Bühler et al. 10.5194/tc-13-3225-2019
- Spatially continuous snow depth mapping by aeroplane photogrammetry for annual peak of winter from 2017 to 2021 in open areas L. Bührle et al. 10.5194/tc-17-3383-2023
- Mapping snow avalanche debris by object-based classification in mountainous regions from Sentinel-1 images and causative indices Y. Liu et al. 10.1016/j.catena.2021.105559
- Automated snow avalanche release area delineation – validation of existing algorithms and proposition of a new object-based approach for large-scale hazard indication mapping Y. Bühler et al. 10.5194/nhess-18-3235-2018
- Snow avalanche detection and mapping in multitemporal and multiorbital radar images from TerraSAR-X and Sentinel-1 S. Leinss et al. 10.5194/nhess-20-1783-2020
- Near-Real Time Automatic Snow Avalanche Activity Monitoring System Using Sentinel-1 SAR Data in Norway M. Eckerstorfer et al. 10.3390/rs11232863
- Automated snow avalanche release area delineation in data-sparse, remote, and forested regions J. Sykes et al. 10.5194/nhess-22-3247-2022
- Avalanche size estimation and avalanche outline determination by experts: reliability and implications for practice E. Hafner et al. 10.5194/nhess-23-2895-2023
- Winter–Spring Prediction of Snow Avalanche Susceptibility Using Optimisation Multi-Source Heterogeneous Factors in the Western Tianshan Mountains, China J. Yang et al. 10.3390/rs14061340
- Automated avalanche mapping from SPOT 6/7 satellite imagery with deep learning: results, evaluation, potential and limitations E. Hafner et al. 10.5194/tc-16-3517-2022
- Determining forest parameters for avalanche simulation using remote sensing data N. Brožová et al. 10.1016/j.coldregions.2019.102976
- Automatic Color Detection-Based Method Applied to Sentinel-1 SAR Images for Snow Avalanche Debris Monitoring A. Karas et al. 10.1109/TGRS.2021.3131853
- Review article: Snow and ice avalanches in high mountain Asia – scientific, local and indigenous knowledge A. Acharya et al. 10.5194/nhess-23-2569-2023
- Grammar Guided Genetic Programming for Network Architecture Search and Road Detection on Aerial Orthophotography V. de la Fuente Castillo et al. 10.3390/app10113953
- Identifying groundwater discharge zones in the Central Mackenzie Valley using remotely sensed optical and thermal imagery B. Glass et al. 10.1139/cjes-2019-0169
- Intercomparison of photogrammetric platforms for spatially continuous snow depth mapping L. Eberhard et al. 10.5194/tc-15-69-2021
Latest update: 17 Apr 2024
Short summary
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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