Articles | Volume 20, issue 6
https://doi.org/10.5194/nhess-20-1783-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-20-1783-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| Highlight paper
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19 Jun 2020
Research article | Highlight paper |
| 19 Jun 2020
| 19 Jun 2020
Snow avalanche detection and mapping in multitemporal and multiorbital radar images from TerraSAR-X and Sentinel-1
Institute of Environmental Engineering, ETH Zurich, Zurich, Switzerland
Raphael Wicki
Institute of Environmental Engineering, ETH Zurich, Zurich, Switzerland
Sämi Holenstein
Institute of Environmental Engineering, ETH Zurich, Zurich, Switzerland
Simone Baffelli
Institute of Environmental Engineering, ETH Zurich, Zurich, Switzerland
Yves Bühler
WSL Institute for Snow and Avalanche Research SLF, Davos Dorf, Switzerland
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Avalanches are important for the mass balance of mountain glaciers, but few data exist on where and when they occur and which glaciers they affect the most. We developed an approach to map avalanches over large glaciated areas and long periods of time using satellite radar data. The application of this method to various regions in the Alps and High Mountain Asia reveals the variability of avalanches on these glaciers and provides key data to better represent these processes in glacier models.
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The South Col Glacier is a small body of ice and snow located on the southern ridge of Mt. Everest. A recent study proposed that South Col Glacier is rapidly losing mass. In this study, we examined the glacier thickness change for the period 1984–2017 and found no thickness change. To reconcile these results, we investigate wind erosion and surface energy and mass balance and find that melt is unlikely a dominant process, contrary to previous findings.
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Julia Glaus, Jan Kleinn, Lukas Stoffel, Pia Ruttner, Katreen Wikstrom Jones, Johan Gaume, and Yves Bühler
EGUsphere, https://doi.org/10.5194/egusphere-2026-999, https://doi.org/10.5194/egusphere-2026-999, 2026
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Yuri Brugnara, Martin Steinbacher, Simone Baffelli, and Lukas Emmenegger
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Julia Glaus, Katreen Wikstrom Jones, Perry Bartelt, Marc Christen, Lukas Stoffel, Johan Gaume, and Yves Bühler
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Avalanches are important for the mass balance of mountain glaciers, but few data exist on where and when they occur and which glaciers they affect the most. We developed an approach to map avalanches over large glaciated areas and long periods of time using satellite radar data. The application of this method to various regions in the Alps and High Mountain Asia reveals the variability of avalanches on these glaciers and provides key data to better represent these processes in glacier models.
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Information on the snow depth distribution is crucial for numerous applications in high-mountain regions. However, only specific measurements can accurately map the present variability of snow depths within complex terrain. In this study, we show the reliable processing of images from aeroplane to large (> 100 km2) detailed and accurate snow depth maps around Davos (CH). We use these maps to describe the existing snow depth distribution, other special features and potential applications.
Adrian Ringenbach, Peter Bebi, Perry Bartelt, Andreas Rigling, Marc Christen, Yves Bühler, Andreas Stoffel, and Andrin Caviezel
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Swiss researchers carried out repeated rockfall experiments with rocks up to human sizes in a steep mountain forest. This study focuses mainly on the effects of the rock shape and lying deadwood. In forested areas, cubic-shaped rocks showed a longer mean runout distance than platy-shaped rocks. Deadwood especially reduced the runouts of these cubic rocks. The findings enrich standard practices in modern rockfall hazard zoning assessments and strongly urge the incorporation of rock shape effects.
Fanny Brun, Owen King, Marion Réveillet, Charles Amory, Anton Planchot, Etienne Berthier, Amaury Dehecq, Tobias Bolch, Kévin Fourteau, Julien Brondex, Marie Dumont, Christoph Mayer, Silvan Leinss, Romain Hugonnet, and Patrick Wagnon
The Cryosphere, 17, 3251–3268, https://doi.org/10.5194/tc-17-3251-2023, https://doi.org/10.5194/tc-17-3251-2023, 2023
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The South Col Glacier is a small body of ice and snow located on the southern ridge of Mt. Everest. A recent study proposed that South Col Glacier is rapidly losing mass. In this study, we examined the glacier thickness change for the period 1984–2017 and found no thickness change. To reconcile these results, we investigate wind erosion and surface energy and mass balance and find that melt is unlikely a dominant process, contrary to previous findings.
Gregor Ortner, Michael Bründl, Chahan M. Kropf, Thomas Röösli, Yves Bühler, and David N. Bresch
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This paper presents a new approach to assess avalanche risk on a large scale in mountainous regions. It combines a large-scale avalanche modeling method with a state-of-the-art probabilistic risk tool. Over 40 000 individual avalanches were simulated, and a building dataset with over 13 000 single buildings was investigated. With this new method, risk hotspots can be identified and surveyed. This enables current and future risk analysis to assist decision makers in risk reduction and adaptation.
Adrian Ringenbach, Peter Bebi, Perry Bartelt, Andreas Rigling, Marc Christen, Yves Bühler, Andreas Stoffel, and Andrin Caviezel
Earth Surf. Dynam., 10, 1303–1319, https://doi.org/10.5194/esurf-10-1303-2022, https://doi.org/10.5194/esurf-10-1303-2022, 2022
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The presented automatic deadwood generator (ADG) allows us to consider deadwood in rockfall simulations in unprecedented detail. Besides three-dimensional fresh deadwood cones, we include old woody debris in rockfall simulations based on a higher compaction rate and lower energy absorption thresholds. Simulations including different deadwood states indicate that a 10-year-old deadwood pile has a higher protective capacity than a pre-storm forest stand.
John Sykes, Pascal Haegeli, and Yves Bühler
Nat. Hazards Earth Syst. Sci., 22, 3247–3270, https://doi.org/10.5194/nhess-22-3247-2022, https://doi.org/10.5194/nhess-22-3247-2022, 2022
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Automated snow avalanche terrain mapping provides an efficient method for large-scale assessment of avalanche hazards, which informs risk management decisions for transportation and recreation. This research reduces the cost of developing avalanche terrain maps by using satellite imagery and open-source software as well as improving performance in forested terrain. The research relies on local expertise to evaluate accuracy, so the methods are broadly applicable in mountainous regions worldwide.
Elisabeth D. Hafner, Patrick Barton, Rodrigo Caye Daudt, Jan Dirk Wegner, Konrad Schindler, and Yves Bühler
The Cryosphere, 16, 3517–3530, https://doi.org/10.5194/tc-16-3517-2022, https://doi.org/10.5194/tc-16-3517-2022, 2022
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Knowing where avalanches occur is very important information for several disciplines, for example avalanche warning, hazard zonation and risk management. Satellite imagery can provide such data systematically over large regions. In our work we propose a machine learning model to automate the time-consuming manual mapping. Additionally, we investigate expert agreement for manual avalanche mapping, showing that our network is equally as good as the experts in identifying avalanches.
Aubrey Miller, Pascal Sirguey, Simon Morris, Perry Bartelt, Nicolas Cullen, Todd Redpath, Kevin Thompson, and Yves Bühler
Nat. Hazards Earth Syst. Sci., 22, 2673–2701, https://doi.org/10.5194/nhess-22-2673-2022, https://doi.org/10.5194/nhess-22-2673-2022, 2022
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Natural hazard modelers simulate mass movements to better anticipate the risk to people and infrastructure. These simulations require accurate digital elevation models. We test the sensitivity of a well-established snow avalanche model (RAMMS) to the source and spatial resolution of the elevation model. We find key differences in the digital representation of terrain greatly affect the simulated avalanche results, with implications for hazard planning.
Adrian Ringenbach, Elia Stihl, Yves Bühler, Peter Bebi, Perry Bartelt, Andreas Rigling, Marc Christen, Guang Lu, Andreas Stoffel, Martin Kistler, Sandro Degonda, Kevin Simmler, Daniel Mader, and Andrin Caviezel
Nat. Hazards Earth Syst. Sci., 22, 2433–2443, https://doi.org/10.5194/nhess-22-2433-2022, https://doi.org/10.5194/nhess-22-2433-2022, 2022
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Forests have a recognized braking effect on rockfalls. The impact of lying deadwood, however, is mainly neglected. We conducted 1 : 1-scale rockfall experiments in three different states of a spruce forest to fill this knowledge gap: the original forest, the forest including lying deadwood and the cleared area. The deposition points clearly show that deadwood has a protective effect. We reproduced those experimental results numerically, considering three-dimensional cones to be deadwood.
Marcel Stefko, Silvan Leinss, Othmar Frey, and Irena Hajnsek
The Cryosphere, 16, 2859–2879, https://doi.org/10.5194/tc-16-2859-2022, https://doi.org/10.5194/tc-16-2859-2022, 2022
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The coherent backscatter opposition effect can enhance the intensity of radar backscatter from dry snow by up to a factor of 2. Despite widespread use of radar backscatter data by snow scientists, this effect has received notably little attention. For the first time, we characterize this effect for the Earth's snow cover with bistatic radar experiments from ground and from space. We are also able to retrieve scattering and absorbing lengths of snow at Ku- and X-band frequencies.
Yves Bühler, Peter Bebi, Marc Christen, Stefan Margreth, Lukas Stoffel, Andreas Stoffel, Christoph Marty, Gregor Schmucki, Andrin Caviezel, Roderick Kühne, Stephan Wohlwend, and Perry Bartelt
Nat. Hazards Earth Syst. Sci., 22, 1825–1843, https://doi.org/10.5194/nhess-22-1825-2022, https://doi.org/10.5194/nhess-22-1825-2022, 2022
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To calculate and visualize the potential avalanche hazard, we develop a method that automatically and efficiently pinpoints avalanche starting zones and simulate their runout for the entire canton of Grisons. The maps produced in this way highlight areas that could be endangered by avalanches and are extremely useful in multiple applications for the cantonal authorities, including the planning of new infrastructure, making alpine regions more safe.
S. Kaushik, S. Leinss, L. Ravanel, E. Trouvé, Y. Yan, and F. Magnin
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2022, 325–332, https://doi.org/10.5194/isprs-annals-V-3-2022-325-2022, https://doi.org/10.5194/isprs-annals-V-3-2022-325-2022, 2022
Animesh K. Gain, Yves Bühler, Pascal Haegeli, Daniela Molinari, Mario Parise, David J. Peres, Joaquim G. Pinto, Kai Schröter, Ricardo M. Trigo, María Carmen Llasat, and Heidi Kreibich
Nat. Hazards Earth Syst. Sci., 22, 985–993, https://doi.org/10.5194/nhess-22-985-2022, https://doi.org/10.5194/nhess-22-985-2022, 2022
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To mark the 20th anniversary of Natural Hazards and Earth System Sciences (NHESS), an interdisciplinary and international journal dedicated to the public discussion and open-access publication of high-quality studies and original research on natural hazards and their consequences, we highlight 11 key publications covering major subject areas of NHESS that stood out within the past 20 years.
Natalie Brožová, Tommaso Baggio, Vincenzo D'Agostino, Yves Bühler, and Peter Bebi
Nat. Hazards Earth Syst. Sci., 21, 3539–3562, https://doi.org/10.5194/nhess-21-3539-2021, https://doi.org/10.5194/nhess-21-3539-2021, 2021
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Surface roughness plays a great role in natural hazard processes but is not always well implemented in natural hazard modelling. The results of our study show how surface roughness can be useful in representing vegetation and ground structures, which are currently underrated. By including surface roughness in natural hazard modelling, we could better illustrate the processes and thus improve hazard mapping, which is crucial for infrastructure and settlement planning in mountainous areas.
Nora Helbig, Michael Schirmer, Jan Magnusson, Flavia Mäder, Alec van Herwijnen, Louis Quéno, Yves Bühler, Jeff S. Deems, and Simon Gascoin
The Cryosphere, 15, 4607–4624, https://doi.org/10.5194/tc-15-4607-2021, https://doi.org/10.5194/tc-15-4607-2021, 2021
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The snow cover spatial variability in mountains changes considerably over the course of a snow season. In applications such as weather, climate and hydrological predictions the fractional snow-covered area is therefore an essential parameter characterizing how much of the ground surface in a grid cell is currently covered by snow. We present a seasonal algorithm and a spatiotemporal evaluation suggesting that the algorithm can be applied in other geographic regions by any snow model application.
Cited articles
Abermann, J., Eckerstorfer, M., Malnes, E., and Hansen, B. U.: A large wet snow avalanche cycle in West Greenland quantified using remote sensing and in situ observations, Nat. Hazards, 97, 517–534, https://doi.org/10.1007/s11069-019-03655-8, 2019. a, b
Airbus: TerraSAR-X Archive, available at: https://terrasar-x-archive.terrasar.com, last access: 16 June 2020. a
Bühler, Y., Hüni, A., Meister, R., Christen, M., and Kellenberger, T.: Automated detection and mapping of avalanche deposits using airborne optical remote sensing data, Cold Reg. Sci. Technol., 57, 99–106,
https://doi.org/10.1016/j.coldregions.2009.02.007, 2009. a, b, c
Bühler, Y., Bieler, C., Pielmeier, C., Frauenfelder, R., Jaedicke, C.,
Schwaizer, G., Wiesmann, A., and Caduff, R.: Improved Alpine avalanche forecast service AAF, Final report, Integrated application program IAP, European Space Agency ESA, SLF, Birmensdorf, NGI, Oslo, available at:
https://www.dora.lib4ri.ch/wsl/islandora/object/wsl:22266 (last access: 16 June 2020), 2014. a
Condat, L.: A Simple Trick to Speed Up the Non-Local Means, working paper
or preprint, available at: https://hal.archives-ouvertes.fr/hal-00512801 (last access: 16 June 2020), 2010. a
Cumming, W. A.: The Dielectric Properties of Ice and Snow at 3.2 Centimeters,
J. Appl. Phys., 23, 768–773, https://doi.org/10.1063/1.1702299, 1952. a, b
Di Tommaso, P., Floden, E. W., Barja, P. P., Palumbo, E., and Notredame, C.:
Nextflow enables reproducible computational workflows, Nat. Biotechnol., 35, 316–319, https://doi.org/10.1038/nbt.3820, 2017. a
Eckerstorfer, M. and Malnes, E.: Manual detection of snow avalanche debris
using high-resolution Radarsat-2 SAR images, Cold Reg. Sci. Technol., 120, 205–218, https://doi.org/10.1016/j.coldregions.2015.08.016, 2015. a, b, c
Eckerstorfer, M., Bühler, Y., Frauenfelder, R., and Malnes, E.: Remote sensing of snow avalanches: Recent advances, potential, and limitations, Cold Reg. Sci. Technol., 121, 126–140, https://doi.org/10.1016/j.coldregions.2015.11.001, 2016. a
Eckerstorfer, M., Malnes, E., and Müller, K.: A complete snow avalanche
activity record from a Norwegian forecasting region using Sentinel-1
satellite-radar data, Cold Reg. Sci. Technol., 144, 39–51,
https://doi.org/10.1016/j.coldregions.2017.08.004, 2017. a
Eckerstorfer, M., Malnes, E., Vickers, H., Müller, K., Engeset, R., and
Humstad, T.: Operational avalanche activity monitoring using radar
satellites: From Norway to worldwide assistance in avalanche forecasting, in:
International Snow Science Workshop, Innsbruck, Austria, 2018. a
ESA: Sentinel-1: ESA's Radar Observatory Mission for GMES Operational Services (ESA SP-1322/1, March 2012), Tech. rep., ESA, Noordwijk, the Netherlands, 2012. a
ESA: Copernicus Open Access Hub, available at: https://scihub.copernicus.eu, last acess: 16 June 2020. a
Frauenfelder, R., Malnes, E., Solberg, R., and Müller, K.: Towards an
automated snow property and avalanche mapping system (ASAM), techreport 20130092-04-R, NGI – Norwegian Geotechnical Institute, https://doi.org/10.13140/RG.2.1.1962.5446, 2015. a
Fung, A. K. and Eom, H. J.: Application of a Combined Rough Surface And Volume Scattering Theory to Sea Ice And Snow Backscatter, IEEE T. Geosci. Remote, GE-20, 528–536, https://doi.org/10.1109/TGRS.1982.350421, 1982. a
Hafner, E. and Bühler, Y.: SPOT6 Avalanche outlines 24 January 2018,
https://doi.org/10.16904/envidat.77, 2019. a
Hamar, J. B., Salberg, A., and Ardelean, F.: Automatic detection and mapping of avalanches in SAR images, in: International Geoscience and Remote Sensing
Symposium, 10–15 July 2016, Beijing, 689–692, https://doi.org/10.1109/IGARSS.2016.7729173, 2016. a
International Commission of Snow and Ice: Avalanche atlas: illustrated
international avalanche classification, Unesco, Paris, available at:
https://unesdoc.unesco.org/ark:/48223/pf0000048004 (last access: 16 June 2020), 1981. a
Jin, Q., Grama, I., and Liu, Q.: Removing Gaussian Noise by Optimization of
Weights in Non-Local Means, in: 2012 Symposium on Photonics and Optoelectronics, SOPO 2012, 21–23 May 2012, Shanghai, https://doi.org/10.1109/SOPO.2012.6270436, 2011. a
Karbou, F., Coléou, C., Lefort, M., Deschatres, M., Eckert, N., Martin, R., Charvet, G., and Dufour, A.: Monitoring avalanche debris in the French
mountains using SAR observations from Sentinel-1 satellites, in: Proceedings
of the International Snow Science Workshop, 7–12 October 2018, Innsbruck, Austria, 344–347, 2018. a
Kendra, J. R., Sarabandi, K., and Ulaby, F. T.: Radar measurements of snow: experiment and analysis, IEEE T. Geosci. Remote, 36, 864–879, 1998. a
Korzeniowska, K., Bühler, Y., Marty, M., and Korup, O.: Regional snow-avalanche detection using object-based image analysis of near-infrared
aerial imagery, Nat. Hazards Earth Syst. Sci., 17, 1823–1836,
https://doi.org/10.5194/nhess-17-1823-2017, 2017. a, b
Lato, M. J., Frauenfelder, R., and Bühler, Y.: Automated detection of snow avalanche deposits: segmentation and classification of optical remote sensing imagery, Nat. Hazards Earth Syst. Sci., 12, 2893–2906,
https://doi.org/10.5194/nhess-12-2893-2012, 2012. a, b
Leader, J.: The relationship between the Kirchhoff approach and small
perturbation analysis in rough surface scattering theory, IEEE T. Anten. Propag., 19, 786–788, 1971. a
Leinss, S., Wiesmann, A., Lemmetyinen, J., and Hajnsek, I.: Snow water
equivalent of dry snow measured by differential interferometry, IEEE J. Sel.
Top. Appl. Earth Obs. Remote Sens., 8, 3773–3790, https://doi.org/10.1109/JSTARS.2015.2432031, 2015. a
Leinss, S., Holenstein, S., and Wicki, R.: Sentinel-1 change detection mosaic
of Switzerland for the avalanche event of January 4th 2018, https://doi.org/10.3929/ethz-b-000376048, 2019. a, b, c
Lucas, C., Leinss, S., Bühler, Y., Marino, A., and Hajnsek, I.: Multipath
Interferences in Ground-Based Radar Data: A Case Study, Remote Sensing, 9,
1260, https://doi.org/10.3390/rs9121260, 2017. a
Matzler, C.: Microwave permittivity of dry snow, IEEE T. Geosci. Remote, 34, 573–581, 1996. a
Meister, R.: Country-wide avalanche warning in Switzerland, in: Proceedings
International Snow Science Workshop, Snowbird, Utah, USA, 30 October–3 November 1994, ISSW 1994 Organizing Committee, Snowbird, UT, USA, 58–71, 1995. a
NASA: Alaska SAR Facility ASF DAAC 2018, available at: https://www.asf.alaska.edu, last access: 16 June 2020. a
Rignot, E., Echelmeyer, K., and Krabill, W.: Penetration depth of
interferometric synthetic-aperture radar signals in snow and ice, Geophys. Res. Lett., 28, 3501–3504, https://doi.org/10.1029/2000GL012484, 2001. a
Rudolf-Miklau, F., Sauermoser, S., Mears, A., and Boensch, M.: The Technical
Avalanche Protection Handbook, Wiley, Berlin, Germany, 2014. a
Schweizer, J., Jamieson, J. B., and Skjonsberg, D.: Avalanche forecasting for
transportation corridor and backcountry in Glacier National Park (BC,
Canada), in: 25 Years of Snow Avalanche Research, 12–16 May 1998, Voss, Norway, 238–243, 1998. a
Schweizer, J., Kronholm, K., and Wiesinger, T.: Verification of regional
snowpack stability and avalanche danger, Cold Reg. Sci. Technol., 37, 277–288, https://doi.org/10.1016/S0165-232X(03)00070-3, 2003. a
Scott, D.: Avalache Mapping: GIS for Avalanche Studies and Snow Science,
Avalanche Rev., 27, 20–21, 2009. a
SLF: Wochenbericht 05.Januar–11. Januar 2018, available at:
https://www.slf.ch/de/lawinenbulletin-und-schneesituation/wochen-und-winterberichte/201718/wob-05-11-januar.html (last access: 16 June 2020), 2018a. a
SLF: Wochenbericht 19.–25. Januar 2018, available at:
https://www.slf.ch/de/lawinenbulletin-und-schneesituation/wochen-und-winterberichte/201718/wob-19-25-januar.html
(last access: 16 June 2020), 2018c. a
SLF: Wochenbericht 26. Januar–01. Februar 2018, available at:
https://www.slf.ch/de/lawinenbulletin-und-schneesituation/wochen-und-winterberichte/201718/wob-26-januar-01-februar.html
(last access: 16 June 2020), 2018d. a
SLF: Avalanche Bulletin, available at:
https://www.slf.ch/en/avalanche-bulletin-and-snow-situation.html#avalanchedanger
(last access: 16 June 2020), 2018e. a
Small, D.: Flattening Gamma: Radiometric Terrain Correction for SAR Imagery,
IEEE T. Geosci. Remote, 49, 3081–3093, https://doi.org/10.1109/TGRS.2011.2120616, 2011. a
Small, D.: SAR backscatter multitemporal compositing via local resolution
weighting, in: International Geoscience and Remote Sensing Symposium, 22–27 July 2012, Munich, Germany, 4521–4524, https://doi.org/10.1109/IGARSS.2012.6350465, 2012. a, b
Techel, F., Jarry, F., Kronthaler, G., Mitterer, S., Nairz, P., Pavšek, M., Valt, M., and Darms, G.: Avalanche fatalities in the European Alps: long-term trends and statistics, Geogr. Helv., 71, 147–159,
https://doi.org/10.5194/gh-71-147-2016, 2016. a
Tiuri, M., Sihvola, A., Nyfors, E., and Hallikainen, M.: The complex dielectric constant of snow at microwave frequencies, IEEE J. Ocean. Eng., 9, 377–382, https://doi.org/10.1109/JOE.1984.1145645, 1984. a
Vickers, H., Eckerstorfer, M., Malnes, E., Larsen, Y., and Hindberg, H.: A
method for automated snow avalanche debris detection through use of synthetic
aperture radar (SAR) imaging, Earth Space Sci., 3, 446–462,
https://doi.org/10.1002/2016EA000168, 2016. a, b, c
Watte, W. P. and MacDonald, H. C.: Snowfield mapping with K-band radar, Remote Sens. Environ., 1, 143–150, https://doi.org/10.1016/S0034-4257(70)80016-5, 1970. a
Werninghaus, R. and Buckreuss, S.: The TerraSAR-X mission and system design,
IEEE T. Geosci. Remote., 48, 606–614, https://doi.org/10.1109/TGRS.2009.2031062, 2010. a
Wesselink, D. S., Malnes, E., Eckerstorfer, M., and Lindenbergh, R. C.:
Automatic detection of snow avalanche debris in central Svalbard using C-band
SAR data, Polar Res., 36, 1333236, https://doi.org/10.1080/17518369.2017.1333236, 2017. a, b, c
Wiesmann, A., Mätzler, C., and Weise, T.: Radiometric and structural
measurements of snow samples, Radio Sci., 33, 273–289, https://doi.org/10.1029/97RS02746, 1998. a
Wiesmann, A., Wegmuller, U., Honikel, M., Strozzi, T., and Werner, C. L.: Potential and methodology of satellite based SAR for hazard mapping,
in: vol. 7, International Geoscience and Remote Sensing Symposium, 9–13 July 2001, Sydney, NSW, Australia, 3262–3264, 2001. a
Winkler, K., Zweifel, B., Marty, C., and Techel, F.: Schnee und Lawinen in den Schweizer Alpen, Hydrologisches Jahr 2017/18, in: WSL Berichte, Vol. 77, SLF – Institut für Schnee- und Lawinenforschung, Davos, WSL – Eidg. Forschungsanstalt für Wald, Schnee und Landschaft, Birmensdorf, 135 pp., 2019. a, b
Xu, X., Tsang, L., and Yueh, S.: Electromagnetic Models of Co/Cross
Polarization of Bicontinuous/DMRT in Radar Remote Sensing of Terrestrial Snow
at X- and Ku-band for CoReH2O and SCLP Applications, IEEE J. Select. Top. Appl. Earth Obs. Remote Sens., 5, 1024–1032, 2012. a
Short summary
To assess snow avalanche mapping with radar satellites in Switzerland, we compare 2 m resolution TerraSAR-X images, 10 m resolution Sentinel-1 images, and optical 1.5 m resolution SPOT-6 images. We found that radar satellites provide a valuable option to map at least larger avalanches, though avalanches are mapped only partially. By combining multiple orbits and polarizations from S1, we achieved mapping results of quality almost comparable to single high-resolution TerraSAR-X images.
To assess snow avalanche mapping with radar satellites in Switzerland, we compare 2 m resolution...
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