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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Articles | Volume XLII-2
https://doi.org/10.5194/isprs-archives-XLII-2-739-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/isprs-archives-XLII-2-739-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
30 May 2018
| 30 May 2018
SUBAQUATIC DIGITAL ELEVATION MODELS FROM UAV-IMAGERY
C. Mulsow
Institute of Photogrammetry and Remote Sensing, Technische Universität Dresden, Dresden, Germany
R. Kenner
WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
Y. Bühler
WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
A. Stoffel
WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
H.-G. Maas
Institute of Photogrammetry and Remote Sensing, Technische Universität Dresden, Dresden, Germany
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ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-3-W2-2020, 129–131, https://doi.org/10.5194/isprs-annals-IV-3-W2-2020-129-2020, https://doi.org/10.5194/isprs-annals-IV-3-W2-2020-129-2020, 2020
M. Elias and H.-G. Maas
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-1-2020, 181–187, https://doi.org/10.5194/isprs-annals-V-1-2020-181-2020, https://doi.org/10.5194/isprs-annals-V-1-2020-181-2020, 2020
C. Mulsow, G. Mandlburger, and H.-G. Maas
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2020, 671–677, https://doi.org/10.5194/isprs-annals-V-2-2020-671-2020, https://doi.org/10.5194/isprs-annals-V-2-2020-671-2020, 2020
Silvan Leinss, Raphael Wicki, Sämi Holenstein, Simone Baffelli, and Yves Bühler
Nat. Hazards Earth Syst. Sci., 20, 1783–1803, https://doi.org/10.5194/nhess-20-1783-2020, https://doi.org/10.5194/nhess-20-1783-2020, 2020
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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.
Benjamin Walter, Hendrik Huwald, Josué Gehring, Yves Bühler, and Michael Lehning
The Cryosphere, 14, 1779–1794, https://doi.org/10.5194/tc-14-1779-2020, https://doi.org/10.5194/tc-14-1779-2020, 2020
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We applied a horizontally mounted low-cost precipitation radar to measure velocities, frequency of occurrence, travel distances and turbulence characteristics of blowing snow off a mountain ridge. Our analysis provides a first insight into the potential of radar measurements for determining blowing snow characteristics, improves our understanding of mountain ridge blowing snow events and serves as a valuable data basis for validating coupled numerical weather and snowpack simulations.
Jürg Schweizer, Christoph Mitterer, Frank Techel, Andreas Stoffel, and Benjamin Reuter
The Cryosphere, 14, 737–750, https://doi.org/10.5194/tc-14-737-2020, https://doi.org/10.5194/tc-14-737-2020, 2020
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Snow avalanches represent a major natural hazard in seasonally snow-covered mountain regions around the world. To avoid periods and locations of high hazard, avalanche warnings are issued by public authorities. In these bulletins, the hazard is characterized by a danger level. Since the danger levels are not well defined, we analyzed a large data set of avalanches to improve the description. Our findings show discrepancies in present usage of the danger scale and show ways to improve the scale.
Yves Bühler, Elisabeth D. Hafner, Benjamin Zweifel, Mathias Zesiger, and Holger Heisig
The Cryosphere, 13, 3225–3238, https://doi.org/10.5194/tc-13-3225-2019, https://doi.org/10.5194/tc-13-3225-2019, 2019
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We manually map 18 737 avalanche outlines based on SPOT6 optical satellite imagery acquired in January 2018. This is the most complete and accurate avalanche documentation of a large avalanche period covering a big part of the Swiss Alps. This unique dataset can be applied for the validation of other remote-sensing-based avalanche-mapping procedures and for updating avalanche databases to improve hazard maps.
F. Liebold, H.-G. Maas, and A. A. Heravi
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W18, 107–113, https://doi.org/10.5194/isprs-archives-XLII-2-W18-107-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W18-107-2019, 2019
T. Kaiser, C. Clemen, and H.-G. Maas
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-5-W2, 35–39, https://doi.org/10.5194/isprs-archives-XLII-5-W2-35-2019, https://doi.org/10.5194/isprs-archives-XLII-5-W2-35-2019, 2019
Robert Kenner, Jeannette Noetzli, Martin Hoelzle, Hugo Raetzo, and Marcia Phillips
The Cryosphere, 13, 1925–1941, https://doi.org/10.5194/tc-13-1925-2019, https://doi.org/10.5194/tc-13-1925-2019, 2019
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A new permafrost mapping method distinguishes between ice-poor and ice-rich permafrost. The approach was tested for the entire Swiss Alps and highlights the dominating influence of the factors elevation and solar radiation on the distribution of ice-poor permafrost. Our method enabled the indication of mean annual ground temperatures and the cartographic representation of permafrost-free belts, which are bounded above by ice-poor permafrost and below by permafrost-containing excess ice.
D. Mader, K. Richter, P. Westfeld, R. Weiß, and H.-G. Maas
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 1053–1059, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1053-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1053-2019, 2019
D. Lin, Z. Dong, X. Zhang, and H.-G. Maas
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W5, 45–51, https://doi.org/10.5194/isprs-annals-IV-2-W5-45-2019, https://doi.org/10.5194/isprs-annals-IV-2-W5-45-2019, 2019
S. C. L. Ribeiro, M. Jarzabek-Rychard, J. P. Cintra, and H.-G. Maas
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W5, 437–444, https://doi.org/10.5194/isprs-annals-IV-2-W5-437-2019, https://doi.org/10.5194/isprs-annals-IV-2-W5-437-2019, 2019
H.-G. Maas, D. Mader, K. Richter, and P. Westfeld
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W10, 113–117, https://doi.org/10.5194/isprs-archives-XLII-2-W10-113-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W10-113-2019, 2019
Andrin Caviezel, Sophia E. Demmel, Adrian Ringenbach, Yves Bühler, Guang Lu, Marc Christen, Claire E. Dinneen, Lucie A. Eberhard, Daniel von Rickenbach, and Perry Bartelt
Earth Surf. Dynam., 7, 199–210, https://doi.org/10.5194/esurf-7-199-2019, https://doi.org/10.5194/esurf-7-199-2019, 2019
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In rockfall hazard assessment, knowledge about the precise flight path of assumed boulders is vital for its accuracy. We present the full reconstruction of artificially induced rockfall events. The extracted information such as exact velocities, jump heights and lengths provide detailed insights into how rotating rocks interact with the ground. The information serves as future calibration of rockfall modelling tools with the goal of even more realistic modelling predictions.
Yves Bühler, Daniel von Rickenbach, Andreas Stoffel, Stefan Margreth, Lukas Stoffel, and Marc Christen
Nat. Hazards Earth Syst. Sci., 18, 3235–3251, https://doi.org/10.5194/nhess-18-3235-2018, https://doi.org/10.5194/nhess-18-3235-2018, 2018
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Coping with avalanche hazard has a long tradition in alpine countries. Hazard mapping has proven to be one of the most effective methods. In this paper we develop a new approach to automatically delineate avalanche release areas and connect them to state-of-the-art numerical avalanche simulations. This enables computer-based hazard indication mapping over large areas such as entire countries. This is of particular interest where hazard maps do not yet exist, such as in developing countries.
M. Kröhnert, R. Anderson, J. Bumberger, P. Dietrich, W. S. Harpole, and H.-G. Maas
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 539–542, https://doi.org/10.5194/isprs-archives-XLII-2-539-2018, https://doi.org/10.5194/isprs-archives-XLII-2-539-2018, 2018
D. Lin, M. Jarzabek-Rychard, D. Schneider, and H.-G. Maas
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 585–591, https://doi.org/10.5194/isprs-archives-XLII-2-585-2018, https://doi.org/10.5194/isprs-archives-XLII-2-585-2018, 2018
K. Richter, D. Mader, P. Westfeld, and H.-G. Maas
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 961–967, https://doi.org/10.5194/isprs-archives-XLII-2-961-2018, https://doi.org/10.5194/isprs-archives-XLII-2-961-2018, 2018
H. Sardemann, A. Eltner, and H.-G. Maas
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 1023–1027, https://doi.org/10.5194/isprs-archives-XLII-2-1023-2018, https://doi.org/10.5194/isprs-archives-XLII-2-1023-2018, 2018
E. Watson, H.-G. Maas, F. Schäfer, and S. Hiermaier
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 1175–1181, https://doi.org/10.5194/isprs-archives-XLII-2-1175-2018, https://doi.org/10.5194/isprs-archives-XLII-2-1175-2018, 2018
F. Liebold and H.-G. Maas
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2, 193–200, https://doi.org/10.5194/isprs-annals-IV-2-193-2018, https://doi.org/10.5194/isprs-annals-IV-2-193-2018, 2018
D. Lin, A. Eltner, H. Sardemann, and H.-G. Maas
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2, 201–208, https://doi.org/10.5194/isprs-annals-IV-2-201-2018, https://doi.org/10.5194/isprs-annals-IV-2-201-2018, 2018
Ellen Schwalbe and Hans-Gerd Maas
Earth Surf. Dynam., 5, 861–879, https://doi.org/10.5194/esurf-5-861-2017, https://doi.org/10.5194/esurf-5-861-2017, 2017
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The simple use of time-lapse cameras as a visual observation tool may already be a great help for environmental investigations. However, beyond that, they have the potential to also deliver precise measurements with high temporal and spatial resolution when applying appropriate processing techniques. In this paper we introduce a method for the determination of glacier motion fields from time-lapse images, but it might also be adapted for other environmental motion analysis tasks.
Karolina Korzeniowska, Yves Bühler, Mauro Marty, and Oliver Korup
Nat. Hazards Earth Syst. Sci., 17, 1823–1836, https://doi.org/10.5194/nhess-17-1823-2017, https://doi.org/10.5194/nhess-17-1823-2017, 2017
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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.
Anna Haberkorn, Nander Wever, Martin Hoelzle, Marcia Phillips, Robert Kenner, Mathias Bavay, and Michael Lehning
The Cryosphere, 11, 585–607, https://doi.org/10.5194/tc-11-585-2017, https://doi.org/10.5194/tc-11-585-2017, 2017
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The effects of permafrost degradation on rock slope stability in the Alps affect people and infrastructure. Modelling the evolution of permafrost is therefore of great importance. However, the snow cover has generally not been taken into account in model studies of steep, rugged rock walls. Thus, we present a distributed model study on the influence of the snow cover on rock temperatures. The promising results are discussed against detailed rock temperature measurements and snow depth data.
Cesar Vera Valero, Nander Wever, Yves Bühler, Lukas Stoffel, Stefan Margreth, and Perry Bartelt
Nat. Hazards Earth Syst. Sci., 16, 2303–2323, https://doi.org/10.5194/nhess-16-2303-2016, https://doi.org/10.5194/nhess-16-2303-2016, 2016
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Simulating medium–small avalanches operationally on a mine service road allows avalanche hazard to be assessed on the mine transportation route. Using accurate data from the snow cover and the avalanche paths, the avalanche dynamic model developed can calculate the avalanche runout distances and snow volumes of the deposits. The model does not predict whether the avalanche is coming or not, but if it comes, the model will predict runout distances and mass of the deposits.
C. Mulsow, H.-G. Maas, and B. Hentschel
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B5, 85–91, https://doi.org/10.5194/isprs-archives-XLI-B5-85-2016, https://doi.org/10.5194/isprs-archives-XLI-B5-85-2016, 2016
Yves Bühler, Marc S. Adams, Ruedi Bösch, and Andreas Stoffel
The Cryosphere, 10, 1075–1088, https://doi.org/10.5194/tc-10-1075-2016, https://doi.org/10.5194/tc-10-1075-2016, 2016
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We map the distribution of snow depth at two alpine test sites with unmanned aerial system (UAS) data by applying structure-from-motion photogrammetry. In comparison with manual snow depth measurements, we find high accuracies of 7 to 15 cm for the snow depth values. We can prove that photogrammetric measurements on snow-covered terrain are possible. Underlaying vegetation such as bushes and grass leads to an underestimation of snow depth in the range of 10 to 50 cm.
C. Vera Valero, Y. Bühler, and P. Bartelt
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhessd-3-2883-2015, https://doi.org/10.5194/nhessd-3-2883-2015, 2015
Manuscript not accepted for further review
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Wet snow avalanches can initiate from large fracture slabs or small point releases. Point
release wet snow avalanches can reach dangerous proportions when they initiate on steep and long avalanche paths and entrain warm moist snow. In this paper we investigate the dynamics of point release wet snow avalanches by applying a numerical model to simulate documented case studies on high altitude slopes in the Chilean Andes. The model simulated correctly flow height, velocity and avalanche run out.
Y. Bühler, M. Marty, L. Egli, J. Veitinger, T. Jonas, P. Thee, and C. Ginzler
The Cryosphere, 9, 229–243, https://doi.org/10.5194/tc-9-229-2015, https://doi.org/10.5194/tc-9-229-2015, 2015
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We are able to map snow depth over large areas ( > 100km2) using airborne digital photogrammetry. Digital photogrammetry is more economical than airborne Laser Scanning but slightly less accurate. Comparisons to independent snow depth measurements reveal an accuracy of about 30cm. Spatial continuous mapping of snow depth is a major step forward compared to point measurements usually applied today. Limitations are steep slopes (> 50°) and areas covered by trees and scrubs.
T. Grünewald, Y. Bühler, and M. Lehning
The Cryosphere, 8, 2381–2394, https://doi.org/10.5194/tc-8-2381-2014, https://doi.org/10.5194/tc-8-2381-2014, 2014
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Elevation dependencies of snow depth are analysed based on snow depth maps obtained from airborne remote sensing. Elevation gradients are characterised by a specific shape: an increase of snow depth with elevation is followed by a distinct peak at a certain level and a decrease in the highest elevations. We attribute this shape to an increase of precipitation with altitude, which is modified by topographical-induced redistribution processes of the snow on the ground (wind, gravitation).
A. Aydin, Y. Bühler, M. Christen, and I. Gürer
Nat. Hazards Earth Syst. Sci., 14, 1145–1154, https://doi.org/10.5194/nhess-14-1145-2014, https://doi.org/10.5194/nhess-14-1145-2014, 2014
Y. Bühler, S. Kumar, J. Veitinger, M. Christen, A. Stoffel, and Snehmani
Nat. Hazards Earth Syst. Sci., 13, 1321–1335, https://doi.org/10.5194/nhess-13-1321-2013, https://doi.org/10.5194/nhess-13-1321-2013, 2013
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