Articles | Volume 15, issue 1
https://doi.org/10.5194/tc-15-69-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-15-69-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
05 Jan 2021
Research article |
| 05 Jan 2021
| 05 Jan 2021
Intercomparison of photogrammetric platforms for spatially continuous snow depth mapping
WSL Institute for Snow and Avalanche Research SLF, Davos Dorf, 7260,
Switzerland
Institute of Geodesy and Photogrammetry, ETH Zurich, Zurich, 8092,
Switzerland
Pascal Sirguey
National School of Surveying, University of Otago, P.O. Box 56, Dunedin,
New Zealand
Aubrey Miller
National School of Surveying, University of Otago, P.O. Box 56, Dunedin,
New Zealand
Mauro Marty
Swiss Federal Institute for Forest, Snow and Landscape Research WSL,
Birmensdorf, 8903, Switzerland
Konrad Schindler
Institute of Geodesy and Photogrammetry, ETH Zurich, Zurich, 8092,
Switzerland
Andreas Stoffel
WSL Institute for Snow and Avalanche Research SLF, Davos Dorf, 7260,
Switzerland
Yves Bühler
WSL Institute for Snow and Avalanche Research SLF, Davos Dorf, 7260,
Switzerland
Related authors
Leon J. Bührle, Mauro Marty, Lucie A. Eberhard, Andreas Stoffel, Elisabeth D. Hafner, and Yves Bühler
The Cryosphere, 17, 3383–3408, https://doi.org/10.5194/tc-17-3383-2023, https://doi.org/10.5194/tc-17-3383-2023, 2023
Short summary
Short summary
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.
Julia Glaus, Katreen Wikstrom Jones, Perry Bartelt, Marc Christen, Lukas Stoffel, Johan Gaume, and Yves Bühler
Nat. Hazards Earth Syst. Sci., 25, 2399–2419, https://doi.org/10.5194/nhess-25-2399-2025, https://doi.org/10.5194/nhess-25-2399-2025, 2025
Short summary
Short summary
This study assesses RAMMS::EXTENDED's predictive power in estimating avalanche runout distances critical for mountain road safety. Leveraging meteorological data and sensitivity analyses, it offers meaningful predictions, aiding near real-time hazard assessments and future model refinement for improved decision-making.
Ghjulia Sialelli, Torben Peters, Jan D. Wegner, and Konrad Schindler
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-G-2025, 829–838, https://doi.org/10.5194/isprs-annals-X-G-2025-829-2025, https://doi.org/10.5194/isprs-annals-X-G-2025-829-2025, 2025
Aaron Cremona, Matthias Huss, Johannes Marian Landmann, Mauro Marty, Marijn van der Meer, Christian Ginzler, and Daniel Farinotti
EGUsphere, https://doi.org/10.5194/egusphere-2025-2929, https://doi.org/10.5194/egusphere-2025-2929, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
Our study provides daily mass balance estimates for every Swiss glacier from 2010–2024 using modelling, remote sensing observations, and machine learning. Over the period, Swiss glaciers lost nearly a quarter of their ice volume. The approach enables investigating the spatio-temporal variability of glacier mass balance in relation to the driving climatic factors.
Hongruixuan Chen, Jian Song, Olivier Dietrich, Clifford Broni-Bediako, Weihao Xuan, Junjue Wang, Xinlei Shao, Yimin Wei, Junshi Xia, Cuiling Lan, Konrad Schindler, and Naoto Yokoya
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-269, https://doi.org/10.5194/essd-2025-269, 2025
Revised manuscript under review for ESSD
Short summary
Short summary
Natural disasters often damage buildings and threaten lives, especially in areas with limited resources. To help improve emergency response, we created a global dataset called BRIGHT using both optical and radar images to detect building damage in any weather. We tested many artificial intelligence models and showed how well they work in real disaster scenes. This work can guide better tools for future disaster recovery and help save lives faster.
Samantha Biegel, Konrad Schindler, and Benjamin D. Stocker
EGUsphere, https://doi.org/10.5194/egusphere-2025-1617, https://doi.org/10.5194/egusphere-2025-1617, 2025
Short summary
Short summary
Our work addresses the predictability of carbon absorption by ecosystems across the globe, particularly in dry regions. We compare 3 different models, including a deep learning model that can learn from past environmental conditions, and show that this helps improve predictions. Still, challenges remain in dry areas due to varying vulnerabilities to drought. As drought conditions intensify globally, it's crucial to understand the varying impacts on ecosystem function.
Pia Ruttner, Annelies Voordendag, Thierry Hartmann, Julia Glaus, Andreas Wieser, and Yves Bühler
Nat. Hazards Earth Syst. Sci., 25, 1315–1330, https://doi.org/10.5194/nhess-25-1315-2025, https://doi.org/10.5194/nhess-25-1315-2025, 2025
Short summary
Short summary
Snow depth variations caused by wind are an important factor in avalanche danger, but detailed and up-to-date information is rarely available. We propose a monitoring system, using lidar and optical sensors, to measure the snow depth distribution at high spatial and temporal resolution. First results show that we can quantify snow depth changes with an accuracy on the low decimeter level, or better, and can identify events such as avalanches or displacement of snow during periods of strong winds.
John Sykes, Pascal Haegeli, Roger Atkins, Patrick Mair, and Yves Bühler
Nat. Hazards Earth Syst. Sci., 25, 1255–1292, https://doi.org/10.5194/nhess-25-1255-2025, https://doi.org/10.5194/nhess-25-1255-2025, 2025
Short summary
Short summary
We model the decision-making of professional ski guides and develop decision support tools to assist with determining appropriate terrain based on current conditions. Our approach compares a manually constructed Bayesian network with machine learning classification models. The models accurately capture the real-world decision-making outcomes in 85–93 % of cases. Our conclusions focus on strengths and weaknesses of each model and discuss ramifications for practical applications in ski guiding.
Jan Magnusson, Yves Bühler, Louis Quéno, Bertrand Cluzet, Giulia Mazzotti, Clare Webster, Rebecca Mott, and Tobias Jonas
Earth Syst. Sci. Data, 17, 703–717, https://doi.org/10.5194/essd-17-703-2025, https://doi.org/10.5194/essd-17-703-2025, 2025
Short summary
Short summary
In this study, we present a dataset for the Dischma catchment in eastern Switzerland, which represents a typical high-alpine watershed in the European Alps. Accurate monitoring and reliable forecasting of snow and water resources in such basins are crucial for a wide range of applications. Our dataset is valuable for improving physics-based snow, land surface, and hydrological models, with potential applications in similar high-alpine catchments.
Mauro Marty, Livia Piermattei, Lars T. Waser, and Christian Ginzler
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-428, https://doi.org/10.5194/essd-2024-428, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
Millions of aerial photographs represent an enormous, resource for geoscientists. In this study, we used freely available historical stereo images covering Switzerland, allowing us to derive four countrywide DSMs at a 1 m spatial resolution across four epochs. Our DSMs achieved sub-metric accuracy compared to reference data and high image matching completeness, demonstrating the feasibility of capturing continuous surface change at a high spatial resolution over different land cover classes.
Andrea Manconi, Yves Bühler, Andreas Stoffel, Johan Gaume, Qiaoping Zhang, and Valentyn Tolpekin
Nat. Hazards Earth Syst. Sci., 24, 3833–3839, https://doi.org/10.5194/nhess-24-3833-2024, https://doi.org/10.5194/nhess-24-3833-2024, 2024
Short summary
Short summary
Our research reveals the power of high-resolution satellite synthetic-aperture radar (SAR) imagery for slope deformation monitoring. Using ICEYE data over the Brienz/Brinzauls instability, we measured surface velocity and mapped the landslide event with unprecedented precision. This underscores the potential of satellite SAR for timely hazard assessment in remote regions and aiding disaster mitigation efforts effectively.
Jaeyoung Lim, Elisabeth Hafner, Florian Achermann, Rik Girod, David Rohr, Nicholas R. J. Lawrance, Yves Bühler, and Roland Siegwart
EGUsphere, https://doi.org/10.5194/egusphere-2024-2728, https://doi.org/10.5194/egusphere-2024-2728, 2024
Short summary
Short summary
As avalanches occur in remote and potentially dangerous locations, data relevant to avalanche monitoring is difficult to obtain. Uncrewed fixed-wing aerial vehicles are promising platforms for gathering aerial imagery to map avalanche activity over a large area. In this work, we present an unmanned aerial system (UAS) capable of autonomously navigating and mapping avalanches in steep mountainous terrain. We expect our work to enable efficient large-scale autonomous avalanche monitoring.
Elisabeth D. Hafner, Theodora Kontogianni, Rodrigo Caye Daudt, Lucien Oberson, Jan Dirk Wegner, Konrad Schindler, and Yves Bühler
The Cryosphere, 18, 3807–3823, https://doi.org/10.5194/tc-18-3807-2024, https://doi.org/10.5194/tc-18-3807-2024, 2024
Short summary
Short summary
For many safety-related applications such as road management, well-documented avalanches are important. To enlarge the information, webcams may be used. We propose supporting the mapping of avalanches from webcams with a machine learning model that interactively works together with the human. Relying on that model, there is a 90% saving of time compared to the "traditional" mapping. This gives a better base for safety-critical decisions and planning in avalanche-prone mountain regions.
B. Xiang, T. Peters, T. Kontogianni, F. Vetterli, S. Puliti, R. Astrup, and K. Schindler
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-1-W1-2023, 605–612, https://doi.org/10.5194/isprs-annals-X-1-W1-2023-605-2023, https://doi.org/10.5194/isprs-annals-X-1-W1-2023-605-2023, 2023
Elisabeth D. Hafner, Frank Techel, Rodrigo Caye Daudt, Jan Dirk Wegner, Konrad Schindler, and Yves Bühler
Nat. Hazards Earth Syst. Sci., 23, 2895–2914, https://doi.org/10.5194/nhess-23-2895-2023, https://doi.org/10.5194/nhess-23-2895-2023, 2023
Short summary
Short summary
Oftentimes when objective measurements are not possible, human estimates are used instead. In our study, we investigate the reproducibility of human judgement for size estimates, the mappings of avalanches from oblique photographs and remotely sensed imagery. The variability that we found in those estimates is worth considering as it may influence results and should be kept in mind for several applications.
Leon J. Bührle, Mauro Marty, Lucie A. Eberhard, Andreas Stoffel, Elisabeth D. Hafner, and Yves Bühler
The Cryosphere, 17, 3383–3408, https://doi.org/10.5194/tc-17-3383-2023, https://doi.org/10.5194/tc-17-3383-2023, 2023
Short summary
Short summary
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
Earth Surf. Dynam., 11, 779–801, https://doi.org/10.5194/esurf-11-779-2023, https://doi.org/10.5194/esurf-11-779-2023, 2023
Short summary
Short summary
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.
Gregor Ortner, Michael Bründl, Chahan M. Kropf, Thomas Röösli, Yves Bühler, and David N. Bresch
Nat. Hazards Earth Syst. Sci., 23, 2089–2110, https://doi.org/10.5194/nhess-23-2089-2023, https://doi.org/10.5194/nhess-23-2089-2023, 2023
Short summary
Short summary
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.
O. Kantarcioglu, K. Schindler, and S. Kocaman
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-M-1-2023, 161–167, https://doi.org/10.5194/isprs-archives-XLVIII-M-1-2023-161-2023, https://doi.org/10.5194/isprs-archives-XLVIII-M-1-2023-161-2023, 2023
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
C. Stucker, B. Ke, Y. Yue, S. Huang, I. Armeni, and K. Schindler
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2022, 193–201, https://doi.org/10.5194/isprs-annals-V-2-2022-193-2022, https://doi.org/10.5194/isprs-annals-V-2-2022-193-2022, 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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Y. Xie, K. Schindler, J. Tian, and X. X. Zhu
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2021, 247–254, https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-247-2021, https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-247-2021, 2021
Nico Lang, Andrea Irniger, Agnieszka Rozniak, Roni Hunziker, Jan Dirk Wegner, and Konrad Schindler
Hydrol. Earth Syst. Sci., 25, 2567–2597, https://doi.org/10.5194/hess-25-2567-2021, https://doi.org/10.5194/hess-25-2567-2021, 2021
Short summary
Short summary
Grain size analysis is the key to understanding the sediment dynamics of river systems and is an important indicator for mitigating flood risk and preserving biodiversity in aquatic habitats. We propose GRAINet, a data-driven approach based on deep learning, to regress grain size distributions from georeferenced UAV images. This allows for a holistic analysis of entire gravel bars, resulting in robust grading curves and high-resolution maps of spatial grain size distribution at large scale.
Elisabeth D. Hafner, Frank Techel, Silvan Leinss, and Yves Bühler
The Cryosphere, 15, 983–1004, https://doi.org/10.5194/tc-15-983-2021, https://doi.org/10.5194/tc-15-983-2021, 2021
Short summary
Short summary
Satellites prove to be very valuable for documentation of large-scale avalanche periods. To test reliability and completeness, which has not been satisfactorily verified before, we attempt a full validation of avalanches mapped from two optical sensors and one radar sensor. Our results demonstrate the reliability of high-spatial-resolution optical data for avalanche mapping, the suitability of radar for mapping of larger avalanches and the unsuitability of medium-spatial-resolution optical data.
Nora Helbig, Yves Bühler, Lucie Eberhard, César Deschamps-Berger, Simon Gascoin, Marie Dumont, Jesus Revuelto, Jeff S. Deems, and Tobias Jonas
The Cryosphere, 15, 615–632, https://doi.org/10.5194/tc-15-615-2021, https://doi.org/10.5194/tc-15-615-2021, 2021
Short summary
Short summary
The spatial variability in snow depth in mountains is driven by interactions between topography, wind, precipitation and radiation. In applications such as weather, climate and hydrological predictions, this is accounted for by the fractional snow-covered area describing the fraction of the ground surface covered by snow. We developed a new description for model grid cell sizes larger than 200 m. An evaluation suggests that the description performs similarly well in most geographical regions.
Angus J. Dowson, Pascal Sirguey, and Nicolas J. Cullen
The Cryosphere, 14, 3425–3448, https://doi.org/10.5194/tc-14-3425-2020, https://doi.org/10.5194/tc-14-3425-2020, 2020
Short summary
Short summary
Satellite observations over 19 years are used to characterise the spatial and temporal variability of surface albedo across the gardens of Eden and Allah, two of New Zealand’s largest ice fields. The variability in response of individual glaciers reveals the role of topographic setting and suggests that glaciers in the Southern Alps do not behave as a single climatic unit. There is evidence that the timing of the minimum surface albedo has shifted to later in the summer on 10 of the 12 glaciers.
Cited articles
Agisoft LLC: Agisoft Metashape User Manual Professional Edition, 1.5, availabe at: https://www.agisoft.com/pdf/photoscan-pro_1_4_en.pdf (last access: 25 October 2019), 2019.
Avanzi, F., Bianchi, A., Cina, A., De Michele, C., Maschio, P., Pagliari,
D., Passoni, D., Pinto, L., Piras, M., and Rossi, L.: Centimetric Accuracy
in Snow Depth Using Unmanned Aerial System Photogrammetry and a
MultiStation, Remote Sens.-Basel, 10, 5,
https://doi.org/10.3390/rs10050765, 2018.
Baggi, S. and Schweizer, J.: Characteristics of wet-snow avalanche
activity: 20 years of observations from a high alpine valley (Dischma,
Switzerland), Nat. Hazards, 50, 97–108,
https://doi.org/10.1007/s11069-008-9322-7, 2008.
Bartelt, P., Buser, O., Vera Valero, C., and Bühler, Y.: Configurational
energy and the formation of mixed flowing/powder snow and ice avalanches,
Ann. Glaciol., 57, 179–188,
https://doi.org/10.3189/2016AoG71A464, 2016.
Basnet, K., Muste, M., Constantinescu, G., Ho, H., and Xu, H.: Close range
photogrammetry for dynamically tracking drifted snow deposition, Cold Reg.
Sci. Technol., 121, 141–153,
https://doi.org/10.1016/j.coldregions.2015.08.013, 2016.
Benassi, F., Dall'Asta, E., Diotri, F., Forlani, G., di Cella, U. M.,
Roncella, R., and Santise, M.: Testing Accuracy and Repeatability of UAV
Blocks Oriented with GNSS-Supported Aerial Triangulation, Remote
Sens.-Basel, 9, 172, https://doi.org/10.3390/rs9020172, 2017.
Beyer, R. A., Alexandrov, O., and McMichael, S.: The Ames Stereo Pipeline:
NASA's Open Source Software for Deriving and Processing Terrain Data, Earth
Space Sci., 5, 537–548,
https://doi.org/10.1029/2018EA000409, 2018.
Bilodeau, F., Gauthier, G., and Berteaux, D.: The effect of snow cover on
lemming population cycles in the Canadian high Arctic, Oecologia, 172,
1007–1016, https://doi.org/10.1007/s00442-012-2549-8, 2013.
Boesch, R., Buhler, Y., Marty, M., and Ginzler, C.: Comparison of digital
surface models for snow depth mapping with UAV and aerial cameras, Int. Arch.
Photogramm., 41, 453–458,
https://doi.org/10.5194/isprs-archives-XLI-B8-453-2016, 2016.
Bühler, Y., Hüni, A., Christen, M., Meister, R., 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.
Bühler, Y., Marty, M., Egli, L., Veitinger, J., Jonas, T., Thee, P., and Ginzler, C.: Snow depth mapping in high-alpine catchments using digital photogrammetry, The Cryosphere, 9, 229–243, https://doi.org/10.5194/tc-9-229-2015, 2015.
Bühler, Y., Adams, M. S., Bösch, R., and Stoffel, A.: Mapping snow depth in alpine terrain with unmanned aerial systems (UASs): potential and limitations, The Cryosphere, 10, 1075–1088, https://doi.org/10.5194/tc-10-1075-2016, 2016.
Bühler, Y., Adams, M. S., Stoffel, A., and Boesch, R.: Photogrammetric
reconstruction of homogenous snow surfaces in alpine terrain applying near-infrared UAS imagery, Int. J. Remote Sens., 38, 3135–3158,
https://doi.org/10.1080/01431161.2016.1275060, 2017.
Bühler, Y., Hafner, E. D., Zweifel, B., Zesiger, M., and Heisig, H.: Where are the avalanches? Rapid SPOT6 satellite data acquisition to map an extreme avalanche period over the Swiss Alps, The Cryosphere, 13, 3225–3238, https://doi.org/10.5194/tc-13-3225-2019, 2019.
Christen, M., Kowalski, J., and Bartelt, P.: RAMMS: Numerical simulation of
dense snow avalanches in three-dimensional terrain, Cold Reg. Sci. Technol.,
63, 1–14, https://doi.org/10.1016/j.coldregions.2010.04.005,
2010.
Cimoli, E., Marcer, M., Vandecrux, B., Bøggild, C. E., Williams, G., and
Simonsen, S. B.: Application of Low-Cost UASs and Digital Photogrammetry for
High-Resolution Snow Depth Mapping in the Arctic, Remote Sens.-Basel, 9, 11,
https://doi.org/10.3390/rs9111144, 2017.
Cullen, N. J., Anderson, B., Sirguey, P., Stumm, D., Mackintosh, A., Conway,
J. P., Horgan, H. J., Dadic, R., Fitzsimons, S. J., and Lorrey, A.: An
11-year record of mass balance of Brewster Glacier, New Zealand, determined
using a geostatistical approach, J. Glaciol., 63, 199–217,
https://doi.org/10.1017/jog.2016.128, 2016.
d'Angelo, P.: Improving Semi-Global Matching: Cost Aggregation and
Confidence Measure, Int. Arch. Photogramm.,
XLI-B1, 299–304,
https://doi.org/10.5194/isprs-archives-XLI-B1-299-2016, 2016.
Deems, J. S. and Painter, T. H.: LiDAR measurement of snow depth: Accuracy
and error sources, in: Proceedings of the 2006 International Snow Science
Workshop, 3–7 October 2016, Telluride, CO, USA, 330–338, 2006.
Deems, J. S., Painter, T. H., and Finnegan, D. C.: Lidar measurement of snow
depth: a review, J. Glaciol., 59, 467–479,
https://doi.org/10.3189/2013JoG12J154, 2013.
De Michele, C., Avanzi, F., Passoni, D., Barzaghi, R., Pinto, L., Dosso, P., Ghezzi, A., Gianatti, R., and Della Vedova, G.: Using a fixed-wing UAS to map snow depth distribution: an evaluation at peak accumulation, The Cryosphere, 10, 511–522, https://doi.org/10.5194/tc-10-511-2016, 2016.
Deschamps-Berger, C., Gascoin, S., Berthier, E., Deems, J., Gutmann, E., Dehecq, A., Shean, D., and Dumont, M.: Snow depth mapping from stereo satellite imagery in mountainous terrain: evaluation using airborne laser-scanning data, The Cryosphere, 14, 2925–2940, https://doi.org/10.5194/tc-14-2925-2020, 2020.
Dong, C.: Remote sensing, hydrological modeling and in situ observations in
snow cover research: A review, J. Hydrol., 561, 573–583,
https://doi.org/10.1016/j.jhydrol.2018.04.027, 2018.
Dreier, L., Bühler, Y., Ginzler, C., and Bartelt, P.: Comparison of
simulated powder snow avalanches with photogrammetric measurements, Ann.
Glaciol., 57, 371–381, https://doi.org/10.3189/2016AoG71A532,
2016.
Eberhard, L. A., Bühler, Y., Marty, M., and Sirguey, P.: Photogrammetric snow depth maps from satellite-, airplane-, UAS and terrestrial platforms from the Davos region (Switzerland), EnviDat, https://doi.org/10.16904/envidat.189, 2020.
Eker, R., Bühler, Y., Schlögl, S., Stoffel, A., and Aydın, A.:
Monitoring of Snow Cover Ablation Using Very High Spatial Resolution Remote
Sensing Datasets, Remote Sens.-Basel, 11, 6,
https://doi.org/10.3390/rs11060699, 2019.
Farinotti, D., Usselmann,
S., Huss,
M., Bauder,
A., and Funk,
M.: Runoff evolution in the Swiss Alps: projections for selected high-alpine catchments based on ENSEMBLES scenarios, Hydrol. Process., 26, 1909–1924, 2012.
Feistl, T., Bebi, P., Dreier, L., Hanewinkel, M., and Bartelt, P.: Quantification of basal friction for technical and silvicultural glide-snow avalanche mitigation measures, Nat. Hazards Earth Syst. Sci., 14, 2921–2931, https://doi.org/10.5194/nhess-14-2921-2014, 2014.
Fierz, C., Armstrong, R. L., Durand, Y., Etchevers, P., Greene, E., McClung, D. M., Nishimura, K., Satyawali, P. K., and Sokratov, S. A.: The International classification for seasonal snow on the ground, IACS, UNESCO, Paris, France, 2009.
Gascoin, S., Kinnard, C., Ponce, R., Lhermitte, S., MacDonell, S., and Rabatel, A.: Glacier contribution to streamflow in two headwaters of the Huasco River, Dry Andes of Chile, The Cryosphere, 5, 1099–1113, https://doi.org/10.5194/tc-5-1099-2011, 2011.
GDAL/OGR contributors: GDAL/OGR Geospatial Data Abstraction software Library, Open Source Geospatial Foundation, available at: https://gdal.org (last access: 15 March 2020), 2020.
Griessinger, N., Mohr, F., and Jonas, T.: Measuring snow ablation rates in
alpine terrain with a mobile multioffset ground-penetrating radar system,
Hydrol. Process., 32, 3272–3282,
https://doi.org/10.1002/hyp.13259, 2018.
Harder, P., Schirmer, M., Pomeroy, J., and Helgason, W.: Accuracy of snow depth estimation in mountain and prairie environments by an unmanned aerial vehicle, The Cryosphere, 10, 2559–2571, https://doi.org/10.5194/tc-10-2559-2016, 2016.
Hirschmuller, H.: Stereo processing by semiglobal matching and mutual
information, IEEE T. Pattern Anal., 30, 328–341,
https://doi.org/10.1109/TPAMI.2007.1166, 2008.
Höhle, J. and Höhle, M.: Accuracy assessment of digital elevation
models by means of robust statistical methods, ISPRS J. Photogramm., 64,
398–406, https://doi.org/10.1016/j.isprsjprs.2009.02.003, 2009.
Hopkinson, C., Demuth, M., Sitar, M., and Chasmer, L.: Applications of airborne LiDAR mapping in glacierised mountainous terrain, IGARSS 2001, Scanning the Present and Resolving the Future. Proceedings, IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217), Sydney, NSW, Australia, 2, 949–951, https://doi.org/10.1109/IGARSS.2001.976690, 2001.
Hopkinson, C., Sitar, M., Chasmer, L., and Treitz, P.: Mapping snowpack
depth beneath forest canopies using airborne lidar, Photogramm. Eng. Rem. S., 70, 323–330, https://doi.org/10.14358/PERS.70.3.323, 2004.
Isenburg, M.: LAStools – efficient LiDAR processing software (version 141017, academic), available at: http://rapidlasso.com/LAStools (last access: 15 March 2020), 2014.
Jonas, T., Marty,
C., and Magnusson,
J. O.: Estimating the snow water equivalent from snow depth measurements in the Swiss Alps, J. Hydrol., 378, 161–167, 2009.
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.
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.
Lopez-Moreno, J. I., Revuelto, J., Alonso-Gonzalez, E., Sanmiguel-Vallelado,
A., Fassnacht, S. R., Deems, J., and Moran-Tejeda, E.: Using very long-range
terrestrial laser scanner to analyze the temporal consistency of the
snowpack distribution in a high mountain environment, J. Mt. Sci.-Engl., 14,
823–842, https://doi.org/10.1007/s11629-016-4086-0, 2017.
Luhmann, T., Robson, S., Kyle, S., and Boehm, J.: Close-Range Photogrammetry
and 3D Imaging, 2 ed., edited by: Luhmann, T., Robson, S., Kyle, S.,
and Boehm, J., De Gruyter, Berlin, Germany and Boston, USA, 2014.
Lundberg, A., Granlund, N., and Gustafsson, D.: Towards automated “Ground
truth” snow measurements – a review of operational and new measurement methods
for Sweden, Norway, and Finland, Hydrol. Process., 24, 1955–1970,
https://doi.org/10.1002/hyp.7658, 2010.
Marti, R., Gascoin, S., Berthier, E., de Pinel, M., Houet, T., and Laffly, D.: Mapping snow depth in open alpine terrain from stereo satellite imagery, The Cryosphere, 10, 1361–1380, https://doi.org/10.5194/tc-10-1361-2016, 2016.
Maune, D. F. and Naygandhi, A.: Digital Elevation Model Technologies and
Applications: The DEM Users Manual, 3 ed., edited by: Maune, D. F. and
Naygandhi, A., American Society for Photogrammetry and Remote Sensing, Maryland,
USA, 2018.
McGrath, D., Sass, L., O'Neel, S., Arendt, A., Wolken, G., Gusmeroli, A.,
Kienholz, C., and McNeil, C.: End-of-winter snow depth variability on
glaciers in Alaska, J. Geophys. Res-Earth, 120, 1530–1550,
https://doi.org/10.1002/2015JF003539, 2015.
McGrath, D., Webb, R., Shean, D., Bonnell, R., Marshall, H. P., Painter, T.
H., Molotch, N. P., Elder, K., Hiemstra, C., and Brucker, L.: Spatially
Extensive Ground-Penetrating Radar Snow Depth Observations During NASA's
2017 SnowEx Campaign: Comparison With In Situ, Airborne, and Satellite
Observations, Water Resour. Res., 55, 10026–10036,
https://doi.org/10.1029/2019WR024907, 2019.
Meyer, J. and Skiles, S. M.: Assessing the Ability of Structure From Motion
to Map High-Resolution Snow Surface Elevations in Complex Terrain: A Case
Study From Senator Beck Basin, CO, Water Resour. Res., 55, 6596–6605,
https://doi.org/10.1029/2018WR024518, 2019.
Nolan, M., Larsen, C., and Sturm, M.: Mapping snow depth from manned aircraft on landscape scales at centimeter resolution using structure-from-motion photogrammetry, The Cryosphere, 9, 1445–1463, https://doi.org/10.5194/tc-9-1445-2015, 2015.
Novac, J.: Quality assessment of elevation data, in: Digital Elevation Model
Technologies and Applications: The DEM Users Manual, 3rd Edition,
edited by: Maune, D. F. and Nayegandhi, A., American Society for Photogrammetry and Remote Sensing, Maryland,
USA, 455–544, 2018.
Painter, T. H., Berisford, D. F., Boardman, J. W., Bormann, K. J., Deems, J.
S., Gehrke, F., Hedrick, A., Joyce, M., Laidlaw, R., Marks, D., Mattmann,
C., McGurk, B., Ramirez, P., Richardson, M., Skiles, S. M., Seidel, F. C.,
and Winstral, A.: The Airborne Snow Observatory: Fusion of scanning lidar,
imaging spectrometer, and physically-based modeling for mapping snow water
equivalent and snow albedo, Remote Sens. Environ., 184, 139–152, https://doi.org/10.1016/j.rse.2016.06.018, 2016.
Prokop, A.: Assessing the applicability of terrestrial laser scanning for
spatial snow depth measurements, Cold Reg. Sci. Technol., 54, 155–163,
https://doi.org/10.1016/j.coldregions.2008.07.002, 2008.
Prokop, A., Schon, P., Singer, F., Pulfer, G., Naaim, M., Thibert, E., and
Soruco, A.: Merging terrestrial laser scanning technology with
photogrammetric and total station data for the determination of avalanche
modeling parameters, Cold Reg. Sci. Technol., 110, 223–230,
https://doi.org/10.1016/j.coldregions.2014.11.009, 2015.
Redpath, T. A. N., Sirguey, P., and Cullen, N. J.: Repeat mapping of snow depth across an alpine catchment with RPAS photogrammetry, The Cryosphere, 12, 3477–3497, https://doi.org/10.5194/tc-12-3477-2018, 2018.
Roze, A., Zufferey,
J.-C., Beyeler, and McClellan,
A.
A.: eBee RTK Accuracy Assessment, White Paper, available at: https://www.sensefly.com/app/uploads/2017/11/eBee_RTK_Accuracy_Assessment.pdf (last access: 2 December 2019), 2017.
Schlatter, A. and Marti, U.: Höhentransformation zwischen LHN95 und den
Gebrauchshöhen LN02, Geomatik Schweiz: Geoinformation und Landmanagement, 103, 450–453, https://doi.org/10.5169/seals-236251, 2005.
Schön, P., Prokop, A., Vionnet, V., Guyomarc'h, G., Naaim-Bouvet, F.,
and Heiser, M.: Improving a terrain-based parameter for the assessment of
snow depths with TLS data in the Col du Lac Blanc area, Cold Reg. Sci.
Technol., 114, 15–26,
https://doi.org/10.1016/j.coldregions.2015.02.005, 2015.
Shaw, T. E., Gascoin, S., Mendoza, P. A., Pellicciotti, F., and McPhee, J.:
Snow Depth Patterns in a High Mountain Andean Catchment from Satellite
Optical Tristereoscopic Remote Sensing, Water Resour. Res., 56, 2,
https://doi.org/10.1029/2019WR024880, 2020.
Shean, D. E., Alexandrov, O., Moratto, Z. M., Smith, B. E., Joughin, I. R.,
Porter, C., and Morin, P.: An automated, open-source pipeline for mass
production of digital elevation models (DEMs) from very-high-resolution
commercial stereo satellite imagery, ISPRS J. Photogramm., 116, 101–117,
https://doi.org/10.1016/j.isprsjprs.2016.03.012, 2016.
Sirguey, P. and Cullen, N. J.: A very high resolution DEM of Kilimanjaro via
photogrammetry of GeoEye-1 images (KILISoSDEM2012), New Zealand Institute of Surveyors, Wellington, N.Z., 19–215, ISSN 0048-0150,
2014.
Spandre, P., François, H., Thibert, E., Morin, S., and George-Marcelpoil, E.: Determination of snowmaking efficiency on a ski slope from observations and modelling of snowmaking events and seasonal snow accumulation, The Cryosphere, 11, 891–909, https://doi.org/10.5194/tc-11-891-2017, 2017.
Steiner, L., Meindl, M., Fierz, C., and Geiger, A.: An assessment of sub-snow GPS for quantification of snow water equivalent, The Cryosphere, 12, 3161–3175, https://doi.org/10.5194/tc-12-3161-2018, 2018.
Stumpf, A., Malet, J. P., Allemand, P., and Ulrich, P.: Surface
reconstruction and landslide displacement measurements with Pléiades
satellite images, ISPRS J. Photogramm., 95, 1–12,
https://doi.org/10.1016/j.isprsjprs.2014.05.008, 2014.
Sturm, M. and Holmgren, J.: An Automatic Snow Depth Probe for Field
Validation Campaigns, Water Resour. Res., 54, 9695–9701,
https://doi.org/10.1029/2018WR023559, 2018.
Sturm, M., Derksen, C., Liston, G., Silis, A., Solie, D., Holm-
gren, J., and Huntington, H.: A reconnaissance snow survey
across northwest territories and Nunavut, Canada, April 2007,
Cold Regions Research and Engineering laboratory, Hanover,
N.H.ERDC/CRREL TR 08-3, 1–80, 2008.
Telling, J., Lyda, A., Hartzell, P., and Glennie, C.: Review of Earth
science research using terrestrial laser scanning, Earth-Sci. Rev.,
169, 35–68, https://doi.org/10.1016/j.earscirev.2017.04.007,
2017.
Thibert, E., Bellot, H., Ravanat, X., Ousset, F., Pulfer, G., Naaim, M.,
Hagenmuller, P., Naaim-Bouvet, F., Faug, T., Nishimura, K., Ito, Y.,
Baroudi, D., Prokop, A., Schon, P., Soruco, A., Vincent, C., Limam, A., and
Heno, R.: The full-scale avalanche test-site at Lautaret Pass (French Alps),
Cold Reg. Sci. Technol., 115, 30–41,
https://doi.org/10.1016/j.coldregions.2015.03.005, 2015.
Toth, C. and Jozkow, G.: Remote sensing platforms and sensors: A survey,
ISPRS J. Photogramm., 115, 22–36,
https://doi.org/10.1016/j.isprsjprs.2015.10.004, 2016.
Vallet, J., Gruber, U., and Dufour, F.: Photogrammetric avalanche volume
measurements at Vallee de la Sionne, Switzerland, Ann. Glaciol., 32,
141–146, https://doi.org/10.1016/j.isprsjprs.2015.10.004, 2001.
Vallet, J., Turnbull, B., Joly, S., and Dufour, F.: Observations on powder
snow avalanches using videogrammetry, Cold Reg. Sci. Technol., 39, 153–159,
https://doi.org/10.1016/j.coldregions.2004.05.004, 2004.
Vander Jagt, B., Lucieer, A., Wallace, L., Turner, D., and Durand, M.: Snow
Depth Retrieval with UAS Using Photogrammetric Techniques, Geosciences, 5,
264–285, https://doi.org/10.3390/geosciences5030264, 2015.
Walter, B., Huwald, H., Gehring, J., Bühler, Y., and Lehning, M.: Radar measurements of blowing snow off a mountain ridge, The Cryosphere, 14, 1779–1794, https://doi.org/10.5194/tc-14-1779-2020, 2020.
Westoby, M. J., Brasington, J., Glasser, N. F., Hambrey, M. J., and
Reynolds, J. M.: “Structure-from-Motion” photogrammetry: A low-cost,
effective tool for geoscience applications, Geomorphology, 179, 300–314,
https://doi.org/10.1016/j.geomorph.2012.08.021, 2012.
Wipf, S., Stoeckli, V., and Bebi, P.: Winter climate change in alpine
tundra: plant responses to changes in snow depth and snowmelt timing,
Climatic Change, 94, 105–121,
https://doi.org/10.1007/s10584-009-9546-x, 2009.
Short summary
In spring 2018 in the alpine Dischma valley (Switzerland), we tested different industrial photogrammetric platforms for snow depth mapping. These platforms were high-resolution satellites, an airplane, unmanned aerial systems and a terrestrial system. Therefore, this study gives a general overview of the accuracy and precision of the different photogrammetric platforms available in space and on earth and their use for snow depth mapping.
In spring 2018 in the alpine Dischma valley (Switzerland), we tested different industrial...
