Articles | Volume 10, issue 3
https://doi.org/10.5194/tc-10-1075-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/tc-10-1075-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Mapping snow depth in alpine terrain with unmanned aerial systems (UASs): potential and limitations
WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
Marc S. Adams
Austrian Research Centre for Forests (BFW), Innsbruck, Austria
Ruedi Bösch
Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland
Andreas Stoffel
WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
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123 citations as recorded by crossref.
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121 citations as recorded by crossref.
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- Marginal snowpacks: The basis for a global definition and existing research needs J. López-Moreno et al. 10.1016/j.earscirev.2024.104751
- Are estimates of wind characteristics based on measurements with Pitot tubes and GNSS receivers mounted on consumer-grade unmanned aerial vehicles applicable in meteorological studies? T. Niedzielski et al. 10.1007/s10661-017-6141-x
- Szorstkość pokrycia terenu jako źródło błędu metody SfM zastosowanej do rekonstrukcji zasięgu pokrywy śnieżnej = Terrain roughness as a source of error with the SfM method applied to the reconstruction of snow cover extent D. Szafert et al. 10.7163/PrzG.2020.3.4
- Obstacle detection in snow covered terrain S. Vecherin et al. 10.1016/j.jterra.2023.05.004
- Accuracy of snow depth estimation in mountain and prairie environments by an unmanned aerial vehicle P. Harder et al. 10.5194/tc-10-2559-2016
- Photogrammetric reconstruction of homogenous snow surfaces in alpine terrain applying near-infrared UAS imagery Y. Bühler et al. 10.1080/01431161.2016.1275060
- Application of Low-Cost UASs and Digital Photogrammetry for High-Resolution Snow Depth Mapping in the Arctic E. Cimoli et al. 10.3390/rs9111144
- Spatio-temporal variability of surface mass balance in the accumulation zone of the Mer de Glace, French Alps, from multitemporal terrestrial LiDAR measurements M. Réveillet et al. 10.1017/jog.2020.92
- Combining Ground‐Penetrating Radar With Terrestrial LiDAR Scanning to Estimate the Spatial Distribution of Liquid Water Content in Seasonal Snowpacks R. Webb et al. 10.1029/2018WR022680
- 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
- Maximum entropy modeling to identify physical drivers of shallow snowpack heterogeneity using unpiloted aerial system (UAS) lidar E. Cho et al. 10.1016/j.jhydrol.2021.126722
- Drone-based ground-penetrating radar (GPR) application to snow hydrology E. Valence et al. 10.5194/tc-16-3843-2022
- Synergistic analysis of satellite, unmanned aerial vehicle, terrestrial laser scanner data and process-based modelling for understanding the dynamics and morphological changes around the snout of Gangotri Glacier, India P. Dhote et al. 10.1016/j.geomorph.2021.108005
- Remote Sensing Techniques for Assessing Snow Avalanche Formation Factors and Building Hazard Monitoring Systems N. Denissova et al. 10.3390/atmos15111343
- Intercomparison of photogrammetric platforms for spatially continuous snow depth mapping L. Eberhard et al. 10.5194/tc-15-69-2021
- Accuracy of Unmanned Aerial Systems Photogrammetry and Structure from Motion in Surveying and Mapping: A Review S. Deliry & U. Avdan 10.1007/s12524-021-01366-x
- European In-Situ Snow Measurements: Practices and Purposes R. Pirazzini et al. 10.3390/s18072016
- Within‐Stand Boundary Effects on Snow Water Equivalent Distribution in Forested Areas R. Webb et al. 10.1029/2019WR024905
- Investigating ANN architectures and training to estimate snow water equivalent from snow depth K. Ntokas et al. 10.5194/hess-25-3017-2021
- The Canadian Hydrological Model (CHM) v1.0: a multi-scale, multi-extent, variable-complexity hydrological model – design and overview C. Marsh et al. 10.5194/gmd-13-225-2020
- Regional snow-avalanche detection using object-based image analysis of near-infrared aerial imagery K. Korzeniowska et al. 10.5194/nhess-17-1823-2017
- Factors Influencing the Accuracy of Shallow Snow Depth Measured Using UAV-Based Photogrammetry S. Lee et al. 10.3390/rs13040828
- Structure from Motion of Multi-Angle RPAS Imagery Complements Larger-Scale Airborne Lidar Data for Cost-Effective Snow Monitoring in Mountain Forests P. Broxton & W. van Leeuwen 10.3390/rs12142311
- Applications of Unmanned Aerial Vehicles in Cryosphere: Latest Advances and Prospects C. Gaffey & A. Bhardwaj 10.3390/rs12060948
- Combined Use of Terrestrial Laser Scanning and UAV Photogrammetry in Mapping Alpine Terrain J. Šašak et al. 10.3390/rs11182154
- Accuracy Assessment of Digital Surface Models from Unmanned Aerial Vehicles’ Imagery on Glaciers S. Gindraux et al. 10.3390/rs9020186
- Snow process monitoring using time-lapse structure-from-motion photogrammetry with a single camera J. Liu et al. 10.1016/j.coldregions.2021.103355
- Repetitive Rockfall Trajectory Testing A. Volkwein et al. 10.3390/geosciences8030088
- Real-Time Alpine Measurement System Using Wireless Sensor Networks S. Malek et al. 10.3390/s17112583
- Passive Microwave Remote Sensing of Snow Depth: Techniques, Challenges and Future Directions S. Tanniru & R. Ramsankaran 10.3390/rs15041052
- Fully-automated estimation of snow depth in near real time with the use of unmanned aerial vehicles without utilizing ground control points B. Miziński & T. Niedzielski 10.1016/j.coldregions.2017.03.006
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Latest update: 21 Nov 2024
Short summary
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.
We map the distribution of snow depth at two alpine test sites with unmanned aerial system (UAS)...