Articles | Volume 10, issue 6
https://doi.org/10.5194/esurf-10-1141-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esurf-10-1141-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
14 Nov 2022
Research article |
| 14 Nov 2022
| 14 Nov 2022
Rockfall trajectory reconstruction: a flexible method utilizing video footage and high-resolution terrain models
Risk Analysis Group, Institute of Earth Sciences, University of
Lausanne, 1015 Lausanne, Switzerland
Geohazard and Earth Observation, Geological Survey of Norway, NGU, 7040 Trondheim, Norway
Michel Jaboyedoff
Risk Analysis Group, Institute of Earth Sciences, University of
Lausanne, 1015 Lausanne, Switzerland
Andrin Caviezel
WSL Institute for Snow and Avalanche Research SLF, 7260 Davos,
Switzerland
Clément Hibert
Institut Terre et Environnement de Strasbourg/ITES, CNRS & University of Strasbourg, 67084 Strasbourg, France
Franck Bourrier
Université Grenoble Alpes, INRAE, ETNA, 38000 Grenoble, France
Jean-Philippe Malet
Institut Terre et Environnement de Strasbourg/ITES, CNRS & University of Strasbourg, 67084 Strasbourg, France
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Cited
12 citations as recorded by crossref.
- Particle tracking in snow avalanches with in situ calibrated inertial measurement units R. Winkler et al. 10.1017/aog.2024.5
- Comparing Flow-R, Rockyfor3D and RAMMS to Rockfalls from the Mel de la Niva Mountain: A Benchmarking Exercise F. Noël et al. 10.3390/geosciences13070200
- Evaluation of rockfall hazard based on UAV technology and 3D Rockfall Simulations M. UTLU et al. 10.30897/ijegeo.1323768
- Deep Learning- and IoT-Based Framework for Rock-Fall Early Warning M. Abaker et al. 10.3390/app13179978
- Quantification of uncertainties in back-analysis of radar-tracked rockfall trajectories A. Xie et al. 10.1016/j.jrmge.2024.08.001
- Machine learning prediction of the mass and the velocity of controlled single-block rockfalls from the seismic waves they generate C. Hibert et al. 10.5194/esurf-12-641-2024
- New Approach for Photogrammetric Rock Slope Premonitory Movements Monitoring M. Núñez-Andrés et al. 10.3390/rs15020293
- Particle trajectories, velocities, accelerations and rotation rates in snow avalanches M. Neuhauser et al. 10.1017/aog.2023.69
- Shape still matters: rockfall interactions with trees and deadwood in a mountain forest uncover a new facet of rock shape dependency A. Ringenbach et al. 10.5194/esurf-11-779-2023
- Implementing close-range remote surveys for the digitally supported rock mass stability analysis S. Mineo et al. 10.1016/j.enggeo.2023.107382
- Highly energetic rockfalls: back analysis of the 2015 event from the Mel de la Niva, Switzerland F. Noël et al. 10.1007/s10346-023-02054-2
- Kinematic assessment of a rockfall disaster: a case study from Batseri Village, Sangla Valley, Himachal Pradesh, India M. Lakherwal et al. 10.1007/s10346-024-02228-6
10 citations as recorded by crossref.
- Particle tracking in snow avalanches with in situ calibrated inertial measurement units R. Winkler et al. 10.1017/aog.2024.5
- Comparing Flow-R, Rockyfor3D and RAMMS to Rockfalls from the Mel de la Niva Mountain: A Benchmarking Exercise F. Noël et al. 10.3390/geosciences13070200
- Evaluation of rockfall hazard based on UAV technology and 3D Rockfall Simulations M. UTLU et al. 10.30897/ijegeo.1323768
- Deep Learning- and IoT-Based Framework for Rock-Fall Early Warning M. Abaker et al. 10.3390/app13179978
- Quantification of uncertainties in back-analysis of radar-tracked rockfall trajectories A. Xie et al. 10.1016/j.jrmge.2024.08.001
- Machine learning prediction of the mass and the velocity of controlled single-block rockfalls from the seismic waves they generate C. Hibert et al. 10.5194/esurf-12-641-2024
- New Approach for Photogrammetric Rock Slope Premonitory Movements Monitoring M. Núñez-Andrés et al. 10.3390/rs15020293
- Particle trajectories, velocities, accelerations and rotation rates in snow avalanches M. Neuhauser et al. 10.1017/aog.2023.69
- Shape still matters: rockfall interactions with trees and deadwood in a mountain forest uncover a new facet of rock shape dependency A. Ringenbach et al. 10.5194/esurf-11-779-2023
- Implementing close-range remote surveys for the digitally supported rock mass stability analysis S. Mineo et al. 10.1016/j.enggeo.2023.107382
2 citations as recorded by crossref.
- Highly energetic rockfalls: back analysis of the 2015 event from the Mel de la Niva, Switzerland F. Noël et al. 10.1007/s10346-023-02054-2
- Kinematic assessment of a rockfall disaster: a case study from Batseri Village, Sangla Valley, Himachal Pradesh, India M. Lakherwal et al. 10.1007/s10346-024-02228-6
Latest update: 22 Nov 2024
Short summary
Rockfall simulations are often performed to make sure infrastructure is safe. For that purpose, rockfall trajectory data are needed to calibrate the simulation models. In this paper, an affordable, flexible, and efficient trajectory reconstruction method is proposed. The method is tested by reconstructing trajectories from a full-scale rockfall experiment involving 2670 kg rocks and a flexible barrier. The results highlight improvements in precision and accuracy of the proposed method.
Rockfall simulations are often performed to make sure infrastructure is safe. For that purpose,...
