The data archive contains site specific geographical data such as DEM and orthophoto as well as the deposition points of manually induced rockfall by releasing differently shaped boulders with 46, 200, 800 and 2670 kg of mass.
Additionally available are all the reconstructed data sets for all trajectories with videogrammetry installed comprising StoneNode data streams for rocks equipped with a sensor. The data set consists of:
Resources (individual zip-archives)
ExperimentalRuns: Archive with all available StoneNode data streams and its respective figure (.mat files)
Input: Archive containing folders
- GNSS: 182 Deposition points of all different weight and shape classes, shape files for release point, cliff and scree line,
- UAS: UAS generated pre- and post-experimental digital surface models and orthophoto of the four most important experimental days and
- VG_Coord: Reconstruction input: Videogrammetry based coordinate list along side with the corresponding sensor/video times
EOTA: Point cloud of cubic EOTA(111) and platy EOTA(221) rock as input for RAMMS::ROCKFALL or other suitable rockfall simulation codes incorporating complex shape files.
Output: Reconstruced trajectory information for all 82 reconstructed trajectories
Video: available video streams for all runs
Further information
Preceeding publications concering the deployed sensors and the reconstruction methods are found in the subsequent references:
A. Caviezel et al., Design and Evaluation of a Low-Power Sensor Device for Induced Rockfall Experiments, IEEE Transactions on Instrumentation and Measurement, 2018, 67, 767-779, http://ieeexplore.ieee.org/document/8122020/
P. Niklaus et al., StoneNode: A low-power sensor device for induced rockfall experiments, 2017 IEEE Sensors Applications Symposium (SAS), 2017, 1-6, http://ieeexplore.ieee.org/document/7894081/
Caviezel, A., Demmel, S. E., Ringenbach, A., Bühler, Y., Lu, G., Christen, M., Dinneen, C. E., Eberhard, L. A., von Rickenbach, D., and Bartelt, P.: Reconstruction of four-dimensional rockfall trajectories using remote sensing and rock-based accelerometers and gyroscopes, Earth Surf. Dynam., 7, 199–210, https://doi.org/10.5194/esurf-7-199-2019, 2019
