Next Article in Journal
Single-Molecule Study of Proteins by Biological Nanopore Sensors
Previous Article in Journal
Evaluating Classifiers to Detect Arm Movement Intention from EEG Signals
Article Menu

Export Article

Open AccessArticle
Sensors 2014, 14(10), 18187-18210;

Semi-Automatic Determination of Rockfall Trajectories

WSL Swiss Federal Institute for Forest, Snow and Landscape Research, Zürcherstr. 111, Birmensdorf 8903, Switzerland
Author to whom correspondence should be addressed.
Received: 9 May 2014 / Revised: 4 September 2014 / Accepted: 19 September 2014 / Published: 29 September 2014
(This article belongs to the Section Physical Sensors)
Full-Text   |   PDF [5917 KB, uploaded 29 September 2014]


In determining rockfall trajectories in the field, it is essential to calibrate and validate rockfall simulation software. This contribution presents an in situ device and a complementary Local Positioning System (LPS) that allow the determination of parts of the trajectory. An assembly of sensors (herein called rockfall sensor) is installed in the falling block recording the 3D accelerations and rotational velocities. The LPS automatically calculates the position of the block along the slope over time based on Wi-Fi signals emitted from the rockfall sensor. The velocity of the block over time is determined through post-processing. The setup of the rockfall sensor is presented followed by proposed calibration and validation procedures. The performance of the LPS is evaluated by means of different experiments. The results allow for a quality analysis of both the obtained field data and the usability of the rockfall sensor for future/further applications in the field. View Full-Text
Keywords: rockfall; trajectory; tracking; measuring rockfall; trajectory; tracking; measuring
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Volkwein, A.; Klette, J. Semi-Automatic Determination of Rockfall Trajectories. Sensors 2014, 14, 18187-18210.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top