Optimising 4-D surface change detection: an approach for capturing rockfall magnitude–frequency
Williams, J.G.; Rosser, N.J.; Hardy, R.J.; Brain, M.J.; Afana, A.A.
Professor Nick Rosser firstname.lastname@example.org
Professor Richard Hardy email@example.com
Dr Matthew Brain firstname.lastname@example.org
We present a monitoring technique tailored to analysing change from near-continuously collected, high-resolution 3D data. Our aim is to fully characterise geomorphological change typified by an event magnitude frequency relationship that adheres to an inverse power law or similar. While recent advances in monitoring have enabled changes in volume across more than seven orders of magnitude to be captured, event frequency is commonly assumed to be interchangeable with the time-averaged event numbers between successive surveys. Where events coincide, or coalesce, or where the mechanisms driving change are not spatially independent, apparent event frequency must be partially determined by survey interval. The data reported has been obtained from a permanently installed terrestrial laser scanner, which permits an increased frequency of surveys. Surveying from a single position raises challenges, given the single viewpoint onto a complex surface and the need for computational efficiency associated with handling a large time series of 3D data. A workflow is presented that optimises the detection of change by filtering and aligning scans to improve repeatability. An adaptation of the M3C2 algorithm is used to detect 3D change, to overcome data inconsistencies between scans. Individual rockfall geometries are then extracted and the associated volumetric errors modelled. The utility of this approach is demonstrated using a dataset of ~ 9 × 103 surveys acquired at ~ 1 hour intervals over 10 months. The magnitude-frequency distribution of rockfall volumes generated is shown to be sensitive to monitoring frequency. Using a 1 h interval between surveys, rather than 30 days, the volume contribution from small (< 0.1 m3) rockfall increases from 67 % to 98 % of the total, and the number of individual rockfall observed increases by over three orders of magnitude. High frequency monitoring therefore holds considerable implications for magnitude-frequency derivatives, such as hazard return intervals and erosion rates. As such, while high frequency monitoring has potential to describe short-term controls on geomorphological change and more realistic magnitude-frequency relationships, the assessment of longer-term erosion rates may be more suited to less frequent data collection with lower accumulative errors.
Williams, J., Rosser, N., Hardy, R., Brain, M., & Afana, A. (2018). Optimising 4-D surface change detection: an approach for capturing rockfall magnitude–frequency. Earth Surface Dynamics, 6(1), 101-119. https://doi.org/10.5194/esurf-6-101-2018
|Journal Article Type||Article|
|Acceptance Date||Jan 23, 2018|
|Online Publication Date||Feb 26, 2018|
|Publication Date||Feb 26, 2018|
|Deposit Date||Jan 23, 2018|
|Publicly Available Date||Jan 29, 2018|
|Journal||Earth Surface Dynamics|
|Peer Reviewed||Peer Reviewed|
|Keywords||Magnitude-frequency, Rockfalls, 4D Monitoring, Terrestrial Laser Scanning, Point Clouds|
Accepted Journal Article
Publisher Licence URL
© Author(s) 2018. This work is distributed under<br /> the Creative Commons Attribution 4.0 License.
Published Journal Article
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