19 Apr 2024
 | 19 Apr 2024
Status: this preprint is open for discussion.

Using UAV-Derived Point Clouds to measure high resolution cliff dynamics in soft lithologies: Demons Bluff, Victoria, Australia

Todd A. Doran, David M. Kennedy, Jak R. McCarroll, Blake M. Allan, and Daniel Ierodiaconou

Abstract. Unoccupied aerial vehicles (UAVs) have revolutionised data collection on the Earth’s surface. Through aerial photogrammetry, very high-resolution digital surface models can be produced enabling contemporary research on landscape stability. There are however significant limitations in the imaging of vertical and overhanging landforms when using aerial platforms for data collection. Rather than reconstructing change from a digital surface model, direct analysis of generated point clouds is likely the key for understanding landform change in these morphologically complex environments. In this study, UAV’s were used to collect aerial imagery generating a high spatio-temporal resolution timeseries comprising of thirty point cloud datasets spanning four years for the steeply sloped Demons Bluff cliff located on the Great Ocean Road, Victoria, Australia. A method was developed to analyse the large quantity of 3D point cloud datasets. It was then possible to capture changes in cliff face morphology, enabling us to enhance our understanding of the erosional processes in coastal cliff environments. Over the study, the retreat rate for the upper half of the cliff face was 0.67 m/year (0.60 m3/m/yr). Cliff erosion was found to be dominated by 9 high magnitude, cliff-top collapses that exceeded 1,000 m3 (up to 9,500 m3) and terminated mid-way down the cliff. Below this point, several slab detachments were observed. Pre-collapse deformation was detected before collapses > 300 m3 with success (75 % of the time) in areas with a complete timeseries over the four-year period. Deformation was observed to occur in two ways, and both were observed to be eventuate in high magnitude (> 1,000 m3) collapse. The first which occurred prior to most high magnitude collapses (> 500 m3) was caused by the expansion of tension cracks behind the cliff-top, and the second which was characterised by smaller collapse volumes (> 100–500 m3) was initiated by rock slabs fracturing and cleaving away from the cliff face and was proceeded by high magnitude failure (< 1000 m3). An additional 14 instances of lean have been identified that are yet to result in collapse and should continue to be monitored to assess the success of this method with the forecasting of future collapse locations. Ultimately, this provides the ability to identify potential locations for future collapses which could aid in the development of an early warning system for cliff collapse to improve the management of volatile cliff environments that pose threats to infrastructure and public safety.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Todd A. Doran, David M. Kennedy, Jak R. McCarroll, Blake M. Allan, and Daniel Ierodiaconou

Status: open (until 12 Jun 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2024-659', Anonymous Referee #1, 25 Apr 2024 reply
    • CC1: 'Reply on RC1', Daniel Ierodiaconou, 02 May 2024 reply
      • CC2: 'Reply on CC1', David M. Kennedy, 16 May 2024 reply
Todd A. Doran, David M. Kennedy, Jak R. McCarroll, Blake M. Allan, and Daniel Ierodiaconou
Todd A. Doran, David M. Kennedy, Jak R. McCarroll, Blake M. Allan, and Daniel Ierodiaconou


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Short summary
This study investigates the use and analysis of 3D models derived from drone data collected by citizen scientists to gain an enhanced understanding of changes in coastal cliff morphology prior to high-magnitude collapse events. The cliff displayed seaward leaning in the upper and middle-regions that led to collapse. This provides a basis for developing an early warning system for cliff collapse that would enhance safety and preservation of infrastructure in coastal cliff landscapes.