the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Using UAV-Derived Point Clouds to measure high resolution cliff dynamics in soft lithologies: Demons Bluff, Victoria, Australia
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.
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RC1: 'Comment on egusphere-2024-659', Anonymous Referee #1, 25 Apr 2024
The paper describes a case study to monitor cliff dynamics over 4 years covered by 30 UAV point clouds. The tools used are standard like point cloud cleaning followed by common M3C2. I am sure the study is highly relevant for understanding the local situation and how it is evolving over time. This might be also very important for the local community. The researchers invested a lot of time and have a great dataset at hand. However, I could not find any new scientific findings / concepts regarding the process of cliff erosion going beyond the description and quantification of the local situation. It remains a case study, as presented in the current manuscript, which is not showing significant relevance and learning moments for probably many others working in the field of 3D monitoring. The senors and methods used are already standard in geosciences.
Citation: https://doi.org/10.5194/egusphere-2024-659-RC1 -
CC1: 'Reply on RC1', Daniel Ierodiaconou, 02 May 2024
I would like to thank RC1 for your constructive comments. Whilst we agree that the tools are standard in terms of point cloud analytics we think the study provides novelty in the application due to the following reasons
-The high frequency data collection over such a period is unique and required in case studies to understand the broader implications of soft cliff retreat and hazard implications. In addition this has been done using innovative citizen science program (see Ierodiaconou et a;. 2022) that received a Eureka Prize in 2020
-Such studies on soft cliffs are unique with such frequency of information and we do provide a conceptual model for the location that we think would be applicable more broadly to soft cliffs globally but would need to be tested. We can make this point more clear in the revised manuscript
-This study has highlighted how a combination of point cloud analysis and high temporal resolution data collection can inform management decision making regarding hazardous coastal cliffs and we demonstrate how this has been put in to practice to improve safety of beach users.
cheers
Daniel Ierodiaconou
Citation: https://doi.org/10.5194/egusphere-2024-659-CC1 -
CC2: 'Reply on CC1', David M. Kennedy, 16 May 2024
We thank the reviewer for their thoughts and comments. They are correct that we utilize a local laboratory in southern Australia to test the methodological approach undertaken. Soft rocky coasts are a relatively understudied geomorphic system and are very sensitive to changes in boundary conditions. The difficulties in obtaining precise and accurate data of their erosion have hindered their study due to their inaccessibility and high rates of change.
This work aims to test the precision and accuracy of drone-based data collection for measuring such environments. It is true that drone-derived photogrammetry is widely used to produce DSM’s in geomorphology, but overhangs and area of shadowing produce many errors for the derived surfaces from vertical imagery. Our point-cloud approach tests an approach for minimizing these errors to enable valid scientific hypotheses to be tested. Another unique aspect of this study is that the collection of our UAV-imagery is conducted primarily by citizen-scientists, and while the precision and accuracy of this method has been proven for sandy beaches it has yet to be properly tested on vertical rapidly eroding cliffs.
We therefore believe the study is globally important. The study proves the unique point-cloud approach is highly relevant for measuring rates of erosion of vertical soft-rock cliffs on the open ocean coast. The field laboratory in southern Australia provides a perfect environment for the methodological exploration. We agree that new models of the processes driving soft rocky coast evolution are unlikely to be produced from the dataset at this stage, yet the results produced in the methodological approach do provide further steps in our understanding of soft cliff evolution.
Citation: https://doi.org/10.5194/egusphere-2024-659-CC2 -
RC2: 'Reply on CC2', Javier Leon, 28 Aug 2024
The manuscript presents a methodology for the analysis of UAV-derived point clouds to describe the dynamics of coastal cliffs. The presented change-detection workflow is not novel, except for the use of UAV-derived point clouds instead of the more commonly TLS-derived point clouds. The analysis is undertaken using CloudCompare and common algorithms such as SOR and M3C2.
In my opinion, the value of the manuscript resides on the use of a unique UAV dataset (50 months of high-res data along 1.5 km). This allows for a detailed description of cliff erosion/deposition and the development of a collapse mechanism model. However, this is not clearly communicated in the introduction, where the aim of the study comes across more like a methodological contribution to change-detection analysis. I suggest this is reworded.
The Discussion needs to be more critical and include a comparison between the advantages/disadvantages of UAV-derived point clouds (photogrammetry) and lidar (TLS and/or UAV). Further, suggest including a limitations/future research paragraph at end of discussion (e.g. use of citizen science programs, etc).
Some specific comments:
- L18 include LOD
- L41 UAV includes UAV-lidar?
- L54: The analysis of point clouds is moving very fast (e.g. Deep learning and individual tree detection ). Suggest you include relevant literature.
- Fig. 1 Suggest removing inset b and including place names.
- Can you merge Fig. 2 and 3? If not, Fig. 3 can improve by adding more detail (e.g. algorithms used).
- Can you adapt Fig. 4 to a cliff example?
- L143 Reword first sentence.
- L143-L155 Break paragraph as hard to follow.
- Fig. 7: Is this better suited as a Discussion figure (e.g. conceptual model based on results)?
- L196: Is this Discussion? Were waves measured? What are high-energy wave conditions?
Citation: https://doi.org/10.5194/egusphere-2024-659-RC2
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RC2: 'Reply on CC2', Javier Leon, 28 Aug 2024
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CC2: 'Reply on CC1', David M. Kennedy, 16 May 2024
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CC1: 'Reply on RC1', Daniel Ierodiaconou, 02 May 2024
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