Scarp edge and vegetation were digitised from the point cloud data. The scarp edge was clearly visible in the 3D point cloud and this resulted in a very accurate scarp edge from each epoch. The vegetation was more difficult to digitise and the seasonal differences impacted on the extent of the vegetation and the colour. The similarity between the soil colour and the dry grass colour impacted on the accuracy digitising of the vegetation 154
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Figure 6.10: Profile 1 extracted from 2010 and absolute difference between 2010 and 2013 shown on the 2013 dataset (axes and legend units are metres).
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Figure 6.11: Profile 2 extracted from 2010 and absolute difference between 2010 and 2012 shown on the 2012 dataset (axes and legend units are metres).
Figure 6.12: Profile 2 extracted from 2012 and absolute difference between 2012 and 2013 shown on the 2013 dataset (axes and legend units are metres).
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Figure 6.13: Profile 2 extracted from 2010 and absolute difference between 2010 and 2013 shown on the 2013 dataset (axes and legend units are metres).
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Figure 6.14: Coastline comparison showing 2010, 2012 and 2013 digitised scarps (top edge).
Chapter Figure 6.15: Coastline comparison showing 2010, 2012 and 2013 digitised vegetation edge.
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The comparison between epochs using the scarp edge (Figure 6.14) provided a good approximation to the change shown in the absolute difference point clouds (Figures 6.5– 6.7). Similarly, the areas that saw minimal erosion overlay each other closely, which indicates that the georeferencing was accurate and the section of scarp that did not erode could be delineated accurately. This was not the case when using the digitised vegetation edge (Figure 6.15). The change was not well represented and significantly under-estimated. The cause of this was the pattern of growth along the scarp edge. The vegetation did not extend to the scarp edge, and in most cases the vegetation shoreline proxy was 100–400 mm landward of the scarp edge. Vegetation edge was a poor proxy for shoreline at the scale of data produce in this UAV-MVS survey. Scarp edge was more representative of the shoreline at this scale and provided a reasonable estimation of change.
6.4
Conclusion
UAV-MVS point clouds representing an eroding section of sheltered coastline were cre- ated from UAV surveys in 2010, 2012 and 2013. The clouds were registered using sur- veyed GCPs and not coregistered due to the lack of common features through time in this natural landscape. The change detection therefore relied on the georeferencing ac- curacy of the individual point clouds to measure absolute difference between the clouds. This illustrates the issues of coregistration in natural landscapes. In these real-world scenarios, common features may not exist naturally that can be used for the coregistra- tion. In a future study, a fourth epoch of this terrain will be measured using the more robust data capture and design guidelines outlined in Chapter 4 and Chapter 5. This should result in a more accurately georeferenced point cloud that will closely match the location of the 2013 dataset (which was captured using the more robust data capture design and registered using more accurate control than the 2010 and 2012 datasets). A second issue faced when comparing these epoch was the occlusion that resulted in sparse portions of the point clouds from 2010 and 2013. These areas resulted in false change. The camera network design needs to be more carefully planned to ensure point cloud completeness. A third issue faced was the absolute difference calculated in the point cloud comparison. This does not give change direction and volumetric change statistics. To understand the direction of change and to calculate volume of erosion and accretion the datasets need to be converted to surfaces and DEM difference used to quantify change. That will be the focus of a future study.
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Three methods of visualising coastal change were evaluated. The first technique com- prised of spatial views of the absolute distance measurements between points in a ref- erence point cloud to the nearest point in the comparison point cloud. These distances were symbolised on the comparison point cloud in a 3D scene. The second technique consisted of profile comparisons extracted from the point clouds. The third was a com- parison of two shoreline proxies, scarp edge and vegetation edge. The spatial views are a comprehensive visualisation of change, however, the differencing tool produced absolute difference, which can lead to difficulties in discerning erosion from accretion. Future research will investigate methods for improving this differencing to provide information on the direction of change. This change direction information will also allow volumet- ric quantification of change. The profile comparisons overcome some of the issues of absolute difference as the two profiles can be compared in the same plot. This allows the analysis of change direction (erosion versus accretion) and the manual measurement of change. Future research will modify the profile comparison algorithm developed in Chapter 4 and Chapter 5 to provide statistics describing change along a profile. The final method of visualising coastal change was the comparison of shoreline proxies. The scarp edge was far more representative of the coast location in each epoch and provided useful estimates of change. Vegetation edge is not useful at this site but may provide a realistic representative shoreline when the vegetation grows at the edge of the eroding terrain.
The UAV-MVS survey technique is a cost-effective tool for coastal monitoring that can provide very high spatial and temporal resolution datasets. The georeferenced point clouds can be used to detect change. The reliability of change detection is closely linked to point cloud completeness and georeferencing accuracy (particularly when coregis- tration is not possible). The guidelines offered in Chapter 4 and Chapter 5 have the potential to improve results and allow for more robust change monitoring. This study has demonstrated that centimetre-scale change can be detected, quantified, and visu- alised. Regular UAV-MVS surveys will provide detailed terrain time series that will give insight into the response of specific coastal morphologies and sediments to changes in sea level.
Thesis context
An overarching aim of this thesis is to prove that UAV-MVS surveys are a viable method for monitoring coastal erosion at scales that are difficult to map using traditional remote
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sensing options. The previous chapters assessed the accuracy of the process and with that knowledge this chapter has been able to evaluate centimetre-level coastal change monitoring options for UAV-MVS point cloud data, specifically cloud differencing, profile comparison and shoreline definition options.
Acknowledgements
The author thanks Chris Sharples (University of Tasmania) for his advice on site selec- tion, coastal geomorphology, vulnerability and monitoring and Vishnu Prahalad (Uni- versity of Tasmania) for his advice on saltmarsh and coastal vulnerability and vegetation types.
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Conclusion
There is a need for rigorous assessment of the accuracy unmanned aerial vehicle structure- from motion (UAV-MVS) terrain mapping to evaluate its potential as a high spatial and temporal resolution coastal monitoring tool. This study assessed the accuracy of UAV-MVS in the context of mapping a natural landscape at centimetre resolution. To evaluate the technique, dense point clouds were derived from images captured of an eroding section of sheltered coastline. UAV-MVS surveys at different time epochs were compared and change detected. Survey design decisions relating to camera calibration, flight planning, ground control distribution, and ground control density were investi- gated. A precise total station survey technique was used to coordinate ground control and verification points in order to assess derived point cloud accuracy. Photogrammet- ric simulations were undertaken to predict accuracy and these were compared to actual survey results to assess those predictions. The impact of establishing ground control using lower precision differential GPS (DGPS) or differential GNSS (Global Naviga- tion Satellite Systems) was also assessed. Profiles were used to compare datasets and visualise point density, accuracy and precision, and to assess the detected change. In addition, shorelines were digitised and compared to assess their suitability for change monitoring at these scales. This study has demonstrated that the UAV-MVS technique is an effective and accurate method for mapping coastal erosion at the centimetre scale when sufficient ground control is used and the camera network is well-designed.
7.1
UAV-MVS Accuracy Assessment
Objective 1: Assess how accurately an area of natural terrain can be mapped using UAV- MVS.
The initial accuracy assessment compared high precision (total station survey) coor- dinates of verification points with coordinates of the same verification points derived from UAV-MVS derived point clouds (Chapter 2). The comparison total station survey verification point coordinates were estimated to be accurate to ∼1 cm horizontally and ∼2 cm vertically. The point clouds were generated using Bundler and PMVS2 and geo- referenced via a Helmert transformation. The initial assessment also compared control point density. The results indicated that, when flying at 40–50 m above ground level (AGL ) and using a well-placed control network, the technique can deliver accuracies
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of ∼2.5 cm horizontally and ∼4 cm vertically. The resulting assessed accuracy is very similar to the precision of the DGPS survey technique used to coordinate the control. It was decided that this similarity between the ground control point (GCP) survey co- ordinate precision and the assessed point cloud accuracy combined with the precision of the verification point coordinates were potentially masking the true accuracy of the technique. The experiment used to assess accuracy in Chapter 4 and Chapter 5 ensured that verification point coordinates were very precise (5–10 times more accurate than the hypothesised accuracy of the UAV-MVS survey technique) and that the impact of GCP precision on model accuracy could be assessed.
The point clouds produced by UAV-MVS have 1–6 points per cm2 when the site is imaged from 25–50 m above ground level (AGL). For such dense datasets to be used in spatial applications they usually need to be converted to surface models that reduce dataset size without losing too much detail. Triangulated meshes are one option for creating a continuous surface from the derived point clouds; another is Poisson surface reconstruction that uses point normal data. Chapter 3 aimed to assess the potential of converting point clouds to surface models prior to change detection by visualising and comparing these two surface representations created from terrestrial MVS (T-MVS) and UAV-MVS. The results indicated that, while these representations were useful for reducing dataset size, the artefacts introduced by the conversion may confuse change detection at the high resolutions possible with UAV-MVS.
Chapter 4 evaluated calibration options in the context of camera network design, GCP survey precision, and GCP density. The results indicate that on-screen checkerboard calibration is the least accurate method. When control is precise (+/- 1–2 mm), the accuracy of results is less sensitive to design choices such as whether or not oblique imagery is included. When control precision is degraded to differential GNSS/DGPS equivalent precision (+/- 22 mm) the sparse control point distribution and poor camera network strength can reduce the object space accuracy of the UAV-MVS model due to poor camera calibration parameter estimation.
Chapter 5 assessed accuracy in the context of ground control survey method and GCP distribution. Verification point locations in the UAV-MVS model were compared to pre- cisely surveyed (+/- 1–2 mm) validation point coordinates to assess whether simulated achievable precision could be verified by an empirical accuracy assessment. When control has differential GNSS/DGPS equivalent precision (+/- 22 mm) the assessed UAV-MVS point cloud accuracy was 10–12 mm (1σ) and the simulated achievable accuracy was ∼9 mm (1σ). When control is precise the point cloud produced by the UAV-MVS survey 164
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is accurate to better than 6 mm (1σ). When very precise ground control coordinates (+/- 1–2 mm) are assumed in the simulation, the resulting predicted object space preci- sion is significantly higher than was achieved in the empirical tests. This suggests that the precision achieved in practice is being limited by residual systematic errors, most probably attributable to inaccuracy in the camera calibration.