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PIV and ICP techniques applied to time series TLS point cloud data from the CCL show that both methods can provide spatially continuous 3D displacement fields. Both methods perform well for the CCL data from the January-May, May-June and January-Jun 2010 time periods (Figure 9 and 10a). One advantage of PIV is that it estimates the displacements at higher resolution and the effects of displacement gradients in the estimation window can be minimized.

For June 2010 to April 2012, the displacement is as large as ~3 m and mismatch between the orientations of ICP and PIV vectors are more prominent (Figure 3.10c). In this case, the PIV displacement field may have larger noise due to the need to use a larger correlation window (6

m) as described in section 1.1 and the larger displacement gradient in the correlation window.

Yet, PIV performs better than ICP; as ICP suffers significantly from problems related to large movements and different data point densities in the two scans. Nevertheless, both ICP and PIV displacement fields reveal the displacement pattern in the active part of the slide from June 2010 to April 2012 (Figure 3.10c).

Differences in performance of ICP vs PIV for the synthetic signal vs the CCL data can be related to the effect of vegetation and shadows. In contrast to the synthetic data, the data from CCL contains backscattering from vegetation particularly in May and June compared to January 2010.

Returns from trees and bushes can be significantly different over time. In PIV, changes in returns from vegetation over time may cause decorrelation and therefore PIV estimates are less sensitive to the vertical growth of trees and bushes over time. In contrast, ICP finds the corresponding match for every single data point and therefore returns from trees and bushes and their shadows increase the estimation errors. Another reason for the poorer match of the ICP estimate with the ground truth measurements can be due to the coarser estimation resolution (larger windows). The ICP estimation is at 5 m grid resolution and therefore the ICP estimation and the ground truth measurements can be as far as 2.5 m in space. This can introduce errors particularly when the displacement gradient in the ICP window is larger.

Table 3.1 Average misfit of the PIV and ICP estimation with different window sizes for a synthetic signal applied to Jan 2010 TLS data and Jan-Jun 2010 TLS data.

–  –  –

The point-to-point ICP matching of large data sets can be very slow, but the point-to-plane ICP we use in our analysis has much faster convergence particularly when the initial position of the data is close to the model and when the input has relatively small noise. When the corresponding shapes start far away from each other, or for noisy point clouds due to trees or shadows, point-toplane ICP tends to oscillate and can fail to converge (Gelfand et al., 2003). Similarly, ICP assumes that one point cloud is a subset of the other. When this assumption is not valid (as might be the case in a deforming landslide), false matches can cause ICP to converge to an incorrect solution or to a local minima (Fusiello et al., 2002). In TLS data, this situation can occur when there are many, similarly shaped features (e.g., trees or surface of uniform slope) or significantly higher displacement gradients than expected. Therefore, some of the vectors in Figure 3.10c are unrealistic (e.g., pointing upwards).

Overall, our results demonstrate that the PIV and ICP methods applied to the TLS data can estimate displacement fields of small spatial extent and can capture the wide spectrum of the displacement field in space (Figure 3.1) and time that can be challenging to measure using other methods such as GPS and InSAR..

3.5 Conclusion

We have applied the particle image velocimetry (PIV) and iterative closest point (ICP) method to terrestrial laser scanning (TLS) data from the toe of the Cleveland Corral landslide (CCL) and derived 3D displacement fields. ICP performed better to recover the synthetic signal applied to one of the TLS data sets. Estimated displacement fields from the CCL, however, agree relatively better with the PIV estimates. This discrepancy can be attributed to the change in returns from vegetation that can affect the ICP estimates more than the PIV estimates. PIV can create displacement fields at higher resolution but the expected maximum displacement is needed a priori for the method to perform well. When the area scanned contains no vegetation and shadows, ICP is preferred. Therefore, instead of these two methods replacing each other, they can complement each other and provide a means to validate the result of one or the other. The methods we demonstrate here should be useful for estimating surface displacements of smaller spatial extent associated with a variety of geologic processes including land subsidence, volcanic activities, ice sheet and glacier movement, in addition to landslides.

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