# Unmanned Aerial Vehicle Depth Inversion to Monitor River-Mouth Bar Dynamics
## 5 Discussion
### Applicability to Bathymetry Monitoring  
Figure 14 shows the relationship between the cross-correlation coefficient and the RMSE for all the transects. There is a general tendency that the error decreases with an increasing cross-correlation coefficient. The correlation is high when intensity signals on the circumference are coherent. Among the four cases, the errors are relatively small in Cases 1 and 4. The two cases were observed during calm weather, but significant swells were propagating to the coast. Case 3 was observed on a windy day just before the typhoon passage, and locally generated wind waves were significant. In Case 2, which is the worst case in terms of accuracy, the cross-correlation is low, especially in the deeper area because the video did a poor job of capturing swell patterns. A possible reason is that wave steepness was low due to the relatively long incident wave. The present method takes advantage of the wave conditions in which swell-originated signals are dominant in the target domain. Therefore, the UAV observation should be performed on a calm day when narrow-band swells propagate from a far-field source. However, the cross-correlation shows significant variation over transects even in the same case affected by different  
Figure 13: Seabed profiles along the survey transects estimated with different video sub-intervals: (**a**) Seabed profiles estimated with different intervals from the start of the record for Case 1, (**b**) Seabed profiles estimated with different intervals from the start of the record for Case 4, (**c**) Seabed profiles estimated with mutually exclusive 1-min video sub-intervals and their average for Case 1 and (**d**) Seabed profiles estimated mutually exclusive 3-min video subsets and their average for Case 1.  
factors. Further studies will be needed to identify the influential factors and optimise the observation setup to extract swell signals systematically.  
The proposed method showed its applicability to monitoring river-mouth bar dynamics. It can estimate the outer-bar bathymetry with a depth error of < 0.5 m under preferable conditions. We also confirmed that depth inversion is possible up to a water depth of ~8 m under a swell period of 7-8 s, beyond which the error will increase rapidly due to high estimation sensitivity to errors in the wave celerity and period. The depth estimation in further deep areas requires high swells with longer wave periods. The inner-bar bathymetry cannot be estimated well due to wave shoaling, breaking and the superposition of refracted waves. The present method does not apply any special treatment to the shallow-water wave transformations. Since wave behaviors and intensity signals are different from those in deeper water [24; 25], there is a possibility of further improvement of the algorithm by accounting for signal characteristics in shallow water.  
The errors along different transects do not show a tendency to increase with the distance from the camera. Therefore, the depth inversion works well to cover the longshore distance of 1 km with the camera settings. The coverage could be further extended with a higher altitude of the UAV, although it requires special permission in Japan. The method enables us to grasp the bar geometry with a several-minute UAV observation, which is especially advantageous in tracking drastic changes due to an extreme event in the short term. The flexible and low-cost method based on UAV provides a new approach for monitoring river-mouth morphodynamics in a wave-dominated environment.