Unmanned Aerial Vehicle Depth Inversion to Monitor River-Mouth Bar Dynamics

Abstract

Monitoring the morphological evolution of a river-mouth bar is of both practical and scientific importance. A large amount of sediment is transported from a river to surrounding littoral cells via a deltaic bar after an extreme weather event. However, it is often not feasible to capture drastic morphological changes in the short term with conventional bathymetric surveys. This paper presents a depth-inversion method based on unmanned aerial vehicle technology to estimate two-dimensional bathymetry from video-sensed swell propagation. The estimation algorithm is tested over four cases with varying wave and bathymetric conditions and is validated with transect survey data. The test results suggest that the method can estimate deltaic-bar topography in front of a river mouth with a root-mean-square error of <0.5 m. The applicable range is limited by wave breaking in the inner bar and up to a depth of ~8 m, where swell intensity signals become ambiguous. A comparison of the different cases shows that the method works better under calm weather conditions with dominant swells propagating from non-local sources. Significant morphological changes of a river-mouth bar due to a powerful typhoon are successfully detected by observations right before and after the event.

Keywords

• Unmanned Aerial Vehicle
• bathymetry
• river mouth
• delta