Laboratory and Field Application of River Depth Estimation Techniques Using Remotely Sensed Data: Annual Report Year 1

Abstract

Abstract : Our long term goal in this and related projects is to maximize the amount of information regarding river flow and morphology that can be deduced from remotely sensed information. In this specific project, our goal is to test and improve the river bathymetry prediction methods developed in previous ONR-funded work. This goal involves further development on computational methods as well as exploration and refinement of methods for remotely measuring water-surface elevations and water-surface velocities. In previous work, we have been able to demonstrate that inversion of the momentum equations and morphodynamics modeling can be used to detect errors and fill gaps in various remotely sensed river bathymetry data sets (i.e., such as those measured using bathymetric LiDAR and various optical correlation techniques using multi- and hyperspectral scanning, as reported in Wright and Brock (2002), Kinzel et al., (2007), Legleiter and Roberts (2005), and Legleiter et al., (2004)). In the current project, we are looking at this problem in a more expansive sense by testing measurement techniques and computational approaches that can be used in the absence of any bathymetric estimate, as would typically be the case in rivers with high suspended sediment loads and/or deep depths, which limit optical techniques. Eventually, we envision that these two different techniques can be rejoined in a manner that is accurate, efficient and capable of providing meaningful error estimates for predicted morphology.