Bedload transport analysis using image processing techniques

Abstract Bedload transport is an important factor to describe the hydromorphological processes of fluvial systems. However, conventional bedload sampling methods have large uncertainty, making it harder understand this notoriously complex phenomenon. In study, a novel, image-based approach, Video-based Tracker (VBT), implemented quantify gravel by combining two different techniques: Statistical Background Model and Large-Scale Particle Image Velocimetry. For testing purposes, we use underwater videos, captured in laboratory flume, with future field adaptation as overall goal. VBT offers full statistics individual velocity grainsize data for moving particles. The paper introduces method which requires minimal preprocessing (a simple quick 2D Gaussian filter) retrieve calculate rate. A detailed sensitivity analysis also carried out introduce parameters method, during was found that simply relying on literature visual evaluation resulting segmented set them correct values. Practical aspects applicability are discussed statistical filter, accounting suspended sediment air bubbles, provided.