Debris-flow monitoring with high-frequency LiDAR scanners: A new method to infer the internal dynamics of debris flows

Abstract

In-situ measurements of debris-flow properties are crucial for understanding their movement mechanisms and quantifying their impact. Here we present the first results of a field monitoring campaign, at Illgraben, Switzerland, to measure debris-flow parameters using high temporal (10 Hz) and spatial resolution LiDAR sensors at several locations along the channel. The point cloud data is projected onto video images to enhance visualization and aid in the interpretation of the measurements. We process the data using machine vision and deep learning based algorithms, and show that this system can accurately measure front and flow surface velocity, flow depth and bed elevation change, as well as the size, style of motion (e.g. rotating or floating without rotation) and trajectories of individual particles. This system thus provides a promising new method for inferring the internal dynamics of debris flows.