Drone video image stabilization and tracer densification for deriving river flow velocity field with Large-Scale Particle Image Velocimetry technique
Topics:
Keywords: Large Scale Particle Image Velocimetry (LSPIV), Unmanned Aerial Vehicle (UAV), Streamflow Velocity, Remote Sensing
Abstract Type: Paper Abstract
Authors:
Rupesh Bhandari, University of Alabama
Hongxing Liu, University of Alabama
Sagy Cohen, University of Alabama
Lei Wang, Louisiana State University
Song Shu, Appalachian State University
Pawan Thapa, University of Alabama
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Abstract
Surface flow velocity along with bathymetric and river width information is vital for estimating river discharge and predicting flood hazards. The conventional method of streamflow velocity observation is expensive, and labor-intensive. Image-based techniques have been developed for the measurement of river velocity, in which there is no direct contact with the water bodies.
In this study, we use the Large-Scale Particle Image Velocimetry (LSPIV) technique to derive instantaneous 2D surface water velocity measurements from a UAV image video. The instability of the UAV platform during the video acquisition adversely influences the accuracy of LSPIV flow velocity measurements.. The reliability and density of natural tracers are an important factor that influences the reliability and spatial resolution of LSPIV flow velocity measurement. In this study, we develop and evaluate new methods to stabilize UAV video images and densify the tracers from multiple images pairs to improve the spatial resolution and accuracy of LSPIV flow velocity measurements. Streamflow velocity measurements obtained from those methods are compared with the in situ field velocity observations from Acoustic Doppler Current Profiler onboard on HYCAT Autonomous Vehicle.
Drone video image stabilization and tracer densification for deriving river flow velocity field with Large-Scale Particle Image Velocimetry technique
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Paper Abstract
