An Evaluation of Image Velocimetry Techniques under Low Flow Conditions and High Seeding Densities Using Unmanned Aerial Systems

Sophie Pearce,Matthew Perks,Flavia Tauro,Dariia Strelnikova,Salvatore Manfreda,Dušan Prodanović,Silvano Dal Sasso,Gernot Paulus,Salvador Peña-Haro,Robert Ljubičić,Salvatore Grimaldi,Jasna Plavšić,Ian Maddock,Alonso Pizarro

doi:10.3390/rs12020232

Abstract

Image velocimetry has proven to be a promising technique for monitoring river flows using remotely operated platforms such as Unmanned Aerial Systems (UAS). However, the application of various image velocimetry algorithms has not been extensively assessed. Therefore, a sensitivity analysis has been conducted on five different image velocimetry algorithms including Large Scale Particle Image Velocimetry (LSPIV), Large-Scale Particle Tracking Velocimetry (LSPTV), Kanade–Lucas Tomasi Image Velocimetry (KLT-IV or KLT), Optical Tracking Velocimetry (OTV) and Surface Structure Image Velocimetry (SSIV), during low river flow conditions (average surface velocities of 0.12–0.14 m s − 1 , Q60) on the River Kolubara, Central Serbia. A DJI Phantom 4 Pro UAS was used to collect two 30-second videos of the surface flow. Artificial seeding material was distributed homogeneously across the rivers surface, to enhance the conditions for image velocimetry techniques. The sensitivity analysis was performed on comparable parameters between the different algorithms, including the particle identification area parameters (such as Interrogation Area (LSPIV, LSPTV and SSIV), Block Size (KLT-IV) and Trajectory Length (OTV)) and the feature extraction rate. Results highlighted that KLT and SSIV were sensitive to changing the feature extraction rate; however, changing the particle identification area did not affect the surface velocity results significantly. OTV and LSPTV, on the other hand, highlighted that changing the particle identification area presented higher variability in the results, while changing the feature extraction rate did not affect the surface velocity outputs. LSPIV proved to be sensitive to changing both the feature extraction rate and the particle identification area. This analysis has led to the conclusions that for surface velocities of approximately 0.12 m s − 1 image velocimetry techniques can provide results comparable to traditional techniques such as ADCPs. However, LSPIV, LSPTV and OTV require additional effort for calibration and selecting the appropriate parameters when compared to KLT-IV and SSIV. Despite the varying levels of sensitivity of each algorithm to changing parameters, all configuration image velocimetry algorithms provided results that were within 0.05 m s − 1 of the ADCP measurements, on average.

Highlights

Monitoring river flow is essential for the development of river science research and management [1]
It was determined that the ratio between surface and depth-averaged velocity is close to unity, and, for that reason, depth-averaged velocity magnitude from the acoustic Doppler current profilers (ADCPs) data was used as an estimate of surface velocity magnitude in all further analyses
This paper explores the sensitivity of five different algorithms to changing feature extraction rates and particle identification and search area/lengths

Summary

Introduction

Monitoring river flow (discharge) is essential for the development of river science research and management [1]. A critical component of computing river flow is velocity This is achieved through using in situ velocity measurement tools such as current meters, acoustic Doppler current profilers (ADCPs) and ultrasonic gauges [2]. Such measurements typically use the velocity-area method to calculate discharge [3]. Determination of flow beyond the gauged maxima typically relies on an extrapolated stage-discharge relationship [4,5]. This often leads to unquantified uncertainty in flow predictions. The use of remote flow monitoring methods could be useful for monitoring a range of river flows [6,7,8]

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