Next Article in Journal
The Determination of Snow Albedo from Satellite Measurements Using Fast Atmospheric Correction Technique
Next Article in Special Issue
A Drone-Based Bioaerosol Sampling System to Monitor Ice Nucleation Particles in the Lower Atmosphere
Previous Article in Journal
Role and Mechanisms of Black Carbon Affecting Water Vapor Transport to Tibet
Previous Article in Special Issue
Rapid Mapping of Small-Scale River-Floodplain Environments Using UAV SfM Supports Classical Theory
Open AccessFeature PaperArticle

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

School of Science and the Environment, University of Worcester, Worcester WR2 6AJ, UK
The Faculty of Civil Engineering, University of Belgrade, Belgrade 11120, Serbia
Photrack AG: Flow Measurements, Ankerstrasse 16a, 8004 Zurich, Switzerland
School of Geography, Politics and Sociology, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
Department for Innovation in Biological, Agro-Food and Forest Systems, University of Tuscia, 01100 Viterbo, Italy
Department of European and Mediterranean Cultures: Architecture, Environment and Cultural Heritage (DICEM), University of Basilicata, 75100 Matera, Italy
Consortium of Italian Universities for Hydrology (CINID), 85100 Potenza, Italy
School of Geoinformation, Carinthia University of Applied Sciences, 9524 Villach, Austria
Department of Civil, Architectural and Environmental Engineering, University of Naples Federico II, via Claudio 21, 80125 Napoli, Italy
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(2), 232;
Received: 17 December 2019 / Revised: 3 January 2020 / Accepted: 6 January 2020 / Published: 9 January 2020
(This article belongs to the Special Issue Unmanned Aerial Systems for Surface Hydrology)
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. View Full-Text
Keywords: image velocimetry; UAS; river flow monitoring; LSPIV; LSPTV; KLT; OTV; SSIV; surface flow velocity image velocimetry; UAS; river flow monitoring; LSPIV; LSPTV; KLT; OTV; SSIV; surface flow velocity
Show Figures

Graphical abstract

MDPI and ACS Style

Pearce, S.; Ljubičić, R.; Peña-Haro, S.; Perks, M.; Tauro, F.; Pizarro, A.; Dal Sasso, S.F.; Strelnikova, D.; Grimaldi, S.; Maddock, I.; Paulus, G.; Plavšić, J.; Prodanović, D.; Manfreda, S. An Evaluation of Image Velocimetry Techniques under Low Flow Conditions and High Seeding Densities Using Unmanned Aerial Systems. Remote Sens. 2020, 12, 232.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop