TY  - JOUR
AU  - Pearce, Sophie
AU  - Ljubičić, Robert
AU  - Peña-Haro, Salvador
AU  - Perks, Matthew
AU  - Tauro, Flavia
AU  - Pizarro, Alonso
AU  - Dal Sasso, Silvano
AU  - Strelnikova, Dariia
AU  - Grimaldi, Salvatore
AU  - Maddock, Ian
AU  - Paulus, Gernot
AU  - Plavšić, Jasna
AU  - Prodanović, Dušan
AU  - Manfreda, Salvatore
PY  - 2020
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/1869
AB  - 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.
PB  - MDPI
T2  - Remote Sensing
T1  - An Evaluation of Image Velocimetry Techniques under Low Flow Conditions and High Seeding Densities Using Unmanned Aerial Systems
IS  - 2
VL  - 12
DO  - 10.3390/rs12020232
ER  - 

@article{
author = "Pearce, Sophie and Ljubičić, Robert and Peña-Haro, Salvador and Perks, Matthew and Tauro, Flavia and Pizarro, Alonso and Dal Sasso, Silvano and Strelnikova, Dariia and Grimaldi, Salvatore and Maddock, Ian and Paulus, Gernot and Plavšić, Jasna and Prodanović, Dušan and Manfreda, Salvatore",
year = "2020",
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.",
publisher = "MDPI",
journal = "Remote Sensing",
title = "An Evaluation of Image Velocimetry Techniques under Low Flow Conditions and High Seeding Densities Using Unmanned Aerial Systems",
number = "2",
volume = "12",
doi = "10.3390/rs12020232"
}

Pearce, S., Ljubičić, R., Peña-Haro, S., Perks, M., Tauro, F., Pizarro, A., Dal Sasso, S., Strelnikova, D., Grimaldi, S., Maddock, I., Paulus, G., Plavšić, J., Prodanović, D.,& Manfreda, S.. (2020). An Evaluation of Image Velocimetry Techniques under Low Flow Conditions and High Seeding Densities Using Unmanned Aerial Systems. in Remote Sensing
MDPI., 12(2).
https://doi.org/10.3390/rs12020232

Pearce S, Ljubičić R, Peña-Haro S, Perks M, Tauro F, Pizarro A, Dal Sasso S, 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. in Remote Sensing. 2020;12(2).
doi:10.3390/rs12020232 .

Pearce, Sophie, Ljubičić, Robert, Peña-Haro, Salvador, Perks, Matthew, Tauro, Flavia, Pizarro, Alonso, Dal Sasso, Silvano, Strelnikova, Dariia, Grimaldi, Salvatore, Maddock, Ian, Paulus, Gernot, Plavšić, Jasna, Prodanović, Dušan, Manfreda, Salvatore, "An Evaluation of Image Velocimetry Techniques under Low Flow Conditions and High Seeding Densities Using Unmanned Aerial Systems" in Remote Sensing, 12, no. 2 (2020),
https://doi.org/10.3390/rs12020232 . .