1. The authors developed a Seeding Distribution Index (SDI) to quantify seeding characteristics on river surfaces that can improve the accuracy of image velocimetry techniques for measuring river flow velocities. 2. Applying the SDI, the authors analyzed video footage from different river field sites to identify the optimal frame windows for image analysis, finding error reductions of 20-39% compared to analyzing full video sequences. 3. The SDI-based method shows potential for improving image velocimetry performances in natural river settings where environmental conditions challenge flow measurements.

1. 1
Seeding metrics for image velocimetry
performances in rivers
S.F. Dal Sasso1 , A. Pizarro2, S. Pearce3, I. Maddock3,
M. T. Perks4, and S. Manfreda2
1 Department of European and Mediterranean Cultures (DICEM), University of Basilicata, Matera, Italy
silvano.dalsasso@unibas.it
2 Department of Civil, Architectural and Environmental Engineering, University of Naples Federico II, Napoli, Italy
alonsovicente.pizarrovaldebenito@unina.it; salvatore.manfreda@unina.it
3 School of Science and the Environment, University of Worcester, Worcester, UK
sog.pearce@worc.ac.uk; i.maddock@worc.ac.uk
4 School of Geography, Politics and Sociology, Newcastle University, Newcastle upon Tyne, United Kingdom
Matthew.Perks@newcastle.ac.uk
EGU21-9229

2. 2
Motivation
The occurrence and distribution of visible features
on the water surface in consecutive frames
represent a key factor
The amount, spatial distribution, and visibility of
natural features on the river surface are
continuously challenging because of
environmental and hydraulic conditions
Even though image-velocimetry techniques are widely used, their accuracy under
field conditions is still an issue of research
Is it possible to increase image velocimetry performances by exploiting
seeding metrics along a video footage?

3. 3
Seeding metrics
background
✓ The dynamics of seeding characteristics on time showed statistical significance
on image-based performances (Dal Sasso et al., 2020).
✓ Pizarro et al. (2020a and 2020b), using numerical simulations, introduced the
Seeding Distribution Index (SDI) as a parameter that synthesises the seeding
conditions, merging seeding and spatial distribution characteristics.
• 𝝆 (seeding density [ppp])
• 𝝆𝒄𝑫∗𝟏 converging seeding density at the Poisson case (D*= 1)
where 𝜌𝑐𝐷∗1 = 1.52E-03 ppp for LSPV technique
• D* (dispersion index) = D/DPoisson=[Var(N)/E(N)]/1
where Var(N) and E(N) are the spatial variance and mean value of the number of tracers
𝑺𝑫𝑰 = ൘
𝑫∗𝟎.𝟏 𝝆
𝝆𝒄𝑫∗𝟏

4. 4
SDI-based method
VIDEO ACQUISITION
VIDEO
RESAMPLING
NOISE SUPPRESSION
(MEDIAN FILTER)
PRE-PROCESSING (1)
VIDEO
STABILISATION
FIELD SURVEY
SDI INDEX
ESTIMATION
AVERAGE
THRESHOLD
Maximisation of frame number
PRE-PROCESSING (2)
CHOICE OF VIDEO
FOOTAGE
IMAGE
BINARIZATION
PROCESSING
IMAGE VELOCIMETRY
APPLICATION

5. 5
LH RH
Frame n. 2400
LH RH
Frame n. 1200
LH RH
Frame n. 550
sector
sub-sectors
LH RH
Sub-sector LH
Sub-sector RH
SDI-based method
at different scales

6. 6
Field experiments
c)
d)
e)
b)
fixed camera UAS
12m
6m
2.5m
UAS
UAS
Case Study
River
discharge
[m3
/s]
Image
System
Benchmark
measurements
(n. locations)
Brenta (Italy) 2.76 fixed (bridge)
Current meter
(n.4)
Noce (Italy) 1.7 mobile (UAS)
Current meter
(n.13)
Bradano
3.97 mobile (UAS)
Current meter
(n.7)
(Italy)
Arrow (UK) 1.46 mobile (UAS)
Current meter
(n.9)
Perks et al., 2020

7. 7
▪ The SDI-based method improved LSPIV performances with a reduction of image
velocimetry errors at sector and sub-sector scales
▪ In such cases, the average surface velocity maps contain details (e.g., velocity fluctuations
and divergences) that are not visible and appreciable in the entire video configuration
(standard approach).
LSPIV Results
River
Number of frames
Entire video Best footage
Brenta 2500 153-378
Noce 200 70-124
Bradano 2496 642
Arrow 799 282
0%
2%
4%
6%
8%
10%
12%
14%
16%
B RE NTA NOCE B RA DA NO A RROW
MAPE
(%)
RIVERS
Entire video
Best footage

8. 8
✓ A priori evaluation of flow seeding conditions can allow to choose the best frame window for
image velocimetry analysis.
✓ We observed an error reduction of 20-39% with respect to the analysis of the full video
(standard case). This beneficial effect appears even more evident when the optimisation is
applied at sub-sector scales, in cases where SDI shows a marked variability along the
cross-section.
✓ The application of the method at the sector and sub-sector scales allowed a significant
reduction in computational time for the analysis, reducing the number frames processed.
✓ The method appears suitable for natural settings where environmental and hydraulic
conditions are extremely challenging and particularly useful for real-time implementations on
gauge-cams, where a vast number of frames is usually recorded and analysed.
✓ In future, this method will be tested on other case studies considering additional seeding
configurations and environmental conditions.
Conclusions

9. 9
Conclusions
Thank you for your interest in our research!
1. Contact the authors for more information
2. A manuscript was recently submitted to Journal of Hydrology, applying field
considerations (Dal Sasso et al., 2021)
3. Codes and data can be downloaded from:
➢ Pizarro, A., Dal Sasso, S. F., Perks, M. T., and Manfreda, S. 2020. Identifying the optimal
spatial distribution of tracers for optical sensing of stream surface flow (Version 0.1), [codes],
OSF, https://doi.org/10.17605/OSF.IO/8EGQW
➢ Dal Sasso SF, Pizarro A, Pearce S, Maddock I, Manfreda S. 2021. Increasing LSPIV
performances by exploiting the seeding distribution index at different spatial scales (Version
0.1). [codes] OSF. https://doi.org/ 10.17605/OSF.IO/3AJNR

10. 10
Related literature
▪ Dal Sasso, S. F., Pizarro, A., & Manfreda, S. (2020). Metrics for the quantification of seeding
characteristics to enhance image velocimetry performance in rivers. Remote Sensing, 12(11), 1789.
▪ Pizarro, A., Dal Sasso, S. F., Perks, M. T., & Manfreda, S. (2020a). Identifying the optimal spatial
distribution of tracers for optical sensing of stream surface flow. Hydrology and Earth System
Sciences, 24(11), 5173-5185.
▪ Pizarro, A., Dal Sasso, S. F., & Manfreda, S. (2020b). Refining image-velocimetry performances for
streamflow monitoring: Seeding metrics to errors minimization. Hydrological Processes, 34(25), 5167-
5175.
▪ Dal Sasso, S. F., Pizarro, A., Pearce. S., Maddock, I. & Manfreda, S. (2021). Increasing LSPIV
performances by exploiting the seeding distribution index at different spatial scales. Journal of
Hydrology, (under review).
▪ Perks M.T., Dal Sasso S.F., Hauet A., Jamieson E., Le Coz J., Pearce S., Peña-Haro S., Pizarro A.,
Strelnikova D., Tauro F., et al. 2020. Towards harmonisation of image velocimetry techniques for river
surface velocity observations. Earth System Science Data 12 (3): 1545–1559

11. 11
Seeding metrics for image velocimetry
performances in rivers
S.F. Dal Sasso1 , A. Pizarro2, S. Pearce3, I. Maddock3,
M. T. Perks4, and S. Manfreda2
1 Department of European and Mediterranean Cultures (DICEM), University of Basilicata, Matera, Italy
silvano.dalsasso@unibas.it
2 Department of Civil, Architectural and Environmental Engineering, University of Naples Federico II, Napoli, Italy
alonsovicente.pizarrovaldebenito@unina.it; salvatore.manfreda@unina.it
3 School of Science and the Environment, University of Worcester, Worcester, UK
sog.pearce@worc.ac.uk; i.maddock@worc.ac.uk
4 School of Geography, Politics and Sociology, Newcastle University, Newcastle upon Tyne, United Kingdom
Matthew.Perks@newcastle.ac.uk
EGU21-9229