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Study Area: Caney Fork
River, North-Central TN
Video Collection
The average flying height for the UAS was approximately 9 m (30
ft) for partial channel field of view (FOV). A reference point (object)
was included in the FOV of the UAS use for calibration purposes in
the analysis. The UAS FOV and flying altitude was limited by FAA
regulations and inhibiting environmental/landscape parameters.
Tracers used at this site included natural debris/foam in the stream,
flowers, teabags, and wood chips. Acoustic Doppler Current Profiler
(ADCP) took measurements concurrently, downstream from UAS
FOV to avoid wake contamination, to be used as validation against
surface velocity estimations.
Video to Image Conversion
Footage taken from UAS flights was converted into individual frames
which would be inputted into PIVlab for the estimations. Using a
command line code (FFMPEG), the video was divided into frames
based on the frame rate of the camera that was used.
9C
9A
9D
9B
C. Jackson1, A. Kalyanpu2, and G. Cervone1
1 Geoinformatics and Earth Observation Laboratory, Department of Geography, and Institute for CyberScience
The Pennsylvania State University, University Park, PA | coj5074@psu.edu ; guc18@psu.edu --- http://geoinf.psu.edu
2 Civil and Environmental Engineering Department, Tennessee Technological University, Cookeville, TN | akalyanapu@tntech.edu
Use of Unmanned Aerial Systems (UAS) for Velocimetry Estimation
ABSTRACT CANEY FORK TRACERS LAB CONTROL TRACERS
Current methods for accurate velocimetry measurements during
flood events are not cost effective and require direct contact with the
stream through fixed or mobile sensors. Image-based methods,
such as Large Scale Particle Image Velocimetry (LSPIV), have been
identified as noncontact techniques for velocimetry estimation, but
require close proximity to the flood which may not be possible or
prove dangerous during an emergency. Increasing popularity in
Unmanned Aerial Systems (UASs) have made the technology cost
effective and efficient means for studying floods, aiding emergency
response efforts to protect lives, properties and the environment.
Direct data measurement from UASs can increase the accessibility
range for velocity measurements, enabling real-time collection at
gaged/ungaged locations. Five locations study locations in
Tennessee were used to test tracer efficiency on the accuracy of
velocimetry estimation. Data collected will assist in informing and
validating future hydrologic models to create short-term forecasts of
flood propagation.
ABSTRACT
INTRODUCTION
Due to the growing threat of climate change, natural hazards are
likely to increase in frequency and magnitude in the coming
decades. Flooding is thought as one of most dangerous and
damaging hazards (Abdelkader et al.2014, Schnebel et al. 2014),
which leaves tremendous physical and emotional causalities in its
wake. Real-time discharge measurements are critical data sources
to for improving flood rating curves and for addressing disconnect
between forecasted and observed velocimetry measurements.
In order to reduce risk, Unmanned Aerial Systems (UASs or drones)
would be the most viable solution for estimating velocity. Traditional
stream observation methods capture measurements for one point
along a river, where as a UAS would be able to provide real-time
estimations over a larger area along the same river. The goal of this
study was to develop and establish a methodology for using UAS
video/imagery for velocimetry estimation.
METHODS
PIVLAB
ACKNOWLEDGEMENTS
Image Processing
• Width: ~82m (270ft)
• Size: Moderate
• Clarity: Low Turbidity
• Land Cover: Agriculture
• Use: Recreational/Fishing
Figures 3A-3D: Tracers used on Caney Fork River to track surface velocity. Natural stream debris (i.e. leaves,
foam, branches) shown in 3A. Images 3B-3D show tracers flowers (3B), teabags (3C), and woodchips (3D).
These tracers were selected because they were non-artificial, biodegradable objects that were in accordance
with Tennessee Department of Environment and Conservation regulations and approved.
This research was made possible through the Pathfinder Fellowship
offered by the Consortium of Universities for the Advancement of
Hydrologic Science, Inc. (CUAHSI). The Pathfinder Fellowship and
CUAHSI are both sponsored and funded by the National Science
Foundation (NSF).
REFERENCES
Abdelkader, M., Shaqura, M., Ghommem, M., Collier, N., Calo, V., & Claudel, C. 2014. Optimal multi-
agent path planning for fast inverse modeling in UAS-based flood sensing applications. In
Unmanned Aircraft Systems (ICUAS), 2014 International Conference. IEEE, 64-71.
Dramais, G., Le Coz, J., Camenen, B., & Hauet, A. 2011. Advantages of a mobile LSPIV method for
measuring flood discharges and improving stage–discharge curves. Journal of
Hydro-Environment Research, 5(4), 301-312.Schnebele, E., Cervone, G., Kumar, S., & Waters, N.
2014. Real time estimation of the Calgary floods using limited remote sensing data. Water,
6(2), 381-398.
Thielicke, W. and Stamhuis, E.J. 2014. PIVlab – Towards User-friendly, Affordable and Accurate
Digital Particle Image Velocimetry in MATLAB. Journal of Open Research
Software, 2(1):e30, DOI: http://dx.doi.org/10.5334/jors.bl
Controlling for environmental conditions that alter the visibility of
tracers will be a one obstacle to overcome. Parameters such as
weather conditions (sunny vs cloudy), tracer density, and flow
conditions should be considered. River cross-sections can be
approximated by using estimated channel (top) width, side slopes
and water depth to creates trapezoidal cross-sections. The product
of stream velocity and the channel cross-sectional area will be
another means to estimate streamflow of the locations. Accounting
for the parameters listed above will aid in standardizing this
methodology so that it may be adapted at any scale in the future.
FUTURE WORK
Caney Fork estimations for each tracer were found to be in
relatively the same velocity range of between each other and across
the number of frames used for analysis. However estimates were
smaller than observed velocity taken by ADCP. The only exception
to this was woodchips, where estimated velocity across the different
frame analyses were double that of the other tracers, but still lower
than the observed. No agreement was found between the two lab
trials and observed velocity. Explanations for these discrepancies
between estimated and observed are outlined below.
Sources of Error:
• Unregulated environmental and technologic parameters
• Irregular tracer densities across FOV and/or ROI
• Masking, ROI, and calibrations dependent upon FOV and what
is in frame
• Estimations not representative of entire channel velocity
• FOV restricted by environmental parameters and to
tracer flow paths
Limitations:
• UAS flight capability to be in accordance with FAA Regulations
• Environmental parameters which inhibit UAS flight:
• Weather, Tree Cover, Infrastructure
• Precision with reference points
DISCUSSION
The straight-forward interface of PIVlab makes it an invaluable tool
for velocity estimation. Estimation results for both lab and field
analyses showed varied estimations that were not reflective of
actual observations. In both instances of video collection, there
were environmental and technological limitations which likely
impacted estimations accuracy. Controlling for such parameters
during flights will improve footage quality for more accurate
estimations. The versatility of the UAS, due to its high mobility and
utility, has potential for uniform data collection across diverse scales
in the future and warrants further investigation into usefulness as a
virtual gage.
CONCLUSION
SURFACE VELOCITY ESTIMATION
3A 3B
3C 3D
Masking, Identifying Region of Interest (ROI), and Filtering
Vector Calibration Velocity Validation
Caney Surface Velocity Lab Control
Figure 7: Velocity vectors generated for each frame are calibrated by a known reference distance within the
image as well as the time step between each frame.
Figures 5A/5B: Frames extracted from UAS video (5A) were processed by converting from an RGB image to a
single band (5B) prior to being used in PIVlab. This was done to reduce environmental noise which may have
influenced tracer visibility.
Figures 6A/6B: Converted frames loaded into PIVlab must be preprocessed using masks and filters as well as
identifying a ROI before any estimation can be done. An ROI was established in the center of the frame and
masks drawn around areas to be excluded from estimation (6A). A high-pass filter was applied to exclude
remaining environmental noise (6B).
4A 4B
4C
Figures 4A-4C: Tracers used in lab control experiment to track surface velocity. Image 4A is no tracer, 4B packing
peanuts, and 4C blue dye. Tracers that were used in the field would have inhibited the flume pump, therefore
dye and packing peanuts were selected because they would not interfere with the pump.
Figures 8A/8B: Post-processing of analyzed frames allows for the user to refine velocity values based on
clustering of plotted vertical (u) and surface (v) velocity values (8A). Mean velocity is calculated from the refined
values in respect to the ROI (8B).
Acoustic Doppler Current Profiler
Table 1: Summary of Caney Fork estimated mean surface
velocities and standard deviations for each tracer by the
number of frames used in each estimation analysis.
7
5A 5B 6A 6B
8B8A
Figures 9A-9D: Histograms of surface velocity frequency in
meters per second for 100 frames by tracer – Flowers (9A),
Natural (9B), Teabags (9C), and Woodchips (9D). Curves of
each graph appear to peak around .01-.02 m/s.
Figure 12: Lab control estimated mean surface velocities for
tracers by the number of frames used in each estimation
analysis with respect to average observed surface velocity.
Table 2: Summary of lab control estimated mean surface
velocities and standard deviations for each tracer by the
number of frames used in each estimation analysis. Blue dye
was omitted from analysis due to issues with its visibility as it
dispersed (diluted?) through the water.
Figure 11: Caney Fork estimated mean surface velocities for
tracers by the number of frames used in each estimation
analysis with respect to average observed ADCP surface
velocity. Average ADCP velocity was calculated for channel
section where the drone footage was collected.
Figure 10: ADCP velocity profile of Caney Fork River
collected concurrently with drone footage. Black bars
represent the section of the channel that was in the field of
view for the drone footage.
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