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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