Problem Statement. How do we assess risk to âflying animalsâ offshore? Assess risk. BACI, BA, Impact-Reference Design, Response-Gradient. Design ...
Behavior Signatures of Birds
An Automated Way to Extract Wing Beat Frequency and FlapGlide Patterns from Thermal Imagery Val Cullinan, Ph.D. Corey Duberstein, M.Sc. Shari Matzner, Ph.D.
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Acknowledgements Signature Discovery Initiative: PNNL National Security Directorate Wind and Water Power Program: DOE Office of Energy Efficiency & Renewable Energy
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Presentation Overview Problem Statement Wing Beat as a Signature Data Extraction Wing Beat Position Conclusions
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Problem Statement How do we assess risk to “flying animals” offshore? Assess risk BACI, BA, Impact-Reference Design, Response-Gradient Design, Resource Selection Function, etc… Presence, abundance Behavior: flight height, avoidance
“Flying Animals” Identification Bird vs. bat Common vs. rare spp Endangered vs. least concern
Offshore Remote Inhospitable Dynamic 4
Wing Beat as a Signature Pennycuick 1996, 2001 allometric model F = mass3/8acceleration1/2wingspan-23/24 wing area-1/3 air density-3/8
Bruderer et al. 2010 Measured 155 species, compiled 45 species (Europe) 4 flight types: Continuous flapping: wading birds, waterfowl, auks, gulls, terns Soaring: storks, pelicans, lg raptors Dynamic soaring: albatrosses, shearwaters Flap-glide: passerines, gulls, terns
Conclusion: Pennycuick pretty reliable for continuous flapping flight 5
Data Extraction
Wings Up
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Wings Down
Wings Up
Wings Glide
Data Extraction Pixel intensity values output
Centroid Centroid of pixel mass output Calculate “hot Hot spot” Spot in pixelated data
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Wing Position UP: w = 41, h = 15 Centroid Hot Spot
NEUTRAL: w = 40, h = 9
DOWN: w = 38, h = 14
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Wing Position Discriminant Analysis Hot spot relative to centroid Frame height Hot spot relative to height and width
1st Root 74% of VAR 91% Correct with crossvalidation Determine up/down cycle and wing beat frequency
Root 1 vs. Root 2 5 4
Neutral
3 2
Down
Root 2
1 0 -1 -2
Up
-3 -4 -5 -6
-5
-4
-3
-2
-1 Root 1
9
0
1
2
3
4
Up Glide Down
Wing Beat Frequency (Hz)
Automated vs. Manual
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Gull
Est. WBF (Hz) Mean (st. dev)
Observer Est. WBF
1 (n = 1)
2.99 Hz
2.5 Hz (n = 1)
2 (n = 3)
3.92 Hz (0.31)
3.34 Hz (0.16) (n = 2)
4 (n = 3)
4.33 Hz (0.63)
3.70 Hz (n = 1)
3.2 ±1.5 Hz
4 3 2
1 0
Observer 10
3.7 ± 1.7 Hz
Modeled
Conclusions Automated extraction possible Advantages = Simplicity Color agnostic ~Range agnostic ~Wind agnostic Acoustic agnostic
Disadvantages Approach or aspect specific? Allometric data for classification Frequency overlap and specificity
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Future Direction Model N.A. pelagic/coastal species Robustness: more data (species, aspect, n, etc.)
Shape Analysis PNNL Signature Discovery Initiative preliminary work shows promise
Combine all attributes for signature specificity
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½W
Range Limitations
Size in Pixels
Camera Specifications AGD FieldPro 5X
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Spectral range: Pixel array: Pixel pitch: FOV: Focal length: Frame rate:
Distance from Camera (m)
3-5 microns 320 x 256 0.03 mm 6 x 4 deg. 30 mm 30 Hz
W
