Behavior Signatures of Birds

An Automated Way to Extract Wing Beat Frequency and Flap-Glide Patterns from Thermal Imagery

Val Cullinan, Ph.D.

Corey Duberstein, M.Sc.

Shari Matzner, Ph.D.

1

Acknowledgements

Signature Discovery Initiative: PNNL National Security Directorate

Wind and Water Power Program: DOE Office of Energy Efficiency & Renewable Energy

2

Presentation Overview

Problem Statement

Wing Beat as a Signature

Data Extraction

Wing Beat Position

Conclusions

3

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

Dynamic4

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

6

Wings

DownWings

Glide

Wings

Up

Wings

Up

Data Extraction

Pixel intensity values output

Centroid of pixel mass output

Calculate “hot spot” in pixelated data

7

Hot Spot

Centroid

Wing Position

8

UP: w = 41, h = 15

Hot Spot

Centroid

NEUTRAL: w = 40, h = 9

DOWN: w = 38, h = 14

Wing Position

Discriminant Analysis

Hot spot relative to centroid

Frame height

Hot spot relative to height

and width

9

Root 1 vs. Root 2

Up

Glide

Down

-6 -5 -4 -3 -2 -1 0 1 2 3 4

Root 1

-5

-4

-3

-2

-1

0

1

2

3

4

5

Ro

ot

2

1st Root 74% of VAR

91% Correct with cross-validation

Determine up/down cycle and wing beat frequency

Neutral

Up

Down

Automated vs. Manual

10

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)

0

1

2

3

4

5

6

Observer Modeled

Win

g B

eat

Freq

uenc

y (H

z)

3.7 ± 1.7 Hz3.2 ±1.5 Hz

Conclusions

11

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

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

12

13 Distance from Camera (m)

Siz

e in P

ixels

Spectral range: 3-5 microns

Pixel array: 320 x 256

Pixel pitch: 0.03 mm

FOV: 6 x 4 deg.

Focal length: 30 mm

Frame rate: 30 Hz

Camera Specifications

AGD FieldPro 5X

Range Limitations½ W

W