Date of Award:

8-2013

Document Type:

Dissertation

Degree Name:

Doctor of Philosophy (PhD)

Department:

Wildland Resources

Committee Chair(s)

R. Douglas Ramsey

Committee

R. Douglas Ramsey

Committee

Nicholas Flann

Committee

David N. Koons

Committee

Christopher M. U. Neale

Committee

Daniel MacNulty

Abstract

Whether a species is rare and requires protection or is overabundant and needs control, an accurate estimate of population size is essential for the development of conservation plans and management goals. Wildlife science has traditionally relied on human observers in airplanes, helicopter, or ground vehicles to count the number of individuals seen during wildlife surveys. However, these traditional surveys of wildlife require significant resources, are difficult to conduct quickly and safely over remote and/or extensive locations, are disruptive to the studied species, and are prone to significant error due to unobserved or missed animals and multiple counts of single animals. One method to correct an observed count of animals is to physically “mark” a certain number of animals prior to an aerial or ground survey of wildlife and record the number of marked animals visually observed during the survey. The proportion of marked animals observed relative to the known number of marked animals in a survey area is the probability of detection, which is then applied to the count of animals from a survey to provide a corrected population size.

My dissertation examined various techniques to improve the probability of detecting animals in remotely sensed aerial imagery. Counting animals in remotely sensed imagery, such as in photographs obtained from an airplane or images from satellites, are advantageous as the images can be acquired for large areas quickly and can reveal spectral information not readily visible by humans (i.e., near infrared and thermal information). In addition, techniques employing computer evaluation have the potential to reduce analysis time, and increase accuracy and precision when estimating animal population sizes.

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