Date of Award:

7-2013

Document Type:

Dissertation

Degree Name:

Doctor of Philosophy (PhD)

Department:

Wildland Resources

Advisor/Chair:

R. Douglas Ramsey

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 censuses in remote locations or over extensive areas are logistically difficult, frequently biased, and time consuming. My dissertation examined various techniques to determine the probability of detecting animals using remotely sensed imagery. We investigated four procedures that integrated unsupervised classification, texture characteristics, spectral enhancements, and image differencing to identify and count animals in remotely sensed imagery. The semi-automated processes had relatively high errors of over-counting (i.e., greater than 60%) in contrast to low (i.e. less than 19%) under-counting errors. The single-day image differencing had over-counting errors of 53% while the manual interpretation had over-counting errors of 19%. The probability of detection indicates the ability of a process or analyst to detect animals in an image or during an aerial wildlife survey and can adjust total counts to estimate the size of a population. The probabilities of detecting an animal in remotely sensed imagery with semi-automated techniques, single-day image differencing, or manual interpretation were high (e.g. ≥ 80%). Single-day image differencing resulted in the highest probability of detection suggesting this method could provide a new technique for managers to estimate animal populations, especially in open, grassland habitats. Remotely sensed imagery can be successfully used to identify and count animals in isolated or remote areas and improve management decisions. Sightability models, used to estimate population abundances, are derived from count data and the probability of detecting an animal during a census. Global positioning systems (GPS) radio-collared bison in the Henry Mountains of south-central Utah provided a unique opportunity to examine remotely sensed physiographic and survey characteristics for known occurrences of double-counted and missed animals. Bison status (detected, missed, or double-counted) was determined by intersecting helicopter survey paths with bison travel paths during annual helicopter surveys. The probability of detecting GPS-collared bison during the survey ranged from 91% in 2011 to 88% in 2012.

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