Master's Theses

Department

Biology

Degree Name

Master of Science (MS)

Abstract

The shortgrass prairie ecoregion in the United States has been reduced to 52% of its historical extent, contributing to reduced habitat for native species. One such species is the Burrowing Owl (Athene cunicularia). The Western Burrowing Owl subspecies (A. c. hypugaea) is listed as a Species of Special Concern in nearly every western and midwestern state, including Kansas where it is designated as a Tier II Species of Greatest Conservation Need. Habitat destruction due to conversion to cropland, increasing use of pesticides, and reduction in burrowing mammal abundance are the primary threats that have led to this status. The objectives of my study were to determine if vegetative structure affected Burrowing Owl nest-burrow selection and to determine if UAS imagery could be used to efficiently and effectively quantify vegetative structure.

Vegetative structure and its effect on burrow selection in Burrowing Owl was measured in two ways. First, structure was quantified with an elevated Daubenmire cover classification scheme. Subsequently, I quantified structure with a photogrammetric technique in which aerial imagery acquired with the aid of an unmanned aerial system (UAS) was used to generate three-dimensional models of the vegetation. Vegetation surrounding both occupied and unoccupied burrows was classified by establishing four 20-m transects oriented to each cardinal direction and centered at the burrow opening. Along each transect, a 1-m x 1-m Daubenmire frame was used to classify vegetation at 2 m, 5 m, 10 m, and 20 m from the burrow. A DJI Phantom 4 Pro was flown over each burrow to collect a series of overlapping images. With the imagery from the UAS, three-dimensional models of vegetative structure were generated. Visual obstruction by vegetation was estimated with these models. Burrowing Owl presence increased with bare ground cover (Z = 2.29, df = 23, p = 0.022) and decreased with forb cover (Z = -2.54, df = 23, p = 0.011). Unoccupied burrows had significantly more obstruction than occupied burrows (X2 = 266, df = 9, p < 0.001). The results of my study suggest that imagery collected by UAS can be used as an effective and efficient method of characterizing vegetative structure and significantly reduce the amount of time and money required to evaluate wildlife and habitat.

Keywords

Remote Sensing, Drone, Grassland Ecology, Phenology

Advisor

Dr. William Stark

Date of Award

Spring 2019

Document Type

Thesis

Rights

© The Author(s)

Comments

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