Date of Award:

1-2018

Document Type:

Thesis

Degree Name:

Master of Science (MS)

Department:

Watershed Sciences

Advisor/Chair:

Joseph M. Wheaton

Abstract

Emerging "Structure-from-Motion" (SfM) photogrammetry techniques encourage faster, cheaper, and more accessible field methods for accurately reconstructing 3D topography. The SfM method consists of collecting sets of overlapping images of the ground surface with a point and shoot camera, and reconstructing surface topography from the images with developed software programs. This research develops and implements a SfM image acquisition method and post-processing workflow as a supplemental technique to the traditional total-station method to aid in monitoring sandbar change in Marble and Grand Canyons along the Colorado River in Arizona. Due to permitting in Grand Canyon National Park, a 4.9 m pole-mounted camera platform was used in this research to mimic the ground perspective of an aerial platform. This research presents an improved understanding of how the low-angle, pole-mounted camera platform affects image acquisition and ultimately 3D reconstructions of the surface topography. Models of ground surfaces always contain some degree of elevation error, or uncertainty. As such, elevation error models are needed to distinguish whether observed changes to topographic features (in this case sandbars) are real or simply due to elevation error. There are many ways to quantify multiple sources of elevation uncertainty, but in this study the sources of elevation uncertainty were considered to vary across the surface and were characterized accordingly. Especially in river environments with complex surface topography (e.g. steep cut banks), and roughness (e.g. vegetation), quantifying the spatially variable elevation uncertainty of the surface representation is critical for interpreting actual changes in surface topography over repeat surveys. This research: used the sandbar images collected in Marble and Grand Canyons with the pole-mounted camera platform to generate SfM, topographic models; calculated spatially variable surface uncertainty derived from slope and roughness using multiple statistical analyses; built an error model that was calibrated based upon the statistical analyses of the spatially variable surface uncertainty; Key findings of this research are: Densely vegetated topography results in high amounts of elevation uncertainty, and without additional information of the surface underlying the vegetation, the SfM tool is less operational in these areas; Bare, exposed topography with low to high slopes that are not covered in black shadows result in lower surface uncertainty, and are areas where SfM is an operational tool for studies of surface change. Complementing existing topographic sampling methods with more efficient and cost- effective SfM approaches will contribute to the understanding of changing responses of the topographic features. In addition, the development and implementation of SfM and corresponding amounts of elevation uncertainty for monitoring geomorphic change will provide a methodological foundation for extending the approach to other geomorphic systems world- wide.

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