Daniel Gerke

Date of Award

12-2019

Degree Name

Master of Science

Department

Geography

First Advisor

Dr. David Lemberg

Second Advisor

Dr. Charles Emerson

Third Advisor

Dr. Adam Matthews

Keywords

UAS, structure-from-motion, LW, management, remote sensing

Access Setting

Masters Thesis-Open Access

Abstract

This research aims to show Unmanned Aerial System (UAS) and Structure-from-Motion (SfM) technology can, in combination, improve on traditional large wood (LW) monitoring techniques. More temporally and economically efficient data collected at a finer spatial resolution and greater spatial extent will increase the effectiveness of management plans and risk assessment for LW by providing decision-makers with a complete picture of the river.

Contemporary practices are too inefficient in time and labor for large-scale monitoring of fluvial LW with anything more than the most general management or risk assessment in mind. The paradigm of river research, the river continuum concept (RCC), where a river’s traits are interpolated between discrete study areas, has shifted to a nested hierarchical structure (Woodget et al., 2017). The nested hierarchical structure paradigm demands higher temporal and spatial resolution combined with greater spatial extent for LW data, as called for in Zorn et al. (2018).

This study revealed UAS-SfM as a plausible option for creating data products capable of enhancing LW monitoring and risk assessment. Orthomosaics of a higher resolution (2.34cm/pixel) than those available via commercially available imagery and a 3D point cloud were both useful for general identification of LW, but both were subject to noise and errors. The sources of the noise and errors were the camera angles used during image collection, overhanging vegetation, and deep water. Changes to data collection techniques can alleviate these issues.

Download

Included in

Geography Commons

Share

COinS