Since 2011, Structure-from-Motion Multi-View Stereo Photogrammetry (SfM or SfM-MVS) has gone from an overlooked computer vision technique to an emerging methodology for collecting low-cost, high spatial resolution three-dimensional data for topographic or surface modeling in many academic fields. This dissertation examines the applications of SfM to the field of fluvial geomorphology. My research objectives for this dissertation were to determine the error and uncertainty that are inherent in SfM datasets, the use of SfM to map and monitor geomorphic change in a small river restoration project, and the use of SfM to map and extract data to examine multi-scale geomorphic patterns for 32 kilometers of the Middle Fork John Day River. SfM provides extremely consistent results, although there are systematic errors that result from certain survey patterns that need to be accounted for in future applications. Monitoring change on small restoration stream channels with SfM gave a more complete spatial perspective than traditional cross sections on small-scale geomorphic change. Helicopter-based SfM was an excellent platform for low-cost, large scale fluvial remote sensing, and the data extracted from the imagery provided multi-scalar perspectives of downstream patterns of channel morphology. This dissertation makes many recommendations for better and more efficient SfM surveys at all of the spatial scales surveyed. By implementing the improvements laid out here and by other authors, SfM will be a powerful tool that will make 3D data collection more accessible to the wider geomorphic community.