The availability of high-resolution, multi-temporal, remotely sensed topographic data is revolutionizing geomorphic analysis. Three-dimensional topographic point measurements acquired from structure-from-motion (SfM) photogrammetry have been shown to be highly accurate and cost-effective compared to laser-based alternatives in some environments. Use of consumer-grade digital cameras to generate terrain models and derivatives is becoming prevalent within the geomorphic community despite the details of these instruments being largely overlooked in current SfM literature.A practical discussion of camera system selection, configuration, and image acquisition is presented. The hypothesis that optimizing source imagery can increase digital terrain model (DTM) accuracy is tested by evaluating accuracies of four SfM datasets conducted over multiple years of a gravel bed river floodplain using independent ground check points with the purpose of comparing morphological sediment budgets computed from SfM-and LiDAR-derived DTMs. Case study results are compared to existing SfM validation studies in an attempt to deconstruct the principle components of an SfM error budget.Greater information capacity of source imagery was found to increase pixel matching quality, which produced eight times greater point density and six times greater accuracy. When propagated through volumetric change analysis, individual DTM accuracy (6-37 cm) was sufficient to detect moderate geomorphic change (order 100 000 m 3 ) on an unvegetated fluvial surface; change detection determined from repeat LiDAR and SfM surveys differed by about 10%. Simple camera selection criteria increased accuracy by 64%; configuration settings or image post-processing techniques increased point density by 5-25% and decreased processing time by 10-30%.Regression analysis of 67 reviewed datasets revealed that the best explanatory variable to predict accuracy of SfM data is photographic scale. Despite the prevalent use of object distance ratios to describe scale, nominal ground sample distance is shown to be a superior metric, explaining 68% of the variability in mean absolute vertical error. Published 2016. This article is a U.S. Government work and is in the public domain in the USA

A 2.5‐km3 debris avalanche during the 1980 eruption of Mount St. Helens buried upper North Fork Toutle River valley and reset the fluvial landscape. Since then, a new drainage network has evolved. Cross‐sectional surveys repeated over nearly 40 years at 16 locations along a 20‐km reach of river valley document channel evolution. We analyze spatial and temporal changes in channel morphology using two new metrics: (1) a shape index that defines the degree of U‐shaped or V‐shaped valley geometry and (2) an alluvial phase space diagram that relates bed degradation or aggradation to increases or decreases in cross‐sectional area. Unlike a simple, linear response model previously proposed, our analysis reveals channel development has been distinctly nonlinear and nonsequential. Rather than following a sequential trajectory of (1) channel initiation and incision, (2) aggradation and widening, and (3) episodic scour and fill with little change in bed elevation, long‐term channel evolution has been more complex with vertical and lateral adjustments intertwined throughout. Our analysis reveals channel evolution has followed a complex trajectory that has migrated nonsequentially through several phase space domains including degradation and aggradation with widening and narrowing, bed‐level fluctuations with little change in cross‐section area, and changes in cross‐sectional area with little change of bed elevation. Persistent channel widening and reworking of the channel bed are responsible for maintaining elevated sediment delivery from this basin. Elevated sediment delivery is likely to persist until valley floor widths greatly exceed that of the channel migration zone, and/or channel slopes and valley walls stabilize.

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