GSA Annual Meeting in Seattle, Washington, USA - 2017

Paper No. 316-7
Presentation Time: 10:10 AM

RAPID CHARACTERIZATION OF STREAMBED MICROTOPOGRAPHY IN MOUNTAIN CHANNELS: METHODS AND APPLICATION USING STRUCTURE-FROM-MOTION PHOTOGRAMMETRY (Invited Presentation)


DIBIASE, Roman A.1, LIU, Xiaofeng2, CHEN, Yunxiang3, MCCARROLL, Nicholas R.1 and NEELY, Alexander B.1, (1)Department of Geosciences, Pennsylvania State University, University Park, PA 16802, (2)Department of Civil and Environmental Engineering, Pennsylvania State University, University Park, PA 16802, (3)Civil and Environmental Engineering, Pennsylvania State University, University Park, PA 16802, rdibiase@psu.edu

Erosion and sediment transport in mountain rivers depend to first order on the size distribution of channel-bed sediment and flow resistance, both of which are encoded in streambed microtopography. Recent advances in structure-from-motion (SfM) multi-view photogrammetry have radically lowered the cost and effort of acquiring high-resolution topographic surveys, which are particularly well-suited for characterizing streambed microtopography at dry or low-flow conditions. Minimal equipment and power requirements make field surveys of typically inaccessible channels feasible, allowing for rapid collection of baseline datasets for quantifying bed-state and assessing landscape change. Here we outline a workflow focused on efficient field data collection of streambed microtopography using ground-based SfM methods. We present field techniques for scaling and referencing datasets with minimal equipment, and evaluate optimal scales of analysis for point-cloud-based processing and mesh generation using statistical properties of streambed microtopography. We apply this methodology to field examples in Pennsylvania, California, and Taiwan to: 1) evaluate connections between streambed microtopography and grain size distribution and particle shape; 2) quantify boulder mobility and erosion using repeat surveys; and 3) integrate field-surveyed streambed microtopography into computational fluid dynamics models to predict flow resistance.