Riverseer: An open-source approach to high-resolution mapping and monitoring of river environments

Authors: Mark A. Fonstad*, University of Oregon, James T. Dietrich, University of Northern Iowa, Aaron Zettler-Mann, South Yuba River Citizens League, Dion Webster, University of Oregon
Topics: Geomorphology, Water Resources and Hydrology, Remote Sensing
Keywords: Rivers, Surveying, Photogrammetry, Monitoring
Session Type: Virtual Paper
Day: 4/8/2021
Start / End Time: 9:35 AM / 10:50 AM
Room: Virtual 3
Presentation File: No File Uploaded

Recent scientific advances demonstrate that river dynamics and habitats vary in the downstream direction not only in a smooth, gradual way, but also in a complex manner at a variety of spatial and temporal scales. Recent research postulates that both high-resolution and river-extent information is necessary to understand fundamental questions of river processes, such as patterns of critical habitat, sediment links, geomorphic-riparian-anthropogenic interfaces, and river instability. Often this mosaic complexity occurs at the very same scales as local river management, and therefore tools are needed for measuring river characteristics at those scales. Most river system analyses are completed using either intensive, small-area surveys, or extensive, low-resolution surveys. Recent advances in 3D mapping based on photography and sonar, coupled with GPS, and (sometimes) aerial drones are beginning to create data sets which allow this extensive-yet-intensive approach to river understanding. We combine these advances with new low-cost, off-the-shelf instruments and easy-to-build, open-source hardware and software to produce new river measurement platforms based on crewed boats, drone boats, and land-based pole platforms. We call this methodological perspective to riverscape mapping “Riverseer”. At the heart of the Riverseer is the idea that high-quality GPS coupled with an inertial measurement unit can provide instantaneous location and orientation information for a suite of instruments such as cameras and sonars. We demonstrate that with this high-precision geotagging, it becomes possible to generate 3D and multivariate river information with no external ground control, and with minimal expertise needed in the field.

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