Seamless Topobathymetric River Mapping Through Multi-Sensor Data Integration: Lidar, Sonar, RTK GNSS and Structure from Motion Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_projects/79407x932

2017

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  • Seamless, high-resolution topobathymetric digital elevation models (DEMs) of rivers are important for generating hydrodynamic models, studying water quality and related ecological issues, modeling flood vulnerability, and numerous other applications. However, such DEMs can be difficult to generate, due to a lack of topographic and bathymetric source data. Even where the source data exist, the disparate data sets can be difficult to merge, due to differences in spatial resolutions, uncertainties, and datums. Advanced surveying technologies, including unmanned aircraft systems (UAS), structure from motion (SfM) photogrammetry, lidar, RTK GNSS, and sonar systems capable of being operated from autonomous survey boats, may assist in filling the riparian data voids, provided robust, reliable techniques can be developed for merging such data. This study investigates new methods of seamless topobathymetric river mapping using RTK GNSS, lidar, single beam echosounder and UAS-derived point clouds. As an example application, this study focuses on the evaluation of a location for a whitewater park on the Rogue River near the prior site of the Gold Ray Dam, which was removed in 2010. Data for the site were collected with a small, multirotor UAS, single beam echosounder, and GNSS. Limited existing airborne lidar data for the site were also available. A data merge approach to remotely measure river geomorphology and the surrounding topography was developed. The approach involves combining the multi-sensor data, based on both uncertainty and distance. The results indicate that the methodology may be viable for low-cost, high-resolution, and high-accuracy topobathymetric river mapping from disparate source data, supporting whitewater park site evaluation, as well as a host of other uses.
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