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Estimation of grain sizes in a river through UAV-based SfM photogrammetry

Wong, Tyler
http://rave.ohiolink.edu/etdc/view?acc_num=osu1658149875487092

Abstract Details

2022, Master of Science, Ohio State University, Environment and Natural Resources.
Unmanned aerial vehicles (UAVs) have an increasingly relevant role in the field of hydrology and water resources management. Their affordability and ease of use in comparison to traditional field-based methods have made research on their applications increase rapidly in the past decade. One application of UAVs to the hydrology of river systems is the estimation of particle sizes within a channel. This project investigated the ability of UAV imagery and Structure-from-Motion (SfM) photogrammetry to estimate grain-size distributions within a reach along the Olentangy River. To do this, we selected a study reach within the Highbanks Metro Park that was approximately 250 m in length and 50 m in width. We flew a DJI Mavic 2 Pro quadcopter UAV and collected imagery of subaerially exposed grains throughout gravels bars within this study reach. These images were processed using a SfM workflow that yielded point clouds and orthomosaics from which we extracted multiple topography-based and image-based metrics to be used as proxies for grain sizes. We then calibrated statistical regression models to predict the D50 and D84 grain size percentiles from these grain size proxies. While previous literature has suggested that topographic roughness metrics outperform image textural metrics for statistical grain size estimation, our study showed that the statistical models that were calibrated based on image textural properties performed better than those that were calibrated based on point cloud roughness properties. This contradiction may reflect the unique nature of our study site where the grains were dominated by smaller particles in comparison to other studies. The smaller grain sizes in our study area would have likely produced less significant topographic signatures in comparison to larger grains, which makes topographic roughness difficult to accurately measure and apply to statistical grain size estimation techniques. The results of this study suggest that topography-based grain size estimation may not be adequate for all sites, and further work on analyzing the range of grain size characteristics for which topography-based and image-based techniques perform better should be done to improve the applicability of these techniques. Doing so will help river scientists and managers to easily assess the physical, chemical, and biological dynamics that occur within rivers.
Steve Lyon (Advisor)
Sami Khanal (Committee Member)
Kaiguang Zhao (Committee Member)
Environmental Science; Geology; Geomorphology; Hydrologic Sciences; Hydrology; Water Resource Management
Fluvial geomorphology; hydrology; grain size estimation; Unmanned Aerial Vehicles; UAVs; drones; remote sensing

Recommended Citations

Citations

  • Wong, T. (2022). Estimation of grain sizes in a river through UAV-based SfM photogrammetry [Master's thesis, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1658149875487092

    APA Style (7th edition)

  • Wong, Tyler. Estimation of grain sizes in a river through UAV-based SfM photogrammetry. 2022. Ohio State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1658149875487092.

    MLA Style (8th edition)

  • Wong, Tyler. "Estimation of grain sizes in a river through UAV-based SfM photogrammetry." Master's thesis, Ohio State University, 2022. http://rave.ohiolink.edu/etdc/view?acc_num=osu1658149875487092

    Chicago Manual of Style (17th edition)

osu1658149875487092
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This open access ETD is published by The Ohio State University and OhioLINK.