Abstract

Bathymetric mapping is an important tool for reservoir management, typically completed before reservoir construction. Historically, bathymetric maps were produced by interpolating between points measured at a relatively large spacing throughout a reservoir, typically on the order of a few, up to 10, meters or more depending on the size of the reservoir. These measurements were made using traditional survey methods before the reservoir was filled, or using sonar surveys after filling. Post-construction issues such as sedimentation and erosion can change a reservoir, but generating updated bathymetric maps is difficult as the areas of interest are typically in the sediment deltas and other difficult-to-access areas that are often above water or exposed for part of the year. We present a method to create complete reservoir bathymetric maps, including areas above the water line, using small unmanned aerial vehicle (sUAV) photogrammetry combined with multi-beam sonar data--both established methods for producing topographic models. This thesis presents methods to create accurate above-water shoreline models using images from sUAVs, processed using a commercial software package and a method to accurately knit sonar and Structure from Motion (SfM) data sets by matching slopes. The models generated by both approaches are point clouds, which consist of points representing the ground surface in three-dimensional space. Generating models from sUAV-captured images requires ground control points (GCPs), i.e., points with a known location, to anchor model creation. We explored issues with ground control spacing, masking water regions (or omitting water regions) in the images, using no GCPs, and incorrectly tagging a GCP. To quantify the effect these issues had on model accuracy, we computed the difference between generated clouds and a reference point cloud to determine the point cloud error. We found that the time required to place GCPs was significantly more than the time required to capture images, so optimizing GCP density is important. We found that we needed to mask water and areas related to distant regions and sky in images used for model creation. This is because water, objects in the far oblique distance, and sky confuse the algorithms that match points among images. Our sonar point clouds, while self-consistent, were not accurately georeferenced. We demonstrate a method using cross-sections of the transition between the above-water clouds and sonar clouds to geo-locate the sonar data and accurately knit the two data sets. Shore line topography models and integration of sonar and drone data is a niche area that leverages current advances in data collection and processing. Our work was applied at three different reservoirs to show that accurate post-construction reservoir bathometry maps can assist with reservoir management. A report is included that compares historical bathymetric maps with the current bathymetric maps at each of the three different reservoirs. A guide to perform the drone surveys is included in the report's appendix.

Degree

MS

College and Department

Civil and Environmental Engineering

Rights

https://lib.byu.edu/about/copyright/

Date Submitted

2021-10-22

Document Type

Thesis

Handle

http://hdl.lib.byu.edu/1877/etd11918

Keywords

reservoir, bathymetry, SfM, photogrammetry, sonar, topography, shoreline, drones

Language

english

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