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Generating Topobathymetry Digital Elevation Model using Crowdsourced Bathymetry: A case of the St. Lawrence River and Ottawa River in Quebec


Generating Topobathymetry Digital Elevation Model using Crowdsourced Bathymetry: A case of the St. Lawrence River and Ottawa River in Quebec

Goswami, Henish (2021) Generating Topobathymetry Digital Elevation Model using Crowdsourced Bathymetry: A case of the St. Lawrence River and Ottawa River in Quebec. Masters thesis, Concordia University.

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The accuracy of two- and three-dimensional hydraulic modelling of free surface flow depends significantly on a complete and accurate geometric description of the river channel and floodplains in the form of a continuous, seamless digital elevation model (DEM). With the advent of airborne Light Detection and Ranging (LiDAR) surveys, high-resolution topographic data is increasingly becoming available. However, bathymetric information for most rivers is not available in ready-to-use digital data formats, mainly because the primary data collection methods, i.e., hydrographic surveys, are costly and time-intensive. The existing methods for generating topobathymetry digital elevation model (TB-DEM) require access to raw data and ground measurements to some extent. This study proposes a simple superposition-based approach to generating a seamless elevation model using terrestrial and bathymetry information available from secondary data sources, including crowdsourcing. It comprises geographic information system (GIS) based interpolation and geoprocessing techniques. An integrated TB-DEM is generated for part of the St. Lawrence River and Ottawa River and the overbank areas on the upstream side of Montreal Island in Quebec. The output DEM is verified using internal and external validation criteria. The upland topography is unaffected by the superposition process, whereas the interpolated bathymetry shows significant positive linear associations with the reference elevation data. The vertical accuracy of bathymetry DEM with respect to Canadian Hydrographic Service Non-Navigational Bathymetric Data-10 (NONNA-10) reference data is 1.43 m in root-mean-squared error. The results of 1-m × 1-m DEM from this study are useful for evaluations of fish habitat health, shoreline stability and drinking-water withdrawal-site selection, and for predictions of river floods, morphological changes, and changes of water quality. The methods are applicable to other sites for generating high-resolution DEMs.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Building, Civil and Environmental Engineering
Item Type:Thesis (Masters)
Authors:Goswami, Henish
Institution:Concordia University
Degree Name:M.A. Sc.
Program:Civil Engineering
Date:1 October 2021
Thesis Supervisor(s):Li, Samuel
Keywords:River bathymetry, topobathymetry, digital elevation model, crowdsource, St. Lawrence River, Ottawa River, Lake St. Louis
ID Code:989089
Deposited By: Henish Goswami
Deposited On:16 Jun 2022 14:40
Last Modified:16 Jun 2022 14:40


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