Automatic processing of Arctic crowd-sourced hydrographic data while improving bathymetric accuracy and uncertainty assessment
| dc.contributor.advisor | Church, Ian | |
| dc.contributor.author | Arfeen, Khaleel | |
| dc.date.accessioned | 2023-03-01T16:24:14Z | |
| dc.date.available | 2023-03-01T16:24:14Z | |
| dc.date.issued | 2019 | |
| dc.date.updated | 2023-03-01T15:02:05Z | |
| dc.description.abstract | Melting sea ice has led to an increase in navigation in Canadian Arctic waters. However, these waters are sparsely surveyed and pose a risk to mariners. Recognizing this issue, the government of Canada has granted funds towards the development of a pilot program to begin collecting bathymetric data through a novel crowd-sourced approach. The project is a coalition between four Canadian partners from across the country; The University of New Brunswick’s Ocean Mapping Group is tasked with the processing of the collected data and this thesis will focus on this aspect. Through an automated approach the data has been processed with the end-product being a final depth measurement with the associated uncertainty. The software is Python based and has been broken down into several modules to complete the task at hand. Utilizing specialized hydrographic equipment, designed to be low-cost and simple to operate, participating communities in the Canadian Arctic have been given the opportunity to collect bathymetric data while traversing their local waterways. As the pilot phase of the project is done, this thesis delves into the steps taken to fulfill the processing goals. The primary motivation surrounds how the processing workflow was completed through automation while mitigating errors and achieving transparency in the uncertainty assessment in the crowd-sourced bathymetric (CSB) data. Particular emphasis is placed upon the issues of collecting valuable hydrographic data from the Arctic with analysis of different methods to process the data with efficiency in mind. These challenges include obtaining a reliable GNSS signal through post-processing, qualification of the GNSS data for vertical reference, utilizing the HYCOM hydrodynamic model to collect sound velocity profiles and the identification and quantification of uncertainty as part of the Total Propagated Uncertainty (TPU) model. Several case study type examples are given where an investigation is conducted using processed collected and/or model data. Discussions surround the results of multi-constellation vs. single-constellation GNSS in the Arctic and the effects on the qualification rate for use as vertical referencing. Similarly, work towards comparing the model used to collect SVP data with equivalent real-world data collected by the Canadian Coast Guard is discussed. Finally, uncertainty has been quantified and assessed for the collected data and the results of the uncertainty assessment are provided using CHS/IHO survey standards as a benchmark. | |
| dc.description.copyright | ©Khaleel Arfeen, 2020 | |
| dc.format | text/xml | |
| dc.format.extent | xii, 12 pages | |
| dc.format.medium | electronic | |
| dc.identifier.uri | https://unbscholar.lib.unb.ca/handle/1882/13751 | |
| dc.language.iso | en_CA | |
| dc.publisher | University of New Brunswick | |
| dc.rights | http://purl.org/coar/access_right/c_abf2 | |
| dc.subject.discipline | Geodesy and Geomatics Engineering | |
| dc.title | Automatic processing of Arctic crowd-sourced hydrographic data while improving bathymetric accuracy and uncertainty assessment | |
| dc.type | master thesis | |
| thesis.degree.discipline | Geodesy and Geomatics Engineering | |
| thesis.degree.fullname | Master of Science in Engineering | |
| thesis.degree.grantor | University of New Brunswick | |
| thesis.degree.level | masters | |
| thesis.degree.name | M.Sc.E. |
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