Ice roughness estimation via remotely piloted aircraft and photogrammetry

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dc.contributor.authorEhrman, James
dc.contributor.examiningcommitteeAsadzadeh, Masoud (Civil Engineering) Shalaby, Ahmed (Civil Engineering)en_US
dc.contributor.supervisorClark, Shawn (Civil Engineering)en_US
dc.date.accessioned2020-11-17T15:27:04Z
dc.date.available2020-11-17T15:27:04Z
dc.date.copyright2020-11-16
dc.date.issued2020-11-13en_US
dc.date.submitted2020-11-13T23:44:05Zen_US
dc.date.submitted2020-11-16T19:24:31Zen_US
dc.degree.disciplineCivil Engineeringen_US
dc.degree.levelMaster of Science (M.Sc.)en_US
dc.description.abstractPhotogrammetry conducted with images obtained via Remotely Piloted Aircraft (RPA) has revolutionized the field of land surface monitoring. It is particularly useful where land surface data collection would otherwise be expensive or dangerous. The monitoring of fluvial ice covers can be time-intensive, dangerous, and costly. Fluvial ice roughness is a sensitive parameter in hydraulic models and is difficult to measure using traditional field methods. This research hypothesizes that the surface roughness of a fluvial ice cover is indicative of subsurface roughness. The hypothesis was tested through a comparison of ice roughness determined through inverse hydraulic modeling and ice roughness determined through statistical analysis of ice surfaces derived through RPA-photogrammetry. Hydraulic and topographic data were collected over two years of field research on the Dauphin River in Manitoba, Canada. Various statistical metrics were used to represent the roughness of the surfaces. Strong correlation was identified in the comparison of ice cover roughness determined through RPA-photogrammetry and roughness calculated via the Nezhikhovskiy equation, as well as ice thickness. Some correlation was observed between ice cover roughness observed through RPA-photogrammetry and ice roughness predicted through inverse hydraulic modeling. The inter-quartile range (IQR) was the most representative roughness metric. The maximum peak value performed better in some cases, but this metric would be heavily influenced by outliers, and was rejected as a representative metric. Three distinct forms of surface ice roughness were noted: rough, smooth, and ridged. Statistical properties of elevation data of fluvial ice covers were calculated, none were found to be normally distributed. K-means clustering analysis was used to group cover into two categories, which were interpreted as rough and smooth ice. The IQR of the rough and smooth categories were 0.07 - 0.12 m and 0.01 - 0.05 m, respectively. RPA-photogrammetry was concluded to be a suitable method for monitoring of fluvial ice covers, but more research is needed to confirm that surface ice cover roughness observed through this method is directly applicable to hydraulic modeling. Other applications of RPA-photogrammetry for the characterization of fluvial ice covers are proposed.en_US
dc.description.noteFebruary 2021en_US
dc.identifier.urihttp://hdl.handle.net/1993/35138
dc.language.isoengen_US
dc.rightsopen accessen_US
dc.subjectPhotogrammetryen_US
dc.subjectIce jamsen_US
dc.subjectHydraulic roughnessen_US
dc.subjectRemotely piloted aircraften_US
dc.subjectRiver iceen_US
dc.subjectIce roughnessen_US
dc.subjectHydraulic modelingen_US
dc.titleIce roughness estimation via remotely piloted aircraft and photogrammetryen_US
dc.typemaster thesisen_US
local.subject.manitobayesen_US
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