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dc.contributor.authorHudson, Rebecca
dc.contributor.otherQueen's University (Kingston, Ont.). Theses (Queen's University (Kingston, Ont.))en
dc.date.accessioned2019-01-10T18:35:01Z
dc.date.available2019-01-10T18:35:01Z
dc.identifier.urihttp://hdl.handle.net/1974/25918
dc.description.abstractUnforeseen ground conditions can have significant impact on the success of any geological project. Therefore, geological site investigation is an essential component of geological field campaigns. Remote sensing techniques have been used extensively for many geological applications, however, their incorporation into geological site investigations have been slow. A framework is required to demonstrate how remote sensing techniques can be incorporated into conventional site investigation practices. Thus, it is the objective of this thesis to assess the contributions of remote sensing datasets and show how to seamlessly integrate these datasets into practical workflows to compliment conventional methods. Case studies using three remote sensing datasets operating at different spatial scales, close-range laser scanning, airborne optical/LiDAR and Synthetic Aperture Radar (SAR), were used. A literature review revealed that close-range 3D laser scanning was a useful tool for the morphological study of geological hand-samples and had been widely applied in the fields of paleontology, rock mechanics and sedimentology. A case study was completed to digitally calculate the grain-size distribution of ore minerals using laser scanning, which produced comparable results to those generated using a scanning electron microscope, the current state-of-practice. The utility of LiDAR reflectance data for improved shadow detection in multispectral images was explored. When combined with the spectral bands as input into shadow classification using Support Vector Machines (SVM), the performance of the classification was improved to greater than 95%. To mitigate the effects of these shadows, four methods of colour correction were tested of which Reinhard’s colour transform performed the best. Shadow mitigation substantially improved subsequent vegetation mapping using the Normalized Difference Vegetation Index (NDVI). It was investigated whether SAR backscatter intensity data could be applied to monitoring temporal changes in northern Canada. Results showed that this data was suitable for monitoring ice cover seasonality, seasonal flooding and seasonal vegetation growth. These case studies make evident that remote sensing datasets can be used to enhance geological site investigation. Creation of workflows, in which suitable remote sensing techniques are integrated, for each site investigation application is recommended as future work.en_US
dc.language.isoenen_US
dc.relation.ispartofseriesCanadian thesesen
dc.rightsQueen's University's Thesis/Dissertation Non-Exclusive License for Deposit to QSpace and Library and Archives Canada*
dc.rightsProQuest PhD and Master's Theses International Dissemination Agreement*
dc.rightsIntellectual Property Guidelines at Queen's University*
dc.rightsCopying and Preserving Your Thesis*
dc.rightsThis publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.*
dc.rightsCC0 1.0 Universal*
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/*
dc.subjectSite Investigationen_US
dc.subjectRemote Sensingen_US
dc.subjectSynthetic Aperture Radaren_US
dc.subjectShadow Correctionen_US
dc.subjectGeologyen_US
dc.subject3D Laser Scanningen_US
dc.titleAssessing the Contributions of Remote Sensing Data in Geological Site Investigationsen_US
dc.typeThesisen
dc.description.degreeMaster of Applied Scienceen_US
dc.contributor.supervisorFotopoulos, Georgia
dc.contributor.departmentGeological Sciences and Geological Engineeringen_US


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Queen's University's Thesis/Dissertation Non-Exclusive License for Deposit to QSpace and Library and Archives Canada
Except where otherwise noted, this item's license is described as Queen's University's Thesis/Dissertation Non-Exclusive License for Deposit to QSpace and Library and Archives Canada