Assessing the Performance of Multispectral Sensors Mounted on Unmanned Aerial Vehicle for the Prediction of Soil Organic Carbon Levels at Field-Scale

dc.contributor.advisorBerg, Aaron
dc.contributor.authorMarch, Michael
dc.date.accessioned2020-01-06T15:35:23Z
dc.date.copyright2019-12
dc.date.created2019-12-11
dc.date.issued2020-01-06
dc.degree.departmentDepartment of Geography, Environment and Geomaticsen_US
dc.degree.grantorUniversity of Guelphen_US
dc.degree.nameMaster of Scienceen_US
dc.degree.programmeGeographyen_US
dc.description.abstractThe quantification of soil organic carbon (SOC) is critical for sustainable agricultural production. Conventional field measurements for assessing SOC content are time-consuming, costly and require large soil sampling efforts. The remote monitoring of SOC using unmanned aerial vehicles (UAVs) possesses the capability to be faster and more economically advantageous when compared to conventional soil sampling methods. This research sought to examine the potential of UAV-mounted multispectral (400-800nm) sensors for SOC prediction at the sub-field scale. To do so, UAV-based imagery was acquired over agricultural fields under bare soil conditions. A total of 806 georeferenced soil samples were collected at 20m intervals for each study site. We used multivariate regression analysis to assess the relationship between SOC and reflectance. The R2 and RMSE were calculated between estimated and observed SOC. Laboratory and UAV reflectance were combined to explore the potential of transferrable models that could estimate SOC across various platforms.en_US
dc.description.embargo2020-12-11
dc.identifier.urihttp://hdl.handle.net/10214/17716
dc.language.isoenen_US
dc.publisherUniversity of Guelphen_US
dc.rights.licenseAll items in the Atrium are protected by copyright with all rights reserved unless otherwise indicated.
dc.subjectmultispectral sensorsen_US
dc.subjectunmanned aerial vehicleen_US
dc.subjectsoil organic carbon levelsen_US
dc.titleAssessing the Performance of Multispectral Sensors Mounted on Unmanned Aerial Vehicle for the Prediction of Soil Organic Carbon Levels at Field-Scaleen_US
dc.typeThesisen_US

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