Is ocean reflectance acquired by ferry passengers robust for science applications?
| dc.contributor.author | Yang, Yuyan | |
| dc.contributor.supervisor | Cowen, Laura Louise Elizabeth | |
| dc.date.accessioned | 2017-12-22T21:16:26Z | |
| dc.date.available | 2017-12-22T21:16:26Z | |
| dc.date.copyright | 2017 | en_US |
| dc.date.issued | 2017-12-22 | |
| dc.degree.department | Department of Mathematics and Statistics | |
| dc.degree.level | Master of Science M.Sc. | en_US |
| dc.description.abstract | Monitoring the dynamics of the productivity of ocean water and how it affects fisheries is essential for management. It requires data on proper spatial/temporal scales, which can be provided by operational ocean colour satellites. However, accurate productivity data from ocean colour imagery is only possible with proper validation of, for instance, the atmospheric correction applied to the images. In situ water reflectance data is of great value due to the requirements for validation and it is traditionally measured with the Surface Acquisition System (SAS) solar tracker system. Recently, an application, 'HydroColor', was developed for mobile devices to acquire water reflectance data. We examine the accuracy of the water reflectance acquired by HydroColor with the help of trained and untrained citizens under different environmental conditions. We used water reflectance data acquired by SAS solar tracker and HydroColor onboard the BC ferry Queen of Oak Bay from July to September 2016. Monte Carlo permutation F-tests were used to assess whether the differences between measurements collected by SAS solar tracker and HydroColor with citizens were significant. Results showed that the HydroColor measurements collected by 447 citizens were accurate in red, green, and blue bands, as well as red/green and red/blue ratios under different environmental conditions. Piecewise models were developed for correcting HydroColor blue/green water reflectance ratios based on the SAS solar tracker measurements. In addition, we found that training and environmental conditions had impacts on the data quality. A trained citizen obtained higher quality HydroColor data especially under clear skies at noon run (12:50-2:30 pm). | en_US |
| dc.description.scholarlevel | Graduate | en_US |
| dc.identifier.uri | http://hdl.handle.net/1828/8919 | |
| dc.language | English | eng |
| dc.language.iso | en | en_US |
| dc.rights | Available to the World Wide Web | en_US |
| dc.subject | Citizen science | en_US |
| dc.subject | Above-water reflectance | en_US |
| dc.subject | Ocean productivity | en_US |
| dc.subject | Robust statistics | en_US |
| dc.title | Is ocean reflectance acquired by ferry passengers robust for science applications? | en_US |
| dc.type | Thesis | en_US |