Estimating phytoplankton size classes from their inherent optical properties

dc.contributor.advisorNicolls, Frederick
dc.contributor.authorBerliner, David Stephen
dc.date.accessioned2020-03-09T11:41:43Z
dc.date.available2020-03-09T11:41:43Z
dc.date.issued2019
dc.date.updated2020-03-09T07:37:25Z
dc.description.abstractPhytoplankton plays a massive role in the regulation of greenhouse gases, with different functional types affecting the carbon cycle differently. The most practical way of synoptically mapping the ocean’s phytoplankton communities is through remote sensing with the aid of ocean-optics algorithms. This thesis is a study of the relationships between the Inherent Optical Properties (IOPs) of the ocean and the physical constituents within it, with a special focus on deriving phytoplankton size classes. Three separate models were developed, each focusing on a different relationship between absorption and phytoplankton size classes, before being combined into a final ensemble model. It was shown that all of the developed models performed better than the baseline model, which only estimates the mean values per size class, and that the results of the final ensemble model is comparable to, and performs better than, most other published models on the NOMAD dataset.
dc.identifier.apacitationBerliner, D. S. (2019). <i>Estimating phytoplankton size classes from their inherent optical properties</i>. (). ,Engineering and the Built Environment ,Department of Electrical Engineering. Retrieved from http://hdl.handle.net/11427/31516en_ZA
dc.identifier.chicagocitationBerliner, David Stephen. <i>"Estimating phytoplankton size classes from their inherent optical properties."</i> ., ,Engineering and the Built Environment ,Department of Electrical Engineering, 2019. http://hdl.handle.net/11427/31516en_ZA
dc.identifier.citationBerliner, D.S. 2019. Estimating phytoplankton size classes from their inherent optical properties. . ,Engineering and the Built Environment ,Department of Electrical Engineering. http://hdl.handle.net/11427/31516en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Berliner, David Stephen AB - Phytoplankton plays a massive role in the regulation of greenhouse gases, with different functional types affecting the carbon cycle differently. The most practical way of synoptically mapping the ocean’s phytoplankton communities is through remote sensing with the aid of ocean-optics algorithms. This thesis is a study of the relationships between the Inherent Optical Properties (IOPs) of the ocean and the physical constituents within it, with a special focus on deriving phytoplankton size classes. Three separate models were developed, each focusing on a different relationship between absorption and phytoplankton size classes, before being combined into a final ensemble model. It was shown that all of the developed models performed better than the baseline model, which only estimates the mean values per size class, and that the results of the final ensemble model is comparable to, and performs better than, most other published models on the NOMAD dataset. DA - 2019 DB - OpenUCT DP - University of Cape Town KW - Electrical Engineering LK - https://open.uct.ac.za PY - 2019 T1 - Estimating phytoplankton size classes from their inherent optical properties TI - Estimating phytoplankton size classes from their inherent optical properties UR - http://hdl.handle.net/11427/31516 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/31516
dc.identifier.vancouvercitationBerliner DS. Estimating phytoplankton size classes from their inherent optical properties. []. ,Engineering and the Built Environment ,Department of Electrical Engineering, 2019 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/31516en_ZA
dc.language.rfc3066eng
dc.publisher.departmentDepartment of Electrical Engineering
dc.publisher.facultyFaculty of Engineering and the Built Environment
dc.subjectElectrical Engineering
dc.titleEstimating phytoplankton size classes from their inherent optical properties
dc.typeMaster Thesis
dc.type.qualificationlevelMasters
dc.type.qualificationnameMSc
Files
Original bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
thesis_ebe_2019_berliner_david_stephen.pdf
Size:
7.12 MB
Format:
Adobe Portable Document Format
Description:
License bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
license.txt
Size:
0 B
Format:
Item-specific license agreed upon to submission
Description:
Collections