Distinguishing cyanobacteria from algae using bio-optical remote sensing

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dc.contributor.advisorShillington, Franken_ZA
dc.contributor.advisorBernard, Stewarten_ZA
dc.contributor.authorMatthews, Mark Williamen_ZA
dc.date.accessioned2014-11-07T09:20:57Z
dc.date.available2014-11-07T09:20:57Z
dc.date.issued2014en_ZA
dc.descriptionIncludes bibliographical references.en_ZA
dc.description.abstractThis study advances the use of remote sensing for eutrophication and cyanobacterial bloom detection in inland and near-coastal waters. The hypothesis that prokaryotic cyanobacteria can be systematically differentiated from algae (or eukaryotic species) on the basis of their distinctive bio-optical features is tested using a novel in situ bio-optical dataset and remotely sensed data from the Medium Resolution Imaging Spectrometer (MERIS). The in situ dataset was collected between 2010 and 201 2 from three optically-diverse South African inland waters. An empirical algorithm, called the maximum peak-height (MPH) algorithm, was developed for operational determinations of trophic status (chlorophyll-α), cyanobacterial blooms and surface scum from MERIS. The algorithm uses top-of-atmosphere data to avoid the large uncertainties associated with atmospherically corrected water leaving reflectance data in optically-complex and turbid waters. The detailed analysis of the variability of the optical properties of the three diverse reservoirs provides new knowledge of the inherent optical properties of South African inland waters which have previously not been described. The study also provides the first detailed investigation of the effects of intracellular gas vacuoles on the optics of phytoplankton using a two-layered sphere model. The results demonstrate how gas vacuoles impart distinctive bio-optical features to cyanobacteria and cause backscattering to be enhanced. An advanced inversion algorithm is developed for detecting phytoplankton assemblage type and size from water leaving reflectance data. The algorithm, based on a direct solution of the equation of radiative transfer using Ecolight-S radiative transfer model, successfully distinguishes between phytoplankton assemblages dominated by small-celled cyanobacteria and those dominated by large-celled dinoflagellate species. It also provides reliable estimates of phytoplankton biomass (chl-α), and the absorption coeficients of phytoplankton and combined non- phytoplankton particulate and dissolved matter. Finally, the application of the MPH algorithm to a time series of MERIS data from 2002 to 2012 in South Africa's 55 largest reservoirs is likely to be the most comprehensive assessment of eutrophication and cyanobacteria occurrence from earth observation data yet performed. The results confirm that widespread cyanobacterial blooms and eutrophication remain issues of critical concern for water quality in South Africa.en_ZA
dc.identifier.apacitationMatthews, M. W. (2014). <i>Distinguishing cyanobacteria from algae using bio-optical remote sensing</i>. (Thesis). University of Cape Town ,Faculty of Science ,Department of Oceanography. Retrieved from http://hdl.handle.net/11427/9311en_ZA
dc.identifier.chicagocitationMatthews, Mark William. <i>"Distinguishing cyanobacteria from algae using bio-optical remote sensing."</i> Thesis., University of Cape Town ,Faculty of Science ,Department of Oceanography, 2014. http://hdl.handle.net/11427/9311en_ZA
dc.identifier.citationMatthews, M. 2014. Distinguishing cyanobacteria from algae using bio-optical remote sensing. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Matthews, Mark William AB - This study advances the use of remote sensing for eutrophication and cyanobacterial bloom detection in inland and near-coastal waters. The hypothesis that prokaryotic cyanobacteria can be systematically differentiated from algae (or eukaryotic species) on the basis of their distinctive bio-optical features is tested using a novel in situ bio-optical dataset and remotely sensed data from the Medium Resolution Imaging Spectrometer (MERIS). The in situ dataset was collected between 2010 and 201 2 from three optically-diverse South African inland waters. An empirical algorithm, called the maximum peak-height (MPH) algorithm, was developed for operational determinations of trophic status (chlorophyll-α), cyanobacterial blooms and surface scum from MERIS. The algorithm uses top-of-atmosphere data to avoid the large uncertainties associated with atmospherically corrected water leaving reflectance data in optically-complex and turbid waters. The detailed analysis of the variability of the optical properties of the three diverse reservoirs provides new knowledge of the inherent optical properties of South African inland waters which have previously not been described. The study also provides the first detailed investigation of the effects of intracellular gas vacuoles on the optics of phytoplankton using a two-layered sphere model. The results demonstrate how gas vacuoles impart distinctive bio-optical features to cyanobacteria and cause backscattering to be enhanced. An advanced inversion algorithm is developed for detecting phytoplankton assemblage type and size from water leaving reflectance data. The algorithm, based on a direct solution of the equation of radiative transfer using Ecolight-S radiative transfer model, successfully distinguishes between phytoplankton assemblages dominated by small-celled cyanobacteria and those dominated by large-celled dinoflagellate species. It also provides reliable estimates of phytoplankton biomass (chl-α), and the absorption coeficients of phytoplankton and combined non- phytoplankton particulate and dissolved matter. Finally, the application of the MPH algorithm to a time series of MERIS data from 2002 to 2012 in South Africa's 55 largest reservoirs is likely to be the most comprehensive assessment of eutrophication and cyanobacteria occurrence from earth observation data yet performed. The results confirm that widespread cyanobacterial blooms and eutrophication remain issues of critical concern for water quality in South Africa. DA - 2014 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2014 T1 - Distinguishing cyanobacteria from algae using bio-optical remote sensing TI - Distinguishing cyanobacteria from algae using bio-optical remote sensing UR - http://hdl.handle.net/11427/9311 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/9311
dc.identifier.vancouvercitationMatthews MW. Distinguishing cyanobacteria from algae using bio-optical remote sensing. [Thesis]. University of Cape Town ,Faculty of Science ,Department of Oceanography, 2014 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/9311en_ZA
dc.language.isoengen_ZA
dc.publisher.departmentDepartment of Oceanographyen_ZA
dc.publisher.facultyFaculty of Scienceen_ZA
dc.publisher.institutionUniversity of Cape Town
dc.titleDistinguishing cyanobacteria from algae using bio-optical remote sensingen_ZA
dc.typeDoctoral Thesis
dc.type.qualificationlevelDoctoral
dc.type.qualificationnamePhDen_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceThesisen_ZA
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