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Distinguishing cyanobacteria from algae using bio-optical remote sensing
Includes bibliographical references.
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| Main Author: | |
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| Other Authors: | |
| Format: | Thesis |
| Language: | English |
| Published: |
Department of Oceanography
2014
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| _version_ | 1867613236585562112 |
|---|---|
| access_status_str | Open Access |
| author | Matthews, Mark William |
| author2 | Shillington, Frank |
| author_browse | Matthews, Mark William Shillington, Frank |
| author_facet | Shillington, Frank Matthews, Mark William |
| author_sort | Matthews, Mark William |
| collection | Thesis |
| description | Includes bibliographical references. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/9311 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:32:56.154Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2014 |
| publishDateRange | 2014 |
| publishDateSort | 2014 |
| publisher | Department of Oceanography |
| publisherStr | Department of Oceanography |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/9311 Distinguishing cyanobacteria from algae using bio-optical remote sensing Matthews, Mark William Shillington, Frank Bernard, Stewart Includes bibliographical references. 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. 2014-11-07T09:20:57Z 2014-11-07T09:20:57Z 2014 Doctoral Thesis Doctoral PhD http://hdl.handle.net/11427/9311 eng application/pdf Department of Oceanography Faculty of Science University of Cape Town |
| spellingShingle | Matthews, Mark William Distinguishing cyanobacteria from algae using bio-optical remote sensing |
| thesis_degree_str | Doctoral |
| title | Distinguishing cyanobacteria from algae using bio-optical remote sensing |
| title_full | Distinguishing cyanobacteria from algae using bio-optical remote sensing |
| title_fullStr | Distinguishing cyanobacteria from algae using bio-optical remote sensing |
| title_full_unstemmed | Distinguishing cyanobacteria from algae using bio-optical remote sensing |
| title_short | Distinguishing cyanobacteria from algae using bio-optical remote sensing |
| title_sort | distinguishing cyanobacteria from algae using bio optical remote sensing |
| url | http://hdl.handle.net/11427/9311 |
| work_keys_str_mv | AT matthewsmarkwilliam distinguishingcyanobacteriafromalgaeusingbioopticalremotesensing |