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Relating an archive of in situ vertical chlorophyll-a profiles to concurrent remotely sensed surface data

Includes bibliographical references (leaves 56-67).

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Bibliographic Details
Main Author: Williamson, Robert I
Other Authors: Field, John G
Format: Thesis
Language:English
Published: Department of Oceanography 2014
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access_status_str Open Access
author Williamson, Robert I
author2 Field, John G
author_browse Field, John G
Williamson, Robert I
author_facet Field, John G
Williamson, Robert I
author_sort Williamson, Robert I
collection Thesis
description Includes bibliographical references (leaves 56-67).
format Thesis
id oai:open.uct.ac.za:11427/6443
institution University of Cape Town (South Africa)
language eng
last_indexed 2026-06-10T12:31:26.417Z
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:11427/6443 Relating an archive of in situ vertical chlorophyll-a profiles to concurrent remotely sensed surface data Williamson, Robert I Field, John G Reason, Chris Applied Marine Science Includes bibliographical references (leaves 56-67). Knowledge of the vertical distribution of phytoplankton in the upper ocean is essential for accurate estimates of primary production. Satellite remote sensing has given scientists an unprecedented view of near-surface chlorophyll distribution and other surface conditions, including sea surface temperature and wind data, from regional to global scales but little information on the dynamics below the surface. As a result estimates of global production tend to use regional profile averages but these methods oversimplify the smaller scale dynamics, particularly in coastal regions where productivity is highly variable on time scales of weeks. A pilot study by computer science honours students in 2006 showed the viability of using a Dynamic Bayesian Network (DBN) in predicting a representative profile per pixel of a satellite map based on a database of time series satellite surface data. In this study, 5813 in situ profiles were obtained from the highly dynamic upwelling region around the southwestern coastline of southern Africa. The samples were collected between 1988 and 2006 between the coast and the continental slope. The region was divided into three sub-regions according to biophysical processes: the west Coast; the west Agulhas Bank; and the east Agulhas Bank. Of the 5813 profiles, 5557 were included in the sub-regions. Two consecutive processes were then applied to the profile database. First, the profiles were clustered using a k-means clustering program which produced 16 representative clusters. 2014-08-13T19:43:13Z 2014-08-13T19:43:13Z 2007 Master Thesis Masters MSc http://hdl.handle.net/11427/6443 eng application/pdf Department of Oceanography Faculty of Science University of Cape Town
spellingShingle Applied Marine Science
Williamson, Robert I
Relating an archive of in situ vertical chlorophyll-a profiles to concurrent remotely sensed surface data
thesis_degree_str Master's
title Relating an archive of in situ vertical chlorophyll-a profiles to concurrent remotely sensed surface data
title_full Relating an archive of in situ vertical chlorophyll-a profiles to concurrent remotely sensed surface data
title_fullStr Relating an archive of in situ vertical chlorophyll-a profiles to concurrent remotely sensed surface data
title_full_unstemmed Relating an archive of in situ vertical chlorophyll-a profiles to concurrent remotely sensed surface data
title_short Relating an archive of in situ vertical chlorophyll-a profiles to concurrent remotely sensed surface data
title_sort relating an archive of in situ vertical chlorophyll a profiles to concurrent remotely sensed surface data
topic Applied Marine Science
url http://hdl.handle.net/11427/6443
work_keys_str_mv AT williamsonroberti relatinganarchiveofinsituverticalchlorophyllaprofilestoconcurrentremotelysensedsurfacedata