Estimating the spatial and temporal variability of primary production from a combination of in situ and remote sensing data a southern Benguela case study

Doctoral Thesis


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University of Cape Town

The aim of this thesis is to produce fine resolution estimates of primary production in three-dimensional space at the temporal scale that these events develop. It is hypothesized that complex relationships among time sequences of physical and biological processes that influence primary production can be automatically discovered from archives of data. This study uses an archive of in situ ship-board data containing subsurface temperature and phytoplankton distribution profiles. Each profile is associated in time and space with satellite remotely-sensed wind, sea surface temperature and surface chlorophyll a data. The bottom depth, season and location of each profile are also recorded. The archive of depth profiles is simplified by mapping each profile onto one of twelve representative profile clusters obtained using the k-means clustering algorithm so that each cluster contains a set of similar profiles and their corresponding data. Relationships between remotely sensed surface features and chlorophyll a profiles are first obtained from a static Bayesian network using same day data. This is then taken further by analysing time-series of satellite data to predict likely temperature and chlorophyll a profiles for each pixel of a 4 km resolution satellite image.

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