In this thesis, optical properties of phytoplankton assemblages are investigated to identify the relationship between Chlorophyll-a spectral signatures and the assemblage abundance. A new algorithm is developed to determine Chlorophyll-a concentrations from three types of waters, including eukaryote dominant waters, cyanobacteria dominant waters and waters that have a mixture of eukaryotes and cyanobacteria; with a flag being proposed for mapping of spatial distributions of surface scums. The result of the algorithm calibration indicates that 79% of the variance within the near concurrent in situ data is explained by the new algorithm, with the MAPE of 16.9%. The validation result shows that the RMSE and MAPE are 0.24 and 16.5%, respectively, with R2=0.69. Regional and global application examples are given to demonstrate the general applicability. This algorithm is applied to a decade of MERIS observations to retrieve phytoplankton bloom dynamics from the Baltic Sea between 2002 and 2011, which are then used for the investigation of ecological responses to the environmental change. The result indicates that phytoplankton blooms have seasonal cycles, alongside high the interannual variability. In addition, a significant increasing spring bloom trend is detected in the Baltic and Bothnian Sea, whereas a decreasing trend is observed in the Baltic Sea during summers. Regarding the ecological responses, the Baltic Proper spring bloom intensities are positively correlated with February Nitrate and Nitrate+Phosphate concentrations, and the correlation with surface layer water temperatures are evident. The summer bloom intensities show strong positive correlations with both spring excess Phosphate and June Phosphate concentrations. Additionally, the summer bloom intensities are correlated with July-August water temperatures, Photosynthetic Active Radiation and wind stress in the Baltic Proper and Gulf of Finland, but the relationship is not detected in the Bothnian Sea. These findings enabled the models to be developed to predict summer bloom intensities.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:647261 |
| Date | January 2015 |
| Creators | Zhang, D. |
| Publisher | University College London (University of London) |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://discovery.ucl.ac.uk/1463324/ |
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