This thesis explores the use of remote sensing to measure the phytoplankton biomass of Lake Balaton, Hungary via the proxy pigment, chlorophyll-a (chl-a). Several scales of spatial and temporal variability are considered, using a ten year time series of Medium Resolution Imaging Spectrometer (MERIS) satellite imagery, ship-mounted Light Detection and Ranging (LiDAR), and water sampling and laboratory measurements from punctual and ongoing campaigns. Existing remote sensing methods are adapted to Lake Balaton for the first time, and novel directions are demonstrated which may be applied to other lakes in the future. Several chl-a retrieval algorithms applied to archive MERIS data are calibrated and validated using an extensive dataset of coinciding in situ measurements and results from each are compared. The application of two atmospheric correction algorithms is also validated and their influence on chl-a retrieval is considered in comparison with the use of un- atmospherically corrected, top-of-Atmosphere (TOA) data. The fluorescence line height (FLH) algorithm applied to TOA MERIS data is found to accurately and robustly retrieve Lake Balaton chl-a (R2 = 0.87; RMSE = 4.19 mg m-3), particularly during high biomass bloom events (chl-a ≥ 10 mg m-3). This algorithm is then applied to the full MERIS archive (2002-2012), resulting chl-a time series are smoothed at the pixel level, and phytoplankton phenology metrics are extracted and mapped. Phenology metric mapping in lakes using MERIS remote sensing is demonstrated and significant spatiotemporal variability in bloom metrics is apparent. Laboratory tank and in situ ship-mounted Ultraviolet Fluorescence LiDAR (UFL) measurements indicate another novel direction for lake remote sensing. Chl-a, as well as total suspended matter (TSM) and coloured dissolved organic matter (CDOM), were measured and cyanobacteria was distinguished from chlorophyta via fluorescence emission spectra. The feasibility of retrieving accurate and quantitative information on Lake Balaton phytoplankton biomass dynamics through the use of remote sensing techniques is confirmed, and the resulting added value for both science and management is highlighted.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:657551 |
| Date | January 2015 |
| Creators | Palmer, Stephanie Catherine Jane |
| Contributors | Balzter, Heiko |
| Publisher | University of Leicester |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://hdl.handle.net/2381/32360 |
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