Phytoplankton Phenology from space: Evaluation and Application of Lake Ecosystem Indicators
Palmer, Stephanie1; Hunter, Peter2; Lankester, Thomas3; Hubbard, Steven3; Tyler, Andrew2; Présing, Mátyás1; Lamb, Alistair3; Balzter, Heiko4; Tóth, Viktor1
1Balaton Limnological Institute, Hungarian Academy of Sciences Centre for Ecological Research, HUNGARY; 2Biological and Environmental Sciences, University of Stirling, UNITED KINGDOM; 3Astrium GEO-Information Services, UNITED KINGDOM; 4Centre for Landscape and Climate Research, University of Leicester, UNITED KINGDOM
Phenology, the timing of periodic natural phenomena, is increasingly recognized as ecologically important across diverse ecosystem types. Long term shifts in seasonality and in the timing of life stages of different species have been linked with the effects of climate change, but must first be extracted from superimposed short term variation and noise. Assessments of the phenology of terrestrial vegetation through remote sensing of vegetation greenness indices (e.g., NDVI, LAI, FAPAR, etc.) are now commonly employed to identify climate-related trends and land use/land cover changes. This makes use of the comprehensive coverage possible through satellite imagery, both spatially and temporally.
The application of remote sensing to further our understanding of lake and coastal marine ecosystems has also been demonstrated and has greatly advanced in recent years, particularly with regards to the quantification of the pigment chlorophyll-a (chl-a) as a proxy for phytoplankton biomass. Phytoplankton phenology has recently begun to be assessed through satellite remote sensing of chl-ain marine ecosystems though remains to be more fully exploited, especially in the context of lake systems. A number of features have been identified as important ecological indicators, namely phytoplankton bloom onset timing and rate, duration, spatial extent, maximum chl-a concentration, and bloom and total annual productivity.
The feasibility of identification, quantification and interpretation of chl-a seasonality in an inland freshwater context are undertaken as part of this study. The full MERIS time series of Lake Balaton has been subset and processed using the Phenology And Vegetation Earth Observation Service (PHAVEOS). Validation of chl-a retrieval is carried out, including the comparison of several potential retrieval algorithms. This is followed by the extraction and analysis of chl-a phenological features. Integration of ancillary data such as lake surface water temperature, meteorological conditions, yearly ice cover parameters and lake bathymetry will follow in an attempt to draw out any covariation or causal relationships so as to improve our overall understanding of the system functioning of Lake Balaton, with satellite derived information at the core.