Optimal algorithms for deriving estimates of phytoplankton biomass in lakes from LANDSAT satellite imagery
The frequency, intensity, and geographical distribution of harmful phytoplankton blooms are on the rise globally. There is a scientific need for estimates of historical and current phytoplankton data. This research develops mathematical algorithms for accurate assessment of surface chlorophyll-a (ch...
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| Format: | Text |
| Language: | English |
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Scholarship@Western
2019
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| Online Access: | https://ir.lib.uwo.ca/etd/6087 https://ir.lib.uwo.ca/context/etd/article/8212/viewcontent/auto_convert.pdf |
| Summary: | The frequency, intensity, and geographical distribution of harmful phytoplankton blooms are on the rise globally. There is a scientific need for estimates of historical and current phytoplankton data. This research develops mathematical algorithms for accurate assessment of surface chlorophyll-a (chl-a), a proxy for phytoplankton biomass, within freshwater lakes. Landsat satellite images (4-5 TM, 7 ETM and 8 OLI) were used to create predictive models (from 1984 to 2017) for seven ecoregions (ranging from the tropics to arctic). Correlation tests for 82 algorithms were conducted to establish the best fit models (linear, exponential, logarithmic, power) for chl-a and environmental parameters (true colour, TSS, and turbidity) that interfere with the chl-a assessment. Three band algorithms involving absorbent and reflective bands multiplied by the near infrared band using power regression provided predictive models across all ecoregions (R2 ranges from 0.40 – 0.81, p < 0.05). These optimized models provide accurate estimates of phytoplankton biomass that can be used to create a 30+-year time series of phytoplankton biomass as a basis for evaluating the effects of global scale changes on phytoplankton blooms. |
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