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Monitoring algal blooms in drinking water reservoirs using the Landsat-8 Operational Land Imager
Citation:
Keith, D., J. Rover, J. Green, B. Zalewsky, M. Charpentier, G. Thursby, AND J. Bishop. Monitoring algal blooms in drinking water reservoirs using the Landsat-8 Operational Land Imager. INTERNATIONAL JOURNAL OF REMOTE SENSING. Taylor & Francis, Inc., Philadelphia, PA, 39(9):2818-2846, (2018).
Impact/Purpose:
One objective of our work is to test and develop algorithms using spectral bands specific to the European Space Agency Sentinel 2 OCLI and US Geological Survey Landsat-8 Operational Land Imager (OLI) sensors. These sensors are optimally oriented at phytoplankton pigment absorption features to estimate the presence and abundance of chlorophyll a (chl a). Chlorophyll a concentrations are used as a proxy for phytoplankton biomass, as an indicator of increased anthropogenic nutrient stress and as a measure of nuisance algal blooms. In this study, we have successfully identified a model for estimating chlorophyll a concentrations using three of the eleven spectral bands of the OLI. We used this approach to forecast the seasonal spatial and temporal variability of chl a for Jordan Lake, North Carolina (a recreational and drinking water source to the Raleigh/Durham metro areas in piedmont North Carolina) and provide a snapshot (1 day) of chl a variability for nine small reservoirs that supply drinking water to communities in southeastern Rhode Island. The impact of this outcome is that we have shown that OLI data, which are collected for the entire continental US at 30 m spatial resolution, can be used to provide information nationally for estimating water quality for freshwater ponds, lakes and drinking water reservoirs at the spatial scales required for monitoring by environmental agencies and managers. When integrated with long established Landsat land cover datasets, opportunities are created to temporally examine historical changes in land cover/land use with coincident impacts on freshwater systems and forecast surface water quality in response to land use at spatial scales previously not available
Description:
In this study, we demonstrated that the Landsat-8 Operational Land Imager (OLI) sensor is a powerful tool that can provide periodic and system-wide information on the condition of drinking water reservoirs. The OLI is a multispectral radiometer (30 m spatial resolution) that allows ecosystem observations at spatial and temporal scales that allow the environmental community and water managers another means to monitor changes in water quality not feasible with field-based monitoring. Using the provisional Land Surface Reflectance product and field-collected chlorophyll-a (chl-a) concentrations from drinking water monitoring programs in North Carolina and Rhode Island, we compared five established approaches for estimating chl-a concentrations using spectral data. We found that using the three band reflectance approach with a combination of OLI spectral bands 1, 3, and 5 produced the most promising results for accurately estimating chl-a concentrations in lakes (R2 value of 0.66; root mean square error value of 8.9 µg l−1). Using this model, we forecast the spatial and temporal variability of chl-a for Jordan Lake, a recreational and drinking water source in piedmont North Carolina and several small ponds that supply drinking water in southeastern Rhode Island.
