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Carbon Cycle & Ecosystems

Cyanobacterial blooms (CYBB) are one of the most important issues concerning environmental agencies, water authorities and public health organizations. Global warming-induced climate change has been considered to be a catalyst for global expansion of harmful CYBB, and this leads to critical considerations on the effect of rising temperature and increasing nutrient runoff on the occurrence of microbial agents, phytoplankton, and CYBB.

To demonstrate the efficiency of new generation satellite hyperspectral HyspIRI datasets in developing strategies for effectively addressing and managing CYBB as well as maintaining the ecological integrity and sustainability of inland drinking water bodies, this study proposes to build simulated HyspIRI datasets with EO-1 Hyperion images and EOS TERRA/ASTER thermal bands and apply them for mapping water temperature, nutrients (nitrite/nitrate, organic nitrogen, ortho-phosphate and organic phosphorus), chlorophyll a (Chl-a), and phycocyanin (PC) of three Central Indiana Reservoirs, correlate the spatial patterns of pigment concentrations, nutrients and temperature via regression analysis and build an early warning system for CYBB prediction through integration of remote sensing mapping with water quality modeling. We will address 1) for a given reservoir, what spectral parameters are more sensitive to Chl-a and PC concentration and what interfering parameters affect the performance of these spectral parameters, 2) for a given pigment, which mapping algorithm has good instrumental, temporal and spatial transferability, 3) what spectral parameters highly correlate to a nutrient constituent in drinking water and whether a correlation is causal; if not, what other water quality parameters are responsible for this correlation, 4) given the fact that temperature and nutrients are important factors for the occurrence of CYBB, whether high correlations can be observed among the spatial patterns of Chl-a, PC, nutrient constituents and temperature in these reservoirs, and 5) whether remote sensing mapping improves the parameterization of water quality models and thus their predictive power.

The result of this research will demonstrate whether HyspIRI data could contribute to the development of new algorithms for mapping Chl-, PC, nutrient constituent and temperature with the addition of thermal bands, the situations where HyspIRI images and corresponding algorithms can be applied and their limitations for mapping Chl-a, PC, nutrient constituents and temperature of inland drinking waters. The result will also help design spectral configuration of future sensors (i.e. HyspIRI) and provide the scientific basis for the new generation of water quality remote sensing. Integrating remote sensing with water quality modeling could be an effective operational tool for water quality mangers to forecast water quality and make science-based decisions on the breakout of CYBB and for posing health advisories or efficient algaecide treatments.

Project PI: Lin Li/Indiana University-Purdue University at Indianapolis

IUPUI Department of Earth Sciences 723 West Michigan Street, Room SL118 Indianapolis, Indiana 46202-5191

Phone: 317-274-0225

Fax: 317.274.7966

Email: ll3@iupui.edu

http://www.geology.iupui.edu/Faculty_Staff/LLi/

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Started: Aug 09, 2010

Last Activity: Dec 09, 2010

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