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COUPLING HYPERSPECTRAL REMOTE SENSING WITH FIELD SPECTROMETRY TO MONITOR INLAND WATER QUALITY PARAMETERS
Citation:
Shafique, N A., F A. Fulk, S M. Cormier, AND B C. Autrey. COUPLING HYPERSPECTRAL REMOTE SENSING WITH FIELD SPECTROMETRY TO MONITOR INLAND WATER QUALITY PARAMETERS. Presented at AVIRIS Earth Science and Applications Workshop, Pasadena, CA, March 5-8, 2002.
Impact/Purpose:
The purpose of this research project is to provide methods, tools and guidance to Regions, States and Tribes to support the TMDL program. This research will investigate new measurement methods and models to link stressors to biological responses and will use existing data and knowledge to develop strategies to determine the causes of biological impairment in rivers and streams. Research will be performed across multiple spatial scales, site, subwatershed, watershed, basin, ecoregion and regional/state.
Description:
Visible to near-infrared, airborne hyperspectral data were successfully used to estimate water quality parameters such as chlorophyll a, turbidity and total phosphorus from the Great Miami River, Ohio. During the summer of 1999, spectral data were collected with a hand-held field spectroradiometer and airborne hyperspectral sensors. Approximately 80 km of the Great Miami River were acquired during a flyover with a Compact Airborne Spectrogrpahic Imager to cover the river and urban/industrial influences around the city of Dayton, Ohio. Instream measurements of water quality data such as turbidity levels, dissolved oxygen concentrations, and Secchi-disk depth were taken on the same day as the flyover. Similarly, water samples were collected for laboratory measurements of chlorophyll a and total phosphorus concentrations in the river. Correlations between water quality parameters and one or a combination of wavebands from the field spectrometry dataset were determined. Based on the selected wavebands, semi-empirical models have been developed for chlorophyll a, turbidity and total phosphorus. With the help of these models, maps of the spatial distribution of these water quality parameters were created from the hyperspectral images of the river. These maps could aid in the development and implementation of total maximum daily load regulations of certain water quality parameters, and identify possible causes of algal blooms in surface waters.