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dc.contributor.authorMayer, Caitlyn Coker
dc.date.accessioned2017-01-17T02:02:09Z
dc.date.available2017-01-17T02:02:09Z
dc.identifier.urihttp://hdl.handle.net/123456789/3482
dc.description.abstractThe Ashepoo, Combahee, Edisto (ACE) Basin in South Carolina is one of the largest undeveloped estuaries in the Southeastern United States. This system is monitored and protected by several government agencies, to ensure its health and preservation. However, as populations in surrounding cities rapidly expand and land is urbanized, the surrounding water systems may decline from influx of contaminants leading to hypoxia, fish kills and eutrophication. Conventional water quality monitoring are timely and costly. Satellite remote sensing methods are used globally to monitor water systems and can produce an instantaneous synopsis of color producing agents (CPAs) including Chlorophyll-α, suspended matter (TSM), and colored-dissolved organic matter by applying bio-optical models. However, the interference of multiple optically active constituents that characterize the water column pose a challenge in complex coastal environments in the southeastern United States. In this study, field, laboratory and historical Land Use Land Cover (LULC) data were collected for summers 2002, 2011, 2015 and 2016. Results indicate higher levels of chlorophyll, ranging from 2.94-12.19 µg/L, and TSM values from 60.4 to 155.2 mg/L between field seasons, with values increasing with time. Results from LULC change analyses indicated a 1.79% increase in developed land from 2001 to 2011, and overall percent change of 0.14%.The field data were combined with <i> in situ hyperspectral radiometric and Landsat OLI satellite data to develop a regionally tiered model that can predict CPA concentrations using traditional band ratio and multivariate approaches. Band ratio models for the hyperspectral dataset (R<sup>2</sup>=0.15 ; RMSE = 2.2 µg/L) and Landsat OLI dataset (R<sup>2</sup> = 0.16; RMSE = 2.1 µg/L) were not successful in predicting CPA variability. However, a stronger model was developed using a multivariate, partial least squares regression to identify wavelengths that are more sensitive to chlorophyll-α (R<sup>2</sup>= 0.49 ;RMSE = 1.8 µg/L) and TSM (R<sup>2</sup> = 0.40; RMSE = 12.9 mg/L). The imbrication of absorption and reflectance features characterizing sediments and algal species in ACE Basin waters make it difficult for remote sensors to distinguish variations among in situ concentrations. Results from this study provide a strong foundation for the future of water quality monitoring and protection of biodiversity in the ACE basin. These technologies provide insight to modeling and prediction methods of water quality and may be used by water managers, and coastal resource managers to respond to environmental concerns more efficiently in the ACE Basin.
dc.titleField Spectroscopy as a Tool for Enhancing Water Quality Monitoring in the ACE Basin, SC
dc.date.updated2017-01-17T02:02:09Z
dc.language.rfc3066en


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