Retrieval of gelbstoff absorption coefficient in Pearl River estuary using remotely-sensed ocean color data

Gelbstoff is the main absorber in natural waters of UV and visible light, and has an increasing absorption with decreasing wavelength in the range between 700 and 350 nm. Gelbstoff is often an important source of interference in the determination of plant pigments (e.g., Chlorophyll a) in coastal waters using remotely- sensed ocean color data. The existence of gelbstoff will affect the transfer of radiance in the water column, and then affect the depth of euphotic layer and photosynthesis. Many biogeochemical processes in the ocean, including trace metal complexation, are influenced by the presence of gelbstoff. Accurate estimates of gelbstoff concentrations in the ocean are critical not only to sea-land interaction studies, but also to biogeochemical and ecological studies. In this study in-situ data of the absorption coefficient of gelbstoff (Ag) and remote sensing reflectance (Rrs) in the Pearl River estuary were collected at 36 stations from 2 cruises conducted in 25-26 January 2003 and 5-6 January 2004 respectively. The Pearl River Estuary is the largest estuarine system in the South China Sea (SCS) region with eight discharge mouths of the river system and dynamic water mixing processes. The in-situ remote sensing reflectance data were calculated into 6 bands responding to the wavelength ranges of SeaWiFS band 1 to band 6 (412 to 670 nm). The relationships of the measured Ag to 240 band combinations to measured Rrs were analyzed, and the band combination with highest correlation coefficients with Ag was used for algorithm development. A local algorithm for retrieval of gelbstoff absorption coefficient was developed and applied to estimate gelbstoff absorption coefficient from atmospheric corrected SeaWiFS data. The accuracy of remotely-sensed retrieval of gelbstoff was evaluated by comparison to the measured data. The spatial distribution of gelbstoff absorption coefficient in the Pearl River estuary waters was revealed by thematic images retrieved from SeaWiFS data. © 2005 IEEE

Development of Satellite-Assisted Forecasting System for Oyster Norovirus Outbreaks

Norovirus outbreaks can cause the closure of oyster harvesting waters and acute gastroenteritis in humans associated with consumption of contaminated raw oysters. The overall goal of this study was to develop a satellite-assisted forecasting system for oyster norovirus outbreaks. The forecasting system is comprised of three components: (1) satellite algorithms for retrieval of environmental variables, including salinity, temperature, and gage height, (2) an Artificial Neural Network (ANN) based model, called NORF model, for predicting relative risk levels of oyster norovirus outbreaks, and (3) a mapping method for visualizing spatial distributions of norovirus outbreak risks in oyster harvest areas along Louisiana coast. The new satellite algorithms, characterized with linear correlation coefficient ranging from 0.7898 to 0.9076, make it possible to produce spatially distributed daily data with a high resolution (1 kilometer) for salinity, temperature, and gage height in coastal waters. Findings from this study suggest that oyster norovirus outbreaks are predictable, and in Louisiana oyster harvest areas, the NORF model predicted historical outbreaks from 1994 - 2014 without any confirmed false positive or false negative predictions when the estimated relative risk level was \u3e 0.6, while no outbreak occurred when the risk level was \u3c 0.5. However, more outbreak data are needed to confirm the threshold for norovirus outbreaks. Gage height and temperature were the most important environmental predictors of oyster norovirus outbreaks while wind, rainfall, and salinity also predicted norovirus outbreaks. The ability to predict oyster norovirus outbreaks at their onset makes it possible to prevent or at least reduce the risk of norovirus outbreaks by closing potentially affected oyster beds. By combining the NORF model with the remote sensing algorithms created in this dissertation, it is possible to map oyster norovirus outbreak risks in all oyster growing waters and particularly in the areas without direct measurements of relevant environmental variables, greatly expanding the coverage and enhancing the effectiveness of oyster monitoring programs. The hot spot (risk) maps, constructed using the methods developed in this dissertation, make it possible for oyster monitoring programs to manage oyster harvest waters more efficiently by focusing on hot spot areas with limited resources