Aquatic Remote Sensing

Over the years, our team developed expertise in the area of aquatic remote sensing, specifically in limnologic (inland or fresh water) and coastal remote sensing. Together with scientists from SUNY – College of Environmental Science and Forestry, SUNY- Univ. at Buffalo, SUNY Brockport, Univ. of Vermont, and Univ. of Tennessee, we explored the utility of chlorophyll extraction techniques to map the spatial and temporal variations in algal blooms in Lake Erie and Lake Ontario with predictions from a hydrodynamic and particle tracking model to determine transport pathways. Using funding from the Kuwait Institute for Scientific Research (KISR), we developed statistical models to identify the factors that contribute the most to the propagation of algal blooms in the Kuwait Bay and its surroundings. These include multivariate regression, hybrid multivariate regression, artificial neural network (ANN), and hybrid artificial neural network model. Currently, we are further developing our statistical modelling expertise and applying these methodologies over the coastal areas of Charlotte County in southwest Florida.