Images are analyzed using several algorithms integrated into the Environmental Visualization software such as Partial Unmixing, Minimum Noise Fraction Transformation, and Spectral Angle Mapper. Regression and accuracy analysis is performed using Neural Networks. The method will complement other airborne or satellite remote sensing technologies being developed to locate the origin of point and non point sources entering the upper Chesapeake Bay Watershed. Federal, State, and Local agencies, as well as Non-Profit organizations need accurate information for identifying impaired waters, prioritizing response actions, and developing long term restoration plans. This study shows a baseline approach that would allow agencies to update their water quality information, using satellite images, track changes and perform trend analysis on a continuous basis.
Remote Sensing Techniques to detect Surface Water Quality Constituents in Coastal and Inland Water Bodies from Point or Non Point Pollution Sources
This study demonstrates that point and non-point pollution sources in coastal and inland water bodies can be identified and monitored using remote sensing techniques so early corrective action can be take to prevent or minimize Harmful Algae Blooms (HAB’s). By tracking these changes in the remote sensing images could be very beneficial in establishing cleanup and restoration efforts for improving water quality on a watershed basis. Water quality collection protocol has its limitation because of poor accessibility to the sites. The proposed remote sensing techniques will validate, and in some cases may replace some conventional methods of water based hydrological monitoring and analysis.