MRRI, Ball State University and Rutgers University-Newark
The current wetland monitoring techniques used in the Meadowlands are labor intensive, time consuming and subjective, hence there is a need for developing cost effective methodologies for accurately determining the health and extent of the remaining open areas, specifically the spatial distribution of Phragmites and its associated mixtures types at the landscape level.
To design and implement a sustainable wetland monitoring and assessment program based on high resolution remote sensing that can be used in the Meadowlands and adopted by other agencies responsible for coastal wetland integrity and reporting.
Use both systems (Hyperspectral and LIDAR) to build a model that will link image values from vegetation types and texture and height to range values of sulfide concentration, salinity and redox-potential in the field. After the model is built and tested, only hyperspectral images will be required for the long term monitoring and assessment.
Sediment chemistry vs. plant community
Image data vs. sediment chemistry
Vegetation classification of hyperspectral imagery