Investigation of epifauna coverage on seagrass blades using spatial and spectral analysis of hyperspectral images

Seagrasses are critical components of marine food webs, yet negatively impacted by excessive coverage of epiphyte (attached algae or plants) or epifauna (organisms or animals). Since excessive nutrients result in excessive epiphyte/epifauna growth, epiphyte/epifauna coverage on seagrasses may serve as an indicator of nutrient status of local marine systems. In this work, the hyperspectral images obtained from seagrass samples are investigated to quantify percent epifauna coverage. The N-FINDR algorithm is utilized for spectral analysis to identify endmembers. Each test spectrum is then correlated with endmembers for classification. The classification results (epifauna or not epifauna) are mapped on a 2D binary image. In spatial analysis, a single image frame from a series of hyperspectral image frames is selected and analyzed using a 2D image processing chain. We show that independent spectral and spatial analysis results produce comparable results.