The impact of human activity on coastal ecosystems is becoming more and more evident across the world. This is leading to a growing need to map, monitor and manage these regions in a sustainable manner.
This pilot study presents a novel mapping technique for shallow-water seafloor habitats: Underwater Hyperspectral Imaging (UHI) from an Unmanned Surface Vehicle (USV). The findings of the pilot study suggest that USV-based UHI may serve as a valuable technique for shallow-water habitat mapping in the future. Deploying an underwater hyperspectral imager on a USV allows the user to acquire high-resolution, georeferenced hyperspectral imagery from a seafloor area that would be difficult to map at the same spatial resolution using other platforms.
By converting the data to pseudo-reflectance and subsequently carrying out SVM classification, we were able to estimate the areal coverage of six spectral classes with an overall accuracy of ~90%. The classification results were achieved using simple means, which shows that USV-based UHI is a robust technique, capable of performing even when certain data elements are sub-optimal.