ALBERT

Description: Highlights: • We present the first hyperspectral image data from the deep seafloor. • The data were acquired with a new UHI in 4200 m water depth. • Supervised classification is able to detect manganese nodules and fauna. • The UHI is a promising tool for high-resolution seafloor exploration and monitoring. Abstract: Hyperspectral seafloor surveys using airborne or spaceborne sensors are generally limited to shallow coastal areas, due to the requirement for target illumination by sunlight. Deeper marine environments devoid of sunlight cannot be imaged by conventional hyperspectral imagers. Instead, a close-range, sunlight-independent hyperspectral survey approach is required. In this study, we present the first hyperspectral image data from the deep seafloor. The data were acquired in approximately 4200 m water depth using a new Underwater Hyperspectral Imager (UHI) mounted on a remotely operated vehicle (ROV). UHI data were recorded for 112 spectral bands between 378 nm and 805 nm, with a high spectral (4 nm) and spatial resolution (1 mm per image pixel). The study area was located in a manganese nodule field in the Peru Basin (SE Pacific), close to the DISCOL (DISturbance and reCOLonization) experimental area. To test whether underwater hyperspectral imaging can be used for detection and mapping of mineral deposits in potential deep-sea mining areas, we compared two supervised classification methods, the Support Vector Machine (SVM) and the Spectral Angle Mapper (SAM). The results show that SVM is superior to SAM and is able to accurately detect nodule surfaces. The UHI therefore represents a promising tool for high-resolution seafloor exploration and characterisation prior to resource exploitation.

Description: Underwater hyperspectral imaging is a relatively new method for characterizing seafloor composition. To date, it has been deployed from moving underwater vehicles, such as remotely operated vehicles and autonomous underwater vehicles. While moving vehicles allow relatively rapid surveying of several 10-1000 m², they are subjected to short-term variations in vehicle attitude that often compromise image acquisition and quality. In this study, we tested a stationary platform that was landed on the seabed and used an underwater hyperspectral imager (UHI) on a vertical swinging bracket. The imaged seafloor areas have dimensions of 2.3 m x 1 m and are characterized by very stable UHI data of high spatial resolution. The study area was the Trans-Atlantic Geotraverse hydrothermal field at the Mid-Atlantic Ridge (26° N) in water depths of 3530-3660 m. UHI data were acquired a 12 stations on an active and an inactive hydrothermal sulfide mound. Based on supervised classification, 24 spectrally different seafloor materials were detected, including hydrothermal and non-hydrothermal materials, and benthic fauna. The results show that the UHI data are able to spectrally distinguish different types of surface materials and benthic fauna in hydrothermal areas, and may therefore represent a promising tool for high-resolution seafloor exploration in potential future deep-sea mining areas.