Proof of concept for a new sensor to monitor marine litter from space.

Worldwide, governments are implementing strategies to combat marine litter. However, their effectiveness is largely unknown because we lack tools to systematically monitor marine litter over broad spatio-temporal scales. Metre-sized aggregations of floating debris generated by sea-surface convergence lines have been reported as a reliable target for detection from satellites. Yet, the usefulness of such ephemeral, scattered aggregations as proxy for sustained, large-scale monitoring of marine litter remains an open question for a dedicated Earth-Observation mission. Here, we track this proxy over a series of 300,000 satellite images of the entire Mediterranean Sea. The proxy is mainly related to recent inputs from land-based litter sources. Despite the limitations of in-orbit technology, satellite detections are sufficient to map hot-spots and capture trends, providing an unprecedented source-to-sink view of the marine litter phenomenon. Torrential rains largely control marine litter inputs, while coastal boundary currents and wind-driven surface sweep arise as key drivers for its distribution over the ocean. Satellite-based monitoring proves to be a real game changer for marine litter research and management. Furthermore, the development of an ad-hoc sensor can lower the minimum detectable concentration by one order of magnitude, ensuring operational monitoring, at least for seasonal-to-interannual variability in the mesoscale.

Increasing the Sentinel-2 potential for marine plastic litter monitoring through image fusion techniques.

Sentinel-2 (S2) images have been used in several projects to detect large accumulations of marine litter and plastic targets. Their limited spatial resolution though hinders the detection of relatively small floating accumulations of marine debris. Thus, this study aims at overcoming this limit through the exploration of fusion with very high-resolution WorldView-2/3 (WV-2/3) images. Various state-of-the-art approaches (component substitution, spectral unmixing, deep learning) were applied on data collected in synchronized acquisitions of plastic targets of various sizes and materials in seawater. The fused images were evaluated for spectral and spatial distortions, as well as their ability to spectrally discriminate plastics from water. Several WV-2/3 band combinations were investigated and five litter indexes were applied. Results showed that: a) the VNIR combination is the optimal one, b) the smallest observable plastic target is 0.6 × 0.6 m2 and c) SWIR bands are important for marine litter detection.

Concentration, anisotropic and apparent colour effects on optical reflectance properties of virgin and ocean-harvested plastics.

We present reflectance measurements collected from virgin and ocean-harvested plastics. Virgin plastics included high and low density polyethylene (HDPE, LDPE), polypropylene (PP) as well as polystyrene (PS). Ocean-harvested plastics were ropes, sheets, foam, pellets and fragmented items previously trawled from the North Pacific Garbage Patch. Nadir viewing angles and plastic pixel coverage were varied to advance our understanding of how reflectance shape and magnitude can be influenced by these parameters. We also investigated the effect of apparent colour of plastics on the measured reflectance from the ultraviolet (UV - 350 nm), visible, near to shortwave infrared (NIR, SWIR - 2500 nm). Statistical analyses indicated that the spectral reflectance of the plastics was significantly correlated to the percentage pixel coverage. There was no clear relationship between the reflectance observed and the viewing nadir angle but dampened materials seemed to be more isotropic (near-Lambertian) than their dry counterparts. A loss in reflectance was also determined between dry and wet plastics. Location of absorption features was not affected by the apparent colour of objects. In general, ocean-harvested plastics shared more identical absorption features (~960, 1215, 1440, 1732, 1920 nm) and had lower reflectance intensity compared to the virgin plastics (~980 nm). Prospects for satellite retrieval of plastic type and pixel plastic coverage are discussed based on Top-of-Atmosphere (TOA) signal simulated through radiative transfer computation using the documented plastic reflectances. Non-linear relationships between TOA reflectance and plastic coverage were observed depending on wavelength and plastic type. Most of the plastics analysed impact significantly the TOA signal but two plastic types did not produce strong signal at TOA (hard fragments, LDPE). Nevertheless, all plastic types produced detectable signals when observations were simulated within the sunglint direction. The measurements collected in this study are an extension to available high quality spectral reference libraries and can support further research in developing remote sensing algorithms for marine litter.

Bio-inspired grasp control in a robotic hand with massive sensorial input.

The capability of grasping and lifting an object in a suitable, stable and controlled way is an outstanding feature for a robot, and thus far, one of the major problems to be solved in robotics. No robotic tools able to perform an advanced control of the grasp as, for instance, the human hand does, have been demonstrated to date. Due to its capital importance in science and in many applications, namely from biomedics to manufacturing, the issue has been matter of deep scientific investigations in both the field of neurophysiology and robotics. While the former is contributing with a profound understanding of the dynamics of real-time control of the slippage and grasp force in the human hand, the latter tries more and more to reproduce, or take inspiration by, the nature's approach, by means of hardware and software technology. On this regard, one of the major constraints robotics has to overcome is the real-time processing of a large amounts of data generated by the tactile sensors while grasping, which poses serious problems to the available computational power. In this paper a bio-inspired approach to tactile data processing has been followed in order to design and test a hardware-software robotic architecture that works on the parallel processing of a large amount of tactile sensing signals. The working principle of the architecture bases on the cellular nonlinear/neural network (CNN) paradigm, while using both hand shape and spatial-temporal features obtained from an array of microfabricated force sensors, in order to control the sensory-motor coordination of the robotic system. Prototypical grasping tasks were selected to measure the system performances applied to a computer-interfaced robotic hand. Successful grasps of several objects, completely unknown to the robot, e.g. soft and deformable objects like plastic bottles, soft balls, and Japanese tofu, have been demonstrated.