Plastic debris has become an abundant pollutant in marine, coastal and riverine environments, posing a large threat to aquatic life. Effective measures to mitigate and prevent marine plastic pollution require a thorough understanding of its origin and eventual fate. Several models have estimated that land-based sources are the main source of marine plastic pollution, although field data to substantiate these estimates remain limited. Current methodologies to measure riverine plastic transport require the availability of infrastructure and accessible riverbanks, but, to obtain measurements on a higher spatial and temporal scale, new monitoring methods are required. This paper presents a new methodology for quantifying riverine plastic debris using Unmanned Aerial Vehicles (UAVs), including a first application on Klang River, Malaysia. Additional plastic measurements were done in parallel with the UAV-based approach to make comparisons between the two methods. The spatiotemporal distribution of the plastics obtained with both methods show similar patterns and variations. With this, we show that UAV-based monitoring methods are a promising alternative for currently available approaches for monitoring riverine plastic transport, especially in remote and inaccessible areas.
Despite recent advances in remote sensing of large accumulations of floating plastic debris, mainly in coastal regions, the quantification of individual macroplastic objects (>50 cm) remains challenging. Here, we have trained an object-detection algorithm by selecting and labeling footage of floating plastic debris recorded offshore with GPS-enabled action cameras aboard vessels of opportunity. Macroplastic numerical concentrations are estimated by combining the object detection solution with bulk processing of the optical data. Our results are consistent with macroplastic densities predicted by global plastic dispersal models, and reveal first insights into how camera recorded offshore macroplastic densities compare to micro- and mesoplastic concentrations collected with neuston trawls.
At present, the distribution of plastic debris in the ocean water column remains largely unknown. Such information, however, is required to assess the exposure of marine organisms to plastic pollution as well as to calculate the ocean plastic mass balance. Here, we provide water column profiles (0–300 m water depth) of plastic (0.05–5 cm in size) concentration and key planktonic species from the eastern North Atlantic Ocean. The amount of plastic decreases rapidly in the upper few meters, from ~ 1 item/m3 (~ 1000 µg/m3) at the sea surface to values of ~ 0.001–0.01 items/m3 (~ 0.1–10 µg/m3) at 300 m depth. Ratios of plastic to plankton varied between ~ 10–5 and 1 plastic particles per individual with highest ratios typically found in the surface waters. We further observed that pelagic ratios were generally higher in the water column below the subtropical gyre compared to those in more coastal ecosystems. Lastly, we show plastic to (non-gelatinous) plankton ratios could be as high as ~ 102–107 plastic particles per individual when considering reported concentrations of small microplastics < 100 μm. Plastic pollution in our oceans may therefore soon exceed estimated safe concentrations for many pelagic species.
Among the emerging applications of remote sensing technologies, the remote detection of plastic litter has observed successful applications in recent years. However, while the number of studies and datasets for spectral characterization of plastic is growing, few studies address plastic litter while being submerged in natural seawater in an outdoor context. This study aims to investigate the feasibility of hyperspectral characterization of submerged plastic litter in less-than-ideal conditions. We present a hyperspectral dataset of eight different polymers in field conditions, taken by an unmanned aerial vehicle (UAV) on different days in a three-week period. The measurements were carried out off the coast of Mytilene, Greece. The team collected the dataset using a Bayspec OCI-F push broom sensor from 25 m and 40 m height above the water. For a contextual background, the dataset also contains optical (RGB) high-resolution orthomosaics.
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