Surveyance of the marine environment for beached plastic debris using a novel, hyperspectral shortwave infrared camera on an uncrewed aerial vehicle (UAV)
Plastic waste has been rapidly accumulating in the natural environment for decades since the commercial introduction of plastic in the 1950s as a disposable material. Millions of tonnes of durable, persistent plastic waste now contaminate global ecosystems, including rivers, urban beaches, remote islands, the deep sea, soils, mountains, and very small pieces (microplastics) have even been detected in the air. Such pervasive plastic debris is a deadly and common threat to wildlife due to ingestion and entanglement. Large debris are particularly problematic not only because of these direct consequences, but because they break down into smaller pieces in the environment. These smaller pieces are more easily ingested by a greater diversity of species and are more difficult to remediate. It is therefore sensible to focus prevention, remediation, and monitoring efforts on large, more easily observable debris. In the past five years, monitoring efforts have increasingly included the investigation of automated plastic detection via remote sensing technologies and techniques. This approach has the potential to reduce observer bias (associated with manual, visual surveys), and realise regular and widespread plastic pollution monitoring, with the ultimate goal of achieving environmentally relevant reductions in plastic production and pollution. This thesis contributes to these aims by i) investigating the performance of novel remote sensing methods for plastic detection, ii) developing and implementing a new plastic detection algorithm, and iii) evaluating for the first time, the impact of operational and environmental variables on these specific plastic detection methods. A large proportion of plastic litter detection investigations have implemented inexpensive and common digital cameras that operate in the visible light spectrum. This approach is often limited in capability/reliability by inherent ambiguity, resulting from two-dimensional imagery of materials of unverifiable chemical composition. This thesis investigates the detection of shoreline plastic litter with the aerial deployment of a specialised, hyperspectral camera that is sensitive to shortwave infrared (SWIR) light and that measures signal from more than ten times as many wavelengths as a conventional digital camera. The rationale for pursuing this research is further detailed in Chapter 1, as well as thesis structure and objectives. The chosen SWIR sensing technology was selected from numerous other sensor technologies introduced in Chapter 2, for its specific relevance to detecting plastic based on chemical composition. Chapter 3 details the functional principles of this technology, which sees widespread use in commercial recycling facilities to successfully sort large volumes of different types of plastic. The criteria for selecting the specific SWIR camera (“OCI-F SWIR” from BaySpec, San Jose, CA, USA) implemented in this work, and an initial evaluation of its capabilities under a controlled lab environment are also presented in Chapter 3, prior to assessing field performance in Chapters 4 and 5. Chapter 4 presents the development of a customised, software-based plastic detection algorithm, informed by results from Chapter 3 and by methods implemented in the literature. Spectral Correlation Mapping (SCM) was selected for its computational efficiency and documented improvement over detection with the commonly applied Spectral Angle Mapping (upon which SCM is based). This method achieved successful classification of plastic and non-plastic pixels within imagery of individual items of polyethylene (PE) and polypropylene (PP) – the most common polymers produced globally – placed on a sandy shoreline. The images were collected with a distance of approximately one metre between the camera and the plastic items to measure a strong signal from the plastic and to inform algorithm parameter selection before increasing image complexity and introducing environmental variability in Chapter 5. Imagery analysed in Chapter 5 was collected on different days to evaluate the impacts on plastic detection performance of: plastic type, cloud cover, beach substrate, plastic’s exposure to the environment (harvested from domestic and environmental sources), and the altitude of the camera above the plastic. The camera was first deployed on a “highline” apparatus and manually conveyed across a fixed transect, at a height of five metres above a variety of plastic items arranged on the beach below. The purpose of this approach was to ensure the collection of data under steady, regular movement, which might not be as easily achieved with an uncrewed aerial vehicle (UAV). SCM was not able to consistently classify plastic pixels in each transect as PE nor PP when SCM was applied in the same way it had been in Chapter 4 (to images recorded at a one metre distance, of individual plastic items). The mathematical factors contributing to this discrepancy were investigated and modifications were subsequently implemented to the application of SCM, to compare segmented spectra. An additional, new algorithm called Reflectance Range Analysis (RRA) was also developed and evaluated. The development of RRA was based on differences observed in the spectral characteristics of plastic items and substrate measured in the aerial imagery, and on recommendations in the literature to develop/implement algorithms that detect the intensity of plastic’s characteristic absorption. Where SCM classifies every image pixel based on spectral similarity to a selected reference, RRA reveals spectral differences between image pixels without requiring similarity to a reference. Chapter 6 concludes this work by summarising the key findings and demonstrating the place of this research in the evolution of plastic detection with SWIR remote sensing. Recommendations are made here for augmenting plastic detection capabilities through further study and improvements to survey and equipment design, as well as leveraging the strengths of multiple remote sensing technologies. The diversity of current, newly available, and upcoming sensor technologies offers numerous opportunities to measure, monitor, and collectively address the problem of human-driven plastic pollution.
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