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Abundance of Plastic-Litter in Hyperspectral Imagery Using Spectral Unmixing in Coastal Environment
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This study tested whether hyperspectral satellite or aerial imagery combined with spectral unmixing algorithms can detect and map microplastic litter in coastal environments. Results showed the approach can identify plastic fragments smaller than a pixel by analyzing mixed spectral signals, offering a scalable monitoring tool. Remote sensing methods like this could greatly reduce the cost and labor of tracking coastal plastic pollution at large spatial scales.
Threatening marine life, coastal erosion, and human health, environmental pollution caused by plastic-litter is a global problem. The process of identifying and mapping plastic-litter is arduous, time-consuming, and expensive. Environmental parameters may be mapped and monitored with the use of hyperspectral remote sensing. Theoretical advances in recent years have shown that hyperspectral imagery may be used to detect and foresee plastics in a variety of geographical settings. Except a few efforts which have used simulated datasets for assessing spectral signatures, there are no studies which have attempted to assess plastic-litter abundance using remote sensing imagery. Plastic-litter are pieces of plastic that are less than five millimetres in size. Sub-pixel methods are required for processing and analysis. In this study, we explore the use of hyperspectral remote sensing to identify and map microplastic pollution. Since plastic-litter are uncommon in pixels, spectral unmixing is employed in conjunction with known endmembers. Mapping and detecting micro-plastic species have yielded varying results. Advancing crucial environmental monitoring application, results suggest that hyperspectral imaging is a potential data source for mapping the plastic litter.
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