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Developing Beach Litter Monitoring System Based on Reflectance Characteristics and its Abundance
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Researchers developed a beach litter monitoring system using optical reflectance characteristics of plastic debris, training a remote sensing model to detect and classify litter items on sandy beach surfaces. The system demonstrated accurate detection of common plastic litter types and offers a scalable, automated alternative to manual beach surveys.
Marine litter is a major global problem; it originates on land and enters the ocean via rivers, coastal erosion, and extreme events. Over time, marine litter collects in coastal areas. As a result, research on litter dispersal and buildup is critical for successful coastal area management. Addressing the knowledge gap is critical for establishing successful solutions to fight that problem. In recent years, a variety of remote sensing techniques have been used to better understand litter abundance, distribution patterns, and dynamics in marine and coastal ecosystems. Marine litter detection and quantification are carried out using aircraft-based imaging systems, satellite images, and Unmanned Aerial Vehicles (UAVs). The purpose of this study was to create a beach litter monitoring system or technical reference using a small UAV and Geographic Information System (GIS), with the test location at Batu Belig Beach, Badung Regency, Bali, Indonesia. The box-plot approach was used to determine the reflectance threshold on the orthophoto. The GIS is used to determine regions with and without litter based on the set threshold values. To verify the model, Slovin's Formula was used to collect the sample, with a confusion matrix indicating an accuracy of 80%. This monitoring system provides a simple approach for identifying and measuring litter, even with only one person handling the entire operation. The outcomes of this analysis indicated that the majority of litter at the study location was made up of white plastic bags and Styrofoam. As a last step, consider portraying litter abundance as a percentage per square meter.
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