RiSIM: River surface image monitoring software for quantifying floating macroplastic transport

Abstract

Reliable and continuous plastic monitoring in rivers is essential for quantifying plastic flux and guiding mitigation efforts. One effective strategy for observing floating plastic transport is image-based monitoring using deep learning models. We developed river surface image monitoring software (RiSIM) to quantify floating macroplastic transport through three core processes: (1) a template matching algorithm, which identifies matching areas in a frame that resemble a template given in the previous frame; (2) deep learning models for plastic detection, classification, and object tracking; and (3) the evaluation of plastic transport rate in terms of both quantity and mass. The RiSIM-derived plastic transport rates were validated through a mark-release-recapture experiment and in-situ visual observation under both non-flood and flood conditions. The temporal variability and composition of the plastic transport rate in terms of quantity and mass were in good agreement with the ground truth data (r = 0.91 and 0.80, respectively). And also, it remained valuable for capturing the temporal variability in plastic transport rate (r = 0.87) via the comparison with in-situ visual observation, indicating that the RiSIM is valuable for assessing the increase in plastic transport rate due to a flood event. In fact, we found a significant relationship (r² = 0.36 for quantity; r² = 0.27 for mass) between daily-mean plastic transport rates and river discharge during flood events over four months. Accordingly, the RiSIM, as a near-field remote sensing technology, is a powerful tool for quantifying plastic transport and managing mis-managed plastic waste in river environments.