Toward Robust River Plastic Detection: Combining Lab and Field‐Based Hyperspectral Imagery

Abstract

Plastic pollution in aquatic ecosystems has increased dramatically in the last five decades, with strong impacts on human and aquatic life. Recent studies endorse the need for innovative approaches to monitor the presence, abundance, and types of plastic in these ecosystems. One approach gaining rapid traction is the use of multi‐ and hyperspectral cameras. However, most experiments using this approach were in controlled environments, making findings challenging to apply in natural environments. We present a method linking lab‐ and field‐based identification of macroplastics using hyperspectral data (1,150–1,675 nm). Experiments using riverbank‐harvested macroplastics were set up in a laboratory environment, and on the banks of the Rhine River. Representative pixel selections of eleven lab‐based images (n = 786,264 pixels) and two field‐based images (n = 40,289 pixels) were used to analyze the differences between these environments. Next, classifier algorithms such as support vector machines (SVM), spectral angle mapper (SAM) and spectral information divergence (SID) were applied, because of their robustness to varying light conditions and high accuracies in mapping spectral similarities. Our results showed that SAM classifiers are most robust in separating plastic pixels from background elements. By applying lab‐based data for plastic detection in field‐based images, user accuracies for plastics to up to 93.6% (n = 8,370 plastic pixels) were attained. This study provides key fundamental insights in linking lab‐based data to plastic detection in the field. With this paper we aim to contribute to the development of future spectral missions to detect and monitor plastic pollution in aquatic ecosystems.

A New Remote Hyperspectral Imaging System Embedded on an Unmanned Aquatic Drone for the Detection and Identification of Floating Plastic Litter Using Machine Learning

“…The high and rapidly increasing levels of plastic litter in aquatic environments represent a serious environmental problem at a global scale, negatively affecting aquatic life and biodiversity, ecosystems, livelihoods, fisheries, maritime transport, recreation, tourism, and economies. To address this problem, the research community is always looking for novel devices, tools, and methods to detect, identify, and quantify plastic litter more rapidly and efficiently [1][2][3][4]. Monitoring methods such as visual counting or sampling using nets are labor-intensive, whereas current remote observation (from spaceborne or airborne platforms) has some limitations in detecting and identifying plastic litter.…”

“…Monitoring methods such as visual counting or sampling using nets are labor-intensive, whereas current remote observation (from spaceborne or airborne platforms) has some limitations in detecting and identifying plastic litter. Therefore, it is necessary to develop an innovative remote sensing system able to automatically detect and identify plastic litter in order to study pollution sources properly, to improve the survey assessments, and to support the implementation of mitigation measures [1][2][3][4][5].…”