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Papers
20 resultsShowing papers similar to On advances, challenges and potentials of remote sensing image analysis in marine debris and suspected plastics monitoring
ClearAdvancing floating macroplastic detection from space using hyperspectral imagery
Researchers evaluated the use of hyperspectral satellite and airborne imagery to detect floating plastic debris in rivers and oceans, addressing major challenges related to plastic spectral properties in field conditions. Remote sensing tools for plastic detection are important for large-scale monitoring of the macro-scale plastic that eventually becomes microplastics.
Assessment of marine litter through remote sensing: recent approaches and future goals
This review classified remote sensing approaches for detecting marine litter — including satellite, aircraft, and drone platforms with optical, infrared, and radar sensors — finding that few studies had reached operational status and that detecting small or submerged litter remains a major technical challenge.
Concept for a hyperspectral remote sensing algorithm for floating marine macro plastics
Researchers developed a reflectance model for how sunlight interacts with floating plastic debris on the ocean surface, accounting for plastic color, transparency, and shape, as a foundational step toward a hyperspectral remote sensing algorithm capable of detecting marine macroplastics from aircraft or satellite.
Detection and Monitoring of Marine Pollution Using Remote Sensing Technologies
This review examined satellite remote sensing technologies for detecting and monitoring marine pollution including oil spills, HABs, plastic debris, and sediment plumes, covering different sensor types and their capabilities. The authors identify current limitations of resolution and revisit frequency as key barriers to routine remote monitoring of microplastic pollution.
Aerial Remote Sensing of Aquatic Microplastic Pollution: The State of the Science and How to Move It Forward
A systematic literature review of aerial remote sensing for aquatic microplastic detection identified three main approaches — spectral characteristics, floating debris imaging, and AI-based analysis — all still largely experimental rather than operational.
Satellite sensors as an emerging technique for monitoring macro- and microplastics in aquatic ecosystems
This review assessed the emerging use of satellite remote sensing technologies for monitoring macro- and microplastic pollution in aquatic ecosystems, evaluating current capabilities and limitations of different satellite sensors for detecting waterborne plastic debris.
Potential of Optical Spaceborne Sensors for the Differentiation of Plastics in the Environment
This study evaluated the potential of optical spaceborne sensors to differentiate plastic types in the environment, assessing whether satellite remote sensing can be used to map and monitor plastic pollution in terrestrial and aquatic ecosystems at scale.
Uncovering Plastic Litter Spectral Signatures: A Comparative Study of Hyperspectral Band Selection Algorithms
This paper is not primarily about microplastics; it focuses on hyperspectral band-selection algorithms to identify the optical spectral signatures of plastic litter under water, primarily as a remote-sensing detection methodology. While relevant to plastic pollution monitoring, it does not assess microplastic abundance, distribution, or ecological/health effects.
On the Potential for Optical Detection of Microplastics in the Ocean
This study examines the potential for optical methods to detect microplastics in ocean water at large spatial scales, noting that while optical detection is promising for overcoming the limitations of discrete water sampling, methods remain in early development and reference libraries of microplastic optical properties are sparse.
Measuring Marine Plastic Debris from Space: Initial Assessment of Observation Requirements
This paper assesses what satellite observation capabilities would be needed to meaningfully monitor marine plastic debris from space, outlining requirements for spatial resolution, spectral bands, and revisit frequency. Developing such a remote sensing capability could revolutionize global tracking of plastic pollution at scales not achievable through ship-based surveys.
Advancing Floating Macroplastic Detection from Space Using Experimental Hyperspectral Imagery
Researchers tested experimental hyperspectral airborne imagery to detect floating macroplastics in rivers and the ocean, demonstrating that combining spectral and spatial features improves detection accuracy over single-band approaches.
Hybrid Deep Learning Approach for Marine Debris Detection in Satellite Imagery Using UNet with ResNext50 Backbone
Despite its title referencing marine debris detection, this paper develops a deep learning computer vision model for identifying marine debris in satellite imagery using a UNet architecture with a ResNext50 backbone — not a study of microplastic pollution itself. It is a remote sensing and machine learning engineering paper, and while the technology could support large-scale ocean plastic monitoring, the paper does not directly examine microplastics or their health effects.
Finding Plastic Patches in Coastal Waters using Optical Satellite Data
Researchers demonstrated for the first time that floating macroplastic patches can be detected in optical data from the European Space Agency's Sentinel-2 satellites, validating detections against ground-truth observations and identifying characteristics that distinguish plastic from other floating material.
Abundance of Plastic-Litter in Hyperspectral Imagery Using Spectral Unmixing in Coastal Environment
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.
The supporting role of Artificial Intelligence and Machine/Deep Learning in monitoring the marine environment: a bibliometric analysis
This review examines the supporting role of artificial intelligence and machine learning in monitoring and managing plastic pollution, covering applications in remote sensing, image-based plastic detection, and predictive modeling of plastic fate. The authors identify deep learning for image classification and satellite-based detection as the most rapidly advancing AI applications in plastic pollution science.
Hyperspectral ultraviolet to shortwave infrared characteristics of marine-harvested, washed-ashore and virgin plastics
Researchers characterized the hyperspectral optical properties of marine-harvested plastic litter across ultraviolet to shortwave infrared wavelengths, generating spectral signatures needed to support remote sensing detection of floating plastic debris. The spectral library produced contributes to developing satellite and airborne monitoring systems for large-scale ocean plastic surveillance.
A Global Review of Progress in Remote Sensing and Monitoring of Marine Pollution
This review examines how remote sensing technology, including satellites and drones, is being used to monitor marine pollution such as oil spills, floating debris, and microplastics. While the technology works well for detecting large-scale pollution, methods for tracking microplastics in the ocean are still in early development. Better monitoring tools are needed to understand the full scope of marine microplastic pollution, which ultimately affects seafood safety and human health.
Hyperspectral remote sensing as an environmental plastic pollution detection approach to determine occurrence of microplastics in diverse environments
Researchers tested whether hyperspectral remote sensing technology could detect microplastics mixed into different environmental surfaces like soil, water, concrete, and vegetation. Using near-infrared and short-wave infrared imaging, they achieved over 90% accuracy in detecting and classifying six common plastic types at concentrations as low as 0.15%. The study suggests that remote sensing could become a practical, large-scale tool for monitoring microplastic pollution across diverse environments.
AI for Monitoring Ocean Plastic Pollution
This review assessed how artificial intelligence technologies—including satellite image analysis, computer vision, and machine learning—are being applied to monitor ocean plastic pollution. The authors found AI can dramatically expand spatial coverage and detection speed compared to traditional ship-based surveys, though ground-truth validation and data standardization remain challenges.
Remotely Sensing the Source and Transport of Marine Plastic Debris in Bay Islands of Honduras (Caribbean Sea)
Researchers used high-resolution Sentinel-2 satellite imagery over Bay Islands, Honduras (2014–2019) and found that patches of floating macroplastics are detectable from space, validating satellite detections against field surveys and demonstrating potential for large-scale marine plastic monitoring.