Real-Time Thickness Measurement of Marine Oil Spill by Fiber-Optic Surface Plasmon Resonance Sensors

Rapid detection of marine oil spills is becoming increasingly critical in the face of frequent marine oil spills. Oil slick thickness measurement is critical in the hazard assessment of such oil leaks. As surface plasmon resonance (SPR) sensors are sensitive to slight changes in refractive index, they can monitor offshore oil spills arising from significant differences in the refractive index between oil and water. This study presents a gold-film fiber-optic surface plasmon resonance (FOSPR) sensor prepared by polydopamine accelerated wet chemical plating for rapid and real-time measurement of oil slick thickness. We examined oil thickness detection at two interfaces, namely, water-oil and air-oil. Detection sensitivity of −1.373%/mm is obtained at the water-oil interface in the thickness range of 0–5 mm; detection sensitivity of −2.742%/mm is obtained at the air-oil interface in the thickness range of 0–10 mm. Temperature and salinity present negligible effects on the oil slick thickness measurement. The fabricated FOSPR sensor has the ability to detect the presence of oil as well as quantify the oil thickness. It has favorable repeatability and reusability, demonstrating the significant potential for use in the estimation of marine oil slick thickness.

Hyperspectral Remote Sensing Detection of Marine Oil Spills Using an Adaptive Long-Term Moment Estimation Optimizer

Marine oil spills can damage marine ecosystems, economic development, and human health. It is important to accurately identify the type of oil spills and detect the thickness of oil films on the sea surface to obtain the amount of oil spill for on-site emergency responses and scientific decision-making. Optical remote sensing is an important method for marine oil-spill detection and identification. In this study, hyperspectral images of five types of oil spills were obtained using unmanned aerial vehicles (UAV). To address the poor spectral separability between different types of light oils and weak spectral differences in heavy oils with different thicknesses, we propose the adaptive long-term moment estimation (ALTME) optimizer, which cumulatively learns the spectral characteristics and then builds a marine oil-spill detection model based on a one-dimensional convolutional neural network. The results of the detection experiment show that the ALTME optimizer can store in memory multiple batches of long-term oil-spill spectral information, accurately identify the type of oil spills, and detect different thicknesses of oil films. The overall detection accuracy is larger than 98.09%, and the Kappa coefficient is larger than 0.970. The F1-score for the recognition of light-oil types is larger than 0.971, and the F1-score for detecting films of heavy oils with different film thicknesses is larger than 0.980. The proposed optimizer also performs well on a public hyperspectral dataset. We further carried out a feasibility study on oil-spill detection using UAV thermal infrared remote sensing technology, and the results show its potential for oil-spill detection in strong sunlight.

Feature Merged Network for Oil Spill Detection using SAR Images

The frequency of marine oil spills has increased in recent years. The growing exploitation of marine oil and continuous increase in marine crude oil transportation has caused tremendous damage to the marine ecological environment. Using synthetic aperture radar (SAR) images to monitor marine oil spills can help control the spread of oil spill pollution over time and reduce the economic losses and environmental pollution caused by such spills. However, it is a significant challenge to distinguish between oil-spilled areas and oil-spill-like in SAR images. Semantic segmentation models based on deep learning have been used in this field to address this issue. In addition, this study is dedicated to improving the accuracy of the U-Shape Network (UNet) model in identifying oil spill areas and oil-spill-like areas and alleviating the overfitting problem of the model; a feature merge network (FMNet) is proposed for image segmentation. The global features of SAR image, which are high-frequency component in the frequency domain and represents the boundary between categories, are obtained by a threshold segmentation method. This can weaken the impact of spot noise in SAR image. Then high-dimensional features are extracted from the threshold segmentation results using convolution operation. These features are superimposed with to the down sampling and combined with the high-dimensional features of original image. The proposed model obtains more features, which allows the model to make more accurate decisions. The overall accuracy of the proposed method increased by 1.82% and reached 61.90% compared with the UNet. The recognition accuracy of oil spill areas and oil-spill-like areas increased by approximately 3% and reached 56.33%. The method proposed in this paper not only improves the recognition accuracy of the original model, but also alleviates the overfitting problem of the original model and provides a more effective monitoring method for marine oil spill monitoring. More importantly, the proposed method provides a design principle that opens up new development ideas for the optimization of other deep learning network models.

APPLICATION OF A GIS BASED APPROACH TO ASSESS THE ENVIRONMENTAL IMPACTS OF THE MV WAKASHIO OIL SPILL IN THE SOUTHEAST OF MAURITIUS

Marine oil spills are regarded as one of the most threatening environmental disasters that can have serious environmental and socio-economic impacts. For islands like Mauritius, such oil spills can have severe repercussions as island communities depend almost entirely on their coastal and marine resources. The MV Wakashio grounded on the coral reef on the south east coast of Mauritius on July 25th 2020, spilling 1000 tons of oil into its clear waters on August 06th 2020. It was the first time the island was faced with such a disaster and in this respect, this study aimed to use a GIS based approach to assess the environmental impacts of the Wakashio oil spill and demonstrate its usefulness in monitoring marine oil spills. SAR imagery was acquired from the Copernicus Platform and ArcGIS was used to process the images. An oil spill map was created using a SAR image dated August 10th 2020. GPS coordinates of the affected sites were recorded and overlaid on a terrain/road network map of Mauritius generated from layers of vector data obtained through the DIVA-GIS portal. The oil spill was mapped on the satellite image using ArcGIS and a vector map of the affected regions was created. From these maps, the short and long term impacts on the environments (marine waters, mangroves, coasts, biodiversity) were examined. This study concludes that GIS is an effective, inexpensive tool that coastal nations around the world, including Mauritius, can use to support management and decision making regarding oil spill preparedness and monitoring as well as disaster management

Using Peer-to-Peer Knowledge Transfer to Build Marine Oil Spill Preparedness Capacity and Foster Resilience Among Indigenous Communities

ABSTRACT Indigenous communities often bear disproportionate risks from marine oil spills because of their close connections to and reliance on marine ecosystems. The impacts of an oil spill on Indigenous people and communities can be far-reaching, even for incidents that might be considered “small” from the perspective of the response community. Building community capacity for oil spill preparedness and response is a critical component to creating resilience within Indigenous communities. While the fundamental elements of capacity are the same for Indigenous communities as for any other coastal community, the approach requires an understanding and respect for Traditional Knowledge, Indigenous governance structures, and existing stewardship networks. Oil spill preparedness and response traditionally follows a top-down approach within both government and industry, because marine oil spills are low frequency, high consequence, highly complex incidents where multiple organizations and jurisdictions must work together. While this reality applies regardless of whether an oil spill impacts Indigenous communities, a top-down approach can be experienced as a threat to self-governance and compromise the effectiveness of capacity-building efforts. There is a significant body of research in support of the concept that resilience to emergencies and disasters among Indigenous people must build upon existing social, cultural, and familial structures in order to be effective. This requires a fundamentally different approach that builds from the ground up with the goal of ultimately meshing with the existing preparedness and response framework. Peer-to-peer learning and knowledge transfer is an approach that has been used in support of a range of initiatives among Indigenous communities, such as human health initiatives. The same approach may provide a mechanism to empower Indigenous communities to enhance both capacity and resilience. This paper presents a case study from ongoing work to connect Indigenous communities from Canada's High Arctic and Pacific Coast in support of marine oil spill preparedness and response.

A Spectral-Spatial Features Integrated Network for Hyperspectral Detection of Marine Oil Spill

Marine oil spills are one of the most serious problems of marine environmental pollution. Hyperspectral remote sensing has been proven to be an effective tool for monitoring marine oil spills. To make full use of spectral and spatial features, this study proposes a spectral-spatial features integrated network (SSFIN) and applies it for hyperspectral detection of a marine oil spill. Specifically, 1-D and 2-D convolutional neural network (CNN) models have been employed for the extraction of the spectral and spatial features, respectively. During the stage of spatial feature extraction, three consecutive convolution layers are concatenated to achieve the fusion of multilevel spatial features. Next, the extracted spectral and spatial features are concatenated and fed to the fully connected layer so as to obtain the joint spectral-spatial features. In addition, L2 regularization is applied to the convolution layer to prevent overfitting, and dropout operation is employed to the full connection layer to improve the network performance. The effectiveness of the method proposed here has firstly been verified on the Pavia University dataset with competitive classification experimental results. Eventually, the experimental results upon oil spill datasets demonstrate the strong capacity of oil spill detection by this method, which can effectively distinguish thick oil film, thin oil film, and seawater.