Review of oil spill remote sensing

Remote sensing of the oil spills caused by ships: A review

Oil spills caused by ships can be categorized as accidental and operational oil spills. For operational oil spills caused by illegal discharge, an efficient remote sensing system for routine surveillance on oil spills needs to be established. For the oil spills caused by ship accidents, the quantitative inversion on some key properties provides useful information for treatment and assessment. The spaceborne synthetic aperture radar is mostly used for both operational and the accidental oil spills due to its effectiveness of oil spill detection under various environments. Optical remote sensing is applied in the cases where the detailed information about oil spills needs to be identified. Some other sensing techniques that may be useful to monitor oil spills caused by ships are also discussed. By jointly utilizing various remote sensing techniques, it is expected to form a comprehensive sensing network and prevent the negative consequences caused by oil pollution from ships.

Feasibility of Oil Spill Detection in Port Environments Based on UV Imagery

Oil spills in ports are particular cases of oil pollution in water environments that call for specific monitoring measures. Apart from the ecological threats that they pose, their proximity to human activities and the financial losses induced by disturbed port activities add to the need for immediate action. However, in ports, established methods based on short-wave infrared sensors might not be applicable due to the relatively low thickness of the oil layer, and satellite images suffer from insufficient spatial resolution, given the agglomeration of objects in ports. In this study, a lightweight ultraviolet (UV) camera was exploited in both controlled experiments and a real port environment to estimate the potential and limitations of UV imagery in detecting oil spills, in comparison to RGB images. Specifically, motivated by the scarce research literature on this topic, we set up experiments simulating oil spills with various oil types, different viewing angles, and under different weather conditions, such that the separability between oil and background (water) could be better understood and objectively assessed. The UV camera was also used to detect real-world oil spills in a port environment after installing it on a vessel for continuous monitoring. Various separability metrics between water and oil, computed in both scenarios (controlled experiments and port environment), show that the UV cameras have better potential than RGB in detecting oil spills in port environments.

Oil spills characterization and modeling using remote sensing and geophysical techniques to protect the highly vulnerable coastal zones in Alexandria, Egypt

Nowadays, oil spills threaten both aquatic and terrestrial environments, especially in regions with intensive oil refining and shipping activities and high environmental sensitivity, such as Alexandria city, Egypt. Oil spill characterization in coastal populous cities is particularly difficult due to large chemical/physical soil heterogeneities and saltwater intrusion, which represent a major challenges for soil remediation and restoration. Recently, the development of inversion algorithms enables electrical resistivity imaging (ERI) to perform detailed characterization of near-surface soil pollution. The study implements an interdisciplinary approach using remote sensing and an advanced time-lapse 2D-inversion scheme for detailed characterization of oil spill patterns around oil refinery sites in the Alexandria coastal zone. The implemented scheme was able to improve the depth of investigation while maintaining the shallow lateral model resolution. The findings indicate that the mapped oil spills constitute a wedge-like form where the oil moves gradually downward, and it then shifts horizontally towards the shoreline with thinning in oil-contaminated zones under control of tidal action and ground surface slope. Consequently, guided by remote sensing observations, in-situ trenches/wells are suggested to withdraw the oil-contaminated water at the maximum deduced oil-contaminated soil thickness. The applied procedures in this study are replicable and can be effectively used as a pre-requisite to remedy oil spills along terrestrial coastal environments worldwide.

A marine oil spill detection framework considering special disturbances using Sentinel-1 data in the Suez Canal

The Suez Canal is a crucial international waterway due to its strategic location. The significant traffic flow not only stimulates economic development along the coast but also leads to a high frequency of oil spill accidents, which negatively impact the ecosystem and natural resources. Synthetic aperture radar (SAR) is an important remote sensing technology for monitoring oil spills, offering all-day and all-weather capabilities. However, special disturbances (SD) caused by imaging conditions, sensor parameters, and other factors can affect image quality, reducing the accuracy and efficiency of oil spill detection. To mitigate the negative impact of SD, an original oil spill detection framework was developed, based on the analysis of these disturbances, to detect oil spills at the northern entrance of the Suez Canal from 2015 to 2019. The framework included an advantageous featureset with SD adaptability and designs a classifier, Boosting Random Support Vector Machine (BRSVM), which combines a boosting strategy with Support Vector Machine (SVM). The study found that the superiority of the featureset was pivotal in oil spill detection. The classification accuracy and F-1 score achieved by BRSVM were 94.72 % and 95.33 %, respectively, outperforming other algorithms in functionality. These results indicate that the proposed framework holds significant potential for applications requiring large-scale, automated oil spill detection.

Fluorescent analyses of sediments and near-seabed water in the area of the WW2 shipwreck "Stuttgart"

Motorship wrecks on the seabed pose a serious threat to the marine environment due to oil leaking from their fuel tanks. Such substances can penetrate the sediments and enter the water. There is a need to analyse bottom water and seabed sediment samples for the content of toxic petroleum substances. Tests were undertaken on samples collected near ​​the site of the World War II shipwreck of the s/s "Stuttgart". The wreck is located in the Baltic Sea, in the Gulf of Gdańsk. To answer whether toxic hydrocarbons from wrecks enter the sea environment, a fluorometric analysis was carried out based on measurements of excitation-emission ultraviolet spectra for sediments and near-seabed water. The results of these analyses indicate the presence of oil substances in the sediments and the bottom water at some sampling points close to the wreck site. Studies have shown that the applied method of the so-called fluorometric indicator allows for determining the sites of water pollution with oil substances hidden in sediments.

Utilizing deep learning algorithms for automated oil spill detection in medium resolution optical imagery

This study evaluates the performance of three typical convolutional neural network based deep learning algorithms for oil spill detection using medium-resolution optical satellite imagery from Sentinel-2 MSI, Landsat-8 OLI, and Landsat-9 OLI2. Oil slick training and validation dataset were created through a semi-automatic labeling approach, based on chronic and accidental oil spill cases reported worldwide. The research enhances UNet, BiSeNetV2, and DeepLabV3+ architectures by integrating attention mechanisms including the Squeeze-and-Excitation module (SE), Convolutional Block Attention Module (CBAM), and a Simple, parameter-free Attention Module (SimAM), analyzing the optimal model for oil spill detection. Notably, UNet integrated with CBAM, especially with sun glint as a feature, significantly outperformed others, achieving a micro-average F1 score of 88.8 %. This research highlights deep learning's potential in optical remote sensing for oil spill detection, stressing its escalating relevance with the growing deployment of medium- to high-resolution optical satellites.

Full-Scale Aggregated MobileUNet: An Improved U-Net Architecture for SAR Oil Spill Detection

Oil spills are a major threat to marine and coastal environments. Their unique radar backscatter intensity can be captured by synthetic aperture radar (SAR), resulting in dark regions in the images. However, many marine phenomena can lead to erroneous detections of oil spills. In addition, SAR images of the ocean include multiple targets, such as sea surface, land, ships, and oil spills and their look-alikes. The training of a multi-category classifier will encounter significant challenges due to the inherent class imbalance. Addressing this issue requires extracting target features more effectively. In this study, a lightweight U-Net-based model, Full-Scale Aggregated MobileUNet (FA-MobileUNet), was proposed to improve the detection performance for oil spills using SAR images. First, a lightweight MobileNetv3 model was used as the backbone of the U-Net encoder for feature extraction. Next, atrous spatial pyramid pooling (ASPP) and a convolutional block attention module (CBAM) were used to improve the capacity of the network to extract multi-scale features and to increase the speed of module calculation. Finally, full-scale features from the encoder were aggregated to enhance the network's competence in extracting features. The proposed modified network enhanced the extraction and integration of features at different scales to improve the accuracy of detecting diverse marine targets. The experimental results showed that the mean intersection over union (mIoU) of the proposed model reached more than 80% for the detection of five types of marine targets including sea surface, land, ships, and oil spills and their look-alikes. In addition, the IoU of the proposed model reached 75.85 and 72.67% for oil spill and look-alike detection, which was 18.94% and 25.55% higher than that of the original U-Net model, respectively. Compared with other segmentation models, the proposed network can more accurately classify the black regions in SAR images into oil spills and their look-alikes. Furthermore, the detection performance and computational efficiency of the proposed model were also validated against other semantic segmentation models.

Charge-Coupled Frequency Response Multispectral Inversion Network-Based Detection Method of Oil Contamination on Airport Runway

Aircraft failures can result in the leakage of fuel, hydraulic oil, or other lubricants onto the runway during landing or taxiing. Damage to fuel tanks or oil lines during hard landings or accidents can also contribute to these spills. Further, improper maintenance or operational errors may leave oil traces on the runway before take-off or after landing. Identifying oil spills in airport runway videos is crucial to flight safety and accident investigation. Advanced image processing techniques can overcome the limitations of conventional RGB-based detection, which struggles to differentiate between oil spills and sewage due to similar coloration; given that oil and sewage have distinct spectral absorption patterns, precise detection can be performed based on multispectral images. In this study, we developed a method for spectrally enhancing RGB images of oil spills on airport runways to generate HSI images, facilitating oil spill detection in conventional RGB imagery. To this end, we employed the MST++ spectral reconstruction network model to effectively reconstruct RGB images into multispectral images, yielding improved accuracy in oil detection compared with other models. Additionally, we utilized the Fast R-CNN oil spill detection model, resulting in a 5% increase in Intersection over Union (IOU) for HSI images. Moreover, compared with RGB images, this approach significantly enhanced detection accuracy and completeness by 25.3% and 26.5%, respectively. These findings clearly demonstrate the superior precision and accuracy of HSI images based on spectral reconstruction in oil spill detection compared with traditional RGB images. With the spectral reconstruction technique, we can effectively make use of the spectral information inherent in oil spills, thereby enhancing detection accuracy. Future research could delve deeper into optimization techniques and conduct extensive validation in real airport environments. In conclusion, this spectral reconstruction-based technique for detecting oil spills on airport runways offers a novel and efficient approach that upholds both efficacy and accuracy. Its wide-scale implementation in airport operations holds great potential for improving aviation safety and environmental protection.

The application of laser‑induced fluorescence in oil spill detection

Over the past two decades, oil spills have been one of the most serious ecological disasters, causing massive damage to the aquatic and terrestrial ecosystems as well as the socio-economy. In view of this situation, several methods have been developed and utilized to analyze oil samples. Among these methods, laser-induced fluorescence (LIF) technology has been widely used in oil spill detection due to its classification method, which is based on the fluorescence characteristics of chemical material in oil. This review systematically summarized the LIF technology from the perspective of excitation wavelength selection and the application of traditional and novel machine learning algorithms to fluorescence spectrum processing, both of which are critical for qualitative and quantitative analysis of oil spills. It can be seen that an appropriate excitation wavelength is indispensable for spectral discrimination due to different kinds of polycyclic aromatic hydrocarbons' (PAHs) compounds in petroleum products. By summarizing some articles related to LIF technology, we discuss the influence of the excitation wavelength on the accuracy of the oil spill detection model and proposed several suggestions on the selection of excitation wavelength. In addition, we introduced some traditional and novel machine learning (ML) algorithms and discussed the strengths and weaknesses of these algorithms and their applicable scenarios. With an appropriate excitation wavelength and data processing algorithm, it is believed that laser-induced fluorescence technology will become an efficient technique for real-time detection and analysis of oil spills.

Emergency response to ecological protection in maritime phenol spills: Emergency monitor, ecological risk assessment, and reduction

Recently, hundreds of maritime accidental spills of hazardous chemicals have raised public concerns, especially for phenol due to its potential of spills and highly toxicity. Therefore, for marine ecological protection, this article prepared specific strategies of emergency response to phenol spills. Through the identification for phenol behavior at sea, migration prediction, emergency monitor, as well as their new methods were reviewed. Further, ecological risk assessment and seawater quality criteria were conducted by using a species sensitivity distribution (SSD) approach, wherein, risk quotient (RQ) indicated phenol of simulated marine spills posed a high risk (RQ > 1) in 30 days. The method with eco-friendliness and high-efficiency for phenol reduction was constructed by combination of dredging equipment such as pneumatic dredgers (Airlift) and bioremediation, where marine microorganisms that degraded phenol were summarized, as well as future research needs. This study provided a guidance for emergency response and policy development of phenol spills.

Combined use of SAR images and numerical simulations to identify the source and trajectories of oil spills in coastal environments

Remote sensing data and numerical simulation are important tools to rebuild any oil spill accident letting to identify its source and trajectory. Through these tools was identified an oil spill that affected Oaxacan coast in October 2022. The SAR images were processed with a standard method included in SNAP software, and the numerical simulation was made using Lagrangian transport model included in GNOME software. With the combining of these tools was possible to discriminate the look-alikes from true oil slicks; which are the main issue when satellite images are used. Obtained results showed that 4.3m3 of crude oil were released into the ocean from a punctual point of oil pollution. This oil spill was classified such as a small oil spill. The marine currents and weathering processes were the main drivers that controlled the crude oil displacement and its dispersion. It was estimated in GNOME that 1.6 m3 of crude oil was floating on the sea (37.2 %), 2.4 m3 was evaporated into the atmosphere (55.8 %) and 0.3 m3 reached the coast of Oaxaca (7 %). This event affected 82 km of coastline, but the most important touristic areas as well as turtle nesting zones were not affected by this small crude oil spill. Results indicated that the marine-gas-pump number 3 in Salina Cruz, Oaxaca, is a punctual point of oil pollution in the Southern Mexican Pacific Ocean. Further work is needed to assess the economic and ecological damage to Oaxacan coast caused by this small oil spill.

Oil species identification based on fluorescence excitation-emission matrix and transformer-based deep learning

After oil spills are found at sea, the identification on oil species can help determine the source of leakage and form the plan of post-accident treatment. Since the fluorometric characteristics of petroleum hydrocarbon reflect its molecular structure, the composition of oil spills could potentially be inferred using the fluorescence spectroscopy method. The excitation-emission matrix (EEM) includes additional fluorescence information in the dimension of excitation wavelength, which could be useful to identify oil species. This study proposed an oil species identification model using transformer network. The EEMs of oil pollutants are reconstructed into sequenced patch input that consists of the fluorometric spectra obtained under the different excitation wavelengths. The comparative experiments show that the proposed model can reduce the incorrect predictions and achieve higher identification accuracies than the regular convolutional neural networks that have been used in the previous studies. According to the structure of transformer network, an ablation experiment is also designed to evaluate the contributions of different input patches and seek for the optimal excitation wavelengths for oil species identification. The proposed model is expected to identify oil species, and even other fluorescent materials, based on the fluorometric spectra collected under multiple excitation wavelengths.

Oil spill detection on X-band marine radar images based on sea clutter fitting model

Oil spills could cause great harm to the natural environment. The ability to identify them accurately is critical for prompt response and treatment. We proposed a sea clutter fitting model of marine radar images for oil spill detection. The model is derived from the geometric structure of the marine radar, the expression of marine radar received power, and the rough surface scattering model of the sea surface. In the denoised marine radar image, the sea clutter fitting model is used to detect coarse oil spills. Then the fine measurement is carried out by mean filter, the Otsu method, and noise reduction. The proposed oil spill detection method was used on radar images sampled after an oil spill accident happened in a coastal region in Dalian, China, on July 21, 2010. The proposed method can detect oil spills without human intervention, and the extracted oil spills are accurate and consistent with visual interpretation.

Formation of oil-particle aggregates in the presence of marine algae

After an oil spill, the formation of oil-particle aggregates (OPAs) is associated with the interaction between dispersed oil and marine particulate matter such as phytoplankton, bacteria and mineral particles. Until recently, the combined effect of minerals and marine algae in influencing oil dispersion and OPA formation has rarely been investigated in detail. In this paper, the impacts of a species of flagellate algae Heterosigma akashiwo on oil dispersion and aggregation with montmorillonite were investigated. This study has found that oil coalescence is inhibited due to the adhesion of algal cells on the droplet surface, causing fewer large droplets to be dispersed into the water column and small OPAs to form. Due to the role of biosurfactants in the algae and the inhibition of algae on the swelling of mineral particles, both the oil dispersion efficiency and oil sinking efficiency were improved, which reached 77.6% and 23.5%, respectively at an algal cell concentration (Ca) of 1.0 × 106 cells per mL and a mineral concentration of 300 mg L-1. The volumetric mean diameter of the OPAs decreased from 38.4 μm to 31.5 μm when Ca increased from 0 to 1.0 × 106 cells per mL. At higher turbulent energy, more oil tended to form larger OPAs. The findings may add knowledge about the fate and transport of spilled oil and provide fundamental data for oil spill migration modelling.

Applications of bubble curtains in marine oil spill containment: Hydrodynamic characteristics, applications, and future perspectives

Although the marine oil spill pollution issue does not bring us to flock in droves as the new emerging oceanic techniques like wave energy converters, remote operated vehicle (ROV), blue ammonia and green hydrogen, the huge pollution risks of the marine oil spills caused by man-made intentional discharge, old equipment, accidental leakage, war and other aspects should arouse our sufficient attention and concern. As the primary countermeasure of emergency response to a marine oil spill, rapid & efficient oil containment is crucial to limit the pollution scope and the subsequent recovery and treatment. Here, we summarized the existing investigations on oil-spill containment with a marked emphasis on the applications of bubble curtains and their working mechanisms. The critical research progress and trends about the remediation techniques and the application of bubble curtains in marine environments were briefly introduced. The article thoroughly analyzed the basic working mechanism of the bubble curtains in oil spill containment, the technical difficulties of the existing methods, the potential application prospects of coupling with the traditional oil containment booms and the critical scientific problems to be studied in the future. Regarding the issues involving insufficient oil retention performance and inconvenient deployment of the existing traditional oil boom under complex and variable sea conditions, the performance and structural optimization of bubble curtain enhanced oil containment boom will get the top priority in developing the next-generation oil containment techniques.

Remote sensing oil in water with an all-fiber underwater single-photon Raman lidar

The detection of oil in water is of great importance for maintaining subsurface infrastructures such as oil pipelines. As a potential technology for oceanic application, an oceanic lidar has proved its advantages for remote sensing of optical properties and subsea materials. However, current oceanic lidar systems are highly power-consuming and bulky, making them difficult to deploy underwater to monitor oil in water. To address this issue, we have developed a compact single-photon Raman lidar by using a single-photon detector with high quantum efficiency and low dark noise. Due to the single-photon sensitivity, the detection of the relatively weak Raman backscattered signal from underwater oil was realized with a laser with a pulse energy of 1 µJ and a telescope with a diameter of 22.4 mm. An experimental demonstration was conducted to obtain the distance-resolved Raman backscatter of underwater oil of different thicknesses up to a distance of 12 m. The results indicate the single-photon Raman lidar's potential for inspecting underwater oil pipelines.

Oil spills detection from SAR Earth observations based on a hybrid CNN transformer networks

Oil spills are the main threats to marine and coastal environments. Due to the increase in the marine transportation and shipping industry, oil spills have increased in recent years. Moreover, the rapid spread of oil spills in open waters seriously affects the fragile marine ecosystem and creates environmental concerns. Effective monitoring, quick identification, and estimation of the volume of oil spills are the first and most crucial steps for a successful cleanup operation and crisis management. Remote Sensing observations, especially from Synthetic Aperture Radar (SAR) sensors, are a very suitable choice for this purpose due to their ability to collect data regardless of the weather and illumination conditions and over far and large areas of the Earth. Owing to the relatively complex nature of SAR observations, machine learning (ML) based algorithms play an important role in accurately detecting and monitoring oil spills and can significantly help experts in faster and more accurate detection. This paper uses SAR images from ESA's Copernicus Sentinel-1 satellite to detect and locate oil spills in open waters under different environmental conditions. To this end, a deep learning framework has been presented to identify oil spills automatically. The SAR images were segmented into two classes, the oil slick and the background, using convolutional neural networks (CNN) and vision transformers (ViT). Various scenarios for the proposed architecture were designed by placing ViT networks in different parts of the CNN backbone. An extensive dataset of oil spill events in various regions across the globe was used to train and assess the performance of the proposed framework. After the detection performance assessments, the F1-score values for the standard DeepLabV3+, FC-DenseNet, and U-Net networks were 75.08 %, 73.94 %, and 60.85, respectively. In the combined networks models (combination of CNN and ViT), the best F1-score results were obtained as 78.48 %. Our results showed that these hybrid models could improve detection accuracy and have a high ability to distinguish oil spill borders even in noisy images. Evaluation metrics are increased in all the combined networks compared to the original CNN networks.

Ultraviolet-induced fluorescence of oil spill recognition using a semi-supervised algorithm based on thickness and mixing proportion-emission matrices

In recent years, marine oil spill accidents have been occurring frequently during extraction and transportation, and seriously damage the ecological balance. Accurate monitoring of oil spills plays a vital role in estimating oil spill volume, determination of liability, and clean-up. The oil that leaks into natural environments is not a single type of oil, but a mixture of various oil products, and the oil film thickness on the sea surface is uneven under the influence of wind and waves. Increasing the mixed oil film thickness dimension and the mix proportion dimension has been proposed to weaken the effect of the detection environment on the fluorescence measurement results. To preserve the relationships between the data of oil films with different thicknesses and the relationships between the data of oil films with different mixing proportions, the three-dimensional fluorescence spectral data of mixed oil films on a seawater surface were measured in the laboratory, producing a thickness-fluorescence matrix and a proportion-fluorescence matrix. The nonlinear variation of the fluorescence spectra was investigated according to the fluorescence lidar equation. This work pre-processes the data by sum normalization and two-dimensional principal component analysis (2DPCA) and uses the dimensionality reduction results as two feature-point views. Then, semi-supervised classification of collaborative training (co-training) with K-nearest neighbors (KNN) and a decision tree (DT) is used to identify the samples. The results show that the average overall accuracy of this coupling model can reach 100%, which is 20.49% higher than that of the thickness-only view. Using unlabeled data can reduce the cost of data acquisition, improve the classification accuracy and generalization ability, and provide theoretical significance and application prospects for discrimination of spectrally similar oil species in natural marine environments.

Real-Time Oil Leakage Detection on Aftermarket Motorcycle Damping System with Convolutional Neural Networks

In this work, we describe in detail how Deep Learning and Computer Vision can help to detect fault events of the AirTender system, an aftermarket motorcycle damping system component. One of the most effective ways to monitor the AirTender functioning is to look for oil stains on its surface. Starting from real-time images, AirTender is first detected in the motorbike suspension system, simulated indoor, and then, a binary classifier determines whether AirTender is spilling oil or not. The detection is made with the help of the Yolo5 architecture, whereas the classification is carried out with the help of a suitably designed Convolutional Neural Network, OilNet40. In order to detect oil leaks more clearly, we dilute the oil in AirTender with a fluorescent dye with an excitation wavelength peak of approximately 390 nm. AirTender is then illuminated with suitable UV LEDs. The whole system is an attempt to design a low-cost detection setup. An on-board device, such as a mini-computer, is placed near the suspension system and connected to a full hd camera framing AirTender. The on-board device, through our Neural Network algorithm, is then able to localize and classify AirTender as normally functioning (non-leak image) or anomaly (leak image).

Proof of concept study for in-situ burn application using conventional containment booms - Design of Burning Tongue

In situ burning (ISB) hasn't been widely used for offshore oil spill response for various reasons. We present a feasibility study for a new ISB method - the Burning Tongue (BT) concept. We conducted scaled experiments in the Ohmsett wave tank to demonstrate its feasibility. We produced a 35-m long "tongue" of burnable oil (average oil thickness 4.2 mm - above the thickness needed for ISB) by towing a conventional boom (with a 12″ (0.3 m) deep skirt) partially filled with crude oil and then released the oil through a 6″ (0.15 m) wide opening at the apex. We found that the boom movement produced a convergence zone just downstream that kept released oil thick and also pulled oil that entrained under the boom skirt into the thick "tongue" of oil. CFD modeling was performed to explain the flow hydrodynamics and the formation of the convergence zone, which indicates the phenomenon is universal. We used small harbor boom only partially filled with oil for this study and believe that a full-scale marine boom filled with oil would achieve an even thicker "burning tongue." The BT concept could make ISB more widely used for oil spill response in offshore areas.

Potential for commercial PlanetScope satellites in oil response monitoring

Extraction of petroleum oil resources may result in oil spills in the aquatic environment. Active and passive satellites are generally limited in either spatial coverage, temporal revisit periods, or spatial resolution when tracking surface oil slicks. PlanetScope passive satellites are reported to have near daily global coverage at a resolution of 3.5 m at nadir. These satellites may complement monitoring and fill temporal gaps by leveraging sun glint caused by the nadir viewing angle. Here, we demonstrate potential for PlanetScope satellite usage by investigating overpass timing and sun glint intensity. The United States potential for use was greatest during summer solstice and at lower latitudes. When combined with other high-resolution active and passive satellites, PlanetScope coverage added an average of 86.3 days each year from January 2018 through December 2020, as demonstrated at the Mississippi Canyon Block 20 Saratoga Platform site in the Gulf of Mexico.

Oil-film extinction coefficient inversion based on thickness difference

The extinction coefficient of oil films on the sea surface was inversion using a physical model based on two-beam interference and the equal-thickness difference method. The coefficient is simplified to a quadratic equation in one variable related to oil-film thickness and incident angle and wavelength of light. Through a laboratory-simulated oil spill experiment, the reflectivities of oil films of different thicknesses were obtained. The extinction coefficients of the oil film under visible light were inversion. The model considered the light beam on the oil-film surface and effects of scattering properties and photon attenuation of the oil film on spectral reflectance.

Offshore Oil Slick Detection: From Photo-Interpreter to Explainable Multi-Modal Deep Learning Models Using SAR Images and Contextual Data

Ocean surface monitoring, emphasizing oil slick detection, has become essential due to its importance for oil exploration and ecosystem risk prevention. Automation is now mandatory since the manual annotation process of oil by photo-interpreters is time-consuming and cannot process the data collected continuously by the available spaceborne sensors. Studies on automatic detection methods mainly focus on Synthetic Aperture Radar (SAR) data exclusively to detect anthropogenic (spills) or natural (seeps) oil slicks, all using limited datasets. The main goal is to maximize the detection of oil slicks of both natures while being robust to other phenomena that generate false alarms, called “lookalikes”. To this end, this paper presents the automation of offshore oil slick detection on an extensive database of real and recent oil slick monitoring scenarios, including both types of slicks. It relies on slick annotations performed by expert photo-interpreters on Sentinel-1 SAR data over four years and three areas worldwide. In addition, contextual data such as wind estimates and infrastructure positions are included in the database as they are relevant data for oil detection. The contributions of this paper are: (i) A comparative study of deep learning approaches using SAR data. A semantic and instance segmentation analysis via FC-DenseNet and Mask R-CNN, respectively. (ii) A proposal for Fuse-FC-DenseNet, an extension of FC-DenseNet that fuses heterogeneous SAR and wind speed data for enhanced oil slick segmentation. (iii) An improved set of evaluation metrics dedicated to the task that considers contextual information. (iv) A visual explanation of deep learning predictions based on the SHapley Additive exPlanation (SHAP) method adapted to semantic segmentation. The proposed approach yields a detection performance of up to 94% of good detection with a false alarm reduction ranging from 14% to 34% compared to mono-modal models. These results provide new solutions to improve the detection of natural and anthropogenic oil slicks by providing tools that allow photo-interpreters to work more efficiently on a wide range of marine surfaces to be monitored worldwide. Such a tool will accelerate the oil slick detection task to keep up with the continuous sensor acquisition. This upstream work will allow us to study its possible integration into an industrial production pipeline. In addition, a prediction explanation is proposed, which can be integrated as a step to identify the appropriate methodology for presenting the predictions to the experts and understanding the obtained predictions and their sensitivity to contextual information. Thus it helps them to optimize their way of working.

Experimental Analysis on the Ultraviolet Imaging of Oil Film on Water Surface: Implication for the Optimal Band for Oil Film Detection Using Ultraviolet Imaging

Passive ultraviolet (UV) imaging is a potential way to detect thin oil film on water surface due to the high reflectivity of oil film under UV bands. This study conducted an outfield imaging experiment on oil film under the UV bands at 300 nm, 310 nm, and 330 nm. The obtained images were visually compared and quantitatively analyzed using imaging quality index (IQI). The experimental results indicated that the UV images obtained under 300 nm are not able to distinguish between oil film and clean water; those obtained under 310 nm achieve high contrast between oil film and clean water, but low average gray value; those obtained under 330 nm have high IQI and thus may be the optimal wavelength for UV imaging of oil film on water surface. This study provides a guidance to the choices of bands for the oil film detection using passive UV imaging method.

Machine-Learning Classification of SAR Remotely-Sensed Sea-Surface Petroleum Signatures—Part 1: Training and Testing Cross Validation

Sea-surface petroleum pollution is observed as “oil slicks” (i.e., “oil spills” or “oil seeps”) and can be confused with “look-alike slicks” (i.e., environmental phenomena, such as low-wind speed, upwelling conditions, chlorophyll, etc.) in synthetic aperture radar (SAR) measurements, the most proficient satellite sensor to detect mineral oil on the sea surface. Even though machine learning (ML) has become widely used to classify remotely-sensed petroleum signatures, few papers have been published comparing various ML methods to distinguish spills from look-alikes. Our research fills this gap by comparing and evaluating six traditional techniques: simple (naive Bayes (NB), K-nearest neighbor (KNN), decision trees (DT)) and advanced (random forest (RF), support vector machine (SVM), artificial neural network (ANN)) applied to different combinations of satellite-retrieved attributes. 36 ML algorithms were used to discriminate “ocean-slick signatures” (spills versus look-alikes) with ten-times repeated random subsampling cross validation (70-30 train-test partition). Our results found that the best algorithm (ANN: 90%) was >20% more effective than the least accurate one (DT: ~68%). Our empirical ML observations contribute to both scientific ocean remote-sensing research and to oil and gas industry activities, in that: (i) most techniques were superior when morphological information and Meteorological and Oceanographic (MetOc) parameters were included together, and less accurate when these variables were used separately; (ii) the algorithms with the better performance used more variables (without feature selection), while lower accuracy algorithms were those that used fewer variables (with feature selection); (iii) we created algorithms more effective than those of benchmark-past studies that used linear discriminant analysis (LDA: ~85%) on the same dataset; and (iv) accurate algorithms can assist in finding new offshore fossil fuel discoveries (i.e., misclassification reduction).

Chronic oiling in global oceans

Ocean oil slicks can be attributed to natural seepages or to anthropogenic discharges. To date, the global picture of their distribution and relative natural and anthropogenic contributions remains unclear. Here, by analyzing 563,705 Sentinel-1 images from 2014-2019, we provide the first global map of oil slicks and a detailed inventory of static-and-persistent sources (natural seeps, platforms, and pipelines). About 90% of oil slicks were within 160 kilometers of shorelines, with 21 high-density slick belts coinciding well with shipping routes. Quantified by slick area, the proportion of anthropogenic discharges was an order of magnitude greater than natural seepages (94 versus 6%), in contrast to the previous estimate quantified by volume during 1990-1999 (54 versus 46%). Our findings reveal that the present-day anthropogenic contribution to marine oil pollution may have been substantially underestimated.

Measuring Floating Thick Seep Oil from the Coal Oil Point Marine Hydrocarbon Seep Field by Quantitative Thermal Oil Slick Remote Sensing

Remote sensing techniques offer significant potential for generating accurate thick oil slick maps critical for marine oil spill response. However, field validation and methodology assessment challenges remain. Here, we report on an approach to leveraging oil emissions from the Coal Oil Point (COP) natural marine hydrocarbon seepage offshore of southern California, where prolific oil seepage produces thick oil slicks stretching many kilometers. Specifically, we demonstrate and validate a remote sensing approach as part of the Seep Assessment Study (SAS). Thick oil is sufficient for effective mitigation strategies and is set at 0.15 mm. The brightness temperature of thick oil, TBO, is warmer than oil-free seawater, TBW, allowing segregation of oil from seawater. High spatial-resolution airborne thermal and visible slick imagery were acquired as part of the SAS; including along-slick “streamer” surveys and cross-slick calibration surveys. Several cross-slick survey-imaged short oil slick segments that were collected by a customized harbor oil skimmer; termed “collects”. The brightness temperature contrast, ΔTB (TBO − TBW), for oil pixels (based on a semi-supervised classification of oil pixels) and oil thickness, h, from collected oil for each collect provided the empirical calibration of ΔTB(h). The TB probability distributions provided TBO and TBW, whereas a spatial model of TBW provided ΔTB for the streamer analysis. Complicating TBW was the fact that streamers were located at current shears where two water masses intersect, leading to a TB discontinuity at the slick. This current shear arose from a persistent eddy down current of the COP that provides critical steering of oil slicks from the Coal Oil Point. The total floating thick oil in a streamer observed on 23 May and a streamer observed on 25 May 2016 was estimated at 311 (2.3 bbl) and 2671 kg (20 bbl) with mean linear floating oil 0.14 and 2.4 kg m−1 with uncertainties by Monte Carlo simulations of 25% and 7%, respectively. Based on typical currents, the average of these two streamers corresponds to 265 g s−1 (~200 bbl day−1) in a range of 60–340 bbl day−1, with significant short-term temporal variability that suggests slug flow for the seep oil emissions. Given that there are typically four or five streamers, these data are consistent with field emissions that are higher than the literature estimates.

A novel deep learning method for marine oil spill detection from satellite synthetic aperture radar imagery

Oil spill discharges from operational maritime activities like ships, oil rigs and other structures, leaking pipelines, as well as natural hydrocarbon seepage pose serious threats to marine ecosystems and fisheries. Satellite synthetic aperture radar (SAR) is a unique microwave instrument for marine oil spill monitoring, as it is not dependent on weather or sunlight conditions. Existing SAR oil spill detection approaches are limited by algorithm complexity, imbalanced data sets, uncertainties in selecting optimal features, and relatively slow detection speed. To overcome these restrictions, a fast and effective SAR oil spill detection method is presented, based a novel deep learning model, named the Faster Region-based Convolutional Neural Network (Faster R-CNN). This approach is capable of achieving fast end-to-end oil spill detection with reasonable accuracy. A large data set consisting of 15,774 labeled oil spill samples derived from 1786C-band Sentinel-1 and RADARSAT-2 vertical polarization SAR images is used to train, validate and test the Faster R-CNN model. Our experimental results show that the proposed method exhibits good performance for detection of oil spills with wide swath SAR imagery. The Precision and Recall metrics are 89.23% and 89.14%, respectively. The average Precision is 92.56%. The effects of environmental conditions and sensor parameters on oil spill detection are analyzed. The expected detection results are obtained when wind speeds and incidence angles are between 3 m/s and 10 m/s, and 21° and 45°, respectively. Furthermore, the computer runtime for oil spill detection is less than 0.05 s for each full SAR image, using a workstation with NVIDIA GeForce RTX 3090 GPU. This suggests that the present approach has potential for applications that require fast oil spill detection from spaceborne SAR images.

User Preferences in Drone Design and Operation

Drones, which were first used in military applications, are now widely used by civilians for various purposes such as for deliveries and as cameras. There has been a lack of research into what drone users expect in terms of drone design and operation from a user perspective. In order to figure out what users want from drones, it is necessary to investigate the perception and design preferences of users with regard to drones. Surveys were conducted to collect data on preferences for various aspects of the design and operation of drone technology. Features relevant to the design and operation of drones were considered. We have identified the underlying factor structures of drone design and operation: outdoor mission type, user interface, military mission type, usefulness, risk, special mission type, and concern. The most important factors that contribute to all the dependent variables are the user interface and usefulness. The fact that drones will be increasingly used in the future is clear; however, the purpose of this study was to find out the areas on which to focus and pay further attention.

Exploring the Potential of Optical Polarization Remote Sensing for Oil Spill Detection: A Case Study of Deepwater Horizon

Oil spills lead to catastrophic problems. In most oil spill cases, the spatial and temporal intractability of the detriment cannot be neglected, and problems related to economic, social and environmental factors constantly appear for a long time. Remote sensing has been widely used as a powerful means to conduct oil spill detection. Optical polarization remote sensing, thriving in recent years, shows a novel potential for oil spill detection. This paper provides a demonstration of the use of open-source POLDER/PARASOL polarization time-series data to detect oil spill. The Deepwater Horizon oil spill, one of the largest oil spill disasters, is utilized to explore the potential of optical polarization remote sensing for oil spill detection. A total of 24 feature combinations are organized to quantitatively study the positive effect of adding polarization information and the appropriate way to describe polarization characteristics. Random forest classifier models are trained with different combinations, and the results are assessed by 10-fold cross-validation. The improvement from adding polarization characteristics is remarkable ((average) accuracy: +0.51%; recall: +2.83%; precision: +3.49%; F1 score: +3.01%, (maximum) accuracy: +0.80%; recall: +5.09%; precision: +6.92%; F1 score: +4.72%), and coupling between the degree of polarization and the phase angle of polarization provides the best description of polarization information. This study confirms the potential of optical polarization remote sensing for oil spill detection, and some detailed problems related to model establishment and polarization feature characterization are discussed for the further application of polarization information.

Oil Spill Identification in Radar Images Using a Soft Attention Segmentation Model

Oil spills can cause damage to the marine environment. When an oil spill occurs in the sea, it is critical to rapidly detect and respond to it. Because of their convenience and low cost, navigational radar images are commonly employed in oil spill detection. However, they are currently only used to assess whether or not there are oil spills, and the area affected is calculated with less accuracy. The main reason for this is that there have been very few studies on how to retrieve oil spill locations. Given the above problems, this article introduces a model of image segmentation based on the soft attention mechanism. First, the semantic segmentation model was established to fully integrate multi-scale features. It takes the target detection model based on the feature pyramid network as the backbone model, including high-level semantic information and low-level location information. The channel attention method was then used for each of the feature layers of the model to calculate the weight relationship between channels to boost the model’s expressive ability for extracting oil spill features.Simultaneously, a multi-task loss function was used. Finally, the public dataset of oil spills on the sea surface was used for detection. The experimental results show that the proposed method improves the segmentation accuracy of the oil spill region. At the same time, compared with segmentation models, such as PSPNet, DeepLab V3+, and Attention U-net, the segmentation accuracy based on the pixel level improved to 95.77%, and the categorical pixel accuracy increased to 96.45%.

Comparison of CNNs and Vision Transformers-Based Hybrid Models Using Gradient Profile Loss for Classification of Oil Spills in SAR Images

Oil spillage over a sea or ocean surface is a threat to marine and coastal ecosystems. Spaceborne synthetic aperture radar (SAR) data have been used efficiently for the detection of oil spills due to their operational capability in all-day all-weather conditions. The problem is often modeled as a semantic segmentation task. The images need to be segmented into multiple regions of interest such as sea surface, oil spill, lookalikes, ships, and land. Training of a classifier for this task is particularly challenging since there is an inherent class imbalance. In this work, we train a convolutional neural network (CNN) with multiple feature extractors for pixel-wise classification and introduce a new loss function, namely, “gradient profile” (GP) loss, which is in fact the constituent of the more generic spatial profile loss proposed for image translation problems. For the purpose of training, testing, and performance evaluation, we use a publicly available dataset with selected oil spill events verified by the European Maritime Safety Agency (EMSA). The results obtained show that the proposed CNN trained with a combination of GP, Jaccard, and focal loss functions can detect oil spills with an intersection over union (IoU) value of 63.95%. The IoU value for sea surface, lookalikes, ships, and land class is 96.00%, 60.87%, 74.61%, and 96.80%, respectively. The mean intersection over union (mIoU) value for all the classes is 78.45%, which accounts for a 13% improvement over the state of the art for this dataset. Moreover, we provide extensive ablation on different convolutional neural networks (CNNs) and vision transformers (ViTs)-based hybrid models to demonstrate the effectiveness of adding GP loss as an additional loss function for training. Results show that GP loss significantly improves the mIoU and F1 scores for CNNs as well as ViTs-based hybrid models. GP loss turns out to be a promising loss function in the context of deep learning with SAR images.

The Application of Satellite Image Analysis in Oil Spill Detection

In recent years, there has been an increasing use of satellite sensors to detect and track oil spills. The satellite bands, namely visible, short, medium infrared, and microwave radar bands, are used for this purpose. The use of satellite images is extremely valuable for oil spill analysis. With satellite images, we can identify the source of leakage and assess the extent of potential damage. However, it is not yet clear how to approach a specific leakage case methodologically. The aim of this study is the remote sensing analysis of environmental changes with the development of oil spill detection processing methods. Innovative elements of the work, in addition to methodological proposals, include the long-term analysis of surface water changes. This is very important because oil is very likely to enter the soil when water levels change. The classification result was satisfactory and accurate by 85%. The study was carried out using images from Landsat 5, Landsat 7, Landsat 8, Sentinel-1, and Sentinel-2 satellites. The results of the classification of the oil stains in active and passive technologies differ. This difference affects the methodology for selecting processing methods in similar fields. In the case of this article, the oil spill that occurred on 29 May 2020 in Norilsk was investigated and compared with data from other years to determine the extent of biodegradation. Due to the tank failure that occurred at the Nornickel power plant on that day, a large amount of crude oil leaked into the environment, contaminating the waters and soil of local areas. Research shows that oil spills may be caused by human error or may be the effect of climate change, particularly global warming.

History of a disaster: A baseline assessment of the Wakashio oil spill on the coast of Mauritius, Indian Ocean

Oil spills from tanker ships provide adverse and irreversible impacts of a pollutant over coastal and marine environments. Using Sentinel-1 and 2 satellite images, this baseline paper presents the detection, assessment, and monitoring of the aground and further oil spill from the Wakashio ship of August 06, 2020, on the Mauritius coast. The oil spill started on August 06, after cracks developed on the hull, and continued until the total breakup of the ship on August 15, 2020. Data shows displacements in ship position of about 100 m, and a maximum change of 80° in orientation (from NS to NE). The remote sensing results were validated using met-ocean observations and reanalysis, which showed winds, waves, and tides of high magnitude at the accident site during the incident period. Analysis of the results of this event using REAS and CMEMS data indicate their usefulness to study similar future oil spills events.

Year-to-year variability of oil pollution along the Eastern Arabian Sea: The impact of COVID-19 imposed lock-downs

This study investigated the year-to-year variability in the occurrence, abundance and sources of oil spills in the Eastern Arabian Sea (EAS) using sentinel-1 imagery and identified the potential oil spills vulnerable zones. The four consecutive year's data acquired from 2017 to 2020 (March-May) reveal three oil spill hot spot zones. The ship-based oil spills were dominant over zone's-1 (off Gujarat) and 3 (off Karnataka and Kerala), and the oil field based over zone-2 (off Maharashtra). The abundance of oil spills was significantly low in zone-1, only 14.30km² (1.2%) during lock-down due to the covid-19 pandemic. Whereas, the year-to-year oil spills over zone's 2 and 3 are not significantly varied (170.29 km² and 195.01 km²), further suggesting the influence of oil exploration and international tanker traffic are in operation during the lock-down. This study further recommends that manual clustering is the best method to study the distribution of unknown oil spills.

A novel lightweight bilateral segmentation network for detecting oil spills on the sea surface

Accidental oil spills from pipelines or tankers have posed a big threat to marine life and natural resources. This paper presents a novel lightweight bilateral segmentation network for detecting oil spills on the sea surface. A novel deep-learning semantic-segmentation algorithm is firstly created for analyzing the characteristics of oil spill images. A Bilateral Segmentation Network (BiSeNetV2) is then selected as the basic network architecture and evaluated by using experimental comparison of the current mainstream networks on detection accuracy and real-time performances for oil samples. Furthermore, the Gather-and-Expansion (GE) layer of the semantic branch in the traditional network is redesigned and the parameter complexity is reduced. A dual attention mechanism is deployed in the two branches of the BiSeNetV2 to solve the problem of inter-class similarity. Finally, experimental results are given to show the good detection accuracy of the proposed network.

Decision Fusion of Deep Learning and Shallow Learning for Marine Oil Spill Detection

Marine oil spills are an emergency of great harm and have become a hot topic in marine environmental monitoring research. Optical remote sensing is an important means to monitor marine oil spills. Clouds, weather, and light control the amount of available data, which often limit feature characterization using a single classifier and therefore difficult to accurate monitoring of marine oil spills. In this paper, we develop a decision fusion algorithm to integrate deep learning methods and shallow learning methods based on multi-scale features for improving oil spill detection accuracy in the case of limited samples. Based on the multi-scale features after wavelet transform, two deep learning methods and two classical shallow learning algorithms are used to extract oil slick information from hyperspectral oil spill images. The decision fusion algorithm based on fuzzy membership degree is introduced to fuse multi-source oil spill information. The research shows that oil spill detection accuracy using the decision fusion algorithm is higher than that of the single detection algorithms. It is worth noting that oil spill detection accuracy is affected by different scale features. The decision fusion algorithm under the first-level scale features can further improve the accuracy of oil spill detection. The overall classification accuracy of the proposed method is 91.93%, which is 2.03%, 2.15%, 1.32%, and 0.43% higher than that of SVM, DBN, 1D-CNN, and MRF-CNN algorithms, respectively.

Satellite Survey of Offshore Oil Seep Sites in the Caspian Sea

This paper presents the results of a long-term survey of the Caspian Sea using satellite SAR and multispectral sensors. The primary environmental problem of the Caspian Sea is oil pollution which is determined by its natural properties, mainly by the presence of big oil and gas deposits beneath the seabed. Our research focuses on natural oil slicks (NOS), i.e., oil showings on the sea surface due to natural hydrocarbon emission from seabed seeps. The spatial and temporal variability of NOS in the Caspian Sea and the possibilities of their reliable detection using satellite data are examined. NOS frequency and detectability in satellite images depending on sensor type, season and geographical region are assessed. It is shown that both parameters vary significantly, and largely depend on sensor type and season, with season being most pronounced in visible (VIS) data. The locations of two offshore seep sites at the Iranian and Turkmenian shelves are accurately estimated. Statistics on individual sizes of NOS are drawn. The release rates of crude oil from the seabed to the sea surface are compared. Detailed maps of NOS are put together, and areas exposed to high risk of sea surface oil pollution are determined.

Application of C-band sentinel-1A SAR data as proxies for detecting oil spills of Chennai, East Coast of India

This paper presents the utilization of Synthetic Aperture Radar (SAR) data for monitoring and detection of oil spills. In this work, a case study of an oil spill has been investigated using C-band Sentinel-1A SAR data to detect the oil spill that occurred on 28 January 2017, near Ennore port, Chennai, India. Oil spill damages marine ecosystems causing serious environmental effects. Quite often, oil spills on the sea/ocean surface are seen nowadays, mainly in major shipping routes. They are caused due to tanker collisions, illegal discharge from the ships, etc. An oil spill can be monitored and detected using various platforms such as vessel-based, airborne-based and satellite-based. Vessel based and airborne methods are expensive with less area coverage. This process also consumes more time. For ocean applications such as oil spill and Ship detection, optical sensors cannot image during bad weather. As SAR is an active sensor, weather independent, and has cloud penetrating capability, the images can be acquired during the day as well as at night. Radar Remote Sensing (RRS) has rapidly gained popularity for monitoring and detection of oil spills and ships for more than a decade. With the availability of the satellite images, detection of oil spill has improved due to its wide coverage and less revisit time. The present paper gives an overview of the methodologies used to detect oil spills on the SAR images using dual-pol Sentinel-1A Level 1 SLC data. This work clearly demonstrates the preprocessing steps of the Sentinel 1A data for oil spill detection. The oil spill was only visible in the VV channel, therefore, for ocean application VV channel image is preferred. SEASAT was the first space-borne SAR mission launched in 1978 by NASA to observe sea surface. The preprocessing was carried out at the European Space Agency (ESA), the Sentinel Application Platform (SNAP) toolbox and Envi 5.1 toolbox. Based on the Sigma naught values, oil spill can be discriminated with the ocean surface. The results obtained with the VV channel are satisfactory and one could map out the oil spill very well. Supervised classifiers SVM and NN were applied on the boxcar filtered 3 × 3 VV channel image to delineate the oil spill. The result of oil spill detection mapping is validated with Supervised SVM and Neural Network classifiers. The results show there is a good agreement between oil spill mapping and classified image using SVM and NN classified images. The Overall Accuracy (OA) obtained using SVM classifier is 98.13% with kappa coefficient as 0.95 and using NN classifier is 98.11% with kappa coefficients 0.95. This technique is considered to be a potential proxy for the detection and monitoring of Oil spills on water bodies. Application of SAR data for oil spill detection is considered to be first of its kind from Indian coasts. This study aims to detect the oil spill occurred due to collision of two LPG tankers with Sentinel-1A SLC data in Chennai coast area.

A Deformable Convolutional Neural Network with Spatial-Channel Attention for Remote Sensing Scene Classification

Remote sensing scene classification converts remote sensing images into classification information to support high-level applications, so it is a fundamental problem in the field of remote sensing. In recent years, many convolutional neural network (CNN)-based methods have achieved impressive results in remote sensing scene classification, but they have two problems in extracting remote sensing scene features: (1) fixed-shape convolutional kernels cannot effectively extract features from remote sensing scenes with complex shapes and diverse distributions; (2) the features extracted by CNN contain a large number of redundant and invalid information. To solve these problems, this paper constructs a deformable convolutional neural network to adapt the convolutional sampling positions to the shape of objects in the remote sensing scene. Meanwhile, the spatial and channel attention mechanisms are used to focus on the effective features while suppressing the invalid ones. The experimental results indicate that the proposed method is competitive to the state-of-the-art methods on three remote sensing scene classification datasets (UCM, NWPU, and AID).

Monitoring offshore oil pollution using multi-class convolutional neural networks

Oil and gas production operations are a major source of environmental pollution that expose people and habitats in many coastal communities around the world to adverse health effects. Detecting oil spills in a timely and precise manner can help improve the oil spill response process and channel required resources more effectively to affected regions. In this research, convolutional neural networks, a branch of artificial intelligence (AI), are trained on a visual dataset of oil spills containing images from different altitudes and geographical locations. In particular, a VGG16 model is adopted through transfer learning for oil spill classification (i.e., detecting if there is oil spill in an image) with an accuracy of 92%. Next, Mask R-CNN and PSPNet models are used for oil spill segmentation (i.e., pixel-level detection of oil spill boundaries) with a mean intersection over union (IoU) of 49% and 68%, respectively. Lastly, to determine if there is an oil rig or vessel in the vicinity of a detected oil spill and provide a holistic view of the oil spill surroundings, a YOLOv3 model is trained and used, yielding a maximum mean average precision (mAP) of ~71%. Findings of this research can improve the current practices of oil pollution cleanup and predictive maintenance, ultimately leading to more resilient and healthy coastal communities.

Numerically modelling the reflectance of a rough surface covered with diesel fuel based on bidirectional reflectance distribution function

Oil spills have become a problem that negatively affects the oceanic environment and maritime transportation. Optical remote sensing technology is a potential method to monitor oil spills by analyzing the reflectance spectra of oil-polluted and clean water surface. In this paper, a numerical model for the reflectance of a rough oil surface is constructed by combining Fresnel reflection and bidirectional reflectance distribution function (BRDF). The way that visible light is reflected from the rough diesel fuel surface is quantitatively described and discussed based on the reflection theory of electromagnetic waves. The simulation result of the proposed model shows reasonable agreement with experimental measurements. With reliable prediction and a low computational complexity, the proposed model is expected to provide a theorical basis for rapid detection of oil spills on rough sea surfaces using optical remote sensing technology.

Fate of MV Wakashio oil spill off Mauritius coast through modelling and remote sensing observations

This study aims at assessing the fate of MV Wakashio oil spill, and the driving forces responsible for possible environmental consequences of polluted coastal region. GNOME simulations were performed, considering various meteo-oceanographic forcings such as (i) winds and currents, (ii) only winds, and (iii) only winds with different diffusion coefficients, and validated with the satellite images. The results revealed that the simulations performed with 'only winds' reasonably match with the satellite observations, indicating that winds are the primary driving forces. The conducive stokes drift is an added contribution to the predominant northwestward drift of the spill. The oil budget analysis suggests that beaching and evaporation together accounted for a significant portion of the spilled oil (1000 tons), in which ~60% of the oil was accounted only for beaching. Our results depict that the diffusion coefficient of 100,000 cm²/s and 3% windages are optimal for oil-spill simulations off the southeastern Mauritius coast.

Intelligent computational techniques in marine oil spill management: A critical review

Effective marine oil spill management (MOSM) is crucial to minimize the catastrophic impacts of oil spills. MOSM is a complex system affected by various factors, such as characteristics of spilled oil and environmental conditions. Oil spill detection, characterization, and monitoring; risk evaluation; response selection and process optimization; and waste management are the key components of MOSM demanding timely decision-making. Applying robust computational techniques based on real-time data (e.g., satellite and aerial observations) and historical records of oil spill incidents may considerably facilitate decision-making processes. Various soft-computing and artificial intelligence-based models and mathematical techniques have been used for the implementation of MOSM's components. This study presents a review of literature published since 2010 on the application of computational techniques in MOSM. A statistical evaluation is performed concerning the temporal distribution of papers, publishers' engagement, research subfields, countries of studies, and selected case studies. Key findings reported in the literature are summarized for two main practices in MOSM: spill detection, characterization, and monitoring; and spill management and response optimization. Potential gaps in applying computational techniques in MOSM have been identified, and a holistic computational-based framework has been suggested for effective MOSM.

Toward monitoring and estimating the size of the HFO-contaminated seabed around a shipwreck using MBES backscatter data

Despite a progressive reduction of oil spills caused by the activity of maritime transportation, the latent sources of pollution still exist. Although the harmful impact of heavy fuel oil (HFO) on the marine environment is widely known, many shipwrecks cause contamination of the surrounding areas. In this paper, an approach to monitor the area of the HFO spill around a shipwreck is made using a bottom backscattering strength (BBS) obtained by a multibeam echosounder (MBES). As a case study, the s/s Stuttgart wreck located in the Gulf of Gdansk (Poland) is verified. Two different measurement campaigns have been carried out in shallow waters using low (190 kHz) and high (420 kHz) MBES frequency. The results indicate that the polluted area around s/s Stuttgart was estimated at 49.1 ha, which is around 18.3% more in comparison to the geological surveys made four years earlier.

Raman lidar for remote sensing of oil in water

We describe a portable Raman lidar system that can remotely detect oil leakages in water. The system has been developed based on a frequency-doubled, Q-switched Nd:YAG laser, operated at 532 nm with a receiver telescope equipped with some filters and photomultipliers. Stand-off detection of oil is achieved in a 6-m-long water tank, which allowed us to considerably increase the survey capability of subsea infrastructures, including both the range observation and target identification.