Oil Spill Detection Research Articles

Spectral Indices Survey for Oil Spill Detection in Coastal Areas

Spectral Indices Survey for Oil Spill Detection in Coastal Areas

Geospatial Analysis of Artisanal Refineries Distribution Pattern in the Central Niger Delta, Nigeria

The study examined the geospatial analysis of geospatial analysis of artisanal refineries distribution pattern in the Central Niger Delta, Nigeria. The study made use of locational data obtained as secondary data from National Oil Spill Monitor and National Oil Spill Detection and Response Agency. Geographic analyses such as nearest neighbour analysis, directional distribution (Standard deviation ellipse), and Mean Center were carried out. Descriptive statistics involving spatial extent, frequency and percentage were employed for the data analysis. The results showed that that a total of 1476 artisanal sites were discovered in the Central Niger Delta. Of this total 55.35% (817) were found in Bayelsa State, 5.49% (81) were found in Delta State and 39.16% (578) were found in Rivers State. The nearest neighbour analysis showed that the distribution pattern of artisanal refineries was significantly clustered in Bayelsa State (Z= 47.5252, p=0.000); Delta State (Z= -10.129, p=0.0000); Rivers State (Z= -35.13333, p=0.0000) and the entire Central Niger Delta (Z= -59.8222, p=0.0000). The mean center of the distribution of artisanal refineries in Bayelsa State was found in Southern Ijaw LGA, Delta State in Uvwie LGA, Rivers State is found in Ikwerre LGA, and the entire Central Niger Delta was found in Ogbia LGA. The directional distribution standard deviation ellipse covered 58.49% in Bayelsa State, 35.06% in Rivers State and 6.35% of dispersion in Delta State. The study can be concluded that the spatial distribution pattern of artisanal refineries in the entire Central Niger Delta was clustered in nature and Bayelsa and Rivers States were more affected. It is recommended to among others that more studies should be done on the proliferation of artisanal refineries in the study area and entire Niger Delta so that the environmental effects resulting from artisanal refinery activities are reduced to the barest minimum.

A Semi-Empirical Threshold Model for Oil Spill Detection by Analyzing Microwave Backscatter of Ocean Surface From Sentinel-1 C-Band SAR Data

A Semi-Empirical Threshold Model for Oil Spill Detection by Analyzing Microwave Backscatter of Ocean Surface From Sentinel-1 C-Band SAR Data

Mineral oil emulsion species and concentration prediction using multi-output neural network based on fluorescence spectra in the solar-blind UV band.

The accurate monitoring of oil spills is crucial for effective oil spill recovery, volume determination, and cleanup. Oil slicks become emulsified under the effects of wind and waves, which increases the consistency of the oil spills. This phenomenon makes oil spills more challenging to handle and exacerbates environmental pollution. In this study, the variation of the solar-blind ultraviolet (UV) fluorescence spectra obtained from simulated oil spills with different oil types and oil-water ratios was investigated. By designing and constructing a multi-angle excitation and detection system, an apparent fluorescence peak of the oil emulsions was observed at around 290 nm under 220 nm excitation. By utilizing competitive adaptive reweighted sampling (CARS) and multi-output neural network algorithms, both the types and concentrations of the emulsified oils were obtained simultaneously. The classification accuracy for identifying the oil type exceeds 98%, and the mean absolute percentage error (MAPE) for concentration regression is around 2%. The results indicate that active solar-blind UV fluorescence could become a supplementary method for on-site oil spill detection to achieve comprehensive monitoring of oil spills. This study provides potential applications for UV-induced fluorescence spectrometry in oil spill on-site monitoring during the daytime.

Mauritius oil spill detection using transfer learning approach for oil spill mapping and wind impact analysis using Sentinel-1 data

Mauritius oil spill detection using transfer learning approach for oil spill mapping and wind impact analysis using Sentinel-1 data

Ecological risk assessment of a coastal area using multi-source remote sensing images and in-situ sample data

Healthy coastal area have high ecological service value. However, because of the uncontrolled utilization of marine resources and the discharge of pollutants, the ecological environments of many coastal areas are faced with ecological risks, such as oil spills and Enteromorpha blooms. Therefore, it is of great significance to monitor and assess the ecological risks in coastal areas. The remote sensing technique can be used to obtain frequent and wide-coverage images of the areas of interest, and has become an effective tool to monitor the ecological risks occurring in coastal areas. In this study, taking Jiaozhou Bay in China as the study area, an innovative ecological risk assessment framework was established using multi-source remote sensing images and in-situ sample data, which fully considers the potential harm of oil spills and Enteromorpha blooms on the ecology of the bay. Specifically, the oil spill risk for Jiaozhou Bay from 2017 to 2019 was identified by a deep learning based method from synthetic aperture radar (SAR) images, and the Enteromorpha bloom risk for the corresponding years was identified by a floating algae index (FAI) threshold method from multi-source optical images. Then, using habitat information and ground observation data about the marine species in the bay, environmental vulnerability maps were generated. Finally, a disaster-causing factors and disaster-bearing factors weighted risk assessment model was developed. The risk source monitoring results show that the occurrence frequency of oil spills in Jiaozhou Bay decreased from 2017 to 2019, while the outbreak intensity of Enteromorpha blooms generally became higher. The ecological risk assessment results reveal that the high risk areas of Jiaozhou Bay are mainly around the transportation regions and the ports, with a higher risk degree in the eastern and southern parts. To support our work, the well-trained oil spill detection network model and some information about the oil spill events that have occurred in Jiaozhou Bay are provided in this paper.

A timely and accurate approach to nearshore oil spill monitoring using deep learning and GIS

Oil spill accidents are a key contributor to marine pollution worldwide. Therefore, timely and effective oil spill detection is crucial for reducing marine pollution and enhancing environmental protection. Against this backdrop, this study explored two methods for performing nearshore on-site oil spill detection and segmentation, namely the U-net and Mask region-based convolutional neural network (R-CNN) methods. The U-net and Mask R-CNN models were revealed to exhibit acceptable and favorable performance, achieving overall accuracy of 77.01 % and 89.02 %, respectively. Subsequently, a verification system based on the Geographic Information System (GIS) was developed to improve the performance of the deep-learning model. With the integration of the verification system, the Mask R-CNN model achieved higher overall accuracy of 90.78 %. The feasibility of applying deep-learning methods to nearshore on-site oil spill monitoring was demonstrated through this study. In addition, the integration of the GIS not only assisted in the provision of oil spill information but also in the improvement of the deep-learning models. The timely, accurate, and effective method for nearshore on-site oil spill monitoring that this study explored can be applied to considerably improve traditional on-site oil spill monitoring, which has received limited academic attention in the last two decades.

Use of Synthetic Aperture Radar (SAR) imagery on oil spill detection: A case study on the MV X-Press Pearl container ship incident in 2021 in Sri Lanka

Oil spills on the ocean from ships have a strong impact on the environment as well as on human societies. The MV X-Press Pearl container ship incident in 2021 became the worst marine environmental disaster in Sri Lanka due to its powerful impact. Oil leakage from the ship to the ocean continued for months. Synthetic Aperture Radar (SAR) imagery has the advantage of penetrating cloud cover, as well as day and night operating capabilities. Therefore, SAR imagery can be used to detect as well as observe the temporal changes of oil spills. The study in this article aimed to detect the oil spill caused by the MV X-Press Pearl container ship on Sri Lanka’s west coast from May to August 2021 using Sentinel-1 SAR data and measure the temporal changes of the oil slick. Three SAR imageries with Vertical-Vertical (VV) and Vertical-Horizontal (VH) polarization from June, July, and August of 2021 have been processed by applying the orbit file, running the multi-looking algorithm, and making the ellipsoid correction. The possible oil spills have been identified from each set of imagery as dark linear shapes with lengths of 3.1 ±0.012 km with an average reflectance of 15.6 decibels for June and 3.6 ±0.012 km with an average reflectance of 16.5 decibels for the other two months, and the shapes correspond with the oil slick classifications from other literature. The results indicate that the VV polarization is the most suitable to detect oil spills, and that the oil spill from the X-Press Pearl continued from June to the end of August 2021.

Novel oil spill indices for sentinel-2 imagery: A case study of natural seepage in Qaruh Island, Kuwait

Oil spills are a paramount and immediate challenge affecting marine ecosystems globally. Effective and timely monitoring tools, such as oil detection indices, offer a swift means to track oil spill spread across vast oceanic expanses. Moreover, these indices enhance data clarity, making it more conducive for machine learning and deep learning algorithms.This study leverages the natural seepage occurring around Qaruh Island, Kuwait as a unique context for the spectral analysis of oil spills using Sentinel-2 multispectral imagery due to repeated occurrences in the same region. This research evaluated 859 single band and 455 multichannel combinations to identify the most effective combinations in oil-water separability, employing the Jeffries-Matusita (JM) distance measure as a key metric. Bands 1, 2, 3, 8A, 11, and 12 consistently featured among the top-performing indices combinations B1−B11B1+B11;B1+B2B3+B11;B1+B2B3+B12;B1+B2B3+B8A affirming the significant effect of oil spills on visible, Near-Infrared (NIR), and Shortwave Infrared (SWIR) bands. Notably, the indices developed in this study outperformed those from prior research in terms of suitability to unsupervised classification algorithms. A significant conclusion of this study is that incorporating a higher number of bands in the analysis did not correlate with an increase in JM values, suggesting that the selection of specific, informative bands is more critical than the volume of input data. These findings underscore the indispensable role of Sentinel-2 imagery in environmental investigations and highlight the potential for focused, efficient analysis using strategic band combinations for effective oil spill detection.•This study identified optimized Sentinel-2 band combinations for oil-water separability, benefiting from naturally occurring spills around Qaruh Island.•The proposed indices outperformed the previous indices for oil spill visualization and clustering.•The new indices highlighted the critical role of specific band selection over the volume of input data for effective oil spill detection.

Use of a Residual Neural Network to Demonstrate Feasibility of Ship Detection Based on Synthetic Aperture Radar Raw Data

Synthetic Aperture Radar (SAR) is a well-established 2D imaging technique employed as a consolidated practice in several oil spill monitoring services. In this scenario, onboard detection undoubtedly represents an interesting solution to reduce the latency of these services, also enabling transmission to the ground segment of alert signals with a notable reduction in the required downlink bandwidth. However, the reduced computational capabilities available onboard require alternative approaches with respect to the standard processing flows. In this work, we propose a feasibility study of oil spill detection applied directly to raw data, which is a solution not sufficiently addressed in the literature that has the advantage of not requiring the execution of the focusing step. The study is concentrated only on the accuracy of detection, while computational cost analysis is not within the scope of this work. More specifically, we propose a complete framework based on the use of a Residual Neural Network (ResNet), including a simple and automatic simulation method for generating the training data set. The final tests with ERS real data demonstrate the feasibility of the proposed approach showing that the trained ResNet correctly detects ships with a Signal-to-Clutter Ratio (SCR) > 10.3 dB.

Tracking the behavior of an accidental oil spill and its impacts on the marine environment in the Eastern Mediterranean

The eastern Mediterranean region is a vital hub for oil transportation and production because of its strategic location between Europe, Asia, and Africa. But its unique attributes, including narrow shipping routes, heavy marine traffic, and proximity to vulnerable ecosystems, render it particularly susceptible to accidental oil spills. In this research, an oil spill detection model, along with bathymetric and oceanographic parameters, was used to track oil spills that occurred at the Syrian Baniyas Station in the Eastern Mediterranean on August 23, 2021. Furthermore, the study employed a pairwise comparison matrix (PWCM) to assess the relative importance of wind speed and direction, water depth, and sea surface temperature (SST) in the dispersion of oil spills. Analysis of Sentinel-1 data obtained prior to, during, and after the incident revealed the accumulation of oil slicks along the Syrian coast from Baniyas to Latakia for up to twenty days. The spilled oil reached the coast of Cyprus six days after the incident. The study determined that wind speed and direction played a critical role in the dispersion of spilled oil, while water depth and SST were comparatively less significant factors in this process. The overall accuracy (OA) and Kappa coefficient (KC) for land, water, and oil slick classes derived from the random forest (RF) algorithm ranged from 90 % to 98 % and from 0.86 to 0.98, respectively. The spread of oil slicks at the incident location was revealed by the decorrelation stretch and band ratios of Sentinel-2 MultiSpectral Instrument (MSI) data. The accidental oil spill could have negative effects on the organic carbon cycle, chlorophyll production, and ecosystem productivity. It is essential to consider the vulnerability of specific regions in the Eastern Mediterranean to oil spills when developing adaptation strategies.

IMPROVING THE ACCURACY OF AN OIL SPILL DETECTION AND CLASSIFICATION MODEL WITH FAKE DATASETS

Abstract. Deep learning is a popular tool for object detection, including oil spill detection. However, acquiring sufficient data for training deep learning models can be challenging, particularly for offshore oil spill accidents. Data augmentation is an effective solution to this issue. This study proposes a data augmentation method using a conditional-GAN model, specifically Pix2Pix, to generate dummy datasets of oil spills. These datasets were used to train the DaNet model for oil detection and classification. Results show that using the dummy datasets improves the mIoU and f1-score to 2.56% and 1.69%, respectively, and enhances the accuracy of classifying of each oil in the model. This approach not only improves the accuracy of the deep learning model but also presents a direction for data enhancement in detection or segmentation tasks for formless objects, such as oil spills, cracks, water seepage, and mildew.

A novel feature enhancement and semantic segmentation scheme for identifying low-contrast ocean oil spills

The oil spill accidents on the sea surface pose a severe threat to the marine environment and human health. This paper proposes a novel Semantic Segmentation Network (SSN) for processing oil spill images so that low-contrast oil spills on the sea surface can be accurately identified. After the detection accuracy and real-time performance of the current SSNs are compared, the basic network architecture of DeeplabV3+ based target detection is analyzed. The standard convolution is replaced by the Omni-dimensional Dynamic Convolution (ODConv) in the Ghost Module Depth-Wise separable Convolution (DWConv) to further enhance the feature extraction ability of the network. Furthermore, a new DeeplabV3+ based network with ODGhostNetV2 is constructed as the main feature extraction module, and an Adaptive Triplet Attention (ATA) module is deployed in the encoder and decoder at the same time. This not only improves the richness of semantic features but also increases the following receptive fields of the network model. ATA integrates the Adaptively Spatial Feature Fusion (ASFF) module to optimize the weight assignment problem in the feature map fusion process. The ablation experiments are conducted to verify the proposed network which show high accuracy and good real-time performance for the oil spill detection.

Detection and identification of oil spill species based on polarization information.

Aiming at the problem of poor oil identification accuracy in existing oil spill detection technologies, the polarization degree model of oil spill on rough sea surface under different azimuths and zenith angles was established based on Fresnel theory. The analytical expressions of visible light polarization degree in calm and fluctuating water surface were derived respectively, and the polarization degree model of oil spill in reflection space was constructed. The effectiveness of the method and its influence on the polarization distribution of oil spill were analyzed by simulation. A portable turntable was designed to test the polarization characteristics of the experiment, and the visible light polarization detection experiment was carried out. The visible light polarization images of five typical oil spills at different observation azimuth and zenth angles were obtained. The differences in the polarization degrees of different oil species were analyzed, and the correctness of the theoretical model was proved by experiments. The polarization detection experiment of visible light pBRDF was completed, which more intuitively showed the variation law of the polarization characteristics of light reflected by different oil spills in different spatial positions. Using polarization information to distinguish oil species is a useful supplement to the traditional oil spill detection method and has important significance to improve the marine pollution control ability.

Remote sensing methods for striped marine oil spill detection in narrow ship channels

Maritime transport is the backbone of international trade and the global economy. With the increasing frequency of maritime transport, oil spills are on the rise and so is the risk of oil spills in shipping lanes. Therefore, timely oil spill monitoring at the ship channel is of far-reaching significance for marine disaster prevention and control. This study selects oil spill in the ship channel of the northern Yellow Sea as an example and proposes an object-oriented fuzzy logic classification method combining selected multi-feature parameters based on Sentinel-2 data. Firstly, sensitive bands for oil spills are selected to perform multi-scale segmentation of the oil spill object. Then, 30 samples of oil spill objects were uniformly selected in the oil spill area and the average fuzzy membership values were calculated using 11 membership functions by combining the preferred spectral, geometric, texture, and custom features. Finally, five fuzzy logic operators are used to achieve the final classification, while the oil spill misclassification information is processed and compared with the pixel-based SVM and RF algorithms for accuracy. The experimental results show that the oil spill detection based on the “AND” operator is the best compared to other fuzzy operators, with an overall accuracy of 85.19% and a KAPPA coefficient of 0.753. Relative to the pixel-based Support Vector Machine (SVM) and Random Forest (RF) algorithms, the overall accuracy was improved by 15.66% and 11.25%, respectively, and the KAPPA coefficients were improved by 0.072 and 0.161, respectively. The method is feasible for oil spill detection at ship channels and provides technical support for future marine oil spill detection work.

A self-evolving deep learning algorithm for automatic oil spill detection in Sentinel-1 SAR images

Oil spill accidents are one of the major problems causing marine pollution, and thus such accidents require rapid detection for early response. In recent years, deep learning algorithms for oil spill detection have been developed for analyzing SAR images. Nevertheless, to generation of deep learning training data using visual inspection is not only a time-consuming and labor-intensive, but also cause bias and lack of diversity in oil slick training data. This has easily led to false positives in challenging SAR images, such as large scale look-alikes, biologic surface films or oil slicks surrounded by look-alikes. In order to accommodate a broader range of SAR oil spill scenes, including those under challenging conditions, it is essential to continuously enhance the performance of oil spill detection algorithms. In this study, a novel self-evolving algorithm for automatic oil spill detection is proposed, which consists of three inter-connected modules: 1) oil spill detection, 2) generation of new training data, and 3) enhancement of deep learning models. The algorithm detected oil slicks automatically while the new SAR image was added, and then the additional high-quality training data was generated by an adaptive thresholding method to increase the performance of deep learning models. In order to detect oil slicks from whole SAR image with oil look-alikes, the variation of the backscattering coefficients near the oil boundary was considered as a key parameter to distinguish oil spills from look-alikes. As a result, after 21 self-evolving training cycles, 27 new training data were generated with an improved F1-score rising from 0.8423 to 0.8896. Despite the existence of look-alikes in various magnitudes, the algorithm successfully identified oil slicks, and the parameters of deep learning models were automatically updated. It was demonstrated that the performance of the algorithm was gradually improved by self-evolving without any human intervention. Additionally, the fundamental limitations of oil spill detection algorithms were mitigated by the proposed approaches, providing more possibilities for subsequent studies.

Establishment and optimization of the three-band fluorometric indices for oil species identification: Implications on the optimal excitation wavelengths and the detection band combinations

BackgroundThe oil pollutants have become a major threat to the ocean environment with the rapid development of social economy and offshore production activities. The detection and identification of petroleum pollutants is the foundation and pre-requisite for controlling offshore oil pollution. Spectroscopic analysis using the ultraviolet–induced fluorescence of oil pollutants is a potential way for oil species identification. Nevertheless, the current fluorescence spectroscopic analysis methods, such as excitation-emission matrix, has high requirements on the detection equipment. There is a great need for rapid oil species identification method when it comes to practical applications. (93) ResultsThis study established the three-band fluorometric index (TBFI) method and examined its capability on the oil species identification. The fluorescence of five different types of oil samples under various excitation wavelengths were excited using a tuneable xenon lamp, and measured using a high-resolution spectrometer. The optimal excitation wavelengths and the corresponding detection band combinations were explored through the iterations of an optimal band combination algorithm through different excitation wavelengths. According to the results, three out of the four TBFIs tested in this study are suitable for oil species identification. They share the same sets of the excitation wavelengths under 365 nm, 375 nm, and 435 nm. The detection band combinations corresponding to each TBFI under each feasible excitation wavelength were also determined in this study. (125) SignificanceThe three-band fluorometric index method proposed in this study provides a feasible way for the rapid oil species identification using fluorescence spectroscopic analysis. The optimal excitation and detection wavelengths determined in this study also provide the theoretical basis for the designs of fluorescence sensors for rapid oil spill detection and identification. (51).

Monitoring of Oil Spill Risk in Coastal Areas Based on Polarimetric SAR Satellite Images and Deep Learning Theory

Healthy coasts have a high ecological service value. However, many coastal areas are faced with oil spill risks. The Synthetic Aperture Radar (SAR) remote sensing technique has become an effective tool for monitoring the oil spill risk in coastal areas. In this study, taking Jiaozhou Bay in China as the study area, an innovative oil spill monitoring framework was established based on Polarimetric SAR (PolSAR) images and deep learning theory. Specifically, a DeepLabv3+-based semantic segmentation model was trained using 35 Sentinel-1 satellite images of oil films on the sea surface from maritime sectors in different regions all over the world, which not only considered the information from the PolSAR images but also meteorological conditions; then, the well-trained framework was deployed to identify the oil films in the Sentinel-1 images of Jiaozhou Bay from 2017 to 2019. The experimental results show that the detection accuracies of the proposed oil spill detection model were higher than 0.95. It was found that the oil films in Jiaozhou Bay were mainly concentrated in the vicinity of the waterways and coastal port terminals, that the occurrence frequency of oil spills in Jiaozhou Bay decreased from 2017 to 2019, and that more than 80 percent of the oil spill events occurred at night, mainly coming from the illegal discharge of waste oil from ships. These data indicate that, in the future, the PolSAR technique will play a more important role in oil spill monitoring for Jiaozhou Bay due to its capability to capture images at night.

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.

Artificial Olfactory System for Distinguishing Oil-Contaminated Soils

Oil-contaminated soils are a major environmental problem for Kazakhstan. Oil spills or leaks lead to profound changes in the physical and agrochemical properties of the soil and the accumulation of hazardous substances. Whilst there are many remote sensing techniques and complex laboratory methods for oil spill detection, developing simple, reliable, and inexpensive tools for detecting the presence of pollutants in the soil is a relevant research task. The study aims to research the possibilities of an electronic nose combining a chemical sensor array with pattern recognition techniques to distinguish volatile organic compounds from several types of hydrocarbon soil pollutants. An electronic nose system was assembled in our laboratory. It includes eight gas metal oxide sensors, a humidity and temperature sensor, an analog-digital processing unit, and a data communication unit. We measured changes in the electrical conductivity of sensors in the presence of volatile organic compounds released from oil and petroleum products and samples of contaminated and uncontaminated soils. The list of experimental samples includes six types of soils corresponding to different soil zones of Kazakhstan, crude oil from three oil fields in Kazakhstan, and five types of locally produced fuel oil (including gasoline, kerosene, diesel fuel, engine oil, and used engine oil). We used principal component analysis to statistically process multidimensional sensor data, feature extraction, and collect the volatile fingerprint dataset. Pattern recognition using machine learning algorithms made it possible to classify digital fingerprints of samples with an average accuracy of about 92%. The study results show that electronic nose sensors are sensitive to soil hydrocarbon content. The proposed approach based on machine olfaction is a fast, accurate, and inexpensive method for detecting oil spills and leaks, and it can complement remote sensing methods based on computer vision.