Oil Spill Mapping Research Articles

Mapping of oil spills in China Seas using optical satellite data and deep learning

Oils spilled into the ocean can form various weathered oils (non-emulsified oil slicks (NEOS), oil emulsions (OE)) which threaten the oceanic and coastal environments and ecosystems. Optical remote sensing has the unique ability to discriminate oil types and quantify oil volumes as their spectral contrasts with oil-free seawater. Here, a deep learning-based model is developed for identification, classification, and quantification of various oil types. Based on the oil-contained datasets collected from 7 satellite sensors from April 2019 to August 2023, the origin, quantity, and spatial distribution of oils spilled from ships and rigs in the China Seas are mapped in detail. We found that oil spill incidents are primarily from ship discharges (85.8 %), while platform leaks lead to more oil emulsions (58.6 % compared to 13.1 % from ships), which illuminates that the drilling oils are the main source of oil spill pollution in China Seas. The spilled oils correlate with major port locations, including offshore Qingdao and Rongcheng, Bohai Bay, the adjacent areas of Beihai, and Hue and Danang in Vietnam. This study provides new insights into the assessment and management of offshore and marine oil spills.

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

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

Self-Supervised Spectral–Spatial Transformer Network for Hyperspectral Oil Spill Mapping

Hyperspectral oil spill mapping aims to distinguish the type of oil spill. Recently, most hyperspectral oil spill detection methods are based on supervised methods that work well with rich training samples. However, in the marine oil spill detection scenario, pixel annotations are difficult and costly. Moreover, the labels obtained by domain experts within a hyperspectral image (HSI) are often scarce. To address these issues, a self-supervised spectral-spatial transformer network is proposed for hyperspectral oil spill mapping. First, we propose a transformer-based contrastive learning network to extract the deep discriminative features. Then, the learned features are transferred to the downstream classification network that is fine-tuned with very few labeled samples. Experiments on hyperspectral oil spill database (HOSD) constructed by ourselves indicate that the proposed method can obtain more promising performance than several state-of-the-art oil spill classification techniques in discriminating different types of oil spills, i.e., thick oil, thin oil, sheen, and seawater.

Oil spill detection based on texture analysis: how does feature importance matter in classification?

ABSTRACT Oil spill mapping and detection represent a relevant issue from an environmental point of view, given the effects on marine ecosystems. This study presents a new feature space assessment protocol for oil spill mapping using the Google Earth Engine (GEE). First, we selected five free Sentinel-1A sensor images from the GEE catalogue. Next, we processed the features evaluated from Gray Level Co-occurrence Matrix (GLCM) spectral and texture data. A recursive protocol that comprises a sequential classification of the evaluated image was also applied, wherein each iteration, the feature with less importance, was removed based on the Gini index. We used the Random Forest algorithm for image classification. Each image was trained on 10,000 points and evaluated for accuracy, with an equal number of points collected independently. Our results showed that the Sum Average (Savg), Convolution Smooth (Smooth), Cluster Shade Shade, and Gray level Correlation (Corr) features were essential to identify oil spills and increase the accuracy values. The best classification results based on the features removal experiment and global accuracy were Angola (0.9960), Trinidad and Tobago (0.9829), Italy (0.9506), Kuwait (0.9547), and Dubai (0.9344). Furthermore, it revealed that the protocol created was essential for better understanding the parameter space to detect oil spills with SAR images.

Monitoring of oil spill in the offshore zone of the Nile Delta using Sentinel data

This study aims to monitor and map the oil spills which occurred from 2019 to 2021 along the northeastern portion of the Nile Delta using Sentinel-1 (SAR) and Sentinel-2 (MSI) data. The examination of VV polarized SAR-C images displayed the presence of the oil spills as dark spots of different sizes. These images were processed using the oil spills detection model in SNAP Toolbox. The oceanographic parameters that may influence the dispersal of oil spills were mapped using GIS technique. This study identified 29 oil spills during the study period in the research area. The largest spill was detected on February 23, 2019, and covered an area of about 10.5 km2. The band ratios and decorrelation stretch methods of available Sentinel-2 data confirmed the results of SAR-C data. The accuracy assessment of spills was achieved using Parallelepiped supervised classification model. The results demonstrated that the overall accuracy (OA) and Kappa coefficient (KC) for seawater, land, and oil spills classes were between 86% and 98% and 0.73% and 0.97%, respectively. The sensitivity zone of oil spills was higher in winter than in summer. This study proved the efficiency of VV polarized data of Sentinel-1 sensor for detection and mapping of oil spills. Several management strategies are needed in the offshore zone of the Nile Delta to limit oil pollution effects on the marine environment.

The Potential of Hyperspectral Image Classification for Oil Spill Mapping

Oil spill mapping is a very challenging problem in marine environmental monitoring. In this paper, the potential of hyperspectral image classification for mapping oil spills is comprehensively investigated. First, several representative hyperspectral image classification methods are reviewed in a general framework. Second, three oil spill mapping cases are designed to analyze the performance of different classification methods in detecting the spatial distribution, classifying the type, and estimating the thickness of oil spills. Finally, the experimental results are analyzed in detail, and some conclusions are given, which bring a comprehensive understanding to scholars who are interested in the fields of hyperspectral remote sensing and oil spill mapping.

Новітній підхід до застосування нормалізованого диференційного вегетаційного індексу для класифікування аерокосмічних зображень за наявності неповних та неточних даних

Solution of different scientific, ecological and agricultural tasks with the use of aerospace im-ages comprises a procedure of image classification. Classification is one of the most important procedures. Nowadays many supervised and unsupervised classification methods are applied in remote sensing. The most accurate results are obtained through the use of supervised classifica-tion methods. In this paper, there are proposed some new approaches to image classification which are based on supervised classification methods and Normalized Difference Vegetation Index (NDVI). Different values of NDVI are noted to correspond to different classes of objects, such as soil, water, roads, sand, green vegetation, oil spills. Application of Vegetation Index is the first step of classification. Using NDVI, it is possible to select special necessary classes. Af-ter the application of NDVI, such classification methods as the parallelepiped method, Demp-ster’s rule, and Inagaki’s combination rule can be used. The current work describes the main advantages of these classification methods. It has been noted that the use of the parallelepiped method allows easy and quick processing of data. The paper also shows that Dempster’s combi-nation rule and Inagaki’s combination rule can deal with inaccurate and incomplete data from different spectral bands. Moreover, these methods can process conflicting information. Demp-ster-Shafer theory has the advantage of high accuracy and simple calculations. In the paper, there is also considered a numerical example where NDVI and Inagaki’s combination rule has been used for detection and mapping of oil spills. Application of Vegetation Index and such su-pervised classification rules as the parallelepiped method, Dempster’s rule, Inagaki’s combina-tion rule can be applied in ecological monitoring, mapping of petroleum spills, and solving ag-ricultural tasks.

Citizen science as a tool for collaborative site-specific oil spill mapping: the case of Brazil.

Many maritime disasters lead to oil pollution, which undermines ecosystem balance, human health, the prosperity of countries and coastal areas across borders, and people's livelihoods. This is a problem that affects the whole world. Governments must strive to ensure that operations in the marine environment are safe and avoid oil pollution by adopting methods that anticipate future scenarios to mitigate the effects of this pollution when it occurs. This study investigates a method of managing contaminated coastal areas, aiming to contribute to the management of the environmental crisis caused by disasters through the use of online collaborative mapping by volunteer collaborators. Volunteer collaborators have been sending georeferenced data and photographs of locations affected by pollution. This information is processed and printed on a cartographic basis created by the web-mapping platform, Google MyMaps©. Photos of 90 locations were plotted on the map, and the pictures demonstrate that the oil slicks that reached the Brazilian coast had very different shapes and consistency. This research can contribute as a participatory monitoring tool during and after oil spills, promoting the oriented preservation of marine ecosystems through citizen science.

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.

Terrestrial oil spill mapping using satellite earth observation and machine learning: A case study in South Sudan

Terrestrial oil spills are a major threat to environmental and human well-being. Rapid, accurate, and remote spatial assessment of oil contamination is critical to implementing countermeasures that prevent potentially lasting ecological damage and irreversible harm to local communities. Satellite remote sensing has been used to support such assessments in inaccessible regions, although mapping small terrestrial oil spills is challenging – partly due to the pixel size of remote sensing systems, but also due to the distinguishability of small oil spill areas from other land cover types. We assessed the usability of freely available Sentinel satellite images to map terrestrial oil spills with machine learning algorithms. Using two test sites in South Sudan, we demonstrated that information from the Sentinel-1 and -2 instruments can be used to map oil spills with more than 90 % classification accuracy. Classification accuracy was significantly increased (>95 %) with the addition of multi-temporal information and spatial predictor variables that quantify proximity to oil production infrastructure such as pipelines and oil pads. The mapping of terrestrial oil spills with freely available Sentinel satellite images may thus represent an accurate and efficient means for the regular monitoring of oil-impacted areas.

MULTIDIMENSIONAL STATISTICAL MODEL FOR DETECTING OIL POLLUTION SITES BASED ON SATELLITE IMAGERY

The article considers an algorithm for determining the statistical model from several inhomogeneous images of the Earth's surface obtained by different sensors (optoelectronic scanning device, synthetic aperture radar (SAR)) over the sea areas. The object of the study are the methods of remote sensing of the Earth used for detection and mapping of oil spills. The aim of the research was to perform testing for a possible variation of the statistical model inside a non-uniform sliding window based on a semi-automatic approach. The proposed algorithm makes it possible to determine the spatial extent of oil production sites and oil pollution in offshore waters using multi-time RSA data and a multi-zone combined image with a spatial resolution of 10 m. First, homogeneous regions are analyzed in the image, and then the model of the analysis zone is expanded to the more general case of inhomogeneous regions that are observed in the analysis windows.

Mapping Oil Spill Using Sentinel-1: Study Case of Karawang Oil Spill

Indonesia as a maritime country has many challenges in developing and making innovations in the marine sector. One of the challenges for Indonesia is how to respond to oil spill disasters that occur because of various events such as blow-out on oil platforms, ship collisions, pipeline leaks, and so on. In the end, marine and coastal pollution cannot be avoided. Karawang is one of the productive northern coastal areas of Java Island with various facilities such as single point mooring, oil wells, tourist areas and historical heritage. On July 15, 2019, an oil-well belonging to Pertamina Hulu Energi (PHE) ONWJ had leaked and polluted the sea and coast not only in Karawang, but also spread to Jakarta waters. As a result of this leakage, of course the impact is very large and varied from ecological, social and economic aspects. In this paper, an oil spill mapping will be carried out to determine the affected areas using sentinel-1 data. Data processing is assisted by open-source software SNAP (Sentinel Application Platform). As the result, August 19, 2019, oil spill seen in Bekasi area. So that oil slick move from Karawang to the west. This oil spill map area can be used in many ways such as clean-up strategy, calculating amount of dispersant, and many more.

Detection of Wakashio oil spill off Mauritius using Sentinel-1 and 2 data: Capability of sensors, image transformation methods and mapping.

Oil spill incidents contaminate water bodies, and damage the coastal and marine environment including coral reefs and mangroves, and therefore, monitoring the oil spills is highly important. This study discriminates the Wakashio oil spill, which occurred off Mauritius, located in the Indian Ocean on August 06, 2020 using the Sentinel-1 and 2 data acquired before, during and after the spill to understand the spreading of the spill and assess its impact on the coastal environment. The interpretation of VV polarization images of Synthetic-Aperture Radar (SAR) C-band (5.404GHz) of Sentinel-1 acquired between July 5 and September 3, 2020 showed the occurrence and distribution of oil spill as dark warped patches. The images of band ratios (5+6)/7, (3+4)/2, (11+12)/8 and 3/2, (3+4)/2, (6+7)/5 of the Sentinel-2 data detected the oil spill. The images of decorrelated spectral bands 4, 3 and 2 distinguished the very thick, thick and thin oil spills in a different tone and showed clearly their distribution over the lagoon and offshore, and the accumulation of spilled oil on the coral reefs and along the coast. The distribution of post-oil spill along the coast was interpreted using the images acquired after 21 August 2020. The accuracy of oil spill mapping was assessed by classifying the SAR-C data and decorrelated images of the MultiSpectral Instrument (MSI) data using the Parallelepiped supervised algorithm and confusion matrix. The results showed that the overall accuracy is on an average 91.72 and 98.77%, and Kappa coefficient 0.84 and 0.96, respectively. The satellite-derived results were validated with field studies. The MSI results showed the occurrence and spread of oil spill having different thicknesses, and supported the results of SAR. This study demonstrated the capability of Sentinel sensors and the potential of image processing methods to detect, monitor and assess oil spill impact on environment.

Marine oil spill detection using Synthetic Aperture Radar over Indian Ocean

Four oil spill events over the Indian Ocean including Chennai, Sharjah, Al Khiran and Mubarak Village are analyzed using Sentinel-1 satellite data. General National Oceanic and Atmospheric Administration (NOAA) Operational Modeling Environment (GNOME) model is utilized for oil spills trajectory production, whereas oil spills weathering processes are modeled using Automated Data Inquiry for Oil Spill (ADIOS). Synthetic Aperture Radar (SAR) based oil spill detection technique provided reliable results at the wind speed between 3 to 9 m/s for all events. Maximum oil spill movement (33 km) from the source point is observed in the Al Khiran, whereas evaporation rate of crude (degraded) oil is observed as high (low). The Near Real Time (NRT) detection of oil spill using SAR imagery needs high computational power, however, provides better results. This study concludes that SAR based oil spill detection is a cost-effective technique and can be utilized for mapping of oil spills.

Spectral similarity algorithm-based image classification for oil spill mapping of hyperspectral datasets

In remote sensing, the compositional information of part of the earth’s surface is statistically evaluated by comparing known field or library spectra with the unknown image spectra, known as spectral matching or spectral similarity analysis. In this research, hybrid spectral similarity algorithms developed based on chi-square distance (CHI or χ2) are used to retrieve useful information from the Hyperion hyperspectral oil spill image covering the area near Liaodong Bay of the Bohai Sea, China. In order to evaluate the discriminability of spectral similarity algorithms, a pixel-level matching is carried out between the reference vectors, viz. Oil Slick (O), Sheen (H), Sea Water (S) and Ship Track (T), collected visually from known areas in the image. The hybrid spectral similarity algorithms are statistically assessed for their performance using the spectral discriminatory measures (i) relative spectral discriminatory power (RSDPW), (ii) relative spectral discriminatory probability (RSDPB) and (iii) relative spectral discriminatory entropy (RSDE). Additionally, the selected hybrid algorithms are used on the Hyperion image subset to perform a pixel-based classification. Classification results revealed that the CHI-based hybrid algorithms performed better than all other hybrid spectral similarity methods. Therefore, the CHI-based hybrid algorithms demonstrated their superior spectral discrimination capacity to classify marine spectral classes for oil spill mapping from the hyperspectral dataset.

Oil Spill Mapping from Kompsat-2 High-Resolution Image Using Directional Median Filtering and Artificial Neural Network

Oil spill accidents in marine environments have a massive impact on ecosystems. Various methods have been developed to detect oil spills using high-resolution optical imagery. However, ocean waves caused by heavy winds occurring in the accident area cause sun glint in the image, and this severely impedes the ability to detect the oil spill area. The objective of this study was to detect oil spill areas from high-resolution optic images using the artificial neural network (ANN) through effective suppression of severe sun glint effects. To enable this, a directional median filter (DMF) was adapted, and its use was compared with that of a traditional low-pass filter. A performance test was conducted using a KOMPSAT-2 image acquired during oil spill accidents that occurred in the Gulf of Mexico in 2010. The proposed method involved two main steps: (i) The sun glint effects caused by the ocean waves were corrected using the DMF; and (ii) the ANN approach was used to detect the oil spill area. The results show the following: (i) The designed DMF, which considers the size and angle of ocean waves, was proficient in correcting the sun glint effect in a high-resolution optical image; and (ii) oil spill areas were efficiently detected using the ANN approach with the proposed filtering method. The oil spill area was classified with accuracies of approximately 98.12% and 89.56% using the receiver operating characteristic (ROC) curve and probability of detection (POD) measurements, respectively. These results show that the accuracy of the proposed method is improved by about 9% compared to the traditional detecting algorithm.

Optimal Model-Based Sensor Placement & Adaptive Monitoring Of An Oil Spill

This paper presents a model based adaptive monitoring method for the estimation of flow tracers, with application to mapping, prediction and observation of oil spills in the immediate aftermath of an incident. Autonomous agents are guided to optimal sensing locations via the solution of a PDE constrained optimisation problem, obtained using the adjoint method. The proposed method employs a dynamic model of the combined ocean and oil dynamics, with states that are updated in real-time using a Kalman filter that fuses agent-based measurements with a reduced-order model of the ocean circulation dynamics. In turn, the updated predictions from the fluid model are used to identify and update the reduced order model, in a process of continuous feedback. The proposed method exhibits a 30% oil presence mapping and prediction improvement compared to standard industrial oil observation sensor guidance and model use.

A machine learning approach to detect crude oil contamination in a real scenario using hyperspectral remote sensing

One of the most ubiquitous and detrimental types of environmental contamination in the world is crude oil pollution. When released into either the aquatic or terrestrial environments, this pollution can negatively impact flora and fauna, as well as human health. Hence, a rapid and affordable spatial assessment of the pollution is favored to limit the spill’s effects. Using airborne hyperspectral remote sensing (HRS) for crude oil detection in terrestrial areas has been investigated in previous studies, which mainly relied on heavily oiled artificial samples. These studies and others based their methodologies on the premise that the spectral features of petroleum hydrocarbon (PHC) are clearly observable, which might not be true in all cases. In this study, we aimed at assessing the true potential of using HRS for terrestrial oil spill mapping in a real disaster site in southern Israel, where laboratory and controlled conditions do not apply. Using the AISA SPECIM Fenix1 K sensor, we collected airborne image of the study site and analyzed the data with advanced data mining techniques. Various challenges and limitations arose from the airborne HRS image being taken two and a half years after the crude oil had been released into the environment and exposed to the surface. Here, no spectral features of PHC were detectable in the spectrum, preventing the use of PHC indices and spectral methods developed by others. Nevertheless, by using standardization techniques, vicarious band selection, dimension reduction, multivariate calibration, and supervised machine-learning, we were able to successfully distinguish between contaminated pixels from non-contaminated ones. Classification accuracy metrics of overall accuracy, sensitivity, specificity, and Kappa yielded good results of 0.95, 0.95, 0.95 and 0.9, respectively, for cross-validation, and 0.93, 0.91, 0.94 and 0.85, for the validation dataset. Classified image and test scenes also showed strong agreement with an orthophoto image taken several days after the disaster, when the pollution was clearly visible. Thus, we conclude that HRS technology can detect PHC traces in an oil spill site, even under the most challenging conditions.

Distributed operation of collaborating unmanned aerial vehicles for time-sensitive oil spill mapping

Abstract Multiple simple agents working together to achieve a common complex goal embodies the underlying theme of swarm concepts, with decentralized decision-making serving as the new frontier for tackling challenges associated with scalability, fault tolerance, and communication constraints. This paper builds on this emerging paradigm to develop a distributed approach (called PSOil) for off-shore oil spill mapping using a team of unmanned aerial vehicles or UAVs. In-flight waypoint planning is achieved via a new particle swarm mechanics-inspired technique, employing a novel combination of anomaly detection for knowledge extraction, and a stochastic occupancy grid approach for timely processing and frugal sharing of knowledge (with net communications