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Monitoring of polycyclic aromatic hydrocarbon contamination at four oil spill sites using fluorescence spectroscopy coupled with parallel factor-principal component analysis
摘要： Fluorescence spectroscopy analysis of oil and environmental samples collected from four oil spill incidents in Canada—a 2016 pipeline spill into the North Saskatchewan River (NSR), Saskatchewan; a 2015 train derailment in Gogama, Ontario; the 1970 sinking of the SS Arrow ship in Chedabucto Bay, Nova Scotia; and the 1970 sinking of the Irving Whale barge in the Gulf of St. Lawrence—permitted assessment of the PAH content of environmentally weathered samples. A recently developed fluorescence fingerprinting model based on excitation–emission matrix-parallel factor analysis-principal component analysis (EEM-PARAFAC-PCA) was applied to (i) evaluate the intensity of the abundant PAH groups in the samples, (ii) investigate changes in the PAH composition of environmental samples over time due to weathering, and (iii) classify the original spilled oil and environmental samples within the already established classes of the fingerprinting PCA model. The environmental sediment samples collected from the Husky Energy spill site show loss of PAHs occurring over the course of 15 months post-spill. However, the extent of weathering depends on several environmental factors rather than solely the time of weathering, the PAH loss was maximum at 15 months. There was a decrease in the PAH content of the environmental samples of Gogama spill collected 20 months post-spill. Almost all of Gogama environmental sediment samples underwent substantial weathering, making PCA classification impractical. The SS Arrow and Irving Whale samples fell within adjacent PCA groups, as they both had a similar type of spilled oil (Bunker C) with similarity in chemical composition.
关键词： EEM-PARAFAC-PCA,fluorescence spectroscopy,environmental monitoring,oil spill,PAH contamination
更新于2025-11-19 16:56:42
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Study of oil-particle-aggregation by digital inline holograph
摘要： This study was performed to visually analyse the relationship between particles and oil in seawater by digital holograph technology. Two types of particles with different sizes were chosen to form oil-particle aggregates (OPAs) in the laboratory to determine whether these particles may be a good approach to safely and harmoniously recover marine environment after an oil spill. The processes were recorded by a digital inline holographic system, which could facilitate a continuous real-time three-dimensional in situ observation of oil droplets and OPAs without touching these particles. The visual results showed that, in the micro scale, the particles formed two different types of OPA after breaking the oil film and transported oil into the bottom of the water, while some OPAs could be resuspended.
关键词： spatial distribution,oil droplet,oil particle aggregation,marine oil spill,digital holographic,digital generated hologram
更新于2025-09-23 15:23:52
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Analysis of impacting factors on polarimetric SAR oil spill detection
摘要： Polarimetric synthetic aperture radar (SAR) oil spill detection parameters conformity coefficient (μ), Muller matrix parameters (|C|, B0), the eigenvalues of simplified coherency matrix (λnos) and the influence of SAR observing parameters, ocean environment and noise level are investigated. Radarsat-2 data are used to make systematic analysis of polarimetric parameters for different incidences, wind speeds, noise levels and the ocean phenomena (oil slick and look likes). The influence of the SAR observing parameters, the ocean environment and the noise level on the typical polarimetric SAR parameter conformity coefficient has been analyzed. The results indicate that conformity coefficient cannot be simply used for oil spill detection, which represents the image signal to the noise level to some extent. When the signals are below the noise level for the oil slick and the look likes, the conformity coefficients are negative; while the signals above the noise level corresponds to positive conformity coefficients. For dark patches (low wind and biogenic slick) with the signal below the noise, polarization features such as conformity coefficient cannot separate them with oil slick. For the signal above the noise, the oil slick, the look likes (low wind and biogenic slick) and clean sea all have positive conformity coefficients, among which, the oil slick has the smallest conformity coefficient, the look likes the second, and the clean sea the largest value. For polarimetric SAR data oil spill detection, the noise plays a significant role. So the polarimetric SAR data oil spill detection should be carried out on the basis of noise consideration.
关键词： oil spill,conformity coefficient,multi-polarimetric SAR,noise
更新于2025-09-23 15:23:52
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Geometrical Properties of Spilled Oil on Seawater Detected Using a LiDAR Sensor
摘要： We report on a small-size light detection and ranging (LiDAR) sensor, which offers the possibility of being used in the field during oil spill incidents. In the present study, we develop an algorithm that can distinguish between seawater and oil through the use of a laser at 905 nm wavelength. We investigate the ability of the sensor to detect three different oil types (light crude, bunker A, and bunker C) through experiments and analyze the differences between the types and volumes of spilled oil (1, 5, 10, 15, 20, 25, 30, 35, 40, and 50 mL). The results showed that our algorithm for detecting oil spills over seawater was successful: the LiDAR sensor was able to detect different oil types and volumes. Spilled oil area coverage ranged by more than 50% of the detected area, and the viscosity of bunker C oil reached up to 73%. In addition, the experimental oil spills were mainly formed of oil films of 1 mm and 2 mm thicknesses, which confirmed geometrical properties. Follow-up research should further investigate the characteristics of oil slick thickness measured by the LiDAR system and undertake field tests to assess the feasibility of using the LiDAR system in pollution incidents.
关键词： LiDAR,algorithm,oil spill detection,oil thickness,seawater
更新于2025-09-23 15:21:01
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A Dynamic Remote Sensing Data-Driven Approach for Oil Spill Simulation in the Sea
摘要： In view of the fact that oil spill remote sensing could only generate the oil slick information at a specific time and that traditional oil spill simulation models were not designed to deal with dynamic conditions, a dynamic data-driven application system (DDDAS) was introduced. The DDDAS entails both the ability to incorporate additional data into an executing application and, in reverse, the ability of applications to dynamically steer the measurement process. Based on the DDDAS, combing a remote sensor system that detects oil spills with a numerical simulation, an integrated data processing, analysis, forecasting and emergency response system was established. Once an oil spill accident occurs, the DDDAS-based oil spill model receives information about the oil slick extracted from the dynamic remote sensor data in the simulation. Through comparison, information fusion and feedback updates, continuous and more precise oil spill simulation results can be obtained. Then, the simulation results can provide help for disaster control and clean-up. The Penglai, Xingang and Suizhong oil spill results showed our simulation model could increase the prediction accuracy and reduce the error caused by empirical parameters in existing simulation systems. Therefore, the DDDAS-based detection and simulation system can effectively improve oil spill simulation and diffusion forecasting, as well as provide decision-making information and technical support for emergency responses to oil spills.
关键词： simulation,DDDAS,detection,oil spill,remote sensing
更新于2025-09-19 17:15:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - On the Optimal Compact Polarimetric SAR Modes and Features for Marine Oil Spill Classification
摘要： In this paper, a unified framework was applied to derive compact polarimetric Synthetic Aperture Radar (SAR) features under general transmit and linear receive polarization conditions. Following the rationale of polarization signature, the characteristics of features derived by transmission of variant roll angles and ellipticity are analyzed. Statistical distance was proposed to quantitatively measure the performance of these compact polarimetric SAR features on marine oil spill classification. Experiment was conducted on a Radarsat-2 quad-pol SAR scene acquired during a controlled oil-on-water exercise.
关键词： SAR,oil spill,compact polarimetric
更新于2025-09-10 09:29:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Assessment of Simulated Compact Polarimetry of the RCM Medium Resolution sar Modes for Oil Spill Detection
摘要： Operational detection and discrimination of oil spills over oceans has received considerable attention due to its impact on marine ecosystem from environmental and political points of view. Synthetic Aperture Radar (SAR) is a valuable instrument for maritime pollution monitoring. The three main requirements for effective operational oil spill detection using SAR are: 1) low noise floor, 2) large area coverage, and 3) maximizing detection and discrimination of pollution and 'lookalike' features, by polarization diversity, multiple frequency, etc. In order to reconcile the advantages of fully polarimetric SAR with larger area coverage, compact polarimetry (CP) acquisitions offer a trade-off between the above mentioned requirements. The future Canadian RADARSAT Constellation Mission (RCM) will enable the acquisition of CP SAR data in wide swath imagery, including ScanSAR modes. In this study, we investigate the potential of CP from three RCM SAR modes for oil spill detection. Results indicate that the RCM MR30 SAR mode has promising oil spill detection performance.
关键词： RCM,compact polarimetric SAR,oil spill
更新于2025-09-10 09:29:36
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Mapping terrestrial oil spill impact using machine learning random forest and Landsat 8 OLI imagery: a case site within the Niger Delta region of Nigeria
摘要： Terrestrial oil pollution is one of the major causes of ecological damage within the Niger Delta region of Nigeria and has caused a considerable loss of mangroves and arable croplands since the discovery of crude oil in 1956. The exact extent of landcover loss due to oil pollution remains uncertain due to the variability in factors such as volume and size of the oil spills, the age of oil, and its effects on the different vegetation types. Here, the feasibility of identifying oil-impacted land in the Niger Delta region of Nigeria with a machine learning random forest classifier using Landsat 8 (OLI spectral bands) and Vegetation Health Indices is explored. Oil spill incident data for the years 2015 and 2016 were obtained from published records of the National Oil Spill Detection and Response Agency and Shell Petroleum Development Corporation. Various health indices and spectral wavelengths from visible, near-infrared, and shortwave infrared bands were fused and classified using the machine learning random forest classifier to distinguish between oil-free and oil spill–impacted landcover. This provided the basis for the identification of the best variables for discriminating oil polluted from unpolluted land. Results showed that better results for discriminating oil-free and oil polluted landcovers were obtained when individual landcover types were classified separately as opposed to when the full study area image including all landcover types was classified at once. Similarly, the results also showed that biomass density plays a significant role in the characterization and classification of oil contaminated and oil-free pixels as tree cover areas showed higher classification accuracy compared to cropland and grassland.
关键词： Spectral bands,Vegetation health indices,Variable importance,Random forest,Oil spill,Landcover
更新于2025-09-04 15:30:14
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Onshore Hydrocarbon Remote Sensing
摘要： Hydrocarbon detection is important for both environment monitoring and hydrocarbon exploration. Hyperspectral imaging and derived spectral indices are used to detect hydrocarbons. With appropriate indices, light hydrocarbons on bare ground are detected. Heavier hydrocarbons are more difficult to detect. Plastic items are very well detected. Shadows and vegetation are generating some false alarms. Detection of hydrocarbon in urban environment, or on bare soils will be possible using spectral indices while detection of hydrocarbon in remote vegetated country areas will be difficult.
关键词： oil spill detection,Hydrocarbon index,hyperspectral imaging,multispectral imaging
更新于2025-09-04 15:30:14