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Different oil spill pollution types could be produced in oil transport and weathering processes. Investigation of these pollution types is beneficial for oil spill recovery and processing. Optical remote sensing techniques play an important role in marine oil spill monitoring and have the ability to identify different oil spill pollution types. Rec...
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... black oil is a kind of thick oil layer floating on the water surface. The visual characteristic is “black” or “low brightness.” It is characterized by strong absorption, low penetrability, and low reflection for incident visible light (Lu et al. 2011b). The curve shape of the reflectance spectrum of floating black oil is monomorphic, and the value is low ( Figure 2). Floating black oil has no specific spectral response characteristics that distinguish it from a background with lower reflectance (Brown et al. 1996). Some research has shown that the floating black oil in the laboratory and the field had flat spectra with no usable features distinguishing it from the background (Taylor 1992). It is easy for optical remote sensing to identify and distinguish floating black oil from background seawater or other oil spill pollution targets. However, it is difficult to determine its thickness by optical remote sensing directly. An oil slick is an important oil spill pollution type floating on the sea surface and can be detected by optical remote sensing. As the oil slick thickness changes, the oil slick shows a variety of visual characteristics. Offshore oil slicks can be divided into two types according to their spectra: very thin oil slick and oil slick. Very thin oil slicks show a different sheen, such as light sheen, silver sheen, or rainbow sheen. Some very thin oil slicks’ spectra were collected for this study using in situ measurement (Figure 3a). Their spectral reflectances were higher than background seawater in the spectral range from 400 nm to 1000 nm and showed “high brightness” in visual characteristics. Some researchers showed that very thin oil slicks can change reflectance at 440 nm (Lu et al. 2009), and the optimal bands to distinguish those oil slicks are located in the range between 350 nm and 440 nm. As the thickness of the oil slick increased, the spectral reflectance decreased (Figure 3b). Another type of oil slick is one in which the thickness is greater than that of the different sheens (very thin oil slick). The spectral reflectance of oil slicks is lower than that of the background water and decreases with an increase in oil slick thickness in the spectral range from 400 nm to 1000 nm (Lu et al. 2008). These different oil slicks can be detected and identified by hyperspectral remote sensors. An oil spill accident occurred in the northern Bohai Sea, an important area for producing oil and gas in China. A timely Hyperion image was collected for this study (Figure 4a). In this image, a very thin oil slick and oil slick can be identified clearly (Figure 4b). In the true color composite image of the Hyperion data, the very thin oil slick was brighter than the background seawater and oil slick; the thick oil slick was darker than background seawater. Spectral sampling analysis showed that the spectral reflectance of the very thin oil slick was higher than the seawater and oil slick, and the oil slick was lower than seawater and the very thin oil slick (Figure 4c). In addition, the latest research shows that the spectra of oil slicks are closely related to the spectra of background water. The optimal bands for oil slicks detected in cases 1 and ...
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Citations
... Crude oil and its emulsions in different states include non-emulsified crude oil, water-in-oil emulsion and oil-inwater emulsion (Lu et al., 2019b;Lu et al., 2020). Once the spilled oil on the sea surface is not removed in time, a series of complex physical and chemical changes, such as diffusion, drift, emulsification, evaporation, dissolution, adsorption precipitation, photooxidation and biodegradation, will occur under the combined action of wind, wave, current and other environmental dynamics, forming water-in-oil (WO) and oil-in-water (OW) emulsions of different concentrations (Lu et al., 2013a). ...
The types of marine oil spill pollution are closely related to source tracing and pollution disposal, which is an important basis for oil spill pollution punishment. The types of marine oil spill pollution generally include different types of oil products as well as crude oil and its emulsions in different states. This paper designed and implemented two outdoor oil spill simulation experiments, obtained the hyperspectral and thermal infrared remote sensing data of different oil spill pollution types, constructed a hyperspectral recognition algorithm of oil spill pollution type based on classical machine learning, ensemble learning and deep learning models, and explored to improve the identification ability of hyperspectral oil spill pollution type by adding thermal infrared features. The research shows that hyperspectral combined with thermal infrared remote sensing can effectively improve the recognition accuracy of different oils, but thermal infrared remote sensing cannot be used to distinguish crude oil and high concentration water-in-oil emulsion. On this basis, the recognition ability of hyperspectral combined with thermal infrared for different oil film thicknesses is also discussed. The combination of hyperspectral and thermal infrared remote sensing can provide important technical support for emergency response to maritime emergencies and oil spill monitoring business of relevant departments.
... Oil spills in waterways can pollute groundwater and freshwater supplies in cities and cause long-term damage to fisheries and wildlife [4]. Oil spills account for about half of total ocean pollution, causing fatal damage to marine life and marine ecology [5,6]. Over the past few decades, many large-scale oil spills have occurred in countries around the world, which has raised the public's awareness of the risks of oil spills in most parts of the world [7,8]. ...
Crude oil spills seriously harm the ocean environment and endanger the health of various animals and plants. In the present study, a totally biodegradable polymer, poly(L-lactic acid) (PLLA), was employed to fabricate highly porous oil absorbent nanofibrous materials by using a combination of electrospinning technique and subsequent acetone treatment. We systematically investigated how the electrospinning parameters affected formation of the porous structure of PLLA nanofibers and demonstrated that PLLA nanofibers with decreased and uniform diameter and improved porosity could be rapidly prepared by adjusting solution parameters and spinning parameters. We also demonstrated that the acetone treatment could obviously enhance the pore diameter and specific surface area of as-optimized electrospun PLLA nanofibers. The acetone treatment could also improve the hydrophobic property of as-treated PLLA nanofiber membranes. All these led to a significant increase in oil absorption performance. Through our research, it was found that the oil absorption of PLLA nanofiber membrane increased by more than double after being treated with acetone and the oil retention rate was also improved slightly.
... Adamo et al. (2009) theoretically described the physical mechanism of remote sensing oil spill monitoring in the visible/near-infrared band. Lu et al. (2013Lu et al. ( , 2020 pointed out that such a false-color image, where short-wave infrared, near-infrared, and red bands are used as the red, green, and blue channels can qualitatively distinguish oil-inwater and water-in-oil. Hu et al. (2009) used Moderate Resolution Imaging Spectroradiometer data to detect a natural oil spill in the Gulf of Mexico for the first time and pointed out the critical reflectivity and critical angle of the oil spill from dark to bright. ...
... (1) Lu et al., 2013;Wen et al., 2018). ...
Accurate detection of an oil spill is of great significance for rapid response to oil spill accidents. Multispectral images have the advantages of high spatial resolution, short revisit period, and wide imaging width, which is suitable for large-scale oil spill monitoring. However, in wide remote sensing images, the number of oil spill samples is generally far less than that of seawater samples. Moreover, the sea surface state tends to be heterogeneous over a large area, which makes the identification of oil spills more difficult because of various sea conditions and sunglint. To address this problem, we used the F-Score as a measure of the distance between forecast value and true value, proposed the Class-Balanced F loss function (CBF loss function) that comprehensively considers the precision and recall, and rebalances the loss according to the actual sample numbers of various classes. Using the CBF loss function, we constructed convolution neural networks (CBF-CNN) for oil spill detection. Based on the image acquired by the Coastal Zone Imager (CZI) of the Haiyang-1C (HY-1C) satellite in the Andaman Sea (study area 1), we carried out parameter adjustment experiments. In contrast to experiments of different loss functions, the F1-Score of the detection result of oil emulsions is 0.87, which is 0.03–0.07 higher than cross-entropy, hinge, and focal loss functions, and the F1-Score of the detection result of oil slicks is 0.94, which is 0.01–0.09 higher than those three loss functions. In comparison with the experiment of different methods, the F1-Score of CBF-CNN for the detection result of oil emulsions is 0.05–0.12 higher than that of the deep neural networks, supports vector machine and random forests models, and the F1-Score of the detection result of oil slicks is 0.15–0.22 higher than that of the three methods. To verify the applicability of the CBF-CNN model in different observation scenes, we used the image obtained by HY-1C CZI in the Karimata Strait to carry out experiments, which include two studies areas (study area 2 and study area 3). The experimental results show that the F1-Score of CBF-CNN for the detection result of oil emulsions is 0.88, which is 0.16–0.24 higher than that of other methods, and the F1-Score of the detection result of oil slicks is 0.96–0.97, which is 0.06–0.23 higher than that of other methods. Based on all the above experiments, we come to the conclusions that the CBF loss function can restrain the influence of oil spill and seawater sample imbalance on oil spill detection of CNN model thus improving the detection accuracy of oil spills, and our CBF-CNN model is suitable for the detection of oil spills in an area with weak sunglint and can be applied to different scenarios of CZI images.
... The use of UV sensors [9][10][11][12], TIR sensors [13][14][15][16], hyperspectral sensors [17][18][19][20] and LiDAR [21][22][23][24] for oil spill detection has been proved to be possible and promising, but the robustness and reliability of these techniques require further investigations. Optical sensors (including visible, near infrared and short-wave infrared bands) [25][26][27][28] and SAR [29][30][31][32] are the most popular types of remote sensing applied for oil spill detection. ...
... Optical remote sensing has proved to have positive value considering that the optical satellites have a lower cost, larger swath width and shorter revisit period compared with satellite-borne radar [27,28]. Optical imagery is highly influenced by frequent clouds and observation angles [38], but this limitation is compensated by the multiple optical sensors onboard and sufficient multiband data from the individual sensor [39]. ...
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.
... Different sensing techniques are usually applied immediately in an attempt to assess and halt the oil spill [4]. Remote sensing methods are affected by environmental conditions such as haze, cloud coverage, temperature variations, and sea conditions [5]- [7]. To handle these limitations, few studies proposed local or contact-based sensors that can provide in situ measurements of the oil-slick thickness. ...
... These values are named the reference or base voltages because they represent the basic capacitance of the sensing cells before being in contact with any material. These values are unit-less because they are converted using an Analog-to-Digital Converter (ADC) embedded in the MPR121 chip; the relationship between the analog and digital voltages is presented in (7). Note that the reference voltages decreases as the cell index increases (cells further away from the control unit). ...
During oil spills, an accurate estimate of the oil-slick thickness at different locations within the affected area is an essential requirement to estimate the spilled amount, assess and enhance the performance of skimmers, and decide on type of remediation techniques to be applied such as
in situ
burning. This requirement cannot be satisfied using remote sensing techniques, which can provide a global assessment of the oil spill extent but not an accurate localized estimate of its thickness, particularly for thicker oil slicks. To solve this problem, we propose a new
in situ
measurement system that works based on a dual-modality sensing concept, combining capacitive and ultrasonic sensors to provide accurate information about the oil-film thickness in real-time. In this article, we describe the design, implementation, and testing of the proposed system, which was realized as two different devices optimized for different sensing use cases, including a handheld and a skimmer-mount device. The handheld device is intended for use in two scenarios: (1) experimental ground truth to verify oil thickness measurements during experiments at testing facilities, and (2) from vessels to obtain sample measurements in the field. The skimmer-mount device is optimized for mounting on skimmers, buoys, or in an oil spill boom apex to provide thickness data continuously under field conditions. The proposed devices were tested at the Cold Regions Research and Engineering Laboratory (CRREL) against different types of oil, in different installation modes, and under static/dynamic liquid conditions. The experimental results demonstrated their ability in providing real-time measurements with high accuracy and precision and without relying on any calibration against different types of water or oil.
... The implementation of optical remote sensing images for detecting oil on the sea surface is a challenging task. Promising results have been demonstrated, based on the analysis of the spectral reflectance of oil on water (Lu et al., 2013). Marine water absorbs strongly in the near infrared band and appears dark on false colour composites. ...
Abstract: The present research is focused on composition, density, distribution
and identification of accumulative areas of floating marine litter (FML) in the
Bulgarian Black Sea coastal, shelf and offshore waters. Macro-debris abundances
were determined in compliance with MSFD protocol for visual observations based
on fixed-width strip transect method. Six floating litter monitoring surveys were
carried out between 2017 and 2019. Over 144 hours of visual observations were
performed in a total of 288 transects, covering an overall survey area of 7.52 km2
.
1320 litter items were identified and classified during the campaigns of which 90%
were detected as plastic materials. The presence of floating debris was observed
throughout the entire study area with density ranging from 0 to 1750 items km-2
and average density of 170 items km-2. Unexpectedly marine litter concentration
patterns were evenly distributed among the studied regions with some specifics at
certain sites.
... Moreover, optical satellite data can be effectively used for oil spill detection [13]. Oil on the water change the spectral characteristics of the water and, thus, can be detected by spectral analysis of the optical satellite data. ...
span>Since oil exploration began, oil spills have become a serious problem. When drilling for oil, there is always a risk of an oil spill. With the new development of technology over the years, oil spill detection has become much easier making the clean-up of a spill to happen much faster reducing the risk of a large spread. In this study, remote sensing techniques were used to detect the Deep-water Horizon oil spill through a change detection method. The change detection method allows the viewer to determine the difference of an area before and after an oil spill as well as detect the irregular difference on a surface. To confirm the effectiveness of change detection method, two approaches were used each showing the differences in the images before and after the spill allowing the size and shape to be identified. The swipe tool in the ArcGIS software was used to visually show the changes. The difference tool was also used to both visually and statistically to investigate the difference before and after the Deep-water Horizon oil spill event.</span
... During the oil weathering progress (e.g., spreading, drift, evaporation, dispersion, emulsification, and biological degradation, Zhong and You, 2011), different types of weathered oils may form, such as nonemulsified oil slicks, water-in-oil (WO) emulsions, and oil-in-water (OW) emulsions. These represent different hazards to the marine environment (Lu et al., 2013(Lu et al., , 2019(Lu et al., , 2020. Timely information about the spatial coverage, concentration, and thickness of these various oil types (non-weathered oil, WO or OW emulsions) is important for a variety of purposes including recovery and cleanup, for example by applying skimming, in situ burning, and chemical dispersants (Zhong and You, 2011;Lu et al., 2019Lu et al., , 2020. ...
Thermal remote sensing has been used in assessing oil spills in the ocean, mostly based on empirical interpretations. This study designs a ground-based experiment to measure brightness temperatures (BTs) of oil-in-water (OW) emulsions with different concentrations and oil-free water as a function of time in 32 consecutive hours. Compared with a previous thermal experiment to measure oil slicks (i.e., non-emulsified oil) with different thicknesses and considering the similarity between oil slicks and water-in-oil (WO) emulsions, it is found that (1) the diurnal response of brightness temperature difference (BTD, between oil samples and oil-free sample) to oil emulsion types (OW or WO) is similar, making it difficult to classify oil emulsion types using thermal remote sensing; (2) in contrast, BTD under thermal balance during the optimal time window appears to be a function of equivalent oil thickness (EOT) (oil volume per area, mm) regardless of oil emulsion type, suggesting that EOT could be estimated from BTD. Application of such experimental results to Landsat imagery over the Deepwater Horizon oil spill in the Gulf of Mexico suggests that although their ability to quantify oil footprint is limited, thermal data show potentials in providing unique information (e.g., estimating EOTs for up to 4 mm without the need of differentiating sub-pixel heterogeneity) to complement optical data in characterizing oil type and oil quantity when the spilled oil is thick (> 0.4 mm).
... Several methods have been proposed to detect oil patches using optical images (Conference et al., 2001;Lu et al., 2013;Merv Fingas, 2018;Merv Fingas & Brown, 2019;Solberg et al., 2007). One of the most widely used of these methods is object-based image analysis (OBIA) (Fan et al., 2015;Kolokoussis & Karathanassi, 2018). ...
The coastal regions in the Persian Gulf are renowned for having the largest oil reserves. Pipelines, as a means of oil transportation, are one of the most common options for transporting petroleum products in these regions. Hence, the risk of oil spill pollution has become a vital challenge for local authorities, which has gradually increased since the last decade. Therefore, it is essential to use a method for timely detection of oil spills to prevent environmental damage. In this study, Sentinel-1 SAR data and Sentinel-2 image have been used for timely detection of oil slicks in the Persian Gulf. The area of oil pollution that extracted from Sentinel-2 (optical data), Sentinel-1 (SAR data), and field data is 114.7, 98.5, and 124.7 square kilometres, respectively. The results indicated that the object-based image analysis (OBIA) method using optical data had better results than using SAR data. In addition, the results indicated that the OBIA could be used as a method to produce ground truth map. Therefore, this research demonstrates the applicability of remote sensing data to recognize oil spill pollution on the surface of the water with the applicability of Sentinel-1 and Sentinel-2 data.
... Oil slicks are common marine pollutants [1,2]. Oil slicks on sea surface can hinder light and heat exchange between the ocean and the atmosphere and affect the absorption, transmission and reflection of electromagnetic waves in the ocean, thus affecting photosynthesis of plants and respiration of organisms inhabiting the sea floor. ...
... Spilled oil first forms a thick black oil slick floating on the sea surface, the thickness of which is difficult to estimate as it is difficult for sunlight and other active detection incident light to penetrate. Black oil slicks are easily detected by optical sensors [1]. The thickness of oil slicks decreases with diffusion, and oil-slick films with different thicknesses and light transmittance are gradually formed, the thickness of which can reach 0.1mm or even thinner [6]. ...
... Their studies indicate that 400-900 nm is an effective waveband for oil-film extraction. Lu et al. [1,9,[16][17][18] systematically designed and performed laboratory experiments -3 -on oil films and oil emulsions. They measured and analyzed the remote-sensing reflectance and absorption of water-in-oil and oil-in-water emulsions with different volumetric concentrations and film thicknesses in the spectral range of 400-2500 nm. ...
Optical identification, classification and quantification of different oil slicks play important roles in oil-slick monitoring and detection. Under the actions of wind, waves and other marine forces, oil slicks on the sea surface mix with seawater to form oil-seawater emulsions. Herein, we focus on the spectral radiative properties of seawater-in-oil emulsions in the visible-infrared region. The complex refractive indices of crude oil are precisely measured using the combined ellipsometry-transmission method, after which the radiative properties of seawater droplets embedded in crude oil and seawater-in-oil emulsions obtained using the traditional and improved Lorenz-Mie theory are compared and analyzed to illustrate the effects of the absorption of host medium. The directional-hemispherical emittance and reflectance for seawater-in-oil emulsions are calculated using the Monte Carlo method. Results show that even though the traditional Mie-calculated radiative properties deviate obviously from the improved Lorenz-Mie theory results, directional-hemispherical reflectance and emittance of seawater-in-oil emulsions show relatively small difference. Besides, increasing the droplet volume fraction and film thickness may generally lead to enhanced scattering of seawater droplets, further leading to enhanced light reflection and reduced light emission. With the increase in both droplet size and absorption index of seawater and crude oil, the effects of seawater droplet absorption and oil absorption on overall attenuation of emulsions increase considerably; they may weaken the reflection and strengthen the emission. Obtaining the key radiative parameters of different oil species and oil emulsions will be helpful for promoting the practical application of quantitative optical remote sensing of oil slicks on sea surface.
