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Change vector in two bands radiometric space 3.3 Oil spill detection Oil spill detection is carried out by the CVA as a binary recognition task in the bi-temporal and multi-spectral domain. The ultimate objective is to isolate anomalies from sea background and discriminate oil spills among the possible look-alikes. We selected two different sensors to investigate oil spill events that differs by oil type, spill overall extension and oil thickness. MODIS data, at 250m spatial resolution, has been selected to investigate the potential of high revisiting frequency (1-2 days) multispectral imagery in oil spill monitoring applications. Despite the low revisiting frequency (16 days), we investigated the effectiveness of OLI data in detecting oil spills of moderate extension and thickness (ca. 2,000 barrels). According to [18,40] uncorrected features at 469, 555 and 649 nm are capable to show significant indications of oil. At the same time optical imagery in the near-infrared (859 nm) proved to be effective in oil spill detection. MODIS band 1 (620-670 nm) and band 2 (841-876 nm) have been selected for the oil detection task over the Gulf of Mexico. The corresponding OLI bands have also been selected to attempt the same detection task, using the same technique, over the other two study areas (Refugio beach and Zakynthos). OLI sensor is called to investigate two peculiar oil spill events: a leakage and a natural outflow both characterized by high degree of oil emulsion with water and reduced spill thickness. The OLI higher resolution with respect to MODIS, is necessary to investigate oil spill of small dimension but the nature of the spill it should be also taken into account. Switching from one sensor to the other just to improve the spatial resolution is not enough to perform the detection the task. In these particular case, the OLI bands 1 (433-453 nm) and 2 (450-515 nm) have also been called in to take advantage of the reflectance of oil at those wavelengths despite the presence of the atmospheric effect [40-41]. The overall procedure workflow is presented in Figure 3. Once the image preprocessing is completed, the CVA takes place. The analysis is carried out on a pixel base, between each data pair constituted by the current image under analysis and the reference image of the area of interest. As result, a change vector is released for each pixel. Then, the magnitude and direction of the change vector are retrieved and submitted for a binary test. Using the reported oil spill from May 17, 2010 as training site, both numerical and angular thresholds have been extracted and employed within the final testing of the previously extracted change vector characteristics. A change pixel is recognized when the magnitude exceeds the numerical threshold, while the nature of the change is
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Oil pollution is one of the most destructive consequences due to human activities in the marine environment. Oil wastes come from many sources and take decades to be disposed of. Satellite based remote sensing systems can be implemented into a surveillance and monitoring network. In this study, a multi-temporal approach to the oil spill detection p...
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Context 1
... example of a change pixel in a bi-dimensional space is depicted in Fig. 2. Thresholds should be determined to discriminate pixels that change from no-change pixels. On the other hand, the direction of the change vector facilitates the discrimination among different types of change that can possibly occur. ...
Context 2
... example of a change pixel in a bi-dimensional space is depicted in Fig. 2. Thresholds should be determined to discriminate pixels that change from no-change pixels. On the other hand, the direction of the change vector facilitates the discrimination among different types of change that can possibly occur. ...
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... Several studies have shown their satellite ability in water quality monitoring [6][7][8], particularly using the bands near 750 nm, which solves the reflectance secondary peak related to pigment concentrations [9]. 2 of 15 Moreover, Sentinel-2 has been utilized for identifying oil spills in both marine and freshwater settings [10,11], and several similar studies with other optical sensors are available, too [12][13][14]. ...
Remote sensing techniques have become pivotal in monitoring algal blooms and population dynamics in freshwater bodies, particularly to assess the ecological risks associated with eutrophication. This study focuses on remote sensing methods for the analysis of 4 Italian lakes with diverse geological origins, leveraging water quality samples and data from the Sentinel-2 and Landsat 5.7–8 platforms. Chl-a, a well-correlated indicator of phytoplankton biomass abundance and eutrophication, was estimated using ordinary least squares linear regression to calibrate surface reflectance with chl-a concentrations. Temporal gaps between sample and image acquisition were considered, and atmospheric correction dedicated to water surfaces was implemented using ACOLITE and those specific to each satellite platform. The developed models achieved determination coefficients higher than 0.69 with mean square errors close to 3 mg/m³ for water bodies with low turbidity. Furthermore, the time series described by the models portray the seasonal variations in the lakes water bodies.
... Prior to the oil boom -Nigeria was an agro-based economy and was relatively, diversified (Liu & Liu, 2020;Yoro & Ojugo, 2019a). Her citizens were self-sufficient in food production -alongside enough to facilitate export (Luciani & Laneve, 2018;Seydi et al., 2021). She had quite a robust economy with functioning laws, institutions (Obinwa, 2022), social and economic infrastructure with limitless job opportunities. ...
The energy market aims to manage risks associated with prices and volatility of oil asset. It is a capital-intensive market that is rippled with chaos and complex interactions among its demand-supply derivatives. Models help users forecast such interactions, to provide investors with empirical evidence of price direction. Our study sought to investigate the reasons for the unexpected plummet in price of the energy market using evolutionary modeling-which seeks to analyze input data and yield an optimal, complete solution that are tractable, robust and low-cost with tolerance of ambiguity, uncertainty and noise. We adopt the Gabillon's model to: (a) predict spots/futures prices, (b) investigate why previous predictions failed as to why price plummet, and (c) seek to critically evaluate values reached by both proposed deep learning model and the memetic algorithm by Okologume and Rotimi (2022).
... Some studies have examined optical satellite sensors for oil spill detection in the oceans (Bonnington et al., 2021;Roberto & Giovanni, 2018;Hu et al., 2021). However, optical sensors are not capable of operating during the nighttime and are affected by cloud coverage. ...
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.
... Because to these SAR detection problems, work has been done on the spectral characterization of oil slicks and their detection with multispectral sensors such as MODIS, MERIS, and Landsat (Al-Ruzouq et al., 2020;Arslan, 2018;Balogun et al., 2020;Chowdhury et al., 2021;De Kerf et al., 2020;Hu et al., 2018;Lu et al., 2020Lu et al., , 2019Luciani and Laneve, 2018;Mohammadi et al., 2021;Ozigis et al., 2019). However, due to weathering processes, the oil in the water can emulsify and/or change its concentration, altering its spectral response, making detection with passive sensors difficult (Bonn Agreement Accord de Bonn (BAOAC), 2007; Ivshina et al., 2015). ...
The method's development to detect oil-spills, and concentration monitoring of marine environments, are essential in emergency response. To develop a classification model, this work was based on the spectral response of surfaces using reflectance data, and machine learning (ML) techniques, with the objective of detecting oil in Landsat imagery. Additionally, different concentration oil data were used to obtain a concentration-estimation model. In the classification, K-Nearest Neighbor (KNN) obtained the best approximations in oil detection using Blue (0.453–0.520 μm), NIR (0.790–0.891 μm), SWIR1 (1.557–1.717 μm), and SWIR2 (1.960–2.162 μm) bands for 2010 spill images. In the concentration model, the mean absolute error (MAE) was 1.41 and 3.34, for training and validation data. When testing the concentration-estimation model in images where oil was detected, the concentration-estimation obtained was between 40 and 60 %. This demonstrates the potential use of ML techniques and spectral response data to detect and estimate the concentration of oil-spills.
... Several investigators have already demonstrated their capabilities for water quality monitoring [28][29][30][31][32][33], particularly in using band 5 at 705 nm, which resolves the secondary reflectance peak related to pigment concentrations [34]. Sentinel-2 was also used to identify oil spills in the sea [35], and several comparable studies are available for other optical sensors [36][37][38]. The visible and infrared reflectance shape features of pure water and oil are relatively similar, and, often, magnitude differences depend heavily on illumination conditions. ...
The purpose of this study was to combine all available information on the state of Lake Pertusillo (Basilicata, Italy), both in the field and published, which included Sentinel-2A satellite data, to understand algal blooms in a lacustrine environment impacted by petroleum hydrocarbons. Sentinel-2A data was retrospectively used to monitor the state of the lake, which is located near the largest land-based oil extraction plant in Europe, with particular attention to chlorophyll a during algal blooms and petroleum hydrocarbons. In winter 2017, a massive dinoflagellate bloom (10.4 × 106 cell/L) of Peridinium umbonatum and a simultaneous presence of hydrocarbons were observed at the lake surface. Furthermore, a recent study using metagenomic analyses carried out three months later identified a hydrocarbonoclastic microbial community specialized in the degradation aromatic and nitroaromatic hydrocarbons. In this study, Sentinel-2A imagery was able to detect the presence of chlorophyll a in the waters, while successfully distinguishing the signal from that of hydrocarbons. Remotely sensed results confirmed surface reference measurements of lacustrine phytoplankton, chlorophyll a, and the presence of hydrocarbons during algal blooms, thereby explaining the presence of the hydrocarbonoclastic microbial community found in the lake three months after the oil spill event. The combination of emerging methodologies such as satellite systems and metagenomics represent an important support methodology for describing complex contaminations in diverse ecosystems.
