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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 17316

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Special Issue Editor

Dr. Francesco Bignami

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Guest Editor

Institute of Marine Science, Italian National Research Council, Italy (CNR-ISMAR)
Interests: physical oceanography; oil spill; ocean physical-biological interaction; ocean remote sensing

Special Issue Information

Dear Colleagues,

As Guest Editor for Remote Sensing, I am very happy to announce this Special Issue on “Remote Sensing Observations for Oil Spill Monitoring.” The Editors and I are warmly inviting you to contribute with state-of-the-art research papers on the monitoring of oil pollution of the marine, coastal, and land environments, in which remote sensing techniques have such a crucial role nowadays given the scientific and technological progress made in the last few decades.

Innovative contributions on remote sensing of oil spills with sensors borne by different platforms (e.g., satellite, aircraft, ship, drones) are welcome, as well as papers on the combined use of remote sensing and modelling for slick displacement and evolution forecasts. Also, we look forward to the presentation on your part of research results on innovative image processing techniques, e.g., employing Artificial Intelligence, for oil detection, slick thickness estimation, oil spill look-alike removal in images, or techniques devoted to speed up image and metadata delivery to clean-up units. Review contributions are welcomed, as well as papers describing new measurement concepts/sensors.

Dr. Francesco Bignami
Guest Editor

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Keywords

Oil spill detection
Remote sensing
Synthetic Aperture Radar
Optical sensors
Microwave sensors
Image processing

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Published Papers (6 papers)

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Research

18 pages, 1671 KiB

Open AccessArticle

A Gamma-Log Net for Oil Spill Detection in Inhomogeneous SAR Images

by Jundong Liu, Peng Ren, Xinrong Lyu and Christos Grecos

Remote Sens. 2022, 14(16), 4074; https://doi.org/10.3390/rs14164074 - 20 Aug 2022

Cited by 1 | Viewed by 1780

Abstract

Due to the complexity of ocean environments, inhomogeneous phenomenon always exist in SAR images of oil spills on the sea surface. In order to address this issue, a universal parameter adaptive Gamma-Log net for detecting oil spills in inhomogeneous SAR images is proposed in this paper. The Gamma-Log net consists of an image feature division module, a correction parameter extraction module, a Gamma-Log correction module and a feature integration module. The normalized input image features are divided into four blocks for correction in the image feature division module. According to the input characteristics, the Gamma-Log correction input parameters are obtained in the correction parameter extraction module. Subsequently, an adaptive method is introduced to adjust the parameters independently by the network to improve efficiency. Then, the input features are corrected in the Gamma-Log correction module by Gamma correction and logarithmic correction. Both correction methods can adjust the gray imbalance in the image and change the overall gray value and contrast. The separated feature blocks are finally reunited together by the feature integration module. In order to avoid information loss, an attention mechanism is added to this module. In the experiments, by adding Gamma-Log Net to multiple semantic segmentation networks, the MIoU and dice indicators increased to some extent, and the HD distance(Hausdorff-95) decreased. Our work demonstrates that the Gamma-Log net can be helpful for oil spill detection in inhomogeneous SAR images. Full article

(This article belongs to the Special Issue Remote Sensing Observations for Oil Spill Monitoring)

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20 pages, 5616 KiB

Open AccessArticle

Distribution, Magnitude, and Variability of Natural Oil Seeps in the Gulf of Mexico

by Carrie O’Reilly, Mauricio Silva, Samira Daneshgar Asl, William P. Meurer and Ian R. MacDonald

Remote Sens. 2022, 14(13), 3150; https://doi.org/10.3390/rs14133150 - 30 Jun 2022

Cited by 9 | Viewed by 2895

Abstract

The Gulf of Mexico is a hydrocarbon-rich region characterized by the presence of floating oil slicks from persistent natural hydrocarbon seeps, which are reliably captured by synthetic aperture radar (SAR) satellite imaging. Improving the state of knowledge of hydrocarbon seepage in the Gulf of Mexico improves the understanding and quantification of natural seepage rates in North America. We used data derived from SAR scenes collected over the Gulf of Mexico from 1978 to 2018 to locate oil slick origins (OSOs), cluster the OSOs into discrete seep zones, estimate the flux of individual seepage events, and calculate seep recurrence rates. In total, 1618 discrete seep zones were identified, primarily concentrated in the northern Gulf of Mexico within the Louann salt formation, with a secondary concentration in the Campeche region. The centerline method was used to estimate flux based on the drift length of the slick (centerline), the slick area, and average current and wind speeds. Flux estimates from the surface area of oil slicks varied geographically and temporally; on average, seep zones exhibited an 11% recurrence rate, suggesting possible intermittent discharge from natural seeps. The estimated average instantaneous flux for natural seeps is 9.8 mL s⁻¹ (1.9 × 10³ bbl yr⁻¹), with an annual discharge of 1.73–6.69 × 10⁵ bbl yr⁻¹ (2.75–10.63 × 10⁴ m³ yr⁻¹) for the entire Gulf of Mexico. The temporal variability of average flux suggests a potential decrease following 1995; however, analysis of flux in four lease blocks indicates that flux has not changed substantially over time. It is unlikely that production activities in the Gulf of Mexico impact natural seepage on a human timescale. Of the 1618 identified seep zones, 1401 are located within U.S. waters, with 70 identified as having flux and recurrence rates significantly higher than the average. Seep zones exhibiting high recurrence rates are more likely to be associated with positive seismic anomalies. Many of the methods developed for this study can be applied to SAR-detected oil slicks in other marine settings to better assess the magnitude of global hydrocarbon seepage. Full article

(This article belongs to the Special Issue Remote Sensing Observations for Oil Spill Monitoring)

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15 pages, 21024 KiB

Open AccessArticle

Oil Spill Identification in Radar Images Using a Soft Attention Segmentation Model

by Peng Chen, Hui Zhou, Ying Li, Bingxin Liu and Peng Liu

Remote Sens. 2022, 14(9), 2180; https://doi.org/10.3390/rs14092180 - 2 May 2022

Cited by 7 | Viewed by 2504

Abstract

Oil spills can cause damage to the marine environment. When an oil spill occurs in the sea, it is critical to rapidly detect and respond to it. Because of their convenience and low cost, navigational radar images are commonly employed in oil spill detection. However, they are currently only used to assess whether or not there are oil spills, and the area affected is calculated with less accuracy. The main reason for this is that there have been very few studies on how to retrieve oil spill locations. Given the above problems, this article introduces a model of image segmentation based on the soft attention mechanism. First, the semantic segmentation model was established to fully integrate multi-scale features. It takes the target detection model based on the feature pyramid network as the backbone model, including high-level semantic information and low-level location information. The channel attention method was then used for each of the feature layers of the model to calculate the weight relationship between channels to boost the model’s expressive ability for extracting oil spill features.Simultaneously, a multi-task loss function was used. Finally, the public dataset of oil spills on the sea surface was used for detection. The experimental results show that the proposed method improves the segmentation accuracy of the oil spill region. At the same time, compared with segmentation models, such as PSPNet, DeepLab V3+, and Attention U-net, the segmentation accuracy based on the pixel level improved to 95.77%, and the categorical pixel accuracy increased to 96.45%. Full article

(This article belongs to the Special Issue Remote Sensing Observations for Oil Spill Monitoring)

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22 pages, 6797 KiB

Open AccessArticle

Improved Classification Models to Distinguish Natural from Anthropic Oil Slicks in the Gulf of Mexico: Seasonality and Radarsat-2 Beam Mode Effects under a Machine Learning Approach

by Ítalo de Oliveira Matias, Patrícia Carneiro Genovez, Sarah Barrón Torres, Francisco Fábio de Araújo Ponte, Anderson José Silva de Oliveira, Fernando Pellon de Miranda and Gil Márcio Avellino

Remote Sens. 2021, 13(22), 4568; https://doi.org/10.3390/rs13224568 - 13 Nov 2021

Cited by 7 | Viewed by 2328

Abstract

Distinguishing between natural and anthropic oil slicks is a challenging task, especially in the Gulf of Mexico, where these events can be simultaneously observed and recognized as seeps or spills. In this study, a powerful data analysis provided by machine learning (ML) methods was employed to develop, test, and implement a classification model (CM) to distinguish an oil slick source (OSS) as natural or anthropic. A robust database containing 4916 validated oil samples, detected using synthetic aperture radar (SAR), was employed for this task. Six ML algorithms were evaluated, including artificial neural networks (ANN), random forest (RF), decision trees (DT), naive Bayes (NB), linear discriminant analysis (LDA), and logistic regression (LR). Using RF, the global CM achieved a maximum accuracy value of 73.15. An innovative approach evaluated how external factors, such as seasonality, satellite configurations, and the synergy between them, limit or improve OSS predictions. To accomplish this, specific classification models (SCMs) were derived from the global ones (CMs), tuning the best algorithms and parameters according to different scenarios. Median accuracies revealed winter and spring to be the best seasons and ScanSAR Narrow B (SCNB) as the best beam mode. The maximum median accuracy to distinguish seeps from spills was achieved in winter using SCNB (83.05). Among the tested algorithms, RF was the most robust, with a better performance in 81% of the investigated scenarios. The accuracy increment provided by the well-fitted models may minimize the confusion between seeps and spills. This represents a concrete contribution to reducing economic and geologic risks derived from exploration activities in offshore areas. Additionally, from an operational standpoint, specific models support specialists to select the best SAR products and seasons for new acquisitions, as well as to optimize performances according to the available data. Full article

(This article belongs to the Special Issue Remote Sensing Observations for Oil Spill Monitoring)

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30 pages, 6075 KiB

Open AccessArticle

Oil Spills or Look-Alikes? Classification Rank of Surface Ocean Slick Signatures in Satellite Data

by Gustavo de Araújo Carvalho, Peter J. Minnett, Nelson F. F. Ebecken and Luiz Landau

Remote Sens. 2021, 13(17), 3466; https://doi.org/10.3390/rs13173466 - 1 Sep 2021

Cited by 9 | Viewed by 2974

Abstract

Linear discriminant analysis (LDA) is a mathematically robust multivariate data analysis approach that is sometimes used for surface oil slick signature classification. Our goal is to rank the effectiveness of LDAs to differentiate oil spills from look-alike slicks. We explored multiple combinations of (i) variables (size information, Meteorological-Oceanographic (metoc), geo-location parameters) and (ii) data transformations (non-transformed, cube root, log₁₀). Active and passive satellite-based measurements of RADARSAT, QuikSCAT, AVHRR, SeaWiFS, and MODIS were used. Results from two experiments are reported and discussed: (i) an investigation of 60 combinations of several attributes subjected to the same data transformation and (ii) a survey of 54 other data combinations of three selected variables subjected to different data transformations. In Experiment 1, the best discrimination was reached using ten cube-transformed attributes: ~85% overall accuracy using six pieces of size information, three metoc variables, and one geo-location parameter. In Experiment 2, two combinations of three variables tied as the most effective: ~81% of overall accuracy using area (log transformed), length-to-width ratio (log- or cube-transformed), and number of feature parts (non-transformed). After verifying the classification accuracy of 114 algorithms by comparing with expert interpretations, we concluded that applying different data transformations and accounting for metoc and geo-location attributes optimizes the accuracies of binary classifiers (oil spill vs. look-alike slicks) using the simple LDA technique. Full article

(This article belongs to the Special Issue Remote Sensing Observations for Oil Spill Monitoring)

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23 pages, 7844 KiB

Open AccessArticle

Marine Oil Slick Detection Using Improved Polarimetric Feature Parameters Based on Polarimetric Synthetic Aperture Radar Data

by Guannan Li, Ying Li, Yongchao Hou, Xiang Wang and Lin Wang

Remote Sens. 2021, 13(9), 1607; https://doi.org/10.3390/rs13091607 - 21 Apr 2021

Cited by 20 | Viewed by 3319

Abstract

Marine oil spill detection is vital for strengthening the emergency commands of oil spill accidents and repairing the marine environment after a disaster. Polarimetric Synthetic Aperture Radar (Pol-SAR) can obtain abundant information of the targets by measuring their complex scattering matrices, which is conducive to analyze and interpret the scattering mechanism of oil slicks, look-alikes, and seawater and realize the extraction and detection of oil slicks. The polarimetric features of quad-pol SAR have now been extended to oil spill detection. Inspired by this advancement, we proposed a set of improved polarimetric feature combination based on polarimetric scattering entropy H and the improved anisotropy A₁₂–H_A₁₂. The objective of this study was to improve the distinguishability between oil slicks, look-alikes, and background seawater. First, the oil spill detection capability of the H_A₁₂ combination was observed to be superior than that obtained using the traditional H_A combination; therefore, it can be adopted as an alternate oil spill detection strategy to the latter. Second, H(1 − A₁₂) combination can enhance the scattering randomness of the oil spill target, which outperformed the remaining types of polarimetric feature parameters in different oil spill scenarios, including in respect to the relative thickness information of oil slicks, oil slicks and look-alikes, and different types of oil slicks. The evaluations and comparisons showed that the proposed polarimetric features can indicate the oil slick information and effectively suppress the sea clutter and look-alike information. Full article

(This article belongs to the Special Issue Remote Sensing Observations for Oil Spill Monitoring)

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