scholarly journals Oil Spill Detection Using Satellite Imagery

2021 ◽  
Vol 2 (4) ◽  
pp. 1-1
Author(s):  
Amber Bonnington ◽  
◽  
Meisam Amani ◽  
Hamid Ebrahimy ◽  
◽  
...  
Keyword(s):  
Change Detection ◽  
Oil Spill ◽  
Deep Water ◽  
Oil Spills ◽  
Detection Method ◽  
Oil Spill Detection ◽  
New Development ◽  
Before And After ◽  
The Difference ◽  
Remote Sensing Techniques

<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>

scholarly journals Semi-Automatic Oil Spill Detection on X-Band Marine Radar Images Using Texture Analysis, Machine Learning, and Adaptive Thresholding

2019 ◽  
Vol 11 (7) ◽  
pp. 756 ◽  
Author(s):  
Peng Liu ◽  
Ying Li ◽  
Bingxin Liu ◽  
Peng Chen ◽  
and Jin Xu
Keyword(s):  
Machine Learning ◽  
Texture Analysis ◽  
Oil Spill ◽  
Oil Spills ◽  
Detection Method ◽  
Adaptive Thresholding ◽  
Oil Spill Detection ◽  
Radar Images ◽  
X Band ◽  
Marine Radar

Oil spills bring great damage to the environment and, in particular, to coastal ecosystems. The ability of identifying them accurately is important to prompt oil spill response. We propose a semi-automatic oil spill detection method, where texture analysis, machine learning, and adaptive thresholding are used to process X-band marine radar images. Coordinate transformation and noise reduction are first applied to the sampled radar images, coarse measurements of oil spills are then subjected to texture analysis and machine learning. To identify the loci of oil spills, a texture index calculated by four textural features of a grey level co-occurrence matrix is proposed. Machine learning methods, namely support vector machine, k-nearest neighbor, linear discriminant analysis, and ensemble learning are adopted to extract the coarse oil spill areas indicated by the texture index. Finally, fine measurements can be obtained by using adaptive thresholding on coarsely extracted oil spill areas. Fine measurements are insensitive to the results of coarse measurement. The proposed oil spill detection method was used on radar images that were sampled after an oil spill accident that occurred in the coastal region of Dalian, China on 21 July 2010. Using our processing method, thresholds do not have to be set manually and oil spills can be extracted semi-automatically. The extracted oil spills are accurate and consistent with visual interpretation.

scholarly journals Assessment of the Physicochemical Parameters of Bodo Creek, Rivers State, Nigeria: A Pre-spill, Post-spill and Post-clean-up Review

2021 ◽  
Vol 25 (8) ◽  
pp. 1505-1512
Author(s):  
D.K. Nkeeh ◽  
A.I. Hart ◽  
E.S. Erondu ◽  
N. Zabbey
Keyword(s):  
Oil Spill ◽  
Physicochemical Parameters ◽  
Oil Spills ◽  
Oxygen Demand ◽  
Total Hardness ◽  
Before And After ◽  
The Difference ◽  
Temperature Levels ◽  
High Level ◽  
Rivers State

Water bodies are a source of ecosystem services such as water supply, production, recreation, and aesthetics. In 2008, two major oil spills took place in Bodo creek. A major challenge with the assessment and monitoring of an environment is the lack of baseline data. However, Bodo Creek has been studied extensively. This paper, therefore, reviews pre-spill, post-spill, and post-clean-up studies on physicochemical parameters in Bodo Creek. This paper revealed that the difference in the levels of the physicochemical parameters including pH, salinity, dissolved oxygen (DO), biochemical oxygen demand (BOD), and temperature in Bodo Creek, before and after the oil spill was not statistically significant (P > 0.05); other physicochemical parameters examined in this paper are alkalinity, total hardness, chemical oxygen demand (COD) and total dissolved solids (TDS). This paper also revealed that pH and temperature were higher in the post-cleanup study, while DO and conductivity were higher in the pre-cleanup study. BOD was significantly higher in the post-spill study than the pre-spill study, indicating a high level of pollution as a result of the oil spill. This review also shows that there are higher pH and temperature levels in post-clean-up studies than the pre-cleanup studies. Pre-clean-up DO and conductivity were higher than the levels in the post-clean-up study.

scholarly journals OILSPILL AND LOOK-ALIKE SPOTS FROM SAR IMAGERY USING OTSU METHOD AND ARTIFICIAL NEURAL NETWORK

2020 ◽  
Vol 4 (11) ◽  
pp. 1-10
Author(s):  
M. Sornam
Keyword(s):  
Image Segmentation ◽  
Oil Spill ◽  
Oil Spills ◽  
Marine Ecosystem ◽  
Dark Spot ◽  
Oil Spill Detection ◽  
Otsu Method ◽  
Artificial Neural ◽  
Propagation Network

Oil spill pollution plays a significant role in damaging marine ecosystem. Discharge of oil due to tanker accidents has the most dangerous effects on marine environment. The main waste source is the ship based operational discharges. Synthetic Aperture Radar (SAR) can be effectively used for the detection and classification of oil spills. Oil spills appear as dark spots in SAR images. One major advantage of SAR is that it can generate imagery under all weather conditions. However, similar dark spots may arise from a range of unrelated meteorological and oceanographic phenomena, resulting in misidentification. A major focus of research in this area is the development of algorithms to distinguish ‘oil spills’ from ‘look-alikes’. The features of detected dark spot are then extracted and classified to discriminate oil spills from look-alikes. This paper describes the development of a new approach to SAR oil spill detection using Segmentation method and Artificial Neural Networks (ANN). A SAR-based oil-spill detection process consists of three stages: image segmentation, feature extraction and object recognition (classification) of the segmented objects as oil spills or look-alikes. The image segmentation was performed with Otsu method. Classification has been done using Back Propagation Network and this network classifies objects into oil spills or look-alikes according to their feature parameters. Improved results have been achieved for the discrimination of oil spills and look-alikes.

scholarly journals Modified S2CVA Algorithm Using Cross-Sharpened Images for Unsupervised Change Detection

2018 ◽  
Vol 10 (9) ◽  
pp. 3301 ◽  
Author(s):  
Honglyun Park ◽  
Jaewan Choi ◽  
Wanyong Park ◽  
Hyunchun Park
Keyword(s):  
Change Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Seasonal Effects ◽  
Detection Accuracy ◽  
Difference Image ◽  
Before And After ◽  
Change Detection Algorithms ◽  
The Difference ◽  
Direction Information

This study aims to reduce the false alarm rate due to relief displacement and seasonal effects of high-spatial-resolution multitemporal satellite images in change detection algorithms. Cross-sharpened images were used to increase the accuracy of unsupervised change detection results. A cross-sharpened image is defined as a combination of synthetically pan-sharpened images obtained from the pan-sharpening of multitemporal images (two panchromatic and two multispectral images) acquired before and after the change. A total of four cross-sharpened images were generated and used in combination for change detection. Sequential spectral change vector analysis (S2CVA), which comprises the magnitude and direction information of the difference image of the multitemporal images, was applied to minimize the false alarm rate using cross-sharpened images. Specifically, the direction information of S2CVA was used to minimize the false alarm rate when applying S2CVA algorithms to cross-sharpened images. We improved the change detection accuracy by integrating the magnitude and direction information obtained using S2CVA for the cross-sharpened images. In the experiment using KOMPSAT-2 satellite imagery, the false alarm rate of the change detection results decreased with the use of cross-sharpened images compared to that with the use of only the magnitude information from the original S2CVA.

scholarly journals Optimization on Emergency Materials Dispatching Considering the Characteristics of Integrated Emergency Response for Large-Scale Marine Oil Spills

2019 ◽  
Vol 7 (7) ◽  
pp. 214 ◽  
Author(s):  
Song Li ◽  
Manel Grifoll ◽  
Miquel Estrada ◽  
Pengjun Zheng ◽  
Hongxiang Feng
Keyword(s):  
Emergency Response ◽  
Oil Spill ◽  
Large Scale ◽  
Oil Spills ◽  
Marine Oil ◽  
Emergency Managers ◽  
Distribution Point ◽  
Dispatching Algorithm ◽  
Marine Oil Spills ◽  
The Difference

Many governments have been strengthening the construction of hardware facilities and equipment to prevent and control marine oil spills. However, in order to deal with large-scale marine oil spills more efficiently, emergency materials dispatching algorithm still needs further optimization. The present study presents a methodology for emergency materials dispatching optimization based on four steps, combined with the construction of Chinese oil spill response capacity. First, the present emergency response procedure for large-scale marine oil spills should be analyzed. Second, in accordance with different grade accidents, the demands of all kinds of emergency materials are replaced by an equivalent volume that can unify the units. Third, constraint conditions of the emergency materials dispatching optimization model should be presented, and the objective function of the model should be postulated with the purpose of minimizing the largest sailing time of all oil spill emergency disposal vessels, and the difference in sailing time among vessels that belong to the same emergency materials collection and distribution point. Finally, the present study applies a toolbox and optimization solver to optimize the emergency materials dispatching problem. A calculation example is presented, highlighting the sensibility of the results at different grades of oil spills. The present research would be helpful for emergency managers in tackling an efficient materials dispatching scheme, while considering the integrated emergency response procedure.

DAMAGE ASSESSMENT IN COASTAL HABITATS: LESSONS LEARNED FROM EXXON VALDEZ

1993 ◽  
Vol 1993 (1) ◽  
pp. 695-697 ◽  
Author(s):  
Thomas A. Dean ◽  
Lyman McDonald ◽  
Michael S. Stekoll ◽  
Richard R. Rosenthal
Keyword(s):  
Optimal Design ◽  
Oil Spill ◽  
Damage Assessment ◽  
Oil Spills ◽  
Lessons Learned ◽  
Exxon Valdez ◽  
Before And After ◽  
And Control ◽  
Alternative Designs ◽  
The Impact

ABSTRACT This paper examines alternative designs for the monitoring and assessment of damages of environmental impacts such as oil spills. The optimal design requires sampling at pairs of impacted (oiled) and control (unoiled) sites both before and after the event. However, this design proved impractical in evaluating impacts of the Exxon Valdez oil spill on nearshore subtidal communities, and may be impractical for future monitoring. An alternative design is discussed in which sampling is conducted at pairs of control and impact sites only after the impact.

A Model Based Approach to Radar Oil Spill Detection

2014 ◽  
Vol 2014 (1) ◽  
pp. 2228-2241
Author(s):  
Torstein Pedersen ◽  
Javier Perez ◽  
Jos Van Heseen
Keyword(s):  
Oil Spill ◽  
Oil Spills ◽  
Radar Image ◽  
Monitoring Systems ◽  
Multiple Sources ◽  
Threshold Detector ◽  
Ir Camera ◽  
Oil Spill Detection ◽  
Model Based ◽  
Primary Driver

ABSTRACT A typical oil spill recovery vessel has been historically outfitted with an oil spill detection (OSD) radar. During an oil spill recovery operation, there is a dedicated operator who is responsible for interpreting information from the radar image. Industry developments over the last several years now require that an OSD radar automatically detect and track an oil spill. There are two primary needs driving this development. The first is that OSD systems and operations are becoming more sophisticated; automatic OSD aids for a more efficient oil spill operation where an operator's attention may be directed to a potential spill. The automatic OSD also aids a multi-sensor system; one such example is where an OSD radar is used to steer an IR camera to a candidate spill for more detailed evaluation or validation. The other primary driver for automatic OSD is for monitoring systems, which serve for early warning. Monitoring systems may be found along coastal installations or oil platforms. The automatic spill detection functionality of an OSD system may be implemented in different levels of sophistication. Perhaps the simplest configuration is one that uses fixed thresholds relative to the image for alarming whether a region in a radar image is a spill or not. The benefit of simple threshold detector is that it is easy to implement in software. The weakness is that it is prone to both lower overall detection rate and high false alarm rate. A more robust automatic spill detection method is one that treats it as an image-processing problem. The paper here presents a model based OSD. Generation of confidence maps is central to the method and provides an indication of the likelihood of oil. Inputs to the confidence maps come from multiple sources, several of which are based on uniquely constructed models. Among these is a histogram comparator, which scans a radar image and compares the data to reference models from real oil spills. A discussion of the methods used focuses on (a) the necessary steps prior to the confidence map construction, (b) how the confidence maps are layered with inputs, (c) how the information in the confidence maps is transitioned into the detection of oil, (d) and finally alarming.

scholarly journals Hyperspectral Remote Sensing Detection of Marine Oil Spills Using an Adaptive Long-Term Moment Estimation Optimizer

2021 ◽  
Vol 14 (1) ◽  
pp. 157
Author(s):  
Zongchen Jiang ◽  
Jie Zhang ◽  
Yi Ma ◽  
Xingpeng Mao
Keyword(s):  
Remote Sensing ◽  
Oil Spill ◽  
Oil Spills ◽  
Heavy Oils ◽  
Marine Oil ◽  
Oil Spill Detection ◽  
Moment Estimation ◽  
Marine Oil Spills ◽  
Oil Films

Marine oil spills can damage marine ecosystems, economic development, and human health. It is important to accurately identify the type of oil spills and detect the thickness of oil films on the sea surface to obtain the amount of oil spill for on-site emergency responses and scientific decision-making. Optical remote sensing is an important method for marine oil-spill detection and identification. In this study, hyperspectral images of five types of oil spills were obtained using unmanned aerial vehicles (UAV). To address the poor spectral separability between different types of light oils and weak spectral differences in heavy oils with different thicknesses, we propose the adaptive long-term moment estimation (ALTME) optimizer, which cumulatively learns the spectral characteristics and then builds a marine oil-spill detection model based on a one-dimensional convolutional neural network. The results of the detection experiment show that the ALTME optimizer can store in memory multiple batches of long-term oil-spill spectral information, accurately identify the type of oil spills, and detect different thicknesses of oil films. The overall detection accuracy is larger than 98.09%, and the Kappa coefficient is larger than 0.970. The F1-score for the recognition of light-oil types is larger than 0.971, and the F1-score for detecting films of heavy oils with different film thicknesses is larger than 0.980. The proposed optimizer also performs well on a public hyperspectral dataset. We further carried out a feasibility study on oil-spill detection using UAV thermal infrared remote sensing technology, and the results show its potential for oil-spill detection in strong sunlight.

scholarly journals Advances in Remote Sensing Technology, Machine Learning and Deep Learning for Marine Oil Spill Detection, Prediction and Vulnerability Assessment

2020 ◽  
Vol 12 (20) ◽  
pp. 3416
Author(s):  
Shamsudeen Temitope Yekeen ◽  
Abdul-Lateef Balogun
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Oil Spill ◽  
Oil Spills ◽  
Biogenic Elements ◽  
Trajectory Prediction ◽  
Oil Spill Detection ◽  
Remote Sensing Technology ◽  
Sensing Technology

Although advancements in remote sensing technology have facilitated quick capture and identification of the source and location of oil spills in water bodies, the presence of other biogenic elements (lookalikes) with similar visual attributes hinder rapid detection and prompt decision making for emergency response. To date, different methods have been applied to distinguish oil spills from lookalikes with limited success. In addition, accurately modeling the trajectory of oil spills remains a challenge. Thus, we aim to provide further insights on the multi-faceted problem by undertaking a holistic review of past and current approaches to marine oil spill disaster reduction as well as explore the potentials of emerging digital trends in minimizing oil spill hazards. The scope of previous reviews is extended by covering the inter-related dimensions of detection, discrimination, and trajectory prediction of oil spills for vulnerability assessment. Findings show that both optical and microwave airborne and satellite remote sensors are used for oil spill monitoring with microwave sensors being more widely used due to their ability to operate under any weather condition. However, the accuracy of both sensors is affected by the presence of biogenic elements, leading to false positive depiction of oil spills. Statistical image segmentation has been widely used to discriminate lookalikes from oil spills with varying levels of accuracy but the emergence of digitalization technologies in the fourth industrial revolution (IR 4.0) is enabling the use of Machine learning (ML) and deep learning (DL) models, which are more promising than the statistical methods. The Support Vector Machine (SVM) and Artificial Neural Network (ANN) are the most used machine learning algorithms for oil spill detection, although the restriction of ML models to feed forward image classification without support for the end-to-end trainable framework limits its accuracy. On the other hand, deep learning models’ strong feature extraction and autonomous learning capability enhance their detection accuracy. Also, mathematical models based on lagrangian method have improved oil spill trajectory prediction with higher real time accuracy than the conventional worst case, average and survey-based approaches. However, these newer models are unable to quantify oil droplets and uncertainty in vulnerability prediction. Considering that there is yet no single best remote sensing technique for unambiguous detection and discrimination of oil spills and lookalikes, it is imperative to advance research in the field in order to improve existing technology and develop specialized sensors for accurate oil spill detection and enhanced classification, leveraging emerging geospatial computer vision initiatives.

Oil spill detection method using X-band marine radar imagery

2015 ◽  
Vol 9 (1) ◽  
pp. 095985 ◽  
Author(s):  
Xueyuan Zhu ◽  
Ying Li ◽  
Haiyang Feng ◽  
Bingxin Liu ◽  
Jin Xu
Keyword(s):  
Oil Spill ◽  
Detection Method ◽  
Oil Spill Detection ◽  
X Band ◽  
Radar Imagery ◽  
Marine Radar
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