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.

Optimal Detection and Estimation of Marine Oil Spills Through Coherent Pluralism

2013 ◽  
Vol 4 (1) ◽  
pp. 84-111
Author(s):  
Kufre Bassey ◽  
Polycarp Chigbu
Keyword(s):  
Remote Sensing ◽  
Environmental Science ◽  
Oil Spills ◽  
False Alarms ◽  
Optical Remote Sensing ◽  
Marine Oil ◽  
Detection And Estimation ◽  
Number Of False Alarms ◽  
Marine Oil Spills ◽  
Remote Sensing Techniques

An important area of environmental science involves the combination of information from diverse sources relating to a similar endpoint. Majority of optical remote sensing techniques used for marine oil spills detection have been reported lately of having high number of false alarms (oil slick look-a-likes) phenomena which give rise to signals which appear to be oil but are not. Suggestions for radar image as an operational tool has also been made. However, due to the inherent risk in these tools, this paper presents the possible research directions of combining statistical techniques with remote sensing in marine oil spill detection and estimation.

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.

Remote sensing of marine oil spills and its applications

2008 ◽  
Author(s):  
Ying Li ◽  
Long Ma ◽  
Shui-ming Yu ◽  
Chuan-long Li ◽  
Qi-jun Li
Keyword(s):  
Remote Sensing ◽  
Oil Spills ◽  
Marine Oil ◽  
Marine Oil Spills

scholarly journals Using Peer-to-Peer Knowledge Transfer to Build Marine Oil Spill Preparedness Capacity and Foster Resilience Among Indigenous Communities

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Elise G. DeCola ◽  
Andrew Dumbrille ◽  
Steve Diggon
Keyword(s):  
Knowledge Transfer ◽  
Indigenous People ◽  
Oil Spill ◽  
Oil Spills ◽  
Low Frequency ◽  
Indigenous Communities ◽  
Peer To Peer ◽  
Top Down ◽  
Marine Oil ◽  
Marine Oil Spills

ABSTRACT Indigenous communities often bear disproportionate risks from marine oil spills because of their close connections to and reliance on marine ecosystems. The impacts of an oil spill on Indigenous people and communities can be far-reaching, even for incidents that might be considered “small” from the perspective of the response community. Building community capacity for oil spill preparedness and response is a critical component to creating resilience within Indigenous communities. While the fundamental elements of capacity are the same for Indigenous communities as for any other coastal community, the approach requires an understanding and respect for Traditional Knowledge, Indigenous governance structures, and existing stewardship networks. Oil spill preparedness and response traditionally follows a top-down approach within both government and industry, because marine oil spills are low frequency, high consequence, highly complex incidents where multiple organizations and jurisdictions must work together. While this reality applies regardless of whether an oil spill impacts Indigenous communities, a top-down approach can be experienced as a threat to self-governance and compromise the effectiveness of capacity-building efforts. There is a significant body of research in support of the concept that resilience to emergencies and disasters among Indigenous people must build upon existing social, cultural, and familial structures in order to be effective. This requires a fundamentally different approach that builds from the ground up with the goal of ultimately meshing with the existing preparedness and response framework. Peer-to-peer learning and knowledge transfer is an approach that has been used in support of a range of initiatives among Indigenous communities, such as human health initiatives. The same approach may provide a mechanism to empower Indigenous communities to enhance both capacity and resilience. This paper presents a case study from ongoing work to connect Indigenous communities from Canada's High Arctic and Pacific Coast in support of marine oil spill preparedness and response.

scholarly journals Ultraviolet remote sensing of marine oil spills: A new approach of HaiYang- 1C satellite

2021 ◽  
Author(s):  
Yingcheng Lu ◽  
Ziyi Suo ◽  
jianqiang liu ◽  
Jing Ding ◽  
Dayi Yin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Oil Spills ◽  
New Approach ◽  
Marine Oil ◽  
Marine Oil Spills ◽  
Ultraviolet Remote Sensing

scholarly journals Feature Merged Network for Oil Spill Detection using SAR Images

2021 ◽  
Vol 13 (16) ◽  
pp. 3174
Author(s):  
Yonglei Fan ◽  
Xiaoping Rui ◽  
Guangyuan Zhang ◽  
Tian Yu ◽  
Xijie Xu ◽  
...  
Keyword(s):  
Oil Spill ◽  
Oil Spills ◽  
Recognition Accuracy ◽  
Original Model ◽  
High Dimensional ◽  
Sar Image ◽  
Sar Images ◽  
Marine Oil ◽  
Marine Oil Spills ◽  
Overfitting Problem

The frequency of marine oil spills has increased in recent years. The growing exploitation of marine oil and continuous increase in marine crude oil transportation has caused tremendous damage to the marine ecological environment. Using synthetic aperture radar (SAR) images to monitor marine oil spills can help control the spread of oil spill pollution over time and reduce the economic losses and environmental pollution caused by such spills. However, it is a significant challenge to distinguish between oil-spilled areas and oil-spill-like in SAR images. Semantic segmentation models based on deep learning have been used in this field to address this issue. In addition, this study is dedicated to improving the accuracy of the U-Shape Network (UNet) model in identifying oil spill areas and oil-spill-like areas and alleviating the overfitting problem of the model; a feature merge network (FMNet) is proposed for image segmentation. The global features of SAR image, which are high-frequency component in the frequency domain and represents the boundary between categories, are obtained by a threshold segmentation method. This can weaken the impact of spot noise in SAR image. Then high-dimensional features are extracted from the threshold segmentation results using convolution operation. These features are superimposed with to the down sampling and combined with the high-dimensional features of original image. The proposed model obtains more features, which allows the model to make more accurate decisions. The overall accuracy of the proposed method increased by 1.82% and reached 61.90% compared with the UNet. The recognition accuracy of oil spill areas and oil-spill-like areas increased by approximately 3% and reached 56.33%. The method proposed in this paper not only improves the recognition accuracy of the original model, but also alleviates the overfitting problem of the original model and provides a more effective monitoring method for marine oil spill monitoring. More importantly, the proposed method provides a design principle that opens up new development ideas for the optimization of other deep learning network models.

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.

scholarly journals A guide to toxicity assessment and monitoring effects at lower levels of biological organization following marine oil spills in European waters

2010 ◽  
Vol 67 (6) ◽  
pp. 1105-1118 ◽  
Author(s):  
C. Martínez-Gómez ◽  
A. D. Vethaak ◽  
K. Hylland ◽  
T. Burgeot ◽  
A. Köhler ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Oil Spill ◽  
Oil Spills ◽  
Biological Effects ◽  
Toxicity Assessment ◽  
Biological Organization ◽  
Marine Oil ◽  
Assessment And Monitoring ◽  
Marine Oil Spills ◽  
Selection Of

Abstract Martínez-Gómez, C., Vethaak, A. D., Hylland, K., Burgeot, T., Köhler, A., Lyons, B. P., Thain, J., Gubbins, M. J., and Davies, I. M. 2010. A guide to toxicity assessment and monitoring effects at lower levels of biological organization following marine oil spills in European waters. – ICES Journal of Marine Science, 67: 1105–1118. The usefulness of applying biological-effects techniques (bioassays and biomarkers) as tools to assist in evaluating damage to the health of marine ecosystems produced by oil spills has been demonstrated clearly during recent decades. Guidelines are provided for the use of biological-effects techniques in oil spill pollution monitoring for the NE Atlantic coasts and the NW Mediterranean Sea. The emphasis is on fish and invertebrates and on methods at lower levels of organization (in vitro, suborganismal, and individual). Guidance is provided to researchers and environmental managers on: hazard identification of the fuel oil released; selection of appropriate bioassays and biomarkers for environmental risk assessment; selection of sentinel species; the design of spatial and temporal surveys; and the control of potential confounding factors in the sampling and interpretation of biological-effects data. It is proposed that after an oil spill incident, a monitoring programme using integrated chemical and biological techniques be initiated as soon as possible for ecological risk assessment, pollution control, and monitoring the efficacy of remediation. This can be done by developing new biomonitoring programmes or by adding appropriate biological-effects methods to the existing monitoring programmes.

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